diff --git a/.coverage b/.coverage new file mode 100644 index 000000000..e1e635c0d Binary files /dev/null and b/.coverage differ diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml new file mode 100644 index 000000000..c707c5a10 --- /dev/null +++ b/.github/workflows/codeql.yml @@ -0,0 +1,94 @@ +# For most projects, this workflow file will not need changing; you simply need +# to commit it to your repository. +# +# You may wish to alter this file to override the set of languages analyzed, +# or to provide custom queries or build logic. +# +# ******** NOTE ******** +# We have attempted to detect the languages in your repository. Please check +# the `language` matrix defined below to confirm you have the correct set of +# supported CodeQL languages. +# +name: "CodeQL Advanced" + +on: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] + schedule: + - cron: '16 19 * * 2' + +jobs: + analyze: + name: Analyze (${{ matrix.language }}) + # Runner size impacts CodeQL analysis time. To learn more, please see: + # - https://gh.io/recommended-hardware-resources-for-running-codeql + # - https://gh.io/supported-runners-and-hardware-resources + # - https://gh.io/using-larger-runners (GitHub.com only) + # Consider using larger runners or machines with greater resources for possible analysis time improvements. + runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }} + permissions: + # required for all workflows + security-events: write + + # required to fetch internal or private CodeQL packs + packages: read + + # only required for workflows in private repositories + actions: read + contents: read + + strategy: + fail-fast: false + matrix: + include: + - language: javascript-typescript + build-mode: none + - language: python + build-mode: none + # CodeQL supports the following values keywords for 'language': 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift' + # Use `c-cpp` to analyze code written in C, C++ or both + # Use 'java-kotlin' to analyze code written in Java, Kotlin or both + # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both + # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis, + # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning. + # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how + # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + # Initializes the CodeQL tools for scanning. + - name: Initialize CodeQL + uses: github/codeql-action/init@v3 + with: + languages: ${{ matrix.language }} + build-mode: ${{ matrix.build-mode }} + # If you wish to specify custom queries, you can do so here or in a config file. + # By default, queries listed here will override any specified in a config file. + # Prefix the list here with "+" to use these queries and those in the config file. + + # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs + # queries: security-extended,security-and-quality + + # If the analyze step fails for one of the languages you are analyzing with + # "We were unable to automatically build your code", modify the matrix above + # to set the build mode to "manual" for that language. Then modify this step + # to build your code. + # ℹī¸ Command-line programs to run using the OS shell. + # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun + - if: matrix.build-mode == 'manual' + shell: bash + run: | + echo 'If you are using a "manual" build mode for one or more of the' \ + 'languages you are analyzing, replace this with the commands to build' \ + 'your code, for example:' + echo ' make bootstrap' + echo ' make release' + exit 1 + + - name: Perform CodeQL Analysis + uses: github/codeql-action/analyze@v3 + with: + category: "/language:${{matrix.language}}" diff --git a/.github/workflows/greetings.yml b/.github/workflows/greetings.yml new file mode 100644 index 000000000..46774343e --- /dev/null +++ b/.github/workflows/greetings.yml @@ -0,0 +1,16 @@ +name: Greetings + +on: [pull_request_target, issues] + +jobs: + greeting: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/first-interaction@v1 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + issue-message: "Message that will be displayed on users' first issue" + pr-message: "Message that will be displayed on users' first pull request" diff --git a/.github/workflows/makefile.yml b/.github/workflows/makefile.yml new file mode 100644 index 000000000..e60fbe0f3 --- /dev/null +++ b/.github/workflows/makefile.yml @@ -0,0 +1,27 @@ +name: Makefile CI + +on: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] + +jobs: + build: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: configure + run: ./configure + + - name: Install dependencies + run: make + + - name: Run check + run: make check + + - name: Run distcheck + run: make distcheck diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml new file mode 100644 index 000000000..6ebda4d03 --- /dev/null +++ b/.github/workflows/python-package-conda.yml @@ -0,0 +1,26 @@ +name: Python Package Conda CI + +on: + push: + branches: + - main + pull_request: + branches: + - main + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v2 + + - name: Set up Miniconda + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + python-version: 3.8 + + - name: Install dependencies + run: conda env update --file environment.yml --name base diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml new file mode 100644 index 000000000..8ec707eae --- /dev/null +++ b/.github/workflows/python-tests.yml @@ -0,0 +1,29 @@ +name: Python Tests + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + test: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.8' # Change this to your desired Python version + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pytest pytest-cov + + - name: Run tests with coverage + run: | + pytest --cov --cov-report=xml diff --git a/.github/workflows/static.yml b/.github/workflows/static.yml new file mode 100644 index 000000000..0ba82305f --- /dev/null +++ b/.github/workflows/static.yml @@ -0,0 +1,43 @@ +# Simple workflow for deploying static content to GitHub Pages +name: Deploy static content to Pages + +on: + # Runs on pushes targeting the default branch + push: + branches: ["master"] + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + +# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. +# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. +concurrency: + group: "pages" + cancel-in-progress: false + +jobs: + # Single deploy job since we're just deploying + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Setup Pages + uses: actions/configure-pages@v5 + - name: Upload artifact + uses: actions/upload-pages-artifact@v3 + with: + # Upload entire repository + path: '.' + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.travis.yml b/.travis.yml index b2f519a5b..cc11ab827 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,15 +1,16 @@ dist: xenial -sudo: false language: python cache: pip python: - "3.6" - "3.7" + - "3.8" + - "3.9" # command to install dependencies install: - python -m pip install -U pip - python -m pip install -e .[dev] # command to run tests script: - - pytest lime - - flake8 lime + - pytest lime_stratified + - flake8 lime_stratified diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 45decc31e..39808d17b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,17 +1,16 @@ ## Contributing -I am delighted when people want to contribute to LIME. Here are a few things to keep in mind before sending in a pull request: +I am delighted when people want to contribute to LIME_Stratified. Here are a few things to keep in mind before sending in a pull request: * We are now using flake8 as a style guide enforcer (I plan on adding eslint for javascript soon). Make sure your code passes the default flake8 execution. * There must be a really good reason to change the external interfaces - I want to avoid breaking previous code as much as possible. * If you are adding a new feature, please let me know the use case and the rationale behind how you did it (unless it's obvious) -If you want to contribute but don't know where to start, take a look at the [issues page](https://github.com/marcotcr/lime/issues), or at the list below. +If you want to contribute but don't know where to start, take a look at the **[issues page](https://github.com/rashidrao-pk/lime_stratified/pulls)**, or at the list below. # Roadmap -Here are a few high level features I want to incorporate in LIME. If you want to work incrementally in any of these, feel free to start a branch. +Here are a few high level features I want to incorporate in LIME_Stratified. If you want to work incrementally in any of these, feel free to start a branch. -1. Creating meaningful tests that we can run before merging things. Right now I run the example notebooks and the few tests we have. -2. Creating a wrapper that computes explanations for a particular dataset, and suggests instances for the user to look at (similar to what we did in [the paper](http://arxiv.org/abs/1602.04938)) -3. Making LIME work with images in a reasonable time. The explanations we used in the paper took a few minutes, which is too slow. -4. Thinking through what is needed to use LIME in regression problems. An obvious problem is that features with different scales make it really hard to interpret. -5. Figuring out better alternatives to discretizing the data for tabular data. Discretizing is definitely more interpretable, but we may just want to treat features as continuous. -6. Figuring out better ways to sample around a data point for tabular data. One example is sampling columns from the training set assuming independence, or some form of conditional sampling. +1. Adding Stratification concept on other data. + + +## Special Thanks: +I would like to say special thanks to **`LIME`** by `Marco` for providing these files. \ No newline at end of file diff --git a/LICENSE b/LICENSE index 32d796bde..2af533e1a 100644 --- a/LICENSE +++ b/LICENSE @@ -20,4 +20,4 @@ DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 000000000..d0c3cbf10 --- /dev/null +++ b/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/README.md b/README.md index b02f20582..4d1f14032 100644 --- a/README.md +++ b/README.md @@ -1,55 +1,94 @@ -# lime -[![Build Status](https://travis-ci.org/marcotcr/lime.svg?branch=master)](https://travis-ci.org/marcotcr/lime) -[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/marcotcr/lime/master) +

Using Stratified Sampling to Improve LIME Image Explanations

+
+

+ Pypi version + Python versions + Documentation + Build status + Coverage + Code quality + License +

-This project is about explaining what machine learning classifiers (or models) are doing. -At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations). -Lime is based on the work presented in [this paper](https://arxiv.org/abs/1602.04938) ([bibtex here for citation](https://github.com/marcotcr/lime/blob/master/citation.bib)). Here is a link to the promo video: -KDD promo video +- This repository contains the changes needed to add the stratified sampling strategy to the original codebase of LIME proposed for the Research Article 'Using Stratified Sampling to Improve LIME Image Explanations'. published at Proceedings of the AAAI Conference on Artificial Intelligence. -Our plan is to add more packages that help users understand and interact meaningfully with machine learning. - -Lime is able to explain any black box classifier, with two or more classes. All we require is that the classifier implements a function that takes in raw text or a numpy array and outputs a probability for each class. Support for scikit-learn classifiers is built-in. +- The rest of Experiments for the proposed strategy are uploaded here at LIME Stratified Examples ## Installation -The lime package is on [PyPI](https://pypi.python.org/pypi/lime). Simply run: +The lime_stratified package is on **[PyPI](https://pypi.org/project/lime-stratified/)**, Simply run: ```sh -pip install lime +pip install lime_stratified +``` +alternatively, it can also be installed using: +```sh +pip install git+https://github.com/rashidrao-pk/lime_stratified.git ``` Or clone the repository and run: ```sh -pip install . +git clone https://github.com/rashidrao-pk/lime_stratified +cd lime_stratified +python setup.py install ``` -We dropped python2 support in `0.2.0`, `0.1.1.37` was the last version before that. +### How to use ? -## Screenshots +- The class `LimeImageExplainer` adds a single parameter `use_stratification` to the `explain_instance` method. When the parameter is False, the code behaves exactly like the original LIME implementation. When `use_stratification=True` the algorithm *StratifiedSampling* is used, as described in the paper. -Below are some screenshots of lime explanations. These are generated in html, and can be easily produced and embedded in ipython notebooks. We also support visualizations using matplotlib, although they don't look as nice as these ones. +```python +# Code example +from lime_stratified.lime import lime_image +lime_explainer = lime_image.LimeImageExplainer(random_state=1234) +explanation = lime_explainer.explain_instance(image_to_explain, + blackbox_model, + use_stratification=True) +``` -#### Two class case, text +## Paper PDF: +**Using Stratified Sampling to Improve LIME Image Explanations** can be found at [LINK](https://ojs.aaai.org/index.php/AAAI/article/view/29397) and also on +. + +# Cite +If you use our proposed strategy, please cite us:
+``` +@inproceedings{rashid2024using, + title={Using Stratified Sampling to Improve LIME Image Explanations}, + author={Rashid, Muhammad and Amparore, Elvio G and Ferrari, Enrico and Verda, Damiano}, + booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, + volume={38}, + number={13}, + pages={14785--14792}, + year={2024} +} +``` +
+The remaining part of this readme is the original redme file of LIME. -Negative (blue) words indicate atheism, while positive (orange) words indicate christian. The way to interpret the weights by applying them to the prediction probabilities. For example, if we remove the words Host and NNTP from the document, we expect the classifier to predict atheism with probability 0.58 - 0.14 - 0.11 = 0.31. +# LIME_Stratified -![twoclass](doc/images/twoclass.png) -#### Multiclass case +[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/rashidrao-pk/lime_stratified/HEAD) -![multiclass](doc/images/multiclass.png) -#### Tabular data -![tabular](doc/images/tabular.png) +This project is about explaining what machine learning classifiers (or models) are doing. +At the moment, we support explaining individual predictions for text classifiers or classifiers that act on tables (numpy arrays of numerical or categorical data) or images, with a package called lime (short for local interpretable model-agnostic explanations based on **stratification approach**). + +LIME_Stratified is based on the work presented in [this paper](https://doi.org/10.1609/aaai.v38i13.29397) ([bibtex here for citation](https://github.com/rashidrao-pk/lime_stratified/blob/master/citation.bib)). Here is a link to the promo video: -#### Images (explaining prediction of 'Cat' in pros and cons) +KDD promo video + +Our plan is to add more packages that help users understand and interact meaningfully with machine learning. + +Lime is able to explain any black box classifier, with two or more classes. All we require is that the classifier implements a function that takes in raw text or a numpy array and outputs a probability for each class. Support for scikit-learn classifiers is built-in. + +We dropped python2 support in `0.2.0`, `0.1.1.37` was the last version before that. - ## Tutorials and API @@ -84,17 +123,11 @@ The raw (non-html) notebooks for these tutorials are available [here](https://gi The API reference is available [here](https://lime-ml.readthedocs.io/en/latest/). -## What are explanations? +### How LIME_Image Works? + -Intuitively, an explanation is a local linear approximation of the model's behaviour. -While the model may be very complex globally, it is easier to approximate it around the vicinity of a particular instance. -While treating the model as a black box, we perturb the instance we want to explain and learn a sparse linear model around it, as an explanation. -The figure below illustrates the intuition for this procedure. The model's decision function is represented by the blue/pink background, and is clearly nonlinear. -The bright red cross is the instance being explained (let's call it X). -We sample instances around X, and weight them according to their proximity to X (weight here is indicated by size). -We then learn a linear model (dashed line) that approximates the model well in the vicinity of X, but not necessarily globally. For more information, [read our paper](https://arxiv.org/abs/1602.04938), or take a look at [this blog post](https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime). - +Documentation ## Contributing diff --git a/build/doctrees/benchmark.doctree b/build/doctrees/benchmark.doctree new file mode 100644 index 000000000..2468bdc97 Binary files /dev/null and b/build/doctrees/benchmark.doctree differ diff --git a/build/doctrees/environment.pickle b/build/doctrees/environment.pickle new file mode 100644 index 000000000..b683b3053 Binary files /dev/null and b/build/doctrees/environment.pickle differ diff --git a/build/doctrees/index.doctree b/build/doctrees/index.doctree new file mode 100644 index 000000000..2852093de Binary files /dev/null and b/build/doctrees/index.doctree differ diff --git a/build/doctrees/lime.doctree b/build/doctrees/lime.doctree new file mode 100644 index 000000000..5050d1482 Binary files /dev/null and b/build/doctrees/lime.doctree differ diff --git a/build/doctrees/lime.tests.doctree b/build/doctrees/lime.tests.doctree new file mode 100644 index 000000000..e3da81ce2 Binary files /dev/null and b/build/doctrees/lime.tests.doctree differ diff --git a/build/doctrees/lime.utils.doctree b/build/doctrees/lime.utils.doctree new file mode 100644 index 000000000..ab38b99ca Binary files /dev/null and b/build/doctrees/lime.utils.doctree differ diff --git a/build/doctrees/lime.wrappers.doctree b/build/doctrees/lime.wrappers.doctree new file mode 100644 index 000000000..8b0c9d54b Binary files /dev/null and b/build/doctrees/lime.wrappers.doctree differ diff --git a/build/doctrees/modules.doctree b/build/doctrees/modules.doctree new file mode 100644 index 000000000..f62ab045b Binary files /dev/null and b/build/doctrees/modules.doctree differ diff --git a/build/doctrees/setup.doctree b/build/doctrees/setup.doctree new file mode 100644 index 000000000..81eccc856 Binary files /dev/null and b/build/doctrees/setup.doctree differ diff --git a/build/html/.buildinfo b/build/html/.buildinfo new file mode 100644 index 000000000..f4429207b --- /dev/null +++ b/build/html/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: a721d384dac6ff9269c7bc4ec71f51f0 +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/build/html/_sources/benchmark.rst.txt b/build/html/_sources/benchmark.rst.txt new file mode 100644 index 000000000..f1f7123ea --- /dev/null +++ b/build/html/_sources/benchmark.rst.txt @@ -0,0 +1,29 @@ +benchmark package +================= + +Submodules +---------- + +benchmark.table\_perf module +---------------------------- + +.. automodule:: benchmark.table_perf + :members: + :undoc-members: + :show-inheritance: + +benchmark.text\_perf module +--------------------------- + +.. automodule:: benchmark.text_perf + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: benchmark + :members: + :undoc-members: + :show-inheritance: diff --git a/build/html/_sources/index.rst.txt b/build/html/_sources/index.rst.txt new file mode 100644 index 000000000..6144fcf91 --- /dev/null +++ b/build/html/_sources/index.rst.txt @@ -0,0 +1,20 @@ +.. lime_stratified documentation master file, created by + sphinx-quickstart on Mon Sep 30 22:54:44 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to lime_stratified's documentation! +=========================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/build/html/_sources/lime.rst.txt b/build/html/_sources/lime.rst.txt new file mode 100644 index 000000000..03d3abdc1 --- /dev/null +++ b/build/html/_sources/lime.rst.txt @@ -0,0 +1,87 @@ +lime package +============ + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + lime.tests + lime.utils + lime.wrappers + +Submodules +---------- + +lime.discretize module +---------------------- + +.. automodule:: lime.discretize + :members: + :undoc-members: + :show-inheritance: + +lime.exceptions module +---------------------- + +.. automodule:: lime.exceptions + :members: + :undoc-members: + :show-inheritance: + +lime.explanation module +----------------------- + +.. automodule:: lime.explanation + :members: + :undoc-members: + :show-inheritance: + +lime.lime\_base module +---------------------- + +.. automodule:: lime.lime_base + :members: + :undoc-members: + :show-inheritance: + +lime.lime\_image module +----------------------- + +.. automodule:: lime.lime_image + :members: + :undoc-members: + :show-inheritance: + +lime.lime\_tabular module +------------------------- + +.. automodule:: lime.lime_tabular + :members: + :undoc-members: + :show-inheritance: + +lime.lime\_text module +---------------------- + +.. automodule:: lime.lime_text + :members: + :undoc-members: + :show-inheritance: + +lime.submodular\_pick module +---------------------------- + +.. automodule:: lime.submodular_pick + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: lime + :members: + :undoc-members: + :show-inheritance: diff --git a/build/html/_sources/lime.tests.rst.txt b/build/html/_sources/lime.tests.rst.txt new file mode 100644 index 000000000..7448701fb --- /dev/null +++ b/build/html/_sources/lime.tests.rst.txt @@ -0,0 +1,53 @@ +lime.tests package +================== + +Submodules +---------- + +lime.tests.test\_discretize module +---------------------------------- + +.. automodule:: lime.tests.test_discretize + :members: + :undoc-members: + :show-inheritance: + +lime.tests.test\_generic\_utils module +-------------------------------------- + +.. automodule:: lime.tests.test_generic_utils + :members: + :undoc-members: + :show-inheritance: + +lime.tests.test\_lime\_tabular module +------------------------------------- + +.. automodule:: lime.tests.test_lime_tabular + :members: + :undoc-members: + :show-inheritance: + +lime.tests.test\_lime\_text module +---------------------------------- + +.. automodule:: lime.tests.test_lime_text + :members: + :undoc-members: + :show-inheritance: + +lime.tests.test\_scikit\_image module +------------------------------------- + +.. automodule:: lime.tests.test_scikit_image + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: lime.tests + :members: + :undoc-members: + :show-inheritance: diff --git a/build/html/_sources/lime.utils.rst.txt b/build/html/_sources/lime.utils.rst.txt new file mode 100644 index 000000000..57f1f43cc --- /dev/null +++ b/build/html/_sources/lime.utils.rst.txt @@ -0,0 +1,21 @@ +lime.utils package +================== + +Submodules +---------- + +lime.utils.generic\_utils module +-------------------------------- + +.. automodule:: lime.utils.generic_utils + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: lime.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/build/html/_sources/lime.wrappers.rst.txt b/build/html/_sources/lime.wrappers.rst.txt new file mode 100644 index 000000000..fbf936a9a --- /dev/null +++ b/build/html/_sources/lime.wrappers.rst.txt @@ -0,0 +1,21 @@ +lime.wrappers package +===================== + +Submodules +---------- + +lime.wrappers.scikit\_image module +---------------------------------- + +.. automodule:: lime.wrappers.scikit_image + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: lime.wrappers + :members: + :undoc-members: + :show-inheritance: diff --git a/build/html/_sources/modules.rst.txt b/build/html/_sources/modules.rst.txt new file mode 100644 index 000000000..deacbf7ff --- /dev/null +++ b/build/html/_sources/modules.rst.txt @@ -0,0 +1,9 @@ +lime_stratified +=============== + +.. toctree:: + :maxdepth: 4 + + benchmark + lime + setup diff --git a/build/html/_sources/setup.rst.txt b/build/html/_sources/setup.rst.txt new file mode 100644 index 000000000..552eb49d6 --- /dev/null +++ b/build/html/_sources/setup.rst.txt @@ -0,0 +1,7 @@ +setup module +============ + +.. automodule:: setup + :members: + :undoc-members: + :show-inheritance: diff --git a/build/html/_static/alabaster.css b/build/html/_static/alabaster.css new file mode 100644 index 000000000..e3174bf93 --- /dev/null +++ b/build/html/_static/alabaster.css @@ -0,0 +1,708 @@ +@import url("basic.css"); + +/* -- page layout ----------------------------------------------------------- */ + +body { + font-family: Georgia, serif; + font-size: 17px; + background-color: #fff; + color: #000; + margin: 0; + padding: 0; +} + + +div.document { + width: 940px; + margin: 30px auto 0 auto; +} + +div.documentwrapper { + float: left; + width: 100%; +} + +div.bodywrapper { + margin: 0 0 0 220px; +} + +div.sphinxsidebar { + width: 220px; + font-size: 14px; + line-height: 1.5; +} + +hr { + border: 1px solid #B1B4B6; +} + +div.body { + background-color: #fff; + color: #3E4349; + padding: 0 30px 0 30px; +} + +div.body > .section { + text-align: left; +} + +div.footer { + width: 940px; + margin: 20px auto 30px auto; + font-size: 14px; + color: #888; + text-align: right; +} + +div.footer a { + color: #888; +} + +p.caption { + font-family: inherit; + font-size: inherit; +} + + +div.relations { + display: none; +} + + +div.sphinxsidebar { + max-height: 100%; + overflow-y: auto; +} + +div.sphinxsidebar a { + color: #444; + text-decoration: none; + border-bottom: 1px dotted #999; +} + +div.sphinxsidebar a:hover { + border-bottom: 1px solid #999; +} + +div.sphinxsidebarwrapper { + padding: 18px 10px; +} + +div.sphinxsidebarwrapper p.logo { + padding: 0; + margin: -10px 0 0 0px; + text-align: center; +} + +div.sphinxsidebarwrapper h1.logo { + margin-top: -10px; + text-align: center; + margin-bottom: 5px; + text-align: left; +} + +div.sphinxsidebarwrapper h1.logo-name { + margin-top: 0px; +} + +div.sphinxsidebarwrapper p.blurb { + margin-top: 0; + font-style: normal; +} + +div.sphinxsidebar h3, +div.sphinxsidebar h4 { + font-family: Georgia, serif; + color: #444; + font-size: 24px; + font-weight: normal; + margin: 0 0 5px 0; + padding: 0; +} + +div.sphinxsidebar h4 { + font-size: 20px; +} + +div.sphinxsidebar h3 a { + color: #444; +} + +div.sphinxsidebar p.logo a, +div.sphinxsidebar h3 a, +div.sphinxsidebar p.logo a:hover, +div.sphinxsidebar h3 a:hover { + border: none; +} + +div.sphinxsidebar p { + color: #555; + margin: 10px 0; +} + +div.sphinxsidebar ul { + margin: 10px 0; + padding: 0; + color: #000; +} + +div.sphinxsidebar ul li.toctree-l1 > a { + font-size: 120%; +} + +div.sphinxsidebar ul li.toctree-l2 > a { + font-size: 110%; +} + +div.sphinxsidebar input { + border: 1px solid #CCC; + font-family: Georgia, serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox input[type="text"] { + width: 160px; +} + +div.sphinxsidebar .search > div { + display: table-cell; +} + +div.sphinxsidebar hr { + border: none; + height: 1px; + color: #AAA; + background: #AAA; + + text-align: left; + margin-left: 0; + width: 50%; +} + +div.sphinxsidebar .badge { + border-bottom: none; +} + +div.sphinxsidebar .badge:hover { + border-bottom: none; +} + +/* To address an issue with donation coming after search */ +div.sphinxsidebar h3.donation { + margin-top: 10px; +} + +/* -- body styles ----------------------------------------------------------- */ + +a { + color: #004B6B; + text-decoration: underline; +} + +a:hover { + color: #6D4100; + text-decoration: underline; +} + +div.body h1, +div.body h2, +div.body h3, +div.body h4, +div.body h5, +div.body h6 { + font-family: Georgia, serif; + font-weight: normal; + margin: 30px 0px 10px 0px; + padding: 0; +} + +div.body h1 { margin-top: 0; padding-top: 0; font-size: 240%; } +div.body h2 { font-size: 180%; } +div.body h3 { font-size: 150%; } +div.body h4 { font-size: 130%; } +div.body h5 { font-size: 100%; } +div.body h6 { font-size: 100%; } + +a.headerlink { + color: #DDD; + padding: 0 4px; + text-decoration: none; +} + +a.headerlink:hover { + color: #444; + background: #EAEAEA; +} + +div.body p, div.body dd, div.body li { + line-height: 1.4em; +} + +div.admonition { + margin: 20px 0px; + padding: 10px 30px; + background-color: #EEE; + border: 1px solid #CCC; +} + +div.admonition tt.xref, div.admonition code.xref, div.admonition a tt { + background-color: #FBFBFB; + border-bottom: 1px solid #fafafa; +} + +div.admonition p.admonition-title { + font-family: Georgia, serif; + font-weight: normal; + font-size: 24px; + margin: 0 0 10px 0; + padding: 0; + line-height: 1; +} + +div.admonition p.last { + margin-bottom: 0; +} + +div.highlight { + background-color: #fff; +} + +dt:target, .highlight { + background: #FAF3E8; +} + +div.warning { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.danger { + background-color: #FCC; + border: 1px solid #FAA; + -moz-box-shadow: 2px 2px 4px #D52C2C; + -webkit-box-shadow: 2px 2px 4px #D52C2C; + box-shadow: 2px 2px 4px #D52C2C; +} + +div.error { + background-color: #FCC; + border: 1px solid #FAA; + -moz-box-shadow: 2px 2px 4px #D52C2C; + -webkit-box-shadow: 2px 2px 4px #D52C2C; + box-shadow: 2px 2px 4px #D52C2C; +} + +div.caution { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.attention { + background-color: #FCC; + border: 1px solid #FAA; +} + +div.important { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.note { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.tip { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.hint { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.seealso { + background-color: #EEE; + border: 1px solid #CCC; +} + +div.topic { + background-color: #EEE; +} + +p.admonition-title { + display: inline; +} + +p.admonition-title:after { + content: ":"; +} + +pre, tt, code { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; + font-size: 0.9em; +} + +.hll { + background-color: #FFC; + margin: 0 -12px; + padding: 0 12px; + display: block; +} + +img.screenshot { +} + +tt.descname, tt.descclassname, code.descname, code.descclassname { + font-size: 0.95em; +} + +tt.descname, code.descname { + padding-right: 0.08em; +} + +img.screenshot { + -moz-box-shadow: 2px 2px 4px #EEE; + -webkit-box-shadow: 2px 2px 4px #EEE; + box-shadow: 2px 2px 4px #EEE; +} + +table.docutils { + border: 1px solid #888; + -moz-box-shadow: 2px 2px 4px #EEE; + -webkit-box-shadow: 2px 2px 4px #EEE; + box-shadow: 2px 2px 4px #EEE; +} + +table.docutils td, table.docutils th { + border: 1px solid #888; + padding: 0.25em 0.7em; +} + +table.field-list, table.footnote { + border: none; + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + +table.footnote { + margin: 15px 0; + width: 100%; + border: 1px solid #EEE; + background: #FDFDFD; + font-size: 0.9em; +} + +table.footnote + table.footnote { + margin-top: -15px; + border-top: none; +} + +table.field-list th { + padding: 0 0.8em 0 0; +} + +table.field-list td { + padding: 0; +} + +table.field-list p { + margin-bottom: 0.8em; +} + +/* Cloned from + * https://github.com/sphinx-doc/sphinx/commit/ef60dbfce09286b20b7385333d63a60321784e68 + */ +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +table.footnote td.label { + width: .1px; + padding: 0.3em 0 0.3em 0.5em; +} + +table.footnote td { + padding: 0.3em 0.5em; +} + +dl { + margin-left: 0; + margin-right: 0; + margin-top: 0; + padding: 0; +} + +dl dd { + margin-left: 30px; +} + +blockquote { + margin: 0 0 0 30px; + padding: 0; +} + +ul, ol { + /* Matches the 30px from the narrow-screen "li > ul" selector below */ + margin: 10px 0 10px 30px; + padding: 0; +} + +pre { + background: #EEE; + padding: 7px 30px; + margin: 15px 0px; + line-height: 1.3em; +} + +div.viewcode-block:target { + background: #ffd; +} + +dl pre, blockquote pre, li pre { + margin-left: 0; + padding-left: 30px; +} + +tt, code { + background-color: #ecf0f3; + color: #222; + /* padding: 1px 2px; */ +} + +tt.xref, code.xref, a tt { + background-color: #FBFBFB; + border-bottom: 1px solid #fff; +} + +a.reference { + text-decoration: none; + border-bottom: 1px dotted #004B6B; +} + +/* Don't put an underline on images */ +a.image-reference, a.image-reference:hover { + border-bottom: none; +} + +a.reference:hover { + border-bottom: 1px solid #6D4100; +} + +a.footnote-reference { + text-decoration: none; + font-size: 0.7em; + vertical-align: top; + border-bottom: 1px dotted #004B6B; +} + +a.footnote-reference:hover { + border-bottom: 1px solid #6D4100; +} + +a:hover tt, a:hover code { + background: #EEE; +} + + +@media screen and (max-width: 870px) { + + div.sphinxsidebar { + display: none; + } + + div.document { + width: 100%; + + } + + div.documentwrapper { + margin-left: 0; + margin-top: 0; + margin-right: 0; + margin-bottom: 0; + } + + div.bodywrapper { + margin-top: 0; + margin-right: 0; + margin-bottom: 0; + margin-left: 0; + } + + ul { + margin-left: 0; + } + + li > ul { + /* Matches the 30px from the "ul, ol" selector above */ + margin-left: 30px; + } + + .document { + width: auto; + } + + .footer { + width: auto; + } + + .bodywrapper { + margin: 0; + } + + .footer { + width: auto; + } + + .github { + display: none; + } + + + +} + + + +@media screen and (max-width: 875px) { + + body { + margin: 0; + padding: 20px 30px; + } + + div.documentwrapper { + float: none; + background: #fff; + } + + div.sphinxsidebar { + display: block; + float: none; + width: 102.5%; + margin: 50px -30px -20px -30px; + padding: 10px 20px; + background: #333; + color: #FFF; + } + + div.sphinxsidebar h3, div.sphinxsidebar h4, div.sphinxsidebar p, + div.sphinxsidebar h3 a { + color: #fff; + } + + div.sphinxsidebar a { + color: #AAA; + } + + div.sphinxsidebar p.logo { + display: none; + } + + div.document { + width: 100%; + margin: 0; + } + + div.footer { + display: none; + } + + div.bodywrapper { + margin: 0; + } + + div.body { + min-height: 0; + padding: 0; + } + + .rtd_doc_footer { + display: none; + } + + .document { + width: auto; + } + + .footer { + width: auto; + } + + .footer { + width: auto; + } + + .github { + display: none; + } +} + + +/* misc. */ + +.revsys-inline { + display: none!important; +} + +/* Hide ugly table cell borders in ..bibliography:: directive output */ +table.docutils.citation, table.docutils.citation td, table.docutils.citation th { + border: none; + /* Below needed in some edge cases; if not applied, bottom shadows appear */ + -moz-box-shadow: none; + -webkit-box-shadow: none; + box-shadow: none; +} + + +/* relbar */ + +.related { + line-height: 30px; + width: 100%; + font-size: 0.9rem; +} + +.related.top { + border-bottom: 1px solid #EEE; + margin-bottom: 20px; +} + +.related.bottom { + border-top: 1px solid #EEE; +} + +.related ul { + padding: 0; + margin: 0; + list-style: none; +} + +.related li { + display: inline; +} + +nav#rellinks { + float: right; +} + +nav#rellinks li+li:before { + content: "|"; +} + +nav#breadcrumbs li+li:before { + content: "\00BB"; +} + +/* Hide certain items when printing */ +@media print { + div.related { + display: none; + } +} \ No newline at end of file diff --git a/build/html/_static/basic.css b/build/html/_static/basic.css new file mode 100644 index 000000000..e5179b7a9 --- /dev/null +++ b/build/html/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: inherit; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, figure.align-center, .figure.align-center, object.align-center { + display: block; + margin-left: auto; + margin-right: auto; +} + +img.align-default, figure.align-default, .figure.align-default { + display: block; + margin-left: auto; + margin-right: auto; +} + +.align-left { + text-align: left; +} + +.align-center { + text-align: center; +} + +.align-default { + text-align: center; +} + +.align-right { + text-align: right; +} + +/* -- sidebars -------------------------------------------------------------- */ + +div.sidebar, +aside.sidebar { + margin: 0 0 0.5em 1em; + border: 1px solid #ddb; + padding: 7px; + background-color: #ffe; + width: 40%; + float: right; + clear: right; + overflow-x: auto; +} + +p.sidebar-title { + font-weight: bold; +} + +nav.contents, +aside.topic, +div.admonition, div.topic, blockquote { + clear: left; +} + +/* -- topics ---------------------------------------------------------------- */ + +nav.contents, +aside.topic, +div.topic { + border: 1px solid #ccc; + padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables ---------------------------------------------------------------- */ + +table.docutils { + margin-top: 10px; + margin-bottom: 10px; + border: 0; + border-collapse: collapse; +} + +table.align-center { + margin-left: auto; + margin-right: auto; +} + +table.align-default { + margin-left: auto; + margin-right: auto; +} + +table caption span.caption-number { + font-style: italic; +} + +table caption span.caption-text { +} + +table.docutils td, table.docutils th { + padding: 1px 8px 1px 5px; + border-top: 0; + border-left: 0; + border-right: 0; + border-bottom: 1px solid #aaa; +} + +th { + text-align: left; + padding-right: 5px; +} + +table.citation { + border-left: solid 1px gray; + margin-left: 1px; +} + +table.citation td { + border-bottom: none; +} + +th > :first-child, +td > :first-child { + margin-top: 0px; +} + +th > :last-child, +td > :last-child { + margin-bottom: 0px; +} + +/* -- figures --------------------------------------------------------------- */ + +div.figure, figure { + margin: 0.5em; + padding: 0.5em; +} + +div.figure p.caption, figcaption { + padding: 0.3em; +} + +div.figure p.caption span.caption-number, +figcaption span.caption-number { + font-style: italic; +} + +div.figure p.caption span.caption-text, +figcaption span.caption-text { +} + +/* -- field list styles ----------------------------------------------------- */ + +table.field-list td, table.field-list th { + border: 0 !important; +} + +.field-list ul { + margin: 0; + padding-left: 1em; +} + +.field-list p { + margin: 0; +} + +.field-name { + -moz-hyphens: manual; + -ms-hyphens: manual; + -webkit-hyphens: manual; + hyphens: manual; +} + +/* -- hlist styles ---------------------------------------------------------- */ + +table.hlist { + margin: 1em 0; +} + +table.hlist td { + vertical-align: top; +} + +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + + +/* -- other body styles ----------------------------------------------------- */ + +ol.arabic { + list-style: decimal; +} + +ol.loweralpha { + list-style: lower-alpha; +} + +ol.upperalpha { + list-style: upper-alpha; +} + +ol.lowerroman { + list-style: lower-roman; +} + +ol.upperroman { + list-style: upper-roman; +} + +:not(li) > ol > li:first-child > :first-child, +:not(li) > ul > li:first-child > :first-child { + margin-top: 0px; +} + +:not(li) > ol > li:last-child > :last-child, +:not(li) > ul > li:last-child > :last-child { + margin-bottom: 0px; +} + +ol.simple ol p, +ol.simple ul p, +ul.simple ol p, +ul.simple ul p { + margin-top: 0; +} + +ol.simple > li:not(:first-child) > p, +ul.simple > li:not(:first-child) > p { + margin-top: 0; +} + +ol.simple p, +ul.simple p { + margin-bottom: 0; +} + +aside.footnote > span, +div.citation > span { + float: left; +} +aside.footnote > span:last-of-type, +div.citation > span:last-of-type { + padding-right: 0.5em; +} +aside.footnote > p { + margin-left: 2em; +} +div.citation > p { + margin-left: 4em; +} +aside.footnote > p:last-of-type, +div.citation > p:last-of-type { + margin-bottom: 0em; +} +aside.footnote > p:last-of-type:after, +div.citation > p:last-of-type:after { + content: ""; + clear: both; +} + +dl.field-list { + display: grid; + grid-template-columns: fit-content(30%) auto; +} + +dl.field-list > dt { + font-weight: bold; + word-break: break-word; + padding-left: 0.5em; + padding-right: 5px; +} + +dl.field-list > dd { + padding-left: 0.5em; + margin-top: 0em; + margin-left: 0em; + margin-bottom: 0em; +} + +dl { + margin-bottom: 15px; +} + +dd > :first-child { + margin-top: 0px; +} + +dd ul, dd table { + margin-bottom: 10px; +} + +dd { + margin-top: 3px; + margin-bottom: 10px; + margin-left: 30px; +} + +.sig dd { + margin-top: 0px; + margin-bottom: 0px; +} + +.sig dl { + margin-top: 0px; + margin-bottom: 0px; +} + +dl > dd:last-child, +dl > dd:last-child > :last-child { + margin-bottom: 0; +} + +dt:target, span.highlighted { + background-color: #fbe54e; +} + +rect.highlighted { + fill: #fbe54e; +} + +dl.glossary dt { + font-weight: bold; + font-size: 1.1em; +} + +.versionmodified { + font-style: italic; +} + +.system-message { + background-color: #fda; + padding: 5px; + border: 3px solid red; +} + +.footnote:target { + background-color: #ffa; +} + +.line-block { + display: block; + margin-top: 1em; + margin-bottom: 1em; +} + +.line-block .line-block { + margin-top: 0; + margin-bottom: 0; + margin-left: 1.5em; +} + +.guilabel, .menuselection { + font-family: sans-serif; +} + +.accelerator { + text-decoration: underline; +} + +.classifier { + font-style: oblique; +} + +.classifier:before { + font-style: normal; + margin: 0 0.5em; + content: ":"; + display: inline-block; +} + +abbr, acronym { + border-bottom: dotted 1px; + cursor: help; +} + +.translated { + background-color: rgba(207, 255, 207, 0.2) +} + +.untranslated { + background-color: rgba(255, 207, 207, 0.2) +} + +/* -- code displays --------------------------------------------------------- */ + +pre { + overflow: auto; + overflow-y: hidden; /* fixes display issues on Chrome browsers */ +} + +pre, div[class*="highlight-"] { + clear: both; +} + +span.pre { + -moz-hyphens: none; + -ms-hyphens: none; + -webkit-hyphens: none; + hyphens: none; + white-space: nowrap; +} + +div[class*="highlight-"] { + margin: 1em 0; +} + +td.linenos pre { + border: 0; + background-color: transparent; + color: #aaa; +} + +table.highlighttable { + display: block; +} + +table.highlighttable tbody { + display: block; +} + +table.highlighttable tr { + display: flex; +} + +table.highlighttable td { + margin: 0; + padding: 0; +} + +table.highlighttable td.linenos { + padding-right: 0.5em; +} + +table.highlighttable td.code { + flex: 1; + overflow: hidden; +} + +.highlight .hll { + display: block; +} + +div.highlight pre, +table.highlighttable pre { + margin: 0; +} + +div.code-block-caption + div { + margin-top: 0; +} + +div.code-block-caption { + margin-top: 1em; + padding: 2px 5px; + font-size: small; +} + +div.code-block-caption code { + background-color: transparent; +} + +table.highlighttable td.linenos, +span.linenos, +div.highlight span.gp { /* gp: Generic.Prompt */ + user-select: none; + -webkit-user-select: text; /* Safari fallback only */ + -webkit-user-select: none; /* Chrome/Safari */ + -moz-user-select: none; /* Firefox */ + -ms-user-select: none; /* IE10+ */ +} + +div.code-block-caption span.caption-number { + padding: 0.1em 0.3em; + font-style: italic; +} + +div.code-block-caption span.caption-text { +} + +div.literal-block-wrapper { + margin: 1em 0; +} + +code.xref, a code { + background-color: transparent; + font-weight: bold; +} + +h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { + background-color: transparent; +} + +.viewcode-link { + float: right; +} + +.viewcode-back { + float: right; + font-family: sans-serif; +} + +div.viewcode-block:target { + margin: -1px -10px; + padding: 0 10px; +} + +/* -- math display ---------------------------------------------------------- */ + +img.math { + vertical-align: middle; +} + +div.body div.math p { + text-align: center; +} + +span.eqno { + float: right; +} + +span.eqno a.headerlink { + position: absolute; + z-index: 1; +} + +div.math:hover a.headerlink { + visibility: visible; +} + +/* -- printout stylesheet --------------------------------------------------- */ + +@media print { + div.document, + div.documentwrapper, + div.bodywrapper { + margin: 0 !important; + width: 100%; + } + + div.sphinxsidebar, + div.related, + div.footer, + #top-link { + display: none; + } +} \ No newline at end of file diff --git a/build/html/_static/custom.css b/build/html/_static/custom.css new file mode 100644 index 000000000..2a924f1d6 --- /dev/null +++ b/build/html/_static/custom.css @@ -0,0 +1 @@ +/* This file intentionally left blank. */ diff --git a/build/html/_static/doctools.js b/build/html/_static/doctools.js new file mode 100644 index 000000000..4d67807d1 --- /dev/null +++ b/build/html/_static/doctools.js @@ -0,0 +1,156 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Base JavaScript utilities for all Sphinx HTML documentation. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", +]); + +const _ready = (callback) => { + if (document.readyState !== "loading") { + callback(); + } else { + document.addEventListener("DOMContentLoaded", callback); + } +}; + +/** + * Small JavaScript module for the documentation. + */ +const Documentation = { + init: () => { + Documentation.initDomainIndexTable(); + Documentation.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS: {}, + PLURAL_EXPR: (n) => (n === 1 ? 0 : 1), + LOCALE: "unknown", + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext: (string) => { + const translated = Documentation.TRANSLATIONS[string]; + switch (typeof translated) { + case "undefined": + return string; // no translation + case "string": + return translated; // translation exists + default: + return translated[0]; // (singular, plural) translation tuple exists + } + }, + + ngettext: (singular, plural, n) => { + const translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated !== "undefined") + return translated[Documentation.PLURAL_EXPR(n)]; + return n === 1 ? singular : plural; + }, + + addTranslations: (catalog) => { + Object.assign(Documentation.TRANSLATIONS, catalog.messages); + Documentation.PLURAL_EXPR = new Function( + "n", + `return (${catalog.plural_expr})` + ); + Documentation.LOCALE = catalog.locale; + }, + + /** + * helper function to focus on search bar + */ + focusSearchBar: () => { + document.querySelectorAll("input[name=q]")[0]?.focus(); + }, + + /** + * Initialise the domain index toggle buttons + */ + initDomainIndexTable: () => { + const toggler = (el) => { + const idNumber = el.id.substr(7); + const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`); + if (el.src.substr(-9) === "minus.png") { + el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`; + toggledRows.forEach((el) => (el.style.display = "none")); + } else { + el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`; + toggledRows.forEach((el) => (el.style.display = "")); + } + }; + + const togglerElements = document.querySelectorAll("img.toggler"); + togglerElements.forEach((el) => + el.addEventListener("click", (event) => toggler(event.currentTarget)) + ); + togglerElements.forEach((el) => (el.style.display = "")); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler); + }, + + initOnKeyListeners: () => { + // only install a listener if it is really needed + if ( + !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && + !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS + ) + return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.altKey || event.ctrlKey || event.metaKey) return; + + if (!event.shiftKey) { + switch (event.key) { + case "ArrowLeft": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const prevLink = document.querySelector('link[rel="prev"]'); + if (prevLink && prevLink.href) { + window.location.href = prevLink.href; + event.preventDefault(); + } + break; + case "ArrowRight": + if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break; + + const nextLink = document.querySelector('link[rel="next"]'); + if (nextLink && nextLink.href) { + window.location.href = nextLink.href; + event.preventDefault(); + } + break; + } + } + + // some keyboard layouts may need Shift to get / + switch (event.key) { + case "/": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.focusSearchBar(); + event.preventDefault(); + } + }); + }, +}; + +// quick alias for translations +const _ = Documentation.gettext; + +_ready(Documentation.init); diff --git a/build/html/_static/documentation_options.js b/build/html/_static/documentation_options.js new file mode 100644 index 000000000..529239f07 --- /dev/null +++ b/build/html/_static/documentation_options.js @@ -0,0 +1,13 @@ +const DOCUMENTATION_OPTIONS = { + VERSION: '1.0', + LANGUAGE: 'en', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false, + SHOW_SEARCH_SUMMARY: true, + ENABLE_SEARCH_SHORTCUTS: true, +}; \ No newline at end of file diff --git a/build/html/_static/file.png b/build/html/_static/file.png new file mode 100644 index 000000000..a858a410e Binary files /dev/null and b/build/html/_static/file.png differ diff --git a/build/html/_static/language_data.js b/build/html/_static/language_data.js new file mode 100644 index 000000000..367b8ed81 --- /dev/null +++ b/build/html/_static/language_data.js @@ -0,0 +1,199 @@ +/* + * language_data.js + * ~~~~~~~~~~~~~~~~ + * + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; + + var step3list = { + icate: 'ic', + ative: '', + alize: 'al', + iciti: 'ic', + ical: 'ic', + ful: '', + ness: '' + }; + + var c = "[^aeiou]"; // consonant + var v = "[aeiouy]"; // vowel + var C = c + "[^aeiouy]*"; // consonant sequence + var V = v + "[aeiou]*"; // vowel sequence + + var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 + var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/build/html/_static/minus.png b/build/html/_static/minus.png new file mode 100644 index 000000000..d96755fda Binary files /dev/null and b/build/html/_static/minus.png differ diff --git a/build/html/_static/plus.png b/build/html/_static/plus.png new file mode 100644 index 000000000..7107cec93 Binary files /dev/null and b/build/html/_static/plus.png differ diff --git a/build/html/_static/pygments.css b/build/html/_static/pygments.css new file mode 100644 index 000000000..04a41742e --- /dev/null +++ b/build/html/_static/pygments.css @@ -0,0 +1,84 @@ +pre { line-height: 125%; } +td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } +span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; } +td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } +span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; } +.highlight .hll { background-color: #ffffcc } +.highlight { background: #f8f8f8; } +.highlight .c { color: #8f5902; font-style: italic } /* Comment */ +.highlight .err { color: #a40000; border: 1px solid #ef2929 } /* Error */ +.highlight .g { color: #000000 } /* Generic */ +.highlight .k { color: #004461; font-weight: bold } /* Keyword */ +.highlight .l { color: #000000 } /* Literal */ +.highlight .n { color: #000000 } /* Name */ +.highlight .o { color: #582800 } /* Operator */ 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*/ +.highlight .ss { color: #4e9a06 } /* Literal.String.Symbol */ +.highlight .bp { color: #3465a4 } /* Name.Builtin.Pseudo */ +.highlight .fm { color: #000000 } /* Name.Function.Magic */ +.highlight .vc { color: #000000 } /* Name.Variable.Class */ +.highlight .vg { color: #000000 } /* Name.Variable.Global */ +.highlight .vi { color: #000000 } /* Name.Variable.Instance */ +.highlight .vm { color: #000000 } /* Name.Variable.Magic */ +.highlight .il { color: #990000 } /* Literal.Number.Integer.Long */ \ No newline at end of file diff --git a/build/html/_static/searchtools.js b/build/html/_static/searchtools.js new file mode 100644 index 000000000..92da3f8b2 --- /dev/null +++ b/build/html/_static/searchtools.js @@ -0,0 +1,619 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/build/html/_static/sphinx_highlight.js b/build/html/_static/sphinx_highlight.js new file mode 100644 index 000000000..8a96c69a1 --- /dev/null +++ b/build/html/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/build/html/benchmark.html b/build/html/benchmark.html new file mode 100644 index 000000000..07732c7b8 --- /dev/null +++ b/build/html/benchmark.html @@ -0,0 +1,124 @@ + + + + + + + + benchmark package — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

benchmark packageÂļ

+
+

SubmodulesÂļ

+
+
+

benchmark.table_perf moduleÂļ

+

A helper script for evaluating performance of changes to the tabular explainer, in this case different +implementations and methods for distance calculation.

+
+
+benchmark.table_perf.interpret_data(X, y, func)Âļ
+
+ +
+
+

benchmark.text_perf moduleÂļ

+
+
+benchmark.text_perf.interpret_data(X, y, func, class_names)Âļ
+
+ +
+
+

Module contentsÂļ

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/build/html/genindex.html b/build/html/genindex.html new file mode 100644 index 000000000..ad46b7e7a --- /dev/null +++ b/build/html/genindex.html @@ -0,0 +1,701 @@ + + + + + + + Index — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ + +

Index

+ +
+ A + | B + | C + | D + | E + | F + | G + | H + | I + | L + | M + | N + | Q + | R + | S + | T + | U + | V + | W + +
+

A

+ + + +
+ +

B

+ + + +
+ +

C

+ + + +
+ +

D

+ + + +
+ +

E

+ + + +
+ +

F

+ + + +
+ +

G

+ + + +
+ +

H

+ + +
+ +

I

+ + + +
+ +

L

+ + + +
    +
  • + lime + +
    • +
  • + lime.discretize + +
    • +
  • + lime.exceptions + +
    • +
  • + lime.explanation + +
    • +
  • + lime.lime_base + +
    • +
  • + lime.lime_image + +
    • +
  • + lime.lime_tabular + +
    • +
  • + lime.lime_text + +
    • +
  • + lime.submodular_pick + +
    • +
  • + lime.tests + +
    • +
  • + lime.tests.test_discretize + +
    • +
+ +

M

+ + +
+ +

N

+ + +
+ +

Q

+ + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

U

+ + +
+ +

V

+ + +
+ +

W

+ + +
+ + + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + +
+ + + + + + \ No newline at end of file diff --git a/build/html/index.html b/build/html/index.html new file mode 100644 index 000000000..d3631b628 --- /dev/null +++ b/build/html/index.html @@ -0,0 +1,110 @@ + + + + + + + + Welcome to lime_stratified’s documentation! — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

Welcome to lime_stratified’s documentation!Âļ

+
+
+
+
+

Indices and tablesÂļ

+ +
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/build/html/lime.html b/build/html/lime.html new file mode 100644 index 000000000..6f5c29af2 --- /dev/null +++ b/build/html/lime.html @@ -0,0 +1,1061 @@ + + + + + + + + lime package — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

lime packageÂļ

+
+

SubpackagesÂļ

+
+ +
+
+
+

SubmodulesÂļ

+
+
+

lime.discretize moduleÂļ

+

Discretizers classes, to be used in lime_tabular

+
+
+class lime.discretize.BaseDiscretizer(data, categorical_features, feature_names, labels=None, random_state=None, data_stats=None)Âļ
+

Bases: object

+

Abstract class - Build a class that inherits from this class to implement +a custom discretizer. +Method bins() is to be redefined in the child class, as it is the actual +custom part of the discretizer.

+
+
+abstract bins(data, labels)Âļ
+

To be overridden +Returns for each feature to discretize the boundaries +that form each bin of the discretizer

+
+ +
+
+discretize(data)Âļ
+

Discretizes the data. +:param data: numpy 2d or 1d array

+
+
Returns:
+

numpy array of same dimension, discretized.

+
+
+
+ +
+
+get_undiscretize_values(feature, values)Âļ
+
+ +
+
+undiscretize(data)Âļ
+
+ +
+ +
+
+class lime.discretize.DecileDiscretizer(data, categorical_features, feature_names, labels=None, random_state=None)Âļ
+

Bases: BaseDiscretizer

+
+
+bins(data, labels)Âļ
+

To be overridden +Returns for each feature to discretize the boundaries +that form each bin of the discretizer

+
+ +
+ +
+
+class lime.discretize.EntropyDiscretizer(data, categorical_features, feature_names, labels=None, random_state=None)Âļ
+

Bases: BaseDiscretizer

+
+
+bins(data, labels)Âļ
+

To be overridden +Returns for each feature to discretize the boundaries +that form each bin of the discretizer

+
+ +
+ +
+
+class lime.discretize.QuartileDiscretizer(data, categorical_features, feature_names, labels=None, random_state=None)Âļ
+

Bases: BaseDiscretizer

+
+
+bins(data, labels)Âļ
+

To be overridden +Returns for each feature to discretize the boundaries +that form each bin of the discretizer

+
+ +
+ +
+
+class lime.discretize.StatsDiscretizer(data, categorical_features, feature_names, labels=None, random_state=None, data_stats=None)Âļ
+

Bases: BaseDiscretizer

+

Class to be used to supply the data stats info when discretize_continuous is true

+
+
+bins(data, labels)Âļ
+

To be overridden +Returns for each feature to discretize the boundaries +that form each bin of the discretizer

+
+ +
+ +
+
+

lime.exceptions moduleÂļ

+
+
+exception lime.exceptions.LimeErrorÂļ
+

Bases: Exception

+

Raise for errors

+
+ +
+
+

lime.explanation moduleÂļ

+

Explanation class, with visualization functions.

+
+
+class lime.explanation.DomainMapperÂļ
+

Bases: object

+

Class for mapping features to the specific domain.

+

The idea is that there would be a subclass for each domain (text, tables, +images, etc), so that we can have a general Explanation class, and separate +out the specifics of visualizing features in here.

+
+
+map_exp_ids(exp, **kwargs)Âļ
+

Maps the feature ids to concrete names.

+

Default behaviour is the identity function. Subclasses can implement +this as they see fit.

+
+
Parameters:
+
    +

  • exp – list of tuples [(id, weight), (id,weight)]

    • +

  • kwargs – optional keyword arguments

    • +
+
+
Returns:
+

list of tuples [(name, weight), (name, weight)â€Ļ]

+
+
Return type:
+

exp

+
+
+
+ +
+
+visualize_instance_html(exp, label, div_name, exp_object_name, **kwargs)Âļ
+

Produces html for visualizing the instance.

+

Default behaviour does nothing. Subclasses can implement this as they +see fit.

+
+
Parameters:
+
    +

  • exp – list of tuples [(id, weight), (id,weight)]

    • +

  • label – label id (integer)

    • +

  • div_name – name of div object to be used for rendering(in js)

    • +

  • exp_object_name – name of js explanation object

    • +

  • kwargs – optional keyword arguments

    • +
+
+
Returns:
+

js code for visualizing the instance

+
+
+
+ +
+ +
+
+class lime.explanation.Explanation(domain_mapper, mode='classification', class_names=None, random_state=None)Âļ
+

Bases: object

+

Object returned by explainers.

+
+
+as_html(labels=None, predict_proba=True, show_predicted_value=True, **kwargs)Âļ
+

Returns the explanation as an html page.

+
+
Parameters:
+
    +

  • labels – desired labels to show explanations for (as barcharts). +If you ask for a label for which an explanation wasn’t +computed, will throw an exception. If None, will show +explanations for all available labels. (only used for classification)

    • +

  • predict_proba – if true, add barchart with prediction probabilities +for the top classes. (only used for classification)

    • +

  • show_predicted_value – if true, add barchart with expected value +(only used for regression)

    • +

  • kwargs – keyword arguments, passed to domain_mapper

    • +
+
+
Returns:
+

code for an html page, including javascript includes.

+
+
+
+ +
+
+as_list(label=1, **kwargs)Âļ
+

Returns the explanation as a list.

+
+
Parameters:
+
    +

  • label – desired label. If you ask for a label for which an +explanation wasn’t computed, will throw an exception. +Will be ignored for regression explanations.

    • +

  • kwargs – keyword arguments, passed to domain_mapper

    • +
+
+
Returns:
+

list of tuples (representation, weight), where representation is +given by domain_mapper. Weight is a float.

+
+
+
+ +
+
+as_map()Âļ
+

Returns the map of explanations.

+
+
Returns:
+

Map from label to list of tuples (feature_id, weight).

+
+
+
+ +
+
+as_pyplot_figure(label=1, **kwargs)Âļ
+

Returns the explanation as a pyplot figure.

+

Will throw an error if you don’t have matplotlib installed +:param label: desired label. If you ask for a label for which an

+
+

explanation wasn’t computed, will throw an exception. +Will be ignored for regression explanations.

+
+
+
Parameters:
+

kwargs – keyword arguments, passed to domain_mapper

+
+
Returns:
+

pyplot figure (barchart).

+
+
+
+ +
+
+available_labels()Âļ
+

Returns the list of classification labels for which we have any explanations.

+
+ +
+
+save_to_file(file_path, labels=None, predict_proba=True, show_predicted_value=True, **kwargs)Âļ
+

Saves html explanation to file. .

+
+
Params:

file_path: file to save explanations to

+
+
+

See as_html() for additional parameters.

+
+ +
+
+show_in_notebook(labels=None, predict_proba=True, show_predicted_value=True, **kwargs)Âļ
+

Shows html explanation in ipython notebook.

+

See as_html() for parameters. +This will throw an error if you don’t have IPython installed

+
+ +
+ +
+
+lime.explanation.id_generator(size=15, random_state=None)Âļ
+

Helper function to generate random div ids. This is useful for embedding +HTML into ipython notebooks.

+
+ +
+
+

lime.lime_base moduleÂļ

+

Contains abstract functionality for learning locally linear sparse model.

+
+
+class lime.lime_base.LimeBase(kernel_fn, verbose=False, random_state=None)Âļ
+

Bases: object

+

Class for learning a locally linear sparse model from perturbed data

+
+
+explain_instance_with_data(neighborhood_data, neighborhood_labels, distances, label, num_features, feature_selection='auto', model_regressor=None)Âļ
+

Takes perturbed data, labels and distances, returns explanation.

+
+
Parameters:
+
    +

  • neighborhood_data – perturbed data, 2d array. first element is +assumed to be the original data point.

    • +

  • neighborhood_labels – corresponding perturbed labels. should have as +many columns as the number of possible labels.

    • +

  • distances – distances to original data point.

    • +

  • label – label for which we want an explanation

    • +

  • num_features – maximum number of features in explanation

    • +

  • feature_selection –

    how to select num_features. options are: +‘forward_selection’: iteratively add features to the model.

    +
    +

    This is costly when num_features is high

    +
    +
    +
    ’highest_weights’: selects the features that have the highest

    product of absolute weight * original data point when +learning with all the features

    +
    +
    ’lasso_path’: chooses features based on the lasso

    regularization path

    +
    +
    +

    ’none’: uses all features, ignores num_features +‘auto’: uses forward_selection if num_features <= 6, and

    +
    +

    ’highest_weights’ otherwise.

    +
    +

    • +

  • model_regressor – sklearn regressor to use in explanation. +Defaults to Ridge regression if None. Must have +model_regressor.coef_ and ‘sample_weight’ as a parameter +to model_regressor.fit()

    • +
+
+
Returns:
+

intercept is a float. +exp is a sorted list of tuples, where each tuple (x,y) corresponds +to the feature id (x) and the local weight (y). The list is sorted +by decreasing absolute value of y. +score is the R^2 value of the returned explanation +local_pred is the prediction of the explanation model on the original instance

+
+
Return type:
+

(intercept, exp, score, local_pred)

+
+
+
+ +
+
+feature_selection(data, labels, weights, num_features, method)Âļ
+

Selects features for the model. see explain_instance_with_data to +understand the parameters.

+
+ +
+
+forward_selection(data, labels, weights, num_features)Âļ
+

Iteratively adds features to the model

+
+ +
+
+static generate_lars_path(weighted_data, weighted_labels)Âļ
+

Generates the lars path for weighted data.

+
+
Parameters:
+
    +

  • weighted_data – data that has been weighted by kernel

    • +

  • weighted_label – labels, weighted by kernel

    • +
+
+
Returns:
+

(alphas, coefs), both are arrays corresponding to the +regularization parameter and coefficients, respectively

+
+
+
+ +
+ +
+
+

lime.lime_image moduleÂļ

+

Functions for explaining classifiers that use Image data.

+
+
+class lime.lime_image.ImageExplanation(image, segments)Âļ
+

Bases: object

+
+
+get_image_and_mask(label, positive_only=True, negative_only=False, hide_rest=False, num_features=5, min_weight=0.0)Âļ
+

Init function.

+
+
Parameters:
+
    +

  • label – label to explain

    • +

  • positive_only – if True, only take superpixels that positively contribute to +the prediction of the label.

    • +

  • negative_only – if True, only take superpixels that negatively contribute to +the prediction of the label. If false, and so is positive_only, then both +negativey and positively contributions will be taken. +Both can’t be True at the same time

    • +

  • hide_rest – if True, make the non-explanation part of the return +image gray

    • +

  • num_features – number of superpixels to include in explanation

    • +

  • min_weight – minimum weight of the superpixels to include in explanation

    • +
+
+
Returns:
+

(image, mask), where image is a 3d numpy array and mask is a 2d +numpy array that can be used with +skimage.segmentation.mark_boundaries

+
+
+
+ +
+ +
+
+class lime.lime_image.LimeImageExplainer(kernel_width=0.25, kernel=None, verbose=False, feature_selection='auto', random_state=None)Âļ
+

Bases: object

+

Explains predictions on Image (i.e. matrix) data. +For numerical features, perturb them by sampling from a Normal(0,1) and +doing the inverse operation of mean-centering and scaling, according to the +means and stds in the training data. For categorical features, perturb by +sampling according to the training distribution, and making a binary +feature that is 1 when the value is the same as the instance being +explained.

+
+
+data_labels(image, fudged_image, segments, classifier_fn, num_samples, batch_size=10)Âļ
+

Generates images and predictions in the neighborhood of this image.

+
+
Parameters:
+
    +

  • image – 3d numpy array, the image

    • +

  • fudged_image – 3d numpy array, image to replace original image when +superpixel is turned off

    • +

  • segments – segmentation of the image

    • +

  • classifier_fn – function that takes a list of images and returns a +matrix of prediction probabilities

    • +

  • num_samples – size of the neighborhood to learn the linear model

    • +

  • batch_size – classifier_fn will be called on batches of this size.

    • +
+
+
Returns:
+

data: dense num_samples * num_superpixels +labels: prediction probabilities matrix

+
+
Return type:
+

A tuple (data, labels), where

+
+
+
+ +
+
+explain_instance(image, classifier_fn, labels=(1,), hide_color=None, top_labels=5, num_features=100000, num_samples=1000, batch_size=10, segmentation_fn=None, distance_metric='cosine', model_regressor=None, random_seed=None)Âļ
+

Generates explanations for a prediction.

+

First, we generate neighborhood data by randomly perturbing features +from the instance (see __data_inverse). We then learn locally weighted +linear models on this neighborhood data to explain each of the classes +in an interpretable way (see lime_base.py).

+
+
Parameters:
+
    +

  • image – 3 dimension RGB image. If this is only two dimensional, +we will assume it’s a grayscale image and call gray2rgb.

    • +

  • classifier_fn – classifier prediction probability function, which +takes a numpy array and outputs prediction probabilities. For +ScikitClassifiers , this is classifier.predict_proba.

    • +

  • labels – iterable with labels to be explained.

    • +

  • hide_color – TODO

    • +

  • top_labels – if not None, ignore labels and produce explanations for +the K labels with highest prediction probabilities, where K is +this parameter.

    • +

  • num_features – maximum number of features present in explanation

    • +

  • num_samples – size of the neighborhood to learn the linear model

    • +

  • batch_size – TODO

    • +

  • distance_metric – the distance metric to use for weights.

    • +

  • model_regressor – sklearn regressor to use in explanation. Defaults

    • +

  • model_regressor.coef (to Ridge regression in LimeBase. Must have)

    • +

  • model_regressor.fit() (and 'sample_weight' as a parameter to)

    • +

  • segmentation_fn – SegmentationAlgorithm, wrapped skimage

    • +

  • function (segmentation)

    • +

  • random_seed – integer used as random seed for the segmentation +algorithm. If None, a random integer, between 0 and 1000, +will be generated using the internal random number generator.

    • +
+
+
Returns:
+

An ImageExplanation object (see lime_image.py) with the corresponding +explanations.

+
+
+
+ +
+ +
+
+

lime.lime_tabular moduleÂļ

+

Functions for explaining classifiers that use tabular data (matrices).

+
+
+class lime.lime_tabular.LimeTabularExplainer(training_data, mode='classification', training_labels=None, feature_names=None, categorical_features=None, categorical_names=None, kernel_width=None, kernel=None, verbose=False, class_names=None, feature_selection='auto', discretize_continuous=True, discretizer='quartile', sample_around_instance=False, random_state=None, training_data_stats=None)Âļ
+

Bases: object

+

Explains predictions on tabular (i.e. matrix) data. +For numerical features, perturb them by sampling from a Normal(0,1) and +doing the inverse operation of mean-centering and scaling, according to the +means and stds in the training data. For categorical features, perturb by +sampling according to the training distribution, and making a binary +feature that is 1 when the value is the same as the instance being +explained.

+
+
+static convert_and_round(values)Âļ
+
+ +
+
+explain_instance(data_row, predict_fn, labels=(1,), top_labels=None, num_features=10, num_samples=5000, distance_metric='euclidean', model_regressor=None)Âļ
+

Generates explanations for a prediction.

+

First, we generate neighborhood data by randomly perturbing features +from the instance (see __data_inverse). We then learn locally weighted +linear models on this neighborhood data to explain each of the classes +in an interpretable way (see lime_base.py).

+
+
Parameters:
+
    +

  • data_row – 1d numpy array or scipy.sparse matrix, corresponding to a row

    • +

  • predict_fn – prediction function. For classifiers, this should be a +function that takes a numpy array and outputs prediction +probabilities. For regressors, this takes a numpy array and +returns the predictions. For ScikitClassifiers, this is +classifier.predict_proba(). For ScikitRegressors, this +is regressor.predict(). The prediction function needs to work +on multiple feature vectors (the vectors randomly perturbed +from the data_row).

    • +

  • labels – iterable with labels to be explained.

    • +

  • top_labels – if not None, ignore labels and produce explanations for +the K labels with highest prediction probabilities, where K is +this parameter.

    • +

  • num_features – maximum number of features present in explanation

    • +

  • num_samples – size of the neighborhood to learn the linear model

    • +

  • distance_metric – the distance metric to use for weights.

    • +

  • model_regressor – sklearn regressor to use in explanation. Defaults +to Ridge regression in LimeBase. Must have model_regressor.coef_ +and ‘sample_weight’ as a parameter to model_regressor.fit()

    • +
+
+
Returns:
+

An Explanation object (see explanation.py) with the corresponding +explanations.

+
+
+
+ +
+
+static validate_training_data_stats(training_data_stats)Âļ
+

Method to validate the structure of training data stats

+
+ +
+ +
+
+class lime.lime_tabular.RecurrentTabularExplainer(training_data, mode='classification', training_labels=None, feature_names=None, categorical_features=None, categorical_names=None, kernel_width=None, kernel=None, verbose=False, class_names=None, feature_selection='auto', discretize_continuous=True, discretizer='quartile', random_state=None)Âļ
+

Bases: LimeTabularExplainer

+

An explainer for keras-style recurrent neural networks, where the +input shape is (n_samples, n_timesteps, n_features). This class +just extends the LimeTabularExplainer class and reshapes the training +data and feature names such that they become something like

+

(val1_t1, val1_t2, val1_t3, â€Ļ, val2_t1, â€Ļ, valn_tn)

+

Each of the methods that take data reshape it appropriately, +so you can pass in the training/testing data exactly as you +would to the recurrent neural network.

+
+
+explain_instance(data_row, classifier_fn, labels=(1,), top_labels=None, num_features=10, num_samples=5000, distance_metric='euclidean', model_regressor=None)Âļ
+

Generates explanations for a prediction.

+

First, we generate neighborhood data by randomly perturbing features +from the instance (see __data_inverse). We then learn locally weighted +linear models on this neighborhood data to explain each of the classes +in an interpretable way (see lime_base.py).

+
+
Parameters:
+
    +

  • data_row – 2d numpy array, corresponding to a row

    • +

  • classifier_fn – classifier prediction probability function, which +takes a numpy array and outputs prediction probabilities. For +ScikitClassifiers , this is classifier.predict_proba.

    • +

  • labels – iterable with labels to be explained.

    • +

  • top_labels – if not None, ignore labels and produce explanations for +the K labels with highest prediction probabilities, where K is +this parameter.

    • +

  • num_features – maximum number of features present in explanation

    • +

  • num_samples – size of the neighborhood to learn the linear model

    • +

  • distance_metric – the distance metric to use for weights.

    • +

  • model_regressor – sklearn regressor to use in explanation. Defaults +to Ridge regression in LimeBase. Must have +model_regressor.coef_ and ‘sample_weight’ as a parameter +to model_regressor.fit()

    • +
+
+
Returns:
+

An Explanation object (see explanation.py) with the corresponding +explanations.

+
+
+
+ +
+ +
+
+class lime.lime_tabular.TableDomainMapper(feature_names, feature_values, scaled_row, categorical_features, discretized_feature_names=None, feature_indexes=None)Âļ
+

Bases: DomainMapper

+

Maps feature ids to names, generates table views, etc

+
+
+map_exp_ids(exp)Âļ
+

Maps ids to feature names.

+
+
Parameters:
+

exp – list of tuples [(id, weight), (id,weight)]

+
+
Returns:
+

list of tuples (feature_name, weight)

+
+
+
+ +
+
+visualize_instance_html(exp, label, div_name, exp_object_name, show_table=True, show_all=False)Âļ
+

Shows the current example in a table format.

+
+
Parameters:
+
    +

  • exp – list of tuples [(id, weight), (id,weight)]

    • +

  • label – label id (integer)

    • +

  • div_name – name of div object to be used for rendering(in js)

    • +

  • exp_object_name – name of js explanation object

    • +

  • show_table – if False, don’t show table visualization.

    • +

  • show_all – if True, show zero-weighted features in the table.

    • +
+
+
+
+ +
+ +
+
+

lime.lime_text moduleÂļ

+

Functions for explaining text classifiers.

+
+
+class lime.lime_text.IndexedCharacters(raw_string, bow=True, mask_string=None)Âļ
+

Bases: object

+

String with various indexes.

+
+
+inverse_removing(words_to_remove)Âļ
+

Returns a string after removing the appropriate words.

+

If self.bow is false, replaces word with UNKWORDZ instead of removing +it.

+
+
Parameters:
+

words_to_remove – list of ids (ints) to remove

+
+
Returns:
+

original raw string with appropriate words removed.

+
+
+
+ +
+
+num_words()Âļ
+

Returns the number of tokens in the vocabulary for this document.

+
+ +
+
+raw_string()Âļ
+

Returns the original raw string

+
+ +
+
+string_position(id_)Âļ
+

Returns a np array with indices to id_ (int) occurrences

+
+ +
+
+word(id_)Âļ
+

Returns the word that corresponds to id_ (int)

+
+ +
+ +
+
+class lime.lime_text.IndexedString(raw_string, split_expression='\\W+', bow=True, mask_string=None)Âļ
+

Bases: object

+

String with various indexes.

+
+
+inverse_removing(words_to_remove)Âļ
+

Returns a string after removing the appropriate words.

+

If self.bow is false, replaces word with UNKWORDZ instead of removing +it.

+
+
Parameters:
+

words_to_remove – list of ids (ints) to remove

+
+
Returns:
+

original raw string with appropriate words removed.

+
+
+
+ +
+
+num_words()Âļ
+

Returns the number of tokens in the vocabulary for this document.

+
+ +
+
+raw_string()Âļ
+

Returns the original raw string

+
+ +
+
+string_position(id_)Âļ
+

Returns a np array with indices to id_ (int) occurrences

+
+ +
+
+word(id_)Âļ
+

Returns the word that corresponds to id_ (int)

+
+ +
+ +
+
+class lime.lime_text.LimeTextExplainer(kernel_width=25, kernel=None, verbose=False, class_names=None, feature_selection='auto', split_expression='\\W+', bow=True, mask_string=None, random_state=None, char_level=False)Âļ
+

Bases: object

+

Explains text classifiers. +Currently, we are using an exponential kernel on cosine distance, and +restricting explanations to words that are present in documents.

+
+
+explain_instance(text_instance, classifier_fn, labels=(1,), top_labels=None, num_features=10, num_samples=5000, distance_metric='cosine', model_regressor=None)Âļ
+

Generates explanations for a prediction.

+

First, we generate neighborhood data by randomly hiding features from +the instance (see __data_labels_distance_mapping). We then learn +locally weighted linear models on this neighborhood data to explain +each of the classes in an interpretable way (see lime_base.py).

+
+
Parameters:
+
    +

  • text_instance – raw text string to be explained.

    • +

  • classifier_fn – classifier prediction probability function, which +takes a list of d strings and outputs a (d, k) numpy array with +prediction probabilities, where k is the number of classes. +For ScikitClassifiers , this is classifier.predict_proba.

    • +

  • labels – iterable with labels to be explained.

    • +

  • top_labels – if not None, ignore labels and produce explanations for +the K labels with highest prediction probabilities, where K is +this parameter.

    • +

  • num_features – maximum number of features present in explanation

    • +

  • num_samples – size of the neighborhood to learn the linear model

    • +

  • distance_metric – the distance metric to use for sample weighting, +defaults to cosine similarity

    • +

  • model_regressor – sklearn regressor to use in explanation. Defaults

    • +

  • model_regressor.coef (to Ridge regression in LimeBase. Must have)

    • +

  • model_regressor.fit() (and 'sample_weight' as a parameter to)

    • +
+
+
Returns:
+

An Explanation object (see explanation.py) with the corresponding +explanations.

+
+
+
+ +
+ +
+
+class lime.lime_text.TextDomainMapper(indexed_string)Âļ
+

Bases: DomainMapper

+

Maps feature ids to words or word-positions

+
+
+map_exp_ids(exp, positions=False)Âļ
+

Maps ids to words or word-position strings.

+
+
Parameters:
+
    +

  • exp – list of tuples [(id, weight), (id,weight)]

    • +

  • positions – if True, also return word positions

    • +
+
+
Returns:
+

list of tuples (word, weight), or (word_positions, weight) if +examples: (‘bad’, 1) or (‘bad_3-6-12’, 1)

+
+
+
+ +
+
+visualize_instance_html(exp, label, div_name, exp_object_name, text=True, opacity=True)Âļ
+

Adds text with highlighted words to visualization.

+
+
Parameters:
+
    +

  • exp – list of tuples [(id, weight), (id,weight)]

    • +

  • label – label id (integer)

    • +

  • div_name – name of div object to be used for rendering(in js)

    • +

  • exp_object_name – name of js explanation object

    • +

  • text – if False, return empty

    • +

  • opacity – if True, fade colors according to weight

    • +
+
+
+
+ +
+ +
+
+

lime.submodular_pick moduleÂļ

+
+
+class lime.submodular_pick.SubmodularPick(explainer, data, predict_fn, method='sample', sample_size=1000, num_exps_desired=5, num_features=10, **kwargs)Âļ
+

Bases: object

+

Class for submodular pick

+

Saves a representative sample of explanation objects using SP-LIME, +as well as saving all generated explanations

+

First, a collection of candidate explanations are generated +(see explain_instance). From these candidates, num_exps_desired are +chosen using submodular pick. (see marcotcr et al paper).

+
+ +
+
+

Module contentsÂļ

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/build/html/lime.tests.html b/build/html/lime.tests.html new file mode 100644 index 000000000..d3c75e674 --- /dev/null +++ b/build/html/lime.tests.html @@ -0,0 +1,329 @@ + + + + + + + + lime.tests package — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

lime.tests packageÂļ

+
+

SubmodulesÂļ

+
+
+

lime.tests.test_discretize moduleÂļ

+
+
+class lime.tests.test_discretize.TestDiscretize(methodName='runTest')Âļ
+

Bases: TestCase

+
+
+check_random_state_for_discretizer_class(DiscretizerClass)Âļ
+
+ +
+
+setUp()Âļ
+

Hook method for setting up the test fixture before exercising it.

+
+ +
+
+test_feature_names_1()Âļ
+
+ +
+
+test_feature_names_2()Âļ
+
+ +
+
+test_feature_names_3()Âļ
+
+ +
+
+test_random_state()Âļ
+
+ +
+ +
+
+

lime.tests.test_generic_utils moduleÂļ

+
+
+class lime.tests.test_generic_utils.TestGenericUtils(methodName='runTest')Âļ
+

Bases: TestCase

+
+
+test_has_arg()Âļ
+
+ +
+ +
+
+

lime.tests.test_lime_tabular moduleÂļ

+
+
+class lime.tests.test_lime_tabular.TestLimeTabular(methodName='runTest')Âļ
+

Bases: TestCase

+
+
+setUp()Âļ
+

Hook method for setting up the test fixture before exercising it.

+
+ +
+
+testFeatureNamesAndCategoricalFeats()Âļ
+
+ +
+
+testFeatureValues()Âļ
+
+ +
+
+test_lime_explainer_entropy_discretizer()Âļ
+
+ +
+
+test_lime_explainer_good_regressor()Âļ
+
+ +
+
+test_lime_explainer_good_regressor_synthetic_data()Âļ
+
+ +
+
+test_lime_explainer_no_regressor()Âļ
+
+ +
+
+test_lime_explainer_sparse_synthetic_data()Âļ
+
+ +
+
+test_lime_explainer_with_data_stats()Âļ
+
+ +
+
+test_lime_tabular_explainer_equal_random_state()Âļ
+
+ +
+
+test_lime_tabular_explainer_not_equal_random_state()Âļ
+
+ +
+ +
+
+

lime.tests.test_lime_text moduleÂļ

+
+
+class lime.tests.test_lime_text.TestLimeText(methodName='runTest')Âļ
+

Bases: TestCase

+
+
+test_indexed_characters_bow()Âļ
+
+ +
+
+test_indexed_characters_not_bow()Âļ
+
+ +
+
+test_indexed_string_callable()Âļ
+
+ +
+
+test_indexed_string_inverse_removing_regex()Âļ
+
+ +
+
+test_indexed_string_inverse_removing_tokenizer()Âļ
+
+ +
+
+test_indexed_string_regex()Âļ
+
+ +
+
+test_lime_text_explainer_good_regressor()Âļ
+
+ +
+
+test_lime_text_tabular_equal_random_state()Âļ
+
+ +
+
+test_lime_text_tabular_not_equal_random_state()Âļ
+
+ +
+ +
+
+

lime.tests.test_scikit_image moduleÂļ

+
+
+class lime.tests.test_scikit_image.TestBaseWrapper(methodName='runTest')Âļ
+

Bases: TestCase

+
+
+test__check_params()Âļ
+
+ +
+
+test_base_wrapper()Âļ
+
+ +
+
+test_filter_params()Âļ
+
+ +
+
+test_set_params()Âļ
+
+ +
+ +
+
+class lime.tests.test_scikit_image.TestSegmentationAlgorithm(methodName='runTest')Âļ
+

Bases: TestCase

+
+
+test_instanciate_felzenszwalb()Âļ
+
+ +
+
+test_instanciate_segmentation_algorithm()Âļ
+
+ +
+
+test_instanciate_slic()Âļ
+
+ +
+ +
+
+

Module contentsÂļ

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/build/html/lime.utils.html b/build/html/lime.utils.html new file mode 100644 index 000000000..077c41879 --- /dev/null +++ b/build/html/lime.utils.html @@ -0,0 +1,126 @@ + + + + + + + + lime.utils package — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

lime.utils packageÂļ

+
+

SubmodulesÂļ

+
+
+

lime.utils.generic_utils moduleÂļ

+
+
+lime.utils.generic_utils.has_arg(fn, arg_name)Âļ
+

Checks if a callable accepts a given keyword argument.

+
+
Parameters:
+
    +

  • fn – callable to inspect

    • +

  • arg_name – string, keyword argument name to check

    • +
+
+
Returns:
+

bool, whether fn accepts a arg_name keyword argument.

+
+
+
+ +
+
+

Module contentsÂļ

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/build/html/lime.wrappers.html b/build/html/lime.wrappers.html new file mode 100644 index 000000000..973e3afa9 --- /dev/null +++ b/build/html/lime.wrappers.html @@ -0,0 +1,179 @@ + + + + + + + + lime.wrappers package — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

lime.wrappers packageÂļ

+
+

SubmodulesÂļ

+
+
+

lime.wrappers.scikit_image moduleÂļ

+
+
+class lime.wrappers.scikit_image.BaseWrapper(target_fn=None, **target_params)Âļ
+

Bases: object

+

Base class for LIME Scikit-Image wrapper

+
+
Parameters:
+
    +

  • target_fn – callable function or class instance

    • +

  • target_params – dict, parameters to pass to the target_fn

    • +
+
+
+
+
‘target_params’ takes parameters required to instanciate the

desired Scikit-Image class/model

+
+
+
+
+filter_params(fn, override=None)Âļ
+

Filters target_params and return those in fn’s arguments. +:param fn: arbitrary function +:param override: dict, values to override target_params

+
+
Returns:
+

dict, dictionary containing variables +in both target_params and fn’s arguments.

+
+
Return type:
+

result

+
+
+
+ +
+
+set_params(**params)Âļ
+

Sets the parameters of this estimator. +:param **params: Dictionary of parameter names mapped to their values.

+
+
Raises :
+
ValueError: if any parameter is not a valid argument

for the target function

+
+
+
+
+
+ +
+ +
+
+class lime.wrappers.scikit_image.SegmentationAlgorithm(algo_type, **target_params)Âļ
+

Bases: BaseWrapper

+
+
Define the image segmentation function based on Scikit-Image

implementation and a set of provided parameters

+
+
+
+
Parameters:
+
    +

  • algo_type – string, segmentation algorithm among the following: +‘quickshift’, ‘slic’, ‘felzenszwalb’

    • +

  • target_params – dict, algorithm parameters (valid model paramters +as define in Scikit-Image documentation)

    • +
+
+
+
+ +
+
+

Module contentsÂļ

+
+
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/build/html/modules.html b/build/html/modules.html new file mode 100644 index 000000000..43d1c008a --- /dev/null +++ b/build/html/modules.html @@ -0,0 +1,269 @@ + + + + + + + + lime_stratified — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +
+

lime_stratifiedÂļ

+
+ +
+
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/build/html/objects.inv b/build/html/objects.inv new file mode 100644 index 000000000..f3594ce2a Binary files /dev/null and b/build/html/objects.inv differ diff --git a/build/html/py-modindex.html b/build/html/py-modindex.html new file mode 100644 index 000000000..758d95833 --- /dev/null +++ b/build/html/py-modindex.html @@ -0,0 +1,224 @@ + + + + + + + Python Module Index — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ + +

Python Module Index

+ +
+ b | + l +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ b
+ benchmark +
    + benchmark.table_perf +
    + benchmark.text_perf +
 
+ l
+ lime +
    + lime.discretize +
    + lime.exceptions +
    + lime.explanation +
    + lime.lime_base +
    + lime.lime_image +
    + lime.lime_tabular +
    + lime.lime_text +
    + lime.submodular_pick +
    + lime.tests +
    + lime.tests.test_discretize +
    + lime.tests.test_generic_utils +
    + lime.tests.test_lime_tabular +
    + lime.tests.test_lime_text +
    + lime.tests.test_scikit_image +
    + lime.utils +
    + lime.utils.generic_utils +
    + lime.wrappers +
    + lime.wrappers.scikit_image +
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + +
+ + + + + + \ No newline at end of file diff --git a/build/html/search.html b/build/html/search.html new file mode 100644 index 000000000..08188d093 --- /dev/null +++ b/build/html/search.html @@ -0,0 +1,116 @@ + + + + + + + Search — lime_stratified 1.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + +
+
+
+ + +
+ +

Search

+ + + + +

+ Searching for multiple words only shows matches that contain + all words. +

+ + +
+ + + +
+ + +
+ + +
+ +
+
+ +
+
+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + +
+ + + + + + \ No newline at end of file diff --git a/build/html/searchindex.js b/build/html/searchindex.js new file mode 100644 index 000000000..7e5a3f947 --- /dev/null +++ b/build/html/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"Indices and tables": [[1, "indices-and-tables"]], "Module contents": [[0, "module-benchmark"], [2, "module-lime"], [3, "module-lime.tests"], [4, "module-lime.utils"], [5, "module-lime.wrappers"]], "Submodules": [[0, "submodules"], [2, "submodules"], [3, "submodules"], [4, "submodules"], [5, "submodules"]], "Subpackages": [[2, "subpackages"]], "Welcome to lime_stratified\u2019s documentation!": [[1, "welcome-to-lime-stratified-s-documentation"]], "benchmark package": [[0, "benchmark-package"]], "benchmark.table_perf module": [[0, "module-benchmark.table_perf"]], "benchmark.text_perf module": [[0, "module-benchmark.text_perf"]], "lime package": [[2, "lime-package"]], "lime.discretize module": [[2, "module-lime.discretize"]], "lime.exceptions 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setup moduleÂļ

