diff --git a/.github/dependabot.yml b/.github/dependabot.yml new file mode 100644 index 0000000..856f418 --- /dev/null +++ b/.github/dependabot.yml @@ -0,0 +1,23 @@ +version: 2 +updates: + - package-ecosystem: "github-actions" + directory: "/" + target-branch: "profsea-v3" + schedule: + interval: "weekly" + day: "monday" + groups: + actions-updates: + patterns: + - "*" + + - package-ecosystem: "pip" + directory: "/" + target-branch: "profsea-v3" + schedule: + interval: "weekly" + day: "monday" + groups: + python-dependencies: + patterns: + - "*" \ No newline at end of file diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml new file mode 100644 index 0000000..f8bedbe --- /dev/null +++ b/.github/workflows/deploy-docs.yml @@ -0,0 +1,68 @@ +# (C) Crown Copyright 2025, Met Office. +# The LICENSE file contains full licensing details. +name: Documentation + +on: + # Runs on pushes targeting the default branch. + push: + branches: ["profsea-v3"] + + # Allows you to run this workflow manually from the Actions tab. + workflow_dispatch: + +# Use a login shell so the Conda environment is activated. +defaults: + run: + shell: bash -leo pipefail {0} + +# 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: + # Build job + build: + runs-on: ubuntu-latest + steps: + - name: Checkout repository + uses: actions/checkout@v6 + + - name: Setup GitHub Pages + uses: actions/configure-pages@v6 + + - uses: mamba-org/setup-micromamba@v3 + with: + environment-file: docs-environment.yml + environment-name: profsea-climate-docs + cache-environment: true + cache-environment-key: env-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles('docs-environment.yml') }} + + - name: Build with Sphinx + run: make --directory=docs html + + - name: Upload artifact + uses: actions/upload-pages-artifact@v4 + with: + path: docs/build/html + + # Deployment job + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + needs: build + steps: + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml new file mode 100644 index 0000000..a1ca01e --- /dev/null +++ b/.github/workflows/pytest.yml @@ -0,0 +1,38 @@ +name: Pytest Suite + +on: + push: + branches: + - 'profsea-v3' + + pull_request: + branches: + - profsea-v3 + +jobs: + test: + name: Run Unit Tests + runs-on: ubuntu-latest + + steps: + # 1. Grab the repository code + - name: Checkout Repository + uses: actions/checkout@v4 + + # 2. Set up the Python environment + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.12" # Adjust this to match your project's target version + cache: 'pip' # Speeds up subsequent runs + + # 3. Install the package and test dependencies + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -e ".[test]" + + # 4. Execute the test suite + - name: Run Pytest + run: | + pytest tests/ -v \ No newline at end of file diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml new file mode 100644 index 0000000..c4caf3d --- /dev/null +++ b/.github/workflows/ruff.yml @@ -0,0 +1,35 @@ +name: Ruff Linting + +on: + push: + branches: + - 'profsea-v3' + pull_request: + branches: + - profsea-v3 + +jobs: + ruff: + name: Code Quality (Ruff) + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.10" + cache: 'pip' + + - name: Install Ruff + run: | + python -m pip install --upgrade pip + pip install ruff + + - name: Run Ruff Linter + run: ruff check --output-format=github . + + - name: Run Ruff Formatter + run: ruff format --check . \ No newline at end of file diff --git a/.gitignore b/.gitignore index 1bedc6d..520e54a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,4 +1,12 @@ # Folders *__pycache__* profsea.egg-info +data/ +.pytest_cache/ +.ipynb_checkpoints/ +.ruff_cache/ +profsea/profsea-assets/ +# Ignore the documentation build directory +docs/build +*.DS_Store diff --git a/ProFSea-environment.lock b/ProFSea-environment.lock deleted file mode 100644 index e00b900..0000000 --- a/ProFSea-environment.lock +++ /dev/null @@ -1,99 +0,0 @@ -# This file may be used to create an environment using: -# $ conda create --name ProFSea-env --file ProFSea-environment.lock -# platform: linux-64 -@EXPLICIT -https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 -https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2023.11.17-hbcca054_0.conda -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-h41732ed_0.conda -https://conda.anaconda.org/conda-forge/linux-64/libgfortran-3.0.0-1.tar.bz2 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-h7e041cc_3.conda 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-https://conda.anaconda.org/conda-forge/linux-64/shapely-1.8.0-py37h48c49eb_0.tar.bz2 -https://conda.anaconda.org/conda-forge/linux-64/cf-units-3.0.1-py37hb1e94ed_2.tar.bz2 -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.2.2-py37h1d35a4c_1.tar.bz2 -https://conda.anaconda.org/conda-forge/linux-64/netcdf4-1.4.1-py37ha292673_200.tar.bz2 -https://conda.anaconda.org/conda-forge/linux-64/cartopy-0.20.0-py37hbe109c4_0.tar.bz2 -https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.2.2-1.tar.bz2 -https://conda.anaconda.org/conda-forge/noarch/iris-3.1.0-pyhd8ed1ab_3.tar.bz2 - diff --git a/ProFSea-environment.yml b/ProFSea-environment.yml deleted file mode 100755 index 2a3e238..0000000 --- a/ProFSea-environment.yml +++ /dev/null @@ -1,14 +0,0 @@ -name: ProFSea-env -channels: - - conda-forge -dependencies: - - numpy - - pandas - - python=3.7.7 - - scipy - - pyyaml - - netcdf4 - - matplotlib - - libwebp>=1.3.2 - - cartopy - - iris \ No newline at end of file diff --git a/README.md b/README.md index b0f37e4..77c5dc9 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,35 @@ -# ProFSea-tool +# ProFSea logo -[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) +[![DOI](https://zenodo.org/badge/713453340.svg)](https://zenodo.org/doi/10.5281/zenodo.10255467) [![tests](https://img.shields.io/github/actions/workflow/status/MetOffice/ProFSea-tool/pytest.yml?branch=profsea-v3&label=Tests&logo=github&logoColor=white)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/pytest.yml) [![lint](https://img.shields.io/github/actions/workflow/status/MetOffice/ProFSea-tool/ruff.yml?branch=profsea-v3&label=Linting)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/ruff.yml) [![docs](https://img.shields.io/github/actions/workflow/status/MetOffice/ProFSea-tool/deploy-docs.yml?branch=profsea-v3&label=Documentation)](https://github.com/MetOffice/ProFSea-tool/actions/workflows/deploy-docs.yml) [![ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json&label=Code%20style)](https://github.com/astral-sh/ruff) -The ProFSea tool should be cited as: Perks, R., & Weeks, J. (2023). MetOffice/ProFSea-tool: v1.0.0 (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10255468 +ProFSea is sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature. It's modular, easy to setup and self-contained - all you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go. -## User Guide -A user guide summarising the underpinning science on sea-level projections and providing guidance on how to set-up and run the Met Office Projecting Future Sea Level (ProFSea) tool can be found here: [10.5281/zenodo.10134070](https://zenodo.org/doi/10.5281/zenodo.10134070). +We simulate sea-level change contributions from: -Users are advised to read this guide before using any products as it describes the data availability, the main outputs from the tool and the caveats and limitations. +- Antartic Ice Sheet 🇦🇶 +- Greenland Ice Sheet 🧊 +- Glacier melt 🏔️ +- Thermal expansion 🌡️ +- Landwater ⛰️ -The ProFSea tool user guide should be cited as: Perks, R. J. (2023). Met Office Projecting Future Sea Level (ProFSea) tool - User Guide (1.1). Met Office. https://doi.org/10.5281/zenodo.10245409 +ProFSea can calculate projections of both global mean sea-level rise and spatially-resolved sea-level change fields: + +

+ output +

+ +All the data required to run the regionalisation module can be found here: [![DOI](https://doi.org/badge/10.5281/20427061.svg)](https://doi.org/10.5281/zenodo.20427061) + +## Developments + +Ongoing developments include: + +- [x] 🛠️ Full spatial field projections of regional sea-level change +- [x] 🛠️ Use any input climate forcing to produce ProFSea projections +- [x] 🛠️ Updates to sea-level components based on the latest evidence in the literature +- [ ] 🛠️ Observational constraints to projection ensembles +- [x] 🛠️ Structural enhancements such as updated workflows, GitHub Actions and general reformatting +- [x] 🛠️ Full code documentation ## Contributors Several people have contributed to the development of the ProFSea tool and User Guide documentation, namely: Rachel Perks, Jacob Cheung, Benjamin Harrison, Katie Hodge, Mathew Palmer, Michael Sanderson, Hamish Steptoe, Jennifer Weeks and Gregory Munday. diff --git a/docs-environment.yml b/docs-environment.yml new file mode 100644 index 0000000..2589e29 --- /dev/null +++ b/docs-environment.yml @@ -0,0 +1,9 @@ +name: profsea-sphinx-docs + +dependencies: + - pre-commit + - pydata-sphinx-theme + - sphinx + - sphinx-design + - nbsphinx + - pandoc diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/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/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..747ffb7 --- /dev/null +++ b/docs/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/docs/source/_static/.gitkeep b/docs/source/_static/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/docs/source/_static/custom.css b/docs/source/_static/custom.css new file mode 100644 index 0000000..61ba797 --- /dev/null +++ b/docs/source/_static/custom.css @@ -0,0 +1,3 @@ +img.transparent-logo { + background-color: transparent !important; +} \ No newline at end of file diff --git a/docs/source/_static/favicon.png b/docs/source/_static/favicon.png new file mode 100644 index 0000000..aaf3849 Binary files /dev/null and b/docs/source/_static/favicon.png differ diff --git a/docs/source/_static/logo.png b/docs/source/_static/logo.png new file mode 100644 index 0000000..aaf3849 Binary files /dev/null and b/docs/source/_static/logo.png differ diff --git a/docs/source/_static/mo-logo-dark.png b/docs/source/_static/mo-logo-dark.png new file mode 100644 index 0000000..18a33b7 Binary files /dev/null and b/docs/source/_static/mo-logo-dark.png differ diff --git a/docs/source/_static/mo-logo-light.png b/docs/source/_static/mo-logo-light.png new file mode 100644 index 0000000..7ab4da0 Binary files /dev/null and b/docs/source/_static/mo-logo-light.png differ diff --git a/docs/source/_static/profsea-logo-dark.png b/docs/source/_static/profsea-logo-dark.png new file mode 100644 index 0000000..a355518 Binary files /dev/null and b/docs/source/_static/profsea-logo-dark.png differ diff --git a/docs/source/_static/profsea-logo-light.png b/docs/source/_static/profsea-logo-light.png new file mode 100644 index 0000000..6a55db7 Binary files /dev/null and b/docs/source/_static/profsea-logo-light.png differ diff --git a/docs/source/_templates/mo_logo.html b/docs/source/_templates/mo_logo.html new file mode 100644 index 0000000..01fd91a --- /dev/null +++ b/docs/source/_templates/mo_logo.html @@ -0,0 +1,7 @@ +
+ +
+
\ No newline at end of file diff --git a/docs/source/api_reference.rst b/docs/source/api_reference.rst new file mode 100644 index 0000000..8eb6a97 --- /dev/null +++ b/docs/source/api_reference.rst @@ -0,0 +1,53 @@ +API Reference +============= + +This page exposes selected Python API docs generated from in-code docstrings. + +Core Models +----------- + +.. automodule:: profsea.components.core.global_model + :members: Global + :undoc-members: + :show-inheritance: + +.. automodule:: profsea.components.core.spatial_model + :members: Spatial, fetch_zenodo_fingerprints + :undoc-members: + :show-inheritance: + +.. automodule:: profsea.components.core.state + :members: ClimateState, SpatialState + +.. automodule:: profsea.components.core.time_projection + :members: time_projection + +Global Components +----------------- + +.. automodule:: profsea.components.global_.antarctica + :members: AntarcticaISMIP6, AntarcticaDynAR5, AntarcticaSMBAR5 + +.. automodule:: profsea.components.global_.greenland + :members: GreenlandAR6, GreenlandSMBAR5, GreenlandDynAR5, load_greenland_calibration + +.. automodule:: profsea.components.global_.glacier + :members: Glacier + +.. automodule:: profsea.components.global_.expansion + :members: ThermalExpansion + +.. automodule:: profsea.components.global_.landwater + :members: LandwaterAR6, LandwaterAR5, load_landwater_projection + +Spatial Components +------------------ + +.. automodule:: profsea.components.spatial.fingerprint + :members: Fingerprint + +.. automodule:: profsea.components.spatial.sterodynamic + :members: SterodynamicCMIP6, SterodynamicCMIP5 + +.. automodule:: profsea.components.spatial.gia + :members: GIA diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..1536fd7 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,96 @@ +# 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 + +import os +import sys + +sys.path.insert(0, os.path.abspath("../..")) + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information + +project = "ProFSea" +copyright = "2026, MetOffice" +author = "isabellaascione" +release = "3.0.0" + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx.ext.autodoc", + "sphinx.ext.napoleon", + "nbsphinx", + "sphinx_design", +] + +napoleon_google_docstring = False +napoleon_numpy_docstring = True +napoleon_include_init_with_doc = True +napoleon_include_private_with_doc = False +napoleon_include_special_with_doc = True +napoleon_use_admonition_for_examples = False +napoleon_use_admonition_for_notes = False +napoleon_use_admonition_for_references = False +napoleon_use_ivar = False +napoleon_use_param = True +napoleon_use_rtype = True + +autodoc_mock_imports = [ + "cartopy", + "dask", + "fair", + "matplotlib", + "numcodecs", + "numpy", + "pandas", + "profsea.emulator", + "profsea.plotting_libraries", + "requests", + "rich", + "rich_argparse", + "scipy", + "xarray", +] + +templates_path = ["_templates"] +exclude_patterns = [] + +nbsphinx_prolog = "" +# Allow nbsphinx to find notebooks outside the docs/source directory +nbsphinx_allow_errors = False + + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "pydata_sphinx_theme" +html_theme_options = { + "show_nav_level": 2, + "header_links_before_dropdown": 4, + "secondary_sidebar_items": [], + "navbar_start": ["mo_logo", "navbar-logo"], + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/MetOffice/ProFSea-tool", + "icon": "fa-brands fa-square-github", + "type": "fontawesome", + } + ], + "logo": { + "image_light": "_static/profsea-logo-light.png", + "image_dark": "_static/profsea-logo-dark.png", + }, +} +html_sidebars = { + "index": [], # This removes the left sidebar on the landing page + "**": ["search-field", "sidebar-nav-bs", "page-toc"], +} +html_static_path = ["_static"] +html_favicon = "_static/favicon.png" +html_css_files = [ + "custom.css", +] diff --git a/docs/source/development_guide.rst b/docs/source/development_guide.rst new file mode 100644 index 0000000..783f77c --- /dev/null +++ b/docs/source/development_guide.rst @@ -0,0 +1,66 @@ +.. _development_guide: + +Development Guide +====================== + +This is the Development Guide page. + +.. _open-an-issue: + +Open an Issue +------------- +Before forking the repository, open an issue in the main repository to describe the changes you plan to make. This helps maintainers track contributions and provide feedback early. Check with the repository owners what is the name of the development branch you need to use. In this guide, the name used for the development branch is generically indicated as "development_branch_name" + +.. _fork-the-repository: + +Fork the Repository +------------------- +First, create a fork of the repository in your GitHub account: + +1. Go to the repository page on GitHub. +2. Click the **Fork** button in the top-right corner. +3. Select your GitHub account as the destination for the fork. + +.. _clone-your-fork: + +Clone Your Fork +--------------- +Clone your forked repository and switch to the `development_branch_name` branch: + +.. code-block:: bash + + git clone git@github.com:/ProFSea-tool.git + cd ProFSea-tool + git checkout + +Replace ```` with your GitHub username. + +Replace ```` with the development branch name provided by the repository owners + +.. _commit-changes-to-your-fork: + +Commit Changes to Your Fork +--------------------------- +Once you are satisfied with your changes, commit and push them to your fork: + +.. code-block:: bash + + git add . + git commit -m "Adding feature: " + git push origin development_branch_name + +Replace ```` with a short summary of your updates. + +.. _create-a-pull-request: + +Create a Pull Request +--------------------- +To contribute your changes to the main repository: + +1. Go to your forked repository on GitHub. +2. Click the **Pull Request** button. +3. Select the ``development_branch_name`` branch of the main repository as the base branch. +4. Select the ``development_branch_name`` branch of your fork as the compare branch. +5. Add a title and description for your pull request. +6. Click **Create Pull Request**. +7. Assign reviewers to the pull request diff --git a/docs/source/documentation.rst b/docs/source/documentation.rst new file mode 100644 index 0000000..e6b536a --- /dev/null +++ b/docs/source/documentation.rst @@ -0,0 +1,82 @@ +How to update the documentation +======================================== + +This is the page describing how to update the sphinx documentation. + +If you want to update the documentation, follow these steps to ensure that your changes are tested locally before contributing: + +1. Initial Steps +----------------------------------- +First of all you need to open an issue, fork and clone the repository. For detailed instructions, refer to the following sections in the :ref:`Development Guide ` page: + +- :ref:`Open an Issue ` +- :ref:`Fork the Repository ` +- :ref:`Clone Your Fork ` + +2. Set Up the Environment +------------------------- +Ensure you have the required dependencies installed to build the documentation. + +2.1 Create a Conda Environment +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +If you are using Conda, create the environment from the ``docs-environment.yml`` file: + +.. code-block:: bash + + conda env create -f docs-environment.yml + conda activate profsea-sphinx-docs + +2.2 Install Dependencies with Pip (Optional) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +If you are not using Conda, install the dependencies with ``pip``: + +.. code-block:: bash + + pip install sphinx pydata-sphinx-theme + +3. Make Changes to the Documentation +------------------------------------- +Edit or add ``.rst`` files in the ``docs/source`` directory. For example: + +- Update ``index.rst`` to include new pages. +- Modify existing ``.rst`` files to update content. +- Add new ``.rst`` files for additional sections. + +4. Build the Documentation Locally +----------------------------------- +Before committing your changes, build the documentation locally to ensure there are no errors: + +.. code-block:: bash + + cd docs + make html + +This will generate the HTML files in the ``docs/build/html`` directory. Open the ``index.html`` file in your browser to preview the changes: + +.. code-block:: bash + + xdg-open build/html/index.html + +5. Test the Documentation +-------------------------- +- Verify that all links work correctly. +- Ensure the side menu and table of contents are updated. +- Check for any warnings or errors in the terminal output during the build process. + +6. Commit Changes and Create a Pull Request +--------------------------------------------- +Once you are satisfied with your changes, commit and push them to your fork. For detailed instructions, +refer to the following sections in the :ref:`Development Guide ` page: +- :ref:`Commit Changes to Your Fork ` +- :ref:`Create a Pull Request ` + +7. Verify Deployment +--------------------- +After your pull request is reviewed and merged, the GitHub Actions workflow will automatically build and deploy the updated documentation. Verify that the changes are live on GitHub Pages: + +1. Go to the **Actions** tab in the main repository. +2. Check the logs for the ``deploy-docs.yml`` workflow to ensure it completed successfully. +3. Visit the GitHub Pages URL to confirm the updates. + +Notes +----- diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..8c7f871 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,139 @@ +.. title:: ProFSea + +.. grid:: 1 1 2 2 + :margin: 4 4 0 0 + :padding: 0 + + .. grid-item:: + :columns: 12 12 8 8 + + .. rst-class:: display-2 font-weight-bold + + ProFSea + + .. rst-class:: display-8 font-weight-light + + (pronouced "pro-fe-see") + + .. rst-class:: lead + + **The Met Office's modular, accessible, and *fast* sea-level rise simulator based on statistical emulations of physical modelling experiments and lines of evidence from the IPCC and across the literature.** + + .. container:: d-flex gap-3 pt-3 + + .. button-ref:: user_guide + :ref-type: doc + :color: primary + :shadow: + :class: font-weight-bold + + Get Started + + .. button-link:: [https://github.com/MetOffice/ProFSea-tool](https://github.com/MetOffice/ProFSea-tool) + :color: secondary + :shadow: + :outline: + :class: font-weight-bold + + View on GitHub + + .. grid-item:: + :columns: 12 12 4 4 + + .. image:: /_static/logo.png + :alt: ProFSea Logo + :class: align-center transparent-logo + :width: 100% + + +---- + +ProFSea makes complex sea-level rise simulations easy and efficient. All you need is global mean surface temperature and ocean heat content forcing anomalies, using any baseline period, and you're good to go. + +We simulate sea-level change contributions from: + +- Antartic Ice Sheet 🇦🇶 +- Greenland Ice Sheet 🧊 +- Glacier melt 🏔️ +- Thermal expansion 🌡️ +- Landwater ⛰️ + +Quick Install +------------- + +ProFSea is available as a Python package. To install it: + +.. code-block:: bash + + pip install profsea + +.. note:: + ProFSea relies on standard scientific libraries including ``numpy``, ``xarray``, and ``dask``. Check the :doc:`user_guide` for detailed dependency requirements. + +---- + +.. grid:: 1 1 2 2 + :gutter: 2 + + .. grid-item-card:: User Guide + :link: user_guide + :link-type: doc + :class-card: sd-rounded-3 + + Learn how to use ProFSea and run the tutorial notebook. + + .. grid-item-card:: API Reference + :link: api_reference + :link-type: doc + :class-card: sd-rounded-3 + + Detailed descriptions of ProFSea functionality by module. + + .. grid-item-card:: Development & Docs + :link: documentation + :link-type: doc + :class-card: sd-rounded-3 + + Guidelines for contributing code and updating this documentation. + + .. grid-item-card:: References + :link: references + :link-type: doc + :class-card: sd-rounded-3 + + Academic and software references for the project. + +Getting in Touch +---------------- + +Whether you need help getting started with ProFSea, found a bug, want the emulator to be more awesome, or just want to chat about sea-level modelling, you have a few options: + +* **Open an issue** on the `GitHub repository `_ if you find a bug, need a missing feature, or spot a typo in this documentation 👀. +* **Start a discussion** on GitHub for general questions about emulations, statistical methods, or setting up a new experiment. + +Citing ProFSea +-------------- + +If you use ProFSea for your research, teaching, or analysis, please credit the project by citing the relevant academic references. See the :doc:`references` page for the full bibliography, methodology papers, and software DOIs. + +.. toctree:: + :caption: User Guide + :maxdepth: 2 + :hidden: + + user_guide + +.. toctree:: + :caption: Development + :maxdepth: 1 + :hidden: + + documentation + +.. toctree:: + :caption: Reference + :maxdepth: 1 + :hidden: + + api_reference + references \ No newline at end of file diff --git a/docs/source/references.rst b/docs/source/references.rst new file mode 100644 index 0000000..6392322 --- /dev/null +++ b/docs/source/references.rst @@ -0,0 +1,4 @@ +References +========== + +This is the References page. diff --git a/docs/source/user_guide.rst b/docs/source/user_guide.rst new file mode 100644 index 0000000..bb57aed --- /dev/null +++ b/docs/source/user_guide.rst @@ -0,0 +1,16 @@ +User Guide +========== + +This is the User Guide page + +Tutorial Notebook +----------------- + +The worked tutorial notebook is available below: + +.. toctree:: + :maxdepth: 1 + + worked_example + development_guide + documentation diff --git a/docs/source/worked_example.ipynb b/docs/source/worked_example.ipynb new file mode 120000 index 0000000..151d4a5 --- /dev/null +++ b/docs/source/worked_example.ipynb @@ -0,0 +1 @@ +../../profsea/tutorial_notebooks/worked_example.ipynb \ No newline at end of file diff --git a/profsea-env.yml b/profsea-env.yml new file mode 100644 index 0000000..1a9735a --- /dev/null +++ b/profsea-env.yml @@ -0,0 +1,29 @@ +name: profsea +channels: + - conda-forge +dependencies: + - cartopy + - dask + - ipykernel + - iris + - matplotlib + - nbsphinx + - netcdf4 + - numcodecs + - numpy + - pandas + - pandoc + - pre-commit + - pydata-sphinx-theme + - pytest + - python=3.12 + - pyyaml + - regionmask + - requests + - rich + - ruff + - scipy + - sphinx + - sphinx-design + - xarray + - zarr diff --git a/profsea/Falkland_example/data/cmip5/STANLEYII_ij_1x1_coords.csv b/profsea/Falkland_example/data/cmip5/STANLEYII_ij_1x1_coords.csv deleted file mode 100644 index 5971a41..0000000 --- a/profsea/Falkland_example/data/cmip5/STANLEYII_ij_1x1_coords.csv +++ /dev/null @@ -1,25 +0,0 @@ -Location: STANLEY II -Latitude: -51.692 -Longitude: -57.821 -Model,i,j,box_lon,box_lat -ACCESS1-0,302,38,302.0,-51.5 -bcc-csm1-1,303,38,303.0,-51.5 -CNRM-CM5,302,38,302.0,-51.5 -CSIRO-Mk3-6-0,302,38,302.0,-51.5 -CanESM2,302,38,302.0,-51.5 -GFDL-ESM2G,303,38,303.0,-51.5 -GFDL-ESM2M,303,38,303.0,-51.5 -GISS-E2-R,302,38,302.0,-51.5 -HadGEM2-CC,303,38,303.0,-51.5 -HadGEM2-ES,303,38,303.0,-51.5 -inmcm4,303,38,303.0,-51.5 -IPSL-CM5A-LR,302,38,302.0,-51.5 -IPSL-CM5A-MR,302,38,302.0,-51.5 -MIROC-ESM,302,38,302.0,-51.5 -MIROC-ESM-CHEM,302,38,302.0,-51.5 -MIROC5,302,38,302.0,-51.5 -MPI-ESM-LR,302,38,302.0,-51.5 -MPI-ESM-MR,303,38,303.0,-51.5 -MRI-CGCM3,302,38,302.0,-51.5 -NorESM1-M,303,38,303.0,-51.5 -NorESM1-ME,303,38,303.0,-51.5 diff --git a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_global.csv b/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_global.csv deleted file mode 100644 index 67d6f48..0000000 --- a/profsea/Falkland_example/data/sea_level_projections/STANLEYII_rcp26_projection_2100_global.csv +++ /dev/null @@ -1,847 +0,0 @@ -year,percentile,exp,antdyn,antsmb,greendyn,greensmb,glacier,landwater,antnet,greennet,sum -2007,5,0.007747830357402563,0.002729416126385331,-0.00018738857907010236,0.0016894025029614568,0.0014912912156432867,0.009886452928185463,0.0002794466563500464,0.002601555286673829,0.0032219790271483363,0.024319663889400545 -2007,10,0.008736629970371723,0.002749401144683361,-0.0001577381044626236,0.0017016869969666004,0.0015035183168947697,0.01020332332700491,0.00028879407909698784,0.0026430128418724053,0.00324442470446229,0.02562091049621813 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-2009,5,0.009925924241542816,0.003181967418640852,-0.0005756449216278269,0.002153712324798107,0.0015587951056659222,0.010254379361867905,0.0008150199428200722,0.002791649312712252,0.003841180296149105,0.029214699009025936 -2009,10,0.011036878451704979,0.0032439231872558594,-0.0004833759739995003,0.002192151267081499,0.0015973133267834783,0.010975790210068226,0.000845813425257802,0.0029193434165790677,0.0039110222132876515,0.03109469817718491 -2009,30,0.014147917740046978,0.0034948461689054966,-0.00032333868439309303,0.0022909934632480145,0.0017007326241582632,0.012485547177493572,0.0009997807210311294,0.0032272590906359255,0.004086900665424764,0.03582932110657566 -2009,33,0.014507969841361046,0.003532136103603989,-0.00030752088059671223,0.002312958473339677,0.0017140804254449904,0.012649044310674071,0.0010228757746517658,0.003271678124292521,0.004108313820324838,0.03635602352733258 -2009,50,0.016064075753092766,0.003748849965631962,-0.00022996746702119708,0.0024063095916062593,0.001796110300347209,0.013508155941963196,0.0011537481332197785,0.0035055707921856083,0.004223189316689968,0.038756343157729134 -2009,67,0.01755238138139248,0.003973476151004434,-0.00015988576342351735,0.0024886783212423325,0.001894370885565877,0.014470420433208349,0.0012846202589571476,0.0037493951733267752,0.004343107922468334,0.04103905962219869 -2009,70,0.01782173477113247,0.004016813402995467,-0.0001469960407121107,0.002510643098503351,0.0019161389209330082,0.014667678158730268,0.0013077154289931059,0.0037959426845191047,0.004366751865018159,0.04149348942155484 -2009,90,0.02100309729576111,0.004339959938079119,-5.453753292385951e-05,0.0026314505375921726,0.002149812411516905,0.016531893424689772,0.0014539845287799835,0.004154370010655839,0.004614380444400013,0.04574705990235089 -2009,95,0.02221948280930519,0.004469077102839947,-2.441359538352117e-05,0.002658906625583768,0.002293102676048875,0.017397857457399368,0.001492476207204163,0.0043016682852794474,0.004758716828655451,0.04771817427390487 -2010,5,0.011036466807126999,0.003405102528631687,-0.0008001990499906242,0.002383620012551546,0.0015909349313005805,0.010370799340307713,0.0010711464565247297,0.0028621499659493566,0.004151605535298586,0.03160007820260944 -2010,10,0.012227976694703102,0.0034890444949269295,-0.0006708314758725464,0.002435928676277399,0.001644184230826795,0.011296790093183517,0.0011140386341139674,0.003039592527784407,0.00424860151251778,0.033805799185938665 -2010,30,0.015564601868391037,0.003828088752925396,-0.00044731363013852395,0.002570436103269458,0.0017893636832013726,0.013248197734355927,0.0013284990563988686,0.0034612940362421796,0.004490448394790292,0.039232348382211055 -2010,33,0.0159507617354393,0.0038785597775131465,-0.00042527844198048115,0.002600326668471098,0.0018074813997372985,0.013455716893076897,0.0013606681022793055,0.0035196542739868164,0.00451978606870398,0.03982664288923843 -2010,50,0.017619702965021133,0.004173104651272297,-0.0003167233517160639,0.0027273616287857294,0.0019220294198021293,0.014552662149071693,0.0015429593622684479,0.0038390401750802994,0.004677285614889115,0.04257657108246349 -2010,67,0.01921592652797699,0.0044823678303509955,-0.00021904123423155397,0.002839451190084219,0.0020598392002284527,0.015747706629335882,0.001725250855088234,0.004175565801674566,0.004843331546289846,0.04520464108645683 -2010,70,0.019504811614751816,0.004542827000841498,-0.00020109109755139798,0.002869341755285859,0.0020889343926683065,0.01599145494401455,0.0017574199009686708,0.004240909416694194,0.004876245697960258,0.04572657800308661 -2010,90,0.02291685715317726,0.005011511035263538,-7.147782889660448e-05,0.0030337399803102016,0.0024167730007320642,0.018289243429899217,0.001961157424375415,0.004756442940561101,0.005221306905150413,0.050585113454144445 -2010,95,0.024221446365118027,0.0052147069945931435,-2.919388862210326e-05,0.0030711032450199127,0.0026162220165133476,0.01937731821089983,0.0020147725008428097,0.0049824361303763,0.0054220715537667274,0.05285678950604051 -2011,5,0.011580809950828552,0.0036261440254747868,-0.0010376267600804567,0.0026120292022824287,0.0016213177586905658,0.0104464590549469,0.0013194996863603592,0.002921492385212332,0.004461290530161932,0.03337878862294019 -2011,10,0.012892026454210281,0.0037327390164136887,-0.0008697424782440066,0.002678736113011837,0.0016900210175663233,0.011570585891604424,0.0013754075625911355,0.0031514297297690064,0.004586647264659405,0.03594331015119678 -2011,30,0.016563870012760162,0.004162145312875509,-0.0005778027116321027,0.002850267803296447,0.0018794205971062183,0.013971872627735138,0.0016549464780837297,0.003692031590617262,0.004897702438756824,0.04218189211678691 -2011,33,0.016988826915621758,0.004226172673515976,-0.0005488611641339958,0.0028883861377835274,0.0019034300930798054,0.014226524513214827,0.0016968772979453206,0.0037631906452588737,0.0049356955301482226,0.04285753955089604 -2011,50,0.018825439736247063,0.004601345397531986,-0.00040720826655160636,0.003050388302654028,0.0020531327463686466,0.015557492151856422,0.001934485393576324,0.004172714667220134,0.00513838604092598,0.046022153706871904 -2011,67,0.020582029595971107,0.005000126007944346,-0.0002795084146782756,0.00319333141669631,0.0022315748501569033,0.016978813726454975,0.002172093605622649,0.004606513801263645,0.005353105859830976,0.04905009061476449 -2011,70,0.02089993841946125,0.0050790454261004925,-0.0002564470923971385,0.003231449518352747,0.0022703062277287245,0.01727275550365448,0.002214024541899562,0.0046934572710597405,0.005396237573586404,0.04964837678708136 -2011,90,0.024654779583215714,0.005712177604436874,-8.722769416635834e-05,0.0034410993102937937,0.00269849575124681,0.019991185516119003,0.0024795865174382925,0.005385389519506135,0.0058442068751901385,0.05521986925741658 -2011,95,0.026090435683727264,0.006004971917718649,-3.080375681747705e-05,0.0034887471701949835,0.002958235563710332,0.021290436387062073,0.0025494713336229324,0.00570445707417093,0.006108361214864999,0.05783235537819564 -2012,5,0.012819323688745499,0.0038450919091701508,-0.0013002261985093355,0.0028389403596520424,0.0016478211618959904,0.010455084359273314,0.0015600797487422824,0.002956699696369469,0.00476907750708051,0.03577534900250612 -2012,10,0.014240577816963196,0.003975007217377424,-0.0010895669693127275,0.0029205738101154566,0.0017339733894914389,0.011796928942203522,0.0016299202106893063,0.003246404812671244,0.0049250290147028865,0.03871398430128466 -2012,30,0.018220558762550354,0.004497014917433262,-0.0007200284744612873,0.0031304885633289814,0.0019729668274521828,0.014697497338056564,0.0019791231025010347,0.003917214984539896,0.005311694426927716,0.04579181682493072 -2012,33,0.018681176006793976,0.004574975022114814,-0.0006830458296462893,0.003177136415615678,0.0020042965188622475,0.014993047341704369,0.002031503478065133,0.004002376226708293,0.005359111120924354,0.04655886352644302 -2012,50,0.020671917125582695,0.005033571273088455,-0.0005045482539571822,0.0033753893803805113,0.0021942530293017626,0.016571104526519775,0.0023283257614821196,0.004505023593083024,0.005610675434581935,0.05013601934479084 -2012,67,0.022575918585062027,0.005526750059798361,-0.0003439585561864078,0.0035503183025866747,0.002418983089737595,0.018236864954233174,0.002625148044899106,0.005040757154347375,0.005878755414159969,0.05357345922326204 -2012,70,0.022920506075024605,0.00562546863220632,-0.00031456921715289354,0.003596966154873371,0.0024673165753483772,0.018579966388642788,0.002677528653293848,0.005150104523636401,0.005933039379306138,0.05424960755917709 -2012,90,0.02699045091867447,0.006441960111260414,-0.0001015606612781994,0.0038535285275429487,0.0030093290843069553,0.021734103560447693,0.003009271342307329,0.006037805491359904,0.006497916067019105,0.06054090707038995 -2012,95,0.028546586632728577,0.00683987233787775,-2.9286128210515647e-05,0.003911837935447693,0.0033414247445762157,0.02325023151934147,0.0030965718906372786,0.006465218681842084,0.006831627967767416,0.0634869208181044 -2013,5,0.014244738966226578,0.004061946179717779,-0.0015966960927471519,0.0030643530189990997,0.0016981662483885884,0.010816581826657058,0.0017928860615938902,0.0029560162220150232,0.005102688551414758,0.03883128772868076 -2013,10,0.015743613243103027,0.004215848632156849,-0.0013380789896473289,0.003161441534757614,0.0018020125571638346,0.012351608369499445,0.0018775764619931579,0.0033090042765252297,0.005290249828249216,0.04203686548571568 -2013,30,0.01994095742702484,0.004832698963582516,-0.0008896735380403697,0.0034110983833670616,0.0020946611184626818,0.015616120770573616,0.0023010284639894962,0.004113615192181896,0.005755482614040375,0.04969107315992005 -2013,33,0.020426731556653976,0.004924966362304986,-0.0008454069029539824,0.003466577734798193,0.002132551970425993,0.015958702396601437,0.002364546060562134,0.004214885761030018,0.005812402979936451,0.05053611986717442 -2013,50,0.022526195272803307,0.005469783209264278,-0.000627555069513619,0.003702364629134536,0.0023652338422834873,0.017755300737917423,0.002724480116739869,0.004815676831640303,0.00611586298327893,0.054440485590021126 -2013,67,0.024534182623028755,0.0060622409218922275,-0.0004313429817557335,0.003910412080585957,0.0026411041617393494,0.019649269059300423,0.0030844141729176044,0.005460367666673847,0.006441213921643795,0.05822976506431587 -2013,70,0.024897588416934013,0.00618209633976221,-0.00039572292007505894,0.003965891432017088,0.0027004596777260303,0.020033824071288106,0.0031479322351515293,0.005593524081632495,0.006507645105011761,0.05897341959644109 -2013,90,0.029189810156822205,0.0072008585557341576,-0.00013581581879407167,0.004271027632057667,0.0033644975628703833,0.02359943278133869,0.0035502114333212376,0.006700445606838912,0.007197301811538637,0.0658560055133421 -2013,95,0.030830930918455124,0.007719407789409161,-4.9211746954824775e-05,0.004340376704931259,0.0037727414164692163,0.025314992573112247,0.0036560744047164917,0.00724978577636648,0.007607888232450931,0.06907740183523856 -2014,5,0.015892239287495613,0.004276706837117672,-0.001879818388260901,0.0032882671803236008,0.0017630320508033037,0.011391282826662064,0.0020179194398224354,0.002964482642710209,0.005450741620734334,0.042366810273961164 -2014,10,0.017387934029102325,0.00445526372641325,-0.0015811462653800845,0.003401339752599597,0.0018869723426178098,0.013071123510599136,0.002118376549333334,0.0033701335196383297,0.005669838166795671,0.0456714068481233 -2014,30,0.021576369181275368,0.005169196054339409,-0.001060834270901978,0.0036920972634106874,0.0022274604532867667,0.016569087281823158,0.0026206630282104015,0.004304061934817582,0.006210821284912527,0.05352719603688456 -2014,33,0.022061113268136978,0.005276146996766329,-0.001008195336908102,0.0037567103281617165,0.002270893193781376,0.016941889077425002,0.0026960058603435755,0.004422789963427931,0.00627725466620177,0.05439660066025681 -2014,50,0.0241561196744442,0.005909981206059456,-0.0007559303776361048,0.004031314514577389,0.0025412742979824543,0.018908094614744186,0.0031229492742568254,0.005123031965922564,0.0066308724926784635,0.05850674584507942 -2014,67,0.026159847155213356,0.006606598282232883,-0.0005281163612380624,0.004273612983524799,0.0028650397434830666,0.020988566819578416,0.0035498926881700754,0.005887461360543966,0.007010257558431476,0.06249652673024685 -2014,70,0.026522481814026833,0.006748929154127835,-0.0004862589412368834,0.004338225349783897,0.002934727119281888,0.02141612712293863,0.0036252355203032494,0.006040505855344235,0.007087722979485988,0.06328446977131534 -2014,90,0.030805593356490135,0.00798887200653553,-0.000185929486178793,0.0046935961581766605,0.0037124447990208864,0.02532110307365656,0.004102407954633236,0.007381180941592902,0.007895627152174713,0.07048782731872053 -2014,95,0.03244323283433914,0.008643578737974167,-8.766335668042302e-05,0.004774361848831177,0.004183367546647776,0.027190981619060032,0.0042279791086912155,0.008069614199484931,0.00837394897826016,0.07389311579463538 -2015,5,0.017370635643601418,0.004489373415708542,-0.0021850518649443977,0.00351068377494812,0.001819094514939934,0.011771785095334053,0.0022351795341819525,0.00295342446770519,0.0057898408500477675,0.04547884239291307 -2015,10,0.018861660733819008,0.00469325203448534,-0.001839303644374013,0.0036402682308107615,0.0019626212306320667,0.013621756806969643,0.0023523208219558,0.00342015631031245,0.006042637582868338,0.04895075588137843 -2015,30,0.023037021979689598,0.005506507121026516,-0.0012395823141559958,0.00397348590195179,0.002357349032536149,0.017454149015247823,0.0029380270279943943,0.00449662865139544,0.006666014133952558,0.05714461475145072 -2015,33,0.023520251736044884,0.005628516934812069,-0.001178052625618875,0.004047533962875605,0.0024084524600766597,0.01786596141755581,0.003025882877409458,0.004629522329196334,0.006742825265973806,0.05803433317894814 -2015,50,0.025608718395233154,0.006354164332151413,-0.0008868009899742901,0.0043622395023703575,0.0027217762544751167,0.020031416788697243,0.0035237332340329885,0.005434655700810254,0.007149776793085039,0.06237044354202226 -2015,67,0.027606191113591194,0.007159822760149839,-0.0006217677082167934,0.004639920778572559,0.0030985691118985415,0.022317582722753287,0.004021583124995232,0.0063189034117385745,0.007588989448267967,0.06659008572576568 -2015,70,0.02796769328415394,0.007325966469943523,-0.0005742505309171975,0.0047139693051576614,0.003180199721828103,0.02279297187924385,0.004109439440071583,0.006498115719296038,0.007678936421871185,0.06741924664238468 -2015,90,0.03223743662238121,0.008806001394987106,-0.0002279469044879079,0.005121234804391861,0.004082545638084412,0.0270682442933321,0.004665860440582037,0.008088253671303391,0.008616724144667388,0.07500444285105914 -2015,95,0.03386996313929558,0.00961238518357277,-0.00011403780081309378,0.005213795229792595,0.004630396957509216,0.029114042408764355,0.004812286701053381,0.008932961376558524,0.009171876590698957,0.07860951451584697 -2016,5,0.018640216439962387,0.004699947312474251,-0.002483146730810404,0.003731601405888796,0.001876448281109333,0.012159384787082672,0.0024446656461805105,0.0029473223257809877,0.006128997216001153,0.048384376177273224 -2016,10,0.02017526887357235,0.004929813556373119,-0.0020918778609484434,0.003878226736560464,0.0020400692941620947,0.014159809798002243,0.002579407999292016,0.0034743912052363165,0.006415502610616386,0.052076086394663434 -2016,30,0.024473926052451134,0.00584463169798255,-0.001414960017427802,0.004255262669175863,0.002486974000930786,0.018300170078873634,0.0032531195320189,0.004690617322921753,0.007121837511658668,0.0607044885342475 -2016,33,0.024971425533294678,0.005982076171785593,-0.0013445350364781918,0.0043390486389398575,0.0025442258920520544,0.018746858462691307,0.0033541761804372072,0.004840501511353068,0.007208958948031067,0.061640961291850545 -2016,50,0.027121564373373985,0.006802333518862724,-0.0010156690841540694,0.0046951379626989365,0.0029000979848206043,0.02109602279961109,0.0039268312975764275,0.00575132470112294,0.007669689133763313,0.06624641621601768 -2016,67,0.029178019613027573,0.007721913112327459,-0.0007159688393585384,0.005009335000067949,0.0033282185904681683,0.023574715480208397,0.004499485716223717,0.006759870913811028,0.008167663742788135,0.07075845940271393 -2016,70,0.02955019846558571,0.007913208566606045,-0.0006629652925767004,0.005093120504170656,0.0034204809926450253,0.024087753891944886,0.004600542597472668,0.006968950852751732,0.008270163391716778,0.07163300631800666 -2016,90,0.033946022391319275,0.009652245789766312,-0.0002705751336179674,0.0055539426393806934,0.004443589597940446,0.028707711026072502,0.005240568425506353,0.008830236960784534,0.009334858856163919,0.07968212433625013 -2016,95,0.03562675788998604,0.010625827126204967,-0.000140847244620091,0.005658674519509077,0.005068389233201742,0.030907876789569855,0.0054089962504804134,0.00984116131439805,0.009963839803822337,0.08354780399240554 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-2010,10,0.010737097842805087,-0.0005356168208781624,-0.0005190031663825572,0.0034881447949547508,0.0022277089473761807,0.009178436250331105,0.0014527486607678608,-0.0026685659750175094,-0.0009252202263497042,0.005951222198431718,0.026932124482598296 -2010,30,0.01376012707874179,-0.00045394064489944,-0.0003460758657316557,0.003726222421137837,0.00242991249305138,0.010765445254886048,0.0017324131909929655,-0.0026685659750175094,-0.0007468190104092745,0.006316886710328639,0.03176882049469915 -2010,33,0.014091478046029808,-0.000445285769894187,-0.0003287257937039202,0.003757275954439939,0.002457085569671162,0.01093116197480637,0.0017743628477552315,-0.0026685659750175094,-0.0007273288812101906,0.006361044343805029,0.032309134809438964 -2010,50,0.015845554190687835,-0.00039717020622646296,-0.00024477344415361655,0.00393777319933963,0.0026126408545774638,0.011826092859173467,0.002012077569408072,0.0008179396157169171,-0.0006256060364666124,0.006601957124674471,0.03510334861573025 -2010,67,0.017740636236034335,-0.00024204207073609088,-0.00016912567593512708,0.004121690922433289,0.002803890959959064,0.012798148170790412,0.002249792594680909,0.0008179396157169171,-0.0005255150735466703,0.00685902653942702,0.037891253399872024 -2010,70,0.01815702973008156,-0.00021642163608979895,-0.0001555115143581659,0.004160973177447996,0.0028438932759459823,0.012997242535030177,0.002291742251443175,0.0008179396157169171,-0.0005062855065438324,0.006909536633647674,0.038435246152776034 -2010,90,0.02200891252309084,-0.00016548484353426601,-5.5189980945216335e-05,0.004432571547265073,0.0032951674035845142,0.014866855076701959,0.0025574237145575226,0.0008179396157169171,-0.0003345597780603279,0.007411590464456383,0.043622012671210374 -2010,95,0.02399759262911975,-0.0001544552622640223,-2.2584944363070527e-05,0.0045343447977598565,0.0035678259871691135,0.015756009841890285,0.002627339809161299,0.0008179396157169171,-0.00026745015627184607,0.0076980961311307805,0.04621914510124496 -2011,5,0.01010493452847004,-0.0006494373706169226,-0.0008018935043600407,0.003750222760557428,0.002194785303156687,0.008483690467079653,0.001720677670903398,-0.002859177830375903,-0.0012410175057284115,0.006244127211949542,0.026274235403463797 -2011,10,0.011338279578462243,-0.0006033912381710354,-0.0006724872967245285,0.003842626083073998,0.0022910802208738657,0.009402318866233987,0.0017935836634188484,-0.002859177830375903,-0.001115584685936836,0.0064373422153420025,0.028712529362601137 -2011,30,0.014658995043486358,-0.0005022916141764085,-0.00044672509056033395,0.004133036156674436,0.0025523105224934933,0.011352900818549887,0.0021581130187561088,-0.002859177830375903,-0.0008869117619929592,0.006898047340135702,0.03425298839892982 -2011,33,0.015011419266462326,-0.0004909359760250687,-0.00042421946161387997,0.004172637442387174,0.0025874141531889963,0.011557401494730475,0.002212792399285198,-0.002859177830375903,-0.0008618688004455636,0.006953585286047411,0.034864849422253576 -2011,50,0.01693522010128945,-0.00043043388541755983,-0.0003147481103520923,0.004402463332330944,0.002789318476148914,0.012638836093462036,0.002522642374093369,0.0008763638739824111,-0.0007349925603854256,0.007258432773370756,0.03803849424488856 -2011,67,0.018988607500120998,-0.00028471348091776706,-0.00021620128731264404,0.004644920836154192,0.0030366252835380947,0.013798120714485674,0.002832492500711538,0.0008763638739824111,-0.0006125812796177966,0.00758287476094737,0.0411810398448826 -2011,70,0.019468979260325432,-0.0002466110033505334,-0.0001982604281573703,0.0046874551242133055,0.0030896483868081215,0.014036316789436456,0.0028871720330506255,0.0008763638739824111,-0.0005893652221224348,0.00764556828355487,0.04178602498648569 -2011,90,0.02363922300413251,-0.0001799209375129263,-6.740967322208198e-05,0.005019862381144451,0.0036769439374636204,0.016245610706394558,0.0032334749282115225,0.0008763638739824111,-0.0003889383753771848,0.008288309847942644,0.047637483144149874 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-2012,33,0.016507816140912474,-0.0005373221042507477,-0.0005283957769392974,0.004594863772430033,0.0027243983866436557,0.01217915650426963,0.002649157638461626,-0.0030497896857342964,-0.0010071970230005542,0.007556549304651271,0.03801256836774221 -2012,50,0.01859966553077102,-0.0004635552707245983,-0.0003904380591439653,0.004878474823416621,0.002979989735933894,0.013463699190652989,0.0030362251615399793,0.0009347881322479053,-0.0008509922845396354,0.0079293412137416,0.04157197910946009 -2012,67,0.020838798129931092,-0.00033105826999704855,-0.00026585239874110054,0.005160263584640257,0.003289835176149263,0.014818004445854337,0.003423292684618333,0.0009347881322479053,-0.0007024145908551483,0.00832876673587039,0.04509151133653364 -2012,70,0.02134277801141143,-0.00027804296372508627,-0.00024317108819139,0.005208726112381527,0.0033581858144058636,0.015100088741587669,0.003491598986002745,0.0009347881322479053,-0.0006748198404718026,0.008407354719575419,0.04577873644587321 -2012,90,0.02588779024034739,-0.00019439198508543114,-7.837101930872365e-05,0.005612790281252945,0.004098577484099669,0.01766239730448964,0.003924204043337373,0.0009347881322479053,-0.00044113716534792093,0.009207998334095314,0.052320316475437556 -2012,95,0.02826680339872837,-0.00017741136369919187,-2.2559967138933828e-05,0.005743213437135214,0.004549063261186544,0.018888154618384684,0.004038047271738067,0.0009347881322479053,-0.0003419887804711829,0.00967507739187498,0.05557009892665286 -2013,5,0.012407807644829155,-0.0008182387419788244,-0.0012339278519813586,0.004398484780709878,0.002300229287583461,0.008783879686557982,0.0023379914709703267,-0.00324040154109269,-0.0017481056774220636,0.0071685338167533546,0.030800996278437694 -2013,10,0.013810574728250502,-0.0007475651773533725,-0.0010347985905913518,0.0045521859666914485,0.002446400057898351,0.010036099059096871,0.002448430967404652,-0.00324040154109269,-0.0015569768331589163,0.007445663225287542,0.03380906563810053 -2013,30,0.017616615982353687,-0.0006008429334237034,-0.0006879067136722678,0.004955652265248462,0.002846144055385578,0.01268571886112176,0.003000628449576278,-0.00324040154109269,-0.001210527795575307,0.008119231214576911,0.0406416955289894 -2013,33,0.018041786781713354,-0.0005839184883174026,-0.0006534192822640471,0.005013290169676527,0.0028992906431384623,0.012964418659909167,0.0030834578062345253,-0.00324040154109269,-0.0011738247562267422,0.008200149039810964,0.041389099976175285 -2013,50,0.02024813132043928,-0.0004968058827095651,-0.0004850900261233468,0.0053508329717523685,0.003212450228830023,0.01442465962567553,0.00355282547631791,0.0009932123905133994,-0.0009887632499050512,0.008647129904354383,0.04527439171765799 -2013,67,0.02264664569478487,-0.00037660486651678845,-0.00033360316914144924,0.005685733463071244,0.0035921387378427417,0.015964325627584115,0.004022193146401294,0.0009932123905133994,-0.0008121330753741395,0.009124295417824847,0.04913404009498608 -2013,70,0.023184860050678255,-0.00031071757761365435,-0.00030603764125417584,0.005743371044979127,0.003674692132820388,0.016284501489104153,0.004105023110299534,0.0009932123905133994,-0.0007794336765135054,0.009219994104404383,0.04987773274616667 -2013,90,0.028045103209093213,-0.00020889804262529753,-0.00010503396457841537,0.006211376661010023,0.004582302213061784,0.019179620698627404,0.004629610452695082,0.0009932123905133994,-0.0005102984603525803,0.010193029389315555,0.05702463661193243 -2013,95,0.030614089226722718,-0.0001888328194324456,-3.817444858747244e-05,0.0063641872088604735,0.005137418147831598,0.020569678568033253,0.004767659785285489,0.0009932123905133994,-0.00039518743361592926,0.010765115986805811,0.06055787274647353 -2014,5,0.013787954503670334,-0.0009078492998107129,-0.0014531391486342588,0.00473395769680235,0.0023915678961419326,0.009253675827384135,0.002631443537028695,-0.003431013396451084,-0.0020069376978193213,0.00766445897694854,0.033684094162604324 -2014,10,0.01523051055148244,-0.0008235284185247615,-0.0012227022300700138,0.0049027230088967335,0.0025607063482506495,0.010618223560760434,0.0027624434205494658,-0.003431013396451084,-0.0017875857727015446,0.007983669165857793,0.03683351319189551 -2014,30,0.019024443509988487,-0.0006507995530070332,-0.0008203796588735065,0.005367163792243254,0.0030262146282227535,0.013460558863563662,0.003417444052633304,-0.003431013396451084,-0.00138627253967639,0.008765230394428988,0.043912403165487786 -2014,33,0.01945086379274726,-0.0006310673554952734,-0.0007799747991696324,0.00544049675149308,0.0030875501820548015,0.013764181173283575,0.003515693965273882,-0.003431013396451084,-0.0013434498494497953,0.008859364705830755,0.044698737959584185 -2014,50,0.021697763298638165,-0.0005300962138453532,-0.0005843810165944629,0.005830363768905318,0.0034528805996264364,0.015364820338300045,0.004072444381097146,0.0010516366487788936,-0.0011310236377004555,0.009376010626804161,0.048757470139255396 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-2015,50,0.02300981916747987,-0.0005636473511601373,-0.0006852430724506475,0.00629311955239139,0.003698415602615769,0.01627859785502364,0.004595081875877688,0.0011100609070443875,-0.0012763062818358217,0.01011164720458041,0.052063770215810594 -2015,67,0.025512241002172233,-0.00042588561063214487,-0.00048088315392832574,0.006744338399442985,0.004212944632063068,0.018143025726340672,0.005244297028935688,0.0011100609070443875,-0.001045536950305836,0.010748861159291552,0.056339766295504835 -2015,70,0.026023414623737334,-0.0003797946652658414,-0.0004437558948606231,0.006825383096462235,0.004324515884625859,0.01852056975006085,0.005358864998267092,0.0011100609070443875,-0.0010042177865186588,0.010877113436591085,0.05717234949213872 -2015,90,0.031219507664442064,-0.00023801510908685918,-0.00017648622747807905,0.007441170336562145,0.005558095826986659,0.021996972475743625,0.006084459101166024,0.0011100609070443875,-0.0006609104705068401,0.01218490889044907,0.06507679222841305 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-2016,90,0.032987871516495945,-0.0002526261180085544,-0.00020884454236752572,0.008055971550921965,0.006052020806444753,0.02332351357193835,0.006833900125799272,0.0011684851653098816,-0.0007338891618259258,0.013193707519437707,0.0691875149445746 -2016,95,0.03577746518775821,-0.00022287084696168044,-0.00010941332177388192,0.00827785648869491,0.006904112863483389,0.0251390579865203,0.007053536402023853,0.0011684851653098816,-0.0005752915287190378,0.014078038544469422,0.073334710401792 -2017,5,0.017203184843063355,-0.0012014133890669882,-0.0021560901475767896,0.005700451994143641,0.0026374441283666837,0.01025965293012662,0.003450978881203436,-0.004002848962526264,-0.002840038762893597,0.009129359561838614,0.041161602610625764 -2017,10,0.01880470642000437,-0.001064141808069809,-0.0018212300138481738,0.005923924965206549,0.002887612220694643,0.012013471911739515,0.0036508364400215506,-0.004002848962526264,-0.0025233140123585647,0.009588736512963757,0.044857361124093144 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-2017,70,0.02915694209560752,-0.000449590837393435,-0.0005863150476904965,0.007931258342271399,0.004993535156027664,0.02063730658896845,0.006648696178853319,0.0012269094235753757,-0.0012353719784218445,0.01257106450326261,0.06467141198471586 -2017,90,0.03500935236029327,-0.000267272102670833,-0.0002489614731501643,0.008676671684921875,0.006566280300929909,0.024655517133662864,0.007598019355524368,0.0012269094235753757,-0.000813914936935936,0.014227579644661148,0.0735959829969092 -2017,95,0.03792779001966119,-0.00023414140287011693,-0.00013641396334852953,0.008925143268921383,0.007521609720510583,0.02658778271549161,0.007847840924522016,0.0012269094235753757,-0.0006398558161140477,0.015217837809951035,0.07799607458048487 -2018,5,0.018245853126049042,-0.001307073503965327,-0.0024163482164051304,0.006027388768552067,0.002700399556837327,0.01042718066141678,0.0037038834076415653,-0.004193460817884657,-0.0031456023256962794,0.009604796155730214,0.04336237220315513 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-2007,50,0.013442737981677055,0.002913743257522583,-7.654178625671193e-05,0.0017701288452371955,0.0015684564714320004,0.011318678501993418,0.00038226827746257186,0.002832439073245041,0.0033452652278356254,0.03152719994614017 -2007,67,0.014706665650010109,0.002984089271631092,-5.35062063136138e-05,0.0017964525613933802,0.001600855146534741,0.011782738026231528,0.0004219948314130306,0.0029089518775435863,0.0033842173870652914,0.033106355798154255 -2007,70,0.01493541244417429,0.002997269877232611,-4.925956091028638e-05,0.0018034722888842225,0.0016080625355243683,0.01188282947987318,0.00042900542030110955,0.002923197029485891,0.0033920287154614926,0.0334307287223055 -2007,90,0.01763715222477913,0.0030866435263305902,-1.887162943603471e-05,0.0018420804990455508,0.0016849985113367438,0.012807788979262113,0.0004734056710731238,0.0030250579366111197,0.003473164769820869,0.0365335078415228 -2007,95,0.018670156598091125,0.0031142900697886944,-8.972326759249077e-06,0.0018508550710976124,0.0017325662774965167,0.013223980087786913,0.00048508995678275824,0.003060682878640364,0.0035207918379455802,0.03787797319309902 -2008,5,0.00942226778715849,0.0029588909819722176,-0.0003722738183569163,0.0019223066046833992,0.0015177444263827055,0.009918984957039356,0.0005511200288310647,0.002709460473852232,0.0035240621014963835,0.027123401036715223 -2008,10,0.010384301654994488,0.003000084310770035,-0.00031190270092338316,0.0019474038854241371,0.0015427843434736132,0.010459965839982033,0.0005707318778149784,0.0027935943799093366,0.0035700987791642547,0.02869841980791534 -2008,30,0.013078315183520317,0.003166733542457223,-0.00020666010095737875,0.0020119398832321167,0.0016108984127640724,0.011618562042713165,0.0006687914719805121,0.0029973652126500383,0.0036848414922133086,0.03265678184252465 -2008,33,0.013390103355050087,0.003191432380117476,-0.00019625165441539139,0.0020262813195586205,0.0016195097705349326,0.011739669367671013,0.0006835003150627017,0.0030262009386206046,0.0036987857951316983,0.03310978279536357 -2008,50,0.01473761722445488,0.003334362991154194,-0.00014519999967887998,0.002087231958284974,0.0016730890492908657,0.012390101328492165,0.0007668508915230632,0.0031801401200937107,0.0037737214006483555,0.03510122626175871 -2008,67,0.016026418656110764,0.0034806386055424815,-9.931124077411368e-05,0.002141011878848076,0.0017373837938066575,0.013119114385917783,0.0008502015843987465,0.0033385501301381737,0.0038522755319718273,0.03696701826731442 -2008,70,0.01625966653227806,0.0035084845032542942,-9.102411422645673e-05,0.0021553533151745796,0.001751153962686658,0.013269010931253433,0.000864910485688597,0.003368482786754612,0.0038676489843055607,0.0373405848396942 -2008,90,0.019014576449990273,0.0037072808481752872,-3.0072495064814575e-05,0.0022342305164784193,0.0019043567590415478,0.014703273307532072,0.0009580671321600676,0.003590644191717729,0.004029212985187769,0.04087927782384213 -2008,95,0.020067911595106125,0.0037783479783684015,-9.629442502046004e-06,0.002252157311886549,0.0019974501337856054,0.015367973549291487,0.0009825819870457053,0.0036765826298506,0.004123428312595934,0.04248074392889976 -2009,5,0.010274065658450127,0.0031868102960288525,-0.0005692910635843873,0.002153712324798107,0.0015511831734329462,0.01009910828433931,0.0008150199428200722,0.0028050837499904446,0.0038332093332428484,0.029350616334522784 -2009,10,0.011347959749400616,0.003250018460676074,-0.00047732802340760827,0.002192151267081499,0.0015892701921984553,0.010831926017999649,0.000845813425257802,0.0029333486745599657,0.0039036007947288454,0.031242374316479984 -2009,30,0.014355217106640339,0.003504558000713587,-0.0003167155955452472,0.0022909934632480145,0.0016932578524574637,0.012393906712532043,0.0009997807210311294,0.003244493762758793,0.004079043201636523,0.03594311278720852 -2009,33,0.014703258872032166,0.00354233036050573,-0.0003008127387147397,0.002312958473339677,0.0017063368577510118,0.012557171313092112,0.0010228757746517658,0.0032890866277739406,0.004100471858400852,0.036462478659814224 -2009,50,0.01620745286345482,0.003761859377846122,-0.00022301705030258745,0.0024063095916062593,0.0017881286912597716,0.013431687373667955,0.0011537481332197785,0.0035254467802587897,0.004215159686282277,0.03882661568786716 -2009,67,0.01764610968530178,0.0039896487211808565,-0.00015299892402254045,0.0024886783212423325,0.0018863402074202895,0.014393134973943233,0.0012846202589571476,0.0037716275546699762,0.004335137617308646,0.04107562266290188 -2009,70,0.01790647953748703,0.004033643938601017,-0.00014021109091117982,0.002510643098503351,0.0019073542673140763,0.014589563012123108,0.0013077154289931059,0.0038195444285520352,0.0043585945153608915,0.041521356368320994 -2009,90,0.020981712266802788,0.0043619126081466675,-4.730471955554092e-05,0.0026314505375921726,0.0021412610076367855,0.01645800843834877,0.0014539845287799835,0.004181875416543335,0.0046054774429649115,0.04569964717666153 -2009,95,0.022157521918416023,0.004492173902690411,-1.602126758371014e-05,0.002658906625583768,0.0022834530100226402,0.017332224547863005,0.001492476207204163,0.00433038741175551,0.004749444650951773,0.047645570761960694 -2010,5,0.011335372924804688,0.0034137119073420763,-0.0008042361587285995,0.002383620012551546,0.001569479936733842,0.00996096059679985,0.0010711464565247297,0.0028763834852725267,0.004131592338671908,0.031343098846264184 -2010,10,0.012474281713366508,0.0034998804330825806,-0.0006716916686855257,0.002435928676277399,0.0016231626505032182,0.01092272037640214,0.0011140386341139674,0.003058027272345498,0.004230084456503391,0.03361100545262161 -2010,30,0.015663601458072662,0.0038453543093055487,-0.0004397040174808353,0.002570436103269458,0.001772689982317388,0.013067109510302544,0.0013284990563988686,0.0034931106289150192,0.004474669927731156,0.03914268588705454 -2010,33,0.016032714396715164,0.003896683161146939,-0.00041662759031169116,0.002600326668471098,0.001792507478967309,0.013290062360465527,0.0013606681022793055,0.0035487159329932183,0.004504478538874537,0.0397384151937149 -2010,50,0.017627974972128868,0.004196232184767723,-0.000305041583487764,0.0027273616287857294,0.0019100001081824303,0.014435678720474243,0.0015429593622684479,0.00387461505306419,0.004663931322284043,0.04248530519544147 -2010,67,0.01915372908115387,0.00451111821923405,-0.00020440792082808892,0.002839451190084219,0.0020494188647717237,0.01566347222775221,0.001725250855088234,0.004214459942886606,0.004832559503847734,0.04509396663677762 -2010,70,0.01942986063659191,0.004572747927159071,-0.00018603239150252204,0.002869341755285859,0.00207995749078691,0.01591876894235611,0.0017574199009686708,0.004282394569600001,0.0048662517219781876,0.045616937745944594 -2010,90,0.02269127406179905,0.005050537642091513,-5.179455547477119e-05,0.0030337399803102016,0.0024151925463229418,0.018270509690046313,0.001961157424375415,0.004807468850049191,0.005217508296482265,0.050437325466191396 -2010,95,0.023938268423080444,0.005255767144262791,-5.994080311211292e-06,0.0030711032450199127,0.002621992025524378,0.019398171454668045,0.0020147725008428097,0.0050360107303276894,0.005425760697107762,0.05269900935381883 -2011,5,0.01258755661547184,0.00363959651440382,-0.0010417817626148461,0.0026120292022824287,0.0016022146446630359,0.010102792596444487,0.0013194996863603592,0.0029397746548056602,0.004443408892257139,0.03395551033463562 -2011,10,0.013784435577690601,0.0037496699951589108,-0.000871053955052048,0.002678736113011837,0.001671685604378581,0.01126901712268591,0.0013754075625911355,0.0031745391024742275,0.004569892887957394,0.03648687543900451 -2011,30,0.017136093229055405,0.0041891224682331085,-0.0005710058030672371,0.002850267803296447,0.0018651392310857773,0.013825938105583191,0.0016549464780837297,0.0037316292873583734,0.00488399596652016,0.04260319561581127 -2011,33,0.0175239946693182,0.004254490467719734,-0.0005412993737263605,0.0028883861377835274,0.0018906547408550976,0.014091147109866142,0.0016968772979453206,0.003801759914495051,0.004922507768496871,0.04326336213038303 -2011,50,0.019200453534722328,0.004637482110410929,-0.0003970886755269021,0.003050388302654028,0.0020427757408469915,0.015461264178156853,0.001934485393576324,0.004220060360239586,0.0051265505608171225,0.04631940215767827 -2011,67,0.020803870633244514,0.005045048967003823,-0.00026714990963228047,0.00319333141669631,0.0022234232164919376,0.01691387651488185,0.002172093605622649,0.004660432845412288,0.005343981068581343,0.04925737610523356 -2011,70,0.021094055846333504,0.0051257970277220006,-0.00024335998750757426,0.003231449518352747,0.002263067988678813,0.017214768193662165,0.002214024541899562,0.004750017251353711,0.005387611221522093,0.04983101422694745 -2011,90,0.0245214756578207,0.0057731568813323975,-6.991623376961797e-05,0.0034410993102937937,0.0026972435880452394,0.019975844770669937,0.0024795865174382925,0.005454560334328562,0.005841541104018688,0.05520343067473732 -2011,95,0.02583194151520729,0.006069128401577473,-1.168072899417904e-05,0.0034887471701949835,0.002965169493108988,0.02130740825086832,0.0025494713336229324,0.005777028450393113,0.0061112570110708475,0.05772272029716986 -2012,5,0.013811971992254257,0.0038644634187221527,-0.0012820952164474873,0.0028389403596520424,0.0016422970220446587,0.010354802943766117,0.0015600797487422824,0.0029992766212671995,0.004762134229531511,0.03667819046204386 -2012,10,0.01508180983364582,0.0039993878453969955,-0.0010737013071775436,0.0029205738101154566,0.0017275080317631364,0.011696481425315142,0.0016299202106893063,0.00328559527406469,0.0049171637743711475,0.03947396023577312 -2012,30,0.018637778237462044,0.004535862244665623,-0.0007074878667481244,0.0031304885633289814,0.0019637443358078597,0.01460636593401432,0.0019791231025010347,0.003966030897572637,0.00530172202270478,0.04613530499336775 -2012,33,0.019049324095249176,0.00461575236171484,-0.0006710535380989313,0.003177136415615678,0.0019951085560023785,0.014902220461517573,0.002031503478065133,0.004053205426316708,0.005348665817873553,0.04685830103699118 -2012,50,0.02082797884941101,0.005085608456283808,-0.0004947070847265422,0.0033753893803805113,0.002182349213398993,0.01647297292947769,0.0023283257614821196,0.004566072602756321,0.0055986211518757045,0.0502197781461291 -2012,67,0.022529134526848793,0.005591439562849702,-0.0003359550901222974,0.0035503183025866747,0.002403837065212432,0.018128657005727293,0.002625148044899106,0.005113495804835111,0.00586478325072676,0.05347386685141828 -2012,70,0.022837011143565178,0.005692790821194648,-0.0003063787240535021,0.003596966154873371,0.0024521765299141407,0.018472920730710028,0.002677528653293848,0.005227342975558713,0.005918369954451918,0.054113245996995823 -2012,90,0.026473358273506165,0.006529770791530609,-9.538193990010768e-05,0.0038535285275429487,0.002985897706821561,0.02160222101956606,0.003009271342307329,0.006134006136562675,0.006476210663095117,0.060030100954463705 -2012,95,0.02786370925605297,0.006932257674634457,-2.3919874092827008e-05,0.003911837935447693,0.003313835710287094,0.023110881447792053,0.0030965718906372786,0.006565365250571625,0.0068056206451728935,0.06279431478469633 -2013,5,0.015280546620488167,0.004088312853127718,-0.0015443468000739813,0.0030643530189990997,0.0016888019163161516,0.010684386175125838,0.0017928860615938902,0.003038609051145613,0.005087736458517611,0.03975069583248114 -2013,10,0.016588643193244934,0.004249033518135548,-0.0012940007727593184,0.003161441534757614,0.001789835630916059,0.012189896684139967,0.0018775764619931579,0.003382223891094327,0.0052725363988429304,0.042746630977489986 -2013,30,0.020251745358109474,0.0048855748027563095,-0.0008578930806834251,0.0034110983833670616,0.0020729871466755867,0.015427292510867118,0.0023010284639894962,0.004191093612462282,0.0057316951337270435,0.04982514012372121 -2013,33,0.020675690844655037,0.004980468843132258,-0.0008145375759340823,0.003466577734798193,0.0021098130382597446,0.015759754925966263,0.002364546060562134,0.004296757921110839,0.005787645471282303,0.050596962589479516 -2013,50,0.022507932037115097,0.00554061122238636,-0.0006031258380971849,0.003702364629134536,0.002334874588996172,0.017529986798763275,0.002724480116739869,0.004908201604848728,0.0060862284153699875,0.054234451410593465 -2013,67,0.024260342121124268,0.006150290630757811,-0.0004129501467105001,0.003910412080585957,0.0026014975737780333,0.019387178122997284,0.0030844141729176044,0.0055701728485291835,0.006404350702650846,0.057763848977629095 -2013,70,0.024577492848038673,0.0062737294472753995,-0.0003783259307965636,0.003965891432017088,0.0026585781015455723,0.019768528640270233,0.0031479322351515293,0.005706168652977794,0.006468834145925939,0.058459646825212985 -2013,90,0.028323397040367126,0.007320377975702286,-0.00012589397374540567,0.004271027632057667,0.0033002041280269633,0.023271748051047328,0.0035502114333212376,0.006837628781795502,0.007138632261194289,0.06483348868787289 -2013,95,0.029755637049674988,0.007845154963433743,-4.112490205443464e-05,0.004340376704931259,0.0036933892406523228,0.024963125586509705,0.0036560744047164917,0.007391141899279316,0.007533663418143988,0.06783768222667275 -2014,5,0.01662345789372921,0.004311145283281803,-0.0018155163852497935,0.0032882671803236008,0.0017540724948048592,0.011280372738838196,0.0020179194398224354,0.0030664439545944333,0.005434273037826643,0.04301788471639156 -2014,10,0.017991479486227036,0.004498607013374567,-0.0015257358551025388,0.003401339752599597,0.0018741115927696228,0.01292260829359293,0.002118376549333334,0.003464116482064128,0.005649811844341457,0.04618074678000994 -2014,30,0.02182239294052124,0.00523825827986002,-0.0010216932278126478,0.0036920972634106874,0.0022027287632226944,0.016361549496650696,0.0026206630282104015,0.004402719480276573,0.0061827467288821936,0.053622314298991114 -2014,33,0.022265758365392685,0.0053486399073153735,-0.0009713359177112579,0.0037567103281617165,0.0022449686657637358,0.016727838665246964,0.0026960058603435755,0.004526247386820614,0.006248184269061312,0.05443825130176265 -2014,50,0.02418193593621254,0.006002491340041161,-0.000727359380107373,0.004031314514577389,0.0025052414275705814,0.018654602579772472,0.0031229492742568254,0.005243040563073009,0.006595137063413858,0.0583382076874841 -2014,67,0.026014626026153564,0.006721601393073799,-0.0005063842399977148,0.004273612983524799,0.0028173751197755337,0.020693971440196038,0.0035498926881700754,0.006023717622156255,0.006965880515053868,0.0621392862754874 -2014,70,0.02634630724787712,0.0068686121143400666,-0.00046595205203630034,0.004338225349783897,0.0028848249930888414,0.021111510694026947,0.0036252355203032494,0.006187218421837314,0.007041030446998774,0.06289063442964107 -2014,90,0.030263813212513924,0.008144979365170002,-0.00017515594663564116,0.0046935961581766605,0.0036316460464149714,0.02494118791073561,0.004102407954633236,0.007555073680123314,0.007820739410817623,0.06971883022342809 -2014,95,0.031761664897203445,0.00880782026797533,-8.008680742932484e-05,0.004774361848831177,0.004085642751306295,0.026772363111376762,0.0042279791086912155,0.00825242690189043,0.008281651232391595,0.07294442859711125 -2015,5,0.017366506159305573,0.004532960243523121,-0.002079466823488474,0.00351068377494812,0.0018132282420992851,0.011716378852725029,0.0022351795341819525,0.0030997053254395723,0.005772088875528425,0.045523549139034 -2015,10,0.018873430788517,0.004748108796775341,-0.0017501550260931253,0.0036402682308107615,0.0019513657316565514,0.013498125597834587,0.0023523208219558,0.0035531908506527543,0.006017377856187523,0.048941876069875434 -2015,30,0.023093316704034805,0.005593914072960615,-0.0011800553184002638,0.00397348590195179,0.0023239082656800747,0.017183198034763335,0.0029380270279943943,0.004623773391358554,0.006625706912018359,0.05703687370987609 -2015,33,0.023581702262163162,0.005720265870913863,-0.0011215585982427,0.004047533962875605,0.0023711302783340216,0.0175820055603981,0.003025882877409458,0.004769174054672476,0.006701157430652529,0.05791284357823315 -2015,50,0.025692438706755638,0.006471247412264347,-0.0008446788997389376,0.0043622395023703575,0.0026674806140363216,0.0196688249707222,0.0035237332340329885,0.005592022149357945,0.00709827896207571,0.06219311218592338 -2015,67,0.027711210772395134,0.007305373558774593,-0.0005927803576923907,0.004639920778572559,0.0030245224665850405,0.02188016287982464,0.004021583124995232,0.006495384615845978,0.007520778498146683,0.06635953935212456 -2015,70,0.028076568618416786,0.0074774413369596,-0.0005478465463966131,0.0047139693051576614,0.0031008378136903048,0.02234296128153801,0.004109439440071583,0.00668697280343622,0.0076060391729697585,0.06717792631097837 -2015,90,0.03239184245467186,0.009003574959933758,-0.0002189711231039837,0.005121234804391861,0.003952709026634693,0.026479313150048256,0.004665860440582037,0.008315211877925333,0.0084965338697657,0.07465752956923098 -2015,95,0.03404177725315094,0.00982025358825922,-0.00011003372019331531,0.005213795229792595,0.004470566287636757,0.028456887789070604,0.004812286701053381,0.009168889533611922,0.009021497331559658,0.0782126677047927 -2016,5,0.018651317805051804,0.004753757268190384,-0.0023512807674705982,0.003731601405888796,0.001855918439105153,0.011920850165188313,0.0024446656461805105,0.003132798708975315,0.006093581416644156,0.0482459348422708 -2016,10,0.020192530006170273,0.004997537471354008,-0.0019789044745266438,0.003878226736560464,0.0020110870245844126,0.013866722583770752,0.002579407999292016,0.0036456503439694643,0.006372261303476989,0.05191325515625067 -2016,30,0.024508439004421234,0.005952541716396809,-0.0013343699974939227,0.004255262669175863,0.0024337880313396454,0.0178974162787199,0.0032531195320189,0.004857043270021677,0.007060783333145082,0.060448012710548935 -2016,33,0.025007937103509903,0.006095345951616764,-0.0012681714724749327,0.0043390486389398575,0.0024872056674212217,0.018328799698501826,0.0033541761804372072,0.005021231321879895,0.007145951967686415,0.06138119077542797 -2016,50,0.02716670371592045,0.006946881301701069,-0.0009550847462378442,0.0046951379626989365,0.002822623122483492,0.02060466632246971,0.0039268312975764275,0.00595385511405766,0.007595171686261892,0.06591380630561616 -2016,67,0.029231412336230278,0.007901605572551491,-0.000670295616146177,0.005009335000067949,0.0032264976762235165,0.02300497664138675,0.004499485716223717,0.006985937128774822,0.008073621348012238,0.07036589432915208 -2016,70,0.029605085030198097,0.008100214321166276,-0.0006195319001562893,0.005093120504170656,0.0033136624842882156,0.023501979373395442,0.004600542597472668,0.0072075197356753044,0.008170087728649378,0.07122667668736539 -2016,90,0.03401855006814003,0.009896163828670979,-0.00024764821864664555,0.0055539426393806934,0.004277213476598263,0.02797578386962414,0.005240568425506353,0.009118438661971595,0.009178611426614225,0.0791542261140421 -2016,95,0.03570602834224701,0.010882454924285412,-0.0001244613384187702,0.005658674519509077,0.0048637050203979015,0.030120916664600372,0.0054089962504804134,0.010140347178094089,0.009773105848580599,0.0829655856708996 -2017,5,0.02026953548192978,0.0049735368229448795,-0.0026249561924487352,0.003951020538806915,0.0019336536061018705,0.012537021189928055,0.0026463791728019714,0.00315349199809134,0.006448140583233908,0.05176977821975015 -2017,10,0.021768666803836823,0.005246895365417004,-0.0022146659437566997,0.004115215037018061,0.002104727551341057,0.014587870799005032,0.0027996397111564875,0.00371848966460675,0.006756717420648784,0.05549945685779676 -2017,30,0.02596673183143139,0.006314140744507313,-0.0015037873527035117,0.004537428729236126,0.0025718828430399297,0.018786277249455453,0.003565941471606493,0.005071714171208441,0.007524915761314333,0.0640988074010238 -2017,33,0.02645258978009224,0.0064738809317350385,-0.0014323430368676782,0.004631254356354475,0.0026323236525058746,0.019237685948610306,0.0036808867007493973,0.005254876450635493,0.007619673386216164,0.06503508828696795 -2017,50,0.02855241298675537,0.007429390214383602,-0.0010860587935894728,0.005030011758208275,0.003004646860063076,0.021653050556778908,0.004332243464887142,0.006299533706624061,0.008121320512145758,0.06967855177936144 -2017,67,0.030560744926333427,0.00851029775105417,-0.0007711967919021845,0.005381856579333544,0.0034503524657338858,0.024210499599575996,0.0049835997633636,0.007470806069322866,0.008652963405475021,0.07426995906746016 -2017,70,0.03092421405017376,0.00873693162575364,-0.0007167526986449958,0.0054756817407906055,0.003549701999872923,0.02473936695605516,0.005098544992506504,0.0077174947946332395,0.008760115830227733,0.075166730734054 -2017,90,0.03521716967225075,0.010822746902704239,-0.00030622168560512364,0.00599172106012702,0.00461588054895401,0.02949514426290989,0.0058265323750674725,0.009933639405062422,0.00987975592724979,0.08330797237576917 -2017,95,0.03685857355594635,0.011994422413408756,-0.00016955693572527048,0.00610900204628706,0.005268503911793232,0.031745828688144684,0.006018107756972313,0.011130439233966172,0.010536304418928921,0.08723732222570105 -2018,5,0.021380890160799026,0.005192299373447895,-0.0029216359835118055,0.00416894257068634,0.00200219196267426,0.012992304004728793,0.0028403187170624733,0.003162941662594676,0.006795489671640098,0.05465789446316194 -2018,10,0.022935254499316216,0.0054961806163191795,-0.0024668530095368624,0.004351234063506126,0.0021903133019804955,0.015171277243644,0.003013014327734709,0.0037858900614082813,0.007136335968971253,0.05856712833920028 -2018,30,0.027287987992167473,0.006678711622953415,-0.001681926310993731,0.004819984547793865,0.0027069145813584327,0.019630011916160584,0.0038764923810958862,0.005285142920911312,0.007989380368962884,0.06758615153376013 -2018,33,0.027791745960712433,0.006855870652943849,-0.0016025180928409101,0.004924151115119457,0.0027756355702877045,0.020107731223106384,0.004006013739854097,0.005488553550094366,0.008094900213181972,0.06858095715753734 -2018,50,0.029968932271003723,0.00791877694427967,-0.0012195026502013206,0.005366859491914511,0.0031869359081611037,0.022687848657369614,0.004739969968795776,0.006652418524026871,0.008651808719150722,0.07348664943128824 -2018,67,0.03205125778913498,0.009131450084969406,-0.0008704272331669927,0.0057574850507080555,0.0036831796169281006,0.025423036888241768,0.005473926197737455,0.007967557176016271,0.009241671129129827,0.07837531387573109 -2018,70,0.03242811560630798,0.009387594275176525,-0.0008103172294795513,0.0058616516180336475,0.0037928109755739556,0.025979829765856267,0.0056034475564956665,0.008242755895480514,0.00936077805235982,0.07932040579617022 -2018,90,0.03687924146652222,0.011783325113356113,-0.00035702457826118915,0.006434568203985691,0.004976685158908367,0.03104409947991371,0.006423751823604107,0.010781583259813488,0.010604447592049837,0.08797548685106449 -2018,95,0.03858111798763275,0.013156159780919552,-0.00020322682394180447,0.006564776878803968,0.005702887196093798,0.03344689682126045,0.0066396212205290794,0.012175223033409566,0.01133186318911612,0.09211874342290685 -2019,5,0.022721588611602783,0.005410044454038143,-0.0032360367709770798,0.0043853651732206345,0.0020391986588947477,0.01311510126106441,0.0030264852102845907,0.0031604247633367777,0.0071169576607644554,0.0573773228097707 -2019,10,0.024371201172471046,0.005745393224060535,-0.002729189395904541,0.00458628311753273,0.0022502425126731396,0.01548346057534218,0.0032195330131798983,0.003855475690215826,0.007496305904351175,0.06161327646113932 -2019,30,0.028990665450692177,0.00704625528305769,-0.001857791212387383,0.005102928727865219,0.002826181310229003,0.020330512896180153,0.004184771794825792,0.005511356212082319,0.008439373271539808,0.07132858083350584 -2019,33,0.02952529489994049,0.007241314956918358,-0.0017684996128082275,0.005217738915234804,0.0029011489171534774,0.02085633100941777,0.004329557530581951,0.005737341823987663,0.00855643104761839,0.07239732780202758 -2019,50,0.0318358950316906,0.008415039628744125,-0.0013434126740321517,0.005705682095140219,0.0033595175482332706,0.023638373240828514,0.00515001080930233,0.0070219687186181545,0.009172888938337564,0.07769084366736934 -2019,67,0.03404582291841507,0.00976506289094687,-0.000955394993070513,0.006136219948530197,0.003912833519279957,0.02657828086987139,0.005970463622361422,0.008483226411044598,0.009827284701168537,0.08295164402690716 -2019,70,0.03444577753543854,0.010052201896905899,-0.0008883664268068969,0.006251030135899782,0.00403442420065403,0.027170580998063087,0.00611524935811758,0.008794157110969536,0.009959728317335248,0.0839694777969271 -2019,90,0.039169661700725555,0.012777895666658878,-0.0003832941874861717,0.006882485933601856,0.005354864522814751,0.032584314793348314,0.007032226305454969,0.011672406952129677,0.011343763442710042,0.0932782621937804 -2019,95,0.04097582772374153,0.0143676633015275,-0.0002146402730431877,0.007025999017059803,0.006162866763770576,0.03516892828047273,0.007273536175489426,0.013280619779834508,0.012157599627971647,0.09776535920100285 -2020,5,0.024134736508131027,0.005626772064715624,-0.00355898542329669,0.004600290209054947,0.00208547244546935,0.013307245448231697,0.0032048781868070364,0.0031487171538174155,0.007446351030375808,0.060274421604117376 -2020,10,0.0258769728243351,0.005994534119963646,-0.0030026815365999937,0.004820362199097872,0.002317892387509346,0.015855735540390013,0.0034191953018307686,0.003915007691830397,0.007863550912588835,0.0647998575004749 -2020,30,0.03075581043958664,0.007416769862174988,-0.002039942890405655,0.005386261735111475,0.002954017836600542,0.021068700589239597,0.004490780644118786,0.005733085563406348,0.008899628114886583,0.07518013240769506 -2020,33,0.03132045641541481,0.007630213852971792,-0.0019429050153121352,0.0055120172910392284,0.0030342251993715763,0.021633028984069824,0.004651518538594246,0.005980399437248707,0.009028313192538917,0.07632021704455838 -2020,50,0.03376079350709915,0.00891817919909954,-0.0014737371820956469,0.006046478636562824,0.003541152458637953,0.02462068200111389,0.005562365986406803,0.007392010185867548,0.0097053786739707,0.0819889125705231 -2020,67,0.03609480336308479,0.010411135861650114,-0.0010470097186043859,0.006518061738461256,0.004152971785515547,0.027751924470067024,0.00647321343421936,0.00900372548494488,0.010425924723967911,0.08761808759067208 -2020,70,0.03651721403002739,0.01073075421154499,-0.000972946640104056,0.0066438172943890095,0.004285371862351894,0.028387075290083885,0.006633951794356108,0.009351924061775208,0.01057311026379466,0.08871189574711025 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-2011,90,0.023829992832615974,-0.0001810866190295754,-5.408197213127563e-05,0.005019862381144451,0.003676694354603399,0.01623749511875813,0.0032334749282115225,0.0008763638739824111,-0.00037845703984136633,0.00828485843212132,0.04795411328394687 -2011,95,0.02587502769660205,-0.00016680515962122985,-9.059275916686284e-06,0.005135228793549446,0.004038604350068798,0.017311368851300957,0.003324607380903336,0.0008763638739824111,-0.0002932260113079914,0.00866296840331992,0.05074220575712343 -2012,5,0.012349708193913102,-0.0007411619059170862,-0.0009917453545257647,0.0040763644256672504,0.0022241852073253414,0.008404583339004979,0.00203440320315189,-0.0030497896857342964,-0.0014728584638352916,0.006681200898781857,0.029411082272880616 -2012,10,0.013726343965716661,-0.0006823771364058784,-0.000829432440653824,0.004194551211450482,0.0023439956741928064,0.009507561150745714,0.002125477816234445,-0.0030497896857342964,-0.0013178434364425344,0.0069128350495216324,0.03205698088439652 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-2012,70,0.021701873111352323,-0.00028183033792731,-0.0002371907487618032,0.005208726112381527,0.003337136805250411,0.015009333629430952,0.003491598986002745,0.0009347881322479053,-0.0006722511688692645,0.008387460271887475,0.046067514057373665 -2012,90,0.02573475367948413,-0.00019607058305932655,-7.352792978786479e-05,0.005612790281252945,0.00406751108561189,0.01755734180844842,0.003924204043337373,0.0009347881322479053,-0.00043819992859173195,0.009178346712479985,0.052195003069038085 -2012,95,0.027963940419070424,-0.00017863966209978547,-1.8467385781507127e-05,0.005743213437135214,0.004511515779610825,0.018778721618574683,0.004038047271738067,0.0009347881322479053,-0.00033940854324637635,0.00964066596575937,0.05524754439505937 -2013,5,0.01366233591362834,-0.0008312661989366493,-0.0011940763440202076,0.004398484780709878,0.0022871788579007283,0.008679246643004464,0.0023379914709703267,-0.00324040154109269,-0.0017172801875646824,0.007148636411091571,0.03194386486501893 -2013,10,0.015059730705246329,-0.0007591912435400694,-0.0010003336541640757,0.0045521859666914485,0.002429477375145457,0.009907589433482885,0.002448430967404652,-0.00324040154109269,-0.0015318601571004985,0.007420479490935959,0.034794711793719665 -2013,30,0.018438434036448598,-0.0006088234141488449,-0.000663290136893637,0.004955652265248462,0.0028169401352200352,0.012528164986889533,0.003000628449576278,-0.00324040154109269,-0.0011940295038187767,0.008086252455905349,0.041246643171640805 -2013,33,0.018847758016735317,-0.0005913018906325111,-0.0006295796460672979,0.005013290169676527,0.0028684402687763435,0.012804800646335126,0.0030834578062345253,-0.00324040154109269,-0.0011586675212333641,0.008165614773745949,0.04195983014803375 -2013,50,0.020811111038923264,-0.0005018462517463948,-0.0004663921449509958,0.0053508329717523685,0.0031710660699048893,0.014240397595616103,0.00355282547631791,0.0009932123905133994,-0.0009766285740089478,0.008606014122760961,0.04557849216869159 -2013,67,0.022923589065670966,-0.00037858698346572984,-0.0003191753601152964,0.005685733463071244,0.0035382207361159368,0.01575177292292371,0.004022193146401294,0.0009932123905133994,-0.0008019739291995191,0.00907469967575489,0.04913420925234148 -2013,70,0.02337098402455449,-0.00031587261360371447,-0.00029230647415707395,0.005743371044979127,0.0036180203200204143,0.016068330969460417,0.004105023110299534,0.0009932123905133994,-0.0007705080002059448,0.009166889847979833,0.0498321109932381 -2013,90,0.027592728758305325,-0.00021118277670672427,-9.719813815422417e-05,0.006211376661010023,0.004494391605875159,0.01891366696646388,0.004629610452695082,0.0009932123905133994,-0.0005031458540731814,0.010112297996665156,0.056438822760332324 -2013,95,0.029982099990360438,-0.00019050467674438653,-3.170374965207366e-05,0.0063641872088604735,0.0050303515662581035,0.020279979898629275,0.004767659785285489,0.0009932123905133994,-0.0003891209005621089,0.010664372362499749,0.05973762714649576 -2014,5,0.01484562956392765,-0.0009253915446538533,-0.0014026370383529185,0.00473395769680235,0.0023791565937209113,0.009155228238843181,0.002631443537028695,-0.003431013396451084,-0.001969428639270686,0.007640819333569241,0.034628482569195755 -2014,10,0.0163078970676288,-0.000838687589049026,-0.0011800449773551516,0.0049027230088967335,0.0025432952993717337,0.010500811875341805,0.0027624434205494658,-0.003431013396451084,-0.0017576138272628135,0.007955609274714592,0.037666233624887614 -2014,30,0.019851011423207818,-0.0006613231805774672,-0.0007904906367566591,0.005367163792243254,0.002992687624164153,0.01329334621030654,0.003417444052633304,-0.003431013396451084,-0.0013675220452575262,0.008726025943146006,0.044496252628741594 -2014,33,0.020292381572723388,-0.0006409202195764611,-0.0007515353068445346,0.00544049675149308,0.003051612725094255,0.0135907422457438,0.003515693965273882,-0.003431013396451084,-0.001326088280870071,0.008818371385715895,0.04525041726130271 -2014,50,0.02234702712222934,-0.0005364725226467297,-0.0005622378989811153,0.005830363768905318,0.0034036386171864314,0.015157451905291806,0.004072444381097146,0.0010516366487788936,-0.0011175157384044933,0.009327515836269796,0.04911693056107863 -2014,67,0.024581221882253885,-0.00040398281495730915,-0.000391508648266115,0.006210750523165267,0.0038318935935339783,0.016817969040677613,0.00462919479692041,0.0010516366487788936,-0.0009189088849673794,0.009870689141043871,0.05292509041620112 -2014,70,0.025048990322090684,-0.00035136791667932625,-0.0003604245621451683,0.006280751883354175,0.003924291664964573,0.01715724798238492,0.004727444709560988,0.0010516366487788936,-0.000882882959776938,0.009977147580543026,0.05367349868911805 -2014,90,0.029521104335784913,-0.00022642321809182757,-0.00013530265010080998,0.006818633323016394,0.00494494654719684,0.020273027678124687,0.0053496956743846295,0.0010516366487788936,-0.0005821617203057602,0.01107234944062037,0.060753184022695944 -2014,95,0.0320638858705759,-0.00020240019348833355,-6.202546560721469e-05,0.007004929104890892,0.005565802331104572,0.02177191067726069,0.005513445225165597,0.0010516366487788936,-0.00045468543613258526,0.011717000142525153,0.06429142797101438 -2015,5,0.015542432350292802,-0.0010247373957076574,-0.0016064601114785416,0.005055860494753963,0.002459630341440622,0.009513252998177445,0.002914758945897001,-0.003621625251809477,-0.0022184109000259683,0.00812203994157952,0.03664736156226215 -2015,10,0.017141676604375242,-0.0009213782739216211,-0.001353734147984908,0.00524346930064616,0.0026466505397621026,0.010969775705961582,0.003067515631098881,-0.003621625251809477,-0.0019787667349970323,0.008478219696134373,0.03996299977459362 -2015,30,0.021030461718142032,-0.0007147433431556895,-0.0009119591178025247,0.005760855303646522,0.003157589984234816,0.013962869306419818,0.0038312987534882847,-0.003621625251809477,-0.0015364600773828886,0.00935584932274669,0.04737937330384049 -2015,33,0.0214947012970224,-0.0006910916007974269,-0.0008679016793946976,0.005863131174459999,0.0032234876924571903,0.014285937910528058,0.003945866115579697,-0.003621625251809477,-0.0014885389950568192,0.009461077110079935,0.04819712394256242 -2015,50,0.023765716384723783,-0.0005720395936874529,-0.0006528324684428414,0.00629311955239139,0.003624443551679663,0.015984836991082694,0.004595081875877688,0.0011100609070443875,-0.001254604190646715,0.010040346364772074,0.052412221851651966 -2015,67,0.026182562202215192,-0.0004310088328411027,-0.0004584831710343363,0.006744338399442985,0.004112654680714912,0.017787877975644403,0.005244297028935688,0.0011100609070443875,-0.0010309920333587744,0.010656996215863714,0.05655240603367664 -2015,70,0.02668736983090639,-0.00038831628742094315,-0.00042327317781760623,0.006825383096462235,0.004217193222304003,0.01815449226666608,0.005358864998267092,0.0011100609070443875,-0.0009905474110614273,0.010779794166595327,0.05737179117272095 -2015,90,0.03154584018476308,-0.0002417919273480353,-0.00016898186567673856,0.007441170336562145,0.005380939698735381,0.02151786850297202,0.006084459101166024,0.0011100609070443875,-0.0006540061165262569,0.012024567323088833,0.06508200299639418 -2015,95,0.03427627720311284,-0.00021432623246502548,-8.50372188510718e-05,0.007637785570467427,0.006091076429351521,0.023147830405832768,0.006275404502238379,0.0011100609070443875,-0.0005151730292472684,0.012759787706786764,0.06891939214164591 -2016,5,0.016662800049036742,-0.0011274563210110864,-0.0018164268010652214,0.0053721741555218805,0.002518254439864101,0.009680882501940654,0.0031879367867152556,-0.003812237107167871,-0.0024756502556416214,0.008583274038421773,0.03890028869834794 -2016,10,0.018305920731276272,-0.0010060287933767677,-0.0015306478267813305,0.005583546987578978,0.0027294227701936766,0.011268059454433524,0.003363645929142918,-0.003812237107167871,-0.0022045938775581593,0.008983683670796284,0.04241047151328947 -2016,30,0.02229817168917507,-0.0007692000515490757,-0.0010312024127842989,0.006184600586725834,0.0033061026555087506,0.014539990792207379,0.004242191337661235,-0.003812237107167871,-0.0017042757337070835,0.009974319552233522,0.05026894094749532 -2016,33,0.022792752950638533,-0.0007420784488559103,-0.0009813615742420212,0.006280897686130596,0.003381711103880517,0.014891709537053138,0.004373973042671985,-0.003812237107167871,-0.0016516717951765413,0.010092541109108129,0.05113209976558353 -2016,50,0.025102866841107605,-0.0006077242193367957,-0.0007381877347463746,0.0067671536732453776,0.0038354917825736776,0.01674605992075627,0.005120737049799548,0.0011684851653098816,-0.001387930873952097,0.010745723800467536,0.0555924045550393 -2016,67,0.027614506143331528,-0.0004593860134005281,-0.0005184338200284021,0.007287182275293309,0.004388092964723404,0.01870048010410804,0.005867500146067124,0.0011684851653098816,-0.0011368194130045748,0.011442644425113283,0.059985382448207704 -2016,70,0.02814580350369215,-0.0004267176050281718,-0.0004786153066218278,0.007374227892529515,0.004505268573988187,0.01909645000516132,0.005999282154697869,0.0011684851653098816,-0.0010915082075817306,0.011578707197351766,0.0608591272871628 -2016,90,0.03318307204246521,-0.00025728884810183057,-0.0001910903617786742,0.008055971550921965,0.005825393101415132,0.022734406259521913,0.006833900125799272,0.0011684851653098816,-0.0007171621506206955,0.012985507736089233,0.06902350107500156 -2016,95,0.03604505956918001,-0.0002262828138078612,-9.617997207299065e-05,0.00827785648869491,0.006624710662163359,0.02449426594338428,0.007053536402023853,0.0011684851653098816,-0.0005631744782894851,0.013818612804159018,0.07307148376827871 -2017,5,0.018062353471051906,-0.0012344261647940773,-0.002028147465412788,0.005700451994143641,0.0026225213260683386,0.010187876355104505,0.003450978881203436,-0.004002848962526264,-0.0027385348891226565,0.009086677695424022,0.04183638904349807 -2017,10,0.019712872318550945,-0.0010932560905296848,-0.001713814445551444,0.005923924965206549,0.0028564657963798287,0.011856503652696243,0.0036508364400215506,-0.004002848962526264,-0.0024391818109647818,0.00952958413426018,0.04540597559501103 -2017,30,0.02359231758769602,-0.0008241653952816021,-0.0011636062191028036,0.006582415016585006,0.0034930208125742482,0.015264266286870675,0.0046501230196321405,-0.004002848962526264,-0.0018864369987560583,0.010631396248901982,0.05336703873086256 -2017,33,0.02408372948858887,-0.0007938608805602607,-0.0011082970368978934,0.006704990445555015,0.003578280703944892,0.015636234905266454,0.004800015961030729,-0.004002848962526264,-0.0018290932769606365,0.010763760307876936,0.05424945234765061 -2017,50,0.026367476553097367,-0.000644231718821768,-0.0008398002267442008,0.007260068648547005,0.004080995866287139,0.017597859266591263,0.005649409902862726,0.0012269094235753757,-0.001538787707578368,0.01149060100430497,0.05880032720152871 -2017,67,0.02887150454241782,-0.0004891962114565234,-0.0005968322354388376,0.007824770822560476,0.004693206411533338,0.01968196585991796,0.00649880323745473,0.0012269094235753757,-0.0012620240468999683,0.012262683608435444,0.06331960741377604 -2017,70,0.029402787426486613,-0.00045800955558831364,-0.0005533167873287659,0.007931258342271399,0.0048248562782318765,0.020099027921903958,0.006648696178853319,0.0012269094235753757,-0.0012127449065260968,0.012414352497479112,0.06422496885048976 -2017,90,0.034460388538241384,-0.00027291399645993253,-0.0002366533573071153,0.008676671684921875,0.006285510087233818,0.023962213938411414,0.007598019355524368,0.0012269094235753757,-0.0008036284294776082,0.0139736052312775,0.07261100249940546 -2017,95,0.037496274605020884,-0.00023826985698324507,-0.00013073302897126577,0.008925143268921383,0.007173242932021193,0.025820185313191885,0.007847840924522016,0.0012269094235753757,-0.0006359346352425541,0.014898853203302271,0.07678792830660774 -2018,5,0.019028907330706717,-0.0013458225582195708,-0.0022551848909416095,0.006027388768552067,0.0027137877013567427,0.01056755250166578,0.0037038834076415653,-0.004193460817884657,-0.0030165877231337675,0.009579225485836822,0.044177228067501684 -2018,10,0.02077561264127494,-0.0011838283270654327,-0.0019085949416604584,0.006261883202922126,0.002972504880433815,0.0123274049910086,0.003929085038394806,-0.004193460817884657,-0.0026878606577395844,0.010068711779413073,0.047945803330900594 -2018,30,0.024799552503228187,-0.0008803939275229412,-0.0013011850898295351,0.007001367469260569,0.0036774693224179093,0.01594599196641216,0.005055093192161008,-0.004193460817884657,-0.0020752389932972935,0.011290343477601652,0.05631417780916911 -2018,33,0.02530707095824182,-0.0008468641022838328,-0.001239710616337401,0.007129037543369594,0.0037721433184999004,0.016342489206495843,0.005223993959795944,-0.004193460817884657,-0.002011387540823071,0.011436238185183183,0.05725031837910318 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-2011,90,0.024014683440327644,0.005966902244836092,-6.91205495968461e-05,0.0034992294386029243,0.0027254330925643444,0.020147496834397316,0.0024795865174382925,0.005641670111799613,0.0059147253166884186,0.055034569231793284 -2011,95,0.025300564244389534,0.006334604695439339,-8.636193342681509e-06,0.0035491634625941515,0.003001577220857138,0.02149893194437027,0.0025494713336229324,0.006032119083101861,0.006192627141717821,0.05755328136292519 -2012,5,0.013517240062355995,0.0038852933794260025,-0.0013041174970567226,0.002865361049771309,0.0016297316178679466,0.010171101009473205,0.0015600797487422824,0.003014154441189021,0.004787169606424868,0.0363343035653088 -2012,10,0.014778546057641506,0.004027960821986198,-0.0010889972327277064,0.0029516038484871387,0.0017170023638755083,0.011536415666341782,0.0016299202106893063,0.0033109181094914675,0.0049481257563456895,0.03913718015246559 -2012,30,0.01831061765551567,0.004592086188495159,-0.0007134063635021448,0.0031733710784465075,0.00196022167801857,0.014564856886863708,0.0019791231025010347,0.004020359338028356,0.005349118262529373,0.045935862135957 -2012,33,0.018719399347901344,0.004676320306025446,-0.0006758983363397419,0.0032226527109742165,0.001992114819586277,0.014874715358018875,0.002031503478065133,0.004114924115128815,0.005397957140812651,0.04666315045098599 -2012,50,0.020486099645495415,0.005175275262445211,-0.0004952334566041827,0.003432099474593997,0.002183576114475727,0.016488246619701385,0.0023283257614821196,0.0046567413955926895,0.0056589661398902535,0.05008336348691955 -2012,67,0.022175824269652367,0.005723798321560026,-0.0003324026765767485,0.003616905305534601,0.0024093652609735727,0.01818103585392237,0.002625148044899106,0.005246118089416996,0.005934542044997215,0.05336436648445669 -2012,70,0.022481631487607956,0.0058358904439955945,-0.0003025465877726678,0.0036661869380623102,0.002460082061588764,0.018527686595916748,0.002677528653293848,0.005370002472773194,0.00599004765972495,0.054027229716302826 -2012,90,0.02609354257583618,0.00680876336991787,-8.506228914484382e-05,0.003937236033380032,0.0030055639799684286,0.021712355315685272,0.003009271342307329,0.006409147579688579,0.006560770072974264,0.060007136870990505 -2012,95,0.027474550530314445,0.007314542308449745,-1.1491801660668531e-05,0.003998837433755398,0.0033442394342273474,0.023249991703778505,0.0030965718906372786,0.006935311794222798,0.006901128345634788,0.06281597865745425 -2013,5,0.015132676810026169,0.00411666464060545,-0.0015486970078200102,0.0031003146432340145,0.0016770695801824331,0.010506565682590008,0.0017928860615938902,0.003077074186876416,0.005124419694766402,0.03961249889780447 -2013,10,0.01644059270620346,0.004287924617528915,-0.0012974385172128677,0.003203677013516426,0.0017797958571463823,0.01203794488683343,0.0018775764619931579,0.003427056421060115,0.005315900081768632,0.042633500743977495 -2013,30,0.02010319009423256,0.004962100647389889,-0.0008555626263841987,0.003469466231763363,0.002065651398152113,0.01535971201956272,0.0023010284639894962,0.004271295212674886,0.005792850395664573,0.04984156463833642 -2013,33,0.020527075976133347,0.005062908898107708,-0.0008112997747957706,0.003528530476614833,0.002103895414620638,0.01570136422291398,0.002364546060562134,0.00438710511662066,0.0058506999630481005,0.050621320099162405 -2013,50,0.02235906384885311,0.00566265732049942,-0.0005983777809888124,0.0037795535754412413,0.0023294996935874224,0.017494864761829376,0.002724480116739869,0.005036963848397136,0.0061625209636986256,0.05430140841053799 -2013,67,0.024111231788992882,0.006330445008352401,-0.0004070596187375486,0.0040010446682572365,0.0025972307194024324,0.0193715918622911,0.0030844141729176044,0.00575213165895548,0.006489574648439885,0.057856188986042986 -2013,70,0.024428339675068855,0.006468503922224044,-0.00037105951923877,0.004060108680278063,0.002656030934303999,0.01975718289613724,0.0031479322351515293,0.005906816804781556,0.006554763996973634,0.05857259726035409 -2013,90,0.028173726052045822,0.007700118236243725,-0.00011667233775369823,0.004384961910545826,0.0033008805476129055,0.023280936852097513,0.0035502114333212376,0.007216451282147318,0.007231329334899783,0.06499834537971766 -2013,95,0.029605768620967865,0.00836548674851656,-3.0315703043015674e-05,0.004458792507648468,0.003699328750371933,0.02498372113332152,0.0036560744047164917,0.007907059683930129,0.007632906630169598,0.06803572672943119 -2014,5,0.016829488798975945,0.0043481760658323765,-0.001829636632464826,0.0033352377358824015,0.0017344130901619792,0.010962268337607384,0.0020179194398224354,0.003111088299192488,0.005475935223512351,0.04312333765310541 -2014,10,0.018148086965084076,0.004549403674900532,-0.0015333117917180061,0.0034565043170005083,0.001853747060522437,0.012655663304030895,0.002118376549333334,0.0035211622598581016,0.0057005391921848055,0.04630146126146428 -2014,30,0.021840602159500122,0.005338210612535477,-0.001019673002883792,0.0037683327682316303,0.002188133541494608,0.01625185031443834,0.0026206630282104015,0.004507158824708313,0.006259503401815891,0.05380445834016427 -2014,33,0.02226795069873333,0.005456316941417754,-0.0009691778104752302,0.0038376280572265387,0.002231540158390999,0.016626043487340213,0.0026960058603435755,0.004645178560167551,0.006327874579001218,0.054627763575699644 -2014,50,0.024114903062582016,0.006161898374557495,-0.0007195415091700852,0.004132132977247238,0.0024974269326776266,0.018602831289172173,0.0031229492742568254,0.005411980419012252,0.006692674709483981,0.058540443977108225 -2014,67,0.025881383568048477,0.006956906197592618,-0.0004947074339725077,0.004391989670693874,0.0028131986036896706,0.02067461423575878,0.0035498926881700754,0.006263645255239682,0.00707754134433344,0.062343413644703104 -2014,70,0.02620108053088188,0.007123012002557516,-0.00045380849041976035,0.004461285192519426,0.0028809471987187862,0.021099988929927347,0.0036252355203032494,0.00644876720616594,0.007153153209947049,0.06308549759560264 -2014,90,0.029977060854434967,0.00864096637815237,-0.00015620363410562277,0.004842408932745457,0.003640657872892916,0.024979694187641142,0.004102407954633236,0.008056519960518926,0.007949409703724087,0.0699147202540189 -2014,95,0.03142079710960388,0.009487438015639782,-5.721960769733414e-05,0.004929027520120144,0.004105629865080118,0.02685219096019864,0.0042279791086912155,0.008927806600695476,0.008419886149931697,0.07316582870844286 -2015,5,0.017872951924800873,0.004579827189445496,-0.0021056639379821718,0.0035701305605471134,0.0017842391971498728,0.011312860064208508,0.0022351795341819525,0.0031525110825896263,0.005821943806950003,0.04586781238322146 -2015,10,0.019300103187561035,0.0048123979941010475,-0.00176658189157024,0.003710085991770029,0.0019223936833441257,0.013151461351662874,0.0023523208219558,0.003621506621129811,0.006080910668242723,0.049321849504485725 -2015,30,0.023296598345041275,0.0057204170152544975,-0.001179371471516788,0.004069970920681953,0.0023054382763803005,0.017054662108421326,0.0029380270279943943,0.004756988608278334,0.006724633951671421,0.05743647182243876 -2015,33,0.023759128525853157,0.005856544286943972,-0.0011213284451514482,0.004149945452809334,0.002355563687160611,0.017466310411691666,0.003025882877409458,0.004917043514724355,0.006802851362153888,0.0583322883863002 -2015,50,0.025758128613233566,0.006672997958958149,-0.0008363813394680619,0.0044898372143507,0.002659519435837865,0.019625820219516754,0.0035237332340329885,0.005803309381008148,0.00722364685498178,0.06259686435805634 -2015,67,0.027670031413435936,0.007603180650621656,-0.0005789885763078928,0.004789741709828377,0.003022577613592148,0.021890610456466675,0.004021583124995232,0.006799459632020444,0.0076660772366449245,0.06677622667164543 -2015,70,0.028016047552227974,0.007799415476620197,-0.0005323281511664391,0.004869716241955757,0.0031006978591904036,0.022350232675671575,0.004109439440071583,0.007016794639639556,0.007752945646643639,0.0675897026900202 -2015,90,0.032102879136800766,0.009631308726966381,-0.00019153508765157312,0.005309575702995062,0.003973783925175667,0.026572374626994133,0.004665860440582037,0.008943326742155477,0.008668448124080897,0.07505128881894052 -2015,95,0.03366547077894211,0.010680394247174263,-8.053356577875093e-05,0.005409543868154287,0.004508966580033302,0.028605666011571884,0.004812286701053381,0.010015321662649512,0.009209373989142476,0.07862821737653576 -2016,5,0.0192322488874197,0.004811618477106094,-0.0023838055785745382,0.003804992651566863,0.001841898774728179,0.011735709942877292,0.0024446656461805105,0.00319040659815073,0.006177793839015067,0.049016562465112656 -2016,10,0.020758869126439095,0.005076907575130463,-0.002004891401156783,0.003964421339333057,0.001998616848140955,0.013724676705896856,0.002579407999292016,0.003720595734193921,0.006471244734711945,0.05273440238379408 -2016,30,0.02503390982747078,0.006108717992901802,-0.0013445659191347664,0.004374380689114332,0.002430281601846218,0.017881717532873154,0.0032531195320189,0.005006199917261256,0.007200732850469649,0.061397623573429885 -2016,33,0.0255286768078804,0.006263591395691037,-0.0012780196266248822,0.004465482663363218,0.0024853999190963803,0.018320602737367152,0.0033541761804372072,0.005188671057112515,0.007289795204997063,0.06235376924159937 -2016,50,0.027667002752423286,0.007195955142378807,-0.0009577961172908545,0.004852666519582272,0.002828353550285101,0.02064702194184065,0.0039268312975764275,0.006199090508744121,0.007766864611767232,0.06696322781499475 -2016,67,0.02971215918660164,0.008269268050789836,-0.0006689384061610325,0.0051942989230155945,0.0032381860073655844,0.023086421191692352,0.004499485716223717,0.00734763516811654,0.008267826077062636,0.07148333112592808 -2016,70,0.03008229285478592,0.008497713692486286,-0.0006158541073091328,0.005285400897264481,0.003326933365315199,0.023589568212628365,0.004600542597472668,0.007598355645313859,0.008365905843675137,0.07236270322464407 -2016,90,0.034453969448804855,0.010671144351363182,-0.00023512591724283993,0.005786462686955929,0.004311460070312023,0.02812519297003746,0.005240568425506353,0.00987963200896047,0.00940058280248195,0.080425617902074 -2016,95,0.03612546995282173,0.011944356374442577,-0.00011020154488505796,0.0059003401547670364,0.004910733550786972,0.030305245891213417,0.0054089962504804134,0.011168814609845838,0.010010719811543821,0.08431326228310354 -2017,5,0.020763233304023743,0.005043548531830311,-0.0026855082251131535,0.00403982400894165,0.0019134991860482841,0.012284976057708263,0.0026463791728019714,0.0032075901981443167,0.006551367929205298,0.05250609767535934 -2017,10,0.022337140515446663,0.005342932417988777,-0.0022625960409641266,0.004219510592520237,0.00209085363894701,0.014406990818679332,0.0027996397111564875,0.0037996380706317723,0.0068792360834777355,0.056417131793568845 -2017,30,0.026744598522782326,0.00650311354547739,-0.0015290488954633474,0.004681561142206192,0.0025735669769346714,0.01879778318107128,0.003565941471606493,0.005243990628514439,0.007700845878571272,0.06545029463595711 -2017,33,0.02725469134747982,0.006677458118647337,-0.0014531207270920277,0.0047842394560575485,0.0026345248334109783,0.019269440323114395,0.0036808867007493973,0.005450834520161152,0.007800189312547445,0.06643785671272781 -2017,50,0.02945924922823906,0.0077307699248194695,-0.0010971198207698762,0.005220620892941952,0.0030184860806912184,0.02175501361489296,0.004332243464887142,0.006591006997041404,0.008336898405104876,0.07130247430177405 -2017,67,0.031567756086587906,0.008955169022083286,-0.000772732135374099,0.005605663172900677,0.0034801389556378126,0.0243736132979393,0.0049835997633636,0.007899810443632305,0.008899114867672324,0.07611922336393036 -2017,70,0.031949352473020554,0.00921790599822998,-0.0007153120241127908,0.005708341021090746,0.00357992025092244,0.024912778288125992,0.005098544992506504,0.008188454783521593,0.009009175142273307,0.07705706658307462 -2017,90,0.03645643591880798,0.011760473251342773,-0.00029076021746732295,0.0062730698846280575,0.004682695493102074,0.02976666819304228,0.0058265323750674725,0.010850046441191807,0.010172609006986022,0.08560222471132875 -2017,95,0.03817971050739288,0.013279324397444725,-0.00015085324412211776,0.006401417311280966,0.0053536612540483475,0.0320851095020771,0.006018107756972313,0.01238184655085206,0.010854358784854412,0.0897138046566397 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-2011,10,0.012058329792134464,-0.0006251863222059517,-0.0006886689747117693,0.0038788099449274327,0.002266075227904148,0.009138002480299707,0.0017935836634188484,-0.002859177830375903,-0.0011453438332560072,0.0064584345147559894,0.029050264573405274 -2011,30,0.01499023161828518,-0.0005144598476530972,-0.0004512318398636932,0.004176952806983848,0.002540213269001963,0.011277967817963464,0.0021581130187561088,-0.002859177830375903,-0.0009042749550800782,0.006939544571184527,0.03454881289909011 -2011,33,0.01534137986842543,-0.0005018566432026738,-0.0004278495660677465,0.004222291181308478,0.0025763814777607997,0.011493460326867527,0.002212792399285198,-0.002859177830375903,-0.0008780731690106571,0.006997732223498736,0.035135333213726805 -2011,50,0.01718070589080453,-0.0004367383734374756,-0.0003132902443220871,0.004459832312283064,0.0027880255893656595,0.01263450776985281,0.002522642374093369,0.0008763638739824111,-0.0007441718042722986,0.007314884901335864,0.038192342134709206 -2011,67,0.018855817704461514,-0.00030631742328225957,-0.00021009280587729975,0.00470590050743787,0.0030449871584709207,0.013837733091821102,0.002832492500711538,0.0008763638739824111,-0.000615322542640525,0.007653323829423133,0.04113944875654435 -2011,70,0.01918915167097002,-0.00025754136521761745,-0.00019106125393513628,0.004751238881762501,0.003101214762053443,0.014087521532750032,0.0028871720330506255,0.0008763638739824111,-0.0005906693788042636,0.00771977633568487,0.04171590784534305 -2011,90,0.02270116546265781,-0.00018277313144258162,-5.3501704840560003e-05,0.005098833084918001,0.0037150174144922107,0.01637544872552535,0.0032334749282115225,0.0008763638739824111,-0.0003833440336799871,0.008389155728506063,0.04711832409858945 -2011,95,0.024334226793050764,-0.00016786974328860112,-6.689706266204649e-06,0.0052197357687873615,0.004087486791278423,0.017468877870822457,0.003324607380903336,0.0008763638739824111,-0.0002970840249716352,0.008778746317824502,0.04973164736772844 -2012,5,0.01196198244523257,-0.0007737036220573874,-0.0010082753849400346,0.004112708950538328,0.002207530598061009,0.008262442657234914,0.00203440320315189,-0.0030497896857342964,-0.0015044212802100031,0.006719719521080999,0.028845227196892104 -2012,10,0.013219638803601264,-0.0007063226643837914,-0.0008413757987285496,0.004242199563021933,0.0023297693252465213,0.009371070582598283,0.002125477816234445,-0.0030497896857342964,-0.0013455302496237888,0.006957892656244308,0.031416627523117295 -2012,30,0.016365969007834793,-0.0005685636896797239,-0.0005514500511103853,0.004612147847125077,0.0026624318653777398,0.011833961966889961,0.0025808516406972105,-0.0030497896857342964,-0.0010516182753498545,0.007544333521805304,0.03751226440325708 -2012,33,0.01674101392105222,-0.0005529479335883673,-0.0005229068965618523,0.004653032691173861,0.00270723949650816,0.012086365095654182,0.002649157638461626,-0.0030497896857342964,-0.0010201819064211298,0.007614727285696311,0.03815916728816557 -2012,50,0.01873147281482816,-0.00047260587411931737,-0.00038304057477376707,0.004958987768981276,0.002965729209062858,0.013396763416612946,0.0030362251615399793,0.0009347881322479053,-0.0008602519623829141,0.008000032343177307,0.041542315447699416 -2012,67,0.020534027546085416,-0.00035946484364857557,-0.00025700993839469105,0.005254283667618094,0.00327763822185448,0.014769973441413547,0.003423292684618333,0.0009347881322479053,-0.0007065294447879451,0.008410006097206718,0.04479662833500258 -2012,70,0.020891256480850277,-0.00029378265098957716,-0.00023379071270122667,0.005307794929811647,0.003346727499728596,0.015054792690138478,0.003491598986002745,0.0009347881322479053,-0.0006777719951192148,0.008490129563062845,0.04543715932918442 -2012,90,0.024673120184987782,-0.00019849919040935155,-6.589448882788513e-05,0.005734394441733124,0.004094036374421817,0.017648436884621414,0.003924204043337373,0.0009347881322479053,-0.0004373317500711655,0.009303642073270436,0.051413251712441835 -2012,95,0.026455196274630725,-0.00018017265935945628,-8.882908802283432e-06,0.005864903241888643,0.004550566252815657,0.018892985056498137,0.004038047271738067,0.0009347881322479053,-0.0003360053342663687,0.009778297825711829,0.054309157008066836 -2013,5,0.013355824761092663,-0.0008755312951377185,-0.0011979167942908107,0.004458231322094543,0.002272761499378878,0.008526635883153631,0.0023379914709703267,-0.00324040154109269,-0.0017441998444274,0.007202515691943483,0.03150938098036472 -2013,10,0.014693421639874577,-0.0007918737265508356,-0.001002210190431586,0.00462186451711228,0.00241604205959895,0.009787670266813793,0.002448430967404652,-0.00324040154109269,-0.0015577338542508846,0.007483731769368312,0.034312933021863674 -2013,30,0.01794978017248213,-0.0006238660329366776,-0.0006612000980154746,0.005044366564044874,0.0028067120817774076,0.012477528643003225,0.003000628449576278,-0.00324040154109269,-0.0012108691744826508,0.008175516148289378,0.040805821997280924 -2013,33,0.018365504255518316,-0.0006050175698571441,-0.0006277666547890857,0.005112764102165491,0.0028587867884292935,0.012754757040196654,0.0030834578062345253,-0.00324040154109269,-0.0011745924879805222,0.008258498838447462,0.041496638958024024 -2013,50,0.02045575246475637,-0.000509149800668296,-0.0004629944775922571,0.005460484722208229,0.0031640134127328312,0.014210666897489399,0.00355282547631791,0.0009932123905133994,-0.0009882290530888664,0.008715470646216009,0.04513175909039345 -2013,67,0.022343662816099823,-0.000385912612869458,-0.00031433571442849933,0.005813969799691837,0.0035327707637501523,0.015736596282435376,0.004022193146401294,0.0009932123905133994,-0.0008096482600511513,0.009197862998420752,0.04862985156337174 -2013,70,0.02272341108173132,-0.0003321410441903334,-0.0002873981609742751,0.005878102953215501,0.003613702052863233,0.016052475784599524,0.004105023110299534,0.0009932123905133994,-0.0007767932741536179,0.009292578454248674,0.04931918166470365 -2013,90,0.026668276676908135,-0.0002144883908865122,-8.998184479882485e-05,0.0063697936096920055,0.004495686161705048,0.018921981367993192,0.004629610452695082,0.0009932123905133994,-0.0005036363393371733,0.010250214053190368,0.055746171707147546 -2013,95,0.028599478569068013,-0.00019259126194043818,-2.3362086424823096e-05,0.00654081715020944,0.005036185555472221,0.020300799956544355,0.004767659785285489,0.0009932123905133994,-0.00038819180268671806,0.010810221619699778,0.05887190239725284 -2014,5,0.01479764774441719,-0.0009844152924042657,-0.0014139833371023165,0.004803575491101715,0.0023499556387443533,0.008901570917927907,0.002631443537028695,-0.003431013396451084,-0.002011252471336928,0.007704453706627124,0.034374033778689105 -2014,10,0.016175421476364134,-0.0008810251974696078,-0.0011857355682161183,0.004979141928685441,0.0025164433157858525,0.010284313959392947,0.0027624434205494658,-0.003431013396451084,-0.0017943018974146926,0.008031007686440673,0.03734200801892502 -2014,30,0.019473775626719,-0.0006809142784940216,-0.0007885309848018762,0.005466585897859166,0.002973008321257695,0.013201583244108011,0.003417444052633304,-0.003431013396451084,-0.0013895133997390723,0.008839489136486578,0.044144712311491974 -2014,33,0.01992400862276554,-0.0006584412002240657,-0.0007489781524959822,0.0055559068336378855,0.0030341214980836857,0.01350785386875952,0.003515693965273882,-0.003431013396451084,-0.001346578109855416,0.008935341726861954,0.04487859362496077 -2014,50,0.02204493523668498,-0.0005466789320608321,-0.0005561825977416232,0.005964684378919447,0.003392336979589362,0.015114617669265824,0.004072444381097146,0.0010516366487788936,-0.0011319232089387985,0.009469076517889901,0.048719315702533406 -2014,67,0.023988024355284868,-0.00041444698007262186,-0.00038261322587577394,0.006384135249245877,0.00382635598975205,0.016797363733312556,0.00462919479692041,0.0010516366487788936,-0.0009264110772862453,0.010032100297761231,0.05244254248936633 -2014,70,0.02438233627155423,-0.00037261646428629055,-0.0003510888103986889,0.0064627825076427154,0.0039189815434355554,0.01714595017160484,0.004727444709560988,0.0010516366487788936,-0.0008887461825461265,0.010141344263540374,0.053174466289814495 -2014,90,0.028401997400075197,-0.00023074073488740346,-0.00012085066541769921,0.00703577416214876,0.004957700423880224,0.020302853498443744,0.0053496956743846295,0.0010516366487788936,-0.0005784989791044678,0.011261755740080513,0.05999710444304166 -2014,95,0.030488129273056983,-0.0002051255510315468,-4.4461956824046745e-05,0.007223038399246785,0.005591756267633365,0.021819823942151637,0.005513445225165597,0.0010516366487788936,-0.00044882716061422716,0.011917775670054379,0.06332994035051884 -2015,5,0.015716381541825832,-0.0010990940315919186,-0.001627099239152841,0.005139781222929659,0.002419501183895799,0.009181589269760683,0.002914758945897001,-0.003621625251809477,-0.0022785228202559724,0.008200938342840608,0.03656796886939404 -2015,10,0.017219481970742342,-0.0009738176036832256,-0.0013670454215343258,0.005342080920382496,0.0026090219022955486,0.010689921713910576,0.003067515631098881,-0.003621625251809477,-0.0020301505763944305,0.008573081818088383,0.03980832713556593 -2015,30,0.020773216911591588,-0.0007396156297271291,-0.0009123714701398582,0.0059147337587031725,0.003131998090439568,0.013854963677161752,0.0038312987534882847,-0.003621625251809477,-0.001566601865857241,0.009499992711389377,0.04717145369381865 -2015,33,0.021255204513296485,-0.0007134043372068,-0.0008667541948602055,0.006007709660762466,0.003201258716734163,0.014189967114751515,0.003945866115579697,-0.003621625251809477,-0.0015172646224049934,0.009609994421745678,0.04797904662238132 -2015,50,0.02354738592170179,-0.0005849514843141191,-0.0006462009537408965,0.006471304791270337,0.0036132523926978256,0.01594453932919957,0.004595081875877688,0.0011100609070443875,-0.0012742210378736795,0.01022105817488108,0.05214282419436288 -2015,67,0.025643277261406184,-0.0004451889090160081,-0.0004474112360271547,0.006967160978649243,0.004109920290116364,0.017784013971560785,0.005244297028935688,0.0011100609070443875,-0.0010399158387507305,0.010868145778922263,0.056195921041403626 -2015,70,0.026071244663745163,-0.00041450012302224397,-0.0004113368596402604,0.007050246693078304,0.0042177443625117834,0.018160594093890748,0.005358864998267092,0.0011100609070443875,-0.0009973393260332133,0.010992719170224331,0.05698697144577265 -2015,90,0.030404983451589943,-0.00024725626267882314,-0.00014813946131811797,0.007702038452589516,0.005410060979619629,0.021598991694105056,0.006084459101166024,0.0011100609070443875,-0.0006487989820801872,0.012276672374607944,0.06440197455077613 -2015,95,0.03264265040829778,-0.0002177755064993832,-6.238659713501553e-05,0.007913830344326174,0.006139805826738794,0.023253081903087332,0.006275404502238379,0.0011100609070443875,-0.0005053784381529596,0.013030587959679716,0.06801315501760971 -2016,5,0.016912954765185714,-0.001219826143022942,-0.0018423498385513302,0.005491570507528632,0.0024981089313096767,0.009524428241462672,0.0031879367867152556,-0.003812237107167871,-0.002550382338868841,0.008708167889258192,0.039134634968674954 -2016,10,0.018509603468328716,-0.0010717421615883647,-0.001550079897007605,0.005706596032539792,0.0027139009238294444,0.011149703465446869,0.003363645929142918,-0.003812237107167871,-0.0022733860887971112,0.009127588927959847,0.042614035987475464 -2016,30,0.022318789448216556,-0.0007995453929540825,-0.0010398239942153542,0.006343919502733911,0.00330164878448983,0.01452478514915077,0.004242191337661235,-0.003812237107167871,-0.0017494619959720586,0.010176060148961278,0.05049025502642723 -2016,33,0.022841295781731604,-0.000769888781925339,-0.0009886405938666991,0.006464991185716309,0.0033795502455573343,0.014887076513335686,0.004373973042671985,-0.003812237107167871,-0.00169485523975396,0.010299870822355619,0.05135354478166973 -2016,50,0.025292096804454923,-0.0006244268662389587,-0.000740622486948478,0.00700592016416411,0.0038426074502891224,0.016777542280263045,0.005120737049799548,0.0011684851653098816,-0.0014230485246813575,0.010991460309598904,0.055817821887596014 -2016,67,0.0275379775788635,-0.0004771123345260937,-0.0005168867414704918,0.007550217138935116,0.0044035502452923715,0.018758605778779528,0.005867500146067124,0.0011684851653098816,-0.0011604141419065944,0.011721690742112682,0.06018223155626336 -2016,70,0.0279940810225904,-0.0004470282100602375,-0.00047616210488311367,0.007653613958379672,0.004524608472138508,0.01916390302874836,0.005999282154697869,0.0011684851653098816,-0.0011131952518311648,0.011862393157687674,0.06103080721788565 -2016,90,0.032633948185667394,-0.00026403493399397354,-0.00018163701526720258,0.008377466242242925,0.005868953047673752,0.022861250938052333,0.006833900125799272,0.0011684851653098816,-0.0007252490807596664,0.013308439336590221,0.06900244218653147 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100644 index 3339337..0000000 Binary files a/profsea/Falkland_example/figures/zos_regression/STANLEYII_bcc-csm1-1_zos_regression_2005-2100.png and /dev/null differ diff --git a/profsea/Falkland_example/figures/zos_regression/STANLEYII_inmcm4_zos_regression_2005-2100.png b/profsea/Falkland_example/figures/zos_regression/STANLEYII_inmcm4_zos_regression_2005-2100.png deleted file mode 100644 index be0f354..0000000 Binary files a/profsea/Falkland_example/figures/zos_regression/STANLEYII_inmcm4_zos_regression_2005-2100.png and /dev/null differ diff --git a/profsea/__init__.py b/profsea/__init__.py new file mode 100644 index 0000000..e7a317c --- /dev/null +++ b/profsea/__init__.py @@ -0,0 +1,23 @@ +import logging + +from rich.logging import RichHandler + +# Tie logger to the "profsea" namespace +logger = logging.getLogger("profsea") + +# Print by default +logger.setLevel(logging.INFO) + +# Use Rich! +rich_handler = RichHandler( + rich_tracebacks=True, + show_time=True, + show_path=False, + markup=True, + log_time_format="[%H:%M:%S]", +) + +formatter = logging.Formatter("%(message)s") +rich_handler.setFormatter(formatter) +logger.addHandler(rich_handler) +logger.propagate = False diff --git a/profsea/aux_data/ISMIP_GIS_calibration.csv b/profsea/aux_data/ISMIP_GIS_calibration.csv new file mode 100644 index 0000000..41ea482 --- /dev/null +++ b/profsea/aux_data/ISMIP_GIS_calibration.csv @@ -0,0 +1,22 @@ +institution,model,b0,b1,b2,b3,b4,b5,sigma +UCIJPL, ISSM1,0.11280511383425385,0.6821503460827252,-0.16400289768763443,0.029789425852056794,-0.009489849321525412,0.00014879916101584456,0.44138133304923294 +UAF, PISM1,0.10798850687485739,2.22736503017021,-0.692345687432411,0.08161212823174324,-0.04641834271523848,0.0004685291256141255,0.5278388471215101 +NCAR, CISM,0.31324843280109604,1.5993142312892896,-0.44199635991569686,0.05872843417961349,-0.030363605622934386,0.00027208434509873314,0.5786469779135103 +MUN, GSM2601,0.26176469764101284,1.9203005829347362,-0.64443813905498,0.06774621868079578,-0.04785788778603006,0.0005414869899329489,0.4804421954085724 +AWI, ISSM1,0.15203622407200434,0.5737828059597607,-0.06009509991088713,0.018698973452613288,-0.0017116999595181426,2.627912853014891e-05,0.5379227283666163 +JPL, ISSMPALEO,0.08192192746084714,0.6728440122964905,-0.12814016340042222,0.022584230858470278,-0.01643152901161038,0.0001855436066229288,0.40578701831731945 +BGC, BISICLES,0.14652938235787794,0.8228410010355418,-0.2424425609220453,0.04248976786373326,-0.01659422359712792,0.0002188231920179362,0.4866165180787471 +GSFC, ISSM,0.17216048421978947,1.940293458477143,-0.6163995281733783,0.07537669095303912,-0.034675558756788405,0.0003814767735637048,0.47471207710632624 +UCIJPL, ISSM2,0.21641138942280236,0.33642105388928734,0.04866982311529178,-0.015205305616245113,-0.004018009867746031,8.458271035394205e-05,0.42536807781497576 +IMAU, IMAUICE2,0.20036011456528868,2.2955765155739787,-0.7112950680247732,0.09145182217310044,-0.044411324549290754,0.00044883827336406057,0.6120063870228036 +VUB, GISMHOMv1,0.39396308078447184,0.8529109232648526,-0.19173147734077922,0.0360613162914003,-0.0023678409809129164,-1.1101471443453192e-05,0.5764299273835812 +IMAU, IMAUICE1,0.2800198843218169,0.8427979750531325,-0.12558217145775608,0.031145488329698878,-0.0023911906890941026,-7.900936685345528e-06,0.7439631769871697 +MUN, GSM2611,0.31961312362156136,0.3908080413807351,0.15162878131265423,-0.042695881436106475,-0.01605741445283293,0.00020608416355116788,0.45394137922358807 +UAF, PISM2,0.2167261842707333,0.22324315646728543,-0.0321449483341798,0.019635585944547174,0.0015780370372677766,-1.0150760214600041e-05,0.42575847295364555 +VUW, PISM,1.3145573843686975e-05,1.5421914530833938,-0.5162788335603761,0.0604074471391165,-0.03655888489420711,0.0004626783801828793,0.3706427862813395 +AWI, ISSM2,0.1513883454153806,0.576742644362664,-0.0562763456969293,0.018041192418930763,-0.0010313859576172035,1.64181719419787e-05,0.539335117427485 +ILTS_PIK, SICOPOLIS2,0.23327050371069546,0.635252150611775,-0.05725160435687826,0.01879619175116609,-0.0052735802812815535,6.885471476181237e-05,0.5382205194008777 +ILTS_PIK, SICOPOLIS1,0.17910675828310474,1.8256763597420758,-0.5246724854860112,0.06912592493435277,-0.03590566483921975,0.0003654973100086778,0.5533500350485601 +AWI, ISSM3,0.0894329235365312,2.117295179930542,-0.6491365405376079,0.08078620843845385,-0.042188539617928456,0.0004211035302041921,0.5679266897268976 +JPL, ISSM,0.19722907443354742,0.727228695939683,-0.1329472066135633,0.028647131912272528,-0.003898102886985555,4.903956863699932e-05,0.5329293558099194 +LSCE, GRISLI2,0.21625621392082017,0.3885561495874317,-0.03848012730510675,0.017660852688523576,-0.006097320636799353,5.743035870686697e-05,0.49405529384710106 diff --git a/profsea/aux_data/cumulative_cmip6_emissions.json b/profsea/aux_data/cumulative_cmip6_emissions.json new file mode 100644 index 0000000..2cf587a --- /dev/null +++ b/profsea/aux_data/cumulative_cmip6_emissions.json @@ -0,0 +1 @@ +{"ssp126": 325.34, "ssp245": 810.20, "ssp370": 1506.5773134877386, "ssp585": 2176.3538993188017} \ No newline at end of file diff --git a/profsea/aux_data/eais_params_expanded.nc b/profsea/aux_data/eais_params_expanded.nc new file mode 100644 index 0000000..370df7c Binary files /dev/null and b/profsea/aux_data/eais_params_expanded.nc differ diff --git a/profsea/aux_data/eais_params_reduced.nc b/profsea/aux_data/eais_params_reduced.nc new file mode 100644 index 0000000..0ff26e9 Binary files /dev/null and b/profsea/aux_data/eais_params_reduced.nc differ diff --git a/profsea/aux_data/pen_params_expanded.nc b/profsea/aux_data/pen_params_expanded.nc new file mode 100644 index 0000000..362b191 Binary files /dev/null and b/profsea/aux_data/pen_params_expanded.nc differ diff --git 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index 0000000..e19121e --- /dev/null +++ b/profsea/components/core/__init__.py @@ -0,0 +1,4 @@ +from .global_model import Global +from .spatial_model import Spatial + +__all__ = ["Global", "Spatial"] diff --git a/profsea/components/core/base.py b/profsea/components/core/base.py new file mode 100644 index 0000000..a997ba1 --- /dev/null +++ b/profsea/components/core/base.py @@ -0,0 +1,200 @@ +from __future__ import annotations + +from abc import ABC, abstractmethod + +import dask.array as da +import numpy as np +import xarray as xr + +from .state import ClimateState + + +class Component(ABC): + @abstractmethod + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """Calculate and return the SLR projection for this component""" + pass + + +class SpatialComponent(Component): + def extract_spatial(self, da_input: xr.DataArray, state) -> xr.DataArray: + if hasattr(state, "target_lats"): + return self._extract_points(da_input, state.target_lats, state.target_lons) + + elif hasattr(state, "grid_lats"): + from profsea.utils.utils import interpolate_to_grid + + return interpolate_to_grid(da_input, state.grid_lats, state.grid_lons) + + else: + raise ValueError("State object is missing required spatial attributes.") + + def _extract_points( + self, da_input: xr.DataArray, target_lats: np.ndarray, target_lons: np.ndarray + ) -> xr.DataArray: + target_lats = np.asarray(target_lats) + target_lons = np.asarray(target_lons) + + # Normalize longitudes to [-180, 180) lazy metadata update + if "lon" in da_input.coords: + da_input = da_input.assign_coords(lon=(((da_input.lon + 180) % 360) - 180)) + target_lons = ((target_lons + 180) % 360) - 180 + + # Extract 1D coordinates eagerly + lats = da_input.lat.values + lons = da_input.lon.values + + # Handle decreasing latitudes + is_descending_lat = False + if lats[0] > lats[-1]: + is_descending_lat = True + lats = lats[::-1] + + # Sort lons + lon_sort_idx = np.argsort(lons) + lons = lons[lon_sort_idx] + + # Sort the lazy array to match the coordinate math + da_input = da_input.isel(lon=lon_sort_idx) + if is_descending_lat: + da_input = da_input.isel(lat=slice(None, None, -1)) + + # Calculate bounded indices locally + lat_idx_hi = np.clip(np.searchsorted(lats, target_lats), 1, len(lats) - 1) + lat_idx_lo = lat_idx_hi - 1 + + # Handle longitude wrapping for global datasets + idx = np.searchsorted(lons, target_lons) + wrap_lo = idx == 0 + wrap_hi = idx == len(lons) + + # Modulo arithmetic perfectly wraps indices over the array bounds + lon_idx_hi = idx % len(lons) + lon_idx_lo = (idx - 1) % len(lons) + + # Extract physical coordinates + lat_lo, lat_hi = lats[lat_idx_lo], lats[lat_idx_hi] + lon_lo, lon_hi = lons[lon_idx_lo], lons[lon_idx_hi] + + # Fix coordinates for wrapped bounds so distance calculations work + lon_lo = np.where(wrap_lo, lons[-1] - 360, lon_lo) + lon_hi = np.where(wrap_hi, lons[0] + 360, lon_hi) + + dlat = np.where(lat_hi == lat_lo, 1.0, lat_hi - lat_lo) + dlon = np.where(lon_hi == lon_lo, 1.0, lon_hi - lon_lo) + + # Calculate weights locally + w_lat_lo = (lat_hi - target_lats) / dlat + w_lat_hi = (target_lats - lat_lo) / dlat + w_lon_lo = (lon_hi - target_lons) / dlon + w_lon_hi = (target_lons - lon_lo) / dlon + + # Build DataArrays for the 4 corners so they broadcast properly with the lazy data + sites = xr.DataArray(np.arange(len(target_lats)), dims=["site"]) + + w_lo_lo = xr.DataArray( + w_lat_lo * w_lon_lo, dims=["site"], coords={"site": sites} + ) + w_hi_lo = xr.DataArray( + w_lat_hi * w_lon_lo, dims=["site"], coords={"site": sites} + ) + w_lo_hi = xr.DataArray( + w_lat_lo * w_lon_hi, dims=["site"], coords={"site": sites} + ) + w_hi_hi = xr.DataArray( + w_lat_hi * w_lon_hi, dims=["site"], coords={"site": sites} + ) + + # Pull the 4 corners + da_lo_lo = da_input.isel( + lat=xr.DataArray(lat_idx_lo, dims="site"), + lon=xr.DataArray(lon_idx_lo, dims="site"), + ) + da_hi_lo = da_input.isel( + lat=xr.DataArray(lat_idx_hi, dims="site"), + lon=xr.DataArray(lon_idx_lo, dims="site"), + ) + da_lo_hi = da_input.isel( + lat=xr.DataArray(lat_idx_lo, dims="site"), + lon=xr.DataArray(lon_idx_hi, dims="site"), + ) + da_hi_hi = da_input.isel( + lat=xr.DataArray(lat_idx_hi, dims="site"), + lon=xr.DataArray(lon_idx_hi, dims="site"), + ) + + # Handle NaNs and renormalization