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+
+ ©2024, Muhammad Rashid. + + | + Powered by Sphinx 7.3.6 + & Alabaster 0.7.16 + + | + Page source +
+ + + + + + \ No newline at end of file diff --git a/build/lib/benchmark/__init__.py b/build/lib/benchmark/__init__.py new file mode 100644 index 000000000..8b1378917 --- /dev/null +++ b/build/lib/benchmark/__init__.py @@ -0,0 +1 @@ + diff --git a/build/lib/benchmark/table_perf.py b/build/lib/benchmark/table_perf.py new file mode 100644 index 000000000..e84c2b090 --- /dev/null +++ b/build/lib/benchmark/table_perf.py @@ -0,0 +1,33 @@ +""" +A helper script for evaluating performance of changes to the tabular explainer, in this case different +implementations and methods for distance calculation. +""" + +import time +from sklearn.ensemble import RandomForestClassifier +from sklearn.datasets import make_classification +from lime.lime_tabular import LimeTabularExplainer + + +def interpret_data(X, y, func): + explainer = LimeTabularExplainer(X, discretize_continuous=False, kernel_width=3) + times, scores = [], [] + for r_idx in range(100): + start_time = time.time() + explanation = explainer.explain_instance(X[r_idx, :], func) + times.append(time.time() - start_time) + scores.append(explanation.score) + print('...') + + return times, scores + + +if __name__ == '__main__': + X_raw, y_raw = make_classification(n_classes=2, n_features=1000, n_samples=1000) + clf = RandomForestClassifier() + clf.fit(X_raw, y_raw) + y_hat = clf.predict_proba(X_raw) + + times, scores = interpret_data(X_raw, y_hat, clf.predict_proba) + print('%9.4fs %9.4fs %9.4fs' % (min(times), sum(times) / len(times), max(times))) + print('%9.4f %9.4f% 9.4f' % (min(scores), sum(scores) / len(scores), max(scores))) \ No newline at end of file diff --git a/build/lib/benchmark/text_perf.py b/build/lib/benchmark/text_perf.py new file mode 100644 index 000000000..f4d359f39 --- /dev/null +++ b/build/lib/benchmark/text_perf.py @@ -0,0 +1,37 @@ +import time +import sklearn +import sklearn.ensemble +import sklearn.metrics +from sklearn.datasets import fetch_20newsgroups +from sklearn.pipeline import make_pipeline +from lime.lime_text import LimeTextExplainer + + +def interpret_data(X, y, func, class_names): + explainer = LimeTextExplainer(class_names=class_names) + times, scores = [], [] + for r_idx in range(10): + start_time = time.time() + exp = explainer.explain_instance(newsgroups_test.data[r_idx], func, num_features=6) + times.append(time.time() - start_time) + scores.append(exp.score) + print('...') + + return times, scores + +if __name__ == '__main__': + categories = ['alt.atheism', 'soc.religion.christian'] + newsgroups_train = fetch_20newsgroups(subset='train', categories=categories) + newsgroups_test = fetch_20newsgroups(subset='test', categories=categories) + class_names = ['atheism', 'christian'] + + vectorizer = sklearn.feature_extraction.text.TfidfVectorizer(lowercase=False) + train_vectors = vectorizer.fit_transform(newsgroups_train.data) + test_vectors = vectorizer.transform(newsgroups_test.data) + rf = sklearn.ensemble.RandomForestClassifier(n_estimators=500) + rf.fit(train_vectors, newsgroups_train.target) + pred = rf.predict(test_vectors) + sklearn.metrics.f1_score(newsgroups_test.target, pred, average='binary') + c = make_pipeline(vectorizer, rf) + + interpret_data(train_vectors, newsgroups_train.target, c.predict_proba, class_names) \ No newline at end of file diff --git a/build/lib/lime/__init__.py b/build/lib/lime/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/build/lib/lime/bundle.js b/build/lib/lime/bundle.js new file mode 100644 index 000000000..b12fab0c1 --- /dev/null +++ b/build/lib/lime/bundle.js @@ -0,0 +1,37089 @@ +var lime = +/******/ (function(modules) { // webpackBootstrap +/******/ // The module cache +/******/ var installedModules = {}; +/******/ +/******/ // The require function +/******/ function __webpack_require__(moduleId) { +/******/ +/******/ // Check if module is in cache +/******/ if(installedModules[moduleId]) +/******/ return installedModules[moduleId].exports; +/******/ +/******/ // Create a new module (and put it into the cache) +/******/ var module = installedModules[moduleId] = { +/******/ exports: {}, +/******/ id: moduleId, +/******/ loaded: false +/******/ }; +/******/ +/******/ // Execute the module function +/******/ modules[moduleId].call(module.exports, module, module.exports, __webpack_require__); +/******/ +/******/ // Flag the module as loaded +/******/ module.loaded = true; +/******/ +/******/ // Return the exports of the module +/******/ return module.exports; +/******/ } +/******/ +/******/ +/******/ // expose the modules object (__webpack_modules__) +/******/ __webpack_require__.m = modules; +/******/ +/******/ // expose the module cache +/******/ __webpack_require__.c = installedModules; +/******/ +/******/ // __webpack_public_path__ +/******/ __webpack_require__.p = ""; +/******/ +/******/ // Load entry module and return exports +/******/ return __webpack_require__(0); +/******/ }) +/************************************************************************/ +/******/ ([ +/* 0 */ +/***/ (function(module, exports, __webpack_require__) { + + /* WEBPACK VAR INJECTION */(function(global) {'use strict'; + + Object.defineProperty(exports, "__esModule", { + value: true + }); + exports.PredictedValue = exports.PredictProba = exports.Barchart = exports.Explanation = undefined; + + var _explanation = __webpack_require__(1); + + var _explanation2 = _interopRequireDefault(_explanation); + + var _bar_chart = __webpack_require__(3); + + var _bar_chart2 = _interopRequireDefault(_bar_chart); + + var _predict_proba = __webpack_require__(6); + + var _predict_proba2 = _interopRequireDefault(_predict_proba); + + var _predicted_value = __webpack_require__(7); + + var _predicted_value2 = _interopRequireDefault(_predicted_value); + + function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + + if (!global._babelPolyfill) { + __webpack_require__(8); + } + + __webpack_require__(339); + + exports.Explanation = _explanation2.default; + exports.Barchart = _bar_chart2.default; + exports.PredictProba = _predict_proba2.default; + exports.PredictedValue = _predicted_value2.default; + //require('style-loader'); + /* WEBPACK VAR INJECTION */}.call(exports, (function() { return this; }()))) + +/***/ }), +/* 1 */ +/***/ (function(module, exports, __webpack_require__) { + + 'use strict'; + + Object.defineProperty(exports, "__esModule", { + value: true + }); + + var _slicedToArray = function () { function sliceIterator(arr, i) { var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i["return"]) _i["return"](); } finally { if (_d) throw _e; } } return _arr; } return function (arr, i) { if (Array.isArray(arr)) { return arr; } else if (Symbol.iterator in Object(arr)) { return sliceIterator(arr, i); } else { throw new TypeError("Invalid attempt to destructure non-iterable instance"); } }; }(); + + var _d2 = __webpack_require__(2); + + var _d3 = _interopRequireDefault(_d2); + + var _bar_chart = __webpack_require__(3); + + var _bar_chart2 = _interopRequireDefault(_bar_chart); + + var _lodash = __webpack_require__(4); + + function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + + function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + var Explanation = function () { + function Explanation(class_names) { + _classCallCheck(this, Explanation); + + this.names = class_names; + if (class_names.length < 10) { + this.colors = _d3.default.scale.category10().domain(this.names); + this.colors_i = _d3.default.scale.category10().domain((0, _lodash.range)(this.names.length)); + } else { + this.colors = _d3.default.scale.category20().domain(this.names); + this.colors_i = _d3.default.scale.category20().domain((0, _lodash.range)(this.names.length)); + } + } + // exp: [(feature-name, weight), ...] + // label: int + // div: d3 selection + + + Explanation.prototype.show = function show(exp, label, div) { + var svg = div.append('svg').style('width', '100%'); + var colors = ['#5F9EA0', this.colors_i(label)]; + var names = ['NOT ' + this.names[label], this.names[label]]; + if (this.names.length == 2) { + colors = [this.colors_i(0), this.colors_i(1)]; + names = this.names; + } + var plot = new _bar_chart2.default(svg, exp, true, names, colors, true, 10); + svg.style('height', plot.svg_height + 'px'); + }; + // exp has all ocurrences of words, with start index and weight: + // exp = [('word', 132, -0.13), ('word3', 111, 1.3) + + + Explanation.prototype.show_raw_text = function show_raw_text(exp, label, raw, div) { + var opacity = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : true; + + //let colors=['#5F9EA0', this.colors(this.exp['class'])]; + var colors = ['#5F9EA0', this.colors_i(label)]; + if (this.names.length == 2) { + colors = [this.colors_i(0), this.colors_i(1)]; + } + var word_lists = [[], []]; + var max_weight = -1; + var _iteratorNormalCompletion = true; + var _didIteratorError = false; + var _iteratorError = undefined; + + try { + for (var _iterator = exp[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { + var _step$value = _slicedToArray(_step.value, 3), + word = _step$value[0], + start = _step$value[1], + weight = _step$value[2]; + + if (weight > 0) { + word_lists[1].push([start, start + word.length, weight]); + } else { + word_lists[0].push([start, start + word.length, -weight]); + } + max_weight = Math.max(max_weight, Math.abs(weight)); + } + } catch (err) { + _didIteratorError = true; + _iteratorError = err; + } finally { + try { + if (!_iteratorNormalCompletion && _iterator.return) { + _iterator.return(); + } + } finally { + if (_didIteratorError) { + throw _iteratorError; + } + } + } + + if (!opacity) { + max_weight = 0; + } + this.display_raw_text(div, raw, word_lists, colors, max_weight, true); + }; + // exp is list of (feature_name, value, weight) + + + Explanation.prototype.show_raw_tabular = function show_raw_tabular(exp, label, div) { + div.classed('lime', true).classed('table_div', true); + var colors = ['#5F9EA0', this.colors_i(label)]; + if (this.names.length == 2) { + colors = [this.colors_i(0), this.colors_i(1)]; + } + var table = div.append('table'); + var thead = table.append('tr'); + thead.append('td').text('Feature'); + thead.append('td').text('Value'); + thead.style('color', 'black').style('font-size', '20px'); + var _iteratorNormalCompletion2 = true; + var _didIteratorError2 = false; + var _iteratorError2 = undefined; + + try { + for (var _iterator2 = exp[Symbol.iterator](), _step2; !(_iteratorNormalCompletion2 = (_step2 = _iterator2.next()).done); _iteratorNormalCompletion2 = true) { + var _step2$value = _slicedToArray(_step2.value, 3), + fname = _step2$value[0], + value = _step2$value[1], + weight = _step2$value[2]; + + var tr = table.append('tr'); + tr.style('border-style', 'hidden'); + tr.append('td').text(fname); + tr.append('td').text(value); + if (weight > 0) { + tr.style('background-color', colors[1]); + } else if (weight < 0) { + tr.style('background-color', colors[0]); + } else { + tr.style('color', 'black'); + } + } + } catch (err) { + _didIteratorError2 = true; + _iteratorError2 = err; + } finally { + try { + if (!_iteratorNormalCompletion2 && _iterator2.return) { + _iterator2.return(); + } + } finally { + if (_didIteratorError2) { + throw _iteratorError2; + } + } + } + }; + + Explanation.prototype.hexToRgb = function hexToRgb(hex) { + var result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(hex); + return result ? { + r: parseInt(result[1], 16), + g: parseInt(result[2], 16), + b: parseInt(result[3], 16) + } : null; + }; + + Explanation.prototype.applyAlpha = function applyAlpha(hex, alpha) { + var components = this.hexToRgb(hex); + return 'rgba(' + components.r + "," + components.g + "," + components.b + "," + alpha.toFixed(3) + ")"; + }; + // sord_lists is an array of arrays, of length (colors). if with_positions is true, + // word_lists is an array of [start,end] positions instead + + + Explanation.prototype.display_raw_text = function display_raw_text(div, raw_text) { + var word_lists = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : []; + var colors = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : []; + var max_weight = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : 1; + var positions = arguments.length > 5 && arguments[5] !== undefined ? arguments[5] : false; + + div.classed('lime', true).classed('text_div', true); + div.append('h3').text('Text with highlighted words'); + var highlight_tag = 'span'; + var text_span = div.append('span').style('white-space', 'pre-wrap').text(raw_text); + var position_lists = word_lists; + if (!positions) { + position_lists = this.wordlists_to_positions(word_lists, raw_text); + } + var objects = []; + var _iteratorNormalCompletion3 = true; + var _didIteratorError3 = false; + var _iteratorError3 = undefined; + + try { + var _loop = function _loop() { + var i = _step3.value; + + position_lists[i].map(function (x) { + return objects.push({ 'label': i, 'start': x[0], 'end': x[1], 'alpha': max_weight === 0 ? 1 : x[2] / max_weight }); + }); + }; + + for (var _iterator3 = (0, _lodash.range)(position_lists.length)[Symbol.iterator](), _step3; !(_iteratorNormalCompletion3 = (_step3 = _iterator3.next()).done); _iteratorNormalCompletion3 = true) { + _loop(); + } + } catch (err) { + _didIteratorError3 = true; + _iteratorError3 = err; + } finally { + try { + if (!_iteratorNormalCompletion3 && _iterator3.return) { + _iterator3.return(); + } + } finally { + if (_didIteratorError3) { + throw _iteratorError3; + } + } + } + + objects = (0, _lodash.sortBy)(objects, function (x) { + return x['start']; + }); + var node = text_span.node().childNodes[0]; + var subtract = 0; + var _iteratorNormalCompletion4 = true; + var _didIteratorError4 = false; + var _iteratorError4 = undefined; + + try { + for (var _iterator4 = objects[Symbol.iterator](), _step4; !(_iteratorNormalCompletion4 = (_step4 = _iterator4.next()).done); _iteratorNormalCompletion4 = true) { + var obj = _step4.value; + + var word = raw_text.slice(obj.start, obj.end); + var start = obj.start - subtract; + var end = obj.end - subtract; + var match = document.createElement(highlight_tag); + match.appendChild(document.createTextNode(word)); + match.style.backgroundColor = this.applyAlpha(colors[obj.label], obj.alpha); + var after = node.splitText(start); + after.nodeValue = after.nodeValue.substring(word.length); + node.parentNode.insertBefore(match, after); + subtract += end; + node = after; + } + } catch (err) { + _didIteratorError4 = true; + _iteratorError4 = err; + } finally { + try { + if (!_iteratorNormalCompletion4 && _iterator4.return) { + _iterator4.return(); + } + } finally { + if (_didIteratorError4) { + throw _iteratorError4; + } + } + } + }; + + Explanation.prototype.wordlists_to_positions = function wordlists_to_positions(word_lists, raw_text) { + var ret = []; + var _iteratorNormalCompletion5 = true; + var _didIteratorError5 = false; + var _iteratorError5 = undefined; + + try { + for (var _iterator5 = word_lists[Symbol.iterator](), _step5; !(_iteratorNormalCompletion5 = (_step5 = _iterator5.next()).done); _iteratorNormalCompletion5 = true) { + var words = _step5.value; + + if (words.length === 0) { + ret.push([]); + continue; + } + var re = new RegExp("\\b(" + words.join('|') + ")\\b", 'gm'); + var temp = void 0; + var list = []; + while ((temp = re.exec(raw_text)) !== null) { + list.push([temp.index, temp.index + temp[0].length]); + } + ret.push(list); + } + } catch (err) { + _didIteratorError5 = true; + _iteratorError5 = err; + } finally { + try { + if (!_iteratorNormalCompletion5 && _iterator5.return) { + _iterator5.return(); + } + } finally { + if (_didIteratorError5) { + throw _iteratorError5; + } + } + } + + return ret; + }; + + return Explanation; + }(); + + exports.default = Explanation; + +/***/ }), +/* 2 */ +/***/ (function(module, exports, __webpack_require__) { + + var __WEBPACK_AMD_DEFINE_FACTORY__, __WEBPACK_AMD_DEFINE_RESULT__;!function() { + var d3 = { + version: "3.5.17" + }; + var d3_arraySlice = [].slice, d3_array = function(list) { + return d3_arraySlice.call(list); + }; + var d3_document = this.document; + function d3_documentElement(node) { + return node && (node.ownerDocument || node.document || node).documentElement; + } + function d3_window(node) { + return node && (node.ownerDocument && node.ownerDocument.defaultView || node.document && node || node.defaultView); + } + if (d3_document) { + try { + d3_array(d3_document.documentElement.childNodes)[0].nodeType; + } catch (e) { + d3_array = function(list) { + var i = list.length, array = new Array(i); + while (i--) array[i] = list[i]; + return array; + }; + } + } + if (!Date.now) Date.now = function() { + return +new Date(); + }; + if (d3_document) { + try { + d3_document.createElement("DIV").style.setProperty("opacity", 0, ""); + } catch (error) { + var d3_element_prototype = this.Element.prototype, d3_element_setAttribute = d3_element_prototype.setAttribute, d3_element_setAttributeNS = d3_element_prototype.setAttributeNS, d3_style_prototype = this.CSSStyleDeclaration.prototype, d3_style_setProperty = d3_style_prototype.setProperty; + d3_element_prototype.setAttribute = function(name, value) { + d3_element_setAttribute.call(this, name, value + ""); + }; + d3_element_prototype.setAttributeNS = function(space, local, value) { + d3_element_setAttributeNS.call(this, space, local, value + ""); + }; + d3_style_prototype.setProperty = function(name, value, priority) { + d3_style_setProperty.call(this, name, value + "", priority); + }; + } + } + d3.ascending = d3_ascending; + function d3_ascending(a, b) { + return a < b ? -1 : a > b ? 1 : a >= b ? 0 : NaN; + } + d3.descending = function(a, b) { + return b < a ? -1 : b > a ? 1 : b >= a ? 0 : NaN; + }; + d3.min = function(array, f) { + var i = -1, n = array.length, a, b; + if (arguments.length === 1) { + while (++i < n) if ((b = array[i]) != null && b >= b) { + a = b; + break; + } + while (++i < n) if ((b = array[i]) != null && a > b) a = b; + } else { + while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) { + a = b; + break; + } + while (++i < n) if ((b = f.call(array, array[i], i)) != null && a > b) a = b; + } + return a; + }; + d3.max = function(array, f) { + var i = -1, n = array.length, a, b; + if (arguments.length === 1) { + while (++i < n) if ((b = array[i]) != null && b >= b) { + a = b; + break; + } + while (++i < n) if ((b = array[i]) != null && b > a) a = b; + } else { + while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) { + a = b; + break; + } + while (++i < n) if ((b = f.call(array, array[i], i)) != null && b > a) a = b; + } + return a; + }; + d3.extent = function(array, f) { + var i = -1, n = array.length, a, b, c; + if (arguments.length === 1) { + while (++i < n) if ((b = array[i]) != null && b >= b) { + a = c = b; + break; + } + while (++i < n) if ((b = array[i]) != null) { + if (a > b) a = b; + if (c < b) c = b; + } + } else { + while (++i < n) if ((b = f.call(array, array[i], i)) != null && b >= b) { + a = c = b; + break; + } + while (++i < n) if ((b = f.call(array, array[i], i)) != null) { + if (a > b) a = b; + if (c < b) c = b; + } + } + return [ a, c ]; + }; + function d3_number(x) { + return x === null ? NaN : +x; + } + function d3_numeric(x) { + return !isNaN(x); + } + d3.sum = function(array, f) { + var s = 0, n = array.length, a, i = -1; + if (arguments.length === 1) { + while (++i < n) if (d3_numeric(a = +array[i])) s += a; + } else { + while (++i < n) if (d3_numeric(a = +f.call(array, array[i], i))) s += a; + } + return s; + }; + d3.mean = function(array, f) { + var s = 0, n = array.length, a, i = -1, j = n; + if (arguments.length === 1) { + while (++i < n) if (d3_numeric(a = d3_number(array[i]))) s += a; else --j; + } else { + while (++i < n) if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) s += a; else --j; + } + if (j) return s / j; + }; + d3.quantile = function(values, p) { + var H = (values.length - 1) * p + 1, h = Math.floor(H), v = +values[h - 1], e = H - h; + return e ? v + e * (values[h] - v) : v; + }; + d3.median = function(array, f) { + var numbers = [], n = array.length, a, i = -1; + if (arguments.length === 1) { + while (++i < n) if (d3_numeric(a = d3_number(array[i]))) numbers.push(a); + } else { + while (++i < n) if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) numbers.push(a); + } + if (numbers.length) return d3.quantile(numbers.sort(d3_ascending), .5); + }; + d3.variance = function(array, f) { + var n = array.length, m = 0, a, d, s = 0, i = -1, j = 0; + if (arguments.length === 1) { + while (++i < n) { + if (d3_numeric(a = d3_number(array[i]))) { + d = a - m; + m += d / ++j; + s += d * (a - m); + } + } + } else { + while (++i < n) { + if (d3_numeric(a = d3_number(f.call(array, array[i], i)))) { + d = a - m; + m += d / ++j; + s += d * (a - m); + } + } + } + if (j > 1) return s / (j - 1); + }; + d3.deviation = function() { + var v = d3.variance.apply(this, arguments); + return v ? Math.sqrt(v) : v; + }; + function d3_bisector(compare) { + return { + left: function(a, x, lo, hi) { + if (arguments.length < 3) lo = 0; + if (arguments.length < 4) hi = a.length; + while (lo < hi) { + var mid = lo + hi >>> 1; + if (compare(a[mid], x) < 0) lo = mid + 1; else hi = mid; + } + return lo; + }, + right: function(a, x, lo, hi) { + if (arguments.length < 3) lo = 0; + if (arguments.length < 4) hi = a.length; + while (lo < hi) { + var mid = lo + hi >>> 1; + if (compare(a[mid], x) > 0) hi = mid; else lo = mid + 1; + } + return lo; + } + }; + } + var d3_bisect = d3_bisector(d3_ascending); + d3.bisectLeft = d3_bisect.left; + d3.bisect = d3.bisectRight = d3_bisect.right; + d3.bisector = function(f) { + return d3_bisector(f.length === 1 ? function(d, x) { + return d3_ascending(f(d), x); + } : f); + }; + d3.shuffle = function(array, i0, i1) { + if ((m = arguments.length) < 3) { + i1 = array.length; + if (m < 2) i0 = 0; + } + var m = i1 - i0, t, i; + while (m) { + i = Math.random() * m-- | 0; + t = array[m + i0], array[m + i0] = array[i + i0], array[i + i0] = t; + } + return array; + }; + d3.permute = function(array, indexes) { + var i = indexes.length, permutes = new Array(i); + while (i--) permutes[i] = array[indexes[i]]; + return permutes; + }; + d3.pairs = function(array) { + var i = 0, n = array.length - 1, p0, p1 = array[0], pairs = new Array(n < 0 ? 0 : n); + while (i < n) pairs[i] = [ p0 = p1, p1 = array[++i] ]; + return pairs; + }; + d3.transpose = function(matrix) { + if (!(n = matrix.length)) return []; + for (var i = -1, m = d3.min(matrix, d3_transposeLength), transpose = new Array(m); ++i < m; ) { + for (var j = -1, n, row = transpose[i] = new Array(n); ++j < n; ) { + row[j] = matrix[j][i]; + } + } + return transpose; + }; + function d3_transposeLength(d) { + return d.length; + } + d3.zip = function() { + return d3.transpose(arguments); + }; + d3.keys = function(map) { + var keys = []; + for (var key in map) keys.push(key); + return keys; + }; + d3.values = function(map) { + var values = []; + for (var key in map) values.push(map[key]); + return values; + }; + d3.entries = function(map) { + var entries = []; + for (var key in map) entries.push({ + key: key, + value: map[key] + }); + return entries; + }; + d3.merge = function(arrays) { + var n = arrays.length, m, i = -1, j = 0, merged, array; + while (++i < n) j += arrays[i].length; + merged = new Array(j); + while (--n >= 0) { + array = arrays[n]; + m = array.length; + while (--m >= 0) { + merged[--j] = array[m]; + } + } + return merged; + }; + var abs = Math.abs; + d3.range = function(start, stop, step) { + if (arguments.length < 3) { + step = 1; + if (arguments.length < 2) { + stop = start; + start = 0; + } + } + if ((stop - start) / step === Infinity) throw new Error("infinite range"); + var range = [], k = d3_range_integerScale(abs(step)), i = -1, j; + start *= k, stop *= k, step *= k; + if (step < 0) while ((j = start + step * ++i) > stop) range.push(j / k); else while ((j = start + step * ++i) < stop) range.push(j / k); + return range; + }; + function d3_range_integerScale(x) { + var k = 1; + while (x * k % 1) k *= 10; + return k; + } + function d3_class(ctor, properties) { + for (var key in properties) { + Object.defineProperty(ctor.prototype, key, { + value: properties[key], + enumerable: false + }); + } + } + d3.map = function(object, f) { + var map = new d3_Map(); + if (object instanceof d3_Map) { + object.forEach(function(key, value) { + map.set(key, value); + }); + } else if (Array.isArray(object)) { + var i = -1, n = object.length, o; + if (arguments.length === 1) while (++i < n) map.set(i, object[i]); else while (++i < n) map.set(f.call(object, o = object[i], i), o); + } else { + for (var key in object) map.set(key, object[key]); + } + return map; + }; + function d3_Map() { + this._ = Object.create(null); + } + var d3_map_proto = "__proto__", d3_map_zero = "\x00"; + d3_class(d3_Map, { + has: d3_map_has, + get: function(key) { + return this._[d3_map_escape(key)]; + }, + set: function(key, value) { + return this._[d3_map_escape(key)] = value; + }, + remove: d3_map_remove, + keys: d3_map_keys, + values: function() { + var values = []; + for (var key in this._) values.push(this._[key]); + return values; + }, + entries: function() { + var entries = []; + for (var key in this._) entries.push({ + key: d3_map_unescape(key), + value: this._[key] + }); + return entries; + }, + size: d3_map_size, + empty: d3_map_empty, + forEach: function(f) { + for (var key in this._) f.call(this, d3_map_unescape(key), this._[key]); + } + }); + function d3_map_escape(key) { + return (key += "") === d3_map_proto || key[0] === d3_map_zero ? d3_map_zero + key : key; + } + function d3_map_unescape(key) { + return (key += "")[0] === d3_map_zero ? key.slice(1) : key; + } + function d3_map_has(key) { + return d3_map_escape(key) in this._; + } + function d3_map_remove(key) { + return (key = d3_map_escape(key)) in this._ && delete this._[key]; + } + function d3_map_keys() { + var keys = []; + for (var key in this._) keys.push(d3_map_unescape(key)); + return keys; + } + function d3_map_size() { + var size = 0; + for (var key in this._) ++size; + return size; + } + function d3_map_empty() { + for (var key in this._) return false; + return true; + } + d3.nest = function() { + var nest = {}, keys = [], sortKeys = [], sortValues, rollup; + function map(mapType, array, depth) { + if (depth >= keys.length) return rollup ? rollup.call(nest, array) : sortValues ? array.sort(sortValues) : array; + var i = -1, n = array.length, key = keys[depth++], keyValue, object, setter, valuesByKey = new d3_Map(), values; + while (++i < n) { + if (values = valuesByKey.get(keyValue = key(object = array[i]))) { + values.push(object); + } else { + valuesByKey.set(keyValue, [ object ]); + } + } + if (mapType) { + object = mapType(); + setter = function(keyValue, values) { + object.set(keyValue, map(mapType, values, depth)); + }; + } else { + object = {}; + setter = function(keyValue, values) { + object[keyValue] = map(mapType, values, depth); + }; + } + valuesByKey.forEach(setter); + return object; + } + function entries(map, depth) { + if (depth >= keys.length) return map; + var array = [], sortKey = sortKeys[depth++]; + map.forEach(function(key, keyMap) { + array.push({ + key: key, + values: entries(keyMap, depth) + }); + }); + return sortKey ? array.sort(function(a, b) { + return sortKey(a.key, b.key); + }) : array; + } + nest.map = function(array, mapType) { + return map(mapType, array, 0); + }; + nest.entries = function(array) { + return entries(map(d3.map, array, 0), 0); + }; + nest.key = function(d) { + keys.push(d); + return nest; + }; + nest.sortKeys = function(order) { + sortKeys[keys.length - 1] = order; + return nest; + }; + nest.sortValues = function(order) { + sortValues = order; + return nest; + }; + nest.rollup = function(f) { + rollup = f; + return nest; + }; + return nest; + }; + d3.set = function(array) { + var set = new d3_Set(); + if (array) for (var i = 0, n = array.length; i < n; ++i) set.add(array[i]); + return set; + }; + function d3_Set() { + this._ = Object.create(null); + } + d3_class(d3_Set, { + has: d3_map_has, + add: function(key) { + this._[d3_map_escape(key += "")] = true; + return key; + }, + remove: d3_map_remove, + values: d3_map_keys, + size: d3_map_size, + empty: d3_map_empty, + forEach: function(f) { + for (var key in this._) f.call(this, d3_map_unescape(key)); + } + }); + d3.behavior = {}; + function d3_identity(d) { + return d; + } + d3.rebind = function(target, source) { + var i = 1, n = arguments.length, method; + while (++i < n) target[method = arguments[i]] = d3_rebind(target, source, source[method]); + return target; + }; + function d3_rebind(target, source, method) { + return function() { + var value = method.apply(source, arguments); + return value === source ? target : value; + }; + } + function d3_vendorSymbol(object, name) { + if (name in object) return name; + name = name.charAt(0).toUpperCase() + name.slice(1); + for (var i = 0, n = d3_vendorPrefixes.length; i < n; ++i) { + var prefixName = d3_vendorPrefixes[i] + name; + if (prefixName in object) return prefixName; + } + } + var d3_vendorPrefixes = [ "webkit", "ms", "moz", "Moz", "o", "O" ]; + function d3_noop() {} + d3.dispatch = function() { + var dispatch = new d3_dispatch(), i = -1, n = arguments.length; + while (++i < n) dispatch[arguments[i]] = d3_dispatch_event(dispatch); + return dispatch; + }; + function d3_dispatch() {} + d3_dispatch.prototype.on = function(type, listener) { + var i = type.indexOf("."), name = ""; + if (i >= 0) { + name = type.slice(i + 1); + type = type.slice(0, i); + } + if (type) return arguments.length < 2 ? this[type].on(name) : this[type].on(name, listener); + if (arguments.length === 2) { + if (listener == null) for (type in this) { + if (this.hasOwnProperty(type)) this[type].on(name, null); + } + return this; + } + }; + function d3_dispatch_event(dispatch) { + var listeners = [], listenerByName = new d3_Map(); + function event() { + var z = listeners, i = -1, n = z.length, l; + while (++i < n) if (l = z[i].on) l.apply(this, arguments); + return dispatch; + } + event.on = function(name, listener) { + var l = listenerByName.get(name), i; + if (arguments.length < 2) return l && l.on; + if (l) { + l.on = null; + listeners = listeners.slice(0, i = listeners.indexOf(l)).concat(listeners.slice(i + 1)); + listenerByName.remove(name); + } + if (listener) listeners.push(listenerByName.set(name, { + on: listener + })); + return dispatch; + }; + return event; + } + d3.event = null; + function d3_eventPreventDefault() { + d3.event.preventDefault(); + } + function d3_eventSource() { + var e = d3.event, s; + while (s = e.sourceEvent) e = s; + return e; + } + function d3_eventDispatch(target) { + var dispatch = new d3_dispatch(), i = 0, n = arguments.length; + while (++i < n) dispatch[arguments[i]] = d3_dispatch_event(dispatch); + dispatch.of = function(thiz, argumentz) { + return function(e1) { + try { + var e0 = e1.sourceEvent = d3.event; + e1.target = target; + d3.event = e1; + dispatch[e1.type].apply(thiz, argumentz); + } finally { + d3.event = e0; + } + }; + }; + return dispatch; + } + d3.requote = function(s) { + return s.replace(d3_requote_re, "\\$&"); + }; + var d3_requote_re = /[\\\^\$\*\+\?\|\[\]\(\)\.\{\}]/g; + var d3_subclass = {}.__proto__ ? function(object, prototype) { + object.__proto__ = prototype; + } : function(object, prototype) { + for (var property in prototype) object[property] = prototype[property]; + }; + function d3_selection(groups) { + d3_subclass(groups, d3_selectionPrototype); + return groups; + } + var d3_select = function(s, n) { + return n.querySelector(s); + }, d3_selectAll = function(s, n) { + return n.querySelectorAll(s); + }, d3_selectMatches = function(n, s) { + var d3_selectMatcher = n.matches || n[d3_vendorSymbol(n, "matchesSelector")]; + d3_selectMatches = function(n, s) { + return d3_selectMatcher.call(n, s); + }; + return d3_selectMatches(n, s); + }; + if (typeof Sizzle === "function") { + d3_select = function(s, n) { + return Sizzle(s, n)[0] || null; + }; + d3_selectAll = Sizzle; + d3_selectMatches = Sizzle.matchesSelector; + } + d3.selection = function() { + return d3.select(d3_document.documentElement); + }; + var d3_selectionPrototype = d3.selection.prototype = []; + d3_selectionPrototype.select = function(selector) { + var subgroups = [], subgroup, subnode, group, node; + selector = d3_selection_selector(selector); + for (var j = -1, m = this.length; ++j < m; ) { + subgroups.push(subgroup = []); + subgroup.parentNode = (group = this[j]).parentNode; + for (var i = -1, n = group.length; ++i < n; ) { + if (node = group[i]) { + subgroup.push(subnode = selector.call(node, node.__data__, i, j)); + if (subnode && "__data__" in node) subnode.__data__ = node.__data__; + } else { + subgroup.push(null); + } + } + } + return d3_selection(subgroups); + }; + function d3_selection_selector(selector) { + return typeof selector === "function" ? selector : function() { + return d3_select(selector, this); + }; + } + d3_selectionPrototype.selectAll = function(selector) { + var subgroups = [], subgroup, node; + selector = d3_selection_selectorAll(selector); + for (var j = -1, m = this.length; ++j < m; ) { + for (var group = this[j], i = -1, n = group.length; ++i < n; ) { + if (node = group[i]) { + subgroups.push(subgroup = d3_array(selector.call(node, node.__data__, i, j))); + subgroup.parentNode = node; + } + } + } + return d3_selection(subgroups); + }; + function d3_selection_selectorAll(selector) { + return typeof selector === "function" ? selector : function() { + return d3_selectAll(selector, this); + }; + } + var d3_nsXhtml = "http://www.w3.org/1999/xhtml"; + var d3_nsPrefix = { + svg: "http://www.w3.org/2000/svg", + xhtml: d3_nsXhtml, + xlink: "http://www.w3.org/1999/xlink", + xml: "http://www.w3.org/XML/1998/namespace", + xmlns: "http://www.w3.org/2000/xmlns/" + }; + d3.ns = { + prefix: d3_nsPrefix, + qualify: function(name) { + var i = name.indexOf(":"), prefix = name; + if (i >= 0 && (prefix = name.slice(0, i)) !== "xmlns") name = name.slice(i + 1); + return d3_nsPrefix.hasOwnProperty(prefix) ? { + space: d3_nsPrefix[prefix], + local: name + } : name; + } + }; + d3_selectionPrototype.attr = function(name, value) { + if (arguments.length < 2) { + if (typeof name === "string") { + var node = this.node(); + name = d3.ns.qualify(name); + return name.local ? node.getAttributeNS(name.space, name.local) : node.getAttribute(name); + } + for (value in name) this.each(d3_selection_attr(value, name[value])); + return this; + } + return this.each(d3_selection_attr(name, value)); + }; + function d3_selection_attr(name, value) { + name = d3.ns.qualify(name); + function attrNull() { + this.removeAttribute(name); + } + function attrNullNS() { + this.removeAttributeNS(name.space, name.local); + } + function attrConstant() { + this.setAttribute(name, value); + } + function attrConstantNS() { + this.setAttributeNS(name.space, name.local, value); + } + function attrFunction() { + var x = value.apply(this, arguments); + if (x == null) this.removeAttribute(name); else this.setAttribute(name, x); + } + function attrFunctionNS() { + var x = value.apply(this, arguments); + if (x == null) this.removeAttributeNS(name.space, name.local); else this.setAttributeNS(name.space, name.local, x); + } + return value == null ? name.local ? attrNullNS : attrNull : typeof value === "function" ? name.local ? attrFunctionNS : attrFunction : name.local ? attrConstantNS : attrConstant; + } + function d3_collapse(s) { + return s.trim().replace(/\s+/g, " "); + } + d3_selectionPrototype.classed = function(name, value) { + if (arguments.length < 2) { + if (typeof name === "string") { + var node = this.node(), n = (name = d3_selection_classes(name)).length, i = -1; + if (value = node.classList) { + while (++i < n) if (!value.contains(name[i])) return false; + } else { + value = node.getAttribute("class"); + while (++i < n) if (!d3_selection_classedRe(name[i]).test(value)) return false; + } + return true; + } + for (value in name) this.each(d3_selection_classed(value, name[value])); + return this; + } + return this.each(d3_selection_classed(name, value)); + }; + function d3_selection_classedRe(name) { + return new RegExp("(?:^|\\s+)" + d3.requote(name) + "(?:\\s+|$)", "g"); + } + function d3_selection_classes(name) { + return (name + "").trim().split(/^|\s+/); + } + function d3_selection_classed(name, value) { + name = d3_selection_classes(name).map(d3_selection_classedName); + var n = name.length; + function classedConstant() { + var i = -1; + while (++i < n) name[i](this, value); + } + function classedFunction() { + var i = -1, x = value.apply(this, arguments); + while (++i < n) name[i](this, x); + } + return typeof value === "function" ? classedFunction : classedConstant; + } + function d3_selection_classedName(name) { + var re = d3_selection_classedRe(name); + return function(node, value) { + if (c = node.classList) return value ? c.add(name) : c.remove(name); + var c = node.getAttribute("class") || ""; + if (value) { + re.lastIndex = 0; + if (!re.test(c)) node.setAttribute("class", d3_collapse(c + " " + name)); + } else { + node.setAttribute("class", d3_collapse(c.replace(re, " "))); + } + }; + } + d3_selectionPrototype.style = function(name, value, priority) { + var n = arguments.length; + if (n < 3) { + if (typeof name !== "string") { + if (n < 2) value = ""; + for (priority in name) this.each(d3_selection_style(priority, name[priority], value)); + return this; + } + if (n < 2) { + var node = this.node(); + return d3_window(node).getComputedStyle(node, null).getPropertyValue(name); + } + priority = ""; + } + return this.each(d3_selection_style(name, value, priority)); + }; + function d3_selection_style(name, value, priority) { + function styleNull() { + this.style.removeProperty(name); + } + function styleConstant() { + this.style.setProperty(name, value, priority); + } + function styleFunction() { + var x = value.apply(this, arguments); + if (x == null) this.style.removeProperty(name); else this.style.setProperty(name, x, priority); + } + return value == null ? styleNull : typeof value === "function" ? styleFunction : styleConstant; + } + d3_selectionPrototype.property = function(name, value) { + if (arguments.length < 2) { + if (typeof name === "string") return this.node()[name]; + for (value in name) this.each(d3_selection_property(value, name[value])); + return this; + } + return this.each(d3_selection_property(name, value)); + }; + function d3_selection_property(name, value) { + function propertyNull() { + delete this[name]; + } + function propertyConstant() { + this[name] = value; + } + function propertyFunction() { + var x = value.apply(this, arguments); + if (x == null) delete this[name]; else this[name] = x; + } + return value == null ? propertyNull : typeof value === "function" ? propertyFunction : propertyConstant; + } + d3_selectionPrototype.text = function(value) { + return arguments.length ? this.each(typeof value === "function" ? function() { + var v = value.apply(this, arguments); + this.textContent = v == null ? "" : v; + } : value == null ? function() { + this.textContent = ""; + } : function() { + this.textContent = value; + }) : this.node().textContent; + }; + d3_selectionPrototype.html = function(value) { + return arguments.length ? this.each(typeof value === "function" ? function() { + var v = value.apply(this, arguments); + this.innerHTML = v == null ? "" : v; + } : value == null ? function() { + this.innerHTML = ""; + } : function() { + this.innerHTML = value; + }) : this.node().innerHTML; + }; + d3_selectionPrototype.append = function(name) { + name = d3_selection_creator(name); + return this.select(function() { + return this.appendChild(name.apply(this, arguments)); + }); + }; + function d3_selection_creator(name) { + function create() { + var document = this.ownerDocument, namespace = this.namespaceURI; + return namespace === d3_nsXhtml && document.documentElement.namespaceURI === d3_nsXhtml ? document.createElement(name) : document.createElementNS(namespace, name); + } + function createNS() { + return this.ownerDocument.createElementNS(name.space, name.local); + } + return typeof name === "function" ? name : (name = d3.ns.qualify(name)).local ? createNS : create; + } + d3_selectionPrototype.insert = function(name, before) { + name = d3_selection_creator(name); + before = d3_selection_selector(before); + return this.select(function() { + return this.insertBefore(name.apply(this, arguments), before.apply(this, arguments) || null); + }); + }; + d3_selectionPrototype.remove = function() { + return this.each(d3_selectionRemove); + }; + function d3_selectionRemove() { + var parent = this.parentNode; + if (parent) parent.removeChild(this); + } + d3_selectionPrototype.data = function(value, key) { + var i = -1, n = this.length, group, node; + if (!arguments.length) { + value = new Array(n = (group = this[0]).length); + while (++i < n) { + if (node = group[i]) { + value[i] = node.__data__; + } + } + return value; + } + function bind(group, groupData) { + var i, n = group.length, m = groupData.length, n0 = Math.min(n, m), updateNodes = new Array(m), enterNodes = new Array(m), exitNodes = new Array(n), node, nodeData; + if (key) { + var nodeByKeyValue = new d3_Map(), keyValues = new Array(n), keyValue; + for (i = -1; ++i < n; ) { + if (node = group[i]) { + if (nodeByKeyValue.has(keyValue = key.call(node, node.__data__, i))) { + exitNodes[i] = node; + } else { + nodeByKeyValue.set(keyValue, node); + } + keyValues[i] = keyValue; + } + } + for (i = -1; ++i < m; ) { + if (!(node = nodeByKeyValue.get(keyValue = key.call(groupData, nodeData = groupData[i], i)))) { + enterNodes[i] = d3_selection_dataNode(nodeData); + } else if (node !== true) { + updateNodes[i] = node; + node.__data__ = nodeData; + } + nodeByKeyValue.set(keyValue, true); + } + for (i = -1; ++i < n; ) { + if (i in keyValues && nodeByKeyValue.get(keyValues[i]) !== true) { + exitNodes[i] = group[i]; + } + } + } else { + for (i = -1; ++i < n0; ) { + node = group[i]; + nodeData = groupData[i]; + if (node) { + node.__data__ = nodeData; + updateNodes[i] = node; + } else { + enterNodes[i] = d3_selection_dataNode(nodeData); + } + } + for (;i < m; ++i) { + enterNodes[i] = d3_selection_dataNode(groupData[i]); + } + for (;i < n; ++i) { + exitNodes[i] = group[i]; + } + } + enterNodes.update = updateNodes; + enterNodes.parentNode = updateNodes.parentNode = exitNodes.parentNode = group.parentNode; + enter.push(enterNodes); + update.push(updateNodes); + exit.push(exitNodes); + } + var enter = d3_selection_enter([]), update = d3_selection([]), exit = d3_selection([]); + if (typeof value === "function") { + while (++i < n) { + bind(group = this[i], value.call(group, group.parentNode.__data__, i)); + } + } else { + while (++i < n) { + bind(group = this[i], value); + } + } + update.enter = function() { + return enter; + }; + update.exit = function() { + return exit; + }; + return update; + }; + function d3_selection_dataNode(data) { + return { + __data__: data + }; + } + d3_selectionPrototype.datum = function(value) { + return arguments.length ? this.property("__data__", value) : this.property("__data__"); + }; + d3_selectionPrototype.filter = function(filter) { + var subgroups = [], subgroup, group, node; + if (typeof filter !== "function") filter = d3_selection_filter(filter); + for (var j = 0, m = this.length; j < m; j++) { + subgroups.push(subgroup = []); + subgroup.parentNode = (group = this[j]).parentNode; + for (var i = 0, n = group.length; i < n; i++) { + if ((node = group[i]) && filter.call(node, node.__data__, i, j)) { + subgroup.push(node); + } + } + } + return d3_selection(subgroups); + }; + function d3_selection_filter(selector) { + return function() { + return d3_selectMatches(this, selector); + }; + } + d3_selectionPrototype.order = function() { + for (var j = -1, m = this.length; ++j < m; ) { + for (var group = this[j], i = group.length - 1, next = group[i], node; --i >= 0; ) { + if (node = group[i]) { + if (next && next !== node.nextSibling) next.parentNode.insertBefore(node, next); + next = node; + } + } + } + return this; + }; + d3_selectionPrototype.sort = function(comparator) { + comparator = d3_selection_sortComparator.apply(this, arguments); + for (var j = -1, m = this.length; ++j < m; ) this[j].sort(comparator); + return this.order(); + }; + function d3_selection_sortComparator(comparator) { + if (!arguments.length) comparator = d3_ascending; + return function(a, b) { + return a && b ? comparator(a.__data__, b.__data__) : !a - !b; + }; + } + d3_selectionPrototype.each = function(callback) { + return d3_selection_each(this, function(node, i, j) { + callback.call(node, node.__data__, i, j); + }); + }; + function d3_selection_each(groups, callback) { + for (var j = 0, m = groups.length; j < m; j++) { + for (var group = groups[j], i = 0, n = group.length, node; i < n; i++) { + if (node = group[i]) callback(node, i, j); + } + } + return groups; + } + d3_selectionPrototype.call = function(callback) { + var args = d3_array(arguments); + callback.apply(args[0] = this, args); + return this; + }; + d3_selectionPrototype.empty = function() { + return !this.node(); + }; + d3_selectionPrototype.node = function() { + for (var j = 0, m = this.length; j < m; j++) { + for (var group = this[j], i = 0, n = group.length; i < n; i++) { + var node = group[i]; + if (node) return node; + } + } + return null; + }; + d3_selectionPrototype.size = function() { + var n = 0; + d3_selection_each(this, function() { + ++n; + }); + return n; + }; + function d3_selection_enter(selection) { + d3_subclass(selection, d3_selection_enterPrototype); + return selection; + } + var d3_selection_enterPrototype = []; + d3.selection.enter = d3_selection_enter; + d3.selection.enter.prototype = d3_selection_enterPrototype; + d3_selection_enterPrototype.append = d3_selectionPrototype.append; + d3_selection_enterPrototype.empty = d3_selectionPrototype.empty; + d3_selection_enterPrototype.node = d3_selectionPrototype.node; + d3_selection_enterPrototype.call = d3_selectionPrototype.call; + d3_selection_enterPrototype.size = d3_selectionPrototype.size; + d3_selection_enterPrototype.select = function(selector) { + var subgroups = [], subgroup, subnode, upgroup, group, node; + for (var j = -1, m = this.length; ++j < m; ) { + upgroup = (group = this[j]).update; + subgroups.push(subgroup = []); + subgroup.parentNode = group.parentNode; + for (var i = -1, n = group.length; ++i < n; ) { + if (node = group[i]) { + subgroup.push(upgroup[i] = subnode = selector.call(group.parentNode, node.__data__, i, j)); + subnode.__data__ = node.__data__; + } else { + subgroup.push(null); + } + } + } + return d3_selection(subgroups); + }; + d3_selection_enterPrototype.insert = function(name, before) { + if (arguments.length < 2) before = d3_selection_enterInsertBefore(this); + return d3_selectionPrototype.insert.call(this, name, before); + }; + function d3_selection_enterInsertBefore(enter) { + var i0, j0; + return function(d, i, j) { + var group = enter[j].update, n = group.length, node; + if (j != j0) j0 = j, i0 = 0; + if (i >= i0) i0 = i + 1; + while (!(node = group[i0]) && ++i0 < n) ; + return node; + }; + } + d3.select = function(node) { + var group; + if (typeof node === "string") { + group = [ d3_select(node, d3_document) ]; + group.parentNode = d3_document.documentElement; + } else { + group = [ node ]; + group.parentNode = d3_documentElement(node); + } + return d3_selection([ group ]); + }; + d3.selectAll = function(nodes) { + var group; + if (typeof nodes === "string") { + group = d3_array(d3_selectAll(nodes, d3_document)); + group.parentNode = d3_document.documentElement; + } else { + group = d3_array(nodes); + group.parentNode = null; + } + return d3_selection([ group ]); + }; + d3_selectionPrototype.on = function(type, listener, capture) { + var n = arguments.length; + if (n < 3) { + if (typeof type !== "string") { + if (n < 2) listener = false; + for (capture in type) this.each(d3_selection_on(capture, type[capture], listener)); + return this; + } + if (n < 2) return (n = this.node()["__on" + type]) && n._; + capture = false; + } + return this.each(d3_selection_on(type, listener, capture)); + }; + function d3_selection_on(type, listener, capture) { + var name = "__on" + type, i = type.indexOf("."), wrap = d3_selection_onListener; + if (i > 0) type = type.slice(0, i); + var filter = d3_selection_onFilters.get(type); + if (filter) type = filter, wrap = d3_selection_onFilter; + function onRemove() { + var l = this[name]; + if (l) { + this.removeEventListener(type, l, l.$); + delete this[name]; + } + } + function onAdd() { + var l = wrap(listener, d3_array(arguments)); + onRemove.call(this); + this.addEventListener(type, this[name] = l, l.$ = capture); + l._ = listener; + } + function removeAll() { + var re = new RegExp("^__on([^.]+)" + d3.requote(type) + "$"), match; + for (var name in this) { + if (match = name.match(re)) { + var l = this[name]; + this.removeEventListener(match[1], l, l.$); + delete this[name]; + } + } + } + return i ? listener ? onAdd : onRemove : listener ? d3_noop : removeAll; + } + var d3_selection_onFilters = d3.map({ + mouseenter: "mouseover", + mouseleave: "mouseout" + }); + if (d3_document) { + d3_selection_onFilters.forEach(function(k) { + if ("on" + k in d3_document) d3_selection_onFilters.remove(k); + }); + } + function d3_selection_onListener(listener, argumentz) { + return function(e) { + var o = d3.event; + d3.event = e; + argumentz[0] = this.__data__; + try { + listener.apply(this, argumentz); + } finally { + d3.event = o; + } + }; + } + function d3_selection_onFilter(listener, argumentz) { + var l = d3_selection_onListener(listener, argumentz); + return function(e) { + var target = this, related = e.relatedTarget; + if (!related || related !== target && !(related.compareDocumentPosition(target) & 8)) { + l.call(target, e); + } + }; + } + var d3_event_dragSelect, d3_event_dragId = 0; + function d3_event_dragSuppress(node) { + var name = ".dragsuppress-" + ++d3_event_dragId, click = "click" + name, w = d3.select(d3_window(node)).on("touchmove" + name, d3_eventPreventDefault).on("dragstart" + name, d3_eventPreventDefault).on("selectstart" + name, d3_eventPreventDefault); + if (d3_event_dragSelect == null) { + d3_event_dragSelect = "onselectstart" in node ? false : d3_vendorSymbol(node.style, "userSelect"); + } + if (d3_event_dragSelect) { + var style = d3_documentElement(node).style, select = style[d3_event_dragSelect]; + style[d3_event_dragSelect] = "none"; + } + return function(suppressClick) { + w.on(name, null); + if (d3_event_dragSelect) style[d3_event_dragSelect] = select; + if (suppressClick) { + var off = function() { + w.on(click, null); + }; + w.on(click, function() { + d3_eventPreventDefault(); + off(); + }, true); + setTimeout(off, 0); + } + }; + } + d3.mouse = function(container) { + return d3_mousePoint(container, d3_eventSource()); + }; + var d3_mouse_bug44083 = this.navigator && /WebKit/.test(this.navigator.userAgent) ? -1 : 0; + function d3_mousePoint(container, e) { + if (e.changedTouches) e = e.changedTouches[0]; + var svg = container.ownerSVGElement || container; + if (svg.createSVGPoint) { + var point = svg.createSVGPoint(); + if (d3_mouse_bug44083 < 0) { + var window = d3_window(container); + if (window.scrollX || window.scrollY) { + svg = d3.select("body").append("svg").style({ + position: "absolute", + top: 0, + left: 0, + margin: 0, + padding: 0, + border: "none" + }, "important"); + var ctm = svg[0][0].getScreenCTM(); + d3_mouse_bug44083 = !(ctm.f || ctm.e); + svg.remove(); + } + } + if (d3_mouse_bug44083) point.x = e.pageX, point.y = e.pageY; else point.x = e.clientX, + point.y = e.clientY; + point = point.matrixTransform(container.getScreenCTM().inverse()); + return [ point.x, point.y ]; + } + var rect = container.getBoundingClientRect(); + return [ e.clientX - rect.left - container.clientLeft, e.clientY - rect.top - container.clientTop ]; + } + d3.touch = function(container, touches, identifier) { + if (arguments.length < 3) identifier = touches, touches = d3_eventSource().changedTouches; + if (touches) for (var i = 0, n = touches.length, touch; i < n; ++i) { + if ((touch = touches[i]).identifier === identifier) { + return d3_mousePoint(container, touch); + } + } + }; + d3.behavior.drag = function() { + var event = d3_eventDispatch(drag, "drag", "dragstart", "dragend"), origin = null, mousedown = dragstart(d3_noop, d3.mouse, d3_window, "mousemove", "mouseup"), touchstart = dragstart(d3_behavior_dragTouchId, d3.touch, d3_identity, "touchmove", "touchend"); + function drag() { + this.on("mousedown.drag", mousedown).on("touchstart.drag", touchstart); + } + function dragstart(id, position, subject, move, end) { + return function() { + var that = this, target = d3.event.target.correspondingElement || d3.event.target, parent = that.parentNode, dispatch = event.of(that, arguments), dragged = 0, dragId = id(), dragName = ".drag" + (dragId == null ? "" : "-" + dragId), dragOffset, dragSubject = d3.select(subject(target)).on(move + dragName, moved).on(end + dragName, ended), dragRestore = d3_event_dragSuppress(target), position0 = position(parent, dragId); + if (origin) { + dragOffset = origin.apply(that, arguments); + dragOffset = [ dragOffset.x - position0[0], dragOffset.y - position0[1] ]; + } else { + dragOffset = [ 0, 0 ]; + } + dispatch({ + type: "dragstart" + }); + function moved() { + var position1 = position(parent, dragId), dx, dy; + if (!position1) return; + dx = position1[0] - position0[0]; + dy = position1[1] - position0[1]; + dragged |= dx | dy; + position0 = position1; + dispatch({ + type: "drag", + x: position1[0] + dragOffset[0], + y: position1[1] + dragOffset[1], + dx: dx, + dy: dy + }); + } + function ended() { + if (!position(parent, dragId)) return; + dragSubject.on(move + dragName, null).on(end + dragName, null); + dragRestore(dragged); + dispatch({ + type: "dragend" + }); + } + }; + } + drag.origin = function(x) { + if (!arguments.length) return origin; + origin = x; + return drag; + }; + return d3.rebind(drag, event, "on"); + }; + function d3_behavior_dragTouchId() { + return d3.event.changedTouches[0].identifier; + } + d3.touches = function(container, touches) { + if (arguments.length < 2) touches = d3_eventSource().touches; + return touches ? d3_array(touches).map(function(touch) { + var point = d3_mousePoint(container, touch); + point.identifier = touch.identifier; + return point; + }) : []; + }; + var Îĩ = 1e-6, Îĩ2 = Îĩ * Îĩ, Ī€ = Math.PI, Ī„ = 2 * Ī€, Ī„Îĩ = Ī„ - Îĩ, halfĪ€ = Ī€ / 2, d3_radians = Ī€ / 180, d3_degrees = 180 / Ī€; + function d3_sgn(x) { + return x > 0 ? 1 : x < 0 ? -1 : 0; + } + function d3_cross2d(a, b, c) { + return (b[0] - a[0]) * (c[1] - a[1]) - (b[1] - a[1]) * (c[0] - a[0]); + } + function d3_acos(x) { + return x > 1 ? 0 : x < -1 ? Ī€ : Math.acos(x); + } + function d3_asin(x) { + return x > 1 ? halfĪ€ : x < -1 ? -halfĪ€ : Math.asin(x); + } + function d3_sinh(x) { + return ((x = Math.exp(x)) - 1 / x) / 2; + } + function d3_cosh(x) { + return ((x = Math.exp(x)) + 1 / x) / 2; + } + function d3_tanh(x) { + return ((x = Math.exp(2 * x)) - 1) / (x + 1); + } + function d3_haversin(x) { + return (x = Math.sin(x / 2)) * x; + } + var Ī = Math.SQRT2, Ī2 = 2, Ī4 = 4; + d3.interpolateZoom = function(p0, p1) { + var ux0 = p0[0], uy0 = p0[1], w0 = p0[2], ux1 = p1[0], uy1 = p1[1], w1 = p1[2], dx = ux1 - ux0, dy = uy1 - uy0, d2 = dx * dx + dy * dy, i, S; + if (d2 < Îĩ2) { + S = Math.log(w1 / w0) / Ī; + i = function(t) { + return [ ux0 + t * dx, uy0 + t * dy, w0 * Math.exp(Ī * t * S) ]; + }; + } else { + var d1 = Math.sqrt(d2), b0 = (w1 * w1 - w0 * w0 + Ī4 * d2) / (2 * w0 * Ī2 * d1), b1 = (w1 * w1 - w0 * w0 - Ī4 * d2) / (2 * w1 * Ī2 * d1), r0 = Math.log(Math.sqrt(b0 * b0 + 1) - b0), r1 = Math.log(Math.sqrt(b1 * b1 + 1) - b1); + S = (r1 - r0) / Ī; + i = function(t) { + var s = t * S, coshr0 = d3_cosh(r0), u = w0 / (Ī2 * d1) * (coshr0 * d3_tanh(Ī * s + r0) - d3_sinh(r0)); + return [ ux0 + u * dx, uy0 + u * dy, w0 * coshr0 / d3_cosh(Ī * s + r0) ]; + }; + } + i.duration = S * 1e3; + return i; + }; + d3.behavior.zoom = function() { + var view = { + x: 0, + y: 0, + k: 1 + }, translate0, center0, center, size = [ 960, 500 ], scaleExtent = d3_behavior_zoomInfinity, duration = 250, zooming = 0, mousedown = "mousedown.zoom", mousemove = "mousemove.zoom", mouseup = "mouseup.zoom", mousewheelTimer, touchstart = "touchstart.zoom", touchtime, event = d3_eventDispatch(zoom, "zoomstart", "zoom", "zoomend"), x0, x1, y0, y1; + if (!d3_behavior_zoomWheel) { + d3_behavior_zoomWheel = "onwheel" in d3_document ? (d3_behavior_zoomDelta = function() { + return -d3.event.deltaY * (d3.event.deltaMode ? 120 : 1); + }, "wheel") : "onmousewheel" in d3_document ? (d3_behavior_zoomDelta = function() { + return d3.event.wheelDelta; + }, "mousewheel") : (d3_behavior_zoomDelta = function() { + return -d3.event.detail; + }, "MozMousePixelScroll"); + } + function zoom(g) { + g.on(mousedown, mousedowned).on(d3_behavior_zoomWheel + ".zoom", mousewheeled).on("dblclick.zoom", dblclicked).on(touchstart, touchstarted); + } + zoom.event = function(g) { + g.each(function() { + var dispatch = event.of(this, arguments), view1 = view; + if (d3_transitionInheritId) { + d3.select(this).transition().each("start.zoom", function() { + view = this.__chart__ || { + x: 0, + y: 0, + k: 1 + }; + zoomstarted(dispatch); + }).tween("zoom:zoom", function() { + var dx = size[0], dy = size[1], cx = center0 ? center0[0] : dx / 2, cy = center0 ? center0[1] : dy / 2, i = d3.interpolateZoom([ (cx - view.x) / view.k, (cy - view.y) / view.k, dx / view.k ], [ (cx - view1.x) / view1.k, (cy - view1.y) / view1.k, dx / view1.k ]); + return function(t) { + var l = i(t), k = dx / l[2]; + this.__chart__ = view = { + x: cx - l[0] * k, + y: cy - l[1] * k, + k: k + }; + zoomed(dispatch); + }; + }).each("interrupt.zoom", function() { + zoomended(dispatch); + }).each("end.zoom", function() { + zoomended(dispatch); + }); + } else { + this.__chart__ = view; + zoomstarted(dispatch); + zoomed(dispatch); + zoomended(dispatch); + } + }); + }; + zoom.translate = function(_) { + if (!arguments.length) return [ view.x, view.y ]; + view = { + x: +_[0], + y: +_[1], + k: view.k + }; + rescale(); + return zoom; + }; + zoom.scale = function(_) { + if (!arguments.length) return view.k; + view = { + x: view.x, + y: view.y, + k: null + }; + scaleTo(+_); + rescale(); + return zoom; + }; + zoom.scaleExtent = function(_) { + if (!arguments.length) return scaleExtent; + scaleExtent = _ == null ? d3_behavior_zoomInfinity : [ +_[0], +_[1] ]; + return zoom; + }; + zoom.center = function(_) { + if (!arguments.length) return center; + center = _ && [ +_[0], +_[1] ]; + return zoom; + }; + zoom.size = function(_) { + if (!arguments.length) return size; + size = _ && [ +_[0], +_[1] ]; + return zoom; + }; + zoom.duration = function(_) { + if (!arguments.length) return duration; + duration = +_; + return zoom; + }; + zoom.x = function(z) { + if (!arguments.length) return x1; + x1 = z; + x0 = z.copy(); + view = { + x: 0, + y: 0, + k: 1 + }; + return zoom; + }; + zoom.y = function(z) { + if (!arguments.length) return y1; + y1 = z; + y0 = z.copy(); + view = { + x: 0, + y: 0, + k: 1 + }; + return zoom; + }; + function location(p) { + return [ (p[0] - view.x) / view.k, (p[1] - view.y) / view.k ]; + } + function point(l) { + return [ l[0] * view.k + view.x, l[1] * view.k + view.y ]; + } + function scaleTo(s) { + view.k = Math.max(scaleExtent[0], Math.min(scaleExtent[1], s)); + } + function translateTo(p, l) { + l = point(l); + view.x += p[0] - l[0]; + view.y += p[1] - l[1]; + } + function zoomTo(that, p, l, k) { + that.__chart__ = { + x: view.x, + y: view.y, + k: view.k + }; + scaleTo(Math.pow(2, k)); + translateTo(center0 = p, l); + that = d3.select(that); + if (duration > 0) that = that.transition().duration(duration); + that.call(zoom.event); + } + function rescale() { + if (x1) x1.domain(x0.range().map(function(x) { + return (x - view.x) / view.k; + }).map(x0.invert)); + if (y1) y1.domain(y0.range().map(function(y) { + return (y - view.y) / view.k; + }).map(y0.invert)); + } + function zoomstarted(dispatch) { + if (!zooming++) dispatch({ + type: "zoomstart" + }); + } + function zoomed(dispatch) { + rescale(); + dispatch({ + type: "zoom", + scale: view.k, + translate: [ view.x, view.y ] + }); + } + function zoomended(dispatch) { + if (!--zooming) dispatch({ + type: "zoomend" + }), center0 = null; + } + function mousedowned() { + var that = this, dispatch = event.of(that, arguments), dragged = 0, subject = d3.select(d3_window(that)).on(mousemove, moved).on(mouseup, ended), location0 = location(d3.mouse(that)), dragRestore = d3_event_dragSuppress(that); + d3_selection_interrupt.call(that); + zoomstarted(dispatch); + function moved() { + dragged = 1; + translateTo(d3.mouse(that), location0); + zoomed(dispatch); + } + function ended() { + subject.on(mousemove, null).on(mouseup, null); + dragRestore(dragged); + zoomended(dispatch); + } + } + function touchstarted() { + var that = this, dispatch = event.of(that, arguments), locations0 = {}, distance0 = 0, scale0, zoomName = ".zoom-" + d3.event.changedTouches[0].identifier, touchmove = "touchmove" + zoomName, touchend = "touchend" + zoomName, targets = [], subject = d3.select(that), dragRestore = d3_event_dragSuppress(that); + started(); + zoomstarted(dispatch); + subject.on(mousedown, null).on(touchstart, started); + function relocate() { + var touches = d3.touches(that); + scale0 = view.k; + touches.forEach(function(t) { + if (t.identifier in locations0) locations0[t.identifier] = location(t); + }); + return touches; + } + function started() { + var target = d3.event.target; + d3.select(target).on(touchmove, moved).on(touchend, ended); + targets.push(target); + var changed = d3.event.changedTouches; + for (var i = 0, n = changed.length; i < n; ++i) { + locations0[changed[i].identifier] = null; + } + var touches = relocate(), now = Date.now(); + if (touches.length === 1) { + if (now - touchtime < 500) { + var p = touches[0]; + zoomTo(that, p, locations0[p.identifier], Math.floor(Math.log(view.k) / Math.LN2) + 1); + d3_eventPreventDefault(); + } + touchtime = now; + } else if (touches.length > 1) { + var p = touches[0], q = touches[1], dx = p[0] - q[0], dy = p[1] - q[1]; + distance0 = dx * dx + dy * dy; + } + } + function moved() { + var touches = d3.touches(that), p0, l0, p1, l1; + d3_selection_interrupt.call(that); + for (var i = 0, n = touches.length; i < n; ++i, l1 = null) { + p1 = touches[i]; + if (l1 = locations0[p1.identifier]) { + if (l0) break; + p0 = p1, l0 = l1; + } + } + if (l1) { + var distance1 = (distance1 = p1[0] - p0[0]) * distance1 + (distance1 = p1[1] - p0[1]) * distance1, scale1 = distance0 && Math.sqrt(distance1 / distance0); + p0 = [ (p0[0] + p1[0]) / 2, (p0[1] + p1[1]) / 2 ]; + l0 = [ (l0[0] + l1[0]) / 2, (l0[1] + l1[1]) / 2 ]; + scaleTo(scale1 * scale0); + } + touchtime = null; + translateTo(p0, l0); + zoomed(dispatch); + } + function ended() { + if (d3.event.touches.length) { + var changed = d3.event.changedTouches; + for (var i = 0, n = changed.length; i < n; ++i) { + delete locations0[changed[i].identifier]; + } + for (var identifier in locations0) { + return void relocate(); + } + } + d3.selectAll(targets).on(zoomName, null); + subject.on(mousedown, mousedowned).on(touchstart, touchstarted); + dragRestore(); + zoomended(dispatch); + } + } + function mousewheeled() { + var dispatch = event.of(this, arguments); + if (mousewheelTimer) clearTimeout(mousewheelTimer); else d3_selection_interrupt.call(this), + translate0 = location(center0 = center || d3.mouse(this)), zoomstarted(dispatch); + mousewheelTimer = setTimeout(function() { + mousewheelTimer = null; + zoomended(dispatch); + }, 50); + d3_eventPreventDefault(); + scaleTo(Math.pow(2, d3_behavior_zoomDelta() * .002) * view.k); + translateTo(center0, translate0); + zoomed(dispatch); + } + function dblclicked() { + var p = d3.mouse(this), k = Math.log(view.k) / Math.LN2; + zoomTo(this, p, location(p), d3.event.shiftKey ? Math.ceil(k) - 1 : Math.floor(k) + 1); + } + return d3.rebind(zoom, event, "on"); + }; + var d3_behavior_zoomInfinity = [ 0, Infinity ], d3_behavior_zoomDelta, d3_behavior_zoomWheel; + d3.color = d3_color; + function d3_color() {} + d3_color.prototype.toString = function() { + return this.rgb() + ""; + }; + d3.hsl = d3_hsl; + function d3_hsl(h, s, l) { + return this instanceof d3_hsl ? void (this.h = +h, this.s = +s, this.l = +l) : arguments.length < 2 ? h instanceof d3_hsl ? new d3_hsl(h.h, h.s, h.l) : d3_rgb_parse("" + h, d3_rgb_hsl, d3_hsl) : new d3_hsl(h, s, l); + } + var d3_hslPrototype = d3_hsl.prototype = new d3_color(); + d3_hslPrototype.brighter = function(k) { + k = Math.pow(.7, arguments.length ? k : 1); + return new d3_hsl(this.h, this.s, this.l / k); + }; + d3_hslPrototype.darker = function(k) { + k = Math.pow(.7, arguments.length ? k : 1); + return new d3_hsl(this.h, this.s, k * this.l); + }; + d3_hslPrototype.rgb = function() { + return d3_hsl_rgb(this.h, this.s, this.l); + }; + function d3_hsl_rgb(h, s, l) { + var m1, m2; + h = isNaN(h) ? 0 : (h %= 360) < 0 ? h + 360 : h; + s = isNaN(s) ? 0 : s < 0 ? 0 : s > 1 ? 1 : s; + l = l < 0 ? 0 : l > 1 ? 1 : l; + m2 = l <= .5 ? l * (1 + s) : l + s - l * s; + m1 = 2 * l - m2; + function v(h) { + if (h > 360) h -= 360; else if (h < 0) h += 360; + if (h < 60) return m1 + (m2 - m1) * h / 60; + if (h < 180) return m2; + if (h < 240) return m1 + (m2 - m1) * (240 - h) / 60; + return m1; + } + function vv(h) { + return Math.round(v(h) * 255); + } + return new d3_rgb(vv(h + 120), vv(h), vv(h - 120)); + } + d3.hcl = d3_hcl; + function d3_hcl(h, c, l) { + return this instanceof d3_hcl ? void (this.h = +h, this.c = +c, this.l = +l) : arguments.length < 2 ? h instanceof d3_hcl ? new d3_hcl(h.h, h.c, h.l) : h instanceof d3_lab ? d3_lab_hcl(h.l, h.a, h.b) : d3_lab_hcl((h = d3_rgb_lab((h = d3.rgb(h)).r, h.g, h.b)).l, h.a, h.b) : new d3_hcl(h, c, l); + } + var d3_hclPrototype = d3_hcl.prototype = new d3_color(); + d3_hclPrototype.brighter = function(k) { + return new d3_hcl(this.h, this.c, Math.min(100, this.l + d3_lab_K * (arguments.length ? k : 1))); + }; + d3_hclPrototype.darker = function(k) { + return new d3_hcl(this.h, this.c, Math.max(0, this.l - d3_lab_K * (arguments.length ? k : 1))); + }; + d3_hclPrototype.rgb = function() { + return d3_hcl_lab(this.h, this.c, this.l).rgb(); + }; + function d3_hcl_lab(h, c, l) { + if (isNaN(h)) h = 0; + if (isNaN(c)) c = 0; + return new d3_lab(l, Math.cos(h *= d3_radians) * c, Math.sin(h) * c); + } + d3.lab = d3_lab; + function d3_lab(l, a, b) { + return this instanceof d3_lab ? void (this.l = +l, this.a = +a, this.b = +b) : arguments.length < 2 ? l instanceof d3_lab ? new d3_lab(l.l, l.a, l.b) : l instanceof d3_hcl ? d3_hcl_lab(l.h, l.c, l.l) : d3_rgb_lab((l = d3_rgb(l)).r, l.g, l.b) : new d3_lab(l, a, b); + } + var d3_lab_K = 18; + var d3_lab_X = .95047, d3_lab_Y = 1, d3_lab_Z = 1.08883; + var d3_labPrototype = d3_lab.prototype = new d3_color(); + d3_labPrototype.brighter = function(k) { + return new d3_lab(Math.min(100, this.l + d3_lab_K * (arguments.length ? k : 1)), this.a, this.b); + }; + d3_labPrototype.darker = function(k) { + return new d3_lab(Math.max(0, this.l - d3_lab_K * (arguments.length ? k : 1)), this.a, this.b); + }; + d3_labPrototype.rgb = function() { + return d3_lab_rgb(this.l, this.a, this.b); + }; + function d3_lab_rgb(l, a, b) { + var y = (l + 16) / 116, x = y + a / 500, z = y - b / 200; + x = d3_lab_xyz(x) * d3_lab_X; + y = d3_lab_xyz(y) * d3_lab_Y; + z = d3_lab_xyz(z) * d3_lab_Z; + return new d3_rgb(d3_xyz_rgb(3.2404542 * x - 1.5371385 * y - .4985314 * z), d3_xyz_rgb(-.969266 * x + 1.8760108 * y + .041556 * z), d3_xyz_rgb(.0556434 * x - .2040259 * y + 1.0572252 * z)); + } + function d3_lab_hcl(l, a, b) { + return l > 0 ? new d3_hcl(Math.atan2(b, a) * d3_degrees, Math.sqrt(a * a + b * b), l) : new d3_hcl(NaN, NaN, l); + } + function d3_lab_xyz(x) { + return x > .206893034 ? x * x * x : (x - 4 / 29) / 7.787037; + } + function d3_xyz_lab(x) { + return x > .008856 ? Math.pow(x, 1 / 3) : 7.787037 * x + 4 / 29; + } + function d3_xyz_rgb(r) { + return Math.round(255 * (r <= .00304 ? 12.92 * r : 1.055 * Math.pow(r, 1 / 2.4) - .055)); + } + d3.rgb = d3_rgb; + function d3_rgb(r, g, b) { + return this instanceof d3_rgb ? void (this.r = ~~r, this.g = ~~g, this.b = ~~b) : arguments.length < 2 ? r instanceof d3_rgb ? new d3_rgb(r.r, r.g, r.b) : d3_rgb_parse("" + r, d3_rgb, d3_hsl_rgb) : new d3_rgb(r, g, b); + } + function d3_rgbNumber(value) { + return new d3_rgb(value >> 16, value >> 8 & 255, value & 255); + } + function d3_rgbString(value) { + return d3_rgbNumber(value) + ""; + } + var d3_rgbPrototype = d3_rgb.prototype = new d3_color(); + d3_rgbPrototype.brighter = function(k) { + k = Math.pow(.7, arguments.length ? k : 1); + var r = this.r, g = this.g, b = this.b, i = 30; + if (!r && !g && !b) return new d3_rgb(i, i, i); + if (r && r < i) r = i; + if (g && g < i) g = i; + if (b && b < i) b = i; + return new d3_rgb(Math.min(255, r / k), Math.min(255, g / k), Math.min(255, b / k)); + }; + d3_rgbPrototype.darker = function(k) { + k = Math.pow(.7, arguments.length ? k : 1); + return new d3_rgb(k * this.r, k * this.g, k * this.b); + }; + d3_rgbPrototype.hsl = function() { + return d3_rgb_hsl(this.r, this.g, this.b); + }; + d3_rgbPrototype.toString = function() { + return "#" + d3_rgb_hex(this.r) + d3_rgb_hex(this.g) + d3_rgb_hex(this.b); + }; + function d3_rgb_hex(v) { + return v < 16 ? "0" + Math.max(0, v).toString(16) : Math.min(255, v).toString(16); + } + function d3_rgb_parse(format, rgb, hsl) { + var r = 0, g = 0, b = 0, m1, m2, color; + m1 = /([a-z]+)\((.*)\)/.exec(format = format.toLowerCase()); + if (m1) { + m2 = m1[2].split(","); + switch (m1[1]) { + case "hsl": + { + return hsl(parseFloat(m2[0]), parseFloat(m2[1]) / 100, parseFloat(m2[2]) / 100); + } + + case "rgb": + { + return rgb(d3_rgb_parseNumber(m2[0]), d3_rgb_parseNumber(m2[1]), d3_rgb_parseNumber(m2[2])); + } + } + } + if (color = d3_rgb_names.get(format)) { + return rgb(color.r, color.g, color.b); + } + if (format != null && format.charAt(0) === "#" && !isNaN(color = parseInt(format.slice(1), 16))) { + if (format.length === 4) { + r = (color & 3840) >> 4; + r = r >> 4 | r; + g = color & 240; + g = g >> 4 | g; + b = color & 15; + b = b << 4 | b; + } else if (format.length === 7) { + r = (color & 16711680) >> 16; + g = (color & 65280) >> 8; + b = color & 255; + } + } + return rgb(r, g, b); + } + function d3_rgb_hsl(r, g, b) { + var min = Math.min(r /= 255, g /= 255, b /= 255), max = Math.max(r, g, b), d = max - min, h, s, l = (max + min) / 2; + if (d) { + s = l < .5 ? d / (max + min) : d / (2 - max - min); + if (r == max) h = (g - b) / d + (g < b ? 6 : 0); else if (g == max) h = (b - r) / d + 2; else h = (r - g) / d + 4; + h *= 60; + } else { + h = NaN; + s = l > 0 && l < 1 ? 0 : h; + } + return new d3_hsl(h, s, l); + } + function d3_rgb_lab(r, g, b) { + r = d3_rgb_xyz(r); + g = d3_rgb_xyz(g); + b = d3_rgb_xyz(b); + var x = d3_xyz_lab((.4124564 * r + .3575761 * g + .1804375 * b) / d3_lab_X), y = d3_xyz_lab((.2126729 * r + .7151522 * g + .072175 * b) / d3_lab_Y), z = d3_xyz_lab((.0193339 * r + .119192 * g + .9503041 * b) / d3_lab_Z); + return d3_lab(116 * y - 16, 500 * (x - y), 200 * (y - z)); + } + function d3_rgb_xyz(r) { + return (r /= 255) <= .04045 ? r / 12.92 : Math.pow((r + .055) / 1.055, 2.4); + } + function d3_rgb_parseNumber(c) { + var f = parseFloat(c); + return c.charAt(c.length - 1) === "%" ? Math.round(f * 2.55) : f; + } + var d3_rgb_names = d3.map({ + aliceblue: 15792383, + antiquewhite: 16444375, + aqua: 65535, + aquamarine: 8388564, + azure: 15794175, + beige: 16119260, + bisque: 16770244, + black: 0, + blanchedalmond: 16772045, + blue: 255, + blueviolet: 9055202, + brown: 10824234, + burlywood: 14596231, + cadetblue: 6266528, + chartreuse: 8388352, + chocolate: 13789470, + coral: 16744272, + cornflowerblue: 6591981, + cornsilk: 16775388, + crimson: 14423100, + cyan: 65535, + darkblue: 139, + darkcyan: 35723, + darkgoldenrod: 12092939, + darkgray: 11119017, + darkgreen: 25600, + darkgrey: 11119017, + darkkhaki: 12433259, + darkmagenta: 9109643, + darkolivegreen: 5597999, + darkorange: 16747520, + darkorchid: 10040012, + darkred: 9109504, + darksalmon: 15308410, + darkseagreen: 9419919, + darkslateblue: 4734347, + darkslategray: 3100495, + darkslategrey: 3100495, + darkturquoise: 52945, + darkviolet: 9699539, + deeppink: 16716947, + deepskyblue: 49151, + dimgray: 6908265, + dimgrey: 6908265, + dodgerblue: 2003199, + firebrick: 11674146, + floralwhite: 16775920, + forestgreen: 2263842, + fuchsia: 16711935, + gainsboro: 14474460, + ghostwhite: 16316671, + gold: 16766720, + goldenrod: 14329120, + gray: 8421504, + green: 32768, + greenyellow: 11403055, + grey: 8421504, + honeydew: 15794160, + hotpink: 16738740, + indianred: 13458524, + indigo: 4915330, + ivory: 16777200, + khaki: 15787660, + lavender: 15132410, + lavenderblush: 16773365, + lawngreen: 8190976, + lemonchiffon: 16775885, + lightblue: 11393254, + lightcoral: 15761536, + lightcyan: 14745599, + lightgoldenrodyellow: 16448210, + lightgray: 13882323, + lightgreen: 9498256, + lightgrey: 13882323, + lightpink: 16758465, + lightsalmon: 16752762, + lightseagreen: 2142890, + lightskyblue: 8900346, + lightslategray: 7833753, + lightslategrey: 7833753, + lightsteelblue: 11584734, + lightyellow: 16777184, + lime: 65280, + limegreen: 3329330, + linen: 16445670, + magenta: 16711935, + maroon: 8388608, + mediumaquamarine: 6737322, + mediumblue: 205, + mediumorchid: 12211667, + mediumpurple: 9662683, + mediumseagreen: 3978097, + mediumslateblue: 8087790, + mediumspringgreen: 64154, + mediumturquoise: 4772300, + mediumvioletred: 13047173, + midnightblue: 1644912, + mintcream: 16121850, + mistyrose: 16770273, + moccasin: 16770229, + navajowhite: 16768685, + navy: 128, + oldlace: 16643558, + olive: 8421376, + olivedrab: 7048739, + orange: 16753920, + orangered: 16729344, + orchid: 14315734, + palegoldenrod: 15657130, + palegreen: 10025880, + paleturquoise: 11529966, + palevioletred: 14381203, + papayawhip: 16773077, + peachpuff: 16767673, + peru: 13468991, + pink: 16761035, + plum: 14524637, + powderblue: 11591910, + purple: 8388736, + rebeccapurple: 6697881, + red: 16711680, + rosybrown: 12357519, + royalblue: 4286945, + saddlebrown: 9127187, + salmon: 16416882, + sandybrown: 16032864, + seagreen: 3050327, + seashell: 16774638, + sienna: 10506797, + silver: 12632256, + skyblue: 8900331, + slateblue: 6970061, + slategray: 7372944, + slategrey: 7372944, + snow: 16775930, + springgreen: 65407, + steelblue: 4620980, + tan: 13808780, + teal: 32896, + thistle: 14204888, + tomato: 16737095, + turquoise: 4251856, + violet: 15631086, + wheat: 16113331, + white: 16777215, + whitesmoke: 16119285, + yellow: 16776960, + yellowgreen: 10145074 + }); + d3_rgb_names.forEach(function(key, value) { + d3_rgb_names.set(key, d3_rgbNumber(value)); + }); + function d3_functor(v) { + return typeof v === "function" ? v : function() { + return v; + }; + } + d3.functor = d3_functor; + d3.xhr = d3_xhrType(d3_identity); + function d3_xhrType(response) { + return function(url, mimeType, callback) { + if (arguments.length === 2 && typeof mimeType === "function") callback = mimeType, + mimeType = null; + return d3_xhr(url, mimeType, response, callback); + }; + } + function d3_xhr(url, mimeType, response, callback) { + var xhr = {}, dispatch = d3.dispatch("beforesend", "progress", "load", "error"), headers = {}, request = new XMLHttpRequest(), responseType = null; + if (this.XDomainRequest && !("withCredentials" in request) && /^(http(s)?:)?\/\//.test(url)) request = new XDomainRequest(); + "onload" in request ? request.onload = request.onerror = respond : request.onreadystatechange = function() { + request.readyState > 3 && respond(); + }; + function respond() { + var status = request.status, result; + if (!status && d3_xhrHasResponse(request) || status >= 200 && status < 300 || status === 304) { + try { + result = response.call(xhr, request); + } catch (e) { + dispatch.error.call(xhr, e); + return; + } + dispatch.load.call(xhr, result); + } else { + dispatch.error.call(xhr, request); + } + } + request.onprogress = function(event) { + var o = d3.event; + d3.event = event; + try { + dispatch.progress.call(xhr, request); + } finally { + d3.event = o; + } + }; + xhr.header = function(name, value) { + name = (name + "").toLowerCase(); + if (arguments.length < 2) return headers[name]; + if (value == null) delete headers[name]; else headers[name] = value + ""; + return xhr; + }; + xhr.mimeType = function(value) { + if (!arguments.length) return mimeType; + mimeType = value == null ? null : value + ""; + return xhr; + }; + xhr.responseType = function(value) { + if (!arguments.length) return responseType; + responseType = value; + return xhr; + }; + xhr.response = function(value) { + response = value; + return xhr; + }; + [ "get", "post" ].forEach(function(method) { + xhr[method] = function() { + return xhr.send.apply(xhr, [ method ].concat(d3_array(arguments))); + }; + }); + xhr.send = function(method, data, callback) { + if (arguments.length === 2 && typeof data === "function") callback = data, data = null; + request.open(method, url, true); + if (mimeType != null && !("accept" in headers)) headers["accept"] = mimeType + ",*/*"; + if (request.setRequestHeader) for (var name in headers) request.setRequestHeader(name, headers[name]); + if (mimeType != null && request.overrideMimeType) request.overrideMimeType(mimeType); + if (responseType != null) request.responseType = responseType; + if (callback != null) xhr.on("error", callback).on("load", function(request) { + callback(null, request); + }); + dispatch.beforesend.call(xhr, request); + request.send(data == null ? null : data); + return xhr; + }; + xhr.abort = function() { + request.abort(); + return xhr; + }; + d3.rebind(xhr, dispatch, "on"); + return callback == null ? xhr : xhr.get(d3_xhr_fixCallback(callback)); + } + function d3_xhr_fixCallback(callback) { + return callback.length === 1 ? function(error, request) { + callback(error == null ? request : null); + } : callback; + } + function d3_xhrHasResponse(request) { + var type = request.responseType; + return type && type !== "text" ? request.response : request.responseText; + } + d3.dsv = function(delimiter, mimeType) { + var reFormat = new RegExp('["' + delimiter + "\n]"), delimiterCode = delimiter.charCodeAt(0); + function dsv(url, row, callback) { + if (arguments.length < 3) callback = row, row = null; + var xhr = d3_xhr(url, mimeType, row == null ? response : typedResponse(row), callback); + xhr.row = function(_) { + return arguments.length ? xhr.response((row = _) == null ? response : typedResponse(_)) : row; + }; + return xhr; + } + function response(request) { + return dsv.parse(request.responseText); + } + function typedResponse(f) { + return function(request) { + return dsv.parse(request.responseText, f); + }; + } + dsv.parse = function(text, f) { + var o; + return dsv.parseRows(text, function(row, i) { + if (o) return o(row, i - 1); + var a = new Function("d", "return {" + row.map(function(name, i) { + return JSON.stringify(name) + ": d[" + i + "]"; + }).join(",") + "}"); + o = f ? function(row, i) { + return f(a(row), i); + } : a; + }); + }; + dsv.parseRows = function(text, f) { + var EOL = {}, EOF = {}, rows = [], N = text.length, I = 0, n = 0, t, eol; + function token() { + if (I >= N) return EOF; + if (eol) return eol = false, EOL; + var j = I; + if (text.charCodeAt(j) === 34) { + var i = j; + while (i++ < N) { + if (text.charCodeAt(i) === 34) { + if (text.charCodeAt(i + 1) !== 34) break; + ++i; + } + } + I = i + 2; + var c = text.charCodeAt(i + 1); + if (c === 13) { + eol = true; + if (text.charCodeAt(i + 2) === 10) ++I; + } else if (c === 10) { + eol = true; + } + return text.slice(j + 1, i).replace(/""/g, '"'); + } + while (I < N) { + var c = text.charCodeAt(I++), k = 1; + if (c === 10) eol = true; else if (c === 13) { + eol = true; + if (text.charCodeAt(I) === 10) ++I, ++k; + } else if (c !== delimiterCode) continue; + return text.slice(j, I - k); + } + return text.slice(j); + } + while ((t = token()) !== EOF) { + var a = []; + while (t !== EOL && t !== EOF) { + a.push(t); + t = token(); + } + if (f && (a = f(a, n++)) == null) continue; + rows.push(a); + } + return rows; + }; + dsv.format = function(rows) { + if (Array.isArray(rows[0])) return dsv.formatRows(rows); + var fieldSet = new d3_Set(), fields = []; + rows.forEach(function(row) { + for (var field in row) { + if (!fieldSet.has(field)) { + fields.push(fieldSet.add(field)); + } + } + }); + return [ fields.map(formatValue).join(delimiter) ].concat(rows.map(function(row) { + return fields.map(function(field) { + return formatValue(row[field]); + }).join(delimiter); + })).join("\n"); + }; + dsv.formatRows = function(rows) { + return rows.map(formatRow).join("\n"); + }; + function formatRow(row) { + return row.map(formatValue).join(delimiter); + } + function formatValue(text) { + return reFormat.test(text) ? '"' + text.replace(/\"/g, '""') + '"' : text; + } + return dsv; + }; + d3.csv = d3.dsv(",", "text/csv"); + d3.tsv = d3.dsv(" ", "text/tab-separated-values"); + var d3_timer_queueHead, d3_timer_queueTail, d3_timer_interval, d3_timer_timeout, d3_timer_frame = this[d3_vendorSymbol(this, "requestAnimationFrame")] || function(callback) { + setTimeout(callback, 17); + }; + d3.timer = function() { + d3_timer.apply(this, arguments); + }; + function d3_timer(callback, delay, then) { + var n = arguments.length; + if (n < 2) delay = 0; + if (n < 3) then = Date.now(); + var time = then + delay, timer = { + c: callback, + t: time, + n: null + }; + if (d3_timer_queueTail) d3_timer_queueTail.n = timer; else d3_timer_queueHead = timer; + d3_timer_queueTail = timer; + if (!d3_timer_interval) { + d3_timer_timeout = clearTimeout(d3_timer_timeout); + d3_timer_interval = 1; + d3_timer_frame(d3_timer_step); + } + return timer; + } + function d3_timer_step() { + var now = d3_timer_mark(), delay = d3_timer_sweep() - now; + if (delay > 24) { + if (isFinite(delay)) { + clearTimeout(d3_timer_timeout); + d3_timer_timeout = setTimeout(d3_timer_step, delay); + } + d3_timer_interval = 0; + } else { + d3_timer_interval = 1; + d3_timer_frame(d3_timer_step); + } + } + d3.timer.flush = function() { + d3_timer_mark(); + d3_timer_sweep(); + }; + function d3_timer_mark() { + var now = Date.now(), timer = d3_timer_queueHead; + while (timer) { + if (now >= timer.t && timer.c(now - timer.t)) timer.c = null; + timer = timer.n; + } + return now; + } + function d3_timer_sweep() { + var t0, t1 = d3_timer_queueHead, time = Infinity; + while (t1) { + if (t1.c) { + if (t1.t < time) time = t1.t; + t1 = (t0 = t1).n; + } else { + t1 = t0 ? t0.n = t1.n : d3_timer_queueHead = t1.n; + } + } + d3_timer_queueTail = t0; + return time; + } + function d3_format_precision(x, p) { + return p - (x ? Math.ceil(Math.log(x) / Math.LN10) : 1); + } + d3.round = function(x, n) { + return n ? Math.round(x * (n = Math.pow(10, n))) / n : Math.round(x); + }; + var d3_formatPrefixes = [ "y", "z", "a", "f", "p", "n", "Âĩ", "m", "", "k", "M", "G", "T", "P", "E", "Z", "Y" ].map(d3_formatPrefix); + d3.formatPrefix = function(value, precision) { + var i = 0; + if (value = +value) { + if (value < 0) value *= -1; + if (precision) value = d3.round(value, d3_format_precision(value, precision)); + i = 1 + Math.floor(1e-12 + Math.log(value) / Math.LN10); + i = Math.max(-24, Math.min(24, Math.floor((i - 1) / 3) * 3)); + } + return d3_formatPrefixes[8 + i / 3]; + }; + function d3_formatPrefix(d, i) { + var k = Math.pow(10, abs(8 - i) * 3); + return { + scale: i > 8 ? function(d) { + return d / k; + } : function(d) { + return d * k; + }, + symbol: d + }; + } + function d3_locale_numberFormat(locale) { + var locale_decimal = locale.decimal, locale_thousands = locale.thousands, locale_grouping = locale.grouping, locale_currency = locale.currency, formatGroup = locale_grouping && locale_thousands ? function(value, width) { + var i = value.length, t = [], j = 0, g = locale_grouping[0], length = 0; + while (i > 0 && g > 0) { + if (length + g + 1 > width) g = Math.max(1, width - length); + t.push(value.substring(i -= g, i + g)); + if ((length += g + 1) > width) break; + g = locale_grouping[j = (j + 1) % locale_grouping.length]; + } + return t.reverse().join(locale_thousands); + } : d3_identity; + return function(specifier) { + var match = d3_format_re.exec(specifier), fill = match[1] || " ", align = match[2] || ">", sign = match[3] || "-", symbol = match[4] || "", zfill = match[5], width = +match[6], comma = match[7], precision = match[8], type = match[9], scale = 1, prefix = "", suffix = "", integer = false, exponent = true; + if (precision) precision = +precision.substring(1); + if (zfill || fill === "0" && align === "=") { + zfill = fill = "0"; + align = "="; + } + switch (type) { + case "n": + comma = true; + type = "g"; + break; + + case "%": + scale = 100; + suffix = "%"; + type = "f"; + break; + + case "p": + scale = 100; + suffix = "%"; + type = "r"; + break; + + case "b": + case "o": + case "x": + case "X": + if (symbol === "#") prefix = "0" + type.toLowerCase(); + + case "c": + exponent = false; + + case "d": + integer = true; + precision = 0; + break; + + case "s": + scale = -1; + type = "r"; + break; + } + if (symbol === "$") prefix = locale_currency[0], suffix = locale_currency[1]; + if (type == "r" && !precision) type = "g"; + if (precision != null) { + if (type == "g") precision = Math.max(1, Math.min(21, precision)); else if (type == "e" || type == "f") precision = Math.max(0, Math.min(20, precision)); + } + type = d3_format_types.get(type) || d3_format_typeDefault; + var zcomma = zfill && comma; + return function(value) { + var fullSuffix = suffix; + if (integer && value % 1) return ""; + var negative = value < 0 || value === 0 && 1 / value < 0 ? (value = -value, "-") : sign === "-" ? "" : sign; + if (scale < 0) { + var unit = d3.formatPrefix(value, precision); + value = unit.scale(value); + fullSuffix = unit.symbol + suffix; + } else { + value *= scale; + } + value = type(value, precision); + var i = value.lastIndexOf("."), before, after; + if (i < 0) { + var j = exponent ? value.lastIndexOf("e") : -1; + if (j < 0) before = value, after = ""; else before = value.substring(0, j), after = value.substring(j); + } else { + before = value.substring(0, i); + after = locale_decimal + value.substring(i + 1); + } + if (!zfill && comma) before = formatGroup(before, Infinity); + var length = prefix.length + before.length + after.length + (zcomma ? 0 : negative.length), padding = length < width ? new Array(length = width - length + 1).join(fill) : ""; + if (zcomma) before = formatGroup(padding + before, padding.length ? width - after.length : Infinity); + negative += prefix; + value = before + after; + return (align === "<" ? negative + value + padding : align === ">" ? padding + negative + value : align === "^" ? padding.substring(0, length >>= 1) + negative + value + padding.substring(length) : negative + (zcomma ? value : padding + value)) + fullSuffix; + }; + }; + } + var d3_format_re = /(?:([^{])?([<>=^]))?([+\- ])?([$#])?(0)?(\d+)?(,)?(\.-?\d+)?([a-z%])?/i; + var d3_format_types = d3.map({ + b: function(x) { + return x.toString(2); + }, + c: function(x) { + return String.fromCharCode(x); + }, + o: function(x) { + return x.toString(8); + }, + x: function(x) { + return x.toString(16); + }, + X: function(x) { + return x.toString(16).toUpperCase(); + }, + g: function(x, p) { + return x.toPrecision(p); + }, + e: function(x, p) { + return x.toExponential(p); + }, + f: function(x, p) { + return x.toFixed(p); + }, + r: function(x, p) { + return (x = d3.round(x, d3_format_precision(x, p))).toFixed(Math.max(0, Math.min(20, d3_format_precision(x * (1 + 1e-15), p)))); + } + }); + function d3_format_typeDefault(x) { + return x + ""; + } + var d3_time = d3.time = {}, d3_date = Date; + function d3_date_utc() { + this._ = new Date(arguments.length > 1 ? Date.UTC.apply(this, arguments) : arguments[0]); + } + d3_date_utc.prototype = { + getDate: function() { + return this._.getUTCDate(); + }, + getDay: function() { + return this._.getUTCDay(); + }, + getFullYear: function() { + return this._.getUTCFullYear(); + }, + getHours: function() { + return this._.getUTCHours(); + }, + getMilliseconds: function() { + return this._.getUTCMilliseconds(); + }, + getMinutes: function() { + return this._.getUTCMinutes(); + }, + getMonth: function() { + return this._.getUTCMonth(); + }, + getSeconds: function() { + return this._.getUTCSeconds(); + }, + getTime: function() { + return this._.getTime(); + }, + getTimezoneOffset: function() { + return 0; + }, + valueOf: function() { + return this._.valueOf(); + }, + setDate: function() { + d3_time_prototype.setUTCDate.apply(this._, arguments); + }, + setDay: function() { + d3_time_prototype.setUTCDay.apply(this._, arguments); + }, + setFullYear: function() { + d3_time_prototype.setUTCFullYear.apply(this._, arguments); + }, + setHours: function() { + d3_time_prototype.setUTCHours.apply(this._, arguments); + }, + setMilliseconds: function() { + d3_time_prototype.setUTCMilliseconds.apply(this._, arguments); + }, + setMinutes: function() { + d3_time_prototype.setUTCMinutes.apply(this._, arguments); + }, + setMonth: function() { + d3_time_prototype.setUTCMonth.apply(this._, arguments); + }, + setSeconds: function() { + d3_time_prototype.setUTCSeconds.apply(this._, arguments); + }, + setTime: function() { + d3_time_prototype.setTime.apply(this._, arguments); + } + }; + var d3_time_prototype = Date.prototype; + function d3_time_interval(local, step, number) { + function round(date) { + var d0 = local(date), d1 = offset(d0, 1); + return date - d0 < d1 - date ? d0 : d1; + } + function ceil(date) { + step(date = local(new d3_date(date - 1)), 1); + return date; + } + function offset(date, k) { + step(date = new d3_date(+date), k); + return date; + } + function range(t0, t1, dt) { + var time = ceil(t0), times = []; + if (dt > 1) { + while (time < t1) { + if (!(number(time) % dt)) times.push(new Date(+time)); + step(time, 1); + } + } else { + while (time < t1) times.push(new Date(+time)), step(time, 1); + } + return times; + } + function range_utc(t0, t1, dt) { + try { + d3_date = d3_date_utc; + var utc = new d3_date_utc(); + utc._ = t0; + return range(utc, t1, dt); + } finally { + d3_date = Date; + } + } + local.floor = local; + local.round = round; + local.ceil = ceil; + local.offset = offset; + local.range = range; + var utc = local.utc = d3_time_interval_utc(local); + utc.floor = utc; + utc.round = d3_time_interval_utc(round); + utc.ceil = d3_time_interval_utc(ceil); + utc.offset = d3_time_interval_utc(offset); + utc.range = range_utc; + return local; + } + function d3_time_interval_utc(method) { + return function(date, k) { + try { + d3_date = d3_date_utc; + var utc = new d3_date_utc(); + utc._ = date; + return method(utc, k)._; + } finally { + d3_date = Date; + } + }; + } + d3_time.year = d3_time_interval(function(date) { + date = d3_time.day(date); + date.setMonth(0, 1); + return date; + }, function(date, offset) { + date.setFullYear(date.getFullYear() + offset); + }, function(date) { + return date.getFullYear(); + }); + d3_time.years = d3_time.year.range; + d3_time.years.utc = d3_time.year.utc.range; + d3_time.day = d3_time_interval(function(date) { + var day = new d3_date(2e3, 0); + day.setFullYear(date.getFullYear(), date.getMonth(), date.getDate()); + return day; + }, function(date, offset) { + date.setDate(date.getDate() + offset); + }, function(date) { + return date.getDate() - 1; + }); + d3_time.days = d3_time.day.range; + d3_time.days.utc = d3_time.day.utc.range; + d3_time.dayOfYear = function(date) { + var year = d3_time.year(date); + return Math.floor((date - year - (date.getTimezoneOffset() - year.getTimezoneOffset()) * 6e4) / 864e5); + }; + [ "sunday", "monday", "tuesday", "wednesday", "thursday", "friday", "saturday" ].forEach(function(day, i) { + i = 7 - i; + var interval = d3_time[day] = d3_time_interval(function(date) { + (date = d3_time.day(date)).setDate(date.getDate() - (date.getDay() + i) % 7); + return date; + }, function(date, offset) { + date.setDate(date.getDate() + Math.floor(offset) * 7); + }, function(date) { + var day = d3_time.year(date).getDay(); + return Math.floor((d3_time.dayOfYear(date) + (day + i) % 7) / 7) - (day !== i); + }); + d3_time[day + "s"] = interval.range; + d3_time[day + "s"].utc = interval.utc.range; + d3_time[day + "OfYear"] = function(date) { + var day = d3_time.year(date).getDay(); + return Math.floor((d3_time.dayOfYear(date) + (day + i) % 7) / 7); + }; + }); + d3_time.week = d3_time.sunday; + d3_time.weeks = d3_time.sunday.range; + d3_time.weeks.utc = d3_time.sunday.utc.range; + d3_time.weekOfYear = d3_time.sundayOfYear; + function d3_locale_timeFormat(locale) { + var locale_dateTime = locale.dateTime, locale_date = locale.date, locale_time = locale.time, locale_periods = locale.periods, locale_days = locale.days, locale_shortDays = locale.shortDays, locale_months = locale.months, locale_shortMonths = locale.shortMonths; + function d3_time_format(template) { + var n = template.length; + function format(date) { + var string = [], i = -1, j = 0, c, p, f; + while (++i < n) { + if (template.charCodeAt(i) === 37) { + string.push(template.slice(j, i)); + if ((p = d3_time_formatPads[c = template.charAt(++i)]) != null) c = template.charAt(++i); + if (f = d3_time_formats[c]) c = f(date, p == null ? c === "e" ? " " : "0" : p); + string.push(c); + j = i + 1; + } + } + string.push(template.slice(j, i)); + return string.join(""); + } + format.parse = function(string) { + var d = { + y: 1900, + m: 0, + d: 1, + H: 0, + M: 0, + S: 0, + L: 0, + Z: null + }, i = d3_time_parse(d, template, string, 0); + if (i != string.length) return null; + if ("p" in d) d.H = d.H % 12 + d.p * 12; + var localZ = d.Z != null && d3_date !== d3_date_utc, date = new (localZ ? d3_date_utc : d3_date)(); + if ("j" in d) date.setFullYear(d.y, 0, d.j); else if ("W" in d || "U" in d) { + if (!("w" in d)) d.w = "W" in d ? 1 : 0; + date.setFullYear(d.y, 0, 1); + date.setFullYear(d.y, 0, "W" in d ? (d.w + 6) % 7 + d.W * 7 - (date.getDay() + 5) % 7 : d.w + d.U * 7 - (date.getDay() + 6) % 7); + } else date.setFullYear(d.y, d.m, d.d); + date.setHours(d.H + (d.Z / 100 | 0), d.M + d.Z % 100, d.S, d.L); + return localZ ? date._ : date; + }; + format.toString = function() { + return template; + }; + return format; + } + function d3_time_parse(date, template, string, j) { + var c, p, t, i = 0, n = template.length, m = string.length; + while (i < n) { + if (j >= m) return -1; + c = template.charCodeAt(i++); + if (c === 37) { + t = template.charAt(i++); + p = d3_time_parsers[t in d3_time_formatPads ? template.charAt(i++) : t]; + if (!p || (j = p(date, string, j)) < 0) return -1; + } else if (c != string.charCodeAt(j++)) { + return -1; + } + } + return j; + } + d3_time_format.utc = function(template) { + var local = d3_time_format(template); + function format(date) { + try { + d3_date = d3_date_utc; + var utc = new d3_date(); + utc._ = date; + return local(utc); + } finally { + d3_date = Date; + } + } + format.parse = function(string) { + try { + d3_date = d3_date_utc; + var date = local.parse(string); + return date && date._; + } finally { + d3_date = Date; + } + }; + format.toString = local.toString; + return format; + }; + d3_time_format.multi = d3_time_format.utc.multi = d3_time_formatMulti; + var d3_time_periodLookup = d3.map(), d3_time_dayRe = d3_time_formatRe(locale_days), d3_time_dayLookup = d3_time_formatLookup(locale_days), d3_time_dayAbbrevRe = d3_time_formatRe(locale_shortDays), d3_time_dayAbbrevLookup = d3_time_formatLookup(locale_shortDays), d3_time_monthRe = d3_time_formatRe(locale_months), d3_time_monthLookup = d3_time_formatLookup(locale_months), d3_time_monthAbbrevRe = d3_time_formatRe(locale_shortMonths), d3_time_monthAbbrevLookup = d3_time_formatLookup(locale_shortMonths); + locale_periods.forEach(function(p, i) { + d3_time_periodLookup.set(p.toLowerCase(), i); + }); + var d3_time_formats = { + a: function(d) { + return locale_shortDays[d.getDay()]; + }, + A: function(d) { + return locale_days[d.getDay()]; + }, + b: function(d) { + return locale_shortMonths[d.getMonth()]; + }, + B: function(d) { + return locale_months[d.getMonth()]; + }, + c: d3_time_format(locale_dateTime), + d: function(d, p) { + return d3_time_formatPad(d.getDate(), p, 2); + }, + e: function(d, p) { + return d3_time_formatPad(d.getDate(), p, 2); + }, + H: function(d, p) { + return d3_time_formatPad(d.getHours(), p, 2); + }, + I: function(d, p) { + return d3_time_formatPad(d.getHours() % 12 || 12, p, 2); + }, + j: function(d, p) { + return d3_time_formatPad(1 + d3_time.dayOfYear(d), p, 3); + }, + L: function(d, p) { + return d3_time_formatPad(d.getMilliseconds(), p, 3); + }, + m: function(d, p) { + return d3_time_formatPad(d.getMonth() + 1, p, 2); + }, + M: function(d, p) { + return d3_time_formatPad(d.getMinutes(), p, 2); + }, + p: function(d) { + return locale_periods[+(d.getHours() >= 12)]; + }, + S: function(d, p) { + return d3_time_formatPad(d.getSeconds(), p, 2); + }, + U: function(d, p) { + return d3_time_formatPad(d3_time.sundayOfYear(d), p, 2); + }, + w: function(d) { + return d.getDay(); + }, + W: function(d, p) { + return d3_time_formatPad(d3_time.mondayOfYear(d), p, 2); + }, + x: d3_time_format(locale_date), + X: d3_time_format(locale_time), + y: function(d, p) { + return d3_time_formatPad(d.getFullYear() % 100, p, 2); + }, + Y: function(d, p) { + return d3_time_formatPad(d.getFullYear() % 1e4, p, 4); + }, + Z: d3_time_zone, + "%": function() { + return "%"; + } + }; + var d3_time_parsers = { + a: d3_time_parseWeekdayAbbrev, + A: d3_time_parseWeekday, + b: d3_time_parseMonthAbbrev, + B: d3_time_parseMonth, + c: d3_time_parseLocaleFull, + d: d3_time_parseDay, + e: d3_time_parseDay, + H: d3_time_parseHour24, + I: d3_time_parseHour24, + j: d3_time_parseDayOfYear, + L: d3_time_parseMilliseconds, + m: d3_time_parseMonthNumber, + M: d3_time_parseMinutes, + p: d3_time_parseAmPm, + S: d3_time_parseSeconds, + U: d3_time_parseWeekNumberSunday, + w: d3_time_parseWeekdayNumber, + W: d3_time_parseWeekNumberMonday, + x: d3_time_parseLocaleDate, + X: d3_time_parseLocaleTime, + y: d3_time_parseYear, + Y: d3_time_parseFullYear, + Z: d3_time_parseZone, + "%": d3_time_parseLiteralPercent + }; + function d3_time_parseWeekdayAbbrev(date, string, i) { + d3_time_dayAbbrevRe.lastIndex = 0; + var n = d3_time_dayAbbrevRe.exec(string.slice(i)); + return n ? (date.w = d3_time_dayAbbrevLookup.get(n[0].toLowerCase()), i + n[0].length) : -1; + } + function d3_time_parseWeekday(date, string, i) { + d3_time_dayRe.lastIndex = 0; + var n = d3_time_dayRe.exec(string.slice(i)); + return n ? (date.w = d3_time_dayLookup.get(n[0].toLowerCase()), i + n[0].length) : -1; + } + function d3_time_parseMonthAbbrev(date, string, i) { + d3_time_monthAbbrevRe.lastIndex = 0; + var n = d3_time_monthAbbrevRe.exec(string.slice(i)); + return n ? (date.m = d3_time_monthAbbrevLookup.get(n[0].toLowerCase()), i + n[0].length) : -1; + } + function d3_time_parseMonth(date, string, i) { + d3_time_monthRe.lastIndex = 0; + var n = d3_time_monthRe.exec(string.slice(i)); + return n ? (date.m = d3_time_monthLookup.get(n[0].toLowerCase()), i + n[0].length) : -1; + } + function d3_time_parseLocaleFull(date, string, i) { + return d3_time_parse(date, d3_time_formats.c.toString(), string, i); + } + function d3_time_parseLocaleDate(date, string, i) { + return d3_time_parse(date, d3_time_formats.x.toString(), string, i); + } + function d3_time_parseLocaleTime(date, string, i) { + return d3_time_parse(date, d3_time_formats.X.toString(), string, i); + } + function d3_time_parseAmPm(date, string, i) { + var n = d3_time_periodLookup.get(string.slice(i, i += 2).toLowerCase()); + return n == null ? -1 : (date.p = n, i); + } + return d3_time_format; + } + var d3_time_formatPads = { + "-": "", + _: " ", + "0": "0" + }, d3_time_numberRe = /^\s*\d+/, d3_time_percentRe = /^%/; + function d3_time_formatPad(value, fill, width) { + var sign = value < 0 ? "-" : "", string = (sign ? -value : value) + "", length = string.length; + return sign + (length < width ? new Array(width - length + 1).join(fill) + string : string); + } + function d3_time_formatRe(names) { + return new RegExp("^(?:" + names.map(d3.requote).join("|") + ")", "i"); + } + function d3_time_formatLookup(names) { + var map = new d3_Map(), i = -1, n = names.length; + while (++i < n) map.set(names[i].toLowerCase(), i); + return map; + } + function d3_time_parseWeekdayNumber(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 1)); + return n ? (date.w = +n[0], i + n[0].length) : -1; + } + function d3_time_parseWeekNumberSunday(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i)); + return n ? (date.U = +n[0], i + n[0].length) : -1; + } + function d3_time_parseWeekNumberMonday(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i)); + return n ? (date.W = +n[0], i + n[0].length) : -1; + } + function d3_time_parseFullYear(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 4)); + return n ? (date.y = +n[0], i + n[0].length) : -1; + } + function d3_time_parseYear(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 2)); + return n ? (date.y = d3_time_expandYear(+n[0]), i + n[0].length) : -1; + } + function d3_time_parseZone(date, string, i) { + return /^[+-]\d{4}$/.test(string = string.slice(i, i + 5)) ? (date.Z = -string, + i + 5) : -1; + } + function d3_time_expandYear(d) { + return d + (d > 68 ? 1900 : 2e3); + } + function d3_time_parseMonthNumber(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 2)); + return n ? (date.m = n[0] - 1, i + n[0].length) : -1; + } + function d3_time_parseDay(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 2)); + return n ? (date.d = +n[0], i + n[0].length) : -1; + } + function d3_time_parseDayOfYear(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 3)); + return n ? (date.j = +n[0], i + n[0].length) : -1; + } + function d3_time_parseHour24(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 2)); + return n ? (date.H = +n[0], i + n[0].length) : -1; + } + function d3_time_parseMinutes(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 2)); + return n ? (date.M = +n[0], i + n[0].length) : -1; + } + function d3_time_parseSeconds(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 2)); + return n ? (date.S = +n[0], i + n[0].length) : -1; + } + function d3_time_parseMilliseconds(date, string, i) { + d3_time_numberRe.lastIndex = 0; + var n = d3_time_numberRe.exec(string.slice(i, i + 3)); + return n ? (date.L = +n[0], i + n[0].length) : -1; + } + function d3_time_zone(d) { + var z = d.getTimezoneOffset(), zs = z > 0 ? "-" : "+", zh = abs(z) / 60 | 0, zm = abs(z) % 60; + return zs + d3_time_formatPad(zh, "0", 2) + d3_time_formatPad(zm, "0", 2); + } + function d3_time_parseLiteralPercent(date, string, i) { + d3_time_percentRe.lastIndex = 0; + var n = d3_time_percentRe.exec(string.slice(i, i + 1)); + return n ? i + n[0].length : -1; + } + function d3_time_formatMulti(formats) { + var n = formats.length, i = -1; + while (++i < n) formats[i][0] = this(formats[i][0]); + return function(date) { + var i = 0, f = formats[i]; + while (!f[1](date)) f = formats[++i]; + return f[0](date); + }; + } + d3.locale = function(locale) { + return { + numberFormat: d3_locale_numberFormat(locale), + timeFormat: d3_locale_timeFormat(locale) + }; + }; + var d3_locale_enUS = d3.locale({ + decimal: ".", + thousands: ",", + grouping: [ 3 ], + currency: [ "$", "" ], + dateTime: "%a %b %e %X %Y", + date: "%m/%d/%Y", + time: "%H:%M:%S", + periods: [ "AM", "PM" ], + days: [ "Sunday", "Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday" ], + shortDays: [ "Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat" ], + months: [ "January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December" ], + shortMonths: [ "Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec" ] + }); + d3.format = d3_locale_enUS.numberFormat; + d3.geo = {}; + function d3_adder() {} + d3_adder.prototype = { + s: 0, + t: 0, + add: function(y) { + d3_adderSum(y, this.t, d3_adderTemp); + d3_adderSum(d3_adderTemp.s, this.s, this); + if (this.s) this.t += d3_adderTemp.t; else this.s = d3_adderTemp.t; + }, + reset: function() { + this.s = this.t = 0; + }, + valueOf: function() { + return this.s; + } + }; + var d3_adderTemp = new d3_adder(); + function d3_adderSum(a, b, o) { + var x = o.s = a + b, bv = x - a, av = x - bv; + o.t = a - av + (b - bv); + } + d3.geo.stream = function(object, listener) { + if (object && d3_geo_streamObjectType.hasOwnProperty(object.type)) { + d3_geo_streamObjectType[object.type](object, listener); + } else { + d3_geo_streamGeometry(object, listener); + } + }; + function d3_geo_streamGeometry(geometry, listener) { + if (geometry && d3_geo_streamGeometryType.hasOwnProperty(geometry.type)) { + d3_geo_streamGeometryType[geometry.type](geometry, listener); + } + } + var d3_geo_streamObjectType = { + Feature: function(feature, listener) { + d3_geo_streamGeometry(feature.geometry, listener); + }, + FeatureCollection: function(object, listener) { + var features = object.features, i = -1, n = features.length; + while (++i < n) d3_geo_streamGeometry(features[i].geometry, listener); + } + }; + var d3_geo_streamGeometryType = { + Sphere: function(object, listener) { + listener.sphere(); + }, + Point: function(object, listener) { + object = object.coordinates; + listener.point(object[0], object[1], object[2]); + }, + MultiPoint: function(object, listener) { + var coordinates = object.coordinates, i = -1, n = coordinates.length; + while (++i < n) object = coordinates[i], listener.point(object[0], object[1], object[2]); + }, + LineString: function(object, listener) { + d3_geo_streamLine(object.coordinates, listener, 0); + }, + MultiLineString: function(object, listener) { + var coordinates = object.coordinates, i = -1, n = coordinates.length; + while (++i < n) d3_geo_streamLine(coordinates[i], listener, 0); + }, + Polygon: function(object, listener) { + d3_geo_streamPolygon(object.coordinates, listener); + }, + MultiPolygon: function(object, listener) { + var coordinates = object.coordinates, i = -1, n = coordinates.length; + while (++i < n) d3_geo_streamPolygon(coordinates[i], listener); + }, + GeometryCollection: function(object, listener) { + var geometries = object.geometries, i = -1, n = geometries.length; + while (++i < n) d3_geo_streamGeometry(geometries[i], listener); + } + }; + function d3_geo_streamLine(coordinates, listener, closed) { + var i = -1, n = coordinates.length - closed, coordinate; + listener.lineStart(); + while (++i < n) coordinate = coordinates[i], listener.point(coordinate[0], coordinate[1], coordinate[2]); + listener.lineEnd(); + } + function d3_geo_streamPolygon(coordinates, listener) { + var i = -1, n = coordinates.length; + listener.polygonStart(); + while (++i < n) d3_geo_streamLine(coordinates[i], listener, 1); + listener.polygonEnd(); + } + d3.geo.area = function(object) { + d3_geo_areaSum = 0; + d3.geo.stream(object, d3_geo_area); + return d3_geo_areaSum; + }; + var d3_geo_areaSum, d3_geo_areaRingSum = new d3_adder(); + var d3_geo_area = { + sphere: function() { + d3_geo_areaSum += 4 * Ī€; + }, + point: d3_noop, + lineStart: d3_noop, + lineEnd: d3_noop, + polygonStart: function() { + d3_geo_areaRingSum.reset(); + d3_geo_area.lineStart = d3_geo_areaRingStart; + }, + polygonEnd: function() { + var area = 2 * d3_geo_areaRingSum; + d3_geo_areaSum += area < 0 ? 4 * Ī€ + area : area; + d3_geo_area.lineStart = d3_geo_area.lineEnd = d3_geo_area.point = d3_noop; + } + }; + function d3_geo_areaRingStart() { + var Îģ00, Ī†00, Îģ0, cosĪ†0, sinĪ†0; + d3_geo_area.point = function(Îģ, Ī†) { + d3_geo_area.point = nextPoint; + Îģ0 = (Îģ00 = Îģ) * d3_radians, cosĪ†0 = Math.cos(Ī† = (Ī†00 = Ī†) * d3_radians / 2 + Ī€ / 4), + sinĪ†0 = Math.sin(Ī†); + }; + function nextPoint(Îģ, Ī†) { + Îģ *= d3_radians; + Ī† = Ī† * d3_radians / 2 + Ī€ / 4; + var dÎģ = Îģ - Îģ0, sdÎģ = dÎģ >= 0 ? 1 : -1, adÎģ = sdÎģ * dÎģ, cosĪ† = Math.cos(Ī†), sinĪ† = Math.sin(Ī†), k = sinĪ†0 * sinĪ†, u = cosĪ†0 * cosĪ† + k * Math.cos(adÎģ), v = k * sdÎģ * Math.sin(adÎģ); + d3_geo_areaRingSum.add(Math.atan2(v, u)); + Îģ0 = Îģ, cosĪ†0 = cosĪ†, sinĪ†0 = sinĪ†; + } + d3_geo_area.lineEnd = function() { + nextPoint(Îģ00, Ī†00); + }; + } + function d3_geo_cartesian(spherical) { + var Îģ = spherical[0], Ī† = spherical[1], cosĪ† = Math.cos(Ī†); + return [ cosĪ† * Math.cos(Îģ), cosĪ† * Math.sin(Îģ), Math.sin(Ī†) ]; + } + function d3_geo_cartesianDot(a, b) { + return a[0] * b[0] + a[1] * b[1] + a[2] * b[2]; + } + function d3_geo_cartesianCross(a, b) { + return [ a[1] * b[2] - a[2] * b[1], a[2] * b[0] - a[0] * b[2], a[0] * b[1] - a[1] * b[0] ]; + } + function d3_geo_cartesianAdd(a, b) { + a[0] += b[0]; + a[1] += b[1]; + a[2] += b[2]; + } + function d3_geo_cartesianScale(vector, k) { + return [ vector[0] * k, vector[1] * k, vector[2] * k ]; + } + function d3_geo_cartesianNormalize(d) { + var l = Math.sqrt(d[0] * d[0] + d[1] * d[1] + d[2] * d[2]); + d[0] /= l; + d[1] /= l; + d[2] /= l; + } + function d3_geo_spherical(cartesian) { + return [ Math.atan2(cartesian[1], cartesian[0]), d3_asin(cartesian[2]) ]; + } + function d3_geo_sphericalEqual(a, b) { + return abs(a[0] - b[0]) < Îĩ && abs(a[1] - b[1]) < Îĩ; + } + d3.geo.bounds = function() { + var Îģ0, Ī†0, Îģ1, Ī†1, Îģ_, Îģ__, Ī†__, p0, dÎģSum, ranges, range; + var bound = { + point: point, + lineStart: lineStart, + lineEnd: lineEnd, + polygonStart: function() { + bound.point = ringPoint; + bound.lineStart = ringStart; + bound.lineEnd = ringEnd; + dÎģSum = 0; + d3_geo_area.polygonStart(); + }, + polygonEnd: function() { + d3_geo_area.polygonEnd(); + bound.point = point; + bound.lineStart = lineStart; + bound.lineEnd = lineEnd; + if (d3_geo_areaRingSum < 0) Îģ0 = -(Îģ1 = 180), Ī†0 = -(Ī†1 = 90); else if (dÎģSum > Îĩ) Ī†1 = 90; else if (dÎģSum < -Îĩ) Ī†0 = -90; + range[0] = Îģ0, range[1] = Îģ1; + } + }; + function point(Îģ, Ī†) { + ranges.push(range = [ Îģ0 = Îģ, Îģ1 = Îģ ]); + if (Ī† < Ī†0) Ī†0 = Ī†; + if (Ī† > Ī†1) Ī†1 = Ī†; + } + function linePoint(Îģ, Ī†) { + var p = d3_geo_cartesian([ Îģ * d3_radians, Ī† * d3_radians ]); + if (p0) { + var normal = d3_geo_cartesianCross(p0, p), equatorial = [ normal[1], -normal[0], 0 ], inflection = d3_geo_cartesianCross(equatorial, normal); + d3_geo_cartesianNormalize(inflection); + inflection = d3_geo_spherical(inflection); + var dÎģ = Îģ - Îģ_, s = dÎģ > 0 ? 1 : -1, Îģi = inflection[0] * d3_degrees * s, antimeridian = abs(dÎģ) > 180; + if (antimeridian ^ (s * Îģ_ < Îģi && Îģi < s * Îģ)) { + var Ī†i = inflection[1] * d3_degrees; + if (Ī†i > Ī†1) Ī†1 = Ī†i; + } else if (Îģi = (Îģi + 360) % 360 - 180, antimeridian ^ (s * Îģ_ < Îģi && Îģi < s * Îģ)) { + var Ī†i = -inflection[1] * d3_degrees; + if (Ī†i < Ī†0) Ī†0 = Ī†i; + } else { + if (Ī† < Ī†0) Ī†0 = Ī†; + if (Ī† > Ī†1) Ī†1 = Ī†; + } + if (antimeridian) { + if (Îģ < Îģ_) { + if (angle(Îģ0, Îģ) > angle(Îģ0, Îģ1)) Îģ1 = Îģ; + } else { + if (angle(Îģ, Îģ1) > angle(Îģ0, Îģ1)) Îģ0 = Îģ; + } + } else { + if (Îģ1 >= Îģ0) { + if (Îģ < Îģ0) Îģ0 = Îģ; + if (Îģ > Îģ1) Îģ1 = Îģ; + } else { + if (Îģ > Îģ_) { + if (angle(Îģ0, Îģ) > angle(Îģ0, Îģ1)) Îģ1 = Îģ; + } else { + if (angle(Îģ, Îģ1) > angle(Îģ0, Îģ1)) Îģ0 = Îģ; + } + } + } + } else { + point(Îģ, Ī†); + } + p0 = p, Îģ_ = Îģ; + } + function lineStart() { + bound.point = linePoint; + } + function lineEnd() { + range[0] = Îģ0, range[1] = Îģ1; + bound.point = point; + p0 = null; + } + function ringPoint(Îģ, Ī†) { + if (p0) { + var dÎģ = Îģ - Îģ_; + dÎģSum += abs(dÎģ) > 180 ? dÎģ + (dÎģ > 0 ? 360 : -360) : dÎģ; + } else Îģ__ = Îģ, Ī†__ = Ī†; + d3_geo_area.point(Îģ, Ī†); + linePoint(Îģ, Ī†); + } + function ringStart() { + d3_geo_area.lineStart(); + } + function ringEnd() { + ringPoint(Îģ__, Ī†__); + d3_geo_area.lineEnd(); + if (abs(dÎģSum) > Îĩ) Îģ0 = -(Îģ1 = 180); + range[0] = Îģ0, range[1] = Îģ1; + p0 = null; + } + function angle(Îģ0, Îģ1) { + return (Îģ1 -= Îģ0) < 0 ? Îģ1 + 360 : Îģ1; + } + function compareRanges(a, b) { + return a[0] - b[0]; + } + function withinRange(x, range) { + return range[0] <= range[1] ? range[0] <= x && x <= range[1] : x < range[0] || range[1] < x; + } + return function(feature) { + Ī†1 = Îģ1 = -(Îģ0 = Ī†0 = Infinity); + ranges = []; + d3.geo.stream(feature, bound); + var n = ranges.length; + if (n) { + ranges.sort(compareRanges); + for (var i = 1, a = ranges[0], b, merged = [ a ]; i < n; ++i) { + b = ranges[i]; + if (withinRange(b[0], a) || withinRange(b[1], a)) { + if (angle(a[0], b[1]) > angle(a[0], a[1])) a[1] = b[1]; + if (angle(b[0], a[1]) > angle(a[0], a[1])) a[0] = b[0]; + } else { + merged.push(a = b); + } + } + var best = -Infinity, dÎģ; + for (var n = merged.length - 1, i = 0, a = merged[n], b; i <= n; a = b, ++i) { + b = merged[i]; + if ((dÎģ = angle(a[1], b[0])) > best) best = dÎģ, Îģ0 = b[0], Îģ1 = a[1]; + } + } + ranges = range = null; + return Îģ0 === Infinity || Ī†0 === Infinity ? [ [ NaN, NaN ], [ NaN, NaN ] ] : [ [ Îģ0, Ī†0 ], [ Îģ1, Ī†1 ] ]; + }; + }(); + d3.geo.centroid = function(object) { + d3_geo_centroidW0 = d3_geo_centroidW1 = d3_geo_centroidX0 = d3_geo_centroidY0 = d3_geo_centroidZ0 = d3_geo_centroidX1 = d3_geo_centroidY1 = d3_geo_centroidZ1 = d3_geo_centroidX2 = d3_geo_centroidY2 = d3_geo_centroidZ2 = 0; + d3.geo.stream(object, d3_geo_centroid); + var x = d3_geo_centroidX2, y = d3_geo_centroidY2, z = d3_geo_centroidZ2, m = x * x + y * y + z * z; + if (m < Îĩ2) { + x = d3_geo_centroidX1, y = d3_geo_centroidY1, z = d3_geo_centroidZ1; + if (d3_geo_centroidW1 < Îĩ) x = d3_geo_centroidX0, y = d3_geo_centroidY0, z = d3_geo_centroidZ0; + m = x * x + y * y + z * z; + if (m < Îĩ2) return [ NaN, NaN ]; + } + return [ Math.atan2(y, x) * d3_degrees, d3_asin(z / Math.sqrt(m)) * d3_degrees ]; + }; + var d3_geo_centroidW0, d3_geo_centroidW1, d3_geo_centroidX0, d3_geo_centroidY0, d3_geo_centroidZ0, d3_geo_centroidX1, d3_geo_centroidY1, d3_geo_centroidZ1, d3_geo_centroidX2, d3_geo_centroidY2, d3_geo_centroidZ2; + var d3_geo_centroid = { + sphere: d3_noop, + point: d3_geo_centroidPoint, + lineStart: d3_geo_centroidLineStart, + lineEnd: d3_geo_centroidLineEnd, + polygonStart: function() { + d3_geo_centroid.lineStart = d3_geo_centroidRingStart; + }, + polygonEnd: function() { + d3_geo_centroid.lineStart = d3_geo_centroidLineStart; + } + }; + function d3_geo_centroidPoint(Îģ, Ī†) { + Îģ *= d3_radians; + var cosĪ† = Math.cos(Ī† *= d3_radians); + d3_geo_centroidPointXYZ(cosĪ† * Math.cos(Îģ), cosĪ† * Math.sin(Îģ), Math.sin(Ī†)); + } + function d3_geo_centroidPointXYZ(x, y, z) { + ++d3_geo_centroidW0; + d3_geo_centroidX0 += (x - d3_geo_centroidX0) / d3_geo_centroidW0; + d3_geo_centroidY0 += (y - d3_geo_centroidY0) / d3_geo_centroidW0; + d3_geo_centroidZ0 += (z - d3_geo_centroidZ0) / d3_geo_centroidW0; + } + function d3_geo_centroidLineStart() { + var x0, y0, z0; + d3_geo_centroid.point = function(Îģ, Ī†) { + Îģ *= d3_radians; + var cosĪ† = Math.cos(Ī† *= d3_radians); + x0 = cosĪ† * Math.cos(Îģ); + y0 = cosĪ† * Math.sin(Îģ); + z0 = Math.sin(Ī†); + d3_geo_centroid.point = nextPoint; + d3_geo_centroidPointXYZ(x0, y0, z0); + }; + function nextPoint(Îģ, Ī†) { + Îģ *= d3_radians; + var cosĪ† = Math.cos(Ī† *= d3_radians), x = cosĪ† * Math.cos(Îģ), y = cosĪ† * Math.sin(Îģ), z = Math.sin(Ī†), w = Math.atan2(Math.sqrt((w = y0 * z - z0 * y) * w + (w = z0 * x - x0 * z) * w + (w = x0 * y - y0 * x) * w), x0 * x + y0 * y + z0 * z); + d3_geo_centroidW1 += w; + d3_geo_centroidX1 += w * (x0 + (x0 = x)); + d3_geo_centroidY1 += w * (y0 + (y0 = y)); + d3_geo_centroidZ1 += w * (z0 + (z0 = z)); + d3_geo_centroidPointXYZ(x0, y0, z0); + } + } + function d3_geo_centroidLineEnd() { + d3_geo_centroid.point = d3_geo_centroidPoint; + } + function d3_geo_centroidRingStart() { + var Îģ00, Ī†00, x0, y0, z0; + d3_geo_centroid.point = function(Îģ, Ī†) { + Îģ00 = Îģ, Ī†00 = Ī†; + d3_geo_centroid.point = nextPoint; + Îģ *= d3_radians; + var cosĪ† = Math.cos(Ī† *= d3_radians); + x0 = cosĪ† * Math.cos(Îģ); + y0 = cosĪ† * Math.sin(Îģ); + z0 = Math.sin(Ī†); + d3_geo_centroidPointXYZ(x0, y0, z0); + }; + d3_geo_centroid.lineEnd = function() { + nextPoint(Îģ00, Ī†00); + d3_geo_centroid.lineEnd = d3_geo_centroidLineEnd; + d3_geo_centroid.point = d3_geo_centroidPoint; + }; + function nextPoint(Îģ, Ī†) { + Îģ *= d3_radians; + var cosĪ† = Math.cos(Ī† *= d3_radians), x = cosĪ† * Math.cos(Îģ), y = cosĪ† * Math.sin(Îģ), z = Math.sin(Ī†), cx = y0 * z - z0 * y, cy = z0 * x - x0 * z, cz = x0 * y - y0 * x, m = Math.sqrt(cx * cx + cy * cy + cz * cz), u = x0 * x + y0 * y + z0 * z, v = m && -d3_acos(u) / m, w = Math.atan2(m, u); + d3_geo_centroidX2 += v * cx; + d3_geo_centroidY2 += v * cy; + d3_geo_centroidZ2 += v * cz; + d3_geo_centroidW1 += w; + d3_geo_centroidX1 += w * (x0 + (x0 = x)); + d3_geo_centroidY1 += w * (y0 + (y0 = y)); + d3_geo_centroidZ1 += w * (z0 + (z0 = z)); + d3_geo_centroidPointXYZ(x0, y0, z0); + } + } + function d3_geo_compose(a, b) { + function compose(x, y) { + return x = a(x, y), b(x[0], x[1]); + } + if (a.invert && b.invert) compose.invert = function(x, y) { + return x = b.invert(x, y), x && a.invert(x[0], x[1]); + }; + return compose; + } + function d3_true() { + return true; + } + function d3_geo_clipPolygon(segments, compare, clipStartInside, interpolate, listener) { + var subject = [], clip = []; + segments.forEach(function(segment) { + if ((n = segment.length - 1) <= 0) return; + var n, p0 = segment[0], p1 = segment[n]; + if (d3_geo_sphericalEqual(p0, p1)) { + listener.lineStart(); + for (var i = 0; i < n; ++i) listener.point((p0 = segment[i])[0], p0[1]); + listener.lineEnd(); + return; + } + var a = new d3_geo_clipPolygonIntersection(p0, segment, null, true), b = new d3_geo_clipPolygonIntersection(p0, null, a, false); + a.o = b; + subject.push(a); + clip.push(b); + a = new d3_geo_clipPolygonIntersection(p1, segment, null, false); + b = new d3_geo_clipPolygonIntersection(p1, null, a, true); + a.o = b; + subject.push(a); + clip.push(b); + }); + clip.sort(compare); + d3_geo_clipPolygonLinkCircular(subject); + d3_geo_clipPolygonLinkCircular(clip); + if (!subject.length) return; + for (var i = 0, entry = clipStartInside, n = clip.length; i < n; ++i) { + clip[i].e = entry = !entry; + } + var start = subject[0], points, point; + while (1) { + var current = start, isSubject = true; + while (current.v) if ((current = current.n) === start) return; + points = current.z; + listener.lineStart(); + do { + current.v = current.o.v = true; + if (current.e) { + if (isSubject) { + for (var i = 0, n = points.length; i < n; ++i) listener.point((point = points[i])[0], point[1]); + } else { + interpolate(current.x, current.n.x, 1, listener); + } + current = current.n; + } else { + if (isSubject) { + points = current.p.z; + for (var i = points.length - 1; i >= 0; --i) listener.point((point = points[i])[0], point[1]); + } else { + interpolate(current.x, current.p.x, -1, listener); + } + current = current.p; + } + current = current.o; + points = current.z; + isSubject = !isSubject; + } while (!current.v); + listener.lineEnd(); + } + } + function d3_geo_clipPolygonLinkCircular(array) { + if (!(n = array.length)) return; + var n, i = 0, a = array[0], b; + while (++i < n) { + a.n = b = array[i]; + b.p = a; + a = b; + } + a.n = b = array[0]; + b.p = a; + } + function d3_geo_clipPolygonIntersection(point, points, other, entry) { + this.x = point; + this.z = points; + this.o = other; + this.e = entry; + this.v = false; + this.n = this.p = null; + } + function d3_geo_clip(pointVisible, clipLine, interpolate, clipStart) { + return function(rotate, listener) { + var line = clipLine(listener), rotatedClipStart = rotate.invert(clipStart[0], clipStart[1]); + var clip = { + point: point, + lineStart: lineStart, + lineEnd: lineEnd, + polygonStart: function() { + clip.point = pointRing; + clip.lineStart = ringStart; + clip.lineEnd = ringEnd; + segments = []; + polygon = []; + }, + polygonEnd: function() { + clip.point = point; + clip.lineStart = lineStart; + clip.lineEnd = lineEnd; + segments = d3.merge(segments); + var clipStartInside = d3_geo_pointInPolygon(rotatedClipStart, polygon); + if (segments.length) { + if (!polygonStarted) listener.polygonStart(), polygonStarted = true; + d3_geo_clipPolygon(segments, d3_geo_clipSort, clipStartInside, interpolate, listener); + } else if (clipStartInside) { + if (!polygonStarted) listener.polygonStart(), polygonStarted = true; + listener.lineStart(); + interpolate(null, null, 1, listener); + listener.lineEnd(); + } + if (polygonStarted) listener.polygonEnd(), polygonStarted = false; + segments = polygon = null; + }, + sphere: function() { + listener.polygonStart(); + listener.lineStart(); + interpolate(null, null, 1, listener); + listener.lineEnd(); + listener.polygonEnd(); + } + }; + function point(Îģ, Ī†) { + var point = rotate(Îģ, Ī†); + if (pointVisible(Îģ = point[0], Ī† = point[1])) listener.point(Îģ, Ī†); + } + function pointLine(Îģ, Ī†) { + var point = rotate(Îģ, Ī†); + line.point(point[0], point[1]); + } + function lineStart() { + clip.point = pointLine; + line.lineStart(); + } + function lineEnd() { + clip.point = point; + line.lineEnd(); + } + var segments; + var buffer = d3_geo_clipBufferListener(), ringListener = clipLine(buffer), polygonStarted = false, polygon, ring; + function pointRing(Îģ, Ī†) { + ring.push([ Îģ, Ī† ]); + var point = rotate(Îģ, Ī†); + ringListener.point(point[0], point[1]); + } + function ringStart() { + ringListener.lineStart(); + ring = []; + } + function ringEnd() { + pointRing(ring[0][0], ring[0][1]); + ringListener.lineEnd(); + var clean = ringListener.clean(), ringSegments = buffer.buffer(), segment, n = ringSegments.length; + ring.pop(); + polygon.push(ring); + ring = null; + if (!n) return; + if (clean & 1) { + segment = ringSegments[0]; + var n = segment.length - 1, i = -1, point; + if (n > 0) { + if (!polygonStarted) listener.polygonStart(), polygonStarted = true; + listener.lineStart(); + while (++i < n) listener.point((point = segment[i])[0], point[1]); + listener.lineEnd(); + } + return; + } + if (n > 1 && clean & 2) ringSegments.push(ringSegments.pop().concat(ringSegments.shift())); + segments.push(ringSegments.filter(d3_geo_clipSegmentLength1)); + } + return clip; + }; + } + function d3_geo_clipSegmentLength1(segment) { + return segment.length > 1; + } + function d3_geo_clipBufferListener() { + var lines = [], line; + return { + lineStart: function() { + lines.push(line = []); + }, + point: function(Îģ, Ī†) { + line.push([ Îģ, Ī† ]); + }, + lineEnd: d3_noop, + buffer: function() { + var buffer = lines; + lines = []; + line = null; + return buffer; + }, + rejoin: function() { + if (lines.length > 1) lines.push(lines.pop().concat(lines.shift())); + } + }; + } + function d3_geo_clipSort(a, b) { + return ((a = a.x)[0] < 0 ? a[1] - halfĪ€ - Îĩ : halfĪ€ - a[1]) - ((b = b.x)[0] < 0 ? b[1] - halfĪ€ - Îĩ : halfĪ€ - b[1]); + } + var d3_geo_clipAntimeridian = d3_geo_clip(d3_true, d3_geo_clipAntimeridianLine, d3_geo_clipAntimeridianInterpolate, [ -Ī€, -Ī€ / 2 ]); + function d3_geo_clipAntimeridianLine(listener) { + var Îģ0 = NaN, Ī†0 = NaN, sÎģ0 = NaN, clean; + return { + lineStart: function() { + listener.lineStart(); + clean = 1; + }, + point: function(Îģ1, Ī†1) { + var sÎģ1 = Îģ1 > 0 ? Ī€ : -Ī€, dÎģ = abs(Îģ1 - Îģ0); + if (abs(dÎģ - Ī€) < Îĩ) { + listener.point(Îģ0, Ī†0 = (Ī†0 + Ī†1) / 2 > 0 ? halfĪ€ : -halfĪ€); + listener.point(sÎģ0, Ī†0); + listener.lineEnd(); + listener.lineStart(); + listener.point(sÎģ1, Ī†0); + listener.point(Îģ1, Ī†0); + clean = 0; + } else if (sÎģ0 !== sÎģ1 && dÎģ >= Ī€) { + if (abs(Îģ0 - sÎģ0) < Îĩ) Îģ0 -= sÎģ0 * Îĩ; + if (abs(Îģ1 - sÎģ1) < Îĩ) Îģ1 -= sÎģ1 * Îĩ; + Ī†0 = d3_geo_clipAntimeridianIntersect(Îģ0, Ī†0, Îģ1, Ī†1); + listener.point(sÎģ0, Ī†0); + listener.lineEnd(); + listener.lineStart(); + listener.point(sÎģ1, Ī†0); + clean = 0; + } + listener.point(Îģ0 = Îģ1, Ī†0 = Ī†1); + sÎģ0 = sÎģ1; + }, + lineEnd: function() { + listener.lineEnd(); + Îģ0 = Ī†0 = NaN; + }, + clean: function() { + return 2 - clean; + } + }; + } + function d3_geo_clipAntimeridianIntersect(Îģ0, Ī†0, Îģ1, Ī†1) { + var cosĪ†0, cosĪ†1, sinÎģ0_Îģ1 = Math.sin(Îģ0 - Îģ1); + return abs(sinÎģ0_Îģ1) > Îĩ ? Math.atan((Math.sin(Ī†0) * (cosĪ†1 = Math.cos(Ī†1)) * Math.sin(Îģ1) - Math.sin(Ī†1) * (cosĪ†0 = Math.cos(Ī†0)) * Math.sin(Îģ0)) / (cosĪ†0 * cosĪ†1 * sinÎģ0_Îģ1)) : (Ī†0 + Ī†1) / 2; + } + function d3_geo_clipAntimeridianInterpolate(from, to, direction, listener) { + var Ī†; + if (from == null) { + Ī† = direction * halfĪ€; + listener.point(-Ī€, Ī†); + listener.point(0, Ī†); + listener.point(Ī€, Ī†); + listener.point(Ī€, 0); + listener.point(Ī€, -Ī†); + listener.point(0, -Ī†); + listener.point(-Ī€, -Ī†); + listener.point(-Ī€, 0); + listener.point(-Ī€, Ī†); + } else if (abs(from[0] - to[0]) > Îĩ) { + var s = from[0] < to[0] ? Ī€ : -Ī€; + Ī† = direction * s / 2; + listener.point(-s, Ī†); + listener.point(0, Ī†); + listener.point(s, Ī†); + } else { + listener.point(to[0], to[1]); + } + } + function d3_geo_pointInPolygon(point, polygon) { + var meridian = point[0], parallel = point[1], meridianNormal = [ Math.sin(meridian), -Math.cos(meridian), 0 ], polarAngle = 0, winding = 0; + d3_geo_areaRingSum.reset(); + for (var i = 0, n = polygon.length; i < n; ++i) { + var ring = polygon[i], m = ring.length; + if (!m) continue; + var point0 = ring[0], Îģ0 = point0[0], Ī†0 = point0[1] / 2 + Ī€ / 4, sinĪ†0 = Math.sin(Ī†0), cosĪ†0 = Math.cos(Ī†0), j = 1; + while (true) { + if (j === m) j = 0; + point = ring[j]; + var Îģ = point[0], Ī† = point[1] / 2 + Ī€ / 4, sinĪ† = Math.sin(Ī†), cosĪ† = Math.cos(Ī†), dÎģ = Îģ - Îģ0, sdÎģ = dÎģ >= 0 ? 1 : -1, adÎģ = sdÎģ * dÎģ, antimeridian = adÎģ > Ī€, k = sinĪ†0 * sinĪ†; + d3_geo_areaRingSum.add(Math.atan2(k * sdÎģ * Math.sin(adÎģ), cosĪ†0 * cosĪ† + k * Math.cos(adÎģ))); + polarAngle += antimeridian ? dÎģ + sdÎģ * Ī„ : dÎģ; + if (antimeridian ^ Îģ0 >= meridian ^ Îģ >= meridian) { + var arc = d3_geo_cartesianCross(d3_geo_cartesian(point0), d3_geo_cartesian(point)); + d3_geo_cartesianNormalize(arc); + var intersection = d3_geo_cartesianCross(meridianNormal, arc); + d3_geo_cartesianNormalize(intersection); + var Ī†arc = (antimeridian ^ dÎģ >= 0 ? -1 : 1) * d3_asin(intersection[2]); + if (parallel > Ī†arc || parallel === Ī†arc && (arc[0] || arc[1])) { + winding += antimeridian ^ dÎģ >= 0 ? 1 : -1; + } + } + if (!j++) break; + Îģ0 = Îģ, sinĪ†0 = sinĪ†, cosĪ†0 = cosĪ†, point0 = point; + } + } + return (polarAngle < -Îĩ || polarAngle < Îĩ && d3_geo_areaRingSum < -Îĩ) ^ winding & 1; + } + function d3_geo_clipCircle(radius) { + var cr = Math.cos(radius), smallRadius = cr > 0, notHemisphere = abs(cr) > Îĩ, interpolate = d3_geo_circleInterpolate(radius, 6 * d3_radians); + return d3_geo_clip(visible, clipLine, interpolate, smallRadius ? [ 0, -radius ] : [ -Ī€, radius - Ī€ ]); + function visible(Îģ, Ī†) { + return Math.cos(Îģ) * Math.cos(Ī†) > cr; + } + function clipLine(listener) { + var point0, c0, v0, v00, clean; + return { + lineStart: function() { + v00 = v0 = false; + clean = 1; + }, + point: function(Îģ, Ī†) { + var point1 = [ Îģ, Ī† ], point2, v = visible(Îģ, Ī†), c = smallRadius ? v ? 0 : code(Îģ, Ī†) : v ? code(Îģ + (Îģ < 0 ? Ī€ : -Ī€), Ī†) : 0; + if (!point0 && (v00 = v0 = v)) listener.lineStart(); + if (v !== v0) { + point2 = intersect(point0, point1); + if (d3_geo_sphericalEqual(point0, point2) || d3_geo_sphericalEqual(point1, point2)) { + point1[0] += Îĩ; + point1[1] += Îĩ; + v = visible(point1[0], point1[1]); + } + } + if (v !== v0) { + clean = 0; + if (v) { + listener.lineStart(); + point2 = intersect(point1, point0); + listener.point(point2[0], point2[1]); + } else { + point2 = intersect(point0, point1); + listener.point(point2[0], point2[1]); + listener.lineEnd(); + } + point0 = point2; + } else if (notHemisphere && point0 && smallRadius ^ v) { + var t; + if (!(c & c0) && (t = intersect(point1, point0, true))) { + clean = 0; + if (smallRadius) { + listener.lineStart(); + listener.point(t[0][0], t[0][1]); + listener.point(t[1][0], t[1][1]); + listener.lineEnd(); + } else { + listener.point(t[1][0], t[1][1]); + listener.lineEnd(); + listener.lineStart(); + listener.point(t[0][0], t[0][1]); + } + } + } + if (v && (!point0 || !d3_geo_sphericalEqual(point0, point1))) { + listener.point(point1[0], point1[1]); + } + point0 = point1, v0 = v, c0 = c; + }, + lineEnd: function() { + if (v0) listener.lineEnd(); + point0 = null; + }, + clean: function() { + return clean | (v00 && v0) << 1; + } + }; + } + function intersect(a, b, two) { + var pa = d3_geo_cartesian(a), pb = d3_geo_cartesian(b); + var n1 = [ 1, 0, 0 ], n2 = d3_geo_cartesianCross(pa, pb), n2n2 = d3_geo_cartesianDot(n2, n2), n1n2 = n2[0], determinant = n2n2 - n1n2 * n1n2; + if (!determinant) return !two && a; + var c1 = cr * n2n2 / determinant, c2 = -cr * n1n2 / determinant, n1xn2 = d3_geo_cartesianCross(n1, n2), A = d3_geo_cartesianScale(n1, c1), B = d3_geo_cartesianScale(n2, c2); + d3_geo_cartesianAdd(A, B); + var u = n1xn2, w = d3_geo_cartesianDot(A, u), uu = d3_geo_cartesianDot(u, u), t2 = w * w - uu * (d3_geo_cartesianDot(A, A) - 1); + if (t2 < 0) return; + var t = Math.sqrt(t2), q = d3_geo_cartesianScale(u, (-w - t) / uu); + d3_geo_cartesianAdd(q, A); + q = d3_geo_spherical(q); + if (!two) return q; + var Îģ0 = a[0], Îģ1 = b[0], Ī†0 = a[1], Ī†1 = b[1], z; + if (Îģ1 < Îģ0) z = Îģ0, Îģ0 = Îģ1, Îģ1 = z; + var δÎģ = Îģ1 - Îģ0, polar = abs(δÎģ - Ī€) < Îĩ, meridian = polar || δÎģ < Îĩ; + if (!polar && Ī†1 < Ī†0) z = Ī†0, Ī†0 = Ī†1, Ī†1 = z; + if (meridian ? polar ? Ī†0 + Ī†1 > 0 ^ q[1] < (abs(q[0] - Îģ0) < Îĩ ? Ī†0 : Ī†1) : Ī†0 <= q[1] && q[1] <= Ī†1 : δÎģ > Ī€ ^ (Îģ0 <= q[0] && q[0] <= Îģ1)) { + var q1 = d3_geo_cartesianScale(u, (-w + t) / uu); + d3_geo_cartesianAdd(q1, A); + return [ q, d3_geo_spherical(q1) ]; + } + } + function code(Îģ, Ī†) { + var r = smallRadius ? radius : Ī€ - radius, code = 0; + if (Îģ < -r) code |= 1; else if (Îģ > r) code |= 2; + if (Ī† < -r) code |= 4; else if (Ī† > r) code |= 8; + return code; + } + } + function d3_geom_clipLine(x0, y0, x1, y1) { + return function(line) { + var a = line.a, b = line.b, ax = a.x, ay = a.y, bx = b.x, by = b.y, t0 = 0, t1 = 1, dx = bx - ax, dy = by - ay, r; + r = x0 - ax; + if (!dx && r > 0) return; + r /= dx; + if (dx < 0) { + if (r < t0) return; + if (r < t1) t1 = r; + } else if (dx > 0) { + if (r > t1) return; + if (r > t0) t0 = r; + } + r = x1 - ax; + if (!dx && r < 0) return; + r /= dx; + if (dx < 0) { + if (r > t1) return; + if (r > t0) t0 = r; + } else if (dx > 0) { + if (r < t0) return; + if (r < t1) t1 = r; + } + r = y0 - ay; + if (!dy && r > 0) return; + r /= dy; + if (dy < 0) { + if (r < t0) return; + if (r < t1) t1 = r; + } else if (dy > 0) { + if (r > t1) return; + if (r > t0) t0 = r; + } + r = y1 - ay; + if (!dy && r < 0) return; + r /= dy; + if (dy < 0) { + if (r > t1) return; + if (r > t0) t0 = r; + } else if (dy > 0) { + if (r < t0) return; + if (r < t1) t1 = r; + } + if (t0 > 0) line.a = { + x: ax + t0 * dx, + y: ay + t0 * dy + }; + if (t1 < 1) line.b = { + x: ax + t1 * dx, + y: ay + t1 * dy + }; + return line; + }; + } + var d3_geo_clipExtentMAX = 1e9; + d3.geo.clipExtent = function() { + var x0, y0, x1, y1, stream, clip, clipExtent = { + stream: function(output) { + if (stream) stream.valid = false; + stream = clip(output); + stream.valid = true; + return stream; + }, + extent: function(_) { + if (!arguments.length) return [ [ x0, y0 ], [ x1, y1 ] ]; + clip = d3_geo_clipExtent(x0 = +_[0][0], y0 = +_[0][1], x1 = +_[1][0], y1 = +_[1][1]); + if (stream) stream.valid = false, stream = null; + return clipExtent; + } + }; + return clipExtent.extent([ [ 0, 0 ], [ 960, 500 ] ]); + }; + function d3_geo_clipExtent(x0, y0, x1, y1) { + return function(listener) { + var listener_ = listener, bufferListener = d3_geo_clipBufferListener(), clipLine = d3_geom_clipLine(x0, y0, x1, y1), segments, polygon, ring; + var clip = { + point: point, + lineStart: lineStart, + lineEnd: lineEnd, + polygonStart: function() { + listener = bufferListener; + segments = []; + polygon = []; + clean = true; + }, + polygonEnd: function() { + listener = listener_; + segments = d3.merge(segments); + var clipStartInside = insidePolygon([ x0, y1 ]), inside = clean && clipStartInside, visible = segments.length; + if (inside || visible) { + listener.polygonStart(); + if (inside) { + listener.lineStart(); + interpolate(null, null, 1, listener); + listener.lineEnd(); + } + if (visible) { + d3_geo_clipPolygon(segments, compare, clipStartInside, interpolate, listener); + } + listener.polygonEnd(); + } + segments = polygon = ring = null; + } + }; + function insidePolygon(p) { + var wn = 0, n = polygon.length, y = p[1]; + for (var i = 0; i < n; ++i) { + for (var j = 1, v = polygon[i], m = v.length, a = v[0], b; j < m; ++j) { + b = v[j]; + if (a[1] <= y) { + if (b[1] > y && d3_cross2d(a, b, p) > 0) ++wn; + } else { + if (b[1] <= y && d3_cross2d(a, b, p) < 0) --wn; + } + a = b; + } + } + return wn !== 0; + } + function interpolate(from, to, direction, listener) { + var a = 0, a1 = 0; + if (from == null || (a = corner(from, direction)) !== (a1 = corner(to, direction)) || comparePoints(from, to) < 0 ^ direction > 0) { + do { + listener.point(a === 0 || a === 3 ? x0 : x1, a > 1 ? y1 : y0); + } while ((a = (a + direction + 4) % 4) !== a1); + } else { + listener.point(to[0], to[1]); + } + } + function pointVisible(x, y) { + return x0 <= x && x <= x1 && y0 <= y && y <= y1; + } + function point(x, y) { + if (pointVisible(x, y)) listener.point(x, y); + } + var x__, y__, v__, x_, y_, v_, first, clean; + function lineStart() { + clip.point = linePoint; + if (polygon) polygon.push(ring = []); + first = true; + v_ = false; + x_ = y_ = NaN; + } + function lineEnd() { + if (segments) { + linePoint(x__, y__); + if (v__ && v_) bufferListener.rejoin(); + segments.push(bufferListener.buffer()); + } + clip.point = point; + if (v_) listener.lineEnd(); + } + function linePoint(x, y) { + x = Math.max(-d3_geo_clipExtentMAX, Math.min(d3_geo_clipExtentMAX, x)); + y = Math.max(-d3_geo_clipExtentMAX, Math.min(d3_geo_clipExtentMAX, y)); + var v = pointVisible(x, y); + if (polygon) ring.push([ x, y ]); + if (first) { + x__ = x, y__ = y, v__ = v; + first = false; + if (v) { + listener.lineStart(); + listener.point(x, y); + } + } else { + if (v && v_) listener.point(x, y); else { + var l = { + a: { + x: x_, + y: y_ + }, + b: { + x: x, + y: y + } + }; + if (clipLine(l)) { + if (!v_) { + listener.lineStart(); + listener.point(l.a.x, l.a.y); + } + listener.point(l.b.x, l.b.y); + if (!v) listener.lineEnd(); + clean = false; + } else if (v) { + listener.lineStart(); + listener.point(x, y); + clean = false; + } + } + } + x_ = x, y_ = y, v_ = v; + } + return clip; + }; + function corner(p, direction) { + return abs(p[0] - x0) < Îĩ ? direction > 0 ? 0 : 3 : abs(p[0] - x1) < Îĩ ? direction > 0 ? 2 : 1 : abs(p[1] - y0) < Îĩ ? direction > 0 ? 1 : 0 : direction > 0 ? 3 : 2; + } + function compare(a, b) { + return comparePoints(a.x, b.x); + } + function comparePoints(a, b) { + var ca = corner(a, 1), cb = corner(b, 1); + return ca !== cb ? ca - cb : ca === 0 ? b[1] - a[1] : ca === 1 ? a[0] - b[0] : ca === 2 ? a[1] - b[1] : b[0] - a[0]; + } + } + function d3_geo_conic(projectAt) { + var Ī†0 = 0, Ī†1 = Ī€ / 3, m = d3_geo_projectionMutator(projectAt), p = m(Ī†0, Ī†1); + p.parallels = function(_) { + if (!arguments.length) return [ Ī†0 / Ī€ * 180, Ī†1 / Ī€ * 180 ]; + return m(Ī†0 = _[0] * Ī€ / 180, Ī†1 = _[1] * Ī€ / 180); + }; + return p; + } + function d3_geo_conicEqualArea(Ī†0, Ī†1) { + var sinĪ†0 = Math.sin(Ī†0), n = (sinĪ†0 + Math.sin(Ī†1)) / 2, C = 1 + sinĪ†0 * (2 * n - sinĪ†0), Ī0 = Math.sqrt(C) / n; + function forward(Îģ, Ī†) { + var Ī = Math.sqrt(C - 2 * n * Math.sin(Ī†)) / n; + return [ Ī * Math.sin(Îģ *= n), Ī0 - Ī * Math.cos(Îģ) ]; + } + forward.invert = function(x, y) { + var Ī0_y = Ī0 - y; + return [ Math.atan2(x, Ī0_y) / n, d3_asin((C - (x * x + Ī0_y * Ī0_y) * n * n) / (2 * n)) ]; + }; + return forward; + } + (d3.geo.conicEqualArea = function() { + return d3_geo_conic(d3_geo_conicEqualArea); + }).raw = d3_geo_conicEqualArea; + d3.geo.albers = function() { + return d3.geo.conicEqualArea().rotate([ 96, 0 ]).center([ -.6, 38.7 ]).parallels([ 29.5, 45.5 ]).scale(1070); + }; + d3.geo.albersUsa = function() { + var lower48 = d3.geo.albers(); + var alaska = d3.geo.conicEqualArea().rotate([ 154, 0 ]).center([ -2, 58.5 ]).parallels([ 55, 65 ]); + var hawaii = d3.geo.conicEqualArea().rotate([ 157, 0 ]).center([ -3, 19.9 ]).parallels([ 8, 18 ]); + var point, pointStream = { + point: function(x, y) { + point = [ x, y ]; + } + }, lower48Point, alaskaPoint, hawaiiPoint; + function albersUsa(coordinates) { + var x = coordinates[0], y = coordinates[1]; + point = null; + (lower48Point(x, y), point) || (alaskaPoint(x, y), point) || hawaiiPoint(x, y); + return point; + } + albersUsa.invert = function(coordinates) { + var k = lower48.scale(), t = lower48.translate(), x = (coordinates[0] - t[0]) / k, y = (coordinates[1] - t[1]) / k; + return (y >= .12 && y < .234 && x >= -.425 && x < -.214 ? alaska : y >= .166 && y < .234 && x >= -.214 && x < -.115 ? hawaii : lower48).invert(coordinates); + }; + albersUsa.stream = function(stream) { + var lower48Stream = lower48.stream(stream), alaskaStream = alaska.stream(stream), hawaiiStream = hawaii.stream(stream); + return { + point: function(x, y) { + lower48Stream.point(x, y); + alaskaStream.point(x, y); + hawaiiStream.point(x, y); + }, + sphere: function() { + lower48Stream.sphere(); + alaskaStream.sphere(); + hawaiiStream.sphere(); + }, + lineStart: function() { + lower48Stream.lineStart(); + alaskaStream.lineStart(); + hawaiiStream.lineStart(); + }, + lineEnd: function() { + lower48Stream.lineEnd(); + alaskaStream.lineEnd(); + hawaiiStream.lineEnd(); + }, + polygonStart: function() { + lower48Stream.polygonStart(); + alaskaStream.polygonStart(); + hawaiiStream.polygonStart(); + }, + polygonEnd: function() { + lower48Stream.polygonEnd(); + alaskaStream.polygonEnd(); + hawaiiStream.polygonEnd(); + } + }; + }; + albersUsa.precision = function(_) { + if (!arguments.length) return lower48.precision(); + lower48.precision(_); + alaska.precision(_); + hawaii.precision(_); + return albersUsa; + }; + albersUsa.scale = function(_) { + if (!arguments.length) return lower48.scale(); + lower48.scale(_); + alaska.scale(_ * .35); + hawaii.scale(_); + return albersUsa.translate(lower48.translate()); + }; + albersUsa.translate = function(_) { + if (!arguments.length) return lower48.translate(); + var k = lower48.scale(), x = +_[0], y = +_[1]; + lower48Point = lower48.translate(_).clipExtent([ [ x - .455 * k, y - .238 * k ], [ x + .455 * k, y + .238 * k ] ]).stream(pointStream).point; + alaskaPoint = alaska.translate([ x - .307 * k, y + .201 * k ]).clipExtent([ [ x - .425 * k + Îĩ, y + .12 * k + Îĩ ], [ x - .214 * k - Îĩ, y + .234 * k - Îĩ ] ]).stream(pointStream).point; + hawaiiPoint = hawaii.translate([ x - .205 * k, y + .212 * k ]).clipExtent([ [ x - .214 * k + Îĩ, y + .166 * k + Îĩ ], [ x - .115 * k - Îĩ, y + .234 * k - Îĩ ] ]).stream(pointStream).point; + return albersUsa; + }; + return albersUsa.scale(1070); + }; + var d3_geo_pathAreaSum, d3_geo_pathAreaPolygon, d3_geo_pathArea = { + point: d3_noop, + lineStart: d3_noop, + lineEnd: d3_noop, + polygonStart: function() { + d3_geo_pathAreaPolygon = 0; + d3_geo_pathArea.lineStart = d3_geo_pathAreaRingStart; + }, + polygonEnd: function() { + d3_geo_pathArea.lineStart = d3_geo_pathArea.lineEnd = d3_geo_pathArea.point = d3_noop; + d3_geo_pathAreaSum += abs(d3_geo_pathAreaPolygon / 2); + } + }; + function d3_geo_pathAreaRingStart() { + var x00, y00, x0, y0; + d3_geo_pathArea.point = function(x, y) { + d3_geo_pathArea.point = nextPoint; + x00 = x0 = x, y00 = y0 = y; + }; + function nextPoint(x, y) { + d3_geo_pathAreaPolygon += y0 * x - x0 * y; + x0 = x, y0 = y; + } + d3_geo_pathArea.lineEnd = function() { + nextPoint(x00, y00); + }; + } + var d3_geo_pathBoundsX0, d3_geo_pathBoundsY0, d3_geo_pathBoundsX1, d3_geo_pathBoundsY1; + var d3_geo_pathBounds = { + point: d3_geo_pathBoundsPoint, + lineStart: d3_noop, + lineEnd: d3_noop, + polygonStart: d3_noop, + polygonEnd: d3_noop + }; + function d3_geo_pathBoundsPoint(x, y) { + if (x < d3_geo_pathBoundsX0) d3_geo_pathBoundsX0 = x; + if (x > d3_geo_pathBoundsX1) d3_geo_pathBoundsX1 = x; + if (y < d3_geo_pathBoundsY0) d3_geo_pathBoundsY0 = y; + if (y > d3_geo_pathBoundsY1) d3_geo_pathBoundsY1 = y; + } + function d3_geo_pathBuffer() { + var pointCircle = d3_geo_pathBufferCircle(4.5), buffer = []; + var stream = { + point: point, + lineStart: function() { + stream.point = pointLineStart; + }, + lineEnd: lineEnd, + polygonStart: function() { + stream.lineEnd = lineEndPolygon; + }, + polygonEnd: function() { + stream.lineEnd = lineEnd; + stream.point = point; + }, + pointRadius: function(_) { + pointCircle = d3_geo_pathBufferCircle(_); + return stream; + }, + result: function() { + if (buffer.length) { + var result = buffer.join(""); + buffer = []; + return result; + } + } + }; + function point(x, y) { + buffer.push("M", x, ",", y, pointCircle); + } + function pointLineStart(x, y) { + buffer.push("M", x, ",", y); + stream.point = pointLine; + } + function pointLine(x, y) { + buffer.push("L", x, ",", y); + } + function lineEnd() { + stream.point = point; + } + function lineEndPolygon() { + buffer.push("Z"); + } + return stream; + } + function d3_geo_pathBufferCircle(radius) { + return "m0," + radius + "a" + radius + "," + radius + " 0 1,1 0," + -2 * radius + "a" + radius + "," + radius + " 0 1,1 0," + 2 * radius + "z"; + } + var d3_geo_pathCentroid = { + point: d3_geo_pathCentroidPoint, + lineStart: d3_geo_pathCentroidLineStart, + lineEnd: d3_geo_pathCentroidLineEnd, + polygonStart: function() { + d3_geo_pathCentroid.lineStart = d3_geo_pathCentroidRingStart; + }, + polygonEnd: function() { + d3_geo_pathCentroid.point = d3_geo_pathCentroidPoint; + d3_geo_pathCentroid.lineStart = d3_geo_pathCentroidLineStart; + d3_geo_pathCentroid.lineEnd = d3_geo_pathCentroidLineEnd; + } + }; + function d3_geo_pathCentroidPoint(x, y) { + d3_geo_centroidX0 += x; + d3_geo_centroidY0 += y; + ++d3_geo_centroidZ0; + } + function d3_geo_pathCentroidLineStart() { + var x0, y0; + d3_geo_pathCentroid.point = function(x, y) { + d3_geo_pathCentroid.point = nextPoint; + d3_geo_pathCentroidPoint(x0 = x, y0 = y); + }; + function nextPoint(x, y) { + var dx = x - x0, dy = y - y0, z = Math.sqrt(dx * dx + dy * dy); + d3_geo_centroidX1 += z * (x0 + x) / 2; + d3_geo_centroidY1 += z * (y0 + y) / 2; + d3_geo_centroidZ1 += z; + d3_geo_pathCentroidPoint(x0 = x, y0 = y); + } + } + function d3_geo_pathCentroidLineEnd() { + d3_geo_pathCentroid.point = d3_geo_pathCentroidPoint; + } + function d3_geo_pathCentroidRingStart() { + var x00, y00, x0, y0; + d3_geo_pathCentroid.point = function(x, y) { + d3_geo_pathCentroid.point = nextPoint; + d3_geo_pathCentroidPoint(x00 = x0 = x, y00 = y0 = y); + }; + function nextPoint(x, y) { + var dx = x - x0, dy = y - y0, z = Math.sqrt(dx * dx + dy * dy); + d3_geo_centroidX1 += z * (x0 + x) / 2; + d3_geo_centroidY1 += z * (y0 + y) / 2; + d3_geo_centroidZ1 += z; + z = y0 * x - x0 * y; + d3_geo_centroidX2 += z * (x0 + x); + d3_geo_centroidY2 += z * (y0 + y); + d3_geo_centroidZ2 += z * 3; + d3_geo_pathCentroidPoint(x0 = x, y0 = y); + } + d3_geo_pathCentroid.lineEnd = function() { + nextPoint(x00, y00); + }; + } + function d3_geo_pathContext(context) { + var pointRadius = 4.5; + var stream = { + point: point, + lineStart: function() { + stream.point = pointLineStart; + }, + lineEnd: lineEnd, + polygonStart: function() { + stream.lineEnd = lineEndPolygon; + }, + polygonEnd: function() { + stream.lineEnd = lineEnd; + stream.point = point; + }, + pointRadius: function(_) { + pointRadius = _; + return stream; + }, + result: d3_noop + }; + function point(x, y) { + context.moveTo(x + pointRadius, y); + context.arc(x, y, pointRadius, 0, Ī„); + } + function pointLineStart(x, y) { + context.moveTo(x, y); + stream.point = pointLine; + } + function pointLine(x, y) { + context.lineTo(x, y); + } + function lineEnd() { + stream.point = point; + } + function lineEndPolygon() { + context.closePath(); + } + return stream; + } + function d3_geo_resample(project) { + var δ2 = .5, cosMinDistance = Math.cos(30 * d3_radians), maxDepth = 16; + function resample(stream) { + return (maxDepth ? resampleRecursive : resampleNone)(stream); + } + function resampleNone(stream) { + return d3_geo_transformPoint(stream, function(x, y) { + x = project(x, y); + stream.point(x[0], x[1]); + }); + } + function resampleRecursive(stream) { + var Îģ00, Ī†00, x00, y00, a00, b00, c00, Îģ0, x0, y0, a0, b0, c0; + var resample = { + point: point, + lineStart: lineStart, + lineEnd: lineEnd, + polygonStart: function() { + stream.polygonStart(); + resample.lineStart = ringStart; + }, + polygonEnd: function() { + stream.polygonEnd(); + resample.lineStart = lineStart; + } + }; + function point(x, y) { + x = project(x, y); + stream.point(x[0], x[1]); + } + function lineStart() { + x0 = NaN; + resample.point = linePoint; + stream.lineStart(); + } + function linePoint(Îģ, Ī†) { + var c = d3_geo_cartesian([ Îģ, Ī† ]), p = project(Îģ, Ī†); + resampleLineTo(x0, y0, Îģ0, a0, b0, c0, x0 = p[0], y0 = p[1], Îģ0 = Îģ, a0 = c[0], b0 = c[1], c0 = c[2], maxDepth, stream); + stream.point(x0, y0); + } + function lineEnd() { + resample.point = point; + stream.lineEnd(); + } + function ringStart() { + lineStart(); + resample.point = ringPoint; + resample.lineEnd = ringEnd; + } + function ringPoint(Îģ, Ī†) { + linePoint(Îģ00 = Îģ, Ī†00 = Ī†), x00 = x0, y00 = y0, a00 = a0, b00 = b0, c00 = c0; + resample.point = linePoint; + } + function ringEnd() { + resampleLineTo(x0, y0, Îģ0, a0, b0, c0, x00, y00, Îģ00, a00, b00, c00, maxDepth, stream); + resample.lineEnd = lineEnd; + lineEnd(); + } + return resample; + } + function resampleLineTo(x0, y0, Îģ0, a0, b0, c0, x1, y1, Îģ1, a1, b1, c1, depth, stream) { + var dx = x1 - x0, dy = y1 - y0, d2 = dx * dx + dy * dy; + if (d2 > 4 * δ2 && depth--) { + var a = a0 + a1, b = b0 + b1, c = c0 + c1, m = Math.sqrt(a * a + b * b + c * c), Ī†2 = Math.asin(c /= m), Îģ2 = abs(abs(c) - 1) < Îĩ || abs(Îģ0 - Îģ1) < Îĩ ? (Îģ0 + Îģ1) / 2 : Math.atan2(b, a), p = project(Îģ2, Ī†2), x2 = p[0], y2 = p[1], dx2 = x2 - x0, dy2 = y2 - y0, dz = dy * dx2 - dx * dy2; + if (dz * dz / d2 > δ2 || abs((dx * dx2 + dy * dy2) / d2 - .5) > .3 || a0 * a1 + b0 * b1 + c0 * c1 < cosMinDistance) { + resampleLineTo(x0, y0, Îģ0, a0, b0, c0, x2, y2, Îģ2, a /= m, b /= m, c, depth, stream); + stream.point(x2, y2); + resampleLineTo(x2, y2, Îģ2, a, b, c, x1, y1, Îģ1, a1, b1, c1, depth, stream); + } + } + } + resample.precision = function(_) { + if (!arguments.length) return Math.sqrt(δ2); + maxDepth = (δ2 = _ * _) > 0 && 16; + return resample; + }; + return resample; + } + d3.geo.path = function() { + var pointRadius = 4.5, projection, context, projectStream, contextStream, cacheStream; + function path(object) { + if (object) { + if (typeof pointRadius === "function") contextStream.pointRadius(+pointRadius.apply(this, arguments)); + if (!cacheStream || !cacheStream.valid) cacheStream = projectStream(contextStream); + d3.geo.stream(object, cacheStream); + } + return contextStream.result(); + } + path.area = function(object) { + d3_geo_pathAreaSum = 0; + d3.geo.stream(object, projectStream(d3_geo_pathArea)); + return d3_geo_pathAreaSum; + }; + path.centroid = function(object) { + d3_geo_centroidX0 = d3_geo_centroidY0 = d3_geo_centroidZ0 = d3_geo_centroidX1 = d3_geo_centroidY1 = d3_geo_centroidZ1 = d3_geo_centroidX2 = d3_geo_centroidY2 = d3_geo_centroidZ2 = 0; + d3.geo.stream(object, projectStream(d3_geo_pathCentroid)); + return d3_geo_centroidZ2 ? [ d3_geo_centroidX2 / d3_geo_centroidZ2, d3_geo_centroidY2 / d3_geo_centroidZ2 ] : d3_geo_centroidZ1 ? [ d3_geo_centroidX1 / d3_geo_centroidZ1, d3_geo_centroidY1 / d3_geo_centroidZ1 ] : d3_geo_centroidZ0 ? [ d3_geo_centroidX0 / d3_geo_centroidZ0, d3_geo_centroidY0 / d3_geo_centroidZ0 ] : [ NaN, NaN ]; + }; + path.bounds = function(object) { + d3_geo_pathBoundsX1 = d3_geo_pathBoundsY1 = -(d3_geo_pathBoundsX0 = d3_geo_pathBoundsY0 = Infinity); + d3.geo.stream(object, projectStream(d3_geo_pathBounds)); + return [ [ d3_geo_pathBoundsX0, d3_geo_pathBoundsY0 ], [ d3_geo_pathBoundsX1, d3_geo_pathBoundsY1 ] ]; + }; + path.projection = function(_) { + if (!arguments.length) return projection; + projectStream = (projection = _) ? _.stream || d3_geo_pathProjectStream(_) : d3_identity; + return reset(); + }; + path.context = function(_) { + if (!arguments.length) return context; + contextStream = (context = _) == null ? new d3_geo_pathBuffer() : new d3_geo_pathContext(_); + if (typeof pointRadius !== "function") contextStream.pointRadius(pointRadius); + return reset(); + }; + path.pointRadius = function(_) { + if (!arguments.length) return pointRadius; + pointRadius = typeof _ === "function" ? _ : (contextStream.pointRadius(+_), +_); + return path; + }; + function reset() { + cacheStream = null; + return path; + } + return path.projection(d3.geo.albersUsa()).context(null); + }; + function d3_geo_pathProjectStream(project) { + var resample = d3_geo_resample(function(x, y) { + return project([ x * d3_degrees, y * d3_degrees ]); + }); + return function(stream) { + return d3_geo_projectionRadians(resample(stream)); + }; + } + d3.geo.transform = function(methods) { + return { + stream: function(stream) { + var transform = new d3_geo_transform(stream); + for (var k in methods) transform[k] = methods[k]; + return transform; + } + }; + }; + function d3_geo_transform(stream) { + this.stream = stream; + } + d3_geo_transform.prototype = { + point: function(x, y) { + this.stream.point(x, y); + }, + sphere: function() { + this.stream.sphere(); + }, + lineStart: function() { + this.stream.lineStart(); + }, + lineEnd: function() { + this.stream.lineEnd(); + }, + polygonStart: function() { + this.stream.polygonStart(); + }, + polygonEnd: function() { + this.stream.polygonEnd(); + } + }; + function d3_geo_transformPoint(stream, point) { + return { + point: point, + sphere: function() { + stream.sphere(); + }, + lineStart: function() { + stream.lineStart(); + }, + lineEnd: function() { + stream.lineEnd(); + }, + polygonStart: function() { + stream.polygonStart(); + }, + polygonEnd: function() { + stream.polygonEnd(); + } + }; + } + d3.geo.projection = d3_geo_projection; + d3.geo.projectionMutator = d3_geo_projectionMutator; + function d3_geo_projection(project) { + return d3_geo_projectionMutator(function() { + return project; + })(); + } + function d3_geo_projectionMutator(projectAt) { + var project, rotate, projectRotate, projectResample = d3_geo_resample(function(x, y) { + x = project(x, y); + return [ x[0] * k + δx, δy - x[1] * k ]; + }), k = 150, x = 480, y = 250, Îģ = 0, Ī† = 0, δÎģ = 0, δĪ† = 0, δÎŗ = 0, δx, δy, preclip = d3_geo_clipAntimeridian, postclip = d3_identity, clipAngle = null, clipExtent = null, stream; + function projection(point) { + point = projectRotate(point[0] * d3_radians, point[1] * d3_radians); + return [ point[0] * k + δx, δy - point[1] * k ]; + } + function invert(point) { + point = projectRotate.invert((point[0] - δx) / k, (δy - point[1]) / k); + return point && [ point[0] * d3_degrees, point[1] * d3_degrees ]; + } + projection.stream = function(output) { + if (stream) stream.valid = false; + stream = d3_geo_projectionRadians(preclip(rotate, projectResample(postclip(output)))); + stream.valid = true; + return stream; + }; + projection.clipAngle = function(_) { + if (!arguments.length) return clipAngle; + preclip = _ == null ? (clipAngle = _, d3_geo_clipAntimeridian) : d3_geo_clipCircle((clipAngle = +_) * d3_radians); + return invalidate(); + }; + projection.clipExtent = function(_) { + if (!arguments.length) return clipExtent; + clipExtent = _; + postclip = _ ? d3_geo_clipExtent(_[0][0], _[0][1], _[1][0], _[1][1]) : d3_identity; + return invalidate(); + }; + projection.scale = function(_) { + if (!arguments.length) return k; + k = +_; + return reset(); + }; + projection.translate = function(_) { + if (!arguments.length) return [ x, y ]; + x = +_[0]; + y = +_[1]; + return reset(); + }; + projection.center = function(_) { + if (!arguments.length) return [ Îģ * d3_degrees, Ī† * d3_degrees ]; + Îģ = _[0] % 360 * d3_radians; + Ī† = _[1] % 360 * d3_radians; + return reset(); + }; + projection.rotate = function(_) { + if (!arguments.length) return [ δÎģ * d3_degrees, δĪ† * d3_degrees, δÎŗ * d3_degrees ]; + δÎģ = _[0] % 360 * d3_radians; + δĪ† = _[1] % 360 * d3_radians; + δÎŗ = _.length > 2 ? _[2] % 360 * d3_radians : 0; + return reset(); + }; + d3.rebind(projection, projectResample, "precision"); + function reset() { + projectRotate = d3_geo_compose(rotate = d3_geo_rotation(δÎģ, δĪ†, δÎŗ), project); + var center = project(Îģ, Ī†); + δx = x - center[0] * k; + δy = y + center[1] * k; + return invalidate(); + } + function invalidate() { + if (stream) stream.valid = false, stream = null; + return projection; + } + return function() { + project = projectAt.apply(this, arguments); + projection.invert = project.invert && invert; + return reset(); + }; + } + function d3_geo_projectionRadians(stream) { + return d3_geo_transformPoint(stream, function(x, y) { + stream.point(x * d3_radians, y * d3_radians); + }); + } + function d3_geo_equirectangular(Îģ, Ī†) { + return [ Îģ, Ī† ]; + } + (d3.geo.equirectangular = function() { + return d3_geo_projection(d3_geo_equirectangular); + }).raw = d3_geo_equirectangular.invert = d3_geo_equirectangular; + d3.geo.rotation = function(rotate) { + rotate = d3_geo_rotation(rotate[0] % 360 * d3_radians, rotate[1] * d3_radians, rotate.length > 2 ? rotate[2] * d3_radians : 0); + function forward(coordinates) { + coordinates = rotate(coordinates[0] * d3_radians, coordinates[1] * d3_radians); + return coordinates[0] *= d3_degrees, coordinates[1] *= d3_degrees, coordinates; + } + forward.invert = function(coordinates) { + coordinates = rotate.invert(coordinates[0] * d3_radians, coordinates[1] * d3_radians); + return coordinates[0] *= d3_degrees, coordinates[1] *= d3_degrees, coordinates; + }; + return forward; + }; + function d3_geo_identityRotation(Îģ, Ī†) { + return [ Îģ > Ī€ ? Îģ - Ī„ : Îģ < -Ī€ ? Îģ + Ī„ : Îģ, Ī† ]; + } + d3_geo_identityRotation.invert = d3_geo_equirectangular; + function d3_geo_rotation(δÎģ, δĪ†, δÎŗ) { + return δÎģ ? δĪ† || δÎŗ ? d3_geo_compose(d3_geo_rotationÎģ(δÎģ), d3_geo_rotationĪ†Îŗ(δĪ†, δÎŗ)) : d3_geo_rotationÎģ(δÎģ) : δĪ† || δÎŗ ? d3_geo_rotationĪ†Îŗ(δĪ†, δÎŗ) : d3_geo_identityRotation; + } + function d3_geo_forwardRotationÎģ(δÎģ) { + return function(Îģ, Ī†) { + return Îģ += δÎģ, [ Îģ > Ī€ ? Îģ - Ī„ : Îģ < -Ī€ ? Îģ + Ī„ : Îģ, Ī† ]; + }; + } + function d3_geo_rotationÎģ(δÎģ) { + var rotation = d3_geo_forwardRotationÎģ(δÎģ); + rotation.invert = d3_geo_forwardRotationÎģ(-δÎģ); + return rotation; + } + function d3_geo_rotationĪ†Îŗ(δĪ†, δÎŗ) { + var cosδĪ† = Math.cos(δĪ†), sinδĪ† = Math.sin(δĪ†), cosδÎŗ = Math.cos(δÎŗ), sinδÎŗ = Math.sin(δÎŗ); + function rotation(Îģ, Ī†) { + var cosĪ† = Math.cos(Ī†), x = Math.cos(Îģ) * cosĪ†, y = Math.sin(Îģ) * cosĪ†, z = Math.sin(Ī†), k = z * cosδĪ† + x * sinδĪ†; + return [ Math.atan2(y * cosδÎŗ - k * sinδÎŗ, x * cosδĪ† - z * sinδĪ†), d3_asin(k * cosδÎŗ + y * sinδÎŗ) ]; + } + rotation.invert = function(Îģ, Ī†) { + var cosĪ† = Math.cos(Ī†), x = Math.cos(Îģ) * cosĪ†, y = Math.sin(Îģ) * cosĪ†, z = Math.sin(Ī†), k = z * cosδÎŗ - y * sinδÎŗ; + return [ Math.atan2(y * cosδÎŗ + z * sinδÎŗ, x * cosδĪ† + k * sinδĪ†), d3_asin(k * cosδĪ† - x * sinδĪ†) ]; + }; + return rotation; + } + d3.geo.circle = function() { + var origin = [ 0, 0 ], angle, precision = 6, interpolate; + function circle() { + var center = typeof origin === "function" ? origin.apply(this, arguments) : origin, rotate = d3_geo_rotation(-center[0] * d3_radians, -center[1] * d3_radians, 0).invert, ring = []; + interpolate(null, null, 1, { + point: function(x, y) { + ring.push(x = rotate(x, y)); + x[0] *= d3_degrees, x[1] *= d3_degrees; + } + }); + return { + type: "Polygon", + coordinates: [ ring ] + }; + } + circle.origin = function(x) { + if (!arguments.length) return origin; + origin = x; + return circle; + }; + circle.angle = function(x) { + if (!arguments.length) return angle; + interpolate = d3_geo_circleInterpolate((angle = +x) * d3_radians, precision * d3_radians); + return circle; + }; + circle.precision = function(_) { + if (!arguments.length) return precision; + interpolate = d3_geo_circleInterpolate(angle * d3_radians, (precision = +_) * d3_radians); + return circle; + }; + return circle.angle(90); + }; + function d3_geo_circleInterpolate(radius, precision) { + var cr = Math.cos(radius), sr = Math.sin(radius); + return function(from, to, direction, listener) { + var step = direction * precision; + if (from != null) { + from = d3_geo_circleAngle(cr, from); + to = d3_geo_circleAngle(cr, to); + if (direction > 0 ? from < to : from > to) from += direction * Ī„; + } else { + from = radius + direction * Ī„; + to = radius - .5 * step; + } + for (var point, t = from; direction > 0 ? t > to : t < to; t -= step) { + listener.point((point = d3_geo_spherical([ cr, -sr * Math.cos(t), -sr * Math.sin(t) ]))[0], point[1]); + } + }; + } + function d3_geo_circleAngle(cr, point) { + var a = d3_geo_cartesian(point); + a[0] -= cr; + d3_geo_cartesianNormalize(a); + var angle = d3_acos(-a[1]); + return ((-a[2] < 0 ? -angle : angle) + 2 * Math.PI - Îĩ) % (2 * Math.PI); + } + d3.geo.distance = function(a, b) { + var ΔÎģ = (b[0] - a[0]) * d3_radians, Ī†0 = a[1] * d3_radians, Ī†1 = b[1] * d3_radians, sinΔÎģ = Math.sin(ΔÎģ), cosΔÎģ = Math.cos(ΔÎģ), sinĪ†0 = Math.sin(Ī†0), cosĪ†0 = Math.cos(Ī†0), sinĪ†1 = Math.sin(Ī†1), cosĪ†1 = Math.cos(Ī†1), t; + return Math.atan2(Math.sqrt((t = cosĪ†1 * sinΔÎģ) * t + (t = cosĪ†0 * sinĪ†1 - sinĪ†0 * cosĪ†1 * cosΔÎģ) * t), sinĪ†0 * sinĪ†1 + cosĪ†0 * cosĪ†1 * cosΔÎģ); + }; + d3.geo.graticule = function() { + var x1, x0, X1, X0, y1, y0, Y1, Y0, dx = 10, dy = dx, DX = 90, DY = 360, x, y, X, Y, precision = 2.5; + function graticule() { + return { + type: "MultiLineString", + coordinates: lines() + }; + } + function lines() { + return d3.range(Math.ceil(X0 / DX) * DX, X1, DX).map(X).concat(d3.range(Math.ceil(Y0 / DY) * DY, Y1, DY).map(Y)).concat(d3.range(Math.ceil(x0 / dx) * dx, x1, dx).filter(function(x) { + return abs(x % DX) > Îĩ; + }).map(x)).concat(d3.range(Math.ceil(y0 / dy) * dy, y1, dy).filter(function(y) { + return abs(y % DY) > Îĩ; + }).map(y)); + } + graticule.lines = function() { + return lines().map(function(coordinates) { + return { + type: "LineString", + coordinates: coordinates + }; + }); + }; + graticule.outline = function() { + return { + type: "Polygon", + coordinates: [ X(X0).concat(Y(Y1).slice(1), X(X1).reverse().slice(1), Y(Y0).reverse().slice(1)) ] + }; + }; + graticule.extent = function(_) { + if (!arguments.length) return graticule.minorExtent(); + return graticule.majorExtent(_).minorExtent(_); + }; + graticule.majorExtent = function(_) { + if (!arguments.length) return [ [ X0, Y0 ], [ X1, Y1 ] ]; + X0 = +_[0][0], X1 = +_[1][0]; + Y0 = +_[0][1], Y1 = +_[1][1]; + if (X0 > X1) _ = X0, X0 = X1, X1 = _; + if (Y0 > Y1) _ = Y0, Y0 = Y1, Y1 = _; + return graticule.precision(precision); + }; + graticule.minorExtent = function(_) { + if (!arguments.length) return [ [ x0, y0 ], [ x1, y1 ] ]; + x0 = +_[0][0], x1 = +_[1][0]; + y0 = +_[0][1], y1 = +_[1][1]; + if (x0 > x1) _ = x0, x0 = x1, x1 = _; + if (y0 > y1) _ = y0, y0 = y1, y1 = _; + return graticule.precision(precision); + }; + graticule.step = function(_) { + if (!arguments.length) return graticule.minorStep(); + return graticule.majorStep(_).minorStep(_); + }; + graticule.majorStep = function(_) { + if (!arguments.length) return [ DX, DY ]; + DX = +_[0], DY = +_[1]; + return graticule; + }; + graticule.minorStep = function(_) { + if (!arguments.length) return [ dx, dy ]; + dx = +_[0], dy = +_[1]; + return graticule; + }; + graticule.precision = function(_) { + if (!arguments.length) return precision; + precision = +_; + x = d3_geo_graticuleX(y0, y1, 90); + y = d3_geo_graticuleY(x0, x1, precision); + X = d3_geo_graticuleX(Y0, Y1, 90); + Y = d3_geo_graticuleY(X0, X1, precision); + return graticule; + }; + return graticule.majorExtent([ [ -180, -90 + Îĩ ], [ 180, 90 - Îĩ ] ]).minorExtent([ [ -180, -80 - Îĩ ], [ 180, 80 + Îĩ ] ]); + }; + function d3_geo_graticuleX(y0, y1, dy) { + var y = d3.range(y0, y1 - Îĩ, dy).concat(y1); + return function(x) { + return y.map(function(y) { + return [ x, y ]; + }); + }; + } + function d3_geo_graticuleY(x0, x1, dx) { + var x = d3.range(x0, x1 - Îĩ, dx).concat(x1); + return function(y) { + return x.map(function(x) { + return [ x, y ]; + }); + }; + } + function d3_source(d) { + return d.source; + } + function d3_target(d) { + return d.target; + } + d3.geo.greatArc = function() { + var source = d3_source, source_, target = d3_target, target_; + function greatArc() { + return { + type: "LineString", + coordinates: [ source_ || source.apply(this, arguments), target_ || target.apply(this, arguments) ] + }; + } + greatArc.distance = function() { + return d3.geo.distance(source_ || source.apply(this, arguments), target_ || target.apply(this, arguments)); + }; + greatArc.source = function(_) { + if (!arguments.length) return source; + source = _, source_ = typeof _ === "function" ? null : _; + return greatArc; + }; + greatArc.target = function(_) { + if (!arguments.length) return target; + target = _, target_ = typeof _ === "function" ? null : _; + return greatArc; + }; + greatArc.precision = function() { + return arguments.length ? greatArc : 0; + }; + return greatArc; + }; + d3.geo.interpolate = function(source, target) { + return d3_geo_interpolate(source[0] * d3_radians, source[1] * d3_radians, target[0] * d3_radians, target[1] * d3_radians); + }; + function d3_geo_interpolate(x0, y0, x1, y1) { + var cy0 = Math.cos(y0), sy0 = Math.sin(y0), cy1 = Math.cos(y1), sy1 = Math.sin(y1), kx0 = cy0 * Math.cos(x0), ky0 = cy0 * Math.sin(x0), kx1 = cy1 * Math.cos(x1), ky1 = cy1 * Math.sin(x1), d = 2 * Math.asin(Math.sqrt(d3_haversin(y1 - y0) + cy0 * cy1 * d3_haversin(x1 - x0))), k = 1 / Math.sin(d); + var interpolate = d ? function(t) { + var B = Math.sin(t *= d) * k, A = Math.sin(d - t) * k, x = A * kx0 + B * kx1, y = A * ky0 + B * ky1, z = A * sy0 + B * sy1; + return [ Math.atan2(y, x) * d3_degrees, Math.atan2(z, Math.sqrt(x * x + y * y)) * d3_degrees ]; + } : function() { + return [ x0 * d3_degrees, y0 * d3_degrees ]; + }; + interpolate.distance = d; + return interpolate; + } + d3.geo.length = function(object) { + d3_geo_lengthSum = 0; + d3.geo.stream(object, d3_geo_length); + return d3_geo_lengthSum; + }; + var d3_geo_lengthSum; + var d3_geo_length = { + sphere: d3_noop, + point: d3_noop, + lineStart: d3_geo_lengthLineStart, + lineEnd: d3_noop, + polygonStart: d3_noop, + polygonEnd: d3_noop + }; + function d3_geo_lengthLineStart() { + var Îģ0, sinĪ†0, cosĪ†0; + d3_geo_length.point = function(Îģ, Ī†) { + Îģ0 = Îģ * d3_radians, sinĪ†0 = Math.sin(Ī† *= d3_radians), cosĪ†0 = Math.cos(Ī†); + d3_geo_length.point = nextPoint; + }; + d3_geo_length.lineEnd = function() { + d3_geo_length.point = d3_geo_length.lineEnd = d3_noop; + }; + function nextPoint(Îģ, Ī†) { + var sinĪ† = Math.sin(Ī† *= d3_radians), cosĪ† = Math.cos(Ī†), t = abs((Îģ *= d3_radians) - Îģ0), cosΔÎģ = Math.cos(t); + d3_geo_lengthSum += Math.atan2(Math.sqrt((t = cosĪ† * Math.sin(t)) * t + (t = cosĪ†0 * sinĪ† - sinĪ†0 * cosĪ† * cosΔÎģ) * t), sinĪ†0 * sinĪ† + cosĪ†0 * cosĪ† * cosΔÎģ); + Îģ0 = Îģ, sinĪ†0 = sinĪ†, cosĪ†0 = cosĪ†; + } + } + function d3_geo_azimuthal(scale, angle) { + function azimuthal(Îģ, Ī†) { + var cosÎģ = Math.cos(Îģ), cosĪ† = Math.cos(Ī†), k = scale(cosÎģ * cosĪ†); + return [ k * cosĪ† * Math.sin(Îģ), k * Math.sin(Ī†) ]; + } + azimuthal.invert = function(x, y) { + var Ī = Math.sqrt(x * x + y * y), c = angle(Ī), sinc = Math.sin(c), cosc = Math.cos(c); + return [ Math.atan2(x * sinc, Ī * cosc), Math.asin(Ī && y * sinc / Ī) ]; + }; + return azimuthal; + } + var d3_geo_azimuthalEqualArea = d3_geo_azimuthal(function(cosÎģcosĪ†) { + return Math.sqrt(2 / (1 + cosÎģcosĪ†)); + }, function(Ī) { + return 2 * Math.asin(Ī / 2); + }); + (d3.geo.azimuthalEqualArea = function() { + return d3_geo_projection(d3_geo_azimuthalEqualArea); + }).raw = d3_geo_azimuthalEqualArea; + var d3_geo_azimuthalEquidistant = d3_geo_azimuthal(function(cosÎģcosĪ†) { + var c = Math.acos(cosÎģcosĪ†); + return c && c / Math.sin(c); + }, d3_identity); + (d3.geo.azimuthalEquidistant = function() { + return d3_geo_projection(d3_geo_azimuthalEquidistant); + }).raw = d3_geo_azimuthalEquidistant; + function d3_geo_conicConformal(Ī†0, Ī†1) { + var cosĪ†0 = Math.cos(Ī†0), t = function(Ī†) { + return Math.tan(Ī€ / 4 + Ī† / 2); + }, n = Ī†0 === Ī†1 ? Math.sin(Ī†0) : Math.log(cosĪ†0 / Math.cos(Ī†1)) / Math.log(t(Ī†1) / t(Ī†0)), F = cosĪ†0 * Math.pow(t(Ī†0), n) / n; + if (!n) return d3_geo_mercator; + function forward(Îģ, Ī†) { + if (F > 0) { + if (Ī† < -halfĪ€ + Îĩ) Ī† = -halfĪ€ + Îĩ; + } else { + if (Ī† > halfĪ€ - Îĩ) Ī† = halfĪ€ - Îĩ; + } + var Ī = F / Math.pow(t(Ī†), n); + return [ Ī * Math.sin(n * Îģ), F - Ī * Math.cos(n * Îģ) ]; + } + forward.invert = function(x, y) { + var Ī0_y = F - y, Ī = d3_sgn(n) * Math.sqrt(x * x + Ī0_y * Ī0_y); + return [ Math.atan2(x, Ī0_y) / n, 2 * Math.atan(Math.pow(F / Ī, 1 / n)) - halfĪ€ ]; + }; + return forward; + } + (d3.geo.conicConformal = function() { + return d3_geo_conic(d3_geo_conicConformal); + }).raw = d3_geo_conicConformal; + function d3_geo_conicEquidistant(Ī†0, Ī†1) { + var cosĪ†0 = Math.cos(Ī†0), n = Ī†0 === Ī†1 ? Math.sin(Ī†0) : (cosĪ†0 - Math.cos(Ī†1)) / (Ī†1 - Ī†0), G = cosĪ†0 / n + Ī†0; + if (abs(n) < Îĩ) return d3_geo_equirectangular; + function forward(Îģ, Ī†) { + var Ī = G - Ī†; + return [ Ī * Math.sin(n * Îģ), G - Ī * Math.cos(n * Îģ) ]; + } + forward.invert = function(x, y) { + var Ī0_y = G - y; + return [ Math.atan2(x, Ī0_y) / n, G - d3_sgn(n) * Math.sqrt(x * x + Ī0_y * Ī0_y) ]; + }; + return forward; + } + (d3.geo.conicEquidistant = function() { + return d3_geo_conic(d3_geo_conicEquidistant); + }).raw = d3_geo_conicEquidistant; + var d3_geo_gnomonic = d3_geo_azimuthal(function(cosÎģcosĪ†) { + return 1 / cosÎģcosĪ†; + }, Math.atan); + (d3.geo.gnomonic = function() { + return d3_geo_projection(d3_geo_gnomonic); + }).raw = d3_geo_gnomonic; + function d3_geo_mercator(Îģ, Ī†) { + return [ Îģ, Math.log(Math.tan(Ī€ / 4 + Ī† / 2)) ]; + } + d3_geo_mercator.invert = function(x, y) { + return [ x, 2 * Math.atan(Math.exp(y)) - halfĪ€ ]; + }; + function d3_geo_mercatorProjection(project) { + var m = d3_geo_projection(project), scale = m.scale, translate = m.translate, clipExtent = m.clipExtent, clipAuto; + m.scale = function() { + var v = scale.apply(m, arguments); + return v === m ? clipAuto ? m.clipExtent(null) : m : v; + }; + m.translate = function() { + var v = translate.apply(m, arguments); + return v === m ? clipAuto ? m.clipExtent(null) : m : v; + }; + m.clipExtent = function(_) { + var v = clipExtent.apply(m, arguments); + if (v === m) { + if (clipAuto = _ == null) { + var k = Ī€ * scale(), t = translate(); + clipExtent([ [ t[0] - k, t[1] - k ], [ t[0] + k, t[1] + k ] ]); + } + } else if (clipAuto) { + v = null; + } + return v; + }; + return m.clipExtent(null); + } + (d3.geo.mercator = function() { + return d3_geo_mercatorProjection(d3_geo_mercator); + }).raw = d3_geo_mercator; + var d3_geo_orthographic = d3_geo_azimuthal(function() { + return 1; + }, Math.asin); + (d3.geo.orthographic = function() { + return d3_geo_projection(d3_geo_orthographic); + }).raw = d3_geo_orthographic; + var d3_geo_stereographic = d3_geo_azimuthal(function(cosÎģcosĪ†) { + return 1 / (1 + cosÎģcosĪ†); + }, function(Ī) { + return 2 * Math.atan(Ī); + }); + (d3.geo.stereographic = function() { + return d3_geo_projection(d3_geo_stereographic); + }).raw = d3_geo_stereographic; + function d3_geo_transverseMercator(Îģ, Ī†) { + return [ Math.log(Math.tan(Ī€ / 4 + Ī† / 2)), -Îģ ]; + } + d3_geo_transverseMercator.invert = function(x, y) { + return [ -y, 2 * Math.atan(Math.exp(x)) - halfĪ€ ]; + }; + (d3.geo.transverseMercator = function() { + var projection = d3_geo_mercatorProjection(d3_geo_transverseMercator), center = projection.center, rotate = projection.rotate; + projection.center = function(_) { + return _ ? center([ -_[1], _[0] ]) : (_ = center(), [ _[1], -_[0] ]); + }; + projection.rotate = function(_) { + return _ ? rotate([ _[0], _[1], _.length > 2 ? _[2] + 90 : 90 ]) : (_ = rotate(), + [ _[0], _[1], _[2] - 90 ]); + }; + return rotate([ 0, 0, 90 ]); + }).raw = d3_geo_transverseMercator; + d3.geom = {}; + function d3_geom_pointX(d) { + return d[0]; + } + function d3_geom_pointY(d) { + return d[1]; + } + d3.geom.hull = function(vertices) { + var x = d3_geom_pointX, y = d3_geom_pointY; + if (arguments.length) return hull(vertices); + function hull(data) { + if (data.length < 3) return []; + var fx = d3_functor(x), fy = d3_functor(y), i, n = data.length, points = [], flippedPoints = []; + for (i = 0; i < n; i++) { + points.push([ +fx.call(this, data[i], i), +fy.call(this, data[i], i), i ]); + } + points.sort(d3_geom_hullOrder); + for (i = 0; i < n; i++) flippedPoints.push([ points[i][0], -points[i][1] ]); + var upper = d3_geom_hullUpper(points), lower = d3_geom_hullUpper(flippedPoints); + var skipLeft = lower[0] === upper[0], skipRight = lower[lower.length - 1] === upper[upper.length - 1], polygon = []; + for (i = upper.length - 1; i >= 0; --i) polygon.push(data[points[upper[i]][2]]); + for (i = +skipLeft; i < lower.length - skipRight; ++i) polygon.push(data[points[lower[i]][2]]); + return polygon; + } + hull.x = function(_) { + return arguments.length ? (x = _, hull) : x; + }; + hull.y = function(_) { + return arguments.length ? (y = _, hull) : y; + }; + return hull; + }; + function d3_geom_hullUpper(points) { + var n = points.length, hull = [ 0, 1 ], hs = 2; + for (var i = 2; i < n; i++) { + while (hs > 1 && d3_cross2d(points[hull[hs - 2]], points[hull[hs - 1]], points[i]) <= 0) --hs; + hull[hs++] = i; + } + return hull.slice(0, hs); + } + function d3_geom_hullOrder(a, b) { + return a[0] - b[0] || a[1] - b[1]; + } + d3.geom.polygon = function(coordinates) { + d3_subclass(coordinates, d3_geom_polygonPrototype); + return coordinates; + }; + var d3_geom_polygonPrototype = d3.geom.polygon.prototype = []; + d3_geom_polygonPrototype.area = function() { + var i = -1, n = this.length, a, b = this[n - 1], area = 0; + while (++i < n) { + a = b; + b = this[i]; + area += a[1] * b[0] - a[0] * b[1]; + } + return area * .5; + }; + d3_geom_polygonPrototype.centroid = function(k) { + var i = -1, n = this.length, x = 0, y = 0, a, b = this[n - 1], c; + if (!arguments.length) k = -1 / (6 * this.area()); + while (++i < n) { + a = b; + b = this[i]; + c = a[0] * b[1] - b[0] * a[1]; + x += (a[0] + b[0]) * c; + y += (a[1] + b[1]) * c; + } + return [ x * k, y * k ]; + }; + d3_geom_polygonPrototype.clip = function(subject) { + var input, closed = d3_geom_polygonClosed(subject), i = -1, n = this.length - d3_geom_polygonClosed(this), j, m, a = this[n - 1], b, c, d; + while (++i < n) { + input = subject.slice(); + subject.length = 0; + b = this[i]; + c = input[(m = input.length - closed) - 1]; + j = -1; + while (++j < m) { + d = input[j]; + if (d3_geom_polygonInside(d, a, b)) { + if (!d3_geom_polygonInside(c, a, b)) { + subject.push(d3_geom_polygonIntersect(c, d, a, b)); + } + subject.push(d); + } else if (d3_geom_polygonInside(c, a, b)) { + subject.push(d3_geom_polygonIntersect(c, d, a, b)); + } + c = d; + } + if (closed) subject.push(subject[0]); + a = b; + } + return subject; + }; + function d3_geom_polygonInside(p, a, b) { + return (b[0] - a[0]) * (p[1] - a[1]) < (b[1] - a[1]) * (p[0] - a[0]); + } + function d3_geom_polygonIntersect(c, d, a, b) { + var x1 = c[0], x3 = a[0], x21 = d[0] - x1, x43 = b[0] - x3, y1 = c[1], y3 = a[1], y21 = d[1] - y1, y43 = b[1] - y3, ua = (x43 * (y1 - y3) - y43 * (x1 - x3)) / (y43 * x21 - x43 * y21); + return [ x1 + ua * x21, y1 + ua * y21 ]; + } + function d3_geom_polygonClosed(coordinates) { + var a = coordinates[0], b = coordinates[coordinates.length - 1]; + return !(a[0] - b[0] || a[1] - b[1]); + } + var d3_geom_voronoiEdges, d3_geom_voronoiCells, d3_geom_voronoiBeaches, d3_geom_voronoiBeachPool = [], d3_geom_voronoiFirstCircle, d3_geom_voronoiCircles, d3_geom_voronoiCirclePool = []; + function d3_geom_voronoiBeach() { + d3_geom_voronoiRedBlackNode(this); + this.edge = this.site = this.circle = null; + } + function d3_geom_voronoiCreateBeach(site) { + var beach = d3_geom_voronoiBeachPool.pop() || new d3_geom_voronoiBeach(); + beach.site = site; + return beach; + } + function d3_geom_voronoiDetachBeach(beach) { + d3_geom_voronoiDetachCircle(beach); + d3_geom_voronoiBeaches.remove(beach); + d3_geom_voronoiBeachPool.push(beach); + d3_geom_voronoiRedBlackNode(beach); + } + function d3_geom_voronoiRemoveBeach(beach) { + var circle = beach.circle, x = circle.x, y = circle.cy, vertex = { + x: x, + y: y + }, previous = beach.P, next = beach.N, disappearing = [ beach ]; + d3_geom_voronoiDetachBeach(beach); + var lArc = previous; + while (lArc.circle && abs(x - lArc.circle.x) < Îĩ && abs(y - lArc.circle.cy) < Îĩ) { + previous = lArc.P; + disappearing.unshift(lArc); + d3_geom_voronoiDetachBeach(lArc); + lArc = previous; + } + disappearing.unshift(lArc); + d3_geom_voronoiDetachCircle(lArc); + var rArc = next; + while (rArc.circle && abs(x - rArc.circle.x) < Îĩ && abs(y - rArc.circle.cy) < Îĩ) { + next = rArc.N; + disappearing.push(rArc); + d3_geom_voronoiDetachBeach(rArc); + rArc = next; + } + disappearing.push(rArc); + d3_geom_voronoiDetachCircle(rArc); + var nArcs = disappearing.length, iArc; + for (iArc = 1; iArc < nArcs; ++iArc) { + rArc = disappearing[iArc]; + lArc = disappearing[iArc - 1]; + d3_geom_voronoiSetEdgeEnd(rArc.edge, lArc.site, rArc.site, vertex); + } + lArc = disappearing[0]; + rArc = disappearing[nArcs - 1]; + rArc.edge = d3_geom_voronoiCreateEdge(lArc.site, rArc.site, null, vertex); + d3_geom_voronoiAttachCircle(lArc); + d3_geom_voronoiAttachCircle(rArc); + } + function d3_geom_voronoiAddBeach(site) { + var x = site.x, directrix = site.y, lArc, rArc, dxl, dxr, node = d3_geom_voronoiBeaches._; + while (node) { + dxl = d3_geom_voronoiLeftBreakPoint(node, directrix) - x; + if (dxl > Îĩ) node = node.L; else { + dxr = x - d3_geom_voronoiRightBreakPoint(node, directrix); + if (dxr > Îĩ) { + if (!node.R) { + lArc = node; + break; + } + node = node.R; + } else { + if (dxl > -Îĩ) { + lArc = node.P; + rArc = node; + } else if (dxr > -Îĩ) { + lArc = node; + rArc = node.N; + } else { + lArc = rArc = node; + } + break; + } + } + } + var newArc = d3_geom_voronoiCreateBeach(site); + d3_geom_voronoiBeaches.insert(lArc, newArc); + if (!lArc && !rArc) return; + if (lArc === rArc) { + d3_geom_voronoiDetachCircle(lArc); + rArc = d3_geom_voronoiCreateBeach(lArc.site); + d3_geom_voronoiBeaches.insert(newArc, rArc); + newArc.edge = rArc.edge = d3_geom_voronoiCreateEdge(lArc.site, newArc.site); + d3_geom_voronoiAttachCircle(lArc); + d3_geom_voronoiAttachCircle(rArc); + return; + } + if (!rArc) { + newArc.edge = d3_geom_voronoiCreateEdge(lArc.site, newArc.site); + return; + } + d3_geom_voronoiDetachCircle(lArc); + d3_geom_voronoiDetachCircle(rArc); + var lSite = lArc.site, ax = lSite.x, ay = lSite.y, bx = site.x - ax, by = site.y - ay, rSite = rArc.site, cx = rSite.x - ax, cy = rSite.y - ay, d = 2 * (bx * cy - by * cx), hb = bx * bx + by * by, hc = cx * cx + cy * cy, vertex = { + x: (cy * hb - by * hc) / d + ax, + y: (bx * hc - cx * hb) / d + ay + }; + d3_geom_voronoiSetEdgeEnd(rArc.edge, lSite, rSite, vertex); + newArc.edge = d3_geom_voronoiCreateEdge(lSite, site, null, vertex); + rArc.edge = d3_geom_voronoiCreateEdge(site, rSite, null, vertex); + d3_geom_voronoiAttachCircle(lArc); + d3_geom_voronoiAttachCircle(rArc); + } + function d3_geom_voronoiLeftBreakPoint(arc, directrix) { + var site = arc.site, rfocx = site.x, rfocy = site.y, pby2 = rfocy - directrix; + if (!pby2) return rfocx; + var lArc = arc.P; + if (!lArc) return -Infinity; + site = lArc.site; + var lfocx = site.x, lfocy = site.y, plby2 = lfocy - directrix; + if (!plby2) return lfocx; + var hl = lfocx - rfocx, aby2 = 1 / pby2 - 1 / plby2, b = hl / plby2; + if (aby2) return (-b + Math.sqrt(b * b - 2 * aby2 * (hl * hl / (-2 * plby2) - lfocy + plby2 / 2 + rfocy - pby2 / 2))) / aby2 + rfocx; + return (rfocx + lfocx) / 2; + } + function d3_geom_voronoiRightBreakPoint(arc, directrix) { + var rArc = arc.N; + if (rArc) return d3_geom_voronoiLeftBreakPoint(rArc, directrix); + var site = arc.site; + return site.y === directrix ? site.x : Infinity; + } + function d3_geom_voronoiCell(site) { + this.site = site; + this.edges = []; + } + d3_geom_voronoiCell.prototype.prepare = function() { + var halfEdges = this.edges, iHalfEdge = halfEdges.length, edge; + while (iHalfEdge--) { + edge = halfEdges[iHalfEdge].edge; + if (!edge.b || !edge.a) halfEdges.splice(iHalfEdge, 1); + } + halfEdges.sort(d3_geom_voronoiHalfEdgeOrder); + return halfEdges.length; + }; + function d3_geom_voronoiCloseCells(extent) { + var x0 = extent[0][0], x1 = extent[1][0], y0 = extent[0][1], y1 = extent[1][1], x2, y2, x3, y3, cells = d3_geom_voronoiCells, iCell = cells.length, cell, iHalfEdge, halfEdges, nHalfEdges, start, end; + while (iCell--) { + cell = cells[iCell]; + if (!cell || !cell.prepare()) continue; + halfEdges = cell.edges; + nHalfEdges = halfEdges.length; + iHalfEdge = 0; + while (iHalfEdge < nHalfEdges) { + end = halfEdges[iHalfEdge].end(), x3 = end.x, y3 = end.y; + start = halfEdges[++iHalfEdge % nHalfEdges].start(), x2 = start.x, y2 = start.y; + if (abs(x3 - x2) > Îĩ || abs(y3 - y2) > Îĩ) { + halfEdges.splice(iHalfEdge, 0, new d3_geom_voronoiHalfEdge(d3_geom_voronoiCreateBorderEdge(cell.site, end, abs(x3 - x0) < Îĩ && y1 - y3 > Îĩ ? { + x: x0, + y: abs(x2 - x0) < Îĩ ? y2 : y1 + } : abs(y3 - y1) < Îĩ && x1 - x3 > Îĩ ? { + x: abs(y2 - y1) < Îĩ ? x2 : x1, + y: y1 + } : abs(x3 - x1) < Îĩ && y3 - y0 > Îĩ ? { + x: x1, + y: abs(x2 - x1) < Îĩ ? y2 : y0 + } : abs(y3 - y0) < Îĩ && x3 - x0 > Îĩ ? { + x: abs(y2 - y0) < Îĩ ? x2 : x0, + y: y0 + } : null), cell.site, null)); + ++nHalfEdges; + } + } + } + } + function d3_geom_voronoiHalfEdgeOrder(a, b) { + return b.angle - a.angle; + } + function d3_geom_voronoiCircle() { + d3_geom_voronoiRedBlackNode(this); + this.x = this.y = this.arc = this.site = this.cy = null; + } + function d3_geom_voronoiAttachCircle(arc) { + var lArc = arc.P, rArc = arc.N; + if (!lArc || !rArc) return; + var lSite = lArc.site, cSite = arc.site, rSite = rArc.site; + if (lSite === rSite) return; + var bx = cSite.x, by = cSite.y, ax = lSite.x - bx, ay = lSite.y - by, cx = rSite.x - bx, cy = rSite.y - by; + var d = 2 * (ax * cy - ay * cx); + if (d >= -Îĩ2) return; + var ha = ax * ax + ay * ay, hc = cx * cx + cy * cy, x = (cy * ha - ay * hc) / d, y = (ax * hc - cx * ha) / d, cy = y + by; + var circle = d3_geom_voronoiCirclePool.pop() || new d3_geom_voronoiCircle(); + circle.arc = arc; + circle.site = cSite; + circle.x = x + bx; + circle.y = cy + Math.sqrt(x * x + y * y); + circle.cy = cy; + arc.circle = circle; + var before = null, node = d3_geom_voronoiCircles._; + while (node) { + if (circle.y < node.y || circle.y === node.y && circle.x <= node.x) { + if (node.L) node = node.L; else { + before = node.P; + break; + } + } else { + if (node.R) node = node.R; else { + before = node; + break; + } + } + } + d3_geom_voronoiCircles.insert(before, circle); + if (!before) d3_geom_voronoiFirstCircle = circle; + } + function d3_geom_voronoiDetachCircle(arc) { + var circle = arc.circle; + if (circle) { + if (!circle.P) d3_geom_voronoiFirstCircle = circle.N; + d3_geom_voronoiCircles.remove(circle); + d3_geom_voronoiCirclePool.push(circle); + d3_geom_voronoiRedBlackNode(circle); + arc.circle = null; + } + } + function d3_geom_voronoiClipEdges(extent) { + var edges = d3_geom_voronoiEdges, clip = d3_geom_clipLine(extent[0][0], extent[0][1], extent[1][0], extent[1][1]), i = edges.length, e; + while (i--) { + e = edges[i]; + if (!d3_geom_voronoiConnectEdge(e, extent) || !clip(e) || abs(e.a.x - e.b.x) < Îĩ && abs(e.a.y - e.b.y) < Îĩ) { + e.a = e.b = null; + edges.splice(i, 1); + } + } + } + function d3_geom_voronoiConnectEdge(edge, extent) { + var vb = edge.b; + if (vb) return true; + var va = edge.a, x0 = extent[0][0], x1 = extent[1][0], y0 = extent[0][1], y1 = extent[1][1], lSite = edge.l, rSite = edge.r, lx = lSite.x, ly = lSite.y, rx = rSite.x, ry = rSite.y, fx = (lx + rx) / 2, fy = (ly + ry) / 2, fm, fb; + if (ry === ly) { + if (fx < x0 || fx >= x1) return; + if (lx > rx) { + if (!va) va = { + x: fx, + y: y0 + }; else if (va.y >= y1) return; + vb = { + x: fx, + y: y1 + }; + } else { + if (!va) va = { + x: fx, + y: y1 + }; else if (va.y < y0) return; + vb = { + x: fx, + y: y0 + }; + } + } else { + fm = (lx - rx) / (ry - ly); + fb = fy - fm * fx; + if (fm < -1 || fm > 1) { + if (lx > rx) { + if (!va) va = { + x: (y0 - fb) / fm, + y: y0 + }; else if (va.y >= y1) return; + vb = { + x: (y1 - fb) / fm, + y: y1 + }; + } else { + if (!va) va = { + x: (y1 - fb) / fm, + y: y1 + }; else if (va.y < y0) return; + vb = { + x: (y0 - fb) / fm, + y: y0 + }; + } + } else { + if (ly < ry) { + if (!va) va = { + x: x0, + y: fm * x0 + fb + }; else if (va.x >= x1) return; + vb = { + x: x1, + y: fm * x1 + fb + }; + } else { + if (!va) va = { + x: x1, + y: fm * x1 + fb + }; else if (va.x < x0) return; + vb = { + x: x0, + y: fm * x0 + fb + }; + } + } + } + edge.a = va; + edge.b = vb; + return true; + } + function d3_geom_voronoiEdge(lSite, rSite) { + this.l = lSite; + this.r = rSite; + this.a = this.b = null; + } + function d3_geom_voronoiCreateEdge(lSite, rSite, va, vb) { + var edge = new d3_geom_voronoiEdge(lSite, rSite); + d3_geom_voronoiEdges.push(edge); + if (va) d3_geom_voronoiSetEdgeEnd(edge, lSite, rSite, va); + if (vb) d3_geom_voronoiSetEdgeEnd(edge, rSite, lSite, vb); + d3_geom_voronoiCells[lSite.i].edges.push(new d3_geom_voronoiHalfEdge(edge, lSite, rSite)); + d3_geom_voronoiCells[rSite.i].edges.push(new d3_geom_voronoiHalfEdge(edge, rSite, lSite)); + return edge; + } + function d3_geom_voronoiCreateBorderEdge(lSite, va, vb) { + var edge = new d3_geom_voronoiEdge(lSite, null); + edge.a = va; + edge.b = vb; + d3_geom_voronoiEdges.push(edge); + return edge; + } + function d3_geom_voronoiSetEdgeEnd(edge, lSite, rSite, vertex) { + if (!edge.a && !edge.b) { + edge.a = vertex; + edge.l = lSite; + edge.r = rSite; + } else if (edge.l === rSite) { + edge.b = vertex; + } else { + edge.a = vertex; + } + } + function d3_geom_voronoiHalfEdge(edge, lSite, rSite) { + var va = edge.a, vb = edge.b; + this.edge = edge; + this.site = lSite; + this.angle = rSite ? Math.atan2(rSite.y - lSite.y, rSite.x - lSite.x) : edge.l === lSite ? Math.atan2(vb.x - va.x, va.y - vb.y) : Math.atan2(va.x - vb.x, vb.y - va.y); + } + d3_geom_voronoiHalfEdge.prototype = { + start: function() { + return this.edge.l === this.site ? this.edge.a : this.edge.b; + }, + end: function() { + return this.edge.l === this.site ? this.edge.b : this.edge.a; + } + }; + function d3_geom_voronoiRedBlackTree() { + this._ = null; + } + function d3_geom_voronoiRedBlackNode(node) { + node.U = node.C = node.L = node.R = node.P = node.N = null; + } + d3_geom_voronoiRedBlackTree.prototype = { + insert: function(after, node) { + var parent, grandpa, uncle; + if (after) { + node.P = after; + node.N = after.N; + if (after.N) after.N.P = node; + after.N = node; + if (after.R) { + after = after.R; + while (after.L) after = after.L; + after.L = node; + } else { + after.R = node; + } + parent = after; + } else if (this._) { + after = d3_geom_voronoiRedBlackFirst(this._); + node.P = null; + node.N = after; + after.P = after.L = node; + parent = after; + } else { + node.P = node.N = null; + this._ = node; + parent = null; + } + node.L = node.R = null; + node.U = parent; + node.C = true; + after = node; + while (parent && parent.C) { + grandpa = parent.U; + if (parent === grandpa.L) { + uncle = grandpa.R; + if (uncle && uncle.C) { + parent.C = uncle.C = false; + grandpa.C = true; + after = grandpa; + } else { + if (after === parent.R) { + d3_geom_voronoiRedBlackRotateLeft(this, parent); + after = parent; + parent = after.U; + } + parent.C = false; + grandpa.C = true; + d3_geom_voronoiRedBlackRotateRight(this, grandpa); + } + } else { + uncle = grandpa.L; + if (uncle && uncle.C) { + parent.C = uncle.C = false; + grandpa.C = true; + after = grandpa; + } else { + if (after === parent.L) { + d3_geom_voronoiRedBlackRotateRight(this, parent); + after = parent; + parent = after.U; + } + parent.C = false; + grandpa.C = true; + d3_geom_voronoiRedBlackRotateLeft(this, grandpa); + } + } + parent = after.U; + } + this._.C = false; + }, + remove: function(node) { + if (node.N) node.N.P = node.P; + if (node.P) node.P.N = node.N; + node.N = node.P = null; + var parent = node.U, sibling, left = node.L, right = node.R, next, red; + if (!left) next = right; else if (!right) next = left; else next = d3_geom_voronoiRedBlackFirst(right); + if (parent) { + if (parent.L === node) parent.L = next; else parent.R = next; + } else { + this._ = next; + } + if (left && right) { + red = next.C; + next.C = node.C; + next.L = left; + left.U = next; + if (next !== right) { + parent = next.U; + next.U = node.U; + node = next.R; + parent.L = node; + next.R = right; + right.U = next; + } else { + next.U = parent; + parent = next; + node = next.R; + } + } else { + red = node.C; + node = next; + } + if (node) node.U = parent; + if (red) return; + if (node && node.C) { + node.C = false; + return; + } + do { + if (node === this._) break; + if (node === parent.L) { + sibling = parent.R; + if (sibling.C) { + sibling.C = false; + parent.C = true; + d3_geom_voronoiRedBlackRotateLeft(this, parent); + sibling = parent.R; + } + if (sibling.L && sibling.L.C || sibling.R && sibling.R.C) { + if (!sibling.R || !sibling.R.C) { + sibling.L.C = false; + sibling.C = true; + d3_geom_voronoiRedBlackRotateRight(this, sibling); + sibling = parent.R; + } + sibling.C = parent.C; + parent.C = sibling.R.C = false; + d3_geom_voronoiRedBlackRotateLeft(this, parent); + node = this._; + break; + } + } else { + sibling = parent.L; + if (sibling.C) { + sibling.C = false; + parent.C = true; + d3_geom_voronoiRedBlackRotateRight(this, parent); + sibling = parent.L; + } + if (sibling.L && sibling.L.C || sibling.R && sibling.R.C) { + if (!sibling.L || !sibling.L.C) { + sibling.R.C = false; + sibling.C = true; + d3_geom_voronoiRedBlackRotateLeft(this, sibling); + sibling = parent.L; + } + sibling.C = parent.C; + parent.C = sibling.L.C = false; + d3_geom_voronoiRedBlackRotateRight(this, parent); + node = this._; + break; + } + } + sibling.C = true; + node = parent; + parent = parent.U; + } while (!node.C); + if (node) node.C = false; + } + }; + function d3_geom_voronoiRedBlackRotateLeft(tree, node) { + var p = node, q = node.R, parent = p.U; + if (parent) { + if (parent.L === p) parent.L = q; else parent.R = q; + } else { + tree._ = q; + } + q.U = parent; + p.U = q; + p.R = q.L; + if (p.R) p.R.U = p; + q.L = p; + } + function d3_geom_voronoiRedBlackRotateRight(tree, node) { + var p = node, q = node.L, parent = p.U; + if (parent) { + if (parent.L === p) parent.L = q; else parent.R = q; + } else { + tree._ = q; + } + q.U = parent; + p.U = q; + p.L = q.R; + if (p.L) p.L.U = p; + q.R = p; + } + function d3_geom_voronoiRedBlackFirst(node) { + while (node.L) node = node.L; + return node; + } + function d3_geom_voronoi(sites, bbox) { + var site = sites.sort(d3_geom_voronoiVertexOrder).pop(), x0, y0, circle; + d3_geom_voronoiEdges = []; + d3_geom_voronoiCells = new Array(sites.length); + d3_geom_voronoiBeaches = new d3_geom_voronoiRedBlackTree(); + d3_geom_voronoiCircles = new d3_geom_voronoiRedBlackTree(); + while (true) { + circle = d3_geom_voronoiFirstCircle; + if (site && (!circle || site.y < circle.y || site.y === circle.y && site.x < circle.x)) { + if (site.x !== x0 || site.y !== y0) { + d3_geom_voronoiCells[site.i] = new d3_geom_voronoiCell(site); + d3_geom_voronoiAddBeach(site); + x0 = site.x, y0 = site.y; + } + site = sites.pop(); + } else if (circle) { + d3_geom_voronoiRemoveBeach(circle.arc); + } else { + break; + } + } + if (bbox) d3_geom_voronoiClipEdges(bbox), d3_geom_voronoiCloseCells(bbox); + var diagram = { + cells: d3_geom_voronoiCells, + edges: d3_geom_voronoiEdges + }; + d3_geom_voronoiBeaches = d3_geom_voronoiCircles = d3_geom_voronoiEdges = d3_geom_voronoiCells = null; + return diagram; + } + function d3_geom_voronoiVertexOrder(a, b) { + return b.y - a.y || b.x - a.x; + } + d3.geom.voronoi = function(points) { + var x = d3_geom_pointX, y = d3_geom_pointY, fx = x, fy = y, clipExtent = d3_geom_voronoiClipExtent; + if (points) return voronoi(points); + function voronoi(data) { + var polygons = new Array(data.length), x0 = clipExtent[0][0], y0 = clipExtent[0][1], x1 = clipExtent[1][0], y1 = clipExtent[1][1]; + d3_geom_voronoi(sites(data), clipExtent).cells.forEach(function(cell, i) { + var edges = cell.edges, site = cell.site, polygon = polygons[i] = edges.length ? edges.map(function(e) { + var s = e.start(); + return [ s.x, s.y ]; + }) : site.x >= x0 && site.x <= x1 && site.y >= y0 && site.y <= y1 ? [ [ x0, y1 ], [ x1, y1 ], [ x1, y0 ], [ x0, y0 ] ] : []; + polygon.point = data[i]; + }); + return polygons; + } + function sites(data) { + return data.map(function(d, i) { + return { + x: Math.round(fx(d, i) / Îĩ) * Îĩ, + y: Math.round(fy(d, i) / Îĩ) * Îĩ, + i: i + }; + }); + } + voronoi.links = function(data) { + return d3_geom_voronoi(sites(data)).edges.filter(function(edge) { + return edge.l && edge.r; + }).map(function(edge) { + return { + source: data[edge.l.i], + target: data[edge.r.i] + }; + }); + }; + voronoi.triangles = function(data) { + var triangles = []; + d3_geom_voronoi(sites(data)).cells.forEach(function(cell, i) { + var site = cell.site, edges = cell.edges.sort(d3_geom_voronoiHalfEdgeOrder), j = -1, m = edges.length, e0, s0, e1 = edges[m - 1].edge, s1 = e1.l === site ? e1.r : e1.l; + while (++j < m) { + e0 = e1; + s0 = s1; + e1 = edges[j].edge; + s1 = e1.l === site ? e1.r : e1.l; + if (i < s0.i && i < s1.i && d3_geom_voronoiTriangleArea(site, s0, s1) < 0) { + triangles.push([ data[i], data[s0.i], data[s1.i] ]); + } + } + }); + return triangles; + }; + voronoi.x = function(_) { + return arguments.length ? (fx = d3_functor(x = _), voronoi) : x; + }; + voronoi.y = function(_) { + return arguments.length ? (fy = d3_functor(y = _), voronoi) : y; + }; + voronoi.clipExtent = function(_) { + if (!arguments.length) return clipExtent === d3_geom_voronoiClipExtent ? null : clipExtent; + clipExtent = _ == null ? d3_geom_voronoiClipExtent : _; + return voronoi; + }; + voronoi.size = function(_) { + if (!arguments.length) return clipExtent === d3_geom_voronoiClipExtent ? null : clipExtent && clipExtent[1]; + return voronoi.clipExtent(_ && [ [ 0, 0 ], _ ]); + }; + return voronoi; + }; + var d3_geom_voronoiClipExtent = [ [ -1e6, -1e6 ], [ 1e6, 1e6 ] ]; + function d3_geom_voronoiTriangleArea(a, b, c) { + return (a.x - c.x) * (b.y - a.y) - (a.x - b.x) * (c.y - a.y); + } + d3.geom.delaunay = function(vertices) { + return d3.geom.voronoi().triangles(vertices); + }; + d3.geom.quadtree = function(points, x1, y1, x2, y2) { + var x = d3_geom_pointX, y = d3_geom_pointY, compat; + if (compat = arguments.length) { + x = d3_geom_quadtreeCompatX; + y = d3_geom_quadtreeCompatY; + if (compat === 3) { + y2 = y1; + x2 = x1; + y1 = x1 = 0; + } + return quadtree(points); + } + function quadtree(data) { + var d, fx = d3_functor(x), fy = d3_functor(y), xs, ys, i, n, x1_, y1_, x2_, y2_; + if (x1 != null) { + x1_ = x1, y1_ = y1, x2_ = x2, y2_ = y2; + } else { + x2_ = y2_ = -(x1_ = y1_ = Infinity); + xs = [], ys = []; + n = data.length; + if (compat) for (i = 0; i < n; ++i) { + d = data[i]; + if (d.x < x1_) x1_ = d.x; + if (d.y < y1_) y1_ = d.y; + if (d.x > x2_) x2_ = d.x; + if (d.y > y2_) y2_ = d.y; + xs.push(d.x); + ys.push(d.y); + } else for (i = 0; i < n; ++i) { + var x_ = +fx(d = data[i], i), y_ = +fy(d, i); + if (x_ < x1_) x1_ = x_; + if (y_ < y1_) y1_ = y_; + if (x_ > x2_) x2_ = x_; + if (y_ > y2_) y2_ = y_; + xs.push(x_); + ys.push(y_); + } + } + var dx = x2_ - x1_, dy = y2_ - y1_; + if (dx > dy) y2_ = y1_ + dx; else x2_ = x1_ + dy; + function insert(n, d, x, y, x1, y1, x2, y2) { + if (isNaN(x) || isNaN(y)) return; + if (n.leaf) { + var nx = n.x, ny = n.y; + if (nx != null) { + if (abs(nx - x) + abs(ny - y) < .01) { + insertChild(n, d, x, y, x1, y1, x2, y2); + } else { + var nPoint = n.point; + n.x = n.y = n.point = null; + insertChild(n, nPoint, nx, ny, x1, y1, x2, y2); + insertChild(n, d, x, y, x1, y1, x2, y2); + } + } else { + n.x = x, n.y = y, n.point = d; + } + } else { + insertChild(n, d, x, y, x1, y1, x2, y2); + } + } + function insertChild(n, d, x, y, x1, y1, x2, y2) { + var xm = (x1 + x2) * .5, ym = (y1 + y2) * .5, right = x >= xm, below = y >= ym, i = below << 1 | right; + n.leaf = false; + n = n.nodes[i] || (n.nodes[i] = d3_geom_quadtreeNode()); + if (right) x1 = xm; else x2 = xm; + if (below) y1 = ym; else y2 = ym; + insert(n, d, x, y, x1, y1, x2, y2); + } + var root = d3_geom_quadtreeNode(); + root.add = function(d) { + insert(root, d, +fx(d, ++i), +fy(d, i), x1_, y1_, x2_, y2_); + }; + root.visit = function(f) { + d3_geom_quadtreeVisit(f, root, x1_, y1_, x2_, y2_); + }; + root.find = function(point) { + return d3_geom_quadtreeFind(root, point[0], point[1], x1_, y1_, x2_, y2_); + }; + i = -1; + if (x1 == null) { + while (++i < n) { + insert(root, data[i], xs[i], ys[i], x1_, y1_, x2_, y2_); + } + --i; + } else data.forEach(root.add); + xs = ys = data = d = null; + return root; + } + quadtree.x = function(_) { + return arguments.length ? (x = _, quadtree) : x; + }; + quadtree.y = function(_) { + return arguments.length ? (y = _, quadtree) : y; + }; + quadtree.extent = function(_) { + if (!arguments.length) return x1 == null ? null : [ [ x1, y1 ], [ x2, y2 ] ]; + if (_ == null) x1 = y1 = x2 = y2 = null; else x1 = +_[0][0], y1 = +_[0][1], x2 = +_[1][0], + y2 = +_[1][1]; + return quadtree; + }; + quadtree.size = function(_) { + if (!arguments.length) return x1 == null ? null : [ x2 - x1, y2 - y1 ]; + if (_ == null) x1 = y1 = x2 = y2 = null; else x1 = y1 = 0, x2 = +_[0], y2 = +_[1]; + return quadtree; + }; + return quadtree; + }; + function d3_geom_quadtreeCompatX(d) { + return d.x; + } + function d3_geom_quadtreeCompatY(d) { + return d.y; + } + function d3_geom_quadtreeNode() { + return { + leaf: true, + nodes: [], + point: null, + x: null, + y: null + }; + } + function d3_geom_quadtreeVisit(f, node, x1, y1, x2, y2) { + if (!f(node, x1, y1, x2, y2)) { + var sx = (x1 + x2) * .5, sy = (y1 + y2) * .5, children = node.nodes; + if (children[0]) d3_geom_quadtreeVisit(f, children[0], x1, y1, sx, sy); + if (children[1]) d3_geom_quadtreeVisit(f, children[1], sx, y1, x2, sy); + if (children[2]) d3_geom_quadtreeVisit(f, children[2], x1, sy, sx, y2); + if (children[3]) d3_geom_quadtreeVisit(f, children[3], sx, sy, x2, y2); + } + } + function d3_geom_quadtreeFind(root, x, y, x0, y0, x3, y3) { + var minDistance2 = Infinity, closestPoint; + (function find(node, x1, y1, x2, y2) { + if (x1 > x3 || y1 > y3 || x2 < x0 || y2 < y0) return; + if (point = node.point) { + var point, dx = x - node.x, dy = y - node.y, distance2 = dx * dx + dy * dy; + if (distance2 < minDistance2) { + var distance = Math.sqrt(minDistance2 = distance2); + x0 = x - distance, y0 = y - distance; + x3 = x + distance, y3 = y + distance; + closestPoint = point; + } + } + var children = node.nodes, xm = (x1 + x2) * .5, ym = (y1 + y2) * .5, right = x >= xm, below = y >= ym; + for (var i = below << 1 | right, j = i + 4; i < j; ++i) { + if (node = children[i & 3]) switch (i & 3) { + case 0: + find(node, x1, y1, xm, ym); + break; + + case 1: + find(node, xm, y1, x2, ym); + break; + + case 2: + find(node, x1, ym, xm, y2); + break; + + case 3: + find(node, xm, ym, x2, y2); + break; + } + } + })(root, x0, y0, x3, y3); + return closestPoint; + } + d3.interpolateRgb = d3_interpolateRgb; + function d3_interpolateRgb(a, b) { + a = d3.rgb(a); + b = d3.rgb(b); + var ar = a.r, ag = a.g, ab = a.b, br = b.r - ar, bg = b.g - ag, bb = b.b - ab; + return function(t) { + return "#" + d3_rgb_hex(Math.round(ar + br * t)) + d3_rgb_hex(Math.round(ag + bg * t)) + d3_rgb_hex(Math.round(ab + bb * t)); + }; + } + d3.interpolateObject = d3_interpolateObject; + function d3_interpolateObject(a, b) { + var i = {}, c = {}, k; + for (k in a) { + if (k in b) { + i[k] = d3_interpolate(a[k], b[k]); + } else { + c[k] = a[k]; + } + } + for (k in b) { + if (!(k in a)) { + c[k] = b[k]; + } + } + return function(t) { + for (k in i) c[k] = i[k](t); + return c; + }; + } + d3.interpolateNumber = d3_interpolateNumber; + function d3_interpolateNumber(a, b) { + a = +a, b = +b; + return function(t) { + return a * (1 - t) + b * t; + }; + } + d3.interpolateString = d3_interpolateString; + function d3_interpolateString(a, b) { + var bi = d3_interpolate_numberA.lastIndex = d3_interpolate_numberB.lastIndex = 0, am, bm, bs, i = -1, s = [], q = []; + a = a + "", b = b + ""; + while ((am = d3_interpolate_numberA.exec(a)) && (bm = d3_interpolate_numberB.exec(b))) { + if ((bs = bm.index) > bi) { + bs = b.slice(bi, bs); + if (s[i]) s[i] += bs; else s[++i] = bs; + } + if ((am = am[0]) === (bm = bm[0])) { + if (s[i]) s[i] += bm; else s[++i] = bm; + } else { + s[++i] = null; + q.push({ + i: i, + x: d3_interpolateNumber(am, bm) + }); + } + bi = d3_interpolate_numberB.lastIndex; + } + if (bi < b.length) { + bs = b.slice(bi); + if (s[i]) s[i] += bs; else s[++i] = bs; + } + return s.length < 2 ? q[0] ? (b = q[0].x, function(t) { + return b(t) + ""; + }) : function() { + return b; + } : (b = q.length, function(t) { + for (var i = 0, o; i < b; ++i) s[(o = q[i]).i] = o.x(t); + return s.join(""); + }); + } + var d3_interpolate_numberA = /[-+]?(?:\d+\.?\d*|\.?\d+)(?:[eE][-+]?\d+)?/g, d3_interpolate_numberB = new RegExp(d3_interpolate_numberA.source, "g"); + d3.interpolate = d3_interpolate; + function d3_interpolate(a, b) { + var i = d3.interpolators.length, f; + while (--i >= 0 && !(f = d3.interpolators[i](a, b))) ; + return f; + } + d3.interpolators = [ function(a, b) { + var t = typeof b; + return (t === "string" ? d3_rgb_names.has(b.toLowerCase()) || /^(#|rgb\(|hsl\()/i.test(b) ? d3_interpolateRgb : d3_interpolateString : b instanceof d3_color ? d3_interpolateRgb : Array.isArray(b) ? d3_interpolateArray : t === "object" && isNaN(b) ? d3_interpolateObject : d3_interpolateNumber)(a, b); + } ]; + d3.interpolateArray = d3_interpolateArray; + function d3_interpolateArray(a, b) { + var x = [], c = [], na = a.length, nb = b.length, n0 = Math.min(a.length, b.length), i; + for (i = 0; i < n0; ++i) x.push(d3_interpolate(a[i], b[i])); + for (;i < na; ++i) c[i] = a[i]; + for (;i < nb; ++i) c[i] = b[i]; + return function(t) { + for (i = 0; i < n0; ++i) c[i] = x[i](t); + return c; + }; + } + var d3_ease_default = function() { + return d3_identity; + }; + var d3_ease = d3.map({ + linear: d3_ease_default, + poly: d3_ease_poly, + quad: function() { + return d3_ease_quad; + }, + cubic: function() { + return d3_ease_cubic; + }, + sin: function() { + return d3_ease_sin; + }, + exp: function() { + return d3_ease_exp; + }, + circle: function() { + return d3_ease_circle; + }, + elastic: d3_ease_elastic, + back: d3_ease_back, + bounce: function() { + return d3_ease_bounce; + } + }); + var d3_ease_mode = d3.map({ + "in": d3_identity, + out: d3_ease_reverse, + "in-out": d3_ease_reflect, + "out-in": function(f) { + return d3_ease_reflect(d3_ease_reverse(f)); + } + }); + d3.ease = function(name) { + var i = name.indexOf("-"), t = i >= 0 ? name.slice(0, i) : name, m = i >= 0 ? name.slice(i + 1) : "in"; + t = d3_ease.get(t) || d3_ease_default; + m = d3_ease_mode.get(m) || d3_identity; + return d3_ease_clamp(m(t.apply(null, d3_arraySlice.call(arguments, 1)))); + }; + function d3_ease_clamp(f) { + return function(t) { + return t <= 0 ? 0 : t >= 1 ? 1 : f(t); + }; + } + function d3_ease_reverse(f) { + return function(t) { + return 1 - f(1 - t); + }; + } + function d3_ease_reflect(f) { + return function(t) { + return .5 * (t < .5 ? f(2 * t) : 2 - f(2 - 2 * t)); + }; + } + function d3_ease_quad(t) { + return t * t; + } + function d3_ease_cubic(t) { + return t * t * t; + } + function d3_ease_cubicInOut(t) { + if (t <= 0) return 0; + if (t >= 1) return 1; + var t2 = t * t, t3 = t2 * t; + return 4 * (t < .5 ? t3 : 3 * (t - t2) + t3 - .75); + } + function d3_ease_poly(e) { + return function(t) { + return Math.pow(t, e); + }; + } + function d3_ease_sin(t) { + return 1 - Math.cos(t * halfĪ€); + } + function d3_ease_exp(t) { + return Math.pow(2, 10 * (t - 1)); + } + function d3_ease_circle(t) { + return 1 - Math.sqrt(1 - t * t); + } + function d3_ease_elastic(a, p) { + var s; + if (arguments.length < 2) p = .45; + if (arguments.length) s = p / Ī„ * Math.asin(1 / a); else a = 1, s = p / 4; + return function(t) { + return 1 + a * Math.pow(2, -10 * t) * Math.sin((t - s) * Ī„ / p); + }; + } + function d3_ease_back(s) { + if (!s) s = 1.70158; + return function(t) { + return t * t * ((s + 1) * t - s); + }; + } + function d3_ease_bounce(t) { + return t < 1 / 2.75 ? 7.5625 * t * t : t < 2 / 2.75 ? 7.5625 * (t -= 1.5 / 2.75) * t + .75 : t < 2.5 / 2.75 ? 7.5625 * (t -= 2.25 / 2.75) * t + .9375 : 7.5625 * (t -= 2.625 / 2.75) * t + .984375; + } + d3.interpolateHcl = d3_interpolateHcl; + function d3_interpolateHcl(a, b) { + a = d3.hcl(a); + b = d3.hcl(b); + var ah = a.h, ac = a.c, al = a.l, bh = b.h - ah, bc = b.c - ac, bl = b.l - al; + if (isNaN(bc)) bc = 0, ac = isNaN(ac) ? b.c : ac; + if (isNaN(bh)) bh = 0, ah = isNaN(ah) ? b.h : ah; else if (bh > 180) bh -= 360; else if (bh < -180) bh += 360; + return function(t) { + return d3_hcl_lab(ah + bh * t, ac + bc * t, al + bl * t) + ""; + }; + } + d3.interpolateHsl = d3_interpolateHsl; + function d3_interpolateHsl(a, b) { + a = d3.hsl(a); + b = d3.hsl(b); + var ah = a.h, as = a.s, al = a.l, bh = b.h - ah, bs = b.s - as, bl = b.l - al; + if (isNaN(bs)) bs = 0, as = isNaN(as) ? b.s : as; + if (isNaN(bh)) bh = 0, ah = isNaN(ah) ? b.h : ah; else if (bh > 180) bh -= 360; else if (bh < -180) bh += 360; + return function(t) { + return d3_hsl_rgb(ah + bh * t, as + bs * t, al + bl * t) + ""; + }; + } + d3.interpolateLab = d3_interpolateLab; + function d3_interpolateLab(a, b) { + a = d3.lab(a); + b = d3.lab(b); + var al = a.l, aa = a.a, ab = a.b, bl = b.l - al, ba = b.a - aa, bb = b.b - ab; + return function(t) { + return d3_lab_rgb(al + bl * t, aa + ba * t, ab + bb * t) + ""; + }; + } + d3.interpolateRound = d3_interpolateRound; + function d3_interpolateRound(a, b) { + b -= a; + return function(t) { + return Math.round(a + b * t); + }; + } + d3.transform = function(string) { + var g = d3_document.createElementNS(d3.ns.prefix.svg, "g"); + return (d3.transform = function(string) { + if (string != null) { + g.setAttribute("transform", string); + var t = g.transform.baseVal.consolidate(); + } + return new d3_transform(t ? t.matrix : d3_transformIdentity); + })(string); + }; + function d3_transform(m) { + var r0 = [ m.a, m.b ], r1 = [ m.c, m.d ], kx = d3_transformNormalize(r0), kz = d3_transformDot(r0, r1), ky = d3_transformNormalize(d3_transformCombine(r1, r0, -kz)) || 0; + if (r0[0] * r1[1] < r1[0] * r0[1]) { + r0[0] *= -1; + r0[1] *= -1; + kx *= -1; + kz *= -1; + } + this.rotate = (kx ? Math.atan2(r0[1], r0[0]) : Math.atan2(-r1[0], r1[1])) * d3_degrees; + this.translate = [ m.e, m.f ]; + this.scale = [ kx, ky ]; + this.skew = ky ? Math.atan2(kz, ky) * d3_degrees : 0; + } + d3_transform.prototype.toString = function() { + return "translate(" + this.translate + ")rotate(" + this.rotate + ")skewX(" + this.skew + ")scale(" + this.scale + ")"; + }; + function d3_transformDot(a, b) { + return a[0] * b[0] + a[1] * b[1]; + } + function d3_transformNormalize(a) { + var k = Math.sqrt(d3_transformDot(a, a)); + if (k) { + a[0] /= k; + a[1] /= k; + } + return k; + } + function d3_transformCombine(a, b, k) { + a[0] += k * b[0]; + a[1] += k * b[1]; + return a; + } + var d3_transformIdentity = { + a: 1, + b: 0, + c: 0, + d: 1, + e: 0, + f: 0 + }; + d3.interpolateTransform = d3_interpolateTransform; + function d3_interpolateTransformPop(s) { + return s.length ? s.pop() + "," : ""; + } + function d3_interpolateTranslate(ta, tb, s, q) { + if (ta[0] !== tb[0] || ta[1] !== tb[1]) { + var i = s.push("translate(", null, ",", null, ")"); + q.push({ + i: i - 4, + x: d3_interpolateNumber(ta[0], tb[0]) + }, { + i: i - 2, + x: d3_interpolateNumber(ta[1], tb[1]) + }); + } else if (tb[0] || tb[1]) { + s.push("translate(" + tb + ")"); + } + } + function d3_interpolateRotate(ra, rb, s, q) { + if (ra !== rb) { + if (ra - rb > 180) rb += 360; else if (rb - ra > 180) ra += 360; + q.push({ + i: s.push(d3_interpolateTransformPop(s) + "rotate(", null, ")") - 2, + x: d3_interpolateNumber(ra, rb) + }); + } else if (rb) { + s.push(d3_interpolateTransformPop(s) + "rotate(" + rb + ")"); + } + } + function d3_interpolateSkew(wa, wb, s, q) { + if (wa !== wb) { + q.push({ + i: s.push(d3_interpolateTransformPop(s) + "skewX(", null, ")") - 2, + x: d3_interpolateNumber(wa, wb) + }); + } else if (wb) { + s.push(d3_interpolateTransformPop(s) + "skewX(" + wb + ")"); + } + } + function d3_interpolateScale(ka, kb, s, q) { + if (ka[0] !== kb[0] || ka[1] !== kb[1]) { + var i = s.push(d3_interpolateTransformPop(s) + "scale(", null, ",", null, ")"); + q.push({ + i: i - 4, + x: d3_interpolateNumber(ka[0], kb[0]) + }, { + i: i - 2, + x: d3_interpolateNumber(ka[1], kb[1]) + }); + } else if (kb[0] !== 1 || kb[1] !== 1) { + s.push(d3_interpolateTransformPop(s) + "scale(" + kb + ")"); + } + } + function d3_interpolateTransform(a, b) { + var s = [], q = []; + a = d3.transform(a), b = d3.transform(b); + d3_interpolateTranslate(a.translate, b.translate, s, q); + d3_interpolateRotate(a.rotate, b.rotate, s, q); + d3_interpolateSkew(a.skew, b.skew, s, q); + d3_interpolateScale(a.scale, b.scale, s, q); + a = b = null; + return function(t) { + var i = -1, n = q.length, o; + while (++i < n) s[(o = q[i]).i] = o.x(t); + return s.join(""); + }; + } + function d3_uninterpolateNumber(a, b) { + b = (b -= a = +a) || 1 / b; + return function(x) { + return (x - a) / b; + }; + } + function d3_uninterpolateClamp(a, b) { + b = (b -= a = +a) || 1 / b; + return function(x) { + return Math.max(0, Math.min(1, (x - a) / b)); + }; + } + d3.layout = {}; + d3.layout.bundle = function() { + return function(links) { + var paths = [], i = -1, n = links.length; + while (++i < n) paths.push(d3_layout_bundlePath(links[i])); + return paths; + }; + }; + function d3_layout_bundlePath(link) { + var start = link.source, end = link.target, lca = d3_layout_bundleLeastCommonAncestor(start, end), points = [ start ]; + while (start !== lca) { + start = start.parent; + points.push(start); + } + var k = points.length; + while (end !== lca) { + points.splice(k, 0, end); + end = end.parent; + } + return points; + } + function d3_layout_bundleAncestors(node) { + var ancestors = [], parent = node.parent; + while (parent != null) { + ancestors.push(node); + node = parent; + parent = parent.parent; + } + ancestors.push(node); + return ancestors; + } + function d3_layout_bundleLeastCommonAncestor(a, b) { + if (a === b) return a; + var aNodes = d3_layout_bundleAncestors(a), bNodes = d3_layout_bundleAncestors(b), aNode = aNodes.pop(), bNode = bNodes.pop(), sharedNode = null; + while (aNode === bNode) { + sharedNode = aNode; + aNode = aNodes.pop(); + bNode = bNodes.pop(); + } + return sharedNode; + } + d3.layout.chord = function() { + var chord = {}, chords, groups, matrix, n, padding = 0, sortGroups, sortSubgroups, sortChords; + function relayout() { + var subgroups = {}, groupSums = [], groupIndex = d3.range(n), subgroupIndex = [], k, x, x0, i, j; + chords = []; + groups = []; + k = 0, i = -1; + while (++i < n) { + x = 0, j = -1; + while (++j < n) { + x += matrix[i][j]; + } + groupSums.push(x); + subgroupIndex.push(d3.range(n)); + k += x; + } + if (sortGroups) { + groupIndex.sort(function(a, b) { + return sortGroups(groupSums[a], groupSums[b]); + }); + } + if (sortSubgroups) { + subgroupIndex.forEach(function(d, i) { + d.sort(function(a, b) { + return sortSubgroups(matrix[i][a], matrix[i][b]); + }); + }); + } + k = (Ī„ - padding * n) / k; + x = 0, i = -1; + while (++i < n) { + x0 = x, j = -1; + while (++j < n) { + var di = groupIndex[i], dj = subgroupIndex[di][j], v = matrix[di][dj], a0 = x, a1 = x += v * k; + subgroups[di + "-" + dj] = { + index: di, + subindex: dj, + startAngle: a0, + endAngle: a1, + value: v + }; + } + groups[di] = { + index: di, + startAngle: x0, + endAngle: x, + value: groupSums[di] + }; + x += padding; + } + i = -1; + while (++i < n) { + j = i - 1; + while (++j < n) { + var source = subgroups[i + "-" + j], target = subgroups[j + "-" + i]; + if (source.value || target.value) { + chords.push(source.value < target.value ? { + source: target, + target: source + } : { + source: source, + target: target + }); + } + } + } + if (sortChords) resort(); + } + function resort() { + chords.sort(function(a, b) { + return sortChords((a.source.value + a.target.value) / 2, (b.source.value + b.target.value) / 2); + }); + } + chord.matrix = function(x) { + if (!arguments.length) return matrix; + n = (matrix = x) && matrix.length; + chords = groups = null; + return chord; + }; + chord.padding = function(x) { + if (!arguments.length) return padding; + padding = x; + chords = groups = null; + return chord; + }; + chord.sortGroups = function(x) { + if (!arguments.length) return sortGroups; + sortGroups = x; + chords = groups = null; + return chord; + }; + chord.sortSubgroups = function(x) { + if (!arguments.length) return sortSubgroups; + sortSubgroups = x; + chords = null; + return chord; + }; + chord.sortChords = function(x) { + if (!arguments.length) return sortChords; + sortChords = x; + if (chords) resort(); + return chord; + }; + chord.chords = function() { + if (!chords) relayout(); + return chords; + }; + chord.groups = function() { + if (!groups) relayout(); + return groups; + }; + return chord; + }; + d3.layout.force = function() { + var force = {}, event = d3.dispatch("start", "tick", "end"), timer, size = [ 1, 1 ], drag, alpha, friction = .9, linkDistance = d3_layout_forceLinkDistance, linkStrength = d3_layout_forceLinkStrength, charge = -30, chargeDistance2 = d3_layout_forceChargeDistance2, gravity = .1, theta2 = .64, nodes = [], links = [], distances, strengths, charges; + function repulse(node) { + return function(quad, x1, _, x2) { + if (quad.point !== node) { + var dx = quad.cx - node.x, dy = quad.cy - node.y, dw = x2 - x1, dn = dx * dx + dy * dy; + if (dw * dw / theta2 < dn) { + if (dn < chargeDistance2) { + var k = quad.charge / dn; + node.px -= dx * k; + node.py -= dy * k; + } + return true; + } + if (quad.point && dn && dn < chargeDistance2) { + var k = quad.pointCharge / dn; + node.px -= dx * k; + node.py -= dy * k; + } + } + return !quad.charge; + }; + } + force.tick = function() { + if ((alpha *= .99) < .005) { + timer = null; + event.end({ + type: "end", + alpha: alpha = 0 + }); + return true; + } + var n = nodes.length, m = links.length, q, i, o, s, t, l, k, x, y; + for (i = 0; i < m; ++i) { + o = links[i]; + s = o.source; + t = o.target; + x = t.x - s.x; + y = t.y - s.y; + if (l = x * x + y * y) { + l = alpha * strengths[i] * ((l = Math.sqrt(l)) - distances[i]) / l; + x *= l; + y *= l; + t.x -= x * (k = s.weight + t.weight ? s.weight / (s.weight + t.weight) : .5); + t.y -= y * k; + s.x += x * (k = 1 - k); + s.y += y * k; + } + } + if (k = alpha * gravity) { + x = size[0] / 2; + y = size[1] / 2; + i = -1; + if (k) while (++i < n) { + o = nodes[i]; + o.x += (x - o.x) * k; + o.y += (y - o.y) * k; + } + } + if (charge) { + d3_layout_forceAccumulate(q = d3.geom.quadtree(nodes), alpha, charges); + i = -1; + while (++i < n) { + if (!(o = nodes[i]).fixed) { + q.visit(repulse(o)); + } + } + } + i = -1; + while (++i < n) { + o = nodes[i]; + if (o.fixed) { + o.x = o.px; + o.y = o.py; + } else { + o.x -= (o.px - (o.px = o.x)) * friction; + o.y -= (o.py - (o.py = o.y)) * friction; + } + } + event.tick({ + type: "tick", + alpha: alpha + }); + }; + force.nodes = function(x) { + if (!arguments.length) return nodes; + nodes = x; + return force; + }; + force.links = function(x) { + if (!arguments.length) return links; + links = x; + return force; + }; + force.size = function(x) { + if (!arguments.length) return size; + size = x; + return force; + }; + force.linkDistance = function(x) { + if (!arguments.length) return linkDistance; + linkDistance = typeof x === "function" ? x : +x; + return force; + }; + force.distance = force.linkDistance; + force.linkStrength = function(x) { + if (!arguments.length) return linkStrength; + linkStrength = typeof x === "function" ? x : +x; + return force; + }; + force.friction = function(x) { + if (!arguments.length) return friction; + friction = +x; + return force; + }; + force.charge = function(x) { + if (!arguments.length) return charge; + charge = typeof x === "function" ? x : +x; + return force; + }; + force.chargeDistance = function(x) { + if (!arguments.length) return Math.sqrt(chargeDistance2); + chargeDistance2 = x * x; + return force; + }; + force.gravity = function(x) { + if (!arguments.length) return gravity; + gravity = +x; + return force; + }; + force.theta = function(x) { + if (!arguments.length) return Math.sqrt(theta2); + theta2 = x * x; + return force; + }; + force.alpha = function(x) { + if (!arguments.length) return alpha; + x = +x; + if (alpha) { + if (x > 0) { + alpha = x; + } else { + timer.c = null, timer.t = NaN, timer = null; + event.end({ + type: "end", + alpha: alpha = 0 + }); + } + } else if (x > 0) { + event.start({ + type: "start", + alpha: alpha = x + }); + timer = d3_timer(force.tick); + } + return force; + }; + force.start = function() { + var i, n = nodes.length, m = links.length, w = size[0], h = size[1], neighbors, o; + for (i = 0; i < n; ++i) { + (o = nodes[i]).index = i; + o.weight = 0; + } + for (i = 0; i < m; ++i) { + o = links[i]; + if (typeof o.source == "number") o.source = nodes[o.source]; + if (typeof o.target == "number") o.target = nodes[o.target]; + ++o.source.weight; + ++o.target.weight; + } + for (i = 0; i < n; ++i) { + o = nodes[i]; + if (isNaN(o.x)) o.x = position("x", w); + if (isNaN(o.y)) o.y = position("y", h); + if (isNaN(o.px)) o.px = o.x; + if (isNaN(o.py)) o.py = o.y; + } + distances = []; + if (typeof linkDistance === "function") for (i = 0; i < m; ++i) distances[i] = +linkDistance.call(this, links[i], i); else for (i = 0; i < m; ++i) distances[i] = linkDistance; + strengths = []; + if (typeof linkStrength === "function") for (i = 0; i < m; ++i) strengths[i] = +linkStrength.call(this, links[i], i); else for (i = 0; i < m; ++i) strengths[i] = linkStrength; + charges = []; + if (typeof charge === "function") for (i = 0; i < n; ++i) charges[i] = +charge.call(this, nodes[i], i); else for (i = 0; i < n; ++i) charges[i] = charge; + function position(dimension, size) { + if (!neighbors) { + neighbors = new Array(n); + for (j = 0; j < n; ++j) { + neighbors[j] = []; + } + for (j = 0; j < m; ++j) { + var o = links[j]; + neighbors[o.source.index].push(o.target); + neighbors[o.target.index].push(o.source); + } + } + var candidates = neighbors[i], j = -1, l = candidates.length, x; + while (++j < l) if (!isNaN(x = candidates[j][dimension])) return x; + return Math.random() * size; + } + return force.resume(); + }; + force.resume = function() { + return force.alpha(.1); + }; + force.stop = function() { + return force.alpha(0); + }; + force.drag = function() { + if (!drag) drag = d3.behavior.drag().origin(d3_identity).on("dragstart.force", d3_layout_forceDragstart).on("drag.force", dragmove).on("dragend.force", d3_layout_forceDragend); + if (!arguments.length) return drag; + this.on("mouseover.force", d3_layout_forceMouseover).on("mouseout.force", d3_layout_forceMouseout).call(drag); + }; + function dragmove(d) { + d.px = d3.event.x, d.py = d3.event.y; + force.resume(); + } + return d3.rebind(force, event, "on"); + }; + function d3_layout_forceDragstart(d) { + d.fixed |= 2; + } + function d3_layout_forceDragend(d) { + d.fixed &= ~6; + } + function d3_layout_forceMouseover(d) { + d.fixed |= 4; + d.px = d.x, d.py = d.y; + } + function d3_layout_forceMouseout(d) { + d.fixed &= ~4; + } + function d3_layout_forceAccumulate(quad, alpha, charges) { + var cx = 0, cy = 0; + quad.charge = 0; + if (!quad.leaf) { + var nodes = quad.nodes, n = nodes.length, i = -1, c; + while (++i < n) { + c = nodes[i]; + if (c == null) continue; + d3_layout_forceAccumulate(c, alpha, charges); + quad.charge += c.charge; + cx += c.charge * c.cx; + cy += c.charge * c.cy; + } + } + if (quad.point) { + if (!quad.leaf) { + quad.point.x += Math.random() - .5; + quad.point.y += Math.random() - .5; + } + var k = alpha * charges[quad.point.index]; + quad.charge += quad.pointCharge = k; + cx += k * quad.point.x; + cy += k * quad.point.y; + } + quad.cx = cx / quad.charge; + quad.cy = cy / quad.charge; + } + var d3_layout_forceLinkDistance = 20, d3_layout_forceLinkStrength = 1, d3_layout_forceChargeDistance2 = Infinity; + d3.layout.hierarchy = function() { + var sort = d3_layout_hierarchySort, children = d3_layout_hierarchyChildren, value = d3_layout_hierarchyValue; + function hierarchy(root) { + var stack = [ root ], nodes = [], node; + root.depth = 0; + while ((node = stack.pop()) != null) { + nodes.push(node); + if ((childs = children.call(hierarchy, node, node.depth)) && (n = childs.length)) { + var n, childs, child; + while (--n >= 0) { + stack.push(child = childs[n]); + child.parent = node; + child.depth = node.depth + 1; + } + if (value) node.value = 0; + node.children = childs; + } else { + if (value) node.value = +value.call(hierarchy, node, node.depth) || 0; + delete node.children; + } + } + d3_layout_hierarchyVisitAfter(root, function(node) { + var childs, parent; + if (sort && (childs = node.children)) childs.sort(sort); + if (value && (parent = node.parent)) parent.value += node.value; + }); + return nodes; + } + hierarchy.sort = function(x) { + if (!arguments.length) return sort; + sort = x; + return hierarchy; + }; + hierarchy.children = function(x) { + if (!arguments.length) return children; + children = x; + return hierarchy; + }; + hierarchy.value = function(x) { + if (!arguments.length) return value; + value = x; + return hierarchy; + }; + hierarchy.revalue = function(root) { + if (value) { + d3_layout_hierarchyVisitBefore(root, function(node) { + if (node.children) node.value = 0; + }); + d3_layout_hierarchyVisitAfter(root, function(node) { + var parent; + if (!node.children) node.value = +value.call(hierarchy, node, node.depth) || 0; + if (parent = node.parent) parent.value += node.value; + }); + } + return root; + }; + return hierarchy; + }; + function d3_layout_hierarchyRebind(object, hierarchy) { + d3.rebind(object, hierarchy, "sort", "children", "value"); + object.nodes = object; + object.links = d3_layout_hierarchyLinks; + return object; + } + function d3_layout_hierarchyVisitBefore(node, callback) { + var nodes = [ node ]; + while ((node = nodes.pop()) != null) { + callback(node); + if ((children = node.children) && (n = children.length)) { + var n, children; + while (--n >= 0) nodes.push(children[n]); + } + } + } + function d3_layout_hierarchyVisitAfter(node, callback) { + var nodes = [ node ], nodes2 = []; + while ((node = nodes.pop()) != null) { + nodes2.push(node); + if ((children = node.children) && (n = children.length)) { + var i = -1, n, children; + while (++i < n) nodes.push(children[i]); + } + } + while ((node = nodes2.pop()) != null) { + callback(node); + } + } + function d3_layout_hierarchyChildren(d) { + return d.children; + } + function d3_layout_hierarchyValue(d) { + return d.value; + } + function d3_layout_hierarchySort(a, b) { + return b.value - a.value; + } + function d3_layout_hierarchyLinks(nodes) { + return d3.merge(nodes.map(function(parent) { + return (parent.children || []).map(function(child) { + return { + source: parent, + target: child + }; + }); + })); + } + d3.layout.partition = function() { + var hierarchy = d3.layout.hierarchy(), size = [ 1, 1 ]; + function position(node, x, dx, dy) { + var children = node.children; + node.x = x; + node.y = node.depth * dy; + node.dx = dx; + node.dy = dy; + if (children && (n = children.length)) { + var i = -1, n, c, d; + dx = node.value ? dx / node.value : 0; + while (++i < n) { + position(c = children[i], x, d = c.value * dx, dy); + x += d; + } + } + } + function depth(node) { + var children = node.children, d = 0; + if (children && (n = children.length)) { + var i = -1, n; + while (++i < n) d = Math.max(d, depth(children[i])); + } + return 1 + d; + } + function partition(d, i) { + var nodes = hierarchy.call(this, d, i); + position(nodes[0], 0, size[0], size[1] / depth(nodes[0])); + return nodes; + } + partition.size = function(x) { + if (!arguments.length) return size; + size = x; + return partition; + }; + return d3_layout_hierarchyRebind(partition, hierarchy); + }; + d3.layout.pie = function() { + var value = Number, sort = d3_layout_pieSortByValue, startAngle = 0, endAngle = Ī„, padAngle = 0; + function pie(data) { + var n = data.length, values = data.map(function(d, i) { + return +value.call(pie, d, i); + }), a = +(typeof startAngle === "function" ? startAngle.apply(this, arguments) : startAngle), da = (typeof endAngle === "function" ? endAngle.apply(this, arguments) : endAngle) - a, p = Math.min(Math.abs(da) / n, +(typeof padAngle === "function" ? padAngle.apply(this, arguments) : padAngle)), pa = p * (da < 0 ? -1 : 1), sum = d3.sum(values), k = sum ? (da - n * pa) / sum : 0, index = d3.range(n), arcs = [], v; + if (sort != null) index.sort(sort === d3_layout_pieSortByValue ? function(i, j) { + return values[j] - values[i]; + } : function(i, j) { + return sort(data[i], data[j]); + }); + index.forEach(function(i) { + arcs[i] = { + data: data[i], + value: v = values[i], + startAngle: a, + endAngle: a += v * k + pa, + padAngle: p + }; + }); + return arcs; + } + pie.value = function(_) { + if (!arguments.length) return value; + value = _; + return pie; + }; + pie.sort = function(_) { + if (!arguments.length) return sort; + sort = _; + return pie; + }; + pie.startAngle = function(_) { + if (!arguments.length) return startAngle; + startAngle = _; + return pie; + }; + pie.endAngle = function(_) { + if (!arguments.length) return endAngle; + endAngle = _; + return pie; + }; + pie.padAngle = function(_) { + if (!arguments.length) return padAngle; + padAngle = _; + return pie; + }; + return pie; + }; + var d3_layout_pieSortByValue = {}; + d3.layout.stack = function() { + var values = d3_identity, order = d3_layout_stackOrderDefault, offset = d3_layout_stackOffsetZero, out = d3_layout_stackOut, x = d3_layout_stackX, y = d3_layout_stackY; + function stack(data, index) { + if (!(n = data.length)) return data; + var series = data.map(function(d, i) { + return values.call(stack, d, i); + }); + var points = series.map(function(d) { + return d.map(function(v, i) { + return [ x.call(stack, v, i), y.call(stack, v, i) ]; + }); + }); + var orders = order.call(stack, points, index); + series = d3.permute(series, orders); + points = d3.permute(points, orders); + var offsets = offset.call(stack, points, index); + var m = series[0].length, n, i, j, o; + for (j = 0; j < m; ++j) { + out.call(stack, series[0][j], o = offsets[j], points[0][j][1]); + for (i = 1; i < n; ++i) { + out.call(stack, series[i][j], o += points[i - 1][j][1], points[i][j][1]); + } + } + return data; + } + stack.values = function(x) { + if (!arguments.length) return values; + values = x; + return stack; + }; + stack.order = function(x) { + if (!arguments.length) return order; + order = typeof x === "function" ? x : d3_layout_stackOrders.get(x) || d3_layout_stackOrderDefault; + return stack; + }; + stack.offset = function(x) { + if (!arguments.length) return offset; + offset = typeof x === "function" ? x : d3_layout_stackOffsets.get(x) || d3_layout_stackOffsetZero; + return stack; + }; + stack.x = function(z) { + if (!arguments.length) return x; + x = z; + return stack; + }; + stack.y = function(z) { + if (!arguments.length) return y; + y = z; + return stack; + }; + stack.out = function(z) { + if (!arguments.length) return out; + out = z; + return stack; + }; + return stack; + }; + function d3_layout_stackX(d) { + return d.x; + } + function d3_layout_stackY(d) { + return d.y; + } + function d3_layout_stackOut(d, y0, y) { + d.y0 = y0; + d.y = y; + } + var d3_layout_stackOrders = d3.map({ + "inside-out": function(data) { + var n = data.length, i, j, max = data.map(d3_layout_stackMaxIndex), sums = data.map(d3_layout_stackReduceSum), index = d3.range(n).sort(function(a, b) { + return max[a] - max[b]; + }), top = 0, bottom = 0, tops = [], bottoms = []; + for (i = 0; i < n; ++i) { + j = index[i]; + if (top < bottom) { + top += sums[j]; + tops.push(j); + } else { + bottom += sums[j]; + bottoms.push(j); + } + } + return bottoms.reverse().concat(tops); + }, + reverse: function(data) { + return d3.range(data.length).reverse(); + }, + "default": d3_layout_stackOrderDefault + }); + var d3_layout_stackOffsets = d3.map({ + silhouette: function(data) { + var n = data.length, m = data[0].length, sums = [], max = 0, i, j, o, y0 = []; + for (j = 0; j < m; ++j) { + for (i = 0, o = 0; i < n; i++) o += data[i][j][1]; + if (o > max) max = o; + sums.push(o); + } + for (j = 0; j < m; ++j) { + y0[j] = (max - sums[j]) / 2; + } + return y0; + }, + wiggle: function(data) { + var n = data.length, x = data[0], m = x.length, i, j, k, s1, s2, s3, dx, o, o0, y0 = []; + y0[0] = o = o0 = 0; + for (j = 1; j < m; ++j) { + for (i = 0, s1 = 0; i < n; ++i) s1 += data[i][j][1]; + for (i = 0, s2 = 0, dx = x[j][0] - x[j - 1][0]; i < n; ++i) { + for (k = 0, s3 = (data[i][j][1] - data[i][j - 1][1]) / (2 * dx); k < i; ++k) { + s3 += (data[k][j][1] - data[k][j - 1][1]) / dx; + } + s2 += s3 * data[i][j][1]; + } + y0[j] = o -= s1 ? s2 / s1 * dx : 0; + if (o < o0) o0 = o; + } + for (j = 0; j < m; ++j) y0[j] -= o0; + return y0; + }, + expand: function(data) { + var n = data.length, m = data[0].length, k = 1 / n, i, j, o, y0 = []; + for (j = 0; j < m; ++j) { + for (i = 0, o = 0; i < n; i++) o += data[i][j][1]; + if (o) for (i = 0; i < n; i++) data[i][j][1] /= o; else for (i = 0; i < n; i++) data[i][j][1] = k; + } + for (j = 0; j < m; ++j) y0[j] = 0; + return y0; + }, + zero: d3_layout_stackOffsetZero + }); + function d3_layout_stackOrderDefault(data) { + return d3.range(data.length); + } + function d3_layout_stackOffsetZero(data) { + var j = -1, m = data[0].length, y0 = []; + while (++j < m) y0[j] = 0; + return y0; + } + function d3_layout_stackMaxIndex(array) { + var i = 1, j = 0, v = array[0][1], k, n = array.length; + for (;i < n; ++i) { + if ((k = array[i][1]) > v) { + j = i; + v = k; + } + } + return j; + } + function d3_layout_stackReduceSum(d) { + return d.reduce(d3_layout_stackSum, 0); + } + function d3_layout_stackSum(p, d) { + return p + d[1]; + } + d3.layout.histogram = function() { + var frequency = true, valuer = Number, ranger = d3_layout_histogramRange, binner = d3_layout_histogramBinSturges; + function histogram(data, i) { + var bins = [], values = data.map(valuer, this), range = ranger.call(this, values, i), thresholds = binner.call(this, range, values, i), bin, i = -1, n = values.length, m = thresholds.length - 1, k = frequency ? 1 : 1 / n, x; + while (++i < m) { + bin = bins[i] = []; + bin.dx = thresholds[i + 1] - (bin.x = thresholds[i]); + bin.y = 0; + } + if (m > 0) { + i = -1; + while (++i < n) { + x = values[i]; + if (x >= range[0] && x <= range[1]) { + bin = bins[d3.bisect(thresholds, x, 1, m) - 1]; + bin.y += k; + bin.push(data[i]); + } + } + } + return bins; + } + histogram.value = function(x) { + if (!arguments.length) return valuer; + valuer = x; + return histogram; + }; + histogram.range = function(x) { + if (!arguments.length) return ranger; + ranger = d3_functor(x); + return histogram; + }; + histogram.bins = function(x) { + if (!arguments.length) return binner; + binner = typeof x === "number" ? function(range) { + return d3_layout_histogramBinFixed(range, x); + } : d3_functor(x); + return histogram; + }; + histogram.frequency = function(x) { + if (!arguments.length) return frequency; + frequency = !!x; + return histogram; + }; + return histogram; + }; + function d3_layout_histogramBinSturges(range, values) { + return d3_layout_histogramBinFixed(range, Math.ceil(Math.log(values.length) / Math.LN2 + 1)); + } + function d3_layout_histogramBinFixed(range, n) { + var x = -1, b = +range[0], m = (range[1] - b) / n, f = []; + while (++x <= n) f[x] = m * x + b; + return f; + } + function d3_layout_histogramRange(values) { + return [ d3.min(values), d3.max(values) ]; + } + d3.layout.pack = function() { + var hierarchy = d3.layout.hierarchy().sort(d3_layout_packSort), padding = 0, size = [ 1, 1 ], radius; + function pack(d, i) { + var nodes = hierarchy.call(this, d, i), root = nodes[0], w = size[0], h = size[1], r = radius == null ? Math.sqrt : typeof radius === "function" ? radius : function() { + return radius; + }; + root.x = root.y = 0; + d3_layout_hierarchyVisitAfter(root, function(d) { + d.r = +r(d.value); + }); + d3_layout_hierarchyVisitAfter(root, d3_layout_packSiblings); + if (padding) { + var dr = padding * (radius ? 1 : Math.max(2 * root.r / w, 2 * root.r / h)) / 2; + d3_layout_hierarchyVisitAfter(root, function(d) { + d.r += dr; + }); + d3_layout_hierarchyVisitAfter(root, d3_layout_packSiblings); + d3_layout_hierarchyVisitAfter(root, function(d) { + d.r -= dr; + }); + } + d3_layout_packTransform(root, w / 2, h / 2, radius ? 1 : 1 / Math.max(2 * root.r / w, 2 * root.r / h)); + return nodes; + } + pack.size = function(_) { + if (!arguments.length) return size; + size = _; + return pack; + }; + pack.radius = function(_) { + if (!arguments.length) return radius; + radius = _ == null || typeof _ === "function" ? _ : +_; + return pack; + }; + pack.padding = function(_) { + if (!arguments.length) return padding; + padding = +_; + return pack; + }; + return d3_layout_hierarchyRebind(pack, hierarchy); + }; + function d3_layout_packSort(a, b) { + return a.value - b.value; + } + function d3_layout_packInsert(a, b) { + var c = a._pack_next; + a._pack_next = b; + b._pack_prev = a; + b._pack_next = c; + c._pack_prev = b; + } + function d3_layout_packSplice(a, b) { + a._pack_next = b; + b._pack_prev = a; + } + function d3_layout_packIntersects(a, b) { + var dx = b.x - a.x, dy = b.y - a.y, dr = a.r + b.r; + return .999 * dr * dr > dx * dx + dy * dy; + } + function d3_layout_packSiblings(node) { + if (!(nodes = node.children) || !(n = nodes.length)) return; + var nodes, xMin = Infinity, xMax = -Infinity, yMin = Infinity, yMax = -Infinity, a, b, c, i, j, k, n; + function bound(node) { + xMin = Math.min(node.x - node.r, xMin); + xMax = Math.max(node.x + node.r, xMax); + yMin = Math.min(node.y - node.r, yMin); + yMax = Math.max(node.y + node.r, yMax); + } + nodes.forEach(d3_layout_packLink); + a = nodes[0]; + a.x = -a.r; + a.y = 0; + bound(a); + if (n > 1) { + b = nodes[1]; + b.x = b.r; + b.y = 0; + bound(b); + if (n > 2) { + c = nodes[2]; + d3_layout_packPlace(a, b, c); + bound(c); + d3_layout_packInsert(a, c); + a._pack_prev = c; + d3_layout_packInsert(c, b); + b = a._pack_next; + for (i = 3; i < n; i++) { + d3_layout_packPlace(a, b, c = nodes[i]); + var isect = 0, s1 = 1, s2 = 1; + for (j = b._pack_next; j !== b; j = j._pack_next, s1++) { + if (d3_layout_packIntersects(j, c)) { + isect = 1; + break; + } + } + if (isect == 1) { + for (k = a._pack_prev; k !== j._pack_prev; k = k._pack_prev, s2++) { + if (d3_layout_packIntersects(k, c)) { + break; + } + } + } + if (isect) { + if (s1 < s2 || s1 == s2 && b.r < a.r) d3_layout_packSplice(a, b = j); else d3_layout_packSplice(a = k, b); + i--; + } else { + d3_layout_packInsert(a, c); + b = c; + bound(c); + } + } + } + } + var cx = (xMin + xMax) / 2, cy = (yMin + yMax) / 2, cr = 0; + for (i = 0; i < n; i++) { + c = nodes[i]; + c.x -= cx; + c.y -= cy; + cr = Math.max(cr, c.r + Math.sqrt(c.x * c.x + c.y * c.y)); + } + node.r = cr; + nodes.forEach(d3_layout_packUnlink); + } + function d3_layout_packLink(node) { + node._pack_next = node._pack_prev = node; + } + function d3_layout_packUnlink(node) { + delete node._pack_next; + delete node._pack_prev; + } + function d3_layout_packTransform(node, x, y, k) { + var children = node.children; + node.x = x += k * node.x; + node.y = y += k * node.y; + node.r *= k; + if (children) { + var i = -1, n = children.length; + while (++i < n) d3_layout_packTransform(children[i], x, y, k); + } + } + function d3_layout_packPlace(a, b, c) { + var db = a.r + c.r, dx = b.x - a.x, dy = b.y - a.y; + if (db && (dx || dy)) { + var da = b.r + c.r, dc = dx * dx + dy * dy; + da *= da; + db *= db; + var x = .5 + (db - da) / (2 * dc), y = Math.sqrt(Math.max(0, 2 * da * (db + dc) - (db -= dc) * db - da * da)) / (2 * dc); + c.x = a.x + x * dx + y * dy; + c.y = a.y + x * dy - y * dx; + } else { + c.x = a.x + db; + c.y = a.y; + } + } + d3.layout.tree = function() { + var hierarchy = d3.layout.hierarchy().sort(null).value(null), separation = d3_layout_treeSeparation, size = [ 1, 1 ], nodeSize = null; + function tree(d, i) { + var nodes = hierarchy.call(this, d, i), root0 = nodes[0], root1 = wrapTree(root0); + d3_layout_hierarchyVisitAfter(root1, firstWalk), root1.parent.m = -root1.z; + d3_layout_hierarchyVisitBefore(root1, secondWalk); + if (nodeSize) d3_layout_hierarchyVisitBefore(root0, sizeNode); else { + var left = root0, right = root0, bottom = root0; + d3_layout_hierarchyVisitBefore(root0, function(node) { + if (node.x < left.x) left = node; + if (node.x > right.x) right = node; + if (node.depth > bottom.depth) bottom = node; + }); + var tx = separation(left, right) / 2 - left.x, kx = size[0] / (right.x + separation(right, left) / 2 + tx), ky = size[1] / (bottom.depth || 1); + d3_layout_hierarchyVisitBefore(root0, function(node) { + node.x = (node.x + tx) * kx; + node.y = node.depth * ky; + }); + } + return nodes; + } + function wrapTree(root0) { + var root1 = { + A: null, + children: [ root0 ] + }, queue = [ root1 ], node1; + while ((node1 = queue.pop()) != null) { + for (var children = node1.children, child, i = 0, n = children.length; i < n; ++i) { + queue.push((children[i] = child = { + _: children[i], + parent: node1, + children: (child = children[i].children) && child.slice() || [], + A: null, + a: null, + z: 0, + m: 0, + c: 0, + s: 0, + t: null, + i: i + }).a = child); + } + } + return root1.children[0]; + } + function firstWalk(v) { + var children = v.children, siblings = v.parent.children, w = v.i ? siblings[v.i - 1] : null; + if (children.length) { + d3_layout_treeShift(v); + var midpoint = (children[0].z + children[children.length - 1].z) / 2; + if (w) { + v.z = w.z + separation(v._, w._); + v.m = v.z - midpoint; + } else { + v.z = midpoint; + } + } else if (w) { + v.z = w.z + separation(v._, w._); + } + v.parent.A = apportion(v, w, v.parent.A || siblings[0]); + } + function secondWalk(v) { + v._.x = v.z + v.parent.m; + v.m += v.parent.m; + } + function apportion(v, w, ancestor) { + if (w) { + var vip = v, vop = v, vim = w, vom = vip.parent.children[0], sip = vip.m, sop = vop.m, sim = vim.m, som = vom.m, shift; + while (vim = d3_layout_treeRight(vim), vip = d3_layout_treeLeft(vip), vim && vip) { + vom = d3_layout_treeLeft(vom); + vop = d3_layout_treeRight(vop); + vop.a = v; + shift = vim.z + sim - vip.z - sip + separation(vim._, vip._); + if (shift > 0) { + d3_layout_treeMove(d3_layout_treeAncestor(vim, v, ancestor), v, shift); + sip += shift; + sop += shift; + } + sim += vim.m; + sip += vip.m; + som += vom.m; + sop += vop.m; + } + if (vim && !d3_layout_treeRight(vop)) { + vop.t = vim; + vop.m += sim - sop; + } + if (vip && !d3_layout_treeLeft(vom)) { + vom.t = vip; + vom.m += sip - som; + ancestor = v; + } + } + return ancestor; + } + function sizeNode(node) { + node.x *= size[0]; + node.y = node.depth * size[1]; + } + tree.separation = function(x) { + if (!arguments.length) return separation; + separation = x; + return tree; + }; + tree.size = function(x) { + if (!arguments.length) return nodeSize ? null : size; + nodeSize = (size = x) == null ? sizeNode : null; + return tree; + }; + tree.nodeSize = function(x) { + if (!arguments.length) return nodeSize ? size : null; + nodeSize = (size = x) == null ? null : sizeNode; + return tree; + }; + return d3_layout_hierarchyRebind(tree, hierarchy); + }; + function d3_layout_treeSeparation(a, b) { + return a.parent == b.parent ? 1 : 2; + } + function d3_layout_treeLeft(v) { + var children = v.children; + return children.length ? children[0] : v.t; + } + function d3_layout_treeRight(v) { + var children = v.children, n; + return (n = children.length) ? children[n - 1] : v.t; + } + function d3_layout_treeMove(wm, wp, shift) { + var change = shift / (wp.i - wm.i); + wp.c -= change; + wp.s += shift; + wm.c += change; + wp.z += shift; + wp.m += shift; + } + function d3_layout_treeShift(v) { + var shift = 0, change = 0, children = v.children, i = children.length, w; + while (--i >= 0) { + w = children[i]; + w.z += shift; + w.m += shift; + shift += w.s + (change += w.c); + } + } + function d3_layout_treeAncestor(vim, v, ancestor) { + return vim.a.parent === v.parent ? vim.a : ancestor; + } + d3.layout.cluster = function() { + var hierarchy = d3.layout.hierarchy().sort(null).value(null), separation = d3_layout_treeSeparation, size = [ 1, 1 ], nodeSize = false; + function cluster(d, i) { + var nodes = hierarchy.call(this, d, i), root = nodes[0], previousNode, x = 0; + d3_layout_hierarchyVisitAfter(root, function(node) { + var children = node.children; + if (children && children.length) { + node.x = d3_layout_clusterX(children); + node.y = d3_layout_clusterY(children); + } else { + node.x = previousNode ? x += separation(node, previousNode) : 0; + node.y = 0; + previousNode = node; + } + }); + var left = d3_layout_clusterLeft(root), right = d3_layout_clusterRight(root), x0 = left.x - separation(left, right) / 2, x1 = right.x + separation(right, left) / 2; + d3_layout_hierarchyVisitAfter(root, nodeSize ? function(node) { + node.x = (node.x - root.x) * size[0]; + node.y = (root.y - node.y) * size[1]; + } : function(node) { + node.x = (node.x - x0) / (x1 - x0) * size[0]; + node.y = (1 - (root.y ? node.y / root.y : 1)) * size[1]; + }); + return nodes; + } + cluster.separation = function(x) { + if (!arguments.length) return separation; + separation = x; + return cluster; + }; + cluster.size = function(x) { + if (!arguments.length) return nodeSize ? null : size; + nodeSize = (size = x) == null; + return cluster; + }; + cluster.nodeSize = function(x) { + if (!arguments.length) return nodeSize ? size : null; + nodeSize = (size = x) != null; + return cluster; + }; + return d3_layout_hierarchyRebind(cluster, hierarchy); + }; + function d3_layout_clusterY(children) { + return 1 + d3.max(children, function(child) { + return child.y; + }); + } + function d3_layout_clusterX(children) { + return children.reduce(function(x, child) { + return x + child.x; + }, 0) / children.length; + } + function d3_layout_clusterLeft(node) { + var children = node.children; + return children && children.length ? d3_layout_clusterLeft(children[0]) : node; + } + function d3_layout_clusterRight(node) { + var children = node.children, n; + return children && (n = children.length) ? d3_layout_clusterRight(children[n - 1]) : node; + } + d3.layout.treemap = function() { + var hierarchy = d3.layout.hierarchy(), round = Math.round, size = [ 1, 1 ], padding = null, pad = d3_layout_treemapPadNull, sticky = false, stickies, mode = "squarify", ratio = .5 * (1 + Math.sqrt(5)); + function scale(children, k) { + var i = -1, n = children.length, child, area; + while (++i < n) { + area = (child = children[i]).value * (k < 0 ? 0 : k); + child.area = isNaN(area) || area <= 0 ? 0 : area; + } + } + function squarify(node) { + var children = node.children; + if (children && children.length) { + var rect = pad(node), row = [], remaining = children.slice(), child, best = Infinity, score, u = mode === "slice" ? rect.dx : mode === "dice" ? rect.dy : mode === "slice-dice" ? node.depth & 1 ? rect.dy : rect.dx : Math.min(rect.dx, rect.dy), n; + scale(remaining, rect.dx * rect.dy / node.value); + row.area = 0; + while ((n = remaining.length) > 0) { + row.push(child = remaining[n - 1]); + row.area += child.area; + if (mode !== "squarify" || (score = worst(row, u)) <= best) { + remaining.pop(); + best = score; + } else { + row.area -= row.pop().area; + position(row, u, rect, false); + u = Math.min(rect.dx, rect.dy); + row.length = row.area = 0; + best = Infinity; + } + } + if (row.length) { + position(row, u, rect, true); + row.length = row.area = 0; + } + children.forEach(squarify); + } + } + function stickify(node) { + var children = node.children; + if (children && children.length) { + var rect = pad(node), remaining = children.slice(), child, row = []; + scale(remaining, rect.dx * rect.dy / node.value); + row.area = 0; + while (child = remaining.pop()) { + row.push(child); + row.area += child.area; + if (child.z != null) { + position(row, child.z ? rect.dx : rect.dy, rect, !remaining.length); + row.length = row.area = 0; + } + } + children.forEach(stickify); + } + } + function worst(row, u) { + var s = row.area, r, rmax = 0, rmin = Infinity, i = -1, n = row.length; + while (++i < n) { + if (!(r = row[i].area)) continue; + if (r < rmin) rmin = r; + if (r > rmax) rmax = r; + } + s *= s; + u *= u; + return s ? Math.max(u * rmax * ratio / s, s / (u * rmin * ratio)) : Infinity; + } + function position(row, u, rect, flush) { + var i = -1, n = row.length, x = rect.x, y = rect.y, v = u ? round(row.area / u) : 0, o; + if (u == rect.dx) { + if (flush || v > rect.dy) v = rect.dy; + while (++i < n) { + o = row[i]; + o.x = x; + o.y = y; + o.dy = v; + x += o.dx = Math.min(rect.x + rect.dx - x, v ? round(o.area / v) : 0); + } + o.z = true; + o.dx += rect.x + rect.dx - x; + rect.y += v; + rect.dy -= v; + } else { + if (flush || v > rect.dx) v = rect.dx; + while (++i < n) { + o = row[i]; + o.x = x; + o.y = y; + o.dx = v; + y += o.dy = Math.min(rect.y + rect.dy - y, v ? round(o.area / v) : 0); + } + o.z = false; + o.dy += rect.y + rect.dy - y; + rect.x += v; + rect.dx -= v; + } + } + function treemap(d) { + var nodes = stickies || hierarchy(d), root = nodes[0]; + root.x = root.y = 0; + if (root.value) root.dx = size[0], root.dy = size[1]; else root.dx = root.dy = 0; + if (stickies) hierarchy.revalue(root); + scale([ root ], root.dx * root.dy / root.value); + (stickies ? stickify : squarify)(root); + if (sticky) stickies = nodes; + return nodes; + } + treemap.size = function(x) { + if (!arguments.length) return size; + size = x; + return treemap; + }; + treemap.padding = function(x) { + if (!arguments.length) return padding; + function padFunction(node) { + var p = x.call(treemap, node, node.depth); + return p == null ? d3_layout_treemapPadNull(node) : d3_layout_treemapPad(node, typeof p === "number" ? [ p, p, p, p ] : p); + } + function padConstant(node) { + return d3_layout_treemapPad(node, x); + } + var type; + pad = (padding = x) == null ? d3_layout_treemapPadNull : (type = typeof x) === "function" ? padFunction : type === "number" ? (x = [ x, x, x, x ], + padConstant) : padConstant; + return treemap; + }; + treemap.round = function(x) { + if (!arguments.length) return round != Number; + round = x ? Math.round : Number; + return treemap; + }; + treemap.sticky = function(x) { + if (!arguments.length) return sticky; + sticky = x; + stickies = null; + return treemap; + }; + treemap.ratio = function(x) { + if (!arguments.length) return ratio; + ratio = x; + return treemap; + }; + treemap.mode = function(x) { + if (!arguments.length) return mode; + mode = x + ""; + return treemap; + }; + return d3_layout_hierarchyRebind(treemap, hierarchy); + }; + function d3_layout_treemapPadNull(node) { + return { + x: node.x, + y: node.y, + dx: node.dx, + dy: node.dy + }; + } + function d3_layout_treemapPad(node, padding) { + var x = node.x + padding[3], y = node.y + padding[0], dx = node.dx - padding[1] - padding[3], dy = node.dy - padding[0] - padding[2]; + if (dx < 0) { + x += dx / 2; + dx = 0; + } + if (dy < 0) { + y += dy / 2; + dy = 0; + } + return { + x: x, + y: y, + dx: dx, + dy: dy + }; + } + d3.random = { + normal: function(Âĩ, Īƒ) { + var n = arguments.length; + if (n < 2) Īƒ = 1; + if (n < 1) Âĩ = 0; + return function() { + var x, y, r; + do { + x = Math.random() * 2 - 1; + y = Math.random() * 2 - 1; + r = x * x + y * y; + } while (!r || r > 1); + return Âĩ + Īƒ * x * Math.sqrt(-2 * Math.log(r) / r); + }; + }, + logNormal: function() { + var random = d3.random.normal.apply(d3, arguments); + return function() { + return Math.exp(random()); + }; + }, + bates: function(m) { + var random = d3.random.irwinHall(m); + return function() { + return random() / m; + }; + }, + irwinHall: function(m) { + return function() { + for (var s = 0, j = 0; j < m; j++) s += Math.random(); + return s; + }; + } + }; + d3.scale = {}; + function d3_scaleExtent(domain) { + var start = domain[0], stop = domain[domain.length - 1]; + return start < stop ? [ start, stop ] : [ stop, start ]; + } + function d3_scaleRange(scale) { + return scale.rangeExtent ? scale.rangeExtent() : d3_scaleExtent(scale.range()); + } + function d3_scale_bilinear(domain, range, uninterpolate, interpolate) { + var u = uninterpolate(domain[0], domain[1]), i = interpolate(range[0], range[1]); + return function(x) { + return i(u(x)); + }; + } + function d3_scale_nice(domain, nice) { + var i0 = 0, i1 = domain.length - 1, x0 = domain[i0], x1 = domain[i1], dx; + if (x1 < x0) { + dx = i0, i0 = i1, i1 = dx; + dx = x0, x0 = x1, x1 = dx; + } + domain[i0] = nice.floor(x0); + domain[i1] = nice.ceil(x1); + return domain; + } + function d3_scale_niceStep(step) { + return step ? { + floor: function(x) { + return Math.floor(x / step) * step; + }, + ceil: function(x) { + return Math.ceil(x / step) * step; + } + } : d3_scale_niceIdentity; + } + var d3_scale_niceIdentity = { + floor: d3_identity, + ceil: d3_identity + }; + function d3_scale_polylinear(domain, range, uninterpolate, interpolate) { + var u = [], i = [], j = 0, k = Math.min(domain.length, range.length) - 1; + if (domain[k] < domain[0]) { + domain = domain.slice().reverse(); + range = range.slice().reverse(); + } + while (++j <= k) { + u.push(uninterpolate(domain[j - 1], domain[j])); + i.push(interpolate(range[j - 1], range[j])); + } + return function(x) { + var j = d3.bisect(domain, x, 1, k) - 1; + return i[j](u[j](x)); + }; + } + d3.scale.linear = function() { + return d3_scale_linear([ 0, 1 ], [ 0, 1 ], d3_interpolate, false); + }; + function d3_scale_linear(domain, range, interpolate, clamp) { + var output, input; + function rescale() { + var linear = Math.min(domain.length, range.length) > 2 ? d3_scale_polylinear : d3_scale_bilinear, uninterpolate = clamp ? d3_uninterpolateClamp : d3_uninterpolateNumber; + output = linear(domain, range, uninterpolate, interpolate); + input = linear(range, domain, uninterpolate, d3_interpolate); + return scale; + } + function scale(x) { + return output(x); + } + scale.invert = function(y) { + return input(y); + }; + scale.domain = function(x) { + if (!arguments.length) return domain; + domain = x.map(Number); + return rescale(); + }; + scale.range = function(x) { + if (!arguments.length) return range; + range = x; + return rescale(); + }; + scale.rangeRound = function(x) { + return scale.range(x).interpolate(d3_interpolateRound); + }; + scale.clamp = function(x) { + if (!arguments.length) return clamp; + clamp = x; + return rescale(); + }; + scale.interpolate = function(x) { + if (!arguments.length) return interpolate; + interpolate = x; + return rescale(); + }; + scale.ticks = function(m) { + return d3_scale_linearTicks(domain, m); + }; + scale.tickFormat = function(m, format) { + return d3_scale_linearTickFormat(domain, m, format); + }; + scale.nice = function(m) { + d3_scale_linearNice(domain, m); + return rescale(); + }; + scale.copy = function() { + return d3_scale_linear(domain, range, interpolate, clamp); + }; + return rescale(); + } + function d3_scale_linearRebind(scale, linear) { + return d3.rebind(scale, linear, "range", "rangeRound", "interpolate", "clamp"); + } + function d3_scale_linearNice(domain, m) { + d3_scale_nice(domain, d3_scale_niceStep(d3_scale_linearTickRange(domain, m)[2])); + d3_scale_nice(domain, d3_scale_niceStep(d3_scale_linearTickRange(domain, m)[2])); + return domain; + } + function d3_scale_linearTickRange(domain, m) { + if (m == null) m = 10; + var extent = d3_scaleExtent(domain), span = extent[1] - extent[0], step = Math.pow(10, Math.floor(Math.log(span / m) / Math.LN10)), err = m / span * step; + if (err <= .15) step *= 10; else if (err <= .35) step *= 5; else if (err <= .75) step *= 2; + extent[0] = Math.ceil(extent[0] / step) * step; + extent[1] = Math.floor(extent[1] / step) * step + step * .5; + extent[2] = step; + return extent; + } + function d3_scale_linearTicks(domain, m) { + return d3.range.apply(d3, d3_scale_linearTickRange(domain, m)); + } + function d3_scale_linearTickFormat(domain, m, format) { + var range = d3_scale_linearTickRange(domain, m); + if (format) { + var match = d3_format_re.exec(format); + match.shift(); + if (match[8] === "s") { + var prefix = d3.formatPrefix(Math.max(abs(range[0]), abs(range[1]))); + if (!match[7]) match[7] = "." + d3_scale_linearPrecision(prefix.scale(range[2])); + match[8] = "f"; + format = d3.format(match.join("")); + return function(d) { + return format(prefix.scale(d)) + prefix.symbol; + }; + } + if (!match[7]) match[7] = "." + d3_scale_linearFormatPrecision(match[8], range); + format = match.join(""); + } else { + format = ",." + d3_scale_linearPrecision(range[2]) + "f"; + } + return d3.format(format); + } + var d3_scale_linearFormatSignificant = { + s: 1, + g: 1, + p: 1, + r: 1, + e: 1 + }; + function d3_scale_linearPrecision(value) { + return -Math.floor(Math.log(value) / Math.LN10 + .01); + } + function d3_scale_linearFormatPrecision(type, range) { + var p = d3_scale_linearPrecision(range[2]); + return type in d3_scale_linearFormatSignificant ? Math.abs(p - d3_scale_linearPrecision(Math.max(abs(range[0]), abs(range[1])))) + +(type !== "e") : p - (type === "%") * 2; + } + d3.scale.log = function() { + return d3_scale_log(d3.scale.linear().domain([ 0, 1 ]), 10, true, [ 1, 10 ]); + }; + function d3_scale_log(linear, base, positive, domain) { + function log(x) { + return (positive ? Math.log(x < 0 ? 0 : x) : -Math.log(x > 0 ? 0 : -x)) / Math.log(base); + } + function pow(x) { + return positive ? Math.pow(base, x) : -Math.pow(base, -x); + } + function scale(x) { + return linear(log(x)); + } + scale.invert = function(x) { + return pow(linear.invert(x)); + }; + scale.domain = function(x) { + if (!arguments.length) return domain; + positive = x[0] >= 0; + linear.domain((domain = x.map(Number)).map(log)); + return scale; + }; + scale.base = function(_) { + if (!arguments.length) return base; + base = +_; + linear.domain(domain.map(log)); + return scale; + }; + scale.nice = function() { + var niced = d3_scale_nice(domain.map(log), positive ? Math : d3_scale_logNiceNegative); + linear.domain(niced); + domain = niced.map(pow); + return scale; + }; + scale.ticks = function() { + var extent = d3_scaleExtent(domain), ticks = [], u = extent[0], v = extent[1], i = Math.floor(log(u)), j = Math.ceil(log(v)), n = base % 1 ? 2 : base; + if (isFinite(j - i)) { + if (positive) { + for (;i < j; i++) for (var k = 1; k < n; k++) ticks.push(pow(i) * k); + ticks.push(pow(i)); + } else { + ticks.push(pow(i)); + for (;i++ < j; ) for (var k = n - 1; k > 0; k--) ticks.push(pow(i) * k); + } + for (i = 0; ticks[i] < u; i++) {} + for (j = ticks.length; ticks[j - 1] > v; j--) {} + ticks = ticks.slice(i, j); + } + return ticks; + }; + scale.tickFormat = function(n, format) { + if (!arguments.length) return d3_scale_logFormat; + if (arguments.length < 2) format = d3_scale_logFormat; else if (typeof format !== "function") format = d3.format(format); + var k = Math.max(1, base * n / scale.ticks().length); + return function(d) { + var i = d / pow(Math.round(log(d))); + if (i * base < base - .5) i *= base; + return i <= k ? format(d) : ""; + }; + }; + scale.copy = function() { + return d3_scale_log(linear.copy(), base, positive, domain); + }; + return d3_scale_linearRebind(scale, linear); + } + var d3_scale_logFormat = d3.format(".0e"), d3_scale_logNiceNegative = { + floor: function(x) { + return -Math.ceil(-x); + }, + ceil: function(x) { + return -Math.floor(-x); + } + }; + d3.scale.pow = function() { + return d3_scale_pow(d3.scale.linear(), 1, [ 0, 1 ]); + }; + function d3_scale_pow(linear, exponent, domain) { + var powp = d3_scale_powPow(exponent), powb = d3_scale_powPow(1 / exponent); + function scale(x) { + return linear(powp(x)); + } + scale.invert = function(x) { + return powb(linear.invert(x)); + }; + scale.domain = function(x) { + if (!arguments.length) return domain; + linear.domain((domain = x.map(Number)).map(powp)); + return scale; + }; + scale.ticks = function(m) { + return d3_scale_linearTicks(domain, m); + }; + scale.tickFormat = function(m, format) { + return d3_scale_linearTickFormat(domain, m, format); + }; + scale.nice = function(m) { + return scale.domain(d3_scale_linearNice(domain, m)); + }; + scale.exponent = function(x) { + if (!arguments.length) return exponent; + powp = d3_scale_powPow(exponent = x); + powb = d3_scale_powPow(1 / exponent); + linear.domain(domain.map(powp)); + return scale; + }; + scale.copy = function() { + return d3_scale_pow(linear.copy(), exponent, domain); + }; + return d3_scale_linearRebind(scale, linear); + } + function d3_scale_powPow(e) { + return function(x) { + return x < 0 ? -Math.pow(-x, e) : Math.pow(x, e); + }; + } + d3.scale.sqrt = function() { + return d3.scale.pow().exponent(.5); + }; + d3.scale.ordinal = function() { + return d3_scale_ordinal([], { + t: "range", + a: [ [] ] + }); + }; + function d3_scale_ordinal(domain, ranger) { + var index, range, rangeBand; + function scale(x) { + return range[((index.get(x) || (ranger.t === "range" ? index.set(x, domain.push(x)) : NaN)) - 1) % range.length]; + } + function steps(start, step) { + return d3.range(domain.length).map(function(i) { + return start + step * i; + }); + } + scale.domain = function(x) { + if (!arguments.length) return domain; + domain = []; + index = new d3_Map(); + var i = -1, n = x.length, xi; + while (++i < n) if (!index.has(xi = x[i])) index.set(xi, domain.push(xi)); + return scale[ranger.t].apply(scale, ranger.a); + }; + scale.range = function(x) { + if (!arguments.length) return range; + range = x; + rangeBand = 0; + ranger = { + t: "range", + a: arguments + }; + return scale; + }; + scale.rangePoints = function(x, padding) { + if (arguments.length < 2) padding = 0; + var start = x[0], stop = x[1], step = domain.length < 2 ? (start = (start + stop) / 2, + 0) : (stop - start) / (domain.length - 1 + padding); + range = steps(start + step * padding / 2, step); + rangeBand = 0; + ranger = { + t: "rangePoints", + a: arguments + }; + return scale; + }; + scale.rangeRoundPoints = function(x, padding) { + if (arguments.length < 2) padding = 0; + var start = x[0], stop = x[1], step = domain.length < 2 ? (start = stop = Math.round((start + stop) / 2), + 0) : (stop - start) / (domain.length - 1 + padding) | 0; + range = steps(start + Math.round(step * padding / 2 + (stop - start - (domain.length - 1 + padding) * step) / 2), step); + rangeBand = 0; + ranger = { + t: "rangeRoundPoints", + a: arguments + }; + return scale; + }; + scale.rangeBands = function(x, padding, outerPadding) { + if (arguments.length < 2) padding = 0; + if (arguments.length < 3) outerPadding = padding; + var reverse = x[1] < x[0], start = x[reverse - 0], stop = x[1 - reverse], step = (stop - start) / (domain.length - padding + 2 * outerPadding); + range = steps(start + step * outerPadding, step); + if (reverse) range.reverse(); + rangeBand = step * (1 - padding); + ranger = { + t: "rangeBands", + a: arguments + }; + return scale; + }; + scale.rangeRoundBands = function(x, padding, outerPadding) { + if (arguments.length < 2) padding = 0; + if (arguments.length < 3) outerPadding = padding; + var reverse = x[1] < x[0], start = x[reverse - 0], stop = x[1 - reverse], step = Math.floor((stop - start) / (domain.length - padding + 2 * outerPadding)); + range = steps(start + Math.round((stop - start - (domain.length - padding) * step) / 2), step); + if (reverse) range.reverse(); + rangeBand = Math.round(step * (1 - padding)); + ranger = { + t: "rangeRoundBands", + a: arguments + }; + return scale; + }; + scale.rangeBand = function() { + return rangeBand; + }; + scale.rangeExtent = function() { + return d3_scaleExtent(ranger.a[0]); + }; + scale.copy = function() { + return d3_scale_ordinal(domain, ranger); + }; + return scale.domain(domain); + } + d3.scale.category10 = function() { + return d3.scale.ordinal().range(d3_category10); + }; + d3.scale.category20 = function() { + return d3.scale.ordinal().range(d3_category20); + }; + d3.scale.category20b = function() { + return d3.scale.ordinal().range(d3_category20b); + }; + d3.scale.category20c = function() { + return d3.scale.ordinal().range(d3_category20c); + }; + var d3_category10 = [ 2062260, 16744206, 2924588, 14034728, 9725885, 9197131, 14907330, 8355711, 12369186, 1556175 ].map(d3_rgbString); + var d3_category20 = [ 2062260, 11454440, 16744206, 16759672, 2924588, 10018698, 14034728, 16750742, 9725885, 12955861, 9197131, 12885140, 14907330, 16234194, 8355711, 13092807, 12369186, 14408589, 1556175, 10410725 ].map(d3_rgbString); + var d3_category20b = [ 3750777, 5395619, 7040719, 10264286, 6519097, 9216594, 11915115, 13556636, 9202993, 12426809, 15186514, 15190932, 8666169, 11356490, 14049643, 15177372, 8077683, 10834324, 13528509, 14589654 ].map(d3_rgbString); + var d3_category20c = [ 3244733, 7057110, 10406625, 13032431, 15095053, 16616764, 16625259, 16634018, 3253076, 7652470, 10607003, 13101504, 7695281, 10394312, 12369372, 14342891, 6513507, 9868950, 12434877, 14277081 ].map(d3_rgbString); + d3.scale.quantile = function() { + return d3_scale_quantile([], []); + }; + function d3_scale_quantile(domain, range) { + var thresholds; + function rescale() { + var k = 0, q = range.length; + thresholds = []; + while (++k < q) thresholds[k - 1] = d3.quantile(domain, k / q); + return scale; + } + function scale(x) { + if (!isNaN(x = +x)) return range[d3.bisect(thresholds, x)]; + } + scale.domain = function(x) { + if (!arguments.length) return domain; + domain = x.map(d3_number).filter(d3_numeric).sort(d3_ascending); + return rescale(); + }; + scale.range = function(x) { + if (!arguments.length) return range; + range = x; + return rescale(); + }; + scale.quantiles = function() { + return thresholds; + }; + scale.invertExtent = function(y) { + y = range.indexOf(y); + return y < 0 ? [ NaN, NaN ] : [ y > 0 ? thresholds[y - 1] : domain[0], y < thresholds.length ? thresholds[y] : domain[domain.length - 1] ]; + }; + scale.copy = function() { + return d3_scale_quantile(domain, range); + }; + return rescale(); + } + d3.scale.quantize = function() { + return d3_scale_quantize(0, 1, [ 0, 1 ]); + }; + function d3_scale_quantize(x0, x1, range) { + var kx, i; + function scale(x) { + return range[Math.max(0, Math.min(i, Math.floor(kx * (x - x0))))]; + } + function rescale() { + kx = range.length / (x1 - x0); + i = range.length - 1; + return scale; + } + scale.domain = function(x) { + if (!arguments.length) return [ x0, x1 ]; + x0 = +x[0]; + x1 = +x[x.length - 1]; + return rescale(); + }; + scale.range = function(x) { + if (!arguments.length) return range; + range = x; + return rescale(); + }; + scale.invertExtent = function(y) { + y = range.indexOf(y); + y = y < 0 ? NaN : y / kx + x0; + return [ y, y + 1 / kx ]; + }; + scale.copy = function() { + return d3_scale_quantize(x0, x1, range); + }; + return rescale(); + } + d3.scale.threshold = function() { + return d3_scale_threshold([ .5 ], [ 0, 1 ]); + }; + function d3_scale_threshold(domain, range) { + function scale(x) { + if (x <= x) return range[d3.bisect(domain, x)]; + } + scale.domain = function(_) { + if (!arguments.length) return domain; + domain = _; + return scale; + }; + scale.range = function(_) { + if (!arguments.length) return range; + range = _; + return scale; + }; + scale.invertExtent = function(y) { + y = range.indexOf(y); + return [ domain[y - 1], domain[y] ]; + }; + scale.copy = function() { + return d3_scale_threshold(domain, range); + }; + return scale; + } + d3.scale.identity = function() { + return d3_scale_identity([ 0, 1 ]); + }; + function d3_scale_identity(domain) { + function identity(x) { + return +x; + } + identity.invert = identity; + identity.domain = identity.range = function(x) { + if (!arguments.length) return domain; + domain = x.map(identity); + return identity; + }; + identity.ticks = function(m) { + return d3_scale_linearTicks(domain, m); + }; + identity.tickFormat = function(m, format) { + return d3_scale_linearTickFormat(domain, m, format); + }; + identity.copy = function() { + return d3_scale_identity(domain); + }; + return identity; + } + d3.svg = {}; + function d3_zero() { + return 0; + } + d3.svg.arc = function() { + var innerRadius = d3_svg_arcInnerRadius, outerRadius = d3_svg_arcOuterRadius, cornerRadius = d3_zero, padRadius = d3_svg_arcAuto, startAngle = d3_svg_arcStartAngle, endAngle = d3_svg_arcEndAngle, padAngle = d3_svg_arcPadAngle; + function arc() { + var r0 = Math.max(0, +innerRadius.apply(this, arguments)), r1 = Math.max(0, +outerRadius.apply(this, arguments)), a0 = startAngle.apply(this, arguments) - halfĪ€, a1 = endAngle.apply(this, arguments) - halfĪ€, da = Math.abs(a1 - a0), cw = a0 > a1 ? 0 : 1; + if (r1 < r0) rc = r1, r1 = r0, r0 = rc; + if (da >= Ī„Îĩ) return circleSegment(r1, cw) + (r0 ? circleSegment(r0, 1 - cw) : "") + "Z"; + var rc, cr, rp, ap, p0 = 0, p1 = 0, x0, y0, x1, y1, x2, y2, x3, y3, path = []; + if (ap = (+padAngle.apply(this, arguments) || 0) / 2) { + rp = padRadius === d3_svg_arcAuto ? Math.sqrt(r0 * r0 + r1 * r1) : +padRadius.apply(this, arguments); + if (!cw) p1 *= -1; + if (r1) p1 = d3_asin(rp / r1 * Math.sin(ap)); + if (r0) p0 = d3_asin(rp / r0 * Math.sin(ap)); + } + if (r1) { + x0 = r1 * Math.cos(a0 + p1); + y0 = r1 * Math.sin(a0 + p1); + x1 = r1 * Math.cos(a1 - p1); + y1 = r1 * Math.sin(a1 - p1); + var l1 = Math.abs(a1 - a0 - 2 * p1) <= Ī€ ? 0 : 1; + if (p1 && d3_svg_arcSweep(x0, y0, x1, y1) === cw ^ l1) { + var h1 = (a0 + a1) / 2; + x0 = r1 * Math.cos(h1); + y0 = r1 * Math.sin(h1); + x1 = y1 = null; + } + } else { + x0 = y0 = 0; + } + if (r0) { + x2 = r0 * Math.cos(a1 - p0); + y2 = r0 * Math.sin(a1 - p0); + x3 = r0 * Math.cos(a0 + p0); + y3 = r0 * Math.sin(a0 + p0); + var l0 = Math.abs(a0 - a1 + 2 * p0) <= Ī€ ? 0 : 1; + if (p0 && d3_svg_arcSweep(x2, y2, x3, y3) === 1 - cw ^ l0) { + var h0 = (a0 + a1) / 2; + x2 = r0 * Math.cos(h0); + y2 = r0 * Math.sin(h0); + x3 = y3 = null; + } + } else { + x2 = y2 = 0; + } + if (da > Îĩ && (rc = Math.min(Math.abs(r1 - r0) / 2, +cornerRadius.apply(this, arguments))) > .001) { + cr = r0 < r1 ^ cw ? 0 : 1; + var rc1 = rc, rc0 = rc; + if (da < Ī€) { + var oc = x3 == null ? [ x2, y2 ] : x1 == null ? [ x0, y0 ] : d3_geom_polygonIntersect([ x0, y0 ], [ x3, y3 ], [ x1, y1 ], [ x2, y2 ]), ax = x0 - oc[0], ay = y0 - oc[1], bx = x1 - oc[0], by = y1 - oc[1], kc = 1 / Math.sin(Math.acos((ax * bx + ay * by) / (Math.sqrt(ax * ax + ay * ay) * Math.sqrt(bx * bx + by * by))) / 2), lc = Math.sqrt(oc[0] * oc[0] + oc[1] * oc[1]); + rc0 = Math.min(rc, (r0 - lc) / (kc - 1)); + rc1 = Math.min(rc, (r1 - lc) / (kc + 1)); + } + if (x1 != null) { + var t30 = d3_svg_arcCornerTangents(x3 == null ? [ x2, y2 ] : [ x3, y3 ], [ x0, y0 ], r1, rc1, cw), t12 = d3_svg_arcCornerTangents([ x1, y1 ], [ x2, y2 ], r1, rc1, cw); + if (rc === rc1) { + path.push("M", t30[0], "A", rc1, ",", rc1, " 0 0,", cr, " ", t30[1], "A", r1, ",", r1, " 0 ", 1 - cw ^ d3_svg_arcSweep(t30[1][0], t30[1][1], t12[1][0], t12[1][1]), ",", cw, " ", t12[1], "A", rc1, ",", rc1, " 0 0,", cr, " ", t12[0]); + } else { + path.push("M", t30[0], "A", rc1, ",", rc1, " 0 1,", cr, " ", t12[0]); + } + } else { + path.push("M", x0, ",", y0); + } + if (x3 != null) { + var t03 = d3_svg_arcCornerTangents([ x0, y0 ], [ x3, y3 ], r0, -rc0, cw), t21 = d3_svg_arcCornerTangents([ x2, y2 ], x1 == null ? [ x0, y0 ] : [ x1, y1 ], r0, -rc0, cw); + if (rc === rc0) { + path.push("L", t21[0], "A", rc0, ",", rc0, " 0 0,", cr, " ", t21[1], "A", r0, ",", r0, " 0 ", cw ^ d3_svg_arcSweep(t21[1][0], t21[1][1], t03[1][0], t03[1][1]), ",", 1 - cw, " ", t03[1], "A", rc0, ",", rc0, " 0 0,", cr, " ", t03[0]); + } else { + path.push("L", t21[0], "A", rc0, ",", rc0, " 0 0,", cr, " ", t03[0]); + } + } else { + path.push("L", x2, ",", y2); + } + } else { + path.push("M", x0, ",", y0); + if (x1 != null) path.push("A", r1, ",", r1, " 0 ", l1, ",", cw, " ", x1, ",", y1); + path.push("L", x2, ",", y2); + if (x3 != null) path.push("A", r0, ",", r0, " 0 ", l0, ",", 1 - cw, " ", x3, ",", y3); + } + path.push("Z"); + return path.join(""); + } + function circleSegment(r1, cw) { + return "M0," + r1 + "A" + r1 + "," + r1 + " 0 1," + cw + " 0," + -r1 + "A" + r1 + "," + r1 + " 0 1," + cw + " 0," + r1; + } + arc.innerRadius = function(v) { + if (!arguments.length) return innerRadius; + innerRadius = d3_functor(v); + return arc; + }; + arc.outerRadius = function(v) { + if (!arguments.length) return outerRadius; + outerRadius = d3_functor(v); + return arc; + }; + arc.cornerRadius = function(v) { + if (!arguments.length) return cornerRadius; + cornerRadius = d3_functor(v); + return arc; + }; + arc.padRadius = function(v) { + if (!arguments.length) return padRadius; + padRadius = v == d3_svg_arcAuto ? d3_svg_arcAuto : d3_functor(v); + return arc; + }; + arc.startAngle = function(v) { + if (!arguments.length) return startAngle; + startAngle = d3_functor(v); + return arc; + }; + arc.endAngle = function(v) { + if (!arguments.length) return endAngle; + endAngle = d3_functor(v); + return arc; + }; + arc.padAngle = function(v) { + if (!arguments.length) return padAngle; + padAngle = d3_functor(v); + return arc; + }; + arc.centroid = function() { + var r = (+innerRadius.apply(this, arguments) + +outerRadius.apply(this, arguments)) / 2, a = (+startAngle.apply(this, arguments) + +endAngle.apply(this, arguments)) / 2 - halfĪ€; + return [ Math.cos(a) * r, Math.sin(a) * r ]; + }; + return arc; + }; + var d3_svg_arcAuto = "auto"; + function d3_svg_arcInnerRadius(d) { + return d.innerRadius; + } + function d3_svg_arcOuterRadius(d) { + return d.outerRadius; + } + function d3_svg_arcStartAngle(d) { + return d.startAngle; + } + function d3_svg_arcEndAngle(d) { + return d.endAngle; + } + function d3_svg_arcPadAngle(d) { + return d && d.padAngle; + } + function d3_svg_arcSweep(x0, y0, x1, y1) { + return (x0 - x1) * y0 - (y0 - y1) * x0 > 0 ? 0 : 1; + } + function d3_svg_arcCornerTangents(p0, p1, r1, rc, cw) { + var x01 = p0[0] - p1[0], y01 = p0[1] - p1[1], lo = (cw ? rc : -rc) / Math.sqrt(x01 * x01 + y01 * y01), ox = lo * y01, oy = -lo * x01, x1 = p0[0] + ox, y1 = p0[1] + oy, x2 = p1[0] + ox, y2 = p1[1] + oy, x3 = (x1 + x2) / 2, y3 = (y1 + y2) / 2, dx = x2 - x1, dy = y2 - y1, d2 = dx * dx + dy * dy, r = r1 - rc, D = x1 * y2 - x2 * y1, d = (dy < 0 ? -1 : 1) * Math.sqrt(Math.max(0, r * r * d2 - D * D)), cx0 = (D * dy - dx * d) / d2, cy0 = (-D * dx - dy * d) / d2, cx1 = (D * dy + dx * d) / d2, cy1 = (-D * dx + dy * d) / d2, dx0 = cx0 - x3, dy0 = cy0 - y3, dx1 = cx1 - x3, dy1 = cy1 - y3; + if (dx0 * dx0 + dy0 * dy0 > dx1 * dx1 + dy1 * dy1) cx0 = cx1, cy0 = cy1; + return [ [ cx0 - ox, cy0 - oy ], [ cx0 * r1 / r, cy0 * r1 / r ] ]; + } + function d3_svg_line(projection) { + var x = d3_geom_pointX, y = d3_geom_pointY, defined = d3_true, interpolate = d3_svg_lineLinear, interpolateKey = interpolate.key, tension = .7; + function line(data) { + var segments = [], points = [], i = -1, n = data.length, d, fx = d3_functor(x), fy = d3_functor(y); + function segment() { + segments.push("M", interpolate(projection(points), tension)); + } + while (++i < n) { + if (defined.call(this, d = data[i], i)) { + points.push([ +fx.call(this, d, i), +fy.call(this, d, i) ]); + } else if (points.length) { + segment(); + points = []; + } + } + if (points.length) segment(); + return segments.length ? segments.join("") : null; + } + line.x = function(_) { + if (!arguments.length) return x; + x = _; + return line; + }; + line.y = function(_) { + if (!arguments.length) return y; + y = _; + return line; + }; + line.defined = function(_) { + if (!arguments.length) return defined; + defined = _; + return line; + }; + line.interpolate = function(_) { + if (!arguments.length) return interpolateKey; + if (typeof _ === "function") interpolateKey = interpolate = _; else interpolateKey = (interpolate = d3_svg_lineInterpolators.get(_) || d3_svg_lineLinear).key; + return line; + }; + line.tension = function(_) { + if (!arguments.length) return tension; + tension = _; + return line; + }; + return line; + } + d3.svg.line = function() { + return d3_svg_line(d3_identity); + }; + var d3_svg_lineInterpolators = d3.map({ + linear: d3_svg_lineLinear, + "linear-closed": d3_svg_lineLinearClosed, + step: d3_svg_lineStep, + "step-before": d3_svg_lineStepBefore, + "step-after": d3_svg_lineStepAfter, + basis: d3_svg_lineBasis, + "basis-open": d3_svg_lineBasisOpen, + "basis-closed": d3_svg_lineBasisClosed, + bundle: d3_svg_lineBundle, + cardinal: d3_svg_lineCardinal, + "cardinal-open": d3_svg_lineCardinalOpen, + "cardinal-closed": d3_svg_lineCardinalClosed, + monotone: d3_svg_lineMonotone + }); + d3_svg_lineInterpolators.forEach(function(key, value) { + value.key = key; + value.closed = /-closed$/.test(key); + }); + function d3_svg_lineLinear(points) { + return points.length > 1 ? points.join("L") : points + "Z"; + } + function d3_svg_lineLinearClosed(points) { + return points.join("L") + "Z"; + } + function d3_svg_lineStep(points) { + var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ]; + while (++i < n) path.push("H", (p[0] + (p = points[i])[0]) / 2, "V", p[1]); + if (n > 1) path.push("H", p[0]); + return path.join(""); + } + function d3_svg_lineStepBefore(points) { + var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ]; + while (++i < n) path.push("V", (p = points[i])[1], "H", p[0]); + return path.join(""); + } + function d3_svg_lineStepAfter(points) { + var i = 0, n = points.length, p = points[0], path = [ p[0], ",", p[1] ]; + while (++i < n) path.push("H", (p = points[i])[0], "V", p[1]); + return path.join(""); + } + function d3_svg_lineCardinalOpen(points, tension) { + return points.length < 4 ? d3_svg_lineLinear(points) : points[1] + d3_svg_lineHermite(points.slice(1, -1), d3_svg_lineCardinalTangents(points, tension)); + } + function d3_svg_lineCardinalClosed(points, tension) { + return points.length < 3 ? d3_svg_lineLinearClosed(points) : points[0] + d3_svg_lineHermite((points.push(points[0]), + points), d3_svg_lineCardinalTangents([ points[points.length - 2] ].concat(points, [ points[1] ]), tension)); + } + function d3_svg_lineCardinal(points, tension) { + return points.length < 3 ? d3_svg_lineLinear(points) : points[0] + d3_svg_lineHermite(points, d3_svg_lineCardinalTangents(points, tension)); + } + function d3_svg_lineHermite(points, tangents) { + if (tangents.length < 1 || points.length != tangents.length && points.length != tangents.length + 2) { + return d3_svg_lineLinear(points); + } + var quad = points.length != tangents.length, path = "", p0 = points[0], p = points[1], t0 = tangents[0], t = t0, pi = 1; + if (quad) { + path += "Q" + (p[0] - t0[0] * 2 / 3) + "," + (p[1] - t0[1] * 2 / 3) + "," + p[0] + "," + p[1]; + p0 = points[1]; + pi = 2; + } + if (tangents.length > 1) { + t = tangents[1]; + p = points[pi]; + pi++; + path += "C" + (p0[0] + t0[0]) + "," + (p0[1] + t0[1]) + "," + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," + p[0] + "," + p[1]; + for (var i = 2; i < tangents.length; i++, pi++) { + p = points[pi]; + t = tangents[i]; + path += "S" + (p[0] - t[0]) + "," + (p[1] - t[1]) + "," + p[0] + "," + p[1]; + } + } + if (quad) { + var lp = points[pi]; + path += "Q" + (p[0] + t[0] * 2 / 3) + "," + (p[1] + t[1] * 2 / 3) + "," + lp[0] + "," + lp[1]; + } + return path; + } + function d3_svg_lineCardinalTangents(points, tension) { + var tangents = [], a = (1 - tension) / 2, p0, p1 = points[0], p2 = points[1], i = 1, n = points.length; + while (++i < n) { + p0 = p1; + p1 = p2; + p2 = points[i]; + tangents.push([ a * (p2[0] - p0[0]), a * (p2[1] - p0[1]) ]); + } + return tangents; + } + function d3_svg_lineBasis(points) { + if (points.length < 3) return d3_svg_lineLinear(points); + var i = 1, n = points.length, pi = points[0], x0 = pi[0], y0 = pi[1], px = [ x0, x0, x0, (pi = points[1])[0] ], py = [ y0, y0, y0, pi[1] ], path = [ x0, ",", y0, "L", d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py) ]; + points.push(points[n - 1]); + while (++i <= n) { + pi = points[i]; + px.shift(); + px.push(pi[0]); + py.shift(); + py.push(pi[1]); + d3_svg_lineBasisBezier(path, px, py); + } + points.pop(); + path.push("L", pi); + return path.join(""); + } + function d3_svg_lineBasisOpen(points) { + if (points.length < 4) return d3_svg_lineLinear(points); + var path = [], i = -1, n = points.length, pi, px = [ 0 ], py = [ 0 ]; + while (++i < 3) { + pi = points[i]; + px.push(pi[0]); + py.push(pi[1]); + } + path.push(d3_svg_lineDot4(d3_svg_lineBasisBezier3, px) + "," + d3_svg_lineDot4(d3_svg_lineBasisBezier3, py)); + --i; + while (++i < n) { + pi = points[i]; + px.shift(); + px.push(pi[0]); + py.shift(); + py.push(pi[1]); + d3_svg_lineBasisBezier(path, px, py); + } + return path.join(""); + } + function d3_svg_lineBasisClosed(points) { + var path, i = -1, n = points.length, m = n + 4, pi, px = [], py = []; + while (++i < 4) { + pi = points[i % n]; + px.push(pi[0]); + py.push(pi[1]); + } + path = [ d3_svg_lineDot4(d3_svg_lineBasisBezier3, px), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, py) ]; + --i; + while (++i < m) { + pi = points[i % n]; + px.shift(); + px.push(pi[0]); + py.shift(); + py.push(pi[1]); + d3_svg_lineBasisBezier(path, px, py); + } + return path.join(""); + } + function d3_svg_lineBundle(points, tension) { + var n = points.length - 1; + if (n) { + var x0 = points[0][0], y0 = points[0][1], dx = points[n][0] - x0, dy = points[n][1] - y0, i = -1, p, t; + while (++i <= n) { + p = points[i]; + t = i / n; + p[0] = tension * p[0] + (1 - tension) * (x0 + t * dx); + p[1] = tension * p[1] + (1 - tension) * (y0 + t * dy); + } + } + return d3_svg_lineBasis(points); + } + function d3_svg_lineDot4(a, b) { + return a[0] * b[0] + a[1] * b[1] + a[2] * b[2] + a[3] * b[3]; + } + var d3_svg_lineBasisBezier1 = [ 0, 2 / 3, 1 / 3, 0 ], d3_svg_lineBasisBezier2 = [ 0, 1 / 3, 2 / 3, 0 ], d3_svg_lineBasisBezier3 = [ 0, 1 / 6, 2 / 3, 1 / 6 ]; + function d3_svg_lineBasisBezier(path, x, y) { + path.push("C", d3_svg_lineDot4(d3_svg_lineBasisBezier1, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier1, y), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier2, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier2, y), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, x), ",", d3_svg_lineDot4(d3_svg_lineBasisBezier3, y)); + } + function d3_svg_lineSlope(p0, p1) { + return (p1[1] - p0[1]) / (p1[0] - p0[0]); + } + function d3_svg_lineFiniteDifferences(points) { + var i = 0, j = points.length - 1, m = [], p0 = points[0], p1 = points[1], d = m[0] = d3_svg_lineSlope(p0, p1); + while (++i < j) { + m[i] = (d + (d = d3_svg_lineSlope(p0 = p1, p1 = points[i + 1]))) / 2; + } + m[i] = d; + return m; + } + function d3_svg_lineMonotoneTangents(points) { + var tangents = [], d, a, b, s, m = d3_svg_lineFiniteDifferences(points), i = -1, j = points.length - 1; + while (++i < j) { + d = d3_svg_lineSlope(points[i], points[i + 1]); + if (abs(d) < Îĩ) { + m[i] = m[i + 1] = 0; + } else { + a = m[i] / d; + b = m[i + 1] / d; + s = a * a + b * b; + if (s > 9) { + s = d * 3 / Math.sqrt(s); + m[i] = s * a; + m[i + 1] = s * b; + } + } + } + i = -1; + while (++i <= j) { + s = (points[Math.min(j, i + 1)][0] - points[Math.max(0, i - 1)][0]) / (6 * (1 + m[i] * m[i])); + tangents.push([ s || 0, m[i] * s || 0 ]); + } + return tangents; + } + function d3_svg_lineMonotone(points) { + return points.length < 3 ? d3_svg_lineLinear(points) : points[0] + d3_svg_lineHermite(points, d3_svg_lineMonotoneTangents(points)); + } + d3.svg.line.radial = function() { + var line = d3_svg_line(d3_svg_lineRadial); + line.radius = line.x, delete line.x; + line.angle = line.y, delete line.y; + return line; + }; + function d3_svg_lineRadial(points) { + var point, i = -1, n = points.length, r, a; + while (++i < n) { + point = points[i]; + r = point[0]; + a = point[1] - halfĪ€; + point[0] = r * Math.cos(a); + point[1] = r * Math.sin(a); + } + return points; + } + function d3_svg_area(projection) { + var x0 = d3_geom_pointX, x1 = d3_geom_pointX, y0 = 0, y1 = d3_geom_pointY, defined = d3_true, interpolate = d3_svg_lineLinear, interpolateKey = interpolate.key, interpolateReverse = interpolate, L = "L", tension = .7; + function area(data) { + var segments = [], points0 = [], points1 = [], i = -1, n = data.length, d, fx0 = d3_functor(x0), fy0 = d3_functor(y0), fx1 = x0 === x1 ? function() { + return x; + } : d3_functor(x1), fy1 = y0 === y1 ? function() { + return y; + } : d3_functor(y1), x, y; + function segment() { + segments.push("M", interpolate(projection(points1), tension), L, interpolateReverse(projection(points0.reverse()), tension), "Z"); + } + while (++i < n) { + if (defined.call(this, d = data[i], i)) { + points0.push([ x = +fx0.call(this, d, i), y = +fy0.call(this, d, i) ]); + points1.push([ +fx1.call(this, d, i), +fy1.call(this, d, i) ]); + } else if (points0.length) { + segment(); + points0 = []; + points1 = []; + } + } + if (points0.length) segment(); + return segments.length ? segments.join("") : null; + } + area.x = function(_) { + if (!arguments.length) return x1; + x0 = x1 = _; + return area; + }; + area.x0 = function(_) { + if (!arguments.length) return x0; + x0 = _; + return area; + }; + area.x1 = function(_) { + if (!arguments.length) return x1; + x1 = _; + return area; + }; + area.y = function(_) { + if (!arguments.length) return y1; + y0 = y1 = _; + return area; + }; + area.y0 = function(_) { + if (!arguments.length) return y0; + y0 = _; + return area; + }; + area.y1 = function(_) { + if (!arguments.length) return y1; + y1 = _; + return area; + }; + area.defined = function(_) { + if (!arguments.length) return defined; + defined = _; + return area; + }; + area.interpolate = function(_) { + if (!arguments.length) return interpolateKey; + if (typeof _ === "function") interpolateKey = interpolate = _; else interpolateKey = (interpolate = d3_svg_lineInterpolators.get(_) || d3_svg_lineLinear).key; + interpolateReverse = interpolate.reverse || interpolate; + L = interpolate.closed ? "M" : "L"; + return area; + }; + area.tension = function(_) { + if (!arguments.length) return tension; + tension = _; + return area; + }; + return area; + } + d3_svg_lineStepBefore.reverse = d3_svg_lineStepAfter; + d3_svg_lineStepAfter.reverse = d3_svg_lineStepBefore; + d3.svg.area = function() { + return d3_svg_area(d3_identity); + }; + d3.svg.area.radial = function() { + var area = d3_svg_area(d3_svg_lineRadial); + area.radius = area.x, delete area.x; + area.innerRadius = area.x0, delete area.x0; + area.outerRadius = area.x1, delete area.x1; + area.angle = area.y, delete area.y; + area.startAngle = area.y0, delete area.y0; + area.endAngle = area.y1, delete area.y1; + return area; + }; + d3.svg.chord = function() { + var source = d3_source, target = d3_target, radius = d3_svg_chordRadius, startAngle = d3_svg_arcStartAngle, endAngle = d3_svg_arcEndAngle; + function chord(d, i) { + var s = subgroup(this, source, d, i), t = subgroup(this, target, d, i); + return "M" + s.p0 + arc(s.r, s.p1, s.a1 - s.a0) + (equals(s, t) ? curve(s.r, s.p1, s.r, s.p0) : curve(s.r, s.p1, t.r, t.p0) + arc(t.r, t.p1, t.a1 - t.a0) + curve(t.r, t.p1, s.r, s.p0)) + "Z"; + } + function subgroup(self, f, d, i) { + var subgroup = f.call(self, d, i), r = radius.call(self, subgroup, i), a0 = startAngle.call(self, subgroup, i) - halfĪ€, a1 = endAngle.call(self, subgroup, i) - halfĪ€; + return { + r: r, + a0: a0, + a1: a1, + p0: [ r * Math.cos(a0), r * Math.sin(a0) ], + p1: [ r * Math.cos(a1), r * Math.sin(a1) ] + }; + } + function equals(a, b) { + return a.a0 == b.a0 && a.a1 == b.a1; + } + function arc(r, p, a) { + return "A" + r + "," + r + " 0 " + +(a > Ī€) + ",1 " + p; + } + function curve(r0, p0, r1, p1) { + return "Q 0,0 " + p1; + } + chord.radius = function(v) { + if (!arguments.length) return radius; + radius = d3_functor(v); + return chord; + }; + chord.source = function(v) { + if (!arguments.length) return source; + source = d3_functor(v); + return chord; + }; + chord.target = function(v) { + if (!arguments.length) return target; + target = d3_functor(v); + return chord; + }; + chord.startAngle = function(v) { + if (!arguments.length) return startAngle; + startAngle = d3_functor(v); + return chord; + }; + chord.endAngle = function(v) { + if (!arguments.length) return endAngle; + endAngle = d3_functor(v); + return chord; + }; + return chord; + }; + function d3_svg_chordRadius(d) { + return d.radius; + } + d3.svg.diagonal = function() { + var source = d3_source, target = d3_target, projection = d3_svg_diagonalProjection; + function diagonal(d, i) { + var p0 = source.call(this, d, i), p3 = target.call(this, d, i), m = (p0.y + p3.y) / 2, p = [ p0, { + x: p0.x, + y: m + }, { + x: p3.x, + y: m + }, p3 ]; + p = p.map(projection); + return "M" + p[0] + "C" + p[1] + " " + p[2] + " " + p[3]; + } + diagonal.source = function(x) { + if (!arguments.length) return source; + source = d3_functor(x); + return diagonal; + }; + diagonal.target = function(x) { + if (!arguments.length) return target; + target = d3_functor(x); + return diagonal; + }; + diagonal.projection = function(x) { + if (!arguments.length) return projection; + projection = x; + return diagonal; + }; + return diagonal; + }; + function d3_svg_diagonalProjection(d) { + return [ d.x, d.y ]; + } + d3.svg.diagonal.radial = function() { + var diagonal = d3.svg.diagonal(), projection = d3_svg_diagonalProjection, projection_ = diagonal.projection; + diagonal.projection = function(x) { + return arguments.length ? projection_(d3_svg_diagonalRadialProjection(projection = x)) : projection; + }; + return diagonal; + }; + function d3_svg_diagonalRadialProjection(projection) { + return function() { + var d = projection.apply(this, arguments), r = d[0], a = d[1] - halfĪ€; + return [ r * Math.cos(a), r * Math.sin(a) ]; + }; + } + d3.svg.symbol = function() { + var type = d3_svg_symbolType, size = d3_svg_symbolSize; + function symbol(d, i) { + return (d3_svg_symbols.get(type.call(this, d, i)) || d3_svg_symbolCircle)(size.call(this, d, i)); + } + symbol.type = function(x) { + if (!arguments.length) return type; + type = d3_functor(x); + return symbol; + }; + symbol.size = function(x) { + if (!arguments.length) return size; + size = d3_functor(x); + return symbol; + }; + return symbol; + }; + function d3_svg_symbolSize() { + return 64; + } + function d3_svg_symbolType() { + return "circle"; + } + function d3_svg_symbolCircle(size) { + var r = Math.sqrt(size / Ī€); + return "M0," + r + "A" + r + "," + r + " 0 1,1 0," + -r + "A" + r + "," + r + " 0 1,1 0," + r + "Z"; + } + var d3_svg_symbols = d3.map({ + circle: d3_svg_symbolCircle, + cross: function(size) { + var r = Math.sqrt(size / 5) / 2; + return "M" + -3 * r + "," + -r + "H" + -r + "V" + -3 * r + "H" + r + "V" + -r + "H" + 3 * r + "V" + r + "H" + r + "V" + 3 * r + "H" + -r + "V" + r + "H" + -3 * r + "Z"; + }, + diamond: function(size) { + var ry = Math.sqrt(size / (2 * d3_svg_symbolTan30)), rx = ry * d3_svg_symbolTan30; + return "M0," + -ry + "L" + rx + ",0" + " 0," + ry + " " + -rx + ",0" + "Z"; + }, + square: function(size) { + var r = Math.sqrt(size) / 2; + return "M" + -r + "," + -r + "L" + r + "," + -r + " " + r + "," + r + " " + -r + "," + r + "Z"; + }, + "triangle-down": function(size) { + var rx = Math.sqrt(size / d3_svg_symbolSqrt3), ry = rx * d3_svg_symbolSqrt3 / 2; + return "M0," + ry + "L" + rx + "," + -ry + " " + -rx + "," + -ry + "Z"; + }, + "triangle-up": function(size) { + var rx = Math.sqrt(size / d3_svg_symbolSqrt3), ry = rx * d3_svg_symbolSqrt3 / 2; + return "M0," + -ry + "L" + rx + "," + ry + " " + -rx + "," + ry + "Z"; + } + }); + d3.svg.symbolTypes = d3_svg_symbols.keys(); + var d3_svg_symbolSqrt3 = Math.sqrt(3), d3_svg_symbolTan30 = Math.tan(30 * d3_radians); + d3_selectionPrototype.transition = function(name) { + var id = d3_transitionInheritId || ++d3_transitionId, ns = d3_transitionNamespace(name), subgroups = [], subgroup, node, transition = d3_transitionInherit || { + time: Date.now(), + ease: d3_ease_cubicInOut, + delay: 0, + duration: 250 + }; + for (var j = -1, m = this.length; ++j < m; ) { + subgroups.push(subgroup = []); + for (var group = this[j], i = -1, n = group.length; ++i < n; ) { + if (node = group[i]) d3_transitionNode(node, i, ns, id, transition); + subgroup.push(node); + } + } + return d3_transition(subgroups, ns, id); + }; + d3_selectionPrototype.interrupt = function(name) { + return this.each(name == null ? d3_selection_interrupt : d3_selection_interruptNS(d3_transitionNamespace(name))); + }; + var d3_selection_interrupt = d3_selection_interruptNS(d3_transitionNamespace()); + function d3_selection_interruptNS(ns) { + return function() { + var lock, activeId, active; + if ((lock = this[ns]) && (active = lock[activeId = lock.active])) { + active.timer.c = null; + active.timer.t = NaN; + if (--lock.count) delete lock[activeId]; else delete this[ns]; + lock.active += .5; + active.event && active.event.interrupt.call(this, this.__data__, active.index); + } + }; + } + function d3_transition(groups, ns, id) { + d3_subclass(groups, d3_transitionPrototype); + groups.namespace = ns; + groups.id = id; + return groups; + } + var d3_transitionPrototype = [], d3_transitionId = 0, d3_transitionInheritId, d3_transitionInherit; + d3_transitionPrototype.call = d3_selectionPrototype.call; + d3_transitionPrototype.empty = d3_selectionPrototype.empty; + d3_transitionPrototype.node = d3_selectionPrototype.node; + d3_transitionPrototype.size = d3_selectionPrototype.size; + d3.transition = function(selection, name) { + return selection && selection.transition ? d3_transitionInheritId ? selection.transition(name) : selection : d3.selection().transition(selection); + }; + d3.transition.prototype = d3_transitionPrototype; + d3_transitionPrototype.select = function(selector) { + var id = this.id, ns = this.namespace, subgroups = [], subgroup, subnode, node; + selector = d3_selection_selector(selector); + for (var j = -1, m = this.length; ++j < m; ) { + subgroups.push(subgroup = []); + for (var group = this[j], i = -1, n = group.length; ++i < n; ) { + if ((node = group[i]) && (subnode = selector.call(node, node.__data__, i, j))) { + if ("__data__" in node) subnode.__data__ = node.__data__; + d3_transitionNode(subnode, i, ns, id, node[ns][id]); + subgroup.push(subnode); + } else { + subgroup.push(null); + } + } + } + return d3_transition(subgroups, ns, id); + }; + d3_transitionPrototype.selectAll = function(selector) { + var id = this.id, ns = this.namespace, subgroups = [], subgroup, subnodes, node, subnode, transition; + selector = d3_selection_selectorAll(selector); + for (var j = -1, m = this.length; ++j < m; ) { + for (var group = this[j], i = -1, n = group.length; ++i < n; ) { + if (node = group[i]) { + transition = node[ns][id]; + subnodes = selector.call(node, node.__data__, i, j); + subgroups.push(subgroup = []); + for (var k = -1, o = subnodes.length; ++k < o; ) { + if (subnode = subnodes[k]) d3_transitionNode(subnode, k, ns, id, transition); + subgroup.push(subnode); + } + } + } + } + return d3_transition(subgroups, ns, id); + }; + d3_transitionPrototype.filter = function(filter) { + var subgroups = [], subgroup, group, node; + if (typeof filter !== "function") filter = d3_selection_filter(filter); + for (var j = 0, m = this.length; j < m; j++) { + subgroups.push(subgroup = []); + for (var group = this[j], i = 0, n = group.length; i < n; i++) { + if ((node = group[i]) && filter.call(node, node.__data__, i, j)) { + subgroup.push(node); + } + } + } + return d3_transition(subgroups, this.namespace, this.id); + }; + d3_transitionPrototype.tween = function(name, tween) { + var id = this.id, ns = this.namespace; + if (arguments.length < 2) return this.node()[ns][id].tween.get(name); + return d3_selection_each(this, tween == null ? function(node) { + node[ns][id].tween.remove(name); + } : function(node) { + node[ns][id].tween.set(name, tween); + }); + }; + function d3_transition_tween(groups, name, value, tween) { + var id = groups.id, ns = groups.namespace; + return d3_selection_each(groups, typeof value === "function" ? function(node, i, j) { + node[ns][id].tween.set(name, tween(value.call(node, node.__data__, i, j))); + } : (value = tween(value), function(node) { + node[ns][id].tween.set(name, value); + })); + } + d3_transitionPrototype.attr = function(nameNS, value) { + if (arguments.length < 2) { + for (value in nameNS) this.attr(value, nameNS[value]); + return this; + } + var interpolate = nameNS == "transform" ? d3_interpolateTransform : d3_interpolate, name = d3.ns.qualify(nameNS); + function attrNull() { + this.removeAttribute(name); + } + function attrNullNS() { + this.removeAttributeNS(name.space, name.local); + } + function attrTween(b) { + return b == null ? attrNull : (b += "", function() { + var a = this.getAttribute(name), i; + return a !== b && (i = interpolate(a, b), function(t) { + this.setAttribute(name, i(t)); + }); + }); + } + function attrTweenNS(b) { + return b == null ? attrNullNS : (b += "", function() { + var a = this.getAttributeNS(name.space, name.local), i; + return a !== b && (i = interpolate(a, b), function(t) { + this.setAttributeNS(name.space, name.local, i(t)); + }); + }); + } + return d3_transition_tween(this, "attr." + nameNS, value, name.local ? attrTweenNS : attrTween); + }; + d3_transitionPrototype.attrTween = function(nameNS, tween) { + var name = d3.ns.qualify(nameNS); + function attrTween(d, i) { + var f = tween.call(this, d, i, this.getAttribute(name)); + return f && function(t) { + this.setAttribute(name, f(t)); + }; + } + function attrTweenNS(d, i) { + var f = tween.call(this, d, i, this.getAttributeNS(name.space, name.local)); + return f && function(t) { + this.setAttributeNS(name.space, name.local, f(t)); + }; + } + return this.tween("attr." + nameNS, name.local ? attrTweenNS : attrTween); + }; + d3_transitionPrototype.style = function(name, value, priority) { + var n = arguments.length; + if (n < 3) { + if (typeof name !== "string") { + if (n < 2) value = ""; + for (priority in name) this.style(priority, name[priority], value); + return this; + } + priority = ""; + } + function styleNull() { + this.style.removeProperty(name); + } + function styleString(b) { + return b == null ? styleNull : (b += "", function() { + var a = d3_window(this).getComputedStyle(this, null).getPropertyValue(name), i; + return a !== b && (i = d3_interpolate(a, b), function(t) { + this.style.setProperty(name, i(t), priority); + }); + }); + } + return d3_transition_tween(this, "style." + name, value, styleString); + }; + d3_transitionPrototype.styleTween = function(name, tween, priority) { + if (arguments.length < 3) priority = ""; + function styleTween(d, i) { + var f = tween.call(this, d, i, d3_window(this).getComputedStyle(this, null).getPropertyValue(name)); + return f && function(t) { + this.style.setProperty(name, f(t), priority); + }; + } + return this.tween("style." + name, styleTween); + }; + d3_transitionPrototype.text = function(value) { + return d3_transition_tween(this, "text", value, d3_transition_text); + }; + function d3_transition_text(b) { + if (b == null) b = ""; + return function() { + this.textContent = b; + }; + } + d3_transitionPrototype.remove = function() { + var ns = this.namespace; + return this.each("end.transition", function() { + var p; + if (this[ns].count < 2 && (p = this.parentNode)) p.removeChild(this); + }); + }; + d3_transitionPrototype.ease = function(value) { + var id = this.id, ns = this.namespace; + if (arguments.length < 1) return this.node()[ns][id].ease; + if (typeof value !== "function") value = d3.ease.apply(d3, arguments); + return d3_selection_each(this, function(node) { + node[ns][id].ease = value; + }); + }; + d3_transitionPrototype.delay = function(value) { + var id = this.id, ns = this.namespace; + if (arguments.length < 1) return this.node()[ns][id].delay; + return d3_selection_each(this, typeof value === "function" ? function(node, i, j) { + node[ns][id].delay = +value.call(node, node.__data__, i, j); + } : (value = +value, function(node) { + node[ns][id].delay = value; + })); + }; + d3_transitionPrototype.duration = function(value) { + var id = this.id, ns = this.namespace; + if (arguments.length < 1) return this.node()[ns][id].duration; + return d3_selection_each(this, typeof value === "function" ? function(node, i, j) { + node[ns][id].duration = Math.max(1, value.call(node, node.__data__, i, j)); + } : (value = Math.max(1, value), function(node) { + node[ns][id].duration = value; + })); + }; + d3_transitionPrototype.each = function(type, listener) { + var id = this.id, ns = this.namespace; + if (arguments.length < 2) { + var inherit = d3_transitionInherit, inheritId = d3_transitionInheritId; + try { + d3_transitionInheritId = id; + d3_selection_each(this, function(node, i, j) { + d3_transitionInherit = node[ns][id]; + type.call(node, node.__data__, i, j); + }); + } finally { + d3_transitionInherit = inherit; + d3_transitionInheritId = inheritId; + } + } else { + d3_selection_each(this, function(node) { + var transition = node[ns][id]; + (transition.event || (transition.event = d3.dispatch("start", "end", "interrupt"))).on(type, listener); + }); + } + return this; + }; + d3_transitionPrototype.transition = function() { + var id0 = this.id, id1 = ++d3_transitionId, ns = this.namespace, subgroups = [], subgroup, group, node, transition; + for (var j = 0, m = this.length; j < m; j++) { + subgroups.push(subgroup = []); + for (var group = this[j], i = 0, n = group.length; i < n; i++) { + if (node = group[i]) { + transition = node[ns][id0]; + d3_transitionNode(node, i, ns, id1, { + time: transition.time, + ease: transition.ease, + delay: transition.delay + transition.duration, + duration: transition.duration + }); + } + subgroup.push(node); + } + } + return d3_transition(subgroups, ns, id1); + }; + function d3_transitionNamespace(name) { + return name == null ? "__transition__" : "__transition_" + name + "__"; + } + function d3_transitionNode(node, i, ns, id, inherit) { + var lock = node[ns] || (node[ns] = { + active: 0, + count: 0 + }), transition = lock[id], time, timer, duration, ease, tweens; + function schedule(elapsed) { + var delay = transition.delay; + timer.t = delay + time; + if (delay <= elapsed) return start(elapsed - delay); + timer.c = start; + } + function start(elapsed) { + var activeId = lock.active, active = lock[activeId]; + if (active) { + active.timer.c = null; + active.timer.t = NaN; + --lock.count; + delete lock[activeId]; + active.event && active.event.interrupt.call(node, node.__data__, active.index); + } + for (var cancelId in lock) { + if (+cancelId < id) { + var cancel = lock[cancelId]; + cancel.timer.c = null; + cancel.timer.t = NaN; + --lock.count; + delete lock[cancelId]; + } + } + timer.c = tick; + d3_timer(function() { + if (timer.c && tick(elapsed || 1)) { + timer.c = null; + timer.t = NaN; + } + return 1; + }, 0, time); + lock.active = id; + transition.event && transition.event.start.call(node, node.__data__, i); + tweens = []; + transition.tween.forEach(function(key, value) { + if (value = value.call(node, node.__data__, i)) { + tweens.push(value); + } + }); + ease = transition.ease; + duration = transition.duration; + } + function tick(elapsed) { + var t = elapsed / duration, e = ease(t), n = tweens.length; + while (n > 0) { + tweens[--n].call(node, e); + } + if (t >= 1) { + transition.event && transition.event.end.call(node, node.__data__, i); + if (--lock.count) delete lock[id]; else delete node[ns]; + return 1; + } + } + if (!transition) { + time = inherit.time; + timer = d3_timer(schedule, 0, time); + transition = lock[id] = { + tween: new d3_Map(), + time: time, + timer: timer, + delay: inherit.delay, + duration: inherit.duration, + ease: inherit.ease, + index: i + }; + inherit = null; + ++lock.count; + } + } + d3.svg.axis = function() { + var scale = d3.scale.linear(), orient = d3_svg_axisDefaultOrient, innerTickSize = 6, outerTickSize = 6, tickPadding = 3, tickArguments_ = [ 10 ], tickValues = null, tickFormat_; + function axis(g) { + g.each(function() { + var g = d3.select(this); + var scale0 = this.__chart__ || scale, scale1 = this.__chart__ = scale.copy(); + var ticks = tickValues == null ? scale1.ticks ? scale1.ticks.apply(scale1, tickArguments_) : scale1.domain() : tickValues, tickFormat = tickFormat_ == null ? scale1.tickFormat ? scale1.tickFormat.apply(scale1, tickArguments_) : d3_identity : tickFormat_, tick = g.selectAll(".tick").data(ticks, scale1), tickEnter = tick.enter().insert("g", ".domain").attr("class", "tick").style("opacity", Îĩ), tickExit = d3.transition(tick.exit()).style("opacity", Îĩ).remove(), tickUpdate = d3.transition(tick.order()).style("opacity", 1), tickSpacing = Math.max(innerTickSize, 0) + tickPadding, tickTransform; + var range = d3_scaleRange(scale1), path = g.selectAll(".domain").data([ 0 ]), pathUpdate = (path.enter().append("path").attr("class", "domain"), + d3.transition(path)); + tickEnter.append("line"); + tickEnter.append("text"); + var lineEnter = tickEnter.select("line"), lineUpdate = tickUpdate.select("line"), text = tick.select("text").text(tickFormat), textEnter = tickEnter.select("text"), textUpdate = tickUpdate.select("text"), sign = orient === "top" || orient === "left" ? -1 : 1, x1, x2, y1, y2; + if (orient === "bottom" || orient === "top") { + tickTransform = d3_svg_axisX, x1 = "x", y1 = "y", x2 = "x2", y2 = "y2"; + text.attr("dy", sign < 0 ? "0em" : ".71em").style("text-anchor", "middle"); + pathUpdate.attr("d", "M" + range[0] + "," + sign * outerTickSize + "V0H" + range[1] + "V" + sign * outerTickSize); + } else { + tickTransform = d3_svg_axisY, x1 = "y", y1 = "x", x2 = "y2", y2 = "x2"; + text.attr("dy", ".32em").style("text-anchor", sign < 0 ? "end" : "start"); + pathUpdate.attr("d", "M" + sign * outerTickSize + "," + range[0] + "H0V" + range[1] + "H" + sign * outerTickSize); + } + lineEnter.attr(y2, sign * innerTickSize); + textEnter.attr(y1, sign * tickSpacing); + lineUpdate.attr(x2, 0).attr(y2, sign * innerTickSize); + textUpdate.attr(x1, 0).attr(y1, sign * tickSpacing); + if (scale1.rangeBand) { + var x = scale1, dx = x.rangeBand() / 2; + scale0 = scale1 = function(d) { + return x(d) + dx; + }; + } else if (scale0.rangeBand) { + scale0 = scale1; + } else { + tickExit.call(tickTransform, scale1, scale0); + } + tickEnter.call(tickTransform, scale0, scale1); + tickUpdate.call(tickTransform, scale1, scale1); + }); + } + axis.scale = function(x) { + if (!arguments.length) return scale; + scale = x; + return axis; + }; + axis.orient = function(x) { + if (!arguments.length) return orient; + orient = x in d3_svg_axisOrients ? x + "" : d3_svg_axisDefaultOrient; + return axis; + }; + axis.ticks = function() { + if (!arguments.length) return tickArguments_; + tickArguments_ = d3_array(arguments); + return axis; + }; + axis.tickValues = function(x) { + if (!arguments.length) return tickValues; + tickValues = x; + return axis; + }; + axis.tickFormat = function(x) { + if (!arguments.length) return tickFormat_; + tickFormat_ = x; + return axis; + }; + axis.tickSize = function(x) { + var n = arguments.length; + if (!n) return innerTickSize; + innerTickSize = +x; + outerTickSize = +arguments[n - 1]; + return axis; + }; + axis.innerTickSize = function(x) { + if (!arguments.length) return innerTickSize; + innerTickSize = +x; + return axis; + }; + axis.outerTickSize = function(x) { + if (!arguments.length) return outerTickSize; + outerTickSize = +x; + return axis; + }; + axis.tickPadding = function(x) { + if (!arguments.length) return tickPadding; + tickPadding = +x; + return axis; + }; + axis.tickSubdivide = function() { + return arguments.length && axis; + }; + return axis; + }; + var d3_svg_axisDefaultOrient = "bottom", d3_svg_axisOrients = { + top: 1, + right: 1, + bottom: 1, + left: 1 + }; + function d3_svg_axisX(selection, x0, x1) { + selection.attr("transform", function(d) { + var v0 = x0(d); + return "translate(" + (isFinite(v0) ? v0 : x1(d)) + ",0)"; + }); + } + function d3_svg_axisY(selection, y0, y1) { + selection.attr("transform", function(d) { + var v0 = y0(d); + return "translate(0," + (isFinite(v0) ? v0 : y1(d)) + ")"; + }); + } + d3.svg.brush = function() { + var event = d3_eventDispatch(brush, "brushstart", "brush", "brushend"), x = null, y = null, xExtent = [ 0, 0 ], yExtent = [ 0, 0 ], xExtentDomain, yExtentDomain, xClamp = true, yClamp = true, resizes = d3_svg_brushResizes[0]; + function brush(g) { + g.each(function() { + var g = d3.select(this).style("pointer-events", "all").style("-webkit-tap-highlight-color", "rgba(0,0,0,0)").on("mousedown.brush", brushstart).on("touchstart.brush", brushstart); + var background = g.selectAll(".background").data([ 0 ]); + background.enter().append("rect").attr("class", "background").style("visibility", "hidden").style("cursor", "crosshair"); + g.selectAll(".extent").data([ 0 ]).enter().append("rect").attr("class", "extent").style("cursor", "move"); + var resize = g.selectAll(".resize").data(resizes, d3_identity); + resize.exit().remove(); + resize.enter().append("g").attr("class", function(d) { + return "resize " + d; + }).style("cursor", function(d) { + return d3_svg_brushCursor[d]; + }).append("rect").attr("x", function(d) { + return /[ew]$/.test(d) ? -3 : null; + }).attr("y", function(d) { + return /^[ns]/.test(d) ? -3 : null; + }).attr("width", 6).attr("height", 6).style("visibility", "hidden"); + resize.style("display", brush.empty() ? "none" : null); + var gUpdate = d3.transition(g), backgroundUpdate = d3.transition(background), range; + if (x) { + range = d3_scaleRange(x); + backgroundUpdate.attr("x", range[0]).attr("width", range[1] - range[0]); + redrawX(gUpdate); + } + if (y) { + range = d3_scaleRange(y); + backgroundUpdate.attr("y", range[0]).attr("height", range[1] - range[0]); + redrawY(gUpdate); + } + redraw(gUpdate); + }); + } + brush.event = function(g) { + g.each(function() { + var event_ = event.of(this, arguments), extent1 = { + x: xExtent, + y: yExtent, + i: xExtentDomain, + j: yExtentDomain + }, extent0 = this.__chart__ || extent1; + this.__chart__ = extent1; + if (d3_transitionInheritId) { + d3.select(this).transition().each("start.brush", function() { + xExtentDomain = extent0.i; + yExtentDomain = extent0.j; + xExtent = extent0.x; + yExtent = extent0.y; + event_({ + type: "brushstart" + }); + }).tween("brush:brush", function() { + var xi = d3_interpolateArray(xExtent, extent1.x), yi = d3_interpolateArray(yExtent, extent1.y); + xExtentDomain = yExtentDomain = null; + return function(t) { + xExtent = extent1.x = xi(t); + yExtent = extent1.y = yi(t); + event_({ + type: "brush", + mode: "resize" + }); + }; + }).each("end.brush", function() { + xExtentDomain = extent1.i; + yExtentDomain = extent1.j; + event_({ + type: "brush", + mode: "resize" + }); + event_({ + type: "brushend" + }); + }); + } else { + event_({ + type: "brushstart" + }); + event_({ + type: "brush", + mode: "resize" + }); + event_({ + type: "brushend" + }); + } + }); + }; + function redraw(g) { + g.selectAll(".resize").attr("transform", function(d) { + return "translate(" + xExtent[+/e$/.test(d)] + "," + yExtent[+/^s/.test(d)] + ")"; + }); + } + function redrawX(g) { + g.select(".extent").attr("x", xExtent[0]); + g.selectAll(".extent,.n>rect,.s>rect").attr("width", xExtent[1] - xExtent[0]); + } + function redrawY(g) { + g.select(".extent").attr("y", yExtent[0]); + g.selectAll(".extent,.e>rect,.w>rect").attr("height", yExtent[1] - yExtent[0]); + } + function brushstart() { + var target = this, eventTarget = d3.select(d3.event.target), event_ = event.of(target, arguments), g = d3.select(target), resizing = eventTarget.datum(), resizingX = !/^(n|s)$/.test(resizing) && x, resizingY = !/^(e|w)$/.test(resizing) && y, dragging = eventTarget.classed("extent"), dragRestore = d3_event_dragSuppress(target), center, origin = d3.mouse(target), offset; + var w = d3.select(d3_window(target)).on("keydown.brush", keydown).on("keyup.brush", keyup); + if (d3.event.changedTouches) { + w.on("touchmove.brush", brushmove).on("touchend.brush", brushend); + } else { + w.on("mousemove.brush", brushmove).on("mouseup.brush", brushend); + } + g.interrupt().selectAll("*").interrupt(); + if (dragging) { + origin[0] = xExtent[0] - origin[0]; + origin[1] = yExtent[0] - origin[1]; + } else if (resizing) { + var ex = +/w$/.test(resizing), ey = +/^n/.test(resizing); + offset = [ xExtent[1 - ex] - origin[0], yExtent[1 - ey] - origin[1] ]; + origin[0] = xExtent[ex]; + origin[1] = yExtent[ey]; + } else if (d3.event.altKey) center = origin.slice(); + g.style("pointer-events", "none").selectAll(".resize").style("display", null); + d3.select("body").style("cursor", eventTarget.style("cursor")); + event_({ + type: "brushstart" + }); + brushmove(); + function keydown() { + if (d3.event.keyCode == 32) { + if (!dragging) { + center = null; + origin[0] -= xExtent[1]; + origin[1] -= yExtent[1]; + dragging = 2; + } + d3_eventPreventDefault(); + } + } + function keyup() { + if (d3.event.keyCode == 32 && dragging == 2) { + origin[0] += xExtent[1]; + origin[1] += yExtent[1]; + dragging = 0; + d3_eventPreventDefault(); + } + } + function brushmove() { + var point = d3.mouse(target), moved = false; + if (offset) { + point[0] += offset[0]; + point[1] += offset[1]; + } + if (!dragging) { + if (d3.event.altKey) { + if (!center) center = [ (xExtent[0] + xExtent[1]) / 2, (yExtent[0] + yExtent[1]) / 2 ]; + origin[0] = xExtent[+(point[0] < center[0])]; + origin[1] = yExtent[+(point[1] < center[1])]; + } else center = null; + } + if (resizingX && move1(point, x, 0)) { + redrawX(g); + moved = true; + } + if (resizingY && move1(point, y, 1)) { + redrawY(g); + moved = true; + } + if (moved) { + redraw(g); + event_({ + type: "brush", + mode: dragging ? "move" : "resize" + }); + } + } + function move1(point, scale, i) { + var range = d3_scaleRange(scale), r0 = range[0], r1 = range[1], position = origin[i], extent = i ? yExtent : xExtent, size = extent[1] - extent[0], min, max; + if (dragging) { + r0 -= position; + r1 -= size + position; + } + min = (i ? yClamp : xClamp) ? Math.max(r0, Math.min(r1, point[i])) : point[i]; + if (dragging) { + max = (min += position) + size; + } else { + if (center) position = Math.max(r0, Math.min(r1, 2 * center[i] - min)); + if (position < min) { + max = min; + min = position; + } else { + max = position; + } + } + if (extent[0] != min || extent[1] != max) { + if (i) yExtentDomain = null; else xExtentDomain = null; + extent[0] = min; + extent[1] = max; + return true; + } + } + function brushend() { + brushmove(); + g.style("pointer-events", "all").selectAll(".resize").style("display", brush.empty() ? "none" : null); + d3.select("body").style("cursor", null); + w.on("mousemove.brush", null).on("mouseup.brush", null).on("touchmove.brush", null).on("touchend.brush", null).on("keydown.brush", null).on("keyup.brush", null); + dragRestore(); + event_({ + type: "brushend" + }); + } + } + brush.x = function(z) { + if (!arguments.length) return x; + x = z; + resizes = d3_svg_brushResizes[!x << 1 | !y]; + return brush; + }; + brush.y = function(z) { + if (!arguments.length) return y; + y = z; + resizes = d3_svg_brushResizes[!x << 1 | !y]; + return brush; + }; + brush.clamp = function(z) { + if (!arguments.length) return x && y ? [ xClamp, yClamp ] : x ? xClamp : y ? yClamp : null; + if (x && y) xClamp = !!z[0], yClamp = !!z[1]; else if (x) xClamp = !!z; else if (y) yClamp = !!z; + return brush; + }; + brush.extent = function(z) { + var x0, x1, y0, y1, t; + if (!arguments.length) { + if (x) { + if (xExtentDomain) { + x0 = xExtentDomain[0], x1 = xExtentDomain[1]; + } else { + x0 = xExtent[0], x1 = xExtent[1]; + if (x.invert) x0 = x.invert(x0), x1 = x.invert(x1); + if (x1 < x0) t = x0, x0 = x1, x1 = t; + } + } + if (y) { + if (yExtentDomain) { + y0 = yExtentDomain[0], y1 = yExtentDomain[1]; + } else { + y0 = yExtent[0], y1 = yExtent[1]; + if (y.invert) y0 = y.invert(y0), y1 = y.invert(y1); + if (y1 < y0) t = y0, y0 = y1, y1 = t; + } + } + return x && y ? [ [ x0, y0 ], [ x1, y1 ] ] : x ? [ x0, x1 ] : y && [ y0, y1 ]; + } + if (x) { + x0 = z[0], x1 = z[1]; + if (y) x0 = x0[0], x1 = x1[0]; + xExtentDomain = [ x0, x1 ]; + if (x.invert) x0 = x(x0), x1 = x(x1); + if (x1 < x0) t = x0, x0 = x1, x1 = t; + if (x0 != xExtent[0] || x1 != xExtent[1]) xExtent = [ x0, x1 ]; + } + if (y) { + y0 = z[0], y1 = z[1]; + if (x) y0 = y0[1], y1 = y1[1]; + yExtentDomain = [ y0, y1 ]; + if (y.invert) y0 = y(y0), y1 = y(y1); + if (y1 < y0) t = y0, y0 = y1, y1 = t; + if (y0 != yExtent[0] || y1 != yExtent[1]) yExtent = [ y0, y1 ]; + } + return brush; + }; + brush.clear = function() { + if (!brush.empty()) { + xExtent = [ 0, 0 ], yExtent = [ 0, 0 ]; + xExtentDomain = yExtentDomain = null; + } + return brush; + }; + brush.empty = function() { + return !!x && xExtent[0] == xExtent[1] || !!y && yExtent[0] == yExtent[1]; + }; + return d3.rebind(brush, event, "on"); + }; + var d3_svg_brushCursor = { + n: "ns-resize", + e: "ew-resize", + s: "ns-resize", + w: "ew-resize", + nw: "nwse-resize", + ne: "nesw-resize", + se: "nwse-resize", + sw: "nesw-resize" + }; + var d3_svg_brushResizes = [ [ "n", "e", "s", "w", "nw", "ne", "se", "sw" ], [ "e", "w" ], [ "n", "s" ], [] ]; + var d3_time_format = d3_time.format = d3_locale_enUS.timeFormat; + var d3_time_formatUtc = d3_time_format.utc; + var d3_time_formatIso = d3_time_formatUtc("%Y-%m-%dT%H:%M:%S.%LZ"); + d3_time_format.iso = Date.prototype.toISOString && +new Date("2000-01-01T00:00:00.000Z") ? d3_time_formatIsoNative : d3_time_formatIso; + function d3_time_formatIsoNative(date) { + return date.toISOString(); + } + d3_time_formatIsoNative.parse = function(string) { + var date = new Date(string); + return isNaN(date) ? null : date; + }; + d3_time_formatIsoNative.toString = d3_time_formatIso.toString; + d3_time.second = d3_time_interval(function(date) { + return new d3_date(Math.floor(date / 1e3) * 1e3); + }, function(date, offset) { + date.setTime(date.getTime() + Math.floor(offset) * 1e3); + }, function(date) { + return date.getSeconds(); + }); + d3_time.seconds = d3_time.second.range; + d3_time.seconds.utc = d3_time.second.utc.range; + d3_time.minute = d3_time_interval(function(date) { + return new d3_date(Math.floor(date / 6e4) * 6e4); + }, function(date, offset) { + date.setTime(date.getTime() + Math.floor(offset) * 6e4); + }, function(date) { + return date.getMinutes(); + }); + d3_time.minutes = d3_time.minute.range; + d3_time.minutes.utc = d3_time.minute.utc.range; + d3_time.hour = d3_time_interval(function(date) { + var timezone = date.getTimezoneOffset() / 60; + return new d3_date((Math.floor(date / 36e5 - timezone) + timezone) * 36e5); + }, function(date, offset) { + date.setTime(date.getTime() + Math.floor(offset) * 36e5); + }, function(date) { + return date.getHours(); + }); + d3_time.hours = d3_time.hour.range; + d3_time.hours.utc = d3_time.hour.utc.range; + d3_time.month = d3_time_interval(function(date) { + date = d3_time.day(date); + date.setDate(1); + return date; + }, function(date, offset) { + date.setMonth(date.getMonth() + offset); + }, function(date) { + return date.getMonth(); + }); + d3_time.months = d3_time.month.range; + d3_time.months.utc = d3_time.month.utc.range; + function d3_time_scale(linear, methods, format) { + function scale(x) { + return linear(x); + } + scale.invert = function(x) { + return d3_time_scaleDate(linear.invert(x)); + }; + scale.domain = function(x) { + if (!arguments.length) return linear.domain().map(d3_time_scaleDate); + linear.domain(x); + return scale; + }; + function tickMethod(extent, count) { + var span = extent[1] - extent[0], target = span / count, i = d3.bisect(d3_time_scaleSteps, target); + return i == d3_time_scaleSteps.length ? [ methods.year, d3_scale_linearTickRange(extent.map(function(d) { + return d / 31536e6; + }), count)[2] ] : !i ? [ d3_time_scaleMilliseconds, d3_scale_linearTickRange(extent, count)[2] ] : methods[target / d3_time_scaleSteps[i - 1] < d3_time_scaleSteps[i] / target ? i - 1 : i]; + } + scale.nice = function(interval, skip) { + var domain = scale.domain(), extent = d3_scaleExtent(domain), method = interval == null ? tickMethod(extent, 10) : typeof interval === "number" && tickMethod(extent, interval); + if (method) interval = method[0], skip = method[1]; + function skipped(date) { + return !isNaN(date) && !interval.range(date, d3_time_scaleDate(+date + 1), skip).length; + } + return scale.domain(d3_scale_nice(domain, skip > 1 ? { + floor: function(date) { + while (skipped(date = interval.floor(date))) date = d3_time_scaleDate(date - 1); + return date; + }, + ceil: function(date) { + while (skipped(date = interval.ceil(date))) date = d3_time_scaleDate(+date + 1); + return date; + } + } : interval)); + }; + scale.ticks = function(interval, skip) { + var extent = d3_scaleExtent(scale.domain()), method = interval == null ? tickMethod(extent, 10) : typeof interval === "number" ? tickMethod(extent, interval) : !interval.range && [ { + range: interval + }, skip ]; + if (method) interval = method[0], skip = method[1]; + return interval.range(extent[0], d3_time_scaleDate(+extent[1] + 1), skip < 1 ? 1 : skip); + }; + scale.tickFormat = function() { + return format; + }; + scale.copy = function() { + return d3_time_scale(linear.copy(), methods, format); + }; + return d3_scale_linearRebind(scale, linear); + } + function d3_time_scaleDate(t) { + return new Date(t); + } + var d3_time_scaleSteps = [ 1e3, 5e3, 15e3, 3e4, 6e4, 3e5, 9e5, 18e5, 36e5, 108e5, 216e5, 432e5, 864e5, 1728e5, 6048e5, 2592e6, 7776e6, 31536e6 ]; + var d3_time_scaleLocalMethods = [ [ d3_time.second, 1 ], [ d3_time.second, 5 ], [ d3_time.second, 15 ], [ d3_time.second, 30 ], [ d3_time.minute, 1 ], [ d3_time.minute, 5 ], [ d3_time.minute, 15 ], [ d3_time.minute, 30 ], [ d3_time.hour, 1 ], [ d3_time.hour, 3 ], [ d3_time.hour, 6 ], [ d3_time.hour, 12 ], [ d3_time.day, 1 ], [ d3_time.day, 2 ], [ d3_time.week, 1 ], [ d3_time.month, 1 ], [ d3_time.month, 3 ], [ d3_time.year, 1 ] ]; + var d3_time_scaleLocalFormat = d3_time_format.multi([ [ ".%L", function(d) { + return d.getMilliseconds(); + } ], [ ":%S", function(d) { + return d.getSeconds(); + } ], [ "%I:%M", function(d) { + return d.getMinutes(); + } ], [ "%I %p", function(d) { + return d.getHours(); + } ], [ "%a %d", function(d) { + return d.getDay() && d.getDate() != 1; + } ], [ "%b %d", function(d) { + return d.getDate() != 1; + } ], [ "%B", function(d) { + return d.getMonth(); + } ], [ "%Y", d3_true ] ]); + var d3_time_scaleMilliseconds = { + range: function(start, stop, step) { + return d3.range(Math.ceil(start / step) * step, +stop, step).map(d3_time_scaleDate); + }, + floor: d3_identity, + ceil: d3_identity + }; + d3_time_scaleLocalMethods.year = d3_time.year; + d3_time.scale = function() { + return d3_time_scale(d3.scale.linear(), d3_time_scaleLocalMethods, d3_time_scaleLocalFormat); + }; + var d3_time_scaleUtcMethods = d3_time_scaleLocalMethods.map(function(m) { + return [ m[0].utc, m[1] ]; + }); + var d3_time_scaleUtcFormat = d3_time_formatUtc.multi([ [ ".%L", function(d) { + return d.getUTCMilliseconds(); + } ], [ ":%S", function(d) { + return d.getUTCSeconds(); + } ], [ "%I:%M", function(d) { + return d.getUTCMinutes(); + } ], [ "%I %p", function(d) { + return d.getUTCHours(); + } ], [ "%a %d", function(d) { + return d.getUTCDay() && d.getUTCDate() != 1; + } ], [ "%b %d", function(d) { + return d.getUTCDate() != 1; + } ], [ "%B", function(d) { + return d.getUTCMonth(); + } ], [ "%Y", d3_true ] ]); + d3_time_scaleUtcMethods.year = d3_time.year.utc; + d3_time.scale.utc = function() { + return d3_time_scale(d3.scale.linear(), d3_time_scaleUtcMethods, d3_time_scaleUtcFormat); + }; + d3.text = d3_xhrType(function(request) { + return request.responseText; + }); + d3.json = function(url, callback) { + return d3_xhr(url, "application/json", d3_json, callback); + }; + function d3_json(request) { + return JSON.parse(request.responseText); + } + d3.html = function(url, callback) { + return d3_xhr(url, "text/html", d3_html, callback); + }; + function d3_html(request) { + var range = d3_document.createRange(); + range.selectNode(d3_document.body); + return range.createContextualFragment(request.responseText); + } + d3.xml = d3_xhrType(function(request) { + return request.responseXML; + }); + if (true) this.d3 = d3, !(__WEBPACK_AMD_DEFINE_FACTORY__ = (d3), __WEBPACK_AMD_DEFINE_RESULT__ = (typeof __WEBPACK_AMD_DEFINE_FACTORY__ === 'function' ? (__WEBPACK_AMD_DEFINE_FACTORY__.call(exports, __webpack_require__, exports, module)) : __WEBPACK_AMD_DEFINE_FACTORY__), __WEBPACK_AMD_DEFINE_RESULT__ !== undefined && (module.exports = __WEBPACK_AMD_DEFINE_RESULT__)); else if (typeof module === "object" && module.exports) module.exports = d3; else this.d3 = d3; + }(); + +/***/ }), +/* 3 */ +/***/ (function(module, exports, __webpack_require__) { + + 'use strict'; + + Object.defineProperty(exports, "__esModule", { + value: true + }); + + var _d = __webpack_require__(2); + + var _d2 = _interopRequireDefault(_d); + + function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { default: obj }; } + + function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + + function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + var Barchart = + // svg: d3 object with the svg in question + // exp_array: list of (feature_name, weight) + function Barchart(svg, exp_array) { + var two_sided = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : true; + var titles = arguments.length > 3 && arguments[3] !== undefined ? arguments[3] : undefined; + var colors = arguments.length > 4 && arguments[4] !== undefined ? arguments[4] : ['red', 'green']; + var show_numbers = arguments.length > 5 && arguments[5] !== undefined ? arguments[5] : false; + var bar_height = arguments.length > 6 && arguments[6] !== undefined ? arguments[6] : 5; + + _classCallCheck(this, Barchart); + + var svg_width = Math.min(600, parseInt(svg.style('width'))); + var bar_width = two_sided ? svg_width / 2 : svg_width; + if (titles === undefined) { + titles = two_sided ? ['Cons', 'Pros'] : 'Pros'; + } + if (show_numbers) { + bar_width = bar_width - 30; + } + var x_offset = two_sided ? svg_width / 2 : 10; + // 13.1 is +- the width of W, the widest letter. + if (two_sided && titles.length == 2) { + svg.append('text').attr('x', svg_width / 4).attr('y', 15).attr('font-size', '20').attr('text-anchor', 'middle').style('fill', colors[0]).text(titles[0]); + + svg.append('text').attr('x', svg_width / 4 * 3).attr('y', 15).attr('font-size', '20').attr('text-anchor', 'middle').style('fill', colors[1]).text(titles[1]); + } else { + var pos = two_sided ? svg_width / 2 : x_offset; + var anchor = two_sided ? 'middle' : 'begin'; + svg.append('text').attr('x', pos).attr('y', 15).attr('font-size', '20').attr('text-anchor', anchor).text(titles); + } + var yshift = 20; + var space_between_bars = 0; + var text_height = 16; + var space_between_bar_and_text = 3; + var total_bar_height = text_height + space_between_bar_and_text + bar_height + space_between_bars; + var total_height = total_bar_height * exp_array.length; + this.svg_height = total_height + yshift; + var yscale = _d2.default.scale.linear().domain([0, exp_array.length]).range([yshift, yshift + total_height]); + var names = exp_array.map(function (v) { + return v[0]; + }); + var weights = exp_array.map(function (v) { + return v[1]; + }); + var max_weight = Math.max.apply(Math, _toConsumableArray(weights.map(function (v) { + return Math.abs(v); + }))); + var xscale = _d2.default.scale.linear().domain([0, Math.max(1, max_weight)]).range([0, bar_width]); + + for (var i = 0; i < exp_array.length; ++i) { + var name = names[i]; + var weight = weights[i]; + var size = xscale(Math.abs(weight)); + var to_the_right = weight > 0 || !two_sided; + var text = svg.append('text').attr('x', to_the_right ? x_offset + 2 : x_offset - 2).attr('y', yscale(i) + text_height).attr('text-anchor', to_the_right ? 'begin' : 'end').attr('font-size', '14').text(name); + while (text.node().getBBox()['width'] + 1 > bar_width) { + var cur_text = text.text().slice(0, text.text().length - 5); + text.text(cur_text + '...'); + if (text === '...') { + break; + } + } + var bar = svg.append('rect').attr('height', bar_height).attr('x', to_the_right ? x_offset : x_offset - size).attr('y', text_height + yscale(i) + space_between_bar_and_text) // + bar_height) + .attr('width', size).style('fill', weight > 0 ? colors[1] : colors[0]); + if (show_numbers) { + var bartext = svg.append('text').attr('x', to_the_right ? x_offset + size + 1 : x_offset - size - 1).attr('text-anchor', weight > 0 || !two_sided ? 'begin' : 'end').attr('y', bar_height + yscale(i) + text_height + space_between_bar_and_text).attr('font-size', '10').text(Math.abs(weight).toFixed(2)); + } + } + var line = svg.append("line").attr("x1", x_offset).attr("x2", x_offset).attr("y1", bar_height + yshift).attr("y2", Math.max(bar_height, yscale(exp_array.length))).style("stroke-width", 2).style("stroke", "black"); + }; + + exports.default = Barchart; + +/***/ }), +/* 4 */ +/***/ (function(module, exports, __webpack_require__) { + + var __WEBPACK_AMD_DEFINE_RESULT__;/* WEBPACK VAR INJECTION */(function(global, module) {/** + * @license + * Lodash + * Copyright JS Foundation and other contributors + * Released under MIT license + * Based on Underscore.js 1.8.3 + * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors + */ + ;(function() { + + /** Used as a safe reference for `undefined` in pre-ES5 environments. */ + var undefined; + + /** Used as the semantic version number. */ + var VERSION = '4.17.11'; + + /** Used as the size to enable large array optimizations. */ + var LARGE_ARRAY_SIZE = 200; + + /** Error message constants. */ + var CORE_ERROR_TEXT = 'Unsupported core-js use. Try https://npms.io/search?q=ponyfill.', + FUNC_ERROR_TEXT = 'Expected a function'; + + /** Used to stand-in for `undefined` hash values. */ + var HASH_UNDEFINED = '__lodash_hash_undefined__'; + + /** Used as the maximum memoize cache size. */ + var MAX_MEMOIZE_SIZE = 500; + + /** Used as the internal argument placeholder. */ + var PLACEHOLDER = '__lodash_placeholder__'; + + /** Used to compose bitmasks for cloning. */ + var CLONE_DEEP_FLAG = 1, + CLONE_FLAT_FLAG = 2, + CLONE_SYMBOLS_FLAG = 4; + + /** Used to compose bitmasks for value comparisons. */ + var COMPARE_PARTIAL_FLAG = 1, + COMPARE_UNORDERED_FLAG = 2; + + /** Used to compose bitmasks for function metadata. */ + var WRAP_BIND_FLAG = 1, + WRAP_BIND_KEY_FLAG = 2, + WRAP_CURRY_BOUND_FLAG = 4, + WRAP_CURRY_FLAG = 8, + WRAP_CURRY_RIGHT_FLAG = 16, + WRAP_PARTIAL_FLAG = 32, + WRAP_PARTIAL_RIGHT_FLAG = 64, + WRAP_ARY_FLAG = 128, + WRAP_REARG_FLAG = 256, + WRAP_FLIP_FLAG = 512; + + /** Used as default options for `_.truncate`. */ + var DEFAULT_TRUNC_LENGTH = 30, + DEFAULT_TRUNC_OMISSION = '...'; + + /** Used to detect hot functions by number of calls within a span of milliseconds. */ + var HOT_COUNT = 800, + HOT_SPAN = 16; + + /** Used to indicate the type of lazy iteratees. */ + var LAZY_FILTER_FLAG = 1, + LAZY_MAP_FLAG = 2, + LAZY_WHILE_FLAG = 3; + + /** Used as references for various `Number` constants. */ + var INFINITY = 1 / 0, + MAX_SAFE_INTEGER = 9007199254740991, + MAX_INTEGER = 1.7976931348623157e+308, + NAN = 0 / 0; + + /** Used as references for the maximum length and index of an array. */ + var MAX_ARRAY_LENGTH = 4294967295, + MAX_ARRAY_INDEX = MAX_ARRAY_LENGTH - 1, + HALF_MAX_ARRAY_LENGTH = MAX_ARRAY_LENGTH >>> 1; + + /** Used to associate wrap methods with their bit flags. */ + var wrapFlags = [ + ['ary', WRAP_ARY_FLAG], + ['bind', WRAP_BIND_FLAG], + ['bindKey', WRAP_BIND_KEY_FLAG], + ['curry', WRAP_CURRY_FLAG], + ['curryRight', WRAP_CURRY_RIGHT_FLAG], + ['flip', WRAP_FLIP_FLAG], + ['partial', WRAP_PARTIAL_FLAG], + ['partialRight', WRAP_PARTIAL_RIGHT_FLAG], + ['rearg', WRAP_REARG_FLAG] + ]; + + /** `Object#toString` result references. */ + var argsTag = '[object Arguments]', + arrayTag = '[object Array]', + asyncTag = '[object AsyncFunction]', + boolTag = '[object Boolean]', + dateTag = '[object Date]', + domExcTag = '[object DOMException]', + errorTag = '[object Error]', + funcTag = '[object Function]', + genTag = '[object GeneratorFunction]', + mapTag = '[object Map]', + numberTag = '[object Number]', + nullTag = '[object Null]', + objectTag = '[object Object]', + promiseTag = '[object Promise]', + proxyTag = '[object Proxy]', + regexpTag = '[object RegExp]', + setTag = '[object Set]', + stringTag = '[object String]', + symbolTag = '[object Symbol]', + undefinedTag = '[object Undefined]', + weakMapTag = '[object WeakMap]', + weakSetTag = '[object WeakSet]'; + + var arrayBufferTag = '[object ArrayBuffer]', + dataViewTag = '[object DataView]', + float32Tag = '[object Float32Array]', + float64Tag = '[object Float64Array]', + int8Tag = '[object Int8Array]', + int16Tag = '[object Int16Array]', + int32Tag = '[object Int32Array]', + uint8Tag = '[object Uint8Array]', + uint8ClampedTag = '[object Uint8ClampedArray]', + uint16Tag = '[object Uint16Array]', + uint32Tag = '[object Uint32Array]'; + + /** Used to match empty string literals in compiled template source. */ + var reEmptyStringLeading = /\b__p \+= '';/g, + reEmptyStringMiddle = /\b(__p \+=) '' \+/g, + reEmptyStringTrailing = /(__e\(.*?\)|\b__t\)) \+\n'';/g; + + /** Used to match HTML entities and HTML characters. */ + var reEscapedHtml = /&(?:amp|lt|gt|quot|#39);/g, + reUnescapedHtml = /[&<>"']/g, + reHasEscapedHtml = RegExp(reEscapedHtml.source), + reHasUnescapedHtml = RegExp(reUnescapedHtml.source); + + /** Used to match template delimiters. */ + var reEscape = /<%-([\s\S]+?)%>/g, + reEvaluate = /<%([\s\S]+?)%>/g, + reInterpolate = /<%=([\s\S]+?)%>/g; + + /** Used to match property names within property paths. */ + var reIsDeepProp = /\.|\[(?:[^[\]]*|(["'])(?:(?!\1)[^\\]|\\.)*?\1)\]/, + reIsPlainProp = /^\w*$/, + rePropName = /[^.[\]]+|\[(?:(-?\d+(?:\.\d+)?)|(["'])((?:(?!\2)[^\\]|\\.)*?)\2)\]|(?=(?:\.|\[\])(?:\.|\[\]|$))/g; + + /** + * Used to match `RegExp` + * [syntax characters](http://ecma-international.org/ecma-262/7.0/#sec-patterns). + */ + var reRegExpChar = /[\\^$.*+?()[\]{}|]/g, + reHasRegExpChar = RegExp(reRegExpChar.source); + + /** Used to match leading and trailing whitespace. */ + var reTrim = /^\s+|\s+$/g, + reTrimStart = /^\s+/, + reTrimEnd = /\s+$/; + + /** Used to match wrap detail comments. */ + var reWrapComment = /\{(?:\n\/\* \[wrapped with .+\] \*\/)?\n?/, + reWrapDetails = /\{\n\/\* \[wrapped with (.+)\] \*/, + reSplitDetails = /,? & /; + + /** Used to match words composed of alphanumeric characters. */ + var reAsciiWord = /[^\x00-\x2f\x3a-\x40\x5b-\x60\x7b-\x7f]+/g; + + /** Used to match backslashes in property paths. */ + var reEscapeChar = /\\(\\)?/g; + + /** + * Used to match + * [ES template delimiters](http://ecma-international.org/ecma-262/7.0/#sec-template-literal-lexical-components). + */ + var reEsTemplate = /\$\{([^\\}]*(?:\\.[^\\}]*)*)\}/g; + + /** Used to match `RegExp` flags from their coerced string values. */ + var reFlags = /\w*$/; + + /** Used to detect bad signed hexadecimal string values. */ + var reIsBadHex = /^[-+]0x[0-9a-f]+$/i; + + /** Used to detect binary string values. */ + var reIsBinary = /^0b[01]+$/i; + + /** Used to detect host constructors (Safari). */ + var reIsHostCtor = /^\[object .+?Constructor\]$/; + + /** Used to detect octal string values. */ + var reIsOctal = /^0o[0-7]+$/i; + + /** Used to detect unsigned integer values. */ + var reIsUint = /^(?:0|[1-9]\d*)$/; + + /** Used to match Latin Unicode letters (excluding mathematical operators). */ + var reLatin = /[\xc0-\xd6\xd8-\xf6\xf8-\xff\u0100-\u017f]/g; + + /** Used to ensure capturing order of template delimiters. */ + var reNoMatch = /($^)/; + + /** Used to match unescaped characters in compiled string literals. */ + var reUnescapedString = /['\n\r\u2028\u2029\\]/g; + + /** Used to compose unicode character classes. */ + var rsAstralRange = '\\ud800-\\udfff', + rsComboMarksRange = '\\u0300-\\u036f', + reComboHalfMarksRange = '\\ufe20-\\ufe2f', + rsComboSymbolsRange = '\\u20d0-\\u20ff', + rsComboRange = rsComboMarksRange + reComboHalfMarksRange + rsComboSymbolsRange, + rsDingbatRange = '\\u2700-\\u27bf', + rsLowerRange = 'a-z\\xdf-\\xf6\\xf8-\\xff', + rsMathOpRange = '\\xac\\xb1\\xd7\\xf7', + rsNonCharRange = '\\x00-\\x2f\\x3a-\\x40\\x5b-\\x60\\x7b-\\xbf', + rsPunctuationRange = '\\u2000-\\u206f', + rsSpaceRange = ' \\t\\x0b\\f\\xa0\\ufeff\\n\\r\\u2028\\u2029\\u1680\\u180e\\u2000\\u2001\\u2002\\u2003\\u2004\\u2005\\u2006\\u2007\\u2008\\u2009\\u200a\\u202f\\u205f\\u3000', + rsUpperRange = 'A-Z\\xc0-\\xd6\\xd8-\\xde', + rsVarRange = '\\ufe0e\\ufe0f', + rsBreakRange = rsMathOpRange + rsNonCharRange + rsPunctuationRange + rsSpaceRange; + + /** Used to compose unicode capture groups. */ + var rsApos = "['\u2019]", + rsAstral = '[' + rsAstralRange + ']', + rsBreak = '[' + rsBreakRange + ']', + rsCombo = '[' + rsComboRange + ']', + rsDigits = '\\d+', + rsDingbat = '[' + rsDingbatRange + ']', + rsLower = '[' + rsLowerRange + ']', + rsMisc = '[^' + rsAstralRange + rsBreakRange + rsDigits + rsDingbatRange + rsLowerRange + rsUpperRange + ']', + rsFitz = '\\ud83c[\\udffb-\\udfff]', + rsModifier = '(?:' + rsCombo + '|' + rsFitz + ')', + rsNonAstral = '[^' + rsAstralRange + ']', + rsRegional = '(?:\\ud83c[\\udde6-\\uddff]){2}', + rsSurrPair = '[\\ud800-\\udbff][\\udc00-\\udfff]', + rsUpper = '[' + rsUpperRange + ']', + rsZWJ = '\\u200d'; + + /** Used to compose unicode regexes. */ + var rsMiscLower = '(?:' + rsLower + '|' + rsMisc + ')', + rsMiscUpper = '(?:' + rsUpper + '|' + rsMisc + ')', + rsOptContrLower = '(?:' + rsApos + '(?:d|ll|m|re|s|t|ve))?', + rsOptContrUpper = '(?:' + rsApos + '(?:D|LL|M|RE|S|T|VE))?', + reOptMod = rsModifier + '?', + rsOptVar = '[' + rsVarRange + ']?', + rsOptJoin = '(?:' + rsZWJ + '(?:' + [rsNonAstral, rsRegional, rsSurrPair].join('|') + ')' + rsOptVar + reOptMod + ')*', + rsOrdLower = '\\d*(?:1st|2nd|3rd|(?![123])\\dth)(?=\\b|[A-Z_])', + rsOrdUpper = '\\d*(?:1ST|2ND|3RD|(?![123])\\dTH)(?=\\b|[a-z_])', + rsSeq = rsOptVar + reOptMod + rsOptJoin, + rsEmoji = '(?:' + [rsDingbat, rsRegional, rsSurrPair].join('|') + ')' + rsSeq, + rsSymbol = '(?:' + [rsNonAstral + rsCombo + '?', rsCombo, rsRegional, rsSurrPair, rsAstral].join('|') + ')'; + + /** Used to match apostrophes. */ + var reApos = RegExp(rsApos, 'g'); + + /** + * Used to match [combining diacritical marks](https://en.wikipedia.org/wiki/Combining_Diacritical_Marks) and + * [combining diacritical marks for symbols](https://en.wikipedia.org/wiki/Combining_Diacritical_Marks_for_Symbols). + */ + var reComboMark = RegExp(rsCombo, 'g'); + + /** Used to match [string symbols](https://mathiasbynens.be/notes/javascript-unicode). */ + var reUnicode = RegExp(rsFitz + '(?=' + rsFitz + ')|' + rsSymbol + rsSeq, 'g'); + + /** Used to match complex or compound words. */ + var reUnicodeWord = RegExp([ + rsUpper + '?' + rsLower + '+' + rsOptContrLower + '(?=' + [rsBreak, rsUpper, '$'].join('|') + ')', + rsMiscUpper + '+' + rsOptContrUpper + '(?=' + [rsBreak, rsUpper + rsMiscLower, '$'].join('|') + ')', + rsUpper + '?' + rsMiscLower + '+' + rsOptContrLower, + rsUpper + '+' + rsOptContrUpper, + rsOrdUpper, + rsOrdLower, + rsDigits, + rsEmoji + ].join('|'), 'g'); + + /** Used to detect strings with [zero-width joiners or code points from the astral planes](http://eev.ee/blog/2015/09/12/dark-corners-of-unicode/). */ + var reHasUnicode = RegExp('[' + rsZWJ + rsAstralRange + rsComboRange + rsVarRange + ']'); + + /** Used to detect strings that need a more robust regexp to match words. */ + var reHasUnicodeWord = /[a-z][A-Z]|[A-Z]{2}[a-z]|[0-9][a-zA-Z]|[a-zA-Z][0-9]|[^a-zA-Z0-9 ]/; + + /** Used to assign default `context` object properties. */ + var contextProps = [ + 'Array', 'Buffer', 'DataView', 'Date', 'Error', 'Float32Array', 'Float64Array', + 'Function', 'Int8Array', 'Int16Array', 'Int32Array', 'Map', 'Math', 'Object', + 'Promise', 'RegExp', 'Set', 'String', 'Symbol', 'TypeError', 'Uint8Array', + 'Uint8ClampedArray', 'Uint16Array', 'Uint32Array', 'WeakMap', + '_', 'clearTimeout', 'isFinite', 'parseInt', 'setTimeout' + ]; + + /** Used to make template sourceURLs easier to identify. */ + var templateCounter = -1; + + /** Used to identify `toStringTag` values of typed arrays. */ + var typedArrayTags = {}; + typedArrayTags[float32Tag] = typedArrayTags[float64Tag] = + typedArrayTags[int8Tag] = typedArrayTags[int16Tag] = + typedArrayTags[int32Tag] = typedArrayTags[uint8Tag] = + typedArrayTags[uint8ClampedTag] = typedArrayTags[uint16Tag] = + typedArrayTags[uint32Tag] = true; + typedArrayTags[argsTag] = typedArrayTags[arrayTag] = + typedArrayTags[arrayBufferTag] = typedArrayTags[boolTag] = + typedArrayTags[dataViewTag] = typedArrayTags[dateTag] = + typedArrayTags[errorTag] = typedArrayTags[funcTag] = + typedArrayTags[mapTag] = typedArrayTags[numberTag] = + typedArrayTags[objectTag] = typedArrayTags[regexpTag] = + typedArrayTags[setTag] = typedArrayTags[stringTag] = + typedArrayTags[weakMapTag] = false; + + /** Used to identify `toStringTag` values supported by `_.clone`. */ + var cloneableTags = {}; + cloneableTags[argsTag] = cloneableTags[arrayTag] = + cloneableTags[arrayBufferTag] = cloneableTags[dataViewTag] = + cloneableTags[boolTag] = cloneableTags[dateTag] = + cloneableTags[float32Tag] = cloneableTags[float64Tag] = + cloneableTags[int8Tag] = cloneableTags[int16Tag] = + cloneableTags[int32Tag] = cloneableTags[mapTag] = + cloneableTags[numberTag] = cloneableTags[objectTag] = + cloneableTags[regexpTag] = cloneableTags[setTag] = + cloneableTags[stringTag] = cloneableTags[symbolTag] = + cloneableTags[uint8Tag] = cloneableTags[uint8ClampedTag] = + cloneableTags[uint16Tag] = cloneableTags[uint32Tag] = true; + cloneableTags[errorTag] = cloneableTags[funcTag] = + cloneableTags[weakMapTag] = false; + + /** Used to map Latin Unicode letters to basic Latin letters. */ + var deburredLetters = { + // Latin-1 Supplement block. + '\xc0': 'A', '\xc1': 'A', '\xc2': 'A', '\xc3': 'A', '\xc4': 'A', '\xc5': 'A', + '\xe0': 'a', '\xe1': 'a', '\xe2': 'a', '\xe3': 'a', '\xe4': 'a', '\xe5': 'a', + '\xc7': 'C', '\xe7': 'c', + '\xd0': 'D', '\xf0': 'd', + '\xc8': 'E', '\xc9': 'E', '\xca': 'E', '\xcb': 'E', + '\xe8': 'e', '\xe9': 'e', '\xea': 'e', '\xeb': 'e', + '\xcc': 'I', '\xcd': 'I', '\xce': 'I', '\xcf': 'I', + '\xec': 'i', '\xed': 'i', '\xee': 'i', '\xef': 'i', + '\xd1': 'N', '\xf1': 'n', + '\xd2': 'O', '\xd3': 'O', '\xd4': 'O', '\xd5': 'O', '\xd6': 'O', '\xd8': 'O', + '\xf2': 'o', '\xf3': 'o', '\xf4': 'o', '\xf5': 'o', '\xf6': 'o', '\xf8': 'o', + '\xd9': 'U', '\xda': 'U', '\xdb': 'U', '\xdc': 'U', + '\xf9': 'u', '\xfa': 'u', '\xfb': 'u', '\xfc': 'u', + '\xdd': 'Y', '\xfd': 'y', '\xff': 'y', + '\xc6': 'Ae', '\xe6': 'ae', + '\xde': 'Th', '\xfe': 'th', + '\xdf': 'ss', + // Latin Extended-A block. + '\u0100': 'A', '\u0102': 'A', '\u0104': 'A', + '\u0101': 'a', '\u0103': 'a', '\u0105': 'a', + '\u0106': 'C', '\u0108': 'C', '\u010a': 'C', '\u010c': 'C', + '\u0107': 'c', '\u0109': 'c', '\u010b': 'c', '\u010d': 'c', + '\u010e': 'D', '\u0110': 'D', '\u010f': 'd', '\u0111': 'd', + '\u0112': 'E', '\u0114': 'E', '\u0116': 'E', '\u0118': 'E', '\u011a': 'E', + '\u0113': 'e', '\u0115': 'e', '\u0117': 'e', '\u0119': 'e', '\u011b': 'e', + '\u011c': 'G', '\u011e': 'G', '\u0120': 'G', '\u0122': 'G', + '\u011d': 'g', '\u011f': 'g', '\u0121': 'g', '\u0123': 'g', + '\u0124': 'H', '\u0126': 'H', '\u0125': 'h', '\u0127': 'h', + '\u0128': 'I', '\u012a': 'I', '\u012c': 'I', '\u012e': 'I', '\u0130': 'I', + '\u0129': 'i', '\u012b': 'i', '\u012d': 'i', '\u012f': 'i', '\u0131': 'i', + '\u0134': 'J', '\u0135': 'j', + '\u0136': 'K', '\u0137': 'k', '\u0138': 'k', + '\u0139': 'L', '\u013b': 'L', '\u013d': 'L', '\u013f': 'L', '\u0141': 'L', + '\u013a': 'l', '\u013c': 'l', '\u013e': 'l', '\u0140': 'l', '\u0142': 'l', + '\u0143': 'N', '\u0145': 'N', '\u0147': 'N', '\u014a': 'N', + '\u0144': 'n', '\u0146': 'n', '\u0148': 'n', '\u014b': 'n', + '\u014c': 'O', '\u014e': 'O', '\u0150': 'O', + '\u014d': 'o', '\u014f': 'o', '\u0151': 'o', + '\u0154': 'R', '\u0156': 'R', '\u0158': 'R', + '\u0155': 'r', '\u0157': 'r', '\u0159': 'r', + '\u015a': 'S', '\u015c': 'S', '\u015e': 'S', '\u0160': 'S', + '\u015b': 's', '\u015d': 's', '\u015f': 's', '\u0161': 's', + '\u0162': 'T', '\u0164': 'T', '\u0166': 'T', + '\u0163': 't', '\u0165': 't', '\u0167': 't', + '\u0168': 'U', '\u016a': 'U', '\u016c': 'U', '\u016e': 'U', '\u0170': 'U', '\u0172': 'U', + '\u0169': 'u', '\u016b': 'u', '\u016d': 'u', '\u016f': 'u', '\u0171': 'u', '\u0173': 'u', + '\u0174': 'W', '\u0175': 'w', + '\u0176': 'Y', '\u0177': 'y', '\u0178': 'Y', + '\u0179': 'Z', '\u017b': 'Z', '\u017d': 'Z', + '\u017a': 'z', '\u017c': 'z', '\u017e': 'z', + '\u0132': 'IJ', '\u0133': 'ij', + '\u0152': 'Oe', '\u0153': 'oe', + '\u0149': "'n", '\u017f': 's' + }; + + /** Used to map characters to HTML entities. */ + var htmlEscapes = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + "'": ''' + }; + + /** Used to map HTML entities to characters. */ + var htmlUnescapes = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + ''': "'" + }; + + /** Used to escape characters for inclusion in compiled string literals. */ + var stringEscapes = { + '\\': '\\', + "'": "'", + '\n': 'n', + '\r': 'r', + '\u2028': 'u2028', + '\u2029': 'u2029' + }; + + /** Built-in method references without a dependency on `root`. */ + var freeParseFloat = parseFloat, + freeParseInt = parseInt; + + /** Detect free variable `global` from Node.js. */ + var freeGlobal = typeof global == 'object' && global && global.Object === Object && global; + + /** Detect free variable `self`. */ + var freeSelf = typeof self == 'object' && self && self.Object === Object && self; + + /** Used as a reference to the global object. */ + var root = freeGlobal || freeSelf || Function('return this')(); + + /** Detect free variable `exports`. */ + var freeExports = typeof exports == 'object' && exports && !exports.nodeType && exports; + + /** Detect free variable `module`. */ + var freeModule = freeExports && typeof module == 'object' && module && !module.nodeType && module; + + /** Detect the popular CommonJS extension `module.exports`. */ + var moduleExports = freeModule && freeModule.exports === freeExports; + + /** Detect free variable `process` from Node.js. */ + var freeProcess = moduleExports && freeGlobal.process; + + /** Used to access faster Node.js helpers. */ + var nodeUtil = (function() { + try { + // Use `util.types` for Node.js 10+. + var types = freeModule && freeModule.require && freeModule.require('util').types; + + if (types) { + return types; + } + + // Legacy `process.binding('util')` for Node.js < 10. + return freeProcess && freeProcess.binding && freeProcess.binding('util'); + } catch (e) {} + }()); + + /* Node.js helper references. */ + var nodeIsArrayBuffer = nodeUtil && nodeUtil.isArrayBuffer, + nodeIsDate = nodeUtil && nodeUtil.isDate, + nodeIsMap = nodeUtil && nodeUtil.isMap, + nodeIsRegExp = nodeUtil && nodeUtil.isRegExp, + nodeIsSet = nodeUtil && nodeUtil.isSet, + nodeIsTypedArray = nodeUtil && nodeUtil.isTypedArray; + + /*--------------------------------------------------------------------------*/ + + /** + * A faster alternative to `Function#apply`, this function invokes `func` + * with the `this` binding of `thisArg` and the arguments of `args`. + * + * @private + * @param {Function} func The function to invoke. + * @param {*} thisArg The `this` binding of `func`. + * @param {Array} args The arguments to invoke `func` with. + * @returns {*} Returns the result of `func`. + */ + function apply(func, thisArg, args) { + switch (args.length) { + case 0: return func.call(thisArg); + case 1: return func.call(thisArg, args[0]); + case 2: return func.call(thisArg, args[0], args[1]); + case 3: return func.call(thisArg, args[0], args[1], args[2]); + } + return func.apply(thisArg, args); + } + + /** + * A specialized version of `baseAggregator` for arrays. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} setter The function to set `accumulator` values. + * @param {Function} iteratee The iteratee to transform keys. + * @param {Object} accumulator The initial aggregated object. + * @returns {Function} Returns `accumulator`. + */ + function arrayAggregator(array, setter, iteratee, accumulator) { + var index = -1, + length = array == null ? 0 : array.length; + + while (++index < length) { + var value = array[index]; + setter(accumulator, value, iteratee(value), array); + } + return accumulator; + } + + /** + * A specialized version of `_.forEach` for arrays without support for + * iteratee shorthands. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Array} Returns `array`. + */ + function arrayEach(array, iteratee) { + var index = -1, + length = array == null ? 0 : array.length; + + while (++index < length) { + if (iteratee(array[index], index, array) === false) { + break; + } + } + return array; + } + + /** + * A specialized version of `_.forEachRight` for arrays without support for + * iteratee shorthands. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Array} Returns `array`. + */ + function arrayEachRight(array, iteratee) { + var length = array == null ? 0 : array.length; + + while (length--) { + if (iteratee(array[length], length, array) === false) { + break; + } + } + return array; + } + + /** + * A specialized version of `_.every` for arrays without support for + * iteratee shorthands. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} predicate The function invoked per iteration. + * @returns {boolean} Returns `true` if all elements pass the predicate check, + * else `false`. + */ + function arrayEvery(array, predicate) { + var index = -1, + length = array == null ? 0 : array.length; + + while (++index < length) { + if (!predicate(array[index], index, array)) { + return false; + } + } + return true; + } + + /** + * A specialized version of `_.filter` for arrays without support for + * iteratee shorthands. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} predicate The function invoked per iteration. + * @returns {Array} Returns the new filtered array. + */ + function arrayFilter(array, predicate) { + var index = -1, + length = array == null ? 0 : array.length, + resIndex = 0, + result = []; + + while (++index < length) { + var value = array[index]; + if (predicate(value, index, array)) { + result[resIndex++] = value; + } + } + return result; + } + + /** + * A specialized version of `_.includes` for arrays without support for + * specifying an index to search from. + * + * @private + * @param {Array} [array] The array to inspect. + * @param {*} target The value to search for. + * @returns {boolean} Returns `true` if `target` is found, else `false`. + */ + function arrayIncludes(array, value) { + var length = array == null ? 0 : array.length; + return !!length && baseIndexOf(array, value, 0) > -1; + } + + /** + * This function is like `arrayIncludes` except that it accepts a comparator. + * + * @private + * @param {Array} [array] The array to inspect. + * @param {*} target The value to search for. + * @param {Function} comparator The comparator invoked per element. + * @returns {boolean} Returns `true` if `target` is found, else `false`. + */ + function arrayIncludesWith(array, value, comparator) { + var index = -1, + length = array == null ? 0 : array.length; + + while (++index < length) { + if (comparator(value, array[index])) { + return true; + } + } + return false; + } + + /** + * A specialized version of `_.map` for arrays without support for iteratee + * shorthands. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Array} Returns the new mapped array. + */ + function arrayMap(array, iteratee) { + var index = -1, + length = array == null ? 0 : array.length, + result = Array(length); + + while (++index < length) { + result[index] = iteratee(array[index], index, array); + } + return result; + } + + /** + * Appends the elements of `values` to `array`. + * + * @private + * @param {Array} array The array to modify. + * @param {Array} values The values to append. + * @returns {Array} Returns `array`. + */ + function arrayPush(array, values) { + var index = -1, + length = values.length, + offset = array.length; + + while (++index < length) { + array[offset + index] = values[index]; + } + return array; + } + + /** + * A specialized version of `_.reduce` for arrays without support for + * iteratee shorthands. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @param {*} [accumulator] The initial value. + * @param {boolean} [initAccum] Specify using the first element of `array` as + * the initial value. + * @returns {*} Returns the accumulated value. + */ + function arrayReduce(array, iteratee, accumulator, initAccum) { + var index = -1, + length = array == null ? 0 : array.length; + + if (initAccum && length) { + accumulator = array[++index]; + } + while (++index < length) { + accumulator = iteratee(accumulator, array[index], index, array); + } + return accumulator; + } + + /** + * A specialized version of `_.reduceRight` for arrays without support for + * iteratee shorthands. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @param {*} [accumulator] The initial value. + * @param {boolean} [initAccum] Specify using the last element of `array` as + * the initial value. + * @returns {*} Returns the accumulated value. + */ + function arrayReduceRight(array, iteratee, accumulator, initAccum) { + var length = array == null ? 0 : array.length; + if (initAccum && length) { + accumulator = array[--length]; + } + while (length--) { + accumulator = iteratee(accumulator, array[length], length, array); + } + return accumulator; + } + + /** + * A specialized version of `_.some` for arrays without support for iteratee + * shorthands. + * + * @private + * @param {Array} [array] The array to iterate over. + * @param {Function} predicate The function invoked per iteration. + * @returns {boolean} Returns `true` if any element passes the predicate check, + * else `false`. + */ + function arraySome(array, predicate) { + var index = -1, + length = array == null ? 0 : array.length; + + while (++index < length) { + if (predicate(array[index], index, array)) { + return true; + } + } + return false; + } + + /** + * Gets the size of an ASCII `string`. + * + * @private + * @param {string} string The string inspect. + * @returns {number} Returns the string size. + */ + var asciiSize = baseProperty('length'); + + /** + * Converts an ASCII `string` to an array. + * + * @private + * @param {string} string The string to convert. + * @returns {Array} Returns the converted array. + */ + function asciiToArray(string) { + return string.split(''); + } + + /** + * Splits an ASCII `string` into an array of its words. + * + * @private + * @param {string} The string to inspect. + * @returns {Array} Returns the words of `string`. + */ + function asciiWords(string) { + return string.match(reAsciiWord) || []; + } + + /** + * The base implementation of methods like `_.findKey` and `_.findLastKey`, + * without support for iteratee shorthands, which iterates over `collection` + * using `eachFunc`. + * + * @private + * @param {Array|Object} collection The collection to inspect. + * @param {Function} predicate The function invoked per iteration. + * @param {Function} eachFunc The function to iterate over `collection`. + * @returns {*} Returns the found element or its key, else `undefined`. + */ + function baseFindKey(collection, predicate, eachFunc) { + var result; + eachFunc(collection, function(value, key, collection) { + if (predicate(value, key, collection)) { + result = key; + return false; + } + }); + return result; + } + + /** + * The base implementation of `_.findIndex` and `_.findLastIndex` without + * support for iteratee shorthands. + * + * @private + * @param {Array} array The array to inspect. + * @param {Function} predicate The function invoked per iteration. + * @param {number} fromIndex The index to search from. + * @param {boolean} [fromRight] Specify iterating from right to left. + * @returns {number} Returns the index of the matched value, else `-1`. + */ + function baseFindIndex(array, predicate, fromIndex, fromRight) { + var length = array.length, + index = fromIndex + (fromRight ? 1 : -1); + + while ((fromRight ? index-- : ++index < length)) { + if (predicate(array[index], index, array)) { + return index; + } + } + return -1; + } + + /** + * The base implementation of `_.indexOf` without `fromIndex` bounds checks. + * + * @private + * @param {Array} array The array to inspect. + * @param {*} value The value to search for. + * @param {number} fromIndex The index to search from. + * @returns {number} Returns the index of the matched value, else `-1`. + */ + function baseIndexOf(array, value, fromIndex) { + return value === value + ? strictIndexOf(array, value, fromIndex) + : baseFindIndex(array, baseIsNaN, fromIndex); + } + + /** + * This function is like `baseIndexOf` except that it accepts a comparator. + * + * @private + * @param {Array} array The array to inspect. + * @param {*} value The value to search for. + * @param {number} fromIndex The index to search from. + * @param {Function} comparator The comparator invoked per element. + * @returns {number} Returns the index of the matched value, else `-1`. + */ + function baseIndexOfWith(array, value, fromIndex, comparator) { + var index = fromIndex - 1, + length = array.length; + + while (++index < length) { + if (comparator(array[index], value)) { + return index; + } + } + return -1; + } + + /** + * The base implementation of `_.isNaN` without support for number objects. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is `NaN`, else `false`. + */ + function baseIsNaN(value) { + return value !== value; + } + + /** + * The base implementation of `_.mean` and `_.meanBy` without support for + * iteratee shorthands. + * + * @private + * @param {Array} array The array to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {number} Returns the mean. + */ + function baseMean(array, iteratee) { + var length = array == null ? 0 : array.length; + return length ? (baseSum(array, iteratee) / length) : NAN; + } + + /** + * The base implementation of `_.property` without support for deep paths. + * + * @private + * @param {string} key The key of the property to get. + * @returns {Function} Returns the new accessor function. + */ + function baseProperty(key) { + return function(object) { + return object == null ? undefined : object[key]; + }; + } + + /** + * The base implementation of `_.propertyOf` without support for deep paths. + * + * @private + * @param {Object} object The object to query. + * @returns {Function} Returns the new accessor function. + */ + function basePropertyOf(object) { + return function(key) { + return object == null ? undefined : object[key]; + }; + } + + /** + * The base implementation of `_.reduce` and `_.reduceRight`, without support + * for iteratee shorthands, which iterates over `collection` using `eachFunc`. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @param {*} accumulator The initial value. + * @param {boolean} initAccum Specify using the first or last element of + * `collection` as the initial value. + * @param {Function} eachFunc The function to iterate over `collection`. + * @returns {*} Returns the accumulated value. + */ + function baseReduce(collection, iteratee, accumulator, initAccum, eachFunc) { + eachFunc(collection, function(value, index, collection) { + accumulator = initAccum + ? (initAccum = false, value) + : iteratee(accumulator, value, index, collection); + }); + return accumulator; + } + + /** + * The base implementation of `_.sortBy` which uses `comparer` to define the + * sort order of `array` and replaces criteria objects with their corresponding + * values. + * + * @private + * @param {Array} array The array to sort. + * @param {Function} comparer The function to define sort order. + * @returns {Array} Returns `array`. + */ + function baseSortBy(array, comparer) { + var length = array.length; + + array.sort(comparer); + while (length--) { + array[length] = array[length].value; + } + return array; + } + + /** + * The base implementation of `_.sum` and `_.sumBy` without support for + * iteratee shorthands. + * + * @private + * @param {Array} array The array to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {number} Returns the sum. + */ + function baseSum(array, iteratee) { + var result, + index = -1, + length = array.length; + + while (++index < length) { + var current = iteratee(array[index]); + if (current !== undefined) { + result = result === undefined ? current : (result + current); + } + } + return result; + } + + /** + * The base implementation of `_.times` without support for iteratee shorthands + * or max array length checks. + * + * @private + * @param {number} n The number of times to invoke `iteratee`. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Array} Returns the array of results. + */ + function baseTimes(n, iteratee) { + var index = -1, + result = Array(n); + + while (++index < n) { + result[index] = iteratee(index); + } + return result; + } + + /** + * The base implementation of `_.toPairs` and `_.toPairsIn` which creates an array + * of key-value pairs for `object` corresponding to the property names of `props`. + * + * @private + * @param {Object} object The object to query. + * @param {Array} props The property names to get values for. + * @returns {Object} Returns the key-value pairs. + */ + function baseToPairs(object, props) { + return arrayMap(props, function(key) { + return [key, object[key]]; + }); + } + + /** + * The base implementation of `_.unary` without support for storing metadata. + * + * @private + * @param {Function} func The function to cap arguments for. + * @returns {Function} Returns the new capped function. + */ + function baseUnary(func) { + return function(value) { + return func(value); + }; + } + + /** + * The base implementation of `_.values` and `_.valuesIn` which creates an + * array of `object` property values corresponding to the property names + * of `props`. + * + * @private + * @param {Object} object The object to query. + * @param {Array} props The property names to get values for. + * @returns {Object} Returns the array of property values. + */ + function baseValues(object, props) { + return arrayMap(props, function(key) { + return object[key]; + }); + } + + /** + * Checks if a `cache` value for `key` exists. + * + * @private + * @param {Object} cache The cache to query. + * @param {string} key The key of the entry to check. + * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. + */ + function cacheHas(cache, key) { + return cache.has(key); + } + + /** + * Used by `_.trim` and `_.trimStart` to get the index of the first string symbol + * that is not found in the character symbols. + * + * @private + * @param {Array} strSymbols The string symbols to inspect. + * @param {Array} chrSymbols The character symbols to find. + * @returns {number} Returns the index of the first unmatched string symbol. + */ + function charsStartIndex(strSymbols, chrSymbols) { + var index = -1, + length = strSymbols.length; + + while (++index < length && baseIndexOf(chrSymbols, strSymbols[index], 0) > -1) {} + return index; + } + + /** + * Used by `_.trim` and `_.trimEnd` to get the index of the last string symbol + * that is not found in the character symbols. + * + * @private + * @param {Array} strSymbols The string symbols to inspect. + * @param {Array} chrSymbols The character symbols to find. + * @returns {number} Returns the index of the last unmatched string symbol. + */ + function charsEndIndex(strSymbols, chrSymbols) { + var index = strSymbols.length; + + while (index-- && baseIndexOf(chrSymbols, strSymbols[index], 0) > -1) {} + return index; + } + + /** + * Gets the number of `placeholder` occurrences in `array`. + * + * @private + * @param {Array} array The array to inspect. + * @param {*} placeholder The placeholder to search for. + * @returns {number} Returns the placeholder count. + */ + function countHolders(array, placeholder) { + var length = array.length, + result = 0; + + while (length--) { + if (array[length] === placeholder) { + ++result; + } + } + return result; + } + + /** + * Used by `_.deburr` to convert Latin-1 Supplement and Latin Extended-A + * letters to basic Latin letters. + * + * @private + * @param {string} letter The matched letter to deburr. + * @returns {string} Returns the deburred letter. + */ + var deburrLetter = basePropertyOf(deburredLetters); + + /** + * Used by `_.escape` to convert characters to HTML entities. + * + * @private + * @param {string} chr The matched character to escape. + * @returns {string} Returns the escaped character. + */ + var escapeHtmlChar = basePropertyOf(htmlEscapes); + + /** + * Used by `_.template` to escape characters for inclusion in compiled string literals. + * + * @private + * @param {string} chr The matched character to escape. + * @returns {string} Returns the escaped character. + */ + function escapeStringChar(chr) { + return '\\' + stringEscapes[chr]; + } + + /** + * Gets the value at `key` of `object`. + * + * @private + * @param {Object} [object] The object to query. + * @param {string} key The key of the property to get. + * @returns {*} Returns the property value. + */ + function getValue(object, key) { + return object == null ? undefined : object[key]; + } + + /** + * Checks if `string` contains Unicode symbols. + * + * @private + * @param {string} string The string to inspect. + * @returns {boolean} Returns `true` if a symbol is found, else `false`. + */ + function hasUnicode(string) { + return reHasUnicode.test(string); + } + + /** + * Checks if `string` contains a word composed of Unicode symbols. + * + * @private + * @param {string} string The string to inspect. + * @returns {boolean} Returns `true` if a word is found, else `false`. + */ + function hasUnicodeWord(string) { + return reHasUnicodeWord.test(string); + } + + /** + * Converts `iterator` to an array. + * + * @private + * @param {Object} iterator The iterator to convert. + * @returns {Array} Returns the converted array. + */ + function iteratorToArray(iterator) { + var data, + result = []; + + while (!(data = iterator.next()).done) { + result.push(data.value); + } + return result; + } + + /** + * Converts `map` to its key-value pairs. + * + * @private + * @param {Object} map The map to convert. + * @returns {Array} Returns the key-value pairs. + */ + function mapToArray(map) { + var index = -1, + result = Array(map.size); + + map.forEach(function(value, key) { + result[++index] = [key, value]; + }); + return result; + } + + /** + * Creates a unary function that invokes `func` with its argument transformed. + * + * @private + * @param {Function} func The function to wrap. + * @param {Function} transform The argument transform. + * @returns {Function} Returns the new function. + */ + function overArg(func, transform) { + return function(arg) { + return func(transform(arg)); + }; + } + + /** + * Replaces all `placeholder` elements in `array` with an internal placeholder + * and returns an array of their indexes. + * + * @private + * @param {Array} array The array to modify. + * @param {*} placeholder The placeholder to replace. + * @returns {Array} Returns the new array of placeholder indexes. + */ + function replaceHolders(array, placeholder) { + var index = -1, + length = array.length, + resIndex = 0, + result = []; + + while (++index < length) { + var value = array[index]; + if (value === placeholder || value === PLACEHOLDER) { + array[index] = PLACEHOLDER; + result[resIndex++] = index; + } + } + return result; + } + + /** + * Converts `set` to an array of its values. + * + * @private + * @param {Object} set The set to convert. + * @returns {Array} Returns the values. + */ + function setToArray(set) { + var index = -1, + result = Array(set.size); + + set.forEach(function(value) { + result[++index] = value; + }); + return result; + } + + /** + * Converts `set` to its value-value pairs. + * + * @private + * @param {Object} set The set to convert. + * @returns {Array} Returns the value-value pairs. + */ + function setToPairs(set) { + var index = -1, + result = Array(set.size); + + set.forEach(function(value) { + result[++index] = [value, value]; + }); + return result; + } + + /** + * A specialized version of `_.indexOf` which performs strict equality + * comparisons of values, i.e. `===`. + * + * @private + * @param {Array} array The array to inspect. + * @param {*} value The value to search for. + * @param {number} fromIndex The index to search from. + * @returns {number} Returns the index of the matched value, else `-1`. + */ + function strictIndexOf(array, value, fromIndex) { + var index = fromIndex - 1, + length = array.length; + + while (++index < length) { + if (array[index] === value) { + return index; + } + } + return -1; + } + + /** + * A specialized version of `_.lastIndexOf` which performs strict equality + * comparisons of values, i.e. `===`. + * + * @private + * @param {Array} array The array to inspect. + * @param {*} value The value to search for. + * @param {number} fromIndex The index to search from. + * @returns {number} Returns the index of the matched value, else `-1`. + */ + function strictLastIndexOf(array, value, fromIndex) { + var index = fromIndex + 1; + while (index--) { + if (array[index] === value) { + return index; + } + } + return index; + } + + /** + * Gets the number of symbols in `string`. + * + * @private + * @param {string} string The string to inspect. + * @returns {number} Returns the string size. + */ + function stringSize(string) { + return hasUnicode(string) + ? unicodeSize(string) + : asciiSize(string); + } + + /** + * Converts `string` to an array. + * + * @private + * @param {string} string The string to convert. + * @returns {Array} Returns the converted array. + */ + function stringToArray(string) { + return hasUnicode(string) + ? unicodeToArray(string) + : asciiToArray(string); + } + + /** + * Used by `_.unescape` to convert HTML entities to characters. + * + * @private + * @param {string} chr The matched character to unescape. + * @returns {string} Returns the unescaped character. + */ + var unescapeHtmlChar = basePropertyOf(htmlUnescapes); + + /** + * Gets the size of a Unicode `string`. + * + * @private + * @param {string} string The string inspect. + * @returns {number} Returns the string size. + */ + function unicodeSize(string) { + var result = reUnicode.lastIndex = 0; + while (reUnicode.test(string)) { + ++result; + } + return result; + } + + /** + * Converts a Unicode `string` to an array. + * + * @private + * @param {string} string The string to convert. + * @returns {Array} Returns the converted array. + */ + function unicodeToArray(string) { + return string.match(reUnicode) || []; + } + + /** + * Splits a Unicode `string` into an array of its words. + * + * @private + * @param {string} The string to inspect. + * @returns {Array} Returns the words of `string`. + */ + function unicodeWords(string) { + return string.match(reUnicodeWord) || []; + } + + /*--------------------------------------------------------------------------*/ + + /** + * Create a new pristine `lodash` function using the `context` object. + * + * @static + * @memberOf _ + * @since 1.1.0 + * @category Util + * @param {Object} [context=root] The context object. + * @returns {Function} Returns a new `lodash` function. + * @example + * + * _.mixin({ 'foo': _.constant('foo') }); + * + * var lodash = _.runInContext(); + * lodash.mixin({ 'bar': lodash.constant('bar') }); + * + * _.isFunction(_.foo); + * // => true + * _.isFunction(_.bar); + * // => false + * + * lodash.isFunction(lodash.foo); + * // => false + * lodash.isFunction(lodash.bar); + * // => true + * + * // Create a suped-up `defer` in Node.js. + * var defer = _.runInContext({ 'setTimeout': setImmediate }).defer; + */ + var runInContext = (function runInContext(context) { + context = context == null ? root : _.defaults(root.Object(), context, _.pick(root, contextProps)); + + /** Built-in constructor references. */ + var Array = context.Array, + Date = context.Date, + Error = context.Error, + Function = context.Function, + Math = context.Math, + Object = context.Object, + RegExp = context.RegExp, + String = context.String, + TypeError = context.TypeError; + + /** Used for built-in method references. */ + var arrayProto = Array.prototype, + funcProto = Function.prototype, + objectProto = Object.prototype; + + /** Used to detect overreaching core-js shims. */ + var coreJsData = context['__core-js_shared__']; + + /** Used to resolve the decompiled source of functions. */ + var funcToString = funcProto.toString; + + /** Used to check objects for own properties. */ + var hasOwnProperty = objectProto.hasOwnProperty; + + /** Used to generate unique IDs. */ + var idCounter = 0; + + /** Used to detect methods masquerading as native. */ + var maskSrcKey = (function() { + var uid = /[^.]+$/.exec(coreJsData && coreJsData.keys && coreJsData.keys.IE_PROTO || ''); + return uid ? ('Symbol(src)_1.' + uid) : ''; + }()); + + /** + * Used to resolve the + * [`toStringTag`](http://ecma-international.org/ecma-262/7.0/#sec-object.prototype.tostring) + * of values. + */ + var nativeObjectToString = objectProto.toString; + + /** Used to infer the `Object` constructor. */ + var objectCtorString = funcToString.call(Object); + + /** Used to restore the original `_` reference in `_.noConflict`. */ + var oldDash = root._; + + /** Used to detect if a method is native. */ + var reIsNative = RegExp('^' + + funcToString.call(hasOwnProperty).replace(reRegExpChar, '\\$&') + .replace(/hasOwnProperty|(function).*?(?=\\\()| for .+?(?=\\\])/g, '$1.*?') + '$' + ); + + /** Built-in value references. */ + var Buffer = moduleExports ? context.Buffer : undefined, + Symbol = context.Symbol, + Uint8Array = context.Uint8Array, + allocUnsafe = Buffer ? Buffer.allocUnsafe : undefined, + getPrototype = overArg(Object.getPrototypeOf, Object), + objectCreate = Object.create, + propertyIsEnumerable = objectProto.propertyIsEnumerable, + splice = arrayProto.splice, + spreadableSymbol = Symbol ? Symbol.isConcatSpreadable : undefined, + symIterator = Symbol ? Symbol.iterator : undefined, + symToStringTag = Symbol ? Symbol.toStringTag : undefined; + + var defineProperty = (function() { + try { + var func = getNative(Object, 'defineProperty'); + func({}, '', {}); + return func; + } catch (e) {} + }()); + + /** Mocked built-ins. */ + var ctxClearTimeout = context.clearTimeout !== root.clearTimeout && context.clearTimeout, + ctxNow = Date && Date.now !== root.Date.now && Date.now, + ctxSetTimeout = context.setTimeout !== root.setTimeout && context.setTimeout; + + /* Built-in method references for those with the same name as other `lodash` methods. */ + var nativeCeil = Math.ceil, + nativeFloor = Math.floor, + nativeGetSymbols = Object.getOwnPropertySymbols, + nativeIsBuffer = Buffer ? Buffer.isBuffer : undefined, + nativeIsFinite = context.isFinite, + nativeJoin = arrayProto.join, + nativeKeys = overArg(Object.keys, Object), + nativeMax = Math.max, + nativeMin = Math.min, + nativeNow = Date.now, + nativeParseInt = context.parseInt, + nativeRandom = Math.random, + nativeReverse = arrayProto.reverse; + + /* Built-in method references that are verified to be native. */ + var DataView = getNative(context, 'DataView'), + Map = getNative(context, 'Map'), + Promise = getNative(context, 'Promise'), + Set = getNative(context, 'Set'), + WeakMap = getNative(context, 'WeakMap'), + nativeCreate = getNative(Object, 'create'); + + /** Used to store function metadata. */ + var metaMap = WeakMap && new WeakMap; + + /** Used to lookup unminified function names. */ + var realNames = {}; + + /** Used to detect maps, sets, and weakmaps. */ + var dataViewCtorString = toSource(DataView), + mapCtorString = toSource(Map), + promiseCtorString = toSource(Promise), + setCtorString = toSource(Set), + weakMapCtorString = toSource(WeakMap); + + /** Used to convert symbols to primitives and strings. */ + var symbolProto = Symbol ? Symbol.prototype : undefined, + symbolValueOf = symbolProto ? symbolProto.valueOf : undefined, + symbolToString = symbolProto ? symbolProto.toString : undefined; + + /*------------------------------------------------------------------------*/ + + /** + * Creates a `lodash` object which wraps `value` to enable implicit method + * chain sequences. Methods that operate on and return arrays, collections, + * and functions can be chained together. Methods that retrieve a single value + * or may return a primitive value will automatically end the chain sequence + * and return the unwrapped value. Otherwise, the value must be unwrapped + * with `_#value`. + * + * Explicit chain sequences, which must be unwrapped with `_#value`, may be + * enabled using `_.chain`. + * + * The execution of chained methods is lazy, that is, it's deferred until + * `_#value` is implicitly or explicitly called. + * + * Lazy evaluation allows several methods to support shortcut fusion. + * Shortcut fusion is an optimization to merge iteratee calls; this avoids + * the creation of intermediate arrays and can greatly reduce the number of + * iteratee executions. Sections of a chain sequence qualify for shortcut + * fusion if the section is applied to an array and iteratees accept only + * one argument. The heuristic for whether a section qualifies for shortcut + * fusion is subject to change. + * + * Chaining is supported in custom builds as long as the `_#value` method is + * directly or indirectly included in the build. + * + * In addition to lodash methods, wrappers have `Array` and `String` methods. + * + * The wrapper `Array` methods are: + * `concat`, `join`, `pop`, `push`, `shift`, `sort`, `splice`, and `unshift` + * + * The wrapper `String` methods are: + * `replace` and `split` + * + * The wrapper methods that support shortcut fusion are: + * `at`, `compact`, `drop`, `dropRight`, `dropWhile`, `filter`, `find`, + * `findLast`, `head`, `initial`, `last`, `map`, `reject`, `reverse`, `slice`, + * `tail`, `take`, `takeRight`, `takeRightWhile`, `takeWhile`, and `toArray` + * + * The chainable wrapper methods are: + * `after`, `ary`, `assign`, `assignIn`, `assignInWith`, `assignWith`, `at`, + * `before`, `bind`, `bindAll`, `bindKey`, `castArray`, `chain`, `chunk`, + * `commit`, `compact`, `concat`, `conforms`, `constant`, `countBy`, `create`, + * `curry`, `debounce`, `defaults`, `defaultsDeep`, `defer`, `delay`, + * `difference`, `differenceBy`, `differenceWith`, `drop`, `dropRight`, + * `dropRightWhile`, `dropWhile`, `extend`, `extendWith`, `fill`, `filter`, + * `flatMap`, `flatMapDeep`, `flatMapDepth`, `flatten`, `flattenDeep`, + * `flattenDepth`, `flip`, `flow`, `flowRight`, `fromPairs`, `functions`, + * `functionsIn`, `groupBy`, `initial`, `intersection`, `intersectionBy`, + * `intersectionWith`, `invert`, `invertBy`, `invokeMap`, `iteratee`, `keyBy`, + * `keys`, `keysIn`, `map`, `mapKeys`, `mapValues`, `matches`, `matchesProperty`, + * `memoize`, `merge`, `mergeWith`, `method`, `methodOf`, `mixin`, `negate`, + * `nthArg`, `omit`, `omitBy`, `once`, `orderBy`, `over`, `overArgs`, + * `overEvery`, `overSome`, `partial`, `partialRight`, `partition`, `pick`, + * `pickBy`, `plant`, `property`, `propertyOf`, `pull`, `pullAll`, `pullAllBy`, + * `pullAllWith`, `pullAt`, `push`, `range`, `rangeRight`, `rearg`, `reject`, + * `remove`, `rest`, `reverse`, `sampleSize`, `set`, `setWith`, `shuffle`, + * `slice`, `sort`, `sortBy`, `splice`, `spread`, `tail`, `take`, `takeRight`, + * `takeRightWhile`, `takeWhile`, `tap`, `throttle`, `thru`, `toArray`, + * `toPairs`, `toPairsIn`, `toPath`, `toPlainObject`, `transform`, `unary`, + * `union`, `unionBy`, `unionWith`, `uniq`, `uniqBy`, `uniqWith`, `unset`, + * `unshift`, `unzip`, `unzipWith`, `update`, `updateWith`, `values`, + * `valuesIn`, `without`, `wrap`, `xor`, `xorBy`, `xorWith`, `zip`, + * `zipObject`, `zipObjectDeep`, and `zipWith` + * + * The wrapper methods that are **not** chainable by default are: + * `add`, `attempt`, `camelCase`, `capitalize`, `ceil`, `clamp`, `clone`, + * `cloneDeep`, `cloneDeepWith`, `cloneWith`, `conformsTo`, `deburr`, + * `defaultTo`, `divide`, `each`, `eachRight`, `endsWith`, `eq`, `escape`, + * `escapeRegExp`, `every`, `find`, `findIndex`, `findKey`, `findLast`, + * `findLastIndex`, `findLastKey`, `first`, `floor`, `forEach`, `forEachRight`, + * `forIn`, `forInRight`, `forOwn`, `forOwnRight`, `get`, `gt`, `gte`, `has`, + * `hasIn`, `head`, `identity`, `includes`, `indexOf`, `inRange`, `invoke`, + * `isArguments`, `isArray`, `isArrayBuffer`, `isArrayLike`, `isArrayLikeObject`, + * `isBoolean`, `isBuffer`, `isDate`, `isElement`, `isEmpty`, `isEqual`, + * `isEqualWith`, `isError`, `isFinite`, `isFunction`, `isInteger`, `isLength`, + * `isMap`, `isMatch`, `isMatchWith`, `isNaN`, `isNative`, `isNil`, `isNull`, + * `isNumber`, `isObject`, `isObjectLike`, `isPlainObject`, `isRegExp`, + * `isSafeInteger`, `isSet`, `isString`, `isUndefined`, `isTypedArray`, + * `isWeakMap`, `isWeakSet`, `join`, `kebabCase`, `last`, `lastIndexOf`, + * `lowerCase`, `lowerFirst`, `lt`, `lte`, `max`, `maxBy`, `mean`, `meanBy`, + * `min`, `minBy`, `multiply`, `noConflict`, `noop`, `now`, `nth`, `pad`, + * `padEnd`, `padStart`, `parseInt`, `pop`, `random`, `reduce`, `reduceRight`, + * `repeat`, `result`, `round`, `runInContext`, `sample`, `shift`, `size`, + * `snakeCase`, `some`, `sortedIndex`, `sortedIndexBy`, `sortedLastIndex`, + * `sortedLastIndexBy`, `startCase`, `startsWith`, `stubArray`, `stubFalse`, + * `stubObject`, `stubString`, `stubTrue`, `subtract`, `sum`, `sumBy`, + * `template`, `times`, `toFinite`, `toInteger`, `toJSON`, `toLength`, + * `toLower`, `toNumber`, `toSafeInteger`, `toString`, `toUpper`, `trim`, + * `trimEnd`, `trimStart`, `truncate`, `unescape`, `uniqueId`, `upperCase`, + * `upperFirst`, `value`, and `words` + * + * @name _ + * @constructor + * @category Seq + * @param {*} value The value to wrap in a `lodash` instance. + * @returns {Object} Returns the new `lodash` wrapper instance. + * @example + * + * function square(n) { + * return n * n; + * } + * + * var wrapped = _([1, 2, 3]); + * + * // Returns an unwrapped value. + * wrapped.reduce(_.add); + * // => 6 + * + * // Returns a wrapped value. + * var squares = wrapped.map(square); + * + * _.isArray(squares); + * // => false + * + * _.isArray(squares.value()); + * // => true + */ + function lodash(value) { + if (isObjectLike(value) && !isArray(value) && !(value instanceof LazyWrapper)) { + if (value instanceof LodashWrapper) { + return value; + } + if (hasOwnProperty.call(value, '__wrapped__')) { + return wrapperClone(value); + } + } + return new LodashWrapper(value); + } + + /** + * The base implementation of `_.create` without support for assigning + * properties to the created object. + * + * @private + * @param {Object} proto The object to inherit from. + * @returns {Object} Returns the new object. + */ + var baseCreate = (function() { + function object() {} + return function(proto) { + if (!isObject(proto)) { + return {}; + } + if (objectCreate) { + return objectCreate(proto); + } + object.prototype = proto; + var result = new object; + object.prototype = undefined; + return result; + }; + }()); + + /** + * The function whose prototype chain sequence wrappers inherit from. + * + * @private + */ + function baseLodash() { + // No operation performed. + } + + /** + * The base constructor for creating `lodash` wrapper objects. + * + * @private + * @param {*} value The value to wrap. + * @param {boolean} [chainAll] Enable explicit method chain sequences. + */ + function LodashWrapper(value, chainAll) { + this.__wrapped__ = value; + this.__actions__ = []; + this.__chain__ = !!chainAll; + this.__index__ = 0; + this.__values__ = undefined; + } + + /** + * By default, the template delimiters used by lodash are like those in + * embedded Ruby (ERB) as well as ES2015 template strings. Change the + * following template settings to use alternative delimiters. + * + * @static + * @memberOf _ + * @type {Object} + */ + lodash.templateSettings = { + + /** + * Used to detect `data` property values to be HTML-escaped. + * + * @memberOf _.templateSettings + * @type {RegExp} + */ + 'escape': reEscape, + + /** + * Used to detect code to be evaluated. + * + * @memberOf _.templateSettings + * @type {RegExp} + */ + 'evaluate': reEvaluate, + + /** + * Used to detect `data` property values to inject. + * + * @memberOf _.templateSettings + * @type {RegExp} + */ + 'interpolate': reInterpolate, + + /** + * Used to reference the data object in the template text. + * + * @memberOf _.templateSettings + * @type {string} + */ + 'variable': '', + + /** + * Used to import variables into the compiled template. + * + * @memberOf _.templateSettings + * @type {Object} + */ + 'imports': { + + /** + * A reference to the `lodash` function. + * + * @memberOf _.templateSettings.imports + * @type {Function} + */ + '_': lodash + } + }; + + // Ensure wrappers are instances of `baseLodash`. + lodash.prototype = baseLodash.prototype; + lodash.prototype.constructor = lodash; + + LodashWrapper.prototype = baseCreate(baseLodash.prototype); + LodashWrapper.prototype.constructor = LodashWrapper; + + /*------------------------------------------------------------------------*/ + + /** + * Creates a lazy wrapper object which wraps `value` to enable lazy evaluation. + * + * @private + * @constructor + * @param {*} value The value to wrap. + */ + function LazyWrapper(value) { + this.__wrapped__ = value; + this.__actions__ = []; + this.__dir__ = 1; + this.__filtered__ = false; + this.__iteratees__ = []; + this.__takeCount__ = MAX_ARRAY_LENGTH; + this.__views__ = []; + } + + /** + * Creates a clone of the lazy wrapper object. + * + * @private + * @name clone + * @memberOf LazyWrapper + * @returns {Object} Returns the cloned `LazyWrapper` object. + */ + function lazyClone() { + var result = new LazyWrapper(this.__wrapped__); + result.__actions__ = copyArray(this.__actions__); + result.__dir__ = this.__dir__; + result.__filtered__ = this.__filtered__; + result.__iteratees__ = copyArray(this.__iteratees__); + result.__takeCount__ = this.__takeCount__; + result.__views__ = copyArray(this.__views__); + return result; + } + + /** + * Reverses the direction of lazy iteration. + * + * @private + * @name reverse + * @memberOf LazyWrapper + * @returns {Object} Returns the new reversed `LazyWrapper` object. + */ + function lazyReverse() { + if (this.__filtered__) { + var result = new LazyWrapper(this); + result.__dir__ = -1; + result.__filtered__ = true; + } else { + result = this.clone(); + result.__dir__ *= -1; + } + return result; + } + + /** + * Extracts the unwrapped value from its lazy wrapper. + * + * @private + * @name value + * @memberOf LazyWrapper + * @returns {*} Returns the unwrapped value. + */ + function lazyValue() { + var array = this.__wrapped__.value(), + dir = this.__dir__, + isArr = isArray(array), + isRight = dir < 0, + arrLength = isArr ? array.length : 0, + view = getView(0, arrLength, this.__views__), + start = view.start, + end = view.end, + length = end - start, + index = isRight ? end : (start - 1), + iteratees = this.__iteratees__, + iterLength = iteratees.length, + resIndex = 0, + takeCount = nativeMin(length, this.__takeCount__); + + if (!isArr || (!isRight && arrLength == length && takeCount == length)) { + return baseWrapperValue(array, this.__actions__); + } + var result = []; + + outer: + while (length-- && resIndex < takeCount) { + index += dir; + + var iterIndex = -1, + value = array[index]; + + while (++iterIndex < iterLength) { + var data = iteratees[iterIndex], + iteratee = data.iteratee, + type = data.type, + computed = iteratee(value); + + if (type == LAZY_MAP_FLAG) { + value = computed; + } else if (!computed) { + if (type == LAZY_FILTER_FLAG) { + continue outer; + } else { + break outer; + } + } + } + result[resIndex++] = value; + } + return result; + } + + // Ensure `LazyWrapper` is an instance of `baseLodash`. + LazyWrapper.prototype = baseCreate(baseLodash.prototype); + LazyWrapper.prototype.constructor = LazyWrapper; + + /*------------------------------------------------------------------------*/ + + /** + * Creates a hash object. + * + * @private + * @constructor + * @param {Array} [entries] The key-value pairs to cache. + */ + function Hash(entries) { + var index = -1, + length = entries == null ? 0 : entries.length; + + this.clear(); + while (++index < length) { + var entry = entries[index]; + this.set(entry[0], entry[1]); + } + } + + /** + * Removes all key-value entries from the hash. + * + * @private + * @name clear + * @memberOf Hash + */ + function hashClear() { + this.__data__ = nativeCreate ? nativeCreate(null) : {}; + this.size = 0; + } + + /** + * Removes `key` and its value from the hash. + * + * @private + * @name delete + * @memberOf Hash + * @param {Object} hash The hash to modify. + * @param {string} key The key of the value to remove. + * @returns {boolean} Returns `true` if the entry was removed, else `false`. + */ + function hashDelete(key) { + var result = this.has(key) && delete this.__data__[key]; + this.size -= result ? 1 : 0; + return result; + } + + /** + * Gets the hash value for `key`. + * + * @private + * @name get + * @memberOf Hash + * @param {string} key The key of the value to get. + * @returns {*} Returns the entry value. + */ + function hashGet(key) { + var data = this.__data__; + if (nativeCreate) { + var result = data[key]; + return result === HASH_UNDEFINED ? undefined : result; + } + return hasOwnProperty.call(data, key) ? data[key] : undefined; + } + + /** + * Checks if a hash value for `key` exists. + * + * @private + * @name has + * @memberOf Hash + * @param {string} key The key of the entry to check. + * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. + */ + function hashHas(key) { + var data = this.__data__; + return nativeCreate ? (data[key] !== undefined) : hasOwnProperty.call(data, key); + } + + /** + * Sets the hash `key` to `value`. + * + * @private + * @name set + * @memberOf Hash + * @param {string} key The key of the value to set. + * @param {*} value The value to set. + * @returns {Object} Returns the hash instance. + */ + function hashSet(key, value) { + var data = this.__data__; + this.size += this.has(key) ? 0 : 1; + data[key] = (nativeCreate && value === undefined) ? HASH_UNDEFINED : value; + return this; + } + + // Add methods to `Hash`. + Hash.prototype.clear = hashClear; + Hash.prototype['delete'] = hashDelete; + Hash.prototype.get = hashGet; + Hash.prototype.has = hashHas; + Hash.prototype.set = hashSet; + + /*------------------------------------------------------------------------*/ + + /** + * Creates an list cache object. + * + * @private + * @constructor + * @param {Array} [entries] The key-value pairs to cache. + */ + function ListCache(entries) { + var index = -1, + length = entries == null ? 0 : entries.length; + + this.clear(); + while (++index < length) { + var entry = entries[index]; + this.set(entry[0], entry[1]); + } + } + + /** + * Removes all key-value entries from the list cache. + * + * @private + * @name clear + * @memberOf ListCache + */ + function listCacheClear() { + this.__data__ = []; + this.size = 0; + } + + /** + * Removes `key` and its value from the list cache. + * + * @private + * @name delete + * @memberOf ListCache + * @param {string} key The key of the value to remove. + * @returns {boolean} Returns `true` if the entry was removed, else `false`. + */ + function listCacheDelete(key) { + var data = this.__data__, + index = assocIndexOf(data, key); + + if (index < 0) { + return false; + } + var lastIndex = data.length - 1; + if (index == lastIndex) { + data.pop(); + } else { + splice.call(data, index, 1); + } + --this.size; + return true; + } + + /** + * Gets the list cache value for `key`. + * + * @private + * @name get + * @memberOf ListCache + * @param {string} key The key of the value to get. + * @returns {*} Returns the entry value. + */ + function listCacheGet(key) { + var data = this.__data__, + index = assocIndexOf(data, key); + + return index < 0 ? undefined : data[index][1]; + } + + /** + * Checks if a list cache value for `key` exists. + * + * @private + * @name has + * @memberOf ListCache + * @param {string} key The key of the entry to check. + * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. + */ + function listCacheHas(key) { + return assocIndexOf(this.__data__, key) > -1; + } + + /** + * Sets the list cache `key` to `value`. + * + * @private + * @name set + * @memberOf ListCache + * @param {string} key The key of the value to set. + * @param {*} value The value to set. + * @returns {Object} Returns the list cache instance. + */ + function listCacheSet(key, value) { + var data = this.__data__, + index = assocIndexOf(data, key); + + if (index < 0) { + ++this.size; + data.push([key, value]); + } else { + data[index][1] = value; + } + return this; + } + + // Add methods to `ListCache`. + ListCache.prototype.clear = listCacheClear; + ListCache.prototype['delete'] = listCacheDelete; + ListCache.prototype.get = listCacheGet; + ListCache.prototype.has = listCacheHas; + ListCache.prototype.set = listCacheSet; + + /*------------------------------------------------------------------------*/ + + /** + * Creates a map cache object to store key-value pairs. + * + * @private + * @constructor + * @param {Array} [entries] The key-value pairs to cache. + */ + function MapCache(entries) { + var index = -1, + length = entries == null ? 0 : entries.length; + + this.clear(); + while (++index < length) { + var entry = entries[index]; + this.set(entry[0], entry[1]); + } + } + + /** + * Removes all key-value entries from the map. + * + * @private + * @name clear + * @memberOf MapCache + */ + function mapCacheClear() { + this.size = 0; + this.__data__ = { + 'hash': new Hash, + 'map': new (Map || ListCache), + 'string': new Hash + }; + } + + /** + * Removes `key` and its value from the map. + * + * @private + * @name delete + * @memberOf MapCache + * @param {string} key The key of the value to remove. + * @returns {boolean} Returns `true` if the entry was removed, else `false`. + */ + function mapCacheDelete(key) { + var result = getMapData(this, key)['delete'](key); + this.size -= result ? 1 : 0; + return result; + } + + /** + * Gets the map value for `key`. + * + * @private + * @name get + * @memberOf MapCache + * @param {string} key The key of the value to get. + * @returns {*} Returns the entry value. + */ + function mapCacheGet(key) { + return getMapData(this, key).get(key); + } + + /** + * Checks if a map value for `key` exists. + * + * @private + * @name has + * @memberOf MapCache + * @param {string} key The key of the entry to check. + * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. + */ + function mapCacheHas(key) { + return getMapData(this, key).has(key); + } + + /** + * Sets the map `key` to `value`. + * + * @private + * @name set + * @memberOf MapCache + * @param {string} key The key of the value to set. + * @param {*} value The value to set. + * @returns {Object} Returns the map cache instance. + */ + function mapCacheSet(key, value) { + var data = getMapData(this, key), + size = data.size; + + data.set(key, value); + this.size += data.size == size ? 0 : 1; + return this; + } + + // Add methods to `MapCache`. + MapCache.prototype.clear = mapCacheClear; + MapCache.prototype['delete'] = mapCacheDelete; + MapCache.prototype.get = mapCacheGet; + MapCache.prototype.has = mapCacheHas; + MapCache.prototype.set = mapCacheSet; + + /*------------------------------------------------------------------------*/ + + /** + * + * Creates an array cache object to store unique values. + * + * @private + * @constructor + * @param {Array} [values] The values to cache. + */ + function SetCache(values) { + var index = -1, + length = values == null ? 0 : values.length; + + this.__data__ = new MapCache; + while (++index < length) { + this.add(values[index]); + } + } + + /** + * Adds `value` to the array cache. + * + * @private + * @name add + * @memberOf SetCache + * @alias push + * @param {*} value The value to cache. + * @returns {Object} Returns the cache instance. + */ + function setCacheAdd(value) { + this.__data__.set(value, HASH_UNDEFINED); + return this; + } + + /** + * Checks if `value` is in the array cache. + * + * @private + * @name has + * @memberOf SetCache + * @param {*} value The value to search for. + * @returns {number} Returns `true` if `value` is found, else `false`. + */ + function setCacheHas(value) { + return this.__data__.has(value); + } + + // Add methods to `SetCache`. + SetCache.prototype.add = SetCache.prototype.push = setCacheAdd; + SetCache.prototype.has = setCacheHas; + + /*------------------------------------------------------------------------*/ + + /** + * Creates a stack cache object to store key-value pairs. + * + * @private + * @constructor + * @param {Array} [entries] The key-value pairs to cache. + */ + function Stack(entries) { + var data = this.__data__ = new ListCache(entries); + this.size = data.size; + } + + /** + * Removes all key-value entries from the stack. + * + * @private + * @name clear + * @memberOf Stack + */ + function stackClear() { + this.__data__ = new ListCache; + this.size = 0; + } + + /** + * Removes `key` and its value from the stack. + * + * @private + * @name delete + * @memberOf Stack + * @param {string} key The key of the value to remove. + * @returns {boolean} Returns `true` if the entry was removed, else `false`. + */ + function stackDelete(key) { + var data = this.__data__, + result = data['delete'](key); + + this.size = data.size; + return result; + } + + /** + * Gets the stack value for `key`. + * + * @private + * @name get + * @memberOf Stack + * @param {string} key The key of the value to get. + * @returns {*} Returns the entry value. + */ + function stackGet(key) { + return this.__data__.get(key); + } + + /** + * Checks if a stack value for `key` exists. + * + * @private + * @name has + * @memberOf Stack + * @param {string} key The key of the entry to check. + * @returns {boolean} Returns `true` if an entry for `key` exists, else `false`. + */ + function stackHas(key) { + return this.__data__.has(key); + } + + /** + * Sets the stack `key` to `value`. + * + * @private + * @name set + * @memberOf Stack + * @param {string} key The key of the value to set. + * @param {*} value The value to set. + * @returns {Object} Returns the stack cache instance. + */ + function stackSet(key, value) { + var data = this.__data__; + if (data instanceof ListCache) { + var pairs = data.__data__; + if (!Map || (pairs.length < LARGE_ARRAY_SIZE - 1)) { + pairs.push([key, value]); + this.size = ++data.size; + return this; + } + data = this.__data__ = new MapCache(pairs); + } + data.set(key, value); + this.size = data.size; + return this; + } + + // Add methods to `Stack`. + Stack.prototype.clear = stackClear; + Stack.prototype['delete'] = stackDelete; + Stack.prototype.get = stackGet; + Stack.prototype.has = stackHas; + Stack.prototype.set = stackSet; + + /*------------------------------------------------------------------------*/ + + /** + * Creates an array of the enumerable property names of the array-like `value`. + * + * @private + * @param {*} value The value to query. + * @param {boolean} inherited Specify returning inherited property names. + * @returns {Array} Returns the array of property names. + */ + function arrayLikeKeys(value, inherited) { + var isArr = isArray(value), + isArg = !isArr && isArguments(value), + isBuff = !isArr && !isArg && isBuffer(value), + isType = !isArr && !isArg && !isBuff && isTypedArray(value), + skipIndexes = isArr || isArg || isBuff || isType, + result = skipIndexes ? baseTimes(value.length, String) : [], + length = result.length; + + for (var key in value) { + if ((inherited || hasOwnProperty.call(value, key)) && + !(skipIndexes && ( + // Safari 9 has enumerable `arguments.length` in strict mode. + key == 'length' || + // Node.js 0.10 has enumerable non-index properties on buffers. + (isBuff && (key == 'offset' || key == 'parent')) || + // PhantomJS 2 has enumerable non-index properties on typed arrays. + (isType && (key == 'buffer' || key == 'byteLength' || key == 'byteOffset')) || + // Skip index properties. + isIndex(key, length) + ))) { + result.push(key); + } + } + return result; + } + + /** + * A specialized version of `_.sample` for arrays. + * + * @private + * @param {Array} array The array to sample. + * @returns {*} Returns the random element. + */ + function arraySample(array) { + var length = array.length; + return length ? array[baseRandom(0, length - 1)] : undefined; + } + + /** + * A specialized version of `_.sampleSize` for arrays. + * + * @private + * @param {Array} array The array to sample. + * @param {number} n The number of elements to sample. + * @returns {Array} Returns the random elements. + */ + function arraySampleSize(array, n) { + return shuffleSelf(copyArray(array), baseClamp(n, 0, array.length)); + } + + /** + * A specialized version of `_.shuffle` for arrays. + * + * @private + * @param {Array} array The array to shuffle. + * @returns {Array} Returns the new shuffled array. + */ + function arrayShuffle(array) { + return shuffleSelf(copyArray(array)); + } + + /** + * This function is like `assignValue` except that it doesn't assign + * `undefined` values. + * + * @private + * @param {Object} object The object to modify. + * @param {string} key The key of the property to assign. + * @param {*} value The value to assign. + */ + function assignMergeValue(object, key, value) { + if ((value !== undefined && !eq(object[key], value)) || + (value === undefined && !(key in object))) { + baseAssignValue(object, key, value); + } + } + + /** + * Assigns `value` to `key` of `object` if the existing value is not equivalent + * using [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * for equality comparisons. + * + * @private + * @param {Object} object The object to modify. + * @param {string} key The key of the property to assign. + * @param {*} value The value to assign. + */ + function assignValue(object, key, value) { + var objValue = object[key]; + if (!(hasOwnProperty.call(object, key) && eq(objValue, value)) || + (value === undefined && !(key in object))) { + baseAssignValue(object, key, value); + } + } + + /** + * Gets the index at which the `key` is found in `array` of key-value pairs. + * + * @private + * @param {Array} array The array to inspect. + * @param {*} key The key to search for. + * @returns {number} Returns the index of the matched value, else `-1`. + */ + function assocIndexOf(array, key) { + var length = array.length; + while (length--) { + if (eq(array[length][0], key)) { + return length; + } + } + return -1; + } + + /** + * Aggregates elements of `collection` on `accumulator` with keys transformed + * by `iteratee` and values set by `setter`. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} setter The function to set `accumulator` values. + * @param {Function} iteratee The iteratee to transform keys. + * @param {Object} accumulator The initial aggregated object. + * @returns {Function} Returns `accumulator`. + */ + function baseAggregator(collection, setter, iteratee, accumulator) { + baseEach(collection, function(value, key, collection) { + setter(accumulator, value, iteratee(value), collection); + }); + return accumulator; + } + + /** + * The base implementation of `_.assign` without support for multiple sources + * or `customizer` functions. + * + * @private + * @param {Object} object The destination object. + * @param {Object} source The source object. + * @returns {Object} Returns `object`. + */ + function baseAssign(object, source) { + return object && copyObject(source, keys(source), object); + } + + /** + * The base implementation of `_.assignIn` without support for multiple sources + * or `customizer` functions. + * + * @private + * @param {Object} object The destination object. + * @param {Object} source The source object. + * @returns {Object} Returns `object`. + */ + function baseAssignIn(object, source) { + return object && copyObject(source, keysIn(source), object); + } + + /** + * The base implementation of `assignValue` and `assignMergeValue` without + * value checks. + * + * @private + * @param {Object} object The object to modify. + * @param {string} key The key of the property to assign. + * @param {*} value The value to assign. + */ + function baseAssignValue(object, key, value) { + if (key == '__proto__' && defineProperty) { + defineProperty(object, key, { + 'configurable': true, + 'enumerable': true, + 'value': value, + 'writable': true + }); + } else { + object[key] = value; + } + } + + /** + * The base implementation of `_.at` without support for individual paths. + * + * @private + * @param {Object} object The object to iterate over. + * @param {string[]} paths The property paths to pick. + * @returns {Array} Returns the picked elements. + */ + function baseAt(object, paths) { + var index = -1, + length = paths.length, + result = Array(length), + skip = object == null; + + while (++index < length) { + result[index] = skip ? undefined : get(object, paths[index]); + } + return result; + } + + /** + * The base implementation of `_.clamp` which doesn't coerce arguments. + * + * @private + * @param {number} number The number to clamp. + * @param {number} [lower] The lower bound. + * @param {number} upper The upper bound. + * @returns {number} Returns the clamped number. + */ + function baseClamp(number, lower, upper) { + if (number === number) { + if (upper !== undefined) { + number = number <= upper ? number : upper; + } + if (lower !== undefined) { + number = number >= lower ? number : lower; + } + } + return number; + } + + /** + * The base implementation of `_.clone` and `_.cloneDeep` which tracks + * traversed objects. + * + * @private + * @param {*} value The value to clone. + * @param {boolean} bitmask The bitmask flags. + * 1 - Deep clone + * 2 - Flatten inherited properties + * 4 - Clone symbols + * @param {Function} [customizer] The function to customize cloning. + * @param {string} [key] The key of `value`. + * @param {Object} [object] The parent object of `value`. + * @param {Object} [stack] Tracks traversed objects and their clone counterparts. + * @returns {*} Returns the cloned value. + */ + function baseClone(value, bitmask, customizer, key, object, stack) { + var result, + isDeep = bitmask & CLONE_DEEP_FLAG, + isFlat = bitmask & CLONE_FLAT_FLAG, + isFull = bitmask & CLONE_SYMBOLS_FLAG; + + if (customizer) { + result = object ? customizer(value, key, object, stack) : customizer(value); + } + if (result !== undefined) { + return result; + } + if (!isObject(value)) { + return value; + } + var isArr = isArray(value); + if (isArr) { + result = initCloneArray(value); + if (!isDeep) { + return copyArray(value, result); + } + } else { + var tag = getTag(value), + isFunc = tag == funcTag || tag == genTag; + + if (isBuffer(value)) { + return cloneBuffer(value, isDeep); + } + if (tag == objectTag || tag == argsTag || (isFunc && !object)) { + result = (isFlat || isFunc) ? {} : initCloneObject(value); + if (!isDeep) { + return isFlat + ? copySymbolsIn(value, baseAssignIn(result, value)) + : copySymbols(value, baseAssign(result, value)); + } + } else { + if (!cloneableTags[tag]) { + return object ? value : {}; + } + result = initCloneByTag(value, tag, isDeep); + } + } + // Check for circular references and return its corresponding clone. + stack || (stack = new Stack); + var stacked = stack.get(value); + if (stacked) { + return stacked; + } + stack.set(value, result); + + if (isSet(value)) { + value.forEach(function(subValue) { + result.add(baseClone(subValue, bitmask, customizer, subValue, value, stack)); + }); + + return result; + } + + if (isMap(value)) { + value.forEach(function(subValue, key) { + result.set(key, baseClone(subValue, bitmask, customizer, key, value, stack)); + }); + + return result; + } + + var keysFunc = isFull + ? (isFlat ? getAllKeysIn : getAllKeys) + : (isFlat ? keysIn : keys); + + var props = isArr ? undefined : keysFunc(value); + arrayEach(props || value, function(subValue, key) { + if (props) { + key = subValue; + subValue = value[key]; + } + // Recursively populate clone (susceptible to call stack limits). + assignValue(result, key, baseClone(subValue, bitmask, customizer, key, value, stack)); + }); + return result; + } + + /** + * The base implementation of `_.conforms` which doesn't clone `source`. + * + * @private + * @param {Object} source The object of property predicates to conform to. + * @returns {Function} Returns the new spec function. + */ + function baseConforms(source) { + var props = keys(source); + return function(object) { + return baseConformsTo(object, source, props); + }; + } + + /** + * The base implementation of `_.conformsTo` which accepts `props` to check. + * + * @private + * @param {Object} object The object to inspect. + * @param {Object} source The object of property predicates to conform to. + * @returns {boolean} Returns `true` if `object` conforms, else `false`. + */ + function baseConformsTo(object, source, props) { + var length = props.length; + if (object == null) { + return !length; + } + object = Object(object); + while (length--) { + var key = props[length], + predicate = source[key], + value = object[key]; + + if ((value === undefined && !(key in object)) || !predicate(value)) { + return false; + } + } + return true; + } + + /** + * The base implementation of `_.delay` and `_.defer` which accepts `args` + * to provide to `func`. + * + * @private + * @param {Function} func The function to delay. + * @param {number} wait The number of milliseconds to delay invocation. + * @param {Array} args The arguments to provide to `func`. + * @returns {number|Object} Returns the timer id or timeout object. + */ + function baseDelay(func, wait, args) { + if (typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + return setTimeout(function() { func.apply(undefined, args); }, wait); + } + + /** + * The base implementation of methods like `_.difference` without support + * for excluding multiple arrays or iteratee shorthands. + * + * @private + * @param {Array} array The array to inspect. + * @param {Array} values The values to exclude. + * @param {Function} [iteratee] The iteratee invoked per element. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new array of filtered values. + */ + function baseDifference(array, values, iteratee, comparator) { + var index = -1, + includes = arrayIncludes, + isCommon = true, + length = array.length, + result = [], + valuesLength = values.length; + + if (!length) { + return result; + } + if (iteratee) { + values = arrayMap(values, baseUnary(iteratee)); + } + if (comparator) { + includes = arrayIncludesWith; + isCommon = false; + } + else if (values.length >= LARGE_ARRAY_SIZE) { + includes = cacheHas; + isCommon = false; + values = new SetCache(values); + } + outer: + while (++index < length) { + var value = array[index], + computed = iteratee == null ? value : iteratee(value); + + value = (comparator || value !== 0) ? value : 0; + if (isCommon && computed === computed) { + var valuesIndex = valuesLength; + while (valuesIndex--) { + if (values[valuesIndex] === computed) { + continue outer; + } + } + result.push(value); + } + else if (!includes(values, computed, comparator)) { + result.push(value); + } + } + return result; + } + + /** + * The base implementation of `_.forEach` without support for iteratee shorthands. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Array|Object} Returns `collection`. + */ + var baseEach = createBaseEach(baseForOwn); + + /** + * The base implementation of `_.forEachRight` without support for iteratee shorthands. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Array|Object} Returns `collection`. + */ + var baseEachRight = createBaseEach(baseForOwnRight, true); + + /** + * The base implementation of `_.every` without support for iteratee shorthands. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} predicate The function invoked per iteration. + * @returns {boolean} Returns `true` if all elements pass the predicate check, + * else `false` + */ + function baseEvery(collection, predicate) { + var result = true; + baseEach(collection, function(value, index, collection) { + result = !!predicate(value, index, collection); + return result; + }); + return result; + } + + /** + * The base implementation of methods like `_.max` and `_.min` which accepts a + * `comparator` to determine the extremum value. + * + * @private + * @param {Array} array The array to iterate over. + * @param {Function} iteratee The iteratee invoked per iteration. + * @param {Function} comparator The comparator used to compare values. + * @returns {*} Returns the extremum value. + */ + function baseExtremum(array, iteratee, comparator) { + var index = -1, + length = array.length; + + while (++index < length) { + var value = array[index], + current = iteratee(value); + + if (current != null && (computed === undefined + ? (current === current && !isSymbol(current)) + : comparator(current, computed) + )) { + var computed = current, + result = value; + } + } + return result; + } + + /** + * The base implementation of `_.fill` without an iteratee call guard. + * + * @private + * @param {Array} array The array to fill. + * @param {*} value The value to fill `array` with. + * @param {number} [start=0] The start position. + * @param {number} [end=array.length] The end position. + * @returns {Array} Returns `array`. + */ + function baseFill(array, value, start, end) { + var length = array.length; + + start = toInteger(start); + if (start < 0) { + start = -start > length ? 0 : (length + start); + } + end = (end === undefined || end > length) ? length : toInteger(end); + if (end < 0) { + end += length; + } + end = start > end ? 0 : toLength(end); + while (start < end) { + array[start++] = value; + } + return array; + } + + /** + * The base implementation of `_.filter` without support for iteratee shorthands. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} predicate The function invoked per iteration. + * @returns {Array} Returns the new filtered array. + */ + function baseFilter(collection, predicate) { + var result = []; + baseEach(collection, function(value, index, collection) { + if (predicate(value, index, collection)) { + result.push(value); + } + }); + return result; + } + + /** + * The base implementation of `_.flatten` with support for restricting flattening. + * + * @private + * @param {Array} array The array to flatten. + * @param {number} depth The maximum recursion depth. + * @param {boolean} [predicate=isFlattenable] The function invoked per iteration. + * @param {boolean} [isStrict] Restrict to values that pass `predicate` checks. + * @param {Array} [result=[]] The initial result value. + * @returns {Array} Returns the new flattened array. + */ + function baseFlatten(array, depth, predicate, isStrict, result) { + var index = -1, + length = array.length; + + predicate || (predicate = isFlattenable); + result || (result = []); + + while (++index < length) { + var value = array[index]; + if (depth > 0 && predicate(value)) { + if (depth > 1) { + // Recursively flatten arrays (susceptible to call stack limits). + baseFlatten(value, depth - 1, predicate, isStrict, result); + } else { + arrayPush(result, value); + } + } else if (!isStrict) { + result[result.length] = value; + } + } + return result; + } + + /** + * The base implementation of `baseForOwn` which iterates over `object` + * properties returned by `keysFunc` and invokes `iteratee` for each property. + * Iteratee functions may exit iteration early by explicitly returning `false`. + * + * @private + * @param {Object} object The object to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @param {Function} keysFunc The function to get the keys of `object`. + * @returns {Object} Returns `object`. + */ + var baseFor = createBaseFor(); + + /** + * This function is like `baseFor` except that it iterates over properties + * in the opposite order. + * + * @private + * @param {Object} object The object to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @param {Function} keysFunc The function to get the keys of `object`. + * @returns {Object} Returns `object`. + */ + var baseForRight = createBaseFor(true); + + /** + * The base implementation of `_.forOwn` without support for iteratee shorthands. + * + * @private + * @param {Object} object The object to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Object} Returns `object`. + */ + function baseForOwn(object, iteratee) { + return object && baseFor(object, iteratee, keys); + } + + /** + * The base implementation of `_.forOwnRight` without support for iteratee shorthands. + * + * @private + * @param {Object} object The object to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Object} Returns `object`. + */ + function baseForOwnRight(object, iteratee) { + return object && baseForRight(object, iteratee, keys); + } + + /** + * The base implementation of `_.functions` which creates an array of + * `object` function property names filtered from `props`. + * + * @private + * @param {Object} object The object to inspect. + * @param {Array} props The property names to filter. + * @returns {Array} Returns the function names. + */ + function baseFunctions(object, props) { + return arrayFilter(props, function(key) { + return isFunction(object[key]); + }); + } + + /** + * The base implementation of `_.get` without support for default values. + * + * @private + * @param {Object} object The object to query. + * @param {Array|string} path The path of the property to get. + * @returns {*} Returns the resolved value. + */ + function baseGet(object, path) { + path = castPath(path, object); + + var index = 0, + length = path.length; + + while (object != null && index < length) { + object = object[toKey(path[index++])]; + } + return (index && index == length) ? object : undefined; + } + + /** + * The base implementation of `getAllKeys` and `getAllKeysIn` which uses + * `keysFunc` and `symbolsFunc` to get the enumerable property names and + * symbols of `object`. + * + * @private + * @param {Object} object The object to query. + * @param {Function} keysFunc The function to get the keys of `object`. + * @param {Function} symbolsFunc The function to get the symbols of `object`. + * @returns {Array} Returns the array of property names and symbols. + */ + function baseGetAllKeys(object, keysFunc, symbolsFunc) { + var result = keysFunc(object); + return isArray(object) ? result : arrayPush(result, symbolsFunc(object)); + } + + /** + * The base implementation of `getTag` without fallbacks for buggy environments. + * + * @private + * @param {*} value The value to query. + * @returns {string} Returns the `toStringTag`. + */ + function baseGetTag(value) { + if (value == null) { + return value === undefined ? undefinedTag : nullTag; + } + return (symToStringTag && symToStringTag in Object(value)) + ? getRawTag(value) + : objectToString(value); + } + + /** + * The base implementation of `_.gt` which doesn't coerce arguments. + * + * @private + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {boolean} Returns `true` if `value` is greater than `other`, + * else `false`. + */ + function baseGt(value, other) { + return value > other; + } + + /** + * The base implementation of `_.has` without support for deep paths. + * + * @private + * @param {Object} [object] The object to query. + * @param {Array|string} key The key to check. + * @returns {boolean} Returns `true` if `key` exists, else `false`. + */ + function baseHas(object, key) { + return object != null && hasOwnProperty.call(object, key); + } + + /** + * The base implementation of `_.hasIn` without support for deep paths. + * + * @private + * @param {Object} [object] The object to query. + * @param {Array|string} key The key to check. + * @returns {boolean} Returns `true` if `key` exists, else `false`. + */ + function baseHasIn(object, key) { + return object != null && key in Object(object); + } + + /** + * The base implementation of `_.inRange` which doesn't coerce arguments. + * + * @private + * @param {number} number The number to check. + * @param {number} start The start of the range. + * @param {number} end The end of the range. + * @returns {boolean} Returns `true` if `number` is in the range, else `false`. + */ + function baseInRange(number, start, end) { + return number >= nativeMin(start, end) && number < nativeMax(start, end); + } + + /** + * The base implementation of methods like `_.intersection`, without support + * for iteratee shorthands, that accepts an array of arrays to inspect. + * + * @private + * @param {Array} arrays The arrays to inspect. + * @param {Function} [iteratee] The iteratee invoked per element. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new array of shared values. + */ + function baseIntersection(arrays, iteratee, comparator) { + var includes = comparator ? arrayIncludesWith : arrayIncludes, + length = arrays[0].length, + othLength = arrays.length, + othIndex = othLength, + caches = Array(othLength), + maxLength = Infinity, + result = []; + + while (othIndex--) { + var array = arrays[othIndex]; + if (othIndex && iteratee) { + array = arrayMap(array, baseUnary(iteratee)); + } + maxLength = nativeMin(array.length, maxLength); + caches[othIndex] = !comparator && (iteratee || (length >= 120 && array.length >= 120)) + ? new SetCache(othIndex && array) + : undefined; + } + array = arrays[0]; + + var index = -1, + seen = caches[0]; + + outer: + while (++index < length && result.length < maxLength) { + var value = array[index], + computed = iteratee ? iteratee(value) : value; + + value = (comparator || value !== 0) ? value : 0; + if (!(seen + ? cacheHas(seen, computed) + : includes(result, computed, comparator) + )) { + othIndex = othLength; + while (--othIndex) { + var cache = caches[othIndex]; + if (!(cache + ? cacheHas(cache, computed) + : includes(arrays[othIndex], computed, comparator)) + ) { + continue outer; + } + } + if (seen) { + seen.push(computed); + } + result.push(value); + } + } + return result; + } + + /** + * The base implementation of `_.invert` and `_.invertBy` which inverts + * `object` with values transformed by `iteratee` and set by `setter`. + * + * @private + * @param {Object} object The object to iterate over. + * @param {Function} setter The function to set `accumulator` values. + * @param {Function} iteratee The iteratee to transform values. + * @param {Object} accumulator The initial inverted object. + * @returns {Function} Returns `accumulator`. + */ + function baseInverter(object, setter, iteratee, accumulator) { + baseForOwn(object, function(value, key, object) { + setter(accumulator, iteratee(value), key, object); + }); + return accumulator; + } + + /** + * The base implementation of `_.invoke` without support for individual + * method arguments. + * + * @private + * @param {Object} object The object to query. + * @param {Array|string} path The path of the method to invoke. + * @param {Array} args The arguments to invoke the method with. + * @returns {*} Returns the result of the invoked method. + */ + function baseInvoke(object, path, args) { + path = castPath(path, object); + object = parent(object, path); + var func = object == null ? object : object[toKey(last(path))]; + return func == null ? undefined : apply(func, object, args); + } + + /** + * The base implementation of `_.isArguments`. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an `arguments` object, + */ + function baseIsArguments(value) { + return isObjectLike(value) && baseGetTag(value) == argsTag; + } + + /** + * The base implementation of `_.isArrayBuffer` without Node.js optimizations. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an array buffer, else `false`. + */ + function baseIsArrayBuffer(value) { + return isObjectLike(value) && baseGetTag(value) == arrayBufferTag; + } + + /** + * The base implementation of `_.isDate` without Node.js optimizations. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a date object, else `false`. + */ + function baseIsDate(value) { + return isObjectLike(value) && baseGetTag(value) == dateTag; + } + + /** + * The base implementation of `_.isEqual` which supports partial comparisons + * and tracks traversed objects. + * + * @private + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @param {boolean} bitmask The bitmask flags. + * 1 - Unordered comparison + * 2 - Partial comparison + * @param {Function} [customizer] The function to customize comparisons. + * @param {Object} [stack] Tracks traversed `value` and `other` objects. + * @returns {boolean} Returns `true` if the values are equivalent, else `false`. + */ + function baseIsEqual(value, other, bitmask, customizer, stack) { + if (value === other) { + return true; + } + if (value == null || other == null || (!isObjectLike(value) && !isObjectLike(other))) { + return value !== value && other !== other; + } + return baseIsEqualDeep(value, other, bitmask, customizer, baseIsEqual, stack); + } + + /** + * A specialized version of `baseIsEqual` for arrays and objects which performs + * deep comparisons and tracks traversed objects enabling objects with circular + * references to be compared. + * + * @private + * @param {Object} object The object to compare. + * @param {Object} other The other object to compare. + * @param {number} bitmask The bitmask flags. See `baseIsEqual` for more details. + * @param {Function} customizer The function to customize comparisons. + * @param {Function} equalFunc The function to determine equivalents of values. + * @param {Object} [stack] Tracks traversed `object` and `other` objects. + * @returns {boolean} Returns `true` if the objects are equivalent, else `false`. + */ + function baseIsEqualDeep(object, other, bitmask, customizer, equalFunc, stack) { + var objIsArr = isArray(object), + othIsArr = isArray(other), + objTag = objIsArr ? arrayTag : getTag(object), + othTag = othIsArr ? arrayTag : getTag(other); + + objTag = objTag == argsTag ? objectTag : objTag; + othTag = othTag == argsTag ? objectTag : othTag; + + var objIsObj = objTag == objectTag, + othIsObj = othTag == objectTag, + isSameTag = objTag == othTag; + + if (isSameTag && isBuffer(object)) { + if (!isBuffer(other)) { + return false; + } + objIsArr = true; + objIsObj = false; + } + if (isSameTag && !objIsObj) { + stack || (stack = new Stack); + return (objIsArr || isTypedArray(object)) + ? equalArrays(object, other, bitmask, customizer, equalFunc, stack) + : equalByTag(object, other, objTag, bitmask, customizer, equalFunc, stack); + } + if (!(bitmask & COMPARE_PARTIAL_FLAG)) { + var objIsWrapped = objIsObj && hasOwnProperty.call(object, '__wrapped__'), + othIsWrapped = othIsObj && hasOwnProperty.call(other, '__wrapped__'); + + if (objIsWrapped || othIsWrapped) { + var objUnwrapped = objIsWrapped ? object.value() : object, + othUnwrapped = othIsWrapped ? other.value() : other; + + stack || (stack = new Stack); + return equalFunc(objUnwrapped, othUnwrapped, bitmask, customizer, stack); + } + } + if (!isSameTag) { + return false; + } + stack || (stack = new Stack); + return equalObjects(object, other, bitmask, customizer, equalFunc, stack); + } + + /** + * The base implementation of `_.isMap` without Node.js optimizations. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a map, else `false`. + */ + function baseIsMap(value) { + return isObjectLike(value) && getTag(value) == mapTag; + } + + /** + * The base implementation of `_.isMatch` without support for iteratee shorthands. + * + * @private + * @param {Object} object The object to inspect. + * @param {Object} source The object of property values to match. + * @param {Array} matchData The property names, values, and compare flags to match. + * @param {Function} [customizer] The function to customize comparisons. + * @returns {boolean} Returns `true` if `object` is a match, else `false`. + */ + function baseIsMatch(object, source, matchData, customizer) { + var index = matchData.length, + length = index, + noCustomizer = !customizer; + + if (object == null) { + return !length; + } + object = Object(object); + while (index--) { + var data = matchData[index]; + if ((noCustomizer && data[2]) + ? data[1] !== object[data[0]] + : !(data[0] in object) + ) { + return false; + } + } + while (++index < length) { + data = matchData[index]; + var key = data[0], + objValue = object[key], + srcValue = data[1]; + + if (noCustomizer && data[2]) { + if (objValue === undefined && !(key in object)) { + return false; + } + } else { + var stack = new Stack; + if (customizer) { + var result = customizer(objValue, srcValue, key, object, source, stack); + } + if (!(result === undefined + ? baseIsEqual(srcValue, objValue, COMPARE_PARTIAL_FLAG | COMPARE_UNORDERED_FLAG, customizer, stack) + : result + )) { + return false; + } + } + } + return true; + } + + /** + * The base implementation of `_.isNative` without bad shim checks. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a native function, + * else `false`. + */ + function baseIsNative(value) { + if (!isObject(value) || isMasked(value)) { + return false; + } + var pattern = isFunction(value) ? reIsNative : reIsHostCtor; + return pattern.test(toSource(value)); + } + + /** + * The base implementation of `_.isRegExp` without Node.js optimizations. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a regexp, else `false`. + */ + function baseIsRegExp(value) { + return isObjectLike(value) && baseGetTag(value) == regexpTag; + } + + /** + * The base implementation of `_.isSet` without Node.js optimizations. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a set, else `false`. + */ + function baseIsSet(value) { + return isObjectLike(value) && getTag(value) == setTag; + } + + /** + * The base implementation of `_.isTypedArray` without Node.js optimizations. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a typed array, else `false`. + */ + function baseIsTypedArray(value) { + return isObjectLike(value) && + isLength(value.length) && !!typedArrayTags[baseGetTag(value)]; + } + + /** + * The base implementation of `_.iteratee`. + * + * @private + * @param {*} [value=_.identity] The value to convert to an iteratee. + * @returns {Function} Returns the iteratee. + */ + function baseIteratee(value) { + // Don't store the `typeof` result in a variable to avoid a JIT bug in Safari 9. + // See https://bugs.webkit.org/show_bug.cgi?id=156034 for more details. + if (typeof value == 'function') { + return value; + } + if (value == null) { + return identity; + } + if (typeof value == 'object') { + return isArray(value) + ? baseMatchesProperty(value[0], value[1]) + : baseMatches(value); + } + return property(value); + } + + /** + * The base implementation of `_.keys` which doesn't treat sparse arrays as dense. + * + * @private + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property names. + */ + function baseKeys(object) { + if (!isPrototype(object)) { + return nativeKeys(object); + } + var result = []; + for (var key in Object(object)) { + if (hasOwnProperty.call(object, key) && key != 'constructor') { + result.push(key); + } + } + return result; + } + + /** + * The base implementation of `_.keysIn` which doesn't treat sparse arrays as dense. + * + * @private + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property names. + */ + function baseKeysIn(object) { + if (!isObject(object)) { + return nativeKeysIn(object); + } + var isProto = isPrototype(object), + result = []; + + for (var key in object) { + if (!(key == 'constructor' && (isProto || !hasOwnProperty.call(object, key)))) { + result.push(key); + } + } + return result; + } + + /** + * The base implementation of `_.lt` which doesn't coerce arguments. + * + * @private + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {boolean} Returns `true` if `value` is less than `other`, + * else `false`. + */ + function baseLt(value, other) { + return value < other; + } + + /** + * The base implementation of `_.map` without support for iteratee shorthands. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} iteratee The function invoked per iteration. + * @returns {Array} Returns the new mapped array. + */ + function baseMap(collection, iteratee) { + var index = -1, + result = isArrayLike(collection) ? Array(collection.length) : []; + + baseEach(collection, function(value, key, collection) { + result[++index] = iteratee(value, key, collection); + }); + return result; + } + + /** + * The base implementation of `_.matches` which doesn't clone `source`. + * + * @private + * @param {Object} source The object of property values to match. + * @returns {Function} Returns the new spec function. + */ + function baseMatches(source) { + var matchData = getMatchData(source); + if (matchData.length == 1 && matchData[0][2]) { + return matchesStrictComparable(matchData[0][0], matchData[0][1]); + } + return function(object) { + return object === source || baseIsMatch(object, source, matchData); + }; + } + + /** + * The base implementation of `_.matchesProperty` which doesn't clone `srcValue`. + * + * @private + * @param {string} path The path of the property to get. + * @param {*} srcValue The value to match. + * @returns {Function} Returns the new spec function. + */ + function baseMatchesProperty(path, srcValue) { + if (isKey(path) && isStrictComparable(srcValue)) { + return matchesStrictComparable(toKey(path), srcValue); + } + return function(object) { + var objValue = get(object, path); + return (objValue === undefined && objValue === srcValue) + ? hasIn(object, path) + : baseIsEqual(srcValue, objValue, COMPARE_PARTIAL_FLAG | COMPARE_UNORDERED_FLAG); + }; + } + + /** + * The base implementation of `_.merge` without support for multiple sources. + * + * @private + * @param {Object} object The destination object. + * @param {Object} source The source object. + * @param {number} srcIndex The index of `source`. + * @param {Function} [customizer] The function to customize merged values. + * @param {Object} [stack] Tracks traversed source values and their merged + * counterparts. + */ + function baseMerge(object, source, srcIndex, customizer, stack) { + if (object === source) { + return; + } + baseFor(source, function(srcValue, key) { + if (isObject(srcValue)) { + stack || (stack = new Stack); + baseMergeDeep(object, source, key, srcIndex, baseMerge, customizer, stack); + } + else { + var newValue = customizer + ? customizer(safeGet(object, key), srcValue, (key + ''), object, source, stack) + : undefined; + + if (newValue === undefined) { + newValue = srcValue; + } + assignMergeValue(object, key, newValue); + } + }, keysIn); + } + + /** + * A specialized version of `baseMerge` for arrays and objects which performs + * deep merges and tracks traversed objects enabling objects with circular + * references to be merged. + * + * @private + * @param {Object} object The destination object. + * @param {Object} source The source object. + * @param {string} key The key of the value to merge. + * @param {number} srcIndex The index of `source`. + * @param {Function} mergeFunc The function to merge values. + * @param {Function} [customizer] The function to customize assigned values. + * @param {Object} [stack] Tracks traversed source values and their merged + * counterparts. + */ + function baseMergeDeep(object, source, key, srcIndex, mergeFunc, customizer, stack) { + var objValue = safeGet(object, key), + srcValue = safeGet(source, key), + stacked = stack.get(srcValue); + + if (stacked) { + assignMergeValue(object, key, stacked); + return; + } + var newValue = customizer + ? customizer(objValue, srcValue, (key + ''), object, source, stack) + : undefined; + + var isCommon = newValue === undefined; + + if (isCommon) { + var isArr = isArray(srcValue), + isBuff = !isArr && isBuffer(srcValue), + isTyped = !isArr && !isBuff && isTypedArray(srcValue); + + newValue = srcValue; + if (isArr || isBuff || isTyped) { + if (isArray(objValue)) { + newValue = objValue; + } + else if (isArrayLikeObject(objValue)) { + newValue = copyArray(objValue); + } + else if (isBuff) { + isCommon = false; + newValue = cloneBuffer(srcValue, true); + } + else if (isTyped) { + isCommon = false; + newValue = cloneTypedArray(srcValue, true); + } + else { + newValue = []; + } + } + else if (isPlainObject(srcValue) || isArguments(srcValue)) { + newValue = objValue; + if (isArguments(objValue)) { + newValue = toPlainObject(objValue); + } + else if (!isObject(objValue) || isFunction(objValue)) { + newValue = initCloneObject(srcValue); + } + } + else { + isCommon = false; + } + } + if (isCommon) { + // Recursively merge objects and arrays (susceptible to call stack limits). + stack.set(srcValue, newValue); + mergeFunc(newValue, srcValue, srcIndex, customizer, stack); + stack['delete'](srcValue); + } + assignMergeValue(object, key, newValue); + } + + /** + * The base implementation of `_.nth` which doesn't coerce arguments. + * + * @private + * @param {Array} array The array to query. + * @param {number} n The index of the element to return. + * @returns {*} Returns the nth element of `array`. + */ + function baseNth(array, n) { + var length = array.length; + if (!length) { + return; + } + n += n < 0 ? length : 0; + return isIndex(n, length) ? array[n] : undefined; + } + + /** + * The base implementation of `_.orderBy` without param guards. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function[]|Object[]|string[]} iteratees The iteratees to sort by. + * @param {string[]} orders The sort orders of `iteratees`. + * @returns {Array} Returns the new sorted array. + */ + function baseOrderBy(collection, iteratees, orders) { + var index = -1; + iteratees = arrayMap(iteratees.length ? iteratees : [identity], baseUnary(getIteratee())); + + var result = baseMap(collection, function(value, key, collection) { + var criteria = arrayMap(iteratees, function(iteratee) { + return iteratee(value); + }); + return { 'criteria': criteria, 'index': ++index, 'value': value }; + }); + + return baseSortBy(result, function(object, other) { + return compareMultiple(object, other, orders); + }); + } + + /** + * The base implementation of `_.pick` without support for individual + * property identifiers. + * + * @private + * @param {Object} object The source object. + * @param {string[]} paths The property paths to pick. + * @returns {Object} Returns the new object. + */ + function basePick(object, paths) { + return basePickBy(object, paths, function(value, path) { + return hasIn(object, path); + }); + } + + /** + * The base implementation of `_.pickBy` without support for iteratee shorthands. + * + * @private + * @param {Object} object The source object. + * @param {string[]} paths The property paths to pick. + * @param {Function} predicate The function invoked per property. + * @returns {Object} Returns the new object. + */ + function basePickBy(object, paths, predicate) { + var index = -1, + length = paths.length, + result = {}; + + while (++index < length) { + var path = paths[index], + value = baseGet(object, path); + + if (predicate(value, path)) { + baseSet(result, castPath(path, object), value); + } + } + return result; + } + + /** + * A specialized version of `baseProperty` which supports deep paths. + * + * @private + * @param {Array|string} path The path of the property to get. + * @returns {Function} Returns the new accessor function. + */ + function basePropertyDeep(path) { + return function(object) { + return baseGet(object, path); + }; + } + + /** + * The base implementation of `_.pullAllBy` without support for iteratee + * shorthands. + * + * @private + * @param {Array} array The array to modify. + * @param {Array} values The values to remove. + * @param {Function} [iteratee] The iteratee invoked per element. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns `array`. + */ + function basePullAll(array, values, iteratee, comparator) { + var indexOf = comparator ? baseIndexOfWith : baseIndexOf, + index = -1, + length = values.length, + seen = array; + + if (array === values) { + values = copyArray(values); + } + if (iteratee) { + seen = arrayMap(array, baseUnary(iteratee)); + } + while (++index < length) { + var fromIndex = 0, + value = values[index], + computed = iteratee ? iteratee(value) : value; + + while ((fromIndex = indexOf(seen, computed, fromIndex, comparator)) > -1) { + if (seen !== array) { + splice.call(seen, fromIndex, 1); + } + splice.call(array, fromIndex, 1); + } + } + return array; + } + + /** + * The base implementation of `_.pullAt` without support for individual + * indexes or capturing the removed elements. + * + * @private + * @param {Array} array The array to modify. + * @param {number[]} indexes The indexes of elements to remove. + * @returns {Array} Returns `array`. + */ + function basePullAt(array, indexes) { + var length = array ? indexes.length : 0, + lastIndex = length - 1; + + while (length--) { + var index = indexes[length]; + if (length == lastIndex || index !== previous) { + var previous = index; + if (isIndex(index)) { + splice.call(array, index, 1); + } else { + baseUnset(array, index); + } + } + } + return array; + } + + /** + * The base implementation of `_.random` without support for returning + * floating-point numbers. + * + * @private + * @param {number} lower The lower bound. + * @param {number} upper The upper bound. + * @returns {number} Returns the random number. + */ + function baseRandom(lower, upper) { + return lower + nativeFloor(nativeRandom() * (upper - lower + 1)); + } + + /** + * The base implementation of `_.range` and `_.rangeRight` which doesn't + * coerce arguments. + * + * @private + * @param {number} start The start of the range. + * @param {number} end The end of the range. + * @param {number} step The value to increment or decrement by. + * @param {boolean} [fromRight] Specify iterating from right to left. + * @returns {Array} Returns the range of numbers. + */ + function baseRange(start, end, step, fromRight) { + var index = -1, + length = nativeMax(nativeCeil((end - start) / (step || 1)), 0), + result = Array(length); + + while (length--) { + result[fromRight ? length : ++index] = start; + start += step; + } + return result; + } + + /** + * The base implementation of `_.repeat` which doesn't coerce arguments. + * + * @private + * @param {string} string The string to repeat. + * @param {number} n The number of times to repeat the string. + * @returns {string} Returns the repeated string. + */ + function baseRepeat(string, n) { + var result = ''; + if (!string || n < 1 || n > MAX_SAFE_INTEGER) { + return result; + } + // Leverage the exponentiation by squaring algorithm for a faster repeat. + // See https://en.wikipedia.org/wiki/Exponentiation_by_squaring for more details. + do { + if (n % 2) { + result += string; + } + n = nativeFloor(n / 2); + if (n) { + string += string; + } + } while (n); + + return result; + } + + /** + * The base implementation of `_.rest` which doesn't validate or coerce arguments. + * + * @private + * @param {Function} func The function to apply a rest parameter to. + * @param {number} [start=func.length-1] The start position of the rest parameter. + * @returns {Function} Returns the new function. + */ + function baseRest(func, start) { + return setToString(overRest(func, start, identity), func + ''); + } + + /** + * The base implementation of `_.sample`. + * + * @private + * @param {Array|Object} collection The collection to sample. + * @returns {*} Returns the random element. + */ + function baseSample(collection) { + return arraySample(values(collection)); + } + + /** + * The base implementation of `_.sampleSize` without param guards. + * + * @private + * @param {Array|Object} collection The collection to sample. + * @param {number} n The number of elements to sample. + * @returns {Array} Returns the random elements. + */ + function baseSampleSize(collection, n) { + var array = values(collection); + return shuffleSelf(array, baseClamp(n, 0, array.length)); + } + + /** + * The base implementation of `_.set`. + * + * @private + * @param {Object} object The object to modify. + * @param {Array|string} path The path of the property to set. + * @param {*} value The value to set. + * @param {Function} [customizer] The function to customize path creation. + * @returns {Object} Returns `object`. + */ + function baseSet(object, path, value, customizer) { + if (!isObject(object)) { + return object; + } + path = castPath(path, object); + + var index = -1, + length = path.length, + lastIndex = length - 1, + nested = object; + + while (nested != null && ++index < length) { + var key = toKey(path[index]), + newValue = value; + + if (index != lastIndex) { + var objValue = nested[key]; + newValue = customizer ? customizer(objValue, key, nested) : undefined; + if (newValue === undefined) { + newValue = isObject(objValue) + ? objValue + : (isIndex(path[index + 1]) ? [] : {}); + } + } + assignValue(nested, key, newValue); + nested = nested[key]; + } + return object; + } + + /** + * The base implementation of `setData` without support for hot loop shorting. + * + * @private + * @param {Function} func The function to associate metadata with. + * @param {*} data The metadata. + * @returns {Function} Returns `func`. + */ + var baseSetData = !metaMap ? identity : function(func, data) { + metaMap.set(func, data); + return func; + }; + + /** + * The base implementation of `setToString` without support for hot loop shorting. + * + * @private + * @param {Function} func The function to modify. + * @param {Function} string The `toString` result. + * @returns {Function} Returns `func`. + */ + var baseSetToString = !defineProperty ? identity : function(func, string) { + return defineProperty(func, 'toString', { + 'configurable': true, + 'enumerable': false, + 'value': constant(string), + 'writable': true + }); + }; + + /** + * The base implementation of `_.shuffle`. + * + * @private + * @param {Array|Object} collection The collection to shuffle. + * @returns {Array} Returns the new shuffled array. + */ + function baseShuffle(collection) { + return shuffleSelf(values(collection)); + } + + /** + * The base implementation of `_.slice` without an iteratee call guard. + * + * @private + * @param {Array} array The array to slice. + * @param {number} [start=0] The start position. + * @param {number} [end=array.length] The end position. + * @returns {Array} Returns the slice of `array`. + */ + function baseSlice(array, start, end) { + var index = -1, + length = array.length; + + if (start < 0) { + start = -start > length ? 0 : (length + start); + } + end = end > length ? length : end; + if (end < 0) { + end += length; + } + length = start > end ? 0 : ((end - start) >>> 0); + start >>>= 0; + + var result = Array(length); + while (++index < length) { + result[index] = array[index + start]; + } + return result; + } + + /** + * The base implementation of `_.some` without support for iteratee shorthands. + * + * @private + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} predicate The function invoked per iteration. + * @returns {boolean} Returns `true` if any element passes the predicate check, + * else `false`. + */ + function baseSome(collection, predicate) { + var result; + + baseEach(collection, function(value, index, collection) { + result = predicate(value, index, collection); + return !result; + }); + return !!result; + } + + /** + * The base implementation of `_.sortedIndex` and `_.sortedLastIndex` which + * performs a binary search of `array` to determine the index at which `value` + * should be inserted into `array` in order to maintain its sort order. + * + * @private + * @param {Array} array The sorted array to inspect. + * @param {*} value The value to evaluate. + * @param {boolean} [retHighest] Specify returning the highest qualified index. + * @returns {number} Returns the index at which `value` should be inserted + * into `array`. + */ + function baseSortedIndex(array, value, retHighest) { + var low = 0, + high = array == null ? low : array.length; + + if (typeof value == 'number' && value === value && high <= HALF_MAX_ARRAY_LENGTH) { + while (low < high) { + var mid = (low + high) >>> 1, + computed = array[mid]; + + if (computed !== null && !isSymbol(computed) && + (retHighest ? (computed <= value) : (computed < value))) { + low = mid + 1; + } else { + high = mid; + } + } + return high; + } + return baseSortedIndexBy(array, value, identity, retHighest); + } + + /** + * The base implementation of `_.sortedIndexBy` and `_.sortedLastIndexBy` + * which invokes `iteratee` for `value` and each element of `array` to compute + * their sort ranking. The iteratee is invoked with one argument; (value). + * + * @private + * @param {Array} array The sorted array to inspect. + * @param {*} value The value to evaluate. + * @param {Function} iteratee The iteratee invoked per element. + * @param {boolean} [retHighest] Specify returning the highest qualified index. + * @returns {number} Returns the index at which `value` should be inserted + * into `array`. + */ + function baseSortedIndexBy(array, value, iteratee, retHighest) { + value = iteratee(value); + + var low = 0, + high = array == null ? 0 : array.length, + valIsNaN = value !== value, + valIsNull = value === null, + valIsSymbol = isSymbol(value), + valIsUndefined = value === undefined; + + while (low < high) { + var mid = nativeFloor((low + high) / 2), + computed = iteratee(array[mid]), + othIsDefined = computed !== undefined, + othIsNull = computed === null, + othIsReflexive = computed === computed, + othIsSymbol = isSymbol(computed); + + if (valIsNaN) { + var setLow = retHighest || othIsReflexive; + } else if (valIsUndefined) { + setLow = othIsReflexive && (retHighest || othIsDefined); + } else if (valIsNull) { + setLow = othIsReflexive && othIsDefined && (retHighest || !othIsNull); + } else if (valIsSymbol) { + setLow = othIsReflexive && othIsDefined && !othIsNull && (retHighest || !othIsSymbol); + } else if (othIsNull || othIsSymbol) { + setLow = false; + } else { + setLow = retHighest ? (computed <= value) : (computed < value); + } + if (setLow) { + low = mid + 1; + } else { + high = mid; + } + } + return nativeMin(high, MAX_ARRAY_INDEX); + } + + /** + * The base implementation of `_.sortedUniq` and `_.sortedUniqBy` without + * support for iteratee shorthands. + * + * @private + * @param {Array} array The array to inspect. + * @param {Function} [iteratee] The iteratee invoked per element. + * @returns {Array} Returns the new duplicate free array. + */ + function baseSortedUniq(array, iteratee) { + var index = -1, + length = array.length, + resIndex = 0, + result = []; + + while (++index < length) { + var value = array[index], + computed = iteratee ? iteratee(value) : value; + + if (!index || !eq(computed, seen)) { + var seen = computed; + result[resIndex++] = value === 0 ? 0 : value; + } + } + return result; + } + + /** + * The base implementation of `_.toNumber` which doesn't ensure correct + * conversions of binary, hexadecimal, or octal string values. + * + * @private + * @param {*} value The value to process. + * @returns {number} Returns the number. + */ + function baseToNumber(value) { + if (typeof value == 'number') { + return value; + } + if (isSymbol(value)) { + return NAN; + } + return +value; + } + + /** + * The base implementation of `_.toString` which doesn't convert nullish + * values to empty strings. + * + * @private + * @param {*} value The value to process. + * @returns {string} Returns the string. + */ + function baseToString(value) { + // Exit early for strings to avoid a performance hit in some environments. + if (typeof value == 'string') { + return value; + } + if (isArray(value)) { + // Recursively convert values (susceptible to call stack limits). + return arrayMap(value, baseToString) + ''; + } + if (isSymbol(value)) { + return symbolToString ? symbolToString.call(value) : ''; + } + var result = (value + ''); + return (result == '0' && (1 / value) == -INFINITY) ? '-0' : result; + } + + /** + * The base implementation of `_.uniqBy` without support for iteratee shorthands. + * + * @private + * @param {Array} array The array to inspect. + * @param {Function} [iteratee] The iteratee invoked per element. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new duplicate free array. + */ + function baseUniq(array, iteratee, comparator) { + var index = -1, + includes = arrayIncludes, + length = array.length, + isCommon = true, + result = [], + seen = result; + + if (comparator) { + isCommon = false; + includes = arrayIncludesWith; + } + else if (length >= LARGE_ARRAY_SIZE) { + var set = iteratee ? null : createSet(array); + if (set) { + return setToArray(set); + } + isCommon = false; + includes = cacheHas; + seen = new SetCache; + } + else { + seen = iteratee ? [] : result; + } + outer: + while (++index < length) { + var value = array[index], + computed = iteratee ? iteratee(value) : value; + + value = (comparator || value !== 0) ? value : 0; + if (isCommon && computed === computed) { + var seenIndex = seen.length; + while (seenIndex--) { + if (seen[seenIndex] === computed) { + continue outer; + } + } + if (iteratee) { + seen.push(computed); + } + result.push(value); + } + else if (!includes(seen, computed, comparator)) { + if (seen !== result) { + seen.push(computed); + } + result.push(value); + } + } + return result; + } + + /** + * The base implementation of `_.unset`. + * + * @private + * @param {Object} object The object to modify. + * @param {Array|string} path The property path to unset. + * @returns {boolean} Returns `true` if the property is deleted, else `false`. + */ + function baseUnset(object, path) { + path = castPath(path, object); + object = parent(object, path); + return object == null || delete object[toKey(last(path))]; + } + + /** + * The base implementation of `_.update`. + * + * @private + * @param {Object} object The object to modify. + * @param {Array|string} path The path of the property to update. + * @param {Function} updater The function to produce the updated value. + * @param {Function} [customizer] The function to customize path creation. + * @returns {Object} Returns `object`. + */ + function baseUpdate(object, path, updater, customizer) { + return baseSet(object, path, updater(baseGet(object, path)), customizer); + } + + /** + * The base implementation of methods like `_.dropWhile` and `_.takeWhile` + * without support for iteratee shorthands. + * + * @private + * @param {Array} array The array to query. + * @param {Function} predicate The function invoked per iteration. + * @param {boolean} [isDrop] Specify dropping elements instead of taking them. + * @param {boolean} [fromRight] Specify iterating from right to left. + * @returns {Array} Returns the slice of `array`. + */ + function baseWhile(array, predicate, isDrop, fromRight) { + var length = array.length, + index = fromRight ? length : -1; + + while ((fromRight ? index-- : ++index < length) && + predicate(array[index], index, array)) {} + + return isDrop + ? baseSlice(array, (fromRight ? 0 : index), (fromRight ? index + 1 : length)) + : baseSlice(array, (fromRight ? index + 1 : 0), (fromRight ? length : index)); + } + + /** + * The base implementation of `wrapperValue` which returns the result of + * performing a sequence of actions on the unwrapped `value`, where each + * successive action is supplied the return value of the previous. + * + * @private + * @param {*} value The unwrapped value. + * @param {Array} actions Actions to perform to resolve the unwrapped value. + * @returns {*} Returns the resolved value. + */ + function baseWrapperValue(value, actions) { + var result = value; + if (result instanceof LazyWrapper) { + result = result.value(); + } + return arrayReduce(actions, function(result, action) { + return action.func.apply(action.thisArg, arrayPush([result], action.args)); + }, result); + } + + /** + * The base implementation of methods like `_.xor`, without support for + * iteratee shorthands, that accepts an array of arrays to inspect. + * + * @private + * @param {Array} arrays The arrays to inspect. + * @param {Function} [iteratee] The iteratee invoked per element. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new array of values. + */ + function baseXor(arrays, iteratee, comparator) { + var length = arrays.length; + if (length < 2) { + return length ? baseUniq(arrays[0]) : []; + } + var index = -1, + result = Array(length); + + while (++index < length) { + var array = arrays[index], + othIndex = -1; + + while (++othIndex < length) { + if (othIndex != index) { + result[index] = baseDifference(result[index] || array, arrays[othIndex], iteratee, comparator); + } + } + } + return baseUniq(baseFlatten(result, 1), iteratee, comparator); + } + + /** + * This base implementation of `_.zipObject` which assigns values using `assignFunc`. + * + * @private + * @param {Array} props The property identifiers. + * @param {Array} values The property values. + * @param {Function} assignFunc The function to assign values. + * @returns {Object} Returns the new object. + */ + function baseZipObject(props, values, assignFunc) { + var index = -1, + length = props.length, + valsLength = values.length, + result = {}; + + while (++index < length) { + var value = index < valsLength ? values[index] : undefined; + assignFunc(result, props[index], value); + } + return result; + } + + /** + * Casts `value` to an empty array if it's not an array like object. + * + * @private + * @param {*} value The value to inspect. + * @returns {Array|Object} Returns the cast array-like object. + */ + function castArrayLikeObject(value) { + return isArrayLikeObject(value) ? value : []; + } + + /** + * Casts `value` to `identity` if it's not a function. + * + * @private + * @param {*} value The value to inspect. + * @returns {Function} Returns cast function. + */ + function castFunction(value) { + return typeof value == 'function' ? value : identity; + } + + /** + * Casts `value` to a path array if it's not one. + * + * @private + * @param {*} value The value to inspect. + * @param {Object} [object] The object to query keys on. + * @returns {Array} Returns the cast property path array. + */ + function castPath(value, object) { + if (isArray(value)) { + return value; + } + return isKey(value, object) ? [value] : stringToPath(toString(value)); + } + + /** + * A `baseRest` alias which can be replaced with `identity` by module + * replacement plugins. + * + * @private + * @type {Function} + * @param {Function} func The function to apply a rest parameter to. + * @returns {Function} Returns the new function. + */ + var castRest = baseRest; + + /** + * Casts `array` to a slice if it's needed. + * + * @private + * @param {Array} array The array to inspect. + * @param {number} start The start position. + * @param {number} [end=array.length] The end position. + * @returns {Array} Returns the cast slice. + */ + function castSlice(array, start, end) { + var length = array.length; + end = end === undefined ? length : end; + return (!start && end >= length) ? array : baseSlice(array, start, end); + } + + /** + * A simple wrapper around the global [`clearTimeout`](https://mdn.io/clearTimeout). + * + * @private + * @param {number|Object} id The timer id or timeout object of the timer to clear. + */ + var clearTimeout = ctxClearTimeout || function(id) { + return root.clearTimeout(id); + }; + + /** + * Creates a clone of `buffer`. + * + * @private + * @param {Buffer} buffer The buffer to clone. + * @param {boolean} [isDeep] Specify a deep clone. + * @returns {Buffer} Returns the cloned buffer. + */ + function cloneBuffer(buffer, isDeep) { + if (isDeep) { + return buffer.slice(); + } + var length = buffer.length, + result = allocUnsafe ? allocUnsafe(length) : new buffer.constructor(length); + + buffer.copy(result); + return result; + } + + /** + * Creates a clone of `arrayBuffer`. + * + * @private + * @param {ArrayBuffer} arrayBuffer The array buffer to clone. + * @returns {ArrayBuffer} Returns the cloned array buffer. + */ + function cloneArrayBuffer(arrayBuffer) { + var result = new arrayBuffer.constructor(arrayBuffer.byteLength); + new Uint8Array(result).set(new Uint8Array(arrayBuffer)); + return result; + } + + /** + * Creates a clone of `dataView`. + * + * @private + * @param {Object} dataView The data view to clone. + * @param {boolean} [isDeep] Specify a deep clone. + * @returns {Object} Returns the cloned data view. + */ + function cloneDataView(dataView, isDeep) { + var buffer = isDeep ? cloneArrayBuffer(dataView.buffer) : dataView.buffer; + return new dataView.constructor(buffer, dataView.byteOffset, dataView.byteLength); + } + + /** + * Creates a clone of `regexp`. + * + * @private + * @param {Object} regexp The regexp to clone. + * @returns {Object} Returns the cloned regexp. + */ + function cloneRegExp(regexp) { + var result = new regexp.constructor(regexp.source, reFlags.exec(regexp)); + result.lastIndex = regexp.lastIndex; + return result; + } + + /** + * Creates a clone of the `symbol` object. + * + * @private + * @param {Object} symbol The symbol object to clone. + * @returns {Object} Returns the cloned symbol object. + */ + function cloneSymbol(symbol) { + return symbolValueOf ? Object(symbolValueOf.call(symbol)) : {}; + } + + /** + * Creates a clone of `typedArray`. + * + * @private + * @param {Object} typedArray The typed array to clone. + * @param {boolean} [isDeep] Specify a deep clone. + * @returns {Object} Returns the cloned typed array. + */ + function cloneTypedArray(typedArray, isDeep) { + var buffer = isDeep ? cloneArrayBuffer(typedArray.buffer) : typedArray.buffer; + return new typedArray.constructor(buffer, typedArray.byteOffset, typedArray.length); + } + + /** + * Compares values to sort them in ascending order. + * + * @private + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {number} Returns the sort order indicator for `value`. + */ + function compareAscending(value, other) { + if (value !== other) { + var valIsDefined = value !== undefined, + valIsNull = value === null, + valIsReflexive = value === value, + valIsSymbol = isSymbol(value); + + var othIsDefined = other !== undefined, + othIsNull = other === null, + othIsReflexive = other === other, + othIsSymbol = isSymbol(other); + + if ((!othIsNull && !othIsSymbol && !valIsSymbol && value > other) || + (valIsSymbol && othIsDefined && othIsReflexive && !othIsNull && !othIsSymbol) || + (valIsNull && othIsDefined && othIsReflexive) || + (!valIsDefined && othIsReflexive) || + !valIsReflexive) { + return 1; + } + if ((!valIsNull && !valIsSymbol && !othIsSymbol && value < other) || + (othIsSymbol && valIsDefined && valIsReflexive && !valIsNull && !valIsSymbol) || + (othIsNull && valIsDefined && valIsReflexive) || + (!othIsDefined && valIsReflexive) || + !othIsReflexive) { + return -1; + } + } + return 0; + } + + /** + * Used by `_.orderBy` to compare multiple properties of a value to another + * and stable sort them. + * + * If `orders` is unspecified, all values are sorted in ascending order. Otherwise, + * specify an order of "desc" for descending or "asc" for ascending sort order + * of corresponding values. + * + * @private + * @param {Object} object The object to compare. + * @param {Object} other The other object to compare. + * @param {boolean[]|string[]} orders The order to sort by for each property. + * @returns {number} Returns the sort order indicator for `object`. + */ + function compareMultiple(object, other, orders) { + var index = -1, + objCriteria = object.criteria, + othCriteria = other.criteria, + length = objCriteria.length, + ordersLength = orders.length; + + while (++index < length) { + var result = compareAscending(objCriteria[index], othCriteria[index]); + if (result) { + if (index >= ordersLength) { + return result; + } + var order = orders[index]; + return result * (order == 'desc' ? -1 : 1); + } + } + // Fixes an `Array#sort` bug in the JS engine embedded in Adobe applications + // that causes it, under certain circumstances, to provide the same value for + // `object` and `other`. See https://github.com/jashkenas/underscore/pull/1247 + // for more details. + // + // This also ensures a stable sort in V8 and other engines. + // See https://bugs.chromium.org/p/v8/issues/detail?id=90 for more details. + return object.index - other.index; + } + + /** + * Creates an array that is the composition of partially applied arguments, + * placeholders, and provided arguments into a single array of arguments. + * + * @private + * @param {Array} args The provided arguments. + * @param {Array} partials The arguments to prepend to those provided. + * @param {Array} holders The `partials` placeholder indexes. + * @params {boolean} [isCurried] Specify composing for a curried function. + * @returns {Array} Returns the new array of composed arguments. + */ + function composeArgs(args, partials, holders, isCurried) { + var argsIndex = -1, + argsLength = args.length, + holdersLength = holders.length, + leftIndex = -1, + leftLength = partials.length, + rangeLength = nativeMax(argsLength - holdersLength, 0), + result = Array(leftLength + rangeLength), + isUncurried = !isCurried; + + while (++leftIndex < leftLength) { + result[leftIndex] = partials[leftIndex]; + } + while (++argsIndex < holdersLength) { + if (isUncurried || argsIndex < argsLength) { + result[holders[argsIndex]] = args[argsIndex]; + } + } + while (rangeLength--) { + result[leftIndex++] = args[argsIndex++]; + } + return result; + } + + /** + * This function is like `composeArgs` except that the arguments composition + * is tailored for `_.partialRight`. + * + * @private + * @param {Array} args The provided arguments. + * @param {Array} partials The arguments to append to those provided. + * @param {Array} holders The `partials` placeholder indexes. + * @params {boolean} [isCurried] Specify composing for a curried function. + * @returns {Array} Returns the new array of composed arguments. + */ + function composeArgsRight(args, partials, holders, isCurried) { + var argsIndex = -1, + argsLength = args.length, + holdersIndex = -1, + holdersLength = holders.length, + rightIndex = -1, + rightLength = partials.length, + rangeLength = nativeMax(argsLength - holdersLength, 0), + result = Array(rangeLength + rightLength), + isUncurried = !isCurried; + + while (++argsIndex < rangeLength) { + result[argsIndex] = args[argsIndex]; + } + var offset = argsIndex; + while (++rightIndex < rightLength) { + result[offset + rightIndex] = partials[rightIndex]; + } + while (++holdersIndex < holdersLength) { + if (isUncurried || argsIndex < argsLength) { + result[offset + holders[holdersIndex]] = args[argsIndex++]; + } + } + return result; + } + + /** + * Copies the values of `source` to `array`. + * + * @private + * @param {Array} source The array to copy values from. + * @param {Array} [array=[]] The array to copy values to. + * @returns {Array} Returns `array`. + */ + function copyArray(source, array) { + var index = -1, + length = source.length; + + array || (array = Array(length)); + while (++index < length) { + array[index] = source[index]; + } + return array; + } + + /** + * Copies properties of `source` to `object`. + * + * @private + * @param {Object} source The object to copy properties from. + * @param {Array} props The property identifiers to copy. + * @param {Object} [object={}] The object to copy properties to. + * @param {Function} [customizer] The function to customize copied values. + * @returns {Object} Returns `object`. + */ + function copyObject(source, props, object, customizer) { + var isNew = !object; + object || (object = {}); + + var index = -1, + length = props.length; + + while (++index < length) { + var key = props[index]; + + var newValue = customizer + ? customizer(object[key], source[key], key, object, source) + : undefined; + + if (newValue === undefined) { + newValue = source[key]; + } + if (isNew) { + baseAssignValue(object, key, newValue); + } else { + assignValue(object, key, newValue); + } + } + return object; + } + + /** + * Copies own symbols of `source` to `object`. + * + * @private + * @param {Object} source The object to copy symbols from. + * @param {Object} [object={}] The object to copy symbols to. + * @returns {Object} Returns `object`. + */ + function copySymbols(source, object) { + return copyObject(source, getSymbols(source), object); + } + + /** + * Copies own and inherited symbols of `source` to `object`. + * + * @private + * @param {Object} source The object to copy symbols from. + * @param {Object} [object={}] The object to copy symbols to. + * @returns {Object} Returns `object`. + */ + function copySymbolsIn(source, object) { + return copyObject(source, getSymbolsIn(source), object); + } + + /** + * Creates a function like `_.groupBy`. + * + * @private + * @param {Function} setter The function to set accumulator values. + * @param {Function} [initializer] The accumulator object initializer. + * @returns {Function} Returns the new aggregator function. + */ + function createAggregator(setter, initializer) { + return function(collection, iteratee) { + var func = isArray(collection) ? arrayAggregator : baseAggregator, + accumulator = initializer ? initializer() : {}; + + return func(collection, setter, getIteratee(iteratee, 2), accumulator); + }; + } + + /** + * Creates a function like `_.assign`. + * + * @private + * @param {Function} assigner The function to assign values. + * @returns {Function} Returns the new assigner function. + */ + function createAssigner(assigner) { + return baseRest(function(object, sources) { + var index = -1, + length = sources.length, + customizer = length > 1 ? sources[length - 1] : undefined, + guard = length > 2 ? sources[2] : undefined; + + customizer = (assigner.length > 3 && typeof customizer == 'function') + ? (length--, customizer) + : undefined; + + if (guard && isIterateeCall(sources[0], sources[1], guard)) { + customizer = length < 3 ? undefined : customizer; + length = 1; + } + object = Object(object); + while (++index < length) { + var source = sources[index]; + if (source) { + assigner(object, source, index, customizer); + } + } + return object; + }); + } + + /** + * Creates a `baseEach` or `baseEachRight` function. + * + * @private + * @param {Function} eachFunc The function to iterate over a collection. + * @param {boolean} [fromRight] Specify iterating from right to left. + * @returns {Function} Returns the new base function. + */ + function createBaseEach(eachFunc, fromRight) { + return function(collection, iteratee) { + if (collection == null) { + return collection; + } + if (!isArrayLike(collection)) { + return eachFunc(collection, iteratee); + } + var length = collection.length, + index = fromRight ? length : -1, + iterable = Object(collection); + + while ((fromRight ? index-- : ++index < length)) { + if (iteratee(iterable[index], index, iterable) === false) { + break; + } + } + return collection; + }; + } + + /** + * Creates a base function for methods like `_.forIn` and `_.forOwn`. + * + * @private + * @param {boolean} [fromRight] Specify iterating from right to left. + * @returns {Function} Returns the new base function. + */ + function createBaseFor(fromRight) { + return function(object, iteratee, keysFunc) { + var index = -1, + iterable = Object(object), + props = keysFunc(object), + length = props.length; + + while (length--) { + var key = props[fromRight ? length : ++index]; + if (iteratee(iterable[key], key, iterable) === false) { + break; + } + } + return object; + }; + } + + /** + * Creates a function that wraps `func` to invoke it with the optional `this` + * binding of `thisArg`. + * + * @private + * @param {Function} func The function to wrap. + * @param {number} bitmask The bitmask flags. See `createWrap` for more details. + * @param {*} [thisArg] The `this` binding of `func`. + * @returns {Function} Returns the new wrapped function. + */ + function createBind(func, bitmask, thisArg) { + var isBind = bitmask & WRAP_BIND_FLAG, + Ctor = createCtor(func); + + function wrapper() { + var fn = (this && this !== root && this instanceof wrapper) ? Ctor : func; + return fn.apply(isBind ? thisArg : this, arguments); + } + return wrapper; + } + + /** + * Creates a function like `_.lowerFirst`. + * + * @private + * @param {string} methodName The name of the `String` case method to use. + * @returns {Function} Returns the new case function. + */ + function createCaseFirst(methodName) { + return function(string) { + string = toString(string); + + var strSymbols = hasUnicode(string) + ? stringToArray(string) + : undefined; + + var chr = strSymbols + ? strSymbols[0] + : string.charAt(0); + + var trailing = strSymbols + ? castSlice(strSymbols, 1).join('') + : string.slice(1); + + return chr[methodName]() + trailing; + }; + } + + /** + * Creates a function like `_.camelCase`. + * + * @private + * @param {Function} callback The function to combine each word. + * @returns {Function} Returns the new compounder function. + */ + function createCompounder(callback) { + return function(string) { + return arrayReduce(words(deburr(string).replace(reApos, '')), callback, ''); + }; + } + + /** + * Creates a function that produces an instance of `Ctor` regardless of + * whether it was invoked as part of a `new` expression or by `call` or `apply`. + * + * @private + * @param {Function} Ctor The constructor to wrap. + * @returns {Function} Returns the new wrapped function. + */ + function createCtor(Ctor) { + return function() { + // Use a `switch` statement to work with class constructors. See + // http://ecma-international.org/ecma-262/7.0/#sec-ecmascript-function-objects-call-thisargument-argumentslist + // for more details. + var args = arguments; + switch (args.length) { + case 0: return new Ctor; + case 1: return new Ctor(args[0]); + case 2: return new Ctor(args[0], args[1]); + case 3: return new Ctor(args[0], args[1], args[2]); + case 4: return new Ctor(args[0], args[1], args[2], args[3]); + case 5: return new Ctor(args[0], args[1], args[2], args[3], args[4]); + case 6: return new Ctor(args[0], args[1], args[2], args[3], args[4], args[5]); + case 7: return new Ctor(args[0], args[1], args[2], args[3], args[4], args[5], args[6]); + } + var thisBinding = baseCreate(Ctor.prototype), + result = Ctor.apply(thisBinding, args); + + // Mimic the constructor's `return` behavior. + // See https://es5.github.io/#x13.2.2 for more details. + return isObject(result) ? result : thisBinding; + }; + } + + /** + * Creates a function that wraps `func` to enable currying. + * + * @private + * @param {Function} func The function to wrap. + * @param {number} bitmask The bitmask flags. See `createWrap` for more details. + * @param {number} arity The arity of `func`. + * @returns {Function} Returns the new wrapped function. + */ + function createCurry(func, bitmask, arity) { + var Ctor = createCtor(func); + + function wrapper() { + var length = arguments.length, + args = Array(length), + index = length, + placeholder = getHolder(wrapper); + + while (index--) { + args[index] = arguments[index]; + } + var holders = (length < 3 && args[0] !== placeholder && args[length - 1] !== placeholder) + ? [] + : replaceHolders(args, placeholder); + + length -= holders.length; + if (length < arity) { + return createRecurry( + func, bitmask, createHybrid, wrapper.placeholder, undefined, + args, holders, undefined, undefined, arity - length); + } + var fn = (this && this !== root && this instanceof wrapper) ? Ctor : func; + return apply(fn, this, args); + } + return wrapper; + } + + /** + * Creates a `_.find` or `_.findLast` function. + * + * @private + * @param {Function} findIndexFunc The function to find the collection index. + * @returns {Function} Returns the new find function. + */ + function createFind(findIndexFunc) { + return function(collection, predicate, fromIndex) { + var iterable = Object(collection); + if (!isArrayLike(collection)) { + var iteratee = getIteratee(predicate, 3); + collection = keys(collection); + predicate = function(key) { return iteratee(iterable[key], key, iterable); }; + } + var index = findIndexFunc(collection, predicate, fromIndex); + return index > -1 ? iterable[iteratee ? collection[index] : index] : undefined; + }; + } + + /** + * Creates a `_.flow` or `_.flowRight` function. + * + * @private + * @param {boolean} [fromRight] Specify iterating from right to left. + * @returns {Function} Returns the new flow function. + */ + function createFlow(fromRight) { + return flatRest(function(funcs) { + var length = funcs.length, + index = length, + prereq = LodashWrapper.prototype.thru; + + if (fromRight) { + funcs.reverse(); + } + while (index--) { + var func = funcs[index]; + if (typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + if (prereq && !wrapper && getFuncName(func) == 'wrapper') { + var wrapper = new LodashWrapper([], true); + } + } + index = wrapper ? index : length; + while (++index < length) { + func = funcs[index]; + + var funcName = getFuncName(func), + data = funcName == 'wrapper' ? getData(func) : undefined; + + if (data && isLaziable(data[0]) && + data[1] == (WRAP_ARY_FLAG | WRAP_CURRY_FLAG | WRAP_PARTIAL_FLAG | WRAP_REARG_FLAG) && + !data[4].length && data[9] == 1 + ) { + wrapper = wrapper[getFuncName(data[0])].apply(wrapper, data[3]); + } else { + wrapper = (func.length == 1 && isLaziable(func)) + ? wrapper[funcName]() + : wrapper.thru(func); + } + } + return function() { + var args = arguments, + value = args[0]; + + if (wrapper && args.length == 1 && isArray(value)) { + return wrapper.plant(value).value(); + } + var index = 0, + result = length ? funcs[index].apply(this, args) : value; + + while (++index < length) { + result = funcs[index].call(this, result); + } + return result; + }; + }); + } + + /** + * Creates a function that wraps `func` to invoke it with optional `this` + * binding of `thisArg`, partial application, and currying. + * + * @private + * @param {Function|string} func The function or method name to wrap. + * @param {number} bitmask The bitmask flags. See `createWrap` for more details. + * @param {*} [thisArg] The `this` binding of `func`. + * @param {Array} [partials] The arguments to prepend to those provided to + * the new function. + * @param {Array} [holders] The `partials` placeholder indexes. + * @param {Array} [partialsRight] The arguments to append to those provided + * to the new function. + * @param {Array} [holdersRight] The `partialsRight` placeholder indexes. + * @param {Array} [argPos] The argument positions of the new function. + * @param {number} [ary] The arity cap of `func`. + * @param {number} [arity] The arity of `func`. + * @returns {Function} Returns the new wrapped function. + */ + function createHybrid(func, bitmask, thisArg, partials, holders, partialsRight, holdersRight, argPos, ary, arity) { + var isAry = bitmask & WRAP_ARY_FLAG, + isBind = bitmask & WRAP_BIND_FLAG, + isBindKey = bitmask & WRAP_BIND_KEY_FLAG, + isCurried = bitmask & (WRAP_CURRY_FLAG | WRAP_CURRY_RIGHT_FLAG), + isFlip = bitmask & WRAP_FLIP_FLAG, + Ctor = isBindKey ? undefined : createCtor(func); + + function wrapper() { + var length = arguments.length, + args = Array(length), + index = length; + + while (index--) { + args[index] = arguments[index]; + } + if (isCurried) { + var placeholder = getHolder(wrapper), + holdersCount = countHolders(args, placeholder); + } + if (partials) { + args = composeArgs(args, partials, holders, isCurried); + } + if (partialsRight) { + args = composeArgsRight(args, partialsRight, holdersRight, isCurried); + } + length -= holdersCount; + if (isCurried && length < arity) { + var newHolders = replaceHolders(args, placeholder); + return createRecurry( + func, bitmask, createHybrid, wrapper.placeholder, thisArg, + args, newHolders, argPos, ary, arity - length + ); + } + var thisBinding = isBind ? thisArg : this, + fn = isBindKey ? thisBinding[func] : func; + + length = args.length; + if (argPos) { + args = reorder(args, argPos); + } else if (isFlip && length > 1) { + args.reverse(); + } + if (isAry && ary < length) { + args.length = ary; + } + if (this && this !== root && this instanceof wrapper) { + fn = Ctor || createCtor(fn); + } + return fn.apply(thisBinding, args); + } + return wrapper; + } + + /** + * Creates a function like `_.invertBy`. + * + * @private + * @param {Function} setter The function to set accumulator values. + * @param {Function} toIteratee The function to resolve iteratees. + * @returns {Function} Returns the new inverter function. + */ + function createInverter(setter, toIteratee) { + return function(object, iteratee) { + return baseInverter(object, setter, toIteratee(iteratee), {}); + }; + } + + /** + * Creates a function that performs a mathematical operation on two values. + * + * @private + * @param {Function} operator The function to perform the operation. + * @param {number} [defaultValue] The value used for `undefined` arguments. + * @returns {Function} Returns the new mathematical operation function. + */ + function createMathOperation(operator, defaultValue) { + return function(value, other) { + var result; + if (value === undefined && other === undefined) { + return defaultValue; + } + if (value !== undefined) { + result = value; + } + if (other !== undefined) { + if (result === undefined) { + return other; + } + if (typeof value == 'string' || typeof other == 'string') { + value = baseToString(value); + other = baseToString(other); + } else { + value = baseToNumber(value); + other = baseToNumber(other); + } + result = operator(value, other); + } + return result; + }; + } + + /** + * Creates a function like `_.over`. + * + * @private + * @param {Function} arrayFunc The function to iterate over iteratees. + * @returns {Function} Returns the new over function. + */ + function createOver(arrayFunc) { + return flatRest(function(iteratees) { + iteratees = arrayMap(iteratees, baseUnary(getIteratee())); + return baseRest(function(args) { + var thisArg = this; + return arrayFunc(iteratees, function(iteratee) { + return apply(iteratee, thisArg, args); + }); + }); + }); + } + + /** + * Creates the padding for `string` based on `length`. The `chars` string + * is truncated if the number of characters exceeds `length`. + * + * @private + * @param {number} length The padding length. + * @param {string} [chars=' '] The string used as padding. + * @returns {string} Returns the padding for `string`. + */ + function createPadding(length, chars) { + chars = chars === undefined ? ' ' : baseToString(chars); + + var charsLength = chars.length; + if (charsLength < 2) { + return charsLength ? baseRepeat(chars, length) : chars; + } + var result = baseRepeat(chars, nativeCeil(length / stringSize(chars))); + return hasUnicode(chars) + ? castSlice(stringToArray(result), 0, length).join('') + : result.slice(0, length); + } + + /** + * Creates a function that wraps `func` to invoke it with the `this` binding + * of `thisArg` and `partials` prepended to the arguments it receives. + * + * @private + * @param {Function} func The function to wrap. + * @param {number} bitmask The bitmask flags. See `createWrap` for more details. + * @param {*} thisArg The `this` binding of `func`. + * @param {Array} partials The arguments to prepend to those provided to + * the new function. + * @returns {Function} Returns the new wrapped function. + */ + function createPartial(func, bitmask, thisArg, partials) { + var isBind = bitmask & WRAP_BIND_FLAG, + Ctor = createCtor(func); + + function wrapper() { + var argsIndex = -1, + argsLength = arguments.length, + leftIndex = -1, + leftLength = partials.length, + args = Array(leftLength + argsLength), + fn = (this && this !== root && this instanceof wrapper) ? Ctor : func; + + while (++leftIndex < leftLength) { + args[leftIndex] = partials[leftIndex]; + } + while (argsLength--) { + args[leftIndex++] = arguments[++argsIndex]; + } + return apply(fn, isBind ? thisArg : this, args); + } + return wrapper; + } + + /** + * Creates a `_.range` or `_.rangeRight` function. + * + * @private + * @param {boolean} [fromRight] Specify iterating from right to left. + * @returns {Function} Returns the new range function. + */ + function createRange(fromRight) { + return function(start, end, step) { + if (step && typeof step != 'number' && isIterateeCall(start, end, step)) { + end = step = undefined; + } + // Ensure the sign of `-0` is preserved. + start = toFinite(start); + if (end === undefined) { + end = start; + start = 0; + } else { + end = toFinite(end); + } + step = step === undefined ? (start < end ? 1 : -1) : toFinite(step); + return baseRange(start, end, step, fromRight); + }; + } + + /** + * Creates a function that performs a relational operation on two values. + * + * @private + * @param {Function} operator The function to perform the operation. + * @returns {Function} Returns the new relational operation function. + */ + function createRelationalOperation(operator) { + return function(value, other) { + if (!(typeof value == 'string' && typeof other == 'string')) { + value = toNumber(value); + other = toNumber(other); + } + return operator(value, other); + }; + } + + /** + * Creates a function that wraps `func` to continue currying. + * + * @private + * @param {Function} func The function to wrap. + * @param {number} bitmask The bitmask flags. See `createWrap` for more details. + * @param {Function} wrapFunc The function to create the `func` wrapper. + * @param {*} placeholder The placeholder value. + * @param {*} [thisArg] The `this` binding of `func`. + * @param {Array} [partials] The arguments to prepend to those provided to + * the new function. + * @param {Array} [holders] The `partials` placeholder indexes. + * @param {Array} [argPos] The argument positions of the new function. + * @param {number} [ary] The arity cap of `func`. + * @param {number} [arity] The arity of `func`. + * @returns {Function} Returns the new wrapped function. + */ + function createRecurry(func, bitmask, wrapFunc, placeholder, thisArg, partials, holders, argPos, ary, arity) { + var isCurry = bitmask & WRAP_CURRY_FLAG, + newHolders = isCurry ? holders : undefined, + newHoldersRight = isCurry ? undefined : holders, + newPartials = isCurry ? partials : undefined, + newPartialsRight = isCurry ? undefined : partials; + + bitmask |= (isCurry ? WRAP_PARTIAL_FLAG : WRAP_PARTIAL_RIGHT_FLAG); + bitmask &= ~(isCurry ? WRAP_PARTIAL_RIGHT_FLAG : WRAP_PARTIAL_FLAG); + + if (!(bitmask & WRAP_CURRY_BOUND_FLAG)) { + bitmask &= ~(WRAP_BIND_FLAG | WRAP_BIND_KEY_FLAG); + } + var newData = [ + func, bitmask, thisArg, newPartials, newHolders, newPartialsRight, + newHoldersRight, argPos, ary, arity + ]; + + var result = wrapFunc.apply(undefined, newData); + if (isLaziable(func)) { + setData(result, newData); + } + result.placeholder = placeholder; + return setWrapToString(result, func, bitmask); + } + + /** + * Creates a function like `_.round`. + * + * @private + * @param {string} methodName The name of the `Math` method to use when rounding. + * @returns {Function} Returns the new round function. + */ + function createRound(methodName) { + var func = Math[methodName]; + return function(number, precision) { + number = toNumber(number); + precision = precision == null ? 0 : nativeMin(toInteger(precision), 292); + if (precision) { + // Shift with exponential notation to avoid floating-point issues. + // See [MDN](https://mdn.io/round#Examples) for more details. + var pair = (toString(number) + 'e').split('e'), + value = func(pair[0] + 'e' + (+pair[1] + precision)); + + pair = (toString(value) + 'e').split('e'); + return +(pair[0] + 'e' + (+pair[1] - precision)); + } + return func(number); + }; + } + + /** + * Creates a set object of `values`. + * + * @private + * @param {Array} values The values to add to the set. + * @returns {Object} Returns the new set. + */ + var createSet = !(Set && (1 / setToArray(new Set([,-0]))[1]) == INFINITY) ? noop : function(values) { + return new Set(values); + }; + + /** + * Creates a `_.toPairs` or `_.toPairsIn` function. + * + * @private + * @param {Function} keysFunc The function to get the keys of a given object. + * @returns {Function} Returns the new pairs function. + */ + function createToPairs(keysFunc) { + return function(object) { + var tag = getTag(object); + if (tag == mapTag) { + return mapToArray(object); + } + if (tag == setTag) { + return setToPairs(object); + } + return baseToPairs(object, keysFunc(object)); + }; + } + + /** + * Creates a function that either curries or invokes `func` with optional + * `this` binding and partially applied arguments. + * + * @private + * @param {Function|string} func The function or method name to wrap. + * @param {number} bitmask The bitmask flags. + * 1 - `_.bind` + * 2 - `_.bindKey` + * 4 - `_.curry` or `_.curryRight` of a bound function + * 8 - `_.curry` + * 16 - `_.curryRight` + * 32 - `_.partial` + * 64 - `_.partialRight` + * 128 - `_.rearg` + * 256 - `_.ary` + * 512 - `_.flip` + * @param {*} [thisArg] The `this` binding of `func`. + * @param {Array} [partials] The arguments to be partially applied. + * @param {Array} [holders] The `partials` placeholder indexes. + * @param {Array} [argPos] The argument positions of the new function. + * @param {number} [ary] The arity cap of `func`. + * @param {number} [arity] The arity of `func`. + * @returns {Function} Returns the new wrapped function. + */ + function createWrap(func, bitmask, thisArg, partials, holders, argPos, ary, arity) { + var isBindKey = bitmask & WRAP_BIND_KEY_FLAG; + if (!isBindKey && typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + var length = partials ? partials.length : 0; + if (!length) { + bitmask &= ~(WRAP_PARTIAL_FLAG | WRAP_PARTIAL_RIGHT_FLAG); + partials = holders = undefined; + } + ary = ary === undefined ? ary : nativeMax(toInteger(ary), 0); + arity = arity === undefined ? arity : toInteger(arity); + length -= holders ? holders.length : 0; + + if (bitmask & WRAP_PARTIAL_RIGHT_FLAG) { + var partialsRight = partials, + holdersRight = holders; + + partials = holders = undefined; + } + var data = isBindKey ? undefined : getData(func); + + var newData = [ + func, bitmask, thisArg, partials, holders, partialsRight, holdersRight, + argPos, ary, arity + ]; + + if (data) { + mergeData(newData, data); + } + func = newData[0]; + bitmask = newData[1]; + thisArg = newData[2]; + partials = newData[3]; + holders = newData[4]; + arity = newData[9] = newData[9] === undefined + ? (isBindKey ? 0 : func.length) + : nativeMax(newData[9] - length, 0); + + if (!arity && bitmask & (WRAP_CURRY_FLAG | WRAP_CURRY_RIGHT_FLAG)) { + bitmask &= ~(WRAP_CURRY_FLAG | WRAP_CURRY_RIGHT_FLAG); + } + if (!bitmask || bitmask == WRAP_BIND_FLAG) { + var result = createBind(func, bitmask, thisArg); + } else if (bitmask == WRAP_CURRY_FLAG || bitmask == WRAP_CURRY_RIGHT_FLAG) { + result = createCurry(func, bitmask, arity); + } else if ((bitmask == WRAP_PARTIAL_FLAG || bitmask == (WRAP_BIND_FLAG | WRAP_PARTIAL_FLAG)) && !holders.length) { + result = createPartial(func, bitmask, thisArg, partials); + } else { + result = createHybrid.apply(undefined, newData); + } + var setter = data ? baseSetData : setData; + return setWrapToString(setter(result, newData), func, bitmask); + } + + /** + * Used by `_.defaults` to customize its `_.assignIn` use to assign properties + * of source objects to the destination object for all destination properties + * that resolve to `undefined`. + * + * @private + * @param {*} objValue The destination value. + * @param {*} srcValue The source value. + * @param {string} key The key of the property to assign. + * @param {Object} object The parent object of `objValue`. + * @returns {*} Returns the value to assign. + */ + function customDefaultsAssignIn(objValue, srcValue, key, object) { + if (objValue === undefined || + (eq(objValue, objectProto[key]) && !hasOwnProperty.call(object, key))) { + return srcValue; + } + return objValue; + } + + /** + * Used by `_.defaultsDeep` to customize its `_.merge` use to merge source + * objects into destination objects that are passed thru. + * + * @private + * @param {*} objValue The destination value. + * @param {*} srcValue The source value. + * @param {string} key The key of the property to merge. + * @param {Object} object The parent object of `objValue`. + * @param {Object} source The parent object of `srcValue`. + * @param {Object} [stack] Tracks traversed source values and their merged + * counterparts. + * @returns {*} Returns the value to assign. + */ + function customDefaultsMerge(objValue, srcValue, key, object, source, stack) { + if (isObject(objValue) && isObject(srcValue)) { + // Recursively merge objects and arrays (susceptible to call stack limits). + stack.set(srcValue, objValue); + baseMerge(objValue, srcValue, undefined, customDefaultsMerge, stack); + stack['delete'](srcValue); + } + return objValue; + } + + /** + * Used by `_.omit` to customize its `_.cloneDeep` use to only clone plain + * objects. + * + * @private + * @param {*} value The value to inspect. + * @param {string} key The key of the property to inspect. + * @returns {*} Returns the uncloned value or `undefined` to defer cloning to `_.cloneDeep`. + */ + function customOmitClone(value) { + return isPlainObject(value) ? undefined : value; + } + + /** + * A specialized version of `baseIsEqualDeep` for arrays with support for + * partial deep comparisons. + * + * @private + * @param {Array} array The array to compare. + * @param {Array} other The other array to compare. + * @param {number} bitmask The bitmask flags. See `baseIsEqual` for more details. + * @param {Function} customizer The function to customize comparisons. + * @param {Function} equalFunc The function to determine equivalents of values. + * @param {Object} stack Tracks traversed `array` and `other` objects. + * @returns {boolean} Returns `true` if the arrays are equivalent, else `false`. + */ + function equalArrays(array, other, bitmask, customizer, equalFunc, stack) { + var isPartial = bitmask & COMPARE_PARTIAL_FLAG, + arrLength = array.length, + othLength = other.length; + + if (arrLength != othLength && !(isPartial && othLength > arrLength)) { + return false; + } + // Assume cyclic values are equal. + var stacked = stack.get(array); + if (stacked && stack.get(other)) { + return stacked == other; + } + var index = -1, + result = true, + seen = (bitmask & COMPARE_UNORDERED_FLAG) ? new SetCache : undefined; + + stack.set(array, other); + stack.set(other, array); + + // Ignore non-index properties. + while (++index < arrLength) { + var arrValue = array[index], + othValue = other[index]; + + if (customizer) { + var compared = isPartial + ? customizer(othValue, arrValue, index, other, array, stack) + : customizer(arrValue, othValue, index, array, other, stack); + } + if (compared !== undefined) { + if (compared) { + continue; + } + result = false; + break; + } + // Recursively compare arrays (susceptible to call stack limits). + if (seen) { + if (!arraySome(other, function(othValue, othIndex) { + if (!cacheHas(seen, othIndex) && + (arrValue === othValue || equalFunc(arrValue, othValue, bitmask, customizer, stack))) { + return seen.push(othIndex); + } + })) { + result = false; + break; + } + } else if (!( + arrValue === othValue || + equalFunc(arrValue, othValue, bitmask, customizer, stack) + )) { + result = false; + break; + } + } + stack['delete'](array); + stack['delete'](other); + return result; + } + + /** + * A specialized version of `baseIsEqualDeep` for comparing objects of + * the same `toStringTag`. + * + * **Note:** This function only supports comparing values with tags of + * `Boolean`, `Date`, `Error`, `Number`, `RegExp`, or `String`. + * + * @private + * @param {Object} object The object to compare. + * @param {Object} other The other object to compare. + * @param {string} tag The `toStringTag` of the objects to compare. + * @param {number} bitmask The bitmask flags. See `baseIsEqual` for more details. + * @param {Function} customizer The function to customize comparisons. + * @param {Function} equalFunc The function to determine equivalents of values. + * @param {Object} stack Tracks traversed `object` and `other` objects. + * @returns {boolean} Returns `true` if the objects are equivalent, else `false`. + */ + function equalByTag(object, other, tag, bitmask, customizer, equalFunc, stack) { + switch (tag) { + case dataViewTag: + if ((object.byteLength != other.byteLength) || + (object.byteOffset != other.byteOffset)) { + return false; + } + object = object.buffer; + other = other.buffer; + + case arrayBufferTag: + if ((object.byteLength != other.byteLength) || + !equalFunc(new Uint8Array(object), new Uint8Array(other))) { + return false; + } + return true; + + case boolTag: + case dateTag: + case numberTag: + // Coerce booleans to `1` or `0` and dates to milliseconds. + // Invalid dates are coerced to `NaN`. + return eq(+object, +other); + + case errorTag: + return object.name == other.name && object.message == other.message; + + case regexpTag: + case stringTag: + // Coerce regexes to strings and treat strings, primitives and objects, + // as equal. See http://www.ecma-international.org/ecma-262/7.0/#sec-regexp.prototype.tostring + // for more details. + return object == (other + ''); + + case mapTag: + var convert = mapToArray; + + case setTag: + var isPartial = bitmask & COMPARE_PARTIAL_FLAG; + convert || (convert = setToArray); + + if (object.size != other.size && !isPartial) { + return false; + } + // Assume cyclic values are equal. + var stacked = stack.get(object); + if (stacked) { + return stacked == other; + } + bitmask |= COMPARE_UNORDERED_FLAG; + + // Recursively compare objects (susceptible to call stack limits). + stack.set(object, other); + var result = equalArrays(convert(object), convert(other), bitmask, customizer, equalFunc, stack); + stack['delete'](object); + return result; + + case symbolTag: + if (symbolValueOf) { + return symbolValueOf.call(object) == symbolValueOf.call(other); + } + } + return false; + } + + /** + * A specialized version of `baseIsEqualDeep` for objects with support for + * partial deep comparisons. + * + * @private + * @param {Object} object The object to compare. + * @param {Object} other The other object to compare. + * @param {number} bitmask The bitmask flags. See `baseIsEqual` for more details. + * @param {Function} customizer The function to customize comparisons. + * @param {Function} equalFunc The function to determine equivalents of values. + * @param {Object} stack Tracks traversed `object` and `other` objects. + * @returns {boolean} Returns `true` if the objects are equivalent, else `false`. + */ + function equalObjects(object, other, bitmask, customizer, equalFunc, stack) { + var isPartial = bitmask & COMPARE_PARTIAL_FLAG, + objProps = getAllKeys(object), + objLength = objProps.length, + othProps = getAllKeys(other), + othLength = othProps.length; + + if (objLength != othLength && !isPartial) { + return false; + } + var index = objLength; + while (index--) { + var key = objProps[index]; + if (!(isPartial ? key in other : hasOwnProperty.call(other, key))) { + return false; + } + } + // Assume cyclic values are equal. + var stacked = stack.get(object); + if (stacked && stack.get(other)) { + return stacked == other; + } + var result = true; + stack.set(object, other); + stack.set(other, object); + + var skipCtor = isPartial; + while (++index < objLength) { + key = objProps[index]; + var objValue = object[key], + othValue = other[key]; + + if (customizer) { + var compared = isPartial + ? customizer(othValue, objValue, key, other, object, stack) + : customizer(objValue, othValue, key, object, other, stack); + } + // Recursively compare objects (susceptible to call stack limits). + if (!(compared === undefined + ? (objValue === othValue || equalFunc(objValue, othValue, bitmask, customizer, stack)) + : compared + )) { + result = false; + break; + } + skipCtor || (skipCtor = key == 'constructor'); + } + if (result && !skipCtor) { + var objCtor = object.constructor, + othCtor = other.constructor; + + // Non `Object` object instances with different constructors are not equal. + if (objCtor != othCtor && + ('constructor' in object && 'constructor' in other) && + !(typeof objCtor == 'function' && objCtor instanceof objCtor && + typeof othCtor == 'function' && othCtor instanceof othCtor)) { + result = false; + } + } + stack['delete'](object); + stack['delete'](other); + return result; + } + + /** + * A specialized version of `baseRest` which flattens the rest array. + * + * @private + * @param {Function} func The function to apply a rest parameter to. + * @returns {Function} Returns the new function. + */ + function flatRest(func) { + return setToString(overRest(func, undefined, flatten), func + ''); + } + + /** + * Creates an array of own enumerable property names and symbols of `object`. + * + * @private + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property names and symbols. + */ + function getAllKeys(object) { + return baseGetAllKeys(object, keys, getSymbols); + } + + /** + * Creates an array of own and inherited enumerable property names and + * symbols of `object`. + * + * @private + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property names and symbols. + */ + function getAllKeysIn(object) { + return baseGetAllKeys(object, keysIn, getSymbolsIn); + } + + /** + * Gets metadata for `func`. + * + * @private + * @param {Function} func The function to query. + * @returns {*} Returns the metadata for `func`. + */ + var getData = !metaMap ? noop : function(func) { + return metaMap.get(func); + }; + + /** + * Gets the name of `func`. + * + * @private + * @param {Function} func The function to query. + * @returns {string} Returns the function name. + */ + function getFuncName(func) { + var result = (func.name + ''), + array = realNames[result], + length = hasOwnProperty.call(realNames, result) ? array.length : 0; + + while (length--) { + var data = array[length], + otherFunc = data.func; + if (otherFunc == null || otherFunc == func) { + return data.name; + } + } + return result; + } + + /** + * Gets the argument placeholder value for `func`. + * + * @private + * @param {Function} func The function to inspect. + * @returns {*} Returns the placeholder value. + */ + function getHolder(func) { + var object = hasOwnProperty.call(lodash, 'placeholder') ? lodash : func; + return object.placeholder; + } + + /** + * Gets the appropriate "iteratee" function. If `_.iteratee` is customized, + * this function returns the custom method, otherwise it returns `baseIteratee`. + * If arguments are provided, the chosen function is invoked with them and + * its result is returned. + * + * @private + * @param {*} [value] The value to convert to an iteratee. + * @param {number} [arity] The arity of the created iteratee. + * @returns {Function} Returns the chosen function or its result. + */ + function getIteratee() { + var result = lodash.iteratee || iteratee; + result = result === iteratee ? baseIteratee : result; + return arguments.length ? result(arguments[0], arguments[1]) : result; + } + + /** + * Gets the data for `map`. + * + * @private + * @param {Object} map The map to query. + * @param {string} key The reference key. + * @returns {*} Returns the map data. + */ + function getMapData(map, key) { + var data = map.__data__; + return isKeyable(key) + ? data[typeof key == 'string' ? 'string' : 'hash'] + : data.map; + } + + /** + * Gets the property names, values, and compare flags of `object`. + * + * @private + * @param {Object} object The object to query. + * @returns {Array} Returns the match data of `object`. + */ + function getMatchData(object) { + var result = keys(object), + length = result.length; + + while (length--) { + var key = result[length], + value = object[key]; + + result[length] = [key, value, isStrictComparable(value)]; + } + return result; + } + + /** + * Gets the native function at `key` of `object`. + * + * @private + * @param {Object} object The object to query. + * @param {string} key The key of the method to get. + * @returns {*} Returns the function if it's native, else `undefined`. + */ + function getNative(object, key) { + var value = getValue(object, key); + return baseIsNative(value) ? value : undefined; + } + + /** + * A specialized version of `baseGetTag` which ignores `Symbol.toStringTag` values. + * + * @private + * @param {*} value The value to query. + * @returns {string} Returns the raw `toStringTag`. + */ + function getRawTag(value) { + var isOwn = hasOwnProperty.call(value, symToStringTag), + tag = value[symToStringTag]; + + try { + value[symToStringTag] = undefined; + var unmasked = true; + } catch (e) {} + + var result = nativeObjectToString.call(value); + if (unmasked) { + if (isOwn) { + value[symToStringTag] = tag; + } else { + delete value[symToStringTag]; + } + } + return result; + } + + /** + * Creates an array of the own enumerable symbols of `object`. + * + * @private + * @param {Object} object The object to query. + * @returns {Array} Returns the array of symbols. + */ + var getSymbols = !nativeGetSymbols ? stubArray : function(object) { + if (object == null) { + return []; + } + object = Object(object); + return arrayFilter(nativeGetSymbols(object), function(symbol) { + return propertyIsEnumerable.call(object, symbol); + }); + }; + + /** + * Creates an array of the own and inherited enumerable symbols of `object`. + * + * @private + * @param {Object} object The object to query. + * @returns {Array} Returns the array of symbols. + */ + var getSymbolsIn = !nativeGetSymbols ? stubArray : function(object) { + var result = []; + while (object) { + arrayPush(result, getSymbols(object)); + object = getPrototype(object); + } + return result; + }; + + /** + * Gets the `toStringTag` of `value`. + * + * @private + * @param {*} value The value to query. + * @returns {string} Returns the `toStringTag`. + */ + var getTag = baseGetTag; + + // Fallback for data views, maps, sets, and weak maps in IE 11 and promises in Node.js < 6. + if ((DataView && getTag(new DataView(new ArrayBuffer(1))) != dataViewTag) || + (Map && getTag(new Map) != mapTag) || + (Promise && getTag(Promise.resolve()) != promiseTag) || + (Set && getTag(new Set) != setTag) || + (WeakMap && getTag(new WeakMap) != weakMapTag)) { + getTag = function(value) { + var result = baseGetTag(value), + Ctor = result == objectTag ? value.constructor : undefined, + ctorString = Ctor ? toSource(Ctor) : ''; + + if (ctorString) { + switch (ctorString) { + case dataViewCtorString: return dataViewTag; + case mapCtorString: return mapTag; + case promiseCtorString: return promiseTag; + case setCtorString: return setTag; + case weakMapCtorString: return weakMapTag; + } + } + return result; + }; + } + + /** + * Gets the view, applying any `transforms` to the `start` and `end` positions. + * + * @private + * @param {number} start The start of the view. + * @param {number} end The end of the view. + * @param {Array} transforms The transformations to apply to the view. + * @returns {Object} Returns an object containing the `start` and `end` + * positions of the view. + */ + function getView(start, end, transforms) { + var index = -1, + length = transforms.length; + + while (++index < length) { + var data = transforms[index], + size = data.size; + + switch (data.type) { + case 'drop': start += size; break; + case 'dropRight': end -= size; break; + case 'take': end = nativeMin(end, start + size); break; + case 'takeRight': start = nativeMax(start, end - size); break; + } + } + return { 'start': start, 'end': end }; + } + + /** + * Extracts wrapper details from the `source` body comment. + * + * @private + * @param {string} source The source to inspect. + * @returns {Array} Returns the wrapper details. + */ + function getWrapDetails(source) { + var match = source.match(reWrapDetails); + return match ? match[1].split(reSplitDetails) : []; + } + + /** + * Checks if `path` exists on `object`. + * + * @private + * @param {Object} object The object to query. + * @param {Array|string} path The path to check. + * @param {Function} hasFunc The function to check properties. + * @returns {boolean} Returns `true` if `path` exists, else `false`. + */ + function hasPath(object, path, hasFunc) { + path = castPath(path, object); + + var index = -1, + length = path.length, + result = false; + + while (++index < length) { + var key = toKey(path[index]); + if (!(result = object != null && hasFunc(object, key))) { + break; + } + object = object[key]; + } + if (result || ++index != length) { + return result; + } + length = object == null ? 0 : object.length; + return !!length && isLength(length) && isIndex(key, length) && + (isArray(object) || isArguments(object)); + } + + /** + * Initializes an array clone. + * + * @private + * @param {Array} array The array to clone. + * @returns {Array} Returns the initialized clone. + */ + function initCloneArray(array) { + var length = array.length, + result = new array.constructor(length); + + // Add properties assigned by `RegExp#exec`. + if (length && typeof array[0] == 'string' && hasOwnProperty.call(array, 'index')) { + result.index = array.index; + result.input = array.input; + } + return result; + } + + /** + * Initializes an object clone. + * + * @private + * @param {Object} object The object to clone. + * @returns {Object} Returns the initialized clone. + */ + function initCloneObject(object) { + return (typeof object.constructor == 'function' && !isPrototype(object)) + ? baseCreate(getPrototype(object)) + : {}; + } + + /** + * Initializes an object clone based on its `toStringTag`. + * + * **Note:** This function only supports cloning values with tags of + * `Boolean`, `Date`, `Error`, `Map`, `Number`, `RegExp`, `Set`, or `String`. + * + * @private + * @param {Object} object The object to clone. + * @param {string} tag The `toStringTag` of the object to clone. + * @param {boolean} [isDeep] Specify a deep clone. + * @returns {Object} Returns the initialized clone. + */ + function initCloneByTag(object, tag, isDeep) { + var Ctor = object.constructor; + switch (tag) { + case arrayBufferTag: + return cloneArrayBuffer(object); + + case boolTag: + case dateTag: + return new Ctor(+object); + + case dataViewTag: + return cloneDataView(object, isDeep); + + case float32Tag: case float64Tag: + case int8Tag: case int16Tag: case int32Tag: + case uint8Tag: case uint8ClampedTag: case uint16Tag: case uint32Tag: + return cloneTypedArray(object, isDeep); + + case mapTag: + return new Ctor; + + case numberTag: + case stringTag: + return new Ctor(object); + + case regexpTag: + return cloneRegExp(object); + + case setTag: + return new Ctor; + + case symbolTag: + return cloneSymbol(object); + } + } + + /** + * Inserts wrapper `details` in a comment at the top of the `source` body. + * + * @private + * @param {string} source The source to modify. + * @returns {Array} details The details to insert. + * @returns {string} Returns the modified source. + */ + function insertWrapDetails(source, details) { + var length = details.length; + if (!length) { + return source; + } + var lastIndex = length - 1; + details[lastIndex] = (length > 1 ? '& ' : '') + details[lastIndex]; + details = details.join(length > 2 ? ', ' : ' '); + return source.replace(reWrapComment, '{\n/* [wrapped with ' + details + '] */\n'); + } + + /** + * Checks if `value` is a flattenable `arguments` object or array. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is flattenable, else `false`. + */ + function isFlattenable(value) { + return isArray(value) || isArguments(value) || + !!(spreadableSymbol && value && value[spreadableSymbol]); + } + + /** + * Checks if `value` is a valid array-like index. + * + * @private + * @param {*} value The value to check. + * @param {number} [length=MAX_SAFE_INTEGER] The upper bounds of a valid index. + * @returns {boolean} Returns `true` if `value` is a valid index, else `false`. + */ + function isIndex(value, length) { + var type = typeof value; + length = length == null ? MAX_SAFE_INTEGER : length; + + return !!length && + (type == 'number' || + (type != 'symbol' && reIsUint.test(value))) && + (value > -1 && value % 1 == 0 && value < length); + } + + /** + * Checks if the given arguments are from an iteratee call. + * + * @private + * @param {*} value The potential iteratee value argument. + * @param {*} index The potential iteratee index or key argument. + * @param {*} object The potential iteratee object argument. + * @returns {boolean} Returns `true` if the arguments are from an iteratee call, + * else `false`. + */ + function isIterateeCall(value, index, object) { + if (!isObject(object)) { + return false; + } + var type = typeof index; + if (type == 'number' + ? (isArrayLike(object) && isIndex(index, object.length)) + : (type == 'string' && index in object) + ) { + return eq(object[index], value); + } + return false; + } + + /** + * Checks if `value` is a property name and not a property path. + * + * @private + * @param {*} value The value to check. + * @param {Object} [object] The object to query keys on. + * @returns {boolean} Returns `true` if `value` is a property name, else `false`. + */ + function isKey(value, object) { + if (isArray(value)) { + return false; + } + var type = typeof value; + if (type == 'number' || type == 'symbol' || type == 'boolean' || + value == null || isSymbol(value)) { + return true; + } + return reIsPlainProp.test(value) || !reIsDeepProp.test(value) || + (object != null && value in Object(object)); + } + + /** + * Checks if `value` is suitable for use as unique object key. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is suitable, else `false`. + */ + function isKeyable(value) { + var type = typeof value; + return (type == 'string' || type == 'number' || type == 'symbol' || type == 'boolean') + ? (value !== '__proto__') + : (value === null); + } + + /** + * Checks if `func` has a lazy counterpart. + * + * @private + * @param {Function} func The function to check. + * @returns {boolean} Returns `true` if `func` has a lazy counterpart, + * else `false`. + */ + function isLaziable(func) { + var funcName = getFuncName(func), + other = lodash[funcName]; + + if (typeof other != 'function' || !(funcName in LazyWrapper.prototype)) { + return false; + } + if (func === other) { + return true; + } + var data = getData(other); + return !!data && func === data[0]; + } + + /** + * Checks if `func` has its source masked. + * + * @private + * @param {Function} func The function to check. + * @returns {boolean} Returns `true` if `func` is masked, else `false`. + */ + function isMasked(func) { + return !!maskSrcKey && (maskSrcKey in func); + } + + /** + * Checks if `func` is capable of being masked. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `func` is maskable, else `false`. + */ + var isMaskable = coreJsData ? isFunction : stubFalse; + + /** + * Checks if `value` is likely a prototype object. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a prototype, else `false`. + */ + function isPrototype(value) { + var Ctor = value && value.constructor, + proto = (typeof Ctor == 'function' && Ctor.prototype) || objectProto; + + return value === proto; + } + + /** + * Checks if `value` is suitable for strict equality comparisons, i.e. `===`. + * + * @private + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` if suitable for strict + * equality comparisons, else `false`. + */ + function isStrictComparable(value) { + return value === value && !isObject(value); + } + + /** + * A specialized version of `matchesProperty` for source values suitable + * for strict equality comparisons, i.e. `===`. + * + * @private + * @param {string} key The key of the property to get. + * @param {*} srcValue The value to match. + * @returns {Function} Returns the new spec function. + */ + function matchesStrictComparable(key, srcValue) { + return function(object) { + if (object == null) { + return false; + } + return object[key] === srcValue && + (srcValue !== undefined || (key in Object(object))); + }; + } + + /** + * A specialized version of `_.memoize` which clears the memoized function's + * cache when it exceeds `MAX_MEMOIZE_SIZE`. + * + * @private + * @param {Function} func The function to have its output memoized. + * @returns {Function} Returns the new memoized function. + */ + function memoizeCapped(func) { + var result = memoize(func, function(key) { + if (cache.size === MAX_MEMOIZE_SIZE) { + cache.clear(); + } + return key; + }); + + var cache = result.cache; + return result; + } + + /** + * Merges the function metadata of `source` into `data`. + * + * Merging metadata reduces the number of wrappers used to invoke a function. + * This is possible because methods like `_.bind`, `_.curry`, and `_.partial` + * may be applied regardless of execution order. Methods like `_.ary` and + * `_.rearg` modify function arguments, making the order in which they are + * executed important, preventing the merging of metadata. However, we make + * an exception for a safe combined case where curried functions have `_.ary` + * and or `_.rearg` applied. + * + * @private + * @param {Array} data The destination metadata. + * @param {Array} source The source metadata. + * @returns {Array} Returns `data`. + */ + function mergeData(data, source) { + var bitmask = data[1], + srcBitmask = source[1], + newBitmask = bitmask | srcBitmask, + isCommon = newBitmask < (WRAP_BIND_FLAG | WRAP_BIND_KEY_FLAG | WRAP_ARY_FLAG); + + var isCombo = + ((srcBitmask == WRAP_ARY_FLAG) && (bitmask == WRAP_CURRY_FLAG)) || + ((srcBitmask == WRAP_ARY_FLAG) && (bitmask == WRAP_REARG_FLAG) && (data[7].length <= source[8])) || + ((srcBitmask == (WRAP_ARY_FLAG | WRAP_REARG_FLAG)) && (source[7].length <= source[8]) && (bitmask == WRAP_CURRY_FLAG)); + + // Exit early if metadata can't be merged. + if (!(isCommon || isCombo)) { + return data; + } + // Use source `thisArg` if available. + if (srcBitmask & WRAP_BIND_FLAG) { + data[2] = source[2]; + // Set when currying a bound function. + newBitmask |= bitmask & WRAP_BIND_FLAG ? 0 : WRAP_CURRY_BOUND_FLAG; + } + // Compose partial arguments. + var value = source[3]; + if (value) { + var partials = data[3]; + data[3] = partials ? composeArgs(partials, value, source[4]) : value; + data[4] = partials ? replaceHolders(data[3], PLACEHOLDER) : source[4]; + } + // Compose partial right arguments. + value = source[5]; + if (value) { + partials = data[5]; + data[5] = partials ? composeArgsRight(partials, value, source[6]) : value; + data[6] = partials ? replaceHolders(data[5], PLACEHOLDER) : source[6]; + } + // Use source `argPos` if available. + value = source[7]; + if (value) { + data[7] = value; + } + // Use source `ary` if it's smaller. + if (srcBitmask & WRAP_ARY_FLAG) { + data[8] = data[8] == null ? source[8] : nativeMin(data[8], source[8]); + } + // Use source `arity` if one is not provided. + if (data[9] == null) { + data[9] = source[9]; + } + // Use source `func` and merge bitmasks. + data[0] = source[0]; + data[1] = newBitmask; + + return data; + } + + /** + * This function is like + * [`Object.keys`](http://ecma-international.org/ecma-262/7.0/#sec-object.keys) + * except that it includes inherited enumerable properties. + * + * @private + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property names. + */ + function nativeKeysIn(object) { + var result = []; + if (object != null) { + for (var key in Object(object)) { + result.push(key); + } + } + return result; + } + + /** + * Converts `value` to a string using `Object.prototype.toString`. + * + * @private + * @param {*} value The value to convert. + * @returns {string} Returns the converted string. + */ + function objectToString(value) { + return nativeObjectToString.call(value); + } + + /** + * A specialized version of `baseRest` which transforms the rest array. + * + * @private + * @param {Function} func The function to apply a rest parameter to. + * @param {number} [start=func.length-1] The start position of the rest parameter. + * @param {Function} transform The rest array transform. + * @returns {Function} Returns the new function. + */ + function overRest(func, start, transform) { + start = nativeMax(start === undefined ? (func.length - 1) : start, 0); + return function() { + var args = arguments, + index = -1, + length = nativeMax(args.length - start, 0), + array = Array(length); + + while (++index < length) { + array[index] = args[start + index]; + } + index = -1; + var otherArgs = Array(start + 1); + while (++index < start) { + otherArgs[index] = args[index]; + } + otherArgs[start] = transform(array); + return apply(func, this, otherArgs); + }; + } + + /** + * Gets the parent value at `path` of `object`. + * + * @private + * @param {Object} object The object to query. + * @param {Array} path The path to get the parent value of. + * @returns {*} Returns the parent value. + */ + function parent(object, path) { + return path.length < 2 ? object : baseGet(object, baseSlice(path, 0, -1)); + } + + /** + * Reorder `array` according to the specified indexes where the element at + * the first index is assigned as the first element, the element at + * the second index is assigned as the second element, and so on. + * + * @private + * @param {Array} array The array to reorder. + * @param {Array} indexes The arranged array indexes. + * @returns {Array} Returns `array`. + */ + function reorder(array, indexes) { + var arrLength = array.length, + length = nativeMin(indexes.length, arrLength), + oldArray = copyArray(array); + + while (length--) { + var index = indexes[length]; + array[length] = isIndex(index, arrLength) ? oldArray[index] : undefined; + } + return array; + } + + /** + * Gets the value at `key`, unless `key` is "__proto__". + * + * @private + * @param {Object} object The object to query. + * @param {string} key The key of the property to get. + * @returns {*} Returns the property value. + */ + function safeGet(object, key) { + if (key == '__proto__') { + return; + } + + return object[key]; + } + + /** + * Sets metadata for `func`. + * + * **Note:** If this function becomes hot, i.e. is invoked a lot in a short + * period of time, it will trip its breaker and transition to an identity + * function to avoid garbage collection pauses in V8. See + * [V8 issue 2070](https://bugs.chromium.org/p/v8/issues/detail?id=2070) + * for more details. + * + * @private + * @param {Function} func The function to associate metadata with. + * @param {*} data The metadata. + * @returns {Function} Returns `func`. + */ + var setData = shortOut(baseSetData); + + /** + * A simple wrapper around the global [`setTimeout`](https://mdn.io/setTimeout). + * + * @private + * @param {Function} func The function to delay. + * @param {number} wait The number of milliseconds to delay invocation. + * @returns {number|Object} Returns the timer id or timeout object. + */ + var setTimeout = ctxSetTimeout || function(func, wait) { + return root.setTimeout(func, wait); + }; + + /** + * Sets the `toString` method of `func` to return `string`. + * + * @private + * @param {Function} func The function to modify. + * @param {Function} string The `toString` result. + * @returns {Function} Returns `func`. + */ + var setToString = shortOut(baseSetToString); + + /** + * Sets the `toString` method of `wrapper` to mimic the source of `reference` + * with wrapper details in a comment at the top of the source body. + * + * @private + * @param {Function} wrapper The function to modify. + * @param {Function} reference The reference function. + * @param {number} bitmask The bitmask flags. See `createWrap` for more details. + * @returns {Function} Returns `wrapper`. + */ + function setWrapToString(wrapper, reference, bitmask) { + var source = (reference + ''); + return setToString(wrapper, insertWrapDetails(source, updateWrapDetails(getWrapDetails(source), bitmask))); + } + + /** + * Creates a function that'll short out and invoke `identity` instead + * of `func` when it's called `HOT_COUNT` or more times in `HOT_SPAN` + * milliseconds. + * + * @private + * @param {Function} func The function to restrict. + * @returns {Function} Returns the new shortable function. + */ + function shortOut(func) { + var count = 0, + lastCalled = 0; + + return function() { + var stamp = nativeNow(), + remaining = HOT_SPAN - (stamp - lastCalled); + + lastCalled = stamp; + if (remaining > 0) { + if (++count >= HOT_COUNT) { + return arguments[0]; + } + } else { + count = 0; + } + return func.apply(undefined, arguments); + }; + } + + /** + * A specialized version of `_.shuffle` which mutates and sets the size of `array`. + * + * @private + * @param {Array} array The array to shuffle. + * @param {number} [size=array.length] The size of `array`. + * @returns {Array} Returns `array`. + */ + function shuffleSelf(array, size) { + var index = -1, + length = array.length, + lastIndex = length - 1; + + size = size === undefined ? length : size; + while (++index < size) { + var rand = baseRandom(index, lastIndex), + value = array[rand]; + + array[rand] = array[index]; + array[index] = value; + } + array.length = size; + return array; + } + + /** + * Converts `string` to a property path array. + * + * @private + * @param {string} string The string to convert. + * @returns {Array} Returns the property path array. + */ + var stringToPath = memoizeCapped(function(string) { + var result = []; + if (string.charCodeAt(0) === 46 /* . */) { + result.push(''); + } + string.replace(rePropName, function(match, number, quote, subString) { + result.push(quote ? subString.replace(reEscapeChar, '$1') : (number || match)); + }); + return result; + }); + + /** + * Converts `value` to a string key if it's not a string or symbol. + * + * @private + * @param {*} value The value to inspect. + * @returns {string|symbol} Returns the key. + */ + function toKey(value) { + if (typeof value == 'string' || isSymbol(value)) { + return value; + } + var result = (value + ''); + return (result == '0' && (1 / value) == -INFINITY) ? '-0' : result; + } + + /** + * Converts `func` to its source code. + * + * @private + * @param {Function} func The function to convert. + * @returns {string} Returns the source code. + */ + function toSource(func) { + if (func != null) { + try { + return funcToString.call(func); + } catch (e) {} + try { + return (func + ''); + } catch (e) {} + } + return ''; + } + + /** + * Updates wrapper `details` based on `bitmask` flags. + * + * @private + * @returns {Array} details The details to modify. + * @param {number} bitmask The bitmask flags. See `createWrap` for more details. + * @returns {Array} Returns `details`. + */ + function updateWrapDetails(details, bitmask) { + arrayEach(wrapFlags, function(pair) { + var value = '_.' + pair[0]; + if ((bitmask & pair[1]) && !arrayIncludes(details, value)) { + details.push(value); + } + }); + return details.sort(); + } + + /** + * Creates a clone of `wrapper`. + * + * @private + * @param {Object} wrapper The wrapper to clone. + * @returns {Object} Returns the cloned wrapper. + */ + function wrapperClone(wrapper) { + if (wrapper instanceof LazyWrapper) { + return wrapper.clone(); + } + var result = new LodashWrapper(wrapper.__wrapped__, wrapper.__chain__); + result.__actions__ = copyArray(wrapper.__actions__); + result.__index__ = wrapper.__index__; + result.__values__ = wrapper.__values__; + return result; + } + + /*------------------------------------------------------------------------*/ + + /** + * Creates an array of elements split into groups the length of `size`. + * If `array` can't be split evenly, the final chunk will be the remaining + * elements. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to process. + * @param {number} [size=1] The length of each chunk + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Array} Returns the new array of chunks. + * @example + * + * _.chunk(['a', 'b', 'c', 'd'], 2); + * // => [['a', 'b'], ['c', 'd']] + * + * _.chunk(['a', 'b', 'c', 'd'], 3); + * // => [['a', 'b', 'c'], ['d']] + */ + function chunk(array, size, guard) { + if ((guard ? isIterateeCall(array, size, guard) : size === undefined)) { + size = 1; + } else { + size = nativeMax(toInteger(size), 0); + } + var length = array == null ? 0 : array.length; + if (!length || size < 1) { + return []; + } + var index = 0, + resIndex = 0, + result = Array(nativeCeil(length / size)); + + while (index < length) { + result[resIndex++] = baseSlice(array, index, (index += size)); + } + return result; + } + + /** + * Creates an array with all falsey values removed. The values `false`, `null`, + * `0`, `""`, `undefined`, and `NaN` are falsey. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to compact. + * @returns {Array} Returns the new array of filtered values. + * @example + * + * _.compact([0, 1, false, 2, '', 3]); + * // => [1, 2, 3] + */ + function compact(array) { + var index = -1, + length = array == null ? 0 : array.length, + resIndex = 0, + result = []; + + while (++index < length) { + var value = array[index]; + if (value) { + result[resIndex++] = value; + } + } + return result; + } + + /** + * Creates a new array concatenating `array` with any additional arrays + * and/or values. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to concatenate. + * @param {...*} [values] The values to concatenate. + * @returns {Array} Returns the new concatenated array. + * @example + * + * var array = [1]; + * var other = _.concat(array, 2, [3], [[4]]); + * + * console.log(other); + * // => [1, 2, 3, [4]] + * + * console.log(array); + * // => [1] + */ + function concat() { + var length = arguments.length; + if (!length) { + return []; + } + var args = Array(length - 1), + array = arguments[0], + index = length; + + while (index--) { + args[index - 1] = arguments[index]; + } + return arrayPush(isArray(array) ? copyArray(array) : [array], baseFlatten(args, 1)); + } + + /** + * Creates an array of `array` values not included in the other given arrays + * using [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * for equality comparisons. The order and references of result values are + * determined by the first array. + * + * **Note:** Unlike `_.pullAll`, this method returns a new array. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {...Array} [values] The values to exclude. + * @returns {Array} Returns the new array of filtered values. + * @see _.without, _.xor + * @example + * + * _.difference([2, 1], [2, 3]); + * // => [1] + */ + var difference = baseRest(function(array, values) { + return isArrayLikeObject(array) + ? baseDifference(array, baseFlatten(values, 1, isArrayLikeObject, true)) + : []; + }); + + /** + * This method is like `_.difference` except that it accepts `iteratee` which + * is invoked for each element of `array` and `values` to generate the criterion + * by which they're compared. The order and references of result values are + * determined by the first array. The iteratee is invoked with one argument: + * (value). + * + * **Note:** Unlike `_.pullAllBy`, this method returns a new array. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {...Array} [values] The values to exclude. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {Array} Returns the new array of filtered values. + * @example + * + * _.differenceBy([2.1, 1.2], [2.3, 3.4], Math.floor); + * // => [1.2] + * + * // The `_.property` iteratee shorthand. + * _.differenceBy([{ 'x': 2 }, { 'x': 1 }], [{ 'x': 1 }], 'x'); + * // => [{ 'x': 2 }] + */ + var differenceBy = baseRest(function(array, values) { + var iteratee = last(values); + if (isArrayLikeObject(iteratee)) { + iteratee = undefined; + } + return isArrayLikeObject(array) + ? baseDifference(array, baseFlatten(values, 1, isArrayLikeObject, true), getIteratee(iteratee, 2)) + : []; + }); + + /** + * This method is like `_.difference` except that it accepts `comparator` + * which is invoked to compare elements of `array` to `values`. The order and + * references of result values are determined by the first array. The comparator + * is invoked with two arguments: (arrVal, othVal). + * + * **Note:** Unlike `_.pullAllWith`, this method returns a new array. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {...Array} [values] The values to exclude. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new array of filtered values. + * @example + * + * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }]; + * + * _.differenceWith(objects, [{ 'x': 1, 'y': 2 }], _.isEqual); + * // => [{ 'x': 2, 'y': 1 }] + */ + var differenceWith = baseRest(function(array, values) { + var comparator = last(values); + if (isArrayLikeObject(comparator)) { + comparator = undefined; + } + return isArrayLikeObject(array) + ? baseDifference(array, baseFlatten(values, 1, isArrayLikeObject, true), undefined, comparator) + : []; + }); + + /** + * Creates a slice of `array` with `n` elements dropped from the beginning. + * + * @static + * @memberOf _ + * @since 0.5.0 + * @category Array + * @param {Array} array The array to query. + * @param {number} [n=1] The number of elements to drop. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Array} Returns the slice of `array`. + * @example + * + * _.drop([1, 2, 3]); + * // => [2, 3] + * + * _.drop([1, 2, 3], 2); + * // => [3] + * + * _.drop([1, 2, 3], 5); + * // => [] + * + * _.drop([1, 2, 3], 0); + * // => [1, 2, 3] + */ + function drop(array, n, guard) { + var length = array == null ? 0 : array.length; + if (!length) { + return []; + } + n = (guard || n === undefined) ? 1 : toInteger(n); + return baseSlice(array, n < 0 ? 0 : n, length); + } + + /** + * Creates a slice of `array` with `n` elements dropped from the end. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to query. + * @param {number} [n=1] The number of elements to drop. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Array} Returns the slice of `array`. + * @example + * + * _.dropRight([1, 2, 3]); + * // => [1, 2] + * + * _.dropRight([1, 2, 3], 2); + * // => [1] + * + * _.dropRight([1, 2, 3], 5); + * // => [] + * + * _.dropRight([1, 2, 3], 0); + * // => [1, 2, 3] + */ + function dropRight(array, n, guard) { + var length = array == null ? 0 : array.length; + if (!length) { + return []; + } + n = (guard || n === undefined) ? 1 : toInteger(n); + n = length - n; + return baseSlice(array, 0, n < 0 ? 0 : n); + } + + /** + * Creates a slice of `array` excluding elements dropped from the end. + * Elements are dropped until `predicate` returns falsey. The predicate is + * invoked with three arguments: (value, index, array). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to query. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {Array} Returns the slice of `array`. + * @example + * + * var users = [ + * { 'user': 'barney', 'active': true }, + * { 'user': 'fred', 'active': false }, + * { 'user': 'pebbles', 'active': false } + * ]; + * + * _.dropRightWhile(users, function(o) { return !o.active; }); + * // => objects for ['barney'] + * + * // The `_.matches` iteratee shorthand. + * _.dropRightWhile(users, { 'user': 'pebbles', 'active': false }); + * // => objects for ['barney', 'fred'] + * + * // The `_.matchesProperty` iteratee shorthand. + * _.dropRightWhile(users, ['active', false]); + * // => objects for ['barney'] + * + * // The `_.property` iteratee shorthand. + * _.dropRightWhile(users, 'active'); + * // => objects for ['barney', 'fred', 'pebbles'] + */ + function dropRightWhile(array, predicate) { + return (array && array.length) + ? baseWhile(array, getIteratee(predicate, 3), true, true) + : []; + } + + /** + * Creates a slice of `array` excluding elements dropped from the beginning. + * Elements are dropped until `predicate` returns falsey. The predicate is + * invoked with three arguments: (value, index, array). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to query. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {Array} Returns the slice of `array`. + * @example + * + * var users = [ + * { 'user': 'barney', 'active': false }, + * { 'user': 'fred', 'active': false }, + * { 'user': 'pebbles', 'active': true } + * ]; + * + * _.dropWhile(users, function(o) { return !o.active; }); + * // => objects for ['pebbles'] + * + * // The `_.matches` iteratee shorthand. + * _.dropWhile(users, { 'user': 'barney', 'active': false }); + * // => objects for ['fred', 'pebbles'] + * + * // The `_.matchesProperty` iteratee shorthand. + * _.dropWhile(users, ['active', false]); + * // => objects for ['pebbles'] + * + * // The `_.property` iteratee shorthand. + * _.dropWhile(users, 'active'); + * // => objects for ['barney', 'fred', 'pebbles'] + */ + function dropWhile(array, predicate) { + return (array && array.length) + ? baseWhile(array, getIteratee(predicate, 3), true) + : []; + } + + /** + * Fills elements of `array` with `value` from `start` up to, but not + * including, `end`. + * + * **Note:** This method mutates `array`. + * + * @static + * @memberOf _ + * @since 3.2.0 + * @category Array + * @param {Array} array The array to fill. + * @param {*} value The value to fill `array` with. + * @param {number} [start=0] The start position. + * @param {number} [end=array.length] The end position. + * @returns {Array} Returns `array`. + * @example + * + * var array = [1, 2, 3]; + * + * _.fill(array, 'a'); + * console.log(array); + * // => ['a', 'a', 'a'] + * + * _.fill(Array(3), 2); + * // => [2, 2, 2] + * + * _.fill([4, 6, 8, 10], '*', 1, 3); + * // => [4, '*', '*', 10] + */ + function fill(array, value, start, end) { + var length = array == null ? 0 : array.length; + if (!length) { + return []; + } + if (start && typeof start != 'number' && isIterateeCall(array, value, start)) { + start = 0; + end = length; + } + return baseFill(array, value, start, end); + } + + /** + * This method is like `_.find` except that it returns the index of the first + * element `predicate` returns truthy for instead of the element itself. + * + * @static + * @memberOf _ + * @since 1.1.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @param {number} [fromIndex=0] The index to search from. + * @returns {number} Returns the index of the found element, else `-1`. + * @example + * + * var users = [ + * { 'user': 'barney', 'active': false }, + * { 'user': 'fred', 'active': false }, + * { 'user': 'pebbles', 'active': true } + * ]; + * + * _.findIndex(users, function(o) { return o.user == 'barney'; }); + * // => 0 + * + * // The `_.matches` iteratee shorthand. + * _.findIndex(users, { 'user': 'fred', 'active': false }); + * // => 1 + * + * // The `_.matchesProperty` iteratee shorthand. + * _.findIndex(users, ['active', false]); + * // => 0 + * + * // The `_.property` iteratee shorthand. + * _.findIndex(users, 'active'); + * // => 2 + */ + function findIndex(array, predicate, fromIndex) { + var length = array == null ? 0 : array.length; + if (!length) { + return -1; + } + var index = fromIndex == null ? 0 : toInteger(fromIndex); + if (index < 0) { + index = nativeMax(length + index, 0); + } + return baseFindIndex(array, getIteratee(predicate, 3), index); + } + + /** + * This method is like `_.findIndex` except that it iterates over elements + * of `collection` from right to left. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @param {number} [fromIndex=array.length-1] The index to search from. + * @returns {number} Returns the index of the found element, else `-1`. + * @example + * + * var users = [ + * { 'user': 'barney', 'active': true }, + * { 'user': 'fred', 'active': false }, + * { 'user': 'pebbles', 'active': false } + * ]; + * + * _.findLastIndex(users, function(o) { return o.user == 'pebbles'; }); + * // => 2 + * + * // The `_.matches` iteratee shorthand. + * _.findLastIndex(users, { 'user': 'barney', 'active': true }); + * // => 0 + * + * // The `_.matchesProperty` iteratee shorthand. + * _.findLastIndex(users, ['active', false]); + * // => 2 + * + * // The `_.property` iteratee shorthand. + * _.findLastIndex(users, 'active'); + * // => 0 + */ + function findLastIndex(array, predicate, fromIndex) { + var length = array == null ? 0 : array.length; + if (!length) { + return -1; + } + var index = length - 1; + if (fromIndex !== undefined) { + index = toInteger(fromIndex); + index = fromIndex < 0 + ? nativeMax(length + index, 0) + : nativeMin(index, length - 1); + } + return baseFindIndex(array, getIteratee(predicate, 3), index, true); + } + + /** + * Flattens `array` a single level deep. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to flatten. + * @returns {Array} Returns the new flattened array. + * @example + * + * _.flatten([1, [2, [3, [4]], 5]]); + * // => [1, 2, [3, [4]], 5] + */ + function flatten(array) { + var length = array == null ? 0 : array.length; + return length ? baseFlatten(array, 1) : []; + } + + /** + * Recursively flattens `array`. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to flatten. + * @returns {Array} Returns the new flattened array. + * @example + * + * _.flattenDeep([1, [2, [3, [4]], 5]]); + * // => [1, 2, 3, 4, 5] + */ + function flattenDeep(array) { + var length = array == null ? 0 : array.length; + return length ? baseFlatten(array, INFINITY) : []; + } + + /** + * Recursively flatten `array` up to `depth` times. + * + * @static + * @memberOf _ + * @since 4.4.0 + * @category Array + * @param {Array} array The array to flatten. + * @param {number} [depth=1] The maximum recursion depth. + * @returns {Array} Returns the new flattened array. + * @example + * + * var array = [1, [2, [3, [4]], 5]]; + * + * _.flattenDepth(array, 1); + * // => [1, 2, [3, [4]], 5] + * + * _.flattenDepth(array, 2); + * // => [1, 2, 3, [4], 5] + */ + function flattenDepth(array, depth) { + var length = array == null ? 0 : array.length; + if (!length) { + return []; + } + depth = depth === undefined ? 1 : toInteger(depth); + return baseFlatten(array, depth); + } + + /** + * The inverse of `_.toPairs`; this method returns an object composed + * from key-value `pairs`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} pairs The key-value pairs. + * @returns {Object} Returns the new object. + * @example + * + * _.fromPairs([['a', 1], ['b', 2]]); + * // => { 'a': 1, 'b': 2 } + */ + function fromPairs(pairs) { + var index = -1, + length = pairs == null ? 0 : pairs.length, + result = {}; + + while (++index < length) { + var pair = pairs[index]; + result[pair[0]] = pair[1]; + } + return result; + } + + /** + * Gets the first element of `array`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @alias first + * @category Array + * @param {Array} array The array to query. + * @returns {*} Returns the first element of `array`. + * @example + * + * _.head([1, 2, 3]); + * // => 1 + * + * _.head([]); + * // => undefined + */ + function head(array) { + return (array && array.length) ? array[0] : undefined; + } + + /** + * Gets the index at which the first occurrence of `value` is found in `array` + * using [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * for equality comparisons. If `fromIndex` is negative, it's used as the + * offset from the end of `array`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {*} value The value to search for. + * @param {number} [fromIndex=0] The index to search from. + * @returns {number} Returns the index of the matched value, else `-1`. + * @example + * + * _.indexOf([1, 2, 1, 2], 2); + * // => 1 + * + * // Search from the `fromIndex`. + * _.indexOf([1, 2, 1, 2], 2, 2); + * // => 3 + */ + function indexOf(array, value, fromIndex) { + var length = array == null ? 0 : array.length; + if (!length) { + return -1; + } + var index = fromIndex == null ? 0 : toInteger(fromIndex); + if (index < 0) { + index = nativeMax(length + index, 0); + } + return baseIndexOf(array, value, index); + } + + /** + * Gets all but the last element of `array`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to query. + * @returns {Array} Returns the slice of `array`. + * @example + * + * _.initial([1, 2, 3]); + * // => [1, 2] + */ + function initial(array) { + var length = array == null ? 0 : array.length; + return length ? baseSlice(array, 0, -1) : []; + } + + /** + * Creates an array of unique values that are included in all given arrays + * using [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * for equality comparisons. The order and references of result values are + * determined by the first array. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @returns {Array} Returns the new array of intersecting values. + * @example + * + * _.intersection([2, 1], [2, 3]); + * // => [2] + */ + var intersection = baseRest(function(arrays) { + var mapped = arrayMap(arrays, castArrayLikeObject); + return (mapped.length && mapped[0] === arrays[0]) + ? baseIntersection(mapped) + : []; + }); + + /** + * This method is like `_.intersection` except that it accepts `iteratee` + * which is invoked for each element of each `arrays` to generate the criterion + * by which they're compared. The order and references of result values are + * determined by the first array. The iteratee is invoked with one argument: + * (value). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {Array} Returns the new array of intersecting values. + * @example + * + * _.intersectionBy([2.1, 1.2], [2.3, 3.4], Math.floor); + * // => [2.1] + * + * // The `_.property` iteratee shorthand. + * _.intersectionBy([{ 'x': 1 }], [{ 'x': 2 }, { 'x': 1 }], 'x'); + * // => [{ 'x': 1 }] + */ + var intersectionBy = baseRest(function(arrays) { + var iteratee = last(arrays), + mapped = arrayMap(arrays, castArrayLikeObject); + + if (iteratee === last(mapped)) { + iteratee = undefined; + } else { + mapped.pop(); + } + return (mapped.length && mapped[0] === arrays[0]) + ? baseIntersection(mapped, getIteratee(iteratee, 2)) + : []; + }); + + /** + * This method is like `_.intersection` except that it accepts `comparator` + * which is invoked to compare elements of `arrays`. The order and references + * of result values are determined by the first array. The comparator is + * invoked with two arguments: (arrVal, othVal). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new array of intersecting values. + * @example + * + * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }]; + * var others = [{ 'x': 1, 'y': 1 }, { 'x': 1, 'y': 2 }]; + * + * _.intersectionWith(objects, others, _.isEqual); + * // => [{ 'x': 1, 'y': 2 }] + */ + var intersectionWith = baseRest(function(arrays) { + var comparator = last(arrays), + mapped = arrayMap(arrays, castArrayLikeObject); + + comparator = typeof comparator == 'function' ? comparator : undefined; + if (comparator) { + mapped.pop(); + } + return (mapped.length && mapped[0] === arrays[0]) + ? baseIntersection(mapped, undefined, comparator) + : []; + }); + + /** + * Converts all elements in `array` into a string separated by `separator`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to convert. + * @param {string} [separator=','] The element separator. + * @returns {string} Returns the joined string. + * @example + * + * _.join(['a', 'b', 'c'], '~'); + * // => 'a~b~c' + */ + function join(array, separator) { + return array == null ? '' : nativeJoin.call(array, separator); + } + + /** + * Gets the last element of `array`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to query. + * @returns {*} Returns the last element of `array`. + * @example + * + * _.last([1, 2, 3]); + * // => 3 + */ + function last(array) { + var length = array == null ? 0 : array.length; + return length ? array[length - 1] : undefined; + } + + /** + * This method is like `_.indexOf` except that it iterates over elements of + * `array` from right to left. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {*} value The value to search for. + * @param {number} [fromIndex=array.length-1] The index to search from. + * @returns {number} Returns the index of the matched value, else `-1`. + * @example + * + * _.lastIndexOf([1, 2, 1, 2], 2); + * // => 3 + * + * // Search from the `fromIndex`. + * _.lastIndexOf([1, 2, 1, 2], 2, 2); + * // => 1 + */ + function lastIndexOf(array, value, fromIndex) { + var length = array == null ? 0 : array.length; + if (!length) { + return -1; + } + var index = length; + if (fromIndex !== undefined) { + index = toInteger(fromIndex); + index = index < 0 ? nativeMax(length + index, 0) : nativeMin(index, length - 1); + } + return value === value + ? strictLastIndexOf(array, value, index) + : baseFindIndex(array, baseIsNaN, index, true); + } + + /** + * Gets the element at index `n` of `array`. If `n` is negative, the nth + * element from the end is returned. + * + * @static + * @memberOf _ + * @since 4.11.0 + * @category Array + * @param {Array} array The array to query. + * @param {number} [n=0] The index of the element to return. + * @returns {*} Returns the nth element of `array`. + * @example + * + * var array = ['a', 'b', 'c', 'd']; + * + * _.nth(array, 1); + * // => 'b' + * + * _.nth(array, -2); + * // => 'c'; + */ + function nth(array, n) { + return (array && array.length) ? baseNth(array, toInteger(n)) : undefined; + } + + /** + * Removes all given values from `array` using + * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * for equality comparisons. + * + * **Note:** Unlike `_.without`, this method mutates `array`. Use `_.remove` + * to remove elements from an array by predicate. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Array + * @param {Array} array The array to modify. + * @param {...*} [values] The values to remove. + * @returns {Array} Returns `array`. + * @example + * + * var array = ['a', 'b', 'c', 'a', 'b', 'c']; + * + * _.pull(array, 'a', 'c'); + * console.log(array); + * // => ['b', 'b'] + */ + var pull = baseRest(pullAll); + + /** + * This method is like `_.pull` except that it accepts an array of values to remove. + * + * **Note:** Unlike `_.difference`, this method mutates `array`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to modify. + * @param {Array} values The values to remove. + * @returns {Array} Returns `array`. + * @example + * + * var array = ['a', 'b', 'c', 'a', 'b', 'c']; + * + * _.pullAll(array, ['a', 'c']); + * console.log(array); + * // => ['b', 'b'] + */ + function pullAll(array, values) { + return (array && array.length && values && values.length) + ? basePullAll(array, values) + : array; + } + + /** + * This method is like `_.pullAll` except that it accepts `iteratee` which is + * invoked for each element of `array` and `values` to generate the criterion + * by which they're compared. The iteratee is invoked with one argument: (value). + * + * **Note:** Unlike `_.differenceBy`, this method mutates `array`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to modify. + * @param {Array} values The values to remove. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {Array} Returns `array`. + * @example + * + * var array = [{ 'x': 1 }, { 'x': 2 }, { 'x': 3 }, { 'x': 1 }]; + * + * _.pullAllBy(array, [{ 'x': 1 }, { 'x': 3 }], 'x'); + * console.log(array); + * // => [{ 'x': 2 }] + */ + function pullAllBy(array, values, iteratee) { + return (array && array.length && values && values.length) + ? basePullAll(array, values, getIteratee(iteratee, 2)) + : array; + } + + /** + * This method is like `_.pullAll` except that it accepts `comparator` which + * is invoked to compare elements of `array` to `values`. The comparator is + * invoked with two arguments: (arrVal, othVal). + * + * **Note:** Unlike `_.differenceWith`, this method mutates `array`. + * + * @static + * @memberOf _ + * @since 4.6.0 + * @category Array + * @param {Array} array The array to modify. + * @param {Array} values The values to remove. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns `array`. + * @example + * + * var array = [{ 'x': 1, 'y': 2 }, { 'x': 3, 'y': 4 }, { 'x': 5, 'y': 6 }]; + * + * _.pullAllWith(array, [{ 'x': 3, 'y': 4 }], _.isEqual); + * console.log(array); + * // => [{ 'x': 1, 'y': 2 }, { 'x': 5, 'y': 6 }] + */ + function pullAllWith(array, values, comparator) { + return (array && array.length && values && values.length) + ? basePullAll(array, values, undefined, comparator) + : array; + } + + /** + * Removes elements from `array` corresponding to `indexes` and returns an + * array of removed elements. + * + * **Note:** Unlike `_.at`, this method mutates `array`. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to modify. + * @param {...(number|number[])} [indexes] The indexes of elements to remove. + * @returns {Array} Returns the new array of removed elements. + * @example + * + * var array = ['a', 'b', 'c', 'd']; + * var pulled = _.pullAt(array, [1, 3]); + * + * console.log(array); + * // => ['a', 'c'] + * + * console.log(pulled); + * // => ['b', 'd'] + */ + var pullAt = flatRest(function(array, indexes) { + var length = array == null ? 0 : array.length, + result = baseAt(array, indexes); + + basePullAt(array, arrayMap(indexes, function(index) { + return isIndex(index, length) ? +index : index; + }).sort(compareAscending)); + + return result; + }); + + /** + * Removes all elements from `array` that `predicate` returns truthy for + * and returns an array of the removed elements. The predicate is invoked + * with three arguments: (value, index, array). + * + * **Note:** Unlike `_.filter`, this method mutates `array`. Use `_.pull` + * to pull elements from an array by value. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Array + * @param {Array} array The array to modify. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {Array} Returns the new array of removed elements. + * @example + * + * var array = [1, 2, 3, 4]; + * var evens = _.remove(array, function(n) { + * return n % 2 == 0; + * }); + * + * console.log(array); + * // => [1, 3] + * + * console.log(evens); + * // => [2, 4] + */ + function remove(array, predicate) { + var result = []; + if (!(array && array.length)) { + return result; + } + var index = -1, + indexes = [], + length = array.length; + + predicate = getIteratee(predicate, 3); + while (++index < length) { + var value = array[index]; + if (predicate(value, index, array)) { + result.push(value); + indexes.push(index); + } + } + basePullAt(array, indexes); + return result; + } + + /** + * Reverses `array` so that the first element becomes the last, the second + * element becomes the second to last, and so on. + * + * **Note:** This method mutates `array` and is based on + * [`Array#reverse`](https://mdn.io/Array/reverse). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to modify. + * @returns {Array} Returns `array`. + * @example + * + * var array = [1, 2, 3]; + * + * _.reverse(array); + * // => [3, 2, 1] + * + * console.log(array); + * // => [3, 2, 1] + */ + function reverse(array) { + return array == null ? array : nativeReverse.call(array); + } + + /** + * Creates a slice of `array` from `start` up to, but not including, `end`. + * + * **Note:** This method is used instead of + * [`Array#slice`](https://mdn.io/Array/slice) to ensure dense arrays are + * returned. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to slice. + * @param {number} [start=0] The start position. + * @param {number} [end=array.length] The end position. + * @returns {Array} Returns the slice of `array`. + */ + function slice(array, start, end) { + var length = array == null ? 0 : array.length; + if (!length) { + return []; + } + if (end && typeof end != 'number' && isIterateeCall(array, start, end)) { + start = 0; + end = length; + } + else { + start = start == null ? 0 : toInteger(start); + end = end === undefined ? length : toInteger(end); + } + return baseSlice(array, start, end); + } + + /** + * Uses a binary search to determine the lowest index at which `value` + * should be inserted into `array` in order to maintain its sort order. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The sorted array to inspect. + * @param {*} value The value to evaluate. + * @returns {number} Returns the index at which `value` should be inserted + * into `array`. + * @example + * + * _.sortedIndex([30, 50], 40); + * // => 1 + */ + function sortedIndex(array, value) { + return baseSortedIndex(array, value); + } + + /** + * This method is like `_.sortedIndex` except that it accepts `iteratee` + * which is invoked for `value` and each element of `array` to compute their + * sort ranking. The iteratee is invoked with one argument: (value). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The sorted array to inspect. + * @param {*} value The value to evaluate. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {number} Returns the index at which `value` should be inserted + * into `array`. + * @example + * + * var objects = [{ 'x': 4 }, { 'x': 5 }]; + * + * _.sortedIndexBy(objects, { 'x': 4 }, function(o) { return o.x; }); + * // => 0 + * + * // The `_.property` iteratee shorthand. + * _.sortedIndexBy(objects, { 'x': 4 }, 'x'); + * // => 0 + */ + function sortedIndexBy(array, value, iteratee) { + return baseSortedIndexBy(array, value, getIteratee(iteratee, 2)); + } + + /** + * This method is like `_.indexOf` except that it performs a binary + * search on a sorted `array`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {*} value The value to search for. + * @returns {number} Returns the index of the matched value, else `-1`. + * @example + * + * _.sortedIndexOf([4, 5, 5, 5, 6], 5); + * // => 1 + */ + function sortedIndexOf(array, value) { + var length = array == null ? 0 : array.length; + if (length) { + var index = baseSortedIndex(array, value); + if (index < length && eq(array[index], value)) { + return index; + } + } + return -1; + } + + /** + * This method is like `_.sortedIndex` except that it returns the highest + * index at which `value` should be inserted into `array` in order to + * maintain its sort order. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The sorted array to inspect. + * @param {*} value The value to evaluate. + * @returns {number} Returns the index at which `value` should be inserted + * into `array`. + * @example + * + * _.sortedLastIndex([4, 5, 5, 5, 6], 5); + * // => 4 + */ + function sortedLastIndex(array, value) { + return baseSortedIndex(array, value, true); + } + + /** + * This method is like `_.sortedLastIndex` except that it accepts `iteratee` + * which is invoked for `value` and each element of `array` to compute their + * sort ranking. The iteratee is invoked with one argument: (value). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The sorted array to inspect. + * @param {*} value The value to evaluate. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {number} Returns the index at which `value` should be inserted + * into `array`. + * @example + * + * var objects = [{ 'x': 4 }, { 'x': 5 }]; + * + * _.sortedLastIndexBy(objects, { 'x': 4 }, function(o) { return o.x; }); + * // => 1 + * + * // The `_.property` iteratee shorthand. + * _.sortedLastIndexBy(objects, { 'x': 4 }, 'x'); + * // => 1 + */ + function sortedLastIndexBy(array, value, iteratee) { + return baseSortedIndexBy(array, value, getIteratee(iteratee, 2), true); + } + + /** + * This method is like `_.lastIndexOf` except that it performs a binary + * search on a sorted `array`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {*} value The value to search for. + * @returns {number} Returns the index of the matched value, else `-1`. + * @example + * + * _.sortedLastIndexOf([4, 5, 5, 5, 6], 5); + * // => 3 + */ + function sortedLastIndexOf(array, value) { + var length = array == null ? 0 : array.length; + if (length) { + var index = baseSortedIndex(array, value, true) - 1; + if (eq(array[index], value)) { + return index; + } + } + return -1; + } + + /** + * This method is like `_.uniq` except that it's designed and optimized + * for sorted arrays. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @returns {Array} Returns the new duplicate free array. + * @example + * + * _.sortedUniq([1, 1, 2]); + * // => [1, 2] + */ + function sortedUniq(array) { + return (array && array.length) + ? baseSortedUniq(array) + : []; + } + + /** + * This method is like `_.uniqBy` except that it's designed and optimized + * for sorted arrays. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {Function} [iteratee] The iteratee invoked per element. + * @returns {Array} Returns the new duplicate free array. + * @example + * + * _.sortedUniqBy([1.1, 1.2, 2.3, 2.4], Math.floor); + * // => [1.1, 2.3] + */ + function sortedUniqBy(array, iteratee) { + return (array && array.length) + ? baseSortedUniq(array, getIteratee(iteratee, 2)) + : []; + } + + /** + * Gets all but the first element of `array`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to query. + * @returns {Array} Returns the slice of `array`. + * @example + * + * _.tail([1, 2, 3]); + * // => [2, 3] + */ + function tail(array) { + var length = array == null ? 0 : array.length; + return length ? baseSlice(array, 1, length) : []; + } + + /** + * Creates a slice of `array` with `n` elements taken from the beginning. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to query. + * @param {number} [n=1] The number of elements to take. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Array} Returns the slice of `array`. + * @example + * + * _.take([1, 2, 3]); + * // => [1] + * + * _.take([1, 2, 3], 2); + * // => [1, 2] + * + * _.take([1, 2, 3], 5); + * // => [1, 2, 3] + * + * _.take([1, 2, 3], 0); + * // => [] + */ + function take(array, n, guard) { + if (!(array && array.length)) { + return []; + } + n = (guard || n === undefined) ? 1 : toInteger(n); + return baseSlice(array, 0, n < 0 ? 0 : n); + } + + /** + * Creates a slice of `array` with `n` elements taken from the end. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to query. + * @param {number} [n=1] The number of elements to take. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Array} Returns the slice of `array`. + * @example + * + * _.takeRight([1, 2, 3]); + * // => [3] + * + * _.takeRight([1, 2, 3], 2); + * // => [2, 3] + * + * _.takeRight([1, 2, 3], 5); + * // => [1, 2, 3] + * + * _.takeRight([1, 2, 3], 0); + * // => [] + */ + function takeRight(array, n, guard) { + var length = array == null ? 0 : array.length; + if (!length) { + return []; + } + n = (guard || n === undefined) ? 1 : toInteger(n); + n = length - n; + return baseSlice(array, n < 0 ? 0 : n, length); + } + + /** + * Creates a slice of `array` with elements taken from the end. Elements are + * taken until `predicate` returns falsey. The predicate is invoked with + * three arguments: (value, index, array). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to query. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {Array} Returns the slice of `array`. + * @example + * + * var users = [ + * { 'user': 'barney', 'active': true }, + * { 'user': 'fred', 'active': false }, + * { 'user': 'pebbles', 'active': false } + * ]; + * + * _.takeRightWhile(users, function(o) { return !o.active; }); + * // => objects for ['fred', 'pebbles'] + * + * // The `_.matches` iteratee shorthand. + * _.takeRightWhile(users, { 'user': 'pebbles', 'active': false }); + * // => objects for ['pebbles'] + * + * // The `_.matchesProperty` iteratee shorthand. + * _.takeRightWhile(users, ['active', false]); + * // => objects for ['fred', 'pebbles'] + * + * // The `_.property` iteratee shorthand. + * _.takeRightWhile(users, 'active'); + * // => [] + */ + function takeRightWhile(array, predicate) { + return (array && array.length) + ? baseWhile(array, getIteratee(predicate, 3), false, true) + : []; + } + + /** + * Creates a slice of `array` with elements taken from the beginning. Elements + * are taken until `predicate` returns falsey. The predicate is invoked with + * three arguments: (value, index, array). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Array + * @param {Array} array The array to query. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {Array} Returns the slice of `array`. + * @example + * + * var users = [ + * { 'user': 'barney', 'active': false }, + * { 'user': 'fred', 'active': false }, + * { 'user': 'pebbles', 'active': true } + * ]; + * + * _.takeWhile(users, function(o) { return !o.active; }); + * // => objects for ['barney', 'fred'] + * + * // The `_.matches` iteratee shorthand. + * _.takeWhile(users, { 'user': 'barney', 'active': false }); + * // => objects for ['barney'] + * + * // The `_.matchesProperty` iteratee shorthand. + * _.takeWhile(users, ['active', false]); + * // => objects for ['barney', 'fred'] + * + * // The `_.property` iteratee shorthand. + * _.takeWhile(users, 'active'); + * // => [] + */ + function takeWhile(array, predicate) { + return (array && array.length) + ? baseWhile(array, getIteratee(predicate, 3)) + : []; + } + + /** + * Creates an array of unique values, in order, from all given arrays using + * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * for equality comparisons. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @returns {Array} Returns the new array of combined values. + * @example + * + * _.union([2], [1, 2]); + * // => [2, 1] + */ + var union = baseRest(function(arrays) { + return baseUniq(baseFlatten(arrays, 1, isArrayLikeObject, true)); + }); + + /** + * This method is like `_.union` except that it accepts `iteratee` which is + * invoked for each element of each `arrays` to generate the criterion by + * which uniqueness is computed. Result values are chosen from the first + * array in which the value occurs. The iteratee is invoked with one argument: + * (value). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {Array} Returns the new array of combined values. + * @example + * + * _.unionBy([2.1], [1.2, 2.3], Math.floor); + * // => [2.1, 1.2] + * + * // The `_.property` iteratee shorthand. + * _.unionBy([{ 'x': 1 }], [{ 'x': 2 }, { 'x': 1 }], 'x'); + * // => [{ 'x': 1 }, { 'x': 2 }] + */ + var unionBy = baseRest(function(arrays) { + var iteratee = last(arrays); + if (isArrayLikeObject(iteratee)) { + iteratee = undefined; + } + return baseUniq(baseFlatten(arrays, 1, isArrayLikeObject, true), getIteratee(iteratee, 2)); + }); + + /** + * This method is like `_.union` except that it accepts `comparator` which + * is invoked to compare elements of `arrays`. Result values are chosen from + * the first array in which the value occurs. The comparator is invoked + * with two arguments: (arrVal, othVal). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new array of combined values. + * @example + * + * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }]; + * var others = [{ 'x': 1, 'y': 1 }, { 'x': 1, 'y': 2 }]; + * + * _.unionWith(objects, others, _.isEqual); + * // => [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }, { 'x': 1, 'y': 1 }] + */ + var unionWith = baseRest(function(arrays) { + var comparator = last(arrays); + comparator = typeof comparator == 'function' ? comparator : undefined; + return baseUniq(baseFlatten(arrays, 1, isArrayLikeObject, true), undefined, comparator); + }); + + /** + * Creates a duplicate-free version of an array, using + * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * for equality comparisons, in which only the first occurrence of each element + * is kept. The order of result values is determined by the order they occur + * in the array. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to inspect. + * @returns {Array} Returns the new duplicate free array. + * @example + * + * _.uniq([2, 1, 2]); + * // => [2, 1] + */ + function uniq(array) { + return (array && array.length) ? baseUniq(array) : []; + } + + /** + * This method is like `_.uniq` except that it accepts `iteratee` which is + * invoked for each element in `array` to generate the criterion by which + * uniqueness is computed. The order of result values is determined by the + * order they occur in the array. The iteratee is invoked with one argument: + * (value). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {Array} Returns the new duplicate free array. + * @example + * + * _.uniqBy([2.1, 1.2, 2.3], Math.floor); + * // => [2.1, 1.2] + * + * // The `_.property` iteratee shorthand. + * _.uniqBy([{ 'x': 1 }, { 'x': 2 }, { 'x': 1 }], 'x'); + * // => [{ 'x': 1 }, { 'x': 2 }] + */ + function uniqBy(array, iteratee) { + return (array && array.length) ? baseUniq(array, getIteratee(iteratee, 2)) : []; + } + + /** + * This method is like `_.uniq` except that it accepts `comparator` which + * is invoked to compare elements of `array`. The order of result values is + * determined by the order they occur in the array.The comparator is invoked + * with two arguments: (arrVal, othVal). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new duplicate free array. + * @example + * + * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }, { 'x': 1, 'y': 2 }]; + * + * _.uniqWith(objects, _.isEqual); + * // => [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }] + */ + function uniqWith(array, comparator) { + comparator = typeof comparator == 'function' ? comparator : undefined; + return (array && array.length) ? baseUniq(array, undefined, comparator) : []; + } + + /** + * This method is like `_.zip` except that it accepts an array of grouped + * elements and creates an array regrouping the elements to their pre-zip + * configuration. + * + * @static + * @memberOf _ + * @since 1.2.0 + * @category Array + * @param {Array} array The array of grouped elements to process. + * @returns {Array} Returns the new array of regrouped elements. + * @example + * + * var zipped = _.zip(['a', 'b'], [1, 2], [true, false]); + * // => [['a', 1, true], ['b', 2, false]] + * + * _.unzip(zipped); + * // => [['a', 'b'], [1, 2], [true, false]] + */ + function unzip(array) { + if (!(array && array.length)) { + return []; + } + var length = 0; + array = arrayFilter(array, function(group) { + if (isArrayLikeObject(group)) { + length = nativeMax(group.length, length); + return true; + } + }); + return baseTimes(length, function(index) { + return arrayMap(array, baseProperty(index)); + }); + } + + /** + * This method is like `_.unzip` except that it accepts `iteratee` to specify + * how regrouped values should be combined. The iteratee is invoked with the + * elements of each group: (...group). + * + * @static + * @memberOf _ + * @since 3.8.0 + * @category Array + * @param {Array} array The array of grouped elements to process. + * @param {Function} [iteratee=_.identity] The function to combine + * regrouped values. + * @returns {Array} Returns the new array of regrouped elements. + * @example + * + * var zipped = _.zip([1, 2], [10, 20], [100, 200]); + * // => [[1, 10, 100], [2, 20, 200]] + * + * _.unzipWith(zipped, _.add); + * // => [3, 30, 300] + */ + function unzipWith(array, iteratee) { + if (!(array && array.length)) { + return []; + } + var result = unzip(array); + if (iteratee == null) { + return result; + } + return arrayMap(result, function(group) { + return apply(iteratee, undefined, group); + }); + } + + /** + * Creates an array excluding all given values using + * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * for equality comparisons. + * + * **Note:** Unlike `_.pull`, this method returns a new array. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {Array} array The array to inspect. + * @param {...*} [values] The values to exclude. + * @returns {Array} Returns the new array of filtered values. + * @see _.difference, _.xor + * @example + * + * _.without([2, 1, 2, 3], 1, 2); + * // => [3] + */ + var without = baseRest(function(array, values) { + return isArrayLikeObject(array) + ? baseDifference(array, values) + : []; + }); + + /** + * Creates an array of unique values that is the + * [symmetric difference](https://en.wikipedia.org/wiki/Symmetric_difference) + * of the given arrays. The order of result values is determined by the order + * they occur in the arrays. + * + * @static + * @memberOf _ + * @since 2.4.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @returns {Array} Returns the new array of filtered values. + * @see _.difference, _.without + * @example + * + * _.xor([2, 1], [2, 3]); + * // => [1, 3] + */ + var xor = baseRest(function(arrays) { + return baseXor(arrayFilter(arrays, isArrayLikeObject)); + }); + + /** + * This method is like `_.xor` except that it accepts `iteratee` which is + * invoked for each element of each `arrays` to generate the criterion by + * which by which they're compared. The order of result values is determined + * by the order they occur in the arrays. The iteratee is invoked with one + * argument: (value). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {Array} Returns the new array of filtered values. + * @example + * + * _.xorBy([2.1, 1.2], [2.3, 3.4], Math.floor); + * // => [1.2, 3.4] + * + * // The `_.property` iteratee shorthand. + * _.xorBy([{ 'x': 1 }], [{ 'x': 2 }, { 'x': 1 }], 'x'); + * // => [{ 'x': 2 }] + */ + var xorBy = baseRest(function(arrays) { + var iteratee = last(arrays); + if (isArrayLikeObject(iteratee)) { + iteratee = undefined; + } + return baseXor(arrayFilter(arrays, isArrayLikeObject), getIteratee(iteratee, 2)); + }); + + /** + * This method is like `_.xor` except that it accepts `comparator` which is + * invoked to compare elements of `arrays`. The order of result values is + * determined by the order they occur in the arrays. The comparator is invoked + * with two arguments: (arrVal, othVal). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Array + * @param {...Array} [arrays] The arrays to inspect. + * @param {Function} [comparator] The comparator invoked per element. + * @returns {Array} Returns the new array of filtered values. + * @example + * + * var objects = [{ 'x': 1, 'y': 2 }, { 'x': 2, 'y': 1 }]; + * var others = [{ 'x': 1, 'y': 1 }, { 'x': 1, 'y': 2 }]; + * + * _.xorWith(objects, others, _.isEqual); + * // => [{ 'x': 2, 'y': 1 }, { 'x': 1, 'y': 1 }] + */ + var xorWith = baseRest(function(arrays) { + var comparator = last(arrays); + comparator = typeof comparator == 'function' ? comparator : undefined; + return baseXor(arrayFilter(arrays, isArrayLikeObject), undefined, comparator); + }); + + /** + * Creates an array of grouped elements, the first of which contains the + * first elements of the given arrays, the second of which contains the + * second elements of the given arrays, and so on. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Array + * @param {...Array} [arrays] The arrays to process. + * @returns {Array} Returns the new array of grouped elements. + * @example + * + * _.zip(['a', 'b'], [1, 2], [true, false]); + * // => [['a', 1, true], ['b', 2, false]] + */ + var zip = baseRest(unzip); + + /** + * This method is like `_.fromPairs` except that it accepts two arrays, + * one of property identifiers and one of corresponding values. + * + * @static + * @memberOf _ + * @since 0.4.0 + * @category Array + * @param {Array} [props=[]] The property identifiers. + * @param {Array} [values=[]] The property values. + * @returns {Object} Returns the new object. + * @example + * + * _.zipObject(['a', 'b'], [1, 2]); + * // => { 'a': 1, 'b': 2 } + */ + function zipObject(props, values) { + return baseZipObject(props || [], values || [], assignValue); + } + + /** + * This method is like `_.zipObject` except that it supports property paths. + * + * @static + * @memberOf _ + * @since 4.1.0 + * @category Array + * @param {Array} [props=[]] The property identifiers. + * @param {Array} [values=[]] The property values. + * @returns {Object} Returns the new object. + * @example + * + * _.zipObjectDeep(['a.b[0].c', 'a.b[1].d'], [1, 2]); + * // => { 'a': { 'b': [{ 'c': 1 }, { 'd': 2 }] } } + */ + function zipObjectDeep(props, values) { + return baseZipObject(props || [], values || [], baseSet); + } + + /** + * This method is like `_.zip` except that it accepts `iteratee` to specify + * how grouped values should be combined. The iteratee is invoked with the + * elements of each group: (...group). + * + * @static + * @memberOf _ + * @since 3.8.0 + * @category Array + * @param {...Array} [arrays] The arrays to process. + * @param {Function} [iteratee=_.identity] The function to combine + * grouped values. + * @returns {Array} Returns the new array of grouped elements. + * @example + * + * _.zipWith([1, 2], [10, 20], [100, 200], function(a, b, c) { + * return a + b + c; + * }); + * // => [111, 222] + */ + var zipWith = baseRest(function(arrays) { + var length = arrays.length, + iteratee = length > 1 ? arrays[length - 1] : undefined; + + iteratee = typeof iteratee == 'function' ? (arrays.pop(), iteratee) : undefined; + return unzipWith(arrays, iteratee); + }); + + /*------------------------------------------------------------------------*/ + + /** + * Creates a `lodash` wrapper instance that wraps `value` with explicit method + * chain sequences enabled. The result of such sequences must be unwrapped + * with `_#value`. + * + * @static + * @memberOf _ + * @since 1.3.0 + * @category Seq + * @param {*} value The value to wrap. + * @returns {Object} Returns the new `lodash` wrapper instance. + * @example + * + * var users = [ + * { 'user': 'barney', 'age': 36 }, + * { 'user': 'fred', 'age': 40 }, + * { 'user': 'pebbles', 'age': 1 } + * ]; + * + * var youngest = _ + * .chain(users) + * .sortBy('age') + * .map(function(o) { + * return o.user + ' is ' + o.age; + * }) + * .head() + * .value(); + * // => 'pebbles is 1' + */ + function chain(value) { + var result = lodash(value); + result.__chain__ = true; + return result; + } + + /** + * This method invokes `interceptor` and returns `value`. The interceptor + * is invoked with one argument; (value). The purpose of this method is to + * "tap into" a method chain sequence in order to modify intermediate results. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Seq + * @param {*} value The value to provide to `interceptor`. + * @param {Function} interceptor The function to invoke. + * @returns {*} Returns `value`. + * @example + * + * _([1, 2, 3]) + * .tap(function(array) { + * // Mutate input array. + * array.pop(); + * }) + * .reverse() + * .value(); + * // => [2, 1] + */ + function tap(value, interceptor) { + interceptor(value); + return value; + } + + /** + * This method is like `_.tap` except that it returns the result of `interceptor`. + * The purpose of this method is to "pass thru" values replacing intermediate + * results in a method chain sequence. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Seq + * @param {*} value The value to provide to `interceptor`. + * @param {Function} interceptor The function to invoke. + * @returns {*} Returns the result of `interceptor`. + * @example + * + * _(' abc ') + * .chain() + * .trim() + * .thru(function(value) { + * return [value]; + * }) + * .value(); + * // => ['abc'] + */ + function thru(value, interceptor) { + return interceptor(value); + } + + /** + * This method is the wrapper version of `_.at`. + * + * @name at + * @memberOf _ + * @since 1.0.0 + * @category Seq + * @param {...(string|string[])} [paths] The property paths to pick. + * @returns {Object} Returns the new `lodash` wrapper instance. + * @example + * + * var object = { 'a': [{ 'b': { 'c': 3 } }, 4] }; + * + * _(object).at(['a[0].b.c', 'a[1]']).value(); + * // => [3, 4] + */ + var wrapperAt = flatRest(function(paths) { + var length = paths.length, + start = length ? paths[0] : 0, + value = this.__wrapped__, + interceptor = function(object) { return baseAt(object, paths); }; + + if (length > 1 || this.__actions__.length || + !(value instanceof LazyWrapper) || !isIndex(start)) { + return this.thru(interceptor); + } + value = value.slice(start, +start + (length ? 1 : 0)); + value.__actions__.push({ + 'func': thru, + 'args': [interceptor], + 'thisArg': undefined + }); + return new LodashWrapper(value, this.__chain__).thru(function(array) { + if (length && !array.length) { + array.push(undefined); + } + return array; + }); + }); + + /** + * Creates a `lodash` wrapper instance with explicit method chain sequences enabled. + * + * @name chain + * @memberOf _ + * @since 0.1.0 + * @category Seq + * @returns {Object} Returns the new `lodash` wrapper instance. + * @example + * + * var users = [ + * { 'user': 'barney', 'age': 36 }, + * { 'user': 'fred', 'age': 40 } + * ]; + * + * // A sequence without explicit chaining. + * _(users).head(); + * // => { 'user': 'barney', 'age': 36 } + * + * // A sequence with explicit chaining. + * _(users) + * .chain() + * .head() + * .pick('user') + * .value(); + * // => { 'user': 'barney' } + */ + function wrapperChain() { + return chain(this); + } + + /** + * Executes the chain sequence and returns the wrapped result. + * + * @name commit + * @memberOf _ + * @since 3.2.0 + * @category Seq + * @returns {Object} Returns the new `lodash` wrapper instance. + * @example + * + * var array = [1, 2]; + * var wrapped = _(array).push(3); + * + * console.log(array); + * // => [1, 2] + * + * wrapped = wrapped.commit(); + * console.log(array); + * // => [1, 2, 3] + * + * wrapped.last(); + * // => 3 + * + * console.log(array); + * // => [1, 2, 3] + */ + function wrapperCommit() { + return new LodashWrapper(this.value(), this.__chain__); + } + + /** + * Gets the next value on a wrapped object following the + * [iterator protocol](https://mdn.io/iteration_protocols#iterator). + * + * @name next + * @memberOf _ + * @since 4.0.0 + * @category Seq + * @returns {Object} Returns the next iterator value. + * @example + * + * var wrapped = _([1, 2]); + * + * wrapped.next(); + * // => { 'done': false, 'value': 1 } + * + * wrapped.next(); + * // => { 'done': false, 'value': 2 } + * + * wrapped.next(); + * // => { 'done': true, 'value': undefined } + */ + function wrapperNext() { + if (this.__values__ === undefined) { + this.__values__ = toArray(this.value()); + } + var done = this.__index__ >= this.__values__.length, + value = done ? undefined : this.__values__[this.__index__++]; + + return { 'done': done, 'value': value }; + } + + /** + * Enables the wrapper to be iterable. + * + * @name Symbol.iterator + * @memberOf _ + * @since 4.0.0 + * @category Seq + * @returns {Object} Returns the wrapper object. + * @example + * + * var wrapped = _([1, 2]); + * + * wrapped[Symbol.iterator]() === wrapped; + * // => true + * + * Array.from(wrapped); + * // => [1, 2] + */ + function wrapperToIterator() { + return this; + } + + /** + * Creates a clone of the chain sequence planting `value` as the wrapped value. + * + * @name plant + * @memberOf _ + * @since 3.2.0 + * @category Seq + * @param {*} value The value to plant. + * @returns {Object} Returns the new `lodash` wrapper instance. + * @example + * + * function square(n) { + * return n * n; + * } + * + * var wrapped = _([1, 2]).map(square); + * var other = wrapped.plant([3, 4]); + * + * other.value(); + * // => [9, 16] + * + * wrapped.value(); + * // => [1, 4] + */ + function wrapperPlant(value) { + var result, + parent = this; + + while (parent instanceof baseLodash) { + var clone = wrapperClone(parent); + clone.__index__ = 0; + clone.__values__ = undefined; + if (result) { + previous.__wrapped__ = clone; + } else { + result = clone; + } + var previous = clone; + parent = parent.__wrapped__; + } + previous.__wrapped__ = value; + return result; + } + + /** + * This method is the wrapper version of `_.reverse`. + * + * **Note:** This method mutates the wrapped array. + * + * @name reverse + * @memberOf _ + * @since 0.1.0 + * @category Seq + * @returns {Object} Returns the new `lodash` wrapper instance. + * @example + * + * var array = [1, 2, 3]; + * + * _(array).reverse().value() + * // => [3, 2, 1] + * + * console.log(array); + * // => [3, 2, 1] + */ + function wrapperReverse() { + var value = this.__wrapped__; + if (value instanceof LazyWrapper) { + var wrapped = value; + if (this.__actions__.length) { + wrapped = new LazyWrapper(this); + } + wrapped = wrapped.reverse(); + wrapped.__actions__.push({ + 'func': thru, + 'args': [reverse], + 'thisArg': undefined + }); + return new LodashWrapper(wrapped, this.__chain__); + } + return this.thru(reverse); + } + + /** + * Executes the chain sequence to resolve the unwrapped value. + * + * @name value + * @memberOf _ + * @since 0.1.0 + * @alias toJSON, valueOf + * @category Seq + * @returns {*} Returns the resolved unwrapped value. + * @example + * + * _([1, 2, 3]).value(); + * // => [1, 2, 3] + */ + function wrapperValue() { + return baseWrapperValue(this.__wrapped__, this.__actions__); + } + + /*------------------------------------------------------------------------*/ + + /** + * Creates an object composed of keys generated from the results of running + * each element of `collection` thru `iteratee`. The corresponding value of + * each key is the number of times the key was returned by `iteratee`. The + * iteratee is invoked with one argument: (value). + * + * @static + * @memberOf _ + * @since 0.5.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The iteratee to transform keys. + * @returns {Object} Returns the composed aggregate object. + * @example + * + * _.countBy([6.1, 4.2, 6.3], Math.floor); + * // => { '4': 1, '6': 2 } + * + * // The `_.property` iteratee shorthand. + * _.countBy(['one', 'two', 'three'], 'length'); + * // => { '3': 2, '5': 1 } + */ + var countBy = createAggregator(function(result, value, key) { + if (hasOwnProperty.call(result, key)) { + ++result[key]; + } else { + baseAssignValue(result, key, 1); + } + }); + + /** + * Checks if `predicate` returns truthy for **all** elements of `collection`. + * Iteration is stopped once `predicate` returns falsey. The predicate is + * invoked with three arguments: (value, index|key, collection). + * + * **Note:** This method returns `true` for + * [empty collections](https://en.wikipedia.org/wiki/Empty_set) because + * [everything is true](https://en.wikipedia.org/wiki/Vacuous_truth) of + * elements of empty collections. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {boolean} Returns `true` if all elements pass the predicate check, + * else `false`. + * @example + * + * _.every([true, 1, null, 'yes'], Boolean); + * // => false + * + * var users = [ + * { 'user': 'barney', 'age': 36, 'active': false }, + * { 'user': 'fred', 'age': 40, 'active': false } + * ]; + * + * // The `_.matches` iteratee shorthand. + * _.every(users, { 'user': 'barney', 'active': false }); + * // => false + * + * // The `_.matchesProperty` iteratee shorthand. + * _.every(users, ['active', false]); + * // => true + * + * // The `_.property` iteratee shorthand. + * _.every(users, 'active'); + * // => false + */ + function every(collection, predicate, guard) { + var func = isArray(collection) ? arrayEvery : baseEvery; + if (guard && isIterateeCall(collection, predicate, guard)) { + predicate = undefined; + } + return func(collection, getIteratee(predicate, 3)); + } + + /** + * Iterates over elements of `collection`, returning an array of all elements + * `predicate` returns truthy for. The predicate is invoked with three + * arguments: (value, index|key, collection). + * + * **Note:** Unlike `_.remove`, this method returns a new array. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {Array} Returns the new filtered array. + * @see _.reject + * @example + * + * var users = [ + * { 'user': 'barney', 'age': 36, 'active': true }, + * { 'user': 'fred', 'age': 40, 'active': false } + * ]; + * + * _.filter(users, function(o) { return !o.active; }); + * // => objects for ['fred'] + * + * // The `_.matches` iteratee shorthand. + * _.filter(users, { 'age': 36, 'active': true }); + * // => objects for ['barney'] + * + * // The `_.matchesProperty` iteratee shorthand. + * _.filter(users, ['active', false]); + * // => objects for ['fred'] + * + * // The `_.property` iteratee shorthand. + * _.filter(users, 'active'); + * // => objects for ['barney'] + */ + function filter(collection, predicate) { + var func = isArray(collection) ? arrayFilter : baseFilter; + return func(collection, getIteratee(predicate, 3)); + } + + /** + * Iterates over elements of `collection`, returning the first element + * `predicate` returns truthy for. The predicate is invoked with three + * arguments: (value, index|key, collection). + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to inspect. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @param {number} [fromIndex=0] The index to search from. + * @returns {*} Returns the matched element, else `undefined`. + * @example + * + * var users = [ + * { 'user': 'barney', 'age': 36, 'active': true }, + * { 'user': 'fred', 'age': 40, 'active': false }, + * { 'user': 'pebbles', 'age': 1, 'active': true } + * ]; + * + * _.find(users, function(o) { return o.age < 40; }); + * // => object for 'barney' + * + * // The `_.matches` iteratee shorthand. + * _.find(users, { 'age': 1, 'active': true }); + * // => object for 'pebbles' + * + * // The `_.matchesProperty` iteratee shorthand. + * _.find(users, ['active', false]); + * // => object for 'fred' + * + * // The `_.property` iteratee shorthand. + * _.find(users, 'active'); + * // => object for 'barney' + */ + var find = createFind(findIndex); + + /** + * This method is like `_.find` except that it iterates over elements of + * `collection` from right to left. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Collection + * @param {Array|Object} collection The collection to inspect. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @param {number} [fromIndex=collection.length-1] The index to search from. + * @returns {*} Returns the matched element, else `undefined`. + * @example + * + * _.findLast([1, 2, 3, 4], function(n) { + * return n % 2 == 1; + * }); + * // => 3 + */ + var findLast = createFind(findLastIndex); + + /** + * Creates a flattened array of values by running each element in `collection` + * thru `iteratee` and flattening the mapped results. The iteratee is invoked + * with three arguments: (value, index|key, collection). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Array} Returns the new flattened array. + * @example + * + * function duplicate(n) { + * return [n, n]; + * } + * + * _.flatMap([1, 2], duplicate); + * // => [1, 1, 2, 2] + */ + function flatMap(collection, iteratee) { + return baseFlatten(map(collection, iteratee), 1); + } + + /** + * This method is like `_.flatMap` except that it recursively flattens the + * mapped results. + * + * @static + * @memberOf _ + * @since 4.7.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Array} Returns the new flattened array. + * @example + * + * function duplicate(n) { + * return [[[n, n]]]; + * } + * + * _.flatMapDeep([1, 2], duplicate); + * // => [1, 1, 2, 2] + */ + function flatMapDeep(collection, iteratee) { + return baseFlatten(map(collection, iteratee), INFINITY); + } + + /** + * This method is like `_.flatMap` except that it recursively flattens the + * mapped results up to `depth` times. + * + * @static + * @memberOf _ + * @since 4.7.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @param {number} [depth=1] The maximum recursion depth. + * @returns {Array} Returns the new flattened array. + * @example + * + * function duplicate(n) { + * return [[[n, n]]]; + * } + * + * _.flatMapDepth([1, 2], duplicate, 2); + * // => [[1, 1], [2, 2]] + */ + function flatMapDepth(collection, iteratee, depth) { + depth = depth === undefined ? 1 : toInteger(depth); + return baseFlatten(map(collection, iteratee), depth); + } + + /** + * Iterates over elements of `collection` and invokes `iteratee` for each element. + * The iteratee is invoked with three arguments: (value, index|key, collection). + * Iteratee functions may exit iteration early by explicitly returning `false`. + * + * **Note:** As with other "Collections" methods, objects with a "length" + * property are iterated like arrays. To avoid this behavior use `_.forIn` + * or `_.forOwn` for object iteration. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @alias each + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Array|Object} Returns `collection`. + * @see _.forEachRight + * @example + * + * _.forEach([1, 2], function(value) { + * console.log(value); + * }); + * // => Logs `1` then `2`. + * + * _.forEach({ 'a': 1, 'b': 2 }, function(value, key) { + * console.log(key); + * }); + * // => Logs 'a' then 'b' (iteration order is not guaranteed). + */ + function forEach(collection, iteratee) { + var func = isArray(collection) ? arrayEach : baseEach; + return func(collection, getIteratee(iteratee, 3)); + } + + /** + * This method is like `_.forEach` except that it iterates over elements of + * `collection` from right to left. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @alias eachRight + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Array|Object} Returns `collection`. + * @see _.forEach + * @example + * + * _.forEachRight([1, 2], function(value) { + * console.log(value); + * }); + * // => Logs `2` then `1`. + */ + function forEachRight(collection, iteratee) { + var func = isArray(collection) ? arrayEachRight : baseEachRight; + return func(collection, getIteratee(iteratee, 3)); + } + + /** + * Creates an object composed of keys generated from the results of running + * each element of `collection` thru `iteratee`. The order of grouped values + * is determined by the order they occur in `collection`. The corresponding + * value of each key is an array of elements responsible for generating the + * key. The iteratee is invoked with one argument: (value). + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The iteratee to transform keys. + * @returns {Object} Returns the composed aggregate object. + * @example + * + * _.groupBy([6.1, 4.2, 6.3], Math.floor); + * // => { '4': [4.2], '6': [6.1, 6.3] } + * + * // The `_.property` iteratee shorthand. + * _.groupBy(['one', 'two', 'three'], 'length'); + * // => { '3': ['one', 'two'], '5': ['three'] } + */ + var groupBy = createAggregator(function(result, value, key) { + if (hasOwnProperty.call(result, key)) { + result[key].push(value); + } else { + baseAssignValue(result, key, [value]); + } + }); + + /** + * Checks if `value` is in `collection`. If `collection` is a string, it's + * checked for a substring of `value`, otherwise + * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * is used for equality comparisons. If `fromIndex` is negative, it's used as + * the offset from the end of `collection`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object|string} collection The collection to inspect. + * @param {*} value The value to search for. + * @param {number} [fromIndex=0] The index to search from. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.reduce`. + * @returns {boolean} Returns `true` if `value` is found, else `false`. + * @example + * + * _.includes([1, 2, 3], 1); + * // => true + * + * _.includes([1, 2, 3], 1, 2); + * // => false + * + * _.includes({ 'a': 1, 'b': 2 }, 1); + * // => true + * + * _.includes('abcd', 'bc'); + * // => true + */ + function includes(collection, value, fromIndex, guard) { + collection = isArrayLike(collection) ? collection : values(collection); + fromIndex = (fromIndex && !guard) ? toInteger(fromIndex) : 0; + + var length = collection.length; + if (fromIndex < 0) { + fromIndex = nativeMax(length + fromIndex, 0); + } + return isString(collection) + ? (fromIndex <= length && collection.indexOf(value, fromIndex) > -1) + : (!!length && baseIndexOf(collection, value, fromIndex) > -1); + } + + /** + * Invokes the method at `path` of each element in `collection`, returning + * an array of the results of each invoked method. Any additional arguments + * are provided to each invoked method. If `path` is a function, it's invoked + * for, and `this` bound to, each element in `collection`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Array|Function|string} path The path of the method to invoke or + * the function invoked per iteration. + * @param {...*} [args] The arguments to invoke each method with. + * @returns {Array} Returns the array of results. + * @example + * + * _.invokeMap([[5, 1, 7], [3, 2, 1]], 'sort'); + * // => [[1, 5, 7], [1, 2, 3]] + * + * _.invokeMap([123, 456], String.prototype.split, ''); + * // => [['1', '2', '3'], ['4', '5', '6']] + */ + var invokeMap = baseRest(function(collection, path, args) { + var index = -1, + isFunc = typeof path == 'function', + result = isArrayLike(collection) ? Array(collection.length) : []; + + baseEach(collection, function(value) { + result[++index] = isFunc ? apply(path, value, args) : baseInvoke(value, path, args); + }); + return result; + }); + + /** + * Creates an object composed of keys generated from the results of running + * each element of `collection` thru `iteratee`. The corresponding value of + * each key is the last element responsible for generating the key. The + * iteratee is invoked with one argument: (value). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The iteratee to transform keys. + * @returns {Object} Returns the composed aggregate object. + * @example + * + * var array = [ + * { 'dir': 'left', 'code': 97 }, + * { 'dir': 'right', 'code': 100 } + * ]; + * + * _.keyBy(array, function(o) { + * return String.fromCharCode(o.code); + * }); + * // => { 'a': { 'dir': 'left', 'code': 97 }, 'd': { 'dir': 'right', 'code': 100 } } + * + * _.keyBy(array, 'dir'); + * // => { 'left': { 'dir': 'left', 'code': 97 }, 'right': { 'dir': 'right', 'code': 100 } } + */ + var keyBy = createAggregator(function(result, value, key) { + baseAssignValue(result, key, value); + }); + + /** + * Creates an array of values by running each element in `collection` thru + * `iteratee`. The iteratee is invoked with three arguments: + * (value, index|key, collection). + * + * Many lodash methods are guarded to work as iteratees for methods like + * `_.every`, `_.filter`, `_.map`, `_.mapValues`, `_.reject`, and `_.some`. + * + * The guarded methods are: + * `ary`, `chunk`, `curry`, `curryRight`, `drop`, `dropRight`, `every`, + * `fill`, `invert`, `parseInt`, `random`, `range`, `rangeRight`, `repeat`, + * `sampleSize`, `slice`, `some`, `sortBy`, `split`, `take`, `takeRight`, + * `template`, `trim`, `trimEnd`, `trimStart`, and `words` + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Array} Returns the new mapped array. + * @example + * + * function square(n) { + * return n * n; + * } + * + * _.map([4, 8], square); + * // => [16, 64] + * + * _.map({ 'a': 4, 'b': 8 }, square); + * // => [16, 64] (iteration order is not guaranteed) + * + * var users = [ + * { 'user': 'barney' }, + * { 'user': 'fred' } + * ]; + * + * // The `_.property` iteratee shorthand. + * _.map(users, 'user'); + * // => ['barney', 'fred'] + */ + function map(collection, iteratee) { + var func = isArray(collection) ? arrayMap : baseMap; + return func(collection, getIteratee(iteratee, 3)); + } + + /** + * This method is like `_.sortBy` except that it allows specifying the sort + * orders of the iteratees to sort by. If `orders` is unspecified, all values + * are sorted in ascending order. Otherwise, specify an order of "desc" for + * descending or "asc" for ascending sort order of corresponding values. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Array[]|Function[]|Object[]|string[]} [iteratees=[_.identity]] + * The iteratees to sort by. + * @param {string[]} [orders] The sort orders of `iteratees`. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.reduce`. + * @returns {Array} Returns the new sorted array. + * @example + * + * var users = [ + * { 'user': 'fred', 'age': 48 }, + * { 'user': 'barney', 'age': 34 }, + * { 'user': 'fred', 'age': 40 }, + * { 'user': 'barney', 'age': 36 } + * ]; + * + * // Sort by `user` in ascending order and by `age` in descending order. + * _.orderBy(users, ['user', 'age'], ['asc', 'desc']); + * // => objects for [['barney', 36], ['barney', 34], ['fred', 48], ['fred', 40]] + */ + function orderBy(collection, iteratees, orders, guard) { + if (collection == null) { + return []; + } + if (!isArray(iteratees)) { + iteratees = iteratees == null ? [] : [iteratees]; + } + orders = guard ? undefined : orders; + if (!isArray(orders)) { + orders = orders == null ? [] : [orders]; + } + return baseOrderBy(collection, iteratees, orders); + } + + /** + * Creates an array of elements split into two groups, the first of which + * contains elements `predicate` returns truthy for, the second of which + * contains elements `predicate` returns falsey for. The predicate is + * invoked with one argument: (value). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {Array} Returns the array of grouped elements. + * @example + * + * var users = [ + * { 'user': 'barney', 'age': 36, 'active': false }, + * { 'user': 'fred', 'age': 40, 'active': true }, + * { 'user': 'pebbles', 'age': 1, 'active': false } + * ]; + * + * _.partition(users, function(o) { return o.active; }); + * // => objects for [['fred'], ['barney', 'pebbles']] + * + * // The `_.matches` iteratee shorthand. + * _.partition(users, { 'age': 1, 'active': false }); + * // => objects for [['pebbles'], ['barney', 'fred']] + * + * // The `_.matchesProperty` iteratee shorthand. + * _.partition(users, ['active', false]); + * // => objects for [['barney', 'pebbles'], ['fred']] + * + * // The `_.property` iteratee shorthand. + * _.partition(users, 'active'); + * // => objects for [['fred'], ['barney', 'pebbles']] + */ + var partition = createAggregator(function(result, value, key) { + result[key ? 0 : 1].push(value); + }, function() { return [[], []]; }); + + /** + * Reduces `collection` to a value which is the accumulated result of running + * each element in `collection` thru `iteratee`, where each successive + * invocation is supplied the return value of the previous. If `accumulator` + * is not given, the first element of `collection` is used as the initial + * value. The iteratee is invoked with four arguments: + * (accumulator, value, index|key, collection). + * + * Many lodash methods are guarded to work as iteratees for methods like + * `_.reduce`, `_.reduceRight`, and `_.transform`. + * + * The guarded methods are: + * `assign`, `defaults`, `defaultsDeep`, `includes`, `merge`, `orderBy`, + * and `sortBy` + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @param {*} [accumulator] The initial value. + * @returns {*} Returns the accumulated value. + * @see _.reduceRight + * @example + * + * _.reduce([1, 2], function(sum, n) { + * return sum + n; + * }, 0); + * // => 3 + * + * _.reduce({ 'a': 1, 'b': 2, 'c': 1 }, function(result, value, key) { + * (result[value] || (result[value] = [])).push(key); + * return result; + * }, {}); + * // => { '1': ['a', 'c'], '2': ['b'] } (iteration order is not guaranteed) + */ + function reduce(collection, iteratee, accumulator) { + var func = isArray(collection) ? arrayReduce : baseReduce, + initAccum = arguments.length < 3; + + return func(collection, getIteratee(iteratee, 4), accumulator, initAccum, baseEach); + } + + /** + * This method is like `_.reduce` except that it iterates over elements of + * `collection` from right to left. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @param {*} [accumulator] The initial value. + * @returns {*} Returns the accumulated value. + * @see _.reduce + * @example + * + * var array = [[0, 1], [2, 3], [4, 5]]; + * + * _.reduceRight(array, function(flattened, other) { + * return flattened.concat(other); + * }, []); + * // => [4, 5, 2, 3, 0, 1] + */ + function reduceRight(collection, iteratee, accumulator) { + var func = isArray(collection) ? arrayReduceRight : baseReduce, + initAccum = arguments.length < 3; + + return func(collection, getIteratee(iteratee, 4), accumulator, initAccum, baseEachRight); + } + + /** + * The opposite of `_.filter`; this method returns the elements of `collection` + * that `predicate` does **not** return truthy for. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {Array} Returns the new filtered array. + * @see _.filter + * @example + * + * var users = [ + * { 'user': 'barney', 'age': 36, 'active': false }, + * { 'user': 'fred', 'age': 40, 'active': true } + * ]; + * + * _.reject(users, function(o) { return !o.active; }); + * // => objects for ['fred'] + * + * // The `_.matches` iteratee shorthand. + * _.reject(users, { 'age': 40, 'active': true }); + * // => objects for ['barney'] + * + * // The `_.matchesProperty` iteratee shorthand. + * _.reject(users, ['active', false]); + * // => objects for ['fred'] + * + * // The `_.property` iteratee shorthand. + * _.reject(users, 'active'); + * // => objects for ['barney'] + */ + function reject(collection, predicate) { + var func = isArray(collection) ? arrayFilter : baseFilter; + return func(collection, negate(getIteratee(predicate, 3))); + } + + /** + * Gets a random element from `collection`. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Collection + * @param {Array|Object} collection The collection to sample. + * @returns {*} Returns the random element. + * @example + * + * _.sample([1, 2, 3, 4]); + * // => 2 + */ + function sample(collection) { + var func = isArray(collection) ? arraySample : baseSample; + return func(collection); + } + + /** + * Gets `n` random elements at unique keys from `collection` up to the + * size of `collection`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Collection + * @param {Array|Object} collection The collection to sample. + * @param {number} [n=1] The number of elements to sample. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Array} Returns the random elements. + * @example + * + * _.sampleSize([1, 2, 3], 2); + * // => [3, 1] + * + * _.sampleSize([1, 2, 3], 4); + * // => [2, 3, 1] + */ + function sampleSize(collection, n, guard) { + if ((guard ? isIterateeCall(collection, n, guard) : n === undefined)) { + n = 1; + } else { + n = toInteger(n); + } + var func = isArray(collection) ? arraySampleSize : baseSampleSize; + return func(collection, n); + } + + /** + * Creates an array of shuffled values, using a version of the + * [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher-Yates_shuffle). + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to shuffle. + * @returns {Array} Returns the new shuffled array. + * @example + * + * _.shuffle([1, 2, 3, 4]); + * // => [4, 1, 3, 2] + */ + function shuffle(collection) { + var func = isArray(collection) ? arrayShuffle : baseShuffle; + return func(collection); + } + + /** + * Gets the size of `collection` by returning its length for array-like + * values or the number of own enumerable string keyed properties for objects. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object|string} collection The collection to inspect. + * @returns {number} Returns the collection size. + * @example + * + * _.size([1, 2, 3]); + * // => 3 + * + * _.size({ 'a': 1, 'b': 2 }); + * // => 2 + * + * _.size('pebbles'); + * // => 7 + */ + function size(collection) { + if (collection == null) { + return 0; + } + if (isArrayLike(collection)) { + return isString(collection) ? stringSize(collection) : collection.length; + } + var tag = getTag(collection); + if (tag == mapTag || tag == setTag) { + return collection.size; + } + return baseKeys(collection).length; + } + + /** + * Checks if `predicate` returns truthy for **any** element of `collection`. + * Iteration is stopped once `predicate` returns truthy. The predicate is + * invoked with three arguments: (value, index|key, collection). + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {boolean} Returns `true` if any element passes the predicate check, + * else `false`. + * @example + * + * _.some([null, 0, 'yes', false], Boolean); + * // => true + * + * var users = [ + * { 'user': 'barney', 'active': true }, + * { 'user': 'fred', 'active': false } + * ]; + * + * // The `_.matches` iteratee shorthand. + * _.some(users, { 'user': 'barney', 'active': false }); + * // => false + * + * // The `_.matchesProperty` iteratee shorthand. + * _.some(users, ['active', false]); + * // => true + * + * // The `_.property` iteratee shorthand. + * _.some(users, 'active'); + * // => true + */ + function some(collection, predicate, guard) { + var func = isArray(collection) ? arraySome : baseSome; + if (guard && isIterateeCall(collection, predicate, guard)) { + predicate = undefined; + } + return func(collection, getIteratee(predicate, 3)); + } + + /** + * Creates an array of elements, sorted in ascending order by the results of + * running each element in a collection thru each iteratee. This method + * performs a stable sort, that is, it preserves the original sort order of + * equal elements. The iteratees are invoked with one argument: (value). + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Collection + * @param {Array|Object} collection The collection to iterate over. + * @param {...(Function|Function[])} [iteratees=[_.identity]] + * The iteratees to sort by. + * @returns {Array} Returns the new sorted array. + * @example + * + * var users = [ + * { 'user': 'fred', 'age': 48 }, + * { 'user': 'barney', 'age': 36 }, + * { 'user': 'fred', 'age': 40 }, + * { 'user': 'barney', 'age': 34 } + * ]; + * + * _.sortBy(users, [function(o) { return o.user; }]); + * // => objects for [['barney', 36], ['barney', 34], ['fred', 48], ['fred', 40]] + * + * _.sortBy(users, ['user', 'age']); + * // => objects for [['barney', 34], ['barney', 36], ['fred', 40], ['fred', 48]] + */ + var sortBy = baseRest(function(collection, iteratees) { + if (collection == null) { + return []; + } + var length = iteratees.length; + if (length > 1 && isIterateeCall(collection, iteratees[0], iteratees[1])) { + iteratees = []; + } else if (length > 2 && isIterateeCall(iteratees[0], iteratees[1], iteratees[2])) { + iteratees = [iteratees[0]]; + } + return baseOrderBy(collection, baseFlatten(iteratees, 1), []); + }); + + /*------------------------------------------------------------------------*/ + + /** + * Gets the timestamp of the number of milliseconds that have elapsed since + * the Unix epoch (1 January 1970 00:00:00 UTC). + * + * @static + * @memberOf _ + * @since 2.4.0 + * @category Date + * @returns {number} Returns the timestamp. + * @example + * + * _.defer(function(stamp) { + * console.log(_.now() - stamp); + * }, _.now()); + * // => Logs the number of milliseconds it took for the deferred invocation. + */ + var now = ctxNow || function() { + return root.Date.now(); + }; + + /*------------------------------------------------------------------------*/ + + /** + * The opposite of `_.before`; this method creates a function that invokes + * `func` once it's called `n` or more times. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {number} n The number of calls before `func` is invoked. + * @param {Function} func The function to restrict. + * @returns {Function} Returns the new restricted function. + * @example + * + * var saves = ['profile', 'settings']; + * + * var done = _.after(saves.length, function() { + * console.log('done saving!'); + * }); + * + * _.forEach(saves, function(type) { + * asyncSave({ 'type': type, 'complete': done }); + * }); + * // => Logs 'done saving!' after the two async saves have completed. + */ + function after(n, func) { + if (typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + n = toInteger(n); + return function() { + if (--n < 1) { + return func.apply(this, arguments); + } + }; + } + + /** + * Creates a function that invokes `func`, with up to `n` arguments, + * ignoring any additional arguments. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Function + * @param {Function} func The function to cap arguments for. + * @param {number} [n=func.length] The arity cap. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Function} Returns the new capped function. + * @example + * + * _.map(['6', '8', '10'], _.ary(parseInt, 1)); + * // => [6, 8, 10] + */ + function ary(func, n, guard) { + n = guard ? undefined : n; + n = (func && n == null) ? func.length : n; + return createWrap(func, WRAP_ARY_FLAG, undefined, undefined, undefined, undefined, n); + } + + /** + * Creates a function that invokes `func`, with the `this` binding and arguments + * of the created function, while it's called less than `n` times. Subsequent + * calls to the created function return the result of the last `func` invocation. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Function + * @param {number} n The number of calls at which `func` is no longer invoked. + * @param {Function} func The function to restrict. + * @returns {Function} Returns the new restricted function. + * @example + * + * jQuery(element).on('click', _.before(5, addContactToList)); + * // => Allows adding up to 4 contacts to the list. + */ + function before(n, func) { + var result; + if (typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + n = toInteger(n); + return function() { + if (--n > 0) { + result = func.apply(this, arguments); + } + if (n <= 1) { + func = undefined; + } + return result; + }; + } + + /** + * Creates a function that invokes `func` with the `this` binding of `thisArg` + * and `partials` prepended to the arguments it receives. + * + * The `_.bind.placeholder` value, which defaults to `_` in monolithic builds, + * may be used as a placeholder for partially applied arguments. + * + * **Note:** Unlike native `Function#bind`, this method doesn't set the "length" + * property of bound functions. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {Function} func The function to bind. + * @param {*} thisArg The `this` binding of `func`. + * @param {...*} [partials] The arguments to be partially applied. + * @returns {Function} Returns the new bound function. + * @example + * + * function greet(greeting, punctuation) { + * return greeting + ' ' + this.user + punctuation; + * } + * + * var object = { 'user': 'fred' }; + * + * var bound = _.bind(greet, object, 'hi'); + * bound('!'); + * // => 'hi fred!' + * + * // Bound with placeholders. + * var bound = _.bind(greet, object, _, '!'); + * bound('hi'); + * // => 'hi fred!' + */ + var bind = baseRest(function(func, thisArg, partials) { + var bitmask = WRAP_BIND_FLAG; + if (partials.length) { + var holders = replaceHolders(partials, getHolder(bind)); + bitmask |= WRAP_PARTIAL_FLAG; + } + return createWrap(func, bitmask, thisArg, partials, holders); + }); + + /** + * Creates a function that invokes the method at `object[key]` with `partials` + * prepended to the arguments it receives. + * + * This method differs from `_.bind` by allowing bound functions to reference + * methods that may be redefined or don't yet exist. See + * [Peter Michaux's article](http://peter.michaux.ca/articles/lazy-function-definition-pattern) + * for more details. + * + * The `_.bindKey.placeholder` value, which defaults to `_` in monolithic + * builds, may be used as a placeholder for partially applied arguments. + * + * @static + * @memberOf _ + * @since 0.10.0 + * @category Function + * @param {Object} object The object to invoke the method on. + * @param {string} key The key of the method. + * @param {...*} [partials] The arguments to be partially applied. + * @returns {Function} Returns the new bound function. + * @example + * + * var object = { + * 'user': 'fred', + * 'greet': function(greeting, punctuation) { + * return greeting + ' ' + this.user + punctuation; + * } + * }; + * + * var bound = _.bindKey(object, 'greet', 'hi'); + * bound('!'); + * // => 'hi fred!' + * + * object.greet = function(greeting, punctuation) { + * return greeting + 'ya ' + this.user + punctuation; + * }; + * + * bound('!'); + * // => 'hiya fred!' + * + * // Bound with placeholders. + * var bound = _.bindKey(object, 'greet', _, '!'); + * bound('hi'); + * // => 'hiya fred!' + */ + var bindKey = baseRest(function(object, key, partials) { + var bitmask = WRAP_BIND_FLAG | WRAP_BIND_KEY_FLAG; + if (partials.length) { + var holders = replaceHolders(partials, getHolder(bindKey)); + bitmask |= WRAP_PARTIAL_FLAG; + } + return createWrap(key, bitmask, object, partials, holders); + }); + + /** + * Creates a function that accepts arguments of `func` and either invokes + * `func` returning its result, if at least `arity` number of arguments have + * been provided, or returns a function that accepts the remaining `func` + * arguments, and so on. The arity of `func` may be specified if `func.length` + * is not sufficient. + * + * The `_.curry.placeholder` value, which defaults to `_` in monolithic builds, + * may be used as a placeholder for provided arguments. + * + * **Note:** This method doesn't set the "length" property of curried functions. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Function + * @param {Function} func The function to curry. + * @param {number} [arity=func.length] The arity of `func`. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Function} Returns the new curried function. + * @example + * + * var abc = function(a, b, c) { + * return [a, b, c]; + * }; + * + * var curried = _.curry(abc); + * + * curried(1)(2)(3); + * // => [1, 2, 3] + * + * curried(1, 2)(3); + * // => [1, 2, 3] + * + * curried(1, 2, 3); + * // => [1, 2, 3] + * + * // Curried with placeholders. + * curried(1)(_, 3)(2); + * // => [1, 2, 3] + */ + function curry(func, arity, guard) { + arity = guard ? undefined : arity; + var result = createWrap(func, WRAP_CURRY_FLAG, undefined, undefined, undefined, undefined, undefined, arity); + result.placeholder = curry.placeholder; + return result; + } + + /** + * This method is like `_.curry` except that arguments are applied to `func` + * in the manner of `_.partialRight` instead of `_.partial`. + * + * The `_.curryRight.placeholder` value, which defaults to `_` in monolithic + * builds, may be used as a placeholder for provided arguments. + * + * **Note:** This method doesn't set the "length" property of curried functions. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Function + * @param {Function} func The function to curry. + * @param {number} [arity=func.length] The arity of `func`. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Function} Returns the new curried function. + * @example + * + * var abc = function(a, b, c) { + * return [a, b, c]; + * }; + * + * var curried = _.curryRight(abc); + * + * curried(3)(2)(1); + * // => [1, 2, 3] + * + * curried(2, 3)(1); + * // => [1, 2, 3] + * + * curried(1, 2, 3); + * // => [1, 2, 3] + * + * // Curried with placeholders. + * curried(3)(1, _)(2); + * // => [1, 2, 3] + */ + function curryRight(func, arity, guard) { + arity = guard ? undefined : arity; + var result = createWrap(func, WRAP_CURRY_RIGHT_FLAG, undefined, undefined, undefined, undefined, undefined, arity); + result.placeholder = curryRight.placeholder; + return result; + } + + /** + * Creates a debounced function that delays invoking `func` until after `wait` + * milliseconds have elapsed since the last time the debounced function was + * invoked. The debounced function comes with a `cancel` method to cancel + * delayed `func` invocations and a `flush` method to immediately invoke them. + * Provide `options` to indicate whether `func` should be invoked on the + * leading and/or trailing edge of the `wait` timeout. The `func` is invoked + * with the last arguments provided to the debounced function. Subsequent + * calls to the debounced function return the result of the last `func` + * invocation. + * + * **Note:** If `leading` and `trailing` options are `true`, `func` is + * invoked on the trailing edge of the timeout only if the debounced function + * is invoked more than once during the `wait` timeout. + * + * If `wait` is `0` and `leading` is `false`, `func` invocation is deferred + * until to the next tick, similar to `setTimeout` with a timeout of `0`. + * + * See [David Corbacho's article](https://css-tricks.com/debouncing-throttling-explained-examples/) + * for details over the differences between `_.debounce` and `_.throttle`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {Function} func The function to debounce. + * @param {number} [wait=0] The number of milliseconds to delay. + * @param {Object} [options={}] The options object. + * @param {boolean} [options.leading=false] + * Specify invoking on the leading edge of the timeout. + * @param {number} [options.maxWait] + * The maximum time `func` is allowed to be delayed before it's invoked. + * @param {boolean} [options.trailing=true] + * Specify invoking on the trailing edge of the timeout. + * @returns {Function} Returns the new debounced function. + * @example + * + * // Avoid costly calculations while the window size is in flux. + * jQuery(window).on('resize', _.debounce(calculateLayout, 150)); + * + * // Invoke `sendMail` when clicked, debouncing subsequent calls. + * jQuery(element).on('click', _.debounce(sendMail, 300, { + * 'leading': true, + * 'trailing': false + * })); + * + * // Ensure `batchLog` is invoked once after 1 second of debounced calls. + * var debounced = _.debounce(batchLog, 250, { 'maxWait': 1000 }); + * var source = new EventSource('/stream'); + * jQuery(source).on('message', debounced); + * + * // Cancel the trailing debounced invocation. + * jQuery(window).on('popstate', debounced.cancel); + */ + function debounce(func, wait, options) { + var lastArgs, + lastThis, + maxWait, + result, + timerId, + lastCallTime, + lastInvokeTime = 0, + leading = false, + maxing = false, + trailing = true; + + if (typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + wait = toNumber(wait) || 0; + if (isObject(options)) { + leading = !!options.leading; + maxing = 'maxWait' in options; + maxWait = maxing ? nativeMax(toNumber(options.maxWait) || 0, wait) : maxWait; + trailing = 'trailing' in options ? !!options.trailing : trailing; + } + + function invokeFunc(time) { + var args = lastArgs, + thisArg = lastThis; + + lastArgs = lastThis = undefined; + lastInvokeTime = time; + result = func.apply(thisArg, args); + return result; + } + + function leadingEdge(time) { + // Reset any `maxWait` timer. + lastInvokeTime = time; + // Start the timer for the trailing edge. + timerId = setTimeout(timerExpired, wait); + // Invoke the leading edge. + return leading ? invokeFunc(time) : result; + } + + function remainingWait(time) { + var timeSinceLastCall = time - lastCallTime, + timeSinceLastInvoke = time - lastInvokeTime, + timeWaiting = wait - timeSinceLastCall; + + return maxing + ? nativeMin(timeWaiting, maxWait - timeSinceLastInvoke) + : timeWaiting; + } + + function shouldInvoke(time) { + var timeSinceLastCall = time - lastCallTime, + timeSinceLastInvoke = time - lastInvokeTime; + + // Either this is the first call, activity has stopped and we're at the + // trailing edge, the system time has gone backwards and we're treating + // it as the trailing edge, or we've hit the `maxWait` limit. + return (lastCallTime === undefined || (timeSinceLastCall >= wait) || + (timeSinceLastCall < 0) || (maxing && timeSinceLastInvoke >= maxWait)); + } + + function timerExpired() { + var time = now(); + if (shouldInvoke(time)) { + return trailingEdge(time); + } + // Restart the timer. + timerId = setTimeout(timerExpired, remainingWait(time)); + } + + function trailingEdge(time) { + timerId = undefined; + + // Only invoke if we have `lastArgs` which means `func` has been + // debounced at least once. + if (trailing && lastArgs) { + return invokeFunc(time); + } + lastArgs = lastThis = undefined; + return result; + } + + function cancel() { + if (timerId !== undefined) { + clearTimeout(timerId); + } + lastInvokeTime = 0; + lastArgs = lastCallTime = lastThis = timerId = undefined; + } + + function flush() { + return timerId === undefined ? result : trailingEdge(now()); + } + + function debounced() { + var time = now(), + isInvoking = shouldInvoke(time); + + lastArgs = arguments; + lastThis = this; + lastCallTime = time; + + if (isInvoking) { + if (timerId === undefined) { + return leadingEdge(lastCallTime); + } + if (maxing) { + // Handle invocations in a tight loop. + timerId = setTimeout(timerExpired, wait); + return invokeFunc(lastCallTime); + } + } + if (timerId === undefined) { + timerId = setTimeout(timerExpired, wait); + } + return result; + } + debounced.cancel = cancel; + debounced.flush = flush; + return debounced; + } + + /** + * Defers invoking the `func` until the current call stack has cleared. Any + * additional arguments are provided to `func` when it's invoked. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {Function} func The function to defer. + * @param {...*} [args] The arguments to invoke `func` with. + * @returns {number} Returns the timer id. + * @example + * + * _.defer(function(text) { + * console.log(text); + * }, 'deferred'); + * // => Logs 'deferred' after one millisecond. + */ + var defer = baseRest(function(func, args) { + return baseDelay(func, 1, args); + }); + + /** + * Invokes `func` after `wait` milliseconds. Any additional arguments are + * provided to `func` when it's invoked. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {Function} func The function to delay. + * @param {number} wait The number of milliseconds to delay invocation. + * @param {...*} [args] The arguments to invoke `func` with. + * @returns {number} Returns the timer id. + * @example + * + * _.delay(function(text) { + * console.log(text); + * }, 1000, 'later'); + * // => Logs 'later' after one second. + */ + var delay = baseRest(function(func, wait, args) { + return baseDelay(func, toNumber(wait) || 0, args); + }); + + /** + * Creates a function that invokes `func` with arguments reversed. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Function + * @param {Function} func The function to flip arguments for. + * @returns {Function} Returns the new flipped function. + * @example + * + * var flipped = _.flip(function() { + * return _.toArray(arguments); + * }); + * + * flipped('a', 'b', 'c', 'd'); + * // => ['d', 'c', 'b', 'a'] + */ + function flip(func) { + return createWrap(func, WRAP_FLIP_FLAG); + } + + /** + * Creates a function that memoizes the result of `func`. If `resolver` is + * provided, it determines the cache key for storing the result based on the + * arguments provided to the memoized function. By default, the first argument + * provided to the memoized function is used as the map cache key. The `func` + * is invoked with the `this` binding of the memoized function. + * + * **Note:** The cache is exposed as the `cache` property on the memoized + * function. Its creation may be customized by replacing the `_.memoize.Cache` + * constructor with one whose instances implement the + * [`Map`](http://ecma-international.org/ecma-262/7.0/#sec-properties-of-the-map-prototype-object) + * method interface of `clear`, `delete`, `get`, `has`, and `set`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {Function} func The function to have its output memoized. + * @param {Function} [resolver] The function to resolve the cache key. + * @returns {Function} Returns the new memoized function. + * @example + * + * var object = { 'a': 1, 'b': 2 }; + * var other = { 'c': 3, 'd': 4 }; + * + * var values = _.memoize(_.values); + * values(object); + * // => [1, 2] + * + * values(other); + * // => [3, 4] + * + * object.a = 2; + * values(object); + * // => [1, 2] + * + * // Modify the result cache. + * values.cache.set(object, ['a', 'b']); + * values(object); + * // => ['a', 'b'] + * + * // Replace `_.memoize.Cache`. + * _.memoize.Cache = WeakMap; + */ + function memoize(func, resolver) { + if (typeof func != 'function' || (resolver != null && typeof resolver != 'function')) { + throw new TypeError(FUNC_ERROR_TEXT); + } + var memoized = function() { + var args = arguments, + key = resolver ? resolver.apply(this, args) : args[0], + cache = memoized.cache; + + if (cache.has(key)) { + return cache.get(key); + } + var result = func.apply(this, args); + memoized.cache = cache.set(key, result) || cache; + return result; + }; + memoized.cache = new (memoize.Cache || MapCache); + return memoized; + } + + // Expose `MapCache`. + memoize.Cache = MapCache; + + /** + * Creates a function that negates the result of the predicate `func`. The + * `func` predicate is invoked with the `this` binding and arguments of the + * created function. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Function + * @param {Function} predicate The predicate to negate. + * @returns {Function} Returns the new negated function. + * @example + * + * function isEven(n) { + * return n % 2 == 0; + * } + * + * _.filter([1, 2, 3, 4, 5, 6], _.negate(isEven)); + * // => [1, 3, 5] + */ + function negate(predicate) { + if (typeof predicate != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + return function() { + var args = arguments; + switch (args.length) { + case 0: return !predicate.call(this); + case 1: return !predicate.call(this, args[0]); + case 2: return !predicate.call(this, args[0], args[1]); + case 3: return !predicate.call(this, args[0], args[1], args[2]); + } + return !predicate.apply(this, args); + }; + } + + /** + * Creates a function that is restricted to invoking `func` once. Repeat calls + * to the function return the value of the first invocation. The `func` is + * invoked with the `this` binding and arguments of the created function. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {Function} func The function to restrict. + * @returns {Function} Returns the new restricted function. + * @example + * + * var initialize = _.once(createApplication); + * initialize(); + * initialize(); + * // => `createApplication` is invoked once + */ + function once(func) { + return before(2, func); + } + + /** + * Creates a function that invokes `func` with its arguments transformed. + * + * @static + * @since 4.0.0 + * @memberOf _ + * @category Function + * @param {Function} func The function to wrap. + * @param {...(Function|Function[])} [transforms=[_.identity]] + * The argument transforms. + * @returns {Function} Returns the new function. + * @example + * + * function doubled(n) { + * return n * 2; + * } + * + * function square(n) { + * return n * n; + * } + * + * var func = _.overArgs(function(x, y) { + * return [x, y]; + * }, [square, doubled]); + * + * func(9, 3); + * // => [81, 6] + * + * func(10, 5); + * // => [100, 10] + */ + var overArgs = castRest(function(func, transforms) { + transforms = (transforms.length == 1 && isArray(transforms[0])) + ? arrayMap(transforms[0], baseUnary(getIteratee())) + : arrayMap(baseFlatten(transforms, 1), baseUnary(getIteratee())); + + var funcsLength = transforms.length; + return baseRest(function(args) { + var index = -1, + length = nativeMin(args.length, funcsLength); + + while (++index < length) { + args[index] = transforms[index].call(this, args[index]); + } + return apply(func, this, args); + }); + }); + + /** + * Creates a function that invokes `func` with `partials` prepended to the + * arguments it receives. This method is like `_.bind` except it does **not** + * alter the `this` binding. + * + * The `_.partial.placeholder` value, which defaults to `_` in monolithic + * builds, may be used as a placeholder for partially applied arguments. + * + * **Note:** This method doesn't set the "length" property of partially + * applied functions. + * + * @static + * @memberOf _ + * @since 0.2.0 + * @category Function + * @param {Function} func The function to partially apply arguments to. + * @param {...*} [partials] The arguments to be partially applied. + * @returns {Function} Returns the new partially applied function. + * @example + * + * function greet(greeting, name) { + * return greeting + ' ' + name; + * } + * + * var sayHelloTo = _.partial(greet, 'hello'); + * sayHelloTo('fred'); + * // => 'hello fred' + * + * // Partially applied with placeholders. + * var greetFred = _.partial(greet, _, 'fred'); + * greetFred('hi'); + * // => 'hi fred' + */ + var partial = baseRest(function(func, partials) { + var holders = replaceHolders(partials, getHolder(partial)); + return createWrap(func, WRAP_PARTIAL_FLAG, undefined, partials, holders); + }); + + /** + * This method is like `_.partial` except that partially applied arguments + * are appended to the arguments it receives. + * + * The `_.partialRight.placeholder` value, which defaults to `_` in monolithic + * builds, may be used as a placeholder for partially applied arguments. + * + * **Note:** This method doesn't set the "length" property of partially + * applied functions. + * + * @static + * @memberOf _ + * @since 1.0.0 + * @category Function + * @param {Function} func The function to partially apply arguments to. + * @param {...*} [partials] The arguments to be partially applied. + * @returns {Function} Returns the new partially applied function. + * @example + * + * function greet(greeting, name) { + * return greeting + ' ' + name; + * } + * + * var greetFred = _.partialRight(greet, 'fred'); + * greetFred('hi'); + * // => 'hi fred' + * + * // Partially applied with placeholders. + * var sayHelloTo = _.partialRight(greet, 'hello', _); + * sayHelloTo('fred'); + * // => 'hello fred' + */ + var partialRight = baseRest(function(func, partials) { + var holders = replaceHolders(partials, getHolder(partialRight)); + return createWrap(func, WRAP_PARTIAL_RIGHT_FLAG, undefined, partials, holders); + }); + + /** + * Creates a function that invokes `func` with arguments arranged according + * to the specified `indexes` where the argument value at the first index is + * provided as the first argument, the argument value at the second index is + * provided as the second argument, and so on. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Function + * @param {Function} func The function to rearrange arguments for. + * @param {...(number|number[])} indexes The arranged argument indexes. + * @returns {Function} Returns the new function. + * @example + * + * var rearged = _.rearg(function(a, b, c) { + * return [a, b, c]; + * }, [2, 0, 1]); + * + * rearged('b', 'c', 'a') + * // => ['a', 'b', 'c'] + */ + var rearg = flatRest(function(func, indexes) { + return createWrap(func, WRAP_REARG_FLAG, undefined, undefined, undefined, indexes); + }); + + /** + * Creates a function that invokes `func` with the `this` binding of the + * created function and arguments from `start` and beyond provided as + * an array. + * + * **Note:** This method is based on the + * [rest parameter](https://mdn.io/rest_parameters). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Function + * @param {Function} func The function to apply a rest parameter to. + * @param {number} [start=func.length-1] The start position of the rest parameter. + * @returns {Function} Returns the new function. + * @example + * + * var say = _.rest(function(what, names) { + * return what + ' ' + _.initial(names).join(', ') + + * (_.size(names) > 1 ? ', & ' : '') + _.last(names); + * }); + * + * say('hello', 'fred', 'barney', 'pebbles'); + * // => 'hello fred, barney, & pebbles' + */ + function rest(func, start) { + if (typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + start = start === undefined ? start : toInteger(start); + return baseRest(func, start); + } + + /** + * Creates a function that invokes `func` with the `this` binding of the + * create function and an array of arguments much like + * [`Function#apply`](http://www.ecma-international.org/ecma-262/7.0/#sec-function.prototype.apply). + * + * **Note:** This method is based on the + * [spread operator](https://mdn.io/spread_operator). + * + * @static + * @memberOf _ + * @since 3.2.0 + * @category Function + * @param {Function} func The function to spread arguments over. + * @param {number} [start=0] The start position of the spread. + * @returns {Function} Returns the new function. + * @example + * + * var say = _.spread(function(who, what) { + * return who + ' says ' + what; + * }); + * + * say(['fred', 'hello']); + * // => 'fred says hello' + * + * var numbers = Promise.all([ + * Promise.resolve(40), + * Promise.resolve(36) + * ]); + * + * numbers.then(_.spread(function(x, y) { + * return x + y; + * })); + * // => a Promise of 76 + */ + function spread(func, start) { + if (typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + start = start == null ? 0 : nativeMax(toInteger(start), 0); + return baseRest(function(args) { + var array = args[start], + otherArgs = castSlice(args, 0, start); + + if (array) { + arrayPush(otherArgs, array); + } + return apply(func, this, otherArgs); + }); + } + + /** + * Creates a throttled function that only invokes `func` at most once per + * every `wait` milliseconds. The throttled function comes with a `cancel` + * method to cancel delayed `func` invocations and a `flush` method to + * immediately invoke them. Provide `options` to indicate whether `func` + * should be invoked on the leading and/or trailing edge of the `wait` + * timeout. The `func` is invoked with the last arguments provided to the + * throttled function. Subsequent calls to the throttled function return the + * result of the last `func` invocation. + * + * **Note:** If `leading` and `trailing` options are `true`, `func` is + * invoked on the trailing edge of the timeout only if the throttled function + * is invoked more than once during the `wait` timeout. + * + * If `wait` is `0` and `leading` is `false`, `func` invocation is deferred + * until to the next tick, similar to `setTimeout` with a timeout of `0`. + * + * See [David Corbacho's article](https://css-tricks.com/debouncing-throttling-explained-examples/) + * for details over the differences between `_.throttle` and `_.debounce`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {Function} func The function to throttle. + * @param {number} [wait=0] The number of milliseconds to throttle invocations to. + * @param {Object} [options={}] The options object. + * @param {boolean} [options.leading=true] + * Specify invoking on the leading edge of the timeout. + * @param {boolean} [options.trailing=true] + * Specify invoking on the trailing edge of the timeout. + * @returns {Function} Returns the new throttled function. + * @example + * + * // Avoid excessively updating the position while scrolling. + * jQuery(window).on('scroll', _.throttle(updatePosition, 100)); + * + * // Invoke `renewToken` when the click event is fired, but not more than once every 5 minutes. + * var throttled = _.throttle(renewToken, 300000, { 'trailing': false }); + * jQuery(element).on('click', throttled); + * + * // Cancel the trailing throttled invocation. + * jQuery(window).on('popstate', throttled.cancel); + */ + function throttle(func, wait, options) { + var leading = true, + trailing = true; + + if (typeof func != 'function') { + throw new TypeError(FUNC_ERROR_TEXT); + } + if (isObject(options)) { + leading = 'leading' in options ? !!options.leading : leading; + trailing = 'trailing' in options ? !!options.trailing : trailing; + } + return debounce(func, wait, { + 'leading': leading, + 'maxWait': wait, + 'trailing': trailing + }); + } + + /** + * Creates a function that accepts up to one argument, ignoring any + * additional arguments. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Function + * @param {Function} func The function to cap arguments for. + * @returns {Function} Returns the new capped function. + * @example + * + * _.map(['6', '8', '10'], _.unary(parseInt)); + * // => [6, 8, 10] + */ + function unary(func) { + return ary(func, 1); + } + + /** + * Creates a function that provides `value` to `wrapper` as its first + * argument. Any additional arguments provided to the function are appended + * to those provided to the `wrapper`. The wrapper is invoked with the `this` + * binding of the created function. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Function + * @param {*} value The value to wrap. + * @param {Function} [wrapper=identity] The wrapper function. + * @returns {Function} Returns the new function. + * @example + * + * var p = _.wrap(_.escape, function(func, text) { + * return '

' + func(text) + '

'; + * }); + * + * p('fred, barney, & pebbles'); + * // => '

fred, barney, & pebbles

' + */ + function wrap(value, wrapper) { + return partial(castFunction(wrapper), value); + } + + /*------------------------------------------------------------------------*/ + + /** + * Casts `value` as an array if it's not one. + * + * @static + * @memberOf _ + * @since 4.4.0 + * @category Lang + * @param {*} value The value to inspect. + * @returns {Array} Returns the cast array. + * @example + * + * _.castArray(1); + * // => [1] + * + * _.castArray({ 'a': 1 }); + * // => [{ 'a': 1 }] + * + * _.castArray('abc'); + * // => ['abc'] + * + * _.castArray(null); + * // => [null] + * + * _.castArray(undefined); + * // => [undefined] + * + * _.castArray(); + * // => [] + * + * var array = [1, 2, 3]; + * console.log(_.castArray(array) === array); + * // => true + */ + function castArray() { + if (!arguments.length) { + return []; + } + var value = arguments[0]; + return isArray(value) ? value : [value]; + } + + /** + * Creates a shallow clone of `value`. + * + * **Note:** This method is loosely based on the + * [structured clone algorithm](https://mdn.io/Structured_clone_algorithm) + * and supports cloning arrays, array buffers, booleans, date objects, maps, + * numbers, `Object` objects, regexes, sets, strings, symbols, and typed + * arrays. The own enumerable properties of `arguments` objects are cloned + * as plain objects. An empty object is returned for uncloneable values such + * as error objects, functions, DOM nodes, and WeakMaps. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to clone. + * @returns {*} Returns the cloned value. + * @see _.cloneDeep + * @example + * + * var objects = [{ 'a': 1 }, { 'b': 2 }]; + * + * var shallow = _.clone(objects); + * console.log(shallow[0] === objects[0]); + * // => true + */ + function clone(value) { + return baseClone(value, CLONE_SYMBOLS_FLAG); + } + + /** + * This method is like `_.clone` except that it accepts `customizer` which + * is invoked to produce the cloned value. If `customizer` returns `undefined`, + * cloning is handled by the method instead. The `customizer` is invoked with + * up to four arguments; (value [, index|key, object, stack]). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to clone. + * @param {Function} [customizer] The function to customize cloning. + * @returns {*} Returns the cloned value. + * @see _.cloneDeepWith + * @example + * + * function customizer(value) { + * if (_.isElement(value)) { + * return value.cloneNode(false); + * } + * } + * + * var el = _.cloneWith(document.body, customizer); + * + * console.log(el === document.body); + * // => false + * console.log(el.nodeName); + * // => 'BODY' + * console.log(el.childNodes.length); + * // => 0 + */ + function cloneWith(value, customizer) { + customizer = typeof customizer == 'function' ? customizer : undefined; + return baseClone(value, CLONE_SYMBOLS_FLAG, customizer); + } + + /** + * This method is like `_.clone` except that it recursively clones `value`. + * + * @static + * @memberOf _ + * @since 1.0.0 + * @category Lang + * @param {*} value The value to recursively clone. + * @returns {*} Returns the deep cloned value. + * @see _.clone + * @example + * + * var objects = [{ 'a': 1 }, { 'b': 2 }]; + * + * var deep = _.cloneDeep(objects); + * console.log(deep[0] === objects[0]); + * // => false + */ + function cloneDeep(value) { + return baseClone(value, CLONE_DEEP_FLAG | CLONE_SYMBOLS_FLAG); + } + + /** + * This method is like `_.cloneWith` except that it recursively clones `value`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to recursively clone. + * @param {Function} [customizer] The function to customize cloning. + * @returns {*} Returns the deep cloned value. + * @see _.cloneWith + * @example + * + * function customizer(value) { + * if (_.isElement(value)) { + * return value.cloneNode(true); + * } + * } + * + * var el = _.cloneDeepWith(document.body, customizer); + * + * console.log(el === document.body); + * // => false + * console.log(el.nodeName); + * // => 'BODY' + * console.log(el.childNodes.length); + * // => 20 + */ + function cloneDeepWith(value, customizer) { + customizer = typeof customizer == 'function' ? customizer : undefined; + return baseClone(value, CLONE_DEEP_FLAG | CLONE_SYMBOLS_FLAG, customizer); + } + + /** + * Checks if `object` conforms to `source` by invoking the predicate + * properties of `source` with the corresponding property values of `object`. + * + * **Note:** This method is equivalent to `_.conforms` when `source` is + * partially applied. + * + * @static + * @memberOf _ + * @since 4.14.0 + * @category Lang + * @param {Object} object The object to inspect. + * @param {Object} source The object of property predicates to conform to. + * @returns {boolean} Returns `true` if `object` conforms, else `false`. + * @example + * + * var object = { 'a': 1, 'b': 2 }; + * + * _.conformsTo(object, { 'b': function(n) { return n > 1; } }); + * // => true + * + * _.conformsTo(object, { 'b': function(n) { return n > 2; } }); + * // => false + */ + function conformsTo(object, source) { + return source == null || baseConformsTo(object, source, keys(source)); + } + + /** + * Performs a + * [`SameValueZero`](http://ecma-international.org/ecma-262/7.0/#sec-samevaluezero) + * comparison between two values to determine if they are equivalent. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {boolean} Returns `true` if the values are equivalent, else `false`. + * @example + * + * var object = { 'a': 1 }; + * var other = { 'a': 1 }; + * + * _.eq(object, object); + * // => true + * + * _.eq(object, other); + * // => false + * + * _.eq('a', 'a'); + * // => true + * + * _.eq('a', Object('a')); + * // => false + * + * _.eq(NaN, NaN); + * // => true + */ + function eq(value, other) { + return value === other || (value !== value && other !== other); + } + + /** + * Checks if `value` is greater than `other`. + * + * @static + * @memberOf _ + * @since 3.9.0 + * @category Lang + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {boolean} Returns `true` if `value` is greater than `other`, + * else `false`. + * @see _.lt + * @example + * + * _.gt(3, 1); + * // => true + * + * _.gt(3, 3); + * // => false + * + * _.gt(1, 3); + * // => false + */ + var gt = createRelationalOperation(baseGt); + + /** + * Checks if `value` is greater than or equal to `other`. + * + * @static + * @memberOf _ + * @since 3.9.0 + * @category Lang + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {boolean} Returns `true` if `value` is greater than or equal to + * `other`, else `false`. + * @see _.lte + * @example + * + * _.gte(3, 1); + * // => true + * + * _.gte(3, 3); + * // => true + * + * _.gte(1, 3); + * // => false + */ + var gte = createRelationalOperation(function(value, other) { + return value >= other; + }); + + /** + * Checks if `value` is likely an `arguments` object. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an `arguments` object, + * else `false`. + * @example + * + * _.isArguments(function() { return arguments; }()); + * // => true + * + * _.isArguments([1, 2, 3]); + * // => false + */ + var isArguments = baseIsArguments(function() { return arguments; }()) ? baseIsArguments : function(value) { + return isObjectLike(value) && hasOwnProperty.call(value, 'callee') && + !propertyIsEnumerable.call(value, 'callee'); + }; + + /** + * Checks if `value` is classified as an `Array` object. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an array, else `false`. + * @example + * + * _.isArray([1, 2, 3]); + * // => true + * + * _.isArray(document.body.children); + * // => false + * + * _.isArray('abc'); + * // => false + * + * _.isArray(_.noop); + * // => false + */ + var isArray = Array.isArray; + + /** + * Checks if `value` is classified as an `ArrayBuffer` object. + * + * @static + * @memberOf _ + * @since 4.3.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an array buffer, else `false`. + * @example + * + * _.isArrayBuffer(new ArrayBuffer(2)); + * // => true + * + * _.isArrayBuffer(new Array(2)); + * // => false + */ + var isArrayBuffer = nodeIsArrayBuffer ? baseUnary(nodeIsArrayBuffer) : baseIsArrayBuffer; + + /** + * Checks if `value` is array-like. A value is considered array-like if it's + * not a function and has a `value.length` that's an integer greater than or + * equal to `0` and less than or equal to `Number.MAX_SAFE_INTEGER`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is array-like, else `false`. + * @example + * + * _.isArrayLike([1, 2, 3]); + * // => true + * + * _.isArrayLike(document.body.children); + * // => true + * + * _.isArrayLike('abc'); + * // => true + * + * _.isArrayLike(_.noop); + * // => false + */ + function isArrayLike(value) { + return value != null && isLength(value.length) && !isFunction(value); + } + + /** + * This method is like `_.isArrayLike` except that it also checks if `value` + * is an object. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an array-like object, + * else `false`. + * @example + * + * _.isArrayLikeObject([1, 2, 3]); + * // => true + * + * _.isArrayLikeObject(document.body.children); + * // => true + * + * _.isArrayLikeObject('abc'); + * // => false + * + * _.isArrayLikeObject(_.noop); + * // => false + */ + function isArrayLikeObject(value) { + return isObjectLike(value) && isArrayLike(value); + } + + /** + * Checks if `value` is classified as a boolean primitive or object. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a boolean, else `false`. + * @example + * + * _.isBoolean(false); + * // => true + * + * _.isBoolean(null); + * // => false + */ + function isBoolean(value) { + return value === true || value === false || + (isObjectLike(value) && baseGetTag(value) == boolTag); + } + + /** + * Checks if `value` is a buffer. + * + * @static + * @memberOf _ + * @since 4.3.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a buffer, else `false`. + * @example + * + * _.isBuffer(new Buffer(2)); + * // => true + * + * _.isBuffer(new Uint8Array(2)); + * // => false + */ + var isBuffer = nativeIsBuffer || stubFalse; + + /** + * Checks if `value` is classified as a `Date` object. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a date object, else `false`. + * @example + * + * _.isDate(new Date); + * // => true + * + * _.isDate('Mon April 23 2012'); + * // => false + */ + var isDate = nodeIsDate ? baseUnary(nodeIsDate) : baseIsDate; + + /** + * Checks if `value` is likely a DOM element. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a DOM element, else `false`. + * @example + * + * _.isElement(document.body); + * // => true + * + * _.isElement(''); + * // => false + */ + function isElement(value) { + return isObjectLike(value) && value.nodeType === 1 && !isPlainObject(value); + } + + /** + * Checks if `value` is an empty object, collection, map, or set. + * + * Objects are considered empty if they have no own enumerable string keyed + * properties. + * + * Array-like values such as `arguments` objects, arrays, buffers, strings, or + * jQuery-like collections are considered empty if they have a `length` of `0`. + * Similarly, maps and sets are considered empty if they have a `size` of `0`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is empty, else `false`. + * @example + * + * _.isEmpty(null); + * // => true + * + * _.isEmpty(true); + * // => true + * + * _.isEmpty(1); + * // => true + * + * _.isEmpty([1, 2, 3]); + * // => false + * + * _.isEmpty({ 'a': 1 }); + * // => false + */ + function isEmpty(value) { + if (value == null) { + return true; + } + if (isArrayLike(value) && + (isArray(value) || typeof value == 'string' || typeof value.splice == 'function' || + isBuffer(value) || isTypedArray(value) || isArguments(value))) { + return !value.length; + } + var tag = getTag(value); + if (tag == mapTag || tag == setTag) { + return !value.size; + } + if (isPrototype(value)) { + return !baseKeys(value).length; + } + for (var key in value) { + if (hasOwnProperty.call(value, key)) { + return false; + } + } + return true; + } + + /** + * Performs a deep comparison between two values to determine if they are + * equivalent. + * + * **Note:** This method supports comparing arrays, array buffers, booleans, + * date objects, error objects, maps, numbers, `Object` objects, regexes, + * sets, strings, symbols, and typed arrays. `Object` objects are compared + * by their own, not inherited, enumerable properties. Functions and DOM + * nodes are compared by strict equality, i.e. `===`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {boolean} Returns `true` if the values are equivalent, else `false`. + * @example + * + * var object = { 'a': 1 }; + * var other = { 'a': 1 }; + * + * _.isEqual(object, other); + * // => true + * + * object === other; + * // => false + */ + function isEqual(value, other) { + return baseIsEqual(value, other); + } + + /** + * This method is like `_.isEqual` except that it accepts `customizer` which + * is invoked to compare values. If `customizer` returns `undefined`, comparisons + * are handled by the method instead. The `customizer` is invoked with up to + * six arguments: (objValue, othValue [, index|key, object, other, stack]). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @param {Function} [customizer] The function to customize comparisons. + * @returns {boolean} Returns `true` if the values are equivalent, else `false`. + * @example + * + * function isGreeting(value) { + * return /^h(?:i|ello)$/.test(value); + * } + * + * function customizer(objValue, othValue) { + * if (isGreeting(objValue) && isGreeting(othValue)) { + * return true; + * } + * } + * + * var array = ['hello', 'goodbye']; + * var other = ['hi', 'goodbye']; + * + * _.isEqualWith(array, other, customizer); + * // => true + */ + function isEqualWith(value, other, customizer) { + customizer = typeof customizer == 'function' ? customizer : undefined; + var result = customizer ? customizer(value, other) : undefined; + return result === undefined ? baseIsEqual(value, other, undefined, customizer) : !!result; + } + + /** + * Checks if `value` is an `Error`, `EvalError`, `RangeError`, `ReferenceError`, + * `SyntaxError`, `TypeError`, or `URIError` object. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an error object, else `false`. + * @example + * + * _.isError(new Error); + * // => true + * + * _.isError(Error); + * // => false + */ + function isError(value) { + if (!isObjectLike(value)) { + return false; + } + var tag = baseGetTag(value); + return tag == errorTag || tag == domExcTag || + (typeof value.message == 'string' && typeof value.name == 'string' && !isPlainObject(value)); + } + + /** + * Checks if `value` is a finite primitive number. + * + * **Note:** This method is based on + * [`Number.isFinite`](https://mdn.io/Number/isFinite). + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a finite number, else `false`. + * @example + * + * _.isFinite(3); + * // => true + * + * _.isFinite(Number.MIN_VALUE); + * // => true + * + * _.isFinite(Infinity); + * // => false + * + * _.isFinite('3'); + * // => false + */ + function isFinite(value) { + return typeof value == 'number' && nativeIsFinite(value); + } + + /** + * Checks if `value` is classified as a `Function` object. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a function, else `false`. + * @example + * + * _.isFunction(_); + * // => true + * + * _.isFunction(/abc/); + * // => false + */ + function isFunction(value) { + if (!isObject(value)) { + return false; + } + // The use of `Object#toString` avoids issues with the `typeof` operator + // in Safari 9 which returns 'object' for typed arrays and other constructors. + var tag = baseGetTag(value); + return tag == funcTag || tag == genTag || tag == asyncTag || tag == proxyTag; + } + + /** + * Checks if `value` is an integer. + * + * **Note:** This method is based on + * [`Number.isInteger`](https://mdn.io/Number/isInteger). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an integer, else `false`. + * @example + * + * _.isInteger(3); + * // => true + * + * _.isInteger(Number.MIN_VALUE); + * // => false + * + * _.isInteger(Infinity); + * // => false + * + * _.isInteger('3'); + * // => false + */ + function isInteger(value) { + return typeof value == 'number' && value == toInteger(value); + } + + /** + * Checks if `value` is a valid array-like length. + * + * **Note:** This method is loosely based on + * [`ToLength`](http://ecma-international.org/ecma-262/7.0/#sec-tolength). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a valid length, else `false`. + * @example + * + * _.isLength(3); + * // => true + * + * _.isLength(Number.MIN_VALUE); + * // => false + * + * _.isLength(Infinity); + * // => false + * + * _.isLength('3'); + * // => false + */ + function isLength(value) { + return typeof value == 'number' && + value > -1 && value % 1 == 0 && value <= MAX_SAFE_INTEGER; + } + + /** + * Checks if `value` is the + * [language type](http://www.ecma-international.org/ecma-262/7.0/#sec-ecmascript-language-types) + * of `Object`. (e.g. arrays, functions, objects, regexes, `new Number(0)`, and `new String('')`) + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is an object, else `false`. + * @example + * + * _.isObject({}); + * // => true + * + * _.isObject([1, 2, 3]); + * // => true + * + * _.isObject(_.noop); + * // => true + * + * _.isObject(null); + * // => false + */ + function isObject(value) { + var type = typeof value; + return value != null && (type == 'object' || type == 'function'); + } + + /** + * Checks if `value` is object-like. A value is object-like if it's not `null` + * and has a `typeof` result of "object". + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is object-like, else `false`. + * @example + * + * _.isObjectLike({}); + * // => true + * + * _.isObjectLike([1, 2, 3]); + * // => true + * + * _.isObjectLike(_.noop); + * // => false + * + * _.isObjectLike(null); + * // => false + */ + function isObjectLike(value) { + return value != null && typeof value == 'object'; + } + + /** + * Checks if `value` is classified as a `Map` object. + * + * @static + * @memberOf _ + * @since 4.3.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a map, else `false`. + * @example + * + * _.isMap(new Map); + * // => true + * + * _.isMap(new WeakMap); + * // => false + */ + var isMap = nodeIsMap ? baseUnary(nodeIsMap) : baseIsMap; + + /** + * Performs a partial deep comparison between `object` and `source` to + * determine if `object` contains equivalent property values. + * + * **Note:** This method is equivalent to `_.matches` when `source` is + * partially applied. + * + * Partial comparisons will match empty array and empty object `source` + * values against any array or object value, respectively. See `_.isEqual` + * for a list of supported value comparisons. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Lang + * @param {Object} object The object to inspect. + * @param {Object} source The object of property values to match. + * @returns {boolean} Returns `true` if `object` is a match, else `false`. + * @example + * + * var object = { 'a': 1, 'b': 2 }; + * + * _.isMatch(object, { 'b': 2 }); + * // => true + * + * _.isMatch(object, { 'b': 1 }); + * // => false + */ + function isMatch(object, source) { + return object === source || baseIsMatch(object, source, getMatchData(source)); + } + + /** + * This method is like `_.isMatch` except that it accepts `customizer` which + * is invoked to compare values. If `customizer` returns `undefined`, comparisons + * are handled by the method instead. The `customizer` is invoked with five + * arguments: (objValue, srcValue, index|key, object, source). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {Object} object The object to inspect. + * @param {Object} source The object of property values to match. + * @param {Function} [customizer] The function to customize comparisons. + * @returns {boolean} Returns `true` if `object` is a match, else `false`. + * @example + * + * function isGreeting(value) { + * return /^h(?:i|ello)$/.test(value); + * } + * + * function customizer(objValue, srcValue) { + * if (isGreeting(objValue) && isGreeting(srcValue)) { + * return true; + * } + * } + * + * var object = { 'greeting': 'hello' }; + * var source = { 'greeting': 'hi' }; + * + * _.isMatchWith(object, source, customizer); + * // => true + */ + function isMatchWith(object, source, customizer) { + customizer = typeof customizer == 'function' ? customizer : undefined; + return baseIsMatch(object, source, getMatchData(source), customizer); + } + + /** + * Checks if `value` is `NaN`. + * + * **Note:** This method is based on + * [`Number.isNaN`](https://mdn.io/Number/isNaN) and is not the same as + * global [`isNaN`](https://mdn.io/isNaN) which returns `true` for + * `undefined` and other non-number values. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is `NaN`, else `false`. + * @example + * + * _.isNaN(NaN); + * // => true + * + * _.isNaN(new Number(NaN)); + * // => true + * + * isNaN(undefined); + * // => true + * + * _.isNaN(undefined); + * // => false + */ + function isNaN(value) { + // An `NaN` primitive is the only value that is not equal to itself. + // Perform the `toStringTag` check first to avoid errors with some + // ActiveX objects in IE. + return isNumber(value) && value != +value; + } + + /** + * Checks if `value` is a pristine native function. + * + * **Note:** This method can't reliably detect native functions in the presence + * of the core-js package because core-js circumvents this kind of detection. + * Despite multiple requests, the core-js maintainer has made it clear: any + * attempt to fix the detection will be obstructed. As a result, we're left + * with little choice but to throw an error. Unfortunately, this also affects + * packages, like [babel-polyfill](https://www.npmjs.com/package/babel-polyfill), + * which rely on core-js. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a native function, + * else `false`. + * @example + * + * _.isNative(Array.prototype.push); + * // => true + * + * _.isNative(_); + * // => false + */ + function isNative(value) { + if (isMaskable(value)) { + throw new Error(CORE_ERROR_TEXT); + } + return baseIsNative(value); + } + + /** + * Checks if `value` is `null`. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is `null`, else `false`. + * @example + * + * _.isNull(null); + * // => true + * + * _.isNull(void 0); + * // => false + */ + function isNull(value) { + return value === null; + } + + /** + * Checks if `value` is `null` or `undefined`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is nullish, else `false`. + * @example + * + * _.isNil(null); + * // => true + * + * _.isNil(void 0); + * // => true + * + * _.isNil(NaN); + * // => false + */ + function isNil(value) { + return value == null; + } + + /** + * Checks if `value` is classified as a `Number` primitive or object. + * + * **Note:** To exclude `Infinity`, `-Infinity`, and `NaN`, which are + * classified as numbers, use the `_.isFinite` method. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a number, else `false`. + * @example + * + * _.isNumber(3); + * // => true + * + * _.isNumber(Number.MIN_VALUE); + * // => true + * + * _.isNumber(Infinity); + * // => true + * + * _.isNumber('3'); + * // => false + */ + function isNumber(value) { + return typeof value == 'number' || + (isObjectLike(value) && baseGetTag(value) == numberTag); + } + + /** + * Checks if `value` is a plain object, that is, an object created by the + * `Object` constructor or one with a `[[Prototype]]` of `null`. + * + * @static + * @memberOf _ + * @since 0.8.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a plain object, else `false`. + * @example + * + * function Foo() { + * this.a = 1; + * } + * + * _.isPlainObject(new Foo); + * // => false + * + * _.isPlainObject([1, 2, 3]); + * // => false + * + * _.isPlainObject({ 'x': 0, 'y': 0 }); + * // => true + * + * _.isPlainObject(Object.create(null)); + * // => true + */ + function isPlainObject(value) { + if (!isObjectLike(value) || baseGetTag(value) != objectTag) { + return false; + } + var proto = getPrototype(value); + if (proto === null) { + return true; + } + var Ctor = hasOwnProperty.call(proto, 'constructor') && proto.constructor; + return typeof Ctor == 'function' && Ctor instanceof Ctor && + funcToString.call(Ctor) == objectCtorString; + } + + /** + * Checks if `value` is classified as a `RegExp` object. + * + * @static + * @memberOf _ + * @since 0.1.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a regexp, else `false`. + * @example + * + * _.isRegExp(/abc/); + * // => true + * + * _.isRegExp('/abc/'); + * // => false + */ + var isRegExp = nodeIsRegExp ? baseUnary(nodeIsRegExp) : baseIsRegExp; + + /** + * Checks if `value` is a safe integer. An integer is safe if it's an IEEE-754 + * double precision number which isn't the result of a rounded unsafe integer. + * + * **Note:** This method is based on + * [`Number.isSafeInteger`](https://mdn.io/Number/isSafeInteger). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a safe integer, else `false`. + * @example + * + * _.isSafeInteger(3); + * // => true + * + * _.isSafeInteger(Number.MIN_VALUE); + * // => false + * + * _.isSafeInteger(Infinity); + * // => false + * + * _.isSafeInteger('3'); + * // => false + */ + function isSafeInteger(value) { + return isInteger(value) && value >= -MAX_SAFE_INTEGER && value <= MAX_SAFE_INTEGER; + } + + /** + * Checks if `value` is classified as a `Set` object. + * + * @static + * @memberOf _ + * @since 4.3.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a set, else `false`. + * @example + * + * _.isSet(new Set); + * // => true + * + * _.isSet(new WeakSet); + * // => false + */ + var isSet = nodeIsSet ? baseUnary(nodeIsSet) : baseIsSet; + + /** + * Checks if `value` is classified as a `String` primitive or object. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a string, else `false`. + * @example + * + * _.isString('abc'); + * // => true + * + * _.isString(1); + * // => false + */ + function isString(value) { + return typeof value == 'string' || + (!isArray(value) && isObjectLike(value) && baseGetTag(value) == stringTag); + } + + /** + * Checks if `value` is classified as a `Symbol` primitive or object. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a symbol, else `false`. + * @example + * + * _.isSymbol(Symbol.iterator); + * // => true + * + * _.isSymbol('abc'); + * // => false + */ + function isSymbol(value) { + return typeof value == 'symbol' || + (isObjectLike(value) && baseGetTag(value) == symbolTag); + } + + /** + * Checks if `value` is classified as a typed array. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a typed array, else `false`. + * @example + * + * _.isTypedArray(new Uint8Array); + * // => true + * + * _.isTypedArray([]); + * // => false + */ + var isTypedArray = nodeIsTypedArray ? baseUnary(nodeIsTypedArray) : baseIsTypedArray; + + /** + * Checks if `value` is `undefined`. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is `undefined`, else `false`. + * @example + * + * _.isUndefined(void 0); + * // => true + * + * _.isUndefined(null); + * // => false + */ + function isUndefined(value) { + return value === undefined; + } + + /** + * Checks if `value` is classified as a `WeakMap` object. + * + * @static + * @memberOf _ + * @since 4.3.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a weak map, else `false`. + * @example + * + * _.isWeakMap(new WeakMap); + * // => true + * + * _.isWeakMap(new Map); + * // => false + */ + function isWeakMap(value) { + return isObjectLike(value) && getTag(value) == weakMapTag; + } + + /** + * Checks if `value` is classified as a `WeakSet` object. + * + * @static + * @memberOf _ + * @since 4.3.0 + * @category Lang + * @param {*} value The value to check. + * @returns {boolean} Returns `true` if `value` is a weak set, else `false`. + * @example + * + * _.isWeakSet(new WeakSet); + * // => true + * + * _.isWeakSet(new Set); + * // => false + */ + function isWeakSet(value) { + return isObjectLike(value) && baseGetTag(value) == weakSetTag; + } + + /** + * Checks if `value` is less than `other`. + * + * @static + * @memberOf _ + * @since 3.9.0 + * @category Lang + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {boolean} Returns `true` if `value` is less than `other`, + * else `false`. + * @see _.gt + * @example + * + * _.lt(1, 3); + * // => true + * + * _.lt(3, 3); + * // => false + * + * _.lt(3, 1); + * // => false + */ + var lt = createRelationalOperation(baseLt); + + /** + * Checks if `value` is less than or equal to `other`. + * + * @static + * @memberOf _ + * @since 3.9.0 + * @category Lang + * @param {*} value The value to compare. + * @param {*} other The other value to compare. + * @returns {boolean} Returns `true` if `value` is less than or equal to + * `other`, else `false`. + * @see _.gte + * @example + * + * _.lte(1, 3); + * // => true + * + * _.lte(3, 3); + * // => true + * + * _.lte(3, 1); + * // => false + */ + var lte = createRelationalOperation(function(value, other) { + return value <= other; + }); + + /** + * Converts `value` to an array. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Lang + * @param {*} value The value to convert. + * @returns {Array} Returns the converted array. + * @example + * + * _.toArray({ 'a': 1, 'b': 2 }); + * // => [1, 2] + * + * _.toArray('abc'); + * // => ['a', 'b', 'c'] + * + * _.toArray(1); + * // => [] + * + * _.toArray(null); + * // => [] + */ + function toArray(value) { + if (!value) { + return []; + } + if (isArrayLike(value)) { + return isString(value) ? stringToArray(value) : copyArray(value); + } + if (symIterator && value[symIterator]) { + return iteratorToArray(value[symIterator]()); + } + var tag = getTag(value), + func = tag == mapTag ? mapToArray : (tag == setTag ? setToArray : values); + + return func(value); + } + + /** + * Converts `value` to a finite number. + * + * @static + * @memberOf _ + * @since 4.12.0 + * @category Lang + * @param {*} value The value to convert. + * @returns {number} Returns the converted number. + * @example + * + * _.toFinite(3.2); + * // => 3.2 + * + * _.toFinite(Number.MIN_VALUE); + * // => 5e-324 + * + * _.toFinite(Infinity); + * // => 1.7976931348623157e+308 + * + * _.toFinite('3.2'); + * // => 3.2 + */ + function toFinite(value) { + if (!value) { + return value === 0 ? value : 0; + } + value = toNumber(value); + if (value === INFINITY || value === -INFINITY) { + var sign = (value < 0 ? -1 : 1); + return sign * MAX_INTEGER; + } + return value === value ? value : 0; + } + + /** + * Converts `value` to an integer. + * + * **Note:** This method is loosely based on + * [`ToInteger`](http://www.ecma-international.org/ecma-262/7.0/#sec-tointeger). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to convert. + * @returns {number} Returns the converted integer. + * @example + * + * _.toInteger(3.2); + * // => 3 + * + * _.toInteger(Number.MIN_VALUE); + * // => 0 + * + * _.toInteger(Infinity); + * // => 1.7976931348623157e+308 + * + * _.toInteger('3.2'); + * // => 3 + */ + function toInteger(value) { + var result = toFinite(value), + remainder = result % 1; + + return result === result ? (remainder ? result - remainder : result) : 0; + } + + /** + * Converts `value` to an integer suitable for use as the length of an + * array-like object. + * + * **Note:** This method is based on + * [`ToLength`](http://ecma-international.org/ecma-262/7.0/#sec-tolength). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to convert. + * @returns {number} Returns the converted integer. + * @example + * + * _.toLength(3.2); + * // => 3 + * + * _.toLength(Number.MIN_VALUE); + * // => 0 + * + * _.toLength(Infinity); + * // => 4294967295 + * + * _.toLength('3.2'); + * // => 3 + */ + function toLength(value) { + return value ? baseClamp(toInteger(value), 0, MAX_ARRAY_LENGTH) : 0; + } + + /** + * Converts `value` to a number. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to process. + * @returns {number} Returns the number. + * @example + * + * _.toNumber(3.2); + * // => 3.2 + * + * _.toNumber(Number.MIN_VALUE); + * // => 5e-324 + * + * _.toNumber(Infinity); + * // => Infinity + * + * _.toNumber('3.2'); + * // => 3.2 + */ + function toNumber(value) { + if (typeof value == 'number') { + return value; + } + if (isSymbol(value)) { + return NAN; + } + if (isObject(value)) { + var other = typeof value.valueOf == 'function' ? value.valueOf() : value; + value = isObject(other) ? (other + '') : other; + } + if (typeof value != 'string') { + return value === 0 ? value : +value; + } + value = value.replace(reTrim, ''); + var isBinary = reIsBinary.test(value); + return (isBinary || reIsOctal.test(value)) + ? freeParseInt(value.slice(2), isBinary ? 2 : 8) + : (reIsBadHex.test(value) ? NAN : +value); + } + + /** + * Converts `value` to a plain object flattening inherited enumerable string + * keyed properties of `value` to own properties of the plain object. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Lang + * @param {*} value The value to convert. + * @returns {Object} Returns the converted plain object. + * @example + * + * function Foo() { + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.assign({ 'a': 1 }, new Foo); + * // => { 'a': 1, 'b': 2 } + * + * _.assign({ 'a': 1 }, _.toPlainObject(new Foo)); + * // => { 'a': 1, 'b': 2, 'c': 3 } + */ + function toPlainObject(value) { + return copyObject(value, keysIn(value)); + } + + /** + * Converts `value` to a safe integer. A safe integer can be compared and + * represented correctly. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to convert. + * @returns {number} Returns the converted integer. + * @example + * + * _.toSafeInteger(3.2); + * // => 3 + * + * _.toSafeInteger(Number.MIN_VALUE); + * // => 0 + * + * _.toSafeInteger(Infinity); + * // => 9007199254740991 + * + * _.toSafeInteger('3.2'); + * // => 3 + */ + function toSafeInteger(value) { + return value + ? baseClamp(toInteger(value), -MAX_SAFE_INTEGER, MAX_SAFE_INTEGER) + : (value === 0 ? value : 0); + } + + /** + * Converts `value` to a string. An empty string is returned for `null` + * and `undefined` values. The sign of `-0` is preserved. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Lang + * @param {*} value The value to convert. + * @returns {string} Returns the converted string. + * @example + * + * _.toString(null); + * // => '' + * + * _.toString(-0); + * // => '-0' + * + * _.toString([1, 2, 3]); + * // => '1,2,3' + */ + function toString(value) { + return value == null ? '' : baseToString(value); + } + + /*------------------------------------------------------------------------*/ + + /** + * Assigns own enumerable string keyed properties of source objects to the + * destination object. Source objects are applied from left to right. + * Subsequent sources overwrite property assignments of previous sources. + * + * **Note:** This method mutates `object` and is loosely based on + * [`Object.assign`](https://mdn.io/Object/assign). + * + * @static + * @memberOf _ + * @since 0.10.0 + * @category Object + * @param {Object} object The destination object. + * @param {...Object} [sources] The source objects. + * @returns {Object} Returns `object`. + * @see _.assignIn + * @example + * + * function Foo() { + * this.a = 1; + * } + * + * function Bar() { + * this.c = 3; + * } + * + * Foo.prototype.b = 2; + * Bar.prototype.d = 4; + * + * _.assign({ 'a': 0 }, new Foo, new Bar); + * // => { 'a': 1, 'c': 3 } + */ + var assign = createAssigner(function(object, source) { + if (isPrototype(source) || isArrayLike(source)) { + copyObject(source, keys(source), object); + return; + } + for (var key in source) { + if (hasOwnProperty.call(source, key)) { + assignValue(object, key, source[key]); + } + } + }); + + /** + * This method is like `_.assign` except that it iterates over own and + * inherited source properties. + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @alias extend + * @category Object + * @param {Object} object The destination object. + * @param {...Object} [sources] The source objects. + * @returns {Object} Returns `object`. + * @see _.assign + * @example + * + * function Foo() { + * this.a = 1; + * } + * + * function Bar() { + * this.c = 3; + * } + * + * Foo.prototype.b = 2; + * Bar.prototype.d = 4; + * + * _.assignIn({ 'a': 0 }, new Foo, new Bar); + * // => { 'a': 1, 'b': 2, 'c': 3, 'd': 4 } + */ + var assignIn = createAssigner(function(object, source) { + copyObject(source, keysIn(source), object); + }); + + /** + * This method is like `_.assignIn` except that it accepts `customizer` + * which is invoked to produce the assigned values. If `customizer` returns + * `undefined`, assignment is handled by the method instead. The `customizer` + * is invoked with five arguments: (objValue, srcValue, key, object, source). + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @alias extendWith + * @category Object + * @param {Object} object The destination object. + * @param {...Object} sources The source objects. + * @param {Function} [customizer] The function to customize assigned values. + * @returns {Object} Returns `object`. + * @see _.assignWith + * @example + * + * function customizer(objValue, srcValue) { + * return _.isUndefined(objValue) ? srcValue : objValue; + * } + * + * var defaults = _.partialRight(_.assignInWith, customizer); + * + * defaults({ 'a': 1 }, { 'b': 2 }, { 'a': 3 }); + * // => { 'a': 1, 'b': 2 } + */ + var assignInWith = createAssigner(function(object, source, srcIndex, customizer) { + copyObject(source, keysIn(source), object, customizer); + }); + + /** + * This method is like `_.assign` except that it accepts `customizer` + * which is invoked to produce the assigned values. If `customizer` returns + * `undefined`, assignment is handled by the method instead. The `customizer` + * is invoked with five arguments: (objValue, srcValue, key, object, source). + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The destination object. + * @param {...Object} sources The source objects. + * @param {Function} [customizer] The function to customize assigned values. + * @returns {Object} Returns `object`. + * @see _.assignInWith + * @example + * + * function customizer(objValue, srcValue) { + * return _.isUndefined(objValue) ? srcValue : objValue; + * } + * + * var defaults = _.partialRight(_.assignWith, customizer); + * + * defaults({ 'a': 1 }, { 'b': 2 }, { 'a': 3 }); + * // => { 'a': 1, 'b': 2 } + */ + var assignWith = createAssigner(function(object, source, srcIndex, customizer) { + copyObject(source, keys(source), object, customizer); + }); + + /** + * Creates an array of values corresponding to `paths` of `object`. + * + * @static + * @memberOf _ + * @since 1.0.0 + * @category Object + * @param {Object} object The object to iterate over. + * @param {...(string|string[])} [paths] The property paths to pick. + * @returns {Array} Returns the picked values. + * @example + * + * var object = { 'a': [{ 'b': { 'c': 3 } }, 4] }; + * + * _.at(object, ['a[0].b.c', 'a[1]']); + * // => [3, 4] + */ + var at = flatRest(baseAt); + + /** + * Creates an object that inherits from the `prototype` object. If a + * `properties` object is given, its own enumerable string keyed properties + * are assigned to the created object. + * + * @static + * @memberOf _ + * @since 2.3.0 + * @category Object + * @param {Object} prototype The object to inherit from. + * @param {Object} [properties] The properties to assign to the object. + * @returns {Object} Returns the new object. + * @example + * + * function Shape() { + * this.x = 0; + * this.y = 0; + * } + * + * function Circle() { + * Shape.call(this); + * } + * + * Circle.prototype = _.create(Shape.prototype, { + * 'constructor': Circle + * }); + * + * var circle = new Circle; + * circle instanceof Circle; + * // => true + * + * circle instanceof Shape; + * // => true + */ + function create(prototype, properties) { + var result = baseCreate(prototype); + return properties == null ? result : baseAssign(result, properties); + } + + /** + * Assigns own and inherited enumerable string keyed properties of source + * objects to the destination object for all destination properties that + * resolve to `undefined`. Source objects are applied from left to right. + * Once a property is set, additional values of the same property are ignored. + * + * **Note:** This method mutates `object`. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Object + * @param {Object} object The destination object. + * @param {...Object} [sources] The source objects. + * @returns {Object} Returns `object`. + * @see _.defaultsDeep + * @example + * + * _.defaults({ 'a': 1 }, { 'b': 2 }, { 'a': 3 }); + * // => { 'a': 1, 'b': 2 } + */ + var defaults = baseRest(function(object, sources) { + object = Object(object); + + var index = -1; + var length = sources.length; + var guard = length > 2 ? sources[2] : undefined; + + if (guard && isIterateeCall(sources[0], sources[1], guard)) { + length = 1; + } + + while (++index < length) { + var source = sources[index]; + var props = keysIn(source); + var propsIndex = -1; + var propsLength = props.length; + + while (++propsIndex < propsLength) { + var key = props[propsIndex]; + var value = object[key]; + + if (value === undefined || + (eq(value, objectProto[key]) && !hasOwnProperty.call(object, key))) { + object[key] = source[key]; + } + } + } + + return object; + }); + + /** + * This method is like `_.defaults` except that it recursively assigns + * default properties. + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 3.10.0 + * @category Object + * @param {Object} object The destination object. + * @param {...Object} [sources] The source objects. + * @returns {Object} Returns `object`. + * @see _.defaults + * @example + * + * _.defaultsDeep({ 'a': { 'b': 2 } }, { 'a': { 'b': 1, 'c': 3 } }); + * // => { 'a': { 'b': 2, 'c': 3 } } + */ + var defaultsDeep = baseRest(function(args) { + args.push(undefined, customDefaultsMerge); + return apply(mergeWith, undefined, args); + }); + + /** + * This method is like `_.find` except that it returns the key of the first + * element `predicate` returns truthy for instead of the element itself. + * + * @static + * @memberOf _ + * @since 1.1.0 + * @category Object + * @param {Object} object The object to inspect. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {string|undefined} Returns the key of the matched element, + * else `undefined`. + * @example + * + * var users = { + * 'barney': { 'age': 36, 'active': true }, + * 'fred': { 'age': 40, 'active': false }, + * 'pebbles': { 'age': 1, 'active': true } + * }; + * + * _.findKey(users, function(o) { return o.age < 40; }); + * // => 'barney' (iteration order is not guaranteed) + * + * // The `_.matches` iteratee shorthand. + * _.findKey(users, { 'age': 1, 'active': true }); + * // => 'pebbles' + * + * // The `_.matchesProperty` iteratee shorthand. + * _.findKey(users, ['active', false]); + * // => 'fred' + * + * // The `_.property` iteratee shorthand. + * _.findKey(users, 'active'); + * // => 'barney' + */ + function findKey(object, predicate) { + return baseFindKey(object, getIteratee(predicate, 3), baseForOwn); + } + + /** + * This method is like `_.findKey` except that it iterates over elements of + * a collection in the opposite order. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Object + * @param {Object} object The object to inspect. + * @param {Function} [predicate=_.identity] The function invoked per iteration. + * @returns {string|undefined} Returns the key of the matched element, + * else `undefined`. + * @example + * + * var users = { + * 'barney': { 'age': 36, 'active': true }, + * 'fred': { 'age': 40, 'active': false }, + * 'pebbles': { 'age': 1, 'active': true } + * }; + * + * _.findLastKey(users, function(o) { return o.age < 40; }); + * // => returns 'pebbles' assuming `_.findKey` returns 'barney' + * + * // The `_.matches` iteratee shorthand. + * _.findLastKey(users, { 'age': 36, 'active': true }); + * // => 'barney' + * + * // The `_.matchesProperty` iteratee shorthand. + * _.findLastKey(users, ['active', false]); + * // => 'fred' + * + * // The `_.property` iteratee shorthand. + * _.findLastKey(users, 'active'); + * // => 'pebbles' + */ + function findLastKey(object, predicate) { + return baseFindKey(object, getIteratee(predicate, 3), baseForOwnRight); + } + + /** + * Iterates over own and inherited enumerable string keyed properties of an + * object and invokes `iteratee` for each property. The iteratee is invoked + * with three arguments: (value, key, object). Iteratee functions may exit + * iteration early by explicitly returning `false`. + * + * @static + * @memberOf _ + * @since 0.3.0 + * @category Object + * @param {Object} object The object to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Object} Returns `object`. + * @see _.forInRight + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.forIn(new Foo, function(value, key) { + * console.log(key); + * }); + * // => Logs 'a', 'b', then 'c' (iteration order is not guaranteed). + */ + function forIn(object, iteratee) { + return object == null + ? object + : baseFor(object, getIteratee(iteratee, 3), keysIn); + } + + /** + * This method is like `_.forIn` except that it iterates over properties of + * `object` in the opposite order. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Object + * @param {Object} object The object to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Object} Returns `object`. + * @see _.forIn + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.forInRight(new Foo, function(value, key) { + * console.log(key); + * }); + * // => Logs 'c', 'b', then 'a' assuming `_.forIn` logs 'a', 'b', then 'c'. + */ + function forInRight(object, iteratee) { + return object == null + ? object + : baseForRight(object, getIteratee(iteratee, 3), keysIn); + } + + /** + * Iterates over own enumerable string keyed properties of an object and + * invokes `iteratee` for each property. The iteratee is invoked with three + * arguments: (value, key, object). Iteratee functions may exit iteration + * early by explicitly returning `false`. + * + * @static + * @memberOf _ + * @since 0.3.0 + * @category Object + * @param {Object} object The object to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Object} Returns `object`. + * @see _.forOwnRight + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.forOwn(new Foo, function(value, key) { + * console.log(key); + * }); + * // => Logs 'a' then 'b' (iteration order is not guaranteed). + */ + function forOwn(object, iteratee) { + return object && baseForOwn(object, getIteratee(iteratee, 3)); + } + + /** + * This method is like `_.forOwn` except that it iterates over properties of + * `object` in the opposite order. + * + * @static + * @memberOf _ + * @since 2.0.0 + * @category Object + * @param {Object} object The object to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Object} Returns `object`. + * @see _.forOwn + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.forOwnRight(new Foo, function(value, key) { + * console.log(key); + * }); + * // => Logs 'b' then 'a' assuming `_.forOwn` logs 'a' then 'b'. + */ + function forOwnRight(object, iteratee) { + return object && baseForOwnRight(object, getIteratee(iteratee, 3)); + } + + /** + * Creates an array of function property names from own enumerable properties + * of `object`. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Object + * @param {Object} object The object to inspect. + * @returns {Array} Returns the function names. + * @see _.functionsIn + * @example + * + * function Foo() { + * this.a = _.constant('a'); + * this.b = _.constant('b'); + * } + * + * Foo.prototype.c = _.constant('c'); + * + * _.functions(new Foo); + * // => ['a', 'b'] + */ + function functions(object) { + return object == null ? [] : baseFunctions(object, keys(object)); + } + + /** + * Creates an array of function property names from own and inherited + * enumerable properties of `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The object to inspect. + * @returns {Array} Returns the function names. + * @see _.functions + * @example + * + * function Foo() { + * this.a = _.constant('a'); + * this.b = _.constant('b'); + * } + * + * Foo.prototype.c = _.constant('c'); + * + * _.functionsIn(new Foo); + * // => ['a', 'b', 'c'] + */ + function functionsIn(object) { + return object == null ? [] : baseFunctions(object, keysIn(object)); + } + + /** + * Gets the value at `path` of `object`. If the resolved value is + * `undefined`, the `defaultValue` is returned in its place. + * + * @static + * @memberOf _ + * @since 3.7.0 + * @category Object + * @param {Object} object The object to query. + * @param {Array|string} path The path of the property to get. + * @param {*} [defaultValue] The value returned for `undefined` resolved values. + * @returns {*} Returns the resolved value. + * @example + * + * var object = { 'a': [{ 'b': { 'c': 3 } }] }; + * + * _.get(object, 'a[0].b.c'); + * // => 3 + * + * _.get(object, ['a', '0', 'b', 'c']); + * // => 3 + * + * _.get(object, 'a.b.c', 'default'); + * // => 'default' + */ + function get(object, path, defaultValue) { + var result = object == null ? undefined : baseGet(object, path); + return result === undefined ? defaultValue : result; + } + + /** + * Checks if `path` is a direct property of `object`. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Object + * @param {Object} object The object to query. + * @param {Array|string} path The path to check. + * @returns {boolean} Returns `true` if `path` exists, else `false`. + * @example + * + * var object = { 'a': { 'b': 2 } }; + * var other = _.create({ 'a': _.create({ 'b': 2 }) }); + * + * _.has(object, 'a'); + * // => true + * + * _.has(object, 'a.b'); + * // => true + * + * _.has(object, ['a', 'b']); + * // => true + * + * _.has(other, 'a'); + * // => false + */ + function has(object, path) { + return object != null && hasPath(object, path, baseHas); + } + + /** + * Checks if `path` is a direct or inherited property of `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The object to query. + * @param {Array|string} path The path to check. + * @returns {boolean} Returns `true` if `path` exists, else `false`. + * @example + * + * var object = _.create({ 'a': _.create({ 'b': 2 }) }); + * + * _.hasIn(object, 'a'); + * // => true + * + * _.hasIn(object, 'a.b'); + * // => true + * + * _.hasIn(object, ['a', 'b']); + * // => true + * + * _.hasIn(object, 'b'); + * // => false + */ + function hasIn(object, path) { + return object != null && hasPath(object, path, baseHasIn); + } + + /** + * Creates an object composed of the inverted keys and values of `object`. + * If `object` contains duplicate values, subsequent values overwrite + * property assignments of previous values. + * + * @static + * @memberOf _ + * @since 0.7.0 + * @category Object + * @param {Object} object The object to invert. + * @returns {Object} Returns the new inverted object. + * @example + * + * var object = { 'a': 1, 'b': 2, 'c': 1 }; + * + * _.invert(object); + * // => { '1': 'c', '2': 'b' } + */ + var invert = createInverter(function(result, value, key) { + if (value != null && + typeof value.toString != 'function') { + value = nativeObjectToString.call(value); + } + + result[value] = key; + }, constant(identity)); + + /** + * This method is like `_.invert` except that the inverted object is generated + * from the results of running each element of `object` thru `iteratee`. The + * corresponding inverted value of each inverted key is an array of keys + * responsible for generating the inverted value. The iteratee is invoked + * with one argument: (value). + * + * @static + * @memberOf _ + * @since 4.1.0 + * @category Object + * @param {Object} object The object to invert. + * @param {Function} [iteratee=_.identity] The iteratee invoked per element. + * @returns {Object} Returns the new inverted object. + * @example + * + * var object = { 'a': 1, 'b': 2, 'c': 1 }; + * + * _.invertBy(object); + * // => { '1': ['a', 'c'], '2': ['b'] } + * + * _.invertBy(object, function(value) { + * return 'group' + value; + * }); + * // => { 'group1': ['a', 'c'], 'group2': ['b'] } + */ + var invertBy = createInverter(function(result, value, key) { + if (value != null && + typeof value.toString != 'function') { + value = nativeObjectToString.call(value); + } + + if (hasOwnProperty.call(result, value)) { + result[value].push(key); + } else { + result[value] = [key]; + } + }, getIteratee); + + /** + * Invokes the method at `path` of `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The object to query. + * @param {Array|string} path The path of the method to invoke. + * @param {...*} [args] The arguments to invoke the method with. + * @returns {*} Returns the result of the invoked method. + * @example + * + * var object = { 'a': [{ 'b': { 'c': [1, 2, 3, 4] } }] }; + * + * _.invoke(object, 'a[0].b.c.slice', 1, 3); + * // => [2, 3] + */ + var invoke = baseRest(baseInvoke); + + /** + * Creates an array of the own enumerable property names of `object`. + * + * **Note:** Non-object values are coerced to objects. See the + * [ES spec](http://ecma-international.org/ecma-262/7.0/#sec-object.keys) + * for more details. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Object + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property names. + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.keys(new Foo); + * // => ['a', 'b'] (iteration order is not guaranteed) + * + * _.keys('hi'); + * // => ['0', '1'] + */ + function keys(object) { + return isArrayLike(object) ? arrayLikeKeys(object) : baseKeys(object); + } + + /** + * Creates an array of the own and inherited enumerable property names of `object`. + * + * **Note:** Non-object values are coerced to objects. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Object + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property names. + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.keysIn(new Foo); + * // => ['a', 'b', 'c'] (iteration order is not guaranteed) + */ + function keysIn(object) { + return isArrayLike(object) ? arrayLikeKeys(object, true) : baseKeysIn(object); + } + + /** + * The opposite of `_.mapValues`; this method creates an object with the + * same values as `object` and keys generated by running each own enumerable + * string keyed property of `object` thru `iteratee`. The iteratee is invoked + * with three arguments: (value, key, object). + * + * @static + * @memberOf _ + * @since 3.8.0 + * @category Object + * @param {Object} object The object to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Object} Returns the new mapped object. + * @see _.mapValues + * @example + * + * _.mapKeys({ 'a': 1, 'b': 2 }, function(value, key) { + * return key + value; + * }); + * // => { 'a1': 1, 'b2': 2 } + */ + function mapKeys(object, iteratee) { + var result = {}; + iteratee = getIteratee(iteratee, 3); + + baseForOwn(object, function(value, key, object) { + baseAssignValue(result, iteratee(value, key, object), value); + }); + return result; + } + + /** + * Creates an object with the same keys as `object` and values generated + * by running each own enumerable string keyed property of `object` thru + * `iteratee`. The iteratee is invoked with three arguments: + * (value, key, object). + * + * @static + * @memberOf _ + * @since 2.4.0 + * @category Object + * @param {Object} object The object to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @returns {Object} Returns the new mapped object. + * @see _.mapKeys + * @example + * + * var users = { + * 'fred': { 'user': 'fred', 'age': 40 }, + * 'pebbles': { 'user': 'pebbles', 'age': 1 } + * }; + * + * _.mapValues(users, function(o) { return o.age; }); + * // => { 'fred': 40, 'pebbles': 1 } (iteration order is not guaranteed) + * + * // The `_.property` iteratee shorthand. + * _.mapValues(users, 'age'); + * // => { 'fred': 40, 'pebbles': 1 } (iteration order is not guaranteed) + */ + function mapValues(object, iteratee) { + var result = {}; + iteratee = getIteratee(iteratee, 3); + + baseForOwn(object, function(value, key, object) { + baseAssignValue(result, key, iteratee(value, key, object)); + }); + return result; + } + + /** + * This method is like `_.assign` except that it recursively merges own and + * inherited enumerable string keyed properties of source objects into the + * destination object. Source properties that resolve to `undefined` are + * skipped if a destination value exists. Array and plain object properties + * are merged recursively. Other objects and value types are overridden by + * assignment. Source objects are applied from left to right. Subsequent + * sources overwrite property assignments of previous sources. + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 0.5.0 + * @category Object + * @param {Object} object The destination object. + * @param {...Object} [sources] The source objects. + * @returns {Object} Returns `object`. + * @example + * + * var object = { + * 'a': [{ 'b': 2 }, { 'd': 4 }] + * }; + * + * var other = { + * 'a': [{ 'c': 3 }, { 'e': 5 }] + * }; + * + * _.merge(object, other); + * // => { 'a': [{ 'b': 2, 'c': 3 }, { 'd': 4, 'e': 5 }] } + */ + var merge = createAssigner(function(object, source, srcIndex) { + baseMerge(object, source, srcIndex); + }); + + /** + * This method is like `_.merge` except that it accepts `customizer` which + * is invoked to produce the merged values of the destination and source + * properties. If `customizer` returns `undefined`, merging is handled by the + * method instead. The `customizer` is invoked with six arguments: + * (objValue, srcValue, key, object, source, stack). + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The destination object. + * @param {...Object} sources The source objects. + * @param {Function} customizer The function to customize assigned values. + * @returns {Object} Returns `object`. + * @example + * + * function customizer(objValue, srcValue) { + * if (_.isArray(objValue)) { + * return objValue.concat(srcValue); + * } + * } + * + * var object = { 'a': [1], 'b': [2] }; + * var other = { 'a': [3], 'b': [4] }; + * + * _.mergeWith(object, other, customizer); + * // => { 'a': [1, 3], 'b': [2, 4] } + */ + var mergeWith = createAssigner(function(object, source, srcIndex, customizer) { + baseMerge(object, source, srcIndex, customizer); + }); + + /** + * The opposite of `_.pick`; this method creates an object composed of the + * own and inherited enumerable property paths of `object` that are not omitted. + * + * **Note:** This method is considerably slower than `_.pick`. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Object + * @param {Object} object The source object. + * @param {...(string|string[])} [paths] The property paths to omit. + * @returns {Object} Returns the new object. + * @example + * + * var object = { 'a': 1, 'b': '2', 'c': 3 }; + * + * _.omit(object, ['a', 'c']); + * // => { 'b': '2' } + */ + var omit = flatRest(function(object, paths) { + var result = {}; + if (object == null) { + return result; + } + var isDeep = false; + paths = arrayMap(paths, function(path) { + path = castPath(path, object); + isDeep || (isDeep = path.length > 1); + return path; + }); + copyObject(object, getAllKeysIn(object), result); + if (isDeep) { + result = baseClone(result, CLONE_DEEP_FLAG | CLONE_FLAT_FLAG | CLONE_SYMBOLS_FLAG, customOmitClone); + } + var length = paths.length; + while (length--) { + baseUnset(result, paths[length]); + } + return result; + }); + + /** + * The opposite of `_.pickBy`; this method creates an object composed of + * the own and inherited enumerable string keyed properties of `object` that + * `predicate` doesn't return truthy for. The predicate is invoked with two + * arguments: (value, key). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The source object. + * @param {Function} [predicate=_.identity] The function invoked per property. + * @returns {Object} Returns the new object. + * @example + * + * var object = { 'a': 1, 'b': '2', 'c': 3 }; + * + * _.omitBy(object, _.isNumber); + * // => { 'b': '2' } + */ + function omitBy(object, predicate) { + return pickBy(object, negate(getIteratee(predicate))); + } + + /** + * Creates an object composed of the picked `object` properties. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Object + * @param {Object} object The source object. + * @param {...(string|string[])} [paths] The property paths to pick. + * @returns {Object} Returns the new object. + * @example + * + * var object = { 'a': 1, 'b': '2', 'c': 3 }; + * + * _.pick(object, ['a', 'c']); + * // => { 'a': 1, 'c': 3 } + */ + var pick = flatRest(function(object, paths) { + return object == null ? {} : basePick(object, paths); + }); + + /** + * Creates an object composed of the `object` properties `predicate` returns + * truthy for. The predicate is invoked with two arguments: (value, key). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The source object. + * @param {Function} [predicate=_.identity] The function invoked per property. + * @returns {Object} Returns the new object. + * @example + * + * var object = { 'a': 1, 'b': '2', 'c': 3 }; + * + * _.pickBy(object, _.isNumber); + * // => { 'a': 1, 'c': 3 } + */ + function pickBy(object, predicate) { + if (object == null) { + return {}; + } + var props = arrayMap(getAllKeysIn(object), function(prop) { + return [prop]; + }); + predicate = getIteratee(predicate); + return basePickBy(object, props, function(value, path) { + return predicate(value, path[0]); + }); + } + + /** + * This method is like `_.get` except that if the resolved value is a + * function it's invoked with the `this` binding of its parent object and + * its result is returned. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Object + * @param {Object} object The object to query. + * @param {Array|string} path The path of the property to resolve. + * @param {*} [defaultValue] The value returned for `undefined` resolved values. + * @returns {*} Returns the resolved value. + * @example + * + * var object = { 'a': [{ 'b': { 'c1': 3, 'c2': _.constant(4) } }] }; + * + * _.result(object, 'a[0].b.c1'); + * // => 3 + * + * _.result(object, 'a[0].b.c2'); + * // => 4 + * + * _.result(object, 'a[0].b.c3', 'default'); + * // => 'default' + * + * _.result(object, 'a[0].b.c3', _.constant('default')); + * // => 'default' + */ + function result(object, path, defaultValue) { + path = castPath(path, object); + + var index = -1, + length = path.length; + + // Ensure the loop is entered when path is empty. + if (!length) { + length = 1; + object = undefined; + } + while (++index < length) { + var value = object == null ? undefined : object[toKey(path[index])]; + if (value === undefined) { + index = length; + value = defaultValue; + } + object = isFunction(value) ? value.call(object) : value; + } + return object; + } + + /** + * Sets the value at `path` of `object`. If a portion of `path` doesn't exist, + * it's created. Arrays are created for missing index properties while objects + * are created for all other missing properties. Use `_.setWith` to customize + * `path` creation. + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 3.7.0 + * @category Object + * @param {Object} object The object to modify. + * @param {Array|string} path The path of the property to set. + * @param {*} value The value to set. + * @returns {Object} Returns `object`. + * @example + * + * var object = { 'a': [{ 'b': { 'c': 3 } }] }; + * + * _.set(object, 'a[0].b.c', 4); + * console.log(object.a[0].b.c); + * // => 4 + * + * _.set(object, ['x', '0', 'y', 'z'], 5); + * console.log(object.x[0].y.z); + * // => 5 + */ + function set(object, path, value) { + return object == null ? object : baseSet(object, path, value); + } + + /** + * This method is like `_.set` except that it accepts `customizer` which is + * invoked to produce the objects of `path`. If `customizer` returns `undefined` + * path creation is handled by the method instead. The `customizer` is invoked + * with three arguments: (nsValue, key, nsObject). + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The object to modify. + * @param {Array|string} path The path of the property to set. + * @param {*} value The value to set. + * @param {Function} [customizer] The function to customize assigned values. + * @returns {Object} Returns `object`. + * @example + * + * var object = {}; + * + * _.setWith(object, '[0][1]', 'a', Object); + * // => { '0': { '1': 'a' } } + */ + function setWith(object, path, value, customizer) { + customizer = typeof customizer == 'function' ? customizer : undefined; + return object == null ? object : baseSet(object, path, value, customizer); + } + + /** + * Creates an array of own enumerable string keyed-value pairs for `object` + * which can be consumed by `_.fromPairs`. If `object` is a map or set, its + * entries are returned. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @alias entries + * @category Object + * @param {Object} object The object to query. + * @returns {Array} Returns the key-value pairs. + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.toPairs(new Foo); + * // => [['a', 1], ['b', 2]] (iteration order is not guaranteed) + */ + var toPairs = createToPairs(keys); + + /** + * Creates an array of own and inherited enumerable string keyed-value pairs + * for `object` which can be consumed by `_.fromPairs`. If `object` is a map + * or set, its entries are returned. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @alias entriesIn + * @category Object + * @param {Object} object The object to query. + * @returns {Array} Returns the key-value pairs. + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.toPairsIn(new Foo); + * // => [['a', 1], ['b', 2], ['c', 3]] (iteration order is not guaranteed) + */ + var toPairsIn = createToPairs(keysIn); + + /** + * An alternative to `_.reduce`; this method transforms `object` to a new + * `accumulator` object which is the result of running each of its own + * enumerable string keyed properties thru `iteratee`, with each invocation + * potentially mutating the `accumulator` object. If `accumulator` is not + * provided, a new object with the same `[[Prototype]]` will be used. The + * iteratee is invoked with four arguments: (accumulator, value, key, object). + * Iteratee functions may exit iteration early by explicitly returning `false`. + * + * @static + * @memberOf _ + * @since 1.3.0 + * @category Object + * @param {Object} object The object to iterate over. + * @param {Function} [iteratee=_.identity] The function invoked per iteration. + * @param {*} [accumulator] The custom accumulator value. + * @returns {*} Returns the accumulated value. + * @example + * + * _.transform([2, 3, 4], function(result, n) { + * result.push(n *= n); + * return n % 2 == 0; + * }, []); + * // => [4, 9] + * + * _.transform({ 'a': 1, 'b': 2, 'c': 1 }, function(result, value, key) { + * (result[value] || (result[value] = [])).push(key); + * }, {}); + * // => { '1': ['a', 'c'], '2': ['b'] } + */ + function transform(object, iteratee, accumulator) { + var isArr = isArray(object), + isArrLike = isArr || isBuffer(object) || isTypedArray(object); + + iteratee = getIteratee(iteratee, 4); + if (accumulator == null) { + var Ctor = object && object.constructor; + if (isArrLike) { + accumulator = isArr ? new Ctor : []; + } + else if (isObject(object)) { + accumulator = isFunction(Ctor) ? baseCreate(getPrototype(object)) : {}; + } + else { + accumulator = {}; + } + } + (isArrLike ? arrayEach : baseForOwn)(object, function(value, index, object) { + return iteratee(accumulator, value, index, object); + }); + return accumulator; + } + + /** + * Removes the property at `path` of `object`. + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Object + * @param {Object} object The object to modify. + * @param {Array|string} path The path of the property to unset. + * @returns {boolean} Returns `true` if the property is deleted, else `false`. + * @example + * + * var object = { 'a': [{ 'b': { 'c': 7 } }] }; + * _.unset(object, 'a[0].b.c'); + * // => true + * + * console.log(object); + * // => { 'a': [{ 'b': {} }] }; + * + * _.unset(object, ['a', '0', 'b', 'c']); + * // => true + * + * console.log(object); + * // => { 'a': [{ 'b': {} }] }; + */ + function unset(object, path) { + return object == null ? true : baseUnset(object, path); + } + + /** + * This method is like `_.set` except that accepts `updater` to produce the + * value to set. Use `_.updateWith` to customize `path` creation. The `updater` + * is invoked with one argument: (value). + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 4.6.0 + * @category Object + * @param {Object} object The object to modify. + * @param {Array|string} path The path of the property to set. + * @param {Function} updater The function to produce the updated value. + * @returns {Object} Returns `object`. + * @example + * + * var object = { 'a': [{ 'b': { 'c': 3 } }] }; + * + * _.update(object, 'a[0].b.c', function(n) { return n * n; }); + * console.log(object.a[0].b.c); + * // => 9 + * + * _.update(object, 'x[0].y.z', function(n) { return n ? n + 1 : 0; }); + * console.log(object.x[0].y.z); + * // => 0 + */ + function update(object, path, updater) { + return object == null ? object : baseUpdate(object, path, castFunction(updater)); + } + + /** + * This method is like `_.update` except that it accepts `customizer` which is + * invoked to produce the objects of `path`. If `customizer` returns `undefined` + * path creation is handled by the method instead. The `customizer` is invoked + * with three arguments: (nsValue, key, nsObject). + * + * **Note:** This method mutates `object`. + * + * @static + * @memberOf _ + * @since 4.6.0 + * @category Object + * @param {Object} object The object to modify. + * @param {Array|string} path The path of the property to set. + * @param {Function} updater The function to produce the updated value. + * @param {Function} [customizer] The function to customize assigned values. + * @returns {Object} Returns `object`. + * @example + * + * var object = {}; + * + * _.updateWith(object, '[0][1]', _.constant('a'), Object); + * // => { '0': { '1': 'a' } } + */ + function updateWith(object, path, updater, customizer) { + customizer = typeof customizer == 'function' ? customizer : undefined; + return object == null ? object : baseUpdate(object, path, castFunction(updater), customizer); + } + + /** + * Creates an array of the own enumerable string keyed property values of `object`. + * + * **Note:** Non-object values are coerced to objects. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category Object + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property values. + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.values(new Foo); + * // => [1, 2] (iteration order is not guaranteed) + * + * _.values('hi'); + * // => ['h', 'i'] + */ + function values(object) { + return object == null ? [] : baseValues(object, keys(object)); + } + + /** + * Creates an array of the own and inherited enumerable string keyed property + * values of `object`. + * + * **Note:** Non-object values are coerced to objects. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category Object + * @param {Object} object The object to query. + * @returns {Array} Returns the array of property values. + * @example + * + * function Foo() { + * this.a = 1; + * this.b = 2; + * } + * + * Foo.prototype.c = 3; + * + * _.valuesIn(new Foo); + * // => [1, 2, 3] (iteration order is not guaranteed) + */ + function valuesIn(object) { + return object == null ? [] : baseValues(object, keysIn(object)); + } + + /*------------------------------------------------------------------------*/ + + /** + * Clamps `number` within the inclusive `lower` and `upper` bounds. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category Number + * @param {number} number The number to clamp. + * @param {number} [lower] The lower bound. + * @param {number} upper The upper bound. + * @returns {number} Returns the clamped number. + * @example + * + * _.clamp(-10, -5, 5); + * // => -5 + * + * _.clamp(10, -5, 5); + * // => 5 + */ + function clamp(number, lower, upper) { + if (upper === undefined) { + upper = lower; + lower = undefined; + } + if (upper !== undefined) { + upper = toNumber(upper); + upper = upper === upper ? upper : 0; + } + if (lower !== undefined) { + lower = toNumber(lower); + lower = lower === lower ? lower : 0; + } + return baseClamp(toNumber(number), lower, upper); + } + + /** + * Checks if `n` is between `start` and up to, but not including, `end`. If + * `end` is not specified, it's set to `start` with `start` then set to `0`. + * If `start` is greater than `end` the params are swapped to support + * negative ranges. + * + * @static + * @memberOf _ + * @since 3.3.0 + * @category Number + * @param {number} number The number to check. + * @param {number} [start=0] The start of the range. + * @param {number} end The end of the range. + * @returns {boolean} Returns `true` if `number` is in the range, else `false`. + * @see _.range, _.rangeRight + * @example + * + * _.inRange(3, 2, 4); + * // => true + * + * _.inRange(4, 8); + * // => true + * + * _.inRange(4, 2); + * // => false + * + * _.inRange(2, 2); + * // => false + * + * _.inRange(1.2, 2); + * // => true + * + * _.inRange(5.2, 4); + * // => false + * + * _.inRange(-3, -2, -6); + * // => true + */ + function inRange(number, start, end) { + start = toFinite(start); + if (end === undefined) { + end = start; + start = 0; + } else { + end = toFinite(end); + } + number = toNumber(number); + return baseInRange(number, start, end); + } + + /** + * Produces a random number between the inclusive `lower` and `upper` bounds. + * If only one argument is provided a number between `0` and the given number + * is returned. If `floating` is `true`, or either `lower` or `upper` are + * floats, a floating-point number is returned instead of an integer. + * + * **Note:** JavaScript follows the IEEE-754 standard for resolving + * floating-point values which can produce unexpected results. + * + * @static + * @memberOf _ + * @since 0.7.0 + * @category Number + * @param {number} [lower=0] The lower bound. + * @param {number} [upper=1] The upper bound. + * @param {boolean} [floating] Specify returning a floating-point number. + * @returns {number} Returns the random number. + * @example + * + * _.random(0, 5); + * // => an integer between 0 and 5 + * + * _.random(5); + * // => also an integer between 0 and 5 + * + * _.random(5, true); + * // => a floating-point number between 0 and 5 + * + * _.random(1.2, 5.2); + * // => a floating-point number between 1.2 and 5.2 + */ + function random(lower, upper, floating) { + if (floating && typeof floating != 'boolean' && isIterateeCall(lower, upper, floating)) { + upper = floating = undefined; + } + if (floating === undefined) { + if (typeof upper == 'boolean') { + floating = upper; + upper = undefined; + } + else if (typeof lower == 'boolean') { + floating = lower; + lower = undefined; + } + } + if (lower === undefined && upper === undefined) { + lower = 0; + upper = 1; + } + else { + lower = toFinite(lower); + if (upper === undefined) { + upper = lower; + lower = 0; + } else { + upper = toFinite(upper); + } + } + if (lower > upper) { + var temp = lower; + lower = upper; + upper = temp; + } + if (floating || lower % 1 || upper % 1) { + var rand = nativeRandom(); + return nativeMin(lower + (rand * (upper - lower + freeParseFloat('1e-' + ((rand + '').length - 1)))), upper); + } + return baseRandom(lower, upper); + } + + /*------------------------------------------------------------------------*/ + + /** + * Converts `string` to [camel case](https://en.wikipedia.org/wiki/CamelCase). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to convert. + * @returns {string} Returns the camel cased string. + * @example + * + * _.camelCase('Foo Bar'); + * // => 'fooBar' + * + * _.camelCase('--foo-bar--'); + * // => 'fooBar' + * + * _.camelCase('__FOO_BAR__'); + * // => 'fooBar' + */ + var camelCase = createCompounder(function(result, word, index) { + word = word.toLowerCase(); + return result + (index ? capitalize(word) : word); + }); + + /** + * Converts the first character of `string` to upper case and the remaining + * to lower case. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to capitalize. + * @returns {string} Returns the capitalized string. + * @example + * + * _.capitalize('FRED'); + * // => 'Fred' + */ + function capitalize(string) { + return upperFirst(toString(string).toLowerCase()); + } + + /** + * Deburrs `string` by converting + * [Latin-1 Supplement](https://en.wikipedia.org/wiki/Latin-1_Supplement_(Unicode_block)#Character_table) + * and [Latin Extended-A](https://en.wikipedia.org/wiki/Latin_Extended-A) + * letters to basic Latin letters and removing + * [combining diacritical marks](https://en.wikipedia.org/wiki/Combining_Diacritical_Marks). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to deburr. + * @returns {string} Returns the deburred string. + * @example + * + * _.deburr('dÊjà vu'); + * // => 'deja vu' + */ + function deburr(string) { + string = toString(string); + return string && string.replace(reLatin, deburrLetter).replace(reComboMark, ''); + } + + /** + * Checks if `string` ends with the given target string. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to inspect. + * @param {string} [target] The string to search for. + * @param {number} [position=string.length] The position to search up to. + * @returns {boolean} Returns `true` if `string` ends with `target`, + * else `false`. + * @example + * + * _.endsWith('abc', 'c'); + * // => true + * + * _.endsWith('abc', 'b'); + * // => false + * + * _.endsWith('abc', 'b', 2); + * // => true + */ + function endsWith(string, target, position) { + string = toString(string); + target = baseToString(target); + + var length = string.length; + position = position === undefined + ? length + : baseClamp(toInteger(position), 0, length); + + var end = position; + position -= target.length; + return position >= 0 && string.slice(position, end) == target; + } + + /** + * Converts the characters "&", "<", ">", '"', and "'" in `string` to their + * corresponding HTML entities. + * + * **Note:** No other characters are escaped. To escape additional + * characters use a third-party library like [_he_](https://mths.be/he). + * + * Though the ">" character is escaped for symmetry, characters like + * ">" and "/" don't need escaping in HTML and have no special meaning + * unless they're part of a tag or unquoted attribute value. See + * [Mathias Bynens's article](https://mathiasbynens.be/notes/ambiguous-ampersands) + * (under "semi-related fun fact") for more details. + * + * When working with HTML you should always + * [quote attribute values](http://wonko.com/post/html-escaping) to reduce + * XSS vectors. + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category String + * @param {string} [string=''] The string to escape. + * @returns {string} Returns the escaped string. + * @example + * + * _.escape('fred, barney, & pebbles'); + * // => 'fred, barney, & pebbles' + */ + function escape(string) { + string = toString(string); + return (string && reHasUnescapedHtml.test(string)) + ? string.replace(reUnescapedHtml, escapeHtmlChar) + : string; + } + + /** + * Escapes the `RegExp` special characters "^", "$", "\", ".", "*", "+", + * "?", "(", ")", "[", "]", "{", "}", and "|" in `string`. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to escape. + * @returns {string} Returns the escaped string. + * @example + * + * _.escapeRegExp('[lodash](https://lodash.com/)'); + * // => '\[lodash\]\(https://lodash\.com/\)' + */ + function escapeRegExp(string) { + string = toString(string); + return (string && reHasRegExpChar.test(string)) + ? string.replace(reRegExpChar, '\\$&') + : string; + } + + /** + * Converts `string` to + * [kebab case](https://en.wikipedia.org/wiki/Letter_case#Special_case_styles). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to convert. + * @returns {string} Returns the kebab cased string. + * @example + * + * _.kebabCase('Foo Bar'); + * // => 'foo-bar' + * + * _.kebabCase('fooBar'); + * // => 'foo-bar' + * + * _.kebabCase('__FOO_BAR__'); + * // => 'foo-bar' + */ + var kebabCase = createCompounder(function(result, word, index) { + return result + (index ? '-' : '') + word.toLowerCase(); + }); + + /** + * Converts `string`, as space separated words, to lower case. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category String + * @param {string} [string=''] The string to convert. + * @returns {string} Returns the lower cased string. + * @example + * + * _.lowerCase('--Foo-Bar--'); + * // => 'foo bar' + * + * _.lowerCase('fooBar'); + * // => 'foo bar' + * + * _.lowerCase('__FOO_BAR__'); + * // => 'foo bar' + */ + var lowerCase = createCompounder(function(result, word, index) { + return result + (index ? ' ' : '') + word.toLowerCase(); + }); + + /** + * Converts the first character of `string` to lower case. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category String + * @param {string} [string=''] The string to convert. + * @returns {string} Returns the converted string. + * @example + * + * _.lowerFirst('Fred'); + * // => 'fred' + * + * _.lowerFirst('FRED'); + * // => 'fRED' + */ + var lowerFirst = createCaseFirst('toLowerCase'); + + /** + * Pads `string` on the left and right sides if it's shorter than `length`. + * Padding characters are truncated if they can't be evenly divided by `length`. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to pad. + * @param {number} [length=0] The padding length. + * @param {string} [chars=' '] The string used as padding. + * @returns {string} Returns the padded string. + * @example + * + * _.pad('abc', 8); + * // => ' abc ' + * + * _.pad('abc', 8, '_-'); + * // => '_-abc_-_' + * + * _.pad('abc', 3); + * // => 'abc' + */ + function pad(string, length, chars) { + string = toString(string); + length = toInteger(length); + + var strLength = length ? stringSize(string) : 0; + if (!length || strLength >= length) { + return string; + } + var mid = (length - strLength) / 2; + return ( + createPadding(nativeFloor(mid), chars) + + string + + createPadding(nativeCeil(mid), chars) + ); + } + + /** + * Pads `string` on the right side if it's shorter than `length`. Padding + * characters are truncated if they exceed `length`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category String + * @param {string} [string=''] The string to pad. + * @param {number} [length=0] The padding length. + * @param {string} [chars=' '] The string used as padding. + * @returns {string} Returns the padded string. + * @example + * + * _.padEnd('abc', 6); + * // => 'abc ' + * + * _.padEnd('abc', 6, '_-'); + * // => 'abc_-_' + * + * _.padEnd('abc', 3); + * // => 'abc' + */ + function padEnd(string, length, chars) { + string = toString(string); + length = toInteger(length); + + var strLength = length ? stringSize(string) : 0; + return (length && strLength < length) + ? (string + createPadding(length - strLength, chars)) + : string; + } + + /** + * Pads `string` on the left side if it's shorter than `length`. Padding + * characters are truncated if they exceed `length`. + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category String + * @param {string} [string=''] The string to pad. + * @param {number} [length=0] The padding length. + * @param {string} [chars=' '] The string used as padding. + * @returns {string} Returns the padded string. + * @example + * + * _.padStart('abc', 6); + * // => ' abc' + * + * _.padStart('abc', 6, '_-'); + * // => '_-_abc' + * + * _.padStart('abc', 3); + * // => 'abc' + */ + function padStart(string, length, chars) { + string = toString(string); + length = toInteger(length); + + var strLength = length ? stringSize(string) : 0; + return (length && strLength < length) + ? (createPadding(length - strLength, chars) + string) + : string; + } + + /** + * Converts `string` to an integer of the specified radix. If `radix` is + * `undefined` or `0`, a `radix` of `10` is used unless `value` is a + * hexadecimal, in which case a `radix` of `16` is used. + * + * **Note:** This method aligns with the + * [ES5 implementation](https://es5.github.io/#x15.1.2.2) of `parseInt`. + * + * @static + * @memberOf _ + * @since 1.1.0 + * @category String + * @param {string} string The string to convert. + * @param {number} [radix=10] The radix to interpret `value` by. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {number} Returns the converted integer. + * @example + * + * _.parseInt('08'); + * // => 8 + * + * _.map(['6', '08', '10'], _.parseInt); + * // => [6, 8, 10] + */ + function parseInt(string, radix, guard) { + if (guard || radix == null) { + radix = 0; + } else if (radix) { + radix = +radix; + } + return nativeParseInt(toString(string).replace(reTrimStart, ''), radix || 0); + } + + /** + * Repeats the given string `n` times. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to repeat. + * @param {number} [n=1] The number of times to repeat the string. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {string} Returns the repeated string. + * @example + * + * _.repeat('*', 3); + * // => '***' + * + * _.repeat('abc', 2); + * // => 'abcabc' + * + * _.repeat('abc', 0); + * // => '' + */ + function repeat(string, n, guard) { + if ((guard ? isIterateeCall(string, n, guard) : n === undefined)) { + n = 1; + } else { + n = toInteger(n); + } + return baseRepeat(toString(string), n); + } + + /** + * Replaces matches for `pattern` in `string` with `replacement`. + * + * **Note:** This method is based on + * [`String#replace`](https://mdn.io/String/replace). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category String + * @param {string} [string=''] The string to modify. + * @param {RegExp|string} pattern The pattern to replace. + * @param {Function|string} replacement The match replacement. + * @returns {string} Returns the modified string. + * @example + * + * _.replace('Hi Fred', 'Fred', 'Barney'); + * // => 'Hi Barney' + */ + function replace() { + var args = arguments, + string = toString(args[0]); + + return args.length < 3 ? string : string.replace(args[1], args[2]); + } + + /** + * Converts `string` to + * [snake case](https://en.wikipedia.org/wiki/Snake_case). + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to convert. + * @returns {string} Returns the snake cased string. + * @example + * + * _.snakeCase('Foo Bar'); + * // => 'foo_bar' + * + * _.snakeCase('fooBar'); + * // => 'foo_bar' + * + * _.snakeCase('--FOO-BAR--'); + * // => 'foo_bar' + */ + var snakeCase = createCompounder(function(result, word, index) { + return result + (index ? '_' : '') + word.toLowerCase(); + }); + + /** + * Splits `string` by `separator`. + * + * **Note:** This method is based on + * [`String#split`](https://mdn.io/String/split). + * + * @static + * @memberOf _ + * @since 4.0.0 + * @category String + * @param {string} [string=''] The string to split. + * @param {RegExp|string} separator The separator pattern to split by. + * @param {number} [limit] The length to truncate results to. + * @returns {Array} Returns the string segments. + * @example + * + * _.split('a-b-c', '-', 2); + * // => ['a', 'b'] + */ + function split(string, separator, limit) { + if (limit && typeof limit != 'number' && isIterateeCall(string, separator, limit)) { + separator = limit = undefined; + } + limit = limit === undefined ? MAX_ARRAY_LENGTH : limit >>> 0; + if (!limit) { + return []; + } + string = toString(string); + if (string && ( + typeof separator == 'string' || + (separator != null && !isRegExp(separator)) + )) { + separator = baseToString(separator); + if (!separator && hasUnicode(string)) { + return castSlice(stringToArray(string), 0, limit); + } + } + return string.split(separator, limit); + } + + /** + * Converts `string` to + * [start case](https://en.wikipedia.org/wiki/Letter_case#Stylistic_or_specialised_usage). + * + * @static + * @memberOf _ + * @since 3.1.0 + * @category String + * @param {string} [string=''] The string to convert. + * @returns {string} Returns the start cased string. + * @example + * + * _.startCase('--foo-bar--'); + * // => 'Foo Bar' + * + * _.startCase('fooBar'); + * // => 'Foo Bar' + * + * _.startCase('__FOO_BAR__'); + * // => 'FOO BAR' + */ + var startCase = createCompounder(function(result, word, index) { + return result + (index ? ' ' : '') + upperFirst(word); + }); + + /** + * Checks if `string` starts with the given target string. + * + * @static + * @memberOf _ + * @since 3.0.0 + * @category String + * @param {string} [string=''] The string to inspect. + * @param {string} [target] The string to search for. + * @param {number} [position=0] The position to search from. + * @returns {boolean} Returns `true` if `string` starts with `target`, + * else `false`. + * @example + * + * _.startsWith('abc', 'a'); + * // => true + * + * _.startsWith('abc', 'b'); + * // => false + * + * _.startsWith('abc', 'b', 1); + * // => true + */ + function startsWith(string, target, position) { + string = toString(string); + position = position == null + ? 0 + : baseClamp(toInteger(position), 0, string.length); + + target = baseToString(target); + return string.slice(position, position + target.length) == target; + } + + /** + * Creates a compiled template function that can interpolate data properties + * in "interpolate" delimiters, HTML-escape interpolated data properties in + * "escape" delimiters, and execute JavaScript in "evaluate" delimiters. Data + * properties may be accessed as free variables in the template. If a setting + * object is given, it takes precedence over `_.templateSettings` values. + * + * **Note:** In the development build `_.template` utilizes + * [sourceURLs](http://www.html5rocks.com/en/tutorials/developertools/sourcemaps/#toc-sourceurl) + * for easier debugging. + * + * For more information on precompiling templates see + * [lodash's custom builds documentation](https://lodash.com/custom-builds). + * + * For more information on Chrome extension sandboxes see + * [Chrome's extensions documentation](https://developer.chrome.com/extensions/sandboxingEval). + * + * @static + * @since 0.1.0 + * @memberOf _ + * @category String + * @param {string} [string=''] The template string. + * @param {Object} [options={}] The options object. + * @param {RegExp} [options.escape=_.templateSettings.escape] + * The HTML "escape" delimiter. + * @param {RegExp} [options.evaluate=_.templateSettings.evaluate] + * The "evaluate" delimiter. + * @param {Object} [options.imports=_.templateSettings.imports] + * An object to import into the template as free variables. + * @param {RegExp} [options.interpolate=_.templateSettings.interpolate] + * The "interpolate" delimiter. + * @param {string} [options.sourceURL='lodash.templateSources[n]'] + * The sourceURL of the compiled template. + * @param {string} [options.variable='obj'] + * The data object variable name. + * @param- {Object} [guard] Enables use as an iteratee for methods like `_.map`. + * @returns {Function} Returns the compiled template function. + * @example + * + * // Use the "interpolate" delimiter to create a compiled template. + * var compiled = _.template('hello <%= user %>!'); + * compiled({ 'user': 'fred' }); + * // => 'hello fred!' + * + * // Use the HTML "escape" delimiter to escape data property values. + * var compiled = _.template('<%- value %>'); + * compiled({ 'value': '''' % bundle + random_id = id_generator(size=15, random_state=check_random_state(self.random_state)) + out += u''' +
+ ''' % random_id + + predict_proba_js = '' + if self.mode == "classification" and predict_proba: + predict_proba_js = u''' + var pp_div = top_div.append('div') + .classed('lime predict_proba', true); + var pp_svg = pp_div.append('svg').style('width', '100%%'); + var pp = new lime.PredictProba(pp_svg, %s, %s); + ''' % (jsonize([str(x) for x in self.class_names]), + jsonize(list(self.predict_proba.astype(float)))) + + predict_value_js = '' + if self.mode == "regression" and show_predicted_value: + # reference self.predicted_value + # (svg, predicted_value, min_value, max_value) + predict_value_js = u''' + var pp_div = top_div.append('div') + .classed('lime predicted_value', true); + var pp_svg = pp_div.append('svg').style('width', '100%%'); + var pp = new lime.PredictedValue(pp_svg, %s, %s, %s); + ''' % (jsonize(float(self.predicted_value)), + jsonize(float(self.min_value)), + jsonize(float(self.max_value))) + + exp_js = '''var exp_div; + var exp = new lime.Explanation(%s); + ''' % (jsonize([str(x) for x in self.class_names])) + + if self.mode == "classification": + for label in labels: + exp = jsonize(self.as_list(label)) + exp_js += u''' + exp_div = top_div.append('div').classed('lime explanation', true); + exp.show(%s, %d, exp_div); + ''' % (exp, label) + else: + exp = jsonize(self.as_list()) + exp_js += u''' + exp_div = top_div.append('div').classed('lime explanation', true); + exp.show(%s, %s, exp_div); + ''' % (exp, self.dummy_label) + + raw_js = '''var raw_div = top_div.append('div');''' + + if self.mode == "classification": + html_data = self.local_exp[labels[0]] + else: + html_data = self.local_exp[self.dummy_label] + + raw_js += self.domain_mapper.visualize_instance_html( + html_data, + labels[0] if self.mode == "classification" else self.dummy_label, + 'raw_div', + 'exp', + **kwargs) + out += u''' + + ''' % (random_id, predict_proba_js, predict_value_js, exp_js, raw_js) + out += u'' + + return out diff --git a/build/lib/lime/lime_base.py b/build/lib/lime/lime_base.py new file mode 100644 index 000000000..cd6ee5329 --- /dev/null +++ b/build/lib/lime/lime_base.py @@ -0,0 +1,212 @@ +""" +Contains abstract functionality for learning locally linear sparse model. +""" +import numpy as np +import scipy as sp +from sklearn.linear_model import Ridge, lars_path +from sklearn.utils import check_random_state + + +class LimeBase(object): + """Class for learning a locally linear sparse model from perturbed data""" + def __init__(self, + kernel_fn, + verbose=False, + random_state=None): + """Init function + + Args: + kernel_fn: function that transforms an array of distances into an + array of proximity values (floats). + verbose: if true, print local prediction values from linear model. + random_state: an integer or numpy.RandomState that will be used to + generate random numbers. If None, the random state will be + initialized using the internal numpy seed. + """ + self.kernel_fn = kernel_fn + self.verbose = verbose + self.random_state = check_random_state(random_state) + + @staticmethod + def generate_lars_path(weighted_data, weighted_labels): + """Generates the lars path for weighted data. + + Args: + weighted_data: data that has been weighted by kernel + weighted_label: labels, weighted by kernel + + Returns: + (alphas, coefs), both are arrays corresponding to the + regularization parameter and coefficients, respectively + """ + x_vector = weighted_data + alphas, _, coefs = lars_path(x_vector, + weighted_labels, + method='lasso', + verbose=False) + return alphas, coefs + + def forward_selection(self, data, labels, weights, num_features): + """Iteratively adds features to the model""" + clf = Ridge(alpha=0, fit_intercept=True, random_state=self.random_state) + used_features = [] + for _ in range(min(num_features, data.shape[1])): + max_ = -100000000 + best = 0 + for feature in range(data.shape[1]): + if feature in used_features: + continue + clf.fit(data[:, used_features + [feature]], labels, + sample_weight=weights) + score = clf.score(data[:, used_features + [feature]], + labels, + sample_weight=weights) + if score > max_: + best = feature + max_ = score + used_features.append(best) + return np.array(used_features) + + def feature_selection(self, data, labels, weights, num_features, method): + """Selects features for the model. see explain_instance_with_data to + understand the parameters.""" + if method == 'none': + return np.array(range(data.shape[1])) + elif method == 'forward_selection': + return self.forward_selection(data, labels, weights, num_features) + elif method == 'highest_weights': + clf = Ridge(alpha=0.01, fit_intercept=True, + random_state=self.random_state) + clf.fit(data, labels, sample_weight=weights) + + coef = clf.coef_ + if sp.sparse.issparse(data): + coef = sp.sparse.csr_matrix(clf.coef_) + weighted_data = coef.multiply(data[0]) + # Note: most efficient to slice the data before reversing + sdata = len(weighted_data.data) + argsort_data = np.abs(weighted_data.data).argsort() + # Edge case where data is more sparse than requested number of feature importances + # In that case, we just pad with zero-valued features + if sdata < num_features: + nnz_indexes = argsort_data[::-1] + indices = weighted_data.indices[nnz_indexes] + num_to_pad = num_features - sdata + indices = np.concatenate((indices, np.zeros(num_to_pad, dtype=indices.dtype))) + indices_set = set(indices) + pad_counter = 0 + for i in range(data.shape[1]): + if i not in indices_set: + indices[pad_counter + sdata] = i + pad_counter += 1 + if pad_counter >= num_to_pad: + break + else: + nnz_indexes = argsort_data[sdata - num_features:sdata][::-1] + indices = weighted_data.indices[nnz_indexes] + return indices + else: + weighted_data = coef * data[0] + feature_weights = sorted( + zip(range(data.shape[1]), weighted_data), + key=lambda x: np.abs(x[1]), + reverse=True) + return np.array([x[0] for x in feature_weights[:num_features]]) + elif method == 'lasso_path': + weighted_data = ((data - np.average(data, axis=0, weights=weights)) + * np.sqrt(weights[:, np.newaxis])) + weighted_labels = ((labels - np.average(labels, weights=weights)) + * np.sqrt(weights)) + nonzero = range(weighted_data.shape[1]) + _, coefs = self.generate_lars_path(weighted_data, + weighted_labels) + for i in range(len(coefs.T) - 1, 0, -1): + nonzero = coefs.T[i].nonzero()[0] + if len(nonzero) <= num_features: + break + used_features = nonzero + return used_features + elif method == 'auto': + if num_features <= 6: + n_method = 'forward_selection' + else: + n_method = 'highest_weights' + return self.feature_selection(data, labels, weights, + num_features, n_method) + + def explain_instance_with_data(self, + neighborhood_data, + neighborhood_labels, + distances, + label, + num_features, + feature_selection='auto', + weight_adjustments=None, + model_regressor=None): + """Takes perturbed data, labels and distances, returns explanation. + + Args: + neighborhood_data: perturbed data, 2d array. first element is + assumed to be the original data point. + neighborhood_labels: corresponding perturbed labels. should have as + many columns as the number of possible labels. + distances: distances to original data point. + label: label for which we want an explanation + num_features: maximum number of features in explanation + feature_selection: how to select num_features. options are: + 'forward_selection': iteratively add features to the model. + This is costly when num_features is high + 'highest_weights': selects the features that have the highest + product of absolute weight * original data point when + learning with all the features + 'lasso_path': chooses features based on the lasso + regularization path + 'none': uses all features, ignores num_features + 'auto': uses forward_selection if num_features <= 6, and + 'highest_weights' otherwise. + weight_adjustments: weights adjustment factors for the data points. + These factors are multiplied with the weights from the distance kernel. + model_regressor: sklearn regressor to use in explanation. + Defaults to Ridge regression if None. Must have + model_regressor.coef_ and 'sample_weight' as a parameter + to model_regressor.fit() + + Returns: + (intercept, exp, score, local_pred): + intercept is a float. + exp is a sorted list of tuples, where each tuple (x,y) corresponds + to the feature id (x) and the local weight (y). The list is sorted + by decreasing absolute value of y. + score is the R^2 value of the returned explanation + local_pred is the prediction of the explanation model on the original instance + """ + + if weight_adjustments is not None: + distances *= weight_adjustments + weights = self.kernel_fn(distances) + labels_column = neighborhood_labels[:, label] + used_features = self.feature_selection(neighborhood_data, + labels_column, + weights, + num_features, + feature_selection) + if model_regressor is None: + model_regressor = Ridge(alpha=1, fit_intercept=True, + random_state=self.random_state) + easy_model = model_regressor + easy_model.fit(neighborhood_data[:, used_features], + labels_column, sample_weight=weights) + prediction_score = easy_model.score( + neighborhood_data[:, used_features], + labels_column, sample_weight=weights) + + local_pred = easy_model.predict(neighborhood_data[0, used_features].reshape(1, -1)) + + if self.verbose: + print('Intercept', easy_model.intercept_) + print('Prediction_local', local_pred,) + print('Right:', neighborhood_labels[0, label]) + return (easy_model.intercept_, + sorted(zip(used_features, easy_model.coef_), + key=lambda x: np.abs(x[1]), reverse=True), + prediction_score, local_pred) diff --git a/build/lib/lime/lime_image.py b/build/lib/lime/lime_image.py new file mode 100644 index 000000000..5f6613da3 --- /dev/null +++ b/build/lib/lime/lime_image.py @@ -0,0 +1,290 @@ +""" +Functions for explaining classifiers that use Image data. +""" +import copy, math +from functools import partial + +import numpy as np +import sklearn +from sklearn.utils import check_random_state +from scipy.special import binom +from skimage.color import gray2rgb +from tqdm.auto import tqdm + + +from . import lime_base +from .wrappers.scikit_image import SegmentationAlgorithm + + +class ImageExplanation(object): + def __init__(self, image, segments): + """Init function. + + Args: + image: 3d numpy array + segments: 2d numpy array, with the output from skimage.segmentation + """ + self.image = image + self.segments = segments + self.intercept = {} + self.local_exp = {} + self.local_pred = {} + self.score = {} + + def get_image_and_mask(self, label, positive_only=True, negative_only=False, hide_rest=False, + num_features=5, min_weight=0.): + """Init function. + + Args: + label: label to explain + positive_only: if True, only take superpixels that positively contribute to + the prediction of the label. + negative_only: if True, only take superpixels that negatively contribute to + the prediction of the label. If false, and so is positive_only, then both + negativey and positively contributions will be taken. + Both can't be True at the same time + hide_rest: if True, make the non-explanation part of the return + image gray + num_features: number of superpixels to include in explanation + min_weight: minimum weight of the superpixels to include in explanation + + Returns: + (image, mask), where image is a 3d numpy array and mask is a 2d + numpy array that can be used with + skimage.segmentation.mark_boundaries + """ + if label not in self.local_exp: + raise KeyError('Label not in explanation') + if positive_only & negative_only: + raise ValueError("Positive_only and negative_only cannot be true at the same time.") + segments = self.segments + image = self.image + exp = self.local_exp[label] + mask = np.zeros(segments.shape, segments.dtype) + if hide_rest: + temp = np.zeros(self.image.shape) + else: + temp = self.image.copy() + if positive_only: + fs = [x[0] for x in exp + if x[1] > 0 and x[1] > min_weight][:num_features] + if negative_only: + fs = [x[0] for x in exp + if x[1] < 0 and abs(x[1]) > min_weight][:num_features] + if positive_only or negative_only: + for f in fs: + temp[segments == f] = image[segments == f].copy() + mask[segments == f] = 1 + return temp, mask + else: + for f, w in exp[:num_features]: + if np.abs(w) < min_weight: + continue + c = 0 if w < 0 else 1 + mask[segments == f] = -1 if w < 0 else 1 + temp[segments == f] = image[segments == f].copy() + temp[segments == f, c] = np.max(image) + return temp, mask + + +class LimeImageExplainer(object): + """Explains predictions on Image (i.e. matrix) data. + For numerical features, perturb them by sampling from a Normal(0,1) and + doing the inverse operation of mean-centering and scaling, according to the + means and stds in the training data. For categorical features, perturb by + sampling according to the training distribution, and making a binary + feature that is 1 when the value is the same as the instance being + explained.""" + + def __init__(self, kernel_width=.25, kernel=None, verbose=False, + feature_selection='auto', random_state=None): + """Init function. + + Args: + kernel_width: kernel width for the exponential kernel. + If None, defaults to sqrt(number of columns) * 0.75. + kernel: similarity kernel that takes euclidean distances and kernel + width as input and outputs weights in (0,1). If None, defaults to + an exponential kernel. + verbose: if true, print local prediction values from linear model + feature_selection: feature selection method. can be + 'forward_selection', 'lasso_path', 'none' or 'auto'. + See function 'explain_instance_with_data' in lime_base.py for + details on what each of the options does. + random_state: an integer or numpy.RandomState that will be used to + generate random numbers. If None, the random state will be + initialized using the internal numpy seed. + """ + kernel_width = float(kernel_width) + + if kernel is None: + def kernel(d, kernel_width): + return np.sqrt(np.exp(-(d ** 2) / kernel_width ** 2)) + + kernel_fn = partial(kernel, kernel_width=kernel_width) + + self.random_state = check_random_state(random_state) + self.feature_selection = feature_selection + self.base = lime_base.LimeBase(kernel_fn, verbose, random_state=self.random_state) + + def explain_instance(self, image, classifier_fn, labels=(1,), + hide_color=None, + top_labels=5, num_features=100000, num_samples=1000, + batch_size=10, + segmentation_fn=None, + distance_metric='cosine', + use_stratification=False, + model_regressor=None, + random_seed=None, + progress_bar=True): + """Generates explanations for a prediction. + + First, we generate neighborhood data by randomly perturbing features + from the instance (see __data_inverse). We then learn locally weighted + linear models on this neighborhood data to explain each of the classes + in an interpretable way (see lime_base.py). + + Args: + image: 3 dimension RGB image. If this is only two dimensional, + we will assume it's a grayscale image and call gray2rgb. + classifier_fn: classifier prediction probability function, which + takes a numpy array and outputs prediction probabilities. For + ScikitClassifiers , this is classifier.predict_proba. + labels: iterable with labels to be explained. + hide_color: If not None, will hide superpixels with this color. + Otherwise, use the mean pixel color of the image. + top_labels: if not None, ignore labels and produce explanations for + the K labels with highest prediction probabilities, where K is + this parameter. + num_features: maximum number of features present in explanation + num_samples: size of the neighborhood to learn the linear model + batch_size: batch size for model predictions + distance_metric: the distance metric to use for weights. + use_stratification: generate data points using stratified sampling + instead of the usual Monte Carlo sampling. + model_regressor: sklearn regressor to use in explanation. Defaults + to Ridge regression in LimeBase. Must have model_regressor.coef_ + and 'sample_weight' as a parameter to model_regressor.fit() + segmentation_fn: SegmentationAlgorithm, wrapped skimage + segmentation function + random_seed: integer used as random seed for the segmentation + algorithm. If None, a random integer, between 0 and 1000, + will be generated using the internal random number generator. + progress_bar: if True, show tqdm progress bar. + + Returns: + An ImageExplanation object (see lime_image.py) with the corresponding + explanations. + """ + if len(image.shape) == 2: + image = gray2rgb(image) + if random_seed is None: + random_seed = self.random_state.randint(0, high=1000) + + if segmentation_fn is None: + segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4, + max_dist=200, ratio=0.2, + random_seed=random_seed) + segments = segmentation_fn(image) + + fudged_image = image.copy() + if hide_color is None: + for x in np.unique(segments): + fudged_image[segments == x] = ( + np.mean(image[segments == x][:, 0]), + np.mean(image[segments == x][:, 1]), + np.mean(image[segments == x][:, 2])) + else: + fudged_image[:] = hide_color + + top = labels + + data, labels, weight_adjustments = self.data_labels(image, fudged_image, segments, + classifier_fn, num_samples, + batch_size=batch_size, + use_stratification=use_stratification, + progress_bar=progress_bar) + + distances = sklearn.metrics.pairwise_distances( + data, + data[0].reshape(1, -1), + metric=distance_metric + ).ravel() + + ret_exp = ImageExplanation(image, segments) + if top_labels: + top = np.argsort(labels[0])[-top_labels:] + ret_exp.top_labels = list(top) + ret_exp.top_labels.reverse() + for label in top: + (ret_exp.intercept[label], + ret_exp.local_exp[label], + ret_exp.score[label], + ret_exp.local_pred[label]) = self.base.explain_instance_with_data( + data, labels, distances, label, num_features, + model_regressor=model_regressor, + weight_adjustments=weight_adjustments, + feature_selection=self.feature_selection) + return ret_exp + + def data_labels(self, + image, + fudged_image, + segments, + classifier_fn, + num_samples, + batch_size=10, + use_stratification=False, + progress_bar=True): + """Generates images and predictions in the neighborhood of this image. + + Args: + image: 3d numpy array, the image + fudged_image: 3d numpy array, image to replace original image when + superpixel is turned off + segments: segmentation of the image + classifier_fn: function that takes a list of images and returns a + matrix of prediction probabilities + num_samples: size of the neighborhood to learn the linear model + batch_size: classifier_fn will be called on batches of this size. + progress_bar: if True, show tqdm progress bar. + + Returns: + A tuple (data, labels), where: + data: dense num_samples * num_superpixels + labels: prediction probabilities matrix + """ + n_features = np.unique(segments).shape[0] + if use_stratification: + data = np.ones([num_samples, n_features], dtype=bool) + weight_adjustments = np.zeros(num_samples, dtype=np.float64) + for i in range(1, num_samples): + q = self.random_state.uniform(0, 1) + data[i,:] = self.random_state.rand(n_features) < q + weight_adjustments[i] = (binom(n_features, np.sum(data[i,:])) + / (2 ** n_features)) * (n_features + 1) + weight_adjustments[0] = 1 + else: + data = self.random_state.randint(0, 2, num_samples * n_features)\ + .reshape((num_samples, n_features)) + data[0, :] = 1 + weight_adjustments = None + labels = [] + imgs = [] + rows = tqdm(data) if progress_bar else data + for row in rows: + temp = copy.deepcopy(image) + zeros = np.where(row == 0)[0] + mask = np.zeros(segments.shape).astype(bool) + for z in zeros: + mask[segments == z] = True + temp[mask] = fudged_image[mask] + imgs.append(temp) + if len(imgs) == batch_size: + preds = classifier_fn(np.array(imgs)) + labels.extend(preds) + imgs = [] + if len(imgs) > 0: + preds = classifier_fn(np.array(imgs)) + labels.extend(preds) + return data, np.array(labels), weight_adjustments diff --git a/build/lib/lime/lime_tabular.py b/build/lib/lime/lime_tabular.py new file mode 100644 index 000000000..880f3d391 --- /dev/null +++ b/build/lib/lime/lime_tabular.py @@ -0,0 +1,730 @@ +""" +Functions for explaining classifiers that use tabular data (matrices). +""" +import collections +import copy +from functools import partial +import json +import warnings + +import numpy as np +import scipy as sp +import sklearn +import sklearn.preprocessing +from sklearn.utils import check_random_state +from pyDOE2 import lhs +from scipy.stats.distributions import norm + +from lime.discretize import QuartileDiscretizer +from lime.discretize import DecileDiscretizer +from lime.discretize import EntropyDiscretizer +from lime.discretize import BaseDiscretizer +from lime.discretize import StatsDiscretizer +from . import explanation +from . import lime_base + + +class TableDomainMapper(explanation.DomainMapper): + """Maps feature ids to names, generates table views, etc""" + + def __init__(self, feature_names, feature_values, scaled_row, + categorical_features, discretized_feature_names=None, + feature_indexes=None): + """Init. + + Args: + feature_names: list of feature names, in order + feature_values: list of strings with the values of the original row + scaled_row: scaled row + categorical_features: list of categorical features ids (ints) + feature_indexes: optional feature indexes used in the sparse case + """ + self.exp_feature_names = feature_names + self.discretized_feature_names = discretized_feature_names + self.feature_names = feature_names + self.feature_values = feature_values + self.feature_indexes = feature_indexes + self.scaled_row = scaled_row + if sp.sparse.issparse(scaled_row): + self.all_categorical = False + else: + self.all_categorical = len(categorical_features) == len(scaled_row) + self.categorical_features = categorical_features + + def map_exp_ids(self, exp): + """Maps ids to feature names. + + Args: + exp: list of tuples [(id, weight), (id,weight)] + + Returns: + list of tuples (feature_name, weight) + """ + names = self.exp_feature_names + if self.discretized_feature_names is not None: + names = self.discretized_feature_names + return [(names[x[0]], x[1]) for x in exp] + + def visualize_instance_html(self, + exp, + label, + div_name, + exp_object_name, + show_table=True, + show_all=False): + """Shows the current example in a table format. + + Args: + exp: list of tuples [(id, weight), (id,weight)] + label: label id (integer) + div_name: name of div object to be used for rendering(in js) + exp_object_name: name of js explanation object + show_table: if False, don't show table visualization. + show_all: if True, show zero-weighted features in the table. + """ + if not show_table: + return '' + weights = [0] * len(self.feature_names) + for x in exp: + weights[x[0]] = x[1] + if self.feature_indexes is not None: + # Sparse case: only display the non-zero values and importances + fnames = [self.exp_feature_names[i] for i in self.feature_indexes] + fweights = [weights[i] for i in self.feature_indexes] + if show_all: + out_list = list(zip(fnames, + self.feature_values, + fweights)) + else: + out_dict = dict(map(lambda x: (x[0], (x[1], x[2], x[3])), + zip(self.feature_indexes, + fnames, + self.feature_values, + fweights))) + out_list = [out_dict.get(x[0], (str(x[0]), 0.0, 0.0)) for x in exp] + else: + out_list = list(zip(self.exp_feature_names, + self.feature_values, + weights)) + if not show_all: + out_list = [out_list[x[0]] for x in exp] + ret = u''' + %s.show_raw_tabular(%s, %d, %s); + ''' % (exp_object_name, json.dumps(out_list, ensure_ascii=False), label, div_name) + return ret + + +class LimeTabularExplainer(object): + """Explains predictions on tabular (i.e. matrix) data. + For numerical features, perturb them by sampling from a Normal(0,1) and + doing the inverse operation of mean-centering and scaling, according to the + means and stds in the training data. For categorical features, perturb by + sampling according to the training distribution, and making a binary + feature that is 1 when the value is the same as the instance being + explained.""" + + def __init__(self, + training_data, + mode="classification", + training_labels=None, + feature_names=None, + categorical_features=None, + categorical_names=None, + kernel_width=None, + kernel=None, + verbose=False, + class_names=None, + feature_selection='auto', + discretize_continuous=True, + discretizer='quartile', + sample_around_instance=False, + random_state=None, + training_data_stats=None): + """Init function. + + Args: + training_data: numpy 2d array + mode: "classification" or "regression" + training_labels: labels for training data. Not required, but may be + used by discretizer. + feature_names: list of names (strings) corresponding to the columns + in the training data. + categorical_features: list of indices (ints) corresponding to the + categorical columns. Everything else will be considered + continuous. Values in these columns MUST be integers. + categorical_names: map from int to list of names, where + categorical_names[x][y] represents the name of the yth value of + column x. + kernel_width: kernel width for the exponential kernel. + If None, defaults to sqrt (number of columns) * 0.75 + kernel: similarity kernel that takes euclidean distances and kernel + width as input and outputs weights in (0,1). If None, defaults to + an exponential kernel. + verbose: if true, print local prediction values from linear model + class_names: list of class names, ordered according to whatever the + classifier is using. If not present, class names will be '0', + '1', ... + feature_selection: feature selection method. can be + 'forward_selection', 'lasso_path', 'none' or 'auto'. + See function 'explain_instance_with_data' in lime_base.py for + details on what each of the options does. + discretize_continuous: if True, all non-categorical features will + be discretized into quartiles. + discretizer: only matters if discretize_continuous is True + and data is not sparse. Options are 'quartile', 'decile', + 'entropy' or a BaseDiscretizer instance. + sample_around_instance: if True, will sample continuous features + in perturbed samples from a normal centered at the instance + being explained. Otherwise, the normal is centered on the mean + of the feature data. + random_state: an integer or numpy.RandomState that will be used to + generate random numbers. If None, the random state will be + initialized using the internal numpy seed. + training_data_stats: a dict object having the details of training data + statistics. If None, training data information will be used, only matters + if discretize_continuous is True. Must have the following keys: + means", "mins", "maxs", "stds", "feature_values", + "feature_frequencies" + """ + self.random_state = check_random_state(random_state) + self.mode = mode + self.categorical_names = categorical_names or {} + self.sample_around_instance = sample_around_instance + self.training_data_stats = training_data_stats + + # Check and raise proper error in stats are supplied in non-descritized path + if self.training_data_stats: + self.validate_training_data_stats(self.training_data_stats) + + if categorical_features is None: + categorical_features = [] + if feature_names is None: + feature_names = [str(i) for i in range(training_data.shape[1])] + + self.categorical_features = list(categorical_features) + self.feature_names = list(feature_names) + + self.discretizer = None + if discretize_continuous and not sp.sparse.issparse(training_data): + # Set the discretizer if training data stats are provided + if self.training_data_stats: + discretizer = StatsDiscretizer( + training_data, self.categorical_features, + self.feature_names, labels=training_labels, + data_stats=self.training_data_stats, + random_state=self.random_state) + + if discretizer == 'quartile': + self.discretizer = QuartileDiscretizer( + training_data, self.categorical_features, + self.feature_names, labels=training_labels, + random_state=self.random_state) + elif discretizer == 'decile': + self.discretizer = DecileDiscretizer( + training_data, self.categorical_features, + self.feature_names, labels=training_labels, + random_state=self.random_state) + elif discretizer == 'entropy': + self.discretizer = EntropyDiscretizer( + training_data, self.categorical_features, + self.feature_names, labels=training_labels, + random_state=self.random_state) + elif isinstance(discretizer, BaseDiscretizer): + self.discretizer = discretizer + else: + raise ValueError('''Discretizer must be 'quartile',''' + + ''' 'decile', 'entropy' or a''' + + ''' BaseDiscretizer instance''') + self.categorical_features = list(range(training_data.shape[1])) + + # Get the discretized_training_data when the stats are not provided + if(self.training_data_stats is None): + discretized_training_data = self.discretizer.discretize( + training_data) + + if kernel_width is None: + kernel_width = np.sqrt(training_data.shape[1]) * .75 + kernel_width = float(kernel_width) + + if kernel is None: + def kernel(d, kernel_width): + return np.sqrt(np.exp(-(d ** 2) / kernel_width ** 2)) + + kernel_fn = partial(kernel, kernel_width=kernel_width) + + self.feature_selection = feature_selection + self.base = lime_base.LimeBase(kernel_fn, verbose, random_state=self.random_state) + self.class_names = class_names + + # Though set has no role to play if training data stats are provided + self.scaler = sklearn.preprocessing.StandardScaler(with_mean=False) + self.scaler.fit(training_data) + self.feature_values = {} + self.feature_frequencies = {} + + for feature in self.categorical_features: + if training_data_stats is None: + if self.discretizer is not None: + column = discretized_training_data[:, feature] + else: + column = training_data[:, feature] + + feature_count = collections.Counter(column) + values, frequencies = map(list, zip(*(sorted(feature_count.items())))) + else: + values = training_data_stats["feature_values"][feature] + frequencies = training_data_stats["feature_frequencies"][feature] + + self.feature_values[feature] = values + self.feature_frequencies[feature] = (np.array(frequencies) / + float(sum(frequencies))) + self.scaler.mean_[feature] = 0 + self.scaler.scale_[feature] = 1 + + @staticmethod + def convert_and_round(values): + return ['%.2f' % v for v in values] + + @staticmethod + def validate_training_data_stats(training_data_stats): + """ + Method to validate the structure of training data stats + """ + stat_keys = list(training_data_stats.keys()) + valid_stat_keys = ["means", "mins", "maxs", "stds", "feature_values", "feature_frequencies"] + missing_keys = list(set(valid_stat_keys) - set(stat_keys)) + if len(missing_keys) > 0: + raise Exception("Missing keys in training_data_stats. Details: %s" % (missing_keys)) + + def explain_instance(self, + data_row, + predict_fn, + labels=(1,), + top_labels=None, + num_features=10, + num_samples=5000, + distance_metric='euclidean', + model_regressor=None, + sampling_method='gaussian'): + """Generates explanations for a prediction. + + First, we generate neighborhood data by randomly perturbing features + from the instance (see __data_inverse). We then learn locally weighted + linear models on this neighborhood data to explain each of the classes + in an interpretable way (see lime_base.py). + + Args: + data_row: 1d numpy array or scipy.sparse matrix, corresponding to a row + predict_fn: prediction function. For classifiers, this should be a + function that takes a numpy array and outputs prediction + probabilities. For regressors, this takes a numpy array and + returns the predictions. For ScikitClassifiers, this is + `classifier.predict_proba()`. For ScikitRegressors, this + is `regressor.predict()`. The prediction function needs to work + on multiple feature vectors (the vectors randomly perturbed + from the data_row). + labels: iterable with labels to be explained. + top_labels: if not None, ignore labels and produce explanations for + the K labels with highest prediction probabilities, where K is + this parameter. + num_features: maximum number of features present in explanation + num_samples: size of the neighborhood to learn the linear model + distance_metric: the distance metric to use for weights. + model_regressor: sklearn regressor to use in explanation. Defaults + to Ridge regression in LimeBase. Must have model_regressor.coef_ + and 'sample_weight' as a parameter to model_regressor.fit() + sampling_method: Method to sample synthetic data. Defaults to Gaussian + sampling. Can also use Latin Hypercube Sampling. + + Returns: + An Explanation object (see explanation.py) with the corresponding + explanations. + """ + if sp.sparse.issparse(data_row) and not sp.sparse.isspmatrix_csr(data_row): + # Preventative code: if sparse, convert to csr format if not in csr format already + data_row = data_row.tocsr() + data, inverse = self.__data_inverse(data_row, num_samples, sampling_method) + if sp.sparse.issparse(data): + # Note in sparse case we don't subtract mean since data would become dense + scaled_data = data.multiply(self.scaler.scale_) + # Multiplying with csr matrix can return a coo sparse matrix + if not sp.sparse.isspmatrix_csr(scaled_data): + scaled_data = scaled_data.tocsr() + else: + scaled_data = (data - self.scaler.mean_) / self.scaler.scale_ + distances = sklearn.metrics.pairwise_distances( + scaled_data, + scaled_data[0].reshape(1, -1), + metric=distance_metric + ).ravel() + + yss = predict_fn(inverse) + + # for classification, the model needs to provide a list of tuples - classes + # along with prediction probabilities + if self.mode == "classification": + if len(yss.shape) == 1: + raise NotImplementedError("LIME does not currently support " + "classifier models without probability " + "scores. If this conflicts with your " + "use case, please let us know: " + "https://github.com/datascienceinc/lime/issues/16") + elif len(yss.shape) == 2: + if self.class_names is None: + self.class_names = [str(x) for x in range(yss[0].shape[0])] + else: + self.class_names = list(self.class_names) + if not np.allclose(yss.sum(axis=1), 1.0): + warnings.warn(""" + Prediction probabilties do not sum to 1, and + thus does not constitute a probability space. + Check that you classifier outputs probabilities + (Not log probabilities, or actual class predictions). + """) + else: + raise ValueError("Your model outputs " + "arrays with {} dimensions".format(len(yss.shape))) + + # for regression, the output should be a one-dimensional array of predictions + else: + try: + if len(yss.shape) != 1 and len(yss[0].shape) == 1: + yss = np.array([v[0] for v in yss]) + assert isinstance(yss, np.ndarray) and len(yss.shape) == 1 + except AssertionError: + raise ValueError("Your model needs to output single-dimensional \ + numpyarrays, not arrays of {} dimensions".format(yss.shape)) + + predicted_value = yss[0] + min_y = min(yss) + max_y = max(yss) + + # add a dimension to be compatible with downstream machinery + yss = yss[:, np.newaxis] + + feature_names = copy.deepcopy(self.feature_names) + if feature_names is None: + feature_names = [str(x) for x in range(data_row.shape[0])] + + if sp.sparse.issparse(data_row): + values = self.convert_and_round(data_row.data) + feature_indexes = data_row.indices + else: + values = self.convert_and_round(data_row) + feature_indexes = None + + for i in self.categorical_features: + if self.discretizer is not None and i in self.discretizer.lambdas: + continue + name = int(data_row[i]) + if i in self.categorical_names: + name = self.categorical_names[i][name] + feature_names[i] = '%s=%s' % (feature_names[i], name) + values[i] = 'True' + categorical_features = self.categorical_features + + discretized_feature_names = None + if self.discretizer is not None: + categorical_features = range(data.shape[1]) + discretized_instance = self.discretizer.discretize(data_row) + discretized_feature_names = copy.deepcopy(feature_names) + for f in self.discretizer.names: + discretized_feature_names[f] = self.discretizer.names[f][int( + discretized_instance[f])] + + domain_mapper = TableDomainMapper(feature_names, + values, + scaled_data[0], + categorical_features=categorical_features, + discretized_feature_names=discretized_feature_names, + feature_indexes=feature_indexes) + ret_exp = explanation.Explanation(domain_mapper, + mode=self.mode, + class_names=self.class_names) + if self.mode == "classification": + ret_exp.predict_proba = yss[0] + if top_labels: + labels = np.argsort(yss[0])[-top_labels:] + ret_exp.top_labels = list(labels) + ret_exp.top_labels.reverse() + else: + ret_exp.predicted_value = predicted_value + ret_exp.min_value = min_y + ret_exp.max_value = max_y + labels = [0] + for label in labels: + (ret_exp.intercept[label], + ret_exp.local_exp[label], + ret_exp.score[label], + ret_exp.local_pred[label]) = self.base.explain_instance_with_data( + scaled_data, + yss, + distances, + label, + num_features, + model_regressor=model_regressor, + feature_selection=self.feature_selection) + + if self.mode == "regression": + ret_exp.intercept[1] = ret_exp.intercept[0] + ret_exp.local_exp[1] = [x for x in ret_exp.local_exp[0]] + ret_exp.local_exp[0] = [(i, -1 * j) for i, j in ret_exp.local_exp[1]] + + return ret_exp + + def __data_inverse(self, + data_row, + num_samples, + sampling_method): + """Generates a neighborhood around a prediction. + + For numerical features, perturb them by sampling from a Normal(0,1) and + doing the inverse operation of mean-centering and scaling, according to + the means and stds in the training data. For categorical features, + perturb by sampling according to the training distribution, and making + a binary feature that is 1 when the value is the same as the instance + being explained. + + Args: + data_row: 1d numpy array, corresponding to a row + num_samples: size of the neighborhood to learn the linear model + sampling_method: 'gaussian' or 'lhs' + + Returns: + A tuple (data, inverse), where: + data: dense num_samples * K matrix, where categorical features + are encoded with either 0 (not equal to the corresponding value + in data_row) or 1. The first row is the original instance. + inverse: same as data, except the categorical features are not + binary, but categorical (as the original data) + """ + is_sparse = sp.sparse.issparse(data_row) + if is_sparse: + num_cols = data_row.shape[1] + data = sp.sparse.csr_matrix((num_samples, num_cols), dtype=data_row.dtype) + else: + num_cols = data_row.shape[0] + data = np.zeros((num_samples, num_cols)) + categorical_features = range(num_cols) + if self.discretizer is None: + instance_sample = data_row + scale = self.scaler.scale_ + mean = self.scaler.mean_ + if is_sparse: + # Perturb only the non-zero values + non_zero_indexes = data_row.nonzero()[1] + num_cols = len(non_zero_indexes) + instance_sample = data_row[:, non_zero_indexes] + scale = scale[non_zero_indexes] + mean = mean[non_zero_indexes] + + if sampling_method == 'gaussian': + data = self.random_state.normal(0, 1, num_samples * num_cols + ).reshape(num_samples, num_cols) + data = np.array(data) + elif sampling_method == 'lhs': + data = lhs(num_cols, samples=num_samples + ).reshape(num_samples, num_cols) + means = np.zeros(num_cols) + stdvs = np.array([1]*num_cols) + for i in range(num_cols): + data[:, i] = norm(loc=means[i], scale=stdvs[i]).ppf(data[:, i]) + data = np.array(data) + else: + warnings.warn('''Invalid input for sampling_method. + Defaulting to Gaussian sampling.''', UserWarning) + data = self.random_state.normal(0, 1, num_samples * num_cols + ).reshape(num_samples, num_cols) + data = np.array(data) + + if self.sample_around_instance: + data = data * scale + instance_sample + else: + data = data * scale + mean + if is_sparse: + if num_cols == 0: + data = sp.sparse.csr_matrix((num_samples, + data_row.shape[1]), + dtype=data_row.dtype) + else: + indexes = np.tile(non_zero_indexes, num_samples) + indptr = np.array( + range(0, len(non_zero_indexes) * (num_samples + 1), + len(non_zero_indexes))) + data_1d_shape = data.shape[0] * data.shape[1] + data_1d = data.reshape(data_1d_shape) + data = sp.sparse.csr_matrix( + (data_1d, indexes, indptr), + shape=(num_samples, data_row.shape[1])) + categorical_features = self.categorical_features + first_row = data_row + else: + first_row = self.discretizer.discretize(data_row) + data[0] = data_row.copy() + inverse = data.copy() + for column in categorical_features: + values = self.feature_values[column] + freqs = self.feature_frequencies[column] + inverse_column = self.random_state.choice(values, size=num_samples, + replace=True, p=freqs) + binary_column = (inverse_column == first_row[column]).astype(int) + binary_column[0] = 1 + inverse_column[0] = data[0, column] + data[:, column] = binary_column + inverse[:, column] = inverse_column + if self.discretizer is not None: + inverse[1:] = self.discretizer.undiscretize(inverse[1:]) + inverse[0] = data_row + return data, inverse + + +class RecurrentTabularExplainer(LimeTabularExplainer): + """ + An explainer for keras-style recurrent neural networks, where the + input shape is (n_samples, n_timesteps, n_features). This class + just extends the LimeTabularExplainer class and reshapes the training + data and feature names such that they become something like + + (val1_t1, val1_t2, val1_t3, ..., val2_t1, ..., valn_tn) + + Each of the methods that take data reshape it appropriately, + so you can pass in the training/testing data exactly as you + would to the recurrent neural network. + + """ + + def __init__(self, training_data, mode="classification", + training_labels=None, feature_names=None, + categorical_features=None, categorical_names=None, + kernel_width=None, kernel=None, verbose=False, class_names=None, + feature_selection='auto', discretize_continuous=True, + discretizer='quartile', random_state=None): + """ + Args: + training_data: numpy 3d array with shape + (n_samples, n_timesteps, n_features) + mode: "classification" or "regression" + training_labels: labels for training data. Not required, but may be + used by discretizer. + feature_names: list of names (strings) corresponding to the columns + in the training data. + categorical_features: list of indices (ints) corresponding to the + categorical columns. Everything else will be considered + continuous. Values in these columns MUST be integers. + categorical_names: map from int to list of names, where + categorical_names[x][y] represents the name of the yth value of + column x. + kernel_width: kernel width for the exponential kernel. + If None, defaults to sqrt(number of columns) * 0.75 + kernel: similarity kernel that takes euclidean distances and kernel + width as input and outputs weights in (0,1). If None, defaults to + an exponential kernel. + verbose: if true, print local prediction values from linear model + class_names: list of class names, ordered according to whatever the + classifier is using. If not present, class names will be '0', + '1', ... + feature_selection: feature selection method. can be + 'forward_selection', 'lasso_path', 'none' or 'auto'. + See function 'explain_instance_with_data' in lime_base.py for + details on what each of the options does. + discretize_continuous: if True, all non-categorical features will + be discretized into quartiles. + discretizer: only matters if discretize_continuous is True. Options + are 'quartile', 'decile', 'entropy' or a BaseDiscretizer + instance. + random_state: an integer or numpy.RandomState that will be used to + generate random numbers. If None, the random state will be + initialized using the internal numpy seed. + """ + + # Reshape X + n_samples, n_timesteps, n_features = training_data.shape + training_data = np.transpose(training_data, axes=(0, 2, 1)).reshape( + n_samples, n_timesteps * n_features) + self.n_timesteps = n_timesteps + self.n_features = n_features + if feature_names is None: + feature_names = ['feature%d' % i for i in range(n_features)] + + # Update the feature names + feature_names = ['{}_t-{}'.format(n, n_timesteps - (i + 1)) + for n in feature_names for i in range(n_timesteps)] + + # Send off the the super class to do its magic. + super(RecurrentTabularExplainer, self).__init__( + training_data, + mode=mode, + training_labels=training_labels, + feature_names=feature_names, + categorical_features=categorical_features, + categorical_names=categorical_names, + kernel_width=kernel_width, + kernel=kernel, + verbose=verbose, + class_names=class_names, + feature_selection=feature_selection, + discretize_continuous=discretize_continuous, + discretizer=discretizer, + random_state=random_state) + + def _make_predict_proba(self, func): + """ + The predict_proba method will expect 3d arrays, but we are reshaping + them to 2D so that LIME works correctly. This wraps the function + you give in explain_instance to first reshape the data to have + the shape the the keras-style network expects. + """ + + def predict_proba(X): + n_samples = X.shape[0] + new_shape = (n_samples, self.n_features, self.n_timesteps) + X = np.transpose(X.reshape(new_shape), axes=(0, 2, 1)) + return func(X) + + return predict_proba + + def explain_instance(self, data_row, classifier_fn, labels=(1,), + top_labels=None, num_features=10, num_samples=5000, + distance_metric='euclidean', model_regressor=None): + """Generates explanations for a prediction. + + First, we generate neighborhood data by randomly perturbing features + from the instance (see __data_inverse). We then learn locally weighted + linear models on this neighborhood data to explain each of the classes + in an interpretable way (see lime_base.py). + + Args: + data_row: 2d numpy array, corresponding to a row + classifier_fn: classifier prediction probability function, which + takes a numpy array and outputs prediction probabilities. For + ScikitClassifiers , this is classifier.predict_proba. + labels: iterable with labels to be explained. + top_labels: if not None, ignore labels and produce explanations for + the K labels with highest prediction probabilities, where K is + this parameter. + num_features: maximum number of features present in explanation + num_samples: size of the neighborhood to learn the linear model + distance_metric: the distance metric to use for weights. + model_regressor: sklearn regressor to use in explanation. Defaults + to Ridge regression in LimeBase. Must have + model_regressor.coef_ and 'sample_weight' as a parameter + to model_regressor.fit() + + Returns: + An Explanation object (see explanation.py) with the corresponding + explanations. + """ + + # Flatten input so that the normal explainer can handle it + data_row = data_row.T.reshape(self.n_timesteps * self.n_features) + + # Wrap the classifier to reshape input + classifier_fn = self._make_predict_proba(classifier_fn) + return super(RecurrentTabularExplainer, self).explain_instance( + data_row, classifier_fn, + labels=labels, + top_labels=top_labels, + num_features=num_features, + num_samples=num_samples, + distance_metric=distance_metric, + model_regressor=model_regressor) diff --git a/build/lib/lime/lime_text.py b/build/lib/lime/lime_text.py new file mode 100644 index 000000000..7055a9b59 --- /dev/null +++ b/build/lib/lime/lime_text.py @@ -0,0 +1,485 @@ +""" +Functions for explaining text classifiers. +""" +from functools import partial +import itertools +import json +import re + +import numpy as np +import scipy as sp +import sklearn +from sklearn.utils import check_random_state + +from . import explanation +from . import lime_base + + +class TextDomainMapper(explanation.DomainMapper): + """Maps feature ids to words or word-positions""" + + def __init__(self, indexed_string): + """Initializer. + + Args: + indexed_string: lime_text.IndexedString, original string + """ + self.indexed_string = indexed_string + + def map_exp_ids(self, exp, positions=False): + """Maps ids to words or word-position strings. + + Args: + exp: list of tuples [(id, weight), (id,weight)] + positions: if True, also return word positions + + Returns: + list of tuples (word, weight), or (word_positions, weight) if + examples: ('bad', 1) or ('bad_3-6-12', 1) + """ + if positions: + exp = [('%s_%s' % ( + self.indexed_string.word(x[0]), + '-'.join( + map(str, + self.indexed_string.string_position(x[0])))), x[1]) + for x in exp] + else: + exp = [(self.indexed_string.word(x[0]), x[1]) for x in exp] + return exp + + def visualize_instance_html(self, exp, label, div_name, exp_object_name, + text=True, opacity=True): + """Adds text with highlighted words to visualization. + + Args: + exp: list of tuples [(id, weight), (id,weight)] + label: label id (integer) + div_name: name of div object to be used for rendering(in js) + exp_object_name: name of js explanation object + text: if False, return empty + opacity: if True, fade colors according to weight + """ + if not text: + return u'' + text = (self.indexed_string.raw_string() + .encode('utf-8', 'xmlcharrefreplace').decode('utf-8')) + text = re.sub(r'[<>&]', '|', text) + exp = [(self.indexed_string.word(x[0]), + self.indexed_string.string_position(x[0]), + x[1]) for x in exp] + all_occurrences = list(itertools.chain.from_iterable( + [itertools.product([x[0]], x[1], [x[2]]) for x in exp])) + all_occurrences = [(x[0], int(x[1]), x[2]) for x in all_occurrences] + ret = ''' + %s.show_raw_text(%s, %d, %s, %s, %s); + ''' % (exp_object_name, json.dumps(all_occurrences), label, + json.dumps(text), div_name, json.dumps(opacity)) + return ret + + +class IndexedString(object): + """String with various indexes.""" + + def __init__(self, raw_string, split_expression=r'\W+', bow=True, + mask_string=None): + """Initializer. + + Args: + raw_string: string with raw text in it + split_expression: Regex string or callable. If regex string, will be used with re.split. + If callable, the function should return a list of tokens. + bow: if True, a word is the same everywhere in the text - i.e. we + will index multiple occurrences of the same word. If False, + order matters, so that the same word will have different ids + according to position. + mask_string: If not None, replace words with this if bow=False + if None, default value is UNKWORDZ + """ + self.raw = raw_string + self.mask_string = 'UNKWORDZ' if mask_string is None else mask_string + + if callable(split_expression): + tokens = split_expression(self.raw) + self.as_list = self._segment_with_tokens(self.raw, tokens) + tokens = set(tokens) + + def non_word(string): + return string not in tokens + + else: + # with the split_expression as a non-capturing group (?:), we don't need to filter out + # the separator character from the split results. + splitter = re.compile(r'(%s)|$' % split_expression) + self.as_list = [s for s in splitter.split(self.raw) if s] + non_word = splitter.match + + self.as_np = np.array(self.as_list) + self.string_start = np.hstack( + ([0], np.cumsum([len(x) for x in self.as_np[:-1]]))) + vocab = {} + self.inverse_vocab = [] + self.positions = [] + self.bow = bow + non_vocab = set() + for i, word in enumerate(self.as_np): + if word in non_vocab: + continue + if non_word(word): + non_vocab.add(word) + continue + if bow: + if word not in vocab: + vocab[word] = len(vocab) + self.inverse_vocab.append(word) + self.positions.append([]) + idx_word = vocab[word] + self.positions[idx_word].append(i) + else: + self.inverse_vocab.append(word) + self.positions.append(i) + if not bow: + self.positions = np.array(self.positions) + + def raw_string(self): + """Returns the original raw string""" + return self.raw + + def num_words(self): + """Returns the number of tokens in the vocabulary for this document.""" + return len(self.inverse_vocab) + + def word(self, id_): + """Returns the word that corresponds to id_ (int)""" + return self.inverse_vocab[id_] + + def string_position(self, id_): + """Returns a np array with indices to id_ (int) occurrences""" + if self.bow: + return self.string_start[self.positions[id_]] + else: + return self.string_start[[self.positions[id_]]] + + def inverse_removing(self, words_to_remove): + """Returns a string after removing the appropriate words. + + If self.bow is false, replaces word with UNKWORDZ instead of removing + it. + + Args: + words_to_remove: list of ids (ints) to remove + + Returns: + original raw string with appropriate words removed. + """ + mask = np.ones(self.as_np.shape[0], dtype='bool') + mask[self.__get_idxs(words_to_remove)] = False + if not self.bow: + return ''.join( + [self.as_list[i] if mask[i] else self.mask_string + for i in range(mask.shape[0])]) + return ''.join([self.as_list[v] for v in mask.nonzero()[0]]) + + @staticmethod + def _segment_with_tokens(text, tokens): + """Segment a string around the tokens created by a passed-in tokenizer""" + list_form = [] + text_ptr = 0 + for token in tokens: + inter_token_string = [] + while not text[text_ptr:].startswith(token): + inter_token_string.append(text[text_ptr]) + text_ptr += 1 + if text_ptr >= len(text): + raise ValueError("Tokenization produced tokens that do not belong in string!") + text_ptr += len(token) + if inter_token_string: + list_form.append(''.join(inter_token_string)) + list_form.append(token) + if text_ptr < len(text): + list_form.append(text[text_ptr:]) + return list_form + + def __get_idxs(self, words): + """Returns indexes to appropriate words.""" + if self.bow: + return list(itertools.chain.from_iterable( + [self.positions[z] for z in words])) + else: + return self.positions[words] + + +class IndexedCharacters(object): + """String with various indexes.""" + + def __init__(self, raw_string, bow=True, mask_string=None): + """Initializer. + + Args: + raw_string: string with raw text in it + bow: if True, a char is the same everywhere in the text - i.e. we + will index multiple occurrences of the same character. If False, + order matters, so that the same word will have different ids + according to position. + mask_string: If not None, replace characters with this if bow=False + if None, default value is chr(0) + """ + self.raw = raw_string + self.as_list = list(self.raw) + self.as_np = np.array(self.as_list) + self.mask_string = chr(0) if mask_string is None else mask_string + self.string_start = np.arange(len(self.raw)) + vocab = {} + self.inverse_vocab = [] + self.positions = [] + self.bow = bow + non_vocab = set() + for i, char in enumerate(self.as_np): + if char in non_vocab: + continue + if bow: + if char not in vocab: + vocab[char] = len(vocab) + self.inverse_vocab.append(char) + self.positions.append([]) + idx_char = vocab[char] + self.positions[idx_char].append(i) + else: + self.inverse_vocab.append(char) + self.positions.append(i) + if not bow: + self.positions = np.array(self.positions) + + def raw_string(self): + """Returns the original raw string""" + return self.raw + + def num_words(self): + """Returns the number of tokens in the vocabulary for this document.""" + return len(self.inverse_vocab) + + def word(self, id_): + """Returns the word that corresponds to id_ (int)""" + return self.inverse_vocab[id_] + + def string_position(self, id_): + """Returns a np array with indices to id_ (int) occurrences""" + if self.bow: + return self.string_start[self.positions[id_]] + else: + return self.string_start[[self.positions[id_]]] + + def inverse_removing(self, words_to_remove): + """Returns a string after removing the appropriate words. + + If self.bow is false, replaces word with UNKWORDZ instead of removing + it. + + Args: + words_to_remove: list of ids (ints) to remove + + Returns: + original raw string with appropriate words removed. + """ + mask = np.ones(self.as_np.shape[0], dtype='bool') + mask[self.__get_idxs(words_to_remove)] = False + if not self.bow: + return ''.join( + [self.as_list[i] if mask[i] else self.mask_string + for i in range(mask.shape[0])]) + return ''.join([self.as_list[v] for v in mask.nonzero()[0]]) + + def __get_idxs(self, words): + """Returns indexes to appropriate words.""" + if self.bow: + return list(itertools.chain.from_iterable( + [self.positions[z] for z in words])) + else: + return self.positions[words] + + +class LimeTextExplainer(object): + """Explains text classifiers. + Currently, we are using an exponential kernel on cosine distance, and + restricting explanations to words that are present in documents.""" + + def __init__(self, + kernel_width=25, + kernel=None, + verbose=False, + class_names=None, + feature_selection='auto', + split_expression=r'\W+', + bow=True, + mask_string=None, + random_state=None, + char_level=False): + """Init function. + + Args: + kernel_width: kernel width for the exponential kernel. + kernel: similarity kernel that takes euclidean distances and kernel + width as input and outputs weights in (0,1). If None, defaults to + an exponential kernel. + verbose: if true, print local prediction values from linear model + class_names: list of class names, ordered according to whatever the + classifier is using. If not present, class names will be '0', + '1', ... + feature_selection: feature selection method. can be + 'forward_selection', 'lasso_path', 'none' or 'auto'. + See function 'explain_instance_with_data' in lime_base.py for + details on what each of the options does. + split_expression: Regex string or callable. If regex string, will be used with re.split. + If callable, the function should return a list of tokens. + bow: if True (bag of words), will perturb input data by removing + all occurrences of individual words or characters. + Explanations will be in terms of these words. Otherwise, will + explain in terms of word-positions, so that a word may be + important the first time it appears and unimportant the second. + Only set to false if the classifier uses word order in some way + (bigrams, etc), or if you set char_level=True. + mask_string: String used to mask tokens or characters if bow=False + if None, will be 'UNKWORDZ' if char_level=False, chr(0) + otherwise. + random_state: an integer or numpy.RandomState that will be used to + generate random numbers. If None, the random state will be + initialized using the internal numpy seed. + char_level: an boolean identifying that we treat each character + as an independent occurence in the string + """ + + if kernel is None: + def kernel(d, kernel_width): + return np.sqrt(np.exp(-(d ** 2) / kernel_width ** 2)) + + kernel_fn = partial(kernel, kernel_width=kernel_width) + + self.random_state = check_random_state(random_state) + self.base = lime_base.LimeBase(kernel_fn, verbose, + random_state=self.random_state) + self.class_names = class_names + self.vocabulary = None + self.feature_selection = feature_selection + self.bow = bow + self.mask_string = mask_string + self.split_expression = split_expression + self.char_level = char_level + + def explain_instance(self, + text_instance, + classifier_fn, + labels=(1,), + top_labels=None, + num_features=10, + num_samples=5000, + distance_metric='cosine', + model_regressor=None): + """Generates explanations for a prediction. + + First, we generate neighborhood data by randomly hiding features from + the instance (see __data_labels_distance_mapping). We then learn + locally weighted linear models on this neighborhood data to explain + each of the classes in an interpretable way (see lime_base.py). + + Args: + text_instance: raw text string to be explained. + classifier_fn: classifier prediction probability function, which + takes a list of d strings and outputs a (d, k) numpy array with + prediction probabilities, where k is the number of classes. + For ScikitClassifiers , this is classifier.predict_proba. + labels: iterable with labels to be explained. + top_labels: if not None, ignore labels and produce explanations for + the K labels with highest prediction probabilities, where K is + this parameter. + num_features: maximum number of features present in explanation + num_samples: size of the neighborhood to learn the linear model + distance_metric: the distance metric to use for sample weighting, + defaults to cosine similarity + model_regressor: sklearn regressor to use in explanation. Defaults + to Ridge regression in LimeBase. Must have model_regressor.coef_ + and 'sample_weight' as a parameter to model_regressor.fit() + Returns: + An Explanation object (see explanation.py) with the corresponding + explanations. + """ + + indexed_string = (IndexedCharacters( + text_instance, bow=self.bow, mask_string=self.mask_string) + if self.char_level else + IndexedString(text_instance, bow=self.bow, + split_expression=self.split_expression, + mask_string=self.mask_string)) + domain_mapper = TextDomainMapper(indexed_string) + data, yss, distances = self.__data_labels_distances( + indexed_string, classifier_fn, num_samples, + distance_metric=distance_metric) + if self.class_names is None: + self.class_names = [str(x) for x in range(yss[0].shape[0])] + ret_exp = explanation.Explanation(domain_mapper=domain_mapper, + class_names=self.class_names, + random_state=self.random_state) + ret_exp.predict_proba = yss[0] + if top_labels: + labels = np.argsort(yss[0])[-top_labels:] + ret_exp.top_labels = list(labels) + ret_exp.top_labels.reverse() + for label in labels: + (ret_exp.intercept[label], + ret_exp.local_exp[label], + ret_exp.score[label], + ret_exp.local_pred[label]) = self.base.explain_instance_with_data( + data, yss, distances, label, num_features, + model_regressor=model_regressor, + feature_selection=self.feature_selection) + return ret_exp + + def __data_labels_distances(self, + indexed_string, + classifier_fn, + num_samples, + distance_metric='cosine'): + """Generates a neighborhood around a prediction. + + Generates neighborhood data by randomly removing words from + the instance, and predicting with the classifier. Uses cosine distance + to compute distances between original and perturbed instances. + Args: + indexed_string: document (IndexedString) to be explained, + classifier_fn: classifier prediction probability function, which + takes a string and outputs prediction probabilities. For + ScikitClassifier, this is classifier.predict_proba. + num_samples: size of the neighborhood to learn the linear model + distance_metric: the distance metric to use for sample weighting, + defaults to cosine similarity. + + + Returns: + A tuple (data, labels, distances), where: + data: dense num_samples * K binary matrix, where K is the + number of tokens in indexed_string. The first row is the + original instance, and thus a row of ones. + labels: num_samples * L matrix, where L is the number of target + labels + distances: cosine distance between the original instance and + each perturbed instance (computed in the binary 'data' + matrix), times 100. + """ + + def distance_fn(x): + return sklearn.metrics.pairwise.pairwise_distances( + x, x[0], metric=distance_metric).ravel() * 100 + + doc_size = indexed_string.num_words() + sample = self.random_state.randint(1, doc_size + 1, num_samples - 1) + data = np.ones((num_samples, doc_size)) + data[0] = np.ones(doc_size) + features_range = range(doc_size) + inverse_data = [indexed_string.raw_string()] + for i, size in enumerate(sample, start=1): + inactive = self.random_state.choice(features_range, size, + replace=False) + data[i, inactive] = 0 + inverse_data.append(indexed_string.inverse_removing(inactive)) + labels = classifier_fn(inverse_data) + distances = distance_fn(sp.sparse.csr_matrix(data)) + return data, labels, distances diff --git a/build/lib/lime/submodular_pick.py b/build/lib/lime/submodular_pick.py new file mode 100644 index 000000000..b8ae62fd0 --- /dev/null +++ b/build/lib/lime/submodular_pick.py @@ -0,0 +1,128 @@ +import numpy as np +import warnings + + +class SubmodularPick(object): + """Class for submodular pick + + Saves a representative sample of explanation objects using SP-LIME, + as well as saving all generated explanations + + First, a collection of candidate explanations are generated + (see explain_instance). From these candidates, num_exps_desired are + chosen using submodular pick. (see marcotcr et al paper).""" + + def __init__(self, + explainer, + data, + predict_fn, + method='sample', + sample_size=1000, + num_exps_desired=5, + num_features=10, + **kwargs): + + """ + Args: + data: a numpy array where each row is a single input into predict_fn + predict_fn: prediction function. For classifiers, this should be a + function that takes a numpy array and outputs prediction + probabilities. For regressors, this takes a numpy array and + returns the predictions. For ScikitClassifiers, this is + `classifier.predict_proba()`. For ScikitRegressors, this + is `regressor.predict()`. The prediction function needs to work + on multiple feature vectors (the vectors randomly perturbed + from the data_row). + method: The method to use to generate candidate explanations + method == 'sample' will sample the data uniformly at + random. The sample size is given by sample_size. Otherwise + if method == 'full' then explanations will be generated for the + entire data. l + sample_size: The number of instances to explain if method == 'sample' + num_exps_desired: The number of explanation objects returned + num_features: maximum number of features present in explanation + + + Sets value: + sp_explanations: A list of explanation objects that has a high coverage + explanations: All the candidate explanations saved for potential future use. + """ + + top_labels = kwargs.get('top_labels', 1) + if 'top_labels' in kwargs: + del kwargs['top_labels'] + # Parse args + if method == 'sample': + if sample_size > len(data): + warnings.warn("""Requested sample size larger than + size of input data. Using all data""") + sample_size = len(data) + all_indices = np.arange(len(data)) + np.random.shuffle(all_indices) + sample_indices = all_indices[:sample_size] + elif method == 'full': + sample_indices = np.arange(len(data)) + else: + raise ValueError('Method must be \'sample\' or \'full\'') + + # Generate Explanations + self.explanations = [] + for i in sample_indices: + self.explanations.append( + explainer.explain_instance( + data[i], predict_fn, num_features=num_features, + top_labels=top_labels, + **kwargs)) + # Error handling + try: + num_exps_desired = int(num_exps_desired) + except TypeError: + return("Requested number of explanations should be an integer") + if num_exps_desired > len(self.explanations): + warnings.warn("""Requested number of explanations larger than + total number of explanations, returning all + explanations instead.""") + num_exps_desired = min(num_exps_desired, len(self.explanations)) + + # Find all the explanation model features used. Defines the dimension d' + features_dict = {} + feature_iter = 0 + for exp in self.explanations: + labels = exp.available_labels() if exp.mode == 'classification' else [1] + for label in labels: + for feature, _ in exp.as_list(label=label): + if feature not in features_dict.keys(): + features_dict[feature] = (feature_iter) + feature_iter += 1 + d_prime = len(features_dict.keys()) + + # Create the n x d' dimensional 'explanation matrix', W + W = np.zeros((len(self.explanations), d_prime)) + for i, exp in enumerate(self.explanations): + labels = exp.available_labels() if exp.mode == 'classification' else [1] + for label in labels: + for feature, value in exp.as_list(label): + W[i, features_dict[feature]] += value + + # Create the global importance vector, I_j described in the paper + importance = np.sum(abs(W), axis=0)**.5 + + # Now run the SP-LIME greedy algorithm + remaining_indices = set(range(len(self.explanations))) + V = [] + for _ in range(num_exps_desired): + best = 0 + best_ind = None + current = 0 + for i in remaining_indices: + current = np.dot( + (np.sum(abs(W)[V + [i]], axis=0) > 0), importance + ) # coverage function + if current >= best: + best = current + best_ind = i + V.append(best_ind) + remaining_indices -= {best_ind} + + self.sp_explanations = [self.explanations[i] for i in V] + self.V = V diff --git a/build/lib/lime/tests/__init__.py b/build/lib/lime/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/build/lib/lime/tests/test_discretize.py b/build/lib/lime/tests/test_discretize.py new file mode 100644 index 000000000..815f588b1 --- /dev/null +++ b/build/lib/lime/tests/test_discretize.py @@ -0,0 +1,177 @@ +import unittest +from unittest import TestCase + +import numpy as np + +from sklearn.datasets import load_iris + +from lime.discretize import QuartileDiscretizer, DecileDiscretizer, EntropyDiscretizer + + +class TestDiscretize(TestCase): + + def setUp(self): + iris = load_iris() + + self.feature_names = iris.feature_names + self.x = iris.data + self.y = iris.target + + def check_random_state_for_discretizer_class(self, DiscretizerClass): + # ---------------------------------------------------------------------- + # -----------Check if the same random_state produces the same----------- + # -------------results for different discretizer instances.------------- + # ---------------------------------------------------------------------- + discretizer = DiscretizerClass(self.x, [], self.feature_names, self.y, + random_state=10) + x_1 = discretizer.undiscretize(discretizer.discretize(self.x)) + + discretizer = DiscretizerClass(self.x, [], self.feature_names, self.y, + random_state=10) + x_2 = discretizer.undiscretize(discretizer.discretize(self.x)) + + self.assertEqual((x_1 == x_2).sum(), x_1.shape[0] * x_1.shape[1]) + + discretizer = DiscretizerClass(self.x, [], self.feature_names, self.y, + random_state=np.random.RandomState(10)) + x_1 = discretizer.undiscretize(discretizer.discretize(self.x)) + + discretizer = DiscretizerClass(self.x, [], self.feature_names, self.y, + random_state=np.random.RandomState(10)) + x_2 = discretizer.undiscretize(discretizer.discretize(self.x)) + + self.assertEqual((x_1 == x_2).sum(), x_1.shape[0] * x_1.shape[1]) + + # ---------------------------------------------------------------------- + # ---------Check if two different random_state values produces---------- + # -------different results for different discretizers instances.-------- + # ---------------------------------------------------------------------- + discretizer = DiscretizerClass(self.x, [], self.feature_names, self.y, + random_state=10) + x_1 = discretizer.undiscretize(discretizer.discretize(self.x)) + + discretizer = DiscretizerClass(self.x, [], self.feature_names, self.y, + random_state=20) + x_2 = discretizer.undiscretize(discretizer.discretize(self.x)) + + self.assertFalse((x_1 == x_2).sum() == x_1.shape[0] * x_1.shape[1]) + + discretizer = DiscretizerClass(self.x, [], self.feature_names, self.y, + random_state=np.random.RandomState(10)) + x_1 = discretizer.undiscretize(discretizer.discretize(self.x)) + + discretizer = DiscretizerClass(self.x, [], self.feature_names, self.y, + random_state=np.random.RandomState(20)) + x_2 = discretizer.undiscretize(discretizer.discretize(self.x)) + + self.assertFalse((x_1 == x_2).sum() == x_1.shape[0] * x_1.shape[1]) + + def test_random_state(self): + self.check_random_state_for_discretizer_class(QuartileDiscretizer) + + self.check_random_state_for_discretizer_class(DecileDiscretizer) + + self.check_random_state_for_discretizer_class(EntropyDiscretizer) + + def test_feature_names_1(self): + self.maxDiff = None + discretizer = QuartileDiscretizer(self.x, [], self.feature_names, + self.y, random_state=10) + self.assertDictEqual( + {0: ['sepal length (cm) <= 5.10', + '5.10 < sepal length (cm) <= 5.80', + '5.80 < sepal length (cm) <= 6.40', + 'sepal length (cm) > 6.40'], + 1: ['sepal width (cm) <= 2.80', + '2.80 < sepal width (cm) <= 3.00', + '3.00 < sepal width (cm) <= 3.30', + 'sepal width (cm) > 3.30'], + 2: ['petal length (cm) <= 1.60', + '1.60 < petal length (cm) <= 4.35', + '4.35 < petal length (cm) <= 5.10', + 'petal length (cm) > 5.10'], + 3: ['petal width (cm) <= 0.30', + '0.30 < petal width (cm) <= 1.30', + '1.30 < petal width (cm) <= 1.80', + 'petal width (cm) > 1.80']}, + discretizer.names) + + def test_feature_names_2(self): + self.maxDiff = None + discretizer = DecileDiscretizer(self.x, [], self.feature_names, self.y, + random_state=10) + self.assertDictEqual( + {0: ['sepal length (cm) <= 4.80', + '4.80 < sepal length (cm) <= 5.00', + '5.00 < sepal length (cm) <= 5.27', + '5.27 < sepal length (cm) <= 5.60', + '5.60 < sepal length (cm) <= 5.80', + '5.80 < sepal length (cm) <= 6.10', + '6.10 < sepal length (cm) <= 6.30', + '6.30 < sepal length (cm) <= 6.52', + '6.52 < sepal length (cm) <= 6.90', + 'sepal length (cm) > 6.90'], + 1: ['sepal width (cm) <= 2.50', + '2.50 < sepal width (cm) <= 2.70', + '2.70 < sepal width (cm) <= 2.80', + '2.80 < sepal width (cm) <= 3.00', + '3.00 < sepal width (cm) <= 3.10', + '3.10 < sepal width (cm) <= 3.20', + '3.20 < sepal width (cm) <= 3.40', + '3.40 < sepal width (cm) <= 3.61', + 'sepal width (cm) > 3.61'], + 2: ['petal length (cm) <= 1.40', + '1.40 < petal length (cm) <= 1.50', + '1.50 < petal length (cm) <= 1.70', + '1.70 < petal length (cm) <= 3.90', + '3.90 < petal length (cm) <= 4.35', + '4.35 < petal length (cm) <= 4.64', + '4.64 < petal length (cm) <= 5.00', + '5.00 < petal length (cm) <= 5.32', + '5.32 < petal length (cm) <= 5.80', + 'petal length (cm) > 5.80'], + 3: ['petal width (cm) <= 0.20', + '0.20 < petal width (cm) <= 0.40', + '0.40 < petal width (cm) <= 1.16', + '1.16 < petal width (cm) <= 1.30', + '1.30 < petal width (cm) <= 1.50', + '1.50 < petal width (cm) <= 1.80', + '1.80 < petal width (cm) <= 1.90', + '1.90 < petal width (cm) <= 2.20', + 'petal width (cm) > 2.20']}, + discretizer.names) + + def test_feature_names_3(self): + self.maxDiff = None + discretizer = EntropyDiscretizer(self.x, [], self.feature_names, + self.y, random_state=10) + self.assertDictEqual( + {0: ['sepal length (cm) <= 4.85', + '4.85 < sepal length (cm) <= 5.45', + '5.45 < sepal length (cm) <= 5.55', + '5.55 < sepal length (cm) <= 5.85', + '5.85 < sepal length (cm) <= 6.15', + '6.15 < sepal length (cm) <= 7.05', + 'sepal length (cm) > 7.05'], + 1: ['sepal width (cm) <= 2.45', + '2.45 < sepal width (cm) <= 2.95', + '2.95 < sepal width (cm) <= 3.05', + '3.05 < sepal width (cm) <= 3.35', + '3.35 < sepal width (cm) <= 3.45', + '3.45 < sepal width (cm) <= 3.55', + 'sepal width (cm) > 3.55'], + 2: ['petal length (cm) <= 2.45', + '2.45 < petal length (cm) <= 4.45', + '4.45 < petal length (cm) <= 4.75', + '4.75 < petal length (cm) <= 5.15', + 'petal length (cm) > 5.15'], + 3: ['petal width (cm) <= 0.80', + '0.80 < petal width (cm) <= 1.35', + '1.35 < petal width (cm) <= 1.75', + '1.75 < petal width (cm) <= 1.85', + 'petal width (cm) > 1.85']}, + discretizer.names) + + +if __name__ == '__main__': + unittest.main() diff --git a/build/lib/lime/tests/test_generic_utils.py b/build/lib/lime/tests/test_generic_utils.py new file mode 100644 index 000000000..02380ad7d --- /dev/null +++ b/build/lib/lime/tests/test_generic_utils.py @@ -0,0 +1,93 @@ +import unittest +import sys +from lime.utils.generic_utils import has_arg + + +class TestGenericUtils(unittest.TestCase): + + def test_has_arg(self): + # fn is callable / is not callable + + class FooNotCallable: + + def __init__(self, word): + self.message = word + + class FooCallable: + + def __init__(self, word): + self.message = word + + def __call__(self, message): + return message + + def positional_argument_call(self, arg1): + return self.message + + def multiple_positional_arguments_call(self, *args): + res = [] + for a in args: + res.append(a) + return res + + def keyword_argument_call(self, filter_=True): + res = self.message + if filter_: + res = 'KO' + return res + + def multiple_keyword_arguments_call(self, arg1='1', arg2='2'): + return self.message + arg1 + arg2 + + def undefined_keyword_arguments_call(self, **kwargs): + res = self.message + for a in kwargs: + res = res + a + return a + + foo_callable = FooCallable('OK') + self.assertTrue(has_arg(foo_callable, 'message')) + + if sys.version_info < (3,): + foo_not_callable = FooNotCallable('KO') + self.assertFalse(has_arg(foo_not_callable, 'message')) + elif sys.version_info < (3, 6): + with self.assertRaises(TypeError): + foo_not_callable = FooNotCallable('KO') + has_arg(foo_not_callable, 'message') + + # Python 2, argument in / not in valid arguments / keyword arguments + if sys.version_info < (3,): + self.assertFalse(has_arg(foo_callable, 'invalid_arg')) + self.assertTrue(has_arg(foo_callable.positional_argument_call, 'arg1')) + self.assertFalse(has_arg(foo_callable.multiple_positional_arguments_call, 'argX')) + self.assertFalse(has_arg(foo_callable.keyword_argument_call, 'argX')) + self.assertTrue(has_arg(foo_callable.keyword_argument_call, 'filter_')) + self.assertTrue(has_arg(foo_callable.multiple_keyword_arguments_call, 'arg2')) + self.assertFalse(has_arg(foo_callable.multiple_keyword_arguments_call, 'arg3')) + self.assertFalse(has_arg(foo_callable.undefined_keyword_arguments_call, 'argX')) + # Python 3, argument in / not in valid arguments / keyword arguments + elif sys.version_info < (3, 6): + self.assertFalse(has_arg(foo_callable, 'invalid_arg')) + self.assertTrue(has_arg(foo_callable.positional_argument_call, 'arg1')) + self.assertFalse(has_arg(foo_callable.multiple_positional_arguments_call, 'argX')) + self.assertFalse(has_arg(foo_callable.keyword_argument_call, 'argX')) + self.assertTrue(has_arg(foo_callable.keyword_argument_call, 'filter_')) + self.assertTrue(has_arg(foo_callable.multiple_keyword_arguments_call, 'arg2')) + self.assertFalse(has_arg(foo_callable.multiple_keyword_arguments_call, 'arg3')) + self.assertFalse(has_arg(foo_callable.undefined_keyword_arguments_call, 'argX')) + else: + self.assertFalse(has_arg(foo_callable, 'invalid_arg')) + self.assertTrue(has_arg(foo_callable.positional_argument_call, 'arg1')) + self.assertFalse(has_arg(foo_callable.multiple_positional_arguments_call, 'argX')) + self.assertFalse(has_arg(foo_callable.keyword_argument_call, 'argX')) + self.assertTrue(has_arg(foo_callable.keyword_argument_call, 'filter_')) + self.assertTrue(has_arg(foo_callable.multiple_keyword_arguments_call, 'arg2')) + self.assertFalse(has_arg(foo_callable.multiple_keyword_arguments_call, 'arg3')) + self.assertFalse(has_arg(foo_callable.undefined_keyword_arguments_call, 'argX')) + # argname is None + self.assertFalse(has_arg(foo_callable, None)) + + +if __name__ == '__main__': + unittest.main() diff --git a/build/lib/lime/tests/test_lime_tabular.py b/build/lib/lime/tests/test_lime_tabular.py new file mode 100644 index 000000000..426079b48 --- /dev/null +++ b/build/lib/lime/tests/test_lime_tabular.py @@ -0,0 +1,651 @@ +import unittest + +import numpy as np +import collections +import sklearn # noqa +import sklearn.datasets +import sklearn.ensemble +import sklearn.linear_model # noqa +from numpy.testing import assert_array_equal +from sklearn.datasets import load_iris, make_classification, make_multilabel_classification +from sklearn.ensemble import RandomForestClassifier +from sklearn.linear_model import LinearRegression +from lime.discretize import QuartileDiscretizer, DecileDiscretizer, EntropyDiscretizer + + +try: + from sklearn.model_selection import train_test_split +except ImportError: + # Deprecated in scikit-learn version 0.18, removed in 0.20 + from sklearn.cross_validation import train_test_split + +from lime.lime_tabular import LimeTabularExplainer + + +class TestLimeTabular(unittest.TestCase): + + def setUp(self): + iris = load_iris() + + self.feature_names = iris.feature_names + self.target_names = iris.target_names + + (self.train, + self.test, + self.labels_train, + self.labels_test) = train_test_split(iris.data, iris.target, train_size=0.80) + + def test_lime_explainer_good_regressor(self): + np.random.seed(1) + rf = RandomForestClassifier(n_estimators=500) + rf.fit(self.train, self.labels_train) + i = np.random.randint(0, self.test.shape[0]) + + explainer = LimeTabularExplainer(self.train, + mode="classification", + feature_names=self.feature_names, + class_names=self.target_names, + discretize_continuous=True) + + exp = explainer.explain_instance(self.test[i], + rf.predict_proba, + num_features=2, + model_regressor=LinearRegression()) + + self.assertIsNotNone(exp) + keys = [x[0] for x in exp.as_list()] + self.assertEqual(1, + sum([1 if 'petal width' in x else 0 for x in keys]), + "Petal Width is a major feature") + self.assertEqual(1, + sum([1 if 'petal length' in x else 0 for x in keys]), + "Petal Length is a major feature") + + def test_lime_explainer_good_regressor_synthetic_data(self): + X, y = make_classification(n_samples=1000, + n_features=20, + n_informative=2, + n_redundant=2, + random_state=10) + + rf = RandomForestClassifier(n_estimators=500) + rf.fit(X, y) + instance = np.random.randint(0, X.shape[0]) + feature_names = ["feature" + str(i) for i in range(20)] + explainer = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True) + + exp = explainer.explain_instance(X[instance], rf.predict_proba) + + self.assertIsNotNone(exp) + self.assertEqual(10, len(exp.as_list())) + + def test_lime_explainer_sparse_synthetic_data(self): + n_features = 20 + X, y = make_multilabel_classification(n_samples=100, + sparse=True, + n_features=n_features, + n_classes=1, + n_labels=2) + rf = RandomForestClassifier(n_estimators=500) + rf.fit(X, y) + instance = np.random.randint(0, X.shape[0]) + feature_names = ["feature" + str(i) for i in range(n_features)] + explainer = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True) + + exp = explainer.explain_instance(X[instance], rf.predict_proba) + + self.assertIsNotNone(exp) + self.assertEqual(10, len(exp.as_list())) + + def test_lime_explainer_no_regressor(self): + np.random.seed(1) + + rf = RandomForestClassifier(n_estimators=500) + rf.fit(self.train, self.labels_train) + i = np.random.randint(0, self.test.shape[0]) + + explainer = LimeTabularExplainer(self.train, + feature_names=self.feature_names, + class_names=self.target_names, + discretize_continuous=True) + + exp = explainer.explain_instance(self.test[i], + rf.predict_proba, + num_features=2) + self.assertIsNotNone(exp) + keys = [x[0] for x in exp.as_list()] + self.assertEqual(1, + sum([1 if 'petal width' in x else 0 for x in keys]), + "Petal Width is a major feature") + self.assertEqual(1, + sum([1 if 'petal length' in x else 0 for x in keys]), + "Petal Length is a major feature") + + def test_lime_explainer_entropy_discretizer(self): + np.random.seed(1) + + rf = RandomForestClassifier(n_estimators=500) + rf.fit(self.train, self.labels_train) + i = np.random.randint(0, self.test.shape[0]) + + explainer = LimeTabularExplainer(self.train, + feature_names=self.feature_names, + class_names=self.target_names, + training_labels=self.labels_train, + discretize_continuous=True, + discretizer='entropy') + + exp = explainer.explain_instance(self.test[i], + rf.predict_proba, + num_features=2) + self.assertIsNotNone(exp) + keys = [x[0] for x in exp.as_list()] + print(keys) + self.assertEqual(1, + sum([1 if 'petal width' in x else 0 for x in keys]), + "Petal Width is a major feature") + self.assertEqual(1, + sum([1 if 'petal length' in x else 0 for x in keys]), + "Petal Length is a major feature") + + def test_lime_tabular_explainer_equal_random_state(self): + X, y = make_classification(n_samples=1000, + n_features=20, + n_informative=2, + n_redundant=2, + random_state=10) + + rf = RandomForestClassifier(n_estimators=500, random_state=10) + rf.fit(X, y) + instance = np.random.RandomState(10).randint(0, X.shape[0]) + feature_names = ["feature" + str(i) for i in range(20)] + + # ---------------------------------------------------------------------- + # -------------------------Quartile Discretizer------------------------- + # ---------------------------------------------------------------------- + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertDictEqual(exp_1.as_map(), exp_2.as_map()) + + # ---------------------------------------------------------------------- + # --------------------------Decile Discretizer-------------------------- + # ---------------------------------------------------------------------- + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertDictEqual(exp_1.as_map(), exp_2.as_map()) + + # ---------------------------------------------------------------------- + # -------------------------Entropy Discretizer-------------------------- + # ---------------------------------------------------------------------- + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertDictEqual(exp_1.as_map(), exp_2.as_map()) + + def test_lime_tabular_explainer_not_equal_random_state(self): + X, y = make_classification(n_samples=1000, + n_features=20, + n_informative=2, + n_redundant=2, + random_state=10) + + rf = RandomForestClassifier(n_estimators=500, random_state=10) + rf.fit(X, y) + instance = np.random.RandomState(10).randint(0, X.shape[0]) + feature_names = ["feature" + str(i) for i in range(20)] + + # ---------------------------------------------------------------------- + # -------------------------Quartile Discretizer------------------------- + # ---------------------------------------------------------------------- + + # ---------------------------------[1]---------------------------------- + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[2]---------------------------------- + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[3]---------------------------------- + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[4]---------------------------------- + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = QuartileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertFalse(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------------------------------------------- + # --------------------------Decile Discretizer-------------------------- + # ---------------------------------------------------------------------- + + # ---------------------------------[1]---------------------------------- + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[2]---------------------------------- + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[3]---------------------------------- + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[4]---------------------------------- + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = DecileDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertFalse(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------------------------------------------- + # --------------------------Entropy Discretizer------------------------- + # ---------------------------------------------------------------------- + + # ---------------------------------[1]---------------------------------- + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[2]---------------------------------- + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=10) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[3]---------------------------------- + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=10) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertTrue(exp_1.as_map() != exp_2.as_map()) + + # ---------------------------------[4]---------------------------------- + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_1 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_1 = explainer_1.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + discretizer = EntropyDiscretizer(X, [], feature_names, y, + random_state=20) + explainer_2 = LimeTabularExplainer(X, + feature_names=feature_names, + discretize_continuous=True, + discretizer=discretizer, + random_state=20) + exp_2 = explainer_2.explain_instance(X[instance], rf.predict_proba, + num_samples=500) + + self.assertFalse(exp_1.as_map() != exp_2.as_map()) + + def testFeatureNamesAndCategoricalFeats(self): + training_data = np.array([[0., 1.], [1., 0.]]) + + explainer = LimeTabularExplainer(training_data=training_data) + self.assertEqual(explainer.feature_names, ['0', '1']) + self.assertEqual(explainer.categorical_features, [0, 1]) + + explainer = LimeTabularExplainer( + training_data=training_data, + feature_names=np.array(['one', 'two']) + ) + self.assertEqual(explainer.feature_names, ['one', 'two']) + + explainer = LimeTabularExplainer( + training_data=training_data, + categorical_features=np.array([0]), + discretize_continuous=False + ) + self.assertEqual(explainer.categorical_features, [0]) + + def testFeatureValues(self): + training_data = np.array([ + [0, 0, 2], + [1, 1, 0], + [0, 2, 2], + [1, 3, 0] + ]) + + explainer = LimeTabularExplainer( + training_data=training_data, + categorical_features=[0, 1, 2] + ) + + self.assertEqual(set(explainer.feature_values[0]), {0, 1}) + self.assertEqual(set(explainer.feature_values[1]), {0, 1, 2, 3}) + self.assertEqual(set(explainer.feature_values[2]), {0, 2}) + + assert_array_equal(explainer.feature_frequencies[0], np.array([.5, .5])) + assert_array_equal(explainer.feature_frequencies[1], np.array([.25, .25, .25, .25])) + assert_array_equal(explainer.feature_frequencies[2], np.array([.5, .5])) + + def test_lime_explainer_with_data_stats(self): + np.random.seed(1) + + rf = RandomForestClassifier(n_estimators=500) + rf.fit(self.train, self.labels_train) + i = np.random.randint(0, self.test.shape[0]) + + # Generate stats using a quartile descritizer + descritizer = QuartileDiscretizer(self.train, [], self.feature_names, self.target_names, + random_state=20) + + d_means = descritizer.means + d_stds = descritizer.stds + d_mins = descritizer.mins + d_maxs = descritizer.maxs + d_bins = descritizer.bins(self.train, self.target_names) + + # Compute feature values and frequencies of all columns + cat_features = np.arange(self.train.shape[1]) + discretized_training_data = descritizer.discretize(self.train) + + feature_values = {} + feature_frequencies = {} + for feature in cat_features: + column = discretized_training_data[:, feature] + feature_count = collections.Counter(column) + values, frequencies = map(list, zip(*(feature_count.items()))) + feature_values[feature] = values + feature_frequencies[feature] = frequencies + + # Convert bins to list from array + d_bins_revised = {} + index = 0 + for bin in d_bins: + d_bins_revised[index] = bin.tolist() + index = index+1 + + # Descritized stats + data_stats = {} + data_stats["means"] = d_means + data_stats["stds"] = d_stds + data_stats["maxs"] = d_maxs + data_stats["mins"] = d_mins + data_stats["bins"] = d_bins_revised + data_stats["feature_values"] = feature_values + data_stats["feature_frequencies"] = feature_frequencies + + data = np.zeros((2, len(self.feature_names))) + explainer = LimeTabularExplainer( + data, feature_names=self.feature_names, random_state=10, + training_data_stats=data_stats, training_labels=self.target_names) + + exp = explainer.explain_instance(self.test[i], + rf.predict_proba, + num_features=2, + model_regressor=LinearRegression()) + + self.assertIsNotNone(exp) + keys = [x[0] for x in exp.as_list()] + self.assertEqual(1, + sum([1 if 'petal width' in x else 0 for x in keys]), + "Petal Width is a major feature") + self.assertEqual(1, + sum([1 if 'petal length' in x else 0 for x in keys]), + "Petal Length is a major feature") + + +if __name__ == '__main__': + unittest.main() diff --git a/build/lib/lime/tests/test_lime_text.py b/build/lib/lime/tests/test_lime_text.py new file mode 100644 index 000000000..d0d2f728e --- /dev/null +++ b/build/lib/lime/tests/test_lime_text.py @@ -0,0 +1,167 @@ +import re +import unittest + +import sklearn # noqa +from sklearn.datasets import fetch_20newsgroups +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics import f1_score +from sklearn.naive_bayes import MultinomialNB +from sklearn.pipeline import make_pipeline + +import numpy as np + +from lime.lime_text import LimeTextExplainer +from lime.lime_text import IndexedCharacters, IndexedString + + +class TestLimeText(unittest.TestCase): + + def test_lime_text_explainer_good_regressor(self): + categories = ['alt.atheism', 'soc.religion.christian'] + newsgroups_train = fetch_20newsgroups(subset='train', + categories=categories) + newsgroups_test = fetch_20newsgroups(subset='test', + categories=categories) + class_names = ['atheism', 'christian'] + vectorizer = TfidfVectorizer(lowercase=False) + train_vectors = vectorizer.fit_transform(newsgroups_train.data) + test_vectors = vectorizer.transform(newsgroups_test.data) + nb = MultinomialNB(alpha=.01) + nb.fit(train_vectors, newsgroups_train.target) + pred = nb.predict(test_vectors) + f1_score(newsgroups_test.target, pred, average='weighted') + c = make_pipeline(vectorizer, nb) + explainer = LimeTextExplainer(class_names=class_names) + idx = 83 + exp = explainer.explain_instance(newsgroups_test.data[idx], + c.predict_proba, num_features=6) + self.assertIsNotNone(exp) + self.assertEqual(6, len(exp.as_list())) + + def test_lime_text_tabular_equal_random_state(self): + categories = ['alt.atheism', 'soc.religion.christian'] + newsgroups_train = fetch_20newsgroups(subset='train', + categories=categories) + newsgroups_test = fetch_20newsgroups(subset='test', + categories=categories) + class_names = ['atheism', 'christian'] + vectorizer = TfidfVectorizer(lowercase=False) + train_vectors = vectorizer.fit_transform(newsgroups_train.data) + test_vectors = vectorizer.transform(newsgroups_test.data) + nb = MultinomialNB(alpha=.01) + nb.fit(train_vectors, newsgroups_train.target) + pred = nb.predict(test_vectors) + f1_score(newsgroups_test.target, pred, average='weighted') + c = make_pipeline(vectorizer, nb) + + explainer = LimeTextExplainer(class_names=class_names, random_state=10) + exp_1 = explainer.explain_instance(newsgroups_test.data[83], + c.predict_proba, num_features=6) + + explainer = LimeTextExplainer(class_names=class_names, random_state=10) + exp_2 = explainer.explain_instance(newsgroups_test.data[83], + c.predict_proba, num_features=6) + + self.assertTrue(exp_1.as_map() == exp_2.as_map()) + + def test_lime_text_tabular_not_equal_random_state(self): + categories = ['alt.atheism', 'soc.religion.christian'] + newsgroups_train = fetch_20newsgroups(subset='train', + categories=categories) + newsgroups_test = fetch_20newsgroups(subset='test', + categories=categories) + class_names = ['atheism', 'christian'] + vectorizer = TfidfVectorizer(lowercase=False) + train_vectors = vectorizer.fit_transform(newsgroups_train.data) + test_vectors = vectorizer.transform(newsgroups_test.data) + nb = MultinomialNB(alpha=.01) + nb.fit(train_vectors, newsgroups_train.target) + pred = nb.predict(test_vectors) + f1_score(newsgroups_test.target, pred, average='weighted') + c = make_pipeline(vectorizer, nb) + + explainer = LimeTextExplainer( + class_names=class_names, random_state=10) + exp_1 = explainer.explain_instance(newsgroups_test.data[83], + c.predict_proba, num_features=6) + + explainer = LimeTextExplainer( + class_names=class_names, random_state=20) + exp_2 = explainer.explain_instance(newsgroups_test.data[83], + c.predict_proba, num_features=6) + + self.assertFalse(exp_1.as_map() == exp_2.as_map()) + + def test_indexed_characters_bow(self): + s = 'Please, take your time' + inverse_vocab = ['P', 'l', 'e', 'a', 's', ',', ' ', 't', 'k', 'y', 'o', 'u', 'r', 'i', 'm'] + positions = [[0], [1], [2, 5, 11, 21], [3, 9], + [4], [6], [7, 12, 17], [8, 18], [10], + [13], [14], [15], [16], [19], [20]] + ic = IndexedCharacters(s) + + self.assertTrue(np.array_equal(ic.as_np, np.array(list(s)))) + self.assertTrue(np.array_equal(ic.string_start, np.arange(len(s)))) + self.assertTrue(ic.inverse_vocab == inverse_vocab) + self.assertTrue(ic.positions == positions) + + def test_indexed_characters_not_bow(self): + s = 'Please, take your time' + + ic = IndexedCharacters(s, bow=False) + + self.assertTrue(np.array_equal(ic.as_np, np.array(list(s)))) + self.assertTrue(np.array_equal(ic.string_start, np.arange(len(s)))) + self.assertTrue(ic.inverse_vocab == list(s)) + self.assertTrue(np.array_equal(ic.positions, np.arange(len(s)))) + + def test_indexed_string_regex(self): + s = 'Please, take your time. Please' + tokenized_string = np.array( + ['Please', ', ', 'take', ' ', 'your', ' ', 'time', '. ', 'Please']) + inverse_vocab = ['Please', 'take', 'your', 'time'] + start_positions = [0, 6, 8, 12, 13, 17, 18, 22, 24] + positions = [[0, 8], [2], [4], [6]] + indexed_string = IndexedString(s) + + self.assertTrue(np.array_equal(indexed_string.as_np, tokenized_string)) + self.assertTrue(np.array_equal(indexed_string.string_start, start_positions)) + self.assertTrue(indexed_string.inverse_vocab == inverse_vocab) + self.assertTrue(np.array_equal(indexed_string.positions, positions)) + + def test_indexed_string_callable(self): + s = 'aabbccddaa' + + def tokenizer(string): + return [string[i] + string[i + 1] for i in range(0, len(string) - 1, 2)] + + tokenized_string = np.array(['aa', 'bb', 'cc', 'dd', 'aa']) + inverse_vocab = ['aa', 'bb', 'cc', 'dd'] + start_positions = [0, 2, 4, 6, 8] + positions = [[0, 4], [1], [2], [3]] + indexed_string = IndexedString(s, tokenizer) + + self.assertTrue(np.array_equal(indexed_string.as_np, tokenized_string)) + self.assertTrue(np.array_equal(indexed_string.string_start, start_positions)) + self.assertTrue(indexed_string.inverse_vocab == inverse_vocab) + self.assertTrue(np.array_equal(indexed_string.positions, positions)) + + def test_indexed_string_inverse_removing_tokenizer(self): + s = 'This is a good movie. This, it is a great movie.' + + def tokenizer(string): + return re.split(r'(?:\W+)|$', string) + + indexed_string = IndexedString(s, tokenizer) + + self.assertEqual(s, indexed_string.inverse_removing([])) + + def test_indexed_string_inverse_removing_regex(self): + s = 'This is a good movie. This is a great movie' + indexed_string = IndexedString(s) + + self.assertEqual(s, indexed_string.inverse_removing([])) + + +if __name__ == '__main__': + unittest.main() diff --git a/build/lib/lime/tests/test_scikit_image.py b/build/lib/lime/tests/test_scikit_image.py new file mode 100644 index 000000000..7c18040cc --- /dev/null +++ b/build/lib/lime/tests/test_scikit_image.py @@ -0,0 +1,128 @@ +import unittest +from lime.wrappers.scikit_image import BaseWrapper +from lime.wrappers.scikit_image import SegmentationAlgorithm +from skimage.segmentation import quickshift +from skimage.data import chelsea +from skimage.util import img_as_float +import numpy as np + + +class TestBaseWrapper(unittest.TestCase): + + def test_base_wrapper(self): + + obj_with_params = BaseWrapper(a=10, b='message') + obj_without_params = BaseWrapper() + + def foo_fn(): + return 'bar' + + obj_with_fn = BaseWrapper(foo_fn) + self.assertEqual(obj_with_params.target_params, {'a': 10, 'b': 'message'}) + self.assertEqual(obj_without_params.target_params, {}) + self.assertEqual(obj_with_fn.target_fn(), 'bar') + + def test__check_params(self): + + def bar_fn(a): + return str(a) + + class Pipo(): + + def __init__(self): + self.name = 'pipo' + + def __call__(self, message): + return message + + pipo = Pipo() + obj_with_valid_fn = BaseWrapper(bar_fn, a=10, b='message') + obj_with_valid_callable_fn = BaseWrapper(pipo, c=10, d='message') + obj_with_invalid_fn = BaseWrapper([1, 2, 3], fn_name='invalid') + + # target_fn is not a callable or function/method + with self.assertRaises(AttributeError): + obj_with_invalid_fn._check_params('fn_name') + + # parameters is not in target_fn args + with self.assertRaises(ValueError): + obj_with_valid_fn._check_params(['c']) + obj_with_valid_callable_fn._check_params(['e']) + + # params is in target_fn args + try: + obj_with_valid_fn._check_params(['a']) + obj_with_valid_callable_fn._check_params(['message']) + except Exception: + self.fail("_check_params() raised an unexpected exception") + + # params is not a dict or list + with self.assertRaises(TypeError): + obj_with_valid_fn._check_params(None) + with self.assertRaises(TypeError): + obj_with_valid_fn._check_params('param_name') + + def test_set_params(self): + + class Pipo(): + + def __init__(self): + self.name = 'pipo' + + def __call__(self, message): + return message + pipo = Pipo() + obj = BaseWrapper(pipo) + + # argument is set accordingly + obj.set_params(message='OK') + self.assertEqual(obj.target_params, {'message': 'OK'}) + self.assertEqual(obj.target_fn(**obj.target_params), 'OK') + + # invalid argument is passed + try: + obj = BaseWrapper(Pipo()) + obj.set_params(invalid='KO') + except Exception: + self.assertEqual(obj.target_params, {}) + + def test_filter_params(self): + + # right arguments are kept and wrong dismmissed + def baz_fn(a, b, c=True): + if c: + return a + b + else: + return a + obj_ = BaseWrapper(baz_fn, a=10, b=100, d=1000) + self.assertEqual(obj_.filter_params(baz_fn), {'a': 10, 'b': 100}) + + # target_params is overriden using 'override' argument + self.assertEqual(obj_.filter_params(baz_fn, override={'c': False}), + {'a': 10, 'b': 100, 'c': False}) + + +class TestSegmentationAlgorithm(unittest.TestCase): + + def test_instanciate_segmentation_algorithm(self): + img = img_as_float(chelsea()[::2, ::2]) + + # wrapped functions provide the same result + fn = SegmentationAlgorithm('quickshift', kernel_size=3, max_dist=6, + ratio=0.5, random_seed=133) + fn_result = fn(img) + original_result = quickshift(img, kernel_size=3, max_dist=6, ratio=0.5, + random_seed=133) + + # same segments + self.assertTrue(np.array_equal(fn_result, original_result)) + + def test_instanciate_slic(self): + pass + + def test_instanciate_felzenszwalb(self): + pass + + +if __name__ == '__main__': + unittest.main() diff --git a/build/lib/lime/utils/__init__.py b/build/lib/lime/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/build/lib/lime/utils/generic_utils.py b/build/lib/lime/utils/generic_utils.py new file mode 100644 index 000000000..ea7eca06b --- /dev/null +++ b/build/lib/lime/utils/generic_utils.py @@ -0,0 +1,39 @@ +import sys +import inspect +import types + + +def has_arg(fn, arg_name): + """Checks if a callable accepts a given keyword argument. + + Args: + fn: callable to inspect + arg_name: string, keyword argument name to check + + Returns: + bool, whether `fn` accepts a `arg_name` keyword argument. + """ + if sys.version_info < (3,): + if isinstance(fn, types.FunctionType) or isinstance(fn, types.MethodType): + arg_spec = inspect.getargspec(fn) + else: + try: + arg_spec = inspect.getargspec(fn.__call__) + except AttributeError: + return False + return (arg_name in arg_spec.args) + elif sys.version_info < (3, 6): + arg_spec = inspect.getfullargspec(fn) + return (arg_name in arg_spec.args or + arg_name in arg_spec.kwonlyargs) + else: + try: + signature = inspect.signature(fn) + except ValueError: + # handling Cython + signature = inspect.signature(fn.__call__) + parameter = signature.parameters.get(arg_name) + if parameter is None: + return False + return (parameter.kind in (inspect.Parameter.POSITIONAL_OR_KEYWORD, + inspect.Parameter.KEYWORD_ONLY)) diff --git a/build/lib/lime/webpack.config.js b/build/lib/lime/webpack.config.js new file mode 100644 index 000000000..b37b258bb --- /dev/null +++ b/build/lib/lime/webpack.config.js @@ -0,0 +1,40 @@ +var path = require('path'); +var webpack = require('webpack'); + +module.exports = { + entry: './js/main.js', + output: { + path: __dirname, + filename: 'bundle.js', + library: 'lime' + }, + module: { + loaders: [ + { + loader: 'babel-loader', + test: path.join(__dirname, 'js'), + query: { + presets: 'es2015-ie', + }, + + }, + { + test: /\.css$/, + loaders: ['style-loader', 'css-loader'], + + } + + ] + }, + plugins: [ + // Avoid publishing files when compilation fails + new webpack.NoErrorsPlugin() + ], + stats: { + // Nice colored output + colors: true + }, + // Create Sourcemaps for the bundle + devtool: 'source-map', +}; + diff --git a/build/lib/lime/wrappers/__init__.py b/build/lib/lime/wrappers/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/build/lib/lime/wrappers/scikit_image.py b/build/lib/lime/wrappers/scikit_image.py new file mode 100644 index 000000000..c99de4667 --- /dev/null +++ b/build/lib/lime/wrappers/scikit_image.py @@ -0,0 +1,114 @@ +import types +from lime.utils.generic_utils import has_arg +from skimage.segmentation import felzenszwalb, slic, quickshift + + +class BaseWrapper(object): + """Base class for LIME Scikit-Image wrapper + + + Args: + target_fn: callable function or class instance + target_params: dict, parameters to pass to the target_fn + + + 'target_params' takes parameters required to instanciate the + desired Scikit-Image class/model + """ + + def __init__(self, target_fn=None, **target_params): + self.target_fn = target_fn + self.target_params = target_params + + def _check_params(self, parameters): + """Checks for mistakes in 'parameters' + + Args : + parameters: dict, parameters to be checked + + Raises : + ValueError: if any parameter is not a valid argument for the target function + or the target function is not defined + TypeError: if argument parameters is not iterable + """ + a_valid_fn = [] + if self.target_fn is None: + if callable(self): + a_valid_fn.append(self.__call__) + else: + raise TypeError('invalid argument: tested object is not callable,\ + please provide a valid target_fn') + elif isinstance(self.target_fn, types.FunctionType) \ + or isinstance(self.target_fn, types.MethodType): + a_valid_fn.append(self.target_fn) + else: + a_valid_fn.append(self.target_fn.__call__) + + if not isinstance(parameters, str): + for p in parameters: + for fn in a_valid_fn: + if has_arg(fn, p): + pass + else: + raise ValueError('{} is not a valid parameter'.format(p)) + else: + raise TypeError('invalid argument: list or dictionnary expected') + + def set_params(self, **params): + """Sets the parameters of this estimator. + Args: + **params: Dictionary of parameter names mapped to their values. + + Raises : + ValueError: if any parameter is not a valid argument + for the target function + """ + self._check_params(params) + self.target_params = params + + def filter_params(self, fn, override=None): + """Filters `target_params` and return those in `fn`'s arguments. + Args: + fn : arbitrary function + override: dict, values to override target_params + Returns: + result : dict, dictionary containing variables + in both target_params and fn's arguments. + """ + override = override or {} + result = {} + for name, value in self.target_params.items(): + if has_arg(fn, name): + result.update({name: value}) + result.update(override) + return result + + +class SegmentationAlgorithm(BaseWrapper): + """ Define the image segmentation function based on Scikit-Image + implementation and a set of provided parameters + + Args: + algo_type: string, segmentation algorithm among the following: + 'quickshift', 'slic', 'felzenszwalb' + target_params: dict, algorithm parameters (valid model paramters + as define in Scikit-Image documentation) + """ + + def __init__(self, algo_type, **target_params): + self.algo_type = algo_type + if (self.algo_type == 'quickshift'): + BaseWrapper.__init__(self, quickshift, **target_params) + kwargs = self.filter_params(quickshift) + self.set_params(**kwargs) + elif (self.algo_type == 'felzenszwalb'): + BaseWrapper.__init__(self, felzenszwalb, **target_params) + kwargs = self.filter_params(felzenszwalb) + self.set_params(**kwargs) + elif (self.algo_type == 'slic'): + BaseWrapper.__init__(self, slic, **target_params) + kwargs = self.filter_params(slic) + self.set_params(**kwargs) + + def __call__(self, *args): + return self.target_fn(args[0], **self.target_params) diff --git a/citation.bib b/citation.bib index 9b1307712..fdfb4f84d 100644 --- a/citation.bib +++ b/citation.bib @@ -1,11 +1,9 @@ -@inproceedings{lime, - author = {Marco Tulio Ribeiro and - Sameer Singh and - Carlos Guestrin}, - title = {"Why Should {I} Trust You?": Explaining the Predictions of Any Classifier}, - booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} International Conference on - Knowledge Discovery and Data Mining, San Francisco, CA, USA, August - 13-17, 2016}, - pages = {1135--1144}, - year = {2016}, -} +@inproceedings{rashid2024using, + title={Using Stratified Sampling to Improve LIME Image Explanations}, + author={Rashid, Muhammad and Amparore, Elvio G and Ferrari, Enrico and Verda, Damiano}, + booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, + volume={38}, + number={13}, + pages={14785--14792}, + year={2024} +} \ No newline at end of file diff --git a/coverage.xml b/coverage.xml new file mode 100644 index 000000000..90cc59569 --- /dev/null +++ b/coverage.xml @@ -0,0 +1,1555 @@ + + + + + + E:\PHD\datacloud_data\repos\lime_stratified + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/doc/conf.py b/doc/conf.py index 4f62a2e31..a69f23237 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -252,7 +252,7 @@ # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'lime.tex', u'lime Documentation', - u'Marco Tulio Ribeiro', 'manual'), + u'Muhammad Rashid', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of @@ -281,7 +281,7 @@ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ - (master_doc, 'lime', u'lime Documentation', + (master_doc, 'lime_stratified', u'lime Documentation', [author], 1) ] diff --git a/doc/images/CV_plots.png b/doc/images/CV_plots.png new file mode 100644 index 000000000..ac78c2a2f Binary files /dev/null and b/doc/images/CV_plots.png differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000001.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000001.JPEG new file mode 100644 index 000000000..af6d781f5 Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000001.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000002.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000002.JPEG new file mode 100644 index 000000000..0a531e191 Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000002.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000003.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000003.JPEG new file mode 100644 index 000000000..26f49a63c Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000003.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000004.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000004.JPEG new file mode 100644 index 000000000..d481d7322 Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000004.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000005.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000005.JPEG new file mode 100644 index 000000000..e1a275155 Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000005.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000006.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000006.JPEG new file mode 100644 index 000000000..0aeb4e70e Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000006.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000007.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000007.JPEG new file mode 100644 index 000000000..eda2b029b Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000007.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000008.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000008.JPEG new file mode 100644 index 000000000..31b6d69ad Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000008.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000009.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000009.JPEG new file mode 100644 index 000000000..835f70393 Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000009.JPEG differ diff --git a/doc/images/ILVRC_2012/ILSVRC2012_test_00000010.JPEG b/doc/images/ILVRC_2012/ILSVRC2012_test_00000010.JPEG new file mode 100644 index 000000000..c5f41b685 Binary files /dev/null and b/doc/images/ILVRC_2012/ILSVRC2012_test_00000010.JPEG differ diff --git a/doc/images/LIME-Image-workflow.png b/doc/images/LIME-Image-workflow.png new file mode 100644 index 000000000..9ba5580c0 Binary files /dev/null and b/doc/images/LIME-Image-workflow.png differ diff --git a/doc/images/LIME-concept-compact.svg b/doc/images/LIME-concept-compact.svg new file mode 100644 index 000000000..091fb50a1 --- /dev/null +++ b/doc/images/LIME-concept-compact.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/doc/images/LIME_Stratified.png b/doc/images/LIME_Stratified.png new file mode 100644 index 000000000..66137ac1d Binary files /dev/null and b/doc/images/LIME_Stratified.png differ diff --git a/doc/images/R2_plots.png b/doc/images/R2_plots.png new file mode 100644 index 000000000..c3d6505cf Binary files /dev/null and b/doc/images/R2_plots.png differ diff --git a/doc/images/binom-shapley.png b/doc/images/binom-shapley.png new file mode 100644 index 000000000..2f41fbc58 Binary files /dev/null and b/doc/images/binom-shapley.png differ diff --git a/doc/images/several_examples.png b/doc/images/several_examples.png new file mode 100644 index 000000000..78ffe79c7 Binary files /dev/null and b/doc/images/several_examples.png differ diff --git a/doc/index.rst b/doc/index.rst index 096b982bd..7e99ab3ee 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -1,18 +1,17 @@ -.. lime documentation master file, created by - sphinx-quickstart on Fri Mar 18 16:20:40 2016. +.. lime_startified documentation master file, created by + sphinx-quickstart on Mon Oct 1 16:20:40 2024. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -Local Interpretable Model-Agnostic Explanations (lime) +LIME with Stratified Sampling (lime_stratified) ================================ In this page, you can find the Python API reference for the lime package (local interpretable model-agnostic explanations). -For tutorials and more information, visit `the github page `_. - +For tutorials and more information, visit `the github page `_. .. toctree:: :maxdepth: 2 - lime + lime_stratified diff --git a/doc/notebooks/.ipynb_checkpoints/Run_Benchmark-checkpoint.ipynb b/doc/notebooks/.ipynb_checkpoints/Run_Benchmark-checkpoint.ipynb new file mode 100644 index 000000000..5179e2254 --- /dev/null +++ b/doc/notebooks/.ipynb_checkpoints/Run_Benchmark-checkpoint.ipynb @@ -0,0 +1,407 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e6f5c6a0", + "metadata": {}, + "source": [ + "## This file will runs experiments on ImageNet Dataset\n", + "\n", + "- this file will \n", + " - find segmentation hyperparamers for all 150 images \n", + " - generate expalantions for all the images in data folder and will save the results as csv file \n", + "