within the Xarray/Dask graph + # Create a boolean mask of valid data + valid_lo_lo = da_lo_lo.notnull() + valid_hi_lo = da_hi_lo.notnull() + valid_lo_hi = da_lo_hi.notnull() + valid_hi_hi = da_hi_hi.notnull() + + # Zero out weights where data is NaN + w_lo_lo_masked = w_lo_lo.where(valid_lo_lo, 0.0) + w_hi_lo_masked = w_hi_lo.where(valid_hi_lo, 0.0) + w_lo_hi_masked = w_lo_hi.where(valid_lo_hi, 0.0) + w_hi_hi_masked = w_hi_hi.where(valid_hi_hi, 0.0) + + # Sum the active weights + w_sum = w_lo_lo_masked + w_hi_lo_masked + w_lo_hi_masked + w_hi_hi_masked + + # Prevent division-by-zero warnings, but we will restore NaNs later where all 4 corners were NaN + w_sum_safe = w_sum.where(w_sum != 0, 1.0) + + # Renormalize weights using the safe denominator + w_lo_lo_norm = w_lo_lo_masked / w_sum_safe + w_hi_lo_norm = w_hi_lo_masked / w_sum_safe + w_lo_hi_norm = w_lo_hi_masked / w_sum_safe + w_hi_hi_norm = w_hi_hi_masked / w_sum_safe + + # Calculate final weighted sum + result = ( + (da_lo_lo.fillna(0) * w_lo_lo_norm) + + (da_hi_lo.fillna(0) * w_hi_lo_norm) + + (da_lo_hi.fillna(0) * w_lo_hi_norm) + + (da_hi_hi.fillna(0) * w_hi_hi_norm) + ) + + # Restore pure NaNs where all 4 corners were originally NaN (where w_sum was 0) + result = result.where(w_sum != 0, np.nan) + + # Re-attach target coordinate metadata + result = result.assign_coords( + lat=("site", target_lats), lon=("site", target_lons) + ) + + return result + + def broadcast_spatiotemporal( + self, temporal_array: da.Array, spatial_array: da.Array + ) -> da.Array: + """ + Dynamically multiplies a temporal series by a spatial pattern. + + Parameters + ---------- + temporal_array: dask.array.Array + 2D array of shape (members, years) + spatial_array: dask.array.Array + Either 2D (members, site) or 3D (members, lat, + """ + # Determine how many spatial dimensions we are dealing with + spatial_dims = spatial_array.ndim - 1 + + if spatial_dims == 1: + # 3D Output: (members, years, site) + return temporal_array[:, :, None] * spatial_array[:, None, :] + elif spatial_dims == 2: + # 4D Output: (members, years, lat, lon) + return temporal_array[:, :, None, None] * spatial_array[:, None, :, :] + else: + raise ValueError( + f"Unexpected spatial dimensions. Expected 1 or 2, got {spatial_dims}." + ) + + @property + @abstractmethod + def global_projection(self): + """Spatial component must define a global_projection attribute or property""" + pass diff --git a/profsea/components/core/global_model.py b/profsea/components/core/global_model.py new file mode 100644 index 0000000..1c44d29 --- /dev/null +++ b/profsea/components/core/global_model.py @@ -0,0 +1,373 @@ +from __future__ import annotations + +import concurrent.futures +import logging + +import numpy as np +import xarray as xr +from rich.console import Console +from rich.progress import track + +from profsea.utils import check_shapes, sample_members_2D, save_components +from profsea.utils.ui import print_global_preflight + +from .base import Component +from .state import ClimateState + +console = Console() +logger = logging.getLogger(__name__) + + +class Global: + """Global sea level rise component emulator. + + Parameters + ---------- + components: Dict + List of SLR components for projections + end_yr: int + End year of the projections. + nt: int + Number of realisations of the input timeseries + num_members: int + Number of realisations of for each component. + Must be a multiple of the number of glacier methods. + tcv: float + Multiplier for the standard deviation in the input fields. + parallel: bool + If True, project SLR components in parallel. + output_percentiles: list|np.ndarray + If not None, calculate percentiles from a 1D list/array for each + component + palmer_method: bool + If True, allow integration to end in any year up to 2300, + with the contributions to GMLSR from ice-sheet dynamics, + Greenland SMB and land water storage held at the 2100 rate + beyond 2100. + random_sample: bool + If True, randomly sample a single ensemble member across all + components + + Attributes + ---------- + endofhistory: int + First year of AR5 projections. + endofAR5: int + Last year of AR5 projections. + nyr: int + Length of projections. + """ + + def __init__( + self, + components: dict[str, Component], + end_yr: int, + nt: int = 100, + num_members: int = 1000, + tcv: float = 1.0, + parallel: bool = True, + output_percentiles: list | np.ndarray = None, + palmer_method: bool = True, + random_sample: bool = False, + dtype: np.dtype | str = np.float32, + ): + self.components = components + self.end_yr = end_yr + self.nt = nt + self.num_members = num_members + self.tcv = tcv + self.parallel = parallel + self.output_percentiles = output_percentiles + self.palmer_method = palmer_method + self.random_sample = random_sample + self.dtype = np.dtype(dtype) + + self.endofhistory = 2006 + self.endofAR5 = 2100 + self.nyr = self.end_yr - self.endofhistory + + # Inject method! + save_components = save_components + + def _arr_to_xr(self, arr_dict: dict[str, np.ndarray]) -> dict[str, xr.DataArray]: + """Convert a dictionary of numpy/dask arrays to xarray DataArrays. + + Parameters + ---------- + arr_dict: dict + Dictionary of arrays, where keys are component names and values are arrays. + + Returns + ------- + dict + Dictionary of xarray DataArrays, where keys are component names and values are xarray DataArrays. + """ + xr_dict = {} + member_dim = "percentile" if self.output_percentiles is not None else "member" + + for name, arr in arr_dict.items(): + member_coords = ( + self.output_percentiles + if self.output_percentiles is not None + else np.arange(arr.shape[0]) + ) + + xr_dict[name] = xr.DataArray( + arr, + dims=[member_dim, "time"], + coords={ + member_dim: member_coords, + "time": np.arange( + self.endofhistory, self.endofhistory + arr.shape[1] + ), + }, + ) + xr_dict[name].attrs["units"] = "m" + + return xr_dict + + def run( + self, + scenario: str, + T_change: np.ndarray, + member_seed: int = 42, + ) -> dict[str, np.ndarray]: + """Run the emulator to project GMSLR components for a specific state. + Parameters + ---------- + scenario: str + Name of the scenario. + T_change: np.ndarray + Array of temperature change values. + member_seed: int + Seed for numpy.random. + """ + + seed_seq = np.random.SeedSequence(member_seed) + run_rng = np.random.default_rng(seed_seq) + + check_shapes(T_change, self.nyr) + + # Standardize T_change shape to (nt, nyr) + if T_change.ndim > 2: + T_change = np.squeeze(T_change) + if T_change.ndim == 1: + T_change = np.expand_dims(T_change, axis=0) + + self.nt = T_change.shape[0] + + print_global_preflight(self, scenario) + + T_change = T_change.astype(self.dtype) + T_ens, T_int_ens, T_int_med = self._calculate_drivers(T_change) + + # Shared physical correlation state + fraction = run_rng.random(self.num_members * self.nt).astype(self.dtype) + + state = ClimateState( + scenario=scenario, + T_ens=T_ens, + T_int_ens=T_int_ens, + T_int_med=T_int_med, + fraction=fraction, + palmer_method=self.palmer_method, + endofAR5=self.endofAR5, + endofhistory=self.endofhistory, + end_yr=self.end_yr, + nyr=self.nyr, + nt=self.nt, + num_members=self.num_members, + dtype=self.dtype, + ) + + # Child RNGs for each component + child_seeds = seed_seq.spawn(len(self.components)) + comp_rngs = { + name: np.random.default_rng(s) + for name, s in zip(self.components.keys(), child_seeds) + } + + results = {} + if self.parallel: + with concurrent.futures.ThreadPoolExecutor() as executor: + futures = { + executor.submit(comp.project, state, comp_rngs[name]): name + for name, comp in self.components.items() + } + for future in concurrent.futures.as_completed(futures): + comp_name = futures[future] + try: + results[comp_name] = future.result() + except Exception as e: + raise RuntimeError(f"Component '{comp_name}' failed.") from e + else: + for name, comp in track( + self.components.items(), description="Projecting components..." + ): + results[name] = comp.project(state, comp_rngs[name]) + + # Random Sampling + if self.random_sample: + random_idx = run_rng.integers(low=0, high=self.nt * self.num_members) + for comp_name, data in results.items(): + if data.ndim > 1: + results[comp_name] = data[random_idx][None, :] + + # Output percentiles + if self.output_percentiles is not None: + logger.info( + f"Sampling {len(self.output_percentiles)} members per component..." + ) + for comp_name, data in results.items(): + results[comp_name] = sample_members_2D( + data, self.output_percentiles, dtype=self.dtype + ) + + self.results = self._arr_to_xr(results) + return self.results + + # def save_components( + # self, + # components: dict[str, xr.DataArray], + # scenario_name: str, + # output_prefix: str = "global", + # output_dir: str = ".", + # output_format: str = "netcdf", + # ) -> None: + # """ + # Stream all global sea level projections to disk in a single file/store. + + # Parameters + # ---------- + # components: dict[str, xr.DataArray] + # Dictionary of component names and their corresponding Xarray DataArrays. + # scenario_name: str + # Name of the scenario you've run the emulator for. + # output_prefix: str + # Prefix for the output file name (default: 'global'). + # output_dir: str + # Directory to save components to. + # output_format: str + # Format to save the output in. Must be either 'netcdf' or 'zarr'. + + # Returns + # ------- + # None + # """ + # ds = xr.Dataset(components) + + # # Add ProFSea version and scenario metadata + # ds.attrs["source"] = "ProFSea v3.0" + # ds.attrs["scenario"] = scenario_name + # ds.attrs["description"] = "Global sea level rise projections" + + # output_format = output_format.lower() + # if output_format not in ["netcdf", "zarr"]: + # raise ValueError("output_format must be either 'netcdf' or 'zarr'.") + + # Path(output_dir).mkdir(parents=True, exist_ok=True) + # encoding = {} + + # # Sort out Zarr encoding + # if output_format == "zarr": + # import numcodecs + # from numcodecs.zarr3 import Blosc + + # compressor = Blosc( + # cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE + # ) + + # # Set the encoding/compression dynamically based on the component's actual dtype + # for name, component in components.items(): + # comp_dtype = ( + # component.dtype.name + # ) # Captures 'float32' or 'float64' dynamically + + # if output_format == "netcdf": + # encoding[name] = {"zlib": True, "complevel": 1, "dtype": comp_dtype} + # elif output_format == "zarr": + # encoding[name] = {"compressor": compressor, "dtype": comp_dtype} + + # file_name = f"{scenario_name}_{output_prefix}" + + # # Stream the computation and write to disk + # if output_format == "netcdf": + # out_path = os.path.join(output_dir, f"{file_name}.nc") + # with console.status( + # "[bold cyan]Computing and saving Global NetCDF...[/bold cyan]", + # spinner="dots", + # ): + # # If using Dask, .compute() is required before .to_netcdf() + # # If arrays are already eager NumPy arrays, .compute() is a harmless no-op + # if hasattr(ds, "compute"): + # ds.compute().to_netcdf(out_path, encoding=encoding) + # else: + # ds.to_netcdf(out_path, encoding=encoding) + + # logger.info( + # f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}" + # ) + + # elif output_format == "zarr": + # out_path = os.path.join(output_dir, f"{file_name}.zarr") + # with console.status( + # "[bold cyan]Streaming computation and saving Global Zarr...[/bold cyan]", + # spinner="dots", + # ): + # ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True) + # logger.info( + # f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}" + # ) + + # # Log the shape of the total_gmslr (or the first available component) + # sample_name = ( + # "total_gmslr" if "total_gmslr" in ds else list(ds.data_vars.keys())[0] + # ) + # dims_str = ", ".join(ds[sample_name].dims) + # logger.info(f"Global output shape was {ds[sample_name].shape} ({dims_str})") + + def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray: + """ + Sum the components in-place to get total GMSLR. + + Parameters + ---------- + components: dict[str, xr.DataArray] + Dictionary of component names and their corresponding Xarray DataArrays. + + Returns + ------- + xr.DataArray + DataArray of the summed global projections. + """ + + iterator = iter(components.values()) + gmslr = next(iterator).copy() + + for comp in iterator: + gmslr += comp + + gmslr.attrs["units"] = "m" + gmslr.attrs["description"] = "Total global mean sea level rise" + components["total_gmslr"] = gmslr + return gmslr + + def _calculate_drivers(self, T_change: np.ndarray) -> tuple: + """Calculate the drivers of GMSLR: temperature change and + thermosteric sea level rise. + + Returns + ------- + T_ens: np.ndarray + Ensemble of temperature changes. + T_int_ens: np.ndarray + Ensemble of time-integral temperature anomalies. + T_int_med: np.ndarray + Median of time-integral temperature anomalies. + """ + T_ens = T_change.copy() + + # Time-integral of temperature anomaly + T_int_ens = np.cumsum(T_ens, axis=1) + T_int_med = np.cumsum(np.median(T_ens, axis=0)) + return (T_ens, T_int_ens, T_int_med) diff --git a/profsea/components/core/local_model.py b/profsea/components/core/local_model.py new file mode 100644 index 0000000..d3ce230 --- /dev/null +++ b/profsea/components/core/local_model.py @@ -0,0 +1,243 @@ +import logging +import warnings +from pathlib import Path + +import dask.array as da +import numpy as np +import xarray as xr +from rich.console import Console +from rich.progress import track + +from profsea.utils import fetch_zenodo_fingerprints, save_components + +from .base import SpatialComponent +from .state import LocalState + +logger = logging.getLogger(__name__) +console = Console() +warnings.filterwarnings("ignore") + +PROFSEA_DIR = Path(__file__).resolve().parent.parent.parent +ZENODO_DOWNLOAD_LINK = ( + "https://zenodo.org/records/20427061/files/profsea-assets.zip?download=1" +) + + +class Local: + """Site-specific (Local) sea level rise component emulator.""" + + def __init__( + self, + components: dict[str, SpatialComponent], + locations: dict[str, tuple[float, float]], + interpolation_method: str = "nearest", + end_year: int = 2301, + baseline_yrs: tuple = (1995, 2014), + output_percentiles: list | np.ndarray = [5, 17, 50, 83, 95], + ) -> None: + """ + Parameters + ---------- + components: dict + Dictionary of spatial components to include in the model. + locations: dict + Dictionary mapping site names to (latitude, longitude) tuples. + Example: {"Aberdeen": (57.144, -2.080)} + interpolation_method: str, optional + Interpolation method to use when interpolating patterns to the target sites. Default is 'nearest'. + end_year: int, optional + The final year of the projections. Default is 2301. + baseline_yrs: tuple, optional + Tuple defining the start and end years of the baseline period. Default is (1995, 2014). + output_percentiles: list or np.ndarray, optional + List or array of percentiles to sample from the ensemble for output. + """ + fetch_zenodo_fingerprints( + zenodo_url=ZENODO_DOWNLOAD_LINK, + data_dir=PROFSEA_DIR, + expected_folder_name="profsea-assets", + ) + + self.components = components + self.end_year = end_year + self.baseline_yrs = baseline_yrs + self.output_percentiles = output_percentiles + self.start_year = 2006 + self.n_years = self.end_year - self.start_year + self.interpolation_method = interpolation_method + + # Parse the locations dictionary + self.site_names = list(locations.keys()) + self.target_lats = [coords[0] for coords in locations.values()] + self.target_lons = [coords[1] for coords in locations.values()] + + if self.output_percentiles is not None and len(self.output_percentiles) > 0: + self.num_members = len(self.output_percentiles) + else: + self.num_members = next( + iter(self.components.values()) + ).global_projection.shape[0] + + logger.info( + f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}" + ) + logger.info(f"Configured for {len(self.site_names)} specific target locations.") + + # Instance method! + save_components = save_components + + def _arr_to_xr(self, arr_dict: dict[str, da.Array]) -> dict[str, xr.DataArray]: + """ + Convert Dask arrays to xarray DataArrays with site coordinates. + + Parameters + ---------- + arr_dict: dict + Dictionary of Dask arrays to convert. Keys should be the component names and values should be Dask arrays of shape (members, time, sites). + + Returns + ------- + dict + Dictionary of xarray DataArrays with site coordinates. Keys are the same as in arr_dict. + """ + xr_dict = {} + member_dim = "percentile" if self.output_percentiles is not None else "member" + + for name, arr in arr_dict.items(): + xr_dict[name] = xr.DataArray( + arr, + dims=[member_dim, "time", "site"], + coords={ + member_dim: self.output_percentiles + if self.output_percentiles is not None + else np.arange(arr.shape[0]), + "time": np.arange(self.start_year, self.start_year + arr.shape[1]), + "site": self.site_names, + "lat": ("site", self.target_lats), + "lon": ("site", self.target_lons), + }, + attrs={ + "units": "m", + "long_name": f"Local {name} sea-level projections", + "source": "ProFSea-Climate v0.1", + }, + ) + return xr_dict + + def _apply_universal_mask(self) -> None: + """ + Identify locations that evaluate to NaN in ANY component (typically driven + by the sterodynamic land mask) and propagate that NaN to ALL components. + This ensures physical consistency: a site over land has no valid components. + + Parameters + ---------- + None + + Returns + ------- + None + """ + if not hasattr(self, "results") or not self.results: + return + + member_dim = "percentile" if self.output_percentiles is not None else "member" + spatial_slices = [ + comp.isel({member_dim: 0, "time": 0}) for comp in self.results.values() + ] + + # Site is valid if it is non-NaN in ALL components + stacked_slices = xr.concat(spatial_slices, dim="component") + valid_site_mask = stacked_slices.notnull().all(dim="component").compute() + + # Inform user of land mask + masked_sites = [ + site + for site, is_valid in zip(self.site_names, valid_site_mask.values) + if not is_valid + ] + if masked_sites: + logger.warning( + f"The following site(s) may be over land: {', '.join(masked_sites)}. " + "Expect NaN values for all components. " + "Either try increasing your grid resolution or check your site coordinates." + ) + + # Apply the mask uniformly + for name in self.results.keys(): + self.results[name] = self.results[name].where(valid_site_mask) + + def run(self, member_seed: int = 42) -> dict[str, xr.DataArray]: + """ + Run the local model to generate site-specific projections. + + Parameters + ---------- + member_seed: int, optional + Seed for random number generation to ensure reproducibility of member sampling. Default is 42. + + Returns + ------- + dict + Dictionary of local projections for each component, where keys are component names and values are xarray DataArrays of shape (n_members, n_years, n_sites). + """ + seed_seq = np.random.SeedSequence(member_seed) + + logger.info( + f"Simulating {len(self.components)} sea-level components for {len(self.site_names)} sites..." + ) + + state = LocalState( + n_years=self.n_years, + n_members=self.num_members, + target_lats=self.target_lats, + target_lons=self.target_lons, + interpolation_method=self.interpolation_method, + output_percentiles=self.output_percentiles, + baseline_yrs=self.baseline_yrs, + ) + + child_seeds = seed_seq.spawn(len(self.components)) + comp_rngs = { + name: np.random.default_rng(s) + for name, s in zip(self.components.keys(), child_seeds) + } + + local_projections = {} + + console.print() + for name, comp in track( + self.components.items(), description="Localising components..." + ): + # Project method should return array of shape (members, time, sites) + lazy_projection = comp.project(state, comp_rngs[name]) + + # Rechunk to optimize for reading full time-series per site + lazy_projection = lazy_projection.rechunk({0: -1, 1: -1, 2: -1}) + local_projections[name] = lazy_projection + console.print() + + self.results = self._arr_to_xr(local_projections) + self._apply_universal_mask() + return self.results + + def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray: + """ + Sum the local components to get total sea-level change. + + Parameters + ---------- + components: dict + Dictionary of xarray DataArrays representing individual components. + + Returns + ------- + xr.DataArray + A single xarray DataArray representing the total sea-level projections, with appropriate attributes. + """ + total_rsl = xr.concat(components.values(), dim="component").sum( + dim="component", skipna=False + ) + total_rsl.attrs["units"] = "m" + components["total_rsl"] = total_rsl + return total_rsl diff --git a/profsea/components/core/spatial_model.py b/profsea/components/core/spatial_model.py new file mode 100644 index 0000000..1303754 --- /dev/null +++ b/profsea/components/core/spatial_model.py @@ -0,0 +1,250 @@ +from __future__ import annotations + +import logging +import warnings +from pathlib import Path + +import dask.array as da +import numpy as np +import xarray as xr +from rich.console import Console +from rich.progress import track + +from profsea.utils import fetch_zenodo_fingerprints, save_components +from profsea.utils.ui import print_spatial_preflight + +from .base import Component +from .state import SpatialState + +logger = logging.getLogger(__name__) +console = Console() +warnings.filterwarnings("ignore") + +PROFSEA_DIR = Path(__file__).resolve().parent.parent.parent +ZENODO_DOWNLOAD_LINK = ( + "https://zenodo.org/records/20427061/files/profsea-assets.zip?download=1" +) + + +class Spatial: + """Spatial sea level rise component emulator.""" + + def __init__( + self, + components: dict[str, Component], + grid_config: dict = None, + grid_interpolation: str = "linear", + end_year: int = 2301, + baseline_yrs: tuple = (1995, 2014), + output_percentiles: list | np.ndarray = [5, 17, 50, 83, 95], + dtype: np.dtype = np.float32, + ) -> None: + """ + Parameters + ---------- + components: dict + Dictionary of spatial components to include in the model. Keys should be the component names and values should be instances of Component subclasses. + grid_config: dict, optional + Dictionary defining the grid configuration with keys 'start_lon', 'end_lon', 'step_lon + 'start_lat', 'end_lat', 'step_lat'. If None, defaults to a 1-degree global grid. + grid_interpolation: str, optional + Interpolation method to use when interpolating patterns to the target grid. Default is 'linear'. + end_year: int, optional + The final year of the projections. Default is 2301. + baseline_yrs: tuple, optional + Tuple defining the start and end years of the baseline period for calculating anomalies. Default is (1995, 2014). + output_percentiles: list or np.ndarray, optional + List or array of percentiles to sample from the ensemble for output. If None, outputs all members. Default is [5, 17, 50, 83, 95]. + dtype: np.dtype, optional + Data type for the output arrays. Default is np.float32. + """ + # Define the path where the data should live + + fetch_zenodo_fingerprints( + zenodo_url=ZENODO_DOWNLOAD_LINK, + data_dir=PROFSEA_DIR, + expected_folder_name="profsea-assets", + ) + + self.components = components + self.end_year = end_year + self.baseline_yrs = baseline_yrs + self.output_percentiles = output_percentiles + self.start_year = 2006 + self.n_years = self.end_year - self.start_year + self.dtype = dtype + + if self.output_percentiles is not None and len(self.output_percentiles) > 0: + self.num_members = len(self.output_percentiles) + else: + self.num_members = next( + iter(self.components.values()) + ).global_projection.shape[0] + + if grid_config is None: + grid_config: dict = { + "start_lon": -179.5, + "end_lon": 179.5, + "step_lon": 1.0, + "start_lat": -89.5, + "end_lat": 89.5, + "step_lat": 1.0, + } + + # Define the grid coordinates + self.grid_lons = np.arange( + grid_config["start_lon"], + grid_config["end_lon"] + grid_config["step_lon"], + grid_config["step_lon"], + ) + self.grid_lats = np.arange( + grid_config["start_lat"], + grid_config["end_lat"] + grid_config["step_lat"], + grid_config["step_lat"], + ) + + logger.info( + f"Baseline period = {self.baseline_yrs[0]} to {self.baseline_yrs[1]}" + ) + + # Log the size of each component and provide an estimate of their memory usage + # Output shape will be (num_members, n_years, n_lats, n_lons) + bytes_per_element = 8 # Assuming float64. Use 4 if strictly float32. + + future_size = ( + self.num_members + * self.n_years + * len(self.grid_lats) + * len(self.grid_lons) + * bytes_per_element + ) / 1e9 + + for name, comp in self.components.items(): + # Warn if memory usage is going to be large + if future_size > 20: + logger.warning( + f"[bold red]The output array for component " + f"[bold blue]'{name}'[/bold blue] requires a large amount " + f"of memory [bold blue]({future_size:.2f} GB)[/bold blue]. " + f"Consider reducing the number of members, the grid " + f"resolution or take percentiles.[/bold red]" + ) + + # Instance method! + save_components = save_components + + def _arr_to_xr(self, arr_dict: dict[str, da.Array]) -> dict[str, xr.DataArray]: + """ + Convert a dictionary of Dask arrays to a dictionary of xarray DataArrays with appropriate coordinates and metadata. + + Parameters + ---------- + arr_dict: Dict[str, da.Array] + Dictionary where keys are component names and values are Dask arrays of shape (n_members, n_years, n_lats, n_lons). + + Returns + ------- + Dict[str, xr.DataArray] + Dictionary where keys are component names and values are xarray DataArrays with dimensions (member, time, lat, lon) and appropriate coordinates. + """ + xr_dict = {} + member_dim = "percentile" if self.output_percentiles is not None else "member" + + for name, arr in arr_dict.items(): + xr_dict[name] = xr.DataArray( + arr, + dims=[member_dim, "time", "lat", "lon"], + coords={ + member_dim: self.output_percentiles + if self.output_percentiles is not None + else np.arange(arr.shape[0]), + "time": np.arange(self.start_year, self.start_year + arr.shape[1]), + "lat": self.grid_lats, + "lon": self.grid_lons, + }, + attrs={ + "units": "m", + "long_name": f"Regional {name} sea-level projections", + "source": "ProFSea-Climate v0.1", + }, + ) + + return xr_dict + + def run(self, member_seed: int = 42) -> None: + """ + Run the spatial model to generate regional sea level projections for each component. + + Parameters + ---------- + member_seed: int, optional + Seed for random number generation to ensure reproducibility of member sampling. Default is 42. + + Returns + ------- + Dict[str, da.Array] + Dictionary of spatial projections for each component, where keys are component names and values are Dask arrays of shape (n_members, n_years, n_lats, n_lons). + """ + print_spatial_preflight(self) + seed_seq = np.random.SeedSequence(member_seed) + + logger.info( + f"Simulating {len(self.components)} sea-level components: {', '.join(self.components.keys())}" + ) + + state = SpatialState( + n_years=self.n_years, + n_members=self.num_members, + grid_lats=self.grid_lats, + grid_lons=self.grid_lons, + grid_interpolation="linear", + output_percentiles=self.output_percentiles, + baseline_yrs=self.baseline_yrs, + dtype=self.dtype, + ) + + child_seeds = seed_seq.spawn(len(self.components)) + comp_rngs = { + name: np.random.default_rng(s) + for name, s in zip(self.components.keys(), child_seeds) + } + + spatial_projections = {} + console.print() # Add a blank line for better readability in the console output + for name, comp in track( + self.components.items(), description="Spatialising components..." + ): + lazy_projection = comp.project(state, comp_rngs[name]) + + # Rechunk before saving to optimize memory during writing + lazy_projection = lazy_projection.rechunk({0: -1, 1: -1, 2: 10, 3: 10}) + spatial_projections[name] = lazy_projection + console.print() + + # Put into xarray datasets for easier saving and metadata handling + spatial_projections_xr = self._arr_to_xr(spatial_projections) + self.results = spatial_projections_xr + return self.results + + def sum_components(self, components: dict[str, xr.DataArray]) -> xr.DataArray: + """ + Sum the spatial components to get total sea-level change. + + Parameters + ---------- + components: Dict[str, xr.DataArray] + Dictionary of spatial component DataArrays. + + Returns + ------- + xr.DataArray + DataArray of the summed spatial projections. + """ + # Using xr.concat preserves all dimensions and coordinates, and summing + # along the new dimension handles the underlying dask arrays cleanly. + total_rsl = xr.concat(components.values(), dim="component").sum( + dim="component", skipna=False + ) + total_rsl.attrs["units"] = "m" + components["total_rsl"] = total_rsl + return total_rsl diff --git a/profsea/components/core/state.py b/profsea/components/core/state.py new file mode 100644 index 0000000..442c731 --- /dev/null +++ b/profsea/components/core/state.py @@ -0,0 +1,52 @@ +from __future__ import annotations + +from dataclasses import dataclass + +import numpy as np + + +@dataclass +class ClimateState: + """ClimateState context object to hold relevant state information for SLR projections.""" + + scenario: str + T_ens: np.ndarray + T_int_ens: np.ndarray + T_int_med: np.ndarray + fraction: np.ndarray # shared correlation array + palmer_method: bool # whether to use Palmer method for time projection + endofAR5: int + endofhistory: int + end_yr: int + nyr: int + nt: int + num_members: int + dtype: np.dtype = np.float32 + + +@dataclass +class SpatialState: + """SpatialState context object to hold relevant state information for spatial SLR projections.""" + + grid_lats: np.ndarray + grid_lons: np.ndarray + n_years: int + n_members: int + grid_interpolation: str + output_percentiles: list[int] | np.ndarray + baseline_yrs: tuple[int, int] + dtype: np.dtype = np.float32 + + +@dataclass +class LocalState: + """LocalState context object to hold relevant state information for local SLR projections.""" + + target_lats: list[float] + target_lons: list[float] + n_years: int + n_members: int + interpolation_method: str + output_percentiles: list[int] | np.ndarray + baseline_yrs: tuple[int, int] + dtype: np.dtype = np.float32 diff --git a/profsea/components/core/time_projection.py b/profsea/components/core/time_projection.py new file mode 100644 index 0000000..b1372e8 --- /dev/null +++ b/profsea/components/core/time_projection.py @@ -0,0 +1,97 @@ +from __future__ import annotations + +from collections.abc import Sequence + +import numpy as np + +from profsea.components.core.global_model import ClimateState + + +def time_projection( + state: ClimateState, + startratemean: float, + startratepm: float, + final, + rng: np.random.Generator, + nfinal: int = 1, + fraction: np.ndarray = None, +) -> np.ndarray: + """Project a quantity which is a quadratic function of time. + + Parameters + ---------- + startratemean: float + Rate of GMSLR at the start in mm yr-1. + startratepm: float + Start rate error in mm yr-1. + final: list | np.ndarray + Likely range in m for GMSLR at the end of AR5. + nfinal: int + Number of years at the end over which final is a time-mean. + fraction: np.ndarray + Random numbers in the range 0 to 1. + + Returns + ------- + np.ndarray + Projection of the quantity. + """ + # Create a field of elapsed time since start in years + timeendofAR5 = state.endofAR5 - state.endofhistory + 1 + time = np.arange(state.end_yr - state.endofhistory, dtype=state.dtype) + 1 + + if fraction is None: + fraction = rng.random((state.num_members, state.nt), dtype=state.dtype) + elif fraction.size != state.num_members * state.nt: + raise ValueError("fraction is the wrong size") + + fraction = fraction.reshape(state.num_members, state.nt) + + # Convert inputs to startrate (m yr-1) and afinal (m), where both are + # arrays with the size of fraction + startrate = ((startratemean + startratepm * np.array([-1, 1])) * 1e-3).astype( + state.dtype + ) # convert mm yr-1 to m yr-1 + finalisrange = isinstance(final, Sequence) + + if finalisrange: + if len(final) != 2: + raise ValueError("final range is the wrong size") + afinal = (1 - fraction) * final[0] + fraction * final[1] + else: + if final.shape != fraction.shape: + raise ValueError("final array is the wrong shape") + afinal = final + + startrate = (1 - fraction) * startrate[0] + fraction * startrate[1] + + # For terms where the rate increases linearly in time t, we can write GMSLR as + # S(t) = a*t**2 + b*t + # where a is 0.5*acceleration and b is start rate. Hence + # a = S/t**2-b/t = (S-b*t)/t**2 + # If nfinal=1, the following two lines are equivalent to + # halfacc=(final-startyr*nyr)/nyr**2 + finalyr = ( + np.arange(nfinal, dtype=state.dtype) - nfinal + 94 + 1 + ) # last element ==nyr + halfacc = (afinal - startrate * finalyr.mean()) / (finalyr**2).mean() + quadratic = halfacc[:, :, np.newaxis] * (time**2) + linear = startrate[:, :, np.newaxis] * time + + # If acceleration ceases for t>t0, the rate is 2*a*t0+b thereafter, so + # S(t) = a*t0**2 + b*t0 + (2*a*t0+b)*(t-t0) + # = a*t0*(2*t - t0) + b*t + # i.e. the quadratic term is replaced, the linear term unaffected + # The quadratic also = a*t**2-a*(t-t0)**2 + + if state.palmer_method: + y = halfacc[:, :, np.newaxis] * timeendofAR5 * ((2 * time) - timeendofAR5) + quadratic[:, :, 95:] = y[:, :, 95:] + + quadratic += linear + + quadratic = quadratic.reshape( + quadratic.shape[0] * quadratic.shape[1], quadratic.shape[2] + ) + + return quadratic diff --git a/profsea/components/global_/__init__.py b/profsea/components/global_/__init__.py new file mode 100644 index 0000000..fd27983 --- /dev/null +++ b/profsea/components/global_/__init__.py @@ -0,0 +1,18 @@ +from .antarctica import AntarcticaDynAR5, AntarcticaISMIP6, AntarcticaSMBAR5 +from .expansion import ThermalExpansion +from .glacier import Glacier +from .greenland import GreenlandAR6, GreenlandDynAR5, GreenlandSMBAR5 +from .landwater import LandwaterAR5, LandwaterAR6 + +__all__ = [ + "AntarcticaDynAR5", + "AntarcticaISMIP6", + "AntarcticaSMBAR5", + "ThermalExpansion", + "Glacier", + "GreenlandAR6", + "GreenlandDynAR5", + "GreenlandSMBAR5", + "LandwaterAR5", + "LandwaterAR6", +] diff --git a/profsea/components/global_/antarctica.py b/profsea/components/global_/antarctica.py new file mode 100644 index 0000000..4b77270 --- /dev/null +++ b/profsea/components/global_/antarctica.py @@ -0,0 +1,304 @@ +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import xarray as xr +from scipy.signal import fftconvolve +from scipy.stats import norm + +from profsea.components.core.base import Component +from profsea.components.core.global_model import ClimateState +from profsea.components.core.time_projection import time_projection + +PROFSEA_DIR = Path(__file__).resolve().parents[2] +AUX_DATA_DIR = PROFSEA_DIR / "aux_data" + +PARAMS_MAP = { + "wais": AUX_DATA_DIR / "wais_params_expanded.nc", + "eais": AUX_DATA_DIR / "eais_params_expanded.nc", + "peninsula": AUX_DATA_DIR / "pen_params_expanded.nc", +} + + +class AntarcticaISMIP6(Component): + """ + ISMIP6 2300 Antarctic ice-sheet emulator with two-timescale response. + + This emulation of the ISMIP6 ice-sheet model ensemble aims to capture + slow, fast and drift responses of the Antarctic ice sheet to GMST. + The slow response is modelled as the impulse response to temperature + forcing, convolved with two exponential decay kernels representing + different ice-sheet response timescales, depending on the region being modelled. + The fast response is modelled as a direct proportionality to the integrated + temperature anomaly, while the drift term captures any linear time-dependent + trends not explained by the temperature forcing. + + Parameters provided are for the WAIS, EAIS and AIS Peninsula regions, since each has + different response characteristics to warming. + """ + + def __init__(self, region: str): + """ + Parameters + ---------- + region: str + The Antarctic region to model. Must be one of "wais", "eais", or "peninsula". + """ + params_path = PARAMS_MAP.get(region.lower()) + if not params_path: + raise ValueError(f"Invalid region calibration: {region}") + + self.param_ds = xr.load_dataset(params_path) + self.n_models = self.param_ds.coords["model"].shape[0] + + def _impulse_response_term( + self, + tas_1d: np.ndarray, + tau1: float, + tau2: float, + gamma: float, + params_1d: np.ndarray, + dt: float, + state: ClimateState, + ) -> np.ndarray: + """Computes the slow response term for a single trajectory.""" + n_time = tas_1d.shape[0] + alpha1, alpha2, _ = params_1d + + forcing_base = np.sign(tas_1d) * (np.abs(tas_1d) ** gamma) + + # decay_factors1 = np.exp(-np.arange(n_time) * dt / tau1) * (dt / tau1) + t_arr = np.arange(n_time, dtype=state.dtype) + decay_factors1 = (t_arr * dt / tau1**2) * np.exp(-t_arr * dt / tau1) * dt + rate_delayed1 = fftconvolve(forcing_base, decay_factors1, mode="full")[:n_time] + term_slow1 = alpha1 * (np.cumsum(rate_delayed1) * dt) + + # decay_factors2 = np.exp(-np.arange(n_time) * dt / tau2) * (dt / tau2) + decay_factors2 = (t_arr * dt / tau2**2) * np.exp(-t_arr * dt / tau2) * dt + rate_delayed2 = fftconvolve(forcing_base, decay_factors2, mode="full")[:n_time] + term_slow2 = alpha2 * (np.cumsum(rate_delayed2) * dt) + + return term_slow1 + term_slow2 + + def _precompute_delayed_rates( + self, tas: np.ndarray, dt: float, state: ClimateState + ) -> tuple[np.ndarray, np.ndarray]: + """ + Precomputes the cumulative delayed rates for all models across all trajectories. + Returns two arrays of shape (n_models, n_traj, n_time). + """ + n_traj, n_time = tas.shape + cum_rate1 = np.zeros((self.n_models, n_traj, n_time), dtype=state.dtype) + cum_rate2 = np.zeros((self.n_models, n_traj, n_time), dtype=state.dtype) + t_arr = np.arange(n_time, dtype=state.dtype) + + for m_idx in range(self.n_models): + tau1 = np.array(self.param_ds.tau1[m_idx].values, dtype=state.dtype) + tau2 = np.array(self.param_ds.tau2[m_idx].values, dtype=state.dtype) + gamma = np.array(self.param_ds.gamma[m_idx].values, dtype=state.dtype) + + # Forcing base is model-specific due to gamma. Shape: (n_traj, n_time) + forcing_base = np.sign(tas) * (np.abs(tas) ** gamma) + + # Decay factors broadcasted to 2D: (1, n_time) + df1 = ((t_arr * dt / tau1**2) * np.exp(-t_arr * dt / tau1) * dt)[ + np.newaxis, : + ] + df2 = ((t_arr * dt / tau2**2) * np.exp(-t_arr * dt / tau2) * dt)[ + np.newaxis, : + ] + + # Vectorized convolution across all trajectories (axes=1) + rate_delayed1 = fftconvolve(forcing_base, df1, mode="full", axes=1)[ + :, :n_time + ] + cum_rate1[m_idx] = np.cumsum(rate_delayed1, axis=1) * dt + + rate_delayed2 = fftconvolve(forcing_base, df2, mode="full", axes=1)[ + :, :n_time + ] + cum_rate2[m_idx] = np.cumsum(rate_delayed2, axis=1) * dt + + return cum_rate1, cum_rate2 + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """ + Projects AIS response using empirical additive bootstrapping aligned + to the ClimateState ensemble size. + """ + tas = np.atleast_2d(np.squeeze(state.T_ens)) + n_traj, n_time = tas.shape + dt = 1.0 + nm = state.nt * state.num_members + + # 1. Precompute expensive convolutions for unique (Model x Trajectory) combos + cum_rate1, cum_rate2 = self._precompute_delayed_rates(tas, dt, state) + tas_int = np.cumsum(tas, axis=1) * dt + + # 2. Generate random model/residual assignments for all members + model_indices = rng.integers(0, self.n_models, size=nm) + all_residuals = np.asarray( + self.param_ds.param_residuals.values, dtype=state.dtype + ) + n_train_scenarios = all_residuals.shape[1] + residual_indices = rng.integers(0, n_train_scenarios, size=nm) + + # 3. Create flat mapping array to match the (Trajectory x Member) layout + # This groups by trajectory: [Traj0_Mem0...Traj0_Mem999, Traj1_Mem0...] + t_indices = np.repeat(np.arange(n_traj, dtype=np.int32), state.num_members) + + # NOTE: If your required grouping is interleaved [Traj0_Mem0, Traj1_Mem0...] + # uncomment the line below instead: + # t_indices = np.tile(np.arange(n_traj, dtype=state.dtype), state.num_members) + + # 4. Vectorized Parameter Extraction + general_p = np.asarray( + self.param_ds.general_params.values[model_indices], dtype=state.dtype + ) + sampled_residuals = all_residuals[model_indices, residual_indices, :] + total_params = general_p + sampled_residuals + + # Slice with [:, 0:1] to maintain a 2D shape (nm, 1) for broadcasting against time + alpha1 = total_params[:, 0:1] + alpha2 = total_params[:, 1:2] + beta = total_params[:, 2:3] + + # 5. Final Vectorized Assembly + term_slow = ( + alpha1 * cum_rate1[model_indices, t_indices] + + alpha2 * cum_rate2[model_indices, t_indices] + ) + term_fast = beta * tas_int[t_indices] + + return term_slow + term_fast + + +class AntarcticaDynAR5(Component): + """ + AR5 Antarctic ice-dynamics response to warming, as a function of + cumulative emissions or scenario. Following the implementation + as given by Jonathan Gregory's ar5gmslr (https://github.com/JonathanGregory/ar5gmslr). + + Requires cumulative emissions total to be specified for a given scenario, + or will default to scenario-based regression, based on rcp scenarios. + """ + + def __init__( + self, d_ant: float = (2.37 + 0.13) * 1e-3, cum_emissions_total: float = None + ): + self.d_ant = d_ant + self.cum_emissions_total = cum_emissions_total + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """Project Antarctic rapid ice-sheet dynamics contribution to GMSLR. + + Parameters + ---------- + fraction: np.ndarray + Random numbers for the dynamic contribution. + + Returns + ------- + np.ndarray + Antarctic rapid ice-sheet dynamics contribution to GMSLR. + """ + # This is a naive solution to calculating the AntDyn contribution + # for any given scenario. Basically linear regressions through existing data + # to find rough relationship between cumulative emissions and AntDyn contribution. + if self.cum_emissions_total: + upper = (0.000110 * self.cum_emissions_total) + 0.375 # in metres + lower = (1.363e-05 * self.cum_emissions_total) + 0.0392 # in metres + final = [lower, upper] + else: + lcoeff = dict( + rcp26=[-2.881, 0.923, 0.000], + rcp45=[-2.676, 0.850, 0.000], + rcp60=[-2.660, 0.870, 0.000], + rcp85=[-2.399, 0.860, 0.000], + ) + lcoeff = lcoeff[state.scenario] + + ascale = norm.ppf(state.fraction).astype(state.dtype) + final = np.exp(lcoeff[2] * ascale**2 + lcoeff[1] * ascale + lcoeff[0]) + final = final.reshape(state.num_members, state.nt) + return ( + time_projection( + state, + 0.41, + 0.20, + final, + rng, + fraction=state.fraction, + ) + + self.d_ant + ) + + +class AntarcticaSMBAR5(Component): + """ + AR5 Antarctic SMB contribution to GMSLR, as a function of global + mean surface temperature change. + + Following the implementation as given by Jonathan Gregory's ar5gmslr + (https://github.com/JonathanGregory/ar5gmslr). + """ + + def __init__(self): + pass + + def project( + self, + state: ClimateState, + rng: np.random.Generator, + ) -> np.ndarray: + """Project Antarctic SMB contribution to GMSLR. + + Parameters + ---------- + T_int_ens: np.ndarray + Ensemble of time-integral temperature anomaly timeseries. + fraction: np.ndarray + Random numbers for the SMB-dynamic feedback. + + Returns + ------- + antsmb: np.ndarray + Antarctic SMB contribution to GMSLR. + """ + # Conversion factor for Gt to m SLE + mSLEoGt = 1e12 / 3.61e14 * 1e-3 + # The following are [mean,SD] + pcoK = [5.1, 1.5] # % change in Ant SMB per K of warming from G&H06 + KoKg = [1.1, 0.2] # ratio of Antarctic warming to global warming from G&H06 + + # Generate a distribution of products of the above two factors + pcoKg = ( + pcoK[0] + + rng.standard_normal([state.num_members, state.nt], dtype=state.dtype) + * pcoK[1] + ) * ( + KoKg[0] + + rng.standard_normal([state.num_members, state.nt], dtype=state.dtype) + * KoKg[1] + ) + meansmb = 1923 # model-mean time-mean 1979-2010 Gt yr-1 from 13.3.3.2 + moaoKg = ( + -pcoKg * 1e-2 * meansmb * mSLEoGt + ) # m yr-1 of SLE per K of global warming + + if state.fraction is None: + fraction = rng.random((state.num_members, state.nt), dtype=state.dtype) + elif state.fraction.size != state.num_members * state.nt: + raise ValueError("fraction is the wrong size") + else: + fraction = state.fraction.reshape((state.num_members, state.nt)) + + smax = 0.35 # max value of S in 13.SM.1.5 + ainterfactor = 1 - fraction * smax + + z = moaoKg * ainterfactor + z = z[:, :, np.newaxis] + antsmb = z * state.T_int_ens + antsmb = antsmb.reshape(antsmb.shape[0] * antsmb.shape[1], antsmb.shape[2]) + return antsmb diff --git a/profsea/components/global_/expansion.py b/profsea/components/global_/expansion.py new file mode 100644 index 0000000..b6945ff --- /dev/null +++ b/profsea/components/global_/expansion.py @@ -0,0 +1,51 @@ +from __future__ import annotations + +import numpy as np + +from profsea.components.core.base import Component +from profsea.components.core.state import ClimateState +from profsea.utils import check_shapes + + +class ThermalExpansion(Component): + """ + Parameters and Attributes + ------------------------- + OHC_change: np.ndarray + Array of ocean heat content change values. + exp_efficiency: float + Sensitivity of thermosteric SLR to ocean heat content change. + """ + + def __init__(self, OHC_change: np.ndarray, distribution_scaler: float = 1.0): + self.OHC_change = OHC_change + self.distribution_scaler = distribution_scaler + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + self.OHC_change = np.asarray(self.OHC_change, dtype=state.dtype) + + # check the shape here + check_shapes(self.OHC_change, state.nyr) + + # Ensure OHC_change is 2D + if self.OHC_change.ndim > 2: + self.OHC_change = np.squeeze(self.OHC_change) + if self.OHC_change.ndim == 1: + self.OHC_change = np.expand_dims(self.OHC_change, axis=0) + + # Sensitivity of thermosteric SLR to ocean heat content change + # From Turner et al. (2023) + mean_eff = 0.113 + std_eff = 0.013 * self.distribution_scaler + + exp_efficiency = ( + rng.normal(loc=mean_eff, scale=std_eff, size=(state.nt, state.num_members)) + * 1e-24 + ).astype(state.dtype) # m/YJ + + ohc_3d = self.OHC_change[:, None, :] + # Efficiency shape: (nt, num_members, 1) + exp_efficiency_3d = exp_efficiency[:, :, None] + + expansion = ohc_3d * exp_efficiency_3d + return expansion.reshape(state.num_members * state.nt, state.nyr) diff --git a/profsea/components/global_/glacier.py b/profsea/components/global_/glacier.py new file mode 100644 index 0000000..4f159f9 --- /dev/null +++ b/profsea/components/global_/glacier.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +import numpy as np + +from profsea.components.core.base import Component +from profsea.components.core.global_model import ClimateState + + +class Glacier(Component): + def __init__(self, glaciermip: int = 2): + self.glaciermip = glaciermip + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """Project glacier contribution to GMSLR. + + Returns + ------- + glacier: np.ndarray + Glacier contribution to GMSLR. + """ + tas = state.T_ens + if tas.ndim > 2: + tas = np.squeeze(tas) + if tas.ndim == 1: + tas = np.expand_dims(tas, axis=0) + + # glaciermip -- False => AR5 parameters, 1 => fit to Hock et al. (2019), + # 2 => fit to Marzeion et al. (2020) + dmzdtref = 0.95 # mm yr-1 in Marzeion's CMIP5 ensemble mean for AR5 ref period + dmz = ( + dmzdtref * (state.endofhistory - 1996) * 1e-3 + ) # m from glacier at start wrt AR5 ref period + glmass = 412.0 - 96.3 # initial glacier mass, used to set a limit, from Tab 4.2 + glmass = 1e-3 * glmass # m SLE + + if self.glaciermip == 1: + glparm = [ + dict(name="SLA2012", factor=3.39, exponent=0.722, cvgl=0.15), + dict(name="MAR2012", factor=4.35, exponent=0.658, cvgl=0.13), + dict(name="GIE2013", factor=3.57, exponent=0.665, cvgl=0.13), + dict(name="RAD2014", factor=6.21, exponent=0.648, cvgl=0.17), + dict(name="GloGEM", factor=2.88, exponent=0.753, cvgl=0.13), + ] + elif self.glaciermip == 2: + glparm = [ + dict(name="GLIMB", factor=3.70, exponent=0.662, cvgl=0.206), + dict(name="GloGEM", factor=4.08, exponent=0.716, cvgl=0.161), + dict(name="JULES", factor=5.50, exponent=0.564, cvgl=0.188), + dict(name="Mar-12", factor=4.89, exponent=0.651, cvgl=0.141), + dict(name="OGGM", factor=4.26, exponent=0.715, cvgl=0.164), + dict(name="RAD2014", factor=5.18, exponent=0.709, cvgl=0.135), + dict(name="WAL2001", factor=2.66, exponent=0.730, cvgl=0.206), + ] + elif not self.glaciermip: + glparm = [ + dict(name="Marzeion", factor=4.96, exponent=0.685, cvgl=0.20), + dict(name="Radic", factor=5.45, exponent=0.676, cvgl=0.20), + dict(name="Slangen", factor=3.44, exponent=0.742, cvgl=0.20), + dict(name="Giesen", factor=3.02, exponent=0.733, cvgl=0.20), + ] + else: + raise KeyError( + "glaciermip must be False (AR5 parameters), 1 (Hock et al., 2019), or 2 (Marzeion et al., 2020)" + ) + + ngl = len(glparm) + model_indices = rng.integers(0, ngl, size=(state.nt, state.num_members)) + + base_factors = np.array([p["factor"] for p in glparm], dtype=state.dtype) + base_exponents = np.array([p["exponent"] for p in glparm], dtype=state.dtype) + base_cvgls = np.array([p["cvgl"] for p in glparm], dtype=state.dtype) + + factors = base_factors[model_indices][:, :, None] + exponents = base_exponents[model_indices][:, :, None] + cvgls = base_cvgls[model_indices][:, :, None] + + r = rng.standard_normal((state.nt, state.num_members), dtype=state.dtype)[ + :, :, None + ] + + T_int_ens_3d = state.T_int_ens[:, None, :] + # Median shape: (1, 1, nyr) + T_int_med_3d = state.T_int_med[None, None, :] + + zgl = self._project_glacier1(T_int_ens_3d, factors, exponents, state) + + # Passes (1, 1, nyr) + (nt, nm, 1) -> Returns (nt, nm, nyr) + mgl = self._project_glacier1(T_int_med_3d, factors, exponents, state) + + # 6. Apply variance and clip using 3D matrix math + glacier = zgl + (mgl * r * cvgls) + glacier += dmz + np.clip(glacier, None, glmass, out=glacier) + + # 7. Flatten to standard 2D output for easy summation + return glacier.reshape(state.nt * state.num_members, state.nyr) + + def _project_glacier1( + self, + T_int: np.ndarray, + factor: np.ndarray, + exponent: np.ndarray, + state: ClimateState, + ) -> np.ndarray: + """Project glacier contribution by one glacier method.""" + scale = 1e-3 # mm to m + # np.where works perfectly with n-dimensional broadcasting + return scale * factor * (np.where(T_int < 0, 0, T_int) ** exponent) diff --git a/profsea/components/global_/greenland.py b/profsea/components/global_/greenland.py new file mode 100644 index 0000000..0ecc215 --- /dev/null +++ b/profsea/components/global_/greenland.py @@ -0,0 +1,222 @@ +from __future__ import annotations + +import functools +from pathlib import Path + +import numpy as np +import pandas as pd +from scipy.stats import truncnorm + +from profsea.components.core.base import Component +from profsea.components.core.global_model import ClimateState +from profsea.components.core.time_projection import time_projection + + +@functools.lru_cache(maxsize=1) +def load_greenland_calibration(): + """Loads the CSV once and keeps it in memory.""" + path = Path(__file__).parents[2] / "aux_data" / "ISMIP_GIS_calibration.csv" + return pd.read_csv(path) + + +class GreenlandAR6(Component): + def __init__(self): + self.df = load_greenland_calibration() + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """Project Greenland ice-sheet contribution to GMSLR. + This follows the IPCC AR6 methodology as closely as possible. + Projections are relative to 1996-2014 baseline. + + Returns + ------- + np.ndarray + Total GIS contribution to GMSLR. + """ + tas = state.T_ens + if tas.ndim > 2: + tas = np.squeeze(tas) + if tas.ndim == 1: + tas = np.expand_dims(tas, axis=0) + + nt = tas.shape[0] + time_delta = np.arange(state.nyr, dtype=state.dtype) + + df = self.df + n_models = len(df) + + model_indices = rng.integers(0, n_models, size=(nt, state.num_members)) + + # Extract parameters and reshape to 3D: (nt, nm, 1) + b0 = df["b0"].values[model_indices][:, :, None].astype(state.dtype) + b1 = df["b1"].values[model_indices][:, :, None].astype(state.dtype) + b2 = df["b2"].values[model_indices][:, :, None].astype(state.dtype) + b3 = df["b3"].values[model_indices][:, :, None].astype(state.dtype) + b4 = df["b4"].values[model_indices][:, :, None].astype(state.dtype) + b5 = df["b5"].values[model_indices][:, :, None].astype(state.dtype) + + # GIS trend values taken from FACTS GitHub repo + trend_mean = 0.19 + trend_std = 0.1 + + # Calculate trend contribution distribution + a_bound = (0.0 - trend_mean) / trend_std + b_bound = (99999.9 - trend_mean) / trend_std # Or just np.inf + trend = truncnorm.ppf( + rng.random((nt, state.num_members)), + a=a_bound, + b=b_bound, + loc=trend_mean, + scale=trend_std, + ).astype(state.dtype) + + trend_sle = (trend[:, :, None] * time_delta[None, None, :]) * 1e-3 + + tas_3d = tas[:, None, :] + + # Calculate GIS contribution rate + dsle = ( + b0 + + (b1 * tas_3d) + + (b2 * tas_3d**2) + + (b3 * tas_3d**3) + + (b4 * time_delta[None, None, :]) + + (b5 * time_delta[None, None, :] ** 2) + ) + + # Now integrate + sle_ens = np.cumsum(dsle, axis=2) * 1e-3 # convert from mm to m + + sle_ens += trend_sle + + # Persist 2100 rate of changeg + if state.end_yr >= 2100: + idx_2100 = 94 + rate = np.diff(sle_ens, axis=2)[:, :, idx_2100 - 1] + sle_ens[:, :, idx_2100 + 1 :] = sle_ens[:, :, idx_2100 : idx_2100 + 1] + ( + rate[:, :, None] * time_delta[None, None, 1 : state.nyr - idx_2100] + ) + + sle_ens = sle_ens.reshape((state.num_members * state.nt, state.nyr)) + return sle_ens + + +class GreenlandSMBAR5(Component): + """ + AR5 Greenland SMB contribution to GMSLR. + """ + + def __init__(self): + self.fgreendyn = 0.5 + self.dgreen = (3.21 - 0.30) * 1e-3 + self.mSLEoGt = 1e12 / 3.61e14 * 1e-3 + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """Project Greenland SMB contribution to GMSLR. + + Parameters + ---------- + state: ClimateState + State object containing relevant information for the projection. + rng: np.random.Generator + Random number generator. + + Returns + ------- + greensmb: np.ndarray + Greenland SMB contribution to GMSLR. + + """ + dtgreen = -0.146 # Delta_T of Greenland ref period wrt AR5 ref period + fnlogsd = 0.4 # random methodological error of the log factor + febound = [1, 1.15] # bounds of uniform pdf of SMB elevation feedback factor + + # random log-normal factor + fn = np.exp(rng.standard_normal(state.num_members, dtype=state.dtype) * fnlogsd) + # elevation feedback factor + fe = ( + rng.random(state.num_members, dtype=state.dtype) * (febound[1] - febound[0]) + + febound[0] + ) + ff = fn * fe + + ztgreen = state.T_ens - dtgreen + + greensmb = ff[:, np.newaxis, np.newaxis] * self._fettweis(ztgreen) + + if state.palmer_method and state.end_yr > state.endofAR5: + greensmb[:, :, 95:] = greensmb[:, :, 94:95] + + greensmb = np.cumsum(greensmb, axis=-1) + + greensmb += (1 - self.fgreendyn) * self.dgreen + + greensmb = greensmb.reshape( + greensmb.shape[0] * greensmb.shape[1], greensmb.shape[2] + ) + return greensmb + + def _fettweis(self, ztgreen: np.ndarray) -> np.ndarray: + """Calculate Greenland SMB in m yr-1 SLE from global mean temperature + anomaly, using Eq 2 of Fettweis et al. (2013). + + Parameters + ---------- + ztgreen: np.ndarray + Global mean temperature anomaly. + + Returns + ------- + np.ndarray + Greenland SMB in m yr-1 SLE. + """ + return ( + 71.5 * ztgreen + 20.4 * (ztgreen**2) + 2.8 * (ztgreen**3) + ) * self.mSLEoGt + + +class GreenlandDynAR5(Component): + """ + AR5 Greenland ice-sheet dynamics contribution to GMSLR. + + NOTE: This is not scenario independent. It will run with either rcp85/ssp585 related projections, + or will default to a temperature independent projection based on AR5. + + This is based on Jonathan Gregory's AR5 implmentation, + which can be found at https://github.com/JonathanGregory/ar5gmslr + + """ + + def __init__( + self, + ): + self.fgreendyn = 0.5 + self.dgreen = (3.21 - 0.30) * 1e-3 + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """Project Greenland rapid ice-sheet dynamics contribution to GMSLR. + + Parameters + ---------- + state: ClimateState + State object containing relevant information for the projection. + rng: np.random.Generator + Random number generator. + + Returns + ------- + np.ndarray + Greenland rapid ice-sheet dynamics contribution to GMSLR. + """ + # For SMB+dyn during 2005-2010 Table 4.6 gives 0.63+-0.17 mm yr-1 (5-95% range) + # For dyn at 2100 Chapter 13 gives [20,85] mm for rcp85, [14,63] mm otherwise + if state.scenario in ["rcp85", "ssp585"]: + finalrange = [0.020, 0.085] + else: + finalrange = [0.014, 0.063] + return ( + time_projection( + state, 0.63 * self.fgreendyn, 0.17 * self.fgreendyn, finalrange, rng + ) + + self.fgreendyn * self.dgreen + ) diff --git a/profsea/components/global_/landwater.py b/profsea/components/global_/landwater.py new file mode 100644 index 0000000..cd434c2 --- /dev/null +++ b/profsea/components/global_/landwater.py @@ -0,0 +1,86 @@ +from __future__ import annotations + +import functools +from pathlib import Path + +import numpy as np +import xarray as xr + +from profsea.components.core.base import Component +from profsea.components.core.global_model import ClimateState +from profsea.components.core.time_projection import time_projection + + +@functools.lru_cache(maxsize=1) +def load_landwater_projection(): + """Loads the NetCDF once and keeps the VALUES in memory.""" + path = ( + Path(__file__).parents[2] / "aux_data" / "ssp_global_landwater_projections.nc" + ) + with xr.open_dataset(path) as ds: + ds.load() + return ds + + +class LandwaterAR6(Component): + def __init__(self): + self.lw_ds = load_landwater_projection() + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """Project land water storage contribution to GMSLR. + This follows the IPCC AR6 methodology as closely as possible. + Projections are relative to 1996-2014 baseline. + + Returns + ------- + np.ndarray + Land water storage contribution to GMSLR. + """ + self.lw_ds = self.lw_ds.astype(state.dtype) + + # Interpolate to annual projections + interp_ds = ( + self.lw_ds.interp(years=np.arange(2005, 2301, 1), method="linear") + .squeeze() + .astype(state.dtype) + ) + + # Base array: Shape (n_samples_in_nc, 296) + lw_base = interp_ds["sea_level_change"].values * 1e-3 + n_samples_nc = lw_base.shape[0] + + # Sample! + sample_indices = rng.integers( + 0, n_samples_nc, size=(state.nt, state.num_members) + ) + + # Resulting shape: (nt, nm, nyr) + lw_ens = lw_base[sample_indices, 1 : state.nyr + 1] + return lw_ens.reshape(state.nt * state.num_members, state.nyr) + + +class LandwaterAR5(Component): + def __init__(self): + self.startratemean = 0.38 + self.startratepm = 0.49 - 0.38 + + def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray: + """Old projection function. Project land water storage + contribution to GMSLR. + + Returns + ------- + np.ndarray + Land water storage contribution to GMSLR. + """ + + # The rate at start is the one for 1993-2010 from the budget table. + # The final amount is the mean for 2081-2100. + nyr = ( + state.endofAR5 - 2081 + 1 + ) # number of years of the time-mean of the final amount + final = [-0.01, 0.09] # AR5 + + return time_projection( + state, self.startratemean, self.startratepm, final, rng, nfinal=nyr + ) diff --git a/profsea/components/spatial/__init__.py b/profsea/components/spatial/__init__.py new file mode 100644 index 0000000..f08d4f2 --- /dev/null +++ b/profsea/components/spatial/__init__.py @@ -0,0 +1,5 @@ +from .fingerprint import Fingerprint +from .gia import GIA +from .sterodynamic import SterodynamicCMIP5, SterodynamicCMIP6 + +__all__ = ["Fingerprint", "GIA", "SterodynamicCMIP5", "SterodynamicCMIP6"] diff --git a/profsea/components/spatial/fingerprint.py b/profsea/components/spatial/fingerprint.py new file mode 100644 index 0000000..8875b15 --- /dev/null +++ b/profsea/components/spatial/fingerprint.py @@ -0,0 +1,174 @@ +from __future__ import annotations + +from pathlib import Path + +import dask.array as da +import numpy as np +import xarray as xr + +from profsea.components.core.base import SpatialComponent +from profsea.components.core.state import SpatialState +from profsea.utils import sample_members_2D + +PROFSEA_DIR = Path(__file__).resolve().parents[2] +FP_DIR = PROFSEA_DIR / "profsea-assets" / "grd-fingerprints" + +FP_PATH_MAP = { + "greendyn": [ + FP_DIR / "greendyn_klemann.nc", + FP_DIR / "greendyn_slangen.nc", + FP_DIR / "greendyn_spada.nc", + ], + "greensmb": [ + FP_DIR / "greensmb_klemann.nc", + FP_DIR / "greensmb_slangen.nc", + FP_DIR / "greensmb_spada.nc", + ], + "greenland": [FP_DIR / "greenland_ar6.nc"], + "landwater": [FP_DIR / "landwater_slangen.nc"], + "wais": [FP_DIR / "wais.nc"], + "eais": [FP_DIR / "eais.nc"], + "antdyn": [ + FP_DIR / "antdyn_klemann.nc", + FP_DIR / "antdyn_slangen.nc", + FP_DIR / "antdyn_spada.nc", + ], + "antsmb": [ + FP_DIR / "antsmb_klemann.nc", + FP_DIR / "antsmb_slangen.nc", + FP_DIR / "antsmb_spada.nc", + ], + "glacier": [ + FP_DIR / "glacier_klemann.nc", + FP_DIR / "glacier_slangen.nc", + FP_DIR / "glacier_spada.nc", + ], +} + + +class Fingerprint(SpatialComponent): + """ + Spatial fingerprint component that applies spatial patterns to global projections. + """ + + def __init__( + self, + global_projection: xr.DataArray, + fingerprint_component: str, + fingerprint_paths: str | Path | list[str | Path] = None, + scaling_factor: float = 1.0, + sample_spatial: bool = False, + ) -> None: + """ + Parameters + ---------- + global_projection: xr.DataArray + A 2D array (members x years) of global projections to apply the fingerprints to. + fingerprint_paths: str, Path, or list of str/Path + Path(s) to NetCDF files containing the spatial fingerprint patterns. Each file should contain a DataArray with dimensions (lat, lon). + scaling_factor: float, optional + Optional multiplier to apply to the fingerprint patterns (e.g., to convert from m to mm). Default is 1.0 (no scaling). + sample_spatial: bool, optional + If True, randomly sample a different fingerprint pattern for each member. If False, use the mean of all provided fingerprints for all members (storyline mode). Default is False. + """ + self._global_projection = da.from_array(global_projection.data, chunks="auto") + self.scaling_factor = scaling_factor + self.sample_spatial = sample_spatial + self.fingerprint_component = fingerprint_component + + # Default paths if not provided (can be overridden by user input) + if fingerprint_paths is None: + # Determine default paths based on the component type + try: + self.fp_paths = [Path(p) for p in FP_PATH_MAP[fingerprint_component]] + except KeyError: + raise ValueError( + f"No default fingerprint paths found for fingerprint component '{fingerprint_component}'. Please provide explicit paths." + ) + # Normalize the input to always be a list of Path objects + elif isinstance(fingerprint_paths, (str, Path)): + self.fp_paths = [Path(fingerprint_paths)] + else: + self.fp_paths = [Path(p) for p in fingerprint_paths] + + if fingerprint_paths is not None: + for p in self.fp_paths: + if not p.exists(): + raise FileNotFoundError(f"Missing fingerprint file: {p}") + + @property + def global_projection(self): + return self._global_projection + + def _load_and_interpolate(self, state: SpatialState) -> da.Array: + """ + Lazily load and regrid all provided fingerprints. + + Parameters + ---------- + state: SpatialState + The state object containing the target grid information. + + Returns + ------- + da.Array + """ + grids = [] + for path in self.fp_paths: + fp_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45}) + fp_interp = self.extract_spatial(fp_da, state) + grids.append(fp_interp.data * self.scaling_factor) + + # Stack them into a 3D array: (n_fingerprints, lat, lon) + return da.stack(grids, axis=0) + + def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array: + """ + Calculate the spatial projection by applying the fingerprints to the global projection. + + Parameters + ---------- + state: SpatialState + The state object containing the target grid information and number of members. + rng: np.random.Generator + Random number generator for sampling fingerprints if sample_spatial is True. + + Returns + ------- + da.Array + A 4D array of shape (members, years, lat, lon) containing the spatial projections for each member and year. + """ + fps = self._load_and_interpolate(state) # Shape: (n_fps, lat, lon) + spatial_shape = fps.shape[1:] + + # Handle the global projection + if state.output_percentiles is not None: + global_proj = sample_members_2D( + self.global_projection, state.output_percentiles + ) + else: + global_proj = self.global_projection + + # Determine the spatial fingerprint for each member + n_fps = fps.shape[0] + + if n_fps == 1: + # Only one fingerprint available + selected_fps = da.broadcast_to( + fps[0], + (state.n_members, *spatial_shape), + ) + elif self.sample_spatial: + # Probabilistic mode: pick a random fingerprint per member + fp_indices = rng.integers(0, n_fps, size=state.n_members) + selected_fps = fps[fp_indices, :, :] + else: + # Storyline mode: take the mean of the available fingerprints + mean_fp = da.nanmean(fps, axis=0) + selected_fps = da.broadcast_to( + mean_fp, + (state.n_members, *spatial_shape), + ) + + # Broadcast and multiply: (members, years) * (members, lat, lon) + return self.broadcast_spatiotemporal(global_proj, selected_fps) diff --git a/profsea/components/spatial/gia.py b/profsea/components/spatial/gia.py new file mode 100644 index 0000000..5821b1d --- /dev/null +++ b/profsea/components/spatial/gia.py @@ -0,0 +1,138 @@ +from __future__ import annotations + +from pathlib import Path + +import dask.array as da +import numpy as np +import xarray as xr + +from profsea.components.core.base import SpatialComponent +from profsea.components.core.state import SpatialState + +PROFSEA_DIR = Path(__file__).resolve().parents[2] +GIA_DIR = PROFSEA_DIR / "profsea-assets" / "gia" + + +class GIA(SpatialComponent): + """ + Handles Glacial Isostatic Adjustment. + GIA accumulates linearly over time and has no global warming driver input. + Supports loading single files, lists of files, or directories of files. + """ + + def __init__( + self, + gia_dir: str | Path = None, + sample_spatial: bool = False, + ) -> None: + """ + Parameters + ---------- + gia_dir: str, Path, or list of str/Path + Path to a directory containing GIA files. + sample_spatial: bool, optional + Whether to sample spatial patterns probabilistically. Default is False. + """ + self.sample_spatial = sample_spatial + + if gia_dir is None: + self.gia_dir = GIA_DIR + else: + self.gia_dir = Path(gia_dir) + + # Dummy property required by the base Spatial architecture + self._global_projection = da.zeros((1, 1)) + + @property + def global_projection(self): + return self._global_projection + + def _load_and_interpolate_rates(self, state: SpatialState) -> da.Array: + """ + Lazily loads all GIA files, regrids them, and stacks them into a + single 3D array of shape (total_models, lat, lon). + + Parameters + ---------- + state: SpatialState + The spatial state containing the target grid information. + + Returns + ------- + da.Array + A Dask array of shape (total_models, lat, lon) containing the regridded GIA rates. + """ + gia_paths = list(self.gia_dir.glob("*.nc")) + + if not gia_paths: + raise FileNotFoundError(f"No GIA NetCDF files found in {self.gia_dir}") + + grids = [] + for path in gia_paths: + gia_da = xr.open_dataarray(path, chunks={"lat": 45, "lon": 45}) + interp_da = self.extract_spatial(gia_da, state) + data = interp_da.data + + # Determine expected spatial dims based on state + spatial_dims = 2 if hasattr(state, "grid_lats") else 1 + + # If the raw file lacks a 'model' dimension, prepend it + if data.ndim == spatial_dims: + data = data[None, ...] + + grids.append(data) + + # Concatenate along the model axis (axis 0) + return da.concatenate(grids, axis=0) + + def project(self, state: SpatialState, rng: np.random.Generator) -> da.Array: + """ + Project the GIA component by multiplying the accumulation time vector with the spatial rates. + + Parameters + ---------- + state: SpatialState + The spatial state containing the target grid information. + rng: np.random.Generator + Random number generator for sampling GIA models if sample_spatial is True. + + Returns + ------- + da.Array + A 4D array of shape (members, years, lat, lon) containing the spatial projections for each member and year. + """ + gia_rates = self._load_and_interpolate_rates(state) + n_patterns = gia_rates.shape[0] + + # Dynamically capture spatial shape (site,) or (lat, lon) + spatial_shape = gia_rates.shape[1:] + + # Calculate the accumulation time vector (mm/yr to m/yr) + midyr = ( + state.baseline_yrs[1] - state.baseline_yrs[0] + 1 + ) * 0.5 + state.baseline_yrs[0] + Tdelta = 2006 - midyr + unit_series = (np.arange(state.n_years) + Tdelta) * 0.001 + + # Broadcast 1D time series to match expected (members, years) signature + temporal_array = da.broadcast_to(unit_series, (state.n_members, state.n_years)) + + # Handle sampling if required + if n_patterns == 1: + selected_gia = da.broadcast_to( + gia_rates[0], + (state.n_members, *spatial_shape), + ) + else: + if self.sample_spatial: + rgiai = rng.integers(n_patterns, size=state.n_members) + selected_gia = gia_rates[rgiai, ...] + else: + mean_gia = da.mean(gia_rates, axis=0) + selected_gia = da.broadcast_to( + mean_gia, + (state.n_members, *spatial_shape), + ) + + # Delegate dimensional multiplication to the base class + return self.broadcast_spatiotemporal(temporal_array, selected_gia) diff --git a/profsea/components/spatial/sterodynamic.py b/profsea/components/spatial/sterodynamic.py new file mode 100644 index 0000000..84c4e1f --- /dev/null +++ b/profsea/components/spatial/sterodynamic.py @@ -0,0 +1,210 @@ +from __future__ import annotations + +import logging +from pathlib import Path + +import dask.array as da +import numpy as np +import xarray as xr + +from profsea.components.core.base import SpatialComponent +from profsea.components.core.state import ClimateState +from profsea.utils import sample_members_2D + +logging.basicConfig(level=logging.WARNING) + +PROFSEA_DIR = Path(__file__).resolve().parents[2] +PATTERNS_DIR = PROFSEA_DIR / "profsea-assets" / "cmip6-patterns" + + +class SterodynamicCMIP6(SpatialComponent): + """ + Parameters and Attributes + ------------------------- + global_projection: np.ndarray + Array of global sea level rise projections to use as input for sterodynamic projection. + """ + + def __init__( + self, + global_projection: xr.DataArray, + patterns_dir: str = None, + sample_spatial: bool = False, + ) -> None: + """ + Parameters + ---------- + global_projection: xr.DataArray + A 2D array (members x years) of global projections to apply the fingerprints to. + patterns_dir: str, optional + Path to directory containing CMIP6 sterodynamic patterns. + sample_spatial: bool, optional + If True, randomly sample a different fingerprint pattern for each member. If False, use the mean of all provided fingerprints for all members (storyline mode). Default is False. + """ + # Convert to dask array for cheap as possible compute + self._global_projection = da.from_array(global_projection.data, chunks="auto") + self.sample_spatial = sample_spatial + + if patterns_dir is None: + self.patterns_dir = PATTERNS_DIR + else: + self.patterns_dir = Path(patterns_dir) + + @property + def global_projection(self): + return self._global_projection + + def _load_CMIP6_slopes(self) -> tuple[xr.DataArray, xr.DataArray]: + """ + Load in the CMIP6 slope coefficients. + + Parameters + ---------- + None + + Returns + ------- + xr.DataArray + A dask array of shape (n_models, n_lats, n_lons) containing the sterodynamic + fingerprint patterns (i.e., regression coefficients) for each CMIP6 model. + xr.DataArray + A dask array of shape (n_models, n_lats, n_lons) containing the land mask for + each CMIP6 model, if present. + """ + slope_files = sorted( + Path(self.patterns_dir).glob("*/zos_regression_ssp585_*.nc"), + key=lambda p: p.name, + ) + + if not slope_files: + raise FileNotFoundError( + f"No NetCDF slope files found in {self.patterns_dir}" + ) + + # Lazily load all files keeping metadata intact + datasets = [ + xr.open_dataset(f, chunks={"lat": 45, "lon": 45})[ + "zos_zostoga_regression_slope" + ] + for f in slope_files + ] + + # Concatenate along a new dimension (representing the ensemble/models) + slopes_stack = xr.concat(datasets, dim="model") + + # Read land mask if present + mask_files = sorted( + Path(self.patterns_dir).glob("*/zos_mask_ssp585_*.nc"), key=lambda p: p.name + ) + + if mask_files: + if len(slope_files) == len(mask_files): + self.land_mask_present = True + + datasets_mask = [ + xr.open_dataset(f, chunks={"lat": 45, "lon": 45})["zos_mask"] + for f in mask_files + ] + mask_stack = xr.concat(datasets_mask, dim="model") + # mask_stack = mask_stack.sum(dim='model', skipna=True) + else: + logging.warning( + "There is a mismatch between number of slope files and mask files. " + "Ignoring mask files." + ) + self.land_mask_present = False + mask_stack = None + else: + self.land_mask_present = False + mask_stack = None + + return slopes_stack, mask_stack + + def _calc_expansion_contribution( + self, rng: np.random.Generator, state: ClimateState + ) -> da.Array: + """ + Calculate the thermal expansion contribution to the regional component of + sea level rise. + + Parameters + ---------- + rng: np.random.Generator + Random number generator for sampling spatial patterns if needed. + state: ClimateState + The state object containing the target grid information and number of members. + + Returns + ------- + da.Array + A dask array of shape (members, years, lat, lon) containing the thermal expansion contribution to the sterodynamic component for each member and year. + """ + # Select slope coefficients based on the MIP + coeffs_da, mask_da = self._load_CMIP6_slopes() + + if self.land_mask_present: # apply land mask + coeffs_da = coeffs_da.where(mask_da == 0.0) + + # Get the data either at sites or on a grid + interp_da = self.extract_spatial(coeffs_da, state) + coeffs = interp_da.data + spatial_shape = coeffs.shape[1:] # either (lat, lon) or (site,) + + if self.sample_spatial: + rand_samples = rng.choice( + coeffs.shape[0], size=state.n_members, replace=True + ) + return coeffs[rand_samples, :, :] + else: + # Calc pattern ensemble mean + mean_coeff = da.nanmean(coeffs, axis=0) + return da.broadcast_to( + mean_coeff, + (state.n_members, *spatial_shape), + ) + + def project(self, state: ClimateState, rng) -> np.ndarray: + """ + Project the sterodynamic component by applying the CMIP6 patterns to the global expansion projection. + + Parameters + ---------- + state: ClimateState + The state object containing the target grid information and number of members. + rng: np.random.Generator + Random number generator for sampling spatial patterns if needed. + + Returns + ------- + np.ndarray + A numpy array of shape (members, years, lat, lon) containing the sterodynamic component for each member and year. + """ + # Calculate percentiles locally without mutating self + if state.output_percentiles is not None: + current_projection = sample_members_2D( + self.global_projection, state.output_percentiles + ) + else: + current_projection = self.global_projection + + expansion_contribution = self._calc_expansion_contribution(rng, state) + + return self.broadcast_spatiotemporal(current_projection, expansion_contribution) + + +class SterodynamicCMIP5(SpatialComponent): + """ + Placeholder for a sterodynamic SLR component based on CMIP5 projections. + """ + + def __init__(self): + pass + + def project(self, state, rng) -> da.Array: + # Dynamically determine the spatial shape to prevent crashes when using LocalState + if hasattr(state, "target_lats"): + spatial_shape = (len(state.target_lats),) + else: + spatial_shape = (state.grid_lats.shape[0], state.grid_lons.shape[0]) + + return da.zeros((state.n_members, state.n_years, *spatial_shape)) diff --git a/profsea/config.py b/profsea/config.py deleted file mode 100644 index 99e26da..0000000 --- a/profsea/config.py +++ /dev/null @@ -1,14 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import os -import pathlib -import yaml - -path = pathlib.Path(__file__).parents[0].as_posix() -print(path) - -with open(os.path.join(path, "user-settings.yml"), "r") as f: - settings = yaml.load(f, Loader=yaml.SafeLoader) diff --git a/profsea/directories.py b/profsea/directories.py deleted file mode 100644 index 346f9e2..0000000 --- a/profsea/directories.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import os - -from profsea.config import settings - - -def makefolder(directory): - """ - Check if subfolder exists, if not then creates the subfolder - :param directory: directory of subfolder - """ - if not os.path.isdir(directory): - os.makedirs(directory) - - -def read_dir(): - """ - Creates multiple directories from user settings - :return: file paths required to run ProFSea tool - """ - root_dir = settings["baseoutdir"] - data_region = settings["siteinfo"]["region"] - - # output data directory - '' adds last / - cmipdir = os.path.join(root_dir, data_region, 'data', 'cmip5', '') - - # output map directory - mapdir = os.path.join(root_dir, data_region, 'figures', 'maps', '') - - # output zos data directory - zosddir = os.path.join(root_dir, data_region, 'data', 'zos_regression', '') - - # output zos figure directory - zosfdir = os.path.join(root_dir, data_region, 'figures', - 'zos_regression', '') - - # output sea level projections data directory - sealev_ddir = os.path.join(root_dir, data_region, 'data', - 'sea_level_projections', '') - - # output sea level projections figure directory - sealev_fdir = os.path.join(root_dir, data_region, 'figures', - 'sea_level_projections', '') - - # output baseline sea level figure directory - base_fdir = os.path.join(root_dir, data_region, 'figures', - 'baseline_sea_level', '') - - return cmipdir, mapdir, zosddir, zosfdir, sealev_ddir, sealev_fdir, \ - base_fdir diff --git a/profsea/plotting_libraries.py b/profsea/plotting_libraries.py deleted file mode 100644 index 2db00e8..0000000 --- a/profsea/plotting_libraries.py +++ /dev/null @@ -1,142 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import numpy as np -import pandas as pd -import re - -from config import settings - - -def calc_xlim(str_id, tg_years, years): - """ - Define the x-axis limits based on tide gauge and projection data - :param str_id: identification variable to define between tide gauge or - sea level projection - :param tg_years: tide gauge years - :param years: sea level projection years - :return: minimum and maximum values - """ - if str_id == 'proj': - xmin = 2005 - elif str_id == 'tide': - xmin = min([min(tg_years), min(years)]) - xmin = np.floor(xmin / 10) * 10 - xmax = settings['projection_end_year'] - - return [xmin, xmax] - - -def calc_ylim(str_id, tg_amsl, df): - """ - Define the y-axis limits based on tide gauge and projection data - :param str_id: identification variable to define between tide gauge or - sea level projection - :param tg_amsl: tide gauge annual mean sea level data - :param df: sea level projection data - :return: minimum and maximum values - """ - # Calculate y-axis limits based on sea level projections only - # i.e. a list of dataframes for each RCP - if isinstance(df, list): - df_all = pd.concat(df, axis=0, ignore_index=True) - # i.e. a single dataframe for one RCP - else: - df_all = df - - ymin = df_all.min(numeric_only=True).min() - ymax = df_all.max(numeric_only=True).max() - - if str_id == 'tide': - tg_ymin = min(tg_amsl) - tg_ymin = np.floor(tg_ymin / 0.1) * 0.1 - # To include the zero line on plots - if tg_ymin > -0.05: - tg_ymin = -0.05 - ymin = min(tg_ymin, ymin) - - return [ymin-0.1, ymax+0.1] - - -def location_string(loc_string): - """ - Re-format location string for use in plotting. - Deals with , - _ ( ) and space - :param loc_string: site location - :return: simplified site location string - """ - loc_title = [] - title_temp = re.split(r"[, |\-|_|)|(|\s]", loc_string) - for string in title_temp: - loc_title.append(string.capitalize()) - loc_title = " ".join(loc_title) - - if loc_title == 'Stanley Ii': - loc_title = 'Stanley II' - - return loc_title - - -def plot_zeroline(ax, xvalues): - """ - Plots a horizontal line where x=0 - :param ax: number of subplot - :param xvalues: range of years - """ - ax.plot(xvalues, [0., 0.], 'grey', linewidth=0.5) - - -def scenario_string(rcp_scen, i): - """ - Re-format scenario string for use in plotting - :param rcp_scen: RCP emission scenario - :param i: loop level; -999 if not required - :return: simplified scenario string - """ - if i >= 0: - rcp_label = rcp_scen[i].upper() - rcp_label = rcp_label[:3] + ' ' + rcp_label[3] + '.' + rcp_label[4:] - else: - rcp_label = rcp_scen.upper() - rcp_label = rcp_label[:3] + ' ' + rcp_label[3] + '.' + rcp_label[4:] - - return rcp_label - - -def ukcp18_colours(): - """ - Define plotting colour schemes as used in UKCP18 Marine Report - :return: UKCP18 colour schemes - """ - rcp_colours = {'rcp85': 'firebrick', - 'rcp45': 'steelblue', - 'rcp26': 'darkblue', - } - - comp_parts_colours = {'antnet': 'b', - 'greennet': 'g', - 'glacier': 'c', - 'landwater': 'purple', - 'sum': 'k', - 'gia': 'orange', - 'ocean': 'r'} - - return rcp_colours, comp_parts_colours - - -def ukcp18_labels(): - """ - Define sea level component labels as used in UKCP18 Marine Report - :return: UKCP18 component labels - """ - comp_parts_labels = {'antnet': 'Antarctica', - 'greennet': 'Greenland', - 'glacier': 'Glaciers', - 'landwater': 'Land Water', - 'sum': 'Local Total', - 'gia': 'GIA', - 'ocean': 'Ocean'} - - return comp_parts_labels diff --git a/profsea/scm_profsea_experiments.py b/profsea/scm_profsea_experiments.py new file mode 100644 index 0000000..5f1645e --- /dev/null +++ b/profsea/scm_profsea_experiments.py @@ -0,0 +1,474 @@ +import argparse +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import xarray as xr +from fair import FAIR +from fair.interface import initialise +from fair.io import read_properties +from rich.console import Console +from rich.progress import track +from rich_argparse import RichHelpFormatter + +# --- NEW IMPORTS --- +from profsea.components.core.global_model import Global +from profsea.components.global_ import ( + AntarcticaISMIP6, + Glacier, + GreenlandAR6, + LandwaterAR6, + ThermalExpansion, +) +from profsea.utils import sample_members_2D + +console = Console() + + +def index_df(df: pd.DataFrame, baseline_start: int, baseline_end: int) -> pd.DataFrame: + meta_cols = [ + "ensemble_member", + "scenario", + "region", + "variable", + "unit", + "climate_model", + ] + existing_meta = [c for c in meta_cols if c in df.columns] + df_indexed = df.set_index(existing_meta) + + years = df_indexed.columns.astype(str).str[:4].astype(int) + df_indexed.columns = years + + mask_full = (years >= 1750) & (years <= 2300) + df_indexed = df_indexed.loc[:, mask_full] + + years_sliced = df_indexed.columns + baseline_mask = (years_sliced >= baseline_start) & (years_sliced <= baseline_end) + + if not baseline_mask.any(): + raise ValueError( + f"No years found in baseline range {baseline_start}-{baseline_end}" + ) + + baseline_means = df_indexed.loc[:, baseline_mask].mean(axis=1) + df_anom = df_indexed.sub(baseline_means, axis=0) + years_final = df_anom.columns + mask_final = (years_final >= 2006) & (years_final <= 2300) + + df_final = df_anom.loc[:, mask_final].reset_index() + return df_final + + +def df_to_arr(df, scenario_order): + meta_cols = [ + "ensemble_member", + "scenario", + "region", + "variable", + "unit", + "climate_model", + ] + existing_meta = [c for c in meta_cols if c in df.columns] + + df = df.set_index(existing_meta) + array = [] + for scenario in scenario_order: + try: + mask = df.index.get_level_values("scenario") == scenario + group_df = df.loc[mask] + except KeyError: + raise ValueError(f"Scenario '{scenario}' not found in the input DataFrame.") + + if group_df.empty: + raise ValueError(f"No data found for scenario '{scenario}'") + + group_sorted = group_df.sort_index(level="ensemble_member") + scenario_data = group_sorted.values + array.append(scenario_data) + + array = np.stack(array, axis=0) + array = array.transpose(2, 0, 1) # (n_scenarios, n_members, n_time) + return array + + +def load_magicc_forcing( + input_path: str, scenarios: list, baseline_start: int, baseline_end: int +) -> tuple[np.ndarray]: + df = pd.read_csv(input_path) + + tas_condition = (df["variable"] == "Surface Air Temperature Change") & ( + df["scenario"].isin(scenarios) + ) + ohc_condition = (df["variable"] == "Heat Content|Ocean") & ( + df["scenario"].isin(scenarios) + ) + tas_df = df.loc[tas_condition] + ohc_df = df.loc[ohc_condition] + + tas_df = index_df(tas_df, baseline_start, baseline_end) + ohc_df = index_df(ohc_df, baseline_start, baseline_end) + tas_arr = df_to_arr(tas_df, scenarios) + ohc_arr = df_to_arr(ohc_df, scenarios) * 1e21 + + # Trim between 2006 and 2300 just to be safe + # The years are after the 6th column, so we can slice by column names + tas_arr = tas_arr[:, :, (tas_df.columns[6] >= 2006) & (tas_df.columns[6] <= 2300)] + ohc_arr = ohc_arr[:, :, (ohc_df.columns[6] >= 2006) & (ohc_df.columns[6] <= 2300)] + + # These are now of shape (time, scenario, 1, member) - we want (scenario, member, time) + tas_arr = tas_arr.squeeze().transpose(1, 2, 0) + ohc_arr = ohc_arr.squeeze().transpose(1, 2, 0) + return tas_arr, ohc_arr + + +def load_fair_forcing( + input_path: str, scenarios: list, baseline_start: int, baseline_end: int +) -> tuple[np.ndarray]: + tas_path = Path(input_path) / "tas.nc" + ohc_path = Path(input_path) / "ohc.nc" + + tas = xr.load_dataarray(tas_path) + ohc = xr.load_dataarray(ohc_path) + + tas_baseline = tas.loc[ + dict(timebounds=np.arange(baseline_start, baseline_end + 1)) + ].mean("timebounds") + ohc_baseline = ohc.loc[ + dict(timebounds=np.arange(baseline_start, baseline_end + 1)) + ].mean(["timebounds"]) + + tas = tas.loc[dict(timebounds=np.arange(2006, 2301))] - tas_baseline + ohc = ohc.loc[dict(timebounds=np.arange(2006, 2301))] - ohc_baseline + + tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds") + ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds") + return tas.values, ohc.values + + +def run_fair( + baseline_start: int, baseline_end: int, scenarios: list, args: argparse.Namespace +) -> tuple[np.ndarray]: + f = FAIR() + f.define_time(1750, 2300, 1) + f.define_scenarios(scenarios) + species, properties = read_properties( + "../data/fair/fair-parameters/species_configs_properties_1.4.1.csv" + ) + f.define_species(species, properties) + f.ch4_method = "Thornhill2021" + df_configs = pd.read_csv( + "../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv", + index_col=0, + ) + f.define_configs(df_configs.index) + f.allocate() + + if args.emissions_file: + f.fill_from_csv( + emissions_file=args.emissions_file, forcing_file=args.forcing_file + ) + else: + f.fill_from_rcmip() + + f.fill_species_configs( + "../data/fair/fair-parameters/species_configs_properties_1.4.1.csv" + ) + f.override_defaults( + "../data/fair/fair-parameters/calibrated_constrained_parameters_1.4.1.csv" + ) + initialise(f.concentration, f.species_configs["baseline_concentration"]) + initialise(f.forcing, 0) + initialise(f.temperature, 0) + initialise(f.cumulative_emissions, 0) + initialise(f.airborne_emissions, 0) + initialise(f.ocean_heat_content_change, 0) + + f.run() + + tas_baseline = f.temperature.loc[ + dict(layer=0, timebounds=np.arange(baseline_start, baseline_end + 1)) + ].mean() + ohc_baseline = f.ocean_heat_content_change.loc[ + dict(timebounds=np.arange(baseline_start, baseline_end + 1)) + ].mean() + + tas = ( + f.temperature.loc[dict(layer=0, timebounds=np.arange(2006, 2301))] + - tas_baseline + ) + ohc = ( + f.ocean_heat_content_change.loc[dict(timebounds=np.arange(2006, 2301))] + - ohc_baseline + ) + + tas = tas.sel(scenario=scenarios).transpose("scenario", "config", "timebounds") + ohc = ohc.sel(scenario=scenarios).transpose("scenario", "config", "timebounds") + return tas.values, ohc.values + + +def plot_samples(tas: np.ndarray, ohc: np.ndarray) -> None: + fig = plt.figure(figsize=(12, 6), layout="constrained") + + ax = fig.add_subplot(121) + ax.plot(tas.T, color="seagreen", alpha=0.05) + ax.set_xlabel("Simulation years") + ax.set_ylabel(r"GMST ($\degree$C)") + ax.plot(np.arange(tas.shape[1]), np.median(tas, axis=0), color="black") + + ax = fig.add_subplot(122) + ax.plot(ohc.T, color="seagreen", alpha=0.05) + ax.set_xlabel("Simulation years") + ax.set_ylabel("OHC (J)") + + fig.savefig("forcing.png", dpi=200) + plt.show() + plt.close() + + +def process_global_ensemble(components: list, percentiles: list, scenario: str) -> None: + for comp, data in components.items(): + sampled_ensemble = sample_members_2D(data, percentiles) + components[comp] = sampled_ensemble + return components + + +def save_to_netcdf(components: dict, filename: str) -> None: + scenarios = list(components.keys()) + comp_names = list(components[scenarios[0]].keys()) + sample = components[scenarios[0]][comp_names[0]] + n_member, n_time = sample.shape + + years = np.arange(2006, 2006 + n_time) + members = np.arange(n_member) + data_vars = {} + for comp in comp_names: + stacked_data = np.stack([components[s][comp] for s in scenarios], axis=0) + data_vars[comp] = xr.DataArray( + data=stacked_data, + dims=["scenario", "member", "year"], + coords={"scenario": scenarios, "member": members, "year": years}, + attrs={"units": "m", "description": f"Sea level contribution from {comp}"}, + ) + + ds = xr.Dataset(data_vars) + ds.attrs = { + "title": "ProFSea GMSLR Projections", + "source": "FAIR v2.2 + ProFSea Emulator", + } + + encoding = {var: {"zlib": True, "complevel": 5} for var in data_vars} + ds.to_netcdf(filename, encoding=encoding) + console.log(f"Successfully saved full ensemble to {filename}") + + +def plot_component( + ax: plt.Axes, + component_dict: dict, + component: str, + scenarios: list, + plot_legend: bool = False, +) -> None: + time = np.arange(2006, 2301) + scenario_colors = { + scenarios[0]: "#800000", + scenarios[1]: "#ff0000", + scenarios[2]: "#fc7b03", + scenarios[3]: "#d3a640", + scenarios[4]: "#098740", + scenarios[5]: "#0080d0", + scenarios[6]: "#100060", + } + for scenario in reversed(scenarios): + ax.fill_between( + time, + component_dict[scenario][component][1], + component_dict[scenario][component][5], + color=scenario_colors[scenario], + edgecolor="none", + alpha=0.3, + ) + ax.plot( + time, + component_dict[scenario][component][3], + label=f"{scenario}", + color=scenario_colors[scenario], + ) + + ax.set_xlabel("Year") + ax.set_ylabel("SLE (m)") + ax.set_title(component) + if plot_legend: + ax.legend(frameon=False, loc="upper left") + + +def main(args): + percentiles = [0, 5, 17, 50, 83, 95, 100] + if args.emissions_file: + scen_df = pd.read_csv(args.emissions_file) + scenarios = scen_df["scenario"].unique().tolist() + else: + # Defaulting to an SSP list to avoid UnboundLocalError + # scenarios = ["ssp119", "ssp126", "ssp245", "ssp370", "ssp534-over", "ssp585"] + scenarios = [ + "Very Low - SSP1 (Marker)", + "Low-to-Negative - SSP2 (Marker)", + "Low - SSP2 (Marker)", + "Medium-to-Low - SSP2 (Marker)", + "Medium - SSP2 (Marker)", + "High-to-Low - SSP5 (Marker)", + "High - SSP3 (Marker)", + ] + + console.log(f"Using scenarios: {scenarios}") + + # if "ssp" in scenarios[0].lower(): + # emissions_path = args.cumulative_emissions_file + # with open(emissions_path) as f: + # cumulative_emissions = json.load(f) + + baseline_start = 1995 + baseline_end = 2014 + + # Updated keys to align with the grouped components + components = {} + for scenario in scenarios: + components[scenario] = { + "total_gmslr": [], + "expansion": [], + "antarctica": [], + "greenland": [], + "glacier": [], + "landwater": [], + } + + if args.input.lower() == "magicc": + tas, ohc = load_magicc_forcing( + args.input_path, scenarios, baseline_start, baseline_end + ) + elif args.input.lower() == "fair": + tas, ohc = load_fair_forcing( + args.input_path, scenarios, baseline_start, baseline_end + ) + elif args.input.lower() == "run_fair": + tas, ohc = run_fair(baseline_start, baseline_end, scenarios, args) + else: + raise ValueError("Input source not recognised.") + + n_iterations = 1000 + rng = np.random.default_rng() + random_indices = rng.integers(0, high=tas.shape[1], size=n_iterations) + + tas_matrix = tas[:, random_indices, :] # Shape: (n_scenarios, 1000, 295) + ohc_matrix = ohc[:, random_indices, :] # Shape: (n_scenarios, 1000, 295) + + sampled_components = {} + for idx, scenario in track( + enumerate(scenarios), + total=len(scenarios), + description="Projecting scenarios...", + ): + tas_scen = tas_matrix[idx, :, :] + ohc_scen = ohc_matrix[idx, :, :] + + slr_components = { + "expansion": ThermalExpansion(OHC_change=ohc_scen), + "greenland": GreenlandAR6(), + "landwater": LandwaterAR6(), + "wais": AntarcticaISMIP6(region="wais"), + "eais": AntarcticaISMIP6(region="eais"), + "pen": AntarcticaISMIP6(region="peninsula"), + "glacier": Glacier(), + } + + global_model = Global( + components=slr_components, + end_yr=2301, + num_members=1000, + dtype="float32", + parallel=False, + ) + projections = global_model.run( + scenario=scenario, + T_change=tas_scen, + member_seed=42, # Only need one seed for the whole matrix operation + ) + global_model.sum_components(projections) + + projections["antarctica"] = ( + projections.pop("wais") + projections.pop("eais") + projections.pop("pen") + ) + + # Process the 1000x295 outputs for saving + sampled_components[scenario] = process_global_ensemble( + projections, percentiles, scenario + ) + + output_dir = Path(args.output_dir) / args.output_filename + save_to_netcdf(sampled_components, output_dir) + + fig = plt.figure(figsize=(16, 8), layout="constrained") + ax = fig.add_subplot(231) + plot_component(ax, sampled_components, "total_gmslr", scenarios, plot_legend=True) + ax = fig.add_subplot(232) + plot_component(ax, sampled_components, "expansion", scenarios) + ax = fig.add_subplot(233) + plot_component(ax, sampled_components, "glacier", scenarios) + ax = fig.add_subplot(234) + plot_component(ax, sampled_components, "antarctica", scenarios) + ax = fig.add_subplot(235) + plot_component(ax, sampled_components, "greenland", scenarios) + ax = fig.add_subplot(236) + plot_component(ax, sampled_components, "landwater", scenarios) + + fig.savefig( + f"{args.output_dir}{args.output_filename.replace('.nc', '_components.png')}", + dpi=300, + ) + plt.show() + + +if __name__ == "__main__": + p = argparse.ArgumentParser(formatter_class=RichHelpFormatter) + p.add_argument( + "--input", + default="run_fair", + required=False, + help="Input climate forcing source (default: FaIR)", + type=str, + ) + p.add_argument( + "--input_path", required=False, help="Path to input climate forcing", type=str + ) + p.add_argument( + "--emissions_file", + required=False, + help="Path to CSV file containing emissions scenarios (optional)", + type=str, + ) + p.add_argument( + "--forcing_file", + required=False, + help="Path to CSV file containing climate forcing time series (optional)", + type=str, + ) + p.add_argument( + "--output_dir", + default="", + help="Directory to save outputs (default: current directory)", + type=str, + ) + p.add_argument( + "--output_filename", + default="gmslr_projections.nc", + help="Filename for output NetCDF (default: gmslr_projections.nc)", + type=str, + ) + p.add_argument( + "--cumulative_emissions_file", + default="cumulative_cmip6_emissions.json", + help="Path to JSON file containing cumulative emissions for SSP scenarios (default: cumulative_cmip6_emissions.json)", + type=str, + ) + main(p.parse_args()) diff --git a/profsea/slr_pkg/__init__.py b/profsea/slr_pkg/__init__.py deleted file mode 100644 index 8476945..0000000 --- a/profsea/slr_pkg/__init__.py +++ /dev/null @@ -1,339 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import os -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import cartopy.crs as ccrs - -from profsea.config import settings -from profsea.slr_pkg import cmip, cubeplot, cubeutils, cubedata, process -from profsea.directories import makefolder, read_dir - - -def abbreviate_location_name(name, chars_not_needed=' ,()'): - """ - Returns an abbreviated version of the location name, by removing the - specified characters. - Check for a comma in the name of the location; then remove the part after - the comma. - :param name: site location - :param chars_not_needed: characters in location name to remove - :return: the name after removing spaces and brackets - """ - if ',' in name: - abbrev_name = name.split(',')[0] - else: - abbrev_name = name[:] - - return ''.join([c for c in abbrev_name if c not in chars_not_needed]) - - -def extract_dyn_steric_regression(models, df, scenarios): - """ - Calculate, plot, and save regression between the global mean ( - thermosteric) and local (sterodynamic) sea level change from CMIP models. - :param models: list of model names to be extracted - :param df: DataFrame of all site location's metadata - :param scenarios: list of RCP scenarios - """ - # Base directory for CMIP "zos" and "zostoga" data - datadir = settings["cmipinfo"]["sealevelbasedir"] - # Dictionary of CMIP models and experiments - zos_dict = cmip.zos_dictionary() - - mstyle_dict = {'rcp26': 'bo', - 'rcp45': 'co', - 'rcp60': 'yo', - 'rcp85': 'ro'} - - # Update as required for regression plots - xmin = -0.05 - xmax = 0.6 - ymin = -0.05 - ymax = 0.6 - - matplotlib.rcParams['font.size'] = 10 - - # For indices of dataframe which will hold the slopes of the sea level - # regressions - iterables = [models, scenarios] - mi = pd.MultiIndex.from_product(iterables, names=['Model', 'Scenario']) - - for loc_name in df.index.values: - # Abbreviate the site location name - loc_abbrev = abbreviate_location_name(loc_name) - - # Empty list used to construct the output dataframe - result = [] - - # Read in the grid indices of the ocean points - in_cmipdir = read_dir()[0] - df = read_ij_1x1_coord(in_cmipdir, loc_abbrev) - - for model in models: - i = df.at[model, 'i'] - j = df.at[model, 'j'] - print(f'Calculating regression for {model} at grid box indices: ' - f'i = {i} and j = {j}') - plt.figure(figsize=(5, 4.5)) - plt.plot([-1, 3.0], [-1, 3.0], 'k:', linewidth=3) - - for scenario in scenarios: - print(f'Scenario: {scenario}') - try: - # dynamic sea level (zos) - zos_date = zos_dict[model][scenario]['driftcorr'] - zos_file = f'{datadir}normalized_zos_Omon_{model}_' \ - f'{scenario}_{zos_date}_driftcorr.nc' - zos = cubedata.read_zos_cube(zos_file)[0][:, j, i] - # -------------------------------------------------------- - # global mean thermosteric (zostoga) - # Extract, and drift-correct CMIP "zostoga" data - # Normal (concatenated) - zostoga_date = zos_dict[model][scenario]['zostoga'] - zostoga_file = f'{datadir}zostoga_Omon_{model}_' \ - f'{scenario}_{zostoga_date}.nc' - zostoga_raw = cubedata.read_zos_cube(zostoga_file)[0] - # piControl (concatenated) - piControl_date = zos_dict[model][scenario]['piControl'] - zostoga_pic_file = f'{datadir}zostoga_Omon_' \ - f'{model}_piControl_{piControl_date}.nc' - zostoga_pic = cubedata.read_zos_cube(zostoga_pic_file)[0] - - regr = process.Regress('linear') - cube_drift, _ = regr.regress_t_scalar(zostoga_pic) - zostoga, _ = regr.detrend_scalar(zostoga_raw, cube_drift) - - # -------------------------------------------------------- - - plotlon = zos.coord('longitude').points[0] - plotlat = zos.coord('latitude').points[0] - - if plotlon > 180: - plotlon -= 360 - - yrs = get_cube_years(zos) - - if model == 'bcc-csm1-1': - index = np.where(yrs >= 2100) - zostoga.data[index] = zostoga.data[index] + \ - zostoga.data[index[0][0] - 1] - - # Calculate regression coefficients for periods 2005-2100 - # and 2050-2100 - idx1 = ((2005 <= yrs) & (yrs <= 2100)) - idx2 = ((2050 <= yrs) & (yrs <= 2100)) - - zostoga.data = zostoga.data - zostoga.data[0:10].mean() - zos.data = zos.data - zos.data[0:10].mean() - - # Calculate the slope and intercepts of linear fits of the - # global and local sea level projections - slope1, _ = np.polyfit(zostoga.data[idx1], zostoga.data[ - idx1] + zos.data[idx1], 1) - slope2, _ = np.polyfit(zostoga.data[idx2], zostoga.data[ - idx2] + zos.data[idx2], 1) - - result.append([i, j, plotlon, plotlat, slope1, slope2]) - - # Add the data points to the figure - leglabel = scenario.upper() - plt.plot(zostoga.data[idx1], - zostoga.data[idx1] + zos.data[idx1], - mstyle_dict[scenario], - markersize=8, markeredgecolor='None', - label=leglabel) - - except IOError: - result.append([i, j, -99, -99, np.nan, np.nan]) - continue - - plt.xlabel('Global thermal expansion (m)') - plt.ylabel('Local sea level (m)') - plt.title(model) - plt.xlim([xmin, xmax]) - plt.ylim([ymin, ymax]) - plt.legend(loc='upper left', numpoints=1, frameon=False) - plt.tight_layout() - - # Create the output figure file directory and filename - out_zosfdir = read_dir()[3] - makefolder(out_zosfdir) - outfigfile = f'{out_zosfdir}{loc_abbrev}_{model}' + \ - '_zos_regression_2005-2100.png' - - # Save the sea level regression figures to file - print(f'SAVING: {loc_abbrev}_{model}_zos_regression_2005-2100.png') - plt.savefig(outfigfile, dpi=200) - plt.close() - - # Save the regression slopes for all models and scenarios - df_out = pd.DataFrame(result, columns=['i', 'j', 'lon', 'lat', - 'slope_05_00', 'slope_50_00'], - index=mi) - df_out['slope_05_00'] = df_out['slope_05_00'].apply( - lambda x: round(x, 4)) - df_out['slope_50_00'] = df_out['slope_50_00'].apply( - lambda x: round(x, 4)) - - # Create the output data file directory and filename - out_zosddir = read_dir()[2] - makefolder(out_zosddir) - outdatafile = f'{out_zosddir}{loc_abbrev}_zos_regression.csv' - - # Save the sea level regressions data to file - df_out.to_csv(outdatafile, na_rep='NA') - - -def get_cube_years(cube): - """ - Extract a numpy array of years from Iris cube - :param cube: iris cube containing local sea level (zos) - :return: array of years in cube - """ - time = cube.coord('time') - dates = time.units.num2date(time.points) - years = [date.year for date in dates] - - return np.array(years, dtype=int) - - -def plot_ij(cube, model, location, idx, lat, lon, save_map=True, rad=5): - """ - Plots the location of the requested site (based on lat, lon - red - cross), the location of the nearest model grid box (based on model indices - i,j - black circle) and the sea surface height above the geoid in meters - (re-gridded onto a 1x1m grid) for each CMIP model. - :param cube: data cube containing the zos field for the CMIP models - :param model: CMIP model name - :param location: site location - :param idx: latitude and longitude of the closest ocean grid point - :param lat: latitude of site - :param lon: longitude of site - :param save_map: determine is maps are saved to file, default is True - :param rad: plotting radius, default is 5 - """ - i, j = idx - - # Define region for plotting - minlon, maxlon = lon - rad * 1.5, lon + rad * 1.5 - minlat, maxlat = lat - rad, lat + rad - - # For sites that cross 180deg. e.g. minlon = 175, maxlon = 185 - if maxlon > 180 or minlon > 180: - maxlon -= 360 - minlon -= 360 - - region = [minlon, minlat, maxlon, maxlat] - - # plotlat, plotlon are the coordinate of the nearest ocean point - plotlat = cube.coord('latitude').points[j] - plotlon = cube.coord('longitude').points[i] - - # targetlat, targetlon are the coordinate of the site - targetlat = lat - targetlon = lon - - if plotlon > 180: - plotlon -= 360 - if targetlon > 180: - targetlon -= 360 - - ax = cubeplot.block(cube, land=False, region=region, cmin=-1, cmax=1, - plotcbar=True, nlevels=25, cent_lon=targetlon, - title='{} (1x1 grid) - SSH above geoid'.format(model)) - - # Transform the points onto the projection used by the map. May not be - # necessary, but is done to avoid possible position errors (i.e. the points - # plotted in the wrong place). - # Set up projections of the map, and the tide gauge and ocean points. - MAP_CRS = ccrs.PlateCarree(central_longitude=targetlon) - SRC_CRS = ccrs.PlateCarree() - - orig_lons = np.array([plotlon, targetlon]) - orig_lats = np.array([plotlat, targetlat]) - - # Transform the points onto the same projection used by the map. - # The function returns x, y and z-coordinates. - # The z-coords are not used, hence the underscore. - new_lons, new_lats, _ = MAP_CRS.transform_points( - SRC_CRS, orig_lons, orig_lats).T - - # Plot symbols showing the ocean point and the tide gauge - ax.plot(new_lons[0], new_lats[0], 'ok') - ax.plot(new_lons[1], new_lats[1], 'xr') - - if save_map: - # Create the output file directory location - out_mapdir = read_dir()[1] - makefolder(out_mapdir) - - # Abbreviate the site location name suitable to use as a filename - loc_abbrev = abbreviate_location_name(location) - figfile = os.path.join(out_mapdir, - f'{loc_abbrev}_{model}_ij_figure.png') - - # Save the CMIP grid box selection map to file - plt.savefig(figfile) - plt.close() - else: - plt.show() - - -def read_ar5_component(datadir, rcp, var, value='mid'): - """ - Return yrs and data for specified global mean sea level component loaded - from file provided as part of supplementary material for AR5 Ch13. - :param datadir: directory containing datafiles provided as supplementary - material for CH13 of AR5. - :param rcp: emissions scenario - :param var: sea level component name e.g. 'greendyn' - :param value: 'mid', 'upper', or 'lower', default is 'mid' - :return: array of yrs and data - """ - f = '%s%s_%s%s.txt' % (datadir, rcp, var, value) - arr = np.loadtxt(f) - - yrs = arr[:, 0] - dat = arr[:, 1] - - return yrs, dat - - -def read_ij_1x1_coord(datadir, location): - """ - Read ij coordinate pair for specified model. - :param datadir: directory in which the CMIP model grid box indices for - each site location are stored - :param location: site location - :return: DataFrame of CMIP model grid box indices - """ - filename = '{}{}_ij_1x1_coords.csv'.format(datadir, location) - - df = pd.read_csv(filename, skiprows=3, header=0, index_col='Model') - - return df - - -def choose_montecarlo_dir(): - """ - Choose the Monte Carlo directory based on the projection end year. - """ - end_yr = settings["projection_end_year"] - if (end_yr >= 2050) & (end_yr <= 2100): - mcdir = settings["short_montecarlodir"] - elif (end_yr > 2100) & (end_yr <= 2300): - mcdir = settings["long_montecarlodir"] - else: - raise ValueError('Projection end year must be between 2050 and 2300') - - return mcdir - - -if __name__ == '__main__': - pass diff --git a/profsea/slr_pkg/cmip.py b/profsea/slr_pkg/cmip.py deleted file mode 100644 index 5d580a1..0000000 --- a/profsea/slr_pkg/cmip.py +++ /dev/null @@ -1,233 +0,0 @@ -# """ -# Copyright (c) 2023, Met Office -# All rights reserved. -# """ - - -def model_dictionary(): - """ - Define a dictionary that contains metadata of CMIP model experiment data - ranges, used in filename convention - :return: model_dict - """ - model_dict = {'ACCESS1-0': {'historical': '185001-200512'}, - 'bcc-csm1-1': {'historical': '185001-201212'}, - 'CanESM2': {'historical': '185001-200512'}, - 'CNRM-CM5': {'historical': '185001-185912'}, - 'CSIRO-Mk3-6-0': {'historical': '185001-200512'}, - 'GISS-E2-R': {'historical': '185001-200512'}, - 'GFDL-ESM2G': {'historical': '186101-186512'}, - 'GFDL-ESM2M': {'historical': '186101-186512'}, - 'HadGEM2-CC': {'historical': '186001-186012'}, - 'HadGEM2-ES': {'historical': '186001-186012'}, - 'inmcm4': {'historical': '185001-200512'}, - 'IPSL-CM5A-LR': {'historical': '185001-200512'}, - 'IPSL-CM5A-MR': {'historical': '185001-200512'}, - 'MIROC-ESM': {'historical': '185001-200512'}, - 'MIROC-ESM-CHEM': {'historical': '185001-200512'}, - 'MIROC5': {'historical': '185001-201212'}, - 'MPI-ESM-LR': {'historical': '185001-200512'}, - 'MPI-ESM-MR': {'historical': '185001-189912'}, - 'MRI-CGCM3': {'historical': '185001-200512'}, - 'NorESM1-M': {'historical': '185001-200512'}, - 'NorESM1-ME': {'historical': '185001-200512'}} - return model_dict - - -def zos_dictionary(): - """ - Define a dictionary that contains metadata of CMIP model experiment data - ranges, used in filename convention. Where a model has not been run for - a specific RCP then 'xxxxxx-xxxxxx' is included. - :return: zos_dict - """ - zos_dict = {'ACCESS1-0': {'rcp26': {'driftcorr': 'xxxxxx-xxxxxx', - 'zostoga': 'xxxxxx-xxxxxx', - 'piControl': 'xxxxxx-xxxxxx'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '030001-080001'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '030001-080001'}}, - 'bcc-csm1-1': {'rcp26': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '000101-050101'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '000101-050101'}, - 'rcp85': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '000101-050101'}}, - 'CanESM2': {'rcp26': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '201501-301101'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '201501-301101'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '201501-301101'}}, - 'CNRM-CM5': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185001-269912'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '185001-269912'}, - 'rcp85': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '185001-269912'}}, - 'CSIRO-Mk3-6-0': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '000101-050101'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '000101-050101'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '000101-050101'}}, - 'GISS-E2-R': {'rcp26': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '333101-453101'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '333101-453101'}, - 'rcp85': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '333101-453101'}}, - 'GFDL-ESM2G': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '186101-210001'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '186101-210001'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '186101-210001'}}, - 'GFDL-ESM2M': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '186101-210001'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '186101-210001'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '186101-210001'}}, - 'HadGEM2-CC': {'rcp26': {'driftcorr': 'xxxxxx-xxxxxx', - 'zostoga': 'xxxxxx-xxxxxx', - 'piControl': 'xxxxxx-xxxxxx'}, - 'rcp45': {'driftcorr': '200512-210101', - 'zostoga': '200512-210101', - 'piControl': '185912-210001'}, - 'rcp85': {'driftcorr': '200512-210101', - 'zostoga': '200512-210101', - 'piControl': '185912-210001'}}, - 'HadGEM2-ES': {'rcp26': {'driftcorr': '200512-230001', - 'zostoga': '200512-230001', - 'piControl': '185912-243606'}, - 'rcp45': {'driftcorr': '200512-230001', - 'zostoga': '200512-230001', - 'piControl': '185912-243606'}, - 'rcp85': {'driftcorr': '200512-230001', - 'zostoga': '200512-230001', - 'piControl': '185912-243606'}}, - 'inmcm4': {'rcp26': {'driftcorr': 'xxxxxx-xxxxxx', - 'zostoga': 'xxxxxx-xxxxxx', - 'piControl': 'xxxxxx-xxxxxx'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185001-235001'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185001-235001'}}, - 'IPSL-CM5A-LR': {'rcp26': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '180001-280001'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '180001-280001'}, - 'rcp85': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '180001-280001'}}, - 'IPSL-CM5A-MR': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '180001-210001'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '180001-210001'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '180001-210001'}}, - 'MIROC-ESM': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '180001-248001'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '180001-248001'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '180001-248001'}}, - 'MIROC-ESM-CHEM': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '184601-210101'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '184601-210101'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '184601-210101'}}, - 'MIROC5': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '200001-267001'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '200001-267001'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '200001-267001'}}, - 'MPI-ESM-LR': {'rcp26': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '185001-285001'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '185001-285001'}, - 'rcp85': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '185001-285001'}}, - 'MPI-ESM-MR': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185001-285001'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185001-285001'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185001-285001'}}, - 'MRI-CGCM3': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185101-235101'}, - 'rcp45': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185101-235101'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '185101-235101'}}, - 'NorESM1-M': {'rcp26': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '070001-120101'}, - 'rcp45': {'driftcorr': '200601-230101', - 'zostoga': '200601-230101', - 'piControl': '070001-120101'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '070001-120101'}}, - 'NorESM1-ME': {'rcp26': {'driftcorr': '200601-210201', - 'zostoga': '200601-210201', - 'piControl': '090101-115301'}, - 'rcp45': {'driftcorr': '200601-210301', - 'zostoga': '200601-210301', - 'piControl': '090101-115301'}, - 'rcp85': {'driftcorr': '200601-210101', - 'zostoga': '200601-210101', - 'piControl': '090101-115301'}}} - return zos_dict diff --git a/profsea/slr_pkg/cubedata.py b/profsea/slr_pkg/cubedata.py deleted file mode 100644 index a7cb154..0000000 --- a/profsea/slr_pkg/cubedata.py +++ /dev/null @@ -1,194 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -from datetime import datetime -import inspect -import os -import cf_units -import iris - -from profsea.slr_pkg import process - - -def _derived(cube, cube_src, var_name=None, derived_type='unknown', - derived_long=None): - """ - Write metadata attributes to a cube describing the derived variable - contained within it. - NOTES: - * Much of this metadata is probably unnecessary. However, derived_type - is currently required in slr.component.Dynamic to recognise when a - scaling map is provided as an input argument. - * Currently the units of the derived variable are unset for - compatibility purposes. - :param cube: cube to which metadata will be written - :param cube_src: list or cubelist of the cubes used to generate the - derived variable - :param var_name: new variable name. If None, the existing variable name - is used. - :param derived_type: short name describing the process for creating the - derived variable - :param derived_long: new long_name attribute. Can either be given as a - regular string or as a formattable string corresponding to the cubes in - cube_src. In the latter case, the derived_var cube attribute will be - used to format the string if present, else the var_name attribute will - be used - i.e. "Product of %s and %s"%(cube1.var_name,cube2.var_name) - If None is given for this keyword, the existing long_name is used - """ - - # Ensure cube_src is an iterable - if type(cube_src) == iris.cube.Cube: - cube_src = [cube_src] - - # New var_name, if applicable - if var_name is None: - var_name = cube.var_name - - # Coordinate information, describing lat/lon coordinate if sliced to scalar - cube_coord = '' - for i_cube in cube_src: - coord_name = [i.name() for i in i_cube.coords()] - if ('longitude' in coord_name and 'latitude' in coord_name) and \ - (len(i_cube.coord('longitude').points) == 1 and - len(i_cube.coord('latitude').points) == 1): - if i_cube.coord('longitude').points[0] < 0: - cube_lon = ('%sW' % i_cube.coord( - 'longitude').points[0]).replace('-', '') - else: - cube_lon = '%sE' % i_cube.coord('longitude').points[0] - if i_cube.coord('latitude').points[0] < 0: - cube_lat = ('%sS' % i_cube.coord( - 'latitude').points[0]).replace('-', '') - else: - cube_lat = '%sN' % i_cube.coord('latitude').points[0] - cube_coord = '%s %s' % (cube_lat, cube_lon) - break - - # Derived history, describing derived variable in a "function(args)" - # format and inheriting previous values set by this function (can get - # rather long..) - derived_hist = [] - for i_cube in cube_src: - if 'derived_hist' in list(i_cube.attributes.keys()): - derived_hist += [i_cube.attributes['derived_hist']] - else: - derived_hist += [i_cube.var_name] - - # Derived variable, describing derived variable as for derived_hist but - # not inheriting any previous values - derived_var = [i_cube.var_name for i_cube in cube_src] - - # Derived period, describing data periods comprising the derived variable - # in a form corresponding to derived_hist such that - # "function_1(args,function_2(args))" corresponds to - # "(dates_function_1,(dates_function_2))" (can also get rather long..) - derived_period = [] - for i_cube in cube_src: - if 'derived_period' in list(i_cube.attributes.keys()): - derived_period += [i_cube.attributes['derived_period']] - elif 'period' in list(i_cube.attributes.keys()): - derived_period += [i_cube.attributes['period']] - else: - try: - period_min = i_cube.coord('time').units.num2date( - i_cube.coord('time').bounds[:, 0].min()) - period_max = i_cube.coord('time').units.num2date( - i_cube.coord('time').bounds[:, 1].max()) - derived_period += ['-'.join(['%04d/%02d' % (i.year, i.month) - for i in [period_min, - period_max]])] - except Exception: - derived_period += ['--'] - - # Check derived_long keyword: if not set then use a generic string, check - # string has sufficient - # string formatting statements if a formattable string is being used - # (contains %s, %i etc) - if derived_long is None: - derived_long = 'Unspecified derived variable based on ' + \ - ', '.join(['%s'] * len(derived_hist)) - elif derived_long.count('%') != len(cube_src) and \ - derived_long.count('%') > 0: - raise TypeError('The number of string formatting statements in ' + - f'derived_long ({derived_long.count("%")} does not' - f' match the number of source cubes ({len(cube_src)})') - - # History attribute entry; add the calling function and arguments - try: - cube_history = str(cube.attributes['history']) - except KeyError: - cube_history = '' - history_callfunc = os.sep.join([inspect.stack()[1][1], - inspect.stack()[1][3]]) - history = '%s: %s(%s)\n' % (datetime.now().ctime(), history_callfunc, - ','.join(derived_var)) - - # Drift correction, a record of the drift-corrected variables - drift_corr = [] - for i_cube in cube_src: - if 'drift_correction' in list(i_cube.attributes.keys()): - drift_corr += [i_cube.attributes['drift_correction']] - - # Apply string formatting where required - if derived_long.count('%') > 0: - derived_long = derived_long % tuple(derived_hist) - derived_hist = '%s(%s)' % (derived_type, ','.join(derived_hist)) - derived_var = '%s(%s)' % (derived_type, ','.join(derived_var)) - derived_period = '(%s)' % (','.join(derived_period)) - - # If a function of several drift-corrected variables, write as "func(a,b)". - if len(drift_corr) > 1: - drift_corr = '%s(%s)' % (var_name, ', '.join(drift_corr)) - - # Otherwise if a function of one drift-corrected variable, i.e. "func(a)", - # write "new_var: old_var" if the derived variable has a different name - elif len(drift_corr) == 1: - parse_result_1 = drift_corr[0].split(': ')[0] - parse_result_2 = drift_corr[0].split('(')[0] - if parse_result_1 != var_name and parse_result_2 != var_name: - drift_corr = '%s(%s)' % (var_name, drift_corr[0]) - else: - drift_corr = drift_corr[0] - - # Write attributes - cube.long_name = derived_long - cube.var_name = var_name - if len(cube_coord) > 0: - cube.attributes['coordinate'] = cube_coord - # REPLACE WITH SPECIFIC CUBE.ADD_HISTORY CALL - cube.attributes['history'] = history + cube_history - cube.attributes['derived_hist'] = derived_hist - cube.attributes['derived_var'] = derived_var - cube.attributes['derived_type'] = derived_type - cube.attributes['derived_period'] = derived_period - if len(drift_corr) > 0: - cube.attributes['drift_correction'] = drift_corr - cube.units = cf_units.Unit(None) - - -def read_zos_cube(cmip_file): - """ - Read cube of CMIP sea level data, applying appropriate time bounds. - Could be: dynamic sea level (zos), global mean thermosteric (zostoga) or - piControl (concatenated) - :param cmip_file: filepath and filename as string - :return: cube of sea level data - """ - cube = iris.load_cube(cmip_file, iris.Constraint()) - - # Guess time bounds if they are not present - if 'time' in [i.name() for i in cube.coords()] and \ - cube.coord('time').bounds is None: - cube.coord('time').guess_bounds() - - # Reject cube if it has an AuxCoord time coordinate - cube = process._reject_auxcoord(cube) - if len(cube) == 0: - raise Exception( - f'The file {cmip_file} did not have a CF-compliant time ' - f'coordinate') - - return cube diff --git a/profsea/slr_pkg/cubeplot.py b/profsea/slr_pkg/cubeplot.py deleted file mode 100644 index 2c546e3..0000000 --- a/profsea/slr_pkg/cubeplot.py +++ /dev/null @@ -1,207 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import cartopy -import cartopy.crs as ccrs -from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER -import matplotlib.pyplot as plt -import matplotlib.ticker as mticker -from matplotlib.colors import BoundaryNorm -import numpy as np -import iris -import iris.plot as iplt - - -def block(cube, **kwargs): - """ - Draw a block color plot for a given cube. Invokes contourf() with - pcolormesh=True. - :param cube: iris.cube.Cube - :param kwargs: See contourf() for other valid keyword arguments - :return: a block color plot for a given cube - """ - - return contourf(cube, pcolormesh=True, **kwargs) - - -def contourf(cube, anom=False, subplot=111, cent_lon=0, land=True, - coast=True, title=None, region=None, plotcbar=True, - charsize=12, cmin=None, cmax=None, nlevels=11, levels=None, - cbarlabel=None, cbar_orien='horizontal', pcolormesh=False, - reproject=False, nx=400, ny=200, proj=None, xticks=None, - yticks=None, cmap=None, **kwargs): - """ - Draw a filled contour plot for a given cube. - :param cube: iris.cube.Cube - :param anom: boolean, use blue/red colormap for anomalies - (def=False) - :param subplot: 3-digit integer specifying subplot position - (def=111) - :param cent_lon: central longitude of the map projection - (def=0) - :param land: boolean, if True, filled land drawn - (def=True) - :param coast: boolean, if True, coastlines drawn - (def=True) - :param title: map title - (def=None) - :param region: 4-element list [E,S,W,N] defining sub-region to plot - (def=None) - :param plotcbar: boolean, if True, colorbar plotted - (def=True) - :param charsize: font size to use - (def=12) - :param cmin: minimum value for colormap/bar - (def=None) - :param cmax: maximum value for colormap/bar - (def=None) - :param nlevels: set number of bands within colormap - (def=11) - :param levels: set minimum, maximum and number of bands within colormap - (def=None) - :param cbarlabel: colorbar label - (def=None) - :param cbar_orien: orientation for colorbar - (def='horizontal') - :param pcolormesh: boolean:, if True, iris.plot.pcolormesh() used for - plotting - (def=False) - :param reproject: boolean, if True, reproject cube to PlateCarree using a - specified number of sample points (nx & ny) before plotting. Used for - fields with 2D latitude/longitude fields - (def=False) - :param nx: number of sample points in x-direction used for cube - re-projection - (def=400) - :param ny: number of sample points in y-direction used for cube - re-projection - (def=200) - :param proj: If not defined, use ccrs.PlateCarree() - (def=None) - :param xticks: plot xticks on colormap - (def=None) - :param yticks: plot yticks on colormap - (def=None) - :param cmap: set colormap to blue/red - (def=None) - :param kwargs: defined in iplt.pcolormesh() and iplt.contourf() - :return: filled contour plot - """ - # Map projection - if reproject: - cube, extent = iris.analysis.cartography.project( - cube, ccrs.PlateCarree(), nx=nx, ny=ny) - - if proj is not None: - map_proj = proj - else: - map_proj = ccrs.PlateCarree(central_longitude=cent_lon) - - # Color map - if cmap is None: - cmap = plt.cm.RdBu_r if anom else plt.cm.jet - - # Create subplot, if specified. - if isinstance(subplot, list): - if len(subplot) != 3: - raise ValueError('"subplot" needs to be defined as a' - ' 3-element list or a 3-digit integer') - - ax = plt.subplot(subplot[0], subplot[1], subplot[2], - projection=map_proj) - else: - ax = plt.subplot(subplot, projection=map_proj) - ax.set_global() - - # Color levels - if levels is None: - if cmin is None: - cmin = cube.data.min() - if cmax is None: - cmax = cube.data.max() - levels = np.linspace(cmin, cmax, nlevels) - - # Plot using pcolormesh or contourf - if pcolormesh: - norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) - plot_handle = iplt.pcolormesh(cube, cmap=cmap, norm=norm, - rasterized=True, **kwargs) - else: - plot_handle = iplt.contourf(cube, cmap=cmap, levels=levels, - rasterized=True, **kwargs) - - # Land and coastlines - if land: - ax.add_feature(cartopy.feature.LAND) - if coast: - ax.coastlines(resolution='50m') - - # Extract region - if region: - if len(region) != 4: - raise ValueError('"Region" needs to be defined as a' - ' 4-element list') - ax.set_ylim([region[1], region[3]]) - x0 = (cent_lon - region[0]) * -1 - x1 = (region[2] - cent_lon) - ax.set_xlim([x0, x1]) - - # Color bar - if title is None: - title = cube.name() - plt.title(title, fontsize=charsize) - - # Color bar - if plotcbar: - cbar = plt.colorbar(plot_handle, orientation=cbar_orien) - cbar.ax.tick_params(labelsize=charsize * 0.85) - if cbarlabel is None: - cbarlabel = cube.units - - cbar.set_label(cbarlabel, fontsize=charsize * 0.85) - - # Tick labels and grid lines - if (xticks is not None) or (yticks is not None): - ax = plt.gca() - - # First do labels - gl = ax.gridlines(draw_labels=True, linewidth=1, color='gray', - linestyle='--') - gl.xlabels_top = False - gl.ylabels_right = False - gl.xlines = False - gl.ylines = False - - if xticks is not None: - gl.xlocator = mticker.FixedLocator(xticks) - gl.xformatter = LONGITUDE_FORMATTER - gl.xlabel_style = {'size': charsize * 0.8} - - else: - gl.xlocator = mticker.FixedLocator([-1e6]) - - if yticks is not None: - gl.ylocator = mticker.FixedLocator(yticks) - gl.yformatter = LATITUDE_FORMATTER - gl.ylabel_style = {'size': charsize * 0.8} - - else: - gl.ylocator = mticker.FixedLocator([-1e6]) - - # Now do grid lines - if xticks is not None: - # xticks += xticks - gl_x = ax.gridlines(linewidth=1, color='gray', - linestyle=':') - gl_x.xlocator = mticker.FixedLocator(xticks) - gl_x.ylines = False - - if yticks is not None: - gl_y = ax.gridlines(linewidth=1, color='gray', - linestyle=':') - gl_y.ylocator = mticker.FixedLocator(yticks) - gl_y.xlines = False - - return ax diff --git a/profsea/slr_pkg/cubeutils.py b/profsea/slr_pkg/cubeutils.py deleted file mode 100644 index d80ad90..0000000 --- a/profsea/slr_pkg/cubeutils.py +++ /dev/null @@ -1,27 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import iris - - -def loadcube(files, ncvar=None, *args, **kwargs): - """ - Load data using iris.load() with optional constraint by netcdf variable - name. - :param files: any sequence of file types accepted by iris.load() - :param ncvar: netcdf variable name - :param args: See iris.load() for other valid arguments - :param kwargs: See iris.load() for other valid keyword arguments - :return: an iris.cube.CubeList object - """ - - cubes = iris.load(files, *args, **kwargs) - - if ncvar: - var_constraint = iris.Constraint(cube_func=lambda c: - c.var_name == ncvar) - cubes = cubes.extract(var_constraint) - - return cubes diff --git a/profsea/slr_pkg/models.py b/profsea/slr_pkg/models.py deleted file mode 100644 index fc91f2f..0000000 --- a/profsea/slr_pkg/models.py +++ /dev/null @@ -1,63 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - - -def cmip5_names(): - """ - List of all available CMIP model names - Ref: Slangen et al. (2014) - :return: List of all CMIP5 models - """ - print(f'Getting data for all CMIP5 models') - - models = ['ACCESS1-0', - 'bcc-csm1-1', - 'CNRM-CM5', - 'CSIRO-Mk3-6-0', - 'CanESM2', - 'GFDL-ESM2G', - 'GFDL-ESM2M', - 'GISS-E2-R', - 'HadGEM2-CC', - 'HadGEM2-ES', - 'inmcm4', - 'IPSL-CM5A-LR', - 'IPSL-CM5A-MR', - 'MIROC-ESM', - 'MIROC-ESM-CHEM', - 'MIROC5', - 'MPI-ESM-LR', - 'MPI-ESM-MR', - 'MRI-CGCM3', - 'NorESM1-M', - 'NorESM1-ME' - ] - - return models - - -def cmip5_names_marginal(): - """ - List of all available CMIP model names that are appropriate for use in - marginal seas e.g. Mediterranean. Marginal sea is where the native grid of - the CMIP model has the 'marginal sea' disconnected from the wider ocean - basin. AR5 recommends excluding these from analysis as values are - unrealistic. - Ref: https://www.ipcc.ch/site/assets/uploads/2018/07/WGI_AR5.Chap_.13_SM. - 1.16.14.pdf - :return: List of CMIP5 models used in AR5 for marginal seas - """ - - print(f'Getting data for subset of CMIP models - marginal selected') - models = ['bcc-csm1-1', - 'CanESM2', - 'GFDL-ESM2M', - 'HadGEM2-CC', - 'HadGEM2-ES', - 'MIROC-ESM', - 'MIROC-ESM-CHEM', - 'MIROC5' - ] - return models diff --git a/profsea/slr_pkg/process.py b/profsea/slr_pkg/process.py deleted file mode 100644 index e219be5..0000000 --- a/profsea/slr_pkg/process.py +++ /dev/null @@ -1,213 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import iris.analysis.cartography -import numpy -import scipy.stats - -from profsea.slr_pkg import cubedata - - -class Regress: - """ - Class to act as an interface to various regression functions. - """ - def __init__(self, method='linear'): - """ - Return an instance linking the appropriate regression functions to - be used as a set of generically-named regression functions, given an - initialization method. - :param method: regression method to be used - """ - self.method = method - - # Associate generic functions with method-specific functions - if self.method == 'linear': - self.detrend_scalar = self._linreg_detrend_scalar - self.regress_t_scalar = self._linreg_regress_t_scalar - # OTHER METHODS - else: - raise Exception( - f'Given regression method is not implemented: {method}') - - def _linreg_detrend_scalar(self, cube_t, cube_slope): - """ - Remove a linear trend from data in one cube with dimensions of (time) - given by the slope in another cube. - :param cube_t: iris.cube.Cube with a variable of dimensions (time) - :param cube_slope: iris.cube.Cube with a dimensionless regression slope - :return: iris.cube.Cube containing the detrended timeseries, - and a numpy.ndarray containing the magnitude of the trend relative to - the starting time corresponding to the times of cube_t - """ - # Require cubes to have expected dimensions - cube_t_coords = [i.name() for i in cube_t.dim_coords] - cube_slope_coords = [i.name() for i in cube_slope.dim_coords] - if not ('time' in cube_t_coords and len(cube_t_coords) == 