\n", + "- you may run the full benchmark on all the 150 images which will take more than one day to run, or run a simplified version on a single image which will take less time.\n", + "\n", + "This code was tested on two windows laptop and a mac-os laptop with following specifications:\n", + "\n", + "```\n", + "3.1. Dell G5 (Corei7 12 CPUs 2.20Ghz, 16GB RAM, NVidia 1060 GPU 6GB)\n", + "3.2. Santech XN2 (Corei9 20 CPUs 2.60Ghz, 16GB RAM, NVidia 4070 GPU 8GB)\n", + "3.3. Macbook Pro (M1, 16GB RAM)\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "d991a68f", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "2875ec2d-6feb-4a5f-b63c-4724e5bd1040", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'lime_stratified'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlime_stratified\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lime_image\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'lime_stratified'" + ] + } + ], + "source": [ + "from lime_stratified.lime import lime_image" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2cf69a8b-6d22-4f67-b68e-88bb40e39a2c", + "metadata": {}, + "outputs": [], + "source": [ + "# !pip install lime_stratified" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e499bcd9-8157-4d19-8893-09ce25b535f8", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'lime_stratified'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[3], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mlime_stratified\u001b[39;00m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'lime_stratified'" + ] + } + ], + "source": [ + "import lime_stratified" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5d8d400f-8fd7-473b-aaa7-944c2cb39e3c", + "metadata": {}, + "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "41598462-02a2-4732-9de0-55a2e93ff678", + "metadata": {}, + "outputs": [], + "source": [ + "os.chdir('E:\\\\PHD\\\\datacloud_data\\\\repos\\\\lime_stratified')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1a0c5dd5", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'utils'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[9], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mimportlib\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mutils\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mut\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdatetime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m date,datetime\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mIPython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdisplay\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m display, HTML\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'utils'" + ] + } + ], + "source": [ + "import os\n", + "import importlib\n", + "import utils as ut\n", + "from datetime import date,datetime\n", + "from IPython.display import display, HTML\n", + "display(HTML(\"\")) # Stretch Notebook Width to 98% size of the Screen\n", + "# It will use the modified code of lime downloaded from https://anonymous.4open.science/r/lime-stratified-BDE5/\n", + "from tensorflow.keras.applications.resnet50 import preprocess_input\n", + "from lime_stratified.lime import lime_image" + ] + }, + { + "cell_type": "markdown", + "id": "9e41377b", + "metadata": {}, + "source": [ + "### Setting Path" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0f2e492", + "metadata": {}, + "outputs": [], + "source": [ + "# Get Current Working Directory and joining subfolders and subfiles path\n", + "Main_dir = os.getcwd()\n", + "DS_path = os.path.join(Main_dir, \"data\")\n", + "result_folder = os.path.join(Main_dir, \"result\")\n", + "json_file = os.path.join(DS_path,\"imagenet_class_index.json\")" + ] + }, + { + "cell_type": "markdown", + "id": "33fac9ec", + "metadata": {}, + "source": [ + "## BlackBox Model" + ] + }, + { + "cell_type": "markdown", + "id": "b258b2d3", + "metadata": {}, + "source": [ + "Load BlackBox Model, here ResNet50 Model is loaded" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c79f3377", + "metadata": {}, + "outputs": [], + "source": [ + "def bb_predict(imgs):\n", + " # On some platform, you will need model.predict(..) instead of model(..)\n", + " return model.predict(preprocess_input(imgs), verbose=False)\n", + "# return model(preprocess_input(imgs))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f855679b", + "metadata": {}, + "outputs": [], + "source": [ + "model_name = 'ResNet50'\n", + "model = ut.load_model(model_name)\n", + "# getting ImageNet class names\n", + "class_names = ut.get_ImageNet_ClassLabels(json_file) " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f623da5", + "metadata": {}, + "outputs": [], + "source": [ + "now = datetime.now()\n", + "print(\"Example Started\\t\\t:\\t\\t\\t\", now.strftime(\"%d/%m/%Y %H:%M:%S\"))" + ] + }, + { + "cell_type": "markdown", + "id": "315ec98a", + "metadata": {}, + "source": [ + "# Hyperparameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5eae7c3", + "metadata": {}, + "outputs": [], + "source": [ + "#######################################################################################################\n", + "## SEGMENTATION PARAMETERS\n", + "seg_algo = 'quickshift'\n", + "segs_list = [50,100,150,200] # Target No of Segments in Image Explanation\n", + "# These hyperparameters can be used to create LIME Image Explanations\n", + "hide_color = [None]\n", + "use_stratification = [False,True]\n", + "num_samples = 1000 # samples in the synthetic neighbourhood needed to compute every explanation\n", + "batch_size = 500 # batch size to compute the synthetic neighbourhood \n", + "# Top Labels: Upto how much labels the explanation is needed, repeat_exp: Upto how much Explanation is needed to be \n", + "# to get an average explanation to remove randomness \n", + "top_labels = 2 # labels for the \n", + "repeat_exp = 10 # aeverage of 10 explanations for the same image to reduce randomness \n", + "# Results of Experiments\n", + "plot_prediction = True # Set it to True if plots for prediction are needed to be plot, Default: False\n", + "plot_segments = True # Set it to True if plots for segments are needed to be plot, Default: False\n", + "plot_explanation = True # Set it to True if plots for explanations are needed to be plot, Default: False\n", + "plot_classification_score = True # Set it to True if plots for classification_score are needed to be plot, Default: False\n", + "plot_heatmap = True # Set it to True if plots for plot_heatmap are needed to be plot, Default: False\n", + "plot_image_mask = True # Set it to True if plots for plot_image_mask are needed to be plot, Default: False\n", + "save_explanations_as_plot = True # Set it to True if plots for explanations are needed to be saved also, Default: False\n", + "\n", + "################### PATHS for Saving results ##########################\n", + "sub_results__ = os.path.join(result_folder,str(segs_list))\n", + "sub_results_ = os.path.join(sub_results__,str(num_samples))\n", + "_ = ut.check_folders(sub_results__)\n", + "############################### \n", + "create_segments_file = True ### IF SEGMENT FILE Needs to be regenerated, SET 'compute_segments' as True\n", + "create_data_file = True ### IF SEGMENT FILE IS LOADED AND COMPUTE EXPERIMENTS ARE REQUIRED, SET 'compute_experiments' as True" + ] + }, + { + "cell_type": "markdown", + "id": "0a96c251", + "metadata": {}, + "source": [ + "### Full Benchmark\n", + "Compute the benchmark by using these pararmeters on the 150 images " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5fc42ea9", + "metadata": {}, + "outputs": [], + "source": [ + "# files = range(0,150,1)" + ] + }, + { + "cell_type": "markdown", + "id": "a7e3bb67", + "metadata": {}, + "source": [ + "### Simplified Benchmark\n", + "\n", + "Use these pararmeters on the single on a image " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eb65dee", + "metadata": {}, + "outputs": [], + "source": [ + "files = range(124,125,1)" + ] + }, + { + "cell_type": "markdown", + "id": "7311db1d", + "metadata": {}, + "source": [ + "### Create Segments 0-100, 100-200, 200-300\n", + "Identify the max_dist hyperparamer needed to get exactly the desired amount of segments in of images" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "96a123b6", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df_seg = ut.segmentation_module(create_segments_file,files,DS_path,result_folder,model,segs_list,seg_algo)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1fc9222", + "metadata": {}, + "outputs": [], + "source": [ + "df_seg" + ] + }, + { + "cell_type": "markdown", + "id": "c4a0ff33", + "metadata": {}, + "source": [ + "### Run the Experiments and Save the CSV File\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a999145c", + "metadata": {}, + "outputs": [], + "source": [ + "df_data = ut.explanation_module(create_data_file,files,df_seg,DS_path,sub_results_,result_folder,segs_list,\n", + " model,model_name,class_names,save_explanations_as_plot,use_stratification,\n", + " plot_prediction,plot_segments,plot_heatmap,plot_image_mask,hide_color,\n", + " num_samples,repeat_exp,top_labels,batch_size,lime_image,bb_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40b79658", + "metadata": {}, + "outputs": [], + "source": [ + "df_data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47a44133", + "metadata": {}, + "outputs": [], + "source": [ + "now = datetime.now()\n", + "print(\"Example Started\\t\\t:\\t\\t\\t\", now.strftime(\"%d/%m/%Y %H:%M:%S\"))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a30a14c9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/notebooks/.ipynb_checkpoints/Tutorial - Image Classification Keras-checkpoint.ipynb b/doc/notebooks/.ipynb_checkpoints/Tutorial - Image Classification Keras-checkpoint.ipynb new file mode 100644 index 000000000..e4fddff1b --- /dev/null +++ b/doc/notebooks/.ipynb_checkpoints/Tutorial - Image Classification Keras-checkpoint.ipynb @@ -0,0 +1,727 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "LgSzX-nVxwkV" + }, + "source": [ + "Here is a simpler example of the use of LIME for image classification by using Keras (v2 or greater)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "colab_type": "code", + "id": "_KS7nU0axwkY", + "outputId": "21a4f2b5-120c-40bd-8ea1-b73edd8bce0a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notebook run using keras: 2.4.3\n" + ] + } + ], + "source": [ + "import os\n", + "import keras\n", + "from keras.applications import inception_v3 as inc_net\n", + "from keras.preprocessing import image\n", + "from keras.applications.imagenet_utils import decode_predictions\n", + "from skimage.io import imread\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np\n", + "print('Notebook run using keras:', keras.__version__)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "w-eyXVAHxwkj" + }, + "source": [ + "# Using Inception\n", + "Here we create a standard InceptionV3 pretrained model and use it on images by first preprocessing them with the preprocessing tools" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 74 + }, + "colab_type": "code", + "id": "GF8hPe1Rxwkl", + "outputId": "fdbae3ad-ddfc-409c-bd3d-3ed8ead94a25" + }, + "outputs": [], + "source": [ + "inet_model = inc_net.InceptionV3()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "GaQ0zT5Txwks" + }, + "outputs": [], + "source": [ + "def transform_img_fn(path_list):\n", + " out = []\n", + " for img_path in path_list:\n", + " img = image.load_img(img_path, target_size=(299, 299))\n", + " x = image.img_to_array(img)\n", + " x = np.expand_dims(x, axis=0)\n", + " x = inc_net.preprocess_input(x)\n", + " out.append(x)\n", + " return np.vstack(out)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "26Le95e5xwk1" + }, + "source": [ + "## Let's see the top 5 prediction for some image" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 398 + }, + "colab_type": "code", + "id": "3sxO8vzlxwk2", + "outputId": "82b2b75b-6bd8-4fab-ea2c-5b365806fb9e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('n02133161', 'American_black_bear', 0.6371605)\n", + "('n02105056', 'groenendael', 0.03181805)\n", + "('n02104365', 'schipperke', 0.029944215)\n", + "('n01883070', 'wombat', 0.028509509)\n", + "('n01877812', 'wallaby', 0.025093526)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "images = transform_img_fn([os.path.join('data','cat_mouse.jpg')])\n", + "# I'm dividing by 2 and adding 0.5 because of how this Inception represents images\n", + "plt.imshow(images[0] / 2 + 0.5)\n", + "preds = inet_model.predict(images)\n", + "for x in decode_predictions(preds)[0]:\n", + " print(x)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "collapsed": true, + "id": "_MCRFeNJxwk9", + "jupyter": { + "outputs_hidden": true + } + }, + "source": [ + "## Explanation\n", + "Now let's get an explanation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "colab_type": "code", + "id": "aP6LjonHxwlA", + "outputId": "07b6f31d-5207-4cf8-e75f-500fb3f8c939" + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import os,sys\n", + "try:\n", + " import lime\n", + "except:\n", + " sys.path.append(os.path.join('..', '..')) # add the current directory\n", + " import lime\n", + "from lime import lime_image" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "2b7ow1YtxwlH" + }, + "outputs": [], + "source": [ + "explainer = lime_image.LimeImageExplainer()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3ADfGCzxxwlO" + }, + "source": [ + "hide_color is the color for a superpixel turned OFF. Alternatively, if it is NONE, the superpixel will be replaced by the average of its pixels. Here, we set it to 0 (in the representation used by inception model, 0 means gray)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 104, + "referenced_widgets": [ + "1fb1b32b563b4cf2bc565e4f150d5886", + "17bb7656e07842dda40e13da228550c8", + "3b0b05ed4a1e4a7492bad6d208b005a1", + "30f68d0a496d4e97a5f9e59b6fce9f5e", + "bfe238fe53be4860b53021934755f0b1", + "f7d2484df9f24bceafc1910e361bc03f", + "059e79101bc04a6dac3d6c8065d5f960", + "e8a5f1d84d784fe7b75b1b965aec3fb8" + ] + }, + "colab_type": "code", + "id": "1L0lWZ8yxwlQ", + "outputId": "ee3a3678-bf31-46ba-dec4-e098c9c55eaa" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "faa50bde7a254cb7986a81274c778be0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "CPU times: user 13min 8s, sys: 1min 12s, total: 14min 21s\n", + "Wall time: 38.9 s\n" + ] + } + ], + "source": [ + "%%time\n", + "# Hide color is the color for a superpixel turned OFF. Alternatively, if it is NONE, the superpixel will be replaced by the average of its pixels\n", + "explanation = explainer.explain_instance(images[0].astype('double'), inet_model.predict, top_labels=5, hide_color=0, num_samples=1000)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "5XAf25PNxwlY" + }, + "source": [ + "Image classifiers are a bit slow. Notice that an explanation on my Surface Book dGPU took 1min 12s" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "uWl908SyxwlZ" + }, + "source": [ + "### Now let's see the explanation for the top class ( Black Bear)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "WXMdU3hwxwla" + }, + "source": [ + "We can see the top 5 superpixels that are most positive towards the class with the rest of the image hidden" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "rgs3IwZnxwlc" + }, + "outputs": [], + "source": [ + "from skimage.segmentation import mark_boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "5LJMsp3Oxwlk", + "outputId": "64b82262-8a45-4232-e844-2e15c5fc2d09" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=True)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "aAFdw9oJxwlr" + }, + "source": [ + "Or with the rest of the image present:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "-6eei5uQxwls", + "outputId": "a9f29754-15aa-43a9-a64d-493f1f01dea2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=False)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "y7aoh_OExwl0" + }, + "source": [ + "We can also see the 'pros and cons' (pros in green, cons in red)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "jHow3M4Oxwl1", + "outputId": "7cc10571-84aa-4e8f-d6cc-80a56cf7d054" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=10, hide_rest=False)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "e5FIt55txwl9" + }, + "source": [ + "Or the pros and cons that have weight at least 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "ExFQHJw_xwl-", + "outputId": "2b7e3bfd-5c66-42ef-eaf7-60506b25dbe3" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AsnzBq/ArdOcwSZmCR6qhrxvX01v8YHFeqOqQPoCDh+uJUV5Tx6+O918LJ4Dsi88NIykXWikE6yk1U8vG4XBDzh1rBW07WLZelx14WzOdzrJeQRxiCtYKrS44F5DoKSVznEe0F/qyEOYZI5UGDDisGHoH8R771O6z1iJ2B/pqzQR12EGeonBGWwcMg4kYF0hpo7cdwDQGSq70Umh+wDiLWlCxOA2gFqOBMTaOcmDt6SkDyZReaKazronldOUYZoIJUBQje/YSR4eSCGEHBlsrOzClGYke20f8aSPXwro8MsURen0CWBsP1xPh5S8y0kADtW9A41JOZIVmwDEz+BGjjttxxjlLSpWWM8Y4YvwWTQx2UmK+0B/fkG1hso7DMODigPdHxBpyOmGixfcTXi1BLLfhU/yryOf3z1i3RwbrqGsheINeG94H0ELpMHHg+fAx8zQQ1gPaAzeSETfCMKBYwnCD04p3M70IYZhxfiDOAUkbxhlq7/SWccOB9fxIb42SF8LxOSVvhDDg7Z4xlrqy1YZFmIylmIWWHdvjp9zFCe8jtba9hGyNdb2nuY4J0LdKS4nNeYZ5YpgmSgoM1u4tSCCEAaWgprDljWgiajtCwGGgwdYy9E7rO/ApFroL9AZYIboDVhSNhdY6l0tlvvM0Ng7jgZIWrvYdvd/tnbWvsK+FE1BVhhCpuULd09X1uuBjIPVEun/DNMy0ajDR0lOj1rpHAd/pRQk2cM4rKobUOgaPklnTirWG2gXbKmGc6a3STMGI3xe8izgqagToTz1bR7dKHEdSKXgje1ou4K2SUmI67ohr753aOsY4cs6k1NDSqCjaoereumzaccYRXESsw/fI6laQRipKsCO1N7a1UpbMNMyU0imlAULpCeMbxoxYb5n8TMmJlBIpX3jcNu70SFXwYnBqoFWagHGepJmtrlTJRCzRCDVXTtsJGyq97SXX0d0RdEToHOY7ZCtogHW9gDaaCsZG1EakFWpZsC5SN4HninUf8vyDb0F0XPKGXCxrvmIk0GRla4W78RnYjRfyyPnhBitX1u5Ij1esA2+OoI5hjDvyPY4EMzA985iWqC3TxaB4rI+kVgkhUJtQpeLHAR89RAO1g83UVokYhilyvzpM2+gqqOzAFWoY3R3XtNL1ijWe3sBYh7GBrIXL65+gd8+Y/Ii4kTWvrPmM8xHrHMEPGD9yqm/IVempYKxnHEa8GyjV4J6C8qaNVB5QreQt4exIWzuqQmuJk1tBLdE61uVMwtFaIqsSzchmO14d1W5srWHalbf3b7m7uwESh2mmro+0pxbzV9nXxgmsa0KeyBvBGHIqpG1Fosf7wDUlMIJdwBEwzpLylS0XOnC5XlEx1K0zOAsVzuuVMHpEDN6PeKP0rPTgCRJoshGcx4khq+A6ZAtSCsModLW0VgjGgjaKV0wvtOoI40TKFeMstVWcjdSS6C3RtKICa66MMeCspZjOSKVLpxvBmM6Wr3gMtazYwVBaY83KfVlorPRmiNZyevcWzR0zD9y+nMkFBixWAyKGRqeSuZZ31MfEPL1kq55WC8fb59gpUkpmbYm8FlwIXNMja21MfqLZjdoywdxizYHeBko3SFdSS0QZuTw+ksqVwzzgZKSmwtYaRZRoDUtt3IZGSLfcjR5r9xQ9uFvksLKcGucxc9MCc9xoY0TWyjgYhnFDtxm7rDw8bJhFGaxnnA/czHfc+JdEe8BFTytXZuNZa6FgCGEgOYcXQ1k2UjeE8YbUVyqW5z0gfqQYjw2BtWVsMczjxKNW1pRgu8dPt0y1EzQwSaWZwCqGah1vlwI4nHUMMWA04JqFlrheLuTtivhIGO44HF9ATWh5Rmv3WBOwZsZaQwwz1HuMP+DiHVUrVibWsjH5j6ltpW6PRCYOhwOPeiXlzLlXTDwS60ZPgvcD3XYmfwOtAJGbQVgatHbhdEm8uj0imng1Pce1BexXL/WvjYqwtErTPerlLe+It7ITcEpldAOaKzUXUkoYZI/YInsPXoSaE7VWtuVKKYVxjjhvcM6zrCu1lr1NLIJzhsF41rRAMDuTC7DW45zjdD6TUqL3jhOAnzICeWIHWtQYcq2IcXs/+c/pMHItWO/ACB323rR1OG8Izjzdn5C3ld4r1u6gkFal5MbpciZGj2gjOKhlpT0BkLUWejNY66mqTww5t7PlsKSt7j16IAwjxhig0yo4PzIOR6Tt5Kb9njq1VrzdgTwRoWmlm0pKK84IHsMYh/2d1EqpnVo6NReWy0paHyk5Y3Bo3TkeqVVy37kcqgpdKcFxqg0k0JzDD54YKs4LmWV/R7WhfdmZfSLE6FnzRq2NrRW0Z+g70JfWDG3Hi0pp5LyRys7N0Fbprew8vVbZtm3v2IiiurNFXfA7sGoFIw1tOzuwlg1hP66JwQW/l4zGkXOldzgtG7XtIB5ALeDDiErAhZkw3qEmIMZijKM1ZYg3hDjjXdwZluoZwyusG6mFL4Hhkhu91y/nnODp6tly2pmjqrTW8MYyj4cdCC2ZVFZMsHRNOFWCOnrv+OFrnwmAilC1Id7SMwRrUWGvvQ2cHy90zVi703q3pWKt3fvqAmtOaCn44AnmyOn8Fm8sDbDqmI4R10GsQ7vQRbDjTCgNKYXoAz4Eai6YKTCOI9IVLdtOaTUGp5bSGzYI23bBxoFhuqXUvd4upVCbfokpVGPpCGAQFdQK1hkm72l0xvkV18snOAPSobczuewTo0vnup0YZW+1uWjwdkCrobtOyg1nARFSabRSGOwtI0dUPcZ2zGzpBpwxGN2zrCEciD6yNUUinJe3MDT84Nnala1WhIHWFGctyA1qdixE+gYYrBPy6UqpUNpG1gvkC7EabErkdk+cj1zFk9yE5GVHwXsllcY0PufSL+gQcfoRx2GhHzrNHjhdGm3JGFupZuHcr5h8oqvh4d07hlyw7bpzPTL01LiuC+flgeWycvd8hnwh0/Dd8K5aPvJHBrcTf071kZw9yh48mgLG8LicGPyBMAxIP7CWz3FYdm6vo6lhGAMln5DmeHc687i+pWgiDAfieIc7Pif6G0b/DObM27d/Bm1DuwHj6U2eypfnGH/AZMH6gGuG3ArW3DC6hycKsqK17fRya7AiWI2kUpERrJ0ZpgPmct7voyqXvtC3wOW6MXHko+kOUeF+3fYuwVfY18IJCJBzxltLrxXvPZflupOIrEFNg6ZEP7G1FRcMac1Y70hrInr3lBUoaV3opeLjRLTQreBV6GWlOYftQtdK75aoO3OsW2H/1+mu0VoBsfjoMWZvC9nu6euVYN3OWiyJwThSumJtxIhQe6NbxZhIyivWWfw4oOIwwZL6whQCU4hkk/FDxFxH4sGznd5w2k6IREreqarSHXNwlJpwDqIZcDZgQ0KtY6ud+8uJvK5MwXN789GOCtP5ycNr1FiO0whU0mqIwwFLI7pAsQsiHo0DdoDWF1yELV+o/UzuHds9YgyZV6g3DDIwTgEV4fJ4YR4MaXOIGmpzuFbp6yPFD/zk3fcRfUWRd/R+ZR4c6h0xeDAGlSP0umsqomf2nbw4nj+3lNlgJaODQZzjWi7k65X1dOKojudjZCsb6gJFKlqF6/mE1c710gmtkGyjFUE8JD8yysAxHEnayKXRAhjr0b5hzURRw3mtNNu5bhWtjsKK4ljKAkmow8gYbinrRi4nrud7qjZupo8QE0E90U9YcUiLTHbADzfQE65DSon59gXeHrA2kPojxtwyjQ5dMoRKTHdYIpjGfX2HGyKjH3lcLsTxln5dqaMj+jvUz9h2wtoARvBmwB5GjDa0j4jAzMTqLdq/eql/LZyAojhj6LXRWttZgE8tttIyvTtQAcn0Dud1I7hI2ioqlfP9idYNqSaMszuRwwpVGrZ1DIY4DpTWEH4qKNpbSga+FLOICKMbqFoZ50grdWcq+oATYQz+KR2vROdZzhfm5zcoQqu6i2lU2NbCMB1oXUip4OOesUzDTHS6OztvyKWRWkXXRKkXrtsVoWLjjDONlldSz0QjRHdHnOe9L28FI44tb2zrgpFGzgZ/GBCGne8/D3sp4xw0uJlu2NIZjJJd2enQdaW2zDFGer1S1FE1U7vFOE9rnbVupJ4ZnMXp3peX4DjMR1qv+KOlnRYmf0C2C04jS7unfmYw8pa2OjhY6rNnPHv+gjnM9ArPphfkXvGDx+oDl3yiupH54CgBirnQg1B7Jl1fc313JqeV4+FIYaC7gFiPF6FhGYIDFOc6SKWUitpOjJ5lu6JOCHZA6dS2ItUCwrZtjMeZTgMHWjNWOyVvuzAHqNXRXGNbCt+8ucMUpTalrBY7QtWNKiOiBXEzVkBsZI7D3mU4Xykie8lhDMYNu/ir7DhQU4OYwuPjPbcILg6oEaQcqaVzNcIQbvFmhHFgNEdWK/SqrC2g6Yr1I8GDsxDHF+S+MdtXUGD2MxrkK9ff1wYT2JZ152dbi7a9Rt0JRLs4Z2uZrRUk7Oq4dV1BdkcRY9zLBLP7tP70Y53b1Weq1NIxdFovOLO3AQFi3AlJKvvxxhis8dTWMN7h3BNJ6CndVzopFVSVeZ6pvaEGck07LoDioqe2jo8DLsSn7ofH+58SfSo5b4Dseona2LaVqh3snhWhDSOKeVL5GRm+1FMY2Uk/OW+UutJ6pva9TsU4agfv/RNrb+cYeB8xTy1PlU6qia4FsZ3z9USIE8YI1owEP1J627saAp29G+PEQFPKlim9IXZXERqxODPi7AjdItqx3lLSRmknvCjRGKYw0FLDiaOVymAHggSECBJBdmfsvCVOB8BQesEpDNEjXSl9pxubEEklA+CM5+7mlmkYCWF4qr/bk+Zjb7t22tO9WHqvXJcT27bSULZUaKrkWtFWabUCBmHDyIpIIqWN0nen0FVwfmSaDrTqULEYF7D2KZCYyOwGtFR6TgxhJNg96HTM/tzsXib2Zli2E1teUVHUOjR41lafgGkLNuLDgRgGwjgxTLc4s2tYMA6VTjzccXP3imG8xYcZ6wKjG5lvnzMPE1LbV669r0cm0DvD6KhlFwb1pk/y146fHFtvGIR8XkhuI7oR05XSE71Xcq+I89RSGW0kms5arnTnicGTc8GHEbt1UIhhZ8VZJ2BkzxJqxXqDcYHgI1u+UnphcB5jHFUUu4MXOB9pYig/1SWLoTxJgn+qN+hiUHGUVpgnx+3tyKUmjOvU1qjFsGwLa76Sth9xyp+yJiFIRfFEa/BOsLZSm1DsisNRcyGOkcrEtjZSWkhScTbzbnmHt7vIKboD4whOKq0VFt0gCL0I0d/xYXjBlldSOWHDI+m60NYBRHbthvWUpmgxlEtCaczHA0YswTr6VqnSd8Wdj6CR+eWRx/vPWdYNt2y8unnJtReOh+dMww2RAD3wYr5hdC8x3rOuF+I4YctIsSeMv2DMLlrq25Uln6jrgDY43r4iTjM5J7guIIYlF4bxhoOLhMMdDzlxqYkYOks90auwGWUMHuMHnF7YUuFdfseyXMhtw+vAYYRzWqFstNJJaf+O98JlvSKtEcRxdzOT44G3TbFLJYYjMUzkslGbMEnnxTyTWmVoys18QybQNBFRuoOcKufLa6wo1+uZT9Y/BWuZ4sTN7ce8XjaONy+YSuSST2Ai43RES8b5ETOO3IaRBz9S/Arygpcf/xK3hzs221jzwv3jD0EGoh7g2TOu9qudwNciE9gVfYVWKh2lWpjmuCO6KZOe9ACqihXIeSNTyVr23nDt1F4YY6DnwtYWrHUM2H+OIl8WfBwwXRjn6UnhFgkxotKpTyQgqxXT9ImWugNiFiVv5z2KK1grTwouxTpPKQ11Dhci1nicj/g4gjMMQyBEz3k5Y61iB//Uww6kVlm216x6IeeEMZ3alZIyk/MY21jbmSWvZJN5k37M4/bA6/MDKuD8vKPdtVH7hTeXzzivJy6XM1ISVndgVamIha12gjmgNeI4EMwLBv+SqB9xGz4iiGVyd7RsGezEbXy+g5ElU7eEqYXttLC8+QKzQf7inrw9Ylrhi+9dsc9fcEo3/P4/ec3hgw937oTcYmqnJ1hWuHlxy8EdWJcLy3bhZorIVqhaQGaMRB7OndP5ylYLsgz0de/AOHWk0llTYSsncDtRa6Mwur2TYK2laExILAoAACAASURBVGdNCzk1aoGlds5lo7FRUuLt+hbNDWMcXTIi657FSaJKoInlcPiAGCPegvcVP8LzYcCbmd4rd89eYuY75ucjrZ5J+YRYiM0weofbKuPNwFDAIkiYcGGmcmHjTE+JqgvHceBa9zH3kliqIFFJ3bH1hRACQxi4bOueSZg9g3A2Mh5uGV9+jB6eMbuJOY48v/kYFzzz4MA2XLDcmIHrw8NXrr+vRSaACOuWuSwbx+MNJVdyg66GXgpdwfg9xaMK+Yl4Yw1sZd1R/NZpgBrLaNzOP88Za4XJeRRDr50whCdJpwVdqdVTciMMDjVCCxFKI44j1+uZrWWm8YCokNsuB95KRYyliODV7BtWlL1vjrH4MCPGQ2vEyXJ3d8fD6REbQbXgB6GJpWqi9opzA9N4y+1xJL0OlJrZauEmBi7lxHScyT2T6wOnUrElMEy/wDAMxDCypTfUkrE+8O78GTd+4MPpG2hJVDKdSq3CGGZi99zOH1KqEKOQqkPsDF3xt3BZzox+3KXCfqRbYX04M1hY7n9C8MOerW2JrA9wHmnmOb/3yT+mmYn/7Td/m2w2fuGfveGjX/42tzHwj37r9/h3/8NXfHz7gnWx/Fk+MdwO6Jq4/3HC3TSiu+Htj17zeb4SXWOdMutJyFsn9EJclTCNtFbY1ntqHqj1HbU7BnnB6Wi4li8oNxNePBWDtM6WHolbpLtb7q8r75YzZttYdMVHSzAOdQObZkwRtBmMicRhxtQrTju5V56HZ8zTkbxWLBE7ThxuTlzXhW5l54LUlaqN02VhOA68qh9yn77ABI/mQpWN++tOzHr1wbe5/KDw5vqGUYW6CuXg6Hg6L8j5QkmROFn+8Lt/zItfmJndkaPvjMaybJnh8Izl+pqqjuZGLMJp27DhSDeWbIQrFSeJ02P6yuX3tXAC+24wjjCPbDlBEVLvLFvhcJiwrUFXwhBZ10R90rKLKCJgimCNwRmDM5aUN8SAN7u8tLWMHwfEWKzq3gXQimXXqVvTsaI48fRLxoojtcJxut3p3mLQp9p4jHHXLHgPonT2HV2cOLLrWB8QH+kNBu959eqO8+MJESUOwjiOpJSxtpPyRhw8qPJsfMXHh4HmLO8+e+BerpxaI7gRI47aGtf8wJYyORs+oPPcz3gbSb6jFPJy5cbORBolnenWU0znsm2U3HA45ukZPXVKEZCK8yPXvHAcnzOFV2j7gtQfmKaJaG/IWyWFSu8rmQqScR7SySI60aPne598wb/5r/1V/sf/5v/ih1+85e/8nb/K+PKv8Zv/7W+z/PC7fOvf+ev85n/9h7z6TmL5sfLRv3JkSsr/8b0/4t/+te8QQufN28Tv/tMf8Cu/dMe//J2P+O5vfcazVwFbMw8NXo2eel4IRUib4sqV3AauS8A8e8ObTzZyV+ZyoU2OkM70cKTWjHF3uGGk9BXZLrta8GGheoOSuHuRKH1F04YzUIuhN0vrFSuFJlClcPCRYZj44u0bvFjUbRQ21nJlNAOBmYfzFa3w7tr5UCOFwI/fvONmuONhSSz1Qj4/INbzZoHJTnz++ie8iCOnXin5c5yJvHg+sPULv/c7f8R3fvWXuebXXN5Wpl/8Bm/fvma6ndlSZS2KDTOPS2GMExoqad0Y3JENS9aF873hh3/0+JXr72vhBHrvO5HDgHvaWCTVhPmp0q8Wght20okx9AZN9407rNk1Bab3fRMS4+k8baiRVub5QOmeXCuHaUBzBzUc5xvWdaXXsqeQudGkcphuoUH0YQeCzI7EO+9R3cfpfGQISi0N8rZvFdVkl/w6T9ed599a4TDNnB8vWLdvWLKuK+M4seaEmAK9YUWhKTfzc0zfqPOBJW/0YBAmhEAuK8DThh27sswZyzBMXKvQauY4ROy2b5xhrDy1NzPlCWMZQ6T1hLTAuhZq23DBY0a7bwxS4HC4Q7cNzE6gMtbjh4GaCxotxgeWhytFhbWsqD7nH/zG/8Sv/6d/i9/95A8YpXKuyifffcutmzjLDZ9/8gV//Ru/yO/83p9hxgPf/e+/i2pjKYk/boHp8Ix/+Dv/J0cz8OqXnvPbv/c55yT88M9+zMfffsYUB85LwQ4HfvFGWZKBbjg/rLTukdOJtA28uZ4Yn8+k1FmXDd8MncBxjmznjfP1jKbOed3puNfHK/NhYLkmXHZYPNaDZW83b7VgqGhTlrSRW8bXjPP7u922BRcDoSacOOZh5P50j7NCiJGCY8mN0zXRy4Xz2vFHoannupz4/vff8isf/zKlPkXwWRhiw4fO49sVMzq0Ct//00+Z7hxSMg/3r/ngxQeUUsipY2QnZ7Xaef3mAT8rlYQ1ga0ors9cTp/wxWc/+cr197VwAgpYEZwVStnVdatsDF1YlgV52qQiTiM08AI5V+I0kNZH5hh3xptzjDGQ1iemVQw0s/eaR4TeVmKYCcbTe8N5T7CBrTZC8DTTOfeFg8yoCtoKVToBwXmPYLHOYceRbjxOBpoatBuadIId6c0QvMH7nah0ergSgqNhaFvn2ctbLJ2tX9B8gnyhq+MQTnwU/grt5Uswn7M9LtyNdyxtZmDgTy6/T06O3Hc24na/0u82bo4vkP4tTlsmr+84xo8Y7co8z4zDRLt/4PXDp+RLYxsvDGHG1Lc8vHlLdwnthg8/eEkfFPPsjm1bWHJD3USqK6Y9ybztwKUb3v0k8Iff+x7fdN/mv/vf/ymPb/6Af/1v/zL/4B9+gdkyJkT+0f/6W0Tzu9BWtDeiecHy/Ss/eXzg4fSO0iofffAhTuF/+fSTvbOBYK3nH//G5zvt2oE5DLz5wYVslHke8e0zfv/lHW9P9ywPjRcfdn78/e/yi9/+Fm9OF158PFJOV378cELNPd/+zq/xq9/5NVwe+ZPv/QkffvQKHV7Sf/yOtL5GVFmv8OZgOUwrNwfLD370J9zePKMmRSbFxg3fHYPtfPcHf8j94x/zr/61f4utvmPyIw/1HbN/xsEf8Hbg3FZ+8PpHtCy8sB8hLvGDP/7k/2HuzWJ1S/Pzrt87ruEb9nROTa7q2d1Jut1tHGM7yBBbNiYJgSiEYARCMUIKF0QK4gIMQiIILiKkiCsuYlkQEAiMEAhDgofQnmLT6W7bHVd1V7nGU3XmPX7DGt+Ri/c7uwN02zKmUa9zUXs4+zu1v7Xe6f9/nt/DxeMBU0de+K5jtm9v+OSPfDfnb93jy7/2ZeLlnh/8x3+AoVWsT5/y5b9/zue+5x9h8+6ed776Ph/+2Iusm2MuLq548fh5+u0j3PWSm3mDSpoKy+BuUDGx7ROaTLor0OaM+4+e8nd/9je5/8X733T8fVtMAuVMXYxAlamJydNITWMMYxYokTELw+BKey/nTFVbko+sViuGbl8Uf0ozuBkpQCmoTYufZowtfX6dwdQNKZZKflVVEDN1VeGjw4WAsoqsY+nTJqiVQSUIfUAuDUIJRM5E59BCYe2KKMBoQ6QcT5RS6MpC8vTTDVICRtJqyRrBxc1V0eprjRUrwv4+j1Z3+ExSaKCuFlgpsMawOHuFeUo052ty6qiUQoiRcXzC0J/w8uKEowSPYk8fzpn9DTpXCBwrFlzHTNqPzH1HiDVi4WhMg3fFamyUYneTuLOWbHxN1VjS7Nm5p1R1ixSZecyQH5PEC3zp7/wKP/gj/wT/+d/4r+hkYre/5r23NP/g1a+xbmsenz/m5OSModtR15Z6tWQRK86fPmTIBddm64r9fk9OCR0zq2XLbvbY5Zqh2zLnzPJoiekzj7cDJ9WC3fUWLzL3336Xo7NTtruZ3dNMtTT8zu+8SRDwysuf5Rf/1y+RbcbHTKOvuL54jUePt7z8kRPu3v0wV5uR7QaGSRYAzaR56WOZp08cq0/B9ulD9k93jNOOFz96BxMVepn44KlD6hmtTrna3qfBMh0JxslzfHaKiILJO3wv6NxEEyv+3jtfYGU1r/3Su9R3LN4brq/hbFHz1/7C3+CPfPiMZVyjXj7jRka+/Lc/z6u/+wl+6I99js//nS9x9d475KR4cvw83fkllw/e5qxZ4o5e4PrNt7n70oKz5+7SzZ6XFwum8Ro3RZ7ogfV8wqPrL/PalwYeffE+t3Teb3B9W3QHSJlFs0QpQ4yFtitzUVgJJZmDp+sGJEVJp41k9r74BCZXzudZMPXDgTIbSD58/eVTIvi5QBtc+bmcBc4VSEOMvnAJVelTZyKIwja0ogAuhNSAZJgd8cAjfPb3n+m7ja4KIfgZ3VeUnrSQqWC8jGWYigDIajgyNYuTU4Q5RonIfp5uX7e1S7SoIWVkNlR2RXCOaRwR0dL1W8IcyEliVU1dHRHmQAw9wRcbdcyhEHJzmesbY1hUK4yypOjRQmOMZmFrht2GsduwuXpK9AGdBf2mx+08bj8ixy33XnvCkCu+8MWvIlAM+w6XIrY6Yl0tubm5oW1bkg9M3tFPI3MMXF5fMztXWJDRAQnvXDlaVcXKPc8zXd8z+xltFZFM13V0/cg89Ww3mwIB8RM32w3dbsMc9rihZ7e9olaSvvNEo7nZb2juLun3E1/5ylfw5xP7YU93fsV7v/02u4ueeacYbzQxdHz1t98gXc9cbwMX/VPc3DHOHdfnj3j86Irj9YsIvcLNNWEOvHfvnOurh/j9Dr+L3Gx7hII8F3u3v+45j5H3X7/krbffICbHm6++g1gYvvLFV7n/+Jw4J8TxistpYPncmquv3ufjH/oUZ097fvdr7/Dbn/8KVbY89+lPcv2b7/K13/gqZ4vvYB5nnrx1weXlUyq1JMya++9ecu/+Iz543PPO2+ek2TOPnn04441f/p0ipBunbzr8viXhI3/Q687ZSf5n/qkfZgoRLRWDHzkylowkZojRo3UBg4AEGalXC/zsCH1fyL9CYARknYqHQGu0VQiXWVSWnBxKtkiRUE0Rb7hpprUGKQRRgDtw246XNSIVk0+ta+YQ0U2FriswAmmWB4GSwMoFSYFQksqsiUCzMKCKT0HoGYlnUQuWpmVwHZWILJaSsG7ZXDxhN+7o+4EPn52yWtRoJCG1dMFSr0/Y3PS89v6XGKee2Xv6aY+fNR/98Ef4zIde5rSWOO956+Fvs5meEsYNH3/lB2mP14w97Ld79ttLMPDi3RZC5OHDx4xpTU6Ss1axaI7pB4GsFFkoRLRENXP5YMvrb7/BvTcGtu/f4HXGkLi62tMuasacMVkgxUw/empjsUYhMwyxiHSqqmG/37NYNHg3YaRCooqNVwpqbej2A0JJmqYlS6jqhjTNxJjIooi16rpmNw00pqZpKuZJoasAKDabDVJFosvUpoWFQsxg6sidsxc4Xj0H1ZLd1Q3tC0fsrq4Qfs9LH3+B1157kxdPjji5c8rkn3DZB56/u2C4CaRoWT7fs2xOeP29V3lu/QrrF+7wqU9/mOunnjfe2vCjf+o5+ljx2q/eY/38S2yfXjA+DHj3Dq/f27AWluRmXvr0x7n/3kN2uz1nJ8fQO3KEk4+esRg99lRw8XSHzJKdk4TYc3Z0FzfNNBg+8sox7z/p+Mgf/w425wNqM/F4N/HxT3+IzeMtynn+6A//ca7efEBsT/nga7/DmOG0WnG+vebzX/j138w5f+//ffx9W+wEcs6MbkZZQ9fvaGzFNM+lIp/CQaNfWhxFiJOIMRayMBxW80jKB4bgIatgPszMKUSkLITXLAr3fg6FFPus+Oh9IcrmEHHBHxBjkZCK+EZkSJT8gBS5JRYX6nC6lSELITBWIZWgrgvBN4QizSVE8uhQWiNkJomEqloq2dA2hUjrkwdZPhZGMEwenzxSBnwYyCSiE6QAbhrohg7vZ7RSnK2PkdlgTE3XX+PmntE7vAzoVnJyvCQ5Q6MtAldQV0pRWYNCHLiOksZWWGFQNJw/mDjaL3j44IrtNDDvR3b9hNCCeZxYW8u07ZnGcFAxFgKzPjjnchbEbsYoTdd15agkFVmkosUQhSz9wvPPI6VE6zKBRB9uHaG1raiU5vryihqNHx1X19eo1BOVph/3pX/uC51q4/fIqPDjQNj1DG4i2sCjizcRwnP1u6/z9PJ9Ls8fcPGgw6BI0vDlL77B041i3hikMJw/ukHN15AET+5teP70U+wuJi4ebumGife/8pC891xePMIEw+P717z7lbd5dP+Ci/ce8Pq7jxm6AR0SUUfm++cEN9HOnnnTs+2uMQL8ZQdtxcWTATkaZmXZ7TasqmUp4LrSWdruB+4+f8x7r9/nyWvv8ujiio++/BzzYDEucfyx53jvC+9wGTPvvvs2I442GYJWyPDNx9+3RU0AiqAijDN1s0BZhTULREiYqmUcewSayhgykZwL7fV0cUQIjug8VgqkMAgi1ghCiGgUdlHBGBAxE8KMNRWqqkgpI0UiKUBrdJJIWWTIzvtCIq4sQmmmVKAMxVUnqYwiHNiE3TRTNTUhKuq6QD/JgUZJZr/HTyNHjeVTr3wnX33zDVZGs0ahq5YcBHsiptWkccne7VirNa0xSOExQrPrB276HW6OZG8IOiJVRbhxXD3tuRYb7Nkxtho4XTzPtJJcXL+F7meCGbFtg7ALgjDI7FFKMqfASduwc0+x1fNUVKXYqiJG1xA8u33mwfsdP/Nf/xyL1pbui5/wAojQ1C3BOUKCOcz4KWArRSQgZLG43mw22KoiKUX0kZN1kQJLBH23Z5x7VqsjTtolF5eXCKvZdnsqrUhzmTiqpim6hBxKIW+eUEoiouRq31OFiJWCfTcwE6hsjc7Qba/R1jAEwTIK3nn1a4WjkDdoVSNQCLnkg3ff4vjoiPtvP4Bx5vytJzSN4tc//xYndskbj2Y+pj7C5aMr/IMrWiGRUfObP/8O5+9ecn31iM989s/ya7/yRebuikf39ty98zIXu4dkl1kqxeAcTBF3UjHcf0zdVIzTnufOXuBosYbhhntvfUAuvQjSlUcqyYOHD7lz9gIpJDb7xLZzjA+uaauWkzvH3H3xRa6uex48eY8P3zlm8/YlcoR+Stx8cM5HPv2dXNzsENsNc/w2Pw6cnR7nH/vRP8lisaAfZ2wlsRJ8FgiXqKqKbV9qAlBMMUoIUihtmsVqTRoHZMwInWiUwiiLJ9BYy6peYESFyyNSFpjo0tb0IZUEISmZOfjODz7t1Wp1EOIMCKMRVcVydVLw5YuGum5JEVTVIJVBiOILMFpiWoPMCeRMjWK9aPnYi3d4/8n7hXPvJziu2KbI9c1TZI6I5NDVgpNFzd1mxfWUeXw98PDqmjDv2HVPmcgY3SJc5vp6w5Fv+fBZxVGr0YsF3/GRl/Fp5OrJnry9h1q/QN8W55ytMtnNtI3EmoyOEH2NEYpWHjFNPROKLYn330r8L//9/8a8ueT+vqcoIyK1NaQg8CFijWR8VsOg2HTXRw3D1LNeH5OCYHu9IYnM0dERu92u4OOFoKkqhOaW17BsVoUU7RKqktgKdC47Cecj0kiICSM0+4NvwypLlpl5HlG2+P4hoYTFzZ66bdhut9S2oV60QPHqt5Wl73vWp8dsup6qqjhbHXF5fUFtLYjEGCNVVRH7AVUZ/Oyw7QJiYuhvWNgl8wTVQhOk4IUPvcx7X3uDs+Upu3FfXAdzzzRG2rYYuo5Xxzy+flrQYMlTLde88vzz+GnPw4vHfPq7Pse9dx9B8nR9z907dw5HqAqRBKq2XF9tOD05wY0Ty+USKQwx9rRHR+yvJ4JweCGRznN2esq+n6mRVC+f8OW//2W2u+7b+DgAt9tvKeUBSuGxdQmA6KceFx1CFAdeCSUJaGuwdUM/zgVrXWmQ5SwvUOiDiSbGyDgPVG2D8BmhJEklbBJkLZlE4QVqXaCOVWUKv9CV40ZKkENmu92WYqE25BwJyRdNgkjoyqKsuo0kczkyjY59v6NdHbPfbcghQEyE5HDeE+aZsdsT3ERyCQmlCJkT/bBlu9kw9df4eWAaeoyS1EKzRDDOke8wEpVAdh6dE+RyxBEpochID2GYmPd75mlAaImPiRQFyAZlDYiaKA1ZLhn3ifla8/ZX3qS/Hpj8SKsVkRLsMnTjoXApCaEU+CY3k3Ik5WIyWi6XTNPEbr8niYzSmmHqi+Q1JYwxzN7TdV1x8NkKZCn+hlwm+P2uB2CapgJEOcSKeV9k4ovFgnHsmYexyGrbhiwocvCcUUbTdXukFBytFqXmQ8Y2NbMPVE3RnMTgClDWTShrGOeppCJlGLoBKQzTEGnXq8Jr7HqiUkQhud7fsJ1GpJS8/dU3UQiuLq7p+vkWjlNV1W1S1qOnT27BHnXVMM4ON43M3vHhj3+S692OOQVm56jrhs3Nlqqq2PcdLgb2u57WVgxTT0KULI6UybJidg6fJ1IShN5hTMPDB/cZ54m+ljx+/z537OKbjr9vi+OAlJKqXYAAnUBry5wmRIaIRArNerVA5HKOXq4KnTfMjpgVteX2QWxtVeyzsgzyuq7JKbE8WjL3e6Q1CGOYYsSuK1ayZjsNZQurFSt9jJD5gAfP4CXyEPygmwofEnL22KbGtHWBQGaKw49MVRnqLHChoMY/8dEPMW23XOx68FsamxmGDXU8o6oCtZCIYCFFaqFRasF+sgybxzy89z5Dngty3TacRsVSzJwI+GgDd1rHKy+t8JXiKkr6p++Q9JrWBOYM0/QBY79AxobZGcKRQFdrooA4PY+eTmiw/Nr9h6he8j/+7M9Bmui3G0QKaGGZp7G4JF2gVgXXPYwjp2drQr9H65KKVNuGcZ7YjA5rinszpoyAkvxUWYILbHZblstFESnF0vZVxHI8O3AcFQqXMm6OTKlnaWpCFvRhRHpPIqMqRW0rLi4uOBLHhDnS+Yl5LguC0pmqafE5crPZc3x0xNiNKJkILmEqSw6ROfTsyUz7kaO7Z3xwdU6j61JhZyaIiL/2TH1Pjom2MXR5Dzkwdzvi6JldR9AVWht0Km3iuq6Z5oDS+nBUibhNR9NWzLFQl292W3wInG8fQPIkIfDDxHK5IMTIOEwkAZObkVEypkizXDCGjmrSzFNinHtcTsxTJvkyae67grsPw8hie804T8Xs9s3G3/9vI/33uATilobaSMOqblm2K0rCObcRTlprlDTM84w7tJieebTrQ7pQCKHksTUNxhQktEiZ6B3WWqqq+nrUmQ9Mw0itDckHgvcl5DHkokp0GUQpEimjbz0Hz+zAwfnbj6WUGKmwWsOBBhNFZr1YMXpHkoF+6Oj6XXE8hkC/70iTR+ZSE2laRVVrsgrstxHizJFtSgfDz0gtaKREK1glRW1aorBoo2hshcwSP7uSr1AZrNVo58jSU+kK4V7G5FcYu4rt48ywi9y73vD4q+/y4MED5OyY+46h6xjHsRizXMlCKIiychlTttQhZZTQnB6XLap3Gecc2+2uxLO5mcvzC2SSTNPEfr8vaHjnbu/hPBdQqhClsBpCIUa5Q0sxhEDfj/T9yDzPKKOpqorKWIZniU6HuLpn9zUcrMDBlYg42xR3pDGmaFJSYuwnalss6NM0oivL7vKa7/++P4FzxRxWLsk4TaQDisz5TIqCpl0gRHmWjK5IZCY344JHSkXTLg525mJJF0KAkkyjO5CDIlIoZu8ZhgHnPDHGwzgoFnMXCtwm+IS1luPlimmcaW3LnMu/lwWMw4wPAWE1PkUyZffkxonNbsvRyTEiffNZ4NtiEijLRabSFRjFnAJkSQ75duAqIZDPLMZZYGwNSiN0wUiTIrXVWKNolg3jrsdWNUYZmqYYYp79ukop3DihUvEfpHmmMvYA4EjUVUtKJSlYGkkWGVXVCK2KICQVb3qMJaSkbdvCoRfFyFQbQSUUOsF2d82oJia/4+RsTdVUDNGz6XcoFNapguXKkGKFiBIXJoYBFhrOlOa0PuHl9RnLybF0E+2wJ/pEngbMskWbBZXSrPQRlcxYK1EqonSDXHwMb76PYf8SD99tePpAMp4/x/0HE+d7x2/91mu8+vrr/B+/8kt04w1Zwvf+Y3+Cj3znJ7nYbrDaUBv79UFxyEb0h/ASkRRhDgWflmXJF8ygq4qYBW27hEOiUNM0t69jbI0QgsVigTUFsmGVIUvB6AtvMouD0SsVgdh6eVQ6N7Pj6uLidtCkkJBSHZ6NjFGaGDJaWVKMLJuWeRogZW66HbN3BRU3O5ILLNq2oLxC5ld+6VfLcS8lQi6D1acin55yKhg8Kbneb7nz4opx7kkiMnlPloLFYoW0FfcePSkTaU7s+46EBG1AaSYXcCEwzqWwKpSGAwswZcVu37PZ7hFWH+L0BDlnHj15ijKWq33H9nzD5dBj2yWeRBCRbbcnIeinuXAGcmZxcsLTiyvI3+Y8AbIoyUEIjFYlQtoczoC6QD8MmhBKu86kRB48NilqDeiS9mLrBiE166oimTLL16aId2prSqxZikjhWZ3eIfsMKjI4j1RF5lstGrwfEVoTXOTZAhhjwtYVCY+xipAywpiy9VQFXFrSkyKL2qBIHD+/pJtG5n5Pzh2qPkatW5Y+8/h8y3zpaAA37KmOSlS1SZLxRtMsMsZ8iLZqCZOjDwM+RW7GSB88m80WqU/ZXd/Q6yPu1ktOFicc3Tykv94xH50g9YInD88Y5og2FU+ffMC2P8WPA5c3T3n3t/Y8SBPbzXvcTB1ruyBNjntvvEU/ThihSpy4jygBSfhCVpaS5AJawendNZeXl+iFgRQJAZq2ZZ4HalOSoPw4McwT1ppbLqT3/kCMHkkRRF0z+5lWV6icmAZH2y4Z5w4ddVEyJsfUDwhbYYwmuBm0BhdJjcV7x8JURdGZM9pIcjZcPHnMcrVgmjuyC0QtiMyEVGCkfd+TUmbKgZUxJfRUlsj3kAJaGVKIWK0ICcI0URvL+ZMRRUWIguQ8y9WSq+0GMXYoJTCVZXIFlmOU5aVXXuKDt94hBIeRDV3fM8yOs7vHOBcw0hBnhzUKa8qOl5gY3MDlbodRluniitHNrOqWqCLvffB+cdEqeZgwNGfodAAAIABJREFUSx1m7iaSkAxdT3QRW6tvOvy+LXYC8qC6q6qKrCX5UHgqMV7j4cEpBcQQQqnW6lLAMZRCICmjKICQNPvDyqIPxCGIyRNiIdHcRp3J8jAao271AlJKyOVt8WkmZ0+MrqT9ugMFOZZWYjxsQcMzzYEQt0iqWiieaxYgPKSxUIVMYd4LK7HWMo8OP5dtYHAzc4ikEA8Mg4plW9EsT2jr0hrVvlCNuvMttC1jjOxmx2ZO7KeENg3RZfIcES4yD3fZ7T1WOp688Q5OJrqbc7rLc67u32eQE7t795jnUvCbg6f3I09uLuhutgc/h7qlMytVEFmCRGU1Wgr6sWOxaEqhNITbewnPOi+U3ANZknylEKQYD8Rmcau3CNFhG0M+aDqqqhyZYk5oU35WKUXTNEBJVq5tVbQd+etF5aIYTTTWlO7ROFI1NcM4Fv3CgTYVUqSqLW3b4l1gjp513ULKVFXFK6+8UhYhpZnHInBy43QQrmncHMhMpOwQMoGSDHPJrww+kWMq4NlDsTSHyPvvv190JLocD0JOrBdrxmkoR5SxFDpvdSvTTAwZoyuQlraqmbxDZ0EXHfLAygBIMRZL/jwUc1EoqdrOfR3I+03H37duaP8BLiEQShGgDOwkYPLUWmGXLQiJMRqjFbUuicRRZpqmYZgGtLKYugElyV0BMYhUioikSK1rzMGSm11EBBBakUM56w5+LHDIXJBUqraEmAsnXiqUscgMprIFPX3Aj9faFhKskWRNOVpkh0KQ4szFZg85k3yikQJZSRIaJWsqWzLjgrZkY1C2ocoBhUFOM0ftEaglTRhRskWhuOkumbtEFzKNyFyaln2uqGzALc5wRrAgIoXGTcfcvyppQINzXE075FS20vunl4R+LE6+7pLJBSpdlYp5zFRSY6yibtvSDRAS4TLW1igtWRwwbEdHRxhjODm7Q8zQVIZKK+LhwZNa4aeC+KrrGlQu90hKjDIEFwiuxGfnHAnOM4aJyU8srUEqhUUW8VCM+ClSG41SBfs2zlPxXzTVoVZTOkEiR4TJ3FxuUZTsyGdbaiVKQCo54F3g7PQ5zGFiR4GQAZMF7fEarRW1rtG25E6slkfgM8SAiwHlErptiSlTNTUiCbLMhQysDFJLpNEFiCMFIpYYdKEAnfEBogxgNdk7VkdLEAIpaoIvnRKtNZtuj8ollcqYimQkOUZcPBybsygdthCwdUsUZaFrTYXhAIJN3yLasBDiHrAHIhByzt8rhDgFfgb4CHAP+Bdyzje/zxxApXTh8zWG7DLy8MvIDFmWDAKhJDkGdAr4KZKt5PT4DuO0A2PIwqBqhVUGRKJtWuRhJQshkfA0qxofHTFEyJmmWoATKOJBQQgChTlYcb2MeErazVLUh3BMTYoSbQRNrUAnWmuYhePuumypjYCuv2JKV1RNpl6fEIUkZoVa3sV6iHFTWkLrmqO1wooFbkzI5ZrFbFnKRJomhvmCm37m6knDf/TTP/9/ee9+5n/499lNjiQUf+xP/hX4k1//3k/8xL/B/ffe4pMf+hhWVbz6pd/i+PSEx5dPuLo5R7w9Ui1aFrJlnv2h8CbJ0mOyIrgZWSmOFgturjb0U8/nPvNZ3n7tDWx9KLrGwJPNB+Aj9fGK9ar06a217LuJ3o0gBTGnw9fLurPvO7RWGCPJoqQ++HmmthWyUmxHR4jw0Y9+gsf3H8AcUMbQzT3BFWKOEw4hDZMrKT8xZ4RVLGvLdjcTkmCaZqQ1t9DVu3fOOL98SpIKmRLXN5c4EuM80S4XjCkjh4l333iLsR8xx4YwzeS2+DWWzRKAs+MThmGimweayqJyQpqADhq0xAEhSe4ct8TJMU5lx6KrhvVizfV+y3IpqLQmuMSnv+d7ee3NN6hUyXc8vXvExfkVWXuqurQBhRCHoJsyucyzozYVMRaZfGMrckwkIrKSKJ1xLmCrEizL/I2jif+/qAn8cM758h/6/CeB/z3n/NeFED95+Pzf+b1eIB224VoLpLKMoUcLdUscnqYZnlV/Y4GFZg3zNKFToKnqw1lIkChbpLpqiqoPyLmk2gpKZb8fAovVEo9ASIlUhVxUH0JBummCJNASVGUQRuK8A0prC2ERWuNCoEkBLS2r1QI6j5UZbcCYhm7q8SKRoqRZtISsQQkm36MRVFphVeLseIWpLWlWmKZl2h2q08mxmTswNWftXf7Nn/5vAfi3+fMsqPgP+O/48X/+P759H/9Tfoq3eY2Zmp/mP+Fv/a3/jH/lL/zLiHXF41ffJRF58vg+OSX8OKC1YpynkoRrLciINYZWWWwtaNuWzWZDoxScrNBSsnlyjV2vGLptkRxLSb1omXc7bFXR9wNSCnbdnpgV0mhUAu9KvmDOmZwEbdvgpiIFN4fui1FFpp1CKBVxY/jq669zulygjMHlSMqp5FEIQEkCico2CCFww4CQghhKrkTb1lRGM8wT4zjQti3n5+eEmKhtzeRmaiUgldTorus4rjSnn/gw3/Pdn+PpB+f84t/9ee6sjskCTs+OmXpH349orZn9xHFd03U7qvWSzEGPYmyRRifYXe84OV7jvS/5FZTjRWsqptmV+HateeNrr7NoG7J3+IMbtVq29H1RVcKICwFrLFKCVppmVXG927NqamrbsLSW3dxR6ZraWMZ+4PjuCU+fPuVo0XzT8fetKAz+OeCHDh//l8Av8/tMAgLQogRk6CwJwoAGmQRT12PrljAPpNkz5YSMAiVjScV9VhAJDm0bktTkqoRupJCpZMGEawkJzewC1jbMhzBJLQPBSOY40cgK5z1VZVBZEl0meIdHs2hWSKsICJAFb10Y+gqjG7ZDoD1WkAIffeHDXO/P2RmHno7YKY9dVJjRsssdT64vEePAkYB5BVMwNPUpU8j0aGTjeXh/x7q2/Nm/+B/evk//Fv86gomZloeM/HP8i1QYPGDRdPw8ZWNYZvw//aN/hjleMD1+l8vzC4yQJO+YD4lAiVL97vuBFxcNLzz/UcaxZ3CRGoOKgmXdIFzGoCALrm9uqCvDennGzWZD7zrOzs5YHR0zjhOTK/4JLUHpzKpd88HjKyafsDmwXh0zzzPTMLBclep9nAIhZlAZEYvWos8zSmsWi5YQHY7AnCM1kFGlOxIySQpsY3H9RGUtUXimCV56/jmuN1dMcURJw9HRUREDiTLRj87T1BU3mw0xRhZtTZ4d/+if/mf54q/+Mr/w1gdkpXnp7os4V35u7mdiEkilGKYRmSIxzKyeO0XO5XjR1FUhTecEOhNC6fNHJZBaIFNGRkeQYITGSsMcAyfHK/quI6ZU6FijJ8tis1aicC2ZPWcnJyR1yKUQgs984qN0u47kPP3YcdK2oCD0M7ptSGHm+bunuMl90/H3h50EMvALQogM/M2c808Bz+ecHx++/wR4/hsOfCH+MvCXAZbLRQFiZoiiBEu65G5NPynEMvUrhcmlWNUPGxa2RmtFziXnx3vPol2Qsyf4sqopIcoZVEv6caaqKqZhQAmJPxQIK2MJIiFy6SVPKeBmj6K0WYx8VhwzZKFISgEJ/yz66RB6ulIKIRJZQtaCKrSMYiTHSNh5UBYGT9yNaOGZ2cNwRFyOJA9zVqjkuXjc8eP/0r8HwF/jJ7EYHtMxMLDmhA07IoljFjzgEYYazZo9e0Z6vsCv8/0/8IMsrMeFmXHfsahq3DgwTRMpRqQs9metNW3dsD49pu93pBhZ1WuMKAacfAjslEpxs+9YHXrOPgXWbYNZVngguBmtDYvFgqurK+7cPT1MMH1RMqbEenVSxFs5I0UmhIjIikwmp4TS8lBkLQG18zjhQ+CF5+9ycXldet2VQYniPzAqE1K8LYTpyhBDxrmR7fUNdVvT93sqYxjmkdViydD3aGPwITOOBTCqlMLj+SOf/iyf/4VfRB7UkM+9cMrDhw+xViMySC0ZxoLFb9qWOA2lQDyXnIN4KHgaWxUNSWUwRjINI1kLVk3DNEwEHyjnzow/AGudK/Wpru9ZtC1+nqkqjUiZfd+jteboaFVw+c8SsZcL3ORKqjbQ1EW3MM0Tlan4ru/+HF/+td+gbutSB/kWTQI/mHN+KIR4DvhFIcQb//A3c875MEH8P67DhPFTAC88/1w+fO222p5iLMW7zG1GQMoCQmRw06E6H9BekBQ0tSGmxLDfYRc11laILEkxY2ypjlpr4eCcU0YSQqEGK9uWMIhQnG3JJ2xt8HNC6APuzM/YUONCQFaaqjIYK29Z/sSIUqXgNAfPjKetjoustnOYtsIrgR4TVZbshp76WHATIMSOH/rsv/r1N+cH4K/yV1FYHCPXPCLgmPC8z1A6BWgyKzyOji2JyECPpYiuFuuWm/Nr2sMDKVIJTdFaM4dATPHQOq2oqordzZ6q1qRQBD8hZSqryVMka0kkY2xdJtq6QfjE3Zeeww0jDy4uCutRFlbkcrlkmmfapmGePEfrBcPsiTGz2WyK2zMmpC6hpyJllFbkW1dnmYxr23CzvaauaxKZ5DwzCRczjbUgJGcnR4UkXdvbLofVVRHqDIWwNO091hzyJTh0JFJxPGqt0UoRs+DkpRex776HNpb9sOfBgwcYa5mdR0kgC1SlyEkQY8bUFWGe8FMkpERVW7p9j9G2dCmAYZhYLVuGaWAaRoZhRCJYrhbM80xda5KDYRiKxqSq8MPEom0Z/UzO0NbleDFFx7pdFJGcaQiUWmsIGa0V3ThQ6eJjkUbz5P5jvuPFl7je3DBN39xA9IeaBHLODw//PRdC/E/A9wFPhRAv5pwfCyFeBM5/3xdKGalLn95ISYyuJPeKjNKW5GdE1qy1YDuNiBjQUuFzZCGgUhJC2d5BUVfFGFEyY6zF54AxCoIgx+IO7N1A3VbIbCCVjEGhyzZrWZUbpHTEZ4WqbWknxlxCRbVEyIQPEzU1gchyaXFJIpXi7f19Xjg+Q1YKd/ku1/sr7psFttcE5XD7np/41/4mAP8uf4Ubdvw3/DhHnBDLuopjy8BAZMc5jyi0u0gmEpEYFG9TNPaOnoEO0OgDQebzv/ALfNcnP8Pl9ppx2pNDaeHVdV2YCBGWiyVaWTbbgc32GlVbVIw0SqEbDTSkELjeDKVmQObO6ojddks/+UJ60pK6bYnec3Oz5Wh1THTl/3MzzAW0YjI2ZPbdroRwApUt0Vk5F3FRSonsQsHHSc04OeZxh9SKhw/vY7IgtjXRR1prDp2hkW4/EEO4hbjcPT4l5YAPASsFwZXBng5MgrIiZnIOaKWolOETn/gUX/ztL/KV3/h1fJyZfKBuG3bd/oCiSyhlSDEhU2YaB+Z+oG1LVyLFSFPXxJRRxt7a2BWGqi4GpMrY0lptqpK05WZWqwaQyDSV7MsM/dhx+txz3NxsqauKSitcTLRKIcURSQrWpyfMbsLvegbvCSlS1xXrZlEyNLynXa/YX1zTD/sC3fk9GoH/r1uEQoiFEGL17GPgx4DXgJ8F/tLhr/0l4H/+/V+stOxSLj1arSyVKitzHEeUT8To2Y07IuUcOMeAUrogorxHSEgx3GoLxmEAMnMYC0OAsvIhDDIkVkoTgyuqP1e+533RuI9TIKDwtoR5htmR4iHVRykEmZQOKkUjWC8jxytLPw40tmEa+hL4ub3EdR3CO+bhhsl7nnYbnuT69ld/m/t4djj2XHPOPb7GEx5wn7e44T0e8ZhrZka27NgyMrDnkksusMCKFotgYmJiZObrM/6rb77Guq2wSkFKtLbFZk12/jYNqdvv8SoxzAPjOHK577hxkWH2XG223PQjdduy73tciFxsOjb9WHwcWfD4+ind0ONzYrVY3+olpLYs1qcYUzP3HqE0i/brku1hmMhJIZ4duXTp+0ulUabwDVpbc+f4DCEtMUtMFFhbREmzd7dbeecTOWZI4KMjPFMSyrLCx9tgGHDTfFhJNVIoFosFX3n1d1g1a/zY03UdOZd4sqqqGIaetrJ454ghMIWIsZZmUaOTgFCi5kMq2gORi/HJWlsUiSEeCNNzwd6nRGsrbFMfouksL5yt0cJztKxZr9f4MHC0rDg6KZ2I1WqBVgJpM02tuH7yhH67Yb1esVgtsG2NPLRABUW56vqOcb9DaBCygHO/2fWH0Qk8D/w9IcQ/AL4I/O2c888Bfx34J4UQbwE/evj8972maWK9WnK93SKMONxgjdUGJNhKl4hoAy5E5jkyu1CQ30RELIVEnQWVkCyrpmzTdTEg+7k4DwVF0z7PM1YevAk6I2RE2YI3K4KSTJoUgYihxHhFVyr2LnaEMGBVprWK43ZJkyNaCXbjltnt8fsN3ZiY04hiRDRnVMsjlrZlmq4B+HH+IgOOixKlwiXnpf2Ix9Fzw0BHV0IzgIRjzxZHYGKgpkVTUFx7dsyMSCTfzfcD8JlPfortzQ4Xy3a1tI4MQhrqask8BUIEkQRGFselkRrnS4GvxGxL3OQxlcWnzDgO1LYiCvDOYXSDtQURprUkpGL1FUlwfHREjAFlTXkIYwILUkFtDTFMxEN8W3QeN3mkFLjgmKdCSU45lsKxUQhbsh61NiW2LQS6YUDr0l7OovhMtJGE6JjmgVdefqls+WUZDcUDAnH2xJy43u1pDxmTw5ywpmWYJ1IWuDkckHbxwB+QWCmY5yLi8TkVzF2KJJmJqUjKlTIkiltVZOj6Pe1ycYi5l+W9k5GjekkcR5JUKGnIMWEFhLnoLBgddVUx+4koFSKpgtkTkeNlQ8oORIHuTFNB8eUcMVURM0mjMUIicyKLb4FsOOf8LvC5b/D1K+BH/kAvJgVaF/KMbWtyzNimYtx15eYCsRtIwZGgYLpVofpGIrOMtKrYNEOGIDIxzlSqZvaZmB2ruib4cuO1lric8W6ktg3BRdrFkt5NGFMx+EIYskozywhaQEyINCOFZN3U1JUhBMfpi2dINPves2ODkonzp/dwscNUa1JT8cLdz/IDn/4xHl5fsXuaOA1lEliyxLDkHu8BUFMfVvqOnpkELFhzzTmOQOSQ0MSEQHGP91hyimXB85wyMhKIrFjwInd5/Obv8qkPfYj9TYc15UGfxwly5vHFFXPwxFRi2WSlODOax24iSEnvPdN1sU6frheHAZkLol2Uo5hQEiJY0ZI5QFvaBlsXLf75+RPaRYvfR+q6xTnHkVyw2XS0Cw0pUVtbCoRCFFt2BnKR69qUiZOnXSy+bkWWEFJGa0Nwnhgiuq0wpsSTZWb85InC0o2B9+4/KsXDaaZuWpyPRSOgNVbC0E+HASwoMLR4yDIsNZST0yMkis3QETw05qBszZkkJdWixNjv9ztkXYObqNuK/b5QpkOM6AT2EH4jrEanEhIaB89iteJ8t+Hk5IS+G/nzf+6f5ktf+BJPz89JIUPw/NGXP8yjqwt2454X12vi0Yqx6/FRIJIulKcYOGlP6bo9KjtS9JyeLem6Ae8jtqq/0cgrw+8PNFi/RVe+lT+K2235s0rrNE0kUXBgzySmMUbIsXDfrSkoskNacA6eeRgPkkmHNhVSFNUWFG5BViVMRFh7mBQ0+6EnCejGjpwP/mAFMWSSoBQEpWSxKP704hbTzC4y9IHr6w3jOBUewcKyG25IYgKhyChy1XBcr0qabywTVsJwn3tUGJbUaDSJgCMSiSjgmmvK4USV1QVBOPzxBGY8PT0zMxt2ZDIbNogyXdJPM3VVoRAoo5lCyaoXzyTO6RlTP5CeIdiSIPpw+157ipmm6waULHZi51zx6FuL1goOoa0xZuZhPDjjikvwmQNxnIvV99Y7IMXtfSlycH/7+Yvf8dKBC9DdvsYzx2FKCVMZlJasVktCCOz3e4SSB2dp0d3XVUHLCSGK/v8gVZ7cfPuMlfchHUJIS7ekmMMiPpWj5dHxMbthZPLFoWm05vTkBCEE3sXDLrO0W7Up/pRxmm4RaUDRulA6WFlA3xdz0TMp8zAMCCn52htvoq2ibhrquua5k1MePXjIvu+wtqY4DAVNXWOtRlJ2uXVzCL6ta+bgMVqy7zukLMatZ8X1b3R9WxiIitZcMLsSPR6jL8UeJVk0LdPYk6VABUU/dJAjMSa0gRAcTdPQuyLgqI3FhYjUGWkOcAitmGLxoRsfcUpipMEnCDmgZQRd0U9bFssGBogSkhKQJCKV0I/j0yPGqaPSpvSOtWYcIvubLfN2R7A96m7i7KMTXbdk469YJUtVNejmiHGcmcbMi3fKJPBf8FP8Gf4U51xgsEQcGsWMQ5IomT8zIyMSTREdF5x4YR9bOm5YsiIjOOKYBzylpoBAABwJhKSpLDEGqrbhZt9RmYo6C9bHKxbZcNPvOTs75XN37/LFV1+jc2VrH8hs9juaxqKFJCU4OT3mydMLjCzt2WcpTb33tFJxenpKN83YaWaaBgJFiJOUQUsQugBdI6m4Ng/Vf5cEOXjIks1my5QCmVRSjlNiWTfM84jLEZEiQsEwjSgtEFkyzDNSGcI8c/iHgEyOGZkl0f+fzL1pjK1rep51vcM3rqGqdu3x7DN2u9tuOrFxTBInlklsE8e2IkUQoyiIyJBIyJESJCQSIiT/gIjR+RVZWEEQkmAJiEBIYEIItmVCW5B2x3ZP7ul0n9Nn2HMNa/jGd+LH8661d5tuYyFHOuton117VdWqVWt93/u9z/Pc93XLtKeqKsIwYaoFIce3BWZUgDQ6ApGJJA1K53nzva9TlxWzn0h1xagiehxwMWEVJOcpjMXNgTEFdtcCVw3J07aN+CmMFeWhgi4vhOWNkjnIeLgwClstePMrbzNF6VkslOXZ/oJOB1pjsQSe7jcs6yX7fieAlWWBGgNFXDCOA+uzU7zxlKag8BOb6z1l5Qm/zan+gVgEUozgEgUW25TMXrzmKUYm50EnVJGYOsFZueAotchTXUxM+x2nixOw0LuORreMrkfpGoPCRkFRGaPwOlCovAFyPWYlCbJ+2KOUwc0JFwexzE4RpazM1GPgutswTY57Ny1TkIhtt7km7HdUNjIbD9NEUa7xumcODsWCYCa+dvkeV48eYmLPSj9v0szINvQWSzSKr/G1XOUrFImZmZIy7w8SniGf4ArHwJoVFsOOLRs6WgosMBB4iVs8efKUD9+7R+cGTm+ds3TQtg2v3H2FZnXK5vIhJ80Z108uWawsu67nje/4CJ/57OepipJSaWKcMWh0ZdiNPeZa7Nib6x261FRFSUwzJydnJB+42u6yNFtSnI0Vg5aOUXDtKaCVeAKqtiKiSF4w86apaRtDNwwkJHQkJs88dRAjQWk0hlKXFEtFP07EAElHKjRaG7RW9H4GhPcQQqKqDMkFtFWM44ytK3bXvVwYNOATSYyDaIEO0FS1xJrHRNvUhM6ho6LQWsxEzpGUYlGvcEOHKQp8546j7nZRo4wspvNejl1lDH52LBYLXD8TopOkqG5iGCbmDK0drjeMuuT85ftwfU20kSGATpE+DJimws2OKTqGrqOuFtxYL4hRdneFFvXlyWmLMYr9o/23PP8+EOUA2aVmjcJ7f9wyQspg0UT0E0olrNWgPDFNksgLtEVN0zRsd3tAH8ETkSTbPTdDkKwBRSQFcSIWTYufBYqpjKbJzZsY5YohnAB5ikVhUEpqwroS+7CuFdM8YAqFLStKC6HzaNWwXFVU5ZKu63BuwoUt15snpKnDltXxV9cUtJzwKq/ykEcsWGAw1GKdoaLCM4sRJI//akoslpqaJUsU0k8wGDSJjh0FBUV+e7/68CGr9ZJlu6K2BSfnN6irJS55Pv6Rj0NTszxZ0TQLbp/fwqBYLZYURZFdbWKIwUi54LNjz4eZpqxYrlci251n2sVCtqCrpXTLlcJYlZ2BFT4FeZw6E6CSKPhSDpht25ZpmkS3oeR4UEnGvhGOuRNRBfpxwEf5XFOW1FWVywahHS/ahhSiuPhSyo0zKT1VChSl7AqqSp6LIR3f/5giSstuyqiScZwpTQlRyp26rqnrWqAyQY7DQ0lKjBATIclxJCe/kkah0bKYzXMuG2ZKbVg1C87Pz1g2LevFkpPVGmstz549wYVZSEgpSGx8iCStQGsKrbh3fs6H7r2MVgpTSIO2qWrqsqJFYTF4/inpBH63bomECy7bcBWkhFUK52ZKndhPIymJX6Df7KmsZZxGiqJgM+6Ip2vC9il11TL7CWc03oklefReYBYkVqVhmjWEjrpq8fuBoq2lK50izB4VDMlYQrT40VNVJdZqTs+WGKM4a1d4J+MYa6C0WjwOduKkOWcRS+ZOU9bnvH7vLpt0iapbnrz/JfrukpPlmrqw/K//x1/nR//Iv41iZEXLz/P3+b18FwHPU0Y6Brbs0Aijv2fCoKip2bLHUOBxjEyccINnPJX6EE+BZkHN8MIb//0/+MPcv/MKy/UZm4sd733189w6vcntV1/jzs0t1BVf+uKnefD1tyFFVPDohMSRe0fY7zBa+Am9G1nVLbVq0VpxfXnNom2IeB48fEcgMHMpU4RhEiCm81ilKZVhCoFxcsTgaNuGpW0oKsVunAUoaiucn7BKUxiLKWF0M7HQtHWJSon9dqYsheq06besVy3DMKHQMmI0uQRJiTAK1FRnMdd6sWToJ7qhoyobpikJ1AUIKmKMZpEs8yR19Bx7cXxOHm2lP+Vm+flzktF2Wdfsd71wEo2hakqsLpi6mf1+oKwUVVFRastsDT4lrrtJkqsXS5xPvPvOI4wSXYAtNHWhiKMnJIVarWhtycVmh6LAb65RWiAuXe15Z9NTpkBRaoZhwM0TH7p5V6YPJFZlxcXwzZOJPxg7AcQfHn3AWoO1It6JUSyb4nUP2YQhWDCtNVM/oAvLdr8FAvvhGmPElir0WXFbDcOAD0IDSgRs2VIUFaZtmUeHibKKT9N0bOREDJPzaA1GS9OnKgsmNzNFn9WMgZScbG9jdh0GAYV6V1CWNS/dugtR4VGsly1VtaCoTjhZrwBIeN7gDW5xm4DigmtWnFDTUlPKNjyf/AbDwEBJSSQMDmWAAAAgAElEQVSi0Xg8e/YoNCUlBQUKhWOGPB3/T/+D/5jv/f3fR9CW6OR3+bYPf4yX33iFOSqWp+eoFHnjjTfYjxMYiXY3GtanJ0cvugbGfqAoimOjztqCk7NTtBYIyyGBqe970eSvlozziPMzXR7nKSO9hRAhZiVjjJHCKLpdT0pyPDRVTYxRkozmGYU56gxMBro2TUPTiFnMZoPSPI8kBGqaUmKaZ8FtzTOTm4/otKoUU03fdTjvGYPIsQCikj8+G5bmHFw7B8+ykUVEgWDoQmS/FyCoLRXaGq72O9AJlw7OPcXkZq43l/jRM/Q5R6OwPLm65ul2xxyRhKuQnjeyU8Jl8EkymtXpihQ9bVmgkkYHOc5jtoEP48SUoihEgR2R3dTR1Mtvee59IBaBlCLBj/ipI8wDYRoBhUuiWUeVKK0xpshbK01btCzKlm4cUNrSjQN1WbLpd7hgmNzIMPUEHQlKFgA3ToIcT3JgDcMgkwJj8T7ifKQsLXNUFJWlLC2FNizammVZUlWJqm2Y+o7BeXTqxb1VaEgdjV1w4/w2HitX7aKlbFecLtZYo6iLNbZYsK5W3K3u8Mmv/UM+hucrfJp73OcZFyy5zYDnmgti7hc4HCMjHkVJlbUEHjE1w0RPSw2I7sFi+F6+i3e55G/8zH9FWay4eLjlbHnKzbMbNIuWl15/ndXt+xSN4fr6EsaZr3/xq8zBc9nvxEAVEo8vLlFJo7R40k0h9XDfjxRtTTcPXO87AqCTwVCQopRjxqq8mEdUoSjLipASISnu3r1NXQpurChkwmOtoV7I9IFk8EmYj2gREw3DhNEVXT+zWLVEENbkMOdegKYoLdWixikJtCnLiqZs2HvZmi8WC2IAW9WCDSs0qrBEo9BKDGIRhY+SSmyt2HAFAuIp2pJhnrC2xCdhSc4uEXw+GQNM3pE0aGVRQRFN7i9RsGpPKIsCrS1FZlCglZCD58jok8TluUicD+WIZjf04BNLb/nwrZvcPT/j9Vt3Obl5SmUL7t+6w3pZg1EiBS8adv2eeZ7ZzjOvvfHqtzz/PhCLgEIz+4CtG8F7K5hcR6kVLki4RYoWHx0OzzAP7LorHB4bJvxwTUiGp5eX2GB4cn0lRp/ZMUwjlMKvH5PDERl0ZFCgdIHSiX7akbQ6QjDrukTpiPd7dBEojKafd6AsJ4tT7p0veeNWQ6d2VOcVJ/du8/Ldb2M3P+Lp5iGmndn7d3l68YwueTb9jsTMcr2UgyZplutzzgrDD3/qX+eX+AQzX+cOH+KEgpYWS81L3MSiaDBkPAQjDoXNO4WCgoYZzYan3OEe38sP8GP8S/w3/AJ/82d/jpfvvExE8/L9lySfwVqKpmYOnm6z59nbD5iYKZqKP/Yjf5w/+Ie/j/56wmnBhDkXBCfmU8a3JxaLlUBMrURnj25kmCeisRR1Q90s6LYT8xTocwZeijm7MSUWTcmTR09pKoF8hhBRxjBMIgdvqhov3T7pF8WKMCR0Uuy2I37STKNjnjzjIH75pERbYMpSRrXjzDgHrnY9T3fX/Nk//xOY2jKPMz7MqOjQyeNGR3Se4CKLuqHUihhlN1hmafPsPVFFkoV9NxCMYjPvSd6w6wdAMyGKQR0FUpui4uL6ihACbVEAkTl4rq73DMHRu4nZJVRC0ppMZLVuJJk7yMSrrCqWbUtTVqwXZ4xBMUXPo92OJ7s9n3v/61xeXvLwass8SdbmuljwcnPGWhWkBGPUVPWKt975+rc8/z4QiwDIiq4MBORFB51ZAr0onpxHJY5UGWMKgp9RKQCavp+YZzGGGKO5unpGynDFmJ6HhxqVUVVZZumcEyClkqbTYZZ8gFhOIaKMlfEQMs/WdUljS1JbZ0xZgakqvPH0YWAYNzjXcb15TD9smacON3V0Y8cwd3ReJKTWKUY98XO/8FP8Ip/ni3yGGUeAYz1fU6OxlBQsaKgpiTg0mjNaPsI/w4/wR/luPs5H+Q4sLRUnAFxcXvLs6hHb/Q5KC7On1JbGlNw4uUFVVbz80n0BgOz2fP5zX+TBOw8osnnrerdFa2ia5ojt0loze49SBpWgKgzKI4q/eebp06d473MJlghKS0Q08h7YHMFmkNj5qqqAxHK5OIbAimS3FF9+ZSCCRtE0Dc6JZkC29FKuyXumUUofLbZVjqs3xrBer/nkJz8pcXfjDEkzZWlvWVrqRhx2B/BISomkVLYfT8RcIrg8CkxBJgf7vhPdgZtJCjwJn8NqtTUkJQuJ1pq2bVk2LXUtEl9rLW6aj3kKgsGT7IuyKdFGSmLvIkoZUSnmWDxrDDdPz3j5/l1UVbBeLtiNE7uhZ/QOlyJkXB5pYBpmFm3Ft7p9IBKIbp7fSH/mT/0YSRumecZoYc7poAgeILLvNiRjGfsNlTWk4ClsyXa8hhRodUX0ibpsKZta3IBhZHV6StQFJsG6rtGFxVal1K6TQ9sClxJGRULSKAXFspKrUejwZc1L65pd2HNycsZZveDeK6/w5oO3sNpz/6XXONFLguv55Nu/itMj/mzD0EOhaz58+/cRxj2X3SWni1c4bU8xZsFyKX2IL3/9s4zpEc5NfNu9P84f+Wf/VW5yg5e4zTUjtznlAU84o+QGJ3gUK1ru8TEcjs/ymzzmAZc8ImFB0g8445wF9/gevpv/kf8CgJ/7L/97tkPHNE3stle8/pGP8tZXv0T/dMPTRw/5zCf/MdswMO/3TI3CpIBOcLXdEpNidI6qrOmzI+38ZMXcTdx/7S6VLdm7kd31njdee51hs+PpZsM4D1RFKaPaQuH8hLKKzdVIaUTiW5aSYGR1yfV+J7LXvqepGoqTmjR4og9EEmWlsWXLPIyMk6DlxiGAgdJYvBcr+q7vqFcL2rYmdiOu1IRxxmQUd9O0YqhqCvZbh1cCHZUpRKKfJlSWlR86/VojuDmlMSnK5MprZp2dkFrArABVaYhRGJizExqQC3KBkol4ZFHVmKgZnQBPFlVJP0xUy5rd5RatLat1g58myoyj2w89dooUdYk1nuXpmjF53MVMYGRxdgPnHN/z+us82e9otCeFxON9xz956/E3TSD6QEwHdK73g5POqrYKHwPdsJW6MURUUYKbBRwxjsRC0+93VG2JdxMpGtw8UKg9vXGctCuZ448TbVMxhYmkqwyv9FCDyqtwsWiIk5MxDp7CViQKljfuE/odZQk3VEvnHVVR0aCZ5h2mbSBZgoGnLtD7kTGNNKEkph1N/SFMrVGDZtHW6MpQ6YqiUeyHK5RtYLYM8571jdsMYw/AMy55mTWRmZGBFSdoHJqGN3idPTu+ylu8xZs85T08EZudhQGLwjNyQcdT/md+nft8B9/NH+Tn//yPH1/zv/ZT/yG/8gv/gDv37vKf/Mx/xF/8c3+BT73zhePnb99YU9oaWxvMvqBtavxuSz+OlJTMzFxsdqxsybAbeX/7gHuvvIyOgeXNMy4ePuZmu+CZFgz7NA9oI5xHqyy2DBRFjcYzjhMpwKB8bhpGyqYhJZj2k+zgtEaTiFhJAgoBayqUgqQNs3IizrEaoxM3b52IUjPAbnIU3tINI6tVi4+K6EXppzEolZjcjEF2en0OltUJyMGxJEgxd11MxMVAW9UMfhT/hgqCvMjqSB8UeI9uCmqticmjVUWKM03VEMaBOI2UZUsig0dGz/qkRZea9rRl2gXc5Knrhn7aMofIqm1Y3VkwdB1vfMdHefetd3H7ASrFol5zu265ce8MFzzrVUM1w87JZOdbnn+/+6f0/7+buK/6YzBpDIbV6iRvPyemec8cOojhaLts2hrnI6iClBViMfvpx7knIBLNwU1YU+ODIsSID0mYeiGSrGU39OiqYJgnktHM/Y6ySgy7Da9//MPE6gapMFRNDXHgYrvB1Ctut7eZUs/Tcce2u2TCMadAVdzgztnHqeuaE/sqN8/vUxRLXrt3n+94/R4nyxMe9Rfs4hbTJM7PblOmmrY95TNv/wIA7/E2H6JhwxXQs+I2HZd8nn/Mb/AFfpVP8JR3qCgpUXIAM6BItLTZZaBIRLa8xa/w91ix5IRTXuJlfuqv/bv8zb/9NykbmVL8zN/6WW5wwv/FP+G/5ef4/u/5Hl5ZnGAXawC22y1TblTpFw6boi252F5z695LuHHi5PwGv/Frn8YUln6ejhTnuq5x0yQ2WufxPoq0OHhMUdG2LTpvpZ0LJC/MgaoqaRp5T90cmXtHDMIbKAoxJhU6YZIRH0NdYYqCqmqIPnF9vSVqxW7oqesKopQKKEO7OCGhSdrwk3/uJ1AKsSAXRkaGMRK8jAm1Bm0OJWSQBcoFlLGZUq2ydDjivUxgMOqoOyjLEucnIkKtroylrht8DKzWJ6iksbbkYrOlvxpodIm1sOt6aYTWa+7evsM4T7z//mOuNh1f+PUvcXW5o25XNFXLsmzRRcm2H9iOM33n+PXtFRdJcT31v8259wEoB27dPE//4o/+EKbQR633OE/oFJiCY5ocGkU0CfyIUZowO1QBLnisVuy6LY2tsShskTJcoqBdnDFHqe/X7YqQBEueQqSyBcEafP78fr9nuWpYLxc0yxJT1VTtKfVCsa4bHvcP+T23PszT6Qk3V7dJCq7697nut+x2He88e5ezl+7w2sv3OavvU9qZe+U9bNHw+ctP8z0f/f3UfkHnJ37py7/EvZsvo9zMSX1C8oqrbeDWrTMaZfj0Z3+Fr751yV/9S/8ZAD/E63yC9ymADrA4akrUkTCgOAzyPCGPuiRIVFMQiCyzFtFiGXE0lGgSf4gf4uf5ee5Q8QTHr/AbvMsX+NP8aX7s+/95PvXZTzNMM8Mw0dAw4SiwTExo4A53eZf3WbclN8/PSU6xXjX0e7Efv/qhN/jC5z4vYa3WYKzl2fWWoqioGsvV1Y5lu5CBZ5QFZh4EgWabirY0x/pZsO9gCqEIKZ0wumB0ntPTNdfX11S2oGkrtpuepBWdG4gRysKgQ6I0NgPYZIw4jiMqWYrC4L3U9xrDHANGKeYo+RNlWTJNjqq0nJ6e8vjJs2Ov6eBDOGj0Dz0UpRQl4n4d5pjNSY7CGEorKUMhKSosTVNx0W1JSK7irbpiM/QM48ydszP22x3tWiL4jNKiW6gM0zRwtlpiygI1e0JK1EWL1TKGfDZM3L57j09++ksf4EDSJDbQ4wuZAnVjKK3OzSSNczPOzRKxBKAkidZqqSfruiUlJVLKPL+OMTHMo3xfHk0JX24iIZ7xmJOIh2Fg2S7kuWiPKQM3TlfoNMkiZC1NYanMLNCKMOP9zOgd225PNz53jSlbUFYWqyrqZElxZuw8F/2OOQa20xalEsknTtd3uHv2GjdP7uJdZI6OotDYIlAtDf/D//Lv8Uu/9NP8Im+TSBhKKgwJiMRsLIqAuPBmHIe3NRIwCDorEeiYCQJMx6PomYDAP+DnAbjGU6H4Gu8yIUy6v/9//iOpsYcBhcbhEd2lByKeyCMecZ97bPuZyha0VUlZlyQjJ8fDhw9ZLpcHIZ0g3eqCcezpuk5ceN1eVHCQr6Ki8QghHPMFBYrSUtb1EfRhyoJpcjRNdewHHHj/h3DbylheunfnqFq0hYZssJIINMHFvRi1Jn6W/DFiUZ5ynsU8O7quyzkM5mhsOxzLshiE4+MURZEbfIoQpHFJbgLOIbJYLPBxRllFZQW6Oo8T0ywRdVqLkapZtLhpZj/PTP1AN424WZb1EBJ+EuR43S54ttvRTx5rS07bU3ZPX2QBf+PtA7EIKAX9OB/BIFEL1LFzDlLIdZvC2OIoolDGUllDZQ24hA6KpqpJ0dPUSzkQMDgVsFZgnN3cM0UvWzyV0EZqwMMkoPAJ0ztWNxv+uY/epbEdq6aiKDWl1nzstVfpCzk5xv4a7zvOqgqnJqa5p1qcUFcFu33P9aYTzBPigzhtlzx4+B6fffeLvPnsLQnmYE3V3EGd3OTurY+gCkXhC6YQeXgRSKOnXtxEl+cANJT0TDTZTlQglByNZc4np8biSdl7IDRegwYUATDU9Izog8sQz4qaBRVL1ngCf4E/w4bIT/PvA+L+A+EWyfOGKXsaLJaCkmdcAPCFt95h6gcm71mcnDIEcQaObqYLgW6acc6jtSzIAUVZWqqmocgEYBUTTVNhraatK5arBudHcYY6J54EbRnmgWmUSca2F71CSoq6KTNxONKeSBrv9uoanxkC+35EqxLIKc3Z59+NfbYpl0zBH0/oopBJiLAFHCiY5lkw9lEmFC/uBpRKNHWVF4aYxVCyMyDrAqL3AitVmm63Q1nLdrulbQo0CbTgwocpUJaW3ns22x6UxfpE0SywxlDkbAIXDdEpHj+6ZLsfaduWR5uBR492PHv87Niw/Ga3D8QiQEo0hQRIAvT7DUonrFEyvovSxQ2TJLQopbCFGEPmEKEwkgAzdqiypO9HsAUhapQq2O8HlH8++ktEcbclT1Ea2rrBRLhxanj1tSVWn/K5i5KvP9hR2pFbqwYfemJSXPczy1TS7/b4LnIzLnhVnRB8YlnUgGbsLVPvCWPFk27iwbMnNOo2+8tLnjz5DG+/9essakXRFizqExapxsSCk/VNbAWDc9QxcHbaUqJRKfA//W9/nVniHZgYEE+Z6CYk7cciGcAh49XTUVUYJPKEGkuFoaUmIiOtQGLM8iaIlJRM9Dzly4zM/BR/hYvNjhusctkhykRp00mY7MDATc6zdUncl9ebHV3XsW4W+DBTVRV1aVmv19iywE+Ouq5p25b9fktwk2zlq0pit71sZUOUXVxZ1AI2yTbmmLI+IGUlqVIUxuDGibEfmHKyk0ahC5ubzzrvIAzJjWgSTVFRKnFd6pxYJbsDhTb55PYyHYiIViJGMq9RZ7t1LsTyTiJGjiGuMcpC0lZlXooFNKIyZu1gKrLaiHkpamptpT+itZQu2apMoQT4kkNnq7qm0OYY4Bp0pF2K92I/9DRFBUVJ3ba0zbdGjn8wFgFAxecmjIORSCmZJStjCVEgjImAthJYEeMsDi3vQCdJfzVAoegnIcGWRUO9WJCSIiYBizZlha1qtKlQyOilXjbMSrDW7uIJ77z7Ja6mkVBGyqKmrpb0bqCfO+h7WrWmCBNBR85WK14/u0ezWGP1gvP1TSpd4V3Ps6uex8+ecrW94MmDCx493nDpn3K12XO7vY1Tiq9fPeGRTixPlqBa7Fxx8/Yr3Dt7lcJHgndM0TMwcUZDwQIFGArRu+dy4ADRUnl3IHsBnfWLiUCkZ2BgOp7QKW/xBWM6ZcuW5wFf5CXu8xK3eJ2X6RixWBwzExMKlf/tUCge8hCdDU5fe/SIxXrFPDrmSTwA3dDj/cwwdkcPPSFiUGgtEmGIGAXzPFLXNUonUgrMk6eqanb7nqg4SpMPKdFCpHZEHwQXbyTzcPaO/TQwjY5xnAVuimJR1axWK0ojJ9tzDUlCkyiMFieykkQhMThlOXFeREBGffInHncNhyi7GORYliZqZMpRdcpoQsz5Ctnif9CrlLak6/aYqClVwWYQmbVOUBU1wzwzucRqfYNuP9H3E8MgjIZbN89ZNi1aJV66cZvkIs4FfIr048j15oPuIiTDFlKSEzomtJKMAKWMiHiUdKUPq61SimSEUW+tzffJAaGVyH1DcGy3G3mTspAkkjIG+5BgoVHaiiOsaBgdFBGWpiHqCYoFTVuyaNf0fU/bNJycnwt4wxtSNLSLG6xu3OLW+iZFUVMVJU1dkqJnv71k7Dd0uz3OBXbTzLxzdLst3nu2l1fsrjdsrraYuhQIiYoUjebsZIlWiv1+e3ydNgyobCf2+Hw1NllGLFyGlBcEhUGiLKQZ6ImMuCNr4LBoHIAlIlCKGCw/zr/Gnp4Ft+jpabMr0eVi4/C9h7815vgxwGazoes6yrJkGAaqqqIoCpZLKdUOmYLey4jw4BoMGb2tFNSZizdNE/0oFKjDbsAkQwygraGuc6I0URb73JBrmkaCbSDjzyyLRjiHu/1GMg9CYHSCEdcoSvN8an44seX3y3FsL+QnwmG8/Tzrz6gcTEs6OhYPn1NKkpi00hgtvoDD44ckeYYHwZNQkC0xegm9RVFoeW5d11E1daZCy+TBT7NYoIlsrq+4e/MW0zRx0W2ZiehvHj6Uf7cPwC0lsepWTUlRWTAlobBgS7SxFLrAe8fsB1lX03zs7iuPqLCCwirL5DVl00rFXFfEZBmCOLNS9qPXZU3yisoYzk5OMclTG8PV6AiqYPH6Hb7z/m1uNCe0Bcyz5BNuNtdor5nqM26+fI+zl7+NZ+kGu8W3c/LSR1m0J3z07hvYoFAK4s6i2sB+vuTiwWf43Huf57O/+gW++umOp08e8c7V13h0+UUebb7G248/xeVuz9nZGbOpuL065fTeml4nbC2LE8BP8K/wfXwUjc6NQBkFSi9AWHghX/UjgbvcoqJgYjpu33MW03M+IyCm5pKJgCLxp/iXsSy4ZmJGdO8Olw1NJi8qjpqCmpJE4Nv5GAtEmfbg4RNunZ0SQqCwkiFQVxUXl9dYU7PfduiizKTpQpKnTYkjslgtmfeevpuJwUDUzENijolp8JS2oGlWOJfY9yOTk3DOqmpk+lFXjG4kjB43eZZVw8mipSlK1JyYnbgXRc1nSSoy+kBQmhTFzOSjnOBp9vgUUCmBit9A6DmM/46LRUykPBa01rIsa6wWkVBEJgwqJLEp5zIhkjKj0LCbBil96kb6MDEJDCeXH40xhOTlOboZq6WJ7ibHvO8Z+kmi7nRkmntO64JXT8+wxnM9fnMHobzzH4ibYKx2m+0xZVYpnYMcLKaVK4I1BmsU0+TwIZGUQVcit8RomuWSpl0TPBSqZh4kmKGpaqbJgdLooPHJgIGRQD/sUJWiWWhWtuG8usHpcsGgIvfOFxTDnmnzgPHyN/nwt30Xtl0Tas3i3sssbt+hOFnjGTBqycVux5lZ43ee7uEVl8MVzy4u0JRoU3FmLNN2Q5hG+mR5/Ph9YRHqQLd9zH73Lhs1cfn0kpvrku7aU5Q1dbOkaaTB9S5v8w6ehnWu8Mk+Q4NCMeOP23KQHviSFSabi6SmrzjhDPJyIPRC2VlYdJ4heP4K/xY1FoOhp0f4BiUjI0J7UBRUWOQq+pAHaArWiGNt5xzVohUzkTF4JcyG0/NTmkXN0801z7Y7cW7m2jZG2F5v2M0DzXrJol7gQ8CFGVQUtSghE6XkcbthImrDfhywCtbLlpNFK3r/JDkKXT+CUmz8SDSKypQMzjMnJyGfGQ+WyJ5/wPuAV+JZ0FpLSlZOPlY5I+HFUuGwEIjWQZqQPka0NthkKFWB1QlrhAsFHEsUY+Q9m0l0cy926MIe8w1CDMSkqE0B2WVbtjVF2dD3QiJSSkGAcXLsN1uSgu32mkVVYYoPeDQ5SkJHS1tAFHDI3PeE4Jnnkb7bSmZ8COAdddESosOlQEiRum5o2hX7bo+L8xEKEnzM6bcj2pYYr6hsjUviMIuFZbmsWZwtuPnSmjt3a9bniaVVDNM1r99taMuGfXrChXuH261lsbzBznU8HXZcTz2qXZHqgi9fvM2D3ds8mp5SThf047swXpDmERM1wRYsdMPNV+4z2Z5UOC4uPk83bjBFyUzk8cMv87XP/Bpf+eovcWvxCmdVTbtc40Lkx//YX+Iv8xf5w3ycZzgmZmpqzrlJgSUw532AyWWCHJjPuMBSMRP4Lr6L38PvzSexlAmHYVmebuNJmLy1j0z8m/wkIyMLFrhcShwMzrIb8BQIXfeaK865yQ6pP/v9nr4fudpueHx1zTgMFNrwxa+9xfzMc4clL7EW9uE8M2eNiEvQVCXb6yueXl2gixIXgyDfraWqa1yacGGWbv3BXpwhJJvNBqMALSO8OQWm6Nn1Aykp5uB5utvQz05Q4YdxYpIr7+RDvhCJi7UwQmwMIR2j5+H5TgA4XtWf267FUyBkocgUHc6N+KiyB+B5SbFaNPhDbLrWRJ1wmcIcU2TfdxgtNmajNW1VUxrLOMxcbncsFgu6YWByTnpohSVlAvTprTPKssTY5xeG33r7QCwCKQrDnugxVjGHGUNAR0ddC6q6bmtMI7bPoqoIaQYjZcToBTDSNC2ltQQU0Rboqsg1maK0GmqJdVbGULYLsAXL9Up2HO1tTFHQRIvrt9y7d4/S1EQ7ksw5V11DwU2aQppwjzYP6PzA6HueXD3G+x2+dGynDT5FigRqGmiU58n2KWO3xy5qlqua9WlL6Qs2+5ln+ydspy1h7tj01zx88HmS1by5n+jtCWO/zY0o+Dqf5u/xSUqkoSbbc8tNbtGwzNEjgmkzR61A5EnOf/kyX+bT/Aav8wZXbPgT/Als7ugbpMGo82xBdgOKCZf1ATMFFeFYaAjqNBIz40CmD1c85TVeBgTOutltuep6WixX1zveffiYApNpyoaIYp5d7r6LCMhqTUgRFzzVoiZ5CaHphoH90KNLzXq9ZrFYyAyexDgP8ktkUGqzaCRzIANDfQqiBvWeEMXoY4ySqHkV0YbMOijQGT93SCdyeZE5FAJH+tQLWoIX+wSHSHFtjCwaWoG12IyVU0ZL8Er+dh8lYDSlxCKb0ozMETDIIjKnQFVVTLOn7zo+9rFvZ+scZAT66D27rqNoS3b9lnFypMLw9OoZT682R2fkN7t9IBaBA0bqsCUDiSQvrM1CkZKgIy46tFX0Y0ezlMZSSomqqo/YKWkeWqbZUVU1PsqUwRiD0yILrZtSLMu5iVQUBY+fPRUZqhIGXIqabuxIoUbhaNqaYHtwBaWtmP3Ivu+4uHzC7Aa8SlgXGYaBbupRzlE2UJQKXVm8bbh34x7r5QJrpQuujGHePaXfPWaye/pxoAt7muacR92OR/s5O/x2ANziFGEHxYwd88zMPOQRoChyrf68WacoKAh4fpAfxOGwFLzNW2g0X+WrDEy5nSj/Fz1BwCK7kxJDS5uHkZ6Smpb2OB2Q7rd+4WcmWuS96YYBWwrqusezoOY+t9S9kPcAACAASURBVFjmBWvCHWPTDl11H2ZUivRzHohmpWCM8Rg+23XiHdjtdseOOxkd1veiEtztdnKVjoKYNyarJ7XkDtgXtvEH+MrBxXh4LsYYdAJbWOkHZErz4fMvLgKH26ERGHLT8fA1MUrz9PCzUsrTHK0YhuE4vo5RfAqjj/QZZHJYIHWSkJ6t93ziNz4NwBQCV5nGbK3FlpIH6b2nPVmhrELb583zb3b7QBiIINHaEk9iDrJNjVpLCaANMYxEH7ElhARVqbN5JGJtRUqKdr1gHj1FDdY6ej/jPNiylggybVkv1uhcN7frNsvXDG4K1OcTdtZcuoEyRdKjd7GnDU9Uz8fuf5gFnveunlKOgVeqNW7qeLx/j7fe/00KZTk9vc1yeY+kErGf6buJsNmweuUut8tEKm6xsAUfUyNqfkroZxa3SxZtS5gfcnl5yeVVz83VG3zko9+JUhWb7QVd8OilNNt+jTdx9PRsGLJWoKSiIeb/wAu2E89zPLlG8wk+gUeuBj/AD/AJPsGbvMkf4A/wST5JkUuAlK93PisGLRUDezxk3IZMEUy+8icSDSWWJZfs6Rh5l3fyCNNgtz0nLEgEajQjfV4AJOnYHwsRxTD1FKYkRWhthVNCBZqUkKS1EnZ/nA11YzldnHA1bCEmotKZsRghgC4LNIOUOklBkhyJ2csVlRRobcXkpFcUgtwfnegPnPeMOQQkxUhMUsuH5GVByTtMEQc9nxjI9ErejYMtGYAYGdIEeUEDMFpESJ54XDD23ZiFYIoSk4GpoIlcI2DUFTWRmHdUEp7qup7v/NhHePe997i1bFAL2F7vMLGmGweM/qeTQPS7euuHmWkOGF0KPkMl6rqiLC1gaJuCFMQAQgpZyVXT1CuqsiVFiy0aqrqlKFusaajKFhcdyWim6OmGkZg0Vb0UbfbtU6pFweOrxzzYGJa376D6gscPdjx7NnKlDKlULE5OuX12j9986zN88dEDHk8Ts3d02y2rynCjaVF+pDFLSlthlmuq8zuUJze53l+iUktdKvYuUZ29xIdf/TCvvFJzUp9RBAlCOa3XrK3m1ftvsFy/hEtiN33y5Al/8vt/EoA1Cx7yhM3RHaC44IJAOAaPhEwcqiiyzNgct/AgJ+Yv88skEh7Pp/gUNkuOD+Iie+z/21waCG3YZkFSQrNikXsLEc8ERJr8SI7AH+L7ZFcmottcSsgcQ77CUSHZkUC22RpShpiaTOU9X6+Px4j3XoCbWpECGNJRT+K9Z3KzaAYioKMQg3TCapODOuUqPk+TjKCPQFtNiop5mEhaypPDTXgRz0eDIDixlJuFh68BjrsI0LLbyGNPWSAk96HUSoRB+ft8lOnMYUQJHHssjkiDoc6nqUJluEyZ5eGJNr9Hd6j5zBe+QhgjV9uevhvzBCJR2oJ5/oCXAyEEdGFzfSgd/bKQemqeR2wRmMZAwuUIbJs5dxa0pqoalLE54FJqJFuWBJRw6H1EK0NVSQBkROVEXsv1fsdi3bLtrollgtUt0jQxo+jmHbfa2xTK4mnxw54ubnjv6fsEG6Aw6KgotUEHcNOEDqCaFrNa4oxBGdke7vd7IoE+BZKtUJSsy3NSSASfuHxyTTIly+aUtm3xPhyDNw63LZZbnLMGVizyQpBwZIY/0v33eTnQ8EJwSTwuCBJyEnMJEI+Tf+CoAXj+2BM6Tx4mpuPjHIJORHOg8+NqFrlM+RgfI5EYmLBoOjoCgZGRlpbi4PZ8AYaqlCIVIvUOyVOXEu5pjXTbZbcwSY2tFSpfDA5in8PVWKi/4Xhy+ujwSUQ+hREZ8EHRl3j+dTovFEoBL5QE5FdWfgaAOm75XxQJvagJgOeSdJA+gMsLXEwe8vcZpTHa5AmJfO+hsRvxOAIJk3dqkpC0ZZubsiHPdzQeOEGzdRNPu5HL0dFPEsQigSof9PARrQgqMk0zdSN68uQdWimm6KQrXHiUKjN2fElVWJrlKdosGKaRRStAiZQUS60YfSTaRKXq3AHX1M1KgjhbjbWK691IcVKSbMWHX7vD/bt3aXjC+dnHebP/MsZWTHVBWy1Jw8C3NSe8Ne646nrKuWFd13TbMwq94vTmKX7y3Fve4aK8pp9G7M2XaYvXeHj5CKUmzm6+ghs6+mRY6IGbi1PcfI1PA06NrMozfHT0u4GzuobTOyj/3AL6L/A9/Oe8ScdAQ03EE5CGXsrLgFBwJEr8QMQRwetBvQYue+hER3jQDljSC0IiIJcGhpmRghpLkTsSgRVtftTIzETFkpEu048q/jv+7lHA1DESSJyypmeDI1BkifOSlpNGMXrJ9CN6qrpgmGcWVUvMSUg2A2SLomCYJrpiYLmoWQwmOyECSSucj5wtK5aLJRe7RxSFlVdEKSEBayOBLGiUSmipHp5LypUSqo8LlFYf6UTPfQHqG0REB6HQYSHy3n/DtEF2N0JvLoxGEdFJoxW4KG7AmAIhQWktWtraaAQrpzBMOCqkNHO5YSsu0IKZiUCiJ1FieYmbDFzIUj043mXD0hiGEPkmLQzgd7ATUEr9LaXUE6XU516474ZS6n9XSn0l/32W71dKqb+hlHpTKfUZpdTv+x2sAUejhS3kWfooEsvJDRhTMAwTMeaDoailHKhzoEVCICBJUdSNKAutlXhynwhRS6S4tuJNUBGb3WpVVWEKTQhO4soqQ7uoeemepowtKcAJFdvdwOwTxhSc2JpxmlBRgTIUVS1U2smzXCzwfsY2gtp2ydEnxxxF2bjfbEErwqyYJ8MQHGVj8WGCmGjac9w8cXUlFtX1YkkqFf/1P/xpAN7mPe5zFyCP6srj/P9FFd9ha3/IKQCOHx/EQvJ16bhlPzgOQRaHQ4Nxzn0ElRuG5th8JJcUMSsWIzXSBLQYKkoWLF/4uZoNV7zK6yxZAoqJmT0jm6Fnymi3FxtuLoScc3C42vINXXnR71vGWZJ9UhJTmNKa7X4jr4sPx6txdF6abCEIlVorMPobtvOHhl6Z5+qC/3re4MvH+fG4PQSN/L8bgc+fZ8yNvYg6ou5kpCm/a8gX6dn743RG+jnxuCMTO/icL2ghez0yJh1RjUKkImXNh6NlzTkr9iGw+m2u97+TcuBvAz/yW+77q8AvppQ+Avxi/jfAjwIfyX/+DeBnfwePLyusDmg1k+KMTzNzmKQZowuMLqmbkqIRKW9zcsLZ8i5Fe0bZnrBandGsTinKlma1RpuWZX3C2eqUZbVkUS5ZVEuaWmEMNMawbBuSlXr37MYJl9cXfOpLnyG2JZ/fJVa3WgrnKbxh8/R9vvyVL9HYhjuLe2Cs+BNqT3N2F1ut2WdO/zx7nnWXvNO9zYOnn0MPMke35S2sMTx88piL/TNU8jx54jGmYp63pJRYcgd37XHjV/nC25/n0eaaO7c+xJ/94b8MwAMe84D3j0EkKm8ZU1YIvHjFV1k6rHnuKTh4C1WeGhwkxgezUYWmAEqptimpMrSkzJW91PNAnhSIi1F6EjM7Jnzen6yoWGYh0YF54Ah8ha+xZ0Q0CZGekfUhNymTfJxzx9DPetEet/oqn+jimku4EFifnAFgNCzrikpbmqrFGEOtlHgQeGH6kCK3bpwTkheMubbH2v2w3Rc7u/QdDs2/bywNXjhuX/hzuO+wkBXGHnsKh+8dnOfm3TtMwaORhQ4kUObQx7EUx4X8QIaYiNijolPe0+kFRYgjMgPv8JAxt1sfccFEzxJzXMy/2e3/sxxIKf0jpdTrv+XuPwn80fzx3wF+Gfh38v1/N8ml/f9WSp0qpe6llB7+9j9D7KoaiCFgMwKciNSkJuLmRHWqCanm1o1zivqEEEtpReVVV9JqClargmG3py4rxsHh8lIblJbQjLbi5HTJEDtMs6RShjrNpGHLg81bXPYP8Vc9ZqxoTzecmBW7MEu6bL3ibHnKNnT4/Z7bN96gxLLb73m0eUpVWspFxU17wpfffQz+beaxZ3utee30Pn67wXmHadbsrt5mDJHNLtIsbc5SSKSkwV2z3zom/7wx9ibvsKVjwudxU8wV4/OrPHDcBRykxeq3fP7QCyiysOggI55wuV8ABXU+cV3ecZCFSAHZQ5ijcyGCTFxo2dOxoGJgwuO4xZor+jxEFOfhhh2O8ehG9LnvYMjb8NLg5hmtLGPmGaIMiohNBuc9yZojnz/9P8y9aaxtyXXf96vatccz3fm+sd97PQ9kNyeRlBTZgmxKhGRbNpAEdoLIcAI4cJIviRMgARJn+uIvSgIEcQDHCOwAgRPBskxBkDUysk2KItlssif23G8e73juGfZclQ9Vdc6+p1+zY8kBXl1c3HP32WefPdRatYb/+i9jwO2bhTHT2QlpamsVJkVOoCJCFaKrmlAp7u8f0Etiat1aQhHhGt64NJqt/LMmOY7rwrsAXYRg1xIAbyFYVwM4VeTUtJbkVSK4c+cOQggqnyVAYqjd6m6oqK3iAozbVtI4Rey3mwUWJCJghy3OcpEZYzbY4ht8w1lvtn1NQkj1MV2I/riBwd2OYN8Ddt3r88DNzn633LaPDCHEXxdCvCyEeLl0RCDSdcVVKiSJUkajNZI0JooVcZxR1wX93hpCBfSjhFjZJpnGGMuJ79hswdYTIGwQME4zpJQMBoNFNNlq/YbN9S3CQCFKTTOfMivGJGkPoTTVbMqkHhMh2UrXIFBI1+IMINCW4izAoExM3hRM6ylKRcjeCN0G5PN9imlBlqTEomVjkLGzNiAKR/R6KUYralNRNxF9ldKLE8pKM8hiMiVoJstIdbNYyZUrJfZ+vlz4+94KWD5g/7N0A7rWggcYRShiYgyGxCkADySqHSjIuw92hdIL4pHKvY9zCZSjRFfYZinCpR99lsIGKO25tdQMGbr0nmMVqlpiZZ/lg+Nxd86glC0Wsn63WBTbNNpGM4LYdgTSLuoehbZVmGeSDsOYJE1p68a2m/d5f1e4Y79nWaimXYfqLl9A93y68QH4aIxgESyUWDCTFJ0sgt20NP+tO2AW9xln29l9tFP4/scndD2S83u8zJyKb/INYmKXYWgpXR/Ljxt/4uyA8U0D/+U/93eNMV8wxnwhjhS9NKKpC7eyKYTUTCYn1E1NKCLKtiZK1u1kTTfA9ShoRQhKWVpspUiTnhXiMKJuBWEgSUMJwlDXJb1+QtM0zOZHDJMMXeWoqiLpJYyyNcJmxG6csJ8fc+voDrO33qMNQ566+DwTqRkXR+ysXeC5K5fYHmxwNL7GNCiI+0NMOOD+tdvMq4qdx57i3/rzf43L21/iy0/8BLqomBclGRHn1s9z8cIuP/8zf4nx9BBVJ6xFZ+lvP87m7mW0SBhun2X7sQusby0tgWMOMVREKCq3eoZ+BXUw4KZj9vmJYolHrN1gLQRo3TreoomcJ2mBQTGamh4ZNdVCeVh8YEXlVFHjoEoNLb5yoaVGAXPmrlmKYY0BkpoQQUyAQmJoCF1Z8nkuUtCyxojArVoSmBYNszy31yEEymHry9qSixRVRS/t2UYfxvH3K9tQNhv0meS28OnJJ5+krmvqtkEryXw+xegGqUIHGHMgp8AKvG21rhYwYCGlJRntKAI3f4HTZcSAtWDd9rqpCBwlemACyy5kXDZswWIkCVGETlUbQDlFazr2gLfs7F8bQ8vo4cO+D9hHIrjGhy5R27iqj4AIQcW/+gKi+0KIs+4BnQWHS4XbwMXOfhfctk8cTdU61JNt9VWXJUEoCcOIWV0QRTFZnNAfDJHSNslUStlorrRFH4FrO2W7u8SkaWpbixlNnMX0eplNIyoJurV/jaaVDjYaRwgxp1AZiRyw3k/Q0ibZdCyZaut7SwmhjtlZW2Mym3B4vM90+oDJse17f3K4Tz0vyPpniMKM9bWYjcEGVVMxn50wLXMqUzPqb1K1gjDOiFXK3fsPUElG20gKrZEqYD4p+eW/Yxl+bLTdlsx6vgArnPWpgBIsVxe/siy3GRfcCxfbNNr5/a7slsClBi0NhgcQebfBvwYfHGyd69AAhtBls2NiKirCheHqW8y2liwFsUgdPmDPuRp2bLHGkIwdLKy7KF17uEWZcMZ8Prflwk4Qm9b6/ScnJ1RVxaDX5/j4GN0sqb6EEFRlQ9bv0y7APTgrwZJzVFW1dDNYru6rnAGm83k//Pu2qelpmjI/bHpRL3ABlbOo7DPW7j57dY1b8bV73S6yPnOmaAwRwQJ5Gbvn6q3EEJijSflXTyry68Bfda//KvC1zvZfclmCLwPjT4oHLIcAYydYWRW2ZFJaLrx+lNomoWFAGPcoqxbdCmQoHdRToVyrKMs7aPOuUgsGoz4oa94ZAW0UUFCTpDFx23Lz3jVkU9JoTV5HiGpIdbVCHwl2JymFiHjn+hvkZczNgzt8cHCLew/2GKkel0bnaauAyeGEMtKU964SzQLOihK99xYH9QO+8Oyz7K6fZ02tEbSGarZPcedDtoZ9bkxmtDIiGawR99bY2NnlaHzE9GQP3Qr274+JRMLf/A/+FgABCttwu1pkA5ZlwfpUhgCW+WZrfhs8L6FGOxNSO3qwkNgJrgUbtYuJaTqfg2W60SMUBYIRI6SbhNKFIiMCWkpqdz7rjFxvxcCRkbQEqIVLYRFwoYs/2MKkkoYZltk3QFLVmtoYqtrCqcMwdCb9UkBtVaFt2VY1LQcHB9aNcb0TtdZIZZuVhqFThB3UXzfA54V5VdC9UvD7eMsBlhaCPRdBq33GxkKEbeGQ8wyc+Fml2y6UNlgLrbXJ8oV1YPGQvibEw6ygdpUcAuOeK4t7KojZoM+M/GMl7xMDg0KIf4gNAm4JIW4B/zXwt4FfEUL8e8B14N90u/8m8PPA+8Ac+GufdHzwphYY3aKMIItcplQowtAG/9LRgI1kk0QNGPRHNMbCQUfDPnVtwSWBsgJSlrapJqJytee29XSaxohQosKUtaikKOdENESBYWym1PdmNCbnx1/8IpQ5jZmxb8ZM9m7xxvuvcna0xfFsQq9nKKYNjbK8glkSk4iQs6OQ/gZcSUNOtKY4vs35Z/8Ur738GoNAoKs+ZRxxdjNmnJdMy+somYPZZHvwOPP96zTzgradsrd3k+p+jjTLYI7F2gdoUjQ1FQ2hCwuGboVuXfqoBQc/lQu/0lcY4oTfpvyESygJ5yL4WgALR7VuxNLCsJPGcgn4lcyDiGISl66y59EjwzIXtcyYEjqboKZBophTkmALZSIiYoeEa10uvKZCYVtrGxfIrGmJk4TWBOTzGtlXIG1ozVbn1dYiFDAtZqRJgqxcAC2KbM5eQpZEnEznC5hv4LD3eVEtlEIgJI1uF12NuorBYwd86zo/j53M2GdhQLo0oIU2e4uMhUXl6d98sM9G+luXAWoXLkFNu4jt0Hkq/jM9UgIUM6YUziIzLiow4UcwivD/LTvwVz7mrT/zkH0N8B9+0jEfNrS7oZZQsnG15bYHoUZhWkPdumouYSsPwzCiyItFw8i6rp12l8yKY0zbEIeSqm1IehbcEtIQByFr/T73j2fEc81eVDAabVEeHdDQ0N/Zpnr3BwRrEeF4zkleczA+RgxDSir66TpGF5yMA1QQkcmQdnJEpQ1JGFLJAUVdoPNjVBkQthFDFTPTLYO1NdZHMcd1Db2YJB6wlvURuqAsjjF1QaM1G3HKQX1Mmy/BQgrhCnls/V3i4KV6IabWKvAWQUiExwN6IY4JKahdlFm7Y9ljlJTYlueNUxx2Mnp3wAcFNdql/uzKZSdt4AJagcO5eZIS666M6NO6yDew8Fn9OXtXoHRuyMzVGHjLwzowttYxzwtSZQjTiCoviMNoAdDRwgphWZUkUbzsMu1WeRFIjGmZFbnFbDQNYWhZimpTnzL1F0LbsQ7cPF9CkKsKY3wbtGWq0GcMvF3WdQq8i+afi2FpwwnnJtUuG+DvsQ+q+velc+P8sUtKFA0jBuQcuIAvi+Bv67CHDxuPBGxYIAilQCqx0LIhilgEZFFAGkb04wEq62MCSaAiojhGqYg4zQhTy0OQJSmhtIzEAQahbXOIKE5p2pa4FxImKZubKVKVPHbuPDy9zu6ZIU1ZUccNg/WL3L51l82Nl0jXL7O2vclod5Orh+9jREiabVCgaQNBj4jdjSscXbvLm69+n/1bd3jl1Rscz/p89qmfpJ0pijYnO7tFScKly2fZzAY00ZAgjZke3ySRQ3pS0OaHVMf7pIni/NY6wfEJW2sBJ1Nbm/9pLmOZA6037xGBrRPRFu2ov8TCe9SO/88n53yYyRKNtsREhEhnfi95CVlkHLwbYRaxgyXzwLKuwPdGtPsZF9wzDOg7q8AwJacgJ6dAEDBkQIztXTBhtvCA/YTvkQKaIQPnprTOLLbB0LwpCWKbJQo60fwir4haHEGrQQXRqapAD+qpG03jeP+Mq+nVrbUWrADrU3wBXUiwhwz7EWhLViO1hNbXFtgCIXwa0digoJ/vqwogcgoR94x8kBC8Wc/iySzjPWIR/LPIgIYMyTaDhYKwfA/SqYSHj0dCCYChMZqiKCirylGApy7qa7V7FCWkkQ38tQ5KLCUEShGHEf3MUjn5nvQ+XRir2DWetKzFrda2uajSjELBgISZCAmoEMSMknWMSBhs9VE6ZKYFgbJuRCQUWdgnr+bUJiSKrCIiiJEiJAlC7t65y0lT0RsGnE2G3D++R9HOMCoiCHvkTUOuE05OCpQJGMWZC2y1BMqmwLa2dkhVQihD/tO/+fcB2GbAkkE4WGh5+7CXXIGAM83DhUDBMlXoEWjtIuln1x+/T+PWcQ9UgSVaULO0CMDHHCAiIne8h7bYKCAk5pDDRQRCuelqKwgL5kwWKUhvCAcLa0JSUxIQcMghvgFZ3TFrUyR394+5sX/IZDazrcycqS4C6dKGelF7YSHlVgEEwoJ4lnEkswgu6qa1KUE4JfT+b1cpgBV4/xyWtZzdme1ZxuVCCfh7Cl6wxWKV9gLvn2fXyvP33idbuxEgjWbAgEOOF9abLwizVsKfDDH4//8QQGAhm2naI0kSjDSEbrWXQWCpohwLrV4Ua9gOrdpUqEAgdY1urdkjAgikRYz1ej36vZ4lJxkYZKjpDQOuHx9ipOBMkBGUOfNZjj4+IAljzl04z2NPr1OZhiTuk6wNeP7Ms2RRhqo1QvRoox6irVnf3KKpC9Sly2xf2OXqe6/wwzslz1zYpNYlzGeE2zvsVwG3mob7e/s05pCimbAbDQhkQpEfcWbzEhu9Zzg+mlHNK84MtvjH/+C/AuAQS6sFllcwxlJYR25lPJ0OFC7AZ9dtha8A1CQuUeetggDPGGzc8XFpP+nshI/iD7TzOQW+/6FxQSvbAq11kxT3vQMyBC0pCQkRCRF9MlpKModKVATMmGEpzOJFRkG5XENIuJjY3jZZJ2FAzDbrKAKapkWqgBJN2TbUbbPI/UsJZVkTBhGJ6xSstY3Qmw5ewLSGUAZ2JRfylJUBp1GCNv1vIddd4V8QujiFEbpAYqNtkzxNi/fZveJu3VGAxbOyUG6P/vTK1zjKV6sYUlJaF7vZ48hS5lHgcQUFFSHJj8ALPiJKwGiNNNoWBaWJbRkVWZpvshSyDMskHNlWVmGIoSFUChFKpExBhMgopjaaspqhTYNII1SsFqWbMlTM5wVtHfHGtRNukzOSipPxfVQjCSZ7fHj7DY7q2xwVLU08YGP7Sap6SjuesTnY4pnwEiJOGDcTbuV7PL5xxHPPZjx//jF2dy7wM1/9KmdnMe+88fu8eVKhWsOdcsbtoyMOpOHB8SHXTz5ko68YqJTRuR2UqpkGmvNP7fC5z3+aT509xxefeIFhuMv9w4a//d/9DX7AD1CujNTQUjj6T+s4LbW8LyWqnbIA21egoCYkpKBykWTbRiSnRCOYLTLJ4ULgpcMfAESojikauNXIB/A0CSkBAfsc46sKbeoywOMWJw5HWFBTuW0FhQOzNASoxXELKnwMvVpUQpoF3LkF5lj26HQBo7btw30gLwxtzMQuCA66qxt8225LOK0tw4/QRLEtPW6NVXCttvR13RiA1vZXtks/X3M6FbtYzTUIIyibZpHh8Mk/4Wy5ZY5lCQDyKd9uDKYbNLSZBOsuTJkSEFC5KIxNG7fuXlpXcU7pgoUPH4+EEvB30xjDweED4jihakqLI3ctqdMkozcYWTM/CNDaLFI1WkBeltS1o3IC0jSxwI8opNEtYSgQoaI0LQ3QG61R1zbS3FQliUzJ0hFEESISNM0U0xjq8QllpYnmJZPjKU0cUNY1IkkpdEmYzEj0Hme3G3oyJDIp6c4W8+KASgUc5AWTYoqKKlRc0R8GrA0iqrk1zNtAIOOQ3iAjGgScNCcIHdPr9QgSxad/7Fn+879lSzA0UGC751p0nw0kJWQIjAPieEGxn/CriMWaWzH36UHcJJPIxdT0PqqfgCEhygm1zTuHC0CSryfwdObrDBmyhnK1BgEhc+YoQlo3sVtaMjIUEaGrTPCT0B/XR8yX1Y4BHibbUDk10boCas0+h4vP7rJDozV121JVtRXaxtAu/HtN01ZLn1lrBzRSiznoG40glug+rQ2+SjgidHH75c9pXOZyW+gE07sM/nPdYC2crusACDsK4rSoOBBTZ7tHd1aOo8E3ovX37ke5AvZ+PwpDWMKF1jREkfXX0l4GgSRUiqSXEYQ2YFOVmkCGRGGyrOfWBiECNLYiMFTW1LNFKLavvJANRloW3Vkxp24aRAOEAUpF9PopUW+TIEnB2H4+lW4QokbLiH4U2+xDUBMQ0Isy6qKiMDlra3Dp3BDRCII4olEGlSpaJTmeHIOcQpujZU2UhAzUAF1JWjnFBJIkS1FxxKRRnDQl0kjCrEfQ71EtcSQolKvFDxZ+XkHBnLmjEVsWlwROIVgswNKk94KzbGEm8Xl/vwp1//erSRdlELpofuhWGw/5nTChpnT05suiJYVi5PgPPE3Z3JGRdie/hydXVAs3xLg4Q0a2cAO8iFl3xnZEShzGoAu80zRJOQAAIABJREFUidzkl0osUoBJkizuhTHGrtZCgC8l7vABaG2WQT4kPTJ8VL4rxB601c3zA/jeEN39TOfHX79/Dn6/wAVCu4Cs1b/d+hC/PXDKuKsgvGvYxY+sjkeCTwDjmFqFpj/s29ZKjYDQNpCQRhLEEUkQ00iX924tfZOurJ/c0NC2DZPZCRKIA8uAF0fS9q0f9smLCaIp6e+sM5sUJCohD3LWn75MT6Q8fnEdNQng9n3u3LvLpZ1d6lrw5PAMzVrBZH6DQAw5u71F0Uxp8gNefrvi7FqPzazPmf47zO4rzpzJ2FNXOJnl6MnbjLaGmCpnPB5zZvcpiltjqG9xVN4mDXe4uP0Yx/M59x4csp5Kpkaw0Ug+uH+XyxeeX94mKmoaavxqZFfKnBIfC1ga395PF26dt9kDCyldGuzeDjDOt7X/LcFH4FODVtxrl170dCQ2dmBrDbbZ4RrXXWt0g8JSZ5XkhESkhKQORbjGiGNmbqWyFo7FN+iFH+yx8hUVUzQ9UiaO+jzABzqtSFirIWDKmMwpKd99qa2tD25tqZpI2YVGCoilotItVeXjTJASOz97KcA++AkfLdDy1lQXmdkV2NgpJ+sALNODrMQEvDVhr8hiAXzdgLczusrYk8TA0ooKUSTEi1Rr6DAjhYsRPWw8GpYAhqotiLOEssopW40JQ1ohkSIiTvoErbEAb2wgx9tmYRgQBgppNMU8R0pJ2kuIQ0WWKJSSpIMhtW4JopYzuxeIZUY/TtnZ2CTMLI+b1hUJPWpaBk1FdHCTdvwBO1HCqL9NFA5QWnBwfBWpEqZHMy70L3LzxpSbByWziWDYC9Facz8/RjcTLmz2iOcV5dER8+kesexTHx8wMDlh20fpAUpV3Lt9j2p8wLA3YFbmnDQV70wOCdOE8cGNxV2qsOEhReBYgKFPTIjAA3xDlixCximGyiEMbYuSCL/+guUF8G7Cqg/qGYrsENgauGWqypu6JQUtJfe5ywZrWI7D1mUiWhIyaloU1puPUMyZsUwnQuYED+wUt2gIvaiea2koKNz+1u9tFoKmiZwBLZFU2HJc41RESEBMQOrWvMphB6Qr5umHMVvDNbQ2pK6c1wu6f+2tE+9c+SBdt1Tbn3t3dPs22rtoRdFiJHwxVssy728Y0HOvlyXhwt27AZkL7sqO0rb7xUS8yGeZkiNdObK3roKPYxThkbEEDHGiEBEImRAnfcgSwiAgTnpEYUYW9miAOMnQbQ1G07QGaoExNbNpThgY2kIjw5B4MABlyOKM3jDi/Jmnadcvs7vxGG+89TKDzXVMk2OKA+5/eI+TtQH1UYlKQ37y0wV3Xr3N7O2vMXrq30b3BKN6l6K5zYfz+7w0v8RaoKmNJktbemJA2d9AnrSsPX7M0f2cdnCRdN5nMNilLT/kWq24fGYTefSAs5f73G5zsrLPwf23eXBb8MzzP8b9a1PQLed3Nnnv9vukvYymWdawezyAzZdbk3GGwZN12vRb41B5VgAiIMVSe/ty4dCtxtqZ8rBcvboAHa8ULP4/ROCpyRpnelqEukUW2lbnuROWoSsrtjyCZlGSbCMyrVMKityRm9cdy8OvfsAiGWqptVgRCo+u8yG3YJFqq93Z2fsSOkFzpCmO4aduDFARE9IwJV5YOt7OkG4FF4vVvivint7LnvNpn96P0qU6vRJeuljGXReLv155T5nhodPilHUnmDB1dt7yWQmnjCxn5Pfpk3DMHB+mBIiURDePMlhIWi5AjUC7GvGqqhYMLFGsCGOb1qmqygZxXNrGCIEWLBpT9gc90jSlampUrJjPZqRxzMG44uh4DyHXOfvYY6T9daSRtvvvMEHommK8z8HdqxxN+4w2c7bDbcZtTq0N6BNOGk3NAW3V0qoI0WpLbKr6JL01Kikp24TcCEQQkg1DQpWBjEjkkFGSsdXfQMV9+tGQSK3TmJpGGBqjkLplvdejP0iJE0VratZP+XfLgmCfkfZcgl4pmIVSMA6m6wuPl0FDX7La/ematN4l6Aa4/IqYugRej95CgYQuo5DSIyTGMx72GTlRbE99Lwh69DAIBgwAvzK6+QAfEabl+SyHF4zWRculW9PEqSsLFv71jIpRL3XBZd9vwaMwLU4/Jl4IfRcX0Q3mdf33rhJY9d09yPdh/A5dRddVMv66bDco1TmX03GAhexgFtdNx3LxwWElIO6U2D9sPBJKADwBgyAMI4LQ9o63rK2WS750+V7LnGoVRN1YY6qqKsIooCxr1z9ekw0y278tyyjrmrsP7nB8MOHm/Qck/R5RmpGORgwDw1os2F7fRZuSoJmwd3SdYG2daB0+PDqhGj+gaFru5O8jZMRJMSZvarIkJgwUpa5Y39glVyllkaCVotfrc1weEkSCw6kmDofQakajdbL+JqlKWBttI4OEXn8TGcRU1TH9VNKWLWErGER9Ro45xw+1mIDdlec0KMjv44OF1pBs8IAfv0Z6U9QLWBfw0g1i4fatnHmpFsJmfyoaSioKCixQKSalx4zcOS+2HbpdWe365i2MriCfroT0wGUreu3ibJbZCy9E9UI4PC/iUni9QgMYkjKe5VRVTdU0zoLw37kELC3v02nI9cOCaw97v3vPuoCobrDPv+/vY/evPyf77JzbS0iXPWohN+6/JXjIULpYUS+2mYJGa4x+xBGDxkASxcQyJBCKVKX00xHDbEgglMULSOGQV4I46tHUEIQWGy4CYXvVBaCiCKTBtCV5U7K1E9CWFeP6Ju30hJvf/hrX5u+imwMOWsnZkw/49K4mClpeuvQiZTni5RuvoceS8Nw2//7zWzzDXZ7qaQ727nJ85y4fjt/j1tH7XBvvkSU7DNd2eHDQUivB1fsPCPrnOXPpCn/46ndJkoRi/Rzpzg61rpkZTV0qrh0eoVSfrc3nOX/pIoP+Bnrvbeo73yY6eJVnzAHx3TlH13+4uE8ZsfNrfeBsGVjyE8iz/4KkoKBFMKXAOGFYtiRfogw9I/HqSuSFzQ+JxQSUaI6Y0LAscU1IXAcEK75HjBmQUlBwxIRjZpxQUNMQE1NSk5AwY7JQav58fEbCCrZf3Tx8RrPG0NUa2OChQpJTuEImnIoTzuvGnad1ETYZEAHJwu1pybDM1JqAgnyhELpWUvf/rnL09341NeivpeV0mnZ1pcbd8eWrrltkX3m8Ru2cKX/smsahRGxsJ0ojjDQEaUQtLFZCgm3Ook/HKrrjkVACMhBobaBVRIFlgpHaoFpDkmSkWQ/lyEONMRRFgVSWo15Ig5ABYaiQSpIkdt+irRhEEWubu5y71EM3LV86+ziNUdx9+x2aKkIX12hVhjl6m6nKufjYFr/0sz+N6I0g1uzvD9kvDWd3nmOkNkknLX2tuRD3aTnicHYboVuyZIswKAnjLTY2L7Jx9hJtccI436Os4Oz6OXr9mJmBcWOJpA+mdzg+uMXTu+f48uWnuSI+5F+7coYXnr7IxZceZ3Buk8P77zATgt/8P/5LAMZMmeKxZb5YZxnB7vrxfnh3wSLLm4Uw+YnmLYguTLiLEvTH9T+NC8mN6DtXQ1JiOyFZuJEFMBkEM3IGDIiI8cSlagFAWkbVE1IMEHbM6LCjlJZpSCtkEyYofKck6YqgQvza760T30bNoif9dxf06VG7uIYkJHcpTW81dZXsapDQr+Z+mz8/P06v0u3i84H78efnU7G4Z9OFTGs8v8PSRukq44bW1YlAScOcHCkFTVUjhbG9N3EBdEDqj08P2vv9CAyjDa0GHYhFbXav1yMIBHEcO1ehUwkmvNlkX4fKMgnHoackt6hCKQzjyQkiCDCiYBg2XF4bcev+VabtHUKpiGTICRJZFBAqBlsJo+xpYrMD8YjDowkkBrVueGK0zhW1hkiHDIIhRpegQ/q9NTb7WzQmwCjNmewsnBgSGRElhiYImOUa7bgOjqeFPUcpOSczBlHDUEr6ap0oGRKXEdMH11A9RWsSTGiFc0ifGNv4w+em/STrCn5XaLsTtXXrEXzUl+0KXPc4p+MCp8uJvakduonrOQp9LwFvpXQtjYDAdSxSLpPhYbT2iXogkP0PZ07bsOAynebNY7vXskT69PU01ERurbTgJU/QWRK5z/jv9k6Iecj96ab8Vu+v3/f06v3R+EBXIXTvaVe5dJ+Zx1r45+ozJ0FgW6pJKQhDRRxHBALSIHTvL7kOgAXp6cPITfx4JJSAkBIpYwbZACkhiTMINDoMkQYiYS8YKWhaiw8XrhojCCxtU5IkFk4sWntVRc5WL2JazvjBux+SZIqNuOTzn9tl+kDxYfkadybf4mhtl+8cGGQY8b03v4eY3+bzV36C/TYmOf88j609T1Ps0eRv85WXzvKpyFBfe4WnjhSJ2aMuSpp6jGohr3PG0wPWN3q8ffVlNiONCkouZyGDeUs0OWAUKqryhM1C8EQ0ZlruIfMJs2jAdyvNb394wGGxzsYTP8nmmacZzw6JXKvsmY2AMGG2WCm7frTP7y9XDR9iEyxj68tuNn6/iGjxuW5wcDlpu9RkNjW1xyENLQWlM7etyWtbxPgVFE6YkxDTUhMSkBJTMidComnou2ZmCaGDHuOE0ef5FYlLRYJV/MDCClgSrFjhWgYYTUfALB9zhVl4+b5pq8GQOER/VxGu+undv937vpjDK4pCdrZ3V/FVZdEd1uI5vb89lkQIm9IER1cmJKbVDi8T8nO/+PMMwpDYdfBSEpQRKMeildcVHzceDSUADPojAiEJ4x5FURAo2ymmaVwCTAqEVK5/QElbWxhoXdnqO08xJgPQRYFCYOKY1jTsbPdIg4hqd0TbSJ4Z56RaMZvA/twwUi3r84K7D+7SpiFpZBiLHk0JQZyRtwOK6Zx6sM1jgxkybJnffJ0RgqqVNEWOMpqmzKl1QjMrUZOcXBT0gpBA9XlmO2MQ9CnLknbcUo8bgnKTsE6YNwXjScChFkxnB6igpa6GlLpGqpJhWHHnu/8zATGSxE1evRDo1VXHD5979sG17krTxQMsiUS7KMHT0Wofc/f5a5+O04tnGLgQoHQpN5vXt5Qw1m+1bEMNMRGFi357AU+ISInxNZGZa4UuwdFkerCP7di7wWDhC4P3v5dmsj0ny5jgA4a+i3NN7Rib7T6u5GyR8usqva6g+3vTdt7rWkv2O5cEoKtj9Tl9VHHIRQXn8jM2lrGgNUMgla2j8OXNdVPze7/z+8zqGmUE2tU7GCFRxoKFHnlLwBigaVHCUl1lWYZuJVpLwjglDkOaqqYsc9q2dmy/Gq0bhAioK9dKyvk+6xsjNjbWODyaU4mSogk4c65PLAwyFvy7f/EX+e73v84w7PPh/fd4fkvzQvQBW6MBr9wSrG1t8PiZHXQMqjRcu/k27735fTAh1ZN/luN6h7vJgDt3Z8S7kpOwpprVNIXgc88+Sb+dkazv87nNlJ5p2CoOSFVGOlK89+5NXr31OsHaLlkaMslGqPgcYTailTVKrvPW1Q+4Md7nmZc+w+jcJk2cMlERBUcIPJ9Ae2qqLf1Uyx3gV8zMpe8eFtleDWY9LDrdfd+mraxq8e8b911+iuXMLfsPIRmW5rqiInGoOQ+l1TSOjtT6xjExJ0zxhc2eHnvO/FTkW2MRjxNmlC6H7yE8IBw4Ryxcj2ixylvronHBNOGsAcupuIyBdPP3Xhn6e9ddobvQ4W58wN/XU/Ob03iM7jG6z8FbYMv/WZj+EkGkbB0MjV64wlYxwHw6ozWGWVMSxjGtNlRNTtnUpEn6EYq07ngkwEJCWF85TfoYETIarJMMh4RBhG4NpdAo4XgDA0mRz1DKEmOBJDAaqVxeVIb0Rn0OH+zRNBV9ucGkOCLe3OF/+KPf4D/56l/goJnys/JxgvNPcf2tH3J+4ymKZsTP/fmfpfzgh4ieIEoHqHcP+c6D79Fr9wiDNf6nX/41dn/8Kf6Nr3yO6vA5vnX/Hpe3H2N84zXMlZCf6o1Z78fI5JiyLrgTP8HhgyPQJZvbBeXJnHNXznDw3gkm0rw22+Pp8ZO8O9tjPDnkia0LjI/nfFA8YDsfUwZ3kGIdHt9lWnuXYMo6IwrGCwgKdFcZC9X1k6lw6aJthuxxsrjnH7ca+fdOBxiXE906GB7IIvE98jyE2K5memGuWwBTiQLXBl3SI0VRs84ac3IaFzT0hTApKRNOFuhH78BYeLNdKSt8+ZRVcPZ7KtcSLXThQAtd9kFUy0ukCYgwNBSUxB0R8DkKgyEiciXWSwFdAoZOr/OrMQG/rXsvV7Myq4rCj7ZzbGv+29VeSEnV+LLoZat2tA2MayzjshCCJIxo2gIahRGGpqoJ5DKNvDoeCUtACEFrNPOyIAwjWw2oBbSaWIWESqFkSKzCRZGHp5MS2hBFkdWYDlxXVgVBFLIxHBDoltmsxBQVjPcY1/cYZUNGgzO0zSFyJqj6O6jNIYP0KcL2mLqokTpjO44IsojAJAx2LqJ6CU07o78+YuNKn5+++ASfXjM8HVUE00NG/QzdTwnjgON2wu3xDEOP2kimrWAzC1DxGk3SkGQxcRzS7/cZRAmiCTmZTjgc75PnOaWumU9LZmrCZDZFhgl/7zf+W4CFKe1NZG/W+jW7u8L7OrSAZSyg+/dhY9WH7U564b7X76McwagHv3iuAVv/L50bAiUVMQkGQ0VFnx7HHGMbbviKxQiPBLSKQjnP3X6XpyazjVDUwj5YpvCWZrnfplxK1WY29OKvdm4FCOoV4VhmClbmaUfYH5aR8Yp31afvHvdhx1w9tg/+dYuZ7PYAJU8H/rqUZ77s+SSfEYqQwAXapZTo5lEvJUYQZymBihACsr5NGRlpzZ2matF1ha4bpNEQWGBn09h21mVZYlqDDAJ6ScyNGzdopzlhGJCIKU8rDdfv8NS5L/H//NH3+f1X7nGHlO//7q/y2JrkzVfeJO5HVLMH/Mq3rrL3zX/C6299l+CFn2HSH5OsraNVnwuPb/Hic5fRBxPELCBNznD39owwHPCDa7/HG9EGB/Ipjg5ztqIdnnvq05TBkMZoTFGg1s4Rr4VMZy1ZHPD8s58m3L7AtYM97s2nHN68Sj65h6iHbPY2eWbrad66/jrbwx437u0TNb5gaIbEuCo+VhwD67H7RGIILo3WfGQVW12ZpJu+AV3+Ot+gRCxQfx7rDrj/9eI8fKrL8hPYXgWNy5bbEKB3Mez5N475wBcIR0RYdqOS1sUqrKAvG6Z5tmKwlYKhqxzwwcDSQZQFy8Iab0koInc2ttlntYgZWMXi78NqFd9qIK8bC+iu8quBv1VXzKz8+PvsXYYwtEK7SmTSaOvy+liAX/lbY/kQLCZAUOmWum0p6oY4Dqma+iPKZHU8IkrA1nNL4aPYEIbhgv89QEAgkaHtABRKa4IKBxkOk5iqqWzFoZQMkh7zac52FCAaRRYqPnspZJSFKFOhmTEJZ+TjMc/2FFktKacTdFHx4kiyvbtFP69oipZZWxD3+oRJTTKKSZMht6/d4Rvfe433Dk/44P4HfGv/hOu3K5IgJokNV29f56Url7m4loEMiPsDkiBBYMjnDSaZsT85IIrnfHDzPu+8cZX57T2Eanly6wKRmDLOQ1QZsJb1qGQDYUt+9y7/9//5n/k7RrmAw5xeSbrpJuXyzV1qroeN5USVH1nJ/Kq4PMbS7rD/BU6Ilrh+//2eg8i7LT16i/Jnj1YMnEvhc+dWWdl8x2lQjW/ZtcQ0eHzDkmRtaZUEi5qBZSqucXaAp/n2gtgNinZRhn6spgx98dRqlqC7z8fFCbwV55Gay3vVLohRV5ucfFz/Ar+vJ0kJhERJiYpC8qpGGOin2UMDlX48GkrAYf/TLIPApkCqqkKFMSqO0ELT6NZqQwzVPHc8AoJWa8qm5ty5M5RVTq5LdkZD1vo95oMB+9mUWPSIgoS4jrl6M+H14l10UfHv/My/zubmLpfOnmV8+4T59H1euGRoZMb50Zh9aiaVQmQjpgiSULB/eMgDkXNnusf6Wp9puM0bx+8zk4ccVzWzyZTX7/+QZ579DE9s9JlN9wkqiEzC3Tzn3vgadR5x2O6h8zVe/eE/5vELDWvb9xn1tzl/7stsrT3Hi+fOc7+IaMU5/sXLv8YvXn4OqUry2T0AUkpil0tedhP+aPApd+DceUfAVk1ZrwCWwvDw4JXftxsU9NBfv3oulcmy50HoprutarMFRik9MnoOWyDxKc4l54Bn111NqwmG9FcUVUvllIJPs1lATYVweIBuZsQrRV+TicM8dBXl6Ws5XQ/QvVdd7sXTU/p09qC73d8vDyn26A0prfne7ZLcFf7Fii6XrkC3AUp338I1WxWR5HA6Jv0RgcFHQgkIBGVV00owMoI2IFSKsiypW9uEpG1bTKttkAMbSBRoVBSQjfoU5ZTRaEC/p9hIcs7tBqxP30XffpV+8ToH732Tb1y7ztHhmDf/4I+Idrb5jdc/4OWy4r3NF3k1eJIf3N2nLDRX//kf8o13x7x340PM8YT58DKv38pZ3zrPhfUnefX2HcrwLHfanONeRBjBlSsvkeRzfutXv8ZeUfH1b72MCRIuKgP5Ie8+eI9/+LX/hd/53jfZurzFiagI9Ji1rU04v8FxWnLYW+f9csxxf0K8domv/+D73PvgDXST8Fv773Lp8Wd46gtf4jd/47/nGFiW3Wi31toYgbUAbMw8cKt025mIXWFYBt48Ui9cEHOsBsW60fCuGyEWx/X9EiW+w5EP4p2QO0y7ICZ26Efbp1A6/kBPYXb62KtBOENJ0VFc0CPCOB/fB/YEy3p7r7SWkGSv6nwK1SMdlqvyw9h4VoX6YUq1u18Xadgd/ju6BChefr0l4NN/q2SnXQbkRjvFpzVSGNB2/9pYro2qLoilIlMh9Y+wBR4RJQD9fh+EIA4j2rZlNs0JAoFUEum6vFR1S1u1iFDR5CXaCIQEoXNUKCjrHBP3Lc23OsQ0AinGmFiyMRySRSOm1TGPbz9LdVJwIdmgiHpcm9xBN8f0dYY+gcnOkJPWUOoHFLLkuzePMGZM3oaIYEagbfPofFJgWk0c9NkYZbRtxWF8n83RBe7svcOtvUMGw3XqNOD+3Xfp64ayrDmcfMDhuOLWeEJZnKALRUtFNshohaQ8uMofvvpdqswQ5lPyWvPevTfJxQM24pjR2cuAZQNYDfQJFwL0JvhSeO3o+qJ+BdOd9zz3wOrK6Ue3sq6bkVjubRVTTcWcGl9AZNN1MY0rNpq6hiWgOeIYSYit42uxTbbFolRZOHvAmunhwoy31oevmfRcf1ZttJ1r8vfHc/R6qI+/Rm8/+evyqEV/RQ8T4sbFErpNQLxC6N6jrs/vrYiusll1JVabnD7MDfArv7UU/P13xC/C/ufdaikls7Ym/Pg48COiBKREt5IgCJEqIIwjIqXAGHRj8WdlXhB2yiGNCmhMQ1uc2Oq8BKRoOSv3Ea2mOFZU032eVBfY609QScH5Fx6n1Iq/+DO/wOuv/CHbX3yBaXjC1ddeJgovcJeCQ1VxNf00W49/jtnd19gd/SlOZjc4/6mvktcVm6M1snCEaCuO9/e5fgRnhhfYKytuz0845F1G556kJy/yh9/7bUbDTTZrwfnRefqbj7G1OaIRFTuDNV798Abh8QMKCqQ8i2p6vP/2W5TTipv5HaLjY2Z3DPlb+7x359dZT3e4UUnOnbsAwJTWFdIsC2zAT3wrGt0Vf1WwV31a/7/Hra+Oh07aFVMXjAvy+c5BLTh+wXpBO6YZ0XMMgxEpGZWLcFglZj+ZETsRX6bmbNPTZdWjQJB3Wpl1XSNfC9B1d/xfn4qzrgF0leTD0IDd6/duju/x0HVXVu9X9xirLsap1KLhVACvawUAH2lssuymvITP++NIGdA0miAQzIucfpzQS7OPPE8/HgkloI0tcmprQ121VG1F1VbIwBq7RVFCqzFNS+BaSLdak0WKfpyRhA3tvGT3TMaFjZQnLq3z9OUdnrl4AR2e49X3rvP3fvhDzm1ss6Z3CM58nt12gzuHx5zs7XFu8wyP7T7J5XXF7z5oWNseYeINNu7NOckP+bHnfpysFIwmOdcOI7I4YxCNGAlDc+N7aCNIjg/J771OMBmy/9YfUDBDVIo07LFx7hmefOLPcP6JL0FQElQ5Ta0pimOONte5euMWlc64f/1NHhsd06sj5ieHVMoQFGPWN88g8xa2dnjn/jf5zu2XF/cuIyFwaD3fVNTHyj1oBk4Dg1ZHN8qtWRJ8+qzAqkCsFhb59wMsIMeGK33rMMt7ZMt5QnY4i3D0pYET+REj+vQXyqelJSZhTkHiOBE803FX4JYQ53BhwnddGOnMf7/ds/yEnfsDdIJ84tRxu/esG8Tr9gM4bRGZU/ezu/p33/NjGWdYCrAfq+Ce1Qj/qrUgpSQMgwUx6sixabWtJomWcYaHjUdCCQgpMNLChANp+78FUUBeFhSl7T8fBAFVW9EI14vGGAwtQ6XptzBvKyZlwImJGQ43CGTIvJjx3b1XuK0mlJsZsyLnrp7SjzLWLj7Lt1/7Oq+9d4OcMVF6jmFyAdXfou3tMC5mBOlFIrnG2+/e5fNfeJHZxjnu3H6DWZ0zHG0xSjc4iSGv9gnlgJHQnB+dpWw1mc5Qsoc2sLGxwZ7OybKM9f42OxvPMVCwO7xCc9wy3Fhnc/1FTN1nY+0rPPuZP8f24BzJWsuVLz/D2u45BtllZsUR90/uU8+PF/euXJjVZiHEy1j36Uq4jxurwb8l6Kc+9b5/3UXI+RXaRtv1qaMGBORUixBcjeYeD0jIKBw56owZBzxA07qmmVbca2fj2ADeMrvgoxHSZQn8Oaya2L7nou/RCMvshjXlffxgyb6sO5/117ds1rJcxbvZl67l0LUYHmYdrAYLuwpCOiUELJqjeoH22xbH8T0SjCu8wxLxNM0yo9A0DaGKyZKMfG5d548bn6gEhBD/uxDigRDijc62/0YIcVsI8QP3+/Od9/4LIcT7QojJVZpMAAAgAElEQVR3hBA/90nHX5yICKgbCSLEmJa6qkiilCBQBMJ2IQILpAiFJIsUo/6A0XbI7tmIn34+47lBwdX397j7g2t8/a33+UfvP2BSR/zUE7/AeG/Ct1/5OoNEUQAv/eQXefHpP83noh7V8AluXPsjwjTj8qVneP+Dt7j2rT/CyIbd6h7T93+LG5MDPvWlr/D5536MqZlwt73BndkdBpWi1Wf4zuG32I0b5Kzg8f5LxKbPtEw5aUvutXP2dEXbThmkEQ09svApdnafJs0eZ7v3Eo9tPE8rdrgpNd95/x3OXXqJxjTMd3rI8wO2+8/zz67+Kp/d/EnevL7s9t7lsbeIuwjf10/x0Rz1EmTkP79czVZ/IqLFymcny/LHr44GDwzyK6Td0/MZxo470ItHQkRLwTYbTMmJCEmJSAgcjtAzDUvX2pxTLopVLA1LdN+S9hwsF6EfIcmpNKCn43IzrrMSny7C6m57mFWwGodZFfrV9/3rh2UJVvfzDMf2tf5ITKCLIVgNGsIyntC2LUII5sUcFUcEf8LswN8HvvqQ7f+jMeYz7vc3AYQQzwN/GXjBfebvCCGCh3z21LBpDmuyCANhEBGp2LaE0stqQaUUSilCKQkDSSt7pArmRYMIQ55Y3+Dy8AH35weUQUh/ELM7PMPZ4SWSZBddzohCw1oSkRvJSXGMTraQxxmBEMwn+wjZkOgJWajQVcX6aMBP7H6at++8j+6fI0o26Q/W6MsBD5qco7pGhRJR52xGfbbXU87uPklZ1pxMQcuAo0nONN9nNj8h6WWcjEvGTcDkJOftd15l3uxx9cY73Ny/zYN771MdlsyqOVG4Qz4pmBzsoXUN9QMuXniKenwa0Rc4gzl05JI+UNY1R08XA3Hq8w9bnVbjCH7fVavCC43n8fNYAYNZrLyeBblypcYCwcxVF9rzt9+5xhBbyiOJiZz/r/DFT/b8PYDJq4olOtBmOjyZmnTHttUWXUuhe03da+9aEqerMU8j+x7mWn1cPKB73NX9HhYg7FoDXXN/NR4QhiFBEBAEAb1euqAP67ZVa+uajY1N8rxcUK0/bHyiEjDG/HPg8JP2c+MXgf/LGFMaY65iW5R/8ZM+5NtDR8px1oUhgVFIIyyRSN3QlBVCW6qxWtcM1/vMDj/kubOSEx0wPo5548M7xMET/O7NO7x59xrHtWHr/GWy0Tnyo4Y06jHYXkcHMcxb7l67w52TQ9af3kTLABUdMDio2Cw0/Widm3s3GIshX/gLX+Xm9Q94853v0qohgRhSJDH9tXVkFaBVyFRrti+/wOXL25RtxZUnX2BzTfA73/4Vxnv7fOf7/4ymyuEkJuKEt37/n/K13/tHEArevPld7h3+Ab30NtWDt7nz/uv8we/9Glff/A7zac5mfw1zoCEc8+btb5Hr9xb3zrf/Miz7A4QuLeiN3O5EX6W4giUHnn/tBcoDcroTuMt756PotirPFvx4wWkW+f6aPhkSTUhMTo0m4Kf4Ci22d4EHBM2YIWCxYktOuxz+vMKOxWFdnmZxXZ5/p5v6LBdtudrFar96vfZeLn397vV3YxEPcwv8Pt3RtRJWj9Hdp+sidD+nXbqvu9oLsYQBd5VCnueu1+Jy/7ptUEnMg8MDZBSyd3TMx40/SUzgPxJCvObcBU+Edx642dnnltv2I4fx0U6hMbqhyouFP9Q0zeLihQoIpULT0oTwpce3ef1uw7CeE57UvPL6O7zcTDl/boOT2XXevPpNbh9XvHX9HrIa8vnNS9SzYwojmI8NiQz4+a/8xzwz+jR1fZPXXrnP9mCO2TKcudKH2hCZQw7vHvHBh++xPy35F7fe47fffIWTmzd45+o7iPkJ62FIIjd5fa6Y6ycwVLx95y6zFoKox7W9H3Kyf5sXLz5OFBrev/sGf+Vv/BJaHFMZ6DcjmlpQ6IDdx9Y5++Qassq58Ng59NSwqYYc1iWzccxrk19FTjr5ZZZcdP61LyCC0zDVT47s85HPrI4ukcmqleFReNbsj4kdccgh44WAlg4t+E/4GjEpLWoBK66dJdFQ0ThHoCTHMhQsgTwVJbWDQXuB7fr+PtDnm254cVwN7vlr7SoE/9dbRQ+LpXTv58Pu6aoLtqoQVo/T3c9nLaIoXBDsAIvVvWkaqqqiaZpOsxS5IOUFazUDlGW5gNV7gNHDxh9XCfyvwBPAZ4C7wC//yx5ACPHXhRAvCyFeLvKSJEsQ0hVuBIqmzVGuu6xFU1kPF2HY3tlgcjzmjeOWg5tjYhHw+ruvU5ice++8yhev/AJplLAWJbSTCXo+5/zZx7h6cJtZVHN4fMSkmXDp8lO8/v63SVXDE2uXUFnG1aZGNy3D0Rmyap3PPjfi2wdv8ESiONm/w/t7c3ZNy7E2mOYujz21xns3HhDFmrdu3uXzT+zQQ3D31rt8983f5vNnR/zpT32O88kWN6/fop5DUq5RNCDCiFbMSPvr7Gxe4dym4XB8jyYz7F7QtLOAT33qRU6mY9bPJMTJJvnejP52uriPtiGpPLVqwTJd1vV3V03crk+8mkLsugXdfbuTv1vAk7h255H71oKSFlusk5Fic/wahaCmYJtN7nPEU7zAOuuuxDd0K7UV6dhVBfgOy97/99wFHg3hU3X2fkTYJiuBcy0C5CK/cJomrHt9S/dhqUyWNQZL4V2laO8eYzV78bD9lm7N6RJl0VE4UgqaprHNUP35dVZ/4YqLJBY05xurLuo5utBjbQhcoPHjxh9LCRhj7htjWmOMBv43lib/beBiZ9cLbtvDjvF3jTFfMMZ8IU0TqrJEIkji2IKG4thaB02LUoq2bVEyIItC6rJi1MsIZwVxGPLB/SPkKKORkjZWHE0OGM9bRDIk7GWcGT0J4RHzck7caur2mDNnN9jeOsfRfk4oMgajDXbObCO1ZCYKQrnNYLcPoscg2+DLL3yWg+Or1Mdv88XP/Flqo2mCmKqOqZv3CNQOqjLcbXLOnHuS+ydHPLF+hcHuZWoBk4MjJvmcqcnZ3t3i1t5t0lFI0zScTMbcP3yAkA1xMqQY3yOIzmLEnI0ra9wtbhNS0zMbBKRkG6PlfXQTt9svoGv6+326YzWS3VUODwtgdbc/bFULCCgoSBxDUUvXKmkW9GI+d99ngCTkU7zImENH9xWRuE5GVgn5QJ8tF7er82nIrj+Prknd3d4VuIdtW60p6LIo+6v3DAN+dDkWPm6F7z6bTxrLAGHneM6k98LsYwI+KOi5A7sxg2579VVqsdbYJqsfN/5YSkAIcbbz718CfObg14G/LISIhRBXgKeA73ziAaVAtoZEKor5zFkEglbYCwmUJEwUWfL/Uvfe0ZFd15nv78bKuYBCDg00gM6ZTZHNJqlmNJVoW9GSLNkzljwaWQ4zY1vWWJLTG42jHGUu27IlSrJoyaIkipRENckWQ7PZOaIbsZFjoXK8af64dYECCOp5vfX+6DlYvdAo1L1VOHXOPnvv79vfVrGoEvHKmKJBKCRBRxxXTy9ncnlmhCSp9CLHx75FxAc9W/sYX57GkwgRjO8k1hZjZ98AjX4/TYntNPp7kUQXi0aKolZA04skxRxuM067N0Bjez8nhqeJehNcGJ9n4trLyFUXgm8rlVyBXKHKpTMnmV9ZgGKQw71vYubadYKNHm47dCc/c+ebefn8CCduDFE2YN4qUVHKtDTuosndRVCHkBLDH4ozsnKDmWTVpnrKAaqlHNn0EsOnv4snprCkSAyetvspZvP1rrtIuUbPcYa8ofjFNhJrrnP9Jqrf5Jvh//X5go2L2pbptBubCdiil2sKxLYb7sULWOQpYqKjIqNh0kEH17nMCstkKLHACmkKNT6BhSMkYvMH3Dg8OBFW+ykYtUSgs/md9+vkLRxK9Zpi8UYq9BorUEBYLS5aGyag1IRJ1v/9P8m1r5/L+jl2/l//mmvJQZsvqSh1gqKmaf/TDQzDWKUMC9iFQvXwoR1Or0cPTNPEsMxVz+GNxn8EIvwacBLoFwRhWhCEXwT+tyAIlwVBuATcC/wagGVZV4EngGvA94GPWZa1uZLB+tdAUVRME8LBIG7VFhdVVXvy9aod/8iqisvlsluLqS7cVQXTF6KoVViZXqC0ZJHJpJHmJ/GGW7jw9HcIa0UuTrzIYuomqXyBZk+MZGEWWZGYW1omu3yVhaU5ppNpLg1fxy150dPLSB6Bgj5FsqgzsTDK8NgE8UiZfOosQ+e+x86muxhLpohEovzMW95LJjfCeHKSXZ13cuK1FwiFupHDbVTECqGwxsGDhwk0t1PMaoSaOpnKDNEYbaYxliCdyeF3R1jKLzG3mKZcLtMeUenZsw9TOExlapmw7Gbn9j6WVzQqFFfnTkOrtQNRVpeaI3O9RqZZi3OdxzfGwRux79XPZhO3104QSqtohIwLFypKzZGXkQgRwI2CQQU3LtRa0k3DVj9eYqF2ypqUqaKi4JT0OnJntpJxGYvqKhnaeWc2LOgImqx9CQirbdWcLe60Na+/g4zjaq+57/UJQ/vvBRdqrRWasG7+6sOGzZKDGxGHdet9Ey/M9pxs0pxzkjuNZF21GF8QBLvJTk1ARxTt9umYthcgSRKC5aBttjEQWeMcvNH4j6AD77Usq9myLMWyrDbLsv7RsqwPWJa1y7Ks3ZZlvc2yrLm65/+hZVk9lmX1W5b1zP/b/WuzgShLtmiIbiEjoEoyhmWiKjIeWUWRJLLZLLFYCJ9UwdQNJtI54r4w/qIHYzrF9lYfVrWAGdtKdS6FKEbpN9tIzi3h9frJFZYwhTIZ2SQcaaS8PMHdOx8mlZshYyyQTI+C20usdQuDyRGU9hjegIdzr1zGE/fT2b6bBk8TPY37efXMGYSiTqgzxsmTJ/G7fEzkhrmRm0ehSqYywmRFQPY2YXkbiTclWLo4xEo1g6n7GE9eQYomqBgqK6lFfEU3Fc1gojCMaEn0bd9Bd/MAim6SSSbIZeeRQjH6YseQ6w56J5Zcq/O3Y9j6mNPWzpNWF+brp//1VGKxhjDUx60yIh5UfLjrREYNKpQpU62l7+wzPE2OEhogoiITIYoXLwYmfnyUsVAJoFPBgxc/fkRM3EhYNbRAwMQFaNhFQhJ2SzW7HdoaQ8JWMXSSfgYSdmtzuVa3oNces/n1DsnahiYdM7BZOCEgUKJAmFBt3tYMhpMgdeZmza13Ppc3TsRtZlSd92MZdht007ThXRcSlmFhWSAIFrpuUNU0DMNuv25arLVP1w1My6x5DAYya5vfKTbabNwSjEEQsAQnkQOyICKK8qp8siFJuBUvHsHC0lPMZmFOyyI0x0ieP4mnkiPR7sEtt7Orb4C4N0Ehs4LP7eHBD36I7ck0k/M3sBpULk2/zPjVzzG8kibiUSlHdKLIBMouGuIRCsUx+reFefbqGV4duoaQVxnobCKa1ZgzDTr790FDlEceeAgvEiuTU0haHo+ooZdLfO3Zf6JQqJBLLzA8foqZ2VcIKz4uXBgm0tKNmjJ45oWvIUtl9u87xuzoNTLpSUYWL6KVV5ALZQIeA1HpIllMUakW6Oxowsw24Xeb6EaOuZkLqzPnrnX2kWtQm71UN9YE2MNxk2H9QqznEDiL2agl1+TVxJi9iCpUKdQgN+d6agTg+ko8qZaQ0zHIkidFBhmFIAG8eKiwQi/dyLjw4MWNghc/blxEkAniRkapmZAyjTQjY5/udsLRqFUdSqi4a3+lVcs8rAmDOC5+fYgjrQYIVs0AsG5OnOFcdTd3IyCg1MhTzt9d7w1sLOPeLGnofN+IIMBactb+PAQcJWTbI6oJpOgWnloPh3VeR93JL2B7CwFfAERlNaMhi29M17kljIAg1GANQUAUZAShtvmNmiUzLUQLwrEQibYYRc1ENUAQqxy+615mc3nCIS/z5RX2DWwj7mnFcntQkfBLKi1b9uBZzpJanGf7lsNIQoDpiX8g6osysTBEWS6ze8c2cmYRS9ORFIlSOodsVEnlKwi4cLmjVJJz+FUvnoCf04MzBORGmhr85CtFXLjJLGRpD7RSVlxUNQPBI6HlMxRKWRrCHkQFwg27iYdkMhWdc0PnaGiM4VdU9JyEOx5H0yuUdYUzZ69y7vKLeAIyyWKJgi6iU0Z2CaieNeKHo7lnn0wObr3RnWcV099I+7XvsZ7lZuv9C3V3cJRw9dWzbr2ba66+dv0iX6vFs8t/SzWKs4FOgRILJCmiIWDgxUWVMgoCbnwEiOCjBRGBbiIkWcTmBdh5hbXkp4ZTIwDUDpI1CvVmoh/OcBSRNoZB9T6RXMunOF0b6oe14WvjyV7/uhvp2xsRhXpUwflyEB7nGhtZ0dbnf0S7x8Yqn0CwjUK1WrU1OHRjlYH4RuPWMALYRUFun63EoxkWkiAiCSKCqNj6g9Uic6kUFwdTZPI6MytJJAuOT86gkSLeuRWpnOY1o0T7nXeyo7ubHT29fOmFf6HhvqNIYgy/tsJrF59kd8c7ac0Mc3z276kkZ5mZO8NyPksi1oZIOwszk/zUgdvJ5y2Wp0ZYyWfJFys8tP9uRvVRRi6dQtQnaRoIEPBZlPUsKU2gWMrTvWsf6cwMuilhpJP83IP/ndJSiquzr1DOZmne0oRebqE7spPxC6eRgjna2xO8adt9dMlhDsb6GBu7ykLuFNcXniW1JGEJbrZ1bWUgdpR9fQ9wuOftq3MnYhfn2DLfDgOeDYvKHpvRVOufJwKOSo/9mIHjAVg1/2INxrONhW0w7FDB0QOQsBesq5ai9OKmmRgKLhpoYZZFooRwAyHctNGChyj9NLKfBDeZ5xhxxrlOIyHOsMwSeTrwIyPiQ8GPt+Y/QJni6oZZIxO/EVpgG8EgvpoP8/oTvB5B0DD4Bv9OjChGXS1FPQXZMXgbR314UN+HYONnUO8Z1OcS7GSlI5Xu6EKYq1cq2H0GS5XK2ufpqA1pFfsQrb3Seoxj/bgljAAAgkRF10ACS7KhDUkA3dIRlRqGalqkMyvsP9jBA4eOEWxppC9nUXbDVGaeam8jajKNLJZR3J1s7zhK0CiTWV4hWHSTKHVSyDeQq6QImHcTCd6H5hWpZJO8NngCRVfwulX2DryJHYluuloOkA8qlFwuWvdtwaPE8BZSpMVlfuFDv8aplwYZu7lMLCbT1NzM1t4dzI9fYWt7CMWyiLR18dyplzHSAjsbDmO5FSamrtLVIuJPxLg55KaSSrN3zyE+8I6jhEMJdu+6k+VCmvnSDRTZzejwea7dGKentZuleYEfnXiZ65fGVqfN3qgmem1hOq7ixqTTZvkA52eptqlVJAK48dV6AIg4uPlaAU91tWx3re2nw9HTcITQ7deTnZgWAwUXBgJ+gnTSh4GJig8ZkQgqPjROMMT3GePR347zGOf50Kd6uMI0//qtT/Lpx36aIbLso41mvGwjQgI/bryE8RLCBag1n2StzsFJDTruu4MSpCisGku5Vl24PjSy7Bbmgu2pLpDEKTCyDe9akdHGAqL6+V0zKK9PCG4METaGCk7t5JoZXyvwAgsF1+prSUh2gl2SkUUJUV5DNDx4fqI3cEsYASfrKVgioiCj63aXIVlSVvFPSVFQXCoRf5BgY4yV5WW8mkpPi4+j23aSQcIU3Nys5lgpjxIISExm56nky0zevEbf3i5kS4aSn9HJK6ixVoSKTDY/Qn4+z/joFapVCbNaxWVGyFoZStYinS0NCKqAt7EJ04J8VUQ0FYJqiMpMhmqqxLIQY0ugEVGvYojQFtzDQEszHlHHzI7g9pVRvCaarlMpL+Nt8BGUvSS1DO1d3bgCzaSKfoZnRpgseFBkPwIKoiizra+V/iadbG6J6YURGqNumiKdq3PnZKodEVBYH9vWowLr5py1hqbOwtNqVF8LiyJlvPgp10pwnPtuzJ47Gn7rX3dNrERAshchZq0DkYccKfxEsBBI0EQYDz/mEgC/+qd30tLSxjt/NYAgGnzst3aTSi8Qa+rggZ/v4wQThGjgJaZxE6x5J1pNXNRJ1EmrbEH7fa2d0k5k7Cx8B56zVv9nrhqAjaM+gehc6+RjNoYFPykXsFGNaLOwYi03Y+Cq6xBVfz9bEt0eumWiGTaKpum17tzO72pVk280bom+A9TiGAEbJXBLKogSxWrFVhwyTcrVCooi4fUFadF9BNu7mBJGsVydLM5N0uhpZ+TyS/iiUcpzKQIRL5Ubs2ilDGIszbG+Ns5eN7hpTrF7ayPffuVHBCthdh7aweLoBPcdeQ+z02niLh/lpI+bpTRWNsuevjux3BIu0USxKsjzQfb1RPjx4A/Yd2w3fm+Ft+17G1956mv4lCBbe1u4sfh9CqVhFnUX+978s2QNNzeS32Zhdp6ezginLryAT1E4sL+VA7tv55d/53/Qt91Lbq6JydnvYVQDJLNFWpraEJpUfuWu3+T8+DippZeJxFSCsTUmtlY7yfTaZtzIhHNGPStw4wK1wwkXFlXsduZ2aOD07DOwmYla3WvYjvWa6+rGjV5TJXLUexyx0CxFclTwIZOjRJYcMXx0E+MbHOevvvxz/PrS7TQ19qL4Z3BLBqW2AK6kieEt4Wv1Iy6tsCUc59NfPcrbqwK/PNHEXneWk7+ZA6CbGEvkax2PxBpj0HkH0qrLbuc3pJq3YEOdTqNWoHby19KrNfxdEATEgImZ25x4tZmIq7OJ6xEXa3VezHU/bzSu9UbVhjzXhyGOsXfuLyNiWc5fYO8jqYaR2FWbNjqiszlCcEt4As5QFGUV8zRNcKkeLMOkqtvdh0QRhIpGKp2h1ednemYMf0cXY6MzzBdmaGhsQhMqZCrLGGaJocIUScPEikRIKX7Skhe/6ufcUIp0cpj99x5hYXwMQ7JYyq2QiPRiGjLJwixV1YUn2kpF8eCumBSSy0wt6jS1N9GW2Mbi+BRTyfMkOrYRTzQRNHx4xCxnR86wmDdYKVSpljIMK+dRLRdT4zfRlCqtiQRRf5CK4UU0Pfz7y19ha6MLsSShqDm2tvTgCgQxZZmKDlPTaV4bGuSVVy9hpnTymTRaYa2JyOtr416vjAOvzwfUx731sbPTMdjCkRNf60EI9RLjTibe9kLKlGtpQxEn8tap4ugcioCKB50qEtCAzDc4zu/99bvJ5FYoabU+Elaeqp4kt1zGE4qxkklz8eJLTCdLiF6Z23p34WmJorkUoo0xbn93AoBxkjQQr3k01dX3W/+3g+MJWLWtaNYkz9cMQP1Yp/SzRs1YFwZsVAtafT6bJwY3Pr4xKblZktJ53PEMNpK9nM/HXeNrrBkWi8pq16dbHSK0QJFlRMvOdqoeL6qk4lJVKloVb8hH2B9CqxQQcmVGBsf5+28+jZGv8Mkv/BHD2RSKInHmxeMUZIPyjWWGh4fxxCWm9TSvPHeFF6dGyJo6sUSURIOL+xNHeOp7f0nc3YRbjvH86WfobE+wUq1y4+w46UwJEYmbzxwHXxWzHOaVM0/xt1/5O8ayc1y4eZJPffRjuIQKhYLB1o4BDI/OwkwWjcOonkOUy2XOPX2WHy1/CcIrDDTvZWfbvawUSrSHIuQrBYZPXaKts5mqlWV8bIGLV86RGl0kpCnMn59hcfEEZ05+Hl/q+0j5ISKGm3u23rE6dTbPTENePXWcDS5gt90QV09CWFtUTldgAWu1LtDh5VdqOLzTH1CooQ82Q1BaJfY4EJy96WScjgU2i9DCi4qOhY5GC51YSGSZYTud3Pb5XXz2L99HPBDhZv463c2dlD3TzGZvsFyYpr/rTqLxRlp6ohzd+056d95G1KUyuzTJQzvfwsFtA0yMJWnqDXPh3D/wod8+xDgzNBIgiLvGFrSgxiIwcOJ/VsXTpBqXEkCW1wg59eq9jsBHPcC2cZOuxenr3f56afH66xwvYeNn4lzr6DPUG2fn/o5gan3RlmMUKjXOpn3/+syIY4Y3H7eEERAEAcmqacnJMoalI4g6GDouRYGSjgudRDjCji3ddPdGeeTObtTWHYRag+wIu+jzBFFUgValii6mKQ9eI9jay0A0gkfJcPyl43g8CXKpPKqqc72cpJjPMZvRuHh9knK1wmh2iXw6RccdA7isIugpiuI8ly+Pc/HScebGszxy7DauPvssh3p8fOeHL3F9/CKSJ8FgfpyxkSyVksDs/DDt8QH0iIudvQfxzDbQ0hpHMiokggn2NndjKh5mJq4huxtYXMwxf3KZRJuMXjGJd0col3Rwibi9PjJlkyvX5ui9Yy/3PfxmZlnlZuHFxIMLCU/NPV/r+mPgNAhbS2LVu6JVdNyoSLWGHwoibkSCqPjw1p6/pu2/psxjk4Dql6KJjmv1lBNpoRMvbny46KOVg4SYYY5h5jnBJfq2JPB54frN4xSLVQLuRga6DyDKbjxmF1dGT5EtZ8iW0vz47HPczI/SEnMha0VeGTyJEY6SSed5dM8+vvD9bxJuivORz91JBJEeGgigIdXemVlDOQQgTCONNGDU3GMNHVFcz6pzBDkcmq4k2aVPsJ72u5FY5AzHTV+vZLQmxuJsfnGT7SchrbYld56z0ejUh3xOq7T6EMIu0VZW30e9wdls3DJGwO/325ZXtGrWGCRZRLA0VFFAtCxaGmPMLC8wkV2iZ6CbleVJREWgfX83sytVPK4oGhHcQRdbW3Yyfv0GsmQSbQpiSRpFKQeKgtcdolRMIbosylqFfKpAVV8i7JXxR/wU/BKyx0dAUrGKFm4/DPT3UiqVaI51IFEhIXrIamPEPSozuRGq+TI7925HVARclRRRbwMUk0TjDezvfhOqy8NKJknVMrhv/1sxcwvogkpPexeWlSMStHC7fDS3ePG68igy+AICuXSGTK7KzKKXStBLqgqDY5P86/c+AUAJC4HqKjLgLEjb9qurc7x+wZm1pKDNnrN7BzgnkRMm6LWNbycPbf68UCsGsv0Ap2aA2qkTJ4QLkzgyXcRoQUZAIY4fMPnwr7+dh967nf/6e4dZSduOuEewMA0Bt0emWI6LTXQAACAASURBVAHJH6C94X5EE0ZnhsisWMheN1MjC+QUFxJuKqUyP3z2abZsacfwRimXc8iKiJbXuMwKZ5jCg4IHDxZ241F7DiBNhgg+LAy70Wddie1GFR8nP2BZFpZp0tofXf/7n5Bs2zjnm5Ukb9yc9XUP9fdf+77e81iDRdeTk5xCrTWx1f8LIELLsjAN+2zBAMUSME3wKCI+t4K/yYsaCTCxMMmsNIMn6OaVmQv0hS0q1XnOv/Ay7copfv79P4ulQ1vXm3jHI8foqAhkNIP+zi10NXRQKS+TKeRJJ70c2N5A2BNBdPnY2d5FwIDnx19i27YdLN68wcTkCHO5FO7bDnCkayfVosCe2/qJx3pxB5Jkl2bo6uyhKxzlyot/zQNvfycHgh185MEmYoKLzMp5VJdAPifij/qpTFWoajP0Nm/jr779p4i6m7AuMT54hdR8loY7ttHW1s72iI8DA110+doZPZtjS8cAueko7qZGLt/4ERdunqI1KrF94BCj//ZhmvFTwG6+YZ8KdkTeRxRhE608+zRyKg5FKthUXRciJnaT0RJlHF0C5zRzoayegU5/QxmZBjx4gBgS06TIAbMU2fv7rbz174+wzDJ3/GkXHZ+Ls2PfYRK9HXzsl5+gnM9RSM9TEMMohpvlzBTnzz8NlgdPk4xULbF0U0fMJrh6bYzmYIBsao5YPIDPLXJnxwF6QzFOvDbInbcdQC4KKLKbX/r9Xbznd5s59KEOspRq3ZArtcDAi45BO124VPugEYTaiSuCU+bibHzqNrkgmswPpdbh9Bvj+M3GxlO6Pm9TP1w1uM+RfK8/wS2smryak8lYMx4bf3Y8BYft8ZM8AGfcEkbAzsjY0yIJItVqFcsyME0d0ZKJxCMUKmUEVaZaKIJYwERC8ZVwF4o0d3eiNYVp8cr4tBzl8ihGvIP+xg4KUpb5zCydTR00RsLs3rmddCZLVq8SVGMUNY1guJu+YDvedAXdL7JSmkaoFEFOMnT9x3gjbhRFoVDK8+NXn6W7qYvp4gyF8jx5oYAiN5IRTCqGjqDcRkd/B5bHT6t/J4X0OK+cewK3CiulLN9+/nlKZo6RmRt0744Tdvuo+EVcUhg8bpqadpHVVtja3cCO7XvJFFIEXWFkRPyeKHolS7GywndP/gCztZshlvDiRa9ReWsYCwnCODWDcg0gql9Yblx17qxNNrLzyToe3LhxIyPX4Cl7c9jcAWo1BAIJArioEkNkGY37Hm3moUf93PvxOKFoBEtR+PDvbaO7awBvUwRByNEVVvmX73ydQLyZXKlCPOgFr0DECrF3z31oyWUyWhYlH8LnVtARSC/NEWjbQyGbQtftqkSECpLhZrtPQBFVUFVEAYSAhKVLCGF7q+Yprm6Dcq1t+nc5jm5uqAK0rBo/X1hj39XKci3LBGGtP8PG87/exXd+3kgGqv+C9WpOQK1z85onB2v8A+f3dnS/vvBrI8JQ7wma/0FDcGsYAezGinKtj7qiqnhVF5aq4pYl5ianaRZEfK1N+Cw/C0Wd1OXXKKRNfurhRwm1uZETh3hl9nnuue82/GKQa9fPUArGba7AUgGhLFFJVkh4Q6SWRzl/bZrYljAJv0Vsa4yMmmA8c4Ob81mkioLqbSRTNol6BM6NnkOKR2lxB6CoMaZ7yUcTzE7OEUrsRhaiXHztByQOHuPx40+zvXWAZk8Lu0MPYshZsrlRDu9+F+FAO6eHHmdr83YGdt+GpTdhGjIRPFwbv8rKcokb4jwpXefut/0mH/yVD7O1+QBWrEzOyhCLbGOuOoSlWkzNXOZLgycBSJIHHMjOAhSe4zoqQRxGoVjjA9gLxqRMBQ/UBEnsotoqFjoWVTTyNW+gShUHYy5jS5cZ6ASIc5MUNzFIYPdBaOhrZPuRR9jmj/LSqSeoiCl8sU62HTxKJj3L1OwEBSvN5JV/IdDQSd+Wo2xvOszerQ9Q0ipcePVVXJUmFi/d5PCxB+lvO4jq8tLTtZsfffdv+PgvfwnZ42NkbpzZuUmS6Lw2Ueb04AtcOvsqlqKQSyXRjAqib/N15kiZ2aW4tsFcK8llnfjGWr2+iGjKsHr62mOzLkWbndD1JzqsNxL1HkG9VoFzr3oUwGRNq8F5jh37r123xnewDbeTFP5JocstYQQEQUCRVXTNsKunJIuSWcXjlSmUi/i8EaZzU4w9dxatL056aoRMqAFBDPLkP30DT4MH7eocUdc2fjD8Fc5ODnNq6TIjlbPE8y5ODJ/h2LE7iG3fwsWrrxKIF+nr9lIpjCMoE5Qq1wkkmtgWaESan0SUJayqm2LRiyTNsX3LMb725J/QFOhgZ6KPA0e2Eg8L7O3ZS8CMEG/YTqqQQVUakPQgf/JHH6d3xwcZmxxlNrXEnQc+QK6SpCyNUKwqJPMaE8efIe5Tef/7/zv/6UN/xZaWHbSqPhpQqS4u8/m//W2OP/sk16cH6fzgO/nUn/0xIW8j8ZWtnHj5WXa372ar0sdnv/TTvPuPf4oiVZxcuESFDsL01Cm7OdCSs0gUBLRahUAQNy5EZNzoCFiIGFAD/+xNU6KCWAP4RCRmWOJdHx3gL7/4S9z5J/sBGJ24zqsnnuPC0gptO7uZHL1GJjPG337uI5SNAlO5cd7xrs8hE+Dbz36BmxkBWaxwaewJskaKM0PzfPpX/pJjd99JyTOJuz9CV4PCrz5wF2a1yK988r10xrrxRwJEJmY5NfwdZNcS2bkUnVsTwAxLUzl27ryLYFjmfZ/Yzq9/8oF1a61KFUGQUWTPavWd/d02CI5gZ703YFkWWDoIdo7FqV1wNu1GL2AzLsZmkN9mUOBm1O7NyF5OIbRZU17e7DXW3++Nxy1hBKhNtEtVV9VQBElEM3QqhkZWL9Lf1YVS1Rl5dZDZYpH0Uoae5i00Sgme/uoP2HfkEYqGhlYGoWoxM3kNCxdqQwSjJHPq5Wuk5zKEIh0UKzqHD/wchXKRiCvKwtAFookAOWmM/NIg89lFMsk8mcI4wdYBnj/9PVriblJKmpAU4NVvPIkn2IdH6cREYC4zjUcvcH1iArlicv87/isrqTmsnECiIURmyaK7pxuf3EC5kmFu8SqSqJJKpfD5XeTTU3QkOpD0Iq6Aj2y5SLG0QqaQRzcWGfzOk5y/dJFotAVNkSnJFoKi0BqVEPMSqZuX+Pin3wxABQs/HjJUucCgPb11i3JtoVhYNdjPxv51zBrLzq7S0+tw6bWuPTIGQVR+6T/tJ+L1cyN9k5nsIg99pButopPJlMktZenp6aUMxIJRQt4IUhW8lRK5pUF8gQ6EaoHhweP4evZhCm7mi0NoqTxnrlykIMeZWVigkjtH7/4Bnjw5zoquMFNIErPgxHdPs/uOVrrCDfijHlw+LwV1jGI1QzzWiKVDpKQR8nho6x7g/Z94x7rlpsgq5bItjOro8dnL0Fr1BDbq/JsCq0nEKnpNzHXzXMBmNOLNNnn96b0xVNjs3vW5hLXPca3kuz5HUW+c1tK9m49bwggIQg0JEARUl8tuN65IlEtVqqZBQFTJpMuYVhVSaQbPzvHQwz9Dc9sOevvbSJ5K0tq+lYpboiHSQYvqocEbI+QKoQsasVCA+ewkQVnA40kQDg9wfuQ0htZMwneAbs9u8kMrKGqeQuA02VSWsiKQnZ8iU5ylXE2xa9+DdIa2EIuHeNu996PPlYjHGplIJWkM2D0Mr1y/QVf/ftKiytTyWXbtOUrcnWAuM4oc8FJO6jR3JSgWywxs2YNVLjB6LUtHvJFczuT2bUeYnUmiV12IukHVsKh4/LjNWbLpKTyeBjwhH5pWZjm/wvz0CqFgC9t6+5mbXIMNLQwydcIjq/Nct+y8eHEhrNYFums6gCpqjT0g1Lr6gtNP0FEnWKIEhoIkyKhk8MUq6IJGJWkgui28HpnB6zfo6OpibnGBEgqm5kdQKpQzeQa6QihyEEuf5/iLz+MLeklWs4jVApnlMb79g29RNUSi/kbypTSnLj2HqkLJEJhcyfLDp06TbNmGx6eSrqSZTi3hkhXyZGnpjHHmhR9TDHnY1n8IQRYwrfKGidCQJAHLMpAkYV0ewF6Pwut6AQqSgGWu9TRopvl1p/3a/L+eFFSP7ddfU79xX/85vZ5UtCZAYqxufpE1BeU1VsAaHLkZFFk/bgkjAAKWid2TUAAsESuvUyhWqFoa+WIOxR/k/jffw4FQjNsf3c8Du+5hKeBjLLfMp373k5RTU6TLSwTNRrY9eJTufY+gFwJ099+GSw1R0TW0SAf37H2QctrD6KlJXn5qAa4o6OctjPMGc19xcfNCJ8JYFW0iietmC6P/oDP85Djff/Iptu+8kzPXbzA47+W1hWWmdYGrZ0e5PnyJvUfu5uMf+DlenpqiPLfCX/zLb+BKxNk28GHmxsc48cJL7O7fx5HOYwwNLXItOUn/wBGeO/kUT33/CUors7w2e4pUcRlvexcDB46QHBtEmzEpZt143EEmZkfY1X+Abe49jLw0xkh5hQujX+foXR/kK3/5Ax794y08+Iv9pKkQqMWrJg433G5PpmDTeTXK6IAbuda6vIoPDzolGojVlHfshNOHeA8mFgfYSisdADR2dXP0oZ9l3+7biYW9+ForhOJR2pq97Nt5FDOd4vTJIa4OLxFp7sfQcoSCcZ659BwvXzlBT3CArf2HmF28wPJShrCg8paHHsZUW+kLRkktphkcWuLMS+dQ20zmxtMcObiNn/vI+3j/p1v43kuPc258DJcqUMqYhGQ/fc3tWJbBA/fcSzVTRAxXODX1LMvL4xx6ZMvacjMtkEVME7v/pciqorUzNoYDpm5h1ZwGBUizvPpcJ2SqL96C9fTfeq5Gfey+8eeNxqH+//WJXWez2wZh7bp6haiN17/RuCWMgP0BqOgWCIKE1+XBE/URjYRobW+idctOyrLJqeev8eLgda5/8TH+/Af/xDOPf4L++BaMgMzXfvQ9UtfyDKfHGXvxFE9+94vIPUEWp8dpbIxSrlSYX7jG8bnzFDML3BHez22JOHq5gK9xAL8s4W3fiysVQc2Eqc4X8AgyYW2J3Oww6pUyf/yR/8z0S1c4/8pFmKwwNz9JOJvFpUUYvHCJm1NztIsrXEneoJKqcnLsNYbGrzO+PE55JcOF8Ys8d/40kiTR19XN1OR1DFeKnr23cXD/Ea5cG6K7/3ZClszXn3qcd7/7l8jmk0TjvahClJ7eJjxNMdp330XHQAB3g4d7m45x/C8+xZXkIsvLIivBMn/01d8gh84+QnhxI9Ty+VDFh5+9tKLgxVPLCaioVKmSI4eMSp4CccI1kyEj4kdBZoppdtZIRO7mKDdGbhBVtxNSm+kNB4hG/SxmTebGx5AlH2FvhAbJh5CD3u67kBWDmanThNp24G0PcHnwEorXg1uIce+OD5BJuXjhx08jFdLMZtIkYltIZZPMXFsil9eJJyzufauP0IoHSbYo5Q2SSyUO3uHCHwoyr09REUxeuPYsoginLrzC8lySG9eXKS1NrK43y7DAsKvuNE1D04yafoWBZa11/VlDBywEEVv7CxslKdcKrZwNDqyGUM4pXp+ccyoPHcMKr0cV4PWhW33I4DzmfHfCNalW/7CW+F2fUPy/wggAyC7Vrh0ATMvAkAS07DKCZjAy+iozyRWyxhKBdBWpoqKeH2TbwFEe+8fHmBu8TrY4w//4hd/k9972Ofbf0cntR7cwdupFIq1xgmaZg3ub8RZzlGdfI5YRyS4Oc6i5EdEVwSUbRNq76SiKBFMVpi48S+rSSWbPvojbXWWrG8qVFfyeBhJNTUTSKbYHmugYbUCUWyhIjcwuFYl6m7m0OI6hWTQ2tKIoBnGPSE9rN3ft34m/EqVUsjjQtIUFIcRQYZJEazP5rMZKNkCw5GHi7Gu85faf5isf+Shnp07T2X2Ahm6FeMDL4plrPPWVrzN6/YdorRF6WraSDrSR2H2I/+fP3s03/+dTdKlespkqhx/sZc9/u5sCRaro6JQoAytkOM80KfK0EceDRJUyAlJNCdBxIWVs6FDkKV6hlTCzlGj5o338zh/+PHfs7+H9b/lFUmYr4zdfQxNdLJcm6drjQZcXCTWGEHWZeEsv8+kJzrz6PBG1nzZBYYu7lUZvC0d2bSO9OEd2scSf/cOXWMqnmEldYaks4M4XmV+YIW/o7HjTDt7z3rt54qsnMUM9iIKL7IKBX1FwiyIL8xGm04sYiwnmJ0cwShXGViYZv7BIWAsgFXRSJYsPffZn2HfvFvwBCdMs2RwBy1gT6rSsdfUD6xp+SiJyDVb0YhdZ17vcFjZNeKMbvlG3cKO600Zpd+e035hAXB9yWKuIjbPhN272+tfdqIm4cdwSRmC1zXKdUqqeytHRkkDVTSqpFeLdbeiuMvHGNiqtFi1v7qc90cMHP/pxMlULdyjAUz98jjMTI2xpvZddTV30tjcwemGISQ2ev3QWQZY4c/xZEm3bwadgiF6Wl8ss5zOcuHqBp6dHuHb9BIlIFLdLZnt7My4lSHxbK13dbjwhH83dPjqbtxJQXZTKGUq4Kc6mCSkLSPkCA23dHHhkL9FAF3MjpyldfY1ytQg04YpE0Y0SnngbbjwEjQCSrCNE/Uh6kaAQ49G7PsSJyy9T9nZQTM/Q25QgoAZZmB5DjDcS8PuZzheoZCtMjQ5R1DNkiwUeftODPH3ymzQnwpTSM0xPrbCQHeGff/RZPvbX2/nSFz/G6Pwp3vs7R3jX++MAXGGOKG5aSBAjioprlX9ereHSJSp4qBJA4BN/8H462wfwNQdZyAk8/q/PIOs63kAZnxphe7yLsNtFwB+nXNJRGyAUiZCulIi2xhicvsiuxFZuXnuVbz7zNLe9+W3kqzpeX4hcJosnlCegRynkkpi6n8zCPFbJTWtnC7OTOl3drcxMDpMqlcgVCyAoZDNlCqkMWsGiKmiU9SKiKHIg0UNfPE5Zt3D7NAxZRa/kUVQVRZGQ5bWNX58P2CjPvxoWGA7HEswaErP6nNomrW/gWg8N1hcZOWQg2Jxx6NzrjTQKNk/6Oa3S139tvOaNxi1hBEDA7XZjmCDLIm6fm0y1RFcogM8dIWeWWFxKYnmbsLb6uffuY3z3W0/zN4/9I6137GNicBK5qjA9MUp+dpihcppAywMc2L6f7c3NeJoEiqkC7e4EcbeL6z88gymBZulMLg2hVUVeeeoFvvrUt5hfsNDUPKpHIiOUKFYUpjPzHDpwlFnDQ0WSmJu5RrZSRlNk9KrG7n23o+sJ8sElZLHC8W/8iP439WMJOTr6t9PUmuDr3/wSmirT5BPwhsNMLk8hRz2ogOTxIkoaRx59J4arwsrKCsdHzqC4XTT1bqWvaSvzQ4PMTIzi8pfxygIv/PAEk8xjWhUawgFGUytMr1ylObqTnt5OXC4PmiVSzpoosk777qNosoWaK6H5PHz0d48CdgluL40EceHFJEozMVzoaHhRiOKhCwk3Xj7/qcf5rQ98mk/+wl8xOrbConYel0ulWg2yPJ/k2K63oJsCYtiiMdxNKV0maLhwUSGdzZEuT/DjC0Ns37UPgxz/+O9fo6erBbniZldbhMHJIeZWVhD8MrIlgNsgI1jMTRZ47ewZqmIaKSdzdWEIt6YQCrRgaiIVvYiiR1iaTyFLXqJBN3fteRcN22+jopVB9UK5BQkNWRBRTAWLmiSXICHL4po6b12zz3qEwBRkLAG6iaOhIG1w/e1VvL4c2AkJ6n+/EULcuKmlVUPz+g5Pa9cJ1KcPnd/Vf3cMkCMYo29S7uwM6TOf+cz/p237/+f4/Oc//5l77r4X3TCQLJNQS5yDXQ1cHlogJxpkqhaizwVKjrHhMb72xAskDvQhrUzyytBrfOZ3vs6Fc0/yyO77+ad/+TPG9DGGz7zAcvUSMzcHedeHPsKZ0SRWTiKei5BKTtvJnHyRpWSZxZkk5UqWOw8dILM4i55P8qajh1mYX0T0K7Q3dbJYXuHun/4Dzk2O0CIIZDNFSsky8fY4E4NX2HHo7Xz36b9nW/9dVHJFvvPdr5ItLnNm+hpd/QLWzQCFQpIfXXiVn7rnGHff9x5OvfwCljoBGYHx+Vk6IgI/OP0iksvCqpRJZgLEEyEuvnaasj6F7HExP7WAXihjmCYlrcCll05RCZaYzgxz4ewYslLh0fs/yTef/HsaFY1yQGZleZGJ9CA/+NZXmZlJ43LB439+mQ9+eAD5235uvGOeblp5J/fxq3yMS7zG7/G7qOQpU8ZFCgV4iZ18mRDvZJGnL+xm/NoNvJEgmakbKNVGlqorFPICO7pv59yFIbq69oKpUdJTnLp0AcHy0NjeSCZbZsueHsYvXuP9P/MrhJv34Qv4cMtVtvbchlhRmJgbRpajHGp5mMtDE2zfoRHrgLkZk3t3f5zp6UEee9//4slTJ7hnxwMMLl/G8LgJ+hQ6wjvJGY3MzX+HcBQqYoRcZZ677jrESnKGQrZMOqMhiHb8LEvyakuv+jyAZVl28ZBp1mBsky/STprsaut0eH1mf7PTeKOB2MgHeL0wy+vHRsOxvkRaet21NnPQ1lcAsD5rzX3mM595bON9bw1PQLBLiGVJwBQl3EaFmzM5lABMjA2SSg0jzs8QkKp84jc+iyevsXjuAnt2v4WmWDd/88SvEhBj/OHjf4Ar2sBWSUcJClRSPiZXvHz5T7/K7U3345opoOc0YnKE6tIMJgVmrQl272gg3hogNTTI4Z7tlNwtLC+t0L9vD8X0Mr5EgJDUzVsfuJ/gSg5Ll6gaMopbQUWkMdaBll/g0fseYnRmnNbmNkS9iW1bDtLWFmZkJMucexI1bhEORPnhqSc59+XHaYx1sLXnGGU9xbZIK5VgI1PjEwT1Ku859l8wilNcvXyF9p4t7Nr+PqaHZqnKOsgGvbv62Nq+iw9/9P2Mzs6iFFyoVYMD++/n9Il/JrdQZUGRGbl4Bb0Q4GjLQeZulIk0epBpAuBLX7zOQP8DXGOeRsK8xmX+kS/QQj/f53lyFFDQWaaMqyYT+kStz4w2cZP7f+oR+hJ9tEe6SKVLhJraKWtZ/MFtNMeasEoVRhYv0RJrw6OohIMmU9NFXCGZI30HCcVcfOHvHuPV55/k8tRpkrk8sZgHo6oR8/sw5DzvfPe76B0o44ovYZVCXHklyxe/93f8/v98go9+/Ru4lf1o6jKSApaWoZovM5EZ5juPfxUtW0ZMiiQUjaZGlXQkgjckEGsI2eVQFjVFa2N1wyuKhCBYq3wVp4pQUW1u/0Vu1DaWI4u+VtDjqAzVu/POqN+gGysK6+P3zYzAxvvUexP1Og+b5SWcDlC3fDhgW14DRbRLilMLCxSMKrLkwS1LlCgQ2dJBd9thFuVl7j10gIEtzTR6BBQN9vRtoYLKgD9IV38IvSpRKYtUSjLxWC+51CQZCowmFxFMkViihcVciifOvILPHcYIBmjp6+Wd73oLZa/J9NQS1yfmSGWyKLKXF589j+Gx+G8PbyPhVxm7OW5zGLQCpmQgGQbnR1+is7GTAmXcagveQIlnXz7NfMFAlgy8IQFBN8kLICfLHN2zl4ujT1NOTtPW2YHRJfPNp/8K1ZWjOd4KwQgffOTXaGvowhPpJLc4QWdLAlfGYDmfo5BLo0oy23pvx8rkqVJkRYZv/PAZ/AEFV1BFUQwq1QypxRXE5gO87dGH8cYbObjvIAD/9v2voMYU3kQ/1xmqwX9LTDKLRB4RmSY6SNCAjsQ/8+rqZ5ZItPLc0yfRZRfz81nS+TwXX32BzHIGsZIG0UVrtAFdNchXCiBU0UolxqcnWF5axte0jXK1xNJigbKWY3rQjdvfQb6YwRP3s/+2t+ILqpTKZdwuL9MjAisTIm9/yy52dUX54vE/58bIc8zPzZDVLVySRDmlUJWr6FUvzTs1MimDvfGdzCxouEU/USnA8nwZw7AQZMEWsrVMNK2m1FPXxsspLV5t8WWsiXkmyaFtYAtuzAPUj41Ennr2JqyhChvDhM0qD51RH27U6xnUJwqdLwc9eKNxy4QD9x87hj8WJBYKUilaFM0isgroZTIL8+Ry48xZN7l26hS7Dm6hu/cQx469jdkl+Pp3/hpvvJ+WmJ/5qzfwSf309+3ljtvfgnuyyogQxr+gsHJpnHNjV8kuT5HXRZ45m2FqcZp9nVtp84WYmFumIRZlR08n4ZAfb2OYcilPeixJxO0l0BCjmi4xPzmJtyHB4swSRx94GG+shdPzL+EvjJLPqHQd2IuVLHOkbxcXrl8nJAbo6XPR37iHzIzJ0rVluvcGyJsxgmKJcF83sVAbi+fO0nvwzYhVFy+fewVdlMmSZ37mIpPZm0R7uom2tWPkvPgtN4XiJZ478SK333MY2RLQlgq0NzRwbXCUpdJN9t7Rz+Vz02zpb2X02qtcGzvL/h1H2Hv3u/n1hhP8Qe8/8YUDAkE0CuRIU2CGCaLIbGU/7fSR4QYRXHTQyY/ZzzMkeICbPPakxvz8JG9+88/zwkvf4CMPP8Tl+WmCDb0sL05juFd48cIZWksF2va0IcgG+XSZtpYOFhaWmRx7kYZAJ/ccuYuhkZvsvDPC3NVpHrz37Vy89BrPnzqJ6vNw5sozGJMmpXmZcrrMb332MV55/jm23XmYxevnUOQygpqmoc1LKVkkNxag1ddPJVTgnl3/mXMLMkPXL+IOuWlqV4mLRdqb95DLzGGYJqIuoWtVRFnCMExM064eVBTFbvklWIiigGFA/Vb5Y2Sqq3X96ysOnaz/Zi7/RuKOnbl//fnvbOD6fgTO4/WKRs7z6rUD6l/bLvquqSN/1rx1wwEBO1MrIlIyDIpVjUp6hdTSItu3bEV1K7R1t7Pz9oM0BnvIGj4ausKkM8v4Ez4Gmg6xMDXMd79/lXe/6+PMDg5SGJzmy3/+GBevn6RpwmT0+gjzxRVU2cNkMcPicoZWl8rcfIqgR+Hy3DxLuSWyxRQFRWM5V2RmYgF0jcN3tOZGxgAAIABJREFUHEF3GwQiIQTJQLAUStkioYZG/PEwmewKndV5Fj0qzZEQsqWQzxcRrCoN0RiZnABWB7MzC7iaA5T0FGp8G1sHPFSLJUKFMJkpk51tfUzdHERU/FimTNTr58bwSa7fuERYjTN0doSb126QMxZR1CweKYApGWiGi4JWQVYCZFNVKotJIpYbQ6sQEAKUc0V27tuJ2xsmHIrz8LZ7ERH4ALuZYZIyZWT86KSwUIkT4CbnAegkRp4SYwwywRg5Vvh97qG7tY9okxeLsq2uJOQR3W7EkkZJKxEIpgj70vzd//o+suCmWs3i8wQZHpsiEFbIVjM0NnXyypmzDJ8fQUkH6epxc+nGDPfe8S6kQoHZ2UkqqQrVHGQLBVyKm3978hsoZhVFDVDIV9ErOpIawiooNMUTLKaLLFUm8BphXr18nhdPfIdsXsPrlpkemaC4lOLS1VG6tvUTbQuh+kVEUa5teAGxpjBUrVYBEwQLyxLwBTzr1qyBVtcnyR5vlPXfmMCr3/IKMnodfLdZsq/+unrIsJ4XUB+S1DMLHWXinzRuCSOAAJLfQ6GQRVQMTLmMEQmRQ+DVSy+yZU8r01qVv/j4H5AJZrnrTW9nf/c7uXJjnMnpqzQ1hTiyPc6bj93Gt0Z/zN2H99ASDPILD7+PoNTFE989zrnzJ1lMpjCqGbIZk0R3FF3wohsKv/vlx+ltaObdRwdoCpWJlKo0J/x0tzWy99BhKu4lTFkkHvKxkpvjLe97K3cevp2ykEKzXAj6KK7QfkbHS/wf5t47Oo7zPPv+zczO9gosyqKD6CQB9l7EIlES1XuxLCmypciWS9xiy4kT23J54zhK7MRVbqq2rN5IUaRISqQodhJgQyPqou5ie5/Zme8PgBRFW0neNznf8X3Onp15dnaePTtz3/Pc7br6hsKIkkavGmLMIjPWNUTbystwaJX4VjfTWFPPX/3NJ5iK+kkNWpEMxby843n2vP0S4/lSDIKdvBZkzaZryIYmqRuYIsAYodhRVgYHkfr86F0DnBkfwW2cxeU3P0I6FSAdyRDMSaxb20jpLAt5s5OxjlFqmorxj/rZe2AfyWyG9p52YLo1NUCMPHGWUY8XM2/jZz9BOjiDA4l2tpBBRCFBCI0a7ORw8w12881PP88L/9qF7DJR3GTi6NGjjEwFuGLVGmSjhBIuZWnLAm696Xqi2XGyGQN5LU11ZRkVzlJUXaRn6BCzyuvwFVnY37mHfNBFz8ld7G1/nS986RvMa1rON7/4c/zBSRbMbmBiZIT9rzxNsbUSJZJgpEehuWk5Qx1+zp5UCARh2dI6dKfC0LERRse78NglCnwmMmaJrs5+RicThEJDjI31YHNZcLgMuFzyjBugI2j582qtaaBrIgYZCso+UJVpajQD65jPbGZfkMz7U+W/cPvi572IiIUPcyxerOgXZhfOyYUrgw8zT3/gUpw74zlWr/8s4PgXYQR0XUNTcng9DpRsDpPLiGQxkU0lyEow0Z3GPT7Glx75Ihajj+7wOF/5/l/jFDIUOvIUB4KUuisJmIJ4c0WMjVkIOWrY1jHIkY5OKiodxBWNouISZLuH0kI7XWeDjMTHyGkK6azMY/tfoWs8zGAoy/6BfoxWHUHKMdgfxm7zsGD9agb8gxhKbERUP6PqCKvWXk4wNEm8qIbyyiV8YtOdmL1mnnjyx6iT49hUA3PmNtNxeBcvvvYkzz/3KHG1k7zmZmBsgMYqL/2jg7jdVRgtThwGjdK8kcYN69i34y1iwV7yzmIsg2ZSGSO9Thu5uELGBCbNxqgm8utf/C2Hj58hn8/jlOK0b9vJQGiQaELkxz96i4nJSXSDRjYNZoudoYGT3PrZNbzJKG8yQACFl9lDiuHz16OMYgJEceGhl5PYsFJMEX2EqMLGAyw/f2w6EGLn252kHeW0+grZd2wv5dXNTAVV4uMpLr+mASEkkkzHmUzoFLrzDHaNo4VF+ocC5BWNR5/8JQ01JUiymSl1iv3te/nNL36Br2o+B9q3MLulkmAgxlV3bWbOijr2HthLVjPiKPFyrGcvJquddEqn+/QoycQYJ7YPULX0frKKAU9dKXZJRolkiAZVBNFNf18UzWTH7bBRUFZI3jxdMqwD9XObyGtgFAXOoY6XVXqY8k/zLTRTyTRJukoVDZzmFOeBSi9Yll/s31+cMTh3TBVVfybpdzGN3J92FF4caJye+0/7BD7gh/4f8A4IglApCMIuQRBOC4JwShCEz8+MFwiCsF0QhJ6Zd8/MuCAIwo8FQegVBKFDEISF//UcEjIael4jE4tjspmRdJV0NoqWSzOZGUYRjRhTGYyywrsH3kKZHCJuc2HKuagtb0A3O1AjQYqrPPhxsmP/ScbSGcp8Jnq6/CTDYWL5JNlohkw2i6znWdVWhq4KqEqSvlMDpBKQSsUp9ZogK5BUsjjdVnKKmWQ4DoIRNRLF622kxOvE2txM3gLhwX4wGOlVYsTjfTSVL8RoFVDjGWzFJjKBQW687AGaPMsw20XO9B3FYHLjblhCaVk95O2UlBbiMHuoal3I+GiUkd5BojY4cuQU0YiCWbQiFjqJWSCYyyBoCmI4gppMYsmKqLqEVZR44LbPk0uJZDJBjFIpZpODsV6RvDFOfTlU1rbQ1DT7/H9/hghuJApwA3Az1djwopKhmGpETDxOD2HiOHCSQyND9Pz3r1l2DQ6ThD3pxz85zlRyDIfVTVZNMhk04K5exdUrbiMayqKkNUb7M7hLNYZjIzT7mgingjy37XkkPc6xriMkMxo1jkKqGuooMkIilsdraQBzntq5K1nQtICqhkpC/gFEQcfmcBFLxggEhygrsRMOWDEpFna/8m84rBaU6AQhxYAuCqRSGrolTS6nokzpTE0EcBt1PBYvRi2PQZIZGxygrNaH1WCYbj5CIyvHyCSnn7gGsogYsGDkIH0zRdTT6qbzAW3YhU/zj0oXTlOsz2Ia60H8kKG4MHD4UeXDHzztzxUjfTDXtGE4hy8xPetHyX9nJaACX9J1fTawHHhIEITZwNeAt3VdbwDentkHuBJomHk9APzsv5pAAPK6SFrV0QSF0HgKhzFDbVUbdtE0TWelN+AxX4ISDbNu1mKWrb2DXWd24vDWM2CRGZ4MU2YsI9o1QnCsn8DgMAOH9hFITdFU7aDA7UZMTJHMpHF4LZRWVWLI6jSX+WjwFGMUzDzz/mFImik22/HNKmFWaQPJVBhZyhH1B2irq2R+w3I0xU3ryhvxROLsfGcXU7Esg6ku/u2HX6esZhmLW5poqV6EnHfgFL3MXbSa5//wM65p/WvkCYgHzlLunUc0MMXxI/uxSBIuh5MNt99G85KN7H7+d5jtXYyMH+aSKxeRMRmYvWwRU8kx6q9cwfo5V6KoEv5UGkF0sL51E1O6Siif4x9f/DWlLi+L2+p47DcPE5mK0LR8LlazAzm3lshQlNDoGItxnf//txFhApHrqcRDMT4aSKNwjL0UUg3AAhYRYIh2OlAx0XQBoIY1A3Gbk5rS+VjEAkRzAlkS6EsO8uybj2P3FWBRBXyeEtRxmSm/gfraUnAVY7BlGezcSSKfI2/VkHIpxvtNSFKCd/a/zW1X388Vd96HvaqaSFJk6HQXja1VVBQ2k4pPoKoaqYiMy+mmuq6Zsgo3G2+7jU8/9A1yuRBlriqqi0rR0jpzmytRTqVpaZuN1acze+5S4nENr8eGJgnIokImoxD1BylscKHmdSRdZKJHQ5hZducwI6OwkFp6OUoOAwUzrMXnuvouLg66uALwwkzCjaw+j4R8ceHRR7kYF8YAjMgzyNHGPwMg8mGSk4+S/w41+Ziu60dntuPAGaAcuA54fOawx4FzTdvXAU/o07IfcAuC4PvP5hCEaZcgEY+j5vLIkTj5vA1fVQUJXSM6MoZk0Dn0/lvIkwGiRoGc10pX1yn6x7rxWebgsVZjtRcyNZ5hMhbFV1RGcGwUp8VDTUsLqp6lpKia1tYGchkBTckh5RRKPA5cZo3Ns5fAZIykNYevupD+3kFymQxlvmKKir3TmVaTCeesOgorSigvriGVUbBrOZyhMIHxMZYtvIQDh3ZS4Crmmhs284k7bkMqdTA0dZzaWi/emjbi/lGWL9lA1/AwubyGw5xBCY3ROmspgakQiVgaNZ9DF3VO7uzFU1JEhacAWXUgoBCbnMBsF3AUF2J3hygs9nHXLXeRTweRRQGbzUZVZSM2k5FQcIwVy67C6Ronn1dobK2lsqQM1SJwmChraDl/Dd6ijxwaj3GIGDGMWMiQo4ce7mU2RzlABJUqiomRwXJByslqLCRvyBKLjTCruYWOowcoKfDgcZoodVdz2N+Nx+0jk0+xYHk1E4FxJkcTaCmVhEHEHw7Su2+c1pYyDCYz+VSOUq+XnJIiODzK0288RWvLQrZveYb5DQsxFnjJKUmWLFqKy+XBW2Inbchh1qH/7BlGEkO89upTOGSRkfFRQpk4RhGKPbXc+cDDFDpKuHTJGrLZOIqikLNnEA0yFoeTXFZDUUUMNXaEaeY78opwng7egY4HB7vpQkOfaQoykEfHhOlPlPY/y/traMQZhQuaiv5vRZ0pW8rNIBRf6IpceM7/NaBRQRBqgAXAAaBE1/VzTezjQMnMdjlc4GCCf2bs4nM9IAjCYUEQDkdjMfR8HtloxmS2gc1EIjPBQO97ZFMhbrn2IQweHSGaoNpoJjjaR2jCT1PVYrq7d7Ojex993YdQQ3k6xgzs2vour7y2jUkhxZneCd47MUDK4ELRY0SnJtFyCslQFNFspnl2OVNhhawcZPOGxRjiWXrH4lTMXkpOBKPRRFyUEHSdREZiLDCFp6yawZ5u2hZeRsPqy/AtnEO1tYSx8ACLGtqImsZ5a9tL/Oydx4hNnSajZsm4HBxvP0B5RRNvvP06jqoxfvbkLzDki7nsmruwFZfx1w9vZvuuf8dAAt1i5Md//0MiBTEkj4fjR49h9ywkK+oc7xsj2pfl8pV3EAlM8MlfPoyYB0l3EhuPksuZKfLW4x+Y5GzfaTIxBaNm58Xnf45sddN7fJrLcM8M6Mg52cIIAI+xBQcWZAxYgAbWMZc5FFOKRh4RiQUzkGIA89ZuQtPSeGwmRqNDnDqtMGVKIRllDB4dEQfRVAD/oQzPvXaA2soWBFlFtaXQJwfQ01n274hw5Eg/+S4NWVYorFyK1Rxmn3834eEOCuZUcuXaBfz29ZfJTY5TWlrGUE8fE4N+hnqD1PiqONgzxuiQibbaFl5/Yifl9nqGh+LEwxPkNI19HW/x2NP/hGQcIGUtobZxMWuvuI+h9jGuu3YFRkuGkjobvmIH/dtGcBuN6EgIkkJOnF75ZBGppBYTMm6cAEwRwoABBeV8au7iOMCfG9fQMGHHdAExyIUrgQvrBC52J84hE1/K2plVxjmGqWnehw+gyv4XewcEQbADLwB/o+t67MLP9Okui/8rU6br+i91XV+s6/pip8OJ2SARmhjFaLZjLrfg6IrT7PThLW/gwOhx4uEghrZZmNsuQyWCrCZ58YkX0QUjy1atYKorxv73+jjb3o7X58VqztE0pwYpbyEcyxKJhsjlIZxSMcg2LA4vFpcVqyhw68ZVzK1opt0fwVJZTlPjXIx6nkRGIZEyktVEJicnqVxWT1FxKYmpPIMjfp7e8hT5oWO8deJ1Lr38RlJTdjIYeOPNrQgGjYmBbuY3X4dokCivnsPwxA4uWbaUO669k0RnmPs+dTVpk8K9X72a3Sd+QX1BE4e69nBswk9KU2jd8AnG0zkyUzmGD7fTc3gfqfY+krkJWte3oKkurl66FnVgGGeslurKVmoUO9aGpciuOWy4+n6Mrhx5xUQqraCl0lSVloFB/tC1uPZTZTz4g9UfGnucY2TJo5Mng8AfeQuwzvjEEjI1AHz9Pz7H5FQ7hlQe3VjBtasfpMgV5ZaFq6k0F6GqBsb844T7oLTZiEuSyKbCOMVCxHwWf2aSjKJx/ZfLWbdxBY2rFtNUU8uBnU/iKy0i3HuWWx/8PDteeJyzYwGKFvlQgzJZwtx3+4PoQxFKqu3kJkVseSNf++4mfv/zX7FqVRVhm0I2qxBLSOiGDIWecsorihBcGbY/9gz9R7fSffhFNqwqoKChjKLqIixGC1d855JpkBRDHqNRn1aTmYKiUsroYIDFzJ3hNZj2vZUZKrNzbTwXdwdeuH3h2BEGyfJB2fA5xT8XYBQxnj/fhQbkXOxhB+9iR2OaGUFARCA3w0WgoZ0ni/kfZwcEQZCZNgBP67r+4szwxLll/sz75Mz4CFB5wdcrZsY+UnRdJ6NkKHK5qG9rJRNPU1VUgbW5jJbF8zk72UWRo5S6pmYsRXUIRhsnR/q468YbKJDcBMeDCEYRg9uMIImkM0kymQy5TJxgNEIKnWw0gpLKYDaZ8LrduJwO8oIByWzFZhWxWU3EU3lMohXNYCIwGcZosZITMgiCjL3IjX9gFLNoJE2eYCRKOjXMnhNnmVNaglnWcEgSHSN7MYhxBo+9hy/toMTehmLQaR/YyURshEO9p0knJLr7Bti1Zx+dYx1sumwdg5Nj1LhdqAgU2yyYbAnAiBrQGB8eoKyqjSs23kz9olV09/hh3M62vW+SyEwQnIqQMU+RDY4gZ6c4fehdBCnJmRPvY5KtGGwK6XiGkrIqjnTv54q5q87/9xtvbsRssTAyOPwn12X6hpZ5hJ/wGf6KK7geA3YyQO/MYu97n/kxHrsHTTSSzob49R9+iopASdVK7MUeIrkYHrsRc1kpmiRgt/poWjYPq8UDWgazLNNc0oqWSnKyt5M/vvIGA4EQrgIv6AUk8ykUTUPK5/EHewgHIvQODWB0eXnu1T/y6W8/iE6I0biAkpyia6wfm6pjlPLMW7sAk5CloFBGzeso8Qi2Igt5UUHWEoyOREhrORylbpTcFGBCS2uIBic2p4wkaihKHkmaVkOAKGkkVGzYiZNCR/tQD8G0c/CnNHAXBu2m96Y/DzGF4QLX6kJDMV1dmPvQ0v5CV0JHx42VDDqleM/XIRqQ0NG4lesRMaD+mTTjhfLfyQ4IwK+BM7quP3rBR68C98xs3wO8csH43TNZguVA9AK34c//CElEFEFVFQ6+t5XG2npKNzUT7swQ6evGnhUZC/sxBDJYgn6UTBZ3JoWlvghvy0ISoovBeJ7JUJjBwCTRZBqT2c727cc5NTiAloijKgIRAWxWJzk9T1lNBYogk9GAwhKG8hJX3byMmO4gmgjRGxpGzkNNXQUlFVUkdA9FnlZmr7yctzu3Ibjj7B/eSlWZnYg/zL2fvRHb3FVcNX8jq1f4iIQUlEojv3z1CWTVTXVNGZpDYM+xHp4//DRXXXkf88uaaJw3l3BwgLaiNZyITNFQtJHLVn6ScL/Kv/zm37j3ri9z+QObCCg5ntm+jbd2voluNTM88A4NspHCxjJa57XSULEUEyFGLZXIwW5GAv0k8360fBSL1cu9d36NsfAwcjLBeye2svFWL3fdXsK3v/YcsYyZwGSaBdc5uOcfVvDxv1vD9x/7KlsZZgtDABTSxm/4V/rJ8SYdJEgC8Jnv3kVkcoJIIEhJQwNyNEzt4mp2HtxB/9AR3A6V08e7WbTKR2NRLV/6+h3s2tbDitUbCWeDZPJmSn1XomXcLGoo487N67n1qvUkzAnuvP5LCKqdo33tjIxGCYez1LfNxVTuwat56RueYGlRM9de+VnmLyhiPKPjD4QZHEthrW5k+44nEdLgKrDx+Vu+ylh3lNOBcZqqSzA225nVWkFRQQn5ogZeeraD4b441goZkybjstmQJBmH24Is2BANAqIgMswIN3MtZmwkUXFjv+hu1qig4kPtw+fi+heX91qx4MTGbOb/2ej/9Hc/yDKc18nzhgI8eFCAOEnm0jaTDMwjIPMir86sUD46PQj/vZXAKuDjwAZBEI7PvDYD/we4TBCEHuDSmX2ALUAf0As8Bnz6v5pA13VMJgsJTcWkmzjVM8CxoUFGjQIjfWPglCgtKiYzehb7nAbWLNqESbZhsOsoeY2+YyeJ51OoQC6vYjAYULI50qkcJpMFXRIQdI0Ci4k0ORRBIB6PU1VZQdncepRcBoNkJhgVGAmFOH2mh6HOIVRVY9gfREGjwCJSVFdHQhcwe4OMTvWiKjqiFmU4NIoketnx8k+YVPKMRqLcd/cN9PtzmNzD+DxutISbElsN0dgQ+Wgeh7eRvp4Q9VW1CEYDkUCSuqIitm17homeDipLW4kFDvH8C28wNRRgaDLERMpIOKBxtm8UOZuiPTTMRCJIJiVhKdG4+tq7COZCzCpoIRTNIUs6uqThdRWx7/A2usdGuGvjjay/aRPRgIlAPM2NNy3l1utvIHAijrfYyvDgIM2LFvNe++vcdN+y89foAb7AO4zzKqcAODgDr6WnwkxMZQhnNIb7hnAWOqkqn8WOHW8QjilE4zFc5jR6zoWoxnjn4B5y2QkGo2NIVhlzOsNQZycObxVxXUNMjfLQF75HcHCE3Tte462tu+k7dAKLU4KcSDwYwH92krwUxtJoI+mUCESCzGqYixRUiSSDGBQrojyJVfRgLa1FMlkYjYySiOsg54hPCNiNNt5/9ySBcIydWzrZfHXDdPDQW4yUhLxNI5bNoYkCmWwCTdXQdI1xAgRIECSEEfE83PuFspTFH9o/55vn+TApqIREEVXIM0xRF2cIAOadD95+sLq4EJ0oTYppQBjXTDxAxoCRBTSjziAP/Y8xBnVd36vruqDrepuu6/NnXlt0XZ/SdX2jrusNuq5fqut6aOZ4Xdf1h3Rdr9N1vVXX9cP/1RyCALqqY7KAmtfx+hyEpnJcWe3g5NB+ilwmRiNRihasZteOFyizGRg928+GuksRAgHkUIJcd4COQx24CqyIoo7J7EA22xBkkUwqiU4eWVHRDXkKNBOKmkXNZ3n3lR28sG0P/rEhRjtHmD2rnKs3Xk1b6yKCgoKtuBRVs7Js01Wc7Amwfe9LZCfj+LVONl16E/50N95WH2s2VbNh83L6+o9y5nCQXEhl9YJlxE+eIUsa8gGyyjgWKY9RjTJ4bA9vvLaPA0eO4TUVcGDXYbb8/m0q3CY6hvo4PXqKb/7tszz3bz/DLmaYHBxkeYGInk2xoNFFNmRgqL0XU9ZAV18P65fdxMP/8C1y/l6WXXoZsjLI8PgEDbZihgcGaD+4j7nFczh7tp1t2/czOhTD66vk5staCaRPUr3uOpprl1FTX8re3c+RVfJEI35WXO84f53WfKwYgK/9/RUAfOw7q7FbRAbOBIimFEb7AphcAj29w5Q5ysllHUhJA2ljBl8mRk4qorO3k7YNFYRifiYDw1QnTHg8HqLhAdTkEJqrnstuW09b0XK++qVfsOgqH41WK8lAjKEDfsa7p3DJNkKigwI5wbf+459YsPxGnv/Jr1GVLFLCjqFQRhNF+vYOMOdaqKgtprSihKJqI6ZsgiPHB6hvXEXjihvpPt6JR49j9pjwek1UVTcSFyNomkrdnBLSSpLNa5Z9CHiki7300EsajVV3frgMRgBe4HUsMBMzmIZ3/z2/xoY8k9SbVjwLecwkWUQjOnlWsRr3eUbHaYL145yhFPeMCTiHNTAN/yqgU4MXCQMqUU5ykmny1RztnJiJEBjhQ/UC/w9G4P8PEQUBm12k0O3B6rLQ2X6MKquTrMVAVdtaxqIhdGOSp1/8Plve3s7D/3gdq268nZde343HYCQ96ef05DB15YXoaZUFdcsZTUZw2JwoWQ2DJGFx2NBtFhLhJGGTiVAoRSQWpLi2maqWRYyG8pSaPESiClMmjYRZ5v3QCJ6aejYuuIqWmqX4rBaUriB16iWMd9Sxa/tblBUsY2Iwxd2X/4BkKIZZKES0yfz68FG2797H8UiGvCXBwKEEZzqHGUgpZPPw1p6naSwT8KbcDCRDfOyh1dSsLsSqVGFOKtx+4038yw8+zcrNizDoUFfiZtI4gq/JTnGRhcnlhQydFYjEZepKmnjmuUexemdxw13f5p03nsJldRDqj+E3pMkmbRRKZkx6lJ++vp3GWQXc+vnLmVO8mOFoimNnXqZByDERHCPaL1Ps8mFRJJoaSlm3dBEAP//pJ1k4r43Pf3U1GWWCG7++mngoRe2cJWTVPE6ziMkQw+zL481XsmrdPIzWIBsWrCPU3ksmpXPmWDfvvNBD3C+iTo4jjhYj5ItZsryV5YtbKW+bRUid5PY7N9DYegnXPujEYvDhKrGwcuF8Hvzy/fzwc5+nrFAjNREiOOQgHA7w5FOPYCy0cs3cYsb6UjQuL6F7W4BlC1o489YAQi5C3+gEmlUjNVjDtdd9k2Pvb8UQO0vD0nKCoQSq3YM/GMJdaMZidlHvdZBOxWioL6NLGWfuggo8JdNchF1ECMwUTL33zFEAnNgwMs3PpDFdVjyLWYiAgkY/3XgpBjKoTDNE1LMIjRQyhWhAjGFi56nkYLrKT2ScCBoK5yDhRTQkdCxYWMpGBFQSpNERyZCmHveMa6Fhx4AR03/qEPxFGAFd15HNJtLpFLqmkk4mCQYH2dlxilhigvREjMHBQdzuYkQpjli+gsn4GXrOvkd7+wF0SaTWWYpRslMsGUgqCgaTcRrC3GhEzebIZDJIgohZklDjCSSLkayaxmqUySWiuAoLMBQbOdnuJ20y4CqpwFfowVviw2i1kLebMFtNfPyhr3DV3R+j50Q7lliMkjkrsMgm3K5C1HyO9Ws3E+wNY1XSRKI9lFld6KIZdynEUzoej4cr122icVUpujlJa8tGxk742dN1BLehjFQ0Tb8/QE2pjUBYpbS6Gc2iIpmsFBXJ+EqauOHjV+PffRBLKEl/13EWrlqNtdhKRWkh/kA7m669C60ghdvpRprKYdRCtF61jrzBQEGBRmQ4hNGaw9lQiclSjGwrYjw1hi4bOHHmJDVNcyguKWR06iyXXn0r133uFNXSAAAgAElEQVSmGNVuRTA4qK0pxucrx2LT2b3lKJPxCAanATVhobylHCEn4rRbGJyIYjU4qCqqoNxTyHtv9nOmY4Kdu97E6JUZj4S4fPkS0m4zv3nmpyhIJHMqnadOoyZVPOYIgxM6geE0B3ceZDIwxeneHiJqAc3FVjSTTm1ZFZLFw6r5bZjNZlbd/0mETBwMEp+4YQXjI2EmQmlyWR09lSaXV6nfMJvvf/0LWORi/ONnsfpETLKOpGSxZkEzZDAlNLJGmVw6Q2FJAVaDhCZLmK0Cy25q5FPfvxf9oqV9jOQFxUK5GS4HAwrgxMlveJYRJs+HAAUErHg4ySAFmJFxMMwoFqwX5fkljJzrZhDOK/c5eNEr2cQ5R0PGhJdC4mTQsWBEZgltfIHPYOIjKJn4CzEC+bxOKB5G0TTMVgOzfLXYbMW8u/MgPouZezfcTIHDgrHCQqGniCLZRHRqirQ4SCIbw2mN01BbgN1pxFFdSDwxhk124HR4SCQjSIKIwWCiqqaKvJpBtogY81nmllVSgsg1axayanYDxzr72XDVbA6f6uT0xBnmVCzEK/kQvCakvEZmNMJzP3iYfVuf5rOX38bqzbfy6nPfZHJ4lFDwEFdd8Un+8NSzrLnkUuavuIzVl9/ARHACR0ikbE4RRSVWxk51kfeW4T86RWF9Be9ueYaC1iL6Th5jwYo11K4robYc6t0bue/K69jxq1dY0rYOY06gyr2OjBrnGw++wo4nDnD/vz7CwJlx/F3vMnV6jNXr1nG2dy85GSKnMtStKyBpVBgZzOLM5RmIRrj0ik0o5gCJUIaTB15kbnkDzRUFJON+Mi6NqloH72/fw+BUP95CD6/uepH60mZ2ntrG0dN76AydIZoKkR6zMHfRbGRU1i5p4JL5raQzY7S/fwKpxsCxvV1ct+IqnnjtdyQUA8ORGFfeXEMyN8wijxGnLFHkrGZB+RyuXLeUTFRmVlkjJT4jh472sK+7g2CnxPGODoq8SVbVLKTAqrPttV9gqbyHY0cPc9cX70FRYzzx76/gkOfxxr4t5GwutHyayz/5OSJShtktjfiPTtHXG8BgNDPZ1cd3v/QxEuko6ckwwXASPS2iZQzUNpdydjRCT2CUqVAEr8tBaLSfQCDAyEAIkzmDGg+hqCfO37sLNn1QeZlHZ5rkbVqG6aWZVpbSQiGu8xBf00yOCg48xIgjYeNBNhJFm+F3OlcHICLO0MxuYgPnqgxEwIGZL/I1KqlBYJoxyo5Ejhhj6FRRgIaB53mOL/FNNl/Q73Gx/EUYAUkS8Xo95BSdcEanaAIMJoGOY4fI2nN0JiI4bVWcOXKIpDGKy16A6o+Q9KdIH1MYGR2HfJ6paJLO471U187G7rARjozz/W9/GlkSQBcJj43htEI0m0ZLa+iagNUESiZOLhdGFGVaaxZSWlJNuC+Mx1yBq8wLGEA3cPi17cQ63sXqtLHi+tUcffcoYsLGssvaeOr57bz39ouIVgPvPvc2xcOTZIa7iYfSEC4lGEuwpHYjUc3AcztexlntJatO0XhjLUYtSng0yR+ffAELFfzHP2/n9NEwcaeLlkWLeOW1l/j4vbdy6OwOSjIruWllPX//6EPc9FcLmbO4jkggDOYc2VAP8xpnsefgcaw2mbGOKSpqCmid38DpYztpqqjlwLE+aquX0N5xgoFwgk98/lu092bQPVmsWRNGGSwGgWKDFWO2kLnVjXRO9sGYTINjNqnxPMdPjVPo0aj1OYlOxVhYVYA4ayFi0sK3v/gSSn+CYrOO7GjA7CrBV5XnE9/ZyOSUn7ef2U7/iTMkU0YSoQi/PfgaKZJcsWoDklJCw7z1VJS6KJZMuFtEfLWNzGu7DsWik84G2N15isce/R6FHifvv/wHJlSVttZSPn73AqLjk7jkLDa7heHePlauLMVartC0sYGI9ThFNQr1c03sGjhOZZWD1o2tVNaUkRE0fLUtnDg0wODRPWhxGTQHU+kEFcWFKBGdqjYPBeVWknqSwGTR+XtXFc0AFOHCgoSGPoPZDCZEujmBh+mCWRNGjJhQyVCAjBmBOCkm6KGZdXiQcWDnHE38dOXBdDjwXfbRQDkmRFTAh4tH+B5TKGhALeWESZBHQSdPmAx2dEZI8M98jx0XAMJcLH8RRsBoMhIeD5JMJ8hP+pHcNhLJMJeuXsKu59/nuT1/IDQaRc9ZSSfCBFNJ5LyXjCJzeCTGcPc4k9kALk8RnrIa2jv2I+RiSCY7kaOjGAodOGwmOrq7kGw2xHQWq8OK1e5CEUUCwVGcpUaaK0rpDY2SSEZZsX4jkWgcSRTRciqKoOIyWbFdsYknv/koZnclgc4eCvVS/u5j32XfsZ2EMlkMssrcWY3s7+lkODeG0Q2f+eo/oYQE/rDjBWSrAYNoxOaxIaoGuqc6kYxWxJIwtz+wkkRmkPeG3uGd07sYmjxB67K5FHmX0TU1ytBxG6tvKieWjyB6Tdz5lXtIBlVyuglfaTEHh97mRNcZjJ4oJ3v68TrcBPJpwhND2IsWkMgliE9G6D41zov/+kectY3sOvQHZFXmO9/4LNHTCnlNJpzOYyksRDBZODY0zhev+ATj4WGSJjPBVA6LzYZ/ZIRwYIpTA7uJVi/h/b2Pc+S1bt498EeiiRiJtJnO9EG8bhMFc1fTdzLP33z+H3DPc7H+js9RpqnsOvQOqq7ikct5+c1X2Xa4k1wqwNy66wjnCnGIGs/9y08IK2E6TxwnJSaYs+BGntrxKtVFFsI6VBUUo9TIvLD7ZYzkqJ01n6ASImvO8v5IAKdjFq8/dYCJQZV6RzNTYwlkOYqIgVAoQoXLx9zllchGieVLlnBmd5rR8T7UfJq2pvkcOTVGJp9FjetEEwrLV84n0hvEV25nziXleC0eAFLkETCwiPnkyCMApZRSjI9uBjlCJxIKObJYMWLCgQsDESY4Tju9DBMijcIHdXgX1v1nySJTgo5IOV4SZKnBzW/5HToiFRRSjW8mcauTJMjf8bfYMfEvfI87uO4j9e8vwghkMhmstmIMooIoOjnccZgjfYcxYmTc72d5dSPhPOiqE4+5hIUmFwe626krb+Kz37kPckZMZiebVyzBZkggS1YqS+dSWOhi14mD6BMJwoEAC9qayMsKl9x3Gzl/mBMDI4TiUfYd7WdyPI7JJlBRUEih7GBeyQLqy2eha9NAEzIGWm6/nLWrbufTv/wtP/yHO/inn7/OrGo7v/0/L/O52z+LR3cgpkTW3XkH+QKJm664BmHEyQNf/hiFZTU4ZAGbI0E6PsSpzgDjgUmUoQwm0UZb/eXs3tHBilVrmYrHWb38EuKZCQ52/I65sy2MHJ7gBz/9G7KSDIYBgv0xLi+fR9slK9m46hJKi1fTWrKOyqoapkZMXLFkDnKhjcW+ZSxet5BPXvVpzFYDmKZwlhh48c3tTPWeoaqynpQ/hE27lNuuuQVBNpBIjHLtTXfwzplJ2vcc5NXhLXjrbAjRCG21daxq28xNt97ErVfcg9lcx+7Xf0+V7GPtHdcz/t5BCuwmfGtM7Hl3G3MWrWayvYvPXX8jh07sJDI2yAtb/51GXxuF+TCSnOP46V2MR4bZ/rtXcbqtFHhzLFizmGJzGV36BEMnjpJIJ7is9Qp+9rWvc2byJL6mYuYvW0OpauP0zh7OdsTIqAqCnMKaMbP/yHHme9YiZTTWbS4lnhnFJqxFMuhMRYtJCnGi6QBvPbefWCpOJpdjIjXO1tAz9HUO4jCJHNh3muKyEqx2NxMTSc7uj/C7b73PO1sPMzaSQMmNUN0w3WBVgUQNBZzlzAylCwwzQYBxrGgoZPFSNpPbNxBGpQ+BFEmO0EE3AyxmBTKeP1vYIyGxlCbKqSBAkK/wbR7hV/TQjopKlgyDTJfjuGboZT/BZzjIXgop5DA9H6l/fxFGQBQgmUsiqgZMJgtGq4uuo6eonFVPWY3C0pVXUGz3YnLkKPNUcqhrP5IIRQ5YufJmnJUFYMpzqPMEhfYy5jX50JM5RINMLOVElswYjUbIpygUZY73tWMpMBHR00SjUXxmI+gKteVV+Ef9bFq1EYMgUl5ViSTJoAmEY2HGwkFiuSiPPPoNvvbphxlLDlDmsnDswCFqS13kRkeRjHle23oYPWkl4sxgcmgEgx2M+4dY3LYCq+oim0kiDiVonl1NOmpAyGm0zbmaElctB07sZehsN/6h0+QSEVp8rby2cwvX3nILP3/sl/hPHeS2Kz6FJA/w4Me+zImjB9i4aiNpU57Dfcd4+P6nCUQPM2/OPVQ6i2g/8yINZXUsW7+JxFQSV0EhwakxlKk+Nq29nANn9nHv9V9Gy0aYM6sF2WwillOYCJtQosO0+DyU1szGGMpy7YrbGc8mmVXdzOG97/DEzt+Si8ukQyk++9B3MIxn+M63f0XXwQmKykoRbSk02cqXP/XvTIz34HPZ6Pf3UTK7hFxZJZff8FdkEirpfJ5cXqXAIjO7ppVfPf4YUWMAg9lEaCpL28JVZDMpGma1Mhk5SsfR98hnkiTiCr7qQoqNBUT9oxQ4S1CjabRoiiP+QUpNJQSHR1CyWVKZHHOXlVBc6MXucaOqGoLowOu1cXZ4Ak1M4Cwq4WzfSfRoArNFx2S2Mzk+glHWySXDNC1zfei+lSUXLdXTbTE6KmkmSZCZyeODOOPhTzKFFRMJEggIyJgxo6GTxTQDCi5h4Go2f0ghL25BfoY/opLkk9xPJz2UUUYvvRiAEUYpwkozVaxkOeu4hiHGZpQ/ywT+j9a//z1V/n8XHZ1kLoggGwlGJ0DTWTpvIV3tr5A0WAh2d3H9usuY8vcgm3KcnQgzERlm4dVz8FSbOLx3DI+o8c5bu7BaLJTMWUR1SyGCOsm8uaWUlXjIydOdYMuv/hLVpeUoFQ4a9BJ0XaOoyUUqG0PV85SWlZCKxWhrbmYsMImu50mNxwiMxLEnQ5jCabK5w7wUHubTd9zCJ7/yU5wtVl7b/jYF+TihcJCuw2+SJEebq46JUYUF9bVoYQNnegbRk+V847YfUl9UQ8/4AFWWZmb51rNo5R1oWphGlw2PJUNqopNQTKJjuJ0ly1q54dobWVfrZdGKe7nmnkexVCxi4aYruf8bn+N7P/4up979A0WSwA/evB+3Vs5Nl2xgMhpnXmMd8al+Xh58hYJKF2hJKn31bNhwN0oyy3yTRMncMr72j/cRkwRQTJS4TXz/4b9lfctcJoIjNJoWYqpycsy/n0f/+sc8/tQLrF98Fat8tTiccQpLy/jO058lIPcz7LZx2y3X0+pqJjLiQJ9I8ds3XiFW4EKylSHKKkd2JfEUBnl8zw6KDRAcUzA7PFg9Kv1+CElD7HvvINFsjLOn9/H8u/vx1C3kbH+envYDPPv8S/SOKGSEBPMqSrnlxnmsLGzl+PtnGQ4k2fvicf7p6kt56ve/Y2XbZoKRNM5CH2LMyujZFAMHeii0OphVWwV5Aa9UiByzUFBioL97ApvLhipaKfOZcHmKiUxGkCQPXQc+wFGIoXBqb5SvPvQbALpJ8gpvYUaeAfKY7g00IqMQpxwHUaI4sJIhziouAcaxYkQhTx/tFFCIkQhmjEgzuX1xpjrgHEd0OW7GsfMaP+MI+1GZZC6VKOjESTPCONvYyR52kiTIT3mcEAkSF2BAXCx/GUZAyzN3dgNxJYTNYsfq0VCyXppXzsGpzCaWjfKDJ76L6nFweF87IjEuu2whbzx7hB/98Yf8/Tc/xiv7OjCVmjna8z4D3XuxWA0svLqF2NAABidYNJFk2SyefuLrzG+uQsy5cNeZKK8uYs7yKykormU8HSOTFZCtBSSlHGoeuvuGSUQz7HlnN893v8cPtz7JPE8VO5/4EZ/40efY//5uThw4StQZ462z3ZQlHKiuHJO5CfKaSMogYJIq+OtrP0lxkZfr1l/NsoWzkNx+KgwFfOFL9/DG1lfZ8/vn0A06Xf4ESQz88N9/j8tXycK2ddTWraR3ZCfP7+3k0Fvf5p0tT7K4fAV333YLZ06/R1LK8fADj5OYrMHUnyVTOsSBSDtnJo5SWX85dXXVtEY8bL7qbsKJAswuOx//xv34igV+/MKT/PHL32fpLUvZWOsmkJriM/d/hQevu4XO3DBP/egwpzvPUiSX8fyTf8RRMp+TPW/RH+jA5qliceEqKtwGMsFRBqf8vPz2s3zrPx7hDwe3Uyp52H/6NF19YyRt3Rw58x6KR+b41hOE+k2ooWFW3nY9kWyCvJqloa6Za9bWERpKMHDkKCVlMrMqvKy/ahNrll1HJDzF0f6zfHz9etrfeJYj+96g+1CMJ0/s5un9b/J3P/kRS4t1nt36OsOKjChqPLPjWbJampFdExidAkeGjuNr9DE+EEC2uhgbHmI8ojCZTDLLbKR93yGCIyrRWIrOoRH6OiaIR1TK3B/gALpaZK6+az2P83UAfJi4kQbqWckbbEEgj4QBEY1ltLKApYCRecwhR5YVXMZWtjLKGCVMxxRO0sNWXqKXGEYUjJjRZlKDOgYENFRgPbfRw/MU0IRAlDRGchgJkkTDTAsl6FhIEqUNMykGsGFC/lO1Oy9/EUZAMhg41X6a+oa5JA0jNM32kejewxsdu+g/eoKmyvl4nGaG+7qZ3byY4uo6uvoihE4OsP+JF9AqNSxmK7mcSiam4nQ6CSWHaWn1ErObcWtp3EYTvfsOo2cdOEvKseky4ZSJUCSJ0e0hHghRWdVCZ2iI4iILSkqjrKkaNThByhymotVFWhxi/tVLCRbkib87zK//+RlS8SydEwmimTHuuvszLL91FSVSLadfnODgvhNYxjKcDXbiLKslFB/AWaPy/K5jjCopggGFb/zoEY5vHecXL3wfYTxL8niYyOH3+NojP+HtJ3/E3gOnGegLQzLCbR9/kP72FBUlcwjLh7GkHTzzt0+weska3jz8G/LuLI8++gbHt6VYPncl2kCO7uEYR4/3oTpaOL37bTavaeDeeYvYvKacrFxJzqIxf/mlqLFRRrN24oEx3j3QQ1/wZZbMW0c4McQrL73Enbf+I2vnryGhBHBXugkHuvj47Z9jyiwz1D+Oo9pHS/E8zvafpna+k/UF1+C0TXHtpctxexSOnuimSCxi2dL5vPrO97FWV/LVe+4m+PI7COkIQW2cwck4n/3hl1mzZBHpXIQ+f4idB/cxr20x7x3egqE6hejxUTC7gPRwilhWQVodoEas4ZHf301b0Rxm1W7gsd9/j87TB5l/7SYWLSzFaHLiXCIxr3URZXIpr2/5Fel4ns5tW1h/5QLMMY2xriM0r1tLOJXB5ZDwjyVw212UlNu55IY1CMUSjXdUcOmnFtFUWs3Y0dPcw/cAGCOLAw8v8nu28BOsWLAhkEHDTBXjjJNFIcoYq1hEN++zgCXoCPQQhBlkoTApHDO1hhoZmKk8yKPQSClFuOjhFG6q6Kef/RzATjGnOYuGhILK9ZSxnCIe4nZ+y07yQAtzCP4Zqvpz8hdhBDQtj9VqpW+8BxkjRqOThlUrwWLkRw9/C9nmoaTMg5Z1sOfwDhbMWcGmm69g6ZIFRJQQ/sQEBsGI1SuSUtLomopojpO2GLEKBhIWKwYxj9ntpe3/Y+69wqO6ti7tt3KVSiWplHMOSEiggMg5mAzGJBsM2GQHjHPCxhiMcw44gAk2GDDJmJwzQgQJoYxylkoqhcq5+sL0Oec73efrfvr/Lzyv1l5V+9k3c4y9n7nmHCMxlZ17d9HWWkh9WxMBASqcPe14IMLDx4eG6jJMRgddGgNoTfTO7k9edSVVzXV0aG9SW1DI+es5qPpkIVYIKcy/T3JmNFJfIT98tQmdh5D5c2awfdvHqMUBvP3io5Td6MIllBDj24vQ4AS2bvoapVJBZFICEUGJ3Cq/iV+wF1n9JnK1xoh//3R27viap59fQVCElJzzZ2nXt/HqYxt54tk1BKUmUVHYQ/qQbF6Ys5jk/gNodVnwE1vYfeQjtrz/G0euvEvaiLE0ltyksLGKkyf347IL2b57PzazJ8lRKVgtQnQ2O+mJ/ti8VGhNnUSro+kbF8fggU9QcL+Qgoo8bBIjLzy/AaFERu6Fi4zqF0d5TxuV9YXorA7wF1HbYEQdEo7Q7UJgEhPTO40NT21G4huHzSynOq8cgTiBwWGxKCL6sX3PN4TGJ/DG608j8PLEwy7FYuog0ObJmOXP4eenJMTXk4jIFFRSCWKhjYLbedidCnqEMoaOn4W3Qs2+PflUdrZSdKOQ+01XGTh6Dr7ISR+ZjFWkQ2pzIJMZyUiKJjUlgWaDnVtlpfSK8+elF99i/KTHUQWokHoEYqEHBVKcTifY7IgkQjw9PWmrrkEVZiMjIoCz399B7iPBLzaYlKF+AHzKSgbzMG1oaKcRNy6iiIAHnX3hBFFHB104ucptHmUqN8mlgfYHh4kSQvGihzZEKBEgIowwlMiRIiASb1ofuEE1oUGKCDlCrFTTTgtKvEklGhlONAQyifHMYhmlXCIAJa00I0P+H/H3tyABoRt0LhNqcysGixmnuY0ycyFCgZjo+dPptMnY/dtFPO0iDmy6SafSyq0/C/j1bC7F1/U8NHICCz4ZieaeFVWsiKReMYyaOJfRjkysJjdqlSeC8DDMXeAYI8GvK4rFH35Lql1FXFYvfFThtLuhqO0eUQlxuGxu/rz4M126Vsoab7LvzZ9JivLj9611DEpS8+yTLyGKNNI7yosqyRX6+nrRdtuIz+AQzv52iov1OWjlheiEvnx7Ko/UkYF4y/VIEKIUWRi5tD8JHv0RaNox+bTz4ovPEynMpvjofmaOVhMcamfpEzPISJ7Pxy/vob2+ivOledTmH2Tx7EeRuG2MThpCReFX2DLjubzpJ/KP32dhWgYXr93k50NPM33Yhzw79yWywvrxVNbjJCTGovI2kdAuwEPVgacqmD6parzxprTCiMouolNTSWbmDLqb81CJQlApDbQ2CQkP80PpI+Xjj9/n+a+f4Gx+JeP6LeZC9UXyzv3A3Bmz+GLgTAqKT1FSdRWZUkjf7BC6ZBKcegMld0/T02ZjwoiB6Nt82Pzam3z//Zf8setHXv1oMyqbhbF9Mgn09GbFs2+xedNXSOxurCYDAm8plgYN2h4tmrZWzAZ/zqzdRuLAdDYs+YAYRRapehmvjFxNzomTfLFjPUlJkdzJy6G9qQi32s2g+H6YzHoKy++Qmapg1rTFNFKOn03KiKHjcDvtRIcr+XrTr9QWNeMp9CAiVoW/U0V86mBkHlacRri4uwaAy4dKuXG0hJKrWgAcSNjD9/zJFrrwI51EiqgmmhiquEQqg3DjRksP2cTxOUcACXrasWElFDUdaNHShhI33kTgxvZApgQa0eFE8OBkoA4jVh5nMQV0kEwUcQRgxshAYsmlnPPUkYwfx7hKApHU0or/371j0O4GHw85Nc02RA4NzR02KptqEXZI8BB4odfVERQZRLdMj9vp4swXxxDRSUpaBAlRcbzy7nPcOHeHrQVfEBPvw8hFUzj252508QrigiNJCk5leEIySqeK0t9q8RILWL1kNguffZqAACnVLWUEhUdzO/cmwzL60mhqZvHi53lh/btEqBJJHdeHW3lnGJjiy4c/b8PU1IQqyM28acswmWQse+dllO4wkkN98FOEEOwbyJmiXOIF3qhqqogPiiWt1wgmLeqH05jE8XNnuJB3nOLb+ciE/jhsd5kyPoEyoZUpC1aS2CsSv1Abd65+xO8HfiLCx48OfQcun1AWrJhBneY2fpgYO2oqxw+f4PHVa1n7wTICJs+kS1vL5IfHU1d3kJwDz7HgsTf58NAuft6xjB6JmhGz+6HpqOfSpUL2FZ0k2CcSvbed8pttOMVOPAPF3Gws47uT6xkVPoHkzCCqr7Xz7pq15BYeZdDwJGKC1ISl9iE1JJnGRhlF+89hD4ygRWfAZenEJNdQqa0mr66a7h4tE6cPwtfsi78igK17KogKjuHw5Zvkt3QxevbD+IT60HZdy/Sp8zGq1bw0dwkafQ+ZmQ/h0Hey8+Yegnx7IRVJ6Gy5wYrlS9j+w2fMfGU6s1Y9xNQXPiC/vByJRMIzs1YgCvWmOE9IarwvzmYHMcoQLBYppfX3sGidDBnoyZC+I3ju6610WW3YlJ3UllwmOyMIa4cFcbgdp03CY6tmUVtRRPRsLxpK9HjI/vcd+PkUMZt3CCSKJtp5g2cYSCYJ9KEJO4f5mSdZggCwYMBMJ2moceOkD4F4IMUGGLDSiJaBpGDEggEejAa7saLnMSaxny3MYx4jSUWAhRZq6UFLJ520YCWYVMJQ0kwXzViJIQIp0IP+P+Lvb0ECAtxUN+poMXUxdfgKjOYODFYjSpmU4ydPI/OSI/CQkRQQwtmzZ0hIjkcuE+EUGOjoNuAlc2JoE/D65g34qJUcPnWAoKAQKqoLCAwMROgvB6GJmF5J+Ht4EhYQxLD0sUSM7YenKgBPhRylwhMvuRfXrtzl/NELCBSepPeORentjV3sZNKMESyZ/zzR/hkExWQicnrg6AzC0G2hqraFaU8tRi5yowpRU1VnIjU1hi5hJ0MWjef6hVxatZ0EeiVT01OAq1hBQloGj82dgdviJDIsmBpBPmKli2DveESeakK91XQo7Ygr7yPocSGS+PLuunksfXI5FkcP/YcuJjx5FmK9hvzSi1iVMi6dP0Nm+giMZjHFlVeR+ISx4/AW7MJuBo18GCEuBo2bgtkl4k7hXWLio2jurua3Q9fpadeQkTQIv1A5rU027G4Jz6z6nhvH7zIwYRw2jwZ+Ov0L/oRS09BAcUc5VS1FJPqnsmLNRjZv30t8dAw1ZQLMTiVuhwgveSDVFZWYnS46u20EylUsGR/Ixbz7/LJ6Ox4hUu7du4SHzY3ZS8quvYfRdmrIuZyLWWOnra2StpYa9M1a8Lbg6SnAy+Uie9RUho2K4tUV79ErKJiVi5/lflsZ3dZWjq/9kOtWIlwAACAASURBVIrWLjau+Qy1KgG9w824h99jSEQ2PT0Sqtq1NHYZUCuikYt6OPjzAURCD6SqKNKyBxDRNxyXzUV7i5kbl86glNgQ9LgQ2oQIvf4a+X3p36bjRXSwm9/owUQnejbyKauZjY4mBLipo4s2ruBLIDV0Y0GMjb+mM6NIpYEmFAiRIuBl0mmjDsUD3cL/qQ0oRcxd7tCH0dTQwjUK0dFDArFIsQAe6NHSjQYlAiJJIRF/9LQ8ECL7z/G3IAGrzUFzQy1pSeHsufwzZQ1tuBrMNHQaUUdouXrhBFZTF94GM/cN14iN9KS0rp7lc5/ES+ji2jEHZcc66P7NhCBdjK5Vw9iXx1J2rp42YwuxYX4MHjOKcQ+n0ycrjWanlXe+28RPn37C4tXvMnD0KJLiw5gwaiYDMzMxCG14GnR89Nqn1JmNbN/1K5s+u8Lh3IM8s3IVxTfzGDF5Ei2ukwzLjGJE9mQi7GLiFAN46dWn+HXHFgJaFAzv05fK2lrcAQp2bfuAxxY9zLG8Xcx+6RH03R1Ej0/h9+8uYqw08seWs2z5dBuPjpxHfU49xy/coLXLzdJXX8PXyxtNUR34eFLasYeW4mpuXPuS4vyjRM9IJSIrC6XRgdFpICHuIbbvP8XVnFyaPZqZmBmGy+pHm6WeOk0PX/y6h303K+jq6aDnmoFBmVnMXjEDnV6L1C+U9rJOegX0QWtoZ9+OlcSPMiKNlJPgCCExKI7rxTdR+XtwbMdF7t06T2z/ME4fOU3fEUNo0dWRHOdBQKCcSK8QbA0NTBs5hkCvWGQRHpRYahk7finB3k6+/PVTOuqcJGakECD+q8Zg8WjlwsHNNJLHR+t+pqe+nY5OLV4qJTGpCQTJZER6CulpaeTSsXK6jLnUlBoYvyKKpupq2vVNPLRoHpf3XSDPUokiSM3bL3zNkrd7I8oOoqbxLEIfM/7ecn67cgm30smtziP0GirHJlUxICMLU0ULTyTNItjDh6EjH2PYlLHc/r4Rn8Aw3DILAJ+x6b/k701aaOQ+J7nOZIYiQcSXHGQlY4lAhBIZZ6nGEyE6LGQSRxKxeKLkJFfxIwgRYhIIx4UYGWamMfLBPKIQATCPyVylCjs2fFHTSQGxKAjAiRhvfuFlWjHRSDOf8w1v8A4N2AlAiAAHCaT+R/z9LUhAJAZ9lwYPhRRPVzAeKj+MChEeYhHnL5yls62L2GARirBI2jV1hKUNwtLtjVtgwyVw0T/YF42+GSE2vH2VjElPx9rdQ0+7GS8/GY0dHQTKZajcYuxSGcmBEdTUXSMsMRCZWMWeP7ciEnljNJrJu36WtrpSHEIXAkzkX7tLoH8UUrkEgcHOK28tY9pjM+lo6EQak8iohx7h+MmvMTs86D14FBcPXiQpNBCn2obR1smoUaNJUHuiD+rB21dJjNqP5OBEUgf2xtDqYNmUJ5g29DHkwT5gdnLwmy8xWeHRscsRuZx0GW8h9A7kuXUbeWLus+RX5mAUaPEKjEapE1J2rpBdm7/EIuthwdLlRCenEyoRYugx4VRKkUsDcJdoMOpc2HX1+IT7kpgUh0siI+uh4cSnp9JSeBc/314cL/6G5Mh4EAkJj/MiI30kzTVt3Mk9TWjSAMxSC0PTByHxNNPddpuoPinU1Wjw843GhR6HQYHBocNT741K4E3vtMEU1VSQNmAQn721hQBfFaVdEgb0SeTrnz4gURxI/LDBWD2l6O61okCEQ2ZG79aT3X8IyX0HovAXYfTpwSAQEqeWUezyIjh2ADEDg7h5pYLecYNJDQ5GFqEgPCGOlKGD0LWKEPrIEUutzFw2HnEXBBmcNHcZGTDcj2GxGbgdNdi0RmL9+5LoG0ZwkpRzOTu4uucoQ5dOx+0r5M9z33G1PI/gEQkERqmxdjiJH/WXpsLsp4cxYulfWgKVdFDzoBnnZ7Zxkw4yiOQaNYThTwReJBJBHa1YcSHBl3GEInhQ+VegeKAaaKAdD8Ywl684xFDiEeJGBDTShgktAiRcJgf5g3Fhf1RE4sEOzqHGi3aqaUPLlQdHld5440SM8l+szv49/hYkAJCa6s+JCzlkjhiLxtWATChE29NNUpsCvyhfbu6qp6qxCl1HJwf3/Uo/s4Wft33D3t1fMm78Kp7bNRrv2ED8xZHMSJ9JUU4FwV5iUmemkJaeiCg8GKnaj7t595CPCcDeocFp8USiaqe1tphe6VFIuxu51X6YyWOG4xMUztpNH1BrbGTekkfQCkyMmZ1BksAPp9TAMG0oXu4o6m/VIMLBhl2voDTUEpkSj1CvITEqg2lL53Lo4EGS0hPxVdlYMGk2YY5o9v25j87rdzl7+R5aQwGTls+jb0Q8ty+X8VP1bmzlUj757SPuVxZz+Mxh5j45lw2vbaCtrQ17rpTYtHC+OvIb14qukSRRkRidxi8f7WLB5MUc//MLrEYHApeCxspriPAja1427Y0udq27jN6l4PjpywgsBj7b9R3bPvkEb5mJYcmP0Cd2IL9fX8+pczfRlXnw1ZXveHbufAi189uBNbR3tmCUWTG0OAmJ8OD4vstEPWxkx6kfOJV7EYRuuiwu7EIXwepgelQ1OAxtPDZhHMfu7OCtT9/iyIV3eOvjU8T7xFEq0rD+yefo7rbz/ZEt9I5PJTI2ghD/GJ5+/TkmPDKLEGUgik4nzw5Ywp8X2xk1eDLPfjWKt6c8QVZmAvktF6ipvMvsJ59DaNRwvaiR19e9hLOpgWPHLpExJpXMsX2ovStG39CAXeBNU4uR5xd9w5efDeduxTmEQS6MDRr0DgFjNjzBB1+/wNpFMxAI5cRm+tFT0UR97R08vazEqVS8sutRLDIdrnY5V4qvAuCPgrW8gwUZVrq5RSkRhOJCzRBGYMH2j1x/CBkGnMQQw5esoJEarFgJJZIySminh2dZji/2B1LmIMaTYHyoQ4OTQgy4EBGIBg0TeQg9Zk7wFX9yiSI0eGLDjJ46jDhx0PZ3bxayO+yoI5KRC1XkXjrAgMFD6OmWIhOaaArwwmq1cvzaTUITQzF16Hn2s9dQzpyKqzGYe7pCzlR+R3RgHGKDgLBgGa9tOcg7096gV/8s8qqaqW6tQdNTS7uxA3//SMpuXMAdk0JIcBJjh41n8MBHefu9lUiECpY+/gl3mpo4ezGP99/4mCu/HeBa/u/4OLRECCKwhEi5cuko4VOHILuuxVPYQsHJYganSajJK8d9r421q9ZwYMsp3l26FnVgAD7BfnhKPJELPblxr4xb14pQZfVjwdiHSR82lPCAYB6bNgrftF5MGTkHc69OeqUOovxiK2Jnf6qLj9NW3cK33x9i9Ng5/Hl4Pb5BXVjF4PJQcGnLEerbBCxb+w1HdhzDL0CJwWqn2+HkxKU8fnnvKJuWvUq/YaF4upz4B3uj8PNG2d6NUp1Ni62Rxc9MpunebbRmNSKlC4lnEPaGch6bvZqUoAAadDfApUUksFHXZKVZo8XoBHOnAMyeeKvbsZt0RAT4EBAQw+7T31Ne28TdpltMHzGXRyaOZf+X+9n46jEunvmZylv56O+3MXlSP+SydpY9tRqHt5v2tjb6SdOYt2AKPgIRwYEBpMb0YuLKbFa//S4WOUQroth+6ic6O5rZtvVdvMP6cSHnEhqXi/f37sBpcKBvaiBU5YvTaOLatQ5ee3kDkRlivLqjaHEWc+H2bixiLywCOb5qGXkVViKiveiTlEF0tD9JD03A6eig7VYLZqsduTucbr2Lm/dq2P3OeVpL6kmJ7c2w5P4AdGBmPe/ShwC8kdJCA3e5RxQOxOip/Bet3fVcZDMnKaSYVfzAJDIJQY4SHTKkXOYY3/ITLoJYyzIGk8VNLtOBkU/4CE8kqLCixhsN8AunuM4dNvIrwUQxiHjSicYJdHEfgJ0Pehr+d/G3IAGRQEB4TBRmcTvabj3n/riMVG5jcFx/isoKMNm1fLzrTTw8fQjx8sfZArf2nyVIHsbhmzuISvbh4KkrXDp/DGelBy0td3B7+BIq9uX+tUI81ErsegcRSb24W3aTwMAoftn1LW0uLc88+RbNNZXEBEZT21JPXtl9NF0VHDm+C6vRRFSIgKyY3ly8fIJolRy7S4HcU8mfZw/x0MypdLVYWPnGZ3iGyOk3ZDgNlTepKMjjsy9/ILf8ErEJafgI49G7BTS11iDxtTB8QjQudw9RYePITPKloDIXcdBATLYOTh16n8zU2WhNN+gzeARaXQc+fjFM6htLn3RPTp4/hZ9nOAaLiILSIgpvFxAQpEYgtWKryWP4oBScEhHtbRDsE063Wcc7Kx/BEjqAlfMnI9CZWDhyNh4eHrhMEpzltzDpAsm9cZ/4kJlY9AZcDjeSngpktiB01ibMNiPnb5Xjsvnhp1YSHBiGw+bEN8CLzsJinpz5FEovP6QuB2oPIUGKQBpqG3FhpuR2C9Xdpbg7vbhd4+TbtasZOXou619YRZzKm/S+8Tz3wkZi/GOh3EZTB4wZspDCpjpKqmuQq3y5d6+IwYmJbPtpO+6ebsKr7CQExOCUWDBZIDWuP4119YR6e+I2lLJmzSec3rcTg95Gl9mAV3E1bYZqfLx9yTt1l+S0QeTd0xLVZzLB6gAqazqpz7+LS66guLAZk0zC/t3n8AsNorKoCpvDSWNBC5OXjiYsyB+JxIlUEomX2heL+58nBq//+Aye+KLGxWuMJRovfiSPjRxix78VEyvp+MdUwH5u4USMDSk++JNEBCuZSzG3WM9mVHgAcvQ4KOMKoMSMjWC8sRDKQLyIwIdicqmjgUtc4wIlgAjZg5JgJjP/I/7+FiTgdrkIq7ASGhGHsL0Rs0cPAruTsPAAeqfFIBOo+HTtAbRaA+d2F1Lflk9IgJK4yZHEqqLRSupYPX0jb21fjcDXjjusg5lfTCFm/ED6SKPwS/WmwyQmP78Ws58NoVzBgLh0EmOC2bBjKZ3CdpJmLGfrmb14iHoYkTaLTzZ+xdErZynMPU5ByTW2b9nH93dOYwvvIkCjZ+3HH7Ft13r8grzJKf6TtutONJ56ho2bybCRccx/YQ5Tnsji6PWdmJQarK1a/KNM5LfdJynah8LTufj4+BEuyeD0me2c2PkNEkU3vcevpE13H4dbzcCp8Vg8W5EHOBjzZAaThy4gILY3RlMzAzyWM2/iNNLkiQT6JREVE8L8+S9ik0dQlFtOWq8svIQC5s+dTXe8kDcWhlLX1kFMoAhHxy1EOheZUfGYohT4JEWz58KX3Lh5h0i5D+FSF6FJcQwPG4g7KRKdw41/gA/dXVbMbk+WPDWfAEkoQ0eEsvrRfQgS1Uzs/w5L5i6kqqSJmfOnUVFRQFpEH8aNTOPVlz9m9TOPIBNV8uaJz6jo0tAtU7Nz73V+++YI+3buY/KS2Tz19gqkHVq+PPYC7dU3kDUHYem00urwol/6ID5852tuHz+F90NTMOvj0ZvtxCU9RHXHHSQuJ/X1YoYPSmbi7CiGj5xGRYeGr5f8wsNr32Hd5gXUVDUQleZPt66Cz6eNZfXaD0mIDuPiyQYe8vfH1SkiKV2E3Dccra+d51e8grZMyCurZrHu7VdZPeRFWtv0DMpOROpuZva8OYx6Ysw/8tjfJOUS96jAym+0kPfgLQyw6N+KiQn4MIU0FjII+KsXYDeF+KBiO+dpop77uMmmL9W04UCEP1LKKKMPk7DRyDZu4I+MRgyMYibRJBKNiM95j74kMpU4QMocRlPEqf+Iv78FCah8PGl2OtB06DleWkt0XAS+chHDh82hqb2TlKFDyIjyQ+qhZ+Ez02kptSKUiKjvaULuqcI7UEbuzUNYmkw4lHawu+kuaUCqguFDsqloaKS6ohCrtAMVNn7+eDdapxvvkGD6DByIb0AoB7Z+RESyidz7FUilPrglAvafuMHpnE56x/fmdkUxnXXV+MtVqPyDiPDz4XrOEewuMY+++DzhQTFEyeTEpffD0N5NmMSb3MYqUpVBmDr17Pn9DE0WHSlXTHgqvHh50iq2/fEJOpsSX3E4o/qk4hciItkzlMPH9hIQ6snZfafx9lEi9/ZlaNJruAU+dNZpsBht1Dra6da46J0RTvG9Kj55ezdit4GYfkF0a4WUnDyLRe7ihfdWkHvsFtP7T6CgqZHTF++jDoqjvKKFtupyYuSBtLS0U9nSgsOnhaa6KmL9QlFbeoh/aBaOjhpSeiWhcCro6HASGhgFchEBAd7IxXakQUJ+/307UQkBOA0BpKYmsvP9vdgETlQ+3gRn9+Xavd001ENn933mLRjGwqdmkzFkEnMXDmBwrD9dZRrWvf86+nYPMKlRefpidxtoaOqipqye+lv1rP/0KK9cfJYFi15l+PRlLH7mXQw9ZnpuXMZfHY1MJSIxtS9iXFSXa0gc2IcXp0+j29BBXNAI7t2uorbKiLTBxr3iRkJDE4mOD8blFBCZKOK9j56g1dJI3z5J4OFLyd0SuvSw5ovxXC+7xaKnHua11z9AIJRhFGnwqO/CP6oXlvZ/fuYfzDn+j3UONzhD0X/J84+/+MuucxTxVNDNUQpp+BfPAYBuqplDNp0PrqW00U4bUmxIEBCOijqu4ksIoQgR0E0HHZRzgXtUcZk/iWYwgSRjRYIRNTNYzJOs/4/4+1uQgL7bwN6L2+hqr6ZXvJLmsmq0Cj8CowezKO0Rgtv9eXjtKCx2PctWvsIfV7Yz870XKS0oQCsUY7aaMdtCGDZwNE6dCGTBeAf5cr32GsrwNG7fyCcqWYWfxAeLQ8l3W3dy7eJ+Jj48DHuHjomj5rHx8U9o1tpYnD2H3w+/g8zlJEqq45U3p2I2dqKt0tCtc9DWUEh+sw/vzt/AE29/wJnCPG788QeNFi2VNWXcu/o1/bNTmTxiKMWnc0ka3ofm+92sWfA8kmYPVv55meS+jzB8ydsUNFwmR1PAjaa7hMYMxsOUxU3tfdS2MC4eOUpRZTHDh89G19TB6vUTKCz6E4GnnJw/dMRL5bQ1tOAURpE0JIV1P7zF98dfo3jPdfziPYlLDMXd7WbW6GlM7BeCRS3H0NKNZ5eNPaUXCRULsCsicWqsDPadxtyJM6mpqGHqggU4vAWcuNHD0McnkNA7mTpbD16hPogs3aSk9aOgpID226XY6npz4PCPDIgbTmZUIrfbf0dicPD6myOYNWQ4rc2liKxiSo5U8sj6WVTUFFKcV8KHG1YT5h/Jo/2XkZERjiNJzJi0dMrL8+mStJJ37zZFtys5k3+cvDulLHhpHLt2HiDnjdOMfy0TuY+CvNIC5DoZl3I66JfQD5vViN0lof5uE89/uJjy2iN4h0Vw5M4eRg0YRlC4Cy/kFDu1JNoCaRL5IRTGU9/aQmbGSJ7Yuh0/sQ9Wix3F0ZuEB3gxZeCTNLW58FK2E50xgCNnLxOe4EGDqRVJbG8Kz3yPQvVP34Hrv5f/L7n94vb4f6yHTpoEQDMCppMOwAVusY8//vGfo7Qgook4fOmNN35ABkPQ48SFlF70QYqKfPIII5UblGJBzDjSWcxscmjFRQfb2EMNRp7jBe5TSPffvTDoQsDn73+DxW3DHexLm6aBT0c/ztzHszBkyVD0VlK19QpBIb34MvdbPGTxVHdcJ2OcFEm9BJtFyDtPjUakFUGPhBQPH6RSHdmBw7D7mwnXhmIVCSm9W8Sna39g4wdP0d7UiCKikZ4qFw+P6MOx0kN898pp6h0N2J1aWtFQW1LCvlOFrHh7I7n5N4kPiUJ4RIC56hwicQuFJ7aAN1yrLmH8Qys4Xr2DHQePcfDKWbq0JmZPfZtMdX88mrS4vMQExYxDqLhLbs5ZHpsRz7rFXyFszaOosZ1TZ69x4punMd3dhqK3lUemjGf+c3NwWSTIrFZef/FXSvXniU45x9YDOYx8YhBZEQPInjKUmnYRLfl3+OqpTYxMErBm1hsIy/V06b1R+/rTqLPzwd6TRCqDcAoDuXq2hkUvPUm62MjksZMZMW0Q/RMGkhKTyt5zZ3D2suHt6+LR3ik4cx0k+Hpz41oh4Un+bDr0OkKXnaykRPoNmc7br7zJoOx01mx+nYrrep5aOo0p0z/gq99/5cPXvqS6Lo8z1/NpqGzju593Et0vlCeeWsEPh9/gmtVMxsRDpERLaKgyUtTSjdtkZnCvYcRFx9E3wYO+Q910tfSw5bc3uJBzlTM/nKK75hQ/nf6ADW9+TWiGD088toR0kR8bln7DjYsVfP78bzQ7isDTxZWKoyz6ZBmWeikBYQomPT2ZlEVjsWlLqc8/iFpqIUVgJjEjGL2omwh1GhdtpbTV3WTrifdp6DqPJCCasvxv2bh+EYMHDySoW82Gd34g39XCgMRkxsxKJ66PiiVvpvPHxR8A6BMKO394F1vrP8E3OGk4AOVU4MdfysWbeZnZD7x8JzAYgN008wvHCCSQPIwEIkSKiDg8+YDN6B4Yjz3HBKRIMWKjjRgKOM9qJqLGjB8KPudDqungAPvojYr/FH8LErA4bGw7vx+h2UVcdCxhob6kDZ1PiH8wLTYX124cZ/b4ubhcJroaDfipLFz74zydnd0Y80uxI6HdqsXuEYHOoOCxyc8QFhBCfaeOhD7J2GQt2E1GglQqHnlkCBK7kpBeEqaPWYjJbWflo3O4eOQnimuOEp+YhN3PH6NZw5CsZIaEBWPSuJm/9DkUQSH8cf8maz47T37lDfpmjeDbl06jDhAQEOqF0OXBoIemYBdJqWi4wL1ff+RswQmSZ4zmUkEerS332b51Ow9lP4q/TIDKKcfTL4bYpAh8/aX4+6mYMXopxXtv02Tq4uSWa5y5c4FqkxGFyI6ytZH7lxzsPfA1Oz9YyNAlq8hKn0Ll/t/pKdEzKDSNkMFT8Ozbm49P3MRgNBMeN4Bh2clEi11EhAYxIFpGc66NndvOUFJfzPjFLxGl8MfiqaNJp8HV4qahuhqL0/SXGnJYCj12Mw6jmWUzHsOplRIXGocjRk1F1U3MmIkJ682g/ktw6xT4RcjQWvTYjW7kXhocLjG+0Q66Wyvw9w6k8E4V0yf0ISZzKBdObqbDpud+Uy27Dp+g5spZSnKsPL7wado6muhymChv6KKzvZsfft2PF97sv3SS1799lj93H+LzX3cya0EWE6cN4Lq1hQZrFWeOniE0SkVNBdTVthDtH09vdSsyTwkuvCk9XobRKiYowItulwO5bxTtjfVYXRJUEhXO9haEEikSqYuz56/iFDvQt3py47aAtAAlV08fZ9HU0XRoNYRLo5kwbCodhQ0MGjqQJl0Db337CY+9PYy1771OXP80jFbZ/5Lv25nADUp4gsEs49N/7J/k+j/Wq1iAGAEDGYMdFwKkhCBGggL5A1v5vRzHiZ0OTCgQE0IiWoScoQgNOk5zETN6YnAgoO4/4k+0bt26///Q/P8Y23dsXyfr38OCUUuozGlD032HwpZKho/LIvkehMwYQ33FLdY8c4z9X/2JSinAqi5jwfBVDJs+n1s3zjImfDo9VjP79+3kxNWjPP/yevYdPkhGSiplRaWMmDWDL7f9yOPzllKrP0NCTBjDklZy5OZe1DH9sdmE2N01HNj4B8roeEKTPKnpEjLuyX7oaqXs+HYNH676gl2ffs2Rk9+hio1HX9tKSLY3Q6e8zB+vPEfWnEc4+Mt3GEt9iO0TQH2LlNAMT67sukZibCKdjm56FAa66ivpNfBhAnz9aPfWEGP1Rp7dw1cfHuFuz22GDkgnO3sak+bPJVgLR3N+49Lln5k49kU6bGqCwiyEWrLwct1CU1tKaEovRi2ahU1uYvjIlWzZtY7QqAhyis6wdcNXrHrqfcZOG8+Xl87i1Emob6shvHcQVZ1hnDj6DWPmLmLGwGV4+PQgFAgIVaUzcfFYzhy/gExspMeahMC/mbLWYh4aNgifoHgkRjUBvSKoKznE3gPHmTp+JouenM+v5+sZnTaQy/dOMC+yP3EDM9C1RzBn3JO465oYHD+NgvpGzIYOYvrGcLPsAhufOs2k+TEUXtQxdGo4t6+fY8TcCRQ338FfqUDX6SA8yMXOw9/Q0NVIysBZnP36Jpu3zqFd7CCrXwZLZnzJ/CfGcqu1hOqK++x89yc+3/Q5XrFB+HvE0qppwWy38Mt733D06gEOnGli9JgxxMYoaG8yEBDlhVvpxWev7uShoakERwUxa+ZQBIIA/tiWg0edm6BYKx0d7az/7DILV03kiWkryY5XcqriFOcu3cVSH8zbb76AAhs+oekUy79HZPfm7C5Pxj7UwdMvDGX/kSQ2v3yL3EUnmC9bC8AyJrKQOl7/F3eASdyjlPn8xA72IcZEK05SmMZcLnKZKkqopAk5Il7nRdqR4ws0UMctqlnPc5joZghS9nISCcnUvNvcsm7dup/+HX9/iy8Bh8CF2ClBILIQ7jSRGDuEip5bnL1xiqz5z6O9U41nRi/ONP1On8xsvLsFNHYaGRYRyZ6jv+CljMQr2oecO2fY8ML7KDs9MekEhIjEJPlE43TJqa7OZWj/IVgNbhQKJX6RwSxdv4LOjgoSAz1oNt1HZQil/5hsAjq7aK1qIMkniDPnbtBuaCIwVsGZomai4qTMXL6UsquXUUWE8eqru/h87ctcL2mg26jFx3Mw02fOZVB6Fp52Bd4uA2tf30i1tpWebiMeDgF38vNpaatEaNTR1FxKq1HG3coqlr+4EpPDRqisH36BCXy+YRMnzm8mO2YaCwavxjc8kdj06XQ336f/ookYzFIqdV0M7xdGv6REFJ31fH/4XbxbnRTknMRX4s0ISydiURe5R25jrLxPZKI3h078zpvvvYa2qozysg48RS5SYpxYm/WUFDfT01hIdbOL+3ddzFqwhoNbD9LUakHosOMQaGio0eGK9sVDqODMyWu4fDo5fvUHPEU2NMWNmNUWBDYrAZFJlBdV8Pzqyfz81Sbqq+9yZP85Nq59F93dFmZMWUhNRR7PfrOIA5tP8cRTI1BgZ+LoRVRqr2PTtJNzQn/IqAAAIABJREFUpwZjqxaVOxiLXIaoUcPRz35CK9TTrfOmq7kFt0jIrq2/0ZHXjoeHgzlTs7l2qwEPLyvO5lb2Hj3MwOxBxMWHc/TWJTxU/nTJ6zlZehGFWcWZnGso/NSEqGQEGVzc6yxm6PAp3C0uxGE3EhIlZfUnn/FDzlFqG2NwuYUkpKWhcAu4UtOBvVuCUOyDj8xCS6uLwvt3GTBgAKoAf8Q2BRqXk8gRUbhlZlqLyvBTyrl+4Z9Fw1pq6X4gR75s/QwA5qLmA74G4DdKGUgfbnEFTzzweCBRFoaLPoSwgR+oo4IQYujDMDyxYkKIHE80hDGWwaj/7h2DLqsNbZeGK7lH8B7hi7bHiairB7+AAHYUb8bZ5KalroE1n7zDhEdGs3zhAlRyGZ/l7mbewhcx6u9zp0lDTO8RrP/5E7qVLta8twSzQYHRZUbmoWDFqHeIj8rCP8GOuCaYa0eaGJveiyCBD7/u343BqqPO3srJq7mMWrgciUPEhKAoFqdOIF0vZUr/RTTpi7jlH0WvFDWGZieNd/OwFWxjUoKEEdnZxNzQ8OLcgRg8url15zjTXlhCcmISW08eICNaTWZqOGr/MIx2GR26+4SlT6A47zb+gU5cxW5CkttIDc9E2FHCN9+9wOa3Xieg3wjGjRpLqU3MjkOb+OblN5C1Ktn1wrNc1ucjdkFBbS55Jbdo87PjIZfyxuff4PJVE+EnJS+xH25HHc1tV8kO9UUYOIw333yDl5e/QmqKHynRCvLba9DrJLR3OwkS25E5EqjYk8MvV75g8oIVZAUFExniRbvGC4XUxI3cn5g0phffvrKG2RkjmR41iP2//UDpuZucuLqZ9jIpn371MzntRVgr2mloNTH31TnUiX14+63H6ZM6kX7Dk/nu87eZ8fBkdDnFrFr7Cl+sO8GgIYvYd3kX1loXc8cuRlCi5LFJk6ip1NBZYuWTFe/w5tvPU1FzlucWbyEmMgabu5Mjd77mve0fUlhwhezo0ezJ/Z6xA4fhVnvy6vInyblWQNkfNnLOHSUouDdq632UhjbSx6bz3vsr0XdX4/RU8PbW70iJT2HDlg1ozW10OTrwVoiYOS+T41u1XMq9xLxV/Xls6eOoEv1Z9exiYiN7EywUY1e6EFouM33GKo6cOYm9zUB7RyPTgqez681XUQlCGfXSNPxDfJB6ibiauweAM5T+Awub1x4CYC9dAFw48dVf+9zBDAiQ4kMEcnoQ4aKKHrzwwkkO97jNj+xkOU+hwgsxEgqpZTKvY/kXFeN/j/8bV+IIgUBwQSAQlAgEgmKBQLD6wf46gUDQ9G8mpf/znjcEAkGlQCAoFwgE4/+PJIAAp9GC01mPwygh0ybn8bAphHs6EF44S5/ZqTwyYyHTR4yhU16KLaYWH3MM1jYPGhrvMrz3NF5ctpJORx6+Sg+efnQi48c+jLHxPiphNGp1NPu2HSHnzl7iFQo8Q2X0yszidm4x/WIGEjxcSae+naazrSx4bi7eQgftHlZePfgdNS4DbfExVJ+5wIcvvkeUxIfXZ3xD6NQkzH37M+Wxr5G5fOifnY4qcRJ5h/PxDVRQ6+9i5eqn+eXXS0weM5fqqjpOn72Boc1NWJiAoIjhvL1iLlMyX+JGwR18Q8MYn7Ubn1gREROfZVqfwZRo5Twy6iV+ubqX8pyrfDdvFRNfHk+h24/+S1/i4THvMnLaHHoCx/HRT2vwaOqho0THfUMbvTNDmTtnIo9OG8WqaW/y7Z7rvPHjBsq2fkJgmheDJo2loc3K6Zwutq5/D3WYH7/98jVXr7Sw5rVlfL9pLw1HW3l+88P8dP5Plj+6laWPvk2YayqThmbz5qR32bTrBK//tIM9168TmBjG5oKfmZw1nscmp/PlomcZNf5ZlL370mS4R9HOHagF8N76jSQHe1ONHrkjAn19Mym9EvCydvDC04N5dclGHOWRTM36jLO5xxg3szcq1SR+PPA1G9csRJE8haWz3sLfGUptQD0FF8TcKbxObF8Hu8/8yronn2P2gnWsGvcCEwb2Ji5gBMWVXcyfuJwlq59HEWBBISwjTJrAlKy1aK4XIWoai0MqIq4zhJ0nPsK3rp4Tbx7EZTeR7B3Cy8+8w9BB6Ty+MJ0XXx+BQBHE7n1buXktl5On8ony1TNnchQ2u4Fmi52eqiKyeg8m72YnASluPss5wJynViHxdXG/oJDlc0bRiot4x38V/1z73XImLwv/x3UUEt6Y+CWZeCIBJuKFH3I+4HVyMdCHcZgxMoNRdGGiiwZWsQIVKoToaCAPfwLR082l/49Cow7gJbfbnQIMBJ4RCAQpD3774l9NSh8QQArwKNAbmABsEggE//lbBJDLJNTWdbDh+Yvcs+Sh13US5KdG3C3j0NECrlw6x9FT18DloLy+BLUwAINOS1tjJUeP7iPUX87nX29BKBJjsDby5zcnaG2tQ+kVA04XbfVVjJ05DTVdnLxewb2CMixGGzqbiZHz5iA1u3BiJmNUP3bt+5ZXVr/Pob2HSEmLJzAknkundxM/8jHGD0ngyonf0cutpCT0p+7jQ2zZ9gbbvjpEnUBLzk87SR83mtpSE1luFVOn9sIvTElgr1h0LidOmYJ2fQN6nYObZ3L54IcD1JWWYmit4F5TLXuu7CMuaRBFdw9gVpk59v1SPvn5A/omejMlOx0NgYTrXFzJv80Xv+zCI0CNweDF7l2reXjQNHq0IvrH2bl8ag/G2m6qqzrQtpYytN9Qvt2whpEZK3nnp714+0XT3thO/0mRhIVCSKwMD5UfZ8+Uc+xyDpfbnVzJKyFx5AhO5V5h7bqX+eirmby6YAnW+nu0lPQQnp7JDzueJSSsP4GRapApaDJWc/LKbRavWkxmagg/TllOUt/h1HdpkHgpUHY6UUkU9FJ6U16QzxOvvcjGly4we94LJA8aRXjfJKa+uog9u3fw4+7nadHouHmrguyMIax57UX2njmOyiuYy/dqqDXlcPaLV3ln+XtYNG4cJjG1ZeVsePcUmRNj2HT4O+pbqxG5JRir7hEel4hCZMYjIp5YVRoao5JzZ46TGDCa4ye/QyoU0TflScp1Hax+Zg1uqxWTTUzNLR1LVz8D/g7SY9LwCQvFRTtuYyvCFB+0XRp+P13F3uOlTJ06ldiEeBIy4thy4nvyTjbi4aejtkCDSu1L+LA0MrOHkZ9/ndhAL7wGzaN8zzIADl84gLFJg59IyZINf50i1GEnhGiURBKImBP/9jYfxVD6k8UEpqIEujFThwY7JlayDhMKblCBAzGq/8aN8P/GlbjF7XbnPVjrgVIg7L+5ZTqwx+12W91udw1/WZT3/++eIRAIcLUaKK9qobk6H6FDxY36UkwtWmYsWE7+oZ/58+IhPIRqyqrK6Wk3ExoZQnb6EKz2Lnx9w/GN9aBNYMbDX8G8tW/Ro+3gbs4d7tXn8/TCp6juLOflV97lYu4F2pvt2O1GpEFKNm3+EVOrE7cObIGdJKf2ITJUiLDdzcyRj+JySBDrLHQKOui2CcgMjMWnN2RGJ/PNbx/Qo5fz8sdruHa6gCe/e4tDl/KoPn2A7jwpjnojsRFh/PDrBvRmCzpzNwqBicCYUB5d0Q+76QRBIT7cLdKicIm5+MsHTM16GoPBgFQYTOb0YQzO6EeLRsT/YO49o6s4tn3f35KW0lLOOWcUQAJJZIRA5JxNMhgwYHKwTTAYMMlgkokGE0wGk7NAIkkCISQhlHPOOa+kpX4fzn77eJ9x9nnv3jvGHZ6fumd3VX36/Xt2Vc2agyasxNDSmjY1TaTtLYT6j2fH5p/44eQqxgf1wy94KsrGLjw8fdDOLyQuMpa85Ah6BPXBevJETt99SqdMg+S2AioSEkgrSENDpGLEF0HIyyXkZ+TgaOzGqmlfcO/nlZw7cpTdv83CUSkCaQGDunswdnR37AYNQ6TWTK+B9viHeqAmCGgZ6VKTnYGW2IITp9cSMM4GsYUrs9aEcWLHDtoaM1DXleA/uB81jQ1oa4rxdO7OD9vm8DT5FVnVhWjWdmHj6ELD52dsv3yRjsosNq/bS2F6C44OEhwtHdEVNGhorubbeWMxkXaxbsVa3Lqb4KgyRENdExNdGY1GBUwd4EzE+Sw+5GnQzc4NH4e+YCogttDBw8mCpKJSJkyYgF83VzLFOuRndtDSUEZqWQyWNrrc/5BKS2kMamJNgsP6M2KEH+UZ1TS2tTBv1irqGtuQ0sGLy7eoU5Px1eRx9OvnTbW0jejod7zOfU9p8TtqsmoQWxihr2fK2u0r+RBVRFHBZwRpMxWyViRKSzwnLSErK4roV09ZvmIrTr0GoiZv42LENhKKr3CXV0STQTmdTF7i/i/cnOcOLtjRDW8MUGccY5BTjye9GcdUminBC1OySeV7uv0b+v4X5wREIpETEAB8+IdruUgkShGJROdEIpHxP3y2QOlfmpXxP4sGKtT56ssFLNo2hlW9Q7Gx0WJCUDfqK2toL41mTtg5QrpCcLVzxFVwJS2tASNjAaPmasw1dbj59CIH9i/EsL2Oc2vOcvuPo6xbewh1C01cnV3Yf+Uwl68vZvn8HRw9vBulUkFFZSE6zVJsPewQo8O5PaeIePkScaMRXV1N9A525WPjZ6JunWXC5El8aH7Eli8m4eHtwMAes0nKTWDjN+vY/MM21u48RaCjDqffvgATY469zeHAszTqHV1JSkrHXCFGrt6GWNlEckINY4Mn4tWgg+JdCvWa2gTYTeSL6aGcPpPEyrXjGDdgJG9vf6Za6czJ67uwx4Q9R+ZRWlPKg6sPmTTMh/XrlrL0h9kc/v4cc5bvx17fDm1PDfZevUO+phQdM21sPUaRX1FKbvNL5vQwY+A4N46fvImaQg0dlRbppXnoazuhY1HOpgN7CBk6kHnd3PhuzToWrVxBR7Q6qixrRg0dz7PnWcxbs4uMQhVHNhZiq+2Li34YToHlVCamExTWD/8+waSXVXPr1yTEug4s2/+QtZs30pDbSJulMznyCgQjOzq1VDw8FU1deif66nmMGzGKoxeu8iEjkYKkBiYO6EudMIBBzrPZ+P1kNCWWhDu6k9fZxtsPt3nw7AmO5sE8u57JzZSb2E0fj4W2KXY+llzZtpMraXHou3Qxa9p2Hj/eh67xEJTKVk4fXYeVKITK0hgu3b3E2ElzsNZXMWTVcDr1VXy7ZQNmHX4s/upripr8Gdl9I9fuHUPWpEOXSp2lu49yZNMv7D9+ioD+IxAp66kuyGP5yn1US7SZ6mqC2KCW8uwmFi4ZjuMAU9oLmpi0cAwXL57nzf1IpOrVjF57DpP8OkQWYmRd2nh5BbNqvD+v038kLzEKIztnIl/c58GzeNb8Mg+AsMGauPoFc+jYyb9wU8dnPlBCLfWI6YYnXuixlpmoaESKBH0sqSSdz/T7PxcBkUikB9wGVguC0AKcBFyBHkAlcOD/b1//6O9rkUiUIBKJElobmrA188fZxYLXcVoc+PkuqE3kwad6bkfnoOrexObjW/h22DJ6fDGATx9SMTB2pqrJDC/v0eBQi2+QDYHu47iWeYnGgipGhw7k+MVHyFDSKm3CwsSVnj28uHrlBDoG1bR3ZLDqx7UUlsfwOCKLtGw5bt11qKqUM0DNjc4qFXX5iYydNYpfrp1j+dj9tFrZsWT2DtKzX1KQnceYFdOQiWUsWDqVtzVScn87y41f7xDUxwEHB10K4mPIja1h0bh5hIdOprpJzLUL0Sw8tglvSyu+33GF9xEfOHXndyJP3WfRtpnotKtz+NQ5whdNJ//yr+iIQVlRyYSRQ3B08cEi0Byv7mNY/O1MZIIuZ+5f4auh09hwaClj5s7CQanBxqFzcbFxorJVwczJ2+nTazJ7UxTYWwciS8vlY1oDSe9LmTh6LlYqdQ7vfkd8dDo/vVlKvKOCnU8f0qqmwNbNl6bKOpSBZjRm6dGobKJF2cSxQ8tplwkcPrOWxQN/p820FVM1CTlvM4m48wcrJk/hXXoU0+f35buf1pIe3YaLsTam2jYEB3tgMiCIjIwyHNx1eZCQzO7re1m9czoO3b0YMt6XnT/NZdeqSWha6rFgzklU1LHr1B0ak9tpVlSSWJJHUUU8S7b+TB/L7tSeSaIqU4/aijrup8SjoW5IN38JXUIN4SO2MnlBMJvX72Xyl/NJbMqhvqWe2ePCOPT7t2S1NNHTbzCyDhF3dhynLKeA384cJfbzTmLz/6Smpou+wTOwshxCWVUVhZWNXL98ge5+fegoy0ZkZ8qtF9fI/ZyO2MWAcxu3cWb9DrRSzDi26SnFJZ2U1MbiLQnhXWQiikw14hPe8CDmJX3sLNhzcCe/HNvMprMvaO2wYkToIkJ7hTJu0mQK8gsZPWw2ziFadBjo8DbxFS3y/9x8VIOM55TgjzvjmUUWlRzlBDb4sI/vsMeSVBKoRp0Snv2fiYBIJNL4hwBcEQThDoAgCNWCIKgEQegCzvCfIX85YP+X5nb/8P2LCYJwWhCEXoIg9NIx0ubKy7M0V4KhvwEGi8x57viBL6fMIfgLLR4eimH30lA83UJYPn0uNtP60t1lBi0O3jxMuYu2tgs+BmNJKXtFzIOXCJ4mdOthT58wPUyRMGrsQlKymwn/Zi1GMhfC+gbRw3kmJ64eRLPTiaePn6Gfno6OeABWPhXk+xly4XIEqVUVdOlrYyZpYN3CKaTFfObr39ZiplAxKGQqaw/c5Pfj2ykuKMJI0xqRhiETxwUjaWlErqeOiZYWlhpGXLpzjfeRV/lwt4RJM8dwwm8Jd6MfkisqoYeNGit+HEu7oxsnt/3KpO6hrP9qM3mnXzFj6jcs6xfK24Im1GvVGTA7gCpZFW7ujgzt5cWN2UtprI1l0bEVHFh3muzSchrNZfx8cxeGjc14GLtQ1ZiEua4j0ecvY67S5Psls1g/oh8/HZrLaJcQeo4YzMf408ya4ceWHgF4DnCj/k0BX0+cTuyTGGhTUHk/m12vDmLl5o+yVUqjkwsXXu/h9K7byFWZVFfI0TFwJCkrju83HmHIzDGkFObT38cdbQspOq0KpFYK6tviCRz0JUXZ5bj46eHoEEzKy/eEOPXihy/PUF1SQNDQAZTkZbJ4wSrW7VhBXXseIiyQGimYt2kBmdlpnL41idtJ1fwZeYFN3x1izI7t7D50kvmz9jFhcijO+k6oSRxJfvArXZ8zaFeI8J/YwqVLZwjw7sHRH0tQGBsxc8o8bl87zMTpXyARGVJVm8H42UGcv3ufiYO/ZsmgL1GTCJg4auAeJiPl4W/0nWiEiYUbP62Yzd37hRQmv2LRwuU8Pf8eTQM/NvyynUeJpxk9vSc9rNsRNXRgYxyC0rmcrSPGMPOnE7Q1N9LVXMeQxcuQdir4dvUp4q4/oTL2BqdvniGxXsWzWwl0NRdy7NguCj/IMXYwwx5v3sb85xbjNGr4RAw9cUBAjTLyCWE6NpiTgkAfenGOSwhksOO/JDD9L4mASCQSAWeBTEEQDv7Fb/2X1ybCP7MlHgAzRCKRlkgkcgbcgfj/aYxOZRfSxny0rY159y4KsVyFpayDqHepDBs8lRuxEaiMtJgyeQayWm3UNeS4+rjz/vp+appqcPf0RWLiSJeGIc59fOjQLKVTIqJTE+gS8yjmCX08exP34SZG5oaMX/EtutVtmOs6kJf7kYzUOF7L6om5/5AmWTtP7iUwd1YgdUWfuPDDSbytfHDt64O6uT6VtTWkFNQzbdJShs3rw9N3+dRWSnkT9Rx7OzXE8jIUul2oS0wwtuhigF8/Kl7G0MPWjS71LuTllTiHBGHepslwX29KhFbSk1KwdfVi98HlJEtzOBJzgwlfrWLF96dxsfcnKS6Nl68+0MPdDTsLKzZv/QH7gEBGhfQiLDycepWSpznX0O0Uo0pqRs+1J069w/j8KYn7r68j7sikKjeSxWs2ELZgPVPXbaYo/h2aPfrTXlxMXKYROmYeqEz7kf40CeceznTRxZCwQUhsXPhy6gxePbyHQt6Ogb4DbS0FKOSV/H75dzpb0rEy1yDq9VPUOlUopMZ8zkklzNWHa9FvkOi0YOvvTFNFI937DWfPxbVUpyfjYeXC84uRRFyNoSI7B5WamHfn41BoqTN37rdYWugSGRXB5j3bmbf+G04ffERCWhxoWvLpLmxcsgN5mxp9uvsTkx7N57RE5O8SKe1s4tDRs5ibdvAovpCyos+oOqUo5ZpcOvwHVpa2nDg2i5LsSkrrO2iuN2Xb6sUYqzli4t4HB1sn3FzbeB9/A5lJBvLURtzcrLAy0CfnRRSNUhEK7UZM9aw4duY42Z+S0HVQYGWvj1yhiaJBTGZjJQlFd1EZONGoaqdLPRuTVjHj1nlTU5CGSF5DrbgGr56mGIolhPjaMWCIG1KZGuYWxmjp1NDWVYZ6jYJ7p18BUFVWjY6lGnQq/8nNFfYhQp0cujDBhQY+84LzaKNHE2/QRoQCgXxqqPrLbsT/ZREA+gFzgLD/shy4TyQSpYpEohRgMLAGQBCEdOAmkAE8A5YJgqD6nwYQVJ2Ya9rSrZs7Bko3BGN97EWGeAV601kmQkNRgW/YUNotxBiYSwgIDCK/KIvvDmxBTyqjtDwbdQNtTDp0qXiRTOiAnrTXdeDpYYmgJqIuLYMeAZ7Ev09i9tTFtDY3oK9uiFaXHFGHA0d+20dyZiwj/dwxMdUjMMQFa0c/liw7gEzcTll9CSKdJjTVunCxduDmrQjmbxhD7LUnKORafE7LRVNNgwLUCXbTRq1OQFsNZJIuZqzayN7ztxCMNTlzazdfjJnLtk3fEPbNMuwk5rh7WDDLdzgvE+7jYe1NxMtouhl3w9bXH6OuBkx6zqRGWkCXrTFtKil6Zlaol9cS+/EV+ZbQWNtKbmICZjZWvLp/Fy+nILT1HXF06YWyupppI2bi6TwIOzdP0t6e48rl3Uyet5ah075g84aFJL96QtLN/SQlplNYnMGIoXP4dt9kNAU12sWdxMdnUlulRmliFnWNpYgEEcbGxoR2C+bKlRtYevXHWmKBpaY3bQ0GVJaW8/T6OYykmjQoWzEzssDCS5vGokoKu2zxtjIlbEFPFFodlFfVoSntpLYrnc4O0NGRUJSdiNSxlvysZhKfpSMrbyLl0wX0NXxoyzLDWc2E1gLILfuETF7Aqct3aM9LIe9TFA0dUhRl9Sz/bgNbVu2jd9BA5m45TkZBLV25jTg6DEJLZEiJPB5DWSm+bj4ItQWITDWQiTpJLE+krbIJX6+hTJuzmo3fnWfctLHMnL4cG/2+aLlao62mR3ObDBPHHvQLC6eHsyfO3bWRI6G7WzdaW5UYqKzZuv0xGoI1TppW0FGFqwnkd9WTmZmKjqCHtm534uPeMKhvOKLmCoLGDEchbcbIxJKm1k60TXWw83Fk1hJnALTkGkRnRBP1MPGf3NjhghEm/MYpavlAOINxx551rEEPMz5wFx20MUCdz0T/74uAIAgxgiCIBEHw/+tyoCAIcwRB8PuHf5wgCJV/abNLEARXQRA8BUF4+v81hjoCCnM3YpNfMnB4KM+2PWLzycMEjh9A8mMJg6ePxcLejYSGFOzVuti9cxvHjp8iKuUu6iI1UmOr8LFyxdHLHIuQIC4ce0CAtzVO+upcu7OboWO6s3bFUoZ6enPt0WXS3kZS1taCoaE+3j0Fxtu5MWvNdK49SaMisQZdLRUNemIunznLV2PXITEzY+TwDbx594C3509x6OYpNi78lpE+EwkfM4JDIXYEaHehramGgZsVvUf2YeaIqZQUOGOhrCbm8F4SPrTS39qU1ft3oNFgRGxiFtI+w7n56BO2sweiVdNIVmYiTsODMO7qQqGq5puTJ5gzrS+rp3/FhcjnFKfVY9vezKpdN4mL+0Rqexwdrx8xa/RMXhz7jW9W/8zygz8j/ZjD9U9vmLtuJ29jnlDeXsrp46dxD13AklkrGemmwZHNd6hMKSemuBVnfUsM9CXUvPrMkAGDeL31Eql5CeTmlNCm1s6TuHSEAjMsrF3pqK7h1Py9XNsez8RBw4jPqaQyPZ/8iFd0JDeTkpSC31BPnBztmD1yKEGWAZz/+SM1FbZUp99AJtRz93AGqBzpEgsUN7Vz6vA1vvxqFrJmKfFXEymtKMYpQIf56wbh7qTPom/ms2ZvAHN6u1PZ3sHIn0ehqi/Fxz0QhaKV9XP3EDTzK/wGjMJY0YPePV1Jyanm4pkj9B3Qi/rsW2ycdIxVPweRU5RCmKoPnSbdeB77JyUdSXi4OzDEZTKB5iFIjGzpNDLGwKQ706auZ/qMuShF9di5u+Dexx9PDytufnMOfaWCLd9u4vKD54SFDGHP+anUCZksnriK3DwpT//I4cDHXxCUeuxcsYMTDcmc3ZZCszSfl48ycNC2pOy9jOqWSh7E5+CZVYyZhR7vXxYgq5HS0dRGTEoUaSUVAMQ9acRd1xwH///kRht7+uKPQBbniOBrfmEc69nPAYYSwjQ2EcltBERUUPG/LwL/N8xQx4zs7BjkCcWYCx24jR+H0lCLmqS3/HbqZ7Lz36OmUCd+/x8Ej+mNvrwDO9tSjv8aiavHUDQ0DLlV+IBLEckcPXqZeVNH01XYwoih08ko/Yyfqwf9pgRj7SUiPu0OTXIZY1dMQK4tZ1T4NHQCLLh9I5pFo3pjrGNFwKj+3D9yhcb2bPY/v0NdbhZVt29g0Sam3c8ccUULUa9jGXtgC26Bbsw/95p8PW3Gmzbw8GAC/byMmeSqz3RNFdV1bSyJ+cjCnqacuHaOb7YtYvHOM3y9+wCJqbvpP7AfR9ccpe+wsaiLDaiqKie+PIMbf97jfXoMwf0HkNFUga2dBaaWmrToOxPx+Afmj1uESKaBvpsvpQ0ddBsezh+PfqC5Bl7XxTA6YCRbz6+jXU9G0tNrmAb6Ehd1itdvn2LgbERHbSNmUjV8AjxY+8dtQnqHYz9tA56ubgxZd4rEzvcMGDXcdMEiAAAgAElEQVSYaYN68+n5Q5oq8sh59wJ/T0+iEz8xZHIPLOw02bvrIOUNTuy//hpjDS3K04t4+CCD2MYiBgSsIfe9khpVIwXZeZQlN2IgsWXHrxMYENCfgrw3iMQaKDK6WP3D15hKtflhzRoma4YxJXwH/Vxd8R++kPc556n7ZMDthhvEpaaj+KBNjVjO3LFTCA3tybuM+1yP3Y+utylpxW8xN9Uk6/VDVo5zwHekPb8cf4jSXgyWKgyNjXAd0Z+h/edQkvMYV48AOuVteHkO5cDxFXwuf4WGjh15eRVkXzlFYmMh9XmdzB+3krCBq2lR1yA41J71y7aiUuTx+Mw7DE190SjWQNqszcOIE/QoM0CmUtFZnYeZtQlmFtMY7z+OuTOWkZP+lLLCckLm96Fv+ECu3r5H2NBghIAe6AnehM3wRlYsw0BPA2t5KH29xjJ9rgcAz6+WUpICA8d6AlBDOte5hggHqihjLaNZwQSSeUIujWjhSScKDDFG9d+UO/9/7W8hAq3tHYgROLDvKm/iK7G11kGZ10JldTWL5oTh5dKH9jIZc2ZPIj7uBZ2ydgqKBXz8DOiq0cPY2QAdqQxRVj4OEg9KsqqwsbEhL7UMpQZcvxhBzONCXOwC0ZNo0y+sLxd3nMcQE5qVTdh6uPDq9yTqO2RUKlu4cewaphoWqKl0qH6eRLcwHwTrFkZs3EZXZSN5egriiuo4/+IJF69EsPH4EWZ/uRgNPXvWzLTnVX4eL86eI6G0HblKho+DCifrAARTGe/iIxk6aQh+g8wordPh6Z1I1m3+jk/xiaSmZdMlMiGrsAwriRltUhltyjY+5SWhbOig98iRCJ0FqPKaCe41HSuDQJJkLfh7DKA6Lg5zQRelshnLnGYOX/8FUVEzFWXVWGoYoiuroq2mCjVjB7q0yzm2bzVhY4fR1KDFh3dviH3/gdsX9pKV+5q8s8c5vmwfuv6FuAyxxVWrE5GemPYKQ/oHDKSlqxLjyk6GhE1Er6ieYweWMnlgKK1dcravXUXJpwZMzIyor/lA7+Fh6KoZodAoQxVVR/6pRAqyUhk33o02DVd6Bdvx+uEfNCqa+OrgAS6+SmTyut1s2ryRZ4fvsnz6FIR2dbR0zJjecxI3jxzmyp83aOtop7i6ggEeOnh69KMus5Co909xdetFcXY5MhmomYro3tuDutoCEhNzsTAxpZtuN5asO86bT8k0yNupryzk2KVD3Hx7lE3f38BCYkhhWQYq7S5WfX+EQ3v34efnR3ZRJrZ2OpTkpPLN4u9JL/qEsb0Fenp6vP30ii4tPXztvSisraT3xAVUdEgpin6LjWcfDv6ykNR3NWgb6lOUI+PUnV9Iy4rCwFEfa309rhyZirkim+L4AqKfJyBV1mCkr0ubVMbJg7fQaNX5F17ePvyPcwvG8hV11PGMe9Sjxhr+YBXrscMPPTqwQ4962mikDBGG/x16wN8ki3Dj5nXbhnd34lzUDTo05Og6tHNsySE2Pb5ITlYGDW11PHpzC5kkn7FjBpGc3kGHrBF5PQzoG8rTow9w8XHg14uP8e+uRS8PM8Yv30VFYTVIakmPTEZa38TY+bORF9SSnvuKDRtOc+DmOkxsDUh5msmuHwdioeVDlV45Zibe5BSnE/2wlALtMiIevmHy8P7sWL8NLTVtmk6/QamoIP7cC8qFN1g41ZJXFk2nlQc5OBGdmca8rdvw0VfjYO4HWpTquBsYEV1RxY9zviS/RkptRzIWhnr8uu01kRl7mD98FSKJiBADY9bO20mGNJ+Ip7dYPHkLKaUv0OiQ0kdPwMs2AJ9Ri/DxMsHJ3puNY7di4l5JQNgSyitfE9ZrBqahA5gxdByZUS/x6ONCk5GC2MxKREXNZOXeZKxRLwrFGrSnpGDla02QRgUXX0ST//gqSzedZOPRq2yYmUZ4nxu8LL9Fu2UF4eNGoyFUYW6vg7xGQmx5Gc6GmmRL2yjLSODouS10n/wlh27+yNjQ7rRraiGrSuD8qct8f2EvPV08+GX/TxSUR+Gs1CS4rw1vjh1FYWlHbvY7altE7PlhPe/eZ5L04iyfCuKYOm881ept9Bg6keyUOFJS0nAMNSXt7TvSG9KQ1oswG2JKR6klsyYuYMmmbYQG9aZJloHYWItchRGBTr2xttekseQ9+m6ahHp/jVdfR5583opmiznX9idwOe44zy++JDMzCZnQyIxBXxL9oYSHqQeZ+OWXVJg18+WkweSX1aAm0+felQvcuBqNhpYJL99EY+Oog76WIYG+wZzZe505P+1n+rDemLtrYK005P7z9/TqZol9gBOlxXl8Li5EVtaKjkKN8sJG/PsZcfhWJBq+Jiz7chF9/X158/Eet6+mAzBpRjUX2Ux3oikjiQt448FObuFDASkc5w7bUNHIbG7wCF8GYIQhWhjSwUVqkTCUcdzZ/uLvm0WoqyvG1siAtioRXYKUyrhGUmUVhLsq0NJ2QVHfia6uMWom1pS3t9HYUIJcKkMQBEaPDWPTjr1kpmXSra8+DY3N3EiK4e2Dh8RFPMbKuhuhI/tg7WDCu7dnScnJQWFsREZaOd62w8kprcTeyYfI2AquxkQjKhfz4ulzRo734M/c25RHPEZX3MLpM++oyVPDUceCpW8P0p5Xj62GgsBgLyJuvUbXSQs9S0/evk9DbCRm0NSV9Jgcjq5YDSu5B53dPahRNWPpF8TJq79R36RAXVfC/fdXMdY1JjI9CbGVFS6BYymoyycpMwU7m15EN1xD3KaLi1kvvlhzG/8R33H40Cbi3uUiSLXw8DUhfHAogokpTYoSCupSqchIIycxllDf4dRVWBIZ9QynAFvytQtQyY2ptvfhWUQyuZJ2FCop9V0mmBlY0SjSpbO5hQOL52Fu7YGDkw6nNkTjZNGNZ8lPaW/spDD7M1mFlUgs8ymqkjJt0gys7PyQiaFLpsBCSyCnUsqda/GkF+cTGDiGfkoBV6u+RL27j42mkiZdMTbaGfQYPZvV6w8weNJsktPfYNOuhbEEHn94xpSfvsEjwJuJQ78i/817vtowm4RIBbHnIggJGkRHXRvLf1xOWa0KIzsTLtx7zvLJs2lpq0RDW46zRgh3jj+jsjWfplpLShpKqC1vRFNsQpNCC31tA1pzS8muT8VGooGf/QDGjBiBuqYuzr59SH79lv7Dw9m99BDpV6IJmdAfS3NjNi/fw+I1P7Frx3SSk19w78FdPn/MQiRuJjdNybjhM6loqWH6RD8qyrOYMvkXPrwuIDYpGSNLA4o7SyksTEBioMPHuCiM+zhz+OApBDMPDPTtKKqoICf9FnL5vxYQtcWHvSyikCesZDX5RKNExSZ+Y/pPvQDQxhwvPLnJWbowREoiYWynEw2q/pGQ9N/Z3yIS2P7Lj9vcezrg7WOPsZEZprq1XD73J1dOpVDWmYudmx2fs3PpKlfRzaMbn2JzMTc0Q6Qpp0//wehYe3Pr2h9M6R/GpIkrMaxPxGn4ZOxtPfjt9Fkcw3qwZsGP6OoaYGutRU/fUFpr4onNyKC1OIuhYf2JePEMTTcJyR/LcHC2pVMGkqY0XqamEOrWl+1rx2NloWTj0ShW9g1H21yM3iA1HCzsePe+HANNR+xsPYh8Ec2KxT+wp6c7D1JK6VBvp5t6OxKvQOozSki/k4SesRplCa34OZug6+/CoeWnyMl5xO6N+1AKUCdPY5zfKEbMXMXRHaup7FBQWydHSIulU16Jv5YV+7etwcKzG6PmenDj9BM+fH7GliknyY6JxsRFH3+PQUhs9bny+QSnf7jHhfMH6JR1klJZhae0mqX7r/DhyQdyC5IJdNRB5O1MdWshk8atRdfChPljt7Lo+8O46jUzLXwpfv7+nLh4gqScZJI/x3Ps5z/pSHvEifMP2T9jBtk4UFmVSO/Onrh4mGGqKESi5UN+bixWGjoUC2l8s/0sD+6+Rt3WEQP1UJyDxZR3tqBj58qQ4IlM/moCNoMc0K2uI7z3Em6e+507Hy/wxajvidyzg3F7viCvIg8jGxO8XB2YOWQNQmULbeqFfHhzDjtHbwydDbh/4Sk/77nO64yzlMRn8rlBjrmZEjcfB9SN7KhsaaWpJoqlcy9w7PpBLC0M2b7uPJoGGWSVVzG893wiqw6RW/+O6cOm0dXVRvrHHAx03FDvrCLhdSKtunW0VdUydeZ4FqwcSVF2A+XN2axZf4jB83oxe7gElZkuzu69+WrLGqKOPEdlJ8e21ZQVX6yhMa+L7QfP42PbhGanG07tVcRVvCbEtwerL91k2aJ13I4yIizgPzIMXbjNM3yZzyEAbuCFDQV4kcLUQf8x6XcdLZJ4wHZWU0QXjznHexIxxpg5fMmR7Wf+vpGASAGx76OpbM/HqaudaWHrWbJuDsmlMdgZm9K/3xC6WVjR1zuY5LgX9PAdhqGLhIGOnty89IZ2Gjm+8zpyM4Gjp79l6+7P9Df0IfXDJ+aPnIxaZSpH73+DrEON/sHDKGipJ6WkCG15DvpW7jQbaoBEh49/prJi0xQ09LJpLkrh1IkYHv5ZhNg2AEWGish8bUSCwPhJI+gQ69OzvzPZShU+ASb8vPMsNeXFKLWlfL/7Oz4ZGxDbXEVrbhNBwTM59+13qLWWIzcrw8lInW827KNQUs3RVZuw6aFiQP9wSgqUWOrr8upFDG1iBbsP70BQF1OVVIunXTV9hk9i8dZt9Fu5hFq5BIVOBM7NzozsZoGplQQTvzCknfDoxG3qpAWcjbyLu4EZ5++epiSlA5saBRF3IhjyzT42zJtMZVUWzn5OFJfkoKuUIxPXgkrE2m8nYuTlTllLJroe3Zh1diYvHkYS6O7E/V9L6O5lRnVeDA5DVrB6006eyQzRqk9Fy9iLxb+tJ/r1bX5/l8umE79i7tWDwT+uQlPbkYTmTzgHqPPrLw+w9QlBX7cXV2+u5/HXa3i67Th9gwYT8zKdYkGH8LAAlm3dTXN8Jy6mYhbtusyfv1xCp8GAelkLiQkpmFmm0CBRUdpUSJeRAXOG/cCqr7bSZ8wgKpWZ2Dh7Inbwp7uDAk11Mca6BuSnFVJa+RRBpIWgbYiQ8ZEGWRtWpvZYqI2nqquCnbeXcHBuBF8N+p1GExtC+k1kxOSF7Nu1AvtwLUx8takrbaWmQo/dSweycsHP2Lob8zIilpKmWp7suMqcdc9RUxOob1Nw9d4ugjb3opu3K82SeoZMX82jxLf8uKoHK3b+hIGnOQu2R9Kk10lE0kt6OeiSk5fIT/PvAFBLHfWUsZOL/2SmO8FM4DIAIYuMANBBnT50x4TeSOiglS5KKaaFNt6R8G/5+1uIgIZIE2cbC5StcnStuvHj8ZPkp6Zg7ehNUkocqZlZGBgYsHLTcsI8+uHhZs/r1/lMnDgFY4kRN+7tpqy6jNLCAnx8rHmfF8fb9gx6jwpkxJLFZH1Mp72qjqg3z2jQFti7ZxOlxUY0yVoIkKmQlVbjbGSL00B97j++hdCmw3czZzLE0JKrkftx7e9Lo4s7r+69pFOkxrwp80guzKRdpkf6pzh8vKYyfeIM/th2BVVRI92NNLFWa8HZ0ZZeo0agVMaio2tBVEYZ2fUdfL/hGv493NDXtcZnqAU6unqgFJi6YjodugrChi3g07uPqKoTcXM3x0CkQmj0x7ZnIKv8+tNQLsMuxBkbLy8K1WQUNTSh3tDClfjT+Nk7MnzuMvTa20l4/4I2aRu2esbI1BpY9tNCct+/Jz76IQZyFSbaErT1REg1TZFqGFFT2wqaMNDEjwWrvuFzTiEaDkboiVTMXj6CmhoZaNQycth8Opv0SIo+yJBenrg42REweAT7ds5g2WAPsuMrMWiDZbtXED7KiQBrf8wcRbw8cZeWGmNESjFmjtbU55Yiau1C0i+MlUf3Ym9vSdKf77H1ccJK35TX8fEklKUSGDoJMxN7wj16EtQ3kKysCrr59uP4sUg+f46lUdqGlm47pe3p9BluztU/blGV14RIpwHt7CTePc1E31wTpYYZfV1dUVfTQEELMq0K+rta0F4pRVNTSaW4Cj01dcqTa+kQKfiQGU9VegaPn//G64fvmb38K5RKJQ2dhTh42CFoK0HfA+16Gb62vViyZD6PEp9T01bL+g3jUAltiDp1eX/nBWoqCTk5NQzpM5YrZzayZPIcRg6eQVV7Ma46ljx+vpzqFGgpl2NsY8DueXf+yYecNnKppI5SWpECkMJnDrMEgA9nmtjCFFLJxgUnyqlBQIk2hsioQAJoYfpv+ft7/A78uG2brgO4+waC2AR7L1OsbUwJ7z6fVpkmyVGXqRG3UlKWSWrpZ3x9vMh/WcPrhjf0dR5BTs5zwsJnMHrMF0g1Nbgfe4DiqkhKSisRKSqol5tjbG9PfWsqowZOo65GzFfz1oBeIE1CI+l5SYwfMpfHx54Q8/wjEj0jrBzCuX/pPn9Gv+TSnsu8uH2c364/Rk1Zy97FG9l+5xT1xQmUF9VS966eRXvHsWjRYn47ehelloBhz2B0X2Zxec8pXHVseJlSxqaRQViYhGLUVUGBqozXv11j65ZD2LV4cD33GcO9tHmf+AkDgw5Mai0ZOn44uW/bGdBNm1pDKVH3fmfhqTMkZSfhbdVFWnUyJbUKtKyC2fHNJY6v6I3hkO8I7tmLkeFTkVtYIdZuR2SnTZD/cF5GxHDs4R1yUuMY7uADfi1YGiuozZGxaMomIu+8pKztI2JHa/wCB7JszkK+nfU1NmVKTNX1icp4x7uSdHTr8pCJ6zn7ZymtRio67hzi8/3bHD6Xhpm0EbOhX5FR+ZI9C49x4O4OnNXc+PXWJX7fdRmxuZjIS2dpVe+kMLEAC8dJpEhPEjrgS5788jv3H8/AxNiD5yn30RbbUtleyI1zC9Fx0WPK6t0kPkrE2MCUB1cu4z9yOEuGT6VFKcNY24/fDuxlcL8vqGrNxkyjiYrGBlaPX8Kfz+KYNEGH9uYO7kVeY97kH0hPe8QIl14MHPo9N99fIr+0A1VzG2Xt2Qy18uLPqLdI26tpq2hg9caFqNTqKW9Mp/BNOlqWZqyfM5+ts9ax+8Rhxvd2xj5kKM8j79HTVZc12xYwaMAYZEILdoYBqFub8jHjIT8tPUh89GOaKmvIUpVj6RJIfWEaLRILst6+xd3PDbFBE/MGHycy3pY/IxTsXiUnO/oUX0/8lVhG85hTSFnPTFbR+y9f9zAyCCMdZ97iyEVUbCeRV8gpQ0ElLWgRuz3h7/s7IJFo0T0oBDV1bZpk1XR2qCHSF7gTcQpVmwILJwmBXU6g1klTNbSLTQkZ2wsjXR0eXD2HupEWufWfUColFH9Ooyujlfy3AmV5lSR8jKaxKhMzfS3Uu+rYvmcXlaV1zFk1mSd3j/I8IQ11hYp69Ro0nVVklatjpC3Q066ZjXMDGKgrIbk8EQt/CxryIlCod7Dm+l2injzDt89oakpbyc6p4ciu27TrG3Lh8R+oqjp5fT+WRb/eZPqkAN7ntlHUUkjv75cTs+8w0Q+eI2sv49sV28lO+Uxdcg6929VxM+xOl1RFVkIW4WNnoCgvpHd4IMNW/YCovg0tXS86WwRcvXyIfvyYtqI2pB2N3H96mMTs8yTl5XP25T6O7j6IhY013X1tKc7MoK1WhrSjC3d/HwrSqrBwcOWL4z9iJDbGWHDjY1UT6tZ6mBg7UV5Sz7CBo7lz8CR/XLiKYNJGjxlzUIh0aJI2YqnooqeHB3Efclk5KYTCxDxOR3zAxKMbP++ZzneXL7Fu2dfUl7di2s0CvfYOxn05CVGbmFMLF3B8+QY09f2Rd2YSNLA7T7dvRqNZm5q2FiLSbmPkYM2rtETMzdywEptTX9dIrVyOOobktD7DwFQPE2sNeg4KJDUtmey8XIpy0zES2zBybDAtjVm0lCsZGTKVrGIZul5euFjY0Yw6RdUyHLQGIFXXQizSJKIggQXHvkVkasjLR9cJGzaJqpY6Ai3sufbbPURyGdK32Zw7/pj8skIEdNAxMyHtQTZSY1+yOqowlnQDE3u0MKTfgJkodE0Z6x3CrYhfsLY0pk1Nncp2BRkPy7jz8RKKFgPK6yqRqAROn/oOEy1Hmquqkau02fDlNjqUanxKjSHm7SU+vq3DdagdRSIJAOEMZT2HEP2lruEHjv23TPnTiyZykaKBHnZ84NG/5e9vIQKCFry+E8Pj58/JyYhk/Zh11CU2cOL0JVKqnuLXaz5VzWI66gwJDZuCn093fHp6UBjRwJ6fhiBrEDi0ditXY9fSVKXFwulLsTWow9xIjfyMIkS2apRkpDNtQTzu9o4YWeUwebg5oc66dFYW4ujqT1FaBAYqO37fOIHbCZEcvX+KdEU5dn38eHf3KPu+34ZYR0Zmaz0zvxtKUvUt1LuMGL9oFWll2Ty99hxzpRkKoYX32ekM1DXDx84AnRBvXLsZ4dpHh+T3afTZNo2Qr0fQWZzFz0eekZz8ktEr1/P1xt8ZOWU6fX38sHZ3Rs1KFxf3KfQZOh5JUS2e0waycsU4YhJjyUwvZe/PN+jpP5oPH14gyJooMs6hus2NwNbuDJren3MvD2Fq6Ii5iQvyrib6B4dx8cJt9v64Fa+e/hy4vAM9iTnjZx4kOMyA97EnOXhwNwd33kawtmLDsfMsWzaYL9csJOb6cdz87Gmr0aXPoH6495mNo7cxui7OhISO4NcXz0nJKKKkVYGVsxmLVs0h8nU8B7d/jWO/QLI/xyKXapCgl8qVd59p1ZNj0mRCRe0jVtzbysLevZGKIXjhRKZuPUJ2USbhw8OwcBCxd/lhNGTaJEV9YnnIEgZNn8Sds1doq9fEIDmBWxHPubDlLvrGxkwYvoRBg8cxcIAzlp6B2HSJWfr1NqoURayaHklNUwUjJ86hRVZB35CFWOn3oLevFxqqMrb9tB07iQV6rQbsuPaRe3vnsmbeXjzH96CpsgAdLSOcdM1paKlixCIf9uyZzZmbdxk74QvsgodjYmyBvmDB9Ut/oHD1JWLpQSoqW2iT1WCk1YSDnx5Pf92P0JhKdqEGWVmVmKjbkBAbQ79+PclrTcfYMozEz0Vo2qRSVtyKtbEzdImobv+Pmf3wMWYAhDHzn+yEsJwrf7l/ylz2s4hLbMGfkXjTk2PE8epfsvv/1USCIPzbh/+3zMHBTJg6eziz5v1I6DBfNh/px2jvdTS2GlDT/ITxYTsRCwKIOuk/IpTgwRJCnIeTV1ZJ0NBBlOQ9Ycv8P4j6sJcrkYnsWnae6Ken2XvvJ+wsvfh5y14UWTB3y1zmLp/NaHdfbmbVs2fRVr7bOYcflh5g/dHBeJcHs2bbAf6MO8Xx0xF8vTWM0ucioi9uJGTGYqJqIzj3YwLf75xBc2cq1VI1DJI6kOe3Yj6wD4JChwfvIhk6uReuQQ6MCB3LTztWYeVmQ8ah9wz9dhIr5x3jwcNdaFnbMHHQcq7e2YK9nj8lynxOHfidQYMGM3lObzxMZ5EVH8Evhw+z9cejvLx0jKXHTnPrzh6m+QzkQ1cdvbt5cvHOCTzkEsyMTXDpN5/8FhknDq1m5Pz5hLqF0yXXYsuVLwixW0xd9DdYDNnMD5s2M/07S9JiagnoOQRZTQy5icOYGV6Hfu95nNz7DQeP/MH5ozsZPmoeNfnxVD7M43aHHHcLd04e38Diw7NYPXMev3y9FTvfYRjYd+IcEkZM7FsiIx4T/6yacStN0ULCw19zePoxBRPDDp4e3k+XTQhrN+zk3fnZxHla0sPUHev27nS1RjJ7+i785rry+FAu+8+tobCskH4+M7j38VsGumzA176JAksxhs0+jOzXg+Bx/ViwbDy+OkG8+XCde0n5jBrRDSOjDqztwpkVsIh1vw1hRtAErqc9wFbUB2sDDZI6M1HvrMRM5sOrzIv4uU9n2eh1XLj3M69j3/L1gsE0V9uhJlHRLCsjPGgheQXJ+HXvj5thXzT11FAJnbiPc2KAk4wdW1/x+FME+75Yz+b7e1HIWqktec3oQfv44cfJxD2uY8KYAMyCupjccwtabWkkF5ciSulENcKSvPz72FjO5+z5H3iy/3fi8xqYOHHlPxnp3kMfjyAVf57p+LccfWIFARyltLMce7Et/XCiGU06KGAWP7BTtC1REIRe/7Xd3yISUApKpi1Zy+CRfvT01cfUyQ4n9yFcS46kqQn2XP2VPbcPsfbIXnr3cWSAiw9G7k54+nqy/cwhJoYvxaeXA52CEY+ePyTxUyzZrcl0VDQT/fwjRuIgjF2MOThtGrL6NvKb04j743fCx4ynVJrMvatbsNZ0wX/9Sk78vBhLl26MChrOjfN/8vjwDjb9fJG3MY+wy8uns6sG/24DMBTckZeXoa2hzYmoJ2gYGmJoK6CjL6ckLZWDK65S21BJSL9+1Da0cu3kE0b3Cefyqu+YPGMauZVZiFQdJMdFcjfyCDYmBugaGmJjbk9aczENnQ3ImpvRkyowDvLBL8QbdTWwtTBBwy2YzznvqahIRNNAhGHPWRg4uyOrLkNHXMFQMz1iUl6gpS2luqGN1qYyzDSVOA1YyO/3/6BTX0phdT2CppSoyNuEuPdnUL9mFJoj8HbWQk/ThmZ5A/UaEu49fI6FnS09Zo9GoiMlyEKD2ty3GOqa8fjAVb49fIo5q8dhbmlN1NNI4t+84tsJvclKf426mRofH3bSiRGbdi1DQ62W4b0GYJn4mEsPjhA8eRvakZeIun4Xubo6aw7vIDH1DA0vlWw4PY3OqkImjpyPkb6EQVYD8fZ1Jf19HAWlaehJ2nn7Kh59iZyGtjpqWhUoW7WpjMzE08WD8kIp2vo6KJoV5JamYWdqQ158GTmfspg85HuU8lrUNJTcfxlBh5om5dW1JCS/wclKha2uknqpB87+LmQ2F5Mc8xldpQYaIjEFJekk5Efz7O1ZsisyGNbPBTcbW1TtAh5mgcQ8uMf9q1dQaojRMDJHU1ObAXITtDQNqExvJK05Hxd3N+Y58QUAACAASURBVATjviQWF3Cv4ANl0iz0re3YtXs9Tk4SruffpaLuX8uTWThoYuRtg3+Y07/4H7Psn9cBHKWsqR578X+c4dOIDAXlhNObn9j2b/n7W4iApYkDO9ePx8ZSQNHSRfTpWMZ/4YVdZwOKhmK8JCWYyDUYNcyd/6e99wyO6koXrtdRS52Uc845ICRAAgSIHASYjEkmGgdyMLYxGJtgDAYHDBgzYAwm55xzFEgIkZRzzqnVkrql7la/P2DuO3PvcO/3znfHUDWsqlO9tc+pOque1vPUPqH3bhvkR2ahHolzC9WGBcirFQwd3AWdpoz44iyGdB/KlisTKaxIwNWqC/dO3SG6vzHvfjGIs7GX6ekVTFDQYLp2jqLeLBafPHtOZd0gvaaUu5e+o0AXhItVAFUaNf1DuvFb/EO2nN5Hk4OKNg6+CBgSn3aFiqR8XAqCmPf5Jt7/aCJSvwoiYiJZt+IbGqs1dAzrTtzuW5QmZ+GkaOQgl1GJ3Jiydh21j2SIRfXM+2YC5ZpSilvKKXuSCroqUvIucnbrGa48O4sk2BWP4b0prEolNvEUu/asxVTswMHdvxCf/pSZv8ynvCKLtbtnY+Pqi7lrBzyNHNCFdyNI7ETyk1JuPtjJzElzUDk2E6yV4mViREuVBqnWjLmjPsbLWU5uWhU+waEEW/qxYN0KugYZMmvhV9iZG/HOkO7cTbzDqQPnsNbkM+DzTyitykZbW0f4pLEs/2MGP+1dwuOW0wRFVtBjoISSfHNWbp+PV4U3sxf60qFvNzTl9czd+jmR744mYNU69qz6GZ2ZGzZN4ejrRZha6PhlwQbGzplBh1EmBGpd0QqBXHx+F5mthBmzfmTT9h/pOn46Q+wmklb7FJmPBstmU7raxmBiLWfNtyvIzikip+QREpcqqouLmLexL+smH+OL/Wuw85EjMxWhlxoRHTSY1mYzGhvqkcskhFgEkJh1hSP3TtG5px/vte9Pi4mKpow0ugeF8VB9Ef/u3Qn0deK9af3o0XU0mY/vMGrEcnoEL0VqYsakab25oVJhUgbebsHU1iZhZShj7MGzxCYfYf3pjZz6NI/nojKKaq9iXVvF2DbhKJuNUTcIuHtY427niTyphnMPltM5OhCAvoPtMQ1zIdzeAV1tzd/lziB+4Wdmsmn9i98TuFi8eArwDV9jgzliLDHGmMEMf2X+vRFFIK84Bw2tONi74uzbCf+OY+g6ogsP7t2kpbkOy4hBmDlpkdUbcuLmYdb+vI3MtFyayxswMTLHt1sIY4ZFo212I6vwFjUFFaTnFSExKeLczUS8IkIwLGzis5+2kleWQ+93YnCOamZ2n1mEdB9NW1EEyuQ46lQqeo3sSaNKimOgC4WqZpYtjcE724Ah7h1wCx+MHlNcnQMY2TmQGm0hJYbxiLR2KJvryIy9g6+/P3UOhpjVp1NWUUtb5858+eUe1CU5NCvLkKoKuX58Jynp6Yidpdg4+WBpY0xOfiIje4xBqTBHatJK055z5CSVIBNn8tNXMeQ/F2NrasCN7BvcKLxMdWMZZiojGtXNfDjqfa7G3ef6gXk0K59R+TCNmO4z+OHYB6TdOcWKDQsJdgmmqG0PViz7EY2BBmutJbvOH+Sdth9x5WwaHdsNpM3grtiZglbuyrH1u3nyJJ/Vm78lvbEC/87D+HLZIDR553iUl42NUwSTZ85nQFQ/jBvbMsZ3O8ZNfTAWDSU8eijZJRl89sVBvO08WOjhy7N76ahPK/h61x9s2DeZUYuHMXN+ez5cd42fevqhyMhg+tT5ONl4Y2LgTYurJcHRVhRcPY5VYyF/PL7Kd1+uoCCvkpAO3cmMK6SispLDZ8/hFRJC2vMsNvyxg/33F3H0qweYGAWxcMhqovsPJcTbi6zb6UyO/oqQnm14lnqJkZHzUKjr6RzuR3fvbnSLHIGnjyOjOvXATBZEJvfYvWMzJq3m+HR2YPfBPUzoN4hlq+YysdcgRs30JMBOzrbfZtOh9xCUzQpswgw4d3IeSzZ8xsFPvmXJ6JsU0kRHJy8Kk5Sc3f4X6mtr2LByAK6ezkgcrUi1K6b89h2MrQORyiXUqiUcuZAKBg3EDBzB5q1bEMmCmdcuimaNC91n9+ar3eP+Ln8ufniXOZ/+/TqI6TzgNukUU8SPXOIsJ16Zf29EEbC2tMbcWMeM5Z9i4inl7Po9FD5qRaUqp0qw5saO/WhlMn7bfIiEu+W4msvZu3YThhZa8uvyCZEGUq1u4uEfS+jYtzPLF/1EqFVbJA1iWjXZ1GRWMWLKZLYeWUfPIe9x4eBZntwsROMiR+Jggp2tjPWrbmOoNcTMvJZjh36noiqfCb3bMCxiKH49fRk2tC/vf/wtn60ZRbW2nOhJS9m+/SKlilwi59TRkpuEkdqfsA6DsCxrwXOcM4u+W4krzRxMT8QFT87lP+Bo5lNcpwzBwyAYsaqViucK0p7lUoQp7gHGRHbpThMydt1JprQ8CRulCJmlnB3XYvF1CedJwmUmTlyCt7GORtwwULjxMOMUES4d6D55G9/u3s3UBXPpEuPMEDc/XEOi8e8bRcHTKxzev4iru9fTr98AYvz6goGaDr2jaVCUEHvuCMXZeaSnZfPzntu0ChZ0HDQYZx9PioqzsbStJz7TAb2qiZ3X4ygpuU/v9u0Ji/ZFbl1NfXkFXYM74Ke1YsyXE3knIIrlv45k7Lu7ePfXH9m86zfuP33OhYM/YdBozGej1hF7rZAyZR5dJv9CXc5F+r73MVIrA5zMXCi4fZbYB8fpP3QCI5fMpObSSRIVqQzsP42fft1CrxExLJu3lvEfjyPt2QOMxErS719GKOzC7hubOfvHFX7c9yGmpi5oFeWMGzWa/dc/J+HJMzLLlSTkHMamxZJGrYomnY7oLr04dusWzeXlXMsr4sKNS/TpPZr42/ks+WETn41bQ0SgIZ0ie+EZ7sePM47h16Et21deRy0pQ9Bm088vhvaD+9MpeCIfDRjO+lNfY6eTcfDILtZtXEl2bTPHb+5mfM+FPKm9xJzxX+DvPgK1IKG+rJgJYybT22co46dNw9UpmhLZcdSaZ8Q+TsbCwpDLD07RXFmGjyz6P3Jn4i92LOgz97/klO/LHwz99WXhgtr/MrnXf/BGvCew+LPPls/8Zhp7vz7Cl993wjnSi5gBU5Cb26ArSaCrvxid3In60mc4h1rj6xKMhU8rcoUxE6Z1oKrBhvzLyTT7izCRWxLlMQClqoRuE0dz/Mh2YgaNxUFwJLUhl1M39hDp3RN5bTFFJqU4SG34edZKjtw7jbG3KR26TOb8kR+4dvY4f/lkD2V5Zfi824frd+uJ6WaG2wAvijJ0BIU50fO9EbyjE0iqlREV2Rfndmb0H9CVnFQtpWWp2CquUB/Qj8OHVtApcigLhy/i2Ik9FDfmMmfyCsRx2Ri0M0Zk0MjzJwpSivMIDw+jQVlM/8GdSbx3myFTP8DENozbd/cQEzWE8NJE7pekUVxizrAhk3mefAQjIytczdyxcwvC36cnm9dOYsmS1RSUZPDOxwtY++UC+g+egam5iPtxd1FSgH1TPb42YZiIbfHt2IUZ36xkyPhB3L11gzO/b+TevTjOnDqJj68FRloFkQ4BBLRxJ2rupwS42xDh05VAt45klupZMH4b9j5hlCsKmDdvEQ9/X0lC2VXesYpBX9eIhmp2XpiPs0pD2rFY1L4OXN7zFbM+f5+zpw6iszUj9ENfpo5cSkebYL5fsIrCNgok1TrSy5MxbDTGMbg7l0+swtHfkQtHthEY1J7y4ie8/+EQOnSeja+nP6XG5WTr0ih+nszg8M+oca4lK+MhfaPepVPEJHad+w4ncSASy2JMLdqiKH2CoWUNlzbW4R0TjEbuxqIpe7h0fx41lVrc1HbMWDCV9PQ0Tl/bx/i5XzC153ia9Sq+2jsDSyc3LJrllFRVsiP+J+wc7egcOYcJQ7oR/c4sdt1YTlTY+yzbNpD3hsWQEfuAjcdPYhbcgEFLMZfKFZw/8jMhg4aytE8PHpam0L/dTHadWkidth4LeVuqlHFE9nDjixUn6fluOF19x3D53q9sWaskz6gdcmMLyi1ukPDIl6iQ/7u2QHeS/y7H8g1msPWbTW/uewKGUjE5hen4xHiQW1xB2vNabt6Iw1piip2zHZ/sPEOVthmpbwDm2irOnjuFiZEOIysTSgrVVNY1IPW2xtpairXUDHuHdjzJTKGiQkV7twCMTZ1xcjam6mkB/o7hmNjZEmbcluRTBzh7eAe5UjnuAa4EWDvwOP06Y8bORVVSw9Zt39BiY03P7h/i1/Qcd2M/VGqwsJOz5/wfNEtrsYiM5tTO23TvOYTz5y8gtTZHrWomqpM7hA5i55YVDPX0xlBmTtydw7SNbIOyII3Dj/cy4JMVZBfnorUwpLS2lKzkGs5fPk6too5GWzFaS0jMfYK5soGKhIc0axVUyprxMPSnnXMPzMWeaBTGZKUV4xMYQlldHbW1tfhKgjCzc6B7n+ncTXjE/ElrCfIIp6y4HHvXSBRNCgIHT8DeyIUHpc8wsHXDzlzG2mWrULQ0omotZ8TwReTkKwhwDENs4Ixdu1AKijNwc7WkRaOntNmaqO79eZ6dQ59JPVn9wwg+mP8ha9ZOws7GFH97F5qMDfHsEk18USu2Nj44ejszbGxPfIs1fDZhLbqyMiZMGMnC6ZtJSb3PtnPjSarOZOanMyk5XseKZesJcgqh58AueNgaY+XsTklDKVq9JfmZGbw/sR/7DlxDqddwP+MBLpZduLb9FM7uzmgdMuhk0YnqJh3oxejUhQS7RFGXW421nTetDU00VekxaIbRo8cSl36b5NiD6HV6mqRqGhv1FFJOkTaPipYWrCwl3Ig/Aq3VJGVexi9oGMraChoaYObShRi0GFDc2szlS4fo0G4ofedE4GjuTY2qlh49/Cioa6TDiN4smN0fP5cg5JiSmX4TvZEWs4YqniRXkfD0Kp6OzmRn1tDQWsfyhT8S2m4s7ez9mDJ1KJoma8K8IwjyimZcv57UNiqxsQrFsEnEJ2PuvzK/vv51EWKJ5pX734iRwO69fywf0mc06YVJNBVaYufqwp0Ll0h+notTiJhHJ1MJi3KkMreRlLgG7hw9x/drtpKV/IgAYwWVEgUBvoMp1ubi2bYrP6xeiciwipqGMjwbbChMOMtTXSOjR07HVWzFyYdH6T7iXbo5d6HUuAZDQzV6uROqVjnnzx4goK0bVW56yhS1fLPmHFdOTaRw9yNSg6wxknjS0lhNWXYZGNQTG5eJl62IApWCD6avxs+5C4YuYh49jae0pJjIjn0JCo3m6PylTPtuD5tXrMYjwJgqlZbKsgpamx5jaGtMgH0wT67ksP/UaaoqKjm36w+0NnJkLXIO7D7B98s2I1IpWXPqNPMXH+KxWo2BtJLK/KNsW3KYjfs+x8fOn5baODr1HIJWJyW1IhZjlQUXflrM7bp9tPPuwf5z27DUWtBaW0f48Onsv7ielmItASFWvNe9HT9vv8XKxYvov3Agvs4mTBk3ngu3bvLh2M18tn4S5hJjXNu4ECAWEyUxRKNvZMXMRWhcihlm2Q1fxybOna8h2NQdJKa4tfXnw2VRzBn+MWGRY7F18+ZCQiVrfv8VW9NQ4tKO49m+F8f3bibU1YuawnpGDvuS9iODyEvNRWYaQkrZfZ4mJOFqYYXG1Zagtm0IcXKmZ1R7sjL1+AbaUXHrd07n7OOTj5bhYGvPmh0/0jsmmoq0AsICwsnNLeFy5gXWL1hFrbKFJ4VXsPVtw5Xzp9j6ww2+3/EplgbZpIsKyUvOwUYuI6zdUHYcXo5IJWLp3O/wcgvgYdY5zt+JxzBeSYtzC+eeX8TdTYevewhVt/O4sHw/AaYZWEQFkVP0lIjArsQ1JGFrqcPNzJYu/uPIyLqLpYMRRnZaRvabQVmuCu9QR75fc4yBnXqgtXCnrPQuN29WYydrZdyQPmyPfYBIKCGvWcOt7Mc8ii/AJ0xg1qALbDk1h9yUKXTr8OS/5NaEnz7GWiPmeuVvJBzMe3NHAgZoCW1ji22LiAplAe5qV35cup0WgzTKC6r4av0GNCoJrra2SI2bef/rmXQMDubjBRtxsQ9BlGdAobiIsoY8zPWWSFtEWDe7k/kojboWGYZBERiZG5KTfJHfT++gi3co127fpKZGTWOZlp4jluLkYMPli/vp0a8NDx8ms6hnP1xcWvjh4PtUKyuZvmcdt++doG9wKI0WEmpq1ES6+6HOkiG1MMfEQUZaZgbLNnzIuWOb2Pv1ZXqHz0ArruP2+VqGTJzJ6u9n4mTijEQbQs6lMzx4vBepTTtuXXzKnPk/0yPEGxMTO1SlWuZNnE51eS7VynRmLJ5HeP9JZEqNMUwo59yufsiLY0lXp1OrEpNadJMjF+9g4daJ0mZ7WpztOPn4Cou/XUKTrpASkTcOLoGcv3sYbaWWLoP7Ep9TxuZjnzBmyGwadJVU1lWx4sA5rEwM+Or7+ZiLFJiINOzetYklH2zhVuox6gxEtMhV+D+rpVbZxJHUQrQSqGhywN/GjudY4xI0iWV//IIQMRAXE0Akw14XSqDEgV1H7jFq7sdMmT6Rg1+vZtCCaEqrWvlxyyKWTVzFiSdJ2Dh1oN5ATZT7AHaeOIalVGBI5wVMHDMTka0M9Z1H1MrqOB97mQmTl+Ps3479x5fRcfAMFr67jMyMJCqbK7C1siElt4D4uJM8yNmKk0MIpi2Qo2jlYPxZ0tOekPrgMPbtQxCJhBePGpt11CsuYdYoYt3Ev3D08FGkElP0JiKe1sdT11RAfFImdfocNh6KxdzMksqcWioUeVRXF7Nw4e9M3DwBv1HT8a51JsDOm+sP1pNy4CqNChmZjfn8vvNj8msfklNQTfKjIi7e38C0sTMoeJ7Kl0sn8uW+mTx+epmSahX+oWrsfdzZfvEXfM3lWNlXcmHzL4xpVWPvLKAW5Oy98zmKKilffLwLgGVbXjwFyKleA8C+BVtZvvB7Mh7c+G/y7w1A16rlyvXd9OkzEc+IMPKFLD7dsACxrQUOvh04+McNbl+6SqNG4NyZbMris8nRa5izdDaxlRJqsqow1kBMx4WUlVcR5SlQXV/Ls/RKvDqFoXjynIiSJvq6TsTd3xGNoZwSUzXqNhEYGhhxL/ZH3ALM6TqiI0WllezduZdvDhwjtagUVfYxvG10pGuKcDKxxshOhIFKg1LaxB8n73DmznYmzf0FL7e21FU8Ye6I4cycuo5Jn0znl1N7aDFqotu8AZh3b48+SYFHn1B8zaxRS+RYNMipLBLRpVMEwz7wZ+nKBTSkJjOm7wjqDaXo620oK0xGbGbJ9p3fUl3WTO9JU/Gz9qbGTMmGz5djZxtJRWY1lnZy4p5dYf2yuRgaeuEQ7oKLXyANd47SZKTm0bkUNM31DOkxisenT2Hq64ajzJHzD45Q01KNvNELnYPAwgVTySoXeJ4lwV0qMH3aauKuHCK/Lp0aXSvK2gpm//qcRwk3aO/hiazVlGenV9BYacuBA4tpArp0M8Gvrpzix+dobMhj7fjeWARH0y/QE3FdK8tX/sRNi2q2rzvP4Q2n+bBbB6rq5JQklHJ28xZs7dwYMfgdfBpcKdbkkZJ3nHtl10muiGfqqJUU7z9BF88QQoZ5EWnrTFlyKScvfcOab79g7+1tlGJCSwWoFOnM/HAFZ4/GY25tg0aj4fz53/GzcKJEW4apoxsexta0AJ6+gUhkUvIKTShurqNSLyMwMIB33Lpgb2VG/IPjJOQWotUZ4WUbwcJfOpGZWUqjsgJBLiIjvYGYqYH8uv08+87upMrXiAbXRpwtYuju35V7Z05iViOiIq0OsaGIQoN6AtytcPD042bsAWrNmrn36B7DJ4/BRCyjzqgVb2MznMwUNNW1Y9rkjYTaLUOnUxHXYMSM+Z/T0KRGqC5kRv9P/iOXVs188RTAy/oLaH2xbNnGeyswMZO8Mv/eiCIg6FqZMvELjl3eT0FiOk3Ojrh19KGqQIGp2Auz3FwGdxqAs4mAY6sxRS1aCjPT6ODriaI+j2Ex7+Bqak92yQ3aRoVjZu9JnaiONuFWePn6MaC9I22C2qN1sMHX0we9uRcFpdf4bvMizJ4raC6uI7U4E3upKRLBBEu5jJz0UhrqRdiZdeDSo0zmb1mN0CzgKLfCS+aJp7MFLt4yjC1COHlwN8/zrpCfkI25WMSXi+fiIyugk3kFtko1WzcvI1fdglev3hSmPqXZtpX8wkxys6rx9fXCrNkeW601BQ0F2Di7czv+EXWqEnq0DSXA3JWNSxZTqspDMFXyXF3Fs5QHNFSloCuBCoN6bj2+RH6pGFOpIcumz8dA20hrfQaRwd0ZNH46AQG+mJhZkVlRy/O6TFZ8cY3qxGwcfT25ciSexgolpU15hPs40vO9QWCqxVBXx9TpSygsLmLgoJHk5x3CXmKBkwzu5vzIiJEDSEzKwKReSe+5M9DK2yB6Wsv2o1voaW9Ls6GSkIj+fDprMb5DlpOefh+NtYY+vSdhWJZKwo3TDJvUl5TUQmKGzkQm1dHJswcVRWKq64rY+d2vrNl9nOSjp2h2MMTeUoKhvBWpgy0fTv0ON/e2xF2JJaBtR8StngR7RBNt1wEfKw+MRU14mklpaqpgSJd59O87mk2nZtFsCh2jYpBbSbCUm1BVX01rgzE69GjkZrRqjShIeo5dq5j9sTvxDQmlXmpMna6JBqUabWsBgkhAr9fT1rUPYUFBOJhaY9yqxScoFDe5CZPHTyakYwxXE48iKWnCSu7O4g/Wo1CIuX0jg3VrtuFgG4BlQTGPH9dS26gipTCf7MREdOoGbqVeokkswkVqT4/Idhy5l0B5WTU3zp/E2c2UwTHTMXXXU1so0KJR42TXCSfh/04dNpXB/9GOf/JiReOSu+dparD8L3n3V96IIlCvauDrtauJ6B6Bb1Aozw+fIv3ObdS6GoyU97Cd6so70TPw7xpJZy9HvvhyGo3FxkwcHEWUiyF+nUOpVGbwTstwrEVmSCO6UVBWiMwplGalkqfBXUhrEXG47nvi7p+jNCUZh1I36s/eYcCyGQztOonWQi3lRS1I6+r4bttq5r43iv49JtEtJIZ5C28R0TmUAdHBtGgcyarPplvkIMLcO7F43UwE+QMsJc5YF6aQnlfGJ1+PI6fIAnWljrTaSjbO/xKr3etI+20Wt56dYO+NWL6avobKhgp+/nYlKhs3psxax9L965n2VQ9c25tiqQ8jNjeWAL8hWIW0Eu7dhmePTuAf1BuVRIa/yI5p83uSfv85jR6+WFq2IJOXs+HJLr7+eiRVuY2UPjpDXGohXibOyBwMmDZkPCJTJYcz9/PRjI/ZMm8TfaJ9MdIqEVoL8bZqx6mj56nNyWFMZ0/mrRlLa7OGy/GnUDkEoK4sx9U7mp07N9N3+K+4tjej29ipCCoxt5d/QtuPOvPbqd20j1zIrfT7PKstZ3afnmQd38blbRdwc+pCe3dT5iyfjb/YEo1EzbGn37Ht/AomDZxN505BuMxwQZBaMHb9aPrPf4f3v/sV95ZQnj2+gqOXPX0nhSDz8GXD/sWM6/c+LXoBebkxsdkP8ek+gLmDPmGk13QaRGJqK2v5+dgiVqzayLxhP9FU0YiafNS1BqRcKgCxlowMBfE1T0hPvIbczgB7Gw8aJcY8fnqFyycOkapOpTRDiasshLLKBkY7DaY1BcYO/RyZiSXTxnxMt7bjCDCyZcyEOVSJijhx+xecbJ0ZOGAJifHxDJnVnaWzP6FnhCsXck+ja20gLV/P5a+O05wgYmznwfyw9ARpj6sIkAQyY4ofEeYh3EyIR9RiiKq5iBljZrFh/Sz821ri6zIApT4fjbKEcvVjPti2mPc3W/Hhvhh2/s0PhSLbLQNg7edxuOhePcfgG1EEmtUq3NqaU1yayJO0A9jJrKgqL2PtJ+soKGqhOjGWxtpmdAY6fKPCMLIzJbyhlT/O3KRd1ynUFNfRXKXmau4lDAzduXj6N8Z9PI8hQ4bgpFGRdXUdF7JucufIYdRU4hXalixdAbtuVzJx4c/Y9AjESt6ESZAZBdpa/vLpT+zZv4sDO1bjFhLEtZtbcMoxxydmLk2NAob1UlLyi7kTfxfl03zUei2Z8deJ+uI7rNQiUi5eYPyKHrTY+/HoWRmrzyxE6NWGSUt2MSqyNyP0LSxZ9xWRo6ewdedJkk8e5sDhhbgauGIvCebelUuofBWkJGt4XFOEulGNpbYJY5kZOjsRpdmlNBoH0znKl1Hd2hIm98RALXDveRIL+y3CTW9IwZ37uPsEsuS7X9GIBeqqlZw9dIHKkmb2/bCb0LAu3DxwBlW6jsInhXjataECJQcvbGHsh5OwkBkzeewsvj80n2SlkmdHL3J0yVc8yywgZuAwjqybisjYixOPb6ExcuGzXXcwkFWzcsJwHiVcJsw1grDh37D+0WPMJZUcOHOBuPsb6DhtBPcu54GnPQ8y7jHebz7ZGQpGzRxEDRYkHL6EugHee3cWxbeSuB8fR0S33ny36CJPd4uJHtKVI3fWEOjpxjsD56CoS2P6sg8pe5CEYGxCUbMZfaZE0dSg49nTJFqdTOjcy4tn6dcRdGYs+OlzbBwc6TGwN13MQlg+byNTP2tH2vU8nG3UOHqG0EwFMiMzatXFNDY20ligxdBEglClodPQaXzyxXLO3jtHQVExR68folJthXN0EHJ3Pyz8HLEytaZBVc9nS4fysPgBCz+dT9y+HZQ0qNDVWlCmKeO79/fz87wVdA3ohMLIFLGDDwF+7enj3Yfvf9lG4DBfCsqayIpLoYO/H+u/CWfEoBi8vfzwtrZk21/2Y2FqwZOsRxhUieneeSruxn+/avHs5S8WMO03Q8QIn/p/kHkveCOKgKOjG1WxWahTDciNV1AvpNL/nWielyTjLLVh345GHMWNJNx7yIDBp+UgtAAAF3pJREFUMWRqUunx/QeU0YSVuy87dh1Eq28lZOBorlw+xuPnFZTmVvHw0RN+On6Y3lHzqS7LwFXpRLDvu+Q2VtIqFliyPhK9wX3WbJjOuJmrqbvbTGBrMP79O+JvZ0FbazOa5Vqq5CBX2dPGORorkwZUei3F235nzKAIvlr1B05tAqhViTmxbQvJRY8prarn6IFC3vtoPDmnSrE3NaLFxIXVuxaizKim0V7M1dNb6RfpysYDy4geOBKZsZR2HbuS8PgOJq6mVD9KIzrQHQ+vQL6atY3vt66inUcHcm6eQ9Z5Mrk5STzMzMKvbQ90JOGkE3Fp1+9s/eNzdp69wbiJK6hosaSNoznXnt7AytyWoR8ORlVaR1hnOd8u/IRfLh+jfa9+zPjyN2wtxKQ+vYmjsQ2nbl0iL7+R5Jx4fD29OPT7X0AmZc/dOA5tvUVtpZJBH32Fg6M3UR4hZP+2jVs3t3F+ZwZGmQraCgKLl20iq/ACKRcuUGnrzrHDq3j/10UsXjETs85m1NdV03C0mP0bv8DRxJrCyhbsjazZuCmWxIoE/C0c8PAyIfvEXSrqCymuamDLts082HuNlLS7VDkI/LBlBlbWAdwsSkXjaoW2sZyLxzdja6Xg2LaFDIsYjZVSS0NjLrsO/sDCsdMwaQKlVIkeU6TONrh6+NNOZYdDmDP5BVoa8stp0JkQJg8AvSEt1a1Edw2FFgkmhsWcv30YP68+FKQkkZ4Yx1dfHsFWJfBO9+Fc2jobTWYmDap6DBubCQ9qB+pK5Ab2eHWPJMo5ilKLVqQiU06lb6PR2ZUrGXc5c2YDDWU6Onfwp0ncgoubL4+uxWPqbsO4aR9TWqei1aM9u29do67OgLzKTE7tWoNGY4yoRc5nK8axbd8mnCWBfHfhMzp/IkbbnExA7whSqnZj7mBKc86r5xh8I4pASXEBTlZuhMk03Nl/G11uIzVVZWiUmSiaRFxcNYhKsY56RQVl1ens+/QBv+zfjZdczqkj3zG8+wBs5W7kFefRv0M3Opn5k58dR6fACLp6WPH97z8gNndCZeNAbeFVEm/uw0QEA7vPx9jEHTcHOypLn4CLDU29XBnQfxxinTuK/Fa6vzuECJ8ghiyYQPyVP5gzrRsGhmpswqO4ffQRufV11OTlUpJdjZ+LiNslj/j+t7N4KSo5fHQ7t8rvoS+ywDSuhL6j5+LWqT/JZfDBtJWs3fQlbSTe9HRqQ31JPeV5ifSfMJpWqYzKCimKQgUevmY8yY7D09SWouoM7D3saKgrIOt5Gkk3nuPSIZJHOalkVtXy27JvqFEbENDDlmdlJZj4SugQOYXqyiLauIdwZOMl9M8MCbHoRvexM7EOsKdJMMLDSkRovyGk5RThaGFJaVIJxg2GlCS1EO5qzdKFn6Kp0tEpZg73Yv/C6Se/UaPJwVoiYKgX8zyolWJpGREdTRn89QXKh8Vg4ysjut90EtJbuHv5LHKncOx1hmgM6untH0x4ujEmYg109GTR3ENMj+pLlbSW5JQnpMQlENhzHFvXHaNKL2L74qX4mNtQVVDIjiOPqXmoxExjQFrVPQobU+gdORA3t0B2XNzLiRuPmNlhON8fPMuNG5dJVT1DpJcjstOQ++Aezu3NyE48TpE+me9/24iJoYwmCxOConVU5jZjZeKAutqcgYOnYGFpjImFBy0ISGV6vum7jC2bNoGBirZhHphKLDjxx3ys3S05euAOsfdqqDRsQSQI1FQ0kZ9VjUJVQVl5Jua+7XHuMRKRkAEyV0LMxjLi88VkpSRjZOTH+oNj6Rw9hZ/3zUctbaCzTx/cbRxITb2P3EzKuTtJkKXn11XfYtUAn4+dTLOgRalQsOW7E1gpHamzbuVG7DG++fAwpXkqOhr2o07phru2iaNP1a/MvzeiCIjERpjpJEQPHc6Ez3/EObIH9Zk1uIcEkFX2lMCBo5BqlNjJLFGYafFwMMG+poVuHm0xN3Hnj4Q9OLv5IhIrSK/Lp7VBiZHaDo1eR6ullO5hUchpxUQvx88hGlmDKaOjP6FTZCds0YGiCmONBSZlsVTcukXW8wSi+kfz2dbfiAjyxlgmoawulSZKmTh6KnK1GP8BETSYi5DqxOjlBgR4OmPWbjAu+mDEBtZ8PGoCZuombl0+QX1NFb0mz+b6rQto7awpeZ5E++F9MVc74mLlSFOwKbYt7tQAYkMVqbef4e5qgaK+GkeZD2YmpkR9PIXEoieoakoIcPTAP9AcRb0NqRmpFFSD2ErKvZRE3hv4AXW5tRhItVQqsjj1YB8OTp4cTzxFD89wFq+ax7XEe8yaOZ56wZT88nxs5RY0G7aiVTRhauWImUSPOCiUsoIGMmtrmT3/B6ortbh6dCD2QQJiJDTUtJCcnUjOoyvcu3iRB1v34uvvxcWbu7iwdxWiYj1ypY7W5vuYu1hSqdPTz7MtZ3emcSbuGFO2H8LGxJ/E5DKePtyLyF2DTGqCtaDCz92dpJubsHH15sSJO7wzaCxfTBlDyrMHaGrKGfvxeirqa7Cx8EAuSFHrrQhyGEGrtpjxAwbSqJbRN3woLl1tqVMo6OYcSWFlCVKPAKytxDh4hWEu9cLLOZgGkYCBoKVKYkhro5ja6nLaO5pwMeUGDvaeSIztUYqa8GupoV5mT1SvYSQVnsTG3R9DGgkP60FyajVX722l65guSFv16JR6DA0sKW6oRyaupTDjAYatBhjL7CjPrkKsN0LtpKawQYpcL+XZtTzO7TxNasUtmvOUmNXI8G3fDifPbhhbwMPSdOTKCmI+mkRMr8E4telKu6FRKIurcPa0RWJjgtzMmLzKUhq02ThZeODqH4BaX8XjxK14uvkyZ87/jwVJ/wz0Gj3n63P58i8biHYWyEtKRmmWT4RXP6qV9Wy7fxiR2B57vS0uuLNy1QEMLSQkC01cfXwHQ+smNn77I908mrl37CtqTCW8N2g4SYlZ/L7rKnWtWkRW+VgqG3iWlkyW+jEB7r0oLyqjQlrPoBGbuBS3H1GrBkt1JW1trLh9/CiHn+zFr20E32xei693L7IKEnD3caBYW8HE3p8S3b0HZ09cwtHNiW49ohnZbz6+Ue1pVCnYUxFPYlw+mZUpOIZ2YP2p9Uj1Wh4c3cPGbTsovJzAj6sPcrVkOxbPcvEN6EhkSAjGcicK6qqRpVfiaxHC0S/W8/PWv5CUp6KgPolibSG//rqF8thCLC2VXDmzhyj/AZjK5Oz5/iT375xnzNjFHN+0ivxLRUgdxbQaqkmNrWLtxhNcSYzHQi6l8xAPku8eICTQm8SqOHS1taz66lNa6mqxdPZC5NxIjUkDx7ZnEN2jK/7dbNj4+RQmv7uUUOeBPL1+jo1Ll9B58BA6RfnQbkxnZAVVDO4dgyo/n02XH7NmZicKS2FH8hUmLYjB27UzEaPb4KBUc2DtQmxCrbGxV/Pb7Y08VybQ0tRIiVyBtC6edb//SFPZbWb++jlWXs70nzgAoTCO+zdP8m7UAExNrHEMsCW9OJ3s5P1oqOOTyYeIGjkBz3c/ZMfVrWirm8lOz6dT76nMem8Xx1P3oilrpZPPQCSFGYzu9gFiWqnIKyV7nxotEpqMmghqH85HI2cjbypHW3CF9nbNjB19GK/Qccg8nNl04lfaSEQ0SDJQGikY6e+KvdwSpxA3KotzkBjoMWuU4m1sz7vvruJ6Vh1btv5MftkNjFobqKp5Rn7aWa5fXMJHi9dy/PxZThy6T9qzh7hF9kGj0WDjEcSBx+sJq4vEbUMS7ioBB405g2Z8Ttbjmxjb2xPs2AY/aSdcO7jw9GEaQbp+JG405I9TY9ket5mTGauIzbiKV6eJZJTdfWX+vRFFwNLUHB5VYaiX0iQxYv602dh5B5P7tIiYru8hqzDHUK+gS+/eaPPTOH5hMxIjA2ipRK7VkaOqY8H7fajKsMYvsjdp6c8pNzVDo6mlQ6A1pg7ZrJ5dhNTEDYnWmkXvfU1qZhpJicfpEN4efVEhUmtzEjIr6NhzOLmxtxk47n2aM7SIjAzIS6skPj4WtVMCKfeykdU3sfLrSegtRdgE2aFvLOXZpXs8ubWf/G07qFCWYG0MfYd/TFNZLo2NSh6fucLYifMYPrgT74yYxpzVO1nyw+dYpypw6ToInYE1cjNPLt+5gXexMRFLP2P8+lVMW/Q5HQ3bUlSRiFxqh6mhmD5jupJXpcewyZCA0DY06MpolQn0mTqRegNXunafTK1WzsYvv8EMS0SGYnpFBLNl/RTu3r9O3+EjGD9wPM+u5RHRZQItUmMOHLrBrad7UImacbS1w0JsQVi7TvQd1R69tJymghZGzZyCQtxIaOd2tDjK6Dp/Dg0KMWGG1jhKxmHZtR8XLq3m0hk9hTWXKO7Um3371iFSiTi95gBx1TUsdQticN8JDJ/1DUWiUiQ1raTdqKLqYTY1xWkkpFyjUS5BhoRjB47Q2c0GO88uJMTdwtQxCk9Pd07fuYakWUp8/HOqM2JprK7Ew88NM5dATlxeRnz+fWrVDeTmGdMvvDtnYn9HX1FI50Y57X07cCX3Co4OdvgFh5Ocn4hfN1fa+wQikYjQKuvY/tteGhUKVn9xl4F9xyNSmdJo1Uq5Kg4Pb2eeVydQo36OubkrN0/v54Y2mQJpJVpFCzXpBqi0Bljb2NFsYIRgI+BsGYyDsxeVNgrMzO1pKTUhJTaDwspYVs7+gt3Pl3AkcwsZRbWoSaNj/6mkPrxF86N8qnwaWVVawKLPV+Bu6Uiruoysqmxs6xwZNWAcBuY6WjKKWbl2F2MHj8VfZkxLnR0SnY64xwosDc2IvbaDbMXTV+bfG1EEqmsU2IZ6Y29uQnldDccO7kJsJKNJVUODrAkzBxOKdXq0RXVETZ+GVYAjvhGhJBcUIbFywl1sRoPQglJRgLejD34+7pw8uYfmFpC7OyOSS7hxdAdtenXHIzgADw8PsrJvYeMRgqogi3v37lGpy4d6GSJDHY6+zshElli5NWNj7E5oVDjn4hJorm+hXt+CzMwGtZ2S+pQaHO1tkFp5IUiNuH43jfYThlOkTMbS1g9TCyNaTZTUV9XQtW8/6hOzadehO93kYhJO7MOvXQc+mvkVv//+LY5ic5TaVB6dSWLGpvVMnzuSnzYso1liQJfQjvyyaQ+2Elt0xgKONv5I7OwxMRZx5dBNDOQmFGdVsWfNz6zfvZOR02NQSSrIL1TQLrATzZo6mlCRmPSYqXMm8+DGE1JTHrN29WqKMhPo13E0T29excjAGk1DAa5Sc3zdfHDza4ejYzDBUR6U55RgKDNAEBly6KfdlDxIRmZYT2ZtEj6BA5izeCI37x1DL/dg9a8f8PP+HeQe2YNpbiGK1GocAt1xbVUh69KHLCMdzfpG/C39KRVUFMbVIbeyINwmADupI1pdLoFt2+EXGolULfD+eyP4fPEBHOzMCbRtQa6tRq9txNVWjL2lJXIbW2IfX+XXzZ/ibOtCStwDNJUqcqvqkEkdKKtIoEavwTPkHZIKM3CVaag3qkWQiKgqS0Ws15ORm46JSExriwhbezty0m5QWvaQFm0LhVW50KBHJPXn1JUthFla8/3ZHxHr7bBz6kFc6n1aVNBq0ECvLuEgbsY12JX2Ee0xqKzEuaMDoX6utBbU0tLUiM7AjLIGLaYyO7Qtelo0jagamnAwNUYiqJGaW9Ld35/Ep+UkPjmKsYEIB7dIDh26QmOjCg8nQ3oH9aDauIWy6nKGuEfgZu1MWnUSo+b2JT6jmKMXtmFuY4OJzBHLViOy8pJfmX9vRBGwsrZkYOcBKHVaevp5EdJ/JKZ2dpy5eBadso5abQtffDaJdF0xT1Nv0cfTnXspJ/lk2AzS8nIJadOJonodOhMpNx4l0CRS4mlnip+zJ1JbEdYloVi7mOMR1JmwwEgS4hWUFT1n+S+fUiKpoNewQdhobNCI1YS2Gcqz0hx00hzCO3WnovApHUN7k5jxFz4ZfZ/Lv8VjbGJGdL0Hbbw6c2DnBtRCIS0ac/IfxWJQV0dZSSFZtXc5mvAN/r59cW/UY9ikZsDAKAZPnk23BSOYOONTAmxVNLiHkVh3jQzdY0xLHZj16Tcc+GYNX4ZG8EHvLqgMtDQ427Fy5iyatOWoFNVkFl/DJaiE7NQapgyN4fm929hbm/DlyR9Zv2AAfX0lBNl7s27LatLyr2OYLSfEvR1PCis5cf4Ytc2pOAsakpX1HNy3kl5dQ/Dr4kwvhzm0DWrLretP2fvbYUpyLmCoMWPu6GVs/OMsZdU6Th7ezYj3phLVvwuaY/cxrG1g5oZJzJ0eROc2/bl/4RKjJ2+g9lY80U4h5FkrmTV1LvN//IawseMpdxShbWpCpSgm4eJNIp2lzFreBZ/oaOLqMjE2tMfAKIyLt27RfuAkjD39CPbQkVeexNEb27maXk2wRyAiCwcksnDic2+z5+pfCPLvhLOtM/YmQZjKFdRqrHExMkVq1I66ZgNW/vQxf7m8l/b23Xlw5igJN2uwljrwJOUkTurO+NtJyaouJLpzD54XVhMY6cOHywdy6dJxkmuVFDWquHL3FNLmbBp0hqQkltE1fCjt27cn3C8KsSaXyvJM+vQZhaasgfikG6gkeh6lP8BZakNayUOeFsVh4uKEg4k9bX3b8I7/ODqHdUOjMETxxAZ7lRt+7gN58uhXdj3cTUyfd+nYpTdRUx3pOH0gXYa0I6c6hbi8h3wUP5dHt+KpSq+i3sWMJT+8w4OEi7RaGPLZhPlEe/dGpFMwKHQs95NqGNBr6ivz742YY1AQhEqgEah63S5/gw1vff4n3jSntz7/Pe56vd72P3e+EUUAQBCEhH80CeLr4q3P/8yb5vTW55/jjbgceMtb3vL6eFsE3vKWf3PepCLwXyY7eM289fmfedOc3vr8E7wx9wTe8pa3vB7epJHAW97yltfAay8CgiAMEAQhXRCELEEQFr8mhzxBEJ4LgvBEEISEl31WgiBcEQQh8+Xnq2dl+N9x+F0QhApBEJL+pu8fOggv2PgyZs8EQWj3J/ksFwSh+GWcngiCMPBv9n3x0iddEIT+/wIfV0EQbgiCkCIIQrIgCPNe9r/OGL3K6bXF6Z9Cr9e/tg0QAdmAFyAGngJBr8EjD7D5T33rgMUv24uB7/7FDtFAOyDpf3IABgIXAAHoBMT9ST7LgUX/4Nigl9+dBPB8+Z2K/pd9HIF2L9umQMbL877OGL3K6bXF6Z/ZXvdIIBLI0uv1OXq9vgU4CAx9zU5/ZSjwx8v2H8Cwf+XJ9Hr9baDmP3W/ymEosFv/ggeAhSAIjn+Cz6sYChzU6/XNer0+F8jixXf7v+lTqtfrE1+2lUAq4MzrjdGrnF7FvzxO/wyvuwg4w9+tmVzEfx/EfxV64LIgCI8EQfjwZZ+9Xq8vfdkuA+xfg9erHF5n3Ga/HF7//jeXSH+qjyAIHkA4EMcbEqP/5ARvQJz+v/K6i8CbQle9Xt8OiAFmCYIQ/bc79S/Gcq/1Mcqb4AD8CngDYUAp8MOfLSAIgglwDJiv1+v/bs6s1xWjf+D02uP0/8LrLgLFgOvf/O3ysu9PRa/XF7/8rABO8GKIVv7X4ePLz4o/2+u/cXgtcdPr9eV6vV6n1+tbge3836Hsn+IjCIIRL5Jtn16vP/6y+7XG6B85ve44/b/yuovAQ8BXEARPQRDEwFjg9J8pIAiCsSAIpn9tA/2ApJcek18eNhk49Wd6veRVDqeBSS/vgHcCFH8zJP6X8Z+uqYfzIk5/9RkrCIJEEARPwBeI/18+twDsAFL1ev2Pf7PrtcXoVU6vM07/FK/7ziQv7uJm8OJO6dLXcH4vXtyxfQok/9UBsAauAZnAVcDqX+xxgBdDRw0vrhXff5UDL+54//IyZs+BDn+Sz56X53vGi39ox785fulLn3Qg5l/g05UXQ/1nwJOX28DXHKNXOb22OP0z29s3Bt/yln9zXvflwFve8pbXzNsi8Ja3/Jvztgi85S3/5rwtAm95y785b4vAW97yb87bIvCWt/yb87YIvOUt/+a8LQJvecu/Of8HvJpmIMvfyIwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=1000, hide_rest=False, min_weight=0.1)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "R2W22gjS1oiJ" + }, + "source": [ + "Alternatively, we can also plot explanation weights onto a heatmap visualization. The colorbar shows the values of the weights." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "qXBM71Bo1UPc", + "outputId": "9f32237b-eb29-4355-a0a3-96163c2360c5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#Select the same class explained on the figures above.\n", + "ind = explanation.top_labels[0]\n", + "\n", + "#Map each explanation weight to the corresponding superpixel\n", + "dict_heatmap = dict(explanation.local_exp[ind])\n", + "heatmap = np.vectorize(dict_heatmap.get)(explanation.segments) \n", + "\n", + "#Plot. The visualization makes more sense if a symmetrical colorbar is used.\n", + "plt.imshow(heatmap, cmap = 'RdBu', vmin = -heatmap.max(), vmax = heatmap.max())\n", + "plt.colorbar()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "YaQK8BZexwmG" + }, + "source": [ + "### Let's see the explanation for the second highest prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ClAt2PowxwmH" + }, + "source": [ + "Most positive towards wombat:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "cROfKjUMxwmI", + "outputId": "37bfe983-d4bc-45b2-bca2-964b91f1258f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(106, positive_only=True, num_features=5, hide_rest=True)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Z2MSbZ6kxwmP" + }, + "source": [ + "Pros and cons:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 287 + }, + "colab_type": "code", + "id": "6HmKJVr1xwmR", + "outputId": "7d369717-cefc-461d-d01f-49389dddd177" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(106, positive_only=False, num_features=10, hide_rest=False)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "g1y8r9Sj0uDD" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "Tutorial - Image Classification Keras.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3.7.5 (tensorflow-gpu)", + "language": "python", + "name": "py375_tf230_gpu" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/notebooks/.ipynb_checkpoints/Tutorial - MNIST and RF-checkpoint.ipynb b/doc/notebooks/.ipynb_checkpoints/Tutorial - MNIST and RF-checkpoint.ipynb new file mode 100644 index 000000000..a5b6707f2 --- /dev/null +++ b/doc/notebooks/.ipynb_checkpoints/Tutorial - MNIST and RF-checkpoint.ipynb @@ -0,0 +1,432 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Overview\n", + "The notebook shows how the ```lime_image``` tools can be applied to a smaller dataset like mnist. The dataset is very low resolution and allows quite a bit of rapid-iteration." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from skimage.color import gray2rgb, rgb2gray, label2rgb # since the code wants color images" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import fetch_openml\n", + "mnist = fetch_openml('mnist_784')\n", + "# make each image color so lime_image works correctly\n", + "X_vec = np.stack([gray2rgb(iimg) for iimg in mnist.data.reshape((-1, 28, 28))],0).astype(np.uint8)\n", + "y_vec = mnist.target.astype(np.uint8)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "fig, ax1 = plt.subplots(1,1)\n", + "ax1.imshow(X_vec[0], interpolation = 'none')\n", + "ax1.set_title('Digit: {}'.format(y_vec[0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Setup a Pipeline\n", + "Here we make a pipeline for processing the images where basically we flatten the image back to 1d vectors and then use a RandomForest Classifier" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.pipeline import Pipeline\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.preprocessing import Normalizer\n", + "\n", + "class PipeStep(object):\n", + " \"\"\"\n", + " Wrapper for turning functions into pipeline transforms (no-fitting)\n", + " \"\"\"\n", + " def __init__(self, step_func):\n", + " self._step_func=step_func\n", + " def fit(self,*args):\n", + " return self\n", + " def transform(self,X):\n", + " return self._step_func(X)\n", + "\n", + "\n", + "makegray_step = PipeStep(lambda img_list: [rgb2gray(img) for img in img_list])\n", + "flatten_step = PipeStep(lambda img_list: [img.ravel() for img in img_list])\n", + "\n", + "simple_rf_pipeline = Pipeline([\n", + " ('Make Gray', makegray_step),\n", + " ('Flatten Image', flatten_step),\n", + " #('Normalize', Normalizer()),\n", + " #('PCA', PCA(16)),\n", + " ('RF', RandomForestClassifier())\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X_vec, y_vec,\n", + " train_size=0.55)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Pipeline(steps=[('Make Gray', <__main__.PipeStep object at 0x0000024B3F533A20>), ('Flatten Image', <__main__.PipeStep object at 0x0000024B3F533828>), ('RF', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n", + " max_depth=None, max_features='auto', max_leaf_nodes=None,\n", + " ...imators=10, n_jobs=1, oob_score=False, random_state=None,\n", + " verbose=0, warm_start=False))])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "simple_rf_pipeline.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "import os,sys\n", + "try:\n", + " import lime\n", + "except:\n", + " sys.path.append(os.path.join('..', '..')) # add the current directory\n", + " import lime" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from lime import lime_image\n", + "from lime.wrappers.scikit_image import SegmentationAlgorithm\n", + "explainer = lime_image.LimeImageExplainer(verbose = False)\n", + "segmenter = SegmentationAlgorithm('quickshift', kernel_size=1, max_dist=200, ratio=0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 3.34 s\n" + ] + } + ], + "source": [ + "%%time\n", + "explanation = explainer.explain_instance(X_test[0], \n", + " classifier_fn = simple_rf_pipeline.predict_proba, \n", + " top_labels=10, hide_color=0, num_samples=10000, segmentation_fn=segmenter)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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RzhcjYjq53+RHGbgoH6x90P/nbG/gvjTALeUKXA28g5zXW81s/u1Inve0eHddBDw3Ip7X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\nSVIvMo9KGu9qX9C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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "temp, mask = explanation.get_image_and_mask(y_test[0], positive_only=True, num_features=10, hide_rest=False, min_weight = 0.01)\n", + "fig, (ax1, ax2) = plt.subplots(1,2, figsize = (8, 4))\n", + "ax1.imshow(label2rgb(mask,temp, bg_label = 0), interpolation = 'nearest')\n", + "ax1.set_title('Positive Regions for {}'.format(y_test[0]))\n", + "temp, mask = explanation.get_image_and_mask(y_test[0], positive_only=False, num_features=10, hide_rest=False, min_weight = 0.01)\n", + "ax2.imshow(label2rgb(3-mask,temp, bg_label = 0), interpolation = 'nearest')\n", + "ax2.set_title('Positive/Negative Regions for {}'.format(y_test[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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QvWS9J9Ra+/ruv1fVoVV1ywzvWu2oqkOm3N3BS/68f2vtja21NyT58QxXKzx3FnNmS1MD\nLDL5Z9GpARaZ/C8ATfMaVdWNkjw2wzs1O6rqiKo6Isk5Sb4zyQOWbL4jyaWttSs3YF4/XFXvrapr\nklyZ4d2hUyZ333zK3V03+fODrbX/XB2vtXZJkr/LsPABC0oNqIFFJv/yv+jUgBpYZPK/OPm3evba\n3T/JbZP8TJLHLbuvZXj36b0zeq7e71Lbb+k/qmrH5Dk/keSXklyS4fMRD0nyi5n+zZLdBXL5yH1X\nJLnHlPtje1EDamCRyb/8Lzo1oAYWmfwvSP41zWv3xAwhemaSWnbfo5KcUFXPmFwmcWGSB1bVt+3l\nXaZeUXw5ybeNjN9x2b8fluTGSR7WWvv33YNV9YDsm48m+UaS24/cd7sM716xuNSAGlhk8i//i04N\nqIFFJv8Lkn+XZ69BVR2Y4fexvbO19rbW2l8svSV5dZJDkzx88pC3Zvien7SXXV+b8aK4MMnNq+qu\nS+Zw2wwf0F/q+smfN1qy3c2TPGVVX9gyrbVrMvxC8/tU1VFL9nmXDJdknLkv+2XrUwNqYJHJv/wv\nOjWgBhaZ/C9W/qu13psZ7E1VPTbDsvIPb62dMXJ/ZVgY4EOttUdMxv4ow7tS75ncbpThl4Wf1Vp7\nzWSbM5Icn6GoLk2ys7V2TlXdIsMS9ZcneWWSm2VYUv6KJPdqre03efxRSc5Lcn6GZeoPSfK0DKvx\n3T3JnVprn5lse3aS1lq7/16+1rsk+fBkH6/M8G7aL0zmf6/W2uem+uaxLagBNbDI5F/+F50aUAOL\nTP4XLP9tjkt3b/VbktOTXJPkwD1sc1qSryX59sm/K8PvS/tYhg/WX5bkjCT3WPKYozIsKHBNhneL\nTlty3wOS/MvksR/P8PmJsaXmH5LknzO8W3Vhkv+W4R2m65McvmS7s5O8b5Vf7z2S/FWSqzIsKvDW\nJEfM+//BbX43NaAGFvkm//K/6Dc1oAYW+Sb/i5V/Z5oBAACgw2eaAQAAoEPTDAAAAB2aZgAAAOjQ\nNAMAAECHphkAAAA69p/3BJLkYVWW8N4EVvyCuQXTWqt5PXepAeZM/llk88x/ogaYP8cAFtlq8u9M\nMwAAAHRomgEAAKBD0wwAAAAdmmYAAADo0DQDAABAh6YZAAAAOjTNAAAA0KFpBgAAgA5NMwAAAHRo\nmgEAAKBD0wwAAAAdmmYAAADo0DQDAABAh6YZAAAAOjTNAAAA0LH/vCfA+rrvcceNjj/4IQ9ZMXbC\nhz88uu3Pvf3tM50TbJTjOvl/yEj+k+TDIzXwdvlni5J/Fp0aYJHJ/2w50wwAAAAdmmYAAADo0DQD\nAABAh6YZAAAAOjTNAAAA0GH17G3ikSecMDr+xCc9aXR8//32WzH23UcfPbrtKeedNzp+0UUXrXJ2\nsL5O6OT/SZ387zeS/yQ5eqQGzpN/Njn5Z9GpARaZ/G8MZ5oBAACgQ9MMAAAAHZpmAAAA6NA0AwAA\nQIemGQAAADqsnr3FPOqRjxwdf/zjHz86PrZKdk9vNb1Tjj12dPxxC7hyHvP3yJEa6OW/l+mese2P\n7eR/EVeOZP7kn0WnBlhk8j8/zjQDAABAh6YZAAAAOjTNAAAA0KFpBgAAgA5NMwAAAHRYPXuTeulL\nXzo6fsxd7jI6vv/+0/1XfvrCC1eMPeLUU0e3/cIXvjDVvmEWejVwl5EamDb/F47kP0lOHakB+Wce\n5J9FpwZYZPK/+TjTDAAAAB2aZgAAAOjQNAMAAECHphkAAAA6NM0AAADQYfXsDXSrW91qdPzpT3/6\nirHv/d7vHd22pnzOsVWyk+Q+L3vZijEr5LGepsl/0q+BafRWiHzZSP4TNcD6kX8WnRpgkcn/2j20\nM37GBj2/M80AAADQoWkGAACADk0zAAAAdGiaAQAAoMNCYBvoMY95zOj4Dx177Jr3fdHOneP7PuWU\n0fEvfvGLa35OmEYv/8fOIP87O/k/Rf7ZJOSfRacGWGTyv37GFghbj8XBnGkGAACADk0zAAAAdGia\nAQAAoEPTDAAAAB2aZgAAAOjYNqtnT7tK2thKa7NyyMEHj44fdeSRa973xRdfPDr+/S960ej4lVde\nuebnhGkdPFIDR84g/8l4DbxI/tlE5J9FpwZYZPK/NrPo0Xr7WMuq2s40AwAAQIemGQAAADo0zQAA\nANChaQYAAIAOTTMAAAB0bMnVs9ey8tlGeOaznjU6vmPHjlXv46KdO0fH33H66aPjW3WFPLanZ43U\nwDT5T5KdnRo4faQG5J/NZD3zv0v+2QIcA1hkG53/4+R/QzjTDAAAAB2aZgAAAOjQNAMAAECHphkA\nAAA6NM0AAADQsSVXz94sjuishHfve997zfu+5wteMDp+9dVXr3nfMCu91SBnUQMvUANscvLPolMD\nLLL1zP85nfzfTP7X5KFreKwzzQAAANChaQYAAIAOTTMAAAB0aJoBAACgw0Jga3CHO9xhdPzGBxww\n1X4+d9llK8a++tWv7tOcYCP1auCAKWrgspH8J2qAzU/+WXRqgEUm/4vFmWYAAADo0DQDAABAh6YZ\nAAAAOjTNAAAA0KFpBgAAgA6rZ6/BAx/4wKm2//rXvz46fq8XvnDF2PXXX79Pc1ruz5/85NHxjx9x\nxIqxXbt2jW77ile8YnT8y1/+8r5PjG1hmhro5f+FI/lPZlMDT+7k/4iR/CfjNSD/9Gz2/E/z+p/I\nP9Pb7DXgGMB6mkX+z+nk/5szyP8tO/n/xU7+r5f/PXKmGQAAADo0zQAAANChaQYAAIAOTTMAAAB0\naJoBAACgY1Ovnn3GvCcwccSOHaPjtz/ssKn2c9bZZ4+O3/vyy1e9j9735Ha3u93o+JX3ve/o+D1u\nc5sVY6210W1f8pKXjI6feeaZo+Pvete7Voz1VuZma9jRqYHDpqiBszv5v3yK/Pf08n/fTv5vM5L/\nZLwGZpH/RA1sZeuZ/2le/5PxY8AsXv8T+afPMWAlNbA41jP/r58y/w8dGbt9J/8v7uT/O6bI/8md\n/P9VJ//v7uR/bGXurcaZZgAAAOjQNAMAAECHphkAAAA6NM0AAADQoWkGAACAjk2xevZmWSW755a3\nvOXo+M0PPXSq/ezcuXPNcxlbNS9Jcumlo8Onnnrq6PijHvWoFWNHHXXU6LaHH3746PjTnva00fH9\n9ttvxdjpp58+um1vxW42l14NHDpFDcwi/z2XziD/yXgNzCL/yXgNyP/WsJ75n/YgPHoMkH/WmWPA\nSmpgcWym/I/2TJ38H76O+X96J//7d/L/9m2Qf2eaAQAAoEPTDAAAAB2aZgAAAOjQNAMAAECHphkA\nAAA6NsXq2Zvd3e9+96m2v/baa0fHL7zwwllMZyq3+vu/Hx3/x/POWzF2eGc1vQMf/eipnvOpT33q\nirEdH//46La/dcEFU+2b+ZhFDcwj/3/fyf95I/lPxleUfPQM8p8kHx+pgQvkf0vo5f9hne3H8v/f\nO/k/el8ntQrTvP4n48eAWbz+J+PHAK//W4djwOo5Bmw/WzX/v9bJ/ylT5P+PZpT/3xjJ/1FbLP/O\nNAMAAECHphkAAAA6NM0AAADQoWkGAACAjmqtzXsOqar5T2IPXn6f+4yOP+95zxsd730xr37Vq0bH\n//q9792Xac1cdcYPPOig0fGjjx5fwuauv/IrK8bOP//80W1POumkVc1tI7TWet+CdbfZa+A+U9bA\nmFd18v/eTZL/noOmzP+vjOQ/Ga8B+R9s1fz/3RTHgJ/o5P/ATZT/sQDM4vU/kf+92ao14BiwkmPA\n9OR/85o2/x+cIv8v3ET5f+cq8u9MMwAAAHRomgEAAKBD0wwAAAAdmmYAAADo0DQDAABAx/7znsBW\ncPkVV4yOX3311aPjhxxyyOj4jh07Zjan9dBbuvC6664bHT/33HNHxw+/6qoVYwcccMC+TotN4IoZ\n1MBmz3/PtPm/aiT/iRrYytYz/5fu+7RmbuwYMIvX/0T+tzrHgJUcAxaH/K80bf733wb5d6YZAAAA\nOjTNAAAA0KFpBgAAgA5NMwAAAHRomgEAAKDD6tmr8Duf/vTo+NMuuWR0/Jhjjhkdf9CDHzw6fuSR\nR64Ye8Mb3zi67a5du0bHr7nmmtHxnoMPPnjV2979bncbHf/sPe85Ov7kW9xixdgf7z8etRMPO2x0\n/LWf/ewqZ8dG+HSnBi6ZogYePEX+k+SNU9TAeub/bp3837OT/1uM5D9J9h+pgcM6+f+s/G8qs8j/\nazr5/3Qn/9McAzb7638yfgzw+r91OAas5BiwOOR/pRd28v/BTv6PH8n/uZ3832OT5t+ZZgAAAOjQ\nNAMAAECHphkAAAA6NM0AAADQoWkGAACADqtnr8G73/3u0fE73elOo+MHHXTQ6PhRRx21YuylJ588\n1Vyuuuqq0fGqGh0/5JBDptr/Wp1//vmj45d0Vsh76Aye84wZ7IM9m6YGpsl/kpw8RQ1s9vwn4zVg\nhdStbT3zP80xYKvm3+v/1ucYsHqOAdvPIud//Kf65IDe9lPkfxZVMYvjyHLONAMAAECHphkAAAA6\nNM0AAADQoWkGAACADk0zAAAAdFg9ew2e+4EPjI5ff/31o+Mnnnji6PhNbnKTVY3tyaGHHjrV9rPw\nH9/4xuj4b37ykyvGPvva1673dFZYj5XzuKEPTFED0+R/T+Nj5pH/b3Ty/8mR/CfJa+dQA6yvaY4B\n2y3/07z+Jxt/DPD6vzEcA1ZyDFgc8r/SnTv5P36D878ev0HBmWYAAADo0DQDAABAh6YZAAAAOjTN\nAAAA0FGttXnPIVU1/0nM0fOOOWbF2CNOOGF021vf+taj45/+1KdGx3vf2M9deumq5rYnF+3cOTp+\n7rnnrnnf8/DO1mpez73INXDMSP6T5IQpauBTnfz3XDqD/O/cZvlv8j8XY6//yXTHAK//azfP1/9k\nsWvAMWBzcAyYD/nfHFaTf2eaAQAAoEPTDAAAAB2aZgAAAOjQNAMAAECHphkAAAA6rJ7Nf3rovCcw\nZ1bPZpFZOZVFPgZYPZtF5xjAIrN6NgAAAKyBphkAAAA6NM0AAADQoWkGAACADk0zAAAAdOw/7wkw\nH4u8SirAIvP6DwDTcaYZAAAAOjTNAAAA0KFpBgAAgA5NMwAAAHRomgEAAKDD6tkL6oyRMSuqAmx/\nY6//iWMAAPQ40wwAAAAdmmYAAADo0DQDAABAh6YZAAAAOjTNAAAA0KFpBgAAgA5NMwAAAHRomgEA\nAKBD0wwAAAAdmmYAAADo0DQDAABAh6YZAAAAOjTNAAAA0KFpBgAAgA5NMwAAAHRomgEAAKCjWmvz\nngMAAABsSs40AwAAQIemGQAAADo0zQAAANChaQYAAIAOTTMAAAB0aJoBAACgQ9MMAAAAHZpmAAAA\n6NA0AwAAQIemGQAAADo0zQAAANChaQYAAIAOTTMAAAB0aJoBAACgQ9MMAAAAHZpmAAAA6NA0AwAA\nQIemGQAAADo0zQAAANChaQYAAIAOTTMAAAB0aJoBAACgQ9MMAAAAHZpmAAAA6NA0AwAAQIemGQAA\nADo0zQAAANChaQYAAIAOTTMAAAB0aJoBAACgQ9MMAAAAHZpmAAAA6NA0AwAAQIemGQAAADo0zQAA\nANChaQYAAIAOTTMAAAB0aJoBAACgQ9MMAAAAHZpmAAAA6NA0AwAAQIemGQAAADo0zQAAANChaQYA\nAIAOTTMAAAB0aJoBAACgQ9MMAAAAHZpmAAAA6NA0AwAAQIemGQAAADo0zQAAANChad4AVfWUqtpV\nVYevYtv7TbY9fiPmtuy571xVZ1bVlVV1fVU9fKPnwPYj/yw6NcAik38WnRrYHhauaa6qJ0/CuPt2\nXVWdX1WvqqrbrNPTtslt6TxOrKon72H7eXhDku9J8vwkT0ryj+v5ZMv+H5benruez7vI5H+PNjT/\nSVJVt6mq36uqz07+L3ZW1anr/byLTA3s0YbVwMj/w/Lb49bruReZ/O/RRv8MdGhV/a+quqCqvlpV\nF1fVqVX1Xev5vItODezRRtfAbarqD6rq8kkNfKSqHr2ez7kW1dq8/l/mYxLQ05K8IMnFSQ5MclyS\nn538+66tta/N+DkryQGttf9YMvbRJJ9vrd1/ZPsbL912I1TVgUm+muTk1tpJG/Scu5KcmaFIl/rn\n1tonNmKoqmn/AAAdoUlEQVQOi0b+u3OcR/4PS/J/k+xK8vtJ/j3J7ZL8QGvtERsxh0WkBrpz3NAa\nqKo7JrnPyF2/nORuSQ5rrV2x3vNYNPLfneNG57+SfDjJdyf5P0k+leTOSZ6V5CtJ7tJau3a957GI\n1EB3jhtdA4ck+ackt07yO0kuT/KYJPdL8vjW2p+t9xymtf+8JzBH72mt/dPk76dV1ZeS/FKSn0ry\nplk+URvemVh1+De6UCZ2v7v2lVntsKpu2lr76l42u6C19iezek5WTf5vaB75f12G78v3t9aunNXz\nsmpq4IY2tAZaaxdn+AF16fYHJnltkvdpmNed/N/QRh8Djk3y/Ume2Vr73SWPuSDJ65P8WJLTZzUX\nRqmBG9roGnhGkh1J7t9a+5vJ9r+b5O+T/L9V9eettW/Oai6zsHCXZ+/BWUkqyZ12D1TVnarqLVX1\nxaq6tqo+VFUPXv7AqvqFqvrXyTZfqqp/qKqfWXL/DT7LUFU7M1z+8CNLLg85a3LfDT7LMLlc5OrJ\nDxPLn/dPq+rSyTtYu8ceVFV/W1XXVNVVVXVGVR2zpy+8qk7K8MNLS/Kbk+e/aMn996yqv6yqr0zm\n8t6q+sFl+9h9ucvxVfWaqro8ySV7et4ljz2wqm6ymm1ZN/K/gfmvqqOT/GSS/9Vau7KqblJVi/wm\n5magBuZ0DFji4UkOSfLHUz6OtZP/jc3/oZM/l785dNnkz+v2NGfWhRrY2Bo4LsOZ9r/ZPTB5c+HN\nSb4zwxnnTUXT/C13nvz5xWS4zj7Jh5L8eJJXZ7i+/yZJ3lFVP7X7QVX19CSvSPKvSZ6T5IVJ/jnJ\n0jAt/yzDc5J8NsknkjwhyROTnLJs+93elOSmSR6ydLJVdVCShyZ5yyRkqaonJTkjydVJnpvkJUnu\nkuQDtefFB96a5BczvFj8yWQ+vzjZ5/ck+dsk35vk1yf7vGOS91fVvUf29ZoMlxu9eLL93jwlybVJ\nrquqj5XPsc2L/G9s/n9s8nV+vqrel+EHpOuq6t1VdYc9PI71owbmcwxY6gkZLg9825SPY+3kf2Pz\n/48ZfvY5uap+tKpuV1X3S/IbSc5J8t49PJb1oQY2tgZukvE3h746mcf37eGx89FaW6hbkicnuT7J\njya5ZZLbJ3lsks8nuSbJbSfb/fZkux9a8tibJbkwyYVLxt6W5LxVPufhS8Y+muSskW3vN9n2+CVj\nlyR587Ltfnqy3Q8vmduXkrx22Xa3TvLlJL+7lzneIcNnK3952fjbMoT6DkvGvjPD5RtnL/sadyV5\nfzJ8Vn4V/xcfSPJfMxT9zyf5l8k+/su8c7Jdb/K/OfKf4fM7uybf93cleXSGz3JeleSCJAfOOyvb\n9aYGNkcNjDz/tyf5WpI/mXdGtvNN/jdP/pM8KMNaFruW3N6d5Kbzzsl2vqmBzVEDGd5o+EaS71o2\n/qeTr+sV887K8tuinmmuJO/LUCCXZHhX5aokj2itfW6yzYOSnNNa+9DuB7VhUYbXJbnjkksdrkxy\nWFV9/zrO9y1JHlxVN10y9tgk/95a++Dk3z+e5OZJ/qyqbrn7luHdqg9neHGYSlXdaLLft7XW/m33\neGvtsgzfs+Oq6uAlD2lJfr9NUr83rbX7ttZe3Vo7o7X2ugzvKv1rkpeVy7XXk/yvwjrnf/fjLm2t\nPaS19uettd9K8vQM73Y/ftr5MhU1sArrfQxY5qeTHBCXZm8E+V+FDcj/FzIshPSrGT5He1KS45P8\n4bRzZWpqYBXWuQZOzdBkv6WqfqiqdlTVrybZvRDqQdPOd70tatPckpyY4RLJH0lyTGvtiNba0sth\n7pDk/JHHfmLJ/clwKc01Sc6p4dcGvLqqxlYEXYvdl2Y8PEmq6mYZivnNS7Y5MsOLwNkZXgR2367I\nEPhb78Pz3nryvBeM3PeJDPlZ/qsRLt6H50mStOED/69O8m3ZjJdlbB/yvzrrmf/rMvw/vGXZ+FuS\nfDPjqwozO2pgdTbyGPCEDGdJ3rOPj2f15H911i3/VbVjMtfXt9Z+o7X2ztbayUmemeTRVfUT+zBf\nVk8NrM661UBr7aNJHpdhMbC/S/LpDFefPifD13HNPsx3XS3ywjP/0L61at4+a619soZFfR6aYWGf\nRyZ5ZlW9uLX24rXuf/IcH66qizMsxf5nGYrmwNywWG6U4UXgiRmWbV9uo1agW+viFbsXDbjFWifC\nHsn/+lht/i+d/HmDubbWdlXVFzNcqsr6UgPrY+pjQA2/l/a4DJcPXj/7KTFC/tfHavP/lAyf6XzX\nsvF3TP784SR/NaM5MU4NrI9VHwNaa39RVe9Icvck+2W48mL3GfGxRn2uFrlp3pt/S3L0yPhdltyf\nJGmtXZfhDNFbalgB921Jfq2qXt76y8ZPe/nam5M8e3IZxGOTXNxaO2fJ/RdmeGfm8621s6bcd8/n\nM3wgv/d92JXpV0fdmyOWPDfzI//rm/+PZJjv7ZcOVtUBSW4V+d8M1MDGHQN2fxzBpdmbh/yvb/5v\nk2G++2X4XOduB0z+9PP5/KmBDTgGTK4y/cjuf1fVj2f43my6xfAW9fLs1Xh3kh+oJUuqTy6H+Pkk\nO1trH5+M3eCM6OQ//xMZgntA+q7NcBnyar0pw7uST0nyE1n5O+T+KsPnMZ5fI7+6pqpuNcVzJRnO\neiU5M8lPLV11r6q+I8MlFR9ore3T5RNj86nhF53/YobP+XxkxYPYSPK/jvnPsFDGFUmeUFU3XjL+\n1Ayvy2fu436ZHTWwvjWw1OOSfKa19n9nsC9mQ/7XN/8XZHitf8yy8cdnaBjWfAaUNVMDG3cM2L3f\nI5P8lyTvbK19elb7nZVFfSer9r5Jfj1DIN5TVa/M8Fmrp2T4DMMjl2x3ZlVdluSDGS6HOCbJs5Kc\nMVkwoOcjSZ5RVb+W4Tr+K1prZ/fm11r756q6MMOS9DfODS/JSGvt6qo6MckbkvxTVf1ZhneIDs+w\nTP3fJXn2Kr7u5f5nhs98fLCqXpNhRbufn8zhucu2Xc33dbdnVdUjkrwzyWeS3C5Dw/BdSZ7YNtkv\nNN9m5H/11iX/rbX/qKr/kWHBlw9U1RszfG+fneFXO/iVO+tLDazeeh0DhgdU3TXJ3ZK8bB/mxr6R\n/9Vbr/z/YZL/nuT3qupeST6WYS2Xn8uwIOrb92GurJ4aWL11OwZU1ccynKH/TIbPNj8jw4mzE/dh\nnuuvbYIlvDfylm8t+36vVWx7xwzv5HwxwztCH0ryk8u2eVqGD91fkeEShguSvDzJwSPPuXSp+dtk\n+OzKlZP7zpqMr1hqfsljTp7c98k9zPn4DO+OfWky5wuSvD7JPffytd5hsu9fGrnv7pN9fiXD7377\n6yQ/sK/f18n2P5ZhwZd/z/BrRr44eY77zTsj2/km/5sj/0se95gMZxS+muFzzr+T5Gbzzsl2vqmB\nTVcDL5s87nvmnY1FuMn/5sl/ktsm+f0MDdN1GX5v72uT3GLeOdnONzWwqWrgjzMsHHZdhsu8X53k\nVvPOSO9Wk0kDAAAAy/hMMwAAAHRomgEAAKBD0wwAAAAdmmYAAADo0DQDAABAh6Z5wVXV+6vqrHnP\nA+ZFDbDI5J9FpwZYZPK/epr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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# now show them for each class\n", + "fig, m_axs = plt.subplots(2,5, figsize = (12,6))\n", + "for i, c_ax in enumerate(m_axs.flatten()):\n", + " temp, mask = explanation.get_image_and_mask(i, positive_only=True, num_features=1000, hide_rest=False, min_weight = 0.01 )\n", + " c_ax.imshow(label2rgb(mask,X_test[0], bg_label = 0), interpolation = 'nearest')\n", + " c_ax.set_title('Positive for {}\\nActual {}'.format(i, y_test[0]))\n", + " c_ax.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "# Gaining Insight\n", + "Can we find an explanation for a classification the algorithm got wrong" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using #9381 where the label was 8 and the pipeline predicted 9\n" + ] + } + ], + "source": [ + "pipe_pred_test = simple_rf_pipeline.predict(X_test)\n", + "wrong_idx = np.random.choice(np.where(pipe_pred_test!=y_test)[0])\n", + "print('Using #{} where the label was {} and the pipeline predicted {}'.format(wrong_idx, y_test[wrong_idx], pipe_pred_test[wrong_idx]))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Wall time: 2.44 s\n" + ] + } + ], + "source": [ + "%%time\n", + "explanation = explainer.explain_instance(X_test[wrong_idx], \n", + " classifier_fn = simple_rf_pipeline.predict_proba, \n", + " top_labels=10, hide_color=0, num_samples=10000, segmentation_fn=segmenter)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true + }, + "outputs": [ + { + "data": { + "image/png": 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Jtdb+dce/q+qwqrp1kncnuUtVHbobh7w6yT8meUmSRyU5NcOrY2+qqlvNYMrs3dQAy0z+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\n//XTNM9QVT2lqm6sqvft4XG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# now show them for each class\n", + "fig, m_axs = plt.subplots(2,5, figsize = (12,6))\n", + "for i, c_ax in enumerate(m_axs.flatten()):\n", + " temp, mask = explanation.get_image_and_mask(i, positive_only=True, num_features=10, hide_rest=False, min_weight = 0.01 )\n", + " c_ax.imshow(label2rgb(mask,temp, bg_label = 0), interpolation = 'nearest')\n", + " c_ax.set_title('Positive for {}\\nActual {}'.format(i, y_test[wrong_idx]))\n", + " c_ax.axis('off')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + }, + "widgets": { + "state": {}, + "version": "1.1.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/notebooks/.ipynb_checkpoints/Tutorial - images - Pytorch-checkpoint.ipynb b/doc/notebooks/.ipynb_checkpoints/Tutorial - images - Pytorch-checkpoint.ipynb new file mode 100644 index 000000000..49eca0b6f --- /dev/null +++ b/doc/notebooks/.ipynb_checkpoints/Tutorial - images - Pytorch-checkpoint.ipynb @@ -0,0 +1,442 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Lime with Pytorch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will show how to use Lime framework with Pytorch. Specifically, we will use Lime to explain the prediction generated by one of the pretrained ImageNet models.\n", + "\n", + "Let's start with importing our dependencies. This code is tested with Pytorch 1.0 but should work with older versions as well." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", + "import torch.nn as nn\n", + "import numpy as np\n", + "import os, json\n", + "\n", + "import torch\n", + "from torchvision import models, transforms\n", + "from torch.autograd import Variable\n", + "import torch.nn.functional as F" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load our test image and see how it looks." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def get_image(path):\n", + " with open(os.path.abspath(path), 'rb') as f:\n", + " with Image.open(f) as img:\n", + " return img.convert('RGB') \n", + " \n", + "img = get_image('./data/dogs.png')\n", + "plt.imshow(img)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to convert this image to Pytorch tensor and also apply whitening as used by our pretrained model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# resize and take the center part of image to what our model expects\n", + "def get_input_transform():\n", + " normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]) \n", + " transf = transforms.Compose([\n", + " transforms.Resize((256, 256)),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " normalize\n", + " ]) \n", + "\n", + " return transf\n", + "\n", + "def get_input_tensors(img):\n", + " transf = get_input_transform()\n", + " # unsqeeze converts single image to batch of 1\n", + " return transf(img).unsqueeze(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load the pretrained model for Resnet50 available in Pytorch." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "model = models.inception_v3(pretrained=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load label texts for ImageNet predictions so we know what model is predicting" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "idx2label, cls2label, cls2idx = [], {}, {}\n", + "with open(os.path.abspath('./data/imagenet_class_index.json'), 'r') as read_file:\n", + " class_idx = json.load(read_file)\n", + " idx2label = [class_idx[str(k)][1] for k in range(len(class_idx))]\n", + " cls2label = {class_idx[str(k)][0]: class_idx[str(k)][1] for k in range(len(class_idx))}\n", + " cls2idx = {class_idx[str(k)][0]: k for k in range(len(class_idx))} " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the predicition for our image." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "img_t = get_input_tensors(img)\n", + "model.eval()\n", + "logits = model(img_t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Predicitions we got are logits. Let's pass that through softmax to get probabilities and class labels for top 5 predictions." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((0.93593013, 239, 'Bernese_mountain_dog'),\n", + " (0.038447894, 241, 'EntleBucher'),\n", + " (0.023756264, 240, 'Appenzeller'),\n", + " (0.0018181818, 238, 'Greater_Swiss_Mountain_dog'),\n", + " (9.1132988e-06, 214, 'Gordon_setter'))" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "probs = F.softmax(logits, dim=1)\n", + "probs5 = probs.topk(5)\n", + "tuple((p,c, idx2label[c]) for p, c in zip(probs5[0][0].detach().numpy(), probs5[1][0].detach().numpy()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are getting ready to use Lime. Lime produces the array of images from original input image by pertubation algorithm. So we need to provide two things: (1) original image as numpy array (2) classification function that would take array of purturbed images as input and produce the probabilities for each class for each image as output. \n", + "\n", + "For Pytorch, first we need to define two separate transforms: (1) to take PIL image, resize and crop it (2) take resized, cropped image and apply whitening." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "def get_pil_transform(): \n", + " transf = transforms.Compose([\n", + " transforms.Resize((256, 256)),\n", + " transforms.CenterCrop(224)\n", + " ]) \n", + "\n", + " return transf\n", + "\n", + "def get_preprocess_transform():\n", + " normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", + " std=[0.229, 0.224, 0.225]) \n", + " transf = transforms.Compose([\n", + " transforms.ToTensor(),\n", + " normalize\n", + " ]) \n", + "\n", + " return transf \n", + "\n", + "pill_transf = get_pil_transform()\n", + "preprocess_transform = get_preprocess_transform()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are ready to define classification function that Lime needs. The input to this function is numpy array of images where each image is ndarray of shape (channel, height, width). The output is numpy aaray of shape (image index, classes) where each value in array should be probability for that image, class combination." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def batch_predict(images):\n", + " model.eval()\n", + " batch = torch.stack(tuple(preprocess_transform(i) for i in images), dim=0)\n", + "\n", + " device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + " model.to(device)\n", + " batch = batch.to(device)\n", + " \n", + " logits = model(batch)\n", + " probs = F.softmax(logits, dim=1)\n", + " return probs.detach().cpu().numpy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's test our function for the sample image." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "239" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_pred = batch_predict([pill_transf(img)])\n", + "test_pred.squeeze().argmax()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import lime and create explanation for this prediciton." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "from lime import lime_image" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "explainer = lime_image.LimeImageExplainer()\n", + "explanation = explainer.explain_instance(np.array(pill_transf(img)), \n", + " batch_predict, # classification function\n", + " top_labels=5, \n", + " hide_color=0, \n", + " num_samples=1000) # number of images that will be sent to classification function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's use mask on image and see the areas that are encouraging the top prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "from skimage.segmentation import mark_boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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EhsbUpc2UWvRcaEwvQIgBYzUfVbLeI2uVkDsjO40D4x1WDH0wuKAhABRCHEAWTqBWrDcMq8C0n6mp0kqhD5EqFhEhuEAIgeOTE2qaoVYkgyeQp4ZzHcZ0PPX4Vf7v3/4Drj635d/49rdTso6f60SwjRg90ho3Tq8rp+Ys/WpgnPaEzmNcQ0rFIXQ99IOhIYgYvGs4LBePB5BKCIYqwi6ND7weXxWKxpwmTjfP8JFPfJhrt56ikch5h7ENZz0VodmG8UpMnVw64datDf0q0g8rck2kNGGHFYNDdyojuODx0VOk4tDdIKeCxZBL4Wi1xpZKQY/NObNeHSEGBMuUMyH0XD65SMkJ7xzbeeR0t0EcpFI0AbpsJXPJkHUHBcE5SzEVZww+OvabLdKEH37/n+Cvf98/5D3v/zpccngfaaLHt9pIVa16a4J1hjrPtNo4Pjnh+ETvmYioi1oLPngQGKdRMzit4XzAx07J06qLM5dMmbdYY4neL65xo1QhL6TsnNSApJxJKWOdYZ5nclEPxFpHLo3gPcFHalHjuN/PgKXvHV3XM+fdOeFVa6UtbPrP/Rf/OlAZhhXGWN7wujeQ0sxut8U5y/ZUf/ahozah1kytFRocrY6pRb0A6wxSMk0qqSRKKXgfcQsRGUOgVuWVrLU4C8ZCjB0pTYQQsE09AyNWiWNraEVJ7NVqhfeezneAsN3uiK6n6zuOugIC3jkQwzRlLl28RM6FNE+4arh+bUMeZ1732OsJrAgusNslrt7c8eSnn2VzY+SPf+XX8JY3fTFPPv8xrA3EfiBNW82YNUdOReexizjvWA0nSNNn3/eeljMYS6maZeqHAWc8phkuHF/CiHDr1g3WQ4cv5YHX46vCKFhnGNPzfPrJ38F1RRnVIkxToosWsMxp5HRzDe8toYushsh2d8pu3NL1HuMCm3lHwOtOgtBozCUhFlLJupD6QMoVnGefMxnBIkrgGMN+mki1YL3HOLXgt/an6h4nYT/u2Yw7SitY5zAItVXCwoKnlBjnCQGGvieXCe8tzUSwmhlxUUmf93zfP+FH/8bXI1SstwhC1ytZp2GDXbIWahyc9+SUsNZR6wucyVmq1RjPZrOl73u6rsO6gDOWbCrOBZwPBCs4Y2lNwFiGYUWplZQyzRisFTAOEUi14TC40FFrRbBY63EuMk+JWmVh+A3DsCKnwjzNdLHHoF5aLZUYA323AuB7/t3/gQ/+4rdhjafvIhb16NS1dthgyTnhjcNHS5GGM42cMpvNKc5pTB+CRyzUmrHWEpf7752n1ooPARFwzi/egRKxITggknJe4m/POI7qmlshhEgr+jzzNEMQgvNE53HiGG/tCNFhrIATfIi4DOM4UrPQmUg6nanbSrRryANHw0U+9vEnmKfGbl+YNo2j4YRv+PpvRGqjpEwrdcl8WJoYHJZ51jl78eIJIXrOMlxSgWYIsYeSsNaCs3gfyHPBO4/1AWss/eqY1XpN94eZknw5INJ46pnfB9kTrMMZi+96etezWq1pTZne6AzBO1rO2Fap40S3GpDaqDnRlnSkVAhdpEljzgmB85+mVVKu1AataprIGkvwEQxkqcxpZtzcwjmvRFsakSbEEMAZjDPE0FFb0508C6lkcBa7iAJqrTRRl65WaJLBekrOjNMLpOM8Z0oe6boeERiGFQJM00gpwjCslDCyLIagsep6Taw1oesGysKZ+BABi4jyIakV+q4jhI6c25Ku1Z1m3I9Y6+n7gc4GRBy5FlqVJa2nO+48zzinxskaR04VbwXQxWtNIywTEKl4H5WTMIZ5Vo8nikNuExnVItgApWi4NI2Thh/Z4r0npcwwDOScEdPIOeOsw1hw3jD4/nwRa+hSaCKUnNRTKBW/PLuSCmmaEFMxFuakrLwPnrx4FyE0zXakkfWwwhhDSQWchrZtSYHXlKBVrDFYJ1hb6TpHng0lJ+oMthjaLFxcPUyrhuefuoV75JinPv0M156/hViHlcRbv/KtXLlyGWOFzeYG87wlt2nJjjlaErrYL9dqNQtUCqY1aI3ShFpUX1HR7FDOVeekady6dUoXB7zvGKd0h+zhfnhVGIWcZzAzXQCHELDEuKI6gzO6I3SdxzoYQsc8zbQGq+Cx0kgpY0qhNCG3ggjYNNMQqghN1Gs445+t9YgYZci9MsV52XVz3umuKXqjS0146xAjTHlivT7GzMpNtNaIMWLhPD1YW8VqFh0p0IWe973zH9/1c//gj38tbSEG97s9IUY2m1NaE3Ip5zl2aXLO4nvnKHMCdJJYY6BpjJxnTTWmkslJDYKESNcN9P2K1irbfSInvUfBeab9zGp9zMULx+z2O1orzGkC0RChSYFmcM4RQw/o+0qDVoTmhDFNep0ieGf1JhsDFozR9JjctlFZqyGWtMb+dKs7u/f0XY9zjmmcETHMU6KIhg+5QQiRlBLOO1xwS6YjMU+zxvQhMM8a9qyGgb7v2Wy2dH1cMjmeeUnbOecBQ82F1TAQo2ZRnHEgUEul7zqaqSoEshYnQq6FPBdcU+NT0oYyg6Vj3k2QHJeOLmNdYD/NzLtrlNy4fPESTz7xJLv9yMNXLvJFX/xmnId53nLz5nPUNmLNmb6lZ8qTkrS9IxdN1VIrpjXSnEhzIsZIv+po0pAZrHM440hzYtyNeOu5cOECVSovrWi5E68KozDNE8899yxdHFTNXR0uqKijloq3jv24VXHKypKXCRVDoKSCawZbocwF6wNYxzQnpjQTu0guBazBh0BNGdu5Jb+fCMExzyM5Z1bDSkUrUgnGUvNMFzukFiyWaU7UmOicIzdRVt1YTHB472m1MoSesY34Xl3ZH/orvwrAe3/6W9TNF2hL+nMIgTRP2Bg0FRqUgMtnaUXnMaK7mDEG69TT2O/3qsmwls2tU4Z+0CxJjFjn1NVvAk0oOTPtR978prdw4+YNDCqUWa+OuHB8gZw1DEKaut4CfVjRWmF91JNid67CDEEn4DAMzGaGI4MxSrKVUiilEGNUnkI0TTlPE2BorZ4/b9WFGErwbMct1jhSyXRDT2vgQsQ4z2q95sbN57HWUVplHEesdfjmaAVqK2y2pxg0Ty9Ydrsdzmootp8mJTiNoYmBKovHotqDViqlZXLWVG+pFUQzKq01atGfZU6E6PX3nMljwceeru+RZhm3M/NuYnArLh09jGsd1im/4Zzu1hcvX+Lylcusp4nWMuujNev1gEwTq3WHbwO1VUoVovdcuPIwKY1UqQupbZHSaLOwPh44bUV1F81iraFKY55Ghn6lHm5QVW8qo2oi/pBSki8bpAnTpAt6v9/R9x4xlb7vGFPCB4NxPdYaTrcjwTtloqUuLmjD4HDOqlFwFtuEaCzeO1oDH4Km6YyjpYLvDKs+st9vyKUw9D3RG2W5j9ecHJ+wH+dFAqtx+zB3WOfpY0SaklbGLTuk9xgxBB9oFzTW3u+2/MyH/jzv+M7/iR9556+ef94P/sK3azgSO9qqkOueXBJNRGPpoIskeM165JzUfXcWZx1djHRLzBycp/NhITutekVFBUMijTyONIEb16+x3WzY7UZ1UZ1nu9lpiIMKiLBqLIMNNPGUuRCcGixrHc55xv1EMmnZ7bO+D3DeqkcyaEqz5oy15lwQVhai6/0/9+dYrQzTNLPf70g5MQwrTk5OmHLCOUfKWbMc3uCcJc0JadCdhU1LFqEZi/OOLvR47zk5uXCulvTeU6sa1FLqkiHSjEHsIjklDIL3Kv4KfkXNFStL2CNCNRZQ4Zf3yp0E3+HcEV1/AkROb264+vQOVy2PvukK1567yfHRJR557GG64YRcA7laXvPI6/nYJz5FKcKlCw/z/PVbPHRzgwmaQZluTUDGu0jveoJxhK5nzCMViH2kTAYfIh5LtBaxQkNINSEYutgxDD05FeLRQCmJvndITeR598Dr8VVhFFbrNV1ckSn4zjKXypQnkmj6qOSqcVYWxHj93XnSlGgYfIwqWnFCBcRoCiwsu5w1Hucsq27FnGZSnoneKUHoHV0XGPqeznuMQDAw73dY7OKyFl3EPjCN0yJ4iRjvSVmJrjxOOBtIZmLoBgwCpfCO7/5fAfjgz/9ZmhhaEaTusTZwcukhLl+6yCc+9fuIFVJOWKfCqyW/R/RL7OscZ+5ucIE+9qpSa0J0mu5LWcMKI43oI6U1BFiv10z7HbXkc4Y+TXqstZq7FxG6occR1DDGgWneqSF16pVIUff6TNevAp2sTP4iIR/HUQVdQEuZeeFPfvTd/wSAktX4KefSGNYrlQt7y/FqxW63J9dMkUpPIPiA1IUnioGUM9vdFhc8LMRg7CK1VsZppi3yZOcdIXZ4H5imicbCHUWnKVlgmia8dxiD7spNqJOmNUWqhk+t4Z2lixGDfvbojzm9WSkp89SnbrJ5fssf//I/yspf4JnTq6QknFx+mCuPvJZrt/Y8+9xN/uhXfDmPvvZNPPvM0wiN3/3dj7HdjRxfDGz2Ow3jVqqFCa5nHkesrapb6S0OC9ZCbjz66CMcnxzx7LVnuHbzOrLkXXOpzNOOGDqMCMdHJ1hnKHmiW4z3g+DVYRT6gbd84ZdyY3+dTz7xSUppmkZyHhx0sTsXAJlFq94ERFQjYI1Xmec000rBiMUZRwwRby3ZFvUinCXGgLQVq/WK/binFcFHRx97ckr0MbDd7+hjRxcH0jwpw76c0xvYpxnbGsRASzPNQEmZvu81DdY0Dn/n9/wyAD/+/q+j1FNN582FughzpvmIImuqFJqpOG+BpurGWjFi6FzEG4+zKnkeJ935wdKq4Iyh5GXyxKD6AnQRe5biI2lLHYihiyui97RYFy2DxwfPNM9M+5n+SEORaZzIWXP/tjbmcSbnoqGMNQhVDVQMtKwxek6ZlFXbgVFRlAqKDO/6kbfx4+/9DX7gnX+f9/3kn+bMhY+doxbDfrNhtntSSqyWdCVU9mnS9CKVPM9st1s+8YlPceHSBR593aPEGFk+4hI1y8IrJGLsWK+UVATlHLy13Lx+g5RmnDG0pu9JRRWMXTw6X0jOW4KB9XCBUgRjBloVblwd2e+E5565yXNPPctJHHjN5UeYbp4ybUakWfbjSOh6Ti5d5tY4s9vPvOXNX4wVy7Xrz/P7H/04jz/+OF/yR97A+oKArBCx+HDEfiqkOTPPO6oUVtKRpwyt0YfAI699PVUK1z+x48atHaUWSi30/UAIkXkqxBDZbkeCt9TSODk+fuD1+KowCtIaVy5e4Z9/5MPMecd6dYz3AStLwYw0TKuYVih5ps4wnu6UOe560lwZp4lKxhmHXXYFJ1CmmSaNsOymmr0wjKcjzlpWfkVKE7klKpXiHBVhP0/UthTGYHDW4awjRI+lx2AwUpAyUVtjtRowFGrNzDVjjeenf+ZbeOc7fpVxtwdjWK2PsMMLevZrt57j2uZZUp10RjeDEbOMDcF4pMIQemgNamUIkSptWQWG0PdMu0kzA3MmTTPeaOVTyRmzeBMYSymV3kWc8WAMMWh8XVKjFZAMm9MNzqgBTWnGe0dqld1uAwjr9Vo1/rVpWrPq78OghWtDGLANcq4McUVnPW3J6JyhVS1+WvUrTM0qM2+NZgzBeebdhhgi/bonVYdb0rV5yqyHFV/4Ba8nho6eiK2WSqP3UWW+ueKdU2LQGNri6QiF/bSHpgo/Q8RYQ9cNuBDZbjZEZwlRszn9agVmxDqwGMbdTG0rTm80Wuq5/uw1nn3yKtE5rly+wMmqZ/PUU4ybU4z3SgBbzQAN655cZp575kmkTbRSVDq92/Mvfuu3+Ze//C2sLxyz2Yw00xhWPde2z2Oo5FQI4ZhmwFlhSo3f/r2Ps9lvuXpzg4jHmcg072nNcPnSMXNKzFPRkKd35ASbzfTA6/FVYRRqLTx0ecXrX/coNza3GMeJnDKtKqmVU6a1ihYze2iGhx+6wtHxCfv9nuevXtPd01hscBjRBVHmpPFg8OxLAWcIMaDOmCX4QJoT3nqmcaSKpq+MNaQpMbuZGKIW9aA5fWssJVdqaUzTDFYJtd12rylOMXjf4YKlFRUEvfc9/4yf+Ik/pXxGa+qKOmXlS1ZXGRGctVijIYyzWvTVikBrdDEwjjtKqxjvwajQabs5xYhh6AdqFeZpwlkPMrs7AAAgAElEQVSDW1JY/bCiD5GGIYpReW9JynJ3qhitRYu8QvSUlEm1MAw93lu6PlLHnXIN0dH1kTRnhtVATol5znRdz36/17oSr3UBobPkksEafvgH/o/zZ/1jH3g7TTJd33NyfMTprWsMsaeWwrgf8cHhrcWYxrjfYqwwTfOiSHV0sVftRoMuRnLOmqq0TglTNPti0ZrxsniOOakAa9UPi6iqIQ3ohNpUDl2w7KcttVXwjpoTF08uUrMjzZUnHn+OdXeFNM5sb26Z9nsuP3aF9VGkycRbvuj17OYdt6ak6lwLTz/9BBcuXuLq1eeAhkgjdp6v/uqv4vqNazz3/FNsThPGWYajgS70tFpYrVfknGhiMM6z2+/Q6t7K6X6Hcx7VyBlC3xGWef/sMzew1mON5eg4kEqjH9bM88yD4lVhFITGzZvP8MgjDzPmmd12f14iO44jxhhy1hJkbyMnJ5c4ProAYlgNR9T8PJcuX8IYYT9u8c6ca+VLztB0omBV/9/FSIg9aZ71d5zWuBN0VxEh7zXm9p2nisWIoeamhTLG4qwhl0wtDWMFay2GRacghnlMrPsjPvCTb+d7/9o/AJy+X+pCSqI1GSI6gTFQzXnZ9zTNGGPpY8+4H0GWRTuVJYWrzL/1VolGGiEGRCJpniit0YcIGKZ5pokhzQnnDSkljo+PGceESGO325PSTN/3eOeI3pHLrAViCfbjntoKnQ147zndbHEh0K10l3XeM+WZOc0E42mmYV2g0HjPD/zvAHzg578V7z3eJ/ZjojbI1WO9pnuxELqgsmMMLngswnbcM096bVpN6RaZMsofGIN3S13EImGutWorjNTOmjzQSlNjU4U8zghC7DpqyTgnKkgSMEFYdx3TtKNVmGbLvBOeeXZkc7OyvhLI40wfAn0IpGnEx4eY0pZpmnRhX7jEar2ilMLDD19hnPYcH6+Y9o0b12acN9y8eZ0rD13hypWHuXl6jZvXb7FaX6FmAavKUYzl+MJFjLXY4MlZuSLnDPOUMVXDzFvTCAjeeZztOD4+XqpgDTltqNbibLjr2rsbXhVGARG221uM88R2uzmvYVcXtS05ZEPXdYzbxGZzyjiOqlpsjRgdJc9LBaTRRYfBBavGAaEfhkXApKRVK1ocpaWmLLLagllKk0/WJ4DVtFJQYs8YlfwaYylNpbvGeLx1xC4gTXAYDCqZDT7y3e9QXqFW1VOIaUt/BYu3jiZgnddiqFm1FdZqRsY5VeyVmilVCLGnNY3znfdaz2AteM847bG2o9Wm1yKCsZbaCmmaSalQRbh8+SJ5SWuWouIia1V96L3DWrS2f+lvYIzozrrUC5yenir34WBMs6roWiX0Hb5z5DQzl4RrwrwU4fzC3/x2pvk6JidiDAyDlhLvxx1GtDKyGktd0qLaq0J1CbFqifiqXyln0TRHv1op77A+WtGkcbo5xXqtU4g+aOUganiddRichisWTgWVfJdMlQK2LYTziu3ulMZS2u3WzKNlt4Fnnt5gcyDtd5S053jdsT8a2Gxu0qohN6OVmaXw8MOXuHD5igrugmOzyfzBk48vfFjFew1hEOi6gcsXH8FvPU89/hyPPnbM+kQrLK3zhC4y7UdV2NaqSkVv8dUA7rxnCJil/PpMF6LqyWHwSNGCvQfFq8IoGGvZ7SdscISglWSt6aQ8K8Q5S/tZmzBWCFF1BmlhVo0taumtuozOGi27joG5ZETaucCIJa8OqnT03i1NPiq1zJTFVV0N/SJi6W9rkKEieuchEFmtjpS0cpaGCkuCixwdHfGXv+t/BOBnf+bPsT4C2W5VHSnqWaj4JrPyHbHruL6bqGXG9h3WCDnPxLhmGDpqTZSal6Ybhd1GWeaCoVWVtspSIBT7AGg5slYDCccnR8QQGdOe2Dn2uz2lFIbVShfdaliKhkCsOTfGAG0xvmYpDLpw6SKlNaZpuygCO9br1aL3mLSMuFTOuv1813f8Hf7jD3wjoIx/WIg/ay2nt3YvdA0yWovgnfYcqKWomMjCPCmBGULQhjfGUGomZct6teLCyQXmnBhWnmnS+Hm9XjNPE60K0XVcvniJvouYWrm1uQVtqVeZG7Hrl8YqqibtwpqaA1ev7/mD33uWG9d2vP6RR5jGmxytVbBWysh+N/ORj3yCVbfiy77oTbiQuHrtBru5EG7e5OZ2w3a3ZRxHjtYD6+MjMND1a+apUMqeftDCq82tHV0Ujo9fg8dTUIPuF91FrZUQj1TpKcJqUJL1TFyVslPP1UBrBqkNZyPWgr1dPXYfvCqMwjQnPvrxT9I8GKcSXdXU2/OmEdaq21tbYc4NFwQfHK4JzuuOpq25LH5pPCJNMwvRBgT9sNZ7jAjSFiOR3KJkTKortwZPO0/ThS4uNRC6KHwMpFzAWlZHupAEIaWkD8MIw1FPt4r83C/8Bb7nu36Jv/KO/5n/5D/9C1y4dELKvYqwjMa8c0pK2AmEoDr9EIN2bHKGXOel05BQitVmJzbgvMbVrVZNCzqPtXDpoQv0fc+NG9d1x61C7DzeGZyDvo9M08RqPSwMvRaCjeNea0OspYsdXdctjVsqR0cnSgZW9RhSypRWmedEzurNOafaAXOWDg4RGfT5/vCP/UmcU/GSs4Z5nLHO0HW9cjmlEL1KrkvOVIHduMU63STM0hHrrIGLsYY5a0ixH3eYtpQtS8M0YegHpnnCGf0s6imNPPvcMwSnnbHOStfLWJcsjkNKYxU7igl4t2a/s1x9+jmOVxd49KHXY5oqFKO3i7krrI6OuX468n/++r/g4qUrPPLYG7h+4xbPPPMsxjmyLCRqbczjxKrvEeDGjZsYAsOw5urzz/IlX/pFHK0CH/39f85jr32NeprRYpd2eiLCeqX3Z9ztqSUx7TNhEWd574ldhwR/my4kkItWo+b5/2NE4zTP7OYMpdH1mg9W76AxjtqgQieeI/ZqHcd5ojcRLFRUWlxLwwVHlYz1DiMW4wxdCBjjKKJ5e2cdadSswbBaLcU+YKzDuYqxdmlxJeoqG7De4hZdggvasSjnGRrkNFNq1vReDOzm/VKqavngz/8rGGN4x3f90h2f+Sc++M3Lrqm7urroauVzSVhntEIxjcTYEaxX9SCVfhiwzrPf7fGddmNyxi4tu7QeI5WJlBIn6xOC90z7kZomxDtKSXgfmBdh1MWLF8+NwH67p7XGdrtbCo8C1jhwhpQq1nr2uw3WG00VFk1N5qWnQ/Q6pUQa7/7e/433/eTXs14NjOOo11i1SKmVwlwnutDhnNPGNlW0piSr5xNjj7EOkcb65IhatcHMnBLGWu3/YC3TNJGzckbDekXKGWMMm9ONFqxVvSfjvGNf25JF0sV0+dJl5pyxRiXCrVbW3Qktd2yu3cLT8QWvfyPWGK5ff5Zb00RdOS5evMhrH3sNt7aGK4907HanXLt1i+Pji9QqC9nsSGNmc3rKMAz03YC3gf08EnzH1avXlFu4coWnnvgUX/VVXwkyYwnsdzdhHnHecOHiMbUKNRVyrgTrWa0j87SlNaEfem1OkxKg6kasJZfC1ZvXuHB8THB/CJ2XXk40aVrlFSxnDUQ1dFClWtd1pDSrlLZCLoUmhdgvNf5LGfGcZ4JpultHDw3K0nXHOK83yxhaacjSN68uefQmshS/CN4YctNW8XVqWO+JXWScJzbbnRZGWe3jKLViUNbdBUOVwjwnQtdRcqM2T4yRv/Ghf4281PS/8y//Mt//H/5DfvT930AXgl6PyHk+XbkNdWX7QQVY0S+pzEWfoLF+A48uWrQ3gpxWnnr6cXLNGDFE7zFV+wl0oaNKJcSwFBFV0jwzjkHj3xjOSbyUEt5Huqg1AillvA+aC4+Wcd5jsbh+wKB9MkUq1moIkMsLTT1a1eyNM06VoMaRRJDa6ONAmmdcUE+h1UozjeA7WhOcQcuT9zONSllazo3jXoneVPT5Gks39MrYN1GOxDnSNAOipGjnaalSs1ZBNhGc1fqZVgTJGWuhGwZuXE9IsrzhsTfy8OWHmMY9N8Tj3YAxjlwSFy5dwPaOEI+4kNZcPX2e8aMjQ1yr4XKwG3e0pX1aLZX9fmJYH+O8zovHH/8kN64/xyOPPExOE6977PVcv3GN4AL76TpjTVy6sFaeyXv6oPPKWANxpYI347AmsBoi+/3IrZu3MM5hmmXo1kvF6uqB1+PL0bj1k8AG/cKFIiJvM8ZcBv428Ea0+9K/+VIdnY21+K5beiBovj3PszZCdZaZSpVKXeLpvutAHLWoaKbWRqsV73saBWNRNV8TSirkuujuraFfKStMU+2BMUpqNiNUg8phEYwP1JbPXdb9NLMfR65fv8GFCxdVj28KEY/zkZJGxlGbfWjLLEOIKqo64wHgBdUhoG28+ogsxVjDUc+cZ1KeSFmr/qwoEbibt7SifR/mScuqp3mmVqF10MdI6CKlauWh1iRUTjenOFTIlVohhJ6cM3NKrFbDeQHVZrdlvVrhrRoMEZiT9l503i9elFsMuNYyWK81E9Ya3am12F/Tgz7yvh/7Gn7o3b8GwI//xNtJs6YiS9FuUBi/lFb3gGXcT9RSzjtQOefY78blGhxVVKJsjVn6TLaFgNZdv5aEj5ZaM+Ok/M7Q9ZRa2W5PaU2LvMQKqUzErqc27STlAKkFHwYunbyGuk+0y5mT9UWcFWJopHnk9HRH1w3gHC70rIdArom+t0iLBGPpesvxyTHbcQSrhs6HgLTGPI8cH5/wyEMPkfOIqTu22w0PPXSB0/0G47yWoW8tF06OKWXGiBB8hw1akZq9Fr7FEBia6POcMyVX5jSx302sj9Z0sSeuBrbb0yWl+YBrWj4LAuKuA6hReJuIXL3ttZ8ErovIjxtj3gVcEpHvv9cYr/uCY/mr7/oGXBc53WygZKK15HnGUMEJzQg2eCRXpDT8EhJ4r+GEdR5jPVPakks6r4ob9xO1Ve3KY4T18YoQ4tLrMNMPAzlpvJwWt1Wlr+ac3FQlX2Tcj9RSOT65oG6b71jHFTduXsU6ME6Y0ryU4c44H1h1vYYh1mlpdtYuQ+9516+ff/6f/cVvoTRNV+7GvYZERV1jJ/DDf+3DAPzQ+/4IgqE2SFkXUwhaj+G9XzoGLeXj0pimkWHoqUXOdQ1d0FBsHPdYZxn6bsnnKy8RrUeqVmla55RYtW7hTjTESnUkl0m5h9jhxCJN2J5uCdYvKkm3EMGJ9/z13wTgve/9WvzS4o1FbRm7jtgpz3GmH7DOaiNeMartEO1GVGrGe60N2Ww3quKLHathpU1mnY67m0ZiF2moh6QNayslJ/pVr23fvKb8clnCMetoLePNJW5eFaJ9iM4eE73haC2I7Hny8at86pOP44Ol6wOr9Yrtbo9zkeOjCxwfHXM0eKzR4jERswjgDGKUE7t1esqqX5N2O2gzTz31KT711HP8iW/8ev7UN30zzz13jXnc8fTTHyH0IyGYhevptLOTc1rDgTBlreURDKXJslGMbLcbTi6c0KphczojFKxr/NiP/tqHReRt91vTr1T48G3ANy2//1fAPwLuaRRA3VWzdOOtTTX0VkQ7J8+Z1cmaECO7cUcfhyVfbShVeYKWJ7COVIo20KhCCIbNdqJUjdVj5xnHysMPXSYGz+9/7GPaOg0lsqzVAhsRdGeqlTlpx6UmldAF3vjmN7HZbNhut5xcOiaPhSnrjuSDZd6eMk4jpVXtVRCjVirOk34PgDRC9Pzgj30Fbmmf9h/dVix1O37kA1/FD3/vC8bjfT/0u3z/j3wZnPU/NFYLlbxfahoq+/2eJo2c54X4c/ild4F1jlLTsgsbfHihlBhgqjPHw/r8ewhCiEuJ+NkyBlrDNsGI8N53/1MAfvCH3oozHuc9IfTarbhoMdmZQfjgB/8Mx8daxehD1O5M88zmdMt6PRB8wPizrEkhro+R1milsV4P7KdRQ4MqNAvBR2LocFY9xlZFu0yp6dLsVVva2wU9Nvqgrd2tAyOUUgnOaVeunMBWTm/NbG8JF48rdFnL51PBWXj0NQ9z6+YtnNPMx4XjYy5fvETKhfXqmL4fcKaQ08xmM6nBttoHczeN7HZbPv6xT/D1f/KrefTNb+GZpx5nP17k0089xxOffIqj4RL+kRWnN55nv7nIdtxRjcF69czmJtRmicEt4at6u7KkWEVkcdbM0unK0YqwWq+J8cG/YP7lMAoC/IrRb2j5z5bW7Y+cdXQWkaeNMa958Ztu/96Hk5PI8889jzjh6Gi9kCKirrh3DEMHYhn3iVIhlUa0ntq03VbsOrq+4/qNm8QYQDTmc15lq3XpyOvDitbgySefpe+WKjrvNOXonfZJXDrgaNMQ0a44Vi0zBp585gnSPJNKpj5fia4nrnuqbUz7kf08ItLwTnPdZ+nBXNN5rlhKo+875jSTx8K7fvQrmKaZ/4e6Nw22NT3L8653+sY17L3PfE5L3VYDYkaxJAZLDHISu3ClHKdSdjnlqgTbcVIxAWRBS+pWt6TuFmpJaDLY2A6V4Y+TKv+h7HIZp7Cxg6AAGxSLCBsJ1K2h+8x7WMM3v0N+PN/ZkoNCmpgQ8VWpdp+js9dee631ve/zPs99X7fLZUqSFxmkxI++VRaEd7z3W/jRJz4BiHzY2owUA30/EoOmUpbxS/IlVAp4r8nzjK7r5ptbbnSjoKoE6PKAY2BntNo4TljnMFomPxrFND7w+LtZVxFhGHn6nZ/gR9/7bShl+dFnf5HnnvtuHn/8d3IjPviB70bPgqYQpKcSJo/X06zoUOcJaQ+w5dM0sVotpemYBqwxlEVBijL5UFZj9cx2cE6qL5+I40i1qMmcIkSPNU5uTKXJbSY+CQLZTDGapoEQIpnLsc4w+J7+LCdMI13bkvyA07kY3nzEuZxLF4/m3VgquRQjdZFTZJYw9jR9wzgO9P3IarlCKccnf+PfcPv2XWLyvOY138SNhy6S54aDwyWnpyvW6wtsNx2f/e0v8K2vfx33tcKpP8KtuxPJTGy7DXmZEwJ0TUOIwrlIKLJMzVBayUEpyxprM1nwO0+RFYztwH778hmNvx/Hh+sppZvzjf+zwA8A/yCldPAl/+Y0pXT4f/cY128s0vf9lW9kCp34+MuKg9VawCPOobRmu99inLj3xDWpadpGqEDWUBQFL916idVqjdZauufaoLUlRjXP5GuUSoxDi1WSdRC8OGlcntO2gl4XWIglc45ds2cYB5RR9ENPJGJzR57LlGS1PBKP/TQwDj3TOIqted7Jq0UhpfzQi2DJChMBBeMo6LKUBKRaViWTlylGljn2uz1FnqMUqAjPPv7x3/HavfWdf5SiKM53dcmokLOy9yObzYayrCjLEvE5tKwPDvA+MvTSTMxcTp6XQkyKAWcsSkHbNLRNR5nnvO3/csP/9Q9/j2gKXE6K8Oa3/BP+9t/+TxlGPzMgNcPYiPV79LNjUfagoReknDWWZtewWC6xxkjzUqlz+KvWin6W9AoMx9IPk7AQpzlnIQVMprl//z7r9Zr14Zqm62SCo8W5WRblDI+1xDQR/EhZZOf8SIXAZHs/8NILNSrWLBcVzg4YNWGxNNuR45NjaXBnThqeKVEUOWVZkmciHGvaRpq0RsaBH/vYr7HZbLlx4xLf/M1fT5Y7lPGsVzWZLTi736Dtkm0zst3t+fN/9s9Q54kYdtw6/izH29uc9SdENMY4hm6QXk+YqGthNeRZSVmvZxqTNNOVVmxPt8RRhGjKwuNP/swfzPEhpXRz/npXKfXTwLcCdx7kPyilrgF3f7fH0FpT1RXdENAqQ2vNYr3CZhn7fSNnQJeDgpgU2mXSLDSGoMRR2gwteVWQlJTJrpBKwvseazO0SthcPA1KpfOjgdaGLMtmnbksEDFGOUaohNaJhOzKiYgrMploaMt6fcA4TLR9gzUKW1hsplFRuvXOKqYwogCX2fMZuzJ6tiQbYXQmKKsaZ2ft/jCKQ7Io5gZV4pm3/cqXfe2W9YIsF4PWOHkiUTr9LkOlRFVWonp8QDCeFYpuftxx9LMEVhqqCSWLqVKkpLh06SJGa/6n//7P8X1/+e+d/9zVak3b9nRDj3MFP/7X/xSTbxgfOCaNpBsVRU1dPxgxz88hF2iLQvwLKkU5FkyerMxZLGrxmISJvJCRpcBDHM5NtF1PVQnfwcfIGHrWB0cURY7RjjxLYtm2UumN/Uj0ki8SvISuhKk4B/X0XY/LMjCW43s7jg6WaGXEExF69s2ETgXL5YK228+NWNF0VGWFUYqu2TNNwq3USjFOI5/+9GdwZuJbX/8NvPprv4ZrV69y7949Xrr1Iu2+x2J5/evewNd+/Wu4ffeYj3/8V/nHP/PTfMPXPERV5WRVJuyF9i7KSBxiuSiJ+0CYhAymVGIYR8ZwhrEyfej7nsVyidYJl8tmNg1/QOBWpVQN6Dlgtgb+BPAM8A+A/wJ43/z17/9ujxNTZAzDbBmWErMfR4KCfhoJ84fJB0/sZafICkkVUlqJYpHE6uhAeHwkFsuKmBL7Zo8PA67IGPweZeUG0drKrm8tSiMOOiU5D1kh3H0fR7CJss5RoxT/ZsaB13VNWZUMnczCs0zGekprQpIQlBATOskO5UPAT56qrjFa5KlTkOyFaZRqzRonuyjSFCwywZM99Rbp4H/4J/8k0+Sx1vLD3/+/8pG/+aeoqgfxaApjHEVeYC2CXkeELw92QwGeCJfBGkcwUNellPdRLMf77QZrDKvlcs5tEOrR2W7DRz/yJ+nahsff8Qv8xf/y7/PRj/wHFOUCrS1N29IPI0obmccbwzSKYrMoZOKx2+1w1s2kqFlglkXxHSiFsRVd17HdngHM8vYHwTWGGAd8kJsgL0rCrEqdJunfmNlk9uCY5kcJwum7Dj8FqiIXrUp6wMacsEsnXoz9RJavyXN5DtYkqgp0kh1aRqoObZaz+UtUIX3Xs99tKXJHVZW8cPPz3Lx5h4ODAy5euMAb/9h3cOXyFckfiYFXvuIGeZ7z/GeeRxnNZz/7WfbtyOVr13j4kWuM/S0+9vM/x2p1yFd/3ddTH8nRUGPJ7AwSQpHNyU9aR0KYONveQxthjSht8EEQ89pAiMNsRX95179rpXAF+OlZAmyB/zml9I+VUv8S+HtKqb8MfB74s7/bgzxg+D9IFwrDSDfcpF7UBIXc9FEqAGZ2AVOkzCsmL028vMjZtw0pCH3XKI3SEWUUaX5x9nMpatDkeUFZynhs8AMBT1WJEi7FiPJzrNjcnbalo+06MVl5z24rGLDoA+tZWNP7abbtKlzhhGwUFbqUsBSZdiqUtvJBMxajHHmdz9mEQlJyhegzImnOBJwvrc8nIn/jp/40znGOM48JtBZPwYN4NJmgzHBarWcakScGRd+P83MxsrvNTauqrIlxBqF6z2a7JURxTfZDzzveIQvUhz78H8pjZhl9P+KDF0KTUaCTvO4J9vtm5iFKn6Zre6ZhpCxLkZ7LEICUgnwGkoeoGYaeYbRY47AuIyqYgqgoR+8Z9w/6H2Z+nWTnTCmKGhQJx8mcxZpaRElVQVk5Tk9PGYYOax0+aowrMdZCqskLh1EZLstJyc8cTAChN4UEbT9gtKbMc6zLUNqw3e8wRiqr69cf4vLlKxweHrGsl+RZRt+3bLcNJEdd5Fy+eJHT42N22xOyQqoCpQIP3bjB/uQ2z3/mRc72A3/k665hDxzd0ImEXjup+sK8SY4T1glxafQTy1UtLtXMyfvcNjKqnl6+9+Hfuafw+3G97nUq/eqvyn9//9u/kbIqOLxwyDu+/2f/rX/31I9/F8/+4M+f//mvvvsbRL5sLOPo+TvP/sbveOw3v/c1eD+ijcZqy/H9YxZVTQiBqi7Z7jYzN8CSFxllVZw37ZSC5XIlC0c7kJj9/sMo5XVEzuOFZEr6mekXU6KYy10do0BKk4z5YoizG9EKIh0xP+VZRuEyMmclySlGds0ebS2JyLNv/WWe+bE3UC8WcrxQ4p3IZ3AIKTF6aNuexbJmGMTKHMLEcrmgaRrpyyc3R+RpVqulKCgn+bNWQiUqcsnxHMZ+TiYSodSTj/087/ngm4QgPQk0VsJTA+M4CKsiJYZhYFkt0WjatmE551Q2O8l0WCyWnJ6cYOdjXt938zhREHVlKWlUwzCRotCvqqoWaKsxc5NUJgtlVc0+jOGcBFUUwhfYzxDcqqrInLgLp2k+KpCRoiOR0w+Re3fPYFqSIrzyoVfS7FsIkbFvsWZimnaEZOiHia7p6NqOC4dHXLt6ibIquHPnFnfu3GZRVxweHHD/zh0+/7nPk1nLoq5p+548r3j0qx/l4Vc+LMKxsacfe9reo0yOdQXXLl/j6776q7l24wa//Gu/xP/xqV9hG14SoVoAnSzrxYKqyglppOsE4bZvOumb1QUzpnQOGyo4PdkQJvjAh//F/68jyd/T9fmXct781KN89Nl/zd983xejJ9/1428SQYcSgdOHHpMF4S8+/ij/43Of4Sff/Rv84HteizWWn3jqV/iBp79NuvJtw/JgwUfe8S/56BP/ije/95shRkxKHC4WlNWCfdvN40gpdyUXUMZ4kLCZJaLoWmEqLFYH4iycAtZk+CCNwYRoKCYvM/Sh7THWolXCkKRLrER4pYziI09+kre+749iE4x9x8HBgZwLh4FuCiRT4rKMOI4SiabEsg3wzsd+kfd8+LvxQcJeYoiE0OHm/HWb5WA8IQnnQaHIXIGPEW1Frdg3s1Ixz2ebuCfEgf2uRauMsqpwmaEbmhmMIjffM++QScjYB0JopSJIgiTLrBHhT17jo6delGJTTorFoqKqZJSclZq6WojJTR3OQBTB9nd9Rz909H3HvlEzRFXhXIENAtgNwdPue/m9sozcZSwLxxAGUlD03SCL3NBBFBqzWNYV+9GTZQmlPVMjkw+jFuT5ZU7v3Sf5QzJt6LqGZrslc4YxDLgcNAaXHdK0Hcu6xI+B0UycnJ2htOLK5W1Xy2YAACAASURBVMtcunSDcYQwNfTdnqODmsuHX4sfRmyWs1wdUq4ucPHqDZbLo3NB1fOf+TQXL12iKCu0y8mympgvuN/tuN/eY9OeidozyyFIiFFIsNlvQQWGPpF8ZHvWUhQlOnq8GSkqjcsyDJaDukTrP2Q4NlCgND/89Ov40Lt+lR9852tFDZiLSSnEiFGKxz70bbRNh9Ej//U7v5G/88wn+fEnRdjz7r/5vTi9weY5mTN8MXoc0jhhrYYUWSxqmaePhr7vyKzFZQ5jNfvdZkaLCS04ITkBcYrkecJpw5Ak3MRqjbOWtpOJAySqZcEwj7/C6CmrgqTFGRhT4kPvkAXvA2//OE8893oymzP2Ekqq4BwsUxSJcZjmrIkcvuQIEXxkGD25kmDdMI0Mo4ztzKwufJCl4LIHjkJ9noKcFGij5wphlMyJLEPVheQ2Rs/9k/t4PwKRPJ9n+/M1+Yl2GMkyLfqNELCFJE93XSv25dnVF0PCGGEjFKkgpTRnPMqxZfIDpAeRfqK1ULOKtO8GlqsleZHPWLhRUG/TIGQqlcicpel29L7FBzVnP8iRxFqDFDmiZ+m7DucKkje0+wmjNXVZsTkduXd3y2q5JBnESdo2UGS4zJLnJSkIqTrPZjl1LmGuzX7PyfEJdVWhjeGRRx6B2PP5F36L0/2Ww/WCRx55JWW9JCmHLZagNF+4dRulLOv1gktXX8m9u3c50hWVyyirFdu24fOf/td8/vOfxmYRpSNZpklJYaIl4glesHXLZUXXBIwWZJ/3HcMo4J4QI5mV3JHVun7Zd+NXxKLwYL6e5xlvftdrRQtvNUWRE/tAPwzsd3uyPCPPKowqgMRbnnsDMUbKLKdeSPLvOPXSaPKRx5759/ixd/7vxH4g5Y4JZDVVmjJ3aCLOCeFoUVXkVmS30zQRQsRHkZUO3cjUjVL2K4WKQXZnH+ibVrQURY5BU+UyT3/gvEMplvVSoBlfcuUup8wLurZFY8ithjjS7BqGbsSPXoxgPlCXC/6Hv/sX+Et/4e9ibEFot4z9iDWlVA3KoI0VPLq2MGPMtTLSk9AKoy3JQJ4zOy57UgSxcUSq3HJyvCEai5rHvO962y//jvfKWAnfZUbMR0QctKiF7qOQ6c0UgwSqpMhLN2+iVGK1Ws1MhpaD9SEkxNTl5L2X3kcxx62nGdXuUSrQdlvGwcvotqzETj317PqRYDzOFLOWRHwz1hiqYsE0JrTO6IcBPyUyu8Bq6DvD2Pbsdw15VnF0dJHKGbab+wQv2Pd8Nt8VZTUfRSLD0OO9l1Tw5Dk9PeXmrZc4Pr7HV3/VoxyuFzz6qldx9/ZNnIa+H6gWB1SLJVOykkMyd4D33cSFSzdYri9z7/59mt5j+w6Va1669Tli7HA2UtQ5xkjyNCqhTcLljixzLKqKzAXqumC32RGJVHmN1pJcNgSPVk6Uwi/z+opYFLTW5IWo2swwzSQdz/H9Y2IK5IXIaYMPIh2uS7yfaFshzrRjK43IuaFGSnKTxsiTT38LP/G+T/CWJx7FFiXdOPDBx//F+c9+94e+lcLlOJNJ5qSxOC0hWz4GsaaWiWkcGcdBrMYz2qqqFxRWyvDMSsyY0XpONhYlnHOap35IcGTP/fib8CHw1F/7ecLoiTqQu5y3v/mfnT+fZz7wBpp9y9HhBalAAuSupCwlRDKzBdeu1PR9RwgjBkVVFud22TQbu6wVrkCWZVgreRJ6hq7EEMUohWQMOK0wymBUIitzQoo8+SNyVHv2g28AFE/9yC/w3g//cbQepXcQPFklZOiubc9t1i4rGMeJaegxyqLNA7K2IyWxHFsn05gpePquhw56Z0kkhkGyDvRM2VZJ3tMUISQ5BvmYaLfN/PtMqFyLwA2DdRbNDOaZ5VEyehTU/2JxkS987gvsN5EsS4RgKOsCAygVqRc1bbNj6HuKoph/dsIYR1UZFguRK4smZeDixSN22y1D3/C5z76Av3aJCwdLXv01X0PhHJuzM166eZNi0RCMY4iK+uAC2mjyRclivebk5JS8KqgXBVGNfOq3n+f45BZFNqJN5PTslLpeUpQVJ5tTstyQGye6mqTOPUDKTizKCq2dTGY0mMxglLz2L/f6img0Xn/FMv1Xb3ktRIWfvAhApnF2JzpBmDlRIK6qI0KQc/+DIFc9j6BcZhkmEQmV2rDIci4crjk5uc9f+6Evim8ee++3kM8Gn6cf+5f/1nN5+oNvPGcCzpg/Dg8PcZlle3o6j8g6CTdRhnGSkr0qcvRMfWKWmxZFxWM/8HN84CfehLWOs+2OIi94Yl4Enn3fG3nq7b/Ae97/xnNl4Y/80D/h2ee+k6dmjBnAM8+9iXc+/s/46E/+Obq+wRglZ992S11XOGcZRkHLRcETn39NKZHn0rhLJKE4jSIQU2lCpZHcWaYhMk1JPrjBM00T73v37Fl4/xsoipKDgyNijBwf36PrGrGkR9FVaK1JCVbrNT5K9iNJ+A/OZefPyfuAtdIQTCHQbrc4m8sUJAbu3b/DOA2s1ksODlaSE5ll2Cxnt93TNB3GZuy2e9aHR2SZo/OildBJoutSHHB2JjBlBeMYUNrhJ80Ln7nPSy9uKYsjsqwGBddvXOXoYEVVOLSCME1yhFBiOqqqkr7rhJyVouRYxsjpyT2GvuX05JhpGsmtoyodly8c8dC1a9y4dpUQA7/1/Auc7FqSy1lfusSooKgrLh5dxmDZ70TjEBm4dfsL7Nsz/LijysDHlqhHXFbwqq/6Kja7M/qhgSAO3ixzmLnBa43j1V/zdZyd7dmcbdnsNgTlybOKFDXvesc/fVmNxq+QRWGV/tIPfBNhFJbBA2GPNhptLVOYAOne1+UKjTk3GEEi+Qlt5OyeQG5M73nVK17Barmg61q+cOsm7STah6HvMAnpSmc5JJnxv/X7BZ320Z/6jzk9PRbd+2wpds4y9j2QuHTxIpvNGSenG3yQqiTLJNjUOcGzpaR4/C1yY7/vr7+JrusJKbGoalRSvO3N4nd45tk3omyaQ0sk8fndT/4Sf+Nv/RlijPzgl3AY3v6u70apKNjuMDGMHUcXjiiris3mDOY0qTzPJfXIe4ZhOI/G01rSr/3oSSFidaRrtxhtKFzJ0EeStQxBEOllWfLMO+QI8danXsvFi5cYxwGlIsrA1A8sq4WYpWJi8uIG1HOojJ+PYca4c3KVVC+WlCIxTJRZgXMFKE3XD3R9Qz80VJVUP0Mnlu0sF0Vm1/ckNLtmz42HHsKHMC+2JSlCXWWk1JNniuWiYrlcEabEbjvy6U+9yAu/fcJ2G6mqA1arNXkheLjDgzV1UeDmCkojC6ox0jvpuo7TszOC96zXS8oiZ+xb9vsNd2/fJMscVVEwdHuqPGO9qFktl1T1gqg0+2nCVDVeaU77PVdvXKUqSjYnZwK4CR5XKJp2w9n2mDhMOK3xoeP6K66I/kKrmd8Y0QqGZpxdpTPkRxuuXL5GWdYC8R17ts2GzBUo5XjyrT/7h2dRuPbQIn3ff/P1ZK5gsajnGfkcOa4lBKNpG6ZpEuORsTMqLGGNIc2Akt1uRwyglaWuCopMkpO00Yx+oh8ltl0paPcNegZ4FEUp8WIonvghGYP+2E9+L10nGK2qqjBa0TYt+/2OixcukjlH2/WM03ROTIpR9PDOZmhtGEfPU2/9hfPf823PvJ66qqlyoULrBM1+DzqI09FYhnGSEI/z3ISJt/7wP+LNb3+90J6KDDdbl2P0GOe4eu06m+2G4/v3WK+WLBYLxnFku91iraUsS/b7PZDICycU45gocsc4tGilsTpntxsoqgI055qR3W7PRz7w6/zAW76B1WqNc5a8stR1web0jDIvyGzG5myDD5F6ZgXE6Al+kp+FRhiCk0i7C0GExehn+bVFKSumMQ1JBaETR0W3n6RyQ1iOLs8lQRzO06KmaeLo4OI8phxlIuMgzyzXrt1AJceLL9zh47/2W3Sto20ljPXgcMliISQsP3kOVysODtaURcFqsWC5XBJjoNnvaZqG7XbHdrvhwoUjMmcYuj2HByuchZP796nrAkPg5P49LhweUpcVy/UKW1SQF0xK81uf/Sx2mbNYLyiLnLt3bhKmHghkuWXynahSg+EbXv2NfOaFTzH5Hq0t/dgzjC0XLq6xRhOnyMnJqTRiQ+La9etUVc2LL73IerVCW+FOWJcTo+apt/7cH56RJElSkCc7EcPE5CURKgHJaEKMpCgUJmeko9x5yX8UqWwhnXatUF6MNcMsqBG+gSVzhZiJjGjVB+1p206OIMkBnv1+z9ue/g7e/65f4rG/+jO87yfehNaloK5cJoEs1hGiHFqKohSQJiLAiVGJ9dpYUpIQmwfX489+GyF4QOHynMw4sW5XFSGKItPHRExqZkEE7t8/4fDwkPd/9D9iuW7nacNA9BNKIRVBCJycnuEymeV779ntdpRlyXK5ZBxHvPczvbmTODmlSCrSDSNGO4qyhmio6nKGiorYat+03LhyAPw6P/Hh3+DxJ76d9eGawe/Z77dM08iyWgBgrWOcxHSTZxldPzFOPSppSZuaj2QSxiM9A200Z9tTYpR/IyyLCDoy+ESdL8hsRr4u6cceHz1WGbSVRuYUPAerFURNjN1c8SnyQhptfT9y++6WqYt84uOfomsUxiy4du0SL770ojQBq4wyL1BxomlaiiKjKsXB6ZyjrmqKvOTk5JRuniKdnBxTZI4b1y+TWQVx4vKlIzabDY+86hVcuXiEVgpnLWVdY4uK6CzbYaSsC8gdMQZOT++x2x9TFoau3YIu6PqOLM8pizWb/RnDNLDZnAoMZs4/jX4ALXaAru3Z7zpcVrLfieJz8iP91FLaDEWg6xrG34N46StiUUizw14n8b+jpD/Qtj14iAiZKLPiVe/mHAKx6Ap0xRpN0IaDiwe07UAzSA6DNopp8hgTWdQLnJYko+AFwpKiVCVZJtFyWfZFFLbExnfEmFit1hhlWC6W4qYMonDTxuCnUYJmlWQdDP2I94H3PCX9ine99zvRyrNaLOQYMvZ4xAZsjcHH6RxXXleSaSjn18TJySkxRer1gswZpqjxQdKnHwh5SJG+a2nahiIXqMs0yf+nQPgIScAcOia0tex3HXme4bJMaNLKMvqWaQrUKkcbQ+7kgwfw5JPfwdC3bE5PKJc5QyuNtu32DIMheInVG4eBcZT/+XGgrhZMo1iQ8zyT3MS+FVtzmsjKghj1HEWnKcqSpt3NjADJ/XDOgYlMQWGsJEL7IJOOvt9RFRZUxGYVebZgHCP37h3zhRdvsd20tNsBGzOuXHqYmEpWqxU31HU2m/tkWcH64ALtviNzmstXLuGsxg8y6q2risOjQ7QxDMPI3Tu3uXz5EhcvXISZIB9DgPn3Oz455urlS6QQKPOC5cEh0TiCNtw8PUVZGWk23ZZpaoEJPw0Yy5wZqkUdO7XcP7uDzRRZ6chdjjWGpvN0gwjpphGKqmYYxXUb48Ru1+H9wDBCURiCF+WqVn/IwK0KRVnU5zh27UTDbZx4AHSUmXSmFVZrtBVz1DR6yqwUnJlP8y4YUWYiRXEgAkzBo+KEsYau22GNJXeGsQ9kDjKbsE6R5ZbtbnP+vDanG1xmCd6z2Z5hkABaZwVDnmIgRAGUxCT2YqOsnPu+5E0wZLRdS2Y1fSuZA3lWCoADmMYOhcJqR9+M52EedVmJzmHoGNodfSsL44PGIQqWq5oYE/3QA5EpTBiTMczg0jBF8SY0PYvFkiLTrA5W1EUmQJfgGbqWGDRFbmn2LUllWKv4az8kOZjve//3sFopFrVjGke6RjImu6bFJnBFjTJCoEqAMhGjCrpJE7yg9iOBXbODGX0nTr5EpiXN6kEeZFWV7Jsd0xg5XC1JXkmCtXNkbiY+KY2Zx66ZsTMLQ6GU4/Rk5LOfucNv/pvnCUFxsDokDhlFVaGxJEb67pgyN6iDFcY6QlIcXLzA4fqI61ePGMeGu3duMYwSDJRiosiFjXF8/4R232ASFLnDWtAqYC1cvHCIVZI1UpYZSYvwS9sSMOjk5mboxDjuOT27zbLOISpyVwroNYygIv20Jy/Eh+M6TZZZ9s2epDVBOdpJ4VzO4rBA2USeWQqn2O0HDlY14+TpW2m6O23OmRkv53r5c4r/j688y6jrmuViQZHlM6ZdU5YFRVlS5DnOGHLnWC2W6KRRSXZBo4Shb7ShncdjeZaxXq6oy2oeK0lU9zgKMq2qS5arJVmeoaw+Z/7tmy/Oc8dRYC3W5UxjoBtGmq6j64dzAKwfR/qux4+B6NM5YBYSTz/3XQC884l/SlUVWCeSZRQooxj9xDCNknBkpPLRSpO5jDzPZnOY0JhT8qQ4nacZucwwjj1Ns6dpdkzz9GHsB9qmJU5eUG5KcXZ6Rtt13Llzh5gizX5PCH5e1Dz7/Y6m3ZKSJy8dSiV+4Pv/IQDPvf+78UGgLMZqjBN3okLPIiVw1rFerSnLmoQAc05OTjHOUJQFZV2wWFQUZYaS9oK8v8bNv7MSFmSK9F3Pwfpw9mBE+rGjHRqm0IvqdOwwRrNer2bru2IaHH7IuHen5ZO//gIf+98+wZ2bDQfLq6hUsN8OGFPgfZDma9/h/TS7RyWhbLlckRclXT+SkmSMTH7i5OREoLNGU5TS8xrGgVu3b3N6ekrbNJydnHB87x7H9+7Rdy277Ya+bWZkvGR9tp2QuGQ8HJimjs3ZCQoJEwJhQ3RdNxvrYPRyBBY3aEBby+g9292e23fuorTl2vXrZIWwN5N+APHJWC0PQBmUtrgsp6oXL/te/MqoFLQSb4K18iJYg00WH4RrIKlBiWSEuOuM5XB9IGTfYaDMC5xzNPs94zRiM0GDy/jLslgIK3+/21NVNdpI1Lu2BrRhmgTFJn4A0QO89enXUlbCJwwxoI0hePAhQhqlx6AMKhrqYnmu4IsxUBSWvCoZ+5EPfPR7SUSefodMNt71vm8HpRknT56VDINMNPb7ltyWLKrVrEr0oGSnGb3AR8tSiFN936OUEv7CJKX5OEpAbGatqC21kQSsouTSpYsMk+fk9IyTk9M5l2FiGHvZYdEoY2j6Hc44NlvBgT/x1GtZLsR0NUwePwlS3GhNUegZn5YIUTD9XT/SDR3KJDBgco0tBC3vg6coM/JC7PC5zYTSXNazAzKQO4cmkVtDVldSdVhRRfaDSK7Ria5vKIpaAKqjZhodfec5PW45O5kgZlw8ukhZrNjttpyeblktFkLT8h7jNNv9Xo5O2hHTA/KzIQWNs1CWC/w4EUJgu91RVYGDgxV/9LWvwWrD5vSY2zdv0jdblnXGNHbcm0ZcOsLEHINoXLQyEvYWItY6NtsNZCMxeo4O1wxDJ8eJZo8yiiIX5mNVVOz3Lduw5/qVq+x2e+lxGCMbU7fl+OSEw4MVCsPQT1hlKaslCY2PYpA7OT3h0UdfRV7kX/be+3LXV8aigDjoxkmMQ1YrwjhJ3Dmz9VcpYedPPV4LhKOuKsGd+TBLjdWMDzOM40SMEogqNOhRZvXz6j0FUS5aa0ko6lqmGe95+y/y9Ie/iyyfICE9g2GYrb2aNNud40wKun7lIbq+p++7GSPeMflATIGoEvvmDG2+WJClCGVd0nYDzW6H9xN5pimLijglTs/O5nAbKMqMMfSs6oXE1s1y2weo8zzPsNYwTRPTOJIZQ5WXpBgYupnzX4g2wWUZ129cozm7P0NKRFtQlDn9MDD5gbzMCH6iKCSw4b3P/hrv/cB3IQnSER/F1FQUFVor6npJVeR07cjYDXTdIGE0uWG5rlFaM8VJ+IEq0TaNVEGZxRkJjLFaPoIiCJIxqPej7OJKoXUCFchzjXVuDmYd0NoJewPFnZtbnK2pissUmWKx6CjLBSfHp0KxUorbd+6QZRnaKkLvGaaJcToT6XMUWnYMCV/nXL18AfDkeUGz24GFGHOMUdy4cY3MWq5ducjVyxe5/dIXCGPHxcM1Vkc2pydk5oCLR0ccrI7IbI7SOcvMEDPN8ekxyvaYLIjgKkpPwWpFt2+IKmIzQ9M0GOMwBpq2YRgG8jzHKM2qWmKvCpvz1q1bqBSoSundKA31YjFLwxPL1QWUyX9P4qWviEUBpGGTZjdeNuO/hqGHCIXLGIdhDg2VsjWmQEyiI5i8CGW0tiyXa9BIrNyMKs9UjtaGelELLj3PsTPK3Fp7/rXrZuLtHD/np8gwDWKrRbr90Xv22z1TP1IXNWMvkuTdZkdZlwydAESNkw/1Ylnjp4lnfuwNvPOxX+SZJ36Z93zoe8SpOUh6tDEa68w5XxAlROmYBMQa/IRCCz5u1jPEGM8TtIwROXNhC4Hdni9Cieg9U4gCMMmL84ZmWYrt3BhHUWgmL8Ew5bJG2MZyTX4iIgtzXlYktOQxzCnIxjqK0jCNEmunrczWQRiQXdey328p5wVs6HuUMgQlIb2jywjRn3Mju64ROXlRME4jkx/JC1E7xhgAg8ISg2W76/k3n/wCTl/ioYcuMPaecUhMo+f09IQbD13l6OiQe/dvsT09o2lbbjx0A2M0292WfhiZvGe73QpkNUBmj+j6nmVdkmcFoZDeidi8I8u6guApsoxXPfII1y5d4N7tm8QwcuFwzfFxjlJBCFE+YaZINImgJDTnypVLePY07TFlLrZzlRJXrl6j6wdOzk4lmmCMRONZ1DXTJAHLfpoo8hKSonCOrCwYux7rMtFNaIkHbJte8kyiaCxOT7YCQH6Z11fGojD35Ix2pDiIcSdGVEqSAp1Jnl5RFsIydJkEisTAOPWC9nKW6EU9J6pGOwtmpFHkrMMYzW63wzgn9tmUKIqSFMWlOI0jTz337Tz7+C/w7vd/B0YJBq5a1CKbTdJh10rNCK+MYeyxVvBsQ9+y221RRrFcLWaVn5CT3/OkeMPf+d5vx+gJpyx1WZDIGH0n1KDSMg2CV4eIMoKaT0S6pidzGSmquVxsqavEen1AN7s3nXXi1Rg6LEkAK1H8HWfbPT54jg7WMj2JUV4bRDGYZZnEtxt1roYEQd5Pkyeh0Sbg8oLJC+IrhpEYJcVbaYeSuk4w9jHhCjfnU+rz6PlpkMUgc+68lxNmnYkxBmulVzKOHYOf0M4RB49zihiU9Bq85v7djk/++vN0jeIV12uiVxiT0bYN1mrW6yWr9YKqznjkkYf57cFz794JB4eHPPzww1RVTT/0jJNnHCa2mw0pwqLKIUXG1QJn5squLNFGs9+f0mw31HXNpQsXGSfPYrFk8eij7LdnlEXGYl3StltiVJztGmynaP0Zm75l023QGSxK8SsoNUFI5FnB2ekZwzBBSuJ+9QGdDLnLiUai8TLjSFFcs0YnEglnNM5Y/CCakLZpOdvsuHDpsozPecCD+AMYSSqlXo1kOzy4XgW8EzgA/gpwb/77J1JK/+h3e6yUkF92Hj3KX8CyWtB3HXGak5qVIMqMlbQkP9uVYxJFV4oWP++8WmnJiExJ8O3jKDFzxtB3LSglo7NulA99CiyrmiyTXfLdb/slfuzH/4Ts0tqgkdFTkWfURcHYD0xTL+f+NLFYVpxtBpwTwIVRmpPjE8I0gVI8/dx38K7Hf4lnnhCF4Dvf/a1kec4wdAypRxFZLw4oq5wYZ2JS9AJ8VRqlPNZms4dBIC1dN/LiF16aE54qtBXXolM5pRF/yDRNoDSLRU3X9yLm0pqYwGUlWhmmMaCtQqUHbMsvlpp5nuN94vT4jCvXamyWoxzkzhL9INbrSSLWE5Fx7Akp4AcvAS4xUOQVVmuG0bM+OJIpS9czjINIdpHf1XfT3GCdzqcRY4owJaqqIC9KEhneK55//guMQ4XRBZARI+x2OzabM6ZxpCgusl6tKUpH5hxZVnB/e8xms6Pvew4PD0Q5q4VEdffOPW7fvcfNW7dY1hXNvqYqMlL05M5SlDmkxH6349bNW7zwmRd49dd8FUYrLl44oFqtJJOkyMmrmmmKHN/fcOfu57h9/5SzdsPBlRX1UUY3ThS5jDAzJ031cRjphwnBDXmODi/xyhsP03Q7dvsTwhTo+pE4JpaLFZHEGKYvVnI+0bUtzmQcrg4wslRj1IOowJcPbv1/vSiklD4FvAZAKWWAl4CfBv4i8JGU0gdf7mOJnv6McpFjrcIojVWaFKLMvucbums7bF5AjPjRC7HXasIkakWtNDpGvBftgRqQsjyJWch5S5q1BduzjSxEToJp/TiRdHaON3/mva+nrAKr5YJxkqRnlRJhGrBGw8wQmAJi51aSIr2oa0HFa0NuM6ZhlPNyN/DEk6/hve/5VwC0+x1F5qjKnDSNxDSxb3dY7TAmm/UaiaQTMUXKohQr8LzYVZXkbMaoaBrRUmhnUUSMBj1H2ltr2bftHNYixxJrLeM0khfFfNOL30QpiXSfegHYvvu5bwMlN+jRxQtEEsvVima3Z980EAby3OFyxzgEQEGKVFVJ27SE6UHfRtwpMSqOj8+oy4UcISIyopyZmP3Yob0kMltnSVoTIwxjh3MO5zTbTc+Ln9uxPfMMbUaKBj8J1VuyI5CAnAhVJf4JkmOzGdjuGu7cvsvVq1fI85yDA8nvEHTaJTC/CSlS1wKFadsOq2G/3ZBSoHCGZt8yjZ5Nu+UTn/gkV65e4nRzxtGFNQcHa2yREVIkYhm84vnPvcide8esLqyoqpIsUyLqUrJhHRwcEKZASgYfoGn3KJOwOuPu7bt0/R7MKJLxweNwFDpjjBNDHFjUK6qqlsyNesXkPSnXZHlODBNp6FlVa9zvYc74+3V8+PeBz6SUPvdA4/57upSaJat6lu8KiWhsO8lKINHMH+xMaSpXnzf+IMwBrWlGdyMuviwyDsPsh5ihoUhIivbCLGyaBmdyVssVYRxRCR77oZ8DhO7Lg3CS+fuN1vhxmkNYM7Q1BBPRpmIcRmKI1HUtDR/gYL1iu9swjQPOONbLA97ylq/lwx/+TdrtjtwYVkdrrJZm4egH+jAS/H62YHdM5wAAIABJREFUH08UZSGOuszMnfKZ1pRkgZAMQQmYSaOSmDmT0fQDRjHbj8VboY3m5GxDVS1wWS6gz25gsawZgxdqtY48+6QsXONsH9ba4pTBulzYkymKByH0GAtVXZPnhn3TUBcLpjiKHC2JsCvLMkJKdJ3YtdtuFOGYikQNaEVelkQSIfhZfi6Ll9FCYG7bFk3Jp37z82yOE8Sa4A0qmdnBuGC9PkQpzRc+/zkJuE2glCHLCglqWSzYN1uGceDuvbs0bcPVK1dFGasM169dY+h7yqJApcg4djijBAy83xHnNCZBqVeElHjxpVs4Z3j+s3Jmf8Urr7Nerbh88Sp5VfNN3/xNfH3SXL5+hXyp+e3P/zrBK6lQh0hVOWlwTp68KGdwT0BFjVEWnznaTsabDxqJKQbCNAoYeOjYTSPTOFEWC8plxb7taZsdFw8PCDpB8GT2D77R+OeB/+VL/vzfKqX+c+BXgR/+3SLjQHgKBxcOmUIvdGFlmEbRcysrcmcUeAIMPXVRSjMuQl5I5kI39ORLS9/t0S7HWkPuSvLJMQUvISRz2dwNI1leUC1q2r4hLxxoaJr2/DkZnRHjA0IPxCTR7tY6MpuJgMZo+qkhLyoynTF0E8HDcnVAP7QkBqqoIWUSVxYiH/7wbwKwqCr8OLE72ZJMwKfZ6ekK0TAk6S20bUu9WtKOPTF0UtHM0Jnloqbvt8KVTImTszOqxRJtM6xzWKsYhhYVE1Zb/Bgpq5qQtEByFQSV2Pc9gw90w0RUX8yAnGJCh4BGpjB5ablzfIuh8xhnmbwioGlnm7FPnhhhDEjHWzvyhWUC2q5jmAJVltN1A1YblquKxgfRTCRNmS9omz06WXQSlWS0EesU1hXstok7L42kkOOcJi8MJMflS5c4unBACBMPPXSVorRYo6kqyeGMMVHWFZevXmHRVKA0TduybxqWi6U0n7OM1bLm07dv0+ZClB4nj81Krlx6iLHvODu5Q+UrIWOFJH2AbmS3nYjzNGtzuuHRR7+K9fIieW65/tAl1gdXWK0vcbK/T/SK3In8WM8J61lusS5icwk+zjJH4Qr8GFBZSZrfE6OtVNAmwZhwyuKs5e7xPWzmyEpLP41MkwjXYhAS1b2TexjzB7goKKUy4E8Dj89/9beAZ5H24bPAh4C/9GW+7zwMZrmaG0RzRNY0TgIP9X6WrwplSNkCFWX+nuUZPkykXs6zVV2RohfSkNaMwyhxaHOitDSxJEPCuhxlDEVR0rVig85yQwqK9zz3nTz5+Md47C3/nGefe6NYjzNhJ06Tp8gL4hxyi4IYRsboMSYjxJHdpiPEEWUSPsro7MKlA6wqeOtjYpl+33v+GIcXZPLgQwAj9GptLJMPKDR5kXHj0nV2zZZ92wjKfJhIIUGM2Dxns91gnMBSlUkYp+mnAR0SxUyQ1s5CDGhlMVHCcjGKQJDXOcHmbCvdbR9EUQc89uzrWC5FLZmSNC37oSMSWK0rNNBoL03QviUpCBGMyqWy0iOTn8jLAu8nQhLGQVnXjCgMgrpflCu2mw3bbcPFC0dcKEua7RaFvOftJKQmZ2te+O07GFac7RrqKnDx4gWODq7Iez8ndpdVzqvWj9B3PbvtjtPTM6zLqet6zvVMtG2LUgnvJ/pesirHcaQqSu7fv491jrZbcOHCIU3boklcu3aZyxcPGbqGYRiZxpGqqhi6nv1+z41r13jFQw+hbODa9RvUVc1md0xeOOq6IqFo9nupREpDXhSS5OUnpikwjB21rUkpst1u2IYNWhnxkXTCxYgkphg43W3odh1VUfLQww/hdjtsZgkxkeeW5VIMdftdS2Yzpgn8lwKA/x+u349K4XuBj6eU7gA8+Drf+D8F/MMv901zktR/B3DtxjJVZYExiWYYMEaRUqDvW4pSKEtKCWO/b+c8wBQZxwmdpploXJCURJ0pNC7LabuWYRwkyj3LpBzOc8pK4srHcaCscvwwSsZAaYlh4rkPvhGtLE89/s+/7C/81NOvE0NS0hiTGIae0lhW6yXaGNFDxCAOvmBIQyR3X+z+9n6kKDIpdpJ09qvFEoWGOc1qGHs221NCiqToscawOFzR7luqOUH4/v171GVNuZCb4igr2OwbIpqkYQyezCq0gqhgebCeF8mE0ZJNobVmHHum4PmpH/vU+XOU3UxI2JP3KO3n2DmP14oUPEZHdruGLCvQOjD0gf1uy3bXUJWO9bpgGjoRzlQSdb9vNkx9T5nlVFnJvmtAaZTWDOOEW1S4smTo5egYoiKGgpu3tty93WGoCVPGxYvXefiVj7BcrMmsfFZ2+y2LWtSv282WmzdvMwwjly9fFpdhP3B6esb161cxRnHnzm222y0XL846Fh9Yr9csVyumaaRtWhZ1wTR5nn/+eR6+cY3D9ZrlQizV165eoeta6jLn8qVLohnxHVWZUZU5UNONPbvdhrwUT4O1Ejk37gaaZo9xos9ROrHZ7eYjcURHGPuBxlpsZqiqiuOZJm60plwuiNHgk+bg6DKjHxl6iSYc+15EYeRMg6IuD2mHlx8w+/uxKPxnfMnR4UEIzPzH/wT45Jf9ri+5lLh2KLKC4vCQ3GaMfU9VSDjrFGbhUUzCGJyTqbOZotx2UvavK/FPDMOAdXY+j0vIZ1WWhCDM/+AFFQ6JFANxPqMlK3LodrdjUS948ulvwVgroSzGEmPk7W/5GM++61f54E/8cUmw0jl5prC2ZBwCRi8hTFhnwCb6oSMvc97xwwJ5edtT34xOEzqKMWhRLwkBfEhzVSRKP0wiqYTLHEoXc6MwUJRSIRlreeUjD7Pd70RaHAJJJ5TRNE3LRKQoHUlDjBBVEsxZnP5P6t401rLsPM971rTHM96pqruqeuZMSRQpijZlR7KdeJAN5E9+BEjkwEDiBEYQCFAgC7ItkrJjyAYcGU4QA/GAJL8CCBYQAUmsJJaVCJQE26IsiqLY7Kmqu4Zbde899wx73mvIj7WrSMuK3YKSgNpAobpOV9+qPufutdf6vvd7HoxJkErQdS0/+YV/zg994ZP8N5/7Bgn7hz73SfI0RHgKglRrBjvS9i1dP6DEjL5twYtp9yJJdUHf9tRtH+nQuaJtKpRR2BBp2lIKZvOcRkSs2aE6YAkTlbkkLwqqukZJgUpiHHhWlPSt5PL8mkyvaCtQwrBeHpOnCSKMjMPI4bCjrmua+kBd11NOJaC1oapbLi+vqKo4PbparbBuJJni9ABZmjIOI6enpxRlrFkNQ0uZpZR5xn634e2375KnCUfrJfNZidaC/W5D10i6bk/wjhs3zyjynMN+iwvRGrZcH7E+OuJq94iAw2iBUnns1owDSZKSpIaqrUjTjK5rEEJQZBlt31OWC0YXx+TXqyVNVZPnM4JXoDWHw55+iPdLLNpqXnvtg9x7+13qqqeqajrbve8b+ncrgymAfwv4j7/p5b8uhPgE8fhw97f8u9/2cs5Fw05bU+QFR8sVi9kM5zyj7dnttzHqKwJ5nkZUeohAEyU1zOc45wjO4URMwHV9P52DY0BEKskwOEIQeBdIk5Jh7HE+Zg+yJAF8tEa5GMoxiUVO7c9ucBj1jaiokCvKck2wcRRaCcP55pzddYUQgtkiAdmRlOHZgvBXfvL3kZUOo1Ps6EBqUBETZp3DuoH5fIb1FhGiLDUzGkRAm+hTbOqWXXNAjpq8LGLunXi0eDokZdIEIQVKaxIj6ZqaxMSdk5QRaooQ/OQXYkHxb3zuS/zIX/00P/GjE4XKecY+Fl7Lsoyxagl2GNBSECzYPgbNVssj3BAVaot5zmxRTEEk8K6Lolkv0CYi2YyRFGVKgwMRR9ntYBlt/CFlNDOJaVI2TQzN3nPYWsr8Bm1Vc3J8jJKCEMY4jxEi2KY6RBKVEILZbMZud8CHGPI6HGqapuGlF+/gnMc7T5EX5HmB1orZLMaa/VNJTfAoFbs0RZZwdHREoiRj39H3sRU7LwuKPKHrag67jnHsyTPJar6IbMcspR8HDvs9ykS7FLhpBHp8hshTWsZJ0BaapkXKaAP3Q0TfDcNAO0QE4DCMOO9pu5bgJaMfsMHS9g3j0JGnOYvZgro50A8dddswumjLer/X72pRCCE0wPFvee0HfqdfR0lJmhqCjdvPe13LenXEMOX6hZIkOsEFO21l49Ov67p43k8SvPOIAIOLA0ZutHR9S1HOyfMSgmboLXUzYExGkhryfIZSAaMFaZpMARYdQR++oCyOGAbH9VXFbhefPD/4n/8p6rrn7/ztn35f/29//gu/H4C/+l99hn5s6IdA3weG3pLnKQ5wBAZvsW7k0FaxAAVcXVwipeTk5Jg00zGa7XysmYwjddOQFznD0E3txBhgegqoC84TlCA1BqPj6G+Sxp2InLpEP/gXP87f/CtfebYg/PDnPolEYKShbxtqd4g7FxHItGGwDolmOT+GKa4ktKBrKvJZTmokiJi4DCIq5JWJxd7gHc6NaCM5OlqBkISgEP4b/kMtFWmSY91A01bMZ4bN5SXb6wax7BhtRzk7Ic8ziiLFaEHf9FSHPW1TM5+VzMqStutRyoCIUtqmaTg5PuXGjZtst9fcuHHCjbMzhr5jGAbm8wVHqzVvv/suC7MkTVOyNKHvaq6uNqSpItOadDZD4JnPc8o8xagSIRZ4O5BlhizLqA57kIpAhh1GZouonENAwLOvKsY+LnzlvJyG9GIBOUqTBxgHsBahNXVdM1ss4uLQRIHxONgYV3cxrj6MPZ3zDH0HIdA1DWNv6YcBk2iC/L2WaATyLKVrImAiMYbN7jqutkUUrAbhGYaevnNkWYH3LqrJ+gEnZGQilGl8wnqPkgYnBHZUeKsJztA2gXtvb6irS7Q2HB0vWSwjkAMC3se2WV11DN0FZbYAFBcXO3b7lrZxpNke7xXf8YlPxPl3E1X2eT4jMdH0dNhvcX7k1p0ZP/u//hI/9hPfh05GutYiZQYYkixFaHCATjXXhy15ntJ0DUkSkWbFbEZdN9RtRzfG6vHYW/oubjm991RVRdVWSCmmseqcfd1O4+dghIjgjaFDC4WSmjRJ+NH/LLZelZD8uR96jaP1Ed55Um3QiQbnMfkMiDKYYRxQxiClheAQUseVGYF3Fu8twVtCiCbkfvQgJFqb6MPs40IkQkASDVBaGepqD0KQphnBRdjMMHq0lpRlQd87Hj64hpBOyVHFycnRdH6PN5aAaIKSceCpqmKdYr1es90dSNOC05MbLBazZ12D2Szn5o0z0iROJiZJhNu+9NJLDBP/IfhIqi7zDCWhPexph54sMVibsNvvyYzkZL3g/OIJF0PHcjVnPj9iNltx2Fc8ubpkf/CgIv0qDsCNPHx0n9PTU6hjkTx2kDzGSKRM4tSrifoB4T3BOWZ5SZqkNHWNtR47HBAutuBn66M4wGVtlAxnBVIohAhs91uKRfG+78VviUXBe4+WsS00WkvdtYzWMowjdkJzPc3mSwludPR9R5oYlIgIcxEku+sdSZ4ijGa0I8MoERisNzx+XHHvnSs2lzVjH6gOOwb3gNkso5wVhBBwzsdcvReIIEh0nGzTScHR+ibyyNB0I3Udny4uQF21EOKUYJJI5kVBXh4Djs3Vlj/4va/yC//nz/OX/uofQavFM16eCx6tLZ4RnaRxRsKOJKmh63oI0T5V5CXWgTAKN0ldZosUO1q6fsC5ccrlKxSCen9gHCyZKBAahAvRKehBBEFwnrHrn733RZohfCA4D87HmHZQFHnE2Y29ZbBxVLwoSxhC1PHJmD/AxTSqFTIat7M5WZlinGWz3eKlIIjY/SEExq7HyjgurUzU2qVJihSBceoqNXWFkoKiLLm+qLk4r5gXz6FlgpJw2G1ZzJaMnSMzKT705FkWHZVKkaYpQmqSBJq2R6uE5XLJ4bDn5OSYoijxzvPkyRO8m5Bw0+46TdJJd3cgTUwUBJFQLksyozlsryFEVF2iRPxe7OJOo65EZFakGWM/8PDRA371y19msXqOXT2yba+jVBi4c+d5yrJksENMQuYp0sSZnlxrvE1w44hODH60cVds4teVQZCqhEHmCK2nDsrAfLFAKx0NVq2NITsCvwONJPAtsijEnn+C0iCUQhmNaxqMZMKUuWnMOYpdx26cUO7QD5YsjSBX5x113aCzFO81XeO52G95+PAe1T7Qt4axz9hcHri+bhldz5XqKMue+WJBYlKE0IyjJ88ypNE0dYerD1gnoslXBZarAh9GdMiQxmBd1LojHHXXoVQ0YRtVUu0dn/n9L3Dn5YfceekYVWhMagiupR86lIktPWTcMSgp6caRvo227CRJcT7E4S9n47BM22GmCcdxiAQiXCDgyYxBCE3btJhUoYKO4+ZSR96DnZycwBf+i+9BERBeYPuRosiRKLwLzyLlEI8Bgx3ouhatJdoA0mFMmMZs/FQsjjTuw97FTIlg6t1fk+rofVRSMjTNRM4S4C0yJOA9idEM02Jv3Uh96Hjrzcf0fWCxLrHWUxQls/kCrVPWqxVVdUmiIoPi7OyUIAVt3WCSDBsgSTM2V1seP3kSJbdVFW/6uokmKyHZ7XYR9ushbLfM5wtWy2VkKCQagqVrW3KjOT4+Yhj6yLZwlrMbx/Rtzf337vPKKy9RFiWHQ4W1EiUNr732Ye689EFWJzf50lee0PU9xkTJzmB7lIpKgBi0EoxDnM6UE0k8yTOCjPWHtm1Zzua4wWGtRwrJfl+jU02ic7K0ZDafAzuGrp/EP4r5fEH4vUZech6urz1ZoXG+xSSK5SqGR/ouziZk2XQDyIDUhlRkJCbiv3ywSKCYlehU44LA25L7dy/46m88pG0DwSc0TcU4BPb7im6I4SDnwVcDVX0ZB4qSFCXj6OpOa4a+BRniuc8OzMo5ACdHa46OTrEo3nnnLr2JTD8tBM5GjLpMDFl2ijIt++uBJ8nA+liRZpZybpDKgBzpXYdODYv5kqZpKRfltHAEqu4Qn6QhDht5JZBZZEr0XUsyQWwFAY2IMw0KRu1p6h4jUxhHEiMojKEaps4I8Lm/8EV+7Mc+y2xiGiotcDbKVgISLyIj04qANCpKYL1EShU9k4NHhAjFtcEig2TwA8MQ6dveRgZEj8BbizIR+iIEqIkd0NZNJDwbg8ciZfR1KgzbneXykcXoHJPGAFM/KkxWoozCBYv1nqbaM5/NOFucMY6W/aGm7Ue2h5q2tzTdQN1FCnXf9/jg2W32lGnK+mhF13RccUWmM+6++y6vvvoKL7/yErMyJxSKarelb1tGr0nynL6puHv3HbbXV9y+dYPjoxU6MayOj7FjRz8OFPmK9fENPn58Sjor2XcVTo4cupGljgyHYWzJcoNOEpyLJuzdtkLLlPmqoB86Rh+lREIZRESy4JVAp5pOjvSi5+zGDdIso3UDtjmQLkqsDAhvGVuHMprfyXbhW2JRaJqef/7P3uH2SyvyMrA+mk+4tUDXOnwIhDAw2p7iZI4SBt9FHfnoRrp2QBcZwkAQEjsovvLlt3jz9Q2HSlDXlq6tGKzDWRinuQIxuRqBOHUZPG0bUWNaGyQarSFJTYxEG02W5hQmYxws2+trPvzRj/FtH/0I7777HufnjxHEzHxd19RNGwnESUlbe56cd0iRcnxaEpxitA0yIVqcVCBLM+zo6NqBxXzB7nrLOPSR4SAF0mgOVU2eFwitKdIMP1g0ivliTtPsGYeBvJwhE43SMQCUpAllkuDHARMk3jk+//k/yOc//wv8+I//In/5x7+HLDMgHOOksXfOQ5AMYQQD+IhvJ3iaposqPylRk+czhGhDymYFajoSjN2IRJCZBCnjANo4DKyXK/phYOh6ykll7yayUNM0jNKRmJLLix27zTAFo2Lr009/zlj2XO8a+rYi2HFydAi6rsM5H61idUPTRrqVUoLFfMF6vabMU3abK27fvkWWxanXYehZLOacnJxMePppd5DFGozvu9h5wGOU5Gi9Qss40Vg3DZ/85CeYL5Z4myOlwVpNP1i+/sbbvPzBV3j38V0urs/pho4FCdZ6RusYDm0cgFMpIiRk2Rxroapa8nziR1QNaZrhRODQVABoFTCF4ehsTTvW2DCgtcH5gBQGIaPA1iSaYRhJ0+x934/fEotC8JKv/cYjNtd7PvCh57BDR5JpkqRg6CxJlrCczRDK4d0AMmbkd4cdCEeSpSDjU8r1mvP7Fbutp2kEh13PMAZAYXTcCkYEl3vmQwBwk+sA4ki0EHHQSoiolZMCttsdzsLxMeR5Sd+1fP1rX+X7/+Sf4o//sT/KG2+8wZtvvs27773H5dWG+/cf0Q8jQkiMSTnsRowekcJS1w3lSqCsZ3QDbvRcP9lgrSP0lnZoSHVCT0eqDEbFFqNLU6QQtIeabvQcLddIZajqBhSksxStYRgd3nZc7q547uyUph8wSjKfz2jbkYsnF/xHf/YV/s5/+zZ/6ce+yF/4i59iuZzhgwfFFOYZyPIEIzTWOqQSkWkxxEGlbJqyfPLee+R5Tp7mCA9aGuxg8c7RjRFAWx3iYjsrizjb4ENc4MyMEDxJauibht31jjQdSYylrXt2+4a80Gw215ye3WQYOnb7LVpB10YR7HJWTkq32JMfrce7GEv3zpEmKXmRcXx8hJJQFjk3jj+AkiBkYFYW7NyIdZbXXnsttmyBuqqptgPejeAsqZG0TU1R5Ny+dYvVxz5MWeRcXD7mwYMHLBYLxq6jqTq++rW3+MpvvM7tl17m1svP03YVSgWK3KClIMsyEJa2jwj7LM0RUlEfKsobS4xyDN2AkppEJxNc2CBkfJjVzQGlFYt5ES1dbYcbLMJpgjHYpgY8Vgh0Ygi/g5mkb4lFgRA5e+cPahazFjekFDNB1+/p+hYha5QwHB3PsX6g6yu8c+R5FmsA1oFMGPqReh+4/17FowcVm00XOw8hOhCjVVoh9GQd0nryG4zPlGsRuhKms7rA+7hjeSo7cXbLOFrW6yOyLMNVAz//j/8Pbj13g+//E3+Uy82WX/nVX+OLX/xFEJrHj8/puxqCRJBzfTXQdxvScmRRe45OE9JMo4Jmd70nTzNSaRjH6KjITc5ysaBrdnjnmJczdJJSb/dUVcU4Om4/f5u6a7g6PGFZzmmqBq0k8yLHTUNhw9CTZSkLGVXvy9WMvWz43I9/D1/4sS9SNy1lWZAX8ylZlzIrSnywWDcwjGMs7uqMsXMMvWNoBxITz/beuXgEsJam61BKYUzE2FtrWS4i96HIy9jI9IKmajBlRpKkse0ZPMvFjMQU9H3gsK+wo0MJjTEJN28+x91778RCn+0jQMfEhQmYOBuKqm652uwZbeQV9uNAnuUoKTgcDrEGIQV1vaduqmfMi3E2IpWinaTBwTu0ADt05KlBq7gwZFnK2dkpRZGhpOC1V1/jN3/zq7z5xpsUWRbhPF3P+uiID3/kQ1g/0rY1s3mOntrsUshJRqQp8gAhxpGdiwyHmL0I9P1AnhU470jzlK6vGWyPMTKO2TuHG0fyVGOHATf09PUB6R02OLL5DKk0Qze879vxW2JR8D7gvaTbOc7v17Q1WNexub5msZzRtHvuv/eE49OC1z5yRpJFAInUkqaydC0UeUbbSL70z97i/r2aroFxUIxjDMQoFbe4T12LcppMjAQjG6m7Uk7BkrhAQMDaMJmbBUJEFPkwDFxdXU6g2ZyHD97lH/yDn+Lk9IRPf+YznJwec3p2xk/91E8zW2S889ZbtHWUroQhIAVkRYJSMA4erQBv8aMjCAtKxRRhbxm6kT4ZsAFGF0NYzSFOxZk8wwk4dA1N3zN4cEiUTugONbjAarGa3I2euhlw/XWsUieKYp6RllHp9jf/y6/yo3/xuzguVwzjDukdOiEGfZBxstFZQqoZ+pEsySb6tqXMYmDJ+ZE8i0SnfrRRha40bdNDIcnSAhHiWPlysSBVOh79dIZ1PVrF9py1gUwYtI46+8V8wdnZDZQUlGUx3VSQ51mExi6XzwjX42hp247NZsNsuY7wHBuht/udp6lrjpeLiS0hJu9mBMFUVQVC8u6770bjkvekWpElisWsoCwUyXJGnmc4N3Lv7jmPH5/z8Y9/lI986MM8fnyOHQfGKeK+Wq944cUXyMqUYZycJUozdiPNOFLO0giJTTKUzFFlwtH6FOstMvRxhkSLCF81KdYOE7BmnCC0kdDtbI9ROYlSSG0wMs7A2OAnr2n4F5CA/7rrW2JRUErxwp1b9O0IePabARccRs6pd462F9ix57s++SlunuZcbu/Fqq0VDL3h/t1rqsOed+4+YnvZ4m2GIMO5wDA4iiLSi5RS9P1Inmfxwxuj9ty5qBgXk20IYteDEIEkTxcEIeLCMo7jZDvqGIecNE35+htf4+/9vb/L2c2bvPzKK3z2s5/m7Xfe5p1336Zu9myvrtlebXCjRVtJ8Jo8m5EmA4IONw6TDlXStx1Ka9p+wDpHP47IqRA5TsRjYT1GGJyA++cPqbqOxXLB1dWOREqGfsRIRZZmCCGn8NaAUD26a5kv59RdTdNVzz6H/WGP1AKjMnaHBkIMjOWZJjVzTKHomx7nwoQu17RNw6zISbK4YxvHgdQkaJPgAzgfODu7SWrS+OfjGdqexERoTlEW+GAZxhapAqk0ZHlG8AlGRyyfd46mqVmMC9I0ovlclrBaraIMhlhP6PuBqqriVtrFBWIcYzDKh8C+qjnstrHzkOfkxYzFYsF6vWK331LVNZvNNVJKVssFQxcJVs/dPOPkeM3RKmcxj4NVD+7f5+233ybPUi4eP+a5GzcoyxmPHtwnTUu+49u/HZMVnN04pRr3aCOp9weksRitUcJElZ+VCGDoO/b7zbS4WdIEBJ7ZfMmTi2tGO2ISidSeWRmne7uuxeg4Idu2DRIZW5Jjx2AHqqbFKYnShvVy/b7vx2+JRSGEwNFqhTrKniHWuq5Bm7j1Y3vJ8VEJNmV/bTFqBd5zfdlx/qjj7ls7Ls6mmGv4AAAgAElEQVQbtoeaYCMuXAgFwaGUwuNRRL9jURSxXz4+3Q3EnISYClVKqanQGNtnUkSkeURzP91lxBl7HzyHuuJQ18yGkbv33uF/+9mf5Qf+9A9QljO+99/4Hrp/3PH4ySO6tiapEjo7MA6Oy4sdq3VKUeaYRBJQDE0VdysqchqTxJCWM6TWdK7DdX0UyZqEIs/ZbeKRQmUJiJgzKIqoTX+q1bPekyUJqUwYRYwp932PaiXz1WzS8MSrnJUTVl4jAlT7Kn5dn5BlCav5Ep/B5eYC5x2zbIazAeuZWAMeHwJ5XiAJJJnBjh4pDVqnECRj18T33jnatqOYzSJ5aLRoEfAhYJRCJSkIwXwxpx96ttstSivUJKe9utxwfHQcW7QhNka1NiwXK5wTXO8qrq+3OC8waULbN3RNLEq+/sYbSCFYrRY8d/MGaZ7jtluGYWS5XHHn9m3yLEVLz9BWpFqhZCBNFBcXTzg9PaFpah49fMi3ffwjCBFp0FdXlzhr0YUgyxNm6yVSS64vNjRNvIkXecqimLG73sewXNchhKbvXITDhJ4sN3H2wwbGsWfsB6qpRpAXJiLYhKZ2nrbt4uBaiKh96yKwWCUZ67ygHXr6YWT/e6374J1nv23IsxC7AhKMiVurebFmtTqiKBVXlxV1Fzi9MeP0dI3rr3nvnV9jczHgbIEfo5p+sCNaytin1SLSgXWCkgJv/TONWl1XkcQkZdyqTtvJJElIEwP4GNqbagtCSKLaUkR3ZHA0bQcIrPMkaco/+Se/xCe/8xN8+rs/zQu3b/HKS6/y67/2Zfp+IOAp5zO69oCUKY8f70hLwXPrPBKTbUc3dhz2O4qiQCcp292WtCjp3Yj2sWKtgiM4zzzPp+EpSYpBOku1u8akJj7xjUGGON5dpgnz5Ig+eEaiTs8Ij5DfWBSyPCFLI/TDOYdSaZT7CsfZ6Qkn6xNunj3PL3zxl3n4+AnjUHF6FPv2bdvT9z3D4Dg6zkgyQ1nMCS4KcESQLGZzrpsGYwzODrGLIjPc6NAmY7EoJg+lJMsKhjF6GoQUZEXOaEeKsqDve4q8oMxnBOfp+u7ZiHeW5aRZT1EU1N1I2w/048hivUBJxWF7zZOLDUq+yWI+4+p6w507t7h37y6H/Z7v/MR3cHp6EqG0OGwquHryiCfnDynyqBJYLOYoKfnkd34Hr776Crdu3eL84SPqqgYX0X7GKMBjEsX1dkNVHbDDQLXvSYSe7OU9dX1gPl+itSRJJdpItIkmrCzN2F4fSExCW19RFCmL2YIwjuhMo1VC349onUwU7BGCIAAuCJQPBOeiG4XfY4VG5wLnjy7Jck1RpORFznK5wk1TeEM10veecmaAjK0SdPWBr3zlde7fvaKuEsbBM4wupu1cIJsXBDzexqKUdhKBYhw8JycnFGVC2zbT8cE9WwyeHheYwjhSigi30PHfKSUQIkyFrp5+GHAuKtbPH53zevE1/uE//J959bWXOT59jhdefJlytoh4sLyITx0sfV/RKclu2zM7hnImSbOMoTkwupGqqdHWoZOoWhuamiyLNwbSIwMsixnD0OOExw0diU5I8pzBDbRdjXWGeV7Q1R1J0KRJjtApqXLIVOCE+xcgrVJJkiQWCAPQthE/r41GCcHj83O8i5gz76/oOk/bewgK5yR5tiSEnv2uI+ni36HrBsZuZDVfk5mYLSEQTUcOBh+zD2leoHUaMe5OU1WWw6GjKGbs9zXeW/q+w5gTkiSq6+umJYwj+8OG9XrFbDYj+DgFa0wsJEs50g3xDK6NQSqNEvDw0TmHQxmTpUajtOall15ksZhPaVlFwEZK8/ECXMfp6RkhBMoiZ+hanIthOqMNwzgwK2d4F01d3sevG2cZOpSWzBdzpI5uz65rY2fHxKNtOXVQrBsic3SMbdKmrVnPn+P5556nayuMUJhU07uBPC2QYsB6jw+CECRSKY6OjtjutgxNgxICO9XM3u/1LbEoKCWZzwtGF9DpDITCOSjLDCnh6rrm4tEVm+trmj6mGWPBb5xs0RYpPQSNsw4hYL/bI6QkTXMSCoYJ/rlcz1BSMNpYCNvvdkgpKYo5eZ4/u0m01pN3QD7zKTIVKYdhpB88fW/REjIvWSpIxcjFm7/Bz+wfcXn1kL/1X/99PvTKHf6T//Df46dKwcWbX+GDZ2CE5le+UvPVJ47tQVA8sTAo0lSRZ0tunN6Ki+FgOdQHQjdyWs7ZXcW0nXDQDz2V6pHCR3XcPmLgBRLpJblM0UIxtrEgiZBU1pL5mKOoLhrysuQnPverAPzI5z+FFE8Tf5EALZVAq0jI3tfXOOep7+/pe0FRKvq+oxr2ZHmOnqcooemqHWdHp6Q6Eqs+8vLLSJmSZopDdUHVtgyjwPcJh92Bk+MM7wVJKhHS04+BN9+45MH9kfMnhudPT0iLVXz/feDNr7/Jax94jWEc2R12lEXOyekpWZrQNA1JktH1HW3bcLxe0/eOcXRsN9cxizCfs17OeO7mKYkWrBclR+sVs7JgVhR0Q89uX/Po4WP82LGaaz74yhkf//hr1IeOLM158OA+i/mc27dvsVqteP3rv8nVdodSmiKJRqZAiZBz7r37gEO9Qaoe7/v4mdgRnSf0NnByducZ/CUER8A983sMw0Axn2NDjUkdJjO0/QYbYovYe4WRirbpUCKZ6l2W3fUeoxWr42OMElxsNhzq/395Cr/rS6noZLjc7GIBr+/ou5bRzrHWst3uuL6OBaLRTW4EwjOW31NAqR0dwU2a9mmWXghBOZshlCT4qCuzxFmLtm2f7RDyoojb2m/KL/TD8IxsHKu+Ducc1o70fR8XID+ynklevZlzusxprefhruadr/4GAlBe8PzNW7z88otcvPnLYA/MMs+dY8Xdq4FD3TB2KXkyYzFXNPU1h90OgUSbaEDu+i7iy8p5lOM6TyDQdSNCeFAdbRfdCcU0ChyIac1h6Ol2e6RUcRs6QlZkMdKN4M9/4ZP8tc99iZ/4/K/8tp/ND3/uQ3RDS5KkEMDaAKRTB2EZwbJ+RKuYuDw6KhntAa0i2/HuvbcAhVCBLBMIHfkF2gjKWdS0zWcFCI8QGV0TeHh/hxvWKFnQDyNZmtI0DccnxzRti9ZxJ3N1dcV89gLjONK2zbOiZJok9F0HQrE+WrF/r2J0gePjU7yzLJYrtE6i6HYmEcIgVUrbB+7ee8TX33iT80dPkMHx8Q+/RJ4o5pNgZb1MSYxmc3XF8zdvstlsuN5sAIFJDUIkdL2nv9pw7/EjrpvHDGLLvt7FQNHk1EiSBKNNBAX7qagt5ATQBa003dhTFBnj2DN0LbP5DKuAEGjaNsJ5ypK8yGmbBh8CaZqwuXhClqVUwnN8sgYRcL/XvA9CfqMYZPqoPWubhqquSIyhmyy72kRzklTqWdAohLjTUEohpcBaP23/n9qiIqRVK4U0ekLBO0bv8M6jtCHN8khsElFxFrFtAqn1NGMRF6LYxmxj+8058FBIxUdfOua7P3LC6UwzhIxf+folX3v0EO+iAckkKTdv3aLtatrDJQvnSYNGizh3IIOi2lXkSUGe5Bz215OXwjOfFeRZQpZEzfxuf4jKdhFrGiY1UQDSOtwYcK5jNotMAetsPLoE6PuB3f7AUbGKAhgp8SIgAvzoj3834+hQyqB1wAeLcyN/7fNf5q9/4XX+3A+9gpKO+WIZt8LSkmaGvEijEavrAQXeo4SMnYixx9mR4BVFMccYRd3tEdLFrfRsQZrmXF9tud5uY8ArETw+b9lc9hgNq9UZdqhiSxHBcrni8uqS88ePuXF2Gm/+vmcxLxACsulznM1iV6Fueq7322lyMHBydgMtBWdnpxx212w2e/puwNnAZrNnu93zT/7ZP43v8WhJleZ18TZXF4/IMs1HP/ASWmtWyyXBRs3dW3fvcn7+OMpcmp7ajzx8dMm2qhnoOb5dossOL+L3Yd8NHK1W5FlG3480dT21G8fIgiCSsggSrUxskfsBk2lMoinKgmFweDTVfk/bduRFSUhj4Kw+7FmvVhitUFpQNRU6TUjd+/c+vK+SpBDi7wshngghvvJNrx0JIf53IcQb08/r6XUhhPhbQog3hRBfFkJ88l/39UMIVE0d+8zD8Ozp76wnTGYkF0Icgjk6nvDYcYGNi4H8Bq2Z8Cy6HKY8wnw+5/jkhKIo4s08tRizvEBrE4tTaYbWBmNSlIrW6zwvUTrOKEgVYSvh6TgdgA8kUnKyyFkXgTQcWKWeV2/fxAi42lwBsZh3dHaLbSV58Kjj+jrw6PxAdRjph4DRCWfHp2gZvQ3WWsZhwBiN9yPBx19raTAqnsnTNMU6z25fxSlKYcjyGQKNEJqjo9N41BDR6LRer1mtltPiGrerAoFWmqZuSdOC+WzJ6clzrFdnJGbGD//YJ6f3UTOOMRPinULKQFFIhBwRYiRJFFluolAVQd+NUSXXT7i2NEEnydQWDRidM1rPaAcCdlLPgx0Fu92Ac4aTk5u88vIHpvc+hrkOVc18tnimAJyVJY8ePuKNN99Cm5TB2onBqXnhhTsxmRhi0fTk9IQ7d17g9OSM6+stb775Nu+994DXv/Z1fvEXf5mf+7mf54tf/EWGceCF28/z2qsvo5Rgc31N3bYEBG0b+R1np2e89tprWOvIs5yymDOOEXRztdnz6MkV28OBclnixchga8pZFjmhJkOgsaNnHMapOJqgVAzXGZ0wn604PjlhtV4jpMS6ka7vIofBB7JyRpLFBGRZzhBSMF/OQATW6yXzsuTs5JT1ekU/9M9Gw/9fXRSA/w7447/ltR8B/lEI4QPAP5p+DZHZ+IHpx58lglz/lZe1lkPV4L3nsN8z9LF6PF8sqNuGpusBiVCaQ9XEkWEf//o+MLkbQ8zjT+BWIUQcUw2BYdpWN009DQ/F4qLzntl8HrVrxHpBZAX2WBfxb/7pcWVCpSdpitL62aRhBFwELjcV9883vHXvIZfbA4N1vHv/Ll72BGE5e+4Ox899jH/+Rss//VrNr71xYFsJ+t7STSh7LRVNXce47Gin2G6D8wHnAlXd4Lx/RuxZr6KduWt7DvsDVVWxWq0py3LqHsQx7BACSZJRzhZsDwcenZ9H1h88a73OyhLv4fGTx1xcPgbhKcuYl//bP/l1itJgUhH5EXKkHw4YQ4ztahA+UBYlRToHHynLwStm8wXDMDIOHjuCs5rNpqY6RIvX0fEyVvuVom0HDoceITRSK07OjkiSlKbtWRwdUbVtBJ46z36/n0Q30DQd548fA3GWxdqoWFsul9w4O0UKgdaGd++9y5d+9Us8eHCf527e5A/9oe/jE5/4dkKwbLcbdvsdH3ztBf7k9/9h/sQf+17+zT/yWb7jOz7Cxz76YdbrFQERR6bTDOs83TCQJDnr9TFpWrA71Gx3B6zzfOb3/T4++wc+SyCi5/I8iw8jJH0fmQ+zWYyVV3XFaOPu005C2ycXl1xdbzjUFVIahDJ44sOs63vGcZqm1BHym0w7t8Vyznw1x6QGrTVZlqGmoN77vd7X8SGE8H8JIV76LS//28D3Tf/83wM/D/z56fX/IcQQwC8LIVa/hdv4L13eB5QyJEmKDIH5fE4ygVZH6+IT3QWqpqUf4uDH0y7BU9JwtP1847VvTqoRAtfXm7jjcC6OIKtJWEpgdI6miww713X44DHak6Qxt6CmuoKfdiVJksSnuXN01vLuRYUdYGxr6r6hChWbBr7y67/BJz7xUYTSLOdzvvPTf4Cf+19+hnuPLkjSOccvPMduuMLaIcI7RcxOGGOYLxZY5/ABrIO2bxl6S5Ybqgn4eXRyTBCB0UYxa5bmzOczfLBxOGocCDiGvseO0Y+R5Tl5qoGA8yO+99hx4PpqQ9db8rmJ9OxUkaTfeLoEOdL0NVmaxxFqGzMcSZZSFgWbqx2L2Yo8yfEWZGnoux6E42pzMUlpU1KdYG3PMIzs9wekdKyWa5JEsd3WLBdHaG2YzUpGO5IVBYO102c3crJecdhvOT8/j7HvxRyTxAGjPM/RWuOcY7fbUVc1s3LOrCy5enJBW1V87CMf4aUXb3FytGK1nNG3FSdHc+6/9x773Z4//H2fZT4rGLuej3/4Var9MUdHc5bzksNuw3M3bpBmOY8fX8ZMiw9IlfLkySWbzY75cs1Hvu3b+NRnvovH24d0XY8TjiePL2NOow2EJL73SddhnaXpakKAsiixPg6iNUOFtTG8Fq3cnvU6Wr7SNCFLE3CBJDUMdsAPntmixAePMppuOiZnJqP13QTEeX/X76amcOPpjR5CeCSEOJtevwW8902/7/702v/johCfzi5SgUzc7htjeHR+jnOe2XLF/tCgtEEpT/CeLMtj0dCOU486FrCUjIbkp0Ek7x1t29JOswwhEOcfhKB3sZBog8P7fupq+Nh5QOBcwCSx6Oide4YCt1M8WhpJN8Dr93e895C4zScwKIsp5rzz1rsMrScro7Hpu777U5y+/DK//uVrzOhxm0ukPuBGSBKNDx3aqBg8EpJmv2ewI4nQdL1lt92zpKQoCrIsIQSLVlCkCXo6Em03F3hcDHARKLKcsY9k4tVyjVOeIk+wQxtD9ojIOyQwn8/ohh6kpO96VuvkG98oOsO5WLyclUdopWi7hrZrSNPYJ1dKMNoOoSzeBqQSJJmh610ccDMZddXS9S3lPEUqxcWTK7TOSfSC6tBjbcnZ2fPk+YKrzYY7L7zIgwf3ud5s6LvIYZjN5lxvr1FKMZ/NGZ2jbRrOnzzh9vPPoZTi8vISpTR5nk29es/tW7d45eUXSYzgsN9gpMXowIc/9DK3b50ytCPHyzltfcC2HXYcOVoumKUpCkjSnPMnlxTFDGUSCDDagbYfuf/wnK4b+MBHbvDRj32Io+MVb93/OgQBQtHULcv5EplIqqai7VqEjkYtpSWH/Z6qaUiTLOICC0Xvo/tk6CJYqMw9Mg2IRE1G6lhcty7a0WwIBO85tDXz2YzdZhtFM4NDS/Xb3nu/3fX/RaHxt0tJ/EuEh2/2PhhjyLIC29ccHa3joJH3HJ2egDZcbLYok2KSOH03+jDd8PG4EGUicSsf4teedgyxaPjUT2mte7aNMiZurwSR+PRUuvo0txCPJpa8KAjEaHPTtNRNi7Mjzo4YIzCznMXsGD9Krndbqu6ASAVzobk433DxaM9Lr9wEYfnAB27z/f/O9/OkueDhO+c8evyAWTag9BxlJOMIWZ7TjwNZXsChQmlDOVtglKNpOoSMrkoffHQcJgnVbo8UgTxP2G6vadqG5XLOer1Ca82inBOKmAfph54i1wgZMWpZlrBYLOgHaJqRzfVICFF08zf+cvRU/OCPfA+jHZEyZXl0RKoNwVoaLxjGBu8sSguMsYzdgXEYwBnKYoW1HSdnK5RK6NoB56ITw6QCLTVd67m63DMrEs4fXWNMRlkGDrbi6uqSxWLFzZs3EQLeeuOKq82G0+MjqmqFMYY0TdlfXdENPfWDir6JzkStIqnq6vIK7xwv3LmDRPKrv/IrHPZXHK/nfPxjH+T28zcIfkTLwOr0lFRLrIyO5oePztkoxUsv3CJLlhwfn9D3A/fuPyTRhrZruf/wMaA4P7/gzosv8uKLdyjLjCcXT7i8vCRPC6wY2Wx3GNlw8+wO5XzO5fWGvCgYx7hbUFrjA0ht2FcHFknO0ekarTIevbtD4sizguBbxqEjS2IdrR8G0iJFp4q2b6i7hqbraLqe5WxGX7ccdgcWi9X7voF/N4vC46fHAiHEc8CT6fX7wJ1v+n23gYe/9T/+Zu9DkechSxNEJhmc5fpwYBwjQKOqO7Ikx7oYVdaTFt4/2w3ESccQfNTHEVelp8VCrWMNYbSTj8/auAglCVIpEmMY7cCAjxN5UhGEwlpPVqip6BerxH3XQIjfcEoG1qsFt2/eJDgJKMplyd379+j6DmcDb997h9ffeotbL95E6UhK+jM/8Geo9zU//VM/zeFqy2w2cnZ7TucP9GOMIV89fsLpzROUgb4eaPYVwXlWi4IxjAyjRaiEqq4mYK0nlRG8orRmfXSEdTaasJzn6uoK24/M53MaN6K6jtTET3/AY53k3r0D796r+OLPv/PsM/r273qFNNMYsyFNDIJAmW8maammmOWYLCNbJeQFaDPS1Duk8agkMLgqhobs04IpSOHJjcF1sTV8587z1E3PoYqmpfkiEovee3CPfbVjGEdmsxc5PTnh8aNHECKu/+z0jK5r2W23DF0Xo9FGcL3dk6Y5UidIJA8ePKLvBt5+4x1CCJyeLPjwh1/ltVduMSvT+DCwcZI0z0fKNMeElDCboZKM6lAjVUpiondz9I7z8ydIkfDw4RP2u5oQAov5gldfvoMxKT4I7t37Ou++9zXS3DHLC5Q+QkpJmit0kCx8jgs9aWlYZAXeL2ibHjcKaBV16yANnJ3M2dd3SZTGhyXj4PFOYvuRfnQs10cI6aOdywraKsaam3qgq3pSoUmTMvIx3uf1u1kUfgb4D4CfmH7+n77p9f9UCPE/Ap8Bdv+qekK8RNSpu+7Z6HI/jEgVOwMgIzzD+mezCdbaZ52HYWCqLcQJRyEEwjMxHRXOOoIPOBvhookppkJchINeXBwY+o5xdNPRIiLhpIychb5vsXbEaE2SZDg7UJYrXn35RbT3/Obrr7NYLFmvj6jqJY8vBtzo2Ow2vHXvbT5Vf2cUnRI4W5/yp//df5+L+4/5pV/8JYrZyKFtENcOiWReliiTxjZj1WB0Std2uGGkaRvmqzmDdQhhSUxE1mdZwfX1lt458iyLRyUpabuO2jucd5hUY1LNWDccGkeyXuCcZegsl4+3fOXXz/n1L204vXmbMEloYUQbjxABoxV5miCFJfgoQ9GJQGpLUQryMvDqa8+zXr2INo40jbMn++s9/ShQBLTRJJnBuwGloxZuNi8oZnMen3fcvvMC1SECVBNjkELSdy2XVxe88vLLLBYzHj9+zHIxZz6PHIYsyyjLGe+99x7z+YJ+GJnN5lNMHr72ta+z21coU/L888/xoQ++wvPPn5IVJXVbc33dcvedt2nqik9/96e4vTYkRpLkOae3bvP4yRX1ddyJXD88oNKUzfaASXIenF9wdXkNzvKpT32M09M12+2Ouu+omz2BnqY7cPPomDQ7iiPsY4cHZouCtm8YbEuzi1HncjGjqXpm8xmdHWKr0llOz5bYoedQXZPplK6zhDCCjKj+Q1WjFBRZzsYT23Ke2MJHsTo+ieG+93m9r0VBCPF/U/emMZql53ne9W5n+7b6aq/eF870cMghqRFFSklsS3Isa4uWRDYcKJbtKPECI0GQf4ID+E9sJ5ARxHHgP4qSIIhjKH+8IbKRDWEsO1q4aEiRM8OZ7pleq7rWbz3ru+THe6pnKNDRwAkC6gCNma6u6q7++jvPeZ/nue/7+jvEoeK2EOIJ8Jf7YvA/CiF+HngE/LH+038V+FHgXaAkUqh/jyv0hiRFXIjEabnvDUtKXXoNbPQv9BkIl/qE2EI46EGwfeBdJB6rDmu7D7YLvTHkMpQjCl+q3kbdSxLwBAHeWYRIIoNSS7IkRRBIsgStI+p70JOdzs4vGG9MyYui7/XirOPtt9/hja9+g+/93OukaUJAcPvWXf7Uz/1pnj094vj0Pc5PaqqyZnNrwO7uNqenJyy9xDvJaDiktCuq2nNxtsJ5yXBcUJYNWicURRHnIGn02qdZVOfVbUMg4IIjHxTs7++yXq3IuhhySxCkZogTitPjp3zty+dsb13BB4HHRjwd4F2kXlsVCE71npEy2st7xobSAaUcDx+8y872iOEwZTLVDEY9QVzHpKKmiwPGnZ1dlIJyXca2UBikbCiGGcPhDrZzbE33MDrl+PQkgn07y87ODrPzM9brNUli2NzcYjwe8fTps5jO1XepPnh293Z5/733Y54iDpSlsSUnF8d0vuT+g5azkxMOnx4yn60YFinXr9/g+maCMYJxmhGkJwhH4xvKiwVajglVx8npAsKcZ4dPaaqaz3/us3z2c6+zXJ7jArjQcT4/p2lrklxRVzWIEE8COtBYizCKNDdIlWOrNdZ5tIbhcISSLabVCOuQzjGd9ABkT3xAOEuejRBSc356hu9nYlmRMRwWFC4H4kPx4mzOZDKMRsCPeH3U7cO/+c/5pT/8bT43AH/xI38HxBlAmibITqK1jgDPEJgv5njvyLIU6xSyDighY0go4cW2wRhN10XiU/89oI3qQ1Kihdb5mBsoRFQyxmm4fPHjco4gRFxDelrKqkSbKBoJvqOta5QI2MrjbEuiFHXTsLWzy3wxZ75Y0rkPrLp13fDmm99kMvk1rl+9yu071/uQWsH3fM/n+fmf/3l+8a//FRI1ZH93AGrO9vYUbx2npwvGwwlZaljM5kip2d+9ws7BDiaVPH72iCzLyfIMay15CCgVpbJGp2wNp5RNTd1UuM5ycn6GFBIRPEpogg9okxCC4h/93YcAtK3tHZrg7OV8Je7+vQ9Mt6cgFavqAucD1jm2tre5d+8ermt59uQR9985oW1OkMaysVmwvT3FGInWK6yrKcs5w1HCaFSQZYbXX98hTQyTDc3zo2Nct6JaeayFjc0xk0mLCIInT56gpGAymZBlOU3T0tQNy+USIWKS0aUVXkrJYjHn4uKczekUe3pK2ZQcPa9ZrS+QghiT3rQYnTAcDsizhNlsRtsdYJKUJM1pm4bTszPOzucs5wvo1pycnPLw8btMt8fsX9nnYG+fT3/qk1jn2dreY7yxzWR7A/FVx2xxTN3NWc1rhICqbCE3+B6gTIjzoUQNCZ1ktlgyHm4yMAOSoPHB0sxr1qsFWinSJMWolI2tTZbLGoFGuujwbRvPuqtABIoij+Ey64ZMFSgMy/nvM5mzlBKTGKSMMA3fE3DSNAUCJlEgs1gRg8L7wHod55kfXkVeuh1BROdYluN691ycO4R+/SmwnYs7+h48GwUol8EAACAASURBVDcYlwahCF9cr9d92lFB1zQ0Vc10MmZjNMVbS9c5xhtTCHDl2g1m8znL81kkZ2vNcrXi/HzGW2+9zZe+9NsURc7mdEyWRi/9D//wj9C2DX//7//3bE2niCTl8ZNHeCuYbmxSFBO06hhvDBjsTfEuIDVIHZhOxsxmpzw9WrOzuxvXjk1M+9Fasy7X0WosJE5A3UThTbWuGA8zFCb2pVXNz/6Z1/jb/83XcGFB0yYIFQtqnJxHJed0c4ONrW3u37/PulnjfUeW5bx87w6fef1TfOXLX2GxWlLWDcHDeLjLnVuf5sbNGzRtxXvvvcujR++xWHnyzDHdlOQpPHv8RUbjnPFozGLeMTs755V7n6apHd567ty+Q1mWrFcrhsPI+7g4O2U4GDCbzdFasbm5ERkQQF1XlOWatu249/LLPFD3Ob84JQtxHmUIjIYDru/vsLO1ycZkzGSyQV2WbE2HXLlxkyw1bE43eevd93jy+Jj3Hj6NisfjOVIGplsFd+/eZTKdsDGe8s477+Nsx6sfv4fOLINOgE+QPmdjkFE3a0LwjDb28AFOz89AGKTMyFRBVVc0dYu3hs4GbGjJsoxhkVNVFRtpDJapqgpBYF176rVHm5gK5oXHqEHEKuJYnUcTl3EBgeLi8ILz89OPfD9+hxQF0ScjBWazGVLJFxuELIuZ/EVe0DQtzkdnmuwTl621UUduNEURX7w4VBG4ntQMUfnYdW1/0/f6c5NgTHSqxZNHxJbHuPKYRty1Na1WPYWpb0+kZO/KAUoneAHr9ZrFcsW67LMCiFLsPM9p6pgbeHF+gXVxPZqYiErPspSf/Imf5OBgyv/5T/8BjZ/jrECrhDwvSBKFtRXgOD4/ItEZw3GO9AFlApk0tFZQVSuSJOHsbEkxGFDXzQt5tydaxfMkmr2EkzTrFiMaphtjHj18B6UG/NCP3WS9eogZdPiux/EhkQFUkpGnhjwzaCXxzoNzjIcjbt+6RVPXHD1/xnK9pPMx+uz23bvc+thLJCZlsap4/OSUVQlbW7d55ZVXmGxs8PDBu7TVc9ZLwfPDU4Lz7O1eRRvJ9tY233z7bU7PjnpArGSxWJFnOUy3eq2IZ7Va8vTZM64cHPQwYjg6OuTK/gE3rl9ldnFGkWYMUs3u7g77B9vs72+xOR0zmQyREmzXEtyIyWQMwpAkQ7zTHD465qtfepNHh+cENEWq2d4dc/P2QdzCpBkPHjzmm2++AyEwuyjZu3LE7v4O2sDW+ACEZZS6fhsmAMHGwS7+MgFMSIrMofI4+0qUwXUOoTSuDhRi0D/oAmkeaezOOUaJQqCom/IFtFZqCMHTdh3GpeAF2kta6xgJDzz9SPfjd0RR8N5T1xVd50iztLedGmxvQIrpuA4Rn1+kadrHZy0IIc4SlJIRAtJPxmOIioireOhvdhnBKTLGlCdJ1kuio+ApeE8I0fOgE0PSI+m6NsJZTBqLQNk0PDs6jX2sgKqMdGvrfW/FjaIoZy3BxO93a2uLra1NUqNRl5AOIUizlO/7vn+FGzd3+MI//Xt88Uv/B21TwmY03rRtFc1PbUUQQGlJE8XpyQlpFldyeZrSdF3kISSRtIWPTs/NyYiz83NcYxFKcv3qbYIVXJydsZJLdnemIAzTac5P/ExGt36bfKiwTYUUEolEdJ4ktNjVjGZ5Dk0LQbAz2WGUjal7yKzzAoHh1q27fPZ7vousKBgMBpzPnhJEw8bmkM9+z+vcvHGbw8PnbG0fUKTXuH//Tebzc6IqWXB+ekK1iqEiQgieP3/O3bt3aZqY2bAx3aTrLJPpJp2z+CpQNy3jyZjRaMRsNmO5WvLw4ft0bcPe7g62BSMVk8GQnekmGxsjpHQ41+K6ut/jC9785n0yrdkYjlicX3B+chrhOOMpRRpI0gST5pxfLJnNnvH+g0fMzxZ4FxWJD58ccuXqAa++8jESMUDJgHUtwcf5TAiQ9nbp1nUQ4iNKIjA+IFzAIOlakErHe6D1QCDRCT64D7XGkWshhMerOHxHCgZ5PE0rKTFSMtgccWXzGvC1j3Q/fkcUhbZtef78iCRJuXr1OmVZMrs4R2rTh6uG3owU1Vpff+O3+fRnvquXtFrSNPTCow9yFp8+iX3y7u71XtIcTwJCRHCM1ubFJsM7R+h5hlKJXowTY7eqPjJcyljphTJ0PlA3JT7EpKHgPXEbGn9/IT1Kq17XnrG3u8vW9iZKxhANge/HH6EHmSZcv/YSP/njf5qdzSt84Qv/mHI9Ay7Q2qN0xmgzwWhNVa6xZUua5GQ6kqZwEi0SimmBMUkkCVU1k9GY0aDAd571eh1nBXVHXTmaqqOuSzY2B0w2xqxLzyc/+RLwNtWq5dW7V1EBquWKcl2zPHqMX82Q5YLEeZTJ2CgyquWCsu4QTiCDJs0LXn7pVfb3DrCuYbWa8fTxe0hp2dvbJ4SW5ydPmc0v2Nza4emjJzinuffyp5GiZbk4I9GRmbi1tc1eMWC5XPLo0SNu3bpFmqbUddxSbWxsIKTg5OQI6z1N09K2LZvTKcE7Dg8PuTg/x9oOawPLVcO6qqi7lnVZoXSfmHR+RlNWOBc4PD5mMsi4dXWPrfGYG1c3uf/kDEFDPthAacPbb7/P8ckJVdnQ1C0iOIpCY308TY4HI5p1g8Th+uG3lBKFxrmYVKV0TLcSQUThURfJT5eKHu8c3lkSk+I6G2/8xmISRegc1oaISLQ2cjy8RDiFNgYlovhOEEhED5v5/QaDee1Tli9+8Zi7t66SJApnNSsRWK/XbGxM4xqwHwR+9Y1ISn7jt7/C1WvXWa1WfWFIX2QhPLj/Dtdv3Obxo/c4Pn7M5uYBbXu5fYgbG2MSqqqiGGQxfUhEVHoUxMR2RCAJ7kPqSKBzDhtAxVRTZPCoJCr/mralWi77DQhRWUkMG93b2yVNFaJ/kwjReypEbFOEyBkPrvOv/uAf4/q1W/zqP/4VHj1+k83NMU1tUUUGUjAaT7B1zc7mDkpKyrJiuV6xubWDTOPgUyFJk4zZ+Yzz49P4d3aO1nbsHVwhzwJpkiGkw2QCgiIxKXUd7bU/8yN/gM/cU7iqwdU1to5pzmVVk48Llk1NJxyz04e886alagPV6hyjAmmiOTk55uF7TyjygqPnR7z/4Ake2N7cZjgcYJ1nMCiYTAse3F9x5+5ttrY2ePL+fZbLkvnFBTtbWzx5/Ji8GLC3t4cbDPjqG29w92MfY7lcMpvNuXbtKuPRhGfPnmCMZrVecX5yyvVrV9FKcXF+Tte1cTbkBaen5xydPufB+3vs7++xXq85fn7MxWwJzpMYyXgyZnB9lzxVDDLPZz51i+P5KXWosG7Eo0fnzJdzlqs1WinGwwGjkeHgYIONjYz9nRvcuXWD5fwC2zXgPME78nwQ20/r6AJ0LpLGhRBIEXF+hIAmUtK0jpBgJTw+dJF1aSHTkXFZdxYlU7I8ByFw3oGO70/p5AvznlTxPW2t/cj343dEUfjtrwj2t0YcnT3lsu95/bVXaRqHsw5eCJUEr33qNb721XgMevrkMcNRgbMNIk04OowaqYMr12POQH8ZY2gahfDRW+AQtB4mhUHh8baNbYTUKJ2ipUKr2Iu54FACfJ/wXPvQZw5G2bNS4AkRiCJEVBxqHa3aRBK0UjGfX4qe8SMg0J88+gmybTt8cKTFkFdf+V6Gwyn/4H/6Ozw9fEDrLLNn50zHAybjFCVjzFw+HOGVYllWsbGynnZdotKcIo3EY7RiOBiyXC2hk6AkWQFpPiIIhfMKaw1N4/nrf/XvAjCaHpALwXAKpmuQrsa1LVXVsF7X1KGlsi1BaVYXhwTrGQYHqWe2POK9t2oSWqabuyzmcyZFwWq9xreWtmzJBiPW65bZfEExLhhtT9B5iswyzs5nFKliOZuRZSmL5RIhJVtbW0w3N7k4P2c0HDK8eoWL83PWqyVbm1Oato5Cp/EIISUXF+eUqxWp1mQbU3wvOjs+OeXd+w95/9FjZM+4vLJ/BW87xgPNzdt3mBSajWFCqhqu7gy5d/sq/+wrD5gtO6qqpa3XTAY5n339Ve7evsq1a7vs7G72JPMthE/46pcusF0g0QaPpqw7tNLRuYtCJppgiV474rbKO48N8VQM8WbWSiMSgRICj6OqLcF76tahdYcKEqNU/Lv0g3OhNHXTYYzC+w7btC9WzB/l+o4oCpfK6Hu3DtiYTPmNN77Bl7/2DV6+fRdnHVUdPQHGqGjB/dC1WpYADPo17ObWbrxh/QehElFGnSNbQWc7gpAIrTHG0HX1JYwLBRilkULF3AsR+/LgYkGQgAwgCX3AaeyBvbNIop07yTKUkKRGYztLlmfcuXOLyWSM6M1b9NOR+LUxR69czqmbmrwdoBLD/t4r/NRP/jv8b1/4h3zlt/8vXL3m+eqMItlBKs9yveRivQJioMqzp0/ROkaprdsVvvMMBiPapgGl6XwgH46o6zVGC9rWIeUEJcZUi0BTfWCYnQw3mKQpw+DJfQPtEt82dEPHehCx6V5EHFnjoQ2eRdNycn7B+Qp0oqhOH9IuTuLmIgPRONzynLXwnJ88J8kKZK6xbU1VrcjzhOFoGKnjCrztOD2Zk0+mLFeS1XrFxmSCFIKzs1Om0ymb0ylNU/Vvn4T1ahXVppeq117anppoirtx7YB7r7zE8ckJR8+P8N6xsznlD/3BP8AgNQwLwUsvfQLsmuePfge7npOYIVe2twjtN5nVF/gQ2JoM+L7vfo0f+aF/mRvXt8lzE/0K2Yjx1lXee+cxbWfBC7yTSKMhxO2X7SxYRwLgwVuPJ/RczcscEBE9OTbiAKqqjZ4e5/vwYLBe0nlLmhi878BGqb9JM6QXtG3XD5a7fkP3+yyjMQahajyS1no+912f5je/8gbffO/+t3ze66+//kLKfPfuTe7ff0iRa7yHuqnY2t59oUv4sAVDShlTlbzFulhBlQi0fWsAgkzB5kYOUtFYT3B9ZqExdCE61/Rl6IpUBBF9Fr5XUvo+PUdJGTMPEHTWsbE55d69ewyKvJ+NwAtYj4iEIktDS826K3nw1mOq1iGkorUV+3uf4Hs/P+Xw6Ts8ffIm1WrGcKyxrqNqa7Y2t5kUBWfHJ5wcnZDnA6bTKUan2A46C/W8ZLXucEEz3ZA42yGcoVtrFosaRcp/8bf+BwB+4c/9WfaGKVuDEYkPSNvgOo3zXRyitk0chimJVgobYmDNYrlie5CizTUu5nOWVYfXgropWVRLRqGhPHoftxxhshyrE2q7YigFsq5w6xWpDNy+cZX17AwZHM4nrNcrzi7OGI9G1OWaa9eucXCwz2KxiIrGNOXk9DmTyRjXxfj02WyGUobJZIOVWFCulnjnGA4Lbly/yqe/6zXKdVwTjgcFm5MxSngmQ8PNGwcYWs6efJWqXqMEXN3b5M71XWbvPGH3YI/Pvf4ZfuAPfp57L11lc5rRdDWJKTDpgKQH0rjORvdYiGxRqVVsDYiGPCUUSvcIQ+iDgUI//+pQUvV6kUDTdH02qMBog7MO1adPBecigDmoF07SCPiWeCFxToFQMbTnI17fEUUBBJ3zLJYVVeNROuH6tRtoFYGv3nta2/Ho4ZcBuHfvZdp2HTFnvsLaGIeepPULA5RSip2da5ycPOHRo3fY3b3eD3wUSnhsU9Imo6ggI3Awzri2O+TZecmy9SiZYkSf4Kwib1BIgReA7E1XXFq3ZRxUQiQrK03Tdnzs5Y/zUz/903zytVf7nMf+CBcu/wshOC5WM37rK7/F48fPeH684PDwAoKOCHcTmGxkbIxzrh28TF09w7OgXp9HhFsIbEw3WF7M2dnepW06BsWAPCuomgakpi5Lug7KswVbB1s0Vcdy5qlOLX/rl/8hAH/53/93kV3K1a0Bk2GKChKNpGs1XidRGEWH8S3KCXSIa91MBIyzSF0wncbUpul4Ew+syjImC13ZY7FcIo1hvlqzLle0peXo5BCyMXUxw29vM94Yc+fGdb5ZrViv5qhE081XdHVDtrVFUzc8evSQV199ldde+ySPHj3m9PSE8WhM23R0ncP5kkfvP2Rve5u2rlkvl7iuRUvomgHlasloPOw9MJL57BRfLygyg/IFSnlcU+LaEi0cmQ4M84TXPn6DRkj+pT/w/bz+6U+ytz0hTTRVXSFV7P/brqMIgaauCd73g0R6mFBE53gflZfOeZTWaGV6e/QlrEi+0N3E+D+LkhrnLOPxkEExYD6bY12cV6VJHgfsIoYKdZ0l+DhjsM4hjUErRdP9PotjC0BV23iMqi1SRnqPNgaTtCAExiTcvPMSWsq+L483pVI6rpVcdEBqbfrgkOhhODi4xeHh+xwfP2Y4mtJ1LRIYDAYv0OiFEVwbDTGupF3N6fwAEGjve6dlPCFE9UMvphXEAU8XEfCJiXFkiEhVSrOcf+OP/3F+5Ef/KKPRIPaOfdYDgp570LBYzvn622/z5Te+zsOHT5hdVNQ1WBubmsmkYD6fc6RKNidw5WDIYJxRdSuaaoU2caB2cX6G9JKDgwMm4yGts9jQx8YpOD8/RxtD1QXWHSxWll/65X8EwF/6934W7efsjLYZJLafvUiaThC8BGQsYMLFdXCQGJVBrxiV3qPzUTzdNA2Jd7RNRZ4N8NNttFGsylXU/C9XlFVLWdes1jXH8zWdc5w1Dav5BXmRs7Ozw9nFGatV1F9sb00ZFDld22C7lm98/etcvXLA3bu38d6xtTHl5PSYzY0NFss5k9GApi5ZL5cYCUWeIIMj2JqTZ09RSnH4/Ij57Iy6XLA9HfE93/VptqZDgm+xbY3rGoo0JTMatOfunQPMaJOPv3ybzEiauqTSBiE7ikEenY4hUtAX8zlZmmKrmCLmrMV3sT11bYsxcdgtWovSpgcoR/6l9yCF6pdTvh9aC6TQlOua1TJmUQqh8ULQOAheIEIMCLL0mSIEvHeEtkMZwbcxKv9zr++MohDA9/kFUZYfd6xxqhqRVz6AEjEHoW0amqZDfKjPCoDrwziyTKOUeaFRuLzi8V1iEh29C6FDdhXjRJBhwXXgY3X2bRPzAIyJx7o+Ii4Q4kYi+OjEVCIafIJH4ZEqQkDHmxN+4Ae+n/FkiFAxi/8ychYfsM5ibcvZ+QXvvPuYx09OOTtfcHY6J02GZNmIJDEYLeiaiqpZUS3WrBcNr3xyD6MNo9EwpjOdL0jTjKQvkGWzikSk5TlKJz37ckSiE84ezpmtG4ZsA/Af/Os/hpWPuLqdk1Y1zcqBjmsygcF1Ci1yAhKtPWku0WmKlhYX4tZGaI3QGmvB2VjAlRBokdM5S9M16KQgCBigyYYw6hzjsibP51RNR9m0lLMZs7MzVGoYDcbgA3VZsmpago1P07qpyXIoy5Kd3V1efuljqCBJjGY6GXN6+hwloV4vaashvmtQAugakqzgbHbG3tVr3Lx5k+7qAYkK3DjYZmtjHF9r25AkMiYl1VFZmIwH5BsDZvUxj967T727w8H+NkU+QAm4mF2QZSOm0/igyZI0nhxDPCooIV60EUbFgJtL5axzTcwZ7bddLsTUKBHiPRDJZKEPFoK6quImQQnaHr4jpEArRXAe5yOTUxDQKt7gmRbY33eDRiGQJkHI+ELF+Kw4NLuElXjvcC5i1KMwCSAO6i6xboFA2zYopUhTgVbmW7gGxsSBkNaaqiqZKhhJSTZM0O2i9y3Ep3hXV5jMkKTxJXKd4xKCqpVAeki0wCgwqaBIFXQOQYfOUoosMMoknW0BjZE9SCae+GNhIbBaLLn/7kOePH7O2dlzvAejU4z2bEwyrC2ZnZ0yOzsj+BmHyZxrt4bkRYZwYExGKAKqECgJXVfjg0Vnhp3dKUoZbOMQXlLOS/7j/+TXXrweP/eZlxlkj3l29pD85gajgaRcLxGpBuFRUjM7r1FhiJEpSgVGI4NIDU2ImowkH6GTFJ0UCJ3gncDkOVokWC/BW1Il0YnCCdAqwwcRszClpFpNqZqOtnWUbcvJbMb5asmybikXS6QUjIaDON9JE6QSDEfDSAlvKiaTMdJFFkeRJwxzjesqlrOA2SwQvqVIEpTwdEHgHh7hveXW7TukeUaRakJbcnT4mPVyxo3b18F4WuvRQdPYuNVxiY/8zcGEgysHbExGpLnChwZfOx68ex/EY27f+QSnxye0TYtCIEKkhSsVqNrId0zTDJR84Snpurhls972p4ZYSC7JAlJEDU6EEcUgIN9Df5HxdQnBRyq4UGilaeqK4CxaKXxbvmhvP8r1nVEUACE0QvgY5qEvQa+R9xhCtIE62fVqwMh/rKs69lNK9iYkge0sZajwHrKU31UUYrhI03UoERhpz5iK3Hick8wry7qJwFmPoOscTSP6AAwP3qHwaCRaBLLg2C401/a2GKSadhUDSBtvSfKWx998A5W+zmAwQhd5DJztw2mbuma9XvLs6VPeevMbHD8/jGlIQCmgq9ccPrnPYnkW26PW0bUzdnclzrYx7yFLUSpFDVPOnp9gEonJErwQJGlK1dZAtD0bDBfrU3783jbvHi64d+MKk25NnmlE62iWaxCWsYhP4eEkxYWKTnVUq4ZVJQjO4pYapQUuxKNu01mk7o+zxCdSkuakwwlVF1CJJhsNEamh9fRP6zkiKIRS4AOhL4R0jomEpEgohGehFGtnOT87Y76oMIkhy2LAzIMHD6iqildffZX97X2MkZyd1ohUczafkWpLpjzSWVTo2JpOKbtAZiTPnj1l58oNNja3qOuW9+6/xfz0OQfbG4wmE5bnRwgdyVVNJ/CtxytPMRrR1B2LxaqPQQOTeIrBEGPmvPXmN+k6zXJZkaUpdB4hVEThGYMZjVkvlzRNTTEaEXPdwRiFC5dBw6LPAAkoGWXL9I1r2zW4PmVJIUj6tXsSmwU0ET6rpSDJFEZo6N3Av+9gMLFTF31cu0IbjZSiZzAolIwocq0SlJa9Qq0DEY/l3vv+eC6jtNl2NE2smkJ8sIXQ2sQXVWkGeUJhApPEM04SlsLjVlGYBAERog6haSq0T8AHhHcIPM56RuMh20PFK1dH7G/mpMJhpmOsdczLFqcd33zj1xnuX43tUdsix2MS9QFHwgcfjUaTgvfeX+C6Okaxz86p1hWr1QLnasbjIYnJqKuK3d1rNG3Fsl4xmo4ZDDMEkjwb0nQ1o3xCVS+5mC9ZrRckSjMpJkgv+cW/+WX+xOdfwiwcF2WN7xp0rhD5CKcSvF5jW0iTIa1tsQpEkVFXHbO2w1vwmY6KRu9julFtUYkkhMiHlD4QmgrpG0JVQW5YLyWqyPFSkU6m1MdHlOsGJzSdA9d5EpNSlS0dki5A0AnjRLO9vY9KEg6PnrNYLhBK0LUtjx4+ZDabcfPGTepRzeHhM8r1DCMtWnqWszOCDqTSkxcZ9fqC09maPDe88cYDnhzPuHr9NmW14umjBwwzw8s/9kP8xm/+FqlyBDSdk9SrBiNr0iSnGA5BeHSaIXVC52pSqV7QpfZ3r9LYhAfrRwQf4/OFiBup4D1GaybjMcvVkvVqQVHEgN2qblAmibkfxPd0ouMgN/SzgthgR65DojXBWlLRp513bRQ8SUWiQItAniZ475gtShASnWQf+W78DikKfZ6Cjkf+S9UhQhF9DZqAwHoLPr5AIfgXtOjLHZ9AvFj3eW8JWAQfZNNFyEsU8BSZwIQlG1lOqqBONV7E2YWkZ0UEgZEB6RtkcIyKFOUtqQzsjAS3D0bc3FSM8pYskSgBZRXlqY2vWRw+pF3O6EZjKucZFEWcNvdCKK0Ssixne2sTLQXWQ1VWQDwlKREYDodxy+A941HB9RvX2N3dZbk+RQBP3n/IxmiT0XhC2moynSKywLqa421LkJ4izykv4lNl++YtXt+7xZXNDfZyhQiwri2J6pjkDaGyeK9wskVLTyYyBlc022tPU9eRQI0nNDVd2yLWa6SIbZu7TAxWCp8nSL0mGE1rOxQZAUXZShw5Kknx1r+YsHcErPNIrRE+YJsOoePpQwqByVJEZai7llC1nJ3N2G8dJyenLBdL7t9/C+EadqYDNscpaWpoyjkosF2N1BIhc/YP9rm7gvuPj3nrrbcI3uK6jo/dvI7pZc8Hd27y+HSGs3GQXZcVjXdk40AIhmdHz1k1DdtbQwpi1GVhBuSjMW/dP6KuOpx1pAiU6MfTUiIB6wNFmnNxcU7QhjxJwcQ0cus8QiqECwjlEN5C18bXBI8RAoUH20DnoYvmJ9uj7zIzROlYJGrr6bzHCY1R5oV246Nc3xlFIRCHIyLgev+CIAJi0yTrVzrRGo3v+rBW139ptPbKF6h4jw8W7+OPD4fYSxkVhNa1tOs1+VSQJ4GuqWltQCiB0RLbBYwW5AayBDSOPBHsThMmeUamBUUi2R97BrJlmOQY02sVRIOSFjrH8vSI5fERG5Mt0klKQESVZH+kqxvLumzZ3NpiZ2evJyunLBZzpICXX7qNDI7Hjx4jpWZjMkCbjI3JNsNhwtn5CRpB26xhkONsxWrRMpqM0HpMUSgCDu8kJyfRTz+cbrGXZEyynIGKopp8ECGuHR6vmj4YxUTpbU+WGu/HtaySAh0uh2ceZzvapqGzHZHFK0CKGMxiO4SKiHobYkBvCILRlWju6WwE9M5m51hnWVdrLgNzl8s1WMuiajl7fsrh8+e4ELUjWnU0jWU+W/GlL36FrDB0Tckw15jdKcPRhNXiHCv6h4AI6ETjMQiluXfvZcZb+5yczWiqkiI13Lp5wPOjp/z4T/woozzna7/1ZVbnp2SpovMNJkvg/BSV7ZCODnj27BhrV0xHO0g8bVOzmlfgLEUqKUON7FrwLj7FswwhDcMkxXvJcH+L2cU5o3HKKM8igLjIsQF8UCAsBI9XoFwTBXJCkA9yOtdRNy0iaLSEQCDN4vvaORFJ3wKkSdBK4pzFtf8ftg9CiP8a+HHgOITwyf5jvwj8a0AL3Af+TAhh1sfAvwm83X/58VSAtgAAIABJREFUr4cQ/vzv9Wd8GOByGZ4ScfA64uBstItG/XcXe/7fNTi5HMJY64grDN97IgzbO/ucnhzx6OEHYqg7BxPyYkzrHY1tEdIgcAxTRWYUuYFpoRgNU4ZFwmSQkGuYDBNoK5RwZKKOpqS2ASdoupi+e5ny5NuKs+eH3PzYx9FS4bzDdRbb1LRtx9nFOUdHhxgtuffyXR49ekLbdhhzi9QoLs5OefzwPbxziGBw1nPv5VeQylMuK+q6ZjQakSYpVVsiUxGzEHVcgQlyXAvPLyr+s7/5v/MX/uSPcH0ro0hSVCDSnkQcZEmpsA7aLiCEQ7g4o5Fa4onsDC1j2KrstzACCF7hZYY3KUJKpJIEKbBthxUOlaQkWiOD6P+NNKEfzHvryLdg4+7HaLqOxWpJnhd01rFeVyRJwpe++hUOj0+RIs5LsmJAuV5HHURV886795lOciQBvznh7GzJ6ckJT588pG0qjJZcO9ji5dsHjDe2aUNGvfZ87O4dXn01p6rWaOFJpGdrlDGfLcmTlNliydHhIaNRis4kk2KCaDuaek5bGRbrEtHm3LmSI70iSwUX6wUnR4+ZpIYszEiEw6jAuirRDAhIysbRioDJUy7OD9maBvI8o0gEZbXCBYlJClwwdF1K2TgIcfaCjuG+CEOaRSq79S5K+qVC9uhEpIp4xabFK4XudRIf9fooJ4X/Fvgvgf/uQx/7X4BfCCFYIcR/CvwCkfkAcD+E8JmP/B0Qj/1RvOFfpDRfRq03TfMiSKXruhhHHsKLoqC1js4wpWnbjpieFEczTdMhRMxxLAYj2qbG+WhQeXA452d/YI9WlHgVraaTQU7TCNomMCkEB1PFeJhQZJpBKpChJaFBJS5aoIXDdwHfeToJLkBnA10nCCjqquTf+gv/IV/74m8g8NRlRZ6m+ACLVXTntV1NW5cM84wb169ycTHjG1//OufnZ+AcwjuyJCcxBX/0h36Qz3/+83zxy/8rq3VJkibEeYzHJBovHXjHxeqU4AzKjXj6/pxf+uVf48/97A+TmYSNQYFwjtDFk5T1sSi7usKH2KZoZRC9Ii54QWM7lJB4rfHKo1Sv8vRxR66kIUjRn/I0rvNUHXgMSTDIYOhCwKiEfmOG7SxVYzHDjKb2lLWlagQiTZCJIU8muK5lYzxFEedKWZLR1g1VWeMCVI0lSxOk0KSJYbWu+eaDhxEjLxKyQY7RkuFkFyFTlEzYHG+zaubMzs+4ev06g60Jo0HKuMjo1muEMJyeXvDs8JiL2YrWOwpyBiIlNQlu5RGiIdeK3ekU4QJt05JkgZ0b+wjpKUJgfnRIGjzCWowzeO/orKWuW5xUPD854eLslETBdDKkXJeMRuMYTxcCUg1JZI43Gb6z6CSJnogoiezX9B4ZVNyIGY0yBudbhJLRS+OiWM8Th8Qf9fo9i8K3A8GEEP7nD/3014Gf+ch/4re7RIxQgw9Arh8uDpd5ikIItAz4nsoLsSgkSYIQiq5nGQYXTeqXQNi2bWPACpeT3HiVjSUJ0TYdkGQGhokkSMneRsbBpkJLR54ERoVEeAHO9bh2GZ/61tJZG9OmjcEFgXMS6wJ/41ejAvO1z36eB/fvg4taCS0l6/WadbWicy1aQV2u8W1HkaV87M5tHhBYzuf4rmU0GHD92h1+8Pu/P5KuqhKPI4SOQT6I5CuV0QpPa6Odd5zvcva84pd++df4uZ/6YSbDAYPMYILHWUtbN7FF8yC1QmmD8AGTJnGn7i/9/HFlhpFkJsF2lrqrcfiYQRDo1XMhZmpaF9kRQiOUprWRh+ECEcIT4kA5eOK8p479dLduyJOMbt1hMoV3gaYusXWD8B4TAs1ySdW2eOu5nCKVVUNZZAzGYwZFhtGS0SAlTxTBt2SJoshTqqplNn+MThcMp3tsb07wzlJXNUq0DDJN1dTsX7nC7Pyc+UXJ4bMZ80XLYO0gaRgNFHhN0u/8y3WNCJqiyMmKMelgyu7edVYnF7St4eGbb6NEINnYonMBZx2TjQwhNBs7FnHHMsgSmnLJ0yff5PjoiHVZMRiOSIoRW7v7FFmB0SoWAqnwfXsGMWtTBItCUBRDnI85j23bxVZLa7x18UHq///dPvzbwK986Oe3hRBfARbAfxRC+Cff7os+zH0Q/SlB9xkE/a+/aCk+UCtqvAgfAsBcpg5Ha7PROtKlnaftGryNx3WjNEqqOGP40NUhaIMhuJpUK0xtGSWAjlLoECAxGmMEiVYIrwjKUAwytFaUZYmte9WZEFHgY1IMHv+7DChKgE5TTJJQLucYY8jzgtGgoB0MWBhDtS5Zr1cEb9kYDzF4lss5w0HOJz/xMvv7W6xWzxDKo1OJcwGhA0J6jJHUjSNPM4bJhPlxy3/+N77Az/3ED5GpAYaEVCrasorKud7Sq3QSBTJS9euwaOxy1ve4uj6ejphdcSm6QkDrL/dGEnovSAjRoiWEiJoS53DE460VEb4TXDQqKdEHswjFKMuQOkp+Q686la4lNBVDI8Eo6rYjMYqagA1x29R1luOzGdIYtvd2GA8KxsOEUaYJrsS1awaZZjlbMpuvqeycO+mYJCjGkzFt51gsFuzvbnP77m3GwzHlYsVi0XBx7pldrFCnLYu14ertPdI0YzTMGQ0HBGEQMkOSUS4t1rdsbG9zPDtkOD0gmKOYyyhENOGlBi8VtvVU5ZoiH9A6CVpw8/YnaaqKk7OzGOF3/20Go/e5deMmB3v7mDSNYcZS4K1HSh0HszLKp1ez2YuZju+9KZcCwHD5b/QRr/9XRUEI8ZcAC/zt/kOHwI0QwpkQ4ruBvyeE+EQIYfG7v/bD3AetTZB9W3AZknLZIlhrXxCmgd708QHjwTmHkJK2jU6yLMtom3gyEP0JJBYPRdvGteSgkKzLmuNZxcbBBqkA0dUo16BDfNrOZoG6gf2dDaYypXGBVMcMSZXoqCRLBgjfIaXD6PgxB4wnYzov+DAoa7mYIVpIkhTdx77lacp0PMF0jiLJ+cabb3JxdsbZyQlVtWY4yNnZHjMZTfju1z/BYKB5+vAZUnVo5UnzJAZ8DFKSRDNSA9rKUi8Cf+2vfgGA3eGUQTokzxVaWapVSZblaCVpXIcMl/29RZukF4l5tNJI+hlBAKP7hOo+Jl/JGEYTvI94euHJkpTO+9iG9Cq+prO9qKqh69fB3kbobjYc0nmPdQ1KJQgVTz8BqNuKupxBvWJqFINBiiviPr/tLJ0LeCRl56kFzE7OOCwyms0JwY7YuLrDxWzO2fMnXDvYY2trB5UNWdcxGWu+mGPShGs3rpJmO6TFAJTi6dOnzM9m7Ew32UwnvP/wKbOyYf58yaqsmNcVL798h09+4uOoLKeqA3mhmV2sePs3f4edvSu0q5KJyaPMufOxrfSWNNPoRIO01K4jFYYGT54nTIZF5Efs7HA3SF4tF5yfn7BaLnjrrTdp2hYpJXsHB+zu7ca4PqmiWM27FxzUznlAUgyGL07dTWv7RedHu/6Fi4IQ4k8RB5B/uE9wJoTQAE3//18SQtwHXga++P/0e11qDS5PB1J+a1XTWsc8Ba1wrfuWINamjZXYudBTnxRdGwDXZy7GY6uRJgpkCJhswGQzZX7+jO++seaVqxMKQLpe6ozAK0XlNIdnS+aLObvTAVf2p+Q6RSUpCKhthRMCkxukFKzWa+q2YZQUL5SYl5f1DuEcrqxxXU3XdBgp2dzYYJoWcCXQNR3Pj56TpSmbm2PGfVbExmRIWZ5zcvKQR0/ewRPRbCZLqFYlWkiC0yirOHuy4OxptJP/tb/4J7m6lSF8QBCfzJ1K8SHe7INhjpTRqdc2NQiBVqJ/+vehHyoazLquQQLGCHwbUDpmXsbtEAihoiRdCJwXhC5GtimiJUQGS6oExigq2+Gtw7YSK+I8py1XcCnYweO7mm51gW5KpqnCG40UGqklCE1nPU0XWLeWi6ZlUVpmz454/uQZt25fY2M8ohht01nH8cUCJxLyfMh0a4gnhtV4IXl2+Jwnzx6hlYxgn6bi+s4+2xsj7n3qDht5ynxdkU/G6CLln33xDc4PT3Av3aaq1rTe0zlI8wF3bt9lc3PK21/9Go8eHZIKRZIYgjQsqwofPPPlgmJQUAwzOtfENlLDcDBA64RsUERT23hCMRzhraVerXj4/nvcf/ddnjx+xnA0YGtrM753xhOmGxO00SAkTsqoctQCFzxKAoZ+vf/Rrn+hoiCE+GHiYPEPhRDKD318BzgPITghxB0iefrB7/kbfmiecImh/wDfRp/S3AuavvX76HMVP4h2L6uSul7T2a7/WNTxG93DRdoOL1Q80gJfejTnj9zy3N0r2JwMKYaeVeeZV551E7HwtrVIPEkiKIo9hNI0Xcf5fM1y1ZAmKVIKynJFWZWs6paDq1e+5a/4mdc/xz/59S9hdML3fva1b/syfO8fgT8P/OxP/zjBW06Oj9icjlgtL3j27AEqndG0K2TiSYsUk2rKtUP3xdAwwC1O+K9+5Xf4K3/2T5BKgZEdUnm61oFP0CZ7McMRUuK8IwDaGCSxKECIJy0VwS2BAD7G5ocg8MEhZIrWSRyK6zgEEwGQEt+0OBeixJZAEAKTGmQff1fkaZ8LEKCNBcJbS5YaCA7b1WQqFvmQG9YOvBUM8gylJNZD23kqPFvDIdvu/6buzWN1S7Ozvt877embznTnW1NXD3bbbWxjg2kCQjiJIhniWIEMECeC4JjgyEFxJockGBMjMRgklISQEISw4lggC5RgrISWARNsgofYadtd3e2qulV1b93pjN+wp3fKH2ufc2817e62Y5L2LpV076lT37j32u+71vP8nsijU0MXE21M3HvjAV3v+fCXf5Dl3h1OLjqKdqAdAsaMuHJg7+CI20fXWe4t2TvY5/z8jKwUX/ebvhLd9/zI9oyP/8w/4mC14vr1Q/YODzl7uuHDNw9xBzPuXDsQ8KsVXsK8qKnnC2LK7B1e48k7x8SUMdmSbOKiH1mfXNC2HYbEnZvXpd9gKxaLZjI5GbQqCFEMaTmCyo7FfJ+PfPker7z8Kg8fPuT07JjNxRnrFNidnfLmp3r2VnssVkv29vcpmhmxh6zl+nBaJkhf6PGFjCQ/WxDMdwIl8Hemff/l6PG3At+tlApABP5gzvn0874KJctTEIQZSJ+BmKZxJVijJ0nsZRHIV0UkhssRZSany+RmK1uNKVLu2cQzM/QdPJeY88GXb/LyUY3OPSebLWndsus8ofds+kRhJRDFuS1VVdMnx8OnZ3zi9YcYaymKkrIQtWTwiXLsyMX51eP/6A//FRZH7+O3fN2vB+DvfOwf0m7O6Dan9LsN7cVa8N1a8R/80T/G//Q3/hb/1u/+nXzFV34Z1nS0uxO0PaUfepYL2IXAt/2+HwHgT/y5j/Lvf/vffc/H+b1/6F9lZksK7dAIqad0lpQUQ5LPRGlFGgNxirU31nIZvVeWJaOXzEwzXfC2YOrjJIqiltCTkFBoQvJkAtY6VAZXFBhtKWwhKyYjk4mcs3ACsoyQnXGMg4iHYowCs4mJaDV+7DHZ49RIqYSAlcYetMijV4slmpZhbFlUNXFWs/MRN4xEb3n64AmvKcPX/qavwRRLsJrz9ZqmLGi7Ddeu30IpjQ8BV5TElHh6cszgP8DcamZNzdnpGXQtrjBsnr5LamHPKsq6oraWWSMrLess/dDy1tsPeOOt+7x46yV01TBsPaEfURZ27cjx6RmL1ZL+/JSh7bhx7YjgBwyFqEWT6B1SlB6OMQXBe0KKOGtYrg5ZLPcY+46uvaDdXXD89Cl913J6esLZ6SlvvfEGKUM9azg6OqSpSpr5jLL+wsNg1OVd9v/Pw1qXl6vDq61Dyll4iGFEoXDWYJVm6Drafisn0LSiuBQ2XW4plNIYYyfga5q8E3nKd7CM44grLE1hOTnfAPCtH73DapZYLRuq5R6Lw1uYouFiN3IxJB6fnHP+9DFxd0qpPT5r7p923HsyUMyP6AZP1w2E4KmtptARkwIv3pzz6pd8iL/6t3+K//7Pfw9dMrRjJHpP6HfkcUceR8bBc3S0x63bkuY8DAPWgnYDIR9zev4Ond+SVOA7/9OfAOBPfu9HKU3DQq/45E++RpUqbsxvU5iKYQgY41DGMYaILZxMYFIm5AqUxoeILQqKosJYN6lCFYaIymFqKkb6vrtiCYLCGouzjrqueR5kgzZYJ0G9WksepEEayCEKWj9l6VeURSUAXaNlu6IURgllKPme7AfGfsv65DH96UOylzxPbR3NcsnZhGgbdp30l6wlkOl7D8ZyttmxHUdUUbIbR863LSFGjNbM6pI7L73A7/iXv5HDG0d03Yah2+Lblna34e6Lt/jAy6/yYx/7P/ixj/0DTEgUpaKZlbItrGrGssLszXnlS17hpZdvQ4q8++BdUsi4suLWrRfYm+/x8J3HPHznMevzLdYUIgv3gdIZwjiI/Dkn7JRXUpQFOQahMxlLiGq66XnZUl821nNG64RKXvBgk6OSlLFKGB2KxDB0bDen9F2LHwf+8x/6iZ/KOX/N570ef/Uv8V/+cVkMnhGSknShp5WARrYA2pjnAmWlgRgnQAVc9iUgJT/5JuyUDxCuxpM558nBlrg5L3m0HXjreMNHrr3ArS/5INfuvMjq2m3KZon3iTYoztZrnj58h0dvvMb5kwds245l0CxaA7MF1bLAblradqAfe/pxQCfFm+/u8LwNwL/z7X+EP/Stv5vNTjj9TWFZNTXL2YIbR9epG4MfT+lCyzgOLBaCIB+HC1KUpbzSlj/xp38zhXOkfkApWJ+dMPOOVTmHMZJMgJRJSPNp9J6sEnuHe5RVzaNHa0JIJBJxHNj2AykrlLZUVUVVTnxJDSpmqlIiyLRSpJgYx4E0DvRBNCAhRVIEZSz1bDbJkjUmZ6ySk3gkkU0iZc0uDuzaFmcdOUVW8/nUnU+MfiSFAeJIihFipNSGmCcUes7kYWBV1+x2LXPniCbRp4hOCa0zKXkOK8ftwz1UUXKx3fFUQTeKA7GoSm7duMbhwZLgW5Jv0aGjyB7lNJuLU955x/L+L/kQj+4f89YbbzHqCNrhbEGOmm030vZPufP+lyRtzClmtYME+wd7WONp+3NuvniN2y/e4Wd/6uOsT3eQM04L0F3bUsbkwaMriTAonKZwWoKGQkSZAqM1KckKuq4EqDKOHitCzavrIqYsBOjLhr0xqNowqytWRqNSgh/6iS/oevyiKAoge9wUM8optAKVJ6mmMZIorA0x2gnLrt7z/3221c7lzy/Hms/3HUCETZC5WSoeDWv+yz/wIjc/8BUc3LyNrZZi5Y6JHDJqtqBqKmaV5eLogPVmg3v3Md5d8M7xgCtLrt24Rj8Ejo+P6XcaRSSkjmo2A54C8N/+xb/+S77/P/Ndv59hfIoPFyzncxZac7654OTkIc2iIGfwKaGMZrHcJ6aRcNrjz0eOmgN0sBjliD6RU8anSFaTJsBl4hjY+Q06jCgfmLuShCakLA2qrAj9jm2faOqSsihQGryXO2wMHpUTtTXUhZWVmw8EFFgZj4W+Q2kj1nat0daRnABzlRY4qcqJMIrbLwwjq1lz1Rty1oCytEPLOPTkGPB+oLSGyhW0XUdOUUQ7iN5CZZiVFjIEEyicYwgBdKbrtsxioDlYcbEb2I0jfUpUlSOrxP0Hb2PwXF/OWV9sePjoES+8/2WOT57y5R/6Su6+9BJvv/NQ+iLGcbYdwUKrITklqLt+4OL4jDD2zOqaB2+/Tjd69o9usNy7xoc+9BV85Ku/gp/8hz9Fd76jUk4Awc5JT0dPeiQDIQwYJZMWsGzanrEbKVxByIkcelk5G4VKotVwRiTpEgAk7BFtC0bfE3NGuwI/hAkG+4UdXxRF4fICDjGgvRiexCKahV2g1XS3T6TENPJ6NqHITLx8Jbbqy8e8TKd+vmhIkcjEBF5pNpMm/Lv/0g/zA3/5t+Kio8gWp0q0VcwXNaptSSmxd1MCV+ZnJ8z2r7F345z08U/x7uNjiA3OlOwvK1oTGfuW63sH/OjH7/Ff/Ue/l7IxGKuwhWW1OmA5P6SuVlT1nN/2Dd/Mf/hdf5n/4j/5etbrx8S8I+QO5wrO1udEZgztSOc9uizYa/ZpTMP5rmVezHHeAgY1RepZBdYaMBaUwjhDHKRhuDcrQVVM1hoZR2bQyl5duMZOE6BsiFHgHSlNNJ8YcTpTOk2hDWMUEE7Uego7lr6E044YBsAQYiCSRHGqNYv5DFdUOK0gBkJIGKMY44jJEaMnejYZn4QTEEIgKqibkmw0q/0VBs3m4oKqLCgKJ4VkWh0VVU0/Brp+xNYNmzHw6Oyc43YnKtI4sul3qDRy98YhprA0ewuu377JoweP0Rpy8JTKoI2IqYIyjDGiTEnhCh49fEJpAmFYsz57Kn6cFLl28ybe9wy+pRs23L57g7sv3OTe9k1UBKs1o/dinOsGTO0wCorCYY2ExVRFiY4jmzFSO5mG+RBkxeQsKmtikCIb/YhhKggKFNJDymgyli7mKxbkF3J8URSFy0NbSY/WKuOsiDJUTqQQRB473fll/ioDdqU1VVlSlCW77VbGk1MhuOw1XDLvLtWR4srUhKxAGVa15aIb2AxQjZkBTxEMd1+4yziMkHvKek5TV/Sbc0LW6KJmtlywWDXce/td7r31kCcnp+SQcTqgi8C/9I1fz1/587d46eXMam+ONZq2HTC2ZjHfY7m6gbLPgJpJRbJKXFyckTMSrR48ZxcXLGyNRSAf1pT0mxF8JvYejaYpG8KYJg/+9IA5yQprHNFWiuvQt7JVU9K7yROkhqnRq1SGLCG/xlqCykQSympiSvhJjpgnA1vpLFmJw1Ncjg6UCGesLcgkdjvRNzR1CWZK+8pSaFKMKC2ZGTpHIE5GKmE2FPOGbr0hjB5tDGMWDYUpHWn0tENL226ZNzUKMWH1fUeKHldUFPOK4/NzhpRYNA7V7FG6ghQys2YhRKpmQb0YWGhHUZYc7O8xdi3Dds1+U1NXFSF5+hBY+0ibMsOu497r9zhaVlw7OqBpGtp2y6MH9zk/22DLGYtVpttsOJwfspjVGKOoXEFRFqTWs263rBYzQfarxDiMJJ/x/UivNqgYmReWbrtmsVxSWUsIkRAGUBrntIyAQyZbQ1aI+clH+R4xOFcyZI1/bnX9+Y4vmqKQpqVxyklOKKNlHBbz1C23KC3Ku+cZCcYYFsvlVSNmGAaYVgnPbxkutQ6AjOTIWFMK9qosgIFv+Xe/k//uL/xJ2l3H+z/4JRAUu/UOow2z5b444TYtqmiYO0scHSkOzOpXePWVV9h2nrYfxVKcE1/9676Uuino2w1hCJjCUdqKhORLZGP5Db/lG67eS1lY7N4eaRyxxnD89AlGwXxWY7MDPLqUplpVlKiyooyKQpcCmyGBTuQs/g+lMlolCiN5E6DYBI2dFJDBi9vUKEWMnuBHQXs5x24bWSyW090flLakrCiamTS3lJ4SwDUhRggRV2hpWiKTil2/Yxh6XFkwr2cTPk/gtloZfPCSgZjle99sLkQMliI5jbRdR+M0piglK8FKHkJKmc1mS+UcxjiqpqTrW/puS+E0ptBUs4r1ZkfMCldaZlqBLdidnHPx5JinD57w+PgJq9mKYYRtG0ijwilHZRxP3r3PuFuzPy/RJLSx9EHJqqHz7HY93UXH2/fepamF4nR04wA/Zoaux/eJ86dnXN+/wbDdcnr8FGe1cCYn1uTeoqF0hsI5xrEDJXkjSilUjKQQpQFZlAzDOCEFpga7syKpR4HVRH85actXn1HKAetkeveZ2p/PdXzRFIWc86Rpz2BkkhCj7BklH0HuRN77q1xINSU65UmVdzl31FpfeSeePy5XDErLXruuGnLKlHUJyAjx3hv3SCHxgVc/xNnJGdvNmuvXjiZ1XmKIoIuaedmQRsvYb7GuoE6Wg8OCZr5kPpsx9jusFXhs8NDvWqhlNKS0NFN/42/9nVev7c/+qW9m0ZyxOTvnYG+fHDPLpqaaO7SFvk+YmDCLGX307M5a9rE4NCZmjFYMeSBFL9uoLI7RlCKFsxKkmiGUN6nqGqWQickwYCZLdApBouT2D6jrhm3bUc/mZKXZ9YM4SSsnpqeJZO29bOu0kRyNrOVz3+wuICWaRTPla0gPoXCCYwMZQ6ckehJtFNYKeTr7EZIHldltd1S2QFnRqsSQyDlijeb0+BQ/jAyjBxKr/X329hZUtePs7ALXVMQhkKfi2FQFH3r5FX7uzfv8vR/+GMe7LduLHR/+0g/SbXbo0dOd7fjExz9Oe7omrgcaW4n12jhkqGdIGNpu4MnpOffftnzkI19GP4xgE5mSo6N9GZMPkXHT83P3f5aH77zD2AqoplCG5Vw+Fw04LavWjCFFphVtxupy8qIqxKsmvImchRTiJwKZUgplRT4Ngm5L0zURxxafIvxaKwp58t5by7QnUhOGSvQKGi0nUgSQ0ZUIb4SA27atyHK1paym0NgMu3Y3OSyFjqsnCKbWGjvxH3PO9P1w9Vq2257DvT1IcHJ8Sk6Bvu+pmoYYArvtjtVixnJRsTkNzJb7lCEzhExGM5vNWC6X+NKiSVhT4GzF8faYofMcHjnKWfke2ekf+Y9/F4vZjju395iVEUKi70Z8DDRNTcgepzR1hnvvvMNBs4dpE2/dO2MVCxauwRpNH3pijjhXikksi74jpUhRlCgk0HaMgdKVzBf76MUe5ERhZSna9R1lWaGMIRjPkDTdMJLQNHVNHzIhjIQp5swYjbXS/R52krqNUuzt7VFYS/DjhFMvhVKcQGnFOI4MfUfVlFcdduscdT3Dk/C9x5mCPnViuXZyU7DGsr64EKn1NJFIMdHMarTRHJ+eMps3JK2pmhptRra7LTEGdiFSVJH33bxOm+DmwQHj01N+/P7fRaeEy5GT+6+jchLAjtFUVSE9FqNpNzsK5QTDVzny/oo8VoYDAAAgAElEQVSL4xP+zx/5+9x68Q5jSqxPj3n/yy+jc+Lu+17hJ//ej/Lo4RNWq0MWy0OqWUNdFBIolKPEEI0jeSI4J5BMES0LskAmTf4FnzPHj48pq5Ll/j67QQAss2ZGu+0YgmDlY4zSS7rcgltDTr/Gtg8yeRgnJeZkvolyByJLeOvoZRSjSEJiNprYe0IYyTmCEzCKmHjClBhdYW26WjmI01KMO8YYco6MY8vz+OvTsy1ltSCbAmMUxycX3LS3KIqCzcU5OXgO9m9jNJTNiqw1fvSUWeTChbXTvwvStNJxRcF8PqPbbemHDluWxOesrCcnTxj6yGyxx3zmSEFm169/8tO8e/KIo2uH1OUCBdysFuQh4TtPNV9xe/8mbhAJtRtrlLMiaVVaXHJZkTK0YeraxxayJo2ZoqhRSjOMA9tOGns5W/o2ok2eHkdRVvOpV3A5Pk7kMUjBCZ7N9oJxGFBZsZjPKauK0A94AqQoTkoljx9DwLhMWTtcAX07EI0WctPoBas/BvIwkvpRinvfYW2mKAratqM0hqapiWNLTsIsHLsdXbAMMdEOHlc4mrrC6Mz+Yk49m2FdQdd7Bp9YKEO57lDaMhhF1JpmVmOGdDUazykzhgABQoh4P+J0ZGYUZRHYO5jB4ZwuBDZvvo1PkaHreON0TVMatvfuQUwcVTOasqJ00lNRRSmGvRgJIYo4TNvJ6xOx2lAaoY5prRnGUYrcbMFhOeP04oJ1FynqObYqaPYO8OocWzesFgvOzs+52G4ZQsCMAY3G/lpbKQCTavEZly5cNQv11FwJV44w9dyUQSi3ihA8Qz9MQwh9VQistTRNg/eevu/f47yEPFmwhX03DCPf/wN/nT/4rX8A6xxNU7HdbYVx5yzDOHJweMSsEZpw2cwEyZ06nNY4J+PAEALOyfhUI5OA2WyG0pI2nBDAyeXRTenPKUfZOyJpP7dv3+F8fULwgWQjQgxXtLuesU+cvnvOnYO7aGfROoOtGWIW01HOBC/LSKWMcAWVoqo1/SCBpWVZMIwe7TQ+yZcwDhFQWK1xzlJVDT6EKzm0UpIMlZwhhEE+h9LinHAIlYFduyUrcGVJ8pIFaq2szKqqmsJTxJsSk8cPfopNi4SxpzAKFMQUcErET1ZbCANOKSpXsqhqVOVo27WQoUdPP2lW6plgyWazhjxBeWJMDGNLVtKhD3GkqcSpGmJm17bMmkpky0rYHVnL+WWskYmIUhSFY9bMxDLvgzS6BxGGxZRI84WIkbRMgbRVzBZLXFlhnQMr42XZ/maK0uJHLx6eLIY9lCJOkx6VMhhLzJlN25O1o56vUEbk1ddv3WK5v8du6Dg7Pma+mvPCqy/x9Gd/hm7ocFFR2QJrfw0WBblzX+oK1HMW6gleybRliAFifE+/4HL8mJJYSqtpC3HpsnweyALyRcvqRArDpRiqLAv291Y8evyX+L7v+81oc8Te3h5MDc66bjg4PMS4YrqD1ox+IPfDRFcusFPATAgBlSNFWYodWYErSqq6IaLo2+eKgu+o54cyediuMRicrtnf26duCvw40G63kpYlRnlCDtx78IT92Tu8evtF6qIQO7KxhOlE1krgKEVRYK3Fe0/pDApFN7TE6PExoYxBWcnOKCqBlrgJXHOpMIUJjpIC7bgjJU9VlZRVTeGsYNlGj0KT/ORi9b2EA1+uoooCHwZykEZvGIWupZVkR5I9OXsqqzFOT/LoTNMUyN5RkROkMLLbeLSWANes5G5aL+YcLJYEZFsjq5dM3cwYfMQ4g9aGk5NTXFGwXCyoqhofAlVpGEJPYUoWiznr9frqhgQR6zSFrVDaMASPK0uqumIcPJU2lFFuU1nLdm3sdmQtRilX1WjrsIU0l7XRZGsIfiQnAaFYK/b+qizZbjfkCT1nrIwvY8oMIaKtpqhqbt+9w8V2zbpt+dS9N/DDADHw0z/709y4dYNX3v8yVdGweXrBk4eP+GXEPnzxFAVr5cOSlfyz0WPO4jvQEx2JrKaT9Jmr8vKiFg2/xHiP4yi9hBzxPlNVFXVdEmOk73t5bLH6XBUgYzTr9QUA3/zNv48f+J//KqvV8ko+vVguJ9y7KPisqjG2xRrHGHt8CBSlpnCG4BN+CFwMHRpZ+saEYM0xfOKTv3D13vux58HD+wyDY9ZIUdlut5AMxsrI9ezpGcMQcK5itXfA2G1Z9wN//x9/nPx1NbevHXF92UwUatnnBy/FNOWe2AV0ymyGSEyKZr6QPWsIV0ErKcvn2Xc9XddJI8xalDIorQgxkpK8F61LrNVkooyHlUVpUd5ZV0nicR4prCOMYirLKeNHz2ZC4eckYTuFFVdfF0d0Cox+YOy25Dhw+841fOzZ21sRUuTJw1P63Ug3DpAD9eEBSRuKqmIMgbTbUM8XLGYLRlfQDyKoSsmzvthQlqIk3NtfsZhVtF0rxW25oG6uS/Bs9KTk0ShuXL/Odruj73oyU1+jLNkNHa6sUNZIYZq4HT4mEU+hWa72aOYLTFUTsIQpJzKNMmEjQwhJogyNIcU4SdwdTk/s0HaURjoKPwxsT88FTd+2rJZLZntCh95GjzEFN29cYzFvODhY4UzFo7cfTyPjX2O5D0qpqVJO8WTTHt9aKzJdrgYLTPUYNZGeL2ErkwEbrfLk8ZfqL1sOgbaSM3VVXfkk0mQMUumZZdsai6An4RO/8PO8733voyocHOxR1RUxXVKkmTgDBc4V5GkkOHQt3WZN6RxD39MPHcPQ432krBsSlpQ1v/j6PQA++tH3c+2GkoDWo4Npjz9ysW4hW44O9whhZDafs9m2GKc5vHaTlFtUWbFrB37kH/0kNw8PePXWAS/fvs7hwaFss4wRCjBOtB0hogfpYnfbLa4oqK0joabGrTRLdSUGoZAiYRywrkBrizNKAl20RSvhz+nJaSl7Y7nr28JIyGlUxJAY+oA1EgTsfWC5OJi+25Hz06cko+hDj++2LAqJVS9LR9KJpDMhJDbdlv39Q67fKXn3/mNwhqZybMeB7bYFJeNC4yxD3/Jot8N7WYmc9eegEf6j9ywXc4auRSPAnr7divBHG+arJTF4SBntDH3fUhSWFMVCjraYUnomShs0Cj96mtKhM6zXwo88OLpGUdfgCpQtMcaRlKRApTES/Ii1ogcJPlzRxoQXEiAHiAPzqsSHQEYzb/a5fiCToAdvvsEQE0nDYjXnztENXv/kJ3HOsEuRT/3cL3D3hZep65rtZv1PpKV9ruOLpijoq8oZQD2zS/fDMLnq5CJUWkl2nlKfURAyOUtDSKlxSpLyz/Bul82jnHFTsSmcY+jaq9cgisln5uzXP/0plvOGvWVDd7iicI6u6xgmhJvWCqMnuOyoMDlRFIaz9Rm7cURlxRg83Tiy3uxQZsfRdcfq8Brf/4Picjw5Pme1t+LwYM4wBOLYYrK4Fi/WF/jYU9clunD4lHBaicpPF1xsOrxPRK14crFhdbBieHRGfd7T1DXWWHJKNGWFdZblfE5dRgqtGH0/0ZfkTnTZ7ApK7njOWro+yFZEIUE4k37Ee482xWTWiWQFVtmJqi3a/mFsSTFBSpSuvtq61ZWCaZVmjKGuKkjCYhx3hjz2FNagy4JhMJycnjGGHkzGo2lb6R1Y5xhyJlvLfLXCe4/SioODA05OTxm9mMKGIZATzGeN9JiMkXjBnGhbaTJrrcUPYx277W66ESmMhe2upa5rZvM5ZVWyblv81BxMU2ZojhGfAu1mSx8812/comzmJKUJCBDXFU56PGJYkIIZs/SC1OV4V2LjYwiMYWBvVokPRUFWYlkfg0dljwW0FVZme7HhxAecMixL0YNsty3333iLGA16srR/occXRVEAuSi99/hxxBXumQJxGn2JFiGJfTpfmsMu/Q3PVHyXfYTLD7ksSwAhMxmD9xIiI70LfbXXNkaWcOPwbK8/9juGVoQnR0f7LJcLcgqMPjD0okOf1SWkRI6erERNRhzxXYuEgMJu17Jeb+nHzKffeJfrt+7yL/4r8hzO1mgcVTmjsGAnUdBuMzCMHlOI0rGsliwODjh+ek6I96ncEWiF0oasDINPbINmf3WNPiZ6DzpkSIp3L9ZorVktEy8ta+q6IJsCpSOaTI5BDDMktE4yNdGapnAY16CMkQ54CtKoI6NJiNwgT1s3JeInNFpJ1Lp1hrKQNNUwypRIeh4SzU6IzJsaq0uS73DOAgWhH+h2O6FA64L5vGSMnvW6JWNwVY2zFmegrgriGBiGnhADSsFysaAsazbbTr6rrieMgaQCddMQssePQShI043IXuldlKx4jMUVlTSoh8Bmdybfb8ysVoes5nNCSKIJUYrjs1OqquBw/whbVngyyjjiJarOB8bRU5SlpEirKa18ai4WhUXlTN/17IInxkRIitB5mqYUI58fSYi4LCXZjrnJF6IyzKoZYRQob2FKcgQzIdvSL2Op8EVRFC4LQJzcjCYJGQiYREwRzQRgYVrqKw362UrhUtKcs2Q+Pu+R0lrh/XjVzJQPSBG8F1fm1Li8tGJziXJJiaYq6dotjx48YDF7P8RI37Z0bUvOicodMA49u+2awmQsQZKkUiDlSDIFTTOn7SM/83M/y9/82E+9573X9YLClmgcq+Uco3oUUBRywuXsaYedhJGUM2xZcbHZsonQ1DXbQSy4wzhwdnrKnds3JZp+uyP6hCscxUS0bofAa/cfUhWaeVOyP68pVKY0FjdRrca+A2OwtpimJwJAMQrMZHPW04oLwLgCHzzDOODHkaJo8MFTFCUxRLyXJKmqtvgxMvqWTbtmvpgzn89Zn5wym9f0vpPinDPaOYp6Rl2WaKXY7M5wpRPfgTISpusc2+0ZxohKcLW3R/Ce3bRtOD09J0aYL/dxLqGip6pLfC/W7lkzw8dAJKONw1iJbA9R/l4UFV3Xo62sgFKO7B8eYLShbz0XJ6cUriLkzNOzpyz2FsyWM8iGPozUixXYgn6IMs0IiRADfhcwuhBq4kQoTynSdS11UZBSoqoqkiloxyRj30HOzyApy6L81Zd+n0nlmJAE9suF8wTlNVOvLf9qGqJ+idyH7wK+hUv7H/xnOee/Pf237wT+7enK+vac8//2+Z5DugGJYeincaL0C3K67HonkopXE4hLWcGzrYPimdbg2dRCTfLdlBTGaIahnWS5E4xUZXSWGHlhLwzPEwKYLxpm85rSFbzxi7/ItaMjYhJtvVKZ7XZDu7ug0Jp+09LnEWcltqtqapJXrH1k147MF/tXBWF/0XC2aXn5xZeAU44OjzC6pKmXlK7m4uKMkMQkpLTEpK03xwT/hPX5Dq2W6Ohpdy05WXxOuKrEWMWbb7xOXZe88tKLVIuCru1kIuIKos886gM2aC4Gz9PzU0oyy7phf7VHXTWo0qCzbCuGkDHJSb9nUiEqk4WUFGVEaVSB0wIf7XYDpqpkCzb5GIbeE40hF4YYvSRyL+ZCGkLMS7tOeg9d7yGMLJqaZtZIwCyKWbPAh5G95ZLdrid5j0+RQpvJYehYb7b4cWAxXxD8Fj9GZrMFWslq0BaGlBNZK3xIJB/ISklQi3WSVRoj1hX0fU9R1VTVjDF4NmsJ0tludqL8nIKOT9bHRKW4du0atirwKWLINLMZylnyZPuOQRrV1paQ89TQ1dI8jImMrPjGlCY0e6AoGnKSXNI4bZfjhBfMWcCoy70ZqrDsdi06yzZZTTkpeaKGq0kT+Znq3s91/EpzHwD+XM75zzz/A6XUh4F/Dfgy4DbwMaXUB3POn4cvnRmjJ6RAYRwg8/6kksBDlZacwglKobSYyZ+fPjwvQPpMm7RkQvZTOK2k8mltKAtpbs7rCgWst7upYMhx95W7NLMGkw2nx+e8++AR129fZ/Seyhn8sOPs9Akljko5vB8YK0PWgeV8SYqZpllwfPGI8/NnAKqoDIvFiqIYKFxm1wVS1gyDJ6WRISZJFAKS9yybmlkhQaRnFpbz63zi4yeEPjH6jHaZmBNnbUBtR2ZVy3a75oPve5EPf+mHaXc9x0/PGENgsXfImARO27c9LvY8PTnFPDljdXiT1UxzWFlmVvoQQSNiKh8gBGJhSBkKK30HnRNhHNEo5mUlOLeENMpSRk2jX++nGfykQAxJMfY9VT2nH0eMqigqGNsLrLP4bovRie1mg1KKsi5JMVGVluViwTgO9K2nsE4EYcZSV471+Q7nSlYLycQonSNYQzdssdaJ5kKDD4miKFgtl4xjj9aKpp5xsWtJStENHqU0hStZzYSA5LIwKI8vzti2HYdHN1is9lit9jk7O+fOjVv0YWTX9xRZIgCNtsKu1AZtJPA1hUDK4Ac/3c9k/5v8yPnpKYcH+0QyTErEbBQhpkkNaogpsX/9kC/7qg9x69Ytnjx6yttv3OPk6TEqKlQnUx6VwUcpgOZXU6fw2XIfPsfxjcAPTADXN5VSvwj8BuDHP8+TEC5zCOU5JyDKs19JU/gIkwz6eeqPTCaeaRt+ifdBCHGa/xqMU1ijOdpb8uk3HwDw8t1rPD15hlFb7R9IBe8DIWV+/rXXKOc10UdCTIwna5689gan6y2urCElPvTh9zM7mIMrKGeWrDRVUbJaFu95LSlnMWYp6MeRqp5RljU5R6q6YfRJxCxGEOx9GNBKcXCwTxguR7Kgpj395bhv6DtyKuiHzL37D1msDrBahFfGGOZFkNwM7XFlTekjvdlx0V2wbRtar9k5w9KNVLMGWxXMypLSOLIfCUqWst3Qo72CJBdXWVUopyGL6zGrjCoUCjdtyS6/K4NBYLpxkOyINBX9S4pW27ao4Jk39XQxGQ4O9+nHnvXmgm4YGLpO5PBJ7oTSnAvEGGSKhWIYR8nVmMJWi6IW4GyIXL95gxCTkKWSaCbaXkRIrpTlfQyRwkoTtmvXdL0npJF+GFmtVqz2VhjrOD05pe8Gzk7OCCoxxoQPoEyJdkICywkx3xmDc8I/CJM4TwrEwHZ9gTVG0PdhpChK2Tb5jrIqSSnRtgPDMPLu/XMen9zj13/1V/P+V16lePkFDhczHr7zEJsC26FFMWWRxEzfd5/zEnz++H/TU/j3lFL/JkJq/o6c8xlwBwmHuTzuTz/7J47ncx+0VugJqvI8FCVPS56cs/QQLn/+3ArgUux0efzSheEK0khOiflsxuHektc+9SYAv//3fBP37v8NVrMKkAJ1/eAab75xjzEkTjZr3nntE2z7lpdu3uDafMHmnWM2n37Azz94wEXouXXtkNvXj9g/2qfznrqa8+TdB5w+fcL1Oy9dvRLB8SsEQxlkOZtFESdSY8U4eqpCTDPZjxgjn1PKmcPDI156ueSnf+JNafhF2VaZAow1jDHhqoa3n5xx//E/4Nr+Pq/cfQFnNIeF53CvwBUzlIK5LqiY8Yk3H/LJ81PaoUIt9whxxESIu5ZSrzksKmZVSbFoaMqC7D1D25KjZ7fbsttuKMsKWxQoJLxEpj2X3+ukObkM48kRRSYNEvYy9h3R96gYJTQmSRozWcsJfa5xpcOHTO9FE7K/XNF1HdZacowkJOMyhpHRjyhrSCSqqqHRSxFXacWsqQV9Poxo44ghTnQjaOYLfIgMPuD7ET8IN1IrRfCRmDMHB0eUsxneBzbblr6TC9h7L1OClFBa7P8GJeNa4yAnhkFGkTJV0MQ8BShHy/Xr16irEk2m61t8aMleeBIxyEhVK4UfB3IK0Hv+rx/7cd5+7ZOM/Qgp4bRlMZuRS31F2Mqkq7TzL+T4lRaFvwD8cWTN/seB70VCYT6b6+KzXqXvzX0wOYf4nkSoGCNZSyXVWl/tiZ4vGiJl1lfc+8vUqM88LpuQz7+knBNf8ZEvB97kv/5T382Xfqjl937TN3Cx+yEA/vC3fQtf+zWan/mZ/5s+BB4dH7PZbXj7nTf5de//IB/9yFfixsyKGsh0KXHn7ovs13N0yHShY4gVTx89ElZh9Pw33/vH+Lbv+KNoI6uVfuzQOrPdtWzbnpRrQgx0Y88YEn5sCa5gXjckAspYjg5XEAvu3J1zdH3J43c34hqNiZwC+/srdv1IlzW6WnJyfIyP5xweHoIfuT1b8eq1fcqq4p1Hj1HDOTms6U7u8/TEEptDFoXDFTVaF5IaneHcBzY+EPqW0jr2Zw1aWRbzhtlySfIyeRnGEUiknFBjh7OWbCQwWGkzjZQTKmeBrBiNyjKqs1aEXWEciD5w3naQ9RVsRbVGRGlKUVYN6+1OTrggSd+ls2Stp5zFRNu3LPf36PoesiWlSNt2aNNhi1IyErSjKBvCJMXOV+5bg1UWqw19u2O322Kd5vDaNcltVBafEvP5gqqQbBKVRWKus8IqC0mUujFlIUYlSWsahh5rCrSx8j7PL2jqCmUl7i3HcLU9zhnKspIJivEiG4+Bqihke+c97bpFkSldgTOGi/NzSueYzRrQJV3b0fNPefqQc3783AX3PwB/a/rrfeCF5371LvDuF/KYKk8Co2fPQUoJZ917bNDPsxRkWmCnacN7a8/zheD5cJnLY7fZ8td+8H+VP2833Lx5g6ODff75f+Y3cnJ2wW//esPJ8THr9TkPj5+yaVvJbgiRT772GtsHT/jauy+x6Vouupb6cMntu7epnMPGJM3H9Y6mrrjYdgzDgCtqvuMPfytnp3+R/aM9Upbit+sGHj894ZVXlmSyEJCVwrqKthtYn6/FValqnpycYXCQKm6/cMjZ+ZZ+F9GmEDViP5AwKO2YrfboBom0f+Pt+9gcue4CF/sVq/09dpsWaxPzsubmtesUFztsbbmxX7MoZ3hl6IwhBkXKAmYNJDrvOXt0TO0MpYZZYcXwU5bYoiYlUNGTfc/Qj7KkN2ZibIrMOqUkK8Ege2yrp8jArMhaoaa7qZC2xKId+oEw9ZZCFHxbYQzRexmDWkcIntGPLFei9tNGE3Ki3fYM/UBVVkI9mpybi/1DtKsYxsAYEopE3w9ELxf5mDPjOFDPam7euEXIAsLd7lqMc2zWG/zoqQoRsI1DQpsCUpowdlOKk9ES2puEHZqTbCHHUaC21lp8CChrp3wNGaV770lBQzIMrWRplK4kxhE3m1GWTGP6hI+RwpRgM0HpSW8iz63/aa8UlFK3cs4Pp79+E/Bz05//F+D7lVJ/Fmk0fgD4x5/3ASeDiEZcYmUpCsc4Nf0ui8JlVsHl5X+JVoPP3DZ85pbivT/TWjM+h6fq+w4FGA3Xrx1wdO1IQko1vHj3DpmEO9dsdluUUaQwcv/dd9AXZ6Sux84Kbt69yWo1wxWG4AdCCuzVR4zzORQV265jGzM3b90E4Oz4nOX+njS+oqIfAihRB+66FnAsZg37qwNOHz9G5Yg14h3Y9C11BR/+qg/y9v2nbNsLjFH03hOGEV1UqATWtXT9gKsc22EkDj0//Qs7dmfHvPjiXVYHR+AK2tjSxYKQBrrzM9TNfSLglWEXM2W1wLhCFJITtQltmS1mKN8zDD2nT4/JWWGLCrRlURbMnZG4Myvd+hSDCINSIsUoachJ+Ag5BGL05DRefrnTthHKqqQfB4y1lJeeFu8BGcHFLDqIetbInb1v2fUjseuYzedcrHdiYS8rMpLyrLQBU3B+dka9WHF4dIOQMhdnZ9N0JUGO7LZbFssFzXzO4D0+RFyWiMAY4mRiuswzVRgUKaepZzKgnEjiZ82SEDx+lMat8CozIUaWqz1cKSg2kjAsc8iSYJ4UeUpSL5zECLi64mzdk5KW7M+JNqZVYjfI+VQah0/g+56qLjHOfd7L8PL4leY+/Dal1FdOV+Q94FunC/PnlVJ/DfgFZGrybZ9/8jA9D0LzydP2QZqNYVLIyQefcyakS5T7pdsxPddbkEf6zKJwGcN9uRKRHp9mVml2vecXX3+dnAK379zkwcMHvPqBD/K+V19mtaz5F/65307Xddx75y3effyInAIvXL/Fwpb0Z+ek6Nk5KOc1C+uwlQIHaoiM0VPNZyQfeefpW5xsH3N489nHoTIy606Zth1Yb3fUM1iu5rRdJBJpu5aylv38YjHj/qN3GMOAb8+oij1UpdmNkawDWjsGHyl0wFlLu11TGI2Pka7tWC3mPD0/R5/1tOWWr7r2Er2OoEoe7y54fLpBu8DT03NeffGQe/fe4fF2y3y+x51rt5jNZngiPoHTlrIqmO81bM9OaYcOlMYrCewZuoF1mymcwVkRPM2biqIRlkDwo2z7YhAeYhbxlMoINDLmSTEKKsuqoigK6vmc7XZL3/fUxnB0eMTTp09IPrDetiilqJs5Y4hsu55N5wkRMbWh8GNHpaAppXht24HtZktGSOEnT58QxpEwjsQUeemlFycIj6Ld9hjr8F6iCtu2xRhNWRZCVVZQNjVJGTbd5PbU4EPkYr2RBLOiRBmRyvsQmS/nVPWMrtsRY6awUkhTzOQQMcYRxoHCGSCR0oi1hoODFScXFwxjRVkWV+d5JKMLx5gysR8herLSgo3/Ao8vZPrwr3+WH/+Pn+P3vwf4ni/4FXCpUxAzjVJciYlSElXipQzaGCP4Lv0sb3J6ToDnmo7qalwJz1BUV9xGFFopdlNH9vt+8IcB+De+6Z9luTdjtTfj5s0jVArMq4o4q3n5zg2RGoeR1XyJConjJ4/pxh1D2xP6njR2JIT4u2jmvPHpt5itllSzJbfu3oXTNSdnZ1fv+9LRSDbsup7tbkdRa0bfMYaMXydiF6ldSeUs88WCG9zAnmvaoSfEng9+2Ysslzd5+O4Zp8drlBbLsLEGZw3DKICO0Rd4pVkrR4ol7WmHefMBh5Xm1o0j1P5tltcCZ+sTPv3gnPPNG/TeMyrFg/v3maGpOMLHHoqKMSneevOE6/tLgck4WOwfsB0iuyEQkiGi2Qw93fkZmkTtLKVT7C8XVIXFFY7aGRhHxujJXpbURmm0QVYQSRqIVWE5vHYkCtHtRriJIfD0+CmD97iyopi0BjErEpZ6vs8wirVbkahLh+k1xBFnLecXa96Ul9sAACAASURBVJaLJRe7jt1mLZOtFMjBU5cFVV3TNA0YxXqzYRgCFZpt105eCRFBVYUTToRRKIWIpDJgDe04gClxRtOPntj2KDVF9fnAwdEh/dCLhoJMnoBAl9Z+oxXKapxT5DzibCTFgfliD58aTk/PmM9uSI+qH8T+7sWtKv1ew+ADKfx/mzr9q3Kk/N4upbqUMV/+kyR+TE1d7cugl8t4uTxJoa8wjJd+ivc85qXxydJ3WwB+z+/6HVRO0ZSWs7PXQR2IdDYMFEYz9i21KyiNwlpNHITU7LShnFecH58LZy9Zgi7oUmTMgaZcUC9n7IaB7XjGJ1+/R+sVP/g3/3cArt+4AUQ0hjB6zk4uODk7RxUGYxOjTxJEK7wuujBw7/49imriMYwDIQ7sX19QlCX33n5CQpKYLs5Psdawv7+i3a5xZclsPqfrB5ZHR7SdJ7Qjr719n5t7DSdDIOuCpKANivlixfF6/H+oe7MYW9P1vuv3Tt+05hp27Xn3cHo4o89gx86JE6GQSEjcIQUUhIJzA5EQIQLnwkPAgFACUiJA4gYCMQiIEIkUrixjTEzsg4fYZz7dp7tP9+49V9WqWvM3vwMX76q9+xhLp0mMdfLui6q99lq1qnat713v8zz//+9P2VXUtkP2HfPLS5pqxcHBBF+XNF3PwcGMzWKOlAJtNJfzZ7QY2mBwMkUIRZLmSBdwtqM3ChSc7WpwllxJJkaiJWQmIUszfNsQbA/e4WyDiJn19NZyfvYszusJpDohz/LoFeg78HGaobSOpYGPgh2lTfRZCEldtZGkbD110yL3/Sq5V4RCYDwaYbPYHwkBdrsSuz+liFTSd13Mx3QeqQw+uBja4i1ZmiONYrfbUgzHOKGp+6hQ1ElGVdUEFU/Abo+qr6oaIVQsUbWi6x1axpRqhKdpW4wGHyy+71DSMxwVeGcp0oStkpyfn3JwdAxEOb9WUTLteg8yjmzVH8H04Q91hRAQQRL9jFe0Jf98jh9ttqBVJD4LGcuMvWwRIWNqTnDu+3YWsWcyxueIqkYCUUQC/PS/+++QpQ84mOSkGn70i59lOJiQCMFuuSY10d8u9kfbEAK+6+k9yCQlUQlGJARp8UaDDqg+YAC85vj6TZ6eXtD2nlu3XuJv/Of/DddPrrFab5GqjNZkqVBI8JK6tqw2DaNpihWObKDZ7VrEOEWphLJcsVq2eBdoqh5kIC8sFk/VR2MT3iN1tIwvl8t4QVhN39bkWcFnXn+Dt955h6ZpaLxg1QXWZxdgo2233O1YeME4y2hCQA9HLJ6eorZbRl5y5+Y1Rsaw3GxYn51RTKZcXMwZFBmT6WHUBEhNCBYhHDookmwEUuGkwwqLFoLQO2wQXNoSZ1t0CBTaMDQpWoFWEVku0Cih8G30KyhACQF9Q++i/yQNNoJl+4Z8GDM7qrrZj0Kh2wNtlJDxdaAzytqS5jll3cXRpFQYk0TbcwBEzL2k79GJwTY9wXbgIh9SJxlKa4aDIX3XkGQJQkV4TqJTjNa4zjMaDOhddEUmOtKo2Stsi6IAEScZV8Y97wJl3aGIoifrPeM0OkqlA7xku+mitmFvU9/UG3oEKIUyCiNVdGJ2PUjPYDKisy90QD9o/VBsCnHtS4F9BSCJPMUQwv62yCNWe5HSFZ35eQPyStkoXny1mClpPnKKiMaQ3TYe4aXS9NaRZCkHkyGT0RCjErwXVLuKRVtx6+aN2IsQNo6ilIlmFecQSlIUA+p6h5LxRdd7hwwCqTSjYkSxrZGd59//G1H8udlsosvOx660tQ6jI8MfJNPpDJ04bFOz2W2Zz5dsNlvG2ZSX792kLjcsLpcEQGuBTmB2WHDn3gkfvvWMfV40UggOjw8JIbBebvFB4q3jxsk1uq7lbD7n8ZNHJNqQKMEwz1gtl/vkIUdblxQHE6bHx2wvl1ysN2TDQ8qup1CGrrPMpjOK2YxtVTJfXNJ2Pa++9BKEMvYWdIa1LYEQ+wY4EhGQYT9hCYqWlCDk3qr9AlArXU/oW7I0YZCn6DxHmT12TnpC3+BtnNcbCeCouxbXSII1ZOIK9e8x3tEFiQ2WNE3J85zOWqqmxrlop9d7qIz3YHtLZx223wcce2jbFrX3xCipGI6GJHtAi05znLfPFYdd1xPqBqRBijjevDJOeW8xOkW0AiWjeSxcTdG8RyDI0pS+80gl0SqWEjoIQpA4Ieisi3JtJxiMp7x6/Rbf/s63mc1mTIYTgo8lc2IMtq2wXffPniEquiD9vt7fm6B8DH4Bold+L37pbYcwCWmaPk+lvjoNXE0jpJARyALPnZFSXm0iL6YUXe8iM08loAwqyRmPJ5S7Gp04lusVxmQIpRHaIHVKlqWU5Y7GWjSaNMvjTF54fN+ijcP3jjQtsCGKYZrl9vlzRpKPeC7mkVLhXKDf4+a881RVw2QypbeefmxZLTaETnG5WDOdDWjP5kglUEaw3S1om5bZQcH7ro8z/b7fG8Mi70Hs9f9103B2fo5zjldf/QRlXdE0DVYElIDxdMrq9AyJ44uf/zwuNfRScz6Z8OS05PxixTerjnGS4L0F8YxXXn+N+XLLxWJLVdX82Jt3OckDi03HvLU8vNxy/NIrtF3FenFJKgXTyYS27QkmJyhF7y1aJ/S9p9//zmKQbMrOB1Y1GAKpUuRKkmuQMhA0qGCRNk4zTBD4vsF3TVQG4om9voCXUfbsbMeuqwlEvUSiDZqAb5uIPfOQJAnBB1Qa31CctwwGBTJ41ps1MknZ7ioGQkXxkdYEFHXTooQEoeg7izQK5zukNgQ8ymi0kXS1JUsitcq6gHU+4v0lVLtdhOEagfWOVCsKnUSJuTBUtqVGxvwM4bl1/RY3T67x9tvv8vCDBwyLAXdu3UIKEdH+StI0LSr5+Jf6D8emAMTjuYvvIMRphNyj2K/GYNa/kD73e1n01Yz36vOwV3AJKZ4j3UGgdYwzk1IwGB1QbhestzuGkxnDySHD8YDFcsW14+s0Tc9quWMymZFmBUFAmhWkeYE2isR76rqKHoAki+NT39O3KqLPRUcxHLEpO4bDEZcf2RTiphQIQXJl+uqtxXvJar0iGSjygYk0Z2sZ5BlbtabtKi5XF1hf0buOvMhQiaApWzZry/0PFtRNxzAr6G0LCJaLVdwIHVjbEwKU5Y5dVbPZ7siSnGeXl7h9PZ2nCVLEVO75+Smf+pHP8fRySZ7HlOneSTaNpeksR7Mx1W7F7/zeV7FmQOsN1a7h3ffe4bN/4k2MFZwttzx89AGPqw1V07J+csqX3nyT6ckxlRZUPiCT+DsKziO1xFsIV6Ru9uIdrfES+mCp+obEOoT3KCHJVcrhJMN2HaJtcc5hhIihrPuSUggJvsX1LjoQQ8wV1QISBNLtHak+TiWumI5X8e0aQaZT6qphNMiRWuPwtFVJY208te3dtlJIkiTH9xG6oxKNUZpdVeIbR55lJDoKpKS4om176qpE6xg3SPAIqTCJIDWaJIAlRsxl4ylpnhMyw41btzAu0Fu4e/suR6MJ1TZqWqSSuODJ8pg/ad0f4vThj2SJKwDrvr8Ae/DHi3f8K4irkOH5hnDVSPzox+c8wcBzzX38+OI04YNncnDCevG3+dd/6l9ju6t55eWXaeqa07NzsiShrktefullBsMRvXOoJEOlaQyrSVLU3ikYo9oTuq7CWUuWSxyKNCtwuw6Eeq63+PSnXkeqBzE8d293i11nsScDJQgcbdMxGkq6pifQkRjoQo/1JXVr8VhMouhsQ5pm9F3P6bPt3oJbMxoN6W0XwaJCYvZ+fec8292WLC8wJuX27THDYcaD+x8SEJRNwxWX8PR8zhekJLQdbdeQFwOCc1ih0QrKto6YfA/ruqd0mlGW8s6TOb/+jxtuHN9mfrFjtStpAiRZQetBKMlb3/0u6WiIzDKmB1OUtNE0ZFKsjGpPR4pz0XfSOYsNMTfRIemURumEYDtK56iraLlOTIbUMeNAhSSG8uxFRAOh0aJBEhAiIHXkPUghcC4e/R0OKeMoVMsIT5UiKiql70h0nIhIIpkJHKkQuAAy2BgYSzztSZ1AZ1F9iu0tSoqIbdufbvuuw1mHuBJoOUvd9iRak6bJcxjLoEipF5sIrhWST7zxBunhAaXtkFIyVAkXjx6D9QzSnIExlE3JaDaGIPaZHJL/D4LGH45NYe9nijX2vvaJdTzPSwohRFS4Ofc8Fu6ql/DRhGnnoykmhoZcsR5jWXEVSR/NQzHr4b//xf+Rn/oLf57D2ZTQe0pf0VQ7jg5msSTwNl4QUqGTjICncxZlEqRymCyjrSva3uE8KJ2gTYwkl1KzrUp+5hdiP+Hi8nJf28nnPQ8pJVoKBqOM6cGEJN3R257gwNvYNTYKevr4OCFJckPbt2SJZjAuSO8NePd4y4fLU5SKMA6pIjx2MMiwvUMpSNKE7W6HkJpbt+6Q5zmf+tQn+cpXfoOmqZmfn0Xpr/c0Xctb3/w2xXTGcDDACcnmYkFn4zuZs5ZrhzPyZEBoSzwakQ3YBsGHW6hkQxk03hQ4DBeLNZNBgZWS9W7H8vQZGMXdG0ekiSY4uHXrJXZNx3ZXMzmcoqRABRUzE0UUKTkpcV6gdQImI1HQ0CJ8z6quybQkl5rMpKgkj2pJHwhdHNdppfaueR/hMlIg97QtaRTa7MtXrt6c3D7mvcNIEcfmoSdVCutalNCgFM73DBIJMsEHiUo01gVc39K6eEJJTFRc9s7FHBMV9upFQ/AQCBAsbdUitQYl2XQdZs/+KKuKpw8fMrYtg9mED979HhmKzdkcaXu0EHgZX++RsanRQHCBNMk+9vX4Q7EpmCQGpzjnUVexWVLucVVXphGFNBobPP4jDUa4kjtHIq7v99Tf8P1jSaV0jPDWem8weuHcW6421HXL5vKcN197CRkcRZ6QGUnblqAU6WCE9Z48z6nrBhc60jTWhDFrgig+SQ0mibSdmDPwQpewXu+eN4GeW0L29CKlAemQWpCIhHrXIr1GSo8Siq7Zkaaa1WpFng5RWU5ZlqSThETD9eMJT+6f4z20fU8iEhBRudm2HVmWYn3LtvRY5xmcDRkMBhwfH5OkGW3XxZyAtKBpWjZ1y9mzZ0y7njRPkVVgOhqRpIZytyLLMiaH11nvGmbTFNNZvO3ZBPjOWc28q3Aipw+G8fCQvvWUTcnb9z8kEbEhrIVhV9eIkLLbbGnKlqfnl8g04ag6xvY9t6/dwvUt2hhGxYDOarbbks7HZm6S5MgkRwYLQVN3DX3nabwgMRqlDYkUeKkQKqFXMoa9iKiCtN4hlLhKIXo+Bpf714e3FudjHJ910TodQtRcKCTWRzCQlnEU6oXHhoAMlkQphDYIZfDe0dd11B342EgPzmF92OPtPXmWkiVXAFeL6zwoEU+qCBSO+++8xXR9iQ+O7WqBDoLJYEiiNN56etejlaapYymV6IhjC/7jHxU+/vDy/8eVZznHJwf7HsBVGKz7vgsfAoQXBqmrfkHkBNoXuRD7jeBK3PRCwxC/jrUWARgj+cxnPgtA17bI/QjKO8udW9fxrqXvyrhztxVd19A7R+88xWCE0RkEiRAR2yWlBKkwSUZe5Pg9QEN+ZPOx+5yAq/WcBIXDhR5ELKFs7/FO0taWaltiW4eWBdt1S+glRT7E9TGVu9xdUleXXDsqODyccO3a0Z5dGZkRTROttlcqwCgRtjx58oSu63jvve9hvWd+eRFTmvuATHPq3kOQ3Ltzh6rc0lYlgzQhMZrxdErZOsxgStlYmt0Gqg2zNPYuVtZw2gkW1qHTgsfvP+Ty2ZzgQyQ2p5J7925jnWdROdatpw2G9x+f8uHjc2SS0znB6fyS+/c/5ODgkBvXr5MYRbVekgjHOFVkwUHbUu1qutYhMQSvqBpLWbUEmWCKMSGbUBdjqtGUpU5ZCMNlUFTZgG44phuOqdOCWif0yuBMilUJwWSIZIBXKRaNVwkqHyJ0ikPFMlJHNePVm0xwHYoebKRNSzwqeIwQGCkwQpJovc+SEM9To73rwFu6tolpaXswbt/1hH1ZU2SGOyeH6LYi7xpuFSk3hhmFDISuoWsrrO1RWlMUQ7wPMbh3fz183PVDcVJI0oQf/2M/yj/+na9zejrf78aRqPTRcWNwL0xRz/0Qe3hrdEiK53+CD9+3qVwZT7x30VnpHOt1ZCf80i//H/zCz32JNE1ZLpe8eu8Ws4MpfVvTd03kJwTiRa8UyX764b2nyDPaVmD6PI6WjEHL2JVerzfsduXznzOKsCJSLI7oXoxWQ4h6fjPSkAS0SAhW0Nuavm/JkjFtX2OMwvWeuqoYDBTb9YpMKWazA4aDlPm8wlmHymK4zrAYUFYVbVfjQ2x0xekHrFYrhFIslytMku7Ve4CS9H3N62+8xpc++2nkuOCrb72NtjFeDjPgydM577z3AWmSRM9KtWNwNMZmKdvOodKcJElYLOY4FzBKo0Tg+PiAo+mIN994k10T2FhB7RxVbdm2nl3vWZUtohhwdrnl4nRB5zyvvvISo0HO+fycWyfXmQxy+s7ifaDzgtB2ZEax6XrmZzEWfrUtGeQ5RycnnG82TCYT0ClSaHzfIl0gEYrgHFoZMiNYlG3MmRAiag2cp3PxFBFDdjTaaHyIQFudpQxMClLtEfixfO06y75ZEPtjwiNCfBzaIPZKOxHiayI1iuB6TKIheAKSGJgWm6aNb1GACdFwl2tBEgLGaMq2xREiNCjEclpITWYy2q6haRqKLP3Y1+MPxaZACHz6U2+Q5Tm//Mu/ym5XxRorRIyVIDYcjQr71F25b0oKEJ4QBOHqP0NEjwR7KOZVGMwVQluIQPCW3kJneu7cucujRw/Z7HZkUrFarGOjSBjGsyFlVSOVwvY1ti1pFAgG6CQ+j041vVNkeRFBJjpaWpu2paq2PH746PmPGQXHkRQTuX/RQhyCQGEQXpBqw/rygjzVHMwO2ZWWi2VJEJF7mOhoyFII6DSZnFAkBU56Dg9HPHwwR4qACC6eMITBKE1LwBgJXYvOEqS0lPWO+rTHmITZwSHWWbwXBA06T/iJH/sMd8YFD4TAdiWj6ZhiVzE6uMPpcMzi/JzxZMLs6ASZ5jw4X6CFZzwsmKh4BO+lwSYpB0VKriyvvfQqp2fnPDtbstps6aUhzQuSvICkopdwtliSDcdYK6h6z4fnF5xvtpwcH+F7+MpXv8VnPvk6x4eHmMQwyFKMTaBpCFnK/bql1xLlA6pteXT/A+bbmrP0AqXg5OiAyXhMYB+nJ2KIjwA+OL1EZjsOr00YJRkWCYmi9100fFkVN8Z9KNFsNsaMp/H11vckQtKUJUq1CO/23aP9JELERqtWARUCveujUM85nPUYKZEhytOt7whXPEYvCCE2rMO+YR4blo6+q8HFmEEvAhIRcW9BYBHIdEDfltRl+QdceH/w+qHYFIqi4M6du8wvV7z55uu8/fZ7LFfbF6Yn756Ht0il8O4KxSb3rPxYasS4bvERmrN9Dmz1zpGmKUmiadto57XbHXmRA/C3/ov/iv/0P/5rPHv4AdvacXQ0wfqO2dE11ps1w32EnJCSRKdkw3wvSIHReETTGPr90U0nhtA2+GB56+1vAfATP/Zp0uxdrAO/P9onJoN9zp/vApmZsF1tmI2PGOUHzC+WBOGZjA6oqjbmC3Ydg+kI5SUiSNabLRt/zmA44k/86Vc5uDbkK7/2Nnk2oSgyTs8eY+npewk+g8xTiAB9Sbk4R6cjbr76Ou99+Ihd19JVPVJabhzlLC7POJSK2WjEeDjgnXff459/43Xu5DXZyyN+877lycWSYSr54ivHPH7Ss1gs+NSNa9j6gsXG40rLZlOjvMEngt/47a/hUDybb2nrLZlJePj0KcPRGB8Co/Eo+lK2W+7evkvnLevVmqYLPD1fkZiEi13Lb339nf3vFj712l1GieG1u/eY7yreP7ugcp6bNwUv3zxhvap5crGhaS6wfc21wwOUFIxGA1577TUQgiJNGWQd2rW89dXvMT0+wguNxvOZV2dIf87o6CYPznbIbMTo8CZdyNi0Bn/aoGQEvUi7xvVlZDtYS5amGKVJTIYyCW3TkrieIjHoPCdREqUEtm8xUmGUJFiLDD2F8HEDUcRAneBAxBLUAq2UCKUIKbgQBXUISd9HeHGqFUPlCUqi9OBjX48/FJtC1/d85Td/m9OzM4SAyWTEZlPu6+IYv3tVCoQQN4DIUXD7HsNVufGi5/CiF7HfWQHrekS/j7n3ASGiUOhqtX3P9OCIurMU4xnr9QXKJKR5Qdf1KKXZbneMRtOoNtMGYzTBO4xxqH0M+xW3IUkU//s//D3+uT/5ObJcM52OWW9roi5T7gVMAkmk+iwvt9y9N0YLqMoqdr991GkUaY6SgcvlZQSUmITtZkeaFhRFRpYZ1rtnmKxncpDi2h7rJGmmUT5yE4WXBOeZDjJCaymKBBsc80cPsG3PttzhO8f1a1OyLOWtd+8zyWY8PJ9zfP0Wj5+d82yx5mggWW/W7OqePB/QlmvOnjRUZU2qAq8e56hO8M37F+TSkeoYctt52O1qTJIzmWiSxDAbDRimKSbL2D07o206QoiMB2sSetcTQhSzrTebSDjyoKXEyphKdTa/hIMZy7Jivt3RCUkbAhfrEmMW3L11m0UfWNfndDawqVskgYvFitOzc27fvsn1wwPMcUbX7sg1TPOUXeepdlvSkDPVLQO/5GwzZ32p0bajlkMGk+ukxYjcKLxwYBJON+e4raNtYlDNvbsvURQS5ztAoLxDhQqtFWmWkKWxEZknKtKuLGQqITMKT0CKgJOBdJDHiHvrMUoTrMe1lizLcAS6YJHK7E/UkCUG7xt6H+g+vkzhh2NT6Lue2cEhN27e5O233mY0mjK/WNK1Dq0NbRdDYKO//grlfjVyfM60RspYK1+Rmq42Br+XFANEibh8brkOwXF0fMzFfM58fsGPfekLzA5nBCRpPqBqW9KsoO0iUHS7W9N2HQMfL8wsS+ma5jlL8CqjUgjBaDTiZ3/6L/Jrv/53nv+sd+7dpK77fd90H5QbAl3bsV5ukXdnpIlitU9yTlJDkqYIF+j6GiNjl1klhumkwFobG5NJoO9qxpOUP/Mv/CjPHm747lv3o27DKyQeHzqsVXRlhexKjkYjWqd4eLEmYMAFdJKQ5AM6F3j7/hN8yGiVQkyGvP7pz3JvMmGzPGXRaWSqEF3NeDRE5wnCafxuzWHiSGiZGs8oUewKzXq7wSaa6cERVdNxubjkaJiA67F9S9dH1oK1ljzLyLOMcrdBJBIhLK9+4hU+fPCQ3a4kG2QoY+h8tKdfLGryNOXRfE4rJMtdg8kHZMMxm7rjW++9h9Q5QWlEklN1jtl4GPMTbMv52RwdPNVl9M4McsWk8Egj6JrA08enXLttKNqGYVdTVnD54H18fozvPV0xpDUJWgrOV0uEVkipOTi5QVJUYHJEWuC7qBvprIXg6ZqOSapp2kDbWVTt6XuLEpJUCYp9pF6qBUantEEhpSGoKHoqUk3XXoX6BLCCNDEELDEvtMdpRQs0zT9jLknnHbODI8rdhqqquXHjFpPxhMVivZc7x96C9zGAJJ4U1Peh2YD9/Xj+b85ZQniBt46ip0CW5iihEALarsW7js9/7tN8/Zt/m//1f/47mCyPXII0Y71eobXZ+yQsWV7wPDBEqheNzt5xZdN03iFEYDAouHP3Dj//M/8mdRtR748evIBi33v55Zga5GxMAXIggtgH1La0bUkxmDEscvq2Z1Bk0SfhekRQjMdT+q6naSvqqqW3nsFAkqWWz37+NnW94xtf3WJUwmBiYkitMmzXW65PEhR7xV+IYE/nHaYoWG5LfN8xGwx55+kcryXl48d89jOfxoxmeJ1xfXKX8vEZ5w/fwwE1KXI8oa96tpcrXj/JuHc85knX83RXxg64NjRdz9HhjCR0TAaK1fycJBsijaFpakLw+OBYrhYYHYVUMbU7YzIdkQ8ynA9stjtMZvBd4GA4Yb2rWZUVQRn6EHkPQmicdyQmZVvVbKoYFaeylJt37nFyPOPR/ffZrpaUZRSCbasNiVEUqqEqW06OxizP5qzGGakwKBJ8b/FB0feO+cWCzl9EDcrelXn7zl3eeOOTDIYjRmOLUklU06qoaNXFGKQidC2l7UkSTS8FWhuiNE1SO8u2DVBb8kRC6OOmLRVaKLT0DHKD9B5ne9IkQRcp2xDY1A112xK0piFyPa96ER9n/ZPmPvwvwBv7u0yBVQjh83vq89vAO/t/+60Qwl/6wd+G5Bvf+DZvvvkqX/7ylwlBMJ1+g4uLBd6DknKfdRgZ+vD9HPuPTiiEiCjxq7EmiOfjotjQcTjVE/GAilQLhLNcnJ8C8Of+1b/I//b3/idMMsKgkcpQNTGZp6xqjg6PKYoiJhRpE8GrfYyo67qe4bCgd90+rSiG0r6U5NStpW4tP/vzf5UQ4Px8zoP7v8iPfP5H2G1WEDTyuTcjkOUaRAr0rJYXpOkAYzTD4ZCmabmcryiyCdeOrnM+f0pZ9kivWC9W2IGlFFtu3Zlw9mzA4myLDA4tPLPJCNlvCVKw3paUTWBXtlgURZbisHQ2gINkMKYsd2xXK/Ii5f133+V4MmXbC54ttpxcO+D66E2ePvqQZ+crGlL0ekefvMyte7fw+Zbv7ua4R0uMMQwHA0xiMMJRKE+mBHaQsisruqZHGc0wjVmfbd/RNH10+HWWt99+G2MMq+0u6llMQnCWclez6AP5IKX3nouLOUobxsMxRZpTli29dTFJerelb1qCEFyuVvRdw+ViRZ6YyGGsKyo6rs8ybkxSRsYzvHmbd5zldN2RTcbYgaTaLNi5hIvLHWpsyMdT8msFj548xm131N19qqYnzQekWc7h0TFPHj+kriqODg956bXPopIcnSTgLc515Em2R8GDkCY6QfE0dYULDokmzRPqukdJSfCOZdOihKArpkqCFQAAIABJREFUK7SKEQZaJwQtqWzsNzgUiVZ/6ODWX+T35T6EEP6Vq8+FEH8TWH/k/u+HED7/sb8DIm7r5Vde4drxNZq6YrOtGI/He7NQHDXGBKl4kUupvq88iEpFefX9PFc5XukerhqR7H0QzvVR2FHsMw73GvQvfOY1vvbt9zg9fcZoXNDZmIbUNw1q768oikEcie5DN64mG7FEESRJAn0MLnXOkaU5xWBCZz1l1TE5gO225Pr16/y1/+Bn+MbX/zp/9s/+JIvFI4yReGejScm2BN9he0fTxgsEpuR5Stv00c3XOdaLFX3dI4PAKAEk0bqbZIwHCV/60bu8/a0P8Y1mg8LaDuU9u6rjYDyjCR1ZIdFpSk0gJIa+dbRtz2q1Jh9kyMpzNB2SG43rK8rtDuU7dpcrBolmOD3korrA1rHc+fXvPOLG1DDNNKFvGQ0HWJ1ENaF33Lx2gN9eMsolaTLDs2M2OqA5n1O23XOUv0kzVqsd2mgCPbtdS9W05HnGblMxGg3J0wyJoq57GtdGu7G1NGVJdnwNPRzSuZbVZoVRMTz35Pp1lssV5RqKJKEqd1wbZbx5c8o3n11wPDuA7YqsrwjVJduy4tG8ZBfg01/4HOdOUa5LurLGlSVqOEEmBTId4TqHNCldgN1mzfbJY4ZnT5iMhpzcOGR5eclv/t//F69+4tPcunkdoyV9gK6tUTpBCbXnSsbTZlaktHWJlIreB4QxgCRIjVMapSVFUqCCRxtF2/Y4wAwSvBB0bYtSgYOPRAz8oPVPlfsg4vn9Xwb+9Md+xj9gKal47ROvY23F6f1TxpMZeZ696BvwUehq5DSFPQvv6pQgpNrX6S9wbcYkXPUbjDG0XRv7DoASMBkMSFPDcrVAy8Bus+ZP/fHP849+86f5B3/vf2A8HmJdh9yXI2meUzc1WRYnFt1eGOL3EA6GoxhDF+KGlKYJQhh8kKSZwSQeH2AymWFtz2w2BeBXfuU3+MmffI00USgl9/HpAaUltu/RSmLSlN5ZRBvNLteOT+jqNtq0RQxWyYcJ145PWCwrql3DZCqYzCQ//uU3WT7r+dpvv/9cJ+ERLMuW04vNHgCaUFU7bNPRVD1tHWk948ltitQw0Ipcw8mkwDUVrqpo+pZV5bn/5AyHJNOKIhnxZLvka+895c2jjLa1DEdjKucRe6x7U5W8fucms6HmH/32t5kdXSMphmTrDa2LeRdKSkZ5gZERFptnQ3rb0/ceJVPyVJHqJEJibTSVbauaJMuQAtq2oW1rslRTVhvquqLpOoyOUFWtdaQqS8VwOCbRLePMUlctXWPJ8GRZwqqrObk2YFDklE1PG3ruvHyTYlNRuYeEdMDFxTnn6x3bsuFoMgCtqdqOum1ZlzvO5udMxgPW2ylNuWO1armcL7l584S7t29QlVvOz+Z84vU3mcyOotlOBEwiGQ4KlmdLBoMh2WCEFholNU3fsa0aqrbl1rVrGBEVnEnQVF3Ltiqp+47BpGC3ucD0f3QuyT8JnIUQ3vvIbS8LIb4GbICfDyH8+g/6IlIp6qpkuZwzmUxZr1ekmUar2JUPIUQ4SrSzgN/7IoKEELn5IlbIQNjTg2OalJQSqQRJYnC+RytFqiW5gk+8eodf+rUrruxu//EcgK/97u/yx7/8E1EeaiQ6ifP+qiyZTqZ0XRsj6BEoFaPVtNSR/iOjvNakWZRqB4XSmnGWIqSOj9tTgq+W8C3j0QF1u8NLR5rnYB1FocAHKuto+hKtBhTFkEE6QAyhd13U0ZsRSmmqsqVrW4QWlG1N21fkieTw5oDxIQz8MHIJm5beexplgIDyPSMtWGxLRA9GxozG5XxOpmGgFW674NbAcPv1ezy9nPHh6SWL0nHi07iJu56L81M6L+hIWdXwvSdLmtEJAUeeSNre8exsgasbDgaa957tOAkFP/6jn2BdbqPhy3kmueQT10acnnY8LjsaV7KrS7wNhEoRrCM1gjyFeWexe6t9VbakaYbJM1bllleO7zLfrEiKIbVf03aW7eWWEDrS8YRNA8rX3EpLjocjmnVLf7Lg5ssjFhdN3LDGDU+e1MhCs6xFdHGqnJt3X6bDcL7+gK7d05+tpfcO11RUVUXvHNlgRFW3PHh4iu06tE5wbsXFpWA0jEE16+2K773/DoPhU1yI/bBRkSK84/z8nMOjYw6OjhgWBdJbns4veHh2zo2DQ8baYfCkStE7iykycmV5+uQ+o+E9ikJxejHn465/2k3hzwN/9yN/fwbcDSFcCiG+BPwDIcSnQwib3//Aj4bBHBzMmJ+fobVE7vFYr7/2Gt/51nd48uR0vykIoio7mqN57m2IEwf/XMsQ7+aJpp0sS8F5lAzkmWEwGHA0nWCE55d+7Xf46//hX2GxnPPw4UPyvKDIRiwWK+r6q4zHQz772c8QfAxJFWlKVdXUdUOSZCRpBHpKJWM8GmI/1Yjd4iTJaJo2jkmFIk0y8sEwshmtZbN58d9ilCdNJWW1iYQiArZzqGJAsDHqvOt7FvUaTclKrSLYVkZF6GgywoiEuuqe04J7LLu6ZCtaxpnn1r1rVKcNzaZmXTYRGmMk1jn6rkOKQKYMRZZFY1LV0nY1x9NrPHt2SmprdssVQSmqXcP1GzdYPTzn3ssvs1mv0CLFqOtsBzmn2zXGjKhVzrrucd2OzgSKwYinz+ZsF4pHIqY2dV3Fg/e/G0d4my1ZkpCpwIHsqX3H47qmsg3jYYpSMVWqbRzDQqIU6E6jexFHwi5K25MswwuBMIbReMbi4RP6LpKgtFQMRxNsEiGp11PNJ2/njBIojGEyMTgRJdhyZxmMUqwreffDZ3C2oWpqiuGQl155jSJJOTg6Yr7aok2K95ambRmlaYSqhECepzitMNKw227p+p7eNWzLHRfLJZ9883UuF5dsyw1JntK2FmM0H7z/PSbDnGq3JR/kbB9sOTqcEfqaXdOiRM+H979L6iqmWYbrGm7fuY3vI+mprlZ88+uXkW36R8FoFEJo4F8CvnR12z4urt1//ntCiPeB14kpUt+3PhoGc3x8FN575x3e/NSbeO8ZjcZ47zk4POTp09N98y06HT+a5/BRw9MVVyEEvy8uYqOx71uUFEgRmMxG3L1zh//zH8YQq5/7q/8Gn3jthGsnh5ycHLPZbBkMJrz08kucn5/z1tvf4t5Ldzg6mqG0wJiMpnnB6Vcq9iOcDXsYh9/3FnQM6ygrpOhB7BFyAZz1WNtSVRXr1YuIukRB01SINJrAyl2N0Qm9DRidoPo9I8CB0JE8vFwuyfNYViilyY2g3FZ4G4VbwQWUSmMmgjG88torfHX+daxsSAuoK8u4iI080LRtS54W9MFH6a8UeBs4m1+gXM/16YC33n9AMRzgTcrF4gHOG3a7NQTL6fk5bVMznoxZrEvqlUVOjuiWW4KzHN++yeXlCuc8VesppkNuHc+4cTRGS8/7q1XkCzqLdj12s2SSZdw8yjhta66NM+7ORgSv+ODxOYhA6yMmPcsymq5HB0HXRSiq84GLxZoiH5JIQ9nHk9W22uClYZSO8H3NjZMxP/HFN1i9/10SCUrm7FrBo/OWqp7zhS++QnYg2b03p2rWJGlCqDtWmy2jqWI6njIYFLS9JTWR27jdbGj2fAdrLVmaMBqMSNOEp8/OkEqzLXc8eNhxdHzIwfERFxcXXC6X9L3l+OiYrBjQWsvJzZvU1vHs7IyyLrl57Sia4ZKEmkBTVxzcvIHEobRifjFnNJvw8ksvs64alNLMz/9oTgp/BvhuCOHx1Q1CiGNgEUJwQohXiLkPH/ygL7Tb7fjG179OVVcMRyO01gyHA5I9w/CKsBT3ghfkpKvewfOgWaK91gePhOibl1AUKUoEXr53m1/51d/iP/mFf4++a7hz9xqDQU5RZBwfH7JYLABJmmTcuHnCxcUl3nckqUFrtX/3T2JJImTMMZQhjiABIQUC+ULYpBKCCwQciHihOl9hnWW32WDbF/l+wzxByoiPq7oGLQ2pzqLYxxiSLGM8jOEjWZ7TNg0mSciLAr8nOFnrOJhMabue+WJO8J58mMUJjlFsmw2zW2NkBrtlQ72x3Lx+g/nFiiQrePDwEVJ6+i6Ke0ajwV5y7siUoHaBdz58wnhUILSBbMTx7ZfIigGnz54wyBJc11DVDZva4naW4dGY61pia8FkPMJ5OL9YIHXC7PiEW8cJY92zK0t26zV5doBsK24eTBinsKl6rFMkOmWaZRxngs1qRag2lK7AqiSGAtv4Dq1NRpJlFIMRAlivt2xWW5qqJtjI/tw0FSLJUeWOWWq4fjDEtz2XlxtSLVgsGwqXsguKRdmwc5paGUgS2rIhNSkySTi/vKB10ecxHg2o2440SZjNJtRNg1SKs7NTurbF9T3j4YjRcECSp7jg0EJjreOdd99ldjBjOBrRW896vabtO+quo9quIvZvPMELiUpSNlVF2TT0ux3ltqSdHWAFDIshrbUsNlt6KQlK0zQ9bVuy2fwhypz/oNyHEMJ/S0yX/ru/7+5/CviPhBCWGEX/l0IIC37Acs5xeXGJdW/Tdj1KKUajIbvddl8eiBcbgojx2lfrqhGJgL7vEEpG9p2ERBtOjg85OT6g3K75lV/9bf7Wf/Zz3L17hFYKrQUHh1PyPLocR6MR7T5tGOD4+AghBFmWYoyBoOI40sQGl/eOQLSrpjrZB9g4pIqhoVFWncSIdedAuEikDvvgUv+ip2ASSQgWFxRGJ/g+kJsc2zqqpqPrPCcn11FSkyYa63rGswlq32BNkgxF5ARMp0OSTLLrd7gkNkTLekNX9fSq4ejmlEHesMsarNsxnhRUjaOzsRcjhcBIxXBYUO1K8rwgNYZtEzMUAg1KNNz7xC1cWTIcDTkoMt64+zlOLxZ89VvfifTi3RbmD7l+OCU/GrNeXKBNgTYJ2WBMFxSffOUOu7MPWcx3SCkxSjEejRDekg/HnD54wnmnkUXKerlh6yB0HcPEgMkQOqdanNPvSrQxNG3DweExxhhu37pFCB6cY7teElofZeh5itOGvoeu3zFIMi4uaja1Z5BnrMvIyHy6dWQ3Dnnn0Tmz4zvIfICtIrg3kQqhFOvtBlnVHB3OsB7SJGU2m3FxeRndst5z/4P7SBFYLpdkSUIxKKiaFrEXSi2Wa9qu5fXXX+fW7Rs8fPQ4ZkoYzWQyZVfXhCRlOJliEWyrJnoyhMYGydvvP2RbNUzGA6aTCZfbmrc+eMSNW9dp2p622ZfRH3P9k+Y+EEL4qT/gtr8P/P2P/ewvHsiurAhCMRgWrNdrFosFWZY9x6/FkkHuHWQfzXmIEtgQAtoYsjxDJ5rLZ1fHpS0xrwb+u//6b/LqKzNm0ynGGLq+4eBgGslPzpHnw+9v/u3HmYlJSbMM5zym90i5F04JER2PIpKACUAXcymucPRpktL1PpKlARUcXbOjL5dsLuIh6y/8i2+ikx5pItwzkRoRBNjAeDhmsV0TvGC9XCOkoyzXDEcD0iSjrTv6zmFtoGw2qACb7ZrpbEphMlbNGqWhriuU0mTDHBkERzcn3L59g6//7nscHgxYbdcoBYNBxIdPs5zrt2+xXm+QMtDbPjIXOhDOM85S1vMF915+ieryAl9taRLNK3fv8OTZnPuPH5FjOdSBN49TPnh8zmpRs+kju8AKyenlkrNnGcd5jk4KWtcySlI+/6nX2Tz4Dt99cs5KJ7Qk0HvSUYF3FU0fyAcjgpkiQuRuOOcRKpCmKffu3iV4T2YUk8kBjx49AANeBNI85dVPvsniYsks0RyEiq9+9S3e04FRKkiHI4RWXG63pIcnyNEB8+2SZX9OOhjhL9dsypK2aynyjNVmw2g8YXZ4wK6quZjPqcqSs/Nz0iylGBRRBp8Yqt2W3hjGRye01rNaLnG5oRgUgGc+n9N1PbtdSZrmDIcjbFtzYzLmfLGh7lqG40Pq7Zrj4+ts1jvcGDbrNU8ullTWUnvB5WKNkxovDIeHY87PLr+PTfqD1g+FolEqidKCXVmy2W4jZmpY/L8walc+w4+uCMWI0uYizymGBU8ePOEv/5V/ixs3rnN4MKOutjTVjh//Y69yMJuRpvFk0Lb1/hcCVVVGutOejSCFRCL3AqhAkQ9p244ytB8hRWmEiurGK5HVFasP4lhMGYUxmtZagnXs6h275Tnby6f82z/7X/KX/9wXwS8xaUoXLGliqFYVoXaIQjA6nHHt6AhvA7ZvccHT1Q2rTYdWhqbsGRZTegtSGRKtmJ9eslytGUwLRCpIsxQ91Gwud6gsoW8bilGKo4MMzpfPMJlhPE7Js4TtpqOuSs6ePmEyyFhvdwQEg9GIruloOssgSQjBczk/57XXXqYfppxfLnDSsKuib+PLX/oMnzwyFCrQlA1nK0uSaLqmjVr/vODZ6SXdMKHsJV4krFdLNrsNg6MTvvedS7Yio/WOSaL5wo98mm7+kOWjM3w6ROsctysZTUast9tYv7cddbVlkOf0bUW57PFdTVluAcn1a9dQUjIeTjjKEz538zqv3zrkK7/xVb723kNuv3ZEUJqF3TLfrTkcjJgOJwymRxzfGnJ2fo61HYhow8/ShO12xenp/9Pem8Zclpz3fb86+zl3v/fde5/hsCkOKZPSSKRFyU4QZTETgDZgBwaSiA4MxB/sIAQcyIqEAOI3J0AcKEgsQLEMyAviBLADM0EMOAhiOKYskiJFDWc4w1l6env3u599q8qHOvdderpnmpKo7gHuf3Cnz3vWp7bn1Kl66v8/wna0GM18plfanhyfsLOzjevY5HnO5sYIFJycnLCIU2RVIh2LbqdDEkeUecHhwQE72zscnYzp9rpURUqtVMOrGRP4ATpuB7Y2NjkscorAYzjo8/HbHyeOY47HEwajIcKAbrfFSy98jN/+17/D0+K5cAoAXuAQhXq6RtNJ6fUEcM7PSBOy+ShW/AqmabB/bx+AF1+4xbVr1/Bdlyhe4Lku2zt7tIIAy7QAhWXZWKaFVBLbqilLHZPgOp4Wtm1EOsoix7Y9pNQxAYapu+yavMLUYqDinO2pVjVFUVJWBbIqQei1GnmWMR0fMT9+wF//6q/zK7/wOSimmI1AgbAslBLsbG9TLXOSMCNaLknqnG67TasdIIVFXsfUssb1PMoC5ouQNNHjFS3PwzBd0jhGhTmOtCmrFM92GQ03ufvwEChRQjIa9Lnx8RHJPEfmOqIyi6om8AtUXdLyXdIkwbAcyrLQI+el5hfEcZgupwxHbW7dus7dg4d899vfYpZJiqrGthx+6sdf5vDuOwz6PTo9haoUSZYgZI4lfOK8Is0LTuKa2rBo2Rbv3rmDaQhKv4/MK8w6w1cwOzlCZgWHYYZSNVF6BEha7RZpUZJn2mFPTk9oXdmFysK00FN7CALbY7M7IE1zjApatmCja7G712HjyiZvHczIpML0PGrXZzY+Jj0+xrN2sAsJIsPzbJQ0oAla63Y7qBCm0wmGZeHaAVWpZ8LKqiZJU2qp6HY7+nMsTsjSDENp1uiqrFnMlwS+R11VzCczHEvrPp6ejBFU7F25BrZPKbXatmNCXWRgGowGXQLfYrQxYnx6yMHBIa3Apd9uURUZaRyStrp028FTt8XnwikYhsHGaIRBSBQlAFRN6PCKnk0HJtVnwUrQfFIIgW1a+J5/th/AMkxc26bb7dLrden2OvQHmt3JQCCrGsvU4rJFrrUJ6lrpcQJHYNsujulqam7TxjQsHAcs28G29Ko2TdugnUK9EkhVNUVZECURilrfv5JkWUG0nBPOTvnrX/11fvE//hxkc1SVUZaQRSWm4yAqg5bfQtYOeZQTLZeYgQNKkqcJSREzn8/wAx8BdDo9LFFqAdPa14N1toftaEaqLCkZjHqYwsI2XQK3RRhPKaqCw8kR/VaPnWub2MonS9/j9GSMYVqN3kIAAtqBr79ha0kiMyxTYHk2tmuxmC35zquv8d6D95gtQypM4jwnTUq+9/rbdMKHeKLieFoxWcTkCNq+S7fbopQV80gPmBpeF5uSQa9FMp8Q5zVG0CWwTdotg55RMd7fJ1cVsxJEVZMUCdvDPsJvUSmYjqcoKTGEwjK0HN6o1+H7b/4AVSsG3QHRbIHfa6EMhSgTxoeHzDYlpUpJhck4jElmS6I0JZeSdjN+dHhwSFVrQZd2q4Oqa6I4YdDvkZcFeVE1tHw1nqs/YceTCYvFkiDw2NrapBV4mMLkZB6TFpoTw3Ishv0+rm1imxZFrpmyhqNNwjjGsQQHB0eMdvYYjUza7TZ5EtLyfGSeYBmKF29e43R6ynI2w1AVtuFwenTI7ZdeIIlmzCYnvPjCLfjt33+q9vhcOAUAQc3u9iZja0q4XGoR0qLQgUlnlN8gMBpatoauRAjMhqjTNs7nYk+OjtjdvYLjeAwGPTrdFo7nUikosxIla7IiI4nnhMuZXtNeSTy/jee6eqGTKTBtD8N2tYS4YeO4LQSyoU0rMCwHsxlgLKuKqsqpqoIyTSiKlLKW5FlCFoaE40O+8iv/PV/9K/82njGlUiVFmVHbbUzbZDlZsLu7TZLk1KWWcLtz55B2r0U2ymm326AMjNzHMF2UJRgMuxgq105MWdiG0MQvswJbOLi2SxnVOC2XwWjAnbsPsAwHS9moqqIsJfN8QbutcDdtkvsSmRRstntstrpkWUXmVEySkLwqGQ5bdIMWnqgJ7IzSqTkYTzmahQw7Pp+6fRPrzl0Sz2CWhHzzTsztGzscRMfMq5RhZ0AdVSyXGa1ejxvXNijznDiXmJjMF0vyAqKsZKNtsNXpUCcLup5HXSUcThIyKUinc5SsSLMcoeYIqfBcnX7XtkmTkMV8Qrqcs79/QK83YNBvocqcOIrZ6g/YHnrc2OowPZgyP51SViWL3KaoFUUtqGuFiWR3b4er166xf3zA17/+dXzXZdQfYAmTP/Hyp1Hfe5XjyYQkz9kYdXjp45/gtdffRJgWRVVzdWPExuaQaLmk1+0wHBSk2RG+CTdHXXyVkS5S5mWNwuJgsqDA4OXbH6OqCtK8wBSS8ckBg8FtzG4bo9YKYIZQGJMpRZYx7Pdp+y2mkykbww3suuaFnT3e2z/lnbc/dBLwDM+FU1BKkacZXtfjxZvXKIuSO3feo2xk4JTSDPzCMBC1aMKXGv4EBZbRvH3cCwEadYZpChzXI/ADfL+lGZSKSjfWLCWKFpR5RJ4tyfOENCux7QChFJZhYbYdTNvGNG0tC47UYbQoZFU0sx1Zo21ZowQkSUJd5yhVs5zPKGpJOJ0STk/5yn/9P/LLX/5ZjGqKqhJNGmM5xEWJaYIqJXmcE4uMXmeDzZ2AMCyoVUmZKQpLYiiTMhHYCHIq3j5+lwoT2/IZdFpgVigHDFfgmg6B5ZNFGUJphl/Xdwn8HihJGC1IohRhCoRj4vVtvvDzn+b03oz4/pg4rDBNDyH0qtS249INAjYHA/JoQr/jIWvFyXJJLU1UJbgy6PFzf/7f52Q547uvv8N7b94lzSXd3gBrXujPgrIkKSoqY8loeI1bV6/wr377d8hFhTvocnAyo1YQRxEh4BsS2/Xob444SB/QcgQyzbEMl1opRp0WaVYRLiIMZeC7DrduXidezFmEEcvZnN7GJts9C1sZ3D+akicm9/cn3BzdwvN8Tmf3UCiiJqjL99soJcjimLt379JuBSSJ/hSYzuYgYT6b8Y1vfov+cMDmpsl4OsVxbNI04fYnPsndh8dkWcJkNsMPbIowofYkmxtD5vMZVpVx4+oOZbSEqiKvtP6jZdscnYwp85x+r4NAcc332Rv1OL77Li+//Ckm4xkP9o8Y9ttE0RLbNrjZHbC91SfPK2aLkFtX9nAbhbTywgD6h+H5cAoofM/HQKFkyc0bV4ijBVmWE6UFUZLq5Z+rGYgLy0AtE2wTTFGwt9U72785DHBcQ6svez625VJVFUWSEYchZR6RxCFlFlHX+js5jpYIYixDzyqYpotp21r8Q0mU0gVmoMirkqquKOPwTK2qrkqE0HEXStVkacYyXDA/PeIXv/obfOUv/CR1eoSFjkQ0TIPaOCeJ2d3e0Y3PbzGbzOi0+ly/dg3TMri/f480qbh5/RqT8ZLlMmPLb1PXCbPZnCCQDDodpBQs5yGdXhukIi9KLNdBCTg6OaLVaaFkTRIn2I5NXqQYwqTt+6R5hmnB1StdEqOmmKXESUSSRdimR+C1UamkdDKKomKyUJqKzjJQysR3XN56/U36ZkqvbXGzLSkCg3o+pawlQ8/HMSziuqLf62D5Hg/uP2RnOKDb8sGqiaVBrWpqFNgW0ySi51p0hYVMS+pKUVeSwPMQhmJre9QwKisMYRG0AnzXJZzPMZTmPryxu4kpaurT++xtD6FrMs2W3Dtd8s/ThI2NPqdpjdPuoOYpVVlhO5rDw7JMEIJ379zhyvXr7O7s6VgVIcilojZN7j7cZ9Af0u/2yfKCN37wFkF7qGn+i4JFGFO8d59+0KaqYe/aBi+9eIuH777DO+/dx1BKx4OYWkhmbzhk/+iE2TJlvoz5/Gd/nK3+kNxxiE2DLd+hsCRXdkYsZjNAES0r0vgd/vSf/jlu3HqB3/7Gt/j+O+9wc2eDsi7pdlrA5Kna43PhFCzTYmd3F1sokjji8PAhnmvSavXwkoLFMqRWCiVX8QoGaP0gTFPQ9i3arsH/8f+9CcCv/rUvcvuzHVzXxvF9LMcjz0uSKGQxmxJFc+oqoy5TsizGNCQmCgM90zCfHGNganpw30UJLVe3kgdHqIb+qiKJIzLb0TMoQlAUBUVRkGUJeZ5BVfKLX/0NADyhV/JVdYGwbJSyKGq9stLv+IyGA/rdPuPTGbKU1KVkY7RJmsfYtkMUxhwenrCYJ2R5geu5xHGCbTkYGDx8eEjQcVFWBYbAMWxMZeIErmaMFhLDgOViiZKaeLTf7eP5DkpK6jLX2pKewNsOsDY73Lt/hJUrirKCusA1fYokw/Y8MqmX9N3PAAAf/0lEQVSYzhbIXIdIm7Q5OTjlrWLCK5+5ykt9l6xlUCmDqpYkBoxnc6qypioygo7HeDLnX3/jmxRFSWXYJErTvitDkJYFjilIpCSpYHZyShhlVEKwfWVEnEaMpxNdE2qDoqiwRI7nbBB4HvPxETf2rrD7yU9w761X8auCLadC2iWd3iaHQYu7R0eklkdltxiMtpguH6IaWfler4OsK65c3dOBYK6P7wXUKKzAJ5vNCNpttrt9LASqrpmFOYso4fA0JCtq2t0ucbQgjnJkqWj7NfW9+/R7HdrtLvvjCdev7NALOkRxjBP4JHlKt91mtoipipw0SekELWQS03Jc8uWCQeAidkeMbciyEs8LGE9mLBZLjk6PMG3FJJxhiBph2Fzd3YHv3X+69vijaOQ/LAzD0GG7k2OGgy5VVSKEpGhUiAb9LsfjeRNLjv6kaAYfbctiY9RnEBj8lf/gBTqtNnvX26hKIYSNZTucTk6ZnxyhypQ8jSmKBKUqUBVVkaAMqKoCyzQwhUGWJkynRyjTxmv52I6rdR2rCtuxsQyDPM8pi4wkDjUFWy3x/KDpdZTEccx0OiFZnHtn3zYxXIc4qcjKmnmYUdoOluMDgizJiEVEkRYcHRxRVYcEgc/G9oh229ehvGnF3rVdBoMus9mYjtGmrjRjT11WdNub9DY6WI5BnmaYygQlmS7nSEM7MUNJijSlFXjEcUhdO2BAVZRkaD1EYZS0hwE7zgh/qwMy4M7r+6RRhu+YjE9TjKCFMA22Rn3qQpLGEW3bYmfYYtM3ScI5O55FWEAiK6ZpxShok6Uprm1jyIrtvaucHO4zHA3YH89JZI3lOChh6hB1BZk0mSwj8kpRVIreoEvge3i+zf7RAbYTkBUFwjCwTJPj40OS0GJ3NGDQ7zE9PSGwBK/8+Gc4uPMDOr6DszUgMTPGWUlUSbZ2dxkNtzgZz+h2d3Ask+ViQZxmlGXB1StXWCxi6kqSI6nLiqDX5+7DQz77qU9jAyeHh/hBi1u9Ee+8e4+yyrWOo6lnsZQQKCFYLhZEizlVVVNKxSItGAyGuEJPzddpQuB5hGFNheS1N3/AZDLmxt4uV3e2KRQIVUIRc2t3i+OjMcswpGUZRLMJ7cDh577wUximxYO7R5rlejl/UvN7H54Lp6CUZDYbk8YJsi7o93vsXdnF83zevfMA33dxbFtHBq7iFmgWUdc1J8cn/O5pzC//Ry/jWD7Uho6nrEree+sNppNj0uUE35ZkaYhC4VgOrutgmIK81MKsQklqWZJlEaKsqJRB0PIQpo3r+mR5Trfb08ubs5S6LIjCOUVRUJaaJtx2bNIsJc9zptMps+ODs3TuH53S8mwKaZBJSCuB4djESYo0JJ22j2d72ENfi6ckKXlZEMULMPWSZ8fxiZMExzW5dmOX2XSMKUzARFUGo61NalESRxFCabr7osjIi5yCDJRer1FVNb7nk6WJlmK3BGVVoaixbJMg8DmN5lSyBKdm0Omxd7PL4b0xRQnKKqmxKKuM3mabMq05OJzSa1vs7O2xsTngB8ev45gmriFoGSU/ttchtIacziM828Q1oJLgtDocTpY47T6G6aDCmCgMceqKYWARpxlpGlOUWkqt3WmTJTEYYChB4PkMeht6Cq8uEQa4rkMUR4zHY6LpmN2eQ216vHUQsrE94t079zjOBL3BCCkLbEfrO3qOxfZoSK/bZtENePedmDe//zq9TovhcJv37u9jmTZRlOC2AmzHZRmG3Ny7olmcXQvbb3F8OMYybEabAzZGPd595y2KrKDb1uHX+/v7oBTDrS1++nOf57XXvsdkOgOhe8C+72JSY4iaUhnsn05YxCl39g/ZGg24sdNnd3uDNEy4ur3BkTphHobUaYjn+LQcE2k4tHpD2hY47kcseAm0QKfjuCilWC5Dtra2tFTsahpSL2aAJqRZewVBUdSkQpOvyqoiKxPEfEp/MeXg7e9To0iiOUU64ziZkyQhtuuztb2HardRQgdICcukLkrSLNbz8FlFUcJy1qOSCtN2idOMqtRqS1EYYRp6ZiGOY6pa6tkLV3MuhNGSKIwJQz3F+ud/9iaySIgLqJQirSRxAa3ARoryTMMxy3Nafpvbt18iyTOOx0fMwym+59Bu9ahri6LMOdh/iOdb+K5FK/Ap8grX9SiyhFxmxFFIXUqyJCdoaw0GwxSkYYHl2mxtben1GHXdSJzVhHGMZYLvKwxhE8WRVspyTJTK2Nrr0OkHFKkiWqZ4js/8ZEaahCRJhW0b5FXNt97Y570Hh4g8xhMeSVIy6joEPYsjaWGZJkVacG27TxrHuH5AVEpO50s8vwVFQaAqWqbik9t9pmnN/jxnkeV0ez2EoWi32nq8I04p6xnDvoHnulSFxPEMWp0WRl0zmc35xIsvsN0SvHc45kFYcZBOSYscd7CJ2ZC5yFoShks2N0cUecx8EtPptPnJz36aV199nd/79nfY2L5CWdfkpcTzfIQysUyLw4NDtoZDRhsjlvGSB3fvouoSWWakyzmpq2cxqEsW8wVB0EIJk6ouqaWkVjVKKNq9DnmuOT+kUgS+o+UIGx2RMM8pZU2aZ/g2+EYPKkm8HPNjP/YxWu02+ydHHE0n7N/fpzPcxjYE0+OHbNnl07fFP+rG/QeBlJI0jfFcj1bgM5me8t1XX8XzPCzHx3ZtHNchy+tG/Wi1ahJqKahls13V2KKg60nq+JisjCiKnPnkmGh+ymIx0ZRf/REzAbIYIoWFMB1s36MsC8IowhQCJQWSmNnkmLysEaZFWSlUXdHrdkmShMB3SFOtvJSmuWYsLgriOObuvXv87b+jI76/8Old8nhCp+UzCRM9NSoVwnLw/DaOU5PmS07GJe+8fYef+dznMCqTJAvx2zaFchBS4DkuYVQyHk+J4pAb13dQjomsajzbxjS0crOqBG3fZ5YsKbKcXr/DIovx+z7TaIFjWLgtlyjSYjV63ER/9zuWwlQSWZYYEkRZ0+v2MQ2L2hJgGZiBTdD1uDrqIm/2mI4z9u/PONofs1xmvHpvgm8rNts2L24OiMucH795DdPIuPvuCShJVUjKOKLd7rDMUopSi7zKokBkMUOzYsc12XNgd3OHRXLIYRlheQ6O6/CJT3ycw/1DJpMFuVRMpzqUt9Xy2NjaIElCyjTBt33G4zF+Ad/8/bfZTw2iaMn2oEXX8xiONkmKnCKNiKI5pqxo+Q7bO7vs7WxTVRWjQY/Xvv8md+7fxe8O6faHtDo9ECaGlFRFRhguqH2XLIkZ9bvURUWRRMzHJwx7Dld3N1kuYk5OZtQITFtLDhZFwXvvvs3W5gbDjRGvfu91JvMZVZYReA69Xo9Fmmlxo6qkKiWFLHn7vXucHHlsD3uMem1e/8GbfOrlT1JLxf7DE4TtEMwyijzlet8jUOmTmt/78Hw4BaVI0xTTdxiPZ5imxdbWNt1eh+k8xMxKfN8ljDLNWyBlIwSjNRvLhrcxilK2BwG+lVFFB0RRRhzGLCanJOFCMy4HAadRgmnZxGGE5beZhyntbg/bdbSqtCmQlcTIMmRdU9RK9yiEVu+pSx1t6TnirPsdhiGmabFcLJlMZ/ztv/OP+dj1DkVSIphiChjPdY/G9XJ8321o0DyCgYNt9TGF5Oa1K/SHPR0w5ZtkRUSULpiPl9SlQbs94uWXP02WJ8ThjOUi5PTwhHarzWg4pE5rhlsbRHHMzetXqWuQQpJPMrqdDvWwxrFt8ixH1rLpMYDtuwjDwJQS1zKwTAuzsvFNB7c2MTAZL5eYrQC35ZAt50SLfWxl0h9tYXt92q0277x1QGWaSGrmVclJVtJt2cTpgo5bkiULAi9AGGAUGT1/wHQZkic5KJM0itgJBD+2O+TjIx+zSLh3ckg8n+B5Np7v0Gq3uHvnPZCC0XBEe9Dn7t37hMuQ3ITj42M6gUuc5gx6G3S7XbLkgKysMTpbXL3yIteGPtubA3JMjmdLwjBio9tGFCkff/FF+t0WjqWZtg2h2N4cscwrxrOI8EHC3p5ehGZJ6PZ7yLpksZxT5zmB57M56NHxHBaLKfFijgx86qLAMQ2m8wVRWiBkrRXQ+z0sy0TKmpsv3MI9blPlOdPTCXlWUudlI4CcstFrUeU582XKYZYwXqTsbbS5sj3gu9//PqONLUZbu6R5xWQ8wVUpbnfAje3OU7fH58IpoBTSMMnrgn6/y3w+pVaC2Tzk6PgEy3LwPYt+12U2j1nJnqEUSiiUaQA1cViQWYLT4iGO7WGZDsV0DGmKkFoePAojatPm9PCQWrjk0uLtuwfsXLnK9u42w1EXz7OQsibLSubzJabtYtsewrS0aKmp6dtrKVmGS9I0Y7GMsW2Xo6Mxv/kPvsa/8YXPcjp5DaWgyEssSw9iCqEwshQnyRmJLt1Oh1ZQkWXNmo9On/sP7hFGCW7g66XUcUmr0+Po8IR+UenoTRRBE9+vezGS+SLCC1oIXDptn6qqCKMpYTynlCllZmtSYKnHDlQtKZIMx7VxhaA3GjCfLBHKRNQK13IwlSDPMr0yzzAYOXrxkhApVVmzDEM2Nju0fA//1oDuhovpd0iTjHQ+gyQG0+bN2ZLAAjyftlXg+QHb20NOlxmlsqhkTplGfHy3x5+8vUOQT+mbEbXpsjiIiKIKo21TpEtk1WY6CfE7baRlEMUpSko2Bl1sQ1EXObmSKCnIyprTuGT8cIHT22TDDhgM2viBQxgn2J5HMp/Q9V3MKqPf74JhUSqTulBUpRY+Rtbc2N2iyktmy4h3332LrZ1dFosFrcCn3fZYzqeIsuLGZz5DkWd0rmwAV9k/PKCoFU5LYXcl6fGEXNbIGuZRysPDCXs7O7Sx8MyaW7vbtFpt7ty5w/HhETtdh89+6jbxckav5ZFlKfuTiPunMXG45GQWam7KPOfoJKLVCrh14yo/9ZmXeeN7r1JWOYW0n7o5Ph9OAbBdk6osmS+1vqFpu6RpqpcEWxatlo1pmiRpRZVo6iutFKxoOgoI00UYkk7bxHcdkmVGneckUY4yXYrUYJkqSlGQqYhC5YwXBftHC+axJExKbtsvUBQWrivIq1Sviy8TLW3mBpS1i8QFoyLJQqI4ZTabE4YpUsJv/oP/k0++dA3JlEIL9lAhqCuB0QyJGEJzMERRTllV9FouZTxBVnByfESW6zUgs6nuNWEITM+m021T1Cme0MXmui6Oq3Utg6BFmtQcHM4YjK5iWiaHJ2PG02MkBe2eR5ZkyFozRFWypNvtEEVLUBITKNKMVtAjT7UAS+C3yJMYx/PJFgnKNDFMn3geUhc1YZQTOL4erE0XYFp4XYtSJvgdg3Z3QDQDo5ZsDveYnEyoqpDdq20s26GzNeRf/ovvYHkOQiq6puJ62+W6byPjHLOumdY2k7imECadTgvPszjc3ydNFdc3RihVAyae59P2bbq+w8P796iLkrKSnE6mvPPeffZ2t7l29SqLZUiVhnjDK4TRkntvv02ZpQy2Roy6PmGU8uDhIe1OF8s0SeMl7cBlb/cKizhhNIgpqpoozbn34D6dbpdFGFIUKY5pYCEIT08xDZhFC70YTUqEYdNut6lNGzsIODw6JQxj8qzkwcEpSZLx6U9+gjRcYAhJEi45OTpgd3uTvi+o85BoPuHowVIvx5c2GBJlGAjLRZkeUkiitGQ6P6Tdcuj0e5zOU+ZFRMv5iDkFpWoWszGqtjENm9FoSJoVmLaD06xpUELQandptXOqKqQsC71eQWoRGAC71aO7pfDaFVWREpYhUZWC65IXNkfjiFqYLIsUOzeJc0UtPMK0ZpmHVMKi1etjiJrR0Mcwc6q6xhYOjuFhOgLDAikqKpUzn0WcjJfcv/cQhcHX/tnXubrTI0m1slTdTFUqsRoX1fEVUun0RGnJeLLk9ks3uH37NnlZEScp9+4/YD6fE7TaOK5DWVUkWcxwa8h8NsNy9WBdvz+glhWu42qJvCgiLWPu3H+HIAhYRAtqaja3NijqlFpJ2t0WSRqfhY0roddzKATLMKHddhGWQVmWZGWOMqBUNaPtDaI0Jy8yZuEcSwiCbgfHgslijlSS0eY2iyghK2rcwGcyn7F3ZRtVFijboetuMXrhBkGrR1ZKJIJbp3uoCsLJgvwkwywrqijDrgwKYfLGYciDeYw0bPr9Aa6vUDlYlgPCJGh3qbMaYZocn4yxtjfIK02fVynFZLbUrFeGjRQmwrR1TInrQmox2trmzp07VEowm4Uslwm2F3A6eUCRZQx6XfbvP+T27ZfIKs3xEKQlVpgi84SsKPFsm1pBrQSb3T69oIXn2hR5xsHBPmGSsLl3ja1+n6PJHFmU9HpdpFSYZkWaJERxzL179+l1AmSZ4bVaSAyyoqTyPIQdYPodomnIRreNzCWLcIFt2fSGI1566WM4piBPE779u9/k4emM/dnvMZ0nyCRhuLn11O3xaUhWrqHp3XfQ5Nu/oZT6NSHEEPhfgZtowoL/UCk1axiefw34IpAAf0kp9Z0Pekar1eFn/uTPABbtdu8swm8xX5xRuXe6HYbDEffu7RNHMUVRoBAoYWBZJn/uSy6f+xNb7GwbuFaOrKDozrGvKMrSYjyNab9kU9SQlgXKMIniAssLuJrrt4rn22zujLBMaLcsPA8c18WxHTq9Ab7vY7ueZlWyLIoyoNPNafdyOu0e8HUeHi34+Z//N/UqubJs1mfo1mcIvbrSMoQWexWS3asb9IdX8T29fsLxMyRDtouS7Z29hqKhJOgF2I7FyfHx2doMgE5nj3a7g+u65HnGYrkkTXI836OSNcIQmBaUdYZpCbruBmG4RDThxnme024HtFoBaarjQooix7UtUBUCzQblOj6TxYKilOxd+ySyVFimgaxyZKVXVvqtDnYYYTsepZIMtmp2NjfwPU1TLmuJ32oj0UIqvf6AnRc+RZVXnB6c8ODNO1zfaOP1O3iywFTguhNu7yiEH/CZn/w0WTLj9OiUVneE8FpIQ89rLuZTZuMTbBNe+NhLeL5PlKQopVm48ywjCAL8Tp8XX3wBx3HIsoQgCBht7XLz+nWMsmQ2W+C3OoRxDChcywRZ4ToWQbeDYdmM50teWsZM5wuKskQoiWkIBr0uW+2ArdGA7c0NwnBB5+23sByPweYWW7tXODg+ZZlmeO0Ox0enLJcRoJiNx1gCblzdY3dnm+l8Tqs/ZG97i5vXdnEcvbjvtddeYzwes1Epdj9WU1cVnVabQb+v1bV6Az77Mz+HQGC7LlFWIIuCnd1t+L13nsopiHPNxSc6hV1gVyn1HSFEB/g28GeBv4SmXvubQohfAgZKqb8hhPgi8J83TuFzwK8ppT73Qc945ZVX1Le+9U3NkiNW9pyTs+q/NO+iUkZDv9ZwKQgTCRhKYYinF7z4UUFKLRnXKFKghLYb1dDGnZHGaLo4pWqEOleXAk20cu5N0N0MQ53J5QlhrLjpEEC1UtRG80BqgirdPZESDEM1QiNNnrLqujTPbGZz9NSvoWnTUc3jVXNec2/E+dQwmuQGJZtd2vZmVcrZaarRMNDENFpgV67KS4kzhr2VKvcZmxYKIQ0wbITZSGipQhPtCOv87a8UBitb6zPWDaFTcbZ9lmGrOn9WGE09axihDcOEhmDHMARCVfoao7mXYSGVJuFZBcyoxgGjal1XhQK1+q7VLOQVYGA1gfqrefXGHKXjE1AKgdS9yqaOrDJIF4PRZL1xgVLgPB1n24Yu13NtFDBt59tKqVc+rA4/DfPSIZqlGaVUKIR4A7gCfAlN0wbwW8C/AP5Gs//vKW3N7wgh+kKI3eY+T36OMJoEnis/XVS6Wm0KsSpeucrSppILXWEwm256c9/m/xf/0+L2ggtVCVitqpCcqZEok0snPGLTJTT7z8qs2SlW6Vk5twt1UzM3Wc2TL9xICf3oS89STcMy32eHlsBrnqgEF08wmqUijUjWedou3mP1Z8MeY5yZs/IwAMaZjzpLwyrkfNW4GiKclc9ZkdEI1VRccYFwV+jcWTkeScOGrUN2NHEOAltxwTea0IznrMpccP7i0M8zm+2VKzDeX2RnO+Slv5VpaOn31WnCatKq7Rdq5Sg1Zd0qh4QAYeqmJIXZ1LJVg+bMMa1ql3nBvvN8Nznz6M2Lb/VcHYmn04LQ9zvP/8ela7WtEKICoV8YT4sfakyhEYX5LPANYHvV0JVSh0KI1UfLFeDBhcseNvs+2Ck0jfvDjF/5zYv1+cLyKP1GvpBZ4sJZ+kWnLrzNmpfwhTufv1XEhcr+JGvEZYdx6ZkrPCE9F06Sj57zmOet3noffC8JYrVS9PLCsQ+159K95GMOXNzX5JhYWbZqVecO43K+rErpUu6eQ6GFgzmr+mflIwWN2sdqMdzKKcpLd9a3uWDL48x4Hy7nxaOpPnfg+ilN5+sSHv1bd8bEhevObTAvXXPumFfHNW+IuJyoVQ1V57VUXrT8EQMuvb+eWG8/GE/tFIQQbTT/4leUUkvx5Cc+7sD7yuai7sP169cxdEjHEy5//410fdRvPXHmiQFRQdNRO89dndniUglczHvFIyXBuYf+IIizN9MHGnvh9PdDvx8/6MTLVevxFV2nwOByRX+cx5KPHLt4joKnKAPRLBxbXXGe9xfrsjzfeenfR595fpEOy276b0qPF9UCBFVTwjYgmp6gfrKhaJy37gN+sBP4YHxQg398jlxMvXG26333edSox/jlVUM/643xSK0U567g8basnOSj9fjDy/NRPJVTEELYaIfwD5VS/6TZfbz6LGjGHU6a/Q+Baxcuvwoc8Agu6j688sorSry/v/xYqIsV72KvVW+h46HtM299yQufn/VIQT3uuR/Q4C/V6Qvd8UvHH3PPswr86JPMD3ulXTLzsS/jsyPi8pH3u+PzA49m5PvOWR255HbP0ycuP+1RV3ZesR/j5C7ebjUGc8GeZvgBE4WgWfOibKRYNSB1oW2ps/s+LhsvdtU/COd2Pf57UV26hXrkPP0M/aX0yPPE5dx49L5n1z7myGWc90Ee3X/JyavmhSjEE6/4IHzoh0Yzm/CbwBtKqb914dDXgC83218G/umF/b8gND4PLD5sPIGV2Rde7E/6nXeKVw7gQqKFTpJqFkKrszfnqgN6wYs+xbOeeEBc+D0uu1c1+on3fCTd6MhMnZ4L2+Lc/kvbzU888rtc4R5j66XfxXuZj/zOrz/vkF+852V7zp9/+Z6i+T1q66NpFO/LpFXZrZ5t6V8zwKyvWN3nom1PyPIfqklcPP9yOb7ftV1+ytmVq7fOWRGoR04Xly5rasClO73f/vMj769Kj6ufvO/Kp8XT9BS+APwnwPeEEN9t9v0y8DeB/00I8ZeB+8BfaI79X+iZh3fQU5L/6VNZ8pSWn59mPPG6P0hG/MHxB3nSB1zzOJ/xEcbTmv/kt5Pu+a1a5XnZXuzF/RFmknjfxtOc/MjeJxXik+75/isun/00tvzRzbw9zezDv+LJVv1bjzlfAX/1D2nXGmtcwEfcM37E8Own9tdYY43nCmunsMYaa1zC2imsscYal7B2CmusscYlrJ3CGmuscQlrp7DGGmtcwtoprLHGGpewdgprrLHGJaydwhprrHEJa6ewxhprXMLaKayxxhqXsHYKa6yxxiWsncIaa6xxCWunsMYaa1zC2imsscYal7B2CmusscYlrJ3CGmuscQlrp7DGGmtcwocqRP2xGCHEKRAD42dtyx8CG3y07YePfho+6vbDjzYNN5RSmx920nPhFACEEL/7NJJWzys+6vbDRz8NH3X74flIw/rzYY011riEtVNYY401LuF5cgq/8awN+EPio24/fPTT8FG3H56DNDw3YwprrLHG84HnqaewxhprPAd45k5BCPHvCSF+IIR4RwjxS8/anqeFEOKuEOJ7QojvCiF+t9k3FEL830KIt5t/B8/azosQQvxdIcSJEOK1C/sea3OjBfo/NOXyqhDiJ56d5We2Ps7+XxVC7Dfl8F0hxBcvHPuvGvt/IIT4d5+N1ecQQlwTQvy/Qog3hBCvCyH+i2b/81UGSqln9kMLAr4LvAA4wO8Dn3yWNv0Qtt8FNh7Z998Cv9Rs/xLw3zxrOx+x708BPwG89mE2o/VA/xlas+3zwDeeU/t/FfgvH3PuJ5v65AK3mnpmPmP7d4GfaLY7wFuNnc9VGTzrnsJPA+8ope4opQrgHwFfesY2/WHwJeC3mu3fAv7sM7TlfVBK/Utg+sjuJ9n8JeDvKY3fAfpCiN0/HksfjyfY/yR8CfhHSqlcKfUeWvD4p39kxj0FlFKHSqnvNNsh8AZwheesDJ61U7gCPLjw98Nm30cBCvjnQohvCyH+s2bftlLqEHQFALaemXVPjyfZ/FEqm7/WdK//7oVPtufafiHETeCzwDd4zsrgWTuFx8kJf1SmQ76glPoJ4M8Af1UI8aeetUF/xPiolM2vAy8CnwEOgf+u2f/c2i+EaAP/GPiKUmr5Qac+Zt+PPA3P2ik8BK5d+PsqcPCMbPmhoJQ6aP49Af53dNf0eNW9a/49eXYWPjWeZPNHomyUUsdKqVopJYH/mfNPhOfSfiGEjXYI/1Ap9U+a3c9VGTxrp/At4CUhxC0hhAP8ReBrz9imD4UQoiWE6Ky2gX8HeA1t+5eb074M/NNnY+EPhSfZ/DXgF5oR8M8Di1UX93nCI9/Yfw5dDqDt/4tCCFcIcQt4CfjmH7d9FyGEEMBvAm8opf7WhUPPVxk8y9HYCyOsb6FHh3/lWdvzlDa/gB7Z/n3g9ZXdwAj4f4C3m3+Hz9rWR+z+X9Bd7BL9FvrLT7IZ3XX9n5py+R7wynNq/99v7HsV3Yh2L5z/K439PwD+zHNg/8+iu/+vAt9tfl983spgHdG4xhprXMKz/nxYY401njOsncIaa6xxCWunsMYaa1zC2imsscYal7B2CmusscYlrJ3CGmuscQlrp7DGGmtcwtoprLHGGpfw/wNyZBPUJ4m7bwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=True, num_features=5, hide_rest=False)\n", + "img_boundry1 = mark_boundaries(temp/255.0, mask)\n", + "plt.imshow(img_boundry1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's turn on areas that contributes against the top prediction." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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Wi8Gy4Fydeg257p/r1QNUfJpUFcYue+zWe1Y2GEas7g4MDAh3Yu1UCYaSkckpSMYCjabB4mu7kUKoANxUFYgn4JEVemIiVAtEV6XnKt6RqqJYMQGaDkNLx8SSXB2raowD8NZ/+t8Bib6foZTmiY97It5PLJcLjNEsDuSzcy0pF1IKpJQgw85slxTFCtBGUWIgl4SPnhgj1jaYCkQ2zpFSZhxHtNYYLQB007R4P+KcQ2exDFTRaKVBK3JMUAqz2QxrLa1tgcJisaQxHW3XstNGKGCNgaIYx8DxY8cJIeKnEZMUp08dEoaJx934eBwznHEsl56TZ5d89EP3cXhm4As+/xl8ypM/jY8+8D60djRdjx8XEjHLhuAjShWMaTDWMOv3KFkiD11nySGA0sQkUaau7zHKorJif/c4qhTOnTvDvG+xMV7zfHxEKAVtzhsckcPq8xdGPC0a0EwMHHAKi8bRMKNhwQFLFrRYFI5DljgsVB8/Vx9dJnCoE8nVNc2yIhAoaEpFIBQrxjrZbTW5E+c4YM2hWLHikGU1zw2qDnpXp4nHM1SAs6cjMGLRZBrWaLep66ypzkqpvvdRsE7cBl1RjbJxYwIejSEdwUzWBBWF5ZAFHR0tLRpXrYKEwW3sElE+glj0zDZKQCBWsXbknmUMCkNbgUtd4dKGqdoCqroOPTNCxU5aOlS10sTyEZQE4Hv/4b/kTT/xdWhl6doGjYGiq2lt0E4Tgscqg200sWSMygQfODw8wBjx6Z2zFA0pBbTWNDUKYY0lpYR1jlLAGFutg0RKEecM0OBDqP63ZRgGMc11wbmGHJOAf+MEruCMpTEWUwzDuSWuMShdwBSsazABhmEghUKrGvzBRFokGj2H0POPXvg2AG75/q9nuYq87affyd/9b5/Gs7/kSykpE30gx1QjH5pcFAbNNAVKKRw7todrZFxToCQgK1zTQfRorcForHWEKWKNRVuHVpputstsPqflrzAk+VBIKecZfa6auZaOjo4ZczKFSKgglyET0CQSIy09Ykj6OtBlijiaC5TC+lNt0HOq9aEqQCk+byAxMTFwruIIlomBQqHBQQUjG1oSua7kpa7u4l1TlUlG2A1yrgBYIoGBiSWJjpZCIjLQ0lVFMqMgZn2k0DNjHVASRZCZ0dV1vdDSEytmIn67sCzXzktHi6OtlkCNy1MYGOpd7mmrqhCXYk140RVenDBVOYmVkLBVoYiCytUVEqfM0tR7oarlBE1Vf2tJsaAdxJhJKTEOIzF6UtBYa/E+0Pc9IQSKyoQQMNqgNBir6G23mcRKFWKM5FKIwYulEBPWWJy1RB/x40hRCaVh8oLKW2cJ1bpwLku0ww/M+xlKKaKPYMS1zUWsh+Q95IRWCm0KWifa1hAmRQyeNIGOihff+i8vHOBv+l540Vt5/Zt/frPpZ3/xj/i9d5xA6cLh4RmmaUHII1ortDZkX2ibrvZVoxSkGFE5Q87EXEhR+BWJjDGaEBIpZ7TKnDt3QNv0WNsyjP4C2sPV5BGhFEKYNn8L4q1pmJHq6liAFotGQpLif8OsrsGeUD3rsgHWdIX/UoUAc/XQBTwUvF0QcvGeQ111A8u6auZq/ovbsA7RzdkVv7X+NDTVlpGfRKqruq797jbhQTG/Lbk6Cwm1aWfFqoYfD8j1OiS4SLUUBMW3NewJoiQ0a2pNqXaPWEWhKgRBJHo6ZmQSC/zmHrkKes7Y5Ri7LFmSiUyMUF2EvAlzGpoa/nXYiibIvR0YN/20XEjGUGhGpgvWKa013ntKzqwOFrKyW0vXdhhjGIeJUhTT6IlF3IeQwbkG7z3GGowz5Aq6TeMkPr1zTFPA+8Cs7+m6jsPDBW3XyJjQlqmG7YyxgCKFyKzvaZpGTHVloECKia5tySoJEUhrTCmEFAlTxGRRPtEfEifQtEzLkZe+6hfhjr8PxsE4wf2n4AkZvvtr4Mf/9QXj3liYpgVnz95PygNaCQ+jcR1jGFEaXGcIUUK1pITKGT95/ORpmoZu1pJLpkygjcEog588w3LAasv+/j6pJC4N/11aHhFKYZzGzd+OFoGrXMUT0sYfpxq4AU8h0+AqGi4scDHpHWAY8YxMNDSEOrAtjkRAY9B1wjsMEwOBwIwZ1LXdoUlMFWAThH3Ek/C0GAKlugxrw96SSfR0DAzVxrDVpZAVc23m5wq+OVzFQtwGjFuH9Gy9flVXYrFoxNJYsUJVxXPIAT197UVTXYtUzyUW1sjAU/gUznAGVSf6nB322a8Yi7gzFkuCqkAiczp8dWfWzyaT6emZmKD2qlAqmBlpqoUWkTClKBhFrmFYgMXiEFBEZ1kMC7Qy+Bho+46cwbgGZSyz+ZwzZx9Aa0PMiWEY0NpgsyFHSDlyuDhAIXH6gma5XGK09Gk1jljrJLJRFCQB40C4BzkmYg6EEIghCsJfPDlFcs6kKJ9x8rjGyt8hEIaIbTrarqNkzbCYmJYjd9z5W3KBeQamERDD9GBbOHEcvv4LYBzhl+QF7PN5TxlHZvMWm3tSTsRUaKxl/7rr8X4glURWhZw1JWbyVJjv9hzkKLyLrNFakUpmGgf6boYxFueE1evjIJyIv6KQ5EMmJRfueP2zeMktv8tZPF0NrXW0DPjq3XdoFAcMOExF5BORXGEsU6e6hNk0haZ6wIKYuw0OkIlYFDMaVhwSiPQ14i54/Zw99lgxVQdALIWetprcsvI0NUQpK6TdTPRcfe0VCyT0aDHV9QBFrJNWrkGmeajuj60BUE/A1ahHwFfzXWMwtDTV5KfCiW5j8ufqTOkKOIYaaj3DKRYcsmRAVwWzYFldnJGGBjZ30JFr5OG8QhJIdGDE49FoPKEeJ0eKRSIhzVRByjUhTFwceMNb/xtmM8U4TqxWS3zw9P2Mvb09xuAxxuCDODvWKozR+MlTMrRtdZtqFCErjbGG1nVYa9nb29+wJa21pCRuQYyJVAooiRg0bUPwHkXBWk2MEWdnpJDQRTFNQdwFpYEiGIcV5qWzLcbs0HZ7QMPB2UNOfmyJSUeSk1Yebrwe+j1IDpKGGx4P7/8gxALf/Bz46Xdw5uwhykkEZTw3AgFrGjrT4ZTBtR1DGEhA0zXEUWFdg0XTaE3RYqn55Cko2qal7zuCjzQ7PTF6us5QkidMy2uej4+Iykuz+Zy2mfH6Nz+XV/PHvIT/wIv4TQ4YCBTGOsQGxGtNWPKGVKOwNBg6HB3igAgXsGdWg4gzWlr22KOvyqWp67DDsEPPDj1dxfEdMLFEk+lwFb9fWyYTI0syoeIT0juhKo0sOQRSRRIimRHx7leVp7ggsCIT2WOHp/CkDUgJ8kDEZE/1nBaDuA26xkocjo6OHea0NDQ1+FjqMYpMU+FMDewyZ2RJImBpcLT4GrhMlQ4lK3+uNppjl52NghPoUbOOeOTqrkj0I6Dq/xWKgYEVSwKegRUrlowMvAJJoI3BM/mRlCK5ZPr5jFLJOLt7uxQgpMDgR0KMOOtw1uKMlRCjgsVywTAMBO9xrhHGIBLazJWebKyh63usc+RSJPqhDK5pN+y+cRzFjSlZVuWcGMdJ7M4CzlqMNlijaZsG6zqUamncPgdnM6fvD7z2Nf+G+z604LEnnnR+QJ8+DaOH49eB7eD+c3D8MfDYJ8Pu9TLCXviNfMlzv40///P3cXiwZFwJr8WZFmc6piEQpkiYAkaJbWu1hlx4zKNv4G989mfx6OtP4JzCWU3TGLQuTOMSa8CawvFje8znDU2jmM+aa56PjwhLYdb1fMpf+wzOrE7zQ3c+kxgiWmt+6GW/ccF+t/M81BF8QDgFaxXgaogtbta1BtGqoRJuJBTnKJWAvGK1WZ07OgKeDseCZcXvezxjRdgz6xj9iqlCeY5c8Y1IoKMjkRmruzKwRNVVe21Oh2onWFpGdojMN/aOqSrBVBdAoWhpKu4hIcA1yWptFZhqeQjpyVWERFWwtvLlN3dM0TKjqa6OcBnEyRmZGJnoqisiFkqsGEmuYcnzloPEXGJVHGkT0l1zJNakKM+EQvESns4dvIuX3vRr3Pb6v83ahG9aQ4qK1eEhk17hvWdWw5WQWPlRwoskwjSxWCx4//s/yP7xfR7zuMfQNI2g9vlIaHgSmnLTtMxnAiqCYA5Wa86ePoP3E0YpcpZjfBQGY9vsoI0ihhFjNU7BvN8nxoJSPTkVzpwcWC0LP/ajvwzPfApv//d/Cvzp+YG6GmAYoO3g+AkYJlhN8JRPk6Sq0w/Ae/8SXvA3+QdvfwM/fOc3QplRisa6HVZjxE+BaVqSSmRWWsIYIGc657jhsY8nlcjp9y85c25JTJGYIl3X41zDNEYa17BYDDirSTGzt7vLtcojQimUnLnu2HX8xz97N1NYMp/tYq3j7rd+OVrJQ40x8rJ/9CvcxjNIwMCyrnpCOR7qim3qmqWrpxxrLN7VlViiF6oyHzUzZvhqosswNyQKK8ZKvynVfzd1FbVouhqDiNWOkfyH9fScCAhvsMOzYmAFKGbsoGmqu6M5xf2c4j48a0xFHflZMxQltCkQaaKvx1MnuaNjZKwKRyalrdNDVvF1Cq5wJrpqI4Gi2bgzayULhxxuFKi0ZfCkagEV5sxZsyPzJp6Ta2aIqfiGRHF6ZrRVAYklIpKTJD/NuhkqBVKMxJzJSuGMZVoe0riGbt7hk8FYsVLCGJj3M/7aEx5P41o6GnTSJDKdbYTmGxLWGAEGlSLHKJwGIqtxBVkYfooGpRVt22Ncw+LwkMZoXNMTY6SbzUANaCOs1mE5kfKMgzOZ7Dt+7Ed/Hr7404TF9fXPhGc/G77/DvjKz4Pjk2SHaSBHmHcQJ7j3o5BHiBFygeUKvvqzeekv/V+89vZv4PBwIKtMP+s4tXgARSL4iHO7ZAVGF0af+ZO/+EsOVwtOnj2kFItRDeO0ImfFieO7TN4zjVFcns4QPBwensftriaPCKWQUuRRJ2Y8/nGP4czhOYZhJPhATllCUz6Qc+KONz6bV978W5vjfppvYcWKBzi1WT2FOyATIlYKjsWyqqv3mkm49p19/f/IQCJWy0BV83qqwJms9j2zuuqnahFMUC2AJSvWIU5Ly5qIZOtauj5vJtPSbFD5WNfjNdlJ1ym9JiblutK3OAaWxOowUdtfVA6FcAIKE2O1FCQ60TGjoyFXJSD3xqOq1RRqmDMTa1Qh4CvGYtG0NKRNXongGZ5QE7s8E4GWjhUrhDSlKoKiCTU0+Sr+/eaZ3X7X88gl0HYde7s7HJw7Rd90pBgZVgPWGazWKJUZVguULozjVFmvhrbpMMYKxtA0hBAEz9MGbcQt8FHwDJQixogqmuAnQkzMur5mEWZKBtpCykKHjmhW44KUE1hDCp5je8dIweCnxGt+8Eio8cu/AFb3wo3XwU4DZYQ33wRPeK+4DgdnRSl87CNw7DicvB/IYtK0Fr7oC+HMKXjgHviur+AVP/Fz3Hb7N9C6jpwis/mMEDy5KJSxLFdL1m7pwWqJMRbvgaJwXYtzBlXgvnvPoLVFK83OrsPHTNfPmabzSvlq8ohQCoXM2bP3csMN1zOEieVitUmRHYYBpRQheJRS3HHnc9jbOc7e3j7f/M1v46f5NhIP8IP8Fm/la1mxwFaDv1QYD9jEzlOdlGJhTPVvU4OXrh4nWYVAReT1ZnVcr/IGRag8vzXTUnGepzDhmbNDRgDJsjk+VVByjR2UzTGwzsxQlcCs6Wo0g82kjYQjyL9GV6AxV1yhwTMSkQQwalu5KjpTP3fZZahRnCUrPBMdXaUmGUI1+xWwqkljbXU2DlhgcLT0dW9xPyamGsbMaCQH4tX89gXP2lrNavCkDCFZtDVSK0GDa53QjlEYZ9EUFsOKaZzouq5mU5pKU0bwA6WwpuZFVApzSklKYfi8LvJAjlmUTSqEQQDkpm1JMWBMEUJSAeUK87ZlHJfkBOOkmZaF1/zgr8HfeQZc93g4twB3DpyDcYDmUeAXElnY6WH/OMxnYhFcfx2MK9idwSrDqQmsgrOn4VHXwXXXw8Ep+M7n88qf/Dnufst/DzqjtQGl2d0/htIa7SwhZBrbYIxiGgMqGVJKnBsHoGCNxeiW3d1dShGgNvhDktYY7bhWeUQoBUphsTjHMI0sFoebHHbJr881hqxo25Zh4Tk8PGAaP6eOAAAgAElEQVQYBt74xq/g5pvfBsDdd38l5aaxrlSWdchuPck7+kpgWnvZkhwlPjN0tKSKRwDssQfVpjAV2BOmoUzWWNWBqh6/YBXi40sbCkdTKVNUKyRQ6lHmSGREVzchVG6FYN6lrvahWhMFR0feMBnX9G2pPziwQteQ4bovqgKT4ucLlnGCY4Qa1lwrTLGC+gpmnldWa4W3pisXCgccsE7fHpiwOAIJV3M9AxMTHsOa9l3lri8FrbjlRb/O6+76W5RSWA1LVJHMyKQ0qWRh4ykpZuJcQ5MEX5p1M4IP+Cwx+tlMcIf5zoxcMgeHB2greQqNdZI5COQYhfiEEXdFw0GBEAMxBlKJoDPOGtp2xmJ5QKamdps506B55S1vhxc8FUKC1RL8CuatKIDDs5CUFEXRuiqC43DiOogBnIHDAB/9MBs6olWyvQBtD8dugIVMxZu+72e4681/h1jTuF3bMK4GtLWolISpaDU2iT2Z05r4p2r6taRNl5JZriJ9bylxnalzbfKIUApKa5arEe0MzkkmWc7id7Ztu0lgsdaitUfpgms0MXpu/+EvoW0atIncxK/zZr5sw13IdXWb6mRcE4yocXUQpuOanJQqhy9WsG9WiUdrhQDUCS94hdCtd/CV9Sd5Cx5Hw061EmQ1jVQG/YYdqev+wo+QIONpRhITmhZNITDRMKenJSHp0qUGYpcsaWiJ9TptDeOaqqCo1yNS2GWHhoaBFQ2mKoW4cYlmlRm5tlXWxCxYk5Qya9r3PsdqXGWxiWbMmVW+x1jdEnGJPk7u+lu8/MXnrYdXvu4Z56sGKclFsEZqDqQYhUykYRonvPc45zDG1H0DPmjmsxn7e/tMwdPPLOMo/vN8PmcaR3IqNKblxLHjdG2DSolzh+cgF6YYYMo0bVcLq8A4DrRuTgqOV77k7dLRk0u44QYYzsK8IvlxgOUEf/Z+aGfwqU8G5+HUGZginD0Li0NYLgR4nPewuyMGYTeHMUJcQS+JV3zTF8O/+D10EYssEvHjhK28i5QSrtkRpmcpzHoBWankKh8MKUqxl5wVJWWMbtAadLnEs7iMPCKUwjh53vuXHyBbUEaTE7Vqjt4UjdBaSRGJHJlCxriCdQaTC8ZmlCq8+S3P4fu/79cBuIvnVP7AehWneuJrTH6tJARAXLsLkiqcK4xWcNUTP28XODwRjkykQsHjq0tQarS+qVEKUScWwz57+AoarlONpw09mw0VSmpCeBSKsAm8lsqy0NXhsRuLJxLqsXCcfTo6znB6owCbajsZoKNhZGRGXzNAJWowsKrWiaatP4l1AZY91jUn5FoFiZg23E/BEjRqc48cDS/lHfKAb38mmKqgtII7nwlGwc2/yzgMpBhprKXremIIpALLYYE2skgoVVOelZJfrZiCuBSrYYnKNW25ZFQu9F3POI0YpWmbFj9OeD9w3/334owmxoBWir7tiEMiJ2EylpiZNS1ROayZs1oeidh/+mdCnmB5Cux6e4SdXTgY4J1/LCHIG58IZ87BvfeBMVAi5AwpwzBCJ6xQzpwFHPRzeOA++IxPhZkDfo8XvehfcMedX4drNFqgEUopzGdyf4blihQ94yrgKjnLWkvTthRXSVYxAo4QJRUtTP+VAY3jNLGcAsRM2zUo1n5jZhikQEXOGWMMTSfacZhGOtWAXrsAihQzb/qRLyWXzIu/7x28meciYT2H1GRaJzebyi/I9MxIrJOfJRgoLD3B49emsqgPi68RjnWcAcTsX09Mi2PJimXNWGyrK/Pd/JsLrvlOnsvIWKMiaxO9PkA86wpRE0OlKtnar1TDhpYVK2ytxiQ8AlWVncJXktEee7gNkDpSEJq0xTHVqMsxjm2UwKoCpguWrMlYYhcppLCMFL8RrodjXfFJ+gxNHVIbhfD6L4FZLyul0kK/MFbM7DufReuEsGSNhVRoXEsKktTTNB1KG0rJzPd2SCnivWfyHqU1MUWM1ozjSAgBtKKfz/AhoJTi8OAQhVic2iiGackqCWDpGplMJ46fYAoBrYQinFNi3u6RQ8vhqXNyDbd8K1yn4PR9ghvMDBw7Bjc+GhYKbmhheQDnzsHuMUhFWIzOwBDg4ECsgbYH7WAa5P8nTwm2cN118JEPwp3/HIC7fvhbmYb7YRowVrF/bJeUCslHQkg4bZnNG6ZxQc6Fru+kOI33gLAb0ZoQIyfPnmJ/dxdn/goqLz2UkkuWLC+nNwVExXUQplrbtng/EWMkJggxkkuk6cS1yKVgjGEKE05lsiq85Seey/d/129e8nw/wlcdAf4EUDufH1FY11mgAosaW03vkUOWm8QoXQlDinU2oSIRa/WktrIPLC+vYNtdP/Z8lFLc9N2/wq38Jq/l2bSV2rxmRYrZH6llaukqzbg5EsrMm+m/jrlUMg4DhcQ9fJhAqMcJqBmJm4nvKrtTuAoTAw5JOHMbzEWiMg1tDV96AhZHR49DVwxDgpCKdQaGKOdIuvCGpwRoKRqZkc9SIGVe+4Pv5FWvfgbGiaWQUyKrjLMtOReMQtKTV5NYRSmCUgzDihADyUdKlHqMbd8JYp8Lfd9jjcGPEiFqZz2mtWSfSEGyIHMpGG3RJZNjoYSA1tD2PWdOe976ll+DW78VHvUoGFZQrNCWlYHo4fg+dAaaHfBzOHgA3jtAMwfvJVw5LMVKiEl+VyPMd8FaaBr48Afg9P3wtl8F4H/78Vsgn8IZx2o8zZA8x/fnUmjYWjrXEkOWGpDNDB88Whm0csz6htVq4NzZcyhjUFnTt3MUmr6fXfN8fCgKt34AKo0PYinl6UqpE8D/CTwJqb70d69U0VlpjW1bSUhxhjAFwjRBAWO0GNAlkUom5UjXtlAMKUpiS0qZnBLWChCnNMSceeNbn030kZASr7zl9wF4A88jsmRtF6xZAZI8xRFuguRJrP+/YmLFwGnOsM8xdA0ZNtVBiQwMtRqBrQQfVw3po+KD3/ztKsFqjQX0dBWmG/FI4ZJ1stWSRSUr2U1atdSgEpumQ6pNxRrhWFsVAgyaWnwl4ugIBCb8puSbJ3DIgjkz1nRwySwdq0thWTM11iQoCdNKzoTUp5D6h+vU8Dv4W7yE34Zbfkcu9s7nwRTEfI5RRouycNuX8JpX/w4/fOfzGFYjKcq5QwgYY1gtB6nSbA2pCEVZK0UMYVMlu21l1U/RYxtNSoFhlMShvu2IKbFYHJBzxllL0QUfR5q2I+WIsXIlJUWs63nhd/6s9Pnlfx/2j4Eu4LKs8AdLWfGNAddB7yB56DTkmu/QadjbFetI96IUnRM3Yhpgd08UTRggLQV3WM8FY+n7GWGh2d/bJcYJVQrOtmiXcdYRbKAUaJyjz4UQAtMUiCEx+ZHVcmS+M6dtOppZz2JxUEOa1yYPlaXw3FLKySPfXwL8u1LKHUqpl9Tvt17uYKUU89kc0zYcHB6C0sxnc8I0kZInpUJWBe0szmZyWGKduARGNbU6jUVpy+gX+MkzTgmtLcNq4vZXvZub+RwUhTkL1tWIPIGOnsC6sEhkXZZFVeTBInx/KSgCO+wxY5dMoasA2xlOYunqEYLIDwzczrs31/j6tzwPVCRFz6vv+EJe/ZJ3citiybyFL2OqUYUlK4BKoxLOxStrO6/ks1hXOPIkGjqE+lwq8iCTsmVOITMy0NAhzELINZlLAFEpLtPXUmvrwnOmKhMhlJtqt0CsNSIUBl8BxTuQKnx38hw0igUDrg4pi+F2/iYv4z/IDbj1N+CHvhiChFM31kInK9hLb/0NXnfHc0FrjBHXoGRxJ3KRakQxaaw2WGcIbSMsPtcw62eM44hqLORECBNKF7RTLKelWAVTIgaPmnVijVhDGDwhJrq+l3RlHXjp9/07ePnXgH4U2B2Z0F2BLsGj5nA4k1yGKVd661lJftrZh53roLeCb7gWyo6QlEoFBrwXVyKO8L4/F4zing/CPfdvxolrO7qcOHM2oS10bce4mgganHGUnLFKsKsQI1oZGtsQc8H1LUor9ncje/t75KQ4PBiEr1H+y0cfvg54Tv37nwHv4ApKAcB7j0Kq8aasJCOuFHKOxCkw25vjmoblsKRr+hqvVsRU6T1hBG3wMUoBjVRwTnH7q2RCreqqPpC4nhM0WP5f3kdgXU8wb2DHNe6QKpgmoUMxu5/EkznkkAUL9tglEBnxG7LPxAEDA3fxJ9z6ms9nd75DjIFxOiPvASgZ11hefvtTMVrTNA2vuOnXL3lPfpAv5FW8c/P9Nt7DrXwm6/JuatNji+R/nMcEwgbkFP5iJFfrRhKr16HbiXV1CRiZ2GWO2gCfzcalWrMoxDIoG4UAcCvv4FV8Ia89ogQ/Tt705bCrYbkE1wgFeJrgQGpzvvGNX0nXVCckRpr5LiVncszM5z2rcRDXIBWyBmcbGid5DCkKWKjVuiSevHch5VyrMMm+jXVS2l0bUIUYE85IjkgKHnR1e85NsJugDWA0+ChEpEdfD2fPCUiaAuzuCjEpRJjtQteDiuAnOBxFMWhJ0GMcJArxvvfDM78InvIpcM+HYTh2gVK48YYncHDmAVaHx1gMS5JSaKvIOTHlQsqaxhnQkhRmtKHUEGsppVZmknqUYMixMJvPaZprT3N6KJRCAX5VyRta/nEt3X7DuqJzKeVjSqlHX3zQ0fc+7O01PHD/AxRT2NmZV1CkEFPEWkPft1A0w8oTE/iYabQlZSm31bQtbddy+sxZmsZBMYToMVZz0yuezt2vfRc/znu4mWeQgY9yHx2WRMZhasjR1DoNtQz3JnQZKrIgE+WjfKTG/QOprtbNJudh4A38MQAvfOlnovVE0wmQGJLfxIpLzHRdy+QnwhB5yWufyjhOuFaiJG3XQCm87hZRCC+//am87mX/ERD6sK0ux4gno5lha/RDVVNfKi20NVdCJrf03wCzWtBlTaQSeLTZ4AbnieKqYhOaV3CeSbqR258hLsBLf5fXHFFeF8jrv1T2URkqHkCIoIVMtlY1N9/8b3nD65+/AQ339nYFdCwT1hj6rqPkgvcBZTVW19oOzhHCRIiF7D2znTmNU6QcscbhXItWmtY2kidBoqlVjEKYSCnTuBbrDFMc4ZVfC8HDaiXUZN1W/kGWSX7dCXEBFlG25wxdC40FP8K4FKUwenETlIM/fQ/ce79EIp72OfD466A1cHwXzuzB/qPgKx4Lv/KHPPGxT+SkVjj1ZD52f6CYwMFwjrZvSQmG5ZKULblECoqmUbUorWBBfT/H2gbrLNMQ6ZoOv5pYHFx7jUZVPoH45SUbUOrGUso9deL/GvC9wC+UUo4d2edMKeX45dq48XE75du+/W8Q0kDOkXk/49jePqvlCuccSmsOFgcY52ianhgkTLlcLTFG0me7ruOjH/soe3v7aK1ZLJYobdDacvdrZAX7AZ6FouBZYZF3HaQ6UR0tq2q6r92GBschi0pYkiShNXVZqMqGPU4Qq5f+Bt4FwIte9dcpWd4lMNvpKCVLiMxIZaEYAijwfqJtOkrRBB/pZz0hBpyzNI1jcbiga1uUkjl120v/4OPu3S18Ph3dZlWXd0l0rLMfz3GOnhk9PZLnsNrwDKYKJjab5K8ApBrpgBVLVgy8eu0CHJW7n1N95VaghJt+HX7yG2RVVVrCdn4J0yjbmlbANZAJo41EIQ6XsuL+gNT7fcMdX4Yxhr7v0VoxVkqvtRZjLOMUpBZiqO9ZKAnTaE6ePMn+/j77x/dZDgOhlilTWtF3PSpL2C6XQIqevmsoudQXy0gx2W//9n8Nt7wA8hx2ZmAnUFIxiwMPp0/JxG6cKLhSRCH0vVxfypXc5KXISszw2++GcwfwuOvhcz8bWgcmwt5cMihPLsHuwtLD4QJ+5nzd47e/7Xs4dXAvZ8fTYucZxzRMTH6kpMB8LpWV2qann+/XakzyYhilFQdnDsheiGjKwktf8cvvLqU8/Wpz+kFbCqWUe+rn/UqptwNfBNy3fv+DUuqxwP1XakNrzWw+Y5gSWjVordnZ38M2DYvFUkp/uxYU5KLQrpE4rDGkSiRbTivaWUdRWeLbnVgSd79aFMILeSpzVF1Ry8Y10BWEk3oGoiAy63dDSP3G9apcyPxTPlTb+yzeciQz7ibkvRUvft3nYpzC+4CzipCEv+Aau4mxKyM5/NoYqdFZoJ/NcbZy9yePKoq266TWYC685tbfv+S9E1KSkKt8TW3S9aoUkg9qNhxJAVHXrpBwDiLrWg+qxjxUtRUKiuu5jp/ka/lOfuHCE+/tC5I+jQK4veWrIS6F9WesmNglC0lnXvGD9W+rzvOamkb2e8NXwQ/8MtZodnbmaKWJKdB2rSiJboYxDucCq2FkNjMYY4k549PI/rETdF2L0Y62KUzjBLYQfcCPnhzl/SIpyktXUugo9R0J4zDimkpIeuAQju3WSEmENMIyQOmEeDQsBCgtRQZePxPrZ7kQCyOVag15+Iu/BBPgC/86fOanw2MeAw88IPkQixGw8PRnwWc/TSo0/cG74Du+Cj798XDzP+EF3/aj/Nw/+y4eWN2PMvI6xH6nJy8SKUgJNqUKk/f4dBZjJfowjiM7u7toXXCtYpxGwnTthVsfVD0FpdS8vnEapdQceD7y8pdfAL617vatwL+6Uju5ZHyaMMZI7rtSjN6TFIzBMwRPyInBT5xbHHLy9ClWfgCjUFYzxYBPkb0Tx9BOuPQ7uzN29+bcdNvnAOBQTCygMu3WZdHW5KI1v7+QakRBIWXPBIzrcBuFAPAjvAeAW9/wDL7vtqdyN3/IzXc8Da01WWW00aRcNsVDS1EEHzHG0TZi0uZcsNaxplZb48ixsh2T8NytsbzmZlEId/MV3Mnf5i6+AoA38dXMmNUoh6WnZ5d9JH9QLIA5s5ruTL0q4RyIm+CYs8O6hPxE4DRnOMnJahWVDb/jzfWcG/mH/0oshX4Hmg5CEL+5JFkN22p2hyDIu1KwWMDkRRF0XQ3LmfO/wIt+4Fc5ODjL2XOnaxg6sFqtmPwkdRSmsb4YBXytjBRCout6sSTGSd76BEQfaJ0jx0gME7ZyHkqBaQqsViM5w2rw3PTimqbfziRqcLiQ9wFlLau+1WIh7OwKJuIaiUAMI9x3n1gIzsJ9H4M/+EP44AclwvA1Xw3P+VJ4whMEn3jC4+BJnyLUaDR84APwrv8Hwgqe9FjYcfBbvwH/oyzoq9WEj56QAqiCMeLWrWtYhuCZpoF7772Hj33sIxweniWmQEwTMY0U5Ul5uuL7Vy+WB2sp3AC8vb4jzwI/U0r5t0qpdwI/q5T6B8CHgG+8UiPrGv7ygCNp8gzTPcx35iQFUwyULBYASh42IdO3M0IMjNNI27UsVktKkuq7RmmUztz9yv8EiPZbsMRUhn9LR09HRCoQJyIz+voKtFxj/FIroKNnXQTlha/5DKbg+ce3/Wde9uYvIsfI/t4OL7/7GcT/j7o3j9btLus8P3ue3uEMd0xuAJMwyiQQGQIouqTRqrat6m7UsrWqQKtVNIUMCYTMNxDGoEhZXeJUq5vWJaVdq7t1qVVaOCC2olWgIoOZkzvk3jO845737j++zz7ngmjFkuUK+66bk3vO+77n3fvdv+f3DN+hWRpt1yGIA5aLFV3n4CYKPYKkOjiuj+cFuJ6P5wREWWTehEhMJBY+o6M3T8DhcA9Kmw/yrQTAoJYsDkVoWE2NBx1DMwzUKZG7JIBfIKCLg6jiw+xBQrmCeTU0zJjTGmvyC467v0mLPgxVDrSN0IqeoxGeZ9nAcqXMAVRWrAsFhiRRhjAowfQtvP+V8CO/wRtv+C1O3/a1lJWP7wX4QUjnQN22lGWlZvKyxg8CPNdEXzrtnH3fUTeWnfk+YeDje5lASWlMkgbs7e1Rljm+H9B0Ll6QHJ5XHIMTqhzoG2UFDkBrmQ7SXnRdBb4gVCm0XOicjx6By07BsePSUsjGdo3WGmf2gUqOI0dgZwfmu/p3GoLTwuWXw+45uOdhAL77B36Gt7/rZeRlTlVWBG4AfU/fqufWVTV+IMWlqqkZTzJ8X+CsrlX5FUQhTf3Ypw9/557Cl+N4wQuc/hMqx3ndW55JksZsbm/yttf9hy943M0feDmnrztseP3gbV9ND/ieT1U1/JvTf85fd1zHM/Hx2WGHERktLSkJc2amG+ATEZIQczef/oLn3vD+51CuS6EXPJ+yrHDNRToMIhMGFbRU6r89saW7bteZuo9GSF3bEcexSZGbk3XXE4UhcRASBj5tI1PRxWqJ6/v0dJy+/g+4g2vJGFkJJO5EdABw1thxTcGIjNKozC01Y0asWFlfPjD6tcuEMUJQtpdMMlxi5ONZUhzQum8cEIrvfYXm9LU1DR0HulbNNd/KhLJUNx5XO+hopO8vltotR2OpE3kexL4yDKGUtBjf/Dv82Ae+URLnnUsQhKRpJtFWT932ttVkIUlTmq6jKEqzaquJY+kLLBdzuk4eDmEgdmFdD6VCyI9cZ/X7u/4neHQf6rH6I6eeAMu1egTFWiVAvRA/oaxhlUtIZXMLThyFNFaGcP6c2JEbG8oeHnhQ2VCWCQkZpfDkq+AJT1QQrAprTjbgRSrDjp2EJz9ZweEZrwTgTW99jgx5W3B7n+loxM23CxB3+60vxvdClqucpmlIshg/UJxyHY/Ai9nbndHW8O67//Dvp6fw5TgeJOL1XMWP8mn+1TsPrSdv5RXMWViP2uV9FhD+OVfxs9zDT9z251x35/PxPZ8fv/n/44d5odX/K8aMiIm4i9/j9TwbF/ksbJr42pLcxpEeMYkRkkvD5um47h1PxfcCwsBjNNlgvV7T1C2+pxl5kibamZ2euqnljrwu8Hwf15EHZdc39I6AV47n8P6b/ozr3/k8/B6qImdjY0N1YVmS1y29lxCEIV1VyRLNcWgtyt/Cx7iTrzPClpCQLTmBBQXhDcRKGCjYIbGNI9VDKA6QipH1DfT4JWtcQhJSAjxyVgcSbYfEKqBooV1bJtBCVSsYtA1EmerwUaKdtHfUsEtDIfsSF9KRdlhn0/oPjp5f5OrqF+qS/8vrflP3wO3X4LcVQeDRtg3rpfQuwjAkCkLGcUDZlvStQ5GXKufLHDqpMYNHWzssq4Yw7HHchnolUNq73vlqbnjLL8IFH5pNraR8JSxB4EFbQgTgQbipQJAlULUKFPv7yo6OHYOjl0MF1Cv1HTYy2HyalUsRTDZhsg0nLofxlkqv5Rzu+RwcPareRBAJDRmNIDdA0xtewnvvPtSjODhu/lpwWm694+MAfP9rnkkcJ7hdQ+NVvOO9/+ULHv7et3/TY16Pj4tM4dgLkv6ffOJqPGLexye4jucf1MmDnZpnu9ia3Ki5Pv/mEu/a2/hm5swMelQinUKfJXNcOnwG7YEIj4AFawpypZk2hnsff3rwetfd8RR6HEPBHVJxy6IiCiO6tiWMI9Z5IedheibjEYvFgjiKzdFoJFThYkHb97zvbYfv98a7roEOfN8jjDwpCjfid8RxSmWSYlk2oioLbr9BN8btvIzCIMsBHi0VPbXhKmMKy3o0igwITa1xYGTmJooiUhXIcyI0kldgXRUJ1NzxpaYOt71YCL7Q1U7ftNoJl0v1DnxPX73QPHodddqrUrV610OYAK6UhzxXgaRrBWzqamUft/wxvP0aeNsXjjrvuP0aHCRSmqUZfuhRNGua1qEue6Mc67qWZY3Th3S9wzrPGU9i6CvWyxrPnZIlJ7nhxp+D7/kWTUA8R3Tn0IM41JgxsklD06hscFxxHOYLnbPvwxWnYDqFNJWy0n2f14KfjvSzbKzRZDxW4FwUGtNOR7C3B48+Km2F0Rg2tmGcwav+GbztldDOwS1FnOodjUDpxNBMUwhSWLVw2xdB+t/4NEhi8BO10U5/HMfhKydTGObrESGv5/kHVOTYFJsLSpYsrXWW4hEDPW/gWiQ5HpEZG7KynoBUAQoknCq+Yw1GJnINwS+cQkPDe/hTbnz319A2rdR8uo7GAkKZV9R5pbTfcXC6lsBzoWkpVmthKWKpLaWR5ukD8w7HYZyNJZpxyREFEUkUk6/XuHhEvgtdxWqxoswrmqoREaxpyZIRP/Ph7+I13/VhPGJa5sZNSA5o3mqcwiC1ptxqcI+61D8Dyw+EUBS0piPFZ5cZnRGvvmRAAGMIuiY3Jh1FglCBYVD36TorKRr1Dc6cAaeHyURlxHwtIZIeBYbAhyi014z13FufJ7jw+16kwNMDb/kjbrn1j7jzzheKTl0XLIqK1msIvFhYDEe8Gd/zSOMRddXjuiFFWdLUPaE/wnehyD3uOP1zer9hKv2DwIPZRQWGulZg6NEuvpgbTNnk1LJEPYe9PTj7COxcgKuv0kK/8ko4d0a9kqKE0YYWfO/r/B10rnmtDGN6DC5eVBlR5BC5cPrboTsDfiftBg+NOp0evF7BKgwsMLSa3swWKkc8VyjMrjVsSAC3vgy+SPDmrzseF0FBO3hiO3Zt5OaGHXboaL+AyusRGGW5Zo0UZwYpsAFyM0wMPDoiRuQs6WnwScgpCWhM31lNqrsMnef1GnMFrky2mq4VNTXpqauKqioJQ1+8DCDNRsR+hOtJ2KMuSjzXNWdjl6ZqCAKXm/+ldvm7eAUNLTfzO7RVQ+e2REHEW15/GOXvePe1rJZrtja3qasKWoiChCSZABASc5LMWI9SUpJjVHdA4xaWwqcw6fbBT2JQg+4si3Lo7acDs0Gy8wN2A4D3Xgs48Kbfg7u/AdzqsFxIDeu/XisdbltNIqpai8ex0aRvN3Bvw67At8c36t7n9j16lRC95NCIAu2Orsulb6npem664Xe56eYX0HY1TuQK4IaHH5hJXxjakNW10aNH18FodISHHniI5azje//pP+Cn/u2vaBGBwCCjDFYL9QDi2H53rwlE6sHI4MploeznyJbKjWIF998HJ49qpPmUpyhj2t9XUByt9Bqdo2zAc02laQq7e+pLjGJwKqEed89CWIHXwf6eso0khco4BksAACAASURBVNmegE9eoOveOwqang9+rcfIfktBKfQUFIZr/xiOx0X5cNkLxv2/+MTzEbhGAiLyFvAP6Lm+IRAnbDEYyg9Gru4BAMk3jUKXBI8REdtM2eUiZzgHyNmhpyWyZTCYrtxoiL3b3/vSA01ArI+2ublJEPrM9/ZwHCjLnK5rwfGo6g7XdUjjCNcVQAaDm8Zxypt/+Ld4N6/AJ2CfBTExNxrn4fQ7X8rNb/k97uSlB8jCN/EfOX3Xy7j5rYdR/Y67XsEtb/1P/CivJmeFh5SiV8xt5KgyKzIAE3DwdfCnHJiYjTE9MlIcahwqInxqOmp6WgJKGk5zCS7iXdcKwruxpd1y54Jq7yDQYi0r6yGgm7zrFBh6Vzd/YBiAztiCvqfduG21oPxI9XzXwsXzKiOmY9iYHE44wkiQ6Fv/UK/1Q0+FD34WgOtv/VqCIMbtXehb+q4k8E2BKYypqhbHDWhql/vuucgjD89J4i3CMOMX/p16F9z2AxAH2sXrWg1SB53jQP2uamU+ga9z2b2gRuTujnAJfiCC1PYWnDwJJ0/onO69DxZr9QyOHtXrZilsHQN8NWDrCijh3EPiU1QLCIFurUAcxnD11VJ6Ko15WXQKtkOD1wvgKU+H/SXsz2ExE+w6TKFzcd72m4+pfHicBIVJ/5pPPOuA3TeYtrg2SBMDT1ZqGRMGYRQdUlR0wcaJGAW54UquYMKInDUPcYa1kYlKcoP7poSWUHsEXM+vA/CjH/of2NvbETGnqQnDgCDwqQqVIkePHGE222d3b0bT9iRxJGBM2xIEvs3DHd76Bi3sd/IKU5vuGZka8g2I73AHL8WhP6AzN7Tcxsf5IN9GR8d1l4CG3sLXIS1GF6k55GyxRULKjH1k9RYQEVHb4i+txzCoXPtWLvV0+HTkxqKUXJ2I4yXy8X4Pnzz8kG5+vvQEq1I7qodS43SkSUTXa8EHQ+vbMAptp5vVsQZuUahW7zvxB8JYXXfH1esVK930aayFl9dabJEQmRQF6kcs4dQpBZbrf4O3nf4G+g6yNKTvC6LQYTxKGY8ntHXPYl7xuc8+zH1/uct83pGmG0wmU37hI5alXf9dh9iJAVnV9wpqnqegsL+vHXg61hixWMNyplIhDPT8fKlSaJSpT5GNdG51DWlm57mEy08o0O7ui5bdNhA7sJ5Js7GsdQ3bHK44bmNQB9xOfx2EgmyEWKQylOixk+o/tL1KidXMrm+Ac/1/+MoJCidfMOr/2SeeQWgGJ531xHMKKwdcVqyoqQ8sWyQV1h8w+Xo6FiwYNA8zA/8KIiI3o8JYkAOEVzV3dwkOweFGMy15z098M3ku05E0TfFch/VqzXK54Mj2EcIgYJ0XVLVgya7r0HUS/Qh8MTerquHmgToM3MA1ZGSkJoHmAisDVLmWKZTUjJkcsB8raq7nV3k91yC1pxDJtLZ0hkY8wWXMmLHDBaaMGdnYcs4c30BNS5aIQRnYLd8TE1CZLoJPxMLEWwEG+7o7BiXmN3y1UIyBD6kv2fK9fRGb/FBEobY71AroGmUDHSiPdbQwqlK1+iB/niSAr1KjLOyhrer7zoFlrQUBWnRRpNcdDsde99bD6/zFx89/5LU8fN95/uSPP0++DlivZca6sTlmNIr53z/8UT3wta+Cjal+z2ikRd21aiiuVmouzmfKBALPpgwTFeEXL+qa0MLFC7C5qVR+OhETNLLAd//9MI7Ue4gjOH8G6kLPi3w1EOmg9eCpz4T7PitWpWvcimoNR6YKVnWn0qNuFAQuu0yB55GH1b/xHQWcIFKmcP1vfSUFhaz/9k9caTdwTG1c/YGY1NIdcP4nJNY3x8g6GN5QSkKDI8IwUWgsEITErMlJSElJ2GefNfmBOhHAkiUJKe9CY553/vgraJoG3w8Ig5C6qilMQSg27ciqaXQfu9B1UgzyPf8AqHT965WevpUXUtMwYcKUDWuKyry9pbZegDADGRk9PRfZZZNNwGXJmgCflpLOztvHp6YlIiMgZMUMxxqPgw38YPEWGJ074FChSSYuDikZ4FEigRnfcJ5L1sTEvAmb59/4Ijh+FJqlOAHrAqZbOvnlWrvp2LrwhY3melfThqLUzu9JGla1bqe6uLPH9BKY027YazRX2E5dFRZEIvUchibadGKNTV9BxHEgMABVUcEbdf3/yXc8mfXSwfO2GI2P8vAjDxOGsLU15sM/fwnZ63XfqgXtBwoMaabzOntGU4chcIWBlJd8xyYmvX5+5RWmMuUcYhTiVMG0rNRfSAKdf7kSUCn2YD1XUClyBb5oCtvH4MyDMNtVUHBdBY1jE5VUnQsPnoFFrut37Jj6PLuPSj06CdWzaXqoO5w7/vgrZ/og5WFd60FQNcBnbSYpQz0cGkwnNx8CF7k/xciTusVjgw3WlAc+DGL6NXh0jBgRWDkyGMX3lpWEhnQMLxFFaeqavMjpup7JZIrneIxHY8pSUNq273E9j6au8E1Tsm5quSU3LXferHHarbwMF9nEBfgmwX7oHSkmpnAFmcnDVRYodtmjozM4skeNSwNWEHi263cUrFmxIiYwTKNnc4ehwKotyArivbRRZIAcN3t8KtYWlCJcPG7iEoeum16sdHlvVzvd2hpt833A043nB5pAVJf8TUeqxT1XabXv6XW6DvpaY7POmnmOq8xhLQNauk7/DgI13FpHd2zTCkMAUCwEgHI61c7hCKoOLuzAw2cP3v7eTsnxo0+k6xMmkwmXO5cxm108DAjf+20QuGoU+q4WcFUqwG1tKjC99xfgW79GFOrtI4OmjEqY4fx2d/TztlV2sLGp8sn1NKnwDQmZzwVtplYPxUd9Bc9VsKvXsH8eQkdBJIisjGlsyuMIF5FmOt84UnBa5GJ3VijYNJXpOXyFCbfKzCRjkGN38Q/QdVrYCgih7WKuECXIGDY5CBqDqapDTU9JZKmwbN2lrZizMPSiZ0IlENoOGeIzZ3bwvmZ7M4LQp20aZvN9PBwCT96GruPSdy1t59A0NV3vEgYBnuNLt/+SD8EjZM2akMGWvScy8xa9v5xB8LSwBquLS0YqVC05JQsKuw5D4xDkE9nRU5h9vLQSwoOG6yAnt6JgxJgYlwkTMkITdGkoWdPhEuOzYk1PyI9cqin5rq+HiQNZoNp1tVItu1prUcSZdb+toeh14MRQu9A4Wmy06upLVMLGj73+7aAF1bZq6q0WutEnYz2/7xUYAkNPOgaMwtHv7Tt7nQB2K7jnPPzkIeDnG7/+yUzS1IJfRZHvkEQezsaE733tP+CnfvpX4KfM6OVn7xK78/xZZSeuMdZe/x79/P/+z/ANT9F5R4FWkNPq6/amglNda5d2O52TnwCeIM69q2BYLWH/nMaNnQOBqTm1lb3GUj2GOIDcVR9mtVS24ARQOwoUmzH4vX4eOLAshQupGwVuz1VAar9IIu9vOP5OhKgv5xERkpEdIBEl0+6SEBtLQWCdiIAJYwbblcHoFCSMImJTS0TIlAmZ1e89HSU5lUmmpSSMGRMit6acNTU1Sw6lsapKYi1+EFFXLXlZscpz8qKkR65ETVVR5AVN1dI1/YHALPTcftfLAbiF3yS1XsigqSBWo2TRJIfhHvwRHiNk0GoM8elp6KlpkWRsgMRnVyxZsaC26UNFaUIrjXVjnINS6Tzn6ehYsTwgf7U05kY9p6cxkdtLdpV3fZ0agp6jXS4wyjOu4RRQhjCZqsGlC6daN/CUCWSxkI1JeNBeoGmsAWmYh6H5mBfCMCSZTTFy67YXeh9Vrht9OlF63zhQBlCGcH4Nn7oPfvuTcNUY/jsxV3/zo5/H82KapqVpGsoip2lqPJuY/PAPvZp3vP1H9N4L21mH3sX3vx1ee8sX3qxVCefOaedfrQTZvnBBf/O1+g7rlTEpHSE381LXyQ8MqJVLtBUrlUCBL8/1e120y7eNrnfXqhxpGk0rzj+qkuKyy5Ql9K1xSDqVFuMNMT1dX5ObbPSY1+LjJlMYCDuNyZDp/weLdyyHOPRz3GSDwdotsZ7CiqWBejyTXNMoc8TIFvySlAwXWb1Lmc+jRlJsju2iANff/nyStMfzfdquxfU8AdvaDvpKWHrHw+k8snisnkDb03UtcewTpQlVUfHuH/1mejpuf70mG7fyIsCloiEioTRw1ZI1EQkjJrg4JoLWWSkh2HJCgofs6QerOGUG8n0sKE3O3bWgURKRcJQjlDTsss8ue+bLUFNSWPdAdKgVCwIC3shv64O5+fkwQrtM3WgH9AMFg9jVjdr1h2PJohLOwOs1nYhc7XYeurmTUE3G5UpZRd9p8Q/jyECCNPieRnagXdBzLGW2BVSslJ04HlQuVIHS6p017NbSStw6AvHk4B6bzRbSeWwavMBlvlwShCGuG9D1Dje+7f164GqlVZGM4Iaf4H2n/wWBF3LdjR/kX7/3On7gTR+A33tAj/2250rFOQsVrOoK+i3oIqDVtXLESqXt9O/5TPiDrpHwa2k9hNVS5xkNmo+p8S+WcPyEAkEQHE5C8rmC0cYE8KCo1awdOCcd4IYq9666UoHjMR6Pk6AwTAjk4CTtII0Z5ZIkB+aOzkg6aqRlZqOivVOsv8GFoLI6PSMjQtbrQ5khPwU5NWv3dshsmnEnH+P2u19OGNXQo55BWeI4joxO2xbfD7QWmpbLjp8iLwqKIjcZ8Zy6aen6ls7pWa72cb3DhKwHEhLreyxoqImQT2VHzx77JuAiMdaKggkjHAsUGlo2VFRE1gmpkbVsiGdirK0FG/1Gyc+HXMZJVlw0ELMakCI/lQeZRkvNO3i5cBun/xje/fKhi6oAsFjqhnUdTRriCNaVdsLcSFGRB9PMgD+19Qv6Qym20DcRklY7GRggyNiTjeEehhLDaRVggkCvtSoF0HFki8eZOfgZxMdUg49yLeqdQ63gc+fPE4Yhru/QFg1lXVPV+wRBTN85fP/3/WP+tw/9Mrzp3fCvbobXnba3VYEPP3n3G/G8hp/94BsJfZ+mqvin//798H2vUED4+b8qgPNXjl86Lf9Iv4CwVdO0s56C6yhYOtaEXRnYyUNZR1kaZ8TVwj8RKYM4e1ZZQmK9GxdlUMOYeLItstXfArz0uAgKoMUyoPFCa/gNN3ZsabF7ED5cBvH1xpYElmuMmQKQk9ODGZ6ocTY4Jku4vD/ANAxfc0zxtu/wXIem7ijrEs8Ug6IoomsalvMldVGRxRlVIUjyYrYgyRLKvMDxHLxAN/VonNHUNXe851puefPHuIM/4E6+3piampUcWsgdtgYHMRQQF8Gx/sChPLuuQGQwLFnjxdRmazdc1c7amB0NIfFBQzMhtSwjIMa1oOJbb+cSSPYw/mtbjdhwhS1w0IL1A0g8qHp937fZOo467vlaPICDJmOh3dNpTeIs1K5ZW4mSrzQSjGPtvE2lRiIG28UDfGh9cQj+7CFwj8KpbcGEy17w6r1dOHUCvutV8OFf408/uwPs8F2vfgWe5zJfzCnKirppmM/n9L3D973mH3Pi2Ban33n64PTjOCFfren7DtfrGGcptA1xGPL//Nwt/PcfugOAH7/xH7G9OWVn5wyO0zKdbHDZ5U8izjbovJiXfdub4X+8GT7/amAJ6x1dk6JSmXHipCY0+3va5atOCk1ZpmvTtfoaJSpvgkClWV7oGsaROBJVA6vCeBg2udmdA4+9p/C4CQoAHgGS+hg8F3vkAh1SkBMb4SdApqbyLZANu4+PuIBDk00mLEPhMYwoZbUuqpBm9WI6lgbZuZkXcfqNv8dt73oxniMZuHSUCTbb9xL/dPTKYRhSVgW+HxBFIWWxZrGY43gO48kI13XN0GbNnTeJG34LL8JDxjEZMTKEzYmJ8PCRayRg5+/YNCE3yPKgjFSyJqNnygY5axpTU9JoNrf8BwYdyn2WNDRsMT0gmenauCbIEjIY5w5NTABTBQVcuTxF8SFgpq2gCwDPBEqV11FX2qViQ9u5rtLnOIVyCAYmXJKvdcP3nW5g38aPldXWQSALtsDR9CHJoHHh0Rw+dS+sHLgsU2/BC7Wr+q4ARtORUvtvewl89h74i/N8+Bf/E//yB19NmmYUZUFVN1RlzXw2o+9glEbcfMNrOP2un+HH3nkdfVdLGs5zWS73WM1nZFnG0e0jVHXDr/7bW6BvOH5inyQOGU0T1us5Xeewv1jh5w7rZv/weroYI7LVWLe1YLq3L8CS+WGI7+HZ1MHVIh9g0vXQhxjAVb6e26Hm72wBR48pYDPoQfw9qDk7jvNU5O0wHFcCtwAbwPcBF+z7N/Z9/6v8DYdOzzkYEw6IsjEjCnI6Bqdmh4jYFryyhGGHVenhG0FowEJ6ByPHodegmnwNOCbAWtlCbBmTEdouedsNH+c9H3glbVNL6xHo2pY4CsnimKooqesCnI62rxmNU/ZnJUEggQvPcdnd2aWtxfq7/a4Xc+tbP35ANLqFryUkoiSnpMChY8oGCZEtV8m811RIAqXBNxiza12TnIqHecQcnlJkdS+z18T4IUMWNSIjpzjItYTlSHCtpzL0FuQMdUmqGUUaN+7sw8lMTasAZQFNKVJQ3WlR02lB960W8rrUgo9SBYaqEVTaDbTDVaWaiKBsIa9tClEfTiP6DupeCMc4AUIFgHsfgjIVgYpQ9/xiAbN9lSLxEWt+BgosoSZRr3jpM3jKUwo2Nzckieeq5n/0/AXOPXqBM2fPMs5S3vC6/5k03qHvGqLAJ04i6HuWiwVnz5zlvnvu46lPuRrPdTiyvUE6mciTJI6I0oy67ti5OOP8ow9w7uIe3zlcz/VCaX5kI8wgUpAtKy1sOqCBzaNw+RNFoV7uqllZVMrIRhOtkbY+zOSaXpgPL4TJBgcdXQf1cYrHLtz63xwU+r7/LPBcAMdxPOAR4P8C/jnw/r7v3/tYX6ujY4d98yAYHJk1MRDIRws6J8c3L8ahthY+QWjFobBoLBAI4OMxkIUCKxpcPObMLBDJVFXmreFB5/2Od1xDkrZMxiOquqHtWpy+p61LfM8F36NvGzMizvEceRKMsgzXdfFcj8gPqcuKqqqo8pIbb3ou77hT5Ks1C2ICUiJ6KjqbfIiLERpeoz8oqQRGEvfRxyPFRU4PDityOhvlCryEQZQk4760ycoQfn1860nERDbpWZlNXE/HLQOK8a4XajeLQjiyredPJuorrFamNxDob6l3R9/ZWHGtG7muD70XO0fBJRlx4BblGh7B9yw7cDS1CPxDIlSV20jShVkBDyxgv4F1CJ2noFQU+tuh3bdDtffGRKPAj6h5ev7co5w4cZwoitjYmNJ1PRsbGxzZPgreZ6DvyLKRXLHXOb4Ly/mMvm+JA4/Vck1dNczWcz75yT/j+Imj7M322dqesrExxY9D2l6UtLJxuPeBhzl/YefwZg99IRgH34uNDV2n3lOGv16qUeuGUoAuluBVuo6l6O24ofVqSsgmwirMl/r/prGAEylolAWk07/VnPHLVT58I3BP3/cPOAPG/W91OJbcDL2C3sZruaYs9Kzsxg5xSa03oP17kF/X6A7kvCTDk9L4EJf+6XAtYKxYERAxYUJro8E3G2AnDDU66jvhJPq+x3NdmkqjrCgMcX2P1utwvZSqrOjajizLqCshDjemE+aLGXVVEngB0/EGb3jD07j77s+wZkGEx4TpQbNQ77eiZcmgfxCb4YuI2QGDB4O6Dy6xuUopo3DMZi5kRWmzFfegfHJx2WVGyoiAyPATJSMyKuNJDJ8EcEgfdn3dxEMq29sibAvdQVmm5uJqBfEIOjV96T2VEqEk68lt0eZWXjidblbXMbhzb7JutmB8X7+7r7ULksBnHoSdHroMGk+/wwsEkppuqhH34AOqs3v0vsNDKbmm7yirkkcvPMpqveLE8ROiyjsel508SVkUJHGM03dUVU7gORIGXi7ozI2pB5Ikpe17Hn7kLEHgce/9qtmveMJlTCcTjh05QZRmPOvZz+IZvcsv/PR38h2vvV3XonF0DcpOprKtlWhxooBKq36A4ws5mdt4c2gkdlai+Y6mF3Wln8UjIRnXhbAem+Zu1TaXmOL+148vV1D4DuDnL/n3DzmO8z3AJ4A3/k2WcaCR5Aab1BSmLuwhX2MJhw5Tc0mDFWTYDYQgzq3V3BE+BUtk6+oRkRARUNtO6FvanFMREpOSsWZFZCjGwZ0JwHNDuk4KPZ4DXd/TNpo8hH6I68jJqKhXRHFK6IaUeU3bwHiyQVGu6SlJOxf6UHZlbcfdd38GgBEpDTUL5vRGaAYILlFm7pGgbMaYNQUdOYMdvYfDmIyC+UHg22WflDGuEaJ9HErWOJYdNHQkZAYi9xhs8pZmQJcbV/Lg6PpD0Ivnyf1o56zGf4Gvmxv3kGbcN7ZTo463G8DIbrE8144YRppSuB5MUkMnNuqORyON5npfi73ppScQOCL1zHt4pII2UtYQGyDo6FHY3tDOeOqE3qfnQhppgnGJzuWJEyfAcVmt1yxXK8ajMY4jJafJOONz586xjiJGo4yqbvDDhONHT1EVOfu750mbVMpYbc/+3j5lXrGY13StdDhmezOuuupqpuMjRJHPZaeOMt04zmR6VG/gTb8Ed71KF8lzda0iX7Z0kQnBhoHOt2oFX3bsM3F9BT2vVxnh+AqcOxf0nMRXgKgLPa4tVT7sXjikhz+G4+8cFBzHCYFvBd5q3/rXwGm0ak8D7wNe8yWed2AGM35CyJoVg0l8TY3EQzWr9w2b4JhpS0VJSHiwQHo6UlJ6Bqs112jVjoUWGGjSSr8jBhm23HoWIR49DnfyMm7id3nzGz7K6bteSt9LPzEMfOq6IY5iBWoT9Ozaiqpr8LyQtqtYzHLarsLxepquxHUDto9u4Dsx179ZjLx38hI2GYRglHYPSMZBLzEi5HIuY8GcJSsaugPos65JxIwZhxbwcsguKHHpifFpjGg1EK4860eAQ2sj2Q6YMaej5QOXuD5x+gVSAOr6Q2GQMgdamBqGwNXnQ7HWzzvAifTRu5Xq3STW1wFUk2XAgEZ0IZmYktFKRKMkEZ0aKyHqQt12P4O/PA9M5BWRtippNo4L09AbczCNYPokLa75Qg284HBGr7JgjeP0cu4q5FVZVRVpnHDx4kX8IGCdj9je3mS1XuPSc/LkMY4d2aTMV5RlRV1VpGlKmRcsl0suP3mSK06dwvFbTl52OVmaMVvsEMUBWZZafmeH61kQ7UzQpVWJ5Gf63nwG7cyyHOMvhGqM07WiRC9yZRZPPKVeSujrs4p8M6FxRdf2Q6j5gsD4Xzu+HJnCNwN/0vf9ebvo54cfOI7zIeD//VJPMiepnwQ4+YJxnxLj0VvaK2eGwgg5akO6pKQUVMQmKiIA0qFTU4+szuQHLXOXkhJZucsBSbYnKZWl6wkRDZXhBXw6au7ipbj43PzWj37JE7759hfgOBD1Lp7XU5YFieczmY5xPY+u6+i7FhyHuvXoy44oOEzLdQ6hraOGHpeUsTX4RGAuKZixZ8VRg4/HiAlr1qRoUV7kAhkZCSk9HVvEzFihggcqGrsaWq9jphYkezyEqLyLj/ODfDU/cYmHBWAaCI4yhaZRAPC8Q9XmthGMd7FSeu620m9czPW9JIBprEASR1qsbSsqb1EoYwgTpcaOoSOr2pCPiRpjDupDtDGcncO5HMigDuHIZfCEJ8nD0W+NxjyXIlIUyYDlzDk18I4dGpTt7e1z2WUn8DyH8+fPMZ/POXIkoqoquqZlOp0ynkyo64r1as0oi6nrhnvvvZcnXn6SzemU8UhS6ydPHCfP12RJxLGjR/F9j7rJSZOQNImAjLwqWCxmRMklO3XbwKJUVhS42t3dXovbt0+rQyPKQQY/Nen5rtO1Go/UT+ld6TI0lY170dckAyIoHUg2Dxu6j+H4cgSF7+SS0mEwgbF//iO4REjxrzkUQ1tiYmI2rZ9QkFpAGIxaBjsT12pwjei6A2enqfEnBg0B1eMOLT0pyUH3YTCCUbeiPcAN9tbcXLNgxIibeA6SiYtt5NnxFn6X07d+gvf++DfIwcqNiEIH30+oyhbPHUNb4wce+D1FmRMlEW97oxpdN/BsXENTFqwZMaYFKxY6fDunockoz4bYGoUtsWVIHj5P4InMWVgA0GJ3cK3/0hk5CstJ1HxtDcjk4nCXsUH/SkC49XkQ9VpQGNuvqbXAywqckd2gjjrmrQt+eghgqlthF9ZLNQ17I/q4DowT9RLKwtyWrURJM934q5UeZz0dsRRdOLcH/gYsEfZ/uq0GaF8rmCxmeu5qoa95bn2JQOrLwAuveSpJUrOxsUHT1oRhJFMhII4i6qrm6NGjpFmG4zhUVU4WR2RJzHy2y7333k8ShWxtThmPMnzfYT7bpVi7FMWcvms5fuIYaZKwmO/T9iVVWTDd3GJza+vw+voOeIk+mdqEXaPAtBhiy7wcZROlNRPbRs3WjalATokFBd9XIK6shOs6fe/qp8C9D4oLsVyJfv0Yj79TUHAcJwW+CfhfL/n2ux3Hea4+be7/op99yaOlJWdJzYqUlC02mJilW03JjP1LoL4RNY2N3nykSTimpaW3/0JrabRHRGipuWsLx7FGZkZlVu4hAbFkbsw1qjcsQ2MdfZfCRn0H5+5ukGWb9I1DEEV4TsC53XPM9pY4jsNoEoJbEGb9QUC4kxcR2+uobPABz4ZQwimOGdHQGIJTZcCgG5EY/XvGAhefxERiBmr5gC9QtiRdRpGwVoSWObloIsOl6exf+UA6Lf6eQ3lyFxPycCTIWva6AadbpsXYa8FPUgMioUZkUxrhxyTZAlckIFoBmLxIz2/sr+tpPOmglDgMYN5p2pAc1w2+vW3EKpts9L6et5irlHAcofpmC6XjHxHEfLVa86QnXkHbdnRtR5qkJEmK73uMRiOaqqZz5N7V9x2e51PVFWkcsrW1Rei51GVBWZb0fcM4S0mTkKJYsZgV1HVJErtsjCdylIsjyrpiVv7zkwAAIABJREFUMZ/jBZdwD1zHtCZs1/cNrZkjxWjXVXZVmfRdZYxNUADsOmVSnaumbt9AuVZgiBKNLNem15iv1Vvw/57Kh77v18D2F33vu/+2ryNzFsmkLZnzADmbbFEZrt8xgI3m9s1B+VpQEBnwpqPDAfNE0Iy/ICdlTEIG+FQ0rKgsBAQkjPDoCXCICG1MObgopGRsUdGyx5IZOT09r+cfsqLkQ6/75cd0bjfc/mIA3sELKVlT0lMiu7aE6KDZV9FY43F5gCPY4SIuLkfYNsm0FsnSSzRmxZqExABeKrlkB+darqFZRWTQrZLqILu6efBxuOmZcOclydytzwMcwxKsobWF5diu21gwG29zEFh8R1nBKNGidxrduE5n7D1TRO5aNQMDF7Y2jO3o2WMdUxbyVFa0lXbOUSABk701TAvteKMj6lWkkf3uUgFhvZLiUZYp9faCL6AMH9k+yvHjJ9jf3+P48SMcP3aMqiyoqorxeMLWxib3Pvggk2BKFEXEUUhZrNjZ2SWKPGLfJxqNcOgYjxOyJCLwMhxnQtdUxHFAHMcsF3NwPXpimqpmNIkZjy8JCsu5xosO6ttUpa5L19mCrwzN2WjXX600Cq4qBY2+UyCtG10n1xVUurUMrDfMQtnoOaHPgaP2YzgeN4jGhIiC3PiBAbvs0YON2HSDV+blFJMaoMmjpqLFNU2EiIEj4RHQ4piSgk9PQE7PA+yy4iI+AVvGo3QZYMU9OQUrCioukCGyyQVmzMjJaYmY0+HxHJ5LTYWHHJgSs1/r6VmwT0vN5Yz49Vs/zi18Pf4BliJGe3lkwUsQ6z3DaYhiHeDikTJixZoVBYUNmiXLIuh2R8fS/rg4RqtOmJMb0kN2eeJXFgbekmr2weG48MarZWwykHZCX/+fjPQOnV43aRCot9AbZ6EzmFXbaHfvGjUFQ0PdOa5eLwg0fnOMBo2rm9cLtEAcR2lza1iKutPumRkC8pE96COTZfMklup7l7g3YbgG+97S+hSbm8oW7JhONw6mBqNRwonjx4jCgDzPCcOArut40pOeRFXVcpvqZGOfJTGeC/liTl6VxGFA04TM5nPiwOXI5oRzFx7lQlUw3RgzHm8xGm2wmC95dOci80UHXsLThzfS1vKTPHoUVp2dZ6HFHrjCIVSlzikMdJ3bVn2CMDLqeicdxxZdi80tZRGNKV7FqcHJe2leTNLHvBYfF0FBUA8HGb02rMiNtFMbfNc7wOYLmSDCj8jUmtg7uMyY2WLzbe7vIkekgPMseYAddllR07NkRsUjjIhNt6A3KJBvy9whpLPRXsoWJ3AJWFNb0JDe4wrVqwUVIS5jUhK2gZZd9nkZV/G7fJSb+UZ8JsgMNqClsyFhjU/EiIyGmpCAAqWKErTPaADZuwnIPTLAkTANtWEvNHcRjbohJmUAeWv6wAE4qbbXB7QYO4PWtp123N4oz6FBjBt7fJZpFOZ2mi6EoW7KztEirCp5G2SRAsVgluKgJmXfH1quDQpJfacb3emN9BOaboCj33dhBeeWkJ7UYnERanE0VWMziKAvjS9hmgRRZJRh1OOwY7lcceTINmma0bUdjz76KF0rKf1h7h2FEX0Py+WCKAwoyxKXkGyaEQc+i/096GVvGHoObd1SFso0VkuH0WhMHMXUZcWZs4/wnz/1KSYbJ5mtal7+LfZGPOCKy3R+TXWoKBW4Cri+D02oMiwMlBEUZuRbGrXbC8FN9NgiV99mMhHkeZVrbOwp5/3bAJfgcRIUBpdkVbrazVrWiEjb22hNP83IqKlx7UxLGmKDL7e0rFjjE9HhU9BxgX3O8ABLekoCamJ2WbBHTk3JDgUZJWMmlwSUjoQYF6k/tSxocBgxxqVnSmqLOcYlYLB1h5YVhSEKISBjScsLeQJXcIYr2LbBaEBv8GaPjtwapRJYdSmM1jzYxLe27AdVqoaCADEcRYCCgU4mXcrBDEbXcqCba3jbHXo6vP1aNOayhmFqYiCDc3Is9ChOZ93t3OpflI4GQ2pu48De0Q08V1GEg27svT0OvcxcpbZDYOga6EPthoHpNIaBdtNFoTFk2cNmpt0xzTRy8yOhAZcXDxWjjx1VMFmtNRHpUdCzYzZfsFgutehXa7JRgu+4zGYzqXB30O/vMx5P2JhO8TyXOPShbyjynCTw2d7eoqpK+q6mbRuOHd+mzFc8/NDDXHnlk8jSjMViSdO4eG7A1Vc/jSue9BQ2jpzQm3jriyGo1VhsykMwWI+uR9WYniP6m8TgWmmR5/KPqIw/4rga5Ua+hFwiE4tlZsY1KEiOJ3zFKS+1wB4dMT6t3czTA5RdxSC5pgXQ28AxNgh0ySAokpIhKXiHjoyHucCnOWPdgJA1S2p65iwprN/fAh0VSy4anUiswxUrZvhU5i0hcZaKEWMAjrDJFkdp8LiP+ykpCQxNIYelBpeAmKN45MypeJSKTTwiGjICXAI4CAABE6asycnIGKzilyyIjOWJ5TKuLfKS3BSpQgMoCY+oV+1YU1pztCbEISVgSc1tXMttfAze9jG45SXSJnB71edNc8iGdNBN6/Q2RTCknTuQljqNxJpaza7eGl+VlQFNb0hFe90A3fgOh9oBq7XtgkbecR0TcQlg1sDZxm54AzDVnt5v4BlYqlMJMhrB5JiC0mKlILdYGTRYR901ktLrO2a7c7IoYnNrg2JdsMMOsR9z/4MPctVVV/JVVz6JUZbQpx7L2T5lnlN3PmGSUK6X3H//fezv7XDq8uNsb23ghwEb29s0dUFZV6TJBpvbx3nm9lGiUcZzXvrd8I5vh+IeBcjekdBKEhxmXGUteXY3go3UdCmtz+BJWFj4DkeBwK3BKeH4cQXwtlKDcWKfZ9dA3irYOl9h1Ok1Jf+F+zjFBgk9m7bwanqKA5qUdIpSxshHsSIykRH5I8bWe3Bp8Pgz7uEv2WWBw4qGgiUVckWsjVfg2B/AWJcdOSsk9hog2RcICQwS7ROTkBJT07DPHk/jq3kWT+dBHuIc53HoWZsrw8qYnQEZOR2PUuASsU1Gj0dt04CBBBYT09BSIDHZGfvUlPS0NgPxWbAiIcXBNxMYYRjGjFkzp6YiYWRgJQWPkJCMkI6KwKDkB8cdvw93XGueByYr5lgpMUiHBRyKoPSdYLRD59yBA8+HohTOYCgJihpRqEMt9qpUiTHd0NeiVN/CvURZaL22LCSDCzPJqzkI699e8nuyEmZrNSOb2taLY/DrTqXIai2SlR0b0wmbm5tkScRsd4dTpy4njsV6raqSyWTMkSNHcF2XprbsIPaJwpCuLDR5oCPwXLY2N/Ddnq7tWK3XPO95z2U8mdI1Ca4b0DQ+ZdXwuc/fy//yw+/WG9g7p4VOqJ2+aQVC6jDNg1DlVwMsLWB0vcRWoljBeb3Ua3m9INLHNuVf2ZvvRNdrZOu2+l2hbwSsL3IN/xuOx0VQ6HH5DGfZZc6TOUlDYTtgagCckCkjHBu+gYDBM2ZgDUYMxdjic44lMzrWOCwoqQzTGOCDpeCDwcwg5SaZlgrggEYsHQPPdn3YZ0aLxi0JGSU5n+PTfAv/kFfxSj7P5/lL7uVBHuIiuzzMWUrTQgiIWFATIOnVFWsyHDw6a5Z27LFr4KKGnDWR0cAjQyuorJIxbM6Kgo4tNnEJWFoJEhHhg71iwUV2OMlR1hYQxozIqfkhnsYHEeSaWz4GNz1fVOPBHr4yLYMkFJy2abVD4UjtqMMguB489JAygigxklOgNLg1QlTfa9duGjXVXM9u9hUE9jsjczzam0FUQ9BITOVThoW7JoNjJ3Sjz/f1HnMzgh2ZdFs5zOQ7lUBNewDTfslLv4YTx7bwXMjShOPbT1YS5PaMspRZW9O0DVdffTW+r3titVyx3K/o2hrahihwydcr0jTh1OWXs/HVTyNLEy5cPM8jjzzCZDKhLgrWy4JPf+Ye/uzPP8upJ33V4Y3u9VrorpVmTqPsoCh17VxPZLPjU9HUi0oB0w91LmFgojOOMgLfE9fBcY112khnIgg0icGau+ElvhuP4XhcBAVwqAk4x4qJ9fhTHMP15zis8AjYYkxDRcGSjpaE2HoALRhicUXPwyw5y5JdCps8aOxZGnZhGHEOgKTBOEWqT2JSqlRROl4bkEjYyX1qGjbZIiampeKj/Ecu5zjfwiu5yD5/zCf5GL8P+JznHCUrwMUhYY+Kkl0iaiZ0bJnki4fPjPmBGmWNpOkTEqZMKJjR0TJmhE/EijlLltS0nOIUK9bs8ChTxqxZ4+MyJrGWbEFFSUzExBghU0b8MM/gx/m0PoJ1rgWbjg1ZF6l+723sVRvjzo/V4CtbkXoCq+1byyQaa4p5nv49dMQnG1r0ieTk5emwln5jGFld3cFkJNPUsoef/8zhLfJHn4HXfg08cJ/Bgy3rCCwwgTISz1OjbXcOvyJ8yDd808tI4hbPdVgsFoSeS+g6rFZzVuvlgeZFPapxPY88L6irir5rVVFVBUkU4HsKDHEccezYUdI0xnMdrr7qav7iLz7NX37+L0njWOI8Rcnm1hZPe/pTgV/T+xsnhuFwtZDTVDt50itLED5NgbX3FQBKk2brTKmpXOncA/cQJVrXKicGPMNqYRoVrZCPnq8A8xiPx0VQ6KyRVtByzvr5DQW77DFhxJo5D/Mo26RczTFCOustuKxpKICUmByXP+EeHmZFAdRGrBqUoZWkq9k2oCKlYCSs3yCcOgQIkFyq+vqacAzaDTtcNKHZhDM8yC/xEY5whGt4IUfY5ijH+Ai/zIiY+7iH3ExXRN2WmpSHan99CAJk9TSAR2S4ikHctQGbp7isqaipCYhpgYUButXqVNO2YAX0bLBxQIZaGepizATRr2PewStkY3f3p+GmF0C2AfVMN1WIUnFcBYC20c1XmrMTIp0TG2Cps1FYU6u2D0PdkOsSUle6Cr31JIZOeV4p0LSlVKCzVL0I51Bq/+Uveha/8wd/ahOJ1BYV5qocyKouipQt1OZP+Su/zTe/6uWUdUfd1PRdzXzWsV6t2J5OyIsCx5GruO97uK7LcrkEx+XBBx+kqiqcriPyPeLQYzJKyVKPcDoiSWLatuaB+89x/vw5nvnMZ/D0pz6N8+fP0dQVtUHcNzY3eMITn3B4o3vYAjUD21FkI9xYk4QslI5C12iigvV5XN90FwyT0NbKHJpCjcemFELS8w41NOvyMNDS//0Sor4ch4fHE7icUswN5lb9B4xZ0ZLj0FDyAp7PCRIu8gCSf3eoCHiYPZbMuY+z7JPTEeMQGyioJTX1Ig+PErlMNbawHCtKBlpRbSzB3mZUrn3fMRBVj0xoNPIsqEmIiPgcn+Gn+SmOcYKv4kpewjXcy73cx72smLPPHvvsMmhA9PgkjIiocCgQdVsCtoNyVE5FQ0tJjWuNyJrOhFslzdYCD3OGJQUTJuwwIzRwU4BHTIxjOEYBu0p8csaMWbFizfLwg1jMDYIbi0zTh8oUYh+CsQxW16VS8/aSHkCa6DGNUXqD8JC63PVK+4PIYNOdxoSBr5Q2S/U7ahN8dU1mrDvEUnQDU/ND/ye8+lUqE+JQGcoH/g+4+Qf1WmUlN6d/J5evum6oa6XQXd8zX65YzPY1eUgSknTEZDJhc3OD2Xyf5WrF7u4eruuyMZ1QFVKwOnniGEe2N9naSJiMM4Ig4JGHH+bee+8liSMunD/PyePHybIRZx95mCjKeM6zn00Qpxw7buzIH/tOWD+gssj3FfQ8TypSoCxsvmvBrVFAppO5zoU9BdrQFWs0yyReW+TSe+x7Ac0wFaa6UABZm/K1H4hW/hiPx0VQ6OnZYgOPmEFirWCNT2j730W2EcFjTkPABtCxR8E5Cu5nxgXW7LMyBJ+kWqDFM20Fz1iQIhPJl3JY+AMa0rGBaGPNO8+GpRJ7Cw5Gei4DyahjYQLpI2ru5z5+g1/nu/luMkZ8HddSUHCeswY1FuS6puUiM9NLSggMWF0ZmnEQrw8JiKxpWFDQUjIYyaYkzKyk8AyiDWKLLpkz2Oo1dMQGCaupEJ26xLP+whew90aZsgHH14KeL9V07EyFeTyFGFFxu1b8/cYETToDHfW9TS96NS/rTgvdN/HQYm1IvFY7+mik8qJu1Ejrey2W8BBSXlYlL/naZ/D7f/hp+MVf+6s30OmfgLe/waTmNw6+vbe3T9s5BFFIXq4p1kv6puazn/88ruOwsTHh5InjRElCu79PVdVMpxtcceoUSRzhux1VviTyPTy3Jwo9Llx4lKNHj7Berzh75gzPeubTcRyHxXzBzs5F2qbBTx3iJGS0OeXZ136P3szaFnESSfthb65rVpg7d9Eq2PalTSQCc3YqFeyGHkEaWKZkALO8ABx9Xr6Ncrte2UeSWnO3/sqbPnR0zFmTWMruAAERPQ5jNtlgixSPHZas6DnKiKNs0rLHQ3ySXcsHuoPxYW27sSr5hspwEEI7NjS2Uy4ZTGQGqzQJx4amsTCwCdRbGIzuXcsbOlpzsXJo6AiJ+EM+zvN4LtdwDU/gcq7kKv6UT1m7U05PBQtcIs4zI8LhJAng0lJQULBgRkqKT8Q++0RklNSIw9niGXJhTILIU4KJuzQs2TNBFce+auSaETJmy0znpAodGEzr4EhCkxi3TMAz9R6nhaNHYPMIHLsMPvYH8h2olrC1pRsvL80dqoXtWAEhHWtisC4VEEZjZRZBYE1Mk1OrW40bJ6l20c5VGWJH09TEacKrXvkiptMp8/mcNEn5pX//m9x6w+soyiXv+tG79eDXi6X/La+8FtedkZcVZV3z/1P35kGWZXl93+fcc9e3v9wqs7LWru6unp4ZeqZnWAViBjBohk1GBsshWYCRJUfIlhQmCIMVCiSDbdlYQiawHbYjCMmWJWxFYAvCSP8gCwUyWMPMMMwM01vtVbm/l2+/6znHf/zOyyr2IpAdPbciO7Nf5tvuu+d3fst36Q176EAzn5xzcjpGB+/Q63YYnY+5enWfBw/uM5/N+OAHXmN7ewsdODSGJlGMTg45OTqglaVEUUSv10UHAa9/8DVu3XqB/f19jg4OWS6WYBqcM0SRBp4RrFnMpeaflbKgFX5CMpdgGwZesToQUFeITAzO55J1LUfSQ+j0vPhNKACmspZGZJb50bD/PJ16qodhBOD3vMe7IigYHEeckRLS8vqCfQYYlKc415RY2kRAygRFwZzP8SaPGbEkpsZSeYSCwpF6OrH1CD5Z9JoayxZbtIjJWfnywVwEg3W5IMfaESH01KLAdxecr/RLSt/nbzAcccSbvME/4f/kFjfZZI9r3KRND4u4XCc+FylZUBAwpaQDtAlISKmYIx4VS0IMITEaTcWS1I9gwRIAfTqe1GUxXtg1JqOi8qPViC4tCj/NSchQJCSIJuP6fV8cOhCabuTr+dynrOtU/+hIGmGtLtiRWKGXVhCQJoC0LzvdtJDGVrH032voDmXhR5HsaoEXGLEe+5C1JJuIIumgL57BF1Ql1jaUZUEUbRHHCSrQfP9f+rO4esFsPuYH/uK/zY/9xP8Mf/unAIiimDAMCYKaoqppGkMYRQQ6RCs4ODxiPm9jnCWMQnQYcuPGdXq9LmVZkMRa8ixTsbnZA1Owvb2Dc452K6MqcoxRtNttojCiqis67Q7WiFOXtfK4F0cYiONV6DkNRS5BOJLN52KCYry6c+1HuPkSunuwd1nGr0pLX8dU0qNRldzPKQm8Wkugnk4kACsPQ/8DOES9KwxmWx9O3Xt+9To1jh49IjQdWrTJyCkYMeGUEWPOWXk0o73ICATUISxIadYpYC22knijVaFPt+nQ8b9xjBkzY0pAQJfuhQUd4BEK6qL5KIeEGXGwzhFLe0MI9AlJ/GvoscFX8a/xE/wUFrjHW/xD/j6nfI6XgYicT/KQ38AS0eMygQc1GWIcPTo+GDbMmSOQa5lOdOjJJkNJi5QAS5c2MyY+N4gQqrmUQPJeoosmaYpmSc6CFRlt/gaflrf21z7kEXW+QRVpGUHqQBqGa0Vmh3D057nUwWkiu5RTsgOenMHGtvQmFLB/U8A4qYbFKeTn0ncorfASNocCeEp8QLIxvD2Fn/wE733fPpe39y/ozeDI85wXX3qRqq7p9Xq0WxlZHJImsecwpNy5e5+z8YQ4aXFwdMYyz2lsI1iEbpdhv8Pe7jZxqBj22mwMB3TaLTqtFkVVMpuvODw4xtYFg27Iyy/ssLPVZTkvSJOMJ08e0+t2uXJln8FgwFvvvMNoMkXrkFYc0sp6dIeX6W/v8JXf9O/A3/wuWB6AXXpNCifns3Hi5BRFEgicR4IazxitPKTZeV0L5cVYYt+/sVqatsuVOEA5X35lqZz/JJbPcDyGvET9rbe/eAxmNeLJcMb0ooFXklPTpaFhwpRzJl6AdJ0KiQDr2txFOuxrarQAnmQxKNp0LpqEkjgL10JUl6zfX1u+cfcUvyCWbhIQxP1a/jXUnpplUNQMCbhFxjYZOZYDltzj8z44KS6zz02uc8qvAHM6WK6iuU/FnBU1a28ozYpz5kw9VCmmRUrhyUxdujxrjltQ+8Km8EJttS879AVaU3QfZ34mEZMDqe8y/KbS4a998nf+cH74tr8QEwkIjQMSP0Hoe+RcLRc7Bjba0Mw9Nj+EB3cALU3EVEHoF3ikhFVZVAJ4UhZUCisHP/kJPvbxL0cHY8qqJk0SVqsVm1ubrPKcMBSdiNFoRLdzjbquyfMVcRRhjSGJY0rvLzHcGDB7tKA2js3Nbaxp6PUHhGFMUawwnQClIgKdkJeO+w8Oeevtdzg6PCFwhve9coMs1nQ7Heq6YdhPiKOQ8WjE5d1dxuMx5+MxoIiSCKViitLynX/6B+V9/sifBDUWcZnAyLkACbShDwbWeZhzIL0DhW8YlqJiXZeSWXQ7cnfnBPJMIE3HViZZgXMSCE5PJFgrC1tDD3H+ImNJKhSRT+cjxPYsZ8WCxQVByKEIvVqR2L7J3HWNQZC2oNT2a78H6weOCjyrQDrwYq9iWLMpE7JnNAi4CCZro1f/ifkxZu4XppzkFppX2eTL2GKbkIqUT3LGGxywlqaPSNhln5wlOWf0sCSsvankcRcsyGiRkTHnnMg3D4VgFZPSpqFhyhzjX49oSkRUNN5BU/SqOrRQKBqai9KlpGLK3Dd0I59jOP5jPsx/xq/+7h/OX38Tvv8FuaB7fa+t0PieQSLCK0WJYA88Hj/1F3RTy27W6krmUczkccJAauMkE3XnyUTuFyv4q/8UkGb6YLBDUy2omwaHot8fcDY64+j4mEs727L4y5Jet+WpGhlRFNPpyFRhuSo5n00wTUPTOLZ2LhEGip2dbebTc8bjGWVRYRrHeDxjMpnxL3/1E0xnc0zdkOiQN9VdRqeHpGnIqy/dIAxDBv0+rjEkUcSd+/c5Ojrm0u4e+apkaWv+0o/+Pfg3vwYo4coxtAvZ5UGC4GDgBVRq75rlfSvbLcBnWC4QaHPgoeNpKBlCuyV9G0KRrcsLwZMkSGYxn8njh/oppT2J/Wj5+Y7nCgpKqZ8CvgU4cc69z9+2gfg+3EDEVL7LOXeuRM75vwY+DqyA73HO/Z6eWoLxX/r6v0L7XUwu88DXzI6QhB6dC7UhB8R+RvCsXnPAUzJ04HfYFh1KKnJy1h4HKS0qKlIyEi/xBlygHDOv4eB881NehftNrzwmYIuMIY6EOS1ibrHLHe4xYsQltlEoNthnQsATCmIiDpmzIKLEERGzwxBNgfXKCiA9CEvtg1hF5LGNhoqEhDkLVixIPPMyJfZBMPSmL0tEJl9EaksKH1yjCyEXjeYv8346DMho0SGjomTGlB9ZS73bEIySOt+KohStwGPva0n74wgWhYcaezlyHUsQGW7KRZqHcnuaCerQNUAju2O7L3W0P7a2dum0h7zzzucIdERd58wXS7qdHlVVYq2l025zeHDIsYaXbr1I1TTESUoYhVy7dpW337kHzmGMYWt7h6tXr9GUJefn59y/e5flfIZyhvt37+GsJc8LXOC4duUyOtAcPHrM+PxcqiMUeV6w0e+xs73D5nCDpjFkaUa71aWuLcZYvv+/+IfyBuZzuDKU89MsoZPKeYkiWXa19erMgUx2ciONxzAWUFOWScCoco9LaOSC7w+lVFgjQlttCSL9DpyNxJ+y5YlRqoHDh1J6PDPN+f2O551T/B3gj/2W234Q+AXn3EvAL/j/B9FsfMl//TlEyPX3PBoa5qz8iG9GRUmLFl16LD0wR3r+IXNWnjIsL18Qh5JQGz96VBfLXsqLyqfVKz8WXAccg6VDl7XtmvV4hZKSxqfoT63cJPDEiJPT2lpNehmOMxY8ZswdDjhjToXhIfexlDgadrjKJu/l18j5BEs+w5wJitKLwawVkVYs6dFDdBNKFqx8BuD8z9aPRQOGDGnRpqBkzpwFCwYMadPGYNAXWZbzuUaPCXMOOfK4DS5Grx3aWOCYY045Bix/na+SD+jH3xIb+kSJ6nBQC5c/QsZnIXKRttqQdL12YCTfuz25+CsriL0mhPFSGJA6hM2+TDa0FiDT+sIMNVs7G8Rxwiov6W1ssMhzkjTFGstsNqOuhfOwWhUcHR8DCqWUuEvXNf1+n0s72wRKEYYRDx885FOf/hRPnjxmb3eXj370I3zgA1+Ccw2TyZjpbMrLL17jmz/+dXzsm76Wb/j6r+K1197De199heFwgEMJZTpJaYylqCriOGM43CRJWkznz+gg/vyvwf/wfwFe+CRLPdQ4kMC4WMo41lmhije1nxQYEZw9PRPru+VCRrprQpQKJHjUnk0Z+qCyztz6XRh0ZYoUhpKRBFomFs95PFem4Jz750qpG7/l5m8HPuJ//rvAPwP+I3/7/+Skg/krSqnBb9Ft/G2HReTPxPPRXVjEVz5RT2kR4liQU1J5stJaw1h2ceEnPL1NNBr0Rfp/zpi1GqPsxMEF9qC+GC2KkJvFEmGJ/U6qfexce0+JknRDjaGXlq9aAAAgAElEQVSg4SELjzhcw4EWjIHP8Xk+wKsoQvp0+SBfzT/lZ3nAKTFdNtljygixe61QPiuJiOjSo0HcJhvEG7OiISViwQJNwAabCPy6IiMjJaNLB0tzQY5yGM/wNL7RmJEhEm+GtctkxTljChoyROROLGmeogoJaoHYJl5UtQmkE54msrONptAZCB+iQYJIUUraPD71prSJx/H72flsLuVEfyjZxkQW1Xf8ia9nMGxTNzVpq0XVNBhjMaZmazhgPptwdHSENYZur0sUxzSNJcsywjDEGMN0OmW5WNJpd+m024xOTskXC977nvdw4/o+WxsDBv0OZb5ga6PL40ePmE1nfN1Hvopup0VdlLzvlVssZptsbHTpd9vMp2P2Ll0iSTOOj8/QWtNYR6ATTk7OGI+n/NCf/xb+8//+Ga3i9Tk4PpOSIHcQ+z5MUUhTsVhKFtDyEwgXyLi3qSRIBL40G3ppvCT2JYHzFOxKgm7Pq0FHHvJs/ajXegLbcx5/mJ7CpfVCd84dKqXWkrn7wKNn/u6xv+13DQqyOxtEFUjS/YiIQ478bj5gxspfqgIhSsl807D2e7qk3NqXG+vU2GLIyck9l0GwCzJYXDcSZcGUfqphLyYPBueXiL4oHSoq1patAQEF8CZTHiFQoRqRVovoco+HVFhSJM/4MB9im5t8lnMiLIYzAuaeuRFifUNRgEcBK2ZU1MSEFDRMmdGnTYsWKTGi8gwtYkKfG004xWI8gEsEa2sqVqwYMMRgaRHTkINHX2gfSMWmT7KykpK/yb945kpJn+Ly2xuysxcr+Ur8nFwrgd7qRhqSWskOVhqPikyF/Vfm0E3kMU5GQo0Oe/C3fwmAnZ3LZFmP0XjM1WvXefLkMefjMWWxIggCOp0u55NztNZ0O11qY8hXK45OTrhyeQ+tNWdnZ2gdkmUpzhicsVzZ3+eFm9eJI8V8NiYKGqLQ8crtm1zZ36bKazb7XfLlnCYvaOqajX6PTpKggTjJODo5o9XqoD1is24q8rLm8cERRVHx0nsu8Ys/99/wtd/6F+S8OSXBc5kLHiEOpM4vcqn5nVeZms2kWRincp5aXqvSOASzbyCzIqgbewMcHcrtppEMwjmvu+CzkPFE7l/5wPKcx/8XjcbfCSXx2+aez/o+RNciUlo0LNlg6IlGlg22gIhTJmhfTzd+4T2LVDReG2HdS1hToh3SNHzKYzAXMObItzRl3BezNl1dlxZSmjRePl2gzSvfKpQ2ZU2EIiKjxyaWwE9IZITYJeSUMafMuMEu0PASV/g4H+eEUw444pAndKjQdNEE1EBKRklFSgtYoIlo0yPy2YxCJGWtb1amxCyYEeDIiJlwzooVfQ/7Cgnp0fXTCOdHmfK+LQ0pMT16lMCKmjE1joC/+2zz8Qf/iKS3QSIz8NATn6yCeiUXZagEwlt4kI6JoDWQILEzkP5CXsl8XTspRYIQcgtnM2g9hTU3tWPeLBiNzuj1Buzu7qIU3Hl7xGg8Zntzg8ViQBRFJEnCbDSiqEqWTxaUK1HCCrXGWMvobIQ1hmtXrxIQ8OlPfpL5bMTmsMv73vsyVy5fwtmaMHAMtrdJwoAmUBgEyzDWmhvX9knjPpubW5RlxYPHB8RhRF7kPD44BjRHR6dcvX6d69ev0m57mvKP/3uQ/Ib0FSZTCFawc1Xq/fOxZFi1zxbC8CkqcTGXjGt7KJDzh8IGFmJU7inRPghUlZQNiRbx1sLrUxSlBIZlLqPfZ5Cev9/xhwkKx+uyQCm1B5z42x8DV5/5uyvAwW+987O+D60PZy71dOWKhnMvKdZgWVD4rMBeAI6BiwW81kFw/r/gJzq+7g99D6H2O7yhIfJNucCnyEKaFgCS9Be0hwdrH4RqViwpLxSS5K+G9LjCLkJxkrHqfR54SLLjLvd4kzvss4tGlJK+l+9lyZKf4WeYM6FDzQ5dCk/yDlGMOGGbLTQyNVixwGEZePv4igZFzMLbyyksCZq1uvWQDRoa74RlGTGiofaaCzWa4kKXWt53wAPmPGTBv+Deb/6gvvpliMbSSMRBNpYTnIYyUkxTGMTQQhSFllPpO8QOzMITd0JJly0yJosisY3HiizZqoTF036CDiIePXnAbDGlqms6netsb21xfHgIztHKMna2dyiKnOlkQlUURFqjI8X5ZEaSZAShgM2ePDmkLCruvn0P5xzbWz1eeeUWL76wT6ediOhKU1IUBVlW004yIpfgOh10