1): - raise Exception('cube_t does not have dimensions of (time)') - if not len(cube_slope_coords) == 0: - raise Exception('cube_slope is not dimensionless') - - # Output cube is based on input cube - cube_detrend = cube_t.copy() - - # Convert times to the units of the slope cube - if cube_detrend.coord('time').units != cube_slope.coord('time').units: - convert_time_units(cube_detrend.coord('time'), - cube_slope.coord('time')) - - # Calculate the trend as a function of time - nparr_trend = cube_detrend.coord('time').points * cube_slope.data - nparr_trend -= nparr_trend[0] - - # Remove trend from the data - cube_detrend.data -= nparr_trend - - # Value of the scalar evolving under the removed trend only - nparr_trend += cube_t.data[0] - - # Metadata - cubedata._derived( - cube_detrend, [cube_t, cube_slope], - derived_type='detrended', - derived_long='%s, minus a linear trend as calculated from %s') - cube_detrend.attributes['drift_correction'] = \ - f'{cube_detrend.var_name}: linear' - - return cube_detrend, nparr_trend - - def _linreg_regress_t_scalar(self, cube_t): - """ - Regress (using a linear regression) data in a cube with dimensions - of (time) against its time coordinate. - :param cube_t: iris.cube.Cube with a variable of dimensions (time) - :return: iris.cube.Cube containing the regression slope, and an - iris.cube.Cube containing the correlation coefficient R^2 - """ - # Require cube to have expected dimensions - cube_t_coords = [i.name() for i in cube_t.dim_coords] - if not (cube_t.ndim == 1 and 'time' in cube_t_coords): - raise Exception('cube_t does not have dimensions of (time)') - if not len(cube_t.coord('time').points) > 1: - raise Exception('The input cube must have at least 2 time ' + - 'coordinates') - - # Output cube is based on cube_t - cube_slope = cube_t.collapsed('time', iris.analysis.MEAN) - cube_corr = cube_slope.copy() - - # Call scipy.stats.linregress on the cube data and its time coordinate - nparr_regr = scipy.stats.linregress(cube_t.coord('time').points, - cube_t.data) - cube_slope.data = nparr_regr[0] - cube_corr.data = numpy.square(nparr_regr[2]) - - # Metadata - cubedata._derived( - cube_slope, [cube_t], var_name='linregslope', - derived_type='linear_regression', - derived_long='Slope of a linear regression of time (x) vs %s (y)') - cubedata._derived( - cube_corr, [cube_t], var_name='linregr2', - derived_type='linear_correlation', - derived_long='Coefficient of determination of time (x) vs %s (y)') - - return cube_slope, cube_corr - - -def convert_time_units(time_in, time_ref): - """ - Convert one time coordinate to the units of another time coordinate. - **METHOD: - - First attempt the iris.coords.Coord.convert_units method. - - If this fails (which it will do for certain units even if the resulting - time coordinates would be valid), use the slightly more forgiving - netCDF4 num2date/date2num method of converting. This will still fail if - the attempted time coordinate conversion results in an illegal date. - :param time_in: iris.coords.Coord (time) coordinate to be converted - :param time_ref: iris.coords.Coord (time) coordinate containing the target - units - """ - # Check that the input coordinates are time coordinates - if not ((type(time_in) is iris.coords.DimCoord or type(time_in) is - iris.coords.AuxCoord) and - (type(time_ref) is iris.coords.DimCoord or type(time_ref) is - iris.coords.AuxCoord)): - raise Exception('The provided inputs are not both iris coordinates') - if not (time_in.standard_name == 'time' and - time_ref.standard_name == 'time'): - raise Exception('The coordinates provided do not have "time" as ' + - 'the standard_name') - - # Time coordinate conversion - try: - # First attempt the iris.coords.Coord.convert_units method - time_in.convert_units(time_ref) - except Exception: - - # Otherwise attempt the slightly more forgiving netCDF4 - # num2date/date2num method - date_points_in = time_in.units.num2date(time_in.points) - date_bounds_in = time_in.units.num2date(time_in.bounds) - - num_points_out = time_ref.units.date2num(date_points_in) - num_bounds_out = time_ref.units.date2num(date_bounds_in) - - time_in.points = num_points_out - time_in.bounds = num_bounds_out - time_in.units = time_ref.units - - date_points_out = time_in.units.num2date(time_in.points) - date_bounds_out = time_in.units.num2date(time_in.bounds) - - # Check dates of converted times against those of original times - if not ((date_points_in == date_points_out).all() and - (date_bounds_in == date_bounds_out).all()): - raise Exception('Unit conversion did not conserve original dates') - - -def _reject_auxcoord(cube_list): - """ - Remove all cubes with AuxCoord time coordinates in a cube list. - :param cube_list: iris.cube.CubeList - :return: iris.cube.CubeList - """ - # Require a cube or cubelist - if not (type(cube_list) == iris.cube.CubeList or - type(cube_list) == iris.cube.Cube): - raise Exception('Input must be a cube or cubelist') - - # Convert cube to cubelist if necessary - if isinstance(cube_list, iris.cube.Cube): - cube_list = iris.cube.CubeList([cube_list]) - - cube_list_out = iris.cube.CubeList(cube_list) # Is this needed? - - for i_cube in cube_list: - # Skip cube if no time coordinate - coord_names = [i.name() for i in i_cube.coords()] - if 'time' not in coord_names: - continue - - # If time coordinate is an AuxCoord, print some info - dc = i_cube.coord('time') - if type(dc) == iris.coords.AuxCoord: - time_min = dc.units.num2date(dc.points.min()) - time_max = dc.units.num2date(dc.points.max()) - print(" * slr.process.reject_auxcoord: Found a cube with an " + - "AuxCoord time coordinate (dates " + - f"{time_min.year}{time_min.month}{time_min.day}-" + - f"{time_max.year}{time_max.month}{time_max.day}),\n" + - " which suggests a problem with the coordinate data. " + - "Skipping this cube..") - - # Remove cube from cubelist - cube_list_out.remove(i_cube) - - # TEMPORARY: Guess time bounds if they are not present - elif dc.bounds is None: - dc.guess_bounds() - - return cube_list_out diff --git a/profsea/slr_pkg/whichbox.py b/profsea/slr_pkg/whichbox.py deleted file mode 100644 index e8419d0..0000000 --- a/profsea/slr_pkg/whichbox.py +++ /dev/null @@ -1,112 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import numpy as np - - -def find_gridbox_indicies(cube, points, xcoord_name='longitude', - ycoord_name='latitude', drop=False): - """ - Find the grid box indices containing the specified points. - **NOTES: - - Points just west of zero degrees longitude can lie outside the - coordinate bounds for some models. - - If this is the case, subtract 360 from the points, so they lie within - the bounds of the model longitude coordinate. - - Similarly, add 360 to any points smaller than the lower limit. - :param cube: iris.cube.Cube containing zos field from CMIP models ( - should be 2D) - :param points: a 2D numpy array of pairs of points (longitude, latitude) - with dimensions (number of points, 2) - :param xcoord_name: name of x-coordinate in the cube - :param ycoord_name: name of y-coordinate in the cube - :param drop: If True, return indices for the points which lie within the - cube boundaries; - If False, return indices of -99 for points which lie outside the - boundaries. - :return: grid box indices of location - """ - # Get the grid box coordinates. Add bounds if not present. - grid_lons = cube.coord(xcoord_name) - grid_lats = cube.coord(ycoord_name) - - if not grid_lons.has_bounds(): - grid_lons.guess_bounds() - if not grid_lats.has_bounds(): - grid_lats.guess_bounds() - - # Find the minimums and maximums of the cube's coordinate boundaries - lon_lims = [grid_lons.bounds.min(), grid_lons.bounds.max()] - lat_lims = [grid_lats.bounds.min(), grid_lats.bounds.max()] - - # Get the lons and lats of the points of interest (as copies) - if isinstance(points, list): - points = np.array(points) - pt_lons, pt_lats = points.T - - if iscoordglobal(grid_lons): - widx, = np.where(pt_lons > lon_lims[1]) - if len(widx) > 0: - pt_lons[widx] -= 360 - widx, = np.where(pt_lons < lon_lims[0]) - if len(widx) > 0: - pt_lons[widx] += 360 - - # Find all points which lie within or on the boundaries of the model grid - # boxes - valid_lats = (pt_lats >= lat_lims[0]) & (pt_lats <= lat_lims[1]) - valid_lons = (pt_lons >= lon_lims[0]) & (pt_lons <= lon_lims[1]) - valid_pts = valid_lats & valid_lons - - if np.sum(valid_pts) == 0: - raise ValueError('find_gridbox_indicies: None of the points lie ' - + 'within the range of the coordinates') - else: - # Find the indices of the grid boxes containing the points - if drop: - idx_lon = [grid_lons.nearest_neighbour_index(x) - for x in pt_lons[valid_pts]] - idx_lat = [grid_lats.nearest_neighbour_index(y) - for y in pt_lats[valid_pts]] - else: - idx_lon = [grid_lons.nearest_neighbour_index(x) - if l else -99 for x, l in zip(pt_lons, valid_pts)] - idx_lat = [grid_lats.nearest_neighbour_index(y) - if l else -99 for y, l in zip(pt_lats, valid_pts)] - idx = np.array([idx_lon, idx_lat]).T - - return idx - - -def iscoordglobal(coord, tol=0.01): - """ - Tests if the longitude coordinate is global. - :param coord: iris.cube.Cube coordinate to be tested - :param tol: decide whether floating point numbers are tolerably equal - :return: True if longitude coordinate is global, otherwise false. - """ - - if not ((0 <= coord.points.min() <= 360) and - (0 <= coord.points.max() <= 360)): - raise ValueError('All points must lie within 0 to 360') - - if not coord.has_bounds(): - coord.guess_bounds() - diff = coord.bounds.max() - coord.bounds.min() - - return (diff > 360.0 - tol) & (diff < 360.0 + tol) - - -def wraplongitude(longitude): - """ - Converts input longitude to values within a range 0 - 360 plus a - specified base. - :param longitude: site location's longitude value - :return: converted longitude - """ - wrapped_lons = ((longitude + 720.0) % 360) - print(f' Converted Longitude from {longitude} to {wrapped_lons}') - - return wrapped_lons \ No newline at end of file diff --git a/profsea/step1_extract_cmip.py b/profsea/step1_extract_cmip.py deleted file mode 100644 index 276c065..0000000 --- a/profsea/step1_extract_cmip.py +++ /dev/null @@ -1,339 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import os -import numpy as np -import pandas as pd - -from profsea.config import settings -from profsea.directories import read_dir, makefolder -from profsea.slr_pkg import abbreviate_location_name, plot_ij # found in __init.py__ -from profsea.slr_pkg import cmip, cubeutils, models, whichbox -from profsea.tide_gauge_locations import extract_site_info - - -def accept_reject_cmip(cube, model, site_loc, cmip_i, cmip_j, site_lat, - site_lon, unit_test=False): - """ - Accept or reject selected CMIP grid box based on a user input. - If CMIP grid box is rejected, search neighbouring grid boxes until a - suitable one is found. - :param cube: cube containing zos field from CMIP models - :param model: CMIP model name - :param site_loc: name of the site location - :param cmip_i: CMIP coord of site location's latitude - :param cmip_j: CMIP coord of site location's longitude - :param site_lat: latitude of the site location - :param site_lon: longitude of the site location - :param unit_test: flag to disable plotting for unit testing purposes - :return: Selected CMIP coords or None if user doesn't confirm grid box - selection - """ - # Plot a map of the site location and selected CMIP grid box for user - # validation. At this stage don't save the figure to file. - if not unit_test: - plot_ij(cube, model, site_loc, [cmip_i, cmip_j], site_lat, site_lon, - save_map=False) - - # Ask user to accept cmip grid box or re-select from neighbouring cells - decision = input(f'Is selected CMIP grid box {cmip_i, cmip_j} ' - f'appropriate for {model}? Y or N: ') - if decision == 'Y' or decision == 'y': - # Save map to file and return CMIP grid box coords - if not unit_test: - plot_ij(cube, model, site_loc, [cmip_i, cmip_j], - site_lat, site_lon) - return cmip_i, cmip_j - elif decision == 'N' or decision == 'n': - print('Selecting another CMIP grid box') - return None, None - else: - raise TypeError('Response needs to be Y or N') - - -def calc_radius_range(radius): - """ - Calculate the maximum distance to search for ocean grid point. - :param radius: Maximum range to search for ocean point - :return: x_radius_range, y_radius_range - """ - # if radius > 1: - # rm1 = radius - 1 - # else: - # rm1 = radius - - x_radius_range = list(range(-radius, radius + 1)) - y_radius_range = list(range(-radius, radius + 1)) - - return x_radius_range, y_radius_range - - -def check_cube_mask(cube): - """ - Check if the cube has a scalar mask. If so, then re-mask the cube to - remove grid points that are exactly equal to 0. - **NOTES: - - Land points have a value of 0, ocean points have non-zero (both - positive and negative) values. - - Because the CMIP models were interpolated onto a 1x1 grid before - the mask was checked some interpolation issues may still arise. - :param cube: cube containing zos field from CMIP models - :return: original cube, or re-masked cube - """ - apply_mask = False - try: - cube.data.mask - apply_mask = not isinstance(cube.data.mask, (list, tuple, np.ndarray)) - except AttributeError: - apply_mask = True - - if apply_mask: - new_mask = (cube.data == 0.0) - print(f'Scalar mask: Re-masking the cube to mask cells = 0') - cube = cube.copy(data=np.ma.array(cube.data, mask=new_mask)) - - return cube - - -def extract_lat_lon(df): - """ - Extracts site location's metadata, calls wraplongitude() to convert - longitude. - :param df: DataFrame of site location's metadata - :return: latitude, longitude, converted longitude - """ - site = df.name - station_id = df['Station ID'] - lat = df['Latitude'] - lon_orig = df['Longitude'] - print(f'Site location: {site}, ID: {station_id}, ' + - f'Lat: {lat:.2f}, Lon: {lon_orig:.2f}') - - lon = whichbox.wraplongitude(lon_orig) - - return lat, lon_orig, lon - - -def extract_ssh_data(cmip_sea): - """ - Get a list of appropriate CMIP models to use. Load SSH data for each - model on a re-gridded 1x1 grid. - :param cmip_sea: variable to distinguish between which CMIP models to use - :return: CMIP model names, and associated SSH data cubes - """ - cmip_dir = settings["cmipinfo"]["sealevelbasedir"] - # Select CMIP models to use depending on whether location is within a - # marginal sea - if cmip_sea == 'all': - model_names = models.cmip5_names() - elif cmip_sea == 'marginal': - model_names = models.cmip5_names_marginal() - else: - raise UnboundLocalError(f'The selected CMIP5 models to use - ' + - f'cmip_sea = {cmip_sea} - is not recognised') - - # Load SSH data for each model on a re-gridded 1x1 grid, store in a list - cubes = [] - cmip_dict = cmip.model_dictionary() - for model in model_names: - print(f'Getting data for {model} model') - cmip_date = cmip_dict[model]['historical'] - cmip_file = f'{cmip_dir}zos_Omon_{model}_historical_{cmip_date}.nc' - cube = cubeutils.loadcube(cmip_file, ncvar='zos')[0] - cubes.append(cube.slices(['latitude', 'longitude']).next()) - - return model_names, cubes - - -def find_ocean_pt(zos_cube_in, model, site_loc, site_lat, site_lon): - """ - Searches for the nearest appropriate ocean point(s) in the CMIP model - adjacent to the site location. Initially, finds the model grid box - indices of the given location. Then, searches surrounding boxes until an - appropriate ocean point is found - needs to be accepted by the user. - **NOTES: - - The GCM data have been interpolated to a common 1 x 1 degree grid - :param zos_cube_in: cube containing zos field from CMIP models - :param model: CMIP model name - :param site_loc: name of the site location - :param site_lat: latitude of the site location - :param site_lon: longitude of the site location - :return: model grid box indices - """ - # Find grid box indices of location - (i, j), = whichbox.find_gridbox_indicies(zos_cube_in, - [(site_lon, site_lat)]) - grid_lons = zos_cube_in.coord('longitude').points - grid_lats = zos_cube_in.coord('latitude').points - - # Check to see if the cube has a scalar mask, and add mask where cmip - zos_cube = check_cube_mask(zos_cube_in) - - # If the CMIP grid box of the exact site location is an ocean point - # Get the user to check if it's appropriate and if so return the indices - if not zos_cube.data.mask[j, i]: - print('Checking CMIP grid box at site location') - i_out, j_out = accept_reject_cmip(zos_cube, model, site_loc, i, j, - site_lat, site_lon) - if i_out is not None: - pt_lon = grid_lons[i_out] - pt_lat = grid_lats[j_out] - return i_out, j_out, pt_lon, pt_lat - - # If no indices are returned then the CMIP grid box is not appropriate - # for use. Check the CMIP grid boxes surrounding the site location - # until an appropriate one is found. - else: - i_out, j_out, pt_lon, pt_lat = search_for_next_cmip(i, j, - zos_cube, - model, - site_loc, - site_lat, - site_lon) - - # If the CMIP grid box of the exact site location is masked, start by - # checking the next set of cmip grid boxes - else: - i_out, j_out, pt_lon, pt_lat = search_for_next_cmip(i, j, zos_cube, - model, site_loc, - site_lat, site_lon) - - return i_out, j_out, pt_lon, pt_lat - - -def ocean_point_wrapper(df, model_names, cubes): - """ - Wrapper script to extract relevant metadata for site location, needed to - select the nearest CMIP grid box. Collates the CMIP model name, i and j - coords selected and lat and lon value of the site. - Writes the results to a single .csv file assuming write=True. - :param df: DataFrame of site location's metadata - :param model_names: CMIP model names - :param cubes: cube containing zos field from CMIP models - """ - - # Get the metadata of either the site location or tide gauge location - for site_loc in df.index.values: - df_site = df.loc[site_loc] - lat, lon_orig, lon = extract_lat_lon(df_site) - - # Setup empty 2D list to store results for each model - # [name, i and j coords, lat and lon value] - result = [] - for n, zos_cube in enumerate(cubes): - model = model_names[n] - i, j, pt_lon, pt_lat = find_ocean_pt(zos_cube, model, site_loc, - lat, lon) - result.append([model, i, j, pt_lon, pt_lat]) - - # Write the data to a file - write_i_j(site_loc, result, lat, lon_orig) - - -def search_for_next_cmip(cmip_i, cmip_j, cube, model, site_loc, site_lat, - site_lon, unit_test=False): - """ - Iteratively check the CMIP grid boxes surrounding the site location - until a suitable option is found. - :param cmip_i: CMIP coord of site location's latitude - :param cmip_j: CMIP coord of site location's longitude - :param cube: cube containing zos field from CMIP models - :param model: CMIP model name - :param site_loc: name of the site location - :param site_lat: latitude of the site location - :param site_lon: longitude of the site location - :param unit_test: flag to disable plotting for unit testing purposes - :return: Selected CMIP coords - """ - grid_lons = cube.coord('longitude').points - grid_lats = cube.coord('latitude').points - - # The radius limit of 7 is arbitrary but should be large enough. - for radius in range(1, 8): # grid boxes - print(f'Checking CMIP grid boxes {radius} box removed ' + - 'from site location') - x_radius_range, y_radius_range = calc_radius_range(radius) - for ix in x_radius_range: - for iy in y_radius_range: - # Search the nearest grid cells. If the new mask is False, - # that grid cell is an ocean point - limit_lo = radius * radius - dd = ix * ix + iy * iy - if dd >= limit_lo: - # modulus for when grid cell is close to 0deg. - i_try = (cmip_i + ix) % len(grid_lons) - j_try = cmip_j + iy - - if not cube.data.mask[j_try, i_try]: - i_out, j_out = accept_reject_cmip( - cube, model, site_loc, i_try, j_try, site_lat, - site_lon, unit_test) - if i_out is not None: - pt_lon = grid_lons[i_out] - pt_lat = grid_lats[j_out] - return i_out, j_out, pt_lon, pt_lat - - -def write_i_j(site_loc, result, site_lat, lon_orig): - """ - Convert the grid indices to a data frame and writes to file. - :param site_loc: name of site location - :param result: grid indices and coordinates of nearest ocean point - :param site_lat: latitude of the site location - :param lon_orig: longitude of site - """ - # Store the grid indices and coordinates in a dataframe - df_out = pd.DataFrame(result, - columns=['Model', 'i', 'j', 'box_lon', 'box_lat']) - - # Create the output file directory location - out_cmipdir = read_dir()[0] - makefolder(out_cmipdir) - - # Abbreviate the site location name suitable to use as a filename - loc_abbrev = abbreviate_location_name(site_loc) - outfile = os.path.join(out_cmipdir, f'{loc_abbrev}_ij_1x1_coords.csv') - - # Save the CMIP grid box selection (i and j coordinates) to file - with open(outfile, 'w') as ofp: - ofp.write(f'Location: {site_loc}' + '\n') - ofp.write(f'Latitude: {site_lat:8.3f}' + '\n') - ofp.write(f'Longitude: {lon_orig:8.3f}' + '\n') - df_out.to_csv(ofp, index=False) - - -def main(): - """ - Find the nearest CMIP model grid box, to the user defined site - """ - print(f'User specified site name(s) is (are):' - f' {settings["siteinfo"]["sitename"]}') - print(f'User specified lat(s) and lon(s) is (are): ' - f'{settings["siteinfo"]["sitelatlon"]}') - if settings["siteinfo"]["sitelatlon"] == [[]]: - print(f' No lat lon specified - use tide gauge metadata if ' - f'available') - print(f'User specified science method is: {settings["sciencemethod"]}') - if {settings["cmipinfo"]["cmip_sea"]} == {'all'}: - print('User specified all CMIP models') - elif {settings["cmipinfo"]["cmip_sea"]} == {'marginal'}: - print('User specified CMIP models for marginal seas only') - - # Extract site data from station list (e.g. tide gauge location) or - # construct based on user input - df_site_data = extract_site_info(settings["tidegaugeinfo"]["source"], - settings["tidegaugeinfo"]["datafq"], - settings["siteinfo"]["region"], - settings["siteinfo"]["sitename"], - settings["siteinfo"]["sitelatlon"]) - - # Find the nearest, appropriate ocean point in CMIP models to specified - # site location - cmip_models, ssh_cubes = extract_ssh_data(settings["cmipinfo"]["cmip_sea"]) - ocean_point_wrapper(df_site_data, cmip_models, ssh_cubes) - - -if __name__ == '__main__': - main() diff --git a/profsea/step2_extract_steric_dyn_regression.py b/profsea/step2_extract_steric_dyn_regression.py deleted file mode 100644 index 599b1b9..0000000 --- a/profsea/step2_extract_steric_dyn_regression.py +++ /dev/null @@ -1,71 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -from profsea.config import settings -from profsea.slr_pkg import extract_dyn_steric_regression # found in __init.py__ -from profsea.slr_pkg import models -from profsea.tide_gauge_locations import extract_site_info - - -def extract_cmip5_steric_dyn_regression(df): - """ - Finds all CMIP model names, then calculates the regression parameters - between global and local sea level projections for the given locations. - :param df: DataFrame of site location's metadata - """ - print('running function extract_cmip5_steric_dyn_regression') - # Specify the emission scenarios - scenarios = ['rcp26', 'rcp45', 'rcp85'] - - # Select CMIP5 models to use - if settings["cmipinfo"]["cmip_sea"] == 'all': - model_names = models.cmip5_names() - elif settings["cmipinfo"]["cmip_sea"] == 'marginal': - model_names = models.cmip5_names_marginal() - else: - raise UnboundLocalError( - 'The selected CMIP5 models to use - cmip_sea = ' + - f'{settings["cmipinfo"]["cmip_sea"]} - ' + - 'is not recognised') - - # Calculate the regression parameters and plot the results - # Note the x and y limits of the plot are set to 0.6 - this can be - # updated in extract_dyn_steric_regression() function - extract_dyn_steric_regression(model_names, df, scenarios) - - -def main(): - """ - Calculate the regression between local sea level change and global mean - sea level rise from thermal expansion. - If the regression slope is steeper/shallower than 1, then the local change - is increasing faster/slower than the global average. - """ - print(f'User specified site name(s) is (are):' - f' {settings["siteinfo"]["sitename"]}') - print(f'User specified lat(s) and lon(s) is (are): ' - f'{settings["siteinfo"]["sitelatlon"]}') - if settings["siteinfo"]["sitelatlon"] == [[]]: - print(f' No lat lon specified - use tide gauge metadata if ' - f'available') - print(f'User specified science method is: {settings["sciencemethod"]}') - if {settings["cmipinfo"]["cmip_sea"]} == {'all'}: - print('User specified all CMIP models') - elif {settings["cmipinfo"]["cmip_sea"]} == {'marginal'}: - print('User specified CMIP models for marginal seas only') - - # Extract site data from station list (e.g. tide gauge location) or - # construct based on user input - df_site_data = extract_site_info(settings["tidegaugeinfo"]["source"], - settings["tidegaugeinfo"]["datafq"], - settings["siteinfo"]["region"], - settings["siteinfo"]["sitename"], - settings["siteinfo"]["sitelatlon"]) - - extract_cmip5_steric_dyn_regression(df_site_data) - - -if __name__ == '__main__': - main() diff --git a/profsea/step3_process_regional_sealevel_projections.py b/profsea/step3_process_regional_sealevel_projections.py deleted file mode 100644 index 0db288e..0000000 --- a/profsea/step3_process_regional_sealevel_projections.py +++ /dev/null @@ -1,542 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import os -import iris -import pickle -import numpy as np -import pandas as pd -from scipy.interpolate import RegularGridInterpolator -from netCDF4 import Dataset - -from profsea.config import settings -from profsea.tide_gauge_locations import extract_site_info -from profsea.slr_pkg import abbreviate_location_name, choose_montecarlo_dir # found in __init.py__ -from profsea.directories import read_dir, makefolder - - -def calc_baseline_period(sci_method, yrs): - """ - Baseline years used for UKCP18 -- 1981-2000 (offset required) - Baseline years used for IPCC AR5 and Palmer et al 2020 -- 1986-2005 - :param sci_method: scientific method used to select GIA estimates, GRD - fingerprints and CMIP regression (based on user input) - :param yrs: years of the projections - :return: baseline years - """ - if sci_method == 'global': - byr1 = 1986. - byr2 = 2005. - # offset not required - G_offset = 0.0 - print("Baseline period = ", byr1, "to", byr2) - elif sci_method == 'UK': - byr1 = 1981. - byr2 = 2000. - G_offset = 0.011 - print("Baseline period = ", byr1, "to", byr2) - - midyr = (byr2 - byr1 + 1) * 0.5 + byr1 - - return yrs[0] - midyr, G_offset - -def calc_future_sea_level_at_site(df, site_loc, scenario): - """ - Calculates future sea level at the given site and write to file. - :param df: Data frame of all metadata (tide gauge or site specific) for - each(all) location(s) - :param site_loc: name of the site location - :param scenario: emission scenario - """ - print('running function calc_future_sea_level_at_site') - # Get the location's latitude and longitude. - loc_coords = [df.at[site_loc, 'Latitude'], df.at[site_loc, 'Longitude']] - - # Set the UKCP*18* random seed so results are reproducible - np.random.seed(18) - - # Directory of Monte Carlo time series for new projections - mcdir = choose_montecarlo_dir() - - # Specify the sea level components to include. The GIA contribution is - # calculated separately. - components = ['exp', 'antdyn', 'antsmb', 'greendyn', 'greensmb', - 'glacier', 'landwater'] - # nesm, nyrs, yrs = get_projection_info(mcdir, scenario) - nesm = 450000 - yrs = np.arange(2007, settings["projection_end_year"] + 1) - nyrs = yrs.size - - # Determine the number of samples you wish to make CHOGOM - # project = 100000, UKCP18 = 200000 - nsmps = 200000 - array_dims = [nesm, nsmps, nyrs] - - # Get random samples of global and regional sea level components - montecarlo_G, montecarlo_R = calculate_sl_components(mcdir, components, - scenario, site_loc, - loc_coords, yrs, - array_dims) - - # Calculate the summary time series of global and local sea level - # change, i.e. mean and percentile values. - G_df, R_df = calculate_summary_timeseries(components, yrs, montecarlo_G, - montecarlo_R) - - # Create the output sea level projections file directory and filename - sealev_ddir = read_dir()[4] - loc_abbrev = abbreviate_location_name(site_loc) - file_header = '_'.join([loc_abbrev, scenario, "projection", - f"{settings['projection_end_year']}"]) - G_file = '_'.join([file_header, 'global']) + '.csv' - R_file = '_'.join([file_header, 'regional']) + '.csv' - - # Save the global and local projections - makefolder(sealev_ddir) - G_df.to_csv(os.path.join(sealev_ddir, G_file)) - R_df.to_csv(os.path.join(sealev_ddir, R_file)) - - -def calc_gia_contribution(sci_method, yrs, nyrs, nsmps, coords): - """ - Calculate the glacial isostatic adjustment (GIA) contribution to the - regional component of sea level rise. - Option to use the generic global GIA estimates or use the GIA estimates - developed for UK as part of UKCP18. - :param sci_method: scientific method used to select GIA estimates, GRD - fingerprints and CMIP regression (based on user input) - :param yrs: years of the projections - :param nyrs: number of years in each projection time series - :param nsmps: determine the number of samples - :param coords: coordinates of location of interest - :return: GIA estimates converted to mm/yr - """ - print('running function calc_gia_contribution') - - nGIA, GIA_vals = read_gia_estimates(sci_method, coords) - - Tdelta, G_offset = calc_baseline_period(sci_method, yrs) - - # Unit series of mm/yr expressed as m/yr - unit_series = (np.arange(nyrs) + Tdelta) * 0.001 - GIA_unit_series = np.ones([nsmps, nyrs]) * unit_series - - # rgiai is an array of random GIA indices the size of the sample years - rgiai = np.random.randint(nGIA, size=nsmps) - - GIA_series = GIA_unit_series.transpose() * GIA_vals[rgiai] - - print('GIA_vals (mm/yr) = ', GIA_series) - - return GIA_series, G_offset - - -def calculate_sl_components(mcdir, components, scenario, site_loc, loc_coords, - yrs, array_dims): - """ - Calculates global and regional component part contributions to sea level - change. - :param mcdir: location of Monte Carlo time series for new projections - :param components: sea level components - :param scenario: emission scenario - :param site_loc: name of the site location - :param loc_coords: latitude and longitude of site - :param yrs: years of the projections - :param array_dims: Array of nesm, nsmps and nyrs - nesm --> Number of ensemble members in time series - nsmps --> Determine the number of samples you wish to make - nyrs --> Number of years in each projection time series - :return: montecarlo_G (global contribution to sea level rise) and - montecarlo_R (regional contribution to sea level change) - """ - print('running function calculate_sl_components') - ncomps = len(components) - - # Numbers of ensemble members, samples, years - nesm, nsmps, nyrs = array_dims - - # Coordinates of location of interest - tlat, tlon = loc_coords - - # Scientific method - sci_method = settings["sciencemethod"] - - # Set up fingerprint interpolators for each component of sea level change - # Note that the first entry in the list is the Slangen, which includes the - # land water estimate - nFPs, FPlist = setup_FP_interpolators(components, sci_method) - - # Use the same resampling across all sea level components (to preserve - # correlations) - resamples = np.random.choice(nesm, nsmps) - rfpi = np.random.randint(nFPs, size=nsmps) - - # The "offset_slopes" scales the global_offset variables into the different - # sea level components. - # These offsets exist because a baseline period of 1981-2000 is used, - # rather than the AR5 baseline period of 1986-2005. - offset_slopes = {'exp': 0.405, - 'antnet': 0.095, - 'antsmb': -0.025, - 'antdyn': 0.120, - 'greennet': 0.125, - 'greensmb': 0.040, - 'greendyn': 0.085, - 'glacier': 0.270, - 'landwater': 0.105} - - # Monte Carlo for regional values (FPs applied) + GIA - montecarlo_R = np.zeros((ncomps + 1, nyrs, nsmps)) - # Monte Carlo for Global values (no FPs applied) - montecarlo_G = np.zeros((ncomps, nyrs, nsmps)) - - # Calculate the GIA contribution to the regional component of sea level - # change - GIA_series, G_offset = calc_gia_contribution(sci_method, yrs, nyrs, nsmps, - loc_coords) - montecarlo_R[-1, :, :] = GIA_series[:, :] - - for cc, comp in enumerate(components): - print(f'cc = {cc:d}, comp = {comp}') - - offset = G_offset * offset_slopes[comp] - - cube = iris.load_cube(os.path.join(mcdir, f'{scenario}_{comp}.nc')) - montecarlo_G[cc, :, :] = cube.data[:nyrs, resamples] + offset - - if comp == 'exp': - if sci_method == 'global': - coeffs = load_CMIP5_slope_coeffs(site_loc, scenario) - rand_coeffs = np.random.choice(coeffs, size=nsmps, - replace=True) - elif sci_method == 'UK': - coeffs, weights = load_CMIP5_slope_coeffs_UK(scenario) - rand_coeffs = np.random.choice(coeffs, size=nsmps, - replace=True, p=weights) - montecarlo_R[cc, :, :] = montecarlo_G[cc, :, :] * rand_coeffs - elif comp == 'landwater': - landwater_FP_interpolator = FPlist[0]['landwater'] - # Interpolate values to target lat/lon - val = landwater_FP_interpolator([tlat, tlon])[0] - montecarlo_R[cc, :, :] = montecarlo_G[cc, :, :] * val - else: - # Initiate an empty list for fingerprint values - FPvals = [] - for FP_dict in FPlist: - # Interpolate values to target lat/lon - val = FP_dict[comp]([tlat, tlon])[0] - FPvals.append(val) - FPvals = np.array(FPvals) - montecarlo_R[cc, :, :] = montecarlo_G[cc, :, :] * FPvals[rfpi] - - return montecarlo_G, montecarlo_R - - -def calculate_summary_timeseries(components, years, montecarlo_G, - montecarlo_R): - """ - Calculates summary timeseries of each sea level rise component, plus sums - of the contributions from Greenland and Antarctica, and sum of all - components. - :param components: sea level components - :param years: years of the projections - :param montecarlo_G: global sea level components - :param montecarlo_R: regional sea level components - :return: DataFrame of global and regional summary timeseries of sea level - """ - print('running function calculate_summary_timeseries') - percentiles = [5, 10, 30, 33, 50, 67, 70, 90, 95] - - # Define lists to store the summary timeseries. They will be converted to - # Pandas dataframes. - R_list = [] - G_list = [] - - for cc, _ in enumerate(components): - # Calculate and store the global component time series - cgout = np.percentile(montecarlo_G[cc, :, :], percentiles, axis=1) - G_list.append(cgout.flatten(order='F')) - - # Calculate and store the regional component time series - crout = np.percentile(montecarlo_R[cc, :, :], percentiles, axis=1) - R_list.append(crout.flatten(order='F')) - - # Set up a multi-level index for the dataframes, consisting of the years - # and percentiles. - iterables = [years, percentiles] - idx = pd.MultiIndex.from_product(iterables, names=['year', 'percentile']) - G_df = pd.DataFrame(np.asarray(G_list).T, columns=components, index=idx) - R_df = pd.DataFrame(np.asarray(R_list).T, columns=components, index=idx) - R_df.rename(columns={"exp": "ocean"}) - - # Store the regional GIA component time series - ncomp = len(components) - crout = np.percentile(montecarlo_R[ncomp, :, :], percentiles, axis=1) - R_df['GIA'] = crout.flatten(order='F') - - # Add sums of contributions from Antarctica and Greenland to the saved - # series. This needs to be calculated across the full montecarlo range not - # a pandas addition. - # calc_future_sea_level_at_site states: components = ['exp', 'antdyn', - # 'antsmb', 'greendyn', 'greensmb', 'glacier', 'landwater'] - antnet_tmpg = np.percentile( - montecarlo_G[1, :, :] + montecarlo_G[2, :, :], - percentiles, axis=1).flatten(order='F') - G_df['antnet'] = pd.DataFrame(antnet_tmpg, columns=['antnet'], index=idx) - greennet_tmpg = np.percentile( - montecarlo_G[3, :, :] + montecarlo_G[4, :, :], - percentiles, axis=1).flatten(order='F') - G_df['greennet'] = pd.DataFrame( - greennet_tmpg, columns=['greennet'], index=idx) - antnet_tmpr = np.percentile( - montecarlo_R[1, :, :] + montecarlo_R[2, :, :], - percentiles, axis=1).flatten(order='F') - R_df['antnet'] = pd.DataFrame(antnet_tmpr, columns=['antnet'], index=idx) - greennet_tmpr = np.percentile( - montecarlo_R[3, :, :] + montecarlo_R[4, :, :], - percentiles, axis=1).flatten(order='F') - R_df['greennet'] = pd.DataFrame( - greennet_tmpr, columns=['greennet'], index=idx) - - # Sum of all components (net sea level change) - montecarlo_Gsum = np.sum(montecarlo_G, axis=0) - montecarlo_Rsum = np.sum(montecarlo_R, axis=0) - # Store the percentiles of the sums of all components to global and local - # sea level change - cgout = np.percentile(montecarlo_Gsum, percentiles, axis=1) - crout = np.percentile(montecarlo_Rsum, percentiles, axis=1) - G_df['sum'] = cgout.flatten(order='F') - R_df['sum'] = crout.flatten(order='F') - - return G_df, R_df - - -def create_FP_interpolator(datadir, dfile, method='linear'): - """ - Generates a scipy Interpolator object from input NetCDF data of - gravitational fingerprints (takes inputs of Latitude and Longitude). - :param datadir: data directory - :param dfile: data filename - :param method: interpolation type --> 'linear' or 'nearest' - :return: 2D Interpolator object - """ - cube = iris.load_cube(os.path.join(datadir, dfile)) - lon = cube.coord('longitude').points - lat = cube.coord('latitude').points - - # Define linear interpolator object: - interp_object = RegularGridInterpolator((lat, lon), cube.data, - method=method, bounds_error=True, - fill_value=None) - - return interp_object - - -def get_projection_info(indir, scenario): - """ - Read in the dimensions of the Monte-Carlo data. These files are all - relative to midnight on 1st January 2007 - :param indir: directory of Monte Carlo time series for new projections - :param scenario: emission scenarios to be considered - :return: Number of ensemble members in time series, number of years in - each projection time series and the years of the projections - """ - sample_file = f'{scenario}_exp.nc' - f = Dataset(f'{indir}{sample_file}', 'r') - nesm = f.dimensions['realization'].size - t = f.variables['time'] - nyrs = t.size - unit_str = t.units - first_year = int(unit_str.split(' ')[2][:4]) - f.close() - - yrs = first_year + np.arange(nyrs) - - return nesm, nyrs, yrs - - -def load_CMIP5_slope_coeffs(site_loc, scenario): - """ - Loads in the CMIP slope coefficients based on linear regression of - 'zos+zostoga' against 'zostoga' for the period 2005 to 2100. - Some models are missing regression slopes for RCP2.6. If so, use RCP4.5 - values instead. - :param site_loc: name of the site location - :param scenario: emissions scenario - :return: 1D array of regression coefficients - """ - print('running function load_CMIP5_slope_coeffs') - - # Read in the sea level regressions - in_zosddir = read_dir()[2] - loc_abbrev = abbreviate_location_name(site_loc) - filename = os.path.join(in_zosddir, f'{loc_abbrev}_zos_regression.csv') - df = pd.read_csv(filename, header=0) - - coeffs = df.loc[(df['Scenario'] == scenario)]['slope_05_00'].values - - # Some models are missing regression slopes for RCP2.6. If so, use RCP4.5 - # values instead - if scenario == 'rcp26': - rcp45_coeffs = df.loc[(df['Scenario'] == - 'rcp45')]['slope_05_00'].values - msgi = np.where(np.isnan(coeffs))[0] - coeffs[msgi] = rcp45_coeffs[msgi] - - return coeffs - - -def load_CMIP5_slope_coeffs_UK(scenario): - """ - Loads in the CMIP5 slope coefficients developed for UKCP18 - :param scenario: emissions scenario - :return: 1D array of slope coefficients and 1D array of weights - """ - print('running function load_CMIP5_slope_coeffs_UK') - in_zosdir_uk = settings["cmipinfo"]["slopecoeffsuk"] - filename_uk = f'{scenario}_CMIP5_regress_coeffs_uk_mask_1.pickle' - - try: - with open(os.path.join(in_zosdir_uk, filename_uk), 'rb') as f: - data = pickle.load(f, encoding='latin1')['uk_mask_1'] - except FileNotFoundError: - raise FileNotFoundError(filename_uk, - '- scenario selected does not exist') - - # Keys are: 'coeffs', 'models', 'weights' - coeffs = data['coeffs'] - weights = data['weights'] - - return coeffs, weights - - -def read_gia_estimates(sci_method, coords): - """ - Read in pre-processed interpolator objects of GIA estimates (Lambeck, - ICE5G or UK specific ones) - :param sci_method: scientific method used to select GIA estimates, GRD - fingerprints and CMIP regression (based on user input) - :param coords: latitude and longitude of tide gauge - :return: length of GIA_vals and numpy array of pre-processed interpolator - objects of GIA estimates - """ - print('running function read_gia_estimates') - # Directories containing GIA data (independent of scenario) - if sci_method == 'global': - gia_file = settings["giaestimates"]["global"] - elif sci_method == 'UK': - gia_file = settings["giaestimates"]["uk"] - else: - raise UnboundLocalError('The selected GIA estimate - ' + - f'{sci_method} - is not available') - - with open(gia_file, "rb") as ifp: - GIA_dict = pickle.load(ifp, encoding='latin1') - - GIA_vals = [] - lat, lon = coords - # The GIA_dict contains interpolator objects - for key in list(GIA_dict.keys()): - val = GIA_dict[key]([lat, lon])[0] - GIA_vals.append(val) - - nGIA = len(GIA_vals) - GIA_vals = np.array(GIA_vals) - - return nGIA, GIA_vals - - -def setup_FP_interpolators(components, sci_method): - """ - Create 2D Interpolator objects for the Slangen, Spada and Klemann - fingerprints - :param components: list of sea level components - :param sci_method: scientific method used to select GIA estimates, GRD - fingerprints and CMIP regression (based on user input) - :return nFPs: length of FPlist and interpolator objects of all sea level - components - """ - print('running function setup_FP_interpolators') - - # Directories for the Slangen, Spada and Klemann fingerprints - slangendir = settings["fingerprints"]["slangendir"] - spadadir = settings["fingerprints"]["spadadir"] - klemanndir = settings["fingerprints"]["klemanndir"] - - # Create empty dictionaries for the Slangen, Spada and Klemann fingerprints - # interpolator objects. - slangen_FPs = {} - spada_FPs = {} - klemann_FPs = {} - - # Only 1 fingerprint for Landwater - comp = 'landwater' - slangen_FPs[comp] = create_FP_interpolator(slangendir, - comp + '_slangen_nomask.nc') - - # Create interpolators for the remaining components. Expansion ('exp') - # is global so no interpolation is needed. - components_todo = [c for c in components if c not in ['exp', 'landwater']] - for comp in components_todo: - slangen_FPs[comp] = create_FP_interpolator(slangendir, - comp + '_slangen_nomask.nc') - spada_FPs[comp] = create_FP_interpolator(spadadir, - comp + '_spada_nomask.nc') - klemann_FPs[comp] = create_FP_interpolator(klemanndir, - comp + '_klemann_nomask.nc') - - if sci_method == 'UK': - # Klemann fingerprints were not used in UKCP18 - FPlist = [slangen_FPs, spada_FPs] - elif sci_method == 'global': - FPlist = [slangen_FPs, spada_FPs, klemann_FPs] - else: - raise UnboundLocalError('The selected GRD fingerprint method - ' + - f'{sci_method} - is not available') - - nFPs = len(FPlist) - - return nFPs, FPlist - - -def main(): - """ - Reads in and calculates global and local (regional) sea level change - (sum total), based on the different contributing factors e.g. thermal - expansion, GIA and mass balance. Writes out the selected emissions scenario - estimates of the various components and their sums. - """ - print(f'User specified site name(s) is (are):' - f' {settings["siteinfo"]["sitename"]}') - print(f'User specified lat(s) and lon(s) is (are): ' - f'{settings["siteinfo"]["sitelatlon"]}') - if settings["siteinfo"]["sitelatlon"] == [[]]: - print(f' No lat lon specified - use tide gauge metadata if ' - f'available') - print(f'User specified science method is: {settings["sciencemethod"]}') - if {settings["cmipinfo"]["cmip_sea"]} == {'all'}: - print('User specified all CMIP models') - elif {settings["cmipinfo"]["cmip_sea"]} == {'marginal'}: - print('User specified CMIP models for marginal seas only') - - print(f'\nProjecting out to: {settings["projection_end_year"]}\n') - - # Extract site data from station list (e.g. tide gauge location) or - # construct based on user input - df_site_data = extract_site_info(settings["tidegaugeinfo"]["source"], - settings["tidegaugeinfo"]["datafq"], - settings["siteinfo"]["region"], - settings["siteinfo"]["sitename"], - settings["siteinfo"]["sitelatlon"]) - - scenarios = ['rcp26', 'rcp45', 'rcp85'] - for scenario in scenarios: - # Get the metadata of either the site location or tide gauge location - for loc_name in df_site_data.index.values: - calc_future_sea_level_at_site(df_site_data, loc_name, scenario) - - -if __name__ == '__main__': - main() diff --git a/profsea/step4_plot_regional_sealevel.py b/profsea/step4_plot_regional_sealevel.py deleted file mode 100644 index 6d968d8..0000000 --- a/profsea/step4_plot_regional_sealevel.py +++ /dev/null @@ -1,974 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import os -import iris -import numpy as np -import pandas as pd -import matplotlib -import matplotlib.pyplot as plt - -from profsea.config import settings -from profsea.tide_gauge_locations import extract_site_info, find_nearest_station_id -from profsea.slr_pkg import abbreviate_location_name, choose_montecarlo_dir # found in __init.py__ -from profsea.surge import tide_gauge_library as tgl -from profsea.directories import read_dir, makefolder -from profsea.plotting_libraries import location_string, scenario_string, \ - ukcp18_colours, ukcp18_labels, calc_xlim, calc_ylim, plot_zeroline - - -def compute_uncertainties(df_r_list, scenarios, tg_years, tg_amsl): - """ - Function to estimate uncertainties following Hawkins and Sutton (2009). - :param df_r_list: regional sea level projections - :param scenarios: emissions scenario - :param tg_years: tide gauge years - :param tg_amsl: annual mean sea level data - :return: years, scenario uncertainty, model uncertainty, internal - variability - """ - nyrs = np.range(2007, settings["projection_end_year"] + 1).size - allpmid = np.zeros([3, nyrs]) - allunc = np.zeros([3, nyrs]) - - # Estimate internal variability from de-trended gauge data - tg_years_arr = np.array(tg_years, dtype='int') - vals = np.ma.masked_values(tg_amsl, -99999.) - IntV = compute_variability(tg_years_arr / 1000., vals / 1.) - - # Get the values of the multi-index: years and percentile values - years, percentiles = multi_index_values(df_r_list) - - for rcp_count, _ in enumerate(scenarios): - # Get the sums of local sea level projections - df_R = df_r_list[rcp_count] - rlow, rmid, rupp = extract_comp_sl(df_R, percentiles, 'sum') - - allpmid[rcp_count, :] = rmid - allunc[rcp_count, :] = (rupp - rlow) * 0.5 - - # Scenario Uncertainty (expressed as 90% confidence interval) - UncS = np.std(allpmid, axis=0) * 1.645 - # Model Uncertainty (already expressed as 90% confidence interval) - UncM = np.mean(allunc, axis=0) - - return years, UncS, UncM, IntV - - -def compute_variability(x, y, factor=1.645): - """ - Estimate internal variability from de-trended gauge data. - :param x: tide gauge temporal data - :param y: tide gauge data - :param factor: multiplication factor - :return: internal variability component of uncertainty - """ - mask = np.ma.getmask(y) - index = np.where(mask is True) - new_x = np.delete(x, index) - new_y = np.delete(y, index) - fit = np.polyfit(new_x, new_y, 1) - fit_data = new_x * fit[0] + fit[1] - stdev = np.std(new_y - fit_data) - - return stdev * factor - - -def extract_comp_sl(df, percentiles, comp): - """ - Get the sums of all components of local sea level projections. - :param df: global or regional DataFrame of sea level projections - :param percentiles: specified percentiles - :param comp: components of sea level - :return: sum of sea level components at lower, middle and upper percentile - """ - # 5th percentile - based on UKCP18 percentile levels - rlow = df.xs(percentiles[0], level='percentile')[comp].to_numpy(copy=True) - # 50th percentile - rmid = df.xs(percentiles[4], level='percentile')[comp].to_numpy(copy=True) - # 95th percentile - rupp = df.xs(percentiles[8], level='percentile')[comp].to_numpy(copy=True) - - return rlow, rmid, rupp - - -def multi_index_values(df_list): - """ - Get the values of the multi-index: years and percentile values. - :param df_list: DataFrame list of regional sea level projections - :return: years and percentile values - """ - df = df_list[0] - proj_years = np.sort(list(set(list(df.index.get_level_values('year'))))) - percentiles_all = np.sort( - [float(v) for v in list(set(list( - df.index.get_level_values('percentile'))))]) - - return proj_years, percentiles_all - - -def plot_figure_one(r_df_list, site_name, scenarios, fig_dir): - """ - Subplot 1 - Local sea level projections for all RCPs - sum total. - Subplot 2 - Local sea level projections for RCP8.5 - sum total and - component parts. - :param r_df_list: regional sea level projections - :param site_name: site location - :param scenarios: emissions scenario - :param fig_dir: figure directory - """ - # Get the values of the multi-index: years and percentile values - years, percentiles = multi_index_values(r_df_list) - - # UKCP18 colour scheme for sea level components - rcp_colours = ukcp18_colours()[0] - - # Calculate the x-axis and y-axis limit - xlim = calc_xlim('proj', [], years) - ylim = calc_ylim('proj', [], r_df_list) - - fig = plt.figure(figsize=(7.68, 3.5)) - matplotlib.rcParams['font.size'] = 7.5 - - # First figure, regional sea level projections and uncertainties under each - # scenario - ax = fig.add_subplot(1, 2, 1) - - for rcp_count, rcp_str in enumerate(scenarios): - # Get the sums of all components of local sea level projections - r_df_rcp = r_df_list[rcp_count] - rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum') - - label = scenario_string(scenarios, rcp_count) - ax.fill_between(years, rlow, rupp, alpha=0.3, - color=rcp_colours[rcp_str], linewidth=0) - ax.plot(years, rmid, color=rcp_colours[rcp_str], label=label) - - plot_zeroline(ax, xlim) - - ax.set_xlim(xlim) - ax.set_ylim(ylim) - ax.yaxis.set_ticks_position('both') - ax.yaxis.set_label_position('left') - - ax.set_title(f'Local sea level - {location_string(site_name)}') - ax.set_xlabel('Year') - ax.set_ylabel('Sea Level Change (m)') - - ax.legend(loc='upper left', frameon=False) - - # Second figure showing the various components of sea level projections - bx = fig.add_subplot(1, 2, 2) - - # UKCP18 colour scheme for sea level components - comp_colours = ukcp18_colours()[1] - # UKCP18 labels for sea level components - comp_labels = ukcp18_labels() - - # Show components for the RCP8.5 scenario (only plot the uncertainty for - # sum and thermal expansion) - for rcp_count, rcp_str in enumerate(scenarios): - if scenarios[rcp_count] == 'rcp85': - break - else: - raise ValueError('RCP8.5 scenario not in list') - - r_df_rcp = r_df_list[rcp_count] - - for comp in ['sum', 'ocean', 'greennet', 'antnet', 'glacier', 'landwater', - 'gia']: - # Get the components of regional sea level projections - clow_85, cmid_85, cupp_85 = extract_comp_sl(r_df_rcp, percentiles, - comp) - colour = comp_colours[comp] - label = comp_labels[comp] - - if comp in ['sum', 'ocean']: - bx.fill_between(years, cupp_85, clow_85, facecolor=colour, - alpha=0.3, edgecolor='None') - - if comp == 'sum': - bx.plot(years, cmid_85, colour, linewidth=1.5, label=label) - else: - bx.plot(years, cmid_85, colour, linewidth=1.5, label=label) - else: - bx.plot(years, cmid_85, colour, linewidth=1.5, label=label) - - plot_zeroline(bx, xlim) - bx.set_xlim(xlim) - bx.set_ylim(ylim) - bx.yaxis.set_ticks_position('both') - bx.yaxis.set_label_position('left') - - bx.legend(loc='upper left', frameon=False) - bx.set_title(f'Sea level components - {scenario_string(rcp_str, -999)}') - bx.set_xlabel('Year') - - # Save the figure - fig.tight_layout() - ffile = f'{fig_dir}01_{site_name}.png' - plt.savefig(ffile, dpi=300, format='png') - plt.close() - - -def plot_figure_two(r_df_list, tg_name, nflag, flag, tg_years, non_missing, - tg_amsl, site_name, scenarios, fig_dir): - """ - Local sea level projections for specified RCPs - sum total, and annual - mean tide gauge observations. - :param r_df_list: regional sea level projections - :param tg_name: tide gauge name - :param nflag: number of flagged years - :param flag: flagged data - :param tg_years: tide gauge years - :param non_missing: boolean to indicate NaN values - :param tg_amsl: annual mean sea level data - :param site_name: site location - :param scenarios: emissions scenario - :param fig_dir:figure directory - """ - # Get the values of the multi-index: years and percentile values - years, percentiles = multi_index_values(r_df_list) - - # UKCP18 colour scheme for RCP scenarios - rcp_colours = ukcp18_colours()[0] - - fig = plt.figure(figsize=(5, 4.5)) - matplotlib.rcParams['font.size'] = 10 - - # Draw regional sea level projections and uncertainties under each scenario - ax = fig.add_subplot(1, 1, 1) - - for rcp_count, rcp_str in enumerate(scenarios): - # Get the sums of all components of local sea level projections - r_df_rcp = r_df_list[rcp_count] - rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum') - - label = scenario_string(scenarios, rcp_count) - ax.fill_between(years, rlow, rupp, alpha=0.3, - color=rcp_colours[rcp_str], linewidth=0) - ax.plot(years, rmid, color=rcp_colours[rcp_str], label=label) - - # Calculate sensible x-axis range and y-axis range based on tide gauge data - # and projections - xlim = calc_xlim('tide', tg_years, years) - df_tmp = pd.DataFrame([rlow, rmid, rupp]) - ylim = calc_ylim('tide', tg_amsl, df_tmp) - - plot_zeroline(ax, xlim) - - # Plot the annual mean sea levels from the tide gauge data - plot_tg_data(ax, nflag, flag, tg_years, non_missing, tg_amsl, tg_name) - - ax.set_xlim(xlim) - ax.set_ylim(ylim) - ax.yaxis.set_ticks_position('both') - ax.yaxis.set_label_position('left') - ax.set_title(f'Local sea level - {location_string(site_name)}') - ax.set_ylabel('Sea Level Change (m)') - ax.set_xlabel('Year') - ax.legend(loc='upper left', frameon=False) - - # Save the figure - fig.tight_layout() - ffile = f'{fig_dir}02_{site_name}.png' - plt.savefig(ffile, dpi=300, format='png') - plt.close() - - -def plot_figure_three(g_df_list, r_df_list, global_low, global_mid, global_upp, - site_name, scenarios, fig_dir): - """ - Subplot 1 - Comparison of IPCC AR5 + Levermann global projections and - global projections developed here. - Subplot 2 - Comparison of global projections developed here and local sea - level projections. - :param g_df_list: global sea level projections - :param r_df_list: regional sea level projections - :param global_low: IPCC AR5 + Levermann global projections 5th percentile - :param global_mid: IPCC AR5 + Levermann global projections 50th percentile - :param global_upp: IPCC AR5 + Levermann global projections 95th percentile - :param site_name: site location - :param scenarios: emissions scenario - :param fig_dir: figure directory - """ - # Get the values of the multi-index: years and percentile values - years, percentiles = multi_index_values(r_df_list) - - # UKCP18 colour scheme for RCP scenarios - rcp_colours = ukcp18_colours()[0] - - # Calculate sensible x-axis range based on projections - xlim = calc_xlim('proj', [], years) - - for rcp_count, rcp_str in enumerate(scenarios): - # Get the sums of all components of global sea level projections - g_df_rcp = g_df_list[rcp_count] - glow, gmid, gupp = extract_comp_sl(g_df_rcp, percentiles, 'sum') - - # Get the sums of all components of global sea level projections - r_df_rcp = r_df_list[rcp_count] - rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum') - - # Calculate sensible y-axis range based on projections - r_ylim = calc_ylim('proj', [], r_df_rcp) - g_ylim = calc_ylim('proj', [], g_df_rcp) - ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])] - - # Plot comparison of global sea level projections - # Check that global is equivalent to the IPCC AR5 + Levermann sea - # level projections - fig = plt.figure(figsize=(7.68, 3.5)) - matplotlib.rcParams['font.size'] = 7.5 - - ax = fig.add_subplot(1, 2, 1) - # IPCC - ax.plot(years, global_mid[rcp_count], color='k', linewidth=1.5, - linestyle='-', label='IPCC AR5 + Levermann') - ax.plot(years, global_low[rcp_count], color='k', linewidth=1.5, - linestyle=':') - ax.plot(years, global_upp[rcp_count], color='k', linewidth=1.5, - linestyle=':') - # Global - ax.plot(years, gmid, color='orange', linewidth=1.5, linestyle='--', - label='Global Total') - ax.fill_between(years, glow, gupp, color='orange', alpha=0.3, - linewidth=0, edgecolor='None') - - plot_zeroline(ax, xlim) - ax.set_xlim(xlim) - ax.set_ylim(ylim) - ax.yaxis.set_ticks_position('both') - ax.yaxis.set_label_position('left') - - ax.set_title(f'Global sea level - {scenario_string(rcp_str, -999)}') - ax.set_ylabel('Sea Level Change (m)') - ax.set_xlabel('Year') - ax.legend(loc='upper left', frameon=False) - - # Plot regional sea level projections, showing both error ranges - bx = fig.add_subplot(1, 2, 2) - # Global - bx.plot(years, gmid, color='k', linewidth=1.5, linestyle='-', - label='Global Total') - bx.plot(years, glow, color='k', linewidth=1.5, linestyle=':') - bx.plot(years, gupp, color='k', linewidth=1.5, linestyle=':') - # Local - bx.plot(years, rmid, color=rcp_colours[rcp_str], linewidth=1.5, - linestyle='--', label=location_string(site_name)) - bx.fill_between(years, rlow, rupp, color=rcp_colours[rcp_str], - alpha=0.3, linewidth=0, edgecolor='None') - - plot_zeroline(bx, xlim) - bx.set_xlim(xlim) - bx.set_ylim(ylim) - bx.yaxis.set_ticks_position('both') - bx.yaxis.set_label_position('left') - - bx.set_title(f'Local sea level - {location_string(site_name)} - ' - f'{scenario_string(rcp_str, -999)}') - bx.set_xlabel('Year') - bx.legend(loc='upper left', frameon=False) - - # Save the figure - fig.tight_layout() - outfile = f'{fig_dir}03_{site_name}_{rcp_str}.png' - plt.savefig(outfile, dpi=300, format='png') - plt.close() - - -def plot_figure_four(r_df_list, site_name, scenarios, fig_dir): - """ - Local sea level projections for all RCPs - sum total. - Same style as Figure 3.1.4 of UKCP18 Marine Report (pg 16). - :param r_df_list: regional sea level projections - :param site_name: site location - :param scenarios: emissions scenario - :param fig_dir: figure directory - """ - # Get the values of the multi-index: years and percentile values - years, percentiles = multi_index_values(r_df_list) - - # UKCP18 colour scheme for RCP scenarios - rcp_colours = ukcp18_colours()[0] - - fig = plt.figure(figsize=(5, 4.5)) - matplotlib.rcParams['font.size'] = 10 - - # First figure, regional sea level projections and uncertainties under each - # scenario - ax = fig.add_subplot(1, 1, 1) - - for rcp_count, rcp_str in enumerate(scenarios): - # Get the sums of regional sea level projections - r_df_rcp = r_df_list[rcp_count] - rlow, rmid, rupp = extract_comp_sl(r_df_rcp, percentiles, 'sum') - - label = scenario_string(scenarios, rcp_count) - plt.plot(years, rmid, color=rcp_colours[rcp_str], linewidth=4.0, - label=label) - plt.plot(years, rupp, color=rcp_colours[rcp_str], linewidth=1.0, - linestyle='--') - plt.plot(years, rlow, color=rcp_colours[rcp_str], linewidth=1.0, - linestyle='--') - - # Calculate sensible x-axis range and y-axis range based on tide gauge data - # and projections - xlim = calc_xlim('proj', [], years) - df_tmp = pd.DataFrame([rlow, rmid, rupp]) - ylim = calc_ylim('proj', [], df_tmp) - - plot_zeroline(ax, xlim) - plt.xlim(xlim) - plt.ylim(ylim) - ax.yaxis.set_ticks_position('both') - ax.yaxis.set_label_position('left') - - plt.title(f'Local sea level - {location_string(site_name)}') - plt.ylabel('Sea Level Change (m)') - plt.xlabel('Year') - plt.legend(loc='upper left', frameon=False) - - # Save the figure - fig.tight_layout() - outfile = f'{fig_dir}04_{site_name}.png' - plt.savefig(outfile, dpi=300, format='png') - plt.close() - - -def plot_figure_five(g_df_list, r_df_list, site_name, scenarios, fig_dir): - """ - Comparison of global mean sea level projections calculated by the tool and - local sea level projections - sum total - Also shown local sea level projections - component parts - Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively. - :param g_df_list: global sea level projections - :param r_df_list: regional sea level projections - :param site_name: site location - :param scenarios: emissions scenario - :param fig_dir: figure directory - """ - # Get the values of the multi-index: years and percentile values - years, percentiles = multi_index_values(r_df_list) - - # UKCP18 colour scheme for sea level components - comp_colours = ukcp18_colours()[1] - # UKCP18 labels for sea level components - comp_labels = ukcp18_labels() - - # Calculate sensible x-axis range and y-axis range based on projections - xlim = calc_xlim('proj', [], years) - r_ylim = calc_ylim('proj', [], r_df_list) - g_ylim = calc_ylim('proj', [], g_df_list) - ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])] - - fig = plt.figure(figsize=(7.68, 3.5)) - matplotlib.rcParams['font.size'] = 7.5 - - # Loop through RCPs for subplots - for rcp_count, _ in enumerate(scenarios): - ax = fig.add_subplot(1, 3, rcp_count + 1) - - # Get the sums of global sea level projections - g_df_rcp = g_df_list[rcp_count] - glow, gmid, gupp = extract_comp_sl(g_df_rcp, percentiles, 'sum') - - ax.plot(years, gmid, color='k', linewidth=1.5, linestyle='--', - label='Global Total') - ax.plot(years, glow, color='k', linewidth=1.5, linestyle=':') - ax.plot(years, gupp, color='k', linewidth=1.5, linestyle=':') - - # Get the sums of local sea level projections - r_df_rcp = r_df_list[rcp_count] - - for comp in ['sum', 'ocean', 'greennet', 'antnet', 'glacier', - 'landwater', 'gia']: - # Get the components of regional sea level projections - clow, cmid, cupp = extract_comp_sl(r_df_rcp, percentiles, comp) - - colour = comp_colours[comp] - label = comp_labels[comp] - - if comp in ['sum', 'ocean']: - ax.plot(years, cmid, colour, linewidth=1.5, label=label) - ax.fill_between(years, cupp, clow, facecolor=colour, alpha=0.3, - edgecolor='None') - else: - ax.plot(years, cmid, colour, linewidth=1.5, label=label) - - plot_zeroline(ax, xlim) - ax.set_xlim(xlim) - ax.set_ylim(ylim) - ax.yaxis.set_ticks_position('both') - ax.yaxis.set_label_position('left') - - ax.set_xlabel('Year') - ax.set_title(f'{location_string(site_name)} - ' - f'{scenario_string(scenarios[rcp_count], -999)}') - if rcp_count == 0: - ax.set_ylabel('Sea Level Change (m)') - - # Put legend to the right of the current axis - ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) - - # Save the figure - fig.tight_layout() - outfile = f'{fig_dir}05_{site_name}.png' - plt.savefig(outfile, dpi=300, format='png') - plt.close() - - -def plot_figure_six(r_df_list, global_low, global_mid, global_upp, - site_name, scenarios, fig_dir): - """ - Comparison of IPCC AR5 + Levermann projections and local sea level - projections - sum total and component parts. - Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively. - :param r_df_list: regional sea level projections - :param global_low: IPCC AR5 + Levermann global projections 5th percentile - :param global_mid: IPCC AR5 + Levermann global projections 50th percentile - :param global_upp: IPCC AR5 + Levermann global projections 95th percentile - :param site_name: site location - :param scenarios: emissions scenario - :param fig_dir: figure directory - """ - # Get the values of the multi-index: years and percentile values - years, percentiles = multi_index_values(r_df_list) - - # UKCP18 colour scheme for sea level components - comp_colours = ukcp18_colours()[1] - # UKCP18 labels for sea level components - comp_labels = ukcp18_labels() - - # Calculate sensible x-axis range and y-axis range based on projections - xlim = calc_xlim('proj', [], years) - r_ylim = calc_ylim('proj', [], r_df_list) - # IPCC AR5 + Levermann projections are read in as an array - g_ylim = [min(global_low[0]-0.1), max(global_upp[2])+0.1] - ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])] - - fig = plt.figure(figsize=(7.68, 3.5)) - matplotlib.rcParams['font.size'] = 7.5 - - # Loop through RCPs for subplots - for rcp_count, _ in enumerate(scenarios): - ax = fig.add_subplot(1, 3, rcp_count + 1) - - # Get the sums of all components of global sea level projections - cmid_g = global_mid[rcp_count] - clow_g = global_low[rcp_count] - cupp_g = global_upp[rcp_count] - - ax.plot(years, cmid_g, color='k', linewidth=1.5, linestyle='--', - label='IPCC AR5 + Levermann') - ax.plot(years, clow_g, color='k', linewidth=1.5, linestyle=':') - ax.plot(years, cupp_g, color='k', linewidth=1.5, linestyle=':') - - # Get the sums of all components of local sea level projections - r_df_rcp = r_df_list[rcp_count] - for comp in ['sum', 'ocean', 'greennet', 'antnet', 'glacier', - 'landwater', 'gia']: - # Get the sums of regional sea level projections - clow, cmid, cupp = extract_comp_sl(r_df_rcp, percentiles, comp) - - colour = comp_colours[comp] - label = comp_labels[comp] - - if comp in ['sum', 'ocean']: - ax.plot(years, cmid, colour, linewidth=1.5, label=label) - ax.fill_between(years, cupp, clow, facecolor=colour, alpha=0.3, - edgecolor='None') - else: - ax.plot(years, cmid, colour, linewidth=1.5, label=label) - - plot_zeroline(ax, xlim) - ax.set_xlim(xlim) - ax.set_ylim(ylim) - ax.yaxis.set_ticks_position('both') - ax.yaxis.set_label_position('left') - - ax.set_xlabel('Year') - ax.set_title(f'{location_string(site_name)} - ' - f'{scenario_string(scenarios[rcp_count], -999)}') - if