nLKYLwl0QhylJElCbQ1HRycEKubg4ITZdIlzjl63x62bV4mihA9/3ffJG3n0BmTGA7M2pKmYea8GmwnQqx35xd6T81AryLU0HhMHW11Y3peswPWlTDCBTC5qI9qMgW/aNkok3asalpX8rEKI2//qpw+/y/GzwHcDf8N//0fP3P7vK6V+GvhyYPp79RPkUN5OvbigLq9JQKmHABdebmXNTWhoLtgJawcHKQUa1tIpsQ8Mxqf+klE0xLR8Iy6gTYtT5p7cZHxpoX1QEZ2F0qMARLBE6NJtBtziOiGWL/AmPfoM2WBBn2NP6Boz5g53+RAfZIA4Be+wzZ/hT3PKMb/ML9OiZs4KhfEgqjaaxI8ZV0Qk/r3XrFjRpeuRmw2xx2WktDhnQonQyaVUCrwIrbRUI0RtsmbFHENMDxGVbzhjwuc44rOM2d69gsOJPBmWo8dvPd/V8O234cXLMLguNuuJV2o6n/mpgpNaN40kWwi9LVy3JfDnI+npfOu3fAO7u5Y4ighUQFnknI1OeeHmTXq9DsfHx/R7XbrdDs5Z0jSl3e7w6NEjut0eZVXT6XSJItFafOONt5jOFuiozeXLe9x++QUuX94mbbVZ5kvOz3Pu37vLarngS7/sQ1wZRsRRQJxlbO9f4fhkxPJcMpHzgzk6SRhP5kRxxpOjU0Zn52AaPvSh97K9PWQymT5zUkrJnDY2Id2QHbz2/gu9luzsjd/Ju32hjS9KwSM0npZuGtjpC6hscS6oz6IR8ZtAS/NysRT8Qpo95aFYvKGPhs0tCRzPeTzvSPIfIE3FLaXUY+CHkWDwvymlvg94CHyn//OfR8aR7yAjye/9/Z/BeWTBWt1QuuXrnX89T7A0rPkLNfUFPmGtzwhruxgpImpqQmpfcDydLoBkGmLMWpN77IEnmYJ/Zut3ZPGgDLyytPNaDBI+2t7ZacQ5PYZktNCEF72ON3mbz/AbfAWvkxDjUNzkFt/N93DAESfcY0xBTsEGbXbY4oxT5v45u3RYsSDHcs4CQ0CHFitKD25q+T5IQsGShAEdWhdTB4Mho8UuOyxZeKdKkXFN6GDQnPGEzzJma/My1sl5dkbOxM7eFayzRDokTRO63S6L+YqmsTjlQBl06Lj/9pvAm/DRV4USPAyh67vlYSBmp7XfxbZ35CJeruTCVxH8l0J02t3boakNm8NLRGHCydmpGPvWDdvb20zGI5bLJXEcsbGxSa/X5cmTA6xbf/ZgnWXn0g73790XPUUM6IayWXF6fkJtV9y5WzE6PeXwySHTyYJOK+Hq1Wtc3YiJIkUvSXGBxSlDaUtW5zPCoIfLa07PZuCmHBw+ocwLvvzLPsyHv+x15vMx5tlCuSokM8gLr5rk/ReaRnAbWSSU53zplbSRABlUUIVPRWL6Xt7dIo9lGlFoCkIZQzoD1gOdOi0wmTx/HEoDuN+RUuU5j+edPvxbv8uvvv53+FsH/IXnfgVIDyDx3AIB+8RY3AULMCWhQRQRBKIEeDyCAJ9Cap8vyG/EPEVdBBDpJkjwESRjSusCwrxGK8rYc+1OWXmxEtlhnUdaekQ8hooYTUHJJjtMmTJl7kOLGLkUlHyBt+jzS1xln5tIXatRfClfzvfxffwY/ykxHXZpA1O2GPoW5IwefVIiD8UO2eUy22wTEfCIh6RkXsKtIcP5waqYzWwyZEVBQY6h4ZSRP18W7bMxcaPS/GMeAFBVDYEW8IxpLEopjGlQQYC1juHWEALNIj/HWEdjDJtbW9y+fZtv/OYPcvD4IZ/7zO8BhFofH7smGUIawevbvjSRY7lakC8sTQODjR79foVyisePH6MDRb/fJ00zyrKiLErm8zlKKdI09e7RiiAImM2mnJ+P2RgOac7OWJUrjo4LFstzAgWr5ZKmrIjCmE6nTZbGTCYTqnqPKE6Ik4yqLDkbjRiNp8ynM6iXnJ6e8eDROwy3euxe3mXv0i6vfcn7aIxlc+sSvcEWb/7az3D7A98BP/lp+A9fFy6IAlaVBAKlvYGOx3foDtSBTGM6GyJF50QwlmkhGpRr5qhOxHNzXgCeKakQ2HidS/BpZYKYXJagW4je5f+/tnF/6CMg8Iy+6MInMicnQcxKI8mNsL6AsDiWPjQ8O4pcsx1B0I8ZmS8c1h0H58efYjwb+ixjPZp0rAlCkrcsWSJqRy0PbC4Y0mPAEOs7FD2Ep36Za0yYMmeCOGeHzFkwZsIbvMkn+TVaZGzQI0V0lv8YH6Oi5B/x99hkiCLhEQ+xKIZs0KJPSE2PNm2GWN+9CHw/Y8IZT1iyzY4fO5YM2SAkZMnyYpxqeGqck5PTI0UTebh2wZ/i/fwvfBbjZpRVjNISUKVzrggCgQsPNre4c+cOy3KJtTVpmvHy7Rf4wOtfwqc/9WlmiznDrV2cNfT6G7z3va9x7fo1yirn3r13ePjwHrPFhNHBw2c+/c9e/PTxb/0Qk9E9Xrn9GmVhsI3lhZsvsFqtWC4WdDotrDWcj87otNtMJlPCULOxMaBuxCSoKHJWqyVVVXP75Ze5q+8wPj8jdZowlLZzt9Pm6u4225sbDPo9+v0BxWrF5rDD5WvXSZOIjeEGb7xzj8ePTrj34IkgHk+mBIFjuNni1q1b9Id9Br0hb799H9PUvPqe24RpQ7tW/PTf+at87vOf5Ef/1s//wRbDD3xU9BbTVBZ3ngvxSQce2uyEiLa0ghOpnPRptDcDxoiu5X/3a7/9sf+T53sJ75KgoHwe4JgwuVjkCuV7CoLuK3013aZNgCguN4iGo7gztC5GkjJSFPISCEpx7Um5xp/L/WIipIkW+B1+nYkIhqGgQiMuTE/dLC+xhybGAksvjiYejutJiCYj8wCkJeec0/jxaOyh3CkJ3863s8eQf87PUjL1eMTYW8JpPzo0nHBETEqHzGdMjpSICkXOgpiYEfMLMNMa3r2miq/JXjL+rYgoGdLjIW+jafONXGe5eEDUrrF1iA5CICBwoOOULInI0ohQB1gj5iS9TpebN25QFgVHxwfMl3NqW5GlGTdv3eLGiy8RRwmzRc6jx2csVrC5eZNv+86P0R8MeHD3Har8mCRVLBbnOHPOpZ19wihga3OLt958k7PRETs7O373X5ClGQw3iT02YbGY8+TggMt7e6SZdP2Pjg65vLvHtav7TM5HtJKUdhKys7PN7t4Wu7ubbAx79Psd4SXVFc506fd7oCLiuIM1IYcPT/j1T36Bh4djHCGtJGRrp8f1m3v0NzZIkpS7dx/x1hfeBueYnK+4dPmInd1twgg2e3v8+I9+N2BQSuGc17i0Busc1loCHWCdQSuNaQw/8GM//a9kTf3ID30cjCK0AZPphNl8wrMB+Pc63hVBwWIpyKkxJN75SKC40iQL/M/rBqJIngcsmEm96MuKxLsupb6T4PxYUp5DGpHO4wtk8pCy5km4izxCPVPEyL+1eUrkg8CKkgPOfHYBuXe3bnwus8481qPShIRNNtlkw+sxSt9EyqaEr+SrucY2v8j/wa/yz6hYAQkxIjEv5Kfcv5eGBM0ZpyREJF5zsaSmQ8/3FvxYjpANuoy84pMi4Co3cSjOGbFgzg5DIGJIxreRUi/fJOtomjInUF7ypbbErqJZTCjn4wtDku3+Nt20R1HWOBNgrBRzN27c4sNf+kHSVot2u8148gSnSgYbHT78pa9z/dpNDg+P2dzao5Vc4c6dLzCdjgk1OKcYn52SL0RURCnF8fExt27doixLyrJkMNygrhv6ww1q02BzR1FW9Po9ut0uk8mE+WLOgwf3qauSSzvb0rcLNP12h+3hBoNBlyAwGFNh6oIwEF+RL7x1hzQMGXS6zMbnjE/PUE7T6g1pJY44iYmSjPH5nMnkgPt3HzIdzbCmxhjDg8eHXN7f49VXXiRWbXTgaEyFs2CNE85SJEpglalFKQ3ZGCPr+Mm/8n0EBNQW0CGNMReSalGosM4Qhr40ttCYGqUs1jXCJg0UWmtSHDoIiIKA9kaXyxtX+KIKChUVxxwRk7DPVVasmDAm8FgCSZtFC0ETkhFf9AwaGhLfX7A81VkMLsqIkDV1OmANaZKSYT3JEGDSWkZF+huZ917I/dRDug1y0Yv0vMCy10XJWoxF+VboGjqdknKJHTbZQLMWc1l3guW+mpirvMS38z1sc5lf5J+wYgKcE2LRpHR9NiQ6CSJvnxL5rGjNqGx5XGhKTUGfHl3fiFwiNaWh9hpONQUrBrTp02OJ5X28BLxJvqh49dY+2kE+X7BaFsyPHmEXE4LVjNhYdJQyaKXk8xmrokYZReBCkqzFyy+9yu6lPRpTslhMePLoHkHQcOnSLs5VHJ8+YTI9Z2NzmycPH2NMyO2XXyNQFfPZiDisyFc5m5tbXGq1mc/nPHz4kBs3bpAkCUVRCDFpMEAFitPTIxprKcuKqqrYGA5x1nB4eMj5eEzT1DSNY74oWeY5RV2xXOXo0CsmjUeUqxxjHIcnJ/TbKTf2L7HZ63Ftf4M7j0coSrL2AB1GvPnmfU5OT8lXJWVRoZyh1QppbIFSjl67S7ksfafK4ZwjCGRAboyjsaDDEOVAOUWgApq6InDqAtFjjcGahjhKMHUjC79siGKNqw1N41BOYZtGHM5tgDKaMIrQKvTXoiNWAdb5pvBzHu+KoLAmQkVExIhCwQLHkiUDhn4MKI1AESyVYNGmzYKFDwzJxdJcMxjX3WgRKVlPH/y0xtfXrYtsQYoWsYORBbjOLJ6iI/FIBxAJNEPgFzXgCVfzi+ddIyvbtLjEDskFdnCdw8ioTvKTjB5X+Qa+k6vc4Of5X3nIF9igR0mDRoRSu/RpKNhmG03Aipw5CzbYJvCNT8maUiZMGHPGmsxVUXOJy2Q4T6IyRCj/bhIKf97+jY99DR+4rTF5iSkKmqKiqmtWeUHWazEvC2plmJw94O0vNOSVI1+MibQjiUNOT094cO8xrazF0fER9+8+xgJbG1t0Om0aY2m3W/SHLe7eWfDCrZtsbg54fP8O8/mK6fk525ubPH70iKzV5tKlS5h2m1//zGe49eKLzOdzJpMpV67s0+v2OTh4TBSFLJYLxqdnXL2yT6g15+MxdV2xXC6xVnF2Nubo7Ji79y+xu3uJ5XLJyfEJ55M5GEscBfT6PdpXd8gSTTu1fOBLbnAyPaNwOY3p8vDhmOl8ynyxJNSaXqdNtxuxtzdgMEjZ3b7GCzeuMZ+e03jTV2cNWdZGKYdrDLUTiEFjpKwIlMIZkbILAaU1YSi8Hq0s1tXiddlAGorHZVE36CAhzTJQCmPFs6M2hsAEKOVFgrUiiqJnNCl+/+NdERTWqTvU5MxJaZGResUCwdau92BBLYpxu9TVIYYS5fkIXOzoAU8JUorStxelnaiogL6HTVsvmiqwpLXgeuDRBgYR1mp8Gu98BiMtTPndujSRVykhS/vsQgBRPVqsBd8kFKxfpww8ZYRpSOjwKl9BhyE/yz/gCXepaJgwZkibPslFupnRxaKZk/vXLzMT7Q1rGmpA06HDnDn4NmUKJHQRwTFNQ0SJ5b/ifwegO9wjU4rOEKK6JDAFpqrI85LlsqBwFXlT4XTI4vwQ11g6zkBimcyPuPdGQUzFcGOH2XRKv9VisVxiq4ZqVZG2uyyXFZPpjFavRXerT5glBGnKaDyhlWjmkwlpmjCbz1FBwObmJsONDc7HY7qdDp39y5yPxywXczY3hpRVIUCnXhcVBJyfj1ktFiRhSDoYYp2gHE9Oz3jnzgPuP3wkZlRac3n3Mrap6bVDrt98gX4rZNCJSXTJ/naH2zf3+b8/fZfJvCbPK6piSb+d8eHXX+XWzX2uXNlhe2eDOInIsk2Ujfn1T57T1I44jLCErIqaUIc4K8zdIA6FJKrks7ROjGUa5wh8KRNFEaEOUbFCK6HK50WDs5aiMoRhjXYBkdbeWEujtUbpkKKsiSKNtTVNWV2MmJ/neFcEhTUyeu0iLZMIfZH6554TELFGKIqxa8kKjXANlsxp0WdNenI+s0iJMRhSMgIEuyCFROhVigq/UIU1GHkEwhqzIGAqCQiypKT7LzW6LG7BBIqwSUzqd+qQhoaUlBe4QZ+eDwPyfpX/v/XUY8WUgoKMNpqIXV7hj/Nn+QV+jk/zyxiWHDOixTYBljlzzlmAf94DnvhzFLH0SMc2XSpKIKTGkdGlYEmEQry6+2h65DhKnhJm+50B/SSh4yyZLaGaY6uSumNYtksaU2OVQUchpYXKWWZlxen4nPECwliTnz2gmp3K5CIFVRrMfMxSWcanx8RpiyALaaqCPF+QZTGdboc4SUSAqak5O52S9YfMFwGL5YJBv0+gFKPRGcPhkI3hkLLM/eUTs1wsCLVGhx716hxNY0gigQRfu7LH7Vde4uT0lKPjI6w1bG8M+do/+jW0k4hOS/HSS++FZsnxw8/RLKfEUYfLW5u46i0mxTnWOTb7bb7yQ+/nY9/4R7h2dYssi4iilDjt0tvc597bj6hqgYFbExBEITiLc46mbqAxrMWabSMlaKCUwMaVwlqFCgKaxhEoyPMKHQQYY7HWSS/BBtS2IYkjrBVqtXMQJSmBVVRVjbUWZWqapiEIvsg0GgPf3LMEVFi/zKFFx2MQxcsx9fiFNQ+hpPCLXEoGy1qTUUJw6HsLa+ah8aDlyFf8FU8FWVJgAzFYLbGsuRQCg17X7WvRFY1DeBbrLOEp0Crw0wxFjWHAkNvcpk12MVJ89uNROMQxs2DJirs8Iseg0FTk7PJevoIhh7zNE75AzoSOB0flFGyyRZ8WI0455ZSMNkOGRCSeuQmFd9E0hAxZO19F1ITMKNAk/AR/H4Af+vN/jkudhM12l9g6gqbE1CHG1qJnWJUo44h0QKg1jTPU1jCbL9hqJ4TRFc6nU+Z5jQ0VRblils/pupLV0X3MvEuUZjRhTNEs6ASKoMgxywVJ4Lh5bZ/lZETgDMbGLJcLRucjet0uxWrJlStX2NvbZTabCaIxSTg9O6bf72HqhigMmUwmaB3R7w9YqBmrxRxrDJ1Oi2tX93ntg+9ntVzinKXXbrHR76GVpd+JuH5tj4iK0eNfJy+WaAX7lzZ44eoOk7cfs7N3iS97/QN89I9+Obdf2mdjmFLWBXHUIkraxFGM1hpTN16+TmGamiDUUhoAQaDRSgufqZa0XikFSrQfjK3RgfZ4EUdZ1mgttPAojDCNQXv1KWcMjTUETl8wScXgO8CqAGM0KE0Uxzzv8a4ICrKALDMvkKb94g99hb/WRTQeY2A8V9H5xdj4vX9t1CppvYwdywunJ+t3e+kONKyo6Ho9BcceKVfocMCKORbt9ZyVR1Gsh6T24hWvd/unUw3goo1ZUvMi7+GP86/zPl7lqYITz3wXAvM5Ez7BJ3jEAcfMOOQcWDtbOfqkDMi4wssUHGCZUTBmLQU/YMCcqccr1LRpk9Ei91lCwYoaWDFjk01KauZYchr+W34OgB/+i/8uQZ2wv9mm30nQTpqidRViwxithaQd2QptFKGT85sqR2QagrDFcOiwrmHY28ACi9WK1XJJdPkSs/mcIIqYLpYsVwuqVcPR6SGkPYrWBLu1RW/Q44VrV3krX7BcTNFxSD1dUBcl6eYmZVHy8OEDXn31Vd7//vfx8OEjzs5O6XV7VGVNXRuMXfHw/gMubW1RFQXL+RxTV4QB1GWb1WJOt9fBNA1RFDCdnGGLGa00QtsWWltMucJUK0JlSENHJ4t5/3uuUaqAr/qaj/D6a+/j0lafJA7Ji1zGikBV17ScoywKnLW+kSglilIeGGelWDbGosOQUEc0xhAEgZdYDAiCwAPHDMY06CDEmIZer0O71WY6mdIY2dCSOMNaQ6DAWUtdNzgrPYbGGIIoItSasv4ik2OTsV7jd9KGgMJX2yI2BiLXJkayT+cAa0KU8dW/wJrXZGfhMKxnA2snKHFZgjbtC3BPC8UVOkSsqJhS00YyDXtRQsh0Yz28XB/KdxYsMU8lvQ2WhIw/wXfxMb6JLm1gjZdcoy4NuVc4+jxv8ik+zwMeMyGnABpf1PRpMWXKESs2gMt0aJOSs6BkQYjmnDHnHrG4x55XeV4rPsjFMGZMSESOYwnMaPgf+ccA/JX/4E8R2inb3S3acYOyDc4FlLXCWV80OXDKkCQJgQuItDQ+rbUE1hJmXVSgqcqS2BqqMidL29jhFmGkWawWWKAzX7DKK1ZFwWJZcDJdUhvDqCxZTM/JWhnb29uMzkcsFgviOGZrc0i7lVFXJU1d8Ruf/zz7l/e4desm1ho2B0NOz07YGAyYzaf0u23KYsVyPhd3tSwmcAbXFJwePEFrzeHxEdPJiGI1Y2vY5Us/+Bqbww7OVjRVgalLWklCGoUQWm69sEfU3eA9L98kjQLKYkUeRqigptXOaEyDdhFVWTKbTkmThCavcM5hmgZbS3lqqopo7QNbNegwEoi2s1gjpUGgpCRclxw4RaBCVsuCxVy0KJUKsUpRGnBWoZyVkgMJKA6HtQZX1ejIc0+e83jXBAV7scDwO70k2jJK9J1Y3zSsPGth3Uxc4wWMV0ZIfXW9Pg1rmrX1j7XWgdTUBOT0UKQXybbIsIi+gvJCI85Tj57iGdZwYRkwVmgEaxkQoXD06PNRPkKPDmsa93reIDTuhoaKEee8zSMeccaIGSOmJHQuhpARyrMzFt7LuuQVLnkhlo4HR81ISIl9gFyxwOKYMUYTI96XXQ9wmjKhpMMWAH/5O76ZJnjI/lZGkheUCwOhjHkj5YQAACAASURBVMkUEabWhCrDERCGliQLCJOEMGgwTrgNKgxRYUjTgGkUURSjlSJUGbVpKOuSMG7hFLQJSTvQrQ29VUGWTcnLmlVZsZpMmIxG6CSi2+6BdRSrFYuywjWymxZlQZrBarVie2eHl196Ee0C4ihk2O9xdnaMDqBYzqnyDrYuxRe3LonTFqPJiEv7V7h+/Tr1/h6xdlzb22Jz0CMKFXVTEscBWZZCUZGmKXGvTTZoMylOeHjvDsXONnu7W7SyNlrB+eScNO0yHLbRgSaNE5z1xjjKoZW6KCMiLQI31nqAnSnF/yaSaZdxohqlnOAMlApQyhGGEUEARZ7LJEErqtpinUMFilBrnLEYK1ZxCicyjUAaKpovxkZjQMxTyxV10TRbKw5bD2OymAvU4JpjsLZ1c773oBGVx9DrDqzDRuRJ0yEhOSuGQNc3I0Nmvnew1iTK/YhUTpG5wDJYP6yEGEUERChasg+gqAlJaOHoEnhOxlorak33wgcWx4I5d3jAY44ZcezHpQkRlgEpDSsmnDFhhGPCIVOu0PHmuhCR+iAqdLKaApF3j9hm6OHMBkXAihU/yi9dnPU/84GXaaePOBg9ILs+oNsOWC3nqCQEZdFByGRcoF2HKEjQ2tHtRqgkonQKpTRx1iWME8K4hQpjrFFEWUaoYhobgG1IdEAYa4yCUKdYp4iiBB0E5IsheVlTVYZVVXE6mTBezJkXFavZnCBQdDttAh0QJzGBVnS6HYypqcqcfr9HYBRKOVpZTCcLMXXOfOKINlooW9GKY7Sy1E5hHhxhbcONmy+QZCmtJMRVK44OH7GcT7h28ypElqqxhC6kbBQ0FhNbwljTavfZu7zHoN8lyTTWldjCcPedO6AecfOF93J2ckpVVrKFOYUOQ7R25FVJ4yxJkoIOLjgldW0wjaGxjc8aJJAEvvcbqABrDUpJaWGtxRoZExPIeXHOEmhFoDShDimLHGcaQq2x1UqC1HMe75KgIMm6QtiPT41eq4tGYkiM8czINYS5oGANWMKn2w0NK3Is4nAmYiRiwSLYA0VJjcbRxdIjJ/OPMqXxbAfRhK79sHOt2SA2dJaQgBBHimGLkCts0iak8oKyJQ0xFY/4DJrXadMlJPPljGAySgqWzDngCW/wG5xw6CHNQi2tWXLIHWaMnimPJuwQYLzDRUqC9l8jTj17ROx3YxJfiAh3JCLinDO+5fYW7xzOuH3tMv16SZaGqMpQzpegGnpKduFOP8G4nFrX5IuSRa5wpsHMQ3SoME5S3bJuCEKfziI7UpxkJJ0+ee3QcUja7aCSiMrid+spymmUFkt6URBLoDb0A4hbMS1lmWnN0jSMRyOms5wojkjTCOssd+/eJc9zXn31VXa3domigNFZgUpCRtMJSdiQaktgGrSr2RwOWdWONAo4OHjC9uVrDDY2KYqKe3feYHp2zN7WgG6/z3x8hApTTG0oa4WtLFZbWt0uZVEzmy2Ik4iqgSi2tNodomjKG194i7oOmc9z0iSB2qKUxjpHFEVE3R7L+ZyyLGh1u4CDAKJIi2OctQSBIgxjFA4dgHOyEYGiqkuMaQiUkOpiJQs99tlrSIAxNWGgiFNNpEKwlrIsMX8AM5h3SVBQrOE8ayGVtYNReIE/jAm9dnPj6dD4+6zlVZzHCqyN5Neya8ZjBfATiICQNjEtHH0sPWLmrPUbQQaNUsyU5ITEF7etXSW6dNhC8wpddslIMEReV3HqF/Fb/Aod9v1rrAjoEfPUR8JiGTJgQIt7zDAUXop97AsGua1Hh5iUgpwdrlB6wFKXHm1vIJvRoaSgS5+cOefMvadkSJ8+AQE/xqf4k4OXiGaG81WBrUvCTKOyLkbH2HBJU0ESd6iaikaDaqUUec2kqrEN2DQURKO1om5UNOg4wLkGZwyBdbgyJ7AlLs8hi1jOA3QrwwaapD+kODlitSwxKhQ1sdoSRwn5qqImoHbgwpheHLK1tYuOYw6PjpnNZyitqKuKhw8eMJlMuH7tOkW34PDwgNVyQhQ0hIFlPhnhQkcSWLJWSrE852yyJMsiPvOZuzw+mbB/9SarfMGTh3fppBEvf/M38v/8y0+QaNEOr01AsSiJgoIkzmh1OqAsYZIShDG1KUgCfeEutbuzT9nE3F0+xFknjUYFOghw1hKFIf1ej/liznIxo9VqY4whL0p0FGMac7EO4lAauc73CvCZZZLExGGIaxoSJQWxqSsBPAWaWEOoHFkSY61hMluBCgjj9LlX47skKKz1FCTlX6MOvfMFQvVVrAVX1+b1a7fodaW+5kbAGmzUoNAe6BRcQIrwzcWIOQMyEqDwI1EZPq69IhQRjgCBrHZJ0B5WvY3iJl2uo+lSkSL6zqK33HgLlgdUTKjpkWNp02Itr76mLqdkbLFBiGIt0IrPkjSODh0/ZbD0aHGVK+yww9wjFR/zgAEbdOl7eTbRfFgyxXocZ4uMlT+jW9dv8PqlG1zeGHAp0ygHy6Ih1jX9rMTlDdZqTFARBpZUpbQvh2wtLWVRkKUhCRZXFtRVhVouCZSjqkrMWjFYa2wWE4RLXBRSNTUejc+qCjBk6DjBNvaiw17jaIwlCEOUdTRljQol+wiUIkoTVB5R1BUurxiNJuxWhtPTM+azOXfuvIEyJdvDNhu9hCSJKFdT0NDUBUEYoIKM3b1dbi3gzqMT3njjDZxtMHXNi9evEnnY894L13l0NsE0Cq1DilVOaQ1pz+FcxMHRMYuyZGuzI3bFFlpRm6zb4407RxR5jWkMCQqtfHs6kHy2sY5WknF+PsaFEVmcQCQEqcZYVKBRxqG0QdkG6krOCZZIKUHQNKVIxNeWqq5pvPVdGnXQoQSJorHU1mJUSKSjC+zG8xzvkqAA60hofDmwdnRKSC8ajHJa6wv0H+AHgvqZ+wgvcP0Fa2+ctQJ0iKgnL8lQZKyZkGtmQ0CDI0KRISVIiCFDsUNM35u7twjYxdKmouOdo60vHjQNYJhzxJwjBmySeMPcyiMqhIjdsKRig022ucTEa1GKfgK8zE0CDI94REDIQNp0DNiiQ8yIU0IUFUsgw5CzoKJLl5AeLTTOMzdOPfehM9zkUpzSTzPaWkA1WbtBBTIWtrpEq0BcmIyFQBNHMb3diEApdKAI3bp5ZjFNTVWW1I3vyCgFgcI6S9PUKB1SN4bGgQoCnFN0LzviMKZuDKu8YDIZ05iGZb5Emm+O+XwJTcMsrxgdn3F4fCxuazog1DVl2TCdLPjkr36atBVRlys6WUi0M6TT7bOYjWlUiNYBKEcYh1gilA65fftlepu7nI4mlPmKVhJx4/oex0dP+JZv+zjdLOOzn/gUi/EZaaKpbUmUxjA+Q6fbJN09Dg5OaJoFw66AyaqyYDHNwTS0koCVKwhqUVT+f6l701jb0vSu7/dOa9rjGe5cc5WrepaDjW2MSBCWEkfBgBSiIDkGhSjggEQkFCXpmChOFECEIYOiSIQospCIkPjChxCUuNXgAbvB3cZNN+6xqm9V3anuGfe0hnfMh3ftc2512nSZmKR7XalunXPPHs9ez3qe//MfhFLoqkJIw7QoiVEyvX3E5cU5s3nJrK4YBktq6hzinVTObEiRqECFgRg8UgjqSY0Ljn6wiKTRMgPzZaXxwRKCwLuIFCBNgVaSEDzB/haOD79BEMxfBH4MsMCbwL+bUrocbeC/BHxlvPlnUko/+e0eYx/9tv8q4wkePf7xo16ywBBH9cH7glHZ8wmyM/O+m8iaiFwMst9AzlIAx20KauZYwujqbBAEpigqFDVwgGJGyZSCBQU1sKCAMUOyoh9FSQMgRiP1a/+GSMcZj3mRD6NR7EPn/BhIf8Y5T3iMQfIGr/IOD7A4DC9RorjglHf5BlkGncVPb/AhJJGWjp6eGTNKSjracRPiSOP7I6gJwHt0/BU+zX/wE/86zx9VNEWJStD3PUpkIEtKhQ9gXUKIgAiCmARSj7mdLs+qWurM1R+7uxQVUVZEUyKkRCpJkgJvHV4EVFFSaI1MYgTKNGkE5qMP1EewfPU1BudYbzfUdYPzgd2uoygKPvdP/jGPn54ihcRUBVUzod3tMg+i6/na19/kYJHl5PFwwdnZhtOTEx4+eBs7dBgtee7OEa+/fIf58hibKvpd5LVXX+EjH6npuh1aRAoZOZpVrC431EXJ5XrDk8ePmc1KdCVZNAuEdQz9CtsZ1rsWYWteuVsjo6IqBRe7NSdP3mVRGqp0SSECRiV2XYtmQkLSDgErEqYuuTh/zNFBoq4rmkLQdltCkpiiISSDcyXtELIZS0ygZY5vEIayKklJ4GNAiFy8pdZ4l52bXUgwWKJS6JEn8UGPD9Ip/AzwPwJ//Znv/SzwyZSSF0L8BeCT5MwHgDdTSt/7gZ8BXHUBWTG4Xx9mq/VhPJklcrRgu851yC8gy5wVevRO2DfbjGvL3EX0dNhR8WjG1j0nNLfsjVsW1AwILIkFgjso5hQ0aCZkW9mCAUUYFRKBvWVsXmaCI+EyTERPy7/Dn+YL/EMEWR5eUxKBNRvOOMPSY2mZUvEC97jgkl/nn3LOGXmbkSnaBQ3/Gr+HH+QH+SyfYks7WsFnryiDHrunwAWnI7w44yEr/hq/yB//8R+lMgXLSYMIgeQ8Mfoc1EQi9B0xCYgRrQxiZMSlKBi8QwlJ1JqoIkoZXIoQ845cSUOSmTAjpSa4SOcgYiiSQSaDSwmjCsaNGd55usFjphVDH2l7TzcIRFkgC0NdLAjOspwfZD6J1FRFhe0HurYnJOgGT1UWSKEpC8N21/PVt97OMfKioJrUGC2ZLm4iZImSBYfzY7bDisvzM+49/zyTowWzScm8qXC7HUIYTk8vePT4KReXW2wMNNRMRElpCsI2IsRArRU3Dw4QIWEHS1ElbrxwGyEjTUqsnjymTBHhPSYYYgw47+l7S5CK905OuDg7pVBwsJjS7lpmsznKlJiUkGpKIWuiqYjOo4siayIyJXL0YYjIpGCfnG0MIVqEknjriCHrKCIZJP6gx7ctCt8qCCal9H898+VngD/4gR/xN3qcccm4t0Z7tjjs/RQz5pDGHsCPLyCf3vuY+b1ZC8A+ENZinzFYiaMEKdKyNz/NgE0FTJEkJLeouDNSo+pxvZi7mUCBwiDHq36GPbPbtBlP4zyC/PfktLyP84O8xZtARkM0kh07dmxxWDTQsyPiaCh5jZd5i8RmxAVmTHieV/g9/O4x6aoly70zezEnX1VY4sjhsMy5yRkdf41f5A//gR9lMZ0wqQwmRYL32D5DsSnmNCalDSImTFnknXoEqTQ+5JUZRlKZAu88vcvFVUsFiZE9l4gpgQ/0NrsIC6Wz61j0hAQ+eEgiM/fi6MnZ53na7QbqosLtHKZSxJAY+hbfD4gYMSkxbDZ01hJ9vEKR2m6gbSom8zmTpsJoyWxSUheKFC1VoWjqkq6zXK7eRZdrpge3OD5cEIOn73qUsEwqTTf03L57l8vzc1YXLY8fXbJaWya7AMXAbKIgaopx59/uekTSNE1N1cwpJwfcvPU825MLrDW8/aWvoESiWB7hQiL4wGJZIYRmecMjXvFMqoKh3fDwwVd5+uQJu7ZjMp1RNDOObt6mqRqMVrkQSEUcxzMQiCQRKfuWNs2UECNag7Uuj1paE31ASUGK/99uH/4oOVNyf7wshPjHwBr4MymlX/hWN3o290G8IMeViro6ofeMReAZtmLmGF4HwCT2wS3XK8d8X3Z0H8qW7vtAl+tqmVeOAosh0Y8W6Z4ZsKc7ZYFThj6LK4akoRnpUS0t+3i5fMWW6JFjsP/Y7o8c0VZiKGhZYTDUNMxosExYY+ho2bEl4VkyxRDZsGJKzcd4ndscseURYr9+Yq+1jBgkPdnNecqCFZb/jp/jD/++f5VKTbLPglTYtsvMuVHSq3SRCTJSjeswRUrZozHETMfd29N57/HB470Fkd3GxYjqIMb8zpSp2UIIYgi4kDEUpMILm3GFkIVKSozGLEIxqyqkzpTf5DLrVAZLGjqmRoJR9NZRGEVPwqc8aDrneXp2iTSG41s3mE8a5tOCWaVJoSXYHZNKs7nccLna0fkVr5RziqSYL+ZYF1iv19y+eczLr77MfDqnXW9ZrwcuziOXF1vUqWW9M9x7+RZlWTGb1symE5IwCFkhqWg3Hh8ty+Njnl4+Znpwh2Se0LUWhCAJiSgNUSq8jXTtjqaeYIMELXjx5Y8xdB0nZ2ecn1/y5Te/wmR2n5deeJE7t25jypKiqIhSEH1ESp2BWZl5stvLyytMJ47aFCVlfuz97+gDHv+vioIQ4qfIIWF/Y/zWY+CFlNKZEOL7gL8thPhoSmn9zbd9NvdBf79JewrwnqOwHxH8CAvujzBuHfYCpDAScyyWnK1QXXUGmdy5Lx7qikaUreANT+lYsqQEBD2KAT0WlEsSPXCbJQeUDCRKsoekGouTYILAIQlj+561mTkLUvBsUFZOjoRizG6KJGpKDlhgyC3qr/MlLjjjjBM6dkypucGcBQu+j48yQfOQR0gcmkhJgSDQjC5NMyZYPD2JP8/PAXBzesCknFLXCq083balqmq0kgzBIdN+vvdok9dYIUS0GuVkKnPyjdZ454jek6JHSUFZFqQY8T77BFZFiYsxjyEji29wHiXBuQEXPFLpfKWXgmo6xcWIDwNKFQgVSCnzUnrb0beX0G85MIrJpCQ0eZ9vnceF7KHVukgv4PLkjMdNxXC4IPkZy3s3uLhccfbeA567c4ujoxuoasquj2hjWK1XmLLguRfuUVY3KJsJKMXDhw9ZnV1y4+CQw3LB/bcfctkOrN7bsG07Vn3H66+/wsc++mFUVdP1ibrRXF5s+co/+iI3bt3FblsWps40ZxcJiaxorDS60CA9fXCUIkvW67pgMW1yfsSNG7yaJB9p15yfn7DdrPnyl7/EYC1SSm7ducPNWzep64ZCKpzriTFkRaSUuJD3Z81kipSSEAKD9Vx7kH3745+7KAgh/ggZgPyR0cGZlNIA2bsspfQ5IcSbwOvwz8o639Oc41V3IL+pqu2TnPZW7s8asQ6jNiJHvOWeYK+b3HscefzIZtwbwU5Y4UYewI4PsaAhLwIZ+Q8RRYfmMRtWrLjJhLscUI9kIWB0Shaj1FuwZUfPwGyMgn/28ONgEegJo7rTIDlkyUFebOFwvMd7VJQcMmdOQ2BgyZSWc054m3f4GpEegR+NYlo0cix7aqRK59CaP/8nf4J7RxUiZo6FEhKnSmLKJ/tkWiNlVurZISdGa3Ut6iYltDKklHAui6uNEUSbUFrhnKUsKpQCIRQhZglwiILksmWbIieZyeQplcAYRecd0Qe8lXihiDFh2y0wEnaIRNfjthfooeWgVESjkUIjtQShcT4yuMTOei4Gy7r1XD56wnsPHvHSy8+xnM9oZsc4H3h6sSaIgrqecnA0JSKYzRdEIXn0+D0ePHoHrSTP3b5NGjqev3Gb4+WMNz7xCsu6ZLXrqBdzdFPyS5/9POePTwjf8zJdt8PGiAtQ1hNeeflVDg8P+Mo/+QLvvPOYUiiKwpCkYdN1xBRZbdY0k4ZmWuHCwG63Y6dhOpmgdUE1abCDYzJf0ExnRO/pt1vevv8N3vz613nw7iOmswlHR4ccLpcczBccLBdoo0FIgpSZ5agFIUWUJBu8in/BsXFCiB8lA4v/Skqpfeb7N4DzlFIQQrxCTp5+69vf4zWesN9EXMe35e9fE5qeeR7sDdmvrd1bWnp27OPhEnudY95CDCPysI+3X9HzlMirNBwypSGyJbIisrtCDTySHDjbcAtBVkGes2PDMHpGClq2tLRssdzh7vte4ffyA/wCn8NQ8EN8/Fu+Cz8E/CTw4/xeEp4TnnDIjC0XPOItFJcMbJFklqZB0xKuRGCGCYET/he+yJ/9Y3+IUgqMdEgVcTZALNCmAgEpJYSUhBhIgDa5sGmV+yvnLEIZYrD5/Y0JbRQpZZ9AIUu0LjIorjMIJhIgJXGwhJAyxZZEEgJTGqRSeO9p6nL0BUhgc4GI3lOVBlLAu55KZRZgqg27ANELJnWFUhIfwbpIR+RoOuU4BJ6cK7oQaUPk/lsP6XrHRz72OvPlPc5WHUU70A4epSymHFgeHnP3+Cbz5Zzl4QGXlxckIfih3/G9yL7n09sLvvBrn+FwseDmzSOWR0dcnGz4yO0jzOGEezcOs/Grzn4J06Kmns4IMbE8usHTd08JMaGSJurIqresz1a0bYcicu/2zYw36IrZrBlFTgopCnzIgrQUQCTDbHrAxz+25OWXXuXx48ecX5yyWV2wjp7dxTnf+GrPcrFktpizPDigaCaEHpIUmTQlr6X9H+T4ICvJbxUE80mgBH5WCAHXq8d/GfivhBB5UQ8/mVI6/yBPxFxpDPZuh3uSUl59Zev29EwRSFdF5HpFmYNms85BX40a6X0TfmYpQqQmcYOa17nBS9RIes7YEmnZjXyIDZFi7EwMWypqegyPueBLPCbbvuRTNIyAY0lH4vLqEX/+7/4Ms+NX+F2//fsA+NlP/QPazQXd5px+t6Fdrem6DiEFf/q/+C/5G/zv/BF+jE/w0VGlcYbknJ6eObDD8yf5NAB/jh/mP+Tvve+9/Mt/4t9moksKObIuYqQ0mhgFQ8yiGSEF0XpCzHOp0jor9WKiLEus6wjeocYTXhdk2W+KFEVNGHEHgcTHvAbV2iASmKJASU2hC4QUoPJmIqWUfQISCCExymCHTB4KIZCCz6IeLXG2RyWHEZZSBEIKRNuDzPToxWyOpGWwLbOqJkxqdi5gBktwmpOHT/myUPz23/H9qGIOWnK5XtOUBW234cbNOwghcd5jipIQIydnpwzue5hqyaSpuTi/gK7FFIrNySNiC0stKOuKWmsmTe60tNH0Q8vb7zzkrbcf8MKdF5FVw7B1+N4iNOxay+n5BbPFnP7ynKHtuHXjGO8GFEVmi8bMd4hhjEZSBd45fAwYrZgvjpjNl9i+o2tXtLsVpycn9F3L+fkZF+fnvP3WW8QE9aTh+PiIpippphPK+oOHwYix8///9dDfb9L8s0dXo0M+jRlxATEuHCUDHS3bqxFiP1I8Gz+fqUx6BCXjqJ1IV/RpiyXbwWvuUvISBXcRLIgsaKhYMuMOioYVlhWR97jkkvcInFOO6owHdNxnoOCYDkfHgMdRIynIJmcvMOVV3uCv8zn+5//hz9JFRWsDwTl8vyPZHcla7OA4Pl5y5+5h1r4PA1qDNAM+nXJ++S6d2xKF55P/6a8A8Bf+8g9TqoaZXPCVz36ZKlbcmt6lUBXD4FHKIJTB+oAuDNZaXEz4VIGQOB/QRUFRVChtMgZA5n+K0RnYh0Dfd1degiDQSmO0oa5r3mcXIxXaFAzDkIuMNHkTlCI+2CziSRmvKIsKIRRKyTyuCIES2WUoup7kBmy/ZX32Hv35Y5LzOOuQ2tDM51yMFm3DrstuRFrjSfS9A6W52OzYWosoSnbWcrlt8SGgpGRSl9x78Xl+77/5+zm6dUzXbRi6La5taXcbnnvhDt/z0qv80qd+kV/61C+gfKQoBc2kZFY0iKrGlhVqOeXlD73Miy/dhRh49PAR0SdMWXHnzvMsp0sev/sej999j/XlFq2KTAt3ntIovB0y/TlFtJRoPQqbgs/uTErjgxil1w4ps3uU1hpSQsqIiC5nc4yKSmLKQdYp0/GHoWO7OafvWpwd+DN/51c+l1L6/m97Pv4LOct/k8e+GOzb/EwUzqLpDD7u/11diZP2AOKeAQl7XALi6COwX1fuyU/7YmJHqfMJFo3Gofg4z3OH17nBCyy4S8kcR6RFcMGaE97lCV/mkods6ZgjmaGA2aiybGkZ6OnpR9n1N9jhyMEnf+xP/RR/4o//W2x2LVoLmkKzaGrmkxm3jm9SNwpnz+l8i7UDs1m2ILfDihhyKy+k5s/9xd9JYQyxHxAC1hdnTJxhUU7BBqLyEEcSeMw02CQiy6MlZVXz5Mka70d/CTuw7QdiEgipqaqKqtRopZASREhUZc7dkEIQQ8TagWgHeu8QQuFjIAYQSlNPJrksC4lKCS3yh9gSSSoSk2QXBnZti9GGFAOL6XRE5yPWWaIfIFhiyJFppVSENFqhp0QaBhZ1zW7XMjWGoCJ9DMgYkTIRo+OoMtw9WiKKktV2x4mAzmYFYlGV3Ll1g6PDOd61RNcifUeRHMJINqtz3n1X89qH3uDJg1PefuttrAwgDUYXpCDZdpa2P+Heay/SdR2VEUxqAxEODpdo5Wj7S26/cIO7L9zj85/7AuvzHaSEkdlhU+oS5yzRO2Rl8D7kDEsjid7jfECoAiUlMUKIkbrKhirWOnQmambimcp4TgyBKHMhkUohasWkrlgoiYgR/s6vfKDz8TuiKMD1YHCddBjzWgo12qepK3mUf98wcJ0h+M339yxT8lncATKxCRKPyVHwNS9wm09wyF00c/ZS7nwfMyoqJmhWHLJmg+E9HCveZcBQcoMb9HhOOaUf0Q9PR8UEOAHgf/qrf+s3fP1/6af/KIM9wfkV8+mUmZRcblacnT2mmRWkBC5GhJLM5geEaPHnPe7SctwcIr1GCUNwkRQTLgaSGDkBJhGsZ+c2SG8RzjM1JRGJjykDVEng+x3bPtLUJWVRICQ4l6+wwTtEitRaURcaLSSDy3G+6Lwe832HkCq7/0iJ1IZoFCJmGnVMCZEi3ma1nx8si0mTTUFizPt4oWmHFjv0pOBxbqDUisoUtF1HiiGTdsh8C5FgUuYYd688hTEM3oNMdN2WSfA0hwtWu4GdtfQxUlWGJCIPHr6DwnFzPmW92vD4yROef+0lTs9O+Ngb38tzL77IO+8+zp8CZbjYWtDQSohG4Dz0/cDq9AJveyZ1zcN33qSzjoPjW8yXN3jjjU/w8d/2CT77Dz5Hd7mjEgZkJhoJKQly5CMp8H5AibxpAc2m7bGdpTAFPkWSz+ZDRglEzBdAozIlve8HYkxIoAnAUAAAIABJREFUJZG6wLqekBLSFLjBj2awH+z4jigK+xN4z1zM7gl+xNSzv4IfDVQy/Shbsl/ffl8krsVR+/vbi4/2hxgXlZl9KNkQeIJlQ8Wa6spDwYzg4ZQaMWY8LMmBK1POmHCDJZdEvsojToEGQ8kBFS3Z6uQmh/w89/mv+fFRvJ1zKBYcMueImgUVU343P8F/9NP/K//5f/IjrNfvEdIOnzqMKbhYXxKYMLSWzjlkWbBsDmhUw+WuZVpMMU4DCpEykq8FaK1yfLkQKKMIQwYMl5MSRMUorcnryARS6KsTV2kxtquKELJ5R4zjiBYCRiZKIymkwgaR+QxSjmHHGZcw0hD8ACh8yL87a7P+fzadYIoKIwUEj/cRpQQ2WFQKKJlXpImEi9knwHtPEFA3JUlJFgcLFJLNakVVFhSFyYVk7I6Kqqa3nq636LphYz1PLi45bXdY12ODZdPvENHy3K0jVKFpljNu3r3Nk4fvISUk7yiFQqpMpvJCYUNAqJLCFDx5/JRSefywZn1xQowBYuDG7ds41zO4lm7YcPe5Wzz3/G3ub7+BCKClxDpHDJ6hG1C1QQkoCoNWOSymKkpksGxsoDaCqBTO+9wxGY1IkuBzkQ0ui+ekkkiRL6il0WOnrelCuvKC/CDHd0RR2B/72HhJGpd8eTWWxU3XGgk5Enf2u4WKkoKSHVsYr9H7n91zH/Y+jXtAMnMVcubBhsS7rPkIUJEYcBQonuO5kSPRUzKloaLncixdNRNmzGi4zyPu85innI8gp0fi+QP8CD/DHV4ksWCKztAYipoZS+bcQnBtqBlFIInIanVBSuRode+4WK2Y6RpNNvnQqqTfWHCJ0Gf/iaZs8DaOGvzxDlNESUWwFqkFUgqGvkVKmY07UiKFTDRiVNEJkSDpDBZqjRdZUiW0JMSIG+mIKUWESPnDJwQ+MqocDYhMnNE6Q7S7XeY3NHUJKrfKIuVCE0NASPIePmWadhZSZc+GYtrQrTd465BKYVPmUKjSEK2jHVradsu0qRFkEVbfd8TgMEVFMa04vbxkiJFZYxDNktIURJ+YNDMODjJ7sJ4NzKShKEsOD5bYrmXYrjloauqqwkdH7z1rF2hjYth13H/zPsfzihvHhzRNQ9tuefLwAZcXG3Q5YbZIdJsNR9MjZpMapQSVKSjKgtg61u2WxWyCkiBFxA6W6BKut/RigwiBaaHptmtm8zmV1ngf8H4AITFG5hWwTyStSIIsfnIh/x5RGFMyJInz32VuzrDHAzL9eJ+klKW/44fyqmdQ7+MAKBQz5uyNUbPgKb2vQ9gzH5+9Xf5elhkrCnqy0uAJp7R0vMaHAHEV1DrhAEvPlhZBwxRNwBAZmPAyr/IyWxztFXEq8tv4MDUFPRs8HoWhpBq5myUJzQ/wb1w9p7LQ6OWSaC1aKU5PnqIETCc1OhnAIcsMqlVFiSgryiAoZJnNO4kgIylJhMgnuBSRQu0tZwUbL9EyYozMZKQYUEIQgsM7m629jGG3Dcxm8/HqD0JqYhIUzSSDW0KiRLYL8yGAD5hCZtCSvKnY9TuGoceUBdN6ktvlAEoqpFA47/DBIVPm8W82K0R0yBhI0dJ2HY2RqKJEopA65yHEmNhstlTGoJShakq6vqXvthRGogpJNalYb3aEJDClZiIF6ILd2SWrp6ecPHzKe6dPWUwWDBa2rSdagRGGShmePnqA3a05mJZIIlJpei9y19A5druebtXxzv1HNHV2cTq+dYiziaHrcX3k8uSCmwe3GLZbzk9PMFpmn8nRa3I5ayiNojAGazsQCq2yx6IIgehDBiCLkmGwCJFt64UQKKMJKTt8oCXB5a1SXhzk9ygmjzYpC1fldWf97Y7vmKJwvT3Ym5XlEeI6WwH8uE3Y26qLkTi09z/cjw57SPKblZTPBtcmoKYhkSgp2WEZCDzmAZHI9/AGF1ywZc1NjskqyMgAZEuThojGsiUHwmoOKWiYM2WCZYceqdGebLMOE8rRySEQ+UF+7Oq5/ZX/5ieYNRdsLi45XB6QQmLe1FRTg9TQ9xEVImo2oQ+O3UXLAdnmTYWEkoIhDcTgQEhIEu8DMQYKo3OQagJf3qaqa4SArhvww4AaJdHRe/q+ZXlwSF03bNuOejIlCcmuHxDSICqTRU+IkdTkCSGfNMYYklTEGNnsVhAjzazBGINzGUMoTLZjg8ySjDHzSaQSaK3p2zYTn6IDkdhtd1S6QGiJ1prgIykFtJKcn57jBstgHRBZHBywXM6oasPFxQrTVITBk8bi2FQFb7z0Ml/8xgP+/t/9FKe7LdvVjo98+HW6zQ5pHd3Fji994Qu052vCeqDRVZZeK0Ne6mU3zrYbeHp+yYN3NB//+EfpBws6kig5Pj7IZq1DwG56vvjg8zx+911sm41qCqGYT/P7IgEjJT7ly1MMeTUZY0LLkr3TSNaqZb+JlLJTiEMQQs7bFDrTpyFbt8WRqRpsi4sBvtuKQpY4OXLO8TUpCa4l0ddMg312wrVpa0s7kpv0GIeWf3afSr0PapHjSJLJUOZqvOgZ2NGNbkU9RywBOOOchKenp6Ih4NmxY8GEORUbPBMOKEkM46AyYTLSnHOvoykwVJxyyoDjCDMG4V7jHD/1H/9BZpMd9+4umZQBfKTvLC54mqbGJ4cRkjrB/Xff5bBZotrI2/cvWISCmWnQStL7npACxpQURYFI5JToGCiKEoGgrEps8JSmZDo7QM6WkCKFzq1o13eUZYVQCq8cQ5R0gyUiaeqa3ie8t/gx5kwpidYZ/R52bZ5dhWC5XFJojXd2tFMvs0txBCEF1lqGvqNqyiuEXRtDXU9wRFzvMKqgj12WXJs8nmilWa9WmWo9biRiiDSTGqkkp+fnTKYNUUqqpkYqy3a3JQTPzgeKKvDK7Zu0EW4fHmJPzvnlB38PGSMmBc4evIlIEZ0ERkmqqsgYi5K0mx2FMFQpcFwZ0sGC1ekZ//DTP8edF+5hY2R9fsprL72ETJHnXnmZz/79n+fJ46csFkfM5kdUk4a6KJCkzL1AEK0ljQ7OEUhCkmRuyDyJOOoXXEqcvndKWZXMDw7YDdmAZdJMaLcdg8+28iGEjCXpcQTXihS/y8aH3CVY9so3ngEWGa/3lpyFuBcD5ch5h8eyT57OjgiZzJTzoiuyn1LuHIbRpG1v+5ZGQBAS7/KAL/I1CuQYqZYlUKesuM0dCgo2XJJwHHIXBZQsSGRJd0m2f8mmcZqCGXHsdAwFUyZ0bOnp0JTsjd8Azs6eMvSByWzJdGKIPu+u3/zK13h09oTjG0fU5QwB3K5mpCHiOkc1XXD34DZmCIgQMLZGGJ0prUJmlVwSxAStH1H70EKSRJsoihohJIMd2HZ53EpJ07cBqdJ4P4Kymo5YATn7UEaS9bngeMdmu8IOAyIJZtMpZVXh+yGH8sSQlZRijOzzHmUSZW0wBfTtQFAyOzdZh475vtNgib2FBLu+Q+tEURS0bUepFE1TE2xLitmz0HY7Oq8ZQqQdHKYwNHWFkomD2ZR6MkGbgq53DC4yE4py3SGkZlCCICXNpEYNmcxljCHFhPUePHgfcM5iZGCiBGXhWR5O4GhK5z2bb7yDi4Gh63jrfE1TKrb370OIHFcTmrKiNBlTEUWJdQMxBLwPmRwmNTFGvA9oqShVdh2TUjJYm4vcZMZROeF8tWLdBYp6iq4KmuUhTlyi64bFbMbF5SWr7ZbBe5TNCJj+busUgJG1OPrSEa8wgQwIhqvNxJUqj2uX55w+7RgYyBPVs6wGTUODw9GPeRKwD6FNowQ7cc45v8qvcodbzFmgRzXkli37dKgByyHHTJgScJRMRh1mN5qmxqutx16ZKcfXNWEywpqaCPT0V6+961uWhxNiCnl2JKf93L17j8v1Gd55og6jY7ig3fXYPnL+6JJ7h88hjUbKBLpmCCmLjlLCu9xGCqGyr6AQVLWkH3JgaVkWDNYhzRh9LgR2CIBAS4kxmqpqcN5f0aGFyMlQ0Si8HwjBUZYaY7IPoVCwa7ckAaYsiS5kjwZtsgiqqsbwlICUghAdbnBjbFrA255CCRAQoseITH7SUoMfMEJQmZJZVSMqQ9uuszO0dfRjqEo9ybZkk0lD8i6DbyEy2JYkMkLvg6WpDHXd4ENi17ZMmirTloXEOUeSZMxFq7wREYKiMEyaCd57nPMIKamGTAwLMRKns0xGknkLJLVgMptjygptDOi8Xs7K1ERRapx1DDYXQD0SycK46RExgdKElNi0PUka6ukCoTK9+uadO8wPluyGjovTU6aLKc+/+iInn/81uqHDBEGlC7T+LiwK++j2fYzbtYR6b4e+Hxkyg/pZvGB/IkYiEj0apu33FtcuTXp8ufFKVBXY4xeJxEMe0rJlxpQzzpAcs2QJI8BZ03DIEWo0NympsQwkBrK7cnFVCvIGP4yqyNwaZpixISDeXxRcRz09ypuH7TrLwGXNwfKAuilwdqDdbjFFScxCeXzy3H/4lIPJu7x69wXqoshyZKXx4wdZimyOUhQFWmucc5QmA7Xd0BKCw4WIUAqh8zteVNm0xBiDUjpbio/rjBTBR09rd8ToqKqSsqopjM62bDZncUSXZ2JcD2NSknOeoihwfiD58XdmPVrrzGT0DpIjJUelJcrIkR6daJqc8A2CFCF6y27jkDIHuCaRr6b1bMrhbI4njzW5e0nUzYTBBZRRSKk4OzvHFAXz2YyqqnHeU5WKwfcUqmQ2m7JerzOI6vNGRBtJoSuEVAzeYcqSqq6wg6OSijKMn1WZxzXb7UgyC6VMVSO1QRclSemsPNUK7ywpZiMUrXXOjChLttsNabSeUzqvL0NMDD4gtaSoau4+d4/Vds26bfnq/bdwwwDB86uf/1Vu3bnFy6+9RFU0bE5WPH38hN9E7MN3TlG4zmkEnlk97gFIeeVwIMa/r1WV15Fw++4gS6nlOCI4EhXV6Lkc6Omv7juDlPkdU0jWrPgSX+IzfIbfxe9kwfxqMzJjfnWlz4yDGkWLxmDpx/FBUqBGUNSzokOSKCjGj7UCFF/i169ee297Hj5+wDAYJo1CScl2u4WoUFpSlSUXJxcMg8eYisXyENttWfcDP/ePvkD6oZq7N465OW8QKZBSnvO9GxmeqSd0HhkTmyEQoqCZzvLM6v1V0EpMmXLcdz1d12UgTGuEUAgp8CEQY27jpSzROkfthpCyqYrMzDttqpx4nCyFNnjr8dZnUpV1bLbZCj9FQWEMhc6qvi5YZPRYN2C7LSkM3L13Axd6lssFPgaePj6n31k6O0Dy1EeHRKkoqgrrPXG3oZ7OmE1mWFPQD5lQFaNjvdpQlplJuDxYMJtUtF2bi9t8Rt3czMGzwRGjQyK4dfMm2+2OvutJjLhGWbIbOkxZIbTKhcl5jNK4EDN5Csl8saSZzlBVjUfjx5zIaGOmJSfwPuZsSaWIIYwUd4ORCTf02NZmAhoCNwxszy+zNX3bspjPmSyzO/Q2OJQquH3rBrNpw+HhAqMqnrzz3rgy/i7LfRDjKabGorA/SbNWwY2l4Pqn81uUZ674zKkN8aqM5MTnvaHr3qQtUY/hKXvOgnjmz16Neckln+GXOWbJK7xChQGWVFRXIisxPqKhwFCQyCvBgZaONSVmJD3vg3AD5bixiEi+zn0AfviHX+PGLZEDWo8Pxxnfslq3kDTHR0u8t0ymUzbbFmUkRzduE1OLKCt27cCnP/NZbh8d8uqdQ166e5Ojw6M8ZilFNmAxWVvvA3LIKHa33WKKglqb/C5ISYhjaa2yQMjHgLcD2hRIqTFK5EAXqZFCQsgzb4j72Thf9XWhcshpEAQfGXqPVpoYc8cwnx1m+rOzXJ6fEJWg9z2u2zIrcqx6WRqijESZ8D6y6bYcHBxx817JowfvgVE0lWFrB7bbFkReFyqjGfqWJ7sdzuVO5KK/BEn2f3SO+WzK0LVIUuY1tNtM/JGK6WJO8A5iQhpF37cUhSaGLCFHalSZMRMhFRKBs46mNMgE63X2jzw8vkFR12AKhC5RyhBFXqtHG/DOonXmg3jnCSHkYNoQCcFD8hAGplWJ8zktddoccPMwb4IefuMthhCJEmaLKfeOb/HmV76CMYpdDHz1i7/Oc8+/RF3XbDdr0gevCd85RWHfJext1vYnaJ/tGa5Owr3aXyC+qSDk/8u+BZY4ujVd27vJcX2ZFZQWR4FhGL0HslQ7L0IbataseJOvMqdhSUPHggJDR8eQJ75xqNiby2aOYIHiggt2o5jL4uiwrNkh2HGMYcEN/rdR5Xh2esliueDocMoweIJtUSmrFlfrFS701HWJLAwuRowUmeUnC1abDuciQQqerjYsDhcMTy6oL3uaukYrTYqRpqzQRjOfTqnLQCEF1vWj+1K+Eu3BLi/yFc9oTdf7PIoIIGa6s1Z53paqGMU6gSRAC40c+QcgGWxLDBFipDR1FvIAdSVA5tKtlKKuKojZi9HuFMn2FFohy4JhUJydX2B9DyrhkLRtxg60MQwpkbRmuljgnENIweHhIWfn51iXRWHD4EkRppMGNZqYBu8JKdK2GWSWUuZEJ23YbXdZL4JAadjuWuq6ZjKdUlYl67bFjeBgDJGyqkkh4KKn3WzpvePmrTuUzZQoJJ5siGsKkzGeLFjIBTOkjAWJ/Xo3x8YH77F+YDmpsg5FQBJZsm69Q6RxU6ezV2a72nDmPEYo5mXmg2y3LQ/eepsQFHKUtH/Q4zuiKEA+KR0Oh2UfF/csA3HPXuCbysCVq/B4P3scIZHGrUA2RMkiKDX6LOyt3uQoiHKjukJh6cnbZoNlx8CWc0445oA5MxIeS851EAgmlGSJd5YPZwDT4mgR46ixo2XNlp7E13jETZ7j943P1+gaiaEqJxQa9EgK2m0GButQRWY6ltWc2eEhpyeX+PCAyhyDFAipSEIxuMjWSw4WN+hDpHcgfYIoeLRaI6VkMY+8OK+p64KkCoTM7NEUfBbMEJEyEkMG0JrCoEyDUCoj4NFnoI4x71uQfwspIaXI5CckUuSodW0UZZHTVL21pLTHPHI0Oz4wbWq0LImuwxgNFPh+oNvtsgu0LJhOS2xwrNctCYWpaozWGAV1VRCsZxh6fPAIAfPZjLKs2Ww7rPMMXY+3nig8ddPgk8NZn12QUh6htDFj6rPIHY/SmKLCOUc/eDa7C5LwhJBYLI5YTKd4HzMnRAhOL86pqoKjg2N0WeFICGUIjFZ1zmOtoyjLnCItEkJkVyutDUWhESnRdz077wgh4qPAd46mKUkpZcEYmVwWYx7HzKgLEQkm1QRvsylvoUpSADVatsXfRKvwHVEU9gUgjCeVGrcHwPj9cIUBvN+d6bpTuA5/3RuxXh8SgRuLwrUTdN5YZFVmuBolsvDKM6EGIg0lHVue8JAZrwGBnpaOlkSk4hBLz441BWnsGQICPw4aBQ1TWgK/xuf523zufa+9rmcUukRiWMynKNEjgKLIH7iUHO2wy2Ek5QRdVqw2WzYBmrpmO2QJ7mAHLs7PuXf3NsuDJZvtjuAipjAUQiGlpB08X37wmKqQTJuSg2lNIRKl0hitCDZg+w6UQusCpfLiN3qPEqBGmbM0YysNKFPgvGOwA85aiqLBeUdRlAQfcC4nSVW1xtmAdS2bds10NmU6nbI+O2cyreldh3MOlRLSGIp6Ql2WSCHY7C4wpcm6A6Eoy4rCGLbbC5TKLMHFcol3jt04NpyfXxICTOcHGBMRwVHVJa7P0u5JM8GFUYurDErnyHYf8tdFUdF1PVLnDiimwMHRIUoq+taxOjunMBU+JU4uTpgtZ0zmE0iK3lvq2QJ0QT+EvM3wER88budRssg7NJm7rhgDXddSFwUxRqqqIqqC1sa89h2y4tXHBEoglCRKsW+dM8sxglI6A4opf+pTzDwOKSXpt1IQ9RvkPvw08O+zl//Bf5ZS+j/Gf/sk8O+R4eI/lVL6P7/dY+zt2PLVN8OF1yf8Nb9xv4G4vt3+HXi2V7jeWuSRwBHJRu8D7bjijFe3kWPLn70XBgT5Ot/QMKVhQk1JwVt8nRscE3D0dOzDYVtWFEh6WnosBsg5kzURMRq+WaYcXBWEg1nDxablpRdeBM45PjpGyZKmnlOamtXqAh+zSEjIHJO23pzi3VPWlzukmCODo921pKhxKWKqEqUF33jrTeq65OUXX6CaFXRth5Yqx5K5xJPeo71kNThOLs8pSczrhoPFkrpqEKVCpjxWDD6hosnI+MhCFCplp6SQV5RKFBiZzUe73YCqqiwMGnUMQ+8ISpEKRQgOIRPz2TQ7DZHFS7suYw9d78BbZk1NM2lywCyCSTPDectyPme364nO4WKgkCr3e8qw3mxxdmA2neHdFmcDk8kMKTITUheKmCJJCpyPROdJQuSgFm3QpiCEgDYFfd9TVDVVNcF6x2adg3S2m11mfgZJDIGz9SlBCG7cuIGuClwMKBLNZIIwmjTKvoOPeZWrS0hpBHRzxKELozZYKmyMozW7pygaUoQh+tHcVhCkYB9m7YH5coIoNLtdi0zZjzGnf+cuQsv9Cp8x5fqDHf+8uQ8A/21K6S89+w0hxEeAPwR8FLgLfEoI8XpK6dv4SyfsaJSeo9/jM1uHvZHrvlvYR8Vddw37EeP63tL/4++B/grI3FuxlCO4OR0TnNfsxus7gOA5nqOhQaE455JHPOEmN7E4KhSOHRc8HQ1dDY5hDHvzzJkTSTTMOOUJl1wbUAWhmM0WFMVAYRK7zhOTZBgcMVqGEHOiEBCdY97UTIocRHqhYT69yZe+cIbvI9YlpEmEFLloPWJrmVQt2+2a1195gY98+CO0u57Tkwus98yWR9gYsX6gb3tM6Dk5O0c9vWBxdJvFRHJUaSY64xBekslUzoP3hEIRExQ64w4yRby1SATTssp2bpEMlMWESHnf7ty4gx8ZiD4KbN9T1VN6a1GioqjAtiu00bhui5KR7WaDEIKyLokhUpWa+WyGtQN96yi0oR86tNLUlWF9ucOYksVsXBsbg9eKbtiitcmcCwnOR4qiYDGfY22PlIKmnrDatUQh6AaHEJLClCwm2QHJpOxBebq6YNt2HB3fYrZYslgccHFxyb1bd+i9Zdf3FAm6tsujwrh2lCoHvkbviQnc4MZrUzZKic5yeX7O0eFBFvyNTMSkBD7EkQ2qCDFycPOIj/5Lb3Dnzh2ePjnhnbfuc3ZyiggC0eUtj0jgQi6A6reSp/Ctch/+GcfvB/7maOD6DSHE14EfAH752zwKfvRUzF+l0RDl+niWl7DnMlx/nf/7rXwVnr1PP96nvJIxS46Zc5ebeDwPecIJl2g0E2YsOESgsCNK8U/5MiX1SFiKWNY85S3O2WLGceMNXmPClL1vc86TKMdkqfG5pDEjIRse01tLVU8oy5qUAlXdYF3MZBaVLdh7PyCF4PDwAD+MK1kxvuqUrtZ9Q9+RYkE/JO4/eMxscYiWmsFalFJMC08MHi8dpqwpXaBXO1bdim3b0DrJzijmxlJNGnRVMClLSmVIzuJFbmW7oUc6ATGfXGVVIYyElFWPSSREMfpmqevWVYgc9euDJww5OyIKSRrRdyEEbdsivGPa1OPJpDg8OqC3PevNim4YGLouj4wxXwkzOOcJwed1H4LBWlyw6DFstSjqbDjrAzdv38KHmJ2lYuZMtH0mIZkyt/fBBwqdQdiuXdP939S9SYxsaXqe9/zTGWLMyOnO91Z1dQ3smWwSpDkAgiwBWnjjjQ1tDHhFA/JO8EKCbMMQBHsj7wzDEAxrZcMGDBiGN5ZgQDZJURTBZjfZXdXVxapbd845YzrjP3nxn8h7u0WLZXFA8ySqbmZkZEZEIs53/v/73vd5W4sLPW3XM5/Pme/NUdpwdXlF23RcX17jRKD3AetAqBxpJEqlVCwRU3PVmMQ/cDH1FVKB6NiuV2ilEvre9WRZnrZNtiEvckII1HVH1/W8fL7k9PJzvv1zP8eX336H7K0HHEzHvHr2Ch0c265GEFJAjI+0bfOvPwXfOP40PYX/WAjxH5BIzX87xngN3COFw+yO58Nt/8rxZu6DfJj8kTvfA+w2D+Hm812P4U2oyptip93x/10YxBv3CEwYc8CMfab8Gr/CS14QgWs2gOCQfY454jGf0xO4ZM0zPmJLzSNuccSUDRdseMEPeMGKljsccJdDFixosJRMOOMFV5xxzKObZ5Jw/AIpk2rPukCISRGXpMaCvrcUWTLNRNujFMiBd3BwcMijt3K+87uPU8PPg5ASlYHSit4HTDHi6dk1z09/g6PFgrfvP8AoyUFmOdjLMNkYIWAiMwrGfPT4FR8vr6i7AjHbw/ke5cFXNblcc5AVjIucbDpilGdEa+nqmugtVbWl2m7I8wKdpWAedlLhmHINU1PtdbIX0SOIhC6FvfRtg7ctwvsUGhNSGjNRpjf0UmJyg3WR1rZY51jM5jRNg9aa6BONUwmBdz297RE6bQuLYsRIzpK4SgrGozKhz7seqQze+YFuBKPJFOs8nXXYtsd2iRsphcBZj4+R/f1D8vEYax2bbU3bpBPYWpumBCEgZMTolHvunEcqAzHQdWkUmaYKEh9jKhpec3x8RFnkSCJNW2NdTbSJJ+FdGqlKIbB9RwwOWsvv//Pf5ukPP6ZvewgBIzXT8ZiYyxvCViSQqT//6cN/C/x90pr97wP/kBQK88e5Lv7Ys/THcx9UjEOzb/fxmsO4M0TtDFKvi4YcPBC7JIjdOPMnD/GvrCJSYfgGX2PFObc5Zs6Ejp5zLtnSsTfYsb/LH9DiOOGCDRue8phv8h6/zLcwpKg5iDQE7vGQxZAxlUaXBeecJFYhlv+G/4K/xX+OVCCVou0bpIxsq5pt3RJiifOOpm/pXcD2Nc5kTMoRAYdQmsODOfiMe/cnHB7POH25IYSI8IEYHIvFnKrtaaJEFjMuLy6wfsnBwQHYnrvjOe8cLciLgmcnp4huSXRrmsvnnF9q/OiAaWbcHZXDAAAgAElEQVQwWYmUWUqNjrC0jo11uLYm14bFeIQUmulkxHg2I9jk4+/6HgiEGBB9g9GaqBJQT8gkgoohIGJMkBUlETGN6rROwi7Xd3jrWNYNxNRgtiEgakXf90ghyIsR622V3g3OE7xLbAcph5zFQN3WzBZ7NG0LUROCp64bpGrQWZ4yEqQhy0e4QYodhUTIhKLXQqOloq0rqmqLNpKDo6OU2yg0NgQmkylF5uj7HhGTxFxGgRYaAkl7EGIiRoWU1tR1LVplSKXT61yuGJUFQqe4t+gdiGSRjhHyvEgTFGWTbNw7iixL2ztrqdc1gkhuMoxSrJZLcmMYj0cgc5q6oeXPefoQYzy9OeGE+EfA/zF8+Rx48MZd7wMvv8jvfC0wGh6DHUHZ/JgN+k0mghh0Am9i3t/83u62N5WRuyMlMXne4iEVG25zi0MWvM0DLllhUFxywZolrzhnQ32T3fAxP2TLGb/Ao6HVWFMy4y53KYZYmNR8rBhRsKKho8NQ8rf5da6v/jsWh3uEmKLYq6bj9PySt9+epUKYLq1oU1A3HevlGkEEUXJ2eY3CQCi4++CA6+WWtvJIlSU1YtsRUAhpGM/3aLoeJSKfPX2Ojp5j41gtCuaLPapNjdaBSV5y++iYbFWhS82tRck0H2OFolEK7wQhJjCrI9BYy/XJBaVR5BLGmU6GnzxHZyUhgPCWaFu6tk9LepW2AUolmXUIgRgCuLTH1jJ5/n0URCkQw9U0hNRdt9bh2g43+BucT/i2TCm8tWkMqg3OWXrbM5sntZ9UEhcD9balazuKvEjUo8G5OV0cIE1B1zt6FxAE2rbD23SS9zHS9x3luOT2rTu4mEC426pGGcNmvcH2liLLiMHTdwGpMghhwNgNKU5KptDe4NPKJqQtZN8nqK3WGuscQiduuTFplG6tJTgJQdHVKUsjNzne95jxmDwHBsq29Z5M5aAjTshBb5IeW/55rxSEEHdijK+GL/9d4PvD5/878D8KIf5rUqPxXeBffqHfOawHHJ6cpHDc4d53RWG3cog3P/N66vDjReEntxQ/fltKoXb8Dr/Hv8NfG6YJKdrtmH0OOUQRUcBD7pECXCUbtkNjs+c5z5BcE2jRZNzmNnPGGBRuCMXd45CeCVCwpWFL5Da3Abi+WDJb7KXGlxe0nQOR1IFVUwOG6XjEYr7P1ekpInq0St6BTVtTFvCVn32Pp8/P2dYrlBK01uK6HpkViADa1DRthykM267Hdy3f+bCiur7g4cP7zPcPwWTUvqbxGS50NMtrxO1FwtUJReUjeTFFmSwpJAdqE1Izno4RtqXrWq7OL4hRoLMCpGaaZ0yMSnFnOnXrg3dJGBQCwfuUhhwSHyE6h/eWGPrdmyzpHwTkRU7bdyityYflubMWSCM4H5MOohyP0pW9ranaHt80jCcTVusKrTJMntSsZZk8DKiM5fU15XTOweEtXIisrq+H6UqA6Km2W6azKaPJhM5arPOYmAz43vnBxKSTVV0kBFCIYeiZdAgDQmnGoxnOWWyfGreJVxlx3jOb72HyhGIjJIZldBHvY2o0Og9RkhmD9w5TFlyvW0KQKftTCLxzSBGouvR+ypXBBrBtS1HmKGO+yGkI/JvnPvwVIcS3hvPtc+DXAWKMPxBC/C/Ah6Spyd/6kycPw+MMa4WdADk1G90wnkyS59Qs3MFYdm7HXbEQN7/pJ4vCboy5W4nsnJSXXPF7/B7v8TYRx11u84IXvMN7fIm3mFPyN/irNDR8zhNeckLE8YA7TMlpWRKwVEBOyXRIs0yP6YcpxZiA5xlPuOSUA17/OUQkzbpDpK471tuKcgyz+YS6SfTpuqnJy7Sfn07HPD95Ru86bH1Nke0hCknVe6J0SGnorCeTDqM19XZNpiTWe5q6YT6dcL5cIq9b6nzLzx49opUeRM5pteL0aoM0jvOrJe88PODzz59xut0ymexx7+gO4/EYi8cGMFKTFxmTvRHb6yvqrgEhsSIF9nRNx7qOZEZhdFqrTUYF2SixBJztEwzEu8RDjAN9M5KgkT4JopQEEdOqIssyysmE7XZL27aUSnF4cMj5+RnBOtbbGiEE5WhC7zzbpmXTWJwHZALs2L6hEDDKU/Ha1h3bzZaIQinN5fkZru9xfY8PnkePHpKXKViw3rYobbDWI6SkrmuUkuR5lqjKAvJRSRCKTTO4PSVY51mtNxiTTFFCBbwPWOeZzCYU5ZimqfA+kulUSIOPROdRyuD6jswoIBBCj9aK/f05l6sVXV+Q59lwriZMocwMfYj4tgdviUImbPwXPL7I9OFv/jE3//f/mvv/A+AffOFnwE6nwI2nwN/0F5IqcSdWSiRn+8aY8sdXCeKNgvBmQO2bTcqdTFoi2GPGmClnnPGcJ9zjLjPGzBlzm0MEjgkFnpK3uIXEYOmZM0MQuOCUhoqOFkdLyixKV7opEz7jyYBjmXGH+8CaS65vXvfO0UhUVE3LtqrISklvG3oXseuAbzylySmMZjKdcotb6KWk7lqcb3nvqw+ZzW7z6uU1VxdrhEyWYaUVRiu6Pm2jepthhWQtDMHn1FcN6vELDgrJnVuHiMVdZkeO6/Uln7xYstx8RmstvRC8eP6cMZKCQ6xvISvog+DJ40uOFzMkAW1guthn23mqzuGCwiPZdC3N8hpJoDSa3AgWsylFpjGZoTQK+p7eW6JNS2olJFKRVhAhlf0i0xwcHRIEVNtN4iY6x/nFOZ21mLwgG7QGPibqRjlZ0PXJ2i0IlLlBtRJ8j9Ga5WrNbDpjVTVUm3XiRQRHdJYyzyjKktFoBEqw3mzoOkeBZNvUg1ciiaCKzCROhBIIQRJJRUAr6r4DlWOUpO0tvm4RYojqs479wwPark0aCiJRJket98nBq6RAaIkxghh7jPYE3zGZ7mHDiKuraybjW6lH1XbJ/m6TWzX1exWddQT3F5s6/Wdy7OREuyNtDH7yI5UHM+Q67wrGrin5pnHqj8ukfK2G1IwZ8XN8mymSAsEIzWM+BfYpyfB0ZEh6akqyYUuTfBQ9NQZFTsGSJTmKDI0jo8HT4xgxpWQ8CKWv+ZjPqRH8r/wTAI5v3SLlXSpcb7m+XHF5vURkCqUDvQ1oKQiJ10XjOj5//jlZMfAY+g7nOxbHU7I85/OnZwRSEtNqeYXWisViTr1dY/Kc8WRC03bMDg+pG4ure3749Dm390Zcdo4oM4KA2gkm0zkX656qr2lcj7Q955eXtPWS/f05oaloe8v+/oL11XlKqzaay/NXdBi6aPAyRwhFlpdIH/GuxxoFCk63DXhHqSRzI9ESCpNR5AWha4nOQvB41yJSZj3WOc5OX6V5PZFcZ5RFmbwCtoeQphlK67Q1CEmwo3TyuyghaeoukZRdoGk7pE7jUjkoQiEym05xReqPxAjbbYUbVikil9i+RwHBB6QyhOhTaEtwFHmJNIrtdsNoMsMLTWOTQlFnBXXdEFVMfIcBVV/XDUKotEXVit56tEwp1YhA23UYDSE6gu1RMjCZjgjeMcozNkpydnbC/uERAH3fo1WSTHsbQKaRrfoLmD78mR6vpwyCVB52noedxyH9l3Btil3c/OtVgWanbnjz2HEc0+8RN+IlieZLfIn3eZcrnrBPSQ78PF9nwpwMwZYVOYqCHDE8p9SsTO4JSU5245B0hOE5pLB6AM0Rd3nJBR2Be7zFf8U/4vatY5arDVJVyZosBxRLkDSNY7lume7lOOEpxprttkPMcpTKqKoly+uO4CNtbUFGylEiRtQ2GZsIAakVMXqur6/TCeE0tmsoixFfe+99Pvz4Y9q2pQ2CZR9ZnV6AS7bdarvlKghmRUEbI3oy5erlCWqzYRokD+4eMzWG6/Wa1ekpo/keFxfnjEcF872DpAmQmhgdQnh0VGTFFKTCS48TDi0E0XpcFFy6Cu86dIyMtGFicrQCrRKyXKBRQhG65FdQgBICbIv1yX+SR5fAsralnMyQJqNu2mEUCn2b2BVKSGIUKF1QNY68LKmaPo0mpcKYLNmeIyBS7iXWojODay3R9eATH1JnBUprJuMJtm/JigyhSGpInWO0xveB6XiM9ckVmelEoyYIlJJpFSLSJGOXEB18pGp6FEn05EJglidHqfRAkGzWfdI2DDb1dbNOKedKoYzCSJWcmL0FGRjPp/TO8kWPn4qikI74xv9f8xRfqxLSqamGW3d05tcNSMGb04WdYWrnjNz1ERIt2nCX+0g0dgCh7DNhzgRDRkBQU3NFzT3uDMXKweC13CVYCSSjAbOmhu9aUtq1RA8A+AZJ4D8jiT/X63Vy2Q0Zjs55jE4Mf5Ds7S3Qmce1DevthvPza9brDbNij7cf3aWp1lxdXqciqQU6g8XBiAePbvH5h6/YQW6lEBwcHRBjZHW9IURJcJ47t47p+47T83Oev3hGpg2ZEkzKguX19ZA85OmaitH+nL2jIzaX11ys1hSTA6reMlKGvncs9haMFgs2dcX51SVdb3nnrbcgVqm3oAucG+iVEiKeTERkHCYsUdGRE4UcrNqvAbXSW6LtKPKMcZmjyxJlBuycDETbElya1xsJ4Gn6Dt9KojMUQ/MtxoAJnj5KXHTkeU5ZlvTOUbcN3kfKIlGR1GDvdtbRO4+zQ8BxgK7rUMNFR0nFZDohGwAtOi/xwd0oDvveEpsWpEGKNN7cGadCcBidIzqBksk8FoUYBE6pV1bkObYPSCXRKm0ldBTEKPFC0Duf5NpeMJ7t8c7te3z/B99nsVgwn8yJIU1yMmNwXY3r+798hqjdAHJHWQ43yoPd8Rq4kvDpKdJ1l0q9Ww3sphG7PErgxhkpb4pIuj0pEj1gSNkLBkXJjDkVDRrPNUsMBQkvb5DkFEO+RItDo8kph8Zncm/sYm1zRjhgxJSWzc0rSSQfcSPmkVLhfcQ6l9xsPlDXLfP5HtYF7MyxvFoTe8Xl1Yq9xZju9BypBMoINtsrurZjsT/iU59yM621aT8ewehs0N1rmrbl9OwM7z3vvPNlqqambVuciCgBs709lienSDw/961v4XODlZqz+ZwXJxVnF0v+oO6ZZRkhOBCv+NJ773J+veHiakNdN/zCBw+5VUau1j3nnePp5Yajt75E19esri7JpWBvPqfrLNGURKWwwaF1hrUBS5rtpyDZnG2ILBswRHKlKJWk1CBlJGpQ0SFdmmaYKAi2JfRtUgYSSL2+SJBJ9uxdz7ZviCS9RKZNGmp3bcKeBciyjBgiKk/GLx8c4/EIGQOr9QqZ5Wy2NWOhkvhIayKKpu1QQoJQ2N4hjcKHHqkNkYAyGm0kfeMoskStcj7ifEh4fwn1dptguEbggifXipHOksRcGGrX0SBTfoYI3Lt9j7u3jvnoox/x9LMnTEZjHty7hxQiof2VpG07VPbFT/WfkqIAaXnuf+wEl0OR2AmYdjJl4CZqPhDewKwl9dauwMib7YNAo7A4dmlTH/FDjtlnwoIJB0wYc8WSY27TYlmyZc6CnBERyBmRM0KjyAg01AQY4LB6oDeohD6nZ8SUNT0Tply+URRCGNYwcXB5hlQQQpAsV0uysaIcm0Rzdo5xWbBRK7q+5nJ5gQs11veUowKVCdqqY71yPP7siqbtmRQjrOsAwfXVEiEEwYNzlhihqrZs64b1ZkuRlby6vMQP++kyz5Ai0rUN52cnfOWb3+Dl5TVlmVKmrZesW0fbOw4XM+rtkn/5e9/BmTFdMNTblh998jFf/5UPME5wer3h6bPPeF6vqduO1YsTvv3BB+zdOqLWgjpEZJZyDqIPSC0JLiHNfBiA/xFyrQkSbHTUtiVzHhECSkhKlXMwL3B9j+g6vPcYIVIoa4xE7xFCQujw1icHYvTp6isgQyD94EgNaSqxYzruLiAaQaFzmrplOi6ROpX+rq5onUurNmPwPjkis6wk2ATdUZnGKM22rgitpywKMp0EUlLsaNuBpq7QOsUNEgNCKkwmyI0mi+BIEXPFbI+8LImF4c69exgfsQ4e3n/I4XROvUmaFqkkPgaKMuVPOv+XMCFq54J8k4/w5hV/13cQw0QCXk8V3vz39f3TKoGbf18Xm0Dgkku+y/f4Bu+zoeFLvE1LwwlnFGQ0VLzN24yZYvEoChT5sP3IUcOKQA0Atp4aT+pOexQ5Izw9DPcE+OpX3kOqJzjvk3AnxqHrLAYyUIbA07U904mkby2RnsxAHy0uVDSdI+AwmaJ3LXleYHvLyavNYMFtmE4nWNcnsKiQmMGv731gs91QlCOMybl/f8ZkUvDk8edEBFXb3nAJT87O+VkpiV1P17eUozHRe5zQaAVV16CNQQRYNZbKa6ZFzscvzvmN3225c3Sf84sty21FGyErRnQBhJJ8+MMfkk8nyKJgb38PJV0yDZkcJ5Pa05PjvaJvHb13uJhyEz2SXmmUzoiup/Kepk6W68wUSJ0yDlTMsH2fioeUjIVGixZJTCE5OvEepBB4n5b+Ho+UaRSqZYKnSjGEHIeeTKeJiCSRmcCTC4GPIKNLgbGk1Z7UGfQOZXOcdSgpErbNp9Wt7Xu884idQMs7ms6SaU2eZzcwlvEop7laJ3CtkHz5/ffJD/apXI+UkonKuHj2HFxgnJeMjaFqK6aLGUQxZHJI/n8IGn86isJuYpBWA2H4/PVJvCMuadQggHY/No94M2HaD+znN/ULktezjB1TwdKxZk0AXnLKAXtEAhU1LVsOWbBDv5aMiSg0BQlG74Zi4DEUdNR0w2YmzSEihgKJZkPF3xn6CReXl8PeLgmw4uB111Iwnhbs7c/J8i3WWaKH4FLX2Chu2ptRSLLS0NmOItOMZyPyR2N+dLTh8+sTlEowDqkkzjnG4wJnPUpBlmdstluE1Ny794CyLPnKV36G3/qt36RtG87PTpP0NwTavuPDP/g+o70Fk/EYLyTriyt6l65k3jmODxaU2ZjYVQQ0ohiziYLPN1DLlipqghnhMVxcrZiPRzgpWW23XJ+8AqN4eOeQPNNED/fuvcW27dlsG+YHeygpUFGlzESRREpeSnwQaJ2BKcgUtHSIYFk2DYWWlFJTmByVlUktGSKxT+M6rVQaPcaQ4DJSIMPQOzAKbdLUwu8uTtEPMe89Rgp8CIhoyZXC+Q4lNCiFD5ZxJkFmhChRmcb5iLcdnU8rlMwkxaX1PgXiqDioFw0xDKP16OjqDqk1KMm67zGIxIqoa14+fcrMdYwXcz770R9RoFifniOdRQtBkCkeMDE20xo6+kieFV/4fPypKApmCE7xpMi43eDQD/0BP5zMEj2wmV83GGEnd05gtGR8fl1IdqsINSDYkggqAVf2WbBhS8TS0LHmjA94C4lnREaBpKMCFDlTHIGSkoYWT09OiSNhXRI9IJIlIiJpxpHRvKFLWK22N02gm5bqQC9SGpAeqQWZyGi2HTJopAwooejbLXmuWS6XlPkEVZRUVUU+z8g03D6a8+LxGSFAZy2ZyEDIRB7qeooix4WOTRVwPjA+nTAejzk6OiLLC7q+TzkB+Yi27Vg3HaevXrHXW/IyR9aRvemULDdU2yVFUTA/uM1q27LYyzG9IzjLOsIPThvO+xovSmw0zCYH2C5QtRUfPf6cTKQxoxaGbdMgYs52vaGtOl6eXSLzjMP6CGct94/v4W2HNobpaEzvNJtNRR8EUmmyrERmJTI6iJqmb7F9oA2CzGiUNmRSEKRCqAyrZAp7EUkF6YJHKMGQzJocp6Qod0hBtz6kOD7nk3U6xqS5UEhcSHxFLdMoNIiAixEZHZlSCG0QyhCCxzZN0h2EQXrvPS7EAW8fKIucItsBXB2+D6AE1ntS1pjn8ccfsre6JETPZnmFjoL5eEKmNMEFrLdopWmbtJXKdMKxxb9s4NaSkhk551zfjBH9sOh+7YfYEZZeC5d2foZdKkRGdlMIdvfYNSN3BcTdBLRIGiqe8oRbHCS9AGlp/og7XHKCJaIHjqPAIAZfw4jpTRtUoIbVQosfPpdD4bKEG0UmpK62kK//5EopQowEEfDRgvCDsCWkqPemByqE8mg5YrPqKErNaG+C7TukjFTbS+gdx4cjDg7mSJFzcXE5YMVSk6nvLc5ZtFZkRuGs48WLF7z11lt88skf4ULg/PIipTTbiMpLmu0KouTRgwd858mndHXF/miKMAq1t8dmtcaM96guntNu1+Aci9mI9dayxBB6gVIenY94/ulT6mbL3l5J43uKccaDO3d58vyMq9oPJ5Lh5fMTTs+u+fLPvEvvBWfnl9jK8s1vfI3xeETT1CwvL8hUQqE766HrqPuYEq4wuGCp+x4rHWYxwoxGRCSNTn4E33fgHCp6smKMkcnS7H3Kp8iVSX0YkjhJyPS9iCAoMWRZ9Elhkhmi84mGrYYUb9+nZqOLoDMkiTmhhEoKTSFBS+zQL/EhpUaH4EAZ+i5tjbVQeO+w3mFUakiPCsNkekDT1Sgis1GeJOciYvuW3iZLoClKRtrQ9A1d3zPOi798WZIZGb/Iz/O7fJcTzm+GkTtGwuvR42tT1Jt+iCR/drypZNwVjF0x6OnZ+S93zsrVwE44ZEE/8ByvueYd7rFgD0uDpR02LAAKgyIbph+BwIiCDoGhJA4lQqPwRFas2VLdvM5IcssJoYYRXURrPbjhkp7fTDVkES0yohNY12BtR5HN6GyDMQpvA01dMx4rNqslhVIsFvtMxjnn5zXeeVQBWikmozFVXdP1DSGmRleafsByuUQoxfX1EpPlg3oPUBJrG957/12+/fWvImcjvvPhR2iX4uUwY168POfjTz4jzzIkAVdvGR/OcEXOpveovCTLMq6uzvE+YpRGicjR0T6He1M+eP8Dtm1k7QSN99SNY9MFtjawrDrEaMzp5YaLkyt6H3jnS28xHZecnZ9x79Zt5uMS2ztCiPRBELuewijWveX8NMXCLzcV47Lk8NYtztZr5vM56BwpNMF2SB/JhCJ6j1aGwgiuqi7lTAiRtAY+0Pu0ikghOxptNCEmoK0ucsYmB6kGBH5iOvS9Y2gWJDCuCIiYfg5tECkRBhHTeyI3iugtJtMQA3F3ORmapm3oUICJBu0DpRZkMWKMpuoSSjjPEiHLe4+QmsIUdH1L27aMivwLn48/FUUBIl/lfQpK/k/+L7bU7ECtYpj6ByJmmC287hIksVMkSZc9HjF4JHb79l0YTGI/7lQOCenSDxDXmo41WwoUS1Yk4pNhxoSKZlhFNDgqWkAwvvFiJPCromA0cJeyGwp1zYbnPLt5lbtSBWLg/iULcYypmIggyLVhdXlBmWv2FwdsK8fFdUUUiXuYaUFwPQoBvaaQc0bZCC8DBwdTnj45R4qIiB7XebQwGKXpiBgjoe/QRYaUjqrZ0pxYjMlY7B/gvCMEQdSgy4xf+oWv8WA24okQuL5iujdjtK2Z7j/gZDLj6uyM2XzO4vAWMi95cnaFFoHZZMRcpSW4lQaX5eyPckrlePetdzg5PePV6TXL9QYrDXk5IitHkNVYCadX1xSTGc4Jahv4/OyCs/WGW0eHBAu/9Z0/5Gs/8x5HBweYzDAucozLoG2JRc7jpsNqiQoR1XU8e/wZ55uG0/wCpeDW4T7z2YzIEKcnMvSAdvvs5BJZbDk4njPNChwSMoUNfTJ8OZUKo3fgPYvFDDPbS9MLa8mEpK0qlOoQwQ/vwmESIVKjVauIihHrLcQ0TvUuYKRExiRPd6En7niMQRBjwr/HYeWcGpYe2zfgU8xgEBGJSLi3KHAIZD7GdhVNVfFFj5+KojBixAMecs6SD3iPj/iEazY348k3wa27UJhd3yGBVv2gEkhY9dc0Z3cDbE3agZyMFP+WthxbSkpOOeeEC77Je7ziMzZ4Dpnj6FlwzIoVkyFCTiDJyCkoYehXTJnSYoahpEZjiLQEHB/yhwD80i98lbz4Ec5DiKkoZKYAUs5f6COFmbNZrlnMDpmW+5xfXBNFYD7dp667lC/Y94z3pqggEVGyWm9YhzPGkym/8lffYf94wm/9s48oizmjUcHJ6XMcFmslhAKKwEhEsBXV1Rk6n3L3nff45PNnbPuOvrZI6bhzWHJ1ecqBVCymU2aTMR//6BP+7fff40HZULw95bcfO15cXDPJJT/3pSOev7BcXV3xlTvHuOaCq3XAV471ukEFQ8gEv/k7v49H8ep8Q9dsKEzG05cvmUxnhBiZzqZIIag2Gx7ef0gfHKvliraPvDxbkpmMi23Hv/jux9gBPvOVdx8yzQzvPnzE+bbm09MLah+4e1fw9t1brJYNLy7WtO0FzjYcH+yjpGA6HfPuu++CEIzynHHRo33Hh9/5I/aODgkidam+9s4CGc6YHt7lyekWWUyZHtyljwXrzhBOWpRMoBfpVnhbJbaDcxR5jlGazBQok9G1HZm3jDKDLksyJVFK4GyHkQqjJNE5ZLSMREgFRJECdaIHkUjbDuikRChFzMHHgPM+mdKsJ8ZArhUTFYhKovT4C5+PPxVFocfyW/wOJ5wigDlT1lQ3HQTeaCzuYCyJo+Bv1Aivqc4/bpbazTbSqsEibvoQSftX01JQA4IOyx6HNDhGLFhxgSIjZ0Q/nPAbtkzZY5dcbYbnYYaVyGssPWQo/gm/x1/5tW9QlJq9vRmrTQODVFsMsltJovpcX254+GiGFlBXdVoNhURVGuUlSkYury8ToMRkbNZb8nzEaFRQFIbV9hWmsMz3c3xncV6SFxoVEjdRBEn0gb1xQewco1GGi57zZ09wnWVTbQm95/bxHkWR8+GPHjMvFjw9O+fo9j2evzrj1dWKw7FktV6xbSxlOaarVpy+aKmrhlxF3jkqUb3gDx5fUEpPrlPIbR9gu20wWcl8rskyw2I6ZpLnmKJg++qUru2JMTEenMmw3hJjwIfAar1OhKMAWkqcTKlUp+eXsL/guqo532zphaSLkYtVhTFXPLx3nysbWTVn9C6ybjokkYurJSenZ9y/f5fbB/uYo4K+21Jq2Ctztn2g3m7IY8me7hiHa4Tx55AAACAASURBVE7X56wuNdr1NHLCeH6bfDSlNIogPJiMk/UZfuPp2hRU8+jhW4xGEh96QKCCR8UarRV5kVHkqRFZZirRrhwUKqMw6QIoRcTLSD4uU8S9CxiliS7gO0dRFHgifXRIZTAqEiIUmSGEFhsi/ReXKfx0FAWLZcEBd7jLR3zElD3OuabHD42+FAKbVgo78fNu5HjDtL6RKu1ITbvCsAPA7o4BXH5TZDo6tqw554Jf4GdZsCAiyRlT05EzomOFRrNhRUfPmIghoyCnpyUxoHYI+STCmjLl7/If8s9+43+4eewHj+7SNBYig4ApQTv7rmd1vUE+XJBniuWQ5JzlhizPET7S2wYjU5dZZYa9+QjnHM4GQhaxfcNsnvPX/sbP8+rpmh9++DixIEPqooTY45yir2pkX3E4ndJ5xdOLFREDPqKzjKwc0/vIR49fEGJBpxRiPuG9r36dR/M56+sTrnqNzBWib5hNJ+gyQ3hN2K44yDwZHXsmMM0U25FmtVnjMs3e/iF123N5dcnhJANvcbajt4m14JyjLArKoqDarhGZRAjHO1/+Ep8/ecp2W1GMC5Qx9MHTe8vFVUOZ5zw7P6cTkuttiynHFJMZ66bnDz/5BKlLotKIrKTuPYvZJOUnuI6z03N0DNSXKRByXCrmo4A0gr6NvHx+wvF9w6hrmfQNVQ2XTz4llEcEG+hHEzqToaXgbHmN0AopNfu37pCNajAlIh8R+qQb6Z2DGOjbnnmuabtI1ztUE7DWoYQkV4LREKmXa4HROV1USGmIKomeRrmm73ahPhGcIM8MEUfKC7V4reiAtv1L5pL0eBYcUrGmpuEO95gz54rVjYla3JzoDCsFdbO9ePPkT7AUNUww3M39YTfrT/j1dB/o6Aj0POUJIzJKRhhKOjwZxdCMNCSfhKNgNKxH9E0R2EFhd6QGP+gax4x4wAP+Hr9OQ0K9P3vyGor96O23U2qQdykFyIOISRPvXEfXVYzGCyajEttZxqMi+SS8RUTFbLaH7S1tV9PUHdYFxmNJkTu+/q37NM2W731ng1EZ47lJIbXKsFltuD3P0rP3PoWQtA0+eMxoxPWmItiexXjCxy/PCVpSPX/O17/2Vcx0QdAFt+cPqZ6fcvb0EzzQkCNnc2xt2Vwuee9WwaOjGS96y8tthZQSqQ1tbzk8WJDFnvlYsTw/IysmSGNo24YYAyF6rpdXGJ2EVForxuOC+d6UclzgQ2S92WIKQ+gj+5M5q23DsqqJymBj4j0IofHBk5mcTd2wrlNUnCpy7j54xK2jBc8ef8pmeU1VJSHYpl6TGcVItdRVx63DGden5yxnBblIbeZgHSEqrPWcX1zRh4ukQRlcmfcfPOT993+G8WTKdOZQKsNah1RJ0apHM5CK2HdUzpJlGisFWpth/iVpvGPTRWgcZSYh2lS0pUILhZaBcWmQIeCdJc8y9ChnEyPrpqXpOqLWtCSu564X8UWOf9Pch/8ZeH+4yx6wjDF+a6A+fwR8PHzvX8QY/6M/+WlIvsf3+YB3+GV+mYhgj+9xwRXJBCUH3YK9UQf+ZOr0bkIhAH2TMLWLdB1mzvjhwyJJxSMfuhAXnHDJHs94Nrgfp4N+wlCTknkqGg45YsQIM2wePC6BR7D0WCaMsAP/eRdK+xYlzdCq/Lv8J0TgjHOePP7HfPNb32S7XkLUSCFuZNBFqUHkgGV5fUGejzFGM5lMaNuOy/Mlo2LO8eFtzs5fUlUWGRSrqyVu7KjEhnsP5py+GnN1ukFGjxaBxXyKtBuiFKw2FVUb2VYdDsWoyPE4ehfBQzaeUVVbNssl5Sjn0x/9iKP5HhsreHW14dbxPrenH/Dy2ee8OlvSkqNXW2z2Nvce3SOUG364Pcc/u8YYw2Q8xmQGIzwjFSiUwI1ztlVN31qU0Uzyghgjne1pW5scfr3jo48+whjDcrPF+4AyGdE7qm3DlY2U4xwbAhcX5yhtmE1mjPKSquqwzqck6e0G23ZEIbhcLrF9y+XVkjIzicPY1NT03F4U3JnnTE1gcvc+H3vHyaqnmM9wY0m9vmLrMy4ut6iZoZztUR6PePbiOX6zpekfU7eWvByTFyUHh0e8eP6Upq45PDjgrXe/jspKdJZBcHjfU2bFgIIHIU1yghJomxofPRJNXmY0jUVJSQye67ZDCUFf1WjVJs+EzohaUrvUb/AoMq3+zMGt/5ifyH2IMf77u8+FEP8QWL1x/09jjN/6ws8AyMl5my9xzDEtNWtqZsyQqGEYuEuQSie5fGMcCako7GzRu5HkrjmZisnOXpV8EKm4eDLKG9+ERnLBGT/gB4wZMWVECkEzWFrUcL8RY3ZsyDcnG2mLIsjI2CHrPZ6CkhFzegIVPXNgQ8VtbvOf8nf43nf/S/76X/9Vrq6eYYwkeJdMSq4jhh5nPW2XThDYoyxzutYmN1/vWV0tsY1FRoFRAsiSdTcrmI0zvv3zD/noDz8ntJo1Cud6VAhs65792YI29hQjic5zGiIxM9jO03WW5XJFOS6QdeBwb0JpNN7WVJstKvRsL5eMM81k74CL+gLXpO3Ob/zgGXf2DHuFJtqO6WSM01lSEwbP3eN9wuaSaSnJswWBLYvpPu3ZOVXX38A1TF6wXG7RRhOxbLcdddtRlgXbdc10OqHMCySKprG0vkt2Y+doq4ri6Bg9mdD7juV6iVEpPPfW7dtcXy+pVjDKMupqy/G04IO7e/zBqwuOFvuwWVLYmlhfsqlqnp1XbCN89We/wZlXVKuKvmrwVYWazJHZCJlP8b1Hmpw+wna9YvPiOZPTF8ynE27dOeD68pLf/uf/N+98+avcu3sboyU2Qt81KJ0lPYOSuOgRIlKMcrqmQkqFDRFhDCCJUuOVRmnJKBuhYkAbRdfZZPMbZwQh6LsOpSL7s4wvevypch+EEAL494C/+oUf8Y85FIp3eQ9HzQknzFgM6dCv+wavVwZpQxD5cdu0GFYDO31CShzI2PUbDIaO7qbvoIA5Y3IM11yhiWxZ8Qkfk2G4xTEzJrgh0l4gyClpaIbJA0PcfdrWpGIwvYmhkyTigsAQkOQ3aBiYs8BhWbAHwD/9p7/Jr/7qu+SZQik5xKdHlJY4a9FKYvIc6x2iS2aX46Nb9E2HjAIpUrBKOck4PrrF1XVNvW2Z7wnmC8kv/vIHXL+y/P7vfHqjkwgIrquOk4v1AADNqOstru1pa0vXJFrPbH6fUW4Ya0Wp4dZ8hG9rfF3T2o5lHXj84hSPpNCKUTblxeaa3//kJR8cFnSdYzKdUfuAGLDubV3x3oO7LCaa/+d3vs/i8JhsNKFYrel8yrtQUjItRxiZYLFlMcE6i7UBJXPKXJHrLEFiXTKVbeqGrCiQArqupesailxT1Wuapqbte8wgYtJaJ6qyVEwmMzLdMSscTd3Rt46CQFFkLPuGW8djxqOSqrV00fLg7buM1jW1f0rMx1xcnHG22rKpWg7nY9Cauutpuo5VteX0/Iz5bMxqs0dbbVkuOy7Pr7l79xYP79+hrjacnZ7z5fc+YL44pGlqgoiYTDIZj7g+vWY8nlCMp2ihUVLT2p5N3VJ3HfeOjzEiKTizqKn7jk1d0die8XzEdn2BsX9xLslfA05jjJ+8cdvbQojfB9bA34sx/saf9EskioaKa86Zs8eKJflATEjOyDepzK/5i7ukKIVGDPv75Np3JKz6LmFKkGHwWDSKHEkJfJkH7DHnKZ/TUA/mp4oP+R7H7PNv8UuDruD1pKGmYo89eroBsLITLOnhGafnozAYikGqnUTYs4HflH4u0g+J2gAidMym+zTdliA9eVmC84xGCkKkdp7WVmg1ZjSaMM7HiAlY3ycdvZmilKauOvquQ2hB1TV0tqbMJAd3x8wOYBwmiUvYdtgQaJVJf8NgmWrB1aZCWDAyZTRen59TaBhrhd9ccW9suP/eI15eLvj85JKrynMr5JRlQfSWi7MT+iDoyVk28Ecvrmmnt4h4ykzSWc+r0yt807I/1nzyasutOOIXf/7LrKpNMnz5wLyUfPl4yslJz/Oqp/UV26YiuEisFdF5ciMoczjvHS4EQoC66sjzAlMWLKsNXzp6yPl6STaa0IQVXe/YXG6IsSefzVm3oELDvbziaDKlXXXYW1fcfXvK1UWbCtas5cWLBjnSXDciuThVyd2Hb9NjOFt9Rt8N9GfnsMHj25q6rrHeU4yn1E3Hk6cnuL5H6wzvl1xcCqaTFFSz2iz5o08/Zjx5iY/JpDUd5YjgOTs74+DwiP3DQyajETI4Xp5f8PT0jDv7B8y0xxDIlUoKyFFBqRwvXzxmOnnEaKQ4uTjnix5/2qLwN4H/6Y2vXwEPY4yXQohvA/+bEOKrMcb1T/7gm2Ew+w8XnHOKZpfkIHmPd/kBP+AFJ7wGraUisHNC7sxOafe10zKkYyd/LoZUaEWkxDBmzCFzDIGH3OctHnKfY57ylJIRI6ZcseS7fIcZE77O14gENApBTk1DQ0tGQUYCeu6GogExTDUEhpyMIpl1hteV1A2Tm63Hmtd/FqMCeS6p6nUiFBFxvUeNxkSXos57a7lqVmgqlmqJH6CnWZ4xnU8xIqOp+xtasMWxbSo2omNWBO49OqY+aWnXDauqTfkFRuK8x/Y9UkQKZRgVRTIm1R1d33C0d8yrVyfkrmF7/f+y9yaxtmXpnddv7b49/W3ffW1kxIvMyMzKdKZJY5epQphJ1QCEyozANmIAEipkqSQXKkYwqhHICFVJJXkAEkIgGQkLMWBUQk6Xu0ynMzMiIyPivXjd7U+/+24tBnuf27x4L/LZWCgS3f/Vuffc3ay19t5rfXs13/f/L1G6Thrn7O7tsXx2xt3791mvlhjCxtR3iXyXk2iFaYZkussqq2jKmNJUeH7I0fE50VznuWhVm8oy5emjD9slvHWEY1k4umKkVWSy5EWWkdY5vcBG11tVqSJvCDwNXQejNDAqga7pyKZ1S7YcBykEwjQJe0Pmzw6pypYJytB0grBPbbUkqbu2wZcPXEILPNOk3zdphGSeNmhxjR/a1E3CR0+O4XRNmmd4QcC9B2/jWTajyYTzZYRh2khZkxcFoW23pCpK4bo2jaFjaiZxFFFWFVWTEyUx08WCL7/7DrP5jChZY7k2RVFjmgaPH31CP3BJ4wjXd4meRkzGQ1SVEecFuqh48umH2E3KwHFoypyD2wfIqmV6ytIlP/zBDF3XOx6PN8Nf2ygIIQzg3wO+tdnWycUV3ffvCSEeAe/Qqkhdw1UxmK1vT9TH/JR3eReJJKSHRDJizBEnnRHY+DBe6jlcDXja8CpsnJs3E40VBRsCtz4hd7jNAbtICiZM2GWHbcbssMWaCJ8+97jHGWd8wI+4y20mDNERmDjknbu0gXExz7BhiNyslGgYOFgYpGgtbQgbavqWtakgJWXF8uJ+WDrkeYqw28jJJM4wDYuqVpiGhV51HAENCKNlHl4sFrhuO6zQdQPXFCRRiqxbnQTVKHTdbjURTJMHbz/g++c/oNZybA+ytKbntRN5YFAUBa7tUSnZuv5qAlkrTs+n6E3F7sDng0dP8QIfadpM509ppEkcr0DVnJydUeQZvX6P+SohW9Zo/QnlIkI1NVsH+8xmS5pGkhYSbxBwa2vI3qSHoUkeLZctv2BTYzQV9XpB33HYnzicFBnbPYc7wxAldR6/OAOhKGRLk+44DnlZYShBWbakqI1UTOcrPDfA0kySqu1ZRekaqZmEdoisMvZ2evzSLzxk+ehDLA10zSUuBM/PCtLsnG/+wgOckUb88TlpvsKyLVRWslxHhAOdQW+A73sUVY1ttryN0XpN3vE71HWNY1uEfohtWxwdn6LpBlES8/RZyWRrzGhrwnQ6ZbZYUFU1W5MtHM+nqGt29vfJ6obj01OSLGF/e9IGw1kWGYo8Sxnt76HRoBs659NzwmGf+/fus0pzdN3g/Oz/m57CrwEfKqVebDYIIbaAuVKqEUI8oNV9ePyzEoqJ+Ut+QEpKQIiBQYCPhdlNNm6Cmi5/w2YJ8vKnZYTeCNO2bsUa4GGjo7jPAe/yNrtsU5GzyzY+Lh4OW4yZM6dlX3TYY4cpMyQlVhfPYHa8jFfZpAWqW4LcTHJqVxybrAtzAe2buyHtfCnX1Fzq+wWuhaYpTNMkLXMMzcQ2nNbZxzSxHIde0IqPOK5LkeeYloXreciOwamuG0b9AUVZcT4/R0mJGzhICbqpE+Vrhrd6aA7Ei5xsXbO/u8f5dInleDx99hxNk22wFYow9InjFCUbHF2QNYqfPjmkF3oIwwQnZOvgHo7nc3J8iO9YNGVOmuWss5omrgkmPXYNjToT9HshjYSz6RzNsBhu7XBry6JnVMRJQrxa4TojtCJlf9SnZ8M6ragbHcuwGTgOW45gvVyi0jVJ41HrFnUjoW7f0IbpYDkOnh8igNUqYr2MyNMMVbfKVes8RVguehIztE12RwGyqJjN1tiGYL7I8RqbWOnMk5y4Mch0EyyLIsmxTRvNsjibTSmaNs6jF/pkRYltWQyHfbI8R9N1Tk9PKIuCpqroBSFh4GO5No1qMIRBXTf89KOPGI6GBGFIVUtWqxVFVZKVJWm0ROgaXq+PFBq6ZbNOU5I8p4pjkiihGI6oBQReQFHXzNcRlaahdIM8ryiKhPX6b9DN+VW6D0qp36NVl/6fXzr83wD+ayFETStF/58qpeb8DDQ0zJhR8xMKKnR0QgJiIjaB1JcGQbv4r92iLv6vuvUCves/tBOGY3YYkbDiPd7lDgdsMene84IRA1wcNDRCQgo6emxgiwkCgdNNE4LeLUdatFSurQmqqDoHauNiklFC51ZtkVF1t6NhwwBhAdqVOQXT0lCqplE6pmEhK4VrutRFQ5qXlKVkZ2cXXTOwLYO6qegN++iaDgosy2mpZJRkMAiwHI24immsluE3ydaUaUWl50z2B/huTuzk1E1Mr++R5g1lXaPrWssrqOkEgUcaJ7iuh22aRHmroaDI0UXO3S/dokkSgjBg5Dk8vPN1TqZzvv+j91v24jiC82fsjge4kx6r+RTD9DBMC8fvUSqdLz+4TXz6hPl5jKZpmLpOLwwRssYNepw8PeSsNNA8m9ViTdSAKksCywTTQRgu6fyMKk4wTJO8yBmNtzBNk4Nbt1o9iaYhWi1QhUQ3DAzXpjFMqgrKKsa3HKbTjHUm8V2HVdJyZB5FDc7emJ8+P2O4dRvN9anTikqCpekIXWcVrdHSjMl4SC3BtmyGwyHT2QzXc5FS8unjT9GEYrFY4FgWnu+R5gWic5SaL1YUZcE777zDrYM9nj1/0WpKmAb9/oA4y1CWTdAfUCOI0ryNyRAGtdL4yaNnRGlOv+cz6PeZRRkfPH7O3q1d8qKiyGucv8mAqNfoPqCU+q1XbPt94PffOPfLM7sgKB0fjxUr5sxxcC6a/ybIacOqDBtORnnhnWhg4nT0aFtM2GLEu7yFi8WUE77O1xkzZMgAE5OSnFG3AiBpcAmuT/51y5ltTKRDg+zWEjaOU+2qh4berXQAnS7Fht7Fxu68HLRu1aOhJKZiwZq2k/Ubf/9dDKtCM1tyT0szEEpAregFPebRCiUFq8UKoTUkyYog9LEthyIrqcqGulYk+RpdwTpaMRgO8EyHZb5CNyDLUnTdwAlcNCWY7Pc5ONjjB3/+MeORzzJaoevg+y19+MBx2T24xWq1RtMUVV21nAsliEbSc2xW53Pu3r9HOpsi04jcMnhw5zaHx+d8+uI5LjVjQ/Huls3jF2cs5xnrquUuqIXGyWzB6bHDlutiWB5FUxBaNt/4yjusn77Ph4dnLA2LAgsqiR16yCYlrxSuH6LMAUJpNI2iaSRCV9i2zd07d1BS4pg6/f6I58+fgglSKGzX5q0vv8t8umBoGYxUyve//wEfG4rQFthBiDB0ZlGEPd5BC0ecRwsW1Rm2HyJnK9ZJQlEWeK7Dcr0m7PUZjkfEacb0/Jw0STg9O8N2bDzfQ8kG0zJJ44jKNOlNdihqyXKxoHFNPN8DJOfn55RlRRwn2LZLEITURcZev8fZfE1WFgS9MVm0Ymtrl/UqpunBerXicLogrWsyKZjNVzSagRQm43GPs9NZ5//yZvhCeDRqXTOLSVgToaER4PEyjdomzvAq1IW50PC6qcJ73OVbfJM9dhkzJCPiFjs84C1GDLG7nkFBhocHQEpCy+7kXAwP2jd+2/X3OgHahOLCN2xDBqthXDhZGd2SJEBBjt6tTRQdHUtMTMwZEUf8Q/47/vNf/wWQC0zbplQ1tmWSLlNU1iA8QTgesj2ZIGtFXRU0SlJmOct1iaGb5ElF4A2oatB0E8vQOT+ZsViu8AcewhbYjo0RGKxnMbpjURU5XmjTUIIDZ4tjTMek17NxHYtoXZKlCadHh/R9h1UUoxD4YUiZl+RljW9ZKCWZnZ/x9tv3qQKbs9mcRjOJ0zZu45e/9VW+PDHxdEWe5JwuayzLoMyL1tff9Tg+mVEGFkmlIYXFarlgHa/xJzt88v6MSDgUsqFvGXzzb71Hef6MxfNTpB1gGC5NnBD2Q1ZR1I7fi5IsjfBdl6pISRYVssxIkgjQ2N3eRtc0ekGfiWvx9f1d3rk15rt/+H3+4uNnHLw9QekG8zriPF4x9kMGQR9/MGHrVsDp2Rl1XYKQSNng2BZRtOTk5ATTasVolosVspGcnZ6xu7uDbZkURcHWZAwKzs7OWCUZsq6QlkEvDEmTmKooOT46Yndnl5OzKb1+j7rMaJTqeDUTPNcDBFLC9mSL47Kg9BxGwwHvPHyHJEk4nc4YjkcIDXo9n7cffIk/+ld/zJviC2EUABwsYkoMLDQEWRdPAJf8jK0T02ct3oZfQUfDwuBtHvAW97nNbVxsYlY42Oywj4/XOSy1PYs2Dk52C5blxSrBRti2HR4UmDhcBnK35dI6c2Z2qxAbtqdWMKb1cZSda7VCUpAz54Qlz/lH/HP+y9/4DpRz9E6gQBgGSgl2d3ao1wVplBOv16RNQS8I8AMPKQyKJqGRDbbjUJWwXEVkaU0jU3zHQdNtsiRBRQWWNKnqDMe0GY+2ePLiGKhQQjIeDrj7zph0WSCL1qMyj1smIU0D1VT4rk2WpmiGRVWV7cx51fILYlnM13NG44D79+/w5OgFP/jen7HIJWXdYBoWv/j19zh+8gnDQZ+wr1C1Is1ThCwwhEtS1GRFyVnS0GgGvmnw6PFjdE1QuQNkUaM3Oa6CxdkJMi85jnKUaoizE0DiBz5ZWVHkrcGenZ/h39qD2kA3aJf2EHimw1ZvSJYVaDX4pmDSM9jbD5nc2uKjowW5VOiOQ2O7LKanZKenOMYuZilB5DiOiZJaS9jSNPR6ISqC+XyGZhjYpkddtSK4Vd2QZhmNVPR6YTscS1LyLEdTLWt0XTWslms816Gpa5azBZbRKoecn00R1Ozfug2mSyVbtW1Lh6bMQdcYD3t4rsF4MmZ6fszR0TG+ZzMIfOoyJ0siMr9HL/DeuC1+IYyChsaEMa2EawpAzYYf+aqgy0ae/mrMQ8uH5OJ2xkF0kQo6NiY9evTp0SNkwOjCb0HSYAA5GSVFNx2oLuIn20lFG4XsqFUMLMDoCNfa4zbMS617cEOFoqGkJO5Urdv0JTklMUsizvlH/HN+5z/4DuRLVJ1TVZDHFbplIWoN3/WRjUURF8TrNbpngZIUWUpaJiyXC1zPRQBh2McQVStg2rjtZJ3pYFqKpq7J04rhuI8uDEzdxrN9omROWZccz04Y+H12b29hKpc8+5TzsymabnR6Cx4ICDy3HcM2klTmGLrAcExM22C1WPP9H/6YT59/ymIdUaOTFAVZWvGj9z8mjF7giJrTec1s1fazAtem1/OpZM0ybidMNaeHScWw75MuZyRFg+b18EydwNfoazXTw0MKVbOoQNQNaZmyMxogXJ9awXw6R0mJJhSG1srhjfshH3z4U1SjGPaGxIsVbt9HaQpRpUyPj1lsSSqVkQmdaZSQLtbEWUYhJYHZOkgdHx1TN62gS+CHqKYhTlKGgz5FVVKU7WpH0zQ4toNhGUxnM1arNZ7nsL29he856ELnbJmQlS0nhmEZjAYDbFPH1A3KoqAoSkbjLaIkwTIER0cnjHf3GY91giCgSCN8x0UWKYameOvebc7n56wXCzRVY2oW5yfHPHz7AWm8YDE7460H9+GP/vKN2uMXwihA63y8xxZT5kSs0VDUlN10oHbFUbn1VRBs6Epal+N2MrDBRxAz54wT9riFhcOQPiE+FjY1bWBU22BzUpZELNAR1EgcAhxsNrSwOg4admdETCx8RBdj0Xo7WujdBGNF3SlOl1SklGRUSApSciIijvlt/lv+q//k38bR5tSqoqxyGjNAN3XWsxV7ezukaUFTtRJujx8fE/R98nFBEASgNLTCRdNtlCEYjnpoqsA0bSxlYGoCwzaoFiWmsLBNmypusHyb4XjI4yfPMTQLQ5mouqaqJMtiRRAo7C2T9JlEpiVbQZ8tv0ee1+RWzSyNKOqK0cin5/k4osEzcyqr4Wg652QRMQpdvvrwHsbjJ6SOxiKN+NPHCQ/v7nIUn7KsM0bhkCauWa9z/H6fu7cnVEVBUrRBbsvVmqKEOK+YBBrbYUiTrug5Dk2dcjxLyaUgmy9RsibLC4RaIqTCsdvrt02TLI1YLWdk6yWHh0f0+0OGAx9VFSRxwvZgyM7I4e52yPxozvJ8TlVXrAqTslGUjaBpFDqSvf1dDm7f5vD0iO9+97u4ts14MMQQOn/rva+hfvRDTmcz0qJgMg55+513+fH7HyJ0g7JuOJiMmWyNiNdr+r2Q0bAky09wdbg37uGqnGyVsawaFAZHsxUlGu89/BJ1XZIVJbqQTM+OGA4fovcCtKZVANOEQpvNKfOc0WBA4PrMZ3Mmowlm0/Bgd59PD8/55OOfuQh4gS+EUVAoCnIcHN7iNhUVj/mU6mIpcuOpoHUrEZvJxta/0UAQYNOjYJqQnAAAIABJREFUYYAJRECOjsDCwcPDxWfDw9g21oyYFRUxBWsKUjIqTDwEqvNDsLpegtn1UWRH59pyOrerHfm1nkNKSkOBomHNghJJxJyIc36b/55/8pt/G62eo+oUhUIzLJKyQtdBVZIiKUhETj+csLXrEUUljaqockVpSDSlU6UtY2RBzcenj6jRMQ2XYeiDXqMs0GyBrVt4hkse5wjVMvzaro3n9kFJonhFGmcIXSAsHWdg8iu/9jXOny5Ink1JohpddxBCous6gWXT8zy2hkOKeMYgdJCN4my9ppE6qhbcGvb51X/w9zlbL/jB+5/w6YdPyApJrz/EWJbtsKCqSMuaWlszHt3m/sEt/vCP/phC1NjDHkdnCxoFSRwTAa4mMW2HwdaYo+w5viWQWYGh2TRKMQ59srwmWsVoSsO1Le7fu0OyWrKKYtaLJf3JFjt9A1NpPDuZU6Q6zw5n3Bvfx3FczhdPUSjizqnLdQOUEuRJwpMnTwh8jzRthwLzxRIkLBcL/uRP/4zBaMjWls50PseyTLIs5eG7X+HJi1PyPGW2WOB6JmWU0jiSrcmI5XKBUefcPdil6jgui7rVfzRMk5OzKVVRMOiHCBS3XZf9cZ/TJ494772vMpsueH54wmgQEMdrTFPjXm/IzvaAoqhZrCLu39rHNltBm6osXtf8PoMvjFFwcdloQN3jFgkrcgpiSmIyNuwHdHMLGxjQLRaW7NPHRtJHsoWH1TkYm7iY2J1ByEmIqIhJu7+bGMeENYKkmz5sYyj1bjDSrnDILoxaUXT9goroQq2qaRX9rgwdctasWHLC7/Av+O1f/xZNdoJB64mo6RqNpqFpAqUUezu7beNzfRazBaE/4M7t2+iGxrPDp2Rpzb07t5lN16zXOdtuQNOkLBZLPE8yDEOkFKyXEWE/AKkoygrDtlACTs5O8EMfJRvSJMW0TIoyQxM6geuSFTm6AQe3eqRaQ7nISNKYNI8xdQfPCVCZpLJyyrJmtlJUjY5jaCil41o2H73/IQM9ox8Y3AskpafRLOdUjWTkuFiaQdLUDPohhuvw/NkLdkdDer4LRkMiNRrVSgZjGszTmL5t0BMGMqtoakVTSzzHQWiK7Z1xx6is0ISB53u4tk20XKKplvvw7t4Wumhozp+xvzOCns48X/P0fM3/laVMJgPOswYrCFHLjLqqMa2Ww8MwdBCCR48fc+vOHfZ297EsC00ICqlodJ0nLw4ZDkYMegPyouQnP/0ILxhh2g5aWbKKEspPnzHwAuoG9m9PePut+7x49AmffPoMTanWH0RvhWT2RyMOT85YrDOW64Rf+ubX2R6MKCyLRNfYdi1KQ3Jrd8xqsQAU8bomSz7h7/ydX+Xu/Qf80Z/8GR988gn3didUTUUv9IHZG7XHL4RRMDDYZQ8TRUrMMS9w0PHp41CyIrqYTdhEQYCkjTMQBBgEaIQ0eGjsEjImxMbsIiEdCipSIlbMiVnSkNOQkZOgd27QWrfSsOT0wpxY3VCiouxWFTbxFZKampSYvHNo0hGU3U9OSkEOVPxO67iJI9pIvropEYaJUgZlI7AsCzd0GY+GDHoDpucLZCVpKslkvEVWJJimRRwlHB+fsVqm5EWJ7dgkSYpptPm/eHGMF9ooowZNYGkmutKxvHb40wiJpsF6tUbJlnh00BvguBZKSpqqaLUlHYGz42FshTx9doJRKMqqhqbE1l3KNMd0HHKpmC9WyKJ1kdYJODs656Nyxre/ccDbA5vc16iVRt1IUg2miyV11VCXOV7oMJ0t+Vd/8qct47RmkqqW9l1pgqwqsXRBKiVpDYuzc6I4pxaCnVtjkixmOp+1NaHRKMsaQxQ41gTPcVhOT7i7f4u9r7zL049+iFuXbFs10qwI+1scez5PTk7IDIfa9BmOt5mvX6A6Wfl+P0Q2NbcO9ltHMNvFdTwaFIbnki8WeEHATm/QRr00DYuoYBWnHJ9H5GVD0OuRxCuSuEBWisBtaJ4+Y9APCYIeh9MZd27t0vdC4iTB8lzSIqMXBCxWCXVZkKUZoecj0wTfsinWK4aejdgbMzUhzyscx2M6W7BarTk5P0E3FbNogSYahGZysLcLP3r2hu3xC4C2QRksOGVEr6NNk5RkGJgM6XHKko3ILGwGDq1mxIQBw84ohLj0CDoTYmJgcc45S05QZBQklKQoauiadUvVVna0KRo5KXNOaCVd2l5GhaSi7sKiNAoKKnJSIjYUbA5e1+uoSEiYMyO9Yp1dU0ezLZK0Jq8allFOZVoYlgsI8jQnETFlVnJydEJdH+N5LpOdMUHgtq68Wc3+7T2Gwx6LxZRQC2jqlrGnqWp6wRb9SYhhaRRZjq50UJL5eonUatIkRlOSMsvwPYckiWgaCzSoy4qcVg9RaBXByGPXGuNuhyA9Hr9/SBbnuJbO9DxD83yErrE9HtCUkiyJCUyD3ZHPlquTRkt2HYOohFTWzLOasReQZxm2aaLJmp39A86ODxmNhxxOl6SywbAslNCpqgJdQS51ZuuYolaUtaI/7OG5Do5rcnhyhGl55GWJ0DQMXef09Jg0MtgbDxkO+szPz/AMwbe//g2OHv+U0LWwtoekes40r4hryfbeHuPRNmfTBb3eLpahs16tSLKcqio5uHWL1SqhqSUFkqaq8foDnrw45ptf/RomcHZ8jOv53O+P+eTRU6q6aHUc9XYVSwmBEoL1akW8WlLXDZVUrLKS4XCELTR0Q9BkKZ7jEEUNNZIff/hTZrMpd/f3ONjdoVQgVAVlwv29bU5PpqyjCN/QiBczAs/iV3/lF9F0g+dPTlqW6/WSN8UXwigoJAumnT5jyYA+++zh4PKI57jYWJidZ+DGb2ETRN1wxhkaJmNuYV0hVIWKT/kJc07JmOEiyYlQqI6mvV3+LKg6I9JGS+TEiE5LwsPp1iJccgp69DvDkXVuSEtKSqpuItLEJCOjoGDOnAVHF9d5eHKO75iUUiOXkNUCzTJJ0gypScLAxTEdzJHbiqekGUVVEicr0NuQZ8tySdIUy9a5fXePxXyKLlrNa1VrjLe3aERFEscI1XqDlmVOURaU5KAMEK3Qreu45FnaSrEbgqpufSkMU8fzXM7jJbWswGoYhn327/U4fjqlrEAZ7RVXdU5/K6DKGo6O5/QDg939fSZbQ356+j6WrmNrAl+r+PJ+SGSMOF/GOKaOrUEtwfJDjmdrrGCApluoKCGOIqymZuQZJFlOliWUVSulFoQBeZqABpoSeI7LsD9pl/CaCqGBbVvEScx0OiWeT9nrWzS6w0dHEZOdMY8eP+U0F/SHY6QsMa1W39GxDHbGI/q9gFXP49EnCR9+8D790Gc02uHTZ4cYukkcp9i+h2nZrKOIe/u3WhZn28B0fU6PpxiayXhryGTc59EnH1HmJb2gdb8+PDwEpRhtb/OvfeeX+PGPf8RsvgDR9oBdt5Um1ERDpTQOz2eskozHh8dsj4fc3R2wtzMhi1IOdiacqDOWUUSTRTiWi2/pSM3C748IDLDsnzPnJaCb2W+76msittnuRvGbgQNcmoINBGU3CGhXCGpyUgRzBsw54gMaFClLShacsiQlwsRlm30UAapzkGr1dyoykm6oUFMCa/rUKHRsEnJqShxsYmL0bmUhIelotCROx7kQdZoPUbfE+g/+9j1kmZKUUCtFVkuSEnzPRIrqQsMxLwp8N+Dhw7dJi5zT6QnLaI7rWAR+n6YxKKuCo8MXOK6Baxv4nktZ1Ni2Q5mnFDIniSOaSpKnBV7QajBouiCLSgzbZHt7uxURaZpO4qwhShIMHVxXoQmTOInxXAvH0lEqZ3s/JBx4lJkiXmc4lsvybEGWRqRpjWlqFHXDn/3kkE+fHyOKBEc4pGnFuGfh9Q1OpIGh65RZye2dAVmSYLsecSU5X65xXB/KEk/V+LriKzsD5lnD4bJglRf0+n2Epgj8oJ3vSDKqZsFooLUCMaXEcjT80EdrGmaLJe++9YAdX/Dp8ZTnUc1RNicrC+zhFnpH5iIbSRSt2doaUxYJy1lCGAZ865tf44c/fJ+/+N73mezcomoaikriOC5C6Ri6wfHRMdujEePJmHWy5vmTJ6imQlY52XpJZrerGDQVq+UKz/NRQqduqlYMRjUooQj6IUVRoGltdKXnWhRVQ6O16tZRUVDJhqzIcU1wtT7UkmQ95ctf/hJ+EHB4dsLJfMbhs0PC0Q6mJpifvmDbrP4KbfELAIkkI8HBwcdlxjk/4Iedy7LbhRZZ5FzSrW56DA2CpvNFbGgwKekhaTglJ6akYMkpMeesmKFjEDBmAUhGSAxatgWHipKImFYdQiBJWHBKQasn0fYnavr0SEnxsMiIycnJKJC0xCsJCU94yj/rPL5/5Wt7FMmM0HeZRSmWY1NLhTAsHDfAshqyYs3ZtOKTjx/zy9/5Dlqtk+YRbmBSKgshBY5lE8UV0+mcOIm4e2cXZenIusExTXStVW5WtSBwXRbpmjIv6A9CVnmCO3CZxysszcD2beJ4TRwnWK6NEu243zIUupLIqkKTIKqGfm+Arhk0hgBDQ/dMvJ7DwbiHvNdnPs05fLbg5HDKep3zw6czXFOxFZi8tTUkqQq+fu82upbz5NEZKEldSqokJghC1nlGWbUir7IsEXnCSK/ZtXX2Ldjb2mWVHnNcxRiOhWVbvPvuOxwfHjObrSikYj5vXXl932GyPSFNI6osxTVdptMpbgl/+pcfc5hpxPGanaFPz3EYjbdIy4Iyi4njJbqs8V2Lnd099nd3qOua8bDPjz/4kMfPnuD2RvQGI/ywD0JHk5K6zImiFY1rk6cJ40GPpqwp05jl9IxR3+Jgb4v1KuHsbEGDQDdbGfqyLPn00cdsb00YTcb88EfvM1suqPMcz7Ho9/usshwlFbKuqCtJKSs+/vQpZycOO6M+437A+z/9kK++9xUaqTh8cYYwLbxFTllk3Bk4eCp7bft7GV8Qo6DIyNCxmLJAx2CbHXqEzInQqXCxicjZsDC1fYdWs7FCsiQhxmQHD5ecmiNichISVpyTsupmDDzOSdExSYgxCFiSEdDvAp0kOqKLtMyRNJSorkdh0A5YWm9LB9FNKJZEROgYrFkzY8E/4/f50p2QMq0QzNEFTJc5ALZT4Lp2R4Pm4A0tTGOALiT3bt9iMOqj6ya6q5OXMXG2Yjld01QaQTDmvfe+Rl6kJNGC9Sri/PiMwA8Yj0Y0WcNoe0KcJNy7c0DTgBSSYpbTC0OaUYNlmhR5gWxk12MA07URmoYuJbahYegGem3i6hZ208Z3TNdrdN/D9i3y9ZJ4dYipdAbjbUxnQOAHfPLREbXe6mws64qzvKLnmyTZitCuyNMVnuMhNNDKnL47ZL6OKNIClE4Wx+x6gi/vjXhn7KKXKU/PjkmWMxzHxHEt/MDnyeNPQQrGozHBcMCTJ8+I1hGFDqenp4SeTZIVDPsTer0eeXpEXjVo4TYHt97i9shlZ2tIgc7pYk0UxUx6AaLMeOettxj0fCyjZdrWhGJna8y6qJkuYqLnKfv7bRCaIaE36CObitV6SVMUeI7L1rBP6FisVnOS1RLpuTRliaVrzJcr4qxEyAapJP1BH8PQkbLh3oP72KcBdVEwP59R5BVNUTEeDlBVxqTvUxcFy3XGcZ4yXWXsTwJu7Qz5wQcfMJ5sM97eIytqZtMZtsqwe0Pu7oRv3B6/EEaBzm+woGRAjyVzGgQLIk44w+jWEAbYLEjYREJsIh8UGik1CSU5gnNeYOFgYFEyBTJadUCLmJgGk3OOabApMPiYI3Y5YIcdRvRwMLqmX7Fk3flGOggMrI5uJSSkQbJmTUbOigQTmxOm/B5/wN/9lW9yPvsxSkFZVBiG1k7gCYWWZ1hpwVj06IUhvleT51ErLR4OePb8KVGcYntuG0qdVPhhn5PjMwZljeu4CBRe598vdIOqlixXMY7nI7AJA5e6roniOVGypJIZVW62pMCyk8VtJGWaY9kmthD0x0OWszVC6YhGYRsWuhIUed5G5mkaY6sNXhIio64a1lHEZCvEdx3c+0N6ExvdDcnSnGy5gDQB3eTDxRrPAByXwChxXI+dnRHn65xKGdSyoMpi3tnr868/3MUr5gz0mEa3WR3FxHGNFpiU2RpZB8xnEW4YIA2NOMlQUjIZ9jA1RVMWFEqipCCvGs6TiumLFVZ/i4npMRwGuJ5FlKSYjkO6nNFzbfQ6ZzDogWZQKZ2mVNSVpGla+be7e9vURcViHfPo0Uds7+6xWq3wPZcgcFgv54iq5u43vkFZ5IS3JsABh8dHlI3C8hVmT5Kdzihkg2xgGWe8OJ6xv7tLgIGjN9zf28H3Ax4/fszp8Qm7PYtvfvUhyXpB33fI84zDWcyz84QkWnO2iFpuyqLg5CzG9z3u3z3gF7/xHj/50Q+p6oJSmm/cGr8gRgFMdGoqliwvfAQyMiwcdAz8LhYh7bwNxMUQol1HiKgQ2AgkITouFik5TScUr7Ap0Vh3y4ubocWUkkNWLJFEVDzkASUGNoKiY1nSSLHxsPCosJHYQE1KREzGgiURGRL4Pf4PvvL2bSRzylawhxpBUwu0bkpEE4pGNsRxQVXX9H2bKpkhazg7PSEvWsquxXyBrhugCXTHJOwFlE2GI9rHZts2lm1h2zae55OlDUfHC4bjA3RD5/hsynR+iqQk6DvkaY5sWoaoWlb0eiFxvAYl0YEyy/G9PkXWCrB4rk+RJliOS75KUbqOprsky4imbIjiAs9y0XRBka1AN3B6BpVMcUONoDckXoDWSLZG+8zOZtR1xN5BgGFahNsj/u9/+X0Mx0JIRU9X3Als7rgmMinQm4Z5YzJLGkqhE4Y+jmNwfHhIlinuTMYo1QA6juMSuCY91+LFs6c0ZUVVS85ncz759Bn7ezvcPjhgtY6oswhndIsoXvP044+p8ozh9phxzyWKM56/OCYIexi6TpasCTyb/b1brJKU8TChrBvirODp82eEvR6rKKIsMyy9DZGLzs/RNVjEqzYYTUqEZhIEAY1uYnoexyfnRFFCkVc8PzonTXO+9pV3yaIVmpCk0ZqzkyP2drYYuIKmiIiXM06er7Esi0yaoEmUpiEMG6U7SCGJs4r58pjAtwgHfc6XGcsyxrd+zoyComHFFNV5D44ZkXW6jFYX06AQ+PTwKaiJqLpOfUPLsGDhYtKnh8KhpiYj6hot2BSYnBDToLMmw0QnQdHgENGwJqLGwGeARsMYF42Cmqab0XA6/iQ6D4WCJTFnrHnGCxQaf8B3Odjtk2ZTdNOikbIV6hCd+RLt2VK1S1NxVjGdrXn49l0ePnxIUdUkacbTZ89ZLpd4foBlW1R1TZonjLZHLBcLDLudrBsMhjSyxrZsqqqmimOyKuHxs0/wPI9VvKKhYWt7QtlkNEoS9HzSLGnLBigBhtlyTa6jlCCwEYZGVVXkVYHSoFIN450JcVZQlDmLaIkhBF4vxDJgtloilWS8tcMqTsnLBttzmS0X7N/aQVUlyrTo2duMH9zF8/vkVUuRc/98H1VDNFtRnOXoVU0d55i1Ril0fnIc8XyZIDWTwWCI7SpUAYZhgdDxgh5N3iB0ndOzKcbOhKKWGLpOrRSzxRqhaSjNRAodoZtouoZu25AZjLd3ePz4MbUSLBYR63WK6Xicz55T5jnDfo/DZy94+PBt8rrlePCyCiPKkEVKXlY4pkmjoFGCrd6Avufj2CZlkXN0dEiUpmzt32Z7MOBktkSWFf1+DykVul6TpSlxkvD06TP6oYeschzfbwewZUXtOAjTQ3dD4nnEpBcgC8kqWmEaJv3RmLff/hKWLiiylO/9+Z/y4nzB4eIvmC9TZJoy2tp+4/b4JiQrt2np3Xdpybf/hVLqd4UQI+B/Ae4BT4B/Xym16Biefxf4e0AK/JZS6vufl4dPyC/zy4BBQJ+NCtSqE3tVKEJCRox5yiEJCSVl58zUBj/1sPkS2+yiYV9M+i0xUVQYTEkIMCmBjBKF3kVlehxQUSFxMNlijAFtVw66hUuLkCEuLibOBYlriUdIQUBBSB/4Li9OVvzar/2bbZRcVbXxGa1FQBMtIYqhiVbsVUj2DiYMRge4jtFyC7o5khE7ZcXO7j4KKKsKr+9hWgZnp6c4tt0qAgFhuE8QhNi2TVHkrNZrsrTAcR1q2SA0gW5A1eTohqBnT4iiNaJzNy6KgiDwOqn3DMM0KcsC2zRA1QhaNijbcpmtVpSVZP/2V5CVwtA1ZF0g6zay0vVDzCjGtBwqJRluN+xuTXCdlqZcNhLXD5C0Qir9wZDdB1+lLmrOj854/uFj7kwCnEGII0t0BbY94+GuQrge3/jW18jTBecn5/i9McLxkVq7rrlazllMzzB1ePClt3FclzjNUEpgd0xVnufhhgPeeusBlmWR5yme5zHe3uPenTtoVcViscL1Q6IkARS2oYOssS0DrxeiGSbT5Zq31wnz5YqyqhBKomuCYb/HduCxPR6yszUhilaEH3+EYTkMt7bZ3rvF0ek56yzHCUJOT85Zr2NAsZhOMQTcPdhnb3eH+XKJPxixv7PNvdt7WJZJr9fjxz/+MdPplEmt2PtSQ1PXhH7AcDBo1bX6Q775y7+KQGDaNnFeIsuS3b0d+ItP3sgoCKU+f/1SCLEH7Cmlvi+ECIHvAf8u8Fu01Gv/VAjxXwBDpdQ/FkL8PeAfdkbhO8DvKqW+83l5fPvbQv35Z1gcfz4hZdNKywOgUKKL6lQCIQSiu92tErlCqQahms0GoCVaubQmdJy1CqUUSkmE0Nrju11106DrHQ9kp6IntLZ7IiVomuqERlS7j03XpctTtUMxpRRC01radFSXveqO69JGbKxcl7sEJbtNbdk3USmbw1SnYSBEVybVLrlpQmu7KmpzmZ0coLqUAxRSA81E6J2ElipbqXZhXL79leo8UxR0NPKbMPZNSptZqIvrBi4fBm3ZOkZoTdNBaF0IuUComk5Pvk1LM5CK9jlsrrEzwKhuCV2oVggU2nuHoKbl4NAuyiMu6gKq9U9AteteSmhduS5lEdvHoHW3Xmvvk9Au9rd/uu9a+1w37Vsp0E3re0qpb/+sOvwmzEvHtCzNKKUiIcRPgFvAv0NL0wbwPwD/EvjH3fb/UbWl+WMhxEAIsdel8xp8C8mfd7dIvv4w4PLxtvoASmjIbpumWpLUtpvelb/7ffVH60KrrlQlYBNVIblQI1E61w5os39dsdo0Ns/soqybitFVgCt1EyFo+W+16wkp0WZ9LS/VNSz9M+XQhd5WamgZm64coHWhIrq+OVpennz90K6Ioj3n4sJVt1+7sFEX16Da7ReNS3QVvbM5XXII1VXc7jgh2vkVsTEcrSmgZcPW2Qi4awhMxRXbqAM2aHVn2Lqmtbl2gO7+XJoC7bOP7GKDvPa/0rVW+n1zmDC6a+0ic9XGULaUdZs7JAQIvW1KUmykBDcNehPT22bUkvFclu/yvutcWHSldde0afDNxbUgLr15xdV6ee26Nt8VQtQgNgPfN8NfaU6hE4X5JvAnwM6moSuljoUQm0HLLeD5ldNedNs+xyhsiFTEzyz8xm5erc/XwqOUuHazxJWj2heduvI2617CV1K+fKuIK5X9daUR1w3GtTw3eM31XDlIvnzMK/LbvPU+Py0JYkPlfT1w7GeW51parzLMV7d1d0xsSrZpVZcG4/p92Tyla3f3Ego0cbUZXz5nKejI7TbBcBujKK+l3CZzpSyvKsZncP1evHzVlwa8zaXrfF3Dy/+3nTFx5bzLMujXzrk0zJv9QrQvhOs3qKuh6rKWyqslf6kA195fr623n483NgpCiICWf/G3lVJr8focX7XjM8/mqu7DnTt30FqXjtec/tmE2vq4YVS4QuUq2piGy0bRfTqX36sJXOnA8dKT4NJCfx7ExZvpcwt75fDPon0/ft6B16vWqyv6JrT8ekV/lcWSL+27eoyCN3gG4iIw7LLxbgp6WZfl5cZrf1/O8/Kk1i27678pgRIajQBB3T1hExBdT7DNWVN0xrvtA765M++rruv1/7/6jly9eu1i02fSeblQr7DLm4Z+0RvjpVopLk3Bq8uyMZIv1+Of/TxfxhsZBSGESWsQ/iel1P/WbT7dDAu6eYezbvsL4PaV0w/gSgBAh6u6D9/+9reV+Gx/+ZVQVyve1V5r+w2EBpgX1vqaFb486qUH9ap8P6fBX6vTV7rj1/a/Is2LCvxyTvrPeqVdK+YrX8YXe8T1PZ81x5c7Xr6Rnzlms+ea2b28PnE9t5dN2WXFfoWRu5rcZg7mSnm66Qd0FGIT86JMpNg0IHWlbamLdF91G6921T8Pl+V69XhRXUtCvXRcm0c7UnopP3H9bryc7sW5r9hzHVfd/V9XFtX1KgRtj/jVZ3wefuZAo1tN+D3gJ0qp/+bKrj8AfrP7/pvA/35l+2+IFr8ErD5/PuEip2sv9td9LjvFGwNw5aJFe0mqC4RWF2/OTQf0ihV9g7xeu0Nc+bzqdm9q9GvTfOm60dprEdr17+Ky/Ne+dx/x0ud6hXtFWa99rqalv/S5PP+yQ341zevlucz/epqi+7xc1pevUXzmJm2e3SZvo/2Idg6iPWOTztWyveaW/5WaxNXjrz/Hz5q267lcnLl561w8AvXS4eLaaV0NuJbSZ8t/ueezVelV9ZPPnPmmeJOewq8A/yHwIyHED7pt/wT4p8D/KoT4j4FnwK93+/5P2pWHT2iXJP+jNyrJG5b88jDttef9dW7EXx9/nZw+55xX2YyfY7xp8V//dmp7fptWeflsr/bi/gZvkvjMlzc5+KWtr3uIr0vzs2dcP/pNyvLmE4k/C2+y+vCHvL5U/9YrjlfAf/b/slw3uMEV/Jxbxp8z/M2Zlxvc4Ab/v8CNUbjBDW5wDTdG4QY3uME13BiFG9zgBtdwYxRucIMbXMONUbjBDW5wDTdG4QY3uME13BiFG9zgBtdwYxRucIMbXMONUbjBDW4/Wl0PAAADuklEQVRwDTdG4QY3uME13BiFG9zgBtdwYxRucIMbXMONUbjBDW5wDTdG4QY3uME13BiFG9zgBtdwYxRucIMbXMONUbjBDf6f9s4gNKojjOO/P6IeVLAaK8FKGyUXT3UREVo8ttXLtjdP9VDoRcEeekjxkquCHgQRLAoqRS+26KXQUgRPtbUliZEQtW2g0ZBUhCoeVPTrYWbNe+tud0NMZh58P1jevG/n8Bu+t9/OzC6MU6LjCVGLIiH9AzwG7qd2mQc9VNsfqj+GqvvDwo7hbTNb16lTFkUBQNL1bo60ypWq+0P1x1B1f8hjDL58cBynhBcFx3FK5FQUTqYWmCdV94fqj6Hq/pDBGLLZU3AcJw9ymik4jpMByYuCpI8kjUu6I2kgtU+3SJqQdEPSkKTrMbZG0o+SbsfrG6k9i0g6LWlG0mgh1tI5ngV6LOZlRFItnflL11b+g5LuxjwMSdpdeO+r6D8u6cM01rNI2ijpiqQxSTclHYjxvHJgZslehAMB/wA2AcuAYWBLSqc5uE8APU2xw8BAbA8Ah1J7NvntBGrAaCdnwnmg3xPObNsBXMvUfxD4skXfLfF5Wg70xedsSWL/XqAW26uAW9EzqxyknilsB+6Y2Z9m9hS4ANQTO82HOnAmts8AHyd0eQUzuwo8aAq3c64DZy3wM7BaUu/imLamjX876sAFM3tiZn8RDjzevmByXWBmU2b2e2w/AsaADWSWg9RFYQPwd+F+MsaqgAE/SPpN0ucxtt7MpiA8AMCbyey6p51zlXKzP06vTxeWbFn7S3oH2ApcI7McpC4KrY4TrsrPIe+ZWQ3YBeyTtDO10GumKrk5AWwG3gWmgCMxnq2/pJXAReALM3v4f11bxBZ8DKmLwiSwsXD/FnAvkcucMLN78ToDfEeYmk43pnfxOpPOsGvaOVciN2Y2bWbPzewF8DWzS4Qs/SUtJRSEb8zs2xjOKgepi8KvQL+kPknLgD3A5cROHZG0QtKqRhv4ABgluO+N3fYCl9IYzol2zpeBT+MO+A7g38YUNyea1tifEPIAwX+PpOWS+oB+4JfF9isiScApYMzMjhbeyisHKXdjCzustwi7wwdT+3TpvImwsz0M3Gx4A2uBn4Db8bomtWuT93nCFPsZ4Vvos3bOhKnr8ZiXG8C2TP3PRb8Rwoeot9D/YPQfB3Zl4P8+Yfo/AgzF1+7ccuD/aHQcp0Tq5YPjOJnhRcFxnBJeFBzHKeFFwXGcEl4UHMcp4UXBcZwSXhQcxynhRcFxnBL/AVjVrBe1yQ8jAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=10, hide_rest=False)\n", + "img_boundry2 = mark_boundaries(temp/255.0, mask)\n", + "plt.imshow(img_boundry2)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/notebooks/.ipynb_checkpoints/Tutorial - images-checkpoint.ipynb b/doc/notebooks/.ipynb_checkpoints/Tutorial - images-checkpoint.ipynb new file mode 100644 index 000000000..f66fbcd45 --- /dev/null +++ b/doc/notebooks/.ipynb_checkpoints/Tutorial - images-checkpoint.ipynb @@ -0,0 +1,508 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Prerequisites: install [tensorflow 1.0](https://www.tensorflow.org/install/) and [scikit-image](http://scikit-image.org/docs/dev/api/skimage.html).\n", + "\n", + "clone this fork of [tf-slim](https://github.com/marcotcr/tf-models) somewhere\n", + "download [the pretrained model](http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz) and put it in tf-models/slim/pretrained/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "slim = tf.contrib.slim\n", + "import sys\n", + "sys.path.append('/Users/marcotcr/phd/tf-models/slim')\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Create a predict fn for inception v3, takes in a list of images and returns a matrix of prediction probabilities" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "from nets import inception\n", + "from preprocessing import inception_preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "session = tf.Session()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "image_size = inception.inception_v3.default_image_size\n", + "def transform_img_fn(path_list):\n", + " out = []\n", + " for f in path_list:\n", + " image_raw = tf.image.decode_jpeg(open(f).read(), channels=3)\n", + " image = inception_preprocessing.preprocess_image(image_raw, image_size, image_size, is_training=False)\n", + " out.append(image)\n", + " return session.run([out])[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from datasets import imagenet\n", + "names = imagenet.create_readable_names_for_imagenet_labels()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "processed_images = tf.placeholder(tf.float32, shape=(None, 299, 299, 3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import os\n", + "with slim.arg_scope(inception.inception_v3_arg_scope()):\n", + " logits, _ = inception.inception_v3(processed_images, num_classes=1001, is_training=False)\n", + "probabilities = tf.nn.softmax(logits)\n", + "\n", + "checkpoints_dir = '/Users/marcotcr/phd/tf-models/slim/pretrained'\n", + "init_fn = slim.assign_from_checkpoint_fn(\n", + " os.path.join(checkpoints_dir, 'inception_v3.ckpt'),\n", + " slim.get_model_variables('InceptionV3'))\n", + "init_fn(session)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "def predict_fn(images):\n", + " return session.run(probabilities, feed_dict={processed_images: images})" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "## Let's see the top 5 prediction for some image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "images = transform_img_fn(['dogs.jpg'])\n", + "# I'm dividing by 2 and adding 0.5 because of how this Inception represents images\n", + "plt.imshow(images[0] / 2 + 0.5)\n", + "preds = predict_fn(images)\n", + "for x in preds.argsort()[0][-5:]:\n", + " print x, names[x], preds[0,x]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "image = images[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "## Now let's get an explanation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "from lime import lime_image\n", + "import time" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "explainer = lime_image.LimeImageExplainer()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "hide_color is the color for a superpixel turned OFF. Alternatively, if it is NONE, the superpixel will be replaced by the average of its pixels. Here, we set it to 0 (in the representation used by inception model, 0 means gray)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "tmp = time.time()\n", + "# Hide color is the color for a superpixel turned OFF. Alternatively, if it is NONE, the superpixel will be replaced by the average of its pixels\n", + "explanation = explainer.explain_instance(image, predict_fn, top_labels=5, hide_color=0, num_samples=1000)\n", + "print time.time() - tmp" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Image classifiers are a bit slow. Notice that an explanation in my macbookpro took 7.33 minutes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Now let's see the explanation for the top class (Bernese mountain dog)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can see the top 5 superpixels that are most positive towards the class with the rest of the image hidden" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "deletable": true, + "editable": true + }, + "outputs": [], + "source": [ + "from skimage.segmentation import mark_boundaries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "temp, mask = explanation.get_image_and_mask(240, positive_only=True, num_features=5, hide_rest=True)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Or with the rest of the image present:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "temp, mask = explanation.get_image_and_mask(240, positive_only=True, num_features=5, hide_rest=False)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "We can also see the 'pros and cons' (pros in green, cons in red)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "temp, mask = explanation.get_image_and_mask(240, positive_only=False, num_features=10, hide_rest=False)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Or the pros and cons that have weight at least 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "temp, mask = explanation.get_image_and_mask(240, positive_only=False, num_features=1000, hide_rest=False, min_weight=0.1)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "### Let's see the explanation for Egyptian cat " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Most positive towards egyptian cat:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "temp, mask = explanation.get_image_and_mask(286, positive_only=True, num_features=5, hide_rest=True)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "deletable": true, + "editable": true + }, + "source": [ + "Pros and cons:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "deletable": true, + "editable": true, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "temp, mask = explanation.get_image_and_mask(286, positive_only=False, num_features=10, hide_rest=False)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/notebooks/ImageExample_MobileNetV2.ipynb b/doc/notebooks/ImageExample_MobileNetV2.ipynb new file mode 100644 index 000000000..f31e41c1a --- /dev/null +++ b/doc/notebooks/ImageExample_MobileNetV2.ipynb @@ -0,0 +1,324 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e6f5c6a0", + "metadata": {}, + "source": [ + "LIME_Stratified" + ] + }, + { + "cell_type": "markdown", + "id": "d991a68f", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f855679b", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow.keras.applications import mobilenet_v2\n", + "from tensorflow.keras.preprocessing import image\n", + "from lime import lime_image\n", + "from skimage.segmentation import mark_boundaries\n", + "import utils as ut\n", + "from lime.wrappers.scikit_image import SegmentationAlgorithm" + ] + }, + { + "cell_type": "markdown", + "id": "459125c6", + "metadata": {}, + "source": [ + "# Load Blackbox ML Model" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ed06b351", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the pre-trained model\n", + "model = mobilenet_v2.MobileNetV2(weights='imagenet')" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "655210e5", + "metadata": {}, + "outputs": [], + "source": [ + "img_path = '../images/ILVRC_2012/ILSVRC2012_test_00000003.JPEG'\n", + "# Preprocess the image\n", + "img = image.load_img(img_path, target_size=(224, 224))\n", + "img_array = image.img_to_array(img)\n", + "img_array = np.expand_dims(img_array, axis=0)\n", + "img_array = mobilenet_v2.preprocess_input(img_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "3534d7df", + "metadata": {}, + "outputs": [], + "source": [ + "def bb_predict(imgs):\n", + " # On some platform, you will need model.predict(..) instead of model(..)\n", + " return model.predict(preprocess_input(imgs), verbose=False)\n", + "# return model(preprocess_input(imgs))" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "f6b65a6f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: [('n02096437', 'Dandie_Dinmont', 0.2808345), ('n02091635', 'otterhound', 0.09810091), ('n02113799', 'standard_poodle', 0.03486853)]\n" + ] + } + ], + "source": [ + "# Make a prediction\n", + "preds = model.predict(img_array)\n", + "decoded_preds = tf.keras.applications.imagenet_utils.decode_predictions(preds, top=3)[0]\n", + "print(\"Predictions:\", decoded_preds)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "15db62da", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "c2de33fc", + "metadata": {}, + "outputs": [], + "source": [ + "def get_segment_number(image, md):\n", + " segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4, max_dist=md, ratio=0.2, random_seed=1234) \n", + " segments = segmentation_fn(image)\n", + " return len(np.unique(segments))\n", + "\n", + "def search_segment_number(image, target_seg_no, init_max_dist=100):\n", + " lmd, rmd = 0, init_max_dist\n", + " lsn = get_segment_number(image, lmd)\n", + " rsn = get_segment_number(image, rmd)\n", + " niter = 0\n", + " while niter<40 and rsn!=target_seg_no:\n", + " niter += 1\n", + " mmd = (lmd + rmd) / 2.0\n", + " msn = get_segment_number(image, mmd)\n", + " if msn <= target_seg_no <= lsn:\n", + " rsn, rmd = msn, mmd\n", + " else:\n", + " lsn, lmd = msn, mmd\n", + " return rmd\n", + "segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4, \n", + " max_dist=search_segment_number(img_array[0], 100), \n", + " ratio=0.2, random_seed=1234) \n", + " \n", + "segments = segmentation_fn(img_array[0])\n", + "def segments_getter(img):\n", + " return segments\n", + "num_segments = len(np.unique(segments))" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "1e0ae694", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7be91f94929d4534b69a822c6394596d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(temp)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "c59586d7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a blank overlay image with the same dimensions as the original image\n", + "overlay_image = np.zeros_like(temp, dtype=float)\n", + "\n", + "# Define colors for positive and negative contributions\n", + "positive_color = np.array([1, 0, 0]) # Red\n", + "negative_color = np.array([0, 0, 1]) # Blue\n", + "\n", + "# Apply colors based on the mask\n", + "alpha = 0.5 # Set alpha for transparency\n", + "for i in range(mask.shape[0]):\n", + " for j in range(mask.shape[1]):\n", + " if mask[i, j] == 1: # Positive contribution\n", + " overlay_image[i, j, :] = positive_color * alpha\n", + " elif mask[i, j] == -1: # Negative contribution\n", + " overlay_image[i, j, :] = negative_color * alpha\n", + "\n", + "# Blend the original image with the overlay image\n", + "highlighted_image = (temp / 255.0) * (1 - alpha) + overlay_image\n", + "\n", + "# Display the original image and the highlighted image with transparency\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 6))\n", + "ax1.imshow(img)\n", + "ax1.set_title(\"Original Image\")\n", + "\n", + "ax2.imshow(highlighted_image)\n", + "ax2.set_title(\"LIME Explanation (Transparent Overlay)\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ddbf799", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/notebooks/ImageExample_ResNet50.ipynb b/doc/notebooks/ImageExample_ResNet50.ipynb new file mode 100644 index 000000000..0014d9849 --- /dev/null +++ b/doc/notebooks/ImageExample_ResNet50.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e6f5c6a0", + "metadata": {}, + "source": [ + "LIME_Stratified" + ] + }, + { + "cell_type": "markdown", + "id": "d991a68f", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f855679b", + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "from tensorflow.keras.applications import mobilenet_v2\n", + "from tensorflow.keras.preprocessing import image\n", + "from lime import lime_image\n", + "from skimage.segmentation import mark_boundaries\n", + "import utils as ut\n", + "from lime.wrappers.scikit_image import SegmentationAlgorithm" + ] + }, + { + "cell_type": "markdown", + "id": "459125c6", + "metadata": {}, + "source": [ + "# Load Blackbox ML Model" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ed06b351", + "metadata": {}, + "outputs": [], + "source": [ + "# Load the pre-trained model\n", + "model = mobilenet_v2.MobileNetV2(weights='imagenet')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "655210e5", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "img_path = '../images/ILVRC_2012/ILSVRC2012_test_00000003.JPEG'\n", + "# Preprocess the image\n", + "img = image.load_img(img_path, target_size=(224, 224))\n", + "img_array = image.img_to_array(img)\n", + "img_array = np.expand_dims(img_array, axis=0)\n", + "img_array = mobilenet_v2.preprocess_input(img_array)\n", + "plt.imshow(img_array[0])\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3534d7df", + "metadata": {}, + "outputs": [], + "source": [ + "def bb_predict(imgs):\n", + " # On some platform, you will need model.predict(..) instead of model(..)\n", + " return model.predict(mobilenet_v2.preprocess_input(imgs), verbose=False)\n", + "# return model(preprocess_input(imgs))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "f6b65a6f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predictions: [('n02096437', 'Dandie_Dinmont', 0.28083292), ('n02091635', 'otterhound', 0.09809891), ('n02113799', 'standard_poodle', 0.03486852)]\n" + ] + } + ], + "source": [ + "# Make a prediction\n", + "preds = model.predict(img_array)\n", + "decoded_preds = tf.keras.applications.imagenet_utils.decode_predictions(preds, top=3)[0]\n", + "print(\"Predictions:\", decoded_preds)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "c2de33fc", + "metadata": {}, + "outputs": [], + "source": [ + "def get_segment_number(image, md):\n", + " segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4, max_dist=md, ratio=0.2, random_seed=1234) \n", + " segments = segmentation_fn(image)\n", + " return len(np.unique(segments))\n", + "\n", + "def search_segment_number(image, target_seg_no, init_max_dist=100):\n", + " lmd, rmd = 0, init_max_dist\n", + " lsn = get_segment_number(image, lmd)\n", + " rsn = get_segment_number(image, rmd)\n", + " niter = 0\n", + " while niter<40 and rsn!=target_seg_no:\n", + " niter += 1\n", + " mmd = (lmd + rmd) / 2.0\n", + " msn = get_segment_number(image, mmd)\n", + " if msn <= target_seg_no <= lsn:\n", + " rsn, rmd = msn, mmd\n", + " else:\n", + " lsn, lmd = msn, mmd\n", + " return rmd\n", + "segmentation_fn = SegmentationAlgorithm('quickshift', kernel_size=4, \n", + " max_dist=search_segment_number(img_array[0], 100), \n", + " ratio=0.2, random_seed=1234) \n", + " \n", + "segments = segmentation_fn(img_array[0])\n", + "def segments_getter(img):\n", + " return segments\n", + "num_segments = len(np.unique(segments))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "1e0ae694", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "58b17dfca6174eb1aae5b988f2ccb3cd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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G8hejW8Q1OtZrdL6tovt7rRwu8K/RUa4+nd76MPqMMTJNE9M0MQxDlUuC94o8lJJUWDqFXL13zHPCEBEL/ujdFf1+XSmddZ4MQbB3sudfhA+IZLwPK1jPrml8qUeP3qQcbMcfWODET//0T//Dw3t1GCZrYxiG6iMtxKALtht3SMX5m1+qLpZBUssm9zViR/1fPZy3hfFgDRH2AqqHFbbaQq9R95+tGcyCd2/hh6010msoDy0j14i7F7zGOBftbbE+5rq/paifAOSssDZiO2cF9s/Xz1GDA1BfoVMvRjUCljlRmMEh3qMeN7UMW7DIBiZQoaewH51y0Wun/do4p3BqEY0CxClsakoCG2ZlfxfWzKV/z/7QXjO1Z91+ZmsXCFVIFkqpUOlpUud8sxyMmawZhcEx8zwTq2BsULT3OBfICK4yTW8wWOmsTBwlTXg58c6zaw6HPfPxTrXg4BodT/PM7SlxP2fAE1wheiFXplvmVIW+RrVKzkieub+feRUDu92owjv6ClsX0jypUMpF/VdjZDqpnWXWdJHemjUMrb5E3SYWQJTNP+sUEtus4gMhBDVCFDbCZ5njHvq3dVzRXb2vKVRApSvp9iCr30UWnrKlp/a7G3WOKmPvmb7de/nxOBblKefUkAD9e1EGFwHpHlxr+37GX12H0Kzv26NMNdi0m2PjXb3l9KZ98/3G1yK6741DVnE1i7BhWTz1E1hEn/54epN6MVW97zbz72L0DHHrWOwFS7/Iprn1n/XXMzHVHKCb+/W/+8qwtkS3hReXaes/64UP0AIwePBO/Wdvum7/njYPW41JzJeGq9qdYEJKD1VPrwElCv2vLdfVvSvfku77c0KzH845EFchMb3+YjXVa5lgs8lxNmOsP6e799m7raHH1bpTGaqhV71gNzqQNQS8YhTLC3XrJMseoNsT3XOWkquyEPEOgnMcdiPjEMk54WtaRc6ZOWVOU1KBhOcwqrAZBs8pCakIyUL+PbiiFhwlkZLj/pjxISJ4YjBZ0wf9UIN6IM2FIgs0uayVQbUCUsHDbmltT9EzUKPRbp76dbBztvvPjjsHfW1hOftdbyMPjtN7LcFL5+hiS6N6P0+RJWBjxSeq8FhZYtJDkzZx6FyJUF3cVXgtVt72/XpYE1ZT/JUtnze940NB/ENuSf1eDHMk2hqb2S+oI7tIwYdQQ58hFdX88jQ3ZmQbV62qBDiGQVd0nufvy6h77cY02HHU/AFz6ttxWzPXtJ6U0irowhiPQVALg3QaUuscodPGxxgVFqF36i4Riu24ccdaGJggCxXLtndz1b/jmGYNQfc+rCynrZD4fkLMNkFvQTTs2lnE5TpcW537ysSIunaqO6uWIb22l0uFszq6+IGEo//YM1Chx2YZVm2mCY2cwcFgDvxmhSzCDJEaAr2GIM8FxaxgE1y7kkPvYT4ny2tTBSrivCN0jmgpCl+vhBWmpKk1bM5w5wwV0qCEaZrwzrHb7bm6vuDmcM1uX8PzU81VEpimRMqZ05zwceDmZuSjdx61d/z0+R239xNfvrwDPD4EEjOZTJIjp5Pn0zlwnyPjKFxdOGLQNIF5VkhqGEZCHDhUUXo8JaZ8Mk9OpT+1MhOiCVylUApLBCpwmo6EGGtAiUeckEpReu4Utp7ZjuO4sqC2yEBvCWxpfDnO6BdiGHFeEJ8AsxweWhErcuz2isHz3gkiXumhnjfPMxQhzQnvPPtRgzmKwJwM6XF1PxeEXJ+roLmHSrC9T6pXaAGOx2MNOltovfAQlegtK73OOnKvHzavFqFtEcF9QMb3G19rIbWMtQnqvCPGgSEOjMNQN1xWhuSl4rYawpvSEpqsCalSr7fWyM8FCvRacf85rBnVNvx5G+L5wIpyDih1g/oH32/P8c61fJItxNZbdiak+vv0sINtKGO8Cs+tgwjseYyIFT/PD665fhfaM9g53vsH1tFqyLIOmNZlnHZ1mG640Fu/svYhbeevSCHNswbe+MUi6QWlWUv1JJC1IG2apn1mG3ZrKW2Y29aylprl09bH2XEFFyI+KAypsBq0kCCpWJgxDGiQLq5CbjiCjyoIRRM5VQHSn+Ad+93AeNgzHEZmFuE8jruarD0hpXBZ6cQ74WqvATW5ZHbeMYfAfhzJRUjFLN9qJXv1X52So1A0J2wAF2twj9N5z5UZDnFQwXM/YShBD8G6/v9uHVk3xAEXFmuyTucDFKL/6ffrdp3aPJ85FxZmb4LKPgNXo1CX66gPCIxXnbPwzymA52ipD1AAkBZR2LkH6ktbWo3NyTmrsR/b70z92fK/h0rq+vMtgtIsXRarUH6wOgl83YWUWxiMjRA8cVDhNIwD424k5ZlpVv+QAwbnG3PthVRKGcNst0JqxVg2RNwLBwvH7n1POWdi1MTieZ4fRNPY9fugBKqVeC76qCdaY0pbIl6mSK0Vxd5VSG0FaM6lwYW+RrZJsQRUYZ6XChf2Y+/XIt14uInhYe7ElsBtkz/UVM+9H5tjaIzaci7M+tFoJ98CQyw03jmHZFGn8zgoE3cLnNQu0mmYmp+mwSS4teXYCy4VvNUCPMMI7NiVllqjriRr1YIwhBpmXojDSAiRGHYcjxO5JM08qLUVXGXCIQSkFFLO6kgHUvVrhThCVm06l0TwruUQxhg4HHbsDpfEw57T6V7XFOFqf8HFYSQNR7zT8HUpGqK+ZybnzOmUGAPsYuBit+d+TkxzQqTOj4+IHyEcOKaZOcMYBUdp0ZiggUpZIItjGHaoKL3De1W+VgEiboEt1a8SNTFbhKFG92Up7dqhJsX3TLOnt17B6vdxr0z11lSv9Cx+6ioKxcK+AyEMiKSq9FYcQAw5WHhJf297xlKVCaOZ/v52XA/vL35PwXsVkiYCtNJKYLbgJ6+QrJXn2ArEBzzEPdzXItKq2ixjebb+uj2c2J9jSMBXGV9rIeW9lkSyIaKx96WWXhmSYuHRuRpQoRFBvjKwGCPiPClpXk8InhgN2tqECG+ESR+5JuIYhnXAhH7enpSUcoMEvfdcXFw0xtnfxzLDp9MEIsShasLOaWkWoeYW1dImIpScmFJWzRVXI43WwrXX0HrGatGO9v00pVqGxcGspN47eM0atBBY2zCwbNp+bsxqNOYMNWIJ9UlpVQtByyIpLJWXUCzSPCvjKaVZNyVnzY0KgRBiy28yyA6v82X3bhZGt5aH/UHf0UFOufmGdK7Qp+uEJGglgYpOUkpu+VE2YtS1mnMtyOPWGvpW6QkhVKLUKhree0IMlKQ5cWmayC6TgmrsGkq8aM3q11uXwUopUU1Lsgh5rrkvzhOdp5Sk1QBEIxGHIXBKielWIwlBUxx4dc/t3ZHp9UvGwXNzuWcfPSHAhJDEMbmIDLXCQck4N4ArpDmRS2aWWSuAiOewu8QjvLo74tixGwI4Fa7TPOND0AjcknEiHMaRU8qkqlx652EYGWMgeEdOMwoxDeRSyKUwz1Ob1wa1x3XE6coC6Rh0Hxg1DENjxL2PxuD57bl2Tw0KMSZf/XIIzml1jxAc01RW1+ytOYPDREBKRrFeAV/DyIv6bL2vqSFZhX1wmjc1p8Q8Z/b7oe3/eZ4RlhJOWp7rYVpKnyrR7xORJdq5D9wYx7FTOF3H63hwbTvPglRCCFr1ZpMg/abxtRZS54xWgwY0IVBqWTaFHkQ6CwNlUL6DFOz3Ir22r4QhUrpzegjJHJcenDIXDJapUVUGMZWSHwi7swEWq/fpUS7VIR86MxUeUnWzy4npzln01kVwGRyxtmIKBQgsJZfW2HMPdUjzs/XPs7U6189qv9NZLz208jAowpzlBkWaz6Ud0P1eRPDian7V2i/W4+gW/bV6xvbcunQLiEOdU28P3b5wZimK1jVUaC3XZ7JV66qLtEXonr9exwdfy3Ql/bRIrU2X6KuhrCwCXaEGyyxWnas8Tn1llm+FHe81eVlpUCpDVaFXcMxJI9TmKYF4pnFmFzXhexYQL7jo8Qm8wDA6ZimQkr4DrvrLajJqnbtUCrmg9QhNeSw0KAxRD2uMnpQUBkQsXWBh7LYLFlorq3noZna1tu07eQjf2lp75x9cy+I0TEGwjam8wX43xbE069siZLfUtNyXdv9Qo0qKSLV0HBZYQoc4BOMbpSB+iQKVygdEpD1Xe14LgxdpSm9v7Wz393au7N8exdGPDfVYC+5+LMcv1ynlhzyZ9/dqWC5LVbFroqNGJYlzZCm8vrttNbtSKivCFCCnGSmF4EEkt1pxmjALMQ4rLQDQGm0OpinX0OWBlDcwXi0KCR2ReGmayul0WuCkes4Sog3jTgMdSi6EWpi1z39KJUOhVYpQIeoxYnTQSsKICDEIuTqaraZYKXPTLhcI0DaTMe8F5tgmHts5plVZDpD3ntevXwNLnkcfPj7PM+I178eIXYQKty5E7b2W1cE5UlrCZnfDCAjTNJNzYj5pvohDGXMTMNKVv+ogGnv23rK17/pgF+ccXnxlNmqgWfi0D77CYIE0q+Ue6zOkeR1aLCL6fQ2QMe3We3UeiwjjblQhFQJlnsgC3vWJ0NXCq3k5vYLjUZgrSSEGtTpcDE0JyDmTJVcEwRF9ZIiR3TgQjUbREk0anudxUuGj8YrihNeTcHV9IB525HzCA4ODU7lHyLxz2PPly3tu74/shpEokTkLwWth25xmsmitzAyckjAMHkdQesTXqPKMF9jFiES1Mqc5UZyoNETI3nF/OhJC5BACSRKZwm63XxSN5Kr+4nFB5zLnXEPjl8ha23tjjA36kJpLFsOwUmIs4dqGwYwFVDHCUWMWNCigJicX0cwwwdXKF66WJgMpru2ZYVhylLwfFcLtKpX0QR6Wx9aCxZzHoX6vlOe2f61WXqh8JpObwmNW3Dair7d8VABTHZla8FgEVV7qc7XqMzVR2a7Z7/k+D22Jqv4hTub9vRuLSmrEEipTDVUzbThpF3nS+2xiiBRX4bbS+QnqtXsT3+7ZGJlb+4seaiaLNWZEs817Ohd0oBFmeXW9XvNb+7tMcNVggWbB1U/E8k7UX2cJsf11euzd+7Xp3r/bKiqtEyQWBdn75Cw4pc8Ve5jjAb3VZ3XHtvcAdfLrGhVSmtu89oK+tzJ6Dbn/rC/d0q7dVYDoy7icG87ZZq3rXH2HBrX12ne/1j382fu0LF+sSFHtuWmobgW9LLRg1QM6rXRlDdTqDs0vZozVnsxRxLVkducDTjSIyDdNXOFcKYVxCHhXUP9Kqf6SXAWmZxcFL0KMhWNUf9VUi51eDQENgc5Kz0WQkkjZM+dQq5vQtomvaEdwCuU6p+s8xEiWTMoz4tQqbAmiNfLSft+unVRjplnNsPq+9zEpo93kCi0Lv/53WcHVNbTTgSzTTmdhiMGACy31tNDyNtEizNKV7gJVTM0qLx3Eom6OtCBDXU1MUwKtCr/l3PU09XDONmHxba+7xlt6V0dzB7hlPvr3W6z7LgqYrz6+5kJqPYoUvFRmW8Nct5APPDRpc66MwunmT7nWGRNp1QvGceyOV8ESayn+HkbsR7/Q5wjDNCG/EaDaPkKjvUxwGRPtF16FQFo0wn5nYAysVhj31Pp0GiBSalVosx62pryev07s2yYn27OcTqf2Xna9GGMLxbfz+xIpzvv+UR+sSQ+PuWpG2HPOG2Fp7yEoXr+d+94H0TMU0/L6+dxmwq+TG8/AtJ3lpf6gqj1vIix7xaJ3ePuglppVaQ9UhIAluXjJqVvTUv9ePQwmUiP5fK0o7xTI885RaiTYMO4YqpDyBZxoVXD1QRb1h5XMYX+ttfLyjEgml4RzGY9j8LAPhSiFGAsHnzn4mTIXnHj2+yumVJjmqUJYqrTkFEg5UEpcoveMqVaBEn1Q8FFENfUM83xCa/uh9fxQ/yxO4d3ed+Loi0WvSGtF4w8UEne+Vp3y34eKi3NOBb2zCiMqaEJNe8EgP6dKYykLAmR0YfveFDzvPVKfwaJUxRQPaPvQFByKtszo+UhvxVhwlO2VrRDq97S9c0+/9p7eh1puzXiQISCqnLkQ1G/boQi++ov7+2yv/YPGPzZCyjkNX7UERKPMHnKxydlmem8j0PphOQN9mLZpXHrNteO8F0Irhl6cwhkdg+yJxp6rQX9xHXhQSmnmfv/8xqBNC7N7N3M+m4BRLTmLlUdZNnUvgHtH/zwvJZxgETQiGh3Xb+7tpjcmalDAlhk4eHB+//72c66Vgo0VFGORdXK+ZFV/fkqJ0+m0ipDq12D7Lsu5D4V5/wzN0iwPKz73z9zDto7Ff2bH2Xo80Gjr2DKVvjJKE2BOIWfvPfOUSCUr3OYUZcgCcynIaSZKJlIU3qp+kJQmDdMve7wrIDO+TAQRDgN4J3iXmPMdMk0wH9mXiac7ody/Zk6F3VhwOTOnhK/vMfiAy/dM9zO7KMQ4EKMF3iQcitcW0XYuu11dE3vPOpWCNLoehqFGzi4KlybQVjRDYwVWa2fz1fOENp8uPqCbLaPtaaZksyzdige0qNB2LiAK1/YFms8NRYGq0DPBHVUpDjEgOa98On35pD4gq1fkSimtlJIJsJ5u7Dj7rJ8nQz4KNBjS1VJdUpWblISAr6WY1spgP9fK8/jK5tQ/NkIKNrH89mH3+zlG2v9Nd5z5NJbP7B5aUXhx3Fro59b66J3ca2Z3zmro/7Xz+1U8B0G5rbPyDHP1zmkbCE2DxFACWKCnBS7rLb3+smsIbfsOD+bu+/xtBL8AtQ/HdoO8aTzUBtfz+/3O/X5je972eueer9FUEeTMuvXHra6zEapbC6m///L7w2v25y9zroqJhQSIqGsB58CpbyQXhfmKg1KVr4LCQ+pzyYgT9cdJQUomuoh3teqBFFwpeJ85jJH4+JpxGMgCF5c3vJoSL+5PWE6RiGNOmTlnnKvh6H6ogUVC9E4hvRbJ5plT0mf3mqArUi0TOb9WPQ2XmovFxqI6h3K0v11/Tdd24gOgz/Wll9ZrLrbRHjyfVVp5M021h6+U1DsN6vJrwIlIs/BaI8wz77d95v6YrbK15UUPFLWNktZ/J5YOcka5esDzzj7d+fGPlZDqicagrmmem8UBS1HJPvR7NdlOcwuKFI0uykuLhyUEU3HlGKyyuoaaihROp+Pq+GaxhXXggcE4fcHZpn2IaK6HaZ/DwDiO3N/ft2sblDFNp+V856pTtLuvHxDRCC7XUbnR0Na0P51Obb4UOlsLArO++v5e9g5v2hhLHseS+GqbrL/2m87fasDb79rvLH6pXvD2wTLDMDQLapu3td3Aa4a/hj+3ydiNMSl5rO69HStLyC9WgR2/DWbprxHCYjX1DPnctUvO5OnEGCPemi5Wnh3CoIEbKVNioARHCVFD30vmlAqn08xpzuxGT4gDqRQNAAl7jRgsCSdaN26IA+8+ecr773+Dx0+fcThccHPzhC9eveJ7L54zHWemaeb5y9d8/OlnfPzp50ypKBQZQLJQyHgfm/AxH3GzPNurmcjQ7xVFsGhCswb8irvbHNNZNufmTaqQplsL79ySJNxbCGIWgp6X8xLB27oqtIdWoTuEAYdfFVt9kAvpFN4WoEZ06RVczQHLCueaMhRc7VrMQ+G0QmdC0CK8XVpIj+YY/dlPX3galuLMC/qifKT5tpM1d3yYcNwPvc/5dTg3vt5Cyq2hul47kVJbiIc1k1gS5lQAWfIjLISayrwSHL2zex08oP6oHiI6HA7t2iumVyzRb+1HWDGVjmlGv4R1NqvIPyzyaBW1U0oN918xVhZi1ATnA3d3x1XuUB/d00NHzi2+sP6Zz1kHQCvBs7UEbKxyp7pon3Owa8+w7dp2/23l6rbBRJjnBZu3NbD26v269LDGQk7Lu/X5MHo9Xe8+cmkL96kFrpUVesXI3vEc3NTPX09vPZ1tj+sDUrbnWkSWKgPq1yw5IyW353NO/RigbeIFyO1/juIjhAhhoPhAcQECiHcUHwi7C3ZDZBwGwnjJdJqYpsTdKfNrv/GbXH/xgv3+wONHT7ibjry6v2WaEnPO3N1PlJw47HeEJDWJVynVFEv1eQScF1yRCk+W1mdKnFlZru1ffec+EML23mLZKt3Iimb6HL8GaXVtbhqE1gVPrZWZraLXlVeqz6mQfFKIfPB4F1Zr+sCKWWLyH6Ac/Xo3hbXzgfbHWbX0ntdsg5f6d+/f75yfyviqJbRYSTXvtWjwlHKb9v6ZzwWs2bx9lfG1FlLm1zhnWhapjeu6zW6M2SbMtIV+wbz3LRu7Fw52n3Ui3mIRGQEcDofm84C1OV9WVtCyEWz0Gk5wDyO7ehx5IbqBnFOLxipb87w6bhUHH7m6umKa0lJ9YcNkl4i/3AjpXCPHPly7ZxR2zLk1WZWeqcm3/fv32LU9l/ln+mfor7MSVEVWf6eUWghua2ZYBWkf2bh9zpWyIw8DbraQSFMsrC6jW/qV9SHtvfbamEkHUdqa2tz2jKMX/KaUbP0aRtdmeQTvCH6pYGDWNt5rKw1Xq8FT43RQiEqcR3yAECk+6O/eId4jIeCHPcNhz+XFJWG4ZJomvnz+ks8//5zvfve7XF58yW6358mT5xRJpDIxp0wuwpSFIoHdOOCD1tO8nxYBoO9tOVxVGw9BAz4alLoIJGuC+YCJ64RQzSgEIYSIc6z8t70StNCuWgTnqjpsaWR5bn2uENxiJThpgjTnpNXqfe1M3O2Vfk/3dAVUuH4ZW0TBe7W0csoPPtcqOksVdXtuo/1eWe8DLXpB2NP39scQmxACxZVl3lnvjV65WibxzbUMt+NrLaS+39hqJ/1Y4KxFs+iZpUYw6QRadN+WeZVSmEVzHUyzd86tIt3e9FzA6prnqjPUai6NsfbllOx8DdpYLA4t8RNYSvNXVFsELRSrZvp+v0NEu90aQ1xrpGvrwjLw7W/7tydy09wMurA8sN6RbNZJe95Nr6mtwOyfoQ8d3x7fQws3NzcrK8yesX+nLbSxgmU7ZaUXbNYwzr7r58GuX0rBO9/yxvoCxT0k2v9EF/FxrbVurdUecjbrth/98xuc6ZxDSqbU6iTeOW3fUfVgjeYrjCEy5UIqSfswOYWI53kmpVkDbgZPHEZcUGjr8+evePnqnjG+5rMvvuT29p4vvvyS4/2R27tbDvczu3HHVDxD1PD0ufZySxkKgUwk1SBWJ9oviwBinYf9ss9UmPd0ov9TeG1p/jcMuzZfzqEw5DC0dzMorrcUzlm3/fza/c8VCtb1Adcq6LO5dl5do7fw+ntsW8vYS0q1crfPZO/YBCuq9Nn+MyFkuVS9lWX8bCvsemXqnBLW05opr6WUxpdKtrYiDucfIkBb9MWqoHyV8Y+XkOq9m2+U/m4loGziesvDeYcXY0iOJX9nyeTmgUvTFs4gZLsnzdEJawLvR8+YaK/xUAtZwwO6Qbavb3kS7XNZCPR0OrWcCbteT0QLITlajbhuA597/q0Ftd309t1ak9pqog99N3bOQ611XR9sefY10W+h1e0zn2NWvbKyXZNzgnr7vL3wfpPFtWJGX02ZPHuffj56BmDvJQiOgDeAptKtd45AIVDwknFlRnLNwUGDPyhJhVz9ESka7ZkLr6s1GkPkxet7bu/ueXU/M8+FROR+FhKJ8e7Efgd7gZS1qro2wtZ0j1I0nBxpYUg1YdZVuOtMpGa1iuisqvZq3bt3kVPtc5E1nfWKSDt6uRDYOpZS8wtde1azTJfz1uc7Z+qAWevuQXrDDxyVj1gkMfQWIlTwlu5l16fLGk6H8/UzHzz3GaHYBKxbv7vxle31vt8e+d2Or7mQ2jAvUajA+6UJmi8LbGZmrWlFPQRTr1AZlcO8tCKWB9UvnG2O6rxl8SFIl0Wd8hIU4XzAh7hiZBY4sWU69msvUO3vHi7ajpwSGWG/P+jbtIgqQSRze3vL69sj1mbarKRt0IJdf4EwFsfqOcbez6+9i/nKtCZiaAyhzX9ZioaapWhhtPYMzq27DPfz1v9tkF4pah32Cki/Wew57DODy/rs/e05vQZ9Tmj3c6YWi29O+jU0/BDW2f6+nePequvfp0cCzGKz0Z8XY8S7CPNMyZk0HxlCIMTAzhUGMqFk3HyPmydi3CF4cgE330NK5OmO5DPzINxPt5QyESQQfCTEHa/vJ06zcJQ9DHv86Lg/3nN3zNyn19xceJ5c+iqcAIIKICda+VzUmipZf9/X3C1xA+40LeiCrTcOCmQx+LevZpIJIbLb7bS6TFbBije0Y2lR0fZMJ6RMkOGXIKoihZQTgYfwe58qYII8Z1USW+POxnec+qsTNddovf5bhcZV69HQFFqPKE0hcE4QD7HSQPa9orwOTTfaN9Rje99+7+52izXaIzxKd2FFzyr0pdWLVLr09LpiLxx7vqXC/82IUz++5kKqi9yqFo71XNJeO2tTd8vkese8EnkVPiJdNNDaeWl5Uc65Wh1cF6+vWtAzTjtWYZRl0e3a2/bQDQIrFjEoK/O8acnNbO4EjKsRRT1TFyMoY74Gna0bL54Ter1FZsS/ZbS90DdG38NvPdNc5rC0DWhzZdfaCuCHAnwtRPpkZFgzcMvl6ss29cy+Z1Dbe5/T+M4JJ1vPFpBDaflwxhh6K+2BUGI9/73vzQrHnsul69fB4NieRkRE/TiAJK344BHydOR0zOweXXIYA0NwfPDkGfvDyH5/IBe4u595/uqO+9NEiMp4dqGQoiIFFC1lFGJE0Bys+3lWhQhA6roPO/AaGNFqaPoAhLr6vtFB8Lp3c05QBB+1xJRBldah1z5DQkupsBFjrQCfEjR676E73d99VZGt9W8pJlt6eyBEFk2y7pNQ95gqdcMwMM2KWhhcrKf41f59kzWuN9P/2Sw1pEQMHdEJUPRkjQptLXqDnt9Ehz3k+MAib9dEy4413thB/94zxgEr1XgOGVm9c1UCvsr4Wgspw2vtj63JagVUjYEYcfYYsVkSy6Keh2Z0g6wFW84J50LbHEYM53wcVhOtZzJbJm730U25MDd73t6HZvdTxmwbyYMsEJkRg0Z0AbjWCtosqeU6yzXXDO+h1tWHzfZCRuckrwi0t7r6axhs0W+oc+Oclbc9tjFtp60bbJ7OQRDnAlV64d8LYjvnTVDIWW20avqmfPQKyfb+IkKWvDrG1tnmsvfd6THy4Ln7IJBecy2iGrmUgkdbype5IGlmPwQudgO76Pngncc8fXLFxcUFKRVevD5yc7Hj9f2R0zQhFLwrJNEK9Rlf24BoXUVrupeLWkMxBqIPuDAgXjtmmVvdudj2gtaadHhrB4+GcWsriQKuMl/vcNbnDdfVpOxhvgUZ0XmL9DReJ1Dhqu9Db+fGm2hQRKFT13yF+hymoM5pamsBjlKcVv/vlKzewrZ7OecanFkfuftxq78Ro6uHeZ89/fXKj+3t7ftsaX27j5xbF2W2ogC9dW9Ciu64/r72jv/ECKntO0pRExjqBDWoQBApDIMKqePx2HKPtGyPY6hFS8ERhqBIvgin44mcZs0v8LpI86z9dC4vr9pCGXMex7HBXH30lZSim7WLiOstqD4AwYVgcbkAZ7Hs/nhfw9VzTkgRhRWtfp/k+v7qs3FONT6cYzALwwRiEU6nBTrKecZ7x+XlRfvsYS+q3Mq5jOPYnvV4PLbjTTDv9/uFSVdBZRtmW0DXNohpgL32u4UAh2FQoRACIY6tjUifXnAubHvrFO+txV7JsOCPeT614IQtTGSfO9R6tWfondk5a+7dyir3TqGvTc5Iu17H0JQWAqX4du1e6TIBJyKUlFqV8/1+x8V+5KMP3+X6YsflfuTxxZ4xeHbR45lwaebLT79Hqq3ijy/vSKeZ68tLhmFkt4u8fDVzPCXmWuzVOan9pBwXgwZgTHMiTSeKd+x3EU9g9B6rUY73hGFHiDtS0ooRp2lSx3tODGME55jzpFUv0mRGF1LABe3JFP2AyBINCNozTYpae5m5zteo+8M7EovlalFvvbLa9mFKjdP2dLClG1NIxzjWYz0h6DGn06ndYxzHRcFUz8FKMelpsgXJdH5uE8aLtbf2ITunyJGrjN/o2EqSbWnoXOBP/359HmEfUKH7dFHAU8q0XnTOUSThi29w6Tnl1j43xf6rjK+1kGpeIZPWS9RCt4A0RpytiGclVtNwdJE1KIJ+cmuOBkFrjCnduAZrmEXSd+Dscdz2nCLVabwto68bTIt6LrBkqXXX3NbCYbG0qolTid0pzK/qZIMrcUtys5UwsecFUbebd3gJNfqpLFns1DyIrjdVpyLZxNMc0+2cPq+pL0G1gRPqfVoNv+p3sHcUaC1TesuuzUUHa6zmuXP090zFaGLR7Fx7HV0Poyj7Xn/X79Zt73vt8IGfqSJMiwVZp8uZklDqQa1Wz2YGl9+tv5au2ZISQKUhh9U3tPYyBcRX+Ewj+ZDM5eGSq4s9l4c9+93AbtQIQDzMOVPSRMkn5klr7FnwRMkFV9TLGrwnerWQCl6rUpSEQwhOcEVLHwUnCFrLLXohusLoYJaivabdgFFUc/qbouL9Qk/eqa9Xap+w6rE36mykJz0UZ+uoTBtnFUBoa4nQ6i2+SZE3Zt+QhAqhCzXsGuUg1trHNci2aGZy/zCyWD9U5KNZ/sZHqlnU07ru31rhRjdDl/z+0JIvXXBHb0G5yrfsX7GbujXytLWmlr26ttikvkfPXy3qtZSCePDFEJwOfdjMsbYz2X56fnzthVQP+bXFh9p2OzSBkMnMaaL4gAtaSTqlxOFwiXeePKtWq0UblTGVXBjiwH7YKdNF22MImktikUm9Zm9a0zYqrZR1kMQ8Wx+mQEpz1YIdpSRyFg6HQ2N0p9Np5Z8IMWobkFKYrS4YsN/viTFyPB4r8/KNyWlBTk+uWn4pQhxVy49D5P6omp+nxr7juLi4wHtHLqkadq42H5TKaLRitWlXpWglgxgdOeu8TNPMxcUF4zg2n5VaXlXgnoEebC2ls1b6MOyt0IIlxLuwdBG2cFnra6Q+IqUa1XjVh+DqLjS4xow9+7yF2W5w9l5wrSqWl8UvcTxOiBS044j6d+w45zJZLLim1251dtcwcqLkTLQweEQTdJGarKz0Sk0YH3cjeTqR5xPvPfsm15eX7GIgTRMvj7dwdYVI4e7+lnk+kfPMfjcyxMhhd0DcSSuWz4XgC+wguIEhCLlahafpiKMQyeTTLc45diHgcsI7x2EQLofCpc/cSiKLJzDWQKNcAyY0em4IAT9G7uc7AOKwx7lCKVqpAh/wu736trIGXRjktfbZ1jVHlcwsiVK70YaoSdkppVpFYilMXOreDM5BDM1qsPmfZrPMogo5EcZhaN6iUlv1hGpFeKfQnoiWvlK+YYqoFWWtBYDr9wtXy6rCOMewG6tfq5BnRUWyVCW29nwzxMiusb+81L02L1X5VZHRJPriPV6WnMtzbofFfwslVSFTLTYBXFbai90cpVJaMYFhGFRQmdJclQbTDKdpXiVIf7/xtRZSZvWIqCocauSSRdao1mQasTLYIpoo62rbiqa1QGPORuxDiO06Dk9wMI4OFyNFhHEYW2SaMVP7fatx94zMxtrpvzbnTSj1JngPdxk8NAxD09r6aDq7p+mLIQRyhRp6eCunRJpn7XLrtemaEW0u2nSu8sVFQDm0yKV04fCo47u1G3BuFa3XdzlVQb7MwQqm2giCwRJkZa11Gc7+JifwUlJInfFW5mmpnmH7pfcJLP6gXhja+1g0V+9HsONXfixZEsftfBXMS5Sj3tnXOWT1fnbfvoL84pt6qEVv/YB2rd1+x3DYUUrmdDpCcZQ8I2VG6ju8ev1K+x1J4ZRK7dU1ksQhPpBywSchJQhhx+gDZXrNYfQ8u9rz+OYZ4+6AFBjGkd3hQEHwwXN1dQFpppxOPH91x3HO3M3w+YvXfPHiNXMt7RPHQCmJOWWGUTvt3t3dktKsSkPUPleLr0bXSmSplGSMUWm64Ose0HVQpbUjk9WeqxdYrXcjkvp3DLo3JOeWEJ3mWfPPYmzQstG/rdey59eKVQhDQ2ZUzLFa24LuvWmeVXEODpJazIpCLIiHuDWMZwFDfUCN7Ytmsbp1S41e8bPflU4rH5V19G5f5Lqfx55HLYriJidLRBXbryh+vtZCyszJasVX5rqEGauhszhgi3XEw2k0UbX/V4TpaNqH92HZFNJhzj7oRvT+AVNdZ653sBSsF2rjUAdWhLQNsz6HW1Pf2cGiTbGuANFjz5YI2peCsSS8cdSmexZ6GmPkNJ3UxxUXAWIFSntoIOfctKXSv3PdwC2MuBMgChEsralt9NaJ9x5veHfnd7F374+zc+1dlw34ENJY1mULAa5x+23I7rbNwfZ67fMe0sGtNvwi4B64wB8oN31k6jiOtSNrWtGSXbs/19X7DMPAxU6Lt87zpGh4nvUawZNz4fX9Ca1Iok0xS/HshkwWBy6QsuBzIWXwYSAQiNMLxui4Ong+eOea66tHXF5csd8fOFxeEoaA845hHLi9O/Lq1S3Di1vuTjP+1ZEXx5lZXpLquwxxoMyFIokxXlBS4nQ61qoNCgn3cc3LPC/D1lxhr67EWZJKr+tz7fd+Lc8KqXofaxpo/tem9NbzTPHrk/nN79z3o2v7tkYYiBjFLC9kqIQgzDkRXdQo3vp9kUIFc5si2vOJPtVjiwAof/EN7nu4J9Z7UQXiw55Qq0jk7vg2N2dKqfVzbg1Mv8r42gspUPzaicMF317ecGVrX6jCp05wVpMV70gpA1UrEI8vnoSWkQlYrol2mi0iuBgaTn6a0wNG2VsS/chFi932oZ62eMb8zNEKa2LoCbAnJJGuqkElmj5QoT9XREi5cJqydpDtmPF+v18JnFJqaR29SwsUERFCjEsDwkpwGlhA7Sa7OLHt+ltGoA5n2mfbn75MS//8Jqhsjna7XXPyzvMMzhHqnFmhXOM5CgcusKw+d4Uzujk/F5Zvz7B1dp9bH+e0skNKhZwyw6BRZua3TGmuEI/SofNOSxNZWaLufr3wNed0TqoI9BGWvQUuIojTNdnt9uwPe+Z0VMu80rVDuP3ypdLeuKvJvto7dqplikKMxBB48fwFQ87kELm4vmIYIiXf8OXxlu98/jG/8OsfkxPMU9HI0VgZKnA8HUkizAI+jghwP2diiIRhYKjHegdhdIRxwIWAzJnjcar5VF7drFXpVIxQavfl9b44Ho8NZu0jYk0Z2q6n0YXRUs+sLRDIrmPK1H6/X9G9rY8JJyvg2qdjqGBT+NCgdkSLBRsMXj3OlYaE6Km5nmvlrCkjqDLiK5xGLXaba+UJm48+ItiQljgM+LC4JPro0J6WzG8Wu+IHtlceNPHs+J3tI7u2zYc9l94nrfp2fb/xtRZS1VTS3ysTUcevEnUTFFX7N9PVmAFoNWFQ/LcdW2prAhYtJVaITzQPsVouep0+j+VNgRNbuMpGz8S3UTt2Xk88W+tIw57RyLYu8bd/lv54Y3ZU5uxdqL6wGukWAkseRPOxrp/tnMu5fm6vuRUw9lk/J2bJ9M/Xfw+0eoRbLe/svIqsYJvFElnnkSzn2dst19vCfNsw8u06bjVFOivNUgP0Oiao2nSp4tQsU1bPsn3HUkqDdfr5Xaa/c3oDcYg4u2etgG/WgADTrH6Nw86YkfoZs4PjnDiMIzF6xDmmLLw+nighEncDhYH7MvDiBK9vJ6YpM00F7wNhGGqHXThNpwpbFeKgEXNzLlxdXjDuAyF4glML3ztFKqasftZUigohIxlRn446/quF2ubM1srmUdfWNHZbsV4A9fPY/9tbpues935s5x/WCo/9/VARgz49xt5vXTDl4Z5YKambLtaceZaeLraIwfZ9eiusV5AeRDzUsVWA27304ktwV/17eaPf/fhaCykNblgmNOeMK1nLvVSYwJhBBQQrx5UWcJGyZksHi8qxYz3VCaqTfzgcyFJIkpiPs1pTZUlmNa1pt9MgC7MkYL05enO419Bh7Xey87Y5O0YUffi6957dmWKjW6avgYrLtaesjcti8Bxnze4/HA7Vz5bMzQssoadsuhA3AYPmr2RZN2js37/XpKzuYM8wtkII0Jbg3fe9Rtfj3vakW60TVPnW5nrDWf9NL7wsrNuaIsYYH9RN7McDyARaUWMfeqjVnm3Zu2oh0GCbLRPZMknnVEHqBVR//6bpOse426mgOE0ENEo11kKnOSeOc8YH2BHJZaZkYazKyjwf2Y174riDGJnmmdcvX/PyOBPHkYtnT3glhU+nHV/eZY4nLRo7DDt28UKDBATwByQfId8TllUkxJGry0soCY+05/Pe8/LuxP1xIhXRXlUVntfYAqUb9YAGNKpOHa3O9b7W0j73ygjArQvL2lh1i3ZrOL2vhN/v1+3c9wLO6Ga32zWUw+Bt56xmnTDVJF/rkL0oKdL+UxpZaKuvfmIdlnVPUpXEZZ/AEkq+tcibPt69T1+MwNIfAG3dMi/Qne6nxTJqkF9VcO24Np+dgJLu95Ug/AHjay6kWE24mZ25lBofI4RhaCLc+tO4oMelankh6hDF14g4qqYiTruauqRaJYsGEUJQLZA1I+4tqh5SMLPciKYv0dOf22PIzrlVro+NPsjAtJetb8zus9LEFeBvm9CExv39fbvWYkG46jDV/IdSaj6WLK3ObZiDNpUFC39TNYe1FWPruMbDe4ZhLT16y8zmpEENLFrcQ4vHNoNrjKcX/r7zX1gQzFZJsJ8eFumt1H4tfLOKqtXkavpDsqK6vf/M1uVhXytTZPq/9fqLn6rvN2XrvYJJ9WSCd3gH85Q0sg6Y5kQ+zUz5Sy72e8Zh5DSfGILn+vJAkcxpOnJ1dck8J+7u75ly4XS6p3wpHE8TLk8EyQyuQIwEL3hJmjqh2Jxu0LDTgrbOMY6Ry2HHRRxIk0afHcaRXDJzKtzdnzhOs+b6VSSjoNCX9TJyzlUEROnZ5q1XWnq4z1WF1dbJ4MA+8MaEkM13P1bz79cBPtt9u1KY3RIspfUyl8oo+/2+5s31HQbW99TrL5+tfa/r/e862jG6Mcuozxk0HtkrVtYtwL63OTHlzKp39MK8f79WFmlTO9NSJNp2r+s3hEjq/L0/aHythRQi1VqqpmX90b4zFfawZmU4RJaWBSZwDJ4xiEvza2I9HorkKlyG1rFTF8tVh+KakHrtqid6M+i2i90z777q9haGAFYbb7sp+pD3nnE+FFLLdSwp9Vz9wH4sz0mDS6Vs4Ex0IxqR9xGP/eZefqAXVFtHbLMyN8Lf5mRrJW5/N+bt3Hpz93NSSWj9Hp1g7T87Z0X1c91gpGa1279KIIvv0srz2LVL7aG0fnabk35dTOgYzfbz2q/9QjOuKUYOjXrT3AFHyoU5ZaZ8X5N1A7l2xo1xQCjkktjvdupLKZl0f9Rk3/t70jxDTlqo1qP+YAdOEk4qrdegJecikhMBOAwjuxgZvEbseYRdjNyfNMR6mmfm2nZCLLKsLpTIsi5tmjuS7ffASlmRRYHpP+/XdLt/7Jhz6MTWqu4Dg/pjzgkVqcw8xkV4re9TYXMsR4/uWZZ3XfhVty82797ziVWJtu59ev6w3f+laFqDdw/3pf3e3BWdwdA9ZH2jZXin0YWpc5f8oPH1FlKboVaRNksvIjhZa0sLeg2NwpuZLGiiQVkTQ1FGIiJttp3XHIiUMhYt2ENR7VzpoulKzVEo6zIl5yAeYOVfMidsb5HAQiRbM//cM/RjKwzMorJrm0UxzRrp9+TpY0rRZ5iOR5B1GPvC/NcRadv79ptABfLa12PaWQ+thBibBtbfC3jICDaWSM80YN1rSo/TaM0tg+mv0a9R7zju578XUg7IaWrnmOW0CB7BqtAbM8slN2u0n7+tIC5AVE1n9Z1FcbVISYE8J5VHohF73mmeqa8ogibLCmmeEXH4EIlhBKcRavurHYf9QPSOIei/3jmO08T98YikGZkmQvAMwRNiJKdCno+Mw4h3DklJE4CDJxdhiIF3Hl+zGyOkmcvdRbPyjnev+eLFS+5PM6kITjQaV+r7KX0sQUohBErW6hAtYKUe1/947yk5YQaLc0uI9jmaepOgOocGLM+1WC7nIkCN1qgBHKnkJpyMjqVCdr4Wii15iZS1od9V33vWsm+GEmzRlHO8wEYLY9/Ql8i6vYxFpPatera8Z0GM1nuvv+72mQCGEPHjV4P7vtpRv4vxcz/3c/zET/wE19fXvPfee/ylv/SX+OVf/uXVMf/cP/fPrUxo5xz/3r/37/2u77VmIksyn6ubkGYx0YpttkUuRphV+HiadVWq1iamxVFxbju+c8wu913j1VuIoIf5zNexgqrqcTHGN5b07zXs/jycW0pAyRqiWp2vb6IJeUVznt7442AYFHM+nY5M01TN/wV+s5FzblBM/6y9tbh9j+3PWuvsiPsNFozNw8paPMNsRB5aO9vn6iOxVvP6hnk/ZwUvz7C+hnOq1MS4+CX0Z32PXsHp5+OsVe7WsFP/fEaTtt7eSWu5sTynWq0xxBYNejpNSn8NhhYcmeAKMQi70XMYHIeolUuKeObsyMVTJDAXtLafcxrg4RwuBEJwjEG4udzz6OrAYQzsh8AYI8F5pMDd3YnjKTHN2r5DpFpRzpARaUXA1wLAqh3oO68Vn2WP9vNkStDWSu3Xuecr55StrRDqaerceLh262AiPXdt7ZfOr7bct9sXbn09d+Z+vWuif3bXHdOfc24f9of1+20bkXxOqTonrO06q3f5AeP33JL6W3/rb/FTP/VT/MRP/AQpJf7j//g/5l/8F/9F/u7f/btcXl624/6df+ff4b/4L/6L9vfFxcW5y/3AsWJG3tXCl1qRwXlHqdFArvSRQpq1Ld5rWSCn1lAjZgScUDrTS3M2HBIM3lrw5RD86nlMQ+7/1udZiNl73/wjPYHY+/S5Ff11YJ0f5L1f5SedO361GcWCFgDxaFlj83Q7tUQdRO8I44FShBcvntd76WbQgJIlyVY1eA0/ts/eFOVoo2mA3XucS27utb7VO3cCye7Tb4oFBlvuLR1cZFCgaY9wPhrS/jUraqtB9s0MzZIJTpn8QhsOasfWkqXzSy3McOvvsvloVkM3LzYXS7O/oXunJX/GO4gilJwADZyhFjgd4wB4SJl5mnldbnl2oyHmw+DxLkNJGq7sHC462Hkinhf3A4XCKUVmHyjeMc9SLYFA9kudt8EVDqHw7MkVu3FgHAJDjMQwMCet2/fll695dT9xmqE434JJTK0qsrbYbX3tHvb52g9kgkrL7+QKeTmndSV7p38v9Pu90tPhm4SQ3c+ez+j/3HEiC431dLqMVAVUUrpXfLcpNf21lKdU37deUbXxdi9pMJ/R0iLg3qw4nkuRMdrqI//Mej8ej4poOAfWwGGrHHdz0r5zDxP33zR+z4XUX//rf33193/73/63vPfee/z8z/88f/Ev/sX2+cXFBR988MH/X/faTrTWGivkGvWTS0aSaP4Tnpb7LRreKlnhOvFOYR/RH5FMcFK1X4MSvFpbXijZIdkRQ1wRuC2saRznNLUeqtoy8B6OOidkGv7bvfu5quo9w7Nr90Jwq1n2P71WZMy7Vy6CW7Qru6YySQeb5zvHWPr3Na233yDe+9bTppWv6TZKz6x7gVNvqjCTyErIKxS29uGZgrFFQ/o16S3ffm3PWV39WgUPS3+h6l+RxYIPfolCizESYnjAfM9ZUPpu63QE4EGFAURbx0cHg/OUpNcIQf0dzsHhsCekjNxru5mSC7e3t5QU2Q17gocxwnz/EicFJ8LoB4Zd5L1Hj9gPE56BF8d7jkkThEPwjEPA53uCczx7fMPjy4Gn1yPXl1damSFEXry85csXX/LFizuOp8TL28QkMIuDMVTINFXmvAhfK0kkuTCneeXb6Rlsv0+81/5GnqXN/OFwWK15r9xsLYLeYu4/6+e9X6de4eppSqpy2JQlp4rdUg5red7gBo7TqdJrH/G70ILSgaWReKL3sNnfb1IQQ1WYe15jYwvjI8uc9AiPrYn5tb3zmpRte7SoP1JzALW4ds6l+VW/mg1Vn+l3cew/1Hjx4gUAT58+XX3+3//3/z3/3X/33/HBBx/wr/6r/yr/6X/6n77RmjqdTpxOp/b3y5cvly8756mIpe4uGnjORetxIbgCVoFgkezGrJa8AJGCWC2gLTTjmoJD3xxxK1DOEjtrM/icidwHR/Sfr67T/Zwzqc9pfDaM6fcbuT+n/6xU63HcLRUsgkX45SVpuW2g7nl6wa3zvHVqr+GALRTXNtlmfrYQ14O58X6zQe24Un/685q0XcElW034nEXa+w77Y/xq7vsgkUUw999bsMU5wdsL1MUS1p/Sr1MpTaBaYqiUDKGDHNEGeXaRcRigBrjkXBM9p0z0hZICnkj0jiQZJxkvwhC0gsKNG/E48pxxLnGfhF0Sxt3AxWGPzzOD97z79Jqri8j1ZdDW9U4QSUzzidd3t7x4fctxKtxPnuTUAht8wMlD+Mjmxoo0S9FipiK+hWuf3aftc9/q9MWNNXPO+tnuT/u7z4nc/ntu3233u9GD1fFbaLDSQ12v82Oh2RZcwfY93YPnebDPN5/3z73dtxagsTqfh0nsvsVisvj+O3eJq0E8tteEh/vsTeP3VUiVUvj3//1/n7/wF/4Cf+pP/an2+b/xb/wb/OiP/ijf+MY3+IVf+AX+w//wP+SXf/mX+R//x//x7HV+7ud+jr/6V//qg8+DZBQfr74nLxqi6wo2J8rnCt475pJrxJ/mWCDCnLVrpvdjXTuhSMYJZAmUWsQzFalt5YO2DCi1jhaLX8qsCvNx2GL3Gm6vcdlx/XwZrGSWQC9M7LNGGPUaDnBdDtI20xyqpZa0UOwwakuJV69eEYfIuNs12GqBI2pkI46cu35bWf0baTo1yGSaJpwPHC4OTUDYvXtI057dxjmfVSN+eqa6Pt60vW19Q+c9u/2e4/HYwm4NZokxIOLp3bDjLuKDY0pzW5uttQmLtWpz02P8vUW1BLEYM7F3ptKYttnIWa2AYRi1fcuGCZxOqkVfXFxQirZcMCszoJF3SCb4ofpeOyjFQ5bCF8+fc7PfMxwOeNTKuLjYk+aJlBOX+8iueJxE7m5PWsSVQjl6jq9m5PKGGA48fucZg9fgiJIFycKjUyHvHT96fUDiJQSPHyNPnz7mw2+8z/vvPGM3Dkx3d/y9736X//sf/Ca/+Q++w/MXr/jex5+BGxG3Y5aR7CJufwAtJNFmrwDRR4YYOJ7uMaQhF1VE4zgChVw2uU9SfT71OG+sXFBLY7N/bI/2Iei9n8Xorg906lvPbAVcz+Dt3OU+fUqIbjKpPEwZurYucQ6GMGB8xXyt+t2SUuG9Z6h7oaS8UnDtuB5NMfrq0zBijKsOw/Z+LZy/Wnp9+xuD++wc8+PP00TwoVaz131zd3/Hbr/XvdjlYB1T0q4UX2H8vgqpn/qpn+IXf/EX+d/+t/9t9fm/++/+u+33P/2n/zQffvgh//w//8/z67/+6/z4j//4g+v87M/+LD/zMz/T/n758iUfffQREUf0gakISdSPJBSKy9XLCMYsipTqU9FFt0DWhSnptZ1ztRilFnkMBrN0OsECmhsuvmDSfWmjrdZtrNg0wSZIRFo1Y5G1Y7Mnut4nYqNpRqalseRC2JD+2O44890JLDX3jFlH3zRasZqHBjPUeobWVM+cfVoQ1DTBCrm2qERYHnWjQXUb6IHQegMDsM3R+2/ohPuqKrmsK2TbYuicS63UvEyZWeN6/wxOqvZnwratZD1W6aNIrZzQ7h9aJJ/plw22Ei2RVBe7CWY70jUteQlk8YAr2qJeW0IIUqA4C/U280FISRNFEasyAnmeQQqKXCeCy4whU0LGh0w6HnF+4LC7IKfM3f1JU2Z3gXF/AZKQkpGQGWJkfxG4vLpkGEf2lwf2+x2HYeT5F18yTxOffvoJv/XZF/zWJ5/xxZevubs/IUSK1LJQTgsy5ZwQ9L1S9Z+p37PuE7egFSFoR17nq0+vtbrpYPyufUdxUmm3s0OM8bfSX1TCXOis7RcWS7ZXXFYWC77RyyqGWNSitf1mlNX4UcdbHBqaLtWHrntOH11blZgFpjpJcFqn1MLrfd3HlFILZ7PyU/fJuq6ameeQgWU6qiB3SypML5gwpd17JCviEXcDpSoSRosu6nrlklvbn1yhQGdo1Q8Yv29C6qd/+qf5X/6X/4X/9X/9X/nmN7/5fY/9yZ/8SQB+7dd+7ayQ2u127Ha7B5+PzjPGSJ5ye3FxhYz2tgHwLmiQAwXna5O4pGZ/8J7gI57aZsIrIwu1VH+RQvSxq3PFwqTEtJXQkle3lSbOmcZmUYlIi+Rr2ss0NUYVTHh0UE5fYbm3yMSEnN60VYA+Z05bCSSF8cZKQKXBKDhHHCI+RnzNiK/maGUEJuR8a5vgvG6AqbOudENnStHuxWBFZdvsdJCCCru19acCsRdqW9ikF1KmHJRpWvnleo3Re20hohNR+3vVJpGIsKgii4WswsskbH1W171DPV7q84JqnyGYw/pY563J4tpaI3F3vFtFe1KvFbwyO0dXdd5VqFY8wfmaazKTSyZLwQWHj5WxS0FKtWDxStcUpuOJODji4HCcgMwuJvyQGXLixavX+PGCR1cHcpp5+XJGsqcwcnF9ILmJ7BIuTIzjyPXlBR++9z7XV5c8enzD/fHEy9ev+eVf+ft8/Omn/Mqv/xovTzMvT4lcHOAZdtekDFMCagPOlGZciHgH03zCOcd+GGrieK5Vu6sxFKwbcILiTT+rCtGCarT9VsA0BxMWwhLRacdFXxWYbqH6AB373RLzLZjFOw0osrXVMkwWPl+DNvpCuV6fX1B+5YPDeq9FEyRFkBai7siYXuwq1AtDCEQfK4RWfUk5ket9NJhqgU3joDxqTnNVWs7XxVwCUqyCu2vzHmLUlIWiXZ+ji1r0uAq0w+HA/f09c9KqG3jPELT+YcmZXVREac4JCU4ru3+F8XsupESEv/JX/gr/0//0P/E3/+bf5Nvf/vYPPOfv/J2/A8CHH374u7rXe0/2fPHlPV4GovPMpahmmTOEDATE5WZhuaJahNRoFBUYvhWrNCZcSqqReA5xlbBNyy4QXMCH0JrnmnlsTHMLH/QFU3vmaURv89aXRZrmuYV19xCDWVQWtdMXcu0j6npzv8EWtZ9R6XBm51wzz0vROmulMuzgA2ETGmujDyro79cLhl4Q9wEIVmnd5qYfCkvmRZAPA87TivNahns/X4ultBB9/3df/aMZQg6s17WTpUdWgzzS1L1TLbHjzDdnglO0TXrfiVSWd+pz3bb+gRDCKiBla0VuGYeud8Cq+teHVYswqwCT4vEuKuOOifuU+Oz1c9575wn7ccfODYjMIImdjwQfCfs9aZcoqfBHv/3j7HY7Lq4uNRJRtI/R6+OR3/7FX1EozcGTq5HdEHn1+jXHu3sOux1DCFgy1tNn7/H03Q/49h//kzx/fc9nL2/5rd/5Lq9v73h9e+TufuKUZk0SdZ4YtT9UKXN1ztsr6lxM09S0cl02zbuiFM0/rHTZV/9fKWgb2jUt3suCSBhsbuiLKaQ9TG30bHvZV8GW85KOUqpiU403S6OstIR23a4KWvB9dZbSkA4PKsCrlR28Bnc5p1aJ8ZpcdN2cgzKEZun3MOUW7ttC7A2u2wRZ2D7V2Vj4UoxR56lLxHUsgTcCrcqPw9FXO7eCVgtO8dXG77mQ+qmf+in+2l/7a/zP//P/zPX1Nd/73vcAePToEYfDgV//9V/nr/21v8a/8q/8Kzx79oxf+IVf4D/4D/4D/uJf/Iv80//0P/27utfjmwOvXh6VSABfpUnvBhfTkJ0pSBv4CE0cBKq2pptTc+pC+9u0Gv2/qwVoWZnVxlDOOST7sdVg7Fl6LNnVUiNbxy2sw63fFDm3esfuOudGY/Le4YraE61TcWfxbJ+/J/j+nfpn7Td0/70d/6D0D+vnrCyi3a8XUmcdvm79vA+F1/KLFSi1iLcebivdOypsplWnG+ZSmZmv1rfBdQb7bYV6zzz6uTk3t33laDvOYFRxBXFWanWpW7dI3oAhXEkSd2nilGd8cYTB4wh4HPtR/Uy7MTLHmZwLjx4/IQRt355lJiVIqXB/nHn+4rZafIHLQ8S5hHdwe3fHPE3aZylE4m7kZtwzDDuud3uSG7nPnsPhBSkLp6ngp4JzGZzX7gWOppR457su2PVzQ0nKEr3Z1qSjg/7fVSrDAvTpOrBcvofAjRreNHpfVk9Ta3nY7b2e6FZKyLl7dEFQgtJUxaDF+6oI+SUfUWj7VBy4IlXnepj+sqW59bPLeq429Lh91uXchRe5el7KGR+0V59UBepBIEb9HDl3r/PDyVc98iuONzHD/+a/+W/4t/6tf4vvfOc7/Jv/5r/JL/7iL3J7e8tHH33Ev/av/Wv8J//Jf8LNzc1XusfLly959OgRf/lf/mf4pV/6Dr/98Ute3c9M+xG3i/jDQN7tKT5SqOVaKCurwDlFkqO1hqc6t1VNwHst1e+xemwGC4SWjyBU3493TbtZYdWu0wTr6Bn7OS3Gzpk3LSMUTlPtfhzHFQxhYysALJfBNEBV+0qrOqDQQlHiqhshxKgadCkWf7Ii9HNFcM/V6YMlZNy0TxNKOefWwfh0OjX2ap9plYTSGLWgVb1t2Dv1Yff2LIWlmeR2ztuzVR6YRQE1j+CcarDtOTtLysLJJbmmHRozKyxFQPt16BNGQwit7UMvfEwxsXeyY7djiWYUiljnYU/JOnNq4VWrkFrSy2Uy92R3z+gcY4g8vrjkg2fPePfJkxpGPnK5G3lxe8vru3u+fPGS++ORV69e8sknn/L69SuePHnCfn/g6uqK/X7Pbhx499GOMXp2IbCPDk/h1ZevyCJkBwlPEuH2lPjy9Wu+ePGK3eGgsKqLvL478fr2xCmpFXJKRXMSncMNNbihhisbLQhoNF8VMnNOBOcYwqL8WHuWcRxXrTnwHnELneSctRlq3RcORV90Nh2pLIqT/btNiG305wIhLEFXImovKP31wRea01VEYfJpmppPSgNunIbdVx7eutADxbsF7qt0F/B4cbUAL+o/j7qHjd/0+79XJPv906Mz/Wj8z3ukLNG2UoVSqH5ce0izGg2JaYE+VeEqpbQAsnmeyege+exX/h4vXrz4vrz/9wXu+37jo48+4m/9rb/1e3KvZ0+v+caHT3l5e+I0z6RcIBXIUluPq3MW54h+DQs5qWZnZWxVmUPcIpT6d2qLiWvFHKUmbFphEge1E7ApT1X4VU3qnObcC5lVfkenidt1+s6wvSDsAyv6Z+2PkQojuE1QhX0fo7Z2cN5rSZZi1uRDyMPGVsD2Y3vc9vyeidN9JiJqCay02/XfvfVlAsGCRaxBYr8p+yr1LWQcs4TVJumVAdN227UrdGH6uDEGYxtk1XhNy/Vd3pPNXR8xtlVizmm8WzqxXBtxiybtq+8qVDhbUYSsy+tFfVpS8HgomePxyOdfPmc6Tdy+eE0MgcEFjvPMaZ45TjPzPHN3eyJJIIwXJPGkop2o7o4T05y4PnikBFws5LkgJXN7OlVB4iGOFDyz+TRX69e9u5Ru3nU/+WbZLiHNIWiT0VwtC2WSSzXvLby3nbumvXdzrVWK6py3I5f92ltazj3sNNvoxFlwjyI2pcJ2LQ+pQeuulWsToYsEXXiAfebEtmi1AZuZvyhjNmfall40h1MWpbLxnu+D7GzhzO2cWZX2PgjPkofVzuuVYnt+34KdtlZaH/Fc5J+Q2n1Pn16T5sL3PvmS29s77u8L4rU+SwmZEoTsHPiAEBqh9oHNLWqvI8zepK+0Us3aKqRYGKRGX/kFz7YNIku5kly1OB8XqKoXmDb6iLTcLWwPddlnNuxZ+y6cW2HVhlP/0wOfVFemqSCQl/e15zlnsVkbe0vyO8doezhzC8fY77AIsmI97tuy6AbdhgD3lmLziVXmYBvTtLlt1esKmC8bRhcYZLnWEBXmyzkzxsDCUhahquuvo9i53hPHofkD7Fn6mmi2bm9iElstd1FACnirKAFjUMrzip1UjVZ9FCE4ghSSWG4bHI9HjvdHPvvsc3Z+ANEmijhN7hx2e6RoKPEQd+wuDhQHmUAqnuPxHqTw6GpQH0jxlPlESYn7+5PO/TBobT7vEY44t0SFFSk4OoVlRSvqjPcSGkxEnV+1roVcO/nilqoSjodw1QP/iv6yUlxchRVXcDM0qM0IsE+lcG6pwN/QkGp5uYUBLIK1FFuWCs6acFksM9c/V6BZUihVtvfF7mcKXDHIs0KUrsJ/skTy2b7d7sue3rZ01yM86i93WBV077Waj3cOSl8wt+67WmRYWFJPtqH9DT1CVkWqv9/4Wgupz774gj/2x/8Yx6nw+Mkn/B//19+nBO1Xk0RhLGzSZYmYO9UIHe89uS7kbhyV0eRMjGqxTNOkJVxq1revG24pi68RY6G2mTcC07yGTJ5m9ZWhFta22rjlHvR1/GxsBVkPDfYt1w0+61tMbAnPPiu5cMqzEpqF2degDzArS88bx5E8aTShRSDCsmlFNP+rlervvjMBYszZAifsXbZlZHomgwPJax8fqNJrgrrPQ7PPbD2HDgq10Qt6mycfA+bVtqjDvlPoNM1NUz+dTjiBMeyQUlaCuX8Gi5qkNhrsrTyDoLbBNcZE+gKz9i7TNLU50uMCpTgkF0pJFJ/AFUqetXp5gOAywTmuDxdc7C+52L9DxjOlzJcvXnM/zRznzG2p/XiHSEkCMwySKyoQtCninLi82FN8IOPx44hH245MqIbtrPKB1coUuDseEedrKIprTMv5wLgbNSAnCEN0OC9IEsgJK9clIsxpafkRY6wZEItgy1LTQ4JfdY7u9wtUhu1qnGRHw8EHYs3xs9SPnDMlFy1q7Fith0UXb5GPXKRWbQew0lN062gtLwTIaAddGnO3Y0GtkyJaUUN7kamfR8M7PSEObb84K+eWUQQkrAOs7PcearfR85MVDN59b33UwLEb903Jm1BLM3YtUny1riyPrVeC7XpbPtaUgq8wvtZC6u7unv1+x+PHN9zeHRm8I3sNJ/VIzeGp/240ra0Gj3PKWDrNv4cRRKRBLVUUtJB1zSdwrXOq6mE1oFmk5iItVYvPWR1bInGbz7bPZMRlYwtxndOe2qbjjJWlB9SaYY6egvprbc/phdQPGueYyHYYDLc9Z/ss/bu159u8rx3TB5c0YV7c4lOUBYayc1o4ufdLPlXo8+Uewp/Lczy83pvWZmtRn1vbfnY8kUIGycosKsPdDZ7DLrAfHeMQeHR9xcXugovxkrs5K5Q3ZQqOuUycqj+ueK/qO4IvFe72rrWoqYq0QuLVAlELUxNlVb67B0JE38P8ZLQKMGbFGiSmloQxrW4+m2W4WEPOrde4h+Ts3+28ObNC2O6HDhZvz9ymua1tf+3tZ1s6c3Uu2t8dVE61cpDleIPy2rG+sy7FLCM7T1rjSIMPofvH1RzGjkdseRjd/baf9++xFlrrYDB7HvHSn7S4TmSBPPt7nv3bnaPxh+NrLaQ+/uw5z1++5nDY8/TpY3aDJzkVHhGvUJ9bM2/YQC1Vi0opgTnIO0KUImTU2gjek7wWyAw+KMx3xnrZaifee+aSKWlu1+6rna9DpOs51t5jE0Jqx2ybJtp9+jD3bai3wRB2vf48q85wmqda1bwQeJg43M9Nj6XDmbpf9bM+VLyHI9t7dcLGeS2xYlXdtzDjNlhipWwUDfjo6xna6Ktj5FKYT4kw7HDBV8tULe0Gq7HI6RACPqzX6UEwhr2La1xjRRPnIqh66/Ic09iG9DtgF3cUlFavDnsudpGnjw88eXTBO08v+dFvvsfV5YGr/QXHl0fuXh75nc+fc3t/IsYdw8vX4G853mlLjOQ1hyYE1xgjUNvAC1LRB2FJC5jnGScqIO09Xah16EIghgGcJ+WlSof5DHPOFVKjCqJFMriaw+eA4DTQwJvg6vjydgzD8ID21nt+fWJKiVLrzW3P8TEoMuKWNejX7wG6seHzrrPAmkzuGH6R0nw8ojdYkACnQUM1THmlrCFSq2jUP4vFLK6Vtl5xO2dF2XGllBXK0e+Xc4qS/e1NmbBnEg1Ycd5pukinEGzPteva+77Jn70dX2sh9Tsfv+bX/v5v8sF77/DsnUc8e3bF62PmWObK7GgTmWXZHMZMLOlNihajtWrgJpB65mMJcE0LqQTzJgZjoy2+rJ2YvXbfE/9WGPWL2TP7LVH19zunPekvtHdozk14QDDOKbTp8ZtadA9LMxn8YO+0ZbrbYJFzVgPdhjhnCbXM9k5Irc7vxqrac/dZ/5yummu5FDy51nKTBvuIaGI31EKnCOIcufoJ8E4TaMW0X5tztb4i4cEm75+rnyd7j77h5TnFpz13KUTnGIcd7z99xM3VjsdXgevLkccXA9eDcOETYXqNO01wN3EdB4ZDAD8wZzilwmd3U20Oqrk3OIWDEaFQiE4DM0L0+Ogq4zS/XQYJrUKCsdMiBXLSpHnQMkodnVtX6GJ7gXVB5eACsxh9+WaJKIwKW2FjyuWW/rdDo1UXgWnHldqyxDtXqyRQGfAart0qG005hPajstAtz1k7MLjKO8zh5WoLOhFp0X1OmVKbS1ev4+q8iXctIblZZ5WGvVtqP26FVK/MnRNAS9mw5Zz+Ott9akETrgaBtKIBQeler+uQshac5/hUH+X6g8bXWkh979M7fvXX/wEfvP8OT5/c8N67N7gvbzk+P+LdWAmdqpUv5YbaZNmiACnnSmM1iU2WmnsCDyoAa6mcqstsLIwmcGxx6iYzYdAz2zXz7PKI6vPZ5+f+7TdRr6lsCa2d1za9tL5HpgkvBFOJuDINg1ts9Im0WyHUR8+dI/w+IGX1bFVhsPMUm+82iixz19+jJ3K7Zl9FwK7X+/Gc68J2q6KxG/eIrOsteq++Lw3PB4/6L40JWfHd6KNCHOYTwRFksVC369TPTa+g9EJqaym29ROHK5khBA7DyPtPn/D08YHrXeGwc1ztHVehMMpEPs1wl5C7xNXhgv0QCLsDxzlxd5pw8lyFsgvUOMCmrSNZfSLO44OrP/bc2q1VogbhZMldZ+Fq/QbdSyWXFu3qaxLrPKmAa4zZ1tlrixOto8gCCdqc9Ru/MmuqcrlVyrZW9nm60Ku2PVjLBTnnWl5kvy+3dE0npNo+qgqLKYOlaNmlECPU0AlXoVHb4757v37PmJAS5+pPDfhpNEHbF+iqsB1bmM3eqffhbhGH/ji7Ro9e9EKqJaubQK9lqbaK6rl75FK0aexXGF9rIfXlLfzCL32Xp+/8Kj/6zXf48/+PP8lvffcLfvFXfov5bqakQhLfNEXTOpSA1Nw1Lcg5hQmbE71+7yrjyTk3Z6vUyLdxGJtuN1gdPHPClkKu1zE83iyvLeHYZ+ZcTyk96A9lw8x4+72vytxvSiOMvo0DIkhWp2sgNCa9hHAvvoPiTDFca6/b6CYLCrDv7Fl67avPW7Kghz5zH++VSVSowJSJPsS9Zzx2j3NdUHe1WG4fXHJWqywOqRUPUlZrqYfzQliiJo0mtNiDarVSu8bmythrTOKDLqL9M9iPKSp9gdwGH3Vw5cO1F3b+nkdXl7z35IYnB8dVyFxHx9UYuTmMPNtfMgTH/XxPvBzZR8/sRyaBfD9zGSOXMXJwjiIwp4JIUvbp0PJKeERSVUi0AkT0gUlmSp7JFIiOYMzWwbgfKAIpW/CAEIMjRN808H6veSpc7UVD+KtgGodBf6+RlbnNw0KHrl7NBa2gYvR1DpozGG0cw0ppsCtaO5jdbldRFVkEa+cWsGs2GqlrJWhpNNfcm+XBmiqfURg5OI2q3KIArhdA3pHSROvpIDWaL3eJ/+Lw4sjkdo4pdHbd1AWI9QKjr1JjYymD9NDK6Rt5+ipARWgRq5Z/asWSmzW4GcYfttbvDxpfayE1ZSh3Mx9/8iX7neef+VPf4DgVnj55wWfTC+7mDKWyPVM9gAq+qzbsFw3FNKNSNC6pUDRvo853W2hqAqddtyP6Jlw6jYtKPNItto0txNePc9+tNOszhLD9bGVV2fduESI9g1ct0hoi2mw9rOywvZflgfX32z5r/z5b+G/1jnWZeiEIGjXWhLBBrXY/DPaw56g/zjX0ZSXAMRgR/WEJJdf7yeoZ2kyYhq8P3ubIAmYW67ezmHTSm8XYz5tV9XBU4abyr8HOdqwxGS8w+MxhhKvLgX2E0QuDcwzOMXrNe4qu1qTUkok1wblwmk7qk5tnXCmEWq1Aq8zV94X2TA7Be4OkBO+lCeq+uGiWTECDKkKwCh0dBFQj7PSVXPvc1ss8MDq/tSuwA02YW+hwmfyFVMwasULJPd01ejMisDWxeTVFaLlkgy4bbfslCR02Pt3V/fR1LPenp31VtDo4b6P4tfu2+WF1bRq9LcEdq6AMZ1c4D3v2/tC+gebWsrf37b97EHDSeFnnX67rpXb2dl6W81d8Szo05weMr7WQSpKJHv7u//3bfPbJK/7f/6+/QNztyM7x3Ve/xIvbIy45xAckBESOmNey1BbV3ikMkbM5pwVKddzGQBwHhk21g2Ec1XLq/DspZ+gKzPaafwhBqyHnxey13KQYI9M0VcttwqobqPPZfV/Nutd4tvDQFgdOKRFDIA5LblGzdqo15JxD5qn60Kqgde6BxgU0SBSRlvzYhwL3x203yLa5o1WJWDl5xaxGtdLmlNRHUnUKEW09olhcwMcRcMwpIQT1pdQw3j602bTunBJjbQzYwx6qKXvKnHRDelcrihccAee1UK5Uq5xaYFRvoE7/gqvdZB1eKi8257QIQ4yIUJm7CqghapHjWQCvTG0+TQzDwH7ck+aMJ3O1g0cHeHYTuT54FVSIVpVwEckBkYALByaZeDWfeDWdeH1/4jvf+5TPn7/iyxevyaeJIGq5zC6QcCz2oPrkog/sx4ExKswzxoAPnp13jLsdMUZe3R2Z5hOFPUMcGYY99/OsBZ/RVijjfsdpSmqhuIDWpQtai08EvBaHzSL4oYY4Ry3hhHS1+zomrHxZ+8WZb+X2/g5DGoyerXNslrUfMASNilz23ryiSdtLZonMpwkfPPv9vuUHWuFYrRm7CCS7j1ldyhOc0s8ZAVWqBdfUqwaLU6tlQHEmyFSYaaWc6s5Afe5b5KGv9lCKztPFxQV3d3fknNnv9w0KN/pfCh3bPDcVqipdy/60c+a8dLZu9QY7/5/xGoNcQwgcTyfSvGmz8obxtRZShEyWyGn2vL7z/MZvfIf9ZeTd9weeXTtuX0F+FZlK4OQiMQrOa68osifnAHmomnBW5uAKnojDa2KhuCXjWtT0nZNWs6jyTaOSqpJXDBpyC96cU26BCotmobkuWpwyoxYLWDHcXBfelPHgO82QxTJqvRltc5kwqwIkmLZaNLEz54URm4Wh/h7VZ11DnKVBMz18ImXJcTBozt5tG0lkhGljG9nXrCgj/vqTa4SXcw5XALQYqBetoBC81KjLBGhX4IKaRS4GXEk6p06FrXOl5bA5J4gXhgijn4nMSNZ6mUVQbgBEPKVAztoVO7oa1STUGnJerYOCapKINvbDUUrdoE4VKee0ZbyTmkvXatZ5RgoRgelILjMl3RMHT4yem3cv2Y0jh91B6RG4cMKjqwsuQ2AgE2q02Dw77k6e22liSplPPn/BF6+OfP7qyO2UOc6ZF3dHbo8T96eJ2Wt5nlwSxRfEWcyWTQQ4POmY8QMMBJwoZeA9BUdq0A4cdiPOB90/0ZOT4/6YyE5wvuaPOU1ol9r/zQ/aL7tMGXzEiafW/F6q8otoCXgE8bnCZpX2RNehzLV0los1fyhokjJLYEFvdPgK2WtrmYoouAqRo6WOTJiYALFcyZwelgCzIBuAMWofKBE03w89VypTVzm7WO6LhaL/k2oVTUn3/zjudI/iGLzutZxSg5sVXtNgFudU+Pb7zypGaHFix+mkgS3RB6X5mhTsnZVmsnm3Pa5oQ6ntQ6xwQaFA9dW6GtQRfWxlkdozsKBLmvRcresYm+/2B42vt5DyhZJ2TMVzd/R857e+y4ffvOGb337K4yvP873j5StPkqCMw4MPpdYD85UhReX03iAkqyARQGrdqo1/L+cuvNT72kZev7OOvsKyMUq3cAsTN8KqQspwlqrN5pyaNbFg44tJTRVUy/7oBBQdFFKtoJQzWaBIZr8Prf2BXndp6qaZ+NXJa8/fC5eK/ce+LE0pFdJaV2K3n62voD1jE7qsoZ9OY6sxKromomHmIUCpZX/0Qp5cmb4L9bkseZKCK5obZkLKOcEHGH0muqytEXAUF9rahjiSipBTIQ5DbZNBnUMoNRAg10aaYE54T84Vt3OWb0QLvnACrqg56HHsyAxkcpoJZcKXE/sQ2e0i33r3UquXj3uGMBJcoJwyh3HgEBwDhSAacZey437yPL+feXV34u/95u/w6Yt7Pnt55G7KJHFkH0hFw6ZjrVStqQna6sOsE0RwpSZoTkWDKnyo0WhCKY4skDCo1bEfB83Bylp1w3k4zgkXhDBU+M17nBtqsrMKMy+iVqmPOIk4NwGZnEuFg4QQlz0jtZa2Br9EvAukNKsVMFgTSL9O9la3Z6O9GDQlJOW5KmxqjdQ01NbxAKmQrJQWbp9TapCjq9ZO74cMtQpKSqn5tmKMev0tvLWBDNvHUBVhIQ6j+p9crWBTivpBZUmata7TrrPqdRmrj020cwPimKdEHJf2Q0XJBxetZuW6AofCitRUuqrEGB4MlW5qoQMftRxdFUb9Xlch1XXzjZ7ovpr4+XoLqQIlq9ab58Kv/fpv4sdv8tEfep/HT655djvz8Rcv8DlCGaqDWx2pUjRJ0/usGFJtsJalUKqfQqB1AjVixjnNCakY+Ar7FlmHaNcSIjFGcsmU1FsR6wria+attclgHba+hSNo99cGaH1FA7pnA4Mh/Cp3oi8Jcz6fwmTH4heJltvD+hmEdY2+XrD1ydPLtWuofxf40Qd8tGfyNRO/wkFSVOgAFeLTVhplOpFFSHlmP3p2gweZEUnkctQeTDjtW+WEgLAPwm5wXF89Yn+45PrmCbd3d0xzYkqF43Hi9d2xZdLPSSsGFMm46jRPkzIt72E3WCj7rJZP8ESVSpCy1rLLBZc9wQX2foe4gg/w0Tc/4tHNBR+895T33nvM9fWBq8OOnDPH+yMvn7/i7u6eTz/+Arwy0hgjQ9ASTj4EfIiIKOR6fzxxd5q4Pc1kYoUnI7jaZ6glUubmdF9osMJd3vPOO+/gKczHO05phpLIITAEZTJXh2scmd24Z54T85yZp5nTnJnuj1xejlztduTjiVNOzHMC74jBUZxQvDCGwjTP5JSIQdWwXEQFGKKFjqtKb0y5ZFU4TLlzNQCH6itLWRMGSm3P0wfO9I1J3zRENP0gYAK2qn2GKECDMXr6tnncRuz2v/f0vt7HyzUsGMn23dxBY+f2f3M9lD630ALBNnu1fz6/5FOB8rtQoUK6iOh+325HzzuMX5jP0WPR0K76oaTR1lcdX3MhVXuzOEd2whev7vn8+Ws++/wVF4cd7zy75up3npOOifsZvCjMUkSrIrvgIBRDIpDim9uvWSzNTHctiKI53zGH7zpHAc5YCSzE4pwt7GL8GONfCEK64zvo4QyR9ITRE/s2oRj3sI7XlgCXjWTPsPbZtOv3z23XPLNE2+fp72Xv1D/HVpjZlcVylKyKQX0ErVIPlISXQmBiEEcsjt2oJavG4Yqri0vte+QCwUMMjjFkhgDXVzfs9gcuLq55dXvHaZq4P83c35949fqO02liTpnbY+I4Fe5OiUSpdWWlrV1bG8kV2hOFJiuzFFNbhVpiK7AfRy53gcePH/Hk5pKnj254fHnB5X7HfgjaxC4G5iFQhlCjDl1ry+CcbxUhTGsu4qrPNeD8gOLStQxSrbKhEHc18Qxq7ZhwqH6d3X5P9LAfB7y7xLuCL8LFbuSwu+AkCUoiELU7tjiFubIQRMPxo2QGCoVCKiogiziCQKQQvSbMlxqxZsRkoJiThRpcrfxdSq2cHpTRIjTYKZeyas0jqkGdTY84pyTaeSv/KG71vbju+OWkFT1vafn7je0z9ZGIW6G2vbarVlbv/zXeonOz8JSefVj+Zi+IwkrgWEDI8jz9fK2Oa/PTCdvueAU3egX7fPTyufG1FlKu7HU1Bs8cCt/57JY5fMwxF/78n/1xvvHhY7744rf57hcT6YsT2V2RJXDMQAjEwYPLeJdrlnmEEnTDV2aSaxFHL6qhDE4rQWgy29JV14Y5S7335JTIpTAEm2ZXBZSrRU+1cy3oYoYQFSPOiVKWpDsTNubc3W6qNh/d8VvC1vYeaJWBLmeoz/7uLR+DELz3Lax7DcMteV7a0nsRaP2/9ry9IxnWQRb9O9p7LNpYQcSRJamQDTviqHkn6f6OwTt2DnI54V3msC+UdI+cJr7x7F2ePLnhR7/1Tf7Ij/84P/LhB9xcXTPEwBgHJN9BmQlhIOfCdErc3R05TRMvb++4uz/y6tVrXr665f448b0vbvn0i5f8zidf8HrKTFkqI/XgFEpTAZXxAqFQ/TdaX8+jGr93sIuBm6sD77/7hCePrvjo3RsOg2PvhNMXX5C/yEyjMl0BhlI4BGHcDcQaem3FZue0tJPLJSBuoPgdYRwZD0CFWtKcKF65kvlQtZd8rRaB187WaWJ3uOD6+prDxSXXV5e88/QRTx9fsh8j9y9eIXNC5pnPPyucjncEGQnF4XLGZyFmOIQ9u5KIxzuu8IwevCskCqmo2PQiXISiwh6IWZW3oli6ohm5Aose9VuJWrAhFNVTvSad+xhIOXOap2pZaU4gRSFdo8u+QPF2/zhXq9lTYbpsUJpOmCuV+2+239bX2jdg7L/bJs3bMfazTbU4d3wvoJbKJ0tngT503lWlRPev8iAv1f9Uq3/4+q7Be2JXG3MM6uPDP8w9WwvE+h7m17bnc+uOEhYcZfDnPxFCCsBHIckJlwtxvODVnee3fvuWP/MnHU8fX/An/thHXPzWc3L+gs/vAsc04MRXP1EBpy0GguwoGUqCGNGFrV1QNdS8aifBYQGlmC/GP9SaHmhG1ddjvYcWwnVVIC1EagLF1rBva2HEawz/XEKrjS2k4Zx/EKloXYV7K0efH2Ic8F2vLHNkm9/IniclTVok+BXx2jPZfGwrLfTzs37O5bOUJ91IQZWHWQrT3T0O2IVAkETIiaudOu/ffefAO89+lCePr3jn2SMuLvY8eXTDu+884tHlwBgzwRWCZGDGOW3FIQHGHezDQMqBq53n7j7wchCudp7jaeawG7g+RC73ke99/pLX9xOf3yZSySQpCKqph6B5VLkUllYKHlpTl0DGcXs88unzL7mf7jiEmceHkcPVnhgdOxfxaVbMP0bmOVNKJpeZ4CMQq8IDIhoxh4/k+3vmKVGyMCfhNAvF+2pVqU9JnFBkxnshRoV1imSlZ1FFa06J17e3/L3f+A12Q+TyYs+TR5dc7EcOYdReTs5zd8rkDHI31eKigVK85gmWgveBi92Ad56deK6u9iQPycPr05HjnEhyT05auLamWOFQ35lQnf5ARv1dwaml7Fyt/FEFkjhVAuIQG8qh3QiC1mite6YvR9VHmq7oH9eiXF3bz1tkQ5W24Jy2KOn2/VJxf1Ee7X7noMaeJ9j+3gopUy63Vc6lFKZZS3sNMTb0R63qDLhaXFsDfzR3c+kcEPwaKmzCuhRqI6vVfMGSqtFQF6GmhlQ3ibkSOoU5eN9gPufVH/lVxtdaSDkHLqjmJyIUd8X9CT79/MTpJMQw8GMfvU9KwpdfvOL1MTCJxzECk9ZAozYklKEFSZSokU5SjBEreUq1nIs54UtRLa4zY830781cu4aFhQKkbBizMfEF29ZjfLPSzBHbh3X2IabrOVlrZ/1nuHXmvFlPOecWxtuPUCulf78Csj2s6VgCJnq/WK9FnoMO7HnOaYqlaIfiIUYNWijCaZoIwMXlgViEQRKP9zturvZ89N4T/vCPf8Q3v/kBj24ODDEwxMBhf6g+o8p4CngSGvlXwHmGwcOgm/wwOA4DDC6zj47TlNgNkd0QGKM666OH2ylxnKVFk2nbCw0nL7lgWdESasRYheGKOO6nifw6cT/d8fTCE+WCvB8IYWDwDoo1xguqHGQVVArl1ehL79S6cAF8qNFa1frMhTkXpSXnavSd+sis0ryvEWOU0nKggleL5O7+yBevX+KcEKPn8fUFF/s97z15xm4Y2I8jpQYITMnCrC3SMoDLDHHgYq/N84oLDPsbkhMmskbC+ol4PxNCIvheCao0CgRfNe8C3g+V9qkQcA3wcTUyzmuicKnV2a1auEXH9vTX74Pt/jH/idVR1WANg7Zq9F4NVjD/co9ebBUy29f2+TnXQP9M9pxbf21/TP9dmmfG2pXALCREqnXj8INGwYpoxKkJuFXJsQpRt/3aNOUFASrNIt1Cmd3z9FZSz4d694NzuDMNPs+Nr7WQCmFC3IQbNULofobsB7yM/PKvfMLp/p6/8JN/BMclo7/ge3/zO7w43XG4uWAicxI6H5QwDCO7YWAWrWuWpeCLa5h4KZk5ae5KtJpfKZPKzDhqCwIppS1GqF0/1RLwjKHrzprUYR1CLziUCasmtRCB5VMdj0dgaX7Yl3p5k4XSlyESWLX06DeD/fTXnOe5wX1tVO2yCM0KE5FmTVrmurXqMCHbQx5bR7J9b/ft21aMo1b1yCXjUbg1DtoyIuaJbzy75keffcgf+pH3eHRzwXsfPOLZOzc8enxFDIrVxyHUdICMFQX2NV1bH6j6Y7pnG4ehtWa5upiYU+L69p7rix3Xh5HLw8jz1/eMv/U5X7y84/MXt0xSQ4RxnFJhSoWL8VA3o1uisqakIcVh4Hh/IkyFV8crLnYjpwLHOeOKJueC9kKbsmfKNX/IQUoT/hCJUaNULdbx8uKCLIH98JwxTEQKKWvO1zhE5pzIecajUXRTzgw+EF3AuVq+RxJlymTAx0GtfCd8eXvHi/s7bk+Z3W7H5cUFd7fPSWliDF4F127k6eOnXO0OXOwveHZz4N3HB6YUOJ0K3/v0OV88/5xPXnzGXdJw+df3idNJmIrgXVZ/W6iNKkUWC2oYNSexZDLKZIchNu38eDw1BulCVwC4kq4FH2wDdAz6W6DoWqVRFM5V57+liBitdP2UZNlzPZLQ/2sQWm8t9Xtwq7DZT99HzTpyW+HoBp87rZiBCLm+o3eeEKKmZFQLsxRhzrqncLQIxFRSe/++i7gFjfjOhbG18Hqh7wNMtVB3szZlgVnFLaXLBj/yVcfXWkhJzaloHReKU7+SH3n56siXzwNZAuO449HNFbsxEP2MoBFexqZaeKXRn+Msoem/pWkdDf6yny1QbRBB/XcdZGGuRiPOtVPT7vkmC2N7m/6rpq10GuOirZ2vYnHud9NmV5pe9yzr65x3FL/p2d2Z62y1xlJKC+lvmwKnMBWOXXRcHXY8vr7k6aNrbm4O3FxdcnE4sBtHtZJq2+5lafpFVuYubvl7eT6P91LhE6mw58w0Rw77gcvDSMqFq4sdx9PM7Rgpk2guGg5qqZxWc815pPpGLCcF0SRMoeB8TSJWfKuGCGs+ntdyEDWR2LdYB3sbHzQQQSulKPwUqwU5BE11KAglz0hOSMmEsFj4FlYRnNKg1ihU30+2uzhqjULh7nTSSkY+qDWYJlIIuBi5GAbiuCOOI+Iccykc58xpEu5PiZe3d7y6u+P2/sixJFIWUha9XoXvvHNVCVQIqVThLoWaJC3g3YrmRUTTAYxpuocBEedobKXE9ft4RdquWXiuCijs/90e3QqfLV2/aZzbr/13Wx6w/THPqGyut0IuipV8Usu6R35sGrf8Rlbfrp/pwX7GBJYG7WzngM3x+vk/AXBfaZ0668T5SAgDcdjz+fOXxJh4eTvjXeTxoxuuL/dcvM4cJRFQ34RhrsoEtYaZhIeTt/X1rKyOdhBtTQ2qaK3mS1+Isy83Y2cbmS1FW7eQnVkn/eZSTUvMwGnHAquAiDcJODu+v64er89k2LRpgHGzkdpm2Xy+hVC2GuNWePUVMpYcq8w898Ts1YJysIueR5c7ntxc8OzJNU9uLrm6OnBxcWC3G6tTvGhUsq8J2YIBtQjVOc92s6kAEdDcIA8xahTdfpiZxsR+p36WlAqPLvfMc2aaMindKvTiPHHw+Dg2IaWO6sI8J672F+p7SVkhPDKH3b423lSNebcbuH99glwIpeBDJA7qQwoOhuCx5NvdEJmTkFJWeEeE/W7gsIOLPchcmFPi/nhsVSV2Y9SqCyEgSbn/4I2OSm2K6cknTZTV5G+F0e6mE1MpzGgX31J9cpfxmpsnT9ldXuLwfPniBcd7x6sXgdv7zN1x5ne+9wV36chduiehwimVQHEB8Y40a07Sfoh4CfhSuD8dybmQ566Cd4jqt8rqSzM/SIxRLXGUyb7JZ9sjCMDa79qSeaXtb0H3cvO7bGCxnvpXAm8jqLZC8QchIdZVut9P20Aj8weZ1QMKNZ+mE871xWQ1z8+V2hakKscL/9tE4OaCuJpnuNnfPS/Sc5125pXQfE1rV8SihC986Z+AArOtMk1R7PnysIPiuD8e+dIlsiv87Z//Vb79zWf8kW+9x4/8yFOSg7//8Zd4hOgKIqpHOiJqktXIFaxA6pIg51QhbgtmwiqGuJj+zvWqCQ4IFbfu+9d4P1ItdYr0kTJ9Qt7amdkXaGxz0CCzdUPFnvmb8FmsuuVcO9+i/BbCk1Zk1Y6FtfZoI8ZYfQJr56+9w1ZD60cfBWjP0EcBUqO+kMyAMFB4erXn+mLHj33wDj/24VN+9MOnvPvOIw6HHYfrS3a7SIih+m6qYGprovNQGotfW5udKQ1QC6RGSs2zu7RmgTkoXDcJuzCwDwMBeHU/8d3XE84PBB/QfSpIScSiQj7kSTfyPHPhMtE7XDriy8Bh9IjLzEWYyXp8HHC5hrWn1DqxRkRbxJ/uwXnGIRCTEH3GM+HlRCgn3HSPL5lDEA6HPYfDnm996wMuDzsuL0Zev3jF/d09jsCcCrf3J45z4TQXXLgnFU0gTUXzDIdxpDjHfZprcIPmsb26O/Lxp5/x6SefacLuXH1+rjBNnjkJt8fCKZ84lUTx9VxCvY6V5xJtVV/pLU8V5g6xBh4JTjQXTYNIVCDth7GFo5uPRCu9d0nirIVVrxzZ/jGFT4pAqHCX0XM93zunFVCoPq9Ks325Mbp7btNHzvmSeyhNzuxTO+dBTzhZQ28qWB0xatCJNWYVtEyXsMnt2syN/eSq8EipwHgNthBYwYJW23EummC84kGuCxZpcKkqQubf+kHjay2kjPc40Z8xetIsTHnmfhLKXeZX/v73uL488E/9+I533rnibjryW598txKipzCgUVna82Vx+i2T7TWECpyrUEtHgLK2RJzziF9Dgd4v+QaL9lRrc2FEVsGEDUzRC57FynkItbXf3MPfz1kuq2l0a9/VYomdOa++8/q4mo3PekNutcjvBwfa9Xrt1jQ00NDlwcGFdzy93PH05oJvvvuID995xDtPb7i5uWS3Gxj3O/zgaj1BA2AbsFo3icMCUx7AGe15qlKCxwfwIsS8Y1chp3lS2O7+mFVJEsfx7h6K8N0XR5xTevLVx1hSITgtUxUk4UshkBgHz26M7ALsomO/18Kw4rRwp1ZJsWZz6ieIThsODiEwBq/MI3hcHJjyzJw8+9FzGB0XoyMlyMURhsj19QU3N1d8+xvvc311waPrA19+/gWvXrymiGOaM6/ujjx/feL1/cQxJSQlrV+IhtO7GBGpNdsaPcD9ceI5r8inI5ILA55MYpZETpFSPOJGZknqvA9WA9G0bGPUufOdCuSsaRtOYXqD4/vINBFqXzBdPPMpUYvoSldxwsY52FuZubR+Us5ZebOlJBk1kIDN/joHr/cIxQ/qn2T7+9yeObevFyG1EVDO/EdnhBSOJKnlLDnnVkrnAyhRqDCydkbwXt8rzUvqjKsRhaloLc0+2rl/J/rntk32FcbXWkiJDCCewQHF8erLjxl2B64fXZOHC0qA73x5YvervwXTa/7wH/9DfPNbT3n+6rt88XzmxSvhvhxIBE6U2htnVsunRsLkea5h6RryW4pAUKsoVOe6PosRpEa2hUYwHquN11ejiNEqLayxWbG9VZbP+x5OBvtN09RF5VVIyS0BDzZ6q0QjZs/DD7AIl8Upq8es+izV6zaQstuMhXWkVL9p7Fi683rt0Yh5q3mWosw5es87jy756Nk1f+xH3uHJ9YH3nlzyzrNrnj2+5PrmQIhB/TuiEW+NuDth1CwrHN4P9YDSAlwW6Da1zVtqUlHc7fFhYBz3BDdysT8hszB6xy54Ys5c7e747NWJu2nmeH9iP+51DstEdIXotNLF5WHg/cdPeP+9Zzx7csMf/yPf4mI/sB89iPqNXr/MtXHgHQMZHwPje+8yxsjlbuCDZzdcXx5499kzMuoU/+TLV9weZ37s2z/K3STcTcIpJXIRUi7EIRCjJ53uQE6U10duonD9+MDxlBAZ+eDJI/7BJ8/Bvebl3T1T0vbzDBHnB/KsdSdLmrGkdO8d0+nE89vX7FA/1yklUhCmiFqihJq2ocUaRLL+IE0QlJNaX4cQGpT35PoaJ0JOJ1zNKyREJHiIA6fTSTvtBtuLqhxoE8aCeF3hHlXo6d9o3PZFCBHvpUZGVui3hm3POatl5V0rB2b0uk3k7VNHLMhoHMd2fxMm8zy36N03WVF9ZPAWxrcAkpwykktVpj0XhwOroKBK+1p/0PbpEoHc73UL1lLhI6t96ZwGavTvtwgcVRhakAqdAlojD6dpwsdwxtt1fnythZRzWgnYKk0TIUQBnyg1b0LE88Wre/7+b5348T/+Y9xc7fnWh+8i+UtevXhhkKwSMcuk6kaqEJm617VzgMbBglUgFvUFmNXRUCU2loNzTQtZjpAKCfoqEKQRfV/OZItJwxqqg6KaOwts0Y8t0W+ht15z2kbhqVa53LtvHqmC0TfNqPW/QTU46ObAUBh7dfvLpPLyiTroRfvneB8J3rEfIjeXB955fM2T6wOPL/fcXOy4PIzsd0P1obgGsVrjBoMuXbMUle08DHKhQTo1nMKWrYMpqiUdUKaQM4f9yJwyORXuD1rt+2LUElVJEqMTxhg5XN1wuRu42A1cjY7L3ci7j694fHPJ1eUenydkzhAHYq2isNtF8qz5QxJVe0/ZIUWY58TxeCI4eLV7VaPeCrevX3F3mpmmzDwJaa6RZUWLC0uG2cGr51/gKBz2O1yFdU6nTHEBYo18q3XwDAJt0FIpuJLxopX/oc6Jg/0YuRwiuxC4HEdOQTiGwjx7SoaUHHOamdJErnUE1VdrCb2qCEUpDINCqk9urone4Smtm62Egbu58NnttCiUXRM9Q0R0D2+Dkjrar9a19/0BNZm3Bm8gUvN61KL23uOCpp54s746SFtkHdVqkXM9tL3dd/Z3r6wZCmNzf67Uku2hnCv3cgZt9xVQZCFko+f1ZlwhP3ZO41QdP1iEZlgpobbvXX9Ob5Gx8I2+w/hXGV9rIRXCjuC0OnZwcLiMlAAz98xSEBkYwsj3nj/nk+9+zv/zL/xp3nl6yZ/7k3+UdPwlfvM3vktx12QXKI7qp3C4rJvB1e6j3i91xLRtuBagldq8rOTMbqc5CojCA/0iFWqhy9C3ybAqzYofNzStlv8JtWitWm/lQWfX3uGrNfm6pLkzZfIXgljrLz3E5pxrDQNTV0iTqumqENNK6eZYpwYEFKjtBPShQ62y0UMS1pFURKpiIbWcTwcHOKvOXcg5cTkM7IbIk4sD7z1+xEfvPePdGw0Df3ZzwfXVgYuLPSX4tkk8q0dHbO4ctb67QC3DYxF9rjJgVwWob4+j71NMynqPjzDEgAyR64sLPFpwN02JnAuPDpEynSinzFUo3FyN/KFvfZMP333Gu48f8fR6z2GMXB9GSjqR5iO/+Z2/xzAE3LMbrq4vGMaBi/2OFDPTcVZoKxVevZyZcuJOEvn4mt3g+fiT7wJaKeHzF7ec5sypwN2pcHtMTDm1UGBt6ZD49JNPiTHyjQ8+1MCDLEwFxHnED9zPVZTXNfEukEpSB71kfM18zymBaP3By8sL3n18zeOLA9eHA3/oWx9xT+Jlmbh9PTFNmde3E89fvODFy8RpKqQsTCXjnBYBHmrdTF9m3rm55tH1FR+885SLw56by8uaCxTBR37z0y/42//fX1e6FOH+eATniME3ctJWL4ViYLTTvDNpiopVSKl1F40OgHEI2mxQMrthh4j2xfIxLBGEdQ+bBWKKngVi9DDcm/bkUjViqdZyOp2qReOrHiccj8cFkuyEolZGn1eNCwU09cCgbhc0uMltFVwV1KWUpZN4FVAGo/bPbDwjRks3WaB5waJol2drvKhGqC7PJyT5JyBwwvuJEAetSubUJ6IImiN6EF/ITHDY4w8f8Gu/9jnlZeGPfvub/Kk/fCIEz//nVz/li7vXlHKJk1GtGjmqZeO8lqAoieBVuMy5UNiB84TZ6b1FS9PkUuoGqQRZrIWH1gm0UvulGjpSSrUWrEa3aDKic0hxS/sE0QKyW8XDOVcrSDwMf+2J2Y41SHCuPaNibWvtWQrpGqGGEGrpDVpuFEKNfdN3spYLc9ZaaX4YKmwmpA7FNGPJglG8D6oOuF5j9RyzRh/FELQMVYDrkLgaA99655IPn17x7NEFl9c7Lg4jVzc3DPtdZaSu+jaE4hxOnDIVAfGRYr18XGrsSZwKKatyoOViqqgTnQ91BS9wqhKe0xBrPGFMxJQY58DVxcic93zw7BFj9BwGx/XFyKPrgR95MnI1JEJ6zf3LW04OXjrP/XRkSjNpdux94HQKDNEj2TFnyOLIIXI/HznmmRd3RxAhOmEIkYwn5KW9dwh7RjQg6DTPOGaCm/Ch4CPMGaZUuMsFyQn/8kQg4vHMpZApzH7i9ey4n4W7qTBnGEJgJxPRJz54fOCwH7g67PjGB+/x6PqKZ4+vOYyRq11k9Fpp/Ppw4JhmbueJL5+/5jQl7o+JTz7f8/FnO377k+fcHhMvbmvrGwpPrgI3lxd868MnvP/OY54+uuLpzZVW7S+0Irafffkl7vSaQ3Ac40DEc9gd0Gz8jNQ29M4NCLla2FItZmcaE8onBefV/xS8WduVKCrCkUQjiUN0pDwzz5mUtaLGOIxtv5lwMmh/W+5Lad8CEwrzlOoxvrUEEkFbh1BUMaiMf7cf6+4rGp3ntekkgISgNeJzahVrctcBNwRp760tN0rlOGrNagPQ3jIqDT7V9j4OqdChGmpaBDiXJbVGe3bVufOegnCaZ6QmVoceSeqh9R8wvtZCyvmirTfMEpHO34BDfA0qjxEZL/j009dcF8+f/aN/hPeePeVYTvwfv/0J7jTh5KKepc0MqUJKW0pnPFWTqAmGUvsOiXNE58lF8128qlbKJCsjdxUy0BYY0qAoq+q9MFjdQM5r07zFfjZYzd58kVbWCTXnBZLb4u6wFlKlElRoUFan3fWhqBbOClA0G920RzP9RGpeDdZpRr/rLbfmr8O0ON82v5OMc54QA6cWArw0fdsHuBrg6dWeR5cjF4eR3W5k3O3YHfb4GBrEYVF8Ot0G2qkAa7lQbQbFHq/+48yObt9Ks2ulS6HSzepCwEcNagjBaymjMbDfDVxf7pjnCUkzNxcDN4eBR4fIEAoun7RlexHmOfPydOSUEpe7kRgdKTnSDN5pzlUBsnPMCFMpHKek+X0e5uJwxamvKUJ0XuFRB7FoIV2H4H0Crwy2uKy17yok9ep+YnDg3cCUM9kJJyfcJccxOaZk/ZQ8I7CPjnce7bi5uuDJo2v+qT/6o7z3zlM+eOcpY4DRgyszUPAiHKeZ+9PEZRCOp5n7Sa2wMieev7glzQXvagizc1wfIu88PvDtb77Lh+8+4dnjK24udpRSON5N3N0J9y5DPuLKRKx5cF6qll4ykk+q5BXbQBV2kmWt7bcFQluQsh6lNxrJRSH94AOScm18WNT5Nqytkz7CbwtpeYv09Z5SZnIVdFTI0vaUq7yiyGLBjIMKqVwDSbzzFSpVekxmMVdmmLtzVSg5C9NbqLzBimv/sVQp0s+bdybk7DnXofalKux9i3m15tYQZW/BfZXxtRZSY9wRap8bVyEm76yLkGq/AS39EkLht7/3OXI38We+POKGPR98+CGH+HcY5UhhJlvvqNYCwFcsP3EYdwSEKI7ZrVtVe+8QavmiWpRVRCO5tO22QYmQZOmxVKpAs4TVXr3QLqa1NJFX4lLIz8qTdI0IharlsBBZRxCryLkQGMaBUEIN8Fgggpwzc05aqt8HtSYc0Lx1tLB/e1ZHLUsH0MF21oQxhECihuvbW8qSJyYpATWKKniCOIYQlMkI3Fxf8vT6kpvLCy73B3bDyH63Y7/baeZ80Ez2Ys/UQPf16DOjoHuHBXmnqTjCksTK8rXYYdRajL4GyUT9WRJoIxf7EScHHl8duLm6YLcfkZS12oP3pJK5u7/ny1evuJ0m5PEj8J6LPDPMHogaheUgO1VCSqkglfTPtnIw1OaKKm6HAIPXmDy8Juv64AgELgfPMSdOr75gkhGRwHGeSBROTpgZSUTyKeEdRO94+uiSR9cD3/7W+zy6uebZkyd89CPf4MmjG64v9tpsMp2qteAZgqfgSYUajKDdhWPwjGPtPZQTUQoXVzsuLq/51kfXvP/shm9/61s8e3LFo6s9Q4DpNHG6nzVgyXn2444hnCDNRBzRCS9fvsBRGHxRupLq7w1aT9G5UK0r5Q0i6/wg8wvb/PaCZ5pOQG27UwMqtMXFAq33EFcfHGGBT0r3oUX62b17X5MRW29x2ecWyGC5kR1x6z++9ufq/FdbwWAnOLf+zPxEffHpbYRhO/uBFNdnsC4RBveZm2Idrfu7H19rIVWK+o7oCkZqDoknmxDIhTJPSJm4LwMvJ8+vfudT3n3/kifvXPD+40um44lPPj9pYzg8czWBxYjcBazegZZZ0Si+1mCvG32UTGs1Lip0UlecsRFCj1WLaJ8ioUaUVa3dLVFwsAgcXfQ3JwRC5xOqv6tFZ9eWmoi5HGMbzDuvRVM7jB6Phlvr1dq92jvWTdnjkmadSSmaaGgmb1FII5dqfaJamkNhUI8KravDnpvLA1cXI4fDwG43sDMBxVIxwrnaZqV2LDXrtT5Fk//27L6zjlzDI2U5wrRuV60xVBFw/z/y/jzaluSq78Q/MWTmOeeObx6qXs0qlWahAUnMCAQ/QRsM2G3Dz9hYGBo3sGyEf21juxvjXjaeFjZtQ7ttY7Ewg1q4sbFNY9MSbbABYSQQoiRVSTUPr+pVvfFO55zMjIjfHzsiMvLc+6oKGlavWs63zrv3npMnh8iIPXz3d++d2FJK5XYb1kpM0Fip/zebWBQTGqvZXGuYNhXBD8pYepE52r5jf7HgxnzBdDbFVJZl56htZJrhJFaqEFjKJ+tzEB5axx2gECpgraGpLeuzWvqaGbCNXLonEHrHYtnT9dB1mr5X7C/nLPue/W7BXuuhX1IDtREo8/zpLU4em3HmxHE21tc4tr3J2mRCY01GB1QkFCmSRS8+qhg+8pkOXvK2Kk2YNqxNJxw/dZpjx49z29kpxzdnHD+2yfqspq6t7K+kJYu8FEaJE2OCx7cdfe9prFj9Bi+ts1C42IxKqYEYpfJYpThQWh9D88By/opROciYMRN1xVM6gnk3/rn6rNToXHlThw3Otk0kkYRMDNBZmrSrxz0sD44+9+r5U2wt3VPeJx0kPeu0X3rcR4zHUUqtLFf1YtvLWkm5vpdJWdSWUsZAZaSFNYHWLejblt4vmU8ucKWt+bX7H+Ut9k7O33aKe86doPE9B5cv06LolKENDociOC/N8nQlmdrRwiF4guvQlRkmfJxUiUba9z3KihuvlEArre9HhVyPYuFJANIBybKRpFpT6bwoyvwl+d7hLPQSdhgUlce7SIKITRJdCPQk2q3OdQEBvGvxwYnCUALnpRJDopAQYY2Krail5Epe+GHMciorAXgvCoW+xyvJRUqUB99HqnZlOb65xontdbY3pmyuTVibTVhbW6OpbU4kRWkwVioCxDp1kLynBACr6CCHpA/Lkad0lYqlygBKqHRL8ThCHqmbCucsfWeoKkNdWzbXG2YTi/czprXFanDdMnqtUk8t9B0L13Ftf58ru/tM19fQxrK+7NBa0XuDSTlBKEKf4ieMhKUIz9JqFqVQWcv6TEu/qrWKqtY0U6kQrq3mllMn6dqeZduzt+84WHiu7++xPz/gys41Ll3dZaddUGvLrKk4fWyd++68hfNnj3F8bcpsJqXGNmYNjRVquTSUNBBzAGUcpcO01YZKGwySBGtcx1ZTsdFM2T5xntvuvIPzt97CsS2Y1oZjazUmZWY5MWRS2r3QliSZucLTz/dolx1bJ4+DB98uqawgEPNOGIuu8FJ8TMJN3pA88z6nfZRrKBEiVgswl+trGHuyMemck3qe0ehLlG7vYkv3wutK388QYboGNZzHe8+yW6JQsZ5ltBjT/A0x3l0k6ZY5SqWX5qMhnFiHpedVzq/UGijV7hTxE7KiyxVLMnw3rJ5SMZcyKF2HkSQuXsr2slZSGgk2LhfSP6YK0qxNg1BEtaZWwg5TasYBiv12ybXnDqg/o8DPuXXrJMfNhPnzCy7ve67NdzlwNUFZIaTEZLjWLTFaUVuDwRKCwXV9DNqDUGi9tC1YwaHTFhQsO8nUTg/RaBO7fjKA4gBBFBzEliJumKzgixyFkDHuoyykkvb6uZ+9xzf/ievpBDlOFDz8vf/1DJ/6zHRkB2mtRCSEVHNpiNn0ro+VNKRGnbQpiol8DIUlCQGj42IMqaCndNNVEPvuiJKzxhDQKN/RGFirDNtrFcc2arbWK9ZnQuFumkpYXlaaAvZyO/GuFBHhl7GA2IBQZW8weWDyR1Kocq3Zsx3wXAboJyZ6hkSnjVT0uqLvK+rWMPWGrZnFe0PwwhBTiEGlo8DqYxuPedvROuiCZt4Fms6zcJ7Ggw0S61RB5o0OBhOKZxYKyz8+Z6mALbXupKV6wAVFPZ0wmVRsrDdMJlJy6dQxgRDbtmNnt2V/v2Nnvsd8Oef6zpRTWzvs7i3wzjKbTjl58hi3nljn+HrFie01Jk3NtKmw3uE7mR9aG1Q00lI1AeejQFOaShtqpTixucGsarj17C0Y27CxfYLt48fY2J7RTHqMCVKhPlasd31PcA6jdWRVBqbWcmx9jTvPn+HE6VMsfcBbRXAtrj3AuZbeOa7udbRBsQw6pjWk5y7PMzFtA4OQHmJEUlbLe0/XuRi3kvlgjKWqatKCLddc6nSb4C7nhuRkqZAhhVxtbCyZizRn40oQocBQRNpaIbckCDCEmAcWC98qhuLOy+XykEItlWmuPsNhZZuUZgpJpE3mP6SY1CqcGCXKIe9yNRaVzr9YLCQP7iVsL2sllSxfMYAc2vsc4E+WklEmQw0HDtrQc7Ccc+nqDZ5o4M7XXmBzu+b8iU16v8P+4gCjKhIHTCBELUmlQYCiRNSQiRJzGbzUufI38WHTYysTChM8mbYhU1tFJRUoDJx8pKG2nY/GyOFJWE4ea+D0qZbX3jfnS75gl+exzOMk3cCz7R3/7oNLru9Ynr1c4ZNAV9BUgbMnu8yAu/I8tK14h5L7EK3SBLUmGFPa2Mh9ZcvUR/g037GUDsqWmIyrUVBbw9pEM5tUrDUV09oyqSx1jP1oozOlNtlvyYpLyyTDkiGg40L2Qa5aIDcNIdFyRfknT4QstNToVVqLRM/S2DHsN61t9BTJSdtt6EXpGw0upjN4jwOBmB10XhIn8yMPhdBCo0L0YknW7HCjA7ssslBDykGKbTqMxVYNVd1Q11W0gKHvHcYsaKoWW8NiaahNjwmw3tR4Z5lOJhzbXmdzrWZtYlmbVtTWUttYiSXGylLJqXxtjGFTrYUeLl2HG7SZYKuG9a0tpusTJhODqT1KOZR3g+ebFLGSXlJSgFYzqSu2N2ZMlaVTioVv8X2FaxXLVrPsWg5aJ0zJPj49lZ7i6kId1l5eg8mASU89pPUlCs7EpqdHeSHW2uyJZGKB92DHbT1W0ZSkDDK1IQxwvilzDxNslwd6qPxSJv+Xyq80YhOkmd4rSy6tGtnjax0r5cH7Gx72TY304vOkwF/K9vuupP7aX/trfN/3fd/ovVe+8pU88MADACwWC777u7+b97///SyXS778y7+cH/7hH+bMmTO/63N1XYetaykJYzTBWqgNqrbRytBUdkZwCtdq3GSB1z26Mjy7s8P82g5f9Oo3cPbEGl/4jpr//Jv3c/n6VSZmIgu486hmgrYV+J4QxPJ0KhAwkvVOoOs8SvdCY00wWN4iyQFhSZVlf8rcpMHCSdbHSj2uWDNQrLJlMdFDhhfK46Xveu85cazj/f/0KU6fkve/j3P8Emt09Px/ucH/qJ7nb/+VizzwcMMf/uYLtIuINaueV9694F/+44vUlSinr/+Wczz4UENVVTGXyQnlWxls0T4gTd6qqlgsFrmxYgheYFStMahIBgCCVPfQSrGx0XBma8aFExucO7HJya0pm+s162s1a7Mm4/o5xpAXSdG+RCsMQ8K1MqLYurYDpTF2kitvN1UVSQ0LbIojup5RxfsBdI8LNGQcvrIWphPadg7Bc2x9FtlfUhGkdw7XLrMlbiuLrjpJNlcWrz1LoA2BnogCWItr2ywMU3BKKal0IMrFYCvLcr4ExILv+56269lve5Ze02HQu1PWvYJ6woHrqJee9bUpVWWlA69qmDQtxjoWC6jUkqkxdJ3H6AnGCIx5cmPKxnrDxqTCqEhYihTRoFMarlx/wlS17rLgNVqzNpuisRhlOJgvUaqjCguqoLAhSD08JekJOnrlta8IsdBEEFxZKP61YXNa0ypDj6JTCOvWK3b3A/OFtO/xC8dBn6qmCLnEOZ8L0ia0JATp15WUVClUJ5NYOaSovpKEbamMrLW5VQ2MPY50Pl3EcubzeVZCQP7usj841NrDqkFchxBkAXifTSYd4bcE2ZcyJSuTEGLVi6EDb7qv1S3R6Y0xVFV1ZHgiV9iJccKuayVpPI5N30uIo+x9l66BqmL30FkPb38gntRrXvMaPvjBDw4nKdo0f9d3fRc/93M/x0//9E+ztbXFd3zHd/C1X/u1/Mqv/Mrv+jwBye/RJsUNBGYQpaBABcmgD9LJVKL+PSgfLVnNYunpe8X6+jonjq9x+vSUK5c9qnXjgrBFQFgrjS8YfjBYQBktKiZy9qNWjLdyn9IlF5bRYZdsiEOUVpAXkgUDoaLsCSXfg/U1x3QiCmGOYgdDR+C3mPCjapsvn+2xNhvybbSG/+ad+1x4Tcu/2dzgTXbBa/slf/gr9vj4J3s+9Etb4nFlq5PimgY3v2x5Pzw1IrRGtr6yr6IUVkFlBFq1WhrWaR2rcCQXr4RGiisYQxTJO0tWn0Ipi51eYf3Ug9mrqoxhuX+KvefuJTBKCyuOPVBvlUwtCk5zfPZDX64EmxgtaQySF5YuO+RXgEjSIZN1hqB4LGSqJPakTYpVDJZ08CE3ritjkl3f03rNwnsWrcNWjmUnLD9lFC4ojNcx6qOystda6gJO6iom1lp0pNhXVvqf6eQ1x++UE19QvsETT17esFtJAE9Vxn1cVylpYGzoiTIfjxvRwwquow9OIF87fLcymt6aXBFCm+QtD+ssz1cfpFZihE/LmpFpTpVKKf2+2tW3fAbj9ayGuRlhupLotBpLTuu3NFpzrCrN5+ToJSjayDn7IjeqPHd5TXLdjOLFRxEtRoSJI7yrEC1DyfMsIP7imlfzxMpr8e4QTHTk9geipKy1nD179tD7N27c4Ed+5Ef4yZ/8Sd75zncC8L73vY9XvepVfPjDH+btb3/7kcdbLpcsl8v8987ODhAtae/Aa5SOGdPeoZwWqERpBEyp0FgCjqB6UA6UJijD3kHPwdJz+vg6p05vctvBJg9f20MFj9GVCNOYyxSzr0hFGyUWkjydwFBaJVpgefJD2dKotNBWqaiyINJ+0QLSZOtLKfB+qLiczhfCMCFuxuwh7y3/KxQfZo1fZ8bdtJxWDmMESrE28Gf/5HXq13u+ilv5c1zljXbJt/+Z63zkY1P+7/+8jfaKkJX34YXpvWe5XObgcbrnUpHDoASSEDNaYbWiMgqjJWfIpP5BRa70cB9Jd8WSK74s2AuxsZN0njWGycZFTtzxEyNBuHPps9l7/l5hWEZjJykUrSTZ2ql0vUlIkq6cLCzUkEStkJqDxJyvQdmF/Fw95I7DgyAUYVjpoeurMRrjhyZ3Oubwee9jVZSBPaaUout7Fr3iwGkOlh3aVhwsHRiLttA7iR+aCDGm+mwCqymaqgajCF4XzTkVxqoRLC3XI/cnfYRKgyzkZ5ohYdkxIgWDwtEqoJVH5TFOMZB4xNGzTwiCo+9aln2gA+ppqjfpJR3ACztXK1l/ch3j8kIDQ1YIS6KoJHcPBgWVFNJQMeZwnKVUUkfV8tNa58K16f1VxZa+Z+pYf6vYDydmTUJW8ED0/E1laLuO3nX5fEcZtT564Ur50b0cJTdWmXly3Wo0T2HwFl0BT6bvrx4v/S21DIcaoy+0/YEoqc985jOcP3+eyWTCO97xDr7/+7+f2267jY9+9KN0XceXfumX5n3vu+8+brvtNn7t137tpkrq+7//+w9BiCDiKWH4Rmlm05lADTrmFqGBGh9ixQHXof0SE8CFmrlXfOgjv82Fs9t82Zfcx8b2SV51L/z2Zz5O37YoPG3o6DrhXeeJmcVjtIiyJSsmeLJ2VBYcgm374AnODYs14cEg38sT1QlQFRD4wYsQ6LouEycgTX5ZmEnBpQWyugDGnlm0/OQogOZ/5gx3nlvyz37wSSov6NKP3XWMh6jpx4WGJEKirFRZNhCi9eb7LnZLjxi6UswmTR6zvutzt1vfi/uQvKPUBNIazfqkZmM6YWttwizFo2pLXdus8FZxcClb5RFe5uABJdh+69YPMTvxCdm32js0l2bHHuCW1/99INAt13jo1/8bNJLfZKtIblFpwelkxOI9KK2GPkYu4KwDE2K7cRluIW6IkFQ+1oADrFJUykQDSOe4i1EK3y9lLpOIH4NAMTrF5RTByTNv2zZj/fv7c57dPeDSzpynn7tEU1dsbqxjjZG40LSmaRq2NzeZGENlFL7bA9+hXMv2+gazZoaxsf2FAVtVGFtB9mwkh0vI3Tpa+nLTibavNGgjifUhrtkQAOdQRkVF3ePpCVSYSPHPzSJBkI/4vEPsEozyGKtYW5vglh3a+QgDi1Zr2575sqP3Ch9SnZR4Dd5nrzdDt1kYe7pWFJ0xwlQ0eviu0YMHIeZKoq2rkUAvPZeRF5Lur3g/QW2lkamlAu9ovvsY/5VYVNxPSWhguVxiq4q6qbPiuXnMZ0BqVluMrCrQtE/ajDZJcKBSAYJCwWotpJIUcihjdqUCK43zF9t+35XU2972Nn70R3+UV77ylTzzzDN83/d9H5//+Z/P/fffz7PPPktd12xvb4++c+bMGZ599tmbHvN7vud7eO9735v/3tnZ4cKFC0M5nGgqKWtl0WaztegbFAtjCrgh1lEXAk9dvYrXHZeu3cLmRs2JE6fZXpuwP3csOimDNFQvIMc+huB5Ac3JiTJ0MvhO5Il9NJBHXiTJjdYqMGk8Z8/M2dlt2NmrCwsu0Vfhtltblkt4/MnB6lpdLBkeSO/ln0mUa+5nws5U8e1vucoEj0PxSRo+QUNKlS23skSKFIeKyjN6fzrmkKXEY8IAd2UrOd12cc1Wa5q6ZlJbmspQW4GarDUjjyGNFSpE1C0p7yRwYlxML7HNVZqNR5luPygn3VPwcKTAxouyawfYUxI3befr2OlT+PYkfXcCrcEgLVhAx4rdw+NNXXwFkjNoHRmm8bp0bKKXWsok80kToTyloyIblCsRyk1zKj23EsLNvpkahEEyjlDQO8eiXdL3PdYaul7o4OLhQdPUHD92jI1pw6yuUH6OwVEbz8Z0LQocUdDKKrSxoM3gu6bnAENxXhX/VsPfSahJf9G0LlS+WR+k+FRQgVx4Kyk6pfLAyPeTkopltVSIEJ3LBaCDl5JFzgV8GBrsjZGMAlKMa1WpNK/zaGdPLsOUapi74nlqUlRoDGtz6L3kiVAoghcjGYyh/cNbMkC9E0N6NXG2jA8P+xcohipzLo869ljhHvV5CSNKoevxOY66r1J5vdj2+66k3v3ud+ffX//61/O2t72N22+/nQ984ANMp9Pf0zGbpslc/XLTlaWe1NJCwBqoNH0IdH0XC4QqgvK40NKjqVqw3qI0dM7R+Z7eaJa7Pf/ulz/Cl3zum3jbG1/Dq+9+iunkOXYffY5OaZyx2Ch4dADndZzMMabgJcPda58Xa67jpQLGpEUxnixKqZGVAowmw32vWPAj/8tn+KF/fiv/4gNnaJomL0jvPbNpzw//nSd46NGab/vu8yMYIm2lRVS8m0XF8A48Sc1XcTtpgR4gpZ4MZa0GiTv0vcN5HYWMCGpbmdHics4xn8/ze03TELwUypxUTSxC28cYjnTSnVSW41ubbK1PWJ9YmomhbixVXWOqKgblCyMheFBeaPIM9de00nSLjtn2Q9zy+n+OUkOjSH6jgvduwWwd6gYaA198A/7iUwBUkz1e+84f4/lH38jjv/VutJpCbXEqWpUqxQeG8Q2AMTXWKKyVdiEyVzq06rHGRHg6SM6QMTTWUtuKyoByQSqKB49zLd55Ki1JsNZonFc4HdlXPnVj9ngvfaXKtWJszckTjs7W+KoWb09pmqamXXa0y5anL11CKcW1G3uc2N5gc32G8gsmlebExoSgNNqK56SNwdYWU09R1uJ00WYhkM03SbTVlNFaFT2foAuFY6TYqetc/n6PpwueKlQCc0Ummw9SAsprYSkGjfRrU4G277ixc4Mre/vMe8fm1ibO9SwWc5yqcMHglGHp5uweHOR4GQxCNd1EoM9Kz5g6e1qJALO6TtMcL4+VCBYpNpT2KUkU3g3mbUI6VuO43nv6ZQtxvqVWHo1tBvhO3MoIIcqK7rt+FI8uu/qm96uqom37TBBZVVYlfyDdU1kxI/gVhYsYoc67iPS4XHS2vMd0/6VC+3+N3be6bW9vc++99/LQQw/xrne9i7ZtuX79+sibunTp0pExrBfbdF1hmkYUlDVgKikwSeI/q2h5OXzoMQgLJYSA1x6nPMHAEsela3s8/MRzbM7WOX/mLNrUPPHcPqqzhN7EwY2QT8wLQse8meRpxEUQ1RDRFi5gnzH2mybPana6UZ6vfvcVzr6643+fnWb9rT3fXD2TY1SJIFLXntMnZXF925++yi//6iaffnh6yJpL24M0/N9s8ARNBJ58vlppzA5L0j2oWFw1YDF8hHX+CYqv5RrnTnd865+6nL0pHzxPPVPz7z+0la/RS4JMpM2Kd5gsp6oSVqRzTuoiGkAZaqOorWZtUjNtLFUs2yT5NwNsGft/kge98DSMUvSdw7uOg705oTlA6VbGfwn87Aw+1kA/gWUFzsCBg0/W8P4zMJugNsF8zpOsH7/IuVf+KruXXsdycRw9q2L+Skk5FoHhC4hXK5OrqHsvuWRGV3FMoXcSp6yrmsb0NCYMBJHsoARMJdXVrdH4Tm7Qe2k5n5iHaR7luWNkTtaVZdZYNqd1NiKqqqI3itYqrl+RenSuW+DcFBccoe+p9NB/TJh0QrSQOKwhRAUS0rwePYOBFDFsCrRFGQtG4Z3A1yDV81EihJSSnMGgUmwyxWYHYZiL1mcAQKFsRVVPcdqx7KHrA8teelkt+sCNPce8i4OXFYp43UpJ/DWvTS2J7qmCAj4qXhNjQAhRK12AinM7tVdfhfdK+G4gKIwp6KseSo5lGbneJBvKrgLp2YQQIlIUYi+18fGO+tnHMmSJRp88RFHCKiMhCSYnDPO9NHbLKhwhhjgqm65xrMAHuG+YFcZYFLA4NF8Ob3/gSmpvb4+HH36Yb/zGb+TNb34zVVXxoQ99iK/7uq8D4MEHH+SJJ57gHe94x+/62LquRUlVVipNWEvk8GZFAYGgegIOoyw2CjmvHFY7gvG0wXPpxj4PPnKR0Hre9TmvYXNji09+5hncvqdbBg7aFkdk+cV1o5WRY4UEy0T4K/bhkQWVCi8KYGhWlFRK8stCRsO0UXzTH7/E4l7DN3Iff+4dT/Ft77h45BgYYHvL8Zf//PNcv17z6YenI4hA64A2UsPiY8z425zNI4OMDkIejvlfJBWlWMbPNYb/zCYfYYO3s8+rb5nz3X/uudE1/Oqvr/HBX9qi66IQSLCStXl2LrtWKLKNpW89vXP43kkcQnumlaGppATPtK6ykpKEZ01K1vJZcA2kjSTMDIpl27FYLLixs4OeHshFemBfwz/bgGcbqKZirXtg3sInKnjyPJw5DrcGeMtF1k9cZO34RT71SyeYX58ynWghi6RkTyVdctOCVtFqtNnSDVEQGKyt6XOsoAcvSmpieyZG4DdTKioNppZ7t8ZIWwMX8L6Pgn5IsE6CLCWRGhWoK8OsqdicScKpirFA11u62vJ8pVl2Pb5vCaHP1q0POrecCc5LbA1yfIaoqNJcES82wm95RoTspsdZT1CWoBQuKSEPXS9VsxvKpn4C841iSKTyVwywn1JCGrAV9dQQes+N+ZK2Cyw72Jt3HCwdV28sJEXCaoIX5e58H3WcwtgaE+eVjizSrpP2HMGJEtMm5iyGmAsZ88G0tnFiDR5Bycw7qgKMNUK6WqWyw5hFqGKOlnM9WksOHgW1W7waEXc6xkT7grJONAoH0sqgAAeSR5STQWJNidAgxJ0Cugwi00zsxOBXyB8JTmxsTef7QpHLGkhFcgcoUFp9+JsY06vb77uS+gt/4S/wh/7QH+L222/n4sWLfO/3fi/GGL7+67+era0tvvmbv5n3vve9HD9+nM3NTb7zO7+Td7zjHTclTbzQ5tcb/GyC1hXSh0iBMpjKUkcYwfsOq8Aaj64EpnOtx2iL1VW0SDzeVDxzbZ+D9ik+902v5sT6cb767Z/LL3z8fq4+8hg9ELQmqIrQLyNzT84rQc4UOE7prVrYUUoqT5sQqD303qFTfTkk8FlZC97j+p4/8tVX+WNfc43bLyx5gCmBwPs5xS+yDURMPvbGmeH42zzJGVJelMAr5UL5rj97mde+44Dv2rjAE9TCwIkSRBPL++OwaKmFVqivCZJvYZCOt0sC7+Uca6TW4J6Gnr/Hs7zu1XPe/08f4R//6Al+/hc3coHcoISaGkLAxvyNtu8lzqcN1XSW4aLNmeX4umbN9sysYVoZJvWEup5QGemCHIJDayOCygcJsMeq8SoETNvT7XcsF/u87vP+LWvbUZn+xEn4xS24AgQtHpQPIi1VBd7AgYdrB7CWFIBsfdfRLjts6zA69jPKi1CYVaauCVqhdUu/vxzgGCzKe7qgWfaerg/4UGG01NU7uWmp9AJVedbWamazmo2tNdZmFbMqto0PioPlAX03p3JtTLEItO0cpRxra9NYWNnJWGrFxlpNVcHm1Ao7VSuqqqZ3jrbr0f2tOBdoJlPJwdUw3dhmczbllpPHmRmD8T3GL7EBKmrqoLHexhYyMuEMEnv0vUNrqcS+XM7pu479g32evbbHE1du8MRTzzBfLAHNqfUtTq9vsj2pmDSWqldoF8A7gjUyb3zszxQ9lVSw2KHoPOx1HdcO5jx15TqXrx+wu+i4ugCvDcpW7B10tM5BZehdSzdfUlnxcCe2QwWpvq67XSqjOXV8M1LgwU7XUBhcB955XO+4Mb9B53okk63GU+OZkQJuyTBMay8lxeYSadGI8JGBLCtsWNEEqRgueXGybyDQDTnmsTeWNFlVCO1cxUroy4NFZnYSPedEeU+RvhR70yFI08oYHgseei9SS5atxiipqOMp2MVoUKkRYwRJvVTYTyZjpa2U84qfO8ZGJNHACM5F+PXFt993JfXUU0/x9V//9Vy5coVTp07xeZ/3eXz4wx/m1KlTAPz9v//30VrzdV/3daNk3t/TVhlUZQXDRuf6OLkQJUFALOVEfRh5gF6FmLmuE5pOUIFF52B/wf68ZXuyxm1nT7P90IQ6yOTxqNjYTyaaN1GkK0tQIRYflS21hghKqJmJDSYoQg7hxsZ7ggcG7zlzquUNr9kf3ebT1FyMCkPARamHMcNzP1MWLLiNjlvOtdz3igWffqhmbS1wxwXHydd61Ks1H2fG9QR/Zls1xdWG+nb5+gm59UaaYh74FI1MRqmqRkPPx2m4b2PJZ71+zutfM+epZ6qRdfj4U4Zr13VuBilCNlZ60OLb4ntqo2isptJB0FutIhHBZPJBuiAFMYAevVYkZ0g7j+uE3rp+7BJrW1fjxKzg/kbKOkCBw8oSJiBc8GUPSzcK2KVWKL6PMQUPKZctMOD5xlhoFqydeDaufqlwwS70zwd6J2WClFI0055jZ3dYuEDXWXTtmdZGKpdXmrqSvlKEgHcy+koF6ZMWy0/VlaWupURUnG7YSgRV0xiapmFt5lkuF/I8jZAntFacPHEchWE6W2fpOzrf0xjFrKmpqgblXSxb4/O5E8pGgr2jh5ZA2ODF4Om7nnbZMt+fc2N3n8vXdnn60mUODhYSt3OGdTtho6oi+hAXRnK/8wNieJ8CKlcCm3beMW9b9uYL9g5adpcC+avGMO88vXdUdUyMD07gOy0tVSpjqK2i1opJXXHL6eNyDwRMvQ7B0C09fdvTdT0u7EueWa9YRqGePFniHCi3FJMdWG/Jq5JzJHZvyRgWb0PmdFY22RhK51BAwRBOiiaGI0r6uUrzMy6anJemhvSKYsCHoVbiZTqlcox92KKi03rUCkS8dRU91AIyzJ9FqBbF73b7fVdS73//+1/w88lkwg/90A/xQz/0Q/+Pz1XXTQz0RcaRS8yfCJcohcKCdzgHOqj8QBPubJQRbFspvIe2czzx7CUaq3jrK+/l1PaM47Vlt1W0ytKbCtO16ODwqlxUSeCT15qggDH5ERjTDyAXWa3r7GofzeIpuYSpEV9gD/gOLvBF7PLDPMl//57n+W//8DW++k/cyZveMOcH/8bT/A/2dv4DWxxknyk1oUhX5PPxyrOF4mdizck3Ip0+KrGOim/jNJ/HAT/OJb7tT13hW7/x6ujq3/u9Z/lXP7eZoYxU202BJGAGqUoutdmsVBSP1Su0iq/UsiQvVLnSkmlFhE/brqVt2zEI3nYwX0gp8KCg92VQRW6x72GpYTFuhSDtWhz9wYIeTa9tjj8ZUwm65QMGy2zrOufe8GMoNUBAFx87zcc/8SVZiFgF5+54jnd9w4f4t//itcx/5VamaxVVrWhUS00rmX3e0neObt5SY6CaMJsssdYwmTScOn2SyaSRyivWUlUWWws8ZasJqpqizISLzzzFfDFnuVjgQ4v3PXfeexeztXWObZ+gdZ7WOS5feobQLpl3rRwz9NSTqUCaCrz2Qg5SR1e2btuW5fKAxcGBxJ5vXOfGzj67e3MuX7nK/sGctdkGy81tiWtpjdcarww+JsivFAmK6yTlrWmsrahrHys3SGUQ71q8azHUOA9u6dHBYfH0iyVGeWYW8C210tx66hQnjm9z6sQxbj13hq2NdW45fTw2M2y5cWPBctkz32tZLluWi5anL1v25nOu7R9wfb9nf+GYd0vp1KsHJVWu3zTXV4vIhhByBYbV9T5myh3OXSphtjIXqqSq3ywmncZTm6FeqPSvG/IY+95HWFdT1bXEsVx7KHYmRxPl41wsfBCX56A0oxIfbMtk34y6P7zY9vKu3acMMewqgiK4wboJPjYn7HGxpkoigNsYtFOqqCKgNQGLJ/DgUxfpQ8uFc5sc36h4wx3nOXjiKtc7z7XOUQePJdBC8nNzIDNb1kSPaYizDpedJl4qxhqtsqMmQiIJpIhRiNkS6X4cmoeZ8oOcBhsIG/ANf/Ia4Sz8o/okD9DQoVhd/iAwnoreVTqejxZsQQcZEdADffa60p4ey2PM+AFOoa0qVIj8f9e7Ov7s6av8iw8cY/+gqJwcX4mxWFlDXYmwray0ptbGxEUlWL4cMuQ0gxRTC87hXY/rOo6ff5izW5+hmszhmQn80hl4uMz1igtH6UHpJVexc3AtwL+8FV6zC599lfOveICNYze4+thng1PgDVU9wU72WD/7IVQU3MEHTH0dbToSBRtg6/gOb33nb5MqSmql2DyxizGeV73pWY6dPMDWko9jK8V00lBXFqs03knypXeB5bLhM7/zFrwTAkerNMtl4GDeEiLl5WCxT+d6lq3DU+FUTdfOCSpQV5Wwqrzn6mNPInR6w6LzLDvHfHeXiTWc3d7g5HrNxrShV5Kz5Qx4LYQjqxJYPQhOj6cPjq7vWLYti8WCvYM9dnZ3uXbjBvN2SecdwUgH7c57OgQ8c0rLC+knplbWSl03eC8dYvvexVePdz06dNQ60FgEHNegrOZg0eKDZ+v4NrPasDHRbG/MWF+bctftF9hYn7KxNmNjbUpTST1CsAQaNpuabtmzWOtYLjuWyxat9tg5aGhqg+IAQkvnAi4U1H81MGlXc45gzG5bVUCrZIqj6NmJDXcoxQQp1FweO6/X+N6Q7G3FxBzFjcZJx2mNKC29yZSP1SycGx3bk2qX6ghXdpDv1WVTNyvY+H+IS+8w6/jo7WWvpEJsZAZkza2RwCcxIz1oL0UHIgvF2hgYjsHHEEKkNkvg8jNPP0vrD3jNPcfY3qh4w53neeS5A/p+wZWuR3tRUr0KEf5L0FMY6SQpSFskdUY5OZqcxMnLYcgABsU6wHMajR55RI/T8I84TSCwPe34V3/iUX6TGX+eWzCIQjZRzSWFpArFVSqr5FUNQMbwUwE9sXVHjFMFAhUVT2P5QSaZgJE2j+fvvvMin//GPf7Nf9hg2VaxYoYw3FKl6+A91mia2lJbGz0qaSaoCytxGJh0hfGqvcO5nt7NOXvLw5y557+IQnn6GHzgNrh0A9T80PyJE2O4y87BdQX/x62w9xzqzdc5d9eDbJ9+jhtPflaujFDVlsnagmO3/QeULjLnPdCOrZKN9X3e8sW/cyTScd8bnuO+Nzx3+IMjtsX+MQ4O/hD7ezXzec+1nRvMF3N2dud0fU/XdVy6fIm9gwOuXt+h6xWdN2xurjGZNBw7to0xotyvXLrCfL5gZ2ePg4VnufRoHzi+tYG7+3amzUnWN5KSIlK/PUFHgRbndYIOPJGG3PeipJYL9g/22dkTJbVsWylhZjVBQxc8PdArhVOpPFMyJMYDVVUNXSeUZR/zn1yk61sdmFTCokw4v6kMtBK3vXDyGMc2Gk5tNNx2y1lOHNviFa+8m9rGBGbXErzD9cvcemK7qehbx2Kto132LJcdrt9lfSoJ7MvW07aO/UWQ5O0VxVPmFSWvaZUkcWQuI4MiKwu+ynQ//N7wfSn7tFriqDR4VSKsKCNGT/CkJpApGZniOkLM70vyShi7LhcoSMcPpPqAUq0+w7ExTUJFlmK8inhsIaAdYTcfub28lZSpEG5ZIWSCh9BHWMPRu2XGvUP0QUIoSnsEUQKpwm8IiuvLBe7Zq/yr//gf+Yo3vpk3ve4VXN31fObZKyweeoJF8CxCECoxg3UhVnKsAhBFtQmRtqoBHR92GMrZJFZN8uzK9RkiQAeJxybKpaICwNEX1on8tkvFt3IHB1GZ9TgCDktVgHe++NagtBSKniGfKCmcsj1ZUnXJmwokb0/AzOTnpWwZT+AHOMUHNrf5wX/0LL/5y1P+9j88g9YUdGPJdaprUVLGDOV9BFZTQlpBZc9LLlpBZFy1iwXN+pPc89Z/ST29Aa2G/+318JkKLl+XWJSx0Md4k9JIyQwLfaQoJ6widRr8xB3wQ6+Br/5VmgtXePWX/jhKxRiZNijTQZl/BfBwA//jOQhRAboWXr2A7730e4HjR1s93eENX/DD/PwvbPJDP3Wc5XKJJ7C2tkbbdyy6lr35Ps476lgeSnvP9cVltArYixdxrhMPJMXhCPS9wWNoqgpjJdbYuZ7W9bhQZRGjUejk6SRHGJfhqa7vmLdL9uf7HBzss7e/T7s4QHdzNmcVylpOn9jk1PENTmyvcXx7jemkYW19wmQqRYuHGIjMsBCkLFoIcGz7OBwTq/D8uXMczBfc96odSbxf9jz97FX2Dxbs7u7Tn97EWMOdd9zG9kbNyc2acyePs742ZdtIzT/XOSoLSgdCJQaT8h5bifFZ9Yr9zuF9S42jITAxmkYpKsB3vQT/C+UEg7JYbWdRsvdSHlW5lQqu7JE3pnEfppgLcqPyOawdqrPnkkWFF6S0kTBHMgoCYgyqIZ9p6LAweIhaa0ykmXfdYJglJAik87PSxLb2SE1BL3LW+cEAVnqsaF9oe1krqURNTWtfrO2A8kqC4LGNbDJaVAREx7guJCXlgeDAecVB63j68gG7bQ+V4ZZTWyzblrMTw7OtYdkjsazkg6SAYy7ZE9C5KoOsai9ZlRHLLSwpBmslS4T82aAIBs9H5/cHVTMwCz9DUxxmvFf5ezpe+VnaNEOdiYEOXI52yJ+Uig4YZTEBPE3F0iruuqelv6x46xvnPPCQ4dq1mGeiovuviAsjxLGMgflYOy9j2WnRKxVLTQlVV6k5s+1nBWpbGHhqChc1LG5AHyls2T0M41c5XCHI/Llh4bEN+PQW2h2wdsfzYhOlba7gt5t4zHjHjzTw6UYyUHUi83j4zVpkglFwXwtNPOcTFVy1WWHkiwhBBiX9fleP3nasbz1Drw54+rkIv2jFfufpvaftO5a9kGo6A1UIWO+BFqUCVgmFPQTPtG6kk7CpUJMarSpmVcP2xhqbG1Nm05qmipXttY4KSmNGJYbGvrbWmqquaKYTUIHNzU0WXtEGzbbr0dZy/MRxjm+us7HWMJtUTCaWSS25YDqSBdLjAWKsWPKX6rqJ60uac9ZNg6kM04OO+aJjMW+ZWE0VvfSqspzaWmNrveHEZsPGbMK0qTAq4NXQ/DCVckqQe6qoplWIXZv9yDCLC1ae74htPY5Hpa2EA8v30v2tbiWtezUmtQoN3iwWdlToYIACY61FX6DdEbLVsVpJAQUduSUvKh+XJGfTdZWzORmW4/l9ZPj9iO1lraR8TEDVscDlpJmgQg9e03ct3ilMzGlQiSqOsH1SYD5Bv9oauq4n+B6tKvoAz+51PHL1gPNXr3Lfnac4NdO0l57ml64vuD7vUKoSryEEXBTptpbum653KCVl+Stt8L6j7ZeYSA2dTCZZkKtIFU2l8dOm0dTULOmzh2MxVDRZsSTKg8djsREKHDzL5NGk7yuGJF2DoYv/0r5DvpTCRcK6ifCiw5E4dknJifclpaZqanr67P2lrVSCn/u2fd7x1kf5M3/+NP/nB9ehcmjlsUaEAkFYZSp4iUlZSR5NlaN1giWIXZA7T991uL6T/KN80gDLDg4C7OyCmYKuxXOKgodeKuKPlJWOAmvRwc4BqKvwT2+DM8fg734UpsW9PVXBf3ce2miV6qRUHOhIvlAOHlTwTSfFc1sHfuoZuC1e64+dhJ/djrRkeZK4Xq7P2ljew8E/uAxfMs/35vFUjTSMvL67I/O5qmiaCd455ns7InSUx9qANYppY5g0MyZNxdpEPJfJZMLWxiZrsxmzumZ9NuWW06fYbCxTa7Aqto8ImspVVC4Wno1evghNTVUZbDVjbWPK6TMnCV7qyS0WSxbzBd4avFL0HqwLWAcbTUNtDZOpJRgIwaGVJSXcWhthXgxBB8SIl1VjrVDfu86ynM8xfsmGCUzXa47NtuM1Wc4dX2dzfcr25oyq1mgTyxhpjakkBi15P3HVKIVVAecDrXfSoDIIyckHYeM6NE4ZTFNhXUAyrRl5O2XrnCEhd6Clp89Td9zVOFJqbVN6VEPC9gAfJpZgCfO1bTv6/LCiS3T5IUczeUZN0+T3XEoDKODLRO5yzmXZk+DHdK+pJVFADKn0HUl0j8o9GYMvYXtZK6mAoDL4gA7QuQ4dehSx3MrIXU6tN4YHJkqqF8PZpZhGL5aU0gQ1kUoUTz/DXa+4h7VZw4WTx1hfXoF5i+tbQDrBpuoLbdvli8seTrwOq232qwRTF8H8nq+/xoVbREG95Q0LeuB9nOB3mNGTYgBDoHqVoTekPw6eXfpGUlhjL2jYSqvYr5yrPM5AvDjM/UtjOsSzViy44jspUVWQTVF3Sinp3GpNFHY6xqLEas7m3mDLrligYXiFfDmxps5NrEEJiA2XnxZN30MfYHcHlnPY34N2Cdda+IcnwcbzOA9XtVSx8Azu+tke/tgVwXmTFxTidfziFH6ngX+2CW9YwtfuiyLdX0g2r5JRzFa668Xc9T0lfeWNb1jwl957mZ//+dM8fXHKqe01iAJMenz1zI1nahRrleHs2ROsr005eWKT2XTCpKmZNBZrjDRA1EKOUN7TWMv2+oSJgkoBTrwYHZI3pWKx1zSu5eAWpActTSmNRoQ+Q3UTS8AqpI6m0qjy+RXzqdwOexyFZ4E4qdbItYaY/G0rQ10ZjC2YoUoVVyz9zXycKml+SZw5yFHj/DPGorWNdRGlg7T3AmElijkMMaWbMezKdvLlfZVKpYw5lYrv5kSDcemzUomlijaj8kvaZDQnnX9cI/AIJt8R96NjwQJJ/g6E3uW1njyt8rpTvmTwEcW5yRitbi97JZVe3ntC1yEcIRfLyyhpZ53KyDBAa4PbHPFTlyrzSpl+qXDQcGVnzmPPPk971x3UteXciS3WruxIZnqbCnZqwViVNBxUK5MGkErZ2kqSa0jZ555p4/nqd1/nTW9sWcTluovmp9nmQabUBZQWxTN9zJPS2adRWXwPv6VSRw5fLMtS2ZXvwQDflApNFfuYeLxBRY1BxURmT/sfVlfFOVP8TQ319qyROnTSPFHidUR4QrirCbct5kCEDcoyQXnzPsp2dbSiysdSZDp678Sr8a3AebtalNTzwPuPD15Y58QLm0Vo0AWYBLi1hT9x7eiVdVnDJyz8+xnsaFFSugOzBDsb7q+KQeVuUVhiVhSj7njVvS33vaLliYeO0R2ss7Z+PM7jQLuUgrILE5g1FRuThvvuuYvjx7e49ZYzrM2mTCcNlU3xC0O7nNO1C7qDOZpAYzTGSTzL+RDDqZLMqaKxokqFlJ/58F+24gET3Mho1vkVE0uTsiuQ5EEoKlYf3ugxx69pwOoY74hpJdZKrpkxKseEU1w4KZ80f3yxblINlqESux7ikDpV3tD44AbBW3gccBiGW1UIq15V2vdmLMEUpyrfy99hMMZLpVAqqbGnNr4eiph4al2SjEfZfzAIshmhZCxz/cFI/iqVU7mvUirK4YER+BJ11MtbSTnloyCSAqPKx8ZlOIJKjdSk5IepjNSLUhqlLc452raldyJ0ZcFKQqYOQkfv9YwnLu9xdec5bpvezz2nTvDm193LXbsHPLm3x1Vl6HvpzCtlWxQYgw/gIk5r4oMNvcP7hSxzLXXU/tgfPuA73nONW887HqbmO7iFLiqqi0wj8aFnEA/RhabP/k2IOUwJfonLFI+npc0+1oRJ9pZMDKwkD6mKpIpVBZb2k5roKn9HzpXI8cN3+mztlyytODb5b9mstdRVReh7MFLRvdIhyudIKyfmbOgYsMrKdDhU8A7fd7iuw68Go5dL6C3UU9CNECcoILlk4ZSel5GserplFGqxJqTWkVARKwZo4I4e/t4l+Hcb8NOb8D8/D69ejuNW5faePfi6AznfNC7h79iFP97DL7wLDmYCD9ZaFNXWTGJYOoBbwkevw2d9ACqhm3/ndz7Ftef3+e0Pfhbz/cB8sWB+cCAQiz9BM1ujWdvg/NmTzNYmbG021I2hqohGXIJQOwxLgt+T+EuwWGVjb67YeBFpz6KVxetY8SAnjwYpIjs8HdBSEFcRMD6WDlMQUnwrqjutFEYZyZWKAzf4U3FPdYQBknaUrihUGtZjAWsh2wicN1ubSLuViXTwRik8JhtaytSidn0dvwtet1LXUDmCrtDGAxaFgSDlgaQDr5zb+wE2S6SFEhob6N86t9sp4byUM1Wy88pSV5nkdQSFPeUo6SOgxLqucwghx8qVQJouKubsicbCyV23TIOP8gGCIjg1XGc8v7U2wp8hGuZiHChSbD3yA5AK7T44iPmOdWXjuj1MHjlqe1krKTGQYwY3yfWPiycGB5USj2OwCkimhNAptc8oESQYirivYtmC7zwPP3sVozR33nKerVnNHcfXWc53OUAwa5egnSQ85fmK2C7WVznZjm05XnGXPKglioeo6ApOXbIiS4ANGOU8HbYzycosfV4qnlVFdNTf4yG++Wf5ngr/bUVU5SvsUHyYGfey5D6WgzcbAqdPdbzu3pZN67I+UskT1vGFyg7R8HwSfODx0QsuLkq8IqdF0ajkjY2vXC4xeXU+KgUjZZMSk0NH2Me5qNwcvLaDV7dwZwuvWQgB4tRpmHXAk0eOE3YbJpsSa6oPgItQbcP0BBxbh8lUrsEi8N/JLSQohEB/+zVcfwXMnkOtPcfp0x1rdctT6xP2g6fBUfUW56QfUz1tqKcNa41lWsUCvhqqkRINUuVeJUawQoXIAA1A9ErKunnJXFDZuRWvSoLv6ailr60BaXmjosHiQ/RWoscSlJFzhdTSZRUOKtZvfvZIZ2KkAG5VFRCaVtjKSFzLDv3AUlGYUFo6UWbEKUauE5gZpjpWqEmKNdV/9aLUC6/pZvTvMtF2laJ+mCo+vI7qxXSYEOFZLSheKslxzGsw9HJbEh0ro2RvthjbIDX3MluwOHfymCQuJsdOh8lwsEqSIbFz8+wpxv+Ft5e3krLgncNExk9VVSinUQ7QEd+NyIkO0PYuanSHsZaqlgZuznk658RKRhp3heBx3RLlND40/KdPPc1jV/cIm+uc2mr48vULPHfxd/B9R+s1TslkwcSHAbHttVgtqdmf6/pcWNYVQjUQ6OgIVKhIMU8iOUF2CXKzVPT0+BybGpMeEoEi4+pAR5c9p/RZ8p5S3tXRcauy+KyK8bDSgxq8pvK4JeNPodhF8e3cyldzg3/E0yTSh1LwuZ+zx9/4n57lp37kFh5/SIKrJlmZOvZbSom8ymedEiAqqD6SJ/rRddN1oqSqKibhFunJiW2kiISHWCRNcLComJBFJieCdgE40D289yq8tZXDffk+vGsBv/7fwycDvP2HRdmsbg+8ET71FtjchnOfgbe/Dz7xZvj0ayTuZJcxFtVBB/ip1BRUUYP4TfjEN8G5D8Mr/6UMh5LySJ1xUg0DjQsBtKZW0OCoVaBSgSp4aiVeh4s2VQgeHTSWmlpN8nNr+x7vWybNjKAVTgd649E2dc8tLLtitvro2fsg1nNAga7p0xzWVogKQIcFKgw1KGnRoUIna1ANxpOKLMfEfk0KRnREQCuD1RW2KeA2q7CVpWqkaaVM09LUUxAkJiXWpIqKEckJi/aJdKqW6vy1UVQaKhWwBHy3lJJVeki0zYnqcUsxpgTBJc8oUdBHRKlc+HWolF62g6+qaqS40ua8p+1aqqoatQWZz+dZQaYK7Ok5BYQwkurwperoVW0ldafvopJSVFZ62bUx1y17eF6eczpncEGQ8lDAhUahlEUpYvwu0HdDB+aXsr28lVQIGGsxQdpgm9hoT6w2qT4RYv+WlIzmfbIWBpxca43xnqANIUh13gAxcVOjfIWrNnl+HviP9z/IH33za3nVhVt58yv3eOzSdT7x5HMoZehjLUAVQqzSHIPMfYdSwlWtKimBpI3BmDkO+Mcc42NMkIoXRGXlUGgaKgS4TDBdYMkSjcZgs6LSJIAsQGYC2szAsehVmTJSJFJEVv6W2JLDpmoeDCzCBCcOGVQ6U85LZXeY3i6L4ePM+Cuc5w1fu+Dz37aPDnDPnUuUgs/+vEe48+7rPPaxt0QhtcpOgpEFFjxbZx7g9MZv07Yt1WQvCqK4tZ2UOep0FoLgI6EiKam0v4/MvnT4uF+ymHX0bE5sw/mTEl96cAfu/SBcfQU8/zroY8zqE3+ILMGtjahngIPzsDmDiYGD0/DRL4fLZ2XfupLvuiClm4wBPZWf1gzHYyL3kp+hwumGlo65Myx8JTlPtiYg8dhELMq9sHJ1lBRj0IRgcUGYj1Id3Eaqts4lkZxyOOUHU6SwvhMNWSN1MAVGNHhthMiQ+SA1XkkyaRe/YTHooFGZ3v4C8Yr0vGLsQ2DE6Np4Yh6XeN/aGDEaVTRp8tRIJI5QWPZiYiklNesCUm1BI8nMuB7lenAdOjgMTvzAaOSselOrXk/yako23yqrr/xO2a03X3VBWy8Ze1pLw9dyX2stk8mE5XKZ4b8Q5Bja6JjPaUAJkSF4gWNNjJuHQKzMozKE6GIybzqPNgmOjfdixbUWRYagVUYXMCVxLITN+1+FJyUGo5YFoIl5SV4arMXamNlqCIEQS92jpdwMgIpFYrUSiCQEopKSmlzaaakwbabstHOuP/kM3Ztez6njJ7jv9vMEpXns0mWcV3ivBK5A3OdBCPTZ9TW2orKKzU3F2kwUx8+zzkeYRkXio6BPSmkQSEkRdXQ01HFZucFzY4hNiRIbqkLoqIRKengZhxqgumQPO0Ksia7yO56UNpjjBagRMSNtJRkjeT0KeIyGx1XDD3zOU7yLnbgv7GG481WXOHt2zlP3vyUefYBx48HiwZXEU/QBa8cf49itHxmffKlh30AbBKLzIUJ+Bc4VkoJKpnnyqiKGFWMo2VG0Go5vwO3n4b57YGcLnroCd34YbtwJT789BigCPPnZceIpmIi3LnhMgLUg51huwsNvEu9M9WAr+a5zoKsYB6tBWaHNZyCtZhz0UjhV0YVA6xSt1xA0tZYk3CHRMq4VBsJjMh6SeglBgu8S41CCcPr0dL3kFuWE+JBQ8+iSxeeSnr7ykf2VvODktUvlfZ/NsNQ1QCC37MsnL5YS3kpGVYhdfOOM1XG+xevRBVSXvKL4X4YT03EGDDOFB1S+Kh0zEsUJ8xAcKkgXg6TQRPEOSqpULiW5ISme0qsq2cdEOPGoihMlPLi6pZgSdqgaoxDvbTKZ4NzQ4BBCjN9HDy+BBEVrHRXJIiGQ2bWdE5jPe09lhn5jOiY3prWttRlM0ogFKz2+9mzYFCGLF9te1kpK9XKj0iVW2kbjHcr16CDl/XUIuBCD3Ujl80zxDKAi9ID3MRjasAiC11bGoEwPfYdqW7SC2m7xkcd3OLAX+dK3vIJbLmyj1D4f/vRzPHXlgEbVOCW5HQfdAq8CEw6waoIJG7Sd55ZzLR/43y5y+qR4PD2C7ojHY2mocTF3qWOJRVNJuUwAJtRR8PfZdwqUNPFI+Sy4dol5J6pH9uzosFhqqpgrJUeSri9JScgiqLBUjBVhEpwDwOmyUmtjO7MyTlWRguaG7+cc/4DTgGOKYYuKv8zjvCIEVBegciK8fS9WrE5V4MEFhZ09yy33/a+Yau/wxPi52+BDZ+HGDaj66KH0EBzYOkJ5doD0+ph/lKpQKC1U9FObcOc52JzCtIZjm3B8G05NYd2APg2/+d3QJ1JGXOl1PWhlE9+zac5FpejdEPPKkjSdP/XdIcKGPUMjT4qfsuj75ZLgerQO9E4sZ7Vw+EmFrmp6L/V0vdeoYJCGFT3JIAmqw+seZQXyDgF8X8QqlSI45CB2yAkEciwkBGnzEALSQiJo6CXnrQtzKZDrFWohKIAzFq0DyvQY02JxmGDoI6khzTQVYv8tgnhHQy0mrAUztdHpNbDwKOdRfYpVOhauY6JqamppVEogKBd/SuNHMR5rSH3L3BQVPJXupFGjCRhjsbaiqhts1WOsPHLtPV4NhIbScypZeiCKq3MO33W5xFDO+1NF/hPSa0xBJj3ooiBtatUij0Zl7z8xXLXSdK4jLDxtLyQQbaUPnkHJ3HM+5zIFxHgGRTuXmocmVLRdAOXQlcUanfuVkeJT3klcK3muFnrn6HyX6/kFFzL9XxdLQOT1EZD4EdvLWklpktaX1eGDTOrgfQa3QkhZ48kiX3Gxi58qfuZ7GXyNGhQfA4Rx5cYej1+6DOY21jfWuPX8aTaevIalR1EVVmU8d5pSSsouWQu3nOtYm0mBzZK8MPg14lUlDl3yb5Kdt7opjhBgUVImTya9N5BDISmm8YviisZ7H7UlRZkyXtJRx95ZeaWKq9ioDjtmWA5oWKKxdcf5u5+in58htNPRNY3uV/VUzVWUdiLgn7kFFrUI0qdPwJUpuJ20sygEn8YmSdNkdscRUipCfE5YflUlyml9CtMGZg00lXhVWgEGlifid4vSA0lwANkDKh9PghET80Clayi/l67z6BEf/SUFKLOVGkKIiZh22DPEZ7MynPn5qOH5Bzii2EB2v0YzYrCQ038MXks+n4dooAzEiPJeE/8zxBCgIv1fniPNrpI6LY5QMWj52AlOkzdTGx2VxkKl64qIRyLgkFCCyMqNBqtK5Bllxs83GSMr22qu1CEvKHmJ8eul95QSX8vDHsluHJ+RBMEGFSCSKUpKeEYlkudbeJWjm4inSqGR1MtL6+TlHnHDo3sb5khGaxJAkQyF8FLuSbaXtZIy1ua4dipxkiZbggAyVh7LHqFXJ3laOAO00LdLvIegDKHrwTsaixAq0Fx85hLz/Rtc+bzXMZtu8OpX3cdHP/UUT/AsbZjgVQILZOE5DCqoeI2Ht0RmGG8itFykf6vstQiQN+yV8PVkFQ0TZKz6Brq6ix4PRM+EI5qwFX/7wnJOsatUpkkxVLBIsFF55nSVhRgu7q+8Vtkma3M+56v/I08/8Fk8/Bu3FMcIxddWJrfT8CtfBJeOw24HzzwP6qrEnUKItHKpXCH7B/GUktekDbHSsJAtAJpGPCJt5GfTRM9LjRfp6t9jLR0/T3/EONiQKCTXlp7n6uTIt5nG9Yh7Jwk5j/fJO0pMrETdjsI8Cazi33AbYwV5lOyVHJeyvA1jKKe4nlLvrXa0SbbB6tANuxT3qBKcFM0eNQjvcs16KQshMalE+VaJeJPu//C5UJHN2PeEWIILbcTYjZ0UnA/SjdkY0DomMyONGVNzzzAw3UqFs5rvlDymtN+qIhtDm+rQ+6t5V/mzUKzblThYIluUZJSVAc/3UcKWGYfJeVZjr9ATcjeC9J3yuZT7rhI+ZGz/K/CkgNFDrxT4TuABa2LEJlaasFrROSkIGbzPrnbycJKFEbyjtiaGLDTeC4xhbEUIjvlyHpvXOX7xY5/hrvMneePd57jt9BrXnp/w0PUg/XkINI2Uxp87jQ/RlT/SCBHxXkViRE8fSQsCzVRYbFZMpXAplZHOymJ85OH4QpCwhKiUbHz8SemkeFgSpgOnLyXmJlWkaGgO3YXO9vBgsCe213COdIWZ8Ax5v0FolcNk6+ucvucXhFUXPzR2nxGDLn0hdgoldniN2AIxQYRc7sLEkkNdP5AoIHpIQNvK+3Ut3+9aJB70YlakGo4BDMSMwnsK6XcSO1veS8GiFD8IxOtOgTHiPZeLO3p+MY6pNRgr0Epi4aWfIiRMbPYpcRc5ncrzQwzdIch9SIhmYTZ8ngXuyqPQauilppChx0e2JhodpIJFQkTkbiRvMSu2IH3GZKgK78IH8Yzj+AQClZ2ifcB68YqVNujIqwh6rCghrQeVd5DeZRqvA733LF3HvHcsXGCuGhbas1RzvLGR1h5Q3uGKgtWJ6VbmPpXjdIjGXSiqcXNERkqv3EoGYVYq3h1+VsU+YmBI0jyFskoeTe+lWHUVe9ulyuakuJkXWWpKpYsom1DMhXR9pcI6imJ/2JC9+fYyV1LRlYwKR4cglk6ichLwKjUF03TeiYXEYVGTAovBS52zEGmpWehoTXCeznkpPRMC9z/6DNoY3nTvBU5tzbj1xBqP3lgQgqcPgRlaHqq3SG7IqrWU7kKEiLDjpPZdRZXhs9LTEmHfF/cw+Er5Xla8lCGPKQkilc8XCsUWRVn+TX7XhyaTQmjuSWklZafiucQ7C/nMpYcmv/usAMdPYjwuSilMNaeaXmXj9H9Bm45DW1/BcgohAd6J8FD4ij5EQZ9UJzEWJLDIUd7JwP5TEcOP13oTI+OQdxVHY/hC3KeU5AN7P15CUlKFT5JiVaXqP3QNg9uidIhB/UE5JSMlUbjT6fO8yLBjmjuHx2MYuSM8uSx4Dr8vuUU657oFHXmhqqDepPypFWp70uXJA9C54G70ioLEfsULjr3HEIJD+s4wlGlNMHpWqep68BJp9UoEdts59udz9uctB/OO3S6w1wX2+0ArBWpIxlwS9CXjzhiTa+iVYxTicxiXIRruN+1bjuFqAu+qUjtKCZTfSfum843XcyjiXCvPtJzvRcwtK1HSe2Rv8qi5sKpQQaSKf4k09Je3ksryRqO8Zz4/QAeP1cOiqKqKmMcnFQ4AbU2mVIZiYqXBtCBWklZ0GJxTLLoWrRTTjU3absGi7/n1+x/h2vUbbJqOV9x6lntuOcmDz34IDgIuTOi7QN/72LBPlJ3rXbYy0ubx9DigJcWhlrR5EXe0BAIzZkdaH6WXIiLNRDbeUGq2z4H34fstLUn8GIxQgSHy+IYqEy4ZA+iUBUMoWnokxlcUEZGgUfalklmsMnEiYV1A/NtiUQxwQrLSzr3qp9g68/S4Z1O5PfI2eOhzYK8D1QmNPBFlkhLoO2HPmSq26sjSAEkgdcOwJLjv2DHxtq5chbPHYFLn6xVPLOZSHdpWnk/aJ3tHgVRWR3RPGCpdpIQ+UiKrGvbNhx/X8SM+Ia0DVaXY22txrqeuG0Iw5F5BEAPvJTSbRvoIJZMt5TDcL0nYFJdTCCWpHqFjs1EfIbIaqklk+Smc1+CF3m2VxqqIaOgQE4UjFB2GMSjhQk2k0etAZWFaz3Bhjgu9dOV1Ab90VFZToTAhzrSk77MbKOWYvA90y5arz11nsVyyXCy5Nt9h72DBU8/usXPg2Ft4Lh8cMO869pYLeidteuZdT0AqvxPHquu6Iw3RXGRWSyuaQeFA16XC0iGn8mXOZeFZJS8tlTrKVPU4Vcp9k6JM++nIaG7bFquEwqSjnDRaEwoWXqqE0bkeFwJd22KVlmcb70NrnctZhWg4jJRQzM1KlTVWafNlOagX217eSgpZNt7HkkjJtR7BLclaHDyMkbZPi5BykFMVCgGsUF6UnlIFM0y+u7u/4IFHn+bu0/dx6sQJXn33BabP7TG/NKdXEyGS5wClGuUZ5EuML89g1Rc2fxT3PpZISqDaoHAG53mA5eLZ8jl0LgVTnjclCie6e1JFiTIc8yGK46Sjl/5ROtrY0k7Wlh9dR1JapboUG9YVxxr2NtUBtt6XP/fPwn4RpwoBbtwBiw0IN6IAD+JFWZ1jCBKTYvCakueQrihi+pJzUMCEYXwvovzSK5Arpq9u6fvphlNMKntLyUOP59Mwqs6OKggdSVEBuoVjH4ONxwkBdi/fye7VMwQ0SsWK5BpwouAzkSJ6WjpZa6PbUoeeT2Eax/3USp+npD/GdJ10JKPEYwraUG1dwZx8OCa2g08IhdfsP38vrl2jM63kSFWJvadIJXXkXNKuo+87XO/wvafreg4WC67e2OXa/h5t37G9tk6lpN8T0WNIXtrwzAcSQWAoLO2co287lsslV65e49renIvP7bIzh/02cL1ztN5x0PWZd+CTwlyJFw3TU/4uKedJ8a+27xAjeexBqeIYZe7UoTYfSujipZJJ3xnBflryIfGl5xeVZRjGY6i+LtU/ZV6JJzuCddV49qx6gKteVQlfvjgda9he1koqKSjf96jBBBmhKoc2NR5AXwijHODrk8CMwWc8trJxkimUtmgMG41mf7HkVz72IF/81tdx6pbb+IoveAu/8YlHefLSb7Fv1un0hKrbJwSN19JczFTjYU9wXtkwcBBRiTYRaGmj16FjtGqIGQ1KAUL0mspk3ooGT6CL1ON07J5AT5eJEMnjMYX4UdEzS/9WY1Ql6SMpwlR9oiRYEBWRREZCvsOA1BksCRpHbldeC49+9fBwfYD5AlgWpAakpEJTSfWIygtU54hljZB9rRVvy3t5P1kyVazT17bgqpiMG0/onFDZ+z7mM0VFM5L8SXGFwUuCAX4McRKiYz4U4jXomlzxPHkuKimoaBRNbsC9P0UwBwA885kv4OrFOwn+OuguljlUaKMKr2kwfLRJkFOpcFSE5Vbvo9jCWKQEyFBevuXomeR4RgClA5vnHuLY3f/q0CG9M3zy57+Fg11L33dUazVW12g7CD2dDEsjwnN+cMD+7h6L+ZLdnQOuXr/Bo08/w7PXrrLse+69626Ob2xwZvsYvjL4oNj2DDUGtQYVJBtBtAVd19K2XVYcrut59vJVLl3b5dGnd9nvDPPesqgm9MDSDwrH6pi5mA/ISOms1u5TStH1MVxQxGxSTL18JSZd6TW1bTuc29oc+1FRSbVtS9/3NI3Ei9P30ma0dBfoF32h6NKnMQez64RqbnS8BiOVzlGxyDBHKD41nh/FWJQV3Q8pzaMMvCO2l7WS8t7F2FMcEGsxsZ6WD4HUaVMsuGRTqrGFk2JUyYVVSrLskazpxGgSrDtm02vxS5a7u2itsHad3/zM0/QB3nDHaV7vFNf2l/zyA9d45voeW3WgJbB0QCwvMrqPCM15QiRJSE7UkOWkqahz7Ec68orvYQciflQvg3Ip41h97uJLfm+gMAykizJM4uLnJvtXSX2lUkpJCKp4H4NITKCSYyBghKiQxLtKijBkiLDcQlzII+s0XVjpKRstJAmNVB2QHhDivZhIFfd+KIuUeAfJOEl9m9LxneSQ5IoTMFjh5SvR0w5tYYAPlRIFFN/OykkqEEvxWxQS3Y9en07HGLx4lINbfgG2HgS9ZH79PnYufT7t/ukosGqs7jHaopVBKZ87PkOhrEJp7ZfX/tKERdr06n3LaWi7Jd2iZX5wgJ1c5563/iea9YtHHkNpz4U3/wf2nr+Fx3/r89ETjdV19jJHrWF8yKkgwUs1mRAcvfcsWs+i8yx7z37raFrPQeewxmA9dMueoAzKWEllU4XxpzTWWLz1UnxWK4JWzNY22HSG7W2LOvCERaD1MoN1oqKj6HvQOCzjWExZ5BWSl5Rq6QmDcID8NN5LJRyRQbHauop12QtBXxIT0nlUYZGX/afSfqWi8iHkxLbs0XhABendpobveifdmQNI3y0vyriOpZm6rhPOSgAKqPDQXCliZmUPLU+QDjQvYXtZK6nBIJTRktjUEMiEArnJInmsyZNbnf6WPZKFH/L+JWctFb/0XUcwBlXNePK5G0wby9tfdTunT27zitvO8ZFHbhD6JdXE0vtYHsS5XO1iuI2B/QZEBSCfeFLybSqDlMA/on+lIr0hKZykrgbYzkORgKlYGY3ssJeUEoUq9knvjP8lYhrFt+MIxWscg4/p/IHx/Q/nCKO//aFJnyWMbLkAbPG+VuPPlYoXGWG2uChzs0OI1PSosHz8qZISS9e+oqTKvw95H6G4VjW4l0lJJbo7VfSU/PA9pWPiefxbL6DZh61PwrFPAtAtT7J3+bPo211gGUvcFOVponeESusgRGPsKAXFC7w3fDayeiMakWHyaOi53rFcLNjd2aF2zzM98TFsFautXNWSsa41rHnUmmfr3KOgl/T924sWEaOzxmGI1x+iORN/l35O0LtA5wLLXpi1Qm4Sm6TvHTpVHYkwpkCAQ8xHGxOLTcuraSZMp7A2C9jpnHW/5OBZcaB1aoiGogtR8avBWzqayRbhOu/zdwePSRdU8fFnFCQFGGDDFO9J50jPeWDlHT63VB5hWI2qYGSm7ykVuzikZN1AUCH2y5OyclRD/C1P6+KxrRJIyvdHcGTw4/y2F9he1krK2Kj5C+aKD7FHjY6N9LQlOCcuedrKiUQB1BTvhzj6CTaRoKWm98NSt8aAsniveeSp55kf7PElb7qXprK88p7bOP6Rh5iqGKhU4+OXW/mohODQFey3IVYl5AIKvyZea/SUTFRnpqCWp/1SOzI5XlS+hfoZLNchhjQs5eE8q/Gu9F3xmvqRwvMxjmbilaUcK52vP2e/FAWYhjE50mIv40kpnpgWmJZFhtHRq6rA9sOXlZL3E5yW8qhG5LkkzMIg2FzsMZW7NarxdYycEj2uWjSC92KJIw1SbHIqPaO6TsojrcKPeDj+O/DqnwGzWB2I4RRBvI2y3cNRZXluNv9ebMu3eUScIQk5gs+B8q7v0WWxXw/8T5vwQAMba/DHr8krjY/zuN7R9z22OswmHa491UzpY6HoAFrWZNv3dM5L+kfvafsea6RwqvUiWBMBozSRZF0rJpNJLHyr2F4uUbqiXVi+7Kuf5bPe/jh/4j3nePTJRjoCm4agLcsoGzSHq0uUCmK4EaJXO+RIlc8mBCko65zDNnUkoIbRc4TBg8q9nELAhf7QNcDYk5EvB/Ea/djQkF5Zii4VtFUwnUnrk7aXChk+1e9TA6VdKtGFI1GFVWVVtin53TjvL2sllTtiRsGSBLhABMRaY7KAQgjZshZZFCdGMZkGJCQBaALtaYokxgAqSHDd6AqPxrnA7txjri/42IOPc+uZ49xx/jTntycsb6n4/C/e5xOPVnzwv0xQWrOzZ/iJ/2ODN71uyRvfuMw+SKmUBgEeSFT0PpeyGbyeUmmMldqwJUUw2NGDIkkRqDym0QMaVNBAyBgfczhvUqLDcQflZbLqHHscY08rAY3jaz4sVNWRiwEgVy43iTBhpGir7Ve+o4ZXgkqSZxMY9k1e2shzSt8vD3eEokr3GSAXMNVWYlCpYoFrpT399Wsw30OkRlSORkHj4ezDcOIRqPbH54y7lfHIEirSOgXli52P2rJ1dvSYDsporJjKBnsqsrxKdCLlR43OM69hdyItUBZjJTfS9yH56EnAyfv17DrTk79N9/gJ2q7htlc/jHk68NDFKV5pOg8Hbct02bHsehZWYXRs+uecxJlj08P0GBNzUWtNPWkIKLRV3Hvufjp/gxu7HXfdc4Pjxzu+8v+zx6ce6PnwR7ZYuI62c1gl3pMpYbIiiTdBbQmekzJEeqSkVuMyYw/osJJaJWMkL6xc84eo4mkdlQhSgszj78710oWmOIePRWeBSPhaMXoYUJYQxucv72XVu0zHDf4m83Jle1krqd71GC/0c50eSpSFPlJtlU+CvrAcwuB1pZgUpO8otBGPRawkqazufYQt0BBiWwzT0Htwved679hbOv7Dr93P57z+bj7rvjt4xdk1zm41fN9fvcS/+NfrfPAjEzSa56/UfPf3nuDPfcsN3hCVVOn1JAEuVl9SRuRKEQn+Sx6SRkUvK4GSRW2vTIYYVIjKrkOy8RMRwhcKB4TEOyi0w9Dg4DEpFBU2xrFSHpTJ15biXum8Q9ULH+HIMeNH4JEjGH9HydPkTWktzZKskTjTpIFlLHHkh0WZIbhkdfp+8KBSBQrNoOySl5YVldx5tmrUyvtlTbKQlGcNMUEbbYSYcfEqPPkYXLsMG1O59sbCWgPbHbzyZ2E2P+KGUxrXQOVXSrrHGmOzg6kiFFkKu3FM6sXM2bCyT7K6dY6xiJJRUWmK92u1ka7K5dfsRF4umd/DZyYpDmS+ZrgyXQIw3XqWs6/9Wfb23sli/w7e8EX/hcmntvn1j7wVj6F1cGN/TmUrtpYzLB4VxDtzXY/re4yJrWrMcO4AKKuZrs2omoZZqLn3cz7FbPPi6Pzv/c6rfOSjazzyyW2e21nSLhx1vYnSRtj5cev7waNJMJtAgRqjYxFrDgvyPBxFDCo9rxHdHFEeqVBtavmBHkOORxEUkj1mSqaeEkLJol3ivMekVh8hSLUdBcpEKNSOlV4gGfaySldbiJRKKsF8+X4U/3XU7gudA6swiAzpXScKC4+ObcnFYhKP1EZh43OsAbEkKAY+CqqgwDshY2il6btIPAhKCit6R20rtJc+PcpWBA2PPb/H9MHHOV47Pu/LPs2Fu68wmXi+4osPuP2Wju/9u5tcvab5gb92g0/f0/ANnOPTTAEd40+xXXfkv7nYXVcjVR5EoaRSSYMCsKSipEMppQGeKxVAiNlQwlVOUFuiM5gIvGkUkpETYsMOTen/CG297DSVPC6pfJCEJwxw5UCOGPiIDTUGj6MbKaTTtz/K2tZ1No5dWXnoxe8Jtkt19rwX4a8j3Fc3UDtRNi5KJE+E+fygXDLzzsdCtCDlS9xwnnTypMxyjONFtraFdg67l2HewaKHZ6/C1evwyBPyGR7uuzMyCw1UNdQvRYnIJfS9wzkflVNEAUIgxETgbFmH3w3KUjyrwlkUwRNbnghmNFo/uvA+S71N76Dzco9l3le8Cfl+y8l7/jXN9Eb+KB1b17sA3Pba3+bc3Z+mahbc+YrL/Onv/DX+yT9f5yO/WfPs1Ru0nceqim59wrKr2bixz6LzbDiYzaZMN3Y497p/g9YdCYkN0eP1EVJsZsWce/AN8OTd8I7/i1e84oC/+bef4Gd+5hS/8mtbXNp19EH6Mo1DBUO/qFz1PBA72Q6rIBEdSiWUfldexi+Rw9JxUi+6khwRgqf3bqQIUvfv8ppS1QjxvEJu22GInlJ8vilWVdWSV7pslwITRmNHKWmDomTYIoGiiEVl20ZFwELeTzlkkByGlXlwk+3lraRcD0pL1WWiZo5Mpjx4UQ6pzMZS2crM2ebxeAppzxHSm7nehzB6BntEBEIwkUAQrRivYG/puHR1lwcfeZIv+RrHHXc1aLPgtlt6bj3X8y//TcNzlzVf+gUHPD6t+SCzCAFAQstV9KtCFuzpqSuSVzVcsRkgn2RVU8KAJf0hwYoD+FYAOvGMQ3vv1A6+PF8x+iSIT628N3yW1KP4WiHfX/l7tNxWPKbpxi7Tjd0jHnrxe8KKdIT4ci2++Le1IhRtJYVnXSml43UmSUU8VoL+fPF7OtmRSkkNx1nVAAFJJF60UpF9dwl7C3jsoiipJ58WBTuJCjYwVryrh/Oavj2OazcGGVAI2ewapAkcFFKJYzAi5EAxHlLM/GE41XheHNJqyRgZlGg5hPlLq9/LSl2z+qEnYKp96lnH7NiDYyWRtr6Cg+NsbOzBcfl8c8ux+bpLbGxV+NCwt2gxesGVyYF0G/aBnf2WgMYoy3T9ADt9ntnxT6LNTahlAVhuQ7sh13v5Vnj6Lrh6nK3jnrd99h6feXCLS894rj/gcG26tQFiKz2HMtVF4lE6C/CMLGvJA0sXEAI4H/IoJ0FfxpBGJASVkIISWlOokRIok+vjedRgxibvVcXnl9ISxOtLRyw8wEzQyKeTNIYYz1KoQ+WS0kTJCu0lWkwvayWluwXOddhK2i9IQlqs/mU8Sktwr9aWStWiyf2Q7AaD6PdAXVcYa9k9WAoBQyvpFBEUWk+GnbX4GF5FxKUCFTqMD9iq5vqB59c+c403/PKX0+1v8jnv+idUtTT2+4d/8wbew3QikFdLS0dFQFHhkBoXPXvRp6pp6GIFiMACg6HG4jEoDA4VbVp5JzUelHsbWHghtpgDBApBwLiQvyvAXKycRoj7lcojqUJRnV2hzgQITF17hfXXF+pRalakJSHh756URVUBDaXQvMkWNGCRvhFJMWjJh6qtKBVbwXQGvYbFvsRATgR47qrEfuo6Kq8pHMyFtODruGCMsOmCkxYbSJsWqX4QvQdFVIhVTMCNk0JBZmCEEKnsDvb2Ye8Arl2FnTnszuHKFdiby71oKzlSzoHv4ihHUsDKInbdFs/+zl+gX65DQIhBxrJWT9Au4NpW6PO9GADGWmo1pdYTKt1gtZWea95hInSTukYHXWFtFdPJXGxoFwWUCuRUg1jPMreC9yL2dKyY0QVP0JIDVpJuMqzRkWoKxbkR6HDcetuvcuF1v3V06SuAq3fAR/8YvO7fwq2/vTIugXYR2Os0++2S53cucWpzg2NrU6Bme63n+HrHq97585y742mUfgHuc7DwyW+C3bNSr3HPSbuXf/eVcMvj8FU/x3/7xy/yri9/jm/4llt48mJF340p4snzgRKyc3R9h7FVochkvijl0Tpg7WBAu048PenkC53r6WI1lJRnGRAozmhNVdeZNJMYnW3bRSVSKDZEn+lYTDcAnXeR+S9esImEDBebxFbK5mIGLhXd7QeUIXleGnJDTWslZOIVuC5dk0iGQMCF8OLrPW4vayUlnpMimChKMxbrMJUkxA1FOwuNHrcyM3tkCGZIZ7Awk/WZKb4MDx01gGHJq/FoLj2/z1NPk60kpWA2LR+MTCYbf8qU0fiCaj7sFSLzD1wRZwKKCNXK+DDEuRKhhPx/Qr9KokRSQul3Ha8pKZjxsQ9DicUYFtc33jMU382je5OjHLEdMtuTmaeGJF1jBqZfZcRTscP9DydKCkYNx0rmXwoE66h4yiDvIfdi5e9kfvpArEYsP3snCqTr5Kex5EaMMb9vuMYSPoL51dey3LsD360TfCWKNDknKySFEnop73dwuAY/ethLjSz2ITUjkMhGMHy2+oQPubirhnLaJa3HlbeV7jE25g8ut+HqGxkaFQI3TkI/gYt3wwK4/ZNQidDW2khya6Kdu8DBfEFFYHd/TqUDa7UCtRzOcf0cXLtNDBytEUqmBm9gcQLcFLwFtRSDxdXizQF1HZhOXSQS5Dsonlda70dQwtWwT6aGp1FbGf9yOOUx3JzifpiwkbzsiGoUHlgiTpTkjtVjDorU59+9j2WoEzyb3fjhYZbxMJVKXqmQZazEL4dreSnby19JgTQQCxEg805wXFVJxnTax/vRg19lnhwKNkbIZDWAm9hLZVJwdpGJfUa1dDZ99vnneOKpKhdvLKdyEkeagIlWqhzNRCW1YFWsu8juK2tNDA68HYmcfJ/kUHQ+f6k0kiocFJXOf4uPpAtlGVa+k/YexmFQdgOV2GVVeFgV6WhdvWi1ieGGRs9jOFCE+Ww15E5VWjysaVRSuYJ4TKhFkWnlIwWlMmyBNTH8Eg6f84Uu0jN4NUk5xXYStJ14TqaKoTs1KLVkIK3c4+6zn8vBlTdGpVY8jxBGcYHV8S1ZWBLzZKj+vzKMSelkmIooGInVBlLMISopEcBDZ+jhQOVMGF1MvLcXGcj5GXj0jw7PIQRR7HoOj78anrwFzj+clZS1hrquqHygdwHXOebzBdr17Ozt0ZjAYqLHrSGu3AUPfBmsTWNVkQaCEa/ZdaBcJNCY2N1QD7FL4u3FuHcIMX8qjXcovImbeFfp56pcyodPrOXRsA4ya1WZ9X0/6vqb9l8lLaT3E3199ZpXFZ9c31DRXeRWoXTS+W6icEqYMs0KCbX4otTWC28vLXL1u9juuOOOsTUWX9/+7d8OwBd90Rcd+uzbvu3bfk/nEg/H0y1bloslrpegt9EG1/b0C4GkCAJhpByENOApuJkeZtu27O8L3dcYQ9M0VFWVA5ApuJmCkolLlzytEAJt55kvHbvzjnd9xSf5hj/9UerG8Qjn+Rm+gOfY5kkMX8cp3scMTQfsoNihwuMw7FOjqTCxHFFiyJniHyQlI9UqHI4u/iuJDClXqfRcSg8qvefw2VPzJGKEnLOjp6Wno8tsw6T+KAgbpZpK5y/LNw1xMrmfCktLTxvBv5fq/o+8n9KDqmt5TWtRTDMF0wATBxOg0aIclh3stSKU7IQcuzFWOvdWNVSxf1RkOImAKpRIBuoLlyFdk/PQOWijF+WiAup6iU8tW7kGFxVfEurlz1XMPn6W8pFjWuugqCLMIgaSxlQWZYQAlABWMbrSvaQ0C0VQajhlXJOmqF4wKCcykqCVzqy1jCrEIfGxHt7oeaYYYSnokSFdJR6QvL30SgYIRGU+NmisAWsCrl3Qdwuca3GhZ9m1PHP5Cs9ducr1nesDEw7kGF0nz6RLKQjRFSu98MaKJ97Yobty3KaNpa6GthlJLqRyRW3b5lJFSWkZa3OH2zSuqdh1ennvscbkY6VXVVVZ8ZWekXOO5XKZz5eOIbfpsyLMhWO7jsViQd/3+ZVYhdljKr6TZGBVVdjKDvMiKrr0KpVf6txbPtIEMABYbajNS/ORft89qd/4jd8YVbe9//77ede73sUf/aN/NL/3Ld/yLfz1v/7X89+z2ez3drKQKhnH+gymGqw/J4tGGZXd0PFXw5HvCWWU7A6H2P9FK5Xd3NTyOUMRhSUUItTnCDi1wCvxiJasc50LXOZpdlB8mIY5Dh1bgw/giYrqIfX7CajiUzlHyEJfftcjT8RG26OE5Eb3ORIfiTIxND5MXlKaWklpDXBm+iwpwvT7+Byr5yq/m+BHSQA+6ipvtmXsY/y2jkm01kaFZYQh55S0gao1NEa8Gg9SSJEVq39FUQADBFd4UtnTCcX3iutLX0neV1qdZXHaNMilwoPD5y/uemXoiw+Gn9lqVao4ROE3FfuF/FZBhCgt32y5j4da5kFS0uXJi+OuburwPQFo27G2fYVqshBjYXESFsePOsJNj3PqdMeddy5otuf0XuCoWikqrTk2cZza7Dl2sqeqinhXHhI1/J0vPD5PFQksqRNzYdIroDIaa1T5xZFXtJrXlD2XI25rNbfpqByq1XMchQKV3tIq4/Co863KwdX9yusR5eOOhiRXvuNTKKb0zlZhxZdok/6+K6lTp06N/v5bf+tvcffdd/OFX/iF+b3ZbMbZs2df8jGXyyXL5TL/vbOzA0Doe5SRihIEoG4IweN8oDKxKFA/DNJqD5dykK21mKik2t7j+oArMNtUJj9ZRcF7govSSAeUMtGKNWIFWcvf/IEZP/mBBT/zkxc5s7HFW7mHX8byGZ5nwu+ggB5hXsiyt0i2UYUawWBCE02Wc0eKQ8XePFFI9JGMYIqGhOm9Kno8gSFuPXg6Fhcp8CV4Jy1CIHuLpLU8eFOQijMFVOHh9dGjS95giNec1K3Lai/Bi3AkRFRuI4+lUFQhRCs9iDKaLiEswVdQOence6IBP4NuV4L33kp+lA9gGjL9PBoN9K18rj30S+g0hOoIBVVck1Jx8DQDtTR6mm2E/HyQnCnt5FS9l+te9SQObZHFVb6jY25UZeVVV0J46FURRHeDvgwDXVpFZmqiC+sYe8gWr/f4FSHkY1WLVNRURaWXPCwT67KZQiAXX47kEEbxvY0Tl/nsr/0ASjt5Xg+8B+bnhzFeVeDGkON4cfumP/003/gnC+GfpicFvKTA2sID0yZWJKkYivqlMS6qlKQE8dVmkwqmtWZSaVSMuZTG+SpxIsmcvkBySg8EBkVQlj0qE6fL+FH50xjDdDodHaeE+UrUSEeShVY6e3xlc8bcUiRfPywWiyxDu1gNJec8pevwASLKtKrAMtW+bSO6Lb35Sk/rhbbfd7iv3Nq25cd//Md5z3veM7ron/iJn+DkyZO89rWv5Xu+53s4ODh4weN8//d/P1tbW/l14cIFIKIwqOwOo7V049RGumwiPzPBYGVSrE6gBAnCYA0cpdAyrdQNvVqMiQ8iSK+Zznn2FrAz14QAU57hDL/KHUy4i9u4kw220HQs6ND0VNGn6FEsSBX4QqEWklpa9YPIe4g6KyuWp31KckUcmZgvJb5SGsuQbeoEyR2uty7dgqtMq0j9ocpzJlhSFWdPOV0CU7qoUG285ptY4KtbUlCr7+Vk3sj0qyvxphoD6xPYnMH2GmytC3yzPIDQibKgBb+U93wH9BDiCz9QwrOmXj1/+UehxJLnFMIAc6U8rRQTIyqzLkKDKuZAJEr6yolCVIY6tSFR5LhThgiVygYNKs4NFVIhlSLUM3glIbpV4vQdZWW/wNMJK9b9UUo2xXdWYntKgbF9rmZAqMSACMUO6WdVQdNAswVPfR489xoAqiowmQSm6TUdfp9MAk3jqWtBSOjW4Okvhf3XwGRN4oLJkFAgpZYUQ11InS5SFOToacS1FYVwMmSBQ5BeNpTVOB60SlzIY8hY5sAg7NPPsg19+SpZfqukCBA4tu/7DEWm6ygbI6byR1qrHG9LeVXZADkCCk7nkevT2agp5Wq6h6Ni6Edtf6DEiX/9r/81169f55u+6Zvye9/wDd/A7bffzvnz5/n4xz/OX/yLf5EHH3yQn/mZn7npcb7ne76H9773vfnvnZ0dLly4IApK64iQ6NihF4wyaJyQEtSQhLpaBLIMXCal1HVdbstxZFAz4v6K+NASNm8MAYXz0vDPhR7lYRk7O6y7pzhjnuJevpMJZ7mfLVr2eYIDYILKnXh7pLysZlU86FgUv1Q+AznB5aoTiaaRPhPFYuL3fFY6kOJPLtbVI9LIVVREsii7GDFKIXId/T0fYx0q+nl94aNZhkTCpFYNJsfPEpHC0pCq/L2oJ5U+zziVGsNNWgm011lhYlUGyQ+YwoETiG+pwe9KQq1tYhv5VoLl7Vwo4QpRYMGJBW11bEf/AtdXfqbi9aXmiz7EmIoqFJAfX3vXi1eVck2OKLiaDx8beioVY1RJ0Cly36YQkjNXQL7y8GAE4WlCKKDeJNgKeCZ51lAE19MnUeeoeH+pkOkQJo8/kpLqusNKfnx38iOkMUuXqUVJGQO+gce/BOYfh5OfeoFjHbG12/DEV0nawbT01AqsoIybJWVlbfS4iitNQxo9SK01y+UyC+KEupRIjgoBFStGpBjUquGc7ndMVDkMJab3VgkYN1Ny6Rr63g8xzHitqbxSGZca4k3l50PjxUPHL56XUOelFUqGMtPxjJHV/0Lrqdj+QJXUj/zIj/Dud7+b8+fP5/e+9Vu/Nf/+ute9jnPnzvElX/IlPPzww9x9991HHqdpmtwjpdwmTQNVhTIWpQ22EkFvCKi+QwWPUQofpFpEGtiyZHxyd1MWdeq32QABAABJREFUt3Q0JTOtTOrb0sXCizEvRCmYrc2yFeucwCqVMbh4PGMtF69O+JpvucDXvnuHP/dnrvEAn+E5FnwVn0PNb/EwlzhA40g0BIlotUisJ1W+G9TK4RYcojRMrjQx1EHPooQFy6xgRCEMycnpfR09JJf9HBGUUlrX4DGZhFEy9lS8qib3rxpHvdJvJa0+KbEQfTa90pn3plv0FEZCZXQmL72k7JoUce1aWCwhNDBtoZ5Hb8qK9+Q6uHIDVAu0EQoE1ivYmMDp43ByWxLbUkC9FLLJ0oYB8lNBlIFRRCczensUf8drDT7Gx2KzxbYFPyG7PWnsogeEJisoeWlMVaHj/C0D5ForgfuUF2UVjTk5yKDwjZGZZ4wB7wVGpxRyQ71Ln61seVepQZmWkNQoypjWk0/P74UecDG2SVmmsU0J2yDjtnMv/Np7hIiSobYI2fkgPycb0NTigYFAir4qTrN6MSEmfccdkrFQkjfynkqM47hv2W22hOvKUENSTGlsS3JKUhqZGg4j7yzlSVlrR55POk7ZhVeGbzjeKLRhDJjhnACTySRf32GPbJhTfQlDMlaIpmi86JzL1dTT80vVSPKxS7blC2x/YErq8ccf54Mf/OALekgAb3vb2wB46KGHbqqkbraZSoLk2lZoYzBVLbIhCKYuHRCGBXRUUPDwBFF4x2jf0qWGwc23USH1PlWeGxgyslAVy6Xmo7/T8ObXxRwLLrFGxSlu5RhrzLAsEYWT5JdZgfPKZaRGqgUGcVAqhjD6VvpMsyrc5fdBBSZwbqgBONjRyV8in2/V0h+TO8pzD1up1NKVJkDyxT2pdNnq0LlHm5FkUprI0AsKpkpgpDYK6OUSlnvQKmgbgQDX1gQitBrWLJw8Bhsz+ayyg9A8yhPIwnT1pVZuKwyf5cK2flC8oYABD916cY74M8Esg4IyaOOxRnoWJXhGaZW9nVL5JLYeHIZfBupy+lv+CyG5TwO0l2fjzbykwNHj9kL7lQpqdVMauhkc3ArzpcAVBECLx+bEd2d9W+bBbFJ8NyTH8Ojzl55nchWPeCaShhJ/PyJWtBqbybHsQrCX8qckTowUNIe9olAogNXzZFjP+0MKavh8zBBM+5XNGAcoclC0yo1rwwzXuzKMpbw84tpfOpP3D1BJve997+P06dN85Vd+5Qvu97GPfQyAc+fO/a7P0UwbMHVWUtVkIu60C3TOETrwoRcIzg/WS3oAqYtlKtSYtX5ItGrZVifD8OCHYCMxca2pa5TqWXa9xOIJ9CHgYrD4S/gYBzzFx3k1FYrTrLHAsRAVF+tIjLHvBO9VDPG1VSWVfif/nTrtHt6G5aZydEq+72MuVkrjTVGm5OfpHFOS/YUXmEYqNW6UI0tbjnHNimFLnpt7cdO62KLwuFncA6LHEhMzm2mE9CbAEqoWdAPTRhTRvIFuCefPwWwdjp2C0yfk8+WeECdCR/bQSiiqlNyK4jMKj6r0npJ1nu7BxxgUkkCavpdiTfom91icKxkWpTBompqqstSVxRiFsZrpdMKkmVBVVRFPGAJUAY00Ch3iGCUrTZhZg2AUaCkGtEPB9CuegTrqub4kufRSdiogOpWYd0rGtHxOiawfYkflbDREQ0XrwgM74hSrz3XFaU9FRUrSxCq8l95LXpY/QomVtfsSwhN3Gima0gC+6ciEgSxxs/1Eno2V581+l7+FvJW9vBISXhmyVcZhVqDRuHvpqmnY/kCUlPee973vffypP/WnRu7qww8/zE/+5E/yFV/xFZw4cYKPf/zjfNd3fRdf8AVfwOtf//rf9Xm01ujKxqC2pnNSDV1HWCJAZhId1V8Hxu5t7s/izSGFNDQmK4RCrKpsgskyy8usxZroDiuolOVjn1jj+3/Q8/Vfs8PWbUt+m0epqPky3sov8FtcYo8lUm2ioyZXbkdFQe8zi66scp7Yf4PiiHKPULT2GI41JsEOwjf1oOrwDCLGkGqxR3Anr1Wxr1Lb+1RMaUwyT9ekiv+BqBiTmjIxsuVefAKHIIJdJ0t+xUvJcjFiY6YhQ296AlMHa53ERc5sQ7snLD4dK3RPNgQq1EDQ0AVJvM0HN2MFla7JQ2Z+ZUEZxza7ljH/xmqJlXUe2kTKsMM5jJE41qHYzeDpBBJDT2Mry3Q6xVpLXddsrG8Cirq2KC2w9Nr6FFvbI61q1GD2aKXxSbgeElYv4r1GD1BrMdaUXvHLsqB64cPk8b2ZF5U9TZAyVpEpaWCorh7PQ4TotGLEd/al51rcVzl/SHMrCnQXonc2bF3n6HuNc0M8KP0s5cVwWh8rMIz3ldsqvI5CTrVtm1vFr8acymoW6Wcpn0oFNyifsfFQysH0nfGxwDlP4gSnIfbe50abIT6zNA4jAoUeWI7S9JEX9pCP2P5AlNQHP/hBnnjiCd7znveM3q/rmg9+8IP8g3/wD9jf3+fChQt83dd9HX/1r/7V39N5tNYYq/Fa41E479ABTAh5rks/LpUzuMdlSNQhJWWtJXgR6YmGubql/a21kkphTK5CnY5njaaPMIGpKj7+Sc3991e89Y1z7j3b8anqUe5W9/A2XsODfJKOq1zBssDQUaFw2ULNFnOmRAw5S77IUlq1U1ItPQFz4oQe7iIDbIrBSwtZSZnYYjEpRhUZeElRDR2DO1IeeroOiusurz/EIw/wosZG/607dP1HDPyQyFlO9nJTgdSCPDdADFoIFSBWtfYSr+rnkWY+hWAFDlzMJS5kQsypEsJ/ttpXoSjvI6RYXGOyPpNgTB6UJmWegomkDK1FceXr17FSxUp9OUUUbkO8QGuNrSqMsjRNjZtOqUyF0Ya6sYA05cOWmkEVQiK9XcKFoqgYCcQERxXCFBV3KYwjNay1o5Th70lJld9NTEmQZ6IMqBp0XyivkAx/+Vur8bGyNbly7ATrZW9rRaivUOcD0Heerh+8zzKelORA2ZhQDItYPfwIg3kE2QE+hByHKj2z1XjXeJjHiqyML6VwxjDMwz4lyy8dp/T08vFNREiiwi3PFwqyRd5fj5WmLCMdp+H/i0rqy77sy450NS9cuMAv/dIv/f6dKCoFKVui6JZCIghKJqcyUbBEq3Y1zqSUtOIIQZgmzgX6vhXxrBS+d9hKqJTWJE8s5FDCctlKB2BjUUoEQherXmgUVkvx19B30rG0a/nu7zvBq+7r+Rs/8EnayRrXeSV/mC/gUZ7hx/hlFlh6ZhAVjAhyURmSpBtiDymTFYmOamgsAVKRpj6rD4OhoaKhoaHmOCeoqKioOckJLJZr3OAYxzjFaUJUHte5zjWuc41rPMMlWjoEKBR1cwB0ONp4Lp8eTlaQKTeKTEUfGH6pSchLmLBJSb3Y5I4dk3GIcOlL7ybCdxXSjiNoiU/RAxNwS8mLSkVsQRQZGpqJKJjkaYQQvfgVQehd4VElyKmgtffLqByVKKPgB5jPeWGSNaXHMIwnUUD0fU9lKqxRONWTIGpTxiOMzG90ElSD4htCCYEQHN73eO9w3uceZSIoB0WFkqLNxpa0nCTgQyH0onIstzKR+YW2QBzb8pYLxWQiy1LF3CWnpAiwT55OiRKUcc7Cetd2+DuzKEuPKr4X4rPv+yPZll2AoDRNYzO8VnZFTsK/VFypLcbqNgojKEUVEajEuBvHiAbls0qSKPcraeollCjk0kFxHEXAcM5FD06ztrY23F/cL5E3chWPMLAcjTG4CB33BQxaGv3aaGlC+RK2l3XtPsHD05a0+korCxW5bisoTdrymlGxEKIPsqgZYA7Zx6/Md7Esx/M2lreP810EdGquKMDcI48bggs8/ZGOjduvsXnhCc5xnI6K85yKlck9scvQcB+FEhqEQ6IzJI9HjX5PvaEgMGXKGhO22WCbLWbMOMUpqpgNdYLjWCz7HEQldYoQSy3tsMNVrnGVa9RUtHTUNLFQUscNKpa07DKno4/swKSqfHGt6ep8vrsyR+tFFVUWYGpFMaxCGSvC/VBqe4KDksBz8ZhHTBCV8qOSYAnjV3nK6JUMMan4zEK8htGLsQWfglehPH5xFwqCCrneWfJkFETrOFFe4v4hZO9L5cvSxVHTXEpmxVgp5v2S8xPS/tFCiwfN+4Vy3I8aR/J3XnBbXaTJRRsdpFBSKb+KMHhq6V4UlOSOoU6jGq6l0E3j+RSK63Fw8hk49jwAzz034+LFKctOk7hZgwMoz9xoaco6ON7JE1LxUqN3mtct+f2yq/EqzLfqdeUUmpV5LworSPsMJSzQxApNsaHxKpHjpv1Hh0s1CqE4P8ndi55z1O3F6KXARBpKrYoVfniK33R7WSsp5X0UwyLmmmoiloHrsgi0yqBChOLkWwV90hMKdzrRiXXsT+WdF8qlHyaD0SZSesGm+uVhyOSfTKa5bpcOqdICBK3ojMF1Cx571PEN37TFn/0zD/OX/9L93M9fouMuvgrNJ3iIT/IIjwJzJNsrPdqeJVKQVmJQsnwkJTflSRkMPUsMhgmNjAGac5zjLKe5hzu4l3s4wXG22SLBhBZDhWGLzehdVXH9SvWIJS1zFnyCT9HTc5JTPMGTXOQil7jMDfZ4ikvsscucA+Ys6ely3pUoTQUR3BvUlnhlVSFgb7qlvKPUN2rVSkYBidnlBoFny89rIMR4UBW/4gcFYyvxZqTGEqhaCtWCnNv10vodh8BNYexFoSQWFrkbIv/D8LcKEfJDKOqESKuOxU1BaPOtcD7T5lXAq4BxYIKw+HK1iMKaTpegEL3hQ2x2qGRsItJCCH32doxSKANLbSSpVkUIPCnSGAtUOAiO3g3dZ0UPxHGPN6mCGut7JUOJVdCmgbjZMw4yZiYpbQUqiqkU8FdK5kJwwBKIz0Nr2ScRUnQA1cpz0kWlipCOw2A0QCF5BxRDYolL+MJ/A+uipD70wbv5xV+8wNUbT9D1PW07kK6aphniUvM5wfcoY7DW5Lp53nuMNvgwVJ4AstdVmYq2XRAI1HWdPbQS5ksJsfP5HKU0dVUXsGNSUgMcV8U14B30fohpy/zX+AhPgjx3pWWNeRVo27kUGI4eYWKRplnf1DWEQN92Ii/jvPIR9SFeT5V68vlIUXcv0DKl2F7eSip6Lt55fHBiaSqwxtKxzDB1uV7GkJ/OC3UU4CzLm2iTk/SSS50nix0L1nGAMk4UuVCB/4JG6QqCjh06lyg85/kgM25lyVt4gqfZ5xqwnus9pBJC6a4NOlMpSl5etHOj/ZKqPGgm1FzgVm7nVl7NfZznLOusM2GCiWVkdaQzTGgyrYEc+5IjVTTczu30eGbMWCL9j9bY5Dmu8BzXRoxCRao1kQgW0VrLSnaw6MpMsJtuUVhSJSuXFT0Vxg87f88P3k363uq5kkWdPZz45iq0mEzmo9zyvEXhunoCleIoUXul2EtZ7ii3hL/JWBzyNAYyz+hSdSo3FckycWh0djh0HrOMR2gt7c2VZmAiRr2hJCbpibmuWaAXl5phuCPGLVdvAH77DPzYGfjKh+DE4uj7zEZI+YB18fvhX1cOUHwWivspXafVLRkcCc5VojBdEDchKrN20TPfb3H9SpyuWP9ljG+V6VbKkFWiQfpdx7HShWJIx+9X4pVKKaqqygpwFQKEIUc0HcOHo7tC5HJHETkKeakFCOP42yrtXuRdXNM6MkadH8GYClA6Fkp4cbMUeNkrKRH+LgSCcwQdrQyt6VWCIsrFNp6cRwXuFOPA5GqJ/eEV29GroTLF6kMrz6GVVMdAe3TQBOVoO83+fuDY5NeZmWfZ5SuY0dBygGaKZWhu5jLDTgSMAGY+5lSJOBqgNSFFWAwTatZZ4wK3cAe3czd3s8UmNTUpebehJgEGqVSSyopPMqwMFTVwlnNRHWoWtIBinU0Uhpo6K7zkKZU1Bgdob2AEpmdzaML2CjoFtRfZ1FlpZFiWC0qw0wi3KDyb5AWsBuLTZ0du8YAJ8jhqO0pBrXpTh44XlY8qlFBMXRiwFIbPbuJthOL8pRA6ZCAhUF/u3UOCdAY6jiiqAW4K8b5zJYB4OEkkHpidA5x5lCKWz49s36Gj8nvwOFzbhs978rCSCsX4ZwixeG88Eiv6pvCIknWYPypgvxfCmULGPgavLlWrj1vX9SzmSyliHctbrRIW5DLUIRmzul9JiFglHJT7p99LqvqIRRcrXIxvJYx+H+JCLyCnCqWaId4ETUa5B6nKyaD0Ds0EpVArBWZfmko6vL2slZQJARNAa0PQBmdi1fK+p9Yabwz98kCwWT2w9Y6aPOl9lCL4qPGVous62rYdKSoVF4HzLltS5WRbtaCE3llTE7vCBs+in/PjPz3l//pFxz/5Xw64/XUtF3mKs2zzbt7Br/A4V1lwwAIrZbyBGQFoY5UJmW6iKBoqJjRUVMyYssEaZznJndzJec7xTt7JBhtMmTGjwZIynMoguMTQ4miQJ3Px3oT1LPCmzDjFGR7lMVocd3Eb26xznRs8zMMsI1gpCslldWextFHNmggRHWL2/eJx+Knz8BcfgTMW/t2Xgz4DzTzmxNhYRSAJq9JSjsrgBb2dm2xKDYywmymy7AEd5Y0NI5nbmARFau+da/O5AL1AayiEUdhVsb6tlld5SuQQA0czu30MbcILCCd5BIzz2pxSaDVEBOVIQaDvmBM3hMdXbjtCYyVLLE+cgFQxMBpl5TU2HuJLW7n3RXukV4jrBZPKsUA1dqCy4nLFm4XnWT4/rRggvuQNUXjC6RmueFc+EljmHezFdh7FkOzuznn+8g6uVyhslC9DqkqC5lJaQGnEJpiuKyqEyOWJrOi6LnpEktfVdd0h76zsGpEg13S8BCmmFhvpekrZp7URL8cffs4lOWOg0Q/nN2a1tFz00qLbJV6YrI+SPJOM/+RYeO9xL7HA7MtaSfmulxIuBT6a2EipCWIpS47KS1jdVq2Lo/ZLgcryo1XFVFq5OlOUoyWrNJiK6/sN+wee3/gY7KLYes2D3KJrLHfzCDdQXKdjh9QWUXr4pk66IYugCs2UhuMcY511jrHFMba5lXPcyZ2c5QxnOEVNk5XEAFSq4v9UTmnwb+T/VOwWVAHVTZiiMBzjOJ7AfdzLFa5yjWvc4AY77LLLLn1UUOl4qdxSQ+BN7OdJeIzCEtzR8IiFB47BjQYub8Ms0o17JwKockW7DX8YYlt9dqVSuZkCUyv7HaWoVj9Pr4B4CqXHpIpXkrbp7+AGQZzycLwaSIFHOmxJqA1Ca4D7xtc14mcgx0tPMT/1KLRFVIf8ymMRv6hUTFlQRPMmxK8XBA4VXkC/Jy0Vi8x2/dHPwLtxmkH6norjli7KQ64TNTJKVo5ZjuPIuyqUWhSww3cC6AM4/mmYtZKqYFtuXNc88MkJTz2tWbQOH4b2PfkuC4N1FcY7ynMZey2DzMlkhOK76Zhlnb5UqLWEBNNxy+OX51wdo5tCdyqaMKPrHl//cN/DMxPZKF5XOQ4+hEz/OuoYN9te1kqqmx9gdOy9VIFRFSoE6dTrHMF7KmMYSqMO2yr1Mk8W7+NilP2rqkIplVuFlJXUu2gIJAsmeV6lJZXgwuAC3rX4GJA0zRStG4xe5+/+UM/r7pvz4//kp9irv4bn+Gqusc9DPMYel9mP7RC7mFwbIlVC4WkIzKg5yRav4T4ucIEL3MIZTvMK7uEEJ5gxo2ag3fYEehID8AXD2BwNjYgqa5hQM+Ee7uFO7uRNvIHLXOUyVwgonuAJHuAB5izo6ElVKDqErHICzz/iMbYzpaXY2g52D+B/vxdObMI5wHppedFGGrlByhiluncAQUchX1iJR9Rdu/mmhu/cTEGlYx3pUalYlikG63UsTGoqUEsRrLoSq973kXpuxDGIRdelVNP4tCGeLhk8qzGOQ3EOhmYvY7ksXlMpr2VW+UjKCfEpJQU3tHexGqwK2EhKCbHKyuA7erTyGBUwasVKT3kbqWTRoj2ajt63BTElKSUvz1XBmKarEIamjSMUFV8arIB4q/nR66Rlh2enk7dbDHLvYeM5ePM/heI+HvzwlO/69vMsgqHDs0RJx4NIvkoyIHkxqcp4CeMlgV8WmC3fS8/PWE3Z7bf8zv7+fqaXp2IJqyWQSrJFWepI6pXGJjkreVYllDjkZCm6bmjzEYIvQiBiqOc2IKRYmqLtOlAqEz+IstUDKnZ51uqlrcmXtZJKgIr2HuUcXSqI6H10wVP+XxgFFGHwfMrCjwBKa6weaKCrlSayFaOiexuP17YiVdK+SUHl97TDqA6UxWNwVISgCMFwfX/BjT0RvGv8Fme5wdv5LI6zzSM8hkLl/rU+K6oAeLZZ5wK38Hl8LndzN2c4zTG22WCdk5xgwgQbYafkIRnUCEEZb+GI3wcvKIGDgz0eqLGEKOKOs0WF4T5eQYXhMs9xhRscMI9XLNlX38A1Ppt91vBjJXnVwD87AZ9oRIgdtFAvYG1fBHpTS1UIQmTIgXTVjVa1S3dVwoDFz6R8dPRk8n4UMBCDhZ3yskpllPbL+5djliz9EEvuJGs/7ubjNdgIVyZmWdtJodRFD5MmCt7h6Uh1f4UKCVIeHs1wyQkMVBitpL+Pdzm+kbwvFHROii4bK1xRFTyNUSiv6FwfuwuoyPryKOfBSbdfq5QkacYLSPEGp+QxmKinR5uK5lDbi+LwBt5/AV69B1/7JDxyFzx+B1y3YBbCcLSp6nn0pLQdFJvWcoxQIcnZWlLgEtsyl/6phG1ZT4dBy3onKikVvdeukzy52/9P2HiMDA/G7Y67Ov5/f+U5fIwJdyg+8UDDj/7Uphw25g3l1kEMcaYkE1L5tTJ+tKooQgj0yw6tVS6sfRRkmOBFUJEbpAoFMsi0supE3/eYWNE/VWlP1zAoMZcV7+o1lkaRcx7vg3QuD+C6Du8FEk7KauTZqTRfg8y7/xrypKS6BOgoEJz3uRyS0j5iqSEPzCorpYwjjSoTaxN10BBTKieFlNwfi6YS+02eFZTus0ep1H7dZ6EegqFbevbmgRs3DJsbj7E9eZK7eSeek5xikwUH7NGRaqWr2KupQnGcLW7nFj6bt3Art3KcY9HDsUyZMEA7xN9XfcpVa7YE+koBnL43CML0WeonJVyyNSyW27iVBXM22WSfBcvYQLGip8LzxezxpX4X9laYcM9W8LPbsKfE+l50cLCE/YUI72Uvgk6FSKyIpYpUYRHLgI8hnGQ5p8+SIEtKaKR0ONqLGgbmJkqq/E76LCm48hg6dny1g6JsO7mvro+KdizlB7DwaEgHyJapys+kJ+DQwcShCaLIlBKqsEL6rUW4zxqN72XcVVARRZUxCk5gOBUCVif4ktiePl6fEuUo8mll/BLc6YJ4kMHDfzoOVxt41zPwxDn4xH2wqWEa40CJOu7DAOuqNMzRs/KxqWWvRMEnA8QXMGEdLzR3RA7kZEYXr9MFqZbf7cP0o7D29KFHf/pMz9f8kZ3Re7/4n9b42Z/fZD4PtN1gEJfypPR6099Dv6bD3kRSJloXnghDvOio3KkwAg50vo70d/K0nXMYZTOdPB2jJG+k/k+lZ5cf4xGeV9M0EKBvW3Lml1KULU9KozYdR/3X4EkFL51UQ7/EO03bdbgQcMFHwy1E91TlwYfhIcLhxaQQQoRieHgpC7uEU7RWMY9wnI9QKqdyEvjQETjAqYqABWUIXgrPTpt1nrw4491/ZMJ/96ev8We+aZeneJ6Ws3wjf5yf45f5JT5CxwTDGjO2uMAJbuEY7+Qt3MHtvJ7XMGVKTU0V2XpxlDjsLyWh6lfeCyu/30T4DpLi0CatEGvu5i46HKf5BNfY5QZ7BAJfyB5/mSc5QQ9XLHz7Bdg1kQxBrCAwBduLZXt9D5YdrM2gqsW6podp7BNltVQoLzv6JS9oFZZLW4YCw6CgIv4+8phWf4IIuLI8T1JSaliAUfqLF1BbcBVMJjBpBeaqNCyVKCil5Nr3l/Jz1sCkhoPiHEj81bcduqry4i4D3IfiUSp6VioKI6XQ2g6RoboGJb19BPBzmX4eaFn24kEZUxGi96vMDFutYZtNiMZe23fZQKOaMa1n7LXgzfXxxNANVFOYKfAdhA6uX4OPAd/1OtjagvUDUdbrDupNmBrptByQeoeTWaytqEWJLfbhynV49BF47nl44jH5bDoVA8Ba2NiS70zjHAleqqZ30SjY2ZNnsrMnnmzXQh3gdcfg71xlpdbzoe0dbz3g3//0I3zf3znDz39wI7e8SOs/PaO2bVksFlnBWGsLj0TkUurBBPFSg2e5XGbPLIUcqmro1VbGi9JxEomiqqost8rOvM6J4ZFkmzGGtm0HYydWjhjiXvbQucp9fVwPoUCWWFFmIa6bpJgFCh17azfbXuZKyuH6PqYzxJbZweMiblp6O0cF6VaDjZBQn1XvgkNKbXWP1eDjUYFIMhUbYsZJ5CUpuk7z1DM1N3aFs7fFZzAsaLmHNRpqpMl8QNEw5TQnucBZbuEcpzjJlGnuljvkKh3tJa3+DCtKaagjMNQ0H93rTZWUyhEzUZVVvCYVzxKY4Lk1xqRwwCULN+xYWVTxnKmOXddHDypa2F0viaHODy+7oogKSPamXlG5b44vFe+Nfh4xnDc9XvyeLiz/8neKn+k++1hUdtnGMjwrJ/OFZ0YxD4uA+qrxJc05bYTsiN6TUMxDqn6Awc6ewFbP4vqeuu/R6wd4L0pKpyZ4zjObrUlvt+laVlLa9VEQRm9BK3p7A2Uvo5SHixN4dA2uaSmq69UwPs7BAfCUEoUR5lB5ieEtBBonaKga+d5BC34h37uxIwbMxefgyWfg2lV4/qooqaaJreEt7EejILENvRel1EcCzv5C5tXOfmxvH+Nfaw38x/XCoS2MvhAkPvrWBdNp4NbzPbPpON/ycKHWw/KghNDyWTKkpwlhUGAvuJXGjB8ShMcpMwOhI3m/5flXleoqyeKmBLJDS6t4I9txKSNy+F4JS77Y9rJWUs7FFsheanc7pXFKGvLpysZFOriVJT48tBw4OseBLKPGnyULQzpbDrunSZmbvq08VK0NSjWEoGQRJPGv5fdAyIJD43k1P8ced3I/99JgWMPk2gxbbHEXt/Ea7uY2buU4x4XeDqPJcPSW6E7lq/AsDnlTq1vpSa3O0HRfIf9V5TK1ZSPFlcOVXkkIg6ejtcA5nYO9hbTQaKOyMlGwJYissvKeSiSJQskcscAObdkTKj0pxp7VUePxQrDg6mu4YXlpBhis62Ch4GAhdHS3ct1BOrqWnnE5J1f7AYGKnVANIfRSBaXrIonESAV/rcEYNk//Z9ZP/vsXH6OXuJ1KV6CAXzsL//xOuHwgnY97A7WSuonOC0nhxh7YWrxgsynrY3ogMaZJEOJMF+D6dbh8WX4+8jhc34FLV2A+j80tD8hebWr33txAiBed0NuDJ1Z+jmMfW9UvYgzMNuLhPlbD/7BRWLlJoAY5zloHH3gabunzva4qpPR32eY9PbebQWhpS205gBEUV25ZARV/wyCD0jnTK9fuI4xYiSVtviy4PRxzFXEZrlvrdM/RqyLE/Lxo+sZjh0g/T5tQ8///5P15sC3Zdd4H/vaQmWe49775vapXEwozSEwiCIIUIIIDTBK0IckabMq0rBbZolo23RaltgaHJFuUmgor1N1ssi0zmh2ySIqyhmg1NbHZnEmLosARgEDMQAE1D2+8954hM/feq/9Ye2fmOe8WWLClCFUgK069c8/Jk7lz5841futbXwRKqus6ZcCxHqzHzWY50mPAGS1CTJEkaShShd2bWLbdBSY7f5cbXGLFKhTMgHCBe72nqQLU31aol637OKs5HJGOEl93leMnfuEcT92o+dN//AXq6xs+xie4jwt8M7+Ln+HTLLjEe/jdvJaHeZirXOeQhnkutZ0qGJWMI6+Dfq4p3wIYKNDwfc+qbBNgwegSnOFdle8KVtAyZ57h8OdZsuAynj/OZ3k9Gz3k378Mv76EthqVS0GApZhPa8DUeszjlVrElQOzVMXkQNFXKVMNGbWeS6hvqvT2vax7roFdRF9RTjtKas/TGl5FKU69L2FguC10ToYJElE0N1OKefugwvFO0DDXgdtRUqHt6NZbnF/gnMk0O2VNRqwVnDMcXv8x/OxpCrzFAIsJ+el03MXbqhef+W2dzS94e97B/+McPNbA8VYVQzS5fCCzN/gcyusC3Nmokn7hpt7j5eNjeLeZqyfZdrmeLKgn1Xaw2WZW+bkqviEc6/R865TPZ7JnJgxktEk0z+kcLA7zegFMbnFTXA5QIwL0XMbcgwyZoummz/8+yu7zMZdPFYQ3brgn+x13y/nKb40xlLzuFJ5+Vmv6GONwWdO82L4CLPKuKNlyDYX+rfAQDoaRZHQjaignkXucqsHUkxFc9lK2l7WSijEogaZXNJ7NRRrOZgUFGvrDDPxlgoYu9sN8OxbQ3mdTJbVbd2DP/D2MN2KAB4sjJYPkfJc1udgt9Yh1GGMxzvKRT8341OMV3/6HbnH5esspT3CZazzKfXyUmxxyhbfxFh7kMpc5YgnYQTlMLf2xhFPy/wtRDkPBZhy+v6dxnc7w5JhFCBfUFDufywRQYbDU1CxYcpELHHJApOKbOOZywVb/xhJ+7twueq4cVybnME4fwLZTL+N0BTOn42hMVkwWuoXOga3OxtTvhP2mj8zkMqZFPvsKqoxvR2FlBTVljZgCJaa60AhDc76ipMqrCNaQYN3D6Skc5zCoDzCPGHMK9jbGWW3y6f3Yfqbr9L0zNOd+g+bg02dMwOfZTg3cmFi1O0J0f128FG0m8GQFP3GgBcquH4uTi8JOMSMyjSqMTa9s9F32fJxTb8i40Rvu4zjfocDNk+YnK5fzRxnaX6ipQmYs8Wa8R4OiSooQtBVUM1WgIbPTkyYBBsmF1yhSy0zu8WQXKBGV3dqkqfIa99/1kvZz2IMnMpE5L+aRmRwy3j9HCfvd00svG69TQ3o/XFzGvjvGPL/DsUeGdUOOFu3I0NGryvFFYMpC8RKWEi9zJaWC1wyyRZAsI1Sfp5ToukBT1TRNw2a7Jaa4e/OCTrpzTls7p5ST07s3sVgli8WCruu0GZnTttzYsXNmcdOnSq3ve4xt8P6AlNtAWLR1R9etqWYNBkeIPWLsAJu/yh3+KD/NKX+AFW/mO3kzNVd4hFewoKHOzTvu3UZLfuT80/T30KqChKK/YFdo31uFvj/r4znKplZVyoRHhooFCx7hEb6Z99LR8xgBzwfGn6Q05gDKQx+z4Knqsc4o5BCXb+BkA6tjFUIHM+jXu23DZw0cieYk6nr0jIzb9apKfmeAzE00ihWGrrhDHil7SsYNXjvWq2VuK4aQ3bB/voaUYBYBB8sAqwR0+ltfKeVTl7Q2iInl+dxzcEvge94EX3UT/vAnuf6mH0fST44t4Ce3YGq0Wn/629y/M7b/13n4R4cMaLoqe7cWNQ6K4DKZpLVEEAwjiMQwzrGI5pDsTL3flEOwRtSwyCS2hAzRrxYQPXQCdqlzIYy/s6XkIOcfUwJXF1w+xB62UcEpVtf1IAEl6T4ebekhea3U+Vo3W/3twudjl2dBIx35wjNgp4wrGxQT/e0ziWwfFNygQr9SxKUIznslYiV7NBPZUqYsRsFZR+Uc3luSBE5PV9R1TV3XhLAbASqKZcyTq/qpnCeESNu2eO+H1xAOzJ7OtCynACAKwGI8B3RdGMZajnOWEu6lH+Zd0X12UKIm5zcRIUhCuo4dSOLn2V7eSip7R0XEOIN6VkYhtjqZIEkIUeHpCZDiaYnSJZlyMMmV94MLvW8BQIopy5Ns0SQAGeqq1B1OimbRg+XcmFDgxgWmab2nNjOMszpuMcMz8JP/YsELty1f+5Ubbtgtz9FzgYdYcIUFFVXmyCv+0oRZjdFKL5Sg+rcZKkan38vkGLDz5E3neue4+5vOj6EajqLtDGFBwxu4xRUepybC0xV8YK5Qc8a5heKVMCqXwarLFnHpsnu8zlDtnCCvc1goiVrf5WdVteuhTTdrwBQE3d4YSgGuDTm/5fSYoXg8AViD60BOJtDm7CV0W2Ux7zqFNbed5l1WrSb/T7YKly7HIzdALNcas5X/eICFxVw5wn/ZGq69CBnr9Cb96lJrzZJMPLyyjsvtk/G6rYWPLeBWfa+SMqhNU5R68WwK/BsmSMfJuihe68A+L6o8yrmNYYTNmfF4hf22HGPnwspxZfzZ4JGa8ZrKq9z3nPMdYOcy/V1+Noxk7yuvsR1HsnhQ5Tgpz+uu6DSmwKIYDGWTxzQNBpf28bvovoQ1UPtKPahYWs1HlU9DFHoq1M0w7eod6bWUy3bOUDc11u6ykcSYBmj4fujxrNzY9HM9DjsRppEVfYS0mxJOzvNnswwcPsuKTV4KKISXvZIahaszQp2NtmCAmEgpYsQQQ2IrnQav8oSl7JLPnTbukxBzNGYkRCUrMkmiCiQp5NYYgzVe60eMHX5njKHNjBMignVqCWqLGUHiFiEN5Mq+meGrA7puO8DeAWIv/JXvu8BXvGXLu79iw+P2lA9yg/fyZZznKvO87IuaAXY+Ka8x/1Q+G5XUqNSm7/O8Dv+f1liVYzP8bldplaYpep6UPTdL4mv4MDP5X3S3Dx3BX3gAZQrYC1G6rLicHwW+LYntPof2anjuRN+fa1TwWK8zMMsgihDVS1ssxmaC08swRj+XLgvZvIqKcjIOql6PY3LFcBLYdDlU1cOtVnMpz78wIg7bVl+3Xsi5k16Va4iqnOaHWlS66dTTICtCk+u+jAFxDPmUJ24r8u3n74e/8RRc/W28pAT8rSvwmwsdW/H4psJgNNt1XioVjNT5PuSw4aAsfD0qZpe9qD5NjLe8xewRD96rGVtm7IRNk94z7d0x/t4yUdRF0RSllMPOkgtgrVFPrFxzMWxC9syLd148rULDtLMI8hpx+brTZvxNGfN0vqpKz9XlPKLzO9dvyc6nK+Ezq/IBUSJqQIKij8XAcj7X0+boDdZQ1w3dtqXdbokSEZuoKodzalCZYX5yKBODEgIkQuyGIl9IWG9pZg2hj0MUKKVE30ft3mAVoj5NZ8DUg5oqLybe0+hBqZKKYz7rjN9KFFypC8z7WGPou/DFAUGHHBuVhIgdPByLMo4nY1BhrZo+RO0ca4b8QdlkiAOnYnUYQwxheF9QNFNRbo3DOovzjgKzdE6twhFllRcBFutdZgwoykE9s2FMKKuANYbQ9YOl8SX8Jtd5gfO8M/snxVspqmRiFeZzwpQtb3+TvX+nn09f02Bi6UGTraUhkDjFE06q6ElU/CZH/F/wfFCLdr/3fvjULCuN8qAxWs37OaCMREPQnk8mW93JqKA83cKdFdSVCo2udFC1Oc+DClXvoW703wH5Z8DN9AmISc/jG817dD2c3oann4NPfVqVShfUExJ03H0YlVuBwRflmFr9NyYNVTkP8zorjAykKHemQM29Yafg1Jj8YBuV0T90GX78PMTN6LlUMxWYfc+Qq/lUpYqxdKktt7DMcQl5Tv8trx1lLuNvzro30/2miiHlfNL+Pjuv/Pl0v6F2bbKdtXinntJZ454et4xrxyvbP4fZvdZ92ZBE71d5vAp61O4+XUospbnZ8k3KuUbnKoX9G6tRQhE2mw3W6K8cwv339fzZ73oS71SpYIXPPeH5a//3CxSpVvLOQ7oie0xJTG6fNgVvJa19ypev+SyLtblkwY4dfKfw8ym90qhszg7LDZGqM/JY+/tN/zU5n3dWh+Kztpe1khrdzt11aLMlU5RXofIZlFHZ9havPvujcBfutSzG3+/yphUlV57l4nYXF780kBvVS06SDpb8eE3GGKadai+y4Rx32MJQAzUqqDKaqQodQ3wM4byzvCbZ+/+o7PTYaTK0tPf9qAjLCMgciWWuHXdxfAi4o57BJ2fwdD1ODmYUTjtC4gwBae140SVs00VlozheaT6qL0qqCHs7FnWKZWgqaKbXLxqOMxZczB5RDzczvPmJZ+B0o4prM7bKpgt6nsUiC2rJ+TO0zqsIt8LEXZBsU2E8LAPZVdjlGm3+jQh8aKH7hwwaEclUP25UUtaOczPGw3a3e0Ji3DvfZ/09vN+7R/v3bfqbQeBP9hvuN7toyuE307FOfjtVskUx7V9LSveGigfrvyjYyfGHh2d/DHu/L/dg8rSRgBcqmEc4Hzl3PnLtas9TLxjtmcmYa9I2J+XJM1grXL28xdmEMwlv4JWP9Hz919xh1oxz+NFPVPzIPzzi7rFjvZkOOB+pYHYmS+oecIbsroOzwBt6W+5VRGPN170lNWfVTZXPdzwp2YXC72wvkRbJyIud7d/h7fj4mHPnznH+wiHWOKqqwlcV88NDcI5kLa1JSqSac0gIdEFpiXR953BfXeGMQUKaxFDV6phCJKcTPcSS+4SxBjfh5DLZq4oxUtU11lrWW+2e6SZxbJEC/zSIKBQ8pTTIl6ZpePtbN/yDH/gUp/6/5JT/jPNcp2ZBxQIlay1WmzCyk44hPpM/k8n3Zso0vqdwihK6t9aq7JPj3vmzUUmVB9ijeSmP4SCT4q6o+DO49GPqTf30EfzV6wxtLGIcBU2V2QRKx06TvQvrYLbUEFq7VUYGgwIOJLMXHMyV3eFwBueO4OgALlzQfNVsBsulKjJQb6YLcPMGHN+Fzz4B6zWsVoxowj4X125HoV81UPJWg0LKnl7Kisrkz2JGsFU1g9Ic9snSxfpMqBpUieX1ly9+ImBktHy8H98X/rcC0ihzWfYb7jHjsUMYkXGFnaHv9bflN2cpqCGMZzUsWzyoshXBXryX8llZS8WjsXYEr5zFCJKt7OHzohTLqxynFIJOPaly/eU41g6tcQjhbAVVIh7eTzzL6fjz5nLOs8t5SmfhQOCrT+D//DjrjeWZ5zy/+3/3CDdv11Re4e5GNNyVohCCEg089GDH/+eHPstykYZTOAsHB2nnlCHA6cry/T94gR/42+fZ3Uy+zQpkCKHTEGMxg43FOZ89MP2s63o2my1VU2Gd3QGQFfk1VQel2DalEUzhckheuf1GBZhEy3OmSMEXN/AhoUQMJ595grt373J0dMSLbS9rT0pSUnZm70kp0bYdxmfoamUzHdIoRJRBOmUGHoM2fCPLgAzAMFrpjSkGVBbekiEGaVRSGMnPvbbiNuVgSH5mEzEljVNnS3nkCtQHQiv1x9ttUAPjvV97zO940yZjCQ6wXMbRZE9qqkSKYipKalRI6QzFJVlJmR3FtFtLNb72/06f5zvDNCRo6FBFdARkWPhRggMDzUyFfJIsRCYCrwis4Wkt583gAps/K4rDZLqbjqwYNvr+tNXamxLum89VYZVz9UGV0mYDN47Vm2r7bJZmYRgNSD1a0QNKzeacFgxoMGMY+xXZyZQVAVrmLP8eUCANY/iTMo0l51M8jYkFXzr7GpN5C5nMhZmcdww378xnEdwhjIplx/1nN2QX4+53Mgkr73u75d+pwpruW76zLoPn0uT6y/Emiql4MEURTT2v4kkV5byzZhjHMFVcU2do+p7JeaYe08ReuPdaDZxYDV//o0ssvvyEq1cCf/B9dzldeayz2WFXBPKnP1Px/l+Z8TXvOuWNX7Ll8qW44zXRz+GZt8DyaTj3OAB3Txw/9bMHfPIz9SijpstBFKQFY92SenD6ed/3OOsZWMet2Sn81ku5lz9wmqfSWrrSzbcUlI/zYIzNhN5j0fI+QcJZXpoUg+clbC9rJRWDCt6Ua0Y26xXGV7i6xvoGYz2VtUhSpgMrFiNmiBwZGCM/ORdkjRnQpXHSLnn6Kjehyu3nY8zEsgVJZMA5S4w9gjJNIxYjpc0HiBSESxoaiVmjuZTGJv7Lb3uWN75B4aweT8MsY/o03DN6P9pQ0CgMCxmg5VPFNb6EwL1AidF7Kkt96jkxBCsSYwEww3cM/7f5nNrwQZn8KnaseV9p4eQ6w8enSqoIlKkwkqg3Ken1KYSzHM+An6lR0meqm00Ld7Y6oqngqTKjdvEiUspeSc7pYMHMRoGJyWizIuhyXsJmZJczo2dSLOvBa0DzZikrNhG1EaaCVkQ9qH2kmLEgYTI+MwpkyEo5f9ZkoMlqpf9aM17vAM03u7mforSL4Jgqiul5CkiiKBdrJ14v4xx+vkDMVElNlUtR9NuMVizjuacJnozH3zdeynimYxjQnIy/6br8oO8psfJ92abgkmkYVD8Yl/oUGCLAx2fwVx6Ev/I4h//Bbf67P/38rvLL2z/8fx/xwV+/ynf84Zu8+13r4fKGrT0PH/+D8NDPD0rqqacr/pu/ch9dlyMYBs1O2YnxnNTY0JKXMk1aMtN1gcoLzvkBVNE0jaaDGZF5+2TY0/eqFJ2SIqREiiVPT0ZGK82WQVkkpts0zzUFZ4gIOIOxXwSME9MCN2sMzXxOFOhTUrSygHVWlY3JLA9JtKA2WyW7hokQB0tKlUixPLb5gZpaIi4/r3a4oQaxxSPThZxKB0yBknfQZ95q7ysElx+KGMOQIjUCW17Jk3wXR7yNQ+ZZ4JfRhsm/iVL/ZLJyMsTsNRUFo+9lKNAtymdw2Kczm18lXJjOOM6+Z5WvLbMMRm32NGErHCZ5wrkXcyuGNFr21o6x6uKdSILudPRcUlalQkZwlRYODuolQ+O8IWwIiphz2csR3cfWev5CQZSYCEMYarWkVYVTbngZK0bpmMocWJl4T7mmqABldDHqeNssOGufmQ/ydQgZdec0vOSzEpqGofqiFL0WN5d7MijKCcKteG074be8FQVUDAXnVKCXLYRRCcBEIeQ1N/VSisKydlchTs9XGBuG0J4ZlcDQtsLseAmDR1XCkGW/qQdYrqN8v+8VTtdXU4/KzTmdrzC5lqJAy2/adjRaUmAnvyMyegIG+OEr8C8O4b95Cs7dC63+Xe9c84N/82ne+KVb2C7hN75WqZ5ip2CdMIPV83DxZPiNMYamqkgxkmLA+xJ+m9Y6FoN3lFvKi+dpGpdtv8JkkafV6PM7pUMC7mGZqKoKDPShy4omYTI9lLJLWMykiLiQ4e53JZ8WCg+K0NhdufB5tpe1khqUQd68U6s85ApofY2L1mIGhv5p19L80VDDMJ5gdImn7rGxudlbaQewZzrthteNgiOyQpCsBHVIJjeN0xEYMVw8H7l2JVLXQuSQU97Jkqt4Kl0UQLaDGMN4+jLD5+XvopDS8D2MYSA5UzFN34fJOZh8pscsYxg7Cw0BB1JWqMoUdonEQxiexswDPLCGJ3o4kVGg7iS996c0W4zTIZKHNgjk/CPjxhu7Y8VnUEH2RPX4JfOcFVdxq4dzlLVjxwUz9c5s/k5gDIPllWcmv6eca2oSDQtk3HcY757HNX2l7FkKWQlPQqDAANe2+Zj7cmCa/yl/l7EUhVSEdPl8Zwww0FZNvZghVAY7D8D+uafHucdj2fvNdA5HN2H3WGedYz+MyeSc020amtz/bP+caXJtw3UOJ1VQ0AsePlPDhfyMGNH7ePEC913cct87j2E9g9tH8PR1LSLu11oz4wxceBbcMSLw3PM1zz5XY4zFmoQYgzNaFxnTuFb16mQy9aOsslZlz8jTV1DKQpKi4O4Nx+2EAIXc/ugMhbKzjHajTdNj7YDLJj99aSrqZa6knNEMTYqRYJRyyFpHXVWYDDfehT0qPVKaeCtF6KfJBEdRhKCvKvXCEtgMiEiooJDsJbF3UxA7utLZK9DwovaBGtgwBioRSwlf1d7zn33LHf7E//4F5jOhw3LInDkN1RA2myqmAoSI5KTM5HtVSDIAJs7KJ8XJQtExmOE7gO3kPPp5OY7QD0oqTeiThA7tGqWwDssBW76LwH/Kkt+P+Yqb8NYPwH/zAPz80YTeRs7gR3NqaZL0s5L0n+ewznqbi+KEAbo9zU+YiWU8DU8Zq14MUa1YyVa0q7LHkJVNDHr+wbqeWPKQx7wezxcjqoxyGNGYCQQ8J/AlKSMGose3Xq+t7zMhajMq3r7L0Pbs7VijeSgEpGfoy2RcZlHv0LydgXqmznVg9G6mgndfCRVPYhpefDGlEye/nX5XPJEdzy3fn5IPtBNlva/gRHJtmpnUt5ndsZT9p7mmsk254HYUe96v73S9lDEODBTTa5t4h8P1GfW6yuNTjFOXr62ATk49/PEH0XBtB76DC4fwX/8f4RW/Aa/8YfgXb4Unr0G6CScncOcWXHwQrvXw3p+BuidG+Evf80o++KH5EMFxTstdRNKOUnFOWUnFjHDwmFnvNd+tTBilJipGIWSDb0TvjbRNBSwmImy3W5IkjMtmqDEDCDWkiDGiAQADxloqa3c8qXLsaQfiofA3j++lbC9rJSXZOhrt09HbKcqjWAw2h/owaac5HCapj5MndVj3Zjf5t2MhWKssEkmG78tvdkyEwaA22Zgba7S0SDg3MbOoj2SgqoTl0vAM76bjLTjcWMm+E6bTHrcyeE2jZzN6OPqdMGWWmC6MqcLa/1xg5zjTfUZlKXkc4+yPXiMkIh1ChWGhe3hRqLcv8yKjUN4XeuWokl+D51SUwRmWuEwmv1xLuSf7VnNKqqCKd1LCN4Vnq6DxpsccxpUmtyO/GfI7abz5+2OTNAkhJXbboU82Y9gBKexcX/EK9gTsIPTNOC7hjDGcdUzu9SLuGY+cvf90DPec4wxlt3+cF9329j/LA3yx78tWvOyirIbC0739XuwY5ZkexjL8TzdrNBcWozKISDaIooVXvxIevqa9sbYPw1Pvgs1FpYDarlVJ2lwsbiL4Xp+NYGhbR9sVgb+LuB05Q7MxtbfUlBBWC21V2RYZtn9po3cz5Q0caZZ0izlXWFg0BJS8gJFAF9Rp2K+Jmp5rSn4rKZG+uBgnzCjIh0nI9ENZ+BSUiskULEZGeiJk0rE33/Cp27wPzRzc4YkClB3LK++XxzM0exfJBIwyKqkYcd4plN2V0hjLZ/l9BN7II3jGI4wABtnxqEbPavxcQRIyhOxKYCANKi/7dMNnu0uzCMhd5WeHffScaeJplbDlGAJM9HTIWZ3jitU9tVyHpDqTecx/D9iPYm2nPaFXhGuZ/Kk1L6OgkZSFeEm8h1FZMAmzlRzRIHANE7domL3h/EXh6eLLebXJeKagBvJ1pJTzWHK2wi3bPcI5n7/ErstYmZwzyt74J+/3lcRZSmrnfHvbdK3v7z8tFC5ztX99RXlOr+0sxXfWPOwr2Rfbd2e8TM5ZACl71zO9pokxOnjkw73cU47WaaH26al6arNa15a18OYvgS99NRw2sHkFvPA+aD8N9hasV7qe6yazV4TBaVR7rbRasdnjGA0l5/ygGIrCMqYYPAVBXJ5RdhRJ8bbGS9hlby+Q9CL/EtzTRVinaVxrAxGCGZXRFGS2cysmSvGLQkmVCSgY/9VqhXEeqTzGCMY7XFUrGVAIuCRYkUyInB9yY0hZUBhrFAQxqTco59n3qmKMuu+LWINukkAuATKVIzkPVZBlxmItPPpIx1/9i8/yioc7wHCNS0QuscBjiQQ2Q2cmjeF0KIqvxwxeTVEcIXtZJQQoKBC2LPbd3JYMAVCGkar67hECkZaUPaZyjPL36LWpGtMx1cAcS43DEXLF1IvcxFEYlDYWQ57JgFH4rYYCG2WXKLmjknchCwvI38mLeCLC0IbcWbANSAWxFMP6HAJKuWYLrXkqmMc4OU5RCtZkTyyNYygdd13h0MvXiCOTRubxl9qcNCqxLhPQmqyZLeNxScq0Ubr+6oLUpVCUtGsYvED0lDtCuMz5FDgxDc9NEX7TbapcppDlfUU1rZMaFCrjeHNbh53xTBFzU4TfNEc2/Xsq+KZKpQi96ffF6JmiL6e/LfOw3U7u02R8xZMaCGWnSjRldv6buR6v0kLuR18B7/wKeOg8HFbw3KdhfQLHN+H0rt7j5pwynNRz+J2/BteeBxf56Z+7wj/55/fx8U8Im/UpXe7GO7pKJTNttTapjxhr8LUnxn4ChNDW8ylKXlZdlmMjO86UbaKqqsFQHxB5KO9eXddjmC730VPFVWpKTTbGx3nfr7faZ1ivfY1Yz4bffnt5KymmmlmIIepEOQspYpKBnDhMIrnde1FQqH9i1APQtagJcmNGy3/w1rJ1MhDLMnpK+y7yfpKQ4fkui2NyAXlbLBJf/mVrmloQKpbMiMwnftToDTHxbBiUTAntdcBjQEXiAsWrydk0YIe4Jaubsk+5At3bZE8s0pOy8jMFUTTxtPTv8t5CVojliIanMTwJBFhVcGMOm4y0QsohJvNVbpQAhWVcsiKIoyIaPIHpb4uHVfYpxyvKK78o4UWXlcoUrJCF/JSZQgBxk+Nm4TUoEMNQwhamyvEsgV9uvs0eX7mOEvaMDF5fOW6ZlzQ5V16v4ykEJcM1Gd5uJsKNF/dCyudFYL9YeG3/831v6iyjbep9DArlbMNu8KAHz/UM5XrW2F7s3MMY9xRvWSP74crhmPueZL4XAkPrkHIciWrkuJk25bx+FR59GF71CNRboIW7N2FzkhVUgGS1R9ZsCYsjuHoCV28CcOPGjN/66AVW69sk0e7IZUwp5YhHbjRoZByXyDjulCQv5cwbmopxDdbu35/8jGbjWaPbaYgqkWXjbsuPnHOn2Pn3ekv7XtQuzVL+zRdDnVRKCVfQXHninNGCtS4mYuqIbYd1Dldl+Ha2CCSOCcgib4rS8V5jun3fj8ZTJtBUzixF97nhGR9vvBgzJAqN1TBeWTxCpM/hLSOChIiESONH9103wyFLIossdkymb9WjjLmgaUiuJxIR7lDxJ4m8gcSfm3g+RUUmLC5bPWnyn5mcoSiYLYmOnnVWVGEASSgoYvwPyN9pSLX4bYlAw/fj+XGggw8+AP/D2+HmXZjljqp9yDRDRSAVLyp7N7ZSi7PbQLsZGScKlNtk5J4kZXAwjOG2AmeXxEBVFEWZyqtGjz9Ai40WGgNs1qMsLdBrN2eUCPkhNl4ZH4xRryUGaE/y2IuXZSdgBCZCFQ31OKPjMSjzRMzeTfHCpozhRSkGYcjLlTqjGBQsQVJFWXptlfPFPS9lP9xX8oJFMUxBJ2W/JAq7H0Kc+bsp2GI//Jfi6KEUCP3+PqBeZr7M8VEwu7V05XdTMMy+JzcFgQz7RZ1ryF7TJNwMGczCOH6REcRhMniFPJ8lZ9lHNYgX5+DCebh6Bf7cf6V51xeegKefhOMX4O7zlA4I1OehXuTfXIGr98Hsx4fLbeqao4NDTtYbYtLedzF7RzFpjZKjwlqvJTViSKTcDshSVVWeAsGYOPCLFsIDkaj59Dxf1li8EYjaM7uQ46pe15x+13VUVYW1Y6PNKJILckHsuJ5LSqM02HTODcZqYX4HHV8sJSK/zfbSVNlk+8Vf/EXe9773cf36dYwx/NiP/djO9yLCX/pLf4n777+f+XzOe97zHj75yU/u7HPr1i2+9Vu/laOjI86fP8+3f/u3c3r6hffBsdZm42uipSdavFgH1tjhQpMIfQza08Qwur3ZMrDYHCXSm+XyyzuPz029JEWtXcgeWhLtD6OLqXhKynZhjceIHWSNqyyucdja4RqHbxy+8bhqN29TQnd+4GRXy3KqbvQvzQupN/WLOH4UuIPlU3h+FM/fxeWX5ZcpiL+UlY4MHljIrxLGC0QCiUCkI9EhbJnzr1jy0yz4Geb8NA0/CxwDEQvUfJiGn0LoiDgilo6vpuUPAQt44BT+g0/C1duKnOu2GmLTG6oPvS+MBD1ao7SBtFUPY4c+KHsjMArMwsggjBBxhKEFRVWPhb0iWSFk8ETss9LsJl5INgQMen4rKvxtVlQp6vhDn2uLJOcY7MRjkjGBX7y4ckxJYz1X2YzV8fpcy2PI85LnxxoGdnAJOm4ptD/52HW5RjM4f6MplpV6geDbyWvqWZR5LXVtksdWUIppAqhJueA5G4yDhrdZsRZFsk8+W841zUdO70uZn9L6vcxbAQVMTxeL5+nGay1jTyanUYV7mTzK9RbFm+dqmBej8+6MznloIWzh/BIefgDe8RXwde+Cr/1K6G7C3Sfg7lOwugWb05wfdGBmsLgIh1dUWaW5Npyc2g5CBj2kTD9pGclr80uEKJGQApJZToz1OOex1uffS9bhYz1U2awBbw2VszinTBTDK9t1GuCWzO7u1BuLmkdKKeGtEtwiaSyxcVadeElDjCakqH+LEGX8Ww3+f0vFvKvVire85S1827d9G7/v9/2+e77/63/9r/N93/d9/NAP/RCPPvoof/Ev/kW+8Ru/kY985CPMZmqlfuu3fivPPPMMP/VTP0Xf9/zRP/pH+Y7v+A7+7t/9u1/QWFRJ7brtBf6Nd7l9BqqocpI5idClpMrHarikKClrPNa6EaSFzc/t2EK5LwLVGOLwHI8LYDRErVox1qoRZRLWJVyVrRgRJBqMM3jncZWfRnSJOR9UDwWx7HlOZRmMwAnHz2L5p/kQayr54TwogIgxX0vizYAufChwjNETzAGD7DWpMotsEHqMbJnLL+F5bhI9qQn2NYip8FhqPoDjw2z5PSQuY7Ak3ovlK6n5ccwjL8DDd+FDD8PHDpUbz+TiVZeFsLEjp13KVq/k3JAvIUKZCBoz5jKsy8Is6XH1chRyXup/Sr6rNNCTrDhyeBjIrRmK9CtKKozQ6MAowAdPJJ/P5/YiU6+ijG/wFPPQS06qXIvGZCbyM3snPs9LMiOmZYDvFwi9G72IphoLpgfS2YkSGvjpLDvhz3zKYSsAj5gm4dA08ZCyIbAPaYecZ7OjN1vmpng7+zkyY8b5LOFKa0cjZpi/4s1my69QfSfJEnjCwxdz6JTs6RmyV2cn8zJVdpP7OW0R731eI0mVFAKXHoRHHobf+Q547UNwcQmf+w04uQm3noPTO+q1iQFTgZ3BwSWYn9OOxcnDaQ+xKBSr5PkhEWNpwe400iFQwApRBCltS0yWNVZllzEGSaOMKmQCJdpjrR06LVirzXzETLRkMRKyghKTlZRACqp8jNE2HzElohTjxGKdI4g6AEN+K4UBiDEFulVW40MvZfuCldR73/te3vve9575nYjwvd/7vfyFv/AX+D2/5/cA8MM//MNcu3aNH/uxH+NbvuVb+OhHP8pP/MRP8Ku/+qt8+Zd/OQDf//3fzzd/8zfzN/7G3+D69esveSzaVmMUWGIiKVpcjEg0iFXAgMZYwXuHzUgoQbH+ldcJTJnbz1lL6MNQ2DtGQnZv9ovFXK11OepR+PtGbj6Scl8pbUkihYj0AUyk27Y7smHDmsQqQ9ALHWtRSAGtj+pz3igSM4CiPFbdyZs5ffo/xQk4f5flo/83rP81av4sPd+G8IYdrwyKsgrEnH/q2WB5igv8HSAgCZ59/1tpj+fEoLUYxiYe+up/Sn24zYG+W8CGQ/4iia8i8qexgz842XytIbw2DaEC2iyYnFXhRjPmYIwwIPYG3ZFDON4pI/o096Q3jSEkVITiNLRl/aQeJ9/ss4AE0w7C5bfleHWt7/d7NpHHXf7Zye9MF3FWUqXGp9TcmBz2K9cU0DmQieCczfS4pRDaOWVJSEn/LYJ7Pwy3n0NKub5uCPO5EXVZQpklxFUUiWHsRZUmv52eo5R9TK+3KOFpwXCZT5HdGrmdPFHeiqKc1sMNoIziTWYPMyV9XAoV1ODlZcVpi/Iif5bDy6kdvcXSIqOPepzlEmSuZMV/+A/B1SNYRtg+BZ9dwc3PQreCcAxswfYgXkEVh+ehWYJfwuwQ7vtX8PA/g8NnuXXnAn/vH/8Bfuu3TjjZ3qCLZdm9SFPB6ZRS7J1x33008nRLqFHe9l1enmNGekp4UD4rwAdrlb2nHNNa9bVUASnp7P4Y9xVkkZMhBW0g+xK2Lzjc9/m2xx57jGeffZb3vOc9w2fnzp3jHe94B7/8y78MwC//8i9z/vz5QUEBvOc978Fay/vf//4zj9u2LcfHxzuvezbZK6o1WXFYTfwNYiN/BqOxO32APx8FfTnKDgJm/7yMLMBjeMVQ6I4GGHW26CWN9VLTLQ1eknoFuw5/Kc7VlwxQcEHE0a9fSVi9hn71asLq1cT1K0AchhMMnwJOd0KGJXBYPLNyPMvTOD6L53HY3CHcTmxvXmBz4yqrG5dZvXCZ0xcu0d+CeDuRjiEcXyKePIBLn8XxOEKLIWSP6ktJPKIX+FAHX7qBN2/hwSxwkmTvptzACbJuUHHmnj9HQSaTKZ8I1UEYp/H9dJuiyybrKS+Y3e/KcT7fb6db8ZAGS+VFtunxdz6fjoGzj1EU7XQcQ1Hy5PhnjXEnZJYVSpmzQVnv/17unZOzrmV63v15vOf85b7szftZ29TjG84to0dIHEO4w5oo303OMR13uW7rJgbRGddlnRomizlcvwZXL0AjEFewuaP0Xf06h6pz+YczqnybnAMtkYP5KVz8LFRb+r7i8aevc/POUe45xRDa01Pvyqd9IMI4nbLz3ZTxYerNyORY0zU1XPHkNzvHOfN23DuWfUT0WZ+le7gaz97+jQInnn32WQCuXbu28/m1a9eG75599lmuXr26OwjvuXjx4rDP/vbX/tpf4y//5b98z+eqmVXP6hobFYT1Vc5tuFz1XASLwTqHmESKiZCJOLUKOiFpzGWpZaJs6gAiu5Ne6gfKZ7oIzI7FAEo2q8+5hhxk0rZBbTk7AUbo5jL8wFG479Lea1ofFXJ+KSFxyd3P/AViexGtTi81EuMDNyqlkSw2De9T9qR6zvH38HwaSNz93Bu58fF3EFc9pIiERIiKAXzmV343zWzGwdE5rK2wVcfRW/86YdZyygvMCHiWCD+I458xM98F3/44fFsWhv/kEnzPw2jNUrELbZmIcRusdCaWLiqMptNXmhsWL0Pd6N3kvojmCkp4rAjlqfdVchJl/8JUUL4rx79nfPsKNf8rjGMunkZpDbLv5U09hHJNxuRbnz2PttXPchh9R7EOoa2JpwgjJ9/+eAv4ZF+hpDiOtUyyOyNMsw+GKOOxOUdYtuI1FY/VmEkzShhCoVOk4VSRTe/VVKGB5ubKWGKva6mq9ZipH5W2YfdaIc9JBrHYqReV12KV10KKcLCE8wdaeBvXsH4Bwgmwhn6l4UDpdQwxwfIQ5rPs+Xot9A2yY0QkEbZdJMRcjTiZy6FmaSetcLYC38nH5zndN6pLO/fpviVvNWWFcE7h6n0XEBGcd8SJp7TjE1iDw+6ca39cU2X1IkRLZ24vC3Tfn//zf54/9af+1PD38fExDz30EKGwSJeIQo5FpxJCESVxLTkl5z2lfxMUj2o0T7UoLeGrGkHd16kVArsWTVVVw5gKKqYsDO3VUpAyGgd2hgy4yJROORoybxqa2a6VUlEhVBOC1kJ/FIEWoUMBEAo5r/n/0t28SDj5/aSwHFRPIhFizerJ30t19DGaS7+K5+dxfJzC3Tdloph6VZYXKJDzFA2xMzlfbXC+VvkqhhgNXSes1z1WIsZGNh/4cqpLieWrfxjMN5D4UuCQwJew4rto3D/DkwE1b1rDf/E0/NgF+NxM70fKCsSX/Myel1QU9yBQJ8K/KJKpRzANLekNZEigT72tHSHIvQzi5bdFKE9pnXa2YtmXYw8LhQFgYNlVDpjRK5r+BnJOKCup4vkX1u++3xW4g1LhXs+vjHOquEoucLrfdBwwMQLM7rxOzzXMqxk/L+HEqddVwn1TwtjhHuX7PFVS0/NNveIyxwWYkfKxCrgEIWf+M5ls9qxKx+NyUWX5DyHd/L0tnrzNgBkLcwNvfCW89pWQ7sJqC+1NWN2EzQpsfk7bVo/hG5gfwGyROynXgINtJpadzFvqe0LfE/teDVkYWr+fxX+nP5MdmXRWAe2+7AopRysKQ4Qo7GokzM3vc05f+YedKq3QTxTOOBaFxpfi45FZoig6YCcUaK3BVC8tkPdvVEndd999ADz33HPcf//9w+fPPfccb33rW4d9nn/++Z3fhRC4devW8Pv9rWkapZjf2/pcUFbIE621mFwXhQimKCpj8d4ps4OBEIrlrQ+SyfvrXEqGTTI08Rr7r0y8EZHJ51O32lL4qkDbNTtrMUY7afZxzBthFXXYNA3NLss9Ho/gs5IqtCgRxRi3KHdej7DC8mkqfpTV7f+c9Qtfh6uVRHXwlkLF6plvZB6W1Oc/gOXXXuQOFi8mWzw25tSPIwVDDJIBVhbnXfb+lIerDxHZtEoEkYTNE2/h4MFPcfEV/4jOPUKwDwGXiLySwB/H81ujknrtBl6zgQ8fwAtzFdpt0pct3kgRWFm5DA9JUVRFmNoxvzHerF1mi2H+YSwC3vOCpvDq8vlUge1Y4Hvb4DXtPsijQpzsO1Umdu+hnSqNlBioocqxipG0Xo+f3cPTt3ec6bWU47qMJBw8zLhb7LuvqMxkHs7yUKehx52w4UTZF1j5VAnFyEiMK/ferzJPwzFTTtbacZ0M8LSshIqS8hYkFxIXQERePsO6H+bKjV5oYTzxQG21OPdLXgFvfxOcPAHtMYRbsLqhLBKV1/N0HTBXJTVbKuzcz4AKktNOzyFOLkuIfUfse0IfdjySfSP5nhBgvjXlN1MP6iyPK0atmfTeq1SJcZBdenvL/dO/vTdDXsrEcWw7vpC5VxlOc2lTtnVjDJV3u4Caz7P9G1VSjz76KPfddx8/8zM/Myil4+Nj3v/+9/Mn/sSfAOCrvuqruHPnDr/+67/O2972NgB+9md/lpQS73jHO77gc5aEHsZgqxpbVUNvJ+MctoRJsgVY2CKcc1TeE0JHiroYvHNY51jlh15vXDGYAynFwbJxzg3vvfcTAsVpjkoVX98XUIMqRwPaDNEYvHW023Ygkizbmi2RNYnADEuNIbJGGSY6Ai2JY+Z8D4bH89kiSTosGuIQCaQYkJTwGNY338jm+M8TU4+I9rEqCiCGHkHwvlSdRy6/7h+QwpbP/NJ76FYHhLZnOZvjnaeq67yYLW3XEWOi2/bYGCFEutMTbn3ikLtP/W4efvfPcuE1P03gf6DniMCGsdXIZPs/PQHrbMX++CX4n+7XECCJsYA3jcWyJecgMqLPhu6+EzDDvkAuYbyQRmu+3LPy+zAZ39RbOkvoTiHcshvG2fF6irfQNKMCKNtUSRZF0/VZWNoxX1eu09oRHDH1BMvf3o9hwen1lwU9VRIhjn2xityYAkpKeG42y9GKPW+pbCWUV+Zw8JomaLuUVIBvNpMQ5iTMWtqThIyknHpU5b6WXlllrFPDYeo1T687BugzqCRVuT5NjTkwmQBXD0swo4HhREsOXnMdXvUAfNO7oL8Dtz8Cm+cVZBHXIEGF7vEmc/g1sLwMsyOYXVYPagukjSoxP9tBD0qMrG+/wPb4Ft36mCTKfFL5aijeLeG4e7wrVD5NUXQpaSqjtDLq+9EDsl4/02ato8ITkeE4xXMyxtB1XTa6VW4VhaR2mOR+VGanu28ZW+lVNT0HebzpJdZJfcFK6vT0lE996lPD34899hgf+MAHuHjxIg8//DB/8k/+Sf7qX/2rvOY1rxkg6NevX+f3/t7fC8Ab3vAGvumbvok/9sf+GD/wAz9A3/d853d+J9/yLd/yBSH7hm2qvfNrJ0SXFZQMN3nSMLCsxSG2PR5vapHsI/uG8505nKnrDZBZ1EnKIzigZ/RBl+G8u8eJQwXUCJ8IdBju4Pg4VtvPYvg09B39+vWk/igrSa1UT0kVq0giYZF+TuqukwYlVTwQyQ0ahejL2CObOw8S+g2nt84jUWvErKtwVY2rtI2AavCIxETf9UjXIX1P6FpV/qdHHD8JbnbC8v7fwPkFnpZw25Lah/BVhW1OcAcvwPVJP6MvXcPbV/ARBycToSFyhgVWrL6JoJ96PtNtgFFPvJzBU9iz/MeburvPWfVE+/uUf6cCfXq8/d+ctQ1r2jIU05RzF4E9Da+V92cdY/rdPWOZzoMpZvmuUivfF9DPvlLcV9xl/7POue8hTe/XwC5idl9Tz9aY7OVkz3oHRbN33vKQFyi6MYwwc7N3zWUOMqw9tqpw5zU8cg0evgIXGrjVwvo29Mda6xd6DblOSWbrBpoD7W9Gg3JFljWXvcDJHCRJdNs1fbdVw3IIX0+naWS32femfrutIP4KZZJOUzl/8dTM5LssB4ZbdsY6ndpiLzHDNEDT5d681YttX7CS+rVf+zW+9mu/dvi75Ir+yB/5I/ztv/23+TN/5s+wWq34ju/4Du7cucO73vUufuInfmKokQL40R/9Ub7zO7+Tr//6r8day+///b+f7/u+7/tChzII+wSQktbaTV3+nedCaIMKZ4smKkOMA0v6ALEFqspDDh+G0A9WRLmZxZop11QsG335nWZi5eYPIjHfeJNrFJIkZnWDr3dvWMk+CQ5lvot0tDh+ixn/NWPcR+hO38qdj30XyWh4McWeJJEQe6TUneSHv1DMYjK3eVbcJRwdc2W8kHjuI99At92y3t7EW/C2omrm1HWD9VV+3IRktnQhcXp8yubkmH6zZulcZqKwfOYX34D/zZ6v+mN/jtnhhpkIz33g32fz5H/E4aWrzB/6NZZv+oe7N/frbsG7b8F3vAJ+bbGL1PKZcUISQ2PCfH8GePQ0pzIFOhTwQ9cxUNxMX30/hrGKAJi+3xe2RVFMwQjF09nxcBhfKV+H8wwNGsvxyrFM9kAGBZGF7KzZ3XdfMZXrmArl6bin3kURPC6HXoZrsKPQLc9t8WD6DAYoxypzUzy2AksvxyoAl+n9KGOY5vtsUU75Ptb16BWV9207NmgsynUg9ZWRp3DqTTmfvahePVhjoGvH+Sq5LZsYWO99o79Zr+HSJXjgIrzn7XDk4MkPQrirQInutjKlbJKWQASBvobZAZy/CvVFZSkJThVUSDDP+b/tdgR6oLnqk+ObtJs1ImFQPtNw3zT1sIMsNmM4sGzW2swSofs3TUNKyh7RRQVUQekIbnA+c/cxtorvQxrVfz6+NRn0MAn3abhQ0yrTVvRFEZUo07QD8EuHTfyvUFJf8zVfc7ZWnVzMd3/3d/Pd3/3dL7rPxYsXv+DC3bM2a+0g+D+vRWHAGjsp7JbhJhoRpj1aRg+IYRFMw3f7W7kRxTXe318N/1xUbA0xlRvMuOBEePpZx3//vdf5mncd887fuR24HkLuFRVYM+Pv4PkoENne/Aq6k9cRuo7YXiGGqKEJY7KBmEZLyEwtLouqFjO2hE4BYxMmQ2/1t4qIDEmbrGmIvwhfixhL4QM0BWUpZMRkImbr1FpDDD3pFJ774LtxdSDGnv75A6Q9pV812OevYD/6PuqH3o87eH64ZzjgP7kJb1nB/3Q+RwinFn7xaOwotPJ6AEZhVXIs+yg+O/ntNJS3H0IrD96+ZV/OUfbdySnl8RWQxNTTGKz4sjjP8PgEhiLfs4AJMP5uuvanXs6Uj3D6+32PTyQb95NQ4v7xpuf01TivU/DEvic65Nv2zjucM+3OM4wMG9Nc3b4hMXiGsMPLJ5NrL4qszPF+KcNgNU4+czaziVhF4117BF73MLz6ekbxxYzoy7GN0k+qbcf+oCVEadzwrIygXNHQarITr61cptC2PV3fK2FABsWcVY85/bdshWxgHwFYDOxBsZXLlVH+vNg28veNtoP+k5ViZpLQ6TxbBk9BFGVTEgaHcS9NUb0s0H0vtlkzhUHu3vBBZImSwg6hvFypVuKuyVlS3EfI6HHuhVGy9/dujZQuhLMnvoQeS3Oy4RqsshnfuGX50X9wmQvnA+/8nVsSKxIrIjWJHsuKQ/5/+PQ4KTVs7ryR1bNfw3a9wiDUrsfhMW7ycMPu6soPpDEGSWM1eoxRQSYIxpbQplI/qVcoJEfm6rL6L9Pn3A7dijVtpLQs1mo4RmIkJsftT78VgK5bMw93qDkhbCvs7UNs91W4C5/BzG5jXM8QfXjPCbyihR8+lxscsqukdoSPQQs58+iKwBravTMqmEEw7gnOfWF69s28V7juKIK9fV9MMezvN9y34iVMxjXdzvKgpltRwg5GBf4ivy3KfWdOYFgz+2E2YxQcUHJ+Uxh5Of5USe2E1fa2qWFQtklEYyfUOP17aiTsz0E5TVG2MZ+/oCinv5kaC4Iy3nuvobrFDB68DG94Fbz2AYjPqPdTRTCRnVYxIeSwh9VjDLRMdjx+ySmyBdNnxOGYkxER+j7Qh0CIEeer8bGFHflUPKoBzZe/n+ajyj7FixkMemMYCLX3PLD9UOLOfOZlYrIcNcZkhvUMhmBUnvvH1Nthd74XBCP/lhgn/l3akgh912OdxYgl9T0pPzChzy0XjBK9OquJQFsQfjHSnpyQkrafsANdMOS+vYOlUYgR1bsIw4T3uZPstIYhhDF3ZbKVrzdlYuUYA64AyxPOehLQd512vKTnYf5btryZ5/lr9KxI3OECifbuwzz2wW9ldduzPv4Uzz79OMv5jEcfeYT5ckHd1OCq3edRYNoqPsZECJG+71QZSYbNGgETiDEQQo+kntgHogFnDMla2pjrqPow1Nw655nNZjTnL1CLYe1rtptTJUlAWCwczluq/iYhbAmntwlygjEBE2/Tbxb060Pa33g31YW3cf7t/zPGT6hwrIWjBaxStkRLiKZiQLwVr6XrGVouTz2UEpYrFv9sNoZ3Bh6/OMLMd7yMiRAtHtnAyFDdq7QKhxwThZjyMQ3sdBF25t4cmwHIYaFpuHBY+GmXVmiKNtwJwaWx42+5pmkIrfyWPIZ9D7QI1inVEKKh0mmo0JhdBogyx27CoTdd+1U1IhOnoTnI4U/G3xuTkXKT8Uv2XqxVkuAyNykrI+szuCaHbX0FtdEwn0QwPo+pSF1RQuErDyvN0de9A66eh0szrYHa3ILVsxn0sIEC4FnlEN/MQyfogzIJf8ao5+t62HRwsoZ3/Dw8/JSev5o0qjCGVM9INaSQMqgwKwCzi+6bKqECfiiypRjfunQlT7GG+Yw1mAyIsJPvC2DirJy7aMwl2zUa9THD96ORLqQcbR1l3xQSP1We3vssY78IPKlRkWS3es8KLlh+M8bvkGxF7GP9S3GaUMJzZudGqGKy97jIUwumFAyPCyofZ2c8DAKl2Mxxks944dZFPv7JQ17x0FM0s08z55doOeKUiud5BOsCzfIGt16ouXXb8LnHn2A5n7GYz7h46SLLgwOqRsCOrZ4H4zmPLYRIjIGxyFcXn6RITP0AuEA0nFeQPoXyKSahT3EgVZrVHmcsVd1QNzV9V7PdqtdonMVXjqoypO6UFDbQnyCsERMIrfKQWSPI3SUpHbB++hVU527SnLulF7BI8JWn8OkKHnNZyBeLmqxodD2oYsrXVbzn4mSVyYBRUbyYVV7224dXl/2mocAilEuvpOmkjwuFyQIb3+80tCu/hQFyv6MkzahYk4zoMDN54Kce9KAo8pZSPvZEeWe01rhY8pj24fCF408AyhwXL0TGf4txMCiBvXmdzvMwP8XbKdcy+Xj6r9lDHzK5jsFzmYyvTG1R+jaH3mTiWVujBb/3X9JOuq9+FTx4Ac7NYJ6UbHizAtkqkCJk8mExaBfeYiAUTzSOrxLnMwKHx3Dxcbj4DBzeGC9R4GOfvMjHP3nAphN6gWgcpnQUKG05bPZwyhyLGun6uSGmDCPfAz6AEmwnSVn551A/u+ALjZ6MMwmMXc2HpZFTFaa813s5RK6mSo69e57PY+1EPb00HfXyVlJKTW8pxXxDb5M9xWCMwXlLzPUHzpn8LNlBljnnsvsqQwMwgL7vCKHH50LgoWmYjHVSJfabktA0s8FqcE4V6BBCHG6KkKJgnSq9zUYtquV8wW9++A103Sv5r77jh3jg+mNc509ylz/Ic3wDT/Efcvnw4/yuL/9Bnn3ubTz57KP8yq//OoumYbte8+pXv5Jr913j3PnzWsdUuUHOFTyBtTZDSjPThjM464h9RwiBzXaVjWGDdWp5NfM5lZtR+YZkLDEJ2zYo/5YknF1gjaOeNdSzhi50yCoXAtYVs7mn9rA9eZYUNpi4xtgtQqQLG3yYYVNHSI52s+TkF/99zr/+A1z98p/X6brWwfd8Bn7kCvzNa9AXYVlQWAUsYaGaK+IqhRE8I5NQD+j+XRhrfIoXUIpCS/ir1PP0/b2KqiiPab5qXzFNlVqBtQ8cgnmfNKEvGsJcMCD6iudQ8huFFDdKZlTJj3wqlnsOISWjABNfjyCIGNXSt46BF3Af8FE8PZ/7fW02OndlbkTQzn5BmRWmXlAxCMo1lCJeN7m2vP4Hb6QYFKWRoy1hw+zJlZCc2Xst5yPrxuC5Tea1KO4wyR35BkiwbnV8hWX+ykV43zfD61+rimr9CehuwY3noLsL6QTYgnTqFUkOM5t5NmI2GVhSwnkOUgfSg6mgMvDKx+Er/in7mwj84I+8kV/9wHleuPscbQh0okLeGrDWYb2y3oxGsEZDYky5NYfQb1uc9ZReeEVJVc5jq4qu32rHhj4MrUsBrLM45xGZKhu0vXuBm3s9ZkoTts+UyQqsJeWQokxqBgsHYBoMKENdV7q0JSFRc90vZXtZK6kYsuuZktJyVBVGIPVB11Gy2KpCUiR0ktexavKhO26eKJULklNWaoWJ6H4+x9tFIMpIL2JEhbkmLXdrpMYQYM71CAzdb/MzqaSr2RNMQuwjN56/wUdT4v/6A5dZLg7wzvH2d3+at3zFD5HomZm7gPCaN3+WxdFNfurn1rQry+2TltU60G4Ca06pKkvduIGtLGaGZJMZNwyJvm+106sRQuxJMVe7Z55A7xucrWiqGd43VK4mBM03qROWm4hYTwSOpWUlHVu6nDc22KoiitD2ke36FJu2eDpMpUYGKRG7LevQ4aNgqgXSrTn9zJy0+gouvuWj1OdPdN6+6gQOA/zgETxZM+RWHCqcQ75JErI1m4VTQX0Jo8B19SjsS5Lblc68WTDGYhkzCkFErWlEDXfJ4akidJGckyjgDKe/7/qJIrD628FLmwjrYSvWE6NnZRKwyO8zArBY8YPHaEeFTdKxFjI4k/Mlzmlxa0pA1LkoXYJhVNwlVFiU6gAASDqPrmEgpy3eYYKBCskYhrq2wWpM44EGhRazsVHumRk9xYLGHKYmfx9y6w5bnlcZe1UVYIcwogFrr16RRGWIn3mFlj/6kDYqfM1FWG7h5DHYfk7Re+0dbckRW70RtlZ6pGTGtWQqsAfgW53LlNcfvSqq5LNhwHC9t+/U/Mj//Eo2rSVE4QP/uuPGzZvE2GFIVMXYzrc+ZB7GlEJ2pLQ5YYJMTaaKRIYyDEspFUg5HK74HV1TbgjLqkfUx47STNFisWJwxhOkz4rL6KMgYHylvwkdjpHcQ0SIqc89RQthbZ4n59S+6nusJGwM+Mqz153oRbeXtZIqkQpjJIt7tZhTjEhOsrtKhUCMiSoj8Ixk17MIi0nYwWQlVSwRY9XTAAaPaAiSZUVVHpwSRtxPWgJDuw0Dg1Vp8k00BXEXE8fHx/Rtx6cfPwBzQFPVXLvyOd75uqc4OIhD9Ob+h29wcP4GwcBmmzhZd6zWHetVi+l6mtrjDmpijkwE53UROqVnMoxQ9WlNVQyZA1ASlhrrDVUzw/saZytCH5TBfVDuelWRxDZ0bGJHFzUvVMKdIUaIPV27xktP5SLGOf1eEinnvrAemxTivH1+Tn/7VRy84mncXNu7m0d77KO34SdmcMdDm5VUgU+XUMhQh2JUoFuZKJIsPH2V4eaRod1EecgT+cljN9cyCN09b2naVynLCGL2tExhbojZcp/A5R0Z6ZXG8ZGVTpFo5bwla208Q21QWVDJjH8PbOV2zF2V5GFRkMbmXFjMeZScx+r6cb++V2+rnhbcZkU/IPqyJ0tW4IMkYxxDUcAlklCKs8nzQbkvJRQ5yZW5rOxAr3kIrTF6mMZkQyFlSLdHPb18j5xVoEdTMdSaNRUsajho4JX3wwPX4OoCpIXNMXQvQFhBWDP0tSpK12c4+QCEcGAbRQXSQ5vHkgLKF9jrGjDjmlmtHT/x0/dzfOLoY+Lm3RPaXiMLxuRD5TSFAFrbGUnFYMqLzJCBWkYjHmBJlIajJqPzzU4UZ4o1ss4QUiKmXr2w3KnAGYvHIcVDymcU1LMzqBekZzHDeEVC7hZsMal8azEur8nYIbn/mqssdtoK5fNsL2slVeew3JC8KxDLLAj0WRld2xgjySR8VjolXzStQzAY+rYd46tZoU2RfEVpxdgPob3y/ZSRYnfTW62LTlSuiHa4FURDl/MZIQSO795BYqcw+cUBP/JD5/nxf7Lk+7/3MzzycLtzVOuWbHvHE8/eIsZP8vgTT9PYyP3XLvPmN75+IBJvO0XM6TPrcdbgnUPQXMNmEwgh0hZ+M6BqLKZy1Iv54PVp12EVPEV+dqsNXbfl9o0XaE/vErZrZgJiLesQCN2a2K+Jd28zc4lm7pF6jrGe2jlFGUoiiSWFlCM+KpSe/VffiJ3N8ItzHL3ilzn/6p+D73kePjqDP/sItEZlT6HG8aJ/p6Sde4vbKvlV+MK6Vr2JwrxADocVhdVP+hmVBnsuK6mSs5mGsFLxoMgtwpMKz3K8xazEYLTRY8lF1bnVyJQRugjmolyMGf/1rQrOroXFIivBUuibQRwisGpHr6Wa6zhKLoukxzDZW0mtdjwuYwpZsTsDVVbC7TZ7mxbq4t6h5zaSufgS2KAeFja3XvHqgRTTLvceGtjAYZxfRL0OEUXSpewZFYaO2OtxTbYEItAbcJUK3lnDwDhevMsrF+Cg1hzTfRfh6AAevq5e1KIGn/Raj59R7eAYw8XlXhjAZK8t5fnow0Qpy8Qz7lXZx1M4XGamEaAdgUAxJm7dusPJ2iPG4r2mEdabLYgibW2yGMkduQ2IMdgcCk0xoXSu+nAHK6x8IhpycYlVY9RVpDz8kAWBNUJI2qjQWYuYhHEQojLeNPWMZBLJJJxTI7wyKPKwj7iQmzF2nS4HZ6i8RYxgbVIpITBzmmroJKqW8Q7XeKw4KnHawfzfVjHvv0tb0fDTv4FB+1NidsV1ziEfMWPFszOWaWHZPUVm2aOaHn+61z4Mffr3FDa6e8zikeX9bIGFlqJfbThmJNFtN5yEGpPmxGB2j5PjQSkaTjctt+4c07cthzPH0dFhxhPk32RGdEkJiwpZNcCLmVW8EBWG1his91jnc13DNK8jw9ktEPpOi35PV/SrDbHb4q3FeI94R4qBFONoVaMtSmKSgeS38IaVsVLqvLoFSRYEWbK5/QDm2TexvPRp/Cs6+Npj+GgNn6rHlVAWxZA/yWtA7PidZEGdxrWx4y1hcv+m/Bp6MpnJHJjJgiv/k8nfk32KsptyChoYBe7kOINCggHlNvrgjA0Kyz3LYyte0pAXK+fJniZZwUoOSRWmb8l5LBvhd7YKFigC3sLQ96nvRoW7U+YwJDvVswgJbA8rC7+UQ6oFWVo8vZ31W2z0yb2aAkVKnmx6L3d+P4bQsWk8ZOXUg7rvClxYwOUlXL8Mhwu4fD4bKEB7CuTQoUzm3thdr7d4kVOSgCTjHJbnp6y7gposodLpsEVDeCGghfUWrXGKgdyGUJ1Vyf3pytLIJ5Z8zMGpypGb8anUfXW6NSc0grnG4ZeyE4cyzCBgc2QlpFCodfXSUsRKxInNSkgVoXUMyk5tHMESccZrZD0ljHVqq5RUS8prMn0ReFIppZ17b3KMX2VGVgR7CkTdUjvmlZzeyOIFSXZjh98Mq2Fy3j3FNPWkzkL+xUlCsVRb6zMhYJJyDKI5LGsMlfcaVQiB0+O7XDh/iflsqde3txk8IcLdky39dsvtyvLg5fNstp2629aqYR4ESQovNykQM+PFjmw3FmPd4AnW9YyqmWntldghClXqLFxe8JvNhs3pKae37xK2p0jf4psK09TMZ7U+RyUMapTVPYRIEoOpnCqqIeGrtReSlZl3gnWJENbcfer13HjuLbzqXd+Pf/Bx+CtPwvdfhI9dzDGS7FVZRq9mIv92TZo05mqcUwu+wMkFLdocb1oWjllgTs2jHfj0RLiSBfrOORnzVCVc5nQN7Ci8QTFNxm1QIWqy5a9WDMMPTQnZdYyADhnDf6DegZiSEFXhGrLHsgzw516AhybX/b9le6yC37wf1tnzKIqtIOx2UnB5rAZlBi8F0D4rmmEislLM4XGMVy+qEIcVz8savZeHB/Cm18P9FxSxd+WceoHtqXqz7Rq2d1VJN6WBJHlhZ5i6jYzgljR+5qyyTMR83qKgrMnhV3IYtVJO6J1NZUXoA12MOSKciKGngBBDWW+Z7FoMhBxAK6FoQ26sag3eahpj7EYimjNCn+uUS2yKbBMRTBSMyUX8KSCSqKSi7wNt21M5h7PKG5NyiNMlLeeZNQ3WWx072qFXYq+NXFPCV54k2kXOeY+tdFRWMjag8tgvBuCEGnD3Cm5JSfNSCKbXRWvyZGN2oZdKAaLCpSibyjo1PHN8XQ2E7H0VL8qMHlA51pSuZEpn4r3XUGROcooxmdxVBchgsOdkr2DotwmL48L5q1y5dIkrFy9y46l3s6ye4cr1v80H/pdH+civX8Nun2dmtxy3K7o2UDvDlQvn2fbCat2yNDPqymmIMy9og3pU205d/CmaWxl5HNY1VM2Mqq6H0KTOlVpazhiIOs83nn6G0+Nj7r5wAxs7LBE/P4/PRkMIHX2/xbQrrEt0xuONx2bghXNWoz8ZRFJksANMv8IQmDVLZpVHmhm3PvENnBw9wdXX/RTmG0/hkR7+xyvwXK3COl8rhc26qkYrmawgnNVQ1tRKjx2D9Vu5Ifms9Upl0WXrnsnfO+/NBBAxUSIxC1efcy5CBm3sWZWmeGt2PI/JlrwBTJXBAI0eJEVN0JOBGX/sBjxSQn2MXoEu8PF4RdkW4IMXuDQBM/xv3a4G+Ms3INpxgZXtJxf6Kr3DUv7eWGgKBVLWYiGO4Ijheoo0LvnCoIrl+hUN7z18P5w7guUCHn4AFgbmHWyegU2Ebs3ghc6DKn7f6lyEoB7ZcK58T9tODYD1JnOWSQ73pTE5jlE4ezLjddcb+LJfhIsvIAJ/6+/cz699cMlJC9GTkXO6RipTYVKAGEje6rQ1BcnXY7NVaI1VeZIM3lssgguBSkq+PaN6Y4/zTsFfMWCM4DNi11iLkaARXSs0iyXOO2bzucoBMbQpqlquapyrqGyFE0fsA3fvHKssNNCnqJ5W6PAWBUxJi7MOP/PMzjXUyzn1fKZAMwDnCDHxyfd/8rddSi9rJYXZrW42ppS/MVCAxBQVUGHGB2XH4xH1loYwXbZkDXBWGE8oCEBNME7Pvb9v+bxUh8cceizswDsMVgJJ0hD2M7airhquXr3G1YsXuHLpAqvjd3L3xmMcHP5Dnvj4VT7+Kw9RxRMa16tBnBTa2faJro+0XWA2SyTvdKz5QUopZDBVpt0349yFPmKNRyqr8NcSbkjaJLIQ1pbEauo7Vnfvsrp7THu6wptIVfLyBhS00isaKHQKg01GW0dbIQa1To1V12xQ+pI92rAFE/G1w5oG6xK3b7yafntAevRXsK9aYx49hR8/UhCAdbCpoHWDd6ZmXBY2xcKwNlPgMDInhNyfrHglyNkhwWmR8LCOJiE3kwVPAVUUhTWlHCq5DZmMafh3ch61loY1gtjRO1quwfWanzKoonnnXXjTLqP+S9lEILVzZJ3nvwylRCvyMzM8F0UB5zzu6Ajm31vB/XvrHd00bM8K/GYBgFgV6msPLSOPYF/CeGWOivtVnrl8fpfUO5o7eNjDg3N4/UW4eF67584FzBbMCtoTRW9uTfbSqswgkYEcKZPFDoAOxvsY+szevmYoaTijNnMo5h3Cyx284hPQbEHg/b95wC/8yyM2oaOEPws0wWJHBvac55LsZSVJYDUSgROdsyQKLkzgk3Y6sMbk9KMQJOIlc/OZpClba6jQXJdJ6oE1Bg7nFXVT0cxrkrVEYznuOnqgb2bUvqGpZpgeum3HyekJ6kPJ4NFbE/HGKplGijhn8POK5WHD7HDB7PBAy26MIVlH1/9bYkH/d2mbkjAC2mrDGFJRXkW2FCBDVkJJRj6p4ol1paodKOEchVWOqsRYQ+UrbU2RIk0zJ6VE3/fUdT2AJQrZbIxjaw9jtaWHZAHjq2qgJIohKI1QEup6xrxe8OjrvpQHH3iIr/vqd3LghbmN+PaUpz7s+fA/+QPcee6Ey5zw0EKY4VnLDG9neCPcunGbW0eHbNtI3Wq7h+XCZ4URcLbCGkvjaxL6ALR9T9v2PP/8LWazBYuDQ5bzOVYMlYf1estmvSVtO2wCZz3takW7OqU/OUU2W6qUwwY2ZQVnSQib7Zb16SmLkFjUNYcH5+jEE4LCX6u6wmk8ABHBh4QQ6NkQ4xrbGVJ/StWtqfstFxbnMPEqd37jz7B4+KeYP/Cz8N8/o0oK4Afvg390aWQjCEapblylCe8Cd54grobbnnK+pwAqpowI1Wzcb7pNWRyMzeOYhqby+9LS3GTU2mwO3UYT7VXNEGosm514cyU0sonZIRT4b5+At50wohqBo/914ToJFS/8wvvoTw6JoafyYI2wPjkh9h19u2W5mCvQ5fSY2AdC21Ev5hjv6SIkPFEqEp7qwimv/I9/DlOdMZ7/+BTet2LHbfub5+DvHUIsBLW95o1KQ8aYFExRyKN9pSi9gxre9mZ49RLe96OwaMf6scETgiG+uDoHv/Fe4EABJe1G822n69GgaJY6tkKqG4Mqp1Iv17UKLlksGYqcS97SGzWKqgq2EU67HSW2ToG7qWNDKMUp1F7XSN9uaSw0laeJVvEoscM4g63rXDelnKPOKdrWZISlhHbo+SRJqdZiSNS1x1jLen2Cgp2E2loqC4dVRZU66hhZNJHKg68sp33Habvl1umakyi84GdqKyTBi5b50BfglMHVDudgZj0uJlyMNLVnfjDn8oP3c3TfZWbnjugrT4iJtm2J1ivA6CVsL2slVfJPhQkYyNGaUqm92ym3LNdpkr7knKbUI7qmdGHZnCwuijCVmqxs0U2ZiYe8VrE6Szgxn1vzQ4U7Txe2pqUEkwSH4b4r13j4gUd45avezNUrV7l84RI+nOL6UyRsoG2JK4cPkbnZclgnerFctssBWm5iz6yuc6gt0neG2NgcudAGjAg479AaimKpCylEum0LYrn9wi02c2042feRvgt4UUhr12/ZrldsVyskBCxCXVc4TC5vMfQxcHx3zelmQ9f1zMhk0DEpfZWxCn0PQugTRqIip13ExOL5agg0GUO0KzosTgTjZ5j+HOHWg/T1l+Mv/Bam0My8dQUnDv7Fgbb5CDIY3oNHZbIiKYqorKEBLp0m4boSlorj78uhSgfYovisABmtJ3EMTw1s3WaSL0ujILXka5WcE8lhLJOAALMaFsDbb0OV81KPbuDSlP8Nts89RNwqknAIn06YT/Ql9H2LpKgvEVL09LccaQvO1tgOjEnYTYMEg+shiac3EFaWFB0xVBjnMcGxbYMy4qO1hAHPzd+6D18rbZYkfVaTwOzKXRZX7u4+y2/bwrGFD1+AU6eeSIqa76kqnYfGjvx6Fy8oCOLKOXjjHXjls3BhDXXH591mCe77JGzvg/Z+WJ1on6l2wxB6NdnYKEbFQG2VRq+7eNnWqPFT2DhchRYBzuDys3DxDtjE8zfP8Zkn76OvLnP++pwlIBkt6RxIDJzevgldR+xaTX+KkEgYo8AMXUqGLiYcgkuagE0S6eMWS4XFKwrQKDK3L1DvxeFQLz2vaubeclhbqn6D31RUVvDecnjhHAtrOWctF7CsxfIsc31mY4JkSX1ie3KKZEBUCJ3m+O5s0OaugrMG57TkRQxEkwhGCA6k9kTjBzL93257WSupkvcZww9o6CErDjKdT0yJ0Pd457SYt1CNFGUBA4u5MQYJcTBovVdLpO97tU5iVJfV+cHDGrn9hK7rBoumKK/SSKxQ5wMDSIOUFJQk0FjHG1/zer7u3V/Pgw+9gaaZQdrQ3blNf3qHqrtLFY9ZujXWrvD2lEtNT1V7zMUjVaQ5r3Xh6AgE+k5DevXM45zNYwVDwlVeoxx9Guu4BNpty2bdsVmdUleOg4MDnK9wruL88hAjwub0lNXxMZvjY1KnhX31YkZTWSqvocLVdsMTzz9H320gdhzMDX0UVm1P1TQY6zQNQKJte1wUrBewnRb9Fb5Dq72JuxiRdgunpzg/Y37U0aXXwd23sfgdfx3rn1Ld80134KuP4dteB1uvVrmIWuNFURkHhEmYz4y5J8lez1RBwQQCXUAA5ThZSfVtBmFUkHKdUeP15xIY0HpVLpwNUWM1FrSgVXRMvsreQ6uf2x4OZ/Aw8Jc+B4eq8KYpL/3AcPzRt7F9/j5SCFhT4W1D7esBWaURTuHk7g1ivyX1W0KKGWC0oqo6FsslVooQjkgF4jxd1xJioE1bsIKZWWKt4bLN5gTBg21IYuhODJ/58S9hXs+Yz2b0ISmVoMB9X/VR5pf3lNQ3rzFfv4U/9TB8toaqh5OTDH2f63wsKuVcnM+U+PXqRXjlNXjb30eufzjPweeXGWa2hi/5BfjsG+ETc7h9K3tGPdSVvqjyfU2DXBm85diPaygEzTHOSnkBYGdaHF0t4Mv+BTz0aQA+8dkH+JF/8nV0M3jFmw00DbZSNoiUIl275YUnn+D4xg2OX3gho2EjKWpLoRgnyjdocBAcIoaQAtu0Vqh+VdPMlvi6Yb6ssVbzVpeuXMbXnrp2nDs44KipOPRg18eYOzdI/RrvLFdf8TCLixc5uHKV5uI1QjXn6X5GFxN9iHR9YrPa8NRjj9GtV/TrFTeff47trVuc3rmh+TGjrD6KkYlswpZ2a+jsAqzD1J7OONrf5l6V7WWtpMq2E/YzikQRZzLMdiyutVatyhKWizEOMfQpJ18q1jRgMuLO5sp3RZ0KyZTq+DFkWHJNZ4I5ROj7fufczlhF8vmKxldcPX+Rh69f56H77sPGQH96TLe+jemOcanFE7BOWM4dZm0xjWUx91g7ozq6SAiBGCOhjywX85G2CZQRw0JMBrGSUx5+8BCLpWZ1cIQuIm0iOIeJQjNfMJsZnLE4I/SQa60sy8WMFLU5o8JSLdvQsW63nKxWIBFnBOPU8hZjlclIhELjUhCQSRI2F/riUr5vuTg6RULYEFKPsRtSMsSonIP9h76B6sITHLz+Z9RTbBL86SdhbVQ5/f1L8C8PRwUjSUlH3aTQcvC0GHMNZVKEnIPIO4poAr0UkqaUQ4RJUWNWFPlRZU0Sgoah8nVhyOjCwo6Rz+WNfn8+wn/+DBxktFt1KwMA0jC87jPfTDx+AJEtKbSk0MLdIyp6jE2KHuvX4GqsdXhjiTnsuD29AxJpaot3mjPdbLaE2LNJIVN6aWE8gMVQOY+3ltnBDMnsLL5WNgVTe/qQ6ILQbvV6vIOmMSwbR2eFPibituPZ3zzPMx9/PdVsoeEqPPe95XNcec3T8B2f1u7MMaqSjxH8MQPYw6nxyeGzuSh3BuefJLU1N3/lzdhY01RevZCUWK829H1P17dc/+onmV/VrttEUSQhNtdZ+YxAdBnkkr2mAowYvCnG3OSgEV02NCzYOudA3biWAOs91WLBucWCRVWRqgqxStqMUfq1LnZQOZIzdJs1MQZi32LMnMok6rqiqmoOjw5Zzg+YzRbU9QJXe+xhhWsabFWzPDikbmYcHB4RYkAQZvOalAJ9t6buOwU5rE+wDVjb09gjmtqxuHQJmc24GwLh7jG9bblhztHHRBcjq+2Wdrvl1CSiFYJTo8tWHuucIgrFYjPlVDJpAFaYrJStr7D1HNe9tND0y1tJTUJqwOBRDeSJMi6jgeg1hwMHhmCRyVK6d9upgxp2HM+xT20/VXbTsB+oEp2GH72zOGuZ+ZrlbM59V65y4fCImfdsVyutP1rfpWGDN5HagXiDNI5QO0LlqCuLqTzN4ZK+7wkxsml7Zk2Vh6wBnpRDikOLozIZTMEnytxhcsI/xYRJQuh66ipqjNxoXbpGRlRJifek3DTOeYP1hk3fsm5btl2LtYbaGRUEVulbkqiS0hos3WJM2CxcbLrXwIgpatuPEMFEeneKdTXGeEz7ALG1zB46wjUbbNXB20/GG/nBOXza6xi2Bm4N+MExFzRNgO8AI/ZzV8Vzz0JrIOrNn0sPhwLnBBpRJdh3+fqTvhKZnSCHBQu1kHcazruU4F2nqqzKaZMhbhY6fmPoXniUeOuVpLAihjWhOyWFE5AtBohdoG8j1gecccpKkMlPQ79VuENVD/JfYo8APRHxXovWMzDCZiCHYHGzGjFCJOFrRVA2swbbR8QWRpKRkchbIVlFlHqTOL25ZPVkTTVbYqwniePo6gn9A3fwbzp+iX2G7gzvwqYiHC/ZPnEfNswxteYTU4ys7q7ouo623xDe/tz48ySZOSR7wwNU3wzRCGXcL/co7a6JgZIq3/pCN2U9WhS0q6QwBnGO5mBJNZshdU20RkGdzlKHQB97rHe4yrNanxJCoOtbjBWsE2aNht7Pnz/PcnnEYr5kNlviZzX1xQN8VeOqisXygLqZsTw4oA8hG34Q+pbN5ph0fJe0XaORhA7TzqhrR1NX2GZO9DXBejZB6KwQKkdnDJ2BDdAZiJUnBk8KXpWOr9S4JKcvshxMRnJ5ncE3tSIFqxpmc4x7afG+l7WSmjJF7G+SBYbmkOxQ7CYZrDAtbiveVfms8mMSPyEDAKIouJJHqGqvciolQtDcQOH5K78XEQVMiBAlqYUBuBBxxlAbx9VzF7j/8lW+7p1fzcxYPvPBDyKnpzgSi7lhcanm4rmKg/mCuI3cDQdIXNHLBreG+bzi2vXzdEG7ad48XulC6CPGe6xxdJ1gYsLEqLkGI8yaMXRl0aK+xoH1jioZNTCtpXY1B9WMZTOnchaTBG8stbMY7xS5XTkWizl148AkPvGRz3H75JQbJyvms4ZFUyF+QfJas5GiYEkczBbD/IXYKeLRaYG1r5zmVazH2ApnjeYmTEBSIKwC/eaU01vPMj84hzuuOL31n3Dxzb/Eudd8aHdB/B9uwLfdBAz80iH8dw+rJ9PtPSgFBZnDjFTZO0qSE71m3GeyFkii4T4rcGDgD9+BP7QX0hpPMqzSe8JT5SuLsr9PtrhZ8MxPvg/CDG8tabsl9R+mXZ8Sui3ddkO3PQVJuuYwJAxpvsQ7j7ce79XT91YIoef2nWOc6P2XqFRWpAwaqWB+cAhoCxqT80pdr1yPbd+qUZJTRbPKcThfEpbaTDN0EelbTk+3IJojvricc+3Ceap6xslKc5Wr1Ybtb72Wp558Hdff91PU549fZN7O3m78y9ezfuw+DioH0tKvN8RkSGJI1PimoV4c4dwnxh/1CVYBFYHZU3IecGN35hRGJdV36mFZsvetoU3wWvhdzRT84mr1ptw8H0+3dR947mTNhYevM798gdnFS7jZDDufa1mIgddicUnwSTiNgR5tiyMSkEzplFJiu93SdYG+C/R9IqBg1hASqRWkXSNsSOaO5om9U+SsiRoYiBYrFfN6iasClV0zdzXRek62jsXiIheu3c/czQnVjHPnrrMhsZZAsz1ls13jnn+W7tZt+jt3sEFwMdLO5/iux8aoHH/eEQ0sDpcsLl+ieeA61A1iPcHPMJt7CsjO3F7WSmrglSpIPWcRm5vvZVhs8SRKNEWMGSC1uuV2yJCLSEsBnOauJAqSBFc8IMaITxxyHOMYSofMAdWXCzoV5q51Rc465vM51y5e5oGr1zj0DYu64fmnnqJBqFPCb09ovKG5dIg7f4HG18oJaoQ29EQEnKNqambzmsN5RR+hj9r6OVmLsQmbQ3tFj6dMKirZkxo8lSGCoSwctnLU1lH5ioNzR8yXB8wWKuz0schz5Cym1gZtYg2rzZptt+F0s2bTtYSofGRaC1VhrCFkJWeMJYoQU6Iv/WyMFjEn74ghUBmbQ4KqCFJMpKBtRqIEMNl67zySaiQZ1k9exciXKtoswaZPnH/gKQ4u3tJJePUW/sNbu6i5spl8cwuQYtrTKUyAE2ayf/k+iSqpmYE3b+HoJWaGz9hSb7n1ocuY5Km8w1pD6mbE0wCpRawlblskRpwVcAlTWcJWW8aISfi6wjc1deWVzy1FStvwruvo+5Ztu8HnNW+zV9n3Hb4OuL6iC1GfmQQYmxVfprFKEeM1UR5MwtZew0d9j4RI2nbEZIjJYo2GcDdxrQXzYqiMYCuLXdTKZ3tX2Dz2KOGgBYQYtPg8hV6L3J0bEGV1XROjKNL25hGm8yTpEdHO0EEsVImj17+gdIfW4A8m0PwuaH8nK6MHO6A5cwi2ADcKyi8JQ/NEY3I9niezJaNxzwoOjuHaR2Fxm21X8WsffQ0ff/xBEsJqs6Y9cWrgbWeY7YY+P0vLuqHCUhtLK/k0TllfjNV1alOintUYn/B1og76zFhnM/GzZHPTEEXwteaixQISSLHFRIM1W1wE7wOVX2uOK3ksMwwNpAbrZ1gzJ4ojGqM8oL5GfCD5SnOnvsJVNdZV2o1bNPyesqS0VjtQOO9wzQybX71rEPfFoKRSQnINUmnoZawZYN65PjvLEDOkFYbSk1z3RO63UgpxcRZnlFhW8+dCldl/Q0wD2jRkvjVlaLADI3pB+RWC1XJCiZEoBltZDg8Pec2rXsVXvPl3sLl5m9O7d/nIB36TQ285VzkO4gkH84rz1YO4aw2z+hBnhZQip9stXUrgHc1iznIx49yiJoglCNRNRRsSqz7gs9dkvM1o5pjDjgJUoxdpDcZI5vTy+Kw457M5l++7n3o2w9cNfbelL+SyGdpvZw0JoTWJG3duc+vObe6uVmz6jigRa6GuFDhiLPQpaBGidXQxEvtA1/V4r2GlGCKhD4QqaKgph05SEkLfE7pOyXATYHuMqwhbBbOk7YaTT1xn88Tr6M2SbQ8vnHS8/ut+huWFrKRes8X8+Sf/7a7NfVDDF7iFrePJn3wY2y84WDT4Kiuq6i5ku7pbaxL/3OF58B6pLd3aI1HRpPP5jOXRAUa0Lm27bvMaN2w2a7q2ZbNdU6GpsNp5kghdiLi6xjpHG3ODPedxvlbWk6Dorcp7cMrzFtMW19SYkJBtS+yDUmS5iui0GFSAtu1pm5b5rGO5XFJVjkVTcXx6ymq14eRDb8X5CiOJzWZFu93Sb06pLBzMGkWNWcu5c+dpu8DN2yc4n6iMENotMSa6PhHw+HrL5d/1IVwzZQ/J/25buH2c+Qk9HBxonooI5OaIiAIqYsj5K8PIj5jD167SwmrjIeWc1MXn4S3/HIDV3QP+3k+9m9vrGWISd+7eIXZrWK9IviLWDZsYwVqO5ksa52lchcHjnKdpZjSzmrqpgIQxDucaZo3FzDTS4JyjqlRRavdsjQQlRMldrV54CC3tZoWYBvwa00NdCU0VsLGHaKnsAUbmxK7CVnOMNGy6xMYIWyP0OILxRONIzmNcjfMNxlX0SYgp4mIgikZUKqelQc57XNPgFgvq5SHezxQc8xK2l7WSyo0uhnrItu80Fuod1jisESit4U22RkrOw2aUX0kQZwNJBHp6QgZfkYwy+6aEEaOsNFkBhoGpQh9axKrLnQzWVmo9YjW3I0INeGvxGGTT8tjHPs6dzz1Be+cO0rXU/Zbl5XMcXD7i8jlYLizzpVAvKnxzgBDwM7hy7UG281u0q2O8CPO5htl8Nua9M2wD2C4hPmSiZmUoKNYWGKzRgmJjI855fFWxWCzwpqayM2aLJc1sTn10RNU0+LombIzWbbRzsJFkA6uTNW3XcbLZ8uztu9w6PuE0Knx6UcHSbVmYCF2DeGVM6CTQp0BmxcR4j68rRSC6nMOOgXZ1SmhbhdGKZKU4RzK8XqvxA2G70bj4rFKDI0aWhxWLhefg8ADzzNfx/N23E7qe2eXHufimXzi70PTf0Hbykeucfuw+BSP0kW3bMZ/NaeoZoKjSGCKx0/YoJ6d3tIalcRweLah9zbxPGFlD25J6zXk45mrzUAiWLabSOiIRYXluySLNcc6DM2xDx+rOHWLXIzFisvdoLTRe8ItKf5uLlp1xzOuKmJQZ36I1b3WFEo5azUVZ53A1+FojF10Hm37DenVC3CirfbvKaEdfqeKxFhFLDIk2JlLf4irHfD7D+8TyoMZXmh/qu5gtd0uqPAHYqsOIE5CTFaGPhL6nTxExAQlbBTVF4eq7n2HxyAm22vNmT2bw86+Du+fg8DD3FHMaurNOXxKAkEN7PoM4thpGiSaH85qsDDLoolnCQuDN/3i3qWEStndOWN1dcdptETkPh3p//bLG1jOOjs5hqprK19TO0/gKJx6LpXKV5qmyFwlD9hMhs9kYQ59QSLuBlFlBBdEKBkEZzZOArYjVAhFHqlp617OxnrhSxveFWGpxzMyMKnmYCX09R6zFG48PkSo46s5qTko86z4QU8I1CyXHNYllA8smcc5HZtJTh460DeAS/Qzwo3f4220vayW1I2NkBE6QwRAGkxmyAZOp5XMCd0x+MvSQKmwSA8dfEpxkctVUOAHzAjHFTxtOr4CvDP63E942ySCBQpZscj3S8e07rJ5/gXByF5cil2cec6Gm8QccHtQslw3zRUXd1NiqJiWLqxoWy0Ns7HAS2M4aKu9Jonk2b4DMAtQmSDYhViGhAMZZYm7tYK0ykBvR+i1rLL7yeFPhbaWJ2FoTneVlQ6cUKE0FnSVa2ISOTbvhzskpJ5sNq65DrMHhqJ3QeKG2ESMBbZXhiSURnRRx6Se8gUq0oEzNoeuIIQxe21CwKGBtVriiIUAwmBg1HOg6jPQ4qyHJ/vZ5NjeO6Nuefrthdt9svHO5nk5h9trSRFIOz4oCSEQkd3XJrPV5AY4lEGkA0BgMm6cOOX3sAuvTNV0f2Gxa+sUB89kcxBJjom87YtsTup6TE0VuzuYOc37JbFZhCViTVNDkNWaSDBy3OtbCo6/0Xs4ZxKrXFSTRd4Gu3RL7HpOUa84koZnVGi3wVpVBzkfpPHhMRooaHM47DRlaDWm52mGchnCsz/xv0RK6nq5tSW1P7AJd24G1GB+QSpWdcT7X12g+RcQRKq0X8t5hjAys+Jr+y+UekugnnIRt22eARiIa7W6QUhbEYmDRYXPYcJi8zQHcPYKn7wcarT1zOVSXMujB5pycIaM61fjElhIBOyop16hnZStYtnC0gSufhWY9kUuJ9nRFu7L0ocOGA5yk3KfO4euK+uAA18wwxlP5itp5nGQvCSUCmAI1tJWOcpPKwAhTrlPBCroepHTlGAmeMdq4FH1JVmBd3yOhZ+s2NPWM+XzDbLbAeo9JSVto5d5UJldOIFnG5mfZWpefZUPjYGaEmUnUKeJDJMSU8+J57J8XsjZuL2slVbZSzDufzXIxYcohLKuABgm6SCuLknlHkrMYb1G8g1EorLEUahvFdCUkUyr1KQ4TG7PCambNThFviIHtdov3ntlsRoiau2mahthD2/eIcZkMMuBixMeAs4IjEeOGunKcP3+OBx+9xtHRAYcXrnB46TLVbK6WrRXoDjIxaM/88IAUArdfeE6BC7OGqm5IGHxvSJn6xxrt4jmbL4gD27Oj77e0XaQvIADLIJyVABfl7rMG6y11U2OMELsZq+PbnK7WvHDjFicnxzzz/A06gWQsRxcvUNlAk1YcLCqWjcdVbughE3Obg9rrwrbWEdGQqCDYjArartcKYMkAlKqqqGud9xiLR+gV3JISMd7Gdmv6qiGsj0liaftI2/b0fVAh/6mOz/ziW9T6RhDbcHB0gSv3P8zl++5jNp9rqKldsdnc5fj2bULb0kRHiD19bLFOFX/TOEJo6buW1HXqMVur3G3xhNj3kIS6MhgTCHGLJEPXddy9c0xlFUV3/vI5fGlUmXrWbUddWeq6op4vdJ2DUsnkfIjNhsfmdJVzAcL2dKPQ864DZzHOMnOe2jpCt8W5ShGa2gSIhHr3eNFcpHX4qkakRsRQz+aaT/AVXduTknDu4iViirTdlhA2pNTjXaPGURXoOg1fYSFKJHYR42qNlqFlCpVXj0mArlOWCJM8ab3JyDihyoK8tjNC17E+OcZUFcY5LT9AiB6CNURj6ahziN/z8V/8UuoPbfmyb3k/voT7fuNr4PkH4GIBvmSLMyXNURlU+ppalVbfF4sUmgN9OKhgdgj1XL0n43TfN/xzePDDSlM12WKIPPu5J9jEQ2YXD6kvHuKuXqC5cgG3PMAdHVFdOsI2c5LJhbiu0q61gj4TOb9Q0MLeV7ncJNGbqNGdFLHJak1hyL5WKZ1AGDoxS6JrN7SbNd3mLt3pbbZ3bpA2KyQFfGhpLMybisOZp/KJWTogJENA6FIkRC13MNIPqOMEzNqOuutpQuR8sBxE4VwfcdsWu9niCntK13Psel0nL2F7WSupHQCEUTh5Eo2Lmvww9yEMYUHnCrOxCkBniyel/VwGBZWLOk1u51C4Pcf4UPGQRnh6gZZ773eQgjs8ftap9Rd72k2LT5E6Bmahw0igDwGDMGsq5vMFzXxJVTfYzPOnQAe1YAelmqHwse+IsSIFh1TaptnZAgrRHEWpNwKrVpEZC47LNRlrMDKyFAzWm2Gw2DVRq0ImIWy3GzabLe22IzqHFMo8o3korQXTOorSlj4lFVDWWUWd5WZqZIWPBeNGxg6VJYkQIsb0w3UUrsNyHXpLEzYFJYwV9a7idk2/7SBp82w7uZZmVjOzDQs3wweDaRPdyZau3dBtNqRND33MzBgdXbvF1RbxlloyB1p0pOQxCF48guZqyMzT3mXGDzJJL5rUrlyFd46mcQP1X/HWTabTMtaNH2ZrvrCmqEdps7dXvA/w3g01fWLUcp/P5oNHnygF60JhtVdBqAaKQfOUIajHYoMWXKckcOcuidzNWbrMrt/mbrnkkHIkxgDW4bMXVlj8Y1QqMeO0IZ6xyraNEyQLU4Uwo2vUQMrMHjEEDVuKtpoJLrNQ1Ymrr31Wi8HVB8XNwi6c/f7PwsEdVU53rsHNB3RejYy1a/oUM/AmxgyiGFyrHBK0uU+WyRyRLoEfFZQI/MK/POLDH12yWkWi76lCICRlCbdB2RokajG5sT1iDeJ0dZbSPVOUTBY7xZZMKGlBMgZrJFdCpLG7Qnmis2GQJGXAijKWR4kESQQSwSSS07k23hKdJTpDn9erS5EA9KJ8gCEFQgzYGDBR7wcpYmVkXq+tobJWCWdV4qCwm5EnNb7EnO3LWkkN4ZaJcEu5C29B/vV9r0WzziE+13xYMupEpakYg8mLT6zSiBiAKCQJpJgGxCDWZPJTBii7lpC4AX6+r6BS7i1jrXJXpRBYn5xSS2QmUYENJFo6jBFms4bZckmzWGBzQjRJRulZo+GYLNFUeAsx9sTQE53D5xoj59SrFNGFrNddlkwOiJrdlzVWC3at26Fx0ukQ7cVlGHJ5grbqWK83dF3IkRJlaTbe0FQVVaXQZw3VqTdlgp7fOa/waO9zbiRmRZSJKLPhASMyUefY4n2leUVGxhDryPMZsdJBMtgUSd2Kfr1GYqBylmZeE7PwOZo7jhaeo7mlpkP6jnZ1k7Zd0W3uDiFO5SDd0m/XGDwiHlMLlZHcskDXl7eOEDJoWFIen4YxjUkkQqbum1E7nZtZoyGtmHrNgxoV3qMxQjZUCrt+QZnleyqiTYbV+sIZQ4xJ24uHhPGOxWKhpMAh0gctiRCK4DOZTDZB1GJeC2y3LaXMu+81B3iy2g7K0DpVxF23wgO1UYWp5w5456gyCKM8QzEGUot61qKtZKx32kA5BLCirRyKosKS8nhC5s8zOMRZorVEY6hmiQff+TjV/EWS8QZ41YfHvz/55XDnEdTjSCPsHoGUaZicBzqQXj9Xy0lftqD6xjDhEMLKwvfHfvwSP/0L59S7Xga6voe+J/U9qWvxXUPqAqFt9V7aTEbiSwNWhi7iJssVSyZfL3kKa0m5x1jKzCFicrE+IMZmEvlsxKSUlZMqqYgQrUG8AzFI7ZHakSpLnw0DW5RUUgNGowmqpGxBYKaQWWIEb6FyVl/W5KyKYEzJpOm6e6ksky9vJUUWljGRSIS2yx0Zxh5Ozqq1PXozOZZutSbCWkXERFEIp6RI47xaKykSoyqZunI7AluAKncRjUnbO4tAYQROooWtenu0xkqsAjqKt9ZvtsTtGm8EW1vOXVxwcDBjsWzwswW2mSHOKUCj60ipx0pPU1cY7xALfewRCdQOjPSkAKED4z2LWUMlhmQcrnE5xFOSqlkIOlXWCW101rYbYu60umxmNPOFUhg5o0LdopDj2OcErXDnZMVqs8U3c+1TZS2hDyRrcXNl1PCVw1fai8b5ir6PpJCIKWFt0oWY+RaN8wSJ9J2ScxaDS5ICWzXHL/ShtKhX5g7nLTPvMUYbrxFaJCTiZots7mLaNdL3yvIcPfPFkrpumNGTVre5+VSrDArG0LcrSB112majWkghsTiquHb5kuYJrTIyaPhky/b0lL7r2Wy3g4FinKeezTg6f6Dep3HUqSYmiElYzubUVUXXbnStBcNyfpgVuq6fvvQnygghLd3pCTl30wet65EJM4IVNQDqptY8pSRO7x7jnBogaVB5UJpM9n2f59pibcAaRyreQy4F8BloEFOk61usKMx4NjvIirynns9wXpWuqzT87HwN2NzoUg0WjQjosxrWa2CNmEYLUg+WGK/rExR9eO7wgNPjE7abDav1lmAtXd3wum94hiuvPcbVL42wFIAHPwYXn9En+fQi/Otv0Li2JKViKui+ulLWkD4AVvNQsyMN9VXn4Pxj8Kp/DMsbdGHJv/z4d/LsTeHxJ5/kt559mu3sBCsgjcXWhrDtsSdbqqrDxi02rpBYQxWwPlBVgaquh3IMazRX553XvC1oY8Ko99o7r2FT64jOZiWl99NERfZaQWu5csp+E6ETQ2sdcb5ALl2CmJG08wVpsaQ9OCI2tdZMxkgyuUtC0kLt3miLeBOFPonmP03Au0A0gd739M7QSUvlE24G27TBJIelJpC+eJQUoFadGFIqBbc2u7yaNLdkeqBct2SzhWpM8UbM0HhTSr1Lfl9CSeT8jMJPs8LLssMUsEXeRdBjDEqtHDuPFZO9uBwCct5QV57DwyXLxYymqbFe2RHE2iGEmWLIWUsYAB/5kM7ZwYOQqKEUn0MHyVi8t1kJjJYe5PBe9pgwEEJA6DFJC2vJnmUyksEZktNZBusdvqoGYafeVS6cTmp/+9zZ1xoznItJeFGVTg7RljE5p+mCSZsIyvzJ2CqlzLuGqPRhHC/NZPLLnm67Vsqg1I/8e8lknjE0HNi3JBIxOM2jh1brbtIW7DgeZxyzRtsaGANtq7RIKfTETuHx/XaTvT+hni+wRqhrl4uSnRrpSehjxFVWjZluXBslxJdQYEgM/XBNNtuhksNtUXIhebaSJ4FbFWim9A+T7IV48Dk0boYFO6z3Ymgp9dfobZVQsymlGZILf3NtobUVBlWWxiask1zfkz3BEjpOGnVQQ1EpqZKkHFYMGJOQGIi1h+RIfqT2cvl5kZSURsg4tqaiOrfi4L57C4CjeE66h5HMLFIAUUYSM3+H+aWndMemhQu3KO0vuDnTOGJWFFjJnZqdFuv6RmuEDm/AuafhwmfZ9Fc53Vzn2eMv4fGbPR972nJq1pgDDV/KvCbOKg28B0G6hPEJ0wZk00Nw2Kp4KWbSrcXiE1TeUGVf3YqoO5US0UVwllhZIpLXTFZSKSrwAjAxqRcdI5s+EkKiw+p1zJeK+jQGWSxIsznM5iSf83+AiIYWg5hM1qFt4kmT1lposbO1mlIR1PPCpCwrs6c+0JCZe+7ZWdvLWkmpYJKBDy+0Gd1DzKExS+UzU7Pz2MphvR3aohuX81CiMfRC/xK228G9njUVznu2200mtFWkjCTRmier4bE+9Ep15JXxuRTzTnM+giZSjQhHR4f4WYVbNlw/rLl40PDah69x/fp9HB4dkJwnGIu3Nnd+iEAgSSCmgJiEcSYTxiqKxrqsbCRgcHircyBOvRiqCqkqUqdknwmNw3uvuaPOOdq+I7QtqT+lPncRqWsOrSHm6ncIGC/MDuYchXMYkzi4cEQ0sNkolN1klJZznsV8jnPag6qI0JQLQ43REF6XEl3f4Z3BO8dyuQRUaHablc53zq/EmPCNhgebphkg9d5nDygavdfesVkfs92suXPrlnYEHZLPlqqegXGECHHbYitPZS2uUs+77zu6ds12c0ztFerdd8I8MxPEvBa2W21hslm3hKBchAfLBV3f04eew4M5s8WCpq7xtdLWtCHS9YF+3bNt13TtSDicBNabFpGWEDT02bYtoALraL5UY8A7tm1LEmGxnJNyvRkpKs9eXofdtkWyp4XoHRQgStAwd+XUu0JZQNSwczhbA467Jystjo2ZHd16Fgcaoq2rmm27IUnM9FQOgyN0iRAjYh0JQy8ZUYu2vmnqOcvDQ/rQaX4uJtp2zXp1SuPm+KoG6bMn5ajqitItOmVvPqZIMI5eKmToMbW7bcJVfvbp/5FeziGmpu9biD1VXPOGy/+At9z3t3TH5U34yh/JQsXBz/1HcOcS0DOwjWiSUBWUm0EDvOn/CfObYOBTz307T9z+97gra9b1TbbLGc0r7ufoviNiiurtOEc3P0DsjE200OVcmIuYGPGSSF1HSh3NwTznfQXXB5wZ+0XVzmNKsea2J1ph64WYi/1jDAoFFy2atwLS9/RdS7taE/oNEnsqY6ncktnhHEymeJvPsLM5br4gKGKKytRINjS2KRADhD5RJfAY+mSJSeXcrK448I7apcGDN5m7r/FeIf9GW5+9JPYrXuZKasiV5NCeJgon2llAUswelMZC1aVRWHESlAUho19cTuDa3JlTMDkpPLZdJmlXWuO0IyYpZkiDgjEk96QpaVaL4J0jpLx48n4WoaorFrXl/MUlFw/nXLl6laPzR8qDVnriZMitFC8mt5kO5ZU6CB0d2ojQ4TJBq6jl6RzilF+teB2jv6H5vChJeU+9o2lqnIGUhcM0FNinDkyvQABr8HVFM1eOsLbt2XanGJ/nJpP79l2g9pqY1Up4C2Kx1mNczNZzNrLFEUXYdi2FqQNjyS1zchW72mMxJfqCdMuesDEGbMWmDaR1i/QJEYuvGmLXEWLC2oqQLHdPt8yjo6qNGi2Z3XuWEs4ru7RzNbPZIZX3GVyQcF6VW9trDmB1uqVrO/o+5TuuhmJV1dRNg/cVKQl379xVOK91uQYvse06bdHt7FCyYIt3L6innS1h9ZaE7bbDeYcHnPGK9hQ0WpCVZ0qR7aZXcAz6kFurgGZjbCb2zZ5sHwhGE/GIyaUIGeCTlFuxqjxV7UgxG1sSiSERstWeRNislQvQSdJ8nBj1HLM3ldt55lopBk65JIkQO5w1zGY1pk+QemLotO+ZBRGboxC6Jo0xXLQV9tKa6rVPcXRthHzfXr2aJ+/8LqRqaLnIXblKmxqCgJEaaxILP+fp7VfCc4JJHXN/k1dd/kmsSZqLeuW/htv3wZNfqguvZFCs1x5UVz8Bl56C+oRV/yDP3vkG7mzfSMecdVrRGkiNxxzMcHXOxeQ+GX21IPkZMq+RypO8Ej5DJBBIornfGB0WB8koGtOYTI5htH4pt/pRJhENuYkpNG7KvCEx4gQtecm5sH7bkvoOSVEJYo0QrHKKGmewwWC6hDVB7yGWKo4Q+LYNpC4SuoD0amBEddxwyeAxeBG8FyoHC1NRo5D6EEGCIAGcly8OJbXT9qKg7DIAIObYfEqKBMqZbQqCTOl1ErXN9n3scaaisl6LgEUf3JS0OLeETxDBWY8zVt3ZpIWjxuZcVWYVd1kRWVGIr3bDjTlSp6G1pq45nHkuXDzi0rkDrly7wuH5I+p5o22lM4RYta26zUIihC4rqo4+dBA7rRg3WrBrnII5nLPYyiPWE5n0u0LBIqOSihmQYWlmDZU1JOeo61qbMxrRfFXfgemwFq0pqhz1bMbyYMlmu+XuaqUL3Y3Ev23X04i2o0YsIkowqzlB9VgUUW2Gh2zTqpJyzlPnnJOxgkQVdClb5imoYWGtU7Jao17UZr1lu1ozr7QvUd3MWXeRPvQ0VUMIkdXphiAVTbL4ykBImL4nClSVp24s1jdUVUNVyEJt1FyL8ZlWqOf4dEMKAYkyhFZIMJ83zGaNhmJi4uT0DpJDy2JcZv8Q6sbr+aomX4vFGJ+BIxFnE945OrMl9IHtVptEGucUeKBtWPO1O1KHsktsNnneNCRrjCWj/xHR4vMoQux6bRJKDs1m5KCWVCQF0DhlIOlaNdi0G0CijxFlchG2bZs7wBrNiCcDRpvyWe+1MwEK+kmigCY1PjTUZ71l5meEsFGwUWxxitDRnC6KBKibSo27w4blq5/hyjc8sWOX3lq/jg8++ceRxRGxbjiuPFuJbENPY2sqC5VreKZ9B88evxnXnXJp/jFecfHnwXZqXL36g3DrJtx4aw4BksPsXtvbX/so8uCvIHhOj1/Np579L+idEExgLYmtgdh4kBkmaF8lssFo3AKxNVI3RFcRnCU5BW8YglJOIYRo1ahAIw5ZvGAxeGNznDwDhgCCIkZFEiH1mk/vw9D/SnpF4UnXa18o0f5O1iRtrVEZ5T62ki3CvgQXabwCNMQYQhdJbSB2vebpeo33mWRwyeBSbtXhhaoyzG1FhceKg1wNlPqErbXJ60vZXtZKaiSJNTufDW2uIbvqigqzRnDG4J3GpoXEzKtSMTFQOUPlFOlXwr4hCUEkJ1VVSBb0W8phgJQtGjKiaths0rBWbmjonQoUZw2HBwc8cv9VXvPQ/VyqhYPKMZvP8FUFGa5rrbZRAPXOJPVI6nUB5SZ61ikGt6AIY4pIMNjSYsHoAxKNGXMMkoWp1eQnqGXbhV57CyU9Xp8ifQz0UaGzUSIpdEp1JpCCUiz5SkOi6ilaElAZkFyQWEKnIRlyqhAv+sDNZjNVkM4MOYcwtHE3uMqr4BMFWFibxlzhQDs1tl2x1YS126iH0fia2DT5c0cfhLaN+KonJktiS/X/J+/fY21dr7NO8Dfey/fNuS5777PP8TW2Yzs3ArGTCjQB1EU7hKrEEYkgQShcxCXqgNTiouQP0kGiOgGkRA1CQiCBqltKo1Ijuqu6oIHuSpHuJBVVY6JcylCQEBxXLk7s42Ofs29rzTm/772M/mOM95tr205styoNFvNonb33usw15/e97zvGeMbzPMMniQ6G1ro0xKGK6of8CNJyDKzrYhW2RGPsaaNUYw4+eOEhKVovr5RKq0otzfuagZizMRej2ITbUtCJjQQypkIM8oQxAyPZD5KYzCzWquPO4XDrjvQ2Ly3nRAx7au2U2v2eNmpv5x5rtx1g/R0zUE7Ovkx5tipMhUYlIpDYGJoxiRm4ovazXVG1xLCrotWEo4FmTv9ynrkWZLD2xly3SM4JbYXemvVOfe/23tC6spsvUeC0LFt/8mI/Ez4FyqeS6GlHSztqmmlTptbCovhYdGGfIlGVkEwO8uryTv7bn/5evvj1/w1f/IZ/ak9076PwO/4u/PxXwa+8y3o3L3wY3vXfwv4RrV/wLz70n/Hs9IWsqiytctLK2iqlN5uzKe5tmZx+nxLzNJPSTE8zTaIx6LL1ixvVeoy9EnvAZACRQDhPH+AMW4PpCUXVqOBqCbio9Z5oxYhCrSK1E7qhSaWZ9+Vg6hIFmksymleToq5fFMys3L539FpbN4iS3qA1E2h33WK6ES3EK0OD6kvrdmbUxqksnOpnFqQ+NZj7azx+9Ed/lG/4hm/gzW9+MyLCP/yH/3D7WimF7/zO7+Rd73oXl5eXvPnNb+aP/tE/yoc//OHnnuPtb3/7J1Gfv+/7vu+zfSle5XiQuZtOgYMLMIZwZelMUZki7LNwOQeu94nrfeR6H7i/C1zvApeTZQ5TsuF9MYpPvD1/4Jm8uRLo9ife96D3jerbvFnZfcLnmIbaWmWeJx4+fIH7Dx5wfe/aWVA+NoARSL3ZK7pBlqptg+2CN9uH4HXwPsaAxkHFtyxrwDN9o3VbbLU3KMDd+U1DJ2Vu8N0p0iZaLtV6LtWrSKskB8uxcXcCsVHkcaak2iHc1Q/itLlIDD7F8EIcVHvZgna8871hu++jotZuTh4B8wvbEhS1oY6DAh28StMuxjCsBl+ZfqexroV1LZTFRl3YR7UBdKWxnBbWZbXg4qw69XuuXUkpIyExAF82pZ697hQSKWamOCEqaDPygXrCYxRuG0nSxtoZvLthoKzna3t3P9hasYplfAyySh+SiObeiGsx3VFXRE071/vZNWUQjWwNdO9bKK1XCyDaXUBt/VxVcZsq9Q+75+0sNDwTXba9q9vsKu7ILII7gyO+xzjLHxCcbv0JlkdgM5piokigIFQJlBBZY+IUIkcJLATWECkxU+PESa545fAlvHL4DXz09jdQ+myap/svw4OPWHB6+GH7894vc6vXPDr8Rl47/iaerG+loJxa41gKp7KylmItCCfzRDXuRWid2BupNXKtpFqI60o8HQmnA+F4SzjeEk8H5HD73AcH8/vjcECOB8LpgPhHOB2Jy4m4HAnLkXg6EtcTuazE9URcF2JZiG0h90rszZzKayV0S8Zo3aqxWumlmHnxuqLLSl9W2ulEPR2pxwPleKCejpTT0f88UNeFJkINgRIjNQglCCdVDq1x2yq3tdhHKRzLaknfZ/D4rCup29tbvvzLv5xv/dZv5Zu+6Zue+9rhcOCnfuqn+It/8S/y5V/+5Tx69Ig/9+f+HN/4jd/IT/zETzz3vX/pL/0lvu3bvm379/X19Wf7Uowyq7plaVug0nNAyQK72NlPjet9ZJ4D19eZ/c4+pmxwWisZVaPcPjoIa4O1K6fSWEpnFT/vGsbiamLwm3Z3l+6DbmZQST07hauX4o3mB0BnuX3GF73ljbxw7x6vu5rYJ+GeFKbdBCG46K8Qm5BCs7I8jIxk3SoqcUp4H8weMdp968phWZhSJoZgm1XtPXWHgUz17wSTnOhzZp5nNAakJXb7C6Z5tgF3zTzbSiumZSoLfanUUzFGTwKN9i4FsWFsQViXwqkBUUhzc28+odROlM6832Maj7r1p3I2ynLKE7H59Fy1MdsxRh90ODwYbdpwKZaJH589Y0pWlR6fPbVDuBZ6aTb8FiXHiYcv7FmXQqs+PkUNyni6PENEjfarhrGv0aYxzxcTdT1xOB68j6Z2iDabcyXNGuRdw9b2k2B9vRQ7IWZCSuzmvUPGkae3ymlV5mTEAxATVza7Jl2VOjQuQOTKgkH1uVpBuby8dOJEMWIBmJlvykS1HoH2RluhlUorldPpYIxRVS4u7zFfXG7svq6dnCb3hXOnendPUVVuD09AhBAzy1JpTbi4vKa3yrIY9Ikac7LSKHT2exMr0yqlKbJCzsY63e8nllacmGP2SXnOaDQt1FpXYk53XqNyXA7ksnzSmdBDYJ0mboCldRsxEQM3Yaa2RtRO18AuzOwmc+IIdUUI/IvHv4+ffvp1fNMX/RleuvigPeHn/zi8zc8uZ6V98GPfwi+89o0cwwOqCIXGk+XIzemWjz16jZvTLctyhFqIvRGL9ZwUmA5Klwhhsn5RV3Np8QolhcAUArVb/1YknpMMT8hCzn7OKSElE8+KuHl0M2PZYK4iy+lEK9UdXMxiihU3ibaR8Ukyva7G4qvZ0KOmxJQIYlq0gaosxxvacqR//KPMpwPz6UD/2MvE44HdFLlNQsiJ/Vw5pcBtX+nLLe02cnz2hFYNijx0OJZfJ7jvve99L+9973s/5dfu37/PD/7gDz73ub/1t/4Wv/W3/lZ+6Zd+ibe97W3b56+vr3njG9/42f765x7b5NlPqKLOFHGHkQIkOrsE+wmuZ/tzNyk5mZaAHD2Z6BycDqwNn0sTLdN21l7EzDLXYiI/I7H082vizLK0HoVln6WvTts1vcFrjx7z87/4i6S3vAG9nLnYG9VbA0bJVQtOkvBp1iu9rdYYxQ6olCK9JZqPCugdtDQII1tqNKksAlWt5FYXIMaYt9dtjWljjcWQiMyE7E6vYtqLTue4HOmtkkMwDVKrHGthqQZVIEYoGcSNKU2koIP/sTH5YzAYaFQJJsSdXJwb0A6tdO8xWl/iDH0ZbbmsfctGarUqJoqJgntvxmrqarT62u3wxIJyipEeLZPvajDEugxnDshOs65dWRcjiyx+sNdemHcTgmwOCPTmdG9YV3fEcNandoh5MtrvaaGcGjFG5mkmENhNs/UJBMQ7WyFG1rba6xGj7dh054XWGmurXN67JIdELdWuaYwMsKy7z11wCFB6NEZYV7T1Tfzc1QgotR3YzfvNI7HWSq3ViDciLgi/U+F3peG2UijPbm5MwLs2At0g4ZQRHxVTayMEJafJNpZWSm0OhVoClvNs633sZz+UL66u6CKUVklpIsXIlCbm3SfTzjudQmEhcER5XBpH4EbNRT8Bu+CWSgotmGZSiZvxxL945fdynV4ja+fzHvw4r7/+aQBuTp/Hh177el65/XIOfc+zrqytclwOPH72hGfHGx49ecJhOXB7uqGVBW3VqdqWKJjvAvQeqF0pTdFkDjHdZ9Z1gdIMKkOF5siHqm7kkwF9DNg0Rxsj37XRgZACeY4WpGo1D0ux2WyrJhpOlIiBmF2zFqONs8d0o7t5NgZ0TI4mKVEXkEq6iMwpM+WZvuxhJ8SrRA4dYmdNK5oDXOxol/doF9ec7l2i+wu4vCLursjLJ1fCn+rx696TevLkCSLCgwcPnvv8933f9/GX//Jf5m1vext/6A/9Ib7927/9uYGBdx/Lsmw0XICnT21xGlU2nEds+EPvBCmDRwy7nZKyS8qlB6h9MhZKCGzZ/VI6U1LHU4UWI10DqypNzWE5RRsXoH6RYxSj3KrNnQqDZAFbABuanRiNndRb58mTJ/ziL32Il65mcrjHg92e5pBGre6WILph0q0VC1K9otqdHBFdy3CGa+i2UKKX9BBYxTLy0htKJIRGyzuGWeWg2EmMxJjJ0YKUxLO1ktI4LUdTmMcE1YL2qVZOrdHUrrO6oDkEm/sTx7gQuzuIfy14gz64iDe5U7bx48Wa7dm1MdqNTBAjIRq0VVtl+L+13qFDjgK90XrFCx20dbegsfd57k0aIFw8qPVVjVwQAkxWOdfajRHVKiLN9WGCzJONFRm4fO+EZGMcSqmEmAhROJ0KIsL+4pK1nMz/rixGp94r85yY0sRSC2BB+WyrNchAwWxwVKllYVkLh+XIxdUeCcK6VlIM2/gT1CG44PN8QtqMlXtrhOhSgWATkJdSqb2S00xyWNV6btXGgofgB7m9JoMm1XR7bjB1ezyi3eZOJYdriRFxMU2tFXNTDwYJd6VXc8Jv9cjV1QWX+z0ejs9Dc0Pg4uKC0jvHmxumKZDzRE6BadpBi2gwKLJrpnWh6MKigWPvPC2dA8LB4cwkwoUbHDdVNAxxs2VR0jP/+uNfy6yVCxo53vLQq6qb5W184KN/iAM7TiSe1s6prNwebnnt5hlPb5/x5OaG03LkdDrCerIgVUz7pbUizSjtpRosWmon7/ZWKZV1g/draa4ftH5x8RlqBusPJqtPBw9CnISilaY2Ky6myHwxmTi/FnJMG5GmxT0akgWmJIgTpmycxgRi5KB+cWHu9Tmbv2MUpFdiaOz3kSllpmw+nr1GmuyI0lBprLLSU4DLS+rFNW1/Sbl3AbtLwtUlcb5gSv8eBKnT6cR3fud38gf/4B/k3r172+f/7J/9s3zlV34lDx8+5J/9s3/Gd33Xd/GRj3yEv/7X//qnfJ7v/d7v5Xu+53s+6fPb0EOvXnQ0bsFxb2HtCdFI6Da4q4dO7Ue0ZeiJOe8JEZoUROwGXBfIi0AJtDbTWiLJTIiQdmHrQU15x9Yv8kwtMIYtbuc+QrPmMRnRDAgSMq89PvDTH/gFohbe/PoX2H3Z2yHMSDhS1fowvWeCZFOW94XWF071yNpXKpWmlV4bulg/hqBmEFps3lDXBXLn2DqlF2pdUMmEmOlqi5QEPSQk70hXkGQixR0ldaqc0FIpbaHpCQ2VWlZOy4mydtZT5ZWbIzdLYU1mnpstLaRXWFfYXxi0sotKkELoFVUTBmpVQoykHKyCc4aZ71WWo0FYvVfyJEzamHeJEGC3y34wKvv9zpq/BWOcddNNkQJpAtUTqoVSFwKVmJW0m4ghk9rwX4DVZ1Udl+LsUPVqSLnIe9CO1sazx7cgkGeb72OiZnOYL0cgVAjVZQ6Wje6uLpkvLw0SrpX1cKKdjIhSStl0fS0Z6YJgB5CVR2YflKYT0xx48OA+y3rk6elA0840ZebdxCoVCcI8T9SqLMcTJZglUU6RsNuRpwxT8qBVqceVulSe3T51+DRuXntZRh+2spyqjUfR6uQy9aF3gX3eU4o5ny+1Il3QHgitE7RSigmSb57dYiF36GgUeuXpcsttPLG/uPDDMhOrQfCvfPg1Ys7sL6/JYUeUDArl5Tfx6J++h6v/6H+El478m//pf82HTm+nnj7Co3iPR2HiQ1OkE1FNSDPacxdlH4SLGMjd9En7GL0/2Xm1NSYRXp8vOX7kW/hXH3kvD/Oerpcc9YpHZeVJPfChwzOO68LpdODRk0fc3t7w9LTSajNEo6qxRpeCNqPV97XQa6dXoAvSA+vxmSUgvQ7Qniqrsxp1G0pJ8MvVIAbzwgQY06qbGKEqROul5x7Q1CCN0USRHBMpGlSLNNv/kzgdOdj5FDISM1PfE2ok1LC1T6K49RGNXhZOZUX2PsG4rG5VFTmk+4S8Y71+AXYX6LQjTvcIeUeKe5RM49/xPKlSCn/gD/wBVJW//bf/9nNf+47v+I7t7+9+97uZpok/9af+FN/7vd/LPM+f9Fzf9V3f9dzPPH36lLe+9a0bPn3Xqghw1pP9taNOJLAs0NgxFjiCRKuK3CtrZOQ28PBsc38WSVvW1WhG+3UMy0ZJWcZov36U1udXJc6+ssnA1vwurXN7OPLxR4/JSXn87EWm3NhNmIdgCETp1GwN6DHioLVK623TsgwoQHs389BoLJ1WilU8vbM0g6lKXWxxxgbTiaDG6BoOBBLdJkqU7vOelHbH5NUa66dlZTk1llNlKY3SjLkmzggKo4oNwckRiRgMBpKRjTPAUqtocGJBkMTm/CEutlZvwrdG75HojXUF639s5IlOqebGoM1IHF2NyURQGHCSdHMRD8Eb8Uag2F6YNzaDW+SZI5FsZJJBWBCHi0xvZ4dG00ZwCyNxivxYp+KUcfUegiAM16PzKnaNUpQziQULvj3a9+hYawIp+O8t1aaXi2zrf8BIYxyN+BUPQ7u0kQ/c1UTtkJznefO2tPlcZwNb9R5K7R2kOWvUXhUbicaIGqj3M/3nW23kIKR4fn0Bc7RX9XEO0R3Z3R0mYody8gpT1cgY7RTgeEF6+QHUPe32jYR2zSWVWBdjHwbzaNReLUApLHSrQprYFGzUqnO1ibJLa2RRdiFR6z1u2zVr3hMkssjK0/XEk3XhtcMTTuvKcjrw7HTDYb3lVBfUrcqg2TnjjFQN6mPcxYmeQlDzAlXESDROVAnDaVjsPEGGrAN67SSf0WV3rtOD/yy2rl2WZTPt/BCLI0h50qISjGab1BGcvjnPACQNhG5arfO6ccDSmb7NNW4ivr28x92D6TyrtzdEFV3NR1VitMHHy7/DIDUC1C/+4i/yQz/0Q89VUZ/q8VVf9VXUWvmFX/gFvuRLvuSTvj7P86cMXncdxu8+7vapTJ/kjVwsI0lYz2FKwn6KJNck3R4bfe2uTxA/H41NZ5PiTTMybtlgpCVnJ9kBWl0AmR1Ks5zT4KVgtiIARFQrp1L48Ec/yun0jBdf2FHqS6Qk7LL1X7RFcmhEJtQt8stqbLOyVlo1V2WC2RYZ1OOCS7Wx1z0EDmo08toLXQMSMruQybuZKezonrlJsn6YjYRQiJEwDXfxSC2wnBpPnxw4HlZOx2IQVjPRchIhhcA8ZfbzxOV+zzxncg5jUgrQadXg1GDNFIOGcrSeWLTpslobc56MCXY0Rtm6NlKOaLCN3lpHG+Rs1OgYA+tyy/H0jLKYh16IBkpJNBFyCBBiJya3rqrKUgqH29stsE5p2irhVoO5P0h1qrx4X1JI887GX8RMSoYRLsutubunSHAvwKHbw5vlrRR6qTbiPUZzgXet2qjrogt9rQ/nBJ+2cjqeuL15xoMXXuRi3pPTxPF04Pb2GQ8v7pNSZCnFWIwpsJ5Wam0c60qM0Uxfo42UN6alj4KX6L+jkXJmf3FhFXOtLMvKfnfBNCXWY6dVmwd1Op3MqLSrwcR5NhmAirWePAmxKrOznA5c7IyGXVolCOymDKOWdfpzjELKJt69uv8AFWFp1QJZNVmE9gZt5fDjn4/Mex5+oRBnRXaBV5+uHGolBZuEcCyFKSeSCEJjbZ1D7+wwx26dkrVfI9y0A9IbvR65JHBBsDE7qhxq4dXjgcenEx968ipLWSjrgcPxlnVdWJcDqPflMN3VPNshHVpgipMF5bVtye+QGoRgiYlEKO2EohubNXgfyoZ9VqZpJufJzp3eOJWT7YHoxlna6W1lTokYTI8YRMgx0ZsFOwkWmBqN4oQLoiXzXRvivW91CnpAzSINg51rWWitkHaTi/ud0SrG9KVX+nLrVPgTpa1G5pl3hDzR/l0FqRGgPvCBD/DDP/zDvPjii5/2Z97//vcTQuD1r3/9Z/377vafPjlgqafAdsGkCxG7UTkF00UFnwIbvOI1bMNL2+BZgri5qVmN2C80a5bgmfDd3z2CZPfsWVW2BqdPnPeMWqAJa2scl5VXHz/m4f09x9M9UrCxD01MN7QWgW5N+lqbOwt08OxenSasjgcohmcb7Vtcu2FBqqlVD6EWtAakRbpDBetaaQWa2RAgOTHHyby9aFYZRFuM3UkJy7pSy2rWQ+I6pVFN+fVU11AMx/pSrVogBlfTJxOHjj5bsOYvdmss6Hs13GqzSaEjI3SlYx8stm6uzLWb8l46rtdxR4Vo9kcb9TlFWrV7bWPpG+vigVlwl2ezQ9qGzXlFXltH1Z0CRlGyVV7bKqT5EMHeGolgWqLufaIuTJMFPR3H+gaznRMxCYFp3lnFSqT3znI6oZOJea8uLm2QYg02z6y3s6+f9g0Or60TTMjmWi9jSA43iNaMqaq4I4tidITmTflgCUEiEMoKTSmlUKtSi5EjBDHRpnSEIYVwxwlsJtuoSptXwtFF6Ay5Qy0gagdsGoa0vnZ7NEF7K0hXpDbC8ZaJS65T51qESwnkriza0W5DMRFzkenYNSko2oVYGjmaLnHpxXpJzUJNlUDMdr7ctoWbtnLsq1UfESQLsUWS2PgO0W6m0S0StTNhZ5B0q7CDwrSTLWEZZ4xEc34hKImzVdHYU0OmELuhEyaat0olh4mYbN+N5xV3nwkhEFrb0I0z8aK7L5915GxrGmIS1IImoj6B3M+WMKQf3f7at8LLvq7jgPO2y7q4JKcazSZEWE/EnGjLZ2YI/FkHqZubG37u535u+/fP//zP8/73v5+HDx/ypje9id//+38/P/VTP8U/+Sf/hNYaL7/8MgAPHz5kmibe97738WM/9mN89Vd/NdfX17zvfe/j27/92/kjf+SP8MILL3y2L2eDUT6xqjpPSbUsRroZswQiU4pMMZKTkILpoBSjzLoVpQsPZTNG3bQadww3TT/kuh+nkN4d03Ge03QOUjbPR+xA1GAU315ZauHJsxtuDieOa2E/mQFqEEyTFCCozaBpPgqht+4UjQEUqc+NsWqtd58ZI2az37RRtDlMA7EWaIHQIirGJjyuK616kFIhaibtEmYj0DbzUxx6aK1TVhsXPgXuvFcXbHrfoTtkBwpNqc0c5kWcIo1BrdK4w2JybEnMo7B3u/4DOkpuEizB4TejBG6jA4Y9zBDxGqsvbZqrzfRWAjUGI8N48G+1mGlCGAxEu746mHa+zob7RaDRmr33lM73Q30ttGajzk2obBou0aGx60QPyK2f55WKU0R7dzYeQsqTQaJETqeV0or3mxJpnjkcnyENri+vDPrcbLr8uZrBeV0SYOM9zN3Dpu06AdBARx1znexQaw75SojmuCJ2+OHrUbtS6MhsB2MtBZGO+JgYg0/9farRrTH00yrc6HOzfJ+1XjEwYCWGycZ3RNfMYZ6vTQKhd6Q15HggxSMX+8KVBC6DklSJqkZ62Xa3Hbo2NMU0aqcmqJijeFGj/0e1taESmTEo8KZXjr2yaKX7eg8SiT2iIRHSZD0bgdgjQTuTMX/MJb7aZOQUkrUQugUIEZDosLSoW5uNpM7/9Bls2od+zc1cNRBJhKhOMnIzYLXe5tAdbsnPyL7soEI7G3NzUOHB9KXQaR7QwWdSCU6QgjG6aDtztxaMr6HqFk19BKlAX41R+OsG9/3ET/wEX/3VX739e/SK/tgf+2N893d/N//oH/0jAL7iK77iuZ/74R/+Yd7znvcwzzN//+//fb77u7+bZVl4xzvewbd/+7c/13P6TB969+Lc+dx5EKE1i+ek7LNy72LmwVXixes9l7vM5T6zn61iWWplH5Wwizw7dhNuRjgEY6ylFGjuIda8Ytn5ELnh/qAq5DzRWvPyOjCMVF1/bXiumNi0idIk0gOsXfjYkyMPn97ywuMbeoFdjlzMM21NrFPiYs5oLRxvbllPJ2pZzRm5Gf5dmo/UblZp9J5Yeqdo5+R/rtqobhO1pIncFuZ+orFSe+PmcKK3gGoiz51JZ0LNLmy2gYaSxEaIZ0GT0Y+1FXZXE1MIzDEyx8CcbKZMHAEgGumkU2lq7soxmNu7OTFYM9gq027q+8V0UtEzRDRYj6RbdRywzx8OR1qt9HJiOR1Z14VlWRkDB9daEOBif0EmkHTMJmo+qDJwebXf1s/QuQE2OgFYu9tfxUj2DDXlGXGPvFoN0osCRTu1dpIfZIP5GVN2arw5NQQgjsrKq87OgAXFg3KlFhM9p/0OiZFpZ+y23jvL8WTv93RknjJB4PFrr7Hbz9y7uGD1qcS9WqVqVHZ7f70rEh3yDsKwu7g5nbhdV1KaLUnY77cqpq0WvKac2IUEa+G4mIaurDZORkQ43h5wy3cmhy53kzmqdIU87WxtCMSgprdzyCtP0VuDjcPxltwKF1OgtgY9kOc9eTdDFPrhxrRfv/JB+vUt+xp52723Mu1nPlADMU1Mexttw6icwBiz68mE2BIoYgFMLnYEzXQVTlgwrGJJ1kmhphlaIs9KxpiwtezMKy+I9Y3Xk81b6t08WHqH3szoSIWowZMqF2uLOceb68RqBCxV7wUCehedCFRPDBrWY81zpKzLRjePSZjzRPJEO8vkSEunsXrSaBIM6ORogJ6tCWi9mtOECFOMHmhszylWucdgLNnWTWM1hYgZDQzJiSLVkmZzwbGpyylle9+nXycx73ve855P2Qcaj1/rawBf+ZVfyT//5//8s/21n/LxifqoccDcJVOIZxspmovEnCNzGtVUIIsgYqSBLtCDXRTrT3eDuXpzGrBBfObawAYzjddi6IVsZAHL2MYNs2wqPNcrO1NKmwpNIqfSeXZcuIyCtugkD8tkphTR1lhLo7hDdtfmG8F0UFXPDMfeO2trFO2UbgHq1CpdEhKEpVZ6EShiM2Ba5bAWVBOqgRYrLQTyutA0ktSsgU51ZfWP0lbyLkJ1py/vSaVoDu4bUQKl1LYRUDw/I4k72LdG7EaV7qN/4+4WtI6GYBtgo98LeQoOUeg27XU5nCjrSnU7IjuUbKMLcAqVroGY3PJ0VM8hmE+hu0cM49Qx18fio2yVVO028qFrYXMdMWmSTaIdJAmHanD9F6jRkLlTqfsy6l0prZiIMp5lDLa2u0OKfbODkjBMp3Tzakti8HOthRoDNYXNXSSE4JX2OdHqLuokgnQLHniVpBpQ75Vt41zARkwwLKnCBhca09IqIsGo7yrNfCLFHTdC2NZmqRXtjrNv79PYmq0ZZV1GdTterztfxBRRIi0AxfV+6wkpB1K54YKVq9i4VKEFMWKBw6mty0aKMvPl4GvB602vUvodhEK7reEag0uOlZjNNy9FEEloDyQxQkalEVpAfBKvaDeWMT7iolkSYqhbBwIh4X1lJ754Uia+ANXhymHCKL5m7LV2h8jdaWe0LlS3vvjorw9/QBxSlhC2Cki3veedUQk+08tYzOM+4JWX4zdg29Veg4xq1W6tkaE8UQ8myu/AJib9NI/Pee++uySJu0HK/m2QSYrCbgpczImLObGbI7scmGNg52LDlAPS7WZnlNTV5r4UgVVJMnsPplCaaSwC0Q7kNEZy2OtKKdnoi7IYQ85ejUFUMfk4hiNjNRVthJhJ8yWHAh997Ya9ZtYpcDytlLqj7SemKaOtcViL0Vxbpy5HIrBPkROOH8vwzeosvVF6pwCn2ni2LKTZGrVtXZmC0jI0sebpk1PBcIRMlpXUGyU25ikxpcCzZ085HY88uXnMs9tnHA5HXnrDNbo2+pMDOQTmFNnlxJSiB3uz4jmdFkJM7OadCQ+7sk+Rrp11WSAJneTZvDXz22LWQ1ECtayUYs3/KWeu9rO7fVglW2vjyeMbq6h6v7MurO8i2PWcJqUzkbPa1GCvbLNklmWh9m4VXauU1uxAF2BKlkx0pdbF7IuqkQh6a0xTYpoyebompok857Nlk5qHI62jzaf1JmOsDc/C3irH45Hr6yumlB2WE9cP2uHR1oZGqw6HHZOoY/7rys2y2HPHyKE3TuuRnHYeSCJF7ffUWo0hpy4iDUpIEZFIcAoyIRGn/caAHMb0IYI2pa72HF1h2l2QUmfOnRSzx71M10Kn2KEZALHr2upKWd39fMpMOZJzZEoBqtIPBx9KOXEe8dJt5HgITPuZHoTaEkI1/drpgNQDsTzlWm9YZc+LuwdelUCcMhpgrWVzhQlpth4SbONcejdSD9FmeoFyqgVR02a2bhNtc7bhqIGOJFu3CQWJZGaD99SSJ/E6ZQh7ayuWwLgGTkQhBbfqwpMbGys01rFVv94+iNkO+2JkrVoXppyYYoJqAmJRaMX6a2O0h6rSQkMDxJiNXDN67t2g+826rWIIx36wWkfPypP0MRhSrP5fSmE3T+Qps64rQielyUyyQ6DHSEiRaZeoGs2J5jN4fE4HqbuPuwFq2OWgFsjM+cCqpxwDop0cYJcios2Hga1QC1IrE1b6TyJGlxWhF2O5SDNmYBLZrObPYmJzWdhEdz7GA880xF2NEcg+/2jQSlETZT59eksvJ+7Fa652mf08k1InZeX2ZFZIS7ODsdVCqY3oAtpDWVmXFQnRGpRJjXrucN/aO1WCvw8ldWhrY2WlhUZDKbKntkhdhaxC7kJPsHbzPjxWWDusqhSUKo22VlIXrnZ7JidCiB+ewcefiAiS3PIJ8+ULTVlXGz0x7fZO1R4sSfc5dJih1uIVstkiLb3w+NFTajXSxhCfTtNMlbiJIXvv1NqIDleasj5RqrH+pHdOayGGRgrJRJ0xoTT8vCFiVXYLgrbhJWeVxTQlwjwbxOKIWS3rVkFuNO7eh6e1N7NtQjHijh/dKtE5JzsoayVkJ5VgEgrFAnGrlaLGVMThZhEh5cS6rCZL8LpNOrS2WB9HbOyKRCGFybU3TrkU5XhazLuwVKqudCIhrwQf27K7mEkpERxurWoi4E5nt59ppVOpxuxD3eUgMjz/othwT8kR0exehwZPMxzdsT2bs8GMvXVCngATSadptsnOa6EKLDQTME8zOq2oNPrpGakeuOhHXpxNgH+snbI079WuoBZQVH2dNiNFCZ2kNj1NWtuMo8V7e0lg6Clx+D4yJjE0slEcrOL2lqpRRpwQ44lF0DZwNXMDFzVbrdrotTAssgSr3gXM2USHJGBUwYYw9LpQ+koTQavB4JMEtBoLMjri0FFjRkp3t51oXoerSTbA+mFzNsPoDnR1SyzUrotAzANv8gYmClM3r8g4ZAOR3W5nV0mgi/U9jbeViPl5JOxXe3xOB6m7pIk7nzyXru5GafipUcCTT7CNYqQJmpWw0u0j9E6WwBQsQGWxICVqjgbSbRQFMSJtOBL3LTCFcB6JMTbnRgTweT4RIUb7HAKtmRt5LY2DNnpRbu6NXkdit+vsqrL4ULsxj6i2xtqaNzE7h1JYS0Hc0VIQC0xqwcoIpU4/7Wo1d1XQQoudLoHKxNqFpQRqMMp8KELtUKJSKu5raD/avAIIGpnzzlhNfkBv2aM3aiVGILhi3sZvtFpdzJstm1Rr2Jt3XXf3DqHW5hBS2Aghh3JiXU+U9cTwDMx52mCnXpsjF+OwiUTP7JoKTe0Q1+biy9idKBC9KYw1hWX0mdWzSHUIw9bUlBNTNnKJ9kZZTrQWCPW8CVtr4PBVcFeSGAPai3WhekfUaPz4+w85buSJgOuOet/6jhh/xdavWAW/LNZvEG+Ii0Cv5j4xCB1GFrGIGmJ0WFI5nBZqU05LYW2N2oM5l+TI1DqSs7Fgu0/THRq0EYxV0Oj2U+pNfXGoEHXY3bSGUcTIKbWyLjbbPIxuO24arKafCpMTN1onY1B6LZUalIq69iZBTgbblyOhnsh95X5QDh2m3qhmx43qihk2Y5C+KqFXD1JKcjdyG4HiJIXus+C6IlpRMSJRoBHpluz2ShqKWz2b6AavOAQME2vW28HdJ2wqbjcxeqvm9bmxHEagAlr14y2Yd2PvpJBBzYW8DRfyUk3rGbP9nj5YfAZTNjUUI0izloWotQ56NzlNDKScTIzcjVwyHiLDwHi4onAOUgy7rXHGGZmn+VEjEhwyN3um8BlGn8/pIPWJj2kapIXilHH1sdRm6RJdu7KbEqip8e9d7ogh05pN2JUu3JsSCaHVwFoSpUWWZmy7Us3nTZtBUKNxMHpRw9opxkgzWbkzyQLJ5wipAj2YKLda4Gm9sxwWZLbBhTe35rFWaiHE1bKTKRFQVu2s3WyWSi0m7L0tHA9HSinM0wWEhsaGSjDfMyywHN0WJ8TAfjJKfK/QXCm+hj1Liyw9cjyYXqXKbMK9ukC1LO50C3WN1D7RtZA8e55SYp8jc4xkEWsOj0M9ZLpabyp3c+GYdzOKsB5ONgoiRKM9SyTQubqaEJSbcMPpuHI8LOx2e5s/1Syj7bGx25uhbSJwTCeWZUFWaE0gqA0gnCYur662vmDrlaUp+2kyxmLMNuqkq03s7VA7G1VaXCOVg3B9eUWOEdHigvBArydaq6zYNSrHE4sY5p+nyYJDimZdBT49W5zKbiLtZVnY7XfkKdG796VENkf7TLJqTRtlNSsliUPsmpja7ELgZFOoowV4RJAcN7SheYNb6+oJj3JaGhZL9wQNBIJl2V1ZDhVNhakpuS3WNA9iGptaOT1ZSJK8Qe/aRKwysS6OwU/azdNvnhIyBVqNSF8JauLzgpkjl35kmidyniBWYjYTZGtVdqKTTqYgrAg9BuaHL1CLcloq7dkTAhNvuw8qnce60uvKokZMGDZfIUCkM2vdetGBYnawqpaIdkVq9d5OMYmDVko92udUid0DTrUgUx0C7mqG0CPR6etqQaj0zbYrequglw51QdrCuHoj0IOgxaD4mJ18o1CXBegkCnky+HjtRp6Yc4CeLQiHxGD/ZjKK3QdLf5JNqlYwBq8iwcXAWMVumr20EW6STzUfUhvoqIyzLpLEglXzWWwigZAnE7GPPpX+OlHQ/3173NVJ3fXwG0LInLNZxsyJlJMzuaIzisTzdPv5FIJReUMghzsTMfGMWt0kcrD2RmYqn/x6/I575jNwW9m+NnppWw+tG9usJQFN5tnVlLBW1mqHSOt2sNnMGf9QG5dQmvWeqioZz1oI57ku2M+1biwqwSEkYXNTsMm5kdqEZbXmftJA10xtjVIEaUKrNu+s9UDXCDIIHrbhzHrF4RsdUdkYYSMpMzcNW+xdXW/Uh++fL+og22Fn9zNu7hUh2OYIMRC7QYrRqcnigPwYbxKCbMMYU4pOdRaouHt9cIZbplTH2kezfwgpxUxJLYAZdk/q5Gi0XWlOme7NZAbY9sc1MHeJGCFEp1D4OvHrcyb9eGBU02TZOupeFVpPi3EwOBEiiFU0I6BuDSScDODPJU5msf543z4/XpctDpvxrF0gGAFoZOq16ZbRb+4iMVCbTdrV0DdCkOpo4D9PKgohkJxIEohMU/KJ2OET3jMbuSO4M4Z6pa1qUg7fgSBi/RttsFaiV6c7VXbAHCCJVX+BRjcCu/eJOqGXDYHpulp12rBhSE2h2ij5psX7bJVSDtYP3JAJJVZjUdZS/HV203L5+9eyGqw22jk6EBZzdqE3glenAkYjF9tLxk6EFIwwMWZB2TXBhPTRBpYOUg53rvvQKCqDvu73WwcJxtb3iJ4hRnsdw/VEbJCq9cTittaCugYuhi0hj9Hs37TLWcZwZ1qFCc3/A4L7xt/H6A6APM/MU+b6+oLr68z1dWa3m5mmSIowpcg+RZDzTKoYI7spc4hKEUjIefxys+Z/rxWNaTt8xuIbAXKMst+YSJiOSjhDf6gf2O38PnrvnI4nphxRhKZCaTYK+rp0G7DpCtHWzVx1LTaksDk5omG9CPVpqiHm7SBsvW5DHO0ADQZ3iVgV4U1tZWIpK09vVlKemTUi7F0EWs3PsAbWKvQWQCNZkhOihJwz8zSTUyCJQm9bxkiIQAd3fphEmFJ2zZQfmF1BxS1xYF1PoAYjTNOEqBmMWpDFRJraNl0JDCZTZTjFxwQhWg8qZQtGIWWWJTgsGwgxM+926AmKWqAzqCd6kMJ6gGVlPS3UpZBT5P71bMGQhupq2XQzU9iQEiEnOrA2E/OKCLv9TFBsLTl71ALz+RAPMWzJi4puhzXrWOPN7XR8Xpdht86asmvDYKT5+pauloTlaD3Y7gLkpkgPTPNE74HWExoj0o1sgnYabrjcOr0UNAoSxryqaPT7bsJzdRhr9IRTsntjkpBETpGUgnU0ohC4uEMOWEBlg41UxaFf7+52K281GYN1MCeRSMgzsVez3womzt6rshdlnyA3G2AaqQ7ZFaJC1A5tQd35feknI0Y1CFWRqiZ810bVYsNHtHIsN9anUjEYsCvRBdut1C3pKGpBKohAdRJDtxAdiJv5cqMStBK8ygOzNcqecAcPZjauptGlk1NCVSibGDmCzcO03xec7edBSh1CV5EtSGkf5J6Btprbjn393EoxMpInbjE+d/YiIDmck5A0oR1qVTcaDpBt9EsQS2RD/3fs3ff/l4c3nfHG4jzP28IIrp6va2E9NY63hdOhc4qRNUcmSfSoTNEif9dArd6EHoeqOzbUavg4HdR7WCrKs9OCBCFnu5mboNex9zwZu8uskhIhTOaW4JTyIRgeze9laYRwzfWVscO6dm5ub7j/YE+TgLiSv0qiEigEekh0MbVNJCFJyfsLFKE0o6U3QCWhAVQqxICGQOlq/YGYkWBmnqdFqTUhBHa7F0gx8ehRQ0mgl4h0WkhUGc34QNLGRGeWYAdQdD1ZB+2dJlaV6R3X42ma2MVo01YRo9d7B0prI+bMPE0sp5XeGsVn/kxT3jbFNGdEmgX/ZnOt5l2mrJHi+hiQs6VIELoUugRSnNjtL0AC+3neKu+1Ves/6qDiqlWKAkka8+Ul6fq+CzCVKXhAVBtQaASIselt1hkhkGUy49QYDZ71tRJzdBMu6N10YrV36ulkfR4rhmitoh2yb1ljLQdPOFxU3pq38s05QzvgtOEQrGo2CYFStDn8Y71bDTaiRXugnnQTPqc007QTamSpR05r4WIyNlg5nph3u62n6FkQksUOYXctULVKOCVrpO+nzG5K1pNqjZSGCL2bm0nw/tMUydNsfZbet4nNirAuR/Pfi4Gwu/QD1bVYu8RSlXZauRaYypH65OPcPnuVm/XEbT1A6Ih0el2Q3sit0Uuh18IJrwrXTtZAQnzStI9mDw2VTqkHYghM00xfDeKcY0JFqcFmoykYmSIYdKer9XmSTEQxiLY7UcgIXZZgVrU+3SY1EEtOzbewk3LynpMlrylbn8j28+QJtPfHwKn8Cs0TRRHytPdzKVtlhp9/EZt+oKYtzD6Z+tz/d+eQrfdv2rY4p21GmTE8hVCxXnSItGh6SMTkKfIZTub93A5S9I35grhifcBn2KIyFlyglk4tkVacGaPDfy9uDVtDdc5uY/Yc48NvijeNO8KyLk6USFtj3dwBAiKJSSbXpoxnDKZQcNrPmE91ZqEZxTQlH+fRzVOudrVmYzRcuYlZtVQRVCLag4lsNRmOnGefmFq9aSn0gDcszTFColFfg0SIExJnpEdKMThPe0TI9B5ZDuYLltOEJGPYabxBxAWK0bLiFGx2VwwOJWp3Vwk7kAMGUQg2kTekSDscGO7MOpxW1fD6M7NNbahgGEMPzxRu7eZwXdfqvS+HHeIYGGcwUO+jeYtj7kIMEyFmdvudmwF3QgpIEzfY2DhZVhUAOUb2u93mQahtoXfzqRtJ59jQW2UUzRsyRoMay7pukFZyDVJATLyLspTFRr94pY7gTX8b4um/xT4keLPHCSf+MoY7RHcg16or60XV3mh+gNrrsOca/nBmv+auBck+l0PiWEzb1KZgPf/SiJNJPAgBtG3WbRJMsGpAszMaYzJj1GwffcCiPRkEJk5vB3o7H4aWMBhVvkezFyrF73WKyOwEqm4VVcqBYzPSRUSRcqLePqI8/TjrejCYLlhlXZcj+MHaSzF9mRghpK8NiJb9R6tEWm9GDgkK7URIkdSDWXC1TkoBDYoES0JVrG2AQ2a9233OTiBJMbGW7q7xPi8tRyJnfZvXQtb3dPg7JUuCtdo9H16S4kQPu3/x3A4JhvaE6kCzCHGebQpAnKhD/+TszxChdTM6mObZNGsSNtTJzkZHixTzqtxFWx+9EUKyxD6IkVqCwcj4uoqBTe/16R6f00FKxHQI3alYtXu1021+UQzCThJSExOZSTpTCOwiTLGTQkN1cSy/kZKrpSlnZ+KYkAzLbeXUOqfaqa1bJit2CNeymu9YGBsdqJ12PJqlUu8oPjZj9B9643DzlNunT7i4mE23VFYOh1uePnuCtIkUYX+xI80T5IzOO3qPHJm4ZeY0sHWxhbybs00aDeK00ZMbSContXlHl7u9YcMxEfbXSN4h+YJpvo+UwPLRj3Pz5MDjxwcevfqMmDL3dg+4ePElHtx/kel+oLQD+mqkHV6hnypSTwTq5lqNRA41UxvcLI00CSkr91JgCpF5N1Eb9KqgmUgkM5EnC0o2L2sxXVUtZLWheE1XynokpuAN5WTXvSdC3NF65dGjE8biu/KZSdYzkWjBed7NW/aImBaoieH8OUGebOMdDyd6j9CzF1WdttxwRDk9gxdeeEBK54zXepR2H3d5h3VcxA838+5TNYue3W5HV2VZF2pUunS02gDN7qa+UXw9M4p4o/Kd8LEL2R0EbCMA5iXY1sV8HXV0xSIV89erazcIMmUCcWu1DAF4oJJi4uKFC9ZivZRTPVqgTpHdRSa1yG0pZp1VEsuhEEOBVkgiTCE6g1Ysm+5WVcXdjpgSJUa6CmuFKe+RpBBWm3tWK/QV7Y21rsjJYLeLywtPhJTQCmhl7Q1SJISZuByRZoMfJF8S9w9Y445T3HH79FU+/uov8+yXf4ZUn3HVVi6K6bPC0Jphww+bQE/BzgTt9FlA7b6Ji36ziK2dINyXYYHU6TlACuTJKxZxkbAnTYOssttfuIdkNqRHQdMOEcijjRhgytMG4w1T4NliC0HCJrod8JvOk9PexTSHEnx8jFtzcTbYjsGstYYwPIRA9uS+NbPoiikZCQSbSi7bOhtQ4rkYwNBLiihMs+9h3xZRtwRl77P0BGNLl/8QKOgOSo//WcYxjA2xSK90EyrarGaMvZJcAe6QhFdBXghvoj5bM/Zs5gVn1U5t1s9JcXzN+0xi1XToViVRLBM2X9SK6rldjjuWD3hsyomrB/eZ88TxcOBqPxncMiXyfEGaLzitRjs/rkrRSA+ZrgFjEHZ6gCbKUgc9XV1pb1QdDQlNs2U2MROnSyTvIO24Oa7c3qx8+OWXuXly5ObpEVJgnvdcv+mS2lfWcqQvkaar9yMyIU3s5gtmCjlapVRaJzSz/RkKkT7uxyhNnWmRctqG5FnGP/zHXMMSAAK05r24Yum262+sgnbLJnS73oKcDWTBPfKa9wxlYxpq6xyOB2JQpmyOImD0WcFg4JwyQZQsM6WaoHhZTJdVXVuCGnQWOtTQ/SAK7v5x1u6NtWlVg1HMLUPXDZo51/Fnn8DhNqBeLaHj2rqEYlC9nXzQuxNGQiSQtuu9VU/RmKxgSVdpFamVlLtNEValYSyvccjZTBqr3jo2/2itSgq+d8QrT3es6DL6tm5cq4MMYCSgHkel7ESAru4yYfBacGbY6NlqHdIO8QF8wfVnTs6ICXGdToiWiBW31+pi54CoGrHGUROiXVvdzhFB1KcgMGhVQLARNsNhY0BwBrR3Jw9AnGa7vqk7+80qaUMVms1yEgEPUuLsUfVlbirc8d6xZDI489ivJ75udQjWvVJvY9046w7vl29nI5yfA694B8rh1V50ZxF7WoPgdQwslfE8Y+XZ4hxsWUK3ZFB8+KrizMJ41ksyWhzizL9P//icD1LnhRSc0aYu63aigo8zlqyoj7vGzTXpzjLT8/OpOttH1QS7mIu2VjNRNbKCHV5BhlBTHdPFmroiMAZ6jVIf7szusV+WgnJ1eUFOgcuLPV/0Be/ktUev8ujRa7z44kNC3jFfXDBf3mfa3+fZ8ZZlPXFzqigRDXvDjbUiLHQxV+eyrtRqc3FizMbEmTMqE1UuiHEipEzaPYA0Qch87MMf5pWPvsq//pl/w3JYqKeCzIHre/d4y1vfyNqe8ey2oRUIzUdWTITpkqvpIVcUZlnoa2Mpldid3TWEvLhjAELVSs6JECGnyYJUEBiu5TLuhWWwEtSYj7Wx1IIGC4aJ7AJQE7IiSmymo7pr2tp7p9dq8IkoOU+kZHKF2hqH4xNCUOYs7n0n5Gm2aoHAbr9jSnag397csJaV28PBbYI8nFg0tSy3mfrfhibZ11u3NRW8p8B2CNh1MjhPzcfPf2oIvZUz7IcYq9EgFfudtZ39B0O0Z+itmi4pG9SiKI3mOplOdCiUoJRaOSyL9dByRf1wguBVF5TuUKKABHPAqM1IKi3AbgoO6AbUvRirmi9hCpHaG8EHXI57m6MH96ZUl3ZcXBhsNTFve8fEyTZZuXbrB+8vLmwiQWtkHG7a79A401MyQ9qYaQFaEnSKaLMRMB33MPQq1ygnQpfgCU5mmExHF16P88EZRnfut5B8XtgIUmAHq4TkNkBxI1UF8fVNYkz/vpu4mMFsBzVdVrpzkCe/dr01ejAPQ2ATDjc1huymU+zWe9rsrEYw6c7OhJFBm7jeX2vHbKiCT67ekOU7hc9dbeogFim6jZY5Z0T2xmRAyohJd8B6VJ/B43M7SG39AttQd4tH688Z9ViSELNpWcKd+TwCJLcFGSK1c9hTE8H1akMn64lSm+mSNKIaKJK3zGZkzJ5uWNY7PhPEYQF1eMEDKAFC4i1veQvXV2YB03rn9nCgKGhIpP0FPSSW1jksK6U0mmsQwAf1dShlZXKbk9ItC26IW8oEbDpppLfInC+JeUeYrqhdWdbGa48e8fFXX2NZDnaIJgi5U/XA//QL/4ZpumCeLnnjO97C/nJmP2Vi2BFjQ05He/+yGjTWze8gxGiH/ieMYFDExrvjQUwF0WAsN+0b3TXckQiMDZSwvkZK0SCJHpAWNqfwyWnkrasREkKgh3D25KuNRqUm6/W1bjh/q8VYe/u9+dA16N0Ev603gyf7SlPLFs2uhufYnDbCRLbmNk18EFwgpYyR8ypLsZljKWeHNouNz8Cg+7b1VUdPjA06CiJ0RpVpJqy9VKOUWwqOaCLE7tN9gzNRlaBidjeqm5MCIdgMr25HgRE3RqVhlGNVE0Tb6A9P8kI0Vqi/dzdts2Gd0bQy5s7hfpcqpucyNgeiPo1Y1QXqjdYqh6WTUmK323l1Y+8rOEuQ6kJbLDmkm+2QhkS6sL3XW6OJyTOsp9vRZHCvTaS1KtOkCVYhRWe7biNEPEjlZJIHc+CXO9HEk6hggXz09tSTMksg/CL62B2rIMa0sPH18NxRxghS3qOTgFfN/jX/0yBsv/YhQs5u6dTpHjSiV3y4VMDulWxJoxdFtv88mNn9tnX/fHTSO5UUzlz2vem9pZH8BBmB3J7biG1h+z3RK774Gap5P6eDlGkMxt2FUX+KGEwhQ/Yxymgv8+90uM8Y6Z1ov/lUYRCRSSAqXZtXY7juqPlrOEOMtnoHhOCLqxsMaRmK2auYa5NRPafZfN6WslKq08pVaV7KV+2cysqpFGqrZv45xjiobA4NIgYzrd2IIV2GaYs1LZVMkwnSDsl7NCRKLRxOC7eHI8eTuVbb2omkuUNs3Bwek9YTUz7yOn0DIc7kaSKFndnDLAnVspFRNtgkBK9wHFawk3SDGCxoGbHDYMCh/xmV8PmfA86KxHOPaYOTZNvQMUZqr5sey27zne/TQecejgC2+XrvrMtKCNHcHiQ5PGuBSBS6Nq/G7iYi59RoG//hAQasCgioN52bE1qMMk8IbljcaNXuXUC3ynyQcXQ7VKznsK2vLYAN41u/CAFnVIXttaofSkZn79vPi1dfEg3XUwnUAXeLLd3mlZv59Om2b0J0UsN52bMpfDy7H4la75YutW6uHkHZZAfSTevXutuYGPNiy9wNIRaHAjvilkASTCu1wYFj5cj5WtVudl8SbYKy+vr7xCCFRBPtnnEqBIgpG/TbdAtEXt46pGoaO1foMcabmMbI90IQ788Eu6AoaPQKOfghPl63Vz9ESzGD/c7AeVbUSKSDw78qlmiErmgItOBuh3IemTMqP/W9xLYf7JPCIPpYAAkaxyn5/J4eS07P1dnWh3fkJIzAi0lJtipKwp3XzYZEfbrH53iQCpz98CwQ6IBfMjaM7ELoc2OJnTUqa+icupDjhDp925IMyx6qWibSgrJoYVWzBemhoKkT9saSQs3xQcAy9qEFgO01BF+AvdmU3KplS05CgLxL5KuJf/uhD5jWqSw2hfj+noOshH5gajvWJyuvHh4TgpXg0y76YL/u/QmbTbUWBRrHUyekifny2rz6QiDP9wjpkpweMF3dJ6bE03LDq4+f8JGPfoRHhyesofDgzdcIxkiSXAgRdsnHubNy76WJF1644sX795H1CjkdOd7cUkrnRm/Zhcy0m4lq7h5xnujdHEDAxL5Tyuxmm0jba3UBrmdevhlrbaylELpl3SEEYs5MYhn5gL3EbW2ano1mxwZdV/OdGyNAphiNTuvzsEqtlArzbs+xdw6nyml5RgyB66t7xJCJIXFzuEVoXFwMZqe5ZoybGYJRn6f5khwjk/cVROB0Orm2p1Hc2PWwnIgxcolXZ71yemZTgXfzbJZHHUIy2EyCIQFBDTaqzZ4rRqs6837PYJK1ZslJnGYEd+5vnkQF6GqD/IpavypNM7EbC25Zi1Gv62r9He2E1YxNb2+PzPPeqr8KEhPT7oJeT9Arpds1OfUKkslJCEQXzMNaFugVWSM9RTQHG3LZGu10ggHRYhBk6+4mH4XD8UTMkTxPiMsBTscT027m6vISUkZCYl0rMoPsJ6b9JTXMnI4djZGLq0tz0NC29TpDwF6DmtnqmQdQt6rFROKRXtzLUcN237dzSIyRGySylNXWRIwbM5ggjDlvu02WIlsw7zF5xTUSiM6UZoRGLSdSMqq6eHAc61twcTcmxJdgvTQdyM4gRnhA6p4gjYl4w8JNG0xT2mjqwYkTtVRHiEY1JFv/TPX596+CMR79oc0Ss5QmX88mzQjirjwqtDvf/2s9PqeDFO6FN3ABO3vcFn6GNAu7ezPTFYQL6FOk5UjLmTUmTvhcGQW0UxUKgaMIJxFWEcOzs8Bk+HOOluVECQQXqgYwB+kQXAluNzL54qvFB93Fyda+mI5qnifyfkaqTVnNfWKa7UP20FNjkROtB2IVoJNy5Er2JirsFZFumVqYCCEhEum5IHlGpyuDoGJiunpASHvCdI3mxKqdR7ePOay3aGjsLjMh77m4ShusJLEgQcmSMIVgpurCqRxY2p59jOTdJWu8QOKK9uRmki4UDDjRxJyVd96DqbWis20Kceiq1kpyplJ0p/jQbFO5JQUkdZcPqwpKqwQNTtc12nCthTFuoqux6pZafXSGbb4oQsqZ1NShMwAbLdEd0lqWlRghR2E5mZv9OOD6lkVCipmYjEpsIzasv6bjfTXXn4TmcPGYrKzIcSFGqxyqZ/FBzCpGgF7N8kgkOoSK9T+CNfdN9O30/FEpBSPnmI2SC6RlQDaB0qubEjezeZLuFe9k3pB+KFXX5IhXPOpJgRFAshcInaYRgjJl6/docyuurUfsb2a8LlUTzTbTd9HMMNdxYkKa6ATW2uhiNPZ2trZzpwwxSKp1lmVlmoxVWWojpEZSyHliihPt9uCCaXVSgFW5nj96smPV/KhqFN3IAjH4ZFtsQvEdDNLj1NCBiReuZ5r1qECMQGNjMiYPGq3b15QhD8H28UbKaNjJMvnrNkLMACHGHt2qO+5UNWrkr45sAers9CB3gpQlPV3aFvTsd1tdGEOw59FR2cr2tnVUZpz3wsZk1rOjjiVx1i+2pMXHGPVAiJ+ZDfrndJBSZxHhrKEYrQdFjrBX8i5w+eIVu0slXSu6U9N47PYsbsffVYiqlm12Y5zcULlBOMVAzcAOZC+EDFOPxJDRkJhj24SfMVmGXn30NmKWTCEEo1KnRJyn7eYNoW8Iwn7DsSFEg1HCLLRcOMnBFmazZvhEJhIoulJ7JQeDGkLeIXEHIZlpad7B/j5xmgl5Yv/gdYScCdNsgs5l4ZUnr9DaSpiV64d7VLMHevHKxrwHaRDCTIgTS79Fj8r+Yse8u8e827FM12ir9PZkg+/ilNFeOS0HSl1AGw+urqEb9Xq/zwaxiL2vVioh74ghkKKNim+xU1bzN6xrIc2RnExYaA4YKwlj+jU1542lFL+u0QNk5bQs5JzNwTtnJAlpmskKGpWyHEEDOe+Mtdcbh8NCSp2alMPhQG+FZV3dZmsH3mNI07SNZokpbfZMA05cqzWhu5iLdymFUi1bLvXIfj+RcqR0rAfailk3hQDO14ohuP9eh9mpzenstr+uxQ4PEeb93gL4WvCz2Ux1XT+ztMJpKSRJBDP/IMTEFCOHkw8ADIEyDFmpQEC8p9Z6Zze5/Libm4KqsL+4sIZ+qUhIW6AyyFDPGj8V6+u2yhQFunn2mZWUMu/tcD8tlaTWE2vdWJOtsZkHqwq1KsfjiXR5SZLAWioxNRvPPk30vKP1ZwbXdp+87XDoyC6DMw9rdbg2BPPtE5eWu6xilyaD9Lq7XvjxrDS61sE4Z54mO5vU4BKTpSRnq1ZCMw1gx900cLh75F5p2Ex1lEAIs8dC3YKi5RzjB/TcJ/MeY/IzsQ/PRMfoJLjW0uNscEFua23zhozxrBuNboggeifYMuLzCMJDd2XJVMM8DgfMiJ5dKCJGQknBbN9ivEsk+9Ufn9tBavzpFhEaA2kfmK8y+UEm7RPpwQyzUnadZ6nZlM2bIxORicg+DW2OC/iAR2li2SmnMMGFMDV4/YsvUDRQSOBO2oEjuANyymZSem44BqZ5Z4dlaTZcLZ3ZaF3dcXsTtOmIbQQRLmbHzAWnTyvr2mlBueWEJpsrlKcd9EA/QU8zMU3cf3jFvLvk8v5DJJoF0OX9F+nSWXXh8Uc/xrPTY8gLEm38R0jmmkAY/TWh6QoKOWVKX2h9NUqyRGroFFGKBC4fvESfAuXJx+0wBJ4dntGa65qC2SBVH32QorMiWzdRr9vD9G6ZcXO6eevNRxK47qIr2iqTw49rKeB2R51O7Y1jKVvVVNYVRMi7Pbvd3pKGZOr30s4VUUzJqc0RCYkwmr1iTfNpmunNDjAjK4TNa84t2zbGnWpH2iAUWADQ3ijdDiaJlmQMdtTtcUUPNtpcgj1XaVZVxRgRdcutcTDV6r/bJQVYJdXVNS5O+c27C5NMtM7qbtaRQIgz836iB2PrtVrJYiNMrq6uKa1zW9btkDNBtPVzolpvhLtN82AHkapTdIL5K1oPxNa2tVatmS9RCD0SW2O9vUVQLi6uzCoItUpKoNRKFkhqCYACtzdHJz15z0YChMjpuFC6MF1fI2Jz0mhKSMKcJi50x3W8oMWCSqcHNa6VjCoayE4tJ6Bx3iD70X+K2L0gWHVle7wZ1OZ287Z/XRqg9neLG+7M0I0tLrjweusdyzgy7Pd2JTCqUA8CQV2NvtVNgLEPQdAY7TnVHGZExdayV25R7B5tvTsZPSbrz0Un/ZxPVfzrGN40vn9AfhK317D11USAbL1xd/IZ2skQxH0IbQ+lGEiT8Jk8PqeDFHiZrO5kEDG35H1idz2TLhL5YiLMoFOnTY0SlAOdQqBIpIpN16wS6Z6hH6dASULLaupwDVwS3ekh+UC40Q/riDRimggepBQBtZk4Esz1WkNA450BZs1HkYfzqJHRKBeBNIkbgdoJPTztVKDIedhYj9aLkBRoKUAS5v1EGB/RXBUkBVovLPXIYX3GYX1mFkmhglSHQWAQmEzb44latL5GoyOhUaWy9mp+gWpjwEPd09K80V+XWmhlpbTCLpiodtB0wSCkFpqzLG2Btzam4rq2SM9QnIptbBlGrRiDrot/qH20rvQA0nXzJhyaGYnZoI4O1ErvNr9q6Ius3xG3TNXGpYezZsXNb3s3ggbDocGvV1OzIlpdvyJOZjCbImNzSjDG3+gZGJGis5vN8kqcRqxqIyiMdKDbIRb6+UAcyYSKGyOJnF014lnro9XZfx2rwqIYIQjrsSWHkFLKaOjE3sBnCQk+uXj0JNwtxZEyovdhoydfZ9mBU9hxkyg5ByntsglPBQvk4hbp6jISq9oUWicmDJZqlUzyIN/94LMZW10qk3pCV92eSoUpJmYyc5ioAbo0mksYth62ysbMFBV6HAvf+8/+XOe+jNt3oS76P7PZxmHOWEOjivH/iYhXjQ45dktKRyUFzpyTQZYZ7D9LxBn3Qjeal50hMVrvR0ckG/Ce3wOHH8859ODpDZjS1/EQiHOu1qR70iXG0rMbnzbo8wxxGoQrHsjt9ztcOl6zBykQ02t+Bo/P6SDVxMwSe+iEqMyXO+brzHwvc/W6S6brid31nmlOTFNkfyFMwVTrqpGiiRLMqmht5t9g5exuW3iTQkLZbTfZzF87amJW8fHw4k1LCViffMxoUcdkbeF21yhEgTGbyEr7AT10z0rtMINApRqTMDs2HUYghFJOJIns93tO/YT2E6eiJA481RO73QUxZk6v/go3p6e8+uwVlqNVOUmqvX5RUjY6pAF8gyJq0EVtFZmEhJGvVgqvPPk44TqRZSZLIk17rh68SCgrlIWj+6AFOnnO7Hcz0qx6WktF6JQU2SUfKd86dbFDPucMMhrLGYlK1E4XIwxICwy2ZOnWRynrarOHQnJiRCDtTbNSAaoN5NvEoKGY8j6YY7tVTZP1iEbP0Q/gs6Ay01phWcsGy9rUUWP+1bUYxf1UiTGTYqZ6IG+9mzN8TJaJ18pSGxIzU5q5vn/PDtyyUuuKdpvjZFZGrvHqEGTCxLRiVjb4OSY24kVD8Gq+23C5EKHZ4b04c06C+3+b95T3F7yqDIH9fs+xVbQUG2qoEEN2o2SjYwSxFC3vZ1IUG6PhXpfmducZNJhgPo2D22aI1Vq4vH8NzWQU6lG0rgbX5jRRu0k+TqfV3PUnc2SPtXN7e0OaE5fxihQiMTSON0fCFIltT18Kkjv391eU2rgpB06iVAJN2pnp6wEjik0TNpefwU5wWCvgfRl793f7LZD8EPeEymUBlkRhAXBojVx4bwmGj6EXv4EywEOXCLjWDEawwV7znZ6UjDNDxJjAIjYuI1oFlSRuM6vMIcdIRD2oo0/nxGNAx+KJFAFyMCfzWs42cua8ESFNXrlZkuEZNsHlMSOpMQafve5wRyakAv0/BHbfaHCqWhYZcyQmyxRDFkIOxCkQciJMEy02arDsKZCIJDREurgCXtQz9tmClGLsMXTLSkdmE1BUF2e1nKsh+7e/PveuCnGIAd0f646CO4IfKufK3pK7kWka/m6W9w5xxLhB0b11mig9dKO4d2VpC1WUtgilV0KIrKfGYblhLUeQbo3ekf3h2pBts9iLU+/SdvGersMGQvCZRab1ybtACoXQ8h3RrMGVZlJqxqriDL+hI5JmY+s3/aJfh+AbHcUrMJtTs91rHci4P9eYjTRIAgOC8O9qtdlYgBAcQffqZtCf/XlCCC70VR+YaEJXuxoWuLvaQdyfEyvbdWrudF5rZYhhjZih23u25xgVVHOyxdC9KSMMIOZvaCd/B7Upz0OXhZyz4tGPks2ZX7zaHOvKD4eu5+AfhEEY9zf4a++1cYD5Kxw91BSDOZpHGxQZAs6A8xETeJByuA863X3t8jSjrbGUg+dw9iJ6V0ovW7VWa4OoTJIZ9QNOla61Ep3yr+NaD3y4K1OciKMS8DOjuT5BGLOQIGy9HRiuNdvD9yK+Bs7Fkr8aiVtgUzkHqU2C4j0ixH38APDxNiJjaodVw548DLPlURFtozT8nWyV1LifW1Q12qL540WGFEZGdR+CuW8MXai/bXFiy+ZoLgNdtHMnjFPQTWJ1IA6KibtFBr697T0v2e5cr7i9Ug1uL/UZPD63g5Rn0zosdlLYfN0ksQUqyQmZZlpYEDExbJKEhoymZPZijIYqaLfGvUEyNiZ6K409QHQ662KZIWFkIhgkh9jEy2i0HclG52VAVd4kHosmOKwVkwlI1VJgz3oDdDO0lOiMuDEPSaH2BRFFo9WBHaX1E1Iaa+vEcgKEtiprPVkFFS2rCbq9K8f3HfICAkL14R/dF20IzmLUAFUJycZnzCETQ0XLRF9OBtMINnY8m+g2pLi5Hvduuq6G0sT6VEFH/0K2IDW0QiImnu16nqk0NlnvQ/i6nTB+qNimG5XqJB7kg1GDmweZjQHnmpcxtPJ0PJo5caskn4OD9xX6FqSM1eXgqFtnNaM1S/X7Z1/vw5apDyGq9QdiCNa38vtpZ5kd6CYSbQRPPlDr0yljk5/p0OYRd2d8gngwVjFNGTA0OOPgU/z9+4Fr7Rf/bzulzziUwTlnltiYtJuisbZwyM/LA8AntOIV5whSPrIkTxNaKye9tcrMSQu927yunK3KrbUyZi5tlkTR+iSj4X83xo6ZW9KUKWdi88Ch5o3Y/GQWLGEQxJImfxYdZ8uoWLYANdh0LscVD9vhfM3H9VJ7pwxRNl752Oh0CybiULO27r0kBe8h3b1PAxLfwqy/rmFHJF6NmTm2WlBxJMDQiH5eI4NRu+0VS1XCqKr8OoHnNJwrpRGkVIJV4josntJ563mA1K3oO4vQt3s49vl/CHDfeIzMLk2ZOCVkioQpEeZIy0LaZeLFBbULnc68i1S10ejdK6kCMKigfTI8uluOo6ok32RdlFKqTd3N1oQnRloZI5nPTcaYI2ig1M4UA7t5ojUjCBxdKxNz8mYrpClvB6DNBrds6HK+NFKGj0OHYdcSyGVGpCGhkgc2HswpfF1vyHFnyu5W2Sdlf3npkGPfdDfbiAQJJmL1TbaKmafmeIHS7JB1AXWO0YJqaOTpkqg7VplZCyynQpBEisI8CdIa7dhoxWjlgvdZEFoAiUYN763YRNtazhk/WLoWoRVlrc17TVsxYFWQRGN2+pwjCYnjcrLkLmW7T8G61r11SjeNjsU3mx0VY/ZxCDDvJnqLtBbom8O5NZjneVgaweF0IoQhHjc3eFn8BSblt/yxj7AehB/7L14CtQM2TZGoptuSkOgSWUv3IGSwYJTEYVks+04TOU1+CJwP0n43e5YAREIw1ugUxau6hkFvQspWldRmbu9IQDVyKjYReN7v6MDampF+ponD0UakxJDIcSJJJOMOEMFcA2yitdr4lfmC07paz7HaoRbcsgmwAYDZ/ATzPKMpknYT+3lmzpnb08GEv7sdp9tbDocjy7KQfeTJxX7HNGWu7j1Ag/U1a+9oWdnP5vzf15XjcSGkhTjdI4dIihmR4teueR/H/AXREXCcOEGgqUHJI+REJ2/UYo4XqsqUJv+J4ZVnSYEFlwj4+hpCadxHEIC02RnhbuW1dpMy5IRWs0UancXgSZf1PitjGOdAD4KvR9kCjLLWSgqBkKK5mrdOKSu4/ix4Dx7t28yxwTg2E9uRbEQGnhT8XApi5JhNVO4JxDiXxlmlKlQn8eU0AuFwZfnMzvfP6SBlBdQAIDyrdK2NBqH7h8YAyby7OkrfhHZmY9JRd2fAkYSRodhJKH001m2ZSMBwXM9g8OrGrJWMWjqChUc9e74gG2snhGgLxbNRxUYedNgqOxu4Z/OerBHpuobu48FDwEKK6V3GYrEJtcZiiqETQ0ejCRglBVozwsBgmY1hZiqDvTagLatYphS9shQXUNsI9RDsqLSFbToqxX0NQyQGyMHHRHfFbKbxDWwX2665wzfime5o/o6v+5HcfPQHijOSIqrWJB8kBj9zHFr1+5kiFw8LF/cbNx8bycoZ/rGNboNUBv4/qgbLXGz6rSCbl6CNAjFW6HyvcvFCRUQpR1gOwv5B5ep1hfgFjXQrvPC2hcPjHcshunTCCwu3RRn+sk2NJq1BaFUNwvPGvgRBwiCUjHs2KiK812m9RQnRx6317WtjPZvtjZNTvHKhd8Q1fk1Bgs0nMxNlO3RHJYpXe+O+G4HBEoUYEkFM42SODpbEaTz3ULb9FLzayomYo42oqGLrBGcfOvpQe2dZV0IMVtlPBjdtZBEUle57zWyueilmexYiKURcUWdM0gGXhk2kxOgBbXj9SDgHFG7RzDaoV5oGp8mWmJ7JBQbbWWViN1s0erV1F2IdaIbXNuJISW0b3L1RHAZsuL3WAV7bz2+GFHeqYfXXMZxKuqifQMMRZFSOcn4/W3XuXozjetz92pYgjeqS7XWeD+jzX72lutXld7GPT/f4nA5SSQNRLdIHDdRFCftAlplVA02VfQi0YIK5GIxaeuyNYQGCGz7mELcqJlmRitmwBMYE1HFmpRBpvfP46PqOKEzzhbH7SvMsshG86hgLZumyzcnZXVwY5VsbU/JS3ANLF1B3+rZsyVhrk5tXllI32CtPPupizLvCHDBIkYsw4B8l7G2Y3toMRgkhE6Ns/RKbamzK8xG4YrOeRVbLfCUkesQqvA45NgKrqeHTDOES0jUyF1J7yiSNiwhSOlIrsnZiSMx5BmlsjfugaFBa6tY49pwtEKxPGDprdWp66dshnKYdrRdaK2bmKiChs9aF2jopz8SUyFPmS/+TD/HW/+gx/++/9RUcP27jOTT5ASVitj/1xL2rK3KKHG6eAZ27Rs1JjH7eCaQsIDZO/W1f9ZQv+z0fA4FXf37mn/7v38Q7f9djfsPXPeX/kd7M/FLja//ir/D+//qt/Nv/7gVOdUWlb4EgSrSRDWp+i+DofZ5pHcqqXKTZRJDRRlnUqqQ0gQin02rXsnYkGYxNt1EM2gqlVGv8p4g62ePpcmtszYQd2DFxW7pBtGm2CrXDlMMGY/eQjeEKhJiJ00zKwSoqz/Zr7dvE5mme0CRokm0idJ5mmjZohSYmVt5dX9B749hPyNShVG5ujiiQ5x0SJlpvPD6eeFZs0Kg8hv3ljocvvcBuF63SCZ2QlDQHlvUAx4ldylwwcxFnTmWh09lNF6w0imprHH8AAQAASURBVDSHv+wMGae2iJuJuWYQsNgF28jzIXRGRwpq/3Bf983oFYFWvR/kM6QEvdNbtNlKop3ULKGMdEozJ5UYfZBrV2KKaFD7fk9QZMAJvRD9922DCVOkS7U0VsT0gSlRa0drQ7JVSF1km+00HOlrb65JjLSRdHulKB0I53Cm3uskCq02h1O9uroDQXff2SOgtxH7Pt05/5l927+fDz9/t0wypWwH5kYAGIFmXCQvndNoqo8UQDesVgQbImhJP3cdrEbm2cQqtP3+Ynst2q0/Ipjrc8qTLVgFSeKIjFUfggkLVSNo9aYxWybUFSpe0Xi1uPljwZ2/yya+s57IcBjwr/dzo1c9QzSfNqNPaxgZ+LnHYvb5as4FoXoKftZEhGTXKpGQCF06cUrm3JF3xDwT00yedmSppDwozJHQGjFEcopspqeerLbuwb3b4W19P92MYVsbWLxNjLWmsG0OY9n5YeJkFbpNKr3/hsI7f9vHefHtt+Rd44v+l7/CcrCx8b/w/oe89isXCPDC5534/Hd9nFf/bebw8Svb2ENkLeY5GN01O2AVzXxdedtv+xiv/+JbYrZ1cvVS4d3f+Ig3fMmJkK0BrqLEoLz5y56Qdp2f/dEXWA7myBHc1ivFUe14ZRKEJMlo+T5RUYBp3pN6J8Zq9xCYZ6Pv195Zykpt1a4JZoar0daUijDcJ4tT9qVjsDdiU119vQ0Y9NwEZ+vRaBR6ghqVGtWdK3w8RxA0uSltCm6xE0Z5i9AZoxtSzIQAc8qs64lSOsXth66ur3n2+JZlOTHPe6t+CnY4Brz3HKm1GbklRqbWLEiJTyauBSkLos0ESmoMvqoNfHTE6BXFzjBYo3oVB+f+TGBch3CnX8coJs5niX8yeMWpYEMXfN8GH0UzaqeNeq4GJcdxbbI5XMSYtkQyeg9HNv2ZbL8vfkKFNnq7d6uZ0dcyEtK5kukDkRq9I9dnBh9XM67AJ5Or7FcmP09Gn/N8Ke58v4izQ/Frfu6pfbrH53SQGndAvUuX3Axyow4jZ+aUnCGPkLwXMxyJR4NDZLv+fVT2jOBhmpKm1hNRCez3l2g3S59WDZaIwRaa9YHstUVjKhjvHN0anDZTKm6fG8V7QDa3gN5xjNhPMc4QgI0SSCA+fiB4hrcFKT1fJbEyUAhOwXK40quv7g3oFs6LUPoYYW1ZphCQ7DY6niGpKGlyq6FpJuaZkGdy35GlkJJXpiEgzayPUrTDa9Nmaaf16gJeHX1oy1KbpQatGqPJyA2Z6BtC/NpIMnsb8UAmwa77vTec+E3v/ch2X7/4P/6wg4fCk1d3PProjiiBh2878u5veIX/4f/yOk5PLi0YuyBSmvUm4pbcmA3T7l7jN77348Ts1xCYX2i86xseo5jFls8jpiG88Tc94XXvfMYv/dR92mmy/plaJ8QCoEDoW7KUkl3j4XCAwDTtvUdaKW54G2JiLYW2FlZj85gx9hSNtt3NkbyqDxxWNQd4zMmhqNH0k0BSOLfWvaIbe8fLCU1Cj1Bjp0YhRIPNkWCwucOLPfpI85y86uhoqwzmmSWVgSkaWlBrodVOkMj11T1unhw5LSuXV9e+XgX1CtwcWyKtm9RDYnWPOJ8v1e13sZ4gVqsUZDJXmX70/slg8JrEIfkOGbCUbaGBToxDfgPmQCyRwt3Ix/s+Q9EeCQZ6KOPIt5EWGwNzBLpovc4YEiEZzBu9NWE/M/R6ZzH4II1IjGcauZMtxoiQ8/fa+4pxWEFZP3pUQsbkuyMADukceCRwl7Xnk2UQMCeXO5pBC87q73QQatjWlHVuz8H20z0+p4OUMZMigwUzmnZgF0AkOv05gUQbrS7iTfruOoYhHGVjpMScnXHmbsmjhNaO9rrdgOrTViFhpDAPGiqe0XvZa1+CLTsHuut1OJMzBjXWhMljY3s2Drag/L2ODGzsgy3XlvMmG4t6bCp16ihb/+2c/YXZYQVn4IQQyM5Mi1u+H5BsB2jUSEqRnBJVnGEVMiFmQsikNNsBp4tlhgkkDiqEbmr07lqxocBvHdNxuKi3Fs9rtRI1QI/0ZFlpdDG0QSl2raRZZRUZ9PBPztZ+hT3/Pa/ny77+MV/2no8C8MrFBf+IL+Rd/+nLfNHv/AVaa3z4Xz7kZ//pWwh58hxm5JTQt9HybNf4/8UDBPjdPOanuce/4R7PmAjAP+LtvJtX+Xxu/G5Y5VLXRumdGKftfjRnasSUkBjI0+SQZDc6Nt6Y90VrvU2rrmqxYK9VecdvfsSX/o7H/Hf/5Rfw6KMXPgDQTJSrowKK2IwvBiPNl+ooF8JgoYkLoiMyB9xjiOp9yTj6QSre2zTpxzYiXLb/2T/H4YcRViy5jIQ0ISpGLc+Z/eUFtds+3O12HJYjpa5U7cwyMTPZPhTQWmnLwklvCPuZECv9dKTnjmZAjEmZwmxtHRGbSoCPScHeVoCtkhoWQVsPhjt/l6FfOldcxj8QehtJhlcYnhT7N4yrYLtW7EywgsQ1fvFM9x/nw2C7Ds+9Me6dAecNaD8IZ8ePwSbV7bVLiD6/TQnSvbp11CDmDcY0j8Tg79P61cPrUkdZ5BWbjdzx9yRsGipLHMfr89/PJ+/JX+vxOR2k7tSVtsF8lMCWTd/5MIIDIH5YB0tx1BX8Vnr7EepiSYMBvcHomVLnvOk2pwhbpVhXadB7w9Zr3VyCvZTWAQ/oKNkHjVbOmzmYDUt0koYnId7DjRtkcMaSw1Yt2LeKN4XHWvLdFMbileeDVBiQzHnBD3p10kgQTOORnFPWzT9OovVzvKwxsWqeSEzErkivhODS6NBcbb+tbwvCyjlQYSr+1s1PsaodfsZd8yDvTXdzhPBKWAY5xcZAnBve59XSgde44KPs+Sgz73gxUV5cEeBI4jUuuX34lHt2RciPGw/fcWNO3gKBkVhAbZ2LhycQuCXxlIlXME+/DzPzUWY+zuxql8CrJD7GBZehc/+tJ1IWbj9+xUBEHrz1hroGnn40+/vCNHYOi3RPoFrTTQ81DoQN5/d1FnPnhTceePEtNzx40w2ve+szVJSXP7K3ibuOBIzkpmIi7dZtXAjudWdLNGzXPoWwTYGVCESruIzWfaZwb8mTL9uxysatGMy0zbHg7n8hgzPdQojkaXYGmnkYxmTkJ7/JRkGPIM36IUJB+pEYToQwEcpCjIEskUSgYobJz58cppMa+8QcJfT8fob4Ue9cd4RhiC4DjUEdeue5vWSu5yOojL08utVsa0qGEBbZCDMbZsgdgsLdda2D2j8C0zj7bA3bpetn4oy/M9z9IQTZbMCsWnItk6r1n4Axk+ouzLdZITnkrH6jxd+PjL+jm/ffoLmPg/Es7f21H5/TQepuyavdzCbnGXaaNswbLAOIMUF05pxgrD4RM7/EDv6x+FYANfPNKEawsLEYlnXijudpvmDwCmspNrL9tGx9lxiSLTg1MkSjj2GatGYbIYhnQ+pzTeM5kEQJTPNsU2lL9UV0FgeLYM1RbWhVm2vjCycMfBk7x6PTRYeOazzBqDTSnZ7XGPEesm3U1MbzgSSbntvX5m4LwvG0kGsgxIk8XxFQ9mUl1wNxUbJWUq+QjUFm+YHP13KPvtqamZo6/b60Timd3qxnt8+JkDJTylbpek/LRP19C1YhR3JOmxO6hvNGKER+iHfyCKFx5Cd5A/+S7PcwkIn8Sz6ff2ntZL7wyz7O/+o3/utffQH63v4AD/hJXs+BlUbjv2LG7TQxL2ujEv80L/HB6SHf8K0f5PanL/j//B9+AyEm8kXht/3xD/D4ly/55//FF5LyxBhSF5zhWXywYamuVwsYNAMUGs3XpsbA/TcUvv5/8/OkbNnu7/imD/LyL13zX/3nX8bShUqHvKNrY+2FU1fWbmJkqY1Umvv0BSYxi5spBWKaSPMMkxFdiLr1QU80o6eHaNpjQFtjkjHkzn1MQrAZTWIi7d6U1qwy7RqIeUdrZvAb8sRFmnj6+CldlBQj++maCxFzwugrh8OBiR2KcDoeSdIoshJOStwv3H/wEhf5ipemK8qixG7ejw2lqjlQIJ2Qm79KdUKLsxE9ePbqN3xA5oIxMOl0LVt1ZbZPuKbPKekMwXdnMAfPCaknVyJEJyWBs4XVruEm8uUO7OoVlsWlEfg86ITxveZP2fuWHiAh0JsxgdM0g6rZivVucLpMeMpv7hvicLpXeOIYX8wjmAdqs3p8Q1vkXAMGf22ikLI5WIyAaefzp398TgepgdeHaBN3tzHuAxMfGQhODR0CQK8gFNiiRkzm+9UtoAji1h12EknCRbfNDr4gNL8hHZv+qr377wg+48aW+jAI9XTcXnurjvviPSq2zMpqPFsEPUWbnDq7BY7DmpuzAMY3m/WMmo9r46TsO5WUELcgdaZ/y50/Uxgjze0QCrDNBBIgZr+iKbAPe2Z25DaRNJCmSO4VjcJ+PRJLQLRCOdp96XZY21gT6zWtdbVelKrNJGoN1ULv1jOLOVlwjOlMgfZstall8YbDs0EhiAXr42mhDBaVX1c70N1tBCMSOGGfuoGmdoU+Ihf8VHwrTuFAWf16Bh/ELkQyL7PH/BEyQqA4DVooxO0/+33dKcH33nTg3d/4i1aJZPhXl/fJbxbe/Xs+xMs/8yZuXrkH21UXYxYqQKarEQxCtiBcVXnp7U944xe9Yj3TS/ix/AbeGm54a3/Gv/ixN/PyL19TJKJpVAcdekBrp7TG0hvVXT9sjnMw1mEMTMky7FOHXq2JNedIniKtrfRu+h7wERQO5Q4dTcU2owhMye5jaIlWVpoqp7XSVIhxpvXVyGp+IKp28jw5C1RYHWKfdgajhlpNQ9QxU96kzClSe0HXW9Znr5F3gZfCfcg7jtq5pXNslUMtLBjZgxRcfNu3XtHG8BVfrzoqQ7ci0u6I6124bRAPRg/HGL2WSPv3YVC8VYhhQ1V8524/42HIqxg2CHZ0r4f3oar3rqMhOM1bmPazluS0Ziw/swVMW4DB4TvbU0LrmEOIDHKTPccQm5/hmjP1fvQqa22bd2fwfTqqyI7aUEkZQft5Mtiv9fisg9SP/uiP8lf/6l/lJ3/yJ/nIRz7CP/gH/4Df+3t/7/b1P/7H/zh/9+/+3ed+5mu/9mv5gR/4ge3fr732Gn/mz/wZ/vE//seEEPjmb/5m/sbf+BtcXV19Vq9FROzgGxej9TMsp+Oe2kIxtlYyiELvBA23TJGY6LVZVRLsBsRw7unEPFguzSoxwaiZnmmQHPLL7k7gIl9B0DrS3+jVuSIteYM1bLocOE+M3Wx9fPEHD8jPUVlF0GbHY5Jpq4DOjKSx5H2xqSvkvfzeEqw71zOm5Hi0ZctDfyGOYYy5OVkys+zZsSMXC1K5ea2ZhF24RAL0cjKDU61bU1mSGYqqWgVV3ZqodmOo9e7zt4INE4wDghlBiqHjYoObIjCau2PqbT0cvb9jkFZBKDS6kfbtGviW7yiVwjgtIsLH2PGEa8xXuqHceq4YWZxOO3Hhgc+dzBE6q/d7rBmvCDNn6KsRuHxx4Yu/5sN0hCOJ/5q387rdkXe/51c4PXvIemOHU69CLd7kJ9BaotdCaatZgIlCqrzwthu+6He+DMCr7PiHvIPYlDesB376/W/gox++ooox70C8vwnaIlUbS1dsnJSirZMkGQN1iibfkMixQZVuvZMo7MjmKdmBFDdIMg7YyOHHJiPxAkmJ2CEE6wv32jjV6iSBbDZkBm0YGUiVNLuXowilVTPkvdx7pu7ZuJo7jKgyRaHXQq+w3j5ivnfBpSoxzZyAR7XxTBczbhZjO1YR3DmPpv08AYNkr1XanX1hQaoOayx/JQPes73ksLpD2Rst3PfZmHun3dbJnV1o67Kf4ceRQJ7ZyGfozwLR8AkZlmV9ey7bN6Be3qrivXvT5229LRm/F0YP6ux8Mc6+8fq4g8qwiYp7wyyYHL6xRPm8VwcEuCHV8gkH0K/y+KyD1O3tLV/+5V/Ot37rt/JN3/RNn/J7vu7rvo7v//7v3/49z/NzX//Df/gP85GPfIQf/MEfpJTCn/gTf4I/+Sf/JH/v7/29z+q1DEGj30FT05dmdOUmTgvHB6WZalxydMsT+0gxm3pezBYTdbNGjJQS/BBrWjfgNU8ZiYFTsUm7EqMfpGxjsgOAsweTmKtAFzuyDKdVRiNetp6NbBZJ5xAzloeaQ7ae3b9FLNDYdwabp8XIrtS1Gp77P5fd+TPeWSNDLyKwQYYMAeZqzxPF+h0BCC2wY88sF7x0/0VSCeizI0wJaqYtr9F6RDXQKtACV1cvELTTe+O0HimtsLiwtNTqcJ+PMAmY/ixna/I2m0pMNP0bvrm9VeCjPDqIiThVLayMPuP7eMBPc8kzKoIwMbujtrCaBS2RzMpKpVKoTMxckjFuXmf2A7EBq9NcCicTnaJM7EgIFyRsnnNjzPQ5sJBJdCL/DW/jLdzwO/kwP8Gb+CAPKex4mUv+S/b85vd8lN/2238eED76gdfzr3/oi7dEI+cJWqTHwNJW9pe3vOcP/o/88r0H/EO+kkLlkiPfzAf52Z94wP/pn30Jz57OVAFiojbrpx1Xq2Brg9ul8exYWFbLtoMERBsigSaZ2ApPT0+4urhinmZqTdwuhZtj43rOTDFxWxpJzIkEccFyCGgKJtLFJsseSiUjZAKaJ5BGr31DMSTN5JiN9OP7rddC641TWQkt0EQ5rScC3RijU6TTefz4EXO6ZZ0PPHjxDaQ58OqjD1N0RXrlTZ/3dtLlNc929zgG4VYCryy3PKsrv7I+sQSLbgJ0lwAEBKqxBZt2ivuEaghGMqARetn6QFu1JHb2nNujtv8sWCiD1TvmVm19O++fh2gJ4iA8AN4jHkHwEw5DRxFsf9cNUhuBYKOvi7A5SPikgJHciQwjAD8nRl8pxC35HUL8rt05AJa0iATybudv6UyEWlv1HnqkVEX9tcUorMv6Kc/1T3x81kHqve99L+9973t/ze+Z55k3vvGNn/JrP/MzP8MP/MAP8OM//uP8lt/yWwD4m3/zb/L1X//1/LW/9td485vf/Jm/GPED1SP+gPrEy3Sjg5uBZ4jmISfJXIDHRY9xcht5O+jPGitrpg6aATqma+rmoRdi34JUGBAzI1s4NwxFMEjCHYEt0RiQkCIuKB7eWgCidmssYA1/BLcTwQ5wo8Y7TEfwCZ7WCN3sjrZKLDy3aOETgtRotuo5SI1+TgherQJJnDIdIpnJPmQyt4y4WmWqNtYEhNYwGKkpq0OTQZXS1PpOzUZVFJ8h1bWzzTR1pGNQ8I2N1Y00qa6hutsUFgG18epNG697+8LFWxZ+kR0fYeJVrIfziW3zPhhSntNa8DHPvObf61sdd4v0O3emnp/pIN5rvGum6YmJVVHKE2ZmlF/mHq9wySP2TCQa0TL96xuuroU3cuDes6e89PbXePLKfdbjjoB68IUHb3rGvdc/4d7rbiHf41UuKBj79AUW7k8rV9eFZ7fW90EdIlUo1XqBTTu1KrUqpfi1dLdtoXNIq60dBMJK9V7VUg2mi9Esp9SrnzAOWV+XEWd/Y3usix1i9O7OGuowqJzFqYr5Deowgg3uxSc+x003tpzo6KE601kseQnSCNJJsRHaif7sCfrsMaiyu5rIuz37eQ8hMdeVZ72ydBtBMwySkiRLhrxvuhGjPGFQUXNgkXMf5vmP8zpjnC2+dkSGBvIM3905PRwWtLPh7h4Fq8zOwP75+bizJjf8e/tZ2f5U/y1ORDyjNnd+P3iii2CeozA8R2GQ1EZLRQmh05zhJ7BV1frceXM+D41Rfff6/OqPX5ee1I/8yI/w+te/nhdeeIHf9bt+F3/lr/wVXnzxRQDe97738eDBgy1AAfzu3/27CSHwYz/2Y/y+3/f7Pun5lmVhWZbt30+fPgUGDdmHrIXgQ+dwf7Ud+/0F+/0Fabcj7/dMFxdITkDfnPrToFyWds5ExCLO6McEMQ2J9QKK2xYpKeUtuIxaevQzAnd0Ne5+PZqKI4jhfnhjQ48qxtyjs5XTOuCA7s10pYVKTD5IDKzh2dlGo4+KCNXNnDL4azxDAZ/6sRmUqsEg5otnZXvEHD5iiOzTjr1estcLYskmhkzRGuDY6IOmkdPSqWunrZXb0w0pCPtpolbL4o+nldoLtRWa0/vFr1OUaAE8CLs8U0tjXRdynECV07qa03RQ5jhvr/20rjSO/Md/6CM8fmPk/8rrMcUXDDK9AtX9HQplu2uj9lm3IAUD0LG7YMa77tIHJL/nPq57A/W8uvXgNw4TI5BHXuYe/3ceMNIgxRrWkcRP8mb+FQ/5g/wsL739VV56+6v8s//b/4IP/+yetRyth0fjt/+nH+BNX/AqYDN8VwKVTPNt/WVf8Rpf+q5HfP//8cv5lV9xqna3w3FdK7V7NbgqdVGWpW/rY4SH21N1CYE5UkzTjtNVBu30stLjjgcyce9yZvgiFz+XtHeywox4Ax4kR8raKL2ySxnRQI/RD0zXXKkfgr2CNoafsnQLHNICtI5093fw/s2960ummJhiQrWABl544YreGu3JR3ntdCTvr7n3+b+Rey++kYvr1/HS1et41hsxXfD0eODZ6WD3XIVEpKuZDC91tdFAoTtJBXpfzxAccifr84ChuiVQtu+NYWwoitdP/Ty/Ce5UXiH6Hm74kyBOPR9kIt/igHrwcar61qM9u7yfs75zDwl/bUHcnYRO93EidIciu0HlG0ltS9twFqp7RDYYdglmPmw2XkTrkWlXcp48uQ/mEnOH1PRrPf5nD1Jf93Vfxzd90zfxjne8gw9+8IP8hb/wF3jve9/L+973PmKMvPzyy7z+9a9//kWkxMOHD3n55Zc/5XN+7/d+L9/zPd/zSZ+3634nWxgZiDcLR/N2UJaH24IQNuZbkOi6ANnICtvF6+f+liVBQzyrmyWfLShx/cPQLFj2GH1EQhuLUgZ1fFQK9lpkmOEIrkq32UWMDGsb8WE5u8Zh5CgDMSCMagLxYInDNudrBeO4/YSLeOcRhwaD7mJfsUDt12cEqxQTsScbg6D2/jfMGTZXAbyXFEJ2LY6xKqua9UpzsstG0fVxHNaba5grh0OUXj31YJBJ6xag1P8ONqvoHV/xjDd8yTPef++KVyVwYkU4q+dH1dO2uul8MawyOldKzeuk0bfqW656Ti0GXyv6584H1/m77/458tZzOBtBESKBAlSE/5438Ra54TfqI975Fb/IG97+MUMAPAu9/7pbjpL5H/h8PsK9LYiO9yfeH5DolO9mvoitmjN884Z2KYXTslIWJ7H0TkiuW4q2hlSE0hVtjZOPwdAGh7WSlsK0cxdJUXdysDfXMQugkOx1pZHBWTq9Zf0SDJ4f126zLRsmh2Ofubgbr7JiiBQ3co0pMU0TF9OO/X5HmjLsJsra6aXS1gMQKLe3LNMzQn5CfnHmImZemK9JGskkFmvMkGJCtdF74+nxKaVXlOpbUs/7jfONvBs0xkV47vu4G7TOW1Cf+xfna+PXx55bn/95kS2p0Dv//8THEPgOsoLqXXhRTAv33C88r1eLq2F7HnwUyUB9gouI/Qb5OxhuF0Z22bxQh19jCDZJYYww+jSP/9mD1Ld8y7dsf3/Xu97Fu9/9br7gC76AH/mRH+FrvuZr/n96zu/6ru/iO77jO7Z/P336lLe+9a12EXzTbkvgzg0cepJRCZggMfgecdEh45A/Y7CyaYZGPokf/H7xg27crgFJ2eHh2gEx6noaEFr3hr4LeUdmbW0f9QzMfnmU4AP30nb84dBgV68HJG5jtNUbrAPiA4MDLJjc2Rhjgd+5rs/Dfba4hpNDHzb84sFWR6AyNlyQZAGe6HjXOLKd+r4lB24+G4fvnds+dTMNHUFq6wTbKwMGlOdHu56PfnX4oakSYyfNnbRrdnjLwhu+5MgXvueW/zNv4OMECgvDXvSc98pGbhjX5W4wka0+Om//8fW+PcOZmuKiAa+4BqzDcz/bfdWcjwg7nLrXYRvdGSN5/CseUgh8oTzl4Re+you89tzzAjzhgn/N52EdmhFcn2dNjTEsYEM4SztbUHWglkZZi/VzXQ6QN83Mxnc3U9rWWKr1XbULh7USU+GiznYdgteMd9CI1m2sPQNq8v15zsiNqq2+n7lzGI+bM8Sgo2AZhJ4Ukw8NNUeGlDK73Y55nohThjlDL9RQqHWllRPleCRMB+J0y3SvswuBe9MloQeiRo4+hNQYi5aatFo4tZXWTQDctBmZ6c49fu6Q1xFbnofYP6l2GAc4I5jwHCT3qR93zjiR57fOnes7RMTnzzks6eWa5aCyabI2DEbH+Wi/K4az68UYkDmkMJsbO5Z0nx/hnHzoGcrdroUTyT6Tx687Bf2d73wnL730Ej/3cz/H13zN1/DGN76RV1555bnvqbXy2muv/ap9rHmeP4l8AX5YiSDNp7SeVvKyUmrZKgnLxswiJcHWt+m4mto31DTbeIvem1VIWGY2Z/MDPJ5u7XvTOAy8WhD3Ns0YhNemO1mgH2cTCInA5JZGIMGCSxQc1rDjLzqu32rYsshRgdXq3Q8ZRZWyFmPCxTRDsN83ElkTAY8A56X43RL7ObzbTgCrGgMpRWzWEuSczJ67WQUBkchE6BnpiRwySbvBrpPh/LfxVXrcMe+vKFopav5qp+XE4yfWpA6c1fECXMyTkT9ETAMUElNKBBEOhwMpWKZ8OJwMYqDxrvc85t3/yePnYMqfvLrg+3mJD3NDJ7DjwpVPoyoSMhPiPabCan0er2gjgR07Etn/hffJ2gifBKebR4x6jX+n16AbacLUdVZnDUJGZnLQcdpIF+N5DUxKZAIrKz/HC3ycF5mZbfKuk987Q7cXOCKbKLdb1++8RxSWUjmthdKEtSmlwXFprGthWRcOt4W22iDQMezz6uKay8sr0ryzaqtWSrMxH68+uyXGTE4TH3rtGdOTpzwrC/cuZx7c35Gaw6O9cTVF7u8iQYWoNupiHzO7iz26muZOQ7T72ZsN51OzN1KvElKOhJS4mGai6xGzBCYR9gGenW6pzaafEQN5N7O7viTPM3J1weWl8OAF4dnTlaUIT159lV0RWo2k6R756gFvffF11Om+UeKJfk/GlVb04edzW4986OYjvHL7iEfLDU3U3TrGJsL3yKha3Fz1eYYSW4ni7YEQEyI8N7+qt+ZQnRMyFOsr4uhOH/rNc4I79sDmHrHppdRnotkgyob1Hs1f2ZL1lE3TOYxzh9+FPXEcHJbNdb/bP3ySsZyDrQcxI88ADjm25hhGkK2n/Jk+ft2D1C//8i/z6quv8qY3vQmA3/7bfzuPHz/mJ3/yJ/nNv/k3A/BDP/RD9N75qq/6qs/quR1itZxXz3y4oLizgeG3SSMpCFmcJKyCio3aHjhqClZF9B62MdlJAlOwuTkanfQQxpgHW4wmsRAGU89el5fV3YmhYsfZeSonjMNyBFNbuaPS8gz+EzLIGMJWWWzw3eaoMXCUkYPpc6nYgEI3MBuDIPEKZWvKugOHONS3fXsYFaUFKVXnPXqFGBFCH6a6goS8zRISJ64Y+mdLfBAxRlUZRZjiqBCHHsqrSb0Du6rpU+K+8nlfdOAN7zxy/brCL7Fj9Urmo0ReAY5eVyTM2snNkrx6UUZtNypO3e7JgDbHT42a1gM9CeOnBQLD9PNcL43a6M6VZgR38UqnMSg5glv6MuDFsK3kSCHwmMzkQQpfGx31kZRnQ1Td3tvzj6qwdjjVzlpNY1U0cnFd+E1vu2VZG8dF+dkPvMiymk7u6vqK3W5GYzAiSm9Q2SjTXRtVK00LNSpPDysaAjIl5sm83CREUoepdGJ08k13kkSwq+YYN2O2kvbBfhNGv6/rGfGIwUfUhBFSld1upmtmf3XJtNsj84SmCU2ZECYjOk2ZmU4ogpYr8m5PTDa3SctKqoUYzHswimkfdUulgGj93gfzNTfLiUNZOWAi8iHutY3klbeyJWCfdCDfQfZsX3Lel2Oze89qUNdDkM1541xtydiUDJzk3N2G84iNkVyGO2tx4JODCOKn0Qh2G/rn33/n9Q2MAWR7n+MNjbbGILDhyeGGrozXKIM3/ekfn3WQurm54ed+7ue2f//8z/8873//+3n48CEPHz7ke77ne/jmb/5m3vjGN/LBD36QP//n/zxf+IVfyNd+7dcC8KVf+qV83dd9Hd/2bd/G3/k7f4dSCn/6T/9pvuVbvuWzY/Zho6tHn8Qqn8gcgue/ivRGXY/sdpGLKXAZDUqrXaw3FRNIO9/iaAfDuMBpOy4au+l8qYZD+mDH2A/bAWX3025IF/HBYZCDkl3ZPejhAzK6W4qbm3lj6NyGUWXXMWjPx6V7xpWmxPBnGfK/zliOig6au9xhAI2F75m/9m74sODiVzfD8bXcxZwxrKea6JooPbIjuuFrIHWH/loBViTvkHwiTBOhZGKP5AljQ7XIRDSW33JCREkpMo9R83kPEulEal/MX00itQtr6xTg/hsLv+fPfpiQrH/z/+T1vMJMJvCUZ9xyQ/G10anc44KJvd+sQKEiFBKdibjJeMO2kRPBRb8zya/UYlRqEjD5dw8CcaOxgBMuBlPQp1TRSQgTCaHeCZYTE4nIwrpl7Wfljd27hrKw+G+7YoT36gqtsmm0ojf1n9/8Jw3cduHpUliWyrp0gsx8yTtX/rP/7ccQgeMp8b/7K+/i0aN7XF1e0PpK64Wnh2csdNaC9V1rJydL/spa6dKQAO3JkduqHDRweRXJU+DiYk9vK7Wc6PjgT00UFdbeSb72iaD/X/L+Pda+LbvrAz9jzrnW3vuc83vc+7u3Hq4qGxfGBhtc2ATsoKRxYTrEoTshpDsSAcktHBopilAbCTVW/4NCdxvhDlJQS6gVgbAQkWlaIEITDGmFDg7YTgw4Tvx+lU3Zt5738Xuds9eac47+Y4wx19q/e8t13ZKlXGVVnXvOb5999l57rTnH4zu+4zvySuuC+rwwtDiknGyooRqd5ThPTJPVcbM26JV7V1dM88SDR4+QMoFk1ulIzzPHdIWUI2m+4fr+iZQOvJIf0sk0tcGEfXlGf/46qUyuhjFZxkOmeUChOZHSgQ+cXuH5eeVu7byuKyHRFDUm7W3sb8mGiqxbrhVlG1sdXu8ZMLPYGrSGfet7XNfVlNFzfkH3z7KRoeMHxMDFcB72H5sC0TXIVB5ohBOKwNUhwOQqEIqxZHVno6K2NKBIYShcKEZgyWKz72qtDKcVcKAL1oYAwq8Z3PdDP/RDfPzjHx//jlrRt37rt/IX/+Jf5Ed+5Ef47u/+bt58802+5Eu+hH/tX/vX+DN/5s9cwHV/7a/9Nf7D//A/5Ju/+ZtHM+9f+At/4Vd7KkYeII1Nu6wr5/OZu9s7eu2DDWTS9GLNtY6jjtWCNURaJjTCIff2MriU+yxk1HI16J8O3+1w4BFJhMPSbjN1pOyyEls4vW2wRu+mMBzU8ajjWLG8EzWM5No4fY3ZWMWgPVWDNyUNTHmLrTxS8jAppWwLVmwhhusCiRUc/7ffiC12+9mZlUzk6CfBerdIQpkKfSqGU6uOseq9m4NN2YMAmclJKRnmw2RtAqk4yyihS/fhiz5SXnfkhV1iWKksZJTsTjozY8DkgQOmJpiYmDwXqTR3TXl8dvFsJRMkXzsLuypnOjakZGb1rCePQCb5vWqsrjlhq8s2/R1nCgeHCd0IuPGqniH18WVmK9Siow6mGBMxcr3moFR2/e6Ib18EUpbauVsa53MDJq6uMv+bf+MTfMmXvcQn5E/wSP4h19MP8+/+gZ/kX3zyg/yzH/56bm+fcl4bc5lNL7DfGks1streqWsd8MXd0+c+6bhzPJw4TAVRm0V0q51Z1SfaJg/ElGPaalhL6izSyLNAF3pV6A316bjJCRmL2BqYcza0RPGM3TKmXGbyfCClCUll3FcYSRtTMSOZyaijIHo+G2tuXS2zQ2hS0DxB8l7KJBzKxIOrG86p89qbbxoKvjfy3gzfXQy6gddx7FxjzW41VtdSjP3pa1teCC5NuaGzrzHFY4jtx33GNvqygpXn9y0y3A2tSW6fvEY4WHwwoEk3IPvxHsILNaaRkbnNGPqT3rQMRIO3OfQ+2m2+2PGrdlLf9E3f9CviiX//7//9L/oaL7/88q+6cfedjhQFfTHGSm8LrTbWZbG+is7W4yGeQqdQ5QrrG0t4d/ndIe2S2t1jbhj9B8vUxdSEE1hnyEblHdRTlKbidSzZ/Q56jaa2fVRlKXaPbIcwztji8YGMxnRKu6KlbvdHfOHoJbEkGjQMpnBFCaI+t5EBBqwYn5Pt84b0i9HlkxE3tkDORg7k7FCmjL6mHu/t8GqSiZKVuQhlnky7UJIZqr7BYJLE2LhvM8H2ma0C1MbmC/p3wRhbE4lC4kBBPfsweG37pJZHW0YSFAdzVH6fRlyZsWbdrW8kABQjPWy04tl/s1JJlj+yUTbUW4uVwkRg/cH722YYbf0v5thCyMlWZyGmKW9Oan+VarO+tFqV6yvhpQfC7/wdb3D9gUf8ON/MFT/Dg/LP+e2/7dM8fHjgR3/8wLresdZMSYUs63jRQWTppqsYAqetOlk/2URhXLPO2mNhsT/G+uQcThWrsklSqis/JJch7+7UendNOBFrM/H1PUlyYFRsL+SCQc9GnrCoPe/ukK9q2WC4hAdyomhdQRuSmkltIdQ0kdQGQ4rfu0kSV/ORe1QLstQTHN9gycdj7IVmNepSY5aUU73Ugw8d5sRX8+6QnSPwo3d9obFXPOjuO9ssvpcjsNBhv5IH6abD507Ksyt2zxs2Mmxf2A5f8G8jwOz6rSRkdFLY0B1xg83JvpvjPa3d13snu3GWJBQXdG1rp95V6t1KvVvpV22whgZ3a7vHvgF3S2OIzeI3LDDd3dMFb9b1R3YXf3tW/JxGyUguJEYYNz8M3LY4LnFgYKOORkqPEQzM2Tjlns7a+1gU46wlXl0HxX6/YMJhJzHa6HaBdERIw0GNTMo12roMxCAirpQTpRSOxyP19kCfDpxTQVNDpJqTS0bzTUmN9CG79xV3SHnDuRPOXHQHeLEW3Emp0xXCTUUlqmACsjPWC7WCx9JGoohMxuC8PNaJZWT2XoUj4s5kT1EPQka8s7ULB30iHGE4f9yhmlGOd92zxKIm1v27r6CdE+ojW5QBJ4oTKXCqxna0JrSWQCa+5Xf/Ev/GN3+KB/dWXmfll/kcr3C7W45CKplptmm4t3d3mDZdNoV7b6AVt6za7DM3H2ipFZbnC+e0cDxc2XwpSVTJ5JQN5ksmlbRE5UfbaFCuycg6PUFLaooxKCXBVFwtRpSldyaw6bHJIPFnd2dmhKtpYsrFmWW2XwJy6t1DA2edmmRRc5jR0InaV1vL+WhZSrJ6FCJI75zKzP10zTHNrN0zcrchar0gROP52OtiQIep/LtjclbvCJzdDtiltd8ZixUfAro1yQoW5ElyXb1hCzDCE5uNiKBlC85dbT3JYMmGTWzBkBBxIevQm7z0oIrB0IwA2Elq8sJXfB5VYn5cCmP4a1WT+p/SYcV0daacjjS7rquN0u4wicEOvRsVVnofRjgNBxTx8uYkxv+G/b7c+PZkj448zraTwjd18kUYCyNthhxG74JFc2YEm8/iMWHH7Iaai4UunkpHdGbSLJvHVYweHs+TQX5oRD4WdHpJCbp6/cuXWi5jgcWwveGg1P4m4XT6tPWbgQdznpqM3EtkCACXUkiYXlrxJukUcOtmw+08I/qDoXxhtUAd1HZ2d2xPHc9+LycKhcxM4YPc8Sq3zBSekPiFQYewfGu7AhvtPDKo7fJbL1DdYkMYdz50vsVfz6qWkUnE35pzijpocqfWhwvrflbhoHScUxrnuJ1T1LYK3S/9SucxhR/m/XyIJ7yPZ9QOrSdECqeT8tLDylt8A4/5CoSJN/gyfpHfygf5cW6un/PVX/Uz/NzPX/Ppz8y76Fd8bWNipT3kwmL3GGGmtc7d7ZmUCserFc2VnjurMjKiRX3itTp5RfER9onmkLUlYmJK+74GTFbO1lRtVgvTnAcJSjHVmbVWUnE1GI/kW7fQJCnkuhi5KoGq0WdisKd2gb6MPSDdKOzSjfmZ1LKYSQpX85EqHox0z4FDbDZntK4GVUc/owhj0CpbYBiiySCDmPKOmYz/e6A6sq1avTAS7pBj3US/3P51bMmOgCuml4+0ULbzSrtVp5uo4QbtxV4YUMp2zu75xp7a9st27l/seM87KYs8PEvoSl9Xzs+BpVNq4pRPJCm0qqy1QV6hKDlNtgkQN9p5W0g7J2W1ocvLmcR5XskXh3Si10l9MwvuZCLbcohuECdQRG08AcmKpM3fP2UZeO2gegqI4Q5IlgGdzfPB7nptQ/U9pdkhDXdUgGFlHdV9ETcjovS2LbY5Ta5uDItWBo0edwBpsjpUzl6TykQXb0Su0nXbTMmcVC6F0+mEtoK2idIrSRtRdYuerO75SVObJdV9IyoW5VlvVCKVy0xqg8jyCDcOTExkThS+ltf4LbwJwM9zj1/iI4iTICw7sm0dgyezw3Q7AANh3sGKlrUHPBdXyXXeidwmlC4yme51pIUFc1vzkK5NbI69734GcXDPfrZPbVWyrc14oqOsdO5QnnLiu/la/nV+lt/Lz7JUYWmZlK+QNNOZ+AR/nCd8hBNP+QQf52f5Gn4f/xc+8P7P8vv/V9/L3/zb/zKf/eyvt6K95IFlalfqaucvARkphmKoUJfO47eesZwbczmih0o/rlxf49fLafTdBjBmwaR+c0alsJ5XgwU9o0shcYfD5R4InNfVlMunQnNiQSmFBtwuC5QDc+ocsun61fUM0pBUUQopLd4wb1lFKQf7iKrA6ga2Q/NBqusZScUhP+GQMi9dPSSvBapyrgtra6wAkkl5ort4ciZo5Dt0xdsuEBlQpdm0NqYmjP3rUxfiT3OOkgWEEu547RQ7lSFVtZGm7Og9ouUN2hu0dWJCdLL6vezmRfnZDwHrmAsWe7B5BuiZazhE9ew4Mj43ae/6eG87KeJiJCTBYZ7oovS1oueG3jX0tsGh+4zsDlOMYVZqr6YmjvH9Te0hmxyQ4M2qIb2yEc+7GuNNfQZe1HZsfkzfRTVRck8MlozrW2m4QgnFY8scbGS8mbpBZfdxFL27qI4UCGireeKc8kieLRoj/DaAdXgDlup79mfz5q2mN7KHNDZr8jQ/Ja8huPMNqkDvnebFf3MLHXEWoAo2VXaakKtrDkVpeUGrfbXlGb0uaDUsvPZKcVmq7ir1KSfaspiahGeoJSXKlHj+lvD/+asf5jd8/Vt85GOPCbA0O7Uhk7jhwPu441/il3g/tyPQMOK4GWucoC7udroDZpngjQZgaCa2UFwBMBh2bdDJo1HXHEcllP/iKwDXcIor6s8KlxN/bbWWybOvAef42cfat74oI2DEdF24QoAzK9/Pl/KJ9JDf+2+/xtXzTyIUTh/6nfw4f5QnvI8Vq4YduKLxiH/Gv8er/BS/Sb6Xb/jtP8VHPvwZ/sbf/Cpu7zJJCiUrvSfO663NwZoL2TPe2nz9d2i1s5xX3nj9Teb7RkmXNINM1L6SpVAy3NWVpJ1VoGgiM5GKQm/01RTpk9iU2qQgrVtABUYtT4meMovaaAu6+mTdxLk2OpFR2R4SOmhlWZ5ZLdVbHJJk6KdN5zJVNGWUiV7vOCtM6do0YfNMXy17OpUjC5Wsz0nNSEMq1mjea/O5Wt7SIibeOhr32QS0YiRJb1F/NWjVbnQ1UoNPrY6gJRbzyIpyZFqGWJgdiICYQeQymH37uxAZSE5MUgmjlqim2mxkEPEg3GvKEMjGdlimi5ei3EGpr9kYToqVN0Kg9t0c72kntaWU9s/QtuvVhvLp+OpQFW0ddZ2vKMzmSLVV3YlkCwn8hu9BnbjoOhySmZyUtqh3X9oy52Ww31aC95f278DAatXhNe+hJepKMSZaR0Rk5xYRlD2+CR6puFDolpkzGoPRkfls5xXmka1OBe68GM+IelRsMzTc9vj4BNHBPpex/HKf6elI6ye0FnTNnHWx2UfdFbDjM8XnjY2gSm8NVSFJIadMyZl6l/i5H37Aoy+54yMfgxs6N0SbbWEm8xLCB6h8JY/HOZ+ZWJnJTGydUi6aS9QLoyoU3D6vezqpInqjFAsVNuQ//j6Pq7B938sqRdPvBhE2/8AbLGJZWWRW44KwZVsVg/jO/jrW8XV0V5n4FCc+m17l9371m3wZK9D5Bb6CX+L3ECrt0ZgtXPMpfisAv4nv5cMf+jwPHzzhMH+lCTWL1Q4voOEUox5AeujJ2b5ozepZ6erEUY6kPJOK1fMQbAioNpIHZlEnKVn9ahpBJDI2W7bq10XIvlZ6SpatdiX1TpfEpKb0rtKYe0eko8mnDqPUdvb6rQUg5rCAGIOeOsEA7X2laSLVChRrwvT+xyyZ7LOZkAyyUWl63yC7gABVdrBYGG4Y4q1tTADwTEaBXSYT127AfL6ojXXngHFkMfHeg5HpBKmNA+9mRMbYDvuT7ueWjBgz7rbu/suGOI19Gucn43cR0Lsx2LIsuHitL3a8p53UcB/dMP56u9hI8ymxvHXL7fyU+/fv2YUs0K+usOmlhmVT8q4uBCEDM5rT1CEmGM6iuTFKWKaw9xnmsJw2rEoSowYHnPa2m+J6dTlbL0MqE+u6WlaRyy5Ft7+cyzT+ZU5kS+OH8OO2ioz+GW+sCRHHx9kyN4Y/cAe0G03fxKDUVtfhVWu1cj/ZgoIsGWn2O5JQ10ZdV5t1REE4QmrIlOiyoPVMWzLa7lg869TuxI9iGzT1bB331QyVJKVXZZoKp8MVJQutr9TFJv0W4Ft5i3/Bib/Bfa458ICZb+HHeMjduNwrme/jt/CYo4mHkrxbytxGMP02RyRu8G0T5+GctkGJ3VXH7QoUfx+b+LoSehOOyNKoo9pkjnDybC57dlXp2KBzO79wl+HsVou1id6rhvCcjvEWjwg3FCYeceQBL/GQl/ll/ihvcQUUqpPxbeZV9fdc6AhX3HDkattfIhwPJ06HzOmw2ATdJNzcu09k2wFfT4fZIvJs94vcSUU5nI7cf/Ay9196ldPxSE7Cevuc9e45y2pEhEOCVAo5J3R9jkh1ur7dl7paDSmVgyuyGHVdMqyiVIXUoJ4rU4EmmSJ+de7OSKqIZI6HgztWU9+3KQGZLJmuC1OZKNNsXXHaWdc7mtjoHskraKarjfuZUqY9q9RmE5O7bm3ciNig0moUcXGmsbH5IlgE8CGsrRoRZprH3rOsRK0k4TX07JT6LYxxB6Vb4KIKtMZAaYZ9YPwc3k6yzQALWakIssVr4qnYv2vbLFfAguLfw0s5sk8QhGIU0hgFMjyqPXlAf+/ieE87qaA1R3YwqN0Kz588I+XE8d6RqR0pcmK6d+QgkOeETB4ZzJ5k7Cv38d0jL1G2ixymLIX5YJeheCYSNScx4xaBjco+ObY/9Hs2opUYhHiRJHr3uW0wj0AZydHbOR0i7mw9AhUxJqIkw7+dPJF80YxxB7GOwts5C8egRI8CHSocixUjpNgOcVJ08sWcM1kL9IJFoTbCQzGCRs4TPbnJl+TOGpqu2EBEG5HQmzHgkiRKyvS2uhNrNg0ZuEK5QplIfIDnfLg95fZnX6ZMt7z86z43nOyZA6uPIBTScADbJQw2VGz86IwPKNCO7v/bZ47BuutuMOL7BvUFWcNeW5xbKIO8YTlVECJiLASk8S51vJ45zz5ec6ZzYOYehSOFI0cecuABK484c01hRr3ZGO8RKyOTzP5Ol4yrMk2UYpMGUirkpHRJfs/bqOtOh2lk0ybLJkyHwnw6cLi6Zr66Zj6dmKZCT4lFbUieEZkSS3zCNFt2U5KvJ0cXFFT8mogP7MyQkpFuLDlJpmBfG5qs0pdrI2ehlORwuaJanXlnzL6uCV2hMTNR6XKABGtqtNTRrDSfnt3RDYZX052sPll6Y8qB+JDByNdsgejWazQ2uKE3KdIkIhPxTMxrO5YhuZXQgIYhKONGKff161p5W/OtEE1aUW/encjIfOwIMo6HsjslHWA4p+3RDWvR8a+tVWbYLwmmJSObSnK51r7Q8Z52Uq1Z7SLm1YT6sig8/vxb3D67pdM4PDlxuLtivjnSe+dwSEibSAdbZJI3GG2fVgezJRyRKX07+y4lkjoMZJzcrUApsA0ICwhIefGeXBRS3TuUbBh8iE0CFIcAjNaq6NpGgTTiKu8X9PXs9ZRwTsE4SjhbyjKp1NRn++jArum7hln/PKVM/jx72yQOi7jBXepKbq6t4E2X1iOVKTKBFpTi009DH3CiTEqriiRTdLDRJsrSOk1XlrVS14pqp4h3MEliWVfW9Y66LKMRGswgHCl8pX6Kr21v8t/+1/8qxwfupMZh5xLZ0D6a23WiEQJIiU0LL5pvA1jt4/lmYJu3BzfPcBpb3WEb+REUiQAYt4b06MPCYUj1v4kGX8vGQnQJYEKZKBw9q5o48IgD1xSOHLjHxD2aj++YnICvrvRnbq7Q/ZqY29xMgogwTwfmOTOVmZIrralX7ey+iKMAx9NhSCWlJJQinO7NnO5fc3r4kMO9e8xXVxyvTiy5WA9cmozslEzT8fZ8RtKBkiyblL4iNKbJ1kVzSH5gKAkoGugUMtn+WGulizUSS2rMIhTJtF5dtGE14ysWYKnCuZ4pbWaaZg4AGWouaGmQHHgVv5tOGDABjs4S06W7S1U16yObk91f23Jbb1zk3Rb0WfapqPcFbnYoHEhAaDZryyfgbjcJsJaQgPm2sRrxtzubMwJwg0glJZsJppu7iQx5L72kvh4MPQriVt8F72ZnbBCthTubqLZs9sW8k7OO/2fgpPZyIHaTvceiWcm6a+f2yXPkKnNoJ7QahNTWak2j+4bb0QuQLpxJKHSXoFzbH1xEEurZyv687PvOBL67zPYiO7Hegj0BNHznlmJ372sIYUjDiX0zvUChCXMq0QsRcN8ONuze9xDTNe2wBmXoxMytoe+lW54Qpjh+H4tUPRrcnHhEhJujVsmsMYH1bmFZV2qrFrVKZi7T6LBv1bKrxOUGfMSZf5Nf4K0fesQP/OiHac+vuHm0zSGDrS5kucRG9o0rFOP6EhsZItzgri3xInvCH93Xn3S8Yny9GG2y+7stp9uXorv/d2MRbppy5vBmhMkJINnzopnMgcRM4cjM9dDeMFhzhwCMu5uG435xoYoTZ7KLDtdeSK25XmM2TT86S11x0UxUQrYLUsnkwwGdJrRktBSYJ+R48gBKUcnU2mnSaClZY7xirSVqUl3J6eokmzAw5WzXy+eNpRwSYiahFMhIJAwxBBSBrs1eW7CMqlk9yEASJZUrkjS6eabLehLe5sKWjDT1OpqI9zHpcDIpCalB1FpF1EXlfU/gssXKcAwuw2Hrced4XmzqHavW6+vigfV4nXc4ZPcfjb2+i5OduhxwimeP2/sM5zVsnA70aGRUuwB79GDaxR7P+QKn947He9tJ+WLYro+MImGvjSY2ong+r/TF4aFmX917pt7+mpc3Td2Yx003tp3/2r3Q/npf3Dw3Wxq6We9wY2KhxGYgfo7IZo8j7278RnAINmHwBXeHbGiA71S2UQj73oUw9vbEAKOEnUMZl3j/HjuTK6B9BHb20M4h7R4cG3RcFLFa3lpXGyFRG63ZWInssEXyfpnWmkF9vlmW24mnrx843V+YtPPq48rnf/HEZ376EQ9f2mjqZyaec/AsgOGogrMZZ7kZ8Twi3misHNHv/v7tboxefEXxWMe/w81tP+/nWwVtff+8yOrCQQX4Fedo9HlzQgapmuyS6Q4m/3dkZn13b/dHQFIVoTHznPcx83i31lxE2L9oJsVlWpIBcfZhi+Ir52TDOcsEyUgOPQtME+lwsFaFrpCyKaKvpgXYMSV0601KG0wQxX8fphePDaJBsl4nEzKODEA34+oTCGIEjIyaWhCpxIYwevAWjklFxv0EoXWrHRra4Pd77J9hHMY+jf1nJQAZWVysGTTsmO7212XA8MVUw7fwSMbrbAZj9zxlxzBmZEm808tfeK/9a7zw5JEiydue+07HsAjv0lO9p52UuJPqzfqUjseDR9qLD81bef74GUyKzJ2HH36Zcpw41HuwrrSMQ3hbNmIhDeMGxQwoPG2dRMaY86q2MWMaLlj6v52ft3n6hngxue2toa0zTaYNlr2PqnukGk2BXbtFSj0MutdvRFjW0BlgOMwcDZDer2Rr1zLLVtvYbhAcNqU5hh64t0jiMB1QhHW9I2chTYVefUPTLApV71tC0W7jBUTV+phqZV3rCA4QceryAW1GMc6TwYPzNKF31vtVW7d6BcJU5qFssSwLz863tHoHdMpU+Okf+CCv/cSX8nv+9z9GvSv813/pN1PSkeNhYlkXltUkp36MX8fP8UFuvTfJmHFmEA2kM8dUnMQQNSJzC3YRExOhKxH3MlTL9xlKZF+dNJyKrYSAqlxKitlNXXe5ps1dGnyo3vwbq104Y/W5TObEPSaumXiAMGMzcG9ozMycSBzJTFh/lq/nCyfoi8ZZiwsrr/EVfIr/Kx/jr/AKP+QzmgrTNHGcbZ09OZ+RlJjnA1km1NdCSkpOQp5spMq9B/e5uneP6eqKWooXqwS5f8PVvRv642eoi5JOh2vSs1vWZ29S6xlqJVPIwNXVRO8rdXlOz8kmFkzWRjIpdJswSs0+OmfCYGus6Ve70lrlcJh8jE2lrTYTyqZmCwadCqqVWQtwAE40ZrSbQLJIQrO4Aro1H2hSKA5toZRg3wBzniieGXYX521BvQ7nOIy7mG7lKDtsxt5s2RfOjrYA0IaMhhTRluPHcyzzn4opySd30ktdhwBt9l5M26tOLNudy75HakxkGMiJiXPHAFObOq6ABfa9RxCWL0QAvtjx3nZSHq0LGLPM6czbVhS0KX2prLd31LuFdrfQz2dSgdQT2iuqk+OyhgUHu29EhogXK/GoroPaKGkQb1Sy54bQY8BwBB08XjAiGDT+YCgphIJyZ4vsRXcU0hQZmTgs52l1yB+pQ0Ge6g0CBBsUl7YT2PU5iAnScgn/tdXAsNahJxfCVMsMjZHkxWdhzKWxAXvhZOytup/SqtYPEqlvSpn5cBj3MfnXlDNZYC6Zw3GyfpiuKCu06tFmQnKi1cLt04mf/+fvt0bhfkTKRDkI7/9Nv8zhQ3d8gi/n8zzglkKonWeHMEN8KLqnxLOOtiNFbHF0ZD9CjMkwtQcZj5nqg6lSrERGtdeU2IDBTTo4Zl3Zb0OVz3UZCF36+BIm4AhcAdcI1wgHEgd3TMWzqSC8hFvaZ7WRBaXxlZgJOvwv85t5UjK/6bf8LPd/4Yr//kdepq7QmnKY4jra+gZFtJFUKGqiw1M6MM/X5HwwA56z3S91nT4R5DibpBYz2ce70BdYrJcv9ULqFcQNdF6RkqEkqtehm2Y0J3r25lPGdhzwt4rQkrCqqVskInORQf9GY+Iu9NORlK5J5QYpJzSfQCZUTC2/SmfVxkJlkWYsWM9l8k6rrmuntt1U57TtRtt/293ZsnTP3kIcwLMUYxez3b8dPG8bKHlWp04EE1Nq8ZKIbCqvtGgY9gyOSErZ4Stpe/93yuL6BQql23P826i8KohsaJIpVliGLPp2JOudjve8k4qsAWBZK3t6ZBKxkdlLZX12S3t+pt+e6Xd3pNmoztoqqpVcrmI+IMWdVEQvIjaYEfVmV6/DJN8J5jgSJJ9Fo97bgzLmt5MwMU7d0nsBcnJVBfsbc3pR81Bol/juWOA4VCIOTklCo4sWaxCOvivEDLyI5QqxvnqgAk5xjf6WcBbtbE6zZY+O6NEmwqorTZr1ZHlzoHY3D902P+HUfMMsHrnSukN4malMaO+0Znp+OQvHUkgyGSvrdIKUuFsXOs84tztrLvRrrpKoNfE//ldfRimZ6+vCNBXKlfIV/8rP8ezlK36Qb+QpZxbTAyCo36ZZHoCdNfgG0WUZ1PJwM7h+etSpmjuneQhOVf9aEFZwJ2XvyHBSdveMl7fx9xpbCG6rS1EmywYGeSNh9cEDwgm4D9yg3B+1qOyNzFu2diFqQ9TZogbVxpmYXnz0eP08/zIyfwX/63/1/8zPvfyQn/ypj9POgla4mg8svXHXXZFErJU4a2HSRJGZOR2Z5/uUckIk2wiMXKiqLNpZUMrxiDEbZ1MvP6xoOyPnyaDCupD7ivTVsoRckUNCijhi4dOvnVikxZyUqBk202w02rxma1yXrqPrDXURY+1o8zuaOo0TOd0nTw/NSZUjKoeRGa9i6h63LJxZXZDW1nreFbRba1TVUSJQH5lx0Ug7hKED/4tMyu5XaxY0h/agLfstvIn1ZY/5Iw6JmjKNB7jisCjC2upwKiKJ7GohgAdLQdLqsVyH4x21611mF85O1erESey+JNmveqWKiSB01IZbtk395lc63tNO6uB9D8Ao4I2aVO++AIXzXWV5c+XZ4zvK1Zny9MycMpoL/V6l54bOFe3WMd9DS88dhBER4n0gItEYjSHivijkgIDimck+qwq1YhFGETduvmJORZzaPn63S/Gb9ysMWBLPtCToqZdVqX0BM+VCDOmzOtYeM7+EF+KxXMx5TYfsPT4VaUrqQS1VVJSaOtk3e3IItDs1oWs3gkpzGNW7+0xUE+sLqyvLckbXBVS5//DBKNY/X86cl4Wnz55Q24IMHTKDCFsT1lqRVHj4vid8w7/1s+QsaMr85L2v4Qk3NDeEhZgbhYu72tVIrjhh85hCGrburqdFvGfMmSVizlBnHc9XVgf/JmY6K411p/O3KdxDZEyJmPtbB4Cow301G9OHkJk4kZiZuGLmyIEbZh6RuSKPycPxCuIjSSzLM1X2tsseN2Zi1Ohi/KOd3+R9SgcE4cu/7FN82//ue/k7f+fL+fEfe0hCXR6oWI1fElq9LaJ3ihTmcuAwHzkcTxxOV6YOLkI1aRcPLp3vppWUIR0K+eFD0nJGnh3I65nUVnKraFvoa0GLoEVMWcLrq90907o0I3jkZNtTMTi/d9Lqc760MznvMqFen1YbeZMSkqGfn9LzgazXRK2vJx1KKkG6WbtB/l112J39PrrYYzjaoE5Ouhi1/vYjAp8cOqMDXsODzQif9nT0Ec2ChmKKGac93VtSBMoWpXaUUQtzZZp97rQPjJOv3sEujrdEXN0+7JmjNG7nQMagyxEYJ+HdHO9pJzUGBb7goLbF4nTQ1SoP612lnivt3OhrR9c+iBQa8sIajohdpHD5bzt20JjqmBUT0J6l+x6l73530bvA9m+G89t/Prn4Pt5571AC2ut6kWm9+DopWVF4i4Zkg0t3r78f1RHjOCRnkloWtREefMOKl87FTJ/9eivj9rg2u69tlk40/fWLuTjFa3QpAQtEiptTQqSMz5Qnw8BrV24e3fLwg0949de9zpKO3HLiDV7mKSeH7oLRpuO/kWnExFs8p9koFdujAbtFXhJAYbiyoIcr8sI7jSs7LoH9Lsyk9Tm18fi+B2ry3xcKV2SOHLnPzIkDVxSuEY4kZobBH1DhZihs9W9nlS5uxwaQqWdq4oNNsjc039zccXPzGvduvgRTo28kcX1DJxV0N1JBqCilkEux4CjHWHrvG3NmhREUjI2Hq3LLYTa2XnX1hVaMqNEsj6W4sUu4oW3ey6O0vhLQmIzPV0mqvn4d2NS2XQGvD/XWHJrq9HqHrs/RdofkaKHYZrlFPdqc1JZFvQiNbVAeY7+h276Lw0zV5WqJLCkqombnd/s0QlvdiB27Px6OYaRBXP6t7l4uWIcXVfOL89md6/gcFw/702QscL34m7c/Br4O3sXxnnZS67oQnP2AquDSuKuCOvayPqks1yv9VtGjIlfQz5WeV7RWor8onF4pZWRlLx62tUO6ZOu5iQXQMDKHuMSRwXvv/FoRJeUgSbQG2c3h7kbG+ezPIYu9uLquX1DDUaNup2zNxCkZ1TYmZo7NLNHzYdBeKbuFmnEJqH13Bw4FQJNOpbFoJasBN+K1gsC0reO+YXIFHgF0fDqoccJSnjieske6yloXeq80hwPmw8Qrx5dH5ra2xR1d4lwa0lf+F3/of+DRB5+CwCf4Kj7BV/KMykLlOXeI15vYqfJFh1JksqZEuNBoO7AssxKjNUYO7eQL5bwTRqpkdxO3nkc1jHEXdSSwmxNis9uAxYVKNABvA++vsQnA0Zh7zQ3vI7kT6Q4HBogZvVaKslApVIrnfMXv+ValcmiMQAka2Zt9swOViVv2mXkqE2U+Mq/VmlWbTcaN8RMlZQ5l5up45HQ6MU8zKReqmjSYqtDE6lKtN+tzVEM7ihRKKjAXU8xXZxH2jtaO1EY+3Cf6jcw5NKuFZVt3SW3dnOvKgo7PNknlKIK0RuodqlFmTJ3PgsxeG6kkUlfWZ5+HtpKnTDoupMOKXh2oWjiTeN7PPOtnnpzP3LGihVFDYmfoI4i2WVs6YPXoDxrVQq879x4kCaVP5nzW7oBsT0wl7qK93Og0sTdjHxapGvKykRicoo7uHJjB+K11Iz/570d5yWvHMRYnEAz7O6/u7iG/3kaZpUS5ZUwsCMfrry+bFNgXO97TTmoP7e05/PsvwJkkViuRLsNIGdnAoykNLDYMsRlw9c65CBy2xjRfYhKuyZ2UWMokGrCdeoEwFm/cGFsJvQckF38rnkVv/4u/iwUXwoxxPuLpn8jl619mgpHiRQFWxjYeEXVkgvvXkf35eL9UrHNVF9R09buUxjm/mNGO3MI9dvdzVgwrt+m+pmLQ2nZe8zz5nBpovVJ73cZVSyJ1IXcoUye7isgK3IE7ipWVOtpmtysPW3OterbRiflQl51E6s8OiCoad5vDZns6eQxg1LFmIlvZuILBqYxB87Zht9eJ5lpzUJnT6H3C6ebqji6o6b6Cdvd/C5Y2yr29enCsthh9gDjb2iGEhLcjiQ8VTGZorHnVgqJY4VvELyPLApxcw6BsR2ddZAzxu+5kAp2AlK3ZVyvRWB1Ztaitld5X8Am/9p6ZEHqmd5PUIliY5ihrh6x+hfLlnRYEutXGWr2Ddoa2YGrS2S+XXTMplh3ukQHf/r7ffE/tLq4qbwv4fIMRmprxN1tG7NlqN6tj6I1ftCgsD+hsWKpdaWL7vq3KuF7h7XZXQfwy718ngl//gOrX6gW+xPbzMI6X+2ifdV72KH7h4z3tpPaNZeGoXnRS0QyaJRscobjBclgmIg0Xd4woYzO2XNyI0P7C3ytJwDuXKXdyc6CwFUcjgvKFGdTSQTcXl1BSJaRE4mzAsg+jqO+bkMO0vXjIbn1sLCFytvoQm3MypHPfmxNZFmPDmFo0FpFpdA5tAJNiJBbr59wYib0bo4qAQ/w93Uf59UnkKSO9ed3YDZzAPM+Ukg2yWzu69DFjSCSRJ3PSssO3LSNaWdxB2cj1clHEbe4GLJOxylEdjqcNh+RXD8vbfIT9cGehMXEZKbbdNYmN3tkcb7AJt8bccFLW+6Q2zg/hQObIxDV5wHoTfUjXhBrFJte0BTYyaidxLtFnBUEeYLdyNm5ZnKO8sKokmnqzw6xJSc3u4xgeoZt9GiZo57TivpqN1Z1Bsz/UZCxRpmSXtTt5QLLtjSADaUdbpVU30MmhcxcyNXFUsw9JTUpMNdkk787WrJ4v3bUgqDupXu+QekbLQg63OuyKjYuRnmKaze6zehayRbHjUHQQlwIaRzwTG5cpdhduM7xR2Gd4aTJnOxyVXTg2cDDuZxp3PJyNiJEutnNyHcS0BSsXfoxRat/d1Ai64me/07vlEqvQ9rrvI2H83FSpfVu3v9LxnnZS0SsksvUOAQMXFxFokNPEXA5IFfq5UZ8t1GOh3U2I+l7onZyFnAsx7CvgwyBnxPuMDE19KUgamUHUVrbR6pupiwUYEGUcYdDjvLMIqzesgsF8MbslHot/B9xHryMV95Ykzzj2GZZ385QJc0RmVHtESHEu3c5XMR55nop/XiF1g2KO6cAxzRzTxHGeKT0xtWR89bbP+DybUrBisRXxW3zmZD0uZFP6SCROcx7BpaGPSj/fUbtyWyt5mpwBmfjo1/8iX/41n+TeS8/H9fz1/Bzv51P8F3yUt5h8VPwMmNJd1JBuvYo0kagod6MjKTNxoKGcWWgj5ovmgETzxwM0i01ZCRhjYtPUS9zRSE4NVya2YYj2jupTfzNH8KzpyKtOlLjHzIHExHmM7/A1hVXBot8qKCDqZrU6fWOsNSdOxISqyPM2pxQ5TvavzVqVnJlK4U4rOQmneabfPUc65FKYpwOHwxXzYSbPhXIoSDEn2po7xKPBeTmLGfgg1ojQRVmT0LrQk3X/lSTMVwd665zvKvNppuSMtEpbzpyfP2EqxfZuqmivtF5Bq9W6tIEupH7LeVmhrhyXzqSdSRtaLVSgNc5r5cwt1ycXt6o3rHeZXpWrm/fRcmJpyq0uPNfFdChlu0a25/KA7qJGtR+rriJbPW3cx1gD1rQuUzJmHqYLKG7Yk6vP0I14Yfs75IvSNrVgRKcyzk07PqbDslHtu8DVXp0tF3b3E+etypiL568rsglRj/FEYuefU95AE+I6AJEEeB363bmo97iTehHWCydlPUWbdE9J2VTFFTOgtVk20RxyGrCU90ilgMS2BWbvF98ty5Du5kn8xqOMHiXZFsiIKF4IUSIii9feZINMfWAzRFv2FY5y7yztV2n3nO31IoOJgMqiqXjfUCHfZ5FbBDzaWFWHQ54oTJKZZWJOk38v1g7q76Psoq94QX/fiKoNCu3kkq1ROEeFSF2yxs1+X83wqGUoKmbAyqzcPHzMow++xSsffos3uEf13h+ls7BwpnL2Os2KqekF/TgYbfusw8x9VG0KyjqUBey3G0TaPefa7uvmMgyuMxJC9FHZoAdTgFAfQd8JmoWwwXsnjAxxoHDNxBUTV95gnIfziawtgObdShlfcdsN6too7yHABOFe0zBRdi12zLDdMaAm/9Q5WSLSxRxYKZlpyuQpmUBsNmM72jDE+wjj3kpE75HDb9DfCOgQWjbcQ4ug0wy5uMit0vJsTbDZFCdUi/c+mpNSGqknWodaDvS10utzjtgaW2snq12b3qwZvc0LqRVqO9PkjMqZ2iu9V2st6JVFqzH7PLvaXyUEdwaeD0UAGL+OhCQ+qG/i2JcxiWDIl8Uak91rkvAC8MhQNzuzy25C7SacleOq2yuH/fSMLYIu2Tkpos1ml6X5+5p9sLKGW9CBbtn7bYjJOAfByR7v7nhPOylgwHzApu0W2mIpM88z8zRzmGdThlCgde+XENpakbWa3I7vvDQ6p3Gtussai3iNaxjSlOh1pXXLxLq6gkOk7HiUlaHWNl4jpTSICnqxIIWcJ0vWu6H9lkDFZ/VmP4cxBHyEgXWsp2lk1XYO3WoutmASKfWx3rpHVKWEk9vBGslVFzqIJibNPCgnDlK4kQPHPHNIhQNibK9uytOKFfP72ITiqsyJHsPmSgfJHI8Hz2CteG9UfKOu99Y4L2dqXbi9vaUL5OMRSuH++x/z8X/7B8jFhg7+l/x2Psc9ltG9sjr12o6VFdNdiHMy7lpCOBOqd9f+GIDQqJzpY5MWAkJrnGmsdEzPIsCz5LnMgZhT1XbNvp0D4mKvcRaTZ0gVI1cI134eR654lcLMxGEYoMwBsVwUE+cJVxtuZat3Nf+r6j+ZGdqPhQyJWx9RY7G7d2zN7hi3Q1tHa6MkjOGGkrOt5eOhUNxBlWOhnAoygxQ1Xb1DgZzpuRhQ2vqIst1iAw2byeTGN9vk6tYbuWSmcqLlCZXEea1oKqTra2q28fEpb23PSzvTugneGiR7TdNEzW/x+U9/lgcl8egw0c/Pyb1ylW2oaO+NpT6nL536/C04ZsgzuZ1pNXGbGs/qmWd94bneUaUPyS4VZzm6aGzvfZsRN4LezTnEJtzmOW1oRls37UxLUjJdnegjoN4rRcoD0AsHE44hSglhw7oqtTZX5BbC/Yzpu7Eqw2H62KGkW317f2xBshnL3rtpIPpJCwHBhiPcXiOGTr6b4z3vpC6ZfHZTc87jC0wSZVnuWNcT01p81EOl92ZNgzkPCRCbSLtlG/ssK2pfY1qui9Lutb1MdUG9JqU7DT5GcXff7xCvu3Vs293tXL5vHJuT8ghNdbcIdDjX/ctt7xWRWhpRUiKa9jaHIoGjx7XtSqEwp8K96ZpTmrmWmaNkJsmU1m0hg5luZTTzJu/qN98Xi9+Ul1NSSrHenNoqa12MHFG3NthemwUL02zGYMosvaMpk7I1VHfgCZ23vJq0kFgRn71klTMfmkKMN7TtVz2HgSMTE6fBjoseqBYFe8ShPHXnVLySZeKt+L8NPDp4JhX6eqFzboKwEzOWsXTEndQV11hD7hWF0+hTSq4cEddjb04aUT9y0g+hqR5R8RYF25mkUY8t/swyHCxsGU30idkn/PznX+Gnf/preOP1V8jJxl7UrqTebAQGmWkuNg8sK6kIeUpGfshKkm4EpCRez9xcKuKQb2SjUeBJbmjZou5oh8iS4FCQDr3ZWtCUYJpAO10brRqDcO1CNCuXqwf0LjxvNi3smDpzbczaIePBUoZko0BUvdFAlaWuVCmck7L0RtWOZruyW9OrG/OUEDfaI6hNlkWu3cHjgWjs6eBeX3fUIqSDtonBDvdpOETfssjQ44vXBWyMiewdg9gE37RlNpZJ7dfArubmiNI2bXxDbvZITu/dyCvj8eSQJJfjONIue9KtjPLFjve0k7qA9WBbECKjjkPrNHdK63pmXW0ekbaKtkZK2Z2URW8BOZiNlouF9mIflg6nEzCPjCm5exADsI5vUVdqvnRAF07KHU+/qMa+/XMPp6wbXSNeYgQzHqZaA2w4poATnEpKJ5mOisMRm9N3G4KoklPhkGbuzddcpZkbmZlUmDQhaYVuOH5VsaKoO+fggWyLXBgTe7OQSqK1lVobz893rOvKsp4RCSKG6yNOB9JUkMPEcj6jyWoBJkOUeMbKE1YEa62yrzC4G9XAKi3mOiwbaa69INwz0Aehs9C8UTdhIq2WmayeRS1YdSdxjXjDZ4w07K7cwPjbcFMm+jpxDcBKdUc0c+KRK0bYLKgQiH2xVhS1pFhX+/9aTpSHk4IRK++yJnNSeTy7+BVq4/nmoheEW0B5/fX38d/+4L/Kkzc+T0nPqNlqIV1tvEqWzDxnQxQSpCJIEbMuSS070ub6WEE42gxUdkKEoj6PaZfpe1ZvsJmhAKSOHLLVTqs5KVJC5uItHEb+r72xdjznVK7LjHZ4rolSO0eajb0RY+6l7ON+3EkRJCHtrG1llcJdMui4itqJazeI+4WAcl93HkY/pzEpN567XQcHjjXi1BfIXy/YuT3uEjOppI/tO/5mjzSBjf0ZnFSxVfM2gswYsWGw49vHvAexKpzhZeA9HC/m9MeU4h3sKQGJvovjPe+k9sce7gsSwpSyQ0eVWhdam6xHo1dqW+nqIqORgrROSeP2jx6m8PqbHIhFUapYVCUyWGeR5TQNSMOyI9tLQW5ob3dQ73DsF+o7Pu/CQQPIWDSRdUEiJUXVpJP2tYZ3et0RffnDmcyUJg7ZIL5TOljm0c2MjnxFleQ1sBwjslNGUqanTM+T6Yl1IxlmH6+AKlUavamrn3vfRvIMJCUOp6M5qSlzRijTjAA/yIf4QT7Ma8wmUQOuolDcrIceRFS8grptpILuziaUDU0uSQgR2u6dU3YxTqwkFiI6T3SfrCtefzI6eR4OKYYaQtDFBRvXbt9hJjFz4MYfn/17GeYjSuwO5owbEzSUF/JgZNzhYPbt/xc0EOvb2g9v3P7V+A38Z7zKP+DAm6CvGtTXG6rN1rFFdDaS3RtxVazXzMbC+3c6aLMgRgK0NKetLimWxZl8Qx9TLevqWMBGMEOdTSkW3Fgqka1hOCU0Z9S8i331BBWaOhczCX0+oeVAbWfOtTKpZdLVJX1MTihByfSSqb1Rz2eOrVFT8z6vTpe+BaTCF9zHm/G2fZW9VrVHNmKvDT6oow1bVnNZe98o72zQnGTf49tr7G3WvsRgfW1GhhgVq/Hab/sIo9a//zzb28uwbyHCba9pIVy0i6j2LZPailTv6njPO6n9RQtKt305Pq3KaJbT7srdvill1ycU8JZEP4L93dtJCiNv8f/a60aPUPcbtr/ptv19wOILTufynm8F5bRfPLvoiPE6jmvjGU+AOrL1LrwTjjzK0+6/bMF73DNexyNBfzxJokhmSoU5TUwpM2mmiFDU9Mh6x8Zxdx2kiSQWPF9ussCoGVCFEuocXhLO2QgVpVDE6lXzfKAlc0APH73B8aUnfFJe5pO8xC9xn8VzAQOojGBgK8AoDuagNup3GHcFd7QO3bgbMJX0aK4VN62ZCiPDsteb0fFz8Q+VSUxklxeCRCUP92DNuJFrG7U8oMCY0ivOG9xU10Mve4u8+/gEeye1rSV7dsBrWwE9nMS+pB5OKr6OfIb7/MLFyjGYR0YrhymSsA/fTWNO3N35XutqwzARfMS47OCuzWDbb3Y06vgwsq3cHnXFgRKY6klDrYVBN4LN9uWiwCKoFKQc6L2xtmpXVvDJBr7XUrYpwKo8v33ObWscXm3OdDdkIhCKuPr7fWyfycgE44GxV8UGp2LPkQg7wreKDCp9PGdvT2JvhqPayEhvP/YO6+I8L675lrXFSri0cZdZ7849vs2+bA4qYMTd2hh7jC94vl/oeE87qYi6wwAGZRxMEFbERkmXkm0UhEuwNDV8vEzFGwgNO5UykeaZ9c6ixtYap6sjh3mirU5P3sN+YsPGaqscpyM5Z57f3kJKFBkJPGAOdOmV4llZSs7OYawaj+SSN7fmkYVFx3jK9t6trRYFJQEpY8zH1qjQRtQUkJ/2TleDQJKNxjWz5A68lJ1j9FeJSGtOE6d84CofuUpHrsSU5IxkoNQOvSlUGz2SWidJ6CeASMeUrHeb1jdObZ2lNpa12biAnDkejmM8xGk2ynGZJt463/L4/ISPf/wfs7yy8lfld/EWnTtv120YzJfc2K+cvV/qjuLcui3DaE4hyMxMRJPsLQuNyq1rQJiKRDTPztzRuCV539JE5jTUJYpDf4WJIwcOHDlw7c5NWF0B0XT1xN3ZtcN8V5iTNEpHNE5bN9aK+LWcmYfj6qMKZqoWUY+KxvLsbrIMp4gDfCZ5FFcjFNhtLUYOuY+ezIFNc0Fkpp3viNjJ1rGakS2ZPM/0JFRR1m4zoVJvTMmyn1QKrasJjLLP6jduoRddGBW1tJ1HZbV9rh48pWLhSW/oebHaSDYyQU/W2G3ZfGHpijIjpwd0hXNdqeVAk8bSnpOLhQdlmlAp3N0tfPKXX+PTb9zy/i//rRymxB0Wlkx0Qtu6t6hhRWZo67VXm17cI9tEQL2+GPC3bIxR3A5lic5L8R1m+38UAASrhyNe6N5lWPv6uapDdpbt2Ric5oxLdyIacOIlQcxew+pYJZXxmhHY7s7GT0nGGtvup5+XxMfdssaUgiz1xY/3tJOSnbFDdROtVC+CijFUpjIxTwemeSZNk0XkSWlZ0eyTY5MZIiUj1o5OSRlj1biJGJmPLTrreUhIKfTIyjzrauqRUsJS6ySoJrp4wdO1AjU55VmC5ROF1IlIm3Xg9FabSCkWlnfWe5YwsjfU61oRFaURLcUYEgVqkDAkKh52zeIjqtewJmYOTJy0MDdlSkGy9i8X8VQ1pyfa6LrSpSK5IRM7lXagm6SS9k6tq03aVSFnq8/kXGyOVip0FWqDu7rw4NV/wUc++NP885sP87l0xec5cgZXHl+89hAbW4gWTGPZdRb27bNxXYxVV0k8c7acIpzJLAhn9uPaF1YyddDDZ264cmdmZApzaFckjtjYjCu/Ss1Xlzkac44hXGSF/TBW1buogk7fx7mamfbV7xkXzs6zTxUKc3h1Knq+DpShkV68/0mGKwz31N3pbZ841lMXG0jYpNPE67HZ9g1iQrUpT+TpyOH6Pvn6hnK4gflInWdkzDCyut0ACDT2ijiKkSJ+38g/bLUyUbGAxwkL2hs5W6DWrE/CnJYjCkl2LRiayeXAvZffz7l1Hr/1hONhIuVMmzqtJFrO1Oo1sKbcKwf0KtFe/yzrsiCnE/OxcJwSfa1UMVV6BWKibpdE7UDKJM1DfbwjVj9Td+67bEgIyN7IWEU2ya5YCTEeZevas/c06FXGNTTf45mL2ynHhuy6hABAziMWiSGRsocAd27S3sJt1sggL6HMRBrv38Xr5kRri27ZIc4G/p9DTWpLox0mSpF2K1lstktOE1OZmcuBUmZSKSaZn6FlpbtitkjBJFUsk0kOkyNG/w5YkOF8vLga+lyYM2IvqT9Sd9w8bsoYFnX57z2iklycmaeIQ0fq5sP6rmzOTjgbERy6dPRDLmPj3n0GVYoIzjDxFvDnWGRmlgR2ArjhpAqTFmYtNkKvQ6HvZPjNSeke29egR1djd0Vg3KyOoMkGG1ZVlqUOR5vyZBlltgF5LTmLjMa53/Khl3+Jr/jqH+W/5N/g53mV526MlcTKc6+nxKwn5bwjLxiZ/JJAkTz6C2jwOSsRJJxJnBFut9geodIpNI4IJzIzV5yIOtgTujudaxInEidspAbAMkgNMzMThSsOPB/mdztiltU2YyoNmCUkm4KIIf4JA04JNt/2U2bydzx4vc2cl32qyL/7eG11d7k/zHV22epXVnIUyMVqUapInsjzkfl0Q7m6hxyuYD7Qp4mWs4+q8N4yEWJsTdeAtUyF3mziZTHeYPMBWuE9qdYekc0Y92TnOoAswbJ39TYKSaQ8c/3wFZbHT3i2CreTqabXcqAVoeVErR76t851mZlPE+2N19HzGXnpIVO+5jgf0brSkyL5tEFf8afq2UnaiATqNkvE6l7qWpYv1pxVDEmJ0KMB0YdkLRpxD5QuOzDXHV+QGnpQyweq471k3TLllLa+UgICHFCgXUNRcVm37fPFt72dE8TUPCJbiqRJogYXFB7/tUTY9cWP97STUk1uqC1aWquxgpJYk14VZcrCsqw86U8oHzkwH645vXyfcu8aPRxJ5UQpJ6ZyJMtshiT7NvdspNeYlGsGdgizegodIzvA2DPwAl7rhSl7hlO/JWoDzgLaLKFFlLtYpbrobG22uFTVFwSA+EZkNNx2uwgkp3erqk/7zS786NDPnh7qjqSz1Q1Sh6IwJx9KrjESUAfrL6GQDFrpvk8kmVJAFmuuXOotra7UdaWtC+fz7TDD6vfMsk8z2CWnUWT93LpwdfMGv+ubvpfj9VO/QDckXuaGAw0TuT0b8Oh0CaNPF+zzm4nPGOk7hmvY0YFnNM9dVpdG6iMrW4bJTkBm5sQVLznDbwWeccU1J665x0xj4pYrZk5MHEclaeZq1IEUa+59TgBrMsKROCzLyRf/tu+6+9mNOWn3t+G2tqpTsPmMylHcWVYSpn1hfVEmJqU+CWsb/OHrd23080JdFrq3CFjsIzZ0UBOH+/d4+OoHKY9eRq6u6NfX6HGG6eBTZz33DosdUb6qn2WM8HiBb7bfSx6pi9hi1aaWwWUnTnjEpmYYyEVommg907I1/c7375Nv7iFXNzxentBa48GhoE3oCXLPtLXx9K03DeIuRx5/6peZ7r/Ey49eRcsVWq64mR+wJEHTJvy8Lz/sxwh1VVrHbYvVXdWJDkEtD+RDsHlZTV24WuI29JF5lV2dKWazRcIysqgIDrEgOY6UJkSMPavq5+fCtmEMIruLbHcsBTanF52goXQR0fLeYYfTe5HN3LpuPVVf5HiPO6lwBmmDD2BXr7Gt30XpScnHien6wHRzTT4dYZ5IZbJFniYSpsW1walB845IxB+X8QTC+4Q2pGU0sv1eh4+CC2PiW1G2KFnHGzAclrKxckKHKwrOsQAs6uxDa/JC/y7eLKXxmISplJ1pHBvBzLpgkMMkmWMuHHJmSjbUcTO1OnT84i39qhHEE5NgsQJ6qJjbZ4heH9vA2jEYyT9bxxbxk945V3jt9h6vTJWXj2c+wmc4YD1GzaG2H+UBt5ywWPzOc6rIssP7h9kOsVehkdwZ4RoTzV8znJQ3lw64zXIOG3nYeU4lgM8DJ4SZiSOZmcRkmYG7ik3YNap1Qcy4dFD7FXL5r53hGL+LzxWfMlTeY3pYHsT0TFSrwksEfSQoJyvWBWaOvLXEZz/1IV7/rLP7/IvWfEBlorpCSyoHyvHEdHUFhyPMMzLPyDRDnr3twY1nkPhS6H/sWiRkc77jc491rCPajz7EGP++EVx0txBjD1jg1pNlF+lwYL6+4fTwIe3zd9z1ztOlW+0qZ+YOvTWW88LpNFGks/aV1FZ0XUkq5LR9JjthHYjWcFAXN1UILoU10MadjM8OsiNWhYNRvyCKbCNvdntt0GJ8X4fdGv8de3J/PffPePvKk/31GwGav1/YxbBWfm/GyKCdHbET99UtWyZsDu1XZjXvj/e2k7I82CZLjmw3iACmnt17RabEdJi5ev9Drj/wiNP7HyE3R7g5kOYTpRyZ5QBqYpE2nmJsCxChdcbCGvUfezJ2B/x33sW6FzyNqJGUx03T7s9xmMMeHeZ+FGFha06+BIXsfaLvyogivhQl7YIfNyI+fTNqau7DCdhAQnKlNntOEq7Kges886Bccz9N3EtWTSmWPFkDr8a8UnU/6OP3eqfXSl3P9LXaNN5s6nSi04Aqn7XnLGvlvCzoNFl9D2Gpjdu28unWuH18w0/81/8Wv+ur/zt+79f9Y/4t/uHFOlgp/Fn+CL/My8ysCI9R+tCcaCMrsPqTAVuNTgEml06ya7f68y1T6tz6XwuJwmkY/BMHEsotjyk8ZubAR7jPkYkTN5hIbIxo35iEfkew6mkEKO/ORcUn2AxOmPMxa5ZQjyhkDhw4cWRmsvtGNAWHMwpB3VsaZypPqJxZeEpjZV0O/H//wb/JG68f6OtTE/itnV5X0mQTkFtK9Jw5PXjA/OAB+d596vGEHmbk+ppUZmvEdofUNWaOiU9YFuRiAOjWv/Pip1dtY+o2OGGjmVhpa53mFPY06tTY3iWZ7p4Y06/c3OfeBz/INBU+eXfL0ydv8NrjJ7ykVzzME6d2RpeV83ILh8KcJ+ZTgVQ5v/kmPHjElA+0lqlJkVRHmQEPPCVlat0m4CImdN29dhA9SPv2lh3TO0QrGRAhrmuivH3cj7C7JhGa4Q2TUQfagNAQpYYts4GtPeZFJmEEE3FX7KN2n07ghDW/1sNZxetGNhdlBD+54JG+m+NX7aT+0T/6R3zXd30X//Sf/lNee+01/tbf+lv8/t//+y8+1Dsdf+7P/Tn+5J/8kwD8ul/36/iFX/iFi99/53d+J3/qT/2pX9W5JC/Wxbhzkb5FZ34aKWd66qxaoVjDH1MhzZNtnjKRciGnYlqUTmGNeHNQPFW3TER2HSwj09oiC4B9Y7E9HllThJGM4W+BU1/CGlsOEP0b+zEe3U6WSL9Dxytc20V0rtsC1DEahF3SFnFVFHLtPbNY1WNKwiTJ6OBsU1EloM7uxYHerCmzN7RVerOZUBFJdZ8fZHW7ZszI3r2PxTInbY11XVha465V6uGAThPp3j1+9va38Xd+9stIZXZjlelro7fKv/KRH+XNw8/xffwOjOI9We2M5nmSuatKHYJCs1+hOrKJyJE6Cyt3VG5ZLSghEVQWc3JHJhInFPWK18othWtmJrq/e+Q41jhs75fZeqbsuo+t/8KxqUbspY8Y93jjCG5/f5lJJaJ5dyO1RzHeXr15BnVHZ+GKn+XX8zd5yI/btVkbba20tQ4DG7UWVbHAq0xQin0dJpgN4qNMkAtIcXUWoAmDmh1GNW2Zku4Ciku3rk4L3+A+xdbSRX+O+itEncXfwyjo9qpVEmk+crz3gKuHL3NH59nnnjAtnbw0iqtPlDnT+8r57ilTAk2F5fwcqSvaOj0XusPsI2BIEWT6nr/4DP59Z7TZPSNybgIpAW+HwetUMmCZwEPCYdnD3VrNojdpH+boFtoQgxLVAmOTXwrCyj6LivO6PNd47UCIxhhNHSZq93FH2rZdj2T39wv5ihePX7WTevbsGR/72Mf4I3/kj/AH/sAfeNvvX3vttYt//72/9/f4tm/7Nv6df+ffuXj8P/qP/iP+6B/9o+Pf9+7d+9Weim1GN6iRRaQBjXlukgRN0FOHkmEqMGWkFPJkRArJ2XXl3LSP1QJBjBhGPxn0s8+e9ocEayew2eGoNgx5fBtOKhbz5aJ+EYqzhRO4YiwQP1kRF+70IyDGXSQXDlV8FY01ojqeF7F5kqhhJHdQTnQW8dqXL1X/W/GeGLSZ9l5v3vzZIxxk9GSJzyLSbo3VTq+taj1nWitLb9y1Rpsm9HgiX9/wqfqIT33mN5MPV6RUSGmi3q3I+pw/9oF/yN1B+Wf8VlZgcSfVnBbQPfTobHmUxZneVzM4VPa1srJ4XiFu6s2xWF0LVo4UrvxdOsLCHRMLJ3cJuJPq7iD3V3j7Givnbet738IQay6qNVtlKqDnzeHJcFKbc0q7V9hCmKjfLSgLnYUDr/ERvpe6Fp7fXdOqtWI0H7Nu3CDLSIwwZxAZpZgs0TQh/p0yGWtW8qYuIGyfacOsGZvODeflnon1vDnnFCsw9lAK2NtQim2bbe8RoUhDyNOB6eqG04MH9Hrm2WcTz6tSls7N0aDpXBJdK8tSyfOE9pnaFnIzh61pb5F3+4kNseBFQ3wBpY2PZt/D7qgSLSKq0caxvZYG5L59TFub3da0jBcaUSg7yxDbEWJSMlvwe1Ei8M+yF5zQ7SUZyuhxXvGO+4B7nMbuPeLOpH3q+IWPX7WT+pZv+Ra+5Vu+5Qv+/gMf+MDFv//23/7bfPzjH+ejH/3oxeP37t1723O/0HE+nzmfz+Pfjx8/BkAcdzIM3W+ky+9LttpSbY3DgxM3739IfnSN3D/C1cx0/5rTgwdMxxMpzVQfIx2GNO7LZspsIyTJrFFjaY2UhGk+jM0yeg12QotmFCxylp3+Vk6JnAuKwwJq8kM5Z5rW0ZgMnuF1ox9H/5Oqslaf6NpNCipnGzOivXsW47F/62SN4QuxsTeKlI0sEY5SOJTCscy8lE7cSxMP8oEbmTilwgnbBK02Y/1oo+pCj6++oH0FtebHOaeY3mFxuzump8+ecl7OnJs14TZJPFNj/fXeWdQYdvrwZdL9+6SHD5FyIE1HpvkKkUyrnelQyesByYUP8Yv8Cf5T/gu+kf+G3+zGrFKYuOMZi2dGoEzMbq7t+jYaC3ecuSVmRC1UKitHjiSUZ7zBExbe4swNR645IrzKzJEDR97iUzSUB3wFW23IADijbpjb34AXOwR2kOR2hDGO+oud7b6yZXdzRQc5YvJ+sEJ26LF4m7AJNykhBXX2fPExK29RecodT1h5C4B/+k++kZ/5sa/k8ZuJup5pvdK8mN89e5LDgXY6occj06P3M730iPzgFbg+0aeJejiCuPSSxD7YqOXVrV8aat5Rk7S+P4ORvMVCYu4Z5shaBdl6ogSfpN07TVdCj671jmbrgexqPX3Pu80pO5aJ933lV3P75gd4/OQJb909542nd7T6jKtUuXcQUlvQXrk6XVNOitwvnEvl3O5Ih2uX9grnylC7ickIL2ZSOcu4+3sx7MgqYrBp6238ZRJTb0yyrZi4ThdOKlkDckri/VZ5TD/OeRq+I/T78QCfF/uV3LYoRubobd1+N2pwafNDOZljHGOKhDRtU80t8A/RbUbN7t3KIr27Z/3/eXz605/m7/7dv8u3fdu3ve13f/bP/lkePXrE133d1/Fd3/VdPtb8nY/v/M7v5MGDB+PrIx/5CAAtd1purLlRU2WV3Vdu1EnJ9w6UB1fML92Qrw/IcbJZNd6Eu6xnzv61tIXaV1ND1p3GFrpjyezwVC8ax/ypuKkKlvan5M7SbkjKaVdoZUwjHf/G+gtiOMQeBjQq66XyhexgjpGCE9tie/6IwvaZm2yRuSkSW+w9SWKWwkEKR8kcJTOrKdAVNQcl3Zsn/Tvd5vjYiIRwehsUGAogcU27Wta0olQxB1VToubCmgvPEc4pU+cDHI7I8Ug6HkmHI2k+DHgpTQfyfKQcr/jk8vV85u6reUnf4khFnLgQo9ZDS8+i830XUvOxiDYeMbh93YnseObUWEgO4hWUztlbfp9jOncM52A8QGFj1eWhPzF5n1Jw77Z8J73Dl4zzZeRCozttfMnu99HUO0UW7J9+62sLvY0F5ezf74BnPOJHOD5/jV/++a/i859+H4/fuqLWTnVRVE1YT2GZ6LmwpkI+XTPde0C6voHTFTof0DKhPk7DANcYzui06ORfDsNpYtQtQiUi1upGV5cLIeceP/vj1n/ozFbJAwZ0LbItqRGoIrRkPVH5+obDg5d48MGPcHr5fcjVfR6vjbfOC8965xY458SahJoTMmeaKGtfvfy0BXz7PUnsR40vxmN7iF9SGrZj2/NeGthdC7Mt8fe+f/fPjaxOIstxh7cRjzdD8w5fQVwZAtQp7exHetvXdsiWmrltu0Bwhq2SnS3abNS7OX5NiRPf/d3fzb17994GC/7xP/7H+fqv/3pefvll/sk/+Sd8x3d8B6+99hp//s//+Xd8ne/4ju/gT/yJPzH+/fjxYz7ykY9wN62klOlaPSOAsV8PSj7NvPKhlzm9cp/TBx4yP7pHuXeiCdyezyyqrIfOlGdO+YqcbIJvySbsSTKaOT6sr/vV7b5wkvho85zoDolEIbTkvBUiwRZVSjvYxGRVGm2gfCo2eqPV6rRUhzGc/l6c6mp9DTFT1aKnVvtYyEYDZRSr4wh4dGwSLJsTsNElHR9WXriWiRsK11o4dXE9Bhsi13tD6wqtWu2prtYz4rOfzElVaJW2LtR1MZ1EnwvVgZoTtWdqgkpiTZm1TCySeLOupPnAdH1NvrlBrq7Jp2skzUiyIYIiiSnPVvbQK/7R42/nS+Yf5n/7vj+OyQsdKYP7txBq4omJ7goRsJIRFm7ZlBZCwNXmT9l4wwVQrpkoriVxyy2wcuYpRw4UMvd5wA33h4vBSeRezif4dUGl6J5ZWLCwY0zt4uP4X9SXtkm6G8N0X4Eyh5g4Ylrqx10mlYdrrpx5SuMOuAWek3mT38Jf4Y3P3uf7vvcP8uTpY1q9ZVnPrN2y3Z6zKX+fTrScOKfEzaP3Mb/8EuXhI+Tqhno40ctEz4kmYv1CbsTE117Y0xEEeiKlfvUFqyR2SUNWK6C9bf0z0Ikw+GtE8qUMuElGv6r/V6CGtBPCfLhhPlzx67/ud/Dp136Jz/zyJ/nsj/8CaXnGWTOPDhmdCk9y5jBljqeJc2rctrNNk88Owbq9LykPBGQLGO2IyQDhLSUXSuZCKWfQ1/d/7c6p9Y3pK2HpE8aYTWwCAR4EWgBp62mUH0bQsz+zywCY/b1R19GN845gvfv6VQZZAu/9GiUTcPRJ4kX9Psa9e3fHr6mT+st/+S/zh/7QH+J4PF48vnc4X/u1X8s8z/yxP/bH+M7v/E4Oh8PbXudwOLzj4x/9xq+hHCaLeAVymRBX1p6vjpS5MN1cUW6OlAdX3H/lIfPpwPE4U+YDZT64I1GT0FEzJIc2mb5WF7QZ+06SLfw+IhmDH5IIXQut28A0VZDeaQrZpfJVlZ5MGyxGvyPGdKLZtjSnIheRT9DPXzxC6ogInsSc4gVRA6zXKGpNIpbd7LrFO0ElN2LElJJNM8oT12nmJtlko0PKzGqZVHLMvztBAm2IC4j27oPmtKGtobVSFxu/0R0qamqK1KsIS0rctk4Vk6+5zYlaJuYH98lXV8w3NxalH4+UgIlUbdErNM/cajeHr/7Z/yV+hPfzWf4G38gbHL3+ZM2vB440VhbOO8dgkU12FT+I+VP49Cb1u9GZOTBz5MgVAswcueYVHvBB7vEBjjwiu0sPgoISnVmXWW9yWvpGXNl/F7YqjB3BMtxnywD76cDJAcbJHaT1QxXP7mxObx868Wc6t7yP7+Pe+k/50X/yMd769H3Ozx5zvnvmcOxqTdeq1JzpOZNOV+TjkeP1FdNLr5AfPIDr++jxCuYTvSSfrBsKCmmrTaqJ0g7DRxit+FCR6VqDvPXt7a+Hbh7NFvmGQsS2SLK7lnGlYt9ZVhPCT1mVSRLHew+4pyDHaw79McuTz/L8rU+TaKytk3TiWg6U4z3qdEUrs0Fh3aTW9nvPjL6Pu/f9qkHoSFvfU+zLUswM71l7YyyHeXN3Pm78UyL6GVXtGq3xt54BJf+sKW+Um2AgWvb3Qj0osj/dSh3mLM0JhcrEBWHFP9dQzPHbYT5KPSCJrNJf223WHgj9YsevmZP6vu/7Pn7yJ3+Sv/7X//oXfe43fMM3UGvlE5/4BF/1VV/1rt/j5Y9+kMPpaPQaEebTESk2FfTq5oo8FXpOyHFCjjPHq4Npws2TfxVaj7h5RdRGNZRukVYXUDJIJxWPZhDEJ4X21h1P3wRSAZP9CY7giP4cUw+6p/dHtdZdZdhvaNzkvu/P/pUPIbT7eJtTu6CTwnDoY6ermU1TOrd5sMdUOKVi9SnJ3shrGzpIFgGYjbZ/Yn7OxvTT3nzaqYuN+v+adouyRZyCoKwCa8q0MpGvb5hurin37lnNcJ6HOw2SJZiTb84oLCkPSPbL+GXez2f5z/l6OieXkzFHVJgA06iII2o/4rmOBS1tl+PY0VjIFCZOxEBAq0fd54qXOHCfmRtihlRErOFUgqyw5UtBYwgjvL+jl3c/2o8D0t2/SkBgwf4LmDHATQPd8NVrjc5CJXFH4nM84Md4uf93/NBP/3u89fo96npLXW3YZNNmQ6zBmlZLhsOBfHVNefCQfHOfdLqBwwmmAzrNRlQKEpLDcsOB8ALMTTjf7V7EY7r/bAPW1rF8LSofnmBckyBVbNe2Xzg3FRcPVmF1o3842mjJVA7w9Et5fjjw9NkzbvWM9pVrNfC05iMtT6bqH87HlV32x4ujNbr6nUvbY/vnjrUYhn9AfYy2lv0QxLhGw0HvxavdSQeEL3jwO15jT/LYjhczPwuA47lbABUZMb2P6yxuv7aAjpH1jdEiw4fL7h9f/Pg1c1J/6S/9JX7bb/ttfOxjH/uiz/3hH/5hUkq8733v+1W9x/TyNfk4GxCXhKuXH6Ji9OJ0cyJPhWmajW5+PHBzdaJkkwXJ2XWovefOvtsFbm01uZFcRhpvhUyHLxAkWcNfEuswD0zdtoRP1BV3Rr6Bet/SeYmb7NHYWJRx6JZJvSj3v2G74cjkcnXtjktpmS0yv1gjvncywjFlDmnikGxMfCGRVUYFhJEV7Ctg7pzYnNiAPHxzhhq3YpRzFdCUWLWyYg21cjiSTic4HtGDfZV5JpfJ4RMzWBEY4K9jH9AYWdshFGYKs7ucheaAWCIGc2yuSYFK9W4iodCdrt5cilVwWVEWQJgpTDzkZe7zKtc8Aq6J6bt7FxQ3Z2+M9+f59kfkbT9HBgZvb/7d/7zVwi6rcNt5hKNaueEn+Ch/hsIzo1Ocz9TzRK0r3UkLTV2NQ4Q0T0zHI/PDlyj3HzA/eoV27yF6dUKvrpA8QYqeP6PmqzPO8LWXymYgNR4XLhhk43PpF1jU47JtUfr20A6y2j0uu2fHYBJB6TnTJbOq0FNG5wNXL72flAs3z29Zn3yOZ8/f4vGa6QvMt5V1VtPr0GA7Xk5KGKfo+9RUKIC+zb8LggW8QwYlciGFJJ4xyQufbXPE2/ulZENcBUG6qb4IVoMfcFtkZC+c69sucUq7LXW5t5L/flPJ8BzYM60Y1BifKeThtnNNpOnXaDLv06dP+Zmf+Znx75//+Z/nh3/4h3n55Zf50i/9UsBqRn/jb/wN/uP/+D9+299///d/Pz/4gz/Ixz/+ce7du8f3f//38+3f/u384T/8h3nppZd+VeeSb47kqwPQIQn9aoYsJip5muhlohyOpGkieaOoTYU1QyeejnZMDsXvoKWk+6jAYTLLFwxaQiKaDUjHlkt6wSREBLGPUiJCGWKzkWLvmHwRnUR0yM7BMM4Nx0x0g06c3aP7X9tP9qe2guxzx8we/3VCmFNmFiNQxKSkBDa0cWSCnk2peuZkj42uHnWyhDsuu84JaGgTI00oLAgtFzQX0nxErq/h6pp8/76RJQ5HcplsKCUZlWwCnU7PTSLbv7XzrD3kJ5//L3n//BPcm36Jj/HTPOB9/CgfJLPQXZzVqoubaryM75EtQ8ys7YQqebjaCZi44h4nTjzk/dzwKgcekrlCfDTHPhsKvbxNttXuZLu4anZsMXVkYUNTYOQEMGLV3bPFAc1tlq8PqCBU1JUFG9l45pYnFN5i5g0+90sv8flPvcyzp43lfMfaKrWv1N4M5hFBU6IcjkzX9zjce0C6vkc+XqGHA306QLaeqO4iyAFRIdtZijCmzY59xS472H2kL+SgtvpUXLu0wUiyc+8XntChbr/eyLYvqk+Vzd1rfClRru5zAO698oynSbiTBKcj/XSfc5rQPIP36qUdoWkz9LEffReLmJqEn0+c63aOOs45HLedpgwHZee8CzL9PQJO2xMcwhvo5RIx+A1cc/Tt19X8qGW/sWdlZHLxPn49U9S5Yv1FMPRiorSDPt4WkL27bOpX7aR+6Id+iI9//OPj31Ff+tZv/Vb+yl/5KwB8z/d8D6rKH/yDf/Btf384HPie7/ke/vSf/tOcz2e+/Mu/nG//9m+/qFO926O8eo98c7IeHYHlMFOmwnSY7EbkRLq5MfYaxZhJIszlYI6hq9sTU2Im3I2UsWBCY85EWK2mIqI2YEwMEqy++iJyjl4gDRFavPEtoo6IuiSgB1tRtW0FqZLKRYF5m/K5a8xVnOq+QYNbPcMN2c5ZJTeZSZIVfL0/S6oS9YxTmjilzEl9wpFAlhgPr6jXn6Sb3BHBglQbzxHyrr1Xq1mJO00xtY3WO4sqd125VVjLTDqdmO7dJz16hXRzj+OjR7ScaSlR8kwJinIyJ9WwmV+4diOiLMvC59cv43tf/9P8rof/CV9Xvod/l3/Aj/JRfkr+MDqqEEbLzxyRwdOMObWT69d1p6hbPnIwIAhr+z2gXPOIj3Cfl/gSPsSJBxx4gHKN+BTfECCK8QUzBmtWoqC+ZVcbBeby2OCrLevr2wob7svuevZPENOpbKB9olI506jAHd2d1Jt8CvR1AH7qn/96/scf+I0sz5/S+h0d5azdpg9PxScFFA7XDzg+fIXjK+9DT1e062vkeI0cDiZ/5M22A3oMTG+HMoS41L72gYYh3NCC/XHRI7Q9iA7Hb683uS7eQA9iT+z8nYz/WnC6tEbtHU2FpJBS4fDgVabrh6TTNXq4pk7XpJsTejjyvFwzzTdGP3c9wnGO8XGG833BG2hofTIcwfjQu78djiI+w3iajHWxEUjMlhQfLprIW7CtkYGxvVaSS1U3vxxvu+YpWdM2Yj2mOwcZ92MPcYYNuLxXStc2amEG17sDT18kU96fi77bZ/5P6Hj8+DEPHjzgd//1P8l0faKqF1inmVwSZTLWTE6J4/Fg+Lwk5ly8EGgkheywkaranCVP27PazdEUgj9CaN8Fs8+KglZLQqOpOCbNAqoU2ca020MB1QEp+YynS3JEwGRTKZuisDvUgQ3HsDn/LNFNfsnQ2XXegzEXgRKj4kVokklqj90nc5MKv6Fccy3ClSRuSMySuMqZpD7QcL0zJYnlbEy+Vnn+5DF1vWNZnvD8/Izzcsfz28dobxjAZ026r9eF29Z5UitvkrmTzPl0BVcn5P595OVHNgrh+tqpuYlrPVoW0tXrWHitEBfgdL2GZr1eWTOv5p/kJfkFnj1+zOn4KT7y4R/kv5Tfwo/wpZijWancoe6QKmfUmYCrD4afvZ8JJ0sIiYXGgYdc8Sof4KPc8JAHvN9Vxm2qruUz04CUVqKVV3zuVAyoV5e1NWcYFaet6RZCBcPMsT26z8aq/2zDIhJXJD5I4grlmpXkA0cyt8BC5QmdO+A5H+avs3z6TX72vzrw6X9xwxufPSHepFprReYDWmbO+UDNM+vhhpe+9Cs5PXoffPhV9HhATwfWo7H5tEybsKk3cavGROON1BKQ0D4ftMDQkY2oCnpQpyihoLCfYdTcYddIQoDjZNOGh7r4SNT0hRJIHlcYZ/kVsfe1affen6WV8+1zlvMtiUrJmcPpypqVS6GVCSXRfNq1jFTJAriA7nqrwxHUZMHrOF4gKgQZwfoKd45MnWi1e270k4k4W1E8aInPrJsvMhV4bIR9XPsX3xsTsx6BtP/1cKzgyjfW7yh+filbzaQ3v9/7jHj3UZdaPbE1G3Z+8pTveuU38tZbb3H//n2+0PGe1u6TMiPz0TuvxWVYEuRsmUISoPgGSaiYMWjd6wwxzGtADzayY0AyOwczvouMSEZyAd2UHCzo2afkWxQXcWREOLHgVCPy2KKkruyM0XYC4tSY7XUlEkHPlPzpvrlVtj/2nMnOPwyA2Ctkkk3ddZLEpLiwz25sQ0w07hV6dXqr9Uj1ttLqQl3uWJc7lvXOPp9YoGDKE7CqurYB9DyjZYarG/Tqin51A8cTMh8gxSSkRPWR960rVdyt7GZiJcywJNmoAZ9efwOfqR/l8ePX+dL1v+eb1r/Lj+Yn/FK+Q6icER5zjbpSnzkJ65cymrfBgXFXkoOeE3DiAQ94hXu8whUPmHg4+p6CR7UV/zeyyJaVb4a5+fVld683QkSsG3t2sAPZZU/s/sZUNMCkcRudO0zq6JaJzyDc0nkMnBF9xs2Tn+KNTys/8z98A8typrY7c8tqWfAkB5uzlCakHEnzNYfjfQ6nh9T5hM6TkyQKTTyjUSPD2HgH33MaYLhnjgFliQzFga5GQEopkQJ3HwyZ+Lq8TnH/Q/4XHBnQDVazn61e3COoi+fFazjpsEsA9jawERFyPpKmmYPeZ613NodunsdnyoPkZKdoe9Bswbg7uwwjJhnvK4qX1cXtthqKvsGYF7/EAtJoiI2sVTxYDXHX+DmQnTF4cXdqAkNPT3EHnwTZ2ag9VTzs2VB7J+51PB62FAvkc6A+uiHA8X7vMj96TzuplBLzNHntQOgl2wypLORieHHz6xdGWsQo6hJNfsG6Ea9RaCxkM+jZv+puMSVH+3OaQJ3Zp40a/U8YVhzR3uYgRtkJEj47ans/w3IzFNlYS+BjB3CGkI3YiCypOoU9i9GZ7Xl4EVVGJNPE2EhVO1obonBKwkmEqyS8Wg7cS4V7KXMEDgqz2lQrajVKee9oXbwX6g7aSm8ry91j7m6f8uTNz3Nbzyy9Mh1s7MKUhNosU60Ci3ae1RW5/wqH6wdM9+/RDgfq6Yp1OqCpsFbLR5pCk4oZJ4Nam2xGIai25tAYDjgXIU0Th/e9zKfXb+Qv/MRf4Pd86Lv5A4/+EqD8Mz7Kf8Lvc7ejqPdLHcgsnGnusAaRxjOZK6644lXu8QGueIkj9zhwf5jQ+JuoXTasChSgYkgxhRtKrkGxl+/cTLKyupKKEgBiGIMXJWvt+RXlCc9YWHjOc4RbMk/5av48D/nF8fxahf/3X/06PvtLJ26fvDnkjqob+d5gOTdoK1xfcTgcOb78Cvfu3eNwOrEmm1yrZM5dqBrCvJsEcrQpV4no3OsbYDJKuzEWrfpMqdZJUZcVN3ioDcfz5vjxN25AZzeoAmRtW8bh4s/zVKj+fCNHCZvZFch2VWuvjCDAFczPdR3SXiW7Pem6BYHBunOnGvfN9v+WsUgOog8egG41ZGDQxFv1zFlC2o0dzC/OAr48Qk+xthj5IfTeBgS4d4JdffxH3IsUK9dqcwNq1O5BR4vI/MKh7M9F1aB2AUreJvj23oeAwUCech5+tu/O64sd720nJZHi+s8pdPZk0Fyj9LxRd3cpMJFe47AfOzppRMV2bI4rjdfasm93QzIIshjt1l6jR4FRtyL5lhVtufHoRmfnpMbOdhgFrDitXtBGCYX1EY33YA5tEbc5MzsPSUYpv0qZ61S4J4V7krgniSNWP5mwIYZZIcW4jV7RttpXPdPqQlsX1uUZ6/k56/k5Tatf5dkWezdpmupwXU8JnQo6z+g8b1DR0HkroOGEfTHLBvENtQ8salWVi2vYS9CdO6UkVA8s0yt88vwxpjdgXVaezsI3P/wRksBC4Qf4TSwuGlQ4uEJDZZ8DJRJHbph5SOYG5UhjYiVYi8EaU9cWDwBxk0yNcw/DYa8czcMv1hX3+cf22QMCjEzM8v/E6qM2Psg/5iU+S+GMsEI/8xP/tKJPbpijsbInPv+pxPMnHa0rKj7qMOor2UkqnnV0hbVWWjPlEOmKNNAuiCjBlTBHrKMGomJBRQt2l3+O1js4fK1qjacBcQ6Z04DWPQOn4/tL3xaFqxvqNTIYxcde+L/Fs9rdPre9t+Wk+zU0EAlRN+TW1mJbyhdirIww7OwheL/DXr+2va/bGoia2Q7KCzTnQlEm7rlu0FwEz1GTGismHkOHUwj1G/b/7erN05eOx9QtoLdtxA2789sfwwmFM32HjGijrnuGxj69DOsqb/u7dzre006qeA1pGP5czFjsioYBB9hU0NGGNL7H86IDWlUpfoFFtwucUtk5KTcafcRNFzfF9xGNzbJatu1bwnfOfsqH7hen2LmM7vORVbkh9h8DDjAixKZG0eme4W3d6ct6RjzDnLJFuffzzH0pvJRmHknhisQNNmAio04BALoaEaItaL2jt4W23LKut6zLHefbx5xvn7HcPrOhkFlIUlAV1tpYa2PpnVqguVp2Oxzp85E+Hehlpk8z3SP0uDFNdXNOblTGgseMgekRhrMWKInamtX1JJHmguRr/vnzf50feuN38/StJ3ztwx/gP3j4fwOFt7jhx/g6Picnztxxw42TJja5WXXy+jX3OPCAzAMaJ1Zml5bF78smP1vdibwI38XZx6MxverSEuwrU7JzY+qvbM/ddNWLSzs946v5z/gy/R/HK9Um/Jm/+TE++Yn3cz9nDiVxyMKcFhLPQbqx1bKgvsZNFSNbMCRWD1xv7zitZ0pbSS7GKM2Nn/rMNqziFx0CojaMuea0GX6grubwNvmgmKItbtj3vUPCWmtY2MtrGY854ajThsB0VgZpxWxC3/Z7fBdzsvYcIRqOS5hPsSwnJXdS/lnFYfumW+Ah6AVd2zISCygi6+i60d9hy5jiGu7bRWLcSJJN6Xw/UFFfcFwpTVs922tzLw4VvHBssnP0ActJsiDEbebQ1qvbvYrXaa1Riq2XUsoI9gPWdKjjwsmOc/DP9T+L8fHzNFtK64sl52KK2q26nIrBJDZFN7NvegynFEKJnr+QFOstGIVey5iyj6g2unNkPdsw8kvzGXFZ8sY6RobEXsOKFyLC8Y6+EH01RVYhni1tpg/26hejKVQySLBp/GySNSr33sgiiGQOkjh69nQETgKHFDUeSFWRrmgzCaT40rqwLs9ZlzvqemZdb2l1QQa7B9pq8EnrarI6anBSz5lUZpgPF5lUl8zQWUMs0tZOjPo1+Rm/TgGB6EZptq54F7IVJ714FJAyMM9GnqmNf7F+Lf/3H/sOUu0c0h3f9hv/c35y+jB/i2/CKOTFxYTsf1Zxysyue9450Ag4Z1BrqD6/qrLxBiNz3wM1ujNURBC1h6DGT1sza/O/6jtDHX+RSXwN/5jfzv+L9/EJPvf4Af/P/+abWd+6pb35hH/xqcZ5rRwRShJ6tvloKkJKmapCa2pSVlgTxbk3miSmU0dag1apdHpSQu1eeqfn7gJS6m3COxYYsGpn7ZsB196prdNi2vVAEZJnTnY9sm5GNYRl9w3usWeTBOPxMthTsZ25tGbnjakmwCZ2HNJkG2ISygrb+5iqRENqJBaR7USmYnt6u6EeAktkf05WUHz22tbkHWSRqCEZN8v3PpttiGzlC817Is5fBMlp1P3MT2yZl0V7OoLjfnHOjGwPtz2RW+7znTi/kIDrvW+i1jZ0b9QGI2pXXwxmu/xc3l0SBbzHnVTOmZQyyUfSJidG7Bteo1AaQ8YgpDq8l8UfE4Gk6tD3tni8huo4bhqQiC1Od1I68rJxc1R1FDUlwygO+aJhB1MOlo3DCbFhJAyu4udh7xOLPLIyYxj5RdFtMYhaBiIe1ShC00aUmidJzkmDGWVSg/gsKuwj5NyTJXpbaW2lOtxn302XT1QvCquqpnRdexhuQXMxcsQ0oaVALmjKDjm5YKoI1pzrfTBxzXYRLrEPRUaUjlp2axlxOO64gUaoyYcDz5ZX+bHH15S186C8yb/bvxuh8SHe4pb3UV0/3HKK5LriyWtYBzqFFUgXJWV1Y6guqHpJgtiMcTyy2TW9eJXLI7KutvsO4UpsDVSU+3yKr+S/A+Dz6/v40U/+epbPPaV//g2e3r2G6MLalapmpD3/t7oR1qRuKtUdNHHuJvzbW0O6SV+t2lh7Q9uKNEF6YemuGqI2tWuRjuxGZYST2pN6rIcuPqwZ/apK7tFwKiP63hfc1dcmsa99P/Wklu2PNRKAlbB2q5P1gPkIAst2ZyIQ4OLeRLhp/2kDWlMPjLZexX1GuL2COAwRxlpB96SNsFEuGbVzWhJ7HndQfXufLeDdvdfANdnOa/ibS4c2VCy2E90cYjj5faqqu/fdvd47Nf9ufzcAw/F43NP9c97t8Z52UqUcKfMRmg/UKLNRpVNinrIRB1qjpImSJh91LiMdRnXDylUpOVGScJwnUFiXSvE0uHgj8KZ5JVSZTNF7lw6Hw+nY1Nyx6LpapDEGvEEuhVIKd8sZVSsQh3x/ycU2jxt7+7zJC83dehjGglPW0NNzI5BSIpdQ1waS0LSydOVgA6G4Vybua+Jhh5tWOSrM3aYZd60Ik01NXW/pdaHVM8+fv05db1lun7Kud6zLQlufmeCsS0vRE8uy0DosDZ6pcifC+eaKdrwmPXiA3jygH67R4zUtmcp004R2FxWSTB7Zn24ZkwjSN4JBkUxOibqu1oPV2oBV0s4YZ4SUCvl6Ih07+XTD3IXrfERy4uv4cb6Gn+Gv8X/iR/kaDsxEhJqxe3HLQiOzMvHMp9sa88/eJwYbdsS1KhrZR3bMzhbEfwa7b9VXSyOy/ID6TIXDRhJWQripEszHTBAonrJwO9wXaErUmxtKOzCXE08eP2PlGU9VqZq46zYhOSVIKmg2QdjHz56y1sq6NHqZ0Twx3T5hzsJVfgB3j3nzsbC2W9LpSGnX3M4zNWfWdqBmYU2YYkU32M96Cw2aD0balDJTmdhVP1iraT+WkkgJSs8bFK/iSG6iBSSGNXFDIU1m+Pu6YmGNG0SErImujaaVMf9oTMROpDyh2HDHlDI5J+d/7hyDCC2ZvWi9kyUZVBWkALIP9DSFSHvp5Kld3Ft3fF53umz+9dPawV+21s3u1FqptVovlP+NaWE2RlXtHZCZeN54TTHCQ7Q77J9fg7CCEhGEYOsjdtv+vKdpGrYmXiNnI2e1ENfVLSsVLmtxKb3983+h4z3tpFKZKdMBshmGNM1GZ+3Z6i4JoJGlUFIYEzEWimcJeYcJZ8eCI9pJKZFyGQ4hGjTtiIlBthaHZL9aJJ80IiN/tkfz+6Fh4lBCNMJFBDIaHtkWQahe2Ll2r6+JM5nYNoGon4u/Qm/jdfEscbB72moZR2NErrWGxJFphdM6dXlOawu9LfQgTPSF1hZat1lDXZsZ6l6pHRa1Hpa7BmueaFOmTTNrKSwkVh+VoMmUL/ogpGwTa2FjQTmj3a5c2xx3S6DJIKSOQSLdWUnjb8UyBQXLhJPQstVKWp4Ak0HKnCkUhCvWcactU+go58i+d7F40FWshuVwBpuGerA7Iwe2FVn3n9CdVWNjnkV0HzrubfQPeXcKm8Z2sOrsWv2Tz3wdP/3ZL+Gzz5/THz+F19/kjWdPaLe3lLWStJHpzNkID5KFdJigZJ7d3Xpdw+sgU+fYF6Z65mp9xt3dE+ZJ6BNYQLJSr65oZeK83tGyjWnv3Zx1iB6DzRRLokiy+U6RaETgYeNshOr9eHaPPeDrfSBqAVsFHN6lIcmxheaAt89HM4xht46iLtIx6E/UGIxxvX0QZ2bFGvbzIDgl/9verO4YyEWoPAxCB93IPKqj0T5fpC6yR8J8P8dPsvvvAO8JxCVdODa7rpfZ0PaaFym5DAswrmkQNeKIxtzucOCexBFDJC8e22W6+D1U2PQMR4tNrHvZnFS8zwsO9Qsd72knlaeJeT56RUigTGbAeyM7a1XEit7Ze2+SCCVtVMmSo3kwqKNbsppzphRzUkNkGIhFPZ7p2H6svAux2TjckQ1ngmH7rXanuTqMoRFRxc3eRoCY8kVABxahhsx/0Dv3qbmObm83BGJOOSoBvVm201fvsdHOUlfC3/Zuaubr3XNaX+h9pdU7ejvbzz2c1GpRpCRarSytclZYOjxvJlfV0kyfj6x54pnic4UypJCHSi6ps9UYLqi6PQyMG7B42FXlq3crplys00m3SbjGhjQDn0ioqMlgSWYp0wuNnkeU01BJTwh3rO4sAuJpoycqqoQjMh1Rs6DefxXG2B43DpybmPFZl5GFbQvGOrbUnVTf9WLZ49Haa8w1pWnm7/zix/mR1z7E5x7/DHef+wznT32G5clj+t2C3t5S7+6o6x2HQ/FgRilXM/lQqK2CJHKefa11rvrC1O44nJ9wdTsz54ocEr3e0c7PyFR0nnlamzmf2VCKJMJxnilSyJLJskMtZpsqkHxdJjVUISXhfL51yK+NPdqaZQyl7zMFpUuynqYR3GEyX9FoiniooGRRaB74hcMTi/oRoUwzSjMqPGdSgnk6+JoL1iE+Lr6OQFZSouSdGJqak08EPVxH870kcRKVxHzAiGvZnFd4HY+yumeGsuvDCwRowIzi9bqdM2JDVPYOqqtuqvN+hIgB2DSCUMywMoG3vuxr5zsSxFitKQ1Hvrd+IuIMyG2AqzE3ecGrfuHjPe2k0uFEmk+jz0PIXn5QCt1rTNsggykXj0giwxFKZFIuAimApxaI+ugDyahGHwXel6GomLEadRzPbAJei1vW+2bIxBdA90wrS9oWTUzjVMDPO4PDdtjkUbVpvMkd0pTMyEaD8Z5J4x/Mrk3aIsWGDX5bKKy9s/TG7VrpvXGsdWw49amk6/nWYnWtdLVBeU0Xal9Y28K6nulLR8/WB7Wqctdh1WSqcZJpeWKdTtT5QJsPtOjh6OYw4jNErQ6P+KrLLqVkWnop7bNZYzA1Oi0ZhFCSkb1GNub1gyZ2XZL60EXsmrj/GscTKq8PhfToR7JamXHwzH3GPb+LqdCIdzwJK5uRjKypDvez1TZsTH0b/VNxRPQcTL7uDuol3uQ/4M9z4vl4XpiEH/38l/Idr/0f+JnX77EsT5jvZU6HR5QvuYecK1I7LJW2LLR1pboahKaEFJBir5ZyocxH2mRwX7m5gVLo00x+cB85naiHo7UMTAfuSqYn6GUC8f5DD7pqD6MPJTH2Xq7KmrpnPrZv8mr3b1CbJa6D15TZFeRxOA0ls1pmBZ65gXQLmAZ4mmw8SM9mJ1avAVmGvdJVuVuXsfdyaUiHtt4NYkL2hu6U8i7TtyDGGm4j2I1MWkctOWZihSP9Qseebafd3mU/aypEafcZTJAuDI5jE6nunb5Wm9jgorPWUrC1Soz94Z5SsfPNntlOqbheZ7t4vxfhykH0YNtL4q+NbGo8afcaGfFQ7Ysf72knBQVSsfQe2DdDGsRiEF64iyxp660axVcnGDDQXQI/E4SQTRn3dHdhI7qI2kc4oYEb4/5mRA37GMP+nSTYXZGCx/sHk8ejTZFRs9pDALFAYJ/B7VLwzeuN84tnNDXdstorta3kblmbxOiNZmMKal9A285RNZ8R1cZX641eIebZtm5a42NqqiR6LvTsZAlJo0YQ+2zEYb7RBaOhB5Y/HJhsH6mJwYpd7GtkJ7v1v2faWTOlRYwILC9ElSudMxa9BqXcBXMGww50ZDQB01lWZfH2OrKeje0WAltKzH+yPqoVY5/FupKLdbg5qVf4PB/ik/xG/hm9Cq+vL/u9tIj5rScf5X946yvp9fMgt8yniemYmfVAcZamrEqvlV4bd8vZjFVK0Qxn6ywXyuFk4yhyIZ2u0JyoKcP1kX6YaTlBychU7PqjPhl2a9wUh+ViCGcabNNO7Z1crTk8GnHVxVoDLUg9xovs4bFAEew1jZm37YdMGtlJEocXfZ8kJzF0VVIM5sMaVq3JNaApy8ZsyoE3IZNA+qiL6tj7Mv5mv6u3RX35u/H7F2CzL3QMyvgLENs7PJOwcjvrNK7L/ms7R9k9L8gWno2JZ25uI7vsQyg79qrve6iP/VnYxd/+RrberL0d/WLHe9pJ2aVzYUVVugPFtrjNSExR20SZnNc/DL2n04GjDk28nWEfcEPckPHuntLHwvbNFiygwT7SbVGEwxm9Fv5mgbc3fXG5u+uVy6a5iMoi43iRcXOJNW9/l8Q6wLNGlFZZW+VcV469kXu3ek43A0JbPIN0qZ3eWNczta6s69m/Fta20lpHq2VoVYI5mYYydhOhZ2/aLbNFpFYUcUzfHbvj+OHUazhV6U43SGxd1EJN9pwqdj971W06KO4WlB0xwRhyC+bIzi9kUhVh9XtrihGr94sF8d+U9zYIb9vsnpu4M2u730WjawgYBem8DUZg3kKm3V/ZKyY638r/g9/BDzCx8F+9+bv5S7/47yNtRVvlyd1TVBIlN/LRFOWnm4m23lKXW1IVUhdy31Qr+rPn1N7N4dCJeqekDNNMS4WWMvk0Q84wFeohowUWbSTtZJziPXTcLGsZMJ57DNVoo+jGIOxqUb7vmZwSPRkZofYVEWw6NTLWfnIViGjcdd6DdS34+01OJ7ey41awNwyw+/Xshi4ATcXqrD5OpGl3Q1vsHJqRKVLKlOiOyHm35zw7b0aSEZERMKfhry/7oELCbe+AXmTgpZTofXssjov9/zbSwTZgEOw849rF30Q9L4/e0i3wi4A6avJjFXqNWy4x8eGkLpiN3eW4dw56nPsX+Czv5nhPOymCnrwzwhkoScgqZIG5ZCcaNNcUw/sXrHmw1TYWTMK0sILymZIVJi/S6929io04okHbRYCMHgnA4xQdm0OBUL/cMh0dGxYJ8oY4pJ2GcwX7TBFZtmaspSwxeZMdFRcCw1SXpskpUdqKtMaz8y0sC/18h/bOsXXW89lgUgDOQEN1tQyqN+5un1DryrLcUdeVWldq6+Oa9W4st+7OSVNx9thET4WeCpqKZT/NJJo0WTPoyggNPKztpnwgPk6lK9XV1dV2FCudqjGEQw1wiyzZM8munbV1WrNovmnnXButZJ7lhb+6fAu/5fon+KaXfohv5B/yJfyiZ2SNSiW7C4mtJ54p2ebeCC+Q+Dwv8/f5uFetPLrfOTB15zTWkH/ePRE6wJP92MSZlZnF/yqjzB7FJ+b5huhSmidT86apaUvOB+aeSN0cVSzAnNqOrm/ZhHZTh6glsYjQkjCXZJkURsLRBkih90Zdz5BnUrY8MYsw5exSYjClaAvPDr+Za85+f1LaHLIRNixbF9mEnAWbZq1dSMnWmcG/ButL78MjdHHChNdr7fqL56ye/Ttcbm5LOC9ng95zobVO7Q3RA4gMJyUp0zPk3DcD7jCWSvfyQFgkIyFkDaIDw7n21sDXc8SgcR12xSlbTymPLNC28RYsl1II+DOIU2GPNvQEy5C5DGBRg55VcHKD2Yc0gujxNBeMtTWM7F6D8Zb28855DswmTmKfOVma5s757fO3vtDxnnZSewo2apukSGLKRj3NAocy0VqlNh0CjLaBDR9vtIuLnJJ12wOGe/dtTAZAt+Ylj5Y8LhVGo2BAMONGEBmPEyT8RsHb0/fo0UrJG1vtSURu2GpFBGYvTqsqdW3+2UtcFFsI8ZoeSbVmpImpOK1cG0+e37Lc3XJ795zeO8fWuH36jEmEWTIpnyE1RBrqQrLPnj72TGodHe61dmgb27B1tRHikump0PJMLwdzXB6h125wo0qjp06XTdU7MkS0I5P1ojW1doJeK61VfFIHFdMjXNXWQcbIMKYObY679c7t3cK6GjjXeudurSzJVKn/4uf+bX7fo+/jX3nwz/id/AN+5zutNS435jtuLxF+it/A3+cbaGLF9MyByHuFqEm04cDwXKp459PWBqzMg6ZjbkmVkcMdxBprSXAqB2o16aLTXBAV6roiaSJlm7Zsjgu0+yTZqZOb0ftrrfTWaA3ryyuJBStupynTRVhUad0kr6ZysKCldqZppuTM0s04z2WiuPxS8Sugkn3sAwabqZLUNDF9kds59ObZmDmpweBz8kDP3eu73UmQCmmru3Tfy+pBkygUEbpWmq40rbTezUmp5VXn5YwiTIcTrVZqq5Z4iUHrpIakTEtQSrcad2T/Tq4L4gpYSSE7HIrbh66KtmbZYcJ7AjdiQh4cRCsdJGD2KQjbb2zdBo3cau1pBD7aNtX1PUQYdmIrb9iaa9qtAd7/F0Fd/H1rjdrdthTPmlJAg9EHacFk9FgZG5KBYgQWKuGswy6N8sXbtQjf6XhPO6lOpbezpbUljaEHGXF2ny1QEWUeEvWQvYcKAY0rIPim11H2RmQswDzFjCmLxDvKKW2NfTEiXWJSBxsDLdJqctkiFcuPQdWVgsWzHY+oavXAw7aBKhx8Xg7rOjbFwRdyDroQis3DYpwvIjDZicnSmNNMnifWqzOVzvN14XOvf5b87DkvP3nGvVy4VybmU0dSg/6cti709cx694xWK+elUavSupKSGeLiDaCaCrfOnDsfT5wPB9Z54k6FtcF57fTDhJaZihmhBoOTHONH0ERaXW/B1bm7Vov6HdrrTS3SbgawPdfGui7mSJd1jLSva/NJygZ/VlXO4i5iXfnezzzkJ3/+30ebAYO5FO/5UObjkVQSTTali2maR7N2KYWpTExl4oNXn+fPfdmfIhp9d2HK7gggMNiCm8LBHu5Nu7/7AL/E4/qQv/wLf5xPL++3KL5YMbz1CpIp+UgSz13ykaYLnbNF8h68k6wueZ0ny6RUua0r59YI/ccGzKmAFFI50hEKSioz4uNpJGVy3D/tHKbDILcUr2cMUpA0ihvFlHRMeo4gs4uQpoySRltHNNZbVF5tjUsmJ3PgvblgVVdqM8beWZvX1RJabW9lBe0rtDuQCroxP7uq3ydBVpunPKXkiiy2X1MuSMpM08GgP0lDRCBQCtzAI9v4zH1QIwA5kX3CQtiUcFLq55BSsty9Oe2e7bWNVGLrYr07s2/8NWNotsgCxS2ciuekFHWjboG2bCdnNffuahT2iyLGGLagWsdEhajd2XmH04klG6QXX7nhsNRWehqCBhawbPnnr3y8p52UZSzdel/UQhD1C6O+QUKWZvQyABA9FrF5BRLOMhuAE7DVnBJbhjK6/70YnATLHlSH6GuMw9gvhjQgOfXs3gqyY5pmPFGwrv3dJ7XT8gxs97ucksGN/l6x8dl/Z4uQDIdMIEqfJpgn5Hjg7JL6cj6zysqaV64EcuqInunrmV7P9GWhtcZ5DScl5MmywKadRU3o89aH5t2hnB2SOzvBYu1pRGChFt5i07IV3fHzFbBMzpsyO93lajqtWnOzQTXKua+cF2syPp/PgwbcW0O7Ulx7scGQ8en1zOdIvPX0S9Bmyhl5mkw1vinHqxN5KrRs1z0hTNNhQLPhoMo0cdsm5OmZLG2zUp4FfUF0w+OSyLd2CQa+w/kUL/PW+hI//eSj3PYjXdpW42ptXLPq10bG2o36h6+jscYSyUWpC9BThmwAY+qg2QR/RSYil895QqSgas3k2WtFfSi62LlHC3kw33x7GVKgEjyN8TsRL6qLa6er7ta/xoWw+mhE717/QExxoqtBwaRkJI3evN4s4IopyWtSgXQYUcDqw9KjQd4QGMsc3CHlbPPdUvZhgE7ykDSMdXz2cevifu5gvYH9bauCoGiLBPxmR9NG1025JqlPIB5Zz45l529o0xEs2I0jSXJmr1VVm3YferhRddhdj1FvS2nUyndtvkMXNUlkcWyvclEzjwgjrKmXRXYo0oZL/srHe9pJdS3GIutubJLLGolQNCAHc1ApNvxukQge+cdjfastiDqVOer4rUN35+YL7k6dXaTJi66dVJycrKZZZ9OpDYacCQkUXx4Db96nvbL5loHrRnOg/zuilZSQ7JlBagYv+AZTzJmlEh/U4DFNmer0/OPxwDwX5ntX5ALLG2/wy5//NHK3kJfK+9fMaYJTaabr5uPEu8CahJ4TPQlpznSFu7Xy5HzHbW3UUlik80w7VZSmFVmuUCqajrA8h7a68G8f/SpEVBdQAcY2VF1o3WEbDwiaNta1U2ujrp21VZ7e3VmdrFbquho8tGOR9eRZpqtbC5D7ShalZOx+iiJt9UADcoOcCodSSGLR9JS7l0IyaEXXSl9v+bm7B3z70/+jIVGKuWBVem0kp8lLtCSI0pJp1iWHtIokj+ah1xb+xFiOClOrqDxFxYxy93pSFoO7Hz97ivZmkFtWctatly5tayao3tkb1g85YCg3vnm2TCJNlq0BOc8kCbUMW2PHcoQ8sWrC3FN2ESlx6McafIfjUg/AmnVnD424EX07eUHVaj0+dkN6Z12b3cvdBk7FVNpVTJHCoK9Gqyv0bjqcav1kQf++pPvHjneoLhlHM4na8NRSyGVimozsk3IhUn7dGdwX4bVOt8xh55eUjZE7bNhOPDZsgqSNSdqi/uo1MvGgyLJNiWVkGeOu32mcizgupJfDMeSF7y86jeR2Zv/7pjr6EjdQerNj+zfoe3RgONLNkX1hpuLbj/e4kxKaOw4BJytYtLhi8EIWM0XRrw++btxQbHALkcQQBWwTObWdFA5CBZNDwQbx2Z/5nCBJFKchq4QCgcGHIem/1UbFswkZi1BHaIkzajZap3qkFjT17ucXkWFLWOe9OGqsdg7R/9W9FlFbI3vzYat3PF8X0nLHs8evc378Bq8/f4t+d4a7M29UOOTOIZlAj/QKWr1OJ3QfSMj5lq6wamPpyqpCq3dUUZZSqH2h1wTnZ9BXaCvTXE2Sxg3LEMWMe6NBZqmgjd4XWl+pbfUo0z5La7p9daVoJUlnyt60GbAhZpCS1zkaMIkN6aNnSnI41XvkUiqDsDHNB5v5E0EFnazdmlDDMKrXEwWifSBWB6irabsq/RgZ201YVwRNdq8rePPo9jri6+kichZsTahnAdjaL1MyQeQkiDPaQs1aXfEkonO7DuqO1ye8Yk6rYfBbSXlkOCkVz6C8eqaQUgHJvvcSSWL6tTMpdWvvQDfOJRLJcvTnqGf7USPuIA6Ho5A8K8gRxIlfgzFA3p4b8JYLQs+SEM0kJlu7JPZdaXY9M0UczkvWfCwpUSbPFgVEm91v9ohMccQiatOb09LOpeMaOdDu0B1CIxvbLyEOq6n3YCVXFt8hJYRt8OvaOqrZ6s3+vHSBsmzvLEPwN7J2GQ4q1ljvfbClI5CITzDuW9zP5AjRrnl4/zkDPt37pJBUejfHe9pJqTLkbgC2UewKYl3qI0KScXm9Qc3VjZ0qGn0vlok4y6pvdHYd0a8MCaQaGleyRVVxe8xpyTBU0aMzsqhxNu4idQNjRvgsOK0zHnas28fOKwrNxySU2HC7V9GQJ3EHVRvndWWaC0mUu+WOdr6l3T7lrcdvcPvkDd44P6Xe3dLubvn8WpmkU2RB1ERGU4qKXfHYNNOa04wTkCfIhdbOaEkOS630ntDlFnpF2sqpVaZcDGKSy+hWd1JQCevR6m2ltoW1LlYA1+4jOQTtEoE5xTe0ZMuU4oqHkwp4pSpUpz7TjRF6mhK9VatBlsmMe8rkPJkhcaKBNQVbpFwk+tOwbCbZldlDe+KGJifPgDMjYxh9OPbBTVncm2GjYdvtGEkgjzlkAfUqqcmArsqUQbeaALo7lySekSmpWP2p9mYjTZJRN1J8XkPLjASA610kq8/MxYxS7WrFb3H5J0mUIaCq8f9NrWBb8BeGbuwrCbakZbMhvGpxm2WhgZTYiB6Dr3pXu3ZYNppzQbsFA3NKWx9jh60FwK6tkRASWQ7DWWeJ8Rz2O7PhNig0k9kAjTxeBy7p2Jp2AQZbcDn2tz+2OTIZ/5ZkqvRjzycZOqDgxJIRZNu6iT0TY30iIBpBQEBuIqNFw4Kf7bzDsYUDCaUbPEDTbTH7/VQHPPoIQraJvYzMSoJgtt+DO82/L3a8p51Uw4xPfNboM9LdFRI1GCCnPCC/oDiLRoYFcRvV/2aYONkig4HBevajfdtcI3bYXXdhiynGu+wWBC88tj/22lhb8TNdpMkXN7kraBtBUbxi7waRtKr0VqGead2039bzU/pyptc77j+84f71zMuHhK4r1JUsRkHv/Q66TSltbbVF3sFi3OyCr6a4kKYZKTPnwxU1Fc55ok4zrUzUq6P1b6TsLq5azcD2z8Dc1ecDWca2ANXmWXnPVlLTcjseD9br0mWDZdX6OvY9I3YvtwjcFCeUnmerxfRKFmHOGMMNZSqzOykz0na/3ZCqUlIm58w0GQHBJraasUhlE5DdcHjdrQLPo3r3DCmeE91Yln9ncWPjmYFgs5gYrx7dX26utLtzBtoWTEgWYghoVvvevZCecrbsR7IVzuMEUxCC1MdXeFamDCo/mDICQTawBcfwU4IZsmrszTQMufra7OPnbQZ79Iax1aw0mK8MyS4xLNXurT9n0ODFVTQUJsmIoyr45+guoWUG26KbUjJBL8+uRCJ+XrFukihFvNtKrQdz7DfZaolKEGJiL2+PxWfyjR+Ait1B3XyAZsviRbtDsrJjBVoMa6i4rfcyl02T04OfUnbQn2wOQqvbFb+HlkylcV772tJl6nd52J7oW5D1KzxRd9DheO13ebynnRQjM9mKevhD48cRvemQaNnjwiMFT5HNhIW/lPoIunn0FsQNF5FRjAe83mD0Wi+r+HnsHI6HwvY2m5GG3ZrY/XFskv9fe98aa+lZ1f9bz/PuMy2XaYXSDiggGBWRSxShTojGxKYtQeMFEyVEwRCJOJhw0RiNgppoFRNNNIjfRD+Aygc0Ipog2BK0giJGAW3AoAVlACG9IHRmv++z/h/W+q213n3OtIN/wsyR/cDpnLP3e3ku636N37Emec33InTKUpUC3vZdhqKNGZNaiC2Lt6ApdBJs+gZyINDloRBvz6BiTcHn5T6ozlYiaWvJj8akupkrh1jklor1iuobTAdXGANDs8+mCWMyqbA1i0bsgDEiJzJ0+ArcMT4GZMxuAjL/RFMzcHK9NXFUYW1BmATKkcnPzM2xv8fkbUIWT/4Ud6LDSlFFtqgzUnVJXpv1LjPzEHNIDDbM1MQmcFU31nBYY1CqbWVuRStvpplazKOEv8b4GOFCHcjDoAwNaB1OwdZ4QnRJawMpPuHeZjvrgoHupkEarRugVgdvXgQsgWTJ34D3Znfzagrbdk4ShVoFLfCRrndb1jDLhab/DC602JkxktbXrZbJxv562jwgQ0y7hawZWBBihw3xKtxDXLzyyD2Rht4zECKIe8u8p+5kufmhKAAGMgT6CpPoya4EkG79uJx5J6N0bUeSpjC4xN6TgQjqxK2JWCSy4zq1P2oplcGsGIJr1qJt3etOJEzBgTfNaxJKVoqodIfC1Yo+SRHNd563O74k8qToqKU9ejusM1kDi9AAgIehOv42ERz0TRI2z11qUzfJbpTKVo7LA4orJy9KO2/t8JppYV28qjrsQM6fPw+2yYgK5vYlos6WIGuUERlLUAQAcywDUQ5JIJHIKA68uzZvah+YF7CS+ZhnyFD3MFhtbowtBAvawUA70dDlSuhyHlgE7YqrzKwE4PxGMGPg/Hwe7DN737lzvo7GCHqcu++8hYLPA7M0azg4HVhi73ZAThxg6hsssGCD3gQb3aKP4czHA/Zdap1axzJmbLdbQyZV6GKFb6dm7caHDiyf2xoMuCYwVLGc30I2G7SNrPfTI/EMkU1DapsrgDZhGVsLyli2QDeJuh0cOCnqkbiLBiNmU3cp1sy409S9s6kRmE3rxebOCDIamdSSTmGtFduiHgq+xDRbszDvc/eds+fJAeAMa7OZXMIXjMWrgOh5J1RwU6G3eXcG4UUaoF0g3dY/oqZjt1xBAfq0sXYn5+9Dnw4gbXLt1XL31NtOLNsZvU84cXAF5oX2jI6mE7rMrhRJEMPu9gojwC0YCC0QDbCgnLGgY4Y0M6sR5qbOXrmevD6G1Rx0RXfTjSx2UIijyZDIYY0brQ0I7/PIxDEANGz6CbCi+dRHMASBfRa+N8D9k/a2qhmFSa1ZCP4y5pATWLliixEmVzKkxYOxK5Pa0DLgh6dDwpQ3MDA5Uz3vNQe1idX4LOa62smXzHBZFhx4Lpf0DNhi7c/FmVJrYkWrna4lLfO0c4H7KxGH2bvBPQVw5lvhCGYUtO8ixrFmUtwIsqPNNIWTj6XnqelMMCQjn2+SrecBbwUd0i+bH3bTQFzyU7UAhKkjpKomUgonWpa9CNCGl+h3SWM4MNJsQ6RtRdowEDeJexWCO1yCgpuPvI9MCksDmD08F8OSftXNM25XtgCSGR3nYQ7kAVksF6s1BbxttLhapjrQ5oZJAR0dlimv0flT0DwwQyFTw9wV5zaWRLkM7+wKgUyCoVsz4aFBF4GKaWPWJHE4iStGkmERfR0LNq1B1BoPQsw8htmZWmtgWSUz4wnGZoNpM6FvJjB3RQoymOmsY7M5sDXBW2q0jqWnNm6Cgb2bmkDvEiHI6ukOs6KWrDN8HPSPuHbkWkBUu3fGvAxEZJZgSeLt5z51q7B9MDX3hQ0sY+uahFqnZB3YUMOAAm0EnDf/X0jKPf2pXYeZYJoYwRXLYbL8K4sMlGZtJyyIwSuJS4Ns1Ko/tC2a9UoBG610j+sy3LNeXBv+AcDCTBH+E4oB4qWLlnHeTJXWwMnObCa8C8RbeIhQe4dbBkyDSgO772UH2DKHrUMYANB8G6mgcu9pAjQm5Be1CRGQNNSS0NUZS6N5F64xDoxmkbzDz4K1ZpjjB6WCLqENUlgdqmhqolF3dc6CMUxwncRqGwJWMFlhgUwGVlmVA6zGoWpq31DoWOAyROyTwvOngCw2QI0MLkiI+d1zaxggRl1xLTCHm2Olxdl3R7or7mcceyYFpFVjmibLHFFGxRh372iY1OphER6Yx8GOnZ77B6RbFSAT8pdkboUXCfXisPzM2mwYlxT3eUW0jLhU03LuAZtlFQE8NAlQA1P/Eua0h0j4Oyyk18xhqgNj3pq5x0oMgGaxjhmTbp1IDpOSLYTQa50p4A5r1QFZDJEnNZFVm0nCNF+oDPtpAhHFtimw3WLMC7bLgoEGmXpI/CINxHkKF0GK/Bd1ptzUWkVMMnkqQXOviEnXTdV9P+QMbq7YdPTJclto0pHefU0OJ63hYJrAvj8WO9EwiVjlbng/O5WI7jQNT2gxckkb2ZsINKNo9L6i3Aux3xf3WQns3zGPSHyNrJNCPKbesOkdm94tonEA8zLH+fQxzCtYJF7ze6gl26KjwfYBTUJyNj3YTavNSoupC0yMiDTFs1gBYIKONDdCtgGrcugmXWF4xVhpF11cK/K/VbNWZkf3s/WwfwzMuoWVaUKUA4MzIWkJP2wH1bSh1aAZ1wTEkakJ0LpYQES38laL9xsTmDBoAVMaJl91PPANcjMY8dbwY/haxIOyOCwvzeBX3eSqUOhwn1TJcYzyaw7H9v2CsQw0b+po2oxdt5BJeTcEVcMDMkD1WPwI4Ve16hlNjBSYEynmCGgEdy3swNAZ5CBhBWIZK5KlGEImLwl/RzCeFZ0u339JMKnWLZ9kclFWNcvMRxPCYaarSS3hVl2EWRZTssM3JW6aETGzAJmAay86ljx4/3eZt1jUQ9xbVddhCpCqh60JpA30bkVYA6BETBNQzgvJcSM806Qq+rHMA7G4duWSmy5WbHS472bZQjGgssQDF3hvKt2GpB4RQjqgi1XOMyZrWmPfeukUl7jRGxbtkSyq2jy3TL3g6GLSahOITmZ5HB4gMRRXbA6CmXcnEjMJq89yDAucELF9Hec/a2VxWgNkQLCgWw0m9/sNjLF12G84IRMwL8ByzgJpBCtNCs3bpX/2Xmza5JVHXHvtDTIW04yXA1gPCifCtmMRDaUk7jCPkFV7tzX1xYQVEdtLEXvvxhlZb90dDsNLOE2AbFhkI6TTeXs+AgE2/YT5IE6YUIGxoKsLHw1R8WRxeG6t29kMAbxppzWEZGhGEnIGaog7b8ZkZ2HA2xxGNELLtx7VoENwQg4siKR1QCxFmsRtyMCkS4mAVC9Gy0Cb2fyYDPrRBV3OQ8StIsRXVzWa+Bk5U7U8L9MQBIpJejJqwNNAFuO8C013Rg9okhMZgJo1RJQkgz4i/0+zMlP0w0ljkEVG3AV41ViF+pVn9dJ6Uws/xyUQr9knXvYoI5fhc2YelXqSujSrV2p1BBE6TVhayLBh+KTCZq28yilEt2hPmXq+I2hSzsJBOhmSesCaSFRzARBCyWpcgIk90DjWTApA+JYAlgPxQ+HmhsSFQB5Xi+xQKBlT74/vEQyjqsbUnOBEAUrJzV3u0gJSMsJHAQzTDviqQFaqcBpTsHfR8a3hW7BvhmtB1LJgEtWYPfFvwdAZFmrrTMq1I/HESCYBriyKkRioIaX1RcA2Juhm4jvvSYFk1Ha7S3MLneFOCJxciADarEp1Kyux6zyfTBSTikvyea4sf9OaQsUkdbD8vyxQFYg77YEiCRtEgMSNp2DVPRQYAxNrwjl1YpebIUAH+4uJ53nAzKzBpIzQz3DiIIn0U7M6iZ3EBOplgjxopFHrEg9LFwvgEAvjH67BsNDvJB005402gKaQYdXsaWZTwLUPNr90QW34vLgGcdOWU1DLX9GAc4WdlTKs22sTiaZZqjcTCAcUJ5pFvHUvGVW7FVmSsQkVGdZhTSBbMxMhAxDUmZS6xWEjsD12VITQbGrRbOrw1YOIujkSxhBryR1ncQl3vlnq6wqiHrgnrqq1wO1ElbwyNPVCkNdMK1iAkyPNsynXBp3wWyOwY4dJuchs8CEWoZnr4/tZkaJqPTxrxE6ITSSYiZT3U+OJeVXGouUiRdAhYFeTyreTcerOai52HG8mNTQ3HCiZ/BImAgBBlE0AJNTDTYIwhjJIGOG5MnbZpk2Yeg9pDSIW+aYD87CGd/ZcL2rqVZybUZlkdGRSDj2LMyeC4SqqxpkoD1+UgGfVGXTxmnTD+uBYDtNsFQjcKGYtrq3FhsbvRogbI6zQwGoMgDdUPL+1dwxr2QFVq4C9WECENf4Dwm8nwAYuiS6z+wCtjbwRWJhmClZVMC3DqkZ4orGYz27j5qjB611jof1s+OqmA7vu3LlzUEygw1tgJX24nwqbIAntKgx2AJMuYS6xvRnwEiVoYr5IaQ2byUN7h0ea9QzMWeBEXVgFDjgxvBp479hurXJ5E8XUGQRjmmFvHardBITJkodnET8xBdpkmkaB7bm5H1GByX13Y9naWYpVLqd8JSoWfh5JaFYlHpIJlgYFzX2YBECx+nZqQfGtNcMB/27rfsllUVzZ4FGac6GWIwUXLEAwKQ/gaab9RaADpUrxe1xIgGtMRvwM55qwEZ/hREezICNVTJME0RxIk3+D4TBryo0luxFY/pHlRtkeosS5szAuPaYN1mfJtCmQfgAhvDAYoAYtZMmhYdoYNJiUReMV4i3mO3TTzJpxOFzPYwF60jcB0EcKH0z8Td8P6YpN2Ara+jWUY3zPlmV4ykFZl99bKVSyXzJ+9VJV5cMy53BZlOde7DjWTCr0Ij+FVbRIlYLNgFwkeGM2zQlYIHX8165aFuv6GjkgktJmtDZ3LJNy8DmHlLJorkvA9FB2cYQTzpGSOkJSGTrMbNm7mzA0k3nd9k2nPJF8USuiaq+1ZNdFrcxK9xWy7g8JoA4rLQQwMdDWwMaDihH9nejnamqEF1C0YfZ/S1yFEyUj2E0EujUHOCVxSqOBKFauIZCCTmO7cmD2jOjeDGz7pJBmpXuadxHFXCQ7l1r71M0cxgohYqbiDcyjMmMx58mm+76KS/rW4mLTjUmNRTyx0lpXDHhQjasnC0wAP2hWBHvqiCqa3Rl6A/d8wEoquUDjAQfdGYYC6JtNlBPSZTGYcVjs/kz4ntC000oFD+ZeMaG7QifNkRZAwMLGBW6HYQNDmze9h+C2gLAJz61aMOZh5vc2xZtI1ohhShxStWg+nrQiztwB1tVC0ypoGSdMuoWv4LMTPWrJYKFTEygwmvl5qIFJ+opDvYzNsT2DmIDC70TdP9ay8khB9NUPTc+hRYXFw88rFiyenhQhQ6GhDHhUXE+aVhNgKzMS1u/yU8751dBxp5GFsWn5zZi5RNSnmRKFsgPCR8cnt6qZatDFogOsBkkl516kkwccx5pJUSUNjYnlX4IBeIY/m54lx4iILIhYZYbVgx2t1Am7WodfMhn7LuUIC6ulTlfkDWeU4u9AAFAFpJKMuZJNEFJkGlAkpBKGjJoT3fsBkRioy6+aT5/VstjnUTogObFkNBoz+IkpYf5xqVT5Tv9MIGEHr3sn0pgYYJpVs+CHRSioeo1BWJtvi8uQtQQHN+PSNKLDzWqK1jYmbasYg+oTetsYPLjPTdQ0HGlWpXxZFixC5mttAg6keRJDcybVPCkXyaSaYNNNBLFIPk/2hDodVfcvuTYpFmgyiWmRwxHdoitJEryWoArg0V+s6dgBsHvyQRPvzSTWfwuIYqQTYUo0EoKbWwIsYtFZBQl8QJWN6FANnxeZLSjlNjOvijij6i5tpxjXQOJrcAhN+OZzyAOILp0EWRUMLaF5yn9ztEizFc21g4xeItg7GQ3xlmtoFPZ6zJFzah5AIdKi3FSsnZJoYWQRJcqY9zhHhGnacFNinYHDmr9zVIuYxpOEFN+v8fkW5MpKEWkOzOdwR/L9oX0FRyIzltX3FHz4vmA0VRAIAFqvRZA0kfddkPVQKyMNvTgedbyZ1GjWvvugT2Y+EZbbWLzECdBkQPoApgU17Eh5Cosd7UY2cYYzFjDNXacJo3sOVUTC2T0H7YpAlF4OlCJT98g0yIDCQlKt3IlCpHsjNPdJEZHcP7Jtw6V/Z58CzOfPR/5DMKvhMr2OIOrzYpUcFu0BMfPiOQxQMN9DMWOIOcjFIwFbT6ncEmwGJhcDBYIHbzZQqOUw+Xo3zONiHTqI+1FSWhcFNl5OB6oWgadWmsrCbd1zIbAW8yshw6S8rsDGGSZE8KATYmvxCCzoAu33GRGlxueYOLUJm1aIs1hEXRPBlQdXmvw9RmCEDuaxAJvuiaTdgkGGLjgIKVvdP+P5djCmDNcWuqTvIquI2N8TLGJSASzbzyXhdWm9n9sGYWhe6fxEG2Fasdp2w4I9BNFskMRvwEpmaUtJgs+2vKwRa1W1PaJGon2Je0fr0HaAZQw3ATbXRpM/9IMDQCycH372vZGVpV5hRzchgg7EhBYKnNYXTS3U2fdu2mwAWKBGY4TpRlyIBMS3e0bLe1pqPuiATFYlpHlB3cj3UYbMp/9paYjADBZyVi3WFDJhFzsE3csSIZiB4RDLCZkgMdQiUiUykO1QhhP3BgG6CWeb4DAIuJk0Ta5tuM/RtbRFMopzKQw5hOnQXtyLqRbRJ/4dTXWT5/hZJQvqXY0Hl8FCpdBvcymEgYSACVq2/TmH7onvoVni4sbFZVP5uOWWW/D0pz8dD33oQ3Httdfiu7/7u3HHHXesrrnvvvtw5swZPPzhD8dDHvIQPOc5z8HHP/7x1TV33nknnv3sZ+NBD3oQrr32WvzkT/4k5nnG5zuah39SggQyJ4V+HVoBSHj5L2tbrYgW/+XnjNDiAQMhYbWWBj7+NP4IpTxz5PO7tVkBCIkTOQeTyJjgmfqV9TYaq5+I1HJApqSdOpkTCa0z7IGgcAS0pnEd0i18u/fJKir0nkVF/bOpd/tpPaK6QpPsPULVLaw5pSWTnJox/mZRUq1PkL6xDrK9Q7w2XJ826JNpSPlzgNY39tO8Gnmb0Nkywn0F07TBpm+wmQ5wMG2w2VgbjRMHB7ji4AROnLgCJw5O4MTBCWuv0fMZXRom6fbTetR8JHxFV1blbrqW4dd0J2r0pwkQ1QIo9/KzqIiha/gxBmtBLtYJd4Eus0eXLu6fNLgikbPSSZ6kvIJIcehyn10p8cX4uvyuFwiWPJ82wfqTNf+3++++/32DxrNqfl2bILzXmUXzMlKs1GH5ZhbZaHDQ452EOevfNPkcexSAFYfH1if3D5VnOMxa7lfP+7wvFPxzMErPn8f3ozeHN/dH9WzPIQHrjjvUrsS1XKqtQrnTaUf4uPyHRMA1irTwFO3NYbpCj8F4wldDCh8u+jr9cNxfvRMBD7SE8L76bvtVosIOkEwyIic1qUv4rCpNba3Q2yR49d4LhasfNT4vTeq2227DmTNn8PSnPx3zPONnfuZncOONN+IDH/gAHvzgBwMAXvayl+HP/uzP8MY3vhFXXXUVXvKSl+B7v/d78dd//dcAgGVZ8OxnPxunTp3C3/zN3+BjH/sYfuiHfgibzQa//Mu//PlMBwe94WDq5rh1k0ODoE3dMp297pUdjAOXU00iy7Jo5CtYrTYP1TQRL5KCYwhCa8PidcbEw0D9+zg05h7555DmIXU0MaiblfIdy7KYxIoRsEztKTvhziEx0p5tYfIjpPHmQGI6XTJCCKMgFQ1e9LQ1ABMEA5MsAdMjcnI0AJe1Dq2vkIN6S0RIJ6mJSoz2yygoC9poXrTV5GY7lua2emFvqyCk9q9imC/DqzPUwpUNMFOcnIi2FbHtCmdIWRBUAGvk6O0PuEZ+jx0ko+ZNm72qwVWY2Fh9esUg3GQayJjIy2dSpJCd/btQwmMrgtNYjEj16SC+t4Z69jvLUtUn8Gha2yQRKiCuTvh624Chr+KJzcSFsQyw15IFHiAFq6GrYqgkiL1vwIoJnEN3zY0+WlWrpAA1097K96PJaI1hMICCARaUuG3ONH43meK8SVoXJZNJDYHIpiKYNtnifc34ETUiuS8mQEq01jjwqgvWep57K6Fl7Z7nUf50Wh8g8Fy9wwR9Rfw1nxXdGlRjb9f36pHzYK7dPM/BaBb3g7LWLcS5cT0XJL7UfL3d77MCS875YpmU6P8mcN3HJz/5SVx77bW47bbb8K3f+q24++678YhHPAKvf/3r8X3f930AgH/913/F133d1+H222/HN3/zN+PP//zP8R3f8R34r//6L1x33XUAgN/5nd/BT/3UT+GTn/wkDg4O7u+VAIB77rkHV111FX74/X+KEycfEgEAUNOuWvNq1jAVH5LAEjxDEuihVkmCwQFMuNUxPGxYIofCnNVWLaGrMTTnbX52SZwDkFRTqxjpwLZCrSOk7TDhqeI+PQ/GyrDC8cyqxFoSIruzqbFEOxAR17x0pGalakSndYtu8nD0LlZqSNz805Txexq5GGMsqUFSm0BhnrpYK20mAjpBN39UX/tcXcLs08ZL7XjF5WHFUUUkmJS9xH7vQeD9jIQaRDExABBlPo8dCJkUzURFjjVTGbJ0lai4ScvhKXivy7JS2or4G6VJnEUE5LjY0WT1tjC3Mgx+WcwHqFBsNpsUKnjOVdtyot1c9WKUqp0tYj6UnJs06LDiu0D6fDiHyct8jTECZ7Q4GDNowPUusQhGuADHe6ogFTtF8y9McFygmLwUmaODR9NSG0lC2sreRm1EnqVrEuJau7WesZSE0NQd2WkSnqYN2PgvtQiHYaEPax1izuBXaiRGg9NMFhfTVFvAOxJfJUgSNHScIiwWmCCDWmkyxDLHJa2/q4LSsHVEgAdJ5V6KZJmiVeSwM1/sfFYZCQVS4oKXmnTNMEAyBanQy+5/cH2tWSTs5+6+F7dc8zW4++67cfLkyQve9//lk7r77rsBAA972MMAAO95z3uw3W5xww03xDVPeMIT8JjHPCaY1O23344nP/nJwaAA4KabbsKLX/xivP/978c3fMM3HHrPuXPncO7cufj7nnvuAZCtERTqcRE0LZjknRDlfwOVZoCSbjjyxlgfnl+DkYDTBB5dNyLDPeL6CnEDjAkJ3BwniEKq9f2UauoxCyWWnc+aEw0RCSknQoObN0WjRjBGMCyjTwJIB7rXYxuOEA2RyQ+lA18j7F3pSfUdsW7Crn3p4qVS3FchlSA68E9TBDMNhqiqmZPgznj43ot05zgt5QkilTRIg/s5jEl1Z1K9lf1fKC8DBacsSGHn+JsTPrOviwcHWPj+YIFWYWCO1W+j1jqUYcyU0UN9Cc1RIYVREaFJEPxyHRGRGfNNLrrW6gCXqvNMBfCWMf4AdxSJBwygEEX71/0NQtRfwrQGtr0xVcsZUyWvfvbeb6k5s2U4OIlbLsDm1SJoqew+z1d3pH4yO5rXvORQGCmLuc1zP2J/IjSFGrCImw5ZAs3zjMhBANsvrhlkWDwrzq/Adv1eJXLaqNUPZUm1JPIMdsjAmR0yJLL6O/M5d4YzCmMkSCElmJgzJMFK6A0hzJ+dzA/BnFZRgEpczXkks00BFeXdlcHlshLu+B4WwRXsRGPfz/hfM6kxBl760pfimc98Jp70pCcBAM6ePYuDgwNcffXVq2uvu+46nD17Nq6pDIrf87ujxi233IJf+IVfOPyFawPieTQsl8h6cCrAvAyXvA7d7Ouwwp1ASs2tXEPUak641ev0ixpxpAbF5FZ1Oa1bAb4ApKgfQXtuHKYGvlTfmHUUXR8sO6wCKFISCXa5xgnboiPMc601y+VpFiY8MLzqhc9CcqW2DeohuzWir7D8yIzPGkDa15qKOGMkUPaGRBo6cGESsPmmSga/pARrMSwW1m4CyAIRbxQiUkK7C2FBmbDyvkoMNFML1AScLhNat5DthYnQsDB3tFJklQyVQzSRHwAK06B7mfKLn7jtYp+8/NLArsR71KAAZgTB9kiLUKAK87VIA2QyOq6EK1sj39MaiyI7wXfCn75TN2fSpOVCoFHAPDvAGZnA/EE9CytDqd34e4C4B86oUylpXhmE8OdClTNGXjfIgGI/pfw4E2vm+zQhiy3Vc66ekWJCnBN1mp8UCvEWF5VI0+RJZ4C6NiJQoGnmJi2MgAQ81NIDU0zj6Y3VS5IWQNfEHfW7ncGUlAbX7AuzHwlZsdPBRCrb86K1FDJsCiZ0NWlBGzinuchgkIpBh5lpNZvb5evfk8YhLFYPNP7XTOrMmTN43/veh3e+853/20dc9Pjpn/5pvPzlL4+/77nnHjz60Y8G4BK4f04Joygzdg2w2nQ9BBRmSy98w8C+pYehaj2iuvo86nEVW4AGFhmAaiBREspAMpegV4dIqU4kJH6ZbHFjqPWXATA8abZ3ccATRz7BZqqSs4Xjtm6+J3beDHcSpeGWKN+7gP2F7f8uxbs5KQiBbxjrjAkR2P0WZA7GFDVLQnG3pKF1hFnGiItNonkRlzCrism6ZFLQEXlCgER4MqhFNKxNf+LRSey3JCmK2C0m89ra7flm+jJzGH9AYuN714TChgbyWVEhMl0E/CRXAdJPlWVoTEtxadX3PVq/hxalGIQnW5iv15kUv6MJ1RlOFFFu9ODwDBFaCv1YGtqJ44IHyASESJrpGCBhgToM5iGStBVD0sCLEs5NvHXGC2cikUPo2mFIRMQbKkL+JKU2J2tGyKAk/rDeoorkeaz4BK8NNhp7bKH2RcsoUbgs7mta6RG+JKzHURqIfxF3ELapLTFHcsVg+XRBMUdTG9JsBwIK1Oa/hFKYdLpUliyS7+T3sWa7YL223TUgtaVdE6c6876Y8b9iUi95yUvw5je/Ge94xzvwFV/xFfH5qVOncP78edx1110rberjH/84Tp06Fde8+93vXj2P0X+8ZnecOHECJ06cOPK7yrmXZQsASeQdOQns4fh1oOhReJSOzuaSqj2jSye6GsVR65zKXA3Qa0Rk4pygUbWBLTO0mYQXBDvUJycUhUEBHubrT2SUIbWn7XYb2tU8G+HeNGv1PcbAdruFCnDQWki1Ywygd2A6wNwssXWZjTw11UgytDBkX79FpJjmNdbSfiORboplq0AXbDYbRHi019vrfUrhYLZ8LrTUzGiaaRGhR4JhCDc1r5DhpLQJpUgFsNgzx2Ih7QKwZh3hIOzrbh6V3qFjcf+e+1X8+RG6y5JGqgW+xEoCDsFg+wk38TDBmkJJcxjTkbX+7DpWKDfEjf5TrTtsGFz0br6YeZ7jOZYL1tAGfTTwZ5BvWMQbnJBbcQOvjsDW8K1BPECBW9yb+2OVTIr7QTOO4RGj3TL4JwU+oDrN7Z7h12iZK8fiHY6rYEbVKNpHqGIsoWuDVS9osqPvi33ETMNwRtp6aDbzdrgwRS2IsBESD3RngqpsdJiaxjqW1zavCbzwLxmUBtwdIr8OzyHY0bxWcIp4cjhplwwytT1dbI40rzLQS8QaYw6xdITwc7vAI2UXgg56gVs+rzJO+yzXLGUOaEmvTFA73HGXdEvKd7QI6XJxEd2fF5NSVfz4j/843vSmN+HWW2/F4x73uNX3T3va07DZbPC2t70Nz3nOcwAAd9xxB+68806cPn0aAHD69Gn80i/9Ej7xiU/g2muvBQC89a1vxcmTJ/HEJz7x85kOhoYLwsDHTRiU8BwcA0BzrJ2XZYWgNE4zkgtcXkbGJH2CKxmathFReyGOiAO2uh/HATNknOHmtuGlUnaYVJ2dp2xFaaaJzmQoNn0iiBrwtY5+wiOMlC0DyBSsNl6fJvu7C6DWFNGa+qoliQr9XtskQvRRFCaVz9VYr/mhxIJLhMUsfbijuZPwryQ4CS2BiCcC70WUpjqRloIDAO0ApLv2IpCRkYG5i/RFwCXcZiHSIUU7AaHkrWSJ7mPgeTpRtO6znm+EtPuz+vTw8km9T1HlwkKwm6XtdNMsBnOdoKHhGSP2aDqZIM1mblFqHuHIaTtctDZhcH1uazKty+D+cLTgCrpyCxoDCujPNZghkw4NxzViA0nWcnR24uYoWxOZMY+aWo5ajzOP7ks/FjUcAF65xKIKvcWGINpQtN4BDzph3FSbzMc5z1sw6VYJC2rad2prpk0sIUjQfO7Lh+UgSmiWqUOo77PC64RQsLwIH4uq2yZICwq+kbn1MncIArYielcky0URD+AuCUgIsDVwotI7isoMiLL8JjHGLlZvknPk+fnknambKwHD5syC07urT+VhKbBHxeIBtyrG58Wkzpw5g9e//vX4kz/5Ezz0oQ8NH9JVV12FK6+8EldddRVe+MIX4uUvfzke9rCH4eTJk/jxH/9xnD59Gt/8zd8MALjxxhvxxCc+ET/4gz+IV7/61Th79ix+9md/FmfOnLmgtnShwWjQ4USlxvYjQnGl/HA4AlRzBREfeYjRPgGaOVSV/AlJmfuf/DMt35PcZZgvTSHVWQlHovV8craJaMgnQoeiT06+lyVW2HuPCuxS0Iu2+NYo2fdANqvqpphAhAF0sR5NsfYiIaZZTS0YQ8w81D0CS5tmFXnKr172SBUh9a2EBceG7jZH0TyLCBzgXhAtJZ8bZ1t2jVN0VmFrpXSKcugEAAZEKO9L30twhh2QsuuKfd4WiNa6JVszaCMCe1pYc5TP9H0yqYhM2s6kMZgETKWoe2DBAVBk1fBYjlGUKhjxy5UoJCECRH1DC8HecZw7YJPAk4mj/LvWDMqxlDW1Ru3INJ7WKMwBVmne96IVHxOFBl8hk7WHjDjf5oxmYZy4eJRjnEmGuRAzgoj3FkxW4nM32LZcc5w3cZhpE0LtzvZtBNDFBtg+EXt3NaVyLmREUtYbz+TZy/rJesQzV4zJfYvEH/s32Up0jZByxvlt0Y4kK8OsBB8gQvrrvLTs8YozpaD5QOPzYlKvfe1rAQDf9m3ftvr8d3/3d/GCF7wAAPAbv/EbaK3hOc95Ds6dO4ebbroJv/3bvx3X9t7x5je/GS9+8Ytx+vRpPPjBD8bzn/98/OIv/uLnMxUAZnqZtFnWOYC2cZPD0ABHQQLVxttITFP3kGR4J1MNABH1GqP+jiyz5ZFkILoAAy3KsIBtMUolZgsaUNcq7KGUyk0aFNfS1nbbeqDhT6LKDg/hdNV5whRBG8uyOHMyn0N0zvRnL6poNc8I5kfq0xSmSd36OhS48mADYMJcqn+HT8RHSFUiFvgQeyXWwG07J1JEkh+sRBEsRUCHQpdsCMnnOuWFiyDO+PMCizQ0yZwZ8BRTnD6TNkHahN69+dww4qMVkf2AQoIcBj2mKZF4zCldehdoazMv0OHVHtQl08kSXbezm0kXjbWTAJgF1qLPZmVQTGp808bCrJfFelyNRaISQ2vZSG87UpKluXU7z+j9IMyWuUYUQs3tdInX9yaJuFgNORVEoSuRrP0Iml0NG8I/VqDDWklU+C5ipBdxsbM02F7EOxRLh3tNMOYS5O3OytadoELdhAbMYw5NhURYOiWJfEcKHMXHpxksyI7KkZMGhWJxYUO9YG+Jisvigx5FKl4GLeg4p2BJ2b4X1bxXBzW9pC0AmgQTruQ9hF9FajTU8jk/XbOc+p46j5jnDmNpzc6e9SAFqfGacCCxhxd6Vn1nbIweNavD4/M29z3QuOKKK/Ca17wGr3nNay54zWMf+1i85S1v+XxeffR8RIzQuFOQ0kuwp9gvhssm4Q+1v3J5Hg7yVprSqAIDWRaJHSI0qQ+kAC/DVsNJqxp1tELyKlLYUSPMfzQHlPlyzoD7iPzgq9ZBAdAkQYJ3ARCFRWAx/J5I5LuorJjhUmmoXmWwEKwwHFglKs8zD40CQF2pCqx2IOft76nBJYzEFz6EZyhBlvOsg/JWTsYoLjsjhsEPEa/Z6CTZNcZI1nVBYqhCm0YRWLhZZEQOGRNHR6zM1iwejGNzXJlyhYxDnMnSzwNneE4AhvhR+b0iobH6Ruc6QUHYrmGZrjTFlm1xZgMnhBL7uj5YEYT5MIWGZLIiJEC2lhSucj503Cc7FN9DgAai6HArQDIFMkIGFUiuVD13r/k5oX7PtykAdsY1YKsMmWKmEXAJQdL0nJp0SgZZozmFSw488YsTH4tVpGodEaRQ9101NCHaZuo57dLdCLQImMqUhjp2zbzJQGhqjLcEMxPiwIoKctkS99XeUXzUURRsd+6HNaoHHse6dp9Kg7U1N8I5L0noAJOkjNO38KkQIOjYZT5AdySDMzB1+zXDlJksu4yBqWfy4xBx+pKaGGGVADQ8YVRUvUhmms/YPiAhpphsguit/00JxyQui+7z5MwqNRt2BOMdoNO81guEScs07DPM3SUxePFS9j2yUoNryYgsUVzCFBEn6oKGyZDe3xfRhI3mFBiDXLL6Rm/dpbNmrQNs1baXUnamCeahobWSiFZpzdiHxtlE48vWsIw5q41gTVgY/r+oJW2rwPo7NZPiKaUuyyB3cPgxk5bV7bUcpHp29ntLAiXWVSmDGegX0yI30H/TvOGgfc4UCGCBGwJBjdD0EGBIDVCoZ5eMio55y8EiAa4wJ/E3WCWbp+Lzpoa3y+wUtodSZEZLX8goQAAWHNQsp4pEk99Vn5Uljw9LLXGtRcEoWJrNAIj5mhadLUJUA0MBz6G095gQAA+qMX+PZp5cnFnBQ7QIZoK9KuBTGXBQzxuZgsKmjRUmlqUGJVFg9juP4D6J/y32PBhNeW69Pu5ra+ZC4TWSsSkIY33GQXt4hjvvkCN+vxCDSsHm4saxZlLukOAfgTyKNbcmokxERtBRCECtfUR0ovTE1KE0hdtzZiccOixaqLugkdvtTs94I1vu6UrtjiOsxBbrQz5qVE1qWRa35UuElrqBAjR27AIKm++JqkfTEgHc0e/MBX2yZ0vDspyPqLXFqyDsRjIBa4dxj3wkFwjUzSN8lxhBMjOJzUu9t9Sg7aKUd2KicBBKahKOpA22+OGEVV07EnGmDGOG1FwWixBBAyPDyGhRECgDQ2QMrzji/a8GgNaiCkjvnRQs4U0RGhkjm3rvGWHIeQ4v29Wq5pDa8tq/47XRljTdECYsCNNzBikAtYZ5VC1FVmbiOkg0jC5raoAi8S+EkXoS2gUFACg1EJRK2GIJxcPWaYwJEXUnklGFqhqMKBl0RlUunuvY+uT1KpMVsreUQtH6xk7cTfhJcB32jsCreJ9JlRGlaGCYd0wtBaBMgzAYL3oXHgiTdWgGh/gZLh5xyTWLpn890TS1LXjgigl9jH+sZ3n0WJm2y3XDE/9RmFi2egm9y/DRze9hGtVKgY9e/VEmxSbeueAixvFmUihZ8YqC6D6KOgugkG86iMnk1P/1auVpDAhkYJCGSpZBCeetA1EwJKKFS+AuH8V7B8y3U9tcqGr4FEKKIjD72lpUs3Z3fhy8aXPGC0wirXuxC7dhchMxAm5Uxt7JfBwRQFvY1jnH3UFCkNpLCOhxAClbFymeEyuPJLJVv0JKdetb+UegqL9EJTUbOCPMH4vwgmuxrFbAdaz+9TdYIU+4puHXJo1ILRIpaQ7dXeuOdhHaSK46jTwjtKxqguGsuN5gBPXTSiW4B4ets2UeSIU79j5ArOwH31IEstgMrrtK2nSg+32i8fuKmcdKnFnx350JG/G21IUIJMgHlOt3SKQvZGVyFM5W1rcESFLrk9hQbuV65sao1h6iuq9VaNHy72EBQcFsxPV3BrIu7AGASjGZF3hE0ZJld5bIz1zYkHLdyiRZtCWTR2g05VqTJro9xmlHW63tgQTunPPFXHnMmZRFP5npwJJEt4C0VPsBt1u3CNEVJ8pdxXvbmJnIumW75CWG9qrkE4rhEmWbOrZQiA70ZUYcidtmdCwhOasjg0xWR21ZBDp5ftG8mBkJijFvIaqYGHgAQRS5hb0faj4ubYAscPPjgjZtoApsB0AvaiZoGvNtkiHOg8QPwLSZrLtv1Oiy9ww3DW0mqyZdYWnXqSoimLdLEPwRSYM2i8biejqg2CKpn5MKDx3vXq+Pfkb4s8fWc4ukFWLntQy3idSN25S7Drah7d1D6Jugz5rXQwDXTCriUmrdHVZ93da1aZt4U3JXM7eOZbGAlE6T1oJ5psMcK9MKsABjoIlJ/7oopukAvXXMs7Vv37A6xJjDHAa4+TEIvxEUmjPbcDOYNozFq6pENfzi9AfQNmZJiLJVyJp/rZi1hlcomQ4mUKqnOXNZyA0t2pDCY5uslmEGb3hC8aBZ2T5fdMEyM2ewu7xEIdTPu/hLyUh1RghVy3nXkqU7LPhximJ2n2HuFEBNsbJXBawNBhRNNgGrU58M8pj/hgywiEAkMaFXYZVf3ADrgsuIdAZZvREmGKq6YcjTONzhbTKVR+axqkUrXrilPMNxaKCshuur2n7IZi52NI0itiPk4Ejjj/qfcQvUm6Pamln0AG4VoXCZe4I1vLWGRQfmcZRue3gcaybVYrPTxCdhxoMLbpKb7e3QSfTNl5Wh22GSC4TSkAjTcY8kYvUgnLBnzhMvHVYqRYHZ9WMmeA76auwhIb34B3HYtH+PQT9OElBJiONjiuRdhq+LiYqAET17vZsCy7UpN1fxPKU2hv0LPJKPVENSgSFAH/7xS6U+nfvnJzoUC3OCkPtZ135Is5P0S6g/A6CpFZAh1r/Lv28+h+B9K8ZU3hNSN/Nr0sRizm6bf2h9F5AQ136pJBMhgZbPKI3XCDCFERH2sFq8kkmXHicYmjuJDRUCaIZ2I/WLXKsb7/iRlCtjK2wfI+8FVkk/fSgxe9RbJOB8By6JI4WjkBDbF07UBG6BSBgV14QVHqZeHkoNIAglcTci0jxST4XpVImzGkbMMg+sW6OXZ++eXe5bCg9Q2yfWgRRg/fyYeuL++un5XYWvNMfJTqHjvEeATCso+6rqeZ51P9MEUqpWAOlDLXOLKjuIyFqMUaJrkzmt9iencL+myTqON5NCakaAO7UpuKfQYNuqimWxEFVLjF08SmokUVBEAU/ebAoUk88KE1MAYtUDzIZMglIQka3d5wWLmntbZuu3NHkBUKtCvZrs6hk234bWBdvtedTWGbSPW6Y9kqADyNBgCamSbebZGXcsS5GqHJgoDXNTguP4nocTm+VfkiGFUSUIzZoIpvs4CXERalMD9n0ZHqRC86X93xFddt9ha2cC425zSBYuTd8ggJ1K3iAxXzEo/28lEjTpViQT3otMPuXCfO4hzKAMj/ykphtSM7I6tvkrLHBHJ6Yy2zqGl2iCAINER+pOO3MSMasCSMjyXDVaymSItvCeYPIJo8uST2+y9oH6hMu/JfJMzEZQhZUUhZwhcceLQNg9hylMzwKw2G2ksBYmcZiZuJmXZ0sQIN44Q6F+Mo4gnrth4vHsuvBgHq4B+moOBQvo6vIjGRZ9qIO8YwWVMN/Ras0WtBFVU5D+qmA9zsDEtR1LJucjJLA6Q/VtHJWkrC3902RS1AazJcsOjvh7rALMYd/ohcaxZlJGRNl2IqOY7DtqHKbSd2nRobRFckZqEOI+qaEaIdX+IASiUv4Mp/mBfS/WYde+W5zgWmfSDni3CQs1tlbvaecVGegO1OxJIzApn4dMM8y6BI19d+7cOVhJnIOVQ9ZviPkTaEWAg42979z52SIfe8d2thYdfTO5pKXJsIr03Vxqph+OZjkAgLDhJP9HAcGIjlVAIGI0r1KgwVwj0k4sJ8SKlW79Gi3aGYnxGsgVw0pjObOx7gychTOKIPx8Dk2jfJ4UrPbhzxFqJaqo1TFtSqkJyrTBGOw9RT/LgEZidAFfMjwXlgSCZSyYzY5lu988aVkkKinwzapipl7ugSCew31gWw6W0zrUu0hMA8taletqzEkL/cmF4PZmcLVA48R5GiI9CGP4SJUhizuEkOvxwBKr+G/fzfPsAmML2Bys1NIOgvFGQIrDlB2VRsF07w8CJg4DNGGSeVL+NEEshYQktrtBAGQdoVFLXruMJeYAX8/9+mEc/DR+NfPgyufL+Yx1xN125DvIqBdPTM8yVobTTXmZhEDDeoesoLKaVtWqePKq8XsUURiZhhHXS0Yh1nX03gMuH2gcbyYVUt5IgmkfA6FhhYoRESmxjS7OBxlT3hxka0dqNmIyVkyR4FQZJrx2VgPrW5FxjSVYXb7DgUWDea0FtGoOscvpUyhO5U4E0EJE1hKrAKVgbQYFNBE0R8SIzShblBNJgr4WIIWzTIJemSShObjM2AVlHoUzEsRZLmrMJ8+rEIajZNDo1yXxOkFGebWQmAFV/90ZL08+gSgZUKwLmmVi6rpIwJvVuVMsq0ZDRcSJ3Yu/Q7NxojEI1wppHlADKTlSHEZMayUJ7krjHEm8Aa/entJ3FcNiCTAtem2O8eABv7eVbZedH3ulxj2rEVaIAs9S7hXKHSWwgffV7VZ43cMW38dyY19zLtE0MPC9RATHm1yYsN3zLUr/7S6jyHXqDpLsYK7m7/xOdj5agZEGOBbmXrVDPfTfwbPyTaznujJL0sxX5qiFMa4xf3ed63kartNqQ1yT1bnVYKTDv8uRGtpR41gzKVHzKVkCqgOfJ20y5NrsdQvgJg16ExqRrgCRxd2pPbMQEgQDCvErgJZ5TnQ4WokXi30JW64zBDR/tJK5OUi1Mucj1GMy42ob5ue9ezJokF6AAMnPAoCkAfCiusND8gGMeU7JyDvVkpWspyOwiEo3S2l+auzbw/n9d5AckAgj5wRFVBsIAYOalMpqLRREQ0J1n8hRQF48Av5jf0dpRcoevIplc1Is9AdVQiqk4L4OihIpQEgwvyPmJIJdCTxWp4Wk+jqLDSDX4037lrozroFnw0HEPC0U2+Cg6kUpthSCowNp4TFipyX0P0xiJLoFKHQZ1lPJK3TUcwq/ZcWzUvm6nk8U663k2J/D1BEF99lb1SBD+fldRP8l17XWFsE4BSutmO8LwNDVPq7OSXUFc2QMzHsMQcVxbUADpkOTwuFBqAmZpuCcAjuCdT5H6/dTj8hiCibRUUATvuBh7qxyo/CKMWUuh9de5qqJW9TuSS9a70U4tVE1d119fjQ+HDWONZOCzsZY3MwtzKQdgDg3sC6QCtUlHOmBdOIERxXLTPS3A1RkoUxjBkTSEc8Yywz1yt3sdEtsN0I8LGJwDAvS8BRLcYmOmtBgdI+mY5VzDB+KIKsRx0gCEmsPICkMinMBw9yLr6aYMtZ7iyOBaJ5nfo2aszE8K6mJOlJ5xCWlPOiqrpm4OTFlBA1JTIXRW5RExQknqzO3covEfHh+0tpKWkxhxf8t74sdKmY4xO6tOJrNQTz6MPYIYTbhfZH/AwmxuEUWM6JuYCZ+ZwUBhaB1MgNBVKQgsx2cq4TJlW3BFGIMXozYrwN46CtjPlKePbeFwjhzvIIR+xyjYV3x+6ltih9Rs1YhwWIrw64Sjb+3+AN3NRYTgkZcp/DcqdZWa6qBAqHt+LHy8wsRxMgz9PVxgtV/tIsj1TcVzQenonnBBDi2zqnwyTmvuBCYe+TMyol6Ksc8A2rXmcOXj3BGoSnQrQQAISxbInrWgre5SAKv50mN1btpspdYAAsJrPE3ApRiWnLItJz/fokwKdXFmFTgv5u6hmtYEO/zo1E9meQHEGAIBBZMsGzPQ8Qb23UY8rOuHyzj32rEUdMQLIvVpdPWEPkrTnA6w67VI7PcdMBj0TGCSA3XXmxa5fCcEESPp3Ko1fzAtQOF8IBMLdcvXrzTrsnAAiDDfflse3ASRz5nXixCsvUevjlIGrKUfnFHEybUQs3XpyCz0R1U8erPvEbVw9kD5K0Z4VBMU09TDBlNIFY/tJbhCdiF7fi7mwUeSBbRrQQpGHEdIqZtOUWvGhJNsMvi83afDATBoDnHCFdmGf/OvfYkzmDuglFamIyZhDj3ifYBKYyBPNmEEvEz7sU/4GHd82K9vACA5k+3AlAosj1MhszGhnasZKTGWJpXPgkcg5aEbARhUwotgNec0xBMYr8WhLaiCsxjWJX+6ALAJ66PJxLz4zO24EnGE/XtoJ5Qbve1JjEfXrtU/IxzVszz7IVZe74c3hAzNOsq+ehqurZeD66CaYftCMJe5527Wta8+7szivTJ2jfNNSjW+oztLgzdmJ1EGxkyE4GlPIygoWuNMs4ODofFelC10N19vJhxzJnUbMxI6R/iN8M1KWCdMUtChjApsbDqwcRqyUto/tb22g/Ti1diLJmX4VFObRG03jxYAkFkHMsMcAbR0LWl5lIvBMt2hsBaaAydvQVJkufu0hURLX9GaF+9Wel9Ix5Zryy0x0B+J4bej8kSktWqKMCqIrEyteFUEmxAosWFOduzzJLCiK9Kx4IGeN6MCc4G7FZProdfZVTJT0o/JwfsKNILChmW/zJGC8Sh9kUzm8IqLUg83+3wK6SBWz4ktLHhcBIeSr8o/YFqWoIzl1o3j79leaxexMkCsP77ElIyMGSCihN9X0skU4+UcBeuoxCwo80oJAQptGQXYWMo0hp0bCFtwoMefEXktlXKZ6Zlms+9koi3qpE2QWRAwEhREmNBzQ2yUrQWDhSSVvMmlpPvtyqGLpgmyyubtzOG79EygDGsDqQx/ZH9knU9391NTkFnYBkLMPvZuEAyZIlzptZK+crMyRm5NsbANGVftCqURBX3gIY1IT40O0e9MBiLYGpTCEoIhuCQ5Q09BRI5aQDKLifvmxghkiAbdUZZQZ1rimhXGPbWnlUUvqiSiWY3rkarlbpJWU0wFTiTXYmeXDP3nbO+OObEcayZlIvlIfVwMG0VABjIQKgOldWTMkcx36XKSvU9n5smAAtnpWqfAFGkiCKFQbWcfTIeS1qE14hjqRF75iqaJohwSnC6g6FKaXXQ5OjS5ygEFprSnQBgd1HhfiWAglI+hpc+STu9SV0SPhKMsio+n60pYpYJnPb4XlosKF+6OlhmtFuwS/rcDHdaSGt5b4j6IakTeVXtGcJrq73InxdHWEwcKsUkmS+3P6gZHEWP+K4L0Kp1QILtkvIlQiZX5kmYEGRQA4UdCR3EdlqqFsno1RZnhyA33ghRJivLVIhYzC3CvNPPklDs1Kq1OF+wazFyLYrm7Sz8nKmdl7XZjz1HvSDxUMkEfFaBEQu7Hqu9cqwploXVKkLgoWWBQghfT8aTzJawfqGIvvrsC31Hs9haSJFVAGl9TsId70UCL2nFrqWjPEW4voAnmnLt3spG450ocBTf1jX7OyQwx3HP5pT/9bUFXh3ejnx70sYvCU1KGDRhJxsRpm7jMKazzClVwD8DIB3W6XPJVh1jWayl+oEj52oPnUE1mF23NXhbnNBeAGQrBZEM6PBqCaZJmE29Cazzr3ScdxDZTNYBd8jAFZuNhfYuC5gnQ81tN1JGh2Ke7SnRvIxTlgxtziwU9mFK0GWuw7SZHKkHFqGkhUCs4GEC+15MejTpSlysaujSES00xBji0DkLzAowJBmLtKylIDAlgpZ6zl36hM0mJVczxzJ83DU8t6kPXYCOMEfCC7QmsYY709PGvyxL1NqLUy+mluGMHpwlCRGhozlBwbTDSBH7AKD4IPysGqxRXwD25ATZn9cyAAUwYWTowIRuTKn6KsvvDJ5gBQjxSh1jUUzTFQDgdfG631bX7TOM5HRHLAXm4nMbOpU9cV4Eq1KiRLRefTbw1iwMezaH+wI1DVOKpi1inZ8pqreW+V9iDTV7W5fuYhFoagPRR2mwjmJb9a/Kx/XUHlsS0FrL75DZTdJMvN63w4PkhEpuMIWVhpHEm4kfVYQLk285qZgnkNUsxE3jqpGwHndo7aKsUQyO7x9lbflm+0fULDZZEtv1ZNLfXXPmDpNfWPPRPz8q9+yocbyZFDUE+8P8PICHR43QcChVx76PgTGAeU6TFHSEgzWkDHqkmaPjTA5u9osAQhSgGRoWxi5FIlE4MlkCMInOGMOCCMRzRNTbMC+WbBylV3yRqQQUSaQQ2xamHgQoic976MAY6YiP/UBqH8Ol1gGAhU+p6cEZot1ijE697Ee056DU7QTN5qSAGKM1RBWgp09CbPJxliICcad088682UeJUYWKaJ/AVhYBEQwRQcFwM0VWUxgRiwXgVZprmjW02SXTHVMINY/CsqKyvmkpJHpGrVWyJxcFepFmJlNRTD01IhFbc3eOLmIFbSGK3lh+yG38a/UnJwMSNCu8Kg7H4R8I4qur5/EBYxiDM18sYc+tD4QPVbRpA5EWPipjUmVeTcM1FxqjJ+soI/pGIlHsQesQWRyf3DzeDS5o4sYRaw+zksMW4SUtEhKRjKoUeNKXV5kMiehRjIrPXgkIqOss2iNS1KovIFOKpNhgFhqFmgMrFGGik/K8QRilllZoggwXqgmo6vP3FI/dfQNQ8tQK40T+hyFJAgk6l46M9YkcmQRchb4vBSblZ+P77VKmOvIHEcpIsXowOhbMQ7NVs7pEScSJzXRzYmEOGAPavF8Q4NJLc3xXK30ERd/0MDFYQAftywOi1v462pNrBZC1lKEYyKoXlUEdIdkxOkfhfi84w7a9WRTmUpEa5SO+dxGEjzDTgAY5AqWCNeBYjZwEgEzK1kvpGWjNJENLzIWHTjOpuh5nMhva5xsmM4mOSpjsXEPUjDU4k9K11dsk9p5ReX6uZs4sfgm/P8yr/ljifUaOpUk3OJUC0gZYmV9cS46IL18fNR97VsPAgoGB3mvAhvumXLIXaR4mPLDpujL9AIclUhdZzDTYSPAziIh7koTJg2Fq6wc1pmh+EDOxNfdnCdyJDhgDbM0T2vxnxaRgOXw0VY9hjMvVCoVp1I1a8GItzVufHL7SgWdV0Oc8dEWlyXZZA3rreU7iZ6oSlvDUG8h4M+Ix4UNX2m+c/w5c5Ufp/zzK+ueQUil+zCLgVQoTFDLczPHjV40anF9IkbQKrmSILEjrM4x3ImAtxN9YN/2i3NgMOAubRrxjtdRiWTgqcGK9Zqxw9P7GsWZScbC+U4trITVriAyHGxPShkuoBIx5LNGfZ10L7BB/s7NYLL9IlAcqK1OLSZVV5qkMxf6mjiStOmUdGegADWaJ0KqqvZiS1hBzkKtrYQwsoP/ASKHE3GxHMnqveRHTClwziZWmU5+PsGssFLl3NzRoC5V/UdrR4UEZisGeTxCgrasapBjGmzxoRYxIYplh5iUGrHhbBSZb0fFL7YNtKyDeGqTgkDrKNRYNNT+jqpmeQvqmREzpOIQVwTp+PveFVFmbOL/iu21eQ6wzdEocGanGOVCD684guN+qtRVLMuXYAwVoJFVpHmnpe+0aXYeEKa41Qe8T5nmLscxAKz2pBWa+bN1wZwxo62ZyY7Sqw5YtVJFRfvmMRIZyzM20+eFMa4Fr3L0XUx3DGFIAy33IyFAG39h3ANC9NFaCFqt2WKUXppbYPHt3M7DjFyNjpex7/bf6vlrLOQ6e8QOSXjunGs8lHnSVpaDK++udw+fd66cJDQtNhSJRFPaCsxHqP5TCjp57iGspF4TJlX5jdWlhLEZgBeuaiPw3rBJu5foSYVJste0fOJIMmJQKl9aiDp3fZR9L1ISzv83UY4wvI/DSsWqHKYUrdn8fK2N77WRkwEVDhL6gQZqi9ufhfJghRzOVvQshTbEBny/Sgbh5wBQJBLL9iCeoZnUCAdxRLl6eSVZzKFJiSGj2DkNDv1ea1RLz78gwoalVAQI2jHQajcUfOJozqZhn7oGCwSjBNm0fxZpLBi0kIwKM+JJIuZbDwJBw8sdiHN0o/qk928457D+AdIcfhEkv9bIRRJjSLXfXubHBn5sfWdgTAsuF83uXIORG3EYQwMrs4MEeOX8Gv0B4xuv9s9/aah8y10psP8OHRwHGhA3brmSKUbm+tVjlcA3QPqMAYHsY6QVkgpIFYQ11nJkyKcvXDzdrWRx8hzQm0ovnuyV8No9ea8RtZ+gRjevaWBNvdc9rRAB1DTAiF2gBSLmjuUAxHM97X6czcB52VgUfkUxTHPYkmE5hAI4f6xJf5TH17JXMjKkG5VoXmgjT4muPokQF7tL6UeFKctH+mKpRiSD8fFo+U4XjP7/LsCfk3aGNA/T7BatzywXpxWFz4FHjWDOpAYssZc23zXQAVXecwpr39WkCoOg6rMAsY8a8tP2yWG24vjkAw56bmGC+kY7h7Ts6zXmatW6uODhhvxRzW/3XJmlH2jrQNj3t2worjrsohniCrCogE6RNUd6n9w5o8zYiLcyN3f0WdvAKYInGf+jWYmBUToGNFaot0V+1COvK/ONzoT+iyRRIqLLE/CnBM9pKW/oCZrU1tZDYAPQNFIJFEd13KclpaGt5niKehS8CbBLHW5k/EY30aGCs1mJrXDBNmwjhTZNI8/8mqs3Dk76DGSCIQhdNAhTVQUYQZp7HlufAIQKZuhNQWYVRG0FIAaG5MEAEDyTvxXTVGpgyYGag/Bmy825gpbXGedthYl4AkQ3adAASb7vHZOS5aEaLUIo2E2STju6CjAXGKGYdESixeIeBFkwJQIlcNBpuFTEYAt39PAHB1DYl+MVxcrL9P7+NHhVkaZi4zvDduISvwIJhzy7aNgURVc+j82afsxdD3Gw2h5hS+CNRtarucoM3RAzasrifMuHNLJEpMA2H2WjaGQflRN4FKr7LYExDeRbfgcm4twmEYloVFocHf2qYVH02/C/hCEBUoUG5aiIzEXXYXetAnbl5nSZhCVwYyChe1s43m+wETCdwMeNYMyk4QhM2Q1WXjCaiiYyBEaptZWs+XGoIfNgK2cMfgRZFVK1vjayii3jdboSY2bgJbEUSY7QR3IzhmdyNEibn2SQASIcXpXWAGxirSD0CdAuAtPWEJsL1douOohYDYIUEDAcGioYSEiMfYoRAgdUcVm3TieAlBFjQggCL8FZed/g8ikCWmEXrYZECJyc2NHk0VUv+as0DI7Q8k4QKsdeNpknyLUGUvtKxJIFl/lZ5P88qdiGWnmRgxD1mZrQ+Vy0WqEI/iTPfeLQTLWS5qsWFKuGmufWWQgYHDYTJhBK2NaRewq7vg38WYduF2LHXVB1sP9MhgFctX9yPWBOl7ZmHE8cjipDSPYNhKq40hOVEI/hHQlsKP4pIll8aZh9pyLUFgxKx6N7QhKhlujn9UIUX+45VKig1RQqD45dCWMhmxRDS8lMDoQTDW+YoO/OKBC1L6PGnCAKO6inUqvGFoPkWmGAr0tCnKZ7Hqa1cw7y3+pBWqThM7cm52m3ivrIWDAoF1kMVC5wbK4H//saxZlIkxHE+OyHaANVmY1S1PAqBMm3fhF9JybwQ0Xhma0Uj6WHBKJOyJxfH44j20CZxGhDm/OmcrgSUkjkPVcSSfQUkbm5eFGDRhuEMWYsTXXqPUinR6zZwWlZAtsrNIlLprtmwMnVJodttzF6xx69LybMKAREwsOQzAYkSNYCu50aEJu7kLcY4NE0O9DEYlxCbl5vwVvf6uYTpj2sMQsnnqivdpkkuW16XBDSuVeYtxet9XtRyQo4EneC2ru5h85WweAQmLyJBFEmmBMHslRzIlKVIwRp+F4BdfsjohMIK3xeTSSIvYia8ahZWeJUJakEJFLGPjKxTwCPBpAQn5dkeKu1DwcUZthAKRcAO2Fktpb43YXrNeI3Di1ddIYGH0Axn941gcnkucXHVbOKV3Ld8pxAQApdQ5smd8sCjoC0aFgEdafUwMJTQPg7nryUcx8eSYLKab8XtoeZj9MftakPhNsChf3bO6fDfq70pc437C/6HwI4vkeg+dnNl+Cs72zasVfLeNxn1huEquI20n5aAhMG9plSLsL0LxLrijoFp2jhA7T6vCCIukTEsmaaPCM/UJC5ZFJeSEqWlZAwkcmOIJ9rSeckW8oVgtYj9haD4b/x6ag8iDfN2u+oYa8EbHSoNiybToa18legc/01tkYQ9iCH8HkqabQI0pWyLEFsAteoGJKaKNHlgF7Gah2YvijYZo1+K1hJVDjrCpBaI4vl1IrxagoApYBI291w8F2hqvocCVrcH50ZCCk1ToGgyH2Sljhr4EpFlBJYwjUmBQ4AmQLAHkAgmrxk3ioQaUpNaH7LuDNDAsEUpq9IeKu8t2xtoUYiylRGzfD5SRGpFjEJjPT/bB/PrTG5+ts+cGBehjHmKhEV1O1igUHeG1YzYQhWTWFDP0CWY2phcI/XoWz6bUbs8aTaQ5D7UhGzj1Yd9tqAwTNwYA9I7eWEwHjPJFq3ZT5nuxqXUzWwjGVUth3SkZgOkEDKIR9xHN2v7mxW7Aq+sNGbG4KtXOclcr5y7gW+ug8+50FAotuxVRyuQw03kRVXtTA+1lrzgONZMyrEIyWTcPh4Yl8QnfRQoBML+TQAr0kAUzSRRaat3UhshMc0piUub5AYJ7JRg+cxwalO2UfFaYAB0QcrMyYRsDUaIBB7FIw6YlIoDwbiG3K9CwhHVIYoktru31KI09rQMLtElfQqwztVj7+NCMp34vOfZuLnKcsZaTrpI7USZ2EzmCTkhMhruoF+kt3hOmJAYLBM7H9fG3oTNERkUQgYLExz4ntXe8UNfK3NlxGHJll7Wt4LhyqSwJpJIOBnxicQiYo/YR8P9Z5bPEJvp56Ixr9gbkUidiO0jPgTRIxwnzgWstWb3114T8U7hlIJYr9eGeA6JaWUcgXuillcG5j9J+J9y/9SDDexlgX6SeJ4aTdGqcBj+qwWB/lPCTwSjgEE0KM9InFHub9l77mUwA34fIosLW5L7FAJlLLUwHTBAYT2P9JkV0ylnSAtEEXAEQG7OWtfaZVDVClM3j6bnRx2DAAAWFklEQVRTUpjaLZh1/wAXrr8UNKmQLo3sWzgpAPZlWDEeIPv0RJWuygT4WyUgiDpqq941gejhDOOEkA9MwhyH6SKXwvpKmRRamR9nISjBnZ7+K1hcCrZuvs6kCMxRk4/Rg+W5hUnKQADNQjwYALuI8h4jcA1hAgpNAXAuHMsNi35ryMTnEmbtVwUJVABimiWAlFwbUbcQDUqXACJBO6ZpGlgUQo23lH0PJG+IVuegNG/rKnQKCtYM9HU3CakcvYFRD7oCEsR7wjwTBOLwqBGphK3UOosmlSARTJOETdUYkkBciywkSuhOYWBNgmPMsxA9wjibh/JJgoR781tyPsFpQF2fBJ+dhakVkTnUd6/mUX0otuElAtOuGyiCYME98SCo1Hhd08VAXUV2L6OQxmfwNRRSfHmDd5bosxJwZMekK+Z5aH11OJwajOuqZIQi2/3wUVYyzZktv1tpJwyiSMYaDGZFQ1IYywLGCKlqle+k5TnlufZqufDaVuuEC3r2hOFFrIf7Tiv2LuNLxNwXUWmtO11wrr2UtgKUPpTMogHIXk/R7bb1yJGZZwPgHv4ul3xUscxeIVqaFYF1hFKnHNFeXWgGNLLZ0NDaFOYy92U6TyGxsF5PM9sxo2pxLRhSn3pUdTeiZRUZVJMxmZO2RNqEb0qTwEoBRmlovXk1aDe7yIQozyL2xCzjTwILCCZfp5srAYh4eaVlxPOk5dpN83FfnFj+UKOkqWSk3h4dRSLdYb4pjRbC0wRSoqjge5QdrtRzgmgoJfFCVHaIvRHOwXwsXrEzGXN5jwXkqBf7Tb8Q58XIOnYkXZaB5nk6Sx6EJ99mpGRIteow4csn8DHs3P726gXKIKLsNzVgaRPR98eJ2ALxv3NP0+aUHCPOm7lTijiXCEopvtjeiHNrAhca/CFfR8Y7BJAhCR/hAur+VxY29rNbIkeMH0rIoswbAjThEdl/KjQQ4fELunRrW+IwGdYVcWHMO4Rq2CZDhLPoOsnrOSdGeFJAsi2jGyHLhDVIpiZQyAaFXA29OrVzm0tTeG87gVU9sXUZBXHzsqyTlG0q67NokmXKakBUwHrtr4cUaNTXRXNqUoR6rkCfmuULXsQ41kwK8CTAiL5Key9wFII0iJBBGcItTh34HCOUDDLoIbZafL8HLXT7rppAhgNpSERBX1yiEQMa1bSHu0AWSEh/SVShMM4FkxabS+fwaLDFkhI5r2DCcELj4crBMCXYCOJ9iVRZ3oX+qOZAx7ytDLzg3pJR0Qk+lm1e4vO1hnO08SOQWTzwBAURXRTI/ynREZHbwcCTGEqBO81zVpnan1uIcY2UEOllD/za4CnqDEIBMd+KfdGCUVJjqoEk0AUirBG3rCVFr5xg8GPJpjoMJtB7UGeL8DTBq2Z8aqlHx/2PpzthkybRpTU1FGdkTqRbo4aDZH6EgbKvWW0k5e00HWUgipIB+5mnr0m94OkOkaK6Eonu+X4QzHbmEnMaKSyu9Xr3X2rOJzKXAuaSEIcA2xrKLoKXNzSv/G3nXU1WZArsbq1uOcik7xSwQJOYw+6ARBUYgZZAofR3AcwhkqKV2HAWHPlXztqK4FL2W+ruFoYG0pvM7VoxqLJpeb/wzeV8saazFSVXVhc+ogglTjMYU/BA41gzKauSUEKZaw5GqdMmsPwFZZ+fApid/W+cWJkwY0g4iEwKDC/136YT9rfXktOhWOYZfTLp9/x2hjU9My3BcM3mOC/zav4k8EA98IapH4SpkkBrREkDiYbAGJcYQW5dyvuoOQ1sZwUwPGDDnfRBuw5H6hmUrzwTvte1Z04FvxQK4FUrFECDNSnqU0mI7Ao0J0RtQkTdSRo4XYkJZjd1MoYkZ5WAjWiEmMxulYApSBMcKkIGRck/FbBoOyK9E5tuZGCixyC0ZL7CHt7ind32WkYQahELh7dAnikZpOcHiRc1VbW6ek288Z0/sontbWMljcKkKKxYRF6abdlAr2+ygG4lXGCybMBGDl7Vi6AXblaut3kdP2V/MAOPxkCfqpHWZ4scCu8uIGlrYisJ1dBAK9MXFxC6O+oh1qrGyL/PswpUdowx//QBOa6XWSq6a2YUMmFBEmUEgwYAt+QApq00oDYpcW3MxoQWQSbRh636Pz3IZVB73DkPMpAiDu/Mib+neU2cFdtnUlJm1szpsFhgY4m+ei68XOjCMsjYbVN8t6OwAQ6d5/2NY82kMkyZH8CiuGjmE4lkOkUSusyjWqMPpW46qU3FX0sPVg0nJRdGqXQSL3tSSrJKxHOJKt6V0kaYBTWTRQF4vbTUivJeM/GoqJdTcRSXCqQ1QEHLNT5PoQTkABTStUuH1FBirylFHgGhse6iVaBZEASAiLILSc42hb6ekPD8Wat2BvRJxXtl9bcU08WuNpDTOxqrRAthFlhx39ZiHz0cBdQQWP+gMql0Th9+zzrghv5R26fYB6QXrgoiXNHunsd3Esflz0LZe436bi3m5hoE7GDzXO1GvcAe0SeynoH9y/ebGEQBKiMVG+Fxh0sdCmGve4a0TxAvYv9i4mnGN39J9evmDDP4wgMSFKimXfDvHQIdqcBhIqeVl6eyq0msV7BizE4HuB4hgyz36M69NJHVMwk5axUxms85FKqufibE/9gZF7wqfyoApeXAqjwaUbErDevwyL0NSogMziJRqZDzwOPYM6kjPjyULBgAKWZrzeRfcnInFrxWvOTMGIjqyFBA1aslSEbhESCbQHqLChPDrxcx+zKlWs5peIgza4lRiu7dCAsb4ykANuQG3H/Dv43yBGAbo6tbYX4mvtP2wOzjSbzksEanSSiImtm6nkEnZd/8uu45PxzmM+yH7N1WLUpW1/I9PDcmTMecVkd8+NyDYLSj72ltl8FI1rEDCZGCLc2XEBpIEJ3Q7QoaVaOicMFkT1QmhRVMxlz87SR/GZCuoWwEUdO8rqy8VFUnCXazknhw37Acral7I8vh73HGZvB02PQiZWYxDyRckAyxhltlXzvsYv1gBXZBwoBTUa0ctY25CKLoPYkqy9jyvwsyOZSM1UROwUSTlNPMUQRJPkPLaQTz5VnFepE+0x3/rP3BYCfSEgkNtIlnjgkgKmYNCWKOIhwdQb6l+Ot8HVU+Z5NUms1JV8yKtA5+svdV1ljxYP05nDnT/3+hwXtjb/g33za07K9ip/zg/Y5jzaRA0kGxX2EHMXKDxuKSdiNieLsKAmmVGsSk5cWhxQ7GfS6EkCKwWE9BgfQegB+RViRgziF1zGiDDBT+XIkEY/X6fL17ci9hXiQINlt51NGat8wgcSqAHwR2ZZ4qkp77v6DeVgSCZTFzpTlzS0QQ4JL/YaJsiNEwdColUQp5UwJwCTSRvKYWCIabUYe66SSYFaXXKlzY0beCrK21tamB81yZ5xx2/Ly5fwMaeWyqI9IIIviGPT3KflbGSpG69Q72myRiWwtyr2juZl/TbLx0kPN+63RPqTbZQSFlKwKssCACm/ZhyZRCh63V5qjsT7Uz6K+0fd15ox+9lGu4m4ZePQR78Rujbc2OUEGB74jJYur5eVZzyrOPICNxS8fQzPcBTdn5vpWbkU+r0b9uJtcwyVF/TiGOpqnDmnIve84glCTU8cRCB3qbPI/TebKHySsAMMFZi+Vj9T67HhWXHRIyonE9UutpjoMjtOfo9Vbuq7lMWWk9YXxX0IpBElw/d7yntLQK1ff9uJhxLJkUN+L8vZ8Nt27o5YWQAMAyz2b26HkUdki6JnaCaOmweGsIjIHWu2XM1/1kPS6BE7KBuWhw1Y4OODEaQHOnOQlXi5I4+fDFAwpqlFO0D/FkVyDBd+kTWAOMjCDXJIcQKwtWaiAEYIRVRLDMM6Q1bwXdC1BSXs6RKT0NA82qo8nR0lZEXgJWIbwsImZYk6ydRfbWk6DuMJ/d50PEGMQYh/bh8BCvO5ea6EBKi0uBjYgKn5d43q4gACBawLfeg9DVhoqEj+12G9qryALBiAAq+joduAAUASOIHTIBHJqt54sjeqyIhZHcqbHIcMEZADMaFg8kSRZefhjdSEIvCJhbwGgyrK4R1chx0B3/064G6p9CoNGKpF5b/839tkhU5TskYSoSef2nKTDFvFhOytvkeEX6Cp9beAHd8jnfXf+13/0UQnuwvyNqsOKZAtonqwPqZ7SoWrX6HQ19lS8H5LkXGAhrCBTR/JV0D4pFR7RXaa6KBZMCgklVDan6iSJS+YhxVIBalKwqZxaNQiWtE4Cdxfbez6zO9kJD9IGuuAzHRz/6UTz60Y++1NPYj/3Yj/3Yj//P8ZGPfARf8RVfccHvjyWTGmPgjjvuwBOf+ER85CMfwcmTJy/1lI7tuOeee/DoRz96v49fgLHfyy/M2O/jF25cznupqrj33nvxqEc96n79XcfS3Ndaw5d/+ZcDAE6ePHnZbf5xHPt9/MKN/V5+YcZ+H79w43Ldy6uuuuoBr7m4bKr92I/92I/92I9LMPZMaj/2Yz/2Yz8u23FsmdSJEyfwqle9CidOXFx3x/04euz38Qs39nv5hRn7ffzCjf8Le3ksAyf2Yz/2Yz/240tjHFtNaj/2Yz/2Yz/+7489k9qP/diP/diPy3bsmdR+7Md+7Md+XLZjz6T2Yz/2Yz/247Ideya1H/uxH/uxH5ftOJZM6jWveQ2+8iu/EldccQWuv/56vPvd777UU7rsx8///M+vqqKLCJ7whCfE9/fddx/OnDmDhz/84XjIQx6C5zznOfj4xz9+CWd8eYx3vOMd+M7v/E486lGPgojgj//4j1ffqype+cpX4pGPfCSuvPJK3HDDDfjgBz+4uubTn/40nve85+HkyZO4+uqr8cIXvhCf+cxnvoiruDzGA+3lC17wgkMwevPNN6+u2e8lcMstt+DpT386HvrQh+Laa6/Fd3/3d+OOO+5YXXMx+HznnXfi2c9+Nh70oAfh2muvxU/+5E9inteNWS+HceyY1B/+4R/i5S9/OV71qlfhH/7hH/DUpz4VN910Ez7xiU9c6qld9uPrv/7r8bGPfSx+3vnOd8Z3L3vZy/Cnf/qneOMb34jbbrsN//Vf/4Xv/d7vvYSzvTzG//zP/+CpT30qXvOa1xz5/atf/Wr85m/+Jn7nd34H73rXu/DgBz8YN910E+6777645nnPex7e//73461vfSve/OY34x3veAde9KIXfbGWcNmMB9pLALj55ptXMPqGN7xh9f1+L4HbbrsNZ86cwd/+7d/irW99K7bbLW688Ub8z//8T1zzQPi8LAue/exn4/z58/ibv/kb/N7v/R5e97rX4ZWvfOWlWNL9Dz1m4xnPeIaeOXMm/l6WRR/1qEfpLbfccglndfmPV73qVfrUpz71yO/uuusu3Ww2+sY3vjE++5d/+RcFoLfffvsXaYaX/wCgb3rTm+LvMYaeOnVKf+3Xfi0+u+uuu/TEiRP6hje8QVVVP/CBDygA/bu/+7u45s///M9VRPQ///M/v2hzv9zG7l6qqj7/+c/X7/qu77rgPfu9PHp84hOfUAB62223qerF4fNb3vIWba3p2bNn45rXvva1evLkST137twXdwEPMI6VJnX+/Hm85z3vwQ033BCftdZwww034Pbbb7+EMzse44Mf/CAe9ahH4fGPfzye97zn4c477wQAvOc978F2u13t6xOe8AQ85jGP2e/r/YwPf/jDOHv27GrfrrrqKlx//fWxb7fffjuuvvpqfNM3fVNcc8MNN6C1hne9611f9Dlf7uPWW2/Ftddei6/92q/Fi1/8YnzqU5+K7/Z7efS4++67AQAPe9jDAFwcPt9+++148pOfjOuuuy6uuemmm3DPPffg/e9//xdx9g88jhWT+u///m8sy7LaWAC47rrrcPbs2Us0q+Mxrr/+erzuda/DX/zFX+C1r30tPvzhD+NbvuVbcO+99+Ls2bM4ODjA1Vdfvbpnv6/3P7g39wePZ8+exbXXXrv6fpomPOxhD9vv7c64+eab8fu///t429vehl/91V/Fbbfdhmc961lYFms2ud/Lw2OMgZe+9KV45jOfiSc96UkAcFH4fPbs2SPhlt9dTuNYturYj89/POtZz4rfn/KUp+D666/HYx/7WPzRH/0Rrrzyyks4s/3YDxs/8AM/EL8/+clPxlOe8hR81Vd9FW699VZ8+7d/+yWc2eU7zpw5g/e9730r//L/tXGsNKlrrrkGvfdDUSof//jHcerUqUs0q+M5rr76anzN13wNPvShD+HUqVM4f/487rrrrtU1+329/8G9uT94PHXq1KGgnnme8elPf3q/tw8wHv/4x+Oaa67Bhz70IQD7vdwdL3nJS/DmN78Zf/VXf7XqbHsx+Hzq1Kkj4ZbfXU7jWDGpg4MDPO1pT8Pb3va2+GyMgbe97W04ffr0JZzZ8Ruf+cxn8G//9m945CMfiac97WnYbDarfb3jjjtw55137vf1fsbjHvc4nDp1arVv99xzD971rnfFvp0+fRp33XUX3vOe98Q1b3/72zHGwPXXX/9Fn/NxGh/96EfxqU99Co985CMB7PeSQ1Xxkpe8BG9605vw9re/HY973ONW318MPp8+fRr//M//vGL6b33rW3Hy5Ek88YlP/OIs5GLHpY7c+HzHH/zBH+iJEyf0da97nX7gAx/QF73oRXr11VevolT24/B4xSteobfeeqt++MMf1r/+67/WG264Qa+55hr9xCc+oaqqP/qjP6qPecxj9O1vf7v+/d//vZ4+fVpPnz59iWd96ce9996r733ve/W9732vAtBf//Vf1/e+9736H//xH6qq+iu/8it69dVX65/8yZ/oP/3TP+l3fdd36eMe9zj93Oc+F8+4+eab9Ru+4Rv0Xe96l77zne/Ur/7qr9bnPve5l2pJl2zc317ee++9+hM/8RN6++2364c//GH9y7/8S/3Gb/xG/eqv/mq977774hn7vVR98YtfrFdddZXeeuut+rGPfSx+PvvZz8Y1D4TP8zzrk570JL3xxhv1H//xH/Uv/uIv9BGPeIT+9E//9KVY0v2OY8ekVFV/67d+Sx/zmMfowcGBPuMZz9C//du/vdRTuuzH93//9+sjH/lIPTg40C//8i/X7//+79cPfehD8f3nPvc5/bEf+zH9si/7Mn3Qgx6k3/M936Mf+9jHLuGML4/xV3/1Vwrg0M/zn/98VbUw9J/7uZ/T6667Tk+cOKHf/u3frnfcccfqGZ/61Kf0uc99rj7kIQ/RkydP6g//8A/rvffeewlWc2nH/e3lZz/7Wb3xxhv1EY94hG42G33sYx+rP/IjP3JI+NzvpR65hwD0d3/3d+Oai8Hnf//3f9dnPetZeuWVV+o111yjr3jFK3S73X6RV/PAY99Paj/2Yz/2Yz8u23GsfFL7sR/7sR/78aU19kxqP/ZjP/ZjPy7bsWdS+7Ef+7Ef+3HZjj2T2o/92I/92I/LduyZ1H7sx37sx35ctmPPpPZjP/ZjP/bjsh17JrUf+7Ef+7Efl+3YM6n92I/92I/9uGzHnkntx37sx37sx2U79kxqP/ZjP/ZjPy7bsWdS+7Ef+7Ef+3HZjv8H4QkY9Nsa5C0AAAAASUVORK5CYII=", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temp, mask = explanation.get_image_and_mask(explanation.top_labels[0], positive_only=False, num_features=5, hide_rest=False)\n", + "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ddbf799", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/notebooks/Paper_Figures.ipynb b/doc/notebooks/Paper_Figures.ipynb new file mode 100644 index 000000000..ae2360b82 --- /dev/null +++ b/doc/notebooks/Paper_Figures.ipynb @@ -0,0 +1,1833 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8e4c9c81", + "metadata": {}, + "source": [ + "# Code used to generate the paper figures\n", + "This notebook contains the code used to generate the paper figures. \n", + "It includes:\n", + " - the code to generate the heatmaps/plots for LIME image with/without stratification\n", + " - the code to generate the plots with the binomial distribution and the Shapley weight function\n", + " - the plot to generate the comparison tables in the Experimental section, starting from the CSV file obtained from the long experiments." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e5383bad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import utils as ut\n", + "import numpy as np\n", + "import pandas as pd\n", + "import scipy.special\n", + "import json, math,cv2\n", + "import tensorflow as tf\n", + "import sys, os, importlib\n", + "import matplotlib.pyplot as plt\n", + "pd.set_option('display.max_columns', None)\n", + "from matplotlib.colors import LinearSegmentedColormap\n", + "from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input\n", + "\n", + "# Stretch Notebook Width to 98% size of the Screen\n", + "from IPython.display import display, HTML\n", + "display(HTML(\"\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "abdcb8b3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "folder created:\tC:\\Users\\rashi\\lime-stratified-examples\\result\\Paper_Figures\n" + ] + } + ], + "source": [ + "Main_dir = os.getcwd()\n", + "DS_path = os.path.join(Main_dir, \"data\")\n", + "result_folder = os.path.join(Main_dir, \"result\")\n", + "paper_figures = os.path.join(result_folder,\"Paper_Figures\")\n", + "json_file = os.path.join(DS_path,\"imagenet_class_index.json\")\n", + "\n", + "ut.check_folders(result_folder)\n", + "ut.check_folders(paper_figures)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "56b4f1ac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BlackBox Model Selected: \t\t ResNet50\n", + "BlackBox Model Layers Count: \t\t 177\n", + "BlackBox Model Weights Count: \t\t 320\n" + ] + } + ], + "source": [ + "# load pre-trained model and data\n", + "model_name = 'ResNet50'\n", + "model = ut.load_model(model_name=model_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0f1ff715", + "metadata": {}, + "outputs": [], + "source": [ + "# getting ImageNet class names\n", + "class_names = ut.get_ImageNet_ClassLabels(json_file) " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "03a1c69c", + "metadata": {}, + "outputs": [], + "source": [ + "from lime_stratified.lime import lime_image\n", + "from lime_stratified.lime.lime_image import LimeImageExplainer\n", + "from lime_stratified.lime.wrappers.scikit_image import SegmentationAlgorithm" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "1bbf3d58", + "metadata": {}, + "outputs": [], + "source": [ + "image_name = 'bird5'\n", + "file = os.path.join(DS_path,image_name+'.png')\n", + "image_to_explain = ut.read_process_image(file,model)" + ] + }, + { + "cell_type": "markdown", + "id": "1f489c4d", + "metadata": {}, + "source": [ + "# Black-box prediction function\n", + "NOTE: depending on tensorflow version, this may require a change highlighted below. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cc190385", + "metadata": {}, + "outputs": [], + "source": [ + "def bb_predict(imgs):\n", + " # On some platform, you will need model.predict(..) instead of model(..)\n", + " return model.predict(preprocess_input(imgs), verbose=False)\n", + "# return model(preprocess_input(imgs))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e057ba3f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted Class\t\t: \t indigo_bunting \n", + "Class Probability\t:\t 0.99494475 \n", + "Predicted Class Index\t:\t 14\n" + ] + } + ], + "source": [ + "\n", + "predicted = bb_predict(np.array([image_to_explain]))\n", + "\n", + "(predicted_cls_idx,f_x,predicted_cls_lbl) = ut.get_class_idx_label_score (predicted,class_names)\n", + "\n", + "# predicted_cls = np.argmax(predicted[0])\n", + "# f_x = predicted[0][predicted_cls]\n", + "print('Predicted Class\\t\\t: \\t',predicted_cls_lbl,'\\nClass Probability\\t:\\t', f_x,'\\nPredicted Class Index\\t:\\t', predicted_cls_idx)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "69005e09", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib import rc\n", + "rc('text',usetex=True)\n", + "\n", + "from skimage.segmentation import mark_boundaries\n", + "seg_algo = 'quickshift'\n", + "max_dist,_,_,_ = ut.search_segment_number(image_to_explain, target_seg_no=50)\n", + "\n", + "segments,num_segments,segmenter_fn = ut.own_seg(image_to_explain,md=max_dist,ks=4,random_seed=1234,ratio=0.2)\n", + "\n", + "fig,axes = plt.subplots(1,2, figsize=(6,3))\n", + "axes[0].imshow(image_to_explain)\n", + "axes[1].imshow(mark_boundaries(image_to_explain, segments))\n", + "plt.suptitle(f'{num_segments} segments')\n", + "plt.savefig(f'{paper_figures}/{image_name}_image_{num_segments}_segments.pdf', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + "plt.savefig(f'{paper_figures}/{image_name}_image_{num_segments}_segments.png', transparent=True,dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9a6caf8f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e8da95fd01ac442583ef4ee3d7d1934f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1000 [00:00" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from skimage.segmentation import mark_boundaries\n", + "# def axis_off(ax):\n", + "# ax.set_xticks([], []) ; ax.set_yticks([], [])\n", + "expl.image = image_to_explain\n", + "heatmap = ut.heatmap_from_beta(segments=segments, beta=beta) \n", + "v = np.max(np.abs(heatmap))\n", + "print(f'v = {v}')\n", + "\n", + "temp_1, mask_1 = expl.get_image_and_mask(expl.top_labels[0], positive_only=True, num_features=1000, hide_rest=True, min_weight=v/2)\n", + "temp_2, mask_2 = expl.get_image_and_mask(expl.top_labels[0], positive_only=True, num_features=1000, hide_rest=False, min_weight=v/2)\n", + "\n", + "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 4))\n", + "ax1.imshow(mark_boundaries(temp_1.astype(np.uint8), mask_1))\n", + "ax2.imshow(mark_boundaries(temp_2.astype(np.uint8), mask_2))\n", + "\n", + "im = ax3.imshow(heatmap, cmap='bwr', vmin=-v, vmax=v)\n", + "fig.colorbar(im, ax=ax3, shrink=0.60)\n", + "ut.axis_off(ax1)\n", + "ut.axis_off(ax2)\n", + "ut.axis_off(ax3)\n", + "ax2.set_title(f'predicted as {class_names[predicted_cls_idx]} f(x)={f_x:.5} g(x)={g_x:.5}')\n", + "plt.savefig(f'{paper_figures}/{image_name}_image_mask_heatmap_single.svg', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + "plt.savefig(f'{paper_figures}/{image_name}_image_mask_heatmap_single.pdf', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + "plt.savefig(f'{paper_figures}/{image_name}_image_mask_heatmap_single.png', transparent=True,dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "e216c1f0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Feature Importance Heatmap generated based on beta values\n", + "heatmap = ut.heatmap_from_beta(segments, beta) \n", + "v = np.max(np.abs(heatmap))\n", + "fig, ax = plt.subplots(1, 1, figsize=(2.5, 2.5))\n", + "im = ax.imshow(heatmap, cmap='bwr', vmin=-v, vmax=v)\n", + "fig.colorbar(im, ax=ax, shrink=0.55, orientation='horizontal', anchor=(.5, 1.5))\n", + "ut.axis_off(ax)\n", + "beta_title = '\\\\widehat{\\\\beta}' if use_stratification else '\\\\beta'\n", + "plt.title(f'$k = {num_segments}$ \\n $CV({beta_title}) = \\\\mathbf{{ {ut.get_CV_beta(beta):.3} }}$')\n", + "plt.tight_layout()\n", + "plt.savefig(f'{paper_figures}/{image_name}_heatmap.svg', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + "plt.savefig(f'{paper_figures}/{image_name}_heatmap.png', transparent=True,dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e0b7667b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Classification Score Plot\n", + "fig, ax = plt.subplots(1, 1, figsize=(2.5, 2.5))\n", + "plt.gca().set_aspect('equal')\n", + "ut.plot_classification_score(ax, expl, X, Y, f_x)\n", + "plt.title(f'$RC(Y) = \\\\mathbf{{ {ut.get_RCY(Y, f_x):.3} }}$')\n", + "plt.tight_layout()\n", + "plt.savefig(f'{paper_figures}/{image_name}_RC_Y.svg', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + "plt.savefig(f'{paper_figures}/{image_name}_RC_Y.png', dpi=150, bbox_inches='tight', pad_inches=0.02)\n" + ] + }, + { + "cell_type": "markdown", + "id": "0e69cb2b", + "metadata": {}, + "source": [ + "# Generate the plot for the binomial distribution and the Shapley weight function (Figure 3)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5eb37c85", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0009765625\n", + "9.5367431640625e-07\n", + "8.881784197001252e-16\n", + "0.00036075036075036075\n", + "2.5774019582069117e-07\n", + "1.5511232553820811e-16\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams.update({\"text.usetex\": True })\n", + "N = 100 # segments\n", + "markers = ['+','o','x']\n", + "fig,axes = plt.subplots(2,2, figsize=(6, 3.2), sharex=True)\n", + "for ii, k in enumerate([10, 20, 50]):\n", + " MD = [ut.pdf_bern(k, s) for s in range(k+1)]\n", + " print(np.min(MD))\n", + " axes[0,0].plot(range(k+1), MD, marker=markers[ii], markersize=3, label=f'$k$={k}', lw=.5)\n", + " axes[1,0].plot(range(k+1), MD, marker=markers[ii], markersize=3, label=f'$k$={k}', lw=.5)\n", + "axes[0,0].set_title('\\\\noindent {\\\\bf (A)} Binomial distribution PMF $\\\\mathcal{B}(k, |m|)$\\\\\\\\ \\\\phantom{niim} for a Bernoulli process $B(0.5)$')#, loc='center')\n", + "axes[1,0].set_xlabel('Number of preserved superpixels $|m|$')\n", + "axes[1,0].set_yscale('log')\n", + "axes[0,0].set_ylabel('Value')\n", + "axes[1,0].set_ylabel('Value (logscale)')\n", + "for ii, k in enumerate([10, 20, 50]):\n", + " MD = [ut.shapley_p(k, s) for s in range(k+1)]\n", + " print(np.min(MD))\n", + " axes[0,1].plot(range(k+1), MD, marker=markers[ii], markersize=3, label=f'$k$={k}', lw=.5)\n", + " axes[1,1].plot(range(k+1), MD, marker=markers[ii], markersize=3, label=f'$k$={k}', lw=.5)\n", + "axes[0,1].set_title('{\\\\bf (B)} Shapley weight function $\\Gamma(k, |m|)$', loc='right')\n", + "axes[1,1].set_xlabel('Number of preserved superpixels $|m|$')\n", + "axes[1,1].set_yscale('log')\n", + "axes[0,1].legend(loc='upper right')\n", + "plt.tight_layout(pad=0.2)\n", + "plt.savefig(f'{paper_figures}/binom-shapley.pdf', transparent=True, pad_inches=0.1, bbox_inches='tight')\n", + "plt.savefig(f'{paper_figures}/binom-shapley.png', transparent=True, pad_inches=0.1, bbox_inches='tight', dpi=150)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3c1b9024", + "metadata": {}, + "source": [ + "# Generate plots for the paper for Figure 4 and 5\n", + "