rcp_count == 0: - ax.set_ylabel('Sea Level Change (m)') - - # Put legend to the right of the current axis - ax.legend(loc='center left', bbox_to_anchor=(1, 0.5)) - - # Save the figure - fig.tight_layout() - outfile = f'{fig_dir}06_{site_name}.png' - plt.savefig(outfile, dpi=300, format='png') - plt.close() - - -def plot_figure_seven(g_df_list, r_df_list, site_name, scenarios, fig_dir): - """ - Subplot 1 - Global sea level projections for RCP8.5 - total and component - parts, including uncertainty range. - Subplot 2 - Local sea level projections for RCP8.5 - total and component - parts, including uncertainty range. - Same style as Figure 4 Howard and Palmer (2019). - :param g_df_list: global sea level projections - :param r_df_list: regional sea level projections - :param site_name: site location - :param scenarios: emission scenario - :param fig_dir: figure directory - """ - # Get the values of the multi-index: years and percentile values - _, percentiles = multi_index_values(r_df_list) - - # UKCP18 colour scheme for sea level components - comp_colours = ukcp18_colours()[1] - # UKCP18 labels for sea level components - comp_labels = ukcp18_labels() - - # Calculate sensible x-axis range and y-axis range - xlim = ([-0.4, 7.4]) - r_ylim = calc_ylim('proj', [], r_df_list) - g_ylim = calc_ylim('proj', [], g_df_list) - ylim = [min(r_ylim[0], g_ylim[0]), max(r_ylim[1], g_ylim[1])] - - # Show components for the RCP8.5 scenario (only plot the uncertainty for - # sum and thermal expansion) - for rcp_count, rcp_str in enumerate(scenarios): - if scenarios[rcp_count] == 'rcp85': - break - else: - raise ValueError('RCP8.5 scenario not in list') - r_df_rcp = r_df_list[rcp_count] - g_df_rcp = g_df_list[rcp_count] - - # ------------------------------------------------------------------------- - # First figure, global sea level projections and uncertainties under - # RCP 8.5 and component parts - fig = plt.figure(figsize=(7.68, 4.5)) - matplotlib.rcParams['font.size'] = 9 - - ax_labels = [] - ax = fig.add_subplot(1, 2, 1) - for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier', - 'landwater']): - # Get the components of regional sea level projections - clow_85_G, cmid_85_G, cupp_85_G = extract_comp_sl(g_df_rcp, - percentiles, comp) - - colour = comp_colours[comp] - label = comp_labels[comp] - ax_labels.append(label) - xpts = [cc - 0.4, cc + 0.4] - - ax.fill_between(xpts, clow_85_G[-1], cupp_85_G[-1], - facecolor=colour, alpha=0.35, label=label) - ax.plot(xpts, [cmid_85_G[-1], cmid_85_G[-1]], linewidth=2.0, - color=colour) - - plot_zeroline(ax, xlim) - ax.set_xticks([0, 1, 2, 3, 4, 5]) - ax.set_xticklabels(ax_labels, rotation=90) - ax.yaxis.set_ticks_position('both') - ax.yaxis.set_label_position('left') - plt.ylim(ylim) - plt.ylabel('Sea Level Change (m)') - plt.title(f'Global sea level - {scenario_string(rcp_str, -999)}') - - # ------------------------------------------------------------------------- - # Second figure, regional sea level projections and uncertainties under - # RCP 8.5 and component parts - bx = fig.add_subplot(1, 2, 2) - bx_labels = [] - for cc, comp in enumerate(['sum', 'ocean', 'antnet', 'greennet', 'glacier', - 'landwater', 'gia']): - # Get the components of local sea level projections - clow_85_R, cmid_85_R, cupp_85_R = extract_comp_sl(r_df_rcp, - percentiles, comp) - - colour = comp_colours[comp] - label = comp_labels[comp] - bx_labels.append(label) - xpts = [cc - 0.4, cc + 0.4] - - bx.fill_between(xpts, clow_85_R[-1], cupp_85_R[-1], facecolor=colour, - alpha=0.35, label=label) - bx.plot(xpts, [cmid_85_R[-1], cmid_85_R[-1]], linewidth=2.0, - color=colour) - - plot_zeroline(bx, xlim) - bx.set_xticks([0, 1, 2, 3, 4, 5, 6]) - bx.set_xticklabels(bx_labels, rotation=90) - bx.yaxis.set_ticks_position('both') - bx.yaxis.set_label_position('left') - plt.ylim(ylim) - plt.title(f'Local sea level - {location_string(site_name)} - ' - f'{scenario_string(scenarios[rcp_count], -999)}') - - # Save the figure - fig.tight_layout() - outfile = f'{fig_dir}07_{site_name}_rcp85.png' - plt.savefig(outfile, dpi=200, format='png') - plt.close() - - -def plot_tg_data(ax, nflag, flag, tg_years, non_missing, tg_amsl, tg_name): - """ - Plot the annual mean sea levels from the tide gauge data. - :param ax: subplot number - :param nflag: number of flagged years - :param flag: flagged data - :param tg_years: tide gauge years - :param non_missing: boolean to indicate NaN values - :param tg_amsl: annual mean sea level data - :param tg_name: tide gauge name - """ - if nflag > 0: - # There are some years with less than min_valid_fraction of flag data; - # plot these annual means as open symbols. - print(f'Tide gauge data has been flagged for attention - ' + - f'{tg_years[(flag & non_missing)]}') - ax.plot(tg_years[(flag & non_missing)], tg_amsl[(flag & non_missing)], - marker='o', mec='black', mfc='None', - markersize=3, linestyle='None', label='TG flagged') - if nflag < len(flag): - ax.plot(tg_years[(~flag & non_missing)], - tg_amsl[(~flag & non_missing)], 'ko', markersize=3, - label=f'{location_string(tg_name)} TG') - - -def read_G_R_sl_projections(site_name, scenarios): - """ - Reads in the global and regional sea level projections calculated from - the CMIP projections (in metres). These data are relative to a baseline - of 1981-2010. - :param site_name: site location - :param scenarios: emission scenarios - :return: DataFrame of global and regional sea level projections - """ - print('running function read_regional_sea_level_projections') - - # Read in the global and regional sea level projections - in_slddir = read_dir()[4] - loc_abbrev = abbreviate_location_name(site_name) - - G_df_list = [] - R_df_list = [] - for sce in scenarios: - G_filename = '{}{}_{}_projection_{}_global.csv'.format( - in_slddir, loc_abbrev, sce, settings["projection_end_year"]) - R_filename = '{}{}_{}_projection_{}_regional.csv'.format( - in_slddir, loc_abbrev, sce, settings["projection_end_year"]) - try: - G_df = pd.read_csv(G_filename, header=0, - index_col=['year', 'percentile']) - G_df.rename(columns={'exp': 'ocean', 'GIA': 'gia'}, inplace=True) - R_df = pd.read_csv(R_filename, header=0, - index_col=['year', 'percentile']) - R_df.rename(columns={'exp': 'ocean', 'GIA': 'gia'}, inplace=True) - except FileNotFoundError: - raise FileNotFoundError( - sce, '- scenario selected does not currently exist') - - G_df_list.append(G_df) - R_df_list.append(R_df) - - return G_df_list, R_df_list - - -def read_IPCC_AR5_Levermann_proj(scenarios, refname='sum'): - """ - Read in the IPCC AR5 + Levermann global sea level projections. - :param scenarios: emission scenario - :param refname: name of the component to plot e.g. "expansion", - "antsmb", "sum" - :return: lower, middle and upper time series of IPCC AR5 + Levermann - global sea level projections - """ - print('running function read_IPCC_AR5_Levermann_proj') - - # Directory of Monte Carlo time series for new projections - mcdir = choose_montecarlo_dir() - - nyrs = np.arange(2007, settings["projection_end_year"] + 1).size - - ar5_low = [] - ar5_mid = [] - ar5_upp = [] - - for sce in scenarios: - reflow = iris.load_cube( - os.path.join(mcdir, sce + '_' + refname + 'lower.nc'))[:nyrs].data - refmid = iris.load_cube( - os.path.join(mcdir, sce + '_' + refname + 'mid.nc'))[:nyrs].data - refupp = iris.load_cube( - os.path.join(mcdir, sce + '_' + refname + 'upper.nc'))[:nyrs].data - - ar5_low.append(reflow) - ar5_mid.append(refmid) - ar5_upp.append(refupp) - - return ar5_low, ar5_mid, ar5_upp - - -def read_PSMSL_tide_gauge_obs(root_dir, data_source, data_type, region, - df_site, site_name, base_fdir): - """ - Read annual mean sea level observations from PSMSL. - :param root_dir: base directory - :param data_source: data source of tide gauge data - :param data_type: data type of tide gauge data - :param region: user specified region (for folder structure) - :param df_site: DataFrame of site metadata - :param site_name: site locaiton - :param base_fdir: figure directory - :return: annual mean sea level data and relevant metadata on missing / - flagged data - """ - # Determine Station ID - indicator for .rlr file - station_id = df_site.loc[site_name, 'Station ID'] - - if station_id == 'NA': - print(f'{site_name} is not a recognised tide gauge on PSMSLs ' + - 'station list') - latitude = df_site.loc[site_name, 'Latitude'] - longitude = df_site.loc[site_name, 'Longitude'] - station_id, tg_name = find_nearest_station_id( - root_dir, data_source, data_type, region, latitude, longitude) - else: - tg_name = location_string(site_name) - - baseline_years = [1981, 2010] - min_valid_fraction = 0.5 - - # Read in the annual mean sea levels, downloaded from PSMSL - tg_years, tg_amsl, _, tg_flag_ex = \ - tgl.read_rlr_annual_mean_sea_level(station_id) - non_missing = ~np.isnan(tg_amsl) - - # Find the years with 'flagged' data, flagged for attention '001' or MTL - # in MSL time series '010' - flag = (tg_flag_ex >= min_valid_fraction) - - nflag = sum(flag) - - # Calculate the baseline sea level and difference between the observed - # period and the baseline. - # Latter will be zero if sufficent observations lie within the baseline - loc_abbrev = abbreviate_location_name(site_name) - baseline_sl, _ = tgl.calc_baseline_sl(root_dir, region, loc_abbrev, - tg_years, tg_amsl, baseline_years) - - # Adjust the tide gauge data to be sea levels relative to the baseline, - # to match the regional projections - tg_amsl = tg_amsl - baseline_sl - - # Convert the tide gauge sea levels from mm to metres. - tg_amsl /= 1000.0 - - return tg_name, nflag, flag, tg_years, non_missing, tg_amsl - - -def main(): - """ - Read in the global and regional sea level projections to create several - graphs. - """ - print(f'User specified site name(s) is (are):' - f' {settings["siteinfo"]["sitename"]}') - print(f'User specified lat(s) and lon(s) is (are): ' - f'{settings["siteinfo"]["sitelatlon"]}') - if settings["siteinfo"]["sitelatlon"] == [[]]: - print(f' No lat lon specified - use tide gauge metadata if ' - f'available') - print(f'User specified science method is: {settings["sciencemethod"]}') - if {settings["cmipinfo"]["cmip_sea"]} == {'all'}: - print('User specified all CMIP models') - elif {settings["cmipinfo"]["cmip_sea"]} == {'marginal'}: - print('User specified CMIP models for marginal seas only') - - # Extract site data from station list (e.g. tide gauge location) or - # construct based on user input - df_site_data = extract_site_info(settings["tidegaugeinfo"]["source"], - settings["tidegaugeinfo"]["datafq"], - settings["siteinfo"]["region"], - settings["siteinfo"]["sitename"], - settings["siteinfo"]["sitelatlon"]) - - # Create the output sea level projections figure directory to use across - # multiple functions - sealev_fdir = read_dir()[5] - makefolder(sealev_fdir) - - rcp_scenarios = ['rcp26', 'rcp45', 'rcp85'] - for df_loc in df_site_data.index.values: - # Read global and regional sea level projections calculated previously - g_df_list, r_df_list = read_G_R_sl_projections(df_loc, rcp_scenarios) - - # Read in the IPCC AR5 + Levermann global sea level projections - ar5_low, ar5_mid, ar5_upp = read_IPCC_AR5_Levermann_proj(rcp_scenarios) - - # Read annual mean sea level observations from PSMSL - tg_name, nflag, flag, tg_years, non_missing, tg_amsl = \ - read_PSMSL_tide_gauge_obs(settings["baseoutdir"], settings[ - "tidegaugeinfo"]["source"], settings["tidegaugeinfo"][ - "datafq"], settings["siteinfo"]["region"], df_site_data, - df_loc, sealev_fdir) - # ------------------------------------------------------------------------- - # Plotting section - comment functions in and out depending on which graphs - # you wish to save - # Figure 1 - # Subplot 1 - Local sea level projections for all RCPs - sum total - # Subplot 2 - Local sea level projections for RCP8.5 - total and - # component parts - plot_figure_one(r_df_list, df_loc, rcp_scenarios, sealev_fdir) - - # Figure 2 - # Local sea level projections for specified RCPs - sum total, - # and annual mean tide gauge observations - plot_figure_two(r_df_list, tg_name, nflag, flag, tg_years, - non_missing, tg_amsl, df_loc, rcp_scenarios, - sealev_fdir) - - # Figure 3 (one output per RCP) - # Subplot 1 - Comparison of IPCC AR5 + Levermann global projections - # and global projections developed here - # Subplot 2 - Comparison of global projections and local sea level - # projections - plot_figure_three(g_df_list, r_df_list, ar5_low, ar5_mid, ar5_upp, - df_loc, rcp_scenarios, sealev_fdir) - - # Figure 4 - # Local sea level projections for all RCPs - sum total - # Same style as Figure 3.1.4 of UKCP18 Marine Report (pg 16) - plot_figure_four(r_df_list, df_loc, rcp_scenarios, sealev_fdir) - - # Figure 5 - # Comparison of global mean sea level projections calculated by the - # tool and local sea level projections - sum total - # Also shown local sea level projections - component parts - # Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively - plot_figure_five(g_df_list, r_df_list, df_loc, rcp_scenarios, - sealev_fdir) - - # Figure 6 - # Comparison of IPCC AR5 + Levermann projections and local sea level - # projections - sum total - # Also shown local sea level projections - component parts - # Subplot 1, 2, 3 - RCP2.6, RCP4.5, RCP8.5 respectively - if settings["projection_end_year"] <= 2100: - plot_figure_six(r_df_list, ar5_low, ar5_mid, ar5_upp, df_loc, - rcp_scenarios, sealev_fdir) - - # Figure 7 - # Subplot 1 - Global sea level projections for RCP8.5 - total and - # component parts, including uncertainty range - # Subplot 2 - Local sea level projections for RCP8.5 - total and - # component parts, including uncertainty range - # Same style as Figure 4 Howard and Palmer (2019) - plot_figure_seven(g_df_list, r_df_list, df_loc, rcp_scenarios, - sealev_fdir) - - -if __name__ == '__main__': - main() diff --git a/profsea/surge/tide_gauge_library.py b/profsea/surge/tide_gauge_library.py deleted file mode 100644 index a358217..0000000 --- a/profsea/surge/tide_gauge_library.py +++ /dev/null @@ -1,175 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -import os -import pandas as pd -import numpy as np -from scipy.stats import linregress - -from profsea.config import settings - - -def calc_baseline_sl(root_dir, data_region, loc_abbrev, years, annual_means, - baseline_years, min_overlapping_years=14): - """ - Calculates or estimates the baseline sea levels, using observed or - modelled values. If there are sufficient observed values, use those to - estimate the baseline sea level, otherwise read in and extrapolate back - from the RCP 2.6 projections, which are nearly linear. - :param root_dir: base directory for all input and output data - :param data_region: region (used for file paths) - :param loc_abbrev: abbreviated name of tide gauge - :param years: Numpy array of available years for requested tide gauge - :param annual_means: annual mean sea level (mm) - :param baseline_years: first and last year of the baseline period, - should be 1981-2000 - :param min_overlapping_years: minimum number of years of the observed - sea levels that must lie inside the baseline period - :return: the baseline sea level and the difference between the observed - period and the baseline - """ - print('running function calc_baseline_sl') - # Check which years from this gauge lie within the baseline period. - common_years = find_common_years(years, baseline_years) - nvalid = 0 - - if len(common_years) >= min_overlapping_years: - obs_amsl = annual_means[(years >= common_years[0]) & - (years <= common_years[-1])] - # Check for number of valid data values - nvalid = sum(~np.isnan(obs_amsl)) - - if nvalid >= min_overlapping_years: - # Calculate the mean sea level for the baseline using these - # observations. - baseline_sea_level = np.nanmean(obs_amsl) - delta_sl = 0.0 - else: - # (a) Get obs years - # (b) read in the RCP2.6 sea level projections - percentile = 50.0 - df_m_rcp26 = read_regional_sea_level_projections( - root_dir, data_region, loc_abbrev, 'rcp26', pcile=50.0) - sl_rcp26 = df_m_rcp26['sum'].values - years_rcp26 = df_m_rcp26['year'].values - - # (c) Fit a straight line to the mid-point estimates - m, c, _, _, _ = linregress(years_rcp26, sl_rcp26) - - # Extrapolate fitted straight line to get values for the baseline - # years and hence the mean value for the baseline. - model_sl_in_baseline = [m * base_year + c for base_year in - list(range(baseline_years[0], - baseline_years[1] + 1))] - model_mean_baseline_sl = np.mean(np.array(model_sl_in_baseline)) - - # Extrapolate fitted straight line to get modelled values for the - # years of the observations, then the mean value for this period - model_sl_in_obs_years = [m * obs_year + c for obs_year in years] - model_mean_obs_sl = np.mean(np.array(model_sl_in_obs_years)) - - # Calculate the change in sea level between the baseline and - # observed periods using the modelled data - delta_sl = model_mean_baseline_sl - model_mean_obs_sl - - # (d) Add modelled SL change from (c) to mean sea level from - # tide gauge data - obs_mean_sl = np.nanmean(annual_means) - baseline_sea_level = obs_mean_sl + delta_sl - - return baseline_sea_level, delta_sl - - -def find_common_years(years, baseline_years): - """ - Finds the years that lie within the specified baseline period - :param years: List of years in the observed data - :param baseline_years: 2-element list holding the first and last years - of the period of interest - :return: list of all common years within the baseline period - """ - common_years = sorted(list(set(list(range(baseline_years[0], - baseline_years[1] + 1))) - & set(years))) - if not common_years: - print(f'No common years found between tide gauge data and ' + - 'baseline years') - else: - print(f'The common years in the tide gauge data are ' + - f'between {common_years[0]} and {common_years[-1]}') - - return common_years - - -def read_regional_sea_level_projections(root_dir, data_region, loc_abbrev, - scenario, pcile=None): - """ - Reads in the regional sea level projections calculated from the CMIP - projections in a previous step. These data are changes in sea level - relative to a baseline of 1981-2010. - :param root_dir: root directory containing all code and input data - :param data_region: region (used for file paths) - :param loc_abbrev: abbreviated name of tide gauge - :param scenario: RCP scenario - :param pcile: If not None, return data for this percentile; otherwise, - return data for all percentiles - :return df: DataFrame containing regional sea level projections, - as annual means. - """ - path = os.path.join(root_dir, data_region, 'data', 'sea_level_projections') - end_yr = settings['projection_end_year'] - filename = os.path.join( - path, f'{loc_abbrev}_{scenario}_projection_{end_yr}_regional.csv') - df = pd.read_csv(filename, header=0, usecols=['year', 'percentile', 'sum']) - - if pcile is not None: - df = df.loc[df['percentile'] == pcile] - df.reset_index(inplace=True, drop=True) - - # Convert the projections from metres to mm. - df['sum'] = df['sum'] * 1000.0 - - return df - - -def read_rlr_annual_mean_sea_level(station_id): - """ - Reads in annual mean sea levels from a .rlrdata file - downloaded - from PSMSL (https://www.psmsl.org/data/obtaining/complete.php and - https://www.psmsl.org/data/obtaining/notes.php). Replaces all -99999 with - NAN values - for use in plotting - :param station_id: PSMSL Station ID for location - :return years: numpy array of available years for requested station - :return annual_means: corresponding annual mean sea level (mm) - :return flag_missing: corresponding flag for missing data - :return flag_exception: corresponding 'flag for attention' - """ - print('running function read_rlr_annual_mean_sea_level') - psmsl_basedir = settings["tidegaugeinfo"]["psmsldir"] - path = os.path.join(psmsl_basedir, 'rlr_annual', 'data') - - filename = os.path.join(path, f'{station_id}.rlrdata') - print(f'Reading in data for {filename}') - rlr_data = np.genfromtxt(filename, dtype=None, skip_header=0, names=[ - 'Year', 'RLR_data', 'Flag_missing', 'Flag_exception'], - delimiter=";", encoding=None) - years = [] - annual_means = [] - flag_missing = [] - flag_exception = [] - for row in rlr_data: - year = row[0] - rlr = row[1] - missing = row[2] - exception = row[3] - years = np.append(years, int(year)) - if float(rlr) != -99999: - annual_means = np.append(annual_means, float(rlr)) - else: - annual_means = np.append(annual_means, np.nan) - - flag_missing = np.append(flag_missing, str(missing)) - flag_exception = np.append(flag_exception, float(exception)) - - return years, annual_means, flag_missing, flag_exception diff --git a/profsea/tide_gauge_locations.py b/profsea/tide_gauge_locations.py deleted file mode 100644 index ced9dd4..0000000 --- a/profsea/tide_gauge_locations.py +++ /dev/null @@ -1,271 +0,0 @@ -""" -Copyright (c) 2023, Met Office -All rights reserved. -""" - -from math import cos, asin, sqrt -import pandas as pd -import os - -from profsea.config import settings - - -def closest(data, v): - """ - :param data: all tide gauges latitude and longitude - :param v: site latitude and longitude - :return: DataFrame row of the closest TG to site lat and lon - """ - return min(data, key=lambda p: distance(v['lat'], v['lon'], - p['lat'], p['lon'])) - - -def distance(lat1, lon1, lat2, lon2): - """ - The haversine formula determines the great-circle distance between two - points on a sphere given their longitudes and latitudes. - Ref: https://en.wikipedia.org/wiki/Haversine_formula - - :param lat1: site latitude - :param lon1: site longitude - :param lat2: TG latitude - :param lon2: TG longitude - :return: distance between points on a sphere - """ - p = 0.017453292519943295 - a = 0.5 - cos((lat2 - lat1) * p) / 2 + cos(lat1 * p) * cos(lat2 * p) * \ - (1 - cos((lon2 - lon1) * p)) / 2 - return 12742 * asin(sqrt(a)) - - -def extract_site_info(data_source, data_type, region, site_name, latlon): - """ - Extract all available tide gauges from station list - Check if site_name is in this list, if so return metadata - If not construct a dataframe in the same format that contains user defined - metadata. - :param data_source: source of tide gauge information - :param data_type: temporal scale of tide gauge data - :param region: region of the world to assess sea level rise - :param site_name: site specific location - :param latlon: site location's latitude and longitude ([] if using tide - gauge) - :return: Data frame of all metadata (tide gauge or site specific) for each - location requested - """ - print('running function extract_site_info') - - # Get a list of all available tide gauges from data_source - df = tide_gauge_locations(region=region, source=data_source, - type=data_type) - - # Create an empty pandas DataFrame to hold site information - dfObj = pd.DataFrame() - for i, location in enumerate(site_name): - # Re-format user defined site name - if data_source == 'PSMSL': - if location == 'Aberdeen': - print(f'CAUTION - There are two gauges available for ' + - 'Aberdeen, using ABERDEEN I (1931-2019)') - site_to_check = 'ABERDEEN I' - else: - site_to_check = location.upper() - else: - raise ValueError("data source not recognised") - - # -------------------------------------------------------------------- - - if site_to_check in df.index: - print(f'{site_to_check} - Site metadata taken from {data_source}') - site_data = df.loc[site_to_check, :] - - if data_source == 'PSMSL': - if 'NEWPORT' in site_to_check: - print(f'CAUTION - There are two Newport gauges listed in' + - 'the file list from PSMSL') - user_input = input('Location of Newport required, UK or ' + - 'US?') - if user_input == 'UK': - print('Newport in the UK selected') - site_data = site_data[~site_data.index.duplicated( - keep='last')].transpose() - else: - print('Newport in the US selected') - site_data = site_data[~site_data.index.duplicated( - keep='first')].transpose() - - df_temp = pd.DataFrame(site_data).transpose() - dfObj = dfObj.append(df_temp) - - elif latlon != [[]]: - print(f'{site_to_check} - Site metadata taken from user input') - data_type = 'user defined' - station_id = 'NA' - latitude = latlon[i][0] - longitude = latlon[i][1] - site_data = [[site_to_check, data_type, station_id, latitude, - longitude]] - - df_temp = pd.DataFrame(site_data, columns=[ - 'Location', 'Dataset type', 'Station ID', 'Latitude', - 'Longitude']) - df_temp = df_temp.set_index('Location') - dfObj = dfObj.append(df_temp) - - else: - raise IndexError(f'No site metadata for this site: {site_to_check}, have you spelled it correctly?') - - return dfObj - - -def find_nearest_station_id(root_dir, data_source, data_type, data_region, - site_lat, site_lon): - """ - Extracts all available tide gauges from data sources, then calculates - the closest tide gauge to the site based on latitude and longitude using - the Haversine formula - ** N.B. Testing only completed on PSMSL station list ** - - :param root_dir: base directory for all input and output data - :param data_source: source of tide gauge information - :param data_type: temporal scale of tide gauge data - :param data_region: region of the world to assess sea level rise - :param site_lat: site latitude - :param site_lon: site longitude - :return: Nearest tide gauge name and station ID - """ - # Get a list of all available tide gauges from data_source, extract - # latitude and longitude - df = tide_gauge_locations(region=data_region, source=data_source, - type=data_type, in_dir=root_dir) - latitude = df.loc[:, 'Latitude'].tolist() - longitude = df.loc[:, 'Longitude'].tolist() - - # Re-format latitude and longitude - temp_data_list = [] - for i in range(len(latitude)): - temp_data_list.append({'lat': latitude[i], 'lon': longitude[i]}) - - # Find the closest latitude and longitude in list to site location - site_location = {'lat': site_lat, 'lon': site_lon} - closest_latlon = closest(temp_data_list, site_location) - - # Extract a dataframe based on the closest latitude and longitude - df_nearest_tg = df.loc[(df['Latitude'] == closest_latlon['lat']) & ( - df['Longitude'] == closest_latlon['lon'])] - tg_station_name = df_nearest_tg.index.values.astype(str)[0] - tg_station_id = df_nearest_tg.loc[tg_station_name, 'Station ID'] - - return tg_station_id, tg_station_name - - -def get_psmsl_gauges(data_type): - """ - Search for available tide gauges from PSMSL, extract metadata using - function read_psmsl_list_of_gauges, re-order into correct format for - future code. - :param data_type: temporal scale of tide gauge data - :return: Re-formatted dataframe of available tide gauges from PSMSL - """ - print('running function get_psmsl_gauges') - - columns_needed = ['Location', 'ID', 'Lat', 'Lon'] - ids = [] - tide_gauges = [] - - for d in data_type: - # Read in the PSMSL station list of all gauges worldwide - psmsl_gauges = read_psmsl_list_of_gauges(d) - - psmsl_basedir = settings["tidegaugeinfo"]["psmsldir"] - - for row in psmsl_gauges.index.values: - idn = int(psmsl_gauges.at[row, 'ID']) - if any(j == idn for j in ids): - continue - ids.append(idn) - tide_gauge_file = f'{idn}.rlrdata' - tide_gauge_file_full = os.path.join( - f'{psmsl_basedir}rlr_{d}', 'data', f'{tide_gauge_file}') - - if os.path.isfile(tide_gauge_file_full): - l = psmsl_gauges.loc[row, columns_needed].tolist() - l.insert(1, f'PSMSL - {data_type[0]}') - tide_gauges.append(l) - - return tide_gauges - - -def read_psmsl_list_of_gauges(data_type): - """ - Read in a list of all gauges available from PSMSL. - :param data_type: temporal scale of tide gauge data, for PSMSL can be - annual, monthly or hourly - :return: Pandas data frame of all available tide gauges from PSMSL - """ - print('running function read_psmsl_list_of_gauges') - - psmsl_basedir = settings["tidegaugeinfo"]["psmsldir"] - - if data_type == 'annual': - station_list_file = 'filelist.txt' - filename = f'{psmsl_basedir}rlr_annual/{station_list_file}' - df = pd.read_csv(filename, sep="; ", header=None, names=[ - 'ID', 'Lat', 'Lon', 'Location', 'num', 'num2', 'flag'], - engine='python') - df.drop(columns=['num', 'num2', 'flag'], inplace=True) - df['Location'] = df['Location'].str.strip() - df = df[['Location', 'ID', 'Lat', 'Lon']] - elif data_type == 'monthly' or 'hourly': - raise ValueError("Monthly or Hourly tide gauge data not available " + - "for PSMSL gauges") - - return df - - -def tide_gauge_locations(**args): - """ - Wrapper to read in a list of all available tide gauges found on PSMSL. - :param args: Argument list - region --> region of the world to assess sea level rise - source --> source of tide gauge information - type --> temporal scale of tide gauge data - :return: Data frame of all available tide gauges from relevant source, - indexed by Location - """ - print('running function tide_gauge_location') - - # Name of columns in dataframe. - columns = ['Location', 'Dataset type', 'Station ID', 'Latitude', - 'Longitude'] - - # Update parameters passed to this function - data_region = None - data_source = None - data_type = None - - if 'region' in args: - data_region = args['region'] - if 'source' in args: - data_source = args['source'] - if 'type' in args: - data_type = args['type'] - - if data_region is None or data_source is None: - raise ValueError("tide_gauge_locations: must specify names of " + - "region and source") - - if data_source == 'PSMSL': - if 'type' not in args: - raise ValueError( - "tide_gauge_locations: must specify data_type when " + - "specifying PSMSL as source (hourly or monthly)") - - if data_source == 'PSMSL': - tide_gauges = get_psmsl_gauges(data_type) - else: - raise ValueError(f'Source {data_source} not known') - - df = pd.DataFrame(tide_gauges, columns=columns) - - return df.set_index('Location') diff --git a/profsea/tutorial_notebooks/worked_example.ipynb b/profsea/tutorial_notebooks/worked_example.ipynb new file mode 100644 index 0000000..f233611 --- /dev/null +++ b/profsea/tutorial_notebooks/worked_example.ipynb @@ -0,0 +1,775 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ef69532c", + "metadata": {}, + "source": [ + "#### Setting up the environment\n", + "The first step is to set up the conda environment. You should use the `profsea-env.yml` environment file, building it with \n", + "\n", + "```bash\n", + "conda env create -f profsea-env.yml\n", + "```\n", + "\n", + "and then activating with `conda activate profsea`.\n", + "\n", + "---\n", + "\n", + "One final step here is to install ProFSea as an editable package, using\n", + "\n", + "```bash \n", + "pip install -e . \n", + "```\n", + "\n", + "while inside the highest level directory." + ] + }, + { + "cell_type": "markdown", + "id": "36f6320e", + "metadata": {}, + "source": [ + "#### Importing components\n", + "\n", + "The next step is to import all the model components you want to run simulations with - a full list is available in the documention [here](https://metoffice.github.io/ProFSea-tool/). This can be any combination of available `global` and `spatial` components, how you build your model is up to you!\n", + "\n", + "To generate a comprehensive set of sea-level projections, we'll run with all the components that contribute to sea-level change: \n", + "- thermal expansion\n", + "- glaciers\n", + "- greenland ice-sheet\n", + "- antarctic ice-sheet\n", + "- landwater \n", + "\n", + "In the example below, we'll run with the configuration being used for SLEIP and Munday et al. (2026), *in prep*." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "868f2127", + "metadata": {}, + "outputs": [], + "source": [ + "from profsea.components.core.global_model import Global\n", + "from profsea.components.global_ import (\n", + " AntarcticaISMIP6,\n", + " Glacier,\n", + " GreenlandAR6,\n", + " LandwaterAR6,\n", + " ThermalExpansion,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "eced6e8d", + "metadata": {}, + "source": [ + "`ProFSea` can run with a single or an ensemble of temperature and ocean heat content forcing trajectories. The trajectories need to be anomalies, relative to some baseline period - `ProFSea` assumes by default this is `1996-2014`\n", + "\n", + "Here we'll just run with an idealised temperature ramp up and stabilisation, and corresponding ocean heat content change, running from `2006` to `2300`:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7c4b26cd", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "t_change = np.concatenate([np.linspace(0.01, 8, 150), np.linspace(8, 8, 145)])\n", + "t_change = t_change[None, :] * np.random.normal(\n", + " loc=1.0, scale=0.1, size=(100, 1)\n", + ") # add some variability across members\n", + "ohc_change = t_change * 1e24 # in Joules\n", + "years = np.arange(2006, 2301)\n", + "years = np.broadcast_to(years, t_change.shape)\n", + "\n", + "plt.figure(figsize=(4, 2))\n", + "plt.plot(years, t_change, color=\"royalblue\", alpha=0.1)\n", + "plt.plot(\n", + " years.mean(axis=0), t_change.mean(axis=0), color=\"royalblue\", label=\"GMST change\"\n", + ")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"GMST change (°C)\")\n", + "plt.show()\n", + "plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "c0375155", + "metadata": {}, + "source": [ + "#### Building the model\n", + "\n", + "To build your model, simply add your components to a dictionary, with whatever names you like, and pass it to the `Global()` class" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a1c9b208", + "metadata": {}, + "outputs": [], + "source": [ + "global_components = {\n", + " \"landwater\": LandwaterAR6(),\n", + " \"greenland\": GreenlandAR6(),\n", + " \"expansion\": ThermalExpansion(\n", + " ohc_change\n", + " ), # only the thermal expansion needs OHC change, so we'll pass it in here\n", + " \"wais\": AntarcticaISMIP6(region=\"wais\"),\n", + " \"eais\": AntarcticaISMIP6(region=\"eais\"),\n", + " \"peninsula\": AntarcticaISMIP6(region=\"peninsula\"),\n", + " \"glacier\": Glacier(),\n", + "}\n", + "global_model = Global(components=global_components, end_yr=2301, num_members=1000)" + ] + }, + { + "cell_type": "markdown", + "id": "7f9cf711", + "metadata": {}, + "source": [ + "now we're ready to run our simulation..." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "84894d79", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
╭──────────────────────── ProFSea Global Run Configuration ─────────────────────────╮\n",
+       "│        Scenario  stabilisation                                                    │\n",
+       "│      Components  landwater, greenland, expansion, wais, eais, peninsula, glacier  │\n",
+       "│       Timeframe  2006 -> 2301                                                     │\n",
+       "│   Ensemble Size  100 inputs × 1000 draws (= 100000)                               │\n",
+       "│          Output  Full Distribution (100000 members)                               │\n",
+       "│  Compute Engine  Parallel Threading                                               │\n",
+       "╰───────────────────────────────────────────────────────────────────────────────────╯\n",
+       "
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\n" + ], + "text/plain": [ + "\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "projections = global_model.run(\n", + " T_change=t_change, scenario=\"stabilisation\", member_seed=42\n", + ")\n", + "global_model.sum_components(projections)\n", + "gmslr = global_model.results[\"total_gmslr\"]" + ] + }, + { + "cell_type": "markdown", + "id": "578b6a8c", + "metadata": {}, + "source": [ + "visualise the global simulations!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5b63dfb5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(4, 3))\n", + "lower = np.percentile(gmslr, 5, axis=0)\n", + "upper = np.percentile(gmslr, 95, axis=0)\n", + "plt.fill_between(\n", + " years[0], lower, upper, color=\"lightcoral\", alpha=0.5, label=\"90% range\"\n", + ")\n", + "plt.plot(years[0], gmslr.mean(axis=0), color=\"darkred\")\n", + "plt.xlabel(\"Year\")\n", + "plt.ylabel(\"GMSLR (m)\")\n", + "plt.show()\n", + "plt.close()" + ] + }, + { + "cell_type": "markdown", + "id": "af51e3f9", + "metadata": {}, + "source": [ + "#### Building the `Spatial` model\n", + "\n", + "Start with the imports:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "47694def", + "metadata": {}, + "outputs": [], + "source": [ + "from profsea.components.core import Spatial\n", + "from profsea.components.spatial import (\n", + " GIA,\n", + " Fingerprint,\n", + " SterodynamicCMIP6,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0a19cc97", + "metadata": {}, + "source": [ + "Now we'll build the model in the same way as the global model, but we'll pass the individual components' global projections to each spatial module. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "09f1fff4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
[13:17:18] INFO     ✓ ProFSea assets found locally!                                                                \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m[13:17:18]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m \u001b[1;32m✓ ProFSea assets found locally!\u001b[0m \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
           INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
╭─────────────────────── ProFSea Spatial Run Configuration ────────────────────────╮\n",
+       "│        Components  sterodynamic, greenland, landwater, wais, eais, glacier, gia  │\n",
+       "│         Timeframe  2006 -> 2301                                                  │\n",
+       "│          Baseline  1995 - 2014                                                   │\n",
+       "│   Grid Resolution  180 × 360 cells                                               │\n",
+       "│            Output  Percentiles: [5, 17, 50, 83, 95]                              │\n",
+       "│  Est. Output Size  ~0.76 GB                                                      │\n",
+       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[34m╭─\u001b[0m\u001b[34m──────────────────────\u001b[0m\u001b[34m \u001b[0m\u001b[1;34mProFSea Spatial Run Configuration\u001b[0m\u001b[34m \u001b[0m\u001b[34m───────────────────────\u001b[0m\u001b[34m─╮\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m Components\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35msterodynamic, greenland, landwater, wais, eais, glacier, gia\u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m Timeframe\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m2006 -> 2301 \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m Baseline\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m1995 - 2014 \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m Grid Resolution\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m180 × 360 cells \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36m Output\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35mPercentiles: [5, 17, 50, 83, 95] \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m│\u001b[0m \u001b[36m \u001b[0m\u001b[36mEst. Output Size\u001b[0m\u001b[36m \u001b[0m\u001b[35m \u001b[0m\u001b[35m~0.76 GB\u001b[0m\u001b[35m \u001b[0m\u001b[35m \u001b[0m \u001b[34m│\u001b[0m\n", + "\u001b[34m╰──────────────────────────────────────────────────────────────────────────────────╯\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+       "
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           INFO     Simulating 7 sea-level components: sterodynamic, greenland, landwater, wais, eais, glacier, gia\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO \u001b[0m Simulating \u001b[1;36m7\u001b[0m sea-level components: sterodynamic, greenland, landwater, wais, eais, glacier, gia\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spatial_components = {\n",
+    "    \"sterodynamic\": SterodynamicCMIP6(\n",
+    "        projections[\"expansion\"],  # passing in the global thermal expansion projection\n",
+    "    ),\n",
+    "    \"greenland\": Fingerprint(\n",
+    "        projections[\"greenland\"], fingerprint_component=\"greenland\"\n",
+    "    ),\n",
+    "    \"landwater\": Fingerprint(\n",
+    "        projections[\"landwater\"],\n",
+    "        fingerprint_component=\"landwater\",\n",
+    "    ),\n",
+    "    \"wais\": Fingerprint(\n",
+    "        projections[\"wais\"],\n",
+    "        fingerprint_component=\"wais\",\n",
+    "    ),\n",
+    "    \"eais\": Fingerprint(\n",
+    "        projections[\"eais\"],\n",
+    "        fingerprint_component=\"eais\",\n",
+    "    ),\n",
+    "    \"glacier\": Fingerprint(\n",
+    "        projections[\"glacier\"],\n",
+    "        fingerprint_component=\"glacier\",\n",
+    "    ),\n",
+    "    \"gia\": GIA(\n",
+    "        sample_spatial=False,\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "# Pass to the spatial model\n",
+    "spatial_model = Spatial(components=spatial_components)\n",
+    "spatial_model.run(member_seed=42)\n",
+    "\n",
+    "total_rsl = spatial_model.sum_components(spatial_model.results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79d407cb",
+   "metadata": {},
+   "source": [
+    "Saving the spatial projections is easy + quick using Zarr format: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "60d81a5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
[13:17:33] INFO     ✓ Successfully saved Zarr: ./stabilisation_projection.zarr                                     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[13:17:33]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ Successfully saved Zarr:\u001b[0m .\u001b[35m/\u001b[0m\u001b[95mstabilisation_projection.zarr\u001b[0m                                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
           INFO     Output shape for 'sterodynamic' was (5, 295, 180, 360) (percentile, time, lat, lon)            \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Output shape for \u001b[32m'sterodynamic'\u001b[0m was \u001b[1m(\u001b[0m\u001b[1;36m5\u001b[0m, \u001b[1;36m295\u001b[0m, \u001b[1;36m180\u001b[0m, \u001b[1;36m360\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0mpercentile, time, lat, lon\u001b[1m)\u001b[0m            \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spatial_model.save_components(\n",
+    "    spatial_model.results, scenario_name=\"stabilisation\", output_format=\"zarr\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8e3a1e6",
+   "metadata": {},
+   "source": [
+    "Plot the output! This takes about 90 seconds, since our data goes from lazy → in memory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e62d28d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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CjZH+gMY8kvWbN1vZBP69A4Kp7x3AcQ+uY27425r7WcZer0KFChXhGR8+fkaKbKWERwWZjClwiPgF9hH1Jh4Vlp1s/1V4hfFjpfll+rZWESz7nqa+X0Cwtgm+463/3irCH0jygzJeBWGRoELF3w6biF9M2s6skZ4r1/C8eg3RKIw5A1RIEbfgIj3mkJWQJmOWOrZhnTCGNLHSP79/B+b1EYKP8AUDOTBl3PNrfDF1zAzVKLFKqFSEMjThmFtY4nv9nd9ehQoVKlSoUKEinMBCOZDwz1BDZVVq6YhYUKI3oRndssRxCKnIVVCOk340KDjTlMbe29hnG3tvY8fRWFRK/zfTj6qY/Hp/vn9Q9stw34ztYyAQlPEhTOsiVagII7AKg9dJSEbr1MiYChUqVKhQoUJFKOKfioz5h3Ch6whqJMjcqI6lo03BqY3zK0piqe9gkWMXmH35bfr3ViJKAUXTzI226X8X/6Jk5kSfLK0ZCyHN09+qawlz45sKFWHsegjuaz+CuTtm7GL9Wwcoiw1MgSEh5pIIU8jC3yJCN2fitbGx0O9iY97xtLFwVaVfZCYkKhkDOt4BEqVArPnM/V5+pU3DAVkJ7fEzOAmYpb6bShJVhDYCew6a+jo1MqZCxV8CjUaDX79+4/uPX/j+46fcfnt5IYKNDSJEsEEEG2uf9yPYwErfI02FChUqVIQK/ilrC3MRVBNLs2Fq5CqgaEqgUphBED0bi2LoR0OCmGJSfgeSjVdv3uPNuw94//EzPnz8gp+/fgkB+fX7N37/9oK1tRWSJ0mIr99+4P3HL7Ld+4+f8PPHT0SOHAmRI0WUWxTej+z9N1JERI1oLf+PbP0bUSJHhL1tNNjafIO1tbX2eEa20+5MhGgINAIRxXrx6h0uXr+HG3ceQwMNbKytYSNEKqL8ff7yLbbvP40T52/iy5cvcozM2iVrvlcEH7foUSIiaeIESJ7EEcmSOCJFkgRInjg+UiR1ROIEsRExognDht51YvX7m/YOj6P3MdBYR8Q/i8BE7oJj3PX+jcw7Y0LHCkNjqc8LTj81v6KzwSTPUKOFoQdzeJCp26qRMRVhBu/ef8KhU5dw+cZ9IUSxHexRwDUjDp66jBFTluPOw6f4/v2n2e9LwkHC9e37D7PICqNGMeyiIl4sO6ROngipkzuiVJGcKFnABUHFx09fhEA6xLDz9Xj/8Utw8dpdXLx2H89fvZXHSbz4PUg4vbz+EB2SyaJ5s6Jfv36wt7dH5MiRfdxsbGzw+/dv/BLSGvDt58+f+PDsOu4/fIq7D55g/9EzePLsldHjlj51MhzZMAUx7G2DfDxUqFCh4l+GSsYstcowdfUTUNTKv6hXgK8NRsG9sVWhMfG4/nMGrzl69houXLuHB4+e4+Pnr/j67TvevPso0a4Xr97i1r3HQjRixIghpICRHgWVK1dG8zYFkDRpUsSJE0e2IflgA1YSDkZ0+Jevu3fvHqJHjy7b8Mb7JFbaNN4v6bX6/ft3X39542c+efIEt2/fxs2bN+Hp6YmrV6/ixr3nsh+b9nlicPcmePLsJd6+/4QPnz7j46ev+Pj5i/wlwVJaJikEJnWKxCia3wXPX7zCqfM3cPr8dVy7/VCejxfHAelSJkHaVEmRKEEc9B8zX/edK1SogLp16yJz5sxImTKlfD/lfUmweFO+u6l4+vSpHEMeh6Di6s37+PLTGvbWf1p8/P71A2/ff8TL1+/w6s07WFsBDraREDOGLRzsoiFqtMja1KhybvAc+5sNR82JaIW14xCYaFxQvkNwFfjoj+3BeYz9em9TPjMQ350RFzU6FnII7qyglcaEUMGHDx9kUnt7ZVPw9KYMY62DAnWCm/IdAkvE/HtdcBw7w8HDL9JlJO3G9BNPqYdPX+PHz9/48fMXfv7SIKZ9dDjlrqbbLlOmTIgaNSpixYqFuHHjyi1DhgwoUqQIUqRIIRP227dvsX//ftja2qJEiRKW/57KPms0KFeuHLZu3Wr0+aQJ4+L124/4/NU73QYgWrRoiB07Nuzs7HQ37ifJIcH9Vwjg6dOnhdxFjBgRWbNmhaurq9z4/a9duyY3Er7nz5/j2bNnvj6fz6dNm9Yi3/X169dCZoOKJvWqIop9fDx+/BivXr3Cy5cv5S/fP6AhpXjx4lg7vTeiRYsi568V/Lne9Cs1A1O1qbyNpV18AtoHYwO3D1Lgz/6Y8f1MniCMvKfNl0faO29v6cYYrxcXvHf1N35//yr3f377JH9/ffuIs4fOyP3v37XfJUpUG0SNot2HyJFtkKlYRblv7Z3Wt4qfDV6x0vvelxAY8/09r/yCsXPMO81u5fVTTy7xQRYdKZM5wjqi92JEP/UenHKWYDx2fzu50wQnofLjumVvSocM5fH+/XsJIPgFNTKmwmJgCnHh+l1o3n2cr+eYTkuaKD5evH4vkShGdapXrw4XFxchKDFjxpRBTomI8S+jZKVKlfK3uWpQwAjY2bNncfHiRV9ELFKkiEiVLCFu3nuMB09e6h5ft24dChcuLIsTc8TvJC0kQUwdBoSPHz/i2LFjmD9/vpAbElVLgQTSlFTtp0+f8PDhQzx48EBIZPr06ZE8eXJpdkvMW7IW6dKlQ5IkSeTxHDlyyH7yO/Kvg4OD7PuFCxdw6NAhufE3JXbv3o0N2wugpocb/hZ8//5DoqRxYsWU84KRx2u37iOGnS0SxDNRZxfMePLsBa5cu42Xr99I1DKS10dYwQqO0b/j0+ev8Lz2GN/eP0Oy+LZIl9gebplj/fMFHhyDLly+hT2HPXH8zCWcvXQbT168xo8f2oVzDLvoKJg7s0gFqpQvhoQJgr7QUfFv4q+KjIUIqw9qOtJY9CsokS9zxPr+Ccj1Wb2ynbEomJXNn9WF3muOnL6MFt3H4MqNezJxM9IzfPhwFChQQFJpnICnT5+OR4+8V+OBQMKECYXUBBVMPQ4bNgyrV68WYmJnGx2/vX7jyxftCpj7yygdCUb27NnlPglGggQJLBJVCq84ceIEbty4gYIFCwoJo4aNJKtRo0ZCsL9+/Yp3794JgT537pzut3KMHwcZ06dCxnQpdTeXzOm0Ez2vWYNzPFARDVOjYfrnuf4q2WBV+/X7T1nRxosbS3vOazS4c+8RPn/9LunmNt0G4e79R0jllExS7Fz1EjyX0qR2km0Z2VXA88YxfmykS+2ETM5pkSJZYlT3KBuocczs1b33d4uf0hVv32n30xTcX1gFCWNH8xEh89x/UO7nHn7e1/Y3R7oiXdHq2v8okbF4meDl4Oy94wFlBsJQVMbKGu71u2DzTu33VSK6zs7OIhlIlCiRRMN5TezduxdHjhyBl9dvVCpTFC0bVEaBnBl1RNbX+WzsOAT2u1swSva3R8WCJUJmQhRbjYypCFFMmb9OiJiHhwd69uwpxIkTGCdsR0dH5MqVCz169MD9+/d1tzt37uDCmaM4dOw03rwNeJJo2LChv88zGsGoGkmBciMh2Ldvn6T/mEpjNOzMmTNwcnLC5DGDUap4YUyfswijJkzXvQ+jdiRfkyZNEr2ZCi34G/Kmj3HjxuHWrVs+Htu5cyeaNm0qOr+MjkDsWDHDpBxBH0dOnsPsRWtx9+ET3L3/GI+fvpDH3csURYL4cbFh6148ff4nQqrg5u17uvt58uTBr+9f8OTpM+TMmRMVK1aUKCePD283rl3BqvXb5EZUdS+trdYNIaxcMBl7Dx7D9Zt3sHbjdh/P8RotX768/I0fPz5yeO0RIvYvg0U/CjgO9O/fH3nz5vURLaR8onfv3kLG508egqnzVqKoRwtky5gGu1dNkuIWRk0ZaVdtZFRYLjJ2dYt5kbGgMO0gDNzBwvCDEhELDS2YnkD6589fuHjtHmxtoyFZoriIHDW60cjXl6/f4Fq+Pa7eeohUyRMiXuwYSJowHpySxodTkrhImTQBMqdPhRjxkspLaLfw5PlLRIkcGbbRo2LmonVYunYH3r3/6GNX1q9fLxOTX+DKslixYj4e4wA3cOBAXZ49oEmLon1qzYwhQfx4SJokEeLEckCc2LGQJ1d2ZMvsjIePn+IeSeG9B9h7gJPU7T+Hz8pKiF1wpUj/Jqxdu1YKDTg2TBjRG5XKlfxzDeqfx3r3fTxveL36ce4bRhgC1IEZi4IZiexS5zhiwmz0GTRGihv4XRj9IJFasGCBpKrSpEmDsmXLStSX5wYjIkuWLJEIIEm/qeB5XLxoYZR2K45mjesjgo2Vyd/fkuCwf/v2HXz6/AWpUiSDnZ2pFbEBRHWM7Luv8diPc8LP7f3ZNtigd+68ef8JFWq3xfHTFyQ6ni9fPmTMmFEWnIyQ8W+8ePFkvGBkeMyYMVi1ahV6tG+KuUvW4NmLV7CNHg1OyRIhd47MyJM9I3LnyIRUKZLASjEWMVU76etxE+a6EDpuQZp3NcFoOWLpyJiZn2dqZCx005QhSbiCekL6S5zCoPDeO604cNF5WdEZ4t6R+Uia2PHP7lhZS7rF0eWPyN4YGJof0qMpWtb3gGNWdxFty8dZW4sYv0CuLKjhUQanzl3G6Elz5bmJEyfKgEVdGF9P7dDnz59FjzR96mSc8fSd8jh58qSI3E0Fo1ktW7bErFmz5P/u7u7o0Kwm7OzssWPPAZy5eBN3796Vm376iCJ6kjjeGC1T/hYtWlT2VYX/YHEBCUrliqUxa8Ig2EWP6n1CGZu0f/tB0ow8ZmbKMjApScXrjH+9YINmbXti4bI1yJYlEzp06iKaxoA0ftT1tWvXToibgmTJkqFEieL4+vUb6tSqjrixYyFa9GiIFjUy7O3sED1qALrB4BgbAhovTfhMn2Oukd/PyPsYHaf9ODf8fY2J+xhcYHRr98ETOHrqIo6eOo9bdx+Ix5++zYw+OF9y3mzVqhWyZcsmWYCjR49KQZKCrm0aYFjvttr/sDjAv3R9GCBjwVbYFpKwMs3T0lLvR6hk7C8mY+TPdFe3pgkvNCKKf/riHTKmTS4DPvH241dUbjkC+09c8vV6GpreP7oAMWLY+zoJuw2ZhTEzVsp9JRplbMAZ1bctztx4ieXLl0tK782bN0KIiOKF8yB9mpTimXX1+i2cOKOt0DKGWA4OeKNHjogqVapgypQpstoMCLSj6Nu3L3bs2IErV67oROKbNm2CW770OH7yPAqWqqzbnvovaj84QGbJkkWqFEnI1BRC4MAJhpECzyNb4JzWyTjZCgdkjJVwvK527DuOydPnYOfu/VLswHQjq2SpMWS0TAFtT3iOde/eHSVLlkTx4iWwcuUKHD9+XJ5PkCA+nj3T2qEQbVu3wKjhg7UfH6B2SiVjIXZczAHPU+U88vopsggSsicv3shCNlq0qHj+8jVadh2Oj58+S2U1q64ZETEW89i0dBJKF82rez+VjIUAVDIWfBeUyWw9OPpDBsYTzNT90Pdh8tKmGg+evob1u09jw55TePTMJ4EhBnWqhV6tq0r6cdOek6jYdJDRt65buQTmj+um/QzF3d4qolELAakKu/0YZy/dxNUbd8T9/tiJs+jWtgFqVHLD3KUbsGX3Ydy+9xjXbvxJ8ymYO3UkalYpjw8fP+Hdh0/yN1LEiPjyzQsvXr5Cuy69cP/BQ4lGcfWogNEWkiu/MGfOHEllKlYQtatVQE6XTHDJ4ix/hUhqfsugOG7qAnhevIqLV27i/sMn/h72Ll26YNSoUf5uo+IPLl26JDYlDL/PGtcPHuWK+jg8JkU5LJ2mMzbgGiNj+qlLxZbA+7lrNx9g3JTZmLdoufyflivbtm2TSBn1jjw/eW2ULVsGi+bPkYrTSpWrYdv2HbI9/68sDIh8efNgz45NCAkEiewFEMny/Xv68zv68XnWv/74B/787SWRg5gx7IxKEfwl9wHtt38wjIAorw+it9qV63cwdMJc2NrayXd69uI1Ll29gSdPn+PFy9e+Xpo9qzOWzBiJVMkSaAmZ3neVhYi514I5xyEQ11mopSIDmmOD0qPXUgQtiNYWIZ+mVMmYWT8sL+apS7ZjxvI9ePnmA5I4xkbFotmQJlcFWYVTkE4jz+vXr2Nw51rInC45kiRMIN5YjGidungbZy/fRpyUeZDC/h1OnbuBTbuOSiTIOW0KjOnXBna20fwkY8TLN59gHTEyHj15jmOnL+DixctiNLpz/zHU9CiNHC6ZUKFWG6NfI21qJ90gG8shJqp5lIVHhVJIki6PL10ZbS4IRrkYFaOQmOceX6/oM5STmff53RWMHNgVHVs28Pdce/z0OZJn8d+vbOnSpahZs2bAv88/DJ5rTCHrp+YUvLh+AA4x7cM9GYNVRHz88gNVajfGvgOH5SFGOZiCZCEIz/9znqe1Fife+0/bj5u374p+8fGTZ1JByQhx6lQptMa8IVStFhbIGKeVb9++y+Lrw4f3+PCBi7EPuHz1Jk6d9sTZC1clisSJimCrssNbFyJrxrThloxRG3bhyk1oaBgSITKGj52Bg0dPwdUlM/LnyYHMOQqI1oyykS1btvh4i9pVyuDN23fSJYTlAeKLbGWFqFEiwSVjauTMlh45s6ZHLAe/J3Mf38MUqGQsHJOxy+tNJmOhUiZrSRuI0HLE1/tBz1y6DddK3XX/ZzqE1U6sBrx8+bIu9B3TLhqsrK3FEV4fJFnJEsVDfteMqFq+MF69/YjqLQaIKJ2C+s2bNwtpq+FeQkSq0aNFRd2qZZDaSSvQ19hEwaoNO1CraVfdeyq2D4qXEifmbJkziAyVKckvX3yLmXPnzi03fh6rygrkzYmz5y7hs57DvqlgyoiVTTRIVZDHNQtmjO6NDGmS+ath4Urz+JnLWLByG1Zt3ou37z6KRQMtLPhd9N349VsI8T5TDjxuTGmSJLKqkN+J6c5/TeRP3QsjRQqyZEyLMQO7ILaDHTKlT+3zuOundpT/m3GdBEua0sjzvuxa9IT+fO+LV29i556DeP78NSJHiYxKFcrAJWsm8z7PVASHS3xQyEwAeq5Pnz7jwqWrOHfxMjzPX4bnhUsSJVc86UzFwB6t8F+Hxsb3OYAiAUtDpCDff+DTF3bX+I4DR89i98GT0jVEubFFWe7smWTBfPjEOdy6+9DHe7CCct269ShTpoyv9+cYQ+0qb0yBczGaP39+mWf52VxI8y/nXppGUwZCuGZNj55ta6OiW/5g14yZO4dv2XMCOw6eweOnr/Di9TttD10bG0SMaINoUaLAKZkjUqdIhDTJ+TchEsaPHbA8xCsEUtNB7J0cEGkTMubsblky9u7S2sBHxkw5IYI6CIU0GQvK+xqDgdP90xdvkCh3fV2JdYK4DnCMFwvOaZMhv2sm5MqaFvNW7cLoGavkQv7vv/+QOnVqSffRD4o3+n3t2rVLXk8NDI04ibQpk6Brz34YMmSIXOiMPHHbahVLYsms0d77YIMlqzahQaseut1q0KAB5s2bp/s/BaqrV63EzXOHMHL8NBw+eV7SixTzM114xvMCXr99L9E7RgwqVaokom8O1BT0k1wx9UNCE9Ea+PD5qxAtCvz9QiLHeKhcrii6tq4nx8NXNZIx13YDcKDde8QTuw+dxqXrdxE5UgTvpuGRECliBDHp5EASIYINrK2spCE5m49zEH7w+DnOXrwpq1g+nzNnLqkoJEn7V8DJglWwXOXTY+n+2c3SzsnkVlrGtrNY+5mgi3B9DKw+SJaN38/7+FwjRM/Ez/b5vIWsLyyxOOa+aLxw/8EjHD1+EkeOn8Kxk566hSEXLIz8cIHC65vjD697RrAN22/R8iF1iqRwSp5Ybm5F8iJ/zkx/vOf8WExZGlxoPXn+Ft9//BBrngNHz2DfkbO4cvO+jzQzwe/FakouyHjjGMVzn2Mn/RQ5BnNhx+PA64NjG8fcgMBjp9gA+fU8F7FczHbq1Eke+3RzB6LqF4KEdEGbAWHbdegMStXthVSpUokZNKPITNUrC1rF4oUFVIoGmYvq4nkzYUi3BmjXb5qM5XFixUD8ODHlfsL4sWRMSeIYRwILYYqg+QeDa1rS8Bk9VDIWnsmY/8/b4NT568hVoZ3Jb0+xOiNZCnjRcOBgaT4d1zlw9u/WAr26tNJ9xus37zBu+kIhJkNGT5ULjL0SGSFjGTfb/PzXpS327D+MoydO696XFx1Xf3Onj0WjFp1QtWpVGaiY5mE0irobkj8OhiwJ58X69uUTOMRNKBWNHMQ4MPN5riaY8uF2jMrQ/4u4dHAl0qVKGigy5gtmDmY/fvzEhWt3sXzDXoybuVL2t02bNujQoYOQzH8Bbm5u4ilGHN82HzmypPvzpErG/koytm3HLrhXqy//TZc2NQoULCxRYpqhchG1fft2bNy4UQgYxwouDtM4JRK5QrpUycUkN3VyR8SOaec7KqIfTQ1mMkaCs2nXEXQbOAU37/4xomaXkMJ5s8G1YFkJQCitzlikRG1kSBX6kAiyMwgtVFhZzhvHS+536waVMHFwe4MvFLpkrGWviZixeIuM3SzA4ljPxTcJGMd2HjeSTRIzjv0k6gwCtKxbFr9/e2HmUq33nl+4eWCOtJ4SqGQsBt6dXwF7u2imscbAHLCgsNGQIF2BfW8Tv+uXb79QoFp3eF6+g/SpkuDTl2/SpoR/2dsxUfw4SJEkAaJGi4KIEWxECM+IDR3O48d1QLSoUSSqEyUCUKPNcKMfy5VH+tTJRXtgGy0aokePJulJepDlyJoBZUsU8q2f8Z5QaHC5fssu3LrzQC6umDHskTihI5o2rIkpMxdh5PjpohHRXwV3btccV6/fwPmLV/H27Xt8MfBkYlSMK0glNK/YXgSEYgVzYfHUIYgb2zvsq/GClean32TMrwHeVE2KIbzTWNzvdTuOYu3WQ6LFS5bcSYTeJJx/KzjRMhKgX3AxZVgXNK9XKeAXG5IP/Ubhpvae9IugWLIhs8F7+SJURqJlAW5nMsEKmm+SsfRSoHyV9CJ+V65ex5IVazBu4jRZIC1evBi1a9eWNBvNnKm15IIuRVJHVCiZHxVK5kU+10yIECkqYBPF57H3+qkjVsbSy0ZJVyDJg6+ok3c3hXkrtqJp5+FCuBYuXChjENt6KX1xQwvcNx7XZcuW6R7jgjNntgxIljgBmtYqi0SORtqjhRIZUyAFZsfPYfXWQ1i3/Qhevn4vXIEt5Rxi2MrYzvOGeTje99JoUCJ/NvTrWAcfP33FrsPncPLcNZw8dx33H7+QNOeXr98l89ChsTuGd2/ot9dkSJCzIODDxy+ImaV6CJExHgzlsaAcGEuRsYBgzj6aStJMhT8Rr2/ffiB21uqSCiP69Omj00gxvchWQlxVMOJE0sJUAH8brjII9sBbOn0Q8ufIgH1HPHHmwjU5maNGiYy0qVIgYzonxIvj4Cutcu/hE4yZt0NWMHzv5Ans0LReFWnZot3OBi9fvcH0ecsxb9lG0VVxlUgixdUNy/yH9euMPDld4FG3rc6RndEiEjOl36SCsm7F0LRhLVy+eh1HTl3Cixcv5LMZTeN3atW0PhImiKftoff+mzzOqF2hvNnhEMMem3cdEkE0jw1vjEQVck2DimWKIXHCBH+qkaRcXDtICVHTmwz8JWPmnsPevx+rTkvX7y0D09Yde8Q6428BB1CeewRX6DRAVVCtQjHU8nBDuRL5Aic5CEDPZQmjxgD1LwFNTqbsj6kEzYzPsBjMJICyz977M3TUBAwYoq0wbt26tZjiMhq2YcMGtGjWRMarDq0bo2KZIsiYOomQGd3xNmLZ4ON7BkPk6+CJCxg8bh4ePv8gXoYcr7hPjNRx3OAYo79g5DZhZfHE6ZjRRGYcFChRJZIZl0xpkCFLTlkQsZKcjyvRO+VWqYgzyhbP6+t43rn/BHU6jpUUIiN9vFEeoqSYC2eKhUL5colEw+hvYey3NIJfv37j/cfPIufoOmqVFJrxmPPY0wanZC4ntGnoLvNSiHiXeYUuWVPJWDgkY2y0XbfjaFldjBgxAt26dfPzbciyGYFZtGiRrybXBF2eWzeqjLv3n8Ahhh0qly+KWDHtsXHHIdx58AyVyhTGoLFzsG3PUbz/8EkibPqoXbUc5k8ZKkRq5/7j6NBzKO49eKwjidSFcXCg8JRwSp4EOVwy4/jpi+I9Rn8mpjLpUH7q1CnZVzbZzprZGZPGDMGYCdOxfvN2Ifl0N6fGjFWUROECebD/0DG5z7QmCwY4WHDwt7ezlegbQX2CYSsep2SJcfHwWm37kRAmY5xknr76iPINeuLG3afi3s72UH8D2GeUba58Pd67Fbq0rO37BSoZC/dkjN5Z9FwbMUYrCyhUqJDIBEhe2rdvL9d+WbcimDxmEBJxEcSI129tf1dzyRgJwcqNu7Bj3wkhH47xYkvTbS4wGWHjX/GB239StncrnNPPCFb/MfMwYvIitGnTVjoqMBrBcUy5cSzhAo5jD3We7D8bFn0GudCmvpb2MdxvLnrp7s8UIK2AqF9TtuNiljdtq7m70iklWpTIiBI1spAeVmU+f/EGew6fkTQiI5qsCubiW3kPLng5RzDCPahbY9/HxEQypqBR51FYsEorYzDEhAGthZCpZCyokbHgQkhFxkI6tWmC/ovRlBpth2Pr3pMSoqbQ3RgoluWkyNJoRQjJwYWEha7xXPUMHqw1l1SiU0oTY9pFcEXFiJZyEXJlReE/+zdSn8AVo0uWDNi+ejYcYsbA3oPH4Va5sS/RqxIyZqUQiRZBXQh7+h07dkysJ3p0bouZ85bItilTJEWblk3RtGEdtGjbFctXr8eMGTNQo0YN3eqsXr16kv7g/s2cOVN6G+r3hqRYlk2pGQmMFjWqr5SngsF9OqNWNXckdoyjJWRKlEy5r5/ONCBjMjjoSNtvv39rf5qoM7XsVrsHjp+9Iua1LHIIr2DlLgd/Q1w7sgqJEsRF1KhR/J/8Dasp/YOp0TBTSYtfbZVMrBwM1L4aTUuG4rgWyONJw+aiFerLtcY+o9RDMmLDBQbPZwrwRw/phWpl8gE2kbzf/LfuetO/rvxrY6U8N2XeGrTrPR5Zs2aV8UAR/pOEMHV4/cA8vHv/CfEylZPtB3dvig5Nq+PU+au4fO2uROcowv/56zdOnbuGrXuOyljJcfFvAcdI/9q+KXM1F07MNuj36FV69nJcZrGRIdHie3t6ekpHCWpyF07qi9oeBjZARsZLY2CGp2HvBULW/aqo5W9MHXAsu4iIFcMOsR3sxRInVkw70RPyL0X8JQtml+yOr/0wF6EYHTM1MmaEJfiNo2euwiVjSsSL828IlEMSXYbMwrpth4Tc0OmbhIeRJX2MHDlSiBijSLx48ubMivGTZ4o/l7ItT35GpRInTiyrJ5JotixasWKFRLNYDckBiu/Di46EzsHBAYcPHxaCxhYwhw7ul1AzkSlDGpQqVgDb9xz6s69dukhVDz+DUbkLFy6gamV3xIsbGy9fv5MUJsGLqFObpujXvZVM2poIdpIyWLh0Jfr16yf7ooCDA7UbtO9goQG/gyEY4q5Vq5ZU8JGI0Zsot2s28S1LkCwDDuzdhpOnPDFg+ET0GTwW+XNnx7gh3ZEsiSMePHiIWPbRkCRR8Fc83n/0XIiYQoAfPnwYZtIg5uL8ed+tqkiQ2VdPxd8JmpS65NeSnmrVqmHChAm65ziRc0I/tmsl7O3tYPXjvdJdMVAg6Zs6fy0GjJ2Htm3bSus0BZy8mA7nImzWkk3Ytlfb3YDoPWKWvIaLWEWKoVSdv3qjXXyyqIApdRYU/Q3gGOkfESM42Q8dOjRQ7815hJkW4tY9bRZEwY79J7Bq414UyJUJboVcYW3lhWu3H+L2/SdIEDcW0qdKiqSJ4uH0hRu4fvuhnCeMPnJhzvmFchZKbfT/8vbqnqdYDL15/1G0Ym/efcTrdx/w/oNWN5w7W3qM798COTKnCbA/cVjFl2/aaHFAMCsypo/5ozuiXhX/DTQtvTokQXnw+CWiR4sCz8u3sGLTfuw9el5KYVMmS4gyRXKibLGc8oPy4mSfPB8lsZZAEL3DdG9jUBo/f+V2/Dd8Fl6//SBEiMebhIWrB06IvO3evRuFCxfGsWNHxZrBEFOnTpX+jKYeSxIbhqqZVlRaD3FFlSqlExrUroLhA7qJzoKggeP+w8exZcc+rNm4U84JDgxccZFsEI4J4uH5i1fiv0Vix+oq6twK5c+NRbMmIkH8uPK98xeviCfPXuLx48eS+uD3ElNME8AKTq7gmKLkpGDsAuUgzlUZCaPiz0Pwu3Rq0xgt61dCwgRxYc1ol69omdef1b0C7y4IfkIvMkZMnLceHfpP87EJBe9NmjSRtCV1buEBpUqVktQxB2imchgB5YKAv+uZXYuQxdnbU8wvGwgTYXb0y6/nTLk2A9jGnLSJqZqx23cf4PDxM4gQMZIsIHjtPn76DI+fPJfzN69rVuTNlQ2JHI0sFEyuBv5thvWH/+MRzZFTZCos97kwZPX0uHHjRDIwefJkIU2F8rmidZPaKO9WEBGt/zS7NtUX7NfPHxg5ZQnGzVqBz1++oUmTppKC0/fsY6RcIQcELSR4TXMxqIBjARd/+gtXpvYYwWOEiBF/jpkqTG9vVrp0afz48Q1NapVHyUI5pW9rtaY9ETeuNrPiF2Wg1ozkWB+UllSoUMHsw//r1y8hcg0bNhT9HAlf6SKuKFMsF3JlSSPtAKW47fM3fP6qFLp9FUIeN1ZMxI0dQ4rdbNkrN6TaaClzhLWNBDlGTFuFeSt3Io1TQmzdd8ayAn59LJnQDTXdi5m8nybpJgwGCfq+XLp+DzdfRZDVEU8Ufddvhs3Lly8vEzRXQPzxOLjp91LkQEJH9YhfHuD5q7cSDk2dIrH4bCVPnABRokTy9rYJ2g+mDOIBfk/v7/jwyQus23YYGdImR9H8rjpS8fnrD5w6dxVL1uzA3GXa9ikpkiZCZufUEqVqULMiIkaOhnY9hmDDlt0+3to5XSpJBzL6FC+Rk/zw/HmZhmQUy1T06tVLVlf8zTkhFyuQA0UK5kPSJInk+Q8fPmLR8tXiN8T+mHly58DbT7/QvEUribAxgkXwd6A3TosWLYRsrV48C1mzZsbWHbtRqVo93eeRjFHbweiRJQ1UqVXjYMxJhDeSCQ7QjB7yuzFCmM4pAdzLFEOZIq5a4bHXd18VmFrt2W/TWnF4/7437z/FiPkHMXeutlm6PujFRHIdlld6TPFky5ZVN7hyUqbW5OGtC1i6Zhsa1CiP2WN7aTc28Zw3hiClIc2BpQdkP4gXK5/ZgitK1GhigDxqwkzMmLfMl88Wowa8Jpk6UsTa9K5iin78sP9Qq2p57VsHZHYaHD5tAE56XsbZ81ekgnrlum3ynZhGihI5sjTJ1se3h0f97jDgR7/RE2cvI2/5FmIezIi4sfGJKXLKGKiDonCdr2FUnIsZki2O/6tXrw6Teq/wDC6wKXWhZERZaDNjwV6sJBT0F+TvzYgjxwUWEnD+ZUqbYyojbMzGcF6mNxtlNIHFz58/ZU6hLIdZGH6OqeB50q5dO/RrlE+Iog8EM0FjqjZaOne5H8MuuhQ0WJSMrZ/ZG6mSOSJd6mTaicSMQdMcMnbu8i10HTRNTDmVgStv3rxiqscfmgMYJ1fqC/QvRE68ik6ApI0NpDnxciX/4/tXxI/jgJdv3ktoXAFdghk9c7C3RcrkCSW/++DxC8SJZY9sGVMjq3NKOCV1lJJtHdM2tuveA9H3n7/x28tLInOcyI6dvYLt+07h/NXbuHH7kZTsNqpRGsdOXxafMIIDzeAezZA2ZVIMmbAQ5y/fxKGNs3HohCfOnL+OWLFiSnVhgbw5YBud1ayR8OXbDzTv0AfL12wO8LCy+okDnqngKcGBkBcAb3SA5mPJkiTG5HFDUbJYIZloxk+ZicXL1yJbloyIEs0ei5csFTfpOnXq+BhcuXrl78bI1OvHN2TCWbJ8LRo199lCSYS7jo7yPCOC/N1Z8cMVMSNhjN7xPSg+ZcSLgwMven4/kjkO7Hydf+DKjiSNKVvePE8fw+Vrt+GazRkbF45FvFjRLELGeL7nrdpbrhN6jymDGKOQYb3tEo8pj7t/WDFrGCqX9Xbh/4fJ2NPnL7F6426cofP8+cu4dvOPqSURw94OPXr+J1WIjN4oVWX60VFOZkzZKwUsxOA+naRvYZF8rn+iPiFIxvSfu37rLjZu3SOT9Jev33S3M+cuwb10EfTv2lRn1vr0+Sts3HEQsR1iiOVOogSxRYzPiKDureGFr1+/I2vxBoiTILEssv0iVCRb9ChcPKU/6rTuL43ZuaBSEfzgmM+FAgsISMaoRw5t3LlzR8ZtSiWUanrlxscYiGB2hzf2L+Zi+MLOGciYNrl8H1pu0Kz71y9tZomcgJk2v8AsVYeBM3D09BXEix0T8eMy6hZTsnH2ttEkQifv99tLutk4p06GtCkTic3UmYs3ce7KHSSMFwtlG/YLHtNXi1UHGbwPV4+7D51B294TEDGKHQYNGoQSJUpo+79ZaPXDr0tyRvNTVgXRG4fEjRMQK/NIABih4TZnz54V4z3+wAo42TunSoSyxfOgXMlCSO2URAam+cs2YuPOwzhy+opszxOD+8z3J4lgKTi1UARF3VxRjB07VlYNjFwpAxJPePpsUe+lrEoUJ2gSkTx58iCbsxPSpk6FdGlT4uDhk+g/bJw8z+bFJDJcsTD6QlJKIuPf6oQDLFcbPHn5uUz98fXM8xOs6qSPkILhQwbgy/ffGDhwoPyf0RIK/7mvWufp37CyskbHjh2lkTdLrTnRcCVLUO/Wtm0blClTTlZS+fLlF2L1/Pkz3L13HxvWr5WOAYYRTkNEjRoNqdOkQeJEibFz53Y5d3jsGNXj8TTnfDhw4IBo5fg77Vo1BXFjehNu/RSm7r4/Ala9Fjqwjohi1Trh7MXrKOlWWs4rVpaS5PIz+X+SyrAI7h81HzSaJEEw1I1tWjIRpYvlC9x7B5Z8WcpDLBAWFvqPK/t/9cZtTJi9VqIHvG5pY8JULm+s0OMYwOuWk5gpLuxM5zOCrPQsVIpsmLqsVN4NSRM7Im6cWKLL5KSQLrUTokSJbHmCFgS8e/UMcZ1LG32O41CubOmQ1zULypYogDQpk6Fyw87YsG2/FOow/cgFtyGoa+3atSsypXfCx69ecq3yOlWjYSoCAhfrTJEy4sz5lsUPnI8NoVTV8nojF2Cwp0zh7OjfqS4uXbuDmm1HCIkqXry4zJPkBvxLXsRMDq9VLpiU7jY8NxnAyZzBCWWK5kLh3JmRukB9y5KxLOmd0Ki6G5rWLudjpRPYAfTeo+eo336opOzIQD98+iJRqwxpkmPVui1hohKGhIAHn15Xyu3EwW3YffC0MGIeBxtra6nkYbl1sTJVJapALRYHY1Y4coDWT0nxPfX/z5+AIVgSGgpmSX54AtHygboJEjuuUEhSmNK7eP4s7tx7qCMr3Af+ilx165MYapRIZnmC8TfkyaDcSIBIDKjbMnYKkHQySqVYWBAzpoyDRyV3TJ+zWOwt/AIrJmfNWyzl7+PHj5fP5sTOCilqjhQSzP1kxGvkyFEoUaIkfntpMGrkMMyePVuiBTx+PPbcRzq9T58+Q153/sJ1OT76x/PGjesoXCi3/J9auGLFiokejRehf+D+UZfC6B2Pw8alUyRlaQkydu32Eyxbvwt7T1wXoiupql/fcPn6bXTu3BmjR3u3nQoDUM5zEkaeiyQFPNc50NBp371EDll8xIoRNL1beCZjPD8OHT+LMZPnYuvO/UIweI43b97cl4wjsDh06JBMBozy8pycN2MCdu8/gmfPX+LT5z8R/WjRoqJ4oTwoW7IgyhQviATxjbeiYnpx94HjSJIoAZIkTIC4cRxMSo/zXPW8eF20boePe2Lj9v3wKFcM8yYNwtMXr8Q1ndtcvXEHO/YelehyvSpuWL/3nIxRXHwyMsFING/UcR09sAsnPS+J/jRDGid8/vIV335qzztOZiwqGjBggI/943VCLay1FWTRSzDjwe1UqPAPvF6ZBWGak4EWBjg433DRzui0MuZxfuFfEivFgHzmtImSISuUKxP2H78g78fxkMbAfoHzKgMbnDMvHd+Kk57XcPTMFRTKnRn7j523LBkj4+Pm/FuzYhG0ql8RuV3Swxoa0wdZve1cK3SS6JOi7SIRoXiQkZywvvJhRIlpUYZNeZ/aBqZOQwpk8TzBGOEj4+eNvw0Ftgp4wilaFTrt80Rj9MrO1lZW7nfu3pPnOKkoBoBMSfJv1qxZ8PnzV9mO0axp06YIOeRnUAvAzy9Z0k28bxRw4s7ukg3p06XBnHkLZUI3bJJLa4o1a9aI9qNgwYLSsDxhwkTYs2cfzp71RJkyJSXapkTSFFAzyMoqRu527Drsa0J59+4tKlZww4P79+TcUaw7KO5lOpCra2NFAozaMbpBwhnLISaqVy6HjOlSIlWKpMiUJinixY0F/P7mQ+BP+BL5e0OfjCnn/5MX71G4UgvcvvdIji11juzvqW/bERIQMnHokPi9kaCTvCtGwlwIKBHYZIkdUaZ4PpQqlhdF8uZANGNhfD+MO40+b6mUpCV6XPq1neF76/3/528vrN64CxOmzpXUHKv0KCIn6Q+KHsZccJzhipyLmoMHD8rKnzpZTio5smVEObfCqObuhtRO3uOQ5jfWb92Lqg07696D/ntJEsZHu6a10Lpxdd3jX79+w4mzl3D4hCcOHffE8dMXhCzxfNXPDMSMYYd37//odpWIPIt5qMnkOcbIFQkUI17GvgMXVjwHuf+MDlN6QaLJY8mFmn6PVy7KGDXTd6QPyyl+FX8Hfvz4IWM0rT54flM7zXS5ubyE1yvHV7oDWJSMMQzHKjXqj8gSeeEw9dagUn7UrVwCjvH9CMdzYFMGYr1B7sAxTyxavQOnz1/Dxau3kSJpQkyeNstol3sVAYMhfP3KIaYalfZChj8zn1PSZIy4MQRLcKBnCnLlypWSYuSkzSgaiRZNBvm8vncMoyazZs2SKJvijcY0L0kQB1tD0sR94eTB9yZI9BI4OqJWzToYPnyInPj8Hob6BA7erETkRTFn3nJfWuGePTthzerlQooV0fy48VNw+NABrF+/RnRl+g3ODc9v6rmYktuzZ49EIbmP/M6saO1YtygiWis+ZHqkTN+bzNBnyjqi+ClxNV+sZg9ZfXESYaUp3zekQeLOVCwXP4kTxoNj/LiIGCGC3KfdR9JECSQVlsYpKdKk8tkSJijO94Lg9N6yVErOjwbgjx4/Q40mXSSyy2uEkRqe82FlsUh5BUkQrzX2hSRx6tetNbp3aCrHhtd9lgLuuHrjLpYsWYL69evrFmhr5o2WjMSEeRskgsCxgulzFuBwocQKYEb1qdHidUCNLlOJvA5JnBjNojaUek2CFjeMQrCdjymRQn4eFwecUxhNGDJkiETU9MHxp1mzZli1ahX6dW6MviNnSraB16kKFeEBCn+yKBnTfzNOVpzAOMGtXbNaUnb0GolhH10E8RS3JYwfW3ppOadLaZSMKeCkdfr8VXTuPwlHTp6XQcDd3V2iFpysVZgOii1JknljqpSkRhE38i/JM4scGOrnb8mIEQdXvyYX/s7UzfG3JstnZIqkjAMxB3UO1gz/EowMKsaEplYKMuJVu3Yd3L9/T8wlWd7O/dTfH34ufdK4ryR6Bw6d9kXGunZphxUrFuua0TLiNmXqPHz8+B4Z0jvJ44zomWIrQbLJ1QxJJvcnQ5oUyOWSQUSZBXNlgkeZgiaTsXEzlqPrwEmhJjzm78eJjMeWv9uUoR1QOK/PtLnZfRn/ETJ25IQnajRsh4iRosjihHrNkAb1etRWkvQwIuffYE4ixkIBLkbe3j0J2+haM+VEGQojQqQoQtaUFl2MfkracdkGZHDWRo9JwKg1DStVviSI3C9eiw2ql8Hy9buQ2DEeps6cJ2lcFSr+WTLGVQ9D0Cwp1seoUaN0rXuYhlL0SZz0qRdwL1UAPdrWhWvW9LrXeGmscOHKLRw+eR4nzl6VEDnTOIagsD00BkEV5oNRL2q5zB3Mufpl5IYTAX9rkkhqvji5cCBmgQDJWdFiJdCmbSe4ZHP1Fel79eol1q5dKVEnJ6dUyJ+/EG7fvoF6davKflEX5lfElYSQ0V5G/Si2ZmRQMWllJIn6FD7HFG7hvC7YtXKiTwsMfej1/dRYRcSrN+/g6tYIcWLFxNmL1xDc4HGhro77ykq0dWtWSoUbKx9njekpBThG4Z+kwMoGG3YcxpZdB/Ds+Wsx3K1WqTRqVykvIvIDR89gz8FjSJMqOZzTpsahY6ewdtMueF64LFYI795/wNv7Z2DrT9WSRYiXKcTMD2KoI2Hez8+cvxwdug+USBDJrOLDZy4MI8HmgNE4w0gRo1AkZbxWlL9cPDCyxB6ATKtwMeLqkkn6ycaJ7SAtwlau3y4RJi5uaazKdCGvFUawKRtQolthBbxeOZfoW4JQr0MdHaN3KlT802RMAS96Ris4YbHyjvqcUf3ay3ODx88X/Q2FwJzAWA1DjQBxduc8Eed3GzQVa7bsx+NnL8X/I1umdMjtkhHZC5SWMLo+GCFjikrF3w0SL6YSOTEwesfTktoRSXlmzo7y5SshpneFpwSljJy2fIy3r1+/4MP79yhQILucmwE1Ambag+kSVr6RfJEcUtNGc0tOWNQFNm7cWPryrZkzDBVLFTSZjHEijpXOTXrrLV+1TiKJlgLfmwskRkM5GTNNxEgKr1eCE2ylUvngUbYo8mZP739qzR8yNnvJBrToMkTuixA8kSOOnTonvlNsS/XoyTPEjhUTr9+80xWUlChSAAXzuYqFQb+h41CjcjksmDosaFGXECBjX7/9RIceAzFv8WppAcSKZ8X42FQwhUaTVC5GGSXm65na5DnE8cxU8FweNXwwJk6ZjsePnwihYysi/t7Ke/uFvDmzSQeKB4+fYtO2vYifwFHGbWpDlcWyX6bJYQEs4iFpfHD/LsqXyI8ufYYL8TTsSqJCxT9NxvTBFBVddln5qFzYXJ3ROVcB7R/aNqmB5vU88PX7L+QuVRfXbt6T1SYHLl5kCqhL4+dxAFMqHngB6os6VfxdIHknETp06DB+/vyjR6PjcyWPqqhQ0QNZsmSDlV5PO+W0ZUSsdi0PXLt2xagVhtKKwz8wtUPBMYlSzWqVce6cJ5auWCMXDveLxrVxY8XAzHF9USxvZr3G40YqKw3IGHH+6l007jBQdJGMwFH4HVRwoUPyyuuHC5rUKZIgY/pUyOKcRsyMU6ZIjOyZ0/nqC2j4f9l9veMq+8/iA72UXaN2/bFoxQbdJrw2Sf7YP5QkmqkkauGYBiZJYCpMiQTxNyEJ4ZhQIK8rtq2Z51v0bswY1EQDUbPTlEbMWpX0Mg1OazZqj5u372Lq1Gk+2nWZCqb/aZjp4pJd5BZp06bDy5cv0LevtvqYBTcshDEHPNephfQ8exqXzhzGu3fvMXr8FMk6xI8XR4jvtRs30bHHQEk1sn0R9V0KFM1oeOn8oELF3wIWoFBfzwi7RcnY1eObRZDspYH43Vy9dhPVG3fG+w8fYW9nC4/yJVG1tnbg3bplM84fWifVN/a2Uf+syq1sZPJdv3UPeg+eiCfPXyBzhrRImzoFNBHt8eLxXcSMaY/v375j1/6junJurgYZllfxd4F6tOYtWuP4scMoWbIMrl27JGJkVmLyRGalIwk6V/T5CxRB794DET9+At3rq1ergCNHDspkxSgYU3Q8v6hxI+g5xmgCowD+gUUDjIbxLxcBhQrkRdZMabFj90Hkz5UNQ/9rCTvb6FJZ6afdhR40JDQ6UhNRDICLVW0Nh7iJhdwFFTUrl8Weg8cxd0J/FC+YExEjWPtuek7iZWK0SEfI9Ipt5DsAUuKd0sXNx0t4nDjx+wemwphqmzF1ApavWi99RHdtWOTTFicgIubf/ptDxPwiYbRx8LLCgiVr0a3PUDgmTCjpXaW/qjlgSo1pwKJFi2HBgkVCgubNm4MpUyYLeWbKkWJ1avfMBcXtI0eOwLrl8zFmwjTpYkFSqxTTMJpMosfSff0KZxUqVIQcKKtR7C9y5ciKU2cvIF8uFxw6dtrC1ZS3jwrp0g1smt/iY3Pa85KUQ89ZtBpPnr2Qp2p4lMGQ3u3FD+vbt6/Ilik94seL7WNQfPX6LeYtWYfL12/h+q17iBwtBmLbR5beaE+fvUTG9Kmxc98R2ZY6ooDc1VWEfXDCoDEr02skTlqD21SoVr02hg8bIHpEbkPyTgJO4Tt9r7jSZ+qbiBUrNmbMXIA8efJj7JjhmDlzCj59+lNur1iwKFEcTpKMUrA0mb079VMdtAbgRMn0ESO7rHqcN3My+g8Zhb492qNn51aw+v0dVt4kLLBkjOgzchYmzV4hei7FADgw4AScJWN6ZM2UDounDvL+wN/BRsYkanT/OTZv34Nuff4UIVCCwPSvcmMUjPo+pnz5+7LClhVzCeLHw4De3VC/ZgXxi/J5oEKPjP3WAKs3bMeAEZNx89Zd1K5eCVNnzvd3wDQGnl8sMKHWkGL7fv36S5UiSRjPS3oFUlMblN+c3l2VK3tIKzIF9PqjDowWO0yhc78ZdXV19fbJU6FCRYiCEhf98WP6uIEoVigvUrsUtzAZu3NCyJhf1VUceF+8eCmD8o8fP5E+l2/BdInCebFo+nDRmOjj06cvWLhyk5CwhI7xsGDZOpw5d1n3PM3UqE9TEX7BSStpUm0rrSpVayGGfQzEiRsXOXPmRu5c2vQfwUpIluxTWJ8kaTLEiBETly76dIHPX6AQVqzUps6+fPmMJ48fw97eTtJj0aPbymcwGPvhw3ts27YFbdu00KUklc4HBKtMGcFRTHIZYWPEh+kmVlLeuXgICWLbmUbGSA4MUl8Cb1Lz/vNP5CnTCG/ffcC8BX4XFPgFXqqceGkzcPLkCezfvAg5M3sX0+i1bPKRigwCGdN9F2/PtLfv3qNcjZa4cPkaHOPHE62Y4k2mj0QJEyBf7pzIl8cV+fPkkGpqkTDoVaDqPtrIYybvtwnfy/C+GEGeu4r1W3Zh3eaduHX7nvjXMbWon9ozBRwXWbnItCD1W6VKlZbzZ/ToUbKgIPGnH5l/ekVzwIgvdYGK+TNNg+nPqEKFirADjjGcO1j9P33sAFSp6IY4KXOHLBnTvqF2IqDLcq9R82XyoJ8VzSUVPLq0Txsl88b37z9Qv1VPbNi2DwnixZHIWGbntGjRqIa01qlSt5VJLUVUhE3QoJJiaOqJSNSnTJmD8hU85DlpaUeSsXcXIkeJgnOex8Sln1ot9iJdsHARbt7Q9vBkeT/dzpMmTYMUTk6InyABbG3t8OjhAxw8uE/OkdSp0yJFCieJiFHIf/36VWTMmBm3b91A/fq1JH9PwkbRPrsTMHVEoT7FzQpodEk945s3r4WMOdhGtggZ01hHxrPXn9GkfR9s33NYmtiy3ZQpzdGpx2JrD0adaIo89L9WKF4475/9CgEyprsvz7HTgxVevHyNR0+f49HjJ/LbumTNLA3lFVmC9tjo9foMJTL2+u1HDB07Xao8lYKDcqWKo1HdaihYqqrZb82iImrKSOTp30ZjUkfHRChQgG2irLB69SqzyZ0KFSqCHyRFLCqk/IWLGWNtuAK7WGK1L/W0lCJQ9sLMy8LpI1CvRXfLkzE7M0L4+ukGpiRfvHqN9GlS+qjqYlqzTLVmIp5dsmSpuK9zgOMEFVYrfVSYfnIyMsCWQzSSLFO2ChIlSoy8+Qr6qFAzTF0x0sXo1uhRQzBx4p+WQY0bt8CAgcMxfvxIjB41FHZ29liydBUquZfRtWyS97O2Rpw4cfHx4wdpLUMdT40atdC6TVt5bPOmTdi3by9OnDgu0TracTD1p7xH7FgOKF60EIrkz4VG9WrA6vcX08mYH62RdOlKG63306S5a9BjwGhUqVJVzDgDglK9XLVSOcydMgzWrObU7wQQmDSlHlnxQcZ0z1v7af3g4zEj72fowebr+OjdN5rmDYq3mUFE7OTZC6hSr514IdapW0989Uj0jXVkMAUsWmA6neL8adOmImHCxPI4o2OZMjkLweaqWIUKFaGDr1+/ShCI3WX0wUg+Mx/K4rtcuXKiUQ4M+F5c3FM/y2g1i6mMFYvNnDgEzdr1CpCMmTUamTtA6m9PvxveDNHpv2G4ffchjhw5ihw5cshjlu4Oz6gcXdXZq1BFyIBRkho16mPt2mX4r9dg1KvfTEiSQry8WAXiHRmjdkd7X/tklKjRhbCUKl1eNGXRbaOjZs26SJJE2+bl4IF98tfGxlpWICRRNB+mOz974FHATB0YU5A8p1gMQAPXWbNmoEOHTmjWvDlatmoNjZcXTp06ISml27dv4ebNW9i/fx/q1KqBEcMGwerXZ2gUMuWlFUpb6SZ6LzIh7V0/Kv8UjqlRSJu1d/AMQLvGVRAnZjTUa91HiCpTWv5ZT7CAgc74b9+9M9n9nQTLlE19VFMa3eDPd/nzXX/6aG1mCoxWSAawrVljjpGWRnz93MVrYWUTEZcun/M1OAcGM2bMkPNry5atSJz4TwoyZcrkkqZklalKxlSoCNlCsLFjx+LG1Yu49+ARnjx9rvNAZUCA8xG1x7SKOXXqNBrXq445C1eIJtlc0Pt0xND+2LJ9r8w9nNdcXTKjRaOakq3IltkZL16+xMNHzBZokC1LBiFjASFwS0MLIn3alFi3ZbfoLmj0Z0nQSJR6EIpqCeosaCZKhkqLAYYUGRVhaT5DlWokznKgSH/NmqUYPmIyKnn86YFnDpydM0mK0ZBQLFi0HDu2bUHWrC5ImjQ5nJxSilicaSOSM/2WUARJOMXO/foPxKhRI+RGcJVy4cIlFC9eAtbeLNHa6rcI9kMCNT1K48Dx86JjY1Nlpak5z0PqyerUqaM7J2kfc+nKdYwcFPBFrcInEidKIKtYSxAxguMJo2qsnDI0wObn0EKCEVf/Im/cjgUsTJkHNkKnQsW/jl+/fkkkmsbc9FXMlT0jChfIA6fkSXDi/C2Zh5hp43zPgAx7NMeNEwuLV6yTXtiMkPt1fbKYS6mMZOZk7dq14n3H1GPa1CkxclBPZEibEtmzZULMGD4jXimiJUaKZNqI+YePn0z6LmalKV/dPaWtpgwCuOLlR7bs3F+qL7NmSo/nL1/hzZt3kkKgOJmDGUP+FOyTLAUGNO+kJkgJG9JMlgeSgx/LxDk5Z86YXjx7Hjx6orVOyJ9fbuyPyKoIpiN4n3nl0OgnGF5Bk1WW2Ldo2QmtWnWGlTfRIalSojr6qUn953WP6W2n0x8p7ihWVrqgjLWVFbp364idO7dKyNg//RXPO1Yy0p+JVX/Dhg3D+QuXpY2TjULGrK2EkF29eg19evdGi+ZNULJQTklVymcraUpWWHrp6bX80z/5pyOzioi9R89jypzl+PL1u0TiPn3+iuOnPMWvq3379vJ9OeAkjBcT29fO06YmDdOARtKUASIoLYmCErEyAWanKf0xc122ejPqt+gaYJrAVHBcYKqTPSHr1qmNsaNHaH3VNL9Rt0FjrFq9ThYF7EVKAsjzkpXBbO/DGyP1SuEDLVf4G/MWkk3HVagI77h06ZIswBlU6dK+Gfp005p0K6BuPWfhCrh15z6yZMqABw8f4+Wr13JNcgFMzS65Bl/PeYEpS2rcmbbktc3xglX41ISSMzAaXrhAbrRpVg/lShU1OXjz4cMnxEnhYlnN2Ku7Z2FvbxtkcS1De4nT59c5dhNVK7rhwNHT0sJD2SV6mW3bvhMuLi4mvzdF4v16dcXajdt178NcLjU3C+fOwKmz50W/Rnge2SIatiPHz2DXvsM4euKM+IJ8/eo9yXqD+qaMGdLCJUtGVKxSS360sNIoOCyCaZoWLVrg1GmuRGx1ZMuQUPlFyvS3M7atkDG9+1u2bEDzZvWlfyDtK0wl6zR53bRpG/LmzQ9rG+0bkpRdunReIqjymdbW2L19I1wyptJ+njcps/715Q8J0yNlRvVj/vVAZA9Lb+sLfaJ25PQFdO8/DsdPX5CHKDjfsnwKsmfJ4JP0mWoDYfD5Ph8zGFT8SycaM7j19/NMGLD864UZJMKoJWMnz5xHfrcaEj31ayxhkUmKFClMrnzkirxOzWpYsXodNq5ZArfiReS4abx+4ey5i+jeexAOHT3pp1G2U1JH/Pz1CwcOn5DHKCjm4lGFin8FjFg9f/5cUoj29vZyU8gUpSOzpo7F4yfPpagvZfqs0l9XeZ6LGgZOeL0unD4Srtm1PVcNwVTh0AkLJOtGEjZo0CDJgmXNkhleGg3ixHLA/YePfbwma+YMKOtWFKlSJseUmQtx+uwF8UicOm4IMmYwwbDZYPw0lYyFSnycPk9Prx3GybMXse/gcSRLmhBVKriJwHbFuq2wt7XFl2/f0bRdL2GqO3bsEObKwYqpG5qB8ockyapdu7aPvnGVKlUSzVCPzq2Q2TkdajVqp5tY8+bKjlZN6wmxSp82FdKm0oYg6QzOmxKOfP7ytUQAI0aMhMtXb+C05wWc8bwsZG3OwuVSzcZKifLly6ukzAgYVWTqZevW9UiWNAVy5s4XbMdp377d6NC+pTjAK7+zKWDUie7806dPFUPWwkUKyz7evHlDqhapNWNYmmnuchWrYtv6xciSKeRMh/O6ZsXBzfPFxy9qlMhmt+T5W8EFlrnnknO61BIx5XhhSMY4VnTs2FF0hezyQVIUkF0EdYk0Fd6weRtq16iKksX+pMW5b9mzZcbmVQvQtG03rFy7Sfc40yiMoLG9Fm8EG14zlcLqXhUq/lbQI5JV8kztk4DxxjS9ISJHjizXCK2NYjnERIZ0qXHsxGncnzpHyAznes7/NJPmXM3Uo1/Zs5Onz6F+80549OQ5evToITcuopie5Hjao1MLPHvxCtmzZkTqlMlx/sJVFCucT2QNyiKyVtWKOHHKE+nTpUYMe/P7y5qDUImM/flwv1fgL16/k+gZd4/NiBlyJKJGjeIjckWiRhsEgj8ODRZpwDhyUA+0bFwHU2YtkoObM3sWJE+a2Kz2K8ZW6AeOnMTA4RNl1UuXbmp7GGHhYE8CQtE40168/avYvn27tMBhWJe/X+8+w1CnbhOj0S7ClxGoQZTsz2t8v76yhxvOnjklAn1+pjkTNSdBpq45KJQv744fP75j9+6dot2iSJNWGazI4flFgf/m9avgmkkbIYPXN210TO7rRci8zyP9VKJRGEtd6lcvGqtu1EdgU5GmRKoMr4UAonz+f34An6df0WlYsckm7ifPo1rDDvj46YtcW7T4GNG/C9o1r+v/vhmgSsNOOHbSE23bdZDI9sOHD4VQTZkyBQkTxEe/Xl0wfvJM/NZYSX9Pv3ogclLhYozPuxUrhOXzp8r4pIVeqhgQgXDLjn3EPoUpcRIvgpEAdovgxKPqVFX8zeD1Ss9GXmcpnZKJK328uHEQP25sxIvHv3HkWvrw8ZN08vno/TdlimSoWLaEXFsXr1xH9vxl5f04vrMQkPY5cWPHgoNDDIwd1kcq4BVQAjBi3HQMGTVZFtWMilHbSelTmVIl8OjxUyyZMx6lSmivx2CB3jgQptOUppAxDsxHT5yVlGKBvDlw/uI13Hv4BPsPHYdr9qzSh6165fJYsmKdsGQ2mF6wYIF4h5B0UfuTNbMzZowfot1nH28eeDImPQc11jhw+DjmLtsoZa0kHfrgAMtS1399tUtyzB6le/bsw45dJ3ysYCxFxl4+f4oBA/6TVCUjHKyoMQc8/ZcvX45mzZqLzq1Zs6biYq7fi5UDiluJ4rh5+zb2bFoukVWVjAU/Gbt64w7yl66DTBnSoHDx0qLbIBrVqSxa0/o13BEtqmk6q7uPXmDgiElYt3m3VOgSjIxSq9WygQcOHD6Js+cuYPjoiVKZ61dfSjYAZ4utR9dOismwT2jJGKu5NmzZhayZMmDZqg2Yu3i1aFbZJk6Fin8BHFfZyo5Vxbze+nRrhzbN6wY6wv/i5WtEiGAj0SljCyUGa5q06Y4Ll67izr0H0gGDunAuuJXPZOUko3MNaldBtw7NkSplCoR/Mubrgy1DzgIEPYNOn0ebrv1w7sIVEdoxskHiwwqJd+8/CitmKJMaMYIMuKp7mcC3XQlAz8LD9+bdZ/z87SXprk9fvqFF2y748OmrVG8Gpg/d3wSmgZydM4q3WNQo0fD6zUtkSJ8J2XPkQo4cuRAvns/j4+14ESAMudr8BTMxeOB/EhVlGNrS6S8SMlZpvnj+DPt2boJT4rhCyPwT9furH/MvIqQfLTN1/43ZU5iiD/P1RiZq0Px674DsK4x4kxnzMCPadhuETTsPSfcN6hBZoq5Y31CmcGTHMuTM5k/qWM+EV8Gnz99w5sJVpEieBGfPX8XhY6ewcese3L3/UJ6vXLEMjp08gwsXLxs1mqYmkSmWPevnGD0+0+csQbvug+VcYuRr0H9t0WPAWGlEz9ZFKlT8C8J69gOmQTV9EVkBnihhXL0teE1ajjNoNBqMmzwHPfoNR+nSpeXGCnpDuQH3p2fPnqI3K1E0P9YtnWnwToHRpwbwPTReJpMxy7iqBkVkayK27tyP4pWaIL9bVSFiJF81alQXHciiWWPx4MoRZMucQbYlEUuZIikWzhiNyuVLaH2SNF6+btoKNO+bAv3HjNyYftK9h9dPWGt+IXbMqEgQNyaSJIyD9KmSYcLIATJpp0uXFtP0TEv/RTA8PGTIYNy/dws/f35G8mSJcejQbrRr2xh582RE40aVsX37JjHfYjSMJMuQaPFi442eLcrt12+ftzp1mkg6iDqvwCCg9GbMmDGlQODps+eYtWAFNBGiAdZRvG+RvG8RobGOIjftfZ83rbu9980YdOeZlzb1qaQ6DW/G9l/fdd8/yHnv383/zzG6/0IerX3e139Mfzv/3scA7EubM2dO0ZAwgsVSc5o5FsyTXQoaXLyv9z/fzchxMnjMNnoUFMqTDVeuXEXVeq2xbsteOGf6I/7t2bmFpBAZGVPWqcr5xyrhrVu3oEiezL7GAd4ePniATr2Gi2ccNS9Mh3btO0qqJN3cfDZaV6HibwMjYOzByjT+82ePsW3tAiyZM0nao/kkOpYN3gwcPkGIGIvGqP1kRxVjuk9WRrJnLscUn1YU3Lfg5zABIcxa3DPCtWjFenT6byjca7aAe62WEp3gAMdyVOaIe3RsgYvHt6O6RzkJXW5bMw/Txg3CrvULcPnENtSoXDZUNBlMT5w/uR8Vy5VCq/ZdpcfivwyGqlnez0ghCQ1Fl7SXYFqZv0+b1g1Rr64H5s2djosXPHWToDng69i3j1YBwQVW6hJ79uzGilVrRfujIvjQsJaHtB1iQ3KC1VB169bF9j2HkDdntkCdJwp27z8ugnrqSNhfUsGQUVOQ0DE+Nm/eLN5uFPxSF8oUe8aMGRHDzhbtW9Qz+p60yOH2LJln14dVq1aJHpGZhX9ZQ6ri7wevlQwZMsi12qd7e5w5uEnE8MGNX79+YfaC5WJVMW3aNH8X1QzcUA9Mn7AE8f8U/YUVWCZNaQyBTF3ee/BY9BbT5q3SVRz52GErKxQvnA+LZ42GQ8w/uh5f25kiQg5qetVYaxildx9s8Bs2SOnsioSJkkglB72JVEsM31i/fr00PWbFLKMS7C9ZuEhJscWQ6hr7GEjomBg5c+b3pRd48+YV5s6digXzp8uKjBq+wHrTBQSSL6bGJ0+eLMQyTy5XdO3UDs5pkiFZkkSw0Xzz0TZJF61SWhb51SZIecpYCtFYeyLDx81JU+rtQ6AQmNSnP+9jmPbn4Hr05AV8+PQJqZySI1PecvIU0x4UAbOZO32BKLLv1KoBhvZp/+f1/kgRDD+n95AJmLdskwj5WerO/qccx99/+FPhxV6oNH3k4N2vawuxoihfqjCyZUpn9DjQFidvqTqyOje3CbwKFeERrI5s07IJNm/bjVLFC2P8yAFwSm5sQWzZ1KSCfQePwc29rvhGMuLl39jNgrsokaxRxb2MBHCS6HXPCDwsl6YMM9bPh46eQsNW3fHgkU8xPDVXFUoVQr7cOWSiLlooD5LSCyikdGpBAKM+MyaPweCRk6TnJvUmjAyp8Al3d3e5UfC/fOUmrFy+GNu2bhBzzW/fvuLTp4+y3YKF65Ezp7ap6/Ll8zF71mQ8efIQkSJFFqsRaoqC05yXv2fFihXlRl+qZk2bwKO6tqrP1jY6GtX2wJAeLaTiV4X5OHryHNp2H4ILl7WN4WPGsBMyxOgVNVf0FKIgl35gbHPClEiSRPFRs3IZXw7YAaFyhZIYO3WhEGuSd9vo0dCkXhUMGTNDfBAZ2eLncBE6a/wA1K1WVgZ0z4vXMGLCHBw4ehbRokaBS5YMKF44D1p3HYRzF6/Je7MCV4WKvx10tWdKnvpNjwqlsXj2RO9uEiE3N3/yLsahabuf23z6JJFuNvCmhVWXdmFTuxlmImMuBSvi0hVt2yJWP1Su4IZGdapIJaVfZeb+VmX69/mWJnJ6+hdlBb5k9Rbs2X9UwqGOjo6idek9YLg4+7KHogrTwQuJnm6795xG4iTJ8PPHLxTIz+4J7yRiwtJpekSFNDg5002dAnOKQ0kQ+nRpjh4dmmojZIrlhZkRsgCjZH5EyALsMemPvszU1xr73KAavNIHqOfA8Vi+bruUorNikSlJLl5ozsu04OHjZ3Hj9n0h2/QCo08cq15ZsJEqRVKcO7AKkSNHMqvHZZaClZEyeRKUqlBN0hyEvV10RIwYAa/fvIdz2pSoX7MCsjinlX3btvuw+BJRJsFmw+zuoTQcpoZt/IRJYkSptFBRoeJvBu1aKDV5+fKlXAstGtdBu5aNMX7KbJy7cEkWSI3r10Cl8m5S7Vi+WiMcPHIS7x9f0rODUcaD34Hahzdv38ExlatIDYxVQDOKzmuShJH707NzK3Rs08SCGjHLRcaCj4z5uXO//wzKeqTo8dPnuHnrnvSqpPN+QOk8f5sOB0dK0t+dsfF1P1uhSkIu6T8WyyEGzh7dhRr1WmD/oaOoWa0ylixfpaYsTQS1YJx8Z81ZjoIFi8Hrt0bSk3Vq05w1W5iJNnKVSL8zaosmD+2EPC7pDdolUZDvs8IyoObZfpIyI1WJxoiVycJ+c4lZQEU7AaVclc+ysoZHvQ7Yuf8Ypk2bLhYQjEByWKIGJU7M6Fg0YySmzF6C799/YNrc5WjUqJE06yboqs/0xKgBndGuWS0/SfOJMxexfQ/J1GvsP3ZeSDQjsWy5RjLN/9OKhpMK/cBYkcVUNHVfTJ2yEIURUZoAs7G7Ui7P9Ahf4+zsLBo0FSr+RdDVgEU2BP0AmQmiTpgZBD5esUQuFC2nvT4Xz56Aah5a3zBLCPtdC5VHVpecRq1jaHe0csUy7Nm8QrzLLI+A9/nDh4+Ikzxb+CFj5iKsk7GxUxege7+Rcr+MWzGsWz5XJrkJ0+ai+3/9RPMSkNO3Ci14inIyTJLECdNnLtHxmZkzJmDGjHGyMqPdQWiDqy8aDJKQfXj7ElePrNY+oZIxf8nY0tVbUb91L/ELdHV11R1LFmM0qesBG2sbjJs2H7EdYuLT5y+YPWeuD/sSWkZwwG3btBYypk+FiqWL6JpvP3z8DEUqNsL9h08lesXWKklTpEaaNGlkcKTvkH8pDhI1eheSgKl6TxUq/Ab9NnkNczFKoTzHbcoAOnfuDI3GC6lSpZbuFaVLFMaGFbMtRsa69hqCtZt3i36N1yj/MtJNiw3eb9mkDiaMHBRMP53lyFjIlxoqKT0zyJFRWwp9mFD6H6ww8pmdWtXH7MnD5f7WHXswatwkiVLUq+khlaBZs2aVVMetW7dCfn/DGXiBMQW4f/8ubN2yFjYRrKWXZN78hWTSplt+WAAJYfPmzaVF1+NnL3xaWehuiu2D9v8BNcQ2es4r9hd+WFLwPFNu/lq1GIHJ6UoT38/Hd/djmx/eTbNZYcsBnAuVqu6l8ePHN1Sr6CbC+B8/fmLp8pXi1G3oI0dCVahwUcxYuA41mnTDSc9Lus/uPmwuvv/UyAr9+YtXuHT1JrZu3Yrx48eLuat/RIxInz69pDlUIqZChf+gHIfRYxIxufysrMRmgj0lacJK0+YRI0Zg/+HjfthJ2AQqfViiaAEpxFGq3all279/H6q6u2H2lJEYPqBHuPjpwqy1xd8AEi96oBFbtu+Vv7EcHHD57CEMGdhHdEZk7yoCBnuOlivvgc6dWmLggB74+PEDVq1cJP5fbEcVFkAzULqzU2RuZWWN12/ehfYuhXk8f/EabXsMEzE8q2rTpk0rC5Vjp85h6cxROHfpGvYe0jbTpj6MUVBD8BxgFwVFN5jGSZuOWLtpl6QZSeTpMRSQ9lSFChWWBYtZWIFOsDCGEWtLL2wK5sslC2Fqi9nLevr06ShVvBCG9usmc3B4KagyL015z1MErkGCmZGrgDQ1Jr9ncEfMfBlhGnFYF9sL7eNPX75FBpcC0kuT3izqRBEweDFTm9C7d2/R+1DLw/ZH1AWEFmgiSH8bCrkNrVhWzRmFSmWL/hHui/nwTz+d7gPsCGHsnDLynL8Iovjf//f2X8tmzBH/45dvGDhqOm7evo+nL14hY7rUqFqxBIrkz4lIkSLi0dOXSJFV23uWoLCf5q/ydhqNpBB5XjAFQP0XX1O9UmkcPnYGd+4/EosJDtJqZEuFipAHfQEZiU6SMD68rCJg0KBBUjl/bM8GZM/ml0znt9mfw4bgG7ey1dkXyTjNmTRI7Ia0CCGxfrjRjAWSDAWZjIV02tJolZnvptD5S1bDyTPn5D4duumpVLJUGTlZWa2lwm/Q6oDaLDbwpnjbkhMtXdZpSvvx40eZ7Ckm57nPEDyJAFdgvKioj5g8bhimzlqEDOlSi9HvqvVbJUrD6E2lssWwYtZwremwuWTMlHPWD/2VKdvpNyQ3SsaMvSYAcujrvf3bxg8Y7j+PP6uSG7bpI/9n71Da28SNZYeIESLg2s270lRYAX8n/jYs9uCATNd7CvHVZtwqVIQOWITDcdqtYDaUqdxACm6Y5cibMysWzmTWyNj48Nvsz7l99z5qNmyLJvVryM3nNe/XGBQC3CBMVlOqZMwHGSvpXh/7Dx3zdZjYD48lwyzTDWxTVRWBw+zZs6UikhE3v8Awu/J8tGhRMah3ZzRvVAtFy9bEyzcfpIKIYOn25UNrkCBebESO6E0WVTJmFhk75XlZTFT14eTkBJeMqfD85Stkz+KMXDky4/7DJzhywhMnPa/K9cPUvxoJU6EidEFqwT7RrKYsmCMtCpWuJrouZhOorT2yaw1cs7uEAEmyCRdkLHhNX8OBMWuwfmclKqBEPiQCocX8aSMxeNQU7N53GPcfPhYfLZ6oTHWxOmzAgAFyq1mzZpioFPxbwQgYS6KXLVsmTutMczWsXRleGo14w61ct83H9iUK50W9Gu5SlZc2dQrpkTpu6jwRmbMlCMWq1I7RVydD/srS1iuva2YM6NYMhXL/6YGog3ffVB8wJnb30vu/tZHnrWyMe2rpVy/7gQCjYgG8PlAO/MbexmD/XTKnw5hBXfH23Xs0rOWO7z9/I1GCeKIBiZE8Fw4f90S8dfHkeNMcmr5kFAyrREyFitDHjRs3pEdrptQJ8eLVa3mMTbop6eA426XXEOzftiIErtffCFGYks0L8ciYBchYuEtTBqAj002U3voapl1mLNkmujGamJI964MtlNasWROCO/3vgJM4Q+j0MVPAgcEhpr04srMbRI6sGZExaw4sXbpUdGqZnNPg7P51si0vndZdB2LWgpViTDp+2gIdwVswbRhOXX4slUX9+vWTx5+c34z4sb2vH72UpEmmxYZkzBw9mcHzhmlKk8iYf/CPjAXi/Qz3/+ad++jadywuXbuFx09fIFliRzx5/hK2tnaiueRChhWPqu5ShYqwgwkTJqB79254dssTvQYOF9NkepCx8pFjJP3/zhzegkwZ0uKvgsF4HrJpSgsSHpPIlzmfHZbImMFj+sTs6bMXGDF+BhYvXyfl+/qgIWVwNsD+F8EIJP1wGP0i2LeMEUnaUjg4OEhF5NChQ1G1QgksnT4YVtYR8PHTZ0SNElnnYXX52m1kKay1WeDAYmyFR/0ZtU7E+IHt0bZRJZ8bKLYUgfHOM4WM+VoQWPtPwiwU5QoUqdOD/ndYtmYb6rX6TwgXFycU8F+/fl20YYMHD7bAzqpQocKSoD9fsWLFkDVjWvEU69hjIKbMXCjPbVu7ALlyZMWtu/eRJWP68BnJ1pjOU4IpTfk7dImXPkzdD4NUYbAjoElIL22kS8vQoyV+bIwf3kc8yAaNmCji4x07dsjz9NFiCkaFZUBLEQq8FbAculChQj4Ghf379yNfzixaIsbHvX7CLhrb7Wh0gvz9h7WWC0TaVMlgbWWNL1+/wUvjhdhxHZEplSOWrN2B6tWrS5/NmYs3/CFjAZ375p7fAVVY6hF/HQnzqwl5KJIwYynLHNkyIIa9rWhNznmeRZ269SRSaWdnh38dXChT3kCtHI1sVagIbdAnsETxoogXNw5mTtJ6bcaJHUv3PFOUtrb2yJopk96rfofMzgWFcwTq836b/N3CTKNwFVq0aV4fI8dPR4kSJcShnx5JqmbMcmAvNaWHGSNWJGbUGxmCVXjDhg5B885D4F62CEoXya0ja6s27sb4WcuxaeEYfP32HW/ffxRR/28vL0SPGhVDxs/F46cvdelPaidIxmyjRVVP80AgtVMyPL2yF2cv3sCufUcxZupC7N27V0g0K1z/FjBJQQdzGkHTF82wvRKjr0eOHJE0z4EDBzBz5kzdcxwrOAkG5bMZxaUkgpXKPJ/ZdYC2IWwBpS4GVZgCGjeznVjypAmxdc18CS4Qr16/kXH22bNnKO1RH6cPbkO6NFpzWBUhSMaCFAEzhsBEuQz9jiwNcyMCevugxGMYIXOIYYe8OV1E5MjVP3t80b5BhWXw6ZM2BTxw4EB069ZNGk8bAwsn+vTpg7nLNspNQetGVTFl7iq5f+SkJ7q0rKn3O2rP83ix7TB88mJMGtIJm3cdwaLV2+Xx9XOHWm5lZtCYXv+xP//3/ZxExUzYzhcCabdhKbCyOFf2zHKr7lEaJT2aSC9JRjCNkenwiOHDh4tpMMHzskWLFkKy2BCdafVK7hVx46a2Y0c8vc4BlJDwtYEFI2tcfJDksQrYvVwpRIseDZ5nTuLVqzcSkVTJmIqAwMVCjx49oPH6LanIWA4xdeMdyT6JGMFuGvsOHkK6NMn/ksiXZWCmZuwU7O1sQ5Z4BecBsNT7BHUSMkg1terUD2cu3sTZs2eDvm8qzAajWBTrsySbrTU4yOhD8bMiHp5Zh4QJ4hg/n7yvhfcfPsHLSwOHmHb+nnv++owZnGM+CkHMMXMNyJjVVBgxcA0O+EU2b915ANdi1dCxU2ch1pYEf9upU6dizepV+PjpE3bs2InkyYNh4jCI2LIBevs2LZDDJSvmLVyK3Xv3y3OsEOWCjAaarZo3RuZMznDJmgVd/hsgkTRPT08xvDUXe/bskcj7yRPH4ZggPoYP6IncuXIgRgx7vHj5Cjdu3sG6TduwdOV6MddVocIvsC1dreqVsGnrbsyYMBT1a3n4eH763KVo17U/hvXvhqWrNmLmxGHInk0/TWkGgotjBJOUiRrwOClcQ9naQkWgQMJLAqwi5MBKVjapNuwVyoazyZIlk9QN042sfuVFRQL2X7t6f4iYP6DeSYVlkcopqRRSsDrL0iC5Y2cHBSlSpECdOnWEmFtabEzzYH7W6NGjUaOqB4YO7COGle/ev9eRMaYrs2XLJvenzpjj4/VM1waGiPF15cqVk8hbs0a10KxBHSRJnFDI78y5i9G2cy/dtoxAqlDh3zlcoUIFXL16BWuWTEdZt6K+CNP2Xdpz+dnzFzh1YKNqxBxUMma0SXd4hqlO46a8zoK49/BxsK/EVWhB0kt/sJHDBxtt2k6fnCWLF+Ld+4+IG9sBxUoXRMlCOVGtfBFEjRrZ9zkTlIpIU931/bGpCNCqwpSUZCD3MTAIqFG6X59HH7d37z8EKUXJAhnKAdjcmP0w9Yl5nDixcfDAPuQvUEgsMxYvXizVYYre0FIYM2aM3FyyZcHYkUO0k5TmN+rUqIxIESOgaauO6Nq1q6TMST6TJE4kt4OHj8rr2VqGLZ9mzZplVueOhg0bSgR4z7pZiByVhNYGL1+9Rrb8ZfDixQshoLwuWMnNfqEqVBjDxYsXUba0m+hl925ZhmyZnY1aO2zffVB00BOnL8DlqzfRtUML6Vwyb/FKNKxTDfHj+bGotcR4GswIiBOZypnUyFgYxN17D+GaO39o78ZfC6YdZ8yYIY2pKYRmtKtQPldsWDIV23YfwIr1O8UhesqUKdi9ezea1HFHTfeSyJQ+JaytND70YSoshzdv32P+sg1CSFI7JZXm4UTsWDGRJpWT3P/+44e0Qjp+WitWD4zlC0XwkydPlubiJDD9+/dH6tSppYceizpIuubOnYvuPf7Dzh3bkS5dWtjaxRQCkzt37kBFooyBChGef5UqlsOyhbN9RN2iRImCurWq4fnzF9iyfRdKlyyORg3rIm4c7aRFgf1/fQdh6Yo18j14LtO8mJW7poA2IdSkHjp2CsWLFtbp8kjECH5X2r3wpkKFMbCAhgsZp+SJsW7pTCRO5Gh0uzPnLsqYO378eNE+du3cAW7udXXPR4wQEZ3bNf3nD7JZmrHXd04EqBkLFYQRhmyJ6MKzl2/glKWonLitWrUKld3629GpUyc5vsUL5YFbsXzwKFcCSRJqBdETpi9Cl37aFFWT2hUxvHcbxIxh5+scEzLmr9mwl2XO1YD6PwakDzN8jQk9LYMz+m3MfoMVe4NGTcekWUuEbFlZWUvUJiCw4pBkmb1dDUE7GEaz2FWBLaqY6qMBLwnPunXrkDhxIvTs3gXly5bBmnXrMWjICCHg9JbjkEiiQjE9J5FzZ07g1evXcPeoihs3bootCpvVk7QFBUyBU5u2fNEcIWSBjZwuX7kWzdt0wPfvP2Sf6ZWXPn16f9+CmsgcOXJIe6lV88boWsa8/fAROQuUQeq06URPRi+3cOkDpcJi+Pz5s4yXjILx79u3b+UvyX+hfDmxfN5E2BnhBecvXUX95l3w8NETGX/4OkaZeX2NHDlSrt1Dhw7CvVxJLJI+lUbgzzURXrJ01IzFdsplWdPX17ePBp6MhYDYN8yRs0B8Z05IXfuMkGbTNB9VYVmw/J8pqZH9O6JTizq+zhlWAj1/+QY/f/5CkkTxfTxnVkTMr3PQlAEkoNRdEFOSpqYGg2OwM0bGLl27g2wF3eW/rLiKGzeu6FBIyEgE+BhNXhm5YZUhW0xx2KLzvl+DGyM6r1+9QvHiRZA6VSo8evQYd+7exYsXL9G0SSM0blgPt27dhluZiqLPqlbVA+s3bMKaNWt1JIskjo2OS7mVwMzpU2VfVq9dhwULF+P48RPIkyePEDhW3hojhAFVMNJ0ePTwQWjbqlnALdVMqBLuM2AoFixeJpHeqSP/Q9OO/hc2sIKSOshD21cibpzY2o+2joj1G7eicauO+PTpM4oUyo89+w6qhOwfBBcibBPXtXMnvHrzFvZ20fH6zTt5zjF+PLRsUhudWjcweu7z+sxdrAq+/vASPRmzDIxAN2nSRBZJLEqJYW+HzBnToUXj2qha0U332iCZX4dVMpYyr0rGwhsZ27r7ECrWbCFally5cgXLbv3L4HHlJEosmjoYNT1K+7zIA2hNpJKxoMGoMa2VDcrXaIFfVpGxa9euIH6CdiLgQqZzx3bo1qWjn9uVLlcJ+/YfkPv7dm/D0OGjsGfvfum+QI0Wyd/atWtFJxY7diwULFAAxYoWQbWqlbFh4ybMmbdQ9peGwStWrED8+N7k3QScOXNGIlML50xD9aoevs43Cp2379yDI8dOyX1GD0kGSxQvgro1q8n+GAOJGFOMx89clHZeokHzA9RIMl2ZMEFc7NywBDHs7YWMKZGzyrUaYefu/UJOkyRJgnnz5pn8/VSEbzBdzQIP9pLMnycH5kwZgahRomDbrv1iWVG6RCEtCfODHPH8sUuUVUgY284x4kzwNay8bNu8PkYN7vmH5Gv0xth/lIyFnGbMhHL9UBfmB/c+mAC3YgXkBGUERyVjllndsYSfqSlGD+7fv697bvz0xajpXtys88NXKtAvGIk+CZEL5HnhbwrSj88zJSXp72d6bxss6QCDfaM+7Olrn23AzCVg9Mm6cuUKxo0dLQNfaurM/Nn3zBkzCBmjMD6jcwasW70co8aMx+ChQ8X8lDKB7Nmz49y5c+jYsSMWLlosN0bmqlbxgHvFCti9Zy/KVaiEYcOGSerGVPB9uSio17gl5i5cgro1qyNu3DiIGcMem7Zux6Sps4RYMYpLMT3TO5zEevcbjL4Dhor/YJkShVC4UH6ZJB88fISoUaPizv2HuPf0HT59+S7HxD9QH7dz504hk+WqNsDWtUtga6clY5EiR4Fz+nRCxi5eOC/XEO01WrZsacavoiK8okOHDrh18wamjx+MSuXd4BAzhoyRDWvrLRz86KvLcYOkK3HCBJKK53lYIK8rFs8ah1kLlst5PLRvZxkPrRTbIE0YbmsYQtm9kEtT/mupy0B+t6/ff8IhWXZZUTAFoiLw4KrO0DCXGpgCOdIiccL4aF7XHXa20UPs/AiK6D80yJjubSxIxnz5o3nfn71oFVp26i/Vq2ztYy4GDBggaRAioaMjpk0ZD7cSfmu69u4/iDLltROLna0tTp88gmRJk8j/z1+4iB69+uPgwYNC5knK2rRpI5qXhg0b4Pr1GyKk7/VfD9SqWR3xHJOITxibluu+p0YjVZkkO2yYzKpEFguw4IDWGSRvmzZtMrpv0aJFRadOnYUAxooVy1fEglod2mww1WkMTH/SBJbO+abg9OnTKFy4EMq6Fcegfj2RPFkSOZ9u3b4LZ5d8QgQZNXvz9q0saljBqSLs482bNzKP0JaHHnY0ZDUFXNDw3MmcIRVWL5pqmjeikWu8UeseWLxig4yxnz5/wa3z+5GEIn9jUTDN30vGwl6aMqyQsuD+kYP4XY6dPo+CpWtJZRc1HSqC8FPoCY95v2X9ShjWqxWi67UlMkaQjEa/AhLS+weLue7783nmaMIMv4sJ14AlCJnR/fJOU46cMAuDx8wQMmZu+y9WE9IHrGSJYhg3ZiSSJk4YoI/RqdNnUaBISfTs2VMqJyuWL4OJ40bp7Ze1pAaHjRiNWbPnSek+03QkR2wTpKBE8WLYtXuPECd6hSmfy/u0pFDgki0bXFyyYfacuUb3hxFbvpbmqiRscbyrJv0DCR79wghGz6ixY3SM+2eu6J7CfxJEwvP4PmRInxbPX7xE0tRZ5DGmYCtVqoRBgwaZtG8qQg+c0qnL6ty5s4jvo0aJJFovkm5GWq9duyakgKlnQxLGRcX06dMRL44Dxo/oi5JFC+jGB1PHAOU6T5w+P6pWqyHnlaOjIzq3aYjeXVtr30cnC/F/7LEKJpF+YBalgYKVTRhMU4ZVhCYRNALndGngnD61rMSp6VArmcwHPZecnZ1FCJ7OKSH2r53sZ3ox0MTLnPPG1ObxZrzGpMHEgue2xVOWevv2/sNHLFi2XsxFzSVioj9p2xYVy5fFovmzJIrj42OUwgyDY5EubWr5S4E+SVd0w8/VeMHG2gq9e3ZFo4b1ULV6HSEjiq9Z4UIFsf/AQSFAXTq1x+ix44SoscpSv+2Wgrbt2qBmtSqSSt2xc5cQJhJBpnNY+Zg4cWLZTvlrCritfjQuKPD68Vn+upcvg1je/QS/fPmqe55VqSR8KsIuqNOiPmvS2CEYNX4GqnmUQ4UyJVCnSXs5z6mDPHTwID59/iypeWoBGUElQWOBDLMGNKiu7lEGw/p1g71dVD9TkaaMSYkc40uq3c7ODm5ubjhw+AT6dGmpHe/MJHiWRkCfazGypvddA4JKxsIY7Oyio3/Pdqhar61Uc7H0XIVpWLVqlfTzY3hewZpZ2tSVirCL5Wu34s79R1i2co3Zr2W5PaNpldwr+CJi/oERJG7PFkNEUu8UpTEw7bl311YkTp5aolGEvZ0dLl84A4eYMYW83Lx5W6JhHh4eMvFRYsDUKfH+3WtvsbMX7O21LbG6dOlichoxuMFxZsnyVahWuSIWzZ2mezxF8qTo3aMTBg8fK5GzJUuWqM7pYRRMWZcqVUp3fo4Y2AMd2zRBiQq1dVEvLhx6dm4JV5fMOH7KE32HjBMZB8kYe7zSu65G5fIYPeQ/7bUUhKwRbVZevX4r9/m5TG+3aardFxXGEUKxOj/gnaJQ4RP5c2dHgvhxUblSeV0PRBX+g+mdatWqCRFjlRoxfWRX5HPNrD3VKBb1vhk9B5Ubq8m4KuKN95Wb9/OsNpOblfWfm/KYObeAXq//vJHtfO23sVswwNh+BRVfv32TiYAGqOaCuixi/4FDfuyvja+oGMHIwMxpk1CuTCnMmz0NdWpV92nGeugwjh47gQsXL0kacvHS5VAEHSRy1atVRkqnFLoo0oxpk5A4UUJxw6e2jGk9RhqI+fPm6VbidWvX1kX0wgpogPzo8VP0693T13NdOrRG44Z1pVqUkRcVYROTJk0SIjZp9ACcPrRZiBjJf7rUKeX5KWMG4uyBdejavhkKF8iNb9+1v6WiqaU3Xbdu3TB19mJ06aWtfDQVxsaBaXOW4vHT55K+r1GjhjSgr+7hvfjwjrYFGBXThJ5OzGLdhswYi0OXjKkwitixHLBl9Vxx4mdFJUO9KgJe3SugNoIRxbqV/3jXqAi7aFS7sqTmacarkCtTwN+Zgz3tGYYPNb9ZeO2a1bB6xWLUqFZFUikKDh4+Arcy7ihasixy5i2M8u5V0bptR13qcc/OLajs4VPEHjNmDKxeuVS0X2wjRP2Xoq1q37GLVGASl69c0UX0wgrYfYD7e+PmLfEW0weJZ/y4cRE/XlzpCqAibIIpSC4A2nbph049BkrqfeL0+Th7/hIG9e6EzM7ppBhFwa692sULvfp43VEeMGLECIwd2gtTZi3Cxq27A7UfPH/adOmPbn1HoGn9avKZvB6mjR2IXNm1C2MVYUXAr0bCTAJXGlt27EOl2q2kckpJp6jwG9u2bZPIBOG5eyEyp09hXB+mnIOmmqL6dc5a4lwOzOrPnM815f0tuAL1bzXpd39NG6zbvAvVGnbQVR7GjBlTWq1Q+2cMnDhYHca035qVy1Agf169D/LCx48f8frNWyFQ1D4lcHTExo2bcfqsp0wQeXLlRJSoUbB23Qbs3rMPDg4xUaliBXhUqiBvUbZCZfkbL1480WVR1JzROT1GDh+CnK7ayKsxFC1ZTlKSnBwV81i3kiWwbs1KITwcbseMHY/efftj8+bNJqcqGTnkfptrLmsKKPJmqooaVZLK0cMGoWCBvEiU0FFE/L36D8Gy5ask4sffRUXYBI3CaT1CWxb2enzx8jWKFy+u8+6rXrmczun+ydPnaNiyK16+fotLV65LGnrIkCFyflIbeeL4Udw+t0e89oxd2/rX8vWbd+FRpxU8Krhh1bqtePbitaTsKRm5cOGCnFuHdyxHLhfv1lpeP8K8e74mMBF/P8blsFVNqRKwQJ8Mleu2gefF67h69apZjYD/RVAczbY3xK/HR4yEi32SMT/JgT+PBTUtF2qDkDmEywLkzL+Sd2PHeM7iNWjRSauxcohpj9+/vRAhgg3u3L0vfRcHD+iL9x8/wcbaWiobX756gx6dmqN330Gib9m7/wDWrt8ibZBI5kjGjKUmGWnmkMftCEbV2G+SkwdBt/31a1bg5ctXWL1mHVavXY+Tp05LZKtLx3YYPLCvP1/aGps2b0XVGnVE6M/OAWy51K/Pfz6Pg0aDchUr48jR42KrkT5DBtGXeXp6CsnkBMimyhRXs4CHUbQ6tWvjw8ePQpwYzdi4cSMsCUZSOMbQdoPaS2PRM06sanQsbIOknfos+sIxPUjvO0amiHy5s2PflmU+tp8ycyE69hwsxSAcPwmehy4uLti5bj4KF/DfeJzncoU6HaXAgzyBhSmsyGTrMYJmrzynSOzix7YP0J8sNMdISxIwfahkLJxDOTHuPXiMzHnLiTCYfjEBlez/6+jXrx+mTp6IZxe3+HxCJWNhmoxxUH/z7pP0poxhZ4vX7z8iZZaius0qly+JzJnSC2kg+UqWJBFSpkiCQ6evi15s5649YmLKqq1kyZKJPQSraZl+JIFg2x/2smSJvaIxZApOqd6k2/iWLVtw5sRhODtn8LGLjx8/lshQvbo1ES9uXH++tPb7kZANHzVOXPaHDRmAurVrIY53uyEFz54/x8JFS7VpnE1bZQIkuI+5XLNj1px5PvSi3KcqHpUwYNAQ3fEKDvB9eazY0JmklpFB+pYlT57crAIJFaHfxJuRY5qHD+/fFXlyuiBjhjS+IqtMR1ap2wrbt2+Xa0fxsmPKc+3iqShXqoiP7bko4Zx08/Y9OVdomdGk7X+yYMqcObMshvXPE15XPI/3rp8FKy9tql4lY6FRTalGxII8mSVPmghzpgxH3aadJDJGsa0Kv8HB4vXb9/jx4yciRfIW33ufi34Zjoak/4yl3z8k3PF9wNROBWZ+T0aAYjt467asbBA1ahRZeCg6lxt37knFpWP8uMiWxRmr1m+TND6vCVYvMppTuXJlP61gmCrRh6GxLCNSJGMkdoZkjOm6Lp3amfxdypcrI7d3794jQeIU6NmrnzzOFCAf+/rxNRLEj69r1cS//NwYMezhki2rfIeSJYpiybIV2LfvgLjrX758RRYZS5evQPTowRch52enTZtWbirCJ1hswahY9qwZsXX1XHG/96vvaZmShRE5ciScPrJLyBgrH0nkiChRomrHAs1vSXnWadoZh4+fkeibPkqWLCn9Jw2vPaa9WeXJ61PfW8zHmKXsj5VN2B9zg5nPqNYW4QBV3cvg9eu3aNdtgOhQ2L7EnD54fzu4QqNOguF5Nrbt3LKWloipCLeYu2SNEDG64DM1t2D2ZNhGj4YHD59g9sKV4uq9dOlSVK9e3SLRYvozEWxJZEk4Z0iPy1euyn0SMaJPv4EYPFBL0Ajuf9EihXy8bujw0eJjpiBdurSiVWNaM1NmbZWmChXGxkJ6VHKxMXPSMGTK4D+pfvLshbcNxRsR8k+bNk0IWZECuVGkYG55vx27D6JTzyHiok9dGatqmfJ/8uSJRKNr1qwpKU76OzJV+fXrVyFhlAIwssouAEDYqR4OqwhezZgaGbMYq2eViktBd9x7oPWRYRSgWbNmsir511secQChO7pLprSoXK4Yurauo52gxaLCOw2m2EGYmDYzCWGhRYclImSB+R6W+u5GopR7Dx5HtUad5ByfM2cOghtPnz4VN/L06dLiwN4diB7du0WWAlOPrR8rcKZWt27bIRMYo1+5crr6TPkZvr+VtejJOnTqKpYA9erWQYN6tXH7zn3kyltAKkgVt3wVKoxFxmgozDSz5+HNSJMqhZ+RMRKxeCldhUBxgdO2RQOUcyuCrJkzyLbFK9bH0RNndTYYZ8+e9REZo8s/i1BoK/T71084JoiHqFEiizVTtUpl4F66iLT38vG5RjRjmpByxA8F7hI2NGMqGbMIlBP169dvuH7rLvYeOIbh42bg3fsPqFWrFmbPni36l38NSj9CTliTh3ZFgxrltE94Hy8fBMxYtWRInZ8hRNoslrI0d3+DWhHqfX/77kMYNWkODh49JcJ6itQdHLQ+XsEJTi7UmXGlP7B/X3Tp3FFL5o19L/+OcWAnFGPvaeS9qtduKBEH6oDM7VSg4t8CyRVF+Q0bNsSw//R6HBs5p6kBIylLkiiBECdSgkUrNmD+8i04fPiwbjsuILiwUMDsA4tNvn37jkL5XLFk9lixZTI6FhmQsTCxCLYKmfFfJWN/EYytGr59/4W5i1ai54DRYgHA9Ny/AnpR9e3bV4wOO7eqg8E9WiFiBP3KSJWMhTcyNmH6QnTpMwK5cmSRJt08p0OyFRhF+rSjYISVeq6CBQvA2toKGZ0zwK1kcfkr++MPGbt+45ZUX7548RIpU6ZAlsyZUbCACX0iTSBjNJ/NmacgZs6ciaZNmwb6e6r4d0C7inHjxuHMwY1InTK5ydcqnfMTpcsnYnxWY9Kdn5YzCpgCpbn29PEDcf3WfUSJFBG9u7aSyK8ClYyFJTKmRsVChJxNmrkYPfqNlJz9v6AjO3funLZC591b9O7UGB2aVtcOAvopSf3Il6k2FiGFEE5tWiRaFtA+m/OdDI+9lQ2u376PjLnLSIugkSNHhlo/Vg6FjAQwIscJiNExRqKoWUuTOjVGjxyCkiWKG33t9Bmz0bVHL0lxspKT1YiMTlSuVBGNGzVAmjSpkDhRokDvW4//+mL5ytV48OCBD+8nFSr8Ars8sMIxpl1U7N64GNGjRzP5Wm3btT9mzFuBxo0bixSG2swyJQvh3IWr2Ldlieg348aJZfS1PhqBq0DIkzGVfIUoFHLx9t17OGUpig4dOmLw4MHSP27iuFEoUaocqlSp4quKLDyDE1H27NmRLGEcrJk7EokTxvvzpHcLIYFeajLUSFg4GIxMJmrBTMaGT18rwmD2mPSl1wplUKy8e/du8Uu6d+8eTh8/JAax+rh95y4yZXWVfpRjx44VKw0WHwwaNEjEyzTjJNgIPVu2bIgcOTLy5MyG/Pny6uQFr1+/wbZd+8WbjFWlFStUkMbjBMlg6nTOKFnS7Z+KgKsIOqjxKliwIMqXLoqFM8b4e83qjwesSi9Vo51WSxbVBt9//MS+zYtNGzPCwdgXklDJ2F8OfZLRo/8oWcVcv35dJgSW6FNTwpURB2+WOf8NYJUQqybP71mI2A4GKwyVjIVbMubkUkqaHDMFF1bBMn36bZGcZcrojFJuJVGubCm45siOe/cfIH3GbGKvwQWQoR7t2rVrEm2jGSajfkyzs3qTRIxVlGnTpMbiJcvx8tUrSQGRlBIZ0qdHzpw5cOHCRZz1PCeWA4ULFw6lI6AiPIKLAka39u3ZhZvntJYVppAxIm+ZBmL0y3Z8i2aOlt6SKhkLPjJmnrWF2tg7zEC5KEjKenZqgUXL10sFDd3EDx86iCc3TyJl5oJYu3ZtmCRjrIJk+yJqD+hWzkmKuh1OeHXq1BEyyUGAUQQFly5dQt68eRE7ll5kQj8NaSDW96W1MxyAghIp+xdWf5YkYYbQ+42YZvZvkAoLYAk/SRUJEW9z5i3AyNFj4erqigTxtRFaJZKlD343ntO8UcNDMBlx5coVbN26VdzR5y1YLNVo1EEmTJhQ7DxatWqFdOnS4fKVa4gUOQo2bdoUbESM1x0d29lKjPtGs1wVfweo9Zo/fz4qVyxt9muXTBuMPkPGS1VkhdJFjWsb/4Zx0MomeOYIM9/DvDTlnRNB702pwqJQCMes+SvQukt/IWQse1+1aBruP3iEHv1GiM7Krx5/wQGeUmziTN0NB3pOWIwCkFhxwGd0QWmrwgmH1gIESdnNmzfFJJCmk/QNY3sNTlisqmvbtq1EGG4d0/b5M5uMGeIfJ2MBrnKDg4wZ/EZesELJKi1FB6X00AsPYFXZzp07ReN2795dDB7YH7XrNrDY+3MRxbQovZ+Ua4qRbkuncdl2iWlYBWxuTgf+kKhiVRH8oAaSXR1o/Lph2UzY2kY3OTL2r4yDCGYyJpExp1wW1oypZCzMQSEcnBzylqyOy1dviPs8V+TTxw/GmEmzpQTZ8/zlEBFGMwVDuw1GvSjyjB83Nm7f0/Y8I5iOcnJywtSpU2WQ2Lt6oo9B4Nyl66hYtwOiR4uKKhWKY/r81UiX3lkmDBI86h92rJyMYgVyal9gxEcszHnWhFH4Ofj6N8BaIBqm3Gc5fd/hkzB28lw5H9jkWIUW6dOnw7Vr13UO5myHxmuKGh5LguSOJG/HxhW4cesOevQZLL/DqFGj1J/iLwG7S7A10t2LB5EoYQJf17HRccDAj8zs8UDVkJtNxtRZ6y8BPWB2rJ2LNs3q6rQqTdr0gFOKpDh/8Wqw63EoMqZYma1qjh09jFXzxuDF9f24dmIjHlzYiSeX92D1vLF48vAOtm5aD6dkiVG2SHZfA0HWjGlx7+xWXDmyFgO7t8LmJRNx8uRJmazpPUUBdI+Bk/Db649AXyVf5oHH3GwixscDS8QUeYPe7e7Dp3AtVgXjp87HmDFjVCJmgLFjx8lfdtzgYphRYjqjWxqUMCRO6Ij8eXKgWcNa6Ny2CSZOnCBNw1X8HWAf1vjx4iChY/ygXccmXtsqEQsc1MhYOIchEbl28w486rQWHzIO3l++fEbNKhUwZ+EK0aG0a9fO4n3n6PTMvmYsIHAvUxSDe7VHqhSmlfGbEhpPk7eqlFfTaX/z5s0oX748zu9fCed0KQO2sVBh3jH3j4wFFsrvAmuxYlmxbiuuXr8tpfHrN25BpkyZ1F/JiPBacelnJJlWGQRJElP7lgIlDFzgLJ4zGVU9yuPbl4/IVaQiokSzk4ID/jahZTWiImhg0qt+/fpSxNWgdmVMHTvI9PSkqZExFQFCjYyFcSgRHVNu5qBSrZa4dfuetEqiKPfLl6+wjmyPfj07yEVJobCl0bp1a3x49xpn96/G8jljkMopqcmvDei7ckBhdVmkSJHk/yxQoL5ox76jFv0O/wr8jYgFFxHzvg0eMx2dew9HMqe06NqtO46fPKMSMT9ATSSLXAiFiNGEs0qVypJWtBRofUPdWIv23bF63WZp0L5g5ljcv3cXWbJkQfZsmaSSVB9c5J05cwafPn2y2H6osDyYDVm0aBH6dGuLEQO6G42O+zkeqFGuEIcaQvjLMGFEH/nLyiyKf/v16ycNXNlonB5kLFUmUVPK5y0BThoNarojQ9qUsDS4Kqdwf8aMGTIpUGBM+4C5S9eL5iiRczHYJc2FiPEyY/WGHRb/fBWWwflL16SFV69evbBy5Upxu1er9vxHjhw5UNnDXe6/ffUM69auxp07d+V6sCSWL1+OPLlyoGGLjpg8YwGyZXbGg6tHsXHFHHz8+Em8/ag5YlSa1jkU93Pf2EKKZtMqwh6YraCJMrs19OzcEnZq4V2Yh0rGgiGSZelolznvU7Joftw6vx8NaleRyBhLm9ngdfLMBbC1/oRp42gMuxjFixaEpcBI1ddv330+GMjQtrHv2K1JWSSIF0t8xhgpo6v0y9fvMGjsbLx49Qbfvv+Q19Zsoq08C07oryj9XVmGEfi7r0o0zJI6MSP6kdmLViNPyRpIlSq1kDEVpqNvvwHyd+WqNXBOnxbjx47G3LlzsXfvXosdRlpyrN+4VRo+d+09FCvWbMaq9VtRqkQhHN+7HjWrlMP8eXNEHrB+3Rpd42na0nCBpyLsgXIU2+hRMfS/Fj6u5fAwZv2r+Kc0Y/+UlsjKBq/fvEWrTn2xYcsu8e1yL1scc6eORK3GHbD3wFHsP3DQImkiVkV+//IeR3cs96O5shn6Az8m/y07D8K9bns0qFkRkezi4+C+Xdi/aQE8L15H0sSOcM5TDsUL58W2VbOC9DsHdaAKC+dYkC0rzN3O2G/q/djLN++R2qUEKleuLJo/tcG1+WArGpIvz9MnECdObBQsXAzPnr/AiRMn4OjoCEuBC7cOHTro/r9/6wrkzZ1d7nOauHn7Hs6cu4z6zbTbpEiWBM9evES6NCmRJpUTcucvKgul9OnT67wD6RX49u1bKShixTfvsyE77WxYkGNp/aoKYNm8iajVqD0Wzx6HapXK4s7de0iRLLH8HioRC3momjEViB3LAV3bNRUxMO0mFq9Yj4NHTqJNs/qws40uAyfTf0HBx48fRQRconDeYD3iZUsWxLA+7TF/2QbRQlQsU1TMXyNGjICsBd0xrF9nrJw3Plj3QYX5uHLtplTadu7cWSVigcSkSZOkPU3vvv1kQl20cJ5YyFjafsKwewCJlgJ+7s49B9GgeUfY29nJY9MnDEX3ji3hkiUjHjx8LB6HrP6kcS1TmewmQD9BEsYkSZIgefLkUizACFyTJk2QK1cuqZRWYTlIF5Z2/4nJa5WKpdFr4Giky1ESdZt1UQ9zGId5DvzhEGEhUhEq8I5qMBLBgZQGrESFGk1x+sBGXDm1Ex17DhH9CcXxDRs2DNTH8H0p5K1XQ6ttMQpzUpaG2+pFZzq3aYQkiRNKarJJ3Wqy7Yz5K/Hz5y/0Hz4J7Vs2MPv3Du8rRbP3Pyiti8x4DX+Hx0+fo133QdI425IVgP8a4sWLJ/YfJDA5c7qiccMGKF6sCFavXo169epZrP8sfydqTAcM0KZGDx09iU+fvyBypEg4f+kKRk+YKTqk7t27S0eC+Su2Ys6EfuJpSDD6defeQ1y9fgvXbmhF/44J4iOWQwxEsIkAGxtrOMSMAccE8RAtmi3cazRGxYoVcf/+fV2BjorA49ChQ3I+0MJi5oQh4jE5asJM+X0uXb3h5+tMHTPD+1gZ1vHXpyn/WTKmwMpGDF879BqJY8eOyUNJEyfE4Z2rECuWAzr0HovZs2eLHmvixIlml7HTgXzokMF4eeuY9rWWdmT2K8Xpff/shavIVUy7on9w+SASxDevlYulB5iQPt+ClYwFBt6/y5dvP5C/VA28ffseK1evRZ48eYL3c/8BZMqYEVevXcPdW9dFr1Wuood0uKC/nyUNc9nYnATQEAMHDpTCC4JaMU78SRI5IpdrNnhUKCU302EjpC1LnhJyblBLyIiZCvPBxTB/F6aZeSznTR4k1biZ85RGg1qVsXT1ZrRtVgd9u7cxOl6oZCx48VelKUNKKP+3gkasezfMRae2TeT/Dx49Qd8h4xApYgRMHdENk0cPwOTJk2VVbC5I1GPGsAsWLyJpObP/JAaPnY12PUeicv0OaNCmNzr2GomDx87gt0b73cYO/Q81KpdDnNgOJgnug0t8H9znW5gtHjBSBj9vyRpcvnoTW7btUImYhVChYkXRbtnb2yFdurS4dvk8GjdqgI4dO0r1nKXAKtdnz57h4sWL4vjPa5xaL4WIEbVr1xbNWgV3D9x9+Bw1GrTBvEUrtf0LA7oJfiN92hRYuXA6Ilp7oVy5crIgDA5j28CA35fN3DkGhVUwdc3oKDV606dPw4iB3bFn/RzR8m3buR/fvn1H1pwFhQSwCIMIyhypzqd/aWQsMD/s3fuPRIioIpCwskHBUtVx/JQnFs0ai+oe2guUYDib+gJe3BRbmwqmTs6cPIpTe1cHvU2G3mtu3nmECjVb4NbdBz42YWUoxb8sqS9SpAjSpkiA5EkTo23zulLV6evjgpGwhMTgZPH9D2pkLIDUpXJMyldvhtdv3uHU2QtB+zwVOhw4cEDMj7NkzoQlixcgaZIkMjanz5gFtWrVlsh2aIF2OfPmzcPgPp3RqU1j019IQqDRYPqcxWjfbQCqViqHlWs3ITRBHawSwciZPSsOHjkufXXDAvliCvns2bNiUcRCCB67cqWKYeywXjIOKtf3jj2H5BqkYbBjPAfs27w4RPc1MOOWJqxnFQKJvyoyRqxctxVps5fAxq178PLVGzEzVWE+1i6ZhiundvkgYkSXdk1Rxq2IiHBN4Oc+BogfP3+a9Rpj0F+BnvK8hAy5y+iIWNZM6XXPcSBi03EaVe7btw8r1m7FfwPHoHDZOqJXURH6WLhsHXbvPwr3ciVCe1f+KhQqVEgaPz9/8RKFi5TQkYZcOXNKU/HQBIkgNWObt+8x+7WMqlfzHo9WrduMkATHLaZ62aSehDJ16tQSeCCquJfFuYtXMH58yBQG3b59W+yISLD1QU9ItoOj4fWQIUOkRynPBfYePrh9hYzpQsT0UKxQHri4uIhhcNd22oyIirANs8hYaPlsEblyZEGJIvkQOXIkpMxaFDGTusCjdktZebNaUIUpP+BvxIkVA6lSeK+g9JvFWlmhfYsGuHnzprg2mwrqRq5cu4U1G3eanMY6fvo82nUfjBxFKiNGshyImyoPoiXMip6Dxss2n79oUxXx4sbG5FF9cXjbUnx65ImNy2dg7NCeSJcqic5n6f2Hj1IR+vrdJ+Qs4oHufUfiwOE/FVqWOg//mhS4X73kTL35A+WYTJ65SMhy974jQuhL/TtwdXWVc//J06fYsnW7HHP7GDEkpRZaIKEZPny43O/dtY2RDfzzstOmLins52KQULzLHjx4gN69e/tIj1oaBQsWROLEiSXiSHNsGktz3CERWzx7ItzLuWHt2rUI7pRo48aNpSiiePHiQgYZ9adshBpYpo3pGxY/tj12b1yM62f3YP6k/mhYpypy58hs1C+Qov0FUwZjeP+uKF2iEEIa4UFSpLGwd2hQv5tZacpXd0+FCQH/xOkLMH7afDx6/Ez+X6RAbuxYNy+0dyt8Qm+CpY+Qc86SPoS6poAl6knix8Tyudrmxv4J7zdt3wuPum3FG6xowTzIkC4lfv38Ja1yUqVIijP7/R749E/qd+8/4OSZC2jZeaCkEOgI/vj+LRw6dlqeT5kiKUYM6IbypYv6q2czNVQdWuQrzOjCzDhGmfOWw2+NtWiKaHGgwrL48eOHnPPUi02ePAkrVq5CgwaNpJm4u7s/Vc3BANqWMFozbNgw9O7WBn27t/O9kTIW+EfmrawlstayQ28sWLpa/Me4MOT0RDE6UzzRo0e36L5zEU/LjTyuWTGoT1ex+4kTO5auJygxZeZ8dO09RPRzwVERzNQzfeSolRvcpxMK5M2J7AXKSaaA35tFEeXLFEexQvmEJP7R3IVQQY4Ki6Qp46Rw/XvSlPpo16I+7pzfh8sntonX1NETZ+BeqyVuG+iLVJiH0RNnyl+uEs0BxbxrNu3E4lV6KQY/oim7DxyHU/Ikov+jyLtrn5GIl8xZ0s5tm9X193P0ResxY9hLt4FNy6cijoOttHQhERs6dKgMYjwXqtRrg5ETZ2HLjn3YtG0vznhewoq1W7B990FppUSE1VWbgrCwD37Br2M0Y/wgPH/2FH17/DEQVWHB4+5NUNKmSSPXQ/UqHihXrixq1KiB//77L8iSAXO9yUjEGNFhD0R/uzv41/FB4yXRnJmThmHutFEoVjAnJo0ehB3rlwgxuXbtmkX3+8aNG+K9SP1pg9rVkMopCeLHIxHjs791t0Z1q8IpRVKUL1va4vtAglehQgUR4J8/uhXNG9WWdGOT+jV0ZLFimeKoWbkc4sVxUInYX45wGRnTx7ZdB1CxprblAyMrl09uD5bKvr8WemTpjOdF5CleWXpa0rJCf4XoH3gK1a9fX1bm9y7u050jCnFSJmpuV9S9qURLNm3a5EOUz1D91WMbTN5t/cm/54DRGDNpjtzngM4VNs8B+hs1b1gTQ8dM021LPyNGFqg7K12iIPp0aw3ndKnxN8FS0bSgEMCyVZtIQQWJsArLgyktt5LFMXb0SJ29wZhxEzBs+EjpI0mCFBJRMTs7O/To1BLdOzRHtGhRA9c+y9djf867C5evIUeBchJl5TgRVPA4sV/jihUrED9eHAwb0BO1q1X4s4H+teO9H3fuPUD+EpXx6vVbLF26FDVr1gzyfnAszJgxIyLaaLB38zLY2UYTyUVJ93piRcQOBqxoTZYkEW6eC4ZrSI2ohRj+6siYPpLrVVfGjRtb1Y8FAdmzZcKowT0xduxYZMmSxeT+dyQ+jEiRBNVr1gVbd+7H169a3RdXtddv3sXsBStRomJ9HDlyRCowFZAYUe8SlBRA7y6tcOf8Xnx+ekFuL26fwKcn57F28VTkyJYJkSJpqyxZ7UXncq5IObCv3bgD2fJXwIRp82Vw/PTps7SQCsvl7OEFyZMlQrw4sUJ7N/5aUJO3ddt2XRTM1tYW/fr0Qs8e3URwbigCtzRopdGqVSv5fC56hIgFAyJ6G8qS+AUVTAXSZHbLli2YMmYwbngeQu1qlQJ8nVPypDh3dLvc119EBvX4XblyBW1bNNQ18d5/6LgQMRZprFy5Uh5r1bROkD6Hjd45xqoI+wjzZCwgf6i0qVJgaN/OmDVxiJTvmhrNUeENg7RB+5YNcXjHKjjYR0Xp0qXRt29fWZWycss/UAS7ePFinLt8U1LGNGItUKY27JJkR6Y8ZdCm6wD81ESQSiAOiAq4sqZQ193NvHZKynmwdsM2OCTLDqcsRZHe1Q2jJ81GDHs7PHj0FIXK1IJHnVb48eOnvIY+RhQHe3h4SOWSgq59RkgaM1byHHBMkxdR42fEgqXBK9oNToRWkY0+HOPHw7bdBy32nVT4RNWqVXHv3n3UqFUHL/TaFjWpVVYWRcFZAch+k/QF27FjB1o0qgX3ciUDH20xms7840eWOmVy2NpGD3LbJEbEeN0fO3YUG5bPRpMG1RElConebyMeaL73Q4qJxgzEsmXLsHOnP8VKJoItoliMMWz0ZCFMhEJo2VLKzc0NzunTyHhsNnjtWllj6uzFiJ3cBXGdciBS7DSy+PyzTQDzZCAKeFT8ZWTMVFNL5XlqJ7q0a4L6tTzkvjnvHR5uIQa9ATFnjizYvna+uDdPnDBeSqoTOjpi6dwJ/upR6E/GFd+pU6dga++A+AmTSZSNgxfTkEePHkWpUj5dupVVm1k2CHoD99dv33X3ScB6DRyLy9duIm6cWMjt6rNNDFfXTFFQGJzYMa60T6pU3g19e7RD1KhRfGx778Fj0/dHhS/YRo8u4XkVwYOiRYti1apVOHL0OOo1/BNppiM+fb6GDBksiyNLo1nDmtJv8vz58xg/vBcmjuyDZIkT6K7JII1hRkgZZQe5smeVaFFgwQhUvjw5cejQQaxdMgP582T3m4AZ3S/tdk0b1JTemnPnzkVQwe9FYvf85Rt07DNWV4hGA+4m9arJ2HRg63L/5zRv0uXr5o05i9cJsWvTRqvle/HylcHrA1E1HdRq7OC4/SUIc5oxcwmIOSv48FSZpiDEhdsGJzd9xM6eu4zh46djy/a9UjnJfpZcZVJ3FVQwh964TmWMHNTd9N+HA7befp67eBXL1myBg0NMpE+TEmXdCkuElKc2hfpRomgNG9kkfcT4mdLgmJOWPrx+a78nB79IkSOKjkzVHgYerTr1k4KKazf+RCBVWB5Mm1EEvmPbZhQqWADW356L83rtJh1Er3f37l0kTZo0yO2RaB9Dzyqm+mtUKY96NTxQvEg+X9GwoLTbMd72zBr9hozDnEWrRENl7jW5YMEC8Q9LljQRls+fikwZvPWhgZkLrKylfdzu3bulIbeywKNWtlq1aoHqr7lw4ULR2xqacJu6P/5FL+0cnYU4NmjQADmyZZYOJZtXabW1fxU0v/8KzViYaxSuXLimTszhkWCZA7++X7CRNANLCoqwc7lmxbol07Fz7yGMmTgLderUEbJDO4lu3bpJ2lFJD1NvdenSJfnLBsb+rezo6cP0p1OKJLrvFJjfk6aw+saw2jfzksFKIWJEwXw55ebrezJEbG2NHC6ZzP5sFb7BNNnilRvRvn179fAEM5gupL5z2vQZQsYInvNTxw5C9oLl0a1TayxfHTid05o1a8Tr6vLly/KetIupU70iJo3si+jRo8k1ZMr1aljIYxY0XsiZPTOGjZkiDcWTJ09u0stYiUgDVepTs2TKgL2bl2qj3/r761ffW39AvSmrHxUDavos8vhQ99q5c2czvxxQt25dkW40b98b799/RNMGNYwTTjOP3cJlWplFnDhx5C91fTTn/ithZRPuCVuYTFOqCLsoWbQAdqxfiEsnd2HSqAHi+8PUZOzYsaWsniL+DBkyCAnLnj27DJz+Gcgy/UlMn7sMVeu3lWhKKpcSyFqgIjLlLY9u/Uapqa5wiK9fv0v6mTpCFcELca+vVg27du/Fnbt3dY+zUrBKxdI4efpcoN6XER9WDbJh+MKZY3HTcx88D23E3CnDtUQsBOHqkln+mqrVolEs/cNo0ePs7IzpE4b5kiEEBlxgHjt2TCKEjDZR88VFXPny5UWOEdjfb/bs2ULK2nTphx79LGOU3LqT1ieSvx/1hbv3H0Gd6gEXK6gIPYS5NOW/GP0KV+lMg1UI7TC27zmIVeu24v7DxyhRJD9aNq4tg8ysBcvlcbZYojGk4YqvRYsWkv4gcaOG4suXL1LuTZLH9Cidr22jRcHM8YNQqrh21W+WkaT+5mHQp+tvBbsgTJm9RLR5QU2RqQgYTN/RSZ66zGljB6BiWa3+Mk22ovj0+bO0jzM1muTp6YnNmzdj2rRpEu05tX8dsmRMZ5GxOCjpylqN22PfoZOi/yLB8AskSilTpkTJogXRq2s75M7pAisrPW1YYHrm6raxxvI1m7Bg8Wrcvf8ATRrURPuWTbB+8w7UbtwWb968CZLJ8bhx48RWiKlELnzNioYp38/7NXmKVcLT569x9epVmbuHD+iBTm2bBC49+7dD8ztMpCnDBRkzBpWghT2ywVPJytpn5nvclDnSFocEbeL0+UK6TIUysObIlhHHdq4IUnohrB2rvxWdew3FpBmLhHzTgFRFyID9C5mS27BhA+rV9MCkUf3RunNfLF6xXiwdAmp0PWfKCPQZNBZPn7+QMb5i2eJo3aQWsmdxttg+Buq6876+X7x8jaz5y4u5LIXvfmHr1q0oW7YsrnseRIqkCUyfbE0dR4x8B88L15CrcDmx7cmb17yqcMPxkynnLM6pMHfqKIMnzSNR/w0YhdETZ+n+P2vScOTNnQOpUiRRtbAhTMz+GZ8xFWEHxrQOHVs3xtSxAzFz/nJJS/bq1UsGUwpMA4KTk5NUL7FNiYrwgWzek/fXr19De1f+KVAbRCE5Bfar1m9Ftfqt0aJRbXnu0KFDRl/DBtksxmEBQJM2PVCoQC7sWr8AT28cxZzJwyxKxIIKWkuwRdvq1av99VBLkyaN/L19J+Qap2dMnwZ2trbYv39/kMdPVj8eOhI0Gw+iV5fWPv7ftG0POLsWh0uB8li4dG2IdmlQYRpUMhbOEeZa5ej7Bnnf2N5jz6bFiBE9EhYvnC9tSFKkSCFtlEjMqL+g2NbYap9pkxoeZS26i6FiHfKPoE61imjfor6kXJj2UhFy4GTOa2nTps3Yf/gkRk2cKX5v69ev121DMtO8eXMhYGnTpsH8eXPx4ukDaWG1cPpIFMqfU4p2fFwbRq7pEIPe5xV2TS3FIf55jnFcYTHR3fv3A/05/m/n2w4jYkRrFC6YW6xEKLW4d++e9JuknpbjlzkoXLiwyD3uPXjk5+eZAmrkMhjpLHL56g00adtDzGVVhC2Rf5irplTxdyJvruzYvm6B3Pe8cAXLNuwXbQrbiygl6Jy89aNrr1+/lr9OyS0jBOf7U4TLyUZF8MHe3k6OcUC+fyqCB4yu0MGd9jM837dv3y4msPQlo9cfu11Qy9e1XRM0b1gDsWOFj2buaVM7iX/d6dOnJV3p1zXOjhsfPwbdsd8c9OvZQdo28bjzmCsO+gSrLE1NX9IMlmBrpKCA4+iVazd97MOYMWN0/x8+bho6tWmCnNmzqNdpGEG4HC3/1YhGWGpgbRYMVtXZMqXFyD6tcPn4Fty7dEgrVgUkZaIfPqcgWanO84FAGP7df/hEKjRjOeXC3oPHMWnmYhw+dkaNkAUD0qdNKW2nzI0KqLAcGPli2x+C3Sa6dOmCl8+fYszQXjh/aC02L52A/zq39EXEHj5+ijkLV+LJsxcW/zmCGpEmuS9aKI9UH/rV4oeRP7ZiK140f+B2MpARslQpkslfLkJevHgh0Tn6em3cuBH58uVDmTJlJGoWEJQFaBz+LkGY50jGTuxdp/t/q/oV8enZFQzs3Un+v37zLhQsVV10vP88NCEc7fUD4WQ2/zsRku1pwhzoUQQvJEwQR6qHJozoiylTpojoWyFkNFckihbUWmCYi2fPX+LitVuYMGMRshRwx43b98QQs233wejUaxhKVm6MiTMX4/W74O3j96+hRJF80gHBw728jhCoCHnQ/4ooVigP3j04i8sntqJtszo+os9v373HirVbMH3pLtRu2RfZCrijZad+cl0YRWi5nntPlgN6dRQjW1YHGsO+ffuQyTkdMjv77HUbqG4AgUgNnjt1UAhYyaL58eTGCXx6ekk6l9CWg5XjAYGt54jYDjEQWHz+/AXnL10V/eaG5TPhXs4NKTMXRLVGXRApYiSsXzZTt+2I8bP+TTmBJhRS7gFATVOqCBNo2aSOtDbq0W+4lNTTOZor+siRIyFiRPNO01t37qNZh77iAE9w8qFOZvr06fL/G7f++DH1HDAa0+YsxZ6NC4VAvHj5BnFixxQ3baYbbt65j8+fvuDrt294+foNfv36javXb0vLkoSOfpfY/8tgb9ANS6ejcdueYkpKB/RJkyYFyqFcRdDSlcSRE2fx5u078R67eOUG7tx7gGs37mDNxh24ePm6LH5Y5UwT59atW2PYsGGI7RAzTB56dsbo0q4pBo2chKZtuurSegp4zUazgKeYuWBfyVw5smL5qo149OQZlswZr4vm0e9tx+6DGD58uPgxGu4zwTGvR48emD9/Plo3revDrNpclPJogBOnzwkRLF2iMK7fvCv2G6w03b9vL758/eYjEnf95h2kT5sq0J+nwjIIt9YWKv4ieK+0mXqwdcwoXj1c1fNWokQJLJw+AjUrm9YqhKdzmpzlJF1AES19yzJlyiRmtM7pUuLK9Tsy6LFpeKJEiYTwpUrlcyBKkSwxBvbqgCUrN2D7buOVaOOH90arJtpqNRV+/xYz5y1Hu+6DxLeKhFhFyIKtjHj+a7x+SQTk1Wtt6j9atGhiVkrtFfvF8lrgooWeZSQL9CtrUreKRaxk/ENgIv7vPnxG0gz5MGDAQOkAooAtiqiHow7q8K41PrzFAtUFwAy7CxYW2CbIIH07I0awwcOrR30QqidPnyNnEXfxfFP6W9LGgpgzZw46dGiPiBEiYPiAbmhYpyoCiwuXryFHwQra+8d3IF2alFiwZLVUUxpDofy5pHG6NCn/l+Q/mpCLiIXbdkgq/l0wcsKBilqjhAkTSjsWuos3adcbtpw8ShfVbXvh8nWcOHMeaVImR75cLjr/sjv3Hko1E4sD6DekgAakJGLEp0+fZPIh6GPGSs4bN24IgYsVKxaGDe6Pus26yP9bNKqJ5m27oVaNKhIRU5AgnrbNiAq/IRHJRjWxZe9JtGvXTo5no0aN1EMWgogbN6402Z40ZghsbaOjZLmq4krPrhnGrGgoPOd16FHBLcz+TjFj2KN86WJSia2QMUbE2DEgfvz4+P7jJw4cPo5CSuuzEAA1YiS4HFsa1K7iK7KV0DE+ju9Zh937D2Pq3JVi88MxiWl8ml/zNSRisYIQkaTeTyFirORMkyqF3C/jVgRl3YpIr1IFpYoXwvbdB5A1cwZVwB9GoEbGQvXoh5OO8yG1irCywZVrt1CnaUdcuqJtxMs2ISRaDLEXzJ0NjetWwbwla7Buyx6diDdhgnhYMG04EiZ0RJ7iVWUlQl2Jfh+7J0+eIHnyZPj58xeaNm2KmTP/6Cb8iigok5kSHeZqln3kqP/o37OdCKBVBIwvX76i039D5XejDrBo0T+kWkXYAqNlJBQ7V08L0UIpcyNkazftRI2G7YXQcEE1atQodO/eHQXyuuLQ0VNCjlYtnIJypYoGfvwyIzJ29fotZMlTSv6bIH5cDOzVUQiWMQwcMRGjJ84Wix+OKW2a1ZPCCnOboOvj0tUbcMmvzR7kzeWCfVuWwcr6z/73GTwGI8ZOk/vTxw9B1izOKFWpgRTaUFt2fO967ef/S9ExcxHI8yiYHPjPwt4uKoIV4YWgqPAfQRgAeUpypduu2wDMXrACo0ePFs0RGwUT7Dv37esXvHz1WgYQhuI/fv4m7UhYss+wv7FS8sGDB2PHjh2YOHGiROACi0qVKol3U5tmddGri++KNBW+wd+zeMX60FhHFqdyFWEPjx49kgXM6ME90KZJrRD/fHMIGQl+soyF4JI9h+iwmAJ3Sp4EqVImx4PHL4WgnT51ApdO7EAMu2jBOw9ZWWPa7EVo322AjFOM6J85fQp3Lx6Cvb1vWQ+7IjRq1U30sGOG9PK7ObiJYPVojMTa/p2sTF+zeJq8t363AKZRK9ZogrPnr6J+/fpYtHC+pExpFkw/xzsXDyFxIkeVjAUDPnwgGXNRHfhVhD9wYGI0rGv7ZkKuWJZPImZraysr94cPHwoRO7h9pRA3rkq5wmTK4uDBg356+rCBMN3Ig0LEiA4dOqBOnTpYuGwd0ruWwrZdB4L0fv8CGKkoW7Iwzp8/L4OSirAHVjNT/F63hjvCOqhxWrd0ulQfkoiVLVUU187swf0HjyXyyspFVorOnOd36yRL4vad+zp7HkbpPn76jA1bdxndtlbVChjUuxMO7ViJZg1rBrk90eOnz+UvCxs2rZytJWIGaNy6G3btPSyCfTY1jxM7Fm7duqWr3tT3JFMROjBfPanv8RQct38RXMEE9y3Ev1Mgf1+9cuMUyZLg+ukdmDVxGNYvm4E4sWJi06ZNOt1IrhxZxLiQJo9c0ffr10+XVgxOFCpUCIsWLcLNW7eRv0BBVGvQToxsVfiP6pXLij6lYsWKQqhVhB3Q049kjFXCoVWkZa79RP7cLti5bi46t22M+dO0vRwdYsbA58+fRROaP38BbN93wswoV+DmoUb1qsu5TV1kw4YNpQho8/a9RtsOcbvuHVsga6YMsAT4WV9fXMXQfl21xM5gzD9xyhPLVm308ZomzVqKLnfhwoXy/3vPvT3QQnPO+MehHnEVYT6iUr92ZZQpWQQXjm1DFffSyJo1K6aNHywDT7eOLVCuVDGpCvv4MWiu1eYiXrx4YjLp5aVBrqKVfVhmqPCNJIkcRft39epV6SHYsWNHSV+qCH1MmDBBNJgdWzdEeAIrJ4f16yJ2KkTSJAmlGIdgoQJTcCEBth769uoGDu1YhUuXLsEhZkys27QD2QtWwPI1m4Ptcx89foZUWQojcbo8OH7KuMnynfvahQ/Hza4dmuukRy1btsSAAQPk/2q3jNCH+ZoxIznwcAOV7ftGWBBsGtOXBUI8y4m+dOnSCGmwJ13//v3x/t0bDO7dUdoB3b77QLQcObNnRv48OVRLGANB6/ip8zFs7HRximcxBbUrQY3srF27Frt27cLFixfx9OlTnfaQkTi6tkeOHHjvpr8ZjFKyT2XLRrUwon9nhBUExvZiycqNaNiqu/zmLLSpUqUK5ozvE7CGNbBZGSP72LZLX8yav1x8xQ4cOCDjUrcOzUXUH9SUpD5oYF25QSecOnVKFob0KrvpuQcxY/qsyKRejJWTJYrkF9LllKkAsmbLjq+f3uKgd1Pyh9eOiw9duJ1D/gLNWPggYyqJCj78BRcSm+qmyVYUs2bNQpMmTUJlH6jFoJBY6RpAMGXKqkxaBTBy175JVRQJZDeBvxEbt+5B03a9YBMhotgU+NVvkNV9JFfUDBoaZtK2gQUZGzasF0NeV5dMyJ7FGYkTxpeJjwaXIyfORoUKFX30C1TxBzxv9+/djSsntoaJRUNQuo3QS61KvbZ4/OQZypQsjC5tGyN69GgWJWKcXHsOGAVrayskSZwQyZIklgVXooQJdJ0/kqbPI71A2R900KBBslijBnZI3y6wBOhbVr9FF5w4fR4bNmwQjW3JkiVlrKHtx8hBPcVOw9g4v2bjdik2ePHy//bOArypuwvjb1ucUqR4gVLc3d3dGTLc3T9gwNBtbMhwZ+hwGO7uTnG3AqVQiru2+Z7zb5Mm6U1yk9xYe37PE1Ku5+p7j4a3Xtq5YSkqVygNp3nOuLmow87Ab5QrxrjOGOPykAWKoBpDjoLqNpFVhuJVqPgkZXPRhUdBsvRmvGLFCtRo3AG/D+8vGjQr+YbsqtSrVVnE/XXq/4coPPrbb7+JAGgSXf7+/sJiRvXiSIip8fX1Rbp06USQ9pkzZ0R2LLmj/hg+AD83qinKCuiTMYMPOvb+VVgQihYtKoaRFYGOCxUVpuXFVFauXIk1a9Zg8ay/nEKIWQtZfjYsn2XTdTwKeoJ/lqzSxK6+eftOdO84tHMtsmbOKAQhbQdd+/RNsaxUDHbo0KFoVLc6ChfMa9X6yf3Zpc8wUcuM7jllyoT34ST3aM6cObF2wzbUrVkFzQwUym5crwZqVq2If1etR55c2VGmZPg1wTgW17CMOQprFDqpZPX80cD65MxQL7akGQqIuBcqLuqM0A2a3o7pLblj6yaY+fcoEQ8XE9AOypayepAb5dffJmPG/GWIHy8uknknF9mzPmlSoflPdZAnZzakS5saIS9ewv/iVdy59xCHj51GwXw50aZ5A7RqWs9ozMv3799RqvrPwhJQplwF4b6it1S69VELIBJ1MVEcX79+HcWKFUP9WpWwZNZfDt8Hdu2/a0WyGLm+azZuj/MXr+LgocNInTq1eDn49uUT9mxZDt/0PujSewiOnTqHOxFxpHSOp0jujQG9OmLIAMvrE+49eAz1m3dBgwYNxMsKdStR319GjBiBP//8U1wrl07uQiJPC0p6uLp1ylnQuudFLzclwxjCzR0BDwORvWBFUXGfLCnODPX+mz17NmZPGoNObZvCmbFn0U/i0ZNnWLTsP2FpqFGlnGhwHdtDmYcC9QvsP+wvIcgK5c+NfLmz48PHT6JhfJ3qFUQRUcqEiymEhISgRLHCokTE8Z0rTbvyooMA08bKzP337z8IQXbn3gMsW75CtF2rWLEivnz5gvXL5uD6zVvo0meoqKlH1fbpMeuVKBF+6d9VZFJaws3b91Chdkth3aX7nLrrCDFr8u/o9b+R6NqhBUYN6SNKVzgUVxJzqjDLf5OMedlNycQYzvpfFt/Ui9LZofIBFDC9bM0mpxdjjsi2HDWkt+5AhQQhWQvWLZmmu2iVCsmSJkaHXsNEXSrqVxoT+PTpk0icIPf+3s1LHSbEXJlEiTxFTa/2PQajTp06wuV98uRJkThQoVZTLJw1ASWKFhRtiR49eiQstwkSxMPHT5/NWs+HDx+xcdseXLh0DRu27BLlKCi+UluIkaXu1zET0bBudcz4+zf2xLgoLiRfGQZRHqbNOg5Cq0590aRJE9E6yRWgDK+TZy7gaXAInAV1jSftj8tug7pWnYkPueVaNqmL9D6pNd0dojskDEqXKIKrV69g44pZ8PUJDzq3tzVM/bE7CtazpJpmG1fOR5UKpdGjWxd8/vxZZE+SIOvUazAa168l2rC1axfedqhQ4aKYv2SNSC6QS4PWfdGx5y/Ye/g0ylesjF27dmlckzoFlevUxZ4Dx4T1zCmga9dVPnKxZl4ZsBhjXJZ9h45j48aN6NGjB5YuXQpXgd6k6c229+Df8CzEPnWQGMPVyx8GPkHhwoWj/YsLBeoXKlRIVKY/tH0FihTI4+jNcnlI0M+Y9JuI2cqXLx8OHTokCkIXL1IQ/xv2u5iGLFkUWE/FYGm6GfPDC61KQeO79x+OUlUaix6W1DGEWizdvHlTJFtQMVspaBpKHlq8bK3sdlInTvvrlMmI450NDVp01WRZMvaFxRjjkjx99kIEshKUgRc/vo17pipIsmTJMHXqVGzdeUCUd3AUjrSC2QSt7g1y2bb7kPiuXr06oitUXoWssVTCokKZoji1dy0K5M7mEEuYQ61htujwogpDZj9f+B/bjkL5c6FXj67iRWvnngPYvn27qHBPrvA3b16jZcuWwkK2Y/dBg4ubMW8pFv67FnHiJ8K0OUtFHCzNZwq6/5Hgm7tohQjyJ8i12bhVd4yfMhfHTp4VtQ+pSOzJM+eRIlMRVKj1s2iqThw5EV5vjLatbE379yVluLQFowS2KqhoJLW7Wbveorgh9VnLkSMHXA1KiCFrReIkie2+7mgjvqxpSB8BBWBHZy5evIj6dWuJemurF05G43rVo3dgPuGAtnrUAeDPUYNRumpjUW6CSrXUqlVLjCtXrhzy58+PVCm9kTVTRlHSxRCHj50RLwbkjjSHe/fu4dWrVyIOsFHLbti4Yi5q/dRBjNu60/ALX55c4aL89p0A4e4kqz3VLaM4tJiS7e0ssGWMcTkOHT0lvgMCAoTp3xUz2SiORJ2VtW3XQZFByNgfyqjMkiVLlDic6ADVaitbtiy8kyXFmf3r7CbEYipFCuVD1ix+mDBhgsiq1K6Nt3jxYjwKDMKVazfRoE41g8t49fq1qFloLmPHjkWCBAmwZO7fOLR9lUaIGSJ+/Hi4cHy7iHsj8ufNKQQYCTEqHCslxGQUXmCsgIu+Msq8iRqzUKjHKfDGSmnCC5etxbBhw6K0/XAVqDJ/586dRQxPjwEjxU2ueJECyJs7vABjtYqlkNw7+okDZ7OKUVumTdv3oUPHzohuUMHRmjWqIVf2TNizfmHMyZh0gFVMHcjt5uaOaeNHoVHLrqhbt67IsFSXS2nYsCFatmyFTZs2IW6cOAYXlSljBly+elXcE8yp+xYUFITSpUujRZN6ov6ZmtMHNsIjdmzcvHUPJ06fE3Fiw/7XQyQdeHom1ExXRKsQ7cqFUzV/33/wCBcv3xDtlKjHJpWEqVC2BNq2aCwK3DLKwZYxxqX4uUMffPv2XYgZV4VusnPmzBFuBWrOXK50MZw+dxE79xxCu24DkT5XWTT4uRvWbdopAmsZ20DxYi9fvUHHjh2j1S6mB/nPP/8sqsNvWjk35ggxJ6BKxTLYsmYB9u/fL9qzaUPXO7VNo8D8r1+/Sc7f/Ke6uHz5sog1k3usKXifChdTLCpB9w2Cyml06TsMxcrXExnns/9ZhouXryNblkw6QkxtKRvQO7yVnNpK73/hCnIUroLm7Xtj8/a96N+/P3wz+GDanMWo37wzW8oUhsUYo3yQrKGPAkHYHz99EplvGTNGj7cyCvZt0ry1qJ79MDBIpMLTzfXV+69o2WkAytduiY+fvir6xu+wQGono1C+HMIdQ66Z6BSsT+Ly3LlzmD5hhMMsrA5LDtEuX2JvIsodVChbEi2bNcCY0SPx/v17zWjqjUodQibPXIjjp85JLqJapbJoXL+mCGOgxvemoN6stEzq6vHHL+EvqAeOnUPbtm2xdetW0a2CMjTVcW1/j/0VOXNklVzWX6MG4dqZPWjdvKH4f+6c2ZApY3rx9/cfoaI23er/tmLVqtW4e/8hgkNeikKoKrhh+55DqFi7BfKUqIGq9Vtj+ZpN4kWTkQ9X4GecC/2bqJ4IyVuiBm7duY/s2bOLmj6O7Edpa+htt3jx4uKBWq58RRTI6YuqFcugYP7cke1/FHjouGxAvwK/fcDw8Vi4fANOnz4jqqi7MleuXBFZk89DnmHsiAHo3OYnOBMxJqjfzR2PHj9BnmJV0b17D5FkpIbisqgEBom0mjVrYtyv3aNYqcjala1gJTRv0Qrjxo0zuioSbFRYNvjuGSRLmgSBQU+FNWvKlCno1q2bsNCRiPJJkxJH9/yHFDLEOW0jbQO9KFIrMbKmdes/HFeu3dKZbsf6JcKq36T9/7Bz506RqEDttShpZN++faLna72aFVG8aEHhFpWzbinWbNiG+PHiiQbojm7ZZQlyK/Dz6zHjUvwvwpROzbhPnQoP5I+uqMXBi5ev8eLFC0yYOh8lqzRG6izFkDlfeaTLURJlqjfDwSPRez/Ykt+H9hZv/9TrT7shuStCFpLbt29j+9p/uLuDg8mQLi1GD+0vSthoW17JErtu3TrROmnFihXoNyS8Fpk2JDjSp0sjxjdt2lRYyagJOPWi1M+yvH//vihrkShC0M2Yu0T8n2LUyHtAmZkk1u4/CMTOvYejrItckZNmLBCuR0qMipciB+KnzIkEqXKhRKVGmLdoFYoWzo/37z9q5qGm58TIsZNFQVoSXhQLRzXWJk6cKLJJr127Jta9/9AJtO0yABlzl0GLjn2x7+Bx0adTLo8eP0HrzgPwU+seov/w9Zt3EV0xzzL24AK8vBIZn1j9lk1vQYb+Vk/HrhLpfccYhG4eVRu0QfbsOUT7kejeT/D169fibYpu4vSWSgKULILkAqCsJ6plRMNq16iEOZN/R+pUKSyyGMVk61jAw8eoWK89EiaIjwOHjxksrOnMXLp0SVglxo7oj37d2sDZcQo3uS2tZW7uwsLUsmNfbN99UAiWMmXK6ExCca9HDx/EldO7o9z7d+8/ghVrNyMk5AX8L17F23fvhUgTPS69vESh61KlSgmrV8lihXB452ox38p1W0Tcae7cuYUgIlavXo2uXTojk18GEdCvDdUhU5e+oJ6t5y+Fz6MNLZuKUzdt20usM2HChMiWLZu4D5FIpF673bsbbn5OcXIUA0cxdDdu3BAWvFrVK4r2TXWqV4xq7XLTPTc2bduDpm16aG3PWpQsXgiuwrt375E8Y0GFG4WzGLMtrvpAtBMUp1CobB3kzZUDG7dsd8mHptLQ5UuxKP379kGcOLGxZc0/yJnNz+zlxGQxRtx7EISqDdvDK4m3ELeenp5wJajEC1kmLh/bhPhxnf8FJSaIMeLr16+o2aSraLdFzb3JRamGhI2vTwr8O3+y0Xs/vYDeDXgo4snO+F/C9gPnMHfuXM34bh1aYPrE0eJvsjrV+bkXDhw4oBlP7kaqJ0buxhN7/xMlONRQ5mW/XyeI0i7d2zUWwfukjSjj8/yNx8JN+vfYYejTrR0G/vonps9dopm3UqVKop4aWe+ofIecexXFMq5fvRhbd+zD9Zt3xEvk4tkTRLKJ/r7T5rdx04TVj0Qp8ebxFdHk3hVwnBhjHIerPlBlMnT0RCxathaPg56ImjqMbs/BmtWr4MnTZxgyoDtKFS+EdGnTIF3aFBbvJpcTaFYKsxu376Nk9ZbC2kAuIleJTyHLB2VP/jFiAH7p01F6vzii5IOriDJtlN5PEfFjdZt0EAWGBw0ahDFjxgiLPr1MdmjVGCOH9DXr3k0B+UtW/IeC+XKTTxPZs/jpCJMtO/YJtx5BGZKTZyzQyZp8+/iy7HWRdY+uA4pRJaG2ePk61K5eCXfvP0C/X37Hg0ePxXRVq1ZFv379hDjTxLOagBIM2rRuhXQ+aXB45xrRfF0b8gSs37ILlcuXQsXaP+P2nfuacfevHMWXr1+F0MyYIR2igxhzsiuBsQq6sRn7uDh+vunEDSGmNHU2hwwZMuD4yTOoW68+Rv05FeVqNEPWghWxYes+ix8w+m1s5HwcipUtb3Jmy4RZE34VvQTPng1vE+MKUPZtlQqlMKhPeDylBu2sQkdlGLpaSy6lszFVYcjgkxpnDm7Cr4N64e+//0azZs1EmAFZzeLFjWP2SzSJnQ6tm4pEnoL5ckWxEO0/dFx8k+BLkyoFRg7poxkXy8yq+hQeoRZXZL3q37MjsmXxQ0DwJ0yeOh03zu3D4jkT8er5U9G6KVeOrAgMDJS1bKrFdujwEVy9fktYvtQ9MYf8MRs9fxmPpm17ok3n/vDJVlwjxIoXLYB1y+YIAZerSBVkK1ABT4NDEB1gy1hMwllueBZCWZSUTUmm819++cXRm+O0kCWbbogU0P3l4xsc2rHabg9ip3ioWvFbyRKQIW9l/NS0OWbNmgVnh44zCfF/5/2N5o1r6+5/qf3gpBYywuFi3g5Wsu27D6BZ216asg+UJUi9K3Nky4w+3duhUrlSiEsCzQooBozixkik/TtvEiqWK4mZS7eJKv2jh/RG1w7W9Z4ktyklEhHfXt4W3+RgO3X2gmhTV6FiZfFCIxfqLTxv3jyxjBpVK2Dbzn2S05GLd+WCSUIMErUatcW+Q8dRtFB+7Nm8zGnr6bGbkrEMZ3iYmrgJULYQmbgrVzbc442BiCvp1asXnt09C69EzhlfYXPxZoEwq/tzDxw5eV5kVxpzKzgDjx8/Rvr06fHzT3Uxb+rvuvFi5ogxJxJuTifKFN4fFy5fx4o1m0T8FZWuoaxDqtZP8VSUZVmjYjEkTJAAf0//R7gkqeI9ferXqhLFlWfspYIsWrZwtc9duAJ9Bo9BpkyZcPOsbnbnvEUr0XvQaFHvLnny5LKXefv2bVGuiCBR+ijwCXbsOSgKfGfJnBGjhvRBs0Z1dDw8Z/0viV6gBLWhKpQ/D6aMo9p64cVvnQV2UzLRjsIF8+LB1aOi3x7FJuzevdvRm+TUVKlSRdyUDx8/7ehNcSkm/TFYPMSoLIGzQ7WcqJzAqv+2itZOjPNTMH8eTez16dOnRc0xsgzNnDkTFy5cwB8TZgpBQ/FYZDG7cPkaOvQYLLIZqZetXPeirWIeK1cojbx582LJkshgfjXkplQXozXFx48fNVX8P3z4oCnn06R+LaxdOhNPb5/GnYsHceXkznAhpgeV3Jgz5Q+NhXHN+q1Im7UYlq3agLETZwoR5Eo46SsI4zCcPO4sbZpU2LRsqnijHDVqlKa6NBMVdcNhZ25CbvO4MwviyLJlzog61cph40bdMgDOSurUqcV38SL5zYuJMhUfZWhaG7u8nS6OTI2CcWQjBvfCywfnRdNwEmHUVWTAgAF48+aNqGhfp04dLFy4EHP+WYJLV26IWmX7D5+Ad8ZCSJW5qAjQP3deNxD/5Jnzon2a+p5IL2KfP4c3LKdhZEkydC8wpwk49aT0P7QeJXKHn3dqKHmofvMu4r5z584dUQtS//5M66FaaRTwT1ZnqoU2cOBA0a+3cIE8uH//Hu7cfyCmJSugb3of3ablEV0O1B6cjm2b4/3Tazh9cBPatQwvcrx4xTqM+WuqCJrfufcQXAWOGWOUwc43zgNHTqFW43YYPny4yE5iokKp9BQke9N/n2hAHH6cnC+IWwqbPYhl/v61m3ahZZfBePDggay0fUdCfU579uyJj08vI7aHlpi117G2gzvT6VyXCv5mEkznL13F2fOXkTRxYgSHPBe1ySgGi6CSNUmTJBa1vvQZM6w/KpUrif7DJwg3pzpwf+j/uuO3cdOFdSxd2tSiMj8x6c9f0btrW9Ebc/aCZTh97hJOnLmI4OBg/NK/G34fPsDi3zFszEThWqXYrY8fP4lh5Hqk6vx+fuFxXiTEqPMAvTg0b1xHxAGv3bgDWbPlELXYqH7aypUrcWr/f8ifJ4dF+50q3jds2RVHT4Qn4RQukFdYF0n82jSuzMA5KtdNGct2W8YwtqNS+VIibXvSpEno27evpkkuEwnF1aVNnRJ+vuH95Rj5XL95TzzUXKG8BRXgJIvDjj2HUb9mRUdvDmMmVG6idIki4qOGhBFlF5JIC3gQiJev34jhwcHP8d+W3cK1SRm05csUE428g54+0ykJsXFLeAgHnRdqIUaULl4Y/67aIETTzdv3JF2MllKrWgUR40XiMU+u7KKNW7+hf4l6atWqVUPKlCmF5Y+YM+U35MmZTfzdpEFNVK7XRoQFLFiwAOfOnECjlj3Qomk9/P5rP7O3w8vLE/u3rhDNzceMm4Ynwc+EhW7GvKWi7I+z4ny9KZ3tDYhxWmsZ3ayyFKiIIUOGCJclE7X21Lgxv2BAL63aUy5iGbOZpUzm7+8z5E+s33YAQUFBopaRM0O38AoVKogHzoFN/0TuK3sfaxtayJzGMuYE2aiUiUmixzNhPLE9JDIuXLqGAvlyCWHXc8BIMR2JovatmqB4kQLInzcncmTNJKr6d+kzTLMsCo5v07yhKJWRMkV4WIOSkDVvwLA/EPQkGA8fBeH123do07wBxo8ZLLZVze8TZuH3CTPRv39/tGjRAkWLFhXDUyRPhoDLB0W3EXOPxex/lkVpN/U55Iau29OJelM6nxhTCme5eBmbCrT+Q//AqnVb8PBRoMtVTbelEGvVqpVwAyyaPSHcuuOiIswmbksZ++LilZsoWrmpSNFv3rw5nJ3169eLJuEGC7/KxZLzJLqKMCV+l52vuw8fPqJy/bZCnBGeCRPg5cMLGgtv9kKVRPsvYuzIgRjYp7PdrL8Uw/blyxckTBApwrSZ9c9yDBw+DkkSJ0Jir0SinyZBAubEntWakhZyuXX3IRq17I479wKE1fGM/0Uc3rESRQrmtet5wWKMxViMEGOPgoKRq0hljBgxUsSPxXTWrFkj3ixJjM37e2jkWyCLsUhk7otGHX8V/U+pxIUrWMc6deqEVatW4sXdk9KWBFkLYjHmymJM4OYhGmrvO3hEtDXq2rGVZtSEqfNw5fotUUW/WaPasoUYtaKjqvvlShWzqA0RxcVVqtNCXEeHti83aJ06e/4y9h48jtt3A0QJkBu3wl2ppw+sFwVuLTl+dG38MXEmfh8/Q/x/5/qFoqq/i4sxqlfk6RSm2pgt0MzZ/6HRXpxRzzRq03E/4IFZtW2iGyEhIciaNStqVi2HJXMm6mUhWVlzKga6KjftOIgm7fri2bNnIt7F2Vm6dCnatWuHD08uCTEmd3+R9UlyWmP7KboG7Sv9u5z0WjLnd5KL0S9vOc3/l8z9W8R5UUyluh/mp09fdL1m6t8dsR4qREtNyYk9G5egYplwN6Sx4x4WFiYySVOmTA6fNKlgDbSNcxauxL2AR/hf745I75MG9uLdexJjRbkdEhP9oWBXgipMx2R+/fVXUehx6rgRdo+LiI7Q23ns2LFcJjnE0jIvVO6gbvNuIgGAYbQhC9u0OYuRKmXkSy5V90+SvgD88pQV1q4EqXIhR5HKIjPSEOs27UDatGkRL148zPxnmayd7O7uLlo+WSvECLLI9e7aBlPHDberEDMHy1497FhzJnpbxTws/NhiHQ5CgTpmFOQ5qF9X0aPv4sWLiGlQLMb48eNFJtKfIwfAO1nSyJH616i67paxN2PtaQx9HIBidchk/IZzl25g7OT5aNOmrdO7KNWoS3A8eBRkdDr9um7T5/6LXfuP4ufOA/E4+HnkPrbz8Xdon1Olf5crPRsNcODwCRQpXw/L125FaBhEp4AbN26IEhq//fYbylesjFhxPdGhQwe8ePkay1ZH1OXT/t0R+2H1f1tRq1Yt0aJtz4FjuBMQaPAYuzlrnTkbY91dxpndlU4rvpwVU9sW6pjjJvOCHNCrE9as34YO7VrhzLmLLvMAVYLJkyeLjNIenVqhU9vmduqz51ouTnN4/+EjGrfuLaqMT58+Ha6C2oJHMSqG0H8AUpPl/YdPinpQ5Oae9c8K/DlqECh2xZYPQ6fIjowpLkkLfjNlbNZo1A5p0qRBQEAA4saNqzOeitQS165dQ83qVUUcWYfWTSTXp27knTt3bnTu3BmzZ8/Gxm17MKhPZ9ObqwpznvPFxkT/X8jECChG5ucm9UQW0atXrxCTWLFiBapWLCP6sjHWs2TVJjx/+Qpr165FggTO2XxYCvUD89v377JLw/jmKS8qt5NVuVGjRti5l12VMQVya2/YshutuwxAziJVMGTUeE2x1pFjp4jvESNGRBFi2hw5ckTUMVs4a3xkYWk97gU8FN+VKlVCwoQJkTRpUgQ8eIxDR0+bVfk/umOmGHO8q0ISJ2zb41SuQEVwkLvTjGNarXJ4kOl///2HmAT1eMuXJ6d0ZpS9r1U7urOsRnsbtbZ57abdqFmzFjJkkH64OCtJkiQR348eP5XVaurwifCK7YMHDxbtaXLlyoWAh4FmPyCl1mPqY3ecxNXuMPR+M1m+WnbqLwrGkkehXIXKmL1ghbCGUTbj5JkLMXr0aHTvbrhIKvWT/Pvvv1GmZBE0qltdd11aqMMmKBGGIHfngn/XolrDdvhr6j8Ig+mMTm3XpbkfucxfvBonz4R3PVAMsS/knWvOoFoYRhEK5M0lihxSaxh6o/v69WuM2LNkvfn8+bOjNyNaQJaBS1dvonz58nA1yIJBGW7PX7w0Oh2JrdF/TUfLjv1Eb8Rx48aJ4WS1+PTpMw4dPWWnLWYcAR3/YWP+xqZte1CjSjncvn0bixYtElau6zfvoPZP7cV0pqzC1Hvy/v37osOHseSRJInDyzm8fBl+XlKBbiruSuV3Rv05Ddt2HYQz7JNeg8agfK0Wmvpm9sb1xRhbwZwMG1rLjFnJIt6A5kwdi9HD+osHTP78+bF//35EdyhD6VNEQ2Cnw4aWCMUsLFrbtmnHflETqUGDBnA1pkyZAnd3N1E/Suph8+r1G8xesBLlarbAn5Pm4K+//sL6zTuFRZV6E3brFp6VfOdeeKNmKSuWU1i5jOHIhBNntbppbQ+dB1NmLcL0uUtErCm5pakcDkFV76k8kL+/P7Zv346OHY0XDy5UqBCGDRuG5Ws2IXexapi7cEWkKNPaD+cjCtBSXCKRLl06sW5qkE68eSfdvFwp5FjJtL0Ku/YeUWClkb//i0yjgJNdSa5AdHM/uug+M+CapnToYQN74syhLUieNBGqVKmCnxrUws2bNxFdefjwIdIqkP7tiigtCo6fPo/cObMiU6ZMcLWm8BMmTEDf7u0k29o0bj8YqbOWxIBhY5HQKxn+/fdfkfRB14v+w6hOjUq6D3Bncy86WZavItut5O8xsAwSTUUrNBCxYenTpxeZjfpQnUYSWZT5aKqkC50zVE7o7NmzKFGqLPoMHoM6P/fC8xe6Mbs79xwS/VMLFiyoM5xql2bJkgX+1wxnVtpTmD27E24R7jf0Dzx5Gp50YE1ds3mLVqJQ+UaoUKe1rHlYjDHRkjy5suHA9lVYMGuCyK6kTB4yi5NJPjpBcRj0yZs7/K2TsY4nT58hg5PWIZIqabJp0yZUrlwZdevWRe3atTFycM8o09HDcffu3WJ8YGCgsBa3bq37gFC7kGb+PSbGCvvoDBVd7dBjMC5fvSH+P3/+fMWWTZmVlERE59WFCxfgk72ESAogCzN91m7cLs5PKWrUqCG25fqtu3A0SZMmFkks1lrryDq498Ax9B40GlevXsXdu3dtIcb06otJfexNDKtFEn2wvYWR3tza/NwI18/tw7QJo7B3zy4ULFgADx6Eu2GiA3SxE3lyZlNmgaaub2s+NkYJ641P2tQIehoeaOzMHDhwQFgVGjZsiE/vX2HJnAlYMec3yTZI0+YuFfE/ixcvFoU3pVg0bxoSeSZEUwkXp92tQc5o6XIUcveJifGjx89GuXLhCU5koSKPgdJQtiTVeaSah+s27kCekrVFQsDbd+81LnB9qF4ZxSr+uzKiRpkdkQr0Xz1/LL69uIFc2bNYtEyq8J+1YBXU/7kLEiVKhDFD++D6mV2y5mXLGBPtocDmrh1a4vi+DYjl4YEiRYqIHo7RAXrAkrk/s59rZf45K2lSJcfDwCdOXR5l69atqFmzpnClnty7Dkd2rkKLJnUN1tYjl0mKFCnExxAPHz1GnlzZNcHWTPSBOizcu3dPZMy+f/9exITZqg4jxYNRdu7x48eF4IsTJzY2rZonXhykoDIXxYoVw+2IOEVXZ9/B46LUB0H7ulnjOpJhA/YRY454KyZVazcLmQsU9rNBKrp94kZsG4fnm94HV07vQeH8udCrp+GUbVeB3FRUC+uX/l2lb65OaL2yB9acqy2b1IOHhzuaNm2K7zLrddnzeA8cOFBYw2pVK4/Ny6agcME8JmNiMmfMIKzBb968kVzuly9fsHvfYVSpWMb4BrAVyzkww4JI5wxZp8hiWr9+fXh6espuDm4NJUqUEBmalJlbr5nx4q4kyG7duY9nL147NCHE2tIYRN2alfHzT3VRvnQxTBo7FJkyppc9r/1+uT1u/GpRZnNhFqr3cS2srcliH6FmGzdmmtQp0abFT6J9hzNbP+QQFBQkBENufRdlNBJW1mLueennmw5rFk0R8S8bNmyAs0A1ndq1/ElkTI78pRdWLpiMuHHjyLp+a1WvIL6XLZPuCbhz507RdYCaP+vALkPXJCKh6fXb9xg6eiLOX7wqsiOpo4QzMnToUASHvETWQlR4diI+fPriVFm65jwv06ZJiaVzJ2Dv5qWiF6Y5OM8vZhg7kSVTeA8/qpHjylBsBrkB8ubi4H0lqVCmOIoXyY+FCxfCWejVq5fIhls0ewKGDuhmlpspbeqU8PHxEcH7Uixfvlw0ZM6RLbOCW8zYm69fv2HbrgOoVPtn5CleDWmyFBUlLCZNmoRSpUo57QEpUCA8jndAzw6Ys2glilRoKKrz2xKKYxs0cgKmzl0qCuA6A45t4Gfs7d3a4E2pt0SbqW3t3+HaQaeG3q7lvqlITadcjzupfWu+BShx4kQan76rQg9Q6vE2a9JvSJc2IvuNrWEmz0u552L3Dj+jXY8hovceZeI6muvXr6N+7Spo8ZP5AfZrNuwQ5U8oZkift2/firIYf4wYoNCWMvbk5p0AzF24HLdu38fVG7fwLOSFiM+qXbsu8uTJI7IVKY7L2UmWLBn+mDgLbbv0Q758+UR1/oArh5AuteE4R2sgz8jUOUvF38tWb8a6JdPMcimqMXU/McfCx5YxJsaRMKKyNLl+XBWqEdWsWTN0atvM0ZsSLWlSv7qwJv36669wBqgILVUqv3bzjlnz3b3/EO17/CIy6aTEGCUDkGXgp/q1FNxaxtbQMRs/ZS7yl6whKul///EdtatXwrlz54Rwnzp1Kjp16uQSQkybrFmz4tixY+LvoaP/ttl6KOFp+PDh4u/L126heJWmog2UIzFLjCnZB8oktgg01o4p0//YLJ4sesTuWHPMbZsQYH5MGWWXEeqCl64GWWsoXqxOlZKRwbhsFZOF3PONAp4n/fY/bN68GQsWLICjocD9xF6e+N+wP01Oq31dTZ65SBTy/P333yWnpT6uxYsUQDqf1IpvM6M8585fRhzvbEiZuShG/DFZxFvdD3iIw8dOY+G/a0TNL2qJ5coULlwYc+fOxer127Bxx37TvSst1AUj+zTHsAFdNRmntZp2wcTpyl7r5ugixZ9GTiHWLFqevQRa9MPaY66cWJMnyu4/eCy+M2d2vRiZdevWiTZPGdKlRYUyxRy9OS6J3POqUd1qaN+yMXr37u3w3p8kDr2Tp8SBI7p9I01dL1T3qEyZMpoaU/oN5qkYbEPtJs+MU1OnaSfxHRamwpUrV0QFfCrdE91o2bKl+G7atg98cpTG4+DnUa9Zqee9GRqAXmSpDtj6f2cgdcrkYtiw36cIF6YjcE3TAMNYwb37D8WF6Ofn59T7kdLSKQtq0KBBKF68uHCbtW3bFhXKlMDV07uRKuIGwtgGOkfa/txAlH6gOk2OpkOHDmbXh8qYwQeXL1+WbOR86NAh8dtqV6+o4FYytiRhgvjim1yRFBMWXfH09BQZzU0b1hLi6KANm9fXq1kJN07vwMyJI7Fj7Xwk906KGBHAb075BIsxpIzNTQowtK1WudaiT7C/JcfckuOqP4/pc0i9X6XPg3sPHiG9TxqnfaOkps1UYX3atGk4c+YMfNKkQtnSxVG9YnEhEJo1qoN48SK2nd2TNg3qV+9nZ8i4OnfqsHCxU7NnubWiWjWrj8Ur1uPw4cOoWFFXdO3Zswe+vr7IlsW5X0qYcF6+eo137z9g5MiRTv8iqVRF/0qVKuGMnx8WL/sP9WpWhlciT3kawkwN4OmZEF3bNbNPcqFTZlPaW7QptYP1t81icSa1PdFboKmPqzViW3te4+eJh+Q+vvswGNly5IKzQa4wChin4Ft64BYumBcHtq1A6RJFoj58o7kIsySkwdJzytj5lDSiIn1IiHWNg63l8ePH8L9wxWxraOkShUVs5J07d3TEGIm6dWtWo0GdqnYpAspYiZs7Nm3bK0oydO5svIhqdGPRokWi0HH5Ou2wafk0ZEyXRveZbc69UIn7pvYyFBRm7KZkYhw3b95Ejhw54ExQDahChQqJchV/jByIwJuncHL/RpQpWZQflg6EisAmS5oYp0/btu6RIU6ePImffvoJGTNmxKvXb7Fw5l9mnQ8kxChzjGINyeJKkDCjbNynz0LQrlUTG249oyTe3snEt9zG09GFihUr4sSJE/j06ROqNWiHx0/Cz+PohsuLMcUSBaxJBFA0+D/6ZWJKoVRCh7lB/mQRoJtZ9uzOUyiV6p3VqVMHnz++x5lDmzGob1ekSpFM9zyKhm2LlEaJZKEowfDuHihWOJ94GNiTr1+/okmTJqJY5+WL5zHlr2G4f/kgqlUy0bJIgr9GDcTJkyfg65tBBPLTuT937hx079gSBfNFWIj5/HJ6ihQMr6Dfp08fyRjA6EyuXLlEjGMYPNC+5zDLPStO3Mje5cUYw5hD0NNnIv7HUONaR0AZUQEBAdi46h/kzO4828WEU61SWRw8eNBgb0dbcOrUKVF2olWzBrhyaie6dWgh4mUsoV6tygi4fBCjh/QRZQ9mT/4NT2+fxrQJoxTfbsZ2jJ8yR3xTFqW9Xw6cgQwZMogSLYeOnY6W1rFoK8YUKanhFNay6Fm3TGnkvik9e/ZcfKdNmxbOwqnjh1ClQinkyRXRY1LKIhbDUKIGnVJWc8o2pB6g6mKU9qq1RCRJnAjubiqrl5cksRcG9umEXWtnoWObZpEJIIaIgeecs0PlLNSkSGGbyvTOTpEiRcT3w0dBxie0gyVLaaKtGNNHsVpnSgo0i5ESaCzW5NQeCw4JF2OpUztPkcs0qVPh0eMnuudFDBVh+ijfgN78FzUqD0HinarV24uXL1+K76KF8im/cKVqMyqNqULfzrSt9kYVhj9HDdS47JwpzMKevI9oYUc9eSVxAdGFmC7GGIa4ePk64sWLB29vb6fZIR8+fkSyJIkdvRmMAShgnuJ0KKvr+HH7tEyhUhRErWrl+bgwgsReibBw9kRRY+z1a8cUJnU02bNnFwk1i5b9h+hGjBJjtnjLDl+wQlX/FcN1rGVKdmUwfnw98OHDR8xZsBwdO3aEh4dzvD2RBeT4yXMorG0B0TqfFO9cwURBzv7t0aoGShUriJo1a+LSpUs234sH92wWtfDoAewwbGmJssrDEAMtaHRfc3NHmZJFhFWoU7sWiIkkTpwYHTp2xs79Wi9FNrCEmeoKo3xbvxgmxpiYzf7DJ0ThxL59+8IZoKruRYuGl67o3La5ozeHMQLFWG1aOQfp06cX7ZGoDpwtoPR96rKwZMUGDOjVgY8Jo4Ofb3qMGTZANAe/detWjNw7nz59EqIsuuFua7Vo6ccabKVcja9U4f6YiuO88WX2sv4k8gy3MjiDVYwe5mShQ9gPnDq4CenTpTW6T2zS39WFsPX1K2ffUpXu8aP64ejRo6KBuC3OiRYtWuC//9Zh0exx6Nm5lfkLsUW8jLWWJkdZrVzdghZhDdMvKt6jc2ukSJ4M8+fPR0xDpVJhy6YNKF+ygNZA08fQHvrD2uU5bQV+m4snZ0f7wWDTfSHnZuThEm2TTBE/WXrx/fbtWziaxYsXi7igXRv/Rcb0aSNEePixMCUKrBVkrnptyWlfZOsOEVTnq2rF0hg4cCBq1Kgh4g+VYs2aNULk/ffvTFGOwvBGethOkEk92MzqTuLE4sYQ1v5mW0HnosR5GD9+PKRI7q0JZo9JXLp0SZS1aFinGqIbrnlXZhgL2LJlC1KlSoW8ecOLJzoKKjpLD/M2bdqgUvlSDt0WxjzIpTzpz2F4+PChqHmkZFeIbt26oXHjxsaFGMNQ38ZyJbFx48YYJ8iePXsmvjP5hb9YRydYjCmNLVJrFa3w7xouTlu45aj6fpIkSRArluMMwtRWh6qqp0ieFONG9Iq0iKlC7eaGVKRjRQwmR9ZMGDG4J8aNG6dIm6QLFy6gfPnySJs6OeZO/MWuVb+jYM36XNEqZghncWMauN/37dEB79+/w/Tp0xWPY6WWbNSCq3Tp0siXLx+yZs0qXh4fPXokCmY72jKWIEF8pE6eTPr4GDhmcu93jrwvshhjYlRaNN1sqICnI5gzZw7KlCmDzJkz49CO1Uge0WuOcT0G9+2MOHHiYPfu3VYtZ9++fUKI+fr6Yv+WZY7NnmRcBooxrV+7Gnbs2GHxMj58+CDEXK9evUTSCN0fqTMJJTiFhIQIEUbnZtWqVUV8Gp2jXl5ewoJ7//592JsfP35g7ty5qF21vOmixS6I08aMuSxqVW7rN1gp1W73WCBTb40eThVbFBefxAVNN6GkSZPadZueP3+O//3vf6K9zcxJvyFO7Fgaq5gzWKaMbYMzxJg5wz7SZt+hE/jy5Qs8PT0t7j05adIkjBw5EpXLl8SqhVORKFHCyAnk7HNn2SeOth45+vfp3+vNnd7CGGJqAm8J/v7+mDZtGrZt2ybuhWlTp0TqVClQpngBjB3RHxXLloCX3kvBqIEdceT4GVy7cQdj/pqCefPmiXALerFUQwKNlkk1+ZQmJCQEHTp0wIMHD7B24cSo+1vqGBjZz3LvJ6amU/LeyGLMVpg4GWyzTiMnjkMeqFI3JQ/Fg6rlcujkJeTPn9/uQoxueO3atYOHhzv+Gj0oQohJBOs7aSCxswkhZyBf7uyyHojUz5KsCcTnz59F0gY1PF7+7xKEvHiF/j3b47dhfcNd5+pjLfc8N+d64GPoPGJUe3oLr+8Ll67i8PGzIuTBGNRQnF5AyYpLkJuxefPmCP3+FZ3bNUPX9i2Q3ie1yfMlWRIvNKhTTXyKF8mPWo3boVGDujh24jQSJUokXJhqYda6dWvF77HdO7fB6VNnsGHZDBTIky2qO1LKCGKNYUSmoFYy6YzFGBMjINfkzp07w8tJKAwF4qdLlw7Dhw9H/PjxRZC3frmCI0eOYNWi6fBOZl8hyNiGNKlTiu/+/fujZ8+eogE3ia3Lly8L1+W///6L4OBgfPz4EQkTJhQPLPo/kSxZMjRrWANdO/yMXNwYnrGAvoPH4OnTp+jSpYtkHS5yPVK3iMDAQHHvy5Ejh8gip5cDsuge2bUWhQvmtUioV6lYBmePbEWlWs2FFYzcmHQNaFt9leTcuXPYuHUPZv09GnWqV3S4JTa8xqBK5z6vBG4qGdUL3717J4qsvQg4C69Elpnlnc0k6BCcwMphFnbb3+btF3POA/U5tWb9VrTu3Ec8LJXOptS+KCnOon379uJvPz8/EeNA1pANK/9BnRp0IwnfHrcwrbg1JW4urnZuuRCGzrdBw8dh2tylQmjRw+iff/4RHRUSJkyAn+rXQPYsfvBJmxpPnj7D8xevkCljOsSPFxetmjfUvZGbaxGz6sfYwMoZ3d2UjsLINd2yUz+s27hDNJQnlyPFe5EVbMyYMSIAn4LcO7VphnQ+aUR81dXrt5EkiReSeCVC6RKFUbJYIQu3KfIc7dhzMHbuPYKvX76gWOH82H84vCp+gwYNRKanUlSpUgXPgx/j1N41iB3LXd75ZqP74efPX1Cowk94EPgEt8/uQPp0PibvF+/ef0Byv6JCDKut5JKbzGLMjrjaAzMaibGy1RohoVdS7N+/H0qzfv16kX2kFmD0NkoWMko7p5vliEE9UKtapXC3LYsxl8PY+Xbu/BWMn7UCBw4cEA+hHm3qIXfOrJIBxgbd7SzGGDOfF2RDOXbKH/8bPgEXL17UDKcXg24dW+Dnn+ojT65syotxrXP32o3bGDB8Ii6c98enz190Mi1DQ0MtjmnThuLSSGjO/nsUOrb+SX6Gq42etSR4U2Yri7fv3ovf1/bnhpgzaZROIXGnEWPOEF/iEhY0VxNmxlB0f3soLsYuXL6G4hXqiLc1emDagoCAAGTKlAkz/v4dXdq3FBfto8dPkDFDOri5RVwT2oVdtS1jroiznL9KWWVk/h5z7y2G7oea5Wiv19zryJIkHqXvz2wVsy965ynFgx08egofP37C5y9fUa5UUWGNtejYmXNNR5xnJB9oG0b9OQWXr9zAgSMnxf8zZswoCltXqFAB1lChTHFR5PXsgf+QyDMhEPYtyjUleU3a8P502v8yytSM7I5xaNsylC5eyLFizBmEV7QQaM7yYFMaRfa5h9XHs/8vo7Bhyy5hsbJVjTG6RugN7sf3bzh/bBdSp9IqW6G+TrQzJ2PSQ8zY+W0oacFZ94+516qJpAyd81g93NBvd/R9wgHHxJWeMQ55FkmdE9YcJ3PPMb3f8jAwCMdOnsXSFetx7sJlPHoUaHEwf2BgIDJkyIClc8ajRaNq8l5u7HS9rN+6D807DhB/eydLgjFD+6BDy0YidlR/u+SKMSdWKAxjPf9t3IbZ85eIDB9bFnuli2zmzJl48fIVAh8H2Ww9DMMwzopveh+0bNoA/86fJqx11vRwPXnypPiuWsH5upQ0rlcdY4b0Fn+/fPUGvQb9hlkLV1q1TLMsYy/vn0YiLy+Xf1thK5mDsPiN0EjshIlllqvRVFRJpxo4FM9lyzpiBQoUQOaMPtizeXl4DIGeFUxcN85q8VFji+2zl0XHnvtW7m+SY+WSY90wYx8qZiWw4/509WdKtHlWWXKeGNjGkpUb4ut3Ffbu3YvUqaO6Tq9fvy7COfLkyaNjDVu5cqVIhFm1ahWePHmCj0EXEMs9zKksY7RsipHLV7oe7j0IFIP8fNOhf492Isj/39WbUL9WZWExI8uYd6bibBljYjZURqBevXo2E2IvXrwQFjGKjaAU8mX/TNUJ5mQYhomJ/DNjvLg/pk+fHhUrVkS/fv1E3O7r16/RtWtX5M6dW2S2v3r1SsSZUZkYuk9Tz9dNmzaJ+baumuPQ9nXGoNptWTNnRN26dUUSRbbMGdF3yFj8MvpvXLt5F9t2HzJrebFi4huMkoXaXLaCvyOQqCItD/UbuofZBWFzZE6P1f9tEm9g1mT3UOVnCv6nmlHU35Jq/AQFBYnK0HSzqF6lPBZM/xNp06QKn8HW14mdLWyyrnsDxSxVllQrN7equb3vYaow2fcOyfVpn7f617zUbzXj97vp7zsRg2d94eTocO93NezeGUPB5w9lc549tAkbtuwUHSu2bdkkynAQFFvllSgR3r1/LwSYn58frl69it+GD0D3Di3g6ZkwPMlJq2+vwU12gFWMWL9lN06evYhu3XuIYuK79h/F48ePRYuzAf37onqlMrZ1U9qyzpgz4DTiLDqKMSks2t8eso/bwqWr0KPfEFEhmt60LIV6WlLfNrJ61ahaUbQQIeGVIb0PalSpgBTJvXVKV2g/PB0WtG/l+ow+fHWqX5t4SGtlDtr7+lJGeFnZGcHYcbCl+zBi2VGSBJS4tzi7u91euMB92uJrzspg/qh44F7AQ+zZfxhFC+VHkUL58TQ4BBOnzsGFy1fx56jBKElZiRGZ5hoxpn8P1dsuySQYW+LmITLlMxesqnG35syZU2SWUnHd27dvI2kSL5w/vBHp0qaW7aZkMaYHi7HoI8bo4siWrzRy5siKnXsOWFUxmd7ayKS+fcNyVKlY1sBDnsWYJCzGDJ9YLMZcGxZjZokx44SGfzm5GPsRqhJxYS27DBLWsJQpU+LKlSsihow6sQzq3RHDBnQV1j1CrhhzTmesA6GD7lBB5gIXt1XYaN9KHbfHT0Lw6HEQZs6eY7YQI0vY8uXLUalSJSHC1MViaT1RhVioc7p1rEx7l2y4bswiJmPZbnJuoApZXaze/8a2w5xtVLy2V5hM611E0LOOdZKuEwV6oNrSMuZK90B7WQit2CdS14GsZ5y5LkuTYSi6+6rfL2NEJiLVEitftoTot/ko8Anc3YAenVoiXmw388vC2AKtZa/btAs9Bo5BpzY/oU/X1ujTrQ1q/tQJy5YtE23QSJj1695WI8TMWg27KZ3cSuZKNyYHF4fVP17bdx9Eo+bt8PDhQ1GvxhxKlCghsjC1yZMrOzasXCAKuUreZLSLulqbOWnLh6MF22XU1Wqq2bmOi0y32KlVcVcG5reLCLbm2Opvn9zrQob4dYPEg5cqGMmt8i9x3mnvY3u/YPA92PkKGZuz7Mjp3aN4LeJ6ZxV/UyxvWFiYJpaMEqH2bFyCiqULRbWMGdpupZ+TEst7EhwCv/yVNdtKUJP1wKBglCtXTvQf3rF2PqpWLK0zH1vGmBjP5SvXNI1mU6RIIVoUmZMpU6lCGeTIlkW0NerWoRUKFcjLmZIMwzBWQp6K1j83wpYd+0UrsaCgINE6ju67dK9+/fatU+3jL1++CgsYdQG4eOkypkyZgrVr1+LDx09iPDVlnz5+eBQhZg7muSnl9oWSq1StDYyNCVmXTr6PZOGgWjln/cN7tjVu3Fi056CWRXKheSgV+8ChY+L/E/8YLiHEJCwTcoP17fAmpxiq0EiXpd5wneNLv91UFXmZyLXASGXTyrXkmLqmjW6DNbFeFgdUa+1n9Tbo7WdhBTO2bgtcTW7WuuLM7cCgPaszZJcby0R1RA09a2vc6SEVwGHyeWfu9khYg/8e+ytW/7dVFHft2bOnGExlLchS5ivRgNvgtil5DKQswyoVfv1jCm7fewh/f3/xLCHhOH36dPw1cgCaNawBz4QJRNC+NZnQTuCHYxjloQvozLnzmv+bYxUjSpYsqfmbauQo0fSWYRiGCSdpksTCvTd58mRR5HX06NEYOXIkChfIg0IFcjvFbqKsycZtemP6vGWYOHEi8uXLhwULFqBjx47o1KkT/terg3BVCiFmJRZZxoz6b7XfjE0pVoXeou2BqXpWdsVVrGU23VeGa4+pyZ41Cz58/CgyX0hQyYUKEFarVg3FChfAsgXT4edrYUkMqbd6C+M4zKo8bQKzc0q1LDAqrWs2ynK0LTX6VhztYRb8LiV/v9x5lLCw6cynVB9BKUukLZAT2yanj6ic32jKEiq3fIq1aO9Pve2WtCApuT4prKw5ZynCImkiftDs7dHZt+HHc+b4oShavj6SJ08u/p8hXRoM7NXeMc3AtZZHRVuXr90sRJi3d3Js2LABNWvWFM3PO3fujCSJvTDj996SMZqWehMVK/qqESuSN2YnFAqu7LqUQo74jUG8fvMG5y5cRKJEXkKMUYV8cwq8UhrymF//Z0SISV9gOu48YynYZmLWvCbOA42g0gyQcbPQz6ykmmHq+XRcFlGnU+K3yfr9Er9LPyPU0mOgxHyS905HuDuVQOoFw5wG50plqhqbx9i+NbTvzNwuKdFi1rmicOawOeuODKkwMK9ESy5jbmOzXJsRUPu4U/vX4ez5K0iRLDGqVy4bKWDsZXTQWiaVpxg0ciJmL1wJT09PDB78CwYNGoTQ0FBky+KHx0+eienq1qgY3hlAQWHMpS2YaIn/hcsi6LJ16wai31nVquEF+oxB/SvJZH7jxg1xoeXNncMu28owDBNTIZGTLYufw0sBvXj5Go1a98K5i1cxY8YM4YaMFy+eGPf3338jOOQFLhzZhNw5sthk/WaJMYPBydaqVRey6JD6d/RJ4zJorCP2f4P3P39JdI2YN2+erBpjVEeMelgm9vKEn28GDB/cBylThJvOpZE4Z90if6tK0gpi6s1R4fNK7n4XLXnkrVvSTSnh2tGZTu5buy3cDprtcVfedWVmYLpFLmWlrGn2QMplKXdbLbFUac8u5S7SeGskSiDIac1m4fUYJeHBwuNlybPGmmeTyXm1azlKXAdSVjND57x0i7BQeftNMXe/1naqVOjQaxjuBDzGkSNHRWkjNdRL86+//kLrZvUjhZjSll22jDHRkUNHjmPi1FmoVau27GKvdLFRK4u9m5cjUULzgv0ZhmEY1+VZyAvs3HdEWMKoZAUlfFGx761btwo3JdU+GzOkt023wXw3pewUaS1fusJp/o6O1XL0+hnDvHv3HvWatMHXr19FBiSlTWtnRhqCpqWWR/MX/ovGDWrDN72PBe2TtONoTE0jFQ/hyBggSwLho17f0mny5haIlPnmKyf42VElRBRar9UB4nZG9hVjNJ5L2xIpkQiic76ZtgTRNBrrmFTMk0iEkAoQl1i3FOaOl1voWHt7nQRJ65fkhB7mWepUVpbMMjcOVm/61KlSYGj/Ljh26jzWrFoh6oipqVKhFNYt+ltME2XZCh4f8yrw3z2KxJ4Jom6IOcF8ci9CU8th5O9XZ8Emx1LXJE6n86JlazBw6Gh8+vQZw4cPF7VrTEHibfDgwaJ2DDFySH/hqjQne9M8tC9i0/XLLMPDwLKMCEJLMOFysKbWlzVJNE4dTiBXXEolSTjqXuAAYSDZiktuxwfJBXoocz+yxbllbHvkrs8Rz0upBAb9c9DQOWnPLiUyISvYjr1HRKsmP18f0a5JvJhbmDUsKvBnKcu9KZmYBV00QUFP8f37DyGsevXqJWu+uHHjYtq0aaIXZYMGDZDeJ43Nt5VhGIZxLqglU/1ale2+XvPclJTO7h47sl+UOY2bbVwl12UwFgBsZRCi02PIHaAgZOGat2g5unXrht695fv4qehgmzZtsGPHDuTMnhWN69fSGmt+c23TFhpjx82SYxpqxrIseRs1shwTx9RsN6WJ68CSOmHG6kdZi0UWOCP7RDJwW8kkKUt/v6VdVWRAff+27DqEIgVyI0dWP1HRXCxOuzRJlPulRMcHk7UtlapHJlVvK8x2CU/muEyVvseaSsSSqsemn7Rji5ABlZ1LZ1m6HpnzcWkLJlrx+s1bvHjxApUrm/dmQ5Wfqb/Y0vlT0aRB7fAaMgzD2IVaTXvg2q17mv+vnD8eTepX473PxBjMtoxpf2sHmxmtmGttBfIo/mdzlL+VlgHFyw2YFzMX7bBxuYtkSZNo0pHlWtJWrlwpSmCMHTkYP/9U38R5Yjx2zFSvREPTW4+pc8cW8T6WW8uMoj2vtfvHGiuDMwTZK9GH0FT8jhMEiT978VLn/16JEhq2FkrtE9llXGxwjzVVhFfJZ4g9Y8Is3W4tK5hOfKe556CbGee+nZ+d0h0B9Ape29wyZmDBRl0RZuwoySagUX64tVW95bU50aw/CgrcvGwZAOrMgcs2Jk6cOPBKlAh3rl8A0M7otK9evULZ0iVx/eZt1K1VFT06tzUj0J2GWZFZp0T9K+1VmDzm1gbtG5tP+/fZKnvR2mtOYjnW7nu5XQbMFDtKuz1lzauUILOwxd2UPwbj6o27SJggPrJl8UX1SqVlPBtkZiXa+n5oURcFC+oQKrXuKNsSahfhZ1CYGUJlhkBzom4/+uep3Hs8+2KYaEezn+pj8ox56NF3MHx8fAxOt27dOiHEThzYiiIF8pi1jtt3A3D56i1kyeyH1f9tRrVK5VCpfNQHCMMwpmneqCbvJiZGY5YYE6UDdNyH5vfBMrsKt5jezN5fZlniLDHZy2tiHBUZb+XGlmVORXVzsZc1zQ5m9gL5cmPh0pUICzP+m4oWLSq+n4U8l71sWmaTNj2wdcc+neGTZ8zHlHGj0LOLcWucBoXf4KTeNOUnDoTasEyHfIy7c6EQCiZHyA3itlGlfkUtPkoH51toITMLmeEvooeqDbfDbO+KcKvZofG1XMyx7Cq0jVH2mUpmj09T1jJtC5nCvS1tXWaLi3gx0Y6kSRIjXry4WLx4MT58+CA5zZkzZ0QNMqJh8w6il6UcqHaZvhBTUyh/Xiu2mmEYhompKOOmlFEx26iqNBqQ6SEdRya5HAW0pRl9+nSsa0bevCLfAmQEWTuqyK2SQdNSy7QZkQH16v3coG4NPH32DP8bMkq0ORo6dCiGDRsmMiSfPXsmmoZfuXJFZymPnz5DoUKGesFFHjdPz4SYM+UP3Ln/ENUqlcf5S1dQMF8eFC2UH15eiWz6S43GBEoVTDa1OIPnpTVWBPOK41pUpsJKzLcomWFNtGPcpt33iTlWBclK9zawTqmTydT7QmIb3cJsEPSt9VvMPg4uUMhcpzSVFdtrsgelkXuZm9x+ueZskBVWPtnePXtU4H8RcBZeiTy15lY6WFf7R2pdZHKzKW1Vx8RGVcn1D6r9KoZb8LtMmuKd4QYTefyDngTDL09kGyRvb2+0b98enz9/xqxZs5AsWTJhOaPvWrVqoXH9mpg3Y6Kix8DYA8KqfafUNsqskm/ZuWMo0cfMlzK5KPSgt+z4G2sa7IBkGjtmR5q1vyysYG4sqz/KNSb1UqnVrN5q15W9M0/tXaFe4daFmpqkcromuBnuwGPudSnLgGNFA3dj6D/bqQJ/cr+iXIGfiZn4pE2Ny2cOoHLtZnj+/Lko6vr3339rxk+ePBn16tXT/N25c2c8eBiI/r06o2LZUsLNyTAMwzD2wEzL2Hl4eXkquHYphWnE7Cyzor/ZFb8NbZ5Sb0GmzLIS0zm1lcxpiXrcX797j9FjJ2HOP0s1w0aMGIFRo0bBwyPiXFOpsHHjRjRu3Dh8KR4eCA0NxabVC1GrWiUF6s/ILYFhQ+uizPPO1ueezSxj1mDgOreblcxRllFLsOCeaPZ+lGmd0fGaGEumMHFftTphwpwab3JwVCcWW1v9JJYvt8+sJeeVyXuxjH0mq8e2if0m1zKmjBiTirOy6MFiIj7MgNvHbPFlatuMHGQpgUbrly3cpKYz5VdXCOMnb/QWZQGBQZg+eyGWLF+Djx8/iXpkFcqWRLMWbZAmTRrs2bERU2f+ozPP+hXzUbdmVctiBKxuXyNzndrHVIlGwzJumpa3gJJ5w3WEKDNxvVkuUKPBdWWN+LN1NqVWbHFUZL4E26k+nDnXibnLMReLz2c5+0ThunuWzisZ42VEjFn1Iq2/bVrzvXtHYqwQizHpo8RiLFo+NDQYjj148/YtTp05j9t372Pt+i04639RU7m/aeN6SJs6Jb59+47CBfOidvXwlkosxliMsRizEBZjUe5B2rAYs9355BatxdiDCwpmjBl+YBp1TyptGVPY5G/SQibXMmZseguRd+HbUpR52Hn9xtdH59vnz18Q8vwF0qROKaxlOjhBNWenwBHtcuy97634jfYLKbAUBY+fI3+r0ZdoY+eLA9vfyV6v/KB2s+ZXGNkZjmYG3qu0jq3JeYz9VnMSEPQzcfXHK6Qb5IoxrsDPxGjix48H3wzpHL0ZDMMwTAzGQjGmcPVwucpUsRUq//Zj1CJmaJyStX1kvBnJe1txtDXICapQyzFXy3kTNTcW0F7WIHP7uDmi5IQlvebkxNIYDKK3vGK8nFR3Q9PZx6qmoEXaHskGBtdh6XlozjkEx2DOfjW3y4OCqMy8TmSHeLh5GPd06bgALSt3ob8+WfHpShwvNzdbiDHaWC7mP24AAD3mSURBVOsOuuXFX43Mpmm67GH7zEhTmHRT2ujma0Z2h9JZK7bGZm0oLMjUNTqNteeYuXWWrF2nwu1CHCowlTpHbBB8bOz8tebcVva6VP/uUCc7LvZ+OXP0yygRavtz3dpzx9osUXPdgW4yi1qbEGpGDT82ec7I10zOUKmTYRiGYRgmxhLLoVYKCy1hdrV+KbFMe1uWrLVGmNPKyhmxpK6XEhYcE22xrK6zY+vWMurlOqoumrnXiSPOSwsrkNsCs4KebdH6yeZYVgfK0ZZ867HmXmQjd7M5mAoFMISbu2LbKSupL8qyPazc39Y9Q1zsKcswDMMwDBO9sE82pVm1OsJkFLBUoPCcPXE2y5LJPpOO7xFo0+MnWa3bRGNjOduj32nBzMr2JhvR2jvu0WT8lBKFnu2Ey1tLHGElcxTmX/uu/5utxQnqRFp7DFRm3k8Uv9+EWpnsoh7uYXsxZpa7ympXmQxRpnQ2llQ1fUuy6JwdpSq1O5sINtYSxaLleVjnptTLPKLrR85Dw6rq9dbiDMcvOrQNMrU/7eC+jAkCxfzfKLXfnejFnTGju4ijjptt1uvEr7IMwzAMwzDRn1g2ewsxpxKuVS41U62NzHM12a0EhrOitHVJCayxCFlQ58ZoMKgJq6JORRkZTYntbr2wpeXL1r/F0eehtdjh3mLe+eSs9zqlrVcmup44qraYM6L0NWbtOe/mYcRKplzwfCTmbq9y91OuwM8wVvDq1Wv8u3wFbt66jXx5cqNTu5aIGzcu71OGYRjGdmLMrFR8e2BOfIm+Snd0kL+5QeGOwhmOsy2QKiQoZQXTqtYcGgacP38eZ8/648CBAzhy5DDev3+vmcXbOzmaNW0MqDzgFvbNNmVDHH3eMg65Xq2zojrBfcQqFNp+ya4YYdbd41yhJIs9+jbbo7SUm4eBQH9T65Z7z3TcvdUqy5jJ7C+l6hnZKjPD0ULH0euPUcGeJmZXn4tu7lC5xw4fFnF53L59B/v378edO7cxd+4cyfmTJ0+Bpk2boGHDBpHndZTkDzsmE7gSMew6cKgrUbKVjhOLA1smiVjUeDzU+mva6qxDCzp1uCJuVoQyRTm2xvaZc+wndlMyjAGePHmCfv36Ytu2bTrD48ePj1+Hj4RvhvS4d+8eChUqhIoVKyJWLHe4hX3n/ckwTLTnXsAjJIgfD2lSp3T0pkQLrBJjVrsp7dk02UrLCeNAzDaXGzjWeueUsIZFjNe2hlEc2KpVKzFr1ix8/foFixcvxvTpM/Dp0yds3rIdqVKlEr1f3SMawKr7wIaFAe4Ry3GjMmP0T3RLColOv0UhF6D1pSRClUtgkrMNMi1CKpUKDwODcOv2PTwOeoI3b9/BM1EipEzhjYzpfZA/by64u5u6l4Y6oKG43HmNJe0YWrZ+c2kDi9f6PafPnMXwPybh27fvqFqpDFo2awi/DD7ytlNdFkelQtDTZzjjfxm5cmRB9ix+cDPVgNrcOolm7E9VWCjKVm+CFy9fY+Gs8WjdvKFyz2w3d+vPGbPK4ziH1YwtYwwTAYmtyVNmYPr0afj8+TOqVq2ODh074d3bV7hw4byY5uHDB0KMMUx0Zfvu/Vi1bjMOHjmB5y9eimH04E/k6YkPHz8ijN46APikSY3VS2ajeNGCcGUuXrmOYaP+wsgh/VGiWCHFlnv+4hVMn7sIK9duQoF8uZAlky8mz1iIsRNnoV3LxhjUtwsyZcwgOS/t49PnLmL56o1Yt2kn3r57LwSZmjIlCuO3X/shf56c+BH6A0mTJDa6LfcfPMK1G3fw6fNnZPJNj/Tp0iJJYi/cfxCIFy9fwd3dDSlTpoRPmlRIkCA+Ah4G4satu7h3/yFev3mLL1/D41+/fvmKJ8HP8OTpMyHEPDw8MPy3SahcvhTSpuH7ojW4qbSPsAHevXuHxIkT48WDy/DySmTfwH1nKEKpVMpvTLDIOVv5Aa0gfDVqK5gIzI/4e9++A+jcuRNevXqF3r17o1OnTsIiNnXqVHz79g2JEnkhd+48WLxkuYgP83AH3D3C30w93CO+Pdzhhh/hiw77HumyVH+rQs22nhi81pS8LpzM2mXOPlKuN6E1+8DDumVIBpWbuSyL+omGz0MP1gNHToq/Dx87haUr1iBPnjyoX78+SpYsidy5cyNdunSIFSuWEAl0jVy/fh0//fQTPn78gH49u6BT+1bwSZtGcvulj4eJchNm/xYpPISAeRz0FN/DwuCdNCmev3wlrHxPngQjdpy4eP/+A/6aOA0PAx8jfvx4GDqwLwoXyo8U3t548/YtXr56hSRJksIrUSKxxKAnTxE3Xlxky5IZfhkzIOT5C3z58hXfvn9DQMAjhIaFinn/WfQvlixfjQwZMmDo0KHo3LmzEC70wjdnzhyMH/cXXr1+gxpVyyOFdzIx7vv3Hwh+FoJHj58Ii+Tnz1/g4+ODdu3aieV4e3ujePHiOHfuHIYPH45r165pfmm+PDlQvXJZFC2UF77pfeCdLInYrjv3HmDR8vXYtuuAjpgzRpw4ccQ9Tx2SQeuNFy+eEOSxY8dG2rRpkSZNGhQuXFicAyWKFRUir1zpEogXLy7ef/iAH9+/48ePULx7/wEfP30Sy6Jz58ePH3Bzd0fcOHHgmTAhkiVNgmJFCqBL++ZI7p1C5oFX4Nyx+hkt//777t17JM+YD2/fvoWXl5eyYkwONhVsrh6YyJiH1INJrtk9YjrhklS7IiO+6QU/LEyF169fI3NmP+TLlw9r164V/6cH0evXb9C9e2/06NkHCRMmFPPQG6RagKnFWKyIbxrn7hYqU4yZn/3jDNnLrlfV3QncZHKXbW0jeUPXhdZ8z0JeYd+h47hzL0BYeA8dOY6tO/aIhyRBD9q2bdti7NixJt1gt2/fxqRJk7By5Up8+PBBXCOZM2VEi+ZN0LRxAyHOVGE/cPHSFUydOQ/3Ax6gaOGCyJY1M3zTp4Vv+nRIkyq5sNCEuzstdxdp9k3E7ydrz7hJM7Bh0zYhoIxB8Z4UkvDnn39i06ZN4rfIgYQKWdClSJ48OX777Td06dJFCC19SJQtXLgQGzZswJcvX4RQoelI5JDw9fPzE2KndOnSkq5gmv7gwYN4/vy5OHZbt27F0aNH8fRp1N9Kwrpv376oU6eO2GaKc338+LEQ1bQeOuahoaEIDg4Ww1+8eIHs2bMjb968YpwpVzStc9q0aTh79qzYLk9PT1Heh35PokSJxHlB55JazJHkoN9MWei0ru3btyNVyhR4cOuC4XWprwODIt+RAs34uclijIk+2FiM0Y3B1ze9sIiNGzcOTZs2xbp163Dm7BWkT59BExNGsBhjMaZzftlBjNHDlqxS5oqx79+/CzfU9Vv34H/xCvYdPIbzF6+KcalTpUDI85fImSMrunTtgVatWokHtdoCYg5v3rzBrl27EBQUhNMnjmLztp3CspLc21s85F+/eQPfDOlQolhhnDt/CQ8fPdaIP81mu7mhWqWyWL9irrDMWCLGTpw6i6079+HQkRO4dOUaPD0Tom2r5qhcrbb4bS9fvkSKFCmE2KEPbQOJh6RJk2qWQ8MePXokRAIZIGh6+n0kHGhaslTRb/P398edO3eQOXNmIT7o+JCwIQESGBgoXuxouL0hYUTHgbafjiWJu2zZspl9TO0Jvfhu2bIFSZMkQdEiBVGyeBEUK1IIWbNkRjqfNOFilsWY5ZYxp3mrVzwRwM5pxY50IdnCAmnLRuJ6b8f6AozEV0hICE6fPiPcK3XrNYSHeywRB1a+fCm0b99evOGtWbMGzZs3R65ceVCnbkM0aNAYvhkzRnFJxoqlaxnTcVOGfo2sMxZhGQt/kws17wEt4VZS6rqy3splh7ILcqH9IFXOxgohZLPrQ8a66NiQm2nQyAmYv3QtkiVNjPKlimLauF+RMlVKyW149+4DLly5LixfW3fsw60794UYIkhUVKlSBTVr1kS1atWEVYzGSVltrIXEC1k7SJSQuCHXWoUKFYRVhKD1Uqbyw4cPhTWGLNH0jCF3Hrk7p0743cTO0U3KIXHUqn1XbN66Q1hyyNJVrlw5NGjQQMRBMc4NWcpOnjwpygfRN33oHCLonEnnkxYZ0vsgU0ZfYXklV+jN23exc/c+lC5ZHM0a10OtquUlzmUrEmMUtJbZyDJ2wYAYk39BO4OrxVWxpYtI9nGx5AGktACTyJA0lBkZSlVa6Y03VIXz/qfRsGFDcT4TJUqUwh9jJ6FZ07pImjSJMPOnTp1ajDt+/DgmTpyIffv2CZdCy5bt8PsfExEnbnjOS+xY7hoRRiUt1N8WiTG5lj+bNsu14BhZcz46WZyaSfSavpucTi76RYBVKrx89Qa3n37D77//Ls5DEik0nF4U6CFF8UN0Y8+VKxf6d2+LcqWL4/CxE2jffSA+fvyEZMmSoV69eiLeK0uWLCLmi0SJM1tHiMGDB4tr7mXQHWHVMgRd0mRte/ToMR48eSliOw8dOoSlS5eiWbNmTv87GeOEhYXh7t27wp1KH7JU0ocskQEBAfj69atwfzZu3BgnTpzAxYsXxcsGiXBfX1/xd8rEcYRwK1q4ANwhJXFs4NY08AwNF2MFWYxFJ1iMWSfGGjaoI9wNGzduFBc5WQry5MmHGzeuCdM+xXno8/HjR/zzzz/43//+h0GDh6NP3wFiOIsx1xRjZJXZe/A4/lm6FgXy5hR1kt68e48u7ZohvU94ADpZWmiag0dOiXGeCROgbOni+PTpM06cOoegp8F49/6jsFzRg5/e1KncQ+qUycWbb7HC+VG3ZmWT20JuRIpnevDwMfYeOo4jx8/gyvXbYj0EPVgWLFggzlOCrLo7d+7EzZs3xXooToisCGrKliqKOfMXi3gfcpu5AiQy9+7di+nTpwtrGrn9rvkfiSKolq1cKwLtAx8HaYLL1ZA4pWuULH5MzMPf3x/r168XL9NkaaXrRP3C3ah+LdSqVgnlyxQXMYrRR4wFnIeXl6dib+xyrDGGBIj1tX1cGVs8yMxwgdkjgcKQ4FKPUw9TCy+Vm4j/In5ECLDQHyohwtTDmjWpg4wZM2LZsmViGL1JU6YSQYG7FLtgiEGDBgnLxJ9/TcLPP7dB7NjuiB0rqmXMXRVpDYtqGfsuI/ZBxjEwtf9NXVcWVQ23r3j6+i0UsWPHklHHyjBUFyvgQaAoEeB/8SruBTzElet3xE2RgpPpLZsg4RL64ztG/NIbXok8sWTFepw6e0E85ClGiGKNKFidKFCggBhOcUbqHqQUxE0PAfqQ5Yrcc1UrlkHO7Flw+24A3n36LuIS6VZLH3LdUZA4Tad2I9KLAIkJKiBMsUckxAoWLGjy91+9elU8jGg+soBZs78cwebNm4U7kcibJzf+W7tSFFNWQ/vrnwWL0af/QDRp0gRly5YVAeFJkiQRx4Esf/Q3w+i/RFPcL8UA37p1S1wXtWpUQcH8eZFauOh/iGF0vVPoCV2TlE2bNLGXKGKbIV1q8ZIl6cEw8/lH4QPJ/QqxGIuesBgzJcbI1H3k8FHRO/L69Wtw9/DAvr27RJbYwIEDNXuy+c9t8d+6FSLA9/Dhw+KhJgW9jffs2VNYKlav3oyKlSqyGLMSql+0fvNuBIc8F2nu1auURc5smTF7wQoMGjFePIjz5MqGEYN7I2Vybzx9FiLqH1EphuCQF2IZiRMlQofWTUQK/aFjp3Htxm1RGfxRYJAYpo47yZsrO7Jl9UOeAsWFO4NceLR8ssCQOBvUrxsWLF0jbtAkoubMW4AyZcqI8TQdZdmSgCPXoCnoLf2vsWPw9u07ZM+WGSlSZxDB1GqhRNtD5xu9GNAnffr0wppli/gtZ+fBgwci248ensSXD6804/zPX0Dvvv/D+QsX0aFDB3HtsQuSMRe6vpctmovV6zbg7r0AUY5EvICFhmpq5ulD5xndB4oXyY+C+XILy1rObH7OJMbOCiUpvSQPswKP7Y+LxajYozenTMvYrn2HMXz0X+KNNW3aNPBOllTEdNDbvPot9vnzF6hUsZzIoDJ7Uw0059Zsg/pvVbglik5ZtfBSn72hYSqERVjB6CFfsWJpBD56KOJnihUrJuK+6AIk106CBAk0q1uxYoXIJMuaLSc+ffyABw/uG3Tx0HrpbZym79d/KOLEVj9c3TUB/GrLmFvol8g3KSOWMWEVUyApRMlYTCWszaYseidO+6PBz13x/sNHEeNBNyp6OyVXIRWopHgQSuunuCkK8FZDVhESLxSATuJGHVNC0HLy58+PrFmzCpFD1iX6psw2Ej+mePbsmbhZypmWUQZ6INIxo5pZc2bPRLt2bUVM0N69+9CxUxfhtqTyGZQAwEKMURK6n9NLgLqsCFm4qdwHhazcv38fZ86cwalTp0RNPbo3tWrWAItmT5CxYN37Ob0UJvcrqqwYe3n/NLw8JW5U2gU1jbSfCR/mBGZ0V3Bt2mQ/yXBFRsRe0Qnqf+EKUqZMgVYduuPMmbOy1hAQIK9CPZ3wZD4mK4XUTVZ9WtKXWniFRQwj0aUtwsSwMJXm77dv3iJvnkziRp83XwE8CXqMly/DLSklSpaFn18W3Ll9E48fP0RISDAKFymBkiXLYvq08SLDiwSXIciVeeXKFRw6fApeXgk18WMEGTaECIuoM6YRYZo6Y991RZj+hWvPjD4F1yF5zUu8jJFIPnT2FlatWiXcwmR5IosTpd+TG2/evHnivKBzj9zCdB7RzZJEEn1TELp+IDrdm/777z/hPiS3Hj+wXQs6fuRqpHpZZDEk9y+VZaBzgDI/KbOZgrUZxlHs3r0bNWrUQIVypbB78ypZiVjaL7QkxrwzFWcx5uxijETHleu3hMWRKidrHiYRDzOq0pwwQXxNfIq9xNitO3eRr0i5yMER7ho1dOOkj9riRDdP+uzcuQvly5cXb7fkgqCHKz1MyYpWpEgRMe+NG9fRsmUrPHsWHJH2XlHU/Xn//p3o30YZUWpBJCXGjh45KqpVly1bDqqI5nDaYozE2s2b17Fq5b+ifYuPT3rMmztNbGuiRIkRP0ECJEvmjbhx4uL7929CqD179hR16jTC1q3rje5Bis+h3zF9+hy0btM6fF+wGNM9f8S3O4KfPcea9Vtx/tI1Uefq9p37YhQJJ6rp1rp1a025AybmQi9NVNaAsuUoPo/EOcW/kSuZxTXjSOi8pGK5dC4unDkOPmlTO4kYu3ci3E2p/wYvaQEzYC2LrtX2LQxwbtiyB7bvPqj5Pz2cGtWrjukTxwhhkj5HCTFcuF3SpUbKFMmxbuM2MezYng0oVqSgzj6mw3n12k0cPXEaNapVQia/CD+3qRZBesHxtBzfTFkREvIcixYtEplfJLrITUTnA6UeHzlyBKdPn0bWrNnE9lGxRspkoRpCT56EWzgMQYLml19+wfz580UQMhUrpBOV5iFzMRVbJQHq5ZUYqVKlFnW+cmTPiePHj2L16hViGVSGgszLFDSdJm06FC5cFC1atEVCTy+8evUSN29cw8UL/jh9+jiCggJ11k+uKBKA5PKiODF686lUqZLJmz/tF3J7pU3rg/XrN2jck4RwUWqsYVp/a6xg5lvGrG0NJInJBAD5ol0XD8106zfvRIt23USsFFms6EPCm4Q6uQ8ZhmGckUuXLomSGfSMmzlzJiqUK4N1KxcjXhwP6U4qxpKyVOEtobwzl2IxZo+3ult3AnDl2nXcvvsA8eLFEdWtY3nEEoGCb96+F81XfTP4IDYFDYaF4tXrtyII+VtEhezV63doAljVNG1YG727tUPZ6k0MrpuCg58+DUFy76TIkT0rvnz9ilu37yLoSbAYT0Jl4p+j0KVDa/hfuIw4cWKLbBI3dw+TYozYd+AQGjRsLGJxyFJF4oUC4CnYWA2dtNOmzRBWra9fv4lpw99scwr3A81H7iaykJFVSe2KKFWqlE5sjroAJe2H5cuX49r1W8K69ub1azwLCcb9e/cQGPhICLMRI4YL6wpVbaaHPb0oXLp0HQcO7sXjwPD4IYLEY/bsuVC0WElUq1pebAsVhSRXl7q9kRzIhUbuS8p+mzJlCnbs2IHp02ehffvwTEwWY1HFWJkq9XHW/6I41hTXpV3lnGEYxtksYPv37xc1y6h8DD076HlXvWolTPxzdLhnylBbO4eJMYmYMVqx0bdyc6xbZlrClAxc1q9ITbuGxBKJmx+hocJSRUKK+p7dvH0fV2/cxuVrtzR1geghT1l36urBFChO+43cdPpWIhpHB5imldvAVQ1Zb8jKQL3LSGCQNYoCYElgUPAyWR/IrErNZEnZazd+pVT9okUKCdFE1qaSJUvAL6MfPn3+IgKlKebr8eMgsR9IOJGF68CBA8K1R4LkcWAgRo0ahdGjR8Pe0HlIAs6Qa4uCLCkjki4kCtwnMWhNMDYdF6p91LFjR7GPCbLYjRs3AbXr1EEstUUsoio/FXxVx4dpx4zpWsP0zNvaF7bM+DGrrWVRkm30rNiSyTi6w3Sm01rHi1dvsHHzdsyYPR+3bt8R+5/OK2Xc7AzDMMpB9ybqITp16lTxop47Vy7UrVsHHdq1Dk/kovtwxP3WTTshy4x7un0tY5bUSrJEjBkYZ3EDXUBYqLr2HYYrN+4JVxn5hym+iWoQkcChwFJ9yBpD/b4o1Z0sNOTmoowg6m5PUGySumEqQS4+asCqFntkJaD9SaKKhtFBopOChBkNowcXZYrROBIY6m86OdT9xkhsyOHYsWPCekXtQcjCQ3V9bty4IcQFxWhINcVVbzdZpmjb9alcubKoTB+doMuARDMJTrLkUOXzffv24/btW6hYqQr69xsgLtYMvr5CDFJtGrVHUyPGRDNcVaQYU18n2hez3jCDzcP1ryeZdckM/j5jiTV6YsuoS1Jr3s9fv+Pa9RvwP38JZ87548xZf1GPi87d6tWri7pQ1PGA60AxDONM/PjxQyQJUbFg4tdff0W/fn3hBt17q3a2e2QiVqjx+7f2PKowO8eMubAYGzp6IibN+AedO3cWAosKgpLgocKL9P8SJUoIaxMJIXoIkwiit/3oEFhKJyT5x0mEklWNjjH9bm2XElWsp/FUPoAEKgm6Tp06uXz6P2VykruRhCmlLlPcGl0saujYFy9eCrVq1UHlKtU0rY/c1L0po7EY+/r9hwi+p+rwVDj127cf+Pz1K56/eIXgZyG4H/AQN2/dxu079zQvCVSws1TpMsL9TBmy9MLAMAzjjPTo0UPEKw8aNBDdunWLrACgp22cVoyp64xFeRjYsvmzhNi6cuMuUngnE7FZ4fNFDSQ2tBxyMR4/eQY79xzA+YtXceXaDWHFIhFG9aMY14QKbVIMGVlh1PVjyOpH4pHctPQhsUTuWrooyLJHFsjMmbMga9bsyJkzl/j29c0In3TpkCRJpCAl0aUvvkl/RSa+qsUYDXeLMo9mOi2xJllnTN8crjWdyV6W5rwQabkXSUxt27EHGzZvx/2AB3gYGKRxyepDApySJihZI2fOnKJYJ1mF5dbxYhiGcTQhISFCfI0fPx59+vTWHWlEjGnfgzX3aD1hpplHS5jJLfrqGg3MtHj//iMKl60r/i5dojAypPPB23fvxRv885ev8OLFKyRLmgT9e3VGksSJ8fbdOzwOeoobt+7iyvWbuHf/gZg3W9bMKFOquMg49E6WDLVr13bwL2OsgfpNUgFXfajFDAkyEmEkukmA0YdaG9Wr3yxKLFN0sHgag1zeV69Ttu0pHDtxBsdOnMLzFy9RqGB+5MmVE3XqNRCBq2TZokxTsgSr9xlZT6P7/mEYJnrz8uVL8U3eHnUXDmfAPDHmFhdwjweVuU02TbgRVe7xdKajWCUKkosS9OvmgYRJPJEgQXyRuZc0eRoEhbwRVrvM2dKhRIoUQvFSuYVe/xsuZqH4FRpGbUxq16knAt/JlUJv90z0gRIV6HyhY0+xd5S9SezZswf7D5xEtmw5osxDNuFv3+S5/PSvV6kLmE5fU5axCCOallXNAw8fBuLo0SMIevwY9+7dFeIxU6ZMwj1epUpVIY70rXMaC5vOD9K9Lm/cuImzZ8/ixs2buHP7Du7dvy8SNOj6IkshdSbo0rWbiOuiZBCGYZjoTo4cOTBkyBDRt5JeOlu1aileOkVimNprEHEvJY+C5q6rNU5993XTGa4uL0XWNHWMLf2p29heETEWGs8bYXENtEOSwNwMS2p3s3v3LvFwIMikRxmKBQsWEpXPa9euI3ZY7959MX58eANQChSmGJV69erptLGhtgY0LQWjO4vyZWxD06ZNsWXLVnz9Gl79Xp/Xr16LorARNWF10PfSm5nYGgVt4aUmUoBFxpnR38HBTzH2j5HYuHGdGEYvDSTAKNN269YtomguWfOoOfLff08T4zTrUS9UZ73hw0gQTp78N0aPHiX+T3GAVI6katVqor0MJZ3QSwnFRjIMw8Qk3Nzc8NdffwlvCVUcoI+aOXPmok2bNnqhTvoxtrGj3H/Dl6uSfDEOiytPZpkVM/YsOAiJDfWmVEiMzZs3F/379xf/b968uYhRodIKly9fFhaCZcuWo0iRoiLbbcWKZSJTkLLfSO1SRiO1zqAPKV3KbqQPuVfIakIPH3rg0QNJ3bSXcW2oQKy61+SSpauRLl0GkXTw/t07pEmTFlmz5dAII2cSY4cO7Uevnh0RK1ZsTJw4QVSj1++JSdcdZb+OGTMGCRN6Yv/+w5rSK9JiTL0+N5QrV1q8yCxZskSc/xQbRhm71GqGzPSU8Us3I8qWJQsZxYAxDMNE1+fErFmzhMeE7nl076O/KYlLGyoVRR4FU0h6RgyIMbqPp0rto2wA/4MHQQYXJrUUOQ8//XnJErBx43/4bcwIYTkgMUU76MKFC2I81dBas3aLzvwXL57H3Dkz8eLFcxG0/fHjB/Hgef36lYiR0Yd+AwUe00OIsiXr1q1rsDk049yQy41EOFlJyQo6deo8VK5SXYhtOv+0zzf9U0+ldYJqn6vGzlE6n1asWIgL/mdQs1YDVKxUXafumfY1qr5gSTfRfCHPn+HOnZtY/98qbN+2EbVq1RIFbk0VRKVkg6pVq2LvviPImze/nrtSd11qOnVsi82bwzsEmIJeZKjYIdcCYxgmuhEQECA6rFDZIj+/TIgdO7wyAhUQJ8MMJW2RkYZqb2bJmhUpUqS0+gVd+9lC+iljRhcUY2pIufr7n8W5s8dFrS8SZfTQonpZvhmjxv9Ib5MK7969FTE49NCmDzWNvnfvBs6dOycKhJKVoG/fvqLoG+Oa0HGm8hRNmzbH9etXkSJFKuTLVwBJkiZD4sRJ4O2dHL4ZMyGTXxb4ZvRDvHjxDYoxegEoVyYv8uQtgBTJUwrLFRW8jR8/ARIkSIgTxw/j1q1ryJQpK+7fv4NevQejV+9BBsXYtm0bMGni76Ihubpmm59fZgwZMliUU9EWUfQ7yHJ1//593Lx5U3zIIkxijK6Hdes2o2y5CrLEGFm+Dh3cp3kZIXFK1w9ZjJMmTaa5rles+BezZs0Q/z906JB42WEYhokOfP/+XYSxkAeN+iZnzx6uHQy9pKv7HztCjJllDvr8JRSxYofKElTa01gmytyQN18x8dHn46cf4vv582f4Z95MZMqcVahcUrvff/zAu3dv8PbNa/HjU6VOgyKFiyFd+gx4fv8egp+9wMOHVDcrSFPwVMp6xrgOJESosTBZSMl9TVXzyVL29MlDXLt6AcHBwZquCOTmy5Y9FwoWKIwcufIiW7acyJo1Bzw9E4nx165eEt9Xr1w0uD5KBiG3HomxMmUq6lx4msBO9/DG6osWzNb0xaSYL5qPigVTgVnqzUnbRh/q50kxYtptsXx80onEg+7d+6JQoSIoUapsRDN0lU6igHrN9LIR/PQJntDnSZB48Qh68jj8979+LV5MaD9Q3056QdFHqsAxwzCMK/Lu3TsR5kQuyj/+GIts2bKL2GG16FLrDZ37t8q0p0RlwuOiXr6aL9/k6QuzLGOXrgTA0zNS2UlZFgxtpPa0UTbcyDhjLFgwA1Mm/yE5jrLFqOYUPWD0fyKl7FNGJX2opAWZKpnoDVmcSKBRcdczZ87g1KlT4v9qIU5xVXS+0IdqZoXHWL0TQi9BwoSiYXliryT4/PkT3r57i3dv36B+g6bo1j08vlEftUh68OAe9u7ZIZqVBz+leIVvwspFyQaxKMEkoScSeSUWy0+WNBmSp0iJDBl8Rc2z5MlTCEsWfZOI/PTpI1auWIozZ08J0UiueHLrqz/6AossYXSuUwuscItYUvGhDhP0IbO8OmmA64QxDBOdCA4OFi+/6hfxbt26Y+iw4ULLCEEVoTu0BZhaK6iM6Bkh5MIMj6dh2pKG+jbny5NRWTels4kxWtetW9dx8eJZHD92EAcO7NKMozii1atXixY2VO6A2hFRbAwdHG7PwhAkXkiQUdsrKgSobklF33Qh37v3AMHBQcLlp3b1ZfClzMRcyJ49t/guWaqccF9KibE3b17j9q3ruH37JgIDHwgL2ZOgQDx69EBcoHKhFwtKRqBG8K9fvUThwkVRunRJ8dZH20QfEo10oZP4okQXEmDqtlYMwzAxlZcvX2L+/H8watRIERdbq3YdVKpUBWVKlxX6wCXF2MXLAfBMGO7OUW+8wQ3SFmoSYktXyFmXwqZ22dCPvnnzKo4dO4CFC2eK3lMTJkywatlMzIbObRJoaqvalStXxIfaSFH5FIplbNuuO1q26oCzZ07g/PkzuHXzOm7euoZnwU81YorKS/j6+or6dvQ3vRhQuQkSVGT1UosqinGgbFC6cOm6I5EYGEgC7pEYPmDAAJ0SFwzDMIxpKBSEWiBRhjolLBHUbaVokWIoVKiwKKGVOQt5JJJLuiQtEWP0N+mS/Hn9lBVj164/FLE1skWYxHTaSM1jLeReqlunlPimoD2yFDCM0tA5TSVVKPFj3rx5wt1Jw8gqRVZZigujb2oVRH0uOVuXYRjGOXj27JmILz558qQIWTl//rzGA0IeBQpdonv3iBFj4O2dIoqe0RZe2iJNDAvT1TwkxnLl9FVWjM2Zuxg+PumFeqTsshs3ruHSRX8RQF+wYFHxwLl96yYOH96HF89D8O79O1Hv6f37tyK4OFfufChWrBQKFS6OZMm8bSLGrl+/gsaNKou/aZspLoY+1BanY8eOouIuPxgZJaEq9/SpUqWKEF4MwzCM6/Djxw+RvX7nzh3xkk0f6ndMsbgZMmQUMbekWZImS4a8efOhTp0GwuNB5bTIe0EfChXJQL2N06bX0Rg2EWOamdzcRCAwuWnU0Eq8k6dAwP17oggnxazQPPShGC1yxVDsFtX6ILJkyYKcOfOKbEfv5CmRInkKEaicOk1a8eNJ7KkxtYnaaf20844fP4ynT4JEnTHKHHv69An27d0pgq+JRF5eSJKYgpmTIVWq5CILk1xEancRfVOQNXVzr1mzpqndwzAMwzBMNOLu3btYuHChSAKkuDP6kOiikBUSYhRSIqVNSD+kS5de1DSjUkrk/uzXt6eypS127dolYmQoZoZEVcmSJUXdL4qlWbN2gwh6nj5tqihQaaiAJKXvk/uQTINnzviLulDPyYr27q3OdKlSpREqk2pEJUrkJbLN6EPV9elbXUSTLHDPIz4kpBJE1IOiDLiEEd/Zs+cUdaeePQsW88eJGw/Pgp/g5o3rwjypbhyqD5kpWYwxDMMwTMwiS5Ysom2SPlQHcsuWLcKdSdnoFPdL36RJ1Fa1q1dvIOBBAM6dPRPea1sGZlnGTCk7a6ANJtVJgcqkSOlDP5qEEgVQ04fWT9/qFH4ShuR2VO8MgmqHqT9Us4m+afu1rXhkQiSzpDbqZVF2BX1ToDW1ZdK2CDIMwzAMw8hFrn5yGjFmDiTGyApGpkK5UHAeWeWoNQJZ9chCRsH96jpM3DSZYRiGYRglkaufXLIhoyXCieLYqHI6fRiGYRiGYZwFd0dvAMMwDMMwTEyGxRjDMAzDMIwDYTHGMAzDMAzjQFiMMQzDMAzDOJBY5mYFMAzDMAzDMMrpJllijEpIUC0v7vPIMAzDMAwjH9JPpkpxyaozpq7tRS2CGIZhGIZhGHmQEDNVkku2GGMYhmEYhmGUhwP4GYZhGIZhHAiLMYZhGIZhGAfCYoxhGIZhGMaBsBhjGIZhGIZxhTpjnE3JMAzDMAyjfDZlLLlCLFH8JPiBr2ZuAsMwDMMwTMyF6owFBAQYFWSyxBjVFyMhlgOVEMstsnCZm5uH+HZ3d9f5v+6wiG+9/4dP46EzTD2PzrLdpObTHaZejtR6dMbprUfzf61l6/8mdw+3yHEeusPctMfpTa89n/707hHLCV+P7rDIaaKu1/j8Ubcpclskfot6H6inUS9H6xho5lf/tohptPeZ/vrDl21kPv316hxX6fm0f2/ksnW/JefT/i1606uXo7MMt6jL1KwvYpz++sOHqfeJ4WVLzQf1MMifT2cao9sUMU79/4hptX6mZru1Rmmmi9wlEvNp1qu7HZLTSCw7cpsQdXvdLFymW0SFHlVo+P9VYZETRQxDRBUfN/X/dcZFTK81LnIZuvNrD9NMI7HMKOO051dPA6n5w4z8loi/w9TL/BE5Tj1Mvb2aabSWHWZs3A/dYWGR61VphulNIzW91DZpptFbjuRvily2KmI69fpV2tsUMb0qVD1Oe77wv8PCpMaFL0MVsZ0qqWk0ywwzuGzNt8rIuIjlSG2L1HrDwsLPEfWvlDz0EeeR1q6ASj2fSnc52sM002iP05tffx3a69Fsm/ZprLdM7XGa9aiMLFMzDlG2N+p35IWvOdXMmJ8IVQ/TW47UsiTXqz9Oa379cerlfQ0FppwLFjrKajGmxh2x4OEW24hg0hJjeiLKPWKcjqjSG2ZMFBkXcYZFoLQYkyECZYgx4+OiihpJMeVhWoxFHWd6fqlxRrfXXDFmRDAZElXawyJFoHliTM6y9bdbdz4jwseYGDMiqgwJNlvMZ7YYkxBVGqGlN429xJie9rWjGAuzUoxFXaZ5YixMATGmL6ZkCB+paWSJsVDDYkxrXNT1ecgQYx5G5tcWN+6GBZN6XKh6msjrXf13WJjUOPWy1P930/kOX6abnqjT2tww6W/dYRHiRv301xInKgPf2tPoCyjtbZASAioj4yKXZXicRtzoLUdnvRLj9JcpNZ/RZVopxsIsFWMyxJS1YixyvfJKuXIAP8MwDMMwjANhMcYwDMMwDONAWIwxDMMwDMM4EBZjDMMwDMMwDoTFGMMwDMMwjANhMcYwDMMwDONAWIwxDMMwDMM4EBZjDMMwDMMwDoTFGMMwDMMwjANhMcYwDMMwDONAWIwxDMMwDMM4EBZjDMMwDMMwDoTFGMMwDMMwjANhMcYwDMMwDONAWIwxDMMwDMM4EBZjDMMwDMMwDoTFGMMwDMMwjANhMcYwDMMwDONAWIwxDMMwDMM4EBZjDMMwDMMwDoTFGMMwDMMwjANhMcYwDMMwDONAWIwxDMMwDMM4EBZjDMMwDMMwDoTFGMMwDMMwjANhMcYwDMMwDONAYpkzcRh+IFQVqd/cECa+VRHD1P/XHRb+HaYK1fk/4a7y0BnmrrPs8HHuYRHLcdOaD7rD3COm1V6W5DjNML1ptNfr5qEzzB1uUZatHuamPU49vcpN51v7bzfNOK3fEuam+zs1/9dadpRxWvOHRgzziFhHxLf2MHePiG3TGufmHrFM9TTu6m+tZWvGuetMI/5W7zv1fDrLNjKf/nq1j6uB+dTbr7ts3W/J+bR/i9706uXoLMMt6jI164sYp7/+8GHqfWJ42VLzQT0M8ufTmcboNqnPUfVP0xrnprvdWqM000XuEon5NOvV3Q7JaSSWHblNiLq9bhYu000VPkB9n1FF3ovUw6AKn8ZN/X+dcRHTa42LXIbu/NrDNNNILDPKOO35NfdDqfnDjPyWiL/D1Mv8ETlOPUy9vZpptJYdZmzcD91hYdr381DpaaSml9omzTR6y5H8TZHLVkVMp16/SnubIqZXharHac8X/ndYmNQ49XMrYtlS02iWGWZw2Zpvne3Vmy80cnvDNMuS/g6fJvwcUQ+RPPQR55HWbFCp51PpLkd7mGYa7XF68+uvQ3s9mm3TPo31lqk9TrMelZFlasYhyvYa+taeX2XG/ESoepjecqSWJWvbwgyPUy/vq9alYrUYixMnDlKnTo2bwQc0OzZ8beqtkLcyhmEYhmGYmETq1KmFjjKGm0ql87pnkC9fvuDbt29KbVuM4d27d0ifPj0CAwPh5eXl6M1hZMDHzLXg4+Va8PFyLfh4WQ8JsXjx4injpqQFmVoYYxgSYizGXAs+Zq4FHy/Xgo+Xa8HHy7ZwAD/DMAzDMIwDYTHGMAzDMAzjQFiM2Zi4ceNi1KhR4ptxDfiYuRZ8vFwLPl6uBR8v+yA7gJ9hGIZhGIZRHraMMQzDMAzDOBAWYwzDMAzDMA6ExRjDMAzDMIwDYTHGMAzDMAzjQFiM2ZCxY8eiVKlSSJAgAZIkSSI5zaNHj1C3bl0kTJgQyZMnR58+fbjTgRORMWNG0f9Q+zNkyBBHbxYTwezZs+Hn5ycKUhcuXBhHjx7lfeOkjB49Osq1RG1iGOfgyJEj4lmUNm1acWw2bdqkM55y/egY0vj48eOjQoUKuHbtmsO2N7rBYsyGUPuoJk2aoHv37pLjQ0NDUbt2bXz8+BHHjh3D6tWrsX79evzvf/+z5WYxZvLbb7/h6dOnms/w4cN5HzoBa9asQb9+/fDrr7/iwoULKFu2LGrWrClecBjnJHfu3DrX0pUrVxy9SUwE9BzKnz8/Zs6cKblPJkyYgMmTJ4vxZ8+eFUK6atWqeP/+Pe9DJaDSFoxtWbx4sSpx4sRRhu/YsUPl7u6uCgoK0gxbtWqVKm7cuKq3b9/yYXECfH19VVOmTHH0ZjASFCtWTNWtWzedYTly5FANGTKE95cTMmrUKFX+/PkdvRmMDEgabNy4UfP/sLAwVerUqVXjxo3TDPvy5Yt4rs2dO5f3qQKwZcyBnDx5Enny5BFmXzXVq1fH169f4e/v78hNY7QYP348vL29UaBAAeF6Josn41joGNA1Uq1aNZ3h9P8TJ044bLsY49y5c0fc78i13Lx5c9y/f593mQsQEBCA4OBgneuNisGWL1+erzeFkN0onFEeOrlTpUqlMyxp0qSiwzuNYxxP3759UahQIXFczpw5g6FDh4ob04IFCxy9aTGaFy9eCDe//vVD/+drxzkpXrw4/v33X2TLlg3Pnj3DH3/8IWJqKe6IXnYY50V9TUldbw8fPnTQVkUv2DKmQBCq/ufcuXOyl0fT60NWYqnhjP2PYf/+/cXbX758+dCpUyfMnTsXCxcuxMuXL/lwOAH61wlfO84LxfM1btwYefPmRZUqVbB9+3YxfOnSpY7eNEYmfL3ZDraMmUmvXr2Eed1UBp4cKADy9OnTOsNev36N79+/R3kDYZzjGJYoUUJ83717l9/mHQhlHnt4eESxgoWEhPC14yJQBjkJM3JdMs6NOuuVrrc0adJohvP1phwsxix4CNBHCUqWLClikCirSH2C79mzR/jiKU2fcb5jSFl7hPYNibE/5Mqna2Tv3r1o2LChZjj9v379+nxIXACKjb1x44bIgmWcG4rxI0FG11fBggU1cZuHDx8WMbWM9bAYsyGUYv/q1SvxTfEtFy9eFMOzZMkCT09PEQyZK1cutG7dGhMnThTTDhw4EJ07d4aXl5ctN42RmWBx6tQpVKxYEYkTJxbp3OS2rFevHjJkyMD70MEMGDBAXDtFihQRLzbz588X11q3bt0cvWmMBHRvozpWdO2QRYVixt69e4e2bdvy/nICPnz4ICz+aig2lp5ZyZIlE8eMysj8+eefyJo1q/jQ31RDs0WLFg7d7miDEimZjDRt27YVKcL6n4MHD2qmefjwoap27dqq+PHjq5IlS6bq1auXSBlmHI+/v7+qePHiIn07Xrx4quzZs4v0/I8fPzp605gIZs2aJcqPxIkTR1WoUCHV4cOHed84Kc2aNVOlSZNGFTt2bFXatGlVjRo1Ul27ds3Rm8VEQM8lqecVPcfU5S3o/kclLqj8Urly5VRXrlzh/acQbvSPowUhwzAMwzBMTIWzKRmGYRiGYRwIizGGYRiGYRgHwmKMYRiGYRjGgbAYYxiGYRiGcSAsxhiGYRiGYRwIizGGYRiGYRgHwmKMYRiGYRjGgbAYYxiGYRiGcSAsxhgmGvLgwQO4ublpWnA5ejmMdfv31q1bojfg+/fvrdqVRYsWxYYNG/hwMIyTwWKMYZyMdu3aiQc0fWLFiiX6wnXv3h2vX7+2+XobNGigMyx9+vSikX2ePHlsuu6DBw+KHqDUB4/63VHvO+pZ+OPHD5uut0KFCqLnnrPz66+/omfPnkiUKJFVyxkxYgSGDBmCsLAwxbaNYRjrYTHGME5IjRo1hAgiy8mCBQuwdetW9OjRw+7b4eHhISwyJAptxbVr11CzZk1htTly5AiuXLmCGTNmIHbs2CwaADx+/BhbtmxB+/btrd7XtWvXxtu3b7F7924lDh3DMArBYoxhnJC4ceMKEZQuXTpUq1YNzZo1w549e3SmWbx4MXLmzIl48eIhR44cmD17tsHlhYaGomPHjvDz80P8+PGRPXt2TJs2TTN+9OjRWLp0KTZv3qyxyh06dEjHjUbWFNqeuXPn6iz7/PnzYpr79++L/9PDvkuXLkiZMiW8vLxQqVIlXLp0yeC27d27F2nSpMGECROEBS5z5sxCjJIIjRMnjma6EydOoFy5cmL7yWLXp08ffPz4UTN++fLlKFKkiLAe0b5r0aIFQkJCYA3G1jl06FCUKFEiyjz58uXDqFGjLDpOUqxduxb58+cX+17NkiVLkCRJEmzbtk0cS7Im/vTTT2Lb6DhmzJgRSZMmRe/evcWx1xbXtWrVwqpVqyzcIwzD2AIWYwzj5JDI2bVrl7AUqfnnn3+E62rs2LG4ceMG/vzzT+GCogexFGohRQ/269evY+TIkRg2bJj4PzFw4EA0bdpUY5GjT6lSpXSW4e7ujubNm2PFihU6w1euXImSJUsiU6ZMUKlUwvoSHByMHTt2wN/fH4UKFULlypXx6tUryW0j4UTrI6uYIchaVr16dTRq1AiXL1/GmjVrcOzYMfTq1Uszzbdv3/D7778L4bdp0yYEBAQI16ulmFpny5Ytcfr0ady7d0/Hykfz0ThLjpMUtF9IZOrz6dMnTJ8+HatXrxbnB4ln2lba7/RZtmwZ5s+fj//++09nvmLFiuHo0aMW7xeGYWyAimEYp6Jt27YqDw8PVcKECVXx4sVT0WVKn8mTJ2umSZ8+vWrlypU68/3++++qkiVLir8DAgLEPBcuXDC4nh49eqgaN26ss9769evrTKO/nPPnz6vc3NxUDx48EP8PDQ1V+fj4qGbNmiX+v3//fpWXl5fqy5cvOsvJnDmzat68eZLb8ePHD1W7du3EelKnTq1q0KCBasaMGaq3b99qpmndurWqS5cuOvMdPXpU5e7urvr8+bPkcs+cOSOW+f79e4P7oHz58qq+fftKjpOzznz58ql+++03zfihQ4eqihYtquhxyp8/v846iMWLF4v57t69qxnWtWtXVYIECXR+b/Xq1cVwbTZv3ix+Ax07hmGcA7aMMYwTQsHs5Bokywu5mshCQ9/E8+fPERgYKNyOnp6ems8ff/yhY6XRh9yLZGFJkSKFmJ6sNo8ePTJruwoWLChcbWo31+HDh4UrkKxqBFnCPnz4AG9vb51tIyuVoW0j1xm58ig2ilyVadOmFZak3LlzC4uZernkmtNeJu0TsvjRsokLFy6gfv368PX1Fa5KCs4nzP2NauSskyxgakshWQVpv6itYpYeJ30+f/4sXJz6kGuSXLpqUqVKJdyTtA7tYfquWnK50m/4+vWrBXuFYRhbYLuoXIZhLCZhwoTIkiWL+JtcUSTOxowZI9xw6kw4ElPFixePImykIHdk//79MWnSJOFSJLEyceJEIfbMhcQGuSYpK4++SaAkT55cjKNto/gvcpnpQzFOxvDx8UHr1q3FhwRLtmzZhICk303L7dq1q4jZ0oeyTSlWimLr6EOxYyQ4SYTRtpH70hJMrZOguDTaDxQ3R6KJxBe5ctXzm3ucpKB9K5VJq+22JihuT2qYfuYkuYtJyJEoYxjGOWAxxjAuAAWEU8YhlbggyxEJF4olU1thTEExQhQDpp2RqW+doWB57WBvQ5AAGT58uLAcUTzSnDlzNOMoPozixSj7kqw0lkLB5yTq1MHytFyKx1ILVH0oTuvFixcYN26cCLQnzp07Z/H65ayToDg8CvAn6xiJsSpVqghrFEHf5h4nQ9ZIivNTiqtXr4rfxjCM88BijGFcAHK5kduOAsBnzpwpsh/JYkPZiiTSyOVE4oMsKAMGDIgyPwmKf//9V5Q0oIxKCu4+e/as+FsNiScaTwVGyc2YOHFiyW2heUjYkfuN6oCRa1ANiRGyvFG9svHjx4tMvydPnoiAchomFYg+b9484ZJt2LChcLt9+fJFbCsJISpxQfzyyy8ic5FqbXXu3FlYDikgnjIxaRqyVJGYpL+7desmBAdZEeVA7kT9oquUVGBqnWpIaNHxIAvclClTdJZj7nGSgqx7nTp1EkLZHIuaMWFOFkSGYZwIRwetMQyji1QgPbFixQpVnDhxVI8ePdL8v0CBAmJY0qRJVeXKlVNt2LBBMjCcAuopSD5x4sSqJEmSqLp3764aMmSICA5XExISoqpatarK09NTzHvw4EGDAeYUsE/D27RpE2U73717p+rdu7cqbdq0qtixY4sg9pYtW2q2Wx9KCmjVqpXKz89PFTduXJW3t7f4LVu2bIkSkK/ePkpuoOD5sWPHasZToHzGjBnFMihAnuY3FRxPAfzqBAntz6hRo2Stk3j9+rVYp37wvPZxk3ucDCU4UJLErl27dAL46VhqQ9usfTylzqXHjx+LYxIYGGhwfQzD2B83+sfRgpBhGIYxDNUmoxpw1hZrHTRokKgDRyUvGIZxHthNyTAM4+RQEV1ybVJvSmtaIlEhXqopxzCMc8GWMYZhGIZhGAfCdcYYhmEYhmEcCIsxhmEYhmEYB8JijGEYhmEYxoGwGGMYhmEYhnEgLMYYhmEYhmEcCIsxhmEYhmEYB8JijGEYhmEYxoGwGGMYhmEYhnEgLMYYhmEYhmHgOP4Pjttmq+drTtEAAAAASUVORK5CYII=",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",
+    "\n",
+    "total_rsl_med = total_rsl[2, -1, :, :]\n",
+    "vmax = np.nanmax(np.abs(total_rsl))\n",
+    "vmin = -vmax\n",
+    "\n",
+    "ax = fig.add_subplot(111, projection=ccrs.PlateCarree())\n",
+    "ax.set_title(\"50th Percentile, Year 2300\")\n",
+    "ax.pcolormesh(\n",
+    "    spatial_model.grid_lons,\n",
+    "    spatial_model.grid_lats,\n",
+    "    total_rsl_med,\n",
+    "    transform=ccrs.PlateCarree(),\n",
+    "    cmap=\"PuOr_r\",\n",
+    "    vmin=vmin,\n",
+    "    vmax=vmax,\n",
+    ")\n",
+    "ax.coastlines()\n",
+    "fig.colorbar(\n",
+    "    mappable=ax.collections[0],\n",
+    "    label=\"Relative Sea Level (m)\",\n",
+    "    orientation=\"horizontal\",\n",
+    "    pad=0.02,\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69021097",
+   "metadata": {},
+   "source": [
+    "Next, we'll drill down on a few grid-boxes of interest. To do this we can use the `Local` model.\n",
+    "\n",
+    "We give the spatial components to our `Local` model, and a dictionary of locations with associated lat/lons: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "5512d606",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
[13:17:52] INFO     ✓ ProFSea assets found locally!                                                                \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[13:17:52]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m \u001b[1;32m✓ ProFSea assets found locally!\u001b[0m                                                                \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
           INFO     Baseline period = 1995 to 2014                                                                 \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Baseline period = \u001b[1;36m1995\u001b[0m to \u001b[1;36m2014\u001b[0m                                                                 \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
           INFO     Configured for 4 specific target locations.                                                    \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Configured for \u001b[1;36m4\u001b[0m specific target locations.                                                    \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
           INFO     Simulating 7 sea-level components for 4 sites...                                               \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Simulating \u001b[1;36m7\u001b[0m sea-level components for \u001b[1;36m4\u001b[0m sites\u001b[33m...\u001b[0m                                               \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
[13:17:54] WARNING  The following site(s) may be over land: Land point. Expect NaN values for all components.      \n",
+       "                    Either try increasing your grid resolution or check your site coordinates.                     \n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[13:17:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mWARNING \u001b[0m The following \u001b[1;35msite\u001b[0m\u001b[1m(\u001b[0ms\u001b[1m)\u001b[0m may be over land: Land point. Expect NaN values for all components.      \n",
+       "\u001b[2;36m           \u001b[0m         Either try increasing your grid resolution or check your site coordinates.                     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from profsea.components.core.local_model import Local\n",
+    "\n",
+    "from profsea.components.core import Spatial\n",
+    "from profsea.components.spatial import (\n",
+    "    SterodynamicCMIP6,\n",
+    "    Fingerprint,\n",
+    "    GIA,\n",
+    ")\n",
+    "\n",
+    "locations = {\n",
+    "    \"Aberdeen\": (57.144, -2.080),\n",
+    "    \"Holyhead\": (53.314, -4.620),\n",
+    "    \"Llandudno\": (53.330, -3.830),\n",
+    "    \"Land point\": (-34.6037, -58.3816),\n",
+    "}\n",
+    "\n",
+    "local_components = {\n",
+    "    \"sterodynamic\": SterodynamicCMIP6(\n",
+    "        projections[\"expansion\"],  # passing in the global thermal expansion projection\n",
+    "    ),\n",
+    "    \"greenland\": Fingerprint(\n",
+    "        projections[\"greenland\"], \n",
+    "        fingerprint_component=\"greenland\"\n",
+    "    ),\n",
+    "    \"landwater\": Fingerprint(\n",
+    "        projections[\"landwater\"],\n",
+    "        fingerprint_component=\"landwater\",\n",
+    "    ),\n",
+    "    \"wais\": Fingerprint(\n",
+    "        projections[\"wais\"],\n",
+    "        fingerprint_component=\"wais\",\n",
+    "    ),\n",
+    "    \"eais\": Fingerprint(\n",
+    "        projections[\"eais\"],\n",
+    "        fingerprint_component=\"eais\",\n",
+    "    ),\n",
+    "    \"glacier\": Fingerprint(\n",
+    "        projections[\"glacier\"],\n",
+    "        fingerprint_component=\"glacier\",\n",
+    "    ),\n",
+    "    \"gia\": GIA(\n",
+    "        sample_spatial=False,\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "local_model = Local(components=local_components, locations=locations)\n",
+    "local_timeseries = local_model.run(member_seed=42)\n",
+    "total_rsl = local_model.sum_components(local_model.results)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "061f92d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "
"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "loc_ts = local_timeseries[\"total_rsl\"].sel(site=[\"Aberdeen\", \"Holyhead\", \"Llandudno\", \"Land point\"], percentile=[17, 50, 83])\n",
+    "fig = plt.figure(figsize=(6, 4), layout=\"constrained\")\n",
+    "ax = fig.add_subplot(111)\n",
+    "for loc in loc_ts.site.values:\n",
+    "    loc_data = loc_ts.sel(site=loc)\n",
+    "    ax.fill_between(loc_data.time, loc_data.sel(percentile=17), loc_data.sel(percentile=83), alpha=0.3)\n",
+    "    ax.plot(loc_data.time, loc_data.sel(percentile=50), label=loc)\n",
+    "ax.set_xlabel(\"Year\")\n",
+    "ax.set_ylabel(\"Relative Sea Level (m)\")\n",
+    "ax.legend(frameon=False, loc=\"upper left\", fontsize=8)\n",
+    "\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "profsea",
+   "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": "3.12.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/profsea/user-settings.yml b/profsea/user-settings.yml
deleted file mode 100644
index 7fce703..0000000
--- a/profsea/user-settings.yml
+++ /dev/null
@@ -1,69 +0,0 @@
-# Site(s) information
-# region: region of the world
-#   N.B. used to create output folder structure
-# sitename: tide gauge name or other site name
-#   N.B. if site_name has an apostrophe use triple quotes
-#       # sitename: '''site'x'''
-# sitelatlon: site location's latitude and longitude
-#   N.B. if tide gauge name: latlon taken from TG metadata
-        # sitelatlon: [[]])
-#   N.B. if other site name: latlon needs to be specified
-        # sitelatlon: [[-51.69, -57.82]])
-siteinfo:
-    region: 'Falkland_example'   # str
-    sitename: ['Stanley II']     # list
-    sitelatlon: [[]]             # list
-
-# Base output directory
-#   N.B. the code will add region to the output directory
-baseoutdir: '/home/user/sl_output/'
-
-# Set the end year of the projection(s)
-projection_end_year: 2300 # int between 2050 and 2300
-
-# Science method
-#   UKCP18 --> sciencemethod: 'UK'
-#   Palmer et al (2020) --> sciencemethod: 'global
-#       N.B. Used to identify which GIA estimates to use
-sciencemethod: 'global'  # str
-
-# Tide gauge information (recommend default below)
-# source: source of tide gauge information
-#   N.B. Also used to identify which TG directory to use
-# datafq: temporal scale of tide gauge data
-#   N.B. not set up monthly files for PSMSL
-# psmsldir: TG base directory
-tidegaugeinfo:
-    source: 'PSMSL'    # str
-    datafq: ['annual'] # list
-    psmsldir: '/home/user/data/PSMSL/'
-
-# CMIP information
-cmipinfo:
-# cmip_sea: for non-UK locations, specify if site is within marginal sea
-#   Not in a marginal sea --> cmip_sea: 'all'
-#   Marginal sea --> cmip_sea: 'marginal'
-    cmip_sea: 'all'     # str
-# sealevelbasedir: Base directory for CMIP "zos" and "zostoga" data
-    sealevelbasedir: '/deposited2023/profsea_tool/cmip5/'
-# slopecoeffsuk: CMIP5 slope coefficients developed for UKCP18
-    slopecoeffsuk: '/deposited2023/profsea_tool/uk_cmip_slope_coefficients/'
-
-
-# Directories for the GIA estimates (independent of scenario)
-#   UKCP18 --> UK specific ones
-#   Global locations --> Lambeck, ICE5G
-giaestimates:
-    global: '/deposited2023/profsea_tool/gia_estimates /global_GIA_interpolators.pickle'
-    uk: '‘/deposited2023/profsea_tool/gia_estimates/Bradley_GIA_interpolator.pickle'
-
-# Directories for the Slangen, Spada and Klemann fingerprints
-fingerprints:
-    slangendir: '/deposited2023/profsea_tool/grd_fingerprints/'
-    spadadir: '/deposited2023/profsea_tool/grd_fingerprints/'
-    klemanndir: '/deposited2023/profsea_tool/grd_fingerprints/'
-
-# Directory of Monte Carlo time series for new projections
-#   N.B. Originally developed by Jonathan Gregory
-short_montecarlodir: '/path/to/2100/montecarlo/simulations' # Required for projections up to 2100
-long_montecarlodir: '/path/to/2300/montecarlo/simulations' # Required for projections from 2100 up to 2300
diff --git a/profsea/utils/__init__.py b/profsea/utils/__init__.py
new file mode 100644
index 0000000..e0fe9da
--- /dev/null
+++ b/profsea/utils/__init__.py
@@ -0,0 +1 @@
+from .utils import *  # noqa: F403
diff --git a/profsea/utils/ui.py b/profsea/utils/ui.py
new file mode 100644
index 0000000..75568b5
--- /dev/null
+++ b/profsea/utils/ui.py
@@ -0,0 +1,88 @@
+from rich.console import Console
+from rich.panel import Panel
+from rich.table import Table
+
+# Central console for package-generated UI elements (not logs)
+ui_console = Console()
+
+
+def print_global_preflight(model, scenario: str) -> None:
+    """Renders the pre-flight summary for the Global model."""
+    table = Table(show_header=False, box=None)
+    table.add_column("Property", style="cyan", justify="right")
+    table.add_column("Value", style="magenta")
+
+    components_list = ", ".join(model.components.keys())
+    ensemble_size = model.nt * model.num_members
+
+    if model.output_percentiles is not None:
+        output_str = f"Percentiles: {model.output_percentiles}"
+    else:
+        output_str = f"Full Distribution ({ensemble_size} members)"
+
+    table.add_row("Scenario", scenario)
+    table.add_row("Components", components_list)
+    table.add_row("Timeframe", f"{model.endofhistory} -> {model.end_yr}")
+    table.add_row(
+        "Ensemble Size",
+        f"{model.nt} inputs × {model.num_members} draws (= {ensemble_size})",
+    )
+    table.add_row("Output", output_str)
+    table.add_row(
+        "Compute Engine", "Parallel Threading" if model.parallel else "Sequential"
+    )
+
+    panel = Panel(
+        table,
+        title="[bold green]ProFSea Global Run Configuration",
+        expand=False,
+        border_style="green",
+    )
+    ui_console.print(panel)
+    ui_console.print()
+
+
+def print_spatial_preflight(model) -> None:
+    """Renders the pre-flight summary for the Spatial model."""
+    table = Table(show_header=False, box=None)
+    table.add_column("Property", style="cyan", justify="right")
+    table.add_column("Value", style="magenta")
+
+    components_list = ", ".join(model.components.keys())
+
+    # Calculate the mathematical grid size: $N_{lat} \times N_{lon}$
+    n_lat = len(model.grid_lats)
+    n_lon = len(model.grid_lons)
+    grid_str = f"{n_lat} × {n_lon} cells"
+
+    # Memory estimation
+    bytes_per_element = 8
+    future_size_gb = (
+        model.num_members * model.n_years * n_lat * n_lon * bytes_per_element
+    ) / 1e9
+
+    if model.output_percentiles is not None:
+        output_str = f"Percentiles: {model.output_percentiles}"
+    else:
+        output_str = f"Full Distribution ({model.num_members} members)"
+
+    table.add_row("Components", components_list)
+    table.add_row("Timeframe", f"{model.start_year} -> {model.end_year}")
+    table.add_row("Baseline", f"{model.baseline_yrs[0]} - {model.baseline_yrs[1]}")
+    table.add_row("Grid Resolution", grid_str)
+    table.add_row("Output", output_str)
+
+    # Color-code the memory warning
+    mem_style = "bold red" if future_size_gb > 20 else "magenta"
+    table.add_row(
+        "Est. Output Size", f"[{mem_style}]~{future_size_gb:.2f} GB[/{mem_style}]"
+    )
+
+    panel = Panel(
+        table,
+        title="[bold blue]ProFSea Spatial Run Configuration",
+        expand=False,
+        border_style="blue",
+    )
+    ui_console.print(panel)
+    ui_console.print()
diff --git a/profsea/utils/utils.py b/profsea/utils/utils.py
new file mode 100644
index 0000000..848fd0f
--- /dev/null
+++ b/profsea/utils/utils.py
@@ -0,0 +1,376 @@
+from __future__ import annotations
+
+import logging
+import os
+import zipfile
+from pathlib import Path
+
+import dask
+import dask.array as da
+import numpy as np
+import requests
+import xarray as xr
+from rich.console import Console
+from rich.progress import (
+    BarColumn,
+    DownloadColumn,
+    Progress,
+    TextColumn,
+    TimeRemainingColumn,
+    TransferSpeedColumn,
+)
+from scipy.spatial.distance import cdist
+
+console = Console()
+logger = logging.getLogger(__name__)
+
+
+def sample_members_2D(
+    array: np.ndarray | da.Array,
+    percentiles: list | np.ndarray,
+    dtype: np.dtype = np.float32,
+) -> np.ndarray | da.Array:
+    """
+    Sample real ensemble members from a 2D numpy or dask array lazily.
+
+    Parameters
+    ----------
+    array: np.ndarray | da.Array
+        Input 2D array of shape (realisation, time).
+    percentiles: list | np.ndarray
+        List of percentiles to sample from the input array.
+
+    Returns
+    -------
+    np.ndarray | da.Array
+        Sampled array of shape (len(percentiles), time). Maintains lazy
+        evaluation if a dask array is provided.
+    """
+
+    def _eager_sample(arr, percs):
+        # Calculate statistical timeseries, then match with closest real timeseries
+        array_percentiles = np.nanpercentile(arr, percs, axis=0)
+        distances = cdist(array_percentiles, arr)
+        mem_indices = np.argmin(distances, axis=1)
+        return arr[mem_indices].astype(dtype)
+
+    if isinstance(array, da.Array):
+        # Tell Dask to delay this operation until the graph is computed
+        lazy_result = dask.delayed(_eager_sample)(array, percentiles)
+
+        # Reconstruct into a Dask array so downstream Xarray operations continue to work lazily
+        shape = (len(percentiles), array.shape[1])
+        return da.from_delayed(lazy_result, shape=shape, dtype=array.dtype)
+
+    else:
+        # Fallback for standard numpy arrays
+        return _eager_sample(array, percentiles)
+
+
+def interpolate(data: da.array, lats: int, lons: int) -> da.array:
+    """
+    Interpolate a 2D dask array to a target grid defined by lats and lons.
+
+    Parameters
+    ----------
+    data: da.array
+        Input 2D dask array to be interpolated.
+    lats: int
+        Number of latitude points in the target grid.
+    lons: int
+        Number of longitude points in the target grid.
+
+    Returns
+    -------
+    da.array
+        Interpolated 2D dask array on the target grid.
+    """
+    original_da = xr.DataArray(
+        data.data,
+        coords=[("lat", data[data.dims[0]].values), ("lon", data[data.dims[1]].values)],
+        name="v",
+    )
+
+    target_lat = np.linspace(-90, 90, lats, endpoint=False) + 0.5
+    target_lon = np.linspace(-180, 180, lons, endpoint=False) + 0.5
+    data_interp = original_da.interp(
+        lat=target_lat, lon=target_lon, method="linear"
+    ).data
+    return data_interp
+
+
+def interpolate_to_grid(
+    data: xr.DataArray,
+    target_lats: np.ndarray,
+    target_lons: np.ndarray,
+    grid_interpolation: str = "linear",
+) -> xr.DataArray:
+    """
+    Interpolate an xarray DataArray to a target grid defined by target_lats and target_lons.
+    Safely handles longitude wrapping mismatches (e.g., [0, 360) vs [-180, 180)).
+
+    Parameters
+    ----------
+    data: xr.DataArray
+        Input xarray DataArray to be interpolated. Must have 'lat' and 'lon' dimensions.
+    target_lats: np.ndarray
+        1D array of target latitude values.
+    target_lons: np.ndarray
+        1D array of target longitude values.
+    grid_interpolation: str, optional
+        Interpolation method to use. Default is 'linear'. Other options include 'nearest', 'cubic', etc.
+
+    Returns
+    -------
+    xr.DataArray
+        Interpolated xarray DataArray on the target grid.
+    """
+    import regionmask
+
+    # Normalize source longitudes to [-180, 180) and sort monotonically
+    data = data.assign_coords(lon=(((data.lon + 180) % 360) - 180))
+    data = data.sortby(["lat", "lon"])
+
+    # Normalize target longitudes to [-180, 180) and sort
+    target_lons_norm = np.sort(((target_lons + 180) % 360) - 180)
+
+    # Pad longitude with one points from each end to handle periodicity in zonal direction
+    data_padded = data.pad(
+        lon=1, mode="wrap"
+    )  # need more padding for higher-order interpolation
+    lon = data.lon.values
+    lon_padded = np.concatenate([[lon[-1] - 360], lon, [lon[0] + 360]])
+    data_padded["lon"] = lon_padded
+    data_padded = data_padded.sortby(["lat", "lon"])
+
+    # Now interpolate
+    data_padded = data_padded.chunk({"lat": -1, "lon": -1})
+    for dim in ["lat", "lon"]:
+        data_padded = data_padded.interpolate_na(
+            dim=dim,
+            method="nearest",
+        )  # this to handle nan values or land mask
+
+    data_interp = data_padded.interp(
+        lat=target_lats, lon=target_lons_norm, method=grid_interpolation
+    )
+
+    # Account for land mask (using regionmask library)
+    land = regionmask.defined_regions.natural_earth_v5_0_0.land_110
+    land_mask = land.mask_3D(data_interp)
+    is_land = land_mask.squeeze("region", drop=True)
+    data_interp = data_interp.where(~is_land)
+
+    return data_interp
+
+
+def check_shapes(array: np.ndarray, n_time: int) -> None:
+    """Check that the input arrays have the correct shape.
+
+    Parameters
+    ----------
+    array: np.ndarray
+        Input array of some kind.
+    n_time: int
+        Expected number of time steps.
+
+    Returns
+    -------
+    None
+    """
+    if array.ndim == 1:
+        array = array[np.newaxis, :]
+
+    if array.shape[1] != n_time:
+        raise ValueError(
+            f"Array should have shape (realisation, time) with time "
+            f"dimension of length {n_time}. Got {array.shape}."
+        )
+
+
+def fetch_zenodo_fingerprints(
+    zenodo_url: str, data_dir: Path, expected_folder_name: str
+) -> None:
+    """
+    Downloads and extracts the ProFSea fingerprint dataset from Zenodo if it doesn't already exist locally.
+
+    Parameters
+    ----------
+    zenodo_url: str
+        The direct download URL for the fingerprint dataset on Zenodo.
+    data_dir: Path
+        The base directory where the dataset should be stored.
+    expected_folder_name: str
+        The name of the folder that should be created when the dataset is extracted. Used to check if the data already exists.
+    """
+    target_dir = data_dir / expected_folder_name
+
+    # 1. Check if data already exists
+    if target_dir.exists() and any(target_dir.iterdir()):
+        logger.info("[bold green]✓ ProFSea assets found locally![/bold green]")
+        return
+
+    # Create the base directory if it doesn't exist
+    data_dir.mkdir(parents=True, exist_ok=True)
+    zip_path = data_dir / "temp_fingerprints.zip"
+
+    logger.info(f"Initiating download from {zenodo_url}...")
+
+    # 2. Stream the download with a rich progress bar
+    try:
+        response = requests.get(zenodo_url, stream=True)
+        response.raise_for_status()  # Raise an error for bad status codes
+
+        total_size = int(response.headers.get("content-length", 0))
+
+        with Progress(
+            TextColumn("[bold cyan]{task.description}"),
+            BarColumn(),
+            DownloadColumn(),
+            TransferSpeedColumn(),
+            TimeRemainingColumn(),
+            console=console,
+        ) as progress:
+            download_task = progress.add_task(
+                "Downloading dataset...", total=total_size
+            )
+
+            with open(zip_path, "wb") as file:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        file.write(chunk)
+                        progress.update(download_task, advance=len(chunk))
+
+    except requests.exceptions.RequestException as e:
+        logger.error(f"[bold red]Failed to download data: {e}[/bold red]")
+        if zip_path.exists():
+            zip_path.unlink()  # Clean up partial downloads
+        raise
+
+    logger.info("Extracting data...")
+    try:
+        with zipfile.ZipFile(zip_path, "r") as zip_ref:
+            # Filter out the __MACOSX directory and its contents
+            valid_members = [
+                member
+                for member in zip_ref.namelist()
+                if not member.startswith("__MACOSX/") and not member.startswith("._")
+            ]
+            zip_ref.extractall(data_dir, members=valid_members)
+
+        logger.info(
+            f"[bold green]✓ Successfully extracted data to {data_dir}[/bold green]"
+        )
+    except zipfile.BadZipFile:
+        logger.error(
+            "[bold red]Error: Downloaded file is not a valid zip archive.[/bold red]"
+        )
+        raise
+    finally:
+        # 4. Clean up the zip file
+        if zip_path.exists():
+            zip_path.unlink()
+
+
+def save_components(
+    self,
+    components: dict[str, xr.DataArray],
+    scenario_name: str,
+    output_prefix: str = "projection",
+    output_dir: str = ".",
+    output_format: str = "zarr",
+    output_dtype: str | None = None,
+    description: str = "Spatial sea level rise projections",
+) -> None:
+    """
+    Stream sea level projections to disk in a single file/store.
+
+    Parameters
+    ----------
+    components: dict[str, xr.DataArray]
+        Dictionary of component names and their corresponding Xarray DataArrays.
+    scenario_name: str
+        Name of the scenario you've run the emulator for.
+    output_prefix: str
+        Prefix for the output file name (e.g., 'projection' or 'global').
+    output_dir: str
+        Directory to save components to.
+    output_format: str
+        Format to save the output in. Must be either 'netcdf' or 'zarr'.
+    output_dtype: str | None
+        Data type to force the output to (e.g., 'float32'). If None, the dtype
+        is dynamically inferred from each individual component.
+    description: str
+        Metadata description attached to the dataset attributes.
+
+    Returns
+    -------
+    None
+    """
+    if not components:
+        logger.warning("No components provided to save.")
+        return
+
+    ds = xr.Dataset(components)
+
+    # Add ProFSea version and scenario metadata
+    ds.attrs["source"] = "ProFSea v3.0"
+    ds.attrs["scenario"] = scenario_name
+    ds.attrs["description"] = description
+
+    output_format = output_format.lower()
+    if output_format not in ["netcdf", "zarr"]:
+        raise ValueError("output_format must be either 'netcdf' or 'zarr'.")
+
+    Path(output_dir).mkdir(parents=True, exist_ok=True)
+    encoding = {}
+
+    # Sort out Zarr encoding
+    if output_format == "zarr":
+        import numcodecs
+        from numcodecs.zarr3 import Blosc
+
+        compressor = Blosc(cname="zstd", clevel=5, shuffle=numcodecs.Blosc.BITSHUFFLE)
+
+    # Set the encoding dynamically, overriding with output_dtype if provided
+    for name, component in components.items():
+        comp_dtype = output_dtype if output_dtype else component.dtype.name
+
+        if output_format == "netcdf":
+            encoding[name] = {"zlib": True, "complevel": 1, "dtype": comp_dtype}
+        elif output_format == "zarr":
+            encoding[name] = {"compressor": compressor, "dtype": comp_dtype}
+
+    file_name = f"{scenario_name}_{output_prefix}"
+
+    # Dynamically adjust console message string based on the prefix
+    log_prefix = "Global " if "global" in output_prefix.lower() else ""
+
+    # Stream the computation and write to disk
+    if output_format == "netcdf":
+        out_path = os.path.join(output_dir, f"{file_name}.nc")
+        with console.status(
+            f"[bold cyan]Computing and saving {log_prefix}NetCDF...[/bold cyan]",
+            spinner="dots",
+        ):
+            # Let xarray handle streaming directly to avoid RAM overload
+            ds.compute().to_netcdf(out_path, encoding=encoding)
+
+        logger.info(f"[bold green]✓ Successfully saved NetCDF:[/bold green] {out_path}")
+
+    elif output_format == "zarr":
+        out_path = os.path.join(output_dir, f"{file_name}.zarr")
+        with console.status(
+            f"[bold cyan]Streaming computation and saving {log_prefix}Zarr...[/bold cyan]",
+            spinner="dots",
+        ):
+            ds.to_zarr(out_path, encoding=encoding, mode="w", compute=True)
+
+        logger.info(f"[bold green]✓ Successfully saved Zarr:[/bold green] {out_path}")
+
+    # Safely get a sample name to log the shape (fixes the scope-leak bug)
+    sample_name = "total_gmslr" if "total_gmslr" in ds else list(ds.data_vars.keys())[0]
+    dims_str = ", ".join(ds[sample_name].dims)
+    logger.info(
+        f"{log_prefix}Output shape for '{sample_name}' was {ds[sample_name].shape} ({dims_str})"
+    )
diff --git a/pyproject.toml b/pyproject.toml
index 0e5bcaf..894724f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,4 +4,45 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "profsea"
-version = "1.0"
\ No newline at end of file
+version = "3.0"
+dependencies = [
+    "numpy",
+    "xarray",
+    "scipy",
+    "dask",
+    "rich",
+    "requests",
+    "regionmask",
+    "netcdf4",
+    "h5netcdf",
+]
+
+[tool.setuptools.packages.find]
+include = ["profsea*"]
+exclude = ["docs*", "tests*"]
+
+[project.optional-dependencies]
+test = [
+    "pytest>=7.0",
+    "pytest-cov",                              # Useful for checking test coverage later
+    "pytest-github-actions-annotate-failures",
+]
+
+# Settings for Ruff formatting
+[tool.ruff]
+line-length = 88
+target-version = "py312"
+extend-exclude = ["*.ipynb"]
+
+[tool.ruff.lint]
+# Pyflakes (F), pycodestyle (E, W), isort (I), and pyupgrade (UP)
+select = ["E", "F", "W", "I", "UP"]
+ignore = [
+    "E501", # Line too long (let the formatter handle this, ignore in linter)
+]
+
+[tool.ruff.format]
+# Use double quotes for strings (standard Python convention)
+quote-style = "double"
+# Indent with spaces
+indent-style = "space"
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/components/__init__.py b/tests/components/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/components/test_glacier.py b/tests/components/test_glacier.py
new file mode 100644
index 0000000..300e362
--- /dev/null
+++ b/tests/components/test_glacier.py
@@ -0,0 +1,54 @@
+import numpy as np
+import pytest
+
+from profsea.components.core.state import ClimateState
+from profsea.components.global_.glacier import Glacier
+
+
+def get_dummy_state(T_change_val: float) -> ClimateState:
+    """Helper to generate a state object with a constant temperature."""
+    T_ens = np.ones((2, 4)) * T_change_val
+    T_int_ens = np.cumsum(T_ens, axis=1)
+    T_int_med = np.cumsum(np.median(T_ens, axis=0))
+
+    return ClimateState(
+        scenario="test",
+        T_ens=T_ens,
+        T_int_ens=T_int_ens,
+        T_int_med=T_int_med,
+        fraction=np.random.rand(10),
+        palmer_method=True,
+        endofAR5=2100,
+        endofhistory=2006,
+        end_yr=2010,
+        nyr=4,
+        nt=2,
+        num_members=2,
+    )
+
+
+def test_glacier_parameter_validation():
+    # Attempting to initialize with an invalid MIP index
+    glacier = Glacier(glaciermip=3)
+    state = get_dummy_state(1.0)
+    rng = np.random.default_rng(42)
+
+    with pytest.raises(KeyError, match="glaciermip must be False"):
+        glacier.project(state, rng)
+
+
+def test_glacier_mass_limit():
+    glacier = Glacier(glaciermip=2)
+
+    # Force a massive, physically impossible temperature spike (1000 degrees)
+    # This ensures the mathematical scaling tries to exceed the physical ice volume
+    extreme_state = get_dummy_state(1000.0)
+    rng = np.random.default_rng(42)
+
+    projection = glacier.project(extreme_state, rng)
+
+    # Calculate the hard limit:
+    max_sle = (412.0 - 96.3) * 1e-3
+
+    # Assert no value in the projection exceeds the hard mass limit
+    assert np.all(projection <= max_sle)
diff --git a/tests/components/test_sterodynamic.py b/tests/components/test_sterodynamic.py
new file mode 100644
index 0000000..3401649
--- /dev/null
+++ b/tests/components/test_sterodynamic.py
@@ -0,0 +1,125 @@
+from pathlib import Path
+from unittest.mock import patch
+
+import numpy as np
+import xarray as xr
+
+from profsea.components.core.state import SpatialState
+from profsea.components.spatial.sterodynamic import SterodynamicCMIP6
+
+
+@patch("profsea.components.spatial.sterodynamic.xr.open_dataset")
+@patch("profsea.components.spatial.sterodynamic.Path.glob")
+def test_load_cmip6_slopes(mock_glob, mock_open_dataset):
+    # Mock file paths (glob is called twice: once for slopes, once for masks)
+    mock_glob.side_effect = [
+        [
+            Path("dummy/zos_regression_ssp585_1.nc"),
+            Path("dummy/zos_regression_ssp585_2.nc"),
+        ],
+        [Path("dummy/zos_mask_ssp585_1.nc"), Path("dummy/zos_mask_ssp585_2.nc")],
+    ]
+
+    # Mock datasets
+    mock_data = xr.DataArray(np.random.rand(5, 5), dims=["lat", "lon"])
+    mock_mask_data = xr.DataArray(np.zeros((5, 5)), dims=["lat", "lon"])
+
+    def mock_open(f, **kwargs):
+        if "regression" in str(f):
+            return {"zos_zostoga_regression_slope": mock_data}
+        elif "mask" in str(f):
+            return {"zos_mask": mock_mask_data}
+
+    mock_open_dataset.side_effect = mock_open
+
+    dummy_global = np.zeros((10, 100))
+    stero = SterodynamicCMIP6(global_projection=dummy_global)
+
+    slopes, masks = stero._load_CMIP6_slopes()
+
+    assert slopes.shape == (2, 5, 5)
+    assert "model" in slopes.dims
+    assert masks is not None
+    assert masks.shape == (2, 5, 5)
+    assert stero.land_mask_present is True
+
+
+@patch("profsea.utils.utils.interpolate_to_grid")
+@patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
+def test_expansion_contribution_storyline_mode(mock_load, mock_interp):
+    # Mock the loaded data and masks
+    mock_coeffs = xr.DataArray(
+        np.array([np.ones((2, 2)), np.ones((2, 2)) * 2, np.ones((2, 2)) * 3]),
+        coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
+    )
+    mock_masks = xr.DataArray(
+        np.zeros((3, 2, 2)),
+        coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
+    )
+    mock_load.return_value = (mock_coeffs, mock_masks)
+
+    # Let the mocked interpolator just return the array passed into it
+    mock_interp.return_value = mock_coeffs
+
+    state = SpatialState(
+        grid_lats=np.array([0, 1]),
+        grid_lons=np.array([0, 1]),
+        n_years=100,
+        n_members=5,
+        grid_interpolation="nearest",
+        output_percentiles=None,
+        baseline_yrs=(1995, 2014),
+    )
+
+    # Test Storyline Mode
+    stero_storyline = SterodynamicCMIP6(
+        global_projection=np.zeros((5, 100)), sample_spatial=False
+    )
+    stero_storyline.land_mask_present = (
+        True  # Simulate the flag being set during the load phase
+    )
+
+    rng = np.random.default_rng(42)
+    result_storyline = stero_storyline._calc_expansion_contribution(rng, state)
+
+    # In storyline mode, it takes the ensemble mean: (1+2+3)/3 = 2
+    assert result_storyline.shape == (5, 2, 2)
+    computed_result = result_storyline.compute()
+    np.testing.assert_array_equal(computed_result, np.ones((5, 2, 2)) * 2)
+
+
+@patch("profsea.utils.utils.interpolate_to_grid")
+@patch.object(SterodynamicCMIP6, "_load_CMIP6_slopes")
+def test_expansion_contribution_sampled_mode(mock_load, mock_interp):
+    mock_coeffs = xr.DataArray(
+        np.array([np.ones((2, 2)), np.ones((2, 2)) * 2, np.ones((2, 2)) * 3]),
+        coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
+    )
+    mock_masks = xr.DataArray(
+        np.zeros((3, 2, 2)),
+        coords=[("model", [0, 1, 2]), ("lat", [0, 1]), ("lon", [0, 1])],
+    )
+    mock_load.return_value = (mock_coeffs, mock_masks)
+    mock_interp.return_value = mock_coeffs
+
+    state = SpatialState(
+        grid_lats=np.array([0, 1]),
+        grid_lons=np.array([0, 1]),
+        n_years=100,
+        n_members=5,
+        grid_interpolation="nearest",
+        output_percentiles=None,
+        baseline_yrs=(1995, 2014),
+    )
+
+    stero_sampled = SterodynamicCMIP6(
+        global_projection=np.zeros((5, 100)), sample_spatial=True
+    )
+    stero_sampled.land_mask_present = True
+
+    rng = np.random.default_rng(42)
+    result_sampled = np.asarray(stero_sampled._calc_expansion_contribution(rng, state))
+
+    assert result_sampled.shape == (5, 2, 2)
+    unique_values = np.unique(result_sampled)
+    assert len(unique_values) > 1
diff --git a/tests/core/__init__.py b/tests/core/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/core/test_global_model.py b/tests/core/test_global_model.py
new file mode 100644
index 0000000..cf3744b
--- /dev/null
+++ b/tests/core/test_global_model.py
@@ -0,0 +1,120 @@
+import numpy as np
+import xarray as xr
+
+from profsea.components.core.base import Component
+from profsea.components.core.global_model import Global
+from profsea.components.core.state import ClimateState
+
+
+class MockGlobalComponent(Component):
+    def project(self, state: ClimateState, rng: np.random.Generator) -> np.ndarray:
+        # Just return an array of 1s with the correct shape
+        return np.ones((state.nt * state.num_members, state.nyr), dtype=state.dtype)
+
+
+def test_calculate_drivers_math():
+    # 2 members, 3 time steps
+    T_change = np.array([[1.0, 2.0, 3.0], [3.0, 4.0, 5.0]])
+
+    global_model = Global(components={}, end_yr=2008, nt=2)
+    T_ens, T_int_ens, T_int_med = global_model._calculate_drivers(T_change)
+
+    # Assert time integrals (cumulative sum over axis 1)
+    np.testing.assert_array_equal(T_int_ens, [[1.0, 3.0, 6.0], [3.0, 7.0, 12.0]])
+
+    # Median of T_change across members is [2.0, 3.0, 4.0]
+    # Cumsum of that median is [2.0, 5.0, 9.0]
+    np.testing.assert_array_equal(T_int_med, [2.0, 5.0, 9.0])
+
+
+def test_run_orchestration():
+    components = {"mock1": MockGlobalComponent(), "mock2": MockGlobalComponent()}
+
+    # 2 time series, 3 members each -> output shape should be (6, nyr)
+    global_model = Global(components=components, end_yr=2010, nt=2, num_members=3)
+
+    # Shape: (nt=2, nyr=4)
+    T_change = np.zeros((2, 4))
+    results = global_model.run(scenario="ssp119", T_change=T_change)
+
+    assert "mock1" in results
+    assert "mock2" in results
+    assert results["mock1"].shape == (6, 4)
+
+
+def test_save_components(tmp_path):
+    # tmp_path is a built-in pytest fixture that creates a temporary directory
+    global_model = Global(components={}, end_yr=2010)
+
+    # Create dummy result: 5 members, 4 years
+    components = {
+        "mock_comp": xr.DataArray(np.random.rand(5, 4), dims=["member", "time"])
+    }
+
+    # Save the output to the temporary directory
+    global_model.save_components(
+        components,
+        output_dir=str(tmp_path),
+        scenario_name="test",
+        output_prefix="global",
+        output_format="netcdf",
+    )
+
+    expected_file = tmp_path / "test_global.nc"
+    assert expected_file.exists()
+
+    # Verify the contents of the NetCDF
+    ds = xr.open_dataset(expected_file)
+    assert "mock_comp" in ds.data_vars
+    assert ds["mock_comp"].shape == (5, 4)
+    assert list(ds.dims) == ["member", "time"]
+
+
+def test_return_types():
+    # Import a number of real components to test their return types
+    from profsea.components.global_ import (
+        AntarcticaDynAR5,
+        AntarcticaISMIP6,
+        AntarcticaSMBAR5,
+        Glacier,
+        GreenlandAR6,
+        GreenlandDynAR5,
+        GreenlandSMBAR5,
+        LandwaterAR5,
+        LandwaterAR6,
+        ThermalExpansion,
+    )
+
+    tas = np.zeros((2, 95))  # 2 trajectories, 4 years
+    ohc = np.zeros((2, 95))
+
+    components = {
+        "AntarcticaISMIP6": AntarcticaISMIP6(region="wais"),
+        "AntarcticaSMBAR5": AntarcticaSMBAR5(),
+        "AntarcticaDynAR5": AntarcticaDynAR5(),
+        "GreenlandAR6": GreenlandAR6(),
+        "GreenlandDynAR5": GreenlandDynAR5(),
+        "GreenlandSMBAR5": GreenlandSMBAR5(),
+        "Glacier": Glacier(),
+        "LandwaterAR5": LandwaterAR5(),
+        "LandwaterAR6": LandwaterAR6(),
+        "ThermalExpansion": ThermalExpansion(OHC_change=ohc),
+    }
+
+    global_model_float64 = Global(
+        components=components, end_yr=2101, nt=2, num_members=3, dtype=np.float64
+    )
+    results = global_model_float64.run(scenario="rcp26", T_change=tas)
+
+    for comp_name, data in results.items():
+        assert isinstance(data, xr.DataArray)
+        assert data.dtype == np.float64
+
+    global_model_float32 = Global(
+        components=components, end_yr=2101, nt=2, num_members=3, dtype=np.float32
+    )
+    results = global_model_float32.run(scenario="rcp26", T_change=tas)
+
+    for comp_name, data in results.items():
+        assert isinstance(data, xr.DataArray)
+        assert data.dtype == np.float32
diff --git a/tests/core/test_local_model.py b/tests/core/test_local_model.py
new file mode 100644
index 0000000..17b1861
--- /dev/null
+++ b/tests/core/test_local_model.py
@@ -0,0 +1,105 @@
+import dask.array as da
+import numpy as np
+import pytest
+import xarray as xr
+
+from profsea.components.core.base import SpatialComponent
+
+
+class DummyState:
+    """Mock state object to hold target coordinates."""
+
+    def __init__(self, lats, lons):
+        self.target_lats = np.atleast_1d(lats)
+        self.target_lons = np.atleast_1d(lons)
+
+
+class MockLocalComponent(SpatialComponent):
+    @property
+    def global_projection(self):
+        # Return a dummy array to satisfy the abstract base class requirement
+        return np.zeros((1, 1))
+
+    def project(self, state, rng):
+        # Return a dummy array of shape (n_members, n_years, n_locations)
+        return np.ones((state.num_members, state.n_years, len(state.target_lats)))
+
+
+class TestLazyLocalExtraction:
+    @pytest.fixture
+    def lazy_grid(self) -> xr.DataArray:
+        """
+        Creates a predictable 3x4 global grid with known values and NaNs.
+        Latitudes: -10, 0, 10
+        Longitudes: -170, -90, 0, 90
+        """
+        lats = np.array([-10.0, 0.0, 10.0])
+        lons = np.array([-170.0, -90.0, 0.0, 90.0])
+
+        # Predictable values 1 through 12
+        data = np.arange(1, 13, dtype=float).reshape(3, 4)
+
+        # Inject NaNs to test coastal weighting
+        data[0, 2] = np.nan  # lat=-10, lon=0
+        data[2, 3] = np.nan  # lat=10,  lon=90
+
+        dask_data = da.from_array(data, chunks=(3, 2))
+
+        return xr.DataArray(
+            dask_data, dims=["lat", "lon"], coords={"lat": lats, "lon": lons}
+        )
+
+    @pytest.fixture
+    def component(self):
+        return MockLocalComponent()
+
+    def test_preserves_lazy_evaluation(self, lazy_grid, component):
+        """Ensure the extraction operations do not eagerly compute the Dask array."""
+        state = DummyState(lats=[5.0], lons=[-45.0])
+        result = component.extract_spatial(lazy_grid, state)
+        assert isinstance(result.data, da.Array), (
+            "Extraction triggered eager evaluation!"
+        )
+
+    def test_standard_bilinear_interpolation(self, lazy_grid, component):
+        """Test a clean extraction in the center of 4 valid nodes."""
+        state = DummyState(lats=[5.0], lons=[-45.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        np.testing.assert_allclose(result.values, [8.5])
+
+    def test_coastal_nan_renormalization(self, lazy_grid, component):
+        """Test extraction where one corner of the bounding box is NaN."""
+        state = DummyState(lats=[-5.0], lons=[-45.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        np.testing.assert_allclose(result.values, [5.0])
+
+    def test_exact_node_strike(self, lazy_grid, component):
+        """Ensure zero-division safeguards work when hitting a coordinate exactly."""
+        state = DummyState(lats=[0.0], lons=[-90.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        np.testing.assert_allclose(result.values, [6.0])
+
+    def test_zonal_periodicity_wrapping(self, lazy_grid, component):
+        """Test extraction across the -180/180 antimeridian line."""
+        state = DummyState(lats=[0.0], lons=[180.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        np.testing.assert_allclose(result.values, [5.3])
+
+    def test_all_nan_handling(self, lazy_grid, component):
+        """Ensure regions completely surrounded by NaNs yield NaN."""
+        lazy_grid *= np.nan  # Make the entire grid NaN
+        state = DummyState(lats=[5.0], lons=[45.0])
+        result = component.extract_spatial(lazy_grid, state).compute()
+        assert np.isnan(result.values[0])
+
+    def test_missing_spatial_attributes(self, lazy_grid, component):
+        """Ensure a ValueError is raised if the state lacks valid spatial targets."""
+
+        class DummyStateEmpty:
+            pass  # A truly empty state object
+
+        state = DummyStateEmpty()
+        with pytest.raises(
+            ValueError, match="State object is missing required spatial attributes"
+        ):
+            component.extract_spatial(lazy_grid, state)
diff --git a/tests/core/test_spatial_model.py b/tests/core/test_spatial_model.py
new file mode 100644
index 0000000..71d22c8
--- /dev/null
+++ b/tests/core/test_spatial_model.py
@@ -0,0 +1,48 @@
+from unittest.mock import patch
+
+import dask.array as da
+import numpy as np
+import xarray as xr
+
+from profsea.components.core.spatial_model import Spatial
+
+
+@patch("profsea.components.core.spatial_model.fetch_zenodo_fingerprints")
+def test_spatial_init_blocks_download(mock_fetch):
+    spatial = Spatial(components={}, end_year=2050)
+    mock_fetch.assert_called_once()
+    assert spatial.end_year == 2050
+
+
+@patch("profsea.components.core.spatial_model.fetch_zenodo_fingerprints")
+def test_arr_to_xr_metadata(mock_fetch):
+    # Initializes with default output_percentiles=[5, 17, 50, 83, 95] (Length 5)
+    spatial = Spatial(components={}, end_year=2010)
+
+    arr = da.zeros((5, 4, 180, 360))
+    arr_dict = {"mock_spatial": arr}
+
+    xr_dict = spatial._arr_to_xr(arr_dict)
+
+    assert "mock_spatial" in xr_dict
+    da_out = xr_dict["mock_spatial"]
+
+    assert isinstance(da_out, xr.DataArray)
+    assert list(da_out.dims) == ["percentile", "time", "lat", "lon"]
+    np.testing.assert_array_equal(da_out.time.values, [2006, 2007, 2008, 2009])
+
+
+@patch("profsea.components.core.spatial_model.fetch_zenodo_fingerprints")
+def test_sum_spatial_components(mock_fetch):
+    spatial = Spatial(components={}, end_year=2010)
+
+    # Two identical arrays of 1s
+    da1 = xr.DataArray(np.ones((2, 2)), dims=["lat", "lon"])
+    da2 = xr.DataArray(np.ones((2, 2)), dims=["lat", "lon"])
+
+    components = {"comp1": da1, "comp2": da2}
+    total = spatial.sum_components(components)
+
+    # 1 + 1 = 2 everywhere
+    np.testing.assert_array_equal(total.values, np.ones((2, 2)) * 2)
+    assert "total_rsl" in components
diff --git a/tests/utils/__init__.py b/tests/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py
new file mode 100644
index 0000000..7ef5f68
--- /dev/null
+++ b/tests/utils/test_utils.py
@@ -0,0 +1,71 @@
+from unittest.mock import MagicMock, patch
+
+import numpy as np
+import pytest
+import xarray as xr
+
+from profsea.utils.utils import check_shapes, interpolate_to_grid, sample_members_2D
+
+
+def test_check_shapes_valid_2d():
+    arr = np.zeros((5, 10))
+    check_shapes(arr, n_time=10)
+
+
+def test_check_shapes_promotes_1d():
+    arr = np.zeros(10)
+    check_shapes(arr, n_time=10)
+
+
+def test_check_shapes_raises_value_error():
+    arr = np.zeros((5, 10))
+    with pytest.raises(
+        ValueError, match="Array should have shape .* time dimension of length 12"
+    ):
+        check_shapes(arr, n_time=12)
+
+
+def test_interpolate_to_grid_longitude_wrapping():
+    # Target grid using -180 to 180 format (includes 180, which should wrap)
+    target_lats = np.array([-45, 0, 45])
+    target_lons = np.array([-10, 0, 180])
+
+    # 1. Setup mock regionmask to act as if there is no land
+    mock_regionmask = MagicMock()
+    mock_land = MagicMock()
+    mock_mask_da = xr.DataArray(
+        np.zeros((1, 3, 3), dtype=bool),
+        dims=["region", "lat", "lon"],
+        coords={"region": [0], "lat": target_lats, "lon": [-180, -10, 0]},
+    )
+    mock_land.mask_3D.return_value = mock_mask_da
+    mock_regionmask.defined_regions.natural_earth_v5_0_0.land_110 = mock_land
+
+    # 2. Setup mock input data
+    lats = np.array([-45, 0, 45])
+    lons_360 = np.array([0, 180, 350])
+    data = xr.DataArray(np.random.rand(3, 3), coords=[("lat", lats), ("lon", lons_360)])
+
+    # 3. Intercept the local import using sys.modules
+    with patch.dict("sys.modules", {"regionmask": mock_regionmask}):
+        result = interpolate_to_grid(data, target_lats, target_lons)
+
+    assert result.shape == (3, 3)
+    expected_lons = np.array([-180, -10, 0])
+    np.testing.assert_array_almost_equal(result.lon.values, expected_lons)
+
+
+def test_sample_members_2D_extracts_real_member():
+    arr = np.array(
+        [
+            [1.0, 1.0, 1.0],  # Min
+            [2.0, 2.0, 2.0],
+            [3.0, 3.0, 3.0],  # Median
+            [4.0, 4.0, 4.0],
+            [5.0, 5.0, 5.0],  # Max
+        ]
+    )
+
+    result = sample_members_2D(arr, percentiles=[50])
+    assert result.shape == (1, 3)
+    np.testing.assert_array_equal(result[0], [3.0, 3.0, 3.0])
diff --git a/ukcp18_example_script.py b/ukcp18_example_script.py
new file mode 100644
index 0000000..de29cda
--- /dev/null
+++ b/ukcp18_example_script.py
@@ -0,0 +1,136 @@
+import cartopy.crs as ccrs
+import matplotlib.pyplot as plt
+import numpy as np
+
+from profsea.components.core.global_model import Global
+from profsea.components.core.spatial_model import Spatial
+from profsea.components.global_ import (
+    AntarcticaDynAR5,
+    AntarcticaSMBAR5,
+    Glacier,
+    GreenlandDynAR5,
+    GreenlandSMBAR5,
+    LandwaterAR5,
+    ThermalExpansion,
+)
+from profsea.components.spatial import GIA, Fingerprint, SterodynamicCMIP6
+
+### Global projections first ###
+global_components = {
+    "expansion": ThermalExpansion(
+        OHC_change=np.linspace(1, 5, 295).reshape(1, -1) * 1e24
+    ),
+    "greenland_dyn": GreenlandDynAR5(),
+    "greenland_smb": GreenlandSMBAR5(),
+    "landwater": LandwaterAR5(),
+    "antarctica_dyn": AntarcticaDynAR5(),
+    "antarctica_smb": AntarcticaSMBAR5(),
+    "glacier": Glacier(),
+}
+
+# Pass to the global model
+global_model = Global(components=global_components, end_yr=2301)
+projections = global_model.run(
+    scenario="rcp85",
+    T_change=np.linspace(1, 5, 295).reshape(1, -1),
+    member_seed=42,
+)
+global_model.sum_components(projections)
+gmslr = global_model.results["total_gmslr"]
+global_model.save_components(
+    global_model.results,
+    scenario_name="rcp85",
+    output_format="zarr",
+    output_prefix="global_",
+)
+
+### Now spatial projections ###
+spatial_components = {
+    "sterodynamic": SterodynamicCMIP6(
+        projections["expansion"],
+    ),
+    "greenland_dyn": Fingerprint(
+        projections["greenland_dyn"], fingerprint_component="greendyn"
+    ),
+    "greenland_smb": Fingerprint(
+        projections["greenland_smb"],
+        fingerprint_component="greensmb",
+    ),
+    "landwater": Fingerprint(
+        projections["landwater"],
+        fingerprint_component="landwater",
+    ),
+    "antarctica_dyn": Fingerprint(
+        projections["antarctica_dyn"],
+        fingerprint_component="antdyn",
+    ),
+    "antarctica_smb": Fingerprint(
+        projections["antarctica_smb"],
+        fingerprint_component="antsmb",
+    ),
+    "glacier": Fingerprint(
+        projections["glacier"],
+        fingerprint_component="glacier",
+    ),
+    "gia": GIA(
+        sample_spatial=False,
+    ),
+}
+
+# Pass to the spatial model
+spatial_model = Spatial(components=spatial_components)
+spatial_model.run(member_seed=42)
+
+spatial_model.sum_components(spatial_model.results)
+spatial_model.save_components(
+    spatial_model.results, scenario_name="rcp85", output_format="zarr"
+)
+
+
+### Plot example ###
+fig = plt.figure(figsize=(10, 4), layout="constrained")
+
+total_rsl = spatial_model.results["total_rsl"][2, -1, :, :]
+vmax = np.nanmax(np.abs(total_rsl))
+vmin = -vmax
+
+ax = fig.add_subplot(121)
+
+# Global projections
+yrs = np.arange(2006, 2301)
+ax.plot(
+    yrs,
+    np.median(gmslr, axis=0),
+    label="Global Projection",
+    color="royalblue",
+)
+# fill between 1 and 4 members
+ax.fill_between(
+    yrs,
+    np.percentile(gmslr, 17, axis=0),
+    np.percentile(gmslr, 83, axis=0),
+    alpha=0.3,
+    color="skyblue",
+)
+ax.set_xlabel("Year", fontsize=14)
+ax.set_ylabel("Global Mean Sea Level Rise (m)")
+
+ax = fig.add_subplot(122, projection=ccrs.PlateCarree())
+ax.set_title("50th Percentile, Year 2300")
+ax.pcolormesh(
+    spatial_model.grid_lons,
+    spatial_model.grid_lats,
+    total_rsl,
+    transform=ccrs.PlateCarree(),
+    cmap="PuOr_r",
+    vmin=vmin,
+    vmax=vmax,
+)
+ax.coastlines()
+fig.colorbar(
+    mappable=ax.collections[0],
+    label="Relative Sea Level (m)",
+    orientation="horizontal",
+    pad=0.02,
+)
+plt.show()