This code may take several minutes to run and will repeat the experiment 10 times to handle randomness in produced results

" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0d943120", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted Class\t\t:\t hyena \n", + "Class Probability\t:\t 0.994619 \n", + "Predicted Class Index\t:\t 276\n", + "k, max_dist\t :\t 50, 8.7890625\n", + "Explanation for\t\t:\t 50_False 1 2 3 4 5 6 7 8 9 10 " + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k, max_dist\t :\t 100, 4.5318603515625\n", + "Explanation for\t\t:\t 100_False 1 2 3 4 5 6 7 8 9 10 " + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "file = os.path.join(DS_path,'ILSVRC2012_test_00000125.JPEG')\n", + "image_to_explain = ut.read_process_image(file,model)\n", + "\n", + "predicted = bb_predict(np.array([image_to_explain]))\n", + "\n", + "(predicted_cls_idx,f_x,predicted_cls_lbl) = ut.get_class_idx_label_score (predicted,class_names)\n", + "f_x = predicted[0][predicted_cls_idx]\n", + "print('Predicted Class\\t\\t:\\t',predicted_cls_lbl,'\\nClass Probability\\t:\\t', f_x,'\\nPredicted Class Index\\t:\\t', predicted_cls_idx)\n", + "\n", + "for k in [50, 100, 150, 200]:\n", + " max_dist,_,_,_ = ut.search_segment_number(image_to_explain, target_seg_no=k)\n", + " segments,num_segments,segmenter_fn = ut.own_seg(image_to_explain,md=max_dist,ks=4,random_seed=1234,ratio=0.2)\n", + "\n", + " print(f'k, max_dist\\t :\\t {k}, {max_dist}') \n", + " for use_stratification in [False, True]:\n", + " sig = f'{k}_{use_stratification}'\n", + " lime_explainer = LimeImageExplainer(random_state=1234)\n", + " beta_arr, rcY_arr = [], []\n", + " print('Explanation for\\t\\t:\\t '+sig, end=' ')\n", + " for repeat in range(10):\n", + " print(repeat+1, end=' ')\n", + " ret = lime_explainer.explain_instance(preprocess_input(image_to_explain), bb_predict,\n", + " labels=class_names, segmentation_fn=segmenter_fn,\n", + " top_labels=3, hide_color=None, use_stratification=use_stratification,\n", + " batch_size=100, num_samples=1000, progress_bar=False)\n", + " \n", + " X, all_Ys, expl = ret # when return (data, labels, ret_exp)\n", + " xpld_cls = expl.top_labels[0]\n", + " Y = all_Ys[:, xpld_cls]\n", + " \n", + " beta_arr.append(ut.get_beta_from_expl(expl)) \n", + " rcY_arr.append(ut.get_RCY(Y, f_x))\n", + " \n", + " beta = np.mean(beta_arr, axis=0)\n", + " \n", + " \n", + " heatmap = ut.heatmap_from_beta(segments, beta)\n", + " v = np.max(np.abs(heatmap))\n", + " fig, ax = plt.subplots(1, 1, figsize=(2.5, 2.5))\n", + " im = ax.imshow(heatmap, cmap='bwr', vmin=-v, vmax=v)\n", + " fig.colorbar(im, ax=ax, shrink=0.55, orientation='horizontal', anchor=(.5, 1.5))\n", + " ut.axis_off(ax)\n", + " beta_title = '\\\\widehat{\\\\beta}' if use_stratification else '\\\\beta'\n", + " plt.title(f'$k = {num_segments}$ \\n $CV({beta_title}) = \\\\mathbf{{ {ut.get_CV_beta(beta):.3} }}$')\n", + " plt.tight_layout()\n", + " plt.savefig(f'{paper_figures}/heatmap_{sig}.svg', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + " plt.savefig(f'{paper_figures}/heatmap_{sig}.png', dpi=150, transparent=True, bbox_inches='tight', pad_inches=0.02)\n", + "# plt.show()\n", + " \n", + " fig, ax = plt.subplots(1, 1, figsize=(2.5, 2.5))\n", + " plt.gca().set_aspect('equal')\n", + " ut.plot_classification_score(ax, expl, X, Y, f_x)\n", + " Y_title = '\\\\widehat{Y}' if use_stratification else 'Y'\n", + " plt.title(f'$RC({Y_title}) = \\\\mathbf{{ {np.mean(rcY_arr):.3} }}$')\n", + " plt.tight_layout()\n", + " plt.savefig(f'{paper_figures}/RC_Y_{sig}.svg', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + " plt.savefig(f'{paper_figures}/RC_Y_{sig}.png', dpi=150, transparent=True,bbox_inches='tight', pad_inches=0.02)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "38b6aa31", + "metadata": {}, + "source": [ + "# Generate the comparison plots (Figure 6)\n", + "This code starts from the .CSV file of the long experiments, which may take more than 1 day to be computed." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "46d61638", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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filename
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" + ], + "text/plain": [ + " hide_color use_stratification num_samples segments max_dist \\\n", + "filename \n", + "1 mean-filled False 1000 50 7.226562 \n", + "1 mean-filled True 1000 50 7.226562 \n", + "1 mean-filled False 1000 100 3.442383 \n", + "\n", + " kernal_size model_name f_x g_x q05_Y q95_Y \\\n", + "filename \n", + "1 4 ResNet50 0.999998 1.158471 0.000845 0.923029 \n", + "1 4 ResNet50 0.999998 1.110057 0.000116 0.999991 \n", + "1 4 ResNet50 0.999998 0.819516 0.000499 0.639730 \n", + "\n", + " q01_Y q99_Y std_Y std_abs_Y r2 maxval \\\n", + "filename \n", + "1 0.000225 0.993214 0.306309 0.306309 0.678968 0.263969 \n", + "1 0.000048 0.999998 0.446534 0.446534 0.898626 0.127983 \n", + "1 0.000117 0.908138 0.206809 0.206809 0.592599 0.142312 \n", + "\n", + " local_pred intercept std_beta std_abs_beta mean_beta \\\n", + "filename \n", + "1 [1.15847106] -0.717964 0.061071 0.054012 0.039567 \n", + "1 [1.11005696] -0.189856 0.025809 0.024511 0.025739 \n", + "1 [0.81951621] -0.580193 0.027363 0.024522 0.014465 \n", + "\n", + " mean_abs_beta q05_beta q95_beta q01_beta q99_beta q25_beta \\\n", + "filename \n", + "1 0.048764 -0.026451 0.152443 -0.054870 0.224645 0.001080 \n", + "1 0.026977 0.001536 0.074437 -0.015488 0.104770 0.007588 \n", + "1 0.018884 -0.012916 0.065831 -0.023956 0.092483 -0.001430 \n", + "\n", + " q75_beta q10_beta q90_beta max_beta min_beta RC_Y \\\n", + "filename \n", + "1 0.066055 -0.016561 0.125614 0.263969 -0.060848 0.992991 \n", + "1 0.036046 0.002734 0.055504 0.127983 -0.015929 0.999952 \n", + "1 0.022001 -0.008370 0.052944 0.142312 -0.026050 0.908023 \n", + "\n", + " cv_abs_beta cv_beta IQR0595_Y IQR0199_Y IQR0595_Y_over_fx \\\n", + "filename \n", + "1 1.107612 1.543461 0.922184 0.992989 0.922186 \n", + "1 0.908604 1.002720 0.999875 0.999950 0.999877 \n", + "1 1.298583 1.891673 0.639231 0.908021 0.639232 \n", + "\n", + " IQR0199_Y_over_fx CV_beta \n", + "filename \n", + "1 0.992991 1.543461 \n", + "1 0.999952 1.002720 \n", + "1 0.908023 1.891673 " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "num_samples = 1000\n", + "segs_list = [50,100,150,200]\n", + "\n", + "# Change the filename if you do not want to use the precomputed results' CSV.\n", + "df = pd.read_csv (f'result/precomputed_results_1000_1_150_[50, 100, 150, 200].csv', sep=';', index_col=0, \n", + " dtype={'Hide_color':str, 'filename':str})\n", + "\n", + "df['IQR0595_Y'] = df.q95_Y - df.q05_Y\n", + "df['IQR0199_Y'] = df.q99_Y - df.q01_Y\n", + "df['IQR0595_Y_over_fx'] = df.IQR0595_Y / df.f_x\n", + "df['IQR0199_Y_over_fx'] = df.IQR0199_Y / df.f_x\n", + "df['RC_Y'] = df.IQR0199_Y_over_fx\n", + "df['CV_beta'] = df.cv_beta\n", + "df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "90b5dec0", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# FIGURE 6 CV PLOTS\n", + "fig, ax = plt.subplots(2,4,figsize=(6,3), sharey=True, sharex=False)\n", + "\n", + "for j, sss in enumerate(segs_list): \n", + " df2 = df[(sss-50 <= df.segments) & (df.segments <= sss) & (df.hide_color =='mean-filled') ].copy()\n", + "\n", + " df_orig = df2[(df2.use_stratification == False)].sort_values(by='filename')\n", + " df_seg = df2[df2.use_stratification == True].sort_values(by='filename')\n", + " \n", + " x1 = df_orig.RC_Y\n", + " y1 = df_orig.CV_beta \n", + "\n", + " x2 = df_seg.RC_Y\n", + " y2 = df_seg.CV_beta \n", + " \n", + " v = np.max([max(x2), max(y2), max(x1), max(y1)])\n", + " for i in range(2):\n", + " if i==0: ax[i,j].set_xlabel('RC($Y$)')\n", + " if i==1: ax[i,j].set_xlabel('RC($\\widehat{Y}$)')\n", + " if j==0 and i==0: ax[i,j].set_ylabel('Monte Carlo\\nCV($\\\\beta$)')\n", + " if j==0 and i==1: ax[i,j].set_ylabel('Stratified\\nCV($\\\\widehat\\\\beta$)')\n", + " ax[i,j].set_xlim(-0.1, 2.1)\n", + " ax[i,j].set_yscale('log'); ax[i,j].set_ylim(0.2, 20)\n", + " ax[0,j].set_xticks([0,1,2]) ; ax[0,j].set_xticklabels(['', '', ''])\n", + " ax[1,j].set_xticks([0,1,2])\n", + " def scatter_cv(ax, x, y):\n", + " H = 1.0\n", + " sel = (y > H)\n", + " ax.scatter(x[sel], y[sel], s=5, c='blue', alpha=0.25)\n", + " sel = (y <= H) \n", + " ax.scatter(x[sel], y[sel], s=4, c='red')\n", + " ax.plot([-10,10], [H,H], ls=':', c='red', lw=1)\n", + " ax.text(1.2, 0.4, f'{np.sum(sel)} images', c='darkred')\n", + " scatter_cv(ax[0,j], x1, y1)\n", + " scatter_cv(ax[1,j], x2, y2)\n", + " ax[0,j].set_title(f'$k={sss}$', fontsize=11)\n", + " \n", + "plt.tight_layout(pad=0.2, w_pad=0)\n", + "plt.savefig(f'{paper_figures}/CV_plots.pdf')\n", + "plt.savefig(f'{paper_figures}/CV_plots.png', transparent=True, dpi=150)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cb926d90", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "######### FIGURE 6 R2\n", + "fig, ax = plt.subplots(1,4,figsize=(6,2), sharey=True, sharex=True)\n", + "\n", + "for j, sss in enumerate(segs_list): \n", + " df2 = df[(sss-50 <= df.segments) & (df.segments <= sss) & (df.hide_color =='mean-filled') ].copy()\n", + "\n", + " df_orig = df2[(df2.use_stratification == False)].sort_values(by='filename')\n", + " df_seg = df2[df2.use_stratification == True].sort_values(by='filename')\n", + " \n", + " x = df_orig.r2\n", + " y = df_seg.r2 \n", + " \n", + " ax[j].set_aspect('equal')\n", + " ax[j].set_xlabel('Monte Carlo $R^2$')\n", + " if j==0: ax[j].set_ylabel('\\nStratified $R^2$')\n", + " ax[j].set_xlim(0,1)\n", + " ax[j].set_ylim(0,1)\n", + " \n", + " sel = (x >= y)\n", + " ax[j].scatter(x[sel], y[sel], s=5, c='blue', alpha=0.50)\n", + " sel = (x < y)\n", + " ax[j].scatter(x[sel], y[sel], s=5, c='green', alpha=0.50)\n", + " ax[j].plot([0,1], [0,1], ls=':', c='black', lw=1)\n", + " ax[j].text(0.05, 0.75, 'stratif.\\nbetter')\n", + " ax[j].text(0.3, 0.05, 'M.C. better')\n", + " ax[j].set_title(f'$k={sss}$', fontsize=11)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{paper_figures}/R2_plots.pdf')\n", + "plt.savefig(f'{paper_figures}/R2_plots.png', transparent=True, dpi=150)" + ] + }, + { + "cell_type": "markdown", + "id": "84a54b4f", + "metadata": {}, + "source": [ + "# Generate plots for the selected examples (Figure 7)\n", + "This code may take a few hours to complete." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8e12e5be", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "************************************************************************************************************************ \n", + "Running for image no:0/5 image id: 114\n", + "Predicted Class\t\t:\t orange \n", + "Class Probability\t:\t 0.7126368 \n", + "Predicted Class Index\t:\t 950\n", + "k = 50 max_dist = 17.1875\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k = 200 max_dist = 5.8837890625\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "************************************************************************************************************************ \n", + "Running for image no:1/5 image id: 147\n", + "Predicted Class\t\t:\t wardrobe \n", + "Class Probability\t:\t 0.31390327 \n", + "Predicted Class Index\t:\t 894\n", + "k = 50 max_dist = 9.375\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k = 200 max_dist = 4.339599609375\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "************************************************************************************************************************ \n", + "Running for image no:2/5 image id: 60\n", + "Predicted Class\t\t:\t milk_can \n", + "Class Probability\t:\t 0.762068 \n", + "Predicted Class Index\t:\t 653\n", + "k = 50 max_dist = 11.42578125\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k = 200 max_dist = 4.034423828125\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "************************************************************************************************************************ \n", + "Running for image no:3/5 image id: 144\n", + "Predicted Class\t\t:\t lynx \n", + "Class Probability\t:\t 0.5038667 \n", + "Predicted Class Index\t:\t 287\n", + "k = 50 max_dist = 12.109375\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k = 200 max_dist = 4.0130615234375\n" + ] + }, + { + "data": { + "image/png": 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\n", 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TFfw+ylZHV7CiYFJcRIeUatKy0xlkjkZj6xjreM+IEXAc+a/mPoowAIu/OyLCxuh7r/jPoeXcGhsxKa699KAEolFfX09hLzuyLGM1m7j9ihlc99AbPPHZSs6ceR1jx4U5cyAQwGII/1YD+ubxzpcLACirqGbd1p3cfc1fcDY7KMrP4+VPF3DqdY+QlD+SadOP69L4eoQTCzdIG2yhI7KfRjSyTEqKuCXWbTU1AEmYTJBl97WshRUr0iLIIbHZWj6H1s0i4fIbsLQ/6paEbDTiacN4HC0wdCTqLrQ3aQ5s7XluN3pbh0aYQDdht9vZsacep9vD6o0lyLLE4CEHcdd9D7dq+9P8HyjZVcU///MWo4YWMfagQdz97NssW7uViqpa5tx5Nas37aCoTy7XXngqADV1jdz22KtYrWYuvHhWp8fXKU6s0wmPUHJyywhJs5n2CbUt2U+vx6WYcPhMODwGFCW+DUxVxcvthj21BlzWDCFj22ytObQGv18Qe309OJ37xNgbqb5riBVEEg8BRWolaSSM1r8PZFlmxgWXMmv2M9Q2uVm3bTfo9K1inzdu3MA3H77OC/dex21XnEtlbRM3P/Iyhx48lCPGj8LrU7ju389x19NvM+qgAaH7MtJSKMrvxbefvkdzc3P049sfX+caK+icTdikZlLNHsxmQdDteoaiDU4azGY8+iQ8tJSftcin9twyXq/gzLsrJHyySdyg+aA1rhXZaUoKGI37zeLXCmVq3rF2C3pE7WwxqvEmsJfww3ff8NhtlzN14mhmnHQ0g3pZWbx4cYs2P/34A388akxIBz7vT9OxJVm54LTjmD5pDM/cdS35uTkMG9SPD79ZhBJcn0tXbyIvO538Xuk8/eTjnR5b17PfvV6SrQFMpg74QBWlpcsnyJJUWYfPJwoMBAKC0+t0YlPQ+T0d9qyYzQh3j0a0GjFHsr70dBxqEo6ABbM5RM+/O2JJ+5Gv6FiOyA0nxI1lGcxmFCVBxL8XvG43KRHWzrzMVGojLaRAQd9CNu2oCP2/deduemWmhf63WsxIssQ/rvwL6Xn9uOi2x5n9+Kv8tm4LR44/mOq6Rhob6ugsuqcTu90YzGZwuts3BFVXC3E2JUVQrU6HlJKCLr13yBgky2CSfSLYH9G0ublt3dFggDSzCyKzRDTLtfa31nkQUgQt9KiFV1FCVu6uQLMDBGk0JgLWdlx1CewVTJl+LG9/+RV/Pn4yHq+Pzxau4r7Hrgh9vmnTJowmM+sqnGz74FssJiPb9jThw4TL48ViMvLl/CUMKsxnT3UtjiYHkqxn9aYdVNc3svi3DSQlWSmy2jo9tu4RcSxFLxqRip/fL6JDNDidyJm9BeeVVa0CX+hjneIjNdVAQ0N8Qk5PR9SscjrD/mCHo+WYgsXtdSm99p71GECW8fdAcEfkHpTA/oFjjjser8/Pfz6Yh4rErXc+QHp6OgCPP/IQe7avY3C/fNb8toyJU46lrKyUVKsBp2ThhodeRQr4yEhNYvjgIm5+5BXczmaumzmD3OxM5rz5EZfMmM6Q/n1556uf+GXxYiYcdliHx9Z963Rb1pn2CNzvx1S6GUfuQGxJtDaO6fVYrfHDoyVJiJ8GPWEnr1bHJsY4Ld4GUWnSaWgRH/J7IeHXPbBx4kknc+JJJ7e4VlVVxda1xVxzgfDxThg1nMtuvZ/rLpnBQQP7AfDZ94vZVF6PUfWyZcceUq1mTpk2iTEjhgBwx18v4rEX32ZI/74cM3EUPyyY/zsTcTxCjXS+tnVvdTUu+0C2bpXIzjaRm+4RebwRUVtWa9joE9ml3Q62pqh407bK+zgc4HaTkt0rFL3Zo/D7ybB5cPhMuDrpxtWq1mpRYwkcGGhoaCA7PTX0v16vw55iY9iAwtC1gM/LoNwUTjh6MgDrN2/n42/mc+LUSYCwfkvB/IM1m3cwYNDBnRpDzwhtsSKlXK6OsR6zOURQer2odhGIiEbSLNWZmcEqIhGGHIOBjm0WsiwoPiUFbDb8frExpKT0cFhyMPzSJjW36re9r8JoFPNJSkqESh9IKCwsZMGyNTQ0iUyXn5asIM2ewjcLloTa/LxsFaMOGhz6f3D/vmzeVsrsR57n4efe5urZj1K6p4an3/6SkjofJ//xlE6NoWeWS+QK9ftFOg+0DF9q4wTAlBSh29rtrb1C2rtWXjojQ1xzu4NGsPaoQ3PLRER0GfDhwdCxQLKOsEVNGtGsWmYzcpSdrT1Iksg9TuDAgl6vZ85LrzPj9FPom5fFuJHDuPTsU7nj8Zf48ZeV2FOsjBw2iC9/WMTMP4sKHh9/Mx+rxYSCsLBmZqTT5PRwy10PhfTsTo2hR2ekEXC0FaotP4jbTa+SX2HAAAJktNm9SR+A9eth6VIsAwfC+PGtG0VbpmMl8watyIoiuF8sZq7XA7JMgz+pXUafkiKqUQYUCV0wh1ELsOqIyC7LtAo1TeDAQWFhIT8vWc69d9/JkhXL+W1zOb3z++B1OalvbObCM09mzcatXH/X4/TrP4B1Gzbh9fq56qKzsacIb8PsB5+ioqJiHxFxRMghFkvHV250H7W16NLThS80HkpKYPFi8azDDsOn6DBEnxgRmdUEgmXbosz2ioLO78HlM6GqYWarDdtoBIO3ucX04kF7tDbuADpQwsdCaQgE2tYw/P6ELnwgY+GCBVSV7+SC007k6Vfe4q4br8JgMNDsdPL3ex4VCSyyHiWgYNDpkE2mEAEDDCgq4N7Z/yAtM4t/3n0vGRltM7RIdF4njuZq2sqX5TARdwXV1bBtWwsRuhVKSsS7osC2bRg+/QB+/hnKy8PXNWjE7HQKt1bkETNGIz7ZhMcTri8QeRqM3w8uuW3lNLKeVqyxRvan7W9t6boJPfjAxvvvvMnMs/5IXk42vXOyMQST1K0WC/5AAEmSGNC3NzNOOoZ7b7mW2vpGqmqEu1VVVVat28TxUyYy4/gp/Ov22zr17M4tHUlq6cLRcnz9fhH/GK+YsxbW1d5ZTQ4HkhJAllvqz1L5rnCwCIjn/fij+FuLz7TZ4h8MrlmyIORXamzUhRh2RO5FaGoOBwQsrcusaIgRQ9Iu4nHhBAEfePB4PNx37z001NVitdlQFQVJkvh1+Sq27ShDURRkWeatj77knNP+yGFjR9HY5ODJF1/j5qsuoX9hAa+89xkGnY71W7YTUFSefe1dJKB///6dGkvXiFiD2x3meM3NbXNhLc5RC/Ywm1v7mIP3S15POOzJaoV168JcOBpeL+rIg6mogFyjO3YNKo1dRlBctNEsaghA2D4XDVkWunRXiS9SZdfr43PzBPZf/OOWvzNl7EjycsZTXVvHfU89x4dffcfW7Tu49IJz+Pd/XiA12cbm7Ts4808nAZCSbGPooAGUlVdgsiYjGXRs2LKV2667mvrGRpoczfywcDGbtmzt1Fi6xwM07mo0ihpZO3fGDq3S5FZtpZrNIh0qWsxVFFiyBMrKwtdzciCqGFkL6ESlSL8fXJm5WKp3tt5MbLaWVKIoWK2t6T06ZyJW5JQmRneF6LTMLwM+fBhwOrtRNTSBfQqPs5m8HHGiQ2Z6Gn1ysqlvclDX6KB/YV9uuvoyFEXhhtvvxh8IoA96Z7aWlLJ5RzmZObn43C5SbDZeefdDRh40lICisLO8gtxe2Z0aS/eWjxahAIIw45lYtdzB2lph4dHrRfv09HDWkV4vCHfdunAdLb9fEHRbwcgGA6by7fTJC4hcY22D0AKQo3Mag88yGVXNbdypJILIKXcUOr9HmAxwYXA2gMOBwdlAKg3YdK64HF2WQedsQud1oSMg/nZHpaolwsD2CTxeX4tURJ/Pxzmnn4pOp2PL9h0A7CzfTX1jE4888yI/L1nOGx98SrPThcFsoSAzjWSLidJduzn5uOlMP+oIjjv6KC4+9yw2b9vOli1bOjyWntPG2nO6Op1hv3FKing3m4Vz2OEQOm9bHDceGhthyRJ8+f0IBGC3PwurH1KNja3PLY4ao4SKLEuYTEJYiNSPzWYx3ECgJWPvrAitczeDoiBpm1J0ZQC3G8nrFdbLyOsaNJtDhNig0+bkdnesskACncaPP/7IF599hi05metvuIHkYI24LVu28OUXX5BfWMTTr75NUX4eW0tKOezQsQAcP20Kc15/j9ysDFJTkklJtaM3mPhu4a8MG9wft8dLycqVNDfUc/6MsygsKODTr7/j+sv7IUkS+Xm5TBw3lkcfepCnnp3TobH2rCBnt8dna35/eMFFclptkZaWtk5c6CjcbgwOkcKllaRusOYKTh+pcMahQL1e6LhaFJfdLqpbJiW1zCbS61vuCTq/p81i7qGkDm2ObcWYR7rm2kssOUDqbR+o+OrLL/jm80857bjpjB8+lKsuvxyv10vxsmU8+O976Z+TRV5aKpLBTE7/IZisVsaPGYXD0czC4pUMO+ggcnNyWL56HX379MFoMnHN5ZexeftOqmrrGTF0CDdcdTlZmRmMPngk/QoK2FqyA1VVeeH1d9Dp9Bj0HT+up3ucOHphGo1CyWtvcWkFsyIJOSOj64cde72wYQNy/mGhqiCpchNsq6Qpuz9ms07kG8eAhAjSiHY1y7IU0om1PSCagLWkC53ZjKo3IPl9rZXqWH/HQ7BwgccrYTSKsrk9VUc7gY5j3jfzOOvEPwCQZrczcsggVq5cyRuvvcoFZ52BLMtkZmSwp6qK0WPGMGToEN778ANQAVlP8dJlpKamcM8dt2MyGqmo3MNr77zL2EMOwe/zI0dJrHqDgbmvvkWfvN4cfdQUNm7eQlMnDgnoHhFreqdGjO0pjJGUEmmZtlqhf38h01ZWdq005IYN9MnJgYIC+PJLMZ6BA0n21+HwpGHYug5GjuxwdxoB62SVlBQplJggy0HiiiRKtxuJHjiZIVhswe024XZDqtWYIOJ9AFVVWhijml0uzGYzElKLypV6vR6/349Op0dFZuXKFQwq6sfMC85j1Zq1mIK7fk52LwIBhaW//UZ5RQVHTjyFux58lEED+tMnL4ev5n3HI/++F13weSnJKdS6Or6euidOa0qkdlxCY6NI+AchVmsvDUZj+IhQjQj0etQhQ2nKH4rnxNOENdrna1v8jDUGEL7j118XBjS7XYjv1dXYAg3w3//Cpk2tbm/r9Art8ToCWIyB1h/sDbjdpKaoQoxPGK32CWZdehkvvPEWW7eXsHjpMhqcLoYPH84fTz2Vtz/6BK/XS2nZLjZuL8HpdPLqiy9x4rTp9MrMREIiOyOD3RXhCh9er5c169ezc9duamrrePiZuUwYO4GjjjiaxcWryMjMYtXadaH2vyxdRmO8mIsY6D4nNhrDRhebTei7Xq9IxzEYBEFq7iKvV3DdSLnUaESrDebzgamwEHbtokW5j3jPhviWJocDsrLEZlBeDg0NQu8uKxObjdUKxxwTMm7Fz6aUMAUfoVN84I7DGa1WPH4dJjzhM1JlWVxTXB0/QylYysiUIOB9hqHDhnHvAw/y/fffc1DfflwzfTqSJHHUUUeRkpzMxx99RGZWJk8/O4dbb7mFs049FUmS8AcCTDh0HK++/S7jx47m8WeexWazsaeyiuv+ej1ff/M1ZdZyTEYT9hez2GYt47LHL+bF119l1dp1/LJ0KbV1dfQt6MuypUs7PN6esU5rIrVeLyxDTqf4W8tcMhoFhbZRjEuSwLZ+KaxaFb6omYe1LAWNgxcUiPObVqyIX+jZ6Qwb0rxecQjxu+8KQrZahRtq8mRUa1JchufxCDU9P18nki80rq+JuJoEgjjZ0OcDU5IQgV1+Q8g0YLIG5fB2K+EFIcvijGTFJySVoLQgOYO7nV4vDij3xolGSaDbyMnJ4eyzz251ffSYMYweMyb0v06W8fp8mIxGZpx+Bk/NnYM1KYnX330Ps8nMEZOP4NwZf0GWZS74y3n8485/cuO112N62QIfy6y/aR3ywTIHDR3GkuJiysorqKur56DhI0LPUFWVXvTqE2+sPWed1qzAWgiSxiFDle/iRDVEXtu2jZjZ9IoiiLWwEAYMgCFDBFEWFsZPcQwmVbBiBbz8MqxcKQgYBDGVlsKyZaEjWjQDcWSSgsEgrlVVif9VWYfDZwr5nx0eAw6XDodLF4rucrmlVpkMDpeOgN4U2+Ic65rRKBLCFAMerxQyHwTMSeKlN4n/9aY2UzwT2Pu44qqrePnNN9heUkLJzlIKi4q48557MBrN9M4vQKfThQxZdfV1DBk8GLPFDJepcIrC0PXDGPzDML6fv4CMzGzOOvPPmK02auvCcdVbrttCKqlxI0C6zokjXSeaSB2vXIZWfTIWMjNR/UHVOV6xaY3bVlcLItYMaP37i3jtWP5ltxvmzIG6uviVR4JjdurTWn0ky+HjXfJ7q6CEvToODDEPYQPxeVNUCV5VDbqbHA6xgVitQvXQao5pUSeRAdkdkKYVJeEm3tcoLCzkkcce4+uvv6Zvdhbby3bx95tuZtbMyyjoU8Dc5+fQK2sVvXr14pU3XgslRkgS+C728uviYibtmUza1gy+9H+B0+kiPT2Tkh3bQgS86/FdNNAQN4ii60RcUUEo3lFL/emo31KLXTQa8cgWkq0Bsbizs2HHjvj3VVWJRX/00WGDmt0uiDg9HdLSYPdu0ZcsC5FbHBnRGqecAkcfTZO7NTVqWU1Wa5ATV0tkmZ1IUnKoVJCqih8i8j06jiMSdfUShoy+2KxVQkbXRHKbLWzsC9oOfMTZIaKQUJt/HyiKwpw5c1ny6xLKysqw29Ooqa1hyODBjDt0LBdeeCHjxo3jww8/xKAzkWRNoqBPAZIkMfOiS/jiq8959e03cblcWCxmHnjkIfr27UvpzjLOuncGgU9Vhn0ylIL8AmoOq6Ouvharxcp3f/oO/Sd6el/Tmz2P79kZb3xdJ2LtgCSIXyw51iqzWkOc2WdNxecB06Z1sGWLMGjFg8ZyAgFByGVlcOihgvD1elG/RwuzKi0VlBavmr7RCBMmxCTgyOnp9eEz0NlTiS1TgQYHvtTeqKp4VGcK7gUCwWdnZYFeT8CeEartZ3A3hQyF0Se3RqK92oMJ9DxuvfU2CvKLmHHW+Xz8yQesWLmcwsIiGhub2b51JzNnziI12U5WVi+Wr/gNvV7Hu++/S21dHSazmbKdpTz9zNO88NyLnHHqWezevZs5LzzDP26ZjU6nQ70UNmzeyJB5g1m3YT2/jlnCYcvHoy8VBDzg0QHQRk35rhGxZoHWoPl1NdalcVrNyqzThVmWZqHW6/F4guHWDkfHQy5VVei56eniHu2oRE1cz88XgSM//ABb42SDDBuGb/go2nLt+v1i6GnUwe5qcSHoNtClx46gbA+KAk36VPQpwcJq3vCzFGNEPekY/f1ewVnF5cVI/4rtdptz4hxmjRFnBc0tnsuln18atx91dljOHzN3DMt3L4/Z7pLRlzD3pLmhZ499bmzcPpddsowxecKoNOuzWTy3/LmY7UbnjqZ4VnHo/3jzgQ7OyQLUwDuZH1NXX8fsO+7lqjWXsMUZdFlqJicfcDj0qy3C+ZuTG//2DzY2beCNLa9y6JeHQm+Y8+uzou10OG7ZNJ4a/iwDLAOZa3iOXldk89/srwD48JD3ws+/M+7wgc4SsSYzagkLGiFrZyFFcmAtzDKWmO31ivaBoN454TARW7xyZfxnm81hju/3CwLWLE4HHRR26+TliUIBixbF7kengz//uUMeH1UlrDJo8wIstbsgJ4e6xp4zKmkbgbbnJbD/YdeuMoYOHd5uO1WFzMxePP/iswSyA9A7fttPP/sY93YvQ4cNp+6gGqjq/Lik6EOh2sLYMWPUZRpxaDqx0ShE2Wh/rdcrcoAjObYmnwaT+DVx1mwGQ+lWUXpHS/yPxIgRqEccifT9dyLLqdUsgkppSgoMGyYituIRsdEIb75JEx07SSFZbhZ+5aD/NoS8POqU1Pg3xoG2/0UjEAjvedEx2tp9sUTpiRPHUly8rMcOcxk7dqy6bNmynurufwLPPvMs27eVkp2dy8Kff+KyS68GoLKykpdfmYvVauaC82aRnJzM0mVLqNizm5WrVnDzzf8CRAGBu+++lTfffI0+ffpw5ZV/RVV1eH1e0tPS2LFjB163ixuTbkL/Sfi581LmcXf93UiShCRJxaqqxhRTuq4Ta6cQRq+2yIyb9PSQCBr6rLoahg/H4w2vO0PlLmGQiqXD9u4NEyZQWgo5k6diKilp7W/VNqLGRvj+e6Ff9xAcahK2bHPoRMUQV3Y6qXenxj07PUatg3YzoNpq93sXuk8gjMsuv4xly5axevVqmp0O5sz9D1ZrEm63G3taGtOmTWHp8p9Zt249u8rKSE5OpU+fvqH7TSYTvXsXsHTpMubOnUte7yKGHTSC1NQ0vvziI3xeLzPqZqD/FnaMLeN54/NMXjWRYxuPYct1W4RO3AY67yfWOFIsAgax0EtKxCuSgDXo9VBdjaJAsk0l2V8XDsboHZQ7dDphTTKZBNEvXUrfPJ9Qm7PbSJhWFMGp29OvO0ENqir8vKFDk4MIZOficIhgsMjuvN5w3S6XS7x8vvgHU0RCqxZiNIpyvNFfr0GvJgoI7COMHSus0E899QQut4u+ffthNlvZVb6b775fyIQJ4/n004+57vrr6Nd/AGXl5Xz8yfsA1NbWUFlZwaJFiyktq8JosvDJJ++zevUKivoNZHzxWAqL+9B0rJttfyjj4FEjubfxHnpf05tdj+9iy3VtM6XOc2LtiNB4sNnCR4vGud9nzxLnJ9XWhovcgVjFFkvr1V5Sgm/8ZPrkq/B5WdtjKywURByPYrxeePll9LOu6bCxSFUJs8eCApBlNIEgWLMvBItF7EFasQGDQXxdFtmDS2l9hCuIPnQ60QavFxzC7iB5vZg0o6FfBq8Xg9GIajSEtJTEqYi/P9IyemHPzKPR6aGvAoMGD+PFl96koqKS9Ru2c8pp5wOweNGPzP7n3wn4/QwYNIz1GzZz3Q2zAThk1KG8/MJ/OHzVBKbUHMnyopV87vmCwwLjeeihB0RhvSAH3vX4rjYjtnq2RJuiCM7ZFsvxejHUV2HQ64UIHXmv0wmnnw4ff9xSl3a5MCyaL/pui/IyM4XPtb24040bsZRtpilnYNwmkW4eEcMiYR8ylA0bWqfyRv6txa1rnrReKa6QKducYgq1NfhdUBmcT0EBLq+uZQHsSN9VFKsPEXdkIkkCPQ5VVXn66WfZsGkzqSnJ3HLzTaxZs5YxYw9jwMCh/Lzgey64WOjHkizz7JznufKvN4fun3DYkSz86Tv+dsfdBAIB3nvnxdBnEnDY8nGMLj2YRb2X8lmvrzj80PHcc89d4TYRhJz6ePyIra4JZ/FkOq+3Q0RMaal4RRrVNBfVsmWxCXXTppZEHwmtxpdWBKC9Suxbt8J//kNy+UaS1/2KFBEpKUmC+GprxVScTkHEJSUirLstISMSIbU9whcl+X2iyJ5eFaqG2y0+b2wMc2Htu2gPWsRZAnsN9/77fprcRo6adiaFA8Zx5VXXMmLEcDZvXouiKJiDR3bU19Wycf0azjp7JuvWhj0smzauISc3HwCdTofb5aaysgJUFe/DtRxaOpo1Qzex9ujt9C4oZP2GEmZeclmLMWiE3LMRW5p4F2lp0d7bEmPbgt8v2Jtm8Y4HLdZRUYRu3Ngo2mdnh2O3U1LgtNNg+3ZYuDB+Xxs2wH33ibOTHngACgrw+USpHoMcwOnUYTa3VutTUgSBtzfNmPTl96MzylBd27Jet9/f2lgXScga19Xmrn1HRmNCnt6L2FFazrEnHAVAWnomZmsqmZmZTDnyMD7+4BVKtm+mqrKC6upKBg0ezuAhI9lRspVXX34Gl6uZ3JxMZDnAihVLcbuc5Bf05dlnHqaocAjHlh1F5fAq1h5RQmHWAHrn98OelsGK4sV89vkXnHTiCaFxSJLEHuJHbHWeE0cvLu3ldHaMM2gLMrpO7JYtYoHm58c340YGlRx3nNBPs7Nbl5/s1Qv69Wv/SIXIovNuNzaDB4PiCVForGH0sjWTmdn+NENfRTS1Nza2dKNFisvadxNtczAaqarViVe9AZ8xCaxW6hwGFDVBxHsLfp+3RTE8p6MJq9XKWWeewSsvP8ePP3xD2Y5VbNlUzNo1IpjlmOP+xJSpJ2E02TAlZdNQ30Cjw4U9szfr1q1i7LhJnPmXWaQ+OJCKMxz89ttili/7heXFv/L8sw+jILFm9dpOjbPzdadjQavwUVQkFqheL9iV2SwWbSTXiUUZeXnCIrRnjwilbGwULHDFitgsLz8f1ZqEVFAgCDFWAQGLBUaMgNWrw4EgOl3rs1ScTli7VmwGEYUKIExfkeV5mpSkFuekt4WKCug1JKq+dqz5aOmM1lThknZCZmYS9fViv3JH1dwXe4AUt7sEegYzLz6fp599kYFDRlFRXsLEiWMxRjAGs9nMRRddwF33PIjPW8dTT9yN0WjGYk3mjLMvQ28wsG7NSpqbHVgsNs6/5Gbu+9dfqayqpk+fQoYcdDAut4urL7qWd9+YQ6/cPvy2/FcGFeV1apw9Z9jSJpeTI97T0/FhwLBtY8va0pLUMiQpGN0QGDocXW4uWK0ErMnIeb2Ryspau4vsdsGtAV/hQAw5OYLYNdanUVxKChx5JPTtK+4ZOVKM7eefRTDIhg3hPn/7DQ47rFWmlcMRjmeRZfH/mjUd/0qcTiheY2LMaKPYbNo6kJ2We1F1tXiPd6hFAnsfEycexoAB/Vm1ahX9+h3b6mQGVVW56eY7OOG0SzEYjNRU7+GLj17irL9cEWqTlJzCkOFj+OmHL2hsqOPci66naMBQVq34hWee/DcZGdl8P+9TJh/1B/ILhBFrwQ+fsfDnn5k8aVKHxrn3vI56vViQmZmCCrT0weiYQkkChwNdfU3YyUpwMccy8NjteAoHh+mhujps2Ao+l8JCIWLn5YlspTPPRB0yVBBzUhK8/bYg/K1bBaVt2gSffx79mBYHm0cajjsKmw0OOYSwEUuj0OjUzKAYHa++QQL7DtnZ2VTX1PHUM8/x0suvthCva2trsWfkYjAIBpaR2YuG+jo2bxSFLbZt2YDJbKVXTj6nnHExqgpFA4YCMPKQCWRm5VJbU0Xlnl307hPeIAYMGsljj/6nw2PcO6cAyXIoIstjy8Bkd8Y/h0mnE8SrcVyvF7kgCamxIbaRS1EwGdVQ/778fhgKCwVRyrKwUBcViRfhGlqSEhDPuOsukWMM4r2+XhBUpP6sKOTniyFr2kFHYbeLfSvL5hIdlESxUq0urtvdQnqoccevMJLAvsN9DzxMo9vKIZPO4NeFX/PytBPo338gGWk2/jX7NhrqwpKiz+dFlfT89OM3fPTui6Rn5nD+rL8DIOt0+H0t17PFasPn91NfW8OSRd8xftI0AJYs/h5zSjYrVqzgkEMOaXeMPUvEmm7nl1rokoG8PqI+c2Vl7NjoSLjdSFs2C0NXLFkyWNJVg6IIQpU0gtBOQUxPF0XbtYE4nXD99SK7KRKNjUInLi+HJ56AK64Aq5WKipbnt3UENhsMTdkFjW6IJwY7nWEZHcKyeoKA90ts2FTC1BMvxOv1sHnjai684k5knY7K3Tu5998PcvDIYTzx4E1kZOVRUb4TW0o62bkFuFwuaqorqN5TTmavPL754h30RiPff/MRg4cewrIl89lZuoWsXnkY9Abmf/8FSxZ/j8fjYepxp2G2JFG+e3eHiLjHxOkAOjxeCZdbCp2moMHvB4+/g4cOaXJrXl6YBer1whJdVCSuR8HrRSQ+aBU3S0rCsY9a3vM//iGCSGKhpkaU8ElPh19+gY0bybWFqw22l8Or14uhjRjk6ZiFXnMpWa2Qk4PPmprgwvspFEXoODVVFfQbcBByUB3Mzu3Dz4uXUNWo57App1JXV4/FlgaSzK6dJfQfcjAGk40ta35kxaKPOO3kozj+2CPYVbKGD999gWanm0MnH8/gYaNITcvkuJPPpnfBAJJT0xk2cjwbVv3MhPHjOzTGTnFiRQGXVxeKA44sdKkhskil5gLV/g+VxejIQtdis71eQSE5OaEOTbI4kCxy4aspqUhWqyDiyNPWO+ICmzRJpDNu2ybY74oVMGkSg4ZObpEevWJF7NuHDgWbyQdyUB8H8Syns2XSRDSC1np/orT0fospR0xg0Y+fUFA0nJIt65h0lPDfNjsaUWUTg4eP56O3n+Hiv96FJEnU11Yxf96HHDn9NI6YdipfvvsE77/9WKi/446ZzgUzr+S0c67C2dzEB288ydgJU1jyy4/sKd/BkMED+OX7N7nrXzeTHrmO20CnxWmtACWE69fFQ9RBhHiMyZjyzSJaq62gDi3bSfMJRxaTVxQoLcVgNhPI6d0iSlF3yCHCiBRJ3bIsrk2eDM8/H/t5u3bBlCnCyKbd++uvZOh0IqVx8mRYs4bC4y+mtLRl93o92JJUHM2GFgFoNluSEPG10kXRSEkJ1RRrz52dwL7DhRecx/Cly/h1yVKOOnwUb794PyZrKg31dZgtNmpr9lDQb0ioqLw9PSvEvSVJwmyxoaoqkiTxn6ee4Zk5L2GyJqMoCtakZKYdP4MP3nyKcy+9HVVVWPjfV7jnrjvIyMjo8Bi7JU7HI2CTKb4xyKMYhPWnrU41hVSrnBldQC8ocutkQTV6Pejef0dEaD32mNB9Fy0KD2LTJrisZThbC2zaFC46r8HrFcXovV6R3lhZSe6aeaF0aA25udDQKMUvWBfvi4iwUGvzSGD/xLhxY7nqysu55e83kpebw7STLmDilD9RV1vF4p++pHT7hpDVuqG+Bn9w03Y2N7Fr5zYuufwGjpp2It8uXMesvz3KgCGjeemZO6ms2MkPX7/HuZfeTnJKGimpGUyYejavvvZWp8bXbcNWW0cvxdLz9Hradn6aTC3FXq30bHTlELcbFi7EVFEBn34qCDHyvrlzhX8nJQWGDxdVMtty8paUtF060mqF/Hz6pTfgyEpl82bxuOZmkUXZKWgutwjEismODDZJpCDuHaiqSmVlJTabjaSkpLjtNMJUUfjms9fRGy2cedGtbNnwG798+zZP3X8NeqMVW2omRqOJ55+cjaOpgezcQraXVXHCWdfxzssP4vN6mH7Sebz27J28+9qTpKeaUNUwoShKAKmTv3WniVhz80bqvpHhvNC2eKir3hNfN3U4hAErGlp7j0e4hRoaROTVm2+2zAOMxIYN8PTTcNVVYhOorBRRXLHqWmsTCQa0t0LwtAjNqGYLNFBYmMqGDWGVN5rh+v1giDQaRJ5ZpRVU0KAoGAy6FqqBzxfeU7SUxuj6gwnC7h4aGxu59MobsKT0ptlRy5SJB3PZpRfj9/u56ZbZVNU58XmcWEwyLp8BRVVRPPVU7K7jomvuA6D/4ENY/MPHTDpmBvW1lZRsXsnpf7mBd195iMuuEFU5XE4HX3/0POMPP57P3nuWc2fdjjUpGa+rhpdfeJmrr7uVCVPPRlECLPnhbV6Y00ZVvBjoFBFLUphoI2M2NPuThliLK9RGs4hBa73Y6RRphLFYUlWVIGKNi3ek7OOnn8Ly5fDXv8KSJYLgTzst7CcGUeb22GPFs2NlPxmNLQg4dFtKANCF3L2RJ6iCuGaw2VqWptWCWWJ8QQZDuIRZU1NLoUBVxa1asJs/WKe7vWStBNrGnfc8yOijL8CWbAdg0bdv8ocdO3jtjXdJyR/PwPH9WbXsR0Dl0LFTAFi17Ad2V30f6mP+1+9w/tX3UVaykerKMvyBAD//8DEZWbkhPdlitaHT6amsKEOWdawq/okd29Yy78sPyMvLY+7TD/Pqa28hyfDCnMext6VuxkCXxOnINejztQ7CcjrD0q8mJkqoGI1S+Cwmo1Fwx8hFHghWztMOZdOgETGIDiVJEHN7Vu6BA2H6dHGP0yksxwcfHA6BdLvhwQehT59wmdvowOi8vHA0WNSY9HrBPcvKhLQeTZuOZgmLPQNdkHDr9FnIKZBqa21MkGUxrcjTZ6Kh1eCK/juBrqHZ6Q4RMIA9qy9lZWWUle/h4KOOB2BP+XYmTzsj1GbQQYfy9UcvMP/rd5k45Y801FezbdNKKspL+MMZV9FYX83Lj11PVlYvFEVBlmWamxrYXbaV3N79KduxGY/bzdFHTuLH+QvYU1nNKX88geuuvarL8+iWTuzzCelUi6jUGGPkiS2hha0owlobWSkuP18QlN0epvq2ak/r9YJavF544YXY5X8iYbcL8VxRwgXmX301fD6ywxEm0OgqIyA+0/zP0XA6sdtTQ3ke0dByRaIt9IoifOYmfUtC1kTozoReRp7bnkDnMXhgP3ZsWUnfAQejKArl24oZNuwM0tO+o66mgrSMHPoNHMmSBZ9x5LEzAFi28EtOOvs6AD5++2m2bVyJx+3iL1cJ8To1LYvDjz2b8UOT+eLtB6hpcFGzp4yM7D44mpuwWFPQSz5q6hpYWSqTmj6Sm25/mH/87RJGjTqkS/PotmErlkEmmjPr9bSsVKHFCqNDV1hIQBErXpeXJwxUsQ4e0wh4wAARkNERNvTzz+J96lQRC9nQIChOrxebhs0Wbmu1thTP8/Nh2rT4R8sgppGUJHIsNEQnesU69MzpBJM9vOsF0IVOeOkoJEk8O6EXdx3XXH059973MEvmLSPg93Lb3y4nLS2N22+9kauuvQmXz4DP04y7uY63n78LWWfAYDQzYdpZAAwYNg5Zp6dk428oSgBZFgvf43bSUB/AZjWRXTSRQMDH0vkfM/LQaRxz2pVsW/cLWzcsZ9KAkQAccfIVPPv8i8x56pAuzaNbRGwwiFeseIZI44ssQwADOrNeiLBmc4hwtfcQtDNUop2x+fmCECsrxVEvHTl822wWGUoHHyxSDe32+C6f9HQYO1b4p8vLYeLENgkYxHDaG0ZAbyJWGWlHs4TPpwsNpyMErOnBocPOpURNgI5AVVW+/e57tpeUMnXKEaFsJEmSuO2Wv7Vqv71kB1mZmdhsSVxz1aUkJyfz5FPPsmDRcmoaw5tyTeVOGmt2kV90EG88czvTT5nJkvmfsWXDctYUp5LZqzd1277g0CmnM/jgw3E01WNLSWfkhOPZvG55yH8sdfOH7JHY6cjaAJoVNbL8aiiGWpHQGY1xD/YOKJII2FizRoi62ko96KDwmUVerwiROvxw+PBDIX8ajZCc3PLwNLtdVO4AGDRIbB5lZcI4puUOR24U6emCKgcNCvun25m0zRomvni/gc7dugyvoohhRFr12yr7owkObT0ngfi4+bY7qQtkk5Hbn5vvfJorLzqFo448PGbb5b+t4O6HX2T8sTNpbqrnyOl/4opZf+HqKy/j6ivh08+/5LW3HkNBx+YNq0hNz2TH5hXk9xvJ91+8zvBx0znuzzdQubuEd5+9lctvfwm9wcjgkRNZNO8tqip2kJXTF4/byeqlPzB01GS+fu8/3Hrd+V2eX48QsWan6ohoF0DXdrB/erpgN8OGCU6qKC19xEajyA22WkUFjy+/hDPPFMedvvee8PcefzyMHy+4d3B3Cdgz0GmRYHl5rWvNala46CohkZBldtYnk5ICqXIzOgJIki5kA4i2x4X6bQMdjZlOEG/XUFlZSWmVl3HTpgKQmTOLl15/Oi4R33vfI8ipA6nYuYU+/Ydz2HEX8eF/F6PT67nwvHM4+cTjGTSgP+fOvI5hE06mqnw7FlsTg0cdTsnG3xhysOg3O7eQoqFj8fu96IOpirbUTByNtVSV78Ce3YfyXSWsWvYDeVk2jjxicpfn2GOcGFqGYep04u/O6mwB2YA8aXJIdZaIY4bV9ONzzw0/5Pbbw8fMaDAa8fkl/F4w5veF/L74/WCy21sexaoRcVsDNhpDh1oMH55EkioMS2azGKeiSK0MUz69BYOW6tiO6J2ZKQQQzeiuic+KEj7KKoHOwev1YjCFpSpJkpDk2Mv+kcefRrYPYcCIyWxatYDK8m0k27PoM2gMi35dyYXnnQPA3Q88yRlXPIxOJ/p57t6L2LLmF1SE6F5XXU5F2RYaaitZW/wjYyafiNvlYPG8d5BkmfHTzmLaqVciSRIfvfhPJh02tltz7NFUxMgF3J0E95aVbCR0bR1/oBFsrNCmYF6zdmuLKrj6ZIx5yei8rnAuc/RmEWVaXr3JFBKfV6wQEvvo0cFcZcBilkNpmNqG5vGAx5SBrx2d124Hi96HW28I7SWRhvzI1M5oP30C8dG7d29wllOzp5T07D6sW/IlR00a3aqdqqr8vHQ9E068BoDxR5/Fp6/ejaoE+MNZ11G2PKLulc4UImCAnILBlG5fh8WawutP3EBmXhEDRx5OWq++lO/YyG+LviAQCGC0JJHZqy/Dx04P3WsxqNxw7ZXdmuN+b9vUEeh8Iamg9dujGNq81esFj2whkN9XiPHBIBSPOZUafypbK5NZvCaZ4k3JVHlTW6VCJyUFy89qUBQMcgCTPoDR2JLQ4h3NouV4WJRmcDpJs/lCxxVH3xOpxmtnNyXQNiRJ4vlnHyXZs5odS17mT1OHckGQo7ZqG7UzepwOBgw7lI+fv5nMDDsVQZdmr/QkqneXAOB2OSgvWceFNz3Pudc9hTU1g2mnX0PfQaOZeupV1NVUMHT0NNKy+uBqdtDsbGL7xmL8fi+L573J2WccHwoK6Sq6zYlDdZT9foxJBiRUAooUYmraGeRd7Rt/OwSs5UVq3FqvF9y3gyWZNeOS2ZaMYk1GR4DN61u7q7WaV5FQVVEAwRBjK9QRdjm0J5VYrYAzfMaTxWbD622f1WpidgJtw2g0cvON17XZRpIk+uensXNzMfkDRlOybhHjDyli/cYFHH7azXhNFmZe/Q+OOGwkGzaXUPrdT1jNOjLS0zl03Bj0BhE+Z7a2rFRqtqawYvFX/PHCO7HZM/n6rQf49NX7yM4t4IhDD+Ki88/t9vw6TcSRQRw6AkKJ04qjm0VlR51eD+b4weQ9hYBswOsGQk4cnTgepgvQwicrKnRtxptEorxcBIEZYhmyZVlkV8milFCcSkOtJQW9XhRQSGCv4rcVq7josusxpeQQ8DqZec5J3Hf3Hbz0yhusKn6Zw8eNwucbjdrrSDJz+wEw9oTreeelOzj10gcZ63Hx6Uu3kzPiFNYvfANlwQcccvhpoKrs3LqKPv1HsqdsM/U1e8jMLeLjl26nV/5ADprwB/QmC4ZAI7ffdmOPzKVzsdOqgsHZEL4QvQIjakZFRyp1GUGZU0VC8vvCeqvR2CFXcXvQ6taDIMp4pcDiYdMmGDlSh0GOYreKIrYWBUxGGZ8vXGI20gXXCooS28qdQI9BVVVmXnkjE066lrx+wwH44OMnmDxxHXk52eTlZDNlypG89c4HyBELWZJk0rJFgo7RZGH4+OPR6Y1gyWHDqoWsK/4Oxe/Colbx3QdPkNtvJGdd+ww6nZ75H/+Hg8YfT/H3byHr9Iw99q+88PKb3Hh918MtNXROJ9Yi9NtKPgjKzpLfh4SKhIpOVjucTheZyaOV9fF4JbE/aAebe73gcHSqgF08NDSE8/Zrazt/Mkp1tSiX3Sb3VBQMhrCeq1XP1Ai6xTz0+kTVy72MhoYGVMkcImCAvsMmcfq5l/POghreWVjLn/48k7TUJLYteZvGuj24nY18/tKtDB9/fLifmt1sWbUAi81OSmY/bJn9yO5zEP+87UZknZ4pp10XMoD1GTCahZ/PxeVsorayDI+rk9yiDfR4tcuAWVRtNOBrYVLVdOV4iLRIRxKyBqORVmFNBr8LP3HSBzuI5GQRgh1dEKQzWLtWeLv6F8ZvY9IHMOmFn9zvF94t7XmSMyIgJBFHudeRmpqKpHjYvWMduX2HAfDL168wdcZssnqL2s82ey8emPs2SQaZsiUvsH1nJQV5WXz60u1M+sNMqndvRVFVavaUMurIs+g3fBKqqvLNW/dwwcwrCehTKd1UTMGgMQAs+uoFTpp5PzZ7Ns6mOr54/noWfPNej8yn54hYy5cl6F7RG1pVrNDEyOh1Gl3TWa8HXX0NHltGaKH7/QhXU8TzPJi6XSVSy42IxYFlWQRwlZe3X8S9vBz6F8WZYMTDdH4vOrMZIqPWIg0NnSTihO+485AkiTlP3svFV9yIOSUXl9OB3mjBmhKuaZWUkkFSahYpGb1ZPP8dBo2aRsGYY9lZ9QTLfnwXj8tJZl4RTkc9/YaLIu9KwIezsZZRx18Pksw37zxA734Ho6oK9uy+2OziYENrchr9h4wkLS2tR+bTM9u+Xk/AaMFnTm5hlfb5W6+waEk81gksADidLTJ99HrCZm69niavKaS/dlY3DgTCZ5EbDCLIIiWl9RFIej1kZITdQG3B6w3ON/rcUwjvUsGJutxSC8mjwWvBY06liWQanIYOzyM1RUVOVNnrEgb0L+LwSYeRk5GExWbn8JOv5OfP5xAI+AkE/Cz87BkKh01k6bevcexf7mLg6GNZ+PnTjJl6Psg6ikYegT27ELezifpqYQndUDyPMVPPo3DYRGypmYw66hymnX0708+ZjSTJoRI+qqribKzhtTfeoaysrNtz6TwRR1OcLKPaklvkLGhBCh3RWWOp114vgqIqKrDgCod0BstONviT0LIatdNBO6NH1tSIYI0tW8LVbe32lkUyQfTrcsH4YU1MnNh2nyEDmaKETx+P/DCCiKOZrZYV2RFx3maDVKWOVKVO7EIJH1OXcMlVt2Ac+GeGn3AHI488l3nv3I/Fls6HT17Kh0/MZNDoY1n27WtMP2c26Tn9SEnP5egzb2X5j2/jqK+i6JDpDJt0BvZehXz87PX8/MVc1v76ORabHYDktBwqd64PPW/AIVP59Nmr2bD4Pb5++SakpHwW7Mxi1t8eZMnS4m7NpfOGLa30awS3kRxNSI4mTIoLk+JC52xC52xCcotSOAFFEqGPEbpuPNuYVum1xp+KK703qtkSEstVJDx+XehEFM3GBR0nYr8fvv1WEM2nnwpizs8XBJyU1KpqDitWIM6G6sw3pRGydrhxVAEDkz7Qor/IfOzIl04Hqf4aUvXNpNJAqrcKXel20Wd9fedyFxMIobGxEb+pl6gTDWT3GUpKZh+SM/KYdtRhLJj3IcnOYuorNkZZpyV2bPiFyafeQGbeQKzJaZx4ySPkDRzH8CNmcPSM2Sz87Cl8XjcGkwVnUy2fPvc3fnr3LtQ9P/PNRy9w9YwJZPcZyrjjryQrfwgH/+FvPDn3jW7Np3M6sba6NOrRTKua2TVKrlXNlo77R4OIbN/YKF5msxQSdaNr6EFL6bW9wJJNm8RQt2wRz4pMfDIYhNgcOQazGXSNddQo7esv1dWQVmATxBVpSY8DTQWOJ7EkJQFVjWG/V7d8dQlosFqteJtrQv+rioKqqgwefyob5r+A2+2mMD8Ho9nMvDf+xcmXPobBZOWrl27F7XIiBXnfzk3L2LLiW+r2lFBTvpX8gWMoPGgybz10Pqnpediz+1JVuoZLrziTGWedBkB9fT1mW1jkk2QZSddxFSoWumfYipSfox3DRmNcX2ggEM74keVwskS8NaoRbluZUhHu4zZRXy8yHTXa2rJFVP7JyhJjiqY5vx9+K0mLGbEVjW3bIDPTRJpNFidCRg5eO43CagW9HlnWtalu2GwgVQdLEiWIt0eh1+s5/fiJvPDOv8joM5Ld21cxfHKwBI8+iQcefoKvFqwjJX8MzvpK3n/yckzWFFRFwZyUyoKPH2XN4o8wJ6Vz5Jn/AGD+O3ey4qd38LqamHDCX7Gl9WL1gnc45uLHeeqlO1mydBlnnX4K48aNRa1bh6N2Irb0XLYu+5jjJ7eO5e7UfLr7heD3C3YZXJyAWLBWa8y15/O1TNnTiLi5ddptCA6H8OEmJ7cs/ROJtjiaBq+39RFP2rNNptjFDSLPeusIHA6w2YI7aywDV5CrynLbu69B8XQ+8iSBdrFhw0a++uYH3v/kv2SO+BPbV36LTqdn/dIvWb/kCyq2LGGNXsdBR1zAwHEn8tO7d3PYn/5GamYBzsZqvnjmMqZfcD/rF3/M2GNmhuKeJ5x0DasXvM3Y4y7jp/fuYdj4U/D7vTib6ijdsRO3nMb82+fyh4nf8+pzj3LvA49Tvb2BGdMmc8pJJ3RrTt23TmuysVZkTjs0LY5PJtbJptGHEkbC5xMEFggIjllfHztvt60+QAxn1SpR/DISzc3w/vthXby7xee0Ml0GvRpfbLDZ2texE1XwehyfffE1N/z7TVa7RmEfdAL1e0rI7nswXrcLFZm03kPx+PwokomB404EQKc3YjBaaKzZRen6RUiyHkd9FZU711BfVRrqu65yB5ZkcWpD3oCxfD73rzTWVrDw40ewpefRf9QfkHQGPp+/Co/Hw12zb+aZx/7dbQKGnuLE0Kqch5qe0Sp/NjLfWEMgIG6z24VOGcm9tYCIyKN9nU5CGUIaIWi1A7QzyqJRUiJKVMfz9fbtK4p9pKXB5s2dd1nFRG1tlzuSZdo/PTKBEJxOJ+vWreelNz/C5/Vw9aXnMXz4cOrq6iguLubDz77k51/X4AnoOeGqVwBIzerLx4+di8tRx8ipF9FcV07puoWoSDTU7MLrbsZgsrJnxxrWLPqQim3LGTbpLI6/8nnW/vQGfp+PNT+/S83uzQCsW/QBR8+4kz07VlOxbSWZfYZxzMwnkGUdrqYafvtmLpNPv51vXriaurq6TpelbQtdJ+JoE3OUS0VqbABzy6MRNKLTEtxVFZJtqoiLRkWWJe32EGeMJZI3Ngr60OuFJ0qzsblcLcsCWa1CIn3tNeFWigfNS1NX1z0C1uuFlTtUGDAWzOYWR7Puzxg3cRp7GvwE/F4UJUBSai+aanfhczuxpKRjMCWhBHy4HfVIkowpKRmdwYzHUY/RmozLUYfi94Csw5qciSzr8bob8bqaMVpT0On0+DwOdHoTJksqjvoKJFnGYsvA73XhdtZjMFoxJdnxuhpRVRWfuxlLcgayToe7qQ6d0YTBlITf6wZUmmp28d8flqI3J1FfsQ1rSgampDQctbXojRZe/+dxJGfk0lxfyREz7qK6dA3lm36lfs82ZJ2BnP5jqC5dw3sP/pm03IHoDGZSsvrh9zjpN1JUBxl97GXs2rSEyaffRt2e7ahqAI/LydolX5CaVUBNxXay+gwNZbFZkjOQZBmd3gB+F3369OnR36lrRBw3QiMCXi/EKOWq6a6hMq+NjUhB03N6elgHjQxLjIbGdbVqOpHitfa3FqP8+edtEzDA9u1iY+iuBGs0iqymFoka0bDZcB4Aqu4VV11LIGUwJ190Bz+9eQuTzpyNwZRE7a4N/PbNHLIKhjNy6sUALHjrH/g8Tqac/zCSJLF56SfoDRbKNy1m95al5A89nPGn3IQkSaz+4WWqd67F62pixNEXsnLeHI6+4FFxSFlVCSu+mcuR59yLz9PMV8/M5A+XPY/BnERN2XrWLXgTr8fB1AseRVVVFr1/N0Mnnkl678GUrPqW5oZKNi/5iJOueZPaXetZ8vkjHH3ew1iSM2iur+CH124irVcRI6dfyvr5r1K3ezP2nP4Mn3IBSsDPwnf/yZjjrua7l6/lqL88SFqOKKg3/41bMJpahvcaTBbmv30nAb+HhuqdDBh3IodMmwVAv0OO5cunL2JcsBCe192M3+dh+VdPcsu1F6DviaD/CHTeT+z1djwqId5DZcFpJdQWYVISKiajSp98NVQeqy1EZiBFw+USJ7lE68Cx0LevOMGlLeNaRxAKH21r4I2NB0R49Kf/nc/B02fhdtSQnjcYg0mklqb3HoKqKgw7IpxYn9F7KCOnXhwy8gwc90f2bF/OqOOuQGc0M2LK+aHPhh1+DiZrKvbsfpSu+YHMghGYrEJiS80qxJQk/jaYksgfejgelzgnOiN/KAaLDWtqL0D4bA+eNpPyzb8AUDhyGk01O8kuPBiAso2LSM8dFNJTk+w5mCwpDJ9yPuWbFqOoCg3VO8gfKmpiyTo9fUcczc4NC7AmZ4YIGKBg+BRcjlp2rP0Jr8vBb/OeQwWOPPd+6qp2kjNgHKnZRaH2RrNY+18+M4sf37qDr5+7CmflRm6bNZXzzjmrp36iEPbecmon2FiWY5SrjUBWphrrPPGYj6mri33EklYPqz0MGyY4f3eJ2O0W/QTQxTaVaz71dqAotI4B/Z2RlmKleuc6TNZUmmrDCdYBv4+A30NdUBcE8HldVO5YFfrfUVuO0ZJM1Y41BLwuaso2hD6rq9iMioLP58KW3htHXbhgv6IE8LrCh7vX7d6COUjUAZ8Hn7sZrzO8rqrL1pKUlguAq7Eand6Io2YXsk5PckY+zsaqFqGO7uZ6KktWkV0wgpryjTRVl+GJSK2tK99Meu4g3M4GGqvDRqsdq75Fkg3sWPsTv37+BFuW/5ep5z/CugVvogQC1O3ewo5V3+L3CXfi5qWfABLHXvY8k8+8k6kXPcGhYw9h+tSjuvRbtAdJ7UTY3tgRI9RlH37YscbZ2XiidOJYMBnjPz+gSDgcghjbE3VNppYnprpc4jji9uxDeXlw0UUiJbGjxQDawsiRIhbb0FjTegex26lzdyzrKs2uCktfO26msSefzLLVq3tMyR47dqy6bNkyfD4f+YMOpXDkdBz1FaAq9Oo3iu0rv8HrbsZiS6dX0WgCPje7Ni4mEPDRe9AEzLZ0dm34mayCEZRt+Bm/z4PeYKLPsMMxmJLYtXERbmcDlqR00vMGUbGtGHuvfmQVjKBk1bf4PM30H3MidRVbqCnbiD27L5kFIyjftBiPswmfx0HhyGkAlK79kcyC4aSk51O++RcMJhvVO9fSe8hEkjMK2PjLe2TmDyWnaAyla39EZzRTv2c7vQeOx9lYQ82udRgtyQwcezKN1aWUb15CWk5/6iu3ozeYSc8dSHNDJU3VO0ES3NzjbEBvtGCyptJYtQOPu5HMjHQsKb3weP2YTUbwNWO02ml2ucnvO4A0q8IzD8/u8KHhsSBJUrGqqjEr6vV4KqLoVU/AltrlKhsadLJKSoqE1dp+FpFOFzaY7dwpIrM6YuDtiHrfGZSXB0+G6abMXFcvYbZlYYln3dvLMBgM7Nn+G//61794/fXFOBwONv/yLpIkkZSUhKdapnbbQvx+P263G51OR01JMRaLhREjRmBqXo7SuI3G+nrcbjflmxYhyzJ6vZ7CwkIyLRKlK99jz549uKvWQXUxjj17cDqdbKjdRE1NDSkpKTRXrGLtT68BIMsyRqMRT80mnE4nRqMRtXEbG2pqMBgMWK1WDDod5Ss+RK/Xk5+Tg7duFSULfsFisWDwG5gyfQI1NZXkDMzBevgfMJvNmM0KslyA5fjBbNu2jRNOuJiMjAzMZjN9+/alpqYGWZbx+/34fD4yMzNpbGykd+/e6HS6btfI6i72DhH7/ej8Hvy0f2yfZpmOBwkVm01ql4idzrDRLJY/OB4qK4U3qKfcsn5/MD+4s9UF9lPMnj2b2bNn7+th7FPEOre4p9IIewKdEqclSaoCduy94STQBfRVVTWrpzpL/Mb7LeL+zp0i4gQSSGD/wwHg7EgggQTaQoKIE0jgAEeCiBNI4ABHgogTSOAAR4KIE0jgAEeCiBNI4ABHgogTSOAAR4KIE0jgAEeCiBNI4ADH/wETSSjI8Nc3ewAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "************************************************************************************************************************ \n", + "Running for image no:4/5 image id: 66\n", + "Predicted Class\t\t:\t ringneck_snake \n", + "Class Probability\t:\t 0.36211467 \n", + "Predicted Class Index\t:\t 53\n", + "k = 50 max_dist = 9.613037109375\n" + ] + }, + { + "data": { + "image/png": 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cFQZPxBaLcnlZgIrIbqftWyB27SyWvhhPW7ywEPjWt0J/790LbNqkPstCSQmwcCF1mdVE6feHDyGU/1Tah12/lsCYIFCLzUXMUWHw/DFCQu6oKIYebq+XdtcMG0b/ttmAyZNpamJenvKxPB7aLRNtwmK1a1iyJJT2uHMn8Oyz0adJOXOGJp40NVFvQalyZWtr6AXFXGXW/aY2YolV84wFa5/zARAcFQbXnSYkNAk3G0DgcoWLkRDqnubm0jay00mXpafTB9nvp+3WeCK6hYU0tTIQAHbtAtasocu8XnVLFwhQt/2VV2igKzsb+M53qIsbuV2s3+XtWa3YbHyGRE5Uhu7JEIRQu9Fmo+KMxOulI4YmTAgFj06coJY7HpYupeWFdu6kAm5roy+KkpLY7iqLVHd00OyqSBFHI9K1LirSLko2OyKHo8LQidhsDlk5JQED1MXOywsFkPx+mpKp140G6GwQ3/52uIABKqjBFIkoUi8jM5O68W63ejCPleXhcKIwdCImhD7AsbqT5COhamvjT0285RbqhssFLIp0KGNennpJHTWYJ6EXQmgATxRpPTKvNxTQczrDhyGKYv9zxHtezkXL0Da0Tp7UHmH2+2lGVzxW2GqlwbDVq8Ot3oQJtP+4s1OfiOV9tHoFZbWG/83K/gJUzGwkk8cTGi3FzsPOKy+0YMSE6JyUZuhE3NurLamDcfZs/MXb/X7gzTf7d+kMG0aHE2rFbA4PqOktRQSoT7wGhAb7K82uKD8H+50HuziIt4uJRZjVPlqEceiQ9giz30+reMTrRnq9/QWcnQ3MnKn9GGPGAD/7GW2jRop2qDKnBIFbYk4cM0CwYuWRRfH0dJ8cPx49oBXJl1+G+meNeGgJobWztOY8jxwJPP44jltmAD/6UXznZP3GkUXlOZwE0W9CEs3Z7eykhem01obq6gL27aOCN5louzFRIU+dSmeDYLB6VUqYzcCcOdi6y0qnUxo+PP7zslI+1dU8OMUxDONL1sbC4wmvFBmLo0fDRxMxdz0eIRMCjB1LrbC8X9hs7h9wYssffhjb85cbqzmHQ380nMNRIbmHwXR1oW8eFjkWi/pMDGoQQi3w/PnKbnRkdU6TCVizBtvPfyU841GeMipHEGifs5YXGUvH5HAMYODCmyaT9javGpFWWH5s1jb3eGJ3OxFCK3EsXKgedMvLowJk/bTjxmFfd1k/re07X4xZ8+b133/2bHSWzkDW3q3BotYczuCQvH0UalZYTno67aqK7C+NFPXkyXQalmjjfgWBBrDmzIFr6pU4fBgYPaK/1+tyARiTEV6V0mwG7Ha43UBWRQWd7qW6mnaJRfrhZjONcPNUSo5BDJyIldxKPZa5rk69sgaDECrMri5qmdk0oF1dISFbrcBXv0pFF2vwvscD7NgBW3U1pi3/AWz+dhxBTuxrnTkTzR3pQABo9OYCtlxkXDcOWdv+X6hvWxDoyyQ/PzSzIg9ucQxgYNvEkV1Q8rl009KASZPCXWM5EyeGhidGIy2NRqxtNirqtLRQ4Mtspn3Bfr9yAoUaDgds/7FOW3KJIKDTm95Pj2430LtoCb0PIFROJ7I6B4eTIIPrTjMxZ2VR13XmTPpge7103G5NDR2k39JCrafWbKrIl0BGBrXIEyYAo0dTEevNbho5EscDExRvIQyzWTVG1doKFM6ZQ9NL1eYU5haZkyBD0yaeODFYLxahAfpFRdTynTpFhxpu2RJ/UgQhNFAVbyE5UQQWL0bd7v6rxowJrjeb6YsiOxveKGP7/WYrFPwMDscwhkbEhYX9l4kizY8eNYqKOFZ7OBpZWdTKp6fTv1lROq0udUUFtu7NUlyVloa+CiV+S6a2kmCCEL1pwK0xJwEGX8R2u/q6hgYafEpLi7+WtclELb18HC4rAs+G/GVm0gCTEsOH46B9PgIKVXjMZmBUsQS4AL8lU/t86rFEzOEkwOCLWC1CLAjAb35DBffss8DmzfH1M9vtyimUPl9oHDGb7zfyWiZOxIU5S9CgMrNieTloH5Mo9s1ko4ns7OgvLw4nAZIrPMpmNLj/flqFQylqHQ27nba1tcwnXFcX3ubOzMQXk5eoTo1aUgLkmGm6qN9s1Zdwpbe4PIejg+QSMYMJeelS7YXZ7XY60bDW7T0eGjVubKR/z5/fNzFDJCUlwKQx3VT8Oq2wzwf1wRUcjgEkp4gBKuRVq4D16+mAhWhWWa+AGSwxxGwGxo9XrRs/qaSnL9dZsuizwqIIfX3UnEsCyS9BiqdKjQLJm3YJ0ADX9OnA+PHA++/TaVZOnw53gwsLqQsdb5ULiwW49lo0diiMYgLNoJQXL+jq0m6FRREwdbTF3hCIL0KtZy5lTtIg+SUc+/4xiHkixj89HiTBobXJLWJGRgZtIy9eTAdFdHTQD3OFz56Nb1SQxULrUI8fj4BCX292NlBo7wG8oe27ndoPbzYDcOuoj623bpfeCc45yYEAiHki6tfTWTljCVmSJBSicLTa+tQQMUMQqNU9cYJGiQsLaaQ5HgETQvuSZ8wAQD1e+XRKgtDfCvciTZdmsjL8gMtAkcmniHG7Ex8lxhkSCCEY//R4AIgpZEmScPInJ5GDHNVqFKklYoba1Ch6mDaNzuQQJEvoQmlpZt+kiCNGAIW2LoCdRhDCBi7FQhShb9pSBhOoPE2TlfaRNyO4gFMaLUJmAq5fX492tKsm8qemiBOtimE207HF8oSQQABji7qRl2dFZydL6giE7ePVIeLsbACOOKaakc/hZLPRv+W1qDkXDUpCznk4Bw6HA6WlpTj787OoX1+P4geK0bi+UbXg+sCLOLLP1ug2nMlE27Z68qzNZjoAIxKfDzlCJ3JyACi0kbVeuiAAaT4dJYjksLK1LhcVMh/pdFETKeTPPv4A0q3HseuNcZj4xUwUP1BM169XP8bgW2Kj84TZAPskqiApioAu3zsSJtwhuKeq81UgjykHWTYs2oAVV6wAADxf9TxWvrNScTsAkH4V6j654vkrsP/CfsXt7i2/F8/f/HzfuSteqFDcDgAq763EFSOvAACs2LICL+x/QXG78hHlqFpR1fe32v0AyXNPOQ/nYGX7XfioZDcNpC6XbfS46u4ABrKfOFpVTC0VM/WIIDNT+7aDQEYG4pu1kcEy1/x+3va9RHA4HJBKnXHtS/R0OFeUlUmVb7+tbWOtbqCaVWbz8nq99FjDh9OIcksL7WaSi4SlUba0aJvmxWYDHn9cXxKG2YwWV3q/QDibLkk+8WGBpVPb3MPRcLk0lfWtWL4clUeOGFZBvqKiQqqsrDTqcByNeDwevF7+Z0z8YmbfspZ5Dfj2juUghIAQUiVJkqJJHxh3Wk87Tq1v1Gym644eDf0NKEemRZH2B2Vk0OICA4HXC1EMF7EgAMMsXX0jmgCgIF8CGhIUsM+nvS43J+WRJAlnf34WE7+YibqZx3FyfhUu//w6DNtZhJM/OdnXZlbDWBHLu0T0wtrKrIsFCM36AMR2T5m1ZvMlnT8fmkb08stDE5MXF8c9VtlsVmimer0wuVwoYIXvGpxxHbsfiQzH5KQM8m6k4geKMf/p+SCEhC2PhTEiltfRSiRoJQj0wT1xgvax6m1XsulCOzpopZB582ixeJOJJokEAtRa19Toj/p6vbB21MNtKe6zxnl5AJqCqo6nT1gNn48L+BIgUsDyfuLIqPXAZWypFVJPhPR0OsWKywUcONB/IrRYMKEuWhQ+IEL++4QJyoMlWP8sCyzJjxmcDDyvrBgORzAvuqF+4LqAuCW+qIkmYIZcyDnrjc7YEgT1AQdGdSHZbHQM4JEj9G+tUVqvlw6YiDaiKRCgfjFL9vD56EvD7Q7NA8ymGO3ooOWCgucnR6pRkJ4ecv2NHuzPro1HpXXT09ODP61/Bs1Njbhm/rVYdPM3h/qSFNEiYAYTcvt6ozK2CNFmfeVJyIkwciR1jwMBOrVpS0vsfUQxPBNLDY+HWlu3mwpY/uIJBKh42bxRckH5/eHdXy4XfWEYNVewz8cFHAeSJOHB+3+EJddfieLZk/Dhzt14vaUFd971/aG+tP4EAF+rL6aAGYQQNEI9Yyu+WRGN3C7WMSwWKsrRo7VV+hBFbeOK3W5aXTPaCH+vN3aXldttXFIGs8Ic3dTX12PEMBtGjSgEIQQ3zr8KB6o+AwC88dpruPOO23DX//ouDh48OMRXChATweRXJhsyDBFI5qIAkRQV0QnRjESL2z+Yk3gHAuFtcY5mLBYL3N2hbjlJkuDz+fHXjRvx+qsv4SulJSjKy8FPfrwKDfJejyGCmIghAgYGMu3S6BzpQEDbjAwA7Zpi9awTxYiJ4bTi8fD+4TjJz8+HbVghtv7rU5SOGYntu/fj1jvuwhOPP4Z/X3Uf8nLtAIAsmw2vvPwSHnr4kaG9YANJDUvsdNJItdY3KHdJL0l+8ehjuHzejXDChgfW/BJXXzMPoij2CRgALhs9CoJwcZXzHzhLHJmPGA+BANDcTINaieQiJ4IWK2yUCyyK3BInyLx584F58/v+vuvue7D53X/g29/4Ovx+Pza/+z6ee/HlIbxC4xnYUUyJdDf5fDTr6ujRoStB4/dry8X2eo0RslERbk4fS5ctQ2+vFy9s3AQJwONr16FQaQaSFGbgnxq93U0uF83YYgMg4qGrC3A41Gd50AKzwFqCD0YlfPB6WYYgSRL27NmDtjYnrrnmatx2+x247fY7+tb39vaioaEBw4cPRzqb6ieFGZw2sdaH3O2mAm5oSKx6h99Pq2Imsr8cNv2qyaR8L4laYTaow+vVXzCfE4YkSfjJT3+G9/6xE9VHT2PFylW4cOFC3/qDBw/h27csxxO/+QOW3XoHPvpox9BdrEEMjv8Wq7qHIFDLuX+/cW3fnp743NxYbWBCqNAkSZ/llG8r/z7kfcPMY1ESMk8A0cSnn36KTFs+rpxzDQCgtHQCfvf7p/H73z0FAFiz5mHct2oNBEGAJEl48skn8bWvXTtk12sEg9cIkz+48hFL7O94BjxEg+VeDx9Ou5vS0mJbOT1CkYtZCVYrSykuECliUYx97/Jr54JWpb29HVm2nL6/LRYr/L7Q9yWY0iAEv39CCDIys9Db24s0rXNhJyFD18XE8q9ZMCc/X/8MDrFwOql7/sknVNDHjoVeFqxqhvwTD7HSUJWsNXuByUvQ6kHu3nPCmDt3Lg58vhvd3TQ1duuHf8dNX7+xb327sw2trQ4AQFeXC42NFxBI8VjE0IZD2cMvCFTEc+cCu3YZ353k99O865YWWjSgpAQYN864Y3u98b+AmMWO90EazIyyFCArKwvr1/8ejz76a3R1uXH77d/BTTcu7Ft/22234vXXNiA3Nx+trQ6YLVn47l0/xBO/fgiTJk0cwiuPn6ETcWSdLTZyqKiIto8TKTQXjby8gZmlUK3NyxlwTp6swS8fWwtTWgYknweFw4fBJw5DbvE4vPafb2Lu3KtgD442u//+H2Hq1Cl45JeP46sLbsG0mXPh9/uw9qmn8epLzw3tjcTJ0IhYrVCeKNKxxKdPA8ePJ16nSgmzmY5ZNhI2nQprGujpHzeimMIlzi8fW4trb74PopiGU19W42zdMcy/4RYAgGvKbKxd9wesWxsqGVlefjm+Mm0Wps2cCwAwmUQIptTNWR9cEWu1UJddBlitwO7dxj/cLhedbDw317hj+v39hzJqgUWjuYATgpisOFf3Jc6dPQmvx40xY0NusS0rB13ubvh8PphMJhBCkJ2dja6OZni63bBYM9DcWI/cnOSqmKqHwfP75O1fLWLOz6eutdGuqctF3XUjMZn0dWWxbiWvl364iBPiTN2XOFnzJUZPmI0ujw8f/eNv8PvpC/Lgvu2oqzuNO+5djaXfXYU/b3gZhBCs/8NafP7JJvzrg9fQfPozPPnEr4b4LuJncPuJ9VbBrKigUWWjKliaTEBWFlBQYMzxGMwS6ynTy4VrGPlFY3DV9csAANfdfDfeef3X+OT9FxGQCHy9Hsy49nsYXkwDmbs/fhNXV1ejrKwMzz37NACgrq4O1dXVmDJlCqxW5Sluk5nBmcYlXmsqCDSS3NBgXMR6yhTqqhsF6+bx+bRbYyOqnnD6SLeE/z8DJA1uMgwWWy5OfPY+SsoFbNn4DCxWG8ZNvhxHvziOsrIyAMATa/+AquMOZOWNQvOp9XjpP36LkUpT/CQxAz8DRCLusCDQaHJZmb5C72pkZRnbF80ErOc+uQU2nLHFeThbcwAAcOrYPri6ulF+/fcxZc5iZA4vxVuvrIU1ZyS6ewPYsvGPeOOvm3G4uho1NTXYW92AOTf9ACNKpqJbsmHRLXdh797PhvaGdKK/xla0FEp5lNWotiyzxk6ntmi1xUIrgChFt9l0oUZkPMkFzArda4G3gQ3nV79YjeV33I2d772KdLMJ0+be3rfuwqljWHzPWuTkFwMAsnOL0OlsxndXPgybVURe8VS42lvw8bsvY+Htj8AkmvGbZ5/Fo6KIWRXlAIDu7m786onfobnNhVFFeXj0oZ8mVYZXfDW2tHyMRsswvbw8Wmt66lRg0qT+1rukJFhr1oBMJ/mLQI8o4y3zG5nhxulj1U8eQem8BzDthh/DjWHYt/Pv2PLGb/HBW+vhC/j6BAwAI0qmovn8SUycuQAzbliFL498iup9H2LWDXdCTEtHT7cL1rxSPPLoE/AGm3D3PfAQegtvxOi598OZeRUe/PdHh+pWFUn8iYh8IAcq0WHiRBpVjhzdZLcDkydTCzx9Onq8BOlmCbjyStpV9f77tA182WXUnWZiSNQasxcBy7jSKi4mYqXqI2ydUvvfZgu1uT0e+rmI29Z68plbuwiKsodh1zvPYeH3ngQhBId2bUKmzQ5LdiGq976LsjnfgCRJqPznRoyffi3qjn2G2uNVsGYXYd/H/w1HcwNmXr0Yn/zjNZRMuxYZYxfiO3fdjxuv+yqqDp3EiK53MeuGO5FbOBZHDydXPfDUea2bzVSstbVUsKNGhWZKqKiAJJj6nv0eLwGQBqFoNNIWLaLil5exFYRQwkc8lTQSteRKCR6CQK/RbA6J0+ulv1ss4S57RgYVvJGzTiQJdadP43+vWYuAOQ+B7hb8evVKzJ5VHrZNR0cHBEFAZmYmHvnVE6jc80/UN7WhzXEep4/uQtHYGTj62bvIHzEeU69chJqD21C57m50u9oQ8Afg6elCW3MjZn19JdztzZh69a2o3vUWtrz6f5BbVILubjd8vR44Gnvw+jsHcNN9L8LZVIdtf1uHhbf/AoHeOOeeHiBSR8QA7TceMwbS8MJwY6VikAIBoCdvBIT8EUhrbQxvIzNBpKfrq/WsJOB4vA82ZxQTsrzULrO40aLdA9VsGUTee38bXn/rfZw9ewaWjGycOlWL7q4uSJIflqxh8Ha14+bbfoi0tHSYrVno7mgBMYkwpVnQ42pB1rBiuDscsGaNwPm6Y+jp7sSO//4TrLZh6HQ2w9l8Ho3nT4KAoLPNAbMlE7a8QjRfqIdotmDvu39GRk4BDuzYCEtWPro6W3Dd955CbmEJAODUwe04d3w3AMA+vATmDDsqt/wW//bD5VHuavBJLREDQH6+bi8y6qTfTAyJuNes31dPhDoywKV33PNATKEziBw6XI0/bdwFMbccWeMmYeLsJZgiSdi87ptY8IPncODD5zB9+b3Yu/kJLFyxASYxDYc+ehWS34dzx3ah/MaH0Xz6EErLv4G8ERPQ3dmCD5+/D9/48WsQzVbsf///wuvpxJzFawAAVe/9Ed0drXA21WDhyg34+I3V+Mb9r8NstcHZWIt/vvZT5BaNh71gTN815o0cj4ba0ETi6cSDF59+BMXFxf3uZyhJvadAEIwP7iYqBiZKPW+XSzxC/d4HH2Hk5cvQWLsfE2cvAUDH99oLxyEzpwCZ9iI0132O8bMWwyTStnF3RzOmXfd9BPy9uKzsOvh6e5A3YgIAoO3ClyiZeSNEM+0zdpw9jBkL7u073/Tr70HT6c+RkTMcAhFQOPZymK008GkvLIUttxhmazYO7ngDAK0Qcujjv6Ch7jCaTlfj860vYMFVk5JOwECqiThYPCApA7SXuCj1MrZkFDobv4TZmo3O1tD0ne6OZggmM7o7HbAXTUDL2aN96yyZuWipP4beni54XG3wez3w+2iQKbugBK31x/q2zcguDPu79fxxiOmZ8HS2wJI1DB2OUPmmgN8XPN94BGDCrk3r8K9Na2HJKsD4WYuxe8sf8ZW8Zvz831YN5FcSN0TSUs0xSMX06VLlu+8O4OVoQxo1WlcCl9kMkLpT6kILBLQHuKIFtVg/dCyUpmux2bTNISWntRUVS5agsrrasEHFFRUVUmVlpVGHU0WSJNz34CM43Z6BmiN7MXLiHLjaW3Du2C7Y7EXILrgMHlcbujtbYB9eguyCy3Dm6MeAJCErfwxaz32B4slXo+XcEYyceBVazx9H85lqFI6diWGjpqCm6l30dLVhfMU3QQQBJyvfQZolCyaTCNFsgWBKQ1q6FcNLZuL04e1Is2TB1VYPMc2CUV+5Gr2eLjSdPgizNRu97edw6otPB/w7iQYhpEqSpAqldclo01KTiyDQNJgQQvDn9U/i/Pnz8Hq/C0mSEAgEsG/flTCZTNi8eTOsI6zYseMEHC1HcPTjJvj9fvhlsYv6458AAGqq3ulb1nRqf9h5muoORL2Oo//6L4wbNw5uRw9uWbIkeB0OFBQUYNbKBzFhwgRMnJjcxQJST8QWi+7AFvH2DKy7y4JMXMS6icxTHhesuLJs2bKhuJyURJc7TQhpBpBALVjOAHCZJEmGDcvi/+OkRfX/rEvEHA4n+eD+H4eT4nARczgpDhcxh5PicBFzOCkOFzGHk+JwEXM4KQ4XMYeT4nARczgpDhcxh5Pi/H9nna8K7U8BLQAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k = 200 max_dist = 3.1622886657714844\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image_no = [\n", + " #orange wardrobe milk_can lynx ringneck_snake\n", + " 114, 147, 60, 144, 66\n", + "]\n", + "for im_idx,ino in enumerate(image_no):\n", + " \n", + " print('*'*120,f'\\nRunning for image no:{im_idx}/{len(image_no)} image id: {ino}')\n", + " file = os.path.join(DS_path,f'ILSVRC2012_test_{ino:08}.JPEG')\n", + " image_to_explain = ut.read_process_image(file,model)\n", + " predicted = bb_predict(np.array([image_to_explain]))\n", + " (predicted_cls_idx,f_x,predicted_cls_lbl) = ut.get_class_idx_label_score (predicted,class_names)\n", + " f_x = predicted[0][predicted_cls_idx]\n", + " print('Predicted Class\\t\\t:\\t',predicted_cls_lbl,'\\nClass Probability\\t:\\t', f_x,'\\nPredicted Class Index\\t:\\t', predicted_cls_idx)\n", + "\n", + " for k in [50, 200]:\n", + " max_dist,_,_,_ = ut.search_segment_number(image_to_explain, target_seg_no=k)\n", + " segments,num_segments,segmenter_fn = ut.own_seg(image_to_explain,md=max_dist,ks=4,random_seed=1234,ratio=0.2)\n", + " print(f'k = {k} max_dist = {max_dist}')\n", + " for use_stratification in [False, True]: \n", + " lime_explainer = LimeImageExplainer(random_state=1234)\n", + " beta_arr, rcY_arr = [], []\n", + " sig = f'{ino}_{k}_{use_stratification}'\n", + " for repeat in range(10):\n", + " ret = lime_explainer.explain_instance(image_to_explain, bb_predict,\n", + " labels=class_names, segmentation_fn=segmenter_fn,\n", + " top_labels=3, hide_color=None, use_stratification=use_stratification,\n", + " batch_size=500, num_samples=1000, progress_bar=False)\n", + " X, all_Ys, expl = ret # when return (data, labels, ret_exp)\n", + " expainled_cls = expl.top_labels[0]\n", + " Y = all_Ys[:, expainled_cls]\n", + " beta_arr.append(ut.get_beta_from_expl(explanation=expl)) \n", + " rcY_arr.append(ut.get_RCY(Y, f_x))\n", + " beta = np.mean(beta_arr, axis=0) \n", + " \n", + " heatmap = ut.heatmap_from_beta(segments=segments, beta=beta)\n", + " fig, (ax1,ax2) = plt.subplots(1, 2, figsize=(4, 2))\n", + " ax1.set_aspect('equal'); ax2.set_aspect('equal')\n", + " ut.axis_off(ax1); ut.axis_off(ax2)\n", + " v = np.max(np.abs(heatmap))\n", + " im = ax1.imshow(heatmap, cmap='bwr', vmin=-v, vmax=v)\n", + " ut.plot_classification_score(ax2, expl, X, Y, f_x, plot_everything=False)\n", + " plt.suptitle(f'{num_segments} $|$ {ut.get_CV_beta(beta):.3} $|$ {np.mean(rcY_arr):.3}', fontsize=24, y=1.05)\n", + " plt.savefig(f'{paper_figures}/dual_{sig}.svg', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + " plt.savefig(f'{paper_figures}/dual_{sig}.png', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "4c1462be", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "************************************************************************************************************************ \n", + "Running for image no:0/2 image id: 19\n", + "Predicted Class\t\t:\t chickadee \n", + "Class Probability\t:\t 0.99888974 \n", + "Predicted Class Index\t:\t 19\n", + "k = 50 max_dist = 11.71875\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k = 200 max_dist = 4.0130615234375\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "************************************************************************************************************************ \n", + "Running for image no:1/2 image id: 57\n", + "Predicted Class\t\t:\t polecat \n", + "Class Probability\t:\t 0.46934342 \n", + "Predicted Class Index\t:\t 358\n", + "k = 50 max_dist = 12.36572265625\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "k = 200 max_dist = 4.583740234375\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "image_no = [\n", + " #orange wardrobe milk_can lynx ringneck_snake\n", + "# 114, 147, 60, 144, 66\n", + " 19,57\n", + "]\n", + "for im_idx,ino in enumerate(image_no):\n", + " \n", + " print('*'*120,f'\\nRunning for image no:{im_idx}/{len(image_no)} image id: {ino}')\n", + " file = os.path.join(DS_path,f'ILSVRC2012_test_{ino:08}.JPEG')\n", + " image_to_explain = ut.read_process_image(file,model)\n", + " predicted = bb_predict(np.array([image_to_explain]))\n", + " (predicted_cls_idx,f_x,predicted_cls_lbl) = ut.get_class_idx_label_score (predicted,class_names)\n", + " f_x = predicted[0][predicted_cls_idx]\n", + " print('Predicted Class\\t\\t:\\t',predicted_cls_lbl,'\\nClass Probability\\t:\\t', f_x,'\\nPredicted Class Index\\t:\\t', predicted_cls_idx)\n", + "\n", + " for k in [50, 200]:\n", + " max_dist,_,_,_ = ut.search_segment_number(image_to_explain, target_seg_no=k)\n", + " segments,num_segments,segmenter_fn = ut.own_seg(image_to_explain,md=max_dist,ks=4,random_seed=1234,ratio=0.2)\n", + " print(f'k = {k} max_dist = {max_dist}')\n", + " for use_stratification in [False, True]: \n", + " lime_explainer = LimeImageExplainer(random_state=1234)\n", + " beta_arr, rcY_arr = [], []\n", + " sig = f'{ino}_{k}_{use_stratification}'\n", + " for repeat in range(1):\n", + " ret = lime_explainer.explain_instance(image_to_explain, bb_predict,\n", + " labels=class_names, segmentation_fn=segmenter_fn,\n", + " top_labels=3, hide_color=None, use_stratification=use_stratification,\n", + " batch_size=500, num_samples=1000, progress_bar=True)\n", + " X, all_Ys, expl = ret # when return (data, labels, ret_exp)\n", + " expainled_cls = expl.top_labels[0]\n", + " Y = all_Ys[:, expainled_cls]\n", + " beta_arr.append(ut.get_beta_from_expl(explanation=expl)) \n", + " rcY_arr.append(ut.get_RCY(Y, f_x))\n", + " beta = np.mean(beta_arr, axis=0) \n", + " \n", + " heatmap = ut.heatmap_from_beta(segments=segments, beta=beta)\n", + " fig, (ax1,ax2) = plt.subplots(1, 2, figsize=(4, 2))\n", + " ax1.set_aspect('equal'); ax2.set_aspect('equal')\n", + " ut.axis_off(ax1); ut.axis_off(ax2)\n", + " v = np.max(np.abs(heatmap))\n", + " im = ax1.imshow(heatmap, cmap='bwr', vmin=-v, vmax=v)\n", + " ut.plot_classification_score(ax2, expl, X, Y, f_x, plot_everything=False)\n", + " plt.suptitle(f'{num_segments} $|$ {ut.get_CV_beta(beta):.3} $|$ {np.mean(rcY_arr):.3}', fontsize=18, y=1.00)\n", + " plt.savefig(f'{paper_figures}/dual_{sig}.svg', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + " plt.savefig(f'{paper_figures}/dual_{sig}.png', dpi=150, bbox_inches='tight', pad_inches=0.02)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5893e5dd", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/notebooks/Tutorial - Image Classification Keras.ipynb b/doc/notebooks/Tutorial - Image Classification Keras.ipynb index 700438d15..ddd24d0f0 100644 --- a/doc/notebooks/Tutorial - Image Classification Keras.ipynb +++ b/doc/notebooks/Tutorial - Image Classification Keras.ipynb @@ -27,7 +27,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Notebook run using keras: 2.4.3\n" + "Notebook run using keras: 2.6.0\n" ] } ], @@ -113,31 +113,28 @@ }, "colab_type": "code", "id": "3sxO8vzlxwk2", - "outputId": "82b2b75b-6bd8-4fab-ea2c-5b365806fb9e", - "scrolled": false + "outputId": "82b2b75b-6bd8-4fab-ea2c-5b365806fb9e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "('n02133161', 'American_black_bear', 0.6371605)\n", - "('n02105056', 'groenendael', 0.03181805)\n", - "('n02104365', 'schipperke', 0.029944215)\n", - "('n01883070', 'wombat', 0.028509509)\n", - "('n01877812', 'wallaby', 0.025093526)\n" + "('n02133161', 'American_black_bear', 0.63726944)\n", + "('n02105056', 'groenendael', 0.03180151)\n", + "('n02104365', 'schipperke', 0.029919019)\n", + "('n01883070', 'wombat', 0.028503727)\n", + "('n01877812', 'wallaby', 0.025099743)\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -155,7 +152,10 @@ "metadata": { "colab_type": "text", "collapsed": true, - "id": "_MCRFeNJxwk9" + "id": "_MCRFeNJxwk9", + "jupyter": { + "outputs_hidden": true + } }, "source": [ "## Explanation\n", @@ -212,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -236,31 +236,23 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "faa50bde7a254cb7986a81274c778be0", + "model_id": "593c7edcff094dc798bd425c4593d777", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))" + " 0%| | 0/1000 [00:00" ] @@ -382,7 +374,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", 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", 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\n", 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", 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\n", 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", 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\n", 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0 } } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/doc/notebooks/Tutorial - images - Pytorch.ipynb b/doc/notebooks/Tutorial - images - Pytorch.ipynb index 49eca0b6f..5aa1c4b49 100644 --- a/doc/notebooks/Tutorial - images - Pytorch.ipynb +++ b/doc/notebooks/Tutorial - images - Pytorch.ipynb @@ -51,7 +51,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -60,9 +60,9 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -122,7 +122,18 @@ "cell_type": "code", "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\XN2\\miniconda3\\envs\\pt\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "c:\\Users\\XN2\\miniconda3\\envs\\pt\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=Inception_V3_Weights.IMAGENET1K_V1`. You can also use `weights=Inception_V3_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + } + ], "source": [ "model = models.inception_v3(pretrained=True)" ] @@ -183,11 +194,11 @@ { "data": { "text/plain": [ - "((0.93593013, 239, 'Bernese_mountain_dog'),\n", - " (0.038447894, 241, 'EntleBucher'),\n", - " (0.023756264, 240, 'Appenzeller'),\n", - " (0.0018181818, 238, 'Greater_Swiss_Mountain_dog'),\n", - " (9.1132988e-06, 214, 'Gordon_setter'))" + "((0.93592995, 239, 'Bernese_mountain_dog'),\n", + " (0.038448, 241, 'EntleBucher'),\n", + " (0.023756305, 240, 'Appenzeller'),\n", + " (0.0018181797, 238, 'Greater_Swiss_Mountain_dog'),\n", + " (9.113315e-06, 214, 'Gordon_setter'))" ] }, "execution_count": 7, @@ -294,6 +305,13 @@ "test_pred.squeeze().argmax()" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -307,9 +325,21 @@ "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'lime_stratified'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[11], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlime_stratified\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lime_image\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'lime_stratified'" + ] + } + ], "source": [ - "from lime import lime_image" + "from lime_stratified.lime import lime_image" ] }, { @@ -361,7 +391,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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" ] @@ -434,7 +464,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.9.0" } }, "nbformat": 4, diff --git a/doc/notebooks/Tutorial - images.ipynb b/doc/notebooks/Tutorial - images.ipynb index fb09c03d5..f66fbcd45 100644 --- a/doc/notebooks/Tutorial - images.ipynb +++ b/doc/notebooks/Tutorial - images.ipynb @@ -15,11 +15,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -46,11 +49,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -60,9 +66,8 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -73,9 +78,8 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -93,9 +97,8 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -107,9 +110,8 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -120,11 +122,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -142,9 +147,8 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -166,36 +170,17 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, + "jupyter": { + "outputs_hidden": false + }, "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "286 Egyptian cat 0.000892741\n", - "242 EntleBucher 0.0163564\n", - "239 Greater Swiss Mountain dog 0.0171362\n", - "241 Appenzeller 0.0393639\n", - "240 Bernese mountain dog 0.829222\n" - ] - }, - { - "data": { - "image/png": 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Xt6iymlfvvIXrM/72v/t3+Vt/4++g0HhryXQxMfLW4YYBN4wMfX/DrkeyLMP7AAFynZOS\nQAlNZgqMzBjaEaMyjMoIAYIH5yKji8yXa5KI6FxjradtO1IEP3qGtidYx9D1BB8+qXoMw4BzES0z\nbkhztNCkJLA2oHVOCODGSLsf2G8bmkOD1poQAs45lusVISSWyxXDMKIzxXy5QGUKYzRlUUJKWGfR\nWYbUiqLMaNsDu801h/0eJSV93+GCZRh6kkg0XcvmsKFt9zg7IJmipH27o20PjN7RDj1JwN1XXyUr\np9Ti6OiIqqrI8/yGVQ9YZyElhsHifaSu5lRVRVFM76MoMpJ3aK3Yt3sO7Z4kIiF5dKapyhKRQCbB\nvJ6R0lQ1cD5QzWaU9XSurMhJUtAPPcMwUpY1KUm0zhBCMZsvcS4ghOb6ckNyCdtbyrzC+0gMYbrm\nGKjmFdV8xuBGhJaMfmR9skZoyb7bcmgbtruRR4923L51i745oDTMqgprLTorEFJRZILbt09Zn6x4\n9uI5V1fX3Lv3FrvNNa/cvcPts9ukJPjg/gdcXL3gV/79/wCtJf/3r/0rXGvo28gX33mDkHp8tNTz\nGW/c+wJf/amfRWWKvKzIsolfcs7Sdz1GZsikaA4dRVGjs5zl+pij01ucnt0mJsHV1Ybl8uhT2+Nn\nIl76s+LLbx+l//l//Ht0Q8OD84dcHtpPdtswOmazOYGE1hlDP1KqmmAjRPBBsFjdYnV0xEcPfshs\nKZHJk+cl+/1m2vnHYSpJiURMYIxG65w8K0lRYt2A1pJhbCYiTBkOhwPeeTKdT1WDqkQg8N6TlGR0\nFqkU3X5PXZYUeT6F5CkxNCNGKRaLJb0daLuOGCHECAlC8CijOD5e048d7dBT1zWHQ0OelRSm4Pp6\nQ1EU5Llmt9lS1zl1UeJDYrPdsT47YrPfUdcVR8dHPHn8mKqqsKObSogxUGQZwUm6tme9XOEFeGc5\nPT7m0O0IQNe11PWSq+2eWkNZZARvMZnmerulLGqG3tPbgcXRAqUN+0PH3bNb9H2PkpJww+soKSFa\nnLcsVrdompa33rrH4ycPEFJgqgyZoO96QpjKnYMdmc/mFHk+cRWjmzghF0EmyrLkoyfPODs7IwLD\nMJGwtcrIdE7bW4rlkvOnT6jyAk9g9Jb5fE70ga7tmFUzmrZjOZux3WxIMqFLw+gsWZ6TxBStHp+e\n0vY9UiqEgOA9UnhyVjx7EPjo/S1vvnpGt79iaA6MXvLjDz7iV/7aL/DlN27xM+/c45u/8R0Wp7fJ\nlkve/OJX+YPv/AGb/SVaGy4uLri+vmSxnBGC5+23v0ZRKk6PXuGf/Z//lLe/chd9tsWYiBsi0UFd\nrpEU2HRB8JDsyGpRMAwdzTASfCCMDpXV7PY7UoLlcklWVPRdiwiO09MFzfbAP/pHv/P7KaWv/2n2\n+LmIFLSeykbr1RI7jggJJsuQwlBkMzJdQ5DkJqc7TGXJos6QKhLlQDs+4/0Pf5ckGkgBKRUxTjX5\nTBvKvKAuauazxU3tOjGOU47lncU7S4oe5yzjOBCcRaREVZVAIssMSkCmp51sXs3oDz37bYOWhqKo\nIUn6bkCiWa2OqfIFIhqUMBhlWC2WZEoTY0BrhZQCrdVUvSBhjEIk8M5xvbkkEfC+Zxha8kLh3IAp\nNIGA1JLgHZnRkCKHwxYpEwlPFJ5yVlDWBUmkif1PkmfPXuDGASUEgjRxM8/PCSGw226py4KqKFFA\nco5x6CgyRYqBzBiCdRil6LuOTGmSFIQUscFjcoNQkkPX3uhDBNfXVzx6/JAfvPfepFcg4dxI8FP9\nPc8NPjjyIiPPc9JNyTaEgMkzIpGymsrL83nNfD6bypZGEr3Hx4gLAR/CT5QFKKWYz2ZoqT6J0LTW\n5FnGerlitVjy9ttvc3J8TApTKjWOPVmWMZvNEMDhsJ8Mrjkw9i1aKIaDpt0K3n/vI/p2z3Z7idaQ\nZxqJoB0sgwsM1jHYkaY7kOWK/f6KW3eOmc9qvvP7v892s+GNN+7hvefevXtICW3b8f4HP+BnvvZT\n/OC77yNijhIaZx31rGRWl1OpNsapvG1ymkN3o+URGAN1bTha15wcLaiqDJJH4DFSok12Q9KLf5Pp\n/RvxuXAKUmY8eXFJVpUU9YzVasV8vkLrnKPjVzDZ/JMdtKrmdH2DysDMBaqwHJ3m3H61YrHUlKXB\n2o6EpShuRCBSMQzDlJtnGSkmqqKk7w603R6IWNsjRMK7kc3VJUPXEr0jxJGqzhAEhr7FDgMX55fc\nPruLETm3z+5iR4s2GVobtrs9ZVGT53P6wbFarxBaojNFZjSL+QzrB2azCutGXly8QGuFEgIlBdZ2\nVFUOwpKSZb2eUc0yVBYZY0877AgMPDn/mNZdsz2c07bXFEaRBjgqj8jQCBVwYUBKEFLx8z//CywX\nNVIEXrx4Sp4bUoyIBMfLikwNyOTItKYsZihKcrPk9VffoK4qbp2d0R0acJ5FXeODZbO7xoeRcew4\nPl0zX81JUjBbLpgf15y9ckKUHm1yDvuGcbDsmj0oEFqxWh0RbKDZ7eiblhgC0miuthtUpqaScAhI\nkdjtrtltrhi7lroo0Eazaw4gE7mWvP3WPfLCUJqMxWyGRHDn1m1Oj4/RUiKIxBiYlQUaiRaSo+UK\nfKA7NIgg2F3tWM8m5x2GHpMkwy7x3Xef8O5v/AFffPMN+uaa49WcN9+4Tbu/pm0a/sm/+H/5vR8+\n4IePzpGm5Opqy8OPPubjj9/n9779m3z3u9/ltddeIc8Nfd9xdnqbqqp5+vQpKQpee/2UBw9+xL3X\nvsBbt7/G1bOBeb1mf33Fi+cfo1SHlorVckmeF5ye3qUdIuvjsyltKTUpDCxmGfNSY1RERkvTXCOV\nIiKpZ9Wntse/MPHSH4WPnqeXG7bfehdhwMaIEJosK6edR4L1novra0ZnKcqMfuwpCjMpy7xg6CNa\nZXRdi84UMUasH5mVC1KMmCwjyzQhBXJd0bY9VVXh3J48M8Qo8dFjjGLo/VSOazvmy8WkPpRgMkNM\nAsTkRG7dPqUfOspyCudShNENSDOp7nznCDJQz2tiSuRFBSpRx5oQA0VWoJVBILGjhRtlndIC5wZ0\nUXJoDvR9izaCtmuYLWZ03cBcJnrfE6ynzEtCCsRRUCwrZC5J3uKUwI49eWm4vDonJEeeG5Rkcoxl\nibOOoR/w9JhsTj+OxJhYLE8YxoEkJFmeY/KMrNCAmHJZVVCXBX4ckcYwdC1KCFrrMHmGkFDNClKI\neO+pqprBdWR5Ttt2GJ0zn2UoIfHWMfqBoiwoqxrnHf040u73FEWF9R6lDN55VosFfTfgfGC5WhND\nou/aSaBIwo4Dx+s155cXn6RQ+2GHC562C2gFECedg1RYaymkpioKgnME68gyg5GaRb1iexUY+4Yv\nvv0mt2+taPcPIQaenz9j7DuMhuXRgv/jn/06q1rwzhd+mrl1XGwuUHlOpktOTnISE4EqBCipuL7c\nUVcVbdvywQeXfOMb3+B/+O//J2RuEZVm7Ebms4qUAsOwZWoniqQk6YaBzbahGUYkFj96RJLU9Qyl\nNGWZ03YdZVYCCu8CTd9+anv8XEQK1lui0rQ2MDhP2w54F/DBszvs6PoGRyRpxXy1JEaQQqGVJjOa\nrhuRIkPrAqkEZVWS5yVlVTKMI0YbpJqkolIpBPpGVFIzm9XkWT6p5YRCoMjzgvl8SVXPyIsS5yPW\nR4SewtJ6XmEKjTaQkgcS/dCidKKsctCR1ekcUwr23Q6XLM3QIDODyTNm8zlZngMCKRVGG/puRCqN\nNgaIaK0wmaFtO8qqRCpFUdZ0fY+QktVyTZXNwMPYjogoWB0dMVss+fI77yCVJC8y5suKLBccmkuG\noaMoc4o8x1nLrbMzqrKEGIk+cehabAwM3pHlGTozXF5fkRUFCEEIk8Q2ywxKCsauw40j0XuGtkPe\nSJGtc/RjBzIQ8Fhrb+TRUBQlCIn3gVlZ0ez22GHiI7zzRCJSSS6vr0lIMpOjlGY+m7NcrJBJI5Ig\nL0oiiaIoUMDQd4xDT/AOOw6kGDjsdwxDN1VfgiOkQNs2NM0BwcQjaKXwNzoOJRVVXhKtp8gytMwZ\nusirr7zCV7/6FlJ6QohkRnN2esJyMePs7AydaRZHhvPNltXRGd5GTo/usL3ck4LCZDnn58/p+pZZ\nPYmI9rv9tJbqiu99/0ekJPkHf+8/ZXu15bU7b+FtwihFmWmCaxBp2ii222sePnzManFCCHqqFImc\niKQfLAiF0gaSoKpnaFVQFDMOu+ZT2+Pnwil0fc/oPV46rB9YzOdkmYEU6PsDQgRQCZNrPInBWtqu\n57CfFiLsScJzdX1OCCPb3ZaLzYaEJK9yxmRZrBZkec58vmS2OEIZxfmL82mRoggB6vkMGzyL+Rrv\nI1pnhAQ2RGbrI/LZnJFE5zrGOLDvt8TkabotEc/gO7JK8dGjH/Pud7/FdrhGZNDZjuXJCqvgMI5E\nIenHgf1hz/rohP2+Y7U6ptkfbpSKjqou0UaTIjjnbkqdiUznaGmmnYEZ/9ZbX+N0dYt6tuDgDhzC\njo+e3efy6pLzF+foDHQWUFmgzEt2ux02WLqh/aQqUeQVRye3uH33FaTRFHXJ5fU5m80F0QcObUNR\n1yTkRDTuDrh+pC4KjtdHRB8oTAYhsVgskFKhpabZtUSfKIuS9tCR65x232CUJhOaxx8/xo+Ok6MT\nUoTFYkGMkTzLuXP79ieOmxhJKXF6dIqUhqKcEYOF4Bj6houLS1LwPH/+DKFgu72mzHLadmCzawBN\nXczY7xqutluQkiwryITmi/feYr04QkuJEoLt1Y7SFJytTtlee86fdJzdOuLoJGe2yEgx8OTJI8ax\n4+hkxSuv3OHnfu7f4Y033+By33JxtUGqnCePz5nNjhhs4P79+7z62h1unZ1yfXVN31nKasH3v/ce\n/+LXfo3l6oRv/+7v8NM/8yXGpkEFRbSR64sdFy8uECmwmldUxpC85ezoiLHruHV0iogSo+fcuvsa\nebXAR4l1iRAF1gV+8ze+zbPHl6yPzj61PX4unEKMCR0VKmoylaEliOTJjGA5q6nyklxKkvcEa9kf\ndqQ0pQTjGNEiJ6RAkeUEm0BA1JHlckkUEZ0p2v5AjFNprxkPROmZrSqaricgCIlJf6Alox0B2B/2\nHNoDbT+p5HZNSzuMqDzDhYiQanIaPlCU5ZRqqEl/X9QFPgaGzuFGN9XNfY/MFFJJQvQgQWWaPC9J\nEeZVhZEGpQzCgAuO1dGaxGRsIoEIESPE1LxEYPAjpixJiJtdcM/HD+7jo0doRTP0jM6x3ewgGdrG\nYooSnUms7ZEKkNAMe5JIiATdoaXdd2g0pc7ItWEcR/JijpYli9mazOT4kMiLknq2QCqNMdmUtjmH\nHRKKjEwUGDWRZ956NJJ5VjG2I9Y6VsdHtP2IdR4lFYXOMUpPPIBICCKZUYgU6F2HjZar7YZxHIje\nEu1I8kxCsSIHGW/SwJzl6oSqXpKiYF4tOD4+Ja9reufQ0hBsmARxo4foGPo9ZV4RXOT6csvmRc/Z\n8W1OTo9YrVYURUZZTySuVIJqVpOXJUWm+dLbb7FcHvGDH7/H6AZ88FxvrwgiEGPkxflzjNbcPr1D\nmU9y9Tff/AJH6zO+/OU3+cP3vkPTt9y9fQYxkClD11jGXiKi4fL5Y+Z5xcliwaI0CLcjFx2235KS\n47Dfk2cFRVHR9T2H9oAdB06PTjhaHmPy7FPb4+fCKUgpKYuSoigQCMahvwkBp+46QSLTAtu3qBQp\nywJlJg5AmwzJFC5pk9O1I8YUSAT92BOix0c3qeJ8jw0Wlxzd2GFKQyASiaAEwzgSb5yNMWYSwsRA\nVVd47xh6S9cPkATOelKYwuWUBClC3w2QJPPFgsVygbWWvummzkHnPuEehEhoBdpItJLkhaHvW8pq\nUgxKIQgh4P2kz9fGYMeRFCLGKBKBJBNBRc6vn+OSJxApyxw79uwPW5wLxCiwzuNDoihqQkoUVYW1\nDqkUpsio5zMuN1fsdjvGoScGj/iJjiN6dDbl8slH7DCitaKqK9qhRUqBc5b5Yk6IiaZpCN5PqUU/\nMfjBe+wwoKSgqioybfDOUeTT+04xkkLAKDl1OIZEpjJc77B2EhRJKWmahqdPn+C8xRjDbrdjv2/Y\nHxqkVFy9mmzyAAAgAElEQVRfXVEWFV3bM3T91LnJ1O0ppUBIgTYKISZ5uVRgcj11lEpBNww3aZFk\nHHu++PY7NAfL1eWerp14HSkgz0qsCzx99nxqkppVSOE5OVlRVgWXVxd8+OA+F9cXWO+4vLwiRUme\n11xfbfF+pK4NWZZYr2e8ce91TBKsqzPCKLn3+tsEH2+qZ5O2Js/LSdHrBiBgbcetsyNMBqvlDPBk\nSiOSpj1E2ibQdpYgAl/68hdwYaTtP3368LkgGqUQSJ0YrWU+qxm6A84FhmEgKEuMAWPAyEgMI0fL\nim7saIcRI3NA0w4jWipO79xhs9tQL2e0XQsism8b+rYlLypUXhCjQGvBoT0gTE5je5QSzJdLrq42\nSCxCS3xwlFV105MqGAfH5YtLci0J1uM7z6qeU2X11DDU9kQSi/WSF+cvEEJxcnKC95Z+HOh9y7zK\n6ZoNVZEx2JGYFLPakKIhrwq8c8TouDz0BO85dAeUVrRdj4xTa/lsteDicM2+b8hMzsG2UxiPgxip\n8gJZZLRty+HQknxiWS1o+w4fA0YVbNuBzJipIjKbRDhD2xGtZ1ZWHPqeBNx/+phlOWdWVizKEhct\nfRrp+oblcsXoLalN9P0UXQkXMQnunJzig8OYjObQcXQ852q7YV5VOOsRQtO3I+ujOUYKxtEwdgNd\nN1LkFYe2weSa3a6hbXec3fAfbXvg9OQWY1cjpCYBZVWyXM7ohpaiLAkhcvH8GV0/Ml/MsW6ge77H\ne0eRaZSSRGnRymDDyKtvvM7TZw+oi4I6n7G9tthB8Iu/8Le5uLhiXke09CxmJf/4W9/hzTduU58e\nY/KcMfQoLbm+3LNczJndOqXIcmb1ghdPn/PFN1+nbQcObc9itaYZrinqxFff+RLPXgyU8znNxUP+\n+i99nUfnD7j95mt8/PADvHPcfXUJUdE2LUdHNYKEMoqqqG54EcXZrVtkWUG77+jGA1oqtttrhjBw\na3YbIQZi7InRf3p7/CyM/M+KlBJ5nrNaH2OdQ0qJMYY8KxjGnqIyJAIxBmIKU4uzyaaeBSFQWlCW\nOYvlDJ0pkgi0fcs4Wrre4j0gNF0/MDpHihFrHYd9SyKRxMRTHJqOECcOwXnPMI5IKTk/P7/pY5dk\nJiM3k/BJqwyBQgpFWdXM5gu0yUgIxtF+Ulvu+2kHrsuC4B0xBa6urrDW4pwliQRS4HE0fcP55QtQ\nmqQEyiiaruHQNmS5YRx7Qgw03YFqNqOu54QQaduO0TrcDdOf0pSWZVlOUZTY4BndQCSybw6EGGm7\njm7o8SkSQiTPC4L35HlOWZUUVUFV10glKMqcfbPn8uqSvu+mCCxMysSUbgg/qdle71Bpmp3grOPF\n8xfs97tp1ERKkwR5HCYtSYiTijTPKcoSISf5udIaJRVd12G0oSyqKbrIcozW5EaxXCyxdgQhyAuD\n8w5iYhgHfLCYXFMvKoaxnyJBAW3TIETC2RHvHb3r2R32hGCx1iKExLmR2WzGg4+f8OjRE7q2RQnQ\nUiJT4rU3XkWZnLbpKYqCw35HDGEqKRM5Wi8oS421DffevEuWCc7O5pydrKnqkuAd3jqaXcuDH/0h\nv/3Nb/LNb/42zbBhVpX0w3BjE1AUGkFEKYUdPSRBCAIhNCarCEHiRmj2PUYrjJFUlWK1Lrlz+4Rc\n52y2W6TSlOXsU9vj58IpSKUpioIsL9Emx8WAyXMeP3mEVJKnz54QlaFarlB5gZEGLQw5BafHp+y7\njr4fQEievHhOUorN5Zb93vHk2Za2T2z3HqjwwfDO21/lZ3/6LzGrj9luDnRtx2Hfcjj0ZEUJUmJj\noJrP6Pue9fqIGCORwE9/7acoqhJtDK++/jpFVfHo0RN8SPgkGG3gcGjxftol+7GjqHJiirz/4x+x\nbffs+g4PbJqGPgQa59iMB967/z5X3ZbnzYaDc7TO4pieRYgB6zwxRawbCSGwKGaooMhkgVYFg404\nFO3gafYdKUqyLKesZ8wXS6SC5aJm7FqMUpwcH5PnU5eii4n79x+QlxX9MHJxeYELnvV8jguOq/0G\nG6dZBbbrqOuSy8vn2KFnv9/T9z2PHz7mztldDpuWw3WDGwISxfH6GDc61ssV1lrmiyV5XnF8dHJD\nfEoQOVLlU1eoSlSLnC996UtorXnt7qtkKqdvOzKlaPd79rstxc0QE1SkHw4UZUYQA33ocHhc9DRD\nSz2fpNbL1YowelRSDIOdpNgxcHn1HKM0bdtwcXXF7uD46MNnON9R5hEtHJkWLGYzXrl7ijGSoijw\nPvCzP/MzrGZrFsWMdTWjysBIS/IbMtVz93bF6a2Se2/d5qvvfIFvf/vbfO+73+f1V7/Am8uHHA8f\n8uD+Jb3L+fpP/TzrekZpauKY2F7tkCi6xmJHuLjYYp2kbR3eCewYyHRJXa8wpSLKSAqaeb7AJEWd\nGZqd5fpFj05/8a3TfybEELneXnG1uZ5q3EJyOBxYrVccmnbSsi/XeCRtN5AX1ZQCmIzN5sDQO8Yx\n0LQjWVYzDgmSJqGRKqdtRoKDph1pm5GxH6iLmtwUjOM4TRDyk0cWSEIMCDGx/l03MA0pCVR1wetv\nvErbt6AgicTHTx5xevc2SUCWZVg7cHHxYqqr30xK2h8OuJsyXz+MuBQwZcFsscB6TxSwbzqSFCQl\ncSGQEGRlOe0S1iKkpO07hFZTVDBYxmakzApEghQFZVnRdcNNY9Y0ecgYQ991U2uytTRNgzEa79w0\n8UkrNpstwzDt3j+JkO7cuUOe5QTrWS2W0/QfranLEi2nMp6R03Sn3W7HbrvllVdeIQZYro6ISaCV\noSxLyhvN/sSTBFJKjOM4Daap5jx7dom1EaUy1us1y+VyImq9QyCY1TOKm80ghYTRktV8yenRKVVZ\nQYgTgdw2CCFvpO3Te83ziafqup5MGTJTTO/YBxSCWydnkCJGazKTk5mctnE4D1WRMZ+VDH3HxYvn\nDH3PbFbyxr3XqMqSqiiQQJHl3D65xenxmmQthRLYvufB/Q95/uwZz55d8r3vfo9vv/sukgXf+Mbf\n5/h4xWKec3ZL0AXBb337e7R2ICsrZvWcW6e3IWqCF5R5TVnOWK+PUVqijWAYW7QWk4o1Onb7lhgV\nKUjaxpG8QjOVVYt8xvH601cfPhecghCCh48/pp6vsDajyjVX11eURUFMUJZz2t6y3R1ISAbrJhl0\nTFxvW8pqSd9Ogo68qkAYsqzGR8Fyueaw3+OTRcmMsqi5f/8++0PH9WZzQ0gJTo+O2Bw2U0twO4Vj\nk6bfUhQlLljKUvH+gw9phgMpCGSmEUYiM0USUxNREok8N0gp6fsegOVyTrdvJ4FRTIx+ZFkvKMuK\n6/2ebXPAekeCm6lBBoRmHFryQuBDYLlecXn+YuogzDJcv6XZHVAio57P2TctScQpBM9yrHUcrY8n\n2bDW0/SnGICIkoLloprGk3lPnhcU5RyHJqlpJFjTdMwXc0ycxr2lGKmqapJiDxZQLOcrnAscrdY4\nF6jrGp0M6/UatokQPXmRQ+Rm/kS8IQ1bZMqRciLujJnCdy0VSsF2f0BlkJFxtF6jpabMSyRwcfWC\nmGd4Fwg2YYRi6HvMjaJUG4kWCsRNc1QKdN1AYQxVkaOFRCQYwjT6bmgGbp2eYcdJCtzJxOZqZL28\nzfmzJ/TzgpmZJO/Be2Z1SZ7lbOzA/KZ0XpcCozXNfodS0zCdwhgsmtXqiHe/80Oafcf9+8/41V/9\nRTKjePz0Absu5+f+2tf5hYPiN9/9fba2o1ysmTcNR6sv8+TFSD84QHJ1ecU7X/kKHz3+gKvtlnld\nk4RjHEcqXeOdZOgHTo+WFHlOVRW4MXH79DZVNee9H/zwU9vj5yJScNazXNfY1HC1u2LfNrzx1hvo\nTLE+usVieYvnTy8Iduq6U5lh1zV8/PwpQU9VhaLKaIeG68MVEYdPjt42oCJSJ5bLBTEG6qIkEnj6\n/CkhRpbLGdaPJBHJ85yrq6ubqxL0/UCelfTdSDe2PHj6gO/98F8zxB4rR67ba17/ypt0caD1PY+e\nPyYky3I1p+lahnGcrrXZk1UFZBqZaZaLOU2z5/zZE4SMJBUnp5OXFFlBrnOOVqfY3hN8oChKdJaT\n1zWjDQSXePXkFcq65vqwoelbQnRwcw9VUbKaL3GD5friEu8nyXc+KzGVpvctJ7ePETKgtOTW2V1W\nqzWz42OK+YLi/6PuTWJky9L7vt+58xA3bkw5vbHqvaoeqnru5tAU2VDTbMGCZJgwbMOCF4QBWxYg\nbwwYlqGVAW/kje2dAe8MLwxNhkmKoDiKoNhkk2Czu9hV3dVd9erNL19mZEx3Hs7gxUmVuZCpsmkb\npbuKfC8jMhCZ59zvfN////tPc05PbjK0A9oRvHjylDxMkGWPHjRKw9FsBQomSUoQRKxWK7tI0pDL\n/RWzRU6axRij8F2BawRqlBwdHdn3mCRMJzlN3RG5gtVsxjROaXY1SZAwS2d4vovvOVy+vCDwfLQ0\nnB6d4LsBWbxAdoar51sCJ6IqWq6uCibuCbPoBN0oTvMjQuFz5/SMPElYzuYWxKMVeZoyjRNW+ZJA\nQBpEGKnoe8HQx0wmt/nsG5/jdHVCHCakcUpVHzByQCjFbDLj8cMPePr0EYfDFW1bIlVH3zZ0VcN2\nvScIV/zyr36Tf/CP/4h33vkhP/7l17n/6qu88/3v8/DZQ+6+8fP8p3/3dwho+MaP/VX+1t/6zwnC\nKUmQcLI6oqthNj2iHwa80OO73/seddWw3e642m5o24a2ren7hhurIz557w6eP/LGZ19jvpxSNQ2q\nL1ifPyRP/Y+8Hj8Wm4Lnefi+Q5z4RFFowZRo4nSC8FxGpZnnOZM4Jk1imq5BeOK6+9vjeJJRNgxD\nSd+XTKeR9Q4gGcYWYxT90BKEHmV1oOlb2r5FC0Pd/Z+yaG00aZowjCNKSXzfo6oq2rZFGYXvW9Ao\nLswWOU1Xs96safqWqi4JgoAsyxCOpTiFUYQRAi8M6MYBZRRxFGLBpzBcG7BQVl7tOx5oO77cXW1J\n45QoCJFKWnbjJGM6nVnpbz/gRQGO59D2Dd61V6BrrdFrmk3p2pY0jhmGjiSJ8AMPI8APfQbZo4wh\nTlIO1wi02XyGMhrX96xFV3h0Q880y7hxdMpn3/gMZydn3Lh5y/aAPB9PuKRxgsBlkFbJ6IcubVdj\njEapkcvLC1wXsmwKxjCOlndgjGGaZchxQGhF37bk0yn5JGc6yYl8/1pBGaDGEc9x8IRHGMRoZciS\nnCiMmUymGOOQJlPyyYrQSzk9voGSmsDzGDvLPujaBqUkUtq7OVojB4UcRqIwRElFVbZEUY7nBmht\nruXTgmEYKKuS8+fnPHn0FM/xSJKUse9YreZUdcFskWOMuWY8eFxeFbz19gfcupHw5puf5LXX7/F7\nv/ctvvl7v8/masuz5z/k7/23f4f/7Bf+BqZ4zs35nFDtUP2e3dUlatQ0VWthsa4F7IZhzOnZLcqy\nRY1W9hyGMWEowQx4ns/5y5cYR+EEPfNFwt07Zyxm/5o1Gl3XwSAsDsu3rMBFtiIOUxwBWvUU7R4/\nDFAjeK4tqZUxOL7ACQLqtmYxm+MLh77vCCOfaJJY953RTPKMIAotQcl3bNNMj4y9xBUuwjHgKIRr\n6IbKAltcQRA54CjGdmRUPWHkUR5KZC/JkgllZQ1Vnu8ymSSEcUQ/jkgh8KMYqfS1KUUThNYl2V/r\nIYQwGKkwgyYQPmgInJDA9cCxaDc5jMRhjFEG1UuSxGLf4kkMRpBNMiZpasGdjo/RBq0VkpF4GoPQ\neJ7AcwRSNgjHIIzBVZD4EWZQBHiYcSB0HeTQkaUJahwJfZ/yUDGdzai7BiU0XuRx684t/MAhz6c4\nAsauJ0kihCto25a+6zHKUB0qpumUwA9wXBfPddHSELg+cWwp1mhLctZaW8m4lmjZ4wkse1FBHMfM\nF0uOT06ZL4+IoxTXEWR5wt17r1A3HYvlkRWtGUMcTxmUpOltpVgc9ux2ew7Fnqo+2M9fWaiu6wgO\n+z1DP6JGw2bdcnG55lCsubh8Tl2XdEPPZrvFdUNOz24itebi6tICa33r38jzKU+fPKfuBk5v3eHu\n/fs4gY/junzytRt84v4dZtMcJY0dX7c1D58/5ubNY26+coPPfOkN7t2/wwfv/inF5oK62XCUTQmd\nAK0krjMSRJpJHOE4hiDykQLSaca+LKnbkqqtUBoeP3lOUVq4rpQDbVvx8uWLj7wePxY9BYvqBs/3\n0Urj+wEYl0+89im+9Yd/QBB4SNNzqApM74Dfo4TA9XzSOKZoK7J8xunJCYfDgW4YGLVgMpsSGIfH\nT5/Sy475cs7Q9ZT7nmQSUtc7bi5P0XKg62p6PYBrWB7NUErR1BVVs+PWrdu8eH6JFwjSLCIKYiIv\n5PjkmO/84E8J/YAsz+k6GLVCC5jMZxgNRsBms2E2y8AxtE1LPp3R1iVxEpNEE9p6oO9GFmdz+q4n\niSLavgbX49byhH3VkGUZKjF0XcdkGnF5eckrR7doOjuqxLjMJ0vapiKdJLSqJ4pjHM8yFbuqsZ9j\n2+PhkoYZvojo6gE/sQ3S3eUlXVHw5HDg/t37OALyLMONAh6eP2FdbXA9jyd//C1ev3cXaRQIQT7P\n6Puesu+RUnN0dIQrYOgku03B0dENjDFsD1uWszlpBE3fkU1yiu2O49MThr4jTAJk17DdbGm6AtcJ\nbD9HjnR9hzK2+bvdHDg5u0GnB3TgMk1XqP2OKEt4cv6INEwYRonCWqOHdkAbxdnZERcv1ty5fZ/L\nyx1Kj9y9NaduOorH75EkOWl4wnI+o9yvWSzu4gnFj77/kOl0CsZleXyEwoq2XARxFHMoDmy2L3nj\nzdf43X/2Fr/+a9/i8rLj/idn/PRXX+Hn/9pfR+DTdC0/+2/8DA8efIAQiovLS979/ltUdcP9L7zB\n9GzBP//NX6UpNhzdyPj8538Sx3cptyOzVU6ve4TrYaQgm84ZZUHXDUyzBQ+frLnabPHihMlkQt00\neMKgTEg+TTgOP/p6/FhUCgaLUJNKMciBXvYUVcWz588YlURqZZtdRjPqkaprkMbgei5D39NUFbti\nz+PnT8lmOXXXEqcZvuPgOKBUjxcIrrYXHIoNQWg9/KHv0TQlcRISp6FVJ2J1E8YYPM9jNpvheS44\nEIQhWms832E6z5BqxBEWBFrXNeM4orTGGBhHSz8WQDaZsJhbVoTNFoBxGLEZAwIv8EEI+ra35fUw\nEocJcRhxtd2TJBOktDkQForjcHx8wjD0BIFnaVJa07cdgR/SVi2+45OElvOAdhDCs1LjXuEQoLSh\nKEtwHfwkxPVsDkYcxVRlRVVVlE1NP3Q0TUU+m4Iw1HWNELDZ7K0/JAxQGLpxwPf8a+2FRdn5foBw\nHIq6ohtHyrKkqmrGfsAYQ1EW9rldRxiGltrkOoRpzDBYjcgoRzbbLf3QX2cuCNJsSlG3KCOoqorN\nbnud9yGI4oAkDUG49INinh+BsUcOIVziZMJ+XxDGMd0wULQ1RVVhGCmrEpTL+bNzhn5kuz1wsd5y\nenpGnudkk4kdoV5DhJVSNG3LgwcPaJuOR4+e07YVngdf+NwtvvZTP8nP/sxP07QVwtM4jmU+nJ6e\n8N3v/ilHqyWbqzXPnz5jHDswCq0Gvv3thzx8dEFZjzRDhxN6di3IDuda2q9GQCr0qDhcHXCIGUcL\nENrtd/h+gOOGKKAfFXXbfeT1+LHYFLQxH7rjRjVSNRUv1xc8f/mCOI3px9GCVdEkWYLEYByB63sU\nRUkST5jOpyjH8HKzRmPn81KNaC2JIx+lRpQZ6cYGpa0Qaj6fkSbWeDTqgSiJrhf0CFilXDu09HLg\n9MaJfa/asN9vWa8vKOuSSZwS+LaXgLCVAY5rRTZhQFNXNFWJGpUlUSMYR4kQLkI4KKVJ0pRkkpCm\nE7quIwgiIs8nTSYIx0VjqU1JOiGOUxzhWZdkEl/z/m2FlaUZSZgQ+RGRH4I2aAkYgcBhHDVKCRzH\nYxgU4hreOipF03e2gbg6JgwihOtcC5sUddtYfcLYU1YF02lG4Ic4jo9wXDo5IFwHNSqm0ylS2gyM\nIIio6hbheDacRVhl5ubqyuZORIE9zghwXAepFbvDHtdz6IaObmjZ7uwm3vU9URRyOFhDU9V1bHYH\nwijBCOu0dQPPbu6eDaIJ/IBRKqbZjEmWg/DJ8wVlVbMvDqyOjxCeIEoTcGAxPyafLJjEKfPZgr4f\nrEvT8az7c7CblxWltUyyCcMw4nouVdWDCjk+nvGlL77Gj33l0yzzHNML5ssZWluydBRFnJ4e8bWv\n/TSybUmjECmlBai4Dmkc8ek3bvL02ZYfvf+Eot7jupJFluAicXwH1xWURYUeDWmYIgfJcnnEdDpj\nuVpxenZ2jeOLqLuWbhyo2uEjr8ePxfEBjCUxa4seNwaqoeZkfsJ2uyUOI+RgFXmtHnDiCOM67MoD\nUkr6okQ5MW3fUfUj03jOy4sts2WK8UACZVtzduuMd773pwRtZM+psxwxjDy5fMb0aAlGsZov2e23\nvHz5giybkOVTpJZMJykZU9q+Jk1jpOxZX10yy1dMkoSubfACD8cVrI6PKA8VWZKxrlr8IOTiYk3k\nx5YjEE8IPZ9nz59x42bOrjiwnK8QCLJpbo1SrsvYWqHPpixYLRdcXr6kbTteuXOX7WZLmiQ0bUkU\nJswXM7YXJUkcATZnwbgCP/IJ/BDf8zGRvcMfr07ZvLxCa4UjsJyFyQRZtVRlwyt37+GGLic3byAc\neP78GX4UEKdTmnbAdT3CZEo/9hihMMJQFAfaYmC+zCjbjq6t8VwfqUe8UNB0DckkhdHqFPb7Lccn\nRxhHgzBEcUjduhzfOsV3HIprdmOYxJboLQRhEhJFAdvDBZP5CqUVh2JruRgGZD9QVjuSaILjKrq+\nZbWY4mc+3djaik/4+DePGfqGceiQsmd1dITrhFw8F+z3B07OznAdwdgdOD+/YJkF7A9bFrM5+2rL\nbr9nMZ+jDAjP4/XXP8XL8+e8fP6QL37xS2gtcF2H+/fv47ouZVPQ1YoH7/+AWzdv8ku/+Cs4rubH\nv/wF/ugPfp+/8R/9bba7LY4c+Kmv/SWePXjI03/wu/RtS1HU+AuH5w8v2Tcb+lKT+AEnxwscHXIo\nCtq+xRue4EcV0+kpda2pqj3z+ZLj0xnloWAxm33k1fixqBQMWIEOttM8yXNGJdkXB6qmBs9BG0E2\nz/Ein35UeJ7tTPueFciMoyKMUrphYF8UHIoKZQxNN+BHke1XAEEYIxyPqm4ZlWFQGoVDUdWU12Xz\nMAwsFovrCmJEeILtfoscrc4gim1nWip7ZpVaXjccDW3X0vc9cRjTt/31qG5CHMe4XmiPCf1wjQ7L\nkFIyjiND3xPEAcIVuI5P4AZgBNoYQFNVBePY43kOu932WvLcWd1CmuAHAUVd0PUdoxo4VAccT+AG\nLjiGXbVnX+wwQrHZbVEa2roj8kO6ztKSh1EhpaaqG0alEJ5rRVRhjNI2Ag7h4vohXddRNxVhGNC2\nDUII5vOcLE9xfYcwCcA1GEez221wHEPTWKVnNp2gteLq6grQCAyXlxd0fUff91RtzWQyIU4TO1UZ\nerzAp65L5ssZ2TTG6AY5VESBY121WiC0II5CJpMExzGMQ8Nut6ZrS9TYY9SIUQOR73B6csIsywnd\nAMd41IVD30Y4niBKIxzXw3Vdzs5OGceRo6Mjuq7DD3xeeeUeV5sdV1cbbt++TdP311GGEq0FT5+c\n8+3vvM3f/0f/mPPLl5S7gmdPHvCdb7/Nr/zyP+Heq2e4wuPdd9/ljTfe4MWL5zx88B6+5zAaOL1z\nj//i7/6XfPftd0gmE7QSGAmJn+ILDyUVQ98zW2SszuYc37QirjBI6GtDV4/IUTIONhcl8h2GrvzI\n6/FjUSkYrfHcEEf4KD0Qej6f/+KXuby6AMehGyTa8QmzlGCe8PCP3iWLJxSlRaDfObrJvqppqpa2\n7JDX0ZLnLy9IIwvpMI6hb3qSJCUwAX4ccNjvUWqk6VoiIA582qFDGoknPMZRsljMiKKI/bhBaZco\niDkUayZRjjHCbhi7K6azCYeyYDadEwQBu8stUirm05xh7Ki7Bs+LkeNAVSmWiyOruXBcFvnMYuAO\ne+sBEAI5SJq2xQwtnicYupY8n1onqXHo6s5CYvseLQq6QTJb5Tx98pi7r9yGQVhWoOvioujHHmUU\nwngo0+Moh+VqiVL2eFXuDuR5DtpazteHSxxXIEdtfQ2uy3ZfIBwf4Qdsd5fM53MuLl8yzTIGDXHo\ncXl1jus6BEFAM9bMZlOOj484FHtrhU9TojhmU7rcvX2LD957n8qtSKYpUmvqfct8OkUrg+MbuqFj\nkIbZIqdqamTXMJlE1xuFw3K5YrcryScpSkk84dC3reV8ei5aDfRdi+taVezVxgaV9Z3GcxOEjikO\nhg/eKzle3WCzfgs51vyln/wZHn6wozjsSJIYR7gMo8XuZ1N78/KDkAcfPOLu3bss5yseSfjOd77N\nK/dfYXV6h9/49W/yP/z3/yN3T1ZoHD77xn2+8OUvMZ0uCKOAhx+8h3JCFmc3eP3+K0zThDiZMD2+\ng5ec8de3D3l6+V2asSGNZggPmlKSpjF1N7JtnlDVHfttgyt8mqYHtcX1NXnmMg7PEO4C2Ukunq4/\n8nr8WGwKNmVUoAaFllyfeQdeeeUeb731Fkoq/DAiTFMu9xfESUJxKEjT1J65BURBwjAojpanbK4u\nCcOA49WKw3ZNmufWdGQMaRiROCESc91ANCTERGFMVe7xfQfnOki2qmvCKLKEnDhmvS4JQo80ihn6\nkcCLcQMPz3OttdaB8rAnSVI8FyI/wnE1fWcRco4fEBmfoWlReiSOUwLfx/M8Xr58SS8NQjpEkcVw\nIQRN2xBHIVIOlGXJ2Ev6vsdohygI8CMfg6FuKxzh4fiWNG2MIUkm1ufge4yDREuJUZokjhilBa1M\nstw+CxgAACAASURBVJS2LjFacHl5CcChLMHXKD3asWtZIlzHciOiiG5ocHzLjej7ns7xCQKPKInQ\npSFJU6RRSK0ZOptz2LctWRJ/iMTL8ilKKU5OTgAHPIf1ZgO+lXHLYaTtW8q6wfMDC4VVCnFtJouC\nGN8PGdqWSZowSMXmqiTLE4su09D3PXk6QTZQNx3a7HEcjyAKMCqhKBRhOOOd773DIn8VLXsWs4zt\n5or1xQtm0yn1YXtdRY3MVyuGUWEcQdN3PH95QZ7n5Islp0crTs5u8PDxA46WSx49esi/+Y2f4e3v\nvcMrt89YHR8hlWGxPCHJUoZ+JIonfPMPv81nvZCLscdF8O4Hz/ja13+OH/vqiocfvEO6cBlETJzM\nKOsK4Xbk8zlBrFlv3qfYNbS1oasFSmluHN+l6ys8EeH6Nhh3MV/ijCHwwUdajR+P48N1AIhWBiUN\nruPx8NFj3nvvPfre+uddz6eTA5vdBlc4NiXa9/ECi2Df7wpQgmmaEQQBxiiiyCeJYoxUNFWFGSWB\n45EnKY5RKDniue6HOvY4tnp7bTSe75GmqS1nqwrXc/E87zop2v7MWb6gHweatkaqkUma0vcNge8T\nRwFR7ON5gjD08AOXsioQQJIkKKXZ7XZ0XYeUlgsYRhGjktdOxwThOpycnNhsSNej63qiKEIpc50Q\nbTX/Gk3XtQhHkE4T5HWic9va7AnfDyyCXruoXtIUNVk2IYwCtKNIpym+7+H7vgXTXOsuhnFgf9ih\nlCVjOw5IOTCMA1Ea4vk27dp3Pa7Wa7SxmoM0nRAGEWmSkSapZSoOiiiyCzK+DgnWSjO2va0s/MBu\nOG3H5eaKcZCs12vG0bpN+966F7VWdN2A0nZq4fsBoxxReiRKIoa2Q40StJVQd50t6Yde0/cj7TDS\n9SObbU0Qzjm9+Wny2SvcufM63VCRJTGrWU6523K1viTL0g9dvDa7wgUESZKSpiknJyfkeY7rBYxa\nc/v2Xd55+wcWdz92fOlznwZHWvHWNAPH4/xig3YCbr9yDz9OPpxmvHr/Pv/Ov/cf8Ok3P8N2e8HQ\nH9htL9lurhjGFo1CmoHL3Tm7YkOaLFjMbnL77HUm6ZyT45sc9gXlvuTl00tU51EVPVIabt+6/a9a\nhh9eH4tKQQgHYQLGsSeKM3ZFSRSHdFVH5DqMfYPrRmyf70lMRqVtKR0GDggXj5A3PnmH9x48RAiX\nk6MVTVuxubxkkNZ4k8QRqutxABG6yIMk9gLiKKIqK8LEoXUMfdfjCg81aIZ+ROPguRH7XY2bhIzj\nwHKyommu8NKI8uk5k0mCkeCnMZ4T0TYDRbW3LkQ/JpA+V9steb5kNl+y2x1wHYnWVvNvjEDgUh72\nCEeA1LRDhe9EmNHB9xK66sAiX7B+dkkQRwjXw3M9pGrpKuuXr+odYRTQtz3DYIgdG54rrxfIPDlh\nu72y4izZY7S9KwS+4Gg1Z1c0OJ6DIy3qPIxtQK1rNHqU9L31L1RdQ9I71lxkDEEcEkUJm/0BPwgZ\nu4EwTtlcrcmzKbdu3sch4lBdIJoW1/VJ/Qwz+nS9Abdn2O4IQp8gEpyc3qB8uSFG8InX7vN8/QKj\nG7rOQY2CsRd0vsN8PmVfbWnGPZ6bUNQDDJossxGBxVVBHE7Qoke6kig7YpB7NoeS09kbVIXhT//4\n+6AEz58/xqgez0sxukPKDiUUq/kJbd2gpAX6+BE4SrJaLKjLPQ/f/yGh4/K4afns5z/D2cmSD97/\nAedPHxGrKdF8ypuf/TI4EcLz2beKx88L3ntyxTf+ys9x75OfQw419199nV5JaDSPHp/zJ2/9Ntly\nwPM94kER0rJYZbBKqIsdoQvC+EznGa4ToXrBKFv8AKQMmeZLhm6gHyTnL9a0y/Ejr8ePRaUghEBr\nWCxWCMeqFY3STNKE2A/BGIrdBkd7TKMlk8kUJTXgYEZNX/VIqVgs5yDs3WyS2rTqUUqE49hUIK0B\nQadHOjnYCiFMMUp/mOF4tFhRlw1qlIz9QJblFFXD+fmlnYxog5AufhBTNSWesTy9sVfIQTLPl2ip\n0cCoFI5wCb2IxWTO2dEx7rU8dugHbpzeQCmF0YambsmzjGK3pyoK6roBBL4bMHSSSTrjZHkDT4S4\njo+U2KbWo2dM4oyxGwk8y3dI0ykClyi0Fm+unYP7XUEcJ3aO74jr8auka0rM2NI2JU1dXR89rhOu\nAx8pO8ahs82sMERJQxKkqF7iYOGj+XyOox2bCaE1fdejNUilefD+Qx4+fs5mu6eqG54+fsZuXbC5\n2pFMcnzf52i5ZBKHuK4gTnx8F46XS5rygBASR1h02uZiQ13XBGGMNNAONXW9w6BJowmO49M0LVVR\nMk0zwjAkyxJGacEwuII4mfPuDx4RejFdXZLGHoFvOD4+JYxSlscn4DmkaczF5YZ8tiTP51xdbdmu\nr7i8vOD8/BnTyYQ4shujGkbef/99hnHkzt1XeO311/A8B4y6FmEluK6P68bksxVKGX7tN3+X+69/\nhiiacrku+ODRM9aXG/J8StetMe5om8i+wA8c0D1DX9E1Fa7WOI4kSAR+NJJNI27fPqEfKnrZ4AcO\nUmkWiwV121BU/5o1GjGGUfa4zgTPc3AVNE1DlsYMw3BtCT7QVgeiyJaZapQMvUaNA2mUUpYH1Dha\nsU1VEy3ntP1A4kUM44gfuLTdSBK5GGWYTnM84eA5DvP5EoHLaraygpm5IU5ipJLXMlyBCDwYFJM0\noe5rXBdcrVFDx2hGsmlGlsRsrjYEnsfZ0Zm1JjseGsVqaV9bdgNIBUogjHX0eY7DyfExdVOwyBdc\nbTa05YBjbBz6NJtx8+w2i+mcQ9MilaRoaqRrmM0WeF7AdDJDKvCcANfxieKIpmkJEzvj9x2XySxm\nGC1evR8kvhfYWHjPw5cjxXaNH6W4boAT2fcV+g6u45Mv51TVaCnZUYTsJavFin1xuJbhQtcMeFIT\nx4KiLsimKUYpfu3Xfo3TG2fc+8QZjtHEqWa+WLDf7+hlh6xriCPCOCAMQtrrmXrb92jHKlyVNlR9\nzaAlQeiy271k6azAOCTRFE+EjHiEkQ8YlBIcrY4oqwbh2s9xGDqU8gi9KUnic7W7QinJyekxcRzj\nCYf9bkNdVwQBeF7MYpHZjFFhK1rf9xmGjul0St+2xFHIk8cPybL8Wt8imC+WGNVTmo59VbFoNwhX\nU3cjXhQjsKrPQy0x2qepBVVREE8Szs5O2e+eY3SN0IL9viSdZmgGpJD0WuGGLsYT4AsUI2GYMl8J\n6qYgmwdkwqVsnzH2MbIYWZ2sKMv/nzYFIcQjoAQUII0xXxFCLIC/D7wCPAL+fWPM7s99HUdwcrKk\nqhqEkcRhCI5he7Wha2pObpyRZfYcrpQkDhKMZ7har/E8OF8/Z7lc4QiPJAoRKiEJIzSSSPt0uqOX\nGjeOCbyAvh4xaLJZxna7JQwj8umMYrdjs7dSUc/1yaczkjAiMg7Hd+7x9PwZg9KU5QYPh0UypY8D\nK0rxXKrdhiyOqKuWpmtsed9I9GgIJzGHokCOisDxcZVHX0gmXmLTjIYR02tevf0qSDj71C3aumUc\nBvpuQJsTBsdQdS0np0tG3eKgEZOEIPApS43nuIx9T1e3RJOEsm7QleLWrVs8f/LUKgI9gSMMbTWy\nmEX0dcXJPCQzmrPlnM445PMlzy7WNrzUDGgjKJsBz5tw2O9Z5FNePH1Blk3Jpxnri3MwDoETMEkD\njCOo9iVy6JjlOV//2Z9BOIKr8pLQDxB+wPqwRsoO10kwBGz2HXpXYxKf733v+3zy1l36sWG6WlB1\nFUEcIcee2fKIOBacX2x49OgpR8czBjnYG0Y4xTiaYeiZz+dsDhuSNMPRLst8hqLly1/4a/yv/8tv\nIEfBcjXn4uKcKAq4desWRVczSULkGFOWB9qu53g6B21oqoo4jrlx4wZ939K1Nc+ePGS2WvDs6QsE\niovnTwldw2E/J01CvvzVr3N+/pwXj79PkOTgzbg8POHeJz7F5eWG5dEd5oucO7dfZV+85OatM/7g\n279BXWwYxpY4yRFCcNhZPcViNeHicIUvBNoztEXLbDonDivU0GOMQKk5eZ6gzQuyNMcEDrM8ZzZb\nAN/5SOv6/43jw9eNMV/4Mxl1/xXwW8aY14Hfuv76z71836duKqLIJ5tMqKqCuqxwXEEYhlZZFoSE\nYcRyPidNUpSUJHFCmqbks4zt9oo0Dhn7FjkoAjcg9EJmUcRpnpP4HlVVkiQxk8QeGfq+Jwh9G0Sr\nFG3TEwYxgR/Qdz1921IXJVkyYTFdYLQFwqhuADkyn2aAwBhB33WkyQSMIM9zPFymaYbRgiCIuLy8\nou9s8IpRGhePF89e4vkB4zCilCIJEzzhMZ/OEdowiVM8T7Dbb9hs14RxRJhEVHVFUx1YX70kjAPO\nL5/TjQ1x6pOkPlHksL26JAoCBIayOGCMYhg7Dvutxd15HlEUE0YxfhiSXgfF/gtYqsEmMxmlrKRX\nG/LFktfuv0bg+6xOjtEClDH0fUccxwRBwHxm3zto1DgShT6T6cSOeT2PMIpJJhNwBYujuZWt1z2e\nH9P3cPlyTxzm7A8txmCPfkoDLo4XcCgLXlysESJCGxsENIyKSZ4gTc++LKm6ln1ZUFYVRin2uy2O\nMCRRyD/9J7/NBw9ekGYZT643yn5oubg8Z7lc0vcdi8Wc1dGCMAjYbrdcXl5+6Orc7Tb4rp2OLWZz\nm/MZR1TlgVk+oakK2qZk/fIl/ahIsxlNrTHKtc7WUBAkI+vDI5LUpayuUKIgiHt6ueVi/YAoCdlt\nC8ZBfpiEXpUjaI+bN24yyTLiNMXxAzzfp6xKG6irJT/3jZ8nDk8JwwVlXXJ5dcG+2NH2H13m/P/F\n8eHfBv7y9eP/Gfgd4O/8eU/QWmO0oioPRFFK5AcYce09SDPqrkM7DkVRkEYd46jwPEhXGW1boPG4\ne+surusyzxfIWFKXHcLTfONrX2e7ueSger71o3dwEWyuLu34sCiZzXOEcDHG2lDf+PSbvPXOO0SJ\nRxA6uAa0hhebK6bplJPZnNXt13jn6QO+f/GUOMhwhCUVd4MijhPSdEo/HBDCRWmbtRhPUl5u1syz\nHKkMcZqTpHNerjc4riGOQup9Rd+NlHXN2UlEcw1tvXXjFZ6/2LApv43jasrdFYtsyv7Q0GtYnd7g\n8vKcZqgYx57QCTia5bR9j1IjevBJQoc0SkhPT1AaStnS9g2dHHj30SWLcEITeGjXZRhHZNsgXXDc\ngKYfqOqe5VJTtR1GQdlWSBORZQlBEmMcgdSSp8+eM5kkTLMMz3M5FHu8MML1bKDLfl8ym82Z5jFt\n2+B6DrNlRhiEhGlC1HZ4gcf26oLl6ZzHz56xPDqlqUayLKOfaoq64O3v/4gf/8pXuXvvFd75wZ9Q\ntobNtuDG7VuMY0eexgSuRxLEJL5H29esL/e8/daWNMrY7zfMFyscM5DnGbdv37ZZl0ohx4G+7emG\nniTNODo6Yjad8t57P2S9XsOpQeAwyTJu37rJL/5v/zuf+cynGPuW/X6NqzuWiyXVYUdRFNy5+yaX\nuw37umJ574i33vtjvvr1n2L9fEPzYk1VntP2G3ZtTtk/R+4r4mXAtjyQTnyW0ymBWdBWJafHd5Gd\nwhhB7CeU2wpfOJy/vKSsN/zmP/slPvH66/iTBV4g8NsCqXra/qPLnP+ilYIBfl0I8W0hxN+8/rcT\nY8z59eOXwMm/7IlCiL8phPhjIcQf19XIeG2kOX/2HN/zWCwWluTj2dGglIrFfMUwjEzimCyNkbIl\nSWPyPMcYxdV6TV3XuK7HqCRaah49fUzZWIqxg4tWhtD3cRzXAkI9H21AOB63777Kj/3EV5nlM2so\nilOGYbAjRK2RSvHo4SOWsyVJnOIEAY7w8L0Qg8s4GpQCrWw68r7c4zgOwnWo6gZjbEUSRrHVQMSR\nLYuNwQ9DfD9EaQjDmEk2Y7E8skpMrZlMUoyWbK/W+IHHfr/HaJfjo1sIN6YdDEE0IQxT0nSC0QrX\ncdCjtoh2Y+ib1mLcugEjoGprksmEQ93R4dAO1pPRtS2uI5DDgOv6XFysqaqSqix4+vQpu/2O05Nj\nwvA6yj3JAPACn/lqQdf3xHFkbcd9f62b0JSHCsfYhm4/dGAkaMNslZLPE27cOWI6DclnEac3l3ie\nS5pMKHYFL19cIIwFuwoX3vzcp/BCQZwlKONhiMjzE4ywuR1xEjPNJpydnXB0tOTo+JSucygOFkjT\nNA3CsX6NMIx48eIl68u1DSUOQrJJxny2ZLVasd1uefr0qW1gVhVhHCOEQxRGvP3OO/zET3yZB+/9\niJOjJbdunCKHlqY68Ed/+Ps8eO+HHOodXujz5ue+wK7cUTU9RTWg1MiLl0+p2g2jajFCcnbzjNnR\nBOEJDAFG+Ayj4ezGCfNpZgN0BkUSTYjcGDkohHZQamAcFQjFs5cfULVbgihAaxvoG//fyJL8i24K\nP22M+RLwV4G/LYT42p/9T2Mtff/SrHtjzP9kjPmKMeYrceKRpCnTLOP09Iyj5crKXKMIz/NwhcMw\n9iRxZLP1+galRoQwuK6tNOQ1hfhw2LPdba/9+fDw6TOeX17ygx++j5IGqcF1fIxx8L2Y3a5BKYem\n6RilZLs9cPfOqywWC+azBVJex571A27gs6trfvijBwyjIvRDpLSLvG17ojC2R4SuYxj666NPz2a9\n5ur6D873QoZBkSSJlT67PtNpziAV9+69jh+EpFlO1TaMUjH0A3Vb0XcNTbPD8yDPEpIk5M7tuzjC\nt7CRIOP8xRohQgI/QRl4+vyc+XJJVbccihLHD+m6nrIo7fzf2I0um80J0wmHoiYKrTXcER5JnOJ7\nMfv9gbbpKMvSajDimDt3b9nPfrzOspT6OnHaGsqMMWhtSNOUtu2pmhbZS/q2+9BzMPTWIKbpkKKn\n6yqU6emHmnRqRWO+H+K5AZ7wuby4srLuOCLLrVmtLEuSOCMMJ8zyFX3TIhC8/8EDnrx4wqHY4fku\nWgkePXzJJ17/JMZY4vV+XzCbzfit3/ptHj9+zDiMFIXd+NZXa4QQ3Lt378PfVZ7P2O/3PHjwAVEU\nsb7a0DQNR0dH3Lp1k6quCAOfJIpYLResFnNunp0yn+e8eu8+rmeTwU5Ob/PLv/yrnN044XC4QukB\nbQxd3zCqAdezztmv/uTXmM+OUdpQ1w0np1azopUmm0yZTDKSeEIapey2e6aTFbdv32W7XXO5PreK\nVGmxc77vfuRFLey6/YtfQoj/GqiA/wT4y8aYcyHEGfA7xphP/nnPPbuVmv/wP34drTTLfEnVNRjX\n2IQgY7kJ55uX1oWXZMxnC4axZxh7mwDleRipGHtJFMXsdgfiZELoRwihQSsWywVNL7lcb/ixz73J\nuz96gMahaDpu3DhlGDr6ruPi4pLT+YKz0yVlvUcEDmGc2mnHICnrmuVkiglcAgNt19F1A8vliqaq\ncIQgjH2LQB8lDi67/R6Ew74qCbyEo/mKxWJOmqacX7wgjEPOz8+ZJvl1s0iTTaeUZcUsTxmHgaKq\nyWY5Tb23d/F+YJ4tGIxhMJqzGzdYnz9nv90wX87wwoBxNCAcQKGUTRESwiUMfcax42i1oioakA7T\nfMqhKJgkExwNfhSybyquLvd85ctf4N1332EYRm7fvk1R7AnDATlaC+8ss76Aw2FLtsyZTqfs1hsr\nNBo6JvNjDvsKr5MI14ArWZ1MOWwKmmakFx2uGxIFMWqQjHrATzw85bCaH1NsGwwuV5srzm6fcWj3\naKERuMTRhKHXJFGKMAZBD66iGQ/ksymy75lGS379V/8E1U/xnZzVasWPHjwjyyfksW83RzlitOLm\njRMC32M2y1kdHaGMBm2oioLz83O67jowyHVYzSecHK+oii23bp7xD//RP+Tf/fl/C9+Bx48fsZrP\n7O95dkrTO0hX8c23v8Wrr3+SIJlw9fIhl+unRD409cirr99mU75A6BDZedw4usGTF+9g3JamcHGE\n5UrcOLtJMplwenzGB+/+iHK3p1c2E+LV1+/xo/ffJZtNSScRRkmKusUol//uv/nut/9M7+//8vp/\nXCkIIVIhRPYvHgN/BXgb+CXgF66/7ReAX/wIr0bbdnRdx6is5t/3A5sIHIV0Q2+pRqFPOkkZRyv6\n6fseKSWuMLiupdwao0nSBNBESQS+ixKC7f5A342EUcTuUKEMuF6AGkb22x11WVFWBVFsbcPDOBKG\nAf04IlwHx3WspTabogR0fYd/fVd1hUN5OFgPh+dRVxVqVCgpGUc7Us2nOfN8RuQHJHGMwXbJg9An\nCHym0wlJmnyomIzihLptCKOYMEpZzFf2bmxAKwXKoakOGDPSdRVlsScIAkAwjgqjbZN2c7WhbVom\nk4xsmqO1wXFdXMdht9naBKIgwPdc8skUlKFtetp2IPQjjk9OuVpfEgQBZVEw9B2TLEGPEjXYY58c\nR6S8PgIOA03TMA62RNfaUFcVrmODaYMgQCtF27QM0sqk1Qi+H9pA2TBiOp2hpWGSZnRdz3yxIIot\n6dn3PYzUFr7aNtRNyWyWImWF44wILJw2TlLqekAqj4vLivW6ZhgNYRgwDBa3N479h/ECxsDp6RlX\nVxuKokIbWG+uqKoKx3Ho+57ddocxhu+//X2GUTJbruiG3krGm4bPf+GLTKc5Tdvymc+8SRzHaK0R\nCFxHEIYp0zzh/QfvM0kS1lcvqJsS3/c4lAfGceDlxQuLJHQMVbvBcXuKcmtzLRyXYRjRGA5VweXl\nOf3YUjUlXafp+p5BFmRZRN/1hL6H6ziEvk/gfvRK4S9yfDgBfk8I8RbwR8CvGGP+KfD3gG8IId4D\nfu7663/FZZHfk+mEoi0JooDQDxh7SVnWDGNHW3d2onB9RBh6q8gzoyR2XYyWeL6P1JLj4xMbr656\nhq5gmiUoKdGqx3cNvRrxIh/jGZrdFd7Y4eqBoak4PTnCBIJ6bMFzGWXPkydP6PvR8hS1pioLHK1t\nFJcTMlsuEZ6D47rWxDQaIi8GBb3sGQHjBgilWcxz/MDHDVw62RFEkeUyZDFSDoz9SBxmluzjGba7\nDW1b0o+W1jyqgd12TZ7mCA+6rkX1ErRA4ZDO5mjlEbkeQnZEvmE5nUA/0OwPJL5HHic42sEoEDiA\nRmjDZn1hO/UuyK5HtwNZ6PPBgx9Q7q9AKV6+eIrnSdRgOOwKlNJcrM9pu5pRjdavUTYYqVGDZhKm\nVPsdZug5OTtlms9wvIB+0LjXDeXVfInRlucojaTvO8ZuZBw1OALtGAZdkcwCetkwmYT4vsANNNHE\nw/UgCEA4I17soF1J17kIvaItZ/zmr3yPxF+xnK1A2FG0HFs8RzDKnqPVAkcYxnHg7iv3WJ2cESfZ\ndUqVpUR/8hOf5ouf/zyH7Z7j1QmvvvoqGkMQhnZ6JUfuv3qX7v+g7s16LUvS87xnxYo1D3vt4exz\nTmZWZlbW1N3VM0WRog0LkO4NAYZ+gvyjfOkbA9aNAcO0KVqUbMmw6G6yOXY3u6sqpzPvec1TRPhi\nHfUVL4oGQZT3XSaQiZ0n94odEd/7Pk/V4NgOV1dXvPrkY1bnl6hREc98NIp4nuB4NvebK5IkIcsW\n2I5L4Evy0xGNzbE4ESU+UhocEeDZM9I44tmT59iuhxP55PUJ4SpsV3P5/AwDuEFE1yiKY4Gq6ylU\nNRgix2cWfv07hf/P0wdjzFfAD/6W398B//zv9Hdpg+cFCFtiGQWPBKQ4SWnaGul4xI5H0zQ0TYPj\nuY8OR4PE0JYNtjuZhYRlY2MmqlJV4jk2VXnCYgqzNF2FGEcco2m6hmdPL5DSwo8jpOdQnI70WqFc\nm7rO8XwPMzQUp5w4nC4+4zic8O3G0NNzOG6Zz+f4XjJp3wR8dXONpUZmYUKazTgcC4Tj0o8DQ36A\nCrBsXNehLEuOh5bEnzFLUrZ3G6LUp29GjkPBfrfl/HLNLFuwPltTBTb3D3dcPDkj9lxGpZHS8Pb9\nm8cdjc/xeEQxEocZ+b4gzxvW6yV5ccLS8PzZE27vbgmjgKpssBtIs4Tj8cigG1aLJa4zxbOfXp5P\nk5R+j43N9bs7BAoNNE1FNl9yOuVkizldO2JbgrJqGMeRcTScnz+hKAq+fPca27EZhp7Q9amahtl6\nRdt1JLMZg1J85/Pv8Gd/9jNoLeJ4BsYgXBs3lBNTwZUcDyV5WYEwtFXFcVDEYciU23YRas1//9/9\n7xit+PannxJHZ0hho9SI67pUdc7zF09xXZ80TQnSOS9fucRJwnq9JkkT6vLEaDrGYSQ6S2mblvX5\nObvdnnGc0ovLRYqwFJ494tiC/eEB+oGXz5/x9Nk5b9+/JssWeMkKLS2acqQ4Vnzy6lN+/vO/xI5b\nhA+W67BcZ9iW4WK2RkiHzf6O88WCOJnhSI9Ba6qqZLk8oyxL5vM5ddWgrKlFnMxdslVC25UEvkMc\nJWglQE0sC9f7+tcE34yYsxBEYYQjHaxH7p30PLphwHamdSvLZniOJIoCfM+ja6bkohAS3wumPr2x\nQBuksHClQFoWcRAihT0BRoepRtuVJVkU4SAoqhxlDJZt/0ZQqoaBqigxSoMCT3o0dcn95o7dYYfj\nOFhm6u8LWzHL4gkIOnQI10G6Ln7k4TgOaJs6r3GkS6/V9EFupwKV0gN1VaGVms60RpPnB8riSJam\npFHM2fKMz7/7fYSQXL97z69++UvGoSfOYsq6wQ8CRjUw9A3zeYrjCBzbno4JliTPc4Qj2WzukNJh\nMV/iui6zWUIYeZRVjus5VNWA50cgHKqmZb8/oLSmrMopnOV5/PZv/aPpQy596naaKjRti9aaJEkQ\nwiaNEywsXn30MUEYMSqN6/k0XY8TuAxaIRzJIc8Joghsm1FphnES4DxsNvhByCkvyMuKeJbR9R1V\nXYMFTdfghh5xnOEKnyTO0Gr4jYj35urIX//smsDJ+PEPfgczWvTtdAxN02kHVpyOXF5eorVCmVi0\nrQAAIABJREFUacPhVLBYrUmzOfYjZHYcNbc3N4xDR9u2E8CnromikPv7e/a7A1ob8lNB341sNltA\ncHl5Qdc3dHXNajmfTOBtTdcNgI0eDE3RkMYxqivRQ8t2/4BwbCxHYruTY9R1HTzPxRhN27ckSUyc\nTPkcR7p07UDTjizPLslWl2SLiCCQNG1JnMbEcczhcKLrKtxAIr3gaz+P34hFwehpGxaEEU8vLwld\nn/ubOywLZvEM17Jp8wJPOgxtRxZFWEoxi0LGtkePYqIGCw9pBMfdPbPYI/J8At/HlfbEBdSTtDaM\nAsqy4OOPPmS1XmNJGzfwJ0sSNmVe0rcdQ9MzNAOBO423gshHuBaHw4koCJmFCdvdjs12g3QcHHfa\nCWgzYluKy4sVH330Mf/qX/23vPr41bRNFRa2FASBh21NP35XSOZphpAGL5Qsz1P6MWfUBdv9Lcd8\nT9sWXCxXfPjBiynIFUZI1+err77keNyx3z9w2h9oipqH+w1xFOG6Hl2rePP6ijQ746c//QlVXVDX\nFe/evZ2mIa5D19dsD1uKctouL5drRmVx/3AgnWdst3uKIufL139D2xQ4to3r+8zmC4QjCYLgESaT\n0vctRXniD//tH5ItZsSzGC9wWK4XjKYFMTKqjnmWEUQhd7c303jXEsRhxNvXb8EYnjx9ShxH3Nze\n0w8ttm1Tty1KKw6ne+bZnIvzl+hGgvIpK8X+OHL9hcf/9K9/QnE44liaNAoo8pZZtqBte+q6Ik1m\n3N/fEwQhbddyefmEU1liOy7jOLDfTwHc88WKpqop8iPZfI6QNq7n8OyDp9RVye31NXrQlHnN5n7H\nL3/xK+5u7zjstpRVTlEUzOcLhCXoB8XxULJanqF0x8XlnGZ3YB6EJEnCoBRhmnC9uZtq/mhuH26R\nrkC6gqI6sj9uMFpTFSXHzZGrqz2r9YdcPv+UU3GiGxocX9Crjm7skK5HGCQY4WCHX5/c+o1YFCwL\nxmFE2pK6rLCUYb1c4doS3/OIgxBHSgQGx7IQBi7Xa8yoCDwX25ZsH3aEfohAMPQdtphKQI4zVZKT\nNOF4OGAZqPoO23cpm5rRKNqhx/XcxyKQz8XFOVmW4bguruMxDOP0gR8mgasxhtPxhB41Uvg4doDR\ngqZpp0bd0BH7LkkU8bDb8JOf/ZT31+847Hb0bUOaxhz2O+I4fCQMRZRlRdsPuH5APEtxPBvpCMLI\npW5yivI/q90kajR0/YjrStJZitZg2y4YgW05eG5AVXUcdkfSdM6zZy+I45gnT885HnfcP9zRNA1F\nUZGfCrquRbqKujuiTcugGoah4fbu6jeXcGEY4bqCxTIhSUNc36fvB6SYCEVD36OUoqpKxrHn8sma\nuq2wXYHSI3mxJ0194sjDsS26pqLOc2bJBNhN4xijJpmNsASL+RzbsRHCnijUo3q81HxU6NWTHr5t\nWiw82trm3ds9dzcn0ihjsYgRVk/fV5yOBZvtgVEZVqtzhkFN39yGiZnZdwx9R1nk9H1HW5cYPeL7\nPnocub295eHhjrpu+NZ3vsWnn37M7/7u7zD2AzfX1/h+wPFwQilF3w0IyyY/5uR5xdBPQF3VjwSe\nz+3dNcYaGUxNnM7YH3P6bqRtGsTUUMOVgigOOR2PHI7HR/Xe9EUmpY3vTo4KrQ1N01LWDV07Utcd\nUZxOQJhxIAhCTseC+/stxrK+9vP4jShEWZaFlPY0grQsbKYjhee46GFgaDtsCY5tM7QtiyTDuBOY\ntG07wmCK695cXRPHEdK2aeqaJF3/RlVm2/ajW9DFCX3qqkIV+eRoUIqH+830HoSN67kMnUD1LVEQ\ncXN/hxs6k1dAa5I0wfSKYRg5Xz+F/2xyHjWu6yKsEVcKjscD7653HJqCfuwZ64Isy3j79g1+NHUe\n7u8eiJMAWzqc8hY11qRJTFtUDGOP7UA6C7D0lE6sqpKmb6bdjbQIgoCqqjDawncDjLGIoxRMj1E1\nYRBi2y5h6HN/t8MS0zHqeBwZ1IgBwtCdlOVGglL0g6EfWnzf4fb2Ftf3KKoSNVQMo8fpdMJLljiP\n05u6qgj8CXRijCbLMmxboPREMBbSwvdd+q4mdCMGy0xNT9WTxhGB9CYfZNvhOw6RP4ljlVFEUYim\nROspZOQFAf2+Q6PwHI0fSppKcHN3JIoumc06Li465oup5XhzfU3ddBR5zXKxoqpyyqKm6UbyU4HS\nI/6j9RqjGboGW0B+OoLqp67DoHHdiXbtSMm3v/1tLMvi/Zuv8D1J37esz9f0Y/sbCVDf1o9gW4fV\nIuNhfyKOIu4ertFORzNsCf2AvKpQbY8rbY67PYnvTReEakQbQ1EUxHGMsCx8P2CxmDMMA1mWcX27\n5X5zjxenLOZn2K6FHge0hjSNKavJso4tWC7+1gzh3/r6RuwUpiy+oGkbqqZCepK7m2uGpkEPAxhF\n3VRgabSAd+/e0DQ1aZIwX84o25yqLvjgg+cMg8JCAhZ3mwf6cZK52o5DOyiatievc2ZZRlM3pPMF\nSTYh1MaxY3E2Q1kDjieI44i6aTg7O6dRI9Lx8aWPHhQCwf3dhkV8hqUk0jj4MkAYCyEEx6KkHzXL\nswVR4uG6sEhnSATnqzVZknE8HAHYbDZYtmQ+O6euB47Hmn4wKO3QdyOusFit5iTzFOEKFB2OI9Cm\nZ7vZsZifsbnfo7TGdeVEbVIWTdlRFzmbh2varuDly5dI6bBcrB6r1RGeO0FbHCdkPj8DJErZZPMz\nPvjgQ9ZnF4w9RGGCxqKsWozlURc9gZ/w6sNPcOV08bs7HAijmFNZYLtT56HrW06HDb5r0TYFeuwx\n44gZR+IgfixRBai+wbWh2O8YmgppG2yp6IeKvi/JZhnSdjBKEQZzbMdQD0fiNOLudsA2F5g+Yz73\nGMecodf8h//zZ8xm5yjgq6+uuLl5oDiVDH1HU5WTkFZrLAuur66oyoIyzznu93zw7BnL9Zrrmxuu\n3r2hKQtU33L5ZM1imeG7kn/+z/4pH796iefaJEnA7/3OPybf77i9eo/rOqzXTwnjBU3bk6YZ67Mz\nLBSn056mqfAti1AIfGOxjFLK4wkZeDRdw3a75fzyCWEy4/rugf2poB81bddwqgpmWcpnn77CFYab\nt18Q+iF6tChOHcvFJUZPtKhnz5+TJhl6/IcZSf69vbRW0zeDHhnUgDKGZJY8JuM0fhDQ9h1eEGAs\ngx+HtI/Fnaqu0NZIXpaTk8F2iOMZnhdMmPKuJc9LomQKPSkFy9kCPY4s53MMoLTC9XySWUzb1wSh\nRzd0j2wGRT9Oi0kcJcRhTF1VwIRzN2pkaFuqoiQ/5hNLQRsQEo3AERLbMH0DJjM812M5X+B7Hkkc\nUzU1tiPphx6tO5q6ADOi1UgaJ4R+iLQlVVmjjcYPPJIkohsq+r6fqEpuQJZlrFYZmg7HGTFWy2wW\nIG0boy1s4fLwsEMrKMuW2WxOGIRIxyZNZygl6FuN64SEQUzoB9RVw2azIU1n5HmJ44RYwmUYBIts\nyWp+Rl02eO4Eck2SBD8IiaMZwzBiDEjbochztJpYFqNSCEuiFNR1R9cO1HVF17VUVYElpgyIMYpx\nbBEwLYB6fGR5TuBSLIPrBQzKZrOpyGYrpG3xcP+A7zsURckPfvhDnj57jhD2Y9agxXEkl5fnKDWQ\nZel0EVhUKK3ZH6atetO0SNfDGIt0NicOQ46HA1qNnPIjlmV4//4tvu/yve99ThyFzJIYpVo8X7LI\nZtORzHGnnWfgo9BYtgUaGDVdPXB5vmSRxsS+z4+//31mcYLtuEhH4vsTSt6WDgaB0RYC8ZsIed3W\n+J6N51gY1ZKmMUkcE0UJh/2JOEywpcRxQ3wv5M2bX3/t5/EbsShgDMIyk/7MdRjNSJQkGGuKLwsp\nsF1J27VY0sZ2HBzPnc7idYWQNsvVirJuqKqaumnQxsKWEu8x/NT1PVK6jw1Gge4GbA19NxCEMVXV\nUtYlSg8U1QlLwKgHjIEoSRC2RD6iuA67HcfjkSxbMPYdWo+0dUmWzTHKcDoUWE5AXtVkcYonJMIY\nBqUxWAhhTxi2QeE4zoRcswWeo1lkAb4HbVMSBC51WWHURHguqoK2b/BCh8C1CX2fLM1omob1+oy6\nzvF9AWbAcyS+7yKlIAhcTsctUnrYwkNYkq6b4tgwofDCIAZjWJ0tmGcJWCNdX3H/cAOWRVHUpOmc\nbLHm7OwZWsH2YUcUhCilUEph2xPpqWk6XMcjiiLqusaTHjc312itHwNdI0ZD105b3f1hxzD0jGqg\nHdoJB+fagJo6EhiOx+Ojc9TBc11c12UYLP7tH/0xwoqIk4i2LSiLCbiz3++JkoiiKphlMdtDSV3X\nfPjhcyzL8OLlByg14EcR1ze3jNqw2x9p+h5lNO/ev6csK549e0YQBAgstpuHR3Sey/nF+cRgsAVP\nnlxi24Kmmv7PojiY3BPWhNyzPZdu7PGDgH/xX/8LzpYXRF7CodqzzzcIDG1RskozPCHp2o6maRlH\njW1L4mi6N7IsC89z8DwH13PYH3ZUVU6WxtTVpC+0bYe6aOjbkSAIKeoTwtL47j9ATuHv82WMBbZD\n1bZYOPSDTZWfpptTo+maDgeHwI05HHLwFUHicqhyvDBCKY1t2YzjBJToh2bK4euGvqrJgghfSvxF\nwFdffYWQI4Gb0g4wD2MWi4z376+wnYiH2wcWTz3a0mA7Gaf2mt3rE0+fXuIg8UOXs9UZtiWQtqbV\nNWHqs7KW3G/u2Gw2PH15QdcW1G3FsYmxLcH+4Ugc9QjXZfNmz8XqjKZvOH+Wccj3BEFA7AhmfkTZ\n1CyePGd7v+dsvcS2Ne2uIU2TifxEzFfv3vHxc4dnL8/ZbPfsdwfm0QpPSpqmZEBjeTbVUCM8qIuc\nWZJxvl6ThDFfffklq/Waq5srln6EqSfaUxyGtKqjGwdmi5QwDDiWB0QguT+dAAs1GtLQoy4L+u0J\n33Hx/YhutNAYLEfTdDVhF+LZAZ2xif0FseczdJpRmcnr6Qg2pz1p6jCi0XpkkZ4xS2bcvruiKI9k\niyVaCPzYxvZG6lYRhZJ3b0b+/b/7OeXJ4vzyQFHmzOZLBvUlRdXz49/+EaNqWK0WfPTqFZZlc32z\nYXusAIt0PmexvqAsa7p24Pb2lvV6zW53wOiRw36PbRmkLeiGkRcvP6StS7748q+5e/+eZ89ecDge\nwPRcPP2QOF4Q+oY4/i4P23ti4KvXb3GdkD/7y1/gRSl/9O/+BCeZyEoX8ye0qkeGEcIO+NnffMH9\n5oYoneP7KZZqcQdJHLr4sY10M4TWMJZoJEJ4XHxwiRoMV1df0VkarSzGweHubsMwjKzWC4bBTNOW\nzcPXfh6/GTuFR98DAMbQtQ1+4OE4LmM/IoxA9SO+4xMFIX0/EgQBSimGcQqkDI/fQOPYo7TC8Tw8\nP6DrR5qmxWjNMHSkSUTb9RjLmtyEXUdxmhBoUkhCPyLwPGZphi2cx1ixxrMlehgYhwHpOtR9i8FQ\nViW2LciyGW3bEEYRgR9OOQZjUVYlRVkSzZLJ8di1aAN11WBpw9i1NEWBHhWu5+F5Hs4jGHZ5NsdY\nhn4cSR7hoNJ22e8ORGFM3XTc32+IopizszXScSYE3TDRimxbEMYeYeiRZUv6ruN4PHJ3fwcalNL4\nfsh+d8Dzp7bpOPZUVUng+RNCvu/wQ59Rj8zmc+bzOfN5RhgFRMnkmxjHydANFsfjnigKUEPLODYM\nQ4sjLbIsIQ4T1usLXjx/STJL8QMfYQvarmcYepTWFEX+GI/WpNmEEqvb7tEQXuNHEY7r8fqLO8zo\n8/FH3+HVy1eMSlEUFVVVU1UV6tHQ3Pcjdd0gbYe27clPJcZYk1l8VLx89YqnT59gWRZvX7/m9Vev\nqeuGh4cNvu9zfX1DWZbc39/Rdz1N0+I4Pn/yJ39K13WPMpaWJ88/4O3VNd2ocIMYx4soyxbbdhl7\nzU9/+qc0TU0/1mx2dwhhIW0HraFuGraHPcoY8jznsD/RNQOffvQpSZTQ1g3DYyZkqDqsZiRxA+62\ne4o8x7dcpO1PUedm5NmTl9jCZegUnnBo8hL5d6g4fSN2Clorbq5uSLKUUY0ssznlKceSDq7j0/cj\n83TO6XDCcX0C1+W0O6C7jiRJ2R32uCKm61r6vpnuGIoS23HIsgzXddjv97i+pO1y9scSNdrYtkPo\nOpRFj+85qFazyM5oyh19U+PbksuzC1zXZ2ynM3wcR2zblqbrkG5L4LvcXL0nTRI+uDzHIFBqxNMB\n89USjeHq5go/8GiagkGBF8RUZUvgSYa6ZiZdRN9xfXPPLE7x0pjD/ojvSRrVgXHw3JBRQVsXxH6I\nKnOE5fFXf/4Lfvjj32Kz2RBHkqo7YuRkmJrHPod8S1eNzIIZm82W2WJG6PjgGU77I+kswfZcmnak\n7ypsoVFqGtG1zUDTdmhrclzmxxNl0bBenZHXNaHvIaSDdke22w1+nHF7fcMnH72krHeUdk4QRgxj\nDcDh1LJcuPTdyPG0x3at6Q7HOPRqYJ5l7LdH+vuG2SJBWQLVWlTNgLEVszTmVFT4Ysbb10c+fPl9\nXr++Zr0+Y+gMwvbY7XY8e/qcebLm008+wzIKoxyU+hVGW1xd3fJP/+nvcf5kzcP9lsN2x8XFBf/y\nX/43/OEf/BvCMOTq6pqXz5/zxz/9E1588IyHu2sEhrrM8Z2Im6t70mjBn//5X+I4k1/k8+98l5ef\nfgfpOzAW+KHPfOnz+//bH/GwOVD3NZ8mIS+fzui6AcVIaEsW2QqBJolCEB6H44bl4gJLCd68fsP9\n7paLF5cTIOeQk7YS3yrIkhTtSZoy50mUsVMjs3RF10y06yRNcKShO+x59vQZDzv1tZ/Hb8hOAZqm\nw7Js7Mf0YRD4uI6L/RhnPp1OVGVJ2zQ4tosaxwljpjRS2iil8AN/MkzFMZ7rIW3J7mFD37RUVYVW\nmrbtWJ+vka7N6XScCjZmKjJprVHDSJ03eNIFrXGlgx5HlNbox/cxm2Wkacr67AzPc2nbhtPxSF3V\n2AI8KUFPmf7ddkMShbRVyTzJsK0JbjoOCtWPJH5I5E0BqyiKGUfF/d2GUcPxeMK2BTB9syFs/DBG\njZrwEQP30atX6H6YSj5aMxiNEziMRlNVLao3VGXD5n6D57roYUQwuREny9bkuLAsgReGRGlMr0YG\npZDOtHOJooj5fI4QNp9//jlZltE2PZvNnjwv8f2A2SwFDOdnZwR+SBjGCCE5nXKaXjE+GqZ+/otf\n0HYt2SKdKu9qII5TPM9nHKc7FiltlBkR1kStUkphjOB0zDEaNtuKKJxzc31HW7ccDgcOhyPCEiyX\nCw6HA2VZcXd3R1XXpLP0UQir2e12fPHlF9zc3BBFIc+ePZtq3kqzWC7I5nM++OADzi8uOD8/J0lT\nLi4vORynnEjTNHiujxAWtzd3vH17hVbwy1/+isO+oO8Nw2goiobbu3tW6zWe6/HJJ5+wWp1RlAWn\nU47nOo8TtBmWZfB8hziKCHwPKS2quprw+a5ks7lnGKapjYWkN4pmaDht7gl8d7p0dQV1ndO0BcIe\nUbpF646hbynzE77nfu1n8RuxKFhikp/ajkeSzGiqGsd22G+3VGWBeHyXrnQY+p4kjid+guNSPLbY\n6rpkVIowDPA8b9o6Ow6q7zntDwhLcMpzPC8gTVMsCyxhqKpy+rXWv/lAhn6IYzv4rgRjUKpHOBbJ\nLMUYg42YMGVYOLZNFATYwqKtK6QlpkacK9FmGqcOXQdK4wmJYwnu3l9R5AVFXrLMFjx/9hyjDJ7r\nUhU1Y2+QwqFrB8Z+eLzMVJRVTRDE5GVNlER4niQKA06nI1kyI0oTpOfhBFMwSwsbhIfrT/o113EB\nwe3dPaeyJIwisCw8P0A4DkVV0o0jfhSiLKj6liAKWa3PqJuavu85W50RRTF93wNTrkRpjet7dF3L\nfL6c0mi2RDguh6LC9WPG0aIZOqQjp5v+3R5LCBzXwbIltu3geRM/w3UnCIsrHeQjHXroDEWe09WK\n//Qf/xxt5CPGbsBxp5+D0YoXL56hjeLh4YEomgS8fdcSxxFtN7Vv0zRlu93StDVFkbNYLLi7veXJ\n5RO6riVJU4SUPHn2AQZr8mdoTV6Uj34Ng+PazOcLpPTw3IBf/c0XfPnVO5qmpygrLEuwXC24WK94\n8eELPvvWt/jd3/0v8NwIx/HYHw7oUTF2iuPxAMbgSgffczkcd2g9NU/jKCQMA9q2RuuRvC7opWFX\nHGmOB5IgpBxq+r6mKg+crRJmqYtlDQSBJFvN6VVPksZf+3n8RhwfDBbnT54xDIbDfsvTxRzLCDzH\nZ76csTtssAScTifmyyW2AGlZWFj4nkerBsIwJktnlNUJY40sFwt0nvNkPidvGjzHJ3+sO19fT2Co\n1WrB7btrbCEI/KmluL2/5aNPPqY8Hiekdj/iRh5l2yPUSBRF+LaLUCnSWJTNdAEosBiHqR7cq4pO\nn7g/dKzXGe/fXtEcC7xLi4/WT7FHqFuDsDR/8+tf044dwSylKe6RwmW9Puf+YU9barpuw3q5oK06\nhJfxq9dvkAheX1+RZRH7/QELG1MKNl/tmS0ygsjFdVweDlvy44F5Np/M3VLTVi2u7yFdlw5N6AdU\nXc0oJFo6XG+2eJ6NUCN11dEOA8qysGzB5cUlP//FX3B3e0+aRLx/947PPv0EIyZzs1tOYZnN4YGq\nbVkHMdnyEm1cjlVO6AZ4vuThYcvl83MGMzJsFV0/IuzJsr252+BmCXEU0TY1ajRksxDLxBg18n//\n+7/iuAv49rdeYJThz/78Z3z+7c/wPJc8z3E+ek4UxZRVjoVmlk1ZibNVyrc++5jN7o7b+1u6piDw\nXNbrJ9xcXyEdm08++YQg8Lm7u6csromTmK5r+Ozb30EPA7uHex5u3uEHEmFr9vs9SsHv/y9/xGKZ\n8fv/8x/wb/7gfyWOQ378o+/z4sUHnF/O+Cf/5T/BDVKiZMkvX/8HQs9D6xZhKe7u7pnNs0cGZEGa\nJijL8PL5h3R5RdnkeInP9rDDeIaGAmeRUFUlLy5XHHcHLNtwOhXESYSmox87bm7esvjed7Bsn4fb\ne5zuH4689PfyEsIiy2bTscH3qOuWsqgx5pGqpEYsMRmUd7steVWiMCijkY98grosefHi+aRmtx9x\n3uMIlsVysXysuAoC18O2bWwL+r7Fth3UCO0w8vDwgONJiqLEApQamM1mj8QkSd8PVGXzG5qNhcay\nLNJ0Btb0jbLZbUnTGcKVIKDuW548vWR9fk5Z1hSnAluIyeQ0TnzAKE7RBjAGrRV5cSIIJiWeY7vT\nkUNKuq7GsqDpWqI0QQiB77vk5YlsnpKfTji2y83NA2XdgGPRDRVCGhQgpCCexcRpirbAsm3KqqZr\nOw77Lbv9nijOOB4rqnbSkxljGLXGdiVNV2MJTRwHzLKE5XpJrwbyoqLtBrSxGFRHEPrM5jEIjeMJ\nFMMkvhkmocqgDMOgp7FlnND2PU3TsNsfSeIUsJG2g1JmIjMNI45tEXkJ9zcdz5685P7+nvvNPVmW\nAdZj1sRBei4vP3zO59/5FmEYMCpNUZbUdYXnOWTZnKdPnwI2Nze37HY7kiTB8xyOxyM/+elPubm5\nmczankdZlhggnc2IZzMWqyVtWzOfz1guV/hewHK5RFiSi/M1l+cXXKzPWa/XE0LPaGxpTaN1IdGj\nJg4m9H0QxCRJimVLlmfnXFycT92c0KduSoZxpOt6xmFk7AbCMMKLPGzLwpM+RVXhByGLxYL8WOA7\nAV3dM3SKD1+84rDLMUIQpSl5WX7t5/EbsVMY+n6iDEsxoa29mL7r8KTH8XhCOi5oRRgHCNvGEtMO\nte97gjChrEukdPji17/Ccx20BcKaNOH9qGBUk78wirCFBcbg+z6OnLaATdsTyxBhC8ZxwEIwDCNg\nqOsa6Qk826dRLZYRjGpE2BZNP1mSRxXguh6DKhjGkappUBoGPTD0I0EacfH0CTSKuq6nTkUSoVVD\nFEXYQcT1wz2+FzyGbBpc3+U73/uc3eb9RGLua+pmRJkJrdWrHmmm+5AoDlAM+L7L2PdgLLqhJwgi\n0iwByxAlEwpeodkddoRxPNGF7cnzKG2wLUl5qnBsDylsxmEkjGKMNYWO8uKEtCyWZ9kU5w28aToy\nKIqyJogi9GgRhD5lU1JVLVGUcDweSJMpXHQ2P5suj7sBx5v6GvPlkvvbK3zH43x1zm6/RSuBlFP4\np+46lJnyJLN0jWW5CDFdnEVRxPZhw/nlGtu2sSxBkiQcD1vevHlDOsuwbYnSI5YFZVmxebCwbUnT\ndMxmM7bbDUWRkyQnZmnK/f0D11dXCCF49uw5d3cbnv3oR7iuS+RILi7OKYojw9DjOJI0jbGE5kc/\n/DHSgfOLNU+fPuHNm6+I4gjpOnS9omtrmroCBmZphuN5FMVh8jscCuJwhut4HOsTw6jwtIcxguPu\nRNuMuGKgGwZ8BGZQSBnQti1B4EyUMSYLuy1sAj+g70aqssZow3az/drP4zdiUbBtm8i3kK5LXXZU\njUZoQd/0WI5NnMZ4wYR7HLWeRkxSEEQeVVkhbUkcxEjbZhw7pKNxHZtjbVF1HVKBMQo9dhjV4YUz\nzlYLttsN88UlY6+5u79jsVzS9jmW5eEHgvy0AUtgKcMscJEazi7O+fLtl6SzGOSUR7++vmY+n4Nl\n4fgeu9OJtrOo24blxZK74540SlnOU559cMlmu2GwNdkyZne/oeo1z1++wlIdTVOhHdjsrtju33N5\n8ZT9ZkevJoy6dD0sIbCl5u6LBz77/CMG3XGqjlxeLGmbivOzOdLz2N7vWM1T2roinS+Iw4CyrJnN\n0mkq0PUUeU7k+by7fos2IUE6436357uff4Zt2xhb0g4tnRq5XK/Y3N+RWD7dODBfprx/f43vhKzT\nGdfvbon8hN1uj+1CHMcU+YZFFlBUBYfjSFcP3N9s+L3/6nf48vVX+GGMEZMhbLvbs0zxlB/SAAAg\nAElEQVRWnK8veX31muxsRdcNSDekHjecTjCff8L17Xtms4RPPv6YeTonjiIsS5Cf9kThjPP1Bb/8\nxV9g2w7bzVsuLi757LNvURYNb97eYFKbWbrk5uaG3cOGYZhyGUopLCGYz+ecr895/+4tnudhCYv/\n4X/813zv88/53qefYjHQtAWff/sHHPZHXNfibL0giEK6vkcIydn5E4QMp4dVWBjT8ubtr8gySV1X\n2KQ83O2R3sBx1zH0A4fjHjMqRmlwnB5TnYijmBGLQCboUQAe1+9uOFss2N1VeOcSx/b5wfe/y+3t\nhKnf7/dUxUDftsTuxGNYZWfAl1/refxGHB8swBiNpUeSICSZxTieRDMSBz5tXWEJQT8O9EPLarWm\nrnuapkcIw36/I5kFGDNOuf8JgAXAqHrqriR+nIs7/jSdGAfDqAW9GhGOTRSGUyNQm0fGwYjr+o8R\nW43/mFTLT3uK04niVHA65oSRj+NOnL/5akGYhBgBrh9hWTaWUtjCRmNQpmOzu5uOP0rRDQPRLJ3i\nsMZMi2LfI6WDdCXPPniKMiNGGtzAwQ9tXE8wjg1CWMzP5ggb/MhH+lPKbdA9++OeuhvosWiNhXE8\nqqZBC4Pju2gzUpY5g+oZzMipKnE9nydPnnA8HknSlLwq2ex3WEbRVB2q0xx2G4xR1E1LXhXU3YAt\nXbBgt93j+QH3uy3704E4lHT1Ht81KHqkYyFdC2NrZquIu+srFlnC2PX0XUuYRKyfPqFsa7zAJ0li\nyvwEWlBXLcJE/PmffjVdNnYtn3/++SR5jSNsKRAWXF9fkZ9y8iInihJ22x13d3eTuu7LN/zVX/+C\noiw4W58xDD3W45FvtVr9pgl6Op14+vQpwziyPFtzdX2NIx1+9IMfcnN9NdXipcPd/QOOK6nbAmMU\ncRKRzdJH/0fI1fu3jH1DUR548+YNdd3z8Wef0nQ9ru9xe3fH/nSk7QeauibwfLqmB1sQBDHjqDmV\nBbvyxGAMfhyRZAmd6pC+ABts32UwMBo4NRVVV3O3eSDPS0BilKQ89TStZvg75BS+EYuCLW2k4yKM\nwAaGUWG7DmEUIEyPa0PdthigHztsIVGjRdsNdGNH0xYoOkbVkc3TCXWNwHU9osjDtqfRV5AkjNb0\njy7LhtOxoulqBtXS9TV5nhNHEbu7WxxboJVB02PbkrptJhza0HBxtmIWJYgRHE8yKMWgFPvjHuna\nBFGIdEPWq3NU03C2WOBIydDX9ENLP4z0bY9wXLS0mS+XVHnJoA3CdbEsGylsurbH9R3iLMCPXFzf\nxhIj0gZpSYJZRNM1HKuCUUAQBQSzEOG5uGFIOM84DR3acegNdErRdA2jHqfFxjbsywOnrsaLIhxf\nEqUhSRZjEERxSp4fCZwQB5eqODG0LXXZMIwD290RKaepgxCC29t7ukFRNFOPIc/3DEM7iXa6DscV\nOB44PhyO97iORZZE1EWO7TgMlkEJzc39DUiBY08gG9uSRN4FN28rwsCh71q2mx2r1Yq8zKmagiI/\n0Lcth/2Wr958Rd/22JZNHMf8za9/zRdfvEE6Lh998go/CJjNZ1OYrGkQQk7His12Sqva058Lk4Qo\njjlbLgGN77n8x//r/+Dm7oaibKibhn6Y+Bz73YGrqxvOz86IQg/BiFINu80NWRpz8eQ5CEkYzyir\nGul6BGHIqC3SJEYYjTD2RAzfHxl6TZDEGBu8OGC+zOjGliD1cCOHoi04f37J/OICJSXv7u6nXIe2\n0JbDMFq4IsL3MjA+o/7/WXVaKcXDbk/khTw9u+TTb3+XN2/f4dqCKJD8xc9/gVITMOP87IztdkcY\nhmTzlFE3k8NBSIwlUKOFH8YcixLfC1jMl5hDztArPDdgNluiNYRhwOXlGVIqivKOs/MUx3UpiwOz\nR+eiUlM2PwhDimZAjRV+4NOOLauLC2ToU5YdL559wvNnr/j5L7+gLmpsT4KeDFa26Xj91Wt+8MN/\nhOoUt3fvWa2XaCy2uwfiICYJQsax5ng64Efh5LHsB2ZRjOtIbKVRwiH2PLabHS5wvHngt3/393h3\nf0NRHjG24CbfcyortBa4fkHTdfjBlOnwbIe6bjmdcoSZciCO6xBHMU3XsF4tOB5ypGVwLYPnWBTH\nA8PQYpuei8sLdsecw+FA4CYcHk4k4RLdagJbwFiThhClGVdXFZ7nIe2Moig43N7w/MPnzOfn7PMD\nYoR27BnHAYWma3ps18OyLPwg5uH2jnSe4vjR44hwxi9/8YAfzMjLirbrcVyHzeaB+WLGcpby/v1b\nosBnuVixP+0Z+54wCFG6IVtk/HK354//+Cd8/4ffJQxDDtt7xnEqRR0OW/7xb/8Wo1LUdYt0bOI4\n4eLyKe/fvUUIwWKx4PJizXF7x36/x7ZtdtsHZmnMn/7k/yGKIk7lifV6xXc+//ZjXV8yX8TM5xlG\nKx42D2y39zy9PJ8WqzfvsWyLWRJjaYu67QjSiCgIiGTAfr9nnx9xjOFnf/mnrBYrkmiOdrupZu5L\ndscNQehTdw1+Osdow+pyjTACWofToUZIeyqRfc3XN2JRGIYRzw3ZHU6UhxzPn6G0QboO17e3ICxc\nZyoR6VFhxpEwDNluNqzOZ2gMZd1MHgajp1YZFuMw4HgBaZJQVi1t09L1DeksQukBjKZt2se6s0Qr\nAGdi77sOme/TjR1Kt9RDxcVqzTAMSC+kGy0cL8HoPZZtc3W14Wf/6R6lbBYXCelCYA0amBagMHK5\nO+4IwpBe9VjCwQC2LRm1mT6QbU0QT5wFIQQPmwdePn9BXbcs5nOkhCxbMPQDerS4u7mlNwOzOJmO\nAHFM5gZYxsKRksB3aep6Gt2Gks1mR5qm6KGfBDtaobWaEqR5QRxFjH2LUYq26tFDTxKGNOXI9v4O\nIxRxGOG7Lq7lUp1qslmC5ygcYTGfRURJxOLzb1HWe+qux/EDEj3iOy51UyGE4Gx9xkM7UjUVtuOQ\nJAlKgx4MIna4uLigaitc10dbA/Nsyft3v0YZCyEFz549RT8GzlzpcNhtydIZ71+/4eHhgcUy49g2\n9HrkbH0G0qUoK5ZnS169esXt7S1BELBcLpDS5oPnz6iaGhC0bY0QUJYFX/zq1zRNhSct8J1Jo/d4\n5HBsidGTPuzliw/o+4HnL57gujarxWwC/HiTNOaLL18zXyt2xZYoDGjqitN+x+l4YLGcczoVpHGK\nJW2ubq8ItSRaP8ExhlkQooaBMAwngrnlst8+kM1m3N3dPuZDGgDKoiIMPJq+wrFdjtsDYzuwWC4w\nw9dX0X8jFgXXdRm6gdVyje/YvH77huVyydXVOzzfMJsl9Lqh7drJ2OR5VI/swOMpJ5n9v9S9WYxt\n2X2f9609z2c+Nd2xum9P7IFNdpM0I9GRrMGQAihPQQwEUawEeUiQ5wSBDTOAFRh+i4EEiQAZkYDE\ngGwgkSzLiC0JigaSEptDN3u6Q9+hplN15rPneeVhlwgmQcS2YwfMfqlTG+eeOlX3rLX3Wv/f//s8\n6qzBNDoXYqM06LaGaCCONrR0DMH5PCPcFTjODeIwxrWcrlHK16hlQytbmlbHcB0qbISuYeg+pycL\ndruAd772iH5vwHqdoxklilCxDYP55SfoioOu6+zCNX/0Jx8gW4XD/TGf/cKQ43t3OTk5oyhroijE\ntk2iqBusR1OXqmi7TlBLZ7XbYGRJV4Y0Td55710O7t5mmSXcunmL09Vjot0WIRuMPEUfuESbNZro\n0O66ohKut2zrkqO9AwLDwNB1ZNVCIxgPxsxm5zR13XWlCoWzszNkW9PrVfSCgDgMsVQNy7V49vQZ\nN4+Ou4ajvsN6u6FMU1558RXWVxGDYUBeLDk7PefgcJ8ii1ht1ghLA03Hchwcz2SxnDMY7yGkJAoj\nDm8cUDUNT54+4Wj/Dkma4XguWZbgew6HwyMePnyEHyiUmcrlaQjS4zK+xPZsfM9jOpkwn19yYzri\n/v2PSaKuWejGjVfZLhcEfsDpbEZv2NGO9/cPaJqG1WLBX/nJv0xdd92t52cznnvhRYbDIe+//wFp\nmjMeeXiOiz4Z8eTJI2RbMRj0cB2T/nBAz/Ho+RqDnkuRZcwuzrlzdEBZF5w+e0Zd1+wf3OTxsxPe\n/NyPMdy7hTmHkzOTeLelyFMCS6PJE4pSoqGiCRXbNinClCenz9AbiWnbFEnKZDImSZLOY+H67LZb\nZKuwXK4wrrthkzCkrQyqOkZVVXqDA66enZJnAkX7/9mkINuWUa9P3TbEaUIv6DFfXlHX5XWKsEbX\nNHa7Etl01QooO6dAlGC7FvP5JQd7h12IJcuQAjSpoaoKpqEThmtcZw/X3md2KskzaPsaR7fGNESo\nqqTKdYRw2YU1aaKhqhqLq5DvfOsTolC/LrE12FaAotXUbYVlSGyz313pCg1NGyCUSyajGwR9H6Eq\nbMItbq+ilBGKJtA1E1Pvbuc26zXDwZSnT54ifUm/N2S9XnMw3cNQdbTtFkXTWC5WxMl9ptMx9x/c\n5/bNQ0zPYra6grxCUwWapjF0A6o4Rdc12rbzXBRphqZ2k8N2u2XYH+B7dtdEVpbIVnJ1tcBxLeJU\ndGTttiHwHSzDxHUsPNfG9kziOKFuavI8pqZiu91g2aCbNopqIDSB49pksiIvSzy9B21F0OvRNjUS\nges4nXmrLtAMlTSNaaqa9SqlPxiQ5hmeYxO4Lp6jsl1kjPojLs8Tjm7s0xv26QcBq9UKx7SYzWb0\ngx6Rv+PO88+xXC45Pj7m4/sP8X2fXRSiajpFXnBxekbbtlRVRVVVPHf8XAeQaST3P37AcDAiy1LS\nJMWxbC4vL3nl5ZeJoi1ZntC2ksloyPziElkbhNslx3fvIqXk0YNPcDwbw9KJooT9g85NmmWdn+Tx\n46ekSYFtuzz4+D7TvQFCV+n1pyRxBKrANS0qrSBKIu5MDlFQ8MdDrjYrLMthOO6xXi85Ojjk4qIr\n3Spo9AMXUQl0TYGmoT8YILMK1w+QUmDonx7c+qMxKUiJaFvSOESq0NKQZQmOY5EXGU1Z0ShdUGi+\nXGDoFlVVYloWpmlRlSXiOujUyTe61yyKAlWR1E2LqpqUhcLF6Zw/+sNHiFZy68YBi6sAoTWgNKDq\n7LYFl7OQNKnRVJX9yTG+/Ty60mCoKZPphMVqRxyW6Fb3s6O4wHM9LBOErnN08y7UGqv1jpf0mwwH\nI6p2Tt1uUIQBsivXqaqKrCp0Q2U4GVAaFU3bUFYVWZaT1AlVXbHZrFGEIM+68FQv6CFbyS6OKMsC\nzzIp0oz96R51VmEoKpZlwzW/oCwKpCKxTJfFYsHxndvUTU1dV4S7kLqqOTq8iSKUToOnq7RNTVXX\nTPf2KcuyC02tO/9lfzggL2Ms2yWLEuI0oqoatmGMbir0Bj2qcIupqGR5gkpNU3e5CVVRuqyGULoJ\nxLEJtyF70z0URYAmWK42aHS2pu12yQfvPqQuWo7vPsfV4opbd25TFAWOZTIY9JC+xWbdqQI3qyUX\n80ve+uxnuXHzBienF4zHE/KswDQMnrtzk29842ssl0tu377N6ekprhtg2Q6r1YbhcERReDRNhWtb\nXF1ekuf5NcvB4OLshPnlBYHjYlk+H390n9FwiKabLC7P6dUBd+7eRtUtoijh2995n6B/gGJ1GIA4\nScC2iJOMz4zvERddbNrz3A7pV9T4QYBsWtIiZzQYdaj8siJvU+zpHu2qoW1bHMejaTspjuf4NLmk\nlRVV03ab5E3JYNi/bhjUP/V4/JGZFBxHpUbH8R3SuKCuCsKkQlg96gYknRqs1x+zCXddUCcvKMuu\nOuEYDskmJgEUW0ExVBokjjmFxsQwff7J7/0Zs4uIvcktVqsNH3ww48mT7oPQ7w05u3hK2yrXbcT7\nQEm4ziirgn7f5ehwQp5VmJpBLhpkJdgmSyzTpCwTAn+A67nY9pCq2GKoCr//B9+mKkN+6qffxD8a\nYagqimLSC3pcXl3g+gbrZEMw6XG5vMBzA1zbIYliHMvmxo1Dzmbn3L51F6U1SdOI2zdukqYhUZph\nKjp50dXGo8sVaZISZTl238e2TZq2RdcMfNunagVH0ymqAufnz7hzp9PKTwd7LM7nmGObNEtI1xX7\n00HXyWm75FlCFIfcOb5LGMesdmtMVTLq+9i6T14K4qQkKzLG00M0XaEfOMwXa5q64ujOTeLdliRO\nkEJcA1gcdkmE6zpo0kCRNVmSoVgqo0mPdLPlabpj2D/g9GSJZY557bWX2X5tx+VsxksvHHNxfkbg\n2WyXC/YPpuy2W1RN4/VXXwXA83o4TogV9DGtFcvFgnvHtzk8OODq6oqqqghcG9O0efDwIZPJlPLa\ncCVlw2I5R9M18izrNiO/8BYKLZPRiPnFBVLC0dFN4rzkhZde4Wh/glAk292O5154kffee8DJ01N0\nZ8cuy9GcBtuxqdqaz7zxMoPpiGJxiWYIVEVlzxuTtZ11W9YNA9snSXM0WqJdN3HGcUJV1WQUmIbD\nPF5w69YtTk6uMLRrwK45INqVeLZglcxJmxRT6X/q8fgjUZIUogNNSlkTRxFZHNFWnReylhLdsmma\nmrKq8P0A1/NopQRBhwdHw3V8kAp11XYR6QbquuaD7z3m5MmO7713ThIrWMaA+XzJxfmMLMs4Pbvg\n4aOHXC1m7E/3cS2PJE7ZrNcoQhJuFziWxtXVJVmaduIYW6coUhA1vjdAV3WqqiSJE8JdQlk2qJqO\n4/n0egcE3iF/8LvvoooAhIHjeGzDiDTPScocqQu28Za6qOh7PpZhdJHp5RIhwHQMdtGWzW5LmufM\nF52sdjKeEtgBeXKtUC8KAj9AEQqK5Pu6syDokWc5eZKCbMjzDMuyyPPk+i4NbM3icHrAwfSAveke\naZpjGAaW7XTVlyhit9tRtTVezyPwXcoqwzCULl0oK1SVTmyzDUl2O3zbJN7uePfb75ImKciWIi9Y\nr1csl0tom64kKCSea3fGpqbs9PW6iq4rZHlOWdbsdjGGZTDd2+fWrVskccJrr71GluXd5mnb0sqG\nuqkpyhzbcmmbhuF4woMHD1iv19cYPYGUkul0yt7elM12RRTt6PW619jtdjiORa8X4Hkeg8EAgeC5\n4+fYbrfoemd/Mk3zet9gnyhK+KVf+q8wTAPHdRGK4PT0pKteNfD222/x+bfeJE5CyqpECInjWuRV\ngeU5NEpLVuVomkLTSGTTotJNFF7QYdUGwyECQZ5X9Htd/Nx1LCzL4uOPP8J2e0gUbt56DlVzKSuF\nvCpBUbBs5/9TFf2/kkMIhcv5Jbqlo2sq4+GI0XDYrT2lpAaCfh+hdPnl7WaDqiiI685HVdVRhIpr\nuxiGjWXZKIqKEDCZ3ubJ0yVf/+MPoLEQwiBLSoqiJUk6l2CWl5yenDM7v8RQLWQjaWu60E4lOHl6\nhi4sbMNkOZ/T6zuMxx5Bz8Cz+vT9KYbioigdLiuKYtrGoJEGtAHUAYPgLst5QdNoJGlnROoPh2im\nSZKnVG2J5zokSYJrO7i2g++41G2N7dpIIViuFgzHQ+IsoahK2qqmTHMs08YwLGzDwTBMjm7eoChK\nijwHIMuzTsVWdqiycLvrXJLrzbVZWqfv92iKirZscUwbTdFAis5lUVYcHR1RNw1lWZJnOaoiKPKU\ntqlI05g8TzEtlbIskG3nvBRtp8nLspS6KqmKEkUIdEVFiG5w5llGWRYUeUbb1mRphoKgkQ1CU4ji\nDNO2yfOSi9k5cZpRVp38RQiu26xbZrMZvV6P8XjEfD7nex9+wOnZOVVVoWo6TdNS1TUPHty/jjhn\nrFYrdF1jsVhcC3I3XF3NOL84Y7XuOAtCCJqmYblccnFxTuB7HB4eoGkaV4sFuyjlrbff5is/cZfV\netnZ0T2bxfyKtpUcP3fMndu3GY1HWJZJmsVkWUojG07OzijriqxIKaqcbbRDSlCkYBD0uqWw0gGI\nNMMgTlPaFkzbAUVlu9vieBaaJrAdH6HpVC1M9g6oKomm6qi6yXi6T5Jkn3o8/kgsH+pasEs97CCj\nqLZUrc3o6IjTZw9okWSaRla1FEnCyJkw9nu4nk9WNCh+TdGW6EJDd1osTUMxDZo24sbBHf7uL/9v\niNanbVTK8oo0zdjuUpqmBiFp6hahKORlyZOLGcZ8gWNrJNma1UYQRwm+H9CuVsjrtV8ch+yNp2ia\nQSMlZ6cX7B0cYloWtawp25JtuAOh0At69KZjHE+QrksWRY2uljiqy8CYUuoFV9sTDFuwd/sWju3z\n7p++i5AtRs9ms93iuz5JnTF2FT55/D2C8ZA9f8zjs2fcHQ2pZUvP9dAqwSLe0OgS27LZLdYc3z5m\nNnvC0d4+qmKwXueYtktbxegE1FsN26tIvZoCiWha6niFE+ikjUIgJOsoxPUsfMfCqnVEo3F+Occ2\nDRbbGQ0WldrQNhk9b8hqu2Ps+KzDHYN+QH/co0gyHMvGsk3SPMd2LSzTIUsLiqQkU1MUTcGWPnql\n4+/7XJzNKFMPVfgMhzqO5xEEKbbvYXoWRZlhmzpPnpwxnU5xfY+mKruLyt5N/vnv/T6b733QsQ4V\nlZs3b3Lzzg0+/vB95vMZx3duMxkPWO7m3d2dqvL4k08wDZMbN4+4feMmtm1h6iaz8zMGPY82ChEK\nzM5O+IM//BN6vTG/9/t/iOvqqKaDbBraomAyDNDcCV4wIegfsk5TKqERliV7vQnS0LGC9hqlbxLH\nIaWEcJuyPxoQeC6r7RUWDqvFjr7nozgWCrC8WqG7FsOjIR9++CGVKXB6AtWecDKfcbA/Ye+2i0rL\nZnaO6SscHO3xryzmLIT4+0KIuRDi/R84NxRC/HMhxMPrr4Pr80II8feEEI+EEO8JIT73ad5EVTZs\nVjVZZEDT3RJZtk0tW5pGBamTJQV1La+ZfQ1VW9EfDcjylPVmiaZ3Ag0raPG9AEWxuP/hHNscsDe9\nxeHhLTabiCStaZoGpOzWLdeSjLqq0LRuM2YXRux28TWks6P6NtdNVGVdMxoMieMY27b5pf/gF/F9\njySJ6PX717h6HdM20Q2Vui4p6prVasvpk0vmVwlpLmgocQKBplZ4to2pGliOTd00HN97nsMbR13s\nGkmap4RxiDfooTsmVduFfvaHh1zNF0z399Ftu8taVKChk2cFQRCQpik3btzADwImBwF7R0M22yWa\nZuM5Awzdo24ahsMA9XqTtxUtlq1gOwq6oWK53f9FmidkWULTlli6TZ6VrJYbbNvFMV2qssHzPTRF\ndI4Gw0TKrqns1q3bJHFHb7YsmyLLKIsMxzYZ9PqMh0OKLMd3PXzPp+f1KdKC+x8+wjQ6rJrn+aw3\nG8JdBFKhKKsO1dZKpATLsGibBk3T+PCjjzBNk+nePq7r4fk+V1dXBEHAG2+8wWQ04sUXX8J1PYqi\n7FR/jsP+wQGO65BnOXEccvvODdq25ebNIwQQDAJ6g6ALJfUtot2cl+7d5HNvvEJvOABFo2olDSqv\nvf4WWV5xen5CXsTMF5fEcYiqqmRJjO86lHlBsi3wnQFFXJGGKZv1jvlyheV4RGlOWhTEWUqUJNSy\noqIiL3OSIqNVWib7Y1bRhqrK8HSN7eISTYEwTej5PZCQRumnmhAAhJR/cShaCPEVIAZ+XUr56vW5\nvwuspZR/RwjxXwADKeV/LoT4OeA/A34O+CLw30gpv/jD3kSvZ8kbNwKmBw57+yYHt4eUbYTraVye\nLdAMjd4oQFd1ZC2wvJooLtCET1Uu8Hs9HN1Aig6Nvl06PHkYsbg0OD+NmS+WGJbWOQIrlSzvdqql\nlCAlutHZrMO4IyeJtsH1bLI0QzccDLOzYAshcT2b47vPI6UgTVPG4ym//Mv/Nbso4h/8xj+irhs+\nfvAAVWkxdIOnT54AELg2ZZ2gWxo37ow5fl6QJDM8RyEMd6i6yvT4gLZVmc9XBKZD3RRcnZzjOx66\na+DoGlfRlqwocVULz3FQq4pWVZns7bNZXqG7BntHhzy+f44iGyzbp9+zyNOSJDvlzu3PcHG+RkHn\no/ee4jp9VD3B7084vuWw3CxRDIWkbChTBcPR0AwFRQFVgC4UmqqkLk0c20QVKt/+9vfY2z+g1Rtu\n3uwcjVVeEifdB1FzDK6eXmAYFo5rcXAwZX41YzwaEYchhh5QFxlW4PLRx4/wdA/N1bl7/AL/6H/+\nBkXaJ0lLxntDXD+gaSVvvv4qDz7+kIP9Kbapo+saXuDzycOHOF6P2XJLK+Hx42coqkq42fD5z7/J\nrVuHyKbC0hTu3LlD09Q8e3ZCGMe4roup65RlwfHxbbI8vm5bF6RxSF1kbLcbppMRVRFyNB3i+z7f\n/fZ3OX7+mC/+G1/h/OIZpunx+JNLfv1/+oeMJ2O++rf/Fr/1z36beXhKmp+hKTqDYZ9weU5VFazn\nLYdHe4RhglBV7jx3h1JW1E1KVXdU5qdPHzOZjKmbEkM3kUKhkDWG3qVnKSrKqkXVFO7euYOqKFyc\nXFJmKXGdMQkO+Jv/5f/+LSnlWz9sPP7QOwUp5R8C6//L6V8Afu368a8B//YPnP912R3fAPpCiIMf\n/jNalosNpycLNMWnbgBFRaga/d4E23TYn+7hOja2Y1FRdTwARcO2ffKsJcoiGllRFJIsFkyG97Ct\nPuvthn7fR1KhaNBSIluBvEZme76P53kdohxBXVSYpknbSITQKMoC2Qp2uwiAppbkWYplGl1iMIn5\n2tf+iMPDPV579TOMJyOEENy8fQfH96mbBk3XQdUoc0lZSKJNydlpQlV40FjsjQ5R0ciilDRO6Hk+\nRZ6hKSqOYTEdTnA0m8DvYVs209GULM0o6pher8/Im5BvC8YHUxq9ZB0vMEwdaKjKgvlyTlkWmKaP\nYboMhx3EVSgtVRVT1AWzizVV2V5TmVtcr4dAxzIMVIWuhVzREKKTAWuaRppmOLaDoSkI2SIkpEmE\nrCrKIkdRBEIIirxkF0aYpnUNlm2oi5oyK9EVgzRNaNuWQa+PY9moCtDCbr1jNQEf/80AACAASURB\nVI+QEnzfZ7FYcXFxTrQLCXc7fD/gyZOnGEaXityFOyzboqpqbty4SZ53zUdHN45QdY1ev8f5+QVV\nVdHrBTx4cJ9PPnlMGIbM53PqukbTVF5++WUWiwVVUbJer5ldzJhfzZESBr0xpmExGU2pywbbdPi5\nn/8F/v6v/gaL+YqiKHj85ITvvPs+6+WG1eKSXbxF0RqE0rlNHNtAti1lWeMHPab7UxzPw/N70Aqa\npqUua4QEISV5mqEJhd1ui2Ub2I7VOUmqiiSJcGwTXQh0VVA2Jcv1nNVyQZkU6LqBbdnkef3DhuH3\nj3/ZjcY9KeXs+vEl8OdOqiPg9Aeed3Z97v92CCH+YyHEO0KId8qyJc1r4riiLCBLSvKsIUlq0gjS\nSNBUgvFor7NIpQlCU5FC4Ht9kCay1ciysgN7GlMuLpbMF1eoqiRJoy69VqS0lGiahuO4jMfj6w9p\n1cFcqgqhqJhmV2MuigJN00nT7ooXhiHrdSdb3W43FEVXEv293/1nrFdzfv7n/yp/9Wd/hv39fcJd\njKFbuK5HVVVstzsCv09TNCwvV1ycRDx9smO7qwh3eeddqCTxJsTSDBQhWF4tiaMY2/WoWkne1GiW\nw9GNmwwnE6I05fDwLj1/QpkKqkZiuhYXswugQVwbrhVVkGQphukzn69o6oZ+32dvf0jQt5lMxsRx\njIKForhkuSCOUybjAbbVyWdMQydLShShU1UddTiKtpydnzMeDTB0BdoWTQg26xVlWaJecyBN1eT1\n118HOldk29asFyvSMMNQLFRVpalb1ss1vufR6/mY175ERdT4vsf5+Tm9Xo/Dg0PqpuLy6pKiyJhO\nR2y3G5IkZrfbsQ0jiqrg61//BuNxp21/+OAhr7/+Buv1hl6vh67rqKpKURRcXFwQhiF5nmOa3XLn\ngw8+QFEU3nvve/R6ffIsp64bNustUZigIDA0i1dffwPD9BhNDvml/+iv8yv/w6/w4Ycf0jYNX/rC\nl/jC25/n9ddeRsoG3RBouriusjU4tsPB0U28no/Xc9jFGxzPZna1JMsKpGxQZEtTVYhWMp6M6Pd7\nHYQn3FIXJYPAR1cg2m07rJ1safOKcLOlrtrrzXaFXhAQBMGnHtz/rzcapZRSCPEv0Jj5/X/3K8Cv\nANiWIWlUirThe999wBe+/FnOFxuKPMNoJ1xcLLia5RhOxtGNIYqrgqixbMkuDEmimsn+gFZm7O/v\n8Wv/7bcpigxJZ3jOkxqh6B3Tryqh7cSz202IonYNWYoiQEhMs4ONCqGgKApVWaNo6vX0qWCaJldX\nc+I4xnU9HMfh5PQZf/Nv/A3+l9/8bSaTMf/eX/t3+frX32GxWnLn+XtURcaTx48pmxrL0KnKFFUG\nGIqkqGqkaEirGtuSmFIh2+xIw4iX7z5PGKfsygp/MOZ0cUptKDCfU0qBq0348MkJr7/yWW77AR+f\nfcTe3pjjW0OuTi7wXQvHVqlkRZJlRE8umIzGmLqCZRh4gU5RKoz3xuwfHTA7X/BjX/kZ4nf/DN3O\nqJsYQ3iEqxV1UdKUGjoNURSCDsguLHZ4OGC92WErBqqmYRsmUVHgKA553RJvU/yjQzzfpCxTBr0B\nd+/co85rkrBEsQS9/pAwiwj6NlQ1spKURU7dKAghODzc4/XXX+fhw4+ZjIf0eh6WqfH06WN+9qd/\nijDc8cLoOc7PZ6zWO0zdhBZu3bpNmqVddSIIeP/99zm+fYMs2pImUVd9uJpjOw4nTz7h1q07xHHC\nB+9/wHq1YLPeYakKlqFimwa+I3hWLrh79zYPn5zy6NET3mxajm7f5se/9OP4PY26qfnoe9+iLjd8\n/nM/RpymzFfnqGaLrpnIuuXsyTOGR2PyNGa8f0i5TBjtj3jT/jKmXtFUEbvNluFgSFlWmLZL2RQY\npkEhclarFWWRcPPWEevFEqEo+K5PYPk0TY2j22yrlJ5roeiC2ePTv2g4/p+Of9k7has/XxZcf/1z\n08Q5cPMHnnfj+txf/CYUga7qtJWgyFre/84zntxfc3WSUhc623XOg48uyeKaOEqQVVeyUWhoqoLN\nbkkUFfjeAQ8/XlLkKmkiyfIO9d40oKgmTQmOG1yblCWu615PCApt2wASy7Suv+/2HJqm6VKGZYGm\naV1DlKaTZRmXlzPyPEPXNFbLFbvdFhrJV378L/HlL/8lnn/uLgcHB/hBn7pt2e525EXRXaXybs/E\nMASu5yAUFdlKlBZUCePhiLZpePjgIXEUsprPUbs2elQp2S5WDIIhaZsxj85556M/QzO7PZcyrWga\niaqqKKqKpmr0+wMmkzHz+YztZoWud3TfwWiAVATr7ZLL5TmPHn2ALiRVXnF6donvTrAdD1XTMCyT\nFliuVqiGiuPb2I6NpMEPHBzXIU4TgqD7G+uGiRTg2i512WBZJnmeUdc148kY23URqophGLSypW5b\nLEdH1SWGpaOoAj+wux16u8tuDEdDfN+jLAt838d1Xcqqsy/pmoaqCKSEi7Nz4jhhs1ozHo2Ioojl\nakXbtnzm1Vc5Ojpkb2+Poig4Pj7GsnR2uy3b7Yb1etURl3WLquyyFLvdll24JQgshkOf/YN9NruQ\nqm3ZbuccP3eDu3fvkKU5dVlyuD9iOh1RVd3Gtqap5HlGnuU4lkVd13iOS13XpFlCXhbsoi1u4DC7\nmqEZKoNeD9k09P0ebd1NKEmUk0YpQrYk0Y7z01MGvR6KhKZuaCQ0dHkRVUAta8IkxPf/9TMafwv4\nxevHvwj85g+c//evqxBfAnY/sMz4fzxUTWEy9Om5Aa45IEtUbHXEdpnz7Em3UdS08NJLL/HGa69g\nqS6yAl0xaGuNKCy5usg5Oy35vd99jyStaBuLqlCpGijy7FqN5qAr2jXSXHQxaLXDw1dVhes5HRW6\nrjskeNug6V093NA0FKULRAlFQdNNWimI45goTtB0g9/57X+Kqggc0+BLX/wcP/ETX+Hw8Aa6YTGd\n7OP5AVJRifOMwHdI4i1SVpRlimF0FYO2aQhcH13r8g5pmiDahp5j8dLzzzEOfKo4puc6tLJFdzUe\nnT4gzFaE8YY0TmhrSZ6X5HkJUkUIAxWdqqgRUiLbbp2vKArL9RWmrdNIBcNSePDofQLb4PzZFXXt\nsFznVC3YrkNWpKCC0CBKE4qyZLlaUhb599H63QZsjlDU76vezetlFK1AQaMoims+BWiGRlGWXSwb\nqGSB4Sh4PRvbNambCss2+fOlh64oaEKwv7dPWVZMJlPiOCUMY3y/x9OnT3n6+DGmYSKE5PDggPVq\nhWma7O/tsVqtePr0KeF2R5alHB0dcPf4Nq+89CK3b95CEQq2aWHbNpZtsVotcV2L4+O7vPLKi+zt\nTbh79y5ZVpDnDZZh8+zRfZJwjW6q7LY7eoHPZDzgs2++yfG9FxFCMBz1kLIhSa6XpapGmedoqoom\nDAa9IevFmtnFCZqhdpbvpkVVNHTN5Px8xnKxoSpbdK2D3LZty3g06cq6cURZFFytVqiaQV1XPPvk\nIXmZs4u3+D33Uw/uH7p8EEL8A+DfBMZCiDPgbwF/B/gNIcR/CDwD/p3rp/8OXeXhEZACf/3TvImq\nqHn1pVfJsh2GA1dXCapiMO69yHq7ZZumvPHGC9y5vQ/tGplDLmu2m5B41+fDb7dczp6g6xcYlktV\npZimTV3pHUHJ1anbFElLUbbcuXPMJ588vB7gEsMwKIuCJEq7ZUT3m6PrOkIIDNNEve6lsCyLtm1J\nkgTLsiiqkmQxx/cqfuef/FPu3XueL37xc7iOwqufucd333+fq/mCV159je/82TdJ6oqyETx6eMpL\nrxxhKA3ImrzMkWqHRndcj9PHDxkOBnz27c8RlRlhEaNkJuig6QLdtImyhFW4I7Acirhl+PyUIs3J\nshzX7VFXNU1VoOk6u3VCFm85vHGLzXJNnknGh2PyxY5tNGO6d5O2rtEbg6vzNYYSsIpKnp0sGI4l\nmt5w94U7PHr0hIPbh2i6RVulGOh4gc5mE1FTU7eS4cGUXZGShTFV3TLuBei6TqCPqSuIooTaKFgu\n1oyGEzRNZblek2YZJC2Tfo+yyVE0A023ieKIzWZDK1Um4z62bfPd77zHz/zMT1OWOWXRBbjiOMV1\nXV577YBHn8x49vQpEsn4YJ+r+Zztx/epipIPP/iQz776Ent7+2i6imPbXF3OaGlR6JaWzz93lxdf\neJ7Z5Tl9z2Z/0me3WaOrgu0mZji5wX//3/09fvan/jLTQct2cUWSNwxHI4o8p6hKbr/4GpoRcH7y\nkFa2xEnE4WSMKgUvvfwSUbREKVu285Aki5FSwbZidukOa39CUdW4lsP5bE5VCQLD5/zkhJtHEyzH\nIgh8lqs1lumSFTGGZqM1EpqG2eU5d164RS1qpGgpyuRTTwqfpvrw16SUB1JKXUp5Q0r5q1LKlZTy\nr0gp70kpf0pKub5+rpRS/qdSyueklK9JKd/5NG9CAkJtQRVomslo1EM3OsxYf9inH/iUac13v3nC\n1UxiuwZeYKGbFut1Ba2Nqiroqk4Wd8JX09Jo2grLcqkrMDSTum5QFY3Tk6ed0Vp03X3lNYCiuzrY\nSBQQKkJoaFrnSlAUDV23aKQkSRMMS6dpa3abLVVRdP0B4Y6v/fHXaFtQRUeMfuG5Y0xTYbfbkKdl\nxxOUNabpcv/hBaY7wA0sensOhahJmoZdmnF4dEDTNuRFg2n3SBuVZVxwNluSNA2mpRFGGwKrIwPv\nDQcYgGc7tE2L61oITcVxbPaGI1xDxdJVmqaiUVt26YrFbo7XH1DnLcvNGbKpkWpFWGxQTUm/Z9IP\nJFlYUSYdyLXnDXGdMfm6xsTHMnyiqEE3fFpFoc1V0rimqArcYR+v30e3dMJoy26XsV6myFrDc7uN\nRBWTrIqwHJumFjhmn7LUsTSPuqwwVAsdA1OzGA97VE3FyekpQgje++ADbNfteBuGRts22LaD73mc\nnjxjOtlDERqzsxlpnJHnGUiFy6s189WW5SZE6CaW54Oi00oFLwg4vHmDV19/Fce1eOGFe3iuQ5GX\nuLbH7HzBaj6nKXLu3JpQ1zvyRhAMp1xezFiuZrSyi6BHUYTjqDy7eEJ6Td1C1AwnQy5XVwynIy4v\n1oi2oCkq+p6Lo+uMvB6eaRPGOVndUEpYbkJmZ3Nsw6ZpGy5mXTkzjnYIJEXVdOxSWyeYDKgEmKZO\nVVekYc7lxeWnnhR+JBKNbStZbbcgG4qyQkqJoiiEu4T+cMhgMGU0camqjMuLhlsvjDg4OMKxWn7z\nH/4W4a5EkQpJHHY4dl10/eZSxbJtqqq67pKzkLIlyzIODw/JyoLNZoNhaKjCIsuybp3qORiGcW0m\nktR1RdHQ+RJUFdPUgRbbtQmzLr4bpwsOwh3f+OY3+U+kiiG7FN0rLz3Pt97d5/T0BNqKJCvYO7xJ\nkcT0e1Mefbzk9os99g4CwjhHVAqX6wXL2Qkvv/wSH9z/hNHhEUmWU6gS0ajYnsNmsWY67CNbSMuC\nXr9HuFixWKy4c/sWrczRTRXZFjw7ecR4MERXA+wgYLS3z3JxTiuhLECVBqrZgq7SDwIOjqZczhes\n5msGwYCwKXjp+c8wHPT40r/1k/zZO9/if/zTX8ULAvYmB4gqRbQeF/NL2rruSpeexq1bU6Joy/Zy\nR5mVWLbDwXSfps3Z7tbcu/cSTx7POLjd5+J8gef20ZWulfrw4BZRCFHYsLh6xGdefZmHDz/m829/\nEc/3URSdt956iziKCLc7qmtZjet4bHc7bty8SRwnhFGMUAUvvvgiT58+RRWC+dUVT56ecTGb4z58\nwHS8x+OHj9jtdnzu85/lc597EynrLlKsqaii5t3vfps4DNkfj3D9MUK0/MLP/xSDYcCP/+TP8M47\nX+Ps9Iw7t/aJopDBaIpl2bRtZyk3q5qmrNmu1+Rxje0HfPTe+5iuA0rN3nhEWeTovkOVNhRpwmDY\n52oxx7UDNCEwdZ3AsbAsk0Dp4/c9PvzwPoFf0gv2aEVLXkvuf3KGYffZJimtAFNYHN9+Hjj5VOPx\nR6L3QcqWs/MZi+WGNM0BhfF4iucHlHVLXpSsVlvKQqKrLroyRjY+3/zTj5CtCsiuFbhpQAiEUMjz\nnL3JhKIoUFT1msXXIdx9P2A0GqMgMM2uZlyVJb1eZ45SBNciks7SY9sWvu/iujaWZdK2LXGcst3u\nuq66LEPSsl4vmM3OrxXv3YaXZZoMB30MQ6NuKwZ9H1Uo17LTHW1h0BQ2bW1fW6J08jTG911msxlt\nXTHsB6RxhCoFsm5wNBvX8vEsC1XAaDAgT2MO9vc42N9D0rDdrUmSkLqtOyQbFbeP7xCFMf3+kPF4\niu/10RWDuqyQjcC2HNprrbyUDcHAJysKpJYTRgviaMvXv/6H3Lq5x2gyRKJSN3+u08vQNIfhaESa\nF+yNDjh7ds6zx8+YnV3SNAptXWIYCk3bEZrjPGcT70jTitFgjOu6CEUyGPSJ4pQ4acjyAt002Ow2\n9Pt9yqIkz0oMw+Dy8pLFcsnl5WUXJBuNUHUDzw8QQlAWHWEqDCNs2+Fg/4AoihlPxiyXa5bLFbZl\nE0chhqHzxS+8zfGdOyAldVkyGY9J45jhIODwcILvmxwe7lPXNbalk+UZVd2Bc4qixLYtNM0EoCgy\nFKFiGjaWbiDrlsANoBUMByOiMGY62SMvclpVQVXAsQyuVleomqAqCi5nF8i2YTY7w7QMPM9BN008\nz+sw/kXDcDDu+BeKgeW61LVkf3rEaLCH5jjoioFoFOLo0/c+/EhMCkIopGlOkhbIazSalHQY8qru\noq1RTBRnLBcxs7OYf/y//hF/8kffZbUKyYvOWNw2DfW1QKMqa4IgoK7rzhHZNOi6RlF0VyzX9dA1\nDc9zO8eEaeI4XTembFsMXccwdBRF0LZdhSK7bt7pWr0ddruQusiRssE0dFbLOVka8dEHH6AoKlJ2\nVKl7zz3XlVd1lXHgYFCh6yp5llEVIFqLZ0+vcG2vawJrus3MLMu5c+cugeviGDr37tylSgpUqdC/\n9lIm8a4DbEhJ23b2Z9sysC0LXVcxDPVaLFIxX84Jej5PnnxCUdTIVmCbnWbPMGx0VcOyutzAwcE+\nN24csIki+mMH9Iavf/OP+eD+t/jWd/+kC3npJmEYEQyHVK1AmC6tMBCqydf/5E959uiUdFviuX22\nm6hjW7QFlm1S1i3z1QbHdzD1a1O2pnW9BqZJWSmEUd2p7ZWuMUsIwe3bdzFNk48++ogwDL/PQiyK\nkqZu0A2DMIrYPzggLbKOp6jpnJ+ds9uF3+8aLfICwzBZrzZMJ2Pe/Owb3Lx5hGWZNE1FnmWslyuK\nPCcM1+zvjXjzzc/w+bfeBODgYA/TNHh2copsu9bmey/c43I2ZzyeslzOr2Pzend3qelMhhN8v3et\nnytwbIfRaIjtdABe09QxHBNES1XmDHoBZZHz2muvcHAwpT/wubyaoet6tzErQQiVOEoos4LFYkGc\nZgR+n4ODG2RlgYqKY7mU5acnL6lf/epX//WM9H+B45d/+W9/1TaN75cGDV1HNwx0Xaesa6Qi2OzW\nhGHGchnxzjsfcnkZUhU6ZSFpkZiGdS1w6a70lm2xXC3RDaMzTTUNutXtZt88usXV1RV5nhGFW+qm\nQbadqHYwGHQbj2V3NXJcB9u2qZsK3+/AKN2dQoymaRzfOiJPC5qyxtA6M3ES7vjs59+m1+t2nMeT\nEc+ePkHTBE4+4/VDg9l6gWL43HvhDg8ffQ/H0ul7JnVVErgOSIX9vSPibUaWFjiGw+X5BfujCa5u\nEIchgdun1/M6UpHvs4u2eNevkScZ4+EQ13XxHI/ddovuOKyWS8bDIVfLOcvVEtsycT2HXZhgqTq2\nZbNYrSiqhNPTE7zAx/IMslyiWIJcVrSqxdAcYOoaQrTcOD5gm+Q8/+ar3P/wMXuTPWSV4Rge08EN\norCgP+iR1Ts0E7bhhqrUEAgm4wGr5YLzixn9vk9W7GilyvvfW/Huu2ekqcnB/ojL2Zw3P/cm3/7W\nd8nzjP2DQ3q9gDzPefHePcq8IIpDHNdjt9tx/9FjLNOh1x9iWJ2bcr1Zc3h0wG67Yro3wdBVXnnp\nmBtHR4xHfabTEbZlYOgamqbw9PFj0jhEVUoMXeG5u7ev0fFLLFNFtoLnX3yZ2ewSy9C5uDjlYO8m\nF5eXDIcDgtEe/cE+7z/8U/zABKVGMRTW2wjD1AnDbWcaEwq9vs/FfIbXC1DahuFwwHq1RVUVzk5P\n6AcucRIynQ6JkxDd0OgNBqxWSyzbYj1fMui7VFXLerulKHOydI6sWqqmJUxCvv3ObvbVr371V37Y\nePyRuFMAGA6HXf++ZWE7FpKOB2BYRteXLiUnp2dczZcIDPKsZb0KaaWgldDUbdfkRAdtaVv5/YGt\nqso1JFXtnAqa1pmpTZOqrkFKJGAYJlmWkaZpV2u3LVzXJc87hLmu69f24u4WWwjBarXurDxqlyir\nq5yHDx5wcXFO27YoomuQevvttxgNe+z7JrcCydFQo61yzi9OONwfQV0T7mLyOMN3fWzLw7GDbs8g\nzvC9AFMzqPIcx7JJs5yibNH17nayqiqEgDRLaKoaGoUib0jCBKSC7/eQCGzboiryTkiiKFRlxTbc\nUZYVhmlSlCVlVVFVXa7eMhQ0dGStousWjtOnbgyu5nMurs6I0h2LxRzLFmw2V8wuz6mqlMB32d8b\n8/prr/DlL3+RH/vxH8N0bHrDHoapYZsWcRhhGSamrfPqa6/Q63n0gj6bTczlLCFNJVXdMBqPSNPs\nmncQYhomq+UKTdO5d+8eq9WKvb09bLubwLfbDXEUce+FexRlV31ZrRa0tDSyxbQsXnnlZV56+UWO\n/w/q3uvXsjQ/z3tWTnvtfHI+p2KH6uo0gUORIjkiQUoEDEsCJciQDRnwheEr/wW+ka98IUOCYdiA\nBRsmZSaJkE1Z5Gg0gZNnuns6d+U6eeewcvyWL9buli900bwQ0NpA4aCAOqeq9t7ft3/hfZ/3xjFh\n6JEkIfP5lOlswmB4TV7UCDkkBdft0G53EaXE0vOZzeZ88sGHaLrKxsYWaZpyeXFVa1uQaDbbFGWF\nrKhESYSiSgTREs9fkhUZmqnXjs7VylaVVZAVbLfFRn+TCgkkGU036vecBJqu0Ww1yMt6HiPKgsD3\nkGUJWQJZAa0CS1dwXZtev8mNoz22tzaQVQkhf3594RfiUpAkVjtuge04eEFAHMeouoYQgmazyXg8\nI05T5kuPcIUqL0WFotZSzkKUdaScJK0YfBlVJa1+gWlaJGnGxuYWQRSiaRrtdhMh6sOtyFpNVi5E\njSDL69BTIaDZbNVpQ5NpnVik6nWOQ5oxnXlQgqmAbUhEfspwOOb09DmssiaR4JV7L6OpKqZU0rdK\nXrm5Qb9pM11MyPOYlutAKZCRydIMTTU5P7/C0A1MTSeNE6ajMRISp2enKJrBwgu4Ggxotbv1oFZS\n0Q2DLC/ptHrMpgvOL69AltGtBkFY6zVUBVRVRtd0qlIQxnVvvFguubq6IvBDfM+vcxqLArVQ0SoF\nTYY0CkmigGUWYHZqH8p4PKTVMMj9BbdvbaFqOd1uE+ScJ8/ep9m2KMucRrOJouo4bgvTNGnYDlmc\n0O40sS195cCscJwu6/1jet1tqkpC0w329w9ZzD1su9YzNByHjz78kMePnqAbBqqhY65ajzzPiMIA\nz/Po9booiopuWPS6a2R5zvbeLk7DIY4ivvPd79Jf65GkCa12C0lWWV/fZLH0+fCjR1yPxpQlZFlJ\nFKYUheD2rbv4vk+RZ7hukyiMCIKAZruN53lYdoNer3ZnzpczZL1CkkrSJCZOE7KiIM1STNum1Wqi\noCErKmsbW8hCIctKJEXFdZvYtkOn22OxmOF5M+IopCoLRFkyGg6QqpLFYoplaViGSavhkuURcexB\nXrC7u8327gaKoXzu8/iF2D4oikKSeAiRr7wNcZ29lxcEUUSapiyXMVUpAxVxUod+yoqykoDWb5RS\nySnSDFEptaFJrrmNnXaXNM9WNt6YLE1wHIv5dIJMvf2wGi5uswuSTxzn9Ne3mM0XqFVFGEZ0mi0M\n3SaOa6u2IkGe5+gKbJgZd7ds1loCcX+bf/XOlN/7Z7/LX/+t38KxXWQq2q0OpmmwDJZUecmhLrFO\nwKyyWPoFjpNw584Onj8j8APipKLf75PGPqUoUGSNzY0NDM1mEWakSUqvV0Nonpw9R1YsLi7mtNoO\nnVabZRjR7PZ4fuYx8Xw+/uQBndYWfcdmMDll+84NvEWAjE4uZSRlgpGnaIZFKRWE0ZzdHZvxckYS\njqmoaPUV4jBCN3LaGxZIFVvtDeQsRCoFWThlf8slSyIKUWduhmnBN7/9r9FUi/56g/l4jmWbaB2T\n2aLirXff5/U3X0A3dKBEIKiEwnCYISsNkjRHVlWGoxnbu9vEaVwrNFstzs7PmUwmSFXB1fUFjmWT\nphn3XnqR2TxgPLzm8OQWjx4/oSxllsuQQqTs7uzy9OkzTg4P6PVbZKVEt7fJaLzg/OySMIr5yU/f\nJssrXMvEsTTWuw6//ItfZTwecefWXd796Q9wGg0GwzFh6KMpOmle0uv1mc19lkHBh2cXfPzoYxpr\nCf5iSK/XoSwqqkKwGM3JswztYAPT6CDSgjzOuZqNaa1tEOaC6cKvw2VcF285JQp8NtbWOT+/QpZU\npuM5e/u7uG6TpRfxwSen7OzuEUdLEn+Cv5wz216QSRJp/h8ZZAVAVWTieHXYZZ1SF8wXCyok4jhB\nUUwkuaJMo9r3L6o6ak6qSLOMdqtFmkIaxciSRAVUom4j0jRFUA/9DMMgiUMsy2SYpjXpRlFwXZco\nSSirCtdpUIk66r6q6lTpLM+pRPkZGDbP83oYqsDRmsWXbvTpuQmTSqMsYkajEYPBgOMjB1lWKClx\nHIczzycpTDRSLCklDGVObuwQp1Oi0MdxrDrBSUiEoc/GWov5Yo7rmCiqZ6L61AAAIABJREFURl4I\nFEWn1XSQNNAVFUmRKSsZy24znc5pNfssfI+k1Gm0XIIkot3vIQsdbxFi2w2SJKXRaFJmgla7jxws\niL0A1zZI4pROp1enb+sGVBW+v0CPG0hYpFlJHE/p9rawDYvJfECR6eimTpYWOJZLLurNgaE7bG+v\nkcQRRZqTpDU6rtFU2NrdIo5jykLgLX3C0EO3dS4uQy6vJFodl1a7xXw+x2k0cewGa2t9FssFxzdO\nkCSJvCjY2dnh6voCwzSwLJM4jqhEgdvs8s7bb+N5PnFS8vWv/yrDyYDNzW2kIubs7Jzd/U1M0+Hy\n6oqLs3NG4xmXl9fMlxFhmCH1ZeYLj+Hlc+69dBdZqUOJhair2rwoGQwGdJodVFUnTmKyrGA4nvJ8\n8hy7pZGnKRKAkAgWHq3mOi2zSRjWgT1pHKJWBTubu4wnPqpmEEQhumGRFyWBH7JYzOi2m1iWw+72\nLlEUcXJ0wmA0QFYkdKuJ0WgTxhm9Voe8CJBlBVWWESt16ed9fCHaB0VRsBwHXa2n0AcH+8jA9dUV\nhqatevgEy1RoNGyUT4M4ENiWhWNYSEKgqzKyXIFUIkkCWanI4piqLDjY2WJ7sw9liWk5DAYjmp0e\nimpgOy5CCECi3e6R5QIhyShaXZKqukpWlhi2XUuisxxZVlFUA0OC126vsd6pWGtovLqnsmHD8OKS\n//Ef/SOkT9FiomI8n/P4csrZMOb0+SWtdgNDdTh9PODOjZtEYUTL6WBqBq22jm3L5GlCr9shE7Uy\ns9dv4bo2rm2ynE8J5ilJkBOlMYqhYGoms8mEw8MbdLsb6KaJ7Vi0Wk2C5YyqKkCScZtNclVQGWAL\niTQoOT6+yd07d/nt3/x1Oq02BSpjf8lwPqEsS6KwIE1j4iRgf+cYXVZZToYYqkGnZSJSwcKL8YOU\ndJmj5iYfv/sAx7YBieF0Qqk06G7fpGk7JOEYo20xD3yePx8QL2VEbjEe2Ny5c5MvfekNdrfXMc0G\n/fU2H338Ma2my3Q2xvOW+L7H1uYGP/jBj2h11iiEjCJrJFHCL33tFwm8BYPrC9yGS9OxMXSNL7/5\nJvvb+3zrW9/nnZ9/wp/8y2/yT//3P+TRsyHf+9H7PHh0TpQV9SGqCuaLGT/4yXvEWcVwNMHSBHku\neOXNX0bVVKaD5+hygWmqPPjwjEfnV7zz0VPeevcTskqnudXk6GSXhmuRlyllJtNp99jcWEeIkmge\n02qZeOGUMJ/hODKWJHFzc4/DnU3iMMe02whTYSmWXE/H6M0+zY11LoYTNN1ga3MTXTZwm+v4acLV\nbMDW9hb3b3yJotLx/YCd7YPPfR6/EJdCWZbMZktEJdB1jcVyiWGaNBsuiqbVhhez1nMfHBx89ikt\nyzJVJUiS5LNoeqS6ivgUqlSWJa7rIkng2BZ5llCWgk9VirIkIavqKkui1kxYVp2647oOsiKRZQmm\nY9cVhxB0Oh2yrKCqKlxTI09jirJgOJqRBiEbvRZSVfH48WMEFbJU/2whBH4qiLCZRgoPnl2DqOi2\nm2RZgmXZBH7MfLZkOpsiy0oNkZ3O6oyGosT3A0bjISLPMDWDLMpouW0M3UCVZSTqPb9hGkRRQlmU\nNCwb17K5c/c2ZsNE03UWsylh6FFJJcPBkGgZMpv7XF5ecnr2lCSNqSro9ddoNJrohgUSyKqg1bbR\ndAXL0TAdlVyUNNsuiiTRX+vX0etxhmM72LZNEIZYjk2z0ySK0ppfMBxTlvXWSEiCosjZ298nL2Ay\nDgmigOVizq1bN5lNF2xsrHN5dYFlWdi2g6op7O/vMx6Pay6G3UBV1Dr7otvFskws02RzY5PA9+j2\nO+zt7fLJJx/z3ns/R9eNVdygxmQy41vf+jaLpY9pWTVb8WiPtqtjmwrr/T79bp+G7aAoElme0+r0\nydIUxzKRJFHLuJsu84VPkuasb2zQ6fbIipzJdIZhWzTbLSzLIAwXPHr8MU6nhWNbJMGSte11Jt4c\nU1dY7zWRyFFkCUOXubh8jkAhL6o6BV3VCJKI7d0dbNtGiIqjwx1URXB885Ber03TbVAkMWEcg1Rz\nLj7v4wtxKVSVRBKnSKsDuPQ9JFnCbDirZN6sRn013FoDL6rVjrbOKdC0+s2QJAmKoiLLMrKioqhK\nDQdNEgaDAb7vk2U5tmWi6zph4NHr9ciyfOVpKEGq8AMP06y99fpq2ClJUm2achyCoG5z6jejTuBH\npGlFEBUMhkuKPKMQJdfXtRfs003K8fExaaXxaBhwtpB4PghIs5xCFExnIyohEYYRRS6Yz5bIikaa\nFkiShKpoLOc+UZhg6CamZdByHSzDJvQiIj8iT1JabpN200U3NDRDQ5NV8ixHlRUyUZAUBX4UoEpg\nKDKqIrG+uY6la7iNJlme8uz5E8oyQ64kNEXHMCziNCXJUxRNQVYkonQJcoHVMFAMhbnvoag6qqbV\nnhFFJogjDk+OGU1naIZGGNapWGfPHlMICctuo0oK7VaLTtdlGU6ZzObYtkMYeliGye3bt1FVlcVi\nwd7+PmmWY9v1kNDzFp/le1xfXbKYz8nzgslkiu95HB0ccPfObbIsRTM0siLj448/YjAc0HAsXrhz\ni/X1HkKkyHJFmafMplOarsOX3nyFV+/d5u6tI/Ik5fbNk5oVMV8SxnH9IVIJ8iSj2WqDJKEbKhtr\nW5yfPsXzxvzar/zVGhYTRpzcvkuSZVSSQIiCitonIUSBJtfA21wI/Pmci/Nn+MGCyXREKSIarkrb\n6WIZLWRZo6rqPI+yTFEUFcOwUA0JWckoizoX1PfnoJVUlLRaLlSfH7LyhZgpJGlGr7/BaHxJWRb0\nOi1kGZyGRTCeoRkmug6j0QhZVpBlBVEJKlGRlSmSVFCUGYZhkKYphmkShwmKJtex80VBp9Pi+voa\nx3GYLaYkUczm5iZJGKFpGnlWYNkGvj8jL8SKGiSDJHBsl7QoaLfbpFGI02iSFwsA4gIipc+TBcwH\nEnnuM5wrCHTCIK4l29Qr0ddefY3u4S3+4C9+QNfQ2Tl6iSzLmHsLNssGhtZmMV2gKxb7ex28ICFJ\nM7rdFksvwlBs4iimzATT+YTNtS5SE4pKxpR01Eriy2++wbvvvQXBjNFwCFTYpoGqqMwnC7a29/Am\nA3RDQZbqg6DKJovBiFGjQaur4/ZckjDk5OYJT56fcX11gWmayKrJ7u4+Dx48wJsPePX1Vykqgdvr\nMhhM2N84JAg8vNmStd4ao/EUR1fp9PpMF0vSVMG1TNbWTbzCJ0mgaaroRhO7rzMYDhiMQvYOXiBJ\nEtb6XX720x8zGAz4zd/8Td79+dvEWU1nHg6HyLLMzs4OZZmzXM7Z2dlBtxxUTefRo0dkuSAIMzqd\nNmVZ8s//+R/z2v17WKbKb/61XyNYeiy9JX/0x79fV2Si5G/81q/w8ku3WOs53LuxUYuupIqOaxEu\n5rzw6ouYpoPjZEyvL9EUi25/hyQXjCZPuR56vPzKLV555WU0TSLxExblnPc++ABRFIRhysb6Nnmc\n0zAblEJjGgZkccT+3g7Wms10OmU4uSTIrlAVjSyBOJqxudNDVm3StE4Am48HKMJGk1IehQ/prq8R\neRFWswuVQLNV2t0ucexDlnzu8/iFqBQURUFIMppuIwRIrIJZrVq1ZeoGRSFwHBchqnqiL6vULYBc\nDxvTeo9NVfeV0oqJoCjKqt2oUFWVqqpWopv6kz8XNdG5LMtVJVFnRNZRXyWypKHrBg3LrpFkRq2s\na7fbqJrMKEj44HTGBxceH1xHPFnIDIOUoqzIipxS1BMeCYk7t++wu3uEZjuMggyh6CiyTBJnGIZN\nWdY4sE+5maqqYlh1vkCW1/DX3Z19VE3DbTZAqvUVqqoSRwluo8F4MsGyTK7Ozzg82McwDKaTObPF\nEg0VS7cQSAilzlTIk5Th+TX91iaz+YyLywGyYmJaJlke0G7amLpG03WZTsdEYUCRpaz1d5hPAzTV\notlqE4Yhhq7SabU4ONilLHM63TZClPzg+z8iSxIMU6bdtFiMZ8RpThRnTIcBRVaS5Sndfoc0k7Ab\nTcq8ZDwec3R0hGVZPH78GD+IODo+odPpcHp6WmddFAXHJ4f4wZIoDAmDAE3TVgPlhNt3bqGqNcY9\nywteffU+9+/fR6Ki3Wqwtdnn5skhr95/kVfu3eZLb76KZSikYUDgLzk+OsRtWFiWwc7ONtPpjEJU\n5HlRczHynDgT6GaDJ8/OuX3nJjvbm9x75V6d6B1H2IZKUWRQQVlIjMcevldwfXlNQcFwNKxDd6KC\n2WyO59Wry6KsEJWCZtoYmkqRZlBJeIs5lm7QbbcwDZt+f5M4L7AbDpZlk6UgyzbX0ymaZqDIKn8Z\nDNIX5FKQEaIiz8DUbdb76zQbLoHn4y+XdNsd8rxEVjRU3UBSFDTDRNVq0g9SbaAq8prSLESdSPxp\nGApAGIbkWR2IkWUZUFODan1EtYKnqBRFuZpX1EKnWrtQZy6qai2nrU1S9c9aJiWPrqa8+3TCuScY\nRAqZpNcvqKjXmfXoWcZtNDg+PKTVaSMUmTSPcGyLNMmRJR1FqUvQUtQtQxjWGLkgDNF1nTiMmU1m\nKJqKoJZaj6cTdEPn8PAASZYYTyd1anUlUeYZ7XYbz/eZTGe03BbX53WyUZYVZEmGoWjEvk+r7bCx\n5dJsOcznHkGUMJ1dk2c+lq3Qats4tsXg+pq222Sjt8FoOCLwQ0LfY39vhyxNoSrJ0wxD1wgCD7dZ\nzxryLMU0deLEhwrcpsNiuWQwmOItQ2RFRVU1ZNkhyyoOD49JkoTFYsHW1janp+cEYYSo+Cz8JQxD\n9lYXn7dYEkUhnU4X07R5fnpGRVW3j2lNKup1u8iyhGUYDAYXiDIjDJa8/PILvPbaPe6/8hKubaAp\nElQlvhfy5OlzwigiTkKQ4fnpGZJUS6slWaMUECUZpVAYDKYIkfPKKy8jywZXV5eYpo5cCqq8RK5k\nFEnFD2LOz6+4GAxIs7odnC0DrobT2tuRRPT6HZJUpqx0ZFnDtm1GgwWe5xOFAWVZfDZwz7Icq+Ew\nmoyoKgnPj3DbXRahT1EKirwCIf37D9+/5/GFuBSg4upqgCKrNN02WVoiySq26XC0f0CZZqytbaDp\nJqCQZjmdTo9SsHJV1hVCUZQoq2pAiHwlV85pNmuDTJLGhGGADGxt7bBcLomTpDba5CmeFyChURQg\nSxpJnKPIOnGcUpZFDVTxa1HM0ltiaDqWrdNc30B2mkxSwdk8pEDBsCxMy+aTTz6BSkJUAgmJ//a/\n+a/ZXV9D0SoG10/42c9+Wm+LREGUL/DCBe12k1bbYWujh9OwsB0Hy7aJ45DR8ArbMimFYOmH7J8c\nMZ4OKIoE2zEZTK74yVvvcLC7iyxJWJbNwcEJR4c38OMQNJlOp09TdeiYTUzN4PjOCUISGJaJbvQp\niyZl2cQPK5puH1HoiELh5PgWR4cnbG1sYxomx4fHBPMlZ48fo0sylqEznUxIkpBnZ49xWiZpHvC1\nr93n5vEhUVA//1GcIZGwt7dOe6PHMkh59vyawdDj9MmCq4sxp8/rluXhg8cIUXFycsLh0RHvvvse\nZSnotNv0+j1EVfHk8TO+8pVfIPADkizFtB3SXKBoBqqmk+UFv/xLf5Wvf/3rLOce//gf/xO++Y1v\ncHl5ga7K2KaGZWicHB0giZws8snShOvBlMU8rNOwi4xpUAfVvvPuB+RCIkehrDRMu82f/N9/higV\n3nj9BUQlkBWHweQcz5uj5BZXTybIpcHu/jb9jRaarbB9sEmYRmiOxeVwTCHDs/NLosJnFo04unmX\nta0DhOLSaOygq102+i16rTZ5nNDtrKPqBiVQEqPqFePZgIU34Wfv/YTNvS3ytCDyU3Td/dyn8Qty\nKdSxcaoqUyHQLZOiLOmvryMrKvP5nCRZ4dDSbCVXVmtqjaquIhwklJWiUZYllNXg0XVdZLl2TVqW\nRZoleEuPLMswDRN/uaTf739WKZRlgSxLpGlCmiYYhk6j4cCqmsjzHHmFEJNlGde2aTUbtJotZKVe\neXz6pAZhyIMHDxCiQpbq/1u31+Vv/s2/XadYSRKqIWGYEoqSYTZsTNug1XZxLHsFJA0xbJtOv4vt\nGOzsbqFqtTPUsm00ra6IwmBJmafkWc72zhbFSs5b5CVFVlAWBbPljMlixtnlGUmaIcqSKI7ISGh2\nGkSByfPHMZfPBeOrBqOrNs+fq6jyHgpbnJ8tSROFrJCIEoOisIjCCoRKEqfEWQ3Vnc5ndNc6WI5B\nLlIUtWK+mKJKEqoqUUgCkZVEQcHhzTtMFwsMw0GUCjIG21tbGKZBKSqGwxF3X7hLu90mzzKyNMU0\nDWzHWblU6+HvX/zF98jSnB98/0d89NEnrK9vEEQho+mM7a1dVEXlrZ++xXe+811kSWFzY5M8L5Al\nmSzL8DwPTVXRNIVmy60hvlIdhntxeYmm6zQaTY6Ojmm1Wjx58hQ/TDi7HKKqOsvFEs1QqRBsbe4g\nkAjiGYdHByRJTqfbY7lcsrbeRVYE23sb6KpgY6eH6qj0eh3CZUB7o0+Y5iiqCbqE3lC4uB6y8CLs\nhkWaBiwWS9I0JfBjrgdXZFlBv9dFk7XVMHRKy20gIdN0GohS1MHGn/PxhRg0VhUcnxySRhHTyYQw\nihFSxdzzGIxGFGVJnAa0Om003fhsBlDbpKVVD16tyvoCRdXQdR1Jkj/Drum6Thh56LqJItcXzfr6\nJlk+QZLrSwVA1VSEKBCri6YosxqJtkK25XmOJEuoUs1r3O6vYZk6mizhNiyWywjKiiTLMQyT0ajO\ng6yq2u0nCsF/9vf/c/6X/+2fshwPUHWNvf11el2T4WTIbn+P68GAo+YhlRAEYcAy8FkGEc2mix8s\nqKQKu2MThQm5yIgiH11SUGUJWZHY2dsjTQNmywWqZFIV9Zq10TDRDZ3JZILh2IRBVqPkTRWqnPNn\ngmdPfLJYQZJyNNNAVoa0Wia6PsJpKKSxR7NhEIeCLEsAl401k3azyXw5xmxY+MslURbjym0MW6es\nSoJwgWNZ5CJByCW6YpEmFRu7B0Tf+S5JVmAoKoZmIsqMZ8+fYZom7W4XRVUxDINOu0MSR2iqRqvZ\nJMkykqSu8FrNNpub2/hRjCJVTGdTmq0eoxV2/dnTp1xfXyNR8Ppr93nppXtIKwitbdt4nodtWeRp\njGPZLFWFO3duc3U5otWyWSyW3Dg6IY1TVFkhiGKiNOP52TkHms1kOuG1+y9AJYjCFN2tuJ6csr25\nQ7PbokhrC74kC0xTRapsSpFiNmUco0lDd7h6Ombmz4jTgjiRkWKfo+1tWv0m4/GCjTWdLAtJophO\nr8F87tNstwnjCCct0VWLSvjYls1itiBDYBW1+jPNP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oT192UpYRgSxRlvfuUX+Z2/91+w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bbv77C3d0AUxWAEwkgs5eE6HgJDLXDZ2ztiMgoptSZoKB69uEajUcMWJUFgM59NybUh0xBm\nBdF8xnQ8paE0dVkyHYxZ31xnf3+fcRgiBOz+v9S9WYxkaXqe95xz/rPHHpERuW+1b71PT8/eTQ6H\nHA7JIYdDWpJFkBJBQhZ8YViAbwwbhu0LX9iQYFiCIVuiSJGWJYJjkuYsnJ7umeme3rururv2JSuz\ncouMjH05cfZzfHGiqscCL/qymUChUKjIBYH8vvP/3/e+z7u3iy5kBoMJmmZQK6iU8iXu7OwipSa5\nOOELT38KkaY44xFVO8/FhTn6vQlemKCrHqoz5MHNO0ihhpRqKIaKaRfwggAvyEJ+hCTTPNhH02Ry\nlkrvuMXyUgM7p2MZFmmcUK1UKBbzaIZBtVohCCKCKCCMIoSh4ycS5x57kivX71Ko1NAtHSRBGAl0\n3eLk5iaD6RB0BTeJyBVNdvYOuHrtFr3uKFOERhENW4UkJpYjOuMBUhKROgOEEnMq79NIm1i9G8yF\nB/T3bmLnTTRZUBQ63c4he80d/ChhGib0pj4dN2R3OKWfimworesoQjAYdBiPeySph5BVJEkBRSCp\nKooQmELFylkEaTZM11WNyI8yXmkQEkUhk6mDEAKSlDiIEbJKEicEQWbtLxZM0jggDn00LcHUUxTp\n44Ywf1JwbGnKt7/9bR7sPWBtdZ1USojjBN1QieJsLvDwdVEcYxjGozmBEBpxFJLEmYwzSRKEKmfr\nIhmSNEFK5dkKM8HzXGQZ4jggTUUGSREynh8yGYe88KUvMJn6lCrzRElKrT7H4aHH1HEI/AmqLiiU\naihCwzTy2JaFIkuk6Sy2TNMI44goijO3pCpj5wzub9+jUskzcVz8IEQRKrIiiOPs2qOrOv32lFwx\nJk497No5HBTUIoz7A+bmatiGwUsvvYoWq6h6TLe3z9LiBq7vk8/ncacDymUVdzpmvVpj0HfQwhDN\nVJnEKZ3BgMfnKtzYbfGPvvkcr7zyDkuyhXu8jSJSJMOg2e1iKxJra0v4qsn/89qrTMdH6PVzhALu\nO4LVsk7/+uUs9k5Kqakw3muxvLpIv+vw1ec3OLp+j+v7t+kPPKREYNWKtFpH5G2TREo4c/IkvdYB\nlbyBbpu4A5fPP32el96+jjP22b5+jfWN5Sx+Pl+ge7zPpD/itbd75PN5vKlL57hNoZTj2k6TIRpX\ndo4RUcyR4zKMIrZ39vAqE372Z7/Ed37wQ+rzdfb2Dzj7xEV+8MZlbu/2mEwCfubSBvsFwdmFeW7c\nu4OnJLhSwqEDN1s9/ov5Evl4jCIPGHou/r0eIhqStwvE0jmsQgEpUPCiAZsLDV597TXmF5fZHYwp\n53KYliD0PAzDJKIMaURBaNRlCTmMaeRzGHGGBUDXGfQGzNl5gsBBQkFRBJZlMhpNSBUZ3dBIogBF\nShmPWtiWAbJF5MsIJSBJXAQRG5ubDIYDep0eidA+djl+Ik4KSBJbW/fRdYPv/fWLCKEghPLIKSlJ\nWSK0IksIWSYMszDaJJ7t+mf4d+mnZg7Z6SAj1kizaLnMTy3he96M/JwNH7Ohm0Gr1cEwc4RRjGUW\nEEJD1RSqtTqKomBaBqPRAEnKNBWyoiDNMPSk2bBRzGTZ0owRUSjkyeXsLPXIUJGV7LqBBGGYbUhU\nVUUVAknSiKOU5dVlmkdthhOffr/P1M10FUXbpmTniIKEYrGI7wUYlka+kGNurk6r1SJfUMgXLeI4\nod8fkcQpuVyBKAyRyJ4mtqXTanewVIOiJiFNJxzu7qMqKqksEYQxiSLzztVrtAcBU62ObOaRELSj\nlB/cPqLpegx9Hz+MOThus9fc5/jgAClNGLSOmSupFMs2qAqSCOiPBhiGge8HlEsl0iQiCjwunjtD\nLl+gWDAY93uoM6XoQqNG4HvEccBRq0UUeVl+RZwwmThomoaiaqyvn+Dezh6qmcNL4O7OPoqhIysS\n3X4PRRXM15cwNYN26xg/jLm5s8Pccp2Dbpdx5LO+uc5eZ4CpGwRRgGWYGDKcOn0CP9GJFAsllilb\nglF7TM7QGPePSLwx4aiDHjjM5SWWyjaxM+TC6RPcvXOT7b1dvv/DV0h0SC0VTxO4QjCMElrjKbu9\nEbv9Mfd6A5puQDuKaYcKw1gm1E00PYdhWNksixBUMIUgDgOSVEI2bVxJpu/HhJJCKAkkRc7yUCSV\n3Qe7j9gfsvbxn/+fiKaQpAknTp7iuec+Q32uhqYJ4iRFEcpHBZ7ZJ5Glj1KnH7ofIRMNKUrGSYCM\nwYAkP/JGpLOj/cNk6yROssYQP7yexOwfHM2gKSlJKmHnirjeFCGyq0G+kMPzXZIZjk3VVBRFoChq\nFm0vxKPv8XBzUqlUKBYLWLZOpVomn7dQRCZKkWXlkX4ijiPG42nmp8jnGI8dOsc9ZEmhXq8zGg7x\n/SmjwRTDMPF9nySRUHUVRUhZ1DsSYTzhV3/983TGfSahD5LCZOygyZnLbzR18aOYg/aQJJao5g2C\nQQ9DljA0HSnNfvbyXI3uyOGDW/cZJBaqZlMwNRJVoXTucfbDGCdISBIZrWSh5iQacxWiyOep8+cw\nhIQzmSDLCokUZeAX08QwLEaDId7UIW9ZnNxYYWf3mJxtc/PGTXb39pFlOLm5ShwGlEsFkiRGyAm6\nGmFZNrKisLS8zKlTJ+l0u/SHA8qVCouLy6ysn2YwGVOdK9NYqNMbjXjtJ2+xurQMCVi2zd7RMQNn\ngmbrWHmd69tbjLyEnd0DNlZXkBOJnCQRO32efOpJPnxwhKIYKFGAFhjZpktJ6QyGhJMhwfEuejwg\nLyCZjJn2uzSqRdY3V6nXK3heDzMnQIlJ5BhZyCiaRtsJkItV2olMM0jYcwJuHw05jiXudkdMEESK\nzjROGPhT0BVkKUEiIZEgSGQMu8zQDXH8ACfwkVQFP4gRWo6UlMlkTLFURtLNj12Pn4hB4//4P/z3\n/903vvl10hR2Huzwpedf4N13PkCo8sxKnSLNUG2KIngkm5BSJDKc2cN4+YdcBuUhtSn9yPcgVAXX\nnSIp2akhZ+cQSrYp0DRBrVLhicefwMqZ+H5EoVCgfXwEqYadt4CE4WCEXaiQzxVnK8+M5uR7Lt1u\nm3w+hztbjTqOgxAyipoxFlTdYGFxkXyhgO8HpCQkaYKhCpBScrZCqaShSAG6kkNJoGBp5HM2kT8l\n9F0ajQX6ox6lWoXzZy7Q3O9mpwwyYIemKaB6bD5W5cLF0+xsD0jjmEbOZOLD0WTKUk1l1Hc4sVjC\nFCmRpHHYGjGJYjSh0qgVGE08qo057tzfp7FY4uXvvcg/+5/+CVXLQDXzGLUlfFdGkQXnTq1glXJs\nrJ3jytVbnCn4GM6IXK7BxEk4GE8xFJkwiGg0aiBLjPsdTq4tcPXK22hmGUs3EIpGa+IRRBFKMmU8\nnOJMHaYTh9XFKr/3u7/NK6+/S4pEvz/AcSa0mseUchoHO/e5feMm1fkFdvaOWFxZ4/i4ycJSA8cN\nuHP/PpGsoGkGsirxwqeeoF6yUdIJW7t9Tq6s0Xc9Rjt36fkSqR/yK198nD/47os0uy6mmbBs5ulM\nE/q+RxClpFqV7eNjAiTefOtDbt3fYm/3gCiKeObpp6mXy1w4c5ZgmkCsY+l5Qj/EMm0SstnWxA2Y\n+BHIgmkAqT3HIFXoJ9D0EnZGHttDj3EoGPgSriIzlQSyqlPQNAq6SkkITCGhywnIMapeIJQ0RvGY\naWJwvzNifxTxzrW/RTi26WxXLITKYDDMsGkKWJZFkmSrRUXIGZAijSCJZzDKbICYPdnTR6udMEpQ\nVB1V6I9CamVZJgxC0jgFZDShks/bmdciidCEjBAZOl4RCl7ggiTPiE2ZvsEPQ0wrWw9qhp4JlmZN\nJQiDR5wEWc6EV0Jkb2+lUqFarbK0uJj9WV7Cti2qtRq2bQKz7p/EGKqJbeaZTHp0WgdISYiuKjTq\nNTTdIF8w0U2DXn9E+7hNKVdHV01Gwz5JFLC0uIAqBKVyiaN2a5aolVAp2CSpIBawvrJIHMXkbA3H\nDfCiBFKJvK7i+x5C1Tk4PGI4GGKXLUa9AWks870XX+X65esYIZy7eIH5lWWCyOf4sI0UG3zmiWdQ\nFcHWfoelOYWK3OfsopY1rUTCNg0mUw/DsFCEwqWLl9hcW+X4sIkqdIaTKYPRGCEkesMxhmVACqqi\nUKvN8/Y7Vzh75jTz8/MkaUq1VkWS4dlnPkU+Z3P+3CkGoyGKKnAnEYuLKwyGA+pLC8RC4EYRQRAw\nGTt8/qmnWCgUmbdLDKce7ngIQYTI5xl6AfW8wo0bV/jMhQpzlsEo0nESmYEXka/lcSYh+BHVWo19\nV2as2dxv9rh32GEawdVrN0jjlEF/iJWvo5kFZEUlZxkoQkIxNCRhY+Zq5IwSppqjVKgyCUBoFkLS\niXyFMNKR9RpjpUgzMrnRlbnZhavHAXfaDltdh2t7x/h+SiqpTEKJzjTg5t4BTS+ilaocywLZyn3s\nevxENAWhCHq9AYosWF/b4MTm6awIff8Rtj1KkkfeiCSJSGZYsiSOQM6Ydw/j5ZIEZDk7lmdORXkG\nW0lQFCnDc89wakmcFZ3nOXTax3heMBNOSYRhNjDMwm/BnfoYhoUqshRfoQlkWcp+niSZfU428AyC\njzQTlmVRrdao1erU5mrk8znW1tfI5S2EkLEtE01TkCUJVRYISUeoCbW5PEnoc/v6VcajDKiaioTK\n3Byt1gBv6jNXrSNkQRxHjEYj7t/bQlV0Uh+uXr2LQoRtCKYjhzQKkBUwhc5c2QIkOp0Jw6lPIhQ0\nWSKfzzGeuoRRhJLEON6EOAZFV7h8/S7luTrLS/N8589fxJIUnn/2U+w32+zt7/Ev/s2/wosiEsui\nULRYqEqUcx5xmBD6MbqukSQRjfkG9bk697e2CEOfom2wu7uDpKrYtoEqFKZuQHcwxHFDcpbJwUGb\n3YM2vu9l0XuFAoGfqVkP9vdnE36D3cMD4iRhb2+fYj5PGic40ymPPf44iioIg4BSuUTn6Jj2YYuD\ngw4Vy+LWndtMh12iVKHT67FQtzi9lON8yeJnzhQZ9gM0W3AwdBn7MqEEzeMeQaLRG/dwXY9ioYIT\nJjQ7A65eu8nt7W1ELk8qaRwdtXjj1R/T3t/BkCGnG9iyhkgkhCqh6wpFW6WoSBQTmYowMQ0bVB0v\nlVA0C1XNoWo5hFFEydUYaUU6Uo6gssw791q8eWePfUfmOFTpo9J2BWEskFQdKXA+dj1+IppCpVpl\ne2eXTqfPr/7aN2m1jzmxuZllCvrB7GmXfQRRptxL04gkDIB4VpQznqOUAU9kWSZM4kcGqSTJGI9J\nkjAeu4RhxHg4Im+ZaKqMlAQINeatt95hNBqSphGTiUO1WqN13ERWBeOxQ72+QLlceQRPiZOQKImI\n4oBCoTBbV2Zva4aBVyjki6wsr3L27DlsO8fFi+f5yle+wje+8Q0uXryAYQpKxRw5Q0VTdJqHbdqD\nfXx5wsQZUi0WaB00GU99/NQnVyrR7/l4E4nxsMN42GWhvoBtlhGqTOgN0f2Q3/4Hv4wujUjjKXYu\nR0kJKUoKItaJk4BpoJOzikTEdPyQ/nSIrqvopsXa8jqWJFNRba58uMWZU6cZDiZMbYs3r15ndbnB\nzv4d9reu8+wLz7M3DvElhWqlyJvbE3z9PKo5h2KX0YXB+bNnsXO5bL5impw8ucHB4T5Li8sYqY9l\nqkzcKXPlAr7jY+cq+Mj0+g4P9jrc2TkmUStcv3WLyXjCYfOQg+Yh+WKBw3YPL0i4u3PINIzwwpBz\nJ9fQpQRTETjtMVvXrjFXKDF1PBbX1vjWd76HK8lMUg2iEZ+9VOfxtTISChYxS0t5tq83KZFiagEP\nQoVvXR7wtV//AqdOXOR+W6aTRly+dZNLFy7wu7/9u+QKBQJVxSdGSAq3PvyAf/t//zE/ef+v6U/v\nkyQdcI+5f/1tTDnFKpcpVEtUSzk0yUNLXWpFgaRGJCrIJggjRVF8olETPR5gGwpIMRN3yiCS6cUq\nQ8lC3dzA3jxNpBWJZVhbazCfX0ZNTDQ9z3yh9LHr8RPRFNI0ZfPECc6cPZ9tFOKYpaV5FCHN0qcz\nNkIax6QPhUsPsVeztChZUh8JmoDZBiBDwcuKhKRkMfeZk1FGminLJCklThJ03USSJG7evsZ4FEAq\nSEmygaAiSOKUMA4p5HMzNWWM77kISSUKYxQ5G3IqQqAockabJoPAGIaZuSmTFNvKMVdrYKgauqaz\ntLTEpccuUirm0FRBb9jFtk0WagskQC5nMhkPcf3MkKULi9SL2FxepGTajAZjxv0p7shF1zL4TJJI\n5Iome/s32DxTp1IxUJWIVJbICZmjg12mbspRu8PcfB09lyevSQSpzMbGBoNhn9FwQH88ZeL5aIYB\nssrGqfPkqnMUKnUKlsGJ02eYX27QO+5RKc7RGYzwopBJonLyqZ/BcyQGe0ekisTY8RhOIyZjh+WF\nBfLOLlY6Zb6xih8kGLbB44+fJ0lTTFVFVSKqpRzFko2uayysLPLeB+8zHLgsLlbRdY1cvogwbBzP\nJ4lTBlOXOE4wTZWd3S1UPTtBFYo5NC0bzuZyNo1qDaNQ5NbWAyzLIhUqx3t7zJd0VL0Akoqm6QzG\nMUoccvcoZeK4NIcxJHDy7Bpm0UBSDcxynubBIS+9/ENG0zFBFJLIglQIVtbXM+WraRKGMUsrS7xx\n+SrvfniLre27qCLFmU5wp0NU3SRJZFwvJoiiTD6fSJiSQs0WVMsaQooYTxxUZHQpxRYyliZRL2sQ\nxghJhcQl9ickgYtCiBcMUEOHUuFv2fbB931W19ayQVOzxebmGr4/JTsFzCTIZCg1ZglYD0Nj4jjN\nmoKs/P+GiimQShJCV5Bms0lJziTImUw6e5MkJTv+y4rCcafN7t59jls9kjh7a9q9HpVqHc8LQMqU\nk5qmEc8yIJI4QUoyYdHDr5/MtiSkCYZhIcsCTdMhBcMwEIpACIVSqUSxWGRtdYW1tWXiJEbPadTq\nFdxxQBRKBIGHqmropoZt54j9BHc84cT6ImkcYKoWpp6j1WyzstygWCpSKVVxY49qLcfZJ9bYPDFP\nkk4Z+THztRJJ7KpGiJgAACAASURBVCE0k0SS6DgOh60eJxplkhiEkEmJ8UOXw06fIEmxcjr3797G\nc6fIcUz3aBdDaLzx+luolsn21i6j3oBESCSSgp/I/OF/+DNC1+PpM2dZXV/C9wJKtRrtTo/ucYsX\nztVYrdm8+uqb6IUKaxvrqEqKM3WJk5Reb0zF0hCElIp5EgI2Tmww1yjjelOkVGZ5eZVCqYxtWqRR\nSJBkxaQKwcTzEYZBuVpjv7lLkCT0B2NUTUaTJKZBiKLp+NMRZq7AdAqWHvJgr8Wlx0+i2wWCJKWU\nzzF0PBYKFifWKth2ma0H9/jcp88RhwHd7oRet8vtrTvs7h2gqTqprOAh8eHNe+i6wZX3bnH3zh5x\nYhJpOqEiePlHL+MMW+hKwo9+/BI//snrdPpjNNVCRSIYOWhxTEEIbDlmsVEFEgzTRldkiqZAyDGh\nO2TQuk9R11FjkKWYwHEQpFSrFkLzqOgR29tXPnY9fiKagqbpjEdjDg/3iZOI4+MWpmnM7NEPVY0f\nHQOytKiPtgsP4+sfviLjJ4SPwl+SJMqSoDRtRneSM5rS/BzPPP04iws1osAhZxuMJz3+/C/+Aw92\nt/FcD8eZYpgajuOwsLDItWvX8X2PKIqzVKY4QhECzTAy9sPMB5EioRsWhmGg61kTKZaKrKyuohsG\ni0tLLC0u8cRjF7Esgyh0aTSKaJbMza3reH7I4vI6mp4nFYJSrUKzvU+726RWL2DYGtVTy5h5uHDh\nFLploCoWj5+4SOfogNCXUMI83fCQk88t8Et/78vk8zr1Shl/6lOar9IJXHbGI+JUIXEcVmybSfOI\n1PeZTn0SVSFnmaiBy+PLZdKdy/zaE4v8Zz97gv1bb/N3vvkrfHDtBq4MvpKixBE5y8SdBiwaPpJu\n8b/+xXtU8jrzcybOtM3C6jwH7V3MxTWsah25aHL7eEh3MkURGl/+mZ9FzVnodp5S6vP8Sh3PGxMO\nhvitNk6o4gQyqqThd5v4W9eRpiMUSSKYBjjTEEWYFAoV3nzzFoXKBqlsUSgWsyTzMObyu++xuLRE\nnCbEaYDpTckVi1TzJonk8/Jr13n9rdt84ek8Ay/kP/35FX7/CwVWpD5HA4XB0MU0FZ68sMpv/fLz\nSKnK8aBLnICQJTQh8EKXWE6JkImikPHY4fW33mboBoz9ADeRePmv/oyXv/unRIlge3+PV958hZt3\n3uHGzde4ee3HpJNbKH6LYNDn3/3pX6GoEo0yFKUuutvk5t0PuHPrMqXEQ2JI2YgomzZL5TlW5gq8\n9pMXaZQM9vevE0n+x67HT0RTEEKgayrtdgvD0GgeHVKrVTKRD8mjqX7WBD6aEWS+Bymj9s5gLNnr\n0mxdmWaR9UIISFPELAxG10QGUFWgXMxx4dxpigWTU6dOsr62zOHBDjdvXCfwI+q1Ofr97iO3pSRJ\nM7NTiGmamatQznBxpCmylG06tBkF6pFdW4hZJoRGqVShWCxiWibyjLQ7Gg5QREoQ+qCkWPkCINGY\nX6TWaJCQUYYs28BxJkRRjOdPQcT0J13m6g2Ojjq8d/l9NNPA0C1GAwdUBS92GUz7zBVzmLLMYq3I\naDzBixP8MCVvFUgkiXqhwKjbQ44TDE1HFSrt/hCVmKN2j9CBg919bly/x9/9hS8xaT1gfXWFjbVl\nLj1+iceefCbznCQRh4dNBmHCkQ/Ngz0OW8eYks6459BtDvjnf/wKb324T7fnkkQJ+wc9nvnMC7ju\nlGK+hK4rnFiqMC8S3DilPLfIYDwhcR1EEjFxXXynyy98+gRJFBCrAmZBP850ilB15urzuF6A7wcE\nQUC9PofjeCiq4OCwRS5XZOzI6JrANlTkeIphqaDA+kKBoqISjcb0uy7uKESYNoaks7PfYr83ZZRa\nNDsjEt9HlaXZgyxhOOhRLGTvaW84ZK5WyYBBSYLnR6SSxNTzWZpv8ORTj2HqJqmc4gYu71x+i5wp\ncCYjrlz7kHeuXeXNyx9w/8EOWzfe4+jOe7i9BzDtcLh3m8vvXiYKA/zxmAcPttl6/zWOWrt0hz1M\n2+bOnQc899nPsdvzPnY9fiKaQpqk/Lv/609o1KuoqoJlGZw+c5JCIZ9h3GfEJHhoikwfCZiYKRgf\n/lvX9UcrSFWWCVwXXQjSJMQwNXRdcPHSWWrVAgvzNZ77zDOcOLHMl7/8BYp5izNnTvDYE6d5593X\n+JM/+WN836fX62IYOrpmousGU9dBVVWCGRw1TrNIdVkWhGGmRbesPJaVR9f1mTcjRdc1DN2gUqlg\nWRZCUbh+7UOuvPMGcuSh6zLFconhZIRm5xiMR7SO2wRhgOs6OKMJR7tN9naO+PDKTTrbe3Snba7f\nv04kJZw+/xhLZ84RK3l2d/eRFBi0I5qHY3abexx3u7T7OwzdEfd3uyTCYOwF6IZCM/aYOmOEopBT\nJDYqNVLHI69Z/MJnnkSoOp5ZZPt4zOZnf57vvPEhf/93/jH37rQ5cXIdu5zji08/hxz5/N7v/0O6\nVoP3OyELp0+RpDK9sYsagWFU2D3ocOPApesIrtx+AGFI3Dnkn/63/w0vvfxD+v0hsTNhwbaYOE3i\nIGR7f5+JN2He9MmnE3Qpoj/xafYcnv3cF5hGAbplEUchaRLwG7/5dW7c+pCz59dYXp3nsNnk8KjF\nwtI8nhdw+vQp/Cjiiz//i8wvmFxcjLhz40OkCNQUVqs6PoK3d0f42hoP0kV24xr/2x/9GW/f7/Dh\n4YSSadFvN6ktr2OaVZyph0gjpDSm1+uiGCZ+FCHFHoYmY+gi+31JYjRdZ+vwgL/8y+9y6uQqbm9C\n5Pq88OUXmIQSkZZn+9hlGMRsnj/Dzz7/DLV6nTjX4Eip8N33t5h6EaeffoZ/+mc/Qh136dy/xnNn\nV6hUFdr9HpdvXOZHP3mDf/V//hF3do4+dj1+IpqCJMH66hoAlmnR6/V47LFLXLp0EW1m/JBlmThO\nUZSswB5aqYVQCXyfNI7R9YxYmyRRZltOopnKUJ7NJVJytsnSQoNSIc/iYgPbsqhWK9kvie9x8tQm\n6+srrK4t0+kcIyvyDAirzNab2ffPnv6ZhVvXdcIgJAjD7ASTpo9kzw/j6hRFgTQmiiJ8zyMKI1zH\nwfemRIGLlMR4nkfr6Bg/CGgdHaHrGkrm/yKOYgr5EksLq0R+QsGqcGdrm8F4TLFcZmtri627W+zv\n7bN1Z4ckltFNk6X6CkokqJQrfOarp+inEgtn1lHkLCw3SmHoTwglDWEVGYzGVIo2g3YThQQ3CPjw\n5l1cL0TTEq5++D7OcMQ3v/7r/Nmf/nsKZRu/+QBp1OXF736bWJY5aB6zO4ZhqjEcjZk4HkEQEXtT\nhv0eQpZxghQviKjValiaxNe/eJ5nNwxUoePGHiRw+0ETUNics1ECF9VQ2KwaPLda4OTSAssFwe99\n42vcvHaTOEgpaCqaUEjClPtb9zANlfZxE1lO0TUDw9AYDIboukG32ydN4f/9q29xcaPGb/3qLyCr\nBnPVInIMRaNArBU4Gktsnlji1HKJYl6j7weYehHHdTGVkN/4+i9y8ckLOFMXXRXISHiOT5TAcDgi\nDBPSKMLSNZYXFslZJprQCAOP3daQaSpTMFSkJOKzzz7LW29c5srN+2ycOYs3jfi557/AgztXae49\n4Ps/eIfLV64y9FwWlpcpyBIX1hucXKmiKjHLJYuJA4tzS6ipxPPPf461EwtsbGxy/rHHP3Y9fiKa\ngu/7WIaB53ocH7d55ulnee/dyxSLRVIeXhf+ZpdX+JC9KGfBK5lVOltgRlE0u2bEM/t0yuLiPOVi\ngeWleepzVeI4Yml5GU03OH/+LIHvsby8zNLiAtVqERko5gtEUYiuGz+FgItJE4kgDDNWJOmjuYYs\nK9j5AupMHv1QQCXNgsGCmbU78H3kNKaQMyGJZjmBWSJWGMTEcYJl24+gLu2jDnEEUiIoVxsopkW3\n02fqTCnXKqSk9Fod1uaXkFAYDhyG3T7MpNznn73A2U+vs33UpGwKRJwQByGSAGcU4CMwLB3L0KhU\nbCIZZF1jrzNkOPXJWxbFcoMXX3yJKzduo+sao16fzlGbVFKZuiGOF9Pp9PC0PK1JgDAMJtMATRdo\nIsRz+pDEjHwHSaQU8hqnlovkUpf5koVQVVZXFrFUlbyiouoap1dWKOVz1Es51koWpxfr9DvHOGHK\nj967xfb+EQsLC0ynPrVqHUlS+PCDawghePONNykUishK1tRdz6PT7TKeTCiViqwsLvBrP/cMneYO\n+VKNYacJCliFPIGA7jRmv9dnNBhRnlvAKBVQVY1+b8Bhb8R3X3yN6WTAXK1ItVyiVq1QLOZAUhGK\nilAkhKKQt3OMxyN832XqOJiGBoZCmMpYeZsT6xsc7O3jOT6WoSOUBMu0+P53v8sLX/w8/+Dv/V0M\nw+K43aF1uEe9UWc8nHL5rbdwp0OcMGZucRmhC/xpwHtvvospK5w9scLTZ08z6rQ/dj1+IlySnXab\nP/yDf8NXfuGr6LrOhx+MeezxS6yvbwAZ/TiKsqaQASt4BFnJfAdZSnUYBo+kznGaIJMZqYSQadSr\n6KrCFz/3LCvLDRq1IqPRCKHqVGtVXN/jhKJlJxBFYvVzK5w53SYKfcqlIpOpi6IICoUiqpqtMxRF\nJZ1FkmmaOvNiaKRyFmYaRln4bRiGKGQpV4nnEUqZ0SnwfUwhUcrpuAUDP56wUD3BaLvHoOVSKEfc\nO3zA1Hc5efoMmpTHdyNOnT/Lza1tPEli3pxD100KJZvIczm1vkrkRty8dZPNU5sUahoPOZa3b9+g\nsbqAP56ymJsy7kWMdBmzkcdpuew2mxhmShTGDDwXo2hTs2xa7ZTp0CFXqfD+q1eo5xQW+yF2ycTK\n63zthS/y7dfeJuxtE7oxjVqVF1+8hmEInn3us/zOf/Jr/C//7J9z8vQJrj5o48VgqSkLcyX0Qo3n\n7Ii97T3GRo3BcI9KOcfSapVFw8ENFMaxyTSB9foiwt+iUVonkHdRcyYf7LfRqkXCNKHvTpj6IWEU\nUyrWyOdLDPodfvjDV0klKdONyNkWKggD9vf3sJWYm1ff4EyhypV3d9HiPJuLZb73ymXmNJNi1WKi\nLJBv5Hn//S0c1aTZPGRzZZ6eJ1GrlBDCIIp8Tq2tsr29g6oLzl18DEsTvPbKj+kPxgyHY6rlGl/9\n6i/y/RdfJAh8FubrbD9o8b//wb/nsUvn2dvd4cKZi3xw7U0mnQe4gcfVe2227u7w3KfOcGZ9DbWk\nE03HOL5HfnGeyXhEfxLRDPOUa6cYjW6xt9Pj5tYxz306z2G7xZv7bUaTv2WDRkhZXl7i5Zd/xPe/\n/wN2dnbZurfFSy+9lP3vT5GcZ2ME0tlOMmMUyMRRdlUIZ0d4Wc5eqOsCTVOpVCpUKiVqtTLFQoFa\nrcbKLAMhly+g6yalagnTNCkVK0hSSqOR/W1ZFoaRAVQNw3iUWynLMoqsZJkPUgZPYXZdiMKsYX0U\nTBMTz+jOaZqSs210Q6fRqBEHPhCh6jK+61PIFaiU6lSrDUCmVKtxcHBEuTZHq33MYDxgPB1QMG3y\npTmiKMGybArlPMVqidL8HCurS1imQNYgJCBMI1Iv5N69eyhFncQIsUoquZzO9p0Wzzy+gCYSnCgk\njmE4iSnYBSxdR+gCwxB0e33Ond1E01RuX7/NifV5Tp7c4LFLj7GwsMg/+S//c9bn87z9+utsbKyB\nBD/+4Q/4zl/+BRubJwnDhLNnzyLJgvWVOo4zIU4k5io5hKFzPHAghdOnT6DbBU6cWCFn2bz+4VWG\n0zFpFKCUGox9mSgJse08d+9vM+j06R4fowqVIPIRmoJmGly6dInpdMry8tIj6TlIRFEW7JukYBoG\nnWmeUnGZ2JdYmrNJQh/bLLHdizjqOly5dgsnSmnMVxkPOizM10jjiH7zkF7ngFdf+QlfePY52p0W\nTz/1OJ969jk2N05y6dJFVhcbBDE4bkyxUuWvv/9DwjAmTSVqxQKFnIGkahx0OvzSN36V5557Al2Y\nGby2YHHxsQt89ktfohektCc9rt29gxskHHWGtDtHdI8H+G7I/uExB4cHXPzUF3ACn1xFYXtvO/NY\n2Da90ehjV+MnpCmA53psbmxg2zlarWN+8pPXabfajwAqHzWFzIQ0I7xnT9+ZhToIAtLZUV0VmTy6\nUilTKhWYm6uyvrZKoZinkM9TLpdYWFigWCyhKALdMLDtHPV6HdvOoesGlm1n8JYwwTStR1wHTdeQ\nyEi9aZoiz9Dtum4QpwlCZOzHNMmo0dn6FHRNR/4pKIyuqrSahwyHA3zPJYhD+oMupmlhWTbO2EXV\nDKIoYTSZkC8WqNbraJZOY77MWr1BHElYdoHm4SGj0ZCtgwc0+y0KeZ31tQUcz8ENfaIowJA1Aj8A\nQ0ZrmBSWcjiRi21YbG4uoWsJQZQQJZn2w5phy/M5g1gRhO6UdOqgA5ZIGXZbOJMR3//RqyRCp1af\n51e++mXSMMAZD/HdKQVLx1YSStUa7W6HKHQgjZBUmXOXznG0/wDNKpLKWQCQIsvsPdji1s1tRlGM\nJwnMks3SUp1Bt8ftZo+3PrxLxbLRZB1ZzSFLMOwNUBSBJIOiKjzY28LImXz1l3+Zp595GlUIdE1H\nkVWqtTK1WgVZhsk04Fvfu8fRcchiw0TIU3QF/CBma+yhl4u0h32cMCSUQSQxeVNBaAqnV1cIvCGG\nroHvk7MsBoM+mq4xdT2uX73G+voaXhAhNJXtB3ssLy0wv7iI58f40yl5WyNME7rjMddv3mR3d5t6\nfYnd/S4KCb4bsv3gAYFuoJWKJJrGOEi4u3PAytI8Z08vo0opreNjrr1/hW+/8j53do+oLdSYJBP2\nj/Zp94+YX174G6rub/74RDQFTVdpd/rcuX0PkJi6IYfNHqOxTzQz63zEaJRmtKKHJ4aYIHCB5NEG\nol6roSDzmc89wy997Sv8+je+zumTKzz3mWdZWFhhaWWNx554mtX1TZZX18nlChRyRRrVJTQjj2bY\nFEsL5POLaIZNKkvkC7VMqagKgiAlShOiNPM5yEJFqNl2ghg0oSGEymQyxrAyrUKUxExdB4mEMAiQ\nSBj1D+kc3EVJQkxTQy/WkITLNBiQJlM0JEzV5KjZJRh7vPvWe0x9l+6oS7mUUGjEswDThOX5JY4e\ntBjsHSI7Q548d4LeqE2cSKiBYNKdkMoapzdXyQlICyn2KZUv/9an0dSEC2fPQJQyZwkW5oqcnrP5\nlRfO8tiZOUq6iVXMQ5Qw7vfpjHwubOR558MdUsmm73e4/fqrvPn6O1TrKxTnG0zGY1zHQ5k6fO28\nxs23X+Xq/SYHe01UCXYeNPn+S28QeCFv3xlw6qkX6Bz3sfMmJ03Bb17Q+PObXd7pSzj9IXqoc3u3\nnUXKqVD1J6yIhM2NBikKUSphKDJmvsTnnv8ipzdXUDWDw+6Ig/0mtmYT+hGqavDCl79EkvqcOn2S\n+sY6p9ca/M//9rtEq48xHIcsLjYoLi8xmk544sImk/4R3718l6uHfeQkod0ecjyZcq/VYbvnMfIc\naidXMUzBjft3OGwf8cMfvcjW/Xscd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NsWtUoTDYPNzTyReBRN1RGuR6tWITPYz6Urb1Mp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\n/wOKT3mSQNNf5gAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "images = transform_img_fn(['dogs.jpg'])\n", "# I'm dividing by 2 and adding 0.5 because of how this Inception represents images\n", @@ -207,9 +192,8 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -220,9 +204,8 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -233,11 +216,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true + "editable": true, + "jupyter": { + "outputs_hidden": false + } }, "outputs": [], "source": [ @@ -247,9 +233,8 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -270,21 +255,16 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "440.970395088\n" - ] + "editable": true, + "jupyter": { + "outputs_hidden": false } - ], + }, + "outputs": [], "source": [ "tmp = time.time()\n", "# Hide color is the color for a superpixel turned OFF. Alternatively, if it is NONE, the superpixel will be replaced by the average of its pixels\n", @@ -324,9 +304,8 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { - "collapsed": true, "deletable": true, "editable": true }, @@ -337,34 +316,16 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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ZW7QuEQuB71XrIFpNrovJOoca3/c5cvT6HStLHIfV+glZynA8RhToMqfQEIQJ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\nA5h+TCFOW/ZjAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "editable": true, + "jupyter": { + "outputs_hidden": false } - ], + }, + "outputs": [], "source": [ "temp, mask = explanation.get_image_and_mask(240, positive_only=True, num_features=5, hide_rest=True)\n", "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" @@ -382,34 +343,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Xt6iymlfvvIXrM/72v/t3+Vt/4++g0HhryXQxMfLW4YYBN4wMfX/DrkeyLMP7AAFynZOS\nQAlNZgqMzBjaEaMyjMoIAYIH5yKji8yXa5KI6FxjradtO1IEP3qGtidYx9D1BB8+qXoMw4BzES0z\nbkhztNCkJLA2oHVOCODGSLsf2G8bmkOD1poQAs45lusVISSWyxXDMKIzxXy5QGUKYzRlUUJKWGfR\nWYbUiqLMaNsDu801h/0eJSV93+GCZRh6kkg0XcvmsKFt9zg7IJmipH27o20PjN7RDj1JwN1XXyUr\np9Ti6OiIqqrI8/yGVQ9YZyElhsHifaSu5lRVRVFM76MoMpJ3aK3Yt3sO7Z4kIiF5dKapyhKRQCbB\nvJ6R0lQ1cD5QzWaU9XSurMhJUtAPPcMwUpY1KUm0zhBCMZsvcS4ghOb6ckNyCdtbyrzC+0gMYbrm\nGKjmFdV8xuBGhJaMfmR9skZoyb7bcmgbtruRR4923L51i745oDTMqgprLTorEFJRZILbt09Zn6x4\n9uI5V1fX3Lv3FrvNNa/cvcPts9ukJPjg/gdcXL3gV/79/wCtJf/3r/0rXGvo28gX33mDkHp8tNTz\nGW/c+wJf/amfRWWKvKzIsolfcs7Sdz1GZsikaA4dRVGjs5zl+pij01ucnt0mJsHV1Ybl8uhT2+Nn\nIl76s+LLbx+l//l//Ht0Q8OD84dcHtpPdtswOmazOYGE1hlDP1KqmmAjRPBBsFjdYnV0xEcPfshs\nKZHJk+cl+/1m2vnHYSpJiURMYIxG65w8K0lRYt2A1pJhbCYiTBkOhwPeeTKdT1WDqkQg8N6TlGR0\nFqkU3X5PXZYUeT6F5CkxNCNGKRaLJb0daLuOGCHECAlC8CijOD5e048d7dBT1zWHQ0OelRSm4Pp6\nQ1EU5Llmt9lS1zl1UeJDYrPdsT47YrPfUdcVR8dHPHn8mKqqsKObSogxUGQZwUm6tme9XOEFeGc5\nPT7m0O0IQNe11PWSq+2eWkNZZARvMZnmerulLGqG3tPbgcXRAqUN+0PH3bNb9H2PkpJww+soKSFa\nnLcsVrdompa33rrH4ycPEFJgqgyZoO96QpjKnYMdmc/mFHk+cRWjmzghF0EmyrLkoyfPODs7IwLD\nMJGwtcrIdE7bW4rlkvOnT6jyAk9g9Jb5fE70ga7tmFUzmrZjOZux3WxIMqFLw+gsWZ6TxBStHp+e\n0vY9UiqEgOA9UnhyVjx7EPjo/S1vvnpGt79iaA6MXvLjDz7iV/7aL/DlN27xM+/c45u/8R0Wp7fJ\nlkve/OJX+YPv/AGb/SVaGy4uLri+vmSxnBGC5+23v0ZRKk6PXuGf/Z//lLe/chd9tsWYiBsi0UFd\nrpEU2HRB8JDsyGpRMAwdzTASfCCMDpXV7PY7UoLlcklWVPRdiwiO09MFzfbAP/pHv/P7KaWv/2n2\n+LmIFLSeykbr1RI7jggJJsuQwlBkMzJdQ5DkJqc7TGXJos6QKhLlQDs+4/0Pf5ckGkgBKRUxTjX5\nTBvKvKAuauazxU3tOjGOU47lncU7S4oe5yzjOBCcRaREVZVAIssMSkCmp51sXs3oDz37bYOWhqKo\nIUn6bkCiWa2OqfIFIhqUMBhlWC2WZEoTY0BrhZQCrdVUvSBhjEIk8M5xvbkkEfC+Zxha8kLh3IAp\nNIGA1JLgHZnRkCKHwxYpEwlPFJ5yVlDWBUmkif1PkmfPXuDGASUEgjRxM8/PCSGw226py4KqKFFA\nco5x6CgyRYqBzBiCdRil6LuOTGmSFIQUscFjcoNQkkPX3uhDBNfXVzx6/JAfvPfepFcg4dxI8FP9\nPc8NPjjyIiPPc9JNyTaEgMkzIpGymsrL83nNfD6bypZGEr3Hx4gLAR/CT5QFKKWYz2ZoqT6J0LTW\n5FnGerlitVjy9ttvc3J8TApTKjWOPVmWMZvNEMDhsJ8Mrjkw9i1aKIaDpt0K3n/vI/p2z3Z7idaQ\nZxqJoB0sgwsM1jHYkaY7kOWK/f6KW3eOmc9qvvP7v892s+GNN+7hvefevXtICW3b8f4HP+BnvvZT\n/OC77yNijhIaZx31rGRWl1OpNsapvG1ymkN3o+URGAN1bTha15wcLaiqDJJH4DFSok12Q9KLf5Pp\n/RvxuXAKUmY8eXFJVpUU9YzVasV8vkLrnKPjVzDZ/JMdtKrmdH2DysDMBaqwHJ3m3H61YrHUlKXB\n2o6EpShuRCBSMQzDlJtnGSkmqqKk7w603R6IWNsjRMK7kc3VJUPXEr0jxJGqzhAEhr7FDgMX55fc\nPruLETm3z+5iR4s2GVobtrs9ZVGT53P6wbFarxBaojNFZjSL+QzrB2azCutGXly8QGuFEgIlBdZ2\nVFUOwpKSZb2eUc0yVBYZY0877AgMPDn/mNZdsz2c07bXFEaRBjgqj8jQCBVwYUBKEFLx8z//CywX\nNVIEXrx4Sp4bUoyIBMfLikwNyOTItKYsZihKcrPk9VffoK4qbp2d0R0acJ5FXeODZbO7xoeRcew4\nPl0zX81JUjBbLpgf15y9ckKUHm1yDvuGcbDsmj0oEFqxWh0RbKDZ7eiblhgC0miuthtUpqaScAhI\nkdjtrtltrhi7lroo0Eazaw4gE7mWvP3WPfLCUJqMxWyGRHDn1m1Oj4/RUiKIxBiYlQUaiRaSo+UK\nfKA7NIgg2F3tWM8m5x2GHpMkwy7x3Xef8O5v/AFffPMN+uaa49WcN9+4Tbu/pm0a/sm/+H/5vR8+\n4IePzpGm5Opqy8OPPubjj9/n9779m3z3u9/ltddeIc8Nfd9xdnqbqqp5+vQpKQpee/2UBw9+xL3X\nvsBbt7/G1bOBeb1mf33Fi+cfo1SHlorVckmeF5ye3qUdIuvjsyltKTUpDCxmGfNSY1RERkvTXCOV\nIiKpZ9Wntse/MPHSH4WPnqeXG7bfehdhwMaIEJosK6edR4L1novra0ZnKcqMfuwpCjMpy7xg6CNa\nZXRdi84UMUasH5mVC1KMmCwjyzQhBXJd0bY9VVXh3J48M8Qo8dFjjGLo/VSOazvmy8WkPpRgMkNM\nAsTkRG7dPqUfOspyCudShNENSDOp7nznCDJQz2tiSuRFBSpRx5oQA0VWoJVBILGjhRtlndIC5wZ0\nUXJoDvR9izaCtmuYLWZ03cBcJnrfE6ynzEtCCsRRUCwrZC5J3uKUwI49eWm4vDonJEeeG5Rkcoxl\nibOOoR/w9JhsTj+OxJhYLE8YxoEkJFmeY/KMrNCAmHJZVVCXBX4ckcYwdC1KCFrrMHmGkFDNClKI\neO+pqprBdWR5Ttt2GJ0zn2UoIfHWMfqBoiwoqxrnHf040u73FEWF9R6lDN55VosFfTfgfGC5WhND\nou/aSaBIwo4Dx+s155cXn6RQ+2GHC562C2gFECedg1RYaymkpioKgnME68gyg5GaRb1iexUY+4Yv\nvv0mt2+taPcPIQaenz9j7DuMhuXRgv/jn/06q1rwzhd+mrl1XGwuUHlOpktOTnISE4EqBCipuL7c\nUVcVbdvywQeXfOMb3+B/+O//J2RuEZVm7Ebms4qUAsOwZWoniqQk6YaBzbahGUYkFj96RJLU9Qyl\nNGWZ03YdZVYCCu8CTd9+anv8XEQK1lui0rQ2MDhP2w54F/DBszvs6PoGRyRpxXy1JEaQQqGVJjOa\nrhuRIkPrAqkEZVWS5yVlVTKMI0YbpJqkolIpBPpGVFIzm9XkWT6p5YRCoMjzgvl8SVXPyIsS5yPW\nR4SewtJ6XmEKjTaQkgcS/dCidKKsctCR1ekcUwr23Q6XLM3QIDODyTNm8zlZngMCKRVGG/puRCqN\nNgaIaK0wmaFtO8qqRCpFUdZ0fY+QktVyTZXNwMPYjogoWB0dMVss+fI77yCVJC8y5suKLBccmkuG\noaMoc4o8x1nLrbMzqrKEGIk+cehabAwM3pHlGTozXF5fkRUFCEEIk8Q2ywxKCsauw40j0XuGtkPe\nSJGtc/RjBzIQ8Fhrb+TRUBQlCIn3gVlZ0ez22GHiI7zzRCJSSS6vr0lIMpOjlGY+m7NcrJBJI5Ig\nL0oiiaIoUMDQd4xDT/AOOw6kGDjsdwxDN1VfgiOkQNs2NM0BwcQjaKXwNzoOJRVVXhKtp8gytMwZ\nusirr7zCV7/6FlJ6QohkRnN2esJyMePs7AydaRZHhvPNltXRGd5GTo/usL3ck4LCZDnn58/p+pZZ\nPYmI9rv9tJbqiu99/0ekJPkHf+8/ZXu15bU7b+FtwihFmWmCaxBp2ii222sePnzManFCCHqqFImc\niKQfLAiF0gaSoKpnaFVQFDMOu+ZT2+Pnwil0fc/oPV46rB9YzOdkmYEU6PsDQgRQCZNrPInBWtqu\n57CfFiLsScJzdX1OCCPb3ZaLzYaEJK9yxmRZrBZkec58vmS2OEIZxfmL82mRoggB6vkMGzyL+Rrv\nI1pnhAQ2RGbrI/LZnJFE5zrGOLDvt8TkabotEc/gO7JK8dGjH/Pud7/FdrhGZNDZjuXJCqvgMI5E\nIenHgf1hz/rohP2+Y7U6ptkfbpSKjqou0UaTIjjnbkqdiUznaGmmnYEZ/9ZbX+N0dYt6tuDgDhzC\njo+e3efy6pLzF+foDHQWUFmgzEt2ux02WLqh/aQqUeQVRye3uH33FaTRFHXJ5fU5m80F0QcObUNR\n1yTkRDTuDrh+pC4KjtdHRB8oTAYhsVgskFKhpabZtUSfKIuS9tCR65x232CUJhOaxx8/xo+Ok6MT\nUoTFYkGMkTzLuXP79ieOmxhJKXF6dIqUhqKcEYOF4Bj6houLS1LwPH/+DKFgu72mzHLadmCzawBN\nXczY7xqutluQkiwryITmi/feYr04QkuJEoLt1Y7SFJytTtlee86fdJzdOuLoJGe2yEgx8OTJI8ax\n4+hkxSuv3OHnfu7f4Y033+By33JxtUGqnCePz5nNjhhs4P79+7z62h1unZ1yfXVN31nKasH3v/ce\n/+LXfo3l6oRv/+7v8NM/8yXGpkEFRbSR64sdFy8uECmwmldUxpC85ezoiLHruHV0iogSo+fcuvsa\nebXAR4l1iRAF1gV+8ze+zbPHl6yPzj61PX4unEKMCR0VKmoylaEliOTJjGA5q6nyklxKkvcEa9kf\ndqQ0pQTjGNEiJ6RAkeUEm0BA1JHlckkUEZ0p2v5AjFNprxkPROmZrSqaricgCIlJf6Alox0B2B/2\nHNoDbT+p5HZNSzuMqDzDhYiQanIaPlCU5ZRqqEl/X9QFPgaGzuFGN9XNfY/MFFJJQvQgQWWaPC9J\nEeZVhZEGpQzCgAuO1dGaxGRsIoEIESPE1LxEYPAjpixJiJtdcM/HD+7jo0doRTP0jM6x3ewgGdrG\nYooSnUms7ZEKkNAMe5JIiATdoaXdd2g0pc7ItWEcR/JijpYli9mazOT4kMiLknq2QCqNMdmUtjmH\nHRKKjEwUGDWRZ956NJJ5VjG2I9Y6VsdHtP2IdR4lFYXOMUpPPIBICCKZUYgU6F2HjZar7YZxHIje\nEu1I8kxCsSIHGW/SwJzl6oSqXpKiYF4tOD4+Ja9reufQ0hBsmARxo4foGPo9ZV4RXOT6csvmRc/Z\n8W1OTo9YrVYURUZZTySuVIJqVpOXJUWm+dLbb7FcHvGDH7/H6AZ88FxvrwgiEGPkxflzjNbcPr1D\nmU9y9Tff/AJH6zO+/OU3+cP3vkPTt9y9fQYxkClD11jGXiKi4fL5Y+Z5xcliwaI0CLcjFx2235KS\n47Dfk2cFRVHR9T2H9oAdB06PTjhaHmPy7FPb4+fCKUgpKYuSoigQCMahvwkBp+46QSLTAtu3qBQp\nywJlJg5AmwzJFC5pk9O1I8YUSAT92BOix0c3qeJ8jw0Wlxzd2GFKQyASiaAEwzgSb5yNMWYSwsRA\nVVd47xh6S9cPkATOelKYwuWUBClC3w2QJPPFgsVygbWWvummzkHnPuEehEhoBdpItJLkhaHvW8pq\nUgxKIQgh4P2kz9fGYMeRFCLGKBKBJBNBRc6vn+OSJxApyxw79uwPW5wLxCiwzuNDoihqQkoUVYW1\nDqkUpsio5zMuN1fsdjvGoScGj/iJjiN6dDbl8slH7DCitaKqK9qhRUqBc5b5Yk6IiaZpCN5PqUU/\nMfjBe+wwoKSgqioybfDOUeTT+04xkkLAKDl1OIZEpjJc77B2EhRJKWmahqdPn+C8xRjDbrdjv2/Y\nHxqkVFy9mmzyAAAgAElEQVRfXVEWFV3bM3T91LnJ1O0ppUBIgTYKISZ5uVRgcj11lEpBNww3aZFk\nHHu++PY7NAfL1eWerp14HSkgz0qsCzx99nxqkppVSOE5OVlRVgWXVxd8+OA+F9cXWO+4vLwiRUme\n11xfbfF+pK4NWZZYr2e8ce91TBKsqzPCKLn3+tsEH2+qZ5O2Js/LSdHrBiBgbcetsyNMBqvlDPBk\nSiOSpj1E2ibQdpYgAl/68hdwYaTtP3368LkgGqUQSJ0YrWU+qxm6A84FhmEgKEuMAWPAyEgMI0fL\nim7saIcRI3NA0w4jWipO79xhs9tQL2e0XQsism8b+rYlLypUXhCjQGvBoT0gTE5je5QSzJdLrq42\nSCxCS3xwlFV105MqGAfH5YtLci0J1uM7z6qeU2X11DDU9kQSi/WSF+cvEEJxcnKC95Z+HOh9y7zK\n6ZoNVZEx2JGYFLPakKIhrwq8c8TouDz0BO85dAeUVrRdj4xTa/lsteDicM2+b8hMzsG2UxiPgxip\n8gJZZLRty+HQknxiWS1o+w4fA0YVbNuBzJipIjKbRDhD2xGtZ1ZWHPqeBNx/+phlOWdWVizKEhct\nfRrp+oblcsXoLalN9P0UXQkXMQnunJzig8OYjObQcXQ852q7YV5VOOsRQtO3I+ujOUYKxtEwdgNd\nN1LkFYe2weSa3a6hbXec3fAfbXvg9OQWY1cjpCYBZVWyXM7ohpaiLAkhcvH8GV0/Ml/MsW6ge77H\ne0eRaZSSRGnRymDDyKtvvM7TZw+oi4I6n7G9tthB8Iu/8Le5uLhiXke09CxmJf/4W9/hzTduU58e\nY/KcMfQoLbm+3LNczJndOqXIcmb1ghdPn/PFN1+nbQcObc9itaYZrinqxFff+RLPXgyU8znNxUP+\n+i99nUfnD7j95mt8/PADvHPcfXUJUdE2LUdHNYKEMoqqqG54EcXZrVtkWUG77+jGA1oqtttrhjBw\na3YbIQZi7InRf3p7/CyM/M+KlBJ5nrNaH2OdQ0qJMYY8KxjGnqIyJAIxBmIKU4uzyaaeBSFQWlCW\nOYvlDJ0pkgi0fcs4Wrre4j0gNF0/MDpHihFrHYd9SyKRxMRTHJqOECcOwXnPMI5IKTk/P7/pY5dk\nJiM3k/BJqwyBQgpFWdXM5gu0yUgIxtF+Ulvu+2kHrsuC4B0xBa6urrDW4pwliQRS4HE0fcP55QtQ\nmqQEyiiaruHQNmS5YRx7Qgw03YFqNqOu54QQaduO0TrcDdOf0pSWZVlOUZTY4BndQCSybw6EGGm7\njm7o8SkSQiTPC4L35HlOWZUUVUFV10glKMqcfbPn8uqSvu+mCCxMysSUbgg/qdle71Bpmp3grOPF\n8xfs97tp1ERKkwR5HCYtSYiTijTPKcoSISf5udIaJRVd12G0oSyqKbrIcozW5EaxXCyxdgQhyAuD\n8w5iYhgHfLCYXFMvKoaxnyJBAW3TIETC2RHvHb3r2R32hGCx1iKExLmR2WzGg4+f8OjRE7q2RQnQ\nUiJT4rU3XkWZnLbpKYqCw35HDGEqKRM5Wi8oS421DffevEuWCc7O5pydrKnqkuAd3jqaXcuDH/0h\nv/3Nb/LNb/42zbBhVpX0w3BjE1AUGkFEKYUdPSRBCAIhNCarCEHiRmj2PUYrjJFUlWK1Lrlz+4Rc\n52y2W6TSlOXsU9vj58IpSKUpioIsL9Emx8WAyXMeP3mEVJKnz54QlaFarlB5gZEGLQw5BafHp+y7\njr4fQEievHhOUorN5Zb93vHk2Za2T2z3HqjwwfDO21/lZ3/6LzGrj9luDnRtx2Hfcjj0ZEUJUmJj\noJrP6Pue9fqIGCORwE9/7acoqhJtDK++/jpFVfHo0RN8SPgkGG3gcGjxftol+7GjqHJiirz/4x+x\nbffs+g4PbJqGPgQa59iMB967/z5X3ZbnzYaDc7TO4pieRYgB6zwxRawbCSGwKGaooMhkgVYFg404\nFO3gafYdKUqyLKesZ8wXS6SC5aJm7FqMUpwcH5PnU5eii4n79x+QlxX9MHJxeYELnvV8jguOq/0G\nG6dZBbbrqOuSy8vn2KFnv9/T9z2PHz7mztldDpuWw3WDGwISxfH6GDc61ssV1lrmiyV5XnF8dHJD\nfEoQOVLlU1eoSlSLnC996UtorXnt7qtkKqdvOzKlaPd79rstxc0QE1SkHw4UZUYQA33ocHhc9DRD\nSz2fpNbL1YowelRSDIOdpNgxcHn1HKM0bdtwcXXF7uD46MNnON9R5hEtHJkWLGYzXrl7ijGSoijw\nPvCzP/MzrGZrFsWMdTWjysBIS/IbMtVz93bF6a2Se2/d5qvvfIFvf/vbfO+73+f1V7/Am8uHHA8f\n8uD+Jb3L+fpP/TzrekZpauKY2F7tkCi6xmJHuLjYYp2kbR3eCewYyHRJXa8wpSLKSAqaeb7AJEWd\nGZqd5fpFj05/8a3TfybEELneXnG1uZ5q3EJyOBxYrVccmnbSsi/XeCRtN5AX1ZQCmIzN5sDQO8Yx\n0LQjWVYzDgmSJqGRKqdtRoKDph1pm5GxH6iLmtwUjOM4TRDyk0cWSEIMCDGx/l03MA0pCVR1wetv\nvErbt6AgicTHTx5xevc2SUCWZVg7cHHxYqqr30xK2h8OuJsyXz+MuBQwZcFsscB6TxSwbzqSFCQl\ncSGQEGRlOe0S1iKkpO07hFZTVDBYxmakzApEghQFZVnRdcNNY9Y0ecgYQ991U2uytTRNgzEa79w0\n8UkrNpstwzDt3j+JkO7cuUOe5QTrWS2W0/QfranLEi2nMp6R03Sn3W7HbrvllVdeIQZYro6ISaCV\noSxLyhvN/sSTBFJKjOM4Daap5jx7dom1EaUy1us1y+VyImq9QyCY1TOKm80ghYTRktV8yenRKVVZ\nQYgTgdw2CCFvpO3Te83ziafqup5MGTJTTO/YBxSCWydnkCJGazKTk5mctnE4D1WRMZ+VDH3HxYvn\nDH3PbFbyxr3XqMqSqiiQQJHl3D65xenxmmQthRLYvufB/Q95/uwZz55d8r3vfo9vv/sukgXf+Mbf\n5/h4xWKec3ZL0AXBb337e7R2ICsrZvWcW6e3IWqCF5R5TVnOWK+PUVqijWAYW7QWk4o1Onb7lhgV\nKUjaxpG8QjOVVYt8xvH601cfPhecghCCh48/pp6vsDajyjVX11eURUFMUJZz2t6y3R1ISAbrJhl0\nTFxvW8pqSd9Ogo68qkAYsqzGR8Fyueaw3+OTRcmMsqi5f/8++0PH9WZzQ0gJTo+O2Bw2U0twO4Vj\nk6bfUhQlLljKUvH+gw9phgMpCGSmEUYiM0USUxNREok8N0gp6fsegOVyTrdvJ4FRTIx+ZFkvKMuK\n6/2ebXPAekeCm6lBBoRmHFryQuBDYLlecXn+YuogzDJcv6XZHVAio57P2TctScQpBM9yrHUcrY8n\n2bDW0/SnGICIkoLloprGk3lPnhcU5RyHJqlpJFjTdMwXc0ycxr2lGKmqapJiDxZQLOcrnAscrdY4\nF6jrGp0M6/UatokQPXmRQ+Rm/kS8IQ1bZMqRciLujJnCdy0VSsF2f0BlkJFxtF6jpabMSyRwcfWC\nmGd4Fwg2YYRi6HvMjaJUG4kWCsRNc1QKdN1AYQxVkaOFRCQYwjT6bmgGbp2eYcdJCtzJxOZqZL28\nzfmzJ/TzgpmZJO/Be2Z1SZ7lbOzA/KZ0XpcCozXNfodS0zCdwhgsmtXqiHe/80Oafcf9+8/41V/9\nRTKjePz0Absu5+f+2tf5hYPiN9/9fba2o1ysmTcNR6sv8+TFSD84QHJ1ecU7X/kKHz3+gKvtlnld\nk4RjHEcqXeOdZOgHTo+WFHlOVRW4MXH79DZVNee9H/zwU9vj5yJScNazXNfY1HC1u2LfNrzx1hvo\nTLE+usVieYvnTy8Iduq6U5lh1zV8/PwpQU9VhaLKaIeG68MVEYdPjt42oCJSJ5bLBTEG6qIkEnj6\n/CkhRpbLGdaPJBHJ85yrq6ubqxL0/UCelfTdSDe2PHj6gO/98F8zxB4rR67ba17/ypt0caD1PY+e\nPyYky3I1p+lahnGcrrXZk1UFZBqZaZaLOU2z5/zZE4SMJBUnp5OXFFlBrnOOVqfY3hN8oChKdJaT\n1zWjDQSXePXkFcq65vqwoelbQnRwcw9VUbKaL3GD5friEu8nyXc+KzGVpvctJ7ePETKgtOTW2V1W\nqzWz42OK+YLi/6PuTWJky9L7vt+58xA3bkw5vbHqvaoeqnru5tAU2VDTbMGCZJgwbMOCF4QBWxYg\nbwwYlqGVAW/kje2dAe8MLwxNhkmKoDiKoNhkk2Czu9hV3dVd9erNL19mZEx3Hs7gxUmVuZCpsmkb\npbuKfC8jMhCZ59zvfN////tPc05PbjK0A9oRvHjylDxMkGWPHjRKw9FsBQomSUoQRKxWK7tI0pDL\n/RWzRU6axRij8F2BawRqlBwdHdn3mCRMJzlN3RG5gtVsxjROaXY1SZAwS2d4vovvOVy+vCDwfLQ0\nnB6d4LsBWbxAdoar51sCJ6IqWq6uCibuCbPoBN0oTvMjQuFz5/SMPElYzuYWxKMVeZoyjRNW+ZJA\nQBpEGKnoe8HQx0wmt/nsG5/jdHVCHCakcUpVHzByQCjFbDLj8cMPePr0EYfDFW1bIlVH3zZ0VcN2\nvScIV/zyr36Tf/CP/4h33vkhP/7l17n/6qu88/3v8/DZQ+6+8fP8p3/3dwho+MaP/VX+1t/6zwnC\nKUmQcLI6oqthNj2iHwa80OO73/seddWw3e642m5o24a2ren7hhurIz557w6eP/LGZ19jvpxSNQ2q\nL1ifPyRP/Y+8Hj8Wm4Lnefi+Q5z4RFFowZRo4nSC8FxGpZnnOZM4Jk1imq5BeOK6+9vjeJJRNgxD\nSd+XTKeR9Q4gGcYWYxT90BKEHmV1oOlb2r5FC0Pd/Z+yaG00aZowjCNKSXzfo6oq2rZFGYXvW9Ao\nLswWOU1Xs96safqWqi4JgoAsyxCOpTiFUYQRAi8M6MYBZRRxFGLBpzBcG7BQVl7tOx5oO77cXW1J\n45QoCJFKWnbjJGM6nVnpbz/gRQGO59D2Dd61V6BrrdFrmk3p2pY0jhmGjiSJ8AMPI8APfQbZo4wh\nTlIO1wi02XyGMhrX96xFV3h0Q880y7hxdMpn3/gMZydn3Lh5y/aAPB9PuKRxgsBlkFbJ6IcubVdj\njEapkcvLC1wXsmwKxjCOlndgjGGaZchxQGhF37bk0yn5JGc6yYl8/1pBGaDGEc9x8IRHGMRoZciS\nnCiMmUymGOOQJlPyyYrQSzk9voGSmsDzGDvLPujaBqUkUtq7OVojB4UcRqIwRElFVbZEUY7nBmht\nruXTgmEYKKuS8+fnPHn0FM/xSJKUse9YreZUdcFskWOMuWY8eFxeFbz19gfcupHw5puf5LXX7/F7\nv/ctvvl7v8/masuz5z/k7/23f4f/7Bf+BqZ4zs35nFDtUP2e3dUlatQ0VWthsa4F7IZhzOnZLcqy\nRY1W9hyGMWEowQx4ns/5y5cYR+EEPfNFwt07Zyxm/5o1Gl3XwSAsDsu3rMBFtiIOUxwBWvUU7R4/\nDFAjeK4tqZUxOL7ACQLqtmYxm+MLh77vCCOfaJJY953RTPKMIAotQcl3bNNMj4y9xBUuwjHgKIRr\n6IbKAltcQRA54CjGdmRUPWHkUR5KZC/JkgllZQ1Vnu8ymSSEcUQ/jkgh8KMYqfS1KUUThNYl2V/r\nIYQwGKkwgyYQPmgInJDA9cCxaDc5jMRhjFEG1UuSxGLf4kkMRpBNMiZpasGdjo/RBq0VkpF4GoPQ\neJ7AcwRSNgjHIIzBVZD4EWZQBHiYcSB0HeTQkaUJahwJfZ/yUDGdzai7BiU0XuRx684t/MAhz6c4\nAsauJ0kihCto25a+6zHKUB0qpumUwA9wXBfPddHSELg+cWwp1mhLctZaW8m4lmjZ4wkse1FBHMfM\nF0uOT06ZL4+IoxTXEWR5wt17r1A3HYvlkRWtGUMcTxmUpOltpVgc9ux2ew7Fnqo+2M9fWaiu6wgO\n+z1DP6JGw2bdcnG55lCsubh8Tl2XdEPPZrvFdUNOz24itebi6tICa33r38jzKU+fPKfuBk5v3eHu\n/fs4gY/junzytRt84v4dZtMcJY0dX7c1D58/5ubNY26+coPPfOkN7t2/wwfv/inF5oK62XCUTQmd\nAK0krjMSRJpJHOE4hiDykQLSaca+LKnbkqqtUBoeP3lOUVq4rpQDbVvx8uWLj7wePxY9BYvqBs/3\n0Urj+wEYl0+89im+9Yd/QBB4SNNzqApM74Dfo4TA9XzSOKZoK7J8xunJCYfDgW4YGLVgMpsSGIfH\nT5/Sy475cs7Q9ZT7nmQSUtc7bi5P0XKg62p6PYBrWB7NUErR1BVVs+PWrdu8eH6JFwjSLCIKYiIv\n5PjkmO/84E8J/YAsz+k6GLVCC5jMZxgNRsBms2E2y8AxtE1LPp3R1iVxEpNEE9p6oO9GFmdz+q4n\niSLavgbX49byhH3VkGUZKjF0XcdkGnF5eckrR7doOjuqxLjMJ0vapiKdJLSqJ4pjHM8yFbuqsZ9j\n2+PhkoYZvojo6gE/sQ3S3eUlXVHw5HDg/t37OALyLMONAh6eP2FdbXA9jyd//C1ev3cXaRQIQT7P\n6Puesu+RUnN0dIQrYOgku03B0dENjDFsD1uWszlpBE3fkU1yiu2O49MThr4jTAJk17DdbGm6AtcJ\nbD9HjnR9hzK2+bvdHDg5u0GnB3TgMk1XqP2OKEt4cv6INEwYRonCWqOHdkAbxdnZERcv1ty5fZ/L\nyx1Kj9y9NaduOorH75EkOWl4wnI+o9yvWSzu4gnFj77/kOl0CsZleXyEwoq2XARxFHMoDmy2L3nj\nzdf43X/2Fr/+a9/i8rLj/idn/PRXX+Hn/9pfR+DTdC0/+2/8DA8efIAQiovLS979/ltUdcP9L7zB\n9GzBP//NX6UpNhzdyPj8538Sx3cptyOzVU6ve4TrYaQgm84ZZUHXDUyzBQ+frLnabPHihMlkQt00\neMKgTEg+TTgOP/p6/FhUCgaLUJNKMciBXvYUVcWz588YlURqZZtdRjPqkaprkMbgei5D39NUFbti\nz+PnT8lmOXXXEqcZvuPgOKBUjxcIrrYXHIoNQWg9/KHv0TQlcRISp6FVJ2J1E8YYPM9jNpvheS44\nEIQhWms832E6z5BqxBEWBFrXNeM4orTGGBhHSz8WQDaZsJhbVoTNFoBxGLEZAwIv8EEI+ra35fUw\nEocJcRhxtd2TJBOktDkQForjcHx8wjD0BIFnaVJa07cdgR/SVi2+45OElvOAdhDCs1LjXuEQoLSh\nKEtwHfwkxPVsDkYcxVRlRVVVlE1NP3Q0TUU+m4Iw1HWNELDZ7K0/JAxQGLpxwPf8a+2FRdn5foBw\nHIq6ohtHyrKkqmrGfsAYQ1EW9rldRxiGltrkOoRpzDBYjcgoRzbbLf3QX2cuCNJsSlG3KCOoqorN\nbnud9yGI4oAkDUG49INinh+BsUcOIVziZMJ+XxDGMd0wULQ1RVVhGCmrEpTL+bNzhn5kuz1wsd5y\nenpGnudkk4kdoV5DhJVSNG3LgwcPaJuOR4+e07YVngdf+NwtvvZTP8nP/sxP07QVwtM4jmU+nJ6e\n8N3v/ilHqyWbqzXPnz5jHDswCq0Gvv3thzx8dEFZjzRDhxN6di3IDuda2q9GQCr0qDhcHXCIGUcL\nENrtd/h+gOOGKKAfFXXbfeT1+LHYFLQxH7rjRjVSNRUv1xc8f/mCOI3px9GCVdEkWYLEYByB63sU\nRUkST5jOpyjH8HKzRmPn81KNaC2JIx+lRpQZ6cYGpa0Qaj6fkSbWeDTqgSiJrhf0CFilXDu09HLg\n9MaJfa/asN9vWa8vKOuSSZwS+LaXgLCVAY5rRTZhQFNXNFWJGpUlUSMYR4kQLkI4KKVJ0pRkkpCm\nE7quIwgiIs8nTSYIx0VjqU1JOiGOUxzhWZdkEl/z/m2FlaUZSZgQ+RGRH4I2aAkYgcBhHDVKCRzH\nYxgU4hreOipF03e2gbg6JgwihOtcC5sUddtYfcLYU1YF02lG4Ic4jo9wXDo5IFwHNSqm0ylS2gyM\nIIio6hbheDacRVhl5ubqyuZORIE9zghwXAepFbvDHtdz6IaObmjZ7uwm3vU9URRyOFhDU9V1bHYH\nwijBCOu0dQPPbu6eDaIJ/IBRKqbZjEmWg/DJ8wVlVbMvDqyOjxCeIEoTcGAxPyafLJjEKfPZgr4f\nrEvT8az7c7CblxWltUyyCcMw4nouVdWDCjk+nvGlL77Gj33l0yzzHNML5ssZWluydBRFnJ4e8bWv\n/TSybUmjECmlBai4Dmkc8ek3bvL02ZYfvf+Eot7jupJFluAicXwH1xWURYUeDWmYIgfJcnnEdDpj\nuVpxenZ2jeOLqLuWbhyo2uEjr8ePxfEBjCUxa4seNwaqoeZkfsJ2uyUOI+RgFXmtHnDiCOM67MoD\nUkr6okQ5MW3fUfUj03jOy4sts2WK8UACZVtzduuMd773pwRtZM+psxwxjDy5fMb0aAlGsZov2e23\nvHz5giybkOVTpJZMJykZU9q+Jk1jpOxZX10yy1dMkoSubfACD8cVrI6PKA8VWZKxrlr8IOTiYk3k\nx5YjEE8IPZ9nz59x42bOrjiwnK8QCLJpbo1SrsvYWqHPpixYLRdcXr6kbTteuXOX7WZLmiQ0bUkU\nJswXM7YXJUkcATZnwbgCP/IJ/BDf8zGRvcMfr07ZvLxCa4UjsJyFyQRZtVRlwyt37+GGLic3byAc\neP78GX4UEKdTmnbAdT3CZEo/9hihMMJQFAfaYmC+zCjbjq6t8VwfqUe8UNB0DckkhdHqFPb7Lccn\nRxhHgzBEcUjduhzfOsV3HIprdmOYxJboLQRhEhJFAdvDBZP5CqUVh2JruRgGZD9QVjuSaILjKrq+\nZbWY4mc+3djaik/4+DePGfqGceiQsmd1dITrhFw8F+z3B07OznAdwdgdOD+/YJkF7A9bFrM5+2rL\nbr9nMZ+jDAjP4/XXP8XL8+e8fP6QL37xS2gtcF2H+/fv47ouZVPQ1YoH7/+AWzdv8ku/+Cs4rubH\nv/wF/ugPfp+/8R/9bba7LY4c+Kmv/SWePXjI03/wu/RtS1HU+AuH5w8v2Tcb+lKT+AEnxwscHXIo\nCtq+xRue4EcV0+kpda2pqj3z+ZLj0xnloWAxm33k1fixqBQMWIEOttM8yXNGJdkXB6qmBs9BG0E2\nz/Ein35UeJ7tTPueFciMoyKMUrphYF8UHIoKZQxNN+BHke1XAEEYIxyPqm4ZlWFQGoVDUdWU12Xz\nMAwsFovrCmJEeILtfoscrc4gim1nWip7ZpVaXjccDW3X0vc9cRjTt/31qG5CHMe4XmiPCf1wjQ7L\nkFIyjiND3xPEAcIVuI5P4AZgBNoYQFNVBePY43kOu932WvLcWd1CmuAHAUVd0PUdoxo4VAccT+AG\nLjiGXbVnX+wwQrHZbVEa2roj8kO6ztKSh1EhpaaqG0alEJ5rRVRhjNI2Ag7h4vohXddRNxVhGNC2\nDUII5vOcLE9xfYcwCcA1GEez221wHEPTWKVnNp2gteLq6grQCAyXlxd0fUff91RtzWQyIU4TO1UZ\nerzAp65L5ssZ2TTG6AY5VESBY121WiC0II5CJpMExzGMQ8Nut6ZrS9TYY9SIUQOR73B6csIsywnd\nAMd41IVD30Y4niBKIxzXw3Vdzs5OGceRo6Mjuq7DD3xeeeUeV5sdV1cbbt++TdP311GGEq0FT5+c\n8+3vvM3f/0f/mPPLl5S7gmdPHvCdb7/Nr/zyP+Heq2e4wuPdd9/ljTfe4MWL5zx88B6+5zAaOL1z\nj//i7/6XfPftd0gmE7QSGAmJn+ILDyUVQ98zW2SszuYc37QirjBI6GtDV4/IUTIONhcl8h2GrvzI\n6/FjUSkYrfHcEEf4KD0Qej6f/+KXuby6AMehGyTa8QmzlGCe8PCP3iWLJxSlRaDfObrJvqppqpa2\n7JDX0ZLnLy9IIwvpMI6hb3qSJCUwAX4ccNjvUWqk6VoiIA582qFDGoknPMZRsljMiKKI/bhBaZco\niDkUayZRjjHCbhi7K6azCYeyYDadEwQBu8stUirm05xh7Ki7Bs+LkeNAVSmWiyOruXBcFvnMYuAO\ne+sBEAI5SJq2xQwtnicYupY8n1onqXHo6s5CYvseLQq6QTJb5Tx98pi7r9yGQVhWoOvioujHHmUU\nwngo0+Moh+VqiVL2eFXuDuR5DtpazteHSxxXIEdtfQ2uy3ZfIBwf4Qdsd5fM53MuLl8yzTIGDXHo\ncXl1jus6BEFAM9bMZlOOj484FHtrhU9TojhmU7rcvX2LD957n8qtSKYpUmvqfct8OkUrg+MbuqFj\nkIbZIqdqamTXMJlE1xuFw3K5YrcryScpSkk84dC3reV8ei5aDfRdi+taVezVxgaV9Z3GcxOEjikO\nhg/eKzle3WCzfgs51vyln/wZHn6wozjsSJIYR7gMo8XuZ1N78/KDkAcfPOLu3bss5yseSfjOd77N\nK/dfYXV6h9/49W/yP/z3/yN3T1ZoHD77xn2+8OUvMZ0uCKOAhx+8h3JCFmc3eP3+K0zThDiZMD2+\ng5ec8de3D3l6+V2asSGNZggPmlKSpjF1N7JtnlDVHfttgyt8mqYHtcX1NXnmMg7PEO4C2Ukunq4/\n8nr8WGwKNmVUoAaFllyfeQdeeeUeb731Fkoq/DAiTFMu9xfESUJxKEjT1J65BURBwjAojpanbK4u\nCcOA49WKw3ZNmufWdGQMaRiROCESc91ANCTERGFMVe7xfQfnOki2qmvCKLKEnDhmvS4JQo80ihn6\nkcCLcQMPz3OttdaB8rAnSVI8FyI/wnE1fWcRco4fEBmfoWlReiSOUwLfx/M8Xr58SS8NQjpEkcVw\nIQRN2xBHIVIOlGXJ2Ev6vsdohygI8CMfg6FuKxzh4fiWNG2MIUkm1ufge4yDREuJUZokjhilBa1M\nstw+CxgAACAASURBVJS2LjFacHl5CcChLMHXKD3asWtZIlzHciOiiG5ocHzLjej7ns7xCQKPKInQ\npSFJU6RRSK0ZOptz2LctWRJ/iMTL8ilKKU5OTgAHPIf1ZgO+lXHLYaTtW8q6wfMDC4VVCnFtJouC\nGN8PGdqWSZowSMXmqiTLE4su09D3PXk6QTZQNx3a7HEcjyAKMCqhKBRhOOOd773DIn8VLXsWs4zt\n5or1xQtm0yn1YXtdRY3MVyuGUWEcQdN3PH95QZ7n5Islp0crTs5u8PDxA46WSx49esi/+Y2f4e3v\nvcMrt89YHR8hlWGxPCHJUoZ+JIonfPMPv81nvZCLscdF8O4Hz/ja13+OH/vqiocfvEO6cBlETJzM\nKOsK4Xbk8zlBrFlv3qfYNbS1oasFSmluHN+l6ys8EeH6Nhh3MV/ijCHwwUdajR+P48N1AIhWBiUN\nruPx8NFj3nvvPfre+uddz6eTA5vdBlc4NiXa9/ECi2Df7wpQgmmaEQQBxiiiyCeJYoxUNFWFGSWB\n45EnKY5RKDniue6HOvY4tnp7bTSe75GmqS1nqwrXc/E87zop2v7MWb6gHweatkaqkUma0vcNge8T\nRwFR7ON5gjD08AOXsioQQJIkKKXZ7XZ0XYeUlgsYRhGjktdOxwThOpycnNhsSNej63qiKEIpc50Q\nbTX/Gk3XtQhHkE4T5HWic9va7AnfDyyCXruoXtIUNVk2IYwCtKNIpym+7+H7vgXTXOsuhnFgf9ih\nlCVjOw5IOTCMA1Ea4vk27dp3Pa7Wa7SxmoM0nRAGEWmSkSapZSoOiiiyCzK+DgnWSjO2va0s/MBu\nOG3H5eaKcZCs12vG0bpN+966F7VWdN2A0nZq4fsBoxxReiRKIoa2Q40StJVQd50t6Yde0/cj7TDS\n9SObbU0Qzjm9+Wny2SvcufM63VCRJTGrWU6523K1viTL0g9dvDa7wgUESZKSpiknJyfkeY7rBYxa\nc/v2Xd55+wcWdz92fOlznwZHWvHWNAPH4/xig3YCbr9yDz9OPpxmvHr/Pv/Ov/cf8Ok3P8N2e8HQ\nH9htL9lurhjGFo1CmoHL3Tm7YkOaLFjMbnL77HUm6ZyT45sc9gXlvuTl00tU51EVPVIabt+6/a9a\nhh9eH4tKQQgHYQLGsSeKM3ZFSRSHdFVH5DqMfYPrRmyf70lMRqVtKR0GDggXj5A3PnmH9x48RAiX\nk6MVTVuxubxkkNZ4k8QRqutxABG6yIMk9gLiKKIqK8LEoXUMfdfjCg81aIZ+ROPguRH7XY2bhIzj\nwHKyommu8NKI8uk5k0mCkeCnMZ4T0TYDRbW3LkQ/JpA+V9steb5kNl+y2x1wHYnWVvNvjEDgUh72\nCEeA1LRDhe9EmNHB9xK66sAiX7B+dkkQRwjXw3M9pGrpKuuXr+odYRTQtz3DYIgdG54rrxfIPDlh\nu72y4izZY7S9KwS+4Gg1Z1c0OJ6DIy3qPIxtQK1rNHqU9L31L1RdQ9I71lxkDEEcEkUJm/0BPwgZ\nu4EwTtlcrcmzKbdu3sch4lBdIJoW1/VJ/Qwz+nS9Abdn2O4IQp8gEpyc3qB8uSFG8InX7vN8/QKj\nG7rOQY2CsRd0vsN8PmVfbWnGPZ6bUNQDDJossxGBxVVBHE7Qoke6kig7YpB7NoeS09kbVIXhT//4\n+6AEz58/xqgez0sxukPKDiUUq/kJbd2gpAX6+BE4SrJaLKjLPQ/f/yGh4/K4afns5z/D2cmSD97/\nAedPHxGrKdF8ypuf/TI4EcLz2beKx88L3ntyxTf+ys9x75OfQw419199nV5JaDSPHp/zJ2/9Ntly\nwPM94kER0rJYZbBKqIsdoQvC+EznGa4ToXrBKFv8AKQMmeZLhm6gHyTnL9a0y/Ejr8ePRaUghEBr\nWCxWCMeqFY3STNKE2A/BGIrdBkd7TKMlk8kUJTXgYEZNX/VIqVgs5yDs3WyS2rTqUUqE49hUIK0B\nQadHOjnYCiFMMUp/mOF4tFhRlw1qlIz9QJblFFXD+fmlnYxog5AufhBTNSWesTy9sVfIQTLPl2ip\n0cCoFI5wCb2IxWTO2dEx7rU8dugHbpzeQCmF0YambsmzjGK3pyoK6roBBL4bMHSSSTrjZHkDT4S4\njo+U2KbWo2dM4oyxGwk8y3dI0ykClyi0Fm+unYP7XUEcJ3aO74jr8auka0rM2NI2JU1dXR89rhOu\nAx8pO8ahs82sMERJQxKkqF7iYOGj+XyOox2bCaE1fdejNUilefD+Qx4+fs5mu6eqG54+fsZuXbC5\n2pFMcnzf52i5ZBKHuK4gTnx8F46XS5rygBASR1h02uZiQ13XBGGMNNAONXW9w6BJowmO49M0LVVR\nMk0zwjAkyxJGacEwuII4mfPuDx4RejFdXZLGHoFvOD4+JYxSlscn4DmkaczF5YZ8tiTP51xdbdmu\nr7i8vOD8/BnTyYQ4shujGkbef/99hnHkzt1XeO311/A8B4y6FmEluK6P68bksxVKGX7tN3+X+69/\nhiiacrku+ODRM9aXG/J8StetMe5om8i+wA8c0D1DX9E1Fa7WOI4kSAR+NJJNI27fPqEfKnrZ4AcO\nUmkWiwV121BU/5o1GjGGUfa4zgTPc3AVNE1DlsYMw3BtCT7QVgeiyJaZapQMvUaNA2mUUpYH1Dha\nsU1VEy3ntP1A4kUM44gfuLTdSBK5GGWYTnM84eA5DvP5EoHLaraygpm5IU5ipJLXMlyBCDwYFJM0\noe5rXBdcrVFDx2hGsmlGlsRsrjYEnsfZ0Zm1JjseGsVqaV9bdgNIBUogjHX0eY7DyfExdVOwyBdc\nbTa05YBjbBz6NJtx8+w2i+mcQ9MilaRoaqRrmM0WeF7AdDJDKvCcANfxieKIpmkJEzvj9x2XySxm\nGC1evR8kvhfYWHjPw5cjxXaNH6W4boAT2fcV+g6u45Mv51TVaCnZUYTsJavFin1xuJbhQtcMeFIT\nx4KiLsimKUYpfu3Xfo3TG2fc+8QZjtHEqWa+WLDf7+hlh6xriCPCOCAMQtrrmXrb92jHKlyVNlR9\nzaAlQeiy271k6azAOCTRFE+EjHiEkQ8YlBIcrY4oqwbh2s9xGDqU8gi9KUnic7W7QinJyekxcRzj\nCYf9bkNdVwQBeF7MYpHZjFFhK1rf9xmGjul0St+2xFHIk8cPybL8Wt8imC+WGNVTmo59VbFoNwhX\nU3cjXhQjsKrPQy0x2qepBVVREE8Szs5O2e+eY3SN0IL9viSdZmgGpJD0WuGGLsYT4AsUI2GYMl8J\n6qYgmwdkwqVsnzH2MbIYWZ2sKMv/nzYFIcQjoAQUII0xXxFCLIC/D7wCPAL+fWPM7s99HUdwcrKk\nqhqEkcRhCI5he7Wha2pObpyRZfYcrpQkDhKMZ7har/E8OF8/Z7lc4QiPJAoRKiEJIzSSSPt0uqOX\nGjeOCbyAvh4xaLJZxna7JQwj8umMYrdjs7dSUc/1yaczkjAiMg7Hd+7x9PwZg9KU5QYPh0UypY8D\nK0rxXKrdhiyOqKuWpmtsed9I9GgIJzGHokCOisDxcZVHX0gmXmLTjIYR02tevf0qSDj71C3aumUc\nBvpuQJsTBsdQdS0np0tG3eKgEZOEIPApS43nuIx9T1e3RJOEsm7QleLWrVs8f/LUKgI9gSMMbTWy\nmEX0dcXJPCQzmrPlnM445PMlzy7WNrzUDGgjKJsBz5tw2O9Z5FNePH1Blk3Jpxnri3MwDoETMEkD\njCOo9iVy6JjlOV//2Z9BOIKr8pLQDxB+wPqwRsoO10kwBGz2HXpXYxKf733v+3zy1l36sWG6WlB1\nFUEcIcee2fKIOBacX2x49OgpR8czBjnYG0Y4xTiaYeiZz+dsDhuSNMPRLst8hqLly1/4a/yv/8tv\nIEfBcjXn4uKcKAq4desWRVczSULkGFOWB9qu53g6B21oqoo4jrlx4wZ939K1Nc+ePGS2WvDs6QsE\niovnTwldw2E/J01CvvzVr3N+/pwXj79PkOTgzbg8POHeJz7F5eWG5dEd5oucO7dfZV+85OatM/7g\n279BXWwYxpY4yRFCcNhZPcViNeHicIUvBNoztEXLbDonDivU0GOMQKk5eZ6gzQuyNMcEDrM8ZzZb\nAN/5SOv6/43jw9eNMV/4Mxl1/xXwW8aY14Hfuv76z71836duKqLIJ5tMqKqCuqxwXEEYhlZZFoSE\nYcRyPidNUpSUJHFCmqbks4zt9oo0Dhn7FjkoAjcg9EJmUcRpnpP4HlVVkiQxk8QeGfq+Jwh9G0Sr\nFG3TEwYxgR/Qdz1921IXJVkyYTFdYLQFwqhuADkyn2aAwBhB33WkyQSMIM9zPFymaYbRgiCIuLy8\nou9s8IpRGhePF89e4vkB4zCilCIJEzzhMZ/OEdowiVM8T7Dbb9hs14RxRJhEVHVFUx1YX70kjAPO\nL5/TjQ1x6pOkPlHksL26JAoCBIayOGCMYhg7Dvutxd15HlEUE0YxfhiSXgfF/gtYqsEmMxmlrKRX\nG/LFktfuv0bg+6xOjtEClDH0fUccxwRBwHxm3zto1DgShT6T6cSOeT2PMIpJJhNwBYujuZWt1z2e\nH9P3cPlyTxzm7A8txmCPfkoDLo4XcCgLXlysESJCGxsENIyKSZ4gTc++LKm6ln1ZUFYVRin2uy2O\nMCRRyD/9J7/NBw9ekGYZT643yn5oubg8Z7lc0vcdi8Wc1dGCMAjYbrdcXl5+6Orc7Tb4rp2OLWZz\nm/MZR1TlgVk+oakK2qZk/fIl/ahIsxlNrTHKtc7WUBAkI+vDI5LUpayuUKIgiHt6ueVi/YAoCdlt\nC8ZBfpiEXpUjaI+bN24yyTLiNMXxAzzfp6xKG6irJT/3jZ8nDk8JwwVlXXJ5dcG+2NH2H13m/P/F\n8eHfBv7y9eP/Gfgd4O/8eU/QWmO0oioPRFFK5AcYce09SDPqrkM7DkVRkEYd46jwPEhXGW1boPG4\ne+surusyzxfIWFKXHcLTfONrX2e7ueSger71o3dwEWyuLu34sCiZzXOEcDHG2lDf+PSbvPXOO0SJ\nRxA6uAa0hhebK6bplJPZnNXt13jn6QO+f/GUOMhwhCUVd4MijhPSdEo/HBDCRWmbtRhPUl5u1syz\nHKkMcZqTpHNerjc4riGOQup9Rd+NlHXN2UlEcw1tvXXjFZ6/2LApv43jasrdFYtsyv7Q0GtYnd7g\n8vKcZqgYx57QCTia5bR9j1IjevBJQoc0SkhPT1AaStnS9g2dHHj30SWLcEITeGjXZRhHZNsgXXDc\ngKYfqOqe5VJTtR1GQdlWSBORZQlBEmMcgdSSp8+eM5kkTLMMz3M5FHu8MML1bKDLfl8ym82Z5jFt\n2+B6DrNlRhiEhGlC1HZ4gcf26oLl6ZzHz56xPDqlqUayLKOfaoq64O3v/4gf/8pXuXvvFd75wZ9Q\ntobNtuDG7VuMY0eexgSuRxLEJL5H29esL/e8/daWNMrY7zfMFyscM5DnGbdv37ZZl0ohx4G+7emG\nniTNODo6Yjad8t57P2S9XsOpQeAwyTJu37rJL/5v/zuf+cynGPuW/X6NqzuWiyXVYUdRFNy5+yaX\nuw37umJ574i33vtjvvr1n2L9fEPzYk1VntP2G3ZtTtk/R+4r4mXAtjyQTnyW0ymBWdBWJafHd5Gd\nwhhB7CeU2wpfOJy/vKSsN/zmP/slPvH66/iTBV4g8NsCqXra/qPLnP+ilYIBfl0I8W0hxN+8/rcT\nY8z59eOXwMm/7IlCiL8phPhjIcQf19XIeG2kOX/2HN/zWCwWluTj2dGglIrFfMUwjEzimCyNkbIl\nSWPyPMcYxdV6TV3XuK7HqCRaah49fUzZWIqxg4tWhtD3cRzXAkI9H21AOB63777Kj/3EV5nlM2so\nilOGYbAjRK2RSvHo4SOWsyVJnOIEAY7w8L0Qg8s4GpQCrWw68r7c4zgOwnWo6gZjbEUSRrHVQMSR\nLYuNwQ9DfD9EaQjDmEk2Y7E8skpMrZlMUoyWbK/W+IHHfr/HaJfjo1sIN6YdDEE0IQxT0nSC0QrX\ncdCjtoh2Y+ib1mLcugEjoGprksmEQ93R4dAO1pPRtS2uI5DDgOv6XFysqaqSqix4+vQpu/2O05Nj\nwvA6yj3JAPACn/lqQdf3xHFkbcd9f62b0JSHCsfYhm4/dGAkaMNslZLPE27cOWI6DclnEac3l3ie\nS5pMKHYFL19cIIwFuwoX3vzcp/BCQZwlKONhiMjzE4ywuR1xEjPNJpydnXB0tOTo+JSucygOFkjT\nNA3CsX6NMIx48eIl68u1DSUOQrJJxny2ZLVasd1uefr0qW1gVhVhHCOEQxRGvP3OO/zET3yZB+/9\niJOjJbdunCKHlqY68Ed/+Ps8eO+HHOodXujz5ue+wK7cUTU9RTWg1MiLl0+p2g2jajFCcnbzjNnR\nBOEJDAFG+Ayj4ezGCfNpZgN0BkUSTYjcGDkohHZQamAcFQjFs5cfULVbgihAaxvoG//fyJL8i24K\nP22M+RLwV4G/LYT42p/9T2Mtff/SrHtjzP9kjPmKMeYrceKRpCnTLOP09Iyj5crKXKMIz/NwhcMw\n9iRxZLP1+galRoQwuK6tNOQ1hfhw2LPdba/9+fDw6TOeX17ygx++j5IGqcF1fIxx8L2Y3a5BKYem\n6RilZLs9cPfOqywWC+azBVJex571A27gs6trfvijBwyjIvRDpLSLvG17ojC2R4SuYxj666NPz2a9\n5ur6D873QoZBkSSJlT67PtNpziAV9+69jh+EpFlO1TaMUjH0A3Vb0XcNTbPD8yDPEpIk5M7tuzjC\nt7CRIOP8xRohQgI/QRl4+vyc+XJJVbccihLHD+m6nrIo7fzf2I0um80J0wmHoiYKrTXcER5JnOJ7\nMfv9gbbpKMvSajDimDt3b9nPfrzOspT6OnHaGsqMMWhtSNOUtu2pmhbZS/q2+9BzMPTWIKbpkKKn\n6yqU6emHmnRqRWO+H+K5AZ7wuby4srLuOCLLrVmtLEuSOCMMJ8zyFX3TIhC8/8EDnrx4wqHY4fku\nWgkePXzJJ17/JMZY4vV+XzCbzfit3/ptHj9+zDiMFIXd+NZXa4QQ3Lt378PfVZ7P2O/3PHjwAVEU\nsb7a0DQNR0dH3Lp1k6quCAOfJIpYLResFnNunp0yn+e8eu8+rmeTwU5Ob/PLv/yrnN044XC4QukB\nbQxd3zCqAdezztmv/uTXmM+OUdpQ1w0np1azopUmm0yZTDKSeEIapey2e6aTFbdv32W7XXO5PreK\nVGmxc77vfuRFLey6/YtfQoj/GqiA/wT4y8aYcyHEGfA7xphP/nnPPbuVmv/wP34drTTLfEnVNRjX\n2IQgY7kJ55uX1oWXZMxnC4axZxh7mwDleRipGHtJFMXsdgfiZELoRwihQSsWywVNL7lcb/ixz73J\nuz96gMahaDpu3DhlGDr6ruPi4pLT+YKz0yVlvUcEDmGc2mnHICnrmuVkiglcAgNt19F1A8vliqaq\ncIQgjH2LQB8lDi67/R6Ew74qCbyEo/mKxWJOmqacX7wgjEPOz8+ZJvl1s0iTTaeUZcUsTxmHgaKq\nyWY5Tb23d/F+YJ4tGIxhMJqzGzdYnz9nv90wX87wwoBxNCAcQKGUTRESwiUMfcax42i1oioakA7T\nfMqhKJgkExwNfhSybyquLvd85ctf4N1332EYRm7fvk1R7AnDATlaC+8ss76Aw2FLtsyZTqfs1hsr\nNBo6JvNjDvsKr5MI14ArWZ1MOWwKmmakFx2uGxIFMWqQjHrATzw85bCaH1NsGwwuV5srzm6fcWj3\naKERuMTRhKHXJFGKMAZBD66iGQ/ksymy75lGS379V/8E1U/xnZzVasWPHjwjyyfksW83RzlitOLm\njRMC32M2y1kdHaGMBm2oioLz83O67jowyHVYzSecHK+oii23bp7xD//RP+Tf/fl/C9+Bx48fsZrP\n7O95dkrTO0hX8c23v8Wrr3+SIJlw9fIhl+unRD409cirr99mU75A6BDZedw4usGTF+9g3JamcHGE\n5UrcOLtJMplwenzGB+/+iHK3p1c2E+LV1+/xo/ffJZtNSScRRkmKusUol//uv/nut/9M7+//8vp/\nXCkIIVIhRPYvHgN/BXgb+CXgF66/7ReAX/wIr0bbdnRdx6is5t/3A5sIHIV0Q2+pRqFPOkkZRyv6\n6fseKSWuMLiupdwao0nSBNBESQS+ixKC7f5A342EUcTuUKEMuF6AGkb22x11WVFWBVFsbcPDOBKG\nAf04IlwHx3WspTabogR0fYd/fVd1hUN5OFgPh+dRVxVqVCgpGUc7Us2nOfN8RuQHJHGMwXbJg9An\nCHym0wlJmnyomIzihLptCKOYMEpZzFf2bmxAKwXKoakOGDPSdRVlsScIAkAwjgqjbZN2c7WhbVom\nk4xsmqO1wXFdXMdht9naBKIgwPdc8skUlKFtetp2IPQjjk9OuVpfEgQBZVEw9B2TLEGPEjXYY58c\nR6S8PgIOA03TMA62RNfaUFcVrmODaYMgQCtF27QM0sqk1Qi+H9pA2TBiOp2hpWGSZnRdz3yxIIot\n6dn3PYzUFr7aNtRNyWyWImWF44wILJw2TlLqekAqj4vLivW6ZhgNYRgwDBa3N479h/ECxsDp6RlX\nVxuKokIbWG+uqKoKx3Ho+57ddocxhu+//X2GUTJbruiG3krGm4bPf+GLTKc5Tdvymc+8SRzHaK0R\nCFxHEIYp0zzh/QfvM0kS1lcvqJsS3/c4lAfGceDlxQuLJHQMVbvBcXuKcmtzLRyXYRjRGA5VweXl\nOf3YUjUlXafp+p5BFmRZRN/1hL6H6ziEvk/gfvRK4S9yfDgBfk8I8RbwR8CvGGP+KfD3gG8IId4D\nfu7663/FZZHfk+mEoi0JooDQDxh7SVnWDGNHW3d2onB9RBh6q8gzoyR2XYyWeL6P1JLj4xMbr656\nhq5gmiUoKdGqx3cNvRrxIh/jGZrdFd7Y4eqBoak4PTnCBIJ6bMFzGWXPkydP6PvR8hS1pioLHK1t\nFJcTMlsuEZ6D47rWxDQaIi8GBb3sGQHjBgilWcxz/MDHDVw62RFEkeUyZDFSDoz9SBxmluzjGba7\nDW1b0o+W1jyqgd12TZ7mCA+6rkX1ErRA4ZDO5mjlEbkeQnZEvmE5nUA/0OwPJL5HHic42sEoEDiA\nRmjDZn1hO/UuyK5HtwNZ6PPBgx9Q7q9AKV6+eIrnSdRgOOwKlNJcrM9pu5pRjdavUTYYqVGDZhKm\nVPsdZug5OTtlms9wvIB+0LjXDeXVfInRlucojaTvO8ZuZBw1OALtGAZdkcwCetkwmYT4vsANNNHE\nw/UgCEA4I17soF1J17kIvaItZ/zmr3yPxF+xnK1A2FG0HFs8RzDKnqPVAkcYxnHg7iv3WJ2cESfZ\ndUqVpUR/8hOf5ouf/zyH7Z7j1QmvvvoqGkMQhnZ6JUfuv3qX7v+g7k16ZVvS87xnxYrVN7kyc+/c\ne5/+nttV3Vs9xUa0YQLS0IAgwCD8C2RN9A/smSceeuSBhp4YsCaGDdOmaFGyJcOmq8hisakqVt17\n2t1nv/ouIjxYWzXi4NIgiOucnQOcg93kioyI732fp2pwbIfLy0tefvwRJ2cXqFERz3w0inie4Hg2\nd+tLkiQhyxbYjkvgS/LjAY3NoTgSJT5SGhwR4Nkz0jjiyaNn2K6HE/nk9RHhKmxXc/HsFAO4QUTX\nKIpDgarrKVQ1GCLHZxZ+9TuF/8/TB2PMK+C7f83fb4F/+Df6v7TB8wKELbGMggcCUpykNG2NdDxi\nx6NpGpqmwfHcB4ejQWJoywbbncxCwrKxMRNVqSrxHJuqPGIxhVmarkKMI47RNF3Dk8fnSGnhxxHS\ncyiOB3qtUK5NXed4vocZGopjThxOF59xHE74dmPo6dkfNsznc3wvmbRvAl5dX2GpkVmYkGYz9ocC\n4bj048CQ76ECLBvXdSjLksO+JfFnzJKUze2aKPXpm5HDULDbbji7WDHLFqxOV1SBzd39LeePTok9\nl1FppDS8ff/mYUfjczgcUIzEYUa+K8jzhtVqSV4csTQ8e/KIm9sbwiigKhvsBtIs4XA4MOiGk8US\n15ni2Y8vzqZJSr/Dxubq3S0ChQaapiKbLzkec7LFnK4dsS1BWTWM48g4Gs7OHlEUBV++e43t2AxD\nT+j6VE3DbHVC23UksxmDUnz2+Wf86Z/+GFqLOJ6BMQjXxg3lxFRwJYd9SV5WIAxtVXEYFHEYMuW2\nXYRa8d/+8/8NoxXf/OQT4ugUKWyUGnFdl6rOefb8Ma7rk6YpQTrnxUuXOElYrVYkaUJdHhlNxziM\nRKcpbdOyOjtju90xjlN6cblIEZbCs0ccW7Db30M/8OLZEx4/OePt+9dk2QIvOUFLi6YcKQ4VH7/8\nhJ/+9M+x4xbhg+U6LFcZtmU4n60Q0mG9u+VssSBOZjjSY9CaqipZLk8py5L5fE5dNShrahEnc5fs\nJKHtSgLfIY4StBKgJpaF6331a4KvR8xZCKIwwpEO1gP3Tnoe3TBgO9O6lWUzPEcSRQG+59E1U3JR\nCInvBVOf3ligDVJYuFIgLYs4CJHCngCjw1Sj7cqSLIpwEBRVjjIGy7Z/JShVw0BVlBilQYEnPZq6\n5G59y3a/xXEcLDP194WtmGXxBAQdOoTrIF0XP/JwHAe0TZ3XONKl12p6I7dTgUrpgbqq0EpNZ1qj\nyfM9ZXEgS1PSKOZ0ecrn3/oOQkiu3r3nFz//OePQE2cxZd3gBwGjGhj6hvk8xXEEjm1PxwRLkuc5\nwpGs17dI6bCYL3Fdl9ksIYw8yirH9RyqasDzIxAOVdOy2+1RWlNW5RTO8jx+/df+3vQmlz51O00V\nmrZFa02SJAhhk8YJFhYvP/yIIIwYlcb1fJquxwlcBq0QjmSf5wRRBLbNqDTDOAlw7tdr/CDkmBfk\nZUU8y+j6jqquwYKma3BDjzjOcIVPEmdoNfxKxHt9eeAvf3xF4GT84Lu/iRkt+nY6hqbptAMramew\nmwAAIABJREFUjgcuLi7QWqG0YX8sWJysSLM59gNkdhw1N9fXjENH27YTwKeuiaKQu7s7dts9Whvy\nY0HfjazXG0BwcXFO1zd0dc3Jcj6ZwNuarhsAGz0YmqIhjWNUV6KHls3uHuHYWI7EdifHqOs6eJ6L\nMZq2b0mSmDiZ8jmOdOnagaYdWZ5ekJ1ckC0igkDStCVxGhPHMfv9ka6rcAOJ9IKv/Dx+LRYFo6dt\nWBBGPL64IHR97q5vsSyYxTNcy6bNCzzpMLQdWRRhKcUsChnbHj2KiRosPKQRHLZ3zGKPyPMJfB9X\n2hMXUE/S2jAKKMuCjz78gJPVCkvauIE/WZKwKfOSvu0Ymp6hGQjcabwVRD7Ctdjvj0RByCxM2Gy3\nrDdrpOPguNNOQJsR21JcnJ/w4Ycf8U/+yX/Gy49eTttUYWFLQRB42Nb043eFZJ5mCGnwQsnyLKUf\nc0ZdsNndcMh3tG3B+fKED54+n4JcYYR0fV69+pLDYctud89xt6cpau7v1sRRhOt6dK3izetL0uyU\nH/3oh1R1QV1XvHv3dpqGuA5dX7PZbyjKabu8XK4YlcXd/Z50nrHZ7CiKnC9f/xVtU+DYNq7vM5sv\nEI4kCIIHmExK37cU5ZE/+Fd/QLaYEc9ivMBhuVowmhbEyKg65llGEIXc3lxP411LEIcRb1+/BWN4\n9PgxcRxxfXNHP7TYtk3dtiit2B/vmGdzzs9eoBsJyqesFLvDyNUXHv/Dv/ghxf6AY2nSKKDIW2bZ\ngrbtqeuKNJlxd3dHEIS0XcvFxSOOZYntuIzjwG43BXDPFic0VU2RH8jmc4S0cT2HJ08fU1clN1dX\n6EFT5jXruy0//9kvuL25Zb/dUFY5RVEwny8QlqAfFId9ycnyFKU7zi/mNNs98yAkSRIGpQjThKv1\n7VTzR3Nzf4N0BdIVFNWB3WGN0ZqqKDmsD1xe7jhZfcDFs084Fke6ocHxBb3q6MYO6XqEQYIRDnb4\n1cmtX4tFwbJgHEakLanLCksZVssTXFviex5xEOJIicDgWBbCwMVqhRkVgedi25LN/ZbQDxEIhr7D\nFlMJyHGmSnKSJhz2eywDVd9h+y5lUzMaRTv0uJ77UATyOT8/I8syHNfFdTyGYZze8MMkcDXGcDwc\n0aNGCh/HDjBa0DTt1KgbOmLfJYki7rdrfvjjH/H+6h377Za+bUjTmP1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1SZYyqpFlNqc8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WrG8a1bSCl59/57OJ6NYelstxE7u42bNEkaP8nDh4+Jowzbdrl/7x2Gow5CV2m1h0ThFlSBa1oU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RcB1QXFB5KouYHrfABJSYV2VF1IU4o8z5FKM4siigrW1tbx/QbNdo80z3E9n8oY4iRlMpnM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\nudEgRSFKJQxFxsyX+OwLX+D05gqqZ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24RiYQZ6Q8h7Cr5bJqemI9CpUytYqApfm7emMM0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\ngg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "editable": true, + "jupyter": { + "outputs_hidden": false } - ], + }, + "outputs": [], "source": [ "temp, mask = explanation.get_image_and_mask(240, positive_only=True, num_features=5, hide_rest=False)\n", "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n" @@ -427,34 +370,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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6nBh55/HDgB9Ghr6/YdcT1lpCiBCh0AU5C5TQWFNipGVoR4yyGGWJEWIA7xOjT8yXa7JI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RnaVM4wU9w2O738GlRmtJXSV4vsciydB1TavdRng+ltB0uiHjRUy60NS+IghNHNekLBVCKRzH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tDy/x/q3ivG0vw88/t9OZ18Tp2qUzl1DhM5nOFwhjMURYpBokSFDV6uwkKCvTB8YQO+cIIB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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "editable": true, + "jupyter": { + "outputs_hidden": false } - ], + }, + "outputs": [], "source": [ "temp, mask = explanation.get_image_and_mask(240, positive_only=False, num_features=10, hide_rest=False)\n", "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n" @@ -472,34 +397,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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Xt6iymlfvvIXrM/72v/t3+Vt/4++g0HhryXQxMfLW4YYBN4wMfX/DrkeyLMP7AAFynZOS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UaNwPeI44CjVoso8rL8ijhhMnHQNA1F1VhfP8H9nT1UM4eXwL2dfRRDR1Ykuv0eiiqYry9h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\nLqQfqpqz+OTkXTsQQmwCFSDTbpdtdOH5PIi95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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "editable": true, + "jupyter": { + "outputs_hidden": false } - ], + }, + "outputs": [], "source": [ "temp, mask = explanation.get_image_and_mask(240, positive_only=False, num_features=1000, hide_rest=False, min_weight=0.1)\n", "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" @@ -527,34 +434,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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f9z/j+/59vu/fFwqF3mIzAoHv7PZy4YMrq/i+QygUptPpkUgkeOyxL7G1tYVt2fR6Pba3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\nfAev+u/Ponugj8Rv0p3LYbQE1UqD7t5emmEAmkY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ZR49V06r10xkhpVy1H0AHDgAbAAv4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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "editable": true, + "jupyter": { + "outputs_hidden": false } - ], + }, + "outputs": [], "source": [ "temp, mask = explanation.get_image_and_mask(286, positive_only=True, num_features=5, hide_rest=True)\n", "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))" @@ -572,34 +461,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": false, "deletable": true, - "editable": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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bPsB4OKCiIrnE5dMtT588ReTIal5TG0MOjrOjI8au48bRKSJJjJ5z4/bzFPWC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4QRM86QwAlXSah4iuc4YYJAsmHLEzzNV3mThzzCZPvuve56LnXX8PwHnmUwHVHOLzAsga7pHARj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\nDH0u/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knX2Kx36AxdMpZFbqzCc899nQtXb6DrGt1mi/pejVioDG2fgRNSrzdxtAzVvodi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\nrA4lVvZ2ufbKS/zJX/4Vv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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "editable": true, + "jupyter": { + "outputs_hidden": false } - ], + }, + "outputs": [], "source": [ "temp, mask = explanation.get_image_and_mask(286, positive_only=False, num_features=10, hide_rest=False)\n", "plt.imshow(mark_boundaries(temp / 2 + 0.5, mask))\n" @@ -608,7 +479,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -622,13 +493,16 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.9.18" }, "widgets": { - "state": {}, - "version": "1.1.2" + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/notebooks/Tutorial Images -.ipynb b/doc/notebooks/Tutorial Images -.ipynb new file mode 100644 index 000000000..9a91844d6 --- /dev/null +++ b/doc/notebooks/Tutorial Images -.ipynb @@ -0,0 +1,69 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Using Lime with Pytorch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we will show how to use Lime framework with Pytorch. Specifically, we will use Lime to explain the prediction generated by one of the pretrained ImageNet models.\n", + "\n", + "Let's start with importing our dependencies. This code is tested with Pytorch 1.0 but should work with older versions as well." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'lime_stratified'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mlime_stratified\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mlime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m lime_image\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'lime_stratified'" + ] + } + ], + "source": [ + "from lime_stratified.lime import lime_image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/doc/notebooks/utils.py b/doc/notebooks/utils.py new file mode 100644 index 000000000..e9aa7ada4 --- /dev/null +++ b/doc/notebooks/utils.py @@ -0,0 +1,543 @@ +import os +import cv2 +import json +import glob +import skimage +import requests +import numpy as np +import pandas as pd +import scipy.special +import matplotlib.pyplot as plt +from datetime import date,datetime +from IPython.display import display, HTML +pd.set_option('display.max_columns', None) +from skimage.segmentation import mark_boundaries +from matplotlib.colors import LinearSegmentedColormap + +# from lime_stratified.lime.wrappers.scikit_image import SegmentationAlgorithm +from lime.wrappers.scikit_image import SegmentationAlgorithm +display(HTML("")) +from tensorflow.keras.applications.resnet50 import ResNet50,preprocess_input +model = ResNet50(weights='imagenet') + +##################################################################################################################### +####################################### BASIC FUNCTIONS ############################################# +##################################################################################################################### +def check_folders(path_): + ''' Args: + path_: Path Verifier ''' + if not os.path.exists(path_): + os.makedirs(path_) + print(f'folder created:\t{path_}') +def axis_off(ax): + ax.set_xticks([], []) ; ax.set_yticks([], []) +##################################################################################################################### +##################################### BLACKBOX MODEL PREDICTION FUNCTION ######################################### +##################################################################################################################### +def get_ImageNet_ClassLabels(json_file=False): + ''' Input: + json_file: Path to JSON File, if file is already downloaded, + filepath can be passed as the input parameter + json_file: if file is not available locally then setting False will download the file automatically. + Output: + class_names: A list containing the 1000 classes names from ImageNet dataset ''' + if os.path.isfile(json_file) == False: + url = "https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json" + r = requests.get(url, allow_redirects=True) + open(json_file, 'wb').write(r.content) + print('ImageNet Classes JSON File Downloaded') + else: + url = json_file +# print('JSON File Loaded') + with open(url) as file: + class_names = [v[1] for v in json.load(file).values()] + return class_names + +def load_model(model_name=None): + if model_name=='ResNet50': + model = ResNet50(weights='imagenet') + if model: + print('BlackBox Model Selected: \t\t',model_name) + print('BlackBox Model Layers Count: \t\t',len(model.layers)) + print('BlackBox Model Weights Count: \t\t',len(model.weights)) + return model + +def read_process_image(filename,model): + ''' Args: + dataset_name: Name of Dataset + filename: Path to Image ''' + rows,cols = model.input.shape[1],model.input.shape[2] + img = cv2.imread(filename, cv2.IMREAD_COLOR) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = cv2.resize(img,[rows,cols]) + return img +def get_class_idx_label_score(predicted,class_names): + ''' + Args: + predicted: Prediction returned by Blackbox model 1x1000 + model_name: Model Name to be used to make predictions + class_names: List of class names for the dataset + Result: + PDI: Predicted Class Index + class_probability: class_probability of current prediction + PDL: Predcited Class Label + ''' + PDI = np.argmax(predicted) + class_probability = predicted[0][PDI] + PDL = class_names[PDI] + return (PDI,class_probability,PDL) +def plot_save_prediction(X_s,PDL,class_probability,result_folder,file_name,save_image=False,plot_everything=False): + ''' + Args: + X_s: Image(3d numpy array) + PDL: Predicted Class Label + class_probability: Class Probablity or Predicirton Score returned by Blackbox Model + curr_results: Path to save the figure + save_image: Plot only if False, Plot and Save if True + Result: ''' + plt.figure(figsize=(3,3)) + plt.imshow(X_s) + if plot_everything: + plt.title(str(PDL)+':'+str(class_probability)) + # plt.title(r'$\alpha > \beta$') + plt.tick_params(axis='x',which='both',bottom=False,top=False,labelbottom=False) + plt.tick_params(axis='y',which='both',left=False,right=False,labelleft=False) + if save_image: + plt.savefig(result_folder+'//Predicted_'+str(PDL)+'_'+str(round(class_probability,3))+'.png',transparent = True,bbox_inches = 'tight',pad_inches = 0.02, dpi = 150) + plt.show() + +##################################################################################################################### +####################################### SEGMENTATION FUNCTIONS ############################################## +##################################################################################################################### +# Create Segments 0-100, 100-200, 200-300 +def segmentation_module(compute_segments,files,DS_path,sub_results_,model,segs_list,seg_algo): + ''' + this function will require following parameters and will return a dataframe from csv file + Args: + compute_segments: Boolean (True or False), if true, then it will compute the hyperparameters according to segments required + files: a range of filename variable, upto which the segmentation parameters are required + DS_path: Path to save or to load the segmentation parameters csv file + model: Blackbox model loaded as variable + model_name blackbox model name as string, to be + segs_list + Result: + df_seg: A Dataframe from CSV File either created after segmentation hyperparameters or from loaded file + ''' + if compute_segments: + now = datetime.now() + print("Segmentation Started\t\t:\t\t\t", now.strftime("%d/%m/%Y %H:%M:%S")) + print('+'*94) + print('| FileName\t | \tTarget Segments | Generated Segments | \tMax Distance | \tKernel Size |') + data_to_csv = dict() + segs_param_table_sucess = [] + for f in files: + print('-'*94) + file_name = f'{f+1:08}' + file = os.path.join(DS_path,'ILSVRC2012_test_'+file_name+'.JPEG') + image = read_process_image(file,model) + for srl in segs_list: + target_seg_no = (srl if isinstance(srl, int) else srl[-1]) + md,ks,random_seed,ratio = search_segment_number(image, target_seg_no=target_seg_no, init_max_dist=100, + init_kernel_size=4,seg_algo=seg_algo) + segments,segs,segmenter_fn = own_seg(image,md=md,ks=ks,random_seed=random_seed,ratio=ratio) + segs_param_table = {'filename':file_name,'seg_algo':seg_algo, 'max_distance':md, + 'kernal_size':ks,'random_seed':random_seed, + 'ratio':ratio,'segments':segs,'target_segs':srl} + segs_param_table_sucess.append(segs_param_table) + print(f'| {file_name}\t | \t {srl}\t\t| \t{segs}\t | \t {md:0.4}\t | {ks}\t |') + df_seg = pd.DataFrame(segs_param_table_sucess) + df_seg.to_csv(f'{sub_results_}//Segmentation_Table_{segs_list}.csv', sep = ';' , index=False) + now = datetime.now() + print("Segmentation Completed\t\t:\t\t\t", now.strftime("%d/%m/%Y %H:%M:%S")) + else: + df_seg = pd.read_csv(f'{DS_path}//Segmentation_Table_{segs_list}.csv', sep = ';') + return df_seg +###################################################################################################### +############################ SUPPORTED FUNCTIONS FOR SEGMENTATION ############################### +###################################################################################################### +def get_segment_number(image, md,ks,seg_algo,random_seed=1234,ratio=0.2): + ''' COMPUTE NO OF SEGMENTS by using hyperparametrs + Args: + image: Image to be segmented (MxNx3) + md: Max Distance + ks: Kernel Size + seg_algo: Algorithm used for segmentation ''' + segmentation_fn = SegmentationAlgorithm(seg_algo, kernel_size=ks, max_dist=md, ratio=ratio, random_seed=random_seed) + segments = segmentation_fn(image) + return len(np.unique(segments)) +def search_segment_number(image, target_seg_no, init_max_dist=100,init_kernel_size=4,seg_algo='quickshift'): + ''' search_segment_number by implementing dichotomic_search + Args: + image: Image to be segmented (MxNx3) + target_seg_no: Target Segments Number + init_max_dist: Initial Max Distance used for creating segments + init_kernel_size: Initial Kernel Size used for creating segments + seg_algo: Algorithm used for segmentation + Return: + rmd:Max Distance Required to create Target Segments Number + init_kernel_size: Kernel Size Required to create Target Segments Number + ''' + random_seed=1234 + ratio=0.2 + lmd, rmd,ks = 0, init_max_dist,init_kernel_size + lsn = get_segment_number(image, lmd,ks,seg_algo,random_seed=random_seed,ratio=ratio) + rsn = get_segment_number(image, rmd,ks,seg_algo,random_seed=random_seed,ratio=ratio) + niter = 0 + while niter<20 and rsn!=target_seg_no: + niter += 1 + mmd = (lmd + rmd) / 2.0 + msn = get_segment_number(image, mmd,ks,seg_algo,random_seed=random_seed,ratio=ratio) +# print(f'{lmd}:{lsn} {mmd}:{msn} {rmd}:{rsn}') + if msn <= target_seg_no <= lsn: + rsn, rmd = msn, mmd + else: + lsn, lmd = msn, mmd + return float(rmd),init_kernel_size,random_seed,ratio + +def own_seg(image,md,ks,random_seed=1234,ratio=0.2,seg_algo='quickshift'): + ''' + skimage.segmentation.quickshift(image, ratio=1.0, kernel_size=5, max_dist=10, + return_tree=False, sigma=0, convert2lab=True, rng=42, *, channel_axis=-1) + ''' + ''' Function to Get Perform Segmentation using Quickshift and slic algorithms. + Args: + X_s: 3d nupmy array + ks: kernal Size,float (Width of Gaussian kernel used in smoothing the sample density. Higher means fewer clusters) + md: Max Dist, float, Cut-off point for data distances. Higher means fewer clusters + seg_algo: Segmentation Algorithm Name, Default: quickshift + ratio: float, optional, between 0 and 1 + sigma: Width for Gaussian smoothing as preprocessing. Zero means no smoothing + Results: + Segments: Segments Created + Segs: Number of Segments + fn_segmentation: Segmenter Function to take iamges and create segments''' + if seg_algo == 'quickshift': + segmenter_fn = SegmentationAlgorithm('quickshift', kernel_size=ks,max_dist=md, ratio=ratio,random_seed=random_seed) + elif seg_algo == 'slic': + segmenter_fn = SegmentationAlgorithm('slic',compactness=md,max_num_iter=ks, ratio=ratio,random_seed=random_seed) + segments = segmenter_fn(image) + segs = np.unique(segments).shape[0] + def fn_segmentation(image): + return segments + return segments,segs,fn_segmentation + +##################################################################################################################### +############################################# PLOT FUNCTIONS ################################################## +##################################################################################################################### +def plot_seg_image(image,segments,md,ks,sub_results,file_name,save_image=False,plot_everything=True,hide_x_y_ticks=True): + ''' + Args: + immgg: Input Image (3d numpy array) + segs: Number of segments + md: Max Distance (Segmentation Parameters) + ks: Kernel Size (Segmentation Parameters) + PDL: Predicted Class Label + class_probability: Class Probablity or Predicirton Score returned by Blackbox Model + file_name: Path to save the figure + save_image: Plot only if False, Plot and Save if True + + Result: + ''' + segs = np.unique(segments).shape[0] + immgg=skimage.segmentation.mark_boundaries(image, segments, color=(1, 1, 0), outline_color=None, mode='outer', background_label=0) + plt.figure(figsize=(3,3)) + plt.imshow(immgg) + if plot_everything: + plt.title(str(segs)+ '_'+str(md)+'_'+str(ks)) + if hide_x_y_ticks: + plt.tick_params(axis='x',which='both',bottom=False,top=False,labelbottom=False) + plt.tick_params(axis='y',which='both',left=False,right=False,labelleft=False) + plt.tight_layout() + if save_image: + plt.savefig(sub_results+'//Segs_'+str(segs)+ '_'+str(md)+'_'+str(ks)+'.png',transparent = True,bbox_inches = 'tight',pad_inches = 0.02, dpi = 150) + plt.show() +def plot_classification_score_examples(explanation,data,labels,class_probability,sub_results,ttl,draw_quantile=False,quantile=[0.05,0.95],save_image=False,plot_points=1000,plot_everything=True,hide_x_y_ticks=True): + ''' Args: + Explanation: Explaination returned by Lime-Image + data: Data returned by LIME-Image (dense num_samples * num_superpixels) + labels: Prediction Probabilities Matrix generated by LIME-Image + class_probability: Class Probability or Prediction Score Returned by BlackBox Model + curr_results: Path to Save the Figure + filenameee: Filename to save the corrosponding Figure with into Relavant Directory + draw_quantile: False, set it to True if quantile plotting on classification score is also needed + quantile: Quantile Upper and Lower bound for classification score on Default [0.05-0.95]''' + colors = ['#6d9eeb','#f9cb9c'] + cm = LinearSegmentedColormap.from_list("Custom", colors) + x = [np.sum(d) / len(d) for d in data] + TL = explanation.top_labels[0] + y =labels[:,TL] + segs = data.shape[1] + nos = data.shape[0] + plt.figure(figsize=(3,3)) + plt.scatter(x[:plot_points],y[:plot_points] , c =y[:plot_points] , cmap = cm, s=20 , lw = 0.5 , edgecolors = 'black') + plt.scatter(x[0],y[0] , c='m' ,marker='x', s =200) + plt.xlim(-0.05,1.05) + plt.ylim(-0.05,1.05) + plt.axhline(class_probability, ls = '--' , lw = 2 , color ='g' ) + if draw_quantile: + q_lower = np.quantile(y,quantile[0]) + q_upper = np.quantile(y,quantile[1]) + plt.axhline(q_lower, ls = '--' , lw = 1 , color ='red' ) + plt.axhline(q_upper, ls = '--' , lw = 1 , color ='blue' ) + if plot_everything: + plt.text(+0.02,y[0]-0.10, '$y=f(\\xi)$' , fontsize='15') + plt.ylabel('$f(\\xi_x)$',fontsize='15') + plt.xlabel('$|x|$' , fontsize='15') + if hide_x_y_ticks: + plt.tick_params(axis='x',which='both',bottom=False,top=False,labelbottom=False) + plt.tick_params(axis='y',which='both',left=False,right=False,labelleft=False) + plt.tight_layout() + if save_image: + plt.savefig(sub_results+'//ClassScore_'+ttl+'.png',transparent = True,bbox_inches = 'tight',pad_inches = 0.02, dpi = 150) + plt.show() + +def plot_heatmap_lime(heatmap,maxval,sub_results,ttl,save_result=False,show_color_bar=False,color_bar_position='right',hide_x_y_ticks=True): + plt.figure(figsize=(3,3)) + plt.imshow(heatmap , cmap='bwr', vmin = -maxval, vmax = maxval )#, cmap='cool') + if hide_x_y_ticks: + plt.tick_params(axis='x',which='both',bottom=False,top=False,labelbottom=False) + plt.tick_params(axis='y',which='both',left=False,right=False,labelleft=False) + if show_color_bar==True: + if color_bar_position == 'right': + plt.colorbar(im,fraction=0.046, pad=0.04, orientation='horizontal') + if color_bar_position == 'bottom': + plt.colorbar(im,fraction=0.046, pad=0.04, orientation='vertical') + if save_result: + plt.savefig(sub_results+'//Heatmap_'+ttl+'.png',transparent = True,bbox_inches = 'tight',pad_inches = 0.02, dpi = 150) + plt.show() +def heatmap_from_beta(segments=None, beta=None): + if segments is not None and beta is not None: + heatmap = np.zeros_like(segments, dtype=np.float32) + for segm, importance in enumerate(beta): + heatmap[ segments==segm ] += importance + return heatmap +def plot_classification_score(ax,explanation,X,Y,class_probability,draw_quantile=False, + quantile=[0.05,0.95],save_image=False,plot_points=1000,plot_everything=True): + colors = ['#6d9eeb','#f9cb9c'] + cm = LinearSegmentedColormap.from_list("Custom", colors) + x = [np.sum(d) / len(d) for d in X] + TL = explanation.top_labels[0] + segs = X.shape[1] + nos = X.shape[0] + ax.scatter(x[:plot_points], Y[:plot_points] , c = Y[:plot_points] , cmap = cm, s=20 , lw = 0.5 , edgecolors = 'black') + ax.scatter(x[0],Y[0] , c='m' ,marker='x', s =200) + ax.set_xlim(-0.05,1.05) + ax.set_ylim(-0.05,1.05) + ax.axhline(class_probability, ls = '--' , lw = 2 , color ='g' ) + if draw_quantile: + q_lower = np.quantile(y,quantile[0]) + q_upper = np.quantile(y,quantile[1]) + ax.axhline(q_lower, ls = '--' , lw = 1 , color ='red' ) + ax.axhline(q_upper, ls = '--' , lw = 1 , color ='blue' ) + if plot_everything: + ax.text(+0.02, Y[0]-0.10, '$y=f(\\xi)$' , fontsize='15') + ax.set_ylabel('$f(\\xi_x)$',fontsize='15') + ax.set_xlabel('$|x|$' , fontsize='15') +def get_img_mask_lime(explanation,TL, sub_results,ttl,min_weight,positive_only=True,save_image=False, num_features=100, hide_rest=False,hide_x_y_ticks=True): + ''' Fuction to Highlight Positive and Negative Features Provided by LIME-Image + Args: + explanation : explanation computed by LIME image Module + TL : Predicted Top Label by LIME-Image Explanation + savepath : Path to Save the Figure + num_features : Features to highlight, (Default: 100) + hide_x_y_ticks: To Hide or to show X and Y Ticks Parameter (Default: True) + ''' + temp, mask = explanation.get_image_and_mask(TL,min_weight=min_weight,positive_only=positive_only, num_features=num_features, hide_rest=hide_rest) + plt.figure(figsize=(3, 3)) + plt.imshow(mark_boundaries(temp/255 / 2 + 0.4, mask)) + if hide_x_y_ticks: + plt.tick_params(axis='x',which='both',bottom=False,top=False,labelbottom=False) + plt.tick_params(axis='y',which='both',left=False,right=False,labelleft=False) + plt.tight_layout() + if save_image: + plt.savefig(sub_results+'//ExpByLime'+ttl+'.png',transparent = True,bbox_inches = 'tight',pad_inches = 0.02, dpi = 150) + plt.show() +##################################################################################################################### +####################################### EVALUATION FUNCTIONS ################################################### +##################################################################################################################### +def evaluate_explanation(explanation,X,all_Ys,beta,f_x,RC_Y,r2_score,data_to_csv,model_name): + ''' + This function will evaluate the explanation produced by LIME-Image and will return a dict of keys and values. + Args: + explanation : explanation computed by LIME image Module + X : data returned and being used by LIME explain_instance module + all_Ys : labels (for generated data) returned and being used by LIME explain_instance module + data_to_csv : A Initial dictionary having keys and values to be saved in .csv file + model_name : Blackbox Model Name as string being used + seg_range : Segments Range (25-50,50-100,100-150,150-200) + ''' + TL = explanation.top_labels[0] + Y =all_Ys[:,TL] + maxval = np.max(np.abs(beta)) + g_x = explanation.local_pred[TL][0] + r2_score = explanation.score[TL] + local_pred = explanation.local_pred[TL] + intercept = explanation.intercept[TL] + data_to_csv['model_name'] = model_name + data_to_csv['f_x'] = f_x + data_to_csv['g_x'] = explanation.local_pred[TL][0] + data_to_csv['q05_Y'] = np.quantile(Y,0.05) + data_to_csv['q95_Y'] = np.quantile(Y,0.95) + data_to_csv['q01_Y'] = np.quantile(Y,0.01) + data_to_csv['q99_Y'] = np.quantile(Y,0.99) + data_to_csv['std_Y'] = np.std(Y) + data_to_csv['std_abs_Y'] = np.std(np.abs(Y)) + data_to_csv['r2'] = r2_score + data_to_csv['maxval'] = maxval + data_to_csv['local_pred'] = local_pred + data_to_csv['intercept'] = intercept + data_to_csv['std_beta'] = np.std(beta) + data_to_csv['std_abs_beta'] = np.std(np.abs(beta)) + data_to_csv['mean_beta'] = np.mean(beta) + data_to_csv['mean_abs_beta'] = np.mean(np.abs(beta)) + data_to_csv['q05_beta'] = np.quantile(beta,0.05) + data_to_csv['q95_beta'] = np.quantile(beta,0.95) + data_to_csv['q01_beta'] = np.quantile(beta,0.01) + data_to_csv['q99_beta'] = np.quantile(beta,0.99) + data_to_csv['q25_beta'] = np.quantile(beta,0.25) + data_to_csv['q75_beta'] = np.quantile(beta,0.75) + data_to_csv['q10_beta'] = np.quantile(beta,0.10) + data_to_csv['q90_beta'] = np.quantile(beta,0.90) + data_to_csv['max_beta'] = np.max(beta) + data_to_csv['min_beta'] = np.min(beta) + data_to_csv['RC_Y'] = RC_Y + data_to_csv['cv_abs_beta'] = np.std(np.abs(beta)) / np.mean(np.abs(beta)) + data_to_csv['cv_beta'] = np.std(beta) / np.mean(beta) + +def get_beta_from_expl(explanation=None): + ''' + Function get_beta_from_expl will compute beta from explanation + Args: + expl: Explanation returned by Strtaified Lime Image Explainer + Result: + beta: Local Exp for Top Label + ''' + if explanation is not None: + n = len(np.unique(explanation.segments)) + beta = np.zeros(n) + for i,v in explanation.local_exp[ explanation.top_labels[0] ]: + beta[i] = v + return beta +def get_RCY(Y,f_x): + ''' + Function get_RCY will take two parameters and will compute the InterQuantile Range (IQR(99-1)) divided by f_x + Args: + Y : Y is the labels returned by Stratified LIME-Image explainer object + f_x : f_x is class probability for the image returned by Blackbox model + Result: + IQR(99-01)/f_x + ''' + return (np.quantile(Y,0.99) - np.quantile(Y,0.01)) / f_x +##################################################################################################################### +################################################# PAPER FIGURE ################################################## +##################################################################################################################### +def shapley_p(k, m): + return 1 / ((k+1) * scipy.special.binom(k, m)) + +def pdf_bern(k, m, p=0.5): + return scipy.special.binom(k,m) * (p ** (m)) * ( (1-p) ** (k-m) ) + +##################################################################################### +def get_CV_beta(beta): + return np.std(beta) / np.mean(beta) + +######################################## COMPUTE EXPLANATION #################################### + +def explanation_module(compute_experiments,files,df_seg,DS_path,sub_results_,result_folder,segs_list,model,model_name,class_names, + save_explanations_as_plot,use_stratification,plot_prediction,plot_segments,plot_heatmap,plot_image_mask, + hide_color,num_samples,repeat_exp,top_labels,batch_size,lime_image,bb_predict): + if compute_experiments: + results_csv = [] + df_data = [] + now = datetime.now() + print("Example Started\t\t:\t\t\t", now.strftime("%d/%m/%Y %H:%M:%S")) + for f in files: + file_name = f'{f+1:08}' + file = os.path.join(DS_path,'ILSVRC2012_test_'+file_name+'.JPEG') + + sub_results = os.path.join(sub_results_,file_name) + if not check_folders(sub_results): print(sub_results_,file_name) + # Read and resize image according to model Input Layer + image = read_process_image(file,model) + image_arr = np.expand_dims(image,axis = 0) + predicted = bb_predict(image_arr) + # Convert the Predicted into Predicted Class Index (PDI), Class Probability, and Predicted Class Label (PDL) + (PDI,f_x,PDL) = get_class_idx_label_score (predicted,class_names) + + # Plot the blackbox model prediction + if plot_prediction: + plot_save_prediction(image,PDL,f_x,sub_results,file_name, + plot_everything=save_explanations_as_plot,save_image=True) + + df_n = df_seg.loc[(df_seg['filename'] == file_name)] + for data, row in df_n.T.iteritems(): + filename_seg,md,ks,sr = row.filename,row.max_distance,row.kernal_size,row.target_segs + segments,segs,segmenter_fn = own_seg(image,md=md,ks=ks) + ######### Plot the segments Created + if plot_segments: + plot_seg_image(image,segments,md,ks,sub_results,file_name,save_image=True) + for hc in hide_color: + for us in use_stratification: + hcc = 'mean-filled' if hc is None else 'zero-filled' + sig = f'{segs}_{hcc}_{us}_{num_samples}' + data_to_csv = dict() + beta_arr, rcY_arr,r2_arr = [], [], [] +######### Fix Random Seed to make benchmark deterministic and reproducible + explainer_lime = lime_image.LimeImageExplainer(random_state=1234) +######### Create Explanation + for repeat in range(repeat_exp): + print(repeat+1, end=' ') + explanation_ret = explainer_lime.explain_instance(image, bb_predict, + hide_color=hc, + top_labels=top_labels,batch_size = batch_size, + use_stratification = us,num_samples=num_samples, + segmentation_fn = segmenter_fn,progress_bar=False) + X, all_Ys,explanation = explanation_ret # split it into 3 variables + predicted_cls = explanation.top_labels[0] + Y = all_Ys[:, predicted_cls] + beta_arr.append(get_beta_from_expl(explanation=explanation)) + rcY_arr.append(get_RCY(Y, f_x)) + r2_arr.append(explanation.score[predicted_cls]) + beta = np.mean(beta_arr, axis=0) + RC_Y = np.mean(rcY_arr) + r2 = np.mean(r2_arr) +########################################################################################################################################### +# Building a Dictionary with Keys and Values to write into Data File ########################################################################################################################################### + data_to_csv = {'filename':str(file_name),'hide_color':str(hcc),'use_stratification':str(us), 'num_samples':str(num_samples),'segments':str(segs),'max_dist':str(md),'kernal_size':str(ks)} +########################################################################################################################################### +# EVALUATING EXPLANATION ########################################################################################################################################### + evaluate_explanation(explanation,X,all_Ys,beta,f_x,RC_Y,r2,data_to_csv,model_name) +########################################################################################################################################### +# PLOTTING CLASSIFICATION SCORE +# This will generate the Classification Score of Linear Regressor ########################################################################################################################################### + if plot_classification_score: + plot_classification_score_examples(explanation,X,all_Ys,f_x,sub_results,sig, + plot_everything=save_explanations_as_plot,save_image=True) +################################################################################################################################## +# PLOT: HEATMAP +# This will generate heatmap plot based on feature importances computed by us from explanation returned by LIME Image Explainer +##################################################################################################################################### + if plot_heatmap: + heatmap = heatmap_from_beta(segments, beta) + plot_heatmap_lime(heatmap,data_to_csv['maxval'],sub_results,sig,save_result=True, + show_color_bar=False,color_bar_position='right') +############################################################################################################################## +# PLOT: GET IMAGE AND MASK BY LIME +# This will generate heatmap plot based on feature importances computed by us from explanation returned by LIME Image Explainer ############################################################################################################################## + if plot_image_mask: + get_img_mask_lime(explanation,predicted_cls, sub_results,sig, + min_weight = data_to_csv['maxval']/2,save_image=True, num_features=5, hide_rest=True) + results_csv.append(data_to_csv) + df_data = pd.DataFrame(results_csv) + df_data.to_csv(f'{result_folder}/results_{num_samples}_{files.start+1}_{files.stop}_{segs_list}.csv', + sep = ';', index=False) + print(f'{file_name} Segs: {segs} Use_Stratification: {us} CV_abs:{data_to_csv["cv_abs_beta"]:0.5} CV: {data_to_csv["cv_beta"]:0.5}') + now = datetime.now() + print("Example Completed\t\t:\t\t\t", now.strftime("%d/%m/%Y %H:%M:%S")) + return df_data + else: + if os.path.exists(f'{result_folder}/results_{num_samples}_{files.start+1}_{files.stop}_{segs_list}.csv'): + df_data = pd.read_csv(f'{result_folder}/results_{num_samples}_{files.start+1}_{files.stop}_{segs_list}.csv', sep = ';') + return df_data + else: + print("Data File Doesn't Exit") \ No newline at end of file diff --git a/environment.yml b/environment.yml new file mode 100644 index 000000000..430dcfa89 --- /dev/null +++ b/environment.yml @@ -0,0 +1,12 @@ +name: my_conda_environment +channels: + - defaults +dependencies: + - python=3.8 + - numpy + - pandas + - scikit-learn + - pytest + - pip + - pip: + - lime # Add pip packages here \ No newline at end of file diff --git a/junit.xml b/junit.xml new file mode 100644 index 000000000..bbe97253f --- /dev/null +++ b/junit.xml @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/lime/lime_base.py b/lime/lime_base.py index d8a093173..cd6ee5329 100644 --- a/lime/lime_base.py +++ b/lime/lime_base.py @@ -141,6 +141,7 @@ def explain_instance_with_data(self, label, num_features, feature_selection='auto', + weight_adjustments=None, model_regressor=None): """Takes perturbed data, labels and distances, returns explanation. @@ -163,6 +164,8 @@ def explain_instance_with_data(self, 'none': uses all features, ignores num_features 'auto': uses forward_selection if num_features <= 6, and 'highest_weights' otherwise. + weight_adjustments: weights adjustment factors for the data points. + These factors are multiplied with the weights from the distance kernel. model_regressor: sklearn regressor to use in explanation. Defaults to Ridge regression if None. Must have model_regressor.coef_ and 'sample_weight' as a parameter @@ -178,6 +181,8 @@ def explain_instance_with_data(self, local_pred is the prediction of the explanation model on the original instance """ + if weight_adjustments is not None: + distances *= weight_adjustments weights = self.kernel_fn(distances) labels_column = neighborhood_labels[:, label] used_features = self.feature_selection(neighborhood_data, diff --git a/lime/lime_image.py b/lime/lime_image.py index ea3940e2a..5f6613da3 100644 --- a/lime/lime_image.py +++ b/lime/lime_image.py @@ -1,12 +1,13 @@ """ Functions for explaining classifiers that use Image data. """ -import copy +import copy, math from functools import partial import numpy as np import sklearn from sklearn.utils import check_random_state +from scipy.special import binom from skimage.color import gray2rgb from tqdm.auto import tqdm @@ -132,6 +133,7 @@ def explain_instance(self, image, classifier_fn, labels=(1,), batch_size=10, segmentation_fn=None, distance_metric='cosine', + use_stratification=False, model_regressor=None, random_seed=None, progress_bar=True): @@ -158,6 +160,8 @@ def explain_instance(self, image, classifier_fn, labels=(1,), num_samples: size of the neighborhood to learn the linear model batch_size: batch size for model predictions distance_metric: the distance metric to use for weights. + use_stratification: generate data points using stratified sampling + instead of the usual Monte Carlo sampling. model_regressor: sklearn regressor to use in explanation. Defaults to Ridge regression in LimeBase. Must have model_regressor.coef_ and 'sample_weight' as a parameter to model_regressor.fit() @@ -195,10 +199,11 @@ def explain_instance(self, image, classifier_fn, labels=(1,), top = labels - data, labels = self.data_labels(image, fudged_image, segments, - classifier_fn, num_samples, - batch_size=batch_size, - progress_bar=progress_bar) + data, labels, weight_adjustments = self.data_labels(image, fudged_image, segments, + classifier_fn, num_samples, + batch_size=batch_size, + use_stratification=use_stratification, + progress_bar=progress_bar) distances = sklearn.metrics.pairwise_distances( data, @@ -218,6 +223,7 @@ def explain_instance(self, image, classifier_fn, labels=(1,), ret_exp.local_pred[label]) = self.base.explain_instance_with_data( data, labels, distances, label, num_features, model_regressor=model_regressor, + weight_adjustments=weight_adjustments, feature_selection=self.feature_selection) return ret_exp @@ -228,6 +234,7 @@ def data_labels(self, classifier_fn, num_samples, batch_size=10, + use_stratification=False, progress_bar=True): """Generates images and predictions in the neighborhood of this image. @@ -248,10 +255,21 @@ def data_labels(self, labels: prediction probabilities matrix """ n_features = np.unique(segments).shape[0] - data = self.random_state.randint(0, 2, num_samples * n_features)\ - .reshape((num_samples, n_features)) + if use_stratification: + data = np.ones([num_samples, n_features], dtype=bool) + weight_adjustments = np.zeros(num_samples, dtype=np.float64) + for i in range(1, num_samples): + q = self.random_state.uniform(0, 1) + data[i,:] = self.random_state.rand(n_features) < q + weight_adjustments[i] = (binom(n_features, np.sum(data[i,:])) + / (2 ** n_features)) * (n_features + 1) + weight_adjustments[0] = 1 + else: + data = self.random_state.randint(0, 2, num_samples * n_features)\ + .reshape((num_samples, n_features)) + data[0, :] = 1 + weight_adjustments = None labels = [] - data[0, :] = 1 imgs = [] rows = tqdm(data) if progress_bar else data for row in rows: @@ -269,4 +287,4 @@ def data_labels(self, if len(imgs) > 0: preds = classifier_fn(np.array(imgs)) labels.extend(preds) - return data, np.array(labels) + return data, np.array(labels), weight_adjustments diff --git a/lime/package.json b/lime/package.json index 298b925d6..48ae1a5f6 100644 --- a/lime/package.json +++ b/lime/package.json @@ -32,7 +32,7 @@ "node-libs-browser": "^0.5.3", "style-loader": "^0.13.1", "webpack": "^1.13.0", - "webpack-dev-server": "^1.14.1" + "webpack-dev-server": "^5.1.0" }, "dependencies": { "d3": "^3.5.17", diff --git a/lime_stratified/environment.yml b/lime_stratified/environment.yml new file mode 100644 index 000000000..2bb1bdb01 --- /dev/null +++ b/lime_stratified/environment.yml @@ -0,0 +1,22 @@ +name: CI + +on: [push, pull_request] + +jobs: + build: + runs-on: ubuntu-latest + + steps: + - name: Checkout code + uses: actions/checkout@v2 + + - name: List files in working directory + run: ls -R + + - name: Set up Conda + uses: conda-incubator/setup-miniconda@v2 + with: + auto-update-conda: true + + - name: Install dependencies + run: conda env update --file .github/workflows/environment.yml --name base \ No newline at end of file diff --git a/make.bat b/make.bat new file mode 100644 index 000000000..dc1312ab0 --- /dev/null +++ b/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..1bcca9650 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,40 @@ +[project] +name = "lime_stratified" +version = "0.0.1" +authors = [ + { name="Muhammad Rashid", email="rashidunito@gmail.com" }, + +] +license = { text = "BSD-2-Clause" } + +description = "Using Stratified Sampling to Improve LIME Image Explanations" +readme = "README.md" +requires-python = ">=3.8" +classifiers = [ + "Programming Language :: Python :: 3", + "Operating System :: OS Independent", +] + +dependencies = [ + "matplotlib", + "numpy", + "scipy", + "tqdm>=4.29.1", + "scikit-learn>=0.18", + "scikit-image>=0.12", + "pyDOE2==1.3.0" +] + +dynamic = ["optional-dependencies"] + +[project.urls] +Homepage = "https://github.com/rashidrao-pk/lime_stratified" +Issues = "https://github.com/rashidrao-pk/lime_stratified/issues" + + +[build-system] +requires = [ + "setuptools", + "some-build-toolkit", +] +build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/setup.py b/setup.py old mode 100755 new mode 100644 index 40b92d2d9..732fb890a --- a/setup.py +++ b/setup.py @@ -1,25 +1,32 @@ from setuptools import setup, find_packages +import some_build_toolkit -setup(name='lime', - version='0.2.0.1', - description='Local Interpretable Model-Agnostic Explanations for machine learning classifiers', - url='http://github.com/marcotcr/lime', - author='Marco Tulio Ribeiro', - author_email='marcotcr@gmail.com', - license='BSD', - packages=find_packages(exclude=['js', 'node_modules', 'tests']), - python_requires='>=3.5', - install_requires=[ - 'matplotlib', - 'numpy', - 'scipy', - 'tqdm >= 4.29.1', - 'scikit-learn>=0.18', - 'scikit-image>=0.12', - 'pyDOE2==1.3.0' - ], - extras_require={ - 'dev': ['pytest', 'flake8'], - }, - include_package_data=True, - zip_safe=False) +def get_version(): + version = some_build_toolkit.compute_version() + return version + +setup( + name='lime_stratified', + version=get_version(), + description='Using Stratified Sampling to Improve LIME Image Explanations', + url='https://github.com/rashidrao-pk/lime_stratified', + author='Muhammad Rashid', + author_email='rashidunito@gmail.com', + license='MIT', + packages=find_packages(exclude=['js', 'node_modules']), + python_requires='>=3.5', + install_requires=[ + 'matplotlib>=3.0,<4.0', + 'numpy>=1.16,<2.0', + 'scipy>=1.2,<2.0', + 'tqdm>=4.29.1,<5.0', + 'scikit-learn>=0.18,<2.0', + 'scikit-image>=0.12,<1.0', + 'pyDOE2==1.3.0' + ], + extras_require={ + 'dev': ['pytest', 'flake8', 'black', 'mypy'], + }, + include_package_data=True, + zip_safe=False +) diff --git a/source/benchmark.rst b/source/benchmark.rst new file mode 100644 index 000000000..f1f7123ea --- /dev/null +++ b/source/benchmark.rst @@ -0,0 +1,29 @@ +benchmark package +================= + +Submodules +---------- + +benchmark.table\_perf module +---------------------------- + +.. automodule:: benchmark.table_perf + :members: + :undoc-members: + :show-inheritance: + +benchmark.text\_perf module +--------------------------- + +.. automodule:: benchmark.text_perf + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: benchmark + :members: + :undoc-members: + :show-inheritance: diff --git a/source/conf.py b/source/conf.py new file mode 100644 index 000000000..e37b41e22 --- /dev/null +++ b/source/conf.py @@ -0,0 +1,38 @@ +import os +import sys +sys.path.insert(0, os.path.abspath('../../lime-stratified')) + + +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = 'lime_stratified' +copyright = '2024, Muhammad Rashid' +author = 'Muhammad Rashid' +release = '1.0' + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.napoleon', # To support Google/NumPy style docstrings +] + +templates_path = ['_templates'] +exclude_patterns = [] + + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = 'alabaster' +html_static_path = ['_static'] + + diff --git a/source/index.rst b/source/index.rst new file mode 100644 index 000000000..6144fcf91 --- /dev/null +++ b/source/index.rst @@ -0,0 +1,20 @@ +.. lime_stratified documentation master file, created by + sphinx-quickstart on Mon Sep 30 22:54:44 2024. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to lime_stratified's documentation! +=========================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/source/lime.rst b/source/lime.rst new file mode 100644 index 000000000..03d3abdc1 --- /dev/null +++ b/source/lime.rst @@ -0,0 +1,87 @@ +lime package +============ + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + lime.tests + lime.utils + lime.wrappers + +Submodules +---------- + +lime.discretize module +---------------------- + +.. automodule:: lime.discretize + :members: + :undoc-members: + :show-inheritance: + +lime.exceptions module +---------------------- + +.. automodule:: lime.exceptions + :members: + :undoc-members: + :show-inheritance: + +lime.explanation module +----------------------- + +.. automodule:: lime.explanation + :members: + :undoc-members: + :show-inheritance: + +lime.lime\_base module +---------------------- + +.. automodule:: lime.lime_base + :members: + :undoc-members: + :show-inheritance: + +lime.lime\_image module +----------------------- + +.. automodule:: lime.lime_image + :members: + :undoc-members: + :show-inheritance: + +lime.lime\_tabular module +------------------------- + +.. automodule:: lime.lime_tabular + :members: + :undoc-members: + :show-inheritance: + +lime.lime\_text module +---------------------- + +.. automodule:: lime.lime_text + :members: + :undoc-members: + :show-inheritance: + +lime.submodular\_pick module +---------------------------- + +.. automodule:: lime.submodular_pick + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: lime + :members: + :undoc-members: + :show-inheritance: diff --git a/source/lime.tests.rst b/source/lime.tests.rst new file mode 100644 index 000000000..7448701fb --- /dev/null +++ b/source/lime.tests.rst @@ -0,0 +1,53 @@ +lime.tests package +================== + +Submodules +---------- + +lime.tests.test\_discretize module +---------------------------------- + +.. automodule:: lime.tests.test_discretize + :members: + :undoc-members: + :show-inheritance: + +lime.tests.test\_generic\_utils module +-------------------------------------- + +.. automodule:: lime.tests.test_generic_utils + :members: + :undoc-members: + :show-inheritance: + +lime.tests.test\_lime\_tabular module +------------------------------------- + +.. automodule:: lime.tests.test_lime_tabular + :members: + :undoc-members: + :show-inheritance: + +lime.tests.test\_lime\_text module +---------------------------------- + +.. automodule:: lime.tests.test_lime_text + :members: + :undoc-members: + :show-inheritance: + +lime.tests.test\_scikit\_image module +------------------------------------- + +.. automodule:: lime.tests.test_scikit_image + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: lime.tests + :members: + :undoc-members: + :show-inheritance: diff --git a/source/lime.utils.rst b/source/lime.utils.rst new file mode 100644 index 000000000..57f1f43cc --- /dev/null +++ b/source/lime.utils.rst @@ -0,0 +1,21 @@ +lime.utils package +================== + +Submodules +---------- + +lime.utils.generic\_utils module +-------------------------------- + +.. automodule:: lime.utils.generic_utils + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: lime.utils + :members: + :undoc-members: + :show-inheritance: diff --git a/source/lime.wrappers.rst b/source/lime.wrappers.rst new file mode 100644 index 000000000..fbf936a9a --- /dev/null +++ b/source/lime.wrappers.rst @@ -0,0 +1,21 @@ +lime.wrappers package +===================== + +Submodules +---------- + +lime.wrappers.scikit\_image module +---------------------------------- + +.. automodule:: lime.wrappers.scikit_image + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: lime.wrappers + :members: + :undoc-members: + :show-inheritance: diff --git a/source/modules.rst b/source/modules.rst new file mode 100644 index 000000000..deacbf7ff --- /dev/null +++ b/source/modules.rst @@ -0,0 +1,9 @@ +lime_stratified +=============== + +.. toctree:: + :maxdepth: 4 + + benchmark + lime + setup diff --git a/source/setup.rst b/source/setup.rst new file mode 100644 index 000000000..552eb49d6 --- /dev/null +++ b/source/setup.rst @@ -0,0 +1,7 @@ +setup module +============ + +.. automodule:: setup + :members: + :undoc-members: + :show-inheritance: