diff --git a/.github/workflows/codecov.yml b/.github/workflows/codecov.yml new file mode 100644 index 00000000..172ab350 --- /dev/null +++ b/.github/workflows/codecov.yml @@ -0,0 +1,41 @@ +name: Coverage (Codecov) + +on: + push: + branches: [ "main", "develop" ] + pull_request: + branches: [ "main", "develop" ] + +jobs: + test: + name: Run tests and upload coverage + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v4 + + - name: Set up Python 3.10 + uses: actions/setup-python@v5 + with: + python-version: "3.10" + + - name: Install pandoc + uses: pandoc/actions/setup@v1 + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install coverage + python -m pip install sphinx myst_parser sphinx-rtd-theme nbsphinx pandoc jupyter jupyterlab + pip install -e . + + - name: Tests with coverage + run: | + coverage run --omit='./results/*','./docs/*','./examples/*' -m unittest discover tests + coverage xml -o coverage.xml + + - name: Upload results to Codecov + uses: codecov/codecov-action@v5 + with: + token: ${{ secrets.CODECOV_TOKEN }} + files: coverage.xml \ No newline at end of file diff --git a/.github/workflows/coveralls.yml b/.github/workflows/coveralls.yml deleted file mode 100644 index 01aff643..00000000 --- a/.github/workflows/coveralls.yml +++ /dev/null @@ -1,34 +0,0 @@ -name: Coveralls - -on: - push: - branches: [ "main", "develop" ] - pull_request: - branches: [ "main", "develop" ] - -jobs: - - build: - name: Build - runs-on: ubuntu-latest - steps: - - - uses: actions/checkout@v4 - - - name: Set up Python 3.10 - uses: actions/setup-python@v3 - with: - python-version: "3.10" - - name: Install dependencies - run: | - python -m pip install --upgrade pip - python -m pip install coverage coveralls - python -m pip install sphinx myst_parser sphinx-rtd-theme - pip install -e . - - - name: Tests with coverage - run: | - coverage run --omit='./results/*','./docs/*','./examples/*' -m unittest discover tests - - name: Coveralls - uses: coverallsapp/github-action@v2.3.6 - diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml index 044a616d..cec03b02 100644 --- a/.github/workflows/run-tests.yml +++ b/.github/workflows/run-tests.yml @@ -26,11 +26,13 @@ jobs: uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} + - name: Install pandoc + uses: pandoc/actions/setup@v1 - name: Install dependencies run: | python -m pip install --upgrade pip python -m pip install flake8 pytest - python -m pip install sphinx myst_parser sphinx-rtd-theme + python -m pip install sphinx myst_parser sphinx-rtd-theme nbsphinx pandoc jupyter jupyterlab pip install -e . - name: Lint with flake8 run: | @@ -41,3 +43,9 @@ jobs: - name: Test with pytest run: | pytest + + + + + + diff --git a/.gitignore b/.gitignore index 5f4041b4..605f2ab1 100644 --- a/.gitignore +++ b/.gitignore @@ -70,6 +70,7 @@ instance/ # Sphinx documentation docs/_build/ +docs/out_docs/ # PyBuilder .pybuilder/ diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 6f412f63..a2940afa 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,9 +1,9 @@ version: 2 build: - os: ubuntu-20.04 + os: ubuntu-22.04 tools: - python: "3.10" + python: "3.12" # jobs: # pre_build: # # - sphinx-apidoc -o docs/_autodoc nnodely mplplots @@ -11,7 +11,7 @@ build: sphinx: configuration: docs/conf.py - fail_on_warning: true + fail_on_warning: false formats: - pdf diff --git a/README.md b/README.md index 3eb45e9b..eb7e78d3 100644 --- a/README.md +++ b/README.md @@ -1,269 +1,151 @@ +

logo

-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) +[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Coverage Status](https://coveralls.io/repos/github/tonegas/nnodely/badge.svg?branch=main)](https://coveralls.io/github/tonegas/nnodely?branch=main) -[![Coverage Status](https://readthedocs.org/projects/nnodely/badge/?version=latest&style=default)](https://nnodely.readthedocs.io/en/latest/) +[![Documentation](https://readthedocs.org/projects/nnodely/badge/?version=stable&style=default)](https://nnodely.readthedocs.io/en/stable/) +[![PyPI](https://img.shields.io/pypi/v/nnodely?color=blue&label=PyPI%20Package)](https://pypi.org/project/nnodely/) - -# Model-Structured Neural Networks for modeling, control and estimation of physical systems +# Neural Network Framekwork for Modelling, Control, and Estimation of Physical Systems Modeling, control, and estimation of physical systems are central to many engineering disciplines. While data-driven methods like neural networks offer powerful tools, they often struggle to **incorporate prior domain knowledge**, limiting their interpretability, generalizability, and safety. -To bridge this gap, we present ***nnodely*** (where "nn" can be read as "m," forming *Modely*) β€” a framework that facilitates the creation and deployment of **Model-Structured Neural Networks** (**MS-NNs**). -MS-NNs combine the learning capabilities of neural networks with structural **priors** grounded in **physics, control and estimation theory**, enabling: - -- **Reduced training data** requirements -- **Generalization** to unseen scenarios -- **Real-time** deployment in real-world applications - -## Why use nnodely? - -The framework's goal is to allow **fast prototyping** of MS-NNs for modeling, control and estimation of physical systems, by embedding prior domain knowledge into the neural networks' architecture. - -#### Core Objectives - -- **Model**, **control**, or **estimate** physical systems with unknown internal dynamics or parameters. -- **Accelerate** the **development** of MS-NNs, which are often hard to implement in general-purpose deep learning frameworks. -- Support researchers, engineers and domain experts to integrate data-driven models into their workflow β€” without discarding established knowledge. -- Serve as a repository of **reusable components** and best practices for MS-NN design **across diverse applications**. - -#### Workflow Overview - -nnodely guides users through **six** structured **phases** to define, train, and deploy MS-NNs effectively: - -1. **Neural Model Definition**: Build the MS-NN architecture using intuitive and modular design functions. -2. **Dataset Creation**: Simplify loading and preprocessing of training, validation, and test data. -3. **Neural Model Composition**: Assemble complex models by combining multiple neural components (e.g., models, controllers, estimators) in a unified training framework. -4. **Neural Model Training**: Train the MS-NN's parameters with user-defined loss functions. -5. **Neural Model Validation**: Assess the performance and reliability of the trained model. -6. **Model Export**: Deploy MS-NNs using standard formats. nnodely supports export to native PyTorch (nnodely-independent) and ONNX for broader compatibility. - - -
- Table of Contents -
    -
  1. - Getting Started -
    2. -
  2. - Basic Example - -
    3. -
  3. - Structure of the Folders - -
    4. -
  4. License
    5. -
  5. References
    6. -
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- - - -## Getting Started -You can install the nnodely framework from PyPI via: - ```sh - pip install nnodely - ``` - -### Applications and use cases - -Some examples of application of nnodely in different fields are collected in the following open-source repository: [nnodely-applications](https://github.com/tonegas/nnodely-applications) +To bridge this gap, we present ***nnodely*** (where "nn" can be read as "m," forming *Modely*) β€” a framework that facilitates the creation and deployment of **Model-Structured Neural Networks** (**MS-NNs**). +MS-NNs combine the learning capabilities of neural networks with structural **priors** grounded in **physics, control, and estimation theory**, enabling: -### How to contribute -Download the source code and install the dependencies using the following commands: - ```sh - git clone git@github.com:tonegas/nnodely.git - pip install -r requirements.txt - ``` +- **Reduced training data** requirements +- **Generalization** to unseen scenarios +- **Real-time** deployment in real-world applications -To contribute to the nnodely framework, you can: -- Open a pull request, if you have a new feature or bug fix. -- Open an issue, if you have a question or suggestion. - -We are very happy to collaborate with you! - -

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- - -## Basic Example - -This example shows how to use nnodely to create a model-structured neural network (MS-NN) for a simple **mass-spring-damper mechanical system**. +In short: - -### Build the neural model +nnodely is not a replacement for a general purpose deep learning frameworks β€” it is a **structured layer on top of them**, purpose-built for physical systems. +

-linearsys + πŸ“– Documentation β€’ + πŸ”¬ Case Studies β€’ + πŸš€ Other Applications

-The system to be modeled is defined by the following equation: -```math -M \ddot x = - k x - c \dot x + F -``` - -Suppose we want to **estimate** the value of the **future position** of the mass, given the initial position and the external force. - -The MS-NN model is defined by a list of inputs and outputs, and by a list of relationships that link the inputs to the outputs. -In nnodely, we can build an estimator in this form: -```python -x = Input('x') -F = Input('F') -x_z_est = Output('x_z_est', Fir(x.tw(1)) + Fir(F.last())) -``` - -Input variables can be created using the `Input` function. -In our system, we have two inputs: the position of the mass, `x`, and the external force exerted on the mass, `F`. -The `Output` function is used to define a model's output. -The `Output` function has two inputs: the first is the name (string) of the output, and the second is the structure of the estimator. - -Let's explain some of the functions used: -1. The `tw(...)` function is used to extract a time window from a signal. -In particular, we extract a time window $T_w$ of 1 second. -2. The `last()` function that is used to get the last force sample applied to the mass, i.e., the force at the current time step. -3. The `Fir(...)` function to build an FIR (finite impulse response) filter with one learnable parameters on our input variable. - -Hence, we are creating an estimator for the variable `x` at the next time step (i.e., the future position of the mass), by building an observer with the following mathematical structure: -```math -x[1] = \sum_{k=0}^{N_x-1} x[-k]\cdot h_x[(N_x-1)-k] + F[0]\cdot h_F -``` -where $x[1]$ is the next position of the mass, $F[0]$ is the last sample of the force, $N_x$ is the number of samples in the time window of the input variable `x`, $h_x$ is the vector of learnable parameters of the FIR filter on `x`, and $h_f$ is the single learnable parameter of the FIR filter on `F`. - -For the input variable `x`, we are using a time window $T_w = 1$ second, which means that we are using the last $N_x$ samples of the variable `x` to estimate the next position of the mass. The value of $N_x$ is equal to $T_w/T_s$, where $T_s$ is the sampling time used to sample the input variable `x`. - -In a particular case, our MS-NN formulation becomes equivalent to the discrete-time response (discretize with Forward-Euler) of the mass-spring-damper system. This happens when we choose the following values: $N_x = 3$, $h_x$ equal to the characteristic polynomial of the system, and $h_f = T_s^2/m$, where $T_s$ is the sampling time and $m$ is the mass of the system. - -However, our formulation is more general and can take better adapt to model mismatches and noise levels in the measured variables. This improved learning potential can be achieved by using a larger number of samples $N_x$ in the time window of the input variable `x`. + -1. The first input is the name of the error (`'next-pos'` in this case). -2. The second and third inputs are the variables whose difference we want to minimize. -3. The fourth input is the loss function to be used, in this case the mean square error ('mse'). +

Table of Contents

+
    +
  1. Getting Started
    2. + +
  2. Structure of the Repository
    3. +
  3. How to contribute
    4. +
  4. License
    5. +
  5. References
    6. +
-In the function `addMinimize`, we apply the `z(-1)` method to the variable `x` to get the next position of the mass, i.e., the value of `x` at the next time step. Indeed, the `z(-1)` function uses the **Z-transform** notation, and `z(-1)` is equivalent to `next()` operator. -The function `z(...)` can be used on an `Input` variable to get a time-shifted value. -Hence, our training objective is to minimize the mean square error between `x_z`, which represents the next position of the mass, and `x_z_est`, which represents the output of our estimator: -```math -\frac{1}{n} \sum_{i=0}^{n} (x_{z_i} - x_{{z\_est}_i})^2 -``` -where `n` represents the number of samples in the dataset. - -Finally, the function `neuralizeModel` is used to create a discrete-time MS-NN model. The input parameter of this function is the sampling time $T_s$, and is chosen based on the data we have available. In this example, we have a sampling time of $T_s = 0.2$ seconds. + +## Getting Started - -### Load the dataset + +### Installation -The training dataset is then loaded. nnodely has access to all the files that are in a source folder. +You can install nnodely from PyPI via: -```python -data_struct = ['time','x','dx','F'] -data_folder = './tutorials/datasets/mass-spring-damper/data/' -mass_spring_damper.loadData(name='mass_spring_dataset', source=data_folder, format=data_struct, delimiter=';') +```sh +pip install nnodely ``` - -### Train the neural network -Using the loaded dataset, we now train the neural model. +Alternatively, you can build it from source by first cloning the repository and installing the requirements and the nnodely library: -```python -mass_spring_damper.trainModel() +```sh +git clone https://github.com/tonegas/nnodely.git +cd nnodely +pip install -r requirements.txt +pip install . ``` - - -### Test the neural model -After training the model, we can test it using a new dataset. Let us create a new simple example to test the model, using the following inputs: + +### Hello, World! +To check if `nnodely` is installed correctly try running the following script. ```python -sample = {'F':[0.5], 'x':[0.25, 0.26, 0.27, 0.28, 0.29]} -results = mass_spring_damper(sample) -print(results) +from nnodely import Input, Output, nnodely, Parameter + +x = Input("x") +l = x.last() +o = (Parameter('A')*l.sw([-2,-1])+Parameter('B')*l).closedLoop(x) +f = Output("fib",o) +model = nnodely() +model.addModel("Fibonacci",f) +model.addMinimize("target", l.sw([-2,-1])+l, f) +model.neuralizeModel(1) +model.loadData("data",{ "x" : list(range(100)) } ) +model.trainModel(prediction_samples = 2, lr = 0.5, num_of_epochs = 500) +model.exportPythonModel(models = "Fibonacci") +print(model({ "x" : [1] },prediction_samples = 20,num_of_samples = 20)) ``` +In the example, the neural network is trained to mimic the Fibonacci series. +Finally, a native pytorch network is exported in a file. -Note that the input variable `x` is a list of 5 samples, as the sampling time $T_s$ is 0.2 seconds and the time window $T_w$ of the input variable `x` is 1 second. For the input variable `F`, we have only one sample, as we are using the last sample of the force applied to the mass. +

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-The resulting output variable is structured as follows: -```shell ->> {'x_z_est':[0.4]} -``` -where the value represents the output of our estimator (i.e., the next position of the mass). + +## Structure of the Repository -Our model can be tested also using a bigger time window: -```python -sample = {'F':[0.5, 0.6], 'x':[0.25, 0.26, 0.27, 0.28, 0.29, 0.30]} -results = mass_spring_damper(sample) -print(results) -``` -where the value of `x` is built using a moving time window. -The resulting model output is structured as follows: -```shell ->> {'x_z_est':[0.4, 0.42]} +```bash +nnodely/ # root directory +β”œβ”€β”€ nnodely/ # source code +β”‚ β”œβ”€β”€ basic/ # core low-level classes +β”‚ β”œβ”€β”€ exporter/ # model export utilities +β”‚ β”œβ”€β”€ layers/ # supported layers +β”‚ β”œβ”€β”€ operators/ # core operators +β”‚ β”œβ”€β”€ support/ # utility functions +β”‚ └── visualizer/ # visualization tools +β”œβ”€β”€ case-studies/ # main case studies +β”œβ”€β”€ docs/ # documentation +β”œβ”€β”€ tests/ # unit and integration tests +β”œβ”€β”€ imgs/ # images used in the documentation +└── mplplots/ # utilities for MatPlotLib ``` -Note that the same output can be obtained by calling the model using the flag `sampled=True`, in this way: -```python -sample = {'F':[[0.5],[0.6]], 'x':[[0.25, 0.26, 0.27, 0.28, 0.29],[0.26, 0.27, 0.28, 0.29, 0.30]]} -results = mass_spring_damper(sample,sampled=True) -print(results) -``` -

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- -## Structure of the Repository - +
+More info about repository structure + ### nnodely Folder This folder contains all the nnodely library files with relative references. -The `Moldey` main class defined in __nnodely.py__, it contains all the main properties of the nnodely object and it derives from five main operators: -1. __composer.py__ contains all the functions to build the networks: `addModel`, `neuralizeModel`, `addConnection`, `addClosedLool` etc.. +The `nnodely` main class defined in __nnodely.py__, it contains all the main properties of the nnodely object and it derives from five main operators, cointained in the folder `operators/`: +1. __composer.py__ contains all the functions to build the networks: `addModel`, `neuralizeModel`, `addConnection`, `addClosedLoop` etc.. 2. __loader.py__ contains the function for managing the dataset, the main function is `dataLoad`. -3. __trainer.py__ contains the function for train the network as the `trainModel`. +3. __trainer.py__ contains the function for training the network as the `trainModel`. 4. __exporter.py__ contains all the function for import and export: `saveModel`, `loadModel`, `exportONNX` etc.. -5. __validator.py__ contains all the function for validate the model ad the `resultsAnalysis`. -All the operators derive from `Network`defined in __network.py__, that contains the shared support functions for all the operators. +5. __validator.py__ contains all the function for validate the model and the `resultsAnalysis`. +6. All the operators derive from `Network` defined in __network.py__, that contains the shared support functions for all the operators. -The folder basic contatins the main classes for the low level functionalities: +The folder `basic/` contains the main classes for the low level functionalities: 1. __model.py__ containts the pytorch template model for the structured network. 2. __modeldef.py__ containts the operation for work with the json model definition. -3. __loss.py__ contatins the loss functions. +3. __loss.py__ contains the loss functions. 4. __optimizer.py__ contains the optimizer calss. 6. __relation.py__ contains all the main classes from which all the layers are derived. The other folders are: -1. exporter that contains the classes for the export functions. -2. support for the support functions. -3. visualizer that contains all the classes related to the visualization. -4. And finally the layers folder. +1. `exporter/` that contains the classes for the export functions. +2. `support/` for the support functions. +3. `visualizer/` that contains all the classes related to the visualization. +4. And finally the `layers/` folder. -The layers folder contains all the layers that can be used in the MSNN. +The `layers/` folder contains all the layers that can be used in the MSNN. In particular, the model structured NN is defined by `Inputs`, `Outputs` and `Parameters`: 1. __input.py__ contains the Input class used for create an input for the network. -2. __ouptut.py__ contains the Output class used for create an output for the network. +2. __output.py__ contains the Output class used for create an output for the network. 3. __parameter.py__ contains the logic for create a generic parameters and constants. The main basic layers without parameters are: @@ -276,7 +158,7 @@ commonly used in the local model as activation function as in [[1]](#1) with rec Using fuzzification it is also possible create a channel coding as presented in [[2]](#2). The main basic layers with parameters are: -1. __fir.py__ this file contains the finite inpulse response filter function. It is a linear operation on the time dimension (second dimension). +1. __fir.py__ this file contains the finite impulse response filter function. It is a linear operation on the time dimension (second dimension). This filter was introduced in [[1]](#1). 2. __linear.py__ this file contains the linear function. Typical Linear operation `W*x+b` operated on the space dimension (third dimension). This operation is presented in [[1]](#1). @@ -285,15 +167,39 @@ This operation is presented in [[1]](#1). 5. __equationlearner.py__ contains the logic for the equation learner. The equation learner is used for learn a relation input outpur following a list of activation functions. The first implementation is presented in [[6]](#6). 6. __timeoperation.py__ contains the time operation functions. The time operation are used for extract a time window from a signal. The derivative operation can be used to implement Physics-informed neural network [[7]](#7) Sobolev learning [[8]](#8). + +### Case Studies Folder +In the case studies folder you can find the main case studies cited in the paper. +Each case study is a jupyter notebook that explains the main functionalities of the library. + + +### Docs Folder +This folder contains all files used to automatically generate the documentation. + ### Tests Folder This folder contains the unit tests of the library. Each file tests a specific functionality. - -### Examples Folder -The files in the examples folder are a collection of the functionality of the library. -Each file presents a specific functionality of the framework. -This folder is useful to understand the flexibility and capability of the framework. + +### Matplotlib Folder +This folder contains the utilities for Matplotlib. + + +### Images Folder +This folder contains the images used in the documentation. + +

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+
+ + +## How to Contribute + +To contribute to the nnodely framework, you can: + +- Open a pull request if you have a new feature or bug fix. +- Open an issue if you have a question or suggestion. + +We welcome contributions and collaborations.

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@@ -305,6 +211,7 @@ This project is released under the license [License: MIT](https://opensource.org ## References + [1] Mauro Da Lio, Daniele Bortoluzzi, Gastone Pietro Rosati Papini. (2019). Modelling longitudinal vehicle dynamics with neural networks. @@ -313,7 +220,7 @@ Vehicle System Dynamics. https://doi.org/10.1080/00423114.2019.1638947 (look the [2] Alice Plebe, Mauro Da Lio, Daniele Bortoluzzi. (2019). On Reliable Neural Network Sensorimotor Control in Autonomous Vehicles. -IEEE Transaction on Intelligent Trasportation System. https://doi.org/10.1109/TITS.2019.2896375 +IEEE Transaction on Intelligent Transportation System. https://doi.org/10.1109/TITS.2019.2896375 [3] Mauro Da Lio, Riccardo DonΓ , Gastone Pietro Rosati Papini, Francesco Biral, Henrik Svensson. (2020). @@ -356,5 +263,10 @@ Mauro Da Lio, Mattia Piccinini, Francesco Biral. (2023). Robust and Sample-Efficient Estimation of Vehicle Lateral Velocity Using Neural Networks With Explainable Structure Informed by Kinematic Principles. IEEE Transactions on Intelligent Transportation Systems. https://doi.org/10.1109/TITS.2023.3303776 -

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+ \ No newline at end of file diff --git a/case-studies/README.md b/case-studies/README.md new file mode 100644 index 00000000..f65f7b18 --- /dev/null +++ b/case-studies/README.md @@ -0,0 +1,28 @@ +# Case Studies + +This folder contains the examples presented in the paper, updated to **nnodely version 1.5.3**. + +The subfolders are organized as follows: + +## 1. `lateral_dynamics` + +Contains three Python notebooks (plus all the other relative files) relative to the lateral vehicle dynamics model: + +- `lateral_dynamics_model.ipynp`: Design of the lateral vehicle dynamics model as presented in the paper +- `lateral_dynamics_control.ipynp`: Design and training of the lateral controller as presented in the paper +- `lateral_dynamics_model_torch.ipynp`: A comparative implementation of the lateral dynamics model developed in native **PyTorch** + +## 2. `mass_spring_damper` + +Contains the file related to the model and control of the mass-spring-damper system: + +- `mass_spring_damper.py` The mass–spring–damper (MSD) system dynamics model and PID controller as the code presented in the paper +- A subfolder containing notebooks that compare the implementation of the same system in **nnodely** and in native **PyTorch** + +## 3. `pinn` + +Provides an example implementation of a Physics-Informed Neural Network (PINN), inspired by the reference article (available at https://doi.org/10.1016/j.jcp.2018.10.045). + +## 4. `neuralODE` + +Contains a preliminary implementation of Neural ODEs within the nnodely framework, illustrating continuous-time modeling via parametric functions and integration operators for the mass-spring-damper-system. \ No newline at end of file diff --git a/case-studies/lateral_dynamics/dataset/test/test_set.csv b/case-studies/lateral_dynamics/dataset/test/test_set.csv new file mode 100644 index 00000000..9c323599 --- /dev/null +++ b/case-studies/lateral_dynamics/dataset/test/test_set.csv @@ -0,0 +1,1106 @@ +time,ax,ay,vx,curv,steer 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+166.2,-0.04018560797,2.624500275,11.66043472,0.0192927495,0,0.06721848249,0.9772968888,1971.963411 +166.25,-0.04099294543,2.645867348,11.66065693,0.01944910658,0,0.06729594618,0.9855079055,1972.675371 +166.3,-0.04170621932,2.666508436,11.66088009,0.01960012656,0,0.06738328189,0.9934590459,1973.389904 +166.35,-0.04231892899,2.686395168,11.66110611,0.01974558155,0,0.06748130172,1.001132488,1974.106921 +166.4,-0.04281719029,2.705456734,11.66134071,0.01988494337,0,0.06759124249,1.00850141,1974.826333 +166.45,-0.04318760708,2.723648071,11.66158867,0.02001786223,0,0.06771473587,1.015546322,1975.548048 +166.5,-0.04341421276,2.740904093,11.66185474,0.02014384457,0,0.0678537935,1.022248149,1976.271968 +166.55,-0.04347674176,2.75716877,11.66214561,0.02026244867,0,0.06801079959,1.028585315,1976.997998 +166.6,-0.04335828125,2.772422314,11.66246891,0.02037350818,0,0.06818839163,1.034543276,1977.726035 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+167.15,-0.02554809302,2.866263628,11.67096519,0.02103392437,0,0.07216209918,1.071828485,1985.837076 +167.2,-0.02231989987,2.86787343,11.6724844,0.02104042919,0,0.0727198571,1.07250452,1986.581007 +167.25,-0.01888220012,2.868376255,11.67417431,0.0210381959,0,0.0733012259,1.072723031,1987.325576 +167.3,-0.0152556086,2.86776495,11.67604256,0.02102716039,0,0.07390191406,1.072488666,1988.07068 +167.35,-0.01146789454,2.866027355,11.67809772,0.02100721099,0,0.07451730222,1.07180512,1988.816219 +167.4,-0.007548679598,2.863191128,11.68034267,0.02097854425,0,0.07514271885,1.070678234,1989.562093 +167.45,-0.003524936968,2.859288216,11.68278217,0.02094138667,0,0.07577361166,1.069114447,1990.308205 +167.5,0.0005694036372,2.85435915,11.68541908,0.02089604742,0,0.07640492171,1.06712079,1991.05446 +167.55,0.004687048495,2.848393202,11.68825245,0.02084246382,0,0.0770290345,1.06470263,1991.800763 +167.6,0.008803996257,2.841400146,11.69128036,0.02078072575,0,0.0776425153,1.061869144,1992.547023 +167.65,0.01290861145,2.833433151,11.69450283,0.02071124251,0,0.07824318856,1.058630586,1993.293151 +167.7,0.01698799431,2.824529409,11.69791412,0.02063432061,0,0.07882925123,1.054997206,1994.039058 +167.75,0.02103160508,2.814704895,11.70151234,0.02055010797,0,0.07939931005,1.050978422,1994.784659 +167.8,0.02503032796,2.803976774,11.70529366,0.02045876599,0,0.0799523294,1.046584129,1995.529872 +167.85,0.02897699736,2.792365313,11.70925426,0.0203604758,0,0.08048757166,1.041825294,1996.274615 +167.9,0.03286541253,2.779894114,11.71338749,0.02025544732,0,0.08100449294,1.036713004,1997.01881 +167.95,0.0366907604,2.766577959,11.71769142,0.02014383186,0,0.08150269836,1.031258941,1997.762382 +168,0.04044944793,2.752447844,11.72215939,0.02002588009,0,0.0819819048,1.025472403,1998.505257 +168.05,0.04413681105,2.73756361,11.72678757,0.01990207906,0,0.08244197071,1.01936996,1999.247364 +168.1,0.0477504544,2.721940517,11.73157024,0.01977257622,0,0.08288285136,1.012963414,1999.988637 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+168.65,0.08223161846,2.509273529,11.79295731,0.01804055982,0,0.08653556556,0.9263417125,2008.070767 +168.7,0.08488140255,2.487030506,11.79920864,0.01786187443,0,0.08676934242,0.917327106,2008.797691 +168.75,0.08745096624,2.46447444,11.80555248,0.01768103493,0,0.08698874712,0.9081782699,2009.523138 +168.8,0.08994223922,2.441594124,11.81198406,0.01749798553,0,0.08719430864,0.8989050984,2010.247075 +168.85,0.09235583991,2.418421984,11.81849861,0.01731298425,0,0.08738653362,0.8895190954,2010.969472 +168.9,0.09469354898,2.394977808,11.82509422,0.01712620133,0,0.08756595105,0.8800317645,2011.690303 +168.95,0.09695510566,2.371287107,11.83176517,0.01693783518,0,0.08773303777,0.8704572916,2012.409542 +169,0.09914426506,2.347372055,11.83850765,0.01674807676,0,0.08788834512,0.8608025908,2013.127165 +169.05,0.1012615338,2.323251486,11.84531975,0.01655707773,0,0.08803230524,0.8510780334,2013.843151 +169.1,0.103307277,2.298942327,11.85219574,0.0163649741,0,0.08816545457,0.841296792,2014.557481 +169.15,0.1052800715,2.274486542,11.85913467,0.0161720923,0,0.08828768879,0.8314670324,2015.270137 +169.2,0.1071885228,2.249900579,11.86613083,0.01597856089,0,0.0884007439,0.8215975761,2015.981102 +169.25,0.1090371013,2.225210428,11.87318325,0.01578458111,0,0.08850558847,0.8116976023,2016.690364 +169.3,0.1108243018,2.200436831,11.88028812,0.01559031515,0,0.08860214055,0.8017758131,2017.397908 +169.35,0.1125536859,2.175598621,11.88744354,0.01539591104,0,0.08869116753,0.7918403149,2018.103725 +169.4,0.1142302528,2.150713682,11.89464664,0.01520150508,0,0.08877364546,0.781898737,2018.807805 +169.45,0.1158593521,2.125798941,11.90189552,0.01500722708,0,0.08885064721,0.7719583511,2019.510139 +169.5,0.1174474061,2.10086298,11.90918732,0.01481315484,0,0.08892338723,0.7620260715,2020.210722 +169.55,0.1190011352,2.075915813,11.91652203,0.01461934441,0,0.08899319172,0.7521144748,2020.909548 +169.6,0.1205287427,2.050995588,11.9238987,0.01442609685,0,0.0890615508,0.7422248721,2021.606614 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/dev/null and b/case-studies/lateral_dynamics/figs/fig_control.png differ diff --git a/case-studies/lateral_dynamics/figs/fig_model.png b/case-studies/lateral_dynamics/figs/fig_model.png new file mode 100644 index 00000000..b500ce76 Binary files /dev/null and b/case-studies/lateral_dynamics/figs/fig_model.png differ diff --git a/case-studies/lateral_dynamics/figs/fig_model_torch.png b/case-studies/lateral_dynamics/figs/fig_model_torch.png new file mode 100644 index 00000000..8bcb3fb3 Binary files /dev/null and b/case-studies/lateral_dynamics/figs/fig_model_torch.png differ diff --git a/case-studies/lateral_dynamics/lateral_dynamics_control.ipynb b/case-studies/lateral_dynamics/lateral_dynamics_control.ipynb new file mode 100644 index 00000000..1d1baa0a --- /dev/null +++ b/case-studies/lateral_dynamics/lateral_dynamics_control.ipynb @@ -0,0 +1,3672 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "# **CONTROL MODEL FOR VEHICLE LATERAL DYNAMICS**", + "id": "4fca82a7205fafb9" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Import packages", + "id": "f4fe99351f149193" + }, + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-12-09T19:50:34.537828Z", + "start_time": "2025-12-09T19:50:31.658706Z" + } + }, + "source": [ + "# Import necessary packages\n", + "import sys, os\n", + "import pandas as pd\n", + "\n", + "# import nnodely modules\n", + "from nnodely import *\n", + "from nnodely.support.utils import *\n", + "from nnodely.support import earlystopping\n", + "from nnodely.support.earlystopping import select_best_model\n", + "\n", + "# import a library for plots\n", + "import matplotlib as mpl\n", + "mpl.rcParams.update(mpl.rcParamsDefault)\n", + "import matplotlib.pyplot as plt\n", + "plt.close('all')\n", + "SMALL_SIZE = 14\n", + "MEDIUM_SIZE = 22\n", + "BIGGER_SIZE = 26\n", + "plt.rc('font', size=SMALL_SIZE) # controls default text sizes\n", + "plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title\n", + "plt.rc('axes', labelsize=SMALL_SIZE) # fontsize of the x and y labels\n", + "plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n", + "plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n", + "plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize\n", + "plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title\n", + "plt.rc('grid', linestyle=\"--\", color='grey')" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>-- nnodely_v1.5.2 --<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Configurations", + "id": "7f7ac76c4718c9be" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T19:50:34.550025Z", + "start_time": "2025-12-09T19:50:34.543029Z" + } + }, + "cell_type": "code", + "source": [ + "\n", + "path_folder = os.path.join('trained_models') # folder to save the model\n", + "lat_dyna_control = nnodely(visualizer=MPLNotebookVisualizer(),seed=1,workspace=path_folder,save_history=False)" + ], + "id": "fc1700c6ad33005a", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Neural Network model", + "id": "a4b4192ab97751ff" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Import NN vehicle model", + "id": "92f39794ca78b53c" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T19:50:34.582276Z", + "start_time": "2025-12-09T19:50:34.559228Z" + } + }, + "cell_type": "code", + "source": [ + "lat_dyna_control.loadModel('vehicle_model')\n", + "lat_dyna_control.neuralizeModel()" + ], + "id": "db30f6144516ecef", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m=============================== Load JSON Model ================================\u001B[0m\n", + "\u001B[32mModel loaded from: trained_models/vehicle_model.json\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[33m[neuralizeModel] Closed loop on Mul77_int33 with sample in the future.\u001B[0m\n", + "\u001B[33m[neuralizeModel] Closed loop on Mul87_int34 with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {'SampleTime': {'dim': 1, 'values': 0.05},\n", + " 'W_fir': {'dim': 1,\n", + " 'sw': 30,\n", + " 'values': [[0.023806948214769363],\n", + " [0.03067079372704029],\n", + " [0.039163894951343536],\n", + " [0.04956628382205963],\n", + " [0.062176525592803955],\n", + " [0.07730474323034286],\n", + " [0.09526325762271881],\n", + " [0.116354800760746],\n", + " [0.14085842669010162],\n", + " [0.16901332139968872],\n", + " [0.20100119709968567],\n", + " [0.23692776262760162],\n", + " [0.2768043279647827],\n", + " [0.3205305337905884],\n", + " [0.3678794503211975],\n", + " [0.4184862971305847],\n", + " [0.4718419909477234],\n", + " [0.5272924304008484],\n", + " [0.5840446949005127],\n", + " [0.6411803960800171],\n", + " [0.6976763010025024],\n", + " [0.7524321675300598],\n", + " [0.8043041825294495],\n", + " [0.8521437644958496],\n", + " [0.894839346408844],\n", + " [0.9313583970069885],\n", + " [0.9607894420623779],\n", + " [0.9823793172836304],\n", + " [0.9955654144287109],\n", + " [1.0]]}},\n", + " 'Functions': {'FFuzzify7': {'centers': [8.0,\n", + " 10.142857142857142,\n", + " 12.285714285714285,\n", + " 14.428571428571429,\n", + " 16.57142857142857,\n", + " 18.714285714285715,\n", + " 20.857142857142858,\n", + " 23.0],\n", + " 'dim_out': {'dim': 8},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FFuzzify8': {'centers': [-2.0, -1.0, 0.0, 1.0, 2.0],\n", + " 'dim_out': {'dim': 5},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FParamFun6': {'code': 'def understeer_corr_local(vx, # input\\n'\n", + " ' A # '\n", + " 'learnable parameter\\n'\n", + " ' ):\\n'\n", + " ' return 1 / (1 + A * torch.pow(vx, '\n", + " '2))\\n',\n", + " 'in_dim': [{'dim': 1, 'sw': 1}, {'dim': [1, 1]}],\n", + " 'map_over_dim': False,\n", + " 'n_input': 2,\n", + " 'name': 'understeer_corr_local',\n", + " 'params_and_consts': []}},\n", + " 'Info': {'SampleTime': 0.05,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [30, 1],\n", + " 'ntot': 31,\n", + " 'num_parameters': 245},\n", + " 'Inputs': {'Mul77_int33': {'closedLoop': 'Add82',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'sw': [-1, 0]},\n", + " 'Mul87_int34': {'closedLoop': 'Add92',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'sw': [-1, 0]},\n", + " 'model_ax_in': {'dim': 1, 'ns': [1, 0], 'ntot': 1, 'sw': [-1, 0]},\n", + " 'model_curv_in': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]},\n", + " 'model_steer_in': {'dim': 1,\n", + " 'ns': [30, 0],\n", + " 'ntot': 30,\n", + " 'sw': [-30, 0]},\n", + " 'model_vx_in': {'dim': 1, 'ns': [1, 1], 'ntot': 2, 'sw': [-1, 1]}},\n", + " 'Minimizers': {'curv_error': {'A': 'SamplePart95',\n", + " 'B': 'Mul72',\n", + " 'loss': 'mse'},\n", + " 'heading_error': {'A': 'Add82', 'B': 'Add92', 'loss': 'mse'}},\n", + " 'Models': 'vehicle_model',\n", + " 'Outputs': {'model_curv': 'Mul72'},\n", + " 'Parameters': {'A_0': {'dim': [1, 1],\n", + " 'init_values': [[9.999999747378752e-06]],\n", + " 'values': [[0.12241744995117188]]},\n", + " 'A_1': {'dim': [1, 1],\n", + " 'init_values': [[9.999999747378752e-06]],\n", + " 'values': [[0.13215704262256622]]},\n", + " 'A_2': {'dim': [1, 1],\n", + " 'init_values': [[9.999999747378752e-06]],\n", + " 'values': [[0.13487723469734192]]},\n", + " 'A_3': {'dim': [1, 1],\n", + " 'init_values': [[9.999999747378752e-06]],\n", + " 'values': [[0.13838686048984528]]},\n", + " 'A_4': {'dim': [1, 1],\n", 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[-0.06875365227460861],\n", + " [-0.06626567989587784],\n", + " [-0.06372548639774323],\n", + " [-0.061116475611925125],\n", + " [-0.05833817645907402],\n", + " [-0.05532317981123924],\n", + " [-0.051966723054647446],\n", + " [-0.048134833574295044],\n", + " [-0.04368661344051361],\n", + " [-0.038527000695466995],\n", + " [-0.03244928643107414],\n", + " [-0.02543233335018158],\n", + " [-0.017468007281422615],\n", + " [-0.008501219563186169],\n", + " [0.001558795920573175],\n", + " [0.01286094356328249],\n", + " [0.02560839056968689],\n", + " [0.03990352153778076],\n", + " [0.05610361695289612],\n", + " [0.07430130988359451],\n", + " [0.09463302791118622],\n", + " [0.1173117607831955],\n", + " [0.1426011621952057],\n", + " [0.17061302065849304],\n", + " [0.20156458020210266],\n", + " [0.23558014631271362]]},\n", + " 'Fir_7': {'dim': 1,\n", + " 'init_fun': {'name': 'init_constant',\n", + " 'params': {'value': 1}},\n", + " 'sw': 30,\n", + " 'values': [[0.06075132265686989],\n", + " [0.05622144788503647],\n", + " [0.04827791452407837],\n", + " [0.037901245057582855],\n", + " [0.02598106488585472],\n", + " [0.013368550688028336],\n", + " [0.0007360622403211892],\n", + " [-0.011336201801896095],\n", + " [-0.02228238433599472],\n", + " [-0.03168044239282608],\n", + " [-0.039212822914123535],\n", + " [-0.0446024127304554],\n", + " [-0.047832805663347244],\n", + " [-0.04878610372543335],\n", + " [-0.04709966853260994],\n", + " [-0.042434681206941605],\n", + " [-0.03468530252575874],\n", + " [-0.023747917264699936],\n", + " [-0.009678727015852928],\n", + " [0.0075911483727395535],\n", + " [0.02810771018266678],\n", + " [0.05173402652144432],\n", + " [0.07855863869190216],\n", + " [0.10853327810764313],\n", + " [0.14130747318267822],\n", + " [0.1765713095664978],\n", + " [0.21406440436840057],\n", + " [0.25343939661979675],\n", + " [0.2944483458995819],\n", + " [0.3369137942790985]]}},\n", + " 'Relations': {'Add34': ['Add', ['Mul12', 'Mul15']],\n", + " 'Add35': ['Add', ['Add34', 'Mul18']],\n", + " 'Add36': ['Add', ['Add35', 'Mul21']],\n", + " 'Add37': ['Add', ['Add36', 'Mul24']],\n", + " 'Add38': ['Add', ['Add37', 'Mul27']],\n", + " 'Add39': ['Add', ['Add38', 'Mul30']],\n", + " 'Add40': ['Add', ['Add39', 'Mul33']],\n", + " 'Add68': ['Add', ['Mul47', 'Mul52']],\n", + " 'Add69': ['Add', ['Add68', 'Mul57']],\n", + " 'Add70': ['Add', ['Add69', 'Mul62']],\n", + " 'Add71': ['Add', ['Add70', 'Mul67']],\n", + " 'Add82': ['Add', ['SamplePart79', 'Mul81']],\n", + " 'Add92': ['Add', ['SamplePart89', 'Mul91']],\n", + " 'Fir10': ['Fir', ['Mul9'], 'Fir_0', None, 0.05],\n", + " 'Fir13': ['Fir', ['Mul9'], 'Fir_1', None, 0.05],\n", + " 'Fir16': ['Fir', ['Mul9'], 'Fir_2', None, 0.05],\n", + " 'Fir19': ['Fir', ['Mul9'], 'Fir_3', None, 0.05],\n", + " 'Fir22': ['Fir', ['Mul9'], 'Fir_4', None, 0.05],\n", + " 'Fir25': ['Fir', ['Mul9'], 'Fir_5', None, 0.05],\n", + " 'Fir28': ['Fir', ['Mul9'], 'Fir_6', None, 0.05],\n", + " 'Fir31': ['Fir', ['Mul9'], 'Fir_7', None, 0.05],\n", + " 'Fuzzify2': ['Fuzzify', ['SamplePart1'], 'FFuzzify7'],\n", + " 'Fuzzify5': ['Fuzzify', ['SamplePart4'], 'FFuzzify8'],\n", + " 'Mul12': ['Mul', ['Fir10', 'Select11']],\n", + " 'Mul15': ['Mul', ['Fir13', 'Select14']],\n", + " 'Mul18': ['Mul', ['Fir16', 'Select17']],\n", + " 'Mul21': ['Mul', ['Fir19', 'Select20']],\n", + " 'Mul24': ['Mul', ['Fir22', 'Select23']],\n", + " 'Mul27': ['Mul', ['Fir25', 'Select26']],\n", + " 'Mul30': ['Mul', ['Fir28', 'Select29']],\n", + " 'Mul33': ['Mul', ['Fir31', 'Select32']],\n", + " 'Mul47': ['Mul', ['ParamFun43', 'Select46']],\n", + " 'Mul52': ['Mul', ['ParamFun48', 'Select51']],\n", + " 'Mul57': ['Mul', ['ParamFun53', 'Select56']],\n", + " 'Mul62': ['Mul', ['ParamFun58', 'Select61']],\n", + " 'Mul67': ['Mul', ['ParamFun63', 'Select66']],\n", + " 'Mul72': ['Mul', ['Add40', 'Add71']],\n", + " 'Mul77': ['Mul', ['SamplePart74', 'SamplePart76']],\n", + " 'Mul81': ['Mul', ['Mul77', 'SampleTime']],\n", + " 'Mul84': ['Mul', ['Add40', 'Add71']],\n", + " 'Mul87': ['Mul', ['Mul84', 'SamplePart86']],\n", + " 'Mul9': ['Mul', ['SamplePart7', 'W_fir']],\n", + " 'Mul91': ['Mul', ['Mul87', 'SampleTime']],\n", + " 'ParamFun43': ['ParamFun',\n", + " ['SamplePart42', 'A_0'],\n", + " 'FParamFun6'],\n", + " 'ParamFun48': ['ParamFun',\n", + " ['SamplePart42', 'A_1'],\n", + " 'FParamFun6'],\n", + " 'ParamFun53': ['ParamFun',\n", + " ['SamplePart42', 'A_2'],\n", + " 'FParamFun6'],\n", + " 'ParamFun58': ['ParamFun',\n", + " ['SamplePart42', 'A_3'],\n", + " 'FParamFun6'],\n", + " 'ParamFun63': ['ParamFun',\n", + " ['SamplePart42', 'A_4'],\n", + " 'FParamFun6'],\n", + " 'SamplePart1': ['SamplePart', ['model_vx_in'], -1, [-1, 0]],\n", + " 'SamplePart4': ['SamplePart', ['model_ax_in'], -1, [-1, 0]],\n", + " 'SamplePart42': ['SamplePart', ['model_vx_in'], -1, [-1, 0]],\n", + " 'SamplePart7': ['SamplePart', ['model_steer_in'], -1, [-30, 0]],\n", + " 'SamplePart74': ['SamplePart', ['model_curv_in'], -1, [0, 1]],\n", + " 'SamplePart76': ['SamplePart', ['model_vx_in'], -1, [0, 1]],\n", + " 'SamplePart79': ['SamplePart', ['Mul77_int33'], -1, [-1, 0]],\n", + " 'SamplePart86': ['SamplePart', ['model_vx_in'], -1, [0, 1]],\n", + " 'SamplePart89': ['SamplePart', ['Mul87_int34'], -1, [-1, 0]],\n", + " 'SamplePart95': ['SamplePart', ['model_curv_in'], -1, [0, 1]],\n", + " 'Select11': ['Select', ['Fuzzify2'], 8, 0],\n", + " 'Select14': ['Select', ['Fuzzify2'], 8, 1],\n", + " 'Select17': ['Select', ['Fuzzify2'], 8, 2],\n", + " 'Select20': ['Select', ['Fuzzify2'], 8, 3],\n", + " 'Select23': ['Select', ['Fuzzify2'], 8, 4],\n", + " 'Select26': ['Select', ['Fuzzify2'], 8, 5],\n", + " 'Select29': ['Select', ['Fuzzify2'], 8, 6],\n", + " 'Select32': ['Select', ['Fuzzify2'], 8, 7],\n", + " 'Select46': ['Select', ['Fuzzify5'], 5, 0],\n", + " 'Select51': ['Select', ['Fuzzify5'], 5, 1],\n", + " 'Select56': ['Select', ['Fuzzify5'], 5, 2],\n", + " 'Select61': ['Select', ['Fuzzify5'], 5, 3],\n", + " 'Select66': ['Select', ['Fuzzify5'], 5, 4]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Model definition", + "id": "b6ef5ba231c37df3" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T19:50:34.845708Z", + "start_time": "2025-12-09T19:50:34.590258Z" + } + }, + "cell_type": "code", + "source": [ + "# ----------------------------------------------------------------\n", + "# Inputs\n", + "# ----------------------------------------------------------------\n", + "curv_in = Input('controller_curv_in') # [1/m] path curvature\n", + "vx_in = Input('controller_vx_in') # [m/s] longitudinal velocity\n", + "steer_in = Input('controller_steer_in') # [rad] steering wheel angle\n", + "ax_in = Input('controller_ax_in') # [m/s^2] longitudinal acceleration\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Hyperparameters\n", + "# ----------------------------------------------------------------\n", + "samples_past_steer = 30 # number of samples in the past for the steering wheel angle prediction\n", + "samples_prediction = 30 # number of samples for future manoeuvre\n", + "n_channels_vx = 8 # number of channels for activation function vx\n", + "chan_vx = list(np.linspace(8, 23, num=n_channels_vx)) # centers of the channels vx\n", + "chan_ax = [-2,-1,0.0,1.0,2.0] # centers of the channels ax\n", + "\n", + "# Exponential weights for normalisation of FIR parameters during training\n", + "W_prev = np.flip(np.array([[np.exp(-(i/(samples_prediction/2))**2)] for i in range(-samples_prediction+1,1)]))\n", + "W_past = np.array([[np.exp(-(i/(samples_past_steer/2))**2)] for i in range(-samples_past_steer+2,1)])\n", + "\n", + "W_fir_init = Constant('W_fir_init', sw = (samples_past_steer-1), values=W_past)\n", + "W_fir_target = Constant('W_fir_target', sw =samples_prediction, values=W_prev)\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Understeer correction function\n", + "# ----------------------------------------------------------------\n", + "def understeer_corr_local_control(vx,curv, # inputs\n", + " A, # constant\n", + " ):\n", + " return curv * (1 + A * torch.pow(vx, 2))\n", + "\n", + "understeer_corr = ParamFun(understeer_corr_local_control)\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Local models\n", + "# ----------------------------------------------------------------\n", + "# Activation functions\n", + "activ_fnc_ax = Fuzzify(centers=chan_ax, functions='Triangular')(ax_in.last())\n", + "activ_fnc_vx = Fuzzify(centers=chan_vx, functions='Triangular')(vx_in.last())\n", + "\n", + "# Understeer coefficient: the value of the constant is taken by the model and is function of the acceleration ax\n", + "A = Constant('A',values=[lat_dyna_control.parameters['A_0'][0][0],lat_dyna_control.parameters['A_1'][0][0],lat_dyna_control.parameters['A_2'][0][0],lat_dyna_control.parameters['A_3'][0][0],lat_dyna_control.parameters['A_4'][0][0]])\n", + "A_ax = Sum( activ_fnc_ax * A ) # Constant A function of ax\n", + "\n", + "# FIR target trajectory\n", + "local_model_target = LocalModel(pass_indexes=True,\n", + " input_function=lambda idx: Fir(output_dimension=1,W_init = 'init_constant',W_init_params={\"value\": 0.01}, W=f'Fir_target_{idx[0]}'))\n", + "# FIR initial condition\n", + "delta_ic = Fir(output_dimension=1, W=\"Fir_InitCondition\", W_init=\"init_constant\", W_init_params={\"value\": 0.01})(steer_in.sw([-samples_past_steer,-1])*W_fir_init)\n", + "\n", + "# Understeer correction\n", + "out = understeer_corr( vx_in.sw([-1,(samples_prediction-1)]),curv_in.sw([-1,(samples_prediction-1)]), A_ax )\n", + "\n", + "# Local model\n", + "delta_target = local_model_target(out*W_fir_target , activ_fnc_vx)\n", + "\n", + "# NN output\n", + "delta = delta_target + delta_ic\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Outputs\n", + "# ----------------------------------------------------------------\n", + "steer_from_target = Output('controller_steer_from_target',delta_target)\n", + "steer_from_ic = Output('controller_steer_from_ic',delta_ic)\n", + "steer_control = Output('controller_steer_out',delta)\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Add controller model and connect to the vehicle model\n", + "# ----------------------------------------------------------------\n", + "lat_dyna_control.addModel('control_steer',[steer_from_target,steer_from_ic,steer_control])\n", + "lat_dyna_control.addClosedLoop(steer_control,steer_in)\n", + "lat_dyna_control.addConnect(steer_control,'model_steer_in')" + ], + "id": "a7afd97b064ac3e", + "outputs": [], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Loss functions definitions", + "id": "deaac585e4481143" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "The loss functions are the same used for the training of the vehicle model: mse error on curvature value and mse value on heading value.", + "id": "1ee10f3e9472c0e3" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Model neuralization", + "id": "5a60203dc386c863" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T19:50:34.888875Z", + "start_time": "2025-12-09T19:50:34.852077Z" + } + }, + "cell_type": "code", + "source": [ + "# Generate the model\n", + "lat_dyna_control.neuralizeModel(sample_time=0.05) # neuralize the model with the chosen sample time" + ], + "id": "c909f3d5f6b02813", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[33m[neuralizeModel] Closed loop on Mul77_int33 with sample in the future.\u001B[0m\n", + "\u001B[33m[neuralizeModel] Closed loop on Mul87_int34 with sample in the future.\u001B[0m\n", + "\u001B[33m[neuralizeModel] Connect on model_steer_in with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {'A': {'dim': 5,\n", + " 'values': [0.12241744995117188,\n", + " 0.13215704262256622,\n", + " 0.13487723469734192,\n", + " 0.13838686048984528,\n", + " 0.14319394528865814]},\n", + " 'SampleTime': {'dim': 1, 'values': 0.05},\n", + " 'W_fir': {'dim': 1,\n", + " 'sw': 30,\n", + " 'values': [[0.023806948214769363],\n", + " [0.03067079372704029],\n", + " [0.039163894951343536],\n", + " [0.04956628382205963],\n", + " [0.062176525592803955],\n", + " [0.07730474323034286],\n", + " [0.09526325762271881],\n", + " [0.116354800760746],\n", + " [0.14085842669010162],\n", + " [0.16901332139968872],\n", + " [0.20100119709968567],\n", + " [0.23692776262760162],\n", + " [0.2768043279647827],\n", + " [0.3205305337905884],\n", + " [0.3678794503211975],\n", + " [0.4184862971305847],\n", + " [0.4718419909477234],\n", + " [0.5272924304008484],\n", + " [0.5840446949005127],\n", + " [0.6411803960800171],\n", + " [0.6976763010025024],\n", + " [0.7524321675300598],\n", + " [0.8043041825294495],\n", + " [0.8521437644958496],\n", + " [0.894839346408844],\n", + " [0.9313583970069885],\n", + " [0.9607894420623779],\n", + " [0.9823793172836304],\n", + " [0.9955654144287109],\n", + " [1.0]]},\n", + " 'W_fir_init': {'dim': 1,\n", + " 'sw': 29,\n", + " 'values': [[0.03067079372704029],\n", + " [0.039163894951343536],\n", + " [0.04956628382205963],\n", + " [0.062176525592803955],\n", + " [0.07730474323034286],\n", + " [0.09526325762271881],\n", + " [0.116354800760746],\n", + " [0.14085842669010162],\n", + " [0.16901332139968872],\n", + " [0.20100119709968567],\n", + " [0.23692776262760162],\n", + " [0.2768043279647827],\n", + " [0.3205305337905884],\n", + " [0.3678794503211975],\n", + " [0.4184862971305847],\n", + " [0.4718419909477234],\n", + " [0.5272924304008484],\n", + " [0.5840446949005127],\n", + " [0.6411803960800171],\n", + " [0.6976763010025024],\n", + " [0.7524321675300598],\n", + " [0.8043041825294495],\n", + " [0.8521437644958496],\n", + " [0.894839346408844],\n", + " [0.9313583970069885],\n", + " [0.9607894420623779],\n", + " [0.9823793172836304],\n", + " [0.9955654144287109],\n", + " [1.0]]},\n", + " 'W_fir_target': {'dim': 1,\n", + " 'sw': 30,\n", + " 'values': [[1.0],\n", + " [0.9955654144287109],\n", + " [0.9823793172836304],\n", + " [0.9607894420623779],\n", + " [0.9313583970069885],\n", + " [0.894839346408844],\n", + " [0.8521437644958496],\n", + " [0.8043041825294495],\n", + " [0.7524321675300598],\n", + " [0.6976763010025024],\n", + " [0.6411803960800171],\n", + " [0.5840446949005127],\n", + " [0.5272924304008484],\n", + " [0.4718419909477234],\n", + " [0.4184862971305847],\n", + " [0.3678794503211975],\n", + " 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12.285714285714285,\n", + " 14.428571428571429,\n", + " 16.57142857142857,\n", + " 18.714285714285715,\n", + " 20.857142857142858,\n", + " 23.0],\n", + " 'dim_out': {'dim': 8},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FFuzzify8': {'centers': [-2.0, -1.0, 0.0, 1.0, 2.0],\n", + " 'dim_out': {'dim': 5},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FParamFun103': {'code': 'def '\n", + " 'understeer_corr_local_control(vx,curv, '\n", + " '# inputs\\n'\n", + " ' '\n", + " 'A, # constant\\n'\n", + " ' ):\\n'\n", + " ' return curv * (1 + A * '\n", + " 'torch.pow(vx, 2))\\n',\n", + " 'in_dim': [{'dim': 1, 'sw': 30},\n", + " {'dim': 1, 'sw': 30},\n", + " {'dim': 1, 'sw': 1}],\n", + " 'map_over_dim': False,\n", + " 'n_input': 3,\n", + " 'name': 'understeer_corr_local_control',\n", + " 'params_and_consts': []},\n", + " 'FParamFun6': {'code': 'def understeer_corr_local(vx, # input\\n'\n", + " ' A # '\n", + " 'learnable 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'ParamFun114': ['ParamFun',\n", + " ['SamplePart111', 'SamplePart113', 'Sum104'],\n", + " 'FParamFun103'],\n", + " 'ParamFun43': ['ParamFun',\n", + " ['SamplePart42', 'A_0'],\n", + " 'FParamFun6'],\n", + " 'ParamFun48': ['ParamFun',\n", + " ['SamplePart42', 'A_1'],\n", + " 'FParamFun6'],\n", + " 'ParamFun53': ['ParamFun',\n", + " ['SamplePart42', 'A_2'],\n", + " 'FParamFun6'],\n", + " 'ParamFun58': ['ParamFun',\n", + " ['SamplePart42', 'A_3'],\n", + " 'FParamFun6'],\n", + " 'ParamFun63': ['ParamFun',\n", + " ['SamplePart42', 'A_4'],\n", + " 'FParamFun6'],\n", + " 'SamplePart1': ['SamplePart', ['model_vx_in'], -1, [-1, 0]],\n", + " 'SamplePart100': ['SamplePart',\n", + " ['controller_vx_in'],\n", + " -1,\n", + " [-1, 0]],\n", + " 'SamplePart106': ['SamplePart',\n", + " ['controller_steer_in'],\n", + " -1,\n", + " [-30, -1]],\n", + " 'SamplePart111': ['SamplePart',\n", + " ['controller_vx_in'],\n", + " -1,\n", + " [-1, 29]],\n", + " 'SamplePart113': ['SamplePart',\n", + " ['controller_curv_in'],\n", + " -1,\n", + " [-1, 29]],\n", + " 'SamplePart4': ['SamplePart', ['model_ax_in'], -1, [-1, 0]],\n", + " 'SamplePart42': ['SamplePart', ['model_vx_in'], -1, [-1, 0]],\n", + " 'SamplePart7': ['SamplePart', ['model_steer_in'], -1, [-30, 0]],\n", + " 'SamplePart74': ['SamplePart', ['model_curv_in'], -1, [0, 1]],\n", + " 'SamplePart76': ['SamplePart', ['model_vx_in'], -1, [0, 1]],\n", + " 'SamplePart79': ['SamplePart', ['Mul77_int33'], -1, [-1, 0]],\n", + " 'SamplePart86': ['SamplePart', ['model_vx_in'], -1, [0, 1]],\n", + " 'SamplePart89': ['SamplePart', ['Mul87_int34'], -1, [-1, 0]],\n", + " 'SamplePart95': ['SamplePart', ['model_curv_in'], -1, [0, 1]],\n", + " 'SamplePart97': ['SamplePart',\n", + " ['controller_ax_in'],\n", + " -1,\n", + " [-1, 0]],\n", + " 'Select11': ['Select', ['Fuzzify2'], 8, 0],\n", + " 'Select118': ['Select', ['Fuzzify101'], 8, 0],\n", + " 'Select121': ['Select', ['Fuzzify101'], 8, 1],\n", + " 'Select124': ['Select', ['Fuzzify101'], 8, 2],\n", + " 'Select127': ['Select', ['Fuzzify101'], 8, 3],\n", + " 'Select130': ['Select', ['Fuzzify101'], 8, 4],\n", + " 'Select133': ['Select', ['Fuzzify101'], 8, 5],\n", + " 'Select136': ['Select', ['Fuzzify101'], 8, 6],\n", + " 'Select139': ['Select', ['Fuzzify101'], 8, 7],\n", + " 'Select14': ['Select', ['Fuzzify2'], 8, 1],\n", + " 'Select17': ['Select', ['Fuzzify2'], 8, 2],\n", + " 'Select20': ['Select', ['Fuzzify2'], 8, 3],\n", + " 'Select23': ['Select', ['Fuzzify2'], 8, 4],\n", + " 'Select26': ['Select', ['Fuzzify2'], 8, 5],\n", + " 'Select29': ['Select', ['Fuzzify2'], 8, 6],\n", + " 'Select32': ['Select', ['Fuzzify2'], 8, 7],\n", + " 'Select46': ['Select', ['Fuzzify5'], 5, 0],\n", + " 'Select51': ['Select', ['Fuzzify5'], 5, 1],\n", + " 'Select56': ['Select', ['Fuzzify5'], 5, 2],\n", + " 'Select61': ['Select', ['Fuzzify5'], 5, 3],\n", + " 'Select66': ['Select', ['Fuzzify5'], 5, 4],\n", + " 'Sum104': ['Sum', ['Mul103']]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Training", + "id": "78870bde708d1090" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Dataset Load", + "id": "d8901687b6eb84c5" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Here, the data for the training, validation, and test sets are loaded and constructed. The dataset is constructed according to the overall architecture of the system, comprising both the model and the controller. Parentheses are used to indicate that multiple inputs refer to the same column of the CSV file.", + "id": "d48eb0e28481f7a2" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T19:50:35.076648Z", + "start_time": "2025-12-09T19:50:34.895611Z" + } + }, + "cell_type": "code", + "source": [ + "lat_dyna_control.loadData('test_set', source='dataset/test',\n", + " format=['', ('controller_ax_in','model_ax_in'),'', ('controller_vx_in','model_vx_in'), ('model_curv_in','controller_curv_in'), ('model_steer_in','controller_steer_in')], skiplines=1)\n", + "\n", + "lat_dyna_control.loadData('training_set', source='dataset/training',\n", + " format=['', ('model_ax_in','controller_ax_in'), '', ('controller_vx_in','model_vx_in'), ('model_curv_in','controller_curv_in'), 'model_steer_in', ''],\n", + " skiplines=1)\n", + "lat_dyna_control.loadData('validation_set', source='dataset/validation',\n", + " format=['', ('model_ax_in','controller_ax_in'), '', ('controller_vx_in','model_vx_in'), ('model_curv_in','controller_curv_in'), 'model_steer_in', ''],\n", + " skiplines=1)" + ], + "id": "6b302b749e7668ee", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m============================ nnodely Model Dataset =============================\u001B[0m\n", + "\u001B[32mDataset Name: test_set\u001B[0m\n", + "\u001B[32mNumber of files: 1\u001B[0m\n", + "\u001B[32mTotal number of samples: 1047\u001B[0m\n", + "\u001B[32mShape of controller_steer_in: (1047, 29, 1)\u001B[0m\n", + "\u001B[32mShape of controller_vx_in: (1047, 30, 1)\u001B[0m\n", + "\u001B[32mShape of controller_ax_in: (1047, 1, 1)\u001B[0m\n", + "\u001B[32mShape of controller_curv_in: (1047, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_ax_in: (1047, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_curv_in: (1047, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_steer_in: (1047, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_vx_in: (1047, 2, 1)\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m============================ nnodely Model Dataset =============================\u001B[0m\n", + "\u001B[32mDataset Name: training_set\u001B[0m\n", + "\u001B[32mNumber of files: 4\u001B[0m\n", + "\u001B[32mTotal number of samples: 12100\u001B[0m\n", + "\u001B[32mShape of controller_vx_in: (12100, 30, 1)\u001B[0m\n", + "\u001B[32mShape of controller_ax_in: (12100, 1, 1)\u001B[0m\n", + "\u001B[32mShape of controller_curv_in: (12100, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_ax_in: (12100, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_curv_in: (12100, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_steer_in: (12100, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_vx_in: (12100, 2, 1)\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m============================ nnodely Model Dataset =============================\u001B[0m\n", + "\u001B[32mDataset Name: validation_set\u001B[0m\n", + "\u001B[32mNumber of files: 2\u001B[0m\n", + "\u001B[32mTotal number of samples: 5560\u001B[0m\n", + "\u001B[32mShape of controller_vx_in: (5560, 30, 1)\u001B[0m\n", + "\u001B[32mShape of controller_ax_in: (5560, 1, 1)\u001B[0m\n", + "\u001B[32mShape of controller_curv_in: (5560, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_ax_in: (5560, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_curv_in: (5560, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_steer_in: (5560, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_vx_in: (5560, 2, 1)\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Training", + "id": "6c9524ead582c921" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T19:50:35.086278Z", + "start_time": "2025-12-09T19:50:35.082620Z" + } + }, + "cell_type": "code", + "source": [ + "# Default training parameters definition\n", + "training_pars = {'num_of_epochs': 5,\n", + " 'val_batch_size': 64,\n", + " 'train_batch_size': 16,\n", + " 'lr': 1e-3,\n", + " 'train_dataset': 'training_set',\n", + " 'validation_dataset': 'validation_set',\n", + " 'models': 'control_steer',\n", + " 'optimizer': 'Adam',\n", + " 'shuffle_data': True,\n", + " 'select_model': select_best_model,\n", + " 'early_stopping': earlystopping.early_stop_patience,\n", + " 'early_stopping_params': {'patience': 3, 'error': 'heading_error'}}" + ], + "id": "fa82ab5c8f2e7f2c", + "outputs": [], + "execution_count": 7 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T19:53:07.861950Z", + "start_time": "2025-12-09T19:50:35.092704Z" + } + }, + "cell_type": "code", + "source": [ + "# First training without few samples in the future\n", + "lat_dyna_control.trainModel(training_params=training_pars,\n", + " prediction_samples=5)" + ], + "id": "e949cd28ae6e34c7", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m======================== nnodely Model Train Parameters ========================\u001B[0m\n", + "\u001B[32mmodels: ['control_steer']\u001B[0m\n", + "\u001B[32mnum of epochs: 5\u001B[0m\n", + "\u001B[32mupdate per epochs: 755\u001B[0m\n", + "\u001B[34mβ””>len(train_indexes)//(batch_size+step)\u001B[0m\n", + "\u001B[32mshuffle data: True\u001B[0m\n", + "\u001B[32mearly stopping: early_stop_patience\u001B[0m\n", + "\u001B[32mearly stopping params: {'error': 'heading_error', 'patience': 3}\u001B[0m\n", + "\u001B[32mprediction samples: 5\u001B[0m\n", + "\u001B[32mstep: 0\u001B[0m\n", + "\u001B[32mclosed loop: {}\u001B[0m\n", + "\u001B[32mconnect: {}\u001B[0m\n", + "\u001B[32mtrain dataset: training_set\u001B[0m\n", + "\u001B[32m\t- batch size: 16\u001B[0m\n", + "\u001B[32m\t- num of samples: 12100\u001B[0m\n", + "\u001B[32m\t- num of first samples: 12080\u001B[0m\n", + "\u001B[32mvalidation dataset: validation_set\u001B[0m\n", + "\u001B[32m\t- batch size: 64\u001B[0m\n", + "\u001B[32m\t- num of samples: 5560\u001B[0m\n", + "\u001B[32m\t- num of first samples: 5550\u001B[0m\n", + "\u001B[32mminimizers: {'curv_error': {'A': 'SamplePart95',\n", + " 'B': 'Mul72',\n", + " 'loss': 'mse'},\n", + " 'heading_error': {'A': 'Add82',\n", + " 'B': 'Add92',\n", + " 'loss': 'mse'}}\u001B[0m\n", + "\u001B[32moptimizer: Adam\u001B[0m\n", + "\u001B[32moptimizer defaults: {'lr': 0.001}\u001B[0m\n", + "\u001B[32moptimizer params: [{'lr': 0.0, 'params': 'A_0'},\n", + " {'lr': 0.0, 'params': 'A_1'},\n", + " {'lr': 0.0, 'params': 'A_2'},\n", + " {'lr': 0.0, 'params': 'A_3'},\n", + " {'lr': 0.0, 'params': 'A_4'},\n", + " {'lr': 0.0, 'params': 'Fir_0'},\n", + " {'lr': 0.0, 'params': 'Fir_1'},\n", + " {'lr': 0.0, 'params': 'Fir_2'},\n", + " {'lr': 0.0, 'params': 'Fir_3'},\n", + " {'lr': 0.0, 'params': 'Fir_4'},\n", + " {'lr': 0.0, 'params': 'Fir_5'},\n", + " {'lr': 0.0, 'params': 'Fir_6'},\n", + " {'lr': 0.0, 'params': 'Fir_7'},\n", + " {'params': 'Fir_InitCondition'},\n", + " {'params': 'Fir_target_0'},\n", + " {'params': 'Fir_target_1'},\n", + " {'params': 'Fir_target_2'},\n", + " {'params': 'Fir_target_3'},\n", + " {'params': 'Fir_target_4'},\n", + " {'params': 'Fir_target_5'},\n", + " {'params': 'Fir_target_6'},\n", + " {'params': 'Fir_target_7'}]\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m=========================== nnodely Training ===========================\u001B[0m\n", + "\u001B[32m| Epoch |\u001B[0m\u001B[32m curv_error |\u001B[0m\u001B[32m heading_error |\u001B[0m\u001B[32m Total |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\n", + "\u001B[32m|----------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| 1/5 |\u001B[0m\u001B[32m7.216e-05|\u001B[0m\u001B[32m3.451e-05|\u001B[0m\u001B[32m2.014e-04|\u001B[0m\u001B[32m5.827e-05|\u001B[0m\u001B[32m1.368e-04|\u001B[0m\u001B[32m4.639e-05|\u001B[0m\n", + "\u001B[32m| 2/5 |\u001B[0m\u001B[32m3.435e-05|\u001B[0m\u001B[32m2.168e-05|\u001B[0m\u001B[32m5.156e-05|\u001B[0m\u001B[32m3.947e-05|\u001B[0m\u001B[32m4.295e-05|\u001B[0m\u001B[32m3.058e-05|\u001B[0m\n", + "\u001B[32m| 3/5 |\u001B[0m\u001B[32m2.631e-05|\u001B[0m\u001B[32m1.536e-05|\u001B[0m\u001B[32m4.149e-05|\u001B[0m\u001B[32m3.209e-05|\u001B[0m\u001B[32m 3.39e-05|\u001B[0m\u001B[32m2.372e-05|\u001B[0m\n", + "\u001B[32m| 4/5 |\u001B[0m\u001B[32m2.243e-05|\u001B[0m\u001B[32m1.184e-05|\u001B[0m\u001B[32m3.738e-05|\u001B[0m\u001B[32m2.927e-05|\u001B[0m\u001B[32m2.991e-05|\u001B[0m\u001B[32m2.056e-05|\u001B[0m\n", + "\u001B[32m| 5/5 |\u001B[0m\u001B[32m1.901e-05|\u001B[0m\u001B[32m9.138e-06|\u001B[0m\u001B[32m3.452e-05|\u001B[0m\u001B[32m2.683e-05|\u001B[0m\u001B[32m2.677e-05|\u001B[0m\u001B[32m1.798e-05|\u001B[0m\n", + "\u001B[32m|----------------------------------------------------------------------|\u001B[0m\n", + "\u001B[1;32m============================ nnodely Training Time =============================\u001B[0m\n", + "\u001B[32mTotal time of Training: 151.05759406089783\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[34mSelected the model at the epoch 5.\u001B[0m\n" + ] + } + ], + "execution_count": 8 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T20:36:41.115404Z", + "start_time": "2025-12-09T19:53:07.919490Z" + } + }, + "cell_type": "code", + "source": [ + "# Training with integral error 4s in the future\n", + "lat_dyna_control.trainModel(training_params=training_pars,\n", + " num_of_epochs=10,\n", + " prediction_samples=80, # 4s in the future\n", + " step=10)" + ], + "id": "beac2f15f1c62932", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m======================== nnodely Model Train Parameters ========================\u001B[0m\n", + "\u001B[32mmodels: ['control_steer']\u001B[0m\n", + "\u001B[32mnum of epochs: 10\u001B[0m\n", + "\u001B[32mupdate per epochs: 453\u001B[0m\n", + "\u001B[34mβ””>len(train_indexes)//(batch_size+step)\u001B[0m\n", + "\u001B[32mshuffle data: True\u001B[0m\n", + "\u001B[32mearly stopping: early_stop_patience\u001B[0m\n", + "\u001B[32mearly stopping params: {'error': 'heading_error', 'patience': 3}\u001B[0m\n", + "\u001B[32mprediction samples: 80\u001B[0m\n", + "\u001B[32mstep: 10\u001B[0m\n", + "\u001B[32mclosed loop: {}\u001B[0m\n", + "\u001B[32mconnect: {}\u001B[0m\n", + "\u001B[32mtrain dataset: training_set\u001B[0m\n", + "\u001B[32m\t- batch size: 16\u001B[0m\n", + "\u001B[32m\t- num of samples: 12100\u001B[0m\n", + "\u001B[32m\t- num of first samples: 11780\u001B[0m\n", + "\u001B[32mvalidation dataset: validation_set\u001B[0m\n", + "\u001B[32m\t- batch size: 64\u001B[0m\n", + "\u001B[32m\t- num of samples: 5560\u001B[0m\n", + "\u001B[32m\t- num of first samples: 5400\u001B[0m\n", + "\u001B[32mminimizers: {'curv_error': {'A': 'SamplePart95',\n", + " 'B': 'Mul72',\n", + " 'loss': 'mse'},\n", + " 'heading_error': {'A': 'Add82',\n", + " 'B': 'Add92',\n", + " 'loss': 'mse'}}\u001B[0m\n", + "\u001B[32moptimizer: Adam\u001B[0m\n", + "\u001B[32moptimizer defaults: {'lr': 0.001}\u001B[0m\n", + "\u001B[32moptimizer params: [{'lr': 0.0, 'params': 'A_0'},\n", + " {'lr': 0.0, 'params': 'A_1'},\n", + " {'lr': 0.0, 'params': 'A_2'},\n", + " {'lr': 0.0, 'params': 'A_3'},\n", + " {'lr': 0.0, 'params': 'A_4'},\n", + " {'lr': 0.0, 'params': 'Fir_0'},\n", + " {'lr': 0.0, 'params': 'Fir_1'},\n", + " {'lr': 0.0, 'params': 'Fir_2'},\n", + " {'lr': 0.0, 'params': 'Fir_3'},\n", + " {'lr': 0.0, 'params': 'Fir_4'},\n", + " {'lr': 0.0, 'params': 'Fir_5'},\n", + " {'lr': 0.0, 'params': 'Fir_6'},\n", + " {'lr': 0.0, 'params': 'Fir_7'},\n", + " {'params': 'Fir_InitCondition'},\n", + " {'params': 'Fir_target_0'},\n", + " {'params': 'Fir_target_1'},\n", + " {'params': 'Fir_target_2'},\n", + " {'params': 'Fir_target_3'},\n", + " {'params': 'Fir_target_4'},\n", + " {'params': 'Fir_target_5'},\n", + " {'params': 'Fir_target_6'},\n", + " {'params': 'Fir_target_7'}]\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m=========================== nnodely Training ===========================\u001B[0m\n", + "\u001B[32m| Epoch |\u001B[0m\u001B[32m curv_error |\u001B[0m\u001B[32m heading_error |\u001B[0m\u001B[32m Total |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\n", + "\u001B[32m|----------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| 1/10 |\u001B[0m\u001B[32m1.346e-05|\u001B[0m\u001B[32m2.467e-06|\u001B[0m\u001B[32m1.081e-03|\u001B[0m\u001B[32m3.308e-04|\u001B[0m\u001B[32m5.473e-04|\u001B[0m\u001B[32m1.666e-04|\u001B[0m\n", + "\u001B[32m| 2/10 |\u001B[0m\u001B[32m1.211e-05|\u001B[0m\u001B[32m2.186e-06|\u001B[0m\u001B[32m4.719e-04|\u001B[0m\u001B[32m2.834e-04|\u001B[0m\u001B[32m 2.42e-04|\u001B[0m\u001B[32m1.428e-04|\u001B[0m\n", + "\u001B[32m| 3/10 |\u001B[0m\u001B[32m 1.19e-05|\u001B[0m\u001B[32m2.095e-06|\u001B[0m\u001B[32m4.269e-04|\u001B[0m\u001B[32m2.913e-04|\u001B[0m\u001B[32m2.194e-04|\u001B[0m\u001B[32m1.467e-04|\u001B[0m\n", + "\u001B[32m| 4/10 |\u001B[0m\u001B[32m1.156e-05|\u001B[0m\u001B[32m1.922e-06|\u001B[0m\u001B[32m4.108e-04|\u001B[0m\u001B[32m2.316e-04|\u001B[0m\u001B[32m2.112e-04|\u001B[0m\u001B[32m1.168e-04|\u001B[0m\n", + "\u001B[32m| 5/10 |\u001B[0m\u001B[32m1.157e-05|\u001B[0m\u001B[32m1.890e-06|\u001B[0m\u001B[32m3.791e-04|\u001B[0m\u001B[32m1.919e-04|\u001B[0m\u001B[32m1.953e-04|\u001B[0m\u001B[32m 9.69e-05|\u001B[0m\n", + "\u001B[32m| 6/10 |\u001B[0m\u001B[32m1.158e-05|\u001B[0m\u001B[32m1.828e-06|\u001B[0m\u001B[32m3.607e-04|\u001B[0m\u001B[32m1.696e-04|\u001B[0m\u001B[32m1.862e-04|\u001B[0m\u001B[32m8.570e-05|\u001B[0m\n", + "\u001B[32m| 7/10 |\u001B[0m\u001B[32m1.154e-05|\u001B[0m\u001B[32m1.845e-06|\u001B[0m\u001B[32m3.389e-04|\u001B[0m\u001B[32m1.798e-04|\u001B[0m\u001B[32m1.752e-04|\u001B[0m\u001B[32m9.084e-05|\u001B[0m\n", + "\u001B[32m| 8/10 |\u001B[0m\u001B[32m1.157e-05|\u001B[0m\u001B[32m1.719e-06|\u001B[0m\u001B[32m3.202e-04|\u001B[0m\u001B[32m1.381e-04|\u001B[0m\u001B[32m1.659e-04|\u001B[0m\u001B[32m6.992e-05|\u001B[0m\n", + "\u001B[32m| 9/10 |\u001B[0m\u001B[32m1.143e-05|\u001B[0m\u001B[32m1.718e-06|\u001B[0m\u001B[32m3.073e-04|\u001B[0m\u001B[32m1.345e-04|\u001B[0m\u001B[32m1.594e-04|\u001B[0m\u001B[32m6.813e-05|\u001B[0m\n", + "\u001B[32m| 10/10 |\u001B[0m\u001B[32m1.115e-05|\u001B[0m\u001B[32m1.688e-06|\u001B[0m\u001B[32m2.714e-04|\u001B[0m\u001B[32m1.218e-04|\u001B[0m\u001B[32m1.413e-04|\u001B[0m\u001B[32m6.175e-05|\u001B[0m\n", + "\u001B[32m|----------------------------------------------------------------------|\u001B[0m\n", + "\u001B[1;32m============================ nnodely Training Time =============================\u001B[0m\n", + "\u001B[32mTotal time of Training: 2612.6510860919952\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[34mSelected the model at the epoch 10.\u001B[0m\n" + ] + } + ], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Plots", + "id": "583107a320c3507e" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### FIR filters weights", + "id": "cebe84713bc43261" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T20:36:41.424463Z", + "start_time": "2025-12-09T20:36:41.159691Z" + } + }, + "cell_type": "code", + "source": [ + "params_fir_target = {}\n", + "params_fir_ic = {}\n", + "sampling_time = 0.05\n", + "\n", + "# Plot\n", + "plt.figure()\n", + "\n", + "for key, value in lat_dyna_control.parameters.items():\n", + " if key.startswith('Fir_target'):\n", + " params_fir_target[key] = lat_dyna_control.parameters[key]\n", + "\n", + " if key.startswith('Fir_InitCondition'):\n", + " params_fir_ic[key] = lat_dyna_control.parameters[key]\n", + "\n", + "for key, value in params_fir_target.items():\n", + " vals = np.array(value).flatten()\n", + " t = np.arange(0, sampling_time * len(vals), sampling_time)\n", + " plt.plot(t,vals * W_prev.flatten(), 'o', label=key)\n", + "\n", + "plt.xlabel('time (s)')\n", + "plt.ylabel('amplitude [m-1 rad-1]')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show(block=False)\n", + "\n", + "plt.figure()\n", + "for key, value in params_fir_ic.items():\n", + " vals = np.array(value).flatten()\n", + " t = np.arange(sampling_time, sampling_time * (len(vals)+1), sampling_time)\n", + " plt.plot(t,np.flip(vals * W_past.flatten()),'o',label=key)\n", + "plt.xlabel('time (s)')\n", + "plt.ylabel('amplitude [-]')\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show(block=False)" + ], + "id": "345b6b768a49e855", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 10 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Analyze model", + "id": "28898a88c513d7e0" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Below is the self-evaluation of the performance of the model and the controller connected as described in the paper on the training, validation, and test sets. The framework automatically computes relevant metrics such as MSE, FVU, and AIC (the latter being particularly useful when comparing this model with alternative controllers). Performance evaluation is conducted over a temporal window of 200 samples, corresponding to 10 seconds. The plot is also generated automatically and displays consecutive 10-second time series segments. Since the errors were computed for both curvature and heading, they are reported separately below.", + "id": "c7bc09da5e69f27d" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T20:40:06.269548Z", + "start_time": "2025-12-09T20:36:41.440312Z" + } + }, + "cell_type": "code", + "source": [ + "lat_dyna_control.analyzeModel('training_set',prediction_samples=200) # 10s prediction\n", + "lat_dyna_control.analyzeModel('validation_set',prediction_samples=200)\n", + "lat_dyna_control.analyzeModel('test_set',prediction_samples=200)" + ], + "id": "eba66fd4f5ce72f7", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m================== nnodely Model Results for training_set =================\u001B[0m\n", + "\u001B[32m| Loss |\u001B[0m\u001B[32m mse |\u001B[0m\u001B[32m FVU |\u001B[0m\u001B[32m AIC |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m lower better |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| curv_error |\u001B[0m\u001B[32m 1.140e-06 |\u001B[0m\u001B[32m 4.404e-03 |\u001B[0m\u001B[32m 2.391e+06 |\u001B[0m\n", + "\u001B[32m|heading_error|\u001B[0m\u001B[32m 5.398e-04 |\u001B[0m\u001B[32m 5.965e-04 |\u001B[0m\u001B[32m 2.393e+06 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| Total |\u001B[0m\u001B[32m 2.705e-04 |\u001B[0m\u001B[32m 2.500e-03 |\u001B[0m\u001B[32m 2.392e+06 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m================= nnodely Model Results for validation_set ================\u001B[0m\n", + "\u001B[32m| Loss |\u001B[0m\u001B[32m mse |\u001B[0m\u001B[32m FVU |\u001B[0m\u001B[32m AIC |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m lower better |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| curv_error |\u001B[0m\u001B[32m 9.094e-07 |\u001B[0m\u001B[32m 3.294e-03 |\u001B[0m\u001B[32m 1.076e+06 |\u001B[0m\n", + "\u001B[32m|heading_error|\u001B[0m\u001B[32m 6.473e-04 |\u001B[0m\u001B[32m 6.261e-04 |\u001B[0m\u001B[32m 1.078e+06 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| Total |\u001B[0m\u001B[32m 3.241e-04 |\u001B[0m\u001B[32m 1.96e-03 |\u001B[0m\u001B[32m 1.077e+06 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m==================== nnodely Model Results for test_set ===================\u001B[0m\n", + "\u001B[32m| Loss |\u001B[0m\u001B[32m mse |\u001B[0m\u001B[32m FVU |\u001B[0m\u001B[32m AIC |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m lower better |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| curv_error |\u001B[0m\u001B[32m 4.818e-07 |\u001B[0m\u001B[32m 1.589e-03 |\u001B[0m\u001B[32m 2.147e+05 |\u001B[0m\n", + "\u001B[32m|heading_error|\u001B[0m\u001B[32m 9.289e-04 |\u001B[0m\u001B[32m 8.999e-04 |\u001B[0m\u001B[32m 2.664e+05 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| Total |\u001B[0m\u001B[32m 4.647e-04 |\u001B[0m\u001B[32m 1.245e-03 |\u001B[0m\u001B[32m 2.405e+05 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 11 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Plot: curvature comparison", + "id": "ff7c6cbf56193020" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T20:44:36.155503Z", + "start_time": "2025-12-09T20:44:31.326411Z" + } + }, + "cell_type": "code", + "source": [ + "data = pd.read_csv(\"dataset/test/test_set.csv\")\n", + "\n", + "sample_test_set = {\n", + " 'controller_vx_in' : np.array(data['vx']),\n", + " 'model_vx_in' : np.array(data['vx']),\n", + " 'controller_curv_in' : np.array(data['curv']),\n", + " 'model_curv_in' : np.array(data['curv']),\n", + " 'controller_steer_in' : np.array(data['steer']),\n", + " 'model_steer_in': np.array(data['steer']),\n", + " 'model_ax_in' : np.array(data['ax']),\n", + " 'controller_ax_in': np.array(data['ax'])\n", + "}\n", + "\n", + "out_nn_test_set = lat_dyna_control(sample_test_set, sampled=False, prediction_samples=1100)\n", + "\n", + "# plot the results\n", + "plt.figure()\n", + "plt.plot(data['steer'],label='telem')\n", + "plt.plot(out_nn_test_set['controller_steer_out'],label='control')\n", + "plt.xlabel('samples')\n", + "plt.ylabel('steer (rad)')\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "plt.figure()\n", + "plt.plot(data['curv'],label='telem')\n", + "plt.plot(out_nn_test_set['model_curv'],label='control')\n", + "plt.xlabel('samples')\n", + "plt.ylabel('curvature (rad)')\n", + "plt.legend()\n", + "plt.grid()" + ], + "id": "f1e195980e10ddfd", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[33m[__call__] Different number of samples between inputs [MAX 1105 = 1105; MIN 1076 = 1076]\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 17 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Export ONNX", + "id": "3d9052e0787f7564" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-09T20:44:55.789536Z", + "start_time": "2025-12-09T20:44:53.780921Z" + } + }, + "cell_type": "code", + "source": [ + "# Save json model\n", + "lat_dyna_control.saveModel('control_model')\n", + "\n", + "# Remove Minimize\n", + "lat_dyna_control.removeMinimize(['heading_error','curv_error'])\n", + "lat_dyna_control.neuralizeModel()\n", + "\n", + "# Export ONNX\n", + "lat_dyna_control.exportONNX(['controller_curv_in', 'controller_vx_in','controller_ax_in','controller_steer_in'],['controller_steer_out','controller_steer_from_ic','controller_steer_from_target'],'controller',models='control_steer')\n" + ], + "id": "d2fd6c9e3bc23d6a", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m=============================== Save JSON Model ================================\u001B[0m\n", + "\u001B[32mModel saved in: trained_models/control_model.json\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[33m[neuralizeModel] Connect on model_steer_in with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {'A': {'dim': 5,\n", + " 'values': [0.12241744995117188,\n", + " 0.13215704262256622,\n", + " 0.13487723469734192,\n", + " 0.13838686048984528,\n", + " 0.14319394528865814]},\n", + " 'W_fir': {'dim': 1,\n", + " 'sw': 30,\n", + " 'values': [[0.023806948214769363],\n", + " [0.03067079372704029],\n", + " [0.039163894951343536],\n", + " [0.04956628382205963],\n", + " [0.062176525592803955],\n", + " [0.07730474323034286],\n", + " [0.09526325762271881],\n", + " [0.116354800760746],\n", + " [0.14085842669010162],\n", + " [0.16901332139968872],\n", + " [0.20100119709968567],\n", + " [0.23692776262760162],\n", + " [0.2768043279647827],\n", + " [0.3205305337905884],\n", + " [0.3678794503211975],\n", + " [0.4184862971305847],\n", + " [0.4718419909477234],\n", + " [0.5272924304008484],\n", + " [0.5840446949005127],\n", + " [0.6411803960800171],\n", + " [0.6976763010025024],\n", + " [0.7524321675300598],\n", + " [0.8043041825294495],\n", + " [0.8521437644958496],\n", + " [0.894839346408844],\n", + " [0.9313583970069885],\n", + " [0.9607894420623779],\n", + " [0.9823793172836304],\n", + " [0.9955654144287109],\n", + " [1.0]]},\n", + " 'W_fir_init': {'dim': 1,\n", + " 'sw': 29,\n", + " 'values': [[0.03067079372704029],\n", + " [0.039163894951343536],\n", + " [0.04956628382205963],\n", + " [0.062176525592803955],\n", + " [0.07730474323034286],\n", + " [0.09526325762271881],\n", + " [0.116354800760746],\n", + " [0.14085842669010162],\n", + " [0.16901332139968872],\n", + " [0.20100119709968567],\n", + " [0.23692776262760162],\n", + " [0.2768043279647827],\n", + " [0.3205305337905884],\n", + " [0.3678794503211975],\n", + " [0.4184862971305847],\n", + " [0.4718419909477234],\n", + " [0.5272924304008484],\n", + " [0.5840446949005127],\n", + " [0.6411803960800171],\n", + " [0.6976763010025024],\n", + " [0.7524321675300598],\n", + " [0.8043041825294495],\n", + " [0.8521437644958496],\n", + " [0.894839346408844],\n", + " [0.9313583970069885],\n", + " [0.9607894420623779],\n", + " [0.9823793172836304],\n", + " [0.9955654144287109],\n", + " [1.0]]},\n", + " 'W_fir_target': {'dim': 1,\n", + " 'sw': 30,\n", + " 'values': [[1.0],\n", + " [0.9955654144287109],\n", + " [0.9823793172836304],\n", + " [0.9607894420623779],\n", + " [0.9313583970069885],\n", + " [0.894839346408844],\n", + " [0.8521437644958496],\n", + " [0.8043041825294495],\n", + " [0.7524321675300598],\n", + " [0.6976763010025024],\n", + " [0.6411803960800171],\n", + " [0.5840446949005127],\n", + " [0.5272924304008484],\n", + " [0.4718419909477234],\n", + " [0.4184862971305847],\n", + " [0.3678794503211975],\n", + " [0.3205305337905884],\n", + " [0.2768043279647827],\n", + " [0.23692776262760162],\n", + " [0.20100119709968567],\n", + " [0.16901332139968872],\n", + " [0.14085842669010162],\n", + " [0.116354800760746],\n", + " [0.09526325762271881],\n", + " [0.07730474323034286],\n", + " [0.062176525592803955],\n", + " [0.04956628382205963],\n", + " [0.039163894951343536],\n", + " [0.03067079372704029],\n", + " [0.023806948214769363]]}},\n", + " 'Functions': {'FFuzzify104': {'centers': [-2.0, -1.0, 0.0, 1.0, 2.0],\n", + " 'dim_out': {'dim': 5},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FFuzzify105': {'centers': [8.0,\n", + " 10.142857142857142,\n", + " 12.285714285714285,\n", + " 14.428571428571429,\n", + " 16.57142857142857,\n", + " 18.714285714285715,\n", + " 20.857142857142858,\n", + " 23.0],\n", + " 'dim_out': {'dim': 8},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FFuzzify7': {'centers': [8.0,\n", + " 10.142857142857142,\n", + " 12.285714285714285,\n", + " 14.428571428571429,\n", + " 16.57142857142857,\n", + " 18.714285714285715,\n", + " 20.857142857142858,\n", + " 23.0],\n", + " 'dim_out': {'dim': 8},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FFuzzify8': {'centers': [-2.0, -1.0, 0.0, 1.0, 2.0],\n", + " 'dim_out': {'dim': 5},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FParamFun103': {'code': 'def '\n", + " 'understeer_corr_local_control(vx,curv, '\n", + " '# inputs\\n'\n", + " ' '\n", + " 'A, # constant\\n'\n", + " ' ):\\n'\n", + " ' return curv * (1 + A * '\n", + " 'torch.pow(vx, 2))\\n',\n", + " 'in_dim': [{'dim': 1, 'sw': 30},\n", + " {'dim': 1, 'sw': 30},\n", + " {'dim': 1, 'sw': 1}],\n", + " 'map_over_dim': False,\n", + " 'n_input': 3,\n", + " 'name': 'understeer_corr_local_control',\n", + " 'params_and_consts': []},\n", + " 'FParamFun6': {'code': 'def understeer_corr_local(vx, # input\\n'\n", + " ' A # '\n", + " 'learnable parameter\\n'\n", + " ' ):\\n'\n", + " ' return 1 / (1 + A * torch.pow(vx, '\n", + " '2))\\n',\n", + " 'in_dim': [{'dim': 1, 'sw': 1}, {'dim': [1, 1]}],\n", + " 'map_over_dim': False,\n", + " 'n_input': 2,\n", + " 'name': 'understeer_corr_local',\n", + " 'params_and_consts': []}},\n", + " 'Info': {'SampleTime': 0.05,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [30, 29],\n", + " 'ntot': 59,\n", + " 'num_parameters': 514},\n", + " 'Inputs': {'controller_ax_in': {'dim': 1,\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'sw': [-1, 0]},\n", + " 'controller_curv_in': {'dim': 1,\n", + " 'ns': [1, 29],\n", + " 'ntot': 30,\n", + " 'sw': [-1, 29]},\n", + " 'controller_steer_in': {'closedLoop': 'Add148',\n", + " 'dim': 1,\n", + " 'local': 0,\n", + " 'ns': [30, -1],\n", + " 'ntot': 29,\n", + " 'sw': [-30, -1]},\n", + 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['Fuzzify2'], 8, 3],\n", + " 'Select23': ['Select', ['Fuzzify2'], 8, 4],\n", + " 'Select26': ['Select', ['Fuzzify2'], 8, 5],\n", + " 'Select29': ['Select', ['Fuzzify2'], 8, 6],\n", + " 'Select32': ['Select', ['Fuzzify2'], 8, 7],\n", + " 'Select46': ['Select', ['Fuzzify5'], 5, 0],\n", + " 'Select51': ['Select', ['Fuzzify5'], 5, 1],\n", + " 'Select56': ['Select', ['Fuzzify5'], 5, 2],\n", + " 'Select61': ['Select', ['Fuzzify5'], 5, 3],\n", + " 'Select66': ['Select', ['Fuzzify5'], 5, 4],\n", + " 'Sum104': ['Sum', ['Mul103']]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m========================== Export Python Torch Model ===========================\u001B[0m\n", + "\u001B[32mModel exported in: trained_models/onnx/controller.py\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m======================== Export Python Onnx Torch Model ========================\u001B[0m\n", + "\u001B[32mModel exported in: trained_models/onnx/controller_onnx.py\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m============================== Export Onnx Model ===============================\u001B[0m\n", + "\u001B[32mModel exported in: trained_models/onnx/controller.onnx\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 18 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/case-studies/lateral_dynamics/lateral_dynamics_model.ipynb b/case-studies/lateral_dynamics/lateral_dynamics_model.ipynb new file mode 100644 index 00000000..08d3a3aa --- /dev/null +++ b/case-studies/lateral_dynamics/lateral_dynamics_model.ipynb @@ -0,0 +1,1399 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "# **FORWARD MODEL OF VEHICLE LATERAL DYNAMICS**", + "id": "4fca82a7205fafb9" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Import packages", + "id": "f4fe99351f149193" + }, + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.200191Z", + "start_time": "2025-12-02T13:54:30.406048Z" + } + }, + "source": [ + "# Import necessary packages\n", + "import sys, os\n", + "import pandas as pd\n", + "\n", + "# import nnodely modules\n", + "from nnodely import *\n", + "from nnodely.support.utils import *\n", + "from nnodely.support import earlystopping\n", + "from nnodely.support.earlystopping import select_best_model\n", + "\n", + "# import a library for plots\n", + "import matplotlib as mpl\n", + "mpl.rcParams.update(mpl.rcParamsDefault)\n", + "import matplotlib.pyplot as plt\n", + "plt.close('all')\n", + "SMALL_SIZE = 14\n", + "MEDIUM_SIZE = 22\n", + "BIGGER_SIZE = 26\n", + "plt.rc('font', size=SMALL_SIZE) # controls default text sizes\n", + "plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title\n", + "plt.rc('axes', labelsize=SMALL_SIZE) # fontsize of the x and y labels\n", + "plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n", + "plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n", + "plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize\n", + "plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title\n", + "plt.rc('grid', linestyle=\"--\", color='grey')" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>-- nnodely_v1.5.2 --<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Configurations", + "id": "7f7ac76c4718c9be" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.215293Z", + "start_time": "2025-12-02T13:54:32.207438Z" + } + }, + "cell_type": "code", + "source": [ + "path_folder = 'trained_models' # folder to save the model\n", + "lat_dyna_model = nnodely(visualizer=MPLNotebookVisualizer(),seed=1,workspace=path_folder,save_history=False)" + ], + "id": "fc1700c6ad33005a", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Neural Network model", + "id": "a4b4192ab97751ff" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Model definition", + "id": "b6ef5ba231c37df3" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.396841Z", + "start_time": "2025-12-02T13:54:32.221943Z" + } + }, + "cell_type": "code", + "source": [ + "# ----------------------------------------------------------------\n", + "# Inputs\n", + "# ----------------------------------------------------------------\n", + "curv_in = Input('model_curv_in') # [1/m] path curvature\n", + "vx_in = Input('model_vx_in') # [m/s] longitudinal velocity\n", + "steer_in = Input('model_steer_in') # [rad] steering wheel angle\n", + "ax_in = Input('model_ax_in') # [m/s^2] lateral acceleration\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Hyperparameters\n", + "# ----------------------------------------------------------------\n", + "samples_past_steer = 30 # number of samples in the past for the steering wheel angle prediction\n", + "n_channels_vx = 8 # number of channels for activation function vx\n", + "chan_vx = list(np.linspace(8, 23, num=n_channels_vx)) # centers of the channels vx\n", + "chan_ax = [-2,-1,0.0,1.0,2.0] # centers of the channels ax\n", + "\n", + "# Exponential weights for normalisation of FIR parameters during training\n", + "W_fir = np.array([[np.exp(-(i/(samples_past_steer/2))**2)] for i in range(-samples_past_steer+1,1)])\n", + "W_constant = Constant('W_fir',sw=30, values=W_fir)\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Understeer correction function\n", + "# ----------------------------------------------------------------\n", + "def understeer_corr_local(vx, # input\n", + " A # learnable parameter\n", + " ):\n", + " return 1 / (1 + A * torch.pow(vx, 2))\n", + "\n", + "understeer_corr = ParamFun(understeer_corr_local)\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Local models\n", + "# ----------------------------------------------------------------\n", + "# Activation functions\n", + "fuzzy_vx = Fuzzify(centers=chan_vx, functions='Triangular')(vx_in.last())\n", + "fuzzy_ax = Fuzzify(centers=chan_ax, functions='Triangular')(ax_in.last())\n", + "\n", + "# FIR input functions\n", + "def Input_Function_Gen(idx):\n", + " def fir_out(fir_input):\n", + " out = Fir(W_init='init_constant', W_init_params={\"value\": 1}, dropout=0.05, W=f'Fir_{idx[0]}')(fir_input)\n", + " return out\n", + "\n", + " return fir_out\n", + "\n", + "local_input_model = LocalModel(pass_indexes=True, input_function=Input_Function_Gen)\n", + "out_input_model = local_input_model((steer_in.sw(samples_past_steer) * W_constant ), fuzzy_vx)\n", + "\n", + "# Parametric functions: Understeer correction\n", + "def Understeer_Function_Gen(idx):\n", + " def osus_out(vx_input):\n", + " A = Parameter('A_' + str(idx[0]), values=[[1e-5]])\n", + " return understeer_corr(vx_input, A)\n", + " return osus_out\n", + "\n", + "local_osus_corr = LocalModel(pass_indexes=True, input_function=Understeer_Function_Gen)\n", + "out_osus = local_osus_corr(vx_in.last(), fuzzy_ax)\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Outputs\n", + "# ----------------------------------------------------------------\n", + "curv_out = Output('model_curv', out_input_model*out_osus) # output of the model\n", + "\n", + "# ----------------------------------------------------------------\n", + "# Add model\n", + "# ----------------------------------------------------------------\n", + "lat_dyna_model.addModel('vehicle_model', curv_out)" + ], + "id": "a7afd97b064ac3e", + "outputs": [], + "execution_count": 3 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Loss functions definitions", + "id": "deaac585e4481143" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.486772Z", + "start_time": "2025-12-02T13:54:32.403013Z" + } + }, + "cell_type": "code", + "source": [ + "# ------------------------------------------------------------------------\n", + "# Targets\n", + "# ------------------------------------------------------------------------\n", + "# Definition of heading as integral of curvature multiplied by the longitudinal speed\n", + "heading_target = Integrate(curv_in.next()*vx_in.next())\n", + "heading_nn = Integrate(out_input_model*out_osus*vx_in.next())\n", + "\n", + "# Add a loss function: mean squared error for the curvature prediction\n", + "lat_dyna_model.addMinimize('curv_error',\n", + " curv_in.next(),\n", + " curv_out,\n", + " loss_function='mse')\n", + "# Add a loss function: mean squared error for the heading prediction\n", + "lat_dyna_model.addMinimize('heading_error',\n", + " heading_target,\n", + " heading_nn)" + ], + "id": "e3cb0383034fec4", + "outputs": [], + "execution_count": 4 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Model neuralization", + "id": "5a60203dc386c863" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.516890Z", + "start_time": "2025-12-02T13:54:32.494761Z" + } + }, + "cell_type": "code", + "source": [ + "# Generate the model\n", + "lat_dyna_model.neuralizeModel(sample_time=0.05) # neuralize the model with the chosen sample time" + ], + "id": "c909f3d5f6b02813", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[33m[neuralizeModel] Closed loop on Mul87_int34 with sample in the future.\u001B[0m\n", + "\u001B[33m[neuralizeModel] Closed loop on Mul77_int33 with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {'SampleTime': {'dim': 1, 'values': 0.05},\n", + " 'W_fir': {'dim': 1,\n", + " 'sw': 30,\n", + " 'values': [[0.023806948214769363],\n", + " [0.03067079372704029],\n", + " [0.039163894951343536],\n", + " [0.04956628382205963],\n", + " [0.062176525592803955],\n", + " [0.07730474323034286],\n", + " [0.09526325762271881],\n", + " [0.116354800760746],\n", + " [0.14085842669010162],\n", + " [0.16901332139968872],\n", + " [0.20100119709968567],\n", + " [0.23692776262760162],\n", + " [0.2768043279647827],\n", + " [0.3205305337905884],\n", + " [0.3678794503211975],\n", + " [0.4184862971305847],\n", + " [0.4718419909477234],\n", + " [0.5272924304008484],\n", + " [0.5840446949005127],\n", + " [0.6411803960800171],\n", + " [0.6976763010025024],\n", + " [0.7524321675300598],\n", + " [0.8043041825294495],\n", + " [0.8521437644958496],\n", + " [0.894839346408844],\n", + " [0.9313583970069885],\n", + " [0.9607894420623779],\n", + " [0.9823793172836304],\n", + " [0.9955654144287109],\n", + " [1.0]]}},\n", + " 'Functions': {'FFuzzify7': {'centers': [8.0,\n", + " 10.142857142857142,\n", + " 12.285714285714285,\n", + " 14.428571428571429,\n", + " 16.57142857142857,\n", + " 18.714285714285715,\n", + " 20.857142857142858,\n", + " 23.0],\n", + " 'dim_out': {'dim': 8},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FFuzzify8': {'centers': [-2.0, -1.0, 0.0, 1.0, 2.0],\n", + " 'dim_out': {'dim': 5},\n", + " 'functions': 'Triangular',\n", + " 'names': 'Triangular'},\n", + " 'FParamFun6': {'code': 'def understeer_corr_local(vx, # input\\n'\n", + " ' A # '\n", + " 'learnable parameter\\n'\n", + " ' ):\\n'\n", + " ' return 1 / (1 + A * torch.pow(vx, '\n", + " '2))\\n',\n", + " 'in_dim': [{'dim': 1, 'sw': 1}, {'dim': [1, 1]}],\n", + " 'map_over_dim': False,\n", + " 'n_input': 2,\n", + " 'name': 'understeer_corr_local',\n", + " 'params_and_consts': []}},\n", + " 'Info': {'SampleTime': 0.05,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [30, 1],\n", + " 'ntot': 31,\n", + " 'num_parameters': 245},\n", + " 'Inputs': {'Mul77_int33': {'closedLoop': 'Add82',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'sw': [-1, 0]},\n", + " 'Mul87_int34': {'closedLoop': 'Add92',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'sw': [-1, 0]},\n", + " 'model_ax_in': {'dim': 1, 'ns': [1, 0], 'ntot': 1, 'sw': [-1, 0]},\n", + " 'model_curv_in': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]},\n", + " 'model_steer_in': {'dim': 1,\n", + " 'ns': [30, 0],\n", + " 'ntot': 30,\n", + " 'sw': [-30, 0]},\n", + " 'model_vx_in': {'dim': 1, 'ns': [1, 1], 'ntot': 2, 'sw': [-1, 1]}},\n", + " 'Minimizers': {'curv_error': {'A': 'SamplePart95',\n", + " 'B': 'Mul72',\n", + " 'loss': 'mse'},\n", + " 'heading_error': {'A': 'Add82', 'B': 'Add92', 'loss': 'mse'}},\n", + " 'Models': 'vehicle_model',\n", + " 'Outputs': {'model_curv': 'Mul72'},\n", + " 'Parameters': {'A_0': {'dim': [1, 1],\n", + " 'init_values': [[9.999999747378752e-06]],\n", + " 'values': [[9.999999747378752e-06]]},\n", + " 'A_1': {'dim': [1, 1],\n", + " 'init_values': [[9.999999747378752e-06]],\n", + " 'values': [[9.999999747378752e-06]]},\n", + " 'A_2': {'dim': [1, 1],\n", + " 'init_values': [[9.999999747378752e-06]],\n", + " 'values': [[9.999999747378752e-06]]},\n", + " 'A_3': {'dim': 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['Mul12', 'Mul15']],\n", + " 'Add35': ['Add', ['Add34', 'Mul18']],\n", + " 'Add36': ['Add', ['Add35', 'Mul21']],\n", + " 'Add37': ['Add', ['Add36', 'Mul24']],\n", + " 'Add38': ['Add', ['Add37', 'Mul27']],\n", + " 'Add39': ['Add', ['Add38', 'Mul30']],\n", + " 'Add40': ['Add', ['Add39', 'Mul33']],\n", + " 'Add68': ['Add', ['Mul47', 'Mul52']],\n", + " 'Add69': ['Add', ['Add68', 'Mul57']],\n", + " 'Add70': ['Add', ['Add69', 'Mul62']],\n", + " 'Add71': ['Add', ['Add70', 'Mul67']],\n", + " 'Add82': ['Add', ['SamplePart79', 'Mul81']],\n", + " 'Add92': ['Add', ['SamplePart89', 'Mul91']],\n", + " 'Fir10': ['Fir', ['Mul9'], 'Fir_0', None, 0.05],\n", + " 'Fir13': ['Fir', ['Mul9'], 'Fir_1', None, 0.05],\n", + " 'Fir16': ['Fir', ['Mul9'], 'Fir_2', None, 0.05],\n", + " 'Fir19': ['Fir', ['Mul9'], 'Fir_3', None, 0.05],\n", + " 'Fir22': ['Fir', ['Mul9'], 'Fir_4', None, 0.05],\n", + " 'Fir25': ['Fir', ['Mul9'], 'Fir_5', None, 0.05],\n", + " 'Fir28': ['Fir', ['Mul9'], 'Fir_6', None, 0.05],\n", + " 'Fir31': ['Fir', ['Mul9'], 'Fir_7', None, 0.05],\n", + " 'Fuzzify2': ['Fuzzify', ['SamplePart1'], 'FFuzzify7'],\n", + " 'Fuzzify5': ['Fuzzify', ['SamplePart4'], 'FFuzzify8'],\n", + " 'Mul12': ['Mul', ['Fir10', 'Select11']],\n", + " 'Mul15': ['Mul', ['Fir13', 'Select14']],\n", + " 'Mul18': ['Mul', ['Fir16', 'Select17']],\n", + " 'Mul21': ['Mul', ['Fir19', 'Select20']],\n", + " 'Mul24': ['Mul', ['Fir22', 'Select23']],\n", + " 'Mul27': ['Mul', ['Fir25', 'Select26']],\n", + " 'Mul30': ['Mul', ['Fir28', 'Select29']],\n", + " 'Mul33': ['Mul', ['Fir31', 'Select32']],\n", + " 'Mul47': ['Mul', ['ParamFun43', 'Select46']],\n", + " 'Mul52': ['Mul', ['ParamFun48', 'Select51']],\n", + " 'Mul57': ['Mul', ['ParamFun53', 'Select56']],\n", + " 'Mul62': ['Mul', ['ParamFun58', 'Select61']],\n", + " 'Mul67': ['Mul', ['ParamFun63', 'Select66']],\n", + " 'Mul72': ['Mul', ['Add40', 'Add71']],\n", + " 'Mul77': ['Mul', ['SamplePart74', 'SamplePart76']],\n", + " 'Mul81': ['Mul', ['Mul77', 'SampleTime']],\n", + " 'Mul84': ['Mul', ['Add40', 'Add71']],\n", + " 'Mul87': ['Mul', ['Mul84', 'SamplePart86']],\n", + " 'Mul9': ['Mul', ['SamplePart7', 'W_fir']],\n", + " 'Mul91': ['Mul', ['Mul87', 'SampleTime']],\n", + " 'ParamFun43': ['ParamFun',\n", + " ['SamplePart42', 'A_0'],\n", + " 'FParamFun6'],\n", + " 'ParamFun48': ['ParamFun',\n", + " ['SamplePart42', 'A_1'],\n", + " 'FParamFun6'],\n", + " 'ParamFun53': ['ParamFun',\n", + " ['SamplePart42', 'A_2'],\n", + " 'FParamFun6'],\n", + " 'ParamFun58': ['ParamFun',\n", + " ['SamplePart42', 'A_3'],\n", + " 'FParamFun6'],\n", + " 'ParamFun63': ['ParamFun',\n", + " ['SamplePart42', 'A_4'],\n", + " 'FParamFun6'],\n", + " 'SamplePart1': ['SamplePart', ['model_vx_in'], -1, [-1, 0]],\n", + " 'SamplePart4': ['SamplePart', ['model_ax_in'], -1, [-1, 0]],\n", + " 'SamplePart42': ['SamplePart', ['model_vx_in'], -1, [-1, 0]],\n", + " 'SamplePart7': ['SamplePart', ['model_steer_in'], -1, [-30, 0]],\n", + " 'SamplePart74': ['SamplePart', ['model_curv_in'], -1, [0, 1]],\n", + " 'SamplePart76': ['SamplePart', ['model_vx_in'], -1, [0, 1]],\n", + " 'SamplePart79': ['SamplePart', ['Mul77_int33'], -1, [-1, 0]],\n", + " 'SamplePart86': ['SamplePart', ['model_vx_in'], -1, [0, 1]],\n", + " 'SamplePart89': ['SamplePart', ['Mul87_int34'], -1, [-1, 0]],\n", + " 'SamplePart95': ['SamplePart', ['model_curv_in'], -1, [0, 1]],\n", + " 'Select11': ['Select', ['Fuzzify2'], 8, 0],\n", + " 'Select14': ['Select', ['Fuzzify2'], 8, 1],\n", + " 'Select17': ['Select', ['Fuzzify2'], 8, 2],\n", + " 'Select20': ['Select', ['Fuzzify2'], 8, 3],\n", + " 'Select23': ['Select', ['Fuzzify2'], 8, 4],\n", + " 'Select26': ['Select', ['Fuzzify2'], 8, 5],\n", + " 'Select29': ['Select', ['Fuzzify2'], 8, 6],\n", + " 'Select32': ['Select', ['Fuzzify2'], 8, 7],\n", + " 'Select46': ['Select', ['Fuzzify5'], 5, 0],\n", + " 'Select51': ['Select', ['Fuzzify5'], 5, 1],\n", + " 'Select56': ['Select', ['Fuzzify5'], 5, 2],\n", + " 'Select61': ['Select', ['Fuzzify5'], 5, 3],\n", + " 'Select66': ['Select', ['Fuzzify5'], 5, 4]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Training", + "id": "78870bde708d1090" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Dataset Load", + "id": "d8901687b6eb84c5" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Here, the data for the training, validation, and test sets are loaded and constructed. The dataset is structured according to the architecture of the network. Specifically, the different columns of the CSV file are mapped to the corresponding Input variables of the network.", + "id": "8ee04d586d61cdd1" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.631856Z", + "start_time": "2025-12-02T13:54:32.525699Z" + } + }, + "cell_type": "code", + "source": [ + "lat_dyna_model.loadData('training_set', source='dataset/training',\n", + " format=['', 'model_ax_in', '', 'model_vx_in', 'model_curv_in', '', '', 'model_steer_in', ''],\n", + " skiplines=1)\n", + "lat_dyna_model.loadData('validation_set', source='dataset/validation',\n", + " format=['', 'model_ax_in', '', 'model_vx_in', 'model_curv_in', '', '', 'model_steer_in', ''],\n", + " skiplines=1)\n", + "lat_dyna_model.loadData('test_set', source='dataset/test',\n", + " format=['', 'model_ax_in', '','model_vx_in', 'model_curv_in', 'model_steer_in'], skiplines=1)" + ], + "id": "6b302b749e7668ee", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m============================ nnodely Model Dataset =============================\u001B[0m\n", + "\u001B[32mDataset Name: training_set\u001B[0m\n", + "\u001B[32mNumber of files: 4\u001B[0m\n", + "\u001B[32mTotal number of samples: 12212\u001B[0m\n", + "\u001B[32mShape of model_vx_in: (12212, 2, 1)\u001B[0m\n", + "\u001B[32mShape of model_ax_in: (12212, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_steer_in: (12212, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_curv_in: (12212, 1, 1)\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m============================ nnodely Model Dataset =============================\u001B[0m\n", + "\u001B[32mDataset Name: validation_set\u001B[0m\n", + "\u001B[32mNumber of files: 2\u001B[0m\n", + "\u001B[32mTotal number of samples: 5616\u001B[0m\n", + "\u001B[32mShape of model_vx_in: (5616, 2, 1)\u001B[0m\n", + "\u001B[32mShape of model_ax_in: (5616, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_steer_in: (5616, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_curv_in: (5616, 1, 1)\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m============================ nnodely Model Dataset =============================\u001B[0m\n", + "\u001B[32mDataset Name: test_set\u001B[0m\n", + "\u001B[32mNumber of files: 1\u001B[0m\n", + "\u001B[32mTotal number of samples: 1075\u001B[0m\n", + "\u001B[32mShape of model_vx_in: (1075, 2, 1)\u001B[0m\n", + "\u001B[32mShape of model_ax_in: (1075, 1, 1)\u001B[0m\n", + "\u001B[32mShape of model_steer_in: (1075, 30, 1)\u001B[0m\n", + "\u001B[32mShape of model_curv_in: (1075, 1, 1)\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Training", + "id": "6c9524ead582c921" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.673595Z", + "start_time": "2025-12-02T13:54:32.668666Z" + } + }, + "cell_type": "code", + "source": [ + "# Default training parameters definition\n", + "training_pars = { 'num_of_epochs': 50,\n", + " 'val_batch_size': 64,\n", + " 'train_batch_size': 16,\n", + " 'lr': 1e-3 ,\n", + " 'train_dataset': 'training_set',\n", + " 'validation_dataset': 'validation_set',\n", + " 'optimizer': 'Adam',\n", + " 'shuffle_data': True,\n", + " 'select_model': select_best_model,\n", + " 'early_stopping': earlystopping.early_stop_patience,\n", + " 'early_stopping_params': {'patience': 5,'error': 'heading_error'}}" + ], + "id": "fa82ab5c8f2e7f2c", + "outputs": [], + "execution_count": 7 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:57:00.869647Z", + "start_time": "2025-12-02T13:54:32.682537Z" + } + }, + "cell_type": "code", + "source": [ + "# First training without samples in the future\n", + "lat_dyna_model.trainModel(training_params=training_pars)" + ], + "id": "e949cd28ae6e34c7", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m======================== nnodely Model Train Parameters ========================\u001B[0m\n", + "\u001B[32mmodels: ['vehicle_model']\u001B[0m\n", + "\u001B[32mnum of epochs: 50\u001B[0m\n", + "\u001B[32mupdate per epochs: 763\u001B[0m\n", + "\u001B[34mβ””>len(train_indexes)//(batch_size+step)\u001B[0m\n", + "\u001B[32mshuffle data: True\u001B[0m\n", + "\u001B[32mearly stopping: early_stop_patience\u001B[0m\n", + "\u001B[32mearly stopping params: {'error': 'heading_error', 'patience': 5}\u001B[0m\n", + "\u001B[32mprediction samples: 0\u001B[0m\n", + "\u001B[32mstep: 0\u001B[0m\n", + "\u001B[32mclosed loop: {}\u001B[0m\n", + "\u001B[32mconnect: {}\u001B[0m\n", + "\u001B[32mtrain dataset: training_set\u001B[0m\n", + "\u001B[32m\t- batch size: 16\u001B[0m\n", + "\u001B[32m\t- num of samples: 12212\u001B[0m\n", + "\u001B[32m\t- num of first samples: 12212\u001B[0m\n", + "\u001B[32mvalidation dataset: validation_set\u001B[0m\n", + "\u001B[32m\t- batch size: 64\u001B[0m\n", + "\u001B[32m\t- num of samples: 5616\u001B[0m\n", + "\u001B[32m\t- num of first samples: 5616\u001B[0m\n", + "\u001B[32mminimizers: {'curv_error': {'A': 'SamplePart95',\n", + " 'B': 'Mul72',\n", + " 'loss': 'mse'},\n", + " 'heading_error': {'A': 'Add82',\n", + " 'B': 'Add92',\n", + " 'loss': 'mse'}}\u001B[0m\n", + "\u001B[32moptimizer: Adam\u001B[0m\n", + "\u001B[32moptimizer defaults: {'lr': 0.001}\u001B[0m\n", + "\u001B[32moptimizer params: [{'params': 'A_0'},\n", + " {'params': 'A_1'},\n", + " {'params': 'A_2'},\n", + " {'params': 'A_3'},\n", + " {'params': 'A_4'},\n", + " {'params': 'Fir_0'},\n", + " {'params': 'Fir_1'},\n", + " {'params': 'Fir_2'},\n", + " {'params': 'Fir_3'},\n", + " {'params': 'Fir_4'},\n", + " {'params': 'Fir_5'},\n", + " {'params': 'Fir_6'},\n", + " {'params': 'Fir_7'}]\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m=========================== nnodely Training ===========================\u001B[0m\n", + "\u001B[32m| Epoch |\u001B[0m\u001B[32m curv_error |\u001B[0m\u001B[32m heading_error |\u001B[0m\u001B[32m Total |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\n", + "\u001B[32m|----------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| 1/50 |\u001B[0m\u001B[32m5.127e+00|\u001B[0m\u001B[32m1.218e+00|\u001B[0m\u001B[32m1.336e+00|\u001B[0m\u001B[32m3.627e-01|\u001B[0m\u001B[32m3.232e+00|\u001B[0m\u001B[32m7.902e-01|\u001B[0m\n", + "\u001B[32m| 2/50 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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[34mSelected the model at the epoch 31.\u001B[0m\n" + ] + } + ], + "execution_count": 8 + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T14:29:18.113953Z", + "start_time": "2025-12-02T13:57:00.898316Z" + } + }, + "cell_type": "code", + "source": [ + "# Training with intergal error 4s in the future\n", + "lat_dyna_model.trainModel(training_params=training_pars,\n", + " num_of_epochs=20,\n", + " prediction_samples=80, # 4s in the future\n", + " step=10)" + ], + "id": "beac2f15f1c62932", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m======================== nnodely Model Train Parameters ========================\u001B[0m\n", + "\u001B[32mmodels: ['vehicle_model']\u001B[0m\n", + "\u001B[32mnum of epochs: 20\u001B[0m\n", + "\u001B[32mupdate per epochs: 457\u001B[0m\n", + "\u001B[34mβ””>len(train_indexes)//(batch_size+step)\u001B[0m\n", + "\u001B[32mshuffle data: True\u001B[0m\n", + "\u001B[32mearly stopping: early_stop_patience\u001B[0m\n", + "\u001B[32mearly stopping params: {'error': 'heading_error', 'patience': 5}\u001B[0m\n", + "\u001B[32mprediction samples: 80\u001B[0m\n", + "\u001B[32mstep: 10\u001B[0m\n", + "\u001B[32mclosed loop: {}\u001B[0m\n", + "\u001B[32mconnect: {}\u001B[0m\n", + "\u001B[32mtrain dataset: training_set\u001B[0m\n", + "\u001B[32m\t- batch size: 16\u001B[0m\n", + "\u001B[32m\t- num of samples: 12212\u001B[0m\n", + "\u001B[32m\t- num of first samples: 11892\u001B[0m\n", + "\u001B[32mvalidation dataset: validation_set\u001B[0m\n", + "\u001B[32m\t- batch size: 64\u001B[0m\n", + "\u001B[32m\t- num of samples: 5616\u001B[0m\n", + "\u001B[32m\t- num of first samples: 5456\u001B[0m\n", + "\u001B[32mminimizers: {'curv_error': {'A': 'SamplePart95',\n", + " 'B': 'Mul72',\n", + " 'loss': 'mse'},\n", + " 'heading_error': {'A': 'Add82',\n", + " 'B': 'Add92',\n", + " 'loss': 'mse'}}\u001B[0m\n", + "\u001B[32moptimizer: Adam\u001B[0m\n", + "\u001B[32moptimizer defaults: {'lr': 0.001}\u001B[0m\n", + "\u001B[32moptimizer params: [{'params': 'A_0'},\n", + " {'params': 'A_1'},\n", + " {'params': 'A_2'},\n", + " {'params': 'A_3'},\n", + " {'params': 'A_4'},\n", + " {'params': 'Fir_0'},\n", + " {'params': 'Fir_1'},\n", + " {'params': 'Fir_2'},\n", + " {'params': 'Fir_3'},\n", + " {'params': 'Fir_4'},\n", + " {'params': 'Fir_5'},\n", + " {'params': 'Fir_6'},\n", + " {'params': 'Fir_7'}]\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m=========================== nnodely Training ===========================\u001B[0m\n", + "\u001B[32m| Epoch |\u001B[0m\u001B[32m curv_error |\u001B[0m\u001B[32m heading_error |\u001B[0m\u001B[32m Total |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\n", + "\u001B[32m|----------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| 1/20 |\u001B[0m\u001B[32m9.480e-06|\u001B[0m\u001B[32m8.776e-08|\u001B[0m\u001B[32m2.279e-04|\u001B[0m\u001B[32m5.188e-05|\u001B[0m\u001B[32m1.187e-04|\u001B[0m\u001B[32m2.598e-05|\u001B[0m\n", + "\u001B[32m| 2/20 |\u001B[0m\u001B[32m9.335e-06|\u001B[0m\u001B[32m1.856e-07|\u001B[0m\u001B[32m2.349e-04|\u001B[0m\u001B[32m1.519e-04|\u001B[0m\u001B[32m1.221e-04|\u001B[0m\u001B[32m7.607e-05|\u001B[0m\n", + "\u001B[32m| 3/20 |\u001B[0m\u001B[32m9.387e-06|\u001B[0m\u001B[32m8.453e-08|\u001B[0m\u001B[32m2.087e-04|\u001B[0m\u001B[32m4.300e-05|\u001B[0m\u001B[32m1.090e-04|\u001B[0m\u001B[32m2.154e-05|\u001B[0m\n", + "\u001B[32m| 4/20 |\u001B[0m\u001B[32m9.402e-06|\u001B[0m\u001B[32m3.337e-07|\u001B[0m\u001B[32m2.348e-04|\u001B[0m\u001B[32m1.644e-04|\u001B[0m\u001B[32m1.221e-04|\u001B[0m\u001B[32m8.237e-05|\u001B[0m\n", + "\u001B[32m| 5/20 |\u001B[0m\u001B[32m9.379e-06|\u001B[0m\u001B[32m1.554e-07|\u001B[0m\u001B[32m2.064e-04|\u001B[0m\u001B[32m6.845e-05|\u001B[0m\u001B[32m1.079e-04|\u001B[0m\u001B[32m3.430e-05|\u001B[0m\n", + "\u001B[32m| 6/20 |\u001B[0m\u001B[32m9.494e-06|\u001B[0m\u001B[32m7.423e-08|\u001B[0m\u001B[32m2.088e-04|\u001B[0m\u001B[32m3.833e-05|\u001B[0m\u001B[32m1.091e-04|\u001B[0m\u001B[32m1.920e-05|\u001B[0m\n", + "\u001B[32m| 7/20 |\u001B[0m\u001B[32m9.541e-06|\u001B[0m\u001B[32m8.349e-08|\u001B[0m\u001B[32m2.260e-04|\u001B[0m\u001B[32m4.111e-05|\u001B[0m\u001B[32m1.178e-04|\u001B[0m\u001B[32m 2.06e-05|\u001B[0m\n", + "\u001B[32m| 8/20 |\u001B[0m\u001B[32m9.233e-06|\u001B[0m\u001B[32m1.709e-07|\u001B[0m\u001B[32m1.944e-04|\u001B[0m\u001B[32m9.026e-05|\u001B[0m\u001B[32m1.018e-04|\u001B[0m\u001B[32m4.522e-05|\u001B[0m\n", + "\u001B[32m| 9/20 |\u001B[0m\u001B[32m 9.5e-06 |\u001B[0m\u001B[32m2.025e-07|\u001B[0m\u001B[32m2.150e-04|\u001B[0m\u001B[32m1.146e-04|\u001B[0m\u001B[32m1.123e-04|\u001B[0m\u001B[32m5.741e-05|\u001B[0m\n", + "\u001B[32m| 10/20 |\u001B[0m\u001B[32m9.468e-06|\u001B[0m\u001B[32m1.847e-07|\u001B[0m\u001B[32m1.977e-04|\u001B[0m\u001B[32m9.271e-05|\u001B[0m\u001B[32m1.036e-04|\u001B[0m\u001B[32m4.645e-05|\u001B[0m\n", + "\u001B[34mStopping the training at epoch 10 due to early stopping.\u001B[0m\n", + "\u001B[1;32m============================ nnodely Training Time =============================\u001B[0m\n", + "\u001B[32mTotal time of Training: 1936.6063182353973\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[34mSelected the model at the epoch 6.\u001B[0m\n" + ] + } + ], + "execution_count": 9 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# ANALYZE MODEL", + "id": "61013b39c2abd32b" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### FIR filters weights", + "id": "5223712c8c0b77f7" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T14:29:18.667992Z", + "start_time": "2025-12-02T14:29:18.428787Z" + } + }, + "cell_type": "code", + "source": [ + "params_fir = {}\n", + "sampling_time = 0.05\n", + "\n", + "# Plot\n", + "plt.figure()\n", + "\n", + "for key, value in lat_dyna_model.parameters.items():\n", + " if key.startswith('Fir_'):\n", + " params_fir[key] = lat_dyna_model.parameters[key]\n", + "\n", + "for key, value in params_fir.items():\n", + " vals = np.array(value).flatten()\n", + " t = np.arange(0, sampling_time * len(vals), sampling_time)\n", + " plt.plot(t,np.flip(vals * W_fir.flatten()), 'o', label=key)\n", + "t = np.arange(0, sampling_time * samples_past_steer, sampling_time)\n", + "plt.xlabel('time (s)')\n", + "plt.ylabel('amplitude [m-1 rad-1]')\n", + "plt.legend()\n", + "plt.grid(True)" + ], + "id": "38e45190d0844097", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 10 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Analyze model", + "id": "856a457e2372543f" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Below is the self-evaluation of the model’s performance on the training, validation, and test sets. The framework automatically computes relevant metrics such as MSE, FVU, and AIC (the latter being particularly useful when comparing this model with alternative models). Performance evaluation is conducted over a temporal window of 200 samples, corresponding to 10 seconds. The plot is also generated automatically and displays consecutive 10-second time series segments. Since the errors were computed for both curvature and heading, they are reported separately below.", + "id": "60f97c83ef7473ce" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T14:33:07.623162Z", + "start_time": "2025-12-02T14:29:18.687232Z" + } + }, + "cell_type": "code", + "source": [ + "lat_dyna_model.analyzeModel('training_set',prediction_samples=200) # 10 s prediction\n", + "lat_dyna_model.analyzeModel('validation_set',prediction_samples=200)\n", + "lat_dyna_model.analyzeModel('test_set',prediction_samples=200)" + ], + "id": "8910741402314add", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m================== nnodely Model Results for training_set =================\u001B[0m\n", + "\u001B[32m| Loss |\u001B[0m\u001B[32m mse |\u001B[0m\u001B[32m FVU |\u001B[0m\u001B[32m AIC |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m lower better |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| curv_error |\u001B[0m\u001B[32m 1.426e-07 |\u001B[0m\u001B[32m 5.633e-04 |\u001B[0m\u001B[32m 2.412e+06 |\u001B[0m\n", + "\u001B[32m|heading_error|\u001B[0m\u001B[32m 2.229e-04 |\u001B[0m\u001B[32m 2.533e-04 |\u001B[0m\u001B[32m 2.414e+06 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| Total |\u001B[0m\u001B[32m 1.115e-04 |\u001B[0m\u001B[32m 4.083e-04 |\u001B[0m\u001B[32m 2.413e+06 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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ZXXfdxXxOqa3GO57Qfbc0njSvQ6UT71hBvobkA0d+hhShTXVNfoCx1ke8pHucSOb3qH1I/vw0MZMmNdGegy+99BJSwdFHH83qlfroNddcw3xYyRc6Vp/aVD1HW115JK09GqEWtHAWGiklBH0Xb+AJBQg0h5xpyYE6nkCQ5gNsaJoTmk23RCznpEumoWWnoJCWoPQHqSI03Qc5USdbtxI0G403iCF0oAmHNLMjos2uQ88h5bH538b7u/Eg/Q5ZPVsaDKUySgpJNOUx2T6cDpQ2zrREKsYQOca2RKFAGEpT1VI6rdDfbQ6lKiJrEqXLIsd9sh7SiwJ/SFEgBZ3Sp1DKpJbGE8kynMo6TDexjhUUNEQrKRRQEqtlOVx9xEu6x4lseQ7279+fBalQcCUFt1FaMXrReExGEDJsUBAPrVCk8zna6spjtKXAWPcCTZTm+ZA2b97MBigpmo8GZKoUeohSQySFQ5rZvfjiiyy6SVrWCEX6e4Iiy+IxS8tBMjKVonBbq+ypqttQKMozXlr6m9AcgbHIhKL1wv1tvL8bD9LvxFpnsZYx2T6cDpQ0zsSC3GOIXGNbIlAUN7lySJCiR5HSpPSRYhr6EJcimCP9LuXmo3x0ZIGkqHbp3uhFkbV0D+RKQ983f5imuw7TTaxjBeW2lKKZKbcjtYPhw4cz5ZPaEX1GhEZ+y9EO0j1OZMtzkCBlkaLTJ0+ezCaB0oSesndIuSbJ8kwZUKRsDJnQ7pNSHpOFHnAkHAo9r66uTupaTzzxRHBwpSWRJ598MuIygJTGJlKZJGKZaYc21NYmtBO0dtlD5UgPioEDB0KpSD6Mkkyi+Zw2Vw5C/zaV0O9QX4m1zlqjjEpFznEm1t+Tsx/KNbYlgpQGhdxFKLXHKaecEvFcSg0TDSkNDb0oOTel1KFlV0q9Q+lDyJJGfsR///03/vnnnyZjRqhMyZLWFts0WZ0pLQ5BqV5okkApp8IRKW1ZW0BJz0EJ6jf0oiV1at/U7ilV3OLFi1m7p/ZOVkhq/7TUnQnPUY0STPU0sIc+7BJByu9GObekfFmRoJluJKhTSpB/QkvEck66oLxnEpTzrSUot106lmHSkU8yGUKXdMl/qCVC89CFyjwdZSQropSLUmllVCpyjjOxIPcYItfYFi90LalslKsumuJICl08y2Tk60j5V2mnJAp4IP8ssmhKbbx57r5MGk9Sxa+//hrMSUhBcZEUR7nbQaahpOdgcyjvJuUmJisj5WKkZWny/ZX8FW+55ZaMafetqjxKgwV1iGQjg6QknLScEi0SlczF0ZJx0hKAxO+//97i7yopko8S+ErLFmQeb8lsLS1vpbJuCck/UKmELhVESjIdOpMliwlBAUm0PZ7SykhBMmvXrg0mfY3FRzKdy1DpjAKWe5yJBbnHELnGtnjrQfrdWPwySa7JJFsm3zBawpPKR9aZVI4n0u/I2RZT3cbjqQ+lj7mpVtAk31ny+w6VW7qfgy1B4zOtFkiJ7GkiFbpUnap2L0cbbVXlMdTxmfwCknG0lUzVNNOIJhT6nUj7nkoz4mHDhrH39ACOVmH0nZyR1slCvk/kGyVFoEn7kYajpXtLFvJdkjoEZfJXkoW2ObSnscSUKVOiOpjT7j6SDyGlWWlpiTsVZaRZazQ/pqeeeirYB0L/rjUJXX5Jt6uHnONMLMg9hsg1tsVbD6HLf9H6L01SaQeLZKFgOClNVvP7oGAhqa+RTyQF3SSDJAc522Kq23is9UGWtND0VG0RKe0RTWjIDzgS1I4oi0Brotfrm6xWhLZ9uZ+jcrbRVlUeaYYumWxpPZ8qnByuIyH5BoSLOiKHVKkx0MM1HPS55DMSjdDr0/Zu4bbLos9a2vqtNQg1e1M0Y7g8dSQjGoxbetAkO9DRRveStY6UWin3XSSoY1BUJzl7pxPaMkzKxUfWG9r2MZyvDFlXHnzwweAMjvzP0gVFokr+LmRdorQP4eqPJgxSGydncXKwVwLkbyhF/lKakXRaH+UcZ2JFzjFEzrGNrJcS5G8VDUonIj1spk+fzvy0muN0Otn2p1IATCQmTZrUonWStoeUrC6U27P58p2Ur4+iaWk8aWm5kfqJFDQSSQ7kA0uW+kxo41I7IGi7znD+u3Qv5F6gJF/81oDy8UqrcCQrar/NoTGexvpkglJaghRXCjqLtgpIY43Uf6idh0aay/0cjaf/KzpghqB9lmkQpY5Oy3G0pzNZSyjZLWnc5AdAPl4kXPL5oJxVFH5PDSIUSqApLeeR0kSmaIpEIxM2dSjaf3XBggXMd4wewtGW/igPE+15SQ2OlAlamgy3Ly1VJqX+CLdHZGtB0Yz0QCK5kgWNnHBpcKdUGKF7W9PDk/aaJbmkKpqOEp5TYlSaMdFAT3txUuOnvHbUSch3i/ykyPJCy1Q04BLU8NMNRTCStYjaGu0TSvtg017JFPVJfnLUXmgQkB4I99133z6RcamE6ofyulF6B3owUKoOephTglvqMyTHb7/9NpgkXhq4Ys2jlw6o3ilpOVnQqO1Rvj9KZi/58JGSRw9gJY8zsSLnGCLn2FZYWMjaOT046DqkXJGDfmgACi2VUfQvuWVQH6b9g0k+9DltPED1RA818lMkCxf5YlHd0l7jkXIK0m/RQ5D8NmkMIOWQ3CmoXZN8SLGcOXNm8Pxw+xVT4JBUf1R+Um5PPfVUNsbRvZNiKu2RTb9H9U3L/JRqqDl0z5JCQfKniHKy/kjjIC0LJ5KGLZVtnCLdqf2QQYACjuj+KcKdlvxpJYLaFFnRaHygeoq28pTt0LhNE33ax53aLvnsSntbU1untkr9j3wOU/kcXLx4MesjNKmgdk99j9oZ9S0abyhohtqhNKkK1+7lfI5Su5cssaQnkHEh1B2GyhZzUE68W9LEu71VS1vzEY2NjcKFF14Y3JO3pVeka9GetdH+jrZFor1vQ7cDDLf3L+FwOJrsd9z8RdtePfPMM022cJJje8JI5ZGI5fdomzJpe65IL9rDl/bLlf7/3HPPCamA9lV96qmn2D7LsdQt7VVKW0Ylu51YItsw0RZrAwYMiFo+2qbrkUceiXiNVG3/JjF//vwmezmHe5GsaXuyZNpQoluuRYO2CYzWDkh2mTDOxIqcY4icY9tPP/3UZGvS5q/Qv6Pt0I4//vgW5URbyUXro2PGjIlJ5larVXjnnXciytTj8bCxK1r5m8slHDabTdhvv/0i/l2ifTeVbZyguunRo0fUe6at+TZt2tRiv41ne0I5xol4ticMlVM4YpXd7bffHrW/0573tFWv9P8bb7xRkJNLLrkkpnZK+2+H29ZT7ucobWsbWu/NX/GM8YpI8kZLI5Q8lmaMNLOm2RlZA2i7LVp6I+sJ7VBAM1eadVFqh3A89NBDzEGdZhk0KyezNV2HZmuUY4k0cskXqSVo5k0WKMo/Rto6+eOQ5Y7KQpY8MjXTNnFKhORGTrg0o6FtpWi2TzMdmqWQtYUsK82XPVJl8aFZEW0JRzM8qgOSJUXDkSzpRfVEOa5oBvTdd9+xJamWEsSmCrI0kGWDZqQkNyonyY1mjbS0TRYgSoFBs9nWgpauyKJCs0eaeUrtnCxKNCOlmStFjCvRpWLQoEFsyYWsXWQtIeuVXDvwpHOciRU5xxA5xzayWkhWa7KuRsuFR+2f7oEst7R6QX2BPiMLBVlxyKpC5Yq2lRxBu+KQdZX6EF1H6lvSfZD1kJa2qW2TxT8SdD6NXbQ0R5Yl+juyYNK1yOebykU7hlA0MlkfIy1tU9sjSx1d46CDDmL3JYfVKdVtnCzmdH1qo/RbVHf0okTX1KbonmlsSGe/UjJPP/00s+6RdVFqc2Slprb75ZdfMlcJ2oY0Vc/BKVOmsNUgcnGi8ZoCeaid0phDv0X9l76j5wqtZqX6OUoWRrLyUx+i86kMVJZE4EiDTOgvVTIeenBJW4bRgNTcz0hFRUVFRSWboa0JJX9amuDQBE2lZVTlsY1Csy3KE0a+jzTzp5lKojMQFRUVFRWVTIP8IckfmSx/ZNWmPdGl6GaV6Chi2VpFXihBLDlUR4Kiyyj6VIo4vfzyy1XFUUVFRUUla6BoYwrqigSl7KIgNmk3HkrerSqOsaNaHrMQ8uWgVDzk00ARkuTLR743lPuRorYoClRKT0Dfkb9Uqvf3VFFRUVFRSRe0gwv5iJNPK/kbUgQ25RGlPL2UVYGegxTpT5DPLn3W1nfjigd1nTJLodQNf/31F3tFgsz1lN6lueI4bdq0hH+XnLcp6EBFRUVFRUUJSiS9IkGpaug52FxxpJRQseyNHYls951ULY9ZCM2sKCch5U0jkzzlPqM8UBRpRZHXNBujqGtaug4XYZhMpB5FklJEmIqKioqKSmvhdrtZAAxFO1MWDXLTkjKMUCQyGU8oofrFF1/MorDDRbZHc/9qiWyPRVaVR5V9G4WqPKqoqKiotGFU5TE6qvKYBJQVnqKUKWO9mldLRUVFRUUlMyDLIK3S0XJ1KnZYy3ZUn8ckIMWRkn6qqKioqKioZB60vSZtoKESH6rymATSfrDU+CiKSy5oE3Xy1aA9V8P5YqioclLbk/yo/U6Vk9qW2k6fa2hoYMaf0H3dVWJHVR6TQFqqJsVRTuWRHH2l5XDaekhFlZPanlKP2u9UOaltqe31OdXlLDHUhX4VFRUVFRUVFZWYUZVHFRUVFRUVFRWVmFGVRwVCe0xT/il1r2lVTmp7Uvud0lDHJ1VGaltSUVP1JOlwm5+fj/r6ell9HlVUVFRUVFRSh/r8Tg7V8qjQCLRXX32VHVVUOantSe13SkIdn1QZqW1JRVUeFZq8lLZSyvbtjZJFlZMqJ7U9qf1OiahjkyqnbEdVHlVUVFRUVFRUVGJGVR5VVFRUVFRUVFRiRg2YUaDDLe2ZvWnTJvTs2VPdc1OVk9qe0oTa71Q5qW2p7fQ5NWAmOVTlMQnUxqeioqKiopJ5qM/v5FCXrRW6ZdOTTz7JjiqqnNT2pPY7JaGOT6qM1LakoiqPCkVN06PKSW1Par9TKur4pMpIbUttG11rF0BFRUVFJfsob3DBzwsoshpg0mtbuzgqKioyoloeVVRUVFRk566vlmPE5N8xfdkuVboqKlmGGjCj0GjrqqoqlJSUqNHWqpzU9pQm1H4nr5zOfX0u5m+uwcvnD8XJgzqiLaG2JeXLSQ2YSQ512VqBcBzHlFI6qqhyUtuT2u8ycXyyuCrQHg2wav1oa6hjuCqnbEddtlaoM/rkyZOz3imdBtgxY8akVE7J/kY20FbaU7KocpJXTrc0PIV5puvRsXwW2hpqW1LllO2oymOGsWjRIkycOBF9+vSB1WqF2WxGr169cOGFF2LmzJloa7z77rswmUzIRB555BGm3Or1euzZs0e2pZhbb70V3bp1g9FoRPfu3XHPPfekJe0T/RbdT+iLytCjRw9ceeWV2LJlS1LXp3sgmVHbpzrv2LEju25FRYUs5d+4cSNmzZqFs846C506dWLlp3tKlP/++w/nnnsuW5Kjfjp48GBMmTIl4p714erujjvugM1mQyZi5F3sqDPltHZRVFRUZEZdts4g35Dbb78dzz//PHQ6HY488kiceuqpTPGgDP3ff/89PvzwQ/ZwfeCBB1q7uIphzZo1sFgsUBqkQJDiSwqKz+fD1KlTcddddyV1TbvdjiOOOAJLly7Fsccei/POOw9LlixhbYYULbo+KSWpRKvV4v777w/+v66uDvPmzcObb76Jr7/+GosXL0bXrl0Tav+nnXYafv75Zxx66KFMwVu/fj3eeust/Pbbb/j3339RWlqaVNn/+ecf/Pnnn+we+vfvn5RCv3r1aowYMQJOp5MpkCR/6qPXXnst++6ll16Kqe6eeeYZVqa//vor4yZJP+EwLPJ1x8iixBVwFRUVhSKoJEx9fT2ZENhRTlwul/Dwww+zo8Q999zDfmvIkCHChg0b9vkbh8Mh/O9//xPuuusuIVOg+zniiCMS/nuST7du3dh1Mo2ZM2eycl955ZVCXl6e0Ldv36Sv+eCDD7JrNm8Dt99+O/v8kUceEVIJ1YXRaAz73bXXXsvK8MADDyR07XfeeYf9/XnnnSfwPB/8fMqUKUE5Jsvq1auFiRMnCrW1tez/dC90T4kwevRoVq4ffvgh+Jnb7RZGjRrFPp8zZ05MdUf/p8+feOIJQSmEG5/C0e/+H4Rud30nbKu2C22NWGXU1mlNOaXq+d1WyLynroJIVeOjhyN1JukhuX79ekGr1QrFxcXCnj17ov5t805YWVkp3HTTTUL37t0Fg8EglJaWCuecc46wYsWKff724osvZvezceNG4ZlnnhH69+/P/oY+J+hBSi96uF533XVC586dWbnefffd4DWWLVsmjBs3TigrKxP0er3QtWtX4frrrxeqqqpiUh7XrVsn3HHHHcLQoUOFoqIi9gDv06cPe4g2Njbu8/fhXlJ5I/1GonLZtGmT8MILLwj9+vVjf0P3RgOf3+8X4mX8+PHsmgsXLmQKC73/66+/hEShttKxY0chJydHsNlsTb4judHnPXv2FFpLeZw+fTq7x2uuuSahax922GHs77ds2bLPfdN9Wa1WNoGSs98lqjxSG6ayjh07dp/v/vjjD/bdpZdeGlPd0f/TUXfJyCkcfj/PFEd6VTa2PQUqFhmptK6cVOUxOVSfRwVCOg+l/5F8o9577z34/X5cddVVaN++fdS/DV2WrKysZEt8L7zwAvOfIn8qWu6m5cNDDjkEs2fPDnuNG264AU888QQOOugg3HzzzRg4cGATvzO6xi+//MKWza+77rpgmaZPn47hw4ezIwWpSH/78ssv47DDDkNtbW2L905le/vtt9GzZ09cfPHFuPrqq1FUVISnnnoKxxxzDLxeb/DcBx98EF26dGHvH3rooeDr9NNPj/obicqF/M8effRRdi9ULuLhhx+O202gpqYG33zzDQYMGIADDzwQF110Efuc7jtRaAl3165dGDlyJPOFDYWW7Q8++GDm3rB9+3a0BtReiGHDhsX9ty6Xiy199+vXj/kDhkLL/tQuaNl34cKF+3wXT8aC5v0uUf744w92pOXn5hx++OGsfmgpOpa6o//T561Zd4nIyen1owzVyIcNFn3byxohV1vKdlQ5ZTBJKp9tmnQtW48ZM4b9zq+//hrXdci6QX9HS96hfP/99+zz3r17N7GaSRY2sihu3bp1n+tJS8THHXfcPlYesizS8munTp32sQ598skn7O/IAhlKOKvgjh072PJecyZNmsTO//DDD+Natg73G4nKpUePHsKuXbuaWC8LCgqE3NzcsGWOxIsvvsiu9+STT7L/06ybLKAWiyXhtvTdd9+FlbEkp+HDh7Pvf/vtNyFVUF2QJfqhhx4Kvm655RZh5MiRgkajYRbpeOQksXLlSlb2k08+Oez3ZCWn799+++0mn0uW6ET7XaKWR8lN4Msvvwz7/QEHHMDk4fV6W6w7gj5Pdd3JvdRYWW8XhIfy2MvfWCm0NdRla+XLSbU8JodqecwAJMf9zp07x5Uq4pNPPkFxcXGTAAbixBNPZNaaDRs2sCCBcBa2aEEN//vf/1j0aCjvv/8+ixZ98skn97EOjR8/nlmcPv300xbLTVGuBoNhn8+vv/56dvz111+RDMnIhSyMHTp0CP6fomgpiKOxsRHr1q2LuQxkYaSEuBMmTGD/J+sYvXc4HDHJKBxk5SAo/140i7R0XqogC/mkSZOCLwrWIVnuv//+GDduXNi6TfbepAT9ze+NgqXolW5iKS8FAFG7ifX80PMyAZfDDrcgxmNqjE2tqSoqKpmPGm2dpaxdu5Yt940dOzZstDF9Tql9KLpz1KhRTb6jpedIUMRn6DK2BEW7ErS8SClPmkNloZ0EpN0EWopCpqX6lStXsgcmPWglaHmvteRCS8zNkRR6iiqOBVpaXbZsGY466qgmkwFaun7ssceYYknpZ1oLuo//+7//2+dzWp6PBVJSSb4SlGZm1apVLF3QmWeeiRdffJG5RaSD/fbbLy2/o7IvdphwuPt9tLNoMF+XWVHiKioqLaMqjwol1EJTVlbGlJ6dO3cyv69YICsgEclHUrKgSeeFEs2vsl27dmH9yMiPj3jllVeilot806IpjzfeeCPzkSRfRvKppHJKVjOyZDXPVxjvllbJyCXcFpSUNkmyuMWC5Nco+TlKUO5C8sMkJZyULbLUxYNktYpknZJ8RSNZt0KVR5Jzospjc3JycpgfKfmTkrJM1l7KUxpP+qSW7k2qq5buLRYSsYwmUl7qQ7m5uTGfH3qeEmhJTg6Pjx31BiOZ1pEp7Kh1YFedCx3yTehSZGn1ttQWUOWUmajL1gqElCWy1EhKEznME5TPLlYkRae8vDzqUng4hShakEGk76TrrFixglkPI72aL2mHQsmeSfkcNGgQU5bJ+kjL4KS4SAEqoZB84s0ZmIxckoVy/tGSOUHBQM0TakvW20QCZ0j5lIIvwslJUjyk8yJBAUTh6i1ZCgoK2MSHFCFKnh0PFDxFk4Rw90ZIn7d0b/H2u0SJVhc0ydi8eTNLnC5NPKKdL+f9yUUscqKAGcJi0CKTmLZkJ9uT+5VZGxTRlrIdVU6Zi6o8KhBapiW/O2m59pJLLmGJi9944w0WKRwNyTJHS3a0xLxgwQLmSxcpInTIkCGylJmsS8TcuXMTvgZFlJKicvTRR+9jmfr777/3OZ/kI22RFqvlL91yCeXLL79k1iW6Nlnfwr2obB988EHcWwmSYkGJqMm/kKy7oZBvHUWQk8IiRae3BlK0fagbQiyQfy25UpBf6datW5t8R+2F3AwoKpmyA8jZ7xKFkn2HRpiHQvUgJQSPpe7o//R5a9ddvHLiqtbhTf2zuMGTeAaB1sDhEccRk16riLaU7ahyylxU5VGB0BLjRx99FFxq7N27N+68807mL3jCCScwy0VzyM/sueeeCy4v0lIA7VJBf0PWu1B++ukntlMHXVeyaibLpZdeypbh7rvvPrbs2hxS1CTLWiQkq+ScOXOaDLo7duxgs/jmkHykpb5Y05ikWy6hSBZFqifaGSXc64wzzmBlo3RH8UCWy8svv5z5GFI6oVBo1yFSQqiOWgtKTUTttrCwEAcccEDcfy/5gVI7CLWEvv7662zSccEFF+wTxEXWa3ol2u9iIdxvkIV19OjRbKvDH3/8Mfg5TQiktE5UV7HUHf2fPr/iiiugFGKSU8MeHKNdhKG+pcgkXGzyLSRtMU2kLbVFVDllLqrPY4ZAwRSkIFL0Kj2cKC8hPYRpe0J6KFMUcnV1NTtvzsYqvDprI/qdcCV6/vmn+NmcOcw6SPsLf/HFF8yyR4Ep8foMRoK2hqMl2XPOOYft4Xv88cczKx9ZQuk3Ka8dbddGClokyN+Qtp376quvmBWJgkpoefm7775j78MF4pBFhrZ7o78jxZosd/T7p5xySsTfoZyRf6ZJLhJkhaAt5mhZmHJgRoIUPJIjKZpnn312XL9BE4xvv/2W3R9tbUcR7rQdIFnAyLKVjkAV2mox1D+SlFaaTFC9k5JE2/Il4uNEy/yfffYZkw21d7LckUzJl5LaANVlc2iLQSLWZXdS2knJpeuSpZ8ebPQZWf4laLvAUJ/dSL/x6quvsgkI5RylKHNq27Q9IcmCMgdQX4i17ihHJ+VMzST8btGC6tE0VeiVzsgdb+F+4wdYtv18qsXWLo6KinJJMtVPmyad2xNKLFiwQLjssstYLkKz2cxy0VGOwPPPP59teUd8PG8r29lh4nvzWS7CG2+8keWrox1fSkpKhLPPPjvqTiqbN28OWy5ph5lorF27lu2YQufRLiyFhYXCwIEDWRnmz5/fYg5G2g3ltttuY/ck7S7z6KOPCh6PZ5/zST603R2dT7u96HS6uHaYkUMulMuQvps1a1ZUuUjbS9L50aD8kl26dGF5ALdt2ybES11dnXDzzTeza0g7/ND/6fdTnUtNyrkZ+qI66dChg3DWWWcJ//zzjyz9olevXqxt0S5Gl19+ecRdl+LN80htN9KuRdKreRuI9ht0PWpT0k5J1A9eeeWViLtphKs7atsNDQ1CpuXm+/PLV1mOx7WTE99+tDX4/f8uY+Ve+s5NSV1HzfOofDmpeR6TQ1UeFdj4KJEyPWQSSahMvPz7eqY83vHFUiGbSVZObQVVTqqc0t2eZn30NFPCVj59vJBJ/PHMeFbuZR823UAgXtQ+p3w5qcpjcnD0T2tbPzMVihylKFbyu0tFdG6iTH/vKQze9BZ2dToWh10VPXWOioqKitzMmvoIxm5+FisKjsLAm7/OGAH/89TpGOmchZUH3IkDzr6vtYuj0gaf35mCGjCjQChymPydYo0gbo7ethvdNBUoQNPIzWwjWTm1FVQ5qXJKe3vyiGMPr8ssn0ed38mOnDEnqeuofU6VU7ajBswoEAo6mDFjBksUTY778aJziQm7YY2cjDsbSFZOSmfatGlsp5uWoACcaEE4SpQTpUSS0iJFg9IaUdBJOlCinJRITHLyimmwBH1yibbTjSGgPOqSVB7VtqTKKdtRlccsxOAR8+lpc0pbuygqSSqPU6dOjencaMqjEiHFMdxONuGirNOlPKrIh8bnzEjlUS+I5daa1P24VVSioSqPWYjVJ+6zbMhTlcdMhnbYoVc2Qul8Et3yUEX5aAKWR86QWcqjkRf3ZTeYk7M8qqhkO6rPowKhfHi9evWKuk1gNHL8YuJsY347ZDPJyqmtoMpJlVO625PWLyphnCGzLHhGQSy30SLuO54oap9T5ZTtqNHWWRat5fHxqHm0J8q4WjRc9Cvyeh7c2kVSUVFpY8x/8gQMd8/BiiEPY+DptyAT8Pl51D7SDaVcA+ov/gP5PYa2dpFU2tjzO5NQLY8KhJytySeMjvFS53CjEDb23lqQ3ZbHZOTUllDlpMop3e1Jxwd8B42ZY3m0e/zIgWh5NOXkJ3Uttc+pcsp2VOVRgVCaB9o+L5EUNHUOLw53v4CzuOegze+IbCYZObUlVDmpckp3e1rEHYAZ/kPBF3ZHpmB3efGO/3i87z8WxtzkMlWofU6VU7ajBsxkGXVOHypRgByrFdDq0dagnPfzNtfAatChf4dc6LTq/EhFJd28y52BXV4XpncenlGWx6d941Fg0eMik7qMqaISjTb1ZN25cyf+7//+D8ceeyy6du0Kg8GAsrIynHXWWZg3bx6ygVqHhx1pAGyLuH08xr/xL055eTZcPr61i6Oi0iZxeEWrpFmfOfkybW5xGd5qUG0qKiot0aZ6yUsvvYSnnnqKRQqSAllaWor169ezfHr0+vjjjzFu3LjWLiY0Gg2GDh3KjvEilK/CQ7qp0Pj2AzAS2Uw4OUkPgEx7cCm1PbUlVDnJKCePHRroYDZkTh902u3oxu1BO33yKc7UtqTKKdtpU9HWX3/9NYqLi3HEEUc0+fzvv//GUUcdhZycHOzevRtGozFjo7Vmfv4qjll9DzZaBqPXnX+hrbGtsgE3P/c2fDorpk+6nEbx1i6Sikqbws8LsE/qiDzOgbqJ/6KgS39kAv/+8QMO/eM87NGUoezBda1dHJUUo8TndybRpp6sZ5555j6KIzFq1CiMHTsWtbW1WLFiBVobr9eL6dOns2O8bNN0wqu+U7G25FhkO+Hk5HA5cYR2Ocbp/mzVsimJZNpTW0KVkzxycnr9MMHN3pssmRNt7XE7YRNMcGmTTxCutiVVTtlOm1Ieo6HXiz6COl3rr+TzPI8lS5awY7ys53rgf77x2NSt9ZffW0NOdt6A531n43WzanWMJieV2NpTW8Lh8eGNvzZiY6WY6itROdF1BrrfxjD3azAWZE7Ghy15B+IA9zuY3OX1pK/V1ttSrKhyylxaX1NSANu2bcOvv/6KDh06YODAgRHPc7vd7BVq9m7+Ofm6kCJKM8/QgUOr1TLF1OPxsIhgCfqMvgv9PPQ3Qt8TdG3avYDOD4WCf+jvq21inrIcg4b9LS3BUzlCrQT093Q+pZMIzdUmfU6fhabhkOOepLLTteK9p+YWDumepOvQUSq7wy2ea9RxTT7PlHtKRT1Jf9u8LJl8T6mop9D2lC33FE89PfPTGrwzZxtmLNuFr646JOI9SYT+bug91duccMMArd4MvyA+ZDKhPzUEgg3NgbEzmXoKbUtttT/Fck8SoddJ5z2pJE6bVx6pwV144YWs8VIwDTWqSDz55JOYNGnSPp8/99xzMJlM7D05kp966qn48ccf2cxTgpbLx4wZg88//xwbN24Mfn7KKadg2LBheOutt1BZWRn22qGN/JprrmF+GpMnT25y3t133818NyrXzUcXzoI5v6zFntke3HPPPdi0aRM++uij4LkUKHTttddi2bJlmDFjRvBzCiSaMGECZs+ezfK4Sch1TxdccAF69+4d9z1NmTIl+BkNHs3v6fnnnw/e0/o1K9GL2wltJTB58p8Ze09y1tPIkWLg1DfffIPNmzdnxT2lsp6oPWXbPcVST4MX3IU39U68uOtMvPbmCtx4Xfh7Ouecc4JyCndPvy1aC2B/8B4Xu/dM6U9rN25h/1+7Yikmr5shSz2RjNp6f4p2T7feemtQTum+p6lTpzYpo0p8tKmAmebQLIUUR4qyvuKKK/DGG29EPT+c5bFLly6oqKgIOtzKMbOjmdX8+fOZL2bzRLwtzezmP3EMDvUtxNqDHkWPY67KytmqdE9OpxNz587FYYcdxq5B58+Z+RVG/HMZtmm7of2dCzLunlJRT/R7//77L4YPH95kcpTsPdU7vXhnzlZcM7YPco26jLeU0G9L7clisbQZ6490TzWPdEcJ6nGa+xE8fu2FOKBLUdh7ot/866+/cMghhwTdfELvacWatdj8xd3wGApxzj3vZUx/+vrtp1C85Ts4eh6PI8+/Pal6or+R2pLZbFb8GNFabY+uTQGrNDZJbSld91RVVcUUVTVgJjHarPJIjeyyyy5jsw+azdAx3lQmSozWWj7pYAwS/sPWo99At8Oz3++xOf/88CFGzr8Omwz90PPe+a1dnKzm8e9X482/N+OwnsX45MpDW7s4KklgszUg55ku7P1X/sPR9ZR7cfDwxFJ9LZ3/J4b8cCqquSIUP7TX0q10/njpKoyp/hRLOk/A0Mtfae3iqKQYJT6/MwlNW1UcL730UqYwnnfeeXjvvfcUlQOPZkoffvhhQj4Zubzoh2nOT257rUwgnJz8bgc7ejWiG4FKcu0poux9PD5fuIO9v3xUj6wQcyrklCnUV+wMvj9LOxv+3SsSlpPXKQbcuDOsD2q8dnbkjLlJX6stt6V4UOWUuShHY0qz4vj++++zhOAffPBBVD/H1oCMweSjEa9RmB7oBRAHbnN+O2Q74eQkeETl0a/NrAeXEttTNP7dVM2WrUtzjRjTLzvaWirklCk01uwKvn/Fdyq2cp0SlpPPLSphHs6MTELvs8mmPLblthQPqpwyF01bXKomxZGcvmlmqDTFMRkanW7kQxy4LW3A8hgOwasqj+lg0dZaTNT+gOvbLYcWbe8ByfMCau1NfakyGXftbnZcyvdi+zuvFhK3JvOuRnb0ZtgETu8Tx06tOXnlUUUl22lT0daPPPIIW6qmnWT69u2Lxx57bJ9zTj/9dAwZMgSZiK2xHsWc+DDTWgrRJvGIDwC/ztLaJclqVm2vwjO6b1Cw0w5sGQb0HIO2RJXdjeGP/8a2wFw56ThoNXtT2GQi3vpydqwU8tmx1pF4Qnl/wPLo1WaW5dHgl5RH1f9NRaUl2pTyuGWLmIrBZrPh8ccfD3tO9+7dW115pGgwSicQb8JyV2MtO3qhg14X2xaLmUxYOXnFPJe8LrMeXEpsT9Ew7piDAs4Or7kU+u6j0NbkVNEgRpnmmnQZrzgSvE1Mw9IIC7py5TCx+xuakJwEaQKnzawJnJF3sqNeBuUxFX0uG1HllLm0qZZNgTH0Ujq0lE55qOLF2VjDjnbOgoKQZL7ZSjg5cT5x2Rqq8hhVTslgc/vQ17NSHD16HQlotG1OTnvqxUlK+7zMWpqNhOAUJ54H6TbhL+0tWFNJ+1GfmZicMtT6bxbEchusBYrrc9mKKqfMpU35PGZSBNqrr74ad6Se217Pjk5N5uwnK7ecND7ReiDoVctjNDklw7ZqBwZzm9h7fdeD0RblVN4oKY/ZYeHnPKKfYr2xjB0tfvH/CckpELTGZ1gftAhinRpzChTX57IVVU6Zi6o8KhBywqds/vE643sDyqO7jSiP4eSk9YsPAM6QWVYPJbanSGyrsWOgRlQe0WlYm5RTZZ0d+bChXW52KI86r6gsOi1ilHWO0JiwnLhA0Jqgz0GmQJkqciCW22wV/T6V1OeyFVVOmUubWrbOdnzOOnb06DJn0JYbbcDyyOlV5TFV7CkvRzEXUC5K+qEtoq1YiWWmK1H/Hylbq5HpSJHG/rwuQA2QJ9gg8Dy4BPLfagPXQgb1QbvDiUJODBKy5CZveVRRyXZUy2MW4XeKCcK9GTTjlxudZHk0Zs6DK9PwVopWR5u+iNb40BYRGsS8iD5jdmQ1MAQUPl1xd3bUc344bOJKRrxIriMwZs4KiMMmTrwJnRptraLSIqryqEBon07aeJ6OceESB3ter6w8ZTV2D579ZR2mzhGj3VMpJx0vKo8addk6qpySwWWvww6hBDZLN7RVOWlse9iRz+2AbOBX3Wi86zsO2g6D4BHEACh7gxhEE6+cdH5x+VdjyBzl0RVQHl0wAFq94vpctqLKKXNRl60VCG2V2Lt377j/7u/8k/Hkxm6Y0HM/DIRy2FXnxEu/b2D+YRePEC0bqZLTM+YbUG0rxwNdjpDtd9pqe4rEXH4AnnW/iP87YhBOR9uU0wp3GT7wHY2jehyJbOAj4Xjs8rkwvaw/HJwZBtjgDLHGxSOnjZqecPgbYcnrikzB5tex+swx6XCGAvtctqLKKXNRLY8KxO1248knn2THeKj0mrBB6AwUKGvQbnCJvkR5Zn3K5bTdX4zVQnfocjJrh53X/tyIh75diZU7E1sqTEV7ikSVTbxOcW52pKmJV060u8yvrr54wHcZtAdfhmyA0i8RVqMOCzVDMNM/DHa/JiE5fWAch4u9d8PeVZ7E8Z9+PxMPv/w2fpm3DKmiTlvE6vON3OsV2eeyFVVOmYuqPCqURFI8NLrEB0CuSVlLJQ1OsVz5MiuP4eTk8vLsSDt/ZBI/rtyDqXO3MittKpAzZUi1TbxWSU52RBrHKyfa09vPi1G0hRYDMh0KjCn1bEcp6pBr0OKdDg/gyYKH4MnrnpCcnB4/O1oM8ixs9Vv7Ch6uuhU5G79DqrAHlOcco3zjhpqmR5VTNqMuW2cRw2u/xzDdNpS5yNeoM5SCr3YbTtL8izKOlnFGpPS3zvV8Db/WA6t3P1JXkSl0q1sAE+dDe6OSHA72xefn8YDnOXQ2VKGs/lmgw2i0NartbnTndsNrLIJBm/nJ+J32OvxmuI29d+hOxcdXHJrU9ezuQNSyQR5FjAsEwWlT6MfstDegEA3IM6iR1ioqsaAqj1nE4Y7fMES3Auuc9EBXzl7D1j0L8IrhRaxroJyAF6f0ty4WpqNY34A93muQSbnObvO8im7GcpS7DgSgLLeDUGjP44HcZvTS7IbfkPmKU6KW1+mG+5EHJ1A9ECjpg0zG0dgIn2CBEV6YzckHuUz3Xgmr0YHaxu9pzxrZMijojKlLOt5+2w9YYnoYK6pJcf45Zb+jopItqMqjQiPQrrnmmrgj9X7nhmOFrz0OKe0LJVHHmzGP3w8+a1/0S6GcyBftC/9o5MGO4/Iyx+ex0e3DZr4MPk6DTqVdFdOeIvmv3ua9Bj2MdXi+bH9kE7HKqabRRvYw8T/WUmQ6DfpiHOl+C7kmLVbEkNexJTlZBSdyOBccZnksha9ar8Xuhl24sVPqrNy8m+oU8MmUIzfZPkfL6HaPD+2yzK84lWOTSnpRlUcFwnEc8vPz2TEe3uePR53Pi187DIaSWGY+BLd42uP63r0xMoVycvt4TPadz96fWtARmUKd3YtLvHfBpNdgbfteimlPkfxqlwq9UWkyA5YiWcqnFGKVU5UTGOR+CycNKMQrpsxxjWgpWCbXqE9aTrRTy1j3s7BwLkwvkiezwia+DGsFK/S57ZAq5pScg0tXDcbEoZ0xtJX7XKPLi6Of+xO1di++vnYEDuiU+W0sHWOTSnpRA2YUCDlaT548OW6Ha4dbclRXVrAIBRgQeWZdSuXk8or3T5h0mdO0ax2elAZfJNqeIj3YiFxT9s07Y5VTdSDaPC8nl55+yHRsgUA7SlOTrJwoWKYSBdgqlMFslsdqRhY4wiJjMMs+v+H2wQ8tTDIs2yfb5/76rwrlDW7m6/nzX7ORzcg5Nqmkl+x7ArRRvH4epf5yOGGEVa8sxalBUh5THAXu9vpYxKhPY4BOw2Wc8liQAZG77rpyTNDORD5oW762FywjJb0niq3Kr69Y0G//G+/rn8VuLwVrJZcf1eEVFT3qfwaZJnBnuKbBrvUhhz+AplhIpfU1VgU6lWgWvonvDF/hAM0W7PyvDMBJrV0kFZV9aP2eoiILDpcH/5huYu89vqMApG6JJ14u3fMYJhuXYFfl/QCuTNnveBqrsMB0rfgf4UyAU5YFNhK6rbPxr/FWbHMOADAKSoarWY/H9O9ij42UxzvQFula+Sem6j+FsZIShE9CpqOp24bR2hVYwifvo+iqr8TDuvfg1OUBOFGW8l3p/xw5eid289chVYwo/whj9StgbbwUgPyuI/HQtfIP7K8Rd+My8E6UV1ahfWnm+HCrtA2UZaJSSRiXQ3T4JgwWZW1PaPXWoJSrh0mmvG+R8LrFHIlemhNpMkNxJHyN5SjjalHA7a1DpeJziEnMPTIFFmQiBfZNOEK7HO2d4h7fmQ4f2NbUq0++Tn31e3CJ7heMxy8ylEwMgvvGPxLT/CNgyC1GqujlWIZTtXNR5C1Ha1Pk3sGO13luxMHuV7Fwt7hyo6KiJFTLowIxGAy4++672TFWnPbAvtbgoNEpK0LPwjeyoyGnKKVy8gSURzcMyKTYPb+tmh29Kcoxl0h7igTvELes82ah8hirnIyeGnbkLdlhDRJcYv/0x6g8RpOTxyley83JMwa5fH628wuxOj918jb47Oyos+S1bp8TBBT5a1gwv7bLgcBWDku21eKkQdmxh3oqxyaV9KJaHhWa96++vp4dY8UTsDw6YVKcE7+FFwdmU15RSuXkdYm/4+EySXWkShOVEb+pUDHtKeK1XA3s6DMoy7qdTjmZvbXsyFmzQ3nk3KLCx+tzk5aT12mTVXm0B4IAaUgz6VK3mmDkHeyoM+e1ap9z2eth5ERL4yH7iynXFm8T21s2IufYpJJeVOVRgXi9XkyZMoUdY8UdmPG7ZBq05QzkyYP4QLHmFadUTj6PmEzYy2XWLFbjEh8OgrlIMe0pIgHlkTfI85BVErHKyegT27PWkh27kWi84tghGHOTlpPfJcrGo5EnobfT5WbjR55egCaFQXCmgPJosOa3ap9rqNnDjk7BgFFdjXhT/yweK7+WBQNmI7KOTSppRV22zhK8Mi8XyUWj04MC2omDLJAyK4/N8QWWrX0ZpjwaPOJSsCYD8iZqPKLyKBizN/dcS5h5UUEy5qTGUpxutB7xfjhT8tZkXyDZtlcrzzjkq1yL5aYrUQuarJyMVGEWnGyp2GRp3XZtq6lgoY71XB66dOyADtql0MOPFevXYeCA7ErKr5LZqJbHLMEn84xfLhrra6DhxCUJnTW1D1veEwiY4YzIJIxe0V9Vl5Na5VoO9D5xksKZss/yGAu0vGYJWKmyRXnUBfz9NDIkPOfd4rW8Wnl2l3EH3HFcKezTFJSTA7FOTTmtqzw66yvYsVGTB85gwW6DmGi9fM2cVi2XikpzVOVRocTrQCz5Gsk145cLe70YDOKCgTanTamcfAHl0a/JLMujxS8qj/rc1G11J5dDuhRYkK3KY0tycnj8yA0oGubc7FAeDf7AMnwc/n6R5CQErJh+nTyTWF/QhzJ1k2KHywVTwM/QImOdJtLnPA2i8ujQiS4RjcWDxC+2z0e2ogbLZCbqsrUCMRqNuOeee+L6G2nQlmvGLxeuRlF5tHM5FMqTUjnxXtHn0afJLMtjTmAZ1JBbpJj2FAlTQNHgsmBbvkTkRNsz5nH2rLI8GvyiMqw35yYtJ8EjXsuvk2enFm8gEtyjSd2k2N5YBynO3GTNa9U+5wtkXvAYxP6l2e9EfLy9Dn/Yh+BoZB9yjk0q6UW1PCoQnuexYcMGdowVvyswaGuVtWztsYnBIA5tTsrlJHglGWSW8mghfyu2ZFaomPbUUmBBPFaqTCEWOZEPb07AhzdbFGgjL96PPkZ/v2hy4jyiYs3LZHn0BzIopHJS7LSJln8X9OB0hlbtc/6A7zofyGbQ9dAz4TjuOVx94flZGZEs59ikkl5U5VGBUOTZRx99FF8EWmDQlmvGL7fy6NbmplxOglfcc5jPIMuj2+dPub9VQu0pAnpBtO7qTNmX5zEWOTns9dAGfHiRJcqjKVCnBnNO0nLiAhM46OVR9vyBNEK+FE6K3TYxYM0Jc+v3Oa9o2ef14jhuNepw+aieGNa1EJzCUrApbWxSSS+q8pgteAMzfpkGbbngHaLy6NGn3lIlBJat+QyyPNodIf5WOcpP/WIURAVdb1bWJCVdeO21e3cxUlgy/kQxByypRhkSZGt8ovIoGOSZXAju1K+ouAO7Jjk0rT92agJGAMgkPxWVVKH6PGYLMs/45UJw1qUvqbRPVB6FFATmpApnwOohZ4LiVGLjTWjgzDDE6B+XbfAOMVWRg7MgPxssQYKAL/1jYIIbo2TY/k8XUB4pUlgOhIAy5QtY4lKBN6A8uhWgPG7XdUejfyiEvJ7IVGh5fV15I7oXW2HSZ842sSrxoSqPCoSWJ0pLS+Napvg973S8sKMvxnUdhmFQDstNB+En7xU4uMMQBOIGUyanxfnH4LWNxTi+8/4YjMygwavFFO+lKDX6cJNWr5j2FA6fn8cpnsfY+yWdhiLbiEVONs6M933HoCgvJ4VZB9OHxy/gfu+l7P2y3IKk5bRT0xEc3xu+nI4yFVBUHgWZfCjDUc8VsL2ztbld0LOV+9wvOafgZ+9wPNblAGQqb/69CU/8sBaj+pTg/cuGR5WBXGOTSoYvWx955JF44IEH5Lxkm4RSF1x77bVxpTDYJRRjsdAX/oIeUBIbuK74zD8W9R1GplxOFVwJ5gn9Yc/rjUzBJhjwof8YTLOcraj2FA6nV9wqjjAbss+iEIuc6vTt8aDvUnxeci2yAYdn784llhjrNJqcPrRehDM9j6C223GylG/vMnjqLI87zH1xs/d6/ND+qlbvc5QKirAaM7d/TZ2zlR3/Xl+F5TtEq26qxyaVDFce582bB79/7wNGJTFIhosXL45Llk6FDjp1Dg875pv1KZcTBZ8QmbRUYnP7Ul5vibSnaMojGQmMuuxzl45FTpIMTFly/w6nC0VoQK7OB71Wk7ScpHEoVkW0JTQBdxwuhcrj3j6oa/U+Z3eJ46XVkJmLgtuqHdhZJ/rQvnvJwRjUOT8tY5NK+pF1BNxvv/2wdas461BJHJ/PhxkzZrBjrAxt+BUTtT+g2LVNUaLv3LAEh2tWoIQToyZTKace9fNxofYXlDn/Q6bgqS/HoZrV6MvtUlR7CofbYcfnhkn4wDAZnF98yGUTscjJ67KjAI1M2coG/LtXYLHpaszU3SKLnCRLplkvj/KjldmHMhwupx06+JAjo/KYaJ97vvoarDFegrL6pchEVu2qx7XaaTiv/TaM7VPU4nK0XGOTSvqRdXpzww034Prrr8fq1asxYMAAOS+t0gLH2b/DAfrVWOs4DAC9lMEFje/gAMM6rG/oBqB/Sn/roIaZGKmfiWW1lGz7eGQC1t3z8KnhMfzXOBDAOCgZl6MBwzXrxP9oUuOfqXS67vwBS02PYvXuEQB+RKbjDuSHdXHyRI6/brsRFmMjGm2fACiWUXlMXfTx8M2v4VbTx5i35yIAL6E1MQlOmDkPDCZl5euNlfIta3Cn/nP4678CPOMBc3Yk0ldJsfLYs2dPjBkzBoceeiiuuuoqHHzwwWjfvn3Y2cfo0aPl/Ok2z1zNEGzwFKBfASlpymEzXwYt74C1sEPKf+s/XR/U+etRkp85Po8Ovxbr+U6oNcoUYJBC7IIJV3tuRgcr8JAmO5ZtE08HlZkP9+ZUFg7Dsa4PMaDUiO9kuF6JUI0CzoYtRnl82D60XoLnG8bi4k6jkCo0gdyKqbRuxso4/A8+dyPeK9sfmciOqlp84x+JAZ2K0E9VHLMaWZVHUhxJUaRQ/WeffTaqyVr1cYgMya1Xr15xRaC9LpyJKq8HP3VUTpwxtYObPVfDzwuY1+PQlMtphvFkLPYejte7HIhMYXXeSFzpaYfze3TFIQpqT+FwCHr8xA9HnxiTSWcaschpbvGZuGLNYFzdpysyNx626TIzDw20RosschrnewR6vwOvFXeXpXxr0RUr+QJcUtAZqeLDwutxze6TcXevAzC8lfvcLq8FHsEEsyUz86jOri/FW97r8O6Yg9EvjWOTSoYrjw8++KDaCGSAIs8mTJiQWJSeghytyRGdFMdUBcw0l5PLy2dcwIw94KyfK6O/lRztKRxSMEQ2RlrHKieXz8+ULYMxOyyP0rhhiWPciCQn6uvrfGXi9SzyTDAc7tSPa3Yfh3rkxLw9Y6r6nNfPw+MTxzBrBvYxMhZsrRbdDLqXWNM6NqmkH1l75MMPPyzn5dos5Dw8e/ZsHH744dDpWq4inheg9TRCB4OiHuz1TnHnFINOkxKFrrmcfGxJUcioSGCb9HBMofIYb3uKBN+4B6do5qCUJyvQ4cg2YpGT05N5E5RoFGyfiZf1H6PGRXbvQ5OSUyJpf1riSOcvOEjrQS5Pdl7yZZaf4KRIxjpNpM85GuowSfcu3DDAos8Mn+1Qau0eFPt2YzeK0SHflNaxSSX9ZM5Ttg1BS/p//vlnzEv7Lq8PS4xXYoPpIljdlVAK9qrtWGC8Gt/r7kyLnF6w340tpgtQuudPZApH7HgNPxvuxNCqGYppT5EwVq3GS4aXcZHtLdnKpiRikdOBVdPwov4l9Kv5A9mApe4/nKz9F73965OWk6uhBrfqPseVuu9km8Dd4H8P/9O/CauvBqnijPqpeEL3Fkqcm1q1z7kcdbhYNxOXaH+CQaGTE7KMfrFwO75dujO4qiRRXr4Ls403Y63pUpg4X1rHJpX0kxJV3263Y9q0aVi6dCkaGhqQl5eHIUOG4PTTT4fVmpm+HErG4XTAwgUsIjItF8mBs74SpVwDdGnyZ9ELYvoYnT5z9hzOd+9CP80OOBHYXlLB8NJWcZrsWLJNhC6O1RilnYvlLrm841oZt7SDS/LBIu76PbhRNw0NggUcN0WGwgGz/EORCzsGyrB1YiQOdf+DHrptWOW9DK2J1yXmR/TAAKWmzLY7XXjtqx/hgQ4nD7yEvBaD39XuFCcgDZp8FGfQFrEqClEev/rqK1x55ZWoq6tjPhAS5BBbUFCAN998E2eeeabcP9umcdv35lBMZUqLeHE3VrOjQ5OLdCRsMAhuNpbpMyjNhcEnPrw1JuXva80HFA2fNnOUc7nR+l2KicyVBa9Yp7w++fvxOG3BtD95Mlm5bvaKO/ksS2G2BjZu0NHcuoYNj1ucQHo45abB8jZU4DfjHfALHLTapsq2q0K03NYZOsiQpEmlTSmPc+bMwfjx46HVanH55Zdj7Nix6NChA/bs2YNZs2Zh6tSp7HsyUx92mHJyESoNjUaDoUOHsmMsuAODNs0GDVrl+I14beJSk1OXmxY5GSE+BHRG5SjQLaH3iw9vrTk1MkqkPUVC8IiWET5LlcdY5CQpjxpD5kxQosEFdnAR4ph0RpKT19koa87IVPhQhsMYUB71MiqPifQ5n9sVtDwqFW+gjMwvs9l3Qu0WdrRbO6d9bFJJP7JqGk888QSMRiP++ecfDB7cNGXMuHHj2B6WI0aMYOdRVnmV8Oj1epx66qkxi8cdGLSdMClq2PHZReXRo5MvijGSnMhKYQ4oj2YFLd23hIkXH94Ga2pklEh7ioQQUDR4XXYoTonISc+LbUybJZZHbcDyGM/2f5Hk5JUmsRqZlEe3Dxx46LW6mLdOTARTYMXCaMpp1T7nC0zOvJySRvGmeINl3Fd1MDSIu5vx+V3TPjappB9Ze+TcuXOZkthccZQYNGgQzj33XGahVImM1+vF9OnT2TEWvA5ReXTLNOOXC8FRx44+Q17K5eR0+2AJKI9GS+qseClTHi15imlPkS/kzGrlMRY56XnR8hJPXkQlo/XHv3d0JDn53KLy6JbJJ9Zb8R82mybgH/3VSBWUqcIUHDdyWrXP+QOKmU/ByqPPI7Z/L/ZdWje7ytlRV9g1/WOTSmYrjw6Hg+0oEw36ns5TiQzP81iyZAk7xoIv4IvmlmnGLxsuUXn0mwpSLieH2wldIGjIkEFJrC2C+MAwWVMjo0TaUyS4gPIoyOAfp0RikZM+sMSpM2ZH4J/eJ9apxpSbtJx4l03WgCpPYEVFSGFSEJfbFRw3jDKOG4n0OZ87YNXTKFl5lCyP+yqPOb5adjTH4Z8q19ikkn5k7ZXdu3fHzJkzo57z22+/sfNU5MMXGLS9CrM8at2i8ogUKY+huEKChhCHFaU1oaTAVoiDsTkndcvWcqHxi2WFPjstj/EEV+hN2aFA024whFaGJVt/YBLrlWnrRrcjYMlM4bjmCvwGYW7lgBk+sPWlT9HKozvs0joFxxbwovJoLVb+VqsqClMeaUl60aJFuPjii7Fr164m3+3evRuXXHIJ+56WtlXkw++Sd9CWC723gR01ltTHWksPGi+58WqVG63YPO2FmRPTC5lzU69gJ4smYKVClloe4wquyBLLoyFg+dbHYXmMhBBI5eSXIe0P4XOJE0JPClNDOQPjBkUPa/Stm16GD1j1/ApWHoMKbjPLY4PTg2LUs/cF7Tq1StlUMjhg5q677sJPP/2EDz74AJ999hl69+7NlqnLy8uxYcMGeDweDB8+nJ2nEhmKVj/iiCPYMa78ewpTHo0B5VFnLUy5nKQ0IW4Yw3jjKBN7Yx0klVFvzlNMe2o5TY2y2lm65ETWFSM8skfmtibGgA9nPPcTUU4ByyMvk/LoD6yoeFI4rklBPi7OCKuM+WgT6XOSYubXKDdHoj+oPDZVcGuqy5HPiYm+TfniFpXpHJtUMtzyaLFY8Ndff7FtCjt37ozVq1ezFD10pP9PmjSJpekxm7Pz4SMXtE3TmDFjYt6uifc4FKk8mvyi5cCQU5RyOUn+UfQQyBRctoZgiiWkMKluvO0p4nWCaWqyQ3GKV07ukIh+Y5Yoj9IyvNFkTVpOnC8QjS+TZVpaUUnluOZ2BvzFYWz1PicEFDM+AyyPza2jjZU72bEeuXGNZXKNTSrpR3ZPZErV8+CDDzJLY319PbZv386O9P8HHniAfd+afPjhh7jqqqtw0EEHsbJQ8vL33nsPSoIstFROOsaCEEguy+uU5fOYw4sKnTGvJOVy8gUeNKlc4pIbl130EXLskzGtddtTS5HGmixJUxOvnFweH0wBy2M8ypaSMUCMcjVarEnLiQuk/ZEroIr3BFxRtKlrb97AuOGWedKZSJ8TfC7F51HlveJkw99s2dpes5sdG7WFrTI2qaQfWdV9Mj1TEvCPPvqI/T83N5e9lMT999+PrVu3oqSkhCUwp/dKg5bHNm7c2GSHnmjIPWjLAZU9R7Cx/GmWvOKUy8krBQ0pLeI8Ch67aHl0aszB5WsltKeI8GLSZq0xcxR0OeXkdDlRwInf6bJEefyFPxgmwYVBlvyk5aQNWB7lCliTdjRKZWooSXmUKzdlMn2uXluM+Xw/1JljT3XTaspjM8ujp24POzoMRa0zNqlktuWR9rDu0qULlMxbb72FLVu2oLKyEldfnbr8YelkQf5xuMRzJ1Z2PCftv21z+zB3YzU2VNj2+fxJ3/l4wnseckq7pbwce8x9cIXnVkwruQqZQq2uBP/zjsNP1jOQCdxkeBg9XB/C0fNEtEXcXh+m+w/DLOGgrAgaohyHN3muxVXeW2HMK036ejVcITbwHeE1J38tggu448jlQxkOO2fBLP9g/GfYH63N8qJjca7nIcztdCmUSnBpXdvUUrvMfAhOcj+Omd1ubaWSqWS05ZGCYZYtWwYlc/TRRyPb2KUpwx/8EBxauF/af/u/8kac9+a/6FRgxj93Hxn8vN7pxRf+MTDoNLg3N/XR1jVcAWbyByG3IHMi/ao07fCq/zSMLSqFch8Xe3F5eZZzz2RUrk9WKnHwBtzovQGluUYsyJCI/miQD6eESZ+8HeHTwqswc/fpeKLrQMiBtKLC61Nn5d1j3Q+3e+/C6NJSHANl1IdJr9zgkVVlp+GapV1xQt/OGBry+U6nHquEHji+tG8rlk4lYy2PFCjz+++/4/3335fzsm0Och4+5ZRTYnYidnrEKDdzKww6NTbRV6XI2lShqHOIvlT5Zn1a5OSQZJDCPXDlhqyzRI5Jr6j2FAmX16/4h1sq5eTySfefHfvwkg+nDmIbNOq0SctJGofk2odaCsDhUuhj6wy0aYvMbTqRPucOtC+jTrnty8lrUYs88Mambg41jsBzIMfQKmOTSvqRtcYoQThFTl166aV46aWXcPDBB7NUPRSUEgr9n4JnMg23281eEg0NDft8Thu8036dtN1SaNZ88gdlUcEeTxP/DvqMpZpp9vmQIUPYtUJ/j6Brk/xCHYx71s3BOdqtKPHkwe1umt2fgoKoHKHbP9HfGwwG+P1++Hy+fT6nz+g7iWj3JA0a+WZdsKxMmaurxCjNcpj0ZcHP6Rqx3hNBZWH+jM22rpLuicq5//77s6OlZjVO18xDD8+BcLv7JnVP8dRTMvfkq92G/bktKBVEf6tU1tOwYcPEwKKQ68R7T4/xL0Kn90Jf3xPu3J4x11Oq7ikV9SS1Jzq3+T012BzQwg+zTtvkOkq/p0j1ZN+zHhtMF8EmmMFhF9xuT8z3RFvN0ufSd1QOh0d8Tzu2SGVK5p4ky6NfZ2ZySmSMaKntNTrcwUmn3PUktSX63Vjq6ditz+FG4+9YW34VeP5uRfYnh1v8bb1WfKZL9zSg+hf01m5FZ7cJgtA1rnqiZx0bDwPlT+c9qShEeSTLowQlA6dXODJVeXzyySdZuqHmPPfcczCZRAVg6NChbKP3H3/8kW27JEG5rEix/vzzz5mDsATNuujBTr6Y5IcpkZ+fj2uvvZZdO7SRX3PNNey7yZMnBz872v8LRupXYm55O0ye/Hvwc+po99xzDzZt2hQMYiJKS0vZtcnFYMaMGcHPe/XqhQkTJmD27NkspZJEtHuq04g+rrs2/4fJk38O3hNqt+ADw2RstHXE5Mnb2OcXXHABy/0Zyz0Rd999N4vUnzJlSov31IfbgP8zTMe8Hcdi8uRVSd1TPPWUzD318q3G98af8PO6wwAclbJ6Ovzww7Fu3Trk5ORg8+bNCd0TL3C4TbMIeZwTz37wFmxcXkL1JGfbS1c9SZhgx0bT69jR2AGTJ5+X8fdk4htxFwf4oI2rnmgziBdeeKHJNrN0Tw/XPAWjoQ6ffn0KFgXS6yRzT4c7K9na2Or1m9G7qirpMSLcPXXxrcdq4y9YUXECZs++tFXraYR/G9pp6rCR96CqqkqR/cngq8bjuh3w7BkBYGDwnsb6fsVo/XIsq+oDj+fwmOvptttuw6uvvsq+S/c9TZ06tUkZVeKDE2QMcwqt0Jagim1tqIFTh3z33XfZ7jeJWB4pQKiiooIFC8llVaDfeP7551mHa044q8JX/3czymyrkXfULRh42Alpna3++f6jGLT1XaxtfzKGT3w+eE9//DINHec+DJu1Kwbd/HVKLI82m43J6ZZbbsH0j19Bt53fAf2Ox/Bz7swI689v7zyIA/d8is2dTsOhV76Ysnqi85555hnceuut7O8SuSeby4cn/zcJFrhw2633wZRTkHWWR6nfUXuiLBHN72nBnzNw+JyJ2K7rinZ3LMyIewqleT2t212PC1+bhTKrBj/ed1bM9UTXoLGT5CSlXqNyND7RG0VCHZacMB0Dhhya9D09+saHqN+9CScddzyOHTUyJZbHuVPvxZhdb2FRyWkYfPU7stWTy+UKtiWatMVST/d98BtWbdyCy48/FGeMGpLwPaWy7S168zqMqPoC/3a8CIde+VLwnl566h5086zHsDNuQs9hR8VcT0TztpSueyIFnRRVUlyl57dKK1keqXKpEsgMnY1Q4w6XpzLc59SYwxH68I7l80h5MUM/f88wHv95bfi48yFhz6fOFe5z6kDhMvtT5wrngxLunnSOcpRy9dihF5r8xgbLEEz0TMaZ+3fCwc1+O5Z7Cm1TLd0THf+0HIOZ3kF4vM8BGBXm/HjuSe56uvqDRdhW48Cjp++PA7uJuUXp818Kx+ParaNxf//+ODTF9SSVPdz1Y7mnBg/wpV+c8E0qahfWFSXdbS9V/UnK/9r8nioKD8Jg1xs4vFcBXsmwe5IIvSdea0A9cpCjN8dVT5LC0Hzcu0V7DzyOBtzfvu8+10rknlZo9sMyvgynlvZl5YvlnkKJ5Z7mlJyL+zcfgAu698WBMtaTpKzQ70cre+jnlZoSrBEECDntIpa9tfvT2rzDMG83j25F4uRAKsubnmPg9h2F2T1HRKyPcJ9HakvpvCeVxJDVM3fs2LF444035LykShxO360RLDIt51yc4H4Sm3tNaPI5RVsTeSkMmAmlIfB7uSkOPkmE9RWNWL27AR6fED5gxqh8Z/G9wTKafRTHtoLDzzFly2dKTd7S1qxTOVjs64G5/P4wWeXJ7esM+FBaUziu1fpN2CG0A29tj9YmEwJmVpkOwgv+s1BRKiqPUjuSIsULLKpC1laQ9anVrl27oO+fSuLQDIr8ZCLNpJrjDCghraE87nSZsUboBkNJj7DKYyqjrUPl1BhUHpWniElRqFajNqzyaE2x8hhvewqHx2XHEZpl0OgobUpT14hsoSU5ZVu0ua5iBZ7QvQm7j4KfxiQtJ0k+ckVbH+icg54aF3IEysFYktpoa5nHzkT63OjG73GQdg+KXHSvHaFEPH5RSTRo9yq4dXYPylANmyYX1jgnInKMTSqtg6xPrWOOOQZ//PEHM9m3VeuEHNDSBDlYx8pPvsthNTpR2/gLUBaafSv11AairQubzTjHbHsJlxr+xu7aKwH0Tbmcnmq4A12N21Be/RqA06Ak7MEUJk2722XVz+E2wxZo6x9K6cMi3vYUDm99OaYanoJLoHq+CdlIS3IqrvgXj+u+hKHxYHLTb/F6NA7e9sUy9GmXiwmHdlWcVVxXtxnn62ZhtW9vcEeicvI6G3AxvoNda4JFf6ws5bvZ8wbaG2qwxXUcgO5IBcNqfsQBujXobBsHoOkEON197hjHD+ij34gVzuMBHAIlkufcjr7cLliFvbvgNDTU4F/TDeJ/fLvi2mFIjrFJpXWQ1T5Ojq/V1dW48sorUVNTAyVCUWMUHEOvL774Yp/P6H1rQ34gFNnd3ME60gOKghjMnAcGU/q3jTvd9gmu1X6LEtQ1+TzftQu9NbuQq/GkRU5WvhH5nAMWBVq+p/rvwfeGe5Dr3NHk8+7ejThY8x9yNC3Xc7raUyS8ga3i3GjdvelbU075Detwge439HMsjul6O2oc+HrxDjz7yzroAn5vSsLvEXcL8WmMScvJWVeB+/Uf4QHdhzAZ5LnXpUJvtl2fITd1bgIDbbNxtW4GSh3rW73P6QVxrNQZlDeGSZxT+TJ+Md6FnpV7s3rY66rY0QN93DsvyTE2qWSB5ZHC6AsKCvDOO++wzc579OgRMc/jb7/9htaAwvybh+j/888/7CVx+eWXo7WJNQcVbZlm4QK5yqzpjRgjxXWCfzry9XZUaK9v8p3RJ+bA1Fnj2+s0XqRIOqtgZ/tom3JT+3vx4vHx2I/bChPnRUMzB20jL6Y60cexr3DC5Ugyp5nPJZbVw2Wv8tiSnDivM669ljesmIt/jddjhflgmA3K29JRCNxPvMpjODl5HWJ/d8CI4pAlzUTx8wKuct/M3i8qSY3VkdDxogKtNVpavc9JyqPWoNy947W8WEaNfm+bcdaLyiMtWxclsOKo5lvMTGRVHmnJWoJmEmvXrmWv5rTmkvZ7773HXtmC026DNE81WeRxVI+VRocT+ZxokcorKmvyndnfyI6ptBpI0O4yuRAfhNa81G+FGK+/4+Wee5iC/2ZhUxlZBAdTeM0haW+Uitcl1rMnAUUjawjseCLEaF2xrf8HZVwt7Ia9OeyUBC8pw832KU4Et1Pc294Jkyzju+SLmGqfYL1fUh5TtwVizGURRL9tXQoUWfmVx73WUY+tmh0d2jwoa+qukkpk7ZWhOZZU0oMrMGgT2jh8TeSgvrocZOv0CxxMzZREWkYm0mEJtDmcKApYX005ylIe7R4fFgj7Qa/hYDDtfSj4/DxyAgpvJiiPfo+oOHmz3PIYDY1PVDQQ8uCMRm6FuEmCv/NwKBKvSzbl0esQ+7ubk2fJ1REIJiM9NJXRx3pBlIFOAcqjAaJipjcq1/Ko40UFVxvSB3wB5dGtU3MltiWUF5qqwiLPaPeBWCLQ3A5ReXRBD1Oa/arsdaKjvY2zIl+zN1qR5wXkBpaRLfmpiZIMlVO9fa8CzZmUNYBJW7Y1D5axO1zI58SHhTm3UDHtqSXl0adVrj9WquWk8YnKfix+XRRJ38ezivWB0gGjoUSkZWteZ0paTl6XqDy6NPIoPq66PVhhnChaMrF3VyS5MfCBLVVN1lbvcwayPHIKVx4DS+uakKV13lHLjl5DfquMTSqtQ8q0Ddr9Y/Hixfj7779T9RNZCy370LZVsSz/eJyBQTu4eJ0+3JKfE9f0YWpzuZHHicpGTkFpyuXktInBOk4K5tAqaxBy1VfhEu1POFO316eWsNnEAZcwWPIU054iIbgDymMWL1u3JCetX1S2OEPLyuO69evQiauCHxoU9qGt3BRIwJLKxzkhCCcnf8CtwauRZxzyOG3I5ZzIpXEkhW5ORojKo0Fm5THePifwPIwBy2NrBD7GqzxqDXvHAcEpjmV+Y0GrjE0qWaI8btmyBaeddhoKCwtx8MEHs8ThEhSUMmDAgCa+kSrhHYgpcj0WR2JvYNlaruWiePAElqo8zawNjbWiA3Wql60lOTlrdrH/N2hSH3gSL/7arXhY/z6u9X3Q5HNXo6jwumBIucIbT3tqyT/On8WWx5bkpA34x2n0LT/cq/+bx467Dd0AYw6UCCctw8dpeQwnJ18gGr/5WJAoHpe0opLayYpJCCiPZmur9jm3xw0tJ6REkZUTKajHEGJ51LrFsUwwF7bK2KSSBcrjtm3bcOihh+KHH35gCuRhhx3WZE/JQw45hO0n+cknn8j5s20aKZChNZRHX8Dq6dE2tcTY60UfGDtZQ9NgCfTU72FHm05Z/o5NlPtmD1VXwFra3GqrVITAsnU2K48toZOUxxgCGoRdy9ixoYASXCOrlMdw8DK3D58z9eMa+R2bAtY+o6l1FXy3M+ASwcqiXMujAaLPY+jSusETCAhLQHlUyVxkVR4feugh1NbW4s8//8SXX37JkoaHQvtRjho1qklaHJXk8Lttsi4XxYMv4Ofka6Y8uhpEy6OdS8+A7GsoZ0enIXX+lYlCyZPDWWTcDnHAdWaK8igtccaYpiYb0UtpXWIITMurWy2e22kIlIrGH8itF4MltSV4t7zKo5RX1JtCNwnaEUdKc2a0tK7y6AncL6GLMSCrNTAGLLV68155mQJp2ThVeWxTyKo8/vzzzzjjjDMwYkRkH59u3bph586dcv5sm8Yn+Rq1gkXIHxjwfLqmCpC7MWB51KYpeMVWwQ5ec+r8K5NW7psp2F67qDy6m32uVDivqBy0ZeXREHhw6kKi5iMpJd29G9j74j7DFaw8isowF5KzL/m0PzJZHoOpoVI3rrkC1k3CKPOydbx43aL83IIenAITyhN8qKU2RF5mnziW6XLURD1tCVlbKe0q07179ISutIytZpOPjsFgwN13382OsS4XebXpf6gLAcVIaKZQeO2iA7Vbl5sWOemdoqWTt7aD0uADvlu+ZvXjkyySWqui2lNLkbmh+d2yjZbkZOBjS+uycfMmdOBqwINDca9hULrlURPnriBh5RSYXAgyWDEJf8CSmcoVFSlTBcHFKQO5+5ykPHo4ZQX8heLyuKDjxHR8phBLrZkXlXC9tbBVxiaVLFAeaTeZ9eujb/O0YsUKdO26d19MlfAKdn19fRN/0UjwAeufvxWVR77ZMp4/kLrBo89Li5yMblF51OY1TcKtBISgdbapjPiA8ujV5SiqPUVCE1Ae5X7IKomW5CRF5upbCGio+m8+O+7WdQZnTG/i/njYquuBuf4B8OWUJS0nafcdOfwnm/pQpm5c80irNtACWl2r9jm3T8Bavgu2cJ2hVNyOvZZaU8iytYXSslH/sBa0ytikkgXKI/k4fvfdd1i+fHnY7yltz++//44TT1TeVl1Kwuv1YsqUKezYEhvzDsENnusxv/RstNZSJvRNFaCNxgF4wnse1pSckBY5TdWcjts8V4PvNgpKQ/AEFOxmStcOY0+85jsF6wuPUFR7isR7eVdjkOsNbOrX+lt3tpacFvN98C/fH/rc6L61Oxq8mOMfgF2FB0PJfGi9BOd570dDx9FJy8kBE8qFAvgSSNeSjgCcSO4FC/m+WMX1bfU+12jthuM9T+F6y/+g9A0pfIIGGp1oKSSl73LPbTjPcx8MHQe0ytik0jrIOt26//77WaDM6NGjcccdd2DDBtHv58cff8ScOXPw3HPPoaSkhH2nIg+7dR0xgx+B0sIeaRdpDfKwhu8Kb06HJp+v1/TAR/5TcFPHPmkpx2xXD9TxnXFlp/5QGpxHnJUL+qbWqg2G/njbZ8I1nXohE7B5BTQgB0ar8tIhpQPaa5kmacTi0t5Rz/3VPQCzvPfj8YMOgJLVR5dP3ALQqE/ehjCj/TX4ZOtJuKVLXxwtQ9mEwMTUn0If2wZDe4zzPIyepVb8HsP5q3bV4+XfN2Bg53xcc0QvWXMTugPbMRpk2Bc8Vdh1hRjtfh4lRgFfB+6dtoZdzXdj73NSvNmBShYrj+TvSEEz48ePxwMPPMA6F81MTj75ZHak5WpSLjt0aKpsqCS3dzJhMezd4SVdfGM+C7d5jsDz/QY3eUjWOcVZZL459f47vMAFf680V3kJrDW+wFJPs1x/Npe480xOCvftlROb25/yfYaVDFmpJMz66H1tY6VY571KlZff8aeVlNZKwGE9S4L3ZNIlP3ZI15JrHJJWNYQUWh6l/bNbqk92rsePS99dgIpGN35cuQcd8k04Y6h8S8xuX8CXMIaytBYuP4dtQnu4QxKENwbGMa2Gg0mGSYhK5iD7k4ByOZLf44wZMzBv3jwWRJOXl8c+p9yPqmNsbMQqp+K6FThOswZlPpqh90M6oS3YCGuzrfcK6tdiIFeNYl3PlJdBo9PhHO0fqEYhCkypXSZPBI20tG9oqkjobLvRmatAgTY9FuNk+915jVOh01WhxElLtsqLak+1nCRFg4i217LL40N1bQ2pAejdTnnK44PfrmQK0Hc3HI7XG65DvrEWexo+BTAqKTlJ23Ca5ZrEStH9KfSxdXljV9i+n7MIJzq+xXs4jlRbvD93a4vKYzx9zrrjb/xquB877LR6cjiUiCuMsu1oqMbV2unwGvLAcYm5o6k6QWaSEjMC5XOklD30igXykVy6dCkuuuiiVBQn4zAajbjnnntiOnd41Ve4wfALFtSRw/GRSCd2d3jr2fjqV/G4cQVW1NFSbf+Uymn86cej35djYYMFGu29UBo6n/gQ1DSL0D2t/CU8bvwbSyruAxC/r1Cq2lMkRvnmoIduJ7b5rkG2Ek1OrsYarDJeynY80QibyNE37Hnbtm/FKuNl2IlSFJvXQGnQMqNkQc4TGlHE2VAT577C4eR0we6ncIVhC2yND1BCtqTLGQzAiaI8un1+GJOwmubv+B3/Gu/F1oaBtPAe9VzjwjfwsP4LHNOVwwWbj0P19nWorB+K0nyLLH2Oc9Wit2YXPIKCJ2bVG3CX7hP4eFKaxZ3jXLW7cLf+UzQINFF6ulXGJpXWQRF25m+++QaXXnppaxdDMfA8z/xF6dgSOzWdsIDvC19O+qP07rc9ht8Mt6F9tbgVm0S1kINdQhH0+alNnUPyWbtlJ/7wD8ZKPT0AlIfOLyqPWlPTqFsPDzgFA7QWeQIM5GpPkXjLfxKe9Z4NXUlm+GjKLSeXvQFWzo18zkbm7ojXqNq6ih01Wh04he2zTq5DkoXQatBiAh7H0e7/gSuO7sMZi5y6eDbgIM1/sCKwa02SzLeOxb3eidhdNibs9z+s2I0BD/6Mmz9dkvBvCM56lHG1yBX2puwJR73Di/J6BwsI6nPgWHya83/4y3gLNi38WbY+t7PgIIxzP4Avi66CUtHWbsI1uhk43vtr8DObX48v/aMx2zCy1cYmlTasPKo0hSLPPvroo5gi0D4zj8M5nodR0+OUtIuxjC9HL81umJpN/m8WbsUI98vQ9kxtJDHJ5/N5W3GJ9y681vExKBG7YES1kAutuWmgyYP629Hf/R6c/c5UVHuKtI3bR94xeMl/Jswl2ZtmK5qc6rWFONz9AiaangeiBEos4PtjiOt1fNjjKSgN8qvjAxlRaHl5k68YG4TOMJotSctpivlKXOW5Ge7SA2Qp6xrdfvjYfxTsJUPCKsGTf1wLE++AacWH2LptS0K/salgBE5yP4Fvym6Met6ibTV4zHsBxue8h3bDToMpt4j5WtdsWSpbn6vXFGCe0B/lOcoL+pOoM5bhLd8JmGfZO65X69rjdu/VeLfo5lYZm1RaD1V5zHDsAUuCxZh+R+tbfdfjHPeDQKehwc94XkBDGgNmnIL4G6U5yguWIe7T344D3a/D1bupP6YU5FNgVWa5Q7EHgmXacsCMzcthh1CKCnN0y+u2GgfqkAtLJ+Xtae1w+9CT24X2qIFZx8kapLEQ/fEzPxz6vPbyBrOE8aEkGdPrfcNkTNa/hZ1L91rC4qEeVqwSuqM+J7pv9uKt4j70w7oVARoNdh94G4a5X8N3ptMgF7QE35I/bWtTae6Fx3wX4rfCccHPGiO4LqlkP2qNZzgOKQq2WdBKOlKXrPJ1Yu9NucXBz20eX9C6kZcG5dElaBUbaR2pflhiXEl5tChraTMcNqcTw7j/4NFYYNDKl54kk5D8e3NbeEhurxXdFDoXKm8bR4e9Eb8bb2fvXfbjcbfuE7ihh0lzpGxZH+QKmOliX4URmirk8hRQJo4zEhuW/o2J2h/QKFiwku+O8hpRuYuXYLR5C8rzhs3k4yrgwG5iKprRBw/DH8OGoMAi364o+dXLcZF2Fjp5aDvLIcoOmAmpY4fdBhPcqvLYBlGVRwVCKY5KS0tjyiP2uu1G5BnrYWv8GEBifieJEBp9aglRjBqrdjM/yHouFyZdapPBk3xu0n+Nw3RLsKLuFgD7QbGW4ZAB1+Z04VPdQ6gTcpCvpchKk2LaUzhc9eX42vgwfGyh4kpkK9HkpCtfyZQtnZfa2GERr3F2xcs4QedFT12XfZSe1sblsKFBsMDMeeDxeHG1TgwS8erfSFpOR3t+Q72Gg5WTJ7PlhQ1vYX/Daqyq7bRPQJnhvxl4QP8hvhLG4hL3XTgK7RBbaGZTyqr/xbXaf9DJSUnSwy+300TvkT3X4H9GFyqs0wF0jcn6Hm+f61L1F8bpp2J+A/lfToAS8dtrUYZq5HN78zn23PIZ1pqex5JKikL/PO1jk0rroSqPCoRSF1x77bUxnVsk1KKYa4DPmN49hx1uD67RTocDRpi4o8idmn1ur69EX81uNKAhqm+YXHIqNXqQ53bAalHetnlknZ3KPQy/XoMc7yBSIdnnDTWVOFjzn3hSC1vdpbs9hcNlr2dHJ8zIzeJBPpqcTDVrmLK12kl5Eu8Pe47Xz+N43+/I0zlQnUNR9MqiUZuPo91voXOBCd94AnspC1oY4oy23kdOgoBHhVcAA7BLkGdysYNrBy3fCK1176qGRE69uAUuXzYY2Apsqd67bV48dKv5BxfoP8OCRmNEha2qfDvaoZbtU27o0S91fc4nbn0paJW5gkL02/Yp/jW9hvlV5F8/QvzQ3RB2E4R0jU0qrYdyHSzaMH6/H4sXL2bHaNCs2ArxIWDMSe0+0s1x2Rtxl/5TTNJPBYe9+5K6GsR9pu1c6nPckXys7kr23ligvH2tScE+mFuHEdrVMIdYZ+31oowaSZmUeU/dZNpTJDwB5dHBKU9BT5echMBD0tdsK85Q9uzZgzxOXLYu6hhfBHN60/To4XGJ5XSTxpeknPwBRZQwWeXZy/tBzQ1suz5fl4CSEkKxexs7duglZljYXutMaG9kjc/VYjqg8s1iuqVyTSlMceyuFG+f4/xiWQSZ9gZPCT6xnoWQXX80nkbxM0Nuq4xNKq2HqjwqEJ/Px5Ks0zEaTrcbJk70nbPkpD7lSyhuu/gw9VMTChnw3A3V7GjXpV6ZJfkUCKK/U06xspYIpYf15d7bcJPvBhhzi4KfOwMysnHyPGjlak+R8DrEunZrslt5jCYnwS0+JPkoFpaaHaI1uYbLB2dIvUU5Ub9N8lnzSsojZ0xaTk7H3lQ3Fos89x3Jh9LlcqEjX87e9+vdh7nILNJeiobG6Ol2wqEN5GDlDJHbdc2eLcGtDFPZ5zQByyOnV67yqAnk3hT0YZRHU16rjE0qrYe6bJ3BOBobAguhgMmSXsuj2yEOGk6YkBOylOm1i4qRW5f6PZDdbg+KaXmcHM5Llac8Nrr9+J0fhnyjHpxu70Pa3SjKyJEGBVsO/JLyqFWeQpQupIckr4+s8NvKN7Jjjb4D9k4VlIO+fAne1z8Ju7MPvG5xQwYPl3zQh8tpQ05gCdyY5E5GzXd/ab514O4ta9GD4+GEESXdD0C+phIG+LCpfDvy8+JLtq8NWPuiKY+u6u3s6LWmdmVDEygLUriXd7JwActjaBn1XlFp1ySoPKpkLoqwPNKe2KNHk9OySjy47KLVzSPooNGn11fG5xIfpi7OtI9TNeE1pF55rKkuh46j3HUccouUt2zdENj3NdfUdI7mtUkKdmYMuH6XqDx6ddlteYwG5wm/R3kovmrRSmW3KG8iw2jYhdHaFejrWwefW1QEvAksWzfH4xBlQ7vvyBH4QHlFf9bexKyKloBbSnMFfY+mDJxGixpOVNMbK3fE/TuxKI9a2y52FPJSW6caXrI8KtfnkQss82tC5KX3i8pj8zy2KtmPIiyPF198MXupiNAA3KtXrxYHYmdg6djJmWR4BMSH1ykqj25N05ky5xSVR78x9cvoturd7EiR3YW6dEugZdy1u3C29k8YtbT7z950KLyd9j4GPGlQsONpT6H8vrYcC7bU4pAeRdAElmy9OuXt1ZwuOekCFhYYIyv82gbRF8+fp8xE6j6XqOT5tWb43OKSrVdjTFpObpct4SXwcLg8XvTQiEvTLlPTyamrVuzzNr0YSNOgL0aZpwLOmp1x/44uoDzqowStmZ1iObQFnVLa57R+UXnUpHAv72TR+MUJh9a4t4wmv9imDJb8tI1NKlmoPF522WUtnqPRaJCXl4d+/frh5JNPRqdOCp2ltyIUgTZhwoSYfdGcnAXpnvdJlsfmyqPGJSpGvGlvOodU4W6oYMd6bRFS/2vxw5WvxDP617HZRYml9+5iwTnEgBmfsVBR7SmUfzZU4+3Zm8ELAkYGgkWi+ftlA9HkpPeJ7V1rjqw8mlgkNqAtpDQ9ykMIWJD9emswyMWrMSQtJ6/TLtsSuORDKU1TjOambc5XLyqPLpO4B7TTUAp41sAb+Dwe9LyzReUxzyMqj+birintc1rew44ag3J9HiVLrda4V14mXqx7vbUgbWOTShYqj++9915wBhEu+o2+C/38hhtuwIMPPoj77w+f+qKtQs7Ds2fPxuGHHw6dLnIVeQLKo6uZApcO+IC1wdssiELnFiNzOUvqFSNXwNpg0yvRwwzwOURZeJpZ7LQOUekVctorqj2FsncPZB04t/iA4KNEGmcD0eRk9ol9zRCSEL85OR7RHcFQqNAJccCC7DfkBpVHf4IBM6Fy8gYsmp5mLiwJFzOwDE5wzXwAOZuozPmtYt/xWtoBNoBrFBX3eDAIorXP0Gzf+dDl82K+GuCA/PbdUtrn9IFla41BuT6P+oDlUReibFsE0YJtTDBgM5GxSSULfR43btzIrInt2rXDE088gT///BNr165lR/p/+/btceqpp2LevHl444030LFjRzz00EP47LPP5CxGxkNpC0hmLaUv8DlFBc7TGsqjW/xtn7bpbxu8osKkC5OfTW78DeKDxG0sgRLhnXVhl3tNbtHyqM0rU1R7CmXMjtfxl+EmHFT+BTTeQERlFH+/bCCanCx+UXk05UVua4V+UXnMKVGm5XFvWpU88AHl0ZdAXsHmcvImsQQeDndgXHORM46m6SNK7xQnXlyuqDz6AkqkwdXUNzIWDLxoSYu0t3dlg53leCTy23dPaZ/TBRRZnYItj0ZerGd9wPpOhqCcgPJoTlB5TGRsUlEGsqr6pASSYrhs2TKmKEr07dsXo0aNwiWXXIIhQ4Zg1qxZuPPOO3HCCSdgwIABePXVVzFu3N79MlViwxdYhvJo0+8nIwQCCHzNgijM/pYtNHLBBSx4PrO4hKU0BKeoSPsMTZc6czyi8mgq7AilkuOpRFdNJWo4N1xesa45Y3pSCykNekjmCY3MAmXJD9/WnG4viiFOFgraK1t55Ey54L2i4uSXQeGT/Cd9MimPnsCqBgXgNFelzG5RQdfndxA/CFjvzc0Ca2KpU0sgR64pJ7zTT9We7ejACfBBC11uO6QSfWDZWhsleKe1MUnL/BZxHHC43LByotJryVOi45BKxlge3377bZx77rlNFMdQysrKcM455+DNN99k/yd/R7JUkrKpkvjSsU/XCr5oHvG3+WYO3jkB5dEYxUIjFwaXqIQhJ7UDe8JIvoLNEujm86I1w1pMgTTK5BPLBJzhnoSK7qdC5xPrmosSLJLNuDx+5NPaKDW1ovBtrbpiVzDyP6cooNgoDMlvkzPlk6Mie8/LsKMJH1Qe5bGaScvg4QJwrH5xQmbIE5V4XUCJzPUGxoI4UgFZISrQpghWM2eVmKanRlMEaOTZszsSekFUHnVG5S5bm4XAhhSB4Bh7Q+3e79KcZ1gly5THHTt2wGiMPhiZTCZ2nkTXrl1Z4leVkErRaDB06FB2jEWB87eC8qgJWB7J+T6UXEEskyU/9cqjJeBjpstLj+9gopYehORAc3s8KA4kNk9XbsqY21MI2/kiLBH6QFvQGfpAMmWtObstj5HkVFdbxRRDIpLlsb5CVDRqKUG4Nr7t/tKFwSf2Wa0lHzZNLlby3VFv6pS0nPiAIurXyqM8+gOTYk8YZXSXUIKtfDsYA5ZAY367Jm4FsWJ32GHkAknTI+wc460T0/Q06EpS3ufqkYMaIQe6kGAUJcHze3czkyy1jkZxHHOhaR7bVI9NKlm4bE2WxGnTpuHRRx9lSmJzSEmk70MjrCsqKlBYqJq8Q9Hr9cw3tCXWWQ/G997L0K90MA5EetEEUpeE7mnq8vrxou8MFHKNuCgNO768oTsPWttIXNx93y3MlIDe07DX0hOgusGOr/ynoUxTh7NLOiqqPYViD+zwYTHoMMl8FypsFXi6+xHIZiLJqcFmxzZ+P+RqvBgQsrtGKFVuDb7yj0JOTi6OgzIxBtKqkM/aUt0heMmzHy7u1A1HJyknj88Hp2CAX6Y8oNLuN94wATgT/XeznZv+7CxuTWgNTFKlSWusOBx2rOM7I5dzoWOEgJn/zEPxrPthHNG9HW5KcZ87R5gMm9uHP8v2hxJxuj2wcqJ11GwVJ8P12gKc77kXnazA02kcm1SUgazq/sSJE1nQDEVOTZ8+HdXVomWIjvR/+nzTpk1NUvr8/fffGDx4sJzFyHi8Xi+TFx2jsUXXAx/5j0ZFyWFINzZYsEMogc+0d1Ze5/DiLf9JeI4/Dzn5qY+A/svZE9P4w2GKM41GutD79k2gW+Hk8KzvXDxvvhFcGva1jqc9hXKM43tM1H6PAl8Fyj0m7BBKIy7vZQuR5FTN5WOc50HcmPd8xL/dLHTAbd5rMK3T7VAq5kBaFUNOIZvoEaZmO7gkIqeF7c5Gf/d7+LbHg7KU0x+I7vc2s2R6/Xxwf+58s2jdzSkQLcFmuMF7Yl/Bon3lj/P8D6cZXo+4JL3Ha8RioS8aSoamvM9J9WHUpXZ5PFHsXj9Ocj+BcZ4HYM4T/dkbfXrM4Q/ACuthaR2bVLJQeaQgGMrZRBudn3HGGSzqmmYWdKT/0+fnnXce7r77bnZ+eXk5TjrpJNxyyy1yFiPj4XkeS5YsYcdYrENWY/pTHHyedykOd7+Izb3Fbc6IOqc4My0w61Oe9NXj41HvFJedCi3KTPFgDPiY6UJyoFU2ig7mpblGxbWnUM7zTcMD+o+Q69oTTNuT0wrtLJ1EkhNNiqR2HYmKQL22S2O9xhsgYg5ExpqsBcHt/4wJKI/N5eQMtI/m+1Anit8T3oeywblXwcg1iXWRX1jE/EwJW13sfo+SEhqtTdfaxfGsyGpIaZ+jlEA+XkxhZ9Qpc/nW4RGwSuiOVfqBQbeMRpc36XEhkbFJRRnI+jTQarV4//33WVT1Bx98gOXLl6OhoYElBSfr4gUXXICjjjoqeD4F1jz/fOTZvEp0iutX4zDNdhQj/T5/0sARuvVeY101BnEbYTWlPgVNXV0tztH+gSohH/mmvW1KSeT4RZ8gc8Avi2io2oVu3B50iuBnpRxFw8Wii43WPNzofRpunQ45wjCqcbQ1agJKRIElshLRWFcFA7xol6fMVCuNbh9yISpleQVFOHr7/2Gi4U9srroSQHLWUmfAatZ8H+pEEQLKY3MfSkfFJvxpuBkVXAm0mpPYZ0a9HpOEy1DnNeBmnwGxhnTREjFhNUYuc4+KX3GJdgu68uSa0xupwu2042vDg3ALBpi4MWQbTlm/JoNDIsqeJC9LyASBq1qH87W/IQ/7AVCm65BK6kiJKeHII49kL5XUckLVu7jHMA8La8nacVCr7NucF2KN0Wyfh+nGB7DBTQPtuan9/T0b8bT+DdQKOdBo7oAy07s0iOldCvcq92UbP8OfxilY2HAigMOhRDx+HjkB53iNKRcXa36ChhPQoJ2MtkiXtW9jgfE9rLSfFrGfnb71cTxqmoMFtQ8DUN5KSp3dix/8Y9BBU4/T8tuzVEzdNeXYHcgvmAwH7/4YR+rnwFd3PiVmky0NGN8sQbizrgJ9NRUwoqmV6hfLydhZ58RFPj1iTeVt2jEHMw33YpejP4BRYc85rG4GhuiXYJlzAGU+RarwuBwYptnA3vOG1CiOy7bXYdKMVSjLN+HVC+L3kPfW7sC12mngNRTdLnrJ5pbPxxP6t7HUTvKjSYhKWyK716EyFLLgHnHEEewYjd1cCSx8J3Bp2qkklPvqJyHHUAOj/QVahGWfOdwe7BSK0aBPfeqcWqcfs/yDabsDjGpBTq2BzelGAcSHYF7RXkus2+USgwsCyY1jwe724dPP3oehajX2P+FqDOvfOyXtSaLR7kBJwDle0Fnxov8Mltbk0jxl7uQjF5HkpLXtRilXj1z9vrtmSZg8tftYmZVEjcODyb7z0THfhNOsJfi64DI8XnUEJnQambScyhz/4RDtUiz0yWMw2GQZjDne8ehRMgxDQj6vMHbD3e6H0avY1CRAo8CiZ8pjXciydksItkr00eyEn4+cj3YBDsAOvwHdS+NXiOPpc04YcafnVlg1PvyfTt5IfVoSf/rndXj77/UoEeqw1tAOFQ2u+C3k1Rtwp/5zbPWRf/kj4kfaUvziPxD+nP2b1FMqxyaVLFce9+zZg0WLFqGuri5i5viLLtrrK6fSrFJ0OowZ0/JM91n9VdjgseHjHoekXYS9+E3ooKnG9pAWtDp3JC50v4QzB3QCLXCmkh26LrjFexcO71aCsQrc1qquthpu5KJAsMEckt7lU+sEXOY+Go8P6I9DYrRgPvv+l7h/x63M+vfFZ1vQ++5PkBfw+ZKzPUnY6msghUG59Pn4P9/ZzBfrCqNyExjLQSQ5fW45D0+4h+DK3kMj2vcvEh6Bz1WLT/ukzkKVDLUOcTJQGPDf286VYbGgxQU5HZOW00zLSfiypieOah/eghcvW4z98J7fiOtLm06S6vwGFsCiy206idlPuxPtNevhryoG+sWmvG/JHYYXPffjoK6d2KJrOF71nYparxe/dB8e9z3E0+fc0GMmfxBy9fKOYzR23PnVcmxe8gd+178Mv7UdLNf8npBrBaWg+sw3Bsac9kHr7krroXjD2w5Xdu2JExIsY7xjk4pykLW1UiqeK664Ap9++mlEB1hq0BRMoSqPkfF4PPj8889ZwnXaOL5lv8P05pWjOrzJewNyhUZMLusX/Fya+edbUl+eapv4MGyo3MXkFU1OrUGl34Iz3a+hc74Rs0Py/omBFRxK8mPL5/b72gq8szEX5dqb0I0rx6u+k+FYvBMXj+gue3uScDbUsKMdZti9QpsIlokmpy0OA1YL3WFt3yvs31EUcDULqslBaZEyI9Ib6uvZVnslgcj/ZKKtm8tpOdcf8/3tMbaUloDDM2djFRZvrcWVo3vB0EJQiDMQzNI8ACdSkMu5js9wiOF3LNpOgTOHxnQP5f5c/MsPQK/CrhHzGkrjWbRAKTn6nNsXiLTWyxss8+3SXfh68U500hSjk64BWk4AOEqyHr/yuEPfHQ/6rsTx7ctweuAzOQJm4h2bVJSDrE8EiqL+6KOP2HaEFFXduXNndbPzBJUzSnlEx2g0BvwOQ4NW0gEN4vP94lJOTn5xmKjU1A8CVY1iWg6frbZFObUGwUjN3KYDdTzR1nRftOREdD58PLqX5uDinfUY2bs4Je1JwmUTl2DtnBVOez36cDtg1ad+u8nWJpKcKluIpK6yBfYl1nAoihJU05qYd/yN+aZbsKWK7GzzMLLxJwzUViHfTT5sHZKSU7hgilA8bjfe+uB9/O7qy3ykLzos+sQnx74ZA7ltyBealstSuRyXan9EgZcWSQ8Ofl5r7oZljT3RgNgTbNdLE90IiqHD42W5IyklWahfdyr6nLehEqdrZkPHkUX1mLh/K+w1A8vVxPijDoW255dApwMBQ2JJyBvCyMvupHYvJKU8xjs2qSgHWbUOmkHQXtW0ZN3STjMqyeHz+fArdw1sBjPyhN8pbjDt0ae0lGkKmS0fte0FnGNYgjrbdQD6pLQMY9c/ieuMM/EZfyyAC1o8f/X6jZjz1Yv4s/R8vHTe0KiRs3JQHZBRYcjvCDyPFxx3o1ZvRZnuA/o26jWWrFqL3Xt2w6zPx7VH9BYtugelft9kjz2wc4Q2B5pdizHTeCe2euh3z0Zbg/fzmOh4G1XaXLQzhnc0qNuxDq/rn8NufVdoNBQIpTxsNhs8ghZek7jke6rjG/TQb8UKF/Wf5JxM+jqXoJ2mEQUCrULsu2y8a/MqvINJWGnojpc3vNOi8nh8xdt4wPgnFlbd06RsJZVz8ZD+A8y3U/7gy4Ofz+1yOabuOAbXF/bG2BjLXFY5BxO0q9DDT+PXvgvX9prdWG66En6Bg0Yr5itOFdrq//B/hlex3UcuBPIE/81dugq6+s0otnbFFaN7AvrkxmOnrY7tMJNn2jtBuHTH/XjGuABLKskH8noZSq2SSchqJycfx+OPP15VHNOAvbEBHbka9NXsZLtatIiMM7ua8m24WPszzrQsbZLPsZ1zE4saLNCkfrtJnacOuZwT2sC2cdHw8wIaPr0CJzu/xd/rq4Iz8lRSsPlHfKx/DGe7vgx+1lBfi4M063CMdjGKClpO1eP85VH8a7wej3ddmBZXAAlvwPLo1lrhdYq5Kj2a7PZ3jERdXQ0u136Pu/Wfojgn/ITYWb4Bx2kXYjS3BErld93h2M89FXMHixHzhkCUtU4GP9brXG/iPcP/UNiwNuz3Dbs3saMWfizfIU5MWkrgvUsogmBuFqAVSOHTPApbctuRllFjYWDNz3hM/y76NMwL+70zsG+zgzOBS/G+1j6PmNnAo5HP4OL96zn8argDj3eck5BrQnMO2zIFq0wTcVz5m022uzRwfhgUvB+3SoYoj/369WOJv1WSg5yITznllLBL/t8t34VR//sdL/2wgP2frAkGU/QHwMZ3JsI7qRTlcz6SpWrcO5Zjkn4qruY/bfK5ySdux6fLSf0SpyGQgLtD934tukbMn/sHDvUvQgnqUYQGfLV4BxrieNAkgrFmLUZoV6OTUBH8rK5iGzs2wgyTJbrCX1tThQPrf4GZ82DogbH5cSXSnsLhd4oPeI8+D35JedRmv/IYTk615WKd2WCGIUKduWt3sqPdkPr93BNld70LPDQoKRUtg3pBtIzrE3jwN5eTRRCVH0OE3KWuqi3s2F+zHa85b0NFffStBF/JuREj3C+jukezbeu8ovIoNNsiUnLbkdx4YsHoJd8/QGsNn0HAGZhAOThryvucP6A8+jh5VkPcbieG1c2EnvNjvwGDZLmmziPKizPvXS0xBnYs0odsgpDqsUklS5XHO+64A99++y02bBBzVqkkBqUtGDZsWNj0BdtqHNhe44TbVhf0S0OU3VwqNy1Hr21fQg8vdL8+SJ7gSVeLp1Ks3zpj0/2rrbyoPJryUv8QNQWUx47d+7aY5sExfyo7/lc8FgUlHeDx+jB3pWgNSRWzDKNwo+c67O5OuQFFGqtEJaOOa3kv93V/fAIT58U2TWf0GHpUytpTOASX+KDw6XPAu8U69cq0b7GSCSenhj1iO6nS7o2Yb46/fg87uk2Rz2ltaNwgOhaIipcRiVseQ+VEgSXWQPJxozV8u+Yb9xoUBmk2Y+PqpVGv74gQMMP5RCVLaNYW+zb+i78NN2HC1vtivgeTX2zX+tzwE12PQ+wDzgSVx3j6XFB5lMnyuHnudBRyjahEIboefLIs1zR4RXlpLIX7bneZxIYH8Y5NKlmqPFKAzHHHHYfhw4fjkUcewXfffYe//vor7EslegTaq6++yo7N2VErDjTtDe69ymMUtv77TfB9ob8alZujD9wtIfB+5Gz/k733FfZs8l0uLyp0lvzUKI/kVE3O+WwHFF60XsxdtDysnEKdugfW/cHeWw++ALeULsQi49XImy3mKksVy5ztMJ0fCW23vVZDV+1udmyMIfjEvG4aO+7pelLUyUGy7SksLvFBIRjyILhEOft0OWht1uxuSKljfTg5SVazBmPkoBKtXVQe/dbU76yUqI/yZNdjeFn/IvqaRKXIIIiWd4PJmpScaGtCKaG8OTeCBSowAakS8jDRcxsW1Fni3r2K0PhEJZUzNP17s55DF00l8r2xr3pZ/eJYZYygPHqDfr/WlPc5aU9uv0zKo2vNT+y4pnAsNDLljZRWlfQ5e5VHi7TdZchnKR+bVBSDrLZiytdEPnA0wD/88MNR9zeOlP9RRVSSKisrwz4otwcsCIU6V0yDG1e+gh1/8A/HXd4r8bitDM0Wg+Ji4dS7cLDzX/Y+d6C4RRjh9XqQx4llyy0sTdmuNoMn/QK9lsMCvajU1NrcURWKZf/8iBFcLRphZbPwBvcMFG62oWN9ckp0S+ypF+unLH/vEpuvQXy4OQ3Rlcfqil3Y37WY7U7TedRFKW1P4ajnzdjMt4eHlCG3KGden76ArHAs2lqDc16bixMHdsDz44ZAr5V/D+BwchJqxWVrt7WplT0Ug1N0TdDkKUd5fOzreVixZD4EgxXVTh9+MwT8MXMLWeAWuUMQepM5KTnZ7Y2wBvyOTTnhLVCagPL4pe5k/OY+EN7d0ZeXn3E+CKPBiSLXO02CyrR+sU9pmimPBou4KaHB74y5/HlCo7j7U4RVEn/A8uhNcNIUT58TvGK5/Vp5lEcKBiK0feTbttUSMAwYAso2tSErKY9clElDCsYmlSxVHh988MGoCqNK8tyy63bcb6jBZpeYbcutjT64FTX+x46zTEej0WthudZOHRx/YmBi05pFGLblLTZg/N37dow6ZG9q2IbaKkgqUU5BipTH2mq2RZZNk4dcwc7K4eOiz6x9y8WAlc2lR2KQzohuw47Gqb88hjV8V8y1uVESIQgi2V0dxth/RLkmHx2se3fwEALLdz5zdMvsxjnTMJzjsVHbE716HYB0M91yJm7yHI4n9xuI7gsfY58JhtazPNLS6OdffoqB8MKg65QSxTESetsOsQz54fMBElZPlXhuQWL9KhXt78Rl1+F+7XqA5ugBV7pqSy8Um/LgcdkhtXpjApbHUGz1tSy+2g8O2ghtROcVJyDtyd/SDhY0I+X7bQ59vr+wAbkaJ6qapYDRBpatuWbpZkyBZVMzjQkxQNbSfIhlyokw0eWdySmP8cB7RaWYl0F59FRuQhm/h/nCdxlKkfTykCOI8jLniaO802mHhRMNQJbcxC2PKpmLrMojWRtVUvsQ7ebbgmJNA3Zz4uzdE2Vw8/t86OzfwZSs/Ycchi/+bsTqXaIVIBHKf3oGPTkByywjMGrCA02+s9VVMuWxgfKiybzFlgQl7a2nRMw6D7SBmao/ShN2uT3Yv/5Pdv95B41jn+XlFcBZMgjeChuWbKvDMQPk39qxqroKT+rEqES/+abg51qnqGQILWxNyG38jR0ry0YjfFrq1G9lJ6UZ0njtra48fr9sK66p/z90NVTA3oesUYluhhY/Oc5d7Ggsjrxrcr5PTOViLY5snUwnW3ftwTBuPXvvMxZA566DAA6Fx93FPnM5aUM8EZM5SeVRikqGBbkRDAd6yT+5fTscvWMZDvKswrbdQ9Ct475pfRwuN8uiEKqoBK/Di0qWtpmfpjlg8TQHAndaoqqyHF0D1lJLQYQdaSS/X0MMmSySJaAU8zIsW+9ePZvtALOW64mBZfJM4v1+HnmkPHKANeCSZG+oBdUCL3CwRLA4q2Q36ZvCK4gFCxbgxBNPREFBAaxWKw499FCWo1Ip6PV6XHDBBewYyq5dO1HMicqfQSMqTz69uGQTjqo9W1jEnVfQ4sD+vfCQbipu3nM34I8/0tjlcuKAulnsvWXMzft876yvZEcbl7rBttpnxEf+ozHPMpr93y3occGEi/eRk8R/S2YzeVGi324HHhf8fHAXcZklGUU6GuWbV7JjDVcArXlv/RjdovKozY28hRrv96N343z2Pv+A41PanlrK41mcY4A2oDxyxtZTHvf8+iq6a8rhNBQhd4B81pRY5FTkE63FORF2l6E8kMWCqEDlt+vcYm7WFXN+wu4dqQ3W2r1ObD+VmnbQ3bMVuG8PuDs2QjNYnEB5nWKdUg5Dvd6QlJwcdWK/t2si93uTX7RaGXOL8IThXVyt+w6bl4gTpObYG/bmVLREUB51zbJLmHPE/kz7r5NPdks0VGxlxzrkgWsWuS3BeUSFlzdEHl9l63Ne0X+d18W/80tzGreI7gmVOf1kWwVsbKxnKXlCV5WkaHR7kqmM4h2bVJRDm1MeZ82ahZEjR2L27NlsS6Srr76a7cU9btw4PPvss1ACGo0GvXv3ZsdQdq1fxI57NO3BBdJW+KPMjGt3ig+pSk0JenUqxTjtHxiBpajbFX80/Jo53zGLQBUK0Pugo/f53tUgKkZ2beqUR2lXiHY68d4bOWtYOQXPXyk6jm/KPRCcbu9DcoR1Fybr3sB+q1JT3407xXx3lYamCb2tHvHBaCiM7Bu3ccVcFKIBNsGM3gcemdL2FIkXbHdihuFetPdsg94vKhoaUxosMGFYsa0Kp9o+Y+/5I+4GIiix5fUO/Dj9U/y3YqFscqIJU2lAMSzu3HSfZYnamnIYAqsAhe2iJ3Bf8OIEDPxlHPLfPBSbVsxFqvBsE8eJitxA8mtSkKx7FTGPW+w/bhjAxdgmIsnJ2yAGC9n04VPeEKZAGzJYC1Bechh7z/0n9s3mOOurgqmRuJBtPdnfBy2PTduANcTnzu1oeULoqNrOjrW6yO4jmoDyCFNeyvtcMIpchmVrXaU4cRXa7Q+5sAXqhIwQBrM4DjgbRcusPcnNKeIdm1SUQ1I11rMn+WT1wubNm4P/j+VFf9Ma0Myf9t6mhkoR32+88QZTGJctW8a2VLz33nuxdas4K21N3G43nnzySXYMxbFNDPLYY+odjAa0w4LNVeF9fWwVYr3U6tvBYjTgLf15uMN7JTY54h+kPCums+OWkjFhZ5reRnE/ZJe2hcHWYw9G88aLr3IjDtOsQjt/4IHFWfH4E09i3S7xt5tTUCEG9vi6N913oneOB+N1f2D/QBS23PgrxSVDR26PJp/n+UVFxFIY2Teueo2YiWCT+QDoDcaUtqew5/r86IctGKjZArtPE7Q81vqMEdtZKvl8zlr86B+OckMX5B56yT5Wv3/+/g1fP38TGp49CCcsvgqdvzwRtRWin2KycqrcuQkaToBTMLAUT+GoC+SBrENu1JyJq+b9isMafmTvLZwb9h8eRKowV4lBcp52g8N+v71C7C9uzoCVO+vjrtdQOfkCaYpcxvCKGF3bEvBFrPAYYO8tBtkdUD8Lft++KyCOwCTUxuXscx3J8ljh1DQps8WSA58gPsqctpbHFm+tqDw2GsO7j7BrB5atGyh4LIF2H2ufo2u7XKIyb+f1SfexUrs49uT1OBBy0Vgt1nE9lxfM/FBeIVrkHZwloTaUyNikkkXKI8/z7BX6f3J4bukV+jfp5Pfff2f7aJ5//vkYMmSv31R+fj5THCldwNSpYk7A1oI64epd9awsdGwySO4Wd0OYVd8OmlrRqmjbtQanP/PdPp2X/l+7SzynTl/G/j+/7Hx84R+DTbbYl6ro75ZtrUavWjE9j7P3vtuvsd+qLg8uX0UaSDbuLEflU8PgfKoPli9bHPY8+owGI+kVek77bd/hE8Pj6FkrRhM6/Vr4vB6c/+JP+1xrY0UjurpFC6utdHCT7zv0GSoe+T1YsnF32N+KpTyRzjFVrxa/KOkbUp4GFAjiA6mSz494HX6HaDmryB8kW3m2VNv3SYUR6Tq1Ng/Ge+7HRO8dmPD+chg84iSlYdm3EdtZvOWJ9UFD+4N/vrIRD/suwY7zZmFznTd4jQXLlmPt5NEY+duZOLP+PfTR7AwqZlv++SLqdaOVJ1ROmzasYcdyTSlW7W4MW+7t28QJWjVXFPX+6/6awt6v1A1gx/0dC4IJyGMlVll3cIg7KDUWHhC2vp6dJvYfCqrY9tpZYeu1JUhO1K5cleL92wylYX+Lrp0TSOlSM/MZXPu7DzVCLopRj03/fLXPfW3ZLtajM2QFQ7qOKeDTWPvHy03KrNVqYIeouLsC+W+jydBRKaZfqtdHLrPOIY5nto1zE273LaWfkX6LrxZl6Ny9LqG6kK61ePVa5kJBfoj6DuEtj4n01z07RYNKvbYo+P0Xf4nL4y4/Em5DhDQ20bMuGSVUJcOUxy1btjCrY48ePZr8P5ZXa/DHH6Kl6dhj9/WbovyUxJ9/ikpSayANJpvevpQp2dvfnhDslFXV1RjgXMzO68PtDDqVl3G1eFL/Juxu3z7X0ewQLW/1DXXs/3lmMbhkV50zpoFEus5/b17MdmdpEMy46Q9fk/Okc7xbxAeSzdYQcbB95pWXUerfA7PgQuWXt+xznnStf6ZcjeI3Bu8zKAl20Sph4sRZqjsQLPOwfuo+93/d8+8jn7OzB2TDL5ObXKekfWecj8cx2P0mFrx7S9jfCi0PfdfSOVumnA3N6yOxZcpZ6OEWl60L+40InnPxc18E/Yaqv7k94nU6Ny5n/6/buS6p8oSeQ8p1uHoNd51qhwfLhN5YZByOR/XvwRJIJj2A2x6xndHfG14/NGEZRmqLXy7aAbePx4AOeSjMMbO/2zrlLFS9djK6fn0KBnhXwCXo8V/hEagc8xQWlpzBrmXftrTFNh1LeX75a7ZYF35jxHv7+R9xQmfzh1fE6P34Z77CENvf7P9rXYVYzvdgFs3tC76Lev/xlpven//M5+gOMcinfs7b+5xD9XeG5m9s40tRIRTgWM2ifeo1lvIQ1K6K65ax91Xl28P+1uO6N2HkRAvjGM0ytiXg3AIxcbV57rPYXCGOF4unXMrakGORuGtVfciSMl2HykhKJ+WKHK5Zt0+ZHZyoPG7csadFGVrLxXG0pqoibJnp2lLuyu7cnojtntqi5fWDsH7Kubjn2Zcx++cvwPuaylFSisKVR/otE9zMclrENSRUF8HyfHwL+/92oRSXvPVP2PNOe2YGVk+ZgMrXTsHWKWfG1F9/+1fczcweSDFG5RurEVfBfNAm3IZYew2MTUXvjUxKCVVJP21qT6D160WTfp8++24SX1ZWhpycnOA54SDTeqh5vaGhYZ/PaUmcnH+9Xm8TCytl0KctmGiWFZrTij6j7+jzukY7HtO/g1d8p2GSZioO0a3FcdrF8Lx0EzTgYeXc2K7phGHa/zDVdzx+8g/HIZo12F+zFQ0Biy79rnSdzUIZvvcPhwUu9v/FuvvYsq9hezk2V3XEGc/MwBXa75HDOeGHBi/7zsBXt5+KLgVGlodTug4t21UKedjCl2GS/n3UNR4Dd65YbhowHtO/jd18MV4WTkN71OBx/Vuwu49vcq90rf7cXpeAzlwlu3Zd41jwRWbm3E3nnKH9Gx/5j8FgbjNGc0vxhP5Ndg79ntkhPhQ38h1xk+da9OZ20Ya52E+zHXavOMuXyn2GZjbm+AfAyrlwvHYRW26RrkMce/QJ6PnjC/jLPxjbhfa4W/cpK0+Dg/w5rcH6uEb3LX7zD8MKvhdu030Ov17T5DqNTg8u0f6IJ30XYKRmFS7U/ox2XB3zD+rc/1DWLug6l+t+xIu+01GMBpyjpQjwptchOd6vm4rNQgfkCQ6M1KxkciU50j2RywVdh8rwse9oTMFp+MIwKShDug61PboOBUYt5XtjNj8I92g/xoP697EUhwUtIXSdR3TvYAG/H2tDD+g+DF6n1ik+APLNeuzv34qrPTezbe2e0r+BwdxG1AhCsK3TdSbp3sN7vuOxQuiOHwz3NimP0Whk8qHPtvHt8Q8/EBdofmUPmkbHMXC7dUHrw3kv/oyTtHNpHyT04nZhiu9UXJC7FMO4Djj7kDPR6HDjEd27+Iofjcf176I9V4fNfBnWH/MujhguLtHt3OmHpmIF/q3QodtrF8AhmPCi70x8fOPx6FNWwOQjtelyoQgPeS/FWZo/g22s2CAGLND92Vw8OqMCs/37wwUjjtCsYH9H9RHaz9qjFl/4RsPEeXCsZmGT+6c2TfVxkfZnpqgZ4MPxmoWYJQzFIGyGf9PfcLsnsvs/98VfcIxmEdv7nNrsw95LWLm7F1vZGEHXOU/7K97xnYj+mm24W/cJKzeVJ7Q+Jmp/wgK+L0rQgCO1y/EYt7c8BoNBzM+nceM0z6PoxlXgM+NjGKrZgEqeD15Hqg9qf4M0m7CE74OHvRez8vQo2bucfLvuMxbFTQzhNrB6bbAfHWzTXo8b3TTlONn9GJvsvq1/GvtzWzFvyJOw/fENOrv+w9ZZj+EB3Sq86j8dOggYpvkvaBWUykPXGcRtxKmeJ2CHCb8bb2fXqfGIOV7pRXVEVPz6ArrMuh6r+G6413s5PrvxWCZDdk2bg5WxDjmsPrpwVay+6m1HASVW1sfot2g8fdx7PrzQ43rdNxjIbUaVZ+8Y3+Bwseu85DsDj2nexRm6f9kLc4GFW//AoEuex/rdoovK1ncuwcGaNVjq74UHfZcF65Sguhii2YBHvRfhfO8DeFA3FUdplzQZy6kuSMG6V/cR+mm2Yy3fGU/6JgSvI7UxGnMhcEy53iMUBscOuicppzK1j1M1c3CIZi26aSqw3N8j2F59BUbWzuptTozTzsKP/kNQI+TjHO0fzJIp5aclGZCMaoUcNjb047ahn34nkxnVB5WbyvTf7lqMe/EXnKCdz9pGDudg9fF5SH2I4+L3+NZzNKzcGFyjnR6ojyNZfUjjngRdl9pw6D0l+8xVSRxOaEPZOcniOHPmTKYgkpNuczp16gSbzYb6enF5MVwqokmTJu3z+d133w2TSXzwDB06FKeeeiqmT5+OJUsCiXkBHHHEESyJ+ocffsiWziVoX0/anomy7FOyVOIb1wC8p38SQzR7zyPIyvLXkGfhXvwxPscpaBSM6KmtRr7GjVMvugadrMCUKeLyGLHCWworX4szdXOxVHswcn0VuFX7IdaiO3xX/IVFr1+LWf5BMAhOWLUcumpqccyVk1C1fkkTC+wGjxUL/T1wmX4WHLrSJvdU0mco5r15B/7h98cefy46aetxPvcDSq/8En99+1HwngjBU40/+UGYZbyNRUo/zV0DP6fDNddcw1wHJk+ejCXeDriTe5ftC72dL8UeFOIHrWipON//GVuefNV7MjycGWs0A9BRKzq25xYU4dabbsDixYsxY8YMbPUXYLm3A3ppduMz/YOYgbFYzInL1UT3vvujw6pX4OH0EKDBYp3oyN9/6CE499Tjg/Xk9PPox69hkZzn6v7Ei7gUtSHbC448/nTkfzeRBR+M0KzGz8JwnKCZh2X6wdjv9pnsnggbb8BqXzscgiW4Wv8DpnAXB69BA+IpF12L/16bgCd8F7LUfKeb1qBQqMWRV0yCp3wjuyfCJ3A4xD+btY1V6Id/NYcEr0Ntr+eBR2DpG9dht8+MAs6Gnbo+GIYVKDn7WWxc8g82bdob6Zvj2YaOXDUqNJ1QpxUjwO0+P/L4KvDFvcE11KJWsMIDHQo5B4b4F6LnZW9h+vt72xixn3cRTtb+i0+F47FD2yt4T/fccw9+nbcM//w0DX5PLTpylbBxRbhAMwMLR3+ExbN/DV4jj6+F2+eAHn40aErh0FjxGF5gVivbpX/gqzkbseW/VZjr6YpBWIUTNQvQnduJH7veh4qd4vLvLn8ufvb0Qz9uK/LhQHudGyU68eFDEZ3U58lHllwdBG89hnJroBV4DNRuwnuaCU3uifrTL28/gC/9R6KXrhr9dZVsP+hTr7gTJmclPvpI3Cd+i78Au3xWXKL5Dv10lZjB7V3VIN/uIWNPwXtTnsJ033CM1G1Ed70NPXxrcJH2R6xHd3zMncnOrfZpYeTr0QN7kKvXYxPXrckYYWjfC9Nfe5i1Hx14/KE7CifwP8Ny5Y+Y8f7eHTp2+vOw2WtBb24HyprlSaRxatH6nZj51Qd7257gxmD/fBhOfpbVkwS1+90+AwRS5o2uJvdEbj8fffktFq5aj988vdEJlbjO+AsOFhbhl/2eYfUk0c6/HQOF1fgJY6DXiQoePXHMejfu8r7C/j+NH4vvfAfiLcMzwb/7UDgJGzX9gv8/wLcIx2vm4RnNtfvcE43XlS8fzRSYUe4X2RL2NP29+AdD8J92YPBcGiPGVz+Ly7x3Y6PQAacbVyFH40W7Tl1xzeWXspUpadzb489BB2EXruE+x+fcyajS7A1yozFi1KKrMd7zMCbp3sHhGtHHVMfxaIAVH5Tcy1aKiN/cvbCLz2Wpu9bqhoLn9tbJMWddiOrPrsFi3QjwgaewhkPYsXyeuwN28vk4XzsLLsNe/9vS0lKMPu0CVL5xNp7xj8daXykO12/CaO1qHHLF0/uM5Xu8WhyGpbhINxOf+o/BOt3AJs+nKW+9i61b1uMArGWZM4ZrN+B77yCs5HuhROtEO72onBX5tqKTsAezNKNRot1rUAkdy2t8WsBvh54Dk9NP/CFYqdvri6nTGzDU8SsqUAQ9eGzQDwo7loe2vQkTJjSpp2SeuRTvcPvtt7P2k5eXWGBUWyYp5ZG2IEzoRzkODzzQNE9gJiiP4SyPXbp0QUVFRbDxJTMLWrWzDjveuRAjuJXwc1p2HlmPio67C+31TpR27osGQwnOfO57tvxDVoHlfE9mFfjitlPRvdjCfpeWSug6R2qWMisGLd3+zB8MjLgRp8w7HzVcPnZdvgIFbwzFLr4Ew7XrmJWus6YSDVcuwn7txVmfdJ2jNYuYguUVNPhLGIzOl32AAR3zWbnXltux+bVzcKRmGRs8afllFj8I3a/+En1LzcF7pWvteud8jOWWMGd4ss5N9x+CLpd9iMHdSlibWLqlkv0eWXMoNQudQ+zQdEJ5p+Nw4HbK8Qec6H4c1+mmsYfFUr4PHvVegE9uPBF9Oxayci/fVh28fz3ng0bg8RN/EDpd9hErN7F2jw3r37wQZ+v+YQqSAX78zg9B1ys+xuCuxcH6WP/2ZThX9zcaBTPMcONH/mB0vuzD4HXWVTiw441zmPvAMM3eKPZf+zyAo86/LejPI5XHwrmYVZLqQ5Ijsb7Kha2vnYUxmhXQQAAPDr/zg9Hj6i/QvyyHzbal6wzkNqE7sxx0w1Z0CF6H2h6VZ8tr57BlJbJUb+Q7YLXQBZ1D7l26ziBuE7NALPH3wg6UsuvU/fs+xq57FCtMB+GSuonMJUBqZ/d7L8U3t52MTnn6Jtchayop+7Qcu1VoHywPWR6Xb6/F1jfGoxtXzvY1/te/H2qQi85XfI5+7a3B6xS/NwJVQj6TIbXFWfxg9OV24kBrJXrePRfLttVg21vns/yFpBx05cpZfXWc+Cn6dxD945Zsq4Xug5PACxoM1/7HrtNJU4WaS/5hbYzks2RzBStzAWwYpV2JBf6+qEA+Ol72Mfp3yGP1RYov1ce2189m9SG1a/q9Hld/jgEdcpv0s6M0S5ivpVvQ4Rf+oOD9MwtMpRNbXjsbI7nV4DkNTPDgW/+hOF/3B1PK+Tu2YE2FE/XvnIkx2hVYy3dBT81u7BaKWLnpOjRGrNljw5op5+Mc3Ww2iaQ+/bcwEN2u/gp9SsSJq1Se4zQLoIMfTrLS8UOC5aH72lRpw7nPzWhWr5fh61tPROd8Q/A69ndOQzUK6AGB43SLsEcoYOXZv1MBG98WbdyDXVMvwVGapXDAhBzYMYsfgk6Xf4b9ykTrJFnNxr/4Mx7Xv40DNFuwjO8VtGDWuXj88s7DuEf7EV71nYKTNXNRxRVgeMDyOGPwazj2RFGxliyzT+jfwgGarcExj67Tt0MhG2NWPn44BvlX4Sf/QSjiGlGKOhg4LyoumRds97S95a63z2P1ReMZG6uEIegy8SMM6V7C+hj5JpOlj+RDVsEVfA/c552IT288LmgxW1tuw843xzGLGu3Uoxd8rN5P1M5n+9GXT/gLOzVlyJ06Frv4Inbv//Gd0FFTE6xTNq7Ve2Iay2lJdxdfjMO0azDX3x8dNdXB64S2sbGa5WyVgNrhXKE/GzuksVyq153vTGAT97N0s5v0+4Fdilg7W7qlCuvfugTn6P7Gar4rWwWY4LkHC4T+uOvYPrhsZDdWH+MC9bF/s/ogC780lte9cxbGapdjDd+FrQ7tEoqa1AeVZ8Pbl7I2HToGd5n4IYZ0L0255bGqqoop36ry2ArL1uGSgofmlgqtMOlzaWeB1lAeaUZERFIOSRksLIycLZ8ehvSK5fNIeauo8Uf6vCDXiku8l4lLzNoRGOabg0d8F2Fa/wPRo0QcuMjuN+32U2B3nwCyuZG36ZdGXfB7Kod0HVpaoSWpJXxv9oB4ret+wDygSKjHLq+TdfoidhXK/ypgJd8dXQOdi17SdbR6v3gdQbzOtFxr8H6tRh3u905s8lv3eS/HNKO4TCYhXusKdh4ty7bT1mGbUIaBudZgmobQcpOVZz3fCYdqVqMzvxOdA4pjLfLwyq2XwO65ELUeN5a9PwUfX3MCUxwJGhTC3T8rU0i586w+PO87mymPhZwdM3wH437fZZhmMTWpDzqHlEdadhPPmdjkOrlmH+7xXoH7tR+wpTSylJHC1v3IS1g7j1YfTeXow70B+ew9ZyKTI91T6H29oH8Z3VEBLSdgB1+CoQufgV1wQqPhkD/kSvaw+8l4F6xwYwnfiy2pn/r+FAy4++4m5ZlqmIxuqMAmoQyP+C5m5alvDOzTnNMBX15+Guzuk4LtbFpIOwutrwd0YpDZftw2XOW5GR83kY+B3esr+hfZ/8uFArZ8N82yt89QVDm1PVJCJb73H4o3UYqXThiCnhyHPKuJXUeSDz1kSO7TcszB6+RYzNgmFKIosHsIILDly64G4z5t7H6daH2j5eZ7PVewe6f6ev7555k1K8ekD1MflzEZ0LVC5aiNWq9e1vbYdbgNmMcPxFO+8ThNO5cp9zWVW6E3dEGlEEh0DTeb7EjlDu1nj/sm4GTtfKawfO8/GI/4LmHlkc7Zp7+GKU+vdrn48vbo9Ur1US4U4qzAxEor+ILloX5BE+jvP3oD070XQxf4rVlSfVhNwd/q19GIr28/FXb3iaxmm49VG0+5E0dPG4otQnssEvrhKu10zPAfypaURxxyQpPrRBvzqN5+spyMb2qG4Urdd+jA1WKdvxPWCF2byDA/x4KLQ+tUGs9yRH9JGvP6dSwKyoeeEjQeft1MPnkWHy6SrsNtwFxhACb5LkZ/fTn6CxtQtW0VuB4d8LFmHE7kv0MxJ46xzeu0V7vo90XnUV3Q3xUG2/S+12nSxoL3JY4d0li+t31MZEvg7H45P+73iO1DOic/x4xp/pFMeSzkbPiNH4olgujm1b7Awn6T6uObKOWW2hAtobOxFD74BQ2bPISWm8pDS/+kPNIY/J00BueIeTylca85ofeU7DNXpZUsj+GCS8gU/Msvv+DCCy/EqFGj0L59e5SXl7PUOGQ+psCUW2+9lZmU0w1FVFNagE8++QTjx49v8h3leuzQoQOOPPJI/PZb+AS24ZRNUkjlnLmwyMxGO1sapOUL6mChnTKe64Q6MNPDp2O+EXisjCk4a8bNxvipq9mAc6J2AWb6h+F279WYdvvJTX4v3HWalyeWc0LP2/LLKzh561NYrR+IAffNjnqtxrpquFZ9j/5rXkCZUImFvW7AQReKW+bRQ4yWR+hh31x5j6VMmyoa0f6V3sw6O/v4n9Cp96Cw55S90otZlv45YSY69to/4v1XrPsX/i1zUTbiPAzcb++Sm5xyZE7oX03GKbtFZaw59fp2qL30H/jeOga9+S349cAp6HLg8fj0zZeayImus/udCRjh+B0zO12P3mfcy37r7+cnYFT9DCzucSWGXfx02N9oXh6by4u8t0egm7ATfwx8CmPOunqfc7Z9cC2OqJ+G30svQo9xk/dpY+Qo/4TuLZykm8+sR1d7b2XfLbr/aBQHtpBsST7B6+jfwkna+fjVPxS3ea8J26bXL56FY+dcwJbM7NevZN83b09y1lnzc5a8djnqXX4MPuc+FHbqjVuffQPfGB9mQSH/8vuJiliYcnteHYV+/EbMHPgseh9xXsJ9MRp0jWuffQ8/Gu+FXTAyV5PQ8khyGn/FDcyKnsxv/flfJR76diUeOe0AFFkNcHh8bGejPu3jyyt6/pv/Ys7Gaswy3IIe/9/eeYC5TWVt+LNlW67TZzLpvfdKQiCE0EtCJ5TQa1h26Z2lQ2B/yrKw9KUGNvTeS4AlQAJJCARI73V6dbf1P+dK9nhm7Gm2bNm+7/MknpE9lnR0JX069xT9Xrzu3w930YN3F65nHSHa95Q/ewqmOL/FiuHXwrb/fHbO7e//ErOEVfjAPwU3+c9vtT0dWU/kmP4yMA5X+i7p8n7R5/78/kMcueICbNP3ROCSn1p9bsXq3zHhjX2ZZ/b3c9djySt3Y1eDhCNOvRTTR0WvdxptPX9/4GEsFO/DDqmIzUpEG9NrdlSg79PD2QPRD0d9gdL+I7p0PLqCGvfvbCIuz2NLAfjMM8+weITly5dj5MjmpQLOPPNMXHbZZdh3331xzDHHpEQ80jpJPJK4bSkeP/300/BnUgm7ODsMoKqKoam/rn5PNHbpctEDFbD7q9mJvOHNn4HdP8GSV4J3zmp9YevIidzRkz30Oeu+s4Gt92Gw9w+4a8tgzi2J/V09c4GRf0XAdxHKd2/GpD7NRVk82zSgxIEtQhFswR3I95dF/Rv6zA59PqzSHhSiKupnwst6Usb+YV3eno5+xnXohXjp+Q0wBpyst3dQb4RPb8bs4Fco8ZWhcfUiNBhyAC9QoG8MT7e1/J69jm7UVw4Of2XTsXHJJUoMeR1rtRf6u+9LD0bf3S8gdwvVMry41Wf2OIpArhx7sC7qGGNj8aNNwKZlLA6TJVeVjAgLx47YJ/w9b/8K7FzG2tu9c070MW0eNQb4HiiSqlGYI6h+zFry99LL8d2GCtzvL8SJRTbccvphwBu3IQeN6H3h63jHYoq63T85BgG1G+GoXdf2WIwD+o4FZ8wCXpO9Rn0ufrOVd5KgcdXV61OIA4YU46urZjKveTyEetR79bIXMW/KaXhn365dzzpCtO/ZZuvFziddzdbwOacv6A/UroKl+zC8M7dzwrHZmH7rF2DXMphzSmKO6Y5+n3fAQGAFUBCshj3K340ZOoQ9FFB3sh5CDc71vASz0YfN4gWd2u6/zJ4OfAZYdL6YY6h/sYOFRFDyVkGwImnCkRM/CS3r/vDDDzNR1lI4hhg9ejR7n6aGUsFBBx3EipS/8sor+OUXudQAQU8e99xzD3Njk8jNZOr08vSus3IXO1HtSlN7yshO1onbf8go1nuVLk7rFzcF77eFYDShuIPCsTPUGeVCwe6K2DX36pSyIc4qOds71Yzo3xun3foKTr7jHcy78w2ceft/cd6tz+G74lPZ+67VH8BjkKdB/Y3RC6gzHHLgvcFZ1rTIJyc4WQrb7pbSkvxJcnza0Pof4FXa3zXDKteIMyh1I1tCY69bD3mdFBv5sXgD7hOaJ+Z0BPqenDx5XVbJGXNMF5f0RINkZiVzyrfJ9RyTSY88OTxit1I2a/hAOUGG4iv7OoIxt9tfIic4mMvlEjlq0b+bbEM6R4eVWFS9NsQrHAmbKD8AeHXyVGSRWUq6EAnk0iQ3IDbIRcgJk0WO/7TpvV3eHjamFc+YReeJe78cBfLDup2UbqB5eR3CYDRit17+zJ51P+NV/0x8GRiP4h79OrWePqXytdUquTCqZ27M7S6H3CHIWdm6uP9zSzbjpR+3oqyuKWmLk4HiccOGDSgsbN6PtCX0fmTmUzKhOAnyjlJQ7YwZM3DhhRfiqquuwtixY7Fu3TomIPv169wJogYkYmnqTI2YjFAbMY/SGUKvtJsy+pNXW4tilHb3mc1+tv0ht55LlZ1cFjmL0q90nYiGU+me4a+WCxhrAUGva3XTLRh3BHvtVbcSfqVtZdBZE9NOhjxZPFo8smAMBiUUBuUs0ZxuTdm+HWHouBnYgyJWF3LdD3I3ombrssvXBZM3erwxYSto3nnH3G8SuoJglYWzqY0xbTAI2GaQz/XKDctVP+9a0j3HjDzUo1EpWG222OCUZO9ZQ3WTmG9JwbD92Gt/12+sD7pamCK65XjcrubvJdFOHaXYYUavfAt8OtmGAa9cmDyZmBRvvdlTGbaRzmRt1oKwq+hMsvASAvF9D2GL6BnucUZ/mNtrlqend21cjVv95+AK4QbYbZ0TrRa7fA2iKemW9S9DkJ0COfJDo6+qtXj89+KN+Ps7q7FDecjiZKh4pMyljz/+uFmiTCQk2uj9oqLYPUXV5sADD2R9ram/9auvvsrKIVBc5qJFi5iQ1AJkP/KGqlFFyS3KFw5/nTw9KZiTLx6JvrPOY1l/A7xrUbl2ScrsFHDIgkWoj+1V9Fnkp3CpXraZVhk5ZgrLCqcEB78yfadzV8e0k1lpk5jjk4uvV9bWo1AnJ60UlHZOPOoFPTYWyW0g/b+81up9o0MedxZ/bPHYo3/zGYte46OHALSH0aKIR6Wnciwqld7P3h2/qH7etWSK53v8Yr4IJ25qShysV1ryNdY0lbdqSf9R+7K6q7loxPb16nkfTeYm8ej1NL9xJ9NOHeXKQ4bgu+tmQVBEr+RNvtgQc+TrhM1fE7YR6ytO50ec4lEQZRFqTIB4pMQyGkNEY230mQl3gTzLE9wr98rukRe79WYsrBE9xxsbop/3ZCeP8nAerGt+DS6rd6OiwcPKFw0v5TGJGS0eqf7Xr7/+yuooUb/oSGiamJavXr2a1VxLJVOmTGEilk5up9OJpUuXYu7cudAKVHKARC29Jhq/Ra7TqGuQb1AGi3xSisHkPqkP6NsXX1kPx4v+Q/Duem/K7KTPlZ96za7dMT8j2WXvpBAxvatFinMsONtwL0Z6nkW9Sb4g6921Me3kKOrFXvODckHjsh1ymRQqvWK0d/4BL39fuXblyNpvsHer3CIvhDlHHncU8xgLS3431rEihLXnKHQFk03JXA62LR4DyhSwrfI31c+7ltgV28PfNB3XoJfPRVdtbPFoEkVsFGXRu3f1YtW2TzAYw8kwvhbiMZl26iwBQQ4HCPqSLx5p/BKOYNM5B2V79BHHuSsIovxgYQh6EjJrEW7nWC+f+y0x9pDPjf18PyIXDehbKIvXziCKlvAYcjdGF49kp03lSpvJhubX4D921WGCbh2GFwqwmKLHJXMyRDxS6R6KK/zoo49YEU6K06DCnvQ6ceJEJtjo/VtvvTWRq+V0Bpt8Exdc8g3KqEzxmZMsHgnvYffhFv85eHxVAF5/avqdm4vlOCWHJ7YwFHJl8Whxa1s8EtYew1nB8hpJ8VS0MU2cXyrvO3UYcjXUoXaHLPjKDD1YR57OMmLC/vjNNI7FyVW+dA6qqyqatitXFqOOoFy2JBY1Y85nr7VTrqRgOHQFMSQelZ7Kscgduj977ev6HZI3uZ53W/9JGOJ+AccH7wsvc1OSE50XDXLoQCzquk1lr8bNX6m6jV4Yo4pHLRMSj1IKxKOtoJTFBn4mTQWCckiBzqSUAQrGJx4NZvl8NgUTs1+NelubvcBLRs9CQNKxEmWrzBfiZElOKO0MOr0eTp18PFwxPI9sGyDvm7XF9XXD1m14S7wN7zacDrhj/z0nA8QjdVmhTOZnn32WZS1TPMO2bdvYK1V6p+WU1RzqxsJJPkKO/HRs9sg3dtGmBGJLyb/YHjGqO8uSLK/34NPf5RjMZOMolqdnC4NNQqclYr4cy2RXpne1zEHDSnD8hJ6w5MoPCWIb08QOR144zq5s12Z49spFzuttnUuWiSTvpH+hUTJjhP93bHn+wvDyHMUrQyWPfJ7Yoq7o2HuBS5cj94hburwNZrs8XWZrRzwOHz0Ru6RCiPBh96qmbjdlFeU48q5Xcf4LP7E4UDUocFDnHiPq3H74AvKDk8cki95AO+KxcMIc9jqk4Wd4W8QjJpJQ8kk6icdgSDx6k59gkZ9fiPN81+Byz4XwBuWHL70Sq2gIxCse7eGOQInApZO3y9MQ3fPYv3dv/KYfHv49v//4rq1H8XB6nLFnHHx6+RpUEChv5kTwbfmRvdZbewFm+dzgZKh4DCVDnH322fjqq69YBXdyS9Mr1U6k5ZFFxDmxUSsYPRSXE4o9ExXPI2XEJRuTQY/T9unDyrIUfXxhl54u47VTTnGPcOZhMEacVGiKMU+Z3tUyZ0/vjwdPHod+veRtNvvrY9qJPAN7DXLSTPWOtRBq5NaF/lwq+9s1eg8ei3X7P8x+HlL3vdyLjrUcK2R144i6yjY8uORtLBrUJc9nCFuOXEGAun14PbFv2maTAWvsU9jPtaveD9upasW7+Mh/Ic7dflNCMoGjkWcxslguorpRDtvwmeTtDjrbyJAHMHjMvqhAHqtPuv6nz1TZPrY9SiW3aOJRS8kykUgGxTERZ4xhV8gxNx3TWpeP2SgUg2mM0/NoMiuFwxMkHsuMPbE22AuNgegSgO7Tm8dczioS/KIbjhH7HNKl9biV2GtvY2zx6DXK8dDddVXYtqsp7jGvXE5kc3eXz1FOhotHTvxQ7TTqCxxvDbWo360kLoRiZyyKl8ak8yOYgqf10yb3Zh1Tprn/h7KPm6bwkmWnvPxi1uqNqKuM7v3M7yZP71Lsj8+d3OnNrmJ2yFn19mB9m3aqMcsi071nPeyNW9nPxpKOFQKOxcBps3Gj7zxc4L0CdW5ZGAmCHnXhhBB1E49sEYH6zvro03IhvIOPYq89dn0G0SAwOwV2yDctf656lRdIlN5sfh0vGe9Bw6albJlkVeJUG9sOj9ALAjbm7ct+rvvtQ9W20ad4Hv0tHqrUvD4lSjzq4owx7OoxpYcCEV5WsJtsZLbmJCRWUbQqmctSYvbrmW434TDvP7CtQB5H0TjmmJOx4uSl6Pa3L2A2Re/e0h4evTwl7XdFF480hq666XaUK6WBtv/5M3tt8Pgx2CMn6zgGyxUGOBlUJLwtqPckeRwje0FH0qePfEPmRM9K37RpE6tJGWqrliiM3UdhqPt5OOx2/Bxxo6X4FndjHWym5IYUlOZZ8H7vv2DNtvfxxe4peFhpX5ksOxkNAl7WHYZ6v4BD3ZJScaw5+flF+DY4hrXb2re2HqWKF0DLWPO64c9gH1Tq8lHYhp2qCifh8zon9nhyYfbkokRXhKL+E+Jad47Nhk/NR6Cy0Ysd1R6MsMgi409hKAy+etg86pWYIQxGE5uOpylyEo95RXLMajRG7TcHz/38Pj4PTsJD9R7UV2xBQaWcfS306VqpoI4yUb8BY4XVWFNOpcsOAHK6o1JyoMHf/vjXTb4AF304FL9W7oP/BYIwCHp1xKNEZW9cSbs+xY3BkjLxSLyIv2OUeQ3WrH8KG1wTw57HeKebRaVeJPVGl4JBNmsQDxaj/PcuX6BNMTxjZHwPUB7BjnqfBR5v9KTI0FiqKD4IX+zcg/JKCVSzYc32cozWyTMhdi4eNUnCz3zqLkMtCO12O3r06IH+/fu3+kcXHU5saKr/5ZdfViWbMdduZgkVNK1CZRLMopFl5w70LESjkJpyCEeceA6uli7De9uMeOzrjUm3038cF+Fe/6nYG8yNWYbmWvNtrC3YXm96xOvaSvrhCO+9mOe5Di63J6adykedhwt8V+PB7UNwjfdCHKV/HN1GxPZGdJRihywYKxubbpr/KLgdc723YLc4EGpzovERjHY/gxpTbOFI9CrKxTulf8P3wZH4ePUevLLwBcAnx0p2GzVL1W10G+Xx5q2XYxxrRpyBiZ4n8WgHOnmMnzoTy8z7YndjkLXlU4NfTBPwQWAfOIXcpF2f4qU6fxSr4LDOPCYl6/cLSqebxmpmo0BOH9zmOxOP65p3NOssppxCnOG9Hid5b4EnAcmFFqM82+Lyqvsg90iP+zDa8x+sL4p+LoXG0p59bsQN/gvwaYXsfS9fswSizo9afR5QwPVCxotHKsdD/ax/+OEHHHrooUycjBkzhv1MtR3pd0qkob7XnNRA0yqELyCxp07y8ulN9FSrY1MFqaBXvhV/P0oOzn7o83XYUpHcqeEim3zBr1Jiz6JRkqMkltQnJuZIbexi06SCs40bBHV+IKqdshAY0ys3IXHJE01bcbz+W3h3/hpelm+Vx161s2ulmTqD29wN9bCiwdv+jfaI0XLc5ye/78WOYAFmeh7AHMPjGNBfXZHrF2Xb+xqqmo0xSiBrD6Ogx9Fj5Hjdd1fE7o4UDy/nXYRLfZehyjEE6UJF6QxWweEHm1xzNNk8VngzRrmfwfoSuUapMacbng8cjncD8T2QWcwW/C84BsuloXAnQDweUvs6PjNdg2GbnoOaWJXrUHsidUJvec7nj911qKTajlu+Yb/vzp8cV/wzJ02mre+88072SnUThw8fzqY0jjvuONxyyy1wuVysCPcbb7zBsq45qcFqEnCb8UX0RBnqdw2Atd9w2EQD6j3+NkWG2pwxrR9qnD70KbSiX5LbinWzAj1RjsYq6iAjC4mWlDhEGOFHZTXd6OXMYS1DyUiiQc+8FHRsYzGs1MGEZl/veuyUirD/YLmGYLwc7fkI00wf4adt5OE4qNmDS00SxKPdLF/aGjzte8eOGt0diz75GnO2P4ONoBjQIRg+bJRqyTIhguZ81u9bUhJkiu1NDyj0oN2eiD9+fA/YfnoEZ//5Gaq2f4SC3k3ZsYmAYkCJRHi6koXZkByPWkysBRQZjQaPbDNzyMPnC3TomLb1sGAUdOyhn67TeZ0vu9iMHKkeQ/Q7Uaf0slf7eLQ3hrrliBjZ3YHcvT9gxTIT1lVLmEYNDwbL1w5OhnseqXPLnDlzmHAMEepCYLFY8Oijj7Kp7BtvvDGRq8046AJD3XrUyEyn75wp/IpDhBVwVcot+ebjNTxufAjSTjlRIFX89aDBOGacXBYnmXY6uf55LDFfhgHrYj/UzGt4HuvNZ2LAn08gXfiv4Xb8JM5HYPfqmHaiWLn/5L+AD8Wb8LV4Jeb0SYywq3IMxbeB0SjXyyWDiCPr38Ay8RKMXiNnY6vJHP+nWGB4GsadcjJKW/QusGJ+6VqcZvgKfwblBKLjJnR8HHYZi5zUpPfIST3FNgNLoPlAfxXqq2MXCg8xrk8+DrJtQqmuCps+flSVBxAdgvB6fUm7PsWLWQigELUwudvOWFf7oaXRG2A2shokTNatwXT8Ap8/PkF7onEJzhE+hqeu/bHRHmtK5+AU781YUnQS1GTfuo/wvPE+DNr+ZtT3I8fSzfZ38YrpHojf3I0HGg/H/oEn0Hv/01TdPo5GxCN1bImMZzQajWhoaGhamV7P6j1S2R5ObKjEwyWXXKJaOYz/iifjet/5qBTlG+XE4GocIfwEVMvZttlmJ8laAo9kgL+NenYGJWuyvUxYLVGoq0exrhZBV12bdhp58i3YaR+F+uGnoLRvYjyPf/Y+FWf6bsCynEPDy+hGWqKrgVEpUK8mE73LcaphMcTKNR36/NDZV+DL4ARca1iEffvnY5/+srBTFUt+s0LuZtGEMcJm5hGqKWt/KppuuJ59r8KV3otx0a4jWdhFIlsGXlJxFzab56HP5teSen2Kh94V/8Ny83xcUXlbStY/zrOcPbQM3PkOs1GO2YDXxTvwouk+uNooV9MRrsAruNX4EgJV8V+nPTn98GNwBPbo1Z1FKfHtxExhFXIb5eSXlkSOpQEHnsUS3Zb5B0EHCbNG94NZKfjPyfBp65KSElRXN9XCKy0txfr165t9xu12s5aAnLYz1am949ixYyEIiW/LtDTnUPxSW4MDdfIN8nPHsVhUNhkH2uV+ptlmp42Dz8aZa6fiqMLukHt3tGbX4NMwYe0YTMwdAHVzcBPH/TnXYn2ZE9fZhqFxxYqYdrL3GAb71V3rL96eB6be3TRlvrvvMThqXU+MzBuqug1/yz8IX9b2QD/rCHSkSty4Ad2x4tw38MmyP/DYUROS4lUTbPL5Z/Y11Te933I5NtUG8VcUoyP1KKbufyju+sWKyj31OOnRrzG2fwkeOGlsYrZfLx/DoN+T1OtTPAhKRxdIqZm27u3fggMNi7GqyogVK1ZgzOjR2Cj1gFsyojhG5ZGO8qNhMnSeWvTVdb7PdEtC0+lqhyptKTkYr2+1YkDOpKjX1six1G3AGPxn+od49KsyFNhMuPzgwapuG0dDnscRI0Zg7dqmnrbTp09nHWcogYb4888/8dprr2HYsMR4NzIVv9+P999/n72qQa4Se0YZ18TveTOxMHAIyoxKv90ss5NDKSUTKXRaUlhYgirkYFed+vF6iaLCNhRrpD6o8Qmqjqe2EnYalDqPhKmgF36X+mG7Ry47oiZbSo/Ao4HjsEHoeM3Kkd3t8Pz5FSyG5EzHGu2yeLQEmjxSG/JnYElwNPa6O/ZcT2EH9xw/GiZBj43VXny0YhM++yMxcWwf9LgCE9xPYFXp8Um9PsWDq/cB6O9eiEss/5eS9euUJCjB18BsFAgGcQz+iaO8C+CMs5rF446/4q++v6HGInfFiodi3y7MEz7HiCp1W1zWF47CW8EZ2GSILgRbjqXzDp2MJdfPwnfXHYi+hdoviZbNJFQ8HnXUUfj222+xe7fc4Py6665j0yj77bcfi2sYPXo0ampqeMxjiukvVGA//W/Ql8tTepQwQ1Bh22yEppaIOkVMR6Nnvvy0v7MmfVq1RfP+JYuBNUvwm3gerthxWXhZnlWe5qxpw86JIpTZnYzknK5iUgr22yL6fXcm4zrEhD75WHj+Pjh+Qi/cfOxEHDoiMVORQUsee2ByBrtWIDoVWEQjJOjbrF+oJnqLLBCNSmenSC+fO86Yx1BtxkR4C7s51+Eu43OYWfMW1CRcEqgTx6NnngVWk2olqDkJIqFH6OKLL8bJJ5+M/Hw5lodc0RTfePfdd7NCoBMnTsRf//pXJjI5qePAxg9xm2khVmw7lYo2oIeuElP1f0CspuGQfTW1igJleNr4AMRqEjefRP1Mz1wzbja8hN6+cjRWj4VN6dWsZSb4V2Go8ANy9yS/bIlZNMOhc8EcaCq7VCQ4MV94D0V1lHm5v6rrLxL9GKrbBks1rT81Nf/aw5IjJxM5pHq5jaNOh2HCbpiFxbDuKO/UuTilfwH7l0goW5/wxCl6kl1NgkhV5QijUszbGGh6yDSHCnLHuU0kqPQIxmy80RkE0ZaQtontkRuoxCz9CvSup7JSE1VdFyeNxSMlyHTr1vymuu++++LDD9VroZWJULzSwIEDVYu7klhdR0Dvk5OZptV+jOtMz2DF9uMAyPXJsslOdhMwQVgOpz92AXCHxYTZhh/RDdXYtn1tWojHsa6l2Nf4OpZWmDFw4NSkZsealAQjMdgU35xnCOA64yIEqINK8Em5j7VKDK5fik/F67F2L1V+OFkT511LbLmy2DMgKBcmN9kwzvMT5hufxordVFT5bKSSYfXf407Dx7DsJaE/PGV26gy2YAMeNf4LlgB5nJuStZKFySK3ETQF3GEb/cO3AN1NW1G/6zGgz8Fd/u7Lqu/GQvM3WLb578DEq+PaToM5OeKxZ+1KPGu6H79X0wPcea3e1/JY4rQN9w1rEMo8mzdvnoorCIlH2SskifIFT/A1TbVkk51sOfL0oRVuIOADhOjTdBVCKboFqlG3ZzMwZga0jmSSj6ve36DueIqCaJPFo0Vq8sA4CmVPm6CT4G6shdmRr976Fa+eNSKeMOXnXQscjhzWFjRsD5MNhly5I47Zo07XmM7Q07kWUw1f4Mc6e0rt1Nmi1EcLP7KfPR4XRKU9YLIwKZ5Hk+QK26hUqkB//V784mreZ335T0uw/OMXIBT2x3l/uaH9L9fL1yXJE3/CqVHxPJoS1Cu7qx5OLY8lTtsk/NGfAl8feughTJkyBTk5OTAYDM060FBa/rp16xK92oyCbPj111+rFpCuE+ULnOCXL0L60O+KmMw2O9lzZfFIeBqbqgW0pMEs39g9lZuRFphlASd46lUdT1FXbZcTB6wR4tFutcMlyXGPDTXqljyy5CpTwsE6zZx3LbGLRrwWnMna6dV75RI75jy5a4zDV4GUY5A98fqAO6V26gw25aGFcNY3ZbEnWzyKkidsI69etqPf3Vz0Vevy8ZlrKNYFO1ZTNKj07ZaU9pnxYLTYwtupJgZRrmZuDEZfj5bHEieJ4pG6yBx44IG4+uqrsXXrViYeI+uOUV/r5557Di+++GIiV5txUPmCb775hr2qgWCWPVJGvywWdYrIMAacWWknu0VEnSRfmBtrY3t8PHYlG71GLq6udULB+5T5qeZ4iobZKotHk86PoE++cVDHljqdfHNtrFVXHNnzQvGEDZCCAU2cdy0he9xruIS106vVyfayF8tjLC+YmiLXkeiMcvKOPuhNqZ06g8EkwiPJHjpXQ/LFo6iEa1jgDtvIL8jiMeBtfn2tF3LwszQMu6wdK5EWNCptZbzxP+SblGlrEeomlIWmx2N5OLU8ljhJFI/33HMPlixZggULFmDPnj04//zzm72fm5vLelt/+umniVwtp5PoFfFoUsSioMTpGCOSG7IJQa9DA+SLnKsN8ajL681exYYdSAcEiyLgAk2F+pOFzdFU3NfV2BQO0aiIR3edutOyuQVyTCpNCTfWpV6IxSLHomT6u+UM9LwSeYw54ILXlfzjFonOGPI8ajdjPRpOpQ6ip7H5NHEyMNmUayv80Cu1JgOK5zHYQvR5fMFmiUntISmeR53fnTAPqQUeOVlLJYzmJk8sJ7NIqHh89dVXmefx2muvZQGw0YJgqQPNtm3td0/gJC+Zwah4qMxp5nlMJI16RdTUxxYaYmF/9upwy6WotI5BEY9iCh4KzGYz69pDuBqbPEChWneeenXFo8VqRaOk1O+sUrd/bzzkm4AC1KGxQZ5ez8srCE/tV+9NrYdbb5DtJ8SYctQqrrB4jK+jS1ewKA/ihAHyA0FAmf6XvM3LfNkrf2XtBkf5fu3YlyueR30Cpq3N1qY41mCL7UokRrO8zSKJVE5GkVDxSKJw0qS2e0c4HA7WxpDTxkHR6zF+/Hj2qgZG5QInKvFoRkVkmCVn1trJo5dvOF5n7KQhR3e5dEpxYK+qT+uJwmTLY6+WYKOq4yka9ODognzTdEd4Ht1Geaz5G9T3Btbp5HHeWFOuifMuGnc478QK88XI2fQx+12n16NKJycS1Zen1sOtN8rnhNBi2joVduoM7tC57Ep+AqDFYoFfku0ycvhQZqOAEJpubu5JLin7nrUbnNrQsXa9OpMiHiPKAHV5O61NBbjdKnq4RUWkmqXo3mutjyVObBJ6xEgYlpW1HQi/ceNGVjCc03bJozlz5rBXNRCVfqGhTNhombHZZiefcsPxe2LfcKjvM90YKCvbVaX9uEcxnLTiVHU8xcKtU8Sjs8kD5DPKYy3gjJ2YlCjqBVk8u2v2auK8i4bPIN/E/Z6mG3itQU7gaqzaiVSiN8nHz9BCPKbCTp3Bo5dFlt+VfM+jaBTgguyx3XfqZGajcKxiC49hmbkf3g9MxS7biE6JR8Ef/3XazGJD5ZkBj1M98WgO1b3UBeDztvY+an0scZIkHqdOncpaDVEXmWhs374dH330EWbM0H6Zk1Ti8/nw3nvvsVc1CDWbZ/EuwQDM9rxmv2ejnXxKPFHAHXuKNz/Hjl26Evbzns2/Q+tY7bIHyyY58d6776o2nmLhVmK9Ir25AaV9m86lvnhsNMp1FH11ezVx3kVjYfebMcC9EKu7nxhe5jQVydtTk9rwCEGJeRRaeI1SYafO4BMUQZ4CzyN53L/HWHwRGI8vvv2R2ShUV1fXIubxz7wZrN3gr92ovm776E3yfhkSUJuRkrU8kMMjPG1c8+JFVMQj4Xa1Xo/WxxInSeLxmmuuQXV1NQ466CCWOBNKv3c6nazTzGGHHcaWXXnllYlcbcYRDAaxcuVK9qoGFsUjRUie+la/Z6Od/MrUUtDT9oW0zCQnNNTv+BNax5Iji0eDLohfV/6k2niKhVcn29QXcROXzPI26d3qJzO4RVmE+Wt3a+K8i4ZosSAIPRoiWoN6LfLMjFSf2lhNg+J5NLYQj6mwU2fwG5Rz2Z2aa9nNpmtwvu8afL+hktlIp4g+qnoQiVtJmAm1L+xozURDi9JJXcWtkz2kXjXFo2hmtUwJT5Tpca2PJU6SioSTR/HRRx/FZZdd1sy7SNPZhCAIeOyxx1ibQk7qsFmt8EkCm0rwOOths3eHVxJgot+pWLFF9kRmEwFD9LikljQ6+gOVPyFQpv1apXZ7DoKSDnoddftNvkfZK1hAq/W7m2yqU8aW4FU/7jlgLQFqAV2jdhNmQj18I/vKB23dgEpAn+LtFkyWqOJR6/iVUABEhAKkokWiXxKi1tUN4fe6WbvBjmZbC0ryiSmYmPAiiq0NBHXweNWrsUgxvOThtMIDjzM7q3lkKgmPUp0/fz5WrVqFSy+9FJMnT2athygglvpe0xNGy/I9nNTcsBqVZAaqhWY1CmiAJWW10bRAqABvezXUpMLB7FWs3QStYzIa4FTir/RIfhHez/Pm4nLvJdhlGxleJtjkqWTRp/440znkou5GZ8cSZlLBSNfPeMT4L4zb/lJ4mT5H3m7RndrtNijdWYxSek0pBo3KVKk3NZ5Hi+JJ9Cu3V0EpV2NoUc3imK33YJN5HibvWdSh7zUo3xOr4HZnucT2EKZ5HkV1bsdiLuP1cPpS5AnmpFF7wuHDh+Phhx9W46uzAvLQUj1MelUDinc5PPgvVPsEfG7rj3y9Dk5YUIAG1iYtG+20N2c0/rtnLyzWEZjQxudMvSfgrT/3w16MgbqX3MSwWdcLhqAHI8eMVW08xVx3/r54b9sujDbIYojQ5/bAmmBv7NSVYJjK6zfkdmevVk+FJs67aBQH9mCm8CNW1zVdik35PVEh5aA2KN90U4UxNG2tlJxJpZ06Q1OMYWo8j7e47sNEcSm+7Hkxs1G4KUOLLGl9QCmerxRj72hhb3OCWgpaFA+pU0XPI+FRHmC9LTrspMNY4sSG97bWINTScebMmaquw2vKhcfnhcsvx5qca7wPOxskvJY3CvItN7vstK3bIXj0j/4429EPx7bxuaIh++C0Dy+Bo9GAiyUpai1TLXGR+A/sqnXj/an7NWsVmgxsotBqSlbqMw2He+/DgFwbDlJ5/dKAmdjvu4eR6+iBDzVy3rVEH+ru5I+Y2h80C5M+fwK9bRb8D6nDYMvD0uAwVkD/oBTbqTPU5g3HO9v2hds0FPukYP1GnQRR50OPojxmq0DRcDzoOxF+a59mD5yCIh5DxdjbQyjoj794/4agmIPHE+ghdfvUDWk5z/5vrKnw4pW80a3e0/pY4sRGlbsJdZdZvnw5y7qO1XbozDPPVGPVGYHX68Vrr72Gk08+mTWOV4OWFw6vWIDGBiecPu3XL1TDTqGn8EihE40+BVbWkabe7cfeOg9Kczt24U8Vof1658OPMfSc41UbT9HoHdyFg/XLYaumMSZP9+dZ5ZIctU71p0ILCwqxQypGQ6N2zruWCEo8nCHCK5VnldddkwQbtYUhtyfmem9hP2+OeFBKhZ06Q1mvQ3HL8p44wlqKU1Kw/tdK/orL1s3FtO16jPB6oS8ejH8FjkcvyYJrIz4XKr6uU+pptofoyMeHwakQfYmJNjvD+SJuMP0M37a/AaPUux/rRSuC8MMVRaRqfSxxkiQe3W43LrjgAixatChm9pSkXIS4eIwN2YjqYUb2BU80Z0jvosi4CfrdFqD3LNhEeShEZn1mk51sRmoJ54TO3XbxasqMHFRohq9iIzav/x2lk7Sd/BU6rlt37lF1PEVjQs2nuMT0LH7YQ2Vo5rBluRZFPLp84WuBWhQ7xLAI8/gDEA1Cys+7lhiVUiahVqFEnmIjekDxB4IwCKkpoCwam9brDQTD9kuFnTqDzZTaa5nXWopdCGJb+XZmo9A56PQ2F08h8Sgo4QEdfeD3+IMIBiUWfhQPPYM7MVG/Hj81qFsSKrTdrhb7nw5jiZMk8Xj99dfj5ZdfxpAhQ3DqqaeiV69eSZ8q43SMqcGVGCeswu/VxwCYhdn+z3CG4VdYdniBoR2rO5ZJDKr6Fr+Zr8D6HZTc8X2bn73WsAgHia9i1cpTgElPQsvMdz2JIaaf8JF0QNLX7bH3wsrgIFTo5ZI5RK7ZgE9M16FAVw9X1fewFvZUbf0kVC8zvo3e2IvqXUNQ2kf2fmoJg9IaVIzIoKXtftG4AKW6KtSXj0R+ab+UbFtkFjAJlvbEt1YgsWaAH5KrNrXZ1krCjFUIYohuO3I9lLV+SPhzBiWLXeig59Fi1OFI/Y+wwAuX6wDYbE01FLvCt4Vz8VT1RByUPx2ToR7Hut/BmcZVcOy6ABjdVM+Uk94kVNmR+3nEiBFsyloUUxvszWmbr62H4+OKEdjPPpT9Ps7/K6YavsaqqqlZabpQUHtHCvDqSkfCVWVCXYP22zkWSzUYqN8NezD5ZTK29zsRN/8yDIfau2G2ssxiMqCbrhr5ugbsrSlXVTySV/N4w/foK+3Eht3rAQ2KR1HpMx+ZBEGexmHCDpSgGturdqdMPJr0wM/ixTDBB2/dSsCcHtHQPWtXYIP5TGyr6gUg+cX8R7h/wY2GT1CttJm0BxvwmXgd+9nvmw+D0k0llDUdKonUHmaDAY8YH4Ggk1BZ/5e4xePenDH4LJiPsREJbWow3PsHJgg/Ymmt2lHOnLQVjxTjeNppp3HhGCfkrZ09e7aqXtvlOQfhf3srMMzan/3+i2Mmvq0tweicMRiL7LOTq+e+GOJ+AcMLC/Fue+sdcyJGrhyAfkEHvoK2+az4TPxj3QGYOnJI0mcB7FGm60jQXWm4EXudwAPGHuim8jZ8apuNmupqTEMxBmngvGtJuDUomj+03Cdeit31AVxj7Am5LH3y0ekF5KKR1YPd5XGm1E5dEeTU0z0V9HP/if0NH+J760HMRlZHHsued0oicp2NyM3Na1Y/M1QSqT30gh7/wxhQGsFgpcB4IuKho00nJ5Ll+Ufgvdr+GGMf0+o9rY8lTmwSesSGDh2KvXu1W5A3XaCyBRMmtFUwJn5CXQ1cXvkitCZ/Jt7ZOgQ3WYYjG+1kNovwwojGDlyUh/UqZF1BtlQ0sgtv6CKsRWodQ7FMsuKAnkOTXg4jNH3XMvZsm3UkNjY2ojpBgf9tsaz4JHxRsRc9pBLsr4HzLlqr0J1SIVwwY1DAR3OYbPl6x1T8WluL6kBqZ3Dm6u5DlUeHZ01yv+1U2akzCN1HYqz7KejMDvySgvWHEmAsBtlWgsWGaf4n4QtI+AEiQv28Qt5mg5I01REuM/wdVY1efBZxPLpKL/9WzNEvQUEVZfrLM1BqsKlwf/x3Yz9cKQ5s9Z7WxxInie0J3333XWzYsCGRX5t1UAYadeKhV7WgqcMxuo0Q6raFpxOJaBlx2WCnUKePjjyFlzjMKLKbEJSANbu1XRfTqpTL+d8Py1QdT9HoUbMCS8S/4taq65stD2UT17nUzyYOJc1UNHg0cd61xJpTgOmeR3Cw5//gR5O4DyUWVTemNuN6u7E/tkjd4Q7oU2qnzuCwWVALO2o9EkssSTZ6ZRraVVcZtlFTJ6Gm6ws9rhIGi9LdKs7kk84yovZ/+Jfp3xhd9h7UxGKMfW/R+ljiqOR5/Pbbb5v9Tgky1L96ypQpuPzyy9kTRU6OPIXQksj2hZzmUOZZeXm5qhloh9S+gbvERVi59QwA+7EEhpG6LRDr6Warvdgwte1klxrxkPHfyHH7AelAml9t8/MX5C3Hgd4X4f1yFnDuo9AqfX2bcYbwOSSnCZJ0QlLXLRoF9NRVwh9snk06Fb9hH2E5jDu9wCh1k7N6WgMYrtsKfRndnIak/LyLVQuTaPQGkGuRRdoo/Rb0EpbCtJtEL8XuIaVJM5Stnko7dQaHKAtv2jynLxAOn0gWepMsBqk4f8hGNpPAKgyEC3JLEsySB9ABohJv3RHMSgZ8y8ztrqBTtlPfonh5oimWKjBJtwa2OvqteWsArY8lTmziOquouGe0Uhs0EG677bY2y3DEqv/ISQ5SqJezT75wTKr5GNeI/8KvWw8FcHDWHQaaxj9OWCL/EvDSXFKbn+9XaMWQip3YXPYztMzAxhU42/g8vgomf2rIbJNvimap+c1pmvd77Gd8B8t30YOluuJxnPN7XCregDXbxqm+rq5gEvT4+uqZLEPYESFyprm+xQzjQizfSXF7x6ds+04KfgKDYTdQ3QPoIyeAaB0SWLcaX0QhatFYOQr2Hn1T4nmkRKMQtwcfQalpM7DzIaDXLEg+F+s5H3medIR/uO/AUPF3bNryT2DgqQkRj4I/MR1rYjG+6iPMFx/H0t2UNneUquvipIl4vOWWWzTfYYMTHUnpaqD3Kzd2Y+gpVN0LiVYRlXp7hOR1QteOeCwavj+wFujlXk9NW4EOdolINnqTrdkUWTIxhRIXWrRTC4hywoDOXa36Noj5coawzVcJLULXz35F8jFqhkUWaoK7Bqlktv9TDDBswaoaErDjkQ6QTY8WfkQxarC1eg+QZPEoiPK1VIwQj/2l7Rik34KV9WXsd7ezAaE0GYu14zGPos4Pu86NgKcxgR5SdT2PutC9JXSv4WQEcYlH8i5yEo/RaMTpp5/OXlVDOaF1ygkdEhlCGp3gibSTxWKGTxJYZqnHVQ+ztW0vy7Bho1Au5aJYV4vqDcuQP1ybYRh6s3xjyhV16o6nKFjCmcQeeH0BmJR4LZhl8aj3qB8vaiuUp3zzApXaOO86iN6q2MhXn9Lt8Ovk+NSA16VJO8XCqbMBUg08jeo/oLTEoIhHixAI28ijl5f53XIbSidEXOK9hp0bj4gd76zi18sPqYkQj6ESQaE2iWqhU+whRHFMpMNY4kQnNa0LOG2i1+sxaNAg9qr206CgxLvoRVurNmnZZCezQQ8XZG+jx9n+hdlmNmK9Sc5ML/vjG2gVgxJPZYZH1fEUDasyHUeCvNHZVOpFb5OFudGrvnjMLZHFI3UPIo9yqs+7jiIoxcNNKRaPAb18TgTIu65BO8XCrYg1b0PyPbcGxaNH09YhG/kFeVnQIx9PZ9CIxcHx+FK/L2t32lH8BlnwBRMgHg3KNd/Ygdq28SAojglDFPGYDmOJEx1+xDSIx+PBggUL2KtqmHPk2mNQpq+Vp0O1LyRatRMVZg6LR5fsHWiP6mK5L4N+qxIrqWHxqPfWqzue2lg34WpsEooGWwF7NfvVF4+FBcVwS0q7v4odqT/vOojBInseTYHU1CoM4VfEYzBCeGvJTrHwCLLH3e+sTZnn0RR0hW3kV2LMg4rnMZR5HCpn1VGCgrnV8ejydpoV8UiJOyrS1vR4OowlTnS4eNQoapcuKOt1OCZ5nsDjRTe1eApNH89jou3kUabovMoFvj1sw2ax1551v5BrBlok1DuZpseSjmCAB7JwczU22dRkD4lH9b1qZpMBFUqnj9ryne1+XislQ4w2uRqgOUWFrlt6HiWfR5N2ioXXEBKPyfc8GhVRFhnzGDAqca1e+TzwVu/CCfpvMVP4rVPfHVRmjJBA8SgqnW7UIixSYzgmtD6WONHh4jFLMbfoLmCwyF4iMY08j4nGo5NvlL4Oeh5HjJuKrwLj8JT/CFTXpXZ6MRYmJe7QmgrxSONL8Wx7nKxOB8OcI/e6tgWTY7MaQRarjZXti0etINpkz6MlmNoWmEEhJB7T66HSb5TFo+RpGnfJwqTUbYxMUpNaiEeh7Hc8YHoClwQWduq7JWXaGr74x4XJHJpeV/faYAytR2WRykkuvCdQllJoM2FYqQN9CpTpaiWxwqTyFIaW8eosgNTxYPSSHCsW5N+B9WUNGLbTg8M1WMlEtDrC7e9SUUnNw2xa30w8WnNl8ehAAxAMUuCTqtvQaCwCAoCnehfSBbND6YuMRrlgYYqqWjSJx/R6qAyY5HEvuZMvHkXF02bSBeBmMxJiWDzqFfHYqLfh68BYNIi9O1dVV4kfDCU6xoNJueazepMq0nRvSa8xxGkbLh41CGWezZ8/X9UMtOmDivDJ5U0Zwiblgmdu0WM3m+zkoym6IOCP6OPbHrfOHokciwEje4SajmkLszU3fCML6OLvh9tZPHoLE24+Z5OX0Z5XzF4FSAi668KZxaptg7kYNKyDdXtSft51FJsiHo0IQPK7wy3vkk0wVLIqohagluwUC8kke9z1SoJKMjFZ7Pg12B9umDAmVPxaif8VfPKD6Z6c0bjUdx2mFBTg6E58d2gcJKLsjckauuZ7IQUDrJe5utP4rUVqOowlTnS4eNRonbLc3Nyk1tA0KbXGTPADAT+LV8s2O/lCZTDcHY8z22+w7EXTKhZ7U9KKz90QLs+RLJyGHCYeg86q8LJchwMuyQSLzouGmgrkqCweA7YSoAbQN+7V3HkXC5sjF0FJxwpJu+trYClIjXiUlASNSPGoJTvFxCyLRyEF2epmqw1zvHezn1fpRRa4oRPl7TH4G5t1iOlswoxOiU0X/M6EldJiY8zVCLPyuxr2YK9S69jGtBhLnKioMl9EAbAfffQRHnzwQdx5553h5W63G2VlZQjSVBWnTfvde++9SQ0kDk1vJiqeJh3t5BeUMhje1CYpJBKr2QyPkm1cV538QtluEo+Eq2ndJoOe9R4mGmvLVd8GnaNUXq9LLtCspfMuFlbRiAYlXtRZn/xahWEM8jboIsSjluwUC73i6TOkQDxS16CQFqpvlO2mt8gzAKJfybbuongM1eNNREk1i9WBgKRDoyTCGZHQplYDBqvOA19Em8t0GUucJInH9957D3369MHs2bNx9dVXNysk/uuvv6J79+5YtGgRks0vv/yCG2+8kfXeLi4uZk861F6RI2MxW5mnI1EFaNORQALLYGgFKkFEBYmJyLjDZLGi8Gj83Xc2ttioPWATjXr5huKqU1/QGvPkLjNWbwXSBbo+NerkeGRXQwrFY7gTVXrFQguhcA3F05fsY0d1Ywm3X3aUCIqYNSnZ88PWP4nfxXNwcuUTXctcToB4FAQBIwKvYKTnOTQa1PP+myM66Lhc2XlvyUQSKh6XLFmCE088EaIo4uGHH8Zpp53W7P0pU6awgqBvvvkmks0777zD6kl9/fXXKC2VPRGcJiwmQ1hkuCPi07KJHdbh+DAwBZViH2QSZbpC7JQK4U7B0/3Wwhl4KXAodhqbt4jbZeiFNcHeaPCqPwthKejJXnP82mxRGItqXT7KpDy4XO7Uex5V7kKSaAxKe0cxoJ5HrS0WCbfgJ3E+sGc1+90Yzp6XxZPO2wCbzoNQ06WOEhKhiarHazUZmtWdVINQPH2oLSMnM0hoYBtNUefl5WH58uUoKipCZWXri/WkSZOwdOlSJJuTTjoJc+bMwejRo9l2kQeU04TZqMeB3rvhkUx4z9oTUbrtZjzLi4/H69um4NqCoZArOGYG54gPYVetG6/bhyR93XazfImpd/ubLX+k6BYs21yFR3LGY4zK22DqPgL7uB+F6CjAt0gfrsh5CGv31uPlvLGdy8hNID5rCf4M9kaVUIh0Qsjtwcpo1Rh7oV8K1l+IOta6tFKpGSsU9MUz/iPgFotxKYnHUOmjUN3GDhIsHoEbfOcBYjcsSJB4rHb6wjGYaqATjDhIehwVHgHv6iPCozhpTULFI4lC8jyScIxF79698e677yLZjBw5EumCyWTC9ddfz16TOdWy19CLPYG6A7qstJNFiT9yq3ghTQU2UT7NfVLyy7rmWeRjU+Ns7vXMs8hxmLUu9Yur59qs2IsCiC695s67jgnv1BWg39VnDuYt64cDc4pxuEbtFA1D6XCc67sWJWYRx6dg/bdabsCuGifuyJfvO5bC3rjLfwbsgkEWj0rCi6R0nukohrye+G/gIJQElSz4OLk8+DyKjVug23070PtAqEWtsQS1Hg+cvuYFw9JhLHGSIB6pxVBOTtsZWzU1NbyPZTtIkoTa2lomwpOZhUbiicSjmk+hWraTxShAhyA8nvQpV9QZUdzoSb4IOWBoMfJtJgwsbu7Lzk2meLTK6/L4g3D7AjDHmCtM1XkXC7sY3WubTEQldo9sp1U7RcOhCO8GT2pst8s8AH9K9XDC1Gp7AkEJglJqR6e07uvsg2Ao4SZexgT/xFBhHVbV7YaahBKDWk6Pp8NY4kQnoa6IAQMG4KeffmrzMz/88AOGDRuWyNVmHD6fD48//jh7TSZzdV/iJsNCSHs61zIrU+w0peIdbDbPw+wNtyCTOMv1Et4x/R2OTR8lfd1Dujlw4sReGN+neQX1/Z2f4XPTNZi49gHVt8EhGnCZ4W38n+EJNOxer7nzLhZHud7Ha6bb0WNj8hMMQ4SENolurdopGg6z8sDg9SIQSH51D1GQb60Nbm9YPHZDFQbpdqDB6YTBJ09n68ydqw9rFQLYT/8bpvt/ZMIrXt5znIqrvBej3KZuSMvZgbdwt+E/kCo2NFueDmOJkwTP4wknnIC77roLzz33HM4555xW799///1YvXo1/vGPfyAdIc9qZAP3urq6Vsv1ej0reEonQ2RJIspsMxgMrCRB5ElPy+i9yOWR62jZMJ6+m57QWpY2ILc//X3Lk5CSl2g7IpfT39PnA4EA/P6mJ/NDpe8w3vAb1pQdAo9nYnh5IvYptO30XYnap9D30GusfQotp2X0Xpv7JMg3HH3AnbJ96shx6tQ+Ue/t4G6M02/EsrodzbYzlfvk0PswWL8T7sbtzY5fR/eps2PvaMOPGIzt2LRrHRzd+kfdp8jxlIrj1HKfugX3Yop+LX6u3cLeT8VxKqr8GV+arkZFdS94PB828w5Frldr1wi7aMQq8Xzk6pyo2PUTHCV9VTtO0fZpuu977Cesg3FPEMHRPSEaBHwhXgOHzoXtO6fApJTsESyOVvva1nEySy4sNMnRjnV1l8Fssca1T6vt++Kb3ZWYpJdDzdQ6Tgf6/4f+hs1YVnUKPJ7WUc6R35PM48TRiHi85pprWCb1+eefj1deeSU8IK699lrmcfz+++8xbtw4XHopRX10nquuuqrVYG2Lyy67DIMHJy7UnLK1b7/99lbLqZ6l2SxnJY4fP54l5nz88cdYuXJl+DMHHHAAKw302muvYePGjeHlVNJowoQJeOaZZ1BeXh71uyMHOVXjp6KqVBsrEoobIfc/PcWFoBPthhtuwKZNm/Dyyy+Hl1OpoksuuQSrVq3C+++/H15ukSZiqb8fHLv9eDXi+xO1T6effjrLtk/0Pj300EMx92ngwIGYN28evvvuO3zzzTdt7tMmfy6u9T2O/Qf2w6QU71Nbx6kz+0S4rQfjGe8klO4pwscR25PKffpNPxxPeW+CrtKAoffe2+l96uzYsxkOg9/VAPdnK6D7dGWb+0TjKRXHqeU+Vfh6YlHwMvSVBmESkJLjVLV7Oybqd0Py6Nj30T5R8mHITok+Toncp0ZlYu35Z55Aoz5XteMUbZ8mV3+ImcZf8OwSDzYNHM72qQZ2+CUBr7yyECdItWzeTy/aO7VPe/ZWYlewD6uM8d5DD6JbSWlc+7TVOwBAAVavWQ/s00+149TLcQRqK8uw+9s/8PH3f4b36corr2w1lpJ1nF544YVm28jpHDopEb7vCKqrq5k4pAMW+XRATw0nn3wyHnvsMeTnd60JsN1uR2Njx+tELV68OGotxz179rBsaxpcVLonHs8jJQBR4fNQrGeiPI+PPvooE8stUdOjdfqzP+PnrTX41yljcdhwuYVcovZJDU9JQ0MDsxONNxLv8T6tvrlyF2585w/MGlaCJ04bmzGexxveXo23ftmDy2b2wyUHDtLEPr2zcgcuf3UVpvTLx0vnTFTd8zj3mZ/wy/Za/PvUMTh4WEnUfQqddzSeHA5Hyj2Pjy7ehEe+3oRTJ/fGghPGpOQ4/bJ2M+55/k3Wa/vpq+ax5fQdDzzwALMT/W0ij1Mi9+moO17BTqceL1w8C8O65ybVo7Xi4bmY2vgVfhhwGfaZdxv7rgPu+xJbq914+dxJGPjf6SiVyrH6yLcxeOz0Th2noX//BL6AhMVX7Iee+Za49unBRR9j3drVOHjaZJw++zDVjtP8V1bhq7XluGvOcJw0US6dFaLlWErWcaqoqGBClYR6e7kanNYkvAcdCUN6gv3Xv/7F4h+rqqrYgZk8eTK6desW13eTWEglNLhDA7y95bF6dcbKKotcTt9FBc3b25aW0MkVbTmdXNGW0wlE/0LYRHmbKRY72ufj2af2tr0r+0TjqqWdWu5T5AWD/rW1Tw6LGA5GT9U+deQ4dWafiByr7BX364So35+Kfcq3ycvqPYFm73d0n9rb9pbL86zy740+tHkOR46nZB+nltueq9jIqcQbpuI4mRyFWCYNR7HU/BoX6/qkpWtEo6U7apxOeGFo9n6ij1PU5UoPaoPkCyeI5lhFoNoNTxCwSnK2tdme1+njREkzNU4fAjp9eBu6uk8HNryPG0xv4Mc9ZwM4TLXjZFESffxS6/2KNZaScpw4XUa1BsaFhYU4/PBQcQdOZ6CnJ5pGogSk0IUnGeQLbvTSlUFqoDZuvZBtdsrz7sXthueQU01PoVORKfTwb8dx+v+hqIL6S2sjWa3A4MPpwhfoUUfelf1VX18PkxMjdZshVVB2a29NnXexKEINjtL/iAFV5Ckdn7L6ry0TZrRmp/aSZlJR6iiodKuSQvUcIzKu610+2Eg86gCLo/OzcFajgBr40OiJP+M6qJQK0qnckrZIV4+hum1AQwGA/mk3ljit4UdLg5D7nby3yc5Am133Cr4TL8fQDc8gG+1khxNnGT7HTPeXyCSG1P2Ih0yPY9zet6AVck0S7jY+i78EXgKS0PruoIYP8aF4E4ZvelZz510surs34N+mf2FO1XMp2wZK0Dhb+ASnBd7XrJ1icURgMe4y/Afi9u9S1uoUEaLsOPdbeNl4N4rWvQpBJ0+jWu2dbwv4kP9u/CBeCv2OZfFvqNIrW6eUDlKLoyv+g0/F6zF4e/Pucukyljgqex5nzepYXw5yyX/5ZXJv0GvWrAkH8bpcrvCys88md73M888/j2wmVLA23P0gywj1jRWlzKrzqFNuEIaAdnp2O/IKWS91vU6Cr7EKxlyVOz6Z5Zu0wVODdMGg9Gc2Ky3tUiUebzO+yI5VMPAv6JUSNOnABP8vmGr4EisqKLv3hKSuW1LaOsLfdC3pHdyFqcLvWFEpl8UJSDrYHZ2PtStEDbrrqlDmqk7ctcGn7hiTRLmzjM6bna1vM5GEisf2kk9CwdapKAZKSTIts6v27t3bbFnWi0djcqYwtIrJYmevZnioei0NWGQCeqUQsUFl70JnoPivWtiQjwY0VJcjX2XxqDPLx1bwp06IdRbRKgsLczB1xy3Ul5hEvtvngVmQY/nSAb9RFiySOwWCRRGPQoR4DJrk7aHp5r95L4Xd4MM9hk42tyZvHR2DAOnS+MdyqEi5EFB3jEkm+fzTqyxSOckjoY+RFL8Q7R91lfnqq6+wzz77sPaFqaivRFnXJFzb+qcVSFxTFljSRbYS5K1XWmdpnUTbSbQqFzhISZlKTRaCIoqNQe14VAW9DvU6WZg46ypVX5/eLAsxYxtjO2XnXQxEm+wttSJ156NobuqA4nE5NWmnWITEms4j1+NNJrrQtTTinJNEeQy6vV68F9wXn5kO6dJ3+/TydwcSIB71YmhWQl3xqFc8j4JSHD1EuowlTmuSMgdBWbEk3j799FMsW7YMd999dzJWm7ZQFhjVuUp2NlhoCkMIaEdkJNNOZkVkMTLI+2pSPFimFHqwotGgk28o7roK1ddlsMjrMrUxdZ+q8y4WFocsHkX4AH9qChobTWY2ZU14FbGiNTvFIiTW9N7ki8c9vQ/HHM+deLPwovAyvVkRs+7aZhUAOotfCS8KeuOvPmJQPPJGla8NoX03tvD8p8tY4rQmqQEsVDvtiCOOYB1oOLGhGlYrVqxoVssqubFx2hIZybKTRRThkeRIDp87tWWhEokgyjcIk4Y8j4TbIN9QvPXqex4NlpCAdmruvIuFzd7Uus7nSr4AYuh08EAp4eV2adJOsdAp3uaW3q5kELCU4FdpIHZKTfVyJZv887jg7zhMvwyjTV3rJx0wyJ5HyRu/51EIiUeVHQaCcv4ZA823OV3GEqc1SY9+pnT83bvVbcKe7lARVKqsH1kgNRkIohIblyaex0TbyWIS4Ib8BOxxZZB4VOKaKPlBS7gNsjjyN8Yf+J+I+MFUnXexsFnMcEpK7dF69W0UC69OPie8Hqcm7RQLfUiwpEA8Hj26BGeal+PxU5ta8YlKXG+Rrg5Pmv6JE73vxpXYCG/8syNGRTyqnSRoVM4/sYXnP13GEifF4pHqOb3++uvo169fMlfL6aSHSu0pDK1iFHRwQb5Ze12ZE9htDCcCaeuhwGeSxWOgkepPqotok9dlhUtOhkoDTAY9GiB7mVwNqcsS9yqeR78nva4LgkU+5qZA8sWjUdCzcjyRsXy2wh7hn5cHB6PWTq0BO0/QmDjxGEoSVFs8hkJnLG14/jlZnG197rnnRl1OTxU7d+5kPSmpntMdd9yRyNVyEoTBYtPk9GayoAu9JyweM6ekhBgWjz4gGAD0nc/wVIOAKN/cdW71vWpmRTwaWJqqh1wuSAdcOhKPNXA3ynFyKfM8shyy9Lrxiza5ALe5xVRpqsgtbmq8cJr3JlzUbwSO7MoXKeIxEYmNolUOHbGoLB5DyV8WenjjZAQJFY/tlboZOnQo69d8/vnnJ3K1GSliqAl8sjPQDErmnUnyZK2d3Dpzs+SATMBib6olF/Q0QK94ZFKNZJZv7nqP+sLIGhE/KHnqoYsiHlN13rWFS28FgoA3heLRHxaPLs3aKRompQB3Krxd0WxUUFgEj2SEqPOhWFeLQrsYV2x6IsSjWRGPZnghBQPQqfRgaQl5/iVXs3J96TKWOCqLx82bN8eMc8zLy2MJM5z2ocyzefPmpa7OYZoUyVbDTl69yG7WiaihphVyc/NYQWKaRnO76mHViHjUW5NXuNtmMbH4QavOA6+zDqK9KZEh1eddW3j0NjYefc7UiUefck4ElGlSLdopGtQ3mrAh+edyNBuJRgO26orQF7vRT7cHPfO6VjNTp4QXCQmo2yraHE11PF2NMNs6X7S8M5UDzDofXG4PLBZzWo0ljsoxj3379o36r3fv3lw4dgKa5qeC68kOIjaFypmEpjez0E4+xfPod6fXFF1bGHSAE/J+uRu0Mx0v2KjPLSD61c8ktpkMaFRCEpwx4gdTdd61hVeQvUz+FIZRMM8jhRl43Zq1UzRCfaNN8FP5hKSuO5aN9hrkuMeFpgXolSfHknYWQZkhMiagY5TVmoNR7mcw0P0SXMo1Qm3Pf2N90/mXLmOJ05r06TWVRVDZgm+++Sbp5QtEq3xRSpc6h2rYabNpML4NjEajkgmcCUhSkCVe1EsWeJRyK1rA4CjGbqkA1VDH2xGJXk/JULKnxxNjCjhV511b+Aw2uCUjfIpwSwV+8jySfZS2pVq0UzTsOfnhGpWeJGT0RxLLRr8VHMpevwqMQ7/i3PgKeycgsdFgEOAV7AhAgNOn3vHUG6kMmiyWXQ1N51+6jCVOgqetX3zxxS7/7ZlnnhnPqjkqYDbbcJzndtbB4INQRl+W8Vre+VhaXYVHC8djPDKHAz0PsjJEn+QMgspdpDtOr30wzfMoBufa8XkSVvdX463Y2xDAs3mjUYr0YFHPG3Har+fi791HYGKKtqHeUIid7kJ4JW0kWnUUu9mE74KjEIAe4zx+xe+cWvofeDYOWViMMaNGY5axa/b0Fw/HPb5TIVl746YElSjzuoJwedX1/p1l+j/saNDhCWMRequ6Jo7mxePZZ5/d6UDXULAsF4/aw2IyYKU0WO6bKukSGxCbJtCFlHB5M+tJ2KCTWNKDU0P7lWORPRG1Ll9S1ldt6oE9cMIVTJ+RbTPLU8YN7tRN673c8yZ88Otu3Fo0AvshfaAWmBfrbmZj/htDAeRJ7NQya3gpJt50JnIsXR+D+sKBeCowG31gTYh4vEr/X+QZd8Nf3h0omQC1KDP3x476RjQk53TnqExcV1HeKUYdKMFo/Pjx7DUVwolw+QJwCPqss5NF8Qa4VZzCSTZkH4fFhAYn4PRoZ7/yrLJ4rEmSeGzv2KbqvGsLh1m+RDeq7BVqC9Eg283jD2rWTm3Zj8RjfZLFd1s2ylXGfVexGOUxkagHwRnST+gnbMcftbsAqCce7aIylj1NxyKdxhIngeLxrLPO4vZUAaPRiDlz5iTdtqJBj7nCYpSgGp7KoXD0HIRss9Ps2ldwn/gKNv95IjDtEWQCZKe/mj9GL98qWLb9DRh8MrRArsWIV4x3sbIl7spRMBeqO5l1WOBrnGT4A6ZdOmDQkZo579pieOPPeMb4H2DLOAD/TMk2mI36ZqJbi3ZqS7DshSfp4lFNG1mFAMboNiKPPVAcHPf3vWM5HrU1VThEbKpDqQZHBBbjSMMaCHtNwPBD0m4scZrD5b4GoULq7733HntNJhROcL7hY1xlfAOB8g3IRjuZ9BJydE7ovZnTnpDs09u9FjOE32Co2w4t3diH6HdgsH4nGmvKVF/fNP8yNr4tlb9r6rxriwKpCgcLK9GzYXXKtmG/qrfwjunvGLljkWbtFIu/e/+J1eK5cKx5LanrVdNG9kAt3hP/jud0d0AKyt7gePjecQSeCxyBamM3qMl09/9wseEDiBVN5186jSVOc7h41CDBYBArV65kr8lmsX4aXvHPQqOpENlop19Lj8eBngfwZY+LkSmQfd70TMWV3ouxPXcytAI9rPxd/zec6r0JVWJP1df3q21fPO6fjTLbEM2dd7FoKJmI63wX4APHSSnbhvxABcbpN8Lu3KVZO8VCFCTYdW4EXMlt76imjURbDnZIRdgk9YDH60lYuJJT5dCIP3P3x1P+o7BX7Btelk5jidOchEeOb9++HXfddRe++OIL7Nq1C16vN+pNg9d10iYvmk/DDpcLb9u1PWWtFpKtGJulWtRALsSbKfyKIdgULMRIk7byHP+wTsJWlxO1fjkxRE1W5h2GT3aOxZ05o5A2FAzEq4EDMUUv18RMBeu7HYWntxRjVP54TEN68UHJRbhu7TG4uNtUjEVmYLHnYbTnX+znlQEh7uqMvXTlmKRbA30djTH1rg+rS4/DS1sm4G9idt5bMo2EisdNmzZhn332QXV1NUaOHAmPx8OKhJvNZvYeuabHjh3Lus1wtEkoqYASZrIRc2j/NZSVnAgM1CKEvAsRwepaiXtMVsZ1yMPiTqNjaxOFlGdbN+YNxpfBAPIEdWPi1MBn74ltkoTqJDycJAuDoIdJ0MMbCLLajPFmkc+pXYh9xI+wdMelFNwBtbApCTMNGkra42hk2vr2229HbW0tvvzyS93m2GwAAENESURBVKxatYotO+ecc/Dnn39iy5YtLDC2sbERb7zxRiJXm3EIgoADDjiAvSYbq1GHPNTD15DcorpasVOpdyuuNryKyWWvI1Mg+0zsLuAg/XLkVKcudi4aE/XrMU/4HMLu5aqvK1fvQm/dXuga92ruvItFjsGP/fW/YpxzScq2wWxQEmb8Ac3aKRZ2UX44SXbCjNo2SmRJsaBBqenrVbeNY67Bh+6ohL5hT3hZOo0ljorikaaqjzzySDYYIus6Et27d8err77Kfr7xxhsTudqMw2AwYObMmew12Vzqehy/mC9Ctz+eRTbaqci3G5ca3sWkmk+QKZB9DrOtw39MD2DsLvkc1AozPYtxl/E55G1frPq69q9+G/8Tr8CUTY9p7ryLRY5Uj5dM9+IOz310MU3JNhT4duF4/bcYWLtMs3aKRd/gNlxjWITRO15O6nrVttGjuA+fma5BcE/05K/OICkNIXQ+dcXjhPK38YP5rzhsZ1MVi3QaSxwVxWNFRQWGDRsW/p0GhNPZ1OZOFEUccsgh+OCDDxK52oyD4kQXLlwYNV5UbfyiMgnirEQ22inUN9YU1H57xo5C9tmxt4r9LPjVvUF0loCotGhzq5/QoDPK7QkRcGvuvGuvPzMLO1DaAyabHjUr8aDpCRxU+7pm7RSL0uAe/MXwHkZXJvdhUG0b9cVuDNHvhL+hMoHiUd3xpTc72KvB33RtTaexxFFRPBYVFbFp6cjfabo6EhKUNTXJzXxLN8hbu3HjxrDXNpn4LUXsVe+qzE47WeSbtTWYOaV6yD61TjkrU/Brp7c1IZll8Sh4ovebTiiKeIxlg1Sed7Gw2XPC/Zl9riTYKAp6URYXhoBHs3aKhWCTE40svrqkrldtG3n1cpqMzxP/w6DOJB9fIULUqYFgkc91U6Dp2ppOY4mjongcPHgwGwghpkyZgk8//ZQlyxDl5eUs3nHgwIGJXC0nkdjkEj1Gt+ypyjYEu7z/jmAd1ZFApuCV5NgvY0BbHlW9RU6eM3jVF0Z6RTzqFRGULu0JG5V8Wld9ah66BaO8fqOUPnYLIeaUsFdbMDXCWy28enksB93xP+TqTbakPFgaLDnsVcygWZ1sJqHi8YgjjsDixYvDnsXLL78c9fX1GDNmDCZPnowhQ4Zgz549+Otf/5rI1XISiGAvZq9mb3aKR6Nd9rwKNE2YDG9YkvArhRUMMaZsU4Ve8fSakuAZ0pnkG64hoC3va1sYBT0aIW+3syFF4jHkeQym39SiNU8Wj1bJBfjTb/tj4RNkQe9PhOdRtCflvDBZZfFo5uIxI4hbPFI5nhDz58/H119/Hc6cokDYRYsWsXI9q1evRrdu3fCvf/0LF1xwQbyrzWhoan/27NkpCSI2OpQndX91VtrJYrWiQVIqpzkzQ0CTfYaMGM1+FoPaEk5GuzytKEZMZamFPjQ9F/Ro7rxrC6dO3m5vY2oeZgyK6DZKXk3bKRo5eYUIKNP+cCXvfFbbRgFB8TwmIEPaoMR5G1W+Nog2edraQkI+tO40Gkuc5sR9xCiL+rTTTsO5556LCRMmsDqPkZx00knsH6fjkPgmW6YCS77cosoWrAeCAUAvZJWd8qwmVEsO1pVCclZCV5j+IRZkp76DhgFrAZOkLc+j6JDFozVQr/q6DIoHzRhDPKbyvGsLN01RkiNcI+JRq3aKRr7djFrYUIAG+BsqYHCUJmW9atsoYJCPiZQAz6NgDolHda8Nok0OUbFJTdPW6TSWOAn2PLrdbjz22GNsWpoGwb///W+eEBMnlHlGNk1FBpo1vxR+SS9P2zao329Ya3bKtxpRBTkr0F1bjkyA7LP4G7lOoFlj4tGiiEebpL7n0WBqWzym8rxrC49evrn7nclN+ghhNMt2E+HRtJ2ikWcxsodBorEmeeez2jYK12ZMQIa0KRyLqO61wWqXxaNZ54PPm35jiZNg8bh37148/vjjmDRpEn755Rf87W9/Q48ePZg3koqFczoPZZ5RclEqMtAK7FbsluSkEalmK7LNTtRhp1YRj84abYvnjkL2qamXPRQWuFJWLzAa1lw5xlSET/VSNAaLrc3Ej1Sed23hNSjiMUXZ1kbFY2uSfJq2U6xuLPX60PmcPPGoto2CiucRCajNaLTIMY+iyg+WVkdTZzlXfW3ajSVOgsWjw+HARRddhKVLl7K4xiuuuAK5ubks1vHQQw9F//79ceedd7Ke1xzt0y3HjB2SnDTjKt+MbIP6rjca5Ngcb10FMoWAEqEiQKIoe2gFR05eOCbN36huTJpRlG+4YpplDfsFWTwGPepP7bdlNxMJ/DTEKcieNXd9ZswkMIy2hNVmNFll8WiGuuLRJIpwK1UfnI28XF+6k9Bs6xEjRuD+++/Hjh078NZbb+Goo47Czp07ceuttzIRSdnYVKqHelxztAm1vSoT5KSZhj1yiaVsw2WUM4D99dHb2KUjQV1T7GrAo50aljlWEXWQb4SNterWFjUp06/GNBNBfpN8c5fcqRGPYshuugD8vvSbXnQZZI+Xr177tWs7ipTA2oxmq+yZtdBDlcoewEYl+cudosoBHI2Kx8gg2GOPPRbvvfce8zjed999rEwP1XycO3cuevbsqcZqMwaj0YjTTz+dvaaCeksP9uqv2JSVdnKKsucV9buQCTA7nTIXHuWp39WYmti5WKVo6hXx6KpT9+YeyiplU+RRanim+ryLRdAoi0ekyPNoMlsiYtxdmrVTLHwmeSYh2Jg88ai2jUKVA/QJqM1ozumGfd3/wgTPE/D61a1t69LJYymU/JVuY4mjsniMhMrzXHPNNayv9fTp01lsQ2Vl5jwBqoFer8egQYPYaypocAxir6bKP5GNdmqw9cUvwQGoMnZHJkD2GTF0MBohst89ztSIkFhU6guxWypAo0vlbM8IEYQohcJTfd7FRPE8qt17OObqlWlrwut2atdOMQiYQy1Xk1eqR20b6UKFvRNQm9FiNmEXilADB1w+dcWjWy+LXp9TFo/pNpY4Tah6xKhA+JNPPsk6zYwbNw5LliyBzWbD2WefreZq0x6qnblgwYJmNTSTSW3pNJzivRkvDv4nstFO20oOxLHeu/BVj4uQCZB97r33XhyKf6O/eyHqlYcDrXBD7n2Y5nkUu/Inqboek8WBOZ47cZjnXkh6o+bOu1j80fd0DHG/gEU9bkjJ+vUGI5ySiHrJAq/Xo1k7xcLt6IvlwcHYKySnTA+hto1chSPxqP8YfCvOjPu7TAY9DHo57tjp80NNHsu9Gkd67sHunLHs93QbS5wmVKnMSV1mnn32Wbz99ttwuVzM2zh16lScd955bNrablemYTgxSWXpgtLSHvgxOAK2SqW4bpbZqdBmYq8VDZlzQSM76Yw2SB4vnN4AtESOWRZydS51b1yiyYBfJblup1fSKX7Y5mixZIjFYoMXRjR41LVPW+yDl1Dv8WOxWIx8jdopFrt7HYGr/hiIk/N74YAkrldNG/mLR+F+/1yMNeYhEf3arjS9iVx/NTxVg4HcIVCLCvtg/CGVozYY4c1Oo7HEUUE8UpLMc889h+effx5btmxhgrG4uBgXX3wxE43Dhw9P1Ko4KjOsVA6gXrNHW9ObyaIkR+4wU1bnlgPIddoX0R1NhiKc3tSJkGjkWGTxWOtSN5HFbGhKGnL7ghAjftcydlG+TDe4U5foIxoFJh7dPnrwSK9uIAU2+TGhsiFzREroXHYl6Fw+Dl+ju6ECG2t2A1BPPNpFebsbU/ggxEkMcV8FKJaRvIxfffUVAoEAi1047LDDmGA85phjeNuhNGRYaQ5KUI0zGl6B75X/wDj3BUBIrxtGPJQ4RLxnuglDN+8Cyr8FSoYhEzgz+C5KjWtg3C4B/Y6GVjjY8zkuNr0F/9rDgX3uVG09RkGHM4XPkIcGeGtHA5Y+SAdKfDvwoPEx2Mopa/jllGyDaJAjnDwqJ1SodT4TZfWZM5NgFQLor9uNbm66LsfvT33bNBvuxnrMMso1ftVitO839BOWIG8PxTz2U3VdHHWJWxGceuqp7JVK8ZxzzjksnrFXr16J2LashTLPqE94qjLQcq1GFBXk4ZTGxaivGY0CjbYoVMtOVOvSDK/cUaN+d9qLx5Cdtj1zOiYJy7C6RjvCkSjS1WKSfh1+qxuseg3P+Yb30V1XiT015wOlfTR13sUiR/BipvAdqlxK4kcKuDbwFPKNO6AvuxfGnvtq0k6x6GFsxPfipcipdAHBXZSlofo61R5Lua7tWCxehRoPzRLFn0Pwge0E/FFXhwlGuUybWoxxLsN042tYVk7S4zTNnnOcJIlH8jLOmjUr3q/iRNzkqNA6vaaKCUP64tJlf8Nl/tUo0Oi0rVp26pYj4izf3+DTifiyz77qlyRIkp2+dRyJ9/cMx/45ozEK2mF76SE4e7MdYwvHYbTK6/pUNx1GfyOmC3JohtbOu2iIRf1wj+9U+M1FuCVF2zAm+Cf6C1vwa2OFZu0Ui6LCQpTo5EzrgKsWgk19Ea62jUSLA3WSFfWwoKlvS9exhqfB1Y2HrsgbjVd3zYROHIopGj7nOO0T933x5Zdf5sIxwVAAMWXHpjKQ+LCRpfguOBon7ToVf39nNbLJTkV2EevRG5uDJajKgJmukJ3+cEzHC4HDsEfsDy0h5Q/A18Hx2CjJ9UXV5AnT2bjJfx4arL01ed5FIye/GE8FZuNlz/SUbcOr9nm4wjsf1db+mrVTLArzcnCi9zZMcz+CSn+0NKnEo7aNjEV9McbzDGZ4/5mQ1n6lQi2G6LbDr3IXnl09DsV1/gux1Hog+z3dxhKniXR3qnBUYr9BRZjSr4D9/NOW5NVH00rh6lDG9V5KmskQkuVd6Czdc80Y0s2OUiVRSU1EYyh2T1s2aIs8qzEcbygnrCSfX2z74e3g/qgzKgX00whBr8M222jsRiH21qdXd6FYWE3ypCHpRkr+ipeza5/AZ+J1KN32AdSEJ8xkDtmTBcHpFDSN8Py5k/H12nLYlGzPbGKydQ9Guj+H8ec/gDnXIBPopqvCZN0aiDUkRgZAKxwxujv7lwzsgh95qIfPRW3d5IejdMi2pjp8/qCEaqcX3XMjip0niVBmejomzBAlOSJLmCmrp4dBueNMOmMxNsWhU/WEUPZ1VwkY5DEledUtRG8z6WGHEzoXbxSS7mSfKuB06un2yCTd1LXGMLESlxreRdXGDQAyQzxOrfsMN4lPYdX22QAORTZyl+tujDevxB+b/wEMuyhtHuQeO30Ce4jLt8oe8WQzMLAJBv06GOsowi79vI8zDGtwtOEbmNbuBoafj3SHvKnPmB5EHurgrRoK2OMr/B80yJ1fJF/8vbLbok/NUqw2n4/NZQOy9hqUKfBpaw1iMplw/fXXs1dOauyky5H7r5tcezPGTjpRLs6vV/kGoWUCenmsBHzutDrvDh1ZiumDimCO8Dglk6PrFuE/pgdQvOdbTdspFmN163Cx4X0U7FqclPUlw0YT9OtYlQJvQ/xhRUGjLB51XnWvDSar7PU1B2QPZzqOJY4MF48ahAKga2trExIIncmoaSdTvpy8YfVWAQF/RthJZ5JvEII/m8WjnDARjCIe+XkXm6AiuuFzpeX1Kfww2EhFsNUnGTZyQ44R9roa4v+ykHj0J0k8SnJP7nQcSxwZLh41iM/nw+OPP85eOamxk72wB3ySAD2CQGNZRtgpdIMwBOQLdzYSEGTxKEURj/y8i41kULKU/e60tJO5RI7xzXNvT8r6kmEjj14Wjz53AuIUTTb2Iqg8K2G2y+LRBnk96TiWODI85pHDiUJprhUHeh9AaffeeCNH/RIyyUCvTFtns3gMKp5HyZe9NugKQUHJhPenZ+2qnN5yZdPiQBngaQCUcyGd8ZJ4DJKej9/zqA/NSqh8bQiJRxP8ygMcr++YrnDPI4cTo8vMDqkEWzOovbdglr0LpmD2CqdgyIMWxfPIaYMIz2M60rNHT5RLOexn3961yAR8iufRnwDPo16Us631AXWPr9XRVNLc46xTdV0cdeHiUaPwAOLU2qlbrnyzrGjwwB9Iz/IkLe1kELl4DHvQAtE9aPy8i4FBtptOsVu62anIbsJGyIXha7b9lpR1qm0jv14WfAHypMaJ0aSIx6C6xbptZjNckmwXV31NWo4ljgyftk4yFNsRCLRf6PeKK65gQcRud3o+6SeLeO0kCELUvqqFNpGVwwgEJZQ3eFJSWy9RiKKIG264Ab+u+FH+PZjFYyrsQfPEtBMntnjUBzxpaScqd/SbZTI2N5RgmK676sWGkmEjv1KbEQmozSgY5fNCCKobe0jX1AZYYIEX7oZa5PdKv7HEySLxSILtvffeY/+WLVuG7du3s4vJiBEjcPbZZ+PCCy9kIkJN6urqUFFRAY+n/ZghEkN+vx8Gg4H3/EyCnehCX1RUhJwceVordJErcYjYXevG3rr0Fo/BYBCbNm2CQZm2NiObxaPiQYsy/Rqy04ABA6DX80mZSHSK6KZpzXS109Lu8/DFn2W4Uz8U41VeVzJsFBDka1IwAeV1BKN8Xhgk9dsEunW03bVwO+vSdixxskQ8bty4ESeeeCLsdjsOOuggzJkzh5UHeP/993HJJZfgo48+YsJSrebsJBx37tzJ1k8ihTxdba2LTigSmvRZfkJBNTuR+KQHCxoLdHyISAH5zFmT4BCN6J6nfts8NaF9pB70p5x+OvtdhE8uPyRkxekfw4PmjmknqjtHDxScJnRGWagIAW/a2mlgiZ2Jx/VlCSht0w7JsFG4sHdCxKO8jQZJ/axnliUepBJD9Wk7ljhZIh4dDgf+/e9/46yzzoLNJntfiAceeAAzZ87EBx98gDfeeAMnnXSSKusngUPCsVevXh0SqCSKyJtmNpu5eFTZThaLhY2PHTt2sOMUKR5H9kj/NmaRmK2Opl98jYCQWfvXEXSKh0UXUN/DkknoTaFpzfTMtiYOGd4NRTYRk/rlIxOQFEGvS0B5HcFkTpp49OoSWGKIkzKywk/cs2dP5mGMFI4E/X7llVeyn7/55htV1k1PVjRVnZuby6egNQoJejo+dJwyud6YxWJltSsJn7MW2YhO8TymswhKBfqQ51HlhAo1mdSvABfMGIDxfTJDPIbqtur98VdP8JeMwqGe+3CpcAvUxqdMtwc8XDymM1khHtsilCxBHiw1CCXHREvKaAu1tifTSJSdQsenI8lM6SiOi4uLYTMbUQv5AcpZF39Ls3REr2SVClGyrUN2Uit8JRPsZgh6uJ06QFLGklLYW5+ArjAmix3rpN7YElS/b7lfKTEU9DTwsZTGZL1CefbZZ5khDj20/Sbt5JmKTHihWMaWy2n6lIQIebBoWtXr9TZrvUTLIqGLC/1ruTx04Yn2eaJlO6dYy2l7aFkilnd027uyvCv7RFC8I0HfGc8+hd6nBByi5XEjkUpJVS2X07Gm72mZCBWKa6XPR0JlKUKxlpFQvA9tS+Ry+nv6PAna0HZFLqdlkWK35dgLQdtNnnfalqOk+1DpMeItUx/YA4G03ic6Jp09TpUF4zHPewP6lPTGrR5Pq30677zzwmMhXfYpKcdJrzxkBz1sOSUZ0nuxrntpsU8qH6fQWApdmxK9TzU5Q/GC/xDAPBHj4twnPeT98PqDzbZTjeP0fv6Z+L+tB+G4nOkYbzLh4osvDs/QJfs4cbpOVovHp556Ch9//DFmzZqFI488st3PL1iwALfffnur5Q8++CCLuyPGjx/PEnLoe1euXMmmxqdPnw6n08ni66qrq5udcDRdSp+heLvIE4WWUfzd3r17mw18EpU08Pfs2dNsG0pLS9kJVV5e3uxk6969O1tfVVVVs5OnpKSEbRMli0RenAoLC9HQ0ID6+qbq2FarFXl5eUws09+EoFhB+tfRfSooKGB20uI+0XbS361YsQIzZszAa6+9xhKtQsyePRsTJkzAM88802x7Tj/9dAwaNIiNgciL0fz585kd7r333mb7RIHhtB7WLlCBLohUroKyDil4PNIuJPpWrVrFkrtCDBw4EPPmzcN3333XLNyi5dgLsf/++7N9/f3331Hms8MJE/715H9wzjEHpe0+HXDAASxeubPH6f2vf8J3wdHotqMe4r33ZsQ+JeM4lQes+Nz7PBwmHZYGAnj99dexbt26tN6ndD9O6/2F+M5/Dg40F2N0nPs0YeJEXCa8BUMwgHvvXarqPn3r7Y9NwRHQ/e9XnDhzEj7//HMsXbo06cfphRdeaLaNnM6hk9KoI/lVV13VoVI3IS677DIMHjw46nuUJHP88cejR48e+OGHH5gg6YrnsXfv3igrKwsnWrR8CqLPUzJG//79mXjsiDeODgkJLBJPLeGexyZPItmM7NStWzdml3g8j1QncsuWLejTpw9LbsokTwl97v7772fxvSc8swLr9jbguTPHY8bQbmm7T131aH386w7Mf2UVxvTMwesXTmm2T7S/Dz30EKsdSg9F6bJPyThOq3fV4YQnl6E0R8Q3V+3PxAHZKZQhm477pOZxoutJaCzR9USNffpo9R5c8fpq7NO/AK+cPyWufaqr3IPip8awn13X7oFe0Kt2nG5+9w+8vmIXLps1AJccMKDVWErWcSLnBglVErWRiZKcDPQ8Pvnkk2hs7HiQLZXniSYeqTQPvUei46uvvuqQcCRocEcrJxBteSiGjgZtSCASsbKCI5c3m06I8flYsTTRlkeuP57lHdn2eJZ3dZ9CwjGefaJXej8UQxmr60Gs5bHKTERbTuuJtpy2IdpyutBFq0NK2xot5jNWfC1t+1FYgnMMy2DffjKEEadlxD51ZrndIm+LNyA1266WP4fGSzrsUzKOU45Vnllx+4Mduu51dNszdeyFxAqtP3SNSfQ+5ZoNKEQtLG5f3PtkycnHQv9B8MKI0wQBomhS7TgNkjbjDOEbdK+eDGBA+PMt/yZV5xMnAxNmaOoxMh6pvX/ksm7Jhx9+yDyOFCu3ePFiVpyUk1zOPfdcdiGh6eTOeJJjsXv3bhZfRA8BNC0+dOhQ3H333RmdOd1VRgf/xKmGxbCUNU3vZBODSxy485iRuOyg6DMSnOjYg7X4P8MTuN3/MDeRRiiu/RXLzfNxV238HVpMZjtu9p+HO/xnwhNUN2FseMNS3Gl8HkPKPlF1PRx1SSvPY7yQcDzhhBNY7B0JR4rZ0CqZWjCV4g4pBoXEI8UsvvPOO5g7d26Xv6+mpgaHH344Cw047rjjmKeZ4mFuvvlm1k2Ivj/bs2dp/yleiF435u2HFVUiBudMxihkH6W5ZpwxrV+7duI0x2oATjJ8i6CkgysQ4HZqh2SMJcqQJhIReWYUmraTkmbUpN4xCB8FpsAjDsYofs6lLWnleYwHCqYl4Zifn8+EY6xYSC1AUxPklcvE7jKvvvoqCz2gGBfav//85z9d/i76+/vuu4+1m3zsscfw5ptvsviZ77//HqeccgrrGrRo0SJkOzQ9Q4Hm9LqrZD88GjgOa0Q5vokT3U6c5lgchbjXdwpu9Z8Ff1DP7dSJc041Ssegv3shjtE/EvdXkcgtMTSiG6rg9ambhby7+0G4xHc5Fjvm8HMujckKz+OaNWuYV4qmSGkq+7///W+rz/Tr14/1udYC9CRJU/QUaJ1pXhASixSvcu2117Jsui+//BJbt25F3759O/1dlLBEYpRCDy666KLwcrIZiUgSjk8//TROPfVUZDMUYE4Zivvttx9yLXJcUK2LT+m3ZSdeZ7U5JrMFz+JYeANBXOD2YeXPP3I7pXgsWUUjJOjh9DYllcTDYuGvsBnc2F49ASgYCbWwmmR7OL1yQgw/59KTrBCPVAImFFsXyxNFaf1aEo80vUvlbjJJPP7xxx/48ccfWVkkSlY688wzmXh87rnncNttt3X6+8jDSMf14IMPbmUnEqMU+7hkyRKWmRct8DpboP2nqfxp06ahwOTHUN02OKop8Wx0qjdNs3bi4rE1VlGA1xlEvcvL7aSBsWQxydc0ty+IYFCCXh/fvcKnowdLN/ye1n3fE4mVbbcEv9fFz7k0JivEI3kbtViRiLbJ5Wvd0YSyrWk5PVFqYeraYhQSImJDU9RnnHEGe6XEJarnReLxlltu6fS+btiwgb3GCkGg5WvXrmWeTZ4YJdPXtRafitdj165eAE6O63hysovRhp3w6irhaRiW6k3hKHGojxofhhUeuJ3TYbXnxWUXH+RZCRJ1atKjahm2mM/Glr39ATTVd+SkF1khHrUKCcQRt3wKrfPHHYeFpxq6CmU+v/TSS6ye1rHHHsuW0bQ8hRMsXLgQX3zxRYe6/EQSKgYeq0ZXaHlk0fBsR3TIfX2tgYZUbwonzVjg/wd6ibuwspyLRy1gNhpxuP4nGHRBVDXUxi8eyfMoAQGfW/UQCMIYVHc9HHVJvVuLkxW8++67rEPBSSedFO7GQ9DUNRFP4gynbcijS10Y6NXsKGTLrFIDub656WLYidMaj16+6fvdDdxOnTjn1IIKebsgV+XwOJu6Z8U3bU2eR3VFncEsZ4mLkpufc2kM9zymeDqYvHrpsJ3xEhKHIbEY4qCDDkLPnj2ZuKTSPVRGqaNQyz0isu1gJKHe49QuK5uh4rnUvouw5sq9wE3wAz4nYLKleOu0aSdOa3yKeJR8TsyZcwo3kQbGkltnhgMueF3xzyT4FfEY8MVfe7ctTGb5mmOW5N7y/JxLT/gjdgqhOEKaDm75z2zQw+tsYK/R3k/2v3jjHamUzmeffRZOTAp176B/lMiyc+dOlvhC09edgeqoEZE9diNZv349KwVBLQezGQoZoLJF9JqTkwevJD8MSM7KVG+aZu3EiWIfwSq/Omu5nTQylkg8El5X/J7HgCIeg2pPW1sVzyM8/JxLY7h41ChOpxOZwvPPP8+SgKhsBXWCafnvrLPO6tLU9dSpU5k4pHjJlglRlCRDyTLTp0/P+sxZsv3KlSvZa57NhGo4mI2cNeUJPMqZZSdOa/wG2WMUdDdwO2lkLHnC4jEBnke9XJMyqLLn0WyVrz9GBJhQ5edcesKnrTmqQqKOsqnJy/jCCy/EzHom7+EPP/yAn3/+GZMmTerQd1NCDE15vPHGG6zv+cUXXxxe5w03yC27LrjgggTuTfpjNgrYBge6oQaN1WWwRW+2wuG0ImiUPY+Sl4SK/DMntfj0IhAEAu74xWMw7HlUVzxarE0Jjr4EbDcnNXDPI0dVvvrqK2zevBkzZsxos1zOOeec0yXv44033ojevXuzkj8nnngirr/+euy7776sEPzs2bNZpxlOc+r1cgyoq7aMm4bTYYJGJT7Ww2/4WotDDXiobmt8BBTPo+RXd9rabBHhU0JnPE4+ltIVLh41CHnpHA5HRhQID4nB9gqwU39ri8XCRJ/L1bE6Y2Qf6k9OhcdJfFKngoceegiVlZW48847mUcyE2wYLxRXSrGmoULplcZSbA52Q4OPZ1u3ZSdOc/QW+aFD763ndurkOacWfiEkHuMPcwqGxaPKCTMRWeJ+j5OPpTSFT1trWDxmAq+88gr715Ep6M7GeYbsRP94qZ/YUIcLKpQf4oWSa7BkQyX+WTQO6jUhS387cZqjt8plnkzeauzH7aSJseQ3KBnw3vg9j0EhJB7V7W1N120SjzlwsrJA/JxLT7jnUYNQkDV5z3jgPrdTIvB6vSyTnV6JPKt8k6hqVPcmke524jTH4Chmr6K3httJI2MpoGTABxPgeQxQ/GQSPI+RWeLOBj6W0hUuHjVKqBc3h9spXiiBaOPGjeGM9IHFdozplYtcixwgz4luJ05zzLmy59EWqON26uQ5pxaSUfY86nzxex6XdDsdJ3puwW+FR0BtvDpZqPpcDXwspSl82pqjubI+W7Zsafdz1OJwzJgxSdmmTOPKQ4awfxxOZ7DkdmOv9mAddztoBEmZttZRwf84qbP3x8+SDpMNciMBNfFSok8gMVninNTAxSNHc+Lxm2++afdz/fr14+KRw0kijvwS9pqPetQFTXB5AxBlBxInRVTljMDbO6bDLw7B5Di/y2SQJyK9fvXrnC6yn4E95RWYmzsaQPvOAo724OJRg1BAMbXUy8ZM4a+//rrDn6UpoWy1U2eD96lsEb1yuJ26iqOkL2b4HkVZwIZuNgGTFnyNh08Zj6PGdOfDKkXn3K4eh2DBqt443tETJ8X5XX2ca3CO8CV6VU8EMAJqstE+Ed/vrcSRxmJ+bUpT+N1Eg5AYstl4z2Fup8RA5UImTJiQoG/LXLid2kZvMMJQ0Bvu8ga84Z8Pt0GE2/gmAC4eUzWWrCa5FBB5geNlYN2PON34EpZVVVPlXSRjuz1+CROm8GtTOsITZjQIZVmXlZXxbGtup4RAGZ+PPfYYzyLmdoqbPgVydu+Z3hvwhO5E9O8/KP4vzUCSdc6ZDXqY4EPQHX9v69qcIXgvMA1bxKFQm0GBTThG/x1MZav4tSlN4Z5HjeL3+1O9CWkBt1PHpvfLy8t5FjG3U9wcoV+G2caPsTgwHtv7HAuDmc+QpPKc61f5P6wzX4J1e4cBWBrXd+0unYXbfKU4Kqc7Toa6TG/8HPubXsfPuxvwW3kRvzalIVw8cjgcDqdDTLBVYLDwHTySCc5BZ3GrpRijWfYEG4PxtxQ0GZSpZJ/6CTPV1gH4X9koNBhLyQWg+vo4iYdPW3M4HA6nQ/Tbby7Wm0fhueBsHD++B7dainH3mIox7qdxkeWBuL+Lsq0FBBBUubc18UeP43GG70YszZ+j+ro46sA9jxpNmCkoKOBZxNxOCcFoNOL0009nrxxup7jGUulwDLzmGzy+eQvy7HKNQU7qzjmLxYI62GD3xV9xov+ej7HRfDVW7x0L4FuoicXYlDBzHr82pSVcPGpUPJrNcvsmDrdTvOj1egwaxBMbuJ0Sg14w8PGkkXMulLXs9MWfba03yEU7DZI3advt9gf5WEpT+LS1RrOtd+/ezbOtuZ0S1upywYIFvOUltxMfTxl2zlnhwgLD07jT/0/K0onruwSjLB71QfVjEAdXf4tV4vk4f8uV/NqUpnDPo0bh/XW5nRKJ2iVDMgVuJ26ndBpLFoMepxoWs58DPjcEU9dDCQSTOWmeR9GoR67OiXJ/A7x+fm1KR7jnkZMUqF81Tce3/EfF0KlH9e23346Ghq73OSVP7XnnnYfu3buzKf+hQ4fi7rvvhs/nS+h+cDgcjlaw2Bzhn12N8dV6NCieR4Ok/jVTEO3s1ZSALHFOauCeR05SGThwIObNm9esFtrHH3+M2267DZ988gm+++471p2hM+zZswdTp07Fjh07cNxxx2Hw4MGsP/bNN9+MZcuW4Z133uHJRxwOJ+MwiyZ4JANEnR9uZx3sSv/xriAYZa+lMQni0WBRxKPkUn1dHHXg4lGDkEeuuLg4IwUPBZGTUIyE4oKmTZuGH3/8kYm+WbNmdcpO559/PrZv347HH38cF198cViYnnbaaVi0aBH7d+qppyJboYzP+fPn82xrbic+njLsnKNroBsiRPjhdXZ95oYwiMq0NdQXj6IiHsWgB/Mv4demdIRPW2uUznrf0hlRFHHggQeynysqKjr1t06nE6+++ioGDBiAiy66qNlF9d5772U/P/3008hmyBa5ubkZ+TCSSLiduJ3ScSy5dLLo87rim7Y2KtPWpiR4Ho2KeDTDza9NaQoXjxqEvGY0FZstSTMUWP7111+zC+24ceM6/Hdkn48++oh5Lg855JBWF+q+ffuy2MclS5YgEIi/lEU625eENE8G4Xbi4ynzzjlPWDzG53kU7AV42X8Q3kDHZn7iwWzNYa8WeHDvggX82pSG8GlrLeBtbP57MAidzykv18fQ94IICMrhC/iBgAfQ6emRLvb3dgTBRMEvynYEqKgbEsmGDRvC09Yk/sjT+Omnn2Lnzp34xz/+gSFDhnTq+zZv3sxeKc4xGrR87dq12Lp1K/NOcjgcTibhJfEoAX53F673EXTr3hun3PEmBL363lKLVfY8EgbenjAt4eJRC9zTvM0XycXu7f3NSc8DI4+Tf17zPvD62UDf/YBzPmz6zD9HA87Kzm3LkfcDUy6Qf976PdB/fySSjRs3sszqlhx99NE4+OCDO/19dXV17JWmiKKRkyM/4dbW1nb6uzkcDkfrePVmIBi/eKSZGyFJkS1mqx3lUg7ckgidPntnhdIZPm3NSSqHHXYY8ziG/pHn8d1338Xq1asxffp0LF26lB8RDofD6SA+QZ62DnjiE4/JxGoWMdnzBPb3Pox6NHkhOekD9zxqgRt3Nfs1JKxCtRBjTluHGDZb/g6ato7k8t+6Nm0dou++UJvCwkLMmTMHVquVxS1SeZ3PP/+8Q39LtunVq1ebnsX2PJPZgMlkwvXXX89eOdxOfDxl1jnnF+RQpaAnvpjHZEJT46JBz5riXDj/Un5tSkO4eNQCJlvz3yUJAb8fBoOBFFL7f0+xj6H4x7a+t7MkON6xLfbZZx/2+tNPP3Xq70JxjOvXr4/6Pi2nC3ifPn2QrdCDCInroqIinnHN7cTHU4adc4GQeOxKjHsK+f32w0DhlTT7FHKWcNIHPm2tQULFs7Ml25qorq4O9/XuKGQfKjpO4pC8lS3tRUkylCxD0+FMiGcp1GWHamDybjvcTnw8Zd45FzBY2avkS6+C2wZBz69NaQwXjxxN8OCDD7LXGTNmdOrvHA4H5s6di02bNuHJJ58MLychecMNN7CfL7hASQDicDicDKPCPhhfBsaj3Ngz1ZvCySKy1x3DSQmRpXqIqqoqVodxxYoVyM/Px3333dfp71ywYAGrE3nJJZfgiy++YF1sqFMNdayZPXs2TjnllATvBYfD4WiD33uciCfXT8B5uf1xVKo3hpM1cPGoUTI1/qNlqR7qLkNJL9TKiwLMOxubSHYqLS1lWdqUbPPhhx/i/fffZwXC77zzTlx77bUZa8vOwJNluJ34eMrMc85qlG/jLl96lrzh16b0RCdlU2BdgqFMXsripcDoUD3BlrjdblbIun///jCb5ZIKHO3BjxOHw0lHnvp2I+75aA2OG9cDD50yPtWbk1H3b05seMyjBiE9T2KG63pup0RASUgULtCZZKRshNuJ2ykdx9Lwik+xRjwL5267DukGP+fSFy4eNQiJRooF5OKR2ykRUMbnyy+/zLOtuZ34eMrAc85oNMGs88EYcCLd4Nem9IXHPHI0x/PPP48tW7a0+zkqLk7xjhwOh5Ot1PaaielLHsaQku54LtUbw8kauHjkaFI8UrZ0e1ByzeGHH56UbeJwOBwtYrLmYCeKkeeX6z1yOMmAi0eNks1FransTkfjZag7AadtKNu8uLiYZ51zOyUEPp60ZSOrUe4E5vKmX7Y1H0vpC8+2jgOebZ058GxrDoeTjvy2oxbHPbYE/Yts+PzKA1K9OWkDz7aOD54wo0EoUaaxsZEnzHA7JYRAIMCKsNMrh9uJj6fMOudG9czBhnuOTEvhyK9N6QsXjxoVj1R7imdbczslAr/fzwqn0yuH24mPp8w659K5CQK/NqUvXDwmCS4EtQ0/PhwOh8PhdIysEY9Uc+u4447DwIED4XA4YLfbMXLkSFxxxRXYuXOnausVBDmYORn1vjhdJ3R8QseLw+FwOBxOdLImpXfRokVYv349pk6diu7duzNP0y+//IKHH36YlYb57rvvmJhMNEajkfVvpmloEq0dnWKgv+Ekx06hMAH6LjpemQaNOXpoSufprWTA7cTtxMcSP+c4HbwOZEtva8qmjdZb+j//+Q/OP/98nHjiiXj99ddVydaiz5F3k7yd9HkSKPxGnnpo6JPHkY5fQ0MDevbsyXuccjgcThbAs63jI2vEY3sDaNy4cVi5cqVqjdXps1ST0OPxtPu9dEjoc+QJ4yJTfTvR3xcVFWWscKSgdPKs77ffflldP7Q9uJ24nfhYyp5zjovH+Mj6O8mHH37IDDFq1CioCQkT+keervbKN3i9Xjz11FO48MILYTKZVN2udCYRdqIYx0ycqo6Exht17Jk2bRoXj9xOfDzxc04z8GtT+pJ14vG1117DH3/8AafTid9//x2ffvop+vfvjzvuuCMp6yeh0p5YIS8a1XkkjxiPfeR24nA4HA5HS2SleHzzzTfDv0+aNIkl05CAbA+aIo2cdia3d8vler2eiUPyMFL7vEgPF7nlyVsWGSlAy+i9yOWR62g5zR2Kl6TPR0Ket1AMXyQkPmk7IpfT39Pn6akvsg5ZaDkti/SOJmKfQttO35WofQp9D71myj6pcZxCf9tyW9J5n9Q4TpHjKVP2SY3jFCJyvem+T4k+TpFjKVP2SY3jFCLye5K5T5wsiXm86qqrOhQzGOKyyy7D4MGDo75XU1PDYhxvuukm5ol86623MGvWrDa/77bbbsPtt9/eavn1118fTsYZP3485syZg/fee69ZDOUBBxyAmTNnYuHChdi4cWN4+ezZszFhwgQ89thjKC8vDy+n7Ni5c+fi/vvvbzbI58+fz+Is77333lbbQLGXjz/+eHgZnWg33HADNmzYwEoVhaCeq5dccgnrgECFbCPXOW/ePNZbmqY5QyRqn04//XQMGjQICxYs4PuUxONE8UTkyaYxv3nzZn6c+PkU19ij69Kzzz6LPXv28GsEv+7FdS2/+uqr8eqrrza7fyTr/vTAAw+w9XckZ4GT5uKRspXpJthRFi9ezAZPW5D3cOjQoexJhG6sbU0pR/M89u7dG2VlZeHBp7Unu0x8WuX7xI8TH3v8fOLXCH4tj+f+RAmsJFS5eMwC8agWVDz8nXfeYR7I4cOHpzxbi06Cjz/+GEcccUTGJ3PEA7cTtxMfT/y80yL82qR9O/Fs6/jImg4zbbFr1y72qhWhRk9P5H6PfIricDvx8cTPOy3Ar0/cRnwscbJCPNbX12Pt2rVR36PYnWXLlrHYSIrH43A4HA6Hw+FkebZ1ZWUlm46mzOphw4axTiLV1dX46aefWFAuTTm/8MILnf7e0Ix/KOs6UVBMIHXEoe/lpXq4nfh4Sg78vON24mMpe8650H2bR+51jayIeaQkm3/84x8sS2vdunVMTFIyRr9+/XDooYfiyiuvRK9evTr9vTt27GAJMxwOh8PhcNKP7du3d+n+n+1khXhUM/aH4iUdDkdC2wiGsrhpUPMSAtxOfDwlB37ecTvxsZQ95xxJHwpp69GjB8vY5nSOrJi2VgsacGo+sYRaGnK4nfh4Sh78vON24mMpO845qpbC6RpcbnM4HA6Hw+FwOgwXjxwOh8PhcDicDsPFowahrLNbb72VZ1pzO/HxxM87zcGvT9xGfCxxeMIMh8PhcDgcDqfDcM8jh8PhcDgcDqfDcPHI4XA4HA6Hw+kwXDxyOBwOh8PhcDoMF48cDofD4XA4nA7DxaOGoF7bRx55JPLy8mCz2TB16lS89tpryGQWLlyIiy66iPUdpyxO6tTz/PPPt9mRgNpJ9u3bl32eWkxec801aGhoiNkF6JFHHsHo0aNhsVhQXFyMU089FZs2bUK6sHPnTvzzn/9krTT79OnDWmuWlpbihBNOwNKlS6P+TTbaiXrk0j7PmDGDdY0wm83MTtOnT8dzzz0Hn8/X6m+y0U7RuO+++9i5R/9+/PHHVu9no51oH0M2aflv5syZUfs033HHHRg8eDAbezQGL7zwQpSVlcVcx8svv4wpU6aw631+fj6OPvporFixAunI22+/jUMOOQSFhYVs//v378+OOXWPyfaxlJFQe0JO6vnqq68ko9EoORwO6YILLpCuvPJKqW/fvtQ6Urr//vulTCW0j0VFReGfn3vuuaifbWhokMaNG8c+c+ihh0rXXXcde6XfJ0+eLLlcrlZ/c/7557P3R44cKV177bXSvHnzJJPJJBUUFEjr1q2T0gHaT9qHgQMHSuedd550/fXXSyeccIIkCIKk1+ulRYsWNft8ttqpvLxcMpvN0owZM9j+3HDDDdLFF18cHldkg0AgIGW7nVry22+/SaIoSjabje3bDz/80Oz9bLUTjZvc3Fzp1ltvbfWv5TWKxtVhhx3G9nnq1KnMRscff7yk0+mkAQMGSGVlZa2+/6677mKfp/XQ9Z6u+3T9p2Px3XffSelCMBiULrzwwvA16pJLLmH7f8YZZ0h9+vSR/ve//0nZPpYyES4eNYDP52MnHV00Vq5cGV5eU1MjDRkyhJ0oW7ZskTKRzz//PLxvCxYsaFM83nLLLex9uuBEE1f33HNPK0FOy0lMeDye8PKPPvoofPFKB958803p66+/brX822+/ZQ8c+fn5ktvtlrLdTnQDj9z+yPNr5syZbF8++OADKdvtFInX65UmTJgg7bPPPuymHE08ZqudSNTRv47w7LPPsn079dRTmZgK8fjjj7PlJK4iIdFjMBjY9Z2u8yHo+k/3geHDhzd70NEy//znP9k+kmj0+/1Rz79sH0uZCBePGuDTTz9lJ8I555zT6r3nn3+evXf77bdLmU5b4pEuyD169JDsdjt7eo2Efqfl9IQfCV3I6fu++eabVt8XEhNbt26V0pnQU/tPP/3Efud2is7DDz/M7EQ3Om6nJsiLRmLl999/l84666xW4jGbx1NnxOO0adPYfrV8yCf7kX3Iq+t0OsPLyStOn3/hhRdafdfZZ58d035ag/aJHl5pHyNFYjSyeSxlIjzmUQN8/fXX7JVi2lpy2GGHsddvvvkG2cz69euxa9cuFr9G8UGR0O+0nGJgIuNryK6h9zLVrkajkb0aDAb2yu0UPWbqk08+YT+PGjWK20mBYuvuvvtu1s1qxIgRUcdXto8nimOkGOx77rkHjz76aNQYY4q1peVDhw5lcXyRUHwkxQE2Njbi559/zrhr/meffYbq6moce+yxCAQCeOutt3DvvffiiSeewIYNG5p9NtvHUqYh33E4KYVOKoICrVtCAf92uz38mWylLRuFln/66afsc71792YX6927dzOxIAhC1M9Hfm86sm3bNnzxxRfo3r07CyYnuJ0Ar9fLbvY0s1JZWYkvv/wSa9aswTnnnIODDjqI20kRRWeeeSbGjRuHa6+9NuYYy/bxtGfPHjZuIpk8eTL++9//YuDAgez3jRs3sgeUtmwU2uf9998//DNd1+n63tbntc7y5cvZKx3rMWPGYN26deH39Ho9rrjiCtx///3s92wfS5kGF48aoLa2lr3m5uZGfT8nJyf8mWylIzaK/FxnP59uUObwGWecwUQAZcqGLq7cTrJ4vP3225t5f66++mosWLAgvCzb7XTLLbewmy7d/KPdmENks51INJLYI/FCQo+E0YMPPoiXXnqJPYT89ttvcDgcXdpn+rmkpKTDn9cqoUxyssuECROwbNkyDB8+HCtXrmSZ5g888AAT2fPnz8/qsZSJ8GlrDifNIC/H2WefjW+//RYXXHABE5GcJuhGT15HmkajKbB///vfeOaZZ1h5FSoTku388MMPzBt08803h6fxOa2h6fxZs2YxkWe1WpmX9sUXX2Tn29atW/H0009nvdnoWkRQ+bB33nmHeWXp/CPR/frrrzPvIwlITubBxaMGCD1ZxXqCohterKevbKEjNor8XGc/n04X63PPPRevvPIK5s2bx2KLIuF2aoJuXL169WJej6eeegpLlixhMX7ZbCe/34+zzjqLTTFef/317X4+W+3UFlSXlqDx1NV9pp8zwUahbaQ6vVTXMhJ6MBkwYACb1q+pqeFjKcPg4lEDtBW7QTE3VDw1VpxIttBefEvLeBoKsqZYwM2bNzMPVHufTxfhSFNpL7zwAiuSS4H8JJAi4XaKTigxIZSokK12omsJbesvv/zCvEWRha9pXBHTpk1jv5MnKVvt1BZFRUXslWLyCBJIdB521Eahn+lY0PW9I5/XKpQkRFBji2iElrtcLj6WMgwuHjXAAQccEM5cawkFEEd+JluhCyk92dLTfuiiHYJ+p+XU0YACrUOQzULvxbIrdSNJJ+FI02Zz585lcVexgsiz2U6xoCzPyOz0bLUTdfQ477zzov4LiZU5c+aw36nzR7baqS1CGddkH4K6nlCXmLVr17Lp7EgofOLzzz9nopq8c5l2zT/wwAPZ659//hk1LpsyrmnfqSsMH0sZRqprBXHkIqpU36qtIuGbN2/OeFPxIuHRoWLBoRp8J510Urv11LK1EC/VKmxsbGy1nJYdfvjhbF/uvvtuKdvtFItodR6z1U5//vln1LFEy0tLS1vVHuxskfC1a9dmTJHwUK3Zp59+utnyO+64gy2n4vPZPJYyFS4eNUK2tiekCw7dtOgfdbqg/Z0+fXp4WeQFiQrJjh07NnzRoDZ9ka2tIovwxmptRS2zQq2t6AKeLoWcaR+oiO5NN90UtV1a5ENHNtuJzp8jjjhCmj9/Prsh0Y2rsLCQ7dv+++/fbN+z1U6dFY/ZaKfQWDrqqKNY55RrrrlGOuaYY9g1mvaLiny3156QWohSe8L+/ftndHvCDRs2SCUlJWxfyF5XXXWVNGvWrPC+7d69O6vHUqbCxaOGWLp0KfOQ5OTkSBaLRZoyZUqrvsWZRuiGFesfvR8JPaVffvnlUu/evdmFnHqn0sWqrq4u6vfTRZ26i9CFhy7KJCTmzp3LLniZYqNoXXmy0U7UZYduwLQPeXl5zLND+3HggQdKTz75ZFSPbTbaqbPiMRvtRO1ATz75ZGnw4MHsekxjiTyOJCCpI1g0qEXobbfdxlrNkrihz5Pw2bNnT8z1LFy4UJo0aRK73lMf7SOPPFJavny5lG5s27aNdcahfabxQePkL3/5i7R3714p28dSpqKj/1I9dc7hcDgcDofDSQ94wgyHw+FwOBwOp8Nw8cjhcDgcDofD6TBcPHI4HA6Hw+FwOgwXjxwOh8PhcDicDsPFI4fD4XA4HA6nw3DxyOFwOBwOh8PpMFw8cjgcDofD4XA6DBePHA6Hw+FwOJwOw8Ujh8PhcDgcDqfDcPHI4XDSipkzZ0Kn0yFdoCZeEydOxKGHHtqlv7/55pvhcDiwd+/ehG8bh8PhdAUuHjkcTsogEdiZf+nIiy++iBUrVuCOO+7o0t9fddVV0Ov1uPXWWxO+bRwOh9MVeG9rDoeTMm677bZWy/75z3+itrY2qliiz2/btg1OpxPDhg2D1gkGgxg4cCB69+6Nb7/9tsvfQwLy4YcfxsaNG9G3b9+EbiOHw+F0Fi4eORyOpujXrx+2bt3KpnvTnQ8//BBHH300nn76aZx//vld/p6VK1diwoQJbAr7zjvvTOg2cjgcTmfh09YcDiftYx6ff/55toxe33//feyzzz6wWq3o2bMn/v73vzMPIPHCCy9g7NixsFgs6NOnD/7v//4v6jpIuD777LOYPn06cnJy2HdNmjSJLesMzz33HNuuE044odV7u3fvxmWXXYbBgwez7cnLy8Pw4cNx8cUXM89rJOPHj8egQYPY/nE4HE6qMaR6AzgcDidRvP322/jss89w7LHHMuFHnr+77rqLicHc3Fz28zHHHMME6Jtvvolrr70W3bp1w5lnnhn+Dvrs6aefjv/+979M2J122mkwmUz4/PPPcd555+GPP/7A/fff3+620PcsXrwYQ4cORX5+frP3aNqdtm/Lli0skea4446D1+vF5s2b8dJLL+Hqq69m2xvJtGnT2Hvr1q3DkCFD+KDhcDipQ+JwOBwN0bdvX5qvjvn+AQcc0Or95557ji0zGo3SsmXLwsvr6uqkkpISyWq1SqWlpdLGjRvD723btk0ymUzS6NGjm33XU089xb7rnHPOkbxeb3i5x+ORZs+ezd77+eef292P33//nX329NNPb/Xee++9x967/PLLW71XX18vud3uVssffvhh9jfPPvtsu+vmcDgcNeHT1hwOJ2OYN28eJk+eHP6dStxQzCF5+ubPn48BAwaE36Mklv322495Ev1+f3j5o48+CpvNhn//+98wGo3h5eR9vPvuu9nP5JVsjx07drBX8mzGgqarW2K32yGKYqvloe8JfS+Hw+GkCj5tzeFwMoZx48a1Wta9e/c23wsEAqyGIsVHksj87bff0KNHD9x3332tPu/z+djrmjVr2t2WyspK9kqxjC2ZMWMGW/e9996LVatWMYF7wAEHsJjHWCWJCgoK2GtFRUW76+ZwOBw14eKRw+FkDJTc0hKDwdDueyFRWF1dzWIVd+7cidtvvz3mehobG9vdlpBX0e12t3qP4hl//PFH3HLLLSzB56OPPgp7Q6+//npccsklrf7G5XKxV0re4XA4nFTCp605HA5HISQwqSMMichY/ygRpj2Ki4vZa1VVVdT3KdubsqfLy8tZKR7ydFJW+F/+8peo0+Kh7wl9L4fD4aQKLh45HA4nIkaSpo7//PNP1NTUxGWXkSNHss4wa9eubfsirNezKXXK/A6Jxvfee6/V50LfM3r0aH68OBxOSuHikcPhcCL429/+xmIfL7jggqjT01ROh0rstAfFOo4ZMwY///xzuM5kiN9//z1qr+rQMrPZ3Oq9pUuXsmn2fffdlx8vDoeTUnjMI4fD4URw0UUXsXhEKii+ZMkSHHzwwSyBhoQdJcqQiHvllVdYJ5z2oPqN1GaRvi9S9FHNyGuuuYbVeqSajYWFhdi0aRPzOJJwpKnrSBoaGth3HHLIISwTnMPhcFIJ9zxyOBxOBKFONa+++iqbev7ggw/w4IMPMsFHwo4KhJOg7AjUkpC8hQsXLmy2/LDDDmMCsa6uDm+99RYeeugh5qGcO3culi9fzrrZREIFzSlhhoQth8PhpBre25rD4XBU5IwzzmCdbqhfN8VUdoX999+feT4pFlMQhIRvI4fD4XQG7nnkcDgcFaGWiOQ1fOSRR7r0919++SW+++47lo3NhSOHw9ECXDxyOByOivTt25fFT3bV61hbW8umyil+ksPhcLQAn7bmcDgcDofD4XQY7nnkcDgcDofD4XQYLh45HA6Hw+FwOB2Gi0cOh8PhcDgcTofh4pHD4XA4HA6H02G4eORwOBwOh8PhdBguHjkcDofD4XA4HYaLRw6Hw+FwOBxOh+HikcPhcDgcDofTYbh45HA4HA6Hw+Ggo/w/ymWVgbYz8HQAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m================= nnodely Model Results for validation_set ================\u001B[0m\n", + "\u001B[32m| Loss |\u001B[0m\u001B[32m mse |\u001B[0m\u001B[32m FVU |\u001B[0m\u001B[32m AIC |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m lower better |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| curv_error |\u001B[0m\u001B[32m 7.635e-08 |\u001B[0m\u001B[32m 2.847e-04 |\u001B[0m\u001B[32m 1.087e+06 |\u001B[0m\n", + "\u001B[32m|heading_error|\u001B[0m\u001B[32m 1.553e-04 |\u001B[0m\u001B[32m 1.550e-04 |\u001B[0m\u001B[32m 1.092e+06 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| Total |\u001B[0m\u001B[32m 7.771e-05 |\u001B[0m\u001B[32m 2.198e-04 |\u001B[0m\u001B[32m 1.089e+06 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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VeENuXIqrBrkO9OUyQPcouf30F+qnpk6dGlLmseCnn37qdKWi6WCa8g7Fueeey547sSAR59aTwL7y+uuvD7tt4PR+X30s9ScUnJWMPjaj475ramrqVz7fZOhokTIgn1eKDg0H+SIqkE9UT5ToNvKNIH8KxaciFNXV1Z3vaVvyaQrmT0MdHUXar1ixgt145GcVrCMnXw56UbSrQmDULPl49QVtE02kZTxlSr4r5J9EkI9KXwojKQQkp3gQGLlI/qVKGb9QKMdNhGsHpLSRv280kK9pOAJvsgMPPLDPzoB8LslPlyKESUkJdc3oGgTLaNAfojlGUpppcELtcuvWrWwQQdHp8biHE4Ea+5lwxKsPGWjfNhDIZ5UUMlIESSYk51BR9eR72hNqsw899BB7Tz7rpByQ3xy11Wj7EzrvvvqTntcwEZAfIfmGkh8kXR/y1e5Z9Yn83cmHnKB2Fe78ScbkS0oGCVJqSAEhH+dg1zuYzGNBNG2ZjC7UN9L594Uazi0Q+l3lXMn/ls4jHNS307OAjvn3339nvuGhDCTJ7GMPPPBAplzT8ZG//I033siqG5IPeyQkuu9MmPJKqT7CEWhBDGahUkYC9Fm0gU/BnIvJAZqcq6MJROrZwQemGSJrQl9Esk2iZBp47BSU1BeUsiNeBI7yKNgnGkI5jhM0go02iCawcwiGYiEmwlkhA7ch5bXnd6P93WhQfoesvoqFq69jVBSicMrrQO/hRKC2fqYv4tGHxKJv6y8UiEWpdCJJAaX8bk8oUISsMmR1pOAssp7SiwLP6OFODzlKBUhpd/rqT4IFOMX6GvYXOjZFeaNz7am8KsGryrahoGAlCooipa6/Mo8F8WjLajm3nr+htG96doabqSPoczpXMhhR6kJKnUWBbGrrY88++2wWzEmDTkoLRzOL9CLjBhmB6L6j2Z9Qx5jovjNhymtfF7gv+sqVFo6eefE2b97MOkglmpUaFo2C6SFOF4YUHsUK9thjj7E8qsq0UiDK95URWF8ETofFgoHIVIlCT9axx+vaBkJRztHS13cCc4RGIhPKMRrsu9H+bjQovxPpNYv0GAd6DycCNfUzkRDrPiRWfVt/oAcfPewUSNGkTAGkdJJiHPjgVSL4Q/0u5XCkiHCywFJksnJu9KJobDoHcmWiz8ndIpnXsL9QfmjKo0kWNIrA/r//+79uLj6KOwG1C7I8B4Mss5RFRYl4p6l6mrKn6077Jpkr15uy01BmhXjlCY11W1bTuQ3kGRCsjw2lvCazj9Xr9czQQhmKKDc4ZSQh1q5dy16UE5lcXSgTCGXL6GnkUPN9NyDldaDQxVdGLJGOwkJxzz33dN5oNCVFiYRDTdkqaaRCHZNCJJaGQIUx2QTedMk+9kA50oNK8V9SI4oPqyKTcD7HPTv0wO/GE/odulcivWbJOEa1Est+JtLfi+V9GKu+rT8ovn2kgFG6m3nz5oXclooVhENJGUcvsgJRSitSZijVEikpNHVLfuQ//vgj87UM7DMCZUpWMrW2aeo7SBEgazWlcCPffyW2gM6J0goRNHUb6hxuueWWTuWOlA6KgwgFpeJLpbaspnML9wwYTH2sXq9ncSf0ovSC1A5pJocGudQe6XrQ4JHuQ3KBoLRgyeo7oyGpZhdlWpWEE9gQ+oOS35H8jpTcmqGgkX4ohg0b1s0/qS8i2SZRBDY6ZYQVDqUjjQeBU+aJyCc7EAJHm1QNqy8C81AGyjwRx0gjfCWAQm3HqFZi2c9EQqz7kFj1bdFC+1KOjZStcIorKZTRTBOSryflXyZrEPk8kn8cWXSVNt4z32Uq9SeB7gCB+Vz7yu2q+P4reWwpn2845Y7oTxBOstqy2s6tZ4CYYvyh52Jf1l76XHnG0gxbrALV4s2ECROYKwHll6Xjp1gKZZBILiI0ME5m35kyyqvSWVFD6CvCvC9olEvQdFa4SGzy/yM/lVDsuuuune8pCXJfJCqSNRIogTiNsggaWfVltlemF+N5bQnFP1StKInjiVBJ7gOtD2QxIiggjpJsq+0YKUiLpoQIKoYQiY9svAmcOktkFHys+5lIiHUfEqu+LdrroPxuJL6MJNeBTO9SxgUqAqIcH1lf49mfKL8Tj7ZIvoSUyF3x7yRlnBLRK8n2aSBKU+XBIH9gxTLZl8zJSkbbq6Ut0zkqfWMizi2WfQoNCKlAi2J5JetkOOhzRZmj76WC+1Wo6xs4qAp138Wq71QG3rG475Iq8cDRJ9UnH4izsjJqotFEOMHQ74Sqe61YBJQqD6QAhOso6bNYZhoYKOT7plT7IJ+rcHWC+zq3gUJTZUplG/KrUZOFuidUl1nh6aefDhsgQFNdin8UObr35WIQj2Ok6mjh/Bnvv//+znsg8HvJJHD6MdGuNrHsZyIh1n1IrPq2aK9DoBtSuPuXBsnBquVECwVjKmnqep4HBasp9xr5xA5UaVPkEK+2qKTAomAe8humIC7Ff5AszqEGIZHKnLj11lsRb0gRVyLTqQpTuFRO1M+H85GM9bnFuk8J7CupDw3HfffdF/R7qcioUaM63/e872Ldd8byvtMkW+tXnNbJL5JyYpLDfyio46YRT7DodWXURJ0aPdyDQeup5GVfBO6foiKDlSukdX2V3kwGV111Ved7iuYNlmuNZEQPg74edAOBOqrbbrut01pJSrWS+zIU1KFRVDOVrEskVDpTycVJ1ivK1xjMv4tGnuSzRdBIu69cgLGEIrGV6R2yrl100UVBrx8NWJQ2TgEWV1xxBdQA+Uwpke+UQiiR1tdY9jOREss+JJZ9W+CDasmSJWG3pYhk5WHz8ccfd6Z4CoSUMyqFqgRgheL222/v0zpLAU6K8tMzSp+mL5X8zjS9Sf1JX25PdJ8oJYlDyYH8+GimIh7Kq2JlIstWJC4DBCnvSsYTyodLpZt7QgNX6ucTMaNFM3lKH0K/SyWFg7kt0UxfX/1hrM8tmrYcCZQXXVHUqaxzqPLftJ58swnansriqpW///3v7NqEg0psK/S872LddyrXjAb11HekbMAW8cILL7BOnDoamg4dOXIkG8lQsm2y3JGfDN0sJLj//e9/LOcbpbJQ6kgrkDOyMp1KShtNiVNELjUu6pxoyoamImjKhpSAcFOvVJOcahhTh03KDE0NB6tLTgoOpY9Q6vaqAYrmpQciyZUsiJTjlB4ulBKDLBckR/qMGiAFFihTWfGY9iCfJuqkaERODxryc6KHDuULpIcRde7kJ0eWJ5quIKVGueESDdU3J2sZtTXquKj2MnVK5CNE00PUXsiCoihd//znP7tN5ccbuj6UZocsITRqpXrZpEzQQ5LuGZIjTVEqRSqUyPNI82gmArrulHOQLIjU9ijfJ/mKKQ956ihDReyqpZ+JlFj2IbHs2yiim9o5PexpP6Tc0RR2YMAJTRWSHx+5xdA9TDXXST60nh7wdJ1ocEp+qpTEnHxQ6dpSMEionJz0WzSYJb9d6gPoIUnuLNSuST6k2JJlTyFYjXcKXFOuHx0/KdcU3U99HJ07Kcak4JN1kH6PrjdZOCl4qid0znRtCJI/ZVQg/06lH6Rp7YGkQaT2RX3uDz/8wHJlKpZWOu++glfpeiv+oHSfkMJIsqdrRwN8CsqjPpMG3dSn96foQzSQIkIuHfQ7dM2pb6RnDJ0LuQqQHysdE8mOjADUfybi3EiOdA/QfUt9I2XeoPs5MCsC3SeRQgM1as90DqREk6GClGg6Rmpf5EZD97QyiKMofdpezcFa//3vf/Hwww+z5wAVQ6DsDtTf0flRTla6BxQXCRqoBCsAEsu+k+472o6eYeRDTwM52ofyDKBrGuhnHZZoS3JFW14wknJgVFP4tNNO66zJ3tcr1L6o7m6471H5O6p9HliONVjtd8Jut3erd9/zRaXy/vWvf0VUoi6a8rChjkchkt+jMpFKecRQL6pDTPXSlf8feughOR5QzeT7779fTk9Pj+jaUn3nhoaGAZdzDCwPG02Jy8mTJ4c9Piqnescdd4TcR7zKbyosWrRILi8vD3uMJOvnn38+5D6iKasYbVnXvkoNhmsHgeUk1dzPREos+5BY9m1ffvllt9LQPV+B36OyzoGlMEPJicpfhrtH586dG5HMqSToiy++GFKmVK6U+q5wx99TLsGwWq3yxIkTQ34vFvfuCy+80Gu/kfSz1GeeffbZYc9r6tSp8qZNm/rs5wZaHlaB+uRddtkl5PFQ6d7//Oc/3cqiBisPG8tzI6ifC7evaGWhlGzOzc0Nu1/6PFzZ577kkKhnxsiOctF9vfLz8+XPP/887n1ndXV1Z7nhYK9oymOrwsuYRjw0rUIjZtL8aWRP2jiNbGgURaMGqhRBI3eaBlciFoP5yZBTuVJBgkYStB+ydpCPCln2FF+0viDLA40gKYUEjRbIH4tGgnQsZMmk0QqV6VQjJDcawdKoisp/krWDrChk7aQRE42OaFQUmPoiXhYvGlFRWTmKHKVrQLKk6HeSJb3oOlG1EpqaIr8wmhLsK6lzvFCSTpNFjORGx0lyo+lusgSQ1YBS+tx8881IFjSFTKNgsqqStUtp52S5IMs2WawoY4IaXVpo1E+uI2TtowAdst7FqgJZIvuZSIllHxLLvo0sn4rVniwk4fJ3UvuncyDrC1kS6V6gdWQdIasWWZ7ouMKVvySoKhhZl+keov0o95ZyHmQ9JdcCatvhpmFpe+q7yEpHljH6HllwaV/k80/HRZUDqUSykgooGNT2yPpN+9h5553ZecV69ommWwNlS+2MXJL6gu4JkjdZO+lakWzpvOk8yUpJGRqoGlTgtHm8oT6Z2gzFBFB/TTMm1L6pzySrNVlIabYh0edG/RxZ7Om5RsGp0RawCQZZXqndkKWfzpXOna4dLWnmizJ+0Ofhyj6rhcWLF7P+h9zMyFJKugDJm+4Xkjk9Q+h+omdGuHLxseo76b6nWROaXaXnAVmt+/sMkEiD7dc3BSkPPTiVko2kVPT0dxEIBAKBQCBQG0J5HaK0tbWxdC7k+0ojKLJ40ihKIBAIBAKBQM2owm1AEFsouThVsAkFRfTSlJYSNXjuuecKxVUgEAgEAkFKICyvgxBKQUKpsMhfh/yIyC+JfLwo9yv5wFDEpJKShj4jf7lI6zkLBAKBQCAQJBMxTzxIoVQYlKKFXqGg9D2UXqmn4vrhhx/2+3fJeZsctwUCgUAgEEQPpdDrq5JiOCi4bM6cORjMCMvrIIQqQFFOUorCpOh4yn1IeUAp1yBFG1LEOkVnkutAsAjbgUSAU9RhImtSCwQCgUAwmKBn6ECySey9994DzpaidoTldRBC6ScoITq9BAKBQCAQCAYTwvKaRKgiDEX5DyTXmUAgEAgEgsRCWUZplpNyl8ajQqUgPMLyGgVOp5Mlgf/jjz9YkmyaiqdkzZTomyL2KfE4JQCOFFJcKyoqojkEgUAgEAgEKoHKI1MBIEFiEZbXKCDfUVI2qbrE+PHjWX5UiuCn+seUmooCleh9pKMwyrVKyi81frPZjFjhdrtZNRuq2U2VNARCfolGtEEhv2Qi2p+QYbxpb29n+gBl7qEKbYLEIiyvUUAlVEnh7KkQer1eHHDAASw6kJTXSMvGKa4CpLjGUnl1uVyd7ghUjlIg5JdoRBsU8ksmov0JGSYK4fKXHISjRjTC0miCWjKpMhVZOQlyJxAIBAKBQCAQxAehvMYo8OrLL79k73fYYYdY7FIgEAgEAoFAEAThNtBPf6p77rmHRRs2NTXh22+/xdq1a3HWWWdhv/32CzuVRa9An5me68m6S0FfHo+HKcUKlKOVLLz02/S7nRdQp2OfBa6nAgXkukCfBf4eQfumaQ7aPhCyKNP36XcDIbcDOo7A9fR92p5+h1wmeq6ndfSZQizOSTl22le8z4leBx98MFvSPgfDOSX6OtFnigyVc0v1c0rkdaL3hxxyCDuewXJOibxOgfcwnd9gOKdEXyc6nkMPPbTbPZzq5xTr6yRIHkJ57QfUaG+//fZuN8U111yDe++9N+z36PPA7yk89NBDMJlMnVWvjjjiCOY7u3Tp0m5Jh+fOnYt33nkHGzdu7Fw/b9487Ljjjnj++efR0NDQuf6UU05hN9gDDzzQ7Sa76KKLmHP5fffd1+0YbrjhBubP+/TTT3euoxv9xhtvxKZNm/DGG290rqdAtYsvvhjLli3DJ5980rmesi5QxoWFCxfihx9+6Fwfy3OicrYkr0ScE1nTB9s5JfI6fffdd50zEoPlnBJ9nSg4lJKND6ZzSsR1evTRR9k5Ke1vMJxTMq6TzWbD//3f/w2qc4rVdXrllVe6HaMgsYhsAzHI00o3CKXQmjJlCj7//POQwVfBLK8UrVhfX9/5nViMbOn9q6++ivPOO6/btqk4sk3GaJ1K81HHdMYZZ7BtB8M5Jfo60b6fffZZJkPFTzzVzymR1ynwHlYsial+Tom8TpR/U7mH6fcGwzkl+joRzz33HE4//fTOezjVzymW14myD5GiTEp1LAOuBZEhLK8DgBo95Xej0SLVEj7++ONx99134/777w+6Pd1owaL/g60PlS82VOqrnuvpxqIbLlS2gWDr6eYOtp7OM9h6uoHp1RO6uenVk4GeU7hjj+U50e+SOwgtlWNI9XNK9HVSXGros57HlKrnFOrY43VOyj08mM5JId7nFHgPBx5XKp9Toq8TKZbUBoPdw6l6Tom4ToLEIJTXGEE5XonBXk9YIBAIBAOHLH2BFkC1QRbHjIwMpsT2nMEbjJCiG02RIUFyEcprjCD3AUI0foFAIBCEgtzFyKLZc8pcbZDCuscee6CqqmrI5DIlCy7Nogo3APUjlNcoWL16NUaOHIn09PRu68lH8u9//zt7T9GZyYYUaHKIF4q0kJ9og6mJuIcHp/xIca2urkZmZiZTkhQfT7Uqr0q2FbUeY6xQ/GnJf5WuDyEUWHUjArai4LbbbmPRkXPmzGFKLDVuaugUpUj+VXvuuSe++uorpKWlRdyRUWSlcPgWCASCwQ9FxpPCSrESg10hTFUllizNpMiOHj067Lbi+Z1cRJGCKDj88MNx4oknYuvWrXjzzTfx4IMPMsV12rRpeOaZZ1hqoEgV13hC01GUlkvt01JqRchPyDDZiDY4+ORHChEdDxksUkFxpcj7mpqabhH4gx26LnR96Dr1zGwgUBfCbSAKdt55Z/ZKBUQCZSG/ZCPaoJCfaH9dKMFZanNlCMdQCNTqiXJ96Hql0rUaagjLq0AgEAiiprrVgQ31FiG5KEkFq+tQRlyf1EAorwKBQCCImmd+2Ij9H1qAh775S0hPIBAkFKG8DkJoqoMKJ4gpDyE/0QZTE7Xfw06PDx8s4VHZu47Mg9pQu/xSxQJJFaSEJVKgRoTyOoidzkWnI+Qn2mBqovZ7eOH6RlhcXpRlm7D7mHyoDbXLL1UIVqFKIFADQnkdpIEy991336APmKEH09y5c+Mqv3j9xmBnqLTBoSq/xcuWYidpHQ6cVACNRn0KotrllyrBWrW1tXEN2jr77LNZH5ufnx+TzBCUHeGcc85BaWkpTCYTJkyYwEq2i8wBgw+hvA4xFi9ezG7ucePGsdJ/lNprzJgxOO200/DNN99gqEGKaapaZ+644w527DQ1Sg+ZWEC5C6ngxogRI1i1GcpnfO2118JqtSLe0G/R+QS+6BhGjRqF888/H5WVlQPaPz0cSWbU9unBVlZWxvZbX18fk+PfuHEjywV9xBFHYNiwYez46Zz6C1VhokT7lMye7tPp06fj6aefDqlMJPLaDdv4Ft433o7zLU/EfN+CoYHFYsE777zD7pPm5mZ8+OGHA9of9YG77bYbXnrpJey+++648sorkZeXh5tuugnHHnvskMycMJgRqbKGCJSr75prrsHDDz8MnU6Hfffdlz1kSfGhxNmfffYZXn/9dfZwv/nmm5N9uKphzZo1vSqqqQHqiKmTpo7f6/XilVdewfXXXz+gfdpsNuy99974888/ceCBB+Kkk07C0qVL8a9//Qs//PADFixYwJS+eE9T0sNGobW1Fb/99huee+45/Pe//8WSJUswfPjwfrX/I488khURmTVrFv72t79h/fr1eP755/Htt9/i119/Zf59A+HHH3/E7bffzs5h0qRJAxpQULujc6brfPzxxzNFm+7Riy++mFX6e/zxx5N27erbnZjuXspMH7mT943JPgVDj7fffpu1WxpwPfLII3jhhRdwwgkn9Ht/1P9t27aNDfAuvPBCto7un5NPPhlvvfUWe9F9IRgkyIKk0dbWRkNBtowlTqdTvu2229hS4cYbb2S/NWPGDHnDhg29vmO32+UHHnhAvv766+VUgc5n7733HtA+6Ps9b4Ng8lMb33zzDTvu888/XzabzfL48eMHvM9bbrmF7bNnG6D/af0999wT8b76I8MRI0bIRqMx6GcXX3wxO4abb75Z7g8vvvgi+/5JJ50k+/3+zvVPP/10pxwHysaNG+VffvmF3UsEnQudU3+YM2cOO66PPvqoc53L5ZL33HNPtv7nn3+O27Xri6+Xrpc9t+TI8q1mWW6tktWIGu9hh8Mhr169mi1TAZ/PJ1dXV7NlPJg1a5as0+nk2tpaeb/99pM1Go1cWVnZr321t7ez+2306NHd7m+C9kn3wD777BPT6xSv57cgMoTymkTi1fjp5qVOW7mJ169fL2u1Wjk/P591FOHo2dk3NDTIV1xxhTxy5EjZYDDIhYWF8nHHHSevWLGi13fPOOMMdj70EP/Xv/4lT5o0iX2H1hP0IKdXS0uLfMkll8jl5eXsuF566aXOfSxbtkw+4YQT5JKSElmv18vDhw+XL730UrmxsTEi5XXdunXytddeK8+cOVPOy8tjHdq4cePYQ9xisfT6frAXHa8iv1AKcn/ksmnTJvnRRx+VJ0yYwL5D50YP2P48HE488US2zz/++EM+55xz2PsFCxbI/YXOtaysTM7MzJStVmu3z+h/Wk8Phv62wYEqrx9//DE7x4suukjuD7Nnz2bf7/lwpOOj88rIyOhUOmNFf5VXasNKu+spv++//559dtZZZ8Xt2vXFG2++yhTXlrvGymqlP+0v3qSa8kqyo74pHjJctWoVa8eHHnoo+/+VV15h/99666392t9XX33Fvn/BBRcE/Vzpc71eb5/7EspraiB8XgchpJe1tbV1+vi8/PLLrFrIBRdcgOLi4rDfJV85hYaGBjbF+uijjzL/OZreIXcDmr4l36KFCxcG3cdll12Ge+65h1UjI7+jqVOndvM7pH18/fXXzG3hkksu6Tymjz/+GLvuuitbki+q8t0nnngCs2fPRktLS5/nTsdG009Ul/qMM85g00fk93T//ffjgAMO6Oa4f+uttzL/QOW98qLjCpRfT/orF/I/vPPOO9m5KNNa5CMZrZsG+Yd98MEHmDx5MnbaaSecfvrpbD2dd3+hKfTt27djjz32YL7QgdD/tJ7cS2harj9tcKBQeyF23HHHqL/rdDqZ6wEFbyjXW4HcLqhd0PTlH3/80euzZPhDf//992w5Z86cXvKjdXQ9yBUgXteuLww1XE6tBdFfi0QR6/YXb+g47W6v6l4Wh6vb/7GSp9JXUawFccwxx7C2Sq5Q/SlHS/cAQf7swaD1FLy3ZcuWAR23QD0In9dBCClo5Pdzww03MGX0p59+YutJwYrWh4iCUG688UamjCp8/vnnOOyww3DWWWdh3bp10Gi6j4GWL1/O/O2C+SaSHyAFntAxURCKQlNTE+vIKDiFPgtUMhRfpVtuuaWXr19PaB+kTBoMhm7ryZeXFFMKEKAgGEVxJEWBOjR6H6hgU6QyyS+WciF/TZINRcISpLRSp0rnRMfW85hD8cYbb7BjVDr+PffckynR7777Lh577DGYzWbEo/Mnf1HarqKiIuo2GCnkvxt4LSgIadGiRfjll1+YP5yiqEcDXSt6IIY7N4LOjWSZbJRrQUuSY6D8yJ+WAtjI75VkRf7rsb52fZFj2cCPpWw61Ep/21+ycHh8mHzLV1A7q+84COkG3YCvzWuvvcb6qaOOOoqty8zMxNFHH83iLv73v/8xv+1ooIEKQenRgqH0icp2gtRHWF6HAErgSHl5ecTfoVHqm2++yVKYBAbQEIceeiizVm3YsKFTMe5pYQwXVPPAAw90U1yJV199lSkq9957by/r2IknnsgsbqTE9gVFeQdTAi+99FK2pI5xIAxELqSsKoorQYo6BRFR1C0pu9FYLUgxPvXUU9n/ZB2k93a7PSIZqbnzpxkCCnpSXhRgSLKcMmUKU14jVfBjcW4UNEWvRKMcRyili46XlHFqN4m+dlaXF+Vebr3KHTltwPsTDD0++ugjNnt13HHHdQsijMUMkmDoICyvgqCsXbuWTbfus88+QaPtaT2l1qLo5p7WKpr6DwV1VoFuBAoU7U3Q9C5ZynpCx0Kpg+hFSl9fUfjkKrFy5Ur2wA6chqLp1WTJhab4e6IMKCiqPhJoanvZsmXYb7/9ug1GqOO/6667WMdP6Z+SBZ0HRQ6TVZDcJ8jKQtbBQGtqOEhhI/kqUJqnVatWMSs3TS2SZZncUhLBxIkTE/I7qcSG2hZMlvg9lFne+z4W9I80vZZZNdUE9Zt1dXXMrUuZRaLjHCiKctpzFoX6NDI+kHJLrlHk7hUpysAt1ACNDCOB2wlSH6G8DlICLVQlJSVM6aqurmZ+f5Gg3OyhfGQVC6KyXSDh/GqLioqC+hFSZ0U8+eSTYY+LfBPDKa+XX34585Gl6VHyXaXjVCxYZMmLNBF2KAvfQOQSbDqfFDvF4jiQjp+mhskPlwYBpOyRpTIaYtX5k/JKclZQfDMjVV57QtOJ5EdM/sSkrJO1m/IUR5O+LNUebMpxhGoTdLx0D2VlZSX8/Oo2r8YMyQenZIIpe+AuCPGkP1b6ZEHXc6DT8fFQXumY6NXTBaq/kN+14r9Oqd1CQe4D1JdHSqDrTzBoPbWH/qTZE6gTdd0tgphAyhpZqhQoYIN8OymfZaR+r4qiRSPvcK4IwRSycEEuoT5T9rNixQrssMMO6A+UbJ6U32nTpjEfyUAFh443UKmKRn6xkstAcTgczGWBoGA0eoVScB966KGo9h1J5x+4XSjI9zYeQTI5OTls4EV+w3/99RdmzJgR8XcpeI8evgM9t0ShHAfNUPR0HSCFdvPmzczvVRn4xOraRYK7ZjVbNphGoSJGCk08CHcPCyKD7plAN6dYQDNipBRT4GEwQ4qSs5r6sGiUVxq4k3JKs17U/wQ+ZyimgdyyaFZMuWcEqY96ex9Bv6HOgfwulenyM888kwV6PPvss8zXKByKZZKmTGmK//fff2e+lKEioqNRIsJB1jWClM7+QhHV1HHtv//+vSxzlEA+XO3uQCtXT/kFkmi5BPLee+8x6xrtm6yPwV50bBQMEW1ZTFJsKBE++ZeSdTsQ+p/Wk8IUacBPOBn2FyXbRLT7JP9qcmWhB1jPaGNqL/TAo0hnyo6hBhSLFE2f9jxXcsVQChLE69qFQ2rZzJb2rFFQM/Fof0MNujfIhSdWg9HAwiqkoFKBkJ4vUm4pGwsFtvbM/hEOMhZQbAQ9A5555pluv6kMYs4777yYnIdAHQjldRBCfoYUka6khRo7diyuu+465i96yCGHMMtNT6iTImudMr1Lo1iK8KfvUBBVIF9++SWLXqb9klU3FlCEPk2D/vOf/2TT3j0hRVHxiw2FEuj1888/d3toVVVVhbTCKH5VgWmEesovkETLJZjLAF2nYB0/vShil46N0o1FAz1Qzj33XOZjSum8AqH/aX00nX84GfYHSg1G7TY3N7dflnnFD5jaQeDDmB509MCjDBQ9gwjJ1YZe8STYb5BFiixT1N4//fTTzvU0IFHSqtG1ite1C4fR2nGf5HYPqlQbsW5/QxG6T8idK1bK63fffcfu4b322ovNhoR7FvQncIsyxNAAjarQUTlYyjRBZWJptmrevHlMuRUMHoQNfYhAwTykoFL0Nj0cyX2AlAAqD0sdCkXhU7oq2k6BcqOSzyKtI4WQrKNUX55SMpFlk0bRsfKFotKc1MlQBCql0jr44IOZlZMswfSbdBzUEZGCGAqa4qKyn++//z6zolEAAE3vkwJA74MFgpEcyKJJ3yPFniyXlD81HImUiwJZkajEJ03LUw7ccB0/yZE6furAo4EGOGTto/OjVGeU4YGm6clHbZdddmF5d+NNz1RZZDmkwQxdd1LSKK1Yf3wZycWCylGSbKi9k+WSZEq+tGSVDGz3ClTilYj04U2DBirBrECKE62jmQ8FKtca6LMd6jcoMI0sUHQ/UJYFattUHpZkQZkz6F5IxrVzuNxwynoYC9RteRWoD0UZDbwfgkHt/YorrmD3Kg3Uew4qQ0H3CAX8kl883SuffPIJM2jQAI7uj2TkbBbEkSQXSRjSJLI8rMLvv/8un3322fLYsWPltLQ0VgWIqkSdfPLJrORosEpSl19+OasURBWvCgoK5GOPPTZsJanNmzcHPS6lwlY41q5dyypG0XZUESU3N1eeOnUqO4ZFixZ12zZY9SuqonX11Vezc1Kqa915552y2+0Our3H45Gvu+46Vu2KShXSNqeeemqn/MJV2IqFXKiiDH02f/78sHJRyvv2VYGGKuJUVFSwUotbt26Vo6W1tVW+8sor2T6UCmckTyq/GA39LQ/bs9oZXZPS0lL5b3/7m/zTTz9FfT7BjmnMmDGsbVEVt3PPPTdk1TnlGCKFrm+oqm3Kq2cbCPUbdKxUWe6YY47prBRH98GTTz4ZsuJRrK5dKDxenzz6xs/kEdd/Ite1dK9WpzZEeVj1l4dVK6LCVmog0Z94KseC0FAUMEUAkx9jLAN8aHqRppBpKjGVIm7VgpCfkGGyUWMb3NZsx54PzIdBp8HaOw6GRqNeS5Ya5UczX0qwXWB+U7VCrldKasJYzySpmUivU7ye34LIEMprEhGNXyAQpAq/bGzCSc/9ilEFGZh/TWjXFcHgUF6HKkJ5TQ2Ez+sghCLnKZE9+Y4q0fQCIT/RBlMHNd7Drm1L8KnhH6j1k0+4upVXNcov1aBJWQqUJT/+ZPiLUuYBiiXoCyoxG4/sLgJ1I5TXQQgFvZCzOiWqFx330JXfhx9+yCp99QUFgIULAlOjDCklmZKWLBz0UFPqp6cSamyD3sZN2EFTiXQ5E2pHjfJLReWVpsQpYCpZyqtS5CQcFMQqlNehh1BeBYJBCimvlE8xEmKtvMYbUlwjKTpBWQZSUXlVI6v0U/Cm+2rsO244Qic6EghiQySDU8HQRSivAsEghSwX9BqMUDqt/pacFfSPzY4MfOvfCbuWTxQiFAgESWXohBAOIWiKZ8yYMSKvnZCfaIMpihrv4QYrr75XZO5eslaNqFF+qUjP8sQCgVoQ2QaSiMg2kHqsr7Mg3ahDcZYROq0Y+wmGDrfffy+a2iw49YRTsOv0Kck+nJRDZBtIDUS2gdRAPH0HIRSsQP5CtBTEVn7nv7YYe9z3Hf7Y0iJEK9rgkLqHT3C8hccMT2KYaz3Ujhrll4oBWxaLJWblYQWCWCKU10EIpYmhKE1aCmInP+rEDW2VyIIdJVliOk20waFzD7u8PuTJrex9VkE51I7a5JeKCOVVoGZEwJZAECHtDg8+0FyHdJMLTt/vAMYL2QmGBI3tDpSgjb3PKhiW7MMRCARDHGF5FQgipKGxDukSD1ox5anf+iQQxIrm+mpoJRk+aCBlFgnBCgSCpCKU10EI1aGeOXNmXOtRz19bj2XbWuH2+kNu89airVi6tWXQyK+1ditbtktZgCE9SUeXGiSiDQ5m1CY/e/N2tmyXzIBG/Un/1Sa/VIWqawkEakTc2YMQvV6PI444gi3jASmsZ738O4588idYXcEDIpZt2Ip3P3wfJz77KzY2WDEY5Gdr3MaWrbqCJB1Z6hDvNhgLnvlhIw56eAFeXLgZakNt8nO31bGlRZeHVEBt8ktFSPHPyckRAwCBKhHK6yDE4/Hg448/Zst4UG9xsqVBq0FuevCHw5aP78V/9Hfh2uHrMLogA4NBft4WrrzajGLatL8yVBObG21YV2eBxam+iHS1yc9rbWJLpz4bqYDa5JeK+P1+tLa2sqVAoDaE8joIoc5m6dKlcet0Ghvq8bbhDjxlfIISBff6vMXiwOy2z2CUvJg3rTTlEoWHkp/cXsOWnvSSJB1Z6hDvNhgL5m55DF8Yrsculm+gNtQmP5+NK69uQw5SAbXJL1Wx2+0x3V9lZSV7HvR8ZWRkYNq0aazks9Xa/5m6mpoanHPOOSgtLYXJZMKECRNw9913i0HMIERkGxBEjbVhK+Zo1sIiZ9LcUq/PV/z6FfaS2mCRMlC889GDRsJ6O1deZXMZUoW6diezLBZmGZGdJqZQA8l3bMYkzTas0Yt0Sn0hOZrZ0mfMjVtbFQwdqPrZqaee2pmSq6GhAV988QUr+fzll19i4cKF0Gqj862ura3FbrvthqqqKhx99NEYN24cS5d20003YdGiRfjwww9TzpAiCI1QXgVR42jpCN7QFSAryOfOdd+y5ba8OZisMwwaCac769nSkJM6qYLIn/OZBZtwzpxRuPnwyck+HFWR5eHWxLT81LmeyULr7Ai8TBfKq2DgjB07limqgbhcLsyePRu//vorUzr33XffqPZ5/fXXY9u2bXj66adx4YUXdirGJ598Mt566y32Oumkk8TlGyQIt4FBCI1Y995776hHrpHiaeXKq8MYPHCpoOVPfhyj9sBgkl+2p4Et0wqGI1Vod3KfP7NJP6ja4EDx+WXkyVwhM6sw6b7a5Kd38wIFmvR8pAJqk18y8fr8+KvWgi1NtqiqZZGVMisrK2HWSqPRiH322Ye9b2xsjOq7VAns7bffxujRo3HBBRd0rqdjv++++9j75557LsZHLEgmwvI6CNHpdJg7d278fsBSyxae9OJeH7XZHJjgXQdIQPGUvTFY5EcZFgrkJnZeOcWpo7xWNC7EedoVGO0+FMC4wdMGB0hTuw35HUn3s4sqoDbUJj+Th8tKl5UamTbUJr9k4vL64fT64JPlqBRRRXlNFG63m5X0pd+dMWNGVN/95ZdfmOX2gAMO6HWOI0aMYL6vP/30E6u4JgY0gwNheR2EUCfw+uuvs2U80Nn59Dmyeiuvm1YuQobkggXpyBkxDalIMPnVt7QiT+KBBNnFI5Eq7ND6Hf6p/w9GWReH3S7W9cvj3QZjmXRfm6W+7BFqk5/Nr0O7nAZjiiivapNfxLht0b98Adky6D2t8zi6VjktmOxdiwrvlqj26/e40NTUxIPe/LH1C9+wYQNzG6DXrbfeiksuuQSTJ0/G6tWr8cADD2D8+OiqF65fv54tyc81GLSe2sKWLVticvyC5CMsr4MQUkQ2btwYc4VEweTkUzo6c2mvz361l+Aq14M4aqwOV6ZogvBg8qu1yjjS+TSmZdvxUlpqRFwTBq+FLXVpoX0V56+rx3MLNjGf2Eml5pRogwOlwe7HfO88lGfKmKfCpPtqk9/5nqvh9PixYCKf1lU7apNfxNzTj2DQ414GpnQExq79BHj3TGDEHOCsz9gqUkK1bx2PTCe3nkfMIQ/AVTGPv9/yMzBqT8QKujaUWaAnhx9+OPbff/+o99fW1jGLkh08lZvZbO62nSD1SU3tQpBUPvLNxmPeo6AZMavXZ3/VO1Apl0I/eg4GE7UWF5qQDWvuZJpPQ6rg9EmwyUboMoMrrx6fH3d9uho/b2zCf5dUYahQ7c7A/d6T8EHJlck+FNXj9PiY4krkZIiMFalGK7Lgg7oGaAcddBAbWCgv8nH96KOPsHLlSuyxxx747bffkn2IApUjLK+CqJ3//+uYAVmegRPH7Nrr8/X13NI3tihzUEm2to0XZig2m5BKXCn/Ha0uD76ZFNxqQgrrxgYb8jMMuGy/xPnEJpu6dhdbFmUZk30oqqfFzqfedRoJWUbxyIgr/+DBsFGhDWjDE+fxfUiabj6va074CaPy05EZTeCmpAMaeEYOjNgd8SQ/P59VRKNytOS3Sumtvvkm8vzLisU1lGW1vb2923aC1Ef0RIMQClaYN28eW8aaRqsbNBOn1UjIz+j+4Pf7ZZzU8BiqtbmYYJ6OwSQ/c+XXuFX3HdL8BwDYEakAXY92B882kJ1hCBpx//Z3f1CcLy7ce0xMMxLEsw3GAmfzNgxDA0oz1Bl8pyb5tTfW4iPDTbBqsyHhEKQCapJfVBgGWI1Qq+OvDvyyzIJNZX06DOlmGoFEvCsqQEPKHguAClCG4wnlaSV+//33qL6n+Loqvq89ofUGgwHDh6vzfhdET4rd2YJIoGjKHXeMj4LV0NqGXaS18GcUMQU2kO11tThF8zVzRvHm3T+o5FfY9BuO132FZZ7efr5qxeLywt/h8hesQME3yzbjOfvlWGsajRlT30mZNhgLZm17AdebPsXyBkqr8wDUhprkZ2+pwUzNJrRTVucUcZlRk/ySidvjwWipGi4YoNdG58+uVL5KJC0tPH1dtJXRZs2axZRTstaSG0JgxgEK0lq3bh1Lw5VygxlBSITPaxRUV1fjkUcewYEHHshGcHSzlJSU4G9/+5uqfHQoqvKpp56KS6StpWYD3jXegVe81/f6bFODBQ94TsAnhkOhy0jdZObB5PezNANPe+fBUZ46vrxtFjveN9yKV40PwOjnbg+BrPzlC+TCiinGBmSYC1KmDcYCt8cNl6yDLlud1dLUJL96qRBnu6/BM9lXIFVQk/ySidflYNlfzJIdUpTWU1Ig6+vrE1pi96GHHmLLvfbaK6rvUUDWiSeeiE2bNuGZZ57pXE+K7I033sjen3feeTE+WkEyEcOQKHj88cdx//33s9J2pMAWFhay6QgqO0ev//znPzjhhBOQbJRye/GItG1rt2CzvxiSKRs9vVrXtGjxlO9IHD6yFB0xqilJMPl94ZyKKu9YvDduNlIFa1sTdtJ0TKPpuvvq1rc78dS2kfhAfgTvHlOBnICpRrW3wVhwi3whalxn4MPpvYMO1YCa5NfgMeA7/47Q5vVOjadW1CS/ZOKQdWj2FyHDIKE/5SW83oA0XHFIlaXQ3NzM8rAuWbIEubm57DkbLVSMYP78+bj44ovxv//9j1XxokpdVLGLXEhIuRUMHoTyGgW77rorS6JMlVsC+fHHH7HffvvhoosuwlFHHcUqhQxW1mlG4yL3wzhpRgXu7fHZ+nrroAzWogdgfUeAT0l26gRstXj1uMB9FcbmANf2SAf15apa5lJQNHwcyqanZiW0gfgCN1joekooyk7stGgq0toRsJWbLjINpBpOr4RWZMBoUle/1TNVFj0zy8vL2TP0hhtu6JdvamlpKZsBpWCvzz77DJ988gkrUHDnnXfiuuuuS1ilMEFiEMprFBxzzDFB1++5557Mn+brr7/GihUrsPPOO2Owwh/6QGFmbwVdU/MnRktujM2fgsFEs8WBHfxrUYs8FAU5b7XS7NbiK/8uaM3O6/XZgrV1bHnA5NSxpsUyet7b4QxcKLIN9ImpYTmO1vyOCaCI89QNxByKUKYBwhRFoFY8GTlyZFyt4aTAvvDCC3Hbv0A9COU1Ruj13CqhBodwOpZTTjml85hiSbONW2Hygyhx5zX/Cw8Yt6LS+TJ1U0hVesqvoXYb/mu8DV5ooNOeilShTck00CNYi6KPz6+8EqfrtSgr4D5mqdQGB0pTfQ3+a7gFzZoC6DVUNld9qEl+o+u/xrmGt/BnK6XBOwKpgJrklyxISUzztkILLYy66GcYyFKZl5cnLJYCVZJ8TWsQsHXrVuZjQ6O+qVOnJvtwoNFomL9PPDhk+5O42LAYrpbLAZzTud7q9KBcrqWZWOQPn4RUpqf8LPW8pGCLJg+FKqzGFJLmTThC8zPGYQcAXbMByzdsxU5YA61Whr+8LOXa4EBpb9iKnTUb0Cw1qjZ6Xk3y07ta2VKT3tuCr1bUJL9k5uQulRtAhQ79Ul6/lFeTytwNBAIFobwOEI/Hg9NOOw0ul4s5mVOKllDQNvTqmTg5cD11umQtoP0GRnnSfsmqS9GzgdMutI4+C1xP+3riiSdw9dVX9zoG2jd1Sj2jcClzAn2ffjcQ8kWi41DWF7kqMVVTiQ1aN3w+X6dD/5bNGzBFcjPrZFrBiG7nGYtzUo6d9hW471icE0Hfp+3pnGw2G5PfpZdeyjpvexOvPNWuK4A5htcp3ueUW7MQjxmewNrmveBydbm81KxcgJ0lGY26EmSlFVKDifk50T4efPBBJkPFBzzW1ykwmERZT+voM4Vg56QMRiy6fGSq4DoFO6fAe5iOp69z6u91iuScDG6uvGoz83odezyv00DOifpW5R6m41ND21OOVTnenlH8tB96RbOe6DkNr6x3s0wDgA8SJC23QAf+fuBxBltP1NXVoaioqJv1lbaPxbH3dU4vv/wyS3MVDtr+yCOPxPTp0yM6p3DrlWOhJX2uXONwbU+QPITyOgCokZ955plYsGABS8NBSmw47r333qD1nCk9iDLCnTlzJqs08sUXX2Dp0qWd21CQ2Ny5c/HOO+8wZ3cFiqKkfIbPP/88i64Ntu/Am4wc4inxNEVmBkJO8lSd5Omnn+5cRx0ypRmh9CNvvPEGW3eMr5klWDPlFGHZsmXMKZ4w+lowRQM0aIqx7pffWJSnQqzOiaYByZoS63MiKHMERakGntPDDz/MMkuU+bjyut1pxOsdv5EK5zQSm9nS6jN02884iR/XFsM4fBqwPpbnVFFRwR7aJMN4XyeCrtOpp56KhQsX9tn28r3bsI8WsBsLVHGdQp2TQiTn1N/rFMk5zfM1sXtek5bLyngm6joN5JyefPLJbu1PDW2PcqZS6VO73Y60tDSW0zRw4EDHR9uQjAOVY5q6p+cDKZKBShQdDylRtbW13c6J0jeSEm1pbWLKq0fWo6Wujs0M0u9RZH+gIkbKKR1TYHUqUt4p6p9+j35XgSpg5eTksMEBfUchKyuLvWJ5TnRtf/nlF/TFsGHDUFxcHNE5USUvq9UKi8US8pzoOOl7lPmAUnaFanuvvPJKn8cmiB+SPNRziQxAcT377LNZA6aOi5Y0egtHMMsrPeQplx7lqYul5ZU6beqYezIQCwRVZGq+bwrKpUa0nPQ5zGNndXZIC999FPttuBtrMnbBuKu+TKqlKJpzCmV5JfldddVVrINd+OwV2LfxP1haejwmn/lEypzT/KcuxcFt72D1iNMw5uQHO9cvv39/7OJfjvW73I7h+18Ul+tE+yBFgWSoNsvrjy/eiP3rXsDywnmYeN6LqrW8Kvdwsi2vVffOxBhUY+u8t1E+88CUsbwq97BaLK8k56qqKowaNYopr/G2vFoaq2H2NMCuNcNUOCqs1dHl8aGu3YlMkw45AT7ypESSYpgMy2sk6/tjYe3L8up0OlFZWckyHmRmZoZse6SQk7JNiq7y/BYkDmF57QfUyM866yy8+uqrOOmkk9j0Rl+Kq9IhBkujFWx9qEAD6iSjWR8qbVew9dRBBFtP50brG60u5IGPWLPyS9kNrLhJaNsq2dJpHsVu7mCBa2o8p57Q+SjraUnHYLRzy4YmpzwlrlPndzume6XMgs71TRYnxvk2Mt/ksilzgu4nVuekHH/P34jVdQrmohNJ29M5GvmbzBJVXKdYnFMkx96fc6IBa7ZsYe0lM6cw5LGr9Zx6tr9kXiel8pOiiIV6ZkS7PlQKKMnXMSjRmTq/G/j7gdjcPrQ6PPD4ZOR1lP1WlDnavudvx+rYoz6nIOtDnVO065VjoSV9rlzL/vR7gvijjvwZKaq4UkGC1157LayfazKgzpKmyWIdadvc0oZ0iXeIuqzCbp+lWbhvkiZ/NFKdnvLLcPFpSWNuOVKJNA9XXnWZXenJN1duQI5kgw8aZAyjQK7UaoOxwOjk11Obo95Sv2qRX7vdjWzY2PvM3CKkCmqRXzLR+Tt82Q19B13ZXdzCnGHsepaRAkeWRZEfVaBGhPLaD1cBUlyPO+44vP7666pTXAnqbMjXKNadjqW5hi3d0AOG7oUI8lzb2DK9ZAJSnZ7yy/VxZSezKPrE2ckk3cf9vQyZXQONts3L2LJeXw7oTSnXBmNBlodfz/S8YVArapFfa2sT9BKfCjdkxbaE8FCQXzAS4alHhTgMMldIdYa0Prc3uhoxWdqCHD8f8Cqo8fkWb4QnZWoglNcouOOOO5hvK/nBjB8/HnfddRcrcRf4+vPPP5FsyEeH/A1jHQ1pb+HT5+2a7G4phpxuL4b5+WcFwyci1QmUn83pQZHMAxxyikcglcjy82wWpuwu5dVXu5ItW7PGpWQbHCg0DZ7rb2HvzYXqtaSrRX7WVq7oO2EA9H0rQWpBLfILpgj29K+NB263C1rJD1KTtX1YXinvs8nvgE7yw6DXdlPiyOd1qClzyvUZiop7KiF8XqOAnLgJila8++67Q1YQmTFjBgYj7vZ6trTrcrqt3161GaMlF3yyhJyyMRhM1DfUYpTEH4CZBaljeXV6fMjp8E9Oz+2KxE1rXceW3sLJGIqQz28huPKarWLlVS04Wvk9b9GYITJ+DgxyYSAfWgrwocj8eFqFfW4HW3qgh0HS9OkykAUne68xZmEoQ4o6XR8lyE+gXoTyGgUUmEWvoYrH0mGFMXZPeN20bR3I07VeW4xSXeqUT42EttqtfCllITuO0+yxpsniQGkQX8UCBx+ApZfHz99VzTQ01KCoYxpcl61en9dkU93qgM3lhaWlY8CqFdHUsaCgoADV1dUs6wC5NShZEGKN02aF1ivDKWnhd3LFNBT29laYfH44qRYXJVHwOTvd5CiDAkXfRxKQnMooGSdIcSXjFKXfEqgbobwKIka2ceXVa+oKACIctX+xZYupHINNHbA1cuW1VVeAbKQOrc31GCbx6T4pjQ82SBkp89ewyPGiEVMwFGmr577Z7ZIZ5kE20IolL/+0Gc/9uBlXl1RiP1KG9N1nWwT9Q0mpRGmWSImNF05LE0w+G9zaDBis4be1tjWjTbbCp02D1sYHt4FWSFLm1Og7HA/I4kqKq0h9pX6E8joIoRQelB8y1qk8dI4mtvSndVde3S08kMuZNRKDTX6uFl6gwGbsmnpPBawtPLG4VcpAppbf5jW12zFW4onFs0rGpGQbHCj2purO6lpqtiUmW3517TxSXQ8f2uR0eAy5SCWSLb9wkGJEL5Y7OyAvbCxZ/tz9mOT6A6vHnIdRh1wQcjuPz4/FT96KqZrVaNnxUuROO7OXNTJe1mG1QT6uwlUgdRDK6yBEGTHTFFUsOx29kwcuaQKi14lX9MfiUuds3DdtPHbE4JLf9+kH4xpnOc7bsQyp5CVq7wi0sWqzoeSFqLZKuMN9PWbmOHCVIT0l2+BA2eLLwxPeIzG2bBgimRj8cmUNvlxZi3P3HI0dhiXO9p5s+dVb+NTxgowDcV/tTrhyylikkqNJsuUXCaQoxUtZqm+zotVjRVpeaWf1xmBsqG7DZc3HYZZpC56ZeRg0AduS2wBVoqIA5cHuNiBIPUSLHITQaJnKHcY6qvUbzMIj3mPgrZjdbf2WJjucMLLyg4NNfrXtLjTDjMzC1Mo0YLe2wyqbuk33brX4scA/HauKj0zZNjhQ1nhK8S/vCdg47pyItn9mwSZ8+Od2fL26q0RmIki2/OrbuPLq9XPXk9yOxPWpQrLll0y8Pj+utJ+NWa4nkT19Xthtl1e1wYJ0OCv2hCa3ottnQ1mGAvUjlFdBxHzhno5HvMdCP3J2tzQrVS18KnpkQcagk2ZtO3+IF5tTJ1iLWG7cCTu4XsQbk5/pXFfdwiOQy3NTJ+VRrKESmJFez2XbWrF0aysMWg1Om5Vag5eB8oz1MvxqvAQeO88VnJMuIq9ThZo2Jxt0ULst6aOdUxsnppWnkke/QCDcBgRR5MdssfOUUfkZXX5kNTVVeF13F6qkEhRlHjLo5HlS89M4WufBcA0pLyVIFRqt/FrlZnW5B+RW/Q9Ha6ow3jR0g29MzetQLvlQmtl3Dsd3/uDBXYdMLUFhVmpZHgcCBfYVoxFmyYGzbC/iKv12mK3/ACJytBAkm63N9s5BqkYT3mWibONbuFFXiWkZZwNI/RzdgqGD8HkdpMQ6UKHV7sZOWIsWKQu5aV0P/sata7G7djUapHpIg8gviuRHwQyH+OYjR2dDk4ke3qlDs40H3BQETPfu2vA+LjAswXI35avdI+7HoMZgmRts92GMsRrVtreAMLkxKE/u+GX34XqdhJ2m3IRkkCz5NTQ1Y6TErfTjvX9honYzKrW8PaUSamx/iUC75kMsNN6LldgTwNyQ21H/Nsv2HXbTrUEz9g+6zVCVoUD9COV1kKb7uPHGG2O6z5bWNrxnvIP/4zsB0PNppk2efLzuvgjTyjJxFgaX/La32PFv77Eo07Tg/BLKZJs6HFz/PE7Tr0OO5XIA3JftT+9oWH1ulCSghG882uBAsbu9cPl1cEl6ZJeEz4zx3YpKHCN/iyydA/7Mi5Bokim/1nqeHs4mm3C3/1QU+hpwdXlqFV5RY/tLFL7mLSiXGlGn5XmeQ7GpwYY3vPths74CJ0zcq9fnQ1mGAvUzeExlgm5Rohs2bGDLWNHa2oxN/hK0SNmAsSvJ0AZ7Oj7w74mtI44edPKraXPgVd9BeC3jLGjS1JxYqTfjXKuwr/ZP5EmWzlrndzn/htM8/0DWuD1Ssg0OlO2tThzqvhc74XVkloafIv16bTOu8VyAPwuPgGbk7kg0yZSfrZGnh6vVluBHzyT8178Xsou6B/OoHTW2v0TxQ8aBOMZ1G1aO7Ep7FYy1te342L873iu5ClLB2F6fD2UZCtSPUF4HIRQd+sYbb8Q0SrTOn4193Q/hvKK3gIDUM9s6grXKc+ObeikZ8qtu4ZaLYrOxz+jedqdHVWmCHnT/Ddd6zu8MriN/ZfJbJvIzDSnZBgdKVWBbDZM+iaZTv/2rGV/5d4Xv8EfDbjsY5edu4blwa/VcYdVrJWQYUqvOuxrbX6LYZEvDEnk8tKXhk5utreUD2wklwUvCDmUZCtSPUF4FUflQ9lR8ymrnYy/NMozKGHwdnLWuEjtJ6zAhwx56G5cXjzx8F+be9h4e+oZXGks27Q4vfvJOwLu+ucgv5xaVxnY7DPAgN10PvXZo3vZVEWZb+H1zMyxOLwtMnFGRWsn5Y4GvbTtbtuiKcIxmAQ4xrVJtrlRBb2rbeTsvzQ6facC15Q/MlNZjh4LUGpgIBITweRVERJONR6/n9cj3eKblGZQb6lHp34XCOwaVNIu3fY73jU9jWRtlUTgg6DbfffEurrE+iEXSzXjsWzMOnFyc0GT24dJ7UXojk54/mFybf8NfpjOwTiJl9kAMRbI3fIAPDa+gwUnnT+01OJv++BqXaOcDIw+Hto9o7cGI1sZz2vp06XjI8G+0+sll5tpkH5YgQg5reQM7ag0oNU4Nu93culdwi3ERtlioylcqlWARCITldVBCVpLCwsKYWktGbnkPnxluxCGtb3auszqcKJEb2fuC4fEPAkq0/HQ2XvbWnxU6RVb2qtfZcpZmDVs+/t16JJvGxjocrfkRB6Z1WYKdHWVuZa0xZdvgQDE0r8cMzSYWgBeOosoPca3+HRzt/xrJIpnyMzrq2dKv423Fpkm9HKBqbH+JwO1y4QLfW7hD/wpKwqTdbnN4MMJbyd7njw5eF3GoylCQGqhq/nDffffFzTffnOzDSHkovcnFF18c0zQn6datmKLZgjyZJ7Um6rZtgE7ywwU9MvPLMdjkZ+p4iGtzgp9bm92Fme4l7P0BR5zCXCPXrf4T62vbkUyc29fgYcPTuNb5eOc6b1stW9qNRSnbBgdKmo0r8NoelYQCsTk9mOr4nb3PmXE4kkUy5Zfh4QNSjcSt9o6OzCKphBrbXyJoqtsKjSTDI2uRWxg6L++GqhqM0PD+LXP49KDbDFUZClIDVSmvv/32G3w+msIQDASS4ZIlS2IqS72riS01mQWd61qqN7Blg6YIGEQ5XhX5mT0N7H9TXnDl9a9VS2CW7HDAiKk774V38p/D98arMf+z/yCZOFu4z6LNkN+5TrJyK7I7rShl2+BAyXVzuaQV946sVli1ZiVKpWZ4oUXBpL2RLJIpv2xfM1tqJf7bbkPqFbVQY/tLBG11PM1ZkyYPkia0L2v9hj/ZskWbD6TnBd1mqMpQkBqoSuOYOHEitmzZkuzDSHm8Xi8++eQTtowVaW4+1ao3dyk/zoZNbNlmGlyVd0huH3/8CfJ83AJlLqSk/r1pXf8LW1anTQC0OowcNR5+WYJt8+/Y2hQ6yCve+Nq5oupJK+xcJ9n59ZMyuhTaVGuDA4GKDpT6ufU5Z9i4kNs1rv6h65oakpdBI1nyIzkVyB33up8HaXqNqRe0prb2lygcbdya2q4Nf82821ewZXNm6HthqMpQkBqoSnm97LLL8NFHH2H16tXJPhRBDzK83F3AmF3cuU5u5j5TzozB4TJw7it/YN8Hv8fCDU3wQEIB+DnnlAZPaK9vWMWWtnweGFF48HW4sewFPOo9Bk//wK3SyUBv5RZGX1ZZ5zqtm9eo12cmRnlVG7UNjSiQuDuHuTS05dW4fRFb2op3xlCksakZmRIP+DN4uLzkzMRY6wUDx2PhA26nLryrR1rLWrZ0508SYhekJKrKNjB69GjMnTsXs2bNwgUXXIBddtkFxcXFQR3G99qrd0UQQXygBPdmuQ2QgMy8ruAlg4XXfkfuiEEh+k2NVlZ1RqeVoJXd0JLvGLQwZQcP2MqwcuVdV9wRrJZRgOMO2gdv//sXvPNHFc7bczRGF2Yi0aQ5uIVRm9Pl22nwcOXVmDU0ldemqr9AQ5A2KQvZaTkh8+OOsC1n79PGzMFQpMHhw+3uv2N8hh1zXNw6pzWHLqMrUBc+W1NErh45dt53GUqE8ipITVSlvJLiSooqS7L+4INhoxyFH05oSG5jxoyJWZRoq8ODPPCE1oHKa7aTJzM3FqZW6dRgUJvb3srzI5Zlp6E8Lx1oAZo1eSgO4s9L2xe5q5hCnz2sK9PCziPzcNSMMqa0FpvD51mMF7lurrwaC7oGFelebkVLN3f5LKdSGxwothpuCW/SlyGUTaq2sQmjZX5NS3eguvDJI1nyq7MB3/h3RmNuDg5u/J6tM+Z2WfBTBbW1v4Rh5/7KPmN2WNeQMh9v57kjQqfIGrIyFKQEqlJeb7nlFnGjxACKDj311FMRK1ra2jFG4oqdIauwU3nL89azDtBckvrKa6vdg5vk56DR+TFcOxGTRhYz5dWiL0SXo0QXDW1WDAP3LyscOaXbZ4+cOBPJwu72olBu4A+msjGd6zNlC1uXlp2fkm1woLgbN7KlLSN0poGta/5AqSSjWcpFXpIVtmTJr8HK/VwLM43IqWvmsy35qefTrrb2lyg0Tu6vLKcFD8IittU1YpzEldzciu59VyBDVYaC1EBVyuttt92W7EMYFJCD/cKFCzFnzhzodAO/xJZmbsnzQAe9iY/o2632Tp/Qwo4qTqlMdasDR2h/YdkDvH4n6jfxKVOHKbi/X03lWhRJfpZpIC1XPT6/2xraMZa0bgBZRaM6LS1m8FK3GbldQVyp1AajxeXlEdJGHY+41rZ2BILmBvdfJiyVPO1ZfcZ4hH70Y3DLb/syzNP8huGaXVHY0Y6yC9XTvlOl/SULvYv3yVKIDAJE/eaVoDCtdskMc5jthqoMBamBqgK2BLGBXCp++OGHmLlW2Fu48mrRmDvrvDds38zyCVKO17ScYLbJ1KK+sZEproQvowT+Zq7seDKCW+Cs23kBgHr9sE6ZqIH66o3MV9cNPZDBFdU2ixUZEreoZSbIbSDWbTBavl1Tj0k3f4kLX1vM/s+0b+vTxUVXzwPw3AWhrVGJIlnyG17zBR43PIFpLd/AJPGSz+kpaHlNdvtLFkYPV151ASkNe2LdzoO1Gk3Bs6gMdRkKUgNVDqdsNhs+/PBD/Pnnn2hvb4fZbMaMGTNw1FFHISMjTNkQQVxwdqRfselyOi1S7XXc4b9JU4AyFSlv/cVSt5ktbZos6AyZyAH3EdVkB39we1p4PkWLSV3+gG21/Dxa9MWdvrqW1kbm+uCHBE2H5XywU1XXAJ3sQbpRC59fRom3mk2B55SHrgRXaOcDElPFDAxVtvny8Zt/YmcxCyvSkalPS/ZhCSIkrcO33WAO4x7UyKsAOsx8ZkYgSEVUp7y+//77OP/889Ha2sr8KhXIaTwnJwfPPfccjjnmmKQe41DDa+HJ+p36rtyB7W2taJTNaDMUQ13qW/9wNnUoo8YS0FlmS1b2vz4/hI9kOw9W82SoKxLb1cgtxo60ruOyt/H0ORZkInsQFZMIR/m6l7HW+CKWt52M7Q13Yhh4Gy4YOS3o9k6XG6N9lUzBLRg7NNNkEe9oDsZy92zcnuNAc3Um7LpcJD5fhqC/ZPq58ppmDu0eZLNZ4ZAN0BSEzvEqEKgdVSmvP//8M0488URotVqce+652GeffVBaWora2lrMnz8fr7zyCvucpjJmz56d7MNVLRqNBjNnzmTLWOC3cuXHY+ryj1pi3AVnuv6NU6cPw11IffytvHSoO6OUye1M/00wuG14Z8I+Qbf/0Hg0HnWNxdnjZiB54Vm9kdv4efjNXX6KDVI+znRfh/EFBvwjRdtgtKRZKplbS1pOISqbbHjMex5mZrbi5MzgD/XKumb86NsfY3X1mBvGOotBLr/6du5esi19MnZ0PYvDxhXiSaQeyW5/yYCMPZVyMRyyDhm5oXPz3uk8Hle7jsRns3YLu7+hKENB6qAq5fWee+6B0WjETz/9hOnTu9dbPuGEE1id5d13351tR5U/BMHR6/U44ogjYiaeVdqJWO05FtNKd4WSWKWmjScyL8kZHG4cSmJ/Uvp80MDu1cCOLBQXBA9o2GAzYKU8Dull6sqT2G53oV1Ogy6vy5+t2WvE9/4ZkHMSE6wVjzYYLblOrsRnlIzHxhYf3vXNRXt5MU4O4eKyvsWPu72nYseyHOyjTX63mAz5UT7nZitlFZHg9vnZukJzat7fyW5/ycDh8eF4183s/aqSrkwjgbTY3Gixky+zBiOLw4clDkUZClIHVQ2pfvnlF6ak9lRcFaZNm4bjjz+eWWgFofF4PPj444/ZMhYs9Y/FY75j0D76sM51te0dymv24PCHS3fwkqqG3HJUNXGXgTS9BmZTcEWmVlHes5OTyzUY5Nt5j+0ITHM9D+3e13Sub3PwdpCdpk/ZNhgNdL7DZH4984dPxOZGnmlhVEHoCfCNDfyajy1SxyR5MuTX0t6O1frTsMh4MWx2nhqvMMuIVCSZ7S9ZcKUUMGg1SDfwLBvBCrEQZdkmpBvCD9KGogwFqYOqlFe73c4qaoWDPqftBKHx+/1YunQpW8aCJpubLXMzDJ3rzq+5Ba/r78YYPw/cSmW8Pj9udJ6Gg1z3Qb/TKWjbugLP6B/CDab/Bs077Pb4cLbzVZyp/RKl6V1+2cmGiix4/TIMWi1KcrM61xsbVuBY7Q+YBJ7rNBXbYDRU19ahsKMUbFrxOKRV/YhdpTUYlxP6WrVU/YU8tGNMgTosjcmQX2tjLXSSH7mSBXNrnsdr+nsw1f4bUpFktr9kQVZVIjtdHzJfevvaBfjCcD1u1r/a5/6GogwFqYOqlNeRI0fim2++CbvNt99+y7YTJI58y1qMlapQYOzqxHbwrMIc7SoUZnYptKlKvcWFNn8aNmlGIL9kBJx1f+Eg7R/YHcuCb99Qh4t1H+M2/avIzVCPZWprMx/UleelQavpeniV132Hf+mfway2LzEUaK3iqYBapRzAZMZxjU/hHeOd2MHLU2EF46iq+7HEdCHmOL7FUMXWyrOKUP7PCvtq7KldiQKtMBSkCv7Kn/Gj4Qo8KD8Ycht37WpM0mzDaA2/1gJBqqIq5ZVcAhYvXowzzjgD27dzH0SFmpoanHnmmexzci0QJC4I4E7Pv/A/43Uotq5m69qdHlzkuQJXuS9CXkXyg1sGSk0bnyKlcq4ajYQN0kjc5DkLvxT8Lej29e0OPO89BJ9r94WkojRCLVtW4hvDtbjb92i39dVSMb73TYc1J/WvVSTYa3nKqyZTORxuH9Z7i7HVX4jCUdNCtnHJw9tAbvlEDFVcSko8jRmPySfi7+4LoR0pAmNTBXdbDSo0DSiU2kJu841/F5zpvhYbx52d0GMTCGJN8iMTArj++uvx5Zdf4rXXXsPbb7+NsWPHMjeBuro6bNiwAW63G7vuuivbThAaytaw9957s+VAsbq8aJMz0IoMmPNKOv09f/VPZj6U6Zmpnze0trYGd+pehKQfAWBfrPcU4HXfAbhoePCE9lWuNNzlPQ27VuThUKgHR906jNNUI0Pu7rf5tWF/LPBMx/+NDa68qb0NRk0Tz3XryBzBfFkv9FyF3HQ9lgwbF7Ik6lGu25EmubBs/CyogWTIz2VpYkurLgfft4+EXx6JG8pSs3peUttfktiYuTPudt2OXYYVIlQY6fJWA/7yz8SZ43fpc39DUYaC1EFVymt6ejoWLFiA+++/H6+++ipWr17NXsTo0aOZRfa6665jGQkEoaFSfnPnzo2JiJptbhzlvhNpei3WlE3ulmmgVEXBSgPBvn0tTtP9Dy0O8rd+CHUW7jtWlpsedPs6JVjLrK7z/9UzAR+5b8SpMyu65d5VArZy0g0p2QajRW/l1bTknBHYFBCIFcoPcFtzh9XVnA2DSvqWZMjP15ESr0FXAr8MkOdJfqY65JFK7S9Z1HnSsFQehwl5FSEDOiubuBvImMK+AxOHogwFqYOq3AYIUkxvueUWZmlta2vDtm3b2JL+v/nmm4XiGgFkoX799dfZMlbBWnkBwVqOquU4Ufsd5hgTFwAUTzba0/CY9yisLTua/V/U8DN2ltaiRM9zXvbE0liFQrSi1Kwuf981rVos9E+Fbtz+3da325wJzzYQyzYYLW/598ONnnPgG3sgNta0sHVji7oC2HpS1aL4CgcfrCSDpMjPzi2vbdpcHKf9HoekrenmO51KJLP9JYsWuzvsIHV7YysulN/D0fpfURZB3zUUZShIHVSlvNL0xCmnnNL5f1ZWFoYNG8aWgsghH76NGzd2q1DWX5qtvOPKDwjMStv2I+7TP4/DnYMj1+4yWx4e8h6P2hmXsf8vsjyO94x3YKR3U9DtZ1U+hd9NF+PA5jegFuhab+sI2BqR310Je9l+CVYYz0GJZWVKtsFoc5V+216ON337IW/sbpi1+g4sNl6AQ7z/C/md9DXv4C3DnThRVk9AWzLkJzm5oi9Dg//TP4tb8AxSlWS1v2RSUfstztF+jtF+XmWvJzWVq/F3/Xu4S/t8RK4AQ1GGgtRBVW4DZrMZFRUhynEKkoK07Vd8brgZDU5yGZjD1sl2Pr0op4Wpn51CbFMsb7npcHk8KJSbWZnQ7GLyge1NmrOOLfW5XVWs1JDj8Xjvx2jRZKIiffduypxZtiBLcsCVlfr+yX1B/qtur59ZDEtzTLDZNiFfsqAgryDkd4yNqzFLswaLtTthKGNwKcort7ZadPkIn7hQoCZ2bP4MZ+t/xR9O8lM+qNfnlqo1bNlgrEBGCBcagSBVUJXySsFYy5YFT08kSF7Z1MmaLdjgz+1cp3Pw6UWEKLWZajle89rXAFI6KrINaKzbjmGSF35ZgrlwWNDvZHsa2DKtoKuKVbLZ0tCKG3RvQi/5AP/l5MHJ1ltdbpjBk/RnJrDCVrKobWjE0Zof4coog+yXUeHbxgYiBaNDB6uZbNVsqQ2oSjYUMXp4lLpW9rKlwxha4ReoD1PH9dNlBjcq+BvWsaU1K3ggqkCQSqjKbeC2227Dd999x4K1BP2HHO3nzZvHlgPFb+OKmtvYVUrQ6GpmS21W6PrZqQIFnz2lewgLjVeiqH0lmmt4pHqzlA29KSNo0EOhn1uec4rVk2+4sXojU1zdMABZpZ3r21uboZX4tJ8pKz8l22A02Gr+wsOGp3G39yFUb1mPTMkJj6xF4fDQZXyzXbwaV3qReh7qyZBfureVLQ1+HsDmSU/d+ztZ7S+ZpPu48mrKDj7oMLV2xCgUjI9of0NRhoLUQVWtkgoUUHTjWWedhccffxy77LILS5XVM0qY/qfgLUFwyJ9pxx13jIl4pI4gDn+Ai0CGh08vGsyp+3BTqGpqw67g56jJGwn76q/Z+1Z9EQqC+IU1NTWiSOIP97zS4G4FycBWs4Etm41lKNF0jUltrXzw4YARaTpjSrbBaGi2u7HQNwVp5jz4Ny/HKApU0ZVjhC54gIrH50eRv55ZZ/NUlBYqGfLL9POqZCYfz9AgZ6Su00Cy2l8yyZQtbJlmDj7DkufcypbpZZHlMh6KMhSkDjq1WV4VqBgBvYIhlNfwUHTo888/j3PPPRcGw8Ai4vVOrthJGV2j+Sw/t9Ck53VZ+FKV5upNzDLplgwwZBbD1VzF1td7MjDc7e4lv+baSpDK3k55b03qCST0NvLgMntG96lve1tH7k4pC2kp2gaj4S+MwmOef+KUscNx8Pb32LqWjNEINcyorW9AhcSVtbyyMVALiZYfzShkk/IjARkdFjxdNs/rnIokq/0lC5/Ph2zZyq5fZm7vQYfV6UGFv4p9XjhyakT7HGoyFKQWqlJe58+fn+xDGBRQdGhDQ0NMokSNbm5l1Xb4t5KPaK7cxjrBrI6iBamMo55PpbUaylAkSZDauf9jkz8jqPws9dx60aItgBnqwdheyZb+XLI1duHuSDxv1/ad11GtbTDaUr9EUZYJukpeacuTF3qatLl6AyhEtA1ZyE5TzxVNtPza7W5c77kIOZIV50i8RK4pNzBbcGqRrPaXLNpbm5Db4R5kzutted22dTMmSQ74oEFWWWRuA0NNhoLUQlXKK1lUKePAjBkzkn0ogp5+cNncRaC5rQ1FEs8bas5Pfcurr5krfY4MnjlAb+P+j6TMBMPVxJVXq1FdU6pmB0/MbyzqPvXtsXHl1akb/JkGiIZ2njmiyGxErpUPTIylvLhGMOwN/Po36YsxNCQUnGaHB1/7d0GWUYdr/O9xC2x+8IBFgfpob65jIZp22Yh0Y+85lqYtq9iyQVuMkgS6DwkEQyJga5999sGzzz6b7MMQBJDl63ARyOHKWltjLVu6oYM2LfUf9/r2rmpMRIazo747egdrEf727WzpTFOP8ur0+FDi40p3zrDuVhWfjVvO3Xr1WBXjyTXVV+B340UYa/0D5V4+0CgYFTrTgLeJK69WU+oPxAZCq5LgPk1CPvg9n1ss0hamCvY23m+1a4Lf586atWzZkq6eIFOBYNBYXouKimAyqavkZiqi1+tZsQdaDlQpygUP4sjM5y4C9pYOy6SUg8JBkCswy8F9XA2FfLo9x8sDnKbP2ieo/HRWfv5ylnqmVLc2WTFc4g+vzNLuyqvs4Mqr15idkm0wWnK9jSiU2rDR60Gm5GCZBopHTQn9BQu/nu4MdSmviZafvWEbjtD8jHRNIfMBp1Rx6bmp6xaUrPaXLJwdvu22EMqrppkHdLpzIg9KHGoyFKQWqrK8HnDAAfj++++Fj80A0Wg0GDt2LFsOhIY2O3I6coRmdFhe7S3c8mrR5SDVIeW80MfPJ6t0HNweH4pk/hCYMG3XoPIzOfj22hz1FCiorapEmuRm/mxSTndrmcbJrWiysStPbyq1wWjwUc5euUNZt/PzrtGWQasPPU2qs/HrKQWkF1MDiZaftmYxHjM8gQOdX7D/W6RsSNrIlBZ7WwMsDdzKrRaS0f6SidvC0/c59MEHqWYrTwGoL4rM33UoylCQWqiqVd53331oamrC+eefj+ZmnktUED0ulwv33nsvWw6E1qYaaMgKAwlSOk+V5WnnFj6Hvivva6pS1eLoslgWj0ZDXTWMkof9/8wr7wSVn7mjQEF6gXqmVNu388CkJn0J0EPh0Lp45DjSc1OyDUZDS1MdDFSkgZRRC1dKm/uYJk138eupz1WXf2ei5dfiM+Fn32S0aLlve7susvt7yUdPQPfQRGQ9ORWLn7kAst8PNZCM9pdM/Db+vHQHUV6pyl6Jhw8ucoaHmYUY4jIUpBaqchs49dRTkZOTgxdffBGvv/46Ro0aFTLP67ff8ohYQeg0JwPF2sSnVNulbORoeVOxuzyol3PgMqV+tabt9XUY25EmScodiebVq5EnG2CX0uHyciUoEIq6zfc1smAWc5F6fMc8jTwwyZpewdJ4BWJQqial56RkG4yG1vptoIRurciEtpX7srqywxceMHu4xSqjQD2W9GTIb6VpRzztuQmXFaxBY/P3sBr6vr8317UiZ8mTMEi8ItdONW/h9/fHYJfjroMaSHT7SyZyRz5un7H3fV7bZscq/wi4NHoMH7VDVPsdSjIUpBaqUl7JZUCBRntr165lr570VGYTCSnVP/74I8tBu2LFCnZzv/TSSzjzzDMx2HB2+LdadblQusQfMg7Cha7JuGryeKR6+ur27Vzps2jMyDKZsUlTgXmul7D3cCNGN/zca/tWuwcHu+5DidSMtysmQC3oWvmUoDe7t0L9H/3f8Hr7DJxcsScGO9ZG7r/cqsnDa4Zj8bh7Ak4fNzvk9pT27SXPARgu1eKoYaErcA2lgK3f0/bA465JOGV6BfrKBvr495X4wnUXri1dhvG5GszZ9DAmrnoIjXudjIJi9Q0GBjNtfhM2+4tZWeSebGp04ALP3zG6MAPfmdUTaCoQDBq3Ab/fH9GLEjIni5tuuollRNiyZQtKS9XlJxdrtsqF+JfnOCwpOKJzXaOVP+TyM1M/abWzgSf2bzeVdZaKJbNqdk7wKVP6vA55qEqfDFNaOtRClSsNa/wV0Bb3Tgm1yDMaH/nnwFA0DoMdV8dgy6bPx58tafjJPxX5YRKyN1hdeNV3AO7zn47c0u75cYcazVY+Nez18ZyehebwgbNtDg8+W1EDB0yYefRVmH3yTVivHYssOLDpzWsTcsyCLj7JPBb7uB/GhvHn9RLLpkY+uzS6ILG5ngWCIaO8pgJUcaSyspIlb77wwguhRig69KKLLhpwlOhGXzGe8B2Nv0ae0rmu2caV14JBoLz6WrmlzpXFq1LVMuUVKMtNDyq/2nZeFra4jwd7IiF/toetB+EQ9/0wzD4/pEUtN92Qkm0wGrztXHm1GIuxvY1fqxH5wVOeBV7voiwjtBp1Zc5ItPzOrb4Ji40XQOrIplGQGT4X6Lcrt8Hl9WN8cSZmVORAq9PBd/D97LOdW77Apj8XIJkko/0lkxYb99XPzeh9n2+tI39YGWMKQ98LwRhqMhSkFqpVXq1WK5YsWcKm6NXE/vvvjxEj1FPTPhjkVpGdnT1g94rGDmtMYVbXg+yyxjvxluFOlDvXI9V53X8wpjqfx7Zdb2X/77Tp33hG/xB28v4ZVH6+zT/hH7o3cKjuD6iF2nYn3D4/dBoJpdndlWqPz4+DvPNxgOYPZOt9KdkGo/pNa11n9bOrtO/gWMOvYQdZTfU12EHahAlZXIlVE4mWX6a3GfmSBec6XsIb+rsxzsWT2oei/Mcb8K3halxYvLbzGCfusj9+yzqABXn6Pr8Osj95M2TJaH/JpEXJ0xtkkLrPX3diufFc7Ov8Jqp9DjUZClIL1SmvZNU88sgjkZubi1122YUVLlD46aefMHny5G6+sYLekB8uZW4YqLO9qfkvjJOqUGzqeghN9K7DLM0a5KRpU17021rssCAdxcO45XWE7U8cpP2D5QkNJr+0mkU4X/cZdvf+BjXleCWryrDcNOi03W/ntvZ2PGT4N54zPASz3p+SbTAa9A6eOYDO9HLdh7hE/3HYB6+ucj4+Nd6EG20PQG0kWn4ZPp7PeYJ/I/bQrkKuKXRJUMooMKp9EcZoajBheHfXqfJjH4BVNmGcew3WfP08kkUy2l8yub/tWnxq+AdK3Ft6fZbn2Aqz5EBefs9wzvAMNRkKUgtVBWxt3boVs2bNYumySIGtra3FL7/80vn5brvthsbGRrz55puYO3cuUg0KQgtMO9Le3t5rPeXUo2kaj8fD/HsVtFotdDod60gCa03TOvoscH3gb/RMc0L7pgd6zw7JYDCw79PvKpzS8hR2Mi7HhqYH4XKVw+3143L3xSiSWnF32UTme+z18khjgvZL+6F1gX7JsTgn5dhpXwM5J8JoNKLN7mIBWERhOv+tp/zHoMCzE/5WNhNY/CX7ncBzWo6xWOM9FGWFs9k+1XBObet/xirjedjo2wF+/97dzrW5tRUrfNORp7VjosYIb8dvJOI6KQSeV3+uEx1H4Hrl2IO1vTQXzxxQpR2GN7z7ITu3DOVhrpPF7kKtnAu7qaTzOBPR9iI5p8Dfiff95HQ6kS1bQJ/8w3cOCv1NuHbY1F7nqpzT2u2tOMV5L3bSb8HDU/dinynnVFAyDD8OOwv7b38axb/dC9fsowFTdtL6CGUZ77aX7H5vrL8SGRontuh17LvKOTncPhzluAkjpDq8PXX/qM5JIfB31diXJ/M6CZKHqpTXW2+9FS0tLfjhhx+w++674/bbb++mvFKj2XPPPZkFNhWhnHl0Tj156KGHOiuLzZw5E0cccQS++OILLF26tHObvffemyns77zzDjZu5FHyxLx587DjjjsyX1zyww2278CbjHyYaCqIRtSB3HDDDWhra8PTTz/duW4Prw4tmkxYvTq2vU3WY5E8HRpZxuM5eez4Pvnkk87tx4wZw9KdLVy4kF1DhVidE1V7oaTZAzkn6rxuvPFG/Lr8Lzynf5Cl/Xr2oZ+QWViOr+zjIcvj4fv4fzBJwMMPP9ztnF7fkI7t/lMxZ81m+L74QhXnlOWtxUFaFzK1fmzatAlvvPFG57Zu8zC84bkexQYtDr3/gYRep4oKngeXZDiQ69TznAoLC3HxxRdj2bJlvdreXl6eLuiz7TlY7j8H0xpq4A9znb7R74PLXBOwc8NWTO04pkS0vUjPSSHe99MDDz6MWyQbLHIafvbyrAu3ZxeEPKf7nnkbLRiNZb5ReOKpp3udk1c2YYxcglGoxbrnT8NbltmkOSS0j3jyySe7tb94t71k9nt33fsAVrj/gRzJghFvfoB/3Dip85ya/WlwYQq2oBS5eQXYsGFDxOd03HHHdcqQdDfK973zjjNU0e+p4Tq98sor3Y5RkFgkOXBIkWTKysqw11574a233mL/k6J3xx13dBsl/f3vf2epqUjJTTZ0g9HNE2mqrGCWV3rI19fXw2w2x9TySh0O3fA9iXRkS9Wnpt81n73/85b9QV4Cq7a345hnFqEoy4BF/zxAlRaIcOcUOFqf//sy7PPZXqwqlfe6bai2+LDvwwth0Er4/bo98Mgjj+Cqq65igwrlnA5+dCE2NNjw0ukzMWdcoSrO6bp3l2D5qlW4aE4Fjj3kgG7n+v1fjbjgjT+xQ5kZ71+wa0KvE+2D7g+SIck73lYVwnPPCFYS9oT89/BbtRt3HTEJJ+xSEfKcTn7hdyza3IyHjt0Bh00tUZWlKPAepuOJ5/20efMGjHplJ2zyl2Bf90PINGqx4raDQp7TnZ+swku/bMUpu5bjlsMmBj2nz7/5Gof9fgZTWm2nfYX08qkJ7SOobyX5Ke0v1Sx60bS9LQ3tmPvQQui1ElbcvC87duWcPl9Zh6veXYGZFdn44JI5UZ0T7YPu4SNPuwA3fLwOa2ut2H9iIR4+cSZMWsT1nFLhOtEsMCnKpFQrz2/BELW8UlWtkSPDJ3+nRpSqFT/oRlMe5H2tDxXhSTdfX+vpPT30aBnK5y/YcdC2yvp6m53vS6dBdhrfj7t1O07SfgvJSNfoAHYD06sndHPTqycDOae+jj2Scwqkyibhes95mF0CHJWeibbNa3GE5ic4Mkexjqin/Oh8ctvXohBZqMjP7DyXZJ/TljY/NsulyBo+jXXCgdvbPf7OII5g+4nndQrXBqO5Tj3PSaFn22uzu3GF51Lm0mJwNiIDeowozAp7nZRsA+X5Wb1+I55tL5Jz6im/eN5PLis3BNQgDydo58ObVgFJOjjkOc1c/ygKdC6MKLikc5ue53T4IYfh/1ZcjoXthRj9mwaPjTJAE5DRId59RFZWVtD2F4+2l+x+z9qht1FGEWUGTzkn/YYvcb/uMzhM+wOYE9U50bP2gsv/jqOeXsQCQ4ldNzyCW189Cf8693Do9ZIq+nK1XSfBEAzYompa69eHj2KnwgDDh/MAG0FwqNOh0eBAjOqdmQYyjV2d//aluFf/As5y/yflRb/ZosPbvn2wZsw57H/Pll/xmOFJXOd/Pqj8rHY73sYN+N10MUoMXLFXA9ua+bFU5PXOO1uw4T2sNJ6NS9sfTMk2GA2Us/V7/0x8oT8Aj1mvxSrTORjp2xL2+B6w/RNvG+7AMN82qI1Eys/RxqdzXTDifv1zuNj7Wtjj2sP6NS7RfYxJ2d2tYIHotRocdPLlWCeNwqfLa/DKL7zi2WBtf8nEUbce52g/w6GGrilvBXPtLzhB9z2mg5eQjgaS3V0fL2OK6+iCDLx5qB5ZWg92nLpDt4GIQIChrrwecMAB+PTTT7F8+fKgn1ParO+++w6HHnpowo8tlaDpD/IN6jnFEg3OquX4wnA97pEf6dpvO3/IOQ25SHWqWrjSR1H6hLeJP1wtprKg8mus2cpSALllHTJz1FGlxury4nLXM7hM+18Mz+gdPCDbm5EpOWHUpmYbjIb6dj7YKkuXkStZ2PuCsjEht293eDATf2E3zVrkm7OgNhIpP3fHfe3WcOuV3ZAfctuWpgbkg5ccHjYmfA2unUbk4V/HTcMuI3Nx0q7DB3X7Syo1f+Jm/Rs4wfNhr4+yrLz6nq4o+oqAFJg3b8OtuEf3HB45ciRmz9oDB1/7Ok6dPbQLegjUgarcBqh61Xvvvcf8Xq+99lrmXE6QI/XPP//MnLsLCgrYZ4L44mrehkmabUiXu6ZjZBt/yHmMoR9uqYK5YTFma9owMp0rOJo2bqVzm4M/ZNtquXLbpMlHqUryHlbVN+EMXUfuRsP/9fpccvDpYJ+xK9p7sGLfvhZHaRYiR1PI/rciDZnm0IOspsZajJa4YmPKG4ahjNvCA9004O3aZSoIuW1d5UpQ/bkG5KEwUykaHZqjZ5bjyOnDhKUujngtPMuGS5/Ty3Ja7N5GRQORU9G7+l5ffPjNd/iH9g8WqKXJcQGGDOSKmXKBSlCV8kr+rl999RVOPPFE3HzzzZ1O44cffjhbkrsAKbfJLMtKUZMUrai4MCjrlNyzc+bMwbnnnotUx9tWy5YOQ9eDTGPnnaScHvrhliocb3kFuxpWoaadch9OhMlazdZr84Irr/YmPrXcZiiEWooCN23nVhUq0ZmW1ltR07pa+Zu04OVuBxNp237AI4an8IVzP/Z/o6YQ4YphttV1XE8pC9m68NWkBj02fl/rwINYfGmh7+/2qjVsWW8oBx8m9I2YYo4vso0PPtzG7vd5Q3MzyiR+bYtGh7eSB3MbG7/5VTY32zriIOQVRm+5FQiGjPKq5HIlv1dKcfHbb7+xIC4KoKH1lPs12U7SpLj2TJFBqbsC03epQXkdqJxkaz1betO7HlF6J+8kpcxIH1vqpN3pQbG/gXXMOaXc8prj3s6WaUVjgspPKSXrMPGodDVgb+DW4BZ9MdKCWIP1bq68atKTYylP5L1a68vCQt8UOI2ZgJeXiA2HvZkrr63aAqjVLp0w+Tm5hd4ouaneBRCmvfgaeEyCLTN8YK0aSPazIlFIDir/Cvh7DFJrNq0CDc1bYUaOObo++7+/rsOp0q/sfeZel8bwaAWCQaq8EhQJePTRR7NXJJCP7J9//onTTz897sf28ssvs5eaochLSuE1EHQd1YrkjK5OL83DO0m9WR0+n/2lutmGMRJXxNMKRrKKQUW+eja9lls2Nrj82nnNd2+GepRXTxN3dbClBbcFmzzcN1GTmZ+SbTAaFhj2xEeeMXjQ8DlFHsGZXhZ2e08LH6zYjeociCVSftoO5TVN7ijUEKa9GNu4td+fPxZqJtHtL5noXLxflnoMOqyKldxYgb4dPLrw+2XU/PY+0iUXLBnDkTV6TkyPVyAYdAFb/eWDDz7AWWedlezDUA2Uq478hQNz1kULpRsiNFldylqmt8NCo5KApf7SsH0LDJKP5XhFVikaG6pZR+2XJRQOGxNUfkYHV16l7PBKUSKx1XPL6wZX8EdTuo8rr8as/JRsg/0J2Mrx8BkD2Rzej9Xfzt1i3GnqbMuJlN8XpoNxred8pEs8HZIhTHvJdHCl39QxQ6FWEt3+komxY4ZFl9V9IOZrWMeWtqzoAqwWVTZjbyfP8e0YeRD8QyBjgyD1GBTKq6A7FGFLFUcGEmmb7u6wTOZ2Ka9mPy9nm5mnHutjf7DUbWLLVl0hoNWhaRufCm2Q8mAwpQWVX4aLW6INubxylBrIcnA/3RZtcGUj08+j7k3mgpRsg9HQ2G5jy1wvV151fVwnra2OLf2Z6lReEym/xZ4xeNc3F1kyl6EpzBRzno/fB+YidbsNJLr9JZN0L1deDT2um7GN93Ny/rio9vfdHysxR8PjOd5e5R0SMhSkHqp0GxAkFwqOy/U1sWn0jAKuBNidLuSCK6/ZBeqxPvYHb/NWtrSaSkFqn7WOl/5r0pcilCqT6+UP7cxC9Siv+d46NvwcFSSfKV1DqldP1zA9W51T47Hkdeu50BvdsHh52quMwvDKlaljMKLNTu2BWCxotfM0a0p7ycghT8neOJ0O5MutbJu8UnUrr0MFj8+PdG8buybaHu4eOQ7ez6WVjI9qf7o1H0An+dGWNw3NLamfFlEwOBGWV0EvqFpRMbgfVU7JCLZsaayDVuLTR+khHm6pgradB185M/jUsqeRT7/bQvhJujweFMjcZSK3RB0PbfJLK+q4RnJu72nBdks7TB2poLLz1WldjBVOt4cpVTmwIb/jOmUXhx9kZLq5W4wpJ7UHYrFgF/sC7CUtZaV1iawQ7aWxZktnruPsArXk3BjaNFicyAWfYcnJ67omXq8Pw3zcxSN/eORpshaub8SBvh/Ye9PM42N+vAJBrBDK6yCEUoxRzeVQpWH7oqGxHmkUecz8W7mCZ2nmPoKtyIKkDV5GL1VIs/NO3W8uZ0tNG7dQeDtyvPaUX2NtNfTkIytLyCni30k2jRYHSjuCzpwzzuj1eVsznz73yFqYMrJTrg1GQ2N9DbMUtSKDFWUg8orDJ8XP8XHFP7NAHdczWfJzuDx4SPMo/s/wHPuf2nhmdn7YXMcNmgJImiRUvlBp+0sm22obOvvqnKKuWYSa6q1sMEJ+/AUVEyPe36I/FmGGZhN80ELa4ZghIUNBaiKU10EIpYi5+OKL+50qprVua2cOTOh5rWx7Cw9YatdGE7eqTjLcXLHT5HDFJd3GLbHavJFB5ddaxx/azZpc1Sju9TXbeNCZLCG/tLfl1dLMfTrb6Rom4eEz0DYYDW0N/PpVy3xGwA4jDGEUdqvTgwJwC21OH0puskiU/Fra2/GLfzLW+bml2iJlhlRM7Y3cPaVNr/6Zl0S2v2TSuImXhLXKJqQHFI1o2LKKLeu0RdAYeB8eyWxO7kZepautbA4MucOGhAwFqYlQXgchPp8PS5YsYcv+YG/sSOBOAU0dWG121Mk5sOhSv0BBlodbLE15fMrY5pXgkA3IKBodVH72hq1dAV4qoaWGpyyqQy5K83qn47e3cp9Om9aMVGyD0WBt4gOrRg3Pc9miCe+n19BQA6PkZe/TO9qA2kiU/JrdOpzq+See0Z2MRtmMVk3owWm1Lwcf+XZHZfYuUDuJbH9JZesvbNGM7iWON9uNeNl7IJZl7x/xrlZUteIA3wL23rzrKUNHhoKURCivgxCv18uKPNCyX9/vSMhvN3ZZWFZn7IrdXE/hhdEPR7wfl9eHbc12FjykFnxkXejwi8zKH8asDac7r8Uk10swT9w7qPyWpc/Grs4n8Z/ym6EWmmq5Qu2SDSjw8/MJZJtuBC53X4rP8s9MyTYYDa5WrryukcZhrutBPFlwS9jtG6x+3Ok5Ba/pjwdUWl0rUfJrtvEp582GCdjZ9W9cV/RsyG2XSFNwhedSrBp7PtROIttfMml2StjoL0VNj3pnS50luM17JlZMvDziff20aiNakAW3ZIRu0mFDRoaC1ERkGxD0Yo00Gos9x2OH0h2geEs1WXkezYIsU8QRzEc9+RMqm+w4bsdS/N/xO6pC0i02JwrA85+aC4ah3uKC2+eHVqNBaU560O/UWDyoRy70hdwyqwa2WIBvfDsiC3aMSu9taaz2ZOFj/+5IK1JPdoR44bdwf+w6XQkq5VLMzA+f47XGpccLvsOwW0EeTuvnb/5VZ8Ez3/+FhvpaDB9Wjkv3m4CS7MjuDTXR0pFpwKTnrgK5GaGniGvbuD9xiTn1znOw8qFxHm5274lxRZn4JmD9liY7W47Mz4h4X19udOIB9x14+IiROJoq1bl4ny8QqJFBYXkdOXIk9tprr2QfxqBhmWc4nvIdheYxR3Wua7Lyh1xemIdbII9+u54prrM1q3D2yjOwcgH3pUo2TU2NcIGfg85cjKoW3smXZpug0wa/HWrb+UO7WEUP7V+dw3Ge5xr8Lk/q9EsOpKnDopaXOfj91aSOUsbNGu7SUphljKigQX+v5+crajDv8YWYtuJevNp0Mq5Ydjief+QWrNrOB0WpRP5f72CJ8Xzs6PiZ/Z+bHrq9uKzN0MKH/Ex1WquHIkq/XNDjPtc2rUM2rKjITYtoPy02N5ZX8fa7xw7qLkAhEAwa5fWMM87A/Pm8IoiAR9qOGTOm31Gif1TySGxvQHWag7c9jLcNd2CybVGf33e7XPAteR0ZcODY9KWYpNkK92/Pq+LS1HtMmOp6AUeY32VTxv5VH+Izw424UvteSPnNqX4R/9C9gbESz1KgFgsykakL7pKR2/AbDtD8gQotT2AeCXXtTtz/5Vrc9vEqbKi3JrUNRoPOwdNeTfWvwVW6dzFJ5nl7Q+FpWI9p0kaMMPHUUNHw84ZGXPHWUri8fqwr56mECqV21LpNqGnl1yQWJEp+srUBeZIVs/xL8Kb+LsyxBdrvunNH41XYaDoNwy1LoHYS2f6SSZuDp8MLtPr7fX48bbsay0znY4TEAzf74vf11UiHk1lwizoGdUNFhoLURFVuA2effXaf22g0GpjNZkyYMAGHH344hg0LP0U4FKHo0FNPPbXf3x/rXIl2KQ25+q78gMOcf2GKZi1W6vlIPxxrFn2DO+SncIGpCI4jXwPe/xw7WH+GzdKKjKzkZito7HB/yDLzQCZ//TpM0WyBTTclpPz2sP8Pw3U1WKfr7yRz7KGiEYAemcbg48+59W/gCsMfWGKjIKZZfe5vc6MN/37qQczy/IqrPJfg/cVVeP/i3TG+uHsgSKLaYDSkdVSDm+lbjl10y7HUE95FZfK2t3Gx8X382UolpXeP+HcaLC7c8+bX8Piycfi0Utx1wgzAfwRsm37BcdIO2Ht87AL6EiU/ycFlV4RWzNauxqKO3MHByPDzAU1GtvqDNhPZ/pLJY57bkWuw4i8P+SHPZOuamilY0wSj7EHBsMhcnWxL38cy4z34U38YgL2HlAwFqYmqlNeXX365c5QXLMiHPgtcf9lll+GWW27BTTfdlNDjVDvkYL9w4ULMmTMHOl10l9jp8eFp/cMokNqxWaYCBTwN00PSGTC6q3FJxW597mN9VR3S/MNgzZmEmVN2xfb/FqMMdVj16+eYecDJSCakgBCFHVOf3xn3x7PuTBw4Yip2DSI/jUaLFzwHoxx1mFc+CWrhMfkBjDduwxpdcMV0I4ZB9rdAmz8yomv+6Euv4RH/g4AW+Cj/XHxfC/zzgxV454LZ/bK8DKQNRovZyxWu76Rdsc5biKmlO4Tdvs2rRY2cByk78hyv1O88/8Yb+Mj7d3xuPhgHHPs6NORmojUhY8I+HY/72JEo+emcXHYfaPbHu47dcFhF8Oh0Cmyc7XqclZD9oizypPfJIpHtL1l4PW5USPUYqamHJafLt3Wbw4BjXP/GSLOE742RuQ34a1ewXNa5+UVDSoaC1EVVbgMbN25k1tSioiLcc889+OGHH7B27Vq2pP+Li4txxBFH4LfffsOzzz6LsrIy3HrrrXj77beTfeiqglKbkMz6k+JkbVU96uVctMiZKK8Y1fng/tExAp/7ZyE7gryYb7ZOwoHu/8PGOf+CpNGgKn8Ptt699iskm+LKj/GC/v+wv4Mfyyp7Fr7z7wj98J2Dyo98R1/xHoB7fKciv0QdOUGphGMe2pAuuZCWGTyf6T2+03GU+07oxvTtC/709xvxYVM5HtKcBfuMs3HvaXNh0muwuLIJv62rTngbjAavz48cKllKJWI9++Em7znIGBU+ldOj0imY7XoCtumRZ2L4alUt0qsWsCpzc8bkwWSI78M8UfIzurnsVmAcPvHvDm1xcMXU4vSyghfNMCMnK3hgo5pIlPySSWPtVlzruRBnuq9FbkemFKK6hbvDFOblRBxce0378ZjjehS5cy8dUjIUpC6qGk6REkqK6bJly5iiqjB+/HjsueeeOPPMMzFjxgzm33rdddfhkEMOweTJk/HUU0/hhBNOSOqxDxY2btmGq933svKwv5XwXANWlxduL/d/zc8w9mnFW17FH4i7jeHXUD9+P6Dxvyhq+RPJxty6Bvtol+JPP7fObWvmHX1FXnrYCOuCTCP0IQK6khGkcbD7fpRITXi2uC5oOjAlipyOOxyUyuzpH8hHVMKEo65H+rRSkCRuG7UGe1Q+haXfHAFMfAhqpbHNhiJY4JT1sLj5rExhpimmAVsOtw93froG1d7jUDzjYJx48H4YLKR5+b1q9fOAn9z04EU4mjvaU4ZBC6NO3dW1hgr1VZvwuzwRkIGHS7vc56pbeZ82LCcyq+uizc2gCU1j4Ujkl/UueCIQqBF1PI07eOGFF3D88cd3U1wDKSkpwXHHHYfnnuOlDMnflSy1pOwKYkN1Dc+ZmaexdlZmam5qxCna/+FwwxKkGcI/uDZsb2YjdXoIVuTxzrNiGrf+jfBtQXtbaJ+6RPCtfm9c5zkPLcMPhs/rwRHtb2Ke5meUm4OP45rrqzBd2oBJWdEH98TT9cEOE+yyCaYCcu3oTqPFCZ/fD43Ut/L69tc/Qva6MXt0Pg6d2lVecs6obFRoGjCt8TPYnDwoRI3UWVw423MtbpcuwhipGgU6O8xpocfkdreXDcaIoj6yEii8sHATUwhIGTjyiOOATPUUqxgomT4eYT7X9xMO0vyOPENwK5t9+2o8pn8cVxveT/ARCkJRWcVnRUxwIydg0DF2zb/xmv4ezPX/GpHwlm7jA5hdRvIiHwJBKqAqy2tVVRWMxvAPFJPJxLZTGD58OJzO2EX5DgYoqG3mzJlsGS31zdSRFSBX25Xjz1a3AXfrX0QTKJ9o+ET91qXvYpXxVvxk2h+SdCBbV1AyHDVSEUpRj8plP2LaXkciWfzhqsBaXw4OG7ErGqs34Rrd23DJeuhy7ggqP8PGr/CR8XYsd5Cv7zyogdq2jmlBqQ2ZBV2BZgotW1ZgjfEsbNGUQ6uhAIzg1LU5cPCa63GysQ0t05/t5ts6bPfj4frhBoyUavHT7wuwx577JawNRkOd1Yvv/TNwQHoDvsUVaIEZknRcyO0bGhrwg+FKNEq5yNTz9hkOi9ODbQteQynG4rqD9+tz8BYrEiE/cgfKkdvhgQZ3S/+G1iCjRRs8aNbTsAlHaH/BBpmnJVM7iWp/ycRVuw4XaX9FvZQHSTq6c31+2wrM1K7EIoMtov3ssPJ+PKffAinjCgDThpQMBamLqlolWVI//PDDkMoorafPAzMM1NfXIzc3fDnIoYZer2e+wbSMljLbanxtuBZHpi/vXOdo4Ung27V9+1DJNcuRJrlhzug+DV+TxafprRt4PslkZxuggK3m6vXsfZ2mEFqtNqj8/G08PZYrvcsqmWzatizHfbpncbL2W2QV9M62YW3cxq5Bmia8r9rPX7+DHaTNyJNsmLwDj1RWkIxZ2JTLfZUdf76f0DYYreWVKNO1s2WbNrz1qLVuK0Zo6jFB2gZJ2/exfTD/V9wpP4H5pqtxeEXfmTZiRSLkZ7XZkCk50IYM5stLmPOCz3q5LTwdmUMf3MdabSSq/SWTrPb1uF7/Fk7Q/dBtfbabz54ZIwjWpEC88ZbfcYB2McZne4ecDAWpi6qU13POOYcFbVF048cff4ymJp7GhZb0P63ftGlTt5RaP/74I6ZPn57Eo1YfHo+HyYuW0ZLnqsJ4TTVyTV3KnLudW1vs+r4HCZkta9lS0yPi2108gy1NTauRLMgXdDfHj5ijWYECkw+2uk1sfauhNKT8tFauvPoz1aO8+mpX4kTd95ilWQ2tufdxuZt46ViLoStyOJjVLX/tG+x91ejjIGX0Tn+kn8ItzeVNP7GHXKLaYDT4ty/H3zQLMMLHz9lmCJ/GydrEZ21atfl97pv8vLMXPQiD5EN7wY7Q5ifOHzAR8mtp5EpOq59HqttgglYffObLZ+V9sduQGoaCRLW/ZGLw8Ol+i677gC3Hx12zMgr7rq63eXsdxoDfE2WTdx9yMhSkLqpSXikIi/LKLVmyBEcffTTLOkCjPlrS/7T+pJNOwg033MC2r6urw2GHHYarrroq2YeuKvx+P5YuXcqW0ZLv5QFAck5Xx+e3cOXVbQxv1ZL9fgxz8wTxuaN36vZZZgUfYBTYNyBZNFudeFT3OF433Is8jQO+5i1svT2jPKT80hxcHrrcyNMqxRu5jT9sWKUwI89XG4i/jfvCOdO7K+WB/LluI2Z5/2Dvh+17YdBtRuzCXQ4mohIbKjcnrA1GQ2nNt3jQ8G+UOnm7c5nCK6+uZi47m7Fvv9Uff/8Dh/u/Z+/zjrgTiSQR8muv38aWTRK/ry1SmJy+dq68eo2pobwmqv0lk0y/hS1dGWWd63weN3Jkvj67sO8c6NWrf4ZGktGgKYIuu3TIyVCQuqhKeaWp21dffRX/+9//cPrpp7PMAlT6lZZUReubb77B66+/3umDQ4FdDz/8MA466KBkH/qgoazDpy07N+DhbqOk14A3Lbxi0NhQgzzw6dth47tPQ5dO4Injh/lrYLfxzjXRNDfWQyfxjliXVQhdO7fW+bNDWygyPfzcTXl9WzEShdbFg2yqdcM7g+oC0Vm5Rc2XGVp5rf31XWZRrDKOQ1r51KDb6LNLsEXPk5xvX/Il1EilLx8LfFOh03DLsD89tLWZ0coVNlcYxV7B9tNzbDq9Mns36IYrWYAHD45mPqtg0WSypU0b2iVA42xhSzlNBPWoATIUZIP7tBoKx3aub2nczpRRr6xBXkHfbdxZ+TtbNmT39p0XCNSMqgK2FPbdd1/2EiQWisQulbiFpaSwa1pV21GFB0GmlgOp27wKpPLWSQUoTu9uxckvqmDBNLlSO7b99ScmzNwTiaa9qeNhLWUiS6tHhoNbKPUhpoNpar3A10hZpGAu7h3VnyxMHq5INGR0PbQCSXdwH2VtgPW8J+Zq7idnGXVw2N9qLtkDI7Ztgr6Str8YauO//r2x1rMjXkt/hv0vZQX32VTQ2XgbkM3hrVJVDS3Yw/IFu/ZZewa3TKc67lY+yHFLJpZuyaELrbzqXbzNSRl9u1sI4k9z3TbkStwIUDC6y22uraEK1Es3SzkoCvDjD0VmI8/UI5d1nykTCNSOqiyvgthZsPfee+9uQUiRsHrTdlbnnKiY3FW5yejmDy5tVvipVuv2dWzZaAgyxS5JeCPvEpzhvh4rHMkpL2lv5S4A1o7Aszw3V/IyS0YHlZ/DYUO2xK0b+aV9Bz8kiuwOnzY5I7iV0ezh52nMD15Uob7FgqlunnO3bOfwGRTME/fh21mXR+X32t82GC217Ty40+zlAyxDTnhrU3qnG0j4ghNrF7yHfMmCJk0B8mcegUSTCPmtNM3ANZ4LYDXyduQ2hA7INHYMmHSZqaG8Jqr9JYvajctQ0DHLVTSc5+MmbB0D9DZt3+4ddD+PdPEYhZxxs4acDAWpjSotr7W1tVi8eDFaW1tDVvcgtwJBcKiU39y5c6MWz9bKdaA6UxY5DVkBD/d0D1eWDObwVi1fA4/et2cFt2Q2jpyHH7ZXYgKPM0g4rg7l1aHPhdflQIHczCxr+eXjgsqvct0ylhyMAlkystTj65fnb2HDzuw0nli+VyBWp7U4uIK2/s8F2IOizCUzskd3VRYLxvBpewPfUJHg7dhcXYVRFRVxbYPRQAUxHHYaXBg6Ffq0vC7/v2DkeLhbTEZheEu6cf2nbFlTcSjytYnvJhMhv7/cRXjftzdmaz/u05813csVJaO5D7cMlZAI+SWT+g1LMEWSYZVNKCntuiddLdyabtP3Pcioqt6G4cpM24TdhpwMBamNqpRXSoV13nnn4a233grpJE4PZ8pHKZTX0Ljdbrzzzjus4IPB0FvBCUVrLQ/KqZEKETjpn+Xj2mZaTviIe2N7JX+T192SqTCqgEc1b26MLP9grPFbuwLPGqo3olSSYZeNKOgR2KDIb3J5BsjeSta3jCC+pcnA5vKiQOI+r6P8HfIOoKW5CXkSzwNbXD4m6D7sm3jy8mrzDGRrwltV9FkFqNYOwzBfNbav+BGjKk6OaxuMhsbmFqwznYkmvxl62csV9iCpwzqPyetHkcwV+5yS0Jb0+pY2zHD8xrYrmZ2cyn2JkF9DR9q4LD9XTJEWWnnN8vM2l5adnFkTNcovmWy186wQVSjGxIDKfz5LXUSBi0Tt+sWg4e12TSnK0sxDToaC1EZVyitlEXjjjTdYOVjKKlBeXs5Gf4LoIAWfUo7RMhqkJh6x3aTtsq74fX7kym1cMcgPPyWb6+ABUKbSCUE/H2P24wjNzxi3nVKvhLf4xQUbtzL40wvQUr0BdDZ12mKM6lH2VZHf6I7gFItePRWVGi0ODANXJAylvYMsaqs2gI66DZnIzuj9QCLSG7ifm790ZmS/mTMdw5qq4dlCSu/JcW2D0dBUuwXkoOKU9MiXuAKWUxTaMlzf2Ihyyc7eZ4dRXjf8+il2lxxo1OSjYHz39EGJIhHyG9W0ANC4O6tsSel5IYODzBTBLgGZIfLAqo1EyC+ZuDsywNQZKtDlNABIVq68+voKXKQZqG28H2jKGIuyIShDQWqjKs2QWbsmT2YuA31V2hLEngLLGrb0B7hCt1takSPx5OzZhaGnZEnJLfVtZw+4guGTgm4zKt2FxwxPwOXQw++7F5oE+1JpHTzRuiazANb64DleA1EKFDjS1PPAbmmsxYiOjAmY0VuRtGz/iy0b9aUIFn7j9flR4VjLrlNuED+3YEgVuwBNn0Pfkrw0Z8For+OW52qpFMPQBCf0MGWE9ttsrtnMlF0L0pFlCq7YE77137Ll1oK9UDCIqwtdZHsKJYYmLPNNCevParG0wixx963sFFFeBzu5Tp41w5bR3f1F7+DZUaTMvq+TvpH3956CyXE5RoFgyCiv5ON68sknC8U1SZj9rcyX0pfeNeXU1lgDUgccMCAtLXQeyNrtm1EmuViKlqLhwS2vxRVj8Kt/Mrb4izCnsRnDihNr0TS6uV+kLqsY/lqu+Dh65HgNRNORcsqbGd6PMpE01/E8pU1yFgqyeYqjQNz13HpuSQt+XlurqzBa4taZ0kmzI/rN/N1OwJzfclHrLsJKjw8mvToCOByN/AHeTlW1fECrlIuSMO4d1np+zVt0Rd3cYgIhK9OwlkXsvXH84M14QoOYlb4RaJEykNERlGgwB59qtjTVg1R9h2xAWo8sIoLEQ5bwUb5K1ldrC7q7BqW5+eySLrtv5TXfxmMUTOVdJWEFglRBVWaFCRMmsMIDgoFBrhbz5s2L2uUipyN61ZDXFejT3t6GGjkPjVJB0JyiCo1beOWsOk1RyCo9Or0B/8i+F9d7z8fm9sT7kKZ3REyn5RSh3mPERn8pvLljQsrP6OBTcxqzepTX1nquvDbK2TCbel9fTStX0LzZwafFq6q2Ybl/FCq1I6FJjywIraykDM6MCnj9wKrtfIo5Xm0wKlq5m4pLx5V4Sx9BKu5mruxajaEf7NXbtmC0vA1+WcKoncOnEYsn8ZZfk82Ncz3X4DDPffDKEjyyFiZz8MFkq8uHD327Y4E2Mku9GkhI+0sS5C5TIXELa9GIQKcB4BvtXnjBewhQFN6a6nC6OqvSFY/bacjJUJD6qKpVXnvttbjwwguxYcMGjB0bPIeloG8otcmOO/KiANHkeC2RuHJXNKoraX2VfhTmuZ7ATsNzEK7C/Sp5DG5z3YY9RqTj6jDbjcrPwKYGGyqbbJgzLnHBHxSsY6agEw2QmV+KV7Vjscg9F4/uwMvWBpPf4QuuQ11LFR6cGJmFMhG4m3hVMAkyJPJF6zGgeEd7GN50V+DYsTzFVU+WOYrwoPtuHDOlDA9F+JsUILnj8BxsaLDC4uxe/zyWbTBaTFaujP6ZNhv3tx+AQ0bl4cYw26+UxmOB5zRML5uEUI/2X2v9eN/9T+yX34Rzc5IXWR9v+W1v5UF9RVlGzLPdA4/Pj59HBFdi6jWFuNJzKaYUmpEq5WAS0f6SxYbqetj8IzFaqsGw8d37r//49ket14mPS4O7bimsr2/HE55LMdO4HReWjx9yMhSkPqpSXilAi6pl7brrrrjyyivZjWM2B/dN22uvvRJ+fKkCRYk+//zzOPfccyOOEl25fite85zKOsTLZuzTzUJD5GWG90Fe3woskcdjp4rw9d/Lc9NggAdNjWRhT1zi/0arqzOoJyu3BNtb6zuPJ5T8tlvGohm5KChQT8BWlUOPb3w7IU1yYXwQf8xf2vJQ75+Nc3uU51UgBZQYWxzd9O9Tp+wIXY/Atli3wWgxO3mRiRZjKbbK6fAXhG97S5yl+M53CO4ZG7yiGLFsux2/+Kdgh4nh9xVv4i2/7S08cK0kOw217ZR1QEJuhinoti0dfUBueupEnCei/SWLH5uz8aTnOmglCRuKus8Ktdgju1Zr65342r8LrGX5uChExpHBLENB6qMq5ZVyypGVh/zObrvtNvY+FKHyvwq4315DQ0NUUaKrN23CJ/7dMVKqxVU5w7opfURBH8rr5kauFI3sSIcViv1tn+B2031YvG5f4PAPEna56tvsGEFlhFhy/0LUtvEps7Kc3sorya22vhHNTq5cl2YHf6gng4Xu8XjCswOuNn2MOT0+c7h9qLfw6zUiLz3o9zfWcQV+bGFvf9lwRKO49rcNRgMlWC/y1bLAM6uGQtM8KMwK30a3NnOFbXgI2RDLq7lbxNTy0IFfiSDe8std+RKWG5/El87j8CcOglGnQZohuBLTbrFABy9yM1JHgYm3/JLJ8ireRvMyDN2ekZRqcoRvC1op00ha+Ef7xnreX48ryhySMhSkPqpSXm+55ZawCqsgflRuJ0toDoq1vOSgwpSNz+Ndw3w02imyPbTFaveaVzFMq8O4rOBTUApKrtgMJ69ulSjqrR4c5XoWu5ab8ERzExbpz8MmeRiKsg4Nur1WduEJ/WPYLhUhJz34Ngy/D1vevBKa7Usgn/IuhpfF1z+2jU3bG5Bp6K1MVldvxdnaL1Ctrwh6zKTwPd18LhwGPdIMb5LdDalKfasFJeABeLOs32CKrh6j5bMoIVvQ7ekBPL5lAdKkHAzP7qn2c9wuFw6r/TfKNKMwvSz4NoOGtm0wS3akSy68bbgDdToasB4SdNMJfz2LDaYX8XMz5bx9NuGHKuhOXS0FkuowsqD7IKy9fgu+Nl4Pp6yH0XRKWLFlVX6NAzStmJStHn9+gSBllVeytgqSg75xLeZqjCg3cMudQo5lPXbR/IXfNd2V2kA8Hg/OcL0Jg96L+syLwv5OZjEPJMrtKGGaKBSLZLY5Gy21mzFBssKisUKrCT5YSpdtOFz7K2qkorADqk3fv4bR61/FVe6LsOSNdfjyiuKQFqxY4HJTjlwZWWm9rYxtm5fgFv1r2KIdDkm6vtfnTU2NqJDqmbXSUxa+PKraaahajxIqMgETZtu/wwTdBqz2hx5kNLS04SntvwAt4Ek7Meg2W9f+jvO1H6Ndm4Gs/FsxmDHaeBo4SZ+G3TRrsQWhC4donHyQoDGJTANqyDTwgOsODDfWYb7+MjIbdH5msbRDK2fBIxnDZt0gDm18AZcatmAl8/6OLN+zQKAmVKW8CmKDXq/HKaecwpaRspv7Z/zT8AcWGLoH+rxtPBYvNk/FCSP2D/nd6sZWfOM7ECO1DdivLLjlSyG/43Mqzep1u6AzJCafb327szNAZb1hMi533Yedy4y4O8i2JLdhOx+C23/zojw/C+eE2e+nm7wo9O4DreTHliY7/rNoK86ZEx9/SbIevqa9A8N0jVhpPLLX51V2LWp9uyItZ3hQb+JtNgl/cz2MHTKteCojV3VtMBpaqnian0ZdCT707YVS7yjMKe0eeR3I9to6bPGPR7HWiuGZwQMF1zR6sci7L4pyMrB/H5XH4k285Zfl5GngajKn4NKayzCqOCdkoOWruZfggupDcN2YSUiVfAPxll+yaKjZgny0IU+yYsTI7kHNdaZR2M/1DMYUpINnKg6dJm2JdyRskFAwcuqQk6FgcCCU10GIRqOJOltDlS8PazTDgfzuyucS9zBs8ufgjBBVs4gtFuBu76mYkJ+FA/pIq5JfNAxuWQeD5EVd7RYUDw/vZhArirZ8ipf1H0G2HYq17cdhnTwcU4qGhZSfNWcsXvIdgiPDuAHQNPzzVRVo956H43YqBxZX4c0FK3H6zoXQm6LzKY2EdqcXhVIbTJIHmXm9p/wX+8biVc+VuGjiGATLUFrd5sZWuRjF+aGVvGS2wWhwduSzbU2rwNMNfGC1pCJ0hPUmZwb+7r4Ns0fn480QVqnfrQV41XsuLpgyGqGHaokh3vLL8/FUS21ZY/CpvwyH5Id2IWm2+9GKLGTkpEZp2P7Kb0uTDf9bU49v19Rh34lFOHfP4GWuk0nVX0txmvs+jJTq8fy0ud0+a7PTrAyQkxHeIFDV4sA17gtg0muwOkRO7kS0QYEgZfO8jh49GmPGjMHmzZs7/4/kRd8RhMblcuHee+9ly0iwuzy413syDnHfB/POx3f7rLFjur0gM3SwxtYmPuU4PD90IIwCVdWq1/CHYEsNr3KVCLJaV2OudhmGy9WdaYKCBWsRJLdP/vcje19iDh2sta7OwhTKdIMWtx0xBeen/4D3XBdgzUeRJqGKjgaLC7u6nsQhrntgLO2d7Iksv+GCtao7zntYiPNOZhuMFl1Hta+mLK6I6zQSctJCW4i2NTv6DNb6q467xowvSv70eDzl57DbUIBW9t5n5Bb4nDDR6ZFGsKuJSORHMxkrqtpw/5drccBDP2Dv//seyz5/HhWV7+GrVYn1yY+U9Rs3wIZ0bEYpSgq65zVu6VBec9PDW0o3KcG1+RnQhHCbSsQ9LBCkrOXV7/d38yfs+X8oRPRj31Cak0hZvGYjXDDACDd2mNw1jeR2e3CE5ws0acwoSOs+yg+kpbYSeWjH8NzQ9eIDadUXo9xdC1tHxaNE8K5rdyz05OHcSQdh4s9v4kJtI8brT6dwlKDbD/Ntg0ZqQ0VGaN/QTWuWYqq0CQXDpiLDqMOs8WXIWWuDf+3z8Dr/Dl2Mra91bU44YWTXKqu4t2NAe+N2SNCGHETkbfoYV+mWIV83LyF+btG0wWh51n8EXnaPw7xhMzFu81ZIGcVhH8R9DrBkGVLdShhRgHHFsbeaq0l+9VUbmVuJXTYiz7IOB2nWYJSGrHXBp5DPa38cNp2MImkcAPWkjeuv/JweH975Yxu2ff8yptl/wSLvQVgvT2D+7xUl+ThBMx/+Y6dDjWyv5yWuK0zOXs/K0k3v4D/697HdQcU1dgm5j601DdDAj9GF4TPDxPseFghSVnmtrKwM+78gMaxYvQoaGDBcqofO2PVwb2ncjrv0L7FqQ0i7JeT399jwL1xu+hG/2ShIiNdJD4c9rRRwL4Ono+JRvKGH1UIrTYuW4MZJe2HOF1dhhH4rlmpCp1y/UPsBZug3YqndHPKhXrj2VXxifA8/+ymydy52PeICVK99AsPkBiz79AlMP/aGmJ4HJRYnhkmNyC7qrlT7fH78x3YeYATa9D+TrbzX90c1fIvjdT/iD3/oqcJUwOeXsaTFBLd/Os5CG74xXoctflLHjgr5nRM23YgrDetR7aSg0N5ToS0N1XjTdw3cRi28eYlpl8mipWotU17rdKWYVvtfnGv4CossfkpkFzRA6HDffBh1HtQYgnmIpxZUIe7yN5diY4MNT+h/YUGZuoIxOH3vEzB3fBGyNXOAullAHyn/ksUYx0rco/sLNebden2W0boeu2pX42eELywwYeWDWGP8BD+7LwYQPB90N5ztqG20Y7XFhJ1H5sFsEj6wguQjfF4F0Nf9iVXGf+Mr3d4ALuiUSHtjDaiQZquUhbwwvqzZTh65nFYYWaCSJ6MUaAO0Vv69eLO5kVvdskw6NqWm8fFKYpn55SG/UyDxXIrphRUht8ls41PXUhGfus5MT8Mf48/DsL/uQdmqZ+E+/AoYTLGbovet/xYP6D5hVhN99jXdPqutrsQwyQ2vrEFhafDiD9kuHqRjKojMQq5WyO2DKqYZdBoYXbyWu7WP0rDFrkpUaBrgygle9KR2w5/I7ShvXJGuTsUlVjhq/2LLVlMF9G7uPqDJCC4/u82CDIlPR2fnhy6rmwqsWDQfZ33ahka3nuUE1k0/H27TXjhkyuFAqeL/rgdGdEXwqwm6FqOkakzVVuL7vN4zJxoXv5ZIC5+jONOyGUbJg6ycvq3okuxH26snoaqhHee7/4ncrAy8ed4sjA2TH1YgGPQ+r4L4QNGhF110UcRRotnWDUiT3DD0iEC3N3Plsl0TOjKdLDPFXr5dzrDIgq+kLP4Q1Dt40Ei8WV+5FSdqv8NR+Vshez3IAfdtNBeUhQxUKAJXcHNKRoZViIjs4Tt0rtv5yEtRjzwUyk347cMnYnoemY1/4njdD5is2QJou1/b5qp1bFmvKYJWH9w3scDH05OZS0arrg1Gw7YNK3CV7l2cZF4Jv4X7JrqMoYOJLDYbhsl8u6LRwaeDLdtWsmWjSR2KfTzlh2bua+7KHoUMD0+DZcwJrpi2N3O5UZBlWnpwxV+N9JTftmXzMeazE3Cb/DR2H52Hr6/cCwcf/jcY9v8nUKpOF4GebFn9O5t1IYon79nrc72bD7g16Xlh91Po5jML5vLwJWRZtoHpehQ1/Y6J0hYM1zajweLEi6++BH8Dz/YhEAxJy+sdd9zRr++Rr8/NN98c8+MZLJB8srOzIy74UOGpZMMYfUn3ICBXG1d2rPrQnWFrcz1ypY466RFmDtBnl7Jlmot3xPHGuvFn3Kd/Hg3WEWhrPIBZ2MhCmVcYPMK6taEGhZKPuUvkFwW3vNraW5DfEfRSOmZa5/rMjAz8Nfk8FK2+H6PWPIOG1otQmBMbK0Wmg1tOnZrefpu2Gm5NazaWIZhK7nTYkdehtOcUj1JdG4wG+4afcYXuA6zHZjRYuQuENz20Fal64ypMlPywIg3mHu4WnTSsZQtHDvl1Jp94yi/Dwgdd2oIxMG//nL1Pzw0+kLO28AFmq2RGUZByxGolUH7Wuk3I/uBUVpChIs2NF0+bBlNa6gSfKWxfuQCTJCs8shYjp/R2GzB6uPKqzww9C2G1tKEEvN8tGR06TRbD2Yai5f9mb/9beBHeOv0kfPTgBTjP+iGqPv0D5We9MrATEghSVXkNVpQgsLMODMxS1tM6obz27WR/33334YYbboDRGD5tSl2LBRMlXip13KQZ3T7ztXPl1W0MrbzWb13HlMEG5KIwPbIo7bQ8/qA0e/mUb9zZ9gdbOIpnwtdYzY63RcpGYQhXiIbqjSwspUnKQWGIPLT1VRtAKmA7MpCb193qN+PIK9Cy5hmUowGfvHk35l10b0xOI9vH5bUlc0YvrzZfE8/Y4cgMrpy11FehtMOClpVboKo2GC1/2vNQ790Ho8pmwlCzmK3ThKkU1LqVW1Vr9RUYG0IZTFcUumJ1+APHU3757iq2TC8dj7xlraxoRVaIWQhnWz1bWjVmFCF16JTfddeg6dXTMQJWrJLGofyi/8KUlppuIc6a1WxZiVKMC+Laku7jPvGGrND3d+3mVczjm1Kf5fThBrL2m5cwWbZgk1yGg0+9BkVmEzKmHg7Liq+wpsmP0E5XAkH8SepQev78+b1ehx12GJuuOPvss/Hyyy/jiy++YMuzzjqLrZ83bx6+++67ZB72oOL3Hz5FrmRlJQVHTOmegly2cauLJy10Z9i+nU8fNem5NTUSsgp5t5frb2FR3vH2d53k4ApO/sQ9YWviLg5tYVwhbI18Wq1FG+a8a3me0QZt70e6xpiBtt1vZO/3qX0Rv/65fIBnwXPKlspckWgrmd3rc0M7V778ucFdAiwd590s5UBKIQtaMD5tGY5/eM+Db7dLkOniAyxDbmjfZG8dt6q2Z4Z2l8h3V7NlZok6LK/xwul0oMTP21Fa3jCWb5nIKQye89hl4VY6hy4bqUjV/57CCNsKtMtpcB75HPJz41ucI56kO/l1qzUE92nP8POZlbTs0P1WyzauANfp+1Y9NSvfYcuN5UehqGP2aPe9D8HOrqdxQdOJnYVfBIIhZ3nde28KEOri+eefx/fff4/FixdjypTuUeunn346rrjiCuy+++448sgje31X0D/qtqxhy41SBaYYugcX6Rwd0/oZoW0u3kauxFnTQysPPckrGYHT3DegQc7BB24f0oy6uEUWv/TmW/iXZgP8kJjVwPXtu+wzWxhXCE8Lt0zZjKHP29mwhS0txuBK+8j9zse2P19DhW0l9B9dBMv4b5GVHjpnbF9sb7UxSy5RUNHbOpjt4MdsKgqeA9nWzF0OLLpchE5Hr36sLi8qO/LZTirNgtfLH+gZRcEf6IShIyesLz+4WwtVeivyNzALZGFFFAUcPA7I7TWQzGWAvv/XNpFsaXbiJvfNmGiox9l+H1tnQRqyTMFTiHk7lFeXIXwQkBrRyh7kLX6cvf+2/BIcPSN1y6C2W9o73ZT8pTODxh6YZStrwxlhArE8ddy9qD0jvG/3pnXLMdGzBj5ZwqQDzupcP7IwExPLC7Gsqg3f/9WA43eOvN8XCGKJqkwwjz76KE488cReiqvC1KlT2ecPP/wwksnvv/+OQw89FDk5OcjIyMCsWbPwzjt8lJpqmFu5VWqbuXd6FSWSW5sVWonTtnElzpsdWnnoSWZ6On7XzsBaeTgarPHJI/jVsq1Y/PR5uL+d0ncBtlEHAVkl8Ha4QrhMYabOLVzRc6WFnlaTW/l5OzOCW6yg0aDg1Odhhwk7ySvx23OXsQdMf6navJZFCNO0f2l+d+sRudL0FTTnaeXnZOsjKl/trFxfiR2kTRiZrUOWQUKBzAPrcktD+/Hm2rlLhak0eIBK/bYN0EoyHLIBBSURPIw9Dmx87TK4766A9PhM2O4Zha0f3UXmcaidtfV2/C5PxOqSI2Fr4fdCmxQmINPOA7o8HcUMUomxvrXIRRu2ohT7nRyq+G1qsGHFoq5grR1618+zW9ugl/hgJDsvtPKqb+HGBl9e+MpZW+e/wJZ/yJN6ZS/ZazztX8aaVQOfURIIBoXyumHDBuTnh3+40ucbN/IbMBmQa8Mee+yBhQsX4vjjj8eFF16I2tpanPD/7J0HdNxU1sf/0vQ+43HvsdN7D5BKDQQIsBBaKAm9LLBAqMtSlhLYZeGj99B7gNA7hBIgQHpvjuPe7fEUT5W+896zHdujcZyQeDSOfufkaDKSNe9d6Un33XfLGWfgf//7H+SAVqulvnJk2x3+YARjI+wBZBkwJWp/WySy1hZbiTN6mcVPk9zzCHbis0xS1RBq3Pt/6ana1YLgB1fiPNWXTCkpmAnLqSzyn/NW7zHAR+Njx4i2rNjHuNkyM+yxlR1DxhBUTXuAfj6q8R188+rCfS6w0Vy8im6rRAdytGx5sI2mhlrYOJYOLC1P2nIYcbM+hfROWd2De0vz6iX4RHcbnufvRV3lLnp9SQCLI8aydzgcRnaE3aOx6rjXl7NMDVWqdPCqPTwSg16UPXYcCne8Ai3IZEIFk+hD7qr/ovKtq/abG8yBkt/mKnbvDE63wN+02xofC76FTWAFQ/cR7HKjubEWJ/A/08+V4xbAatpz9b/urP0fri7Hcz8WoSXIFMTe5sdKHk7OTQNNc4dNjNrvbmQrEMT9y9BN7IHdxybd+vTYvt2+QBD9Kz+ln82HXRB1D04tsOFH7T9wx86zITSw8ykoHNTKa0pKCvVxjfWCJxW4yP7k5PjU2CYvwosvvpimUvrxxx/x7LPPUoV1zZo1GDhwIG699Vbs2hX/wUzk53K59qgoffPlByjkK+nLf/zh0Qnera35UI1Jsf1Zk4PM4mfaS1/BY7QbcIXqQwRKmD/q/oL0+eNX/osT8SMi4BE+/XUYznsHMDNlVe1j1gvOFFt5NbQwRU/dTRCQ2c9e/Fpn9xbngiPmYdXQG9EomnH7phzc+sE6eAPMz3Bv8JeuodtqOODs4jZQs4tZz0nQnN4knc6Ia/VfDhtTZXUP7i2GClKAAWjJmIimKubnW8cngVNJu55UlbA0cMRiHUux91UxtwKXLvZkhSKKKH7pYmQ3r6I+lIsHP4SyK4vxWsq1NDNFxtbX4Fvx5l/r4AGWX9rWN3GG6nuMTgoi3JpNxK+LPaFRteYO5faQfklubHn3Lpi5FmxXFWLCrN3L3j3F1RLCx2sqcP0rS7Hwnltgfe8sPPTZKtz8fnysjb4d7L7fzveD0RStnHqa2Phu5iwxM1SQlZ+MMJvIOXKjy0u3sernz5DN1cILA5zDj466B0fnp9LsE4TazWyCoKBwUCuvZ599NtauXUuDsohC2JHVq1fT79evX4+5c0lFo96HBIoRqy9p5+jRuyPzSUoWoriSCNeXX45/+pBQKISnnnqKbrsjsJr5f25FLnTmztaXHTVu2EWWeqVWsLYn+u/I1op6pIpMGXTpMyWPicWx4W9xo+ZtaMt+xf7k599+wdw65ufWOPEGqIee0L6PtE/jb33Iq5Ik20u+s4SYFaORj31MUrh1yVWbvsd+jzn9n/hq+geo4px48/dSHPngUpQ9cjTq69jv9IQUF3tpLtNNhcqwW0Elv128bR39TComxWov52G/5VY59uo6Heh7cG/wB8Po72UW6HDOYSgtZsGCDaqUmP3euPYP+rlclYldTUHJY3zVbCWncQ/3cOmPryK/4lNq/fpq1KM47cwLUZBqxakX/wuv6M6kx3i//c9+cR84EPIjzGx4DQ9onkNGuHy3H7TKHlN+aj9bfWnmpJ8BcqRi11ZMqHuffg5M+yd4lWqPf0MUtE2VzXju2/W449Fn8dy9V8K+eA4W7vgb7lU9i8NVa3AUvxIfrq5AcS/LYXuNB9lu9j4sMY+UvA4Vlexaejgz1pe7JI9Zs3krVeiJH2ujLivm9RRXswnY9pSj8MwLL0fdg6Q4SIWRTQSbi1gmFwWFg7rCFkmdRYK1PvvsM2phJf6kxBpbW1sLr9dLHzBHHXUU7rjjjri0jwSTEY455piofTNnslKjP/zwA+IJeSA1udlDaWOFC3aLCf26lDokx5TV1GN8aCWdvtQ4J3Yq6kr2n/fQu1imZ0tk9e8vwGXhi7FkwQnt5yLH/OOxN/CZToRP1KLunatwSfiSTse0HdfR0mjSqen+CvsEvNsUAc/ndFOFu/tzdD3G5Q3A+PWN1NK23Twe/Y+9udP+kx/8BEu0NbTPzRu+xMlrUqP6dNKDH+NXXT0NfHAtfxUnL+Oijpnz4Af4U88sUq7vHsbJX7n22O+JI4fh5Wwfbnl/HSyuLQgGSjD9oT/x0vwJcBi1kv1qO0dFXSOmCptom9wZh0X16R71h3Qk1wfUmEv62KW95JhXtWW03+6i5UwOPbxO+3IdyDGhYID+v7jei0GZnVM97etvVW9fhUO4BgShxqVfevDP1n43BjicHaPfd6lfp8dUhsy4PMYxT2vWACrAVVsRUzZudzPSl7LUfh9Y5+LUk+e07zdoVdAdfgP++YkBn4an4amdDUi3GQ6Y/PZErN9p8vrxcXgSTY13y9f1uLu13+76qqh+t8nmHW1d63j5CievST/g9013x/S07yXv3Y1MLowVwkCY+8+I+TukzPDO0jK4tyyFrnQZBoc3YR5XwnxHO7wZtxjGolabhamenVjuHYLP1lfiihnd+4zuTXu76zfZf93Dz+N/GpburbmhVvJaLfnxT0zTAr4Ij5KnT8Utoejn9cOvvoeXdcT1KAkVi87B+RLH1DY0YnTzD/RZExhyGvDTGsn21luHAC2fAhWr9rpP+0s2Cgc3slJe9Xo9vvrqK2q9fOWVV6gVtqSkhFo2J0yYgHPPPRfnn3/+AUnc3RO2bWOWngEDopfI09PTYTab24+JB20vnHNUX8OoMmHbCxfg08ghuPioUUixGaFSaVDvB+78aC3mq77EVHUNPKIe/6qYiFfrvO0PBvLAuFr9AcpFJ/QI4ljVCvAc+f7Y9t8ix8xXfUE/V4sOzFStBMc91+mYtvbcqH4Tw/mdKBdT2h+qRXlz8Mj20ZgtZmJQObPwxnp4k3Ms1DyHMfx2rBL6Sz6YyTG3qF/Hmer1NPDmqvq/4cmGlvZjvttUjen8amwXsxAUNBjDbafn7Nqnf6tfhAbsgXkEvxomTSDqmJvVb1JXixBUOJ5fDq0mItnv+arPcLRqJXaJae1t/vb66fhkZT4W/myFWBvGry/djNNUP2KzmNepX23nuFX9GgTwMKiD9HqoUod2agvpQzJYfkcNQpJ9It/tFDMgCBwGcuVRx7T91s3q11DIVZH3Fh6OnIDbrlsQJeOeXAdyzDBSBYw/B2c/+gUWLzhpn85zvuoLnKT6BVvEHHrMvRlsiXKLaSLujLwCBzz0/ypEYva7rTADBzHmMSmtpYDzuJqYsrlf/SxGquvpNXig9hCMb/B1au/CD/+ECaPQBC3efuFBfC+MkZgYfYKLVZ9gNL+Djpk/hME482+nYsz4w3osvz291DueYxS3AyuF/rgtfBFtC1GE7wvPRYZNj3/5Frb77JJSyLFkQ65PpejEUG5XTNmcrvoeh/OrqGvMrV0msG3HLFC/jVSuERGour1vYt0Te+p32/6LHnwNn+s+o8rXDzgErz/2Zbv82n7nNvUrMHEBZHN1mM0Vg+fETuuQ1aId5QK5c0zUUjnatwqDWlbSfcO16/Dehmuwc3jGX1bI29pzu/plpHGNNGd0136Tvz+H/waFHLOsTlWtw0I++lr14yrxfPg4mNFCnyekym3XY05SsfFTDyuO4Vd0Oma3bF7FRHULygUnrvjKgxM7JKDpeJ1Cgg0htQpp3i3YWetGvxTLfr+WbGL+AlK5JtpmqfMoHLzISnklEMV03rx59J/cID5oBKJMS2G1WtuPkSIQCNB/bTQ3N0d9T/xpST5bslRDfHzbUKlUUKvV1DWhow8S+Y7sI98Ti+s9mkW4NXQhvlLfhAx1A05X/wT8tLsNxF5wl2YQXggfh5PEX6l14ibNu/D4T6O/R36XWH1IANCJgXuRxLnwle5m+jJtCLJ2tk0eKuHEJP/jOEb1J27TvIHhfDFVo4hvcCQSoe05lN+Ae8Pn4lrVuzhH/Q1tX5P7cFh17E3x2ZoSjNtwD8012yha8Gj4b3j72hPQP83aqU9k3xPhk3Ea/0P7OQIWdvt6WoK4Xf0SPohMw3B+F3yiDleoP4LHP7e9Tz9trcFSYQqWC0MhgsNnpE+I7lMy14xBgZeRgQZ8ob8Fw8XOfSKyUXECPWYctwVv6BZimLj7PORakIfy4fxKvBw5FmmcC7NVv+JezfP0RcGLEZw4OguFqUfD+tyDKBPTUcDXwCiG8Jzmf2j6YRcCE46CB3m4T/Mc3ggfReVLIP2ayK2DIAxuv07kutwRnIeFYRMuV38oeZ1G8kW4PTQfpWIqntQ8guFc9HX6h3oxHgqfgW91C2DlWjCOfxg//ZKHzJlntffpavV7WBSeRX2JX9MubO9T2z1JznOF+kN8FpmEpcIY2DUB/EvzGprcR9FrRe5rjz+EU1U/4vHwKRjPb8VN6rdoPz0txyAQYNeTnGcCvxlvRo6iCuWxqj/xT/WrGNWwmSol3v4nYOTq/+He0FzcGL4EV6g+wmTV+qjrRGSxMHgWXowchzmqpZ1kQ/pEGMYV46Qgu/+e0vwfRnJFNCFRxz6RUrSLhenoL1SiUkzCHZpX0eQ+plOf5qm+xHOR4+nv/BoZjIfUj0f16QLV5/hcOARXaT6i352GnyF8/AI2lN6GYacsgNsXoBPPjyOHYauYAw0XwJ2al9Hsm9l+HqKAznn0a/xN9RMm8pupMnhn6Hy8cfWxKEy10ut0j+YF/CIMxy2Ri3G3+sX26/T7DubiMzjNhGG7duG20AU4J3QrntQ8imH8znbZkD6RvhN5/Td0BlW0/6t9FqOxHfWC0P6sIn0ik71/hi/EPPVX0AoR/FvNxmbQqqGBPqRPc1Xf4IHwmfiH+n3M4b+nik2z76hOsvm7+gP8GBmFlcJAXKt+r32Mh+06+ozbWtmI0x75CofyGzGO34pMrg7/DF2Ed68+BnlOY/t5hvLFeDlyDD3Gz1va7z8hyUCvEznvU+HZ9DdG8iwLRaUmF+HcyXAOmYYi3VAUvX0j/hs+C/+nfQLjeOYPvZkfgMJIEYbyJVBX/Q+nPmjAiarfYEAAhVw57gmfh/euPRY5dn37dTrj0S9xpup72DkPfZ78O3xe+3UifXJ5WnCu6ivcGZ6H2apf8C/VK1Q2Ls8R7c82ch+/F5mKPzCETkbmqH9qf96QZxsZ4+SYLL4Oy4QRGMaRIEYBo/gdqGm9nm3nWSP2x0uB43A8/xsGacrp9W0MMVcacp2IbH6KDIcvrEceV0Xv8zXq6Z2u972aF/BCeBa9P4fwJZik2opd5ZsRdoxp7xO5B9YJBfgqMgH/Ukf3icjm9Ee/wrH8HzTP8ARuM+4Ks3t4QLqd9on81qXqj3F7eD7GctvwpOZh3KdBp+c+gRTwaHvGt0H+ntx75DlQVNOMLLsBKp5r/77tGdHGX3nnKsQPTtzfEQF9GOIu8PXXX1Prav/+0ctGWVlZ8Hg8MRVY4hZx1113RX1PooqJ1ZkwZswYzJ49Gx999BFWrdq9JEPy2s6YMQOvvfZap2wLxA947NixePLJJ6l7BeHzwCA8oX4QSWimihoPkT7Q1IhAhzAiooibwpfCzPmRpNHQQT37vMuRZQL1s2tDKwZwk/gktiEfb/GntH9PXDmmnTQXy5/+Ozbww+hv2Hk/rEIjjr7kLtRtW9XuPsFH3FjAvYwmmPE7xmI1z0qpFoUd+C2Ug5HcDug5AU2iATkqN9I0IYydchROPHJypz41BwMYwJVgAFeK5aopCHG7I2CPPvVcpL1zHP4pXoPiiB3HareA4/hOffokMBi1ghlHazbjOdW/sUXsJ9mnFc9cge38IPhFNcx8SLJPhMHCZkzFb/idG4813PBO1yl5wBh8+swdOJ9bgnSuEY8LZ2IYV4SUSxbjxw9fb+/T1rATM8RluFD1+W7rTysu3o6GsA73ChdiNDbiSH4FCrgKrD3jd+QlWztdp6Ag4njhS/Acjy9Vx0j2qVi1O4VWrD6tCSThaO43jFDtwhR+HWpEG57lzsOUGUfRPi199iba735cFV7jT8c08deoPvkFEaMiq5DJ1WOZahqC3O4lb1JrvtwLvL/oIZzAfU/vmy9Vx2Ge8BrK53yJr997tf1YfziEW/jnYUAQ/xEvhFF04VrV23QSUz7/D6xYtADb+MHwilrouDDSxFrJPiVHypEllqGJT8EuviDqOn397B2o5ZLRLOqQxLWgUNiCcZc+2alPxEeQDzdgl+DEAI0PIV4f1af3Xn4afxM+xFh+O5ZHBmEUX4TVp/yALz9c3H6sO6JCfUikvt4+GKAHU7CJFd9zwY/4ebsLvy79FKPF9QhwOnAqM44Uv8dP457GplXL289TGxQQIFHnfD2EDqnP2vq0/Lkb0BBW4XT+G6zEENi5FnqdHnv2WfwayMVwTR0GqetwpvABClCCB3A5Irw2qk8fvbL7HiOMDf8C5xlPdbpOEUFAQyiMhzVP0eX21yLHYod6KL33rrjiCnz87TIs/+k7nBD5FIeoNmGJMB2TuLVRfUqKlMEguGHnvNigHh/13Hvq+RexdVcJMoRy8ByH6aoNqBX02Jx8EtxNzC+XUBhej28io2FUqZGm9kv2aWs4GaZIA6aqN2EqfkfF6V916pNGDIIPMUU/R+VCGZ8DP2fGSHE1TsF3WCMU4APueBSHDEjnGpCr8iBf3AHPhAVd+lSN5eFc+hy2azVQcXxUn8rKynCWsJhO/L+MTMRIfhveTLq+vU9Bkccb/tEQwWOcqgQjidtThz4RQwqpJNaRBcKT+EKYhPXqce3fqTVa9Pf9AnBq/MYfQp/XBIs9CdddcxW9Tit//galERuKIkmYrfoDZ3AfR12nQeJWWCM1OIb/Ez8KI3GEajVeEE9D4eHntvepprwEkWADtQZn8C6M4rZ06hOhILwBKyMFyOAa4NOkg+NUUX0Ki8BJwscYz2/DOiGfXvfFHZ7ZRBG95ZZbaJai119/vf37tnvvjz9X4Pz3WPXIY7RbMX5AFs455xzq/tfxGbGv71wSrL1gwQL6vieGK4XeRbbKK5kZ1dXVdbJUdiQ3N0aN8gPInDlzsHjxYvz5558YN273g6ENi8UCh8NBXR16annNyclBTU1N+83/V2aBG8qbULboXMzg16CSS0O+WIovhQnIvuBVDM1k1uKtNT6UPDsH07l1EDmm2H4njEb+pW9jWKaV/i6ZGZPlSmL1Gc9voUuPt4cuoDPjfKeJKrvlzSGc8uDHuFvzIrXsrRUKcEdoHt5bMBs5dh29fsTndtcL5+EY1QoYuSA2RHJQhCzaHrH0d4z9bi7KhGSUiKk4TLURv0SGUguC68LlGJmb1KlPyXDhUNUm/BIZgnrYJPs0g18HFQRqGezap4n3L4WrJUxdGE5R/dxtn/6teRFO3od6wYg7u/SpTTZ3aV6i1h1iuSb9bjsPuRabq73Y+fQcJMGDyaoNdDn158hQ9Lt8MQamGOj1I7Ih/ZrJ/0EtW+vFfmgQzcjnazGG20brsBPIpW7zkvk1MgTmS784INeJ3jfcKlj4AKoFKyKchiqg60bdjiEnXkP7VPTUaThctQ4WrgWbI9nYjkzkX9a5TztfOB9/U7PI6D+FIagQbchpvVbkvt5Q0YxNT5+L09U/0rQ+ZNn6J3EEci95FwNTmQWtrT25XDVG8Tup/HQIUZlsHnYddIcv2GOfenKdSpsCOPXBj+gxbef5V2g+PlhwIrKsmqjrpEGE5u0l91Z2lz4VP3MGdU04QbWcXkdikas48U1kj5jWqU/H8b/TiUqLqMPXkdFI04VxaORPrE85AcKJj2HTs+fjDPWPKBFS4OZsKBYcyL74bQxOM7efp2nRqThctRarhEIM5UtRJdrRMG8ZRuQktd97xJWEjJetQha2ilnIuvB1DHhxJF0G/+moD/GPTyrwb80iqmz/KQxqt94S2ZA+kUIQc/73Eb232mRzW2g+3r/ueGTbtFF9Wi0WUiWDXNNfjvkYUyaMo8rF2pIG7HruLHr9yHNgu5DJ2tOlT0UvzMOp6mVUkW8RNfhBZDImfSLPuNXFdWh64WTMUK3DNiELBXwlnVxVz/8DQzKs7eepWHQ2ZvIrEOI02CHmokhw0vtvVF5y+3Wazq2BlovQ58RSQfrem8n9ATUv0OvUdr3r136Fo1ZdiWIhDb8KQ3AIvxn9+CqsihQinW9AzYUrOvUp+aVDYYUPNs5H+63hwu3Xqa1PpS/MhQNuTFFtwEYhF8ViGjIvfLO9T8//XIz/fr0daWjAO7p/0+vQdq3arJTEKt32HCDXar2Qh1tDF+Gdq4+h13O3tfNruspBVqfazvPm1cdhYKaj/ToRVyk1JyAiAt8Lo6CZ9R9MHT2EvpdIn8oXnQNn671F2kss0b8VXI3xZ9/RqU/Er3oAX4E/I/1RA0enPpHzpL40CXqEaPqvLUI29FyQyoZcJ9Kn1cW19DoQ5w3yDF0Z6Y9Kcp4L3mh/7u/J8vr95irMf2kF7AYNflowFTqNar9aXol+QhRlRXmND7JzGyABWyRyn6SiimWWJzcnuQF7mzZfV2J57aq8klyvxOo6cWJ0Dr6OA02qTrnU92QwSREr7yP5ngRnzQtdQJd/VqoPw9jIL3RpcInF1H5+syGMW0MXM58kjvkk0WP0GjqIyXEkQIT4iXkDx4NkeSTJoBZ38Unql6ylL3lv4DjqVUhSxL/X4RgywGl7wvOp9fEQ1WbsELPwrzBrTziZpd+ycr723JjEMrxByENu65Juxz6R5UkC8f/stk98dJ+I9YIoroTxFz6Ceq262z41uY+gFprZ8y7Hex0C3kifusomX+I8xH/rttCFuEf9PH3RDuDLMZCvgCbcCK2WPXjb+qXSRGibiQj+Ez4Lb19zHP5obMbaX76Es/gTzFIthx1euEU9FobPxCMH6jqFLmDyE7djldgfayMFuFnzNoyb34X6lOtpn/4Vvghv8XdjCFeK9WI+7g6fhyU6dfs9Sc5zV3geJqs2Io1rwlLuELwZnIjFHa+VXoN7w2djlup3avn/PDIed4bnY4lB235MW3vuVTOf1RyeWcG2msZj0N9uBafS7LFPPblO/ZLVVOH1Bma1n+eDLsd0vU7t91aXPpHvrle/jQoxCZlcA+4KnYNZyeNQ2KVPHc9zR/gC3HuoDvjjfPSv/QoVXAteisykyivxw13EnYOPQwOx2Khv/y2NVod6kSkBJvihEsNszGjZ8nrbvXeH+iV6TAFXSavZPbD5J6q4NsKKyYdMweLBgXbZ9JOQTUGKuV1+bbJZ0o1sRnPbsEboRycb5p/uhnbyZ/QYi1FHZbNA9RYNDsvjqjE3eAve6tAncp67w+fiJNWv1Hr7SWQiuycsJtonek01GtSJbOw4ODcdL0SGuZqu983F4DSgFu33+BPxUWAAvf/ImGm7Th2ffeS5IXXv0eskdr7eljEzgFVAPl+NZ0PHUWspgQQQ0rao1J2u0zqhH0ZyO6nySiZpRGlvu04Em9mA80MX0KV1sp8ogo8FT8aNZmP7ebLXPopzVWpoRs2Be/KfktdqUGZSp2uVJ3GtyHgg/qLEfaTtenY8T9t16vgMJRPh2V99jOljh9H2MNlcSH2GCQO5Mhqsa2jeEdWnpzQPYwAqUCam4A5yLTv0ichmjVCIQVwZVV7JOXaIGVQ25Dp1vA43qd7AZBW5nsX4R+ByvNxh3LXR9jzsynsrypGCRswaPRoW027nXdLWtvb+1XeuQvyQleWVpMMi5V/JjXX44Yfj448/xqhRo2gw1MqVK+kyHjHj5+Xl4cUXmTLTm3z55Zc49thjMX/+fCxatKjTPhJkRvx0iVvA7bff3qPzEcsrWSLZnzO3tmwDVPk67/KY2QZ6K4qT/Na2D/+DY0r/Dyu14+C45GP6W1VVZUh/muU4eD98GLXW/REZiItCCySd+7ctfQPHrF+AzaqB0F2+dK/6tLasCbMfXwanSYsV/zp6j20m1nGybEXcOaQeij3tN2lPZekOjPr8bzRYpU6bjaTzXgOfPaZH12F7jRvLt1WhevMvNEfpYaOHH9Dr1LEtrpoyTFoyhVphmi76DfbsIfSYyhfPxWHeb/F1xqXof+odktfB/fRMjAyvw9PCqTj8sgfpyzXqt546EsMjm/DN4LtReNSFsa9nwA3fynegN9sw8ujzAJX0C+ZAsjfR8sveuBeXep9BqZCCD6d+jL8bvgT+eIH66pbOXgyXLr3TebJsepTfOxz9UIEd0x9DZOBx6P/sAGqdvQdXYs4lCzrJj/zOLf97HG/p7kOl6IgZ8Eba4nxuLDJQh6XjnoC2bBkOq34DK+wzMe4f7xwQ2TTuXIXDvj6ZFo7YcNQrGDblpPZjmrwBZCwaR5fZ1x39BkZMPj7qPPrHR9L2fjf5NfQbc0TUM+C8B9/ET3pWKevj8ATc1iUDSsf2EB9P8gw88+KrouS3r4FW4YiAkn8PpROC+cEF1M95umodzeBwW/hCySC0N7T3UkV6cXgy7g6fH7O9lYtvwNGNb6Ocz0D6TSug0plQXbELjmfGUr/QktO/RO7QQ3Ag6dpvMpF467nHOj0HyTHEdzj5+XH0Wm0ScmA0mpF382+dzlP8xrU4vOFtfO+Yg/y5j0gGYz2veRATVFvxfngy9XmVzPLREoR60ZEYKu7A79nzMfGi/+tRX1y+EG5Y+AAe5x9C84j5SD5t/xcQOhDvb4UEtbzefffddLt8+XIMGcKWKk455RSqDLa0tOD666+ny/ZdFcfe4sgjj0RBQQHeeOMNXH311e25XsnNe99999GZ2HnnnYd4QgY/cWgnISFkeUVK+erNaE3yW+Kk44DS/8PgwHporOyWsztSaWJ38pLWDzse2PILrBoBS66JjiYl/48MHAasB1IjVUiSaH93fdpWXIpT+R+hSe692uZt7RmeNRY/Rl5H8KvzkB0sg/D8EQgMPB66ceegX79ppHxZzHP0T7XQf5g8oNfa206WDas+HYkx4dXY9ct7sJ9+G5t0JA8kyUxh9xVLypx895ulAGhchyQ0tS9bdj3mF/tQoH4TbK5NMc/DsAEF1yGe9GS8tCstR1yK2o/eRA5fi8blr0OYqALvYm5EOXYNcpKir3eRYwr6Nb4D38YvMOLw81DDOZCKBujElij5kd+54czjgA/ug5NYpS95l1oOO1uU2eflydORUfcezFvfQ457Nf1OPfi4vygN6X5Tsmbgt9V/wyF178H0/W0QDpkFXq1hxySb8Ie2EOmhBnC1pKhGZ+WVHLNJk4aMUB3soVrJZ8DL152BwBM30zLJSSc/gCX5gyWPa5uAkmeglPz2qk8dUKt4lPLZKBArcb7uB0Sco4C6dbAkZ2LJOZ2fW+QzUcbqn30eCO6CddB0LDlO+tlGyDznPtQ8+hWyhEpsWHQ5hl32MnYuuQdpXBhbNEMx6AArrh3b0oaUy17bMStMQ5Hh/RFu0Yi8QHEn/yZyTG1qPtAA2IJVkn0msil7eQng3gpL9hAsOS2WbEz4ZsTfMXTttRhW9hYEz7/Am/dcJfCj1WW4hPuQuoc4rfteXU1BvsiqSAEpuUocp4ni2kabYdhgMODxxx9HZmYmdSuIB8Qi/Pzzz1O/mGnTpuGSSy6hCjWxDm/dupUqsPn5ZIEyvtDyqykpcUsp1pX8IRPQDCP1Wdy1mVXU0uu0cIH5hyVZ2NYmuGK+OFJyWMARCX7weViO1Z7i3v4r/qd9Gtc3dw5s6C35TZs8GetmfYCPI4eChwDd1o+BN8+A8EA+xEXHAt/cCZTJL9l3U86RdKvf+U37d9rWspJWL1sylUJwFtJtBt8UW4YZLHDP3LARfYmjR+bjVW42/Xx2cDEWc0dj7bHvY+2x7+G3Or1kdTXtYJYjOrP+N6oENKhZOeZkA4sm78qwgUy+xCKXawrHHDO2SWfT7Xj3d9RnkrgMDD2cFVM4UAw84z4aVJcfKcHaJQ912ue2siBXsWaT5N969cyVKNggHTNQkGpBLc8UF2ekrltF9EA9A5sNrAqbPdIAczL7bA3Vx5yAtZXVtYrubtub5EzGlsMepBP6YdUfoviBQ3FIDbOQBycvQDzoTob+1NHt96BRbIHoZqm82tClEMcEwOpnFRi7QmShtrMKhtZQ99fykFnnYjPyYEILSpb0LMf7xuVf0mwmYV4L7tAre/Q3ComFrJRXYsEkls02aBoaD8vlSCCWWOI28O2338aphaDuDETJnjx5Mt5++20a9Z2Wloa33nqLKrJygFiASbSlXHxySIWbUh2zHjZs+739+2aeLbW0PRttpKJXDC8Wm4OEoLAHXE0pS1/TE8jkZ02lB8uFwfBnTIqb/I6bNJIGkF1ufRwvh49GmZgMPhIAV/Ir8PPDKPn9YwTDrcECLU3AusWAb3d0bjxIGceUsIKWtQh5WalgRw7LM5sRLqXlJqXQtSq42aqGmDK0FzCf8azAtpjXPBHRa1TwjzqfBpmR0su5Py1A/ad3oenTOyG8eio83z/c+Q9++C8Kh46ngUpOoR7++hK49cy1YFhOkqT89AYTXK1joalGWtEjDBp3BNbqd5cA2Tn4Emh0HRJ3HgCSUtKxduBV9HPB+kfQUl/avi/sZFWZjE3SubBDZqbMcK7df9MVl5qVdfbWxe73gXwGRpzs3ibBeWoLK7VsDNTGPl7fWrmwZc9jeerM0/B57nVUgc33MwV/afJZGDHjVMSD7mRoKmDP0gyOeE0DzRWdr6ktg02wkiOsEqEUvJVNVrQt3VcaJL7K64beQD/nbH8d4SrpyU8bm6uaMbPhDfo5POJMwLLbVUeh7yAr5TU1NRWNjewlSSC+rl2T/vv9fvh8PsQTEpRFKoARZZu0hbg5nHHGGZALJJKS+Ah3jKiMN+4klk5KqGDLlwSvii2hcgKLFCWVsXze2Hlya1TsIdRYSpYde8bq0iZ84BqEc4U7kXTGE3GV34hsG574xznInvsE7ip4C8eF/4cbQxfjnfB0XPOHA2P+/RUueeVP/PT1B8B7FwIv7Nk/90AyZOgoFCODRtnvWvkV/S6zcDh9uZIo6obacsm/c7bWTU8PlyPcmkeyK7mDx9EgFSu8aCiPX2GPA8EphwzG8+FZ9PMh/CZaWnSaah3NqGFoLmqfVIUqNwDf34OMr6/ADp6E2QBl639EyJxNP/sqt8S8B5t4phS566SvAYHjeRRc8Q5+L7waq8YupGWKe4NJp12LzVwhvbb1r5wPCKwPxnQ2gbXEsMZx9hy61Xml9xN8emaVDjXG7veBHMPC8NMw2P8izVsrqphLljXCFDgpRAOzFKt6oLwSZl3wL/x8+Lv4LPUSfDPuKUy9/EnEi+5kmDvsULolgZnT/Q+iyDCi0/6UbGZlt8ODZpd033UOZrk2BVhAZnfMnH0mlmI8zSZT8173luiffvwWM1RraHCvfvq1ezy3QmIiK5/XoUOHYsuWLe3/J9bNJUuW4Ndff8Whhx6KTZs24Z133sHgwWwGryANycRAgt2GDRvWnow93mhJkFLl63C4di8Tt2jsIMWs+EAzTcBP3AqaaithNNslz9FoLgRcOxCo2NCj3/xyQxXu/nAt/XzCyAwaeBFv+fE8hyOHpNF/3sAYLN1Si++31KB0Sy28ngC+2lgNji9GsjoH23zDUfvzTsw/LJ/+XW9DEnuXWMciv/lTeLb8BEw9g1r9KvlkZIi1qC7eBGcaUzg6kpE/iCqmZDJSWbodGQW7q4K1YTYasUWVj0HCDlRuXIak7N15aBMdkhLo1oxzcXVFKlJIiWVejZDIISTw2LUxDWtu/wK+UAT9UYYP9SaoDCmoMjuQ31yK6vJd4IkSVwWom0vovSh1D3rUTiBYBn9j5+XarpitSZh4Losl6C2IS1DZkY8h5+tTkO1aAc+X98B83B1wZjGFJkmoYwot37lfOidLf2gJxLbWhcwZIOHyXHN5XMZwTloy/NDR9H6cyFxAkoUGCBEBvCraFsSbmduANrjbKNMdZIl+2oyjAfIvznQnw6QkJ6rgRDrqkcy5UdLYgjFs/kUxWYnHu5kqr7Wl22G1RWfhMSWzSZotsmfl1arXoO6wfyG47G/IrP0Z7nWfwTKCTRA74guGkb+R5SeuyZ2F9KTdK7kKfQtZWV6PP/54miKrspI9kG+66SZqoZgyZQr1vRkxYgSampri5vOqsO+kDWLLTHmhIoRbc/IFtUxJFX11aOKYFdbTEPtl3LbsqKnfs+X1rd+KsPnNm/E//78wyubDTcfKb8JDlOnjR2bgwTmj8PutR+Ljv0/BdUcPRG32MTg+9ACubTwV768si4vi2kYkh5Uu5et2y7xBw5b7PFW7E3d3RKfV0ahpQl1JbJ/Watsoug3u/BV9jfMnF+Ij4TC84JuKFzyH4hXvIXizZSJ+8efBG4xQT4ltYjbGtzyGc1yXYseI6zE88ALe5GZBl8z85kman1i06JLpNtxcBTly5OTD8Jz17/SzeflDaC76E870bOoeoYaAcFO08mlNZ36SzkjsZWTOyqx1Wl98+p2bxIJ/KsRkiAFPu99nU4N0mzVm5uagD8VeUUpUqnTseg3ky1BSH70aWqdiVnJXpfRzwpHKJitOsan9ndAds4+Yho90J9LPvo9vAiLRf/PRn8XIE8ro55RZip7Ql5GV5fWyyy7D6aefThP9E0ggFPFvvffee1FUVERzq1511VVUyVVILDLyhyIgaqDnQigt2YqcwmEQiD8Yeab7GuBR2YFIDVqaYltdjNnDgSLA6d2+R58nfHYDrlGzQKP3st6E2nIa5AxRUIlbAfl39ZEDUOP247O1lXCYtHH3e52xUo96ZGO1IFJrrM+YDQTXIlzPymtKUa/LRT9/GVoqd6+kdIXLmQg0vg9b3e6qNn2Fk0ZnUgtsSygCFcdRualVrVueg0GrQmWTH2c/9xv+KG7EuLwChKHG1mo3rCOZUkDSSsUibEyhFki4Y4+XeMKq9l2H/3usHDOxDGe8Uof/ne7GYCQhB7VoqtqJ5KTOhWacWcxP0g43/N5m6E3R6Ye0DmatM/nj0+8Usw7/VL+Godwu1Hn+hiZYaHuJ7zHx9+2KzsqUV1O47ymvHusAoPZPTOPWQrfpYeDIzi4ObhLc5tkBf00s5TWLVq8j6fhq6yqRktF94SGtmseg0/+N+le/RVqwBJveX4ghc3anpfSHInjsxxLUBBfiyYl1ODqdpWJU6JvIyvJKArRI8FNHB3GS9/XTTz+lLgNffPGForj28MVRWFgom2wDBF6tRrmaWU3qdq6jW9G42x/Mp2ETlqAr9ksppZClusqKlCMU3F36sSPEUv/lW4/jTP4b6vMkzH4C6nMW744KSxD5pVr0mDe5H04azWQWLwbnZaNWkw23P4ItVURbAiI29pJRN8cOrGmxsuU6rj72RCN12HS6zQ1th+DfHZjZFyD3zqB0C0bn2OmEZGimFQPTLChMMSPPaaLXd1SOHRdOJXISsX3zevp3RbVe2NKZEkeS2wdi+ICLZmbVUrXEDhaKN6TQwfSL7scC+yNo9kdw7dtrUMszZc5dHT3xsdmdcIssoKy2nPkGd8XUaq2zh2vjMobJJHO8ajut+lTlV6ORZ24B7lrpsWC0s/6axWYkGnuSIZfKVrPy+GqMaWBFKToStLFnAN8g7dOuUqtRz7HnflP1rh61aUT/PPxeeBX1u/9wXS3Wl7eOD1HEU99tRnlTC5xWM6acMK9nnVRIWGSlvCrsH4jyT2o4yyXbQBuNxn7wijo01THXAN6U3O4PFtCxl4Dgif1SSs/uT1NukeTZxRtZyq2u/Pz7n7ig8VH62TvpH+DHnkPeOH1CfvGA5LYcm8deMCt2MUugJplZBo0+tjwnBZ/GorLNntjW2cL+g1EpOukycvnGZTgYOWdSLs5ULcVzTRfhds2r+Fh9E3zFv8MlmroNyFK3RlAb/PJVXgljch1Ycs3htGxqVrAIVaKTlkpt8kdnmCBKUp2KRfA3V0nfN4505lLhFBsRCYfiMoY/NJ2GfwSvgLXwUHi07BkWy/fYnMQmGVbRC6Gb9sqRPcnQkjOiveLZIv7UqKwh6lQWoGdyx06r5yK+28SK203gYVeOPnsBbk97HE8Hj8VZz/6GV3/ZgbKHD0fOzzfTieCtxw+hKxsKfRvZKa/ESfzhhx+mEf2kakXHMm6kAhdJ3UFyqip0L8OlS5fGpYRudywfdif16/tUdQT9fyR5MN4MH47lmgmIGNhLgPPGdt4nARGrzNPwXmQqNlRH+1iFwhHov1oAC9eCUstoWI75Z5+SX7w41l6OJzX/h4I/7qT/1+VPwn9Cp+NlIbb7ji5nHF4KH4MPhGndKsavJV2JOYHb8Vvw4AysSLXqEcgYRxcGhnLFGMKXoLl4Nb7RzKAp1aq80mnEdI4MhEUeYRllFImFRsXjnFEWfKm7GR8Hx+Ko4INYbZ0heewG8yF4LzIFFUG95H5nahY2CblYJgxHQ0NDXMZwefqRWCJMgceQDr8+DbWiDW6f9EqQLYkp4z7o4HH1LGhLLuxJhukDx2Na4GFMDTyCxzxHICR0vlctuaPwmzAEf4Zjj+06fR62CNlo8klnJZFCrVbhhvlnYFK/JLgDYTz10c/Ibl6F2fwy3DomhNmjWMo1hb6NrJRXUkWL5FFdsGABdu3aRZXXjtVr+/XrR8vCvvIKq62sIA1JbfLDDz/IKlUWIS8zDSJ4bK9hS8Rc1jjcEr4Y73DHYUvBPEzwP4F3ki7u9hxrx9yD60OX45v66Corv3z8IiZEViMADRxnP0fWpfqU/OLFwCQVZql+x4DGn+j/U/OH4MnIyVjsHbE7N20XUvsNx53heXjBc0jMYwiRgcfjD3Ew/iiLb/q7eDJo+ATUilasFfrhwuD1+FE7DW8nX0lrwhdHpKsJafrPwIDAK7hQ7Fkp6nhzyPCBWC0UIgJmEatujq7eRPg57++4PnQFNoC5TnSFGDPm6R/GeaFbUBHQxWUMp1nZ79Y0+/HT0DswIfAUvjWwQhNSwYujhNcxPLAIja25eROFPckwxWFFjTqD+moTat2dr2n6kENwZvBfuMt3Gtx+aavzp/3vxMzgf7BS37Mc3G3YDBq8euEk3Hb8EAzPtmOR9Qp8c9QXuPj0k/fqPAqJi6yUV1KhatmyZVi4cCGqqqpw0UUXddpP6ghPnz4dX375ZdzaqLDv9E9llbR21DLldXy+A8tvPRIf/n0yTElpqIUDdb7Yig5hXD5bwl6+swFCh5m+2+3CoDUL6ectBfNhzug7qZfiTe7wybgvdBauCVyGlkCYBq3oNTxdJaxoapH8m1SLDmpEEBFElDR4Y557Yj92PX8rim9BhngyscCJNUIhdZ/4VhiHDW499YntTslLsRnpRLDeG0Qo0v2YkQMFySacyy3EV8J4+v8Gr3S/Mu3M5zXWfUVIt7FjqpqlrZ0Hmjy9H0fwK2EvX4psB1NIia9lLEwG1t4mX2K5DewJ4uaR42DZFwpQjvou+ZpJeqtkM1P0d9ZJPwPS2u/zvb+WJIDroqkFePbvJ+GC6xZi1tSJsorzUDiIlFdSsYpYXm+88UZ6E0rdiKQCV0lJ99VVFORJrl2HpzUP423hBjTX19BqRGlWPd0mm7WSs/eujMtzwKJTIcuzARs2ssAvwup37kU66lDNpWDI6T0rIajQM9JSkrHEeBp+FYZiY1UzHZcTbU04nF+FmjLpSGJyTCbXiJHcDlTsjJ3abGI/J6ap1uPi5sdRvfa7g/KSDM+0YTNXgHyOpX8qrvMhzaKlUewtDdK+gElGLc1cQKjzdD9m5AAJdBqWZcUYbhs+196Ec3dcH1N5VSMMbzcp8zKsTOGpaoqPtX6gsAOLtA/imIqnkOVgiml5Y+y22Izs2dbU0reUV8Ipuj/xsOYJ/FvzEjQb3o3aX5Bigh4BlFRIpzYjz//uJmkKCgmhvBKldPx4NjOPhcVioZWtFGJDyuiOGTOGbuWEQa/FeNU2DOV3obq0cwqlDE0LblO/iosbu5TP7IJOrcJTjjewRHc73D+x1CzlJUUYW/IS/Vw18RZo9MzC29fkF09GZrM8vGvL2Ni7Pvw8XtT+F/zO7yWPJ7L7p2kJPtL9C6YNb8Y8r1mnxnnWlThX/Q0aV76PgxFiQfI6RyCPq8Lx/G84pv41zGj+CKv1l+LoogdiKoMLDa/iHe1dcO1clTArLwI4DOFLkR6QDuIZGNqErbrz8c/Ka2Ke52/+97FKdwkGr38wLmPYmMRyGJvDjcjR+fC65l481fz3mOWSz4u8jxc1D0C7M35lzfeFnshwuGoXTlEtgxEBiK7oidZVoRexWT8fSetekPz7wtAWfKG9CddVXLdf267Q95FVnleimNbUdF/neMeOHbRggUL3Kcdmz2Z16eXGi9bLsaEujLOFNLBYVEayRYuL1J+TYFGEAy1Qd1OD3TbiWPh//AzrqgIY4g2i9O0FyOIC2KYZgpEz5/dp+cWLSSkRmLb8DO2GDcDkG9FkKsBGXy0aAnxMGepzRqGmaCUaWrr3O3T1Ox4vrRXQFB4L+ZWS6B10eePxZ+0g3K1ZhCTOgx81bPWAC8e26I3kdmIwvwWrqkgAKysmIWdG6apxtPpdnBu8GbwlDS9LHJOUmg2eE2ERXFQZJCVuu2I2aOHgPNB4KuMyhq3JLH2dVWyG0elAropV/Kuvr4YzhSm2HRkg7MR41Rr83iC9SiFXeiLD5uwZuL+URBmEMdsdXdZXa0sD6iCp2BKSrGb040tRH068VGIK8UVWpqVDDjmElqMjVbSkKC0txWeffYZp02JHMCsAoVAIH330Ed3KjdKMmVgqjMFOd5dyg850PBM+AXeHzkG9J7b/GGH4jNNxvm0RFgZOw7z/vYWJnu9osmvtCQ9Ivuz6kvzixXhjBR7RPonpVS/S//8xcAFmBRfiZ8PhkscT2X0fHoOJgSfxNH9Gt+fOmXACDe56pTKnkx/zwUR+XgFuCl+KEpGlwGpQJWOQ/yVczsd2gfk+5VxcGbwamzVDkAiQClv9uGr8JIzE775oJY+QnFWIcf6nMDLwHM0NK0Vz/5NwTOABPGa6Mi5jOCklg+YZVUGAKuTFrarrcHrgXyj3Sj97VqfMxg2hS7DZNAGJRE9kqCuYjKcjs/G9MAYGf7RrgH/EORjjfxr3qC6X/Ht79mA6mTkrcCsCYSVAViFBldcbbrgBjY2NOPLII2ngVluKDp/PRyttzZw5k3533XXKEkN3CIKAVatW0a3cyEliFtXSLj5iKhWPFwzz8UJkFmpaus/Rx6nUuHXONBo0tMbnxB3h87Fh4BXIG8WS3vdl+cWL9AHMnSczUolgi6c94rrKJR1oQWTXUMJcQ4paA/RiQRL5m7QqNHiDWNeWdLwbSLGEOU//goWfb0JfgRQxIBSLLC+owVOCALSodgc6ZVzpSF3W4fhUOAQ7A9GVqORIVnomDGD3S0tIoHXou6LXaSEYk2kwWmWz9CTWkZqDrWJO1AS4t8ZwksWIBljoZ1ddOTY7j8Lv4hCUuaV/qz7tMLwbmYGdXHwLjuwtPZFhW7ncEjEVtlB0zuG87Gw0wkqfAVITU7vNhuXcaHo99xTvoKAgW+WVWFQff/xxrFu3jn4m2Qfa3AmOOeYYbN++HU8++SQtE6uQmPQ3tmA2/wuySj+N2pfaloLGvefIU1Kd6LOrp+LWWYNx8iV3YORcdq8oHBjSM3NQL9rokm7FtpU0PymBKFexsHJsX6MvhEZvsFufzxkDnRjHbYH7qz1fR9fvb+C2iiuRvfF59BVINL6RlIxVMYuk08NyWYciIpWfFBk2/R4j8+VEhsNAK2idyC/D5aqP0FS2tdtsApUxJkYZHfbHUuwPJCRQzsUxH/DmugpktUbclzdKXwe7QUO3rj6WbaDNGDGYK8EEfjNEIQQEOxslsh0GaFU8AmFBMiMDCexse+7vS8YBhYMXWfm8Ei6//HLMmDEDTz/9NJYvX04TUZN8r5MmTaIFCoYNU+oVJzL9uXKcon0cpQ0kkTSpiLKbQmMAPLcD3moHMIRZoPZUfvKSlL8WnKXQM8hLplzXD87gajTuXI3cQgu+0S6AupZEvG+U/BsNJ+BB4ys4NPIH6lc9AMeUOTHPf9xAC47Zdg+0pRGg+nwgrZtxXvY7RvFF8Bom9pnLRwo2rLr9aCx7fzuwcTGsnmLcpn4Ng7gSNO5IQtLIQ6P+JtcQwDH8H+hXTZS5sZA7Fp0aW/gkXMx9hpH8ThSVTQcKhkYddzr/PdI0P0G95WxgULQPO1F2LlN9hCzUoblhDGxO5mrRm7jVDiBcipbGKozQaWBRfQtDcTkw7ZKoY9NVHkzn1yC9gfiEjkZfwqhV40Xdf5GBepQKKTC5KqBK6d/pvr7e8jUGeFegdgOQMzW6sMlxurXQqDbAV2IA8lgBGwWFhLK8tjFkyBA88sgj+O2332g1rT///BNPPPGEorj2EJVKRfPhkq3cSMpiYVppQg1EobOP0+m+N2l0esb2dxBP5Cy/eNJsZSVfI5Xr4Uxyoj9fgRyxQrJMZ5sMs3QtyOLq4S3vfol/6vBCfC8yBazpt+6LkNgaWYCMKrNvKQIkk4YxlZXetQcrMUmzHVNV69FSLW2hzEElntU+jLmuZ5EoE6BmdTK8IrMYB5qqJY8bjJ20KIamZq3kfpJa7wLNVzRDRUOX3KK9NYZbtKx4RNBVjdGh1bhP8wKGVn0oeWxOy3q8rH0AJ9cnxnXaWxk2qFgANbGqS5UznqDehiNUqxEskS7pPSv0DW7UvAN1+fL91HKFgwFZKq8Kfw1ShYZYrzuW1pULKZn5NNhBy4XRVNfZwV8wM2sr75XOCdhbyFl+8YRLZ1YyY9NWGrRCypOqOBGNNWUxZei3tVZKqpNWMtqwGTXYkMKsMlqSLzIiXZJSCIeRG2RR28kD964qTyJgy2ClNJOEOrQYmEUxGCvXawZTdJPFBkQSpJSxT58CP1je00iztPIqWlh5T7UnOnq9jUYVKyftqSmJyxgO6dnvC54a6FPy6WdrQLq9Ogs71hhxI5HoqQzdutb7FGq466Pv1aCdPQO4eulnQMjInvtic3yf+wqJhSyVV1Jd69NPP8Xrr79OS8FK/VOITTAYxGuvvUa3ckOr06Gh1V+ssapzrkfeyvz99C3dp0s7mOUXT6x5zNKZ4d9BX2j1nJ3+v6m6NKYMRSdbQjQ1F+3x/GnjTkSdaIUxWA/skM6JWVW0FgYuCI+oR+7AkehrpGbkISSqaLWt2gBTGoRmaaXImZrNjuUENEpcAzlCFJVwa4lY0Rcd4ENQ2Vlgk75FWrkluHWpdBtoKI3LGBbNzNrIeWtgy2TV/NLDlZK5Xo02dqxFTCzltacyDBjZc1vkOPgbo9OXadJYAjyLW/oZIFqY8quOs9FCIbGQlWnJ7/fj4osvxltvvRUzwpE46JPlp/POO6/X25coEBmRfLjxCGboCWSZKTnSBE8tsZpMbv9e52AWF1OoLo6tk7/84kXuwLEQPuHg4JrhbqhEkzoZaeEG+OpLYspwwJGTgQ1ASnDXHs9//OhcfPD5ZMxXfQ7Xb6/ANjC6XnzNxh9B7pJiTX8M74OW8SSLAWVwIgc1aAqqQPS8WC91tUaDSs6BDNShoWonkrOYJVbOcOY0iGCVwVQ+6XFuTM6lW2so9iQ2aEwHfIAQI3/ogR7DKjNTnrX+eqTlDaKrSWauBQ21FUhKy+50rNnOLK8W0UtXDvgEuW97KkPBkkVzuZJy0GFX9L1qyx0OrAAyQjGs5LZWo4VfejKjoCCFrEbRzTffTK2tAwcOxFlnnYXs7Gxl6bYP4tGmAC3bEGjovNxsTs6hW0ekPk4tU+gOm82KSi4ZGahF+fa18GmSgDDx+4utZKTmM1cDm+hG2F0LtSV2gRG7UYuK/FOA0s9h2vkl4Gsg5Yw6HVNa04Bs0YqGlL4TrNURMjGvU6ciJ1KDgKjZowWySZOCjFAdvLV7nhzIAY0tHSowX3ddQHqcW9Py6DY5QrLbi0QoUccIxLWAKEzdFCo4kGjtTOEyBOuhN5hQxTlpeeqaXZujlFeLg93zJFNHs7sBVgdTfPsKakc2sBO0ylbQE32vZhaOoFsHmtFUWwl7l0IO+iQmL0ucjRYKiYWslNd33nkHQ4cOxYoVK6DTsfQZCn2PAPFxaiFWk87LofY0prza4IEQbAGvjV1lSyE+1OpzkeGvhbtsEyLaJHYdPbEtJunOJJSLycji6lC7cx0yRnYfTTzp0BnYuCuPlhCOrFsM1aTO0dsPNx+BqwMT8Pyhfc9loA2PLgPwrW/X2UzhhpjHeom/YWgTgjGWz+WG3pEBLZnxEMUv1Ch5jDOD+ZAauQA8zQ0w21hwVEfUdqbwGPyxFfsDiTEpk1pbEWHBinXabKQH6+CpJLmNj+p0rF5voEFqJs4PT0Ntn1NeDSlssmHnPGiQcAUxmm2oQgrSUUvdfroqr5YUxWihkOA+r6Sy1rHHHqsorn8R4o944oknytZqLZiZe4DK29lq4nSmtlubmmri9zKWu/ziidfMlqaFuu2IGFqVCl99TBlqtRpUadjLqamUZQnojumDUvCFmlXtau6SdYDkMy2q8wIcj/H92T3UFwm2jg+nkT2ebZHG2MeaWhWBGMvncsPszIYBLP+vNSzdL7PZgsbWIgANFTsljzG0rtLEci040GPYnDsaAwMv44TIf+n/vWbm6hCuk/brbOZYf7yuxFka76kM7WlssuGAB0+kSVeEq9EzBdddFv0McKTntiu/Xm/3BU0UFGSpvA4aNAjV1fGZSfclSGqTsWPHyjbVk9qeKbkcqlGrUMs56GeXRAR7byF3+cWV1gAsvasIMDFfPnVLfbcybG5VeEMxUj51RKPiYZlwNg1EcjSug1izuX3fj3+uAQcB4/McNDtBXyXiKMQOIQNhHXOZsMILMSSdAJ+zsxe/zhO/8bI3OJyp0Lcqr2Z4gbB0kYsGnt1b7urOQZ1t2NpcC4R65lrQy2M4xWZEGGr4ghF4A2EIdqbAaVzS7fWqmPLqb06cpfGeyjAlLYtmHiFuES1N0sq518oyDgg1rOpeRyy2ZPhbjRYNVdJ+sQoKsi8P++GHH9JKWgr7DokOJZXI5Botr2vzcQpGP+hcrSlwfPXxs7zKXX7xRJ81HJuFHOwIJ0PVGnGtCzZ0K8NIElN4NY09G9d/mzoaP4oss0H1N4+wL8NBHPXLXCzTXY2z8/ZcQjaRcQ88FUcG/4f/a5qGgMisXu46ad9OXQqbGFj9sdNKyYlkiw6PhP+GoMgUoohb2nLq1rKl9ZYYz4HkVtcCHULwNtX0+hg26dS0IhqBlDXVprJ73OKTVr5aVKyEb9CTOP78PZWh1ahFPVgGmYhbOriQS2E5og1SWUc4Dg08W8VpqlaUV4WeEdd10R9//LHT/0mA1syZMzFx4kT84x//oLM+Ul1LClI+VkEaEh1aW1sr22j5toAMpxBthfCS5N8tQLAxfi9jucsvnjiGHoEZnz0AncDjDas7pu9iRxnq0gcBRYDDJ70E3BWnWYfigfOB7SuQsvUtREouwBaPAfaIQFNITZ50GPoyWQ7m690Y4lGvsyMTdXDVlcLamgO2I7YMZtFKiSRGmiGHUYtvMZEqOxloQHN9JRwO5gLQET/JceuPnU3AbDKhXrTCyTWjoXInTI60Xh/Dt+jeRYG4Cb7tatiymHKWGpZ+bgW1NpIIFZEEUl57KkMSZNjIO5AmNuIk33uA79SoQEtz1lCWdcQvbZl2a5OBQFVrBhoFBZkrryQBMrnxu0IGy5133im5r41IpHN1JoXEIalVeTWjBQGfCzojm7UTAoZUFgTkjk8UsUL3kFrlap6jtcoDOmYtsUS6t4QmkVQ5vxAFq5paUKFmSeq74+STT8dnD76BmeKveP+Lr/ByYBpKAg/g8oFuXJ60+37pi2TZmfLqE7Vw8Q5kCnXw1ksrp2m5LMeoBT54mura0zLJFZ7nkGLRod5vRQbXgOa6cjh2VxNtJ2LJABoBvptCBQ2qZDiFZrhJoYKhh6C3Gc4VY4xqA9bUbkfusHPod3Z40NxUC6u9c1aNsJblRBZ8sf2XExmPxgkEi5CPSgQay6HrorymtWYcSBNq4fe5oTcyN4pOk5XAeoQaFOVVIQGU19tvv71bBVWhb2Kz2VEt2uERDTA3NiCtg/IqkvyJDaTKVnwLFShIQ2qV5zqNKKr1oEkw0+9soitmSiNCbl4BLSpgJtHWVdtgzh7WI+urePz/4dwPluCXouEk5AUWvQ2nnDq7z1+adJser2nvwxBuF4r4gYBASqlKT+bMFhu1YjrhQm3JFtkrr4Sx+iqUtKRCI4Thb4mATWUlChWUAHpfbIsydS3wF8EfJxejZcmn4cXiSZhmGIVRFhtq4UAKGlFTvAnW0Z2V14ie+fJz/ib0Rfy6ZAgBDquFQjhFC83F3JHk1CwahOfg3KgsWo9+ww/ttD9szQWaAN6lKK8KCaC8Euuqwv5Ho9Fg7ty5dCtHOJ7HidoXUOMO4BMkoeOCH08sLjT5d/wCG+Quv3hzK17CobrP8EfJtbgnNBcNogX3BIIw6nWSMtTxPO5SX4iyFh2uCljR0yRXx08YhIj2XNR/tx1mvRp3nDiUKnZ9HRK0lsx74YQbn2hHYL0/GSmqHIyKcXy1OhMtIQ3qGxog/zIFwHFYhnciM7BUGI0HzCMk+6XKGI73Vk1BLTcEZOoiRbV1OJZ6W+ATOlvxemsM16ZNxUdFu5ATYROGnbpBKGupg7fRjShjsoEprwgmTjT93sjw+/zrMO+Ps2gQ23DBFqW8EiPV56aTUN4UxFiPLuo+JT6xW4qzURFiE2IFhT2h5ALqg/A8j/79JdbiZASxrBHltc7TOdrYWzgL41ekoCA1B+/EqW2JIL94YjbqYCJBKs278ApOQlAQcK0vAqM+tgw3pZ2I34oaMKuZ67HySpg9KpP+O9h4ynYNttT4kZ03Gt80NOByTSFOiHHsE/mP4dP1NbhdPRTjIX+arQMg1Prp55pm6WwDln7jcX3oCjgCGlwW4zwb+1+Cx0qOwDmaXMyKwxhOteo79eGtwgfw/spy3IBBmNLl2LL+52DAhjGYZs/EJCQGeyNDm92BMGrbA9ikWJl/CRavKMN1XguO7LKPG3UGZi7LQXpIj9P/cssVDgZklW1AYf8QCASwcOFCupUryWbm91jv6RzJ6nA4UQcbarwskXk8SAT5xZOygfNwROBBvG46D87W69jgDXYrw37JzKJSVOuNQ4sTj2b7UGwWcyFyuyPaY5GdxGRb2uhDIlCePQs/iszeWu1mSmxXMqytQWu+EPwh6fiGNit8lcvfszEcDuK95TvQ5Ns/GQiytH4cya9AVs339P95SSa63VUffY9bLSaEoEZTCytqkAjszXMw2cxWXazwIFi5XvKYwhR2n26vibY+ZzuM7fdDIKzEsyjsGUV57aPIPc3T3wIf4hPtrUjf8nKn70kwx55e1r2B3OUXT2wZBSgSM7GrOYJRugrM4FehpW5XtzIcYo/gOH45Une818utTUzSWseBGIkgCc3gGnd2G0RHKG2QzgUrN1LMOhzKb8AX2ptw2tYbJY+xGtSwakRkc7WorpOuMJbRqrzWNTXveQyHWlD+5AngPrkac5/7LaZCvDfkRorxgvZ/mFP/DP1/fjJTwErqPJKljwn7S3GW23Mwh6/F/2kex6Oax1G4+VnJY/onG9CPq4S54mdJY4Zew9Ng7crGxLiPFeKL4jagEBdS+WYM54vxh2tXlPJ6q/p1ZIj18DWOhtGRrlwhmUbDlze24G7Nyxij/QOrSmzAmFhemcBgfQPO0z4CVw1JfXdrL7Y2MRmgqcXlqo+QVm/EIv0iVFaRcXCy5LEDVeV4V3sntKVE4Y1WDOQGGeMRkcdgvhQVAWklkvhILtHcigLVLmzcbgcyovuezdVhte5iaBojgFgVM2AQQgQ1L56NrIblOIbXw10QhF7z14sXmJ1ZdGsXmHI9UF2N77XXQl9F+rSj07FONFLlzkj12hnoaziNakxV/YIWUYvyFmm1YpDBhe911yPYrIYYuQycStPper+sexBD+A0o2vYykDKzF1uvkIgoyqtCXCjJORHPlaRjkHUsJnT43qRV4WTVL0jlGlFZVaworzIky67HVar3kRusQZ0pG+tbatEs7A7WkiI1fziNRC5GJmaHAuA13R9/sJPP1+BCzVv4uWUc+0KI7UaT5rAhn98Kf0gDURBoQKScSdULuEb9Hs4L3oSQKRNvxjiuWZOCgL8cHpd08GZyaibsHFuiD3gboTN3Ts/UhuubB5Fa8R2t4vT2gAdx4YlH7Zd+ONJYfloT/Ij43cjIyIadZ1UD/d5m6E27c5TbdBr6XIsIHEQhAo7vW9X7rKnZ7cGb1/m/kDwmLbc/6kQrqsQkZNRVw5nGitW0YVMFYQ23wFctXWJXQaEjivLaByHRoZdffrmso+VVqUOxVAiDC3ZO7UNm4G9qT4HLF8JJsKO1cnuvkgjyiyc2oxbz1F/RBPF/t72HT2rn4HrrQEzvRobZack4OnIPQhERE7wCsljaS4UYWFNY2dc0sRr9/a9A5DXYJog0T2pX0nP64+/Bq1AqpuAlXxAOs7wzMjjtNoBrxI/CKKi9gBCjX2/1uwdvra7HTeYhmChxHofdjuPCD6IsbMdnAR1yzNH3n1qtxrsbvZgravGi7QpcfNbc/dYPh90Bn6iDkQvQctaOnMGYjzux2e/ES80iBjEXWIrVmYq7Q3Phghl3+IOwGNnqhZzZm+dgst2O5yPH08/XRpZIHqPTaDBd+yKq3AF8EDCBZYnezTf51+PKtTU4VjsJfbsMicL+QN5TdIV9giiANptN1jl02wJ96rsE+hB+cJyGRZHjUBmJj4aTCPKLN7Vq5s6hC7G8lV0DUbrKkOaHTWI+gTuVoK09Yk1nymu+WEnTD0UEEY0x/CX1ej2Wm2ZgjdgfpU3SAVByIsWiB1qLNoUF6WcAwekg6aU4VLmkfSDJveWz9YcbRqoQSd1/b/1RhnuqJuE44SEcd+4NNA3Z/kKtVqGGYypYc80u+pt1zgmohBMljZ2vg15vwGvciVgcmY4mf2JU7tub5yBxwzBpmVKz6EcAAGQkSURBVGz9Ag9EpAPTcpzsGVAm4ddqyh2FHWIWttUnTlCbQvzg5eok/tlnn+Ghhx7C3Xff3f693+9HTU0NBEGIa/vkDpHf/fffL+ugo2RtEH/jf8SMpiUxI1dru6TR6i0SQX7xxqNjyqu+tTRsky+0RxmSjANqhFFWIV3yU2E3dmsS9R/UcBE4DGyJua5LZo6O5CRQ0BYJxnJxFpzC/4S/qz5Aw/Y/JI9Lt7E+VUpkE2g/pjVdFfG/7gi57+5c+CDu/2Iz/f+5M6egX2u0+/6kQZ1Kt/7WgEVSwCNWxgFSGpfgSpCMA3v7HBxrqKHZF9yiAfBKu3rktGYVkMqM0ZaNoKhOyUiikIDK60cffYTc3FyceOKJWLBgQadCBmvXrkVGRgbeeuutuLZR4a+TrI3gIe3T+EfoeYiRzv58uYYARnPbIVZJp1xRiD8hEystMdL3Ow1SOXPXbXv8m5PxPTbp5mPUyn/1QgsTG5tRg2qRJba/il+M1zT3IrT125jHH2KqwjzVFxC2fQ25Qyx5HrUDp6uWYoHmXYSLf5E8rhDleErzMM4u323A6MqR+q24R/0CTOtf67xDFDFDWIoxwRUYmm7BvMPycSDw6Ng4CDeyKl8TtLtwg/otpG6NfkeRzBzT+TXw1sWnItiB5nJuMc2+QCZcLY3SFeGmiitololDV98Sta/QKuAK1YeY3/goQhHFQKWQQMrrsmXLcNppp0Gn0+GRRx7B2Wef3Wn/xIkTadLk995T0u0kOo4UZrnjORHeps6lYCe3fIclutsxdqd0yhWF+COamTeyKdyEfnw17IHYZTzbsCRn0hebxRedVkshWsGrBXObGYydmKLagEjtlphiOkxYgTs1ryCz9JOEEKVX60QAzBIZapBW5lJMahyn+gOjA3/GPM8wTRnOUX+LtKqlnb6v+G0xZvK/4ynN/+HuY7OhkvCp3R+0GNg44JrL6HYIX4or1R+hoDZ6EnFV4Dm8rH0A6tJf0RcJ6Fn8ggAOzXUVksc4LXqaZcLh7ZyNgZBhN+FGzduYq/oG5RVMngoKCaG8EhcBu92OFStW4O9//zsGDBgQdcz48eOxZs2auLRPYf9h1OvRLLIlJHcjq8zShsra6k8ZxxKxCt3D21rL+Apsic8Ykc612RF7zjC6TQmV0/RFCt1TL9rotoVjy+cRT+zxoE1llZDM3sSoDR/Up6DNtqZyS7uRODNZEVE7SXzfIl1W1ZA5lG6TWjpPiB7YkoLnwrPwjXMuxg0uwIFCsLJ0WVoPszQa0weytgeila+AhmUfCHul89YmOhEjc6HgIcLXIK28WtML6dYZip7s8nozqnl2jtqidQe0rQqJj6yU1+XLl+Okk05CcnLnCPSO5OTkoKpqz1aegxmtVoubb76ZbuVMM8dqkvuaOiuvOgcrB2oJxUd5TRT5xRNDEktzYxVcdGsSPXuUYVb+QAREDXQIIVBX3MstTiyI3FROttQdEFlSGC6GHyHBmjlw98QgARBMqdCATWCMPuk2k2p7HpH5tDZUShdpSM4fQbfpkUoIIeYjv7KkEV/sDON+4VyMnnsPDiSaJBZYZ/Szd5IzdxDdpgq1iLS2p42wllnSRV9iKK97+xzkLMyFQgSHBqFDqoUOJGezSZYFXgg+5i/fkXp9Ht36Kjb9hZYrHAzISnklZeis1t258aRoamqiNZcVYkOqlLhcLrqVM14Vu9Z+d+eXsjmJWfUsrYpRb5Mo8osn5hSW4zJFrKdbq+il+Su7k6HTYkApx6zqtcWKP3N3ELm1LcNGWkWo9jNZS5GW32qBRDNamqOVArnBW9JgAguyckhY4QgkX209z2TgqpZ2NcnM7kcVXDUnoKp4I/V1feSbbXTfrCHO9kC2A4UljSlb9hDL75qWkUfTZ6k4EXVlrB1tCHqmvHIt8r8++/Ic1LSuxqghYKN1suQx6clJqBeZ0aK+Itp1wG9jyi1Xt/UvtFzhYEBWWmBBQQH++EM68rSNX3/9FYMHD+61NiUioVAITz31FN3KmRY1U16D7s4vZWsSm8HTl1u49yP+E0V+8cSexixO6ahv911u8bC0WbFkSPw4a7RM6XWXKZaV7iBy21LOJm+q1gIF2mBspcdud6AOzM2getdGyB2tLQ1Ozk0/J4lNEIPSEeYuTQrd+upKYqarKtUw94KqTb9g14+v4cKd12G8aht027874GPYmVGw+1nld0Gl4lGpYhO0htIuPsoGFoCnCuweJ3Jmb5+DhlajQwrXhNoYmTFIyrwaFXu+N1Vsj96fxizXRne0YqugIFvl9dRTT6VBWy+++KLk/gcffBDr16/HGWec0ettU9j/BDXMEhH2dlFeHcmIiCzAwt/c2aVAQR447EnwiAYYEaSWJinfZSl8FqZoRGoVy8oeZQW29KoXWVohY2taMinoxEDN/C9d5bEDu+SCwZGJNK4BzSStEmlzlbRbgE/fFs0f2x2iMZlVIROKfwH3y/9hmmodLkjfCQt/4Ce+malOakmsFu1wNzDra6OOudS0VHW+x3kTywmrCcZnRelAY2ktl5vENeOkkey6SdGkTe+UXqwjzoLRdJsdKKK5jRUUEkJ5veGGGzBkyBBcdNFFOProo/Httyw1zI033oipU6fipptuwujRo2kwl0LiE9YxSxG6+IBZDVo0gS0teRrZC0FBXpCKSPV8Ei0n39yqZHm7+C5L4mRBmDqXtLKisJsAx/w9bSJTdqyR7i12biOzagdrOi9XyxFLchb0XJiWCiU0lku3OdKa1QJu6QAggq5wCt2Ob/gEuYHtND9u/syr0BsYtWocq3oekwJPohStSpmZrUoIDZ3vcY2J9VUX3nNwYyKSlJJJjQ4qiMjXR+dxbcNnYDENaIxWXjMGjocgckjnGrBrl/KMUEgQ5dVsNuOnn37CmWeeiaVLl+Lnn3+m/jbE4vrLL7/g9NNPxzfffENTaSl0TyIEGwn6JEkfMGJFcnHMpcDb2DmNVm+RCPKLN81qdv08HMsa4W+u36MMDZnM5cfhV9Jl7QlRY8IWIRuVYBHYFni6zdIQsrEAL75R/sFwyXYrXKIRjSJLTO+tkq5nz9mYNU/nk84bShg65WQ0YHesxC+2WSjMy+m1MZzuYH0ob2otlJDEVhd07s6uDlors7waI4ljed0bGTqtRtS3uq64amOnuhItTHnlPVIZByyoUDPLddWW5fvQYoWDBVkprwSHw4HXX3+dZhQgVbZee+01WrigoqICb775Jt2v0D1Eub/lllvkr+QbmfKjlvAB87QFc7l6X3lNGPnFmWLTKHwdGQsPx17eQU/9HmWYnM/SZSUL9RADzOdRIRoitxuuvw4zg//BNcHL21MQhbpJl6VOGZAw6bJSLDrcGLoERSJTZMIN0gq3rjWrhSkQ+zlgMBiwefRt1H1lPTcAg89a2KtjOMvOXB/KW6tG6dLYdbD7O+evNVpZ8JlZSIz7fm9lSHLpNnLs/dxcF9vNQ2Vn11zXIr2qVm8dQreh0pX70GqFgwXZKa9tOJ1OHHvssbRQwQknnIC0tNg+NL3B6tWrceutt2LmzJlISUmh1sEZM2ZAjpDyudu3b5d9GV21iT3otKFo5dWvtksGc/UGiSK/ePNr3mW4OLQAbp5ZW0Kexj3KMC8rC7Uim5i4Sjb0cosTByK32vJd0Kg4RKBqt1A211XuMV2WMyj/dFkmnRo/qg7FVpEppyq3dKECUyqzJieFu3dJOezkSxG6oQSDbvsdWRmZvTqGp6jW4S3t3Rix+i76f0cWU15TI9U0+0EbJgcLPrOKHogJ8GzZFxm6W1djWhpi34P61gmJJSh9TYW0kXRrrFeeDwoJqLzKjSVLlmDhwoXUnSE9nfk2yRUSHUqs13KPltdYmCVCL+EDFmjNiRjpJrflwS6/eOM0sSVFd2u+XqGD73IsGeo1KhSr2LJqfZFiWYkFkdsbb7zRLuOmVjcaT0PsHNepecxilYxGBHyuhLC+lolMoTPFyPWalM6UVweaEQm2LsvHwGbWQ6Pie30Mpxk4HMJvQqprLf1/Rk4h9dsk+Yyb63f76lrtzP2DpPXydsjMIVf2RYY+HXumh1yx71NLKvMJdgj1nZT7Nuz9J9JtTstmJWhLISYs+7VMOOKII3p0HLF6tgVz9RZz5szB7NmzMWLECNTX1yMjozWQQGGf0VtT0SCa0Swwn8mOhPUOwA2IXTIRKMgHh7FNeW1NSN7D/JWN5gFA8xoEytjLXiE2N3OvYKruO5S1BgO1uGIHMDqTU9AgWpDEuVGzazNyhkyStWjHmBtgdDVhs5CDUiETUnWwnMlpNADLwAXRWLULybnyS5OoyR2Pa/64AirbADxEFHGjEdWcA2loQG3pdliTmd+u3miCX9RAz4VoZg6zlVkp+xJhQwoiHg5BX2zXiOT0PKrca7kwrRqnsrAJTBu5ww9D+GMe6Vw9tmzdgEGDh/dCyxUSDVkpr8SquSellQRwkW1vM2wY89VT2H9ocsdhbOBZ2I0arO6yTzSw4AZVN4nZFeLLEPev2KS7Bm8EZ+Lf4XORajoEPVGXdhWejVm/jcVY2yQc2PpHiY9FI8AZcOMD7hisDBWgQJWBWOobeS5u0Q4DH2gC3+gGyz0Qze87G/D8T0U4tNCJ+ZOZFTweTFetxwTVx5gafARagcdmQaRZLDpC8qa+rz4OjQFgRguH2LUX40dKRjY+FKYgybM7uGmNfiKC3kbYvCIKO1wfUlVQjwZ4m+qAPJbTtC+xpuBiXFw2E3OTCzA+xjHJNjPujZyDRsGEmwM8UtnCTTsqvQVLzcdio0uN5HIPBslvvqIgA2TlNkB8a6T+kapa3333HSZNmoTTTjsNwWDvJ65PJMhDss0vV84QpZXgaglB6JLTryR/Dsb4n8aLKTf1ersSRX7xxmixUYuYR9BjUeQ4rOXZsvWeZJiePwQbxXxsqOlcPlMBUfJbnn42Zgbux2LzXNwVPh9FGuZPGYtX8+/DGcHbsU5oU5miCSxfhPnbroJmzatxFXkkZSi2CxngICIYFlDrkb4f3nNeigfDZ6A02H31xXiN4dwktnLU4A3C7WdL7J/l34y/h67B+nDnKcQVlsfR3/8Kqk3yV1z3RYZJdjv10a5pjj22SaGCT42n4H1hGipbVJLHrB/3b/wnfCZ+qFKyvigkgOU1FqRkLAmO+vLLL+my/b333ovbb78diQYpf0v+tdHc3Bz1PSl9q9FoqJ9RR0d5lUoFtVpNFfeO5frId2Rf1+8vu+wyeq6Ov0cg5yYPo64TAJIShfx9V/8mEmlK2tG1UhI5PhKJIBwOR31PviP72ojVJ7OGzZ1Is2td3nZllvTJZHWgEVbUecPtfSDn6I0+kb+58MIL6Zacc2/6tLfXqbf69FeuU6w+cZljMCXwf/BrmT2s3sPuY3IOcv42GXa8fuS3C5JY9PKWKjdaWvzQ63Wy6ZOcrhPJd/3I90XYIooYyLGXfI2rhf5trD7lJjEXjqKa5va2du2Ttno1Jqk2QtQcQvfH694Tsidg/u83Q6vmEQxHUFrnRpJBFdWnDBuJ5m9CWYMnZp+6XqeOY7jtnjxQfdLxHKabdiHXvwXl643oN+IwpFuY0lXVer3a+sQZ7QijiSq6cr732r6/5JJL6L6evp/I9SNUN7fQv4nVp3SbDlXNfpTWuzE41RDVp9FZzBz7+856+P1+2nY5PiMU4kdCKK9tWCwWHHfccbQCVyIqryTg6667WERqRx566CHo9Swh+ZgxY6hv7eeff45Vq1a1HzN9+nSqwL/zzjvYsWN36bwTTzwRY8eOxfPPP4/a2t3Rm8RKTQo9kHN3HGSXX345bDYb7r///k5tuPnmm2kda1IOsA0y0EmqlKKiIuq43waZjV9xxRVYs2YNPv744/bvCwsLcc4559D8vD/88EP799316RndI8gWq/DS/82Enze39ynJxPzENu0owf33f0k/z507F/3795d9n/bmOiVynw45YhbKxFQYAn6M4Uph2eXB/fd/TfvUr18/mp+548ujrU/vvPAEZvN+HCZswAv3f4yLb31INn2S03Ui3ztHHks/19VUIwVhrFv2BT4Pb4/Zp3wn8xz98c+1uH/dEsk+nRjZTNfc2qpXxe3e+24FgMG4CS9hju4H7Fx1LT5fPySqT5kWB7K5Gvz29QZULG2R3XUifTpP8x2OjHyN9z8qwduf/ojN4RRwyIGroRaff17U3qfaILk+SahxB2R975E+nXXWWXj33XexdevWHo+nbb99gUc178FRTZ4FR8fsU27ycNj4Lfh1yXKs+FAV1acv33wWZgzBaN+fWLhwKW699XbZPSNefvnlTnJX6F04seOUIgG48sor8cILL9DZ2N5y/fXXR81eu+Oaa67BgAHRy3QkBy0J2CI39578dPdkec3JyUFNTQ21Lu8vSxH5jYcffpg+xLoiN4te+b0jkSeWY9Mxr6Fg3DHtfVq3oxRrXlmAVG0QR9z0bq9aILxeL5XftddeSycVfc2it78sECGBw/C7vsZQrhif6W5FPeww37K1/RzkpUpk2JYnsmOffnlwDg4P/YDNQ6/BoDl3yaZPcrlObWN4wjEnY/0njyNd68d54kfYoh+Fguu/jdmn9WtXIPnDs0EWNew3b5Tsk3dhfyShGZtO/AhDxk2P271X0ejFtP/9jH+pX8WF6s+xMmsuRsx7JKpPv73zIKZsXYjVpikYcvX7PbpO5NnaNoZJ+w50n7557hbMqn0em1JmoeCil7Dqp08w9qeLUabORc7Nf7T3afHbL8O87QPo+x2Cw8+5RZb3Xtv35Bxdx/CextOOkjIULmLxIf7riqAx2SX7tOylf+KIyuewOvkEDLl4kWSfNv7fyRjT8gt+zrkMky+4X3bPiLq6OqooE6W67f2t0HsklOWVzLDITDA/n6VP2VueeeYZqpj0FOJfK6W87itkoEklfJb6ngymval4Euv7WAmmpb4ng1vqezK4pb4nA5j86woZ3ORfV6T69LzpEpQ0+HCRaRCGdPgNh1GD89VfA+RZwouARt9rfWr7nmzb5Lo3feqL10mq7eSXrtJ+jORIDcrEZNTCgdFabScfOal7m/y/OPUoPLrLgXTNaAzmONn0SW7XKUUXwY2ad7AsPBQCz0GMMJeBWH1Kz8xGOlcHiEBIDEGjN3fqk6exhiquhMz+o+LSJwK5RtlOK57VPoxyIQlHBv6LSdZRGCvRJ21SNgKihvrFdv2NPV2nrvffgeoTR6pq1QJ6bxn9G2daDrRcBEmRuk7XqZ+6FoepfsHqBpXs7702ZXlv3k9Z6em4K3Qu6kQb7uG00Lees2ufIo4CbCzPQ7WYhNEx3onh/kehdM02bGoQMCWBnhEKB6HyesEFF0h+T2ZK5eXl1NxPZkf//ve/9+n8Ho/nL7ZQYX+z034ofq6rw8ld0mXZHE48Gj4ZTaIFN0cEaKWfKwpx5hTVz0jlajE8wKwnG0MRWu99TwQHHo+HdhTieF8GTu+FdiYq1mSWko8ENfUPvIp+Vgu6SxKYlpqBc8V/Y10gDe+6RAzYPeejVO9YA6LOViIZGTaWSzlekMwCIa0N9oAHO8QspLikk+HzA2di0NKXkGsy4UfIE0Naf2ALYPOzsqiO3KGY7H8E1VwSNkWE9vyzkdzJuHfD2Qirh2M0+h56rRqLNSfC7Q/jmhbA1iWTQBveASdh1so8HKpzYmaMc+UdeSkm/jEIqOdwgqul1fdZQUGGyutLL73U7f5BgwbRpX8SyKAQGzJDJf49iRAtb2sN0mr0dl4Oshp0+L/I6SBJCC4N8kjr8hI+kCSS/OKNV2WDSSiFmhMRFjk0+kJUed2TDIdksGW29RXyT6YfD9rkl2K3wSUa4eTcEMDTQJ9u/47n4U4dj6bSJmyt9mBAWmftobmUVS2q1uZBDpmqvaYcFASX0c+lDdJFCNIdJAiNQ5XL3+NUib09hpOyWXWzJKEBCPqQZDGhVpWGcESg/q1tJWRNuaPwXMSLLL8Bd0De7KsMSfEJorySfvfvmgerlVQrs5RWN8d2/0u1mzEx34nfixvwwapyXDGj/172QKEvIyvldefOnZLfk2UBu91OA7YU9gxZziCO6YnAIK4URtXPMFeRpcx+nawydqOWvqwbfUGkWXtPe00k+cWbgMYKLgxY1GE0hjRo9Abpi3pPMhyZbaNVkwoaV6G5NBXWHCWPckfa5EeUtSLYkcSxpf6mlhCtOkTqyMdiQKoZq0ubsK2GJIrvrKKGqjfRrdcWO5VWbxKx5yOn6X1cqVqCHG8tQr6x0Bg7W4RTLXoQ/SkYEejzwGmWXj6P5xjOycyikwwb50Ogdgd0WSOQZtNRhZxkHGhTXjNs+nalTZDIaysn9lWGIw11KOQ3wF9uAgqlS6intz7Pu1NeCaeOy8LK4hpULH8f4tTrwKlkpbIoxBFZ3Ql5eXmQK5s3b26P6mxpaWn/bt68eT22HPcWxM2CuFhMmTJF0rdHTozx/YqrNc9iRcUJAM7qtG+gvglBXxk8tQXkaddrbUok+cWbMCnj2wIs5J/CYO1ONBc9CGTN3qMMycTkPvM7OC78HXb95oM15764tF+udJSfS5WEvEgV7lM/hzyuBq6K/kjKjp0ndKK5FgPVryFrYzJw1GOd9pkbmfKqSpNH1SKNsxApxc24SP0ZHJwHlaXbkTGoc3p7kkrrP4ZXMCC8FU3bLXCOnia7MWw3abGRS4cNRagr3YysrBE4R/0dUjUrEdzmB/JOpcelmHUYxhcjC7WorxuHlFSW8UGO7KsM/xb8GNO0S7BqRzMwTVp5JcaIj7T/RD5XDU/lDzBnMMt1V2YNT8foT/6JQS2l2PFjOgoPP2+f+6PQt5BVkQI5QzIMkNQY5B9JnUGorq5u/05OaTNIJCVJBdIxolKucEZWIlETjC4tel34ebyvuxP6oi96tU2JJL94E9ExK1k614B8vrq9pnlPZOh2soAhlJOUSQqd5NpBfl5NEtScgMNUGzFZtQHe2pJuhdXf3IKL1Z9hbGPncUOsuI+FTsJDodNgHSKtVPQ2lswB4DkRNSK7j5oqtkkeN5QvxWi+CL6q7bIcw2RpvV6bTT97K1kfxoqbcIpqGfiqdZ0S9D+tfZQGqjUV707LJEf2VYYRYyrdcr7YpYxNOjXsnA9Wzoem6tKYx1kMWpSkHU0/G355kFQy2qu2KPRd4mpWeuWVV/b5b887r3dnYCTfW4JlFUsIVGaW4F4XbIraF9TYgAAQ9jbEoWUKPcLAlI4WmnsAtFZ5T9HmTQSqgeTm9eylxCtzaSn8umQ6DpppqFU1fI1sghCL9P5jQKK60sUahFqaoTGwVYtadwCf+wbjS24wLh8gD8trZnoGXg4fjXy+BoNQhpZaadcxjy4NCG9AoJEFRMmRFnMO0ABE6lhO0LAxBSCeG57OSly9Ngs5wSp4Kkj+VJbHty/BWdLpVteN8kpoUjuRG6mGty628kooPPF6ND//FjJDu1DzyytInbJ7tVPh4CWuyitZct9bZ/A2h/3eVl4VDgx6awrdGsPRgTthnQMgCSJ89Yr4ZQpvdNAtSWUEDhC8Pb9WOUPGoWW5FibBA7FuC7jU3eVlFXYT0TuJ5govWEaOYPPuZO9SpKdnoV60wcm5ULF9DfJGTKXfry1jY6wgxQyDVrosZ29DSqueGJ6PW9SvYzq/BkLjLsnjQqZ0IgCguRxyRXD0o8qrppn1QTAxC6S6pfP18pjzgYYViNRKW5kTHa2TlcQ1B7pXXr3aFOpyFGjs/poW5GTjvaQzcWrjImiX3g1MOA3Q7U4Bp3BwElfllVTKUtj/kAA3UjWEbOWOwc4e8GaBBaR0RDAwxYhriXYpOJAkkvzijdrE3D4irR5IvL+xxzIcmpWMFeJATOHWw7XpO9gV5bWdTvIzOel3frC8khF3TbfXhEzuy3SFcAZXom7Lb+3Ka9OqJTiar0Jm1lGQU7YRm0GDsiCbxGrd0lY40ZoJ1AAaT/dW53iOYV1qf2AHYPWxPqisLFjOEOisvApJhVTJ1bqKIGf2VYaWVBZ46wh3P8kKGNKo8hpprtzjOQeffDN2vfAJ8sI1qPnsPqSeovjIH+zEVXk9//zz4/nzfRaSbJmUu0sEzA6mvFpFd9TScbs/bCDapeBAkkjyizc6C1OsIArU8qpqVV57IkNi/dthGospLevh2/w97NOv7I0mJwQd5acyM8Uu2Pq4FnuwEuFOGQ2UrwRX/kf7d2N2PovTtNvxm54s606GXDh7Ui4MxQOBKsDil1ZktA7mT2rcgzUvnmPYkcWC6JyRGiAchNbOls/Noc5uT/r0QcB2wN7Sve9yvNlXGTqzmPJqhg+Crwl8l+wRbQjmdKrEq7q4VUgxLC8NL+RegwvL/gnHmmchTrsIXGspZIWDE8W01AchhRw++uijqFJ6csTqZMqrihPh93R+yKtMzB9WG+pd5TWR5BdvDFamvKrByjBqW32XeypDIY9ZBe01y5VgjA50lJ+21bWmzeVe3bJn5VWffwjdprnW0m1LIIzfAgXYIWQgazQLgJELNx07GDMnT6Cfk8PSllVjMstEYwt1b3WO5xjOzMlHqZCCVcIAhHyNMCZl0e9tkc7PNUfOULpNj1QAgnyDQvdVhqlJSWgU2bJ+Y2Vs67Lalkm3+paeTUiOPfUCLBNHQIMQyt++dq/apND3UJTXPgipz7xq1apOdZrlisVohEdkORCbG6okrXoGCX/YA0kiyS/eGG1MsTKJLZ18l3sqw5zhh8Ej6mGMNAPV63uhxYlBR/np7SydkkZkBQq0gT0HMOaOmk63WUIl3HWlWLajHv8MzcN5xieRnbs7n7JcSM1mZbgt8MHvju6fPY0pr0lCIy2RK8cxnGo14GjxMZwWvAPlQTMsKa3KKzwQQ7vzmWbl94df1ECLMFw0aEue7KsMSUaFWp49F5qqimMep0tiyqs51L17QRtZDiMqD7kDYZFHds1S1K/5fK/apdC3kJ3yWlpaiksvvZRW9jAYDO31iTv+U3Jv9h2If56LY8UnSN31juhaLU5S/rAK8sCS1HqNOKa8mvbyWk0oSMPvwmD62b3pmwPQwsTHnJSBTUIumsCyBuhDe/YBT01Nx2aeKYRFP7+HbzYx69ZRQ1JlWTmOFKEhQWaEmpItUftTMnKo0kJShrnr9+wjGQ+IXPOSSDUwoLjeC6czDUGRBcZ5OmSIMOq0KOKZMl619U/0RVwatqLmq5UOwCNYkllgl51YpnuYyeeUmUfhMyNzZQh/eDX8DfLNPqFwECmvRUVFGDt2LF544QWYzWYEAgHk5uZi4MCBVGElmQZGjhyJqVPZUqNC38CjYi9lv6vzDNzkaFWMRK+ypCxTLFYHVT43C+xFZBNcPX4R0eONGmwxT6Kfgxs+OWDtTGTszlQcF7wfLwqsCrw50rOViOpMFpjl2PAyQuveBw8BRw9lfphyVPxq1czC7KpkqaY6otdpUcuxAM7GSul0WnIg18kyQpTWe6HXqtsnHK66zqtKtWY2YWvZ1TdzHLcYWbBauDG2X68jLZdujfBD8Pds0ksqy405936UIB1pQg2+fvk++EPydb1QOEiU17vuugsulwvffvst1qxZQ7+bP38+Nm3ahOLiYuo87vV6sXjx4ng3VdYQ6/T06dPpNhHwqZlDf6C5c45QWxJ7makgQGjpPb/XRJNfPOF4FS7X3INbwxfT/xu4IPxe117JMNCf5bp01K8EPD1bQuzrdJSf08SyDNRGmCJkE109msxlTT8fIVGF3FAR/oeH8bLpMRxW2BpgJ0PceqbwBOqkl5obVWwy6+7GmhfvMXwUvwI/aa/BhN+vpv/38GxVydvUeVUpnDqCbvV1GyBX/ooMIxYWYMe7Y6fBSnYmobnVZay7QgVdycnMQMMpb+MDYRquq56Jn7f1PLe0Qt9BVsrrN998g1mzZtEB00ZbYYCMjAy8/fbb9POtt94atzYmAsRKTYoqJIp7RVBjl0xwb7eYqT8kwdvFpeBAkmjyizd2owY+6NuvVVNN6V7JcPTw4Vgr9AMPEeKWz3qhxfKno/z0GhWMWhUawRQhdQ8nc/0HDMG3qSyjC/WxnHIleF5+LgNttJiY9Z5rklZOPTq2FB3swVJxvMZwks2CHL4WVi9TwL2tE/OWLsqrOX8s3ab7tuzVSkVv8ldkqG7NDmHwxXbx0Kh41HFsMuWq2bvMC6NHjUbGvJfwzxNH4qih8i2xq3CQKK91dXUYPJgtpxDIoPH5fO3/1+l0OProo/HJJ8ryYncEg0G89tprdJsIhPRO1IsW+IJCVE1zV+sL29Pl4X8gSTT5xRuHkVkG61uXdV21ZXslw0MKnPgCk/F1ZCx2RVh6tIOdrvJ7UPM0ftZe3W6p6hrcGIsjL/sflh71Mbad9TMmHX4S5ExL8nB8FxmNIjAltivB1qVowVUu2zGsz5+EOYHbscBwN2uHtnVVyd05Q0TOkPHUKm4XXTGrisWbvyJDQ3Ie9VEOh7sPrmvWMOXVV7/3vqvkuTFvsvyCDxV6B1mZlpKTk6lbQMf/E3eBjhCFtqmpd1MnJRrEWr1jx46EKWf764DrcE7ZSZiXlI9pXfa5ybKbWAtvF3/YA0miyS/e3OD5D0bpfsZT3FlwBYEZXCry90KGxLK4tXAentxUgwWegfh7r7Ra3nS9B62qMFIjLrwcORZhkcMRIRWks2dGW7dmTOk6quRJy4DZuHRlLg7hkzBHYn992mQ8VhWAQTsO42U6hrPS0/CHOBjGJhX97XLLaFQ0BwEVsxq3keF0YB0/AIaIG97t2zA6VX45S/+KDE2Fh2LQZy/DqtJjVTfH/eCYg5fLDsN0/XAM+0utVTjYkJXldcCAAXSwtDFx4kR8+eWXNJCLUFtbS/1dSSYChb5nuWvwRs/wF9pux2j/MyhJOiwOLVPoCVoVDz0XwjL1JLwUORYlkb33qzx8MHu5f7u59yzsicQnyRfg6MB/8Jj2AtwdPhc1YDmQ+xIZNuZ2UtG0O61URwL9jsD/wqfjp8hwyJVshxHEM8MXjKDWE8CG3LNxVehqrNGzoMSOAWqvD3oURwf/i6/dLPNAXyLLaUYEKjT6QnD7Y1tfqzNm4ANhKorCfe9+VjiIlNfjjjsO33//fbtl9R//+AfcbjfNMDBhwgSadaCqqgpXXXVVvJuqcACU10ZftPIqWDLRBAsafUpEqVz5qd/VmBJ4BH4zy2tZ0xzY63Mc0aq81pZug+vXl/d7GxOdkL0Q28Rs6DTqmBO9RCfTzlwivK56CKFgTOW2yiWt3MoB4up0inkjbla/gab1X9FsGoSmlmgFbnx/luf01x17LjqRaFj1mvZAw131u13/upJmYde0WsbXVEGexF15Jemw2rj88suxdOnS9uhG4iz+1ltvIS8vD+vXr0daWhoeffRRXHwxi2xWkIa4Vpx44okJE3CUEyrGG5p7cHn1HVH77N0otgeKRJNfvFHZs1EmpsAk+jGW2wptzeq9lmGGzYBp2RyWaq+F7curgcY9R5T3ZbrKz2lm40DLCUhBIzwN8sx1+ldIt+nxjXYBVmguhGvX6uj9Fi2yuRpkuFbuMdtCPMfwMZo1uEz9CcQdP8Bu0NIUZS2e6PRmk/sza+Pm0irUVssvX+lfleGVxq+xWHsnQitfj3lMjjGAGfxqZFR//xdaqnAwEve3M8kicPbZZ+OCCy6gOV4nTeq8vDJnzhz6T6HnEOWfyDJRsBpUGKzaiIYgS1LekbGRtZio/gipOycCU2/qlfYkmvzkkG2AMD74O27QPYT15WOhUp2x1zI8fMwQ/PrFUFj1Kozy925VNbnR9R7M42twlep9FPobcbL+W6zeciYw/Rn0JYh/bovKDIiAq7oYjv4TO+1Pt2jwo/ZampXC13Q6jK0VmuQ2hkO2PMAHcI3F6O/+Hdt1F6G4nPi0royyNN+cvAznuZ9HyScnI+XCZyEn/qoMCzSNGM9vxeqq2JXz8sRyvKT9D6rqycrLNfv8WwoHH3G3vPr9fjz55JPULYAMlCeeeEIJyPqLkOhQItNEiZY3pRbi6uCVuEGIDtXJi5TgXPU3yKz/tdfak2jyize5oZ24Wf0mHMFKlItOVAqOfZLh8SMzcGH4Rpzkvgkl2v44mOkqv0zU4XrNYugjHkREDpFA7KXYROYhx20Y4l+EzbboIDOLyYhypGKnkIb6uhrZjmEuiQVfGT27oLckgefEmIUl8goHw8gFEKzeLLuUWX9VhlV5s3Fl8Gp8YZgV8xhrah7WC/nYKOT/hZYqHIzEXXmtrq7GU089hfHjx2P16tW4+uqrkZmZSa2xpFiBwt5DokNJcFuiRMvbHA58JEzGt8FhUdVSvCmj8Uj4FPxqnNFr7Uk0+cWb5FAVLlN/jIxwGSYHHsMtwuX7JMNUix4TClnOxg9X7zkdUl+mq/yMDiaXnWIaBgRexUvJ16Evok/KRgv0qHSxcsNdmW99FocHH0Ypz/KIynEMmzJYWV5HsBzqzBGY4H8Ss/nHJY8dd/ipmBv6F2Y334j1FfIqg/1XZWjMH4dPhUOwwhM7/Z0zswAnBO/DBf5/IBBW4hoUEkh5tVgsuPTSS7F8+XLq13rttdfCZrNRX9djjjkG/fr1w913343S0p5X4FBILCw6NdStydO7+rZGMsbi4fAcLOUPiVPrFPaE3sYqH+WKFXRb7w0iGN5zBSgpThnDlJLFK8sgCMrkoQ2jg+U4TUc9BPCo9+x9UFwiQHyfCZUxAngy7N3vlwPJ2YPo1iT6YFeFUAs76v2ipBKYajchecSRxF6LRT/LM9/rvtLPaaLbnXWxVwkcRg3NVrKvgZ4KBy9xV147MnToUDz44IMoKyvD+++/j+OPPx7l5eW44447qBJLshGQVFmhUPeJjxUSC5I25kjDVpyu+h7u6p2S/pQk5YqCPDE52pTXSug1PF393FflYtaIdJh1aqSYdWjoxSA9uWNPSoUgcnCiuc9mGyAM1DXgPvVzmLH535L7062tGQea5au85qQmoVJk1ka9h+UpD0VEmj5LigtaE+1/t3oLSld83mnfos9+xvM/FaEpAcdCXrKRBmOd1PIBPI1VMZ/9qVYd/Vwdw9quoCDLgK1YjuInn3wy/UfcCl555RW8+OKLNOfrV199BafTiZoaJR9kLDQaDebOnUu3icJVeBvDNRuxoWQkMHBo+/dJBhUKuArk0JfA1F5pSyLKL55Y7GxJ28b78ID6JUzgf0fTxrv2SYZGrRo/3DADTjN7oR2sdL0HkywGNMKMZK4Jj2seQarLB4g/kbc/+hLpJg4z1N/D52YW1q5MDf+Kc7TPwLthLHD4C7IcwyQ91nYuDRlogK9mF27QfIlM1MBdVQhT3u4Kkm2MyrHjvAFh/KPkOhg/CUPMWwYuuT88xX/ggt+Px9LIKGzOWIxD+ncudHCg+asyJOmy/qV9A4UoQ/GWmTAfMlvyuDvwDA7R/YCidbcB/ZQSJQoJaHmVgqTHuuGGG/D2229j8uTJdOmlvr7v5cXbn/A8j/79+9NtouDTssT2oabOKYCStBF8p1uAZ0O3AsHeCVJJRPnFE4PVSa2CBKe6BZlcAwJ1O/dZhge74ip1Dxq1KjTCCgfnwQmq5ZgoroPo73uVBh3pLGG/UWwB/NE+oEl6EaP4IiR5tsl6DD/quIUGnm1wHI5Zqt9ximoZWmo7V4vsyLwTD8dWMRd60Y+mV84BvHVwfcCyq4T0SZhUyFY3epP9IcMaHQvE8pbFzjhg1PCwcC0INzG3IwWFniDrtzMpUPDMM8/QSlujR4/GsmXLYDKZMG/evHg3TdaQ3LkLFy7slENX7gT1LOdhxF3d6XubzY6gyPL++t29UyI2EeUXV1RquDnm3+bVsOsoNBQrMtyP9yBZXnXzNiTBDY/Ils499Z3HSl8gPSUZLtFIP4ebonOfGpw5dGsJ1sh6DJtTcmjgWWmDDz6VhbWpObbRpSDVio2H/BcNohmO5k3AfwuR5VoBr6hDZOqN9Pr3NvtDhh4ryxoi1m6OeUzExFZuOE/fy12scJApr6TK1rnnnktzwF5xxRX4888/af7X5557DpWVlXjhhdjLRQqMREvzFDExywLv7fxSMus1tMIWwd3Qe64iiSa/eOPhW1/QWgfd6jzligz38z3o09ih48JobB0PzX2wUEGyWYcqka3CuKqjC1VYU5klzynU7zG1VDzH8PmH5uPpc8Zh1ogMtKhZ/uqgu/sVw/OPnYyHM/6LUoE9C4kS/5jzXzhmyqGIF39ZhqnMTcLkim0p56wsGFHt63uTMYWDwOeVBGkRv9aXXnoJxcXF1D0gJSUFl112GS688EIMGTIk3k1UOIDwZjb71rR0tq5SixNnQSqa4GvqHcurwt7jU9kAoRIRDbPA2gLKEuD+xq9NAoJAM8ykkC5amvrey17Fc2hQJwNCKdw1u8DU2N0kZzC3Aj2CCLjroLP2/nJ6T5jYb3d6qGKNDQgAEV/9Hvt+20Vn4ImvR2HHljVIye6P608YA741E0siYsoaBmwAUgPFbLIhYUHW2lmxCWNAeb4rJJDySnxZFy1ahO+++w6RSIT618ycOZMqrCeddJJSovMgQWNns29DsCFqn1dlBSLEBU55uMkVP3lBhwBOxYI7UiNV4LBv6bIUpAnrkgAP4AabIARcfTNo1atLA4jLa0N0ekS71Yw60YZkzoXGymKky1R57UhYZ6fXTfQ17vFYnVqF644bBpB/fYDUfsMRFnmY4IPYXA7OFp2f1+hk39nCSiyLQgIpr2eddRbdklRY8+fPp/6s2dndJ6BW6B4SHXr55ZcnVLS8oTWPpSUcrbzSZbcIWXar65W2JKL84k1Ia6clMXUIISBqoONCuPCMExQZ7sd7UDQ6gXqi17GAtlAv+YD3NkFjOlVeRVd0oQqyElOvciJZcMFVswvpgybIfgwLejvdcv49K69yYn/IMDfFjmKkoz8q0LhrPZJGRr/bbWnMmu4QGiFGwuBUcVdLFBIAXg7K6zfffIMdO3bgtttuUxTX/QB5wJNCD/Fw8t9XrMlZdOsQGwGhs8UuSBQj4hfr6Z2ZeSLKL95E9GyZ1BKqQymXTj8HGksVGe7He5A3sUX0IFqVCW/fVF5FK1tGVnukc4M2a1jKKH9dtE+sLMewgfmBqwPSJWLlyv6QoVbNo0LNlFPXrrWSxySnZdGSx2pOgKex7/lxK/RR5fX111/HEUccEe9m9CmIk/3999+fUAEz9hSmvGoQQaCLktqWiaBrMNeBIhHlF3eMbS/oJtRqWUT4b1+8q8hwf96DtixsEPLgbc3swLf0zWVWrYNZ5wx+aeXVb2CTo0hTeUKMYZWJTey0wcRKbba/ZNhkLqTbcPUmyf1GvQ51HDNQNFWV/KXfUjh4iLvyqqBAsJlNNE0MwVXbOUWO0BrMpUSjypeQrQC/C4NQyqXBZ2EVg5KQWMukckfIORTHBxdio4YV8dAE+qZ8Dcm5dGsLSVuWI2bmYsS7E8NKp261mOvD0XlrDwaEZFYuV9+4NeYxTTyTkbvLs19BIRaK8qogC0hEbSPPrHfuus4WFXVrKhW9v3d8XhX2Hk//E3F68A68rjkNopPldkwhDpoK+w2HUUu3NRGWKssQ7JvKq721UIFF9ABBb9R+lZ2t0uhbpC2zckNnYYqZMeLGwYgug022knw7Y6Y382hZ4J2/IbY1XUGhI4ryqiAbPGq2vNbSJX+lPom9rCwxLDEK8cdhYooVqcFuyGC5HbM55XrtT5JaZVwZZisUpnBiLUP3lNTklPZCDGEJ1wB9a6EC8x4KFcgFo425PZnFg1N5Tc4fRn1aTaIXcEtPOAIG5scckQjSU1CQQgnr64NotVrcfPPNdJtIeLUpqAta4Wnxd/relMxeVnahMWauwP1JosovntgNLIio0RtESv4w4AcgjWuEIBJ/OaXc6/64B9Nselw6rQB6r47mzrSJTb0yHnqbZLMebwmTIYjAMX4RTK3ZjSWVWWaTInWxc4fKaAwb7cyqaCDJXkN+QMMUc7mzv2TYLz0JK8SBCIlqjPc1Q9e6ktaRkDkTxMtI41bcBhR6hqK89kFIgQeXy4Xk5GR5RNv2kA/zb8fbK8pwvW0gDunwfVJaWzBXGKK3Dpz5wOZ2TFT5xRMHmvGr7u+wix4IGeX4V2geSsUU/McXQWpivKtlhdQ9aNVrcMusISivS8P7a6egCVbMj4TAqeOvoO1vF6InTH9HeVMLhiI1SnlNysjHo+GTUS06cVc4DLVEKic5jWGbzYkfIyPgggkzg35oE0R53V8ydJq0OIK/G83+ML5ABti6TGdcOUfi2h2A3TgGo/9SqxUOFhS3gT5IKBTCU089RbeJRIqVPdRr3J1raafYLTg88D+M8D9PX9gHmkSVXzyxOZKRwTXAwAURctfjW/NsLBXGYFtdfGrLJzrd3YNOmw3Xha7Av0PnoDnYNydX6Tb2LKhydV6FISTb7XhUOAOvR45ErS8s+zFsMWhwfvgWXBW6Gi7BgERhf8mQKL4FKczVpag22oeZYMoZiQ+EqfjNx9KkKSjsCUV5VZANKRa2vFzbRXklVWeaDLlww4gaT/xT3yhEo9HqcJr4AA71P4YGwYh+yUb6/c466ZeVwr6j16hga3XTqHFHK3d9gXSrDhb40FRTJmmZTWud6FZKKLdyg7SXWM0JrpaD8/lVkMLSu+2qlg7izHOy50Vpg49afBUU9oSivCrIhgKxFG9o7sFFpTdH7Uu1sJdVdbP8X1YHK1XGgaiEE00BASOsLTiB/xW67Z/Gu1l9knSzGmloQENdNfoixwW/xjr9RZiw7g7J/UPMXkziNqG5fEuvt21fsBs14CHA5fHhYGScqZa6FZ3128mS+7MdRhzGb8DJ4c/RWBNdFlhBoSuK8tpHkUOgwt7iMOlwmGojBgXXR+2bqVmJe9UvQLt5Sa+0JRHlF29evmAilt18BEZk2TBWU4zHtY9hYumL8W5WwtLdPXhH+BEs1/8dhk1voy+itrcG9QQ8kvvPD72Nt3V3w7b1/YQYw/eGHkSR/hwYNi1GIrG/ZJiWmUfdiuzhWsDfLLma8G/da7hH8yIatv+5X35ToW+jBGz1QXQ6HW655RYkGrb0AlwTvAJNqiS8JAjg+N1zqyF8CY5Tf4uN5WR56eID2o5ElV+8KWz1ayM48kfhz9UDUYb+YLHhCvvzHgwaUhD28Qh4+mbieyF/OgYvfxEj0tPxrsT+gCUXRY3paAhHB2vJcgyrdaSuL8LeBiQK+1OGuZkZmB24G3XabCzTWSDlqb3FMAY73U7o/WqwTNEKCrFRLK99EEEQsH37drpNJJxJdnwoTMEPoaHwBCOd9jWnH4pHwqfgN8P0A96ORJWfnEjPH4zTgndigfdchCKKHPf3Pfh7wd8xIPAKPneej75IWpIVfuhi+rQWD7oIRwQfwhLjaQkxhj9JvxLj/E/hz/QzkCjsTxkSn9b1KERFQIdaj3QQ59J+1+Hi0AKswpC//HsKfR9Fee2DkOjQ119/XRaRtnuDUauGWaeWzDigzjsUD4fn4JvQiAPejkSVn5xINqigQgRhQaRBGAr79x5Mslkhgu+7AVs2Q7uPu0ASvnYho3V/laslIcaw2pqKetjQlEDJN/anDEnQLfFr7S7jQFvQ1q565XmhsGcU5VVBVhxuLMKZqu/gLt3Q6fvctmjURuXBlgiQCGsbF4AWIewqr4h3c/ocqTHSyvUVUi06LFC/g+f5hWgu+j1qf7pNlzDZBgh2A/MddfkOzmwDhKm2GtyhfhmmX/4ruT/XSTISiGiq61xhUUFBCkV5VZAV54gf437N81AV/9jp+9wkIy03mu/6AyFP4viNHcxcov4Em3TzkPr7f+LdlD5HpsaDxzWP4Iaa6MwcfQGNischmm2YrloLd/mmqP3pZhU+1N6G933zIbTI3+83P1KMO9UvYcJBHMA42NyC+eovkVEmnYGkUO/BWt3FeLpmLhCRzt+roNCGorz2QUhS6JSUlLhXltkX/DpWPSvS3Hn2nWLWYZH2v3hVcx8at/12QNuQyPKTC0R2otYKFSdC59oR7+YkHHu6B51WM05QLceEyGog2DdXI9xaVlvLXxedOinVbkE/rgqpXBOaqktkP4ZT0YB56q8wwvU9EoX9LUNL5gC6tfkriUNt1P7c3DyoEaGVFN1V2/bLbyr0XRTltQ9C0ptcccUVskoV01MiJvbC4r3VUcvQtRpWJtZTvvGAtiGR5ScXiOyGTplFPye1FMe7OQnHnu7BlOQUtIhsn6+hHH2RFkM63UZcZZKW2VrOST+7aoplP4a1FtZWQ0T+VuIDJcO07P4IiSpoEALc0a4BFoMOJTx7xlfvWLNfflOh76Ior32QSCSClStX0m3CYWEvLI2vJmpXo6mQbiNVB1Z5TWj5yQQiu4YA80tMEhoAvyveTUoo9nQPmvUa1MJBPzf10aTugpmVClVJKDoEl4at0nhrS2U/hvWtyqtZcCNR2N8yLEyzoVxMpp+DddKrMfWGfLr1HmADhULioyivfZBwOIyPP/6YbhMNrY29sIzBuqh9kZShdKtr2HxA25DI8pMLRHYrfluGatFO/y/lt6jw1+7BJhVTiLx10ZbJvgBvZ1Y4XUuV5H6fPo1uw42lsh/DJjtTtI3wA+HECNra3zIk5b8rOHbNGsu2Sh4TtDPXAtRK71dQaENRXnsASRXy3nvv4fzzz8eQIUNgNpthsVgwadIkPPXUU7KZ3fcF9E6mvFrD0UFZltyRdJvSUiTpM6UgLzScgFI+m36uK46umqbw1/BomRUr0Ng3sznonTl0awnWSu4Pm1qrcDXLv/8WmxOCyHxHxZZGHIwQ31mXgT0P3JXbJY/RpA+mW4tb8ZNX6B5Fee0BO3bswGmnnYb3338fgwYNwpVXXom5c+eirKyM+gSdfPLJEMXoXIQKe4/FyR5uNtEVFXGaWTgcAVENg9gCsVHxo0wEGgysvpa/UrG87m/8+lTJ4Ma+giWF3Ts2oVHaWmllllmNT9oyKyfsZj2awdL9+VzRq0oHC0FLLt0KDUWS++35o+g2M1SsZBxQ6BZFee0BxMr6xBNPoKqqCkuWLMEDDzyAp59+Glu3bsX48ePxySefYPHixbKa4RYWFsom0nZvcKZlIizy4CEi1Nz5pVSQ5sAmkb3QGrcuO2BtSGT5yYU2GQZszE+Zr5e2tCjs+z0oWJjlUeXumwFbKWmZdLJKngWihN+rNolNdE3+zsGdchzDeo0KLrDyyZ4maUuy3DgQMtQkF9Ctzi3tp53TfzjcogF6BOEu75zrW0GhI4ry2gOysrKohdVkIkmUd0P+f91119HPP/zwA+QCiQ4955xzZBNpuzckmfSog41+dtV09uXTqnnsNDLXgeYtPx2wNiSy/ORCmwzblwE90pYWhX2/B1UOtqyu9/VNy2u63YhqkQWleSXSZZlSmBXPEa5NiDHs5q10629ODMvrgZChIY1NZh0B6QmXzajDNhU7pnrTr/vtdxX6Hory+hfRaDR0q1azsqZygDjYL126VDbBCnsDSYnVyCfRzx6JQJRA1kS6NVYtP2BtSGT5yYU2GVqzmPKaEqpQlgH38z2od7LIbFsw2vLYFyDWylqe+fU2V++K2u/IaO0/3BC75LqV4xhuUVnoNuhODOX1QMjQnsUCsqyCCwh4JI+ptw6hW3/Jiv32uwp9D/loXAnKokWL6PaYY47Z47GBQID+a6O5uTnqe57nqUJMgsSEDkFJKpWKKsjBYLCTfy35juzr+D05F7EEH3rooVHBZOTcZBmIHN8RMrsmf9+1jrVOp6Pt6Pg9+XtyPDl3xwdb2/fku46/u7d9alYnAaEd1NrSUV7kHMlDZiCynUOqvxiB6q2APW+/98nn81H5jR07Fnq9fr/0Seo6tfWJnKtjPxPlOnXXJ3JeIsPT511K85EauCBNj6NJHZiwferN69RxDBOk+mRutTwmCfUItHih0uhk3ad9uU7NJB1WaBM8NcWd2kn6lJqcCq+og4kLoKFiB8wZA9v71HEMk/bJoU8tKisQBgKtyqvcrxM5R0cZ7o/x5LTb0SwaYeV8iDSVImwviOqTmDEaaHoXxrq17X2W6zNCIX4oyutf4Nlnn8Xnn3+OI444ArNmsYTs3bFw4ULcddddUd8/9NBDVEkijBkzBrNnz6bnXbVqVfsx06dPx4wZM/DOO+/QALI2TjzxRPpwef7551FbWyt57o6D7PLLL4fNZsP999/f6bibb74ZLpeLZk9ogwz0W265BUVFRXj99dfbvydVV4gbxZo1a2gqlTaIfxRZZvr55587uVHsbZ8O1TC3gaL1y7F49e6k3iRIbvTgQiz/cAgOU23E10/egBX82APWp4cffni/9SnWdSJ96t+/f0Jep+76lJPDlrTffvEZzBEzMIzbhZINy5Fjz0/YPvX2dWojVp/WbtiCQaIKWi6C/3vgDoybcYLs+7S312mEmj0LKreswJvr7o/qUxGSUIBKvPHi02jgU9v7RGIU2sawXPo0PCwCKqChjLnQyP06zZkzp5MM98d4EkTgTDEZVq4ETRU78OyiJdF96jcW2ARk+bfjPwvvhcCpZPmMePnllzvJXaF34cSDKEz++uuvj5q9dsc111yDAQNa8851gQRp/e1vf0NmZiZ+/fVXZGS0pm3ZS8srecnX1NTAarXuV8sreeCQAd8VOVgg9tSnH56/GaNr3se23DMw/py7O7WdnOvZ/92KS9xPoMmQC8PVy6Ha+R1WhnIxavCg/dInr9dL5Xfttdcqltd9tFIS2ZKXKpHhisfOxuGhn7Bt5A3of8o/ZX3vycX61XEMk/ZI9Yn8ZtW9w5DL1aDspMVIH3G4rPu0L9fpzdcXQVX0DaxDj8Sxp14Y1adV90zDmPAarJtwPwYedUF7n8iztW0My8XyuvTZBZhZ/wrWZZyKEZcukv11IudoG8P7y/JKePzB2xD2NeO4My7BoEHDo/rkbgmh6IHDsE3IwrTLH4cjOU2Wz4i6ujqqKBOluu39rdB7HFSW12eeeYYqJj2FpMeSUl4/++wzui8tLQ3fffddjxTXtoHW9hDY0/dtvrRdieU83/F7MhjJbLJtUMZqS1fI4Jb6npxH6nsygMm/rpDBLeUD3NM+re1/GS4pPQrnOvMwWeJ3TRPmourbN/BbeDhmI4Sqrx7CsMb1eHbI87jqzNl/uU8Gg4HKj2w7+jT/lT7t6Xuptsj9OnX3fds9SGT4U8Z8LNx2As50zsCAGG1PhD715nXqOIZj9Ym08WvtkQi1eHAoZ0d26zFy7dO+XCdX7lF4eGsuztTl4CSJ8y9znIx3KsbjMOMIjOiwX2oMx7tPXlt//FgzAgFNLkYkwHUiypyUDDv2qadt7/j9Hyl/w29FDRgUScdIibbYTDpcZ/0fdtZ58bLXgOlZuoR6Rij0DgeV8urxSDuI7w2ffvopTj31VCQnJ+P7779HQUFnnx05QAYhWQZJVFKt7EFU65a2ks+eOBDTv3sCDW7guyVF+Kb2Whwj/oLDCkbvl99PdPnJgY4yNGUPx9at27GtXvER2xf5dce3qfPwy456PCxmgGXI7Ftk2Jg7VaXLL7m/IvNovFlagpQwy3kr5zFcnjUL123sjzNtOTga8udAyTDLboRF3wxfMHZxnysP7w+SoGtohmLRVJBGyTawD4prUlISVVyJH5IcITPmjz76KGqJJVFIMTPltcYt/cKy6DW4+mhWKvajNRXwRXi0DDkVcyYwP8uDXX5yoKMMC1JYirkdtT1f9TjY6ek9mGEz0G1Fk/RYSXTSrTpY4YW+cQsgRCs7GVam3Fa5WmQ/hm1GZtlr8smnTd1xoGR4/6kjsO7OmTh7Egs4lOK0cdk4dVw2LSmroCCForz2EOLMTRRXh8NBFddYvrBygPjtEMfzjv47iUS6tgVvae/Gg7WXAjFcss8/LB//nDUEI7NtuGByPzxy5pj9lkw70eUnBzrKsMBpwiWqj3F21X+Bg7Q05oG6B7NtGmSgHqGqvlnBLMOqxQrdZXjG/XfAHV1JK9euxjhuC3JKP5T9GLYZmPLa7EuMicaBkqFGpagdCn+dg8ptYF/ZvHkzTjnlFOowT6IP33zzzahj8vPzMW/evLi0r6/hdCQhl98EEL2VKDtGlve1I0RRvXhaAf2nIG/yU8yYr/4SGWIDvBWbYSpk6Z8U/jqjw2twrf4qlBb1I3HQfU6kaQ4TquGASfRD31wLg42VhG1jsFON93R3AS5AbLkKnIEVNZAj6UIV1uouAldFJtkV8W6OgkJCoyivPYCUhW2L9HzrrbckjyFpNRTldf/gsJpwafAfaIYJi6AHWxhVSFSIxel1/hgEQ0EcFzSjc04Ihb+COTUfIVGFUKRvJo2x6NSYJj6CpiCH7/T90XWq2i87C6uFQjSJZgyprUVarnyVV5PFQfOb0kl5JAyolNevgsK+ooyeHkCsrYmUUYxEWBJlWirSMhEw69T4jptEX8gNQQ5Zxt79/USXnxzoKsPvUs/Hn7saURB0KsrrPsgvFvacoRgUeBkmvRbr0PcgKyxOmxlNtV5UufwoSDFHlYy+0fEwtlZ78KLPjjQZj+Gk5DQcEXgQXt6CX8HL3mdPjjJUUGhD7uNHYR8gaUCIwi2nkrV7+8KyG1kakkZv70eoJ7r85EBXGfZLZkFbO5WgrX2SXyyykkwQwMPtD8PVkhiBQHtLW1BaVbO0r+jwTFbI4I/iBlmPYadFjyIxE9URC1x++ZStjYUcZaig0IaivPZBSFLl1157LaHL103RbsPZqm8RLO99e1JfkF+86SrDfk49crhqcKW/xrtpCUFP70GjVo1kM5volTb40BeZyq/FK5qFyF/5gOT+KQOS6bZq40+k9qpsx7BOrYK9NeNATYw0gHJCjjJUUGhDmVL1QYiLAylnl0iuDl05NfIlpmi+x/qSZGDilF797b4gv3jTVYbD9XX4SXct/Lt0gHguMa/Hu4myZm/uwUv032J88Gvgz3OArGvR18jUBTBNtQ7FjdL3zLSBKbhXswjTXatx0Vuj8dx542U7hudrv4c9uA2+XWYgfTrkjFxlqKBAUCyvCrIkqLPTreiti3dTFPYDqXmDEBZ56BGA2KxEWu9P8nRujOW3g6vegL6IJonlbzYFaiT3J5t1aBo+H99FxmBNmWu/pcw7EMwQf8f56q8hVq6Jd1MUFBIaxfKqIEsEvYOmv0HLbj82hcQlL8WBUjEF/bhquCu2wNol5ZHCviPa84F6QOPe1WczKqwT8tHMZyElxjGXzTkeP46eiH/KvACAT58GBADBVR7vpigoJDSK8toHIQ72J554YkI72osGlttV5e/9pPZ9QX7xpqsMDVoVKtVZ6CdUo7F0I6xDjoh3E2XN3tyDupQCYAdgbembCpEjIx/HB+9DslaHP2Mco+I5HD4oVfZjOGTOoJNylVv+qw9ylaGCAkFxG+iDkNQmY8eOTegUJ7zJSbeaQO8rr31BfvFGSoYuYx7dBqq2xbFlicHe3IPWTFbtzxmuliyhmuikt5aArfMEEAwLCT2GRQtbcdC3VELuyFWGCgoERXntg5Do0CeffDKho0Q1FhZBrA8T34HepS/IL95IyTBkI1WgAL5xRxxblhjszT2Ynl2AoKiCBmFE+uBydJJJi2uPGoj/nDoSQg+Dh+Q6htUO5r9rCVRD7shVhgoKBGU9oA9CokNra2sTOkpU36q8GsPNvf7bfUF+8UZKhurUAUA5YPYUx7VticDe3IPpdhNKkIJ+qEJj2VYkO3LRlyABWNccxazLiT6GLWn5dOsI10LuyFWGCgoExfKqIEv0dua/ZhGbyVM03s1R2A9Ys4eiSEhHEbKUa7ofIf6eteoM+tlVobhkyJmULLb6YEILIr6meDdHQSFhUZRXBVliTWKFHrUIA0FPvJujsB/IzO2PI4IP4UL/tbS8u8L+o1nPfCkDtUWKWGVMqtOJJpFVm2uoVK6VgsK+oiivfRCNRoO5c+fSbaJit1oREFn7g+76Xv3tviC/eCMlw5wkI7USRkQRdR7Fj25v5dcdQStzFeCbFJeMfZFfb0Hu/xqerSo1VmyHnJGrDBUUCIrPax/k/9u7E6iqyrUP4A8zKJMCChiigKIfkkOO1wEb1K4N6lIzc8pCzWypZWatTMXKeZlc7VpZCM7awvowbamZw9JyCvT6oYEo4JAzckAQZNjfel465x455wACnj2c/28t3LL3Zp99nvNuePa738He3p7CwsJIzTzdnOkGeVAA5dC9uzeosW9FWzFr0EL85GYuhk4O9nRk1jPUxMOF7O2VO5C8EjxqGXTwaUl0jcj13uXHel5qoeRr+I5rENH9TCq+nk5KpuQYAqDmVYOKi4tp4cKFYqlWnNzk23mI/xfkmp9Z53HRQvzkZimG/l6uSFzrED9LGjSt6NDUqFj544fa+jVc4F5xI253R9k1r0qOIQCSV43SwvAmdx186IbkTQX3rf/LUwvxkxtiaL34NW5Wkbx6STqi4vw6vrI2KLX8lTcKFUvXPOU38VBqDAGQvIJiLfb5jLoV/5uyGveS+1QAFO2JgKa0snQwfVzyBhWWaG+iAi1xaRZJh8oiKcWujdynAqBaaPMKiuXdwFksdfdx9w9QFS83J4pzHkV3C0todL49tXVHvJSqaXgXev7nj8izwJGGSZIYxxYAHg1qXjWIe4dOnjxZ9b1Evd0qzj+3sMSqr6uV+MkJMbR+/IJ9KoZgyr5TQLZOyeWvpW9D4v6KeUWldCtfue1JlRxDACSvGsR38l5eXqq/o+9e/Bttc46hyLRYq76uVuInJ8TQ+vEL95boKbs0KrrwG9k6JZc/F0cHcaPhSQWUlaXcsV6VHEMAJK8axI3sFy1apPrG9j4OBdTVPo0a5Vt31iCtxE9OiKH14xdll0yJLjH0ZLp1b/aUSOnl723nnfQf1wnkdWwpKZXSYwi2DckrKJbOvwdNfjCNfvQaK/epACiem39ruiL50vXyRnKfClTDxbdimtiyvOuIFUAtoMMWKJajbwj9XN6Nupc3lvtUABTPK6wb9drzLwp0dSU0HFC2hu0GUvv/+JC/VwDtlvtkAFQINa+gWHJ12AJQoxZ/d9j6S1dERRguS9EiW/iTjtzp/M18Kigulft0AFTHTpIkSe6TsFV5eXmiQbxOpyNPT896Oy5/pNxOydnZWdWN7c9k36TVX6+k4AbFNOvjxdyDwCqvq5X4yQkxtH78+GeejNlD+UWltHd6b2rlX3+/U9RGDeWvx8J9dE1XRFsndqduIT6kNGqIoRb/fkPNoOZVg/iXDl9Qar8vaeTmQP92/hfNKv2aqDjPaq+rlfjJCTG0fvw4wfjQ7X/puMvbJP22kmyZGsrfyx0CaVS35uTVQJlDUakhhmC7kLxqUElJCa1evVos1YzvZu9LFRMVFOffsdrraiV+ckIM5Ykf3/A1sculstsZZMvUUP4++mdb+nxIJLVRaA25GmIItgvJKyiWh4sj5VLFVEH5OTflPh0AxSvya0/7yjrSebtguU8FAOCxwWgDoFj8GDTfzoMCKIcK827JfToAilfWagC9eTqAetn50stynwwAwGOCmleN4kb2WlDg4CGWxXnWazagpfjJCTG0fvxC/NyphU8DCvByJVuH8ocYgnZhtAEZobdi9X5fOJB6FB+hsx0+of8Z/L4VPhUAAICq4e+3vFDzqkHl5eWUkZEhlmr3wMlLLMsKcqz2mlqKn1wQQ8QP5U/dcA2DkiF51SDuHbpx40ZN9BItdfEWS+n+Xau9ppbiJxfEEPFD+VM3XMOgZEheQdHKXSvmabcrsl7yCgAAAMqF5BUULb9xJK0tHUD/59ZF7lMBAAAABUDyqtEhpvz8/DQxpV9BYA+KKR1HB537Wu01tRQ/uSCGiB/Kn7rhGgYlw2gDMkJvxertOP0XvbftFD0d3oS+GdvZCp8KAABA1fD3W16oedWgsrIySk5OFku1eyEygNI/+6dVE1ctxU8uiCHih/KnbriGQcmQvGpQaWkp7dixQyzVzt7ezuqP77UUP7kghogfyp+64RoGJUPyCgAAAACqgeQVAAAAAFQDyasG8WP20NBQ9JZH/FAGVQrXMOInN5RBUDKMNiAj9FYEAABQH/z9lhdqXjXa0P7AgQPocIT4oQyqFK5hxE9uKIOgZEheNTrEycGDBzHUE+KHMqhSuIYRP7mhDIKSIXkFAAAAANVA8lpDGzdupCFDhoiOUB4eHuTu7k4RERH07rvv0tWrVx/vpwQAAAAAgmPFAqqzZcsWOn/+PHXv3p0CAgJIkiQ6deoUxcbGUnx8PB0+fFgks0pgb29PHTt2FEtA/FAG1QfXMOInN5RBUDKMNlBDRUVF5OrqarL+u+++o+joaBo2bBh9//33jxR89FYEAABQH/z9lheq5mrIXOLKhg8fLpYZGRmkFCUlJZSUlCSWgPihDKoPrmHET24og6BkSF7raOfOnWLZrl07Uory8nJKSUkRS0D8UAbVB9cw4ic3lEFQMrR5fUTbtm2js2fPUmFhIaWmptLu3bupZcuWNH/+/MfzCQEAAACAAZLXWiSviYmJhu87d+4sOnNxAlud4uJi8aWn0+nE8vbt24b13EjeyclJPLIxrjl1cHAgR0dHevDggegsZvgAHR3FNuP1fCxuo8ttcirjY/O0f7y/MWdnZ/HzlZsauLi4iPMwXs8/z/vzOIA8kHXl9byOt+nVx3vSnzsfyziGj+M9FRQUiPjdunVLNBfRwnuy9ufEx9DHkM9NC+/Jmp+T8TXM56OF92TNz4njZlz+tPCerP058TEqX8Nqf0/1+TnduXNHfG+8DazHpjpszZgxw+Riqcq0adOoVatWZrfl5uaKR/Mff/yxqIndvn07PfPMM1Ueb968eRQTE/PI5w0AAADKc/nyZXriiSfkPg2bY1PJK4/NyrVqNbV//37q27dvlfvwHX54eLi4E8vMzBR3cDWteeW7vJycHPLx8RF3hfWFzykoKEhcVJ6envV2XFuB+CGGckMZRPzkhjJYNU6d8vPzKTAwEMNSysCmmg3cu3ev3o/JySGP/frjjz+KEQfatm1rcV9+xKF//KLn7e1d7+dkfG5IXhE/OaEMIn4of+qGa9gyLy8vK34SYAyjDdSDv/76SyyrqnUFAAAAgLpD8loD/GggLS3N7La4uDg6fvy4aBsbFhZWDx8JAAAAAFhiU80Gaot7FXJzAB5ZoE2bNtSsWTO6e/cunThxgpKTk8VjlYSEBFIKbpowd+5ckyYKgPihDKoDrmHET24og6BkNtVhq7a4k9eSJUvowIEDlJ6eLpJZHnKjRYsW1L9/f3rvvffQ2xAAAADACpC8AgAAAIBqoM0rAAAAAKgGklcAAAAAUA0krwAAAACgGkheNYRHPxg4cKCY+KBhw4Zi8oRt27bJfVqKwx3teEYzc1/mZlTjWdHmz58vhkNzdXUVM6pMnDiRbt68SVq1YcMGmjRpkhhhg3sdc2zi4+OrnI2HOy4GBweL/TnGM2fOtDgxCM8ut3LlSoqMjCQ3Nzfy8/OjkSNH0sWLF8kWY8hTR1sqk/yVlZVl9ud2795NUVFR5OHhIUY9efrpp2nfvn2kdlevXqUVK1aIDrHNmzcXHWT9/f1p6NChdOzYMbM/gzJY+/ih/IHaYKgsjeCpbAcMGCCSq1dffVX8MUtMTKQRI0aIaWJnzJgh9ykqbmaU6dOnm6znpKtykjVo0CCRJPDNAP/yP3/+PH377bciSTh69KhIvLRm9uzZlJ2dTb6+vhQQECD+X9VoHJxAnTp1Svyx5CQ0JSWFli1bRgcPHqRDhw6JcmmMkzqOYUREBE2dOlVM9ME3Wnv27BEx5RsFW4qh3rhx40zKoKWZ+Dg5HjNmjCh/r7/+uli3detW6tevn4jlsGHDSK34xmbx4sUUGhoqyhS/R77ueCZD/tq0aZP43aaHMli3+Omh/IFq8FBZoG4lJSVSaGio5OLiIqWkpBjW5+bmSq1bt5acnZ2lrKwsWc9RSYKDg8VXTcTFxfFQctLIkSOl8vJyw/rVq1eL9RMnTpS0aO/evYYys3DhQvFe165da3bfOXPmiO2zZs16aD1/z+sXLFjw0Ppff/1VrO/Tp49UXFxsWL9r1y6xvn///pKtxXDu3Lli+/79+2t07JycHMnb21vy9fWVLl++bFjP/+d1/JWXlyepVWJionTgwAGT9YcOHZKcnJykRo0aSUVFRYb1KIN1ix/KH6gNklcN2L17t/jDN378eJNt8fHxYltMTIws56b25LVHjx4ifpWTf05kQ0JCpIYNG0qFhYWSllWVeHEcAgMDJXd3d+nevXsPbePveT3HyRjfCPDxDh48aHK8vn37im3Z2dmSltR38vr1119bvK7nzZsntiUkJEhaxDc3/P5OnDghvkcZrFv8GMofqA3avGoAT57A+PFQZdyUgPHjW3i4HSu3P1ywYAGtWrXKbDuwoqIisT48PFy05TTG7RD58Sw/rjx58qTNhpYfRfIj/549e4p21sb4e17P7Vi56YpxedVvq8zWyys3seDHvUuXLhWPdy21Gbbla97JyUksHR0rWr2hDNYtfsZQ/kAt0OZVA/iXNzPXTpAb6bu7uxv2gQrXr1+n8ePHPxSOLl260ObNm0U7MXbhwgXR5tVS+0v9eo5t7969bTK0VZU9/XpuL8z7BQUFiWT/2rVr1K5dO3JwcDC7v/FxbQ1P61y5rWtsbCyNHTu2xnHXcgwvXbpEv/zyi2hDzJ39GMpg3eJnDOUP1AI1rxqg0+kMnZDM4V7I+n2ARNLKna1u3LghkinuXMQdX3i0hmeffZby8/NrHFfj/WzRo8YIMTWvffv2FBcXJ2qp79+/T5mZmaLTDdfwc2espKSkGsddq+WypKREXKf81IRrp/U3PyiDdYsfQ/kDtUHNK9icyrULHTp0oHXr1on/r1+/ntasWSOGfQKwliFDhjz0PY848M4771Dbtm1F8xQeueDll1+22Q+En4BwEs+PtSdMmCCSMKi/+KH8gdqg5lUD9LUvlmpaePxDSzVj8PDwTezIkSM1jqvxfrboUWOEmD4afhLAzVjOnDljiGV1cdRaueTE64033hDDO40ePZq++uqrh7ajDNYtflVB+QOlQvKqAVW1ceO2ndzpQwvjZj5uPB4n46YELCQkhOzt7S22HayurZ0tqK59ZeUYcUctbm/Hj8XLysqq3R/+Wy4LCwtrFHctxZATL27mk5CQIMYP5k6WfE0aQxmsW/yqY8vlD5QLyasG8ADxjAd4r4w7yxjvA5bpRxzQDxLPMz917dqV0tLSTAaY52Hm9u7dK5IxnkHJVvEfKJ5xjGur9Um/Hn/P61u2bCk6a+lxWdRvs1Re+/TpY4WzVz6OU2pqqihn+iTCVq55feLFTXp4QH1u0mOpkx/KYO3jVxVbLn+gcHKP1QX1M0kBj6VZ1SQFmZmZCLUkSefOnZMKCgrMrvf39zcZf9RWJykwhkkKHm8MeTKBtLQ0k/U8frB+TNzKYzjzJAVeXl6anaSgrKxMGjdunHjvw4cPF7/jqoJJCmofP5Q/UCM7/kfuBBoe3/SwXGPI03Rietj/zuG9fPlyUbPHY7dyjUJ6ejrt2rVL9Mb96KOPxNivxrUXAwcONEwPy7UJGRkZtH37dlFDy7W1WpwelqduPXz4sPg/t7dMTk4W47KGhYWJdb169aLo6GhD7QxvO336tBh3tFOnTmJ/rpXh4cd4vFGuxTbGnUb008O+8MILYvgsntqUh3X7/fffqXXr1mQrMczKyhJNVDhW3EGLh7fjkTB4SKMrV66IIY34+vbx8bE4Pax+qk+O4e3bt8Vy+PDhpObrNCYmRpSHadOmmR2TdPDgwaKzJUMZrH38UP5AleTOnqH+HDt2THr++eclT09Pyc3NTeratau0ZcsWhNgIT5n4yiuvSK1atRJxcnR0FDWugwYNEjOVmcPTKPKsRTwFL9di8/7R0dHS9evXNRtbfa2NpS/eboxr+adPny4FBQWJ6SebN28uzZgxw2LtH9cMxcbGShEREeKJgY+PjzRixAgpIyNDsrUY6nQ6acqUKVKXLl0kPz8/USY9PDzE9btkyZIqZ3D7+eefpd69e4uZ3ng2s6ioKDEtrdZjZ64WG2WwdvFD+QM1Qs0rAAAAAKgGOmwBAAAAgGogeQUAAAAA1UDyCgAAAACqgeQVAAAAAFQDySsAAAAAqAaSVwAAAABQDSSvAAAAAKAaSF4BAAAAQDWQvAIAAACAaiB5BQCb0rdvX7KzsyO1kCSJnnrqKerfv3+tfn727Nnk4eFBN27cqPdzAwCQA5JXAFAtTkIf5UuN1q1bR8nJyTR//vxa/fyMGTPI3t6e5s6dW+/nBgAgBzuJb+sBAFRo3rx5JutWrFhBOp3ObLLG+1+6dIkKCwupTZs2pHTl5eUUGhpKQUFBdOjQoVofhxPY2NhYunDhAgUHB9frOQIAWBuSVwDQlBYtWlB2drZ43K52O3fupBdffJHWrFlD0dHRtT5OSkoKderUSTQh+PTTT+v1HAEArA3NBgCAbL3Na3x8vFjHyx07dlC3bt2oQYMG1KxZM/rkk09EDShLSEig9u3bk5ubGzVv3pyWLl1q9jU4cY6Li6OePXuSp6enOFbnzp3Fukexdu1acV5Dhw412Xbt2jWaNm0atWrVSpyPt7c3tW3blt566y1R82ysY8eOFBYWJt4fAIDaOcp9AgAASvHDDz/Qnj17aPDgwSLx5JrPzz77TCSjXl5e4v+DBg0SCXBiYiJ98MEH1LRpUxo7dqzhGLzvqFGjaPPmzSKxfO2118jZ2Zn27t1Lb775Jp09e5aWLVtW7bnwcfbv30/h4eHUqFGjh7Zxswc+v6ysLNGRa8iQIfTgwQPKzMyk9evX0/vvvy/O11iPHj3EtvT0dGrdunU9Rg0AwMq4zSsAgFYEBwdzewGL26Oioky2r127VqxzcnKSjh8/blifl5cnNWnSRGrQoIHk7+8vXbhwwbDt0qVLkrOzsxQZGfnQsb755htxrPHjx0sPHjwwrC8uLpZeeuklse3kyZPVvo/U1FSx76hRo0y2JSUliW3Tp0832Zafny8VFRWZrI+NjRU/ExcXV+1rAwAoGZoNAAD8bfTo0dSlSxdDPHiIKW5zyjWdkydPppCQEMM27kTVq1cvUZNaWlpqWL9q1Spq2LAhffnll+Tk5GRYz7Wvn3/+ufg/18pW58qVK2LJNbuWcHOBytzd3cnFxcVkvf44+uMCAKgVmg0AAPytQ4cOJrEICAiocltZWZkYQ5Xbx3KSe+bMGQoMDKTFixeb7F9SUiKWf/75Z7Uxv3PnjlhyW9bK+vTpI1570aJFdPr0aZFgR0VFiTavloYEa9y4sVjevn0bnzcAqBqSVwCAv3HnKpNfko6O1W7TJ6V3794VbVWvXr1KMTExFuNaUFBQbcz1tapFRUUm27g969GjR2nOnDmig9muXbsMtcEffvghvf322yY/c//+fbHkzmMAAGqGZgMAAPVEn+DyjFicxFr64o5Y1fHz8xPLnJwcs9t5tAMePeDWrVtiKCyu6eVREaZMmWK2WYL+OPrjAgCoFZJXAIB6wm1k+dH9uXPnKDc3t07HioiIEDNjpaWlVbkf78NNGnjkA33SmpSUZLKf/jiRkZF1Oi8AALkheQUAqEdTp04VbV8nTJhgtnkAD2fFQ1xVh9u6Pvnkk3Ty5EnDOLN6qampop1tZfp1rq6uJtuOHTsmmjn84x//eMR3BACgLGjzCgBQjyZNmiTao/KEBkeOHKHnnntOdODixJI7anESuWnTJjETWHV4/Fae5paPZ5x08pixM2fOFGO98pitPj4+dPHiRVHjyokrNx0wdu/ePXGMfv36iZEQAADUDDWvAAD1SD9T19atW8Wj/59++omWL18uEk5OLHmCAk5oa4KnhOXa0g0bNjy0fsCAASJBzcvLo+3bt9MXX3whamhHjBhBf/zxh5jNyxhPqMAdtjixBgBQOzse7FXukwAAAPPGjBkjZvrKzs4WbWpro3fv3qLml9viOjg4INQAoGqoeQUAUDCekpZrTVeuXFmrn9+3bx8dPnxYjEaAxBUAtADJKwCAggUHB4v2s7WtddXpdKKpArefBQDQAjQbAAAAAADVQM0rAAAAAKgGklcAAAAAUA0krwAAAACgGkheAQAAAEA1kLwCAAAAgGogeQUAAAAA1UDyCgAAAACqgeQVAAAAAFQDySsAAAAAkFr8Px77aImuxkUIAAAAAElFTkSuQmCC" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m==================== nnodely Model Results for test_set ===================\u001B[0m\n", + "\u001B[32m| Loss |\u001B[0m\u001B[32m mse |\u001B[0m\u001B[32m FVU |\u001B[0m\u001B[32m AIC |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m small better |\u001B[0m\u001B[32m lower better |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| curv_error |\u001B[0m\u001B[32m 9.887e-08 |\u001B[0m\u001B[32m 4.011e-04 |\u001B[0m\u001B[32m 1.872e+05 |\u001B[0m\n", + "\u001B[32m|heading_error|\u001B[0m\u001B[32m 1.387e-04 |\u001B[0m\u001B[32m 1.989e-04 |\u001B[0m\u001B[32m 1.907e+05 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| Total |\u001B[0m\u001B[32m 6.941e-05 |\u001B[0m\u001B[32m 3.e-04 |\u001B[0m\u001B[32m 1.889e+05 |\u001B[0m\n", + "\u001B[32m|-------------------------------------------------------------------------|\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 11 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Plot: curvature comparison", + "id": "8b9f1ebb69812b43" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T14:33:10.620735Z", + "start_time": "2025-12-02T14:33:07.689031Z" + } + }, + "cell_type": "code", + "source": [ + "data = pd.read_csv(\"dataset/test/test_set.csv\")\n", + "\n", + "sample_test_set = {\n", + " 'model_steer_in': np.array(data['steer']),\n", + " 'model_vx_in' : np.array(data['vx']),\n", + " 'model_curv_in' : np.array(data['curv']),\n", + " 'model_ax_in' : np.array(data['ax'])\n", + "}\n", + "\n", + "# Model inference\n", + "out_nn = lat_dyna_model(sample_test_set, sampled=False, prediction_samples=-1)\n", + "\n", + "# Plot\n", + "plt.figure()\n", + "plt.plot(out_nn['model_curv'])\n", + "plt.plot(np.array(data['curv'])[30:])\n", + "plt.xlabel('time (s)')\n", + "plt.ylabel('curvature [m-1]')\n", + "plt.grid(True)" + ], + "id": "7db898659ee8183f", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[33m[__call__] Different number of samples between inputs [MAX 1105 = 1105; MIN 1076 = 1076]\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 12 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# SAVE MODEL", + "id": "e52ef4f02f48fb3f" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T14:33:10.650450Z", + "start_time": "2025-12-02T14:33:10.634850Z" + } + }, + "cell_type": "code", + "source": [ + "# Save json model: will be used in the controller training\n", + "lat_dyna_model.saveModel('vehicle_model')" + ], + "id": "8cf9e78e5e359eeb", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m=============================== Save JSON Model ================================\u001B[0m\n", + "\u001B[32mModel saved in: trained_models/vehicle_model.json\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 13 + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/case-studies/lateral_dynamics/lateral_dynamics_model_torch.ipynb b/case-studies/lateral_dynamics/lateral_dynamics_model_torch.ipynb new file mode 100644 index 00000000..4bd1ef67 --- /dev/null +++ b/case-studies/lateral_dynamics/lateral_dynamics_model_torch.ipynb @@ -0,0 +1,965 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4fca82a7205fafb9", + "metadata": {}, + "source": [ + "# **FORWARD MODEL OF VEHICLE LATERAL DYNAMICS**" + ] + }, + { + "cell_type": "markdown", + "id": "00de8ab2", + "metadata": {}, + "source": [ + "This notebook reproduces the same example presented in *lateral_dynamics_model.ipynb*, but this time the implementation is done directly in **Torch** rather than **nnodely**. \n", + "\n", + "Here, a Model-Structured Neural Network for vehicle lateral dynamics is presented. The dataset used to train the network was collected in CarMaker14 using a 14 Dof vehicle model, the Kia_EV6_demo, an electric car. The training and validation set were collected on a custom track with a spread range of cruvature values and different corner types, from constant radius corner to clothoids. Three different driver profiles were adopted in order to cover a larger values of accelerations, \"normal\", \"aggressive\" and \"prudent\" respectively. Every dataset was then mirrored in order to avoid offsets in the training, assuming that the vehicle can be approximated to be reasonably symmetric in right and left corners.\n", + "Finally, the test telemetries were collected on a different track to proove the robustness of the trained network." + ] + }, + { + "cell_type": "markdown", + "id": "f4fe99351f149193", + "metadata": {}, + "source": [ + "## Initialization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.200191Z", + "start_time": "2025-12-02T13:54:30.406048Z" + }, + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using cpu device\n" + ] + } + ], + "source": [ + "# Import necessary packages\n", + "import os\n", + "import numpy as np\n", + "import torch\n", + "from torch import nn\n", + "from torch.utils.data import DataLoader, Dataset, ConcatDataset\n", + "\n", + "# import a library for plots\n", + "import matplotlib as mpl\n", + "mpl.rcParams.update(mpl.rcParamsDefault)\n", + "import matplotlib.pyplot as plt\n", + "plt.close('all')\n", + "SMALL_SIZE = 14\n", + "MEDIUM_SIZE = 22\n", + "BIGGER_SIZE = 26\n", + "plt.rc('font', size=SMALL_SIZE) # controls default text sizes\n", + "plt.rc('axes', titlesize=MEDIUM_SIZE) # fontsize of the axes title\n", + "plt.rc('axes', labelsize=SMALL_SIZE) # fontsize of the x and y labels\n", + "plt.rc('xtick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n", + "plt.rc('ytick', labelsize=SMALL_SIZE) # fontsize of the tick labels\n", + "plt.rc('legend', fontsize=SMALL_SIZE) # legend fontsize\n", + "plt.rc('figure', titlesize=BIGGER_SIZE) # fontsize of the figure title\n", + "plt.rc('grid', linestyle=\"--\", color='grey')\n", + "\n", + "# Congigurations\n", + "device = \"cpu\"\n", + "print(f\"Using {device} device\")\n", + "\n", + "torch.manual_seed(1)\n", + "np.random.seed(1)\n", + "\n", + "path_folder = 'trained_models' # folder to save the model" + ] + }, + { + "cell_type": "markdown", + "id": "a4b4192ab97751ff", + "metadata": {}, + "source": [ + "# MS-NN model" + ] + }, + { + "cell_type": "markdown", + "id": "b6ef5ba231c37df3", + "metadata": {}, + "source": [ + "The structure of the Model-Structured Neural Network is derived from the description of the lateral dynamic performance of the vehicles through the understeering gradient that describe the correction to be applied with respect to the kinematic (or Ackermann) steering. In particular, denoting with $\\delta$ the steering angle at wheels (note that $\\delta = \\delta_{sw}/\\tau_{sw}$, where $\\delta_{sw}$ is the steering wheel angle and $\\tau_{sw}$ is the steering ratio), the relationship between steer angle and curvature $\\kappa$ is:\n", + "$$\n", + "\\rho = \\frac{\\delta}{1+A v_x^2}\n", + "$$\n", + "where the parameter $A$ is generally called \"understeering gradient\". \n", + "\n", + "The **FirFuzzyLayer** implements the equivalent of a *LoacalModel* composed of *FIR filters* activated by *Fuzzy* **TriangularMembershipFunction** on forward speed takes into account the dynamic response of the system that is function of the speed. \n", + "The **UsFuzzyLayer** implements another *LocalModel* that applies the algebraic relation above as function of the learnable parameter **A** that varies as function of the longitudinal acceleration. \n", + "Finally, the **LateralDynamics** evaluates the output curvature as the product of the two local models outputs.\n", + "\n", + "![](figs/fig_model_torch.png)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0ee3eeaa", + "metadata": {}, + "outputs": [], + "source": [ + "# triangular activation function\n", + "class TriangularMembershipFunction(nn.Module):\n", + " def __init__(self,centers: list) -> None:\n", + " super().__init__()\n", + " self.centers = torch.tensor(centers, dtype=torch.float32) # (C,)\n", + " self.C = self.centers.numel()\n", + "\n", + " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", + " \"\"\"\n", + " x: (batch,1) \n", + " out: (batch,C) -> membership per channel/centre\n", + " \"\"\"\n", + " # get sizes\n", + " batch = x.size(0)\n", + " C = self.C\n", + " centers = self.centers\n", + "\n", + " # triangural membership function initalization\n", + " phi = torch.zeros(batch,self.C)\n", + " x = x.squeeze(1) # (C,)\n", + "\n", + " # evaluate channel width and ceneters\n", + " x_contig = x.contiguous()\n", + " idx0 = torch.clip(torch.bucketize(x_contig,centers)-1, 0, C-2) # [0, C-2] \n", + " idx1 = idx0 + 1 # [1, C-1]\n", + " c0 = centers[idx0] # left center\n", + " c1 = centers[idx1] # right center\n", + " ch_w = c1 - c0 # channels distance\n", + "\n", + " b = torch.arange(batch)\n", + "\n", + " phi[b,idx0] = torch.clip( (c1 - x) / ch_w , 0 , 1 ) # left channel weight\n", + " phi[b,idx1] = torch.clip( (x - c0) / ch_w , 0 , 1 ) # right channel width\n", + "\n", + " return phi # (batch, C)\n", + "\n", + "\n", + "class FirFuzzyLayer(nn.Module):\n", + " def __init__(self,channels: list,window: int) -> None:\n", + " super().__init__()\n", + " self.C = len(channels)\n", + " self.phi_layer = TriangularMembershipFunction(channels) # activation functon weights\n", + " self.fir_layer = nn.Conv1d( # parallel fir filters\n", + " in_channels=self.C,\n", + " out_channels=self.C,\n", + " kernel_size=window,\n", + " groups=self.C,\n", + " bias=False) \n", + " nn.init.constant_(self.fir_layer.weight, 1.0)\n", + " self.W_fir = torch.tensor([[np.exp(-(i/(window/2))**2)] for i in range(-window+1,1)],dtype=torch.float32).view(1,1,-1)\n", + " \n", + " def forward(self, x: torch.Tensor, a: torch.Tensor) -> torch.Tensor:\n", + " \"\"\"\n", + " x: (batch,window) -> input signal with time windows\n", + " a: (batch,1) -> activation function input\n", + " out: (batch,1) -> interpolated fir output\n", + " \"\"\"\n", + " # evaluate membership function\n", + " phi = self.phi_layer(a) # (batch, C)\n", + "\n", + " # replicate input for Conv1d\n", + " x_fir = x.unsqueeze(1)*self.W_fir # (batch, 1, window)\n", + " x_fir = x_fir.repeat(1,self.C, 1) # (batch, C, window)\n", + " y = self.fir_layer(x_fir) # (batch, C, 1)\n", + " # evaluate weightet output\n", + " y = y.squeeze(-1) # (batch, C)\n", + " y = phi * y\n", + "\n", + " return y.sum(dim=1).unsqueeze(-1) # (batch,1)\n", + " \n", + "class UsFuzzyLayer(nn.Module):\n", + " def __init__(self,channels: list) -> None:\n", + " super().__init__()\n", + " self.C = len(channels)\n", + " self.phi_layer = TriangularMembershipFunction(channels)\n", + " self.A = nn.Parameter(1e-5*torch.ones(1,self.C)) \n", + "\n", + " def forward(self, x: torch.Tensor, a: torch.Tensor) -> torch.Tensor:\n", + " \"\"\"\n", + " x: (batch,1) -> input signal\n", + " a: (batch,1) -> activation input\n", + " out: (batch,1) -> output\n", + " \"\"\"\n", + " # evaluate membership function\n", + " phi = self.phi_layer(a) # (batch,1)\n", + "\n", + " # replicate for channels\n", + " x = x.repeat(1,self.C) # (batch,C)\n", + "\n", + " # evaluate parametric function\n", + " y = 1 / (1 + self.A * x**2) \n", + " y = phi * y\n", + "\n", + " return y.sum(dim=1).unsqueeze(-1) #(batch,1)\n", + " \n", + "\n", + "class LateralDynamics(nn.Module):\n", + " def __init__(self, chan_vx: list, chan_ax: list, window: int) -> None:\n", + " super().__init__()\n", + " self.fir_layer = FirFuzzyLayer(chan_vx,window)\n", + " self.us_layer = UsFuzzyLayer(chan_ax)\n", + "\n", + " def forward(self, steer: torch.Tensor, vx: torch.Tensor, ax: torch.Tensor) -> torch.Tensor:\n", + " \"\"\"\n", + " steer: (batch,window) -> steer time window \n", + " vx: (batch,1) -> vx last\n", + " ax: (batch,1) -> ax last\n", + " out: (batch,1) -> rho next\n", + " \"\"\"\n", + " # apply fir\n", + " rho_kin = self.fir_layer(steer,vx)\n", + " # apply us correction\n", + " corr_kus = self.us_layer(vx,ax)\n", + "\n", + " return rho_kin * corr_kus" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a7afd97b064ac3e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-02T13:54:32.396841Z", + "start_time": "2025-12-02T13:54:32.221943Z" + } + }, + "outputs": [], + "source": [ + "# ----------------------------------------------------------------\n", + "# Hyperparameters\n", + "# ----------------------------------------------------------------\n", + "samples_past_steer = 30 # number of samples in the past for the steering wheel angle prediction\n", + "n_channels_vx = 8 # number of channels for activation function vx\n", + "chan_vx = list(np.linspace(8, 23, num=n_channels_vx)) # centers of the channels vx\n", + "chan_ax = [-2,-1,0.0,1.0,2.0] # centers of the channels ax\n", + "\n", + "prediction = 80 # 4s prediction\n", + "\n", + "# ----------------------------------------------------------------\n", + "# MODEL\n", + "# ----------------------------------------------------------------\n", + "model = LateralDynamics(chan_vx,chan_ax,samples_past_steer)" + ] + }, + { + "cell_type": "markdown", + "id": "55eb656e", + "metadata": {}, + "source": [ + "## Training, validation and test datrasets" + ] + }, + { + "cell_type": "markdown", + "id": "34bdc37f", + "metadata": {}, + "source": [ + "In this section the datasets used in training, validation and test are created. Differently from nnodely, where the time windows are automatically generated, it is important to pay attention to the dimensions while creating the datasets. Below, different dataset for non-recurrent and recurrent training are created. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "75ad310a", + "metadata": {}, + "outputs": [], + "source": [ + "data_training = \"dataset/training/*.csv\"\n", + "data_valid = \"dataset/validation/*.csv\"\n", + "data_test = \"dataset/test/*.csv\"\n", + "\n", + "head = ['ax', 'vx', 'curv', 'steer']\n", + "sampling = 0.05\n", + "\n", + "\n", + "# loading of csv datasets\n", + "import pandas as pd\n", + "import glob\n", + "def load_csv_list(data_path: str):\n", + " dfs = []\n", + " for file in glob.glob(data_path):\n", + " df = pd.read_csv(file)\n", + " df = df[head].reset_index(drop=True)\n", + " dfs.append(df)\n", + " return dfs\n", + "\n", + "class VehicleDataset(Dataset):\n", + " def __init__(self, df: pd.DataFrame, past_samples: int, prediction: int):\n", + " self.lb = past_samples\n", + " self.lf = prediction\n", + " self.df = df\n", + "\n", + " self.steer = torch.tensor(df[\"steer\"].values, dtype=torch.float32)\n", + " self.vx = torch.tensor(df[\"vx\"].values, dtype=torch.float32)\n", + " self.ax = torch.tensor(df[\"ax\"].values, dtype=torch.float32)\n", + " self.curv = torch.tensor(df[\"curv\"].values, dtype=torch.float32)\n", + "\n", + " def __len__(self):\n", + " return len(self.df) - self.lb - self.lf - 1\n", + "\n", + " def __getitem__(self, i):\n", + " steer = self.steer[i : i + self.lb + self.lf]\n", + "\n", + " if self.lf > 0:\n", + " vx = self.vx[i + self.lb -1: i + self.lb + self.lf -1]\n", + " ax = self.ax[i + self.lb -1: i + self.lb + self.lf -1]\n", + " curv = self.curv[i + self.lb : i + self.lb + self.lf]\n", + " else:\n", + " vx = self.vx[i + self.lb -1].unsqueeze(-1)\n", + " ax = self.ax[i + self.lb -1].unsqueeze(-1)\n", + " curv = self.curv[i + self.lb].unsqueeze(-1)\n", + "\n", + " return steer, vx, ax, curv\n", + "\n", + "# Concatenate datasets \n", + "def build_dataset(csv_path, past_samples, prediction):\n", + " dfs = load_csv_list(csv_path)\n", + " datasets = [\n", + " VehicleDataset(df, past_samples, prediction)\n", + " for df in dfs\n", + " if len(df) > past_samples + prediction + 1\n", + " ]\n", + " return ConcatDataset(datasets)\n", + "\n", + "# non recurrent dataset\n", + "train_dataset = build_dataset(data_training, samples_past_steer, prediction=0)\n", + "valid_dataset = build_dataset(data_valid, samples_past_steer, prediction=0)\n", + "\n", + "train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n", + "valid_dataloader = DataLoader(valid_dataset, batch_size=64, shuffle=True)\n", + "\n", + "# recurrent dataset\n", + "train_dataset_rec = build_dataset(data_training, samples_past_steer, prediction)\n", + "valid_dataset_rec = build_dataset(data_valid, samples_past_steer, prediction)\n", + "\n", + "train_dataloader_rec = DataLoader(train_dataset_rec, batch_size=64, shuffle=True)\n", + "valid_dataloader_rec = DataLoader(valid_dataset_rec, batch_size=64, shuffle=True)\n", + "\n", + "# test set\n", + "test_dataset = build_dataset(data_test, samples_past_steer, prediction=0)\n", + "test_dataloader_analyse = DataLoader(test_dataset, batch_size=64, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "id": "a71e1f61", + "metadata": {}, + "source": [ + "## Training" + ] + }, + { + "cell_type": "markdown", + "id": "771b72d3", + "metadata": {}, + "source": [ + "The training is done in two steps. The first training has not prediction samples in the future, that corresponds to train the network uniquely on the curvature loss function. In this way the network weights are adjusted. The second step of training is more a refining of the network weigths, including a future time window on which the integral error is evaluated.\n", + "\n", + "Two loss terms are defined, both of them use the MSE as loss function. \n", + "The first loss is evaluated on the curvature error: the next-step curvature predicted by the model is compared to the dataset curvature. \n", + "The second loss is evaluated on the heading error*: both reference heading and predicted heading are evaluated starting form the curvature values. \n", + "\n", + "*assuming steady-state curvature, the following relation holds: $\\psi = \\int \\kappa v_x \\, \\mathrm{d}t$, where $\\psi$ denotes the heading angle.\n", + "\n", + "The second loss term provided more robustness to the network, reducing possible biases. The dependency to forward speed, moreover, weights the loss term reducing the bias espetially for high speed, where a bias is more determinant in the tracking error of the desired trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5e8d2a56", + "metadata": {}, + "outputs": [], + "source": [ + "# Loss function and optimizer\n", + "loss_fn = nn.MSELoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "80045e78", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| EPOCHS | train_loss | valid_loss |\n", + "| 1 | 1.205848e+01 | 3.993303e+00 |\n", + "| 2 | 3.601545e+00 | 2.272017e+00 |\n", + "| 3 | 2.269297e+00 | 1.560794e+00 |\n", + "| 4 | 1.623210e+00 | 1.166077e+00 |\n", + "| 5 | 1.231791e+00 | 9.115995e-01 |\n", + "| 6 | 9.694558e-01 | 7.314101e-01 |\n", + "| 7 | 7.785340e-01 | 5.986812e-01 |\n", + "| 8 | 6.348477e-01 | 4.952880e-01 |\n", + "| 9 | 5.227449e-01 | 4.137729e-01 |\n", + "| 10 | 4.325497e-01 | 3.469233e-01 |\n", + "| 11 | 3.591937e-01 | 2.921307e-01 |\n", + "| 12 | 2.987542e-01 | 2.463117e-01 |\n", + "| 13 | 2.482705e-01 | 2.072196e-01 |\n", + "| 14 | 2.057386e-01 | 1.743322e-01 |\n", + "| 15 | 1.700803e-01 | 1.459871e-01 |\n", + "| 16 | 1.399665e-01 | 1.219467e-01 |\n", + "| 17 | 1.145317e-01 | 1.010900e-01 |\n", + "| 18 | 9.316733e-02 | 8.357940e-02 |\n", + "| 19 | 7.517885e-02 | 6.819770e-02 |\n", + "| 20 | 6.023755e-02 | 5.558151e-02 |\n", + "| 21 | 4.778022e-02 | 4.462547e-02 |\n", + "| 22 | 3.758923e-02 | 3.550482e-02 |\n", + "| 23 | 2.931881e-02 | 2.794297e-02 |\n", + "| 24 | 2.265477e-02 | 2.176316e-02 |\n", + "| 25 | 1.734488e-02 | 1.671030e-02 |\n", + "| 26 | 1.320375e-02 | 1.269140e-02 |\n", + "| 27 | 9.982055e-03 | 9.503939e-03 |\n", + "| 28 | 7.504168e-03 | 7.023965e-03 |\n", + "| 29 | 5.625284e-03 | 5.138575e-03 |\n", + "| 30 | 4.195398e-03 | 3.697327e-03 |\n", + "| 31 | 3.120406e-03 | 2.636478e-03 |\n", + "| 32 | 2.311825e-03 | 1.860454e-03 |\n", + "| 33 | 1.705874e-03 | 1.295019e-03 |\n", + "| 34 | 1.249782e-03 | 8.950270e-04 |\n", + "| 35 | 9.079855e-04 | 6.093815e-04 |\n", + "| 36 | 6.536414e-04 | 4.103271e-04 |\n", + "| 37 | 4.659053e-04 | 2.724742e-04 |\n", + "| 38 | 3.283628e-04 | 1.794563e-04 |\n", + "| 39 | 2.290913e-04 | 1.178826e-04 |\n", + "| 40 | 1.583847e-04 | 7.676189e-05 |\n", + "| 41 | 1.086100e-04 | 5.077305e-05 |\n", + "| 42 | 7.452647e-05 | 3.443727e-05 |\n", + "| 43 | 5.146370e-05 | 2.447852e-05 |\n", + "| 44 | 3.615109e-05 | 1.834351e-05 |\n", + "| 45 | 2.613800e-05 | 1.459893e-05 |\n", + "| 46 | 1.963191e-05 | 1.217828e-05 |\n", + "| 47 | 1.540853e-05 | 1.051233e-05 |\n", + "| 48 | 1.259156e-05 | 9.319271e-06 |\n", + "| 49 | 1.071819e-05 | 8.425278e-06 |\n", + "| 50 | 9.450373e-06 | 7.736369e-06 |\n", + "| 51 | 8.544468e-06 | 7.234268e-06 |\n", + "| 52 | 7.901305e-06 | 6.807302e-06 |\n", + "| 53 | 7.416667e-06 | 6.457940e-06 |\n", + "| 54 | 7.034882e-06 | 6.163034e-06 |\n", + "| 55 | 6.720832e-06 | 5.903926e-06 |\n", + "| 56 | 6.436727e-06 | 5.667749e-06 |\n", + "| 57 | 6.168953e-06 | 5.425480e-06 |\n", + "| 58 | 5.908444e-06 | 5.178713e-06 |\n", + "| 59 | 5.645844e-06 | 5.046294e-06 |\n", + "| 60 | 5.375260e-06 | 4.794419e-06 |\n", + "| 61 | 5.107968e-06 | 4.535123e-06 |\n", + "| 62 | 4.828490e-06 | 4.287488e-06 |\n", + "| 63 | 4.544723e-06 | 4.031118e-06 |\n", + "| 64 | 4.263380e-06 | 3.839551e-06 |\n", + "| 65 | 3.979495e-06 | 3.521478e-06 |\n", + "| 66 | 3.707824e-06 | 3.273343e-06 |\n", + "| 67 | 3.429362e-06 | 3.125194e-06 |\n", + "| 68 | 3.166739e-06 | 2.852911e-06 |\n", + "| 69 | 2.907662e-06 | 2.671098e-06 |\n", + "| 70 | 2.665252e-06 | 2.332261e-06 |\n", + "| 71 | 2.443775e-06 | 2.320320e-06 |\n", + "| 72 | 2.236160e-06 | 2.076525e-06 |\n", + "| 73 | 2.041818e-06 | 1.824754e-06 |\n", + "| 74 | 1.873725e-06 | 1.677864e-06 |\n", + "| 75 | 1.725128e-06 | 1.769001e-06 |\n", + "| 76 | 1.593802e-06 | 1.591576e-06 |\n", + "| 77 | 1.475845e-06 | 1.369824e-06 |\n", + "| 78 | 1.379302e-06 | 1.465899e-06 |\n", + "| 79 | 1.297395e-06 | 1.308736e-06 |\n", + "| 80 | 1.233196e-06 | 1.213152e-06 |\n", + "| 81 | 1.177609e-06 | 1.340199e-06 |\n", + "| 82 | 1.129489e-06 | 1.241996e-06 |\n", + "| 83 | 1.091999e-06 | 1.350001e-06 |\n", + "| 84 | 1.064996e-06 | 1.215024e-06 |\n", + "| 85 | 1.037280e-06 | 1.151030e-06 |\n", + "| 86 | 1.018500e-06 | 1.131976e-06 |\n", + "| 87 | 9.930868e-07 | 1.137796e-06 |\n", + "| 88 | 9.750614e-07 | 1.266192e-06 |\n", + "| 89 | 9.549960e-07 | 1.125168e-06 |\n", + "| 90 | 9.368780e-07 | 1.153786e-06 |\n", + "| 91 | 9.215902e-07 | 1.120187e-06 |\n", + "| 92 | 9.064754e-07 | 1.049562e-06 |\n", + "| 93 | 8.884964e-07 | 1.069193e-06 |\n", + "| 94 | 8.656354e-07 | 1.071534e-06 |\n", + "| 95 | 8.474267e-07 | 1.011096e-06 |\n", + "| 96 | 8.311083e-07 | 9.008651e-07 |\n", + "| 97 | 8.032037e-07 | 1.172527e-06 |\n", + "| 98 | 7.757734e-07 | 8.968937e-07 |\n", + "| 99 | 7.627752e-07 | 7.495522e-07 |\n", + "| 100 | 7.379058e-07 | 1.060575e-06 |\n", + "| 101 | 7.121067e-07 | 9.034667e-07 |\n", + "| 102 | 7.002885e-07 | 9.284777e-07 |\n", + "| 103 | 6.750601e-07 | 6.723834e-07 |\n", + "| 104 | 6.297265e-07 | 7.253778e-07 |\n", + "| 105 | 6.110112e-07 | 8.118105e-07 |\n", + "| 106 | 5.863349e-07 | 8.956012e-07 |\n", + "| 107 | 5.582208e-07 | 6.649525e-07 |\n", + "| 108 | 5.417131e-07 | 6.324120e-07 |\n", + "| 109 | 5.115345e-07 | 6.490774e-07 |\n", + "| 110 | 4.772005e-07 | 7.314286e-07 |\n", + "| 111 | 4.523723e-07 | 5.651769e-07 |\n", + "| 112 | 4.344899e-07 | 7.990565e-07 |\n", + "| 113 | 4.163532e-07 | 5.831500e-07 |\n", + "| 114 | 3.700234e-07 | 5.238173e-07 |\n", + "| 115 | 3.464892e-07 | 3.960559e-07 |\n", + "| 116 | 3.261640e-07 | 3.322188e-07 |\n", + "| 117 | 3.272612e-07 | 3.995878e-07 |\n", + "| 118 | 2.779281e-07 | 3.986440e-07 |\n", + "| 119 | 2.532952e-07 | 3.935416e-07 |\n", + "| 120 | 2.370212e-07 | 2.654797e-07 |\n", + "| 121 | 2.180676e-07 | 2.576516e-07 |\n", + "| 122 | 1.970586e-07 | 2.338232e-07 |\n", + "| 123 | 1.850989e-07 | 1.690015e-07 |\n", + "| 124 | 1.662572e-07 | 2.530447e-07 |\n", + "| 125 | 1.554526e-07 | 1.801642e-07 |\n", + "| 126 | 1.541599e-07 | 1.610781e-07 |\n", + "| 127 | 1.403137e-07 | 1.081776e-07 |\n", + "| 128 | 1.368817e-07 | 1.763919e-07 |\n", + "| 129 | 1.142252e-07 | 8.304040e-08 |\n", + "| 130 | 1.064391e-07 | 1.869386e-07 |\n", + "| 131 | 1.078645e-07 | 7.290388e-08 |\n", + "| 132 | 8.960386e-08 | 1.032055e-07 |\n", + "| 133 | 1.055897e-07 | 8.360829e-08 |\n", + "| 134 | 9.835559e-08 | 8.713607e-08 |\n", + "| 135 | 7.627369e-08 | 7.361678e-08 |\n", + "| 136 | 8.809436e-08 | 7.581416e-08 |\n", + "| 137 | 8.236222e-08 | 1.884940e-07 |\n", + "| 138 | 7.659095e-08 | 7.755157e-08 |\n", + "| 139 | 7.638957e-08 | 7.455683e-08 |\n", + "| 140 | 6.080827e-08 | 6.616238e-08 |\n", + "| 141 | 6.887925e-08 | 1.022889e-07 |\n", + "| 142 | 7.571003e-08 | 5.598343e-08 |\n", + "| 143 | 6.667938e-08 | 5.446819e-08 |\n", + "| 144 | 7.271347e-08 | 6.338221e-08 |\n", + "| 145 | 6.150606e-08 | 8.034650e-08 |\n", + "| 146 | 6.495895e-08 | 6.901670e-08 |\n", + "| 147 | 6.104783e-08 | 1.048582e-07 |\n", + "| 148 | 5.851796e-08 | 5.648495e-08 |\n", + "| 149 | 6.977191e-08 | 4.805939e-08 |\n", + "| 150 | 8.714432e-08 | 6.990093e-08 |\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ------------------------------------------------------------------------\n", + "# Non-recurrent training\n", + "# ------------------------------------------------------------------------\n", + "EPOCHS = 150\n", + "\n", + "train_loss_list = []\n", + "valid_loss_list = []\n", + "\n", + "def inference(steer: torch.Tensor, vx: torch.Tensor, ax: torch.Tensor, curv: torch.Tensor):\n", + "\n", + " # Make one-step prediction\n", + " output = model(steer,vx, ax)\n", + " # Compute the loss\n", + " loss = loss_fn(output, curv)\n", + "\n", + " return loss\n", + "\n", + "\n", + "\n", + "def train_one_epoch():\n", + " avg_loss = 0.0\n", + "\n", + " for i, data in enumerate(train_dataloader):\n", + " steer, vx, ax, curv = data\n", + " \n", + " # Zero your gradients for every batch!\n", + " optimizer.zero_grad()\n", + "\n", + " # compute loss and its gradient\n", + " loss = inference(steer,vx,ax,curv)\n", + " loss.backward()\n", + "\n", + " avg_loss += loss.item()\n", + " # Adjust learning weights\n", + " optimizer.step()\n", + "\n", + " return avg_loss / len(train_dataloader)\n", + "\n", + "\n", + "print('| {:^7} | {:^12} | {:^12} |'.format('EPOCHS','train_loss','valid_loss'))\n", + "for epoch in range(EPOCHS):\n", + " # Make sure gradient tracking is on, and do a pass over the data\n", + " model.train(True)\n", + " avg_loss = train_one_epoch()\n", + "\n", + " # Set the model to evaluation mode, disabling dropout and using population\n", + " # statistics for batch normalization.\n", + " model.eval()\n", + " avg_vloss = 0.0\n", + " \n", + " with torch.no_grad():\n", + " for i, vdata in enumerate(valid_dataloader):\n", + " steer, vx, ax, curv = vdata\n", + " \n", + " vloss = inference(steer,vx,ax,curv)\n", + " avg_vloss += vloss.item()\n", + " avg_vloss /= len(valid_dataloader)\n", + " \n", + " # save the total losses\n", + " train_loss_list.append(avg_loss)\n", + " valid_loss_list.append(avg_vloss)\n", + "\n", + " print('| {:^7} | {:^12.6e} | {:^12.6e} |'.format(epoch+1, avg_loss, avg_vloss))\n", + " epoch += 1\n", + "\n", + "plt.figure()\n", + "plt.title('Non recurrent training')\n", + "plt.plot(train_loss_list, label='train loss')\n", + "plt.plot(valid_loss_list, '--', label='validation loss')\n", + "plt.yscale('log')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "# Save the weights\n", + "torch.save(model.state_dict(), path_folder + \"/model.pt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "42eb6289", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| EPOCHS | train_loss | valid_loss |\n", + "| 1 | 1.377889e-04 | 2.693818e-05 |\n", + "| 2 | 2.623087e-05 | 4.141323e-05 |\n", + "| 3 | 2.588223e-05 | 2.720195e-05 |\n", + "| 4 | 2.534634e-05 | 2.554914e-05 |\n", + "| 5 | 2.738388e-05 | 2.576481e-05 |\n", + "| 6 | 2.765964e-05 | 3.931546e-05 |\n", + "| 7 | 2.544308e-05 | 2.646154e-05 |\n", + "| 8 | 2.837828e-05 | 3.363704e-05 |\n", + "| 9 | 2.730536e-05 | 4.175908e-05 |\n", + "| 10 | 2.756178e-05 | 2.300445e-05 |\n", + "| 11 | 3.432054e-05 | 2.018997e-05 |\n", + "| 12 | 2.904275e-05 | 3.317036e-05 |\n", + "| 13 | 2.574582e-05 | 1.755240e-05 |\n", + "| 14 | 2.493574e-05 | 2.667926e-05 |\n", + "| 15 | 2.635224e-05 | 3.085884e-05 |\n", + "| 16 | 2.881143e-05 | 4.300633e-05 |\n", + "| 17 | 2.687611e-05 | 2.373896e-05 |\n", + "| 18 | 2.842827e-05 | 3.746086e-05 |\n", + "| 19 | 2.865635e-05 | 2.058030e-05 |\n", + "| 20 | 2.820284e-05 | 2.592496e-05 |\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ------------------------------------------------------------------------\n", + "# Recurrent training\n", + "# ------------------------------------------------------------------------\n", + "EPOCHS = 20\n", + "\n", + "curv_loss_list = []\n", + "head_loss_list = []\n", + "curv_vloss_list = []\n", + "head_vloss_list = []\n", + "\n", + "def inference_rec(steer: torch.Tensor, vx: torch.Tensor, ax: torch.Tensor, curv: torch.Tensor):\n", + "\n", + " # recurrent training\n", + " psi0_ref = 0\n", + " psi_ref = 0\n", + " psi0_comp = 0\n", + " psi_comp = 0\n", + " loss_curv = 0\n", + " loss_head = 0\n", + "\n", + " for j in range(prediction):\n", + " steer_k = steer[:,j:j+samples_past_steer]\n", + " vx_k = vx[:,j].unsqueeze(-1)\n", + " ax_k = ax[:,j].unsqueeze(-1)\n", + " curv_k = curv[:,j].unsqueeze(-1)\n", + "\n", + " # Make one-step prediction\n", + " output = model(steer_k,vx_k, ax_k)\n", + " # Compute the loss\n", + " loss_curv += loss_fn(output, curv_k)\n", + " psi_ref = psi0_ref + curv_k*vx_k*sampling\n", + " psi_comp = psi0_comp + output*vx_k*sampling\n", + " loss_head += loss_fn(psi_ref,psi_comp)\n", + "\n", + " psi0_comp = psi_comp\n", + " psi0_ref = psi_ref\n", + "\n", + " loss_curv = loss_curv / prediction\n", + " loss_head = loss_head / prediction\n", + "\n", + " loss = loss_curv + loss_head\n", + "\n", + " return loss, loss_curv, loss_head\n", + "\n", + "\n", + "\n", + "def train_one_epoch_rec():\n", + " avg_loss = 0.0\n", + " avg_curv_loss = 0.0\n", + " avg_head_loss = 0.0\n", + "\n", + " for i, data in enumerate(train_dataloader_rec):\n", + " steer, vx, ax, curv = data\n", + " # Zero your gradients for every batch!\n", + " optimizer.zero_grad()\n", + "\n", + " # compute loss and its gradient\n", + " loss, loss_curv, loss_head = inference_rec(steer,vx,ax,curv)\n", + " loss.backward()\n", + "\n", + " avg_loss += loss.item()\n", + "\n", + " avg_curv_loss += loss_curv.item()\n", + " avg_head_loss += loss_head.item()\n", + "\n", + " # Adjust learning weights\n", + " optimizer.step()\n", + "\n", + " return avg_loss / len(train_dataloader_rec), avg_curv_loss / len(train_dataloader_rec), avg_head_loss / len(train_dataloader_rec)\n", + "\n", + "\n", + "print('| {:^7} | {:^12} | {:^12} |'.format('EPOCHS','train_loss','valid_loss'))\n", + "for epoch in range(EPOCHS):\n", + " # Make sure gradient tracking is on, and do a pass over the data\n", + " model.train(True)\n", + " avg_loss, avg_curv_loss, avg_head_loss = train_one_epoch_rec()\n", + "\n", + " curv_loss_list.append(avg_curv_loss)\n", + " head_loss_list.append(avg_head_loss)\n", + "\n", + " # Set the model to evaluation mode, disabling dropout and using population\n", + " # statistics for batch normalization.\n", + " model.eval()\n", + " avg_vloss = 0.0\n", + " avg_vloss_curv = 0.0\n", + " avg_vloss_head = 0.0\n", + " # Disable gradient computation and reduce memory consumption.\n", + " with torch.no_grad():\n", + " for i, vdata in enumerate(valid_dataloader_rec):\n", + " steer, vx, ax, curv = vdata\n", + " \n", + " vloss, vloss_curv, vloss_head = inference_rec(steer,vx,ax,curv)\n", + " avg_vloss += vloss.item()\n", + " avg_vloss_curv += vloss_curv.item()\n", + " avg_vloss_head += vloss_head.item()\n", + "\n", + " avg_vloss /= len(valid_dataloader_rec)\n", + " avg_vloss_curv /= len(valid_dataloader_rec)\n", + " avg_vloss_head /= len(valid_dataloader_rec)\n", + " head_vloss_list.append(avg_vloss_head)\n", + " curv_vloss_list.append(avg_vloss_curv)\n", + " \n", + " print('| {:^7} | {:^12.6e} | {:^12.6e} |'.format(epoch+1, avg_loss, avg_vloss))\n", + " epoch += 1\n", + "\n", + "\n", + "plt.figure()\n", + "plt.title('Recurrent training')\n", + "plt.plot(curv_loss_list, label='train curvature loss')\n", + "plt.plot(curv_vloss_list, '--', label='validation curvature loss')\n", + "plt.yscale('log')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "plt.figure()\n", + "plt.title('Recurrent training')\n", + "plt.plot(head_loss_list, label='train heading loss')\n", + "plt.plot(head_vloss_list, '--', label='validation heading loss')\n", + "plt.yscale('log')\n", + "plt.xlabel('Epochs')\n", + "plt.ylabel('Loss')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()\n", + "\n", + "\n", + "# Save the weights\n", + "torch.save(model.state_dict(), path_folder + \"/model.pt\")" + ] + }, + { + "cell_type": "markdown", + "id": "7e8a9f4c", + "metadata": {}, + "source": [ + "## Test set" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "90ec7fe7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# inference\n", + "model = LateralDynamics(chan_vx,chan_ax,samples_past_steer)\n", + "model.load_state_dict(torch.load(path_folder + \"/vehicle_model_torch.pt\"))\n", + "\n", + "curv_targ = []\n", + "curv_out = []\n", + "\n", + "for i,data in enumerate(test_dataloader_analyse):\n", + " steer, vx, ax , curv = data\n", + "\n", + " model.eval()\n", + " curv_ = model(steer, vx, ax)\n", + "\n", + " curv_targ.append(curv)\n", + " curv_out.append(curv_)\n", + "\n", + "curv_targ = torch.cat(curv_targ,dim=0).squeeze().numpy()\n", + "curv_out = torch.cat(curv_out,dim=0).squeeze().detach().numpy()\n", + "\n", + "# Plot comparison\n", + "plt.figure(figsize=(12,6))\n", + "plt.plot(sampling*np.linspace(0,len(curv_targ),len(curv_targ)),curv_targ, label='Target curvature')\n", + "plt.plot(sampling*np.linspace(0,len(curv_out),len(curv_out)),curv_out, '--', label='Predicted curvature')\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Curvature (1/m)')\n", + "plt.title('Curvature Prediction vs Target')\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "788217cb", + "metadata": {}, + "source": [ + "## Fir weights" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "16604a2f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sampling = 0.05\n", + "W_fir = torch.tensor([[np.exp(-(i/(samples_past_steer/2))**2)] for i in range(-samples_past_steer+1,1)],dtype=torch.float32).squeeze(-1)\n", + "\n", + "for name, param in model.named_parameters():\n", + " if name.startswith('fir_layer'):\n", + " fir_param = param\n", + "\n", + "plt.figure()\n", + "\n", + "for i in range(fir_param[:,0,0].detach().numpy().size):\n", + " weight_loc = fir_param[i,0,:]*W_fir\n", + " weight_loc = weight_loc.detach().numpy()\n", + " time = np.linspace(0,sampling*(len(weight_loc)-1),len(weight_loc))\n", + " plt.plot(time, np.flip(weight_loc),'o',label=f\"Fir_{i}\")\n", + "plt.legend()\n", + "plt.grid(True)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "nnodely", + "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": "3.10.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/case-studies/mass_spring_damper/PID_test.mlx b/case-studies/mass_spring_damper/PID_test.mlx new file mode 100644 index 00000000..0e8ff46e Binary files /dev/null and b/case-studies/mass_spring_damper/PID_test.mlx differ diff --git a/case-studies/mass_spring_damper/comparison/PID_test_nnodely.csv b/case-studies/mass_spring_damper/comparison/PID_test_nnodely.csv new file mode 100644 index 00000000..340fbcc4 --- /dev/null +++ b/case-studies/mass_spring_damper/comparison/PID_test_nnodely.csv @@ -0,0 +1,2001 @@ +Time,target,autotune,est_est,estimate +0,1,0.017951206612974,0,0.0197528204324663 +0.01,1,0.0713020535082324,0.0845516166766174,0.0782087325802734 +0.02,1,0.140772776002296,0.113324183940263,0.153643140966683 +0.03,1,0.207281418411051,0.287444471158552,0.224822047777887 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+19.99,-0.984000000000001,-0.945599666403992,-0.948859104854265,-0.948699589337934 diff --git a/case-studies/mass_spring_damper/comparison/mass_spring_damper_nnodely.ipynb b/case-studies/mass_spring_damper/comparison/mass_spring_damper_nnodely.ipynb new file mode 100644 index 00000000..19e7df06 --- /dev/null +++ b/case-studies/mass_spring_damper/comparison/mass_spring_damper_nnodely.ipynb @@ -0,0 +1,778 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6efa68b1", + "metadata": {}, + "source": [ + "# Mass Spring Damper Example\n", + "This example shows how to fit a simple linear model.\n", + "The data was created previously and loaded from file.\n", + "The data are the position of the mass and the force applied.\n", + "The neural model mirrors the structure of the physical model.\n", + "The network build estimate the future position of the mass." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "d5da1888", + "metadata": {}, + "outputs": [], + "source": [ + "from nnodely import *\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "2b7aba7d", + "metadata": {}, + "source": [ + "# Definition of the Model-Structured Neural Netrok\n", + "A discrete-time neural approximation of the dynamics is given by\n", + "$x_{k+1} = h_x^⊀ x_k + h_F^⊀ F_k$,\n", + "where the subscript $(Β·)_k$ indicates the value of a variable at time step $k$, and bold symbols denote vectors corresponding to temporal windows, i.e., $x_k = [x_{kβˆ’n}, \\dots , x_k]$ and $F_k = [F_{kβˆ’n}, \\dots , F_k]$. The parameters $h_x$ and $h_F$ represent the **FIR** filters associated with $x$ and $F$." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f49e9026", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;32m================================ nnodely Model =================================\u001b[0m\n", + "\u001b[32m{'Constants': {},\n", + " 'Functions': {},\n", + " 'Info': {'SampleTime': 0.01,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [10, 1],\n", + " 'ntot': 11,\n", + " 'num_parameters': 20},\n", + " 'Inputs': {'F': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'x': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'x_t': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]}},\n", + " 'Minimizers': {'x[t]': {'A': 'SamplePart8', 'B': 'Add6', 'loss': 'mse'}},\n", + " 'Models': 'neural_msd',\n", + " 'Outputs': {'x_n': 'Add6'},\n", + " 'Parameters': {'PFir3W': {'dim': 1,\n", + " 'init_fun': {'name': 'init_negexp',\n", + " 'params': {'first_value': 0.1,\n", + " 'lambda': 3,\n", + " 'size_index': 0}},\n", + " 'tw': 0.1,\n", + " 'values': [[0.004978707060217857],\n", + " [0.006948345340788364],\n", + " [0.009697197005152702],\n", + " [0.013533527962863445],\n", + " [0.018887560814619064],\n", + " [0.026359714567661285],\n", + " [0.03678794577717781],\n", + " [0.051341712474823],\n", + " [0.07165312767028809],\n", + " [0.10000000149011612]]},\n", + " 'PFir5W': {'dim': 1,\n", + " 'tw': 0.1,\n", + " 'values': [[0.8822692632675171],\n", + " [0.9150039553642273],\n", + " [0.38286375999450684],\n", + " [0.9593056440353394],\n", + " [0.3904482126235962],\n", + " [0.600895345211029],\n", + " [0.2565724849700928],\n", + " [0.7936413288116455],\n", + " [0.9407714605331421],\n", + " [0.13318592309951782]]}},\n", + " 'Relations': {'Add6': ['Add', ['Fir2', 'Fir5']],\n", + " 'Fir2': ['Fir', ['TimePart1'], 'PFir3W', None, 0],\n", + " 'Fir5': ['Fir', ['TimePart4'], 'PFir5W', None, 0],\n", + " 'SamplePart8': ['SamplePart', ['x_t'], -1, [0, 1]],\n", + " 'TimePart1': ['TimePart', ['x'], -1, [-0.1, 0]],\n", + " 'TimePart4': ['TimePart', ['F'], -1, [-0.1, 0]]}}\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n" + ] + } + ], + "source": [ + "# The time history for x\n", + "T_x = 0.1\n", + "\n", + "# Define the neural model\n", + "x = Input('x') # MSNN input (Mass position)\n", + "F = Input('F') # MSNN input (Force)\n", + "x_free = Fir(W_init = 'init_negexp', W_init_params = {'first_value':0.1,'size_index':0,'lambda':3})(x.tw(T_x))\n", + "x_force = Fir(F.tw(T_x))\n", + "x_n = Output('x_n', x_free + x_force)\n", + "\n", + "# Add the neural models to the nnodely structure\n", + "msd = Modely(seed=42, workspace='saved')\n", + "msd.addModel('neural_msd', x_n)\n", + "\n", + "# These functions are used to impose the minimization objectives.\n", + "# Here it is minimized the error between the future position of x get from the dataset\n", + "# and the estimator designed using the neural network.\n", + "# The minimization is imposed via MSE error.\n", + "x_t = Input('x_t') # Real position\n", + "msd.addMinimize('x[t]', x_t.next(), x_n)\n", + "\n", + "# Nauralize the model and getting the neural network.\n", + "# The sampling time depends on the datasets.\n", + "msd.neuralizeModel(0.01)" + ] + }, + { + "cell_type": "markdown", + "id": "9c628955", + "metadata": {}, + "source": [ + "# Data loading and Training\n", + "With this lines a dataset in CSV format is loaded from the folder β€œdata”. The column are interpreted using the variable format where the second column is $x$ and $x_r$ and the forth column is $F$. The CSV files are used to build a dataset with a tensor structure compliant with the network architecture. All time windows for each input are generated automatically, and temporal consistency across different files is guaranteed.\n", + "\n", + "A set of parameters default_par are defined and used for training the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "414b7be5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33m[check_names] The name 'simulations' is already in defined as Dataset but it is overwritten.\u001b[0m\n", + "\u001b[1;32m============================ nnodely Model Dataset =============================\u001b[0m\n", + "\u001b[32mDataset Name: simulations\u001b[0m\n", + "\u001b[32mNumber of files: 100\u001b[0m\n", + "\u001b[32mTotal number of samples: 199100\u001b[0m\n", + "\u001b[32mShape of F: (199100, 10, 1)\u001b[0m\n", + "\u001b[32mShape of x: (199100, 10, 1)\u001b[0m\n", + "\u001b[32mShape of x_t: (199100, 1, 1)\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n", + "\u001b[33m[_setup_recurrent_variables] The value of the prediction_samples=0 but the network has no recurrent variables.\u001b[0m\n", + "\u001b[1;32m======================== nnodely Model Train Parameters ========================\u001b[0m\n", + "\u001b[32mmodels: ['neural_msd']\u001b[0m\n", + "\u001b[32mnum of epochs: 80\u001b[0m\n", + "\u001b[32mupdate per epochs: 1088\u001b[0m\n", + "\u001b[34mβ””>(n_samples-batch_size)/batch_size+1\u001b[0m\n", + "\u001b[32mshuffle data: True\u001b[0m\n", + "\u001b[32mtrain dataset: simulations_train\u001b[0m\n", + "\u001b[32m\t- batch size: 128\u001b[0m\n", + "\u001b[32m\t- num of samples: 139370\u001b[0m\n", + "\u001b[32mvalidation dataset: simulations_val\u001b[0m\n", + "\u001b[32m\t- batch size: 128\u001b[0m\n", + "\u001b[32m\t- num of samples: 39820\u001b[0m\n", + "\u001b[32mtest dataset: simulations_test\u001b[0m\n", + "\u001b[32m\t- num of samples: 19910\u001b[0m\n", + "\u001b[32mminimizers: {'x[t]': {'A': 'SamplePart8',\n", + " 'B': 'Add6',\n", + " 'loss': 'mse'}}\u001b[0m\n", + "\u001b[32moptimizer: Adam\u001b[0m\n", + "\u001b[32moptimizer defaults: {'lr': 0.0005}\u001b[0m\n", + "\u001b[32moptimizer params: [{'params': 'PFir3W'}, {'params': 'PFir5W'}]\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n", + "\u001b[1;32m================= nnodely Training =================\u001b[0m\n", + "\u001b[32m| Epoch |\u001b[0m\u001b[32m x[t] |\u001b[0m\u001b[32m Total |\u001b[0m\n", + "\u001b[32m| |\u001b[0m\u001b[32m Loss |\u001b[0m\u001b[32m Loss |\u001b[0m\n", + "\u001b[32m| |\u001b[0m\u001b[32m train |\u001b[0m\u001b[32m val |\u001b[0m\u001b[32m train |\u001b[0m\u001b[32m val |\u001b[0m\n", + "\u001b[32m|--------------------------------------------------|\u001b[0m\n", + "\u001b[32m| 1/80 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"\u001b[32m================================================================================\u001b[0m\n", + "\u001b[1;32m========================== Export Python Torch Model ===========================\u001b[0m\n", + "\u001b[32mModel exported in: saved/msd_preliminary.py\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n" + ] + } + ], + "source": [ + "data_struct = ['time', ('x','x_t'), '', 'F']\n", + "msd.loadData(name = 'simulations',\n", + " source = '../msd-data/data',\n", + " format = data_struct, delimiter = ';')\n", + "\n", + "# Neural network train\n", + "param_model = {'num_of_epochs' : 80,\n", + " 'train_batch_size' : 128,\n", + " 'lr' : 0.0005,\n", + " 'splits' : [70,20,10]}\n", + "msd.trainModel(training_params = param_model)\n", + "\n", + "# Save the neural model\n", + "msd.exportPythonModel(name = 'msd_preliminary')" + ] + }, + { + "cell_type": "markdown", + "id": "d9143f71", + "metadata": {}, + "source": [ + "## Closed-Loop Evaluation\n", + "\n", + "The model is rolled out autoregressively, using its predictions as inputs at each step, and the resulting trajectory is compared with the ground truth." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dde885f5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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f31/t27ev+tBDD6k5OTn2bfLy8tQLLrhA9ff3VwEZzl0I4VYUVf1DHwEhhBBCCAcYMGAA4eHhLF++XOsoQgjRpuSZLCGEEEK0itlspqGhodG61atXk5KSwvjx47UJJYQQGpI7WUIIIYRolYyMDCZPnsy1115LTEwMBw8e5I033iAwMJC9e/cSGhqqdUQhhGhTMvCFEEIIIVolODiYwYMH8/bbb1NYWIivry8XXHABzz77rBRYQogOSe5kCSGEEEIIIYQDyTNZQgghhBBCCOFAUmQJIYQQQgghhAPJM1nnYLVaycnJwd/fH0VRtI4jhBBCCCGE0IiqqlRWVhITE4NOd+b7VVJknUNOTg5xcXFaxxBCCCGEEEK4iOPHj9OpU6czfl2KrHPw9/cHbBcyICBA0yxms5lly5YxdepUjEajplnaK7nGziXX1/nkGjuXXF/nk2vsXHJ9nU+usXNpfX0rKiqIi4uz1whnIkXWOZzoIhgQEOASRZaPjw8BAQHyj9ZJ5Bo7l1xf55Nr7FxyfZ1PrrFzyfV1PrnGzuUq1/dcjxHJwBdCCCGEEEII4UBSZAkhhBBCCCGEA0mRJYQQQgghhBAOJM9kCSGEEEII4eJUVaWhoQGLxaJ1FE2ZzWYMBgN1dXVOuRZ6vR6DwdDqqZukyBJCCCGEEMKF1dfXk5ubS01NjdZRNKeqKlFRURw/ftxpc9j6+PgQHR2Nh4dHi48hRZYQQgghhBAuymq1kp6ejl6vJyYmBg8PD6cVF+7AarVSVVWFn5/fWScDbglVVamvr6ewsJD09HS6du3a4nNIkSWEEEIIIYSLqq+vx2q1EhcXh4+Pj9ZxNGe1Wqmvr8fLy8vhRRaAt7c3RqORY8eO2c/TEjLwhRBCCCGEEC7OGQWFOD1HXGv5bgkhhBBCCCGEA0mRJYQQQgghhBAOJEWWEEIIIYQQwq0lJCSwcOFCrWPYSZElhBBCCCGEEA4kRZZwKeW1Zo6X1FBT36B1FCGEEEII0Ybq6+u1juAwUmQJl7A+tYhLX9/AgCeWMfa5VfR/fBm3fbCN1IIqraMJIYQQQogWGD9+PHfddRd33XUXgYGBhIWF8be//Q1VVQFbF78nn3yS66+/noCAAG699VYA1q1bx9ixY/H29iYuLo758+dTXV1tP25hYSEXXXQR3t7eJCYm8tFHH2ny+s5G5skSmrJaVV5YfohXV6U1Wm+2qCzdl8+qQ4U8f1k/Lh4Qq1FCIYQQQgjXM/M/6yisNLX5ecP9Pfnh7jFN3v5///sfN998M1u2bGHbtm3ceuutxMfHc8sttwDwr3/9i0cffZS///3vAKSlpXH++efz1FNP8e6771JYWGgv1N577z0A7rjjDgoLC1m1ahVGo5H58+dTUFDg+BfbClJkCU39c+lB3lxz1L6cFO5L1wh/tmeWUlhpor7Byr2f7cJiVZk9qJOGSYUQQgghXEdhpYm8ijqtY5xTXFwcL774Ioqi0L17d/bs2cOLL75oL7ImTpzI/fffb99+3rx5XHPNNdx7770AdO3alZdffplx48bx+uuvk5GRwYoVK9i0aRPDhw8H4J133qFnz55t/trORoosoZmvd2TZC6yhukM8H7uWztV7UI7XYQ3rxnfhE3jwaH8aVAN/+WoPiWG+DIwP1ji1EEIIIYT2wv093eK8I0aMQFEU+/LIkSN54YUXsFgsAAwZMqTR9ikpKezevbtRF0BVVbFaraSnp3Pw4EEMBgODBw+2f71Hjx4EBQW14NU4jxRZQhNZpTU8+t0+9Fh4yPAptxkWQ+HJr+tyd3AJOxgS3JMrS/9EjiWM+z7bxZJ7zsPbQ69dcCGEEEIIF9CcLnuuzNfXt9FyVVUVt912G/Pnzz9l2/j4eA4ePNhW0VpFBr4Qmnjs+/1Umcw8Y3jbVmCd4B0CwQn2xU7mdCZG1QCQUVzDwhWH2zipEEIIIYRoqc2bNzda3rRpE127dkWvP/2H5oMGDWL//v0kJyef8sfDw4MePXrQ0NDA9u3b7fscOnSIsrIyZ76MZpMiS7S5zUeLWXEgnzv133GFYY1tpc4A056BBw7DPSlw83KI6IUy5zNumHMdHgbbj+q769M5XlKjYXohhBBCCNFUmZmZLFiwgEOHDvHJJ5/wn//8h3vuueeM2//5z39mw4YN3HXXXezatYsjR47w3XffcddddwHQvXt3Jk2axJ/+9Cc2b97M9u3bmTdvHt7e3m31kppEiizRplRV5eklttu8Ffhg0XkAClz6Doy8A/RG24Zxw+D29dBlPMkRfswbkwjYRh3893K5myWEEEII4Q6uv/56amtrGTZsGHfeeSf33HOPfaj20+nXrx9r1qzh8OHDjB07loEDB/Loo48SExNj3+bVV18lOjqacePGMXv2bG699VYiIiLa4uU0mTyTJdrUutQiUo6XAbA1/FK48hbI3gq9Z526se7kZwC3jUvi4y2ZVNXU8u2ubO4Yn0TXSP+2CS2EEEIIIVrEaDSycOFCXn/99VO+lpGRcdp9hg4dyrJly854zMjISH744Qd0v3uveN1117U6qyPJnSzRpt5ae3K49rsndkUf3QeG3HjO/QKNVt6JX87XHn8H1crbv6Y7M6YQQgghhBAtJkWWaDMHciv49UgRAHEh3pzfJ6rpO397B4Mz3qKfLp2LdBv4Zmc2BZWuPzeEEEIIIYToeKTIEm3mg03HuFy/mtv0P/CnERHodco597EbdL29eb/hC1RLPR9uPOb4kEIIIYQQwiFWr17NwoULtY6hCSmyRJuorbfw865j3G/4goeNn3D1hgugtrTpB+gyDrqMByBeV8gFuk18vi0Li1V1TmAhhBBCCCFaSIos0SaW7M1lYsNaohRbYaV0Hg3ewc07yLg/25vzDD+RV1HL2sOFZ9lBCCGEEEKItidFlmgTn287zjX6lSdXjD7z/AhnFD8SYgYC0EeXwQjdAT7dmumghEIIIYQQQjiGFFnC6fIr6qjI2MlAXSoAalRf2zxYzaUoMPIu++L1+mWsPFBASXW9o6IKIYQQQgjRalJkCadbsieXq3S/2JeVwTfYCqaW6HUx+Nomm5us206AtZyl+/IckFIIIYQQQgjHkCJLON2y3ZlcrF8PgNXgDX0vb/nB9EYYcDUAHoqFS/S/8uPuHEfEFEIIIYQQwiGkyBJOVVBRh0/WGgKVGgCUnjPBK7B1Bx1wLQA1eOFNPRvTiimsNLU2qhBCCCGEEA4hRZZwqiV787hAt8m+rPS5tPUHDe8GV3/KW8OW8IrlEqwq/Lw3t/XHFUIIIYQQDjN+/HjuvfderWPYtWUeKbKEU63cl8Uk3U4ALB6BkDTRMQfuPp1pA5Ptiz/uliJLCCGEEKK9qa93zwHOpMgSTlNlamBjRgWTTP/i38Zb0J13Pxg8HHb8HlH+dAnzBWDbsVLKatzzH6EQQgghRHtzww03sGbNGl566SUURUFRFNLS0rj55ptJTEzE29ub7t2789JLL52y36xZs/jHP/5BTEwM3bt3B2DDhg0MGDAAHx8fJkyYwLfffouiKOzatcu+7969e5k+fTp+fn5ERkZy3XXXUVRUdMY8GRkZTnv9BqcdWXR461OLMFtUCgmitPcNKGP6OPT4iqIwqWcEub8ewMPawOpDhcwaGOvQcwghhBBCuKwNr8DGV8+9XXR/mPNp43UfXwW5Kefed+SdMOquc2/3By+99BKHDx+mT58+PPHEEwAEBwfTqVMnvvjiC0JDQ9mwYQO33nor0dHRXHHFFfZ9V65cSUBAAMuXLwegoqKCmTNnMmPGDD788EMOHDjAww8/3Oh8ZWVlTJw4kXnz5vHiiy9SW1vLn//8Z6644gp++eWX0+YJDw9v9utqKimyhNOsPlRgb0/o4YQf4tIMbi94ivs8V/C+ZSorD3aTIksIIYQQHYepEiqbMMpy4GneH9UUNW1fU2XzcwGBgYF4eHjg4+NDVFSUff3jjz9ubycmJrJx40Y+//zzRkWWr68vb7/9Nh4eth5Qb7zxBoqi8N///hcPDw86depEaWkpt912m32fV155hYEDB/L000/b17377rvExcVx+PBhunXrdto8ziJFlnAKVVVZfagQAA+DjpFdwhx/Eg9/QjJ/RlEsnK/bwquH8jFbrBj10gtWCCGEEB2Apz/4x5x7O5/TvA/zCWvavp7+zc91Fq+++irvvvsumZmZ1NbWUl9fz4ABAxpt07dvX3uBBXDo0CH69euHl5cXVqsVgGHDhjXaJyUlhVWrVuHn53fKOdPS0ujWrZtDX8e5SJElnOJQfiUXVH1FlKGEwpgJeOutgN6xJ/ENRek8CjJ+JUGXT4Qpk60ZJYxKckJBJ4QQQgjhakbd1aKufMCp3QfbwKeffsoDDzzACy+8wMiRI/H39+f5559n8+bNjbbz9fVt9rGrqqqYOXMm//znP0/5WnR0dIszt5QUWcIp1hwq5Gr9LyTpcrEWLgPT5eAT4vgTdTsfMn4FYKJuJ78cGCNFlhBCCCGEC/Dw8MBisdiX169fz6hRo7jjjjvs69LS0s55nO7du/Phhx9iMpkwGo0AbN26tdE2gwYN4quvviIhIQGD4fQlzh/zOJP0qxJOkXYwhSSdbVh1U/RQ5xRYYCuyfjNZv4N1qUXOOY8QQgghhGiWhIQENm/eTEZGBkVFRXTt2pVt27axdOlSDh8+zN/+9rdTiqXTmTNnDlarlVtvvZUDBw6wcuVK/v3vfwO2gdAA7rzzTkpKSrj66qvZunUraWlpLF26lBtvvNFeWP0xz4muh84gRZZwOLPFSlD2avuyV68ZzjtZWDKE2ubLGqwcJjcvl8JKk/POJ4QQQgghmuSBBx5Ar9fTq1cvwsPDmTZtGrNnz+bKK69k+PDhFBcXN7qrdSYBAQH88MMP7Nq1i0GDBvHUU0/x17/+FQAvLy8AYmJiWL9+PRaLhalTp9K3b1/uvfdegoKC0Ol0p82TmZnptNcu3QWFw+3OKmeouse+rHSd7NwTdjsfNr6CQbEyTrebDWljuHiAjDIohBBCCKGlbt26sXHjxkbr3nvvPd57771G65555hl7e9GiRac91qhRo0hJScFqtVJRUcEPP/yA0WgkPj7evk3Xrl35+uuvm5XHWdzqTtbatWuZOXMmMTExKIrCt99+e9btV69ebZ9s7Pd/8vLy2iZwB7U5LZ/hugMA1HmEQEQv556w2zR7c7x+F78ekS6DQgghhBDtyfvvv8+6detIT09n8eLFPPzww1xxxRV4e3trHe203OpOVnV1Nf379+emm25i9uzZTd7v0KFDBAQE2JcjIiKcEU/8Jv/AJgKUWgAsncfAb31lnSZuBKrRF8VczRjdXp47XIiqqvY+usJ9ZBbXcCCvggaLSkKYDz2jAtDp5PsohBBCdHR5eXk8+uij5OXlERkZyWWXXdZoTixX41ZF1vTp05k+fXqz94uIiCAoKMjxgcQpTA0WgvI32O+R+nSf5PyTGjxQEsbAkaXUY8BSmUdaYTXJEafOkyBc09rDhbyw7BApWeWN1seH+PCn8UlcMSQOvRRbQgghRIf10EMP8dBDD9m7CwYEBNiftXJFblVktdSAAQMwmUz06dOHxx57jNGjR59xW5PJhMl0cuCEiooKAMxmM2az2elZz+bE+bXOcTbbM0oZqu61LzfEj4K2yDvu//g8+HYeWVsDKKw5lE/nYM9mH8YdrrE7++P1bbBYeXrJIT7YfPy022eW1PDw13v4flc2L13Zj2Afj9NuJ06Sn2HnkuvrfHKNnUuur/M5+hqbzWZUVcVqtTp1NDx3oaqq/W9nXQ+r1YqqqpjNZvT6xvO8NvX7qqgnkroZRVH45ptvmDVr1hm3OXToEKtXr2bIkCGYTCbefvttPvjgAzZv3sygQYNOu89jjz3G448/fsr6jz/+GB8fH0fFb7dWHjfzXOHteClmSvVhrO37gvO7C/4mqxqe32373KBvsJV5PeQXkSuzqPDBER07i09+ChXro9I3RCXGmsP+Km82VYTavxbprXJ3bwv+Ri3SCiGEENowGAxERUXRqVMnPD2b/wGyaD6TyURWVhZ5eXk0NDQ0+lpNTQ1z5syhvLy80eNIf9Sui6zTGTduHPHx8XzwwQen/frp7mTFxcVRVFR01gvZFsxmM8uXL2fKlCn2idhczbxFmzCkr2KUbj+XjOyJ/9T/a7NzW60qI/65mtIaM0HeRjb/ZXyzn+dxh2vszn5/ff+++DBfbM8GwKhX+NsFPbhqSCcURUH/1Y3oDv5AWcRwFhTP5JfqLgD0jPLno5uH4u/VIW7Ct4j8DDuXXF/nk2vsXHJ9nc/R19hisXD06FHCw8MJDQ099w7tnKqqVFZW4u/v77Tn74uLiyksLKRLly6n3MmqqKggLCzsnEVWh3unMmzYMNatW3fGr3t6ep72UwKj0egyv4xcKcvvWawqO7JqqLQOZo/vKG6aManNB58YmhDCsv35lNXWk1FqonuUf4uO46rXuL34JiWfL7ZnE0EpwwypXHrtHUzo8bsBaUozAAgq2My7bOZ939k8Vj2bA3mVPPrDAf5z9UAZ2OQc5GfYueT6Op9cY+eS6+t8jrrGRqOR4OBgioqK0Ol0+Pj4dOj/A61WK/X19ZhMJoc/k6WqKjU1NRQVFREcHGyfg+v3mvo97XBF1q5du4iOjtY6Rrt0pKCSSpPtlurgzsFt/wugIpc7LB9yl8dqfrEOZEt6nxYXWcJ58mvhX4sPEkUxn3o8RZyhBH3ALOB3RVbPC8FcDSVHAbje8jUhXgXMr7udH3fnMjIplGuGd9YkvxBCCNHWoqKiACgoKNA4ifZUVaW2thZvb2+nvdcMCgqyX/OWcqsiq6qqitTUVPtyeno6u3btIiQkhPj4eB5++GGys7N5//33AVi4cCGJiYn07t2buro63n77bX755ReWLVum1Uto17YfK7W3B3cO1iTDgGPvgQ7qMbIovYTrRiZokkOcntWq8lmaHq+GSj72+AcJunywAssfhbnfn9xw/F9g7AOw6VVY8TioFi5kHRUGI//XMI+nFx9gYo8IogNdc24MIYQQwpEURSE6OpqIiIgOP2iJ2Wxm7dq1nHfeeU65G2s0Gk/pItgSblVkbdu2jQkTJtiXFyxYAMDcuXNZtGgRubm5ZGZm2r9eX1/P/fffT3Z2Nj4+PvTr148VK1Y0OoZwnIwje7lYt47tancGxY9s+wAB0aghSSglafRT0kg5mivzZbmYb3blcLRS5S3j63TR/TYpeHAizHr91I31Bhh9D4Qmw+fXg7WBOYZV7FG78En9JB7/fj9vXDe4bV+AEEIIoSG9Xu+QAsCd6fV6Ghoa8PLycukur25VZI0fP56zjdOxaNGiRssnxtMXbSMoczmPeLwHgLlQgc5z2zyD0nkUlKThoViIqT7AseLxJIT5tnkOcao6s4WFK1O5Qb+UKfodtpXewXD9dxAYe+Yde1wAs96Ar+cB8Ljxf6RYk/h5H6xPLWJ0clgbpBdCCCGEaDrXncFLuJWiKhNdak/Oj2WMG6JNkM6j7M1hugNsSS/RJoc4xYebjqGvyOIBw+cnV85+G4Kb8GxVv8th+O0AeNDA3422LsHPLT101g9ehBBCCCG0IEWWcIgdGSUM0R0CoE7vB+E9tQnyuyJrqO4Qm6XIcgmVdWZeXZXK48ZF+Cq/TZEw5GboOrnpB5nyJIT3RO0ygdcD7wcg5XgZS/flOyGxEEIIIUTLSZElHOLokT2EKxUAVIYPBAcPqdlkQZ1R/W2jRw7WHWZHeqE2OUQjn2zJpFvtbibrdwKg+kXB5L837yAGD7jhR5TrvuHa6ePsq19YdgirVe5mCSGEEMJ1SJElHMKSscne9kkarV0QRUHpbDu/r2LCv2w/RVWmc+wknMlssfLeunQeMn5qX2eZ8FfwCmz+wXzDQFGY1DOCQfFBABwpqGLVIRnSVgghhBCuQ4os0WoWq0pk2U77sm/yGA3TAJ1Pjmw4THeQnZll2mUR/LQnl7yKWj5umESRPoIKr1jUPpe36piKonDH+GQMNBBAFW+tPeqgtEIIIYQQrSdFlmi1o4VV9FaPAGBBB7GDtA3U+eSdtOG6g+zILD3LxsKZVFXlnXXpqOj4ynoehy77hS1d7gVdK4eftVqZaFnHGu+H+KvhIzanl5ByvMwRkYUQQgghWk2KLNFq+zJy6aZkAVDqmwweGg+ZHtad6qF3c0v9Ah4038qOY1JkaSUlq5zdWeUA9IkNYGhSJNWeka0/cH0VusX3EavmMUu/jiiK+e+vcjdLCCGEEK5BiizRasWpW9ArtoEHGqIHapwG0OnwveAp9geMpQx/dmeV02Cxap2qQ/ps68nJwa8fkeC4iaG9AmCobd4sD8XCTYafWbovT56/E0IIIYRLkCJLtFpaYTUbLL2oVL0JSBqudRy7gb8NjFBrtnAwr1LbMB1QtamB3bu28azhLfp55HJBv2jHnmD47aD3BGCOfiXeliq+2Jbl2HMIIYQQQrSAFFmiVcwWK18VxTPH/Fcu8v0In6HXah3JblB8sL29U57LanOL9+RyqXUpVxlW873ufnxTf3DsCfwiYMAcW1Op40r9Kj7ZkinDuQshhBBCc1JkiVY5nF+JqcHWFa9vXAgYPDVOdNLwoHJm6dYxX/81O2SEwTb3zZY0LtGvA8Cq94Qu4x1/kpF32ZvX6FdyvKSK9WlFjj+PEEIIIUQzSJElWmXPb4MaAPTr1IJ5j5yo5/p7WOjxGguMX3L42HGt43QoWaU1hGctI1ipAkDpfTF4B59jrxYIS4ZE28TECbp8xuj2SpdBIYQQQmhOiizRKnszCwFb96y+sa5VZOnihtnbIWV7ZVCENvTj7lyu0q+yLyuD5jrvZENvtjev1a9g2f48qkwNzjufEEIIIcQ5SJElWqVH2jvs9LyN9z2eoa/Rxe4gdBpqbw5UUtklXQbbzNYd2xml3w+AObBLo7nLHK77DPCLAmCybjsB5mKW7s1z3vmEEEIIIc5BiizRYnVmCzHVBwhWqjhPtwcfHz+tIzXWaYi9OUh3hN1ZZdpl6UDSCqvoWbTMvmwcch04auj209EbYdD1WPUeLLEOw1sx8e2ubOedTwghhBDiHKTIEi12KLeCvkoaADU6fwjponGiPwhOxOIdCsAAXSp7smSEwbbw464cLtJvOLmi7+XOP+mIP6Hcf5jn/B/mmBrF+tQi8ivqnH9eIYQQQojTkCJLtFhG+mHCFdvAFyVBfZx7t6IlFAVdnK3LYJBSTVnWQVRVhvd2tr27NtJNZ7uTZIoZBkFxzj+pTwiKTzCzBsYCYFXh+105zj+vEEIIIcRpSJElWqw6fbu9rcQO1DDJmSm/G/wiybSfrNJaDdO0f6kFVfQpPzngheeAK9r0/LMGxNjbP+7JbdNzCyGEEEKcIEWWaDFD4R57O6jLkLNsqaHfDX4xSDnCnuzys2wsWmvFgXxebZjFvPr7SY2YBr0ubtPzdwn3o3+UJ9N1m9l7vJicMimqhRBCCNH2pMgSLWK1qoRVHbYv+3Z2zTtZxAxCxdaNsa/uKCky+IVTLd+fTz1GVlgHo7/iPfCLaNsA297l88rred3jJcbo9vKzjDIohBBCCA1IkSVa5FhJDd1JB6BW8YWgBG0DnYmnH+bY4ay19GWFZTC7j8udLGcpqjKxI9M2uEhyhB+JYb5tH8InDE9LNQDTdZulyBJCCCGEJqTIEi2SmnGMWKUYgGL/bqBz3R8lj1uW8pD347xkuZS92eVYrTL4hTP8cqCAE+OKTOkVqU2IrlPAaCvupum3sfNYAQWVMsqgEEIIIdqW674zFi6t9OjJQS8sEX00TNI0fTsFAlBpaiC9uFrjNO3T7pRtvGJ8mYt065nWxUubEEZv6DYNgGCliuHKAZbty9cmixBCCCE6LCmyRIssq+7KZNNzzK+/E6/Bc7SOc079fyuyAPZkSZdBR6szWwjKXMqF+k287PEq/Qq/1y7M7wbbmCFdBoUQQgihASmyRIvsza0mVe3Eao9xRPQYqXWcc+rXKQgFK52UAhn8wgk2HS1mAtvsy7oeF2gXputUVKMPANP0W9lytICymnrt8gghhBCiw5EiSzRbcZWJvArbcy49owNQXG0S4tMYvu9JUjxvYY3HfRw6Lt3HHG37vkMMVFIBqAzoCqFJ2oXx8EHpOgWAUKWSwRxgzeFC7fIIIYQQosORIks024HcSnu7V0yAhkmazlMPAUotekVFzd1Ng8WqdaT25fDP6BTbqBfG3jM1DgP0mmVvXqDbxC8HC7TLIoQQQogOR4os0WzZqbu4z/Al03RbGRjiJt2wYk7O49XTmsqRgioNw7Qv2WW19KrebF/26q1hV8ETuk5FNdgG35ii387qgwVSWAshhBCizRi0DiDcj5KxjnsMXwOQWx0EuOhExL/3uyKrr+4o+3Iq6BntHnfhXN2vB3OYodsLQK0hEO8YF/h58PRDSRxHfsY+vq/tR31DFTsyyxiWGKJ1MiGEEEJ0AHInSzSbT+kBezsseaiGSZohohdWnQcA/ZSj7MuREQYdJXvPWgKUWgBq48eBTq9xot9c/h7rpy/lHw3XUouXdBkUQgghRJuRIks0S53ZQqe6I/ZlY0xfDdM0g8EDa6RtPq8kXS5Hs3I1DtQ+NFis+GevsS8H9Z2uYZo/8PBlXLdwTozL8stBGfBECCGEEG1DiizRLGn55XRXjgNQaIwFL/fpcmfoNMje1uelYLWqGqZpH1Kyyhhp3Wlf1iVP0jDNqUL9PBkYFwTA4fwqjpfUaBtICCGEEB2CFFmiWXKO7sNbsQ12URHUQ+M0zRRzssjq2nCETHnD3WprDhfxF/MtPGe+gqNxl4B/pNaRTjGxRwQxFNFVyWLVIekyKIQQQgjnkyJLNEtV5m57WxfZW8MkLfCHwS/2ynNZrbYhtYh9aiKvWWbhfdnrWsc5VVUB8/Zdzwav+fzZ8AkrD0iRJYQQQgjnkyJLNIuu8OSgFwEJ/TVM0gJh3bDovQFIUnLZl1OhcSD3VlPfQEpWGQCJYb5EB3prG+h0fMLwNJUAMEa3l51Hc6gzWzQOJYQQQoj2Toos0SxBlYft7ZCEAdoFaQm9gbJZHzDW9CLT65+RIquVtmWUYrbYnmsb0SVU4zRnoNOhdJsGgJdiZqh1N1vSSzQOJYQQQoj2Toos0WSVdWbiGzIAMOGJEpKobaAWCOkzmWqfOEBhX3Y5qiqDX7TUgf27uEX/I72VdEZ2CdI6zpl1Pzni4STdTn49UqhhGCGEEEJ0BDIZsWiyI/mV7LX2o5hjBAX4k+wq8yE1g6Io9I4J4NcjRRRX15NfYSIq0EvrWG7JcGQZjxg/BqCyHOAuTfOcUeI4VL0HiqWe8/S7ef9wIVygdSghhBBCtGdyJ0s02aH8Kh5tuJHL6h9j3ch3tI7TYr1jAu3tvdky+EVLVJka6Fy5zb7s32OChmnOwcMHpfMoADopRdQXHKagok7jUEIIIYRoz6TIEk12KK/S3u4W5a9hktaZrG7iL4ZPeMbwX3kuq4W2pRUwTLENglJlCILwntoGOpekk/N3nafbzdojRRqGEUIIIUR7J0WWaLLD+SeLrO6R7ltk9T36FrcbfuAK/WqOZOVrHcctpe/dSIBSC0Bl5AjQufivkuTGRZY8lyWEEEIIZ3Lxd0bClRzPs80xFObnQaifp8ZpWs6j0wAA9IqKKWevtmHclJKx1t4O6DXpLFu6iIheqP7RAIzU7WfL4RysVhn0RAghhBDOIUWWaJKiKhNfN9zFZs87eNn4itZxWkWJHmBvR1QforS6Xrswbqi81kxi1Q77sm/3iRqmaSJFQUmaiBkj261doaaY/bnSVVQIIYQQziFFlmiSo8cyCFfKiVTKiNa7+ZvT6H72Zm8lQ57LaqZtqXkMVQ4BUGEMh9AkjRM10aS/8+WktVxrfoRcQvlVnssSQgghhJNIkSWapORoir1tDu2hYRIHiOyDigJAb10G+3JkhMHmyN6/Dh/FBEBl9EhQFI0TNZF/JCN7drYvynNZQgghhHAWKbJEk5hzTz675Nmpr4ZJHMDTj/pA20TKPZTjHMgu0TiQezFkrrO3A3u6wfNYv9M51Ie4EG8AtmWUUlPfoHEiIYQQQrRHUmSJJvEqOWhvh3UZqGESxzD+NviFp2KmMmuftmHcSG29hV/KY/jOMopiJRi/nm7wPNbvKIrCmORwdFjxtlSwLaNU60hCCCGEaIekyBLnpKoqEbVH7cu+nfpomMYxdNH97e2g8gNUm+SORlOkZJWxomEA95jv4vne30FQvNaRmqe6mLuLn2K75+08ZXyX9WnyXJYQQgghHE+KLHFOOWU1JJMJQKEhCjzdd44suz8MfnEwTwa/aIptGSe7Vg5JDNUwSQt5BRJVuJ5gpYpRun1sSpXnsoQQQgjheFJkiXPKTDuA728DHZT5d9U4jYNE9ScvZCj/bZjBL9aB7JcRBptk6++61w1NCNYwSQvpDegSxwIQqlRizt1LeY1Z41BCCCGEaG+kyBLnVHZst71tDe+lYRIH8g0l++Iv+EfDtayz9pVh3JvAYlWpOrYLb+oI9/ckPsRH60gtkzjO3hyp7GVzerGGYYQQQgjRHhm0DiBcn5p3cmAIH3cfWfB3ekT5oyigqsjEtE1wMLeMd3gMX886DhmHoCiTtY7UMl1OFlmjdftYm1bM1N5RGgYSQgghRHsjd7LEOb1rncEM09PcY76T8D7uNZrc2fh6GkgM8wXgYF4lZotV40SuLXXvNoKUaoyKBX9fb63jtFx4D6y+EQAM0x1kS2q+xoGEEEII0d5IkSXOSlVV9hea2a8msDNwCl4hsVpHcqheUf7EUkic5ThHC6u1juPSatNOzo/lkThGwyStpCjofrub5afU4V24i8JKk8ahhBBCCNGeSJElziqnvI6aegsAXSP8NE7jYNXF/Cv9EtZ73cPfDB+yL6dc60QuS1VVgou22ZfD+4zXLowj/O65rDG6vWw6Ks9lCSGEEMJxpMgSZ5VaUGVvJ7e3IssnBL3e9k+gty6d/dlSZJ1JVmktfSwHAKhTPDHEDtA2UGv97rmsUfp9bJD5soQQQgjhQFJkibMqTd3KHfrvmKLbRu/AdtalSlFQI20DeYQrFeRkZWibx4XtO7CXWMV2t6cgoD/ojRonaqWgeKzBXQDopBSyObVA40BCCCGEaE9kdEFxVl6Zq3nI+BkA6eYEYICWcRzOo9MAyFwLgJK/B1Wdom0gF1VxaK29rcSP0DCJ4+hmLuT+n3L4KssfTCaySmvoFOymw9ILIYQQwqXInSxxVt5lqfZ2eJf+GiZxkuiTrynBnEZOeZ2GYVyXb94We9vtn8c6ocs44noMBhQANqbJc1lCCCGEcAwpssQZqapKWF06ABZ0+EX30DiRE0T1szd76zI4kFupYRjXVF5rplvdXgAa0OOV2D7uZAGMSgqzt6XIEkIIIYSjSJElzqi4qo5ENRuAQkMUGL00TuQEoUk06G1zPvVSjsmkxKexOyMf6293e/J8uoOHr8aJHGdAXBDeRj0AG9KKUVVV40RCCCGEaA+kyBJndDz9MD6KbbCLMt8uGqdxEp2ehrBeACTo8snIztM4kOvZkV3LtPrnGFj3BodHPKN1HIfyqMjgHyGL+czjCfpV/Up6kcyVJoQQQojWkyJLnFHJsT32dkNIdw2TOJdn3AB725K758wbdlA7MksBKCWArn2GaZzGwYqPMrv8fYbrDjJOt5v10mVQCCGEEA4gRZY4I0veAXvbK6aXhkmcS4myDeNeo3qir86n2qxxIBditarsOl4GQJifB52CvbUN5GidR2LV2YajH6XbyyYpsoQQQgjhADKEuzgjj9LD9nZYYr+zbOnmel7EK0cj+PcOK1Z0xNdYtE7kMtKLqymvtVWdA+KCURRF40QO5uGL0mkIZG4kUZdPetpBrNaB6HTt7HUKIYQQok3JnSxxRmG16fZ2UHxvDZM4mW8oEYn9sP72zyFbHsuxS9+9gWUeD/Ks4S0uCEg99w5uSOky3t7uZdrF4QIZYVIIIYQQreNWRdbatWuZOXMmMTExKIrCt99+e859Vq9ezaBBg/D09CQ5OZlFixY5PWd7UFFnZqu5CzutyRwzJLSrEeVOp1dMgL2dVS13MU6oPbqBbrpsrjKspr9HttZxnCPxPHtzpG4fG1Kly6AQQgghWsetiqzq6mr69+/Pq6++2qTt09PTueCCC5gwYQK7du3i3nvvZd68eSxdutTJSd1fakEVjzfM5ZL6J3ij14dax3G6rpF+GH7rIpYtRZadX+EOezu693ln2dKNxQ7BarA9azZat4+NaUUaBxJCCCGEu3OrZ7KmT5/O9OnTm7z9G2+8QWJiIi+88AIAPXv2ZN26dbz44otMmzbNWTHbhdSCKns7OcJPwyRtw7MsnScCviWy+jBfm8ZSZ56M0WjUOpamqk0NJJn2gwJ1eOL9u1EY2xWDB0rnUZC2kiillLz0vVisQ9DLc1lCCCGEaCG3KrKaa+PGjUyePLnRumnTpnHvvfeecR+TyYTJZLIvV1TYJqc1m82YzdoOO3fi/G2R41Buub2dGOql+Wt3NqX0GHPqPgM9HFfDOZBTxsDOoVrH0tTeg4cZrhQCkOvTnU5WwNq6n4O2/BluDl3nMejTVgLQ35zC7swS+sQGnGMv1+Sq17i9kOvrfHKNnUuur/PJNXYura9vU8/brousvLw8IiMjG62LjIykoqKC2tpavL1PHY76mWee4fHHHz9l/bJly/Dx8XFa1uZYvny508+xeb8K2O7kZO7Zwk+Hz769uzM2VDHjt3Yv3TG++mULuZGqppm0ln90O8N/ax/XdWL3Tz857Nht8TPcHIE1Osb/1h6l28d7P61nUqx7f/9d7Rq3N3J9nU+usXPJ9XU+ucbOpdX1rampadJ27brIaomHH36YBQsW2JcrKiqIi4tj6tSpBARo+8m22Wxm+fLlTJkyxeld2TqnnEeYZwHpdGL4zJ9QDB5OPZ8rqEt7Cq+aHHopx/g2uBMzZvTVOpKmVvznR3u7y8iLiRw24yxbN01b/gw3i9VC6XdpPLorgI3W3vT2jGTGjEFap2oRl73G7YRcX+eTa+xccn2dT66xc2l9fU/0cjuXdl1kRUVFkZ+f32hdfn4+AQEBp72LBeDp6Ymnp+cp641Go8v8Q3F2lrr6BhIsxwhSqvHSgYd3+x5Z0C62PxzJwU+poyznCEaje77JdgRVVYms2GNfjuk7Hp0Df+Zc6d+TjZGgy19m05GVFFWa2HqsFHR6jHq3GhuoEde7xu2LXF/nk2vsXHJ9nU+usXNpdX2bek73fQfRBCNHjmTlypWN1i1fvpyRI0dqlMg9ZGRmEKTYJosq8k7UOE3bMcb0t7c9ivZjsbp3d7HWyCqqoKeaBkChPgpdQJTGiZxPURRGJdmew6upt7A7q/wcewghhBBCnJ5bFVlVVVXs2rWLXbt2AbYh2nft2kVmZiZg6+p3/fXX27e//fbbOXr0KA899BAHDx7ktdde4/PPP+e+++7TIr7bKE7fbW+bgrtqmKSNRfezN7uqR8ko7rizEh/duwlvpR6A4pD+59i6/RjZ5eRgJzKUuxBCCCFayq2KrG3btjFw4EAGDhwIwIIFCxg4cCCPPvooALm5ufaCCyAxMZHFixezfPly+vfvzwsvvMDbb78tw7efQ13OfnvbGNlTwyRtLOpkkdVLOca+nKb1uW2P1pWHcXX9IzxvvgJzr0u1jtNmxsTomK7bzGOGRWxOzT/3DkIIIYQQp+FWz2SNHz8eVT1zF65Fixaddp+dO3c6MVX7Yyg5OZRgcEIHGvwhsBNmYyBGczm9dcd4N6eCi/rHaJ1KE1uy60ix9mYjvbl1+FSt47SZ2M2P87rH5wAszRxNnXkkXka9xqmEEEII4W7c6k6WaBuBVUft7YguHaerGIqCJbIPABFKGVnH0zUOpI06s4X9ObbnkZLCfQn06TgP7SpdxtnbQ9Q97Mws0y6MEEIIIdyWW93JEs5ntljp1HAMFChRggnxDdE6Upsy9L6IDzN9SbEksCfPhKqqKIqidaw2tS+nArPFdsd4YHywxmnaWOJ59uZo/T42HC1mZFLHnpRaCCGEEM0nd7JEI8ezjhOm2J5FKvRK0DaMBtQhN/OB9/V8YRnPsRojBZUmrSO1ueN713Gdfhm9lQwGdfLTOk7bCoqnITABgIHKEbanZmmbRwghhBBuSYos0Ujh0ZMjC9YGdaCRBX8n9nfTgu3vgINf+Kb+wJPGRSz2/D/GqDu0jtPmDEm2LoOeSgPG7C3U1DdonEgIIYQQ7kaKLNHIjoYELjI9yf31t1Pd7RKt42iik+/JwVX25XS8uZLCy04W2rF9x51ly3bqd10Gh7GPbRmlGoYRQgghhDuSIks0crC4gd1qEl9ZzyOs51it42gi1kclnFLG63ZxJLtA6zhtKq+kgh7WVADy9dHo/SM0TqSB3xVZo3R72Xi0WMMwQgghhHBHUmSJRlILqgDQKZAQ5qNxGm1MLPmYrV53ssjjORqyU7SO06aO7t2El2IGoCi4A40s+Xt+ETSE2eaH66ukk3LkmMaBhBBCCOFupMgSdlarSlqhrcjqHOqLp6Fjzg9U4xVlb4dWHqSyzqxhmrZVnbrB3tbFDdMwibYMSeMB0CsqfnmbqOhAPwNCCCGEaD0psoRdTkEh11m/Z7xuJ4ODa7WOo5ly7872dm/lGAdyKzVM07a8C04OdBHVu2N2FwUgaQKZvn34T8MsUq0xbE0v0TqREEIIIdyIFFnCLj9tF48YP2aRx/NcW/eJ1nE0U+HdCetv/zR66TLsE/O2d2aLlYTa/QDU4UFw4kCNE2mo2zT2T/+KFxqu4Kgaw8Y0eS5LCCGEEE0nRZawq8rab28r4d01TKIti84TU2AXALopWRzI7hh3MVLT0uikFAKQ5d0T9EaNE2lrRJcQTsxDvUGKLCGEEEI0gxRZ4qSiQ/amX1xvDYNozxDTF7DNlVSdtVfjNG0jb/+v9nZN5CANk7iGIB8PekUHALA/t4LS6nqNEwkhhBDCXUiRJex8K9Ls7cgu/TRMoj1d9MnX71uyn/oGq4Zp2sbeUj0/WYaRpwbjnzxK6zguYVSXELooOYzU7WNzutzNEkIIIUTTGLQOIFxHhMk2VHUtnviFJ2gbRmNq1MkiqzsZpBZU0SsmQMNEzvdVUTwZ5nvxMCjsHT5V6zjas1pYcOBKHvE8TqY1nLfTZnB+n2itUwkhhBDCDcidLAFAcVk5sWo+AHnGONB17B8NNbKPvd1bl8G+dj74RUl1PRnFNQD0iQnEwyifv6DTYwyNByBeV0j6kX0aBxJCCCGEu+jY76SFXXbaHvSKCkCFX5LGaVyATygmnyhMqoEGVc/+3AqtEzlVyvEye3tgfLB2QVyMIWmCvR1dupWCyjoN0wghhBDCXUiRJQCoOH5yZEFrWDcNk7iO2uuW0Nv0LteYH2FfTvsusg6mHcUD24S7A+ODtA3jShLPszdH6/ax6WjHGGlSCCGEEK0jRZYAwJJ/0N72ju6pYRLXERTdhfBAPwAO5FSgqqrGiZyn777n2ON5M197PNqhJ6I+RewgGgy+AIzS7WNjaqHGgYQQQgjhDqTIEgAcq/NijzWBWtWD8A4+suDv9f5tsItKUwPHS9pn8WG1qsRV78NTaaC37hhR0Z20juQ69EaUzraRFsOVcnJTd2mbRwghhBBuoUlPt8+ePbvZB37jjTeIiIho9n5CG2/WTia7fjQBXjpS4jv2HFm/1ys6gBUHCgDYn1tOfKiPxokcLyPzGF2UPACOe3Yj2eCpcSLXok8aD2nLAehcsZ2csiuICfLWNpQQQgghXFqT7mR9++23eHh4EBgY2KQ/ixcvpqqqytnZhYNUmxrILrPdpUmKCEDp4CML/t6Mqq950/hvvvZ4tN0+l5W97+QkxFXhMgnxKRo9l7WXjWkyX5YQQgghzq7J4zS//PLLTb4z9eWXX7Y4kGh76UXV9nZyuJ+GSVxPQvEaeui3AfD+8WNAd20DOUHDsc32tk/ScA2TuKjIPpg9gzGaShmhO8ATqQVcOli6VAohhBDizJp0y2LVqlWEhIQ0+aBLliwhNja2xaFE20otOHnXMSlCiqzf8+w0wN625u7RLogTBZWk2Ntx/cZpmMRF6XTouoylQvVhk7Un+9KOtetBUIQQQgjRek26kzVuXPPeeI0ZM6ZFYYQ2gna9wRqPTzmixuJn+Dsg82SdoET3t7djag9TXGUi1K/9PLNUVWuiq/kwKFCohBH+2+S7ojH9zJe4q/Iga1NLwQxphdUkywcSQgghhDiDJncX/KOCggIKCgqwWq2N1vfrJyPTuRvPkkN01hXQmQJyQuSB/kaiT/4899IdY39uBWO7hmsYyLFS921lgGKbYDcvoC/t55U5mE8IY7pF2oosYO3hQimyhBBCCHFGzR7hYPv27fTp04fo6Gj69evHgAEDGDhwoP1v4X6CqtMBsKoKkV36apzGxYR1w6LzAKC3ktHuBr8oO7Te3rbEDtEwies7r9vJEnTtEZkvSwghhBBn1uw7WTfddBPdunXjnXfeITIyEkVRnJFLtJGGBgtxluOgQIEunChPX60juRa9kfqQ7ngX7SFRySM1K5/21J2yvuCwvR3WfbSGSVxf90h/IgM8Kauo5NDRdOrMg/Ey6rWOJYQQQggX1Owi6+jRo3z11VckJyc7I49oY1nHj5Kg2IZvL/ZOJErjPK7IM24gFO1Bp6iYsncDo7SO5BCqqvJ/1Vfzf3WTGOWVzsKeI7SO5NKUsmMs8niOBM+dfGEZx7aMSYzpGqZ1LCGEEEK4oGZ3F5w0aRIpKSnn3lC4hcL0kyPm1Qa1nzs0jqT73XNZQeUHqalv0DCN42SV1lJUZaKIQMrip6DzkOfxzsonlO7VO/BW6hmv28XawwVaJxJCCCGEi2r2nay3336buXPnsnfvXvr06YPRaGz09Ysuushh4YTz1WXvs7cNkT01TOLCok4WWT2VDA7mVTIoPljDQI6x83iZvT0wLkizHG7D0x9L3HB0meuI1xWSenA3XNBL61RCCCGEcEHNLrI2btzI+vXrWbJkySlfUxQFi8XikGCibehKTj6TExjXR8MkLiyqD4c6Xcb/0gPZZu3G9TkV7aPIyiy1twfGB2kXxI0Yu02BzHUAdCrZQH7FLCIDvDROJYQQQghX0+zugnfffTfXXnstubm5WK3WRn+kwHI/AZXp9nZ0cv+zbNmBefhSOfl5PrZM4rAa125GGBy6/2meNLzLbN1aBsT6ax3HPSRPtjfH6Xaz5rCMMiiEEEKIUzW7yCouLua+++4jMjLSGXlEG1JVlRhzJgDFBOEVEKpxItfVIzqAEwNp7s91/yLLZDYztmYl1xlW8IjnZwT5yt2YJonsTb13BAAjdfvZcDBb40BCCCGEcEXNLrJmz57NqlWrnJFFtLG88lpurb+XP5tv4aeQa7WO49L8PA0khNqGtz+YW0GDxXqOPVzb0f078f9tVMkcv94gUzE0jaJg6DYFAB/FRG3aOixWVeNQQgghhHA1zX4mq1u3bjz88MOsW7eOvn37njLwxfz58x0WTjhXamE129XubLd0JyApUes4Lm9IBHQq2Y3eaiG9aAxdI923i13RwXX2dn20TELcHLqukyHlIwAGm3eQklXWLp7RE0IIIYTjtGh0QT8/P9asWcOaNWsafU1RFCmy3EhaQZW9nRzhp2ESN2Cu5Z9HZ6PzsLDXmsC+nBvdusjS52yzt4O7ySTEzdJlPFZ06LAyUbeTbw7kS5ElhBBCiEaaXWSlp6efeyPhFlILTxZZSeFSZJ2V0ZuawCT8yg/TXTnO4uxCGBirdaoWi66wzXVnVvXE9Wkfkyu3GZ8QLLFD0WVvpgE9G/enw7QeWqcSQgghhAtp9jNZov0IOraMsbrdRFFMcriv1nFcnr7TIACMioWaYzs1TtNyubnZJKpZABzzSMLoJQV2cxkv+Ce3hbzD+fX/ZEe+lazSGq0jCSGEEMKFNKnIWrBgAdXV1U0+6MMPP0xJSUmLQ4m2cU3pa3zg8SzLvf5MkI+H1nFcnnfnk88u+RTtRlXdc8CD4ymr7e2ysMHaBXFnMQPp2+fklAcrDxRoGEYIIYQQrqZJRdZLL71ETU3TP6l99dVXKSsra2km0QbKy0qJpgiAXGO8jC7XFLGD7M3khiPklNdpGKbl6tM32NteSdJVsKUm9Tw5jcWKA/kaJhFCCCGEq2nSM1mqqtKtWzeUJr4Rb85dL6GNnLQ9BP7WrvDromkWtxHZB4tiQK820E85yu7jZcQGeWudqtmCi3fY2/H9J2qYxL31iPInNsib7LJadh7No8rUgJ9nsx9zFUIIIUQ71KR3BO+9916zDyyTFbu28sy99rY1tJuGSdyIwZPqoB4ElO4lScnh+2PZTO8brXWqZqk2NfB07WyGKT3o7V3C5PBOWkdyW0p9Fc8GfEVUzSoOq7H8eniY2/w8pBZUsXTDFmqz9mFRwRDVm4nDBzJQRkkUQgghHKJJRdbcuXOdnUO0MUvBQXvbJ7aXhknciyFuMJTuRaeoVKdvA9xrjqmU42Wst/RiPb2Y0yueyVoHcmcGb0aUL8aoKyVKLeHJfVkuX2TVmS089/MhDm78kY89/nHyC8Xw6+4+PN3lXu648mJ5RlMIIYRoJRldsIPyLk+1t8MT+2mYxL34JAy1t70L92CxutfgF9uOldrbQzrLXYtW0RvQdZsGgL9SS9Xh1TRYrBqHOrMqUwM3vreVd9ens9XanWK18TxvY/V7eTDjNt59+TFyymq1CSmEEEK0E1JkdVChtRkAmFQjEXFdtQ3jTmIHYUHHfmtnSiyepP5uQmd38Psia7AUWa2m7znD3h5Zv4kt6a45qqrFqnL3xzvYeLQYAJ3BgwNJ86geNp+64XdT6W3rNmpULCyoe5Wv33iU8hqzlpGFEEIItyZFVgdUW1NDrDUXgGxDJxS9PKzfZOE9WXTer8yof4ZPLJNIOV6mdaIms1qsJBz7im7KccJ9jcSH+Ggdyf0lTcKi8wTgfP1WluzJ0jjQ6X309bdsOJQNQICXgU9vHcGY6x/Dd8aTeE1/Cv/7d1A5YJ59+ztq/8t7773mttMUCCGEEFqTIqsDykrdjUGxdWsq9UvWOI2b0enonXDyuZudblRkpR/ZzRPKGyzz/DNveP2nyaOFirPw9ENNtj3ZFq6UU7Bntct1Id114BAX7rmLjzyeJlxXyRvXDj51gAuDJ/6zXqB8yD0A6BSVuQXP8dWa7RokFkIIIdyfFFkdUE5hIQes8ZhUAw2h3bWO43b6xgai+60+cac7Wfl719jb1kh5Ds9RDH1n29tj6n91qS6D9WYL1V/dTYhSxRDdYRYlrmBUctgZtw+84HEKOk0FIFWN4dWVB8iW57OEEEKIZmt2kVVdXc3f/vY3Ro0aRXJyMl26dGn0R7i+TeauTK9/ll6m96gddIvWcdyOr6eBbpG2QQPS80uorbdonKhpdMc329uB3cdomKSd6Xb+77oMbuFnF+oyuPLHjxjdYPu+lymB9LjqmbPvoChEzHmTn2Lu5sr6R0k3h/DskoNn30cIIYQQp2j2wzjz5s1jzZo1XHfddURHR0uXIzd0JL8SAAt6kmIiNE7jhsx1PK2+TKhHCllqOHtzxjI0IUTrVOcUXbELgHpVT0K/sdqGaU88/VC7ToFDPxKuVJC/5xcsF/VDr9P2d2NlTR1dU547uTzhHwT5h597R58QRl/3d4L+tZqS6np+SMlh7sjODHGDn3EhhBDCVTS7yFqyZAmLFy9m9OjRzsgj2sCR30bE8/HQExvkrXEaN2T0oocpBR9dAaFqBZ9lFrt8kVWYn01n1TbwQYZHV7p5+2mcqH0x9J0Nh36kWvUksC6bbRklDO8Sqmmmrd+/wTSOA5Dh1ZOEsdc2ed9AbyP3T+3GI9/YJi3/9/LDfHzLCKfkFEIIIdqjZncXDA4OJiTEtd9QijOrrbeQWVIDQNcIP3Qaf9rurhqibfNl+Sl15Kft1DjNuR3b+Yu9XRY6SMMk7VTXaWwe+iKDTW/wmWUC36fkaBqnvkElKe1/9mXPGU9DM3sdXDkkjoRQH4YoB7kq8zF2pGn7moQQQgh30uwi68knn+TRRx+lpqbGGXmEk2Ud2sZK4wLeNP6bS7xk5LCW8k0eaW8bs7dpmKRpzGknB73w6nqehknaKU8/ek++HsVoGxb/h5QcTA3aPatXmnOA5N/uYqV79yG638RmH8Og1/FqzFK+9HyCi/Qb2ffjK46OKYQQQrRbzS6yXnjhBZYuXUpkZCR9+/Zl0KBBjf4I11aavpMuujym6bfRw6NI6zhuSx9/sutUF9N+CirqNExzbuHFtoLaqiokDp6scZr2yc/TwPl9ogCoqGvglwMFmuRQVZWexcvsy7qRd7T4WN3GX21vTyz5lNS80rNsLYQQQogTmv1M1qxZs5wQQ7QVS95+e9sntreGSdxcdD8aFCMG1cxA5QjbjpUyo2/0uffTQGlxAV0s6aBAhiGRLkFNGPxAtMjsQbF8szMbf2r4YVsa0zX4mUjZu5vR6k5QoFAXTufRV7b4WMbY/mSGjiW++FdilWI+XfoRyXPvcmBaIYQQon1qdpH197//3Rk5RBvxKjtsb4cnDdAuiLszeFIV0oegYtudwa9S0122yNqdmkmNdQjDdQcoChuCTLTgPKP8C3nL51XOs2zhybS5FFcNJdTPs00zvL3Hykvmv3Cj/meiek8kXN/sX/ONhE68G774FYCEo59QZbodP8/WHVMIIYRo71r8P+X27ds5cOAAAL1792bgwIEOCyWcJ7w2HYAa1ZPIuK4ap3FvXokjoNg26EVd+iZglLaBzmB1gTfvme9Dwco7o/toHadd01tNTLWuBwUu0a3l+5Qcbhyd2GbnzymrZcWhIizWvhzyGcz6Wc1/FuuPfHtOocijE2H1WYxQ9vLd2rVcPKX1xxVCCCHas2Y/k1VQUMDEiRMZOnQo8+fPZ/78+QwePJhJkyZRWFjojIzCQWqrK4mx5gOQbYxHp9drnMi9eSWefC4rtDTFZScl3nS0xNZQdAzu2knbMO1d9ABMId0BGKI7zNqNG1FVtc1O/+GmY1istvNdPTQOD6MD/o3rdDQMvvnk8rZ3W39MIYQQop1rdpF19913U1lZyb59+ygpKaGkpIS9e/dSUVHB/PnznZFROEh2ago6xfYGrMwvSeM07UDcMHuzH0fYdbxMuyxnUF5j5mBeBQA9owII9DZqnKidUxQ8h1xnXxxd9j1b0kva5NS1pgY+3pIJgF5RuWqo4wrqqPNuwoSt2+N5das4mC2D5gghhBBn0+wi6+eff+a1116jZ8+e9nW9evXi1VdfZcmSJQ4NJxyrNGO3vW0J7a5hknYiIIbtff7KbNNj3Gx+kO3H2ubNdHPsOJyOUTUDMLyLzG/XJgZcg0VnK0gu16/h0w2H2uS0m3/+gP82PMI03VaGhFgI93fgs2DeQWRHTwIgWKli7+ovHXdsIYQQoh1qdpFltVoxGk/9NNxoNGK1Wh0SSjiHJf+Ave0dK8/mOELI+DvYoXbDhAfbjrne8NaeGxeyx3MenxifYmJomdZxOgafEOgzG4BApQbvg19TUOncIf6tVpXQlDcZqjvMmx4vMidor8PPET56rr2tS1tp75YohBBCiFM1u8iaOHEi99xzDzk5OfZ12dnZ3HfffUyaNMmh4YRjeZeeHFkwIqm/hknaj4RQH8L8PADYcawUq4u98Qwt3oqnYmakfj99usq4gm1FP/xWe/sa3TI+25zp1PPt2LicvlbbhyjHDfEQ7vjpGfx7TmZZ4GXMNj3Ggpq5rEuVLoNCCCHEmTS7yHrllVeoqKggISGBpKQkkpKSSExMpKKigv/85z/OyCgc5FXrpTxivokP1OlEyciCDqEoCoM7BwO2CWiPFFRpnOikivISksypAGTo4gkOd80h5tul2EGYIm0jrvbWHSNl41LqzM4bGKXh15ft7bJ+t4CiOP4kegPWKU+xQ+0GKPyYknPOXYQQQoiOqtlDuMfFxbFjxw5WrFjBwYMHAejZsyeTJ092eDjhOLX1FpZXxKKqsfSPCeI6XbPra3EGU0MK6aRfzEBdGtvSe9I9yl/rSACkbVnCQMXWhTc/dBgJ2sbpcDxH3Q7f3AbAVfVf8dWOi7hmeGeHnyclZQdDa9eBAiVKEN0m3cDRFascfh6A8d0j8PXQU11vYfmBfMwWK0a9/C4RQggh/qhF82QpisKUKVOYMmWKo/MIJ0krrOLESNJdI/y0DdPOTCr8H5cabYO+PHtgC4x0jW55pkMr7W3PbtKVt831nk39ssc5Vqmw2DKC7WuOcuWQOAwOLkoqlj6F/rdRQ3O6XU93D2+HHv/3vIx6JvSI4MfduZTVmNl8tIQxXcOcdj4hhBDCXTWpyHr55Ze59dZb8fLy4uWXXz7rts4exv3VV1/l+eefJy8vj/79+/Of//yHYcOGnXbbRYsWceONNzZa5+npSV2dcx9Cd0WH8yvt7W6RUmQ5kn/38ZBhK7KMWRtR1StRnNFdq5liSjYB0KDqSB5+vsZpOiCDBx7zlvDEl7n8mlYCJTUs3pPLxQNiHXaK3bu2Mqr6F1CgAj96XHw/zn4q8OKunoTt+5np+i0cX3spdH3IyWcUQggh3E+TiqwXX3yRa665Bi8vL1588cUzbqcoilOLrM8++4wFCxbwxhtvMHz4cBYuXMi0adM4dOgQERERp90nICCAQ4dODqHsCm9+tVB95FdG6o5z0BpH10jX6M7WXugTx9jbfcx7OVJQRTeNr3He8VTirdkAHPHoQc8AGb5dE8EJ/GmCH7+mbQbgX8sOcX6fKDwNrZ8kWFVVypc8ab+LldljHn18gjCbza0+9tmMCa1kivF9AMxZS7BYH0Sv65i/V4UQQogzaVKRlZ6eftp2W/v3v//NLbfcYr879cYbb7B48WLeffdd/vKXv5x2H0VRiIqKasuYLmlA+n+5zmM7AAX+ezRO085E9KLOEIBXQwXDdAf5PrVQ8yLr+NbFnPipL4sec9ZthXONTApldHIo61OLOV5Sy0ebMrlpTGKrj7v2lyWMM60BoEwJoMfF97f6mE3hnTCMEn04IZZChllT2HXkGIO7J7TJuYUQQgh30exnsp544gkeeOABfHx8Gq2vra3l+eef59FHH3VYuN+rr69n+/btPPzww/Z1Op2OyZMns3HjxjPuV1VVRefOnbFarQwaNIinn36a3r3PPLyxyWTCZDLZlysqKgAwm81O/4T4XE6cvyU5ouvSACjBn6DQSM1fi6tq6TU2RQ/D6/gKgpUqMg5sxTwszhnxmkw5enLgA/8eE13m+92an2F39sDkrpSk7eBhw8e8v/JSLu53GwHep8432FQ19Q28sKmcKsswLtBvoWDA3fgavBv9nnLmNS6Om0JIxsd4KBYyNnxFvy7O7SbuStrLz3B5rZmdx8vILa/D06Cje6Q/PaP80bnAXcn2co1dlVxf55Nr7FxaX9+mnldRVbVZXfj1ej25ubmndM8rLi4mIiICi8U5wxTn5OQQGxvLhg0bGDlypH39Qw89xJo1a9i8efMp+2zcuJEjR47Qr18/ysvL+de//sXatWvZt28fnTp1Ou15HnvsMR5//PFT1n/88cenFJbuoqG2gksP3gXADqU3xwf8WeNE7U+X/CX0zfkEgKcsc+k9eBJavVexqtBp5zMMVQ5QpXqxvP9r6PQtGuNGOEhI1SFGH3kaHSp7rQk8E/g4VyS3/Afkmwwdq3NtA2hcFnCA83p1QVVa3wWxqXzLDzL56NMArGAY1QPvarNzi9YpMcH+jEx6V/zKIN1hwpVyalRP9qiJrNaNxCumH8MiFfTa11pCCOGSampqmDNnDuXl5QQEBJxxu2a/81JV9bTPNaWkpBAS4lrPfYwcObJRQTZq1Ch69uzJm2++yZNPPnnafR5++GEWLFhgX66oqCAuLo6pU6ee9UK2BbPZzPLly5kyZQpGY9M/BT+48UewjbZPXVhvZsyY4aSE7q+l15jcWHjXVmQNZR9Rg/5Or2htfl4O5FZy0SYDURRzWeca7pl5kSY5TqfF19fdWafR8NbXeBQfoI8ug74lSwjq/jijkkKbfagdmWWs2bQFAA+DjltvnEdimK/9621yja3TqHz2P/irlQxT95A/ZBQJEUHOOZeLceef4Z/25PH8t/t4xLqKawwnRx9FgSRymcUG9mQn8IHpPu659jIi/D01yenO19gdyPV1PrnGzqX19T3Ry+1cmlxkBQcHoygKiqLQrVu3RoWWxWKhqqqK22+/vflJmygsLAy9Xk9+fn6j9fn5+U1+5spoNDJw4EBSU1PPuI2npyeenqf+x2I0Gl3mH0pzs9RknXwGSx/Vx2Vehytr9ve70yDqjMF4mUsZqdvHF+lF9I9v/htoR9iUUQpAHqGE9Rvrkt9vV/r31DaMcMkrWN+egg4r9xm+4sZvRtP/nqsI8vFo8lFKq0w88cV6VNV27e6f0o1u0UGnP6NTr7GRjIgx+OcvIUCpZd32VXS96Aonncs1udvP8GurU3nuZ9sgUAu5lEsM6/DCjMkzFKO5AoPV1k2+ry6DJ4sf4JlXj3DtbX8hOUK750vd7Rq7G7m+zifX2Lm0ur5NPWeTi6yFCxeiqio33XQTjz/+OIGBgfaveXh4kJCQ0OiukaN5eHgwePBgVq5cyaxZswCwWq2sXLmSu+5qWlcVi8XCnj17OtydHH3hfns7MHGghknaMZ2O+s7n4ZX6HQFKLYUHN8C47ppEWXWw0N4e2y1ckwziNDoNQRlxB2x6BU/FzN9qn+fBjzvzxk3nNWl0vgaLlRX//TMf1n7B33Q3ktNpOvPGajcnm2/v6ZBvm7pAPbIM6FhFljt5d126vcACGDuwN9Yh36OL7Ym3VyBYzJC6guqfH8O39CCeipnHLK/w3Bt1XPanx+kSLtN+CCFEczW5yJo7dy4AiYmJjBo1SpPKccGCBcydO5chQ4YwbNgwFi5cSHV1tX20weuvv57Y2FieeeYZwDZIx4gRI0hOTqasrIznn3+eY8eOMW/evDbPrqWgyiMAWFSFuG5SZDmL/4BZLE8rYEV9Hzbk+HG/xYrRwRPPnktFrYmtGSUAdA71ocvvupEJ7SkTH8F86GeMpan01GVy0bGn+ds3gTx1Sf+zDjhgtaq8/+4r3FC2CJ2i8rLHKxRPuVjTodOjB1+A9Zd70KHSrWIj1aYGfD3l2T9Xs2H7LhKW3kMgd1COHw9O684d45Mad/vXG6H7dHyTJlH3w4N4pSyiXtVztM6Pef/bxrd3jSbASz6NF0KI5mjS/4gVFRX255EGDhxIbW0ttbW1p93Wmc8tXXnllRQWFvLoo4+Sl5fHgAED+Pnnn4mMjAQgMzMTne7km9rS0lJuueUW8vLyCA4OZvDgwWzYsIFevXo5LaOrsTaYiTMfAwWydDF09pM5spxF6TObH/Yk8n1KDlhgx7FShndp2y6Dqas+Ypnhn6yyDqS283Uddl44l+Xhg3HOx1jenIC+oZqZ+k1U7/w7Dzb8nX/M7o+X8dTBK6pMDfzvvde5JfcxdL/NiZXV/z7iuw5t6/SNKL5hZPr0pnPNXroqWazds4/zhvTXNJNoLKuwhIAfbmKUPo2PlX+wZvh/uWNC8pl3MHjgdclL1PpG8PQuL34u6QFF1Sz4LIX/Xj9Yfp8IIUQzNKnICg4Oto8oGBQUdNpftCcGxHDW6IIn3HXXXWfsHrh69epGyy+++OJZJ0/uCHKP7iNWsQ01WeiTTGeN87R3E3qE24osYNWhwjYvsqyHfqaLLo8uuiXsibqqTc8tmii8O/rL3sb62bXoVAtXGVaj7IGLchbwl+k9GNctAr1OwWyxsmxvDtk/Psvt9R/aJx3O6Xwx8Rf/TeMXYVPe/1b+vHYPqy39mZip47whWicSJ6iqyp7/LWA6tuk7wj3q+dOErk3a13vqI9wyuIbvX1lHea2ZFQfy+XTrca4eFu/MyEII0a40qcj65Zdf7CMHrlq16hxbC1dyPDeHSmscSUoOptCeWsdp987rGo6igKrC6kMF/GV6jzY7t7WhgaRy25xx1aoX3YZPa7Nzi2bqMQPdpW9j/WoeVqvKF5bzOJxfxU2LtuHvaWCUbxadqvZyOcu5QHccfvtcKz/+AmKufxd0bdsN9UySJ1zDN78upx4r61ILz72DaDOrV/7EtMpvQQETHnhf9wmKT9NHAI4P9eHFK/tz06JtALzz4xrGJF9KXIh7TmUihBBtrUlF1rhx407bFq5vY30yL9X/Ew/MvDLgzJMwC8cI9fNkSIwnPrlb8C2oJa98GFGBXm1y7iO7VtMd27Cih/2GMNDTu03OK1qoz2x0PiEUZuynbl9fyLZ97ypNDTys/pME3cmRVK0olA9bQOT5f3WZAgvAx8PA4PhgNh4t5nhJLZnFNcSHyptwrZWUVxK37s/27qXH+99LcufmP487sUckcwZFErv7JW5TfuQ/n1dznxNHERZCiPak2f9b//zzz6xbt86+/OqrrzJgwADmzJlDaWmpQ8OJ1juUVwlAPUa6xsdonKYDaKjng7K5/M/jnzxq/IDVB/Pa7NSlO3+wt81JU9vsvKIVuowncuIdfH/nGN68bjDT+0QRG+SNVTn5+VdFaH+UG5cQPONRlyqwThjTNczeXpdapGESccK2L54lmeMAZHp2I/milk9A/7ekI9xp+B6DYmVmzkusP5TrqJhCCNGuNft/7AcffNA+CdeePXtYsGABM2bMID09vdEkvsI1HMyzfa+8jDripZuH8xk8MMUMAyBKKeXo7g1tduroXNvkolZVocuIWW12XtF6Op3CtN5RvH7tYNb/ZSJdJs+DqU/BbWsJuHstSmfnTY/RWmMS/Jig28lfDR+gbl+kdZwOLysnh2HH3wNsvwt8L38N9C0f9dF70FUUBQ8AIFmXQ8rX/6LBYnVEVCGEaNeaXWSlp6fbR+f76quvmDlzJk8//TSvvvoqS5YscXhA0XI19Q0cK6kBoHukv6bDPXck/v0vtreDs1ZQ3+D8NyTHDm6ns9X2yfUhj16ExcgQJ25t7AIYdTdEu/5ofX2C6nnP43nmGZbQs2AxVquqdaQO7fBXTxCkVAOwP3w6ocmtHIVSUQiZ/W/74jV1H/PDpj1n2UEIIQS0oMjy8PCgpsb2xn3FihVMnWrrlhQSEmK/wyVcQ9bu1Sw3PsArxpe52Hf/uXcQDqHrfr69PV7dyoY053ehyt3wmb1dntixJtsW2tIHx5Nn7ARAX/UIBzOlO5lWjmWkMaroSwBMGIm//GmHHFcXN5ii5EsBCFRqqFq1sE0+PBJCCHfW7CJrzJgxLFiwgCeffJItW7ZwwQUXAHD48GE6derk8ICi5crTtpKsy+FC/SZ6eJdpHafj8I+iLMR2B6KnLpPNO3c6/ZSRWUvt7YSxMnS7aFvlUaMBMCoWMnYs1zhNx/Xe1gJebriEIjWAA52uICAy0WHHDpv5JGZsExLPNi/mx427HXZsIYRoj5pdZL3yyisYDAa+/PJLXn/9dWJjYwFYsmQJ559//jn2Fm1JyTv5n2BQkkxg05Z8+860t3WHlmBxYheqjMxjhFoKADho6EFU3FkmGxXCCQL7TLG3laOrtQvSgRVU1vHxrjJes8zifF6jy2VPOvYEgbGU9bwaAF/FRPXqhZjl2SwhhDijZj8NGx8fz48//njK+o4+6a8rCqk4AECDqiO+Zyv75YtmMfa+ENY8BcCYhs1sSS9hZJJzJiZenGZmoekNRuv2cmX/zrTdzFxC2ET2nYxliQ49VrpUbqPObMHLqNc6Vofy3voM6n8rei4dkUxAkON/34Sf/xfMBz7BiJnZDT+xfPsBZgyTqUGEEOJ0WjQesMVi4auvvuKpp57iqaee4ptvvsFisTg6m2gFs6mWuIZMADL1cfj5+WucqIMJ70GVr23wieG6A6zf6ZyuNaqq8kNKDmYMrLYOoM+42U45jxBno/gEk+3dDYDuSiZ7Dx3ROFHHUme28PFm2+97o17hptGO6ybYSGAsxd2vBGx3swpWv4WqykAnQghxOs0uslJTU+nZsyfXX389X3/9NV9//TXXXnstvXv3Ji0tzRkZRQtkHd6OUbEVvoW+3TRO0wEpCsYBlwOgU1R0+79zyrDH+3IqOPjbXGgD44OIk2H6hUZqYsfY2wV7VmqYpOPZvvwTbjV/QCyFzOwXQ2SA8yZAj5y6gDxdJE+Zr+GFkjFszZD5MYUQ4nSaXWTNnz+fpKQkjh8/zo4dO9ixYweZmZkkJiYyf/58Z2QULVByZKu93RDRT8MkHZfngCspNkTwRsNMFtf2cspErd9uO/nBxqWDZOAZoZ3Q3hPtbWNW280PJyBw5xvcafietZ73Mq9bjVPPpYQmsenClbxtuYBKfHj716NOPZ8QQrirZhdZa9as4bnnniMkJMS+LjQ0lGeffZY1a9Y4NJxoOTUnxd72TxykYZIOLLwbWy9ey7MNV5OmxvL1jmyHHr7eZOK2nbN5x/g8M4zbmdkvxqHHF6I5wnqOpeG3/1I6V+2SIb7bSOreLfQx2+atytbH0rP/CKefc0a/WKJ+u1u2/EA+mcXOLeyE46kq5JTVsupgAYt357LqUAG55bVaxxKiXWn2wBeenp5UVlaesr6qqgoPDw+HhBKtF1B+wN6O6+X8/3TF6U3sGUmQj5GyGjNL9+VRWWfG38vokGPvW/sVAyllkr6UYL8AAn0cc1whWkLxCmB74DR2FevYbO3JndnlDO4crHWsdq9w1eucGE80v9s1xOta9Kh1s3gYdFw3sjPPLz2EqsKnWzN56HwZcscd1NZb+HjNHvJ3rcG6/S2SlByiaKBE9WexGs+BwLH0HD6Na0Yk4u0hg9cI0RrNLrIuvPBCbr31Vt555x2GDRsGwObNm7n99tu56KKLHB5QNJ+1wUx8fRookEUUnULCtI7UYXkYdFzUP4b3Nx7D1GDlpz25XDk03iHHVrYvsrcNg65xyDGFaI2jo5/jma9td1WGppdIkeVkNdUV9C1aAgrUqJ70nH5bm537iiFx/LhiJZcrv+C1WY95yocY9c4v8ETLrdiVSu73TzLH8hPeSj38oYYazT6oXsLhFa+y4NfbufjiKzi/T5Q2YYVoB5r9G/Hll18mKSmJkSNH4uXlhZeXF6NHjyY5OZmXXnrJGRlFM2WW1HJN/f/xd/NcVodeoXWcDu/SQZ2IpIT5+q/Z8+v3DhmN61jqXvrVbgEgj3B6nyejCgrtDU882Y18c3qxhkk6hv2/fIKfYuvitS9kMn6Bzpkm4nTCvRW+9HySmww/M9u6jDUph9vs3KJ5GixWHvt+H499to4rLIttBdZvrOho0DXuhdRNl01BVQO3f7idv327V+ZDE6KFmn0nKygoiO+++47U1FQOHLB1SevZsyfJyTIBqqvYl1fNdrU72y3dubdnV63jdHj9dEfZ4DUfPVZ+KU1lR+Y1rf6EP2fZy3RWbMXasaSriTJIV0GhvcQwX8L8PCmqMrEtoxSLVUWvU7SO1W557Pvc3vYbdl3bntzgQVnXS/E9tAgvxUzur+/DoKfbNoM4J1ODhfmf7GTpvnwgnP9aZnCr4ScOB42jx6wHMHQahM7gCVWFcHQVtevf4GCFke113QH4YNMxjpfW8No1g/DxaPZbRiE6tCbfybJarfzzn/9k9OjRDB06lLfffpvJkyczc+ZMKbBczN6ccnu7d0yghkkEgBLdnzrvSADG61L4bvXGVh2vsqKM3vk/AGBSjfScfmerMwrhCIqiMDwxmE5KIVPNv3AwI0vrSO1WUe4xetduByBHiaD7sKltniF6wsnuiUOLv+d4cXWbZxBnZrGq3P3xiQLLNoda2PkPwx0bSUu8HjV2CBg8bRv7hUO/K/C+fSUD7vmCpy/pi4fB9hZx9aFC/vS/DZgaZD5UIZqjyUXWP/7xD/7v//4PPz8/YmNjeemll7jzTnlz54r2Zp8ssvrEBmiYRACg0+M5/CZbU1Hpkvq/Vo3ilPL9ywQottG89oRMISBM+swL13Gb5RPWed7Dvz3eICvlF63jtFtpK99Df+JudsxMdPq2H6RAF9WLnID+APTQHWfDmp/bPIM4PVVV+eZ//0Z30PaBnLdRzztzh3LVmJ4owQln3lFRULwCmDM8nvdvGoa/p4HzdVv4R9aNPP3BYixWmXxaiKZqcpH1/vvv89prr7F06VK+/fZbfvjhBz766COsVumr60pUSwP9j3/IUOUgsT5W+zC7QluGYfMw62zfiyt1v/DBiu0tOk5VdRXdU9+1L0dMXeCQfEI4SmjyEHtbzVinYZL2zT/jZEETO/4GzXL4jLjJ3vba/7lDnjkVrbfipy+5KOMfvGZ8iZsNP/PmdYM5r1t4s44xokso30wu51XjS3RSipiXvoDXFm92UmIh2p8mF1mZmZnMmDHDvjx58mQURSEnJ8cpwUTL5KSm8ADv84XnEyz0egtFkechXIJPCA0D5wLgrdTjv+u/5JQ1/27Wjm9eIpxSAFL8ziO+51CHxhSitaL7TrK3Y8p3YpVPvh0utaCK2VUPMb/+Lr7zmkV8V+0mnA8afBl1iq3L2Tjzr+zKyNcsi7A5fOQwg7YswEOxoFNU5iSZOK9ry0YZTh48mdog2yMhcbpCBmy5nxV75X2fEE3R5CKroaEBL6/Gd0WMRiNms9nhoUTLFRxcb2/XRQ7UMIn4I+/z7sGi2B4cnqtbwqKfNzRr/4KKOl46EsZmq20+muAZjzg8oxCtpfMPJ8dom6agl5rG0Rx50+1oS/bkUocn31tHUTj679qG8fQjP3oyAEFKNQfWfKVtng6utq6eqk9vJlSpAOBIwAiSrn8NWvqBq08Ifjd9R43RNnLoWP1e0r98mOwWfEgoREfT5CJLVVVuuOEGZs+ebf9TV1fH7bff3mid0Jbl+MluaP5JwzVMIk4RGIt54I0A+Cgmuu5byM7M0ibv/uySg2w3deLK+r/xetLrxMsk08JFlYXbugwaFCuZe6TLoKMt2Ztnb0/vG61hEpuIMXPt7ciMb6lvkMcItLLxo8cZZNkNQJEulPh5H4C+laMCBsbiPed9LL+9ZbxZ/Y53P/pQuoYKcQ5NLrLmzp1LREQEgYGB9j/XXnstMTExjdYJbYWU2X65WlSFhD4jNU4j/shr8v9hMvgDcKnuVz7+/NMmvSFZeSCfr3dmAxDo7cGVsy9zak4hWsO7yyh725TeutE0RWMZRdXsz7XdpegfF0RskLfGicC7+yQq9LZpKXqqqaw9kK1xoo7p0L4URmW+CdjeA5guehPPgAiHHFtJHEv9+L8CtgGcbsj/J5+tP+CQYwvRXjX544333nvPmTmEA5jrqog3Z4ACGbp4koJCzrmPaGM+IRgm/h8se5gsNYy04jqe/ukAj13U+4y75BcV89jnGwDbhJEPnd+dEF+PM24vhNZi+o6H325ghRTv1DRLe5Oz5Hn+bdzKEsswhvVykcnm9Qayhj7C82vz+dXal6m7C5jcN07rVB2KxWKl7pu78FJsj3Ds6TSHAQOmOPQc3ufdS9m+JQQVbiVOVwjLHyWv74dEBcoAW0KcTpPvZAnXd3zfJgyK7a5IQcCZ37QLbelH3Ebu0Ie52PIcO9RuLNqQwUebj5122/IaE4fevJ5PrA8wTDnAlF6RzBkW38aJhWgez4hkypQgAHo0HKCkqk7bQO1IbMbXzNav478e/2ZGF9eZhLzblJvZ4zOcBgysPFBAtalB60gdytYf36J/g60nS54ugt7X/tPxJ9HpCZrzNiad7e7pFazgw6++cfx5hGgnpMhqR0qObLK3rdGDNEwizkqnJ/qCv/DQxSeHun7km70sXHGYBsvJroOHc0rY/OJVnGdeRyeliHc9X+D5C+JlxEjh+hSF/EDbiHeBSg2H9mzTOFD7kJe2i84W2wcy+w09ie2crHGikwx6Hef3sc3ZZ2qw8svBAo0TdRzVtSbid75gXy4d/yxGb3/nnCw4Aet5fwGgCm9SUw+yIa3IOecSws218mlI4Ur0OTvs7eDuo86ypXAFVw+LJ72omrfWHqWTUsjotY/x6OZZ+MX3x6/8EJMK/sdUne0NVQM6Ki94g+hQx/SvF8LZ1LjhVJVuYac1mdTsQuQJ0dbLWv8pJ6YeL44/X9MspzOjbzQfbsoE4KeUbGb2j9E4Ucfw5rpjLDfdxyOGDwny86HPeZc69XzeY+9kf2Ym1+8fQhGBpH23j5/uGYtRL5/bu4vyGjOb04s5nJlDTVkBVlVF9Y0kITqMYYkhdAnzlQ90HUCKrHYkqnIPALWqB116DTnH1sIVPDy9B4FeBvqsfpahusMMNT8Hab998bf/r+oxUj79VaKHXqxZTiGaK2TCHfTf2g8LeoYVhXCj1oHagZDM5fZ23OirNUxyesMTgpnrs4Gx5vX4pdZTbVqLr6e8zXCmwkoTb61No07tzA2WR1hxXRvMnag30uPaF4h9fQNFx8s4UlDFl9uzuFq6sru8bUcL2bbiU6KyfmaEso+pSuMRjo9tj2CDtTdPB17KhLHncemgTnh76DVK6/7kY4d2oqyyirXmXqRZo0k1dsfL01PrSKIJFEXhzhGhDAs7/XxzRb5dUW/8mfDhV7ZxMiFaJyIkhNgQW5ellKwyGda7lcrzj9GlIRWAI7ouJCR11zjRqfR6PXd4LGayficjdPtYv3O31pHavbfWplFntv3bunZEAgkxbdPbQadTePTCXvblhSsOU1tvaZNzi+Y7XlLDi2++Rdii0dye81dm6dYRpZw6hUxnXQFXG1ZRWlLEX7/dy7jnV/F9So4M199CUmS1E9uzqvlzw61Mqn+B7/u+onUc0Rw+IXjftR6u+Qrz0NupSJpJ5YBbsM75irD7N+PZWe5KCvc0uLNtWG9Tg9U+7LhomfQNJyf5zY0cr12QczB1u8jeLtv6hYZJ2r+i0lI+3JQOgKdBxx3jk9r0/IM7BzOlVyRBVHJTzbt8uXpLm55fNM33KTlMf+lXvs4wEKWU2NebdD6UhA2hPOkiKpIvojJsIBbFyDFDAjvUrgAUVJqY/8lO5v1vG2U19Vq9BLcl9/Hbia0ZJz+RGJwUqWES0SI6PXSdjLHrZFxnvDAhWmdQ52C+2ZmNgpVdadkMiAvSOpLbMqQutbeDBl50li21FTv6atj9EgDJRSuoNj0pXQadJP2zP/O1soGXdJcSPfQyIgLafij1R3sXEZh2HwFKDV9vqKV8zOcE+sj/Yq7AalV5avEB3l1vK8SriOQ9/aVcEpJB6KR78ew6CU/DH6aDMdfSuSKHH+vCeHH5YVb+NoDNmNTnee7F/sy54S76xMqcuE0ld7LaiW0ZJz+dGNxZ5scSQmhvVGAp7xn/yS7PW4lJeVnrOG6roa6K5OrtABQQTM9BYzVOdGb6yJ7keXUBYJBymPU7UjRO1D6V5mfSN/dreuqO86LxNf40PEiTHHE9h9kHvLhYXcUXS1dqkkM0Vt9g5a23X+O99Wn2dZcMjOXaB/9D1F1LMfacDn8ssACM3hCaRJ/YQN65YShvXTeYq7y3cKNhKU+bn2P5W39hw5HCNnwl7k2KrHagrraGjKxsABLDfAn3l+exhBDaS4iNZoI+hUClhsiyXdKvv4XStizBC9tzm0cCR2E0uPadofruv+syuO1LDZO0X6nfPmufeHh75KVERGk0+bNPCPUj5gOgV1Qid71Cec3pnzEWbcNisbLqlT9xe87/8ajhA/Q6ePqSvvz7iv74e5+msDqLqb2j+Fufkx/i36d8TOr7d/HLgTxHx26XpMhqB47tWMYWwy0s8fgzNwftOPcOQgjRBvQBkeQZbMN497CmkV1Upm0gN/VVZS8uNj3Byw2zsPS5Qus45xQ7eo693aXoF+rMMiCCI9VWlNIz92sA6lQjyRf/n6Z5AsfdSbXe1oVsBuv4duVaTfN0ZKqqsvLNB5hW9ikANxqW8tn5MGd4y+fY9J39Mubxf7UvX6//meOf3MsmmR/tnKTIagcqDq1Fp6j01B0nKdxX6zhCCGFXEjwQAE/FzNE9GzRO455WHiwkRU1moeUK+o6aoXWcc9JHdCffwzac90AOsXXfIY0TtS/7F/8HP2oB2BF8PlGxGg+d7umHedifANvdrKDtL1NlatA2Uwe14sN/MrXgHfvy4aFPMOS8C1p3UEXBOP5BLBe9hhVboTZXt4S97y9gT1Z5647dzkmR1Q745W+zt2P6TdAwiRBCNGZMHGFv16RKkdVcGUXVpBVWA7bR3IJ9m9fdRys1XWyTJesVlfyt32mcpv2wmuuJO7zIvhw2ZYF2YX4naNyd1Oj8ALhAXct3q+TfelvbtvILJqY+a1/e1+chul1wj8OOrx90DdaZr9gLrXnKd/z43j8oqKhz2DnaGymy3Jylvo4E0wEAcggnPqGrxomEEOKkmL7j7e2AIunO3Fy//Da6F8DEHu4zcmz08MvsbWvOLixWeR7PEQ6sfJ8ItRiAbZ7D6dZ7kMaJfuMVQN2Q2wEwKFa8N78k3UTbUEbqAZLX3otesf07S4m/jt6XPeLw8xgGX4vl/Oftyw82/JfX3vmvfK/PQIosN5eRshpvbHMXZPoPaHGfWyGEcAbf2N5U4w1AF9NB+c+4mTptfpx5+sV0U44zoUe41nGazKvzUD4JvZvzTC/yUO317Dp+6sSnoplUFZ/tr59cHHmXhmFOFTLhbmp1tkcWLrCuYckmGVmyLdTVVmP+5BqClCoAdvuNpt8NLzntfMYRt1Az+GRBfWfZ8zz5jXyAdjpSZLm5kr3L7W1LwnjtggghxOno9OT49gIgSinh8JGDGgdyH6bKYiZVfMtfjR/xhud/6B7pr3WkptPpMIy8jUzVdvdt2f58jQO5v+O7V5NoTgXgkC6JQWMv1DjRH3gHUdX7GgBMGNm0cS1WuYPpdHsW3UtXi22o9mxdNMm3foCi0zv1nD4XPE1F56lkqpHcWP8gH+0o4PuUHKee0x1JkeXmAnNP9nuOHzxdwyRCCHF6pqiTXZoKDqzXMIl7ydiy2N7953jIKLfrqTCpZyS63yIv3ydFVmv9eLCCJZahNKg68nrcgF7vem/hwiffwyL/2xhp+g+flXRljcyp5FRb9h8lMe9nwDbSZP2l/8MnINT5J9bpCbj6HXZM/469qm1evP/7eg+ZxTXOP7cbcb1/oaLJ6qpK6WKyfSqcrnSiU+cuGicSQohTBSSPtLct2Ts1TOJe6g4us7eNPaZomKRlQnw9GJoQAsDRompS8ys1TuS+6swWXj/gxZ/M9zHB8gr9z79B60inF9iJmPMX2LsIv/NrusaB2q/KOjP3fZ/B+aZ/stwyiB3d7iWx9/C2C+AVwMXDezBrgG2ajipTA3d/uhOzxdp2GVycFFlu7Oi2ZRgU2w9zTvAwt/uUUwjRMcT0Hc+frXcywfQCT1Rfdu4dBKgqsUW2ngp1qpFuQ6dqHKhlLutcy4OGT1nu8SD7NvygdRy39ePuXCrqbMOiD+vXh6CAAI0TndmknpEkhPoAsC61iP05FRonap9eWHaY7LJaigjknU7/YMRVbT9fmqIoPHVJXzqH+qDHwtCcj/hgxbZz79hBSJHlxnLSD2BWbf1uDV1l6HYhhGsy+IWSETuTdDWa7PI6GfK3CUoydhP22yhy+zz6EhYcpG2gFpoQmM+dhu/pqstGf3iJ1nHc1sebj9nbc4ZrPC/WOeh1CjeNSQQghAp+WLlK40Ttz56sct7fmAGAl1HH85cPQKdR91E/TwOvXxDKlx6P81fjR8Ssf4TDeVJYgxRZbm1h1ST6m/7LDfUPkTTU9SeoFEJ0XAPjg+3tncfLtAviJrK3/Whvl8Wcp2GS1gkbeAFmDAAMrFlPQXmtxoncT/quNcRkLcFIAz2i/BkUH6R1pHO6rG8wz3u9xwbPu5l4ROZSciRLfR37PnoIf9U2muA9k7oRF+KjaaZe8VF097A9f3e+bgvff/QfmbYBKbLcVl55HXuzK6jBi6Lo8wgLC9M6khBCnNGAuCB7e5cUWedkzDj56X9Iv/M1TNJKXgFkBQ0FIFYpZtfWNRoHcj91vzzPKx7/YYPn3fypj8UtHg3w8Q1gvHcaXoqZobpDLP9l+bl3Ek2y+/MnuKr2U1Z6PsCckEPMG5uodSTwi8Bw0UL74ryKV/l45Wbt8rgIKbLc1MqDJ0dqmuRGE1QKITqmgbE+TNZt50HDp3Ta95bWcVyaWl9DQrVtjqFcNZRe/YZqnKh1DD1P9rSo3SddBpujrjSHrhW2ETmt6Jg4aoTGiZpIUTCMvN2+6Lf7PRkQwQEqcg7TM9X2+zOIKq6ZPByji4wy6dFvNiWJMwEIUqoJ/vVxjpd07NEGXeM7I5pt5b5ce3tKLymyhBCuLdLfk9c8XuJOw/eMqvhJupKcxfGdy/H6bZL5w/7D8DQaNE7UOrHDZtnbiSXrMDXIhNRNlbbyXQzYipO94Rfi7+OtcaKmCx5+DTW/TU48zbKW1bsOaZzI/WV9/iBemAFYF3oZvQeN1jhRYyGXvUSNPhCAC3Ub+OKz9zVOpC0pstxQTUURzx27goXGV7jEbx+9Y1x3lCEhhADA4EmWVzcAEpVc0o5lahzIda2ujOUh8y38aBlOfbL7z3+oC44nxzMJgH5KKjv2HdY4kZtQVQIPfW5fDB1zg3ZZWsLTj9JulwPgpZjJX/O2xoHcW07KSnqVrQagQA2i59XPaBvodHxDUaY9aV+cnftvVu05dpYd2jcpstxQ+qYfCFPKmaXfwKVBR9yif7YQQlSHD7S3c/at0zCJa1t+zMLnlgncZb6HxFGXah3HIWoST87zVbD9ew2TuI+iw5voZLa9QU3R9aRfv8EaJ2q+6El329tjy77nSG6ZdmHcmdWK6aeH7Yu7ku8kMtw1n8X3Hno9RaFDAEjQ5XPsuyepM3fMu9dSZLmjAydHnfLte4GGQYQQoul8Ek9OlGk+tkXDJK6rzmxhc3oJALFB3iSF+2qcyDGih86yt4OyV6Kq0l30XPLWvGNv53e5FJ3O/T5Q1YUnkxU6CoDOugI2L//8HHuI0znyyyISTbbulkeIZ/Rl92ic6CwUhdArX6EB2xRDc8xf88niFRqH0oYUWW7GWl9Lj0rbBJXFBNBrpPt3JRFCdAyd+p4cijyoNEXDJK5rR2Yp9Q22Z3BGJYW2m54KvonDKNfZntXo07Cf1LxyjRO5NtVcS+ecnwCoUT3pOWmuxolaLnj8nfZ257SPqDI1aJjG/aj1NQRueNq+nDP8r/h6e2qY6NyUiJ5UDPoTDaqOjy2TeG1rBceKq7WO1eakyHIzat5OvBTbQ4+HQibi6eHa/9CEEOIEz7AESpUgALqZD1FZa9I2kAsq3PIl5+lS8KaOUcmhWsdxHJ2elB73Ma/+fsaYXuKXw8VaJ3JpxzZ8iT+2N6VbfcYSFx2hcaKW8+09nRKPaADGKrtYuW6DxoncS+pPLxFhtc1BtcUwmDHTrtA4UdOEnP8Ii/p9xGMNN1Bo8eHZJQe1jtTmpMhyM3Glm+xtv8FXa5hECCGaSVHI8+8DQKBSw5H9cjfrj4ak/of3Pf7Jds8/MTLeT+s4DhU38VZWWAdTixcrDxZoHcel1W87OSqbtf8cDZM4gE5P/cAbSbdG8oT5OhalVEt30SZSVZXlh8spU32xqgr1Ex5F7y7dRj18uOrCqYT52W4GLNmbx5bfukJ3FFJkuZHSguMMtOwGII9Qeo+Yco49hBDCtVhihtjbJYfWa5jE9dQWHyfWkgXAUX0iUaFB2gZysMQwX7qE2Z4x236slPIas8aJXFOd2cJDlVfzRsNMdqjdGDp+ptaRWi1qyr08GPUu71qms7PAytaMUq0juYWl+/J4rmQsY00v8U//PzN61DitIzWLn6eBB6Z2sy8//cNurB1o+g4pstxI2rK3MCi2vvppMTPR6/UaJxJCiOYJ6T7K3jbkbtMwies5tm2pvV0QNkzDJM4zsYet25vFqrLmSKHGaVzTLwcL2FUXybMNV/NBr//i5+WhdaTWM3hy7chE++KHmzrusN5NZbGq/Hu5bbqDSnwYceHNbvmM5uVD4ugT6cU8/WJeKb6Znzbt1jpSm5Eiy01YGxpIzPzS1lYV4iffoXEiIYRovuieI9lFN95tOJ8vqgdIt6HfqU9dbW97dZugXRAnmpzkwyzdOl42/gfThje1juOSvt2ZbW/PHhSrYRLHOr9PFME+RgCW7M2lqEqeyTybH3fncDi/CoBB8UGM7x6ucaKW0esUXo9dxl+NH9FJKcK84klq6jvG4CdSZLmJzXv3U2qxzfS+23sIcV26a5xICCGaT/EK4MX4V3mi4XoW1/Qiq/T/27vv8Kiq/A3g752angDppBFaKCEEhBB6DUXpAiKiuIiKWBDXH7orIKsr9l0La8GCXUQBFaQ3KaGGTggkQEJJIYH0ZDKZOb8/RgYjpDIzd2byfp4nz3Nmcsubw+Fmvrn3nlsmdyS74Z+3HwCgE2q07jJQ5jTW0aWZAf/V/A+jlImIzF6PSoNR7kh2paBMj20ppjN8vh5a9Gxpn89CaggXtRIT7whFK+ki/il9jm1b1te+UiNVWXQF5Wv+AV+YZuH8e0JbhzyLdV3o8Dkok9wAAKMNm7Dyt7UyJ7INFlkOYvlpI4ZXLMIY3b9Q1ONZueMQETVY51Afc/vQhXzZctiT4qxUBBqzAADJqij4NfWRN5CVaPxbIksdBgDoLE7h6JlzMieyL6lr/ou/YRWCkYu7OgU5ziQHdTTdPwWbtP+HaaoNcD/yaaO6P6c+zv38CibpV+F37WzMCkpBz1YOXmx7BqC0x9MAAIUk0PLQImTlO/8f2FhkOYjXxnfCu5NiIDWNxB09+ssdh4iowWLDfMztQxm8AR4ALiTd+Kt+nn9cDUs6voKwQQAApSRw6cCvMqexI0IgLHkJnlN/j+3apzG2nXM8iPrP/DsloFjhCQAYWLkLu4+fljmR/TEW5yI09RsAgBJGDBw4VOZEltFs0FO4qgkGAPSQTmDdT5/WsobjY5HlINRKBYZ3DMS0NkaolPxnIyLHdf1MViDyUHY2Ud4wdqIy7Xdz262Nc14qeF3AHaPNbY/0zTImsS/XUnbCz5ANAEhSdkKnVuEyJ7ICjRtyW90NANBKelze5vwftOvr/G9vwwWm+9U2uQ1Dl47tZU5kISotNMNeNr/sl/4uTl5w7slv+GmdiIhsysdVjW1uc7HH5Qk8ffVl6CoNckeSlxAIvroPAFAqtGjb1bGmaa4vnza9USyZngHWpeIALlwpkDmRfcja9ZW5nRs5yqHvwalJyOBZ5nZc3kpcvlYiYxr7IsoL4J/8BQBAL5TwGviMU40Dj9hxyPTpAgBoocjGwR/fcOrJj1hkERGRbUkSKlxMU3kHSNdw5kyKzIHkVVBYhF/03XDKGIoT6g5o6uVcDyG+iVKNy769AJgeSn1y30aZA9kBgx7BF02TAZQLNVr3nSxzIOtR+bdGuk8PAEC4lIM9G5bLnMh+XNjwPjyEaUbBzZqB6NW1s7yBLE2S0Gz8mzDCVDiOyv8KO4857yWjLLKIiMjmdIFdzO0ryTtkTCK/vRdLsbDyAQyreA3ro/8rdxybcIu+09w2nFonYxL7cOXIOniLQgDAXk0c2oQFyZzIurz6PGJu+576CnrOMgnoy+B9+GMAgEFIUPZ9Ggonm/gEADShXXEpzHTJsLdUisurX3XaWUZZZBERkc15tYo3t8WFxv1Q4t1peeZ2XOtAGZPYTvM7RsLwx0eQqMJdKNE1jufmVOfq3m/N7eI242RMYhtNOo/CVaXpuU+9jQexc3+SzInkd3nLR/A25gMAtql6YUDP+JpXcGAhdy9CoeSJDypH4qXCEfjhwEW5I1mFwxVZixcvRkREBFxcXBAXF4d9+/bVuPzy5csRFRUFFxcXREdH47fffrNRUiIiqk5wxz7mtl/BURmTyG/PWVORpZCA7i2aypzGNiS3pshwjwYAREqXkXT4oMyJZFRRirDsLQCAfOGOTv3HyxzIBpQqFHa4D4BpSu+CXUtkDiSzygq47F9sflnWY7ZTT3ImeQUjbcoevFY5GcVww9sbT6PYCf/Q4lD/gsuWLcOcOXOwYMECJCUlISYmBkOHDkVOTs4tl9+9ezcmT56M6dOn49ChQxgzZgzGjBmD48eP2zg5ERH9mdrLH5lK03S+bQxpuJJfJHMieVy9koVrWekAgA7B3vB2VcucyHZKOj2A1/UTMVT3KlZnuMgdRzaZ+1bAFeUAgH2ufRDq5yNvIBsJG/woKqEEALQo2IezOY3zGAAAF1KPwKg3jYEdUlcM7u/cM4wCQGyrEIyINp25zy3W4ePtaTInsjyHKrLefvttzJgxAw8++CDat2+PDz/8EG5ubvjss89uufw777yDYcOG4dlnn0W7du3w0ksvoUuXLnj//fdtnJyIiP4qz6cTANNUzmeP75E5jTwyd36BvS6PY7PmGUz2PSt3HJtqNXAaPlOMQ4oIw5bTVxrtg2mLD/5gbhs6NIKzWH9QeAXiYMtZeKjiGYyrWIhv912QO5Js3j2mQW/dO3hRfz9yujwNF7VS7kg28X9Do6BWmu47W7njIDKvFcucyLJUcgeoq4qKChw8eBDPP/+8+T2FQoHBgwcjMfHWz1lJTEzEnDlzqrw3dOhQrFq1qtr96HQ66HQ68+vCQtONqHq9Hnq9/jZ+gtt3ff9y53Bm7GPrYv9an0P1cUhXIM806UHh6V3Qx/WXN08dWLp/pXOm52O1VGQiJyzEMf7dLEQJoGdkM2xJuYIrRToczshDdHNvxxrDt0kIgSfKZqCjPgr9FEfRtecwq//c9tS/LUY9h99TfofBaMTygxfw1MBIpygw6tPHmQXlWHnoEiqhxQr1XXiyf1+7+LexhebeGky7ww+uBz7Eo4pfsXpZGsZOf67W9eQew3Xdr8MUWbm5uTAYDAgICKjyfkBAAE6dOnXLdbKysm65fFZWVrX7WbRoERYuXHjT+xs2bICbm1sDklvexo2c7tba2MfWxf61PkfoY2WRFh2vty/ucah7Zi3Sv8KIgYWmG/6vCg9kZBUgx4H6wBL89BLwxyVjH69OxPDQG7OMOcIYvl3ni4BT+SqcQj8c9eiDWTu32mzf9tK/MT4K7M9VoKCsEq9+swHd/Z3njGZd+njFOQUqjaYLy3o0q8COLRusHcuudCs8jQT1jwCAgZkf4/PvWyPAy7VO68o1hktLS+u0nMMUWbby/PPPVzn7VVhYiNDQUCQkJMDLy0vGZKbKeePGjRgyZAjU6sZz3b4tsY+ti/1rfQ7VxwY9yl99GUIApQYFEoYOs/ubvS3Zv/mp++B52PTLOlkbg3Gj7rJERIfSpaAMR9/6GoMUSQgsUWPEiHcdawzfppd/OwUgAwAwbUA0RnRtbvV92lv/BmbkY9IS0yRm58vVeHHEYJkT3b669nFB+hG8feAUgCZwUSvwr6n90cxdY7ugdmEE0j5ORMsrm+ErFSIgezOGT1pc40OY5R7D169yq43DFFm+vr5QKpXIzs6u8n52djYCA2895W1gYGC9lgcArVYLrVZ70/tqtdouDkaAfWVxVuxj62L/Wp9D9LFajVciluCrUxIqocJvV3VoHyzvH7PqyhL9m3N8M/z+aJcE97L/fy8rCG0CLHd5GR6iBAWlbsgreh3NPE2TYDjEGL4NBqPAb8dNn1E0SgXujGlu05/XXvq3e6QvpjQ7jSGFK9El9zRSLx1Au4gQuWNZRI19LAQqfp6NjYqT+FHVF+e7/hOBPu62DWgnQia+gYrF3aFBJYYVrcTBozMRf0eXWteTawzXdZ/2/SfDP9FoNOjatSs2b95sfs9oNGLz5s2Ij7/1swTi4+OrLA+YTi1WtzwREdlW85adUPnH3/sOXbgmcxrbUmXsNLebdhwkYxIZKdW42KwXANODSU/sbTyXSp1f9x6eLXsXvRXHMKBN00Y1s+SfSZKEB5qeQH/lEXhJZTi94VO5I9lE6emtCC46Bo1kQFdlKqb1ay93JNlo/VriQptpprakR/m6+U7xgGKHKbIAYM6cOViyZAm++OILJCcnY+bMmSgpKcGDDz4IALj//vurTIzx1FNPYd26dXjrrbdw6tQpvPjiizhw4AAef/xxuX4EIiL6k9iwJub2oYx8+YLYmkGPsKIjAIBs4YMO0XfIHEg+rh3vNLcNyY3nnjTt0a8wUbUdX2sWYVIrg9xxZBUy5MbnsnaXlqOorELGNLZxdd2r5vaB0AcR3KRxnsW6LnLsPBRI3gCAAZU7sHnjrzInun0OVWRNmjQJb775JubPn4/OnTvj8OHDWLdunXlyi4yMDGRmZpqX79mzJ7799lt8/PHHiImJwY8//ohVq1ahY8eO1e2CiIhsqEOwl3kK30MZjedMVu7pRPOzkVJcY+GqdZir9y0utNtoVP7xcaRNwU6U652/4KjIPo2Q8tMAgOMiEvHd4mROJC+3sM4472Z6OHUb6QJ2b10tcyLr0p3fh5BrewEA50UAeoycLnMi+UmuPsjv8az5dfM9/0JxuWMX2w5VZAHA448/jvT0dOh0OuzduxdxcTcOTNu2bcPSpUurLD9hwgSkpKRAp9Ph+PHjGDFihI0TExFRdVzUSjzdZBeWqN/E5wUPoaC4TO5INpF95MZlceUhvWRMIj+FexOku5memRYhZeHo0YMyJ7K+izu+NLdT/YfCVeP405bfLlXcDHNbc+hzCOE8swz+Vc5vr5jbOwPuQ8sAH/nC2JHwwTNxWRMBAOiIVGz/6QN5A90mhyuyiIjIufRVJ2OIMglhiis4c2Kf3HFsIvdKDnTCdA+Ob8chMqeRn77VUHO74IjjXyZUIyHgfvpnAIBRSGgWN1nmQPYhpNc9KJBME9/0qtiFI8mnZU5kHZWXjyE0xzRVf6Zoiti7HpU5kR1RqqAcfqMADTr9NTILHPcPbyyyiIhIVqJ5N3O74PRuGZPYzj9K7kEn3RJMrnwR7TtEyx1HdmE9xpnbQVnb4cQnMVB+4TACKkzTtidJUejembcwAABUWmS1nAgA0EgGXNrykcyBrCNzzSJze1OTSegQ5i9jGvsTEHsnTjYZiLf0d+Ne3fN4c73jFtsssoiISFb+7Xqb25os579U7MLVUlzKL4MOGiAsHi5qXirmFhyFTJVp2u5o4ynkFRXLnMh6Lu382tzOCB4BrYr//tdFDH0cRpju0Yy9sgp5hXV76KujMOamIfjSWgBAnvBEm2GzZE5kn4If+h5faiahHFqsOHQRxy8VyB2pQVhkERGRrPzbdEPFH9O4h5ScgNHoxKcxACSm5Znb8S2byZjEvlwNGQgAUElGKHOOypzGSoxG+Jz9BQCgF0oEx98jcyD7ovVrgTTvngCAYCkP+zd+L3Miy7qw/h0oYZqafJ3nOHRv6xzPA7M0H3ctnhjYCgAgBPDKb8kOeY8eiywiIpKVpHbBBW1rAEALXEb6pYsyJ7KuxLRcc5tF1g1+3SfgZ0NPPFnxOH4srf1BpI6o9OxuNKvMAQDsU3TCHe1byZzI/nj2Md2jtNcYhbVnip3mjy5CCDxzdQz+oZ+O48YIhCY8AUmS5I5lt6bGhyOsqRsA4MLZk9i5x/EuJW+8c8YSEZHdKPaLBS4mAwAuHtuBFqH3ypzIOoSuGP88NQYJ6tbYJMUjJmS43JHshn/7vvigGXAqqwhSiUBesQ6BTZzrAb0Ze39G1B/trLC7oFLyb91/FdjlTszd/zGWpXsAFcD41Fz0beMnd6zbtis1DwculeEABuGQ3xj8Fs0CuyZalRLzEsKQ9uN8PKhch+QNbVDa5Xe4aR3nmMD/3UREJDvXFj3M7Yp0551hMPv4dvgiH8OV+zHCMw0aFX8N/9nAKNMkAAIStp3OrWVpx/Oa7m6M072IzyuHokWvCXLHsU8KJQb06Wt++fWedBnDWM7iranm9qyBrXgWqw4GdwzBGJckaKVKdBYnsXXZO3JHqhce3YmISHYh0f3M7SZ5h+ULYmV5xzea25XhvWtYsnEa1O7GTGtbUq7ImMTyrpVUYEdqHpJEGyxxfwSdW4XJHcluDWoXAH9PLQBgU3K2Q0/jDQCHUi8i8azpXswWvu4Y3jFI5kSOQVJpgeFvAgCSjWH4LEWD1JwimVPVHYssIiKSnZtfOPKkpgCA1vpTKCmvkDmRdbhfTjS3g2P4fKy/6hzaBMGulRiq2I/4tP+ivKJS7kgWs/Z4Fir/uL9oZEwwFAqeyaiOWqnAPd3DAAj0kI5j58ZVckdquLJ8tP62B95Sf4CW0iXM7NcSSv7b11lg1zuxKupNjKx4Ga1i+6Opu1buSHXGe7KIiEh+koRdwQ9g1/liHDK2wsKLhYhv5St3KosSZdcQqjM98+W0CEO7Vi1lTmR/lAoJn7m9jyhhumR0z/4d6NFrgMypLOPXI5fN7ZExwTImcQxTOrpizI6/I1KRiRMnWkE3egK0asf72Jqz+V34G4swXrkDKrULhsc+JHckhzNs/N8Q3rMQsWFNAAB6vV7mRHXDM1lERGQXdLHTscwwAKdFKA5dzJc7jsVdPrLFPH1zuldXTnpQDWOboeZ20eGVMiaxnPykFZhz8QlMVW5ATDMDOgR7yR3J7gUEhkDl4g4A6CBSsXvLLzInaoDyAngkmR6qXCkUqIx/kvdhNoCLWmkusBwJ/6WJiMgu/PmX6KGMfPmCWEnByU3mtmjRR8Yk9i08fgKMwnQ5VeSVzag0GGVOdPuu7fkW3RSn8ZJ6KR4Iv8pJD+pCkmDoceNhve7733e46dzzt74LN6PpwdprFP0wol8vmRORLbHIIiIiuxDp6w5vV9P0vIcy8h3y4ZM18coy3Y9lEBJCY3k/VnU0TYKRojRNb90SF3Hs8H6ZE90mXRGCc34HAOQJT0T3HiVzIMfRot9UXFGYJkPpXnkQ+/f9LnOiulNXFsPj8CcATA+eLo57Gq4apcypyJZYZBERkV1QKCT0aK5GX8UR3F/+FS5lZckdyWKMRTkIqTgHAEiWItE2PFTmRPYt3aubuZ174CcZk9y+vP0/QQsdAGC3tjdaBzeVOZEDUaqR12mG+WXFtv/IGKZ+Ai6tg6uxBADwq2IAxg7kWazGhkUWERHZjUcN3+FLzWt4UrUKGUe2yR3HYi4evnGp4EWfbpxZrhaVgV3N7ZCsTQ53mdifle7/2tyu6DBRxiSOqc2wx1AgeQIA4su24+TJYzInqoPSPLS/ugEAUCGUqIh/Gm4ax5u0g24PiywiIrIbLi1vPDuq4twuGZNY1npjHAbrXsd8/QPQR42RO47dM7j64bzadMlgO5GGk6eOy5yoYUTBRTQvOAAAOGcMQHzfYTIncjwKFw9ktLwPAKCSjMhZ/5bMiWp3bdN/4IpyAMAvysEY3T9e5kQkBxZZRERkN8I635iuu2lukoxJLGv32TykihB8aRiKqFg+hLguCiJuFCRZe5bLmKThLu/4EgqYzsId8E5AcBM3mRM5ptYj56AMpucjxeWvwfn08/IGqknxFXgfXwoA0Ak1KnvO4b1YjRSLLCIishvuvqHIVAQCANpUnkZBYbHMiW6f3mDEvnNXAQC+Hlq08veQOZFjaB4/wdz2v7jB8SZCEQKq4z+YX7rdca+MYRybi7c/TjcfBwBwlSqwe913Mieq3uGrKjyjm4EMox9WKYdgTL9uta9ETolFFhER2ZVsn1gAgIukR+pRx79k8NilApRUGAAA8S2bcfruOvIO7YADLj3wfuVoPF92H05nO1bBrb90BAHlpslODoq26BvHD9u3o9W4F7AZcRihewUvnO+EtCv2Nx6EEHhl7SmsNsZjcMWbqOz7HFzUPIvVWLHIIiIiu6KMuHH/QtFpx5myuTq6rW/gadWP6C4lo1ekj9xxHMrxPh/izcpJOCFaYM2xTLnj1MuZQ9tRKUwfs04HjICni1rmRI7NvVkIUvr/DydFBIwCeHfzGbkj3WRzco75rLW3iwpje0TJnIjkxCKLiIjsSnD0jfuy3LMPyJjEAoRA24xleEq1Aks1r6NHhI/ciRzK8OggXD/xt/rIZYe6ZHBxYR/00C3GQv1UNO/NSwUt4YH4CDRxMxWrvxy5jNQc+zmbVVlWiEVrk82vR4YZoVbyY3Zjxn99IiKyK80iopEP05TNrcpPQKfXy5yo4SpyTqOpIRcAcFTRDuH+PvIGcjABXi6Ia2F6rtTZ3BKcuFwoc6K6KSjTY2NyNnLhjV9cRiO+Qyu5IzkFd60KD/dtCQCQhBE/r14lb6DrjEYUfDgcz+X/Cy2lS4gN9Uanpo7zBwGyDhZZRERkXyQJFz06AQCaSEVIPXlI5kANdylpvbl9xS+O92M1wMiYYIRIOXhM+TPObf5E7jh1svZYJioqjQBM+XlGw3Lujw/HeLdD2Kh5Fk+lP4GUY/vkjoSyfZ+jWcFxDFEexDvqxXhuaBvwvzrxfz0REdkdfUg8ThjDsbQyAUcyS+WO02D61G3mtlvbgfIFcWAjwoGd2tn4P/UyRJ370v4fTFxRilUHzppfjo1tLmMY5+OuVeHeiFK0VGRCJRlRtvo5CKNRvkClVyE2vmh+uSHkCXQJbyJfHrIbLLKIiMjueA6YjTsrFuHFymnYku0ud5yGMRoReHU/AKBAuPH5WA3UJDAcaRrTBAKtxXkk28GZi5pc2f4R/pc9BS+ovkIvvzJ0CvGWO5LTiZ7wT2RJfgCAzrqDSNr8Qy1rWM+1X16Am8F0GetqY09MuJv335EJiywiIrI7Lf09zDe4H0i/Zv9nL26h/NIxeAnTh6+jqmg0b8rnYzVUcesx5nZu4rfyBamNEBBJX6GpVIyHVGsxtoMPLxG1Ao2rO7Lj/mF+7bd7ISp05TbPIS7sh/cp03gsEq64Ej8PoU35wGkyYZFFRER2R5Ik3BFhmvAgv1Rvl8/Eqc2lpLXm9rWAHjImcXyRA+6DQZiKlcisdaisNMic6NYqL+yHf1kaAOCgsQ0G9ukrcyLn1SlhGpLVHQAAYeIykr5/2bYB9OUoXPYwFDD9AegL7WTcOyTOthnIrrHIIiIiu9QtwnRfgx/ycTw5uZal7VDaFnPTu/0QGYM4Pk/fUJx2Mz2kOhRZOLZvs8yJbi1z68fm9snAMWjqrpExjXOTFAqo73zNXHzHnv0Ql1KP2mz/Retfgnex6d67o8YW6DTuWWhVfPAw3cAii4iI7FJvrxxs0zyN/S6Pwe/IYrnj1I++HCGFplkRL4umiIntLnMgx6dvP97cLt37pYxJqqErhu/51QBMl45F9JsicyDn16pzH+wLmAQA0Ep6FC2fCWG0/llOcWE/3A/8DwCgEyqsazUffaOCrb5fciwssoiIyC61at0eIdIVAEDz/AMO9SDanCIdnqp4DN9WDsAut8HwcdfKHcnhRQ26H6Uw9WNM/iYUFBbInKiqggPL4CrKAABbVL3Rs124zIkah+ipr+MSAgAAUbrjOPzT61bf5+Gdv0EB04yGn6om4pG7R1p9n+R4WGQREZFd0rh745ymDQCghbiISxczZE5UdzvPF2GdsTv+UTkD6Z3/Lnccp6Bx88KpZqbLLj2kMhzf+JXMif5ECOh2f2R+WdJhCpQKTnhhCx6e3sgZYCqsdEKNlceu4lxuidX2d+JyASad6I6HKp7BZkMs2o+fB+8/Jukh+jMWWUREZLcK/jRhRMafHuxr73aeyTW3e7f2lTGJc/Hp9aC5rU1ZKWOSqioz9sK/JAUAcMQYiX4Dh8mcqHGJ7TcGqwNnYmzFQnxZ0R9PfnfI/DBoSyoq12PWN0moqDRik7ErtnV9H/3b8zJBujUWWUREZLe82g0wt8W532VMUndCCOxINRVZbholuoTxwaSWEhk7CJs1AzCn4lFMLXwMZ7KL5I4EAMje9J65fTBgApr7uMqYpnEa+LeXUe5rmm3w2KUCvLzmpEW3byjOxdPLjuB8nunh6NHNvfHCXe0sug9yLiyyiIjIbkXEDkKFMM3YFZJ/0CHuy8o4shXdS7bDB0XoEdkMGhV/1VqMJCG973+wwtgXZXDB8oMX5U4EAPg93xc5wgd5whNRgx+QO06j5KZR4d17YqFRmv6/fZmYjvVrfgQscMwQ6btR+XY0Ak9/DQDwdFFh8b1dOJsg1YhHfiIislsaN0+c00YBAMJxGRnpaTInql1F4hIs1ryLJO2jGOOfLXccpzMmtjnUStP9TssPXEC5Xt5nZp3KKsTzVxLQS/cu5rq/jPg2vHxMLh2be+PfYztCghHPq77B0P3Tcfb7Z2+r0BLnd0H/xThojaV4Wf05RqsS8cGUrghrxocOU81YZBERkV0rCrpxX9bFpA0yJqkDoxH+V3YDAEqhRfvYeJkDOZ+m7hoM7xgEALhWqseaI/KezfoyMR0AoIcKffv0hyRxwgs5TbgjFP+OLcQjqjUAgMiUJTj/9SzAUFnvbYkTq6D/chw0RtOskdsNndB35IO8z5LqhEUWERHZNe92g8xt6fwOGZPUTnfpKLyN+QCAQ4qOaBnYVN5ATur+HmHoIp3Gu+r30Gb9fbLluFZSgZVJlwAAHloVxnUJkS0L3XDPhHvxU9Az5tcRad/g0ntDIYrqeGa5sgL6DS9CWv4ANMZyAMA2QwzODV6C8XEtrRGZnBCLLCIismstOvdHhVBBJ9TIKyq16/uyLh9cY27nBvTiWQ0r6RreBG+5LcUoZSKi9Udx5rAMxbfRiORlL6CpPgsAcHfXEHhoVbbPQTdRKCSMnTEPy5o/j0ph+qjbPP8ASv/TFYXbFwP6sluvKASQshZl7/eCevd/zG+vMPTGhYQlmNYvyhbxyUmwyCIiIrumcnHHv4PfQyfdEjxR9jBSc4rljlQtKW2Tue3dcaiMSZybpFDgSrv7za/zt75v8wy6k6vRM+MjbNc+jcdUv+ChPi1snoGqp1BImDB9LlZEf4hs4QMAcDcWwWvrP1D6Wlvkf/cwRFm+efmKigqU/OcO4Lt74Jp/GgCgF0q8Je6Dx6RPMLVPWxl+CnJkLLKIiMjuNW8XBx00AIDEs3kyp6lGWT5Ciw4DAM6LQHSN7SZvHifXcdhDKIQ7ACAmfyNyLp613c6FQMHGNwAAKskI74jOCGnCiRDsjUIhYeLdk5A8ei3WSH3N77tVFsAnZRl6vLYdw/77Owa9tQ0dFm7Cjms3HrdwxBiJZ5v8B6NnvYqEP+4BJKoPFllERGT34iNv3Gi+O9U+i6ycw79BCdMDUE+4x8PbXSNzIufm5uGN48ETAAAayYDzv75ms31Xpm6Ff8FRAMApYyj6jrjXZvum+uvfpT16PfsTPm77KX419kK5UOOa8EB2uQqnsoqQdqUEeoPACkNv7DR0wFzVXBwdvhJvPXk/Wvl7yh2fHBQvHiYiIrvXPtgLPm5q5JfqsSctG5WVlVCp7OtXWOGRNfD/o21olSBrlsai9ei/o+x/38JVqkDHrJUozH0RXr5WPusgBPLXLMD1sn+r332YGext3X3SbfNx0+DhyXcjr3gkfjx0Dmkn9qN5viuuFOugUkho7uMKj+ZjUNLxUbzU1p/Pt6PbZl+/oYiIiG5BqZDwUHA6ItN/QG9xHKcPf4v2dwyQO9YNRgMCcn4HABQLF7SN4/1YtuAXEIrdfqPQM/dHuEk67P/lDXT729tW3WdF8jr45t84ixU3aoZV90eW1cxDi/v6RAF9orBA7jDk1FimExGRQ+jRpAgjlPvgJZXi2pG1csepoqS0BEv0w5FkbIW9yi5oE8yp220l9M7/g14oAQBRGd+h+FqO9XZmNKJo7Yvml5sCpqNLeDPr7Y+IHBaLLCIicgjhcSPN7SaZ9vW8rJ3ppXhXPxrjKv6FbdGLOHW7DYW2aIv9PsMAAJ4oxekfF1ptX7rD36NZ0SkAwDFjBPqPftBq+yIix8Yii4iIHIJfSGtcUDQHALTRn8K1q7kyJ7ph66kbZ08GtONMZLYWMnYhyoUaJ43h+PBiOK6WVFh+J7piVKybb365LWQmOob4WH4/ROQUWGQREZHDyPLrBcA0bfaZPatlTmMihMDWFFORpVUpqsyESLYRFtEaS9p+jDsr/o0Nug54b8sZi+8j80IqrujUAIDNxi64c+x9Ft8HETkPFllEROQw3NvfmLXPcHpTDUvaTuqRnWhadBqAQM+WzeCqUcodqVGaeNcIaNWm+by+TExHcmahRbf/YmIlEnSv4iX9fTjT+XlE+nlYdPtE5FxYZBERkcNo2W0oKoTpg3Rk/m4Io1HmRIBm60Ks1T6P3donMDKSBZZcArxcMKt/KwCAwSgwf+URGPU6i2x7zdFMrD+RjUqo8LPrWEwZYUczWxKRXWKRRUREDkPr5oUUt1gAQADykHZst7yBSq8ipOAgAEAPFfrEdpA3TyP3cL9ItPB1R0fpLOZlPYGUb5657W3mFhRj3s/Hza/nj2wPTxf1bW+XiJwbiywiInIoJS1uPIPqyoFV8gUBkL3/JyhhOpt22KMv/LxcZM3T2GlVSiwaGojlmn+hk+Ic2p3/Cpf3rWjw9oxntqDyve7wL00FAAzvGIiRnTixCRHVjkUWERE5lMhed994kXVUviAAyo6sNLel9qNlTELX9YiOwqbmj5lfe6x9Erqc1Ppv6Fo6dMumIbDyElZq5iPO7TJeGtOR0/MTUZ2wyCIiIofi37wF3vN4EoN1r2Ny0ZO4eK1UniBl+Wh+dS8A4JJohi7xg+TJQTcZ/MA87FT1AAB4iSIULBkFY1E9HlJclIWST+6Ca2UBAGC3sQMev2cUfD201ohLRE6IRRYRETme2PuRKkIASNh0MluWCFcP/wI1KgEAB1x7IaSpuyw56GauWhX8p37yxxgB/PWXkPv+YBivZdS+8tWzKPl4GNxLTMumGYNwvu9/0adNgDUjE5GTYZFFREQOZ0iHGx94NybLU2QVJt2416ey7UhZMlD12oSHImfUt8gUTQEA/rp0FL/fF+Unfrv1CkJAHF+B8g8GwL3oHADgovDFN23fw98Gx9gqNhE5CRZZRETkcNoGeCK0qSsAYO/ZqygotcxU3XVWXoigK7sAAFeEN2LiE2pZgeTQs2sMjg35HudFIADAy3ANLssn4+z3cyGEuLHgiVUo/XAQpB8fhIs+HwBw2tgcS1q+h39OHsz7sIio3lRyByAiIqovSZIwtpUKxoMrcZdyD1LXTUfXcU/bbP9XD/6IAFQAABJd+mBUoI/N9k31k9A7DtvcViH955noJx0CAPzfEX9cOLsZd4Q3hValwMi0zzCg4qB5nbWGbkju9grm39UNSgULLCKqPxZZRETkkIaHC7Q7uhwAcCJlFQDbFVmbc32hNfTGMMV+VHacaLP9UsP079IO50NX450v30bz/P04IKKAQh3WHMsEAKiVbTBAvR2njKH4QnMPBkx4CHM6BMqcmogcGYssIiJySG0798HF1YEIEVmIKj+Ca9kX0CQg1Or7FQJYcq4pzukfwzypHJv7DLH6Pun2Rfh54Mk587ApOQf996YjMS0PukrTM842Gbvi756tEROfgBe6hsJdy49HRHR7eBQhIiKHpFAqkB40DCGXl0IpCZzZ9g26T3rO6vu9UAKcyzNNG9+pRTCCfNysvk+yDEmSMKR9AIa0D4Cu0oCsgnJUGgX8PbXwdFHLHY+InAgnviAiIoflHz/F3PZK/dkm+zyQe+NX55jOzW2yT7I8rUqJ8GbuaOnnwQKLiCyORRYRETmsVh274bzCdIlglP4krqSfsur+9JePISJ3K9xQDo1SgeHRQVbdHxEROSYWWURE5LAkScLF0FHm1xmbP7bq/q5segcLlZ9ir3YWZrbMhbcrz4AQEdHNWGQREZFDixj0ECqF6ddZ2IVVEIZK6+yoLB+BF9cCAASAnj37W2c/RETk8FhkERGRQwsJi8Rh1+4AAD+Rh1O7VlllP3mJX0ErTA893qQegO5tQ6yyHyIicnwssoiIyOEZYu4DAJQKLY6fOGb5HRiNMO77xPxSFzMVksSH1BIR0a05TJF19epVTJkyBV5eXvDx8cH06dNRXFxc4zr9+/eHJElVvh599FEbJSYiIluJGTgRC6TH0F23GP+81APXSiosuv3Sk2vhV34eALDf2BYDeve16PaJiMi5OEyRNWXKFJw4cQIbN27E6tWr8fvvv+Phhx+udb0ZM2YgMzPT/PX666/bIC0REdmSi1YLZZepKIYbKiqN+GZvukW3X7DpLXN7u/sINPPQWnT7RETkXByiyEpOTsa6devwySefIC4uDr1798Z7772H77//HpcvX65xXTc3NwQGBpq/vLy8bJSaiIhsaVrPCCj+uIJv6e50lOsNFtluZcZ+BOUfBACkGYPgGRpjke0SEZHzUskdoC4SExPh4+ODO+64w/ze4MGDoVAosHfvXowdO7badb/55ht8/fXXCAwMxMiRIzFv3jy4ublVu7xOp4NOpzO/LiwsBADo9Xro9XoL/DQNd33/cudwZuxj62L/Wl9j7uMgLzUS2gdg3YlsSMXZ2LXhR/RNGHfb272y5mUE/9He2mwiAt0VjbJ/baUxj2FbYP9aH/vYuuTu37ru1yGKrKysLPj7+1d5T6VSoWnTpsjKyqp2vXvvvRfh4eEIDg7G0aNHMXfuXKSkpGDFihXVrrNo0SIsXLjwpvc3bNhQY3FmSxs3bpQ7gtNjH1sX+9f6Gmsft4NAR9UKTFeuRcV+DdaVCxhVrg3enkpXgL5Z+wAJyBJNUOTdCX5ovP1rS+xj62L/Wh/72Lrk6t/S0tI6LSdrkfXcc8/htddeq3GZ5OTkBm//z/dsRUdHIygoCIMGDUJaWhpatmx5y3Wef/55zJkzx/y6sLAQoaGhSEhIkP1SQ71ej40bN2LIkCFQq/kATGtgH1sX+9f62MfA7reWwrW8Aq6oQERZEqIm/bvB21p24CJ6J/0XM1W/Qvi2wSMTRzT6/rU2jmHrYv9aH/vYuuTu3+tXudVG1iLrmWeewbRp02pcJjIyEoGBgcjJyanyfmVlJa5evYrAwMA67y8uLg4AkJqaWm2RpdVqodXefEOzWq22m/8o9pTFWbGPrYv9a32NuY+9hy+AfsXvUEsGRKZ+AUPJHLj41P13xXWlFZVYvO0sCuCBVysn46exPc192pj711bYx9bF/rU+9rF1ydW/dd2nrEWWn58f/Pz8al0uPj4e+fn5OHjwILp27QoA2LJlC4xGo7lwqovDhw8DAIKCghqUl4iI7F/nmFhs2TQCA4t+hTvKkfL9M2j76Df13s77W1KRWVAOABgY5Y+u4U14jwUREdWJQ8wu2K5dOwwbNgwzZszAvn37sGvXLjz++OO45557EBxsuh350qVLiIqKwr59+wAAaWlpeOmll3Dw4EGcP38ev/zyC+6//3707dsXnTp1kvPHISIiKwsduxAFwnQfbdus1cg5vK5e62dv/wS/7tgPANAoFXjhznYWz0hERM7LIYoswDRLYFRUFAYNGoQRI0agd+/e+Pjjj83f1+v1SElJMd+MptFosGnTJiQkJCAqKgrPPPMMxo8fj19//VWuH4GIiGykdWRLbA17wvxa9essGItyaljjhooTqxGw9RmsVf0dU5Sb8Ei/SET6eVgrKhEROSGHmF0QAJo2bYpvv/222u9HRERACGF+HRoaiu3bt9siGhER2aFB9z6Dg6+vQVdxHE0Nucj4ZArCnlgDqDTVriOyT8Cw4lEAgIdUjiAPJab3b2WryERE5CQc5kwWERFRfXi6alE5dglyhA8AIKxgH85+81T1K1w5jdJPR8LVUAQAWGfsjkEPzIOrRmmDtERE5ExYZBERkdOK69Qeu2LfQpnQIMPoh6mneuCrxPNVrnyA0QBD0jfQfdgf7hV5AIDDxkiUDn8X7YK9ZUpORESOzGEuFyQiImqIMaPH45OiUnx+QuAyfDHv5xNYdyILj7QzoPOpN6G6cgJu5dm4fr7qhDEcB3p/gofiOdkFERE1DIssIiJyapIk4aH7piLnt2Qs2XEOALArNQ+qs0fwhWZLlWV/NvRE/qA3Mb1fezmiEhGRk2CRRURETk+SJPzzzvboFtEUL605iQtXy+AD071XOqHGDmNHbPYej7sn3IfR4U1kTktERI6ORRYRETUaCR0CMTDKHztSc3HobCheLR4K9yaB6NHKH6+EN4EkSXJHJCIiJ8Aii4iIGhWVUoEBbf0xoK2/3FGIiMhJcXZBIiIiIiIiC2KRRUREREREZEEssoiIiIiIiCyIRRYREREREZEFscgiIiIiIiKyIBZZREREREREFsQii4iIiIiIyIJYZBEREREREVkQiywiIiIiIiILYpFFRERERERkQSyyiIiIiIiILIhFFhERERERkQWxyCIiIiIiIrIgFllEREREREQWpJI7gL0TQgAACgsLZU4C6PV6lJaWorCwEGq1Wu44Tol9bF3sX+tjH1sX+9f62MfWxf61Pvaxdcndv9drgus1QnVYZNWiqKgIABAaGipzEiIiIiIisgdFRUXw9vau9vuSqK0Ma+SMRiMuX74MT09PSJIka5bCwkKEhobiwoUL8PLykjWLs2IfWxf71/rYx9bF/rU+9rF1sX+tj31sXXL3rxACRUVFCA4OhkJR/Z1XPJNVC4VCgZCQELljVOHl5cX/tFbGPrYu9q/1sY+ti/1rfexj62L/Wh/72Lrk7N+azmBdx4kviIiIiIiILIhFFhERERERkQWxyHIgWq0WCxYsgFarlTuK02IfWxf71/rYx9bF/rU+9rF1sX+tj31sXY7Sv5z4goiIiIiIyIJ4JouIiIiIiMiCWGQRERERERFZEIssIiIiIiIiC2KRRUREREREZEEssuzM4sWLERERARcXF8TFxWHfvn01Lr98+XJERUXBxcUF0dHR+O2332yU1PEsWrQI3bp1g6enJ/z9/TFmzBikpKTUuM7SpUshSVKVLxcXFxsldiwvvvjiTX0VFRVV4zocv/UTERFxUx9LkoRZs2bdcnmO35r9/vvvGDlyJIKDgyFJElatWlXl+0IIzJ8/H0FBQXB1dcXgwYNx5syZWrdb3+O4M6upj/V6PebOnYvo6Gi4u7sjODgY999/Py5fvlzjNhtyrHFmtY3jadOm3dRfw4YNq3W7HMcmtfXvrY7JkiThjTfeqHabHMM31OWzWXl5OWbNmoVmzZrBw8MD48ePR3Z2do3bbejx25JYZNmRZcuWYc6cOViwYAGSkpIQExODoUOHIicn55bL7969G5MnT8b06dNx6NAhjBkzBmPGjMHx48dtnNwxbN++HbNmzcKePXuwceNG6PV6JCQkoKSkpMb1vLy8kJmZaf5KT0+3UWLH06FDhyp9tXPnzmqX5fitv/3791fp340bNwIAJkyYUO06HL/VKykpQUxMDBYvXnzL77/++ut499138eGHH2Lv3r1wd3fH0KFDUV5eXu0263scd3Y19XFpaSmSkpIwb948JCUlYcWKFUhJScGoUaNq3W59jjXOrrZxDADDhg2r0l/fffddjdvkOL6htv79c79mZmbis88+gyRJGD9+fI3b5Rg2qctns6effhq//vorli9fju3bt+Py5csYN25cjdttyPHb4gTZje7du4tZs2aZXxsMBhEcHCwWLVp0y+UnTpwo7rzzzirvxcXFiUceecSqOZ1FTk6OACC2b99e7TKff/658Pb2tl0oB7ZgwQIRExNT5+U5fm/fU089JVq2bCmMRuMtv8/xW3cAxMqVK82vjUajCAwMFG+88Yb5vfz8fKHVasV3331X7XbqexxvTP7ax7eyb98+AUCkp6dXu0x9jzWNya36+IEHHhCjR4+u13Y4jm+tLmN49OjRYuDAgTUuwzFcvb9+NsvPzxdqtVosX77cvExycrIAIBITE2+5jYYevy2NZ7LsREVFBQ4ePIjBgweb31MoFBg8eDASExNvuU5iYmKV5QFg6NCh1S5PVRUUFAAAmjZtWuNyxcXFCA8PR2hoKEaPHo0TJ07YIp5DOnPmDIKDgxEZGYkpU6YgIyOj2mU5fm9PRUUFvv76a/ztb3+DJEnVLsfx2zDnzp1DVlZWlTHq7e2NuLi4asdoQ47jVFVBQQEkSYKPj0+Ny9XnWEPAtm3b4O/vj7Zt22LmzJnIy8urdlmO44bLzs7GmjVrMH369FqX5Ri+tb9+Njt48CD0en2V8RgVFYWwsLBqx2NDjt/WwCLLTuTm5sJgMCAgIKDK+wEBAcjKyrrlOllZWfVanm4wGo2YPXs2evXqhY4dO1a7XNu2bfHZZ5/h559/xtdffw2j0YiePXvi4sWLNkzrGOLi4rB06VKsW7cOH3zwAc6dO4c+ffqgqKjolstz/N6eVatWIT8/H9OmTat2GY7fhrs+DuszRhtyHKcbysvLMXfuXEyePBleXl7VLlffY01jN2zYMHz55ZfYvHkzXnvtNWzfvh3Dhw+HwWC45fIcxw33xRdfwNPTs9ZL2TiGb+1Wn82ysrKg0Whu+sNLbZ+Pry9T13WsQWWzPRHZkVmzZuH48eO1XgMdHx+P+Ph48+uePXuiXbt2+Oijj/DSSy9ZO6ZDGT58uLndqVMnxMXFITw8HD/88EOd/qpH9fPpp59i+PDhCA4OrnYZjl9yFHq9HhMnToQQAh988EGNy/JYUz/33HOPuR0dHY1OnTqhZcuW2LZtGwYNGiRjMufz2WefYcqUKbVOMMQxfGt1/WzmKHgmy074+vpCqVTeNFtKdnY2AgMDb7lOYGBgvZYnk8cffxyrV6/G1q1bERISUq911Wo1YmNjkZqaaqV0zsPHxwdt2rSptq84fhsuPT0dmzZtwkMPPVSv9Th+6+76OKzPGG3IcZxuFFjp6enYuHFjjWexbqW2Yw1VFRkZCV9f32r7i+O4YXbs2IGUlJR6H5cBjmGg+s9mgYGBqKioQH5+fpXla/t8fH2Zuq5jDSyy7IRGo0HXrl2xefNm83tGoxGbN2+u8pfoP4uPj6+yPABs3Lix2uUbOyEEHn/8caxcuRJbtmxBixYt6r0Ng8GAY8eOISgoyAoJnUtxcTHS0tKq7SuO34b7/PPP4e/vjzvvvLNe63H81l2LFi0QGBhYZYwWFhZi79691Y7RhhzHG7vrBdaZM2ewadMmNGvWrN7bqO1YQ1VdvHgReXl51fYXx3HDfPrpp+jatStiYmLqvW5jHsO1fTbr2rUr1Gp1lfGYkpKCjIyMasdjQ47fVmGzKTaoVt9//73QarVi6dKl4uTJk+Lhhx8WPj4+IisrSwghxNSpU8Vzzz1nXn7Xrl1CpVKJN998UyQnJ4sFCxYItVotjh07JtePYNdmzpwpvL29xbZt20RmZqb5q7S01LzMX/t44cKFYv369SItLU0cPHhQ3HPPPcLFxUWcOHFCjh/Brj3zzDNi27Zt4ty5c2LXrl1i8ODBwtfXV+Tk5AghOH4txWAwiLCwMDF37tybvsfxWz9FRUXi0KFD4tChQwKAePvtt8WhQ4fMM9u9+uqrwsfHR/z888/i6NGjYvTo0aJFixairKzMvI2BAweK9957z/y6tuN4Y1NTH1dUVIhRo0aJkJAQcfjw4SrHZZ1OZ97GX/u4tmNNY1NTHxcVFYm///3vIjExUZw7d05s2rRJdOnSRbRu3VqUl5ebt8FxXL3ajhNCCFFQUCDc3NzEBx98cMttcAxXry6fzR599FERFhYmtmzZIg4cOCDi4+NFfHx8le20bdtWrFixwvy6Lsdva2ORZWfee+89ERYWJjQajejevbvYs2eP+Xv9+vUTDzzwQJXlf/jhB9GmTRuh0WhEhw4dxJo1a2yc2HEAuOXX559/bl7mr308e/Zs879HQECAGDFihEhKSrJ9eAcwadIkERQUJDQajWjevLmYNGmSSE1NNX+f49cy1q9fLwCIlJSUm77H8Vs/W7duveUx4XofGo1GMW/ePBEQECC0Wq0YNGjQTf0eHh4uFixYUOW9mo7jjU1NfXzu3Llqj8tbt241b+OvfVzbsaaxqamPS0tLRUJCgvDz8xNqtVqEh4eLGTNm3FQscRxXr7bjhBBCfPTRR8LV1VXk5+ffchscw9Wry2ezsrIy8dhjj4kmTZoINzc3MXbsWJGZmXnTdv68Tl2O39Ym/RGMiIiIiIiILID3ZBEREREREVkQiywiIiIiIiILYpFFRERERERkQSyyiIiIiIiILIhFFhERERERkQWxyCIiIiIiIrIgFllEREREREQWxCKLiIiIiIjIglhkERGR05o2bRrGjBlj8/0uXboUkiRBkiTMnj27TutMmzbNvM6qVausmo+IiKxLJXcAIiKihpAkqcbvL1iwAO+88w6EEDZKVJWXlxdSUlLg7u5ep+XfeecdvPrqqwgKCrJyMiIisjYWWURE5JAyMzPN7WXLlmH+/PlISUkxv+fh4QEPDw85ogEwFYGBgYF1Xt7b2xve3t5WTERERLbCywWJiMghBQYGmr+8vb3NRc31Lw8Pj5suF+zfvz+eeOIJzJ49G02aNEFAQACWLFmCkpISPPjgg/D09ESrVq2wdu3aKvs6fvw4hg8fDg8PDwQEBGDq1KnIzc2td+b//e9/aN26NVxcXBAQEIC77777druBiIjsEIssIiJqVL744gv4+vpi3759eOKJJzBz5kxMmDABPXv2RFJSEhISEjB16lSUlpYCAPLz8zFw4EDExsbiwIEDWLduHbKzszFx4sR67ffAgQN48skn8a9//QspKSlYt24d+vbta40fkYiIZMbLBYmIqFGJiYnBCy+8AAB4/vnn8eqrr8LX1xczZswAAMyfPx8ffPABjh49ih49euD9999HbGwsXnnlFfM2PvvsM4SGhuL06dNo06ZNnfabkZEBd3d33HXXXfD09ER4eDhiY2Mt/wMSEZHseCaLiIgalU6dOpnbSqUSzZo1Q3R0tPm9gIAAAEBOTg4A4MiRI9i6dav5Hi8PDw9ERUUBANLS0uq83yFDhiA8PByRkZGYOnUqvvnmG/PZMiIici4ssoiIqFFRq9VVXkuSVOW967MWGo1GAEBxcTFGjhyJw4cPV/k6c+ZMvS738/T0RFJSEr777jsEBQVh/vz5iImJQX5+/u3/UEREZFd4uSAREVENunTpgp9++gkRERFQqW7v16ZKpcLgwYMxePBgLFiwAD4+PtiyZQvGjRtnobRERGQPeCaLiIioBrNmzcLVq1cxefJk7N+/H2lpaVi/fj0efPBBGAyGOm9n9erVePfdd3H48GGkp6fjyy+/hNFoRNu2ba2YnoiI5MAii4iIqAbBwcHYtWsXDAYDEhISEB0djdmzZ8PHxwcKRd1/jfr4+GDFihUYOHAg2rVrhw8//BDfffcdOnToYMX0REQkB0kIIeQOQURE5EyWLl2K2bNnN+h+K0mSsHLlyirP9yIiIsfCM1lERERWUFBQAA8PD8ydO7dOyz/66KPw8PCwcioiIrIFnskiIiKysKKiImRnZwMwXSbo6+tb6zo5OTkoLCwEAAQFBcHd3d2qGYmIyHpYZBEREREREVkQLxckIiIiIiKyIBZZREREREREFsQii4iIiIiIyIJYZBEREREREVkQiywiIiIiIiILYpFFRERERERkQSyyiIiIiIiILIhFFhERERERkQX9P+rUKX1zN9oZAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the network performance on the test dataset\n", + "samples = msd.getSamples(dataset='simulations', window=2000-10, index=50*2000-450)\n", + "result = msd(samples, sampled=True, prediction_samples=2000-10, closed_loop={'x':'x_n'})\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "t = np.arange(len(result['x_n'])) * 0.01\n", + "plt.plot(t, result['x_n'], label=\"pred\", linewidth=2)\n", + "plt.plot(t, np.array(samples['x_t'])[:,0,0], '--', label=\"target\", linewidth=2)\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title(\"Model Rollout vs Target\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Position [m]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "05862476", + "metadata": {}, + "source": [ + "# Recurrent Training\n", + "To improve the fitting and enhance robustness, a recurrent training procedure is performed. In this case the network predict the system dynamics for a prediction horizon of 15 s. The framework enables fast prototyping of recurrent networks: in this case, the estimated position $x_n$ is fed back as the input $x$ for the next step." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "06d03f5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1;32m======================== nnodely Model Train Parameters ========================\u001b[0m\n", + "\u001b[32mmodels: ['neural_msd']\u001b[0m\n", + "\u001b[32mnum of epochs: 10\u001b[0m\n", + "\u001b[32mupdate per epochs: 54\u001b[0m\n", + "\u001b[34mβ””>len(train_indexes)//(batch_size+step)\u001b[0m\n", + "\u001b[32mshuffle data: True\u001b[0m\n", + "\u001b[32mprediction samples: 1500\u001b[0m\n", + "\u001b[32mstep: 500\u001b[0m\n", + "\u001b[32mclosed loop: {'x': 'x_n'}\u001b[0m\n", + "\u001b[32mconnect: {}\u001b[0m\n", + "\u001b[32mtrain dataset: simulations_train\u001b[0m\n", + "\u001b[32m\t- batch size: 128\u001b[0m\n", + "\u001b[32m\t- num of samples: 139370\u001b[0m\n", + "\u001b[32m\t- num of first samples: 34370\u001b[0m\n", + "\u001b[32mvalidation dataset: simulations_val\u001b[0m\n", + "\u001b[32m\t- batch size: 128\u001b[0m\n", + "\u001b[32m\t- num of samples: 39820\u001b[0m\n", + "\u001b[32m\t- num of first samples: 9820\u001b[0m\n", + "\u001b[32mtest dataset: simulations_test\u001b[0m\n", + "\u001b[32m\t- num of samples: 19910\u001b[0m\n", + "\u001b[32m\t- num of first samples: 4910\u001b[0m\n", + "\u001b[32mminimizers: {'x[t]': {'A': 'SamplePart8',\n", + " 'B': 'Add6',\n", + " 'loss': 'mse'}}\u001b[0m\n", + "\u001b[32moptimizer: Adam\u001b[0m\n", + "\u001b[32moptimizer defaults: {'lr': 1e-05}\u001b[0m\n", + "\u001b[32moptimizer params: [{'params': 'PFir3W'}, {'params': 'PFir5W'}]\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n", + "\u001b[1;32m================= nnodely Training =================\u001b[0m\n", + "\u001b[32m| Epoch |\u001b[0m\u001b[32m x[t] |\u001b[0m\u001b[32m Total |\u001b[0m\n", + "\u001b[32m| |\u001b[0m\u001b[32m Loss |\u001b[0m\u001b[32m Loss |\u001b[0m\n", + "\u001b[32m| |\u001b[0m\u001b[32m train |\u001b[0m\u001b[32m val |\u001b[0m\u001b[32m train |\u001b[0m\u001b[32m val |\u001b[0m\n", + "\u001b[32m|--------------------------------------------------|\u001b[0m\n", + "\u001b[32m| 1/10 |\u001b[0m\u001b[32m2.421e-04|\u001b[0m\u001b[32m5.139e-06|\u001b[0m\u001b[32m2.421e-04|\u001b[0m\u001b[32m5.139e-06|\u001b[0m\n", + "\u001b[32m| 2/10 |\u001b[0m\u001b[32m2.042e-06|\u001b[0m\u001b[32m6.135e-07|\u001b[0m\u001b[32m2.042e-06|\u001b[0m\u001b[32m6.135e-07|\u001b[0m\n", + "\u001b[32m| 3/10 |\u001b[0m\u001b[32m1.060e-06|\u001b[0m\u001b[32m3.639e-07|\u001b[0m\u001b[32m1.060e-06|\u001b[0m\u001b[32m3.639e-07|\u001b[0m\n", + "\u001b[32m| 4/10 |\u001b[0m\u001b[32m 9.58e-07|\u001b[0m\u001b[32m2.904e-07|\u001b[0m\u001b[32m 9.58e-07|\u001b[0m\u001b[32m2.904e-07|\u001b[0m\n", + "\u001b[32m| 5/10 |\u001b[0m\u001b[32m8.406e-07|\u001b[0m\u001b[32m2.644e-07|\u001b[0m\u001b[32m8.406e-07|\u001b[0m\u001b[32m2.644e-07|\u001b[0m\n", + "\u001b[32m| 6/10 |\u001b[0m\u001b[32m8.806e-07|\u001b[0m\u001b[32m3.605e-07|\u001b[0m\u001b[32m8.806e-07|\u001b[0m\u001b[32m3.605e-07|\u001b[0m\n", + "\u001b[32m| 7/10 |\u001b[0m\u001b[32m8.805e-07|\u001b[0m\u001b[32m2.615e-07|\u001b[0m\u001b[32m8.805e-07|\u001b[0m\u001b[32m2.615e-07|\u001b[0m\n", + "\u001b[32m| 8/10 |\u001b[0m\u001b[32m8.637e-07|\u001b[0m\u001b[32m2.719e-07|\u001b[0m\u001b[32m8.637e-07|\u001b[0m\u001b[32m2.719e-07|\u001b[0m\n", + "\u001b[32m| 9/10 |\u001b[0m\u001b[32m8.409e-07|\u001b[0m\u001b[32m2.659e-07|\u001b[0m\u001b[32m8.409e-07|\u001b[0m\u001b[32m2.659e-07|\u001b[0m\n", + "\u001b[32m| 10/10 |\u001b[0m\u001b[32m9.033e-07|\u001b[0m\u001b[32m2.436e-07|\u001b[0m\u001b[32m9.033e-07|\u001b[0m\u001b[32m2.436e-07|\u001b[0m\n", + "\u001b[32m|--------------------------------------------------|\u001b[0m\n", + "\u001b[1;32m============================ nnodely Training Time =============================\u001b[0m\n", + "\u001b[32mTotal time of Training: 383.07360458374023\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n", + "\u001b[34mThe selected model is the LAST model of the training.\u001b[0m\n", + "\u001b[1;32m=============================== Save JSON Model ================================\u001b[0m\n", + "\u001b[32mModel saved in: saved/msd_final.json\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n" + ] + } + ], + "source": [ + "# Refine weights with recurrent train\n", + "msd.trainModel(num_of_epochs = 10, prediction_samples = 1500, step = 500, lr=0.00001, closed_loop={'x':'x_n'}, training_params = param_model)\n", + "\n", + "# Save the neural model in json format\n", + "msd.saveModel(name = 'msd_final')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa11498e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show the network performance on the test dataset after recurrent training\n", + "samples = msd.getSamples(dataset='simulations', window=2000-10, index=50*2000-450)\n", + "result = msd(samples, sampled=True, prediction_samples=2000-10, closed_loop={'x':'x_n'})\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "t = np.arange(len(result['x_n'])) * 0.01\n", + "plt.plot(t, result['x_n'], label=\"pred\", linewidth=2)\n", + "plt.plot(t, np.array(samples['x_t'])[:,0,0], '--', label=\"target\", linewidth=2)\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title(\"Model Rollout vs Target\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Position [m]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "64e9b4cb", + "metadata": {}, + "source": [ + "# Definition of the PID controller network\n", + "The **Integrate** and **Differentiate** layers directly implement the integral and derivative actions. The resulting neural **PID** model is added to the list of models and trained in series with the forward model: the controller output **F_PID** is connected to the system input $F$, while two closed-loops ensure both the dynamic evolution $('x':'x_n')$ and the update of the measured position $('x_m': 'x_n')$." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "49ffc72b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33m[neuralizeModel] Connect on Sub13_int14 with sample in the future.\u001b[0m\n", + "\u001b[33m[neuralizeModel] Closed loop on Sub13_int13 with sample in the future.\u001b[0m\n", + "\u001b[1;32m================================ nnodely Model =================================\u001b[0m\n", + "\u001b[32m{'Constants': {'SampleTime': {'dim': 1, 'values': 0.01}},\n", + " 'Functions': {},\n", + " 'Info': {'SampleTime': 0.01,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [10, 1],\n", + " 'ntot': 11,\n", + " 'num_parameters': 23},\n", + " 'Inputs': {'F': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'Sub13_int13': {'closedLoop': 'Add20',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'sw': [-1, 0]},\n", + " 'Sub13_int14': {'connect': 'Sub13',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [2, 0],\n", + " 'ntot': 2,\n", + " 'sw': [-2, 0]},\n", + " 'x': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'x_m': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]},\n", + " 'x_t': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]}},\n", + " 'Minimizers': {'x[t]': {'A': 'SamplePart8', 'B': 'Add6', 'loss': 'mse'}},\n", + " 'Models': {'PID': {'Constants': ['SampleTime'],\n", + " 'Functions': [],\n", + " 'Inputs': ['x_m', 'x_t', 'Sub13_int14', 'Sub13_int13'],\n", + " 'Outputs': ['F_PID'],\n", + " 'Parameters': ['D', 'I', 'P'],\n", + " 'Relations': ['SamplePart12',\n", + " 'SamplePart10',\n", + " 'Sub13',\n", + " 'SamplePart28',\n", + " 'SamplePart26',\n", + " 'Sub29',\n", + " 'Div31',\n", + " 'Mul34',\n", + " 'Mul19',\n", + " 'SamplePart17',\n", + " 'Add20',\n", + " 'Mul23',\n", + " 'Mul15',\n", + " 'Add24',\n", + " 'Add35']},\n", + " 'neural_msd': {'Constants': [],\n", + " 'Functions': [],\n", + " 'Inputs': ['F', 'x', 'x_t'],\n", + " 'Outputs': ['x_n'],\n", + " 'Parameters': ['PFir5W', 'PFir3W'],\n", + " 'Relations': ['TimePart4',\n", + " 'Fir5',\n", + " 'TimePart1',\n", + " 'Fir2',\n", + " 'Add6',\n", + " 'SamplePart8']}},\n", + " 'Outputs': {'F_PID': 'Add35', 'x_n': 'Add6'},\n", + " 'Parameters': {'D': {'dim': 1, 'init_values': [0.5], 'values': [0.5]},\n", + " 'I': {'dim': 1, 'init_values': [0.5], 'values': [0.5]},\n", + " 'P': {'dim': 1, 'init_values': [0.5], 'values': [0.5]},\n", + " 'PFir3W': {'dim': 1,\n", + " 'init_fun': {'name': 'init_negexp',\n", + " 'params': {'first_value': 0.1,\n", + " 'lambda': 3,\n", + " 'size_index': 0}},\n", + " 'tw': 0.1,\n", + " 'values': [[-0.18469615280628204],\n", + " [-0.12608873844146729],\n", + " [-0.06660982221364975],\n", + " [-0.005940185859799385],\n", + " [0.05625353008508682],\n", + " [0.1205686405301094],\n", + " [0.18780383467674255],\n", + " [0.2590898871421814],\n", + " [0.3359234631061554],\n", + " [0.4205183684825897]]},\n", + " 'PFir5W': {'dim': 1,\n", + " 'tw': 0.1,\n", + " 'values': [[-1.662617978581693e-05],\n", + " [4.8189936933340505e-05],\n", + " [7.177559018600732e-05],\n", + " [5.9527203120524064e-05],\n", + " [0.00011585278843995184],\n", + " [0.00019496057939250022],\n", + " [0.00014330405974760652],\n", + " [0.00018421869026497006],\n", + " [0.00017740165640134364],\n", + " [8.003542461665347e-05]]}},\n", + " 'Relations': {'Add20': ['Add', ['SamplePart17', 'Mul19']],\n", + " 'Add24': ['Add', ['Mul15', 'Mul23']],\n", + " 'Add35': ['Add', ['Add24', 'Mul34']],\n", + " 'Add6': ['Add', ['Fir2', 'Fir5']],\n", + " 'Div31': ['Div', ['Sub29', 'SampleTime']],\n", + " 'Fir2': ['Fir', ['TimePart1'], 'PFir3W', None, 0],\n", + " 'Fir5': ['Fir', ['TimePart4'], 'PFir5W', None, 0],\n", + " 'Mul15': ['Mul', ['Sub13', 'P']],\n", + " 'Mul19': ['Mul', ['Sub13', 'SampleTime']],\n", + " 'Mul23': ['Mul', ['Add20', 'I']],\n", + " 'Mul34': ['Mul', ['Div31', 'D']],\n", + " 'SamplePart10': ['SamplePart', ['x_t'], -1, [0, 1]],\n", + " 'SamplePart12': ['SamplePart', ['x_m'], -1, [0, 1]],\n", + " 'SamplePart17': ['SamplePart', ['Sub13_int13'], -1, [-1, 0]],\n", + " 'SamplePart26': ['SamplePart', ['Sub13_int14'], -1, [-1, 0]],\n", + " 'SamplePart28': ['SamplePart', ['Sub13_int14'], -1, [-2, -1]],\n", + " 'SamplePart8': ['SamplePart', ['x_t'], -1, [0, 1]],\n", + " 'Sub13': ['Sub', ['SamplePart10', 'SamplePart12']],\n", + " 'Sub29': ['Sub', ['SamplePart26', 'SamplePart28']],\n", + " 'TimePart1': ['TimePart', ['x'], -1, [-0.1, 0]],\n", + " 'TimePart4': ['TimePart', ['F'], -1, [-0.1, 0]]}}\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n" + ] + } + ], + "source": [ + "x_m = Input('x_m') # measured position\n", + "kp = Parameter('P', values=0.5)\n", + "ki = Parameter('I', values=0.5)\n", + "kd = Parameter('D', values=0.5)\n", + "e = x_t.next()-x_m.next()\n", + "c = e*kp+Integrate(e)*ki+Differentiate(e)*kd\n", + "controlForce = Output('F_PID', c)\n", + "msd.addModel('PID',controlForce)\n", + "\n", + "# Neuralization of the whole models\n", + "msd.neuralizeModel()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "20554e08", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33m[_setup_recurrent_variables] Closed loop on variable 'x_m' with sample in the future.\u001b[0m\n", + "\u001b[1;32m======================== nnodely Model Train Parameters ========================\u001b[0m\n", + "\u001b[32mmodels: ['PID']\u001b[0m\n", + "\u001b[32mnum of epochs: 20\u001b[0m\n", + "\u001b[32mupdate per epochs: 166\u001b[0m\n", + "\u001b[34mβ””>len(train_indexes)//(batch_size+step)\u001b[0m\n", + "\u001b[32mshuffle data: True\u001b[0m\n", + "\u001b[32mprediction samples: 500\u001b[0m\n", + "\u001b[32mstep: 500\u001b[0m\n", + "\u001b[32mclosed loop: {'x': 'x_n', 'x_m': 'x_n'}\u001b[0m\n", + "\u001b[32mconnect: {'F': 'F_PID'}\u001b[0m\n", + "\u001b[32mtrain dataset: simulations_train\u001b[0m\n", + "\u001b[32m\t- batch size: 128\u001b[0m\n", + "\u001b[32m\t- num of samples: 139370\u001b[0m\n", + "\u001b[32m\t- num of first samples: 104370\u001b[0m\n", + "\u001b[32mvalidation dataset: simulations_val\u001b[0m\n", + "\u001b[32m\t- batch size: 128\u001b[0m\n", + "\u001b[32m\t- num of samples: 39820\u001b[0m\n", + "\u001b[32m\t- num of first samples: 29820\u001b[0m\n", + "\u001b[32mtest dataset: simulations_test\u001b[0m\n", + "\u001b[32m\t- num of samples: 19910\u001b[0m\n", + "\u001b[32m\t- num of first samples: 14910\u001b[0m\n", + "\u001b[32mminimizers: {'x[t]': {'A': 'SamplePart8',\n", + " 'B': 'Add6',\n", + " 'loss': 'mse'}}\u001b[0m\n", + "\u001b[32moptimizer: Adam\u001b[0m\n", + "\u001b[32moptimizer defaults: {'lr': 0.05}\u001b[0m\n", + "\u001b[32moptimizer params: [{'params': 'D'},\n", + " {'params': 'I'},\n", + " {'params': 'P'},\n", + " {'lr': 0.0, 'params': 'PFir3W'},\n", + " {'lr': 0.0, 'params': 'PFir5W'}]\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n", + "\u001b[1;32m================= nnodely Training =================\u001b[0m\n", + "\u001b[32m| Epoch |\u001b[0m\u001b[32m x[t] |\u001b[0m\u001b[32m Total |\u001b[0m\n", + "\u001b[32m| |\u001b[0m\u001b[32m Loss |\u001b[0m\u001b[32m Loss |\u001b[0m\n", + "\u001b[32m| |\u001b[0m\u001b[32m train |\u001b[0m\u001b[32m val |\u001b[0m\u001b[32m train |\u001b[0m\u001b[32m val |\u001b[0m\n", + "\u001b[32m|--------------------------------------------------|\u001b[0m\n", + "\u001b[32m| 1/20 |\u001b[0m\u001b[32m8.146e-03|\u001b[0m\u001b[32m2.809e-03|\u001b[0m\u001b[32m8.146e-03|\u001b[0m\u001b[32m2.809e-03|\u001b[0m\n", + "\u001b[32m| 2/20 |\u001b[0m\u001b[32m2.879e-03|\u001b[0m\u001b[32m1.704e-03|\u001b[0m\u001b[32m2.879e-03|\u001b[0m\u001b[32m1.704e-03|\u001b[0m\n", + "\u001b[32m| 3/20 |\u001b[0m\u001b[32m1.899e-03|\u001b[0m\u001b[32m1.252e-03|\u001b[0m\u001b[32m1.899e-03|\u001b[0m\u001b[32m1.252e-03|\u001b[0m\n", + "\u001b[32m| 4/20 |\u001b[0m\u001b[32m1.464e-03|\u001b[0m\u001b[32m1.001e-03|\u001b[0m\u001b[32m1.464e-03|\u001b[0m\u001b[32m1.001e-03|\u001b[0m\n", + "\u001b[32m| 5/20 |\u001b[0m\u001b[32m1.169e-03|\u001b[0m\u001b[32m8.403e-04|\u001b[0m\u001b[32m1.169e-03|\u001b[0m\u001b[32m8.403e-04|\u001b[0m\n", + "\u001b[32m| 6/20 |\u001b[0m\u001b[32m1.022e-03|\u001b[0m\u001b[32m7.237e-04|\u001b[0m\u001b[32m1.022e-03|\u001b[0m\u001b[32m7.237e-04|\u001b[0m\n", + "\u001b[32m| 7/20 |\u001b[0m\u001b[32m8.838e-04|\u001b[0m\u001b[32m6.375e-04|\u001b[0m\u001b[32m8.838e-04|\u001b[0m\u001b[32m6.375e-04|\u001b[0m\n", + "\u001b[32m| 8/20 |\u001b[0m\u001b[32m7.799e-04|\u001b[0m\u001b[32m5.701e-04|\u001b[0m\u001b[32m7.799e-04|\u001b[0m\u001b[32m5.701e-04|\u001b[0m\n", + "\u001b[32m| 9/20 |\u001b[0m\u001b[32m7.004e-04|\u001b[0m\u001b[32m5.161e-04|\u001b[0m\u001b[32m7.004e-04|\u001b[0m\u001b[32m5.161e-04|\u001b[0m\n", + "\u001b[32m| 10/20 |\u001b[0m\u001b[32m 6.36e-04|\u001b[0m\u001b[32m4.715e-04|\u001b[0m\u001b[32m 6.36e-04|\u001b[0m\u001b[32m4.715e-04|\u001b[0m\n", + "\u001b[32m| 11/20 |\u001b[0m\u001b[32m5.913e-04|\u001b[0m\u001b[32m4.336e-04|\u001b[0m\u001b[32m5.913e-04|\u001b[0m\u001b[32m4.336e-04|\u001b[0m\n", + "\u001b[32m| 12/20 |\u001b[0m\u001b[32m5.436e-04|\u001b[0m\u001b[32m4.017e-04|\u001b[0m\u001b[32m5.436e-04|\u001b[0m\u001b[32m4.017e-04|\u001b[0m\n", + "\u001b[32m| 13/20 |\u001b[0m\u001b[32m5.085e-04|\u001b[0m\u001b[32m3.735e-04|\u001b[0m\u001b[32m5.085e-04|\u001b[0m\u001b[32m3.735e-04|\u001b[0m\n", + "\u001b[32m| 14/20 |\u001b[0m\u001b[32m4.667e-04|\u001b[0m\u001b[32m3.496e-04|\u001b[0m\u001b[32m4.667e-04|\u001b[0m\u001b[32m3.496e-04|\u001b[0m\n", + "\u001b[32m| 15/20 |\u001b[0m\u001b[32m4.448e-04|\u001b[0m\u001b[32m3.281e-04|\u001b[0m\u001b[32m4.448e-04|\u001b[0m\u001b[32m3.281e-04|\u001b[0m\n", + "\u001b[32m| 16/20 |\u001b[0m\u001b[32m4.097e-04|\u001b[0m\u001b[32m3.093e-04|\u001b[0m\u001b[32m4.097e-04|\u001b[0m\u001b[32m3.093e-04|\u001b[0m\n", + "\u001b[32m| 17/20 |\u001b[0m\u001b[32m 3.97e-04|\u001b[0m\u001b[32m2.922e-04|\u001b[0m\u001b[32m 3.97e-04|\u001b[0m\u001b[32m2.922e-04|\u001b[0m\n", + "\u001b[32m| 18/20 |\u001b[0m\u001b[32m3.682e-04|\u001b[0m\u001b[32m2.772e-04|\u001b[0m\u001b[32m3.682e-04|\u001b[0m\u001b[32m2.772e-04|\u001b[0m\n", + "\u001b[32m| 19/20 |\u001b[0m\u001b[32m3.532e-04|\u001b[0m\u001b[32m2.634e-04|\u001b[0m\u001b[32m3.532e-04|\u001b[0m\u001b[32m2.634e-04|\u001b[0m\n", + "\u001b[32m| 20/20 |\u001b[0m\u001b[32m3.304e-04|\u001b[0m\u001b[32m2.512e-04|\u001b[0m\u001b[32m3.304e-04|\u001b[0m\u001b[32m2.512e-04|\u001b[0m\n", + "\u001b[32m|--------------------------------------------------|\u001b[0m\n", + "\u001b[1;32m============================ nnodely Training Time =============================\u001b[0m\n", + "\u001b[32mTotal time of Training: 2534.1204628944397\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n", + "\u001b[34mThe selected model is the LAST model of the training.\u001b[0m\n", + "The PID controller kp = [16.827768325805664] ki = [18.563440322875977] kd = [8.177507400512695]\n" + ] + } + ], + "source": [ + "# Train the PID controller\n", + "msd.trainModel(models = 'PID',\n", + " closed_loop = {'x' : 'x_n', 'x_m' : 'x_n'},\n", + " connect = {'F' : 'F_PID'},\n", + " prediction_samples = 500,\n", + " step = 500, num_of_epochs = 20,\n", + " lr = 0.05,\n", + " training_params = param_model)\n", + "\n", + "# Print the parameter of the PID\n", + "print(f\"The PID controller kp = {msd.parameters['P']} ki = {msd.parameters['I']} kd = {msd.parameters['D']}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1707c46", + "metadata": {}, + "outputs": [], + "source": [ + "msd.exportPythonModel(name = 'msd_final_with_PID')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "aa79aba8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33m[check_names] The name 'test_control' is already in defined as Dataset but it is overwritten.\u001b[0m\n", + "\u001b[1;32m============================ nnodely Model Dataset =============================\u001b[0m\n", + "\u001b[32mDataset Name: test_control\u001b[0m\n", + "\u001b[32mNumber of files: 1\u001b[0m\n", + "\u001b[32mTotal number of samples: 2001\u001b[0m\n", + "\u001b[32mShape of x_t: (2001, 1, 1)\u001b[0m\n", + "\u001b[32m================================================================================\u001b[0m\n", + "\u001b[1;32m============== nnodely Model Results for test_control =============\u001b[0m\n", + "\u001b[32m| Loss|\u001b[0m\u001b[32m mse |\u001b[0m\u001b[32m FVU |\u001b[0m\u001b[32m AIC |\u001b[0m\n", + "\u001b[32m| |\u001b[0m\u001b[32m small better |\u001b[0m\u001b[32m small better |\u001b[0m\u001b[32m lower better |\u001b[0m\n", + "\u001b[32m|-----------------------------------------------------------------|\u001b[0m\n", + "\u001b[32m| x[t]|\u001b[0m\u001b[32m 3.347e-02 |\u001b[0m\u001b[32m 2.534e-02 |\u001b[0m\u001b[32m -1.073e+03 |\u001b[0m\n", + "\u001b[32m|-----------------------------------------------------------------|\u001b[0m\n", + "\u001b[32m|Total|\u001b[0m\u001b[32m 3.347e-02 |\u001b[0m\u001b[32m 2.534e-02 |\u001b[0m\u001b[32m -1.073e+03 |\u001b[0m\n", + "\u001b[32m|-----------------------------------------------------------------|\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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2jRkzhg0bNvCf//ynzWzfkydPJikpibFjx5KYmMiWLVt48cUXOfPMMwkPDwfgH//4B0uWLGHUqFFce+21DBgwgMrKStasWcP3339PZWWl+/WJiori1VdfJTw8nNDQUEaNGnXE5tbhw4cDcN9993HppZdiNBo5++yz2/06//DDD9x0001cdNFF5OTkYLfbee+999zJW1fv+6RJk7jiiit4/vnn2bFjB6effjpOp5Ply5czadIkbrrpJoYOHcr06dN57bXXqK6uZsKECfz222+88847TJs2jUmTJh3zPIc67rjj0Ov1PPHEE9TU1GA2m91zSh3JPffcwwcffMAZZ5zBLbfcQkxMDO+88w55eXl8+umnrQYiSJKbj0epSZLfWL16tTAYDOLmm29utd1ut4sTTjhBpKSkuOdqOdLweSGEWLFihRg+fLgwmUyHHUrf3nmEhFCHy+fm5gqj0SgSExPF9ddf32ZengkTJhx2qPf06dNFRkbGMc+xdetWMX78eBEcHCwA95Bq1/D5srKyVvu/9dZbhx3e/emnn4px48aJ0NBQERoaKnJzc8WNN97Yav6lI1m2bJn7NcvOzhavvvqq+/wH++qrr8SQIUNEUFCQyMzMFE888YR4880328RTUlIizjzzTBEeHi4A971qbm4Wd9xxh0hOThbBwcFi7NixYuXKlW3u59y5c8X48eNFbGysMJvNonfv3uKuu+4SNTU1reIpLS0VN954o0hPTxdGo1EkJSWJU045Rbz22mut9vvyyy/FgAEDhMFgaNcw7YcfflikpqYKnU7X5tqO9Trv3r1bXH311aJ3794iKChIxMTEiEmTJonvv/++1TmOdN/bw263iyeffFLk5uYKk8kk4uPjxRlnnCFWr17t3sdms4k5c+aIrKwsYTQaRXp6upg1a5Zobm5uVVZGRoY488wz25zjcL9jr7/+usjOznZPreAaSn+kMoRQ5yC68MILRVRUlAgKChIjR44U33zzTat95PB56WCKEB7oXSlJ3dzEiROx2Wx8+eWXmEwmIiIijnlMQ0MDTU1N3HzzzXz99dfU19f7IFJJkiSpI2Q9oSS104oVK4iPj+dPf/pTu/a/7777iI+P53//+5+XI5MkSZI6S9YISVI7rF692j3HSXx8PEOHDj3mMdu3b3d3CDYYDK1WrZckrTkcjladvA8nLCzMY9MhSJK/komQJElSD5Sfn3/M+bIefPDBLi0iLEmBQI4akyRJ6oGSkpJYtGjRUfc5dHSdJHVHskZIkiRJkqQeS3aWliRJkiSpx5JNY8fgdDopKioiPDzc5wtxSpIkSZLUOUII6urqSElJOepkmjIROoaioiLS09O1DkOSJEmSpE7Yu3fvURcflonQMbim19+7d2+7JtFrL5vNxsKFC5k8eTJGo9Fj5fqT7n6N3f36oPtfo7y+wNfdr1FeX+fV1taSnp7u/hw/EpkIHYOrOSwiIsLjiVBISAgRERHd8s0N3f8au/v1Qfe/Rnl9ga+7X6O8vq47VrcW2VlakiRJkqQeSyZCkiRJkiT1WDIRkiRJkiSpx5KJkCRJkiRJPZZMhCRJkiRJ6rFkIiRJkiRJUo8lEyFJkiRJknosmQhJkiRJktRjyURIkiRJkqQeSyZCkiRJkiT1WDIRkiRJkiSpx5KJkCRJkiRJPZZMhCRJkiRJ6rFkIhQoNnwCDpvWUUiSJElStyITIX8kBOxbc+Dxtm/h02vgoyvBbtUuLkmSJEnqZmQi5I92LYbXT4Ylj6mPhQBDEESmgyJvmSRJkiR5ivxU9Ue/zgUEWOrUx7lT4aZVMPWfoDf4PBxFUfjiiy/8phxJkiRJ8hSZCPmZkvzt3Pz812Q/V4f53KdIT0/n7LPPZvHq7Qd2ctihYpd2QR7D7NmzOe6449psLy4u5owzzvBJDE1NTcTExBAXF4fFYunQsc3Nzdx4443ExsYSFhbGBRdcQGlpaZdjmj17NoqiuP9FRkZy0kknsWzZMp/HtmfPHs4880xCQkJISEjgrrvuwm63H/WYNWvWcNpppxEVFUVsbCzXXXcd9fX1rfZZvHgxY8aMITw8nKSkJP72t78ds1xJkiQtyUTIj+Tn5zN81Gh+yLPx5PlZbNiwkQULFjBp0iRuvPFGdafGSnhvGrx1BtSVtKtcq/Xw/YpsNt92vk5KSsJsNvvkXJ9++ikDBw4kNze3w7VQt99+O19//TUff/wxy5Yto6ioiPPPP98jcQ0cOJDi4mKKi4tZuXIlffv25ayzzqKmpsZnsTkcDs4880ysVisrVqzgnXfe4e233+aBBx444jFFRUWceuqp9OnTh19//ZUFCxawadMmrrrqKvc+69atY+rUqZx++un88ccffPjhh3z11Vfcc889HYpPkiTJp0QAWbZsmTjrrLNEcnKyAMTnn39+zGOWLFkihg0bJkwmk+jdu7d46623OnTOmpoaAYiamprOBX0EVqtVfPHFF8Jqtbq3nXHGGSI1NkzUzwoX4tt7Wu1fVVWl/mCpFwUPDxPn9DOIULNehIeHi4suukiUlJS4933wwQfF0KFDxeuvvy4yMzOFoihCCCEA8fLLL4uzzz5bhISEiAcffFAIIcQXX3whhg0bJsxms8jKyhKzZ88WNpvNXd6hr/Xdd98t+vbtK4KDg0VWVpa4//773dfx1ltvCaDVvzfeeOOw5axfv15MmjRJBAUFiZiYGHHttdeKuro69/PTp08X5557rnjyySdFUlKSiImJETfccEOr1+xIJk6cKF599VXxyiuviNNOO+2Y+7tUV1cLo9EoPv74Y/e2LVu2CECsXLmy1b6Hu4dH47ovB9u7d68AxG+//ebR2I5m/vz5QqfTtXrPvPLKKyIiIkJYLJZW+7qu8eWXXxYJCQnC4XC4n1u/fr0AxI4dO4QQQsyaNUuMGDGi1fFfffWVCAoKErW1te2Oz5c6eg8DTXe/PiG6/zXK6+u89n5+B1SNUENDA0OHDuWll15q1/55eXmceeaZTJo0ibVr13Lbbbfxl7/8he+++87LkR4jrvIGthTXArCluJa88gYqKytZsGABN54UR6hJgbQTWh0TFRUFgNMQzLkfNVPZrGPZlUEsmnMuu3fv5pJLLmm1/86dO/n000/57LPPWLt2rXv77NmzOe+889iwYQNXX301y5cv58orr+TWW29l8+bNzJ07l7fffptHH330iPGHh4fz9ttvs3nzZp577jlef/11nnnmGQAuueQS7rjjDgYOHMiePXt46623uOiii9qU0dDQwJQpU4iOjub333/n448/5vvvv+emm25qtd+SJUvYtWsXS5YscddcvP3220d9fXft2sXKlSu5+OKLufjii1m+fDkFBQVHPcZl9erV2Gw2Tj31VPe23NxcevXqxcqVK9tVRntZLBbeeustoqKi6Nevn8diy8zMZPbs2UcsZ+XKlQwePJjExET3tilTplBbW8umTZuOGKvJZEKnO/AnIzg4GICffvrJvU9QUFCr44KDg2lubmb16tXHvD5JkvzI/q3QVKV1FD4RUInQGWecwSOPPMJ5553Xrv1fffVVsrKyeOqpp+jfvz833XQTF154oftDWwt55Q1M+tdSLpqrfnBdNHclk/61lB9XbUAIQW5Qubpj6vGHPX7x4sVs2Lyd/771GsNT9Iyq+Yp37/szy5Yt4/fff3fvZ7Vaeffddxk2bBhDhgxxb//Tn/7EjBkzyM7OplevXsyZM4d77rmH6dOnk52dzWmnncbDDz/M3Llzj3gN999/P2PGjCEzM5Ozzz6bO++8k48++ghQP/jCwsIwGAwkJSURHR3t/sA82H//+1+am5t59913GTRoECeffDIvvvgi7733Xqs+L9HR0bz44ovk5uZy1llnceaZZ7J48eKjvsZvvvkmZ5xxBtHR0cTExDBlyhTeevNNsDWBtbH1zrYmsNS7/5XszcdkMhEVbGi1PTEhnpJ9e9THtuajnv9oNmzYQFhYGGFhYQQHB/Ovf/2LDz74gIiIiGMeW1JSosbWkhS7JCYmUlJyoJm0d+/exMXFHbWcg5MgVxmu5w5n4sSJlJSU8OSTT2K1WqmqqnI3eRUXFwNqMrVixQo++OADHA4H+/bt46GHHmq1jyRJAWD5U/DyKHj++HZ3wQhkvh+C5EMrV65s9e0Z1D/Wt9122xGPsVgsrTrX1taqNTc2m80jfWrqGpsx6wVOAbet1CMAENzwfss3ZqeDShHB8H9uBNp+O69d9RVKWBzjF8Zxh34aNxm+IHPt4xiCgpk65wPCBu+navl2RFgcI5/+rc3x7+/S88Wsee7He1b8zg/LlnPfgw8d2Ek4EXYrGXd+is6ofsO/7r1V3P6rEYCGLT9Su+prbNXFCGszwulAZw4hq6XcquXbaSyuJefvCwE9t61cCAqtyqlcPA9reBqDHlnqPq2zuQGn08n0O+4noVc2n60uxGmIp8/9C/ib/gNSlHJs+ZvYU1bLqgdOIBgLwVgxK1ZM2NHhRHHaefuFMv51eghP338NLzjOp0Hpz3fPvcgDPEMN4Rxvfe3A62F8lLG6g17nDTZwWOHx1NYvXFE9rNwAj7/JfxyncL/9GhAQH6Tnt1UP89wzT7t3XbduHb169Wrz2jscDnJycvjss8/U90JdHR9//DEXXXQRixYtYvjw4W2OOZir0/Gh70MhBA6Hw719wYIFh93P/To7nQghWj3v+tlutx92e05ODv/+97+5++67mTVrFnq9nptuuonExER3WZMmTeIf//gH//d//8cVV1yB2Wzm3nvvZfny5TidTp/3SWsPV0z+GJsnLN5cwgOr9IRmlzIhN/HYBwQgX95DIQTXvf8Hdc123r96BAa99+sSfP0e/WJ1Pmcs/hdhAE2VOH5/C+dJd3rtfN68vvaW2a0ToSN9862traWpqemwNRWPP/44c+bMabN94cKFhISEeCSuf46ED3frWFF64JdIH50CwNZyJ8kiHSGUwx4rREvHGwFP2y9kiLKL8foNhGDBjBWhZlYoxiD3zwdTDK23C1szkeP+REjOmLY7600H9hXqOS37tlD21b+IGvdnorOOR2cOUROj379ocz71oXodOuEEYICSzwRdLb8omylVynjXeB9v2M/kK+cYXIffa/wvqYZ0PiMNdAaEgPG69QzQFfCDrpkq4WCEbjuHM3+3jaI6J1d8Uo/gLZy83RKMk8W7QxiRTes4D4k5KUzB6oDqZkFU0IF7UNogSApTWr0WQ5RdhFqbiU5N51//+pd733Xr1rFx48Y2se3YsYPm5ma2bz8Q+7hx4/jggw+45557uP322w97TS4FBQVYrVY++ugjwsLCWm2vqqpi/vz5Rz3epa6ujh07drTa31ULt3PnzsOWs2jRIiIjI5k7dy7V1dWYzWYUReHZZ5+lurrafUxOTg7vvPMOVVVVhIaGsn//fkCtEWpvfFpYtGiR1iF4xSubddTYdDw97w8adju1DserfHEP622wdLv6sfneFwtIbPsR4jW+eo+u2riVizhQc978y5t8XzfA6+f1xvU1NjYeeye6eSLUGbNmzWLmzJnux7W1taSnpzN58uR2NV8cy5biWi6auxKTInh4uJ0n1umwOhVev34Cj/6czUu/53HNjCGsPHNCq+NqaqqJjIxi2RIHl1/8bz75c29S09JQmoay/pmTqW2u5duhq8m+5X6eFD+zoGYj3/+tdRkpT8A/zh/IGQeVfc6SYfRJdfD0Py47YswHH/fqi+t4JyODlV++7H7+jlt+Zt56Aytbzvc8y/iy1MnPkzZQvXkJqboKjLUF6IDbDJ8xzWjk9QQrf1vfTG+7g0dPMjHrxAksXvQd05/Xkd03l9j0HM4crKOmpoa3/jaBoK13UtNchWXTV9itRVSe+RDCGIwwBIMhCKEzgs7AyzfdzbSzTdx20/8hTGEIo5owPPfUP3mpzs6rN77GSuNBCa19FMUHZUaptbUYP+jPJznPcuZZZwNqcrBnzhj63vo5xSNGcLJOz0q9CftzM0mnhI1D/0O/UVOOee9XrVrFli1bmDp1aqvtERERREdHt9l+qLFjx/Lwww9jMBjc+27bto2ysjJmzJjBqFGjjhkDgE6n45NPPmHEiBEkJCQA8MYbbxAREcG1117bamSfzWZj0aJFnHbaaRiNxlblvP322wQFBXHXXXe1aa5zmT17Nunp6dx0003o9fp2xedLR7u+QNdkdXDXb0sAJ3kNBk45bRJmo//dg67y5T38Y281rFJr2jMHncCkfvFePR/4/j1aunEhAOtDRjO46TdCrfuZOnawOqGvF3jz+lwtOsfSrROhpKSkNnOslJaWEhERcdjaIACz2XzYId5Go9EjNyk8JAiLQwE9RJjAJhSsToVecRG8esUgxt6fx6QHvuGh4CkMGTIEu93OokWLeOWVV9iyZQuXnHc2TwwezMwbr+PZZ5/Fbrdzw0ITE7JMjDFuhq+vINxwPEa9juTosDbnjwkLbrX9kYfmcNZZZ5HbtzcXXnghOp3OXaPxyCOPtDlu+NBBPFa4l2XffcMJJ5zAvG++4bv536AoqOW+eQaDdyzjhYImCj5/hLQIBWFSUAwttSkRaZB7An8e3osHf3mF6asHMfvM0ZSt/Z0HZ93NFVdcQdq9bwMQvPgqLEaDWu7oK9X782UehjIHMSe07YBdVlbGwh9+5KuvvmLC5NNbPddcew3nnXceJoOBmFavS+vXKDkhnmuuuYaH58yhd1YWERER/O3OOxg9ejRnTW09B9JKXQpNdj2KKaRd7w29Xo/D4aCiogJQa2Y+/PBDtmzZwj333HPMMuLi4rjmmmu4++67SUhIICIigptvvpnRo0czbtw4936nnHIK5513XpuO5y5Tp05lwIABXH311fzzn/+kpKSEBx98kBtvvNFd0/Tbb79x5ZVXupvZjEYjc+fOZcyYMYSFhbFo0SLuuusu/vGPfxAff+DD4Mknn+T0009Hp9Px2Wef8eSTT/LRRx+16UTtbzz1++1PftpdhdWh1gI12Zys3VfPuL5H7jsW6HxxD/fVWAihmUbMlJZXYGxeABljIf7Ygx26yhfXZ3M4+ajheHYoIZgMGQxJtkDRGoz7foe4bK+e2xvX197yunUiNHr06DbV8YsWLWL06NEaRQRZcaEsuXMidY3N5P3xEx//dTThIUFkxYWCvpg114Xy6P4TuOOOOyguLiY+Pp7hw4fzyiuvAOrszF9++SU333wz48ePR6fTcfrpp/PCvc/Cgmuh8DdYvR7s0e2KZ8qUKXzzzTc89NBDPPHEExiNRnJzc/nLX/5y2P3POeccbr/9dm668UYsjbWc2T+Uv9//ILNdzYlR6VzQ38BnuyKZ9H4T1Q1W3nj0Nq65/jaYk6nOjj1tGiHAdz/8iVtvvZUTTj2XkJAQLrjgAp5++unDnrc93n33XUJDQznllFPaPHfKKacQHBzM+++/zy233HLUcp555hl0Oh0XXHABFouFKVOm8PLLL7fZ7y7z3ylsbOKjuMHtjnHTpk0kJycDEBISQu/evXnllVe48sor23V8e2LbtWsX5eXlRyxDr9fzzTffcP311zN69GhCQ0OZPn26u2MzqFXK27Zta9XG/ttvv/Hggw9SX19Pbm4uc+fO5YorrmhV9rfffsujjz6KxWJh6NChfPnllz6bRFNqbdm2staPt+/v1omQL+SVN/KM8WX6KwXELW8GRx2MuQUmP6x1aB6xr6qJnc4kdpJEeI2BRwaMRilaA3t/gaGXHLuAAKUIcbieJP6pvr6enTt3AjBs2DCefvppJk2aRExMDL169WLWrFns27ePd999F1CHzw8aNIgbb7yRq6++mh9++IFbbrmFefPmMWXKsZsyQK1ai4yMpKamxiNNYy42m4358+czderUA1nrY2lgrYOb10Bs744XWrIR/ncZVO+B7Ilw5ZddD9TaqCZX+T9DUCSMaallcNjhH73A1gDXr4DEger2uhIwBkNQ5OGvsRsZ98QPFFY18dF1IxmZ7f0qci1093vYna9v0r+WklfewPGxTtZU6OibEMaimROOfWCA8eU9vPV/f3DXlgtJU8pZFHYOp0XugyGXwqjrvHZOX17f0m37ueqtA6OPN1xQQ/i86yF9FFyz0Cvn9Ob1tffzO6BqhFatWsWkSZPcj119eaZPn87bb79NcXExe/bscT+flZXFvHnzuP3223nuuedIS0vjjTfeaHcS5HN/y1MTifDkzh2fNAiuWwZf3wKT7j+wvXwnOCyQMACUw3fCBtQewFV5UPQHFK2Fvb/CvjXgbKkViOl9IBHSG+C8VyGqF8QdVC0cntS52ANZwHyVkHqKgooG8sobMOgUzurlZG2ljh3769lX3URqlA97+HYzxeWVpClqbevzzgs57dpzNY7Is4pLirlMv5jtzjRWi37sMWQxEGD/FvXz4WifHwEsoBKhiRMncrQKrMNNtDdx4kT++OMPL0blQXojRHWxQ1pIDFzyfuttK56HNe/AqXNg3G3qtopdsOpNsDVC/X6oKoCqfLVG6lARqWo7eObY1r8MA87pWqwa+M9//sNf//rXwz6XkZFxxAkFD2e29SkyTbuxlT4Jvc/yq9iknu3H7Wqz2LBeUcQGlTE0LZI/9tbw4/YyLhvZdmoHqX10FTsAqBDhbK42YnM4MfpgCL2v2ArX8rjx3+xyJnOK9Sm2OxIZqDOApRZq9qpffLuhgEqEpE4SDtCbIbbPgW37t8DKF9vuqzdB4iBIOQ5Sh6sJUHRmt/kmcM455xxxdFVHq2VTnKX00RWx3Vp/7J3bwZOxST3b0pb+QRP6xkF9GeP7xvHH3hqWbtsvE6FOqm60kmgpABPsFqk4nIJ9VU1kRijqxKyhsVqH2GX76hwscQylWFGb+vOq7HDcn8EcDkr3G3HoIhMhf7H9O9jwidq3Z9ifPVv2uS/BGf9s/UaOSFY7+ZlCITgGojMgKgNissFg8uz5/Uh4eDjh4eEeKcuh6FomdfLM/CyejE3quSx2Byt2qSMTT+obS/4fML5vHM/9sIufd1Z0u1oMXymoaKS3rgiA/UEZYAX7T8/D2ifg+CvhnOc1jrDrFjVkM9f2N8bnxMP2MgoqGuDSwL+uY5GJkL/YtwY2fKQmJp5OhEAt92Cpw9V/Uqc5W1aoUYRD40gk6YBV+VU02RzEh5vpnxROPjAoJYKYUBOVDVbWFFQxKjvway98Lb+igT6Kmgg1hGdDLexzRNEHAfs3axxd1zmcgr2V6gSEE3Pi+XF7GfkV7ZuQMNDJrwX+ou9kOO1h6N/1viaSb7gSIWQiJPmRZS39gybkxKMoCgZHE4bv7+eVsDfopZS6n5c6pqCikT7KPgBEbA4Amxxp6pP7t4AzsGfuLqpuIthRh8mg48SWRLmgokHtF9pYqS7C2k3JGiF/kTZc/ScFjAM1QoH9B1DqXpZuU5c1mZCj9vMYuudN9NW/Mgr42PQLN295FU7P1TDCwLSnrIZMRV2ANCi5P6yvZE19rNqv0lqvdiaOztA4ys7bW1LC+qDrqFIiMUeqSwRVN9qo2/0b4e9NVkcz39E9kyFZIyRJnSRrhCR/U1TdxPbSenQKjOsTBzV7Sav+FQBhCCIEC7qyjeyvbdY40sDTtH8XJsWBXR9MfJo6z9vuSqs6mASgcrd2wXlA9d4tACg6PSGhESRGqCss5IuW9ToVPdgtRzo8oMlEyF9s/w6K16kTFUoB4UCNkJxISPIPrmHzQ9OjiA41odu1GABn+okof/mem2Lm8otzAD/uOPLM49Lhmap3AWCL6k1GvDqoYW9VI87olqUnAjwRspZsA6A6WK3VyohV+5XurjfCfSUwcxMY2i4/1R3IRMgf2C3w34th7ni1ilUKCLJGSPI3rv4/E3PUxXSV3UsAENmTIGkwQ/rnttpPap/aZps6dB4wJOaQHBGEyaDD5hA0hLZMRxDgiZC+ypXoqYldZqy6OHV+eaO6WkA3JhMhf9BYqf6v6NVlLKSA4FRafn2cMhGStGdzOPmppaZnQr94cNhQ8n8EWhIhDvQb2rV9Ew6nrMlsrz0VjfRpGTpvTOyPTqfQK0ZNFEqNKepOlXlahecRYfVqoqeL7wscqBEqqGjQLCZfkYmQP2hqSYSCo7rNxIU9gUMOn5f8yB97qqmz2IkOMTI4NRIKV6FY6rDowxBJQwE4LjmYRUF/Y764kc1bA3/It6/kVzTwrP18noj8OwxQl9Vw15iIlmWFArhGyOkUJNr2AhCR2h+AzJZEKL+iAbbOg7fPgu/naBajN8lEyB80Van/B8doG4fUIXLUmORPlm1XR4ud1DcevU6Blv5BZeGDQKdOpmowB0NQFFahJ2/9T5rFGmgKKhopFAnsTz0N4tW1FV2JwlZLnLpTVV7ADqEvqWkik2IAotNbEqE4NdErqGhUP6Pyl6vrUHZDMhHyB66msRCZCAUS2UdI8ifu/kH91OYvdqqJ0P6Iwa322znqUUZYXuXNikE+jS+Q5ZWrzUOuWiCAjDg1EVpfFwE6A9iboa5Yk/i6qmjvbkIUCw50GGKzgANNYxUNVhqCWxYCrynUKkSvkomQP3A3jclEKJAs04/hWfv5NMQN0ToUqYfbX9fMxn21gFojREOF+9t7WUTrhGfY8aOoJZR1hdVUNlh9HmsgcpRs5gb9Fwx3rnNvcyVFuyubA34IfXWhOj9QmSHZvcRSmNlAXJg6SqzQ2VLrVVOoTrDYzchEyB+4m8aitY1D6pClhrE8a7+QurjjtA5F6uGWb1c7SQ9KjSA+3Ax5SwGBSBhAs7H135WkyCByk8IRApa3NKdJR5dctYq7jR8xaO8H7m2uprE9FY2IyJaRY9V7tAivy+z71aHztSGtJ4R0JXs7LJGAAvYmaOh+Uy/IRMgfyKYxSZK64OBlNQBIGwlTHsdxwl8Pu/818Vv5wvR3opbP9lGEgavBYmdjcyyfO8ZizDnVvT05MgijXsHqcLJ/7ANw4+8w6AINI+08wyFD511czWP5VTYIb+kUXhOYyd7RyETIH7ibxmSNUCCJE5X0VQoxNFdoHYrUgzmcguU7XP2D1PmDiEqH0Tcgjjv8As6DU0I5TreLXpUrcMph9EdVUNHIj86hPGS8jeBx17u3G/Q60qPVGpNdIh3ic8AYpFWYXRLe0DJHUkLfVtvdI+MqGiEyXd1YvdensfmCTIT8QWNL05isEQooV9v+wyLz3STs/FjrUKQebH1hNVWNNsKDDAxLj2rXMdknTMUhFLLYx44dW7wbYIBzzaPjqh05WMbBiUKAEkKQaFM7QUekDWj1nKtDeEFFg5pcg7qmWjcjEyF/IIfPB6RGQigXETj13XPaeSkwuJrFxvWJw6DXwbYFsOY9qCs54jGmsGjyg9RZpgvXLPBJnIFKXWy1mN6xbX/PXclRWcleWPZPWPSgr8Prsv11Fi6z3McVtnuJyxnZ6rks91xCskZI8jbZNBaQXjJfwwjLq5T0n6F1KFIP1qZ/0K+vwFc3wabPj3pcY+oYAPQFP3s1vkBnLdrAUvMdzN59WZvnslpqTIorqmHJo7DypYCbSyi/vIFiYtkTNRJjSFSr53q11HiV1VmwhKWqG2WNkOQVTbJpLJB1w9GkUoCoarCybm810LKsBkDmSZA6AnqfctRjkwarz/duWkdts82bYQY0Ub4DAEtYWpvnXE1j62tCYNjlMP4ucATWlAT5R2n6iww2EhOqDqcvUVreX92wRsigdQAScPsmNRmSNUIBRS6GImlt+c5ynAL6JYaTHNmyMOb4O9V/ALYjJzjxA8bj+FJHulLGkrXrmHTiCB9EHHhCalvWEIvr2+Y51xD63ZXNOG99EZ0u8P4q6Hcu5A7DckLMpwEj2zyfERtCZYOVAkcsGSBrhCQv0RshLEH9XwoY02zz+ND0EAk7P9Q6FKmHWratpVnMVRvUEeZwikPV5RTKNv7gybC6jSarg0SbOlw8JDm3zfOp0cHodQrNNif76yy+Ds8jkop/4GbDFxxvX3fY591LiTRFqRuaq8FS55vgfEQmQpLUSSnOEkbpthJU3z2nnZf8m9MpDiyr4eoftON7aK5pfxm91H5CwUW/IGQbbxt7KhvJVtRlM4KT+7V53qjXkRat1sQV7K+E8h1QVeDTGLvqR8dg/mM/BdFr9GGfdzX/7aoGxt4GUx7zWWy+IhMhrdUUwmd/hUUPaB2J1EGipXFMrj4vaWFLSS3l9RZCTHqGZ0arfTf+cwE82ResDe0qI2mIOkHgYPtGduyv92a4ASm/vI4sRR19p8TlHHYfV9+asF+fgRdHwM/P+iq8LhNC8J+6Ydxnv4aIgZMPu0+rVehPmwOjbwRzuC/D9DqZCGlMqS+F9f875ggPyf84Fdeiq4E1SkTqHpa2NIuN6R2L2aB3rzZP8lAwte34ejimrDE40ZGpK+X3dRu8FWrAKi/Ka1mMVH9gPbFDuCYd3ONajyuAOhOX11tpsDrQKZAeE3zYfVw1QgUBPFfSschESGMiPAVOexjG3KJ1KFIHCWQiJGmnzbD5ltXm6XP00WKtBEVQEaH2faneutSD0XUPzSXqGlw1QalH7MPpqhHa0RylbgigzsSFRfsYoOSTHamoyfRhuKYIKKltpqm2Ul3Mt2y7L8P0OpkIaS0iGcbeAiOv1ToSqYOcrqYxZN8Kybdqm22sKVCn3ZiQkwAOO+xepj55jGHzhzJmnwRAdNlqGix2j8YZ6HQVOwFojsw+4j5ZcWqNycb6luaimn1ej8tTLFsXMd98Ly85Hz3iPlEhJiKD1SSwfvkr8NpEWPGcjyL0DZkISVInHagRkn2EJN9asbMcu1OQHReqTnq3bzVYaiAoClKP71BZkSf9levMT/B323RW7pLr5h0srD4fAF1826HzLq4aoT+q1YQIax0013o7NI9wlKlzJNWFZR51P1fzXzGxEJ4MxvY1vQYKmQhprbYYClernaalgOJo+fVRZNOY5GOuZrHxrmYxV/+g3pNAd/gmjiNRYnuT0H8sDvTuciVoth0YOh+a0v+I+6VFB6NToMxqxGmOVDfWBkatkKlmNwCO6N5H3c+V7K0MOw3u2ApT/+n12HxJJkIa023+DN44GRY/pHUoUgcJpWXyNDnsWPIhIUTb+YNc/YM62CzmMiFHXbV+6fb9chh9i8KqA0Pnw1LaziHkYjboSYlSOxo3ByepGwMkEYpqVBM9U+LhR8S5ZHaDxWWPRiZCWnMNc23nKA/Jf7j6CMnO0pIv7dxfT1FNM2aDjtHZsdBYCUVr1Cd7n9ypMseF7OUfpjc4r+Y/3fbDrqP2lFaRjNpUeKSh8y6uIeY1RjWhDIR+QsLpJNmhtkRE9xpw1H1dNUIFFe2bliHQyERIa9aWuTtkIhRwhLtpTPYRknzHNWx+VHYsQUY97F6qJuPx/SEytVNlBjfv51LdD5yr/5ml2/Z7MNrAtbvazmDLv3k8/TUIjTvqvq4h5vuVWHVDbZG3w+uy6vIiwmjCIRSSMo5c4wWQGXfQEPoPLoPnjoOybT6I0jdkIqQxpaRlWvMjzFEh+S+nu7O0bEqQfKfNsPldnRg2f6iM0axLv4JH7X9mmUyEAPVDv5EgDGlDQTn6GmKuGqG9jpb1IgOgaWx/wRYASpV4goJDjrqvq0aoqKYJZ1U+VOUF1DQBxyITIY0pTdXqDzFHHp4p+SdXIqQgm8Yk32i02vktrxJoSYSEOKh/UOeaxQAIjsZ85mP84DyeX/IqabbJWs6jrcp+qMyWuXZ2NgdOZ+n6IrVGp8J07FrE2FAT4WYDQkBTUKK6MQBqvdpLJkJaszer/xuCtI1D6rCdumzetZ9GdeKJWoci9RC/7K7A6nCSFh1M7/hQ2L8F6orVvx8ZY7pUdr/EcJIigmi2Od3JVk82tuRdHjH8m4HOHcfc19WZeHNDmLohAJIEW9kuABrCeh1zX0VRyGhpHqsytPSDCoBrbC+ZCGnN3rJiscGsbRxSh602HMcD9hmUZU3TOhSph3D1D5qQE4+iKOC0Q7+pkDMFjIdfIqG9FIeVvyTncY1+nvs8PZXV7mS0ZQWXGxaTpjt2UpgeE4KiwC5LS41QAHSWNtbkA+CMbl9rhKtmrIQYdUM3mvLFoHUAPZ7Dqv6vl4lQoDlGtwFJ8rg2/YOSh8BlH3imcGsD1xTciWIUXLB1Mpx99JFE3VlhVSNz7WcxxLCXv/Yeccz9g4x6kiOCKKhJZOP5ixnU7+idj/1BRJPaxyco4ehzCLm411SzRTECZI2Q5EGyaSxgmYSFWGrQWwNjFlkpsOWXN1BQ0YhRrzCmz9FHMXVKSAzOeDX5Saxazd7KnjuMvqCikfnOE/ki5mqUdg5kyYgNxY6B7fYkMId5N8CuEoIku1prFZXWr12HHFhTLULdIBMhyWNcNUKyaSzgnG5bzOqg6+n76/1ahyL1AK7aoBEZMYSZDeoq55V5Hj2HvmXdsRN1W/hxR89tHnN1lM5sR0dpF9cQ80CYh6mmqpxgoX4JT8psX+2V67VYXxc4/aDaK+ASoZdeeonMzEyCgoIYNWoUv/322xH3ffvtt1EUpdW/oCA/qnkRAsVdIyQToUAjXBMqylFjkg+45vdxzyb9y8vw/HGw6EHPnSRzHACjdFt6dD+hxsJNjNVtoH9kU7uPcdWYxOZ9BV/dDDu/91Z4XZbfYCTX8jbTDC8REhrRrmNcTWPra1qG2ltqwFLnrRB9KqASoQ8//JCZM2fy4IMPsmbNGoYOHcqUKVPYv//I815ERERQXFzs/ldQUODDiI9OJw5a6VkmQgHna+PpZDb/hy3jXtQ6FKmba7Y5WLlbneXY3T+ouQZ0Bkg5znMn6qWOPOunK2Trzl1Y7T0zyc/e9zn/MT3O6ZXt73/lShQSK1fBmndh7+/eCq/L8isasGPAFN++/kEA8eFmQkx66kQwDlP3ah4LqETo6aef5tprr2XGjBkMGDCAV199lZCQEN58880jHqMoCklJSe5/iYmJPoz46FonQn5UUyW1i6IogOwxLXnf7/mVNNucJEaYyU0KVzdOexnuzoOc0z13otBYRILaT2iQfROrC6o8V3YAiWzIB8CYcPSlNQ7mmktonmUoTLwXsid4IzSPyC9Xm+9cyVt7KIrirvVqCnbNJeT/o+PaI2BGjVmtVlavXs2sWbPc23Q6HaeeeiorV6484nH19fVkZGTgdDo5/vjjeeyxxxg4cOAR97dYLFgsFvfj2lq1I6zNZsNms3ngSnCXZ9cF0XjzZoyKA5wKeLB8f+B6vTz5uvkTgTqjtN1u77bX2N3vYaBc35ItpQCc1CcOu/2gL1D6liHzR4i/M9en6zUG/f7NjNJtYcnWEkb0al/TiVY8fQ9tDifJ9kJQIDSlX7vLTQ43AvB10xAeGDaJqBCjR/6me+M9mrPlRZ427sSpXInN1v7Rgb2ig9hSXEuVPo4wdmCv2ovoYlze/B1sb5kBkwiVl5fjcDja1OgkJiaydevWwx7Tr18/3nzzTYYMGUJNTQ3/+te/GDNmDJs2bSItLe2wxzz++OPMmTOnzfaFCxcSEtL+7LldFIVFP63ybJl+aNGiRVqH4BU5dduZaVyI4ftezN95jtbheFV3vYcu/n5989bqAYWw+j3Mn1+AzmnFqTO1+/iOXF9ydTAjUTtMX706j4H2nR0PWAOeuoeVTXauQO1usWZ7MdaC+e0+NtKop8am8MHXi8gI90g4bp58j/ap+ImB+p18VDyM+fPb3xphr9YBOnbWm0kHdqxeyvZ9UR6JyRu/g42N7eu4HjCJUGeMHj2a0aNHux+PGTOG/v37M3fuXB5++OHDHjNr1ixmzpzpflxbW0t6ejqTJ08mIsJz34xsNhuLFi3itNNOw2g0eqxcf9Ldr3HzltWc2fwbFSYTEVOnah2OV3T3exgI17evuonSlcvRKXDThacSaVYwPDsAEZ2J48J3IDz5iMd26voaRsKzL5Cr20tTYx3Dx51FYoT/Nt17+h6uXv0bhq1OGgni1Gl/7tCEYe8X/87q/Ar6pUVxSqodkTm+y/F44z1657rNpFjyOXviRfTt1/4aofpVhSz+cjOrIk5j/HEn0zd9NH1ShnUpFm/+DrpadI4lYBKhuLg49Ho9paWlrbaXlpaSlJTUrjKMRiPDhg1j584jf8Mxm82YzW07LhuNRo/fpCBrJeZFf0MflgCn/N2jZfsTb7x2/sC1+rwOZ7e8voN113vo4s/Xt2J3MQDH94omLiJE7YTbVIkinOiiUkGnP2YZHbq+qGR1JfuyLZyo28KKvHFcPCK9K5fgE566h42l6pIaZaY0Mkztr3UDyIoLZVN+MVOWttQQ37MXgjzzBdpT11fbbOPLxiHAEG7oM6BDZWYnqNcyr2kgd42b1OVYDuaN38H2lhcwnaVNJhPDhw9n8eLF7m1Op5PFixe3qvU5GofDwYYNG0hOPvI3KF8Kslej/+NdWPc/rUOROkNx/fr0zJE1km8s294ybP7Q1eazJ7YrCeqUlo6+43Qb3fMX9RR21xpcoRkdPjYjNpRGgmjU+e9cO3ta5jmKCzMRHtSxxMM1V1JhVRM2R/f5uxcwiRDAzJkzef3113nnnXfYsmUL119/PQ0NDcyYMQOAK6+8slVn6oceeoiFCxeye/du1qxZw+WXX05BQQF/+ctftLqEVpoNUTjG3wMnXq91KFInOFyrz4vu8wdB8i9Wu5Ofd7YMm3fNH+San6bPKd47cbb6bX+cbgPLt5dh70YfesdirNkNgDOmfWtwHcw16eB+pWXm71r/W4+rLH8jp+pWMzKy4zPiJ4YHYTbowGmjfNsvsO1bL0ToewHTNAZwySWXUFZWxgMPPEBJSQnHHXccCxYscHeg3rNnDzrdgdyuqqqKa6+9lpKSEqKjoxk+fDgrVqxgwAD/WEOn2RSD86TL0ftplbx0dK4JFWUiJHnLmj1V1FvsxIaaGJQSCY2VsG+1+mRvLyZCmWNxZk/ii93JNDRbWFdYzfCMGO+dz49ENO4BwJTQt8PHZrQMRy90RJNJvl/WCAXv+IY3TC/zu/104IIOHavTKWTGhlJUWk/yR2eoG2ft8/8lRY4hoBIhgJtuuombbrrpsM8tXbq01eNnnnmGZ555xgdRST2Rq48QMhGSvMTVLDU+Jx6dToHdS9X3W3x/iEz13onN4eiu/ILt/12DY30xy7aV9YhEyOEUJNmLQIGotI4vnOqaS2iPPUr9dPXDREhfrS7LYo/K7NTxGbEhbCsNoSY0k8ioOHV26QBPhAKqaay7CbJWQulGaCjXOhSpE5yuyRSF0DYQqdtatu2Q1eZ3tvQP8maz2EEmtpy3p/QTKiqvIhm1KTK2V/8OHx9mNhAXZqZYxKobavyvaSysQa3xMib06dTxrmTv2dz/wrWLIcI/+tx2hUyENJRRsRTjGxNhyWNahyJ1gkDtqKoIh8aRSN3R/tpmNhfXoihwUt84NeHe5eNEKFVwtm4FG/dVUVFvOfYBAa6sYCs6RVBPCPqw+E6VkRkbQgkttWd+WCMUb1Nng45Mad+q84dyNf8VBMDisu0lEyENmez16g/B0doGInWKUy66KnmRqxZmcGoksWFm2L8Z6orBEOxeE8yrnE7i3zmJF0wvMojdLN/R/Wuuy8uKqRRhlJvSOjR/0MEyYkMpctUI+dkSFA111cRRDUBCRsdrvOBAh/D8igZPhaU5mQhpyOhoeSPJRCggCfeoMdk0JnmeKxGaeGizWOY4MPpggkOdDrLGUxqSQ6jS3COax34X/Tne8hr/HTi302VkxoZQIlpqhGr8KxEqyVdXYagmjMiYztV4uWqERlXNQzw7BObf7bH4tCITIQ0ZHU3qD2YPz8Uu+YRTcY0ak01jkmfZHU53DUzbYfOn+i6QC98i78IFrHAO4sftZTid3Tvpz29p7klL6HzH8Iy40AN9hKx10NzxYereUrNvOwD7DSmdLiM5MhiTXodTCJTqAqjK81R4mpGJkIbcH6D6js1eKvkHpxw1JnnJusIaappsRAQZGJoWBdYG2NOyuLSP+gcBoDdwfK9owswGKhqsbCyq8d25NVDQ0tzjav7pjMzYEBoJopaWMvyoeaypTE1aGoI7P+JQr1NIjwmm2E9rvTpDJkIa0rkTITmPUCCq1kUxzzGSqqRxWocidTOuZqiT+sZj0OugfAcYgyGqF8R2brRPZ5kMOiZmhRJNrXsUW3fkdAoeq76Lt4xP0NtY2elyMlqSqH1O/0sUlKp8AOyRvbpUTtbBtV5+OGlkR8lESEM6YW/5IeCmc5KAfH0mN9puY9ewv2kditTNuBIhd7NYynFw126Y/k2nO/F22ooXeK5gGrcYPu/W/YRKKyoZoWxjkn4diXGxnS4nMthITKiJItEyu3R9iYci7Lqghr0AGGI7Pmv2wTJiQw/UCDXXgKW+q6FpSiZCGlJkjZAkSYeoqLewvrAaOGj+IAC9AaI7vv5Vl0VnoRc2JurWsmZPFTWNNt/H4AP51VYutd7PE+abMYTHdamsjNgQ7rZdx8Jz18Cwyz0UYdfFWNTh/OFJvbtUTmZsCPWE0Ki4mv/8b5qAjpCJkIbcTWM6mQgFLiH7CEke9dPOcoSA3KRwEiOCwGHXdtLO7AmgM5KlKyWDYn7a2T2H0edX2fjFOYAtSed0uazM2FAqiGRnjf90Lm+y2EgS6gK+8b06N4eQi6v5r1TpHs1jMhHS0IEaIdk0Fohy7VvJD/ozo76ZrHUoUjfi6oczsV+CumHN2/DsYFjxojYBmcMhcywAk3RrWbZ9vzZxeFm+BzpKu7gnHSz3n0kHC4uLqSACG3oiEjO7VJbrNdprb5n6xY/6QXWGTIQ0dKCPkKwRCkTutcbkhIqShzidgh93HLKsxq4lULMXHBrO7NxXTfYn6taybHsZohvOnRWdv4CL9EsZGFLV5bIyY0NJpYwpux6BL2/senAesLvBxFjLC1wc+xmKoWsjlVOigjDolAMdwv1oZFxnyERIQ7KPUGDbpc/m+OZXWXXaZ1qHInUTm4pqKa+3EmrSMzyj5dv2+a/Dnz+BQRdqF1hLInSibgt1tTVsLanTLhYvGVP+CU8aX2OgfUuXy8qIDUGvODm56TvY8KlfrEfomhogPS6yy2UZ9DrSY0IOGjkmEyGpk2QfocDmUAxUEoHdHKV1KFI34Wp2GtMnDpOh5c+zKQT6nqZNR2mX2D4QnYlJsTNWt7HbjR4TQpBoVz/MO7Pq/KEyY0MpETE8bbsQ65R/+kU/QtdkkZktzXZdlRkbQjH+N0VAZ8hESEM/9b0X219XQuIArUORJMkPuJfV6Ne55Q+8RlGg7xSgpZ9QN5tPaH9lJYmK2iQWl9H1v8dRIUaCgoJ53nE+eenngU7f5TK7asyuZ/nQ9BAn2n71SHn+vKZaR8lESENN5niI66tOlCYFnEhnNXMMb9FnzaNahyJ1AzVNNtbsqQZgfN+WROiTa2DRg1BXql1gLq5+Qvq1rCqooN5i1zggz9mfpzaH1RCOKbzzcwi5KIpCVpx/LU6a2rCZUbqtJIV4pnbKn9dU6yiZCElSJ4WIJqYbFpGc94nWoUjdwM87y3E4Bb3jQ0mPCVGTn42fwM/PguIHf6ozx4IhmBSlkt7OPazoRsPo64q2AVBm6vzSE4fKiA0lmQrsOxZD2TaPldsZzTYH91su5xbrTUTlTvBImRlxoRSKeOYGXQPnvghO7Zv/OssPfrt6KLuFQYX/Qbf8SXB0zwnKujtbS98unVPeP6nrXM1NE3Jahs3v+kH9P3kohPlBU5kxWJ1TCDhZ90e36idkK9sBQH2o5/phZcaG8FfD15y59gZY94HHyu2MwqpGNjkz+cE4nuikTI+UmRkbigUTzzSchhhwLugCN50I3MgDXVMVvcu+UxMhRfv2Y6njHKjzP+mcNr8YFSIFLiFE2/5Buxar//tytfljaWkeO1W/ulsNozfVqIuROqKyPFamugxFSzObxk1H+S3zGWXEhqB4aImW1Khg9DqFZpuT/XUaTu3gATIR0oreyM6EM3COuj6gM+mezMZBE2E6HdoFIgW87aX1lNQ2E2TUMTIrRm1mcNUI9fbhavPH0u8MhCGIKiIprqpnd7l/9H/pqvDGPQCYEnM8VmZmnP8ML6/as5Hp+u84NWSnx8o0GXSkRgWTpRRTs/pTKN3ssbJ9TX4CayUklk2pl+E8ZY7WkUid5Di4Jk82j0ldsHSbOmz+xOxYgox6KF4LjRVgCof0kdoGd7CIFJS/5fNWr8dxoGdpNxg9JoQgwVYMQFRq15aeOJg6qkrtTOys0XYJCnPhCuYY3+HsBs/2Z8yIDeEv+vnkLLsBNn3u0bJ9SSZCktRJdg6a/8lh1S4QKeC5V5t3zSa983v1/+wJ/jfhqjHYHWd36CdUUV1NQsvQ+YTMrs8h5BIbaqLGlAiAUlukaWdiXY1a4+WM9OxcVFlxoWwTaRSFDYSwBI+W7UsyEdKKrYlgazk0dJ+RFz2Ng4NqhBzdZyix5FsNFju/51cCB60vtmOh+n9f/1zHbkK/eNKUMtbtLqLZFtjNwiX56oiuWkIxd3HV+YMpikJIbCpOoaA4bdCgXdIY2rgXAHN8tkfLzYgN5V3HFB5NfhFGXuvRsn1JJkIaUfb9zuRNMzH8Z5rWoUidJBQddtHyKyRrhKROWrmrAptD0CsmRJ31t6EcClepT/Y9TdvgjqDvkv/jJ/OtjHGu4ZfdFVqH0yU1ReqIsQpjssfLTouLZD9R6gONVmi32p3E2UoAiEzp49GyXbNU5wV4XzGZCGnF3vLBqe/a4neStuyuWiHZR0jqpKUty2pMyIlXR/TsXAwISBwMESnaBncESkwWTnT0VooCvp9QaZ2VDc5MKsI91z/IJTM2RPORY4VVjaQr6nssMtmziVBGyyr0BRUNCKcjYEfPykRIKy01CEJv1jgQqSucrl+hAP0DIGlLCOFOJNz9g1zNYjn+2SwGwOibWXL2z7zoOI8fA7yf0BJxPGdbH2PNcQ97vOyDO0xrNXJsX3EJUYpaY6NEZ3q07PSYYIyKnfncDI8kQnO1R8v3FZkIacXRMu+CQdYIBTJ3+uMHiypKgSevvIHCqiZMeh2je8eq0zC4Okr7af8gAMITOWFgXww6hd3lDexpWdAzELlWZXfVbnhSZqu5hLRpGqtsafqr00WCOcyjZZsNehIiwwlXGtV+UAG61IZMhLQim8YCnqIcVCOErBGSOs416uqErGhCzQbYt1r9Vh0UBakjNI3tWCKCjByfEY0ZK8tamvcCjRCCvPJ6APfaYJ50cNOYQ6MkoXF/PgD1wd5pZvWn+ZI6SyZCWnHIRKg7+N3Zj8qEUSCbOKVOaNMsljoC/vojnPMC6A1HOdIPVOXzbOMsFpnuYtm2wEyEqhss/CRmsMh0F73Mnu/wGx9uplyn3ltb5R6Pl98ejir1vPbwNK+U3x1WoZeJkEYUdyIkP0AD2dW2u/lj4jsQ6bnFGqWeodnmcI+4cg+b1+nUtcUGnKNhZO0UlkhSw1Z66cqo2r0Kiz3whtEX7tlFpNJIlq6EIA8OnXdRFMX9t8HeVO/x8tvDWKc2yemje3ml/KzY0IBfhV4mQlqRfYQkqUf7Na8Si91JcmQQfRM823fDJ4zBKC3D+yc5f2F1fpXGAXXczuYwTrI8wyOxT3itBs6eOJT+zW/y8cgPvVL+0dgcTiKt6tD50ETPraN2sIxY2TQmdZZd1ghJUk/mWlbDPWx+42fw+f/B7qXaBtYByoBzAThD95v7egJJfoWFvSKR5pRRXjtHelwETQRRoEGH8qLqJpJRJ+0N91IilBl3oGlMyERI6pCWGiEh+wgFLEWB7013ctIXo6Bsm9bhSAGmzbIamz6DdR/Anl81jKqD+k7GoTPRW1dM/pbVWkfTYd4cMebimnQwv8L3kw7mVzSyX0RTpsShi/bs8houvWIO1Ag5q7VdU62zZCKkFdlZuluIVBowWWvAKZfYkNpvb2Uju8sa0OsUxvZt6Zty4o0w5mbof5a2wXVEUASO7JMBGFD9A8U1TRoH1DH9Cz/kdsPHDDJ47wM8IzaU6/Rfc0PhXbDje6+d53AKKhq41nYH92Z9CEmDvXKOIKMeR7g6K7dSuy8g51STiZAWGith6zz1591L1MdSQLrY+gArpnwDMb21DkUKIK7aoOG9ookIallUNWM0TH4EEgdqGFnHmQZNA9TmsUCbXHFs/UJuNXxOplLitXNkxoUwQFfASMdaHCUbvXaew8kvV5vjXLVS3hIUq45I0zks0BR4fcVkIqSFr2+Fqnz158o89bEUkPJEMo0RvcEYpHUoUgBxD5vvF69xJB7Q73QcioF+ukK2bAic5rGaRhspohSAuF6eX17DJTE8iM+ZxF226yhJmui18xyOq+kv0wtzJB0sLS6aMhGhPqjZ69VzeYNMhLRQ+DtO9PyY84A6IV/h71pHJEmSj1jtTlbsUjuwuvsHLXtSnVHaHoCL9wZHU586DoCYggXYHIExy/rekmJiFHVIe3CC92p0dTqF4phRfOyYyE7h22k2cku+5CfzLUzY85JXz6N2mG5p4q2WiZDUHmkngM5AVWgf0BnUx1LAUVC4Rj+f7I3PQ22R1uFIAWJVQSWNVgdxYWYGJEeotcNLHoH/XAy2wFyqInzYBQCcIlaydm+1tsG0U/kedYBDjS4SzOFePdfBi5P6isMpCGvYS5pSTpTe4tVzZcaGsM+VCMkaIaldzn4OcqaoP+dMUR9LAWmGYQHZW16BumKtQ5EChKt/0PicOHQ6Bba3LLLa60QIjtIusC7Q9T8LBzoG6gpYt3aN1uG0S2PJTgCqzd6ZcflgfWKMjNVtIGLbJ14/l0tRdROv2U7nEvscgsde79VzZcSG8o59Cncqd0DumV49lzcEXCL00ksvkZmZSVBQEKNGjeK333476v4ff/wxubm5BAUFMXjwYObPn++jSI8iJAbOf039+fzX1MdSQHIKRf0hAEdKSNpYduiyGtta/ib1O0OjiDwgJIbyOHUuHv32rzUOpn0clXkAWMLTvX6urCgd/zE9zrT8h8Hqm1q/gopGqoigPPo49Im5Xj1XRmwIv4r+fNI0nBpTslfP5Q0BlQh9+OGHzJw5kwcffJA1a9YwdOhQpkyZwv79h5/Ia8WKFVx22WVcc801/PHHH0ybNo1p06axcaNve+5L3ZfAlQgFRr8ISVslNc1sLalDUeCkvvHQXAP5P6lP9puqbXBdFHzc+dSKYKpq6imr825TjCeY61rW/orxzkSDB0tNSqZWBKsPfNR05Jq3KNOLcyS5hJgMJISbW503kARUIvT0009z7bXXMmPGDAYMGMCrr75KSEgIb7755mH3f+655zj99NO566676N+/Pw8//DDHH388L774oo8jl7orp0yEpA5wDS8fmhZFTKgJdi4Gpw3iciA2sKdgiBh1BVfE/JfnHeezfIf/D6OPbFZnQQ5J7OP1c2XEhrBPqDWArkVQvW1vWQ2zDP/hQsd8n3TC7xtjYIrud/j9Da+fy9P8fHnjA6xWK6tXr2bWrFnubTqdjlNPPZWVK1ce9piVK1cyc+bMVtumTJnCF198ccTzWCwWLJYD32Zqa2sBsNls2Gy2LlxBa66yPFmmv+n+1yjUUX+A3W5DdMPr7O730NfXt2SrOlz7pD4x2Gw29FvnoQMcfafg9EIMvr0+A2P6JrGuOI8lW0s5e3CiD87ZuWusa7aT7CgGHUQk9fb66xMXYmAbcfRnD1WF24nMmtjuYzt7D6tL8pllmIejcCE25z3g5WvMjNLxaOkzsB5sU/4CpvbVRHnzPdreMgMmESovL8fhcJCY2PqXKzExka1btx72mJKSksPuX1Jy5MmzHn/8cebMmdNm+8KFCwkJ8fykVIsWLfJ4mf6mu15jTY3e/fMvK1dQsaH7TozZXe+hiy+uzyFg6VY9oGAo28638zZz+pb5mIAV5ZFUerH/oq/un7EWQM+uzWv4Zl4hOsUnpwU6do1F9XauVSoAWLmpgObttd4Ky82iU0dV7Vy3gtK6jnfQ7ug9rN23Xf1fH82P3y7o8Pk6qrpSYYVjAJjDqPn2a6zGiA4d7433aGNj+/pjBUwi5CuzZs1qVYtUW1tLeno6kydPJiKiYzf2aGw2G4sWLeK0007DaDR6rFx/0t2v8fWClTjL1RqhE0eNRGSM0zgiz+vu99CX17e6oIqmX34nKtjIXy86DcPenzGsbUSExHLihTeDTn/sQjrI1/fP1tzA+TtPJFMpZl2fnxnQz3sTFbrP2YlrXPrLbxh2OLFg4uRzLgPF+71EPs3/CaogMcjG8Knt7w/WmetzOgU/r/oF9GBO7MPUDpyvs5SNJfzpw/s5PjmKD88d2e7jvPkedbXoHEvAJEJxcXHo9XpKS0tbbS8tLSUpKemwxyQlJXVofwCz2YzZ3HZFeKPR6JU/JN4q159012tUFMXdR8ig10M3vEaX7noPXXxxfT/vVpceOCknniCzCXaq34CVvlMwmr07M7mv7p/RGIUjOIbmpnIKNv/G0EGDvH7OA+du/zU2luUDUGVKJsnU9u+9N+ijM6AKjPX7OnUvOnJ9RdVNJDr3gx6C4rLQ+eDeZyeoFQV7Khu9fn0dKbM9AqaztMlkYvjw4SxevNi9zel0snjxYkaPHn3YY0aPHt1qf1Cr3460vyR1lBw1JrVXq9Xmhegew+YPY8uoJzjeMpe3y71fG9RZe+oUFjhOoCTuRJ+dMyRBHZ0W1uz9OcfyKxpIVdTZy3XRvbx+PlA7hANU1DdTVxtY640FTCIEMHPmTF5//XXeeecdtmzZwvXXX09DQwMzZswA4Morr2zVmfrWW29lwYIFPPXUU2zdupXZs2ezatUqbrrpJq0uQepmZCIktUd5vYX1hTWAOpEiZdugKg/0Juh9ssbRedbxw0bQSBBr91ZT3eifS4Ysb8ri/2y3UzDyQZ+dMyZFHRUY5agAu3enFyioaHQnQkR6f54kgPAgI9eGLGObeTqOLwNr/cyASoQuueQS/vWvf/HAAw9w3HHHsXbtWhYsWODuEL1nzx6Kiw9k22PGjOG///0vr732GkOHDuWTTz7hiy++YJAPq2ul7ktBJkJS+7iGkw9MiSAhPOhAbVDWBDCHaRiZ56VEBZOTGIZTwIot/rncgi/n2HFJTUmnUajNcM4q774uB9cIEeWbRAggKDwGk+LA6aMpAjwlYPoIudx0001HrNFZunRpm20XXXQRF110kZejknqqA/MIaRuH5N/azCY96HzQGyEmsOcOOpLLUkoZVfUIph9iYPhSrcNppdFqp6muCgj2aSKUEh1MPnH0YR+VxbuIi/fe/EV7yupJbRkV56saIQB9dC+oAVP9Pp+d0xMCqkZIkvxNvkikLqIPmDw/tYLUPTidgh93HLLafHQmjLkZcgN7NukjGdyvLwN0BWQ1rEPUH37mf60UVDSyyHwXW4JmEFm7zWfnNeh1VBjU1ovqop1ePVdVeRFmxabWWEf4bsX7sES1H1SItcwnkzh6ikyEJKkLbrLdyorJX0PGGK1DkfzUhn01VDZYCTcbOD4jWutwfGLwoCFsENnocVL8y8dah9PKnrIq4qghGCuEH3kEsTc0BKdQJcKorq332jmEENirCgFwhCSAweS1cx0qPikNizCiQ0Bt4NQKyURIkiTJi1yjxcb2icOo18GSx2Htf8FSp3Fk3mM26NkWo3YCd2z6UuNoWttdaWeA5S0eyX4fQmJ9eu7lff/GMMtrLAo/x2vn2F9nIcahvud0UR2fuLErsuLDKRTqxJG+WlPNE2QiJEmd5V54XnYQko7MPWy+Xzw0VsLyf8EX14OfNRl5mm7QNABSqn6HhgptgzlIQUUDVoyEJvcDxYdTXwO94sLVGMq9twJ9fnkDyYo6y70uMsVr5zmcjJhQ9rUkQpaKfJ+euytkIiRJXfCs8UXGfXcm7FqidSiSH6pptPHHHnVOlfGu/kHj74IB0wJ+kdVjGXHccDY6M9HjpHmj/9QKuUeMxfm+X19mXGirGLyhoKKRahHKdtMASBritfMcTmSIkXJ9AgA1xXk+PXdXBNyoMUnyJ6lKOWH1ed26mUPqvOU7y3AK6JsQRmpUMBAME+/ROiyf6BUbwhtB4xhkzaduzacEjbpa65AAGLL/Sy4ybmRw49WAj5uOwgXvGP9BalUlwv4Hihf67+RXNPClcxwRA/7MwxN8P1VMU0gKNIK1PN/n5+4sWSMkSV0w23YVv45/FzLGah2K5Idcw+Yn9ovXOBJtNPU5C4CY0pVqs6DGmm0OBlrWcYF+OSnWAp+fPyU+lhN1m+mjFFLupRqTggq12c0107OvCddwfdlHSJJ6hk0ik6r4ERDq206Xkv8TQhy0rEYC7PkVNn8JVu81i/ibQUOGs8XZCz0OxNZ5WofDnspG0hT1ngS3LHnhSyajgcfMt3GZ9T7ym7wzh1F+RQMKTrLifDdH0sFMsRkABDUWaXL+zpCJkCR1kiInU5SOYktxHfvrLAQb9YzIjIaVL8JHV8Lyp7QOzWdOzI7lOzEKgMa1n2kcjdqR2JUIKVG+WYPrULsTJ7PSOZC8Gs//4RBCsKeins3mqxk/bxLUl3n8HMcSkZwNQKR1PzgDY8Z9mQhJUhecrFtDxs73YP9WrUOR/IyrNmh071iChAV2fq8+0f9sDaPyrWCTnuLUKQAE7f0Rmqo1jadwfxWJSksM0ZmaxOCazdobHabL660EWSoJVqwYGooh2PfzViWmZmEXOozYoL7U5+fvDJkISVIX/Fm/mP7rH4d9q7QORfIzy7arw+Mn9otXkyBbI0T1guTjtA3Mx/oOHM42Zxp6YYdt32oaS1XpbgCsuhBNkgSAQSGVXKxfQlTBdx4vu6CigXIiONf8Bsq1P4De9+OhMuMj+avtds6xPEyzKcrn5+8MmQhJUhfYXAMvHYEznbzkffUWO6vy1WHzE3LiYdPn6hP9z/H53DVam9gvnm+dI9kvorA2a9s/ytYykqkpNFWz+zDIsZV/Gl9nVNknHi87v6IRgY6w+HRIGebx8tsjKsTI76ZRrBe92VNj1ySGjpKJkCR1gQ29+oPDpm0gkl/5eWc5dqcgMzaEjDBxoCZk0AXaBqaB3vFhfBV6MSdaXuSnKG2bBZVqdSST8OFCpIeKTOkLQKyt2OOTsRa0NLdl+HAx2UMpiuKeLymvPDAGBshESJI6SVHAKmuEpMM4MFosXk2C7E0Qk63Zt3QtKYrCiblpONG5pxPQgsXuILxZXf/KHOf7EWMu8b1yAEimnIpazyYK+RWNnKP7mUuq5kLBCo+W3REjwiuZof8W4yb/WmfuSGQiJEldYBcyEZJaE0IcNH9QAmxo+TAYfFGPaxZzmdAyq/bybSWwf4smMeytbHKPGAuKz9QkBgBzVAoWTOgVQXHBDo+WXVDRwGn61Qzd8x4U/eHRsjtiuCGfB43vkVUgEyFJ6vZk05h0qF1lDeyrbsJk0DEqCdi1WH1i0IWaxqWlMb1jSdLV8L+GaxBzJ2gyE3tBxUFD56MzfH5+N0WhzKCuel9V5LlESAhBXnkDSS3rjBHh23XGDmZMGcQ8x0h+1Q/XLIaOkImQJHWB7CwtHWrpNnW02KisGEJ2zgOnHZIGQ3yOxpFpJzzISGZGJvUiCKti0mS6ifyKRtKUcvWBRnMIudSFqEt7WPbv8liZ1Y026prt7gVXifDt8iEHi8k6jhttt/GCNTCmipCJkCR1gewjJB2qVf+gDS0jg3pwbZDLhH6JXG27i5tSP4L0E3x+/sL9lSS45hCK0rBGCLBHtJy/2nPLfORVNKDD6Rc1Qq7O0kU1TVjsDs3iaC+ZCElSJykcXCMkm8YkaLI6+DVP/SA6JdUOBT+rT/TA0WKHmpATT75I5qfdtTTbfP/huK+ihjftp7M3eYpmcwi5GFo6a4fU7/FYmQUVDcRRgwEnKHoIT/JY2R0VG2oi3KwjXlSxr7hEszjaSyZCktQFNiETIemAX/IqsNqdpEYFk1m8ABDQazREaTdc21/0Tw4nIdxMk83BqrxKn/cT2lql8JD9SopOe0XzTusRyeoQ+mhrkceG0OeXN5KiVKgPwpNBp/dIuZ2hKAqvmp7nt6Absa79SLM42qtd007W1tZ2uOCIiIgOHyNJgUb2EZIO5hotNj4nHuX4K8EcBmHafTP3J4qiMCEnnrI/vmbgJ3dCn5Fw8Ts+ObfV7qSwSl2VPVOjxUgPFpeu9hdLFaVUN9qIDjV1ucyCCv/oKO1iCU2BarCW7dY6lGNqVyIUFRWF0oEMWlEUtm/fTnZ2dqcDk6RAYJVNY9JBXP2DJvaLh+AoGHG1tgH5mQn94pm7JpJoaxHsWAjWRjCFeP28+6qbiBeV6IxmEsK6nnR0lTmuZWFSpZH1RUVE983scpn5FY0c76oRikztcnld5YzsBdWgr/FcPyhvafdCJJ988gkxMTHH3E8IwdSpU7sUlCQFigaCsJhjMZu0/5YpaaugooG88gYMOoUxvWO1DscvjesTxy1kUSjiSLOVq2uwDTjH6+fNr2jgfuP7nK3/BX4th9E3eP2cR2UKoUoXTbSziorCbeCBRKigooEzXYlQhPaJkDk+GwogtHGf1qEcU7sSoYyMDMaPH09sbPt+ubOzszEajV0KTJL8naIofOA4hXFTZ3LmUO2Gqkr+4ceW2qATe4US/uEF6irzx08Hg/Y1EP4iKsTEsF4xzN83iusM82DzFz5JhArKG0jHoj6I9I/f1dKQfhTWllBa2fGuJ4eqabRR1Wgj2ehqGtM+EYpM6QNArK1I40iOrV2dpfPy8tqdBAFs3LiR9HTZOVCSpJ5jaUv/oCtjNkHeMvjpWU07rPqrCTnxzHeMUh9s/w5sTV4/Z35FI9fY7uKfJyyDnCleP197LBnxMmdbH+MXW58ul5XfssZYL4O60K8/NI0lZvQDIJxGrHWVGkdzdB0eNfbuu+9isVjabLdarbz77rseCUqSJCmQWOwOVuxSmyUyhp8Bp/8Dxt8pE6HDmJATz1rRm2IRC9Z62PWD18/pShTS42PAYPb6+dojM1btG5Vf0djlslzXl6L4T41QQkw0ZSIKgLK927QN5hg6nAjNmDGDmpqaNtvr6uqYMWOGR4KSpEAxStnCiT9Oh29mah2KpKFV+VU02RzEh5vJyc6EE6+HEfLv4eEMTo0kJtTMfMdIdcOmL7x+zoKWZCMj1vsds9vLtUJ8QXl9l8tSr09QED4M0kdBpPYtMsrBS4ns265xNEfX4URICHHYEWSFhYVERkZ6JChJChSRSj2xFaugZIPWoUgaOng26Y6MsO2JdDqF8X3jmOdqHtv2LdjbtjJ4it3hJKlqNR8YH2Hw9pe8dp6OyhJ7+N50Jx86bqemsWujTtUaIYVfjnscrlkI4YmeCbKL6oLV/lhNHlxKxBvaPWps2LBhKIqCoiiccsopGAwHDnU4HOTl5XH66ad7JUhJ8kcKsNbZh9Ujn2b4gH5ahyNpaOm2/ZiwcXf5fbD2zzD4QtDLASNHMqFfPDPX9qFciSHOWgm7lkA/73x+FFU305u9jNZvRlRpu7TGwYLDY+ijK8IudGwur2FIr7hOl+WPNV4A9oh0qAeqPDeDtje0OxGaNm0aAGvXrmXKlCmEhYW5nzOZTGRmZnLBBXIaealn2U80xSlDIdM/RqJIvldU3cT20nrO0/9KQuly+GE3DL5I67D82kl94xHo+Np2AjMM36mjx7yUCOVXNLgXW9V01flDhSXxcPRjLCwJ5q7KZoZ0YR3YgooGTNjIjA7yXHweoI/NgiII8uBSIt7Q7kTowQcfBCAzM5NLLrmEoCD/esElSZK04Bo2f33wYrCj9g3St/tPa48UF2ZmcGok84tGqYnQ1vlgt3plqoGCigbSFPUeab3qfCs6HbUp49hbXEh+RedHztU12yivt3KNfiED37xaff+d+ZQHA+28sOQ+sAGiLMVah3JUHe4jNH36dJkESVKLWGpIKZyvDgOWeqRl28sYouwix74N9CY4/iqtQwoIE/vFs1rkUKOPAUuNOuWAF+RXNB6UCPlRjRAHlvtwjfrqDFezWLapBkU4wBjskdg8IT5NXUok0VmK3ea/s++3KxGKiYmhvLy83YX26tWLggL/n1Zbkrqqr24fx6+6Cxb+XetQJA3YHE5+2lHOlYZF6oaB50NYvLZBBYgJOfE40fGt4wRETDbYuj6M/HDyyxtI9ccaIWCosoPbDZ+Qum9Bp8twJVFfxP8fzNwCY27xVHhdFp+SxcX2hxlleYmiGv9dj7Fd9bfV1dV8++237R4VVlFRgcPh6FJgkhQIHKLlu4RwahuIpIk/9lRjtFRydtBKdcPI67QNKIAclx5FeJCBB5svI+fcCRyfcewlnDqjuLySeKVl9mY/S4R6N29mnOEzFtWOBu7pVBmuGqH0uHC/WGz1YDqDgerYoVSV1pNf2UgvP1jw9nDa3ZA9ffp0b8YhSQFHUcBJy1BpIRP/nmjZ9v1cql+CGRukHA9pw7UOKWAY9DpO6hvH/A0lLNte7pVEyOEUiKo9YASnKRxdcJTHz9EVkal9AUhyFFPXbCM8qOMjDfPL1RqhzFj/TDIyYkPZXlpPQUUD4J+1pe1qGnM6nR3+J1eel3oCp+tXyCkToZ7ox60l/NnwvfpA1gZ12MScBACWbi9T5xIq8+wMxMU1TSSKUsDPRoy1CElSp93IVEopKO9cP6GCikZ0ODl/9wOw8H6fLFnSEeNMO/i74T0itn6kdShH1OHO0kfT2OidNl5J8lcO16+QENoGIvnc/rpmUkqXkqpU4AyOhYHnaR1SwBmfo9YQ2PetRfwzG96d5tHfpYKKxgND5/2sozQAURk4UQhXmiguKuxUEfkVDcRTTdq++fDLK2qHfT/SXyngGsO3pJct1TqUI+pwInTKKaewb9++Ntt//fVXjjvuOE/EJEkBQzaN9VzLt5dzpX4hALrh08EoR9N2VFJkELlJ4ex0pqj9SoUTatt+vnRWvr8OnXcxBlFtVGeBri3a2uHDG6129tdZSFHUde4IT/a79e2MmSfyun0q3zjHaB3KEXU4EQoKCmLIkCF8+OGHgNpsNnv2bE466SSmTp3q8QAlyZ+5m8ZkZ+keZ9uG3xmr36S+B0ZcrXU4AWtCTjwWTPwz6w111FOk5yYnLWg1dN4PEyGgPlSNy1a2s8PHujpK9wlqWf/TzzpLA8T1Hcmj9st5v244Dqd/1px3OBGaN28eDz30EFdffTV/+tOfGDduHK+//jrffPMNzz77rBdCVFVWVvLnP/+ZiIgIoqKiuOaaa6ivP/pidRMnTnQvC+L693//939ei1HqeWQfoZ7J4RT0zv8AgOqMyRCl/SKXgWpCP7V57LN884EaVg/JL/fzGiHAEZUFgLE6r8PHujpK54a0fBb6YSKUEhWMSa/D6nBSXONf/ZdcOjX96Y033khhYSFPPPEEBoOBpUuXMmaMd6u9/vznP1NcXMyiRYuw2WzMmDGD6667jv/+979HPe7aa6/loYcecj8OCfGvtVikwOaQNUI90vrCairsZhoMQUSOv0HrcALaiIwYQkx6yuutbC6uZVByGDisHpkYML+igf85TiYu9yTSEgd4IFrPMyf0gXwIb9zb4WPzW2qEsszV0ABEpHo0Nk/Q6xQGRVmhcjdFhdmkRffXOqQ2OlwjVFVVxQUXXMArr7zC3Llzufjii5k8eTIvv/yyN+IDYMuWLSxYsIA33niDUaNGMW7cOF544QX+97//UVRUdNRjQ0JCSEpKcv+LiIjwWpxSzyP7CPVMy7aX8U/7pdzf+xP02eO1DiegmQw6xvRWFxyt+uF5eKqf2um3i5xOQUFFI/9znIxjyuMQ458jmSNTcwFIduyj0Wrv0LEFLZMppuqq1A1+WCMEMMf5Ap+ZZ+Pc9q3WoRxWh2uEBg0aRFZWFn/88QdZWVlce+21fPjhh9xwww3MmzePefPmeTzIlStXEhUVxYgRI9zbTj31VHQ6Hb/++ivnnXfk0Rr/+c9/eP/990lKSuLss8/m73//+1FrhSwWCxaLxf24tladiMtms2Hz4BThrrI8Waa/6e7XKIRwN40J4fTrKeQ7q7vfw85e39Jt+wE4oW8aNnvHPrx8KVDu37g+MXy/pZQt+5s4qaEM55ZvcJx4c7uOPdI1Ftc0Y7E7MegUEkINfvsamOLVBC1DKWVXSQ25ya2/rB/tHuaVq01isQ51ZJw9NBHhh9fZGNoLmn7HUbarzXV48z3a3jI7nAj93//9H/fddx863YHKpEsuuYSxY8cyY8aMjhbXLiUlJSQkJLTaZjAYiImJoaSk5IjH/elPfyIjI4OUlBTWr1/P3/72N7Zt28Znn312xGMef/xx5syZ02b7woULvdKstmjRIo+X6W+66zVWV+ndNUJ2q4X58+drHJH3dNd76NKR6zPUFWIobAb6Yduznvml670XmIf4+/1zNgMY+HdZf64NUtAVrWbRF+/TbGr/JIuHXuOOGoV4aulnqmLJvGpsBv+ccFDntHImChFKE999N5/dCYdvtTjcPdy2Tw8omOrUZrUVG/OpyvO/v0PC2tLMWbbtiH8nvfEebe+UPh1OhP7+98OvqZSWltbhC7nnnnt44oknjrrPli1bOlTmwa677sAEZ4MHDyY5OZlTTjmFXbt20bt378MeM2vWLGbOnOl+XFtbS3p6OpMnT/Zos5rNZmPRokWcdtppGI0dn000EHT3a3xv36+U1JchUDAYjd1y1GR3v4edub7St/7MJ+bveNd8GZed94KXI+yaQLp/7+/9id3lUBUzlJjKtZyabsU5/Ni/U0e6xo9WFXL6tmd5mLdxWqbiOOddb4bfJdWb7iPGXkpWXFCbvyNHur5mm4NbVy5Gh5NwZzUAo6dc6Jf9hDYva4Sf3ieJcka28/o8wdWicyyd6iztKXfccQdXXXXVUffJzs4mKSmJ/fv3t9put9uprKwkKSmp3ecbNWoUADt37jxiImQ2mzGbzW22G41Gr/wh8Va5/qS7XqOi01EoEvj63A2cMyyd7neFB3TXe+jS7usTgj1NQcQKE7a+UwPmNQmE+zehXwK7y/P5xXQiU1mLfvu36E/8a7uPP/Qa91Zb0CGoM8QSHpOFzo+vvzB2DL8VFVLUwBHv06HXl1fZDEBmUAOK0w6KDmNUGug1/Vg/rLjMAfATJDmK0Ov06PRtuyd74z3a3vI0fcXi4+OJjz/22iOjR4+murqa1atXM3y4upbPDz/8gNPpdCc37bF27VoAkpOTOxWvJEk9m1PALXVX0mw5m9eO898J4gLRhJx43vo5n7cqBjEVIH85NFVBcHSnyiuoaOBbxxQyJ93OjDGZngzV43ad+Ai3f7iOExtjaF/PqAND54+LbIQaICzJL5MggMT0HBxCIVSxUFq6l8QU/5rl26NLbHhL//79Of3007n22mv57bff+Pnnn7npppu49NJLSUlRe8nv27eP3NxcfvvtNwB27drFww8/zOrVq8nPz+err77iyiuvZPz48QwZMkTLy5EkKUBtLq6lvN6C0xTO8MzOfUBLh3didixmg47f62KwRPcDpx12dL7fSN7Bi5Eqnp2fyNMyWhZMdU2Q2B75LSPG+of67xxCLgZzMPt1aqVHWUHnu7t4S0AkQqCO/srNzeWUU05h6tSpjBs3jtdee839vM1mY9u2be7OUSaTie+//57JkyeTm5vLHXfcwQUXXMDXX3+t1SVI3VAEDYz47Tb435+1DkXytuq9bFzzMyAY0zsOs8G/ljIIdEFGPSdmxwKwKWKcunFL5/5eCyHcSUVGrP/PHaeuHC9ortlPs619U3G45hAKj0mEQRdA70lejLDrKszqjOENxds1jqStTtWj2e12li5dyq5du/jTn/5EeHg4RUVFREREEBYW5ukYAYiJiTnq5ImZmZmIgxbrS09PZ9myZV6JRZIAFMCAg5Ti76EYdbFIP//mKXXBb3O5dPULNBpOx9jvn1pH0y1NyIln2fYyPm0axvEAOxerq6l3cHLFsjoLiq2BH81/I+2bXLjiUzC07fvpL6KbC9kcdDV2oWNPxZnkJB17YI5rDiFD1lgYfom3Q+yyprAMaF6DvXy31qG00eEaoYKCAgYPHsy5557LjTfeSFmZOn35E088wZ133unxACXJnzUQxLrB98NZz8oV6LszpxPnhk8A+NXZnwl9j923Ueq4iS3LbXy8LxZnRCrYGmD30g6Xk1/RSKpSTi+lDF3pBr9OggCUiBSCsBJKM/uK2rfobH65WiOUGQA1XgAiWl1KxFybr20gh9HhROjWW29lxIgRVFVVERx8IEs/77zzWLx4sUeDkyR/Z8FEftalMGIG6AKmpVnqqH2r0dUVUyuCyY8eS68A+fAJNFlxoaTHBGN1CAoTTlY3bvmmw+X4/arzhzIG8XDW++Ra3mFn/bGTNovdQVHLul2Zwc0BsdZhcGJfACKaOr6UiLd1+C/38uXLuf/++zGZTK22Z2Zmsm9f+zJZSZKkgLLjOwB+dA5lTK7/dkoNdIqiMCFHrRX6Xpygbtw2HxwdX3oioBIhICy5LzYM7k7QR7O3sgkhIMxsIPZ/Z8DD8VC42gdRdl5UWj8AkuxFCKd/rc3Y4UTI6XTicLTNPgsLCwkPD/dIUJIUKHQ4iSv7FXYt6fAfaylwiO0LAPjBcZz7g1ryjgk56ioC7xalIIJjICod6o+8gsDh5Fc0ku5KhKIzPRyhd7hGjrUnEXL1D8qICUapL1PXOgzz7/dlYoa6ppoBB+XlZRpH01qHE6HJkyfz7LPPuh8rikJ9fT0PPvhgt5xZV5KORFHAiJ2xK6+B96aBrf1DX6UAUluEUrIBp1BYoTvePbJJ8o4xvWMx6hXyq6wU/Pkn+OuPEJnWoTICsUZooGMbTxlf4eTiN4+5r3tqgLgwmLUXZm71yxmlD2YKDuWc4LcYYHmTvAb/mtyyw4nQU089xc8//8yAAQNobm7mT3/6k7tZ7FjLZUhSd+M4+FdI+Fd1r+QhOxYCsFb0pl92FkFGOWzem0LNBk7IVNcYW1Jg7fDxQgjyyxsDLhFKMdRygX45I22/YrEfvc9Pq6kBdHqISFb/93ORcamA0q5aL1/qcCKUlpbGunXruPfee7n99tsZNmwY//jHP/jjjz/aLIwqSd2dUyZC3V/+zwAsdw6WzWI+4nqdl21vSWYsddBc065jKxqs1Fvs9FJalmUKkEQoIk1tOspSSth7jIkVXYlEZqx/LiR7JJnuiSP9KxHq1DxCBoOByy+/3NOxSFLAca0+D8hEqJtyFqxAB/zmzOWhfjIR8oUJ/eJ5/Nut/LK7Avv3D2NY+TxMuhfG3X7MYwsqGoikniil5cM2QPoIKTHZONARpjRTvC+fPomDj7ivq0bouIaf4OOF0OdUGOb/k7qO1G9nrPFlDFuzYMrrWofj1uFE6N13j76C75VXXtnpYCQp8CgIFBREQAxhlTqoei+62kLsQsf+iMFkxwXWN/BA1S8xnKSIIEpqm9ltCSfHYYWite06Nr+8kQylVH0QlgSmALlnBjOVxiTibUXUFm6B4w+fCFntTgqr1EQotXEzbPoMQuMCIhFKC7ExTP87u2v9q7N0hxOhW2+9tdVjm81GY2MjJpOJkJAQmQhJPYarLkgoehRhlzVC3dGelQBsFJmMyu2FImcO9wnXMPoPV+3lC+so7r7xN4jLadexBRUNBxKhmCwvRul5taGZxFcX4Sg78jIU+6qbcAoINuoJaW65Tj9eZ+xgUb1HMPuHKykUvXhdCL/5fepwH6GqqqpW/+rr69m2bRvjxo3jgw8+8EaMkuTXhOuXWcgaoW5n768A/O7MdQ/rlnxjQksz5He7LRDfr93L1+RXHFQjFB1YiZAjujcApuojL0Ph6h+UERuCUlukbvTzEWMuyamZvOM8ne8tA6ho6HhHeG/xyFS4ffv25R//+Eeb2iJJ6gkELaM1ZNNYt1Olj2eTM4P19GV0bzls3pfG9olDr1PYVdbA3sqWzsPtmIivdY1Qthcj9DxjglrrFdFQcMR9CsoP6ijtToQCo0YoyKgnJVJdkcKfOkx7bE0Ag8FAUVGRp4qTpIAhlJZfI9k01u18GX4JZ1ofp7zXVMLMnRpbInVSZLCR43tFAbBicx58fBU83R9szUc9Lr+ikY8cE9k/+u/Q52TvB+pBkekDAEix78VqP/zfk3z30PnggEuEAEZFVnGObgU1O37ROhS3Dv9mf/XVV60eCyEoLi7mxRdfZOzYsR4LTJIChkyEui3X8O2JcrSYJibkxPN7fhXf72rkkorf1Bmm836EnMmH3b+qwUpNk43fySV80ulg8v+5dQ4W3UtNhNKUMvZW1JCVGN1mH1fTWE6EA+zqemOEJ/ssxq6a5lzEeNMHrNlVBqf6xyTMHU6Epk2b1uqxoijEx8dz8skn89RTT3kqLknye66OfrJGqHtqrq/i992lgM7dX0XyrQk5Cfxr4XZW7KrAccIU9KvfVNceO0Ii5EoSkiKCCA6wJAhACU+mkWBClCb2F2whK3FMm31cQ+f7mCvVDaHxYAxus5+/ErF9oAyCa47cD8rXOpwIOf1ssTRJ0ppwtTDLPkLdStm8R1mte4t/h15Mv0T/+Oba0wxMiSAuzER5vZXtkSfRnzdh+wIQ4rCdpwsqGomlhotDNkFpMiQO0CDqLlAUyszpZFi207BvC9A6EbI7nO7+UulKhboxMt3HQXZNcFI/2ArRzXu0DsXNY32EJKmnkjVC3VNT0SbMip3ExBS/Gebb0+h0CuP7qrVxX9f1BlMY1BVD8drD7p9f0cDxuh3MrH4Uvrjeh5F6Tn1YJgCOsp1tniuqbsbuFJgNOqJsLR3CowIrEYppaf5LcO5HHKO/l6+0q0Zo5syZ7S7w6aef7nQwkhSIFk36irOPSwNzhNahSB50o/NuGpr3MHv4KK1D6dEm9Ivnsz/28cOOGu7ufTJs+Qq2fQspw9rsW1DRiBUjJRGDSUoeqkG0XVeTfgr/LtVT5sjmtEOeO3jovK62UN0YYDVCqakZ1IpgIpQmaoq2E5LSX+uQ2pcI/fHHH+0qTH5rknoimykSgtt2apQCV2FVIzvKGtHr4hk1oLfW4fRo4/rEoSiwtaSOmhNPI3LLV2o/oUn3ttk3v6KBP5xD+eO0GZwxOHA6ELcy+EIe/iWd7IZQ7jnkqQJ3IhQK1S1NSwGWCAWbDeTpUhggdlFRsClwEqElS5Z4Ow5JkiS/8eP2cgCGpUcRGWzUOJqeLTbMzJDUSNYV1rDEeRzTFB2UbIDqvW2ahQ6syh4gy2ochmth0j2VjdgdrZvbXUPnM2NDYN9edWOANY0BVJh7QfMumkqOPIO2L8k+QpLUSa76zwFbn4evboHKPE3jkTyneO13vGh8nhvCf9Q6FAmY0E+d1XtRnh3SW5oqty9otU9tk43KBgtG7GTEhvg6RI9JiggiwdDAELGN4rLKVs/llx9UI5R7Fgy6AOL6aRFmlzRFqDN+i4q2/aC00KkZwlatWsVHH33Enj17sFpbT5P92WefeSQwSQoUafu+hcZCGHZ5wK1tJLVltTtRitZwlv4XqkWS1uFIqPMJPb94B8t3lOE45XT0e1aqzWMjr3XvU1DZSDzVrAy6GcPrfeGGlaALvCH0Op3CN8ZZJBjK+WPXAJJiD0wK6eojlBkbCn3v1CrELlNi+8J+CKn1jy+PHa4R+t///seYMWPYsmULn3/+OTabjU2bNvHDDz8QGRnpjRglya/tzLoCJt0fULO7Ske2Zk8VqQ51xt6ItFyNo5EAhqZFEhlspLbZzpbIlol785ZDc617n4KKRvroijDgBKctIJMgl3JzL/aJWCoqy93bHE7B3kp1AsVArvECCElRa7Fi/WQIfYcToccee4xnnnmGr7/+GpPJxHPPPcfWrVu5+OKL6dWrlzdilCS/tivrTzDhLohM0zoUyQOWbS8jS1cMgC6ur8bRSAAGvY5xfeMAWFgSAbF91GRn1w/ufQoqm+ij7FMfBGBz0cE+H/g8Yy0vsFI5MDKupLYZq8OJUa+QEmSBmsKAnbvMNYQ+UtRAU7W2wdCJRGjXrl2ceeaZAJhMJhoaGlAUhdtvv53XXnvN4wFKkiT50tJtZaQr6tIagbZ6eXc2MUedT2jp9jLod4a6tE35DvfzBZWN5CgtQ8rjc7QI0WMy4tWpOA5emNTVETw9JgT9li/hmYHwvz9pEl9X9UqKp0SoI20bi7dpHE0nEqHo6Gjq6uoASE1NZePGjQBUV1fT2Njo2egkyZ+19JYObiqBorVQV6JpOFLXldY2s724igSq1A2RqdoGJLlNaEmE1hfWUDH0Orhrl1oT22JPRSODdS19TpKGaBGix7hGjrlGiYGa6AFkxYaqTYI6A0QE5vsz1Gxgty6T7c5USiurtA6n44nQ+PHjWbRoEQAXXXQRt956K9deey2XXXYZp5xyiscDlCR/l7vjVXhtAvzxntahSF304/YyEqlCrwjQGSE0QeuQpBYJEUH0T1ZrSpYX6SEkptXzhRW19Fda+pwcZrLFQJIZauUD4yO8VXMNDrsdOGRqgLG3wP37YfLDWobZJc8kPMpk65NsMmk/8WW7EyFXzc+LL77IpZdeCsB9993HzJkzKS0t5YILLuDf//63d6KUJD8mlJbBlwHaXi8dsGx7GcmuNZwiUkAnZxjxJxNbFr9dtr3swEaHlWYHxDbuxqzYEOYIiMnWKELPSE5I4HjdDtKVMsr3qUPMXYlQZlxLR2mdHkyBO19SRlwYoPbt0lq7f8uHDBnCqFGj+PTTTwkPD1cP1um45557+Oqrr3jqqaeIjpaz60o9j1NpGZ3itGsbiNQldoeT5TvKSXEvZik7v/sbV/PYj9vLcJbthDdPx/D6BMqbBINamsWUlOMOuyBrINEbDBTp1Zmxq/ZsBg40jQXyZJEHy4pTr+PgflBaaXcitGzZMgYOHMgdd9xBcnIy06dPZ/ny5d6MTZICgnAlQg6btoFIXbKusIaaJhtZpmp1Q4D2v+jOju8VTZjZQEWDlc11QbBvNUrFDuz1ZZyo26LulDpC2yA9pCJIHYXdVLoDp4A9LTUnWeEOeG0SfHINOAL3y1ff0Ca+Mt3HA9vP13zB6nYnQieddBJvvvkmxcXFvPDCC+Tn5zNhwgRycnJ44oknKCmRHUWlnkVp6S19oGkscP8oSQeaW46PavmGKjtK+x2TQceY3rEALMlrgovexnbTWnba4zlJt17dqffJRykhcDRFqM17usqd1FrBYndi0CmkOEqgaA3sXgr6Ts2J7BdSklPIVfYQ7awi2FZ57AO8qMMN4KGhocyYMYNly5axfft2LrroIl566SV69erFOeec440YJcmvCaXl10j2EQporkSoj7lG3SBrhPzSxJblNpZuL4PcMyEyjciG3cQrtVj1IQeW4Ah0ceoUAGF1eZQ1q1+60qKDMdS0jIwL8H5QveIjuMZ2F6dYnqRap223mi71BOzTpw/33nsv999/P+Hh4cybN89TcUlSwHDKGqGAV9FgZX1hNQCJyD5C/mx8jjqx4h97qqhpVJujIy1FWISB0pRTwWDSMjyPCW1ZlT3eUkB5s7otMy4UKnerDwI8EYoIMrI5eAS7RCplFm1nAe90IvTjjz9y1VVXkZSUxF133cX555/Pzz//7MnYJCkgCJ3sLB3oft5ZgRDQPzkC48Cz1cUsY+Ws0v4oLTqEPglhOAX8tLMcGis52f4jAoXG4ddrHZ7HxGUNAiBWVFHX6Fp1PvTA4s4BngjBgaVCXDVeWulQA2NRURFvv/02b7/9Njt37mTMmDE8//zzXHzxxYSGdo+e7JLUUUKOGgt4y3eoazpNyImH8XcdY29JaxNy4tm5v55l2/czrlc6n9vH8HfndD7IOUHr0DwmJSGBEhFDklKJqCsGwtXEYburRijwZz0fFlHHKP1X9KsQwBmaxdHuROiMM87g+++/Jy4ujiuvvJKrr76afv0Cez0XSeoK1wjdA8PnZR+hQOQUsHyn2hzmGp4t+beJ/eL59095LNtexuUj03nfcRpRwUYiQ4xah+YxBr2Offo0kpyVhDXvA3LIjAmB/S2j4+ID//M3J6SBS4z/o7wx5tg7e1G7EyGj0cgnn3zCWWedhV4fuKv6SpKnyT5CgW1fg9pHKMxsYHhqsLpUSkgs6LvPh2p3c0JmDEFGHaW1FhZtKQWgV2ywxlF5XmVIFtSvJ1tRFwHODm6Apkp1nbW4wF5PDSA8LRfWQ5yoxGapA6M2CVG7+wh99dVXnHvuuTIJkqRDyKaxwLalWq3aG9M7FlPR7/BUP3hlrMZRSUcTZNQzOlsdRv+/39WFVjNiQrQMySssUX0A6KPsQ6dAivWg/kHGwE/8UpNTKRPqsilU7tIsjoCZP/7RRx9lzJgxhISEEBUV1a5jhBA88MADJCcnExwczKmnnsqOHTuOfaAkdYBMhALblmr1z+CEfvFgqVW/bYfGaRyVdCyuZsyyeivQPRMhXUIuAH2UIlKigjFWbFWfSOivYVSekxkbyg5nGrucyVgaazWLI2ASIavVykUXXcT117d/VMA///lPnn/+eV599VV+/fVXQkNDmTJlCs3NzV6MVOpp9qROhZvXwJlPaR2K1EG1TTby69SfJ+TEQ/+z4e8V8KePtA1MOibXfEIurhFI3Ul4xnE8bLucv9tnqIleqbrcBgkDtQ3MQyJDjNygn80p1qfIC9VuodyASYTmzJnD7bffzuDBg9u1vxCCZ599lvvvv59zzz2XIUOG8O6771JUVMQXX3zh3WClHsE14NNmjIDY3hAmVyoPND/vqsCJQu/4UNKiXYtZ6sAcpm1g0jFlxoW2Sn66YyKUmpLCvx1TWe4cQmZsCBT+pj6RrP2K7Z7Sq2URWdeisloImESoo/Ly8igpKeHUU091b4uMjGTUqFGsXLlSw8gkSfIXrtFi4/vKprBAdPAov+7YNJYWHYKu5RtXv3ALlG9XH/Q6UbugPMx131yLymohcBcqOQbX2meJiYmtticmJh51XTSLxYLFYnE/rq1V2y1tNhs2m+cW1XSV5cky/U13v0YhBABhVVtwLPw3IrIX4vjpGkflWd39Hq7cpSZCozMjsdls6H58AqV8O87hVyMyAr/DdHe/f2N7x/DuygKC9YIwY/e7TgXoF2mnb+1vDG1IwH7uKygVu3Aaw6GbXOv/jctgkFLIxSOSPX7/2lueponQPffcwxNPPHHUfbZs2UJubq6PIoLHH3+cOXPmtNm+cOFCQkI8/41j0aJFHi/T33TXaywv1wE6qrcuR1/5IuVhufxc0j3noemu97CqXg8o5G1aQ1MejN3+FXEN21jdlErRphqtw/OY7nr/HALGJ+lIDRV8//33WofjFVeHrOCi5lep2N6becqDwBCYP1/rsDwqKQR+XPKDx8ttbGxfLZOmidAdd9zBVVddddR9srM7N414UlISAKWlpSQnJ7u3l5aWctxxxx3xuFmzZjFz5kz349raWtLT05k8eTIRERGdiuVwbDYbixYt4rTTTsNo7J7zlXT3a/x4/yqoqSSq3zgcDhvR0b2ZOmKq1mF5VHe/hw+uXQJ2G6PHjKF/ShSGPQ9DAwwbexrHZZ6kdXhd1t3vH8Dp3fwabbUjcLz4b0KnPcXUzDHqqMZuxJvvUVeLzrFomgjFx8cTH++db9BZWVkkJSWxePFid+JTW1vLr7/+etSRZ2azGbPZ3Ga70Wj0yi+Zt8r1J931GnUtjfd10QPQj1Snh++us2x113vomh3caDCo19eoNpUZIpKgG11vd71/B+u21xiRwE859zEma1z3vL4W3rh/7S0vYFLLPXv2sHbtWvbs2YPD4WDt2rWsXbuW+vp69z65ubl8/vnnACiKwm233cYjjzzCV199xYYNG7jyyitJSUlh2rRpGl2FJEn+pKWbl8phV2ftBQjtnk2cUmCqDgn8BVb9WcB0ln7ggQd455133I+HDVPnHFiyZAkTJ04EYNu2bdTUHGjXv/vuu2loaOC6666jurqacePGsWDBAoKCgnwau9S9KQ4L1OxTq6wjko99gOQ3BGompCjKgSQIBUK0XftIkiTfCZhEyLXq/dGIVl/v1D9uDz30EA899JAXI5N6urjqtfDMtRDfH278RetwpM5qUFegJyQGdN21kVOSpEMFTNOYJPkruehq4HJ9d1IAGsrUByFyTiFJ6klkIiRJnaS0zC0t1xoLfIrCgaYx2SwmST2KTIQkqYsOJEIObQOROqxVY3pzy1DboEgtQpEkSSMyEZKkLnIga4QCnaIAzS0DLWQiJEk9ikyEJKmLhE72EQpUrcZXuBIhs+cmTpUkyf/JREiSuuhA01j3WPunJ3EPn0cBi2wak6SeSCZCktRFTqVl9lKHrBEKaKnDYehlkHKc1pFIkuRDATOPkCT5nZblGZw6VyJk1S4WqXNcTWMKMPRS9Z8kST2KrBGSpC5yuBMhyyGdTqRAoWgdgCRJmpGJkCR1kTsRAtlhOsC0SlsbK8HWrFUokiRpRCZCktRFzoMTIdk8FpAUBZg7Hh5NhMLVWocjSZIPyURIkrrI3VkaZCIUYFqtTyhHjUlSjyQ7S0tSJ7n6lQhFDzMWgMEEpjBNY5I65kBfaQXuzgdrnbyHktTDyERIkrpIoEDGaK3DkLpKp5O1QZLUA8mmMUmSeiz36vNy2Jgk9VgyEZIkT1j1Fvz8HDSUax2J1AnGmnz46EpYeL/WoUiS5GMyEZIkT1j2BCx6AGr3aR2J1AGuPkL6+mLY/CVsW6BpPJIk+Z7sIyRJndSqOWXAudBcKxfsDDCuUWN6W726QfYRkqQeRyZCkuQJZzyhdQRSF+jcQ+dlIitJPY1sGpOkLpPLagQ6xZUIyRo9SepxZCIkSZ7gsIO1ARw2rSOROkFna1R/kHMISVKPIxMhSfKEt6fCYymw/TutI5E6wD183u5KhEK0C0aSJE3IREiSOkk5eM1yvUn9Xy6xEZB0rkTIKBMhSeppZCIkSZ6gb1lvTCZCAcW9xIa7aSxUs1gkSdKGTIQkqYuEQNYIBSjX8HmdvVndIGuEJKnHkYmQJHmCrBEKaAdqhGQiJEk9jUyEJMkT9Gb1fzlqLKC0aRozyqYxSeppZCIkSZ3UamZp2TQW0HRy1Jgk9VhyZmlJ8gRX05hdJkKBxDV83po2GlNEIkSkahuQJEk+JxMhSeoiAbJGKMDVj7mHsGg5maIk9USyaUySPMHg6iMkEyFJkqRAIhMhSfIE96gx2Vk6ULiGzis41QRWyDXjJKknkomQJHXSwX2lDzSNWbQIReqCDKWUpBez4B8ZWociSZIGZCIkSZ4g5xEKOK4KIBN29QfXPZQkqUeRnaUlqYuEAAacB4mDIaqX1uFIHbRDpLL/+q0khJm0DkWSJA3IREiSPCGuj/pPChjC/b8OgiIgVE6mKEk9kWwakySpx1Na9/j6//buPL6pKv8b+OdmadrSja5ppZSWrYDADxmpxQ2hlAIiKAPiOGiVQWEAB5d5AJ8RREfQGWYcRUXHKS0Myii/AecBHLCyKhYqS0WwVkBoWVrK1n1Lk/P8ERIa2qZJm/Rm+bxfr7yS3Jxz8j09Cfly7rn3EpEXYSJE1E5S01NLXz0N5K0HTn4pX0BkF9NRY/2kQgRkPw98/abMERGRHJgIETnCuW+Bz2YB37wjdyRkI9OusVipFH7HPgZ+/FzWeIhIHm6TCL322msYPnw4/P39ERISYlOd9PR0SJJkcUtLS3NuoOSdgmKAXilAzBC5IyE7mY8aM50Uk4i8itsslm5oaMCUKVOQnJyMjIwMm+ulpaUhMzPT/Fyj4T925FgCAuhxl/FGbsN0+LwG10+CqeRRY0TeyG0SoaVLlwIAsrKy7Kqn0Wig1WqdEBEReQIf6XoixBkhIq/kNrvG2mv37t2IjIxE3759MXv2bFy5ckXukIjIBYjrq4S4a4zIu7nNjFB7pKWl4aGHHkJ8fDxOnTqFF198EWPHjkVOTg6USmWLderr61Fff+MyCRUVFQAAnU4Hnc5x15EyteXINl2Np/dRCAMAQK/Xo/FMDpQfTwaCY9H41NcyR+Y4njyGOp0eAOBzfdeYQaGG3sP66cnjZ+LpfWT/Ot52W2RNhBYuXIg33njDapn8/HwkJia2q/1p06aZHw8cOBCDBg1Cz549sXv3bowaNarFOsuXLzfvhmvqiy++gL+/f7visCY7O9vhbboaT+1jaakCgAL5+fnYd/Yk7m2oRk35ZWR/7nlHH3niGOoMAKAyrxEqunAR33ng2AGeOX438/Q+sn/2q6mpsamcrInQ888/j/T0dKtlEhISHPZ+CQkJCA8Px8mTJ1tNhBYtWoTnnnvO/LyiogKxsbFITU1FUFCQw2LR6XTIzs7G6NGjoVZ75jWOPL2Pm68dBq5eRmJiPwxPiAV+Wgo/HyXGjRsnd2gO48ljWKfT44UDO8xrhGJ79MItaZ4zdoBnj5+Jp/eR/Ws/0x6dtsiaCEVERCAiIqLT3u/cuXO4cuUKoqOjWy2j0WhaPLJMrVY75UPorHZdiaf2UZKMS+yUSiXUGuNsoaTXeWRfPXEM9deXSJrWCCl9/KD0sD6aeOL43czT+8j+ta9NW7jNYumioiLk5eWhqKgIer0eeXl5yMvLQ1VVlblMYmIiNm3aBACoqqrC73//e+zfvx9nzpzBjh07MHHiRPTq1QtjxoyRqxvkqXj1ebdjOnzeF9fHTOUrXzBEJBu3WSy9ePFirFmzxvx8yBDjiet27dqFESNGAAAKCgpQXl4OwPi/9KNHj2LNmjUoKytDTEwMUlNT8eqrr/JcQuQQTa+wYT4HDRMhtxMkXV9H4BssbyBEJAu3SYSysrLaPIeQ6dpBAODn54ft27c7OSqi65omQkLclCWRKzIdPh+EauMGJkJEXsltdo0RuSoBWJ6V2NAoVyhkB9P/m/JFHBq73wV0jZM3ICKSBRMhIkdomgg11rdejlzOXxqnoubhfwMJI+QOhYhkwESIyBGaLrRtrJMvDrKZaPKYezKJvBcTIaJ2svjtVChuzArpauUIh9pNtF2EiDwWEyEiR1H7Ge85I+QWhBBQwICTmukIWNkPqL0md0hEJAMmQkQdZVp1q7qeCHFGyG2o0QiVZIBUexVQuM1BtETkQPzmEznKmNcAgx4I7iZ3JGQDAaABKtxV/xa2z0tCF3UXuUMiIhkwESJylIG/lDsCsoMQgIAC50QE0DXBuM6LiLwOv/lE7STxUCMiIrfHRIjIUc4fBn7cCpSdlTsSsoUwnlX6RdVH8PnqdbmjISKZMBEicpRdrwH/+hVweq/ckZCNgqRqPKXaCtW378sdChHJhGuEiDrIfBaa8L5AbRngFyJfMGQzAQE19MYnPGKMyGvx20/kKGnL5I6A7KQyJ0JqeQMhItlw1xhRO3GptHsTwngeIQCAkv8nJPJWTISIyCsJcEaIiJgIETnOvreAv/YHdr4mdyRkI/OMENcIEXktJkJEHWS6wgZ0tUDFeaDmsqzxkG2EENBIOuNjla/M0RCRXJgIETmK6ceU1xpzG4GoMT7wDZQ3ECKSDRMhonZqdmJptb/xnomQWxAAgqTriZAmWNZYiEg+TISIHEV9fUaosU7eOMgmQgBB5hmhIHmDISLZcIUgkaOo/Iz3uhrnvUddBZD7AXBql/FSHgoFEBwLxA0H+k8Covo77709kC8ajA9Ms3lE5HWYCBF1kPnM0mpTIuTEGSGFCtj/vuWC7GtngDNfAXveALonAyMWAgkjnBeDhxAQ8DEtllb6yBwNEcmFiRCRo5h3jTlxjZCPP5C2HGioBqJuBYQeuFQAnPgC+GkbUJQDrJ0I9EoBxq0AQuOdF4sHuCRC8J0hAbeGdJc7FCKSCRMhonaSbj63tDMWSwsBbH0O6J0K9B1r3DZoqmWZ7ncAQx8HKoqBr98EDq4GTn4JrBoOjHwJSHoaUCgdF5OnEMA6/Wh8pE/BT3eMAf9CRN6Ji6WJHMV8+LwDd40d32hMbP73SaDmqvWyQdHAuD8Bcw4AcXcZ1yptXwT8e4bj4iEi8jBMhIgcRe2ExdKJ9wND04ERiwD/UNvqhPUEHt8M3P83wCcQGDLdcfF4ENPaLl4zjsi7cdcYUQcJ06mlTYmQIw+fV2mACW81OX21jRQK4BdPAP0nWiZQF/KAyP6AiouDhQBeVmVhtPIQpLzFwO3pcodERDLgjBCRo5gPn6+1P3G5We01yzaanb3RRk2ToGtngDUTgNWpQFVph8LzFOFSOW6RrkDiSTCJvBZnhIgcxTcYGP2KMSESBkBq5/JbIYCPHzY+nvA2EJnomPjKzhoXTSt9AL+ujmnTjQkIvN74K/xDPx4b+k3gYmkiL8VEiKi9ml1iwxe483cdb/f0HuDsAePia18HXvoh/m5g1tfGx0q18b6xwXgIvmm3npc5JyJQjHAgIEruUIhIJtw1RuRqDvzdeD9kuvFIMEcK7ma8mexYCnw4ynguIi/T0b2XROQZmAgROdKFPODM18YTHrbHtULgp/8aHw97ymFhtaj2GvD9BqD0OPD3EcChNV6XHUxR7sZTys3A1Z/lDoWIZMJEiKiDLFKHj6YAWePb/8N6KNO4vihhBBDRxwHRWeHXFXj6K+N76WqAzc8A/3wQKCty7vu6CAHgMeUXWKD6F6Srp+QOh4hkwjVCDqLX66HT6Wwur9PpoFKpUFdXB71e78TI5GPqo16vh1qtljuczhHWq/0LkQ0G4Oinxse/eNJxMVkTGAX8ehOQ8w6w6zXg513Ae8lAysvALzz7RIxCCKhx/bvHa40ReS0mQh0khEBJSQnKysrsrqfVanH27FlI7T002sWZ+vjzzz+ja9eu0Gq1HtXXFnvy5H/b32BRDlBxHtAEAb3HtL8deykUwJ3PAH3HAf9vrjGOz18A8j6ClPLHzotDBj64/p8XJkJEXouJUAeZkqDIyEj4+/vb/ENvMBhQVVWFgIAAKBSeuYfSYDCgsrISCoUCly8br5YeHe3gxb+e5Nj/Gu/7PXDjAq6dKbwXkP458O0/gB2vABeOQLV2PIaGJAFlA4CIXp0fkxMJAfhIjcYnSo28wRCRbJgIdYBerzcnQWFhYXbVNRgMaGhogK+vr0cnQg0NDQgKCoJCoUBpaSkiIyOhVPKMLc3odcDxz4yPB06WLw6FAkh6ChgwCdj5KsThf6Jb2QGI94YBg6cB979pPNu1h/CBMRESPNM2kdfyzF/gTmJaE+Tv7y9zJK7P9DeyZx2Vu7A40GrXcuDdO4xHYNnj9F6g9irQJQLocY9D42uXgEjggZVonLETpYG3QhJ64yJqD0qCAECDBuMDzggReS3OCDmAJ617cRav+RtVlwKX8oHKYvvq/bTdeN93HKB0oa+ldiByev0fjB8cBZWmyUkXK4qNR8jdNt14mL+bjq/GtEZIjl2RROQS3GJG6MyZM5gxYwbi4+Ph5+eHnj17YsmSJWhoaLBar66uDnPmzEFYWBgCAgIwefJkXLx4sZOiJk/X4m+/T4Dxvr7S9oaEAH7aZnzcpxMXSdtB3DIUiB58Y8ORfwIXvwfyN1v+Ia6edptzEQmDgK90PRFSMREi8lYu9F/P1v34448wGAz44IMP0KtXLxw7dgwzZ85EdXU1VqxY0Wq9Z599Flu3bsWGDRsQHByMuXPn4qGHHsK+ffs6MXryKpog4319he11GuuAhHuBn/cA8fc6Jy5HS3raeJqAoJgb2yqKgbf/BwjqZjw3UeztQMxtQGS/G5f0cCX6JhdaZSJE5LXcIhFKS0tDWlqa+XlCQgIKCgqwatWqVhOh8vJyZGRk4OOPP8bIkSMBAJmZmejXrx/279+PO+64o1Nid2U5OTm46667kJaWhq1bt9pcr6ioCLNnz8auXbsQEBCAxx9/HMuXL4dK5RYfJ+fSBBrv66tsr6P2Ax5YaZxJcZddTL7BwLCZlttKjxsPQ684B+StM94AY5IR2R8I7208z1JYT6BrvDGJ6hJhvBCsDKTG+htPmAgReS23/eUqLy9HaGhoq68fOnQIOp0OKSkp5m2JiYno3r07cnJymAgByMjIwLx585CRkYELFy4gJiamzTp6vR7jx4+HVqvFN998g+LiYjz22GNQq9VYtmxZJ0Tter45dQWN1/cGJRbXYCSAwuKL2Lr7pKxxOYJBb8CP5yWc3XsaCmVbe9J7QDV8J6LL83BL2UFEVhxHROUP0DRWAxcOG2830Usq/P2ebyAkYzI0pCgLITWFyI+ehJJg4664oNpziL2aA73CBwZJDb1CBb3CBwIKc+IoLM7qZHxcFDrc/Hp45Y/wb7iMa/4JqPQzfs7Vl3/ATACNQuGaM1ZE1CncMhE6efIkVq5caXW3WElJCXx8fBASEmKxPSoqCiUlJa3Wq6+vR339jf8pVlQYd3HodLpmRzzpdDoIIWAwGGAwGOzqg7i+jsJUv7NVVVXhk08+QW5uLoqLi5GZmYlFixa1WW/btm344Ycf8MUXXyAqKgqDBg3C0qVLsWjRIixevBg+PjcOQ765j0II6HQ6jzl8XqM0/sju/ukydv9kPE9SmqIcI32Ai5cv40/b2r6QqR/q0FO6gB9EDxhcdsmeEluKTthRPhRAKoBUSDAgXipBH+kcEqRixEvFiFeUIFYqRTjKcUUE4I3tNxLG9eod6Kf8Ae8Vdcdmg3GWJk2Ri/d9/mZ31D3qPoIpKXpH/TbuVx7AYt3jWKsfc73dQ5jpA9RCA6UHHs0I3DhK0xOP1jTx9D6yfx1vuy2yJkILFy7EG2+8YbVMfn4+EhMTzc/Pnz+PtLQ0TJkyBTNnzrRSs32WL1+OpUuXNtv+xRdfNDtMXqVSQavVoqqqCg0NDRBCoE5nX1JTe6WsI+Ga+aoVdh2ZtW7dOvTu3RvR0dF48MEH8eKLL+K3v/1tm23s2bMH/fv3h5+fnzlJvPPOO1FRUYHc3FwMGjSoWZ3Kyko0NDSgtrYWe/fuRWNjo32dc1F9BXBXlAJNh7xbowaoBaJUtUgKbvuzcFvjcSyo/QtOKeLxYpdXnRitnLS4Bi0OATjUZKskDOiCaiQF3fg77deNwmnDrVB26Y4khXF7ZGMQDuhuh0o0QoVGqKCDSuihvH55DAktL85OCjeYZ4Qa6yNxqjEeoYGBSFIb2w3SR+DH+j4oDLoDddnZju+2C8n28P4Bnt9H9s9+NTU1NpWThJDvEI9Lly7hypUrVsskJCSYZxkuXLiAESNG4I477kBWVpbVExHu3LkTo0aNwrVr1yxmheLi4jB//nw8++yzLdZraUYoNjYWly9fRlBQkEXZuro6nD17Fj169ICvry9qGhpx68vyfFiPvTwa/j6257V33303pkyZgmeeeQaNjY245ZZb8Mknn2DEiBFW6z399NMoLCzEtm3bzNtqamoQGBiILVu2YOzYsebtQghUVlYiMDAQ9fX1OHPmDGJjY+Hr6xnrMXQ6HbKzszF69GjztdSk84egyhoDERyLxrlH2mxDOvJPKHcshug3Cfrxbzo7ZLu11EdPwv65P0/vI/vXfhUVFQgPD0d5eXmz3++mZJ0RioiIQEREhE1lz58/j/vuuw9Dhw5FZmZmm2djHjp0KNRqNXbs2IHJk41n6i0oKEBRURGSk5NbrafRaKDRND+5mlqtbjZIer0ekiRBoVCYb3Kx5/0LCgqQm5uLTZs2QaFQwMfHBw8//DAyMzPNC8tbI0mSuc9N37ulGEy7/EzlJUlq8e/o7iz6FGj8PEt15bb1c9iTwNDHIDVUQuHCfxdPHLem2D/35+l9ZP/a16Yt3GKN0Pnz5zFixAjExcVhxYoVuHTpkvk1rVZrLjNq1CisXbsWw4YNQ3BwMGbMmIHnnnsOoaGhCAoKwrx585CcnOy0hdJ+aiV+eMW288AYDAZUVlQiMCjQIQmUn9r2dTcZGRlobGy0WBwthIBGo8E777yD4ODgVutqtVrk5uZabDOdm8k0Fl7NN8R4X19hvGyGLYtwlar2X7GeiIg6xC0SoezsbJw8eRInT55Et27dLF4z7dnT6XQoKCiw2Cf45ptvQqFQYPLkyaivr8eYMWPw3nvvOS1OSZJs3j1lMBjQ6KOEv4+qU2eSGhsbsXbtWvzlL39BamqqxWuTJk3C+vXrMWvWrFbrJycn47XXXjNfNwwwjk9QUBD69+/v1Njdgm+TJLKuHOgS3nrZxgaA17giIpKVqx6mYiE9PR1CiBZvJj169IAQwmKNi6+vL959911cvXoV1dXV2Lhxo9fPWmzZsgXXrl3DjBkzcOutt1rcJk+ejIyMDKv1U1NT0b9/f0yfPh3fffcdtm/fjj/84Q+YM2dOi7sUvY5SBWiuJ0O1ZdbLfvF/gb8NAo5+6vSwiIioZW6RCJHjZGRkICUlpcXdX5MnT8bBgwdx9OjRVusrlUps2bIFSqUSycnJ+PWvf43HHnsMr7zyijPDdi93/Q5Iedlydqglp78Cygo97kKmRETuxC12jZHjbN68udXXhg0bBlsOIoyLi8Pnn3/uyLA8y93Pt12m6vrFWQGgx93OjYeIiFrFGSEiOZz5yngfNRDwb/0M6URE5FxMhMjCrFmzEBAQ0OLN2iJqaqKqFLiQB5Sfa73Mma+N9/GcDSIikhN3jZGFV155BS+88EKLr1k7IRU1sWsZcCgTuHcBcN+LLZcxJUI97uq8uIiIqBkmQmQhMjLSfFg8tVOgFgjQGq/E3pLKi8DlnwBIQPfWT+5JRETOx0SIyNFGLDTeWlO4z3gfdSvXBxERyYxrhIg6G3eLERG5DCZCRJ3NNCPU40554yAiIiZCRA5Xew3IHAe8cztw/cKzZlWXgEs/Gh/HMREiIpIb1wgROZpPAFD4DQAB1FwGAposPjfNBkUO4PogIiIXwBkhIkdTqoEuEcbHFRcsXyvOM95ztxgRkUtgIuSl0tPTIUmS+RYWFoa0tDSr1xlr6urVq3j00UcRFBSEkJAQzJgxA1VVVU6O2o2EdDfeXzttuX3UEuCZPCB5bqeHREREzTER8mJpaWkoLi5GcXExduzYAZVKhfvvv9+muo8++iiOHz+O7OxsbNmyBXv37sVTTz3l5IjdSFgv4/2VU5bbJQkIjQe6xnV+TERE1AzXCHkxjUYDrVYLANBqtVi4cCHuvvtuXLp0CREREa3Wy8/Px7Zt2/Dtt9/iF7/4BQBg5cqVGDduHFasWIGYmJhOid+lhfU03l/9Wd44iIjIKs4IOUNDtW03Xc2Nx/rGG/X1jddfr7WtXQeoqqrCunXr0KtXL4SFhVktm5OTg5CQEHMSBAApKSlQKBQ4cOCAQ+Jxe6EJxvumM0IbnwbW/8p4HTIiInIJnBFyhmVtz4goAIQ03TAlCxjwoPHxj5uBDelA3F3AE1tvlPnbQKDmSvPGXi5vV5hbtmxBQEAAAKC6uhrR0dHYsmULFArr+XFJSUmzy3CoVCqEhoaipKSkXbF4nPDexvtLPwJCGJPa/M2Arhq49/fyxkZERGacEfJi9913H/Ly8pCXl4fc3FyMGTMGY8eORWFhodyhub/I/oDKF6grM84K+fgDv8kGRr4ERP+P3NEREdF1nBFyhhcvtFnEYDCgorISQYGBxhkYpebGi4kTjG1IN+Wp8793aJhdunRBr169zM//8Y9/IDg4GB9++CH++Mc/tlpPq9WitLTUYltjYyOuXr1qXnPk9ZRqIGYIUJQDnN4NhPcCogYYb0RE5DI4I+QMPl1su6n9bzxWNslJlarrr/vZ1q6DSJIEhUKB2tpaq+WSk5NRVlaGQ4cOmbft3LkTBoMBSUlJDovH7fVJM97v/KPlGjAiInIZTIS8WH19PUpKSlBSUoL8/HzMmzcPVVVVmDBhgtV6/fr1Q1paGmbOnInc3Fzs27cPc+fOxbRp03jEWFODphp3j9VeA/49A2islzsiIiK6CXeNebFt27YhOjoaABAYGIjExERs2LABI0aMaLPuRx99hLlz52LUqFFQKBSYPHky3n77bSdH7GaCYoAH3gF2LwP0OkDfAKg0bdcjIqJOw0TIS2VlZSErK6vd9UNDQ/Hxxx87LiBPNWiK8UZERC6Ju8aIiIjIazERomaWLVuGgICAFm9jx46VOzwiIiKH4a4xambWrFmYOnVqi6/5+fm1uJ2IiMgdMRGiZkJDQxEaGip3GERERE7HXWNERETktZgIOYAQQu4QXB7/RkRE5IqYCHWAWq0GANTU1Mgciesz/Y1MfzMiIiJXwDVCHaBUKhESEmK+7pa/vz8kSbKprsFgQENDA+rq6tq82ru7MhgMqK+vx5UrV3D58mWEhIRAqVTKHRYREZEZE6EOMl1k9OaLkLZFCIHa2lr4+fnZnDy5m6Z97Nq1Ky/ISkRELoeJUAdJkoTo6GhERkZCp9PZXE+n02Hv3r245557PHZ3kamPo0aNgq+vr9zhEBERNcNEyEGUSqVdu32USiUaGxvh6+vrsYmQqY/cHUZERK7KMxenEBEREdmAiRARERF5LSZCRERE5LW4RqgNphMBVlRUOLRdnU6HmpoaVFRUeOwaIU/vo6f3D/D8PrJ/7s/T+8j+tZ/pd7utE/oyEWpDZWUlACA2NlbmSIiIiMhelZWVCA4ObvV1SfDaB1YZDAZcuHABgYGBDj3fT0VFBWJjY3H27FkEBQU5rF1X4ul99PT+AZ7fR/bP/Xl6H9m/9hNCoLKyEjExMVZPXMwZoTYoFAp069bNae0HBQV55Ie7KU/vo6f3D/D8PrJ/7s/T+8j+tY+1mSATLpYmIiIir8VEiIiIiLwWEyGZaDQaLFmyBBqNRu5QnMbT++jp/QM8v4/sn/vz9D6yf87HxdJERETktTgjRERERF6LiRARERF5LSZCRERE5LWYCBEREZHXYiLkRO+++y569OgBX19fJCUlITc312r5DRs2IDExEb6+vhg4cCA+//zzTorUfsuXL8ftt9+OwMBAREZGYtKkSSgoKLBaJysrC5IkWdx8fX07KWL7vPzyy81iTUxMtFrHncYPAHr06NGsj5IkYc6cOS2Wd/Xx27t3LyZMmICYmBhIkoTPPvvM4nUhBBYvXozo6Gj4+fkhJSUFJ06caLNde7/HzmKtfzqdDgsWLMDAgQPRpUsXxMTE4LHHHsOFCxesttmez7kztTWG6enpzeJNS0trs113GEMALX4fJUnCn//851bbdKUxtOV3oa6uDnPmzEFYWBgCAgIwefJkXLx40Wq77f3u2oqJkJN88skneO6557BkyRIcPnwYgwcPxpgxY1BaWtpi+W+++QaPPPIIZsyYgSNHjmDSpEmYNGkSjh071smR22bPnj2YM2cO9u/fj+zsbOh0OqSmpqK6utpqvaCgIBQXF5tvhYWFnRSx/QYMGGAR69dff91qWXcbPwD49ttvLfqXnZ0NAJgyZUqrdVx5/KqrqzF48GC8++67Lb7+pz/9CW+//Tbef/99HDhwAF26dMGYMWNQV1fXapv2fo+dyVr/ampqcPjwYbz00ks4fPgwNm7ciIKCAjzwwANttmvP59zZ2hpDAEhLS7OId/369VbbdJcxBGDRr+LiYqxevRqSJGHy5MlW23WVMbTld+HZZ5/F5s2bsWHDBuzZswcXLlzAQw89ZLXd9nx37SLIKYYNGybmzJljfq7X60VMTIxYvnx5i+WnTp0qxo8fb7EtKSlJPP30006N01FKS0sFALFnz55Wy2RmZorg4ODOC6oDlixZIgYPHmxzeXcfPyGE+N3vfid69uwpDAZDi6+70/gBEJs2bTI/NxgMQqvVij//+c/mbWVlZUKj0Yj169e32o693+POcnP/WpKbmysAiMLCwlbL2Ps570wt9fHxxx8XEydOtKsddx7DiRMnipEjR1ot48pjePPvQllZmVCr1WLDhg3mMvn5+QKAyMnJabGN9n537cEZISdoaGjAoUOHkJKSYt6mUCiQkpKCnJycFuvk5ORYlAeAMWPGtFre1ZSXlwMAQkNDrZarqqpCXFwcYmNjMXHiRBw/frwzwmuXEydOICYmBgkJCXj00UdRVFTUall3H7+GhgasW7cOTz75pNWLC7vT+DV1+vRplJSUWIxRcHAwkpKSWh2j9nyPXUl5eTkkSUJISIjVcvZ8zl3B7t27ERkZib59+2L27Nm4cuVKq2XdeQwvXryIrVu3YsaMGW2WddUxvPl34dChQ9DpdBbjkZiYiO7du7c6Hu357tqLiZATXL58GXq9HlFRURbbo6KiUFJS0mKdkpISu8q7EoPBgPnz5+POO+/Erbfe2mq5vn37YvXq1fjPf/6DdevWwWAwYPjw4Th37lwnRmubpKQkZGVlYdu2bVi1ahVOnz6Nu+++G5WVlS2Wd+fxA4DPPvsMZWVlSE9Pb7WMO43fzUzjYM8Yted77Crq6uqwYMECPPLII1YvZGnv51xuaWlpWLt2LXbs2IE33ngDe/bswdixY6HX61ss785juGbNGgQGBra528hVx7Cl34WSkhL4+Pg0S87b+m00lbG1jr149XnqsDlz5uDYsWNt7pdOTk5GcnKy+fnw4cPRr18/fPDBB3j11VedHaZdxo4da348aNAgJCUlIS4uDp9++qlN/0NzNxkZGRg7dixiYmJaLeNO4+fNdDodpk6dCiEEVq1aZbWsu33Op02bZn48cOBADBo0CD179sTu3bsxatQoGSNzvNWrV+PRRx9t84AEVx1DW38XXAFnhJwgPDwcSqWy2Ur4ixcvQqvVtlhHq9XaVd5VzJ07F1u2bMGuXbvQrVs3u+qq1WoMGTIEJ0+edFJ0jhMSEoI+ffq0Gqu7jh8AFBYW4ssvv8RvfvMbu+q50/iZxsGeMWrP91hupiSosLAQ2dnZVmeDWtLW59zVJCQkIDw8vNV43XEMAeCrr75CQUGB3d9JwDXGsLXfBa1Wi4aGBpSVlVmUb+u30VTG1jr2YiLkBD4+Phg6dCh27Nhh3mYwGLBjxw6L/1E3lZycbFEeALKzs1stLzchBObOnYtNmzZh586diI+Pt7sNvV6P77//HtHR0U6I0LGqqqpw6tSpVmN1t/FrKjMzE5GRkRg/frxd9dxp/OLj46HVai3GqKKiAgcOHGh1jNrzPZaTKQk6ceIEvvzyS4SFhdndRlufc1dz7tw5XLlypdV43W0MTTIyMjB06FAMHjzY7rpyjmFbvwtDhw6FWq22GI+CggIUFRW1Oh7t+e62J3Bygn/9619Co9GIrKws8cMPP4innnpKhISEiJKSEiGEENOnTxcLFy40l9+3b59QqVRixYoVIj8/XyxZskSo1Wrx/fffy9UFq2bPni2Cg4PF7t27RXFxsflWU1NjLnNzH5cuXSq2b98uTp06JQ4dOiSmTZsmfH19xfHjx+XoglXPP/+82L17tzh9+rTYt2+fSElJEeHh4aK0tFQI4f7jZ6LX60X37t3FggULmr3mbuNXWVkpjhw5Io4cOSIAiL/+9a/iyJEj5qOmXn/9dRESEiL+85//iKNHj4qJEyeK+Ph4UVtba25j5MiRYuXKlebnbX2PXaV/DQ0N4oEHHhDdunUTeXl5Ft/J+vr6VvvX1ue8s1nrY2VlpXjhhRdETk6OOH36tPjyyy/FbbfdJnr37i3q6urMbbjrGJqUl5cLf39/sWrVqhbbcOUxtOV3YdasWaJ79+5i586d4uDBgyI5OVkkJydbtNO3b1+xceNG83NbvrsdwUTIiVauXCm6d+8ufHx8xLBhw8T+/fvNr917773i8ccftyj/6aefij59+ggfHx8xYMAAsXXr1k6O2HYAWrxlZmaay9zcx/nz55v/HlFRUWLcuHHi8OHDnR+8DR5++GERHR0tfHx8xC233CIefvhhcfLkSfPr7j5+Jtu3bxcAREFBQbPX3G38du3a1eJn0tQHg8EgXnrpJREVFSU0Go0YNWpUs37HxcWJJUuWWGyz9j3uTNb6d/r06Va/k7t27TK3cXP/2vqcdzZrfaypqRGpqakiIiJCqNVqERcXJ2bOnNksoXHXMTT54IMPhJ+fnygrK2uxDVceQ1t+F2pra8Vvf/tb0bVrV+Hv7y8efPBBUVxc3KydpnVs+e52hHT9TYmIiIi8DtcIERERkddiIkRERERei4kQEREReS0mQkREROS1mAgRERGR12IiRERERF6LiRARERF5LSZCROR20tPTMWnSpE5/36ysLEiSBEmSMH/+fJvqpKenm+t89tlnTo2PiOzHq88TkUuRJMnq60uWLMFbb70Fuc4FGxQUhIKCAnTp0sWm8m+99RZef/11t7l+F5G3YSJERC6luLjY/PiTTz7B4sWLUVBQYN4WEBCAgIAAOUIDYEzU7LnqdXBwMIKDg50YERF1BHeNEZFL0Wq15ltwcLA58TDdAgICmu0aGzFiBObNm4f58+eja9euiIqKwocffojq6mo88cQTCAwMRK9evfDf//7X4r2OHTuGsWPHIiAgAFFRUZg+fTouX75sd8zvvfceevfuDV9fX0RFReGXv/xlR/8MRNRJmAgRkUdYs2YNwsPDkZubi3nz5mH27NmYMmUKhg8fjsOHDyM1NRXTp09HTU0NAKCsrAwjR47EkCFDcPDgQWzbtg0XL17E1KlT7XrfgwcP4plnnsErr7yCgoICbNu2Dffcc48zukhETsBdY0TkEQYPHow//OEPAIBFixbh9ddfR3h4OGbOnAkAWLx4MVatWoWjR4/ijjvuwDvvvIMhQ4Zg2bJl5jZWr16N2NhY/PTTT+jTp49N71tUVIQuXbrg/vvvR2BgIOLi4jBkyBDHd5CInIIzQkTkEQYNGmR+rFQqERYWhoEDB5q3RUVFAQBKS0sBAN999x127dplXnMUEBCAxMREAMCpU6dsft/Ro0cjLi4OCQkJmD59Oj766CPzrBMRuT4mQkTkEdRqtcVzSZIstpmORjMYDACAqqoqTJgwAXl5eRa3EydO2LVrKzAwEIcPH8b69esRHR2NxYsXY/DgwSgrK+t4p4jI6bhrjIi80m233YZ///vf6NGjB1Sqjv1TqFKpkJKSgpSUFCxZsgQhISHYuXMnHnroIQdFS0TOwhkhIvJKc+bMwdWrV/HII4/g22+/xalTp7B9+3Y88cQT0Ov1NrezZcsWvP3228jLy0NhYSHWrl0Lg8GAvn37OjF6InIUJkJE5JViYmKwb98+6PV6pKamYuDAgZg/fz5CQkKgUNj+T2NISAg2btyIkSNHol+/fnj//fexfv16DBgwwInRE5GjSEKu07MSEbmZrKwszJ8/v13rfyRJwqZNm2S5NAgRtY4zQkREdigvL0dAQAAWLFhgU/lZs2bJeiZsIrKOM0JERDaqrKzExYsXARh3iYWHh7dZp7S0FBUVFQCA6Ohom69RRkSdg4kQEREReS3uGiMiIiKvxUSIiIiIvBYTISIiIvJaTISIiIjIazERIiIiIq/FRIiIiIi8FhMhIiIi8lpMhIiIiMhrMREiIiIir/X/AdhhLpzYrUe2AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Test the controller on step and triangular signal\n", + "tt = np.linspace(0, 5, int(5/0.01), endpoint=False)\n", + "data_target = np.concat([np.ones(511,dtype=np.float32)*1.0, # Step\n", + " -2*np.ones(500,dtype=np.float32)*1.0,\n", + " 2 * np.abs( 2 * (tt*1/5 - np.floor(tt*1/5 + 0.5)) ) - 1,\n", + " 2 * np.abs( 2 * (tt*1/2.5 - np.floor(tt*1/2.5 + 0.5)) ) - 1])\n", + "msd.loadData('test_control', {'x_t': data_target})\n", + "\n", + "vis = MPLNotebookVisualizer()\n", + "vis.setModely(msd)\n", + "vis.showResult('test_control')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/case-studies/mass_spring_damper/comparison/mass_spring_damper_torch.ipynb b/case-studies/mass_spring_damper/comparison/mass_spring_damper_torch.ipynb new file mode 100644 index 00000000..62616256 --- /dev/null +++ b/case-studies/mass_spring_damper/comparison/mass_spring_damper_torch.ipynb @@ -0,0 +1,737 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e4702b44", + "metadata": {}, + "source": [ + "# Mass Spring Damper System: A PyTorch Implementation\n", + "\n", + "This notebook presents a PyTorch implementation of the mass–spring–damper system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25bcecdc", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import numpy as np\n", + "from pathlib import Path\n", + "from tqdm import tqdm\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "b9d7042d", + "metadata": {}, + "source": [ + "# FIR Filter and System Model Definition\n", + "\n", + "This section defines the system architecture. \n", + "First, a learnable **FIR** filter is implemented in PyTorch to model linear temporal dynamics. \n", + "Then, the mass–spring–damper system is constructed by combining two **FIR** modules that separately capture the free response and the force response of the system." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "66130322", + "metadata": {}, + "outputs": [], + "source": [ + "class FIR(nn.Module):\n", + " def __init__(self, window_size, init_negexp=False):\n", + " super().__init__()\n", + " self.window_size = window_size\n", + " self.weight = nn.Parameter(torch.zeros(1, 1, window_size))\n", + "\n", + " if init_negexp:\n", + " t = torch.arange(window_size).float()\n", + " w = torch.exp(-3*t)\n", + " w = 0.1 * w / w.sum()\n", + " self.weight.data[:] = w.flip(0)\n", + " else:\n", + " self.weight.data.uniform_(.0, 1.0)\n", + "\n", + " def forward(self, x):\n", + " # x: (B, T)\n", + " y = F.conv1d(x.unsqueeze(1), self.weight)\n", + " return y.squeeze(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "22a726a7", + "metadata": {}, + "outputs": [], + "source": [ + "class NeuralMSD(nn.Module):\n", + " def __init__(self, window_dim):\n", + " super().__init__()\n", + " self.window_dim = window_dim\n", + " self.x_free = FIR(window_dim, init_negexp=True)\n", + " self.x_force = FIR(window_dim)\n", + "\n", + " def step(self, x, u):\n", + " return self.x_free(x) + self.x_force(u)\n", + " \n", + " def forward(self, x, u):\n", + " return self.step(x, u)\n", + "\n", + " def rollout(self, x, u_seq, horizon):\n", + " preds = []\n", + "\n", + " for k in range(horizon):\n", + " u_k = u_seq[:, k:k+self.window_dim]\n", + "\n", + " x_next = self.step(x, u_k) # (B,1)\n", + "\n", + " preds.append(x_next.squeeze(1))\n", + "\n", + " # shift window\n", + " x = torch.cat([x[:, 1:], x_next], dim=1)\n", + "\n", + " return torch.stack(preds, dim=1) # (B,H)" + ] + }, + { + "cell_type": "markdown", + "id": "c4f5f5d7", + "metadata": {}, + "source": [ + "# Dataset and DataLoader\n", + "\n", + "In PyTorch, the dataset must be defined explicitly. In this case, it is designed to handle sliding input windows, future prediction horizons, and to prevent sequences from crossing file boundaries." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48cc11a1", + "metadata": {}, + "outputs": [], + "source": [ + "def load_simulation_file(path):\n", + " data = np.loadtxt(path, delimiter=';')\n", + "\n", + " time = data[:,0]\n", + " x = data[:,1]\n", + " v = data[:,2]\n", + " u = data[:,3]\n", + "\n", + " return x, v, u\n", + "\n", + "from torch.utils.data import Dataset\n", + "from torch.utils.data import DataLoader\n", + "from torch.utils.data import random_split\n", + "\n", + "class MSDSimDataset(Dataset):\n", + " def __init__(self, folder, window_dim, horizon=1, stride=1):\n", + " self.window_dim = window_dim\n", + " self.data = [] # file list\n", + " self.index_map = [] # global window map\n", + " self.horizon = horizon\n", + " self.stride = stride\n", + "\n", + " files = sorted(Path(folder).glob(\"*.txt\"))\n", + "\n", + " # load all files\n", + " for f in files:\n", + " x, v, u = load_simulation_file(f)\n", + "\n", + " x = torch.tensor(x, dtype=torch.float32)\n", + " v = torch.tensor(v, dtype=torch.float32)\n", + " u = torch.tensor(u, dtype=torch.float32)\n", + "\n", + " self.data.append((x, v, u))\n", + "\n", + " # build sliding window map\n", + " for file_id, (x, _, _) in enumerate(self.data):\n", + " T = len(x)\n", + " max_start = T - self.window_dim - self.horizon\n", + "\n", + " for start in range(0, max_start, self.stride):\n", + " self.index_map.append((file_id, start))\n", + "\n", + " def __len__(self):\n", + " return len(self.index_map)\n", + "\n", + " def __getitem__(self, idx):\n", + " file_id, start = self.index_map[idx]\n", + " x, v, u = self.data[file_id]\n", + "\n", + " W = self.window_dim\n", + " H = self.horizon\n", + "\n", + " return (\n", + " x[start : start + W],\n", + " x[start + W : start + W + H],\n", + " u[start : start + W + H - 1]\n", + " )\n", + "\n", + "WINDOW_DIM = 10\n", + "HORIZON = 1\n", + "\n", + "dataset = MSDSimDataset(\"../msd-data/data\", window_dim=WINDOW_DIM, horizon=HORIZON)\n", + "n = len(dataset)\n", + "n_train = int(0.7*n)\n", + "n_val = int(0.2*n)\n", + "n_test = n - n_train - n_val\n", + "\n", + "train_ds, val_ds, test_ds = random_split(dataset, [n_train,n_val,n_test])\n", + "\n", + "train_loader = DataLoader(train_ds, batch_size=128, shuffle=True)\n", + "val_loader = DataLoader(val_ds, batch_size=128, shuffle=False)\n", + "test_loader = DataLoader(test_ds, batch_size=128, shuffle=False)" + ] + }, + { + "cell_type": "markdown", + "id": "eb36b056", + "metadata": {}, + "source": [ + "# Training Loop\n", + "\n", + "This section defines the training and validation workflow. The model is optimized using Adam with MSE loss, and the best weights are saved according to the validation performance." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f057c758", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 80 | train loss: 1.043e-07 | val_loss: 6.713e-10 | best_val_loss: 1.629e-11: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 80/80 [08:28<00:00, 6.35s/it]\n" + ] + } + ], + "source": [ + "model = NeuralMSD(window_dim=WINDOW_DIM)\n", + "opt = torch.optim.Adam(model.parameters(), lr=5e-4)\n", + "loss_fn = nn.MSELoss()\n", + "\n", + "best_val_loss = float('inf')\n", + "\n", + "train_losses = []\n", + "val_losses = []\n", + "\n", + "pbar = tqdm(range(80))\n", + "for epoch in pbar:\n", + " model.train()\n", + " total_loss = 0\n", + "\n", + " for x, x_next, u in train_loader:\n", + " pred = model.step(x, u)\n", + " loss = loss_fn(pred, x_next)\n", + "\n", + " opt.zero_grad()\n", + " loss.backward()\n", + " opt.step()\n", + "\n", + " total_loss += loss.item()\n", + " train_losses.append(total_loss / len(train_loader))\n", + "\n", + " model.eval()\n", + " val_loss = 0\n", + "\n", + " with torch.no_grad():\n", + " for x, x_next, u in val_loader:\n", + " pred = model.step(x, u)\n", + " loss = loss_fn(pred, x_next)\n", + " val_loss += loss.item()\n", + "\n", + " val_loss /= len(val_loader)\n", + "\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " torch.save(model.state_dict(), \"saved/best_model.pth\")\n", + " val_losses.append(val_loss)\n", + " pbar.set_description(f\"Epoch {epoch+1} | train loss: {total_loss/len(train_loader):.3e} | val_loss: {val_loss:.3e} | best_val_loss: {best_val_loss:.3e}\")" + ] + }, + { + "cell_type": "markdown", + "id": "19c483e5", + "metadata": {}, + "source": [ + "## Closed-Loop Evaluation\n", + "\n", + "The model is rolled out autoregressively, using its predictions as inputs at each step, and the resulting trajectory is compared with the ground truth." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "de1ab317", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, _, u = dataset.data[50]\n", + "\n", + "best_model = NeuralMSD(window_dim=WINDOW_DIM)\n", + "best_model.load_state_dict(torch.load(\"saved/best_model.pth\"))\n", + "\n", + "preds = best_model.rollout(x[:WINDOW_DIM].unsqueeze(0), u.unsqueeze(0), horizon=len(x)-WINDOW_DIM).squeeze(0)\n", + "\n", + "target = x[WINDOW_DIM:]\n", + "t = np.arange(len(preds)) * 0.01\n", + "plt.figure(figsize=(12,6))\n", + "plt.plot(t, preds.detach().numpy(), label=\"pred\", linewidth=2)\n", + "plt.plot(t, target.numpy(), '--', label=\"target\", linewidth=2)\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title(\"Model Rollout vs Target\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Position [m]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "2df1679d", + "metadata": {}, + "source": [ + "# Recurrent Training\n", + "\n", + "In this stage, the model is trained in a recurrent fashion over a 15 s prediction horizon. A sequence-based dataset is built to generate input windows and multi-step targets, enabling rollout predictions over the entire horizon. \n", + "\n", + "During training, the model iteratively feeds its own predictions back as inputs, and the loss is computed over the full sequence." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b309a2c4", + "metadata": {}, + "outputs": [], + "source": [ + "# Recurrent dataset\n", + "HORIZON = 1500\n", + "\n", + "dataset = MSDSimDataset(\"../msd-data/data\", window_dim=WINDOW_DIM, horizon=HORIZON, stride=5)\n", + "n = len(dataset)\n", + "n_train = int(0.7*n)\n", + "n_val = int(0.2*n)\n", + "n_test = n - n_train - n_val\n", + "\n", + "train_ds, val_ds, test_ds = random_split(dataset, [n_train,n_val,n_test])\n", + "\n", + "train_loader = DataLoader(train_ds, batch_size=128, shuffle=True)\n", + "val_loader = DataLoader(val_ds, batch_size=128, shuffle=False)\n", + "test_loader = DataLoader(test_ds, batch_size=128, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "598b2bed", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 10 | train loss: 9.789e-07 | val_loss: 1.008e-06 | best_val_loss: 9.293e-07: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 10/10 [08:37<00:00, 51.74s/it]\n" + ] + } + ], + "source": [ + "# Recurrent train\n", + "model = best_model\n", + "opt = torch.optim.Adam(model.parameters(), lr=1e-5)\n", + "loss_fn = nn.MSELoss()\n", + "\n", + "best_val_loss = float('inf')\n", + "\n", + "train_losses = []\n", + "val_losses = []\n", + "\n", + "pbar = tqdm(range(10))\n", + "for epoch in pbar:\n", + " model.train()\n", + " total_loss = 0\n", + "\n", + " for x, x_next, u_seq in train_loader:\n", + " pred = model.rollout(x, u_seq, HORIZON)\n", + " loss = loss_fn(pred, x_next)\n", + "\n", + " opt.zero_grad()\n", + " loss.backward()\n", + " opt.step()\n", + "\n", + " total_loss += loss.item()\n", + " train_losses.append(total_loss / len(train_loader))\n", + "\n", + " model.eval()\n", + " val_loss = 0\n", + "\n", + " with torch.no_grad():\n", + " for x, x_next, u_seq in val_loader:\n", + " pred = model.rollout(x, u_seq, HORIZON)\n", + " loss = loss_fn(pred, x_next)\n", + " val_loss += loss.item()\n", + "\n", + " val_loss /= len(val_loader)\n", + "\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " torch.save(model.state_dict(), \"saved/best_model.pth\")\n", + " val_losses.append(val_loss)\n", + " pbar.set_description(f\"Epoch {epoch+1} | train loss: {total_loss/len(train_loader):.3e} | val_loss: {val_loss:.3e} | best_val_loss: {best_val_loss:.3e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "76765ce2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot some predictions\n", + "x, _, u = dataset.data[50]\n", + "\n", + "best_model = NeuralMSD(window_dim=WINDOW_DIM)\n", + "best_model.load_state_dict(torch.load(\"saved/best_model.pth\"))\n", + "\n", + "preds = best_model.rollout(x[:WINDOW_DIM].unsqueeze(0), u.unsqueeze(0), horizon=len(x)-WINDOW_DIM).squeeze(0)\n", + "\n", + "target = x[WINDOW_DIM:]\n", + "t = np.arange(len(preds)) * 0.01\n", + "plt.figure(figsize=(12,6))\n", + "plt.plot(t, preds.detach().numpy(), label=\"pred\", linewidth=2)\n", + "plt.plot(t, target.numpy(), '--', label=\"target\", linewidth=2)\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.title(\"Model Rollout vs Target\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Position [m]\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "10d492fc", + "metadata": {}, + "source": [ + "# PID Controller and Closed-Loop Simulation\n", + "\n", + "Here, a trainable **PID** controller is coupled with the plant model to simulate the system in closed loop. The control input is computed from the tracking error and applied recursively at each time step." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "bd1ae187", + "metadata": {}, + "outputs": [], + "source": [ + "class PID(nn.Module):\n", + " def __init__(self, dt):\n", + " super().__init__()\n", + "\n", + " self.kp = nn.Parameter(torch.tensor(0.5))\n", + " self.ki = nn.Parameter(torch.tensor(0.5))\n", + " self.kd = nn.Parameter(torch.tensor(0.5))\n", + "\n", + " self.dt = dt\n", + "\n", + " # Internal state for integral and derivative (initialized in reset_state)\n", + " self.register_buffer(\"integ_state\", None, persistent=False)\n", + " self.register_buffer(\"prev_error\", None, persistent=False)\n", + "\n", + " def reset_state(self, batch_size, device=None):\n", + " device = device or self.kp.device\n", + " self.integ_state = torch.zeros((batch_size, 1), device=device)\n", + " self.prev_error = torch.zeros((batch_size, 1), device=device)\n", + "\n", + " def forward(self, e):\n", + " \"\"\"\n", + " e: (B,1)\n", + " return: (B,1)\n", + " \"\"\"\n", + "\n", + " # Eular integration\n", + " self.integ_state = self.integ_state + self.dt * e\n", + "\n", + " # Euler derivative\n", + " deriv = (e - self.prev_error) / self.dt\n", + "\n", + " self.prev_error = e\n", + "\n", + " u = (\n", + " self.kp * e +\n", + " self.ki * self.integ_state +\n", + " self.kd * deriv\n", + " )\n", + "\n", + " return u" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "93db9677", + "metadata": {}, + "outputs": [], + "source": [ + "class ControlledSystem(nn.Module):\n", + " def __init__(self, plant, pid):\n", + " super().__init__()\n", + " self.plant = plant\n", + " self.pid = pid\n", + "\n", + " def forward(self, x0, target):\n", + " x = x0\n", + " u_seq = torch.zeros((x0.shape), device=x0.device)\n", + " xs = []\n", + " self.pid.reset_state(batch_size=x0.shape[0], device=x0.device)\n", + "\n", + " for k in range(target.shape[1]):\n", + " e = target[:,k] - x[:, -1]\n", + " # shift control sequence and append new control\n", + " u_seq = torch.cat([u_seq[:, 1:], self.pid(e.unsqueeze(1))], dim=1)\n", + " x_next = self.plant.step(x, u_seq)\n", + " \n", + " # shift state sequence and append new state\n", + " x = torch.cat([x[:, 1:], x_next], dim=1)\n", + " xs.append(x_next.squeeze(1))\n", + "\n", + " return torch.stack(xs, dim=1)" + ] + }, + { + "cell_type": "markdown", + "id": "df31b583", + "metadata": {}, + "source": [ + "# PID Dataset and Controller Training\n", + "\n", + "This section trains the **PID** controller in closed loop using rollout simulations. The plant dynamics are fixed, and only the **PID** gains are optimized to minimize the trajectory tracking error over the full horizon." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "117169a8", + "metadata": {}, + "outputs": [], + "source": [ + "# PID dataset\n", + "HORIZON = 500\n", + "\n", + "dataset = MSDSimDataset(\"../msd-data/data\", window_dim=WINDOW_DIM, horizon=HORIZON, stride=5)\n", + "n = len(dataset)\n", + "n_train = int(0.7*n)\n", + "n_val = int(0.2*n)\n", + "n_test = n - n_train - n_val\n", + "\n", + "train_ds, val_ds, test_ds = random_split(dataset, [n_train,n_val,n_test])\n", + "\n", + "train_loader = DataLoader(train_ds, batch_size=128, shuffle=True)\n", + "val_loader = DataLoader(val_ds, batch_size=2048, shuffle=False)\n", + "test_loader = DataLoader(test_ds, batch_size=2048, shuffle=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b3c44a9f", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 20 | train loss: 3.254e-04 | val_loss: 3.056e-04 | best_val_loss: 3.056e-04: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 20/20 [20:29<00:00, 61.49s/it]\n" + ] + } + ], + "source": [ + "pid = PID(dt=0.01)\n", + "# Use the best model from the previous training as plant\n", + "system = ControlledSystem(best_model, pid)\n", + "\n", + "# Only train the PID parameters, keep the plant fixed\n", + "opt = torch.optim.Adam(pid.parameters(), lr=0.05)\n", + "loss_fn = nn.MSELoss()\n", + "\n", + "best_val_loss = float('inf')\n", + "\n", + "train_losses = []\n", + "val_losses = []\n", + "\n", + "pbar = tqdm(range(20))\n", + "for epoch in pbar:\n", + " system.train()\n", + " total_loss = 0\n", + "\n", + " for x, x_next, u in train_loader:\n", + " pred = system(x, x_next)\n", + " loss = loss_fn(pred, x_next)\n", + "\n", + " opt.zero_grad()\n", + " loss.backward()\n", + " opt.step()\n", + "\n", + " total_loss += loss.item()\n", + " train_losses.append(total_loss / len(train_loader))\n", + "\n", + " system.eval()\n", + " val_loss = 0\n", + "\n", + " with torch.no_grad():\n", + " for x, x_next, u in val_loader:\n", + " pred = system(x, x_next)\n", + " loss = loss_fn(pred, x_next)\n", + " val_loss += loss.item()\n", + "\n", + " val_loss /= len(val_loader)\n", + "\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " torch.save(pid.state_dict(), \"saved/best_controller.pth\")\n", + " val_losses.append(val_loss)\n", + " pbar.set_description(f\"Epoch {epoch+1} | train loss: {total_loss/len(train_loader):.3e} | val_loss: {val_loss:.3e} | best_val_loss: {best_val_loss:.3e}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e355c9aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Trained PID parameters: kp=16.196, ki=18.643, kd=8.131\n" + ] + } + ], + "source": [ + "# Print the parameter of the PID\n", + "print(f\"Trained PID parameters: kp={pid.kp.item():.3f}, ki={pid.ki.item():.3f}, kd={pid.kd.item():.3f}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8324968e", + "metadata": {}, + "source": [ + "## Controller Performance Evaluation\n", + "\n", + "The learned PID controller is tested in closed loop on a reference trajectory and compared with the nnodely implementation to assess tracking performance." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8834a9ec", + "metadata": {}, + "outputs": [], + "source": [ + "best_pid = PID(dt=0.01)\n", + "best_pid.load_state_dict(torch.load(\"saved/best_controller.pth\"))\n", + "best_system = ControlledSystem(best_model, best_pid)\n", + "\n", + "tt = torch.linspace(0, 5, int(5/0.01))\n", + "data_target = torch.cat([torch.ones(511,dtype=torch.float32)*1.0, # Step\n", + " -2*torch.ones(500,dtype=torch.float32)*1.0,\n", + " 2 * torch.abs( 2 * (tt*1/5 - torch.floor(tt*1/5 + 0.5)) ) - 1, # Triangle\n", + " 2 * torch.abs( 2 * (tt*1/2.5 - torch.floor(tt*1/2.5 + 0.5)) ) - 1])\n", + "best_system.eval()\n", + "preds = best_system(torch.zeros_like(data_target[1:WINDOW_DIM+1].unsqueeze(0)), data_target[WINDOW_DIM+1:].unsqueeze(0))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "06f385ce", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = pd.read_csv(\"PID_test_nnodely.csv\")\n", + "\n", + "plt.figure(figsize=(12,6))\n", + "t = torch.arange(len(preds.squeeze(0).detach().numpy())) * 0.01\n", + "plt.plot(t.numpy(), data_target[WINDOW_DIM+1:].numpy(), '-', label=\"Target\", linewidth=1, color='black')\n", + "plt.plot(t.numpy(), df['estimate'].to_numpy(), '--', label=\"nnodely PID\", linewidth=3, color='tab:blue')\n", + "plt.plot(t.numpy(), preds.squeeze(0).detach().numpy(), '-.', label=\"PyTorch PID\", linewidth=3, color='tab:orange')\n", + "plt.xlim(0, 20)\n", + "plt.ylim(-3.5, 1.5)\n", + "plt.grid()\n", + "plt.title(\"PID Controller Performance Comparison\")\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"Position [m]\")\n", + "plt.legend(loc='lower right', fontsize=24)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/case-studies/mass_spring_damper/comparison/saved/best_controller.pth b/case-studies/mass_spring_damper/comparison/saved/best_controller.pth new file mode 100644 index 00000000..151ec21a Binary files /dev/null and b/case-studies/mass_spring_damper/comparison/saved/best_controller.pth differ diff --git a/case-studies/mass_spring_damper/comparison/saved/best_model.pth b/case-studies/mass_spring_damper/comparison/saved/best_model.pth new file mode 100644 index 00000000..62577baa Binary files /dev/null and b/case-studies/mass_spring_damper/comparison/saved/best_model.pth differ diff --git a/case-studies/mass_spring_damper/comparison/saved/msd_final_with_PID.json b/case-studies/mass_spring_damper/comparison/saved/msd_final_with_PID.json new file mode 100644 index 00000000..8a4622e2 --- /dev/null +++ b/case-studies/mass_spring_damper/comparison/saved/msd_final_with_PID.json @@ -0,0 +1,114 @@ +{"Constants": {"SampleTime": {"dim": 1, "values": 0.01}}, + "Functions": {}, + "Info": {"SampleTime": 0.01, + "nnodely_version": "1.5.2", + "ns": [10, 1], + "ntot": 11, + "num_parameters": 23}, + "Inputs": {"F": {"dim": 1, "ns": [10, 0], "ntot": 10, "tw": [-0.1, 0]}, + "Sub13_int13": {"closedLoop": "Add20", + "dim": 1, + "local": 1, + "ns": [1, 0], + "ntot": 1, + "sw": [-1, 0]}, + "Sub13_int14": {"connect": "Sub13", + "dim": 1, + "local": 1, + "ns": [2, 0], + "ntot": 2, + "sw": [-2, 0]}, + "x": {"dim": 1, "ns": [10, 0], "ntot": 10, "tw": [-0.1, 0]}, + "x_m": {"dim": 1, "ns": [0, 1], "ntot": 1, "sw": [0, 1]}, + "x_t": {"dim": 1, "ns": [0, 1], "ntot": 1, "sw": [0, 1]}}, + "Minimizers": {"x[t]": {"A": "SamplePart8", "B": "Add6", "loss": "mse"}}, + "Models": {"PID": {"Constants": ["SampleTime"], + "Functions": [], + "Inputs": ["x_m", "x_t", "Sub13_int14", "Sub13_int13"], + "Outputs": ["F_PID"], + "Parameters": ["D", "I", "P"], + "Relations": ["SamplePart12", + "SamplePart10", + "Sub13", + "SamplePart28", + "SamplePart26", + "Sub29", + "Div31", + "Mul34", + "Mul19", + "SamplePart17", + "Add20", + "Mul23", + "Mul15", + "Add24", + "Add35"]}, + "neural_msd": {"Constants": [], + "Functions": [], + "Inputs": ["F", "x", "x_t"], + "Outputs": ["x_n"], + "Parameters": ["PFir5W", "PFir3W"], + "Relations": ["TimePart4", + "Fir5", + "TimePart1", + "Fir2", + "Add6", + "SamplePart8"]}}, + "Outputs": {"F_PID": "Add35", "x_n": "Add6"}, + "Parameters": {"D": {"dim": 1, + "init_values": [0.5], + "values": [8.177507400512695]}, + "I": {"dim": 1, + "init_values": [0.5], + "values": [18.563440322875977]}, + "P": {"dim": 1, + "init_values": [0.5], + "values": [16.827768325805664]}, + "PFir3W": {"dim": 1, + "init_fun": {"name": "init_negexp", + "params": {"first_value": 0.1, + "lambda": 3, + "size_index": 0}}, + "tw": 0.1, + "values": [[-0.18469615280628204], + [-0.12608873844146729], + [-0.06660982221364975], + [-0.005940185859799385], + [0.05625353008508682], + [0.1205686405301094], + [0.18780383467674255], + [0.2590898871421814], + [0.3359234631061554], + [0.4205183684825897]]}, + "PFir5W": {"dim": 1, + "tw": 0.1, + "values": [[-1.662617978581693e-05], + [4.8189936933340505e-05], + [7.177559018600732e-05], + [5.9527203120524064e-05], + [0.00011585278843995184], + [0.00019496057939250022], + [0.00014330405974760652], + [0.00018421869026497006], + [0.00017740165640134364], + [8.003542461665347e-05]]}}, + "Relations": {"Add20": ["Add", ["SamplePart17", "Mul19"]], + "Add24": ["Add", ["Mul15", "Mul23"]], + "Add35": ["Add", ["Add24", "Mul34"]], + "Add6": ["Add", ["Fir2", "Fir5"]], + "Div31": ["Div", ["Sub29", "SampleTime"]], + "Fir2": ["Fir", ["TimePart1"], "PFir3W", null, 0], + "Fir5": ["Fir", ["TimePart4"], "PFir5W", null, 0], + "Mul15": ["Mul", ["Sub13", "P"]], + "Mul19": ["Mul", ["Sub13", "SampleTime"]], + "Mul23": ["Mul", ["Add20", "I"]], + "Mul34": ["Mul", ["Div31", "D"]], + "SamplePart10": ["SamplePart", ["x_t"], -1, [0, 1]], + "SamplePart12": ["SamplePart", ["x_m"], -1, [0, 1]], + "SamplePart17": ["SamplePart", ["Sub13_int13"], -1, [-1, 0]], + "SamplePart26": ["SamplePart", ["Sub13_int14"], -1, [-1, 0]], + "SamplePart28": ["SamplePart", ["Sub13_int14"], -1, [-2, -1]], + "SamplePart8": ["SamplePart", ["x_t"], -1, [0, 1]], + "Sub13": ["Sub", ["SamplePart10", "SamplePart12"]], + "Sub29": ["Sub", ["SamplePart26", "SamplePart28"]], + "TimePart1": ["TimePart", ["x"], -1, [-0.1, 0]], + "TimePart4": ["TimePart", ["F"], -1, [-0.1, 0]]}} \ No newline at end of file diff --git a/case-studies/mass_spring_damper/comparison/saved/msd_final_with_PID.py b/case-studies/mass_spring_damper/comparison/saved/msd_final_with_PID.py new file mode 100644 index 00000000..1d916b74 --- /dev/null +++ b/case-studies/mass_spring_damper/comparison/saved/msd_final_with_PID.py @@ -0,0 +1,119 @@ +import torch + +def nnodely_basic_model_update_state(data_in, rel): + data_out = data_in.clone() + max_dim = min(rel.size(1), data_in.size(1)) + data_out[:, -max_dim:, :] = rel[:, -max_dim:, :] + return data_out + +def nnodely_basic_model_timeshift(data_in): + return torch.cat((data_in[:, 1:, :], data_in[:, :1, :]), dim=1) + +class TracerModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.all_parameters = {} + self.all_constants = {} + self.all_constants["SampleTime"] = torch.tensor(0.009999999776482582, requires_grad=False) + self.all_parameters["D"] = torch.nn.Parameter(torch.tensor([8.177507400512695]), requires_grad=True) + self.all_parameters["I"] = torch.nn.Parameter(torch.tensor([18.563440322875977]), requires_grad=True) + self.all_parameters["P"] = torch.nn.Parameter(torch.tensor([16.827768325805664]), requires_grad=True) + self.all_parameters["PFir3W"] = torch.nn.Parameter(torch.tensor([[-0.18469615280628204], [-0.12608873844146729], [-0.06660982221364975], [-0.005940185859799385], [0.05625353008508682], [0.1205686405301094], [0.18780383467674255], [0.2590898871421814], [0.3359234631061554], [0.4205183684825897]]), requires_grad=True) + self.all_parameters["PFir5W"] = torch.nn.Parameter(torch.tensor([[-1.662617978581693e-05], [4.8189936933340505e-05], [7.177559018600732e-05], [5.9527203120524064e-05], [0.00011585278843995184], [0.00019496057939250022], [0.00014330405974760652], [0.00018421869026497006], [0.00017740165640134364], [8.003542461665347e-05]]), requires_grad=True) + self.all_constants["SamplePart10"] = torch.tensor([[1.0]], requires_grad=True) + self.all_constants["SamplePart12"] = torch.tensor([[1.0]], requires_grad=True) + self.all_constants["SamplePart17"] = torch.tensor([[1.0]], requires_grad=True) + self.all_constants["SamplePart26"] = torch.tensor([[0.0, 1.0]], requires_grad=True) + self.all_constants["SamplePart28"] = torch.tensor([[1.0, 0.0]], requires_grad=True) + self.all_constants["SamplePart8"] = torch.tensor([[1.0]], requires_grad=True) + self.all_constants["TimePart1"] = torch.tensor([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]], requires_grad=True) + self.all_constants["TimePart4"] = torch.tensor([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]], requires_grad=True) + self.all_parameters = torch.nn.ParameterDict(self.all_parameters) + self.all_constants = torch.nn.ParameterDict(self.all_constants) + + def update(self, closed_loop={}, connect={}, disconnect=False): + pass + + def forward(self, kwargs): + getitem = kwargs['x_m'] + relation_forward_sample_part12_w = self.all_constants.SamplePart12 + einsum = torch.functional.einsum('bij,ki->bkj', getitem, relation_forward_sample_part12_w); getitem = relation_forward_sample_part12_w = None + getitem_1 = kwargs['x_t'] + relation_forward_sample_part10_w = self.all_constants.SamplePart10 + einsum_1 = torch.functional.einsum('bij,ki->bkj', getitem_1, relation_forward_sample_part10_w); getitem_1 = relation_forward_sample_part10_w = None + sub = einsum_1 - einsum; einsum_1 = einsum = None + getitem_2 = kwargs['Sub13_int14'] + update_state = nnodely_basic_model_update_state(getitem_2, sub); getitem_2 = None + relation_forward_sample_part28_w = self.all_constants.SamplePart28 + einsum_2 = torch.functional.einsum('bij,ki->bkj', update_state, relation_forward_sample_part28_w); relation_forward_sample_part28_w = None + relation_forward_sample_part26_w = self.all_constants.SamplePart26 + einsum_3 = torch.functional.einsum('bij,ki->bkj', update_state, relation_forward_sample_part26_w); relation_forward_sample_part26_w = None + sub_1 = einsum_3 - einsum_2; einsum_3 = einsum_2 = None + all_constants_sample_time = self.all_constants.SampleTime + truediv = sub_1 / all_constants_sample_time; sub_1 = None + all_parameters_d = self.all_parameters.D + mul = truediv * all_parameters_d; truediv = all_parameters_d = None + mul_1 = sub * all_constants_sample_time; all_constants_sample_time = None + getitem_3 = kwargs['Sub13_int13'] + relation_forward_sample_part17_w = self.all_constants.SamplePart17 + einsum_4 = torch.functional.einsum('bij,ki->bkj', getitem_3, relation_forward_sample_part17_w); getitem_3 = relation_forward_sample_part17_w = None + add = einsum_4 + mul_1; einsum_4 = mul_1 = None + all_parameters_i = self.all_parameters.I + mul_2 = add * all_parameters_i; all_parameters_i = None + all_parameters_p = self.all_parameters.P + mul_3 = sub * all_parameters_p; sub = all_parameters_p = None + add_1 = mul_3 + mul_2; mul_3 = mul_2 = None + add_2 = add_1 + mul; add_1 = mul = None + getitem_4 = kwargs['F'] + relation_forward_time_part4_w = self.all_constants.TimePart4 + einsum_5 = torch.functional.einsum('bij,ki->bkj', getitem_4, relation_forward_time_part4_w); getitem_4 = relation_forward_time_part4_w = None + size = einsum_5.size(0) + relation_forward_fir5_weights = self.all_parameters.PFir5W + size_1 = relation_forward_fir5_weights.size(1) + squeeze = einsum_5.squeeze(-1); einsum_5 = None + matmul = torch.matmul(squeeze, relation_forward_fir5_weights); squeeze = relation_forward_fir5_weights = None + to = matmul.to(dtype = torch.float32); matmul = None + view = to.view(size, 1, size_1); to = size = size_1 = None + getitem_5 = kwargs['x'] + relation_forward_time_part1_w = self.all_constants.TimePart1 + einsum_6 = torch.functional.einsum('bij,ki->bkj', getitem_5, relation_forward_time_part1_w); getitem_5 = relation_forward_time_part1_w = None + size_2 = einsum_6.size(0) + relation_forward_fir2_weights = self.all_parameters.PFir3W + size_3 = relation_forward_fir2_weights.size(1) + squeeze_1 = einsum_6.squeeze(-1); einsum_6 = None + matmul_1 = torch.matmul(squeeze_1, relation_forward_fir2_weights); squeeze_1 = relation_forward_fir2_weights = None + to_1 = matmul_1.to(dtype = torch.float32); matmul_1 = None + view_1 = to_1.view(size_2, 1, size_3); to_1 = size_2 = size_3 = None + add_3 = view_1 + view; view_1 = view = None + getitem_6 = kwargs['x_t']; kwargs = None + relation_forward_sample_part8_w = self.all_constants.SamplePart8 + einsum_7 = torch.functional.einsum('bij,ki->bkj', getitem_6, relation_forward_sample_part8_w); getitem_6 = relation_forward_sample_part8_w = None + return ({'F_PID': add_2, 'x_n': add_3}, {'SamplePart8': einsum_7, 'Add6': add_3}, {'Sub13_int13': add}, {'Sub13_int14': update_state}) + +class RecurrentModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.Cell = TracerModel() + self.inputs = ['x_m', 'x_t', 'F', 'x', ] + self.states = dict() + + def forward(self, kwargs): + n_samples = min([kwargs[key].size(0) for key in self.inputs]) + self.states['Sub13_int14'] = kwargs['Sub13_int14'] + self.states['Sub13_int13'] = kwargs['Sub13_int13'] + results = {'F_PID':[], 'x_n':[], } + X = dict() + for idx in range(n_samples): + for key in self.inputs: + X[key] = kwargs[key][idx] + for key, value in self.states.items(): + X[key] = value + out, _, closed_loop, connect = self.Cell(X) + for key, value in results.items(): + results[key].append(out[key]) + for key, val in closed_loop.items(): + self.states[key] = nnodely_basic_model_timeshift(self.states[key]) + self.states[key] = nnodely_basic_model_update_state(self.states[key], val) + for key, val in connect.items(): + self.states[key] = nnodely_basic_model_timeshift(val) + return results diff --git a/case-studies/mass_spring_damper/mass_spring_damper.py b/case-studies/mass_spring_damper/mass_spring_damper.py new file mode 100644 index 00000000..60122de7 --- /dev/null +++ b/case-studies/mass_spring_damper/mass_spring_damper.py @@ -0,0 +1,105 @@ +from nnodely import * + + +# This example shows how to fit a simple linear model. +# The model is a mass spring damper. +# The data was created previously and loaded from file. +# The data are the position of the mass and the force applied. +# The neural model mirrors the structure of the physical model. +# The network estimates the future position of the mass. + +# The time history for x +T_x = 0.1 + +# Define the neural model +x = Input('x') # MSNN input (Mass position) +F = Input('F') # MSNN input (Force) +x_free = Fir(W_init = 'init_negexp', W_init_params = {'first_value':0.1,'size_index':0,'lambda':3})(x.tw(T_x)) +x_force = Fir(F.tw(T_x)) +x_n = Output('x_n', x_free + x_force) + +# Add the neural models to the nnodely structure +msd = Modely(seed = 42, workspace = 'saved') +msd.addModel('neural_msd', x_n) + +# These functions are used to impose the minimization objectives. +# Here it is minimized the error between the future position of x get from the dataset +# and the estimator designed using the neural network. +# The minimization is imposed via MSE error. +x_t = Input('x_t') # Real position +msd.addMinimize('x[t]', x_t.next(), x_n) + +# Nauralize the model and getting the neural network. +# The sampling time depends on the datasets. +msd.neuralizeModel(0.01) + +# Data load carica i file CSV e costruisce automaticamente il dataset compatibile con la struttura della rete. +data_struct = ['time', ('x','x_t'), '', 'F'] +msd.loadData(name = 'simulations', + source = 'msd-data/data', + format = data_struct, delimiter = ';') + +# Neural network train +default_par = {'num_of_epochs' : 80, + 'train_batch_size' : 128, + 'lr' : 0.0005, + 'splits' : [70,20,10]} +msd.trainModel(training_params = default_par) + +# Save the neural model in json format +msd.saveModel(name = 'msd_preliminary') + +# Show the network performance on the test dataset +msd.analyzeModel(splits = [70,20,10]) +vis = MPLVisualizer() +vis.setModely(msd) +vis.showResult("simulations_test") + +# Refine weights with recurrent train and analyze the model showing the performance (mse, FVU, AIC). +msd.trainAndAnalyze(num_of_epochs = 10, prediction_samples = 1500, step = 500, lr=0.00001, + closed_loop={'x':'x_n'}, training_params = default_par) + +# Show the network performance on the test dataset on recurrent +vis.showResult("simulations_test") +# Save the neural model in json format +msd.saveModel(name = 'msd_final') + +# Definition of the PID controller network +x_m = Input('x_m') # measured position +kp = Parameter('P', values=0.5) +ki = Parameter('I', values=0.5) +kd = Parameter('D', values=0.5) +e = x_t.next()-x_m.next() +c = e*kp+Integrate(e)*ki+Differentiate(e)*kd +controlForce = Output('F_PID', c) +msd.addModel('PID',controlForce) + +# Neuralization of the whole models +msd.neuralizeModel() + +# Train the PID controller +msd.trainModel(models = 'PID', + closed_loop = {'x' : 'x_n','x_m' : 'x_n'}, + connect = {'F' : 'F_PID'}, + prediction_samples = 500, + step = 500, num_of_epochs = 20, + lr = 0.05, + training_params = default_par) + +# Print the parameter of the PID +print(f"The PID controller kp = {msd.parameters['P']} ki = {msd.parameters['I']} kd = {msd.parameters['D']}") + +# Test the controller on step and triangular signal +import numpy as np +tt = np.linspace(0, 5, int(5/0.01), endpoint=False) +data_target = np.concatenate([np.ones(511,dtype=np.float32)*1.0, # Step + -2*np.ones(500,dtype=np.float32)*1.0, + 2 * np.abs( 2 * (tt*1/5 - np.floor(tt*1/5 + 0.5)) ) - 1, + 2 * np.abs( 2 * (tt*1/2.5 - np.floor(tt*1/2.5 + 0.5)) ) - 1]) +msd.loadData('test_control', {'x_t': data_target}) +msd.analyzeModel('test_control', + prediction_samples = 2000, + closed_loop = {'x' : 'x_n','x_m' : 'x_n'}, + connect = {'F' : 'F_PID'}, + batch_size = 1) +vis.showResult('test_control') \ No newline at end of file diff --git a/case-studies/mass_spring_damper/msd-data/data/1.txt b/case-studies/mass_spring_damper/msd-data/data/1.txt new file mode 100644 index 00000000..1b836ad5 --- /dev/null +++ b/case-studies/mass_spring_damper/msd-data/data/1.txt @@ -0,0 +1,2001 @@ +0;0.0192574774141414;0.0419367541414499;0 +0.01;0.0196739012595751;0.0412805176962757;0 +0.02;0.0200837119519013;0.0406130348364797;0 +0.03;0.020486798011891;0.0399345249759165;0 +0.04;0.0208830501676899;0.0392452104568002;0 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"cell_type": "markdown", + "id": "6e093f24", + "metadata": {}, + "source": [ + "# Neural ODE Implementation in nnodely: Mass Spring Damper\n", + "\n", + "This section presents a Neural ODE model in nnodely for learning the continuous-time dynamics of the mass spring damper." + ] + }, + { + "cell_type": "code", + "id": "67c44ae2", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-27T15:24:11.101524Z", + "start_time": "2026-02-27T15:24:10.484610Z" + } + }, + "source": [ + "import os\n", + "from nnodely import *\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from nnodely.support import earlystopping\n", + "from nnodely.support.odeint.adjoint import odeint_adjoint\n", + "\n", + "def init_random_range(indexes, params_size, dict_param={'min_value': 0.0, 'max_value': 1.0}):\n", + " import numpy as np\n", + " min_val = dict_param.get('min_value', 0.0)\n", + " max_val = dict_param.get('max_value', 1.0)\n", + " return np.random.uniform(low=min_val, high=max_val)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>-- nnodely_v1.5.2 --<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "0f2d4f10", + "metadata": {}, + "source": [ + "# Neural ODE Function\n", + "\n", + "At this stage, nnodely uses the **ParamFun** block to wrap a PyTorch module and employ it as the Neural ODE dynamics function.\n", + "\n", + "The function below defines the continuous-time state evolution. It maps the current state window to its time derivative through a structured neural model that acts as the learnable vector field. In particular, the dynamics are modeled using two FIR filters applied to the position and input-force windows, and the next velocity is computed as the sum of their contributions." + ] + }, + { + "cell_type": "code", + "id": "dcc7da2c", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-27T15:24:11.108984Z", + "start_time": "2026-02-27T15:24:11.102685Z" + } + }, + "source": [ + "def ode_func_torch(t, state, weight_fir):\n", + " import torch\n", + " import torch.nn.functional as F\n", + " # state: (B, W, 2)\n", + " # B = batch size\n", + " # W = window length\n", + " # 2 channels = [position_window, force_window]\n", + " #\n", + " # weight_fir: FIR weights applied independently to each channel\n", + "\n", + " # Apply two independent FIR filters (grouped convolution):\n", + " # this produces one-step predictions for each channel\n", + " # (B, W, 2) -> (B, 2, W) -> conv -> (B, 2, 1)\n", + " out = F.conv1d(state.transpose(1, 2), weight_fir, groups=2)\n", + "\n", + " # Restore time dimension ordering\n", + " # (B, 2, 1) -> (B, 1, 2)\n", + " out = out.transpose(1, 2)\n", + "\n", + " # Combine the two FIR contributions:\n", + " # next_velocity = free_response + forced_response\n", + " out[:, :, 0] = out[:, :, 0] + out[:, :, -1]\n", + "\n", + " # Set the force prediction to zero, for the intermediate time steps done in the integration process (if a variable step integrator, such as Dopri5, is used)\n", + " out[:, :, -1] = 0.0\n", + "\n", + " # Shift the sliding window forward by one step\n", + " # drop the oldest sample and append the new prediction\n", + " # resulting shape: (B, W, 2)\n", + " out = torch.cat((state[:, 1:, :], out), dim=1)\n", + "\n", + " return out" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "de1088d6", + "metadata": {}, + "source": [ + "# Neural ODE Model Definition in nnodely\n", + "\n", + "This section defines the Neural ODE model using nnodely’s modeling interface. \n", + "The system state together with the force input are concatenated and passed to a learnable Neural ODE block that represents the continuous-time dynamics.\n", + "\n", + "Trainable parameters define the neural vector field, while multiple outputs are connected in closed loop and optimized through MSE loss on position. Finally, the model is neuralized and exported for training and simulation." + ] + }, + { + "cell_type": "code", + "id": "616f6513", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-27T15:24:11.312554Z", + "start_time": "2026-02-27T15:24:11.275795Z" + } + }, + "source": [ + "model_name = 'neuralODE_msd'\n", + "msd = Modely(seed=42, workspace='saved')\n", + "\n", + "x = Input('x') # MSNN input (Mass position)\n", + "F = Input('F') # MSNN input (Force)\n", + "T_x = 0.1\n", + "init_value = 0.1\n", + "weight_fir = Parameter('weight_fir', dimensions=(2, 1, 10), init=init_random_range, init_params={'min_value': -init_value, 'max_value': init_value})\n", + "paramFun = ParamFun(ode_func_torch)\n", + "neuOde = NeuralODE(func=paramFun, dt=0.01, rtol=1e-7, atol=1e-9, method='dopri5')\n", + "state = Concatenate(x.tw(T_x), F.tw(T_x))\n", + "ans = neuOde(state, weight_fir)\n", + "x_est = TimePart(Select(ans, 0), 0.09, 0.1)\n", + "x_est.closedLoop(x)\n", + "\n", + "# Output and loss\n", + "x_n = Output('x_n', x_est)\n", + "\n", + "x_t = Input('x_t')\n", + "msd.addModel('msd', [x_n])\n", + "msd.addMinimize('x[t]', x_t.next(), x_n, loss_function='mse')\n", + "msd.neuralizeModel(sample_time = 0.01)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[33m[neuralizeModel] Closed loop on x with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {},\n", + " 'Functions': {'FNeuralODE5': {'atol': 1e-09,\n", + " 'code': 'def FNeuralODE5(state, *weights):\\n'\n", + " ' from '\n", + " 'nnodely.support.odeint.adjoint import '\n", + " 'odeint_adjoint as odeint\\n'\n", + " ' def ode_func_torch(t, state, '\n", + " 'weight_fir):\\n'\n", + " ' import torch\\n'\n", + " ' import torch.nn.functional as '\n", + " 'F\\n'\n", + " ' # state: (B, W, 2)\\n'\n", + " ' # B = batch size\\n'\n", + " ' # W = window length\\n'\n", + " ' # 2 channels = '\n", + " '[position_window, force_window]\\n'\n", + " ' #\\n'\n", + " ' # weight_fir: FIR weights '\n", + " 'applied independently to each channel\\n'\n", + " ' \\n'\n", + " ' # Apply two independent FIR '\n", + " 'filters (grouped convolution):\\n'\n", + " ' # this produces one-step '\n", + " 'predictions for each channel\\n'\n", + " ' # (B, W, 2) -> (B, 2, W) -> '\n", + " 'conv -> (B, 2, 1)\\n'\n", + " ' out = '\n", + " 'F.conv1d(state.transpose(1, 2), '\n", + " 'weight_fir, groups=2)\\n'\n", + " ' \\n'\n", + " ' # Restore time dimension '\n", + " 'ordering\\n'\n", + " ' # (B, 2, 1) -> (B, 1, 2)\\n'\n", + " ' out = out.transpose(1, 2)\\n'\n", + " ' \\n'\n", + " ' # Combine the two FIR '\n", + " 'contributions:\\n'\n", + " ' # next_velocity = '\n", + " 'free_response + forced_response\\n'\n", + " ' out[:, :, 0] = out[:, :, 0] + '\n", + " 'out[:, :, -1]\\n'\n", + " ' \\n'\n", + " ' # Set the force prediction to '\n", + " 'zero, for the intermediate time steps '\n", + " 'done in the integration process (if a '\n", + " 'variable step integrator, such as '\n", + " 'Dopri5, is used)\\n'\n", + " ' out[:, :, -1] = 0.0\\n'\n", + " ' \\n'\n", + " ' # Shift the sliding window '\n", + " 'forward by one step\\n'\n", + " ' # drop the oldest sample and '\n", + " 'append the new prediction\\n'\n", + " ' # resulting shape: (B, W, 2)\\n'\n", + " ' out = torch.cat((state[:, 1:, '\n", + " ':], out), dim=1)\\n'\n", + " ' \\n'\n", + " ' return out\\n'\n", + " ' \\n'\n", + " ' ans = odeint(lambda t, y: '\n", + " 'ode_func_torch(t, y, *weights), state, '\n", + " 't=torch.tensor([0.0, 0.01]), '\n", + " 'rtol=1e-07, atol=1e-09, '\n", + " \"method='dopri5', \"\n", + " 'adjoint_params=list(weights))\\n'\n", + " ' return ans[-1]\\n',\n", + " 'dt': 0.01,\n", + " 'method': 'dopri5',\n", + " 'name': 'FNeuralODE5',\n", + " 'rtol': 1e-07}},\n", + " 'Info': {'SampleTime': 0.01,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [10, 1],\n", + " 'ntot': 11,\n", + " 'num_parameters': 20},\n", + " 'Inputs': {'F': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'x': {'closedLoop': 'TimePart8',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [10, 0],\n", + " 'ntot': 10,\n", + " 'tw': [-0.1, 0]},\n", + " 'x_t': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]}},\n", + " 'Minimizers': {'x[t]': {'A': 'SamplePart10', 'B': 'TimePart8', 'loss': 'mse'}},\n", + " 'Models': 'msd',\n", + " 'Outputs': {'x_n': 'TimePart8'},\n", + " 'Parameters': {'weight_fir': {'dim': [2, 1, 10],\n", + " 'init_fun': {'code': 'def '\n", + " 'init_random_range(indexes, '\n", + " 'params_size, '\n", + " \"dict_param={'min_value': \"\n", + " \"0.0, 'max_value': 1.0}):\\n\"\n", + " ' import numpy as np\\n'\n", + " ' min_val = '\n", + " \"dict_param.get('min_value', \"\n", + " '0.0)\\n'\n", + " ' max_val = '\n", + " \"dict_param.get('max_value', \"\n", + " '1.0)\\n'\n", + " ' return '\n", + " 'np.random.uniform(low=min_val, '\n", + " 'high=max_val)\\n',\n", + " 'name': 'init_random_range',\n", + " 'params': {'max_value': 0.1,\n", + " 'min_value': -0.1}},\n", + " 'values': [[[-0.025091975927352905,\n", + " 0.0901428610086441,\n", + " 0.04639878869056702,\n", + " 0.01973169669508934,\n", + " -0.06879626959562302,\n", + " -0.06880109757184982,\n", + " -0.0883832797408104,\n", + " 0.07323522865772247,\n", + " 0.02022300288081169,\n", + " 0.04161451384425163]],\n", + " [[-0.09588310122489929,\n", + " 0.09398197382688522,\n", + " 0.06648852676153183,\n", + " -0.0575321763753891,\n", + " -0.06363500654697418,\n", + " -0.06331909447908401,\n", + " -0.03915155306458473,\n", + " 0.004951286129653454,\n", + " -0.013610996305942535,\n", + " -0.04175417125225067]]]}},\n", + " 'Relations': {'Concatenate4': ['Concatenate', ['TimePart1', 'TimePart3']],\n", + " 'NeuralODE5': ['NeuralODE',\n", + " ['Concatenate4', 'weight_fir'],\n", + " 'FNeuralODE5'],\n", + " 'SamplePart10': ['SamplePart', ['x_t'], -1, [0, 1]],\n", + " 'Select7': ['Select', ['NeuralODE5'], 2, 0],\n", + " 'TimePart1': ['TimePart', ['x'], -1, [-0.1, 0]],\n", + " 'TimePart3': ['TimePart', ['F'], -1, [-0.1, 0]],\n", + " 'TimePart8': ['TimePart', ['Select7'], 0.1, [0.09, 0.1]]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "3d40b3f8", + "metadata": {}, + "source": [ + "# Data Loading\n", + "\n", + "This section loads the training, validation, and test datasets into nnodely. \n", + "Each dataset contains time series of the force input and the position. \n", + "\n", + "The data are read from CSV files and registered within the framework to be used for model training and evaluation." + ] + }, + { + "cell_type": "code", + "id": "f0f3f7be", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-27T15:24:13.507162Z", + "start_time": "2026-02-27T15:24:13.191582Z" + } + }, + "source": [ + "data_struct = ['time', ('x_t', 'x'), '', 'F']\n", + "msd.loadData(name = 'simulations',\n", + " source = 'data',\n", + " format = data_struct, delimiter = ';')" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m============================ nnodely Model Dataset =============================\u001B[0m\n", + "\u001B[32mDataset Name: simulations\u001B[0m\n", + "\u001B[32mNumber of files: 100\u001B[0m\n", + "\u001B[32mTotal number of samples: 199100\u001B[0m\n", + "\u001B[32mShape of x: (199100, 10, 1)\u001B[0m\n", + "\u001B[32mShape of F: (199100, 10, 1)\u001B[0m\n", + "\u001B[32mShape of x_t: (199100, 1, 1)\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "a737b085", + "metadata": {}, + "source": [ + "# Training\n", + "\n", + "This section trains the Neural ODE model using nnodely’s built-in training pipeline. \n", + "The model is optimized with Adam on the training dataset and evaluated on a separate validation set." + ] + }, + { + "cell_type": "code", + "id": "7fab61e7", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-27T15:36:05.871626Z", + "start_time": "2026-02-27T15:24:39.965159Z" + } + }, + "source": [ + "msd.exportPythonModel(name = model_name)\n", + "msd.importPythonModel(name = model_name)\n", + "msd.neuralizeModel(sample_time=0.01)\n", + "param_model = {'num_of_epochs' : 20,\n", + " 'train_batch_size' : 128,\n", + " 'lr' : 1e-3,\n", + " 'splits' : [70, 20, 10]}\n", + "msd.trainModel(training_params = param_model, minimize_gain=None)\n", + "\n", + "# Save the neural model\n", + "msd.exportPythonModel(name = model_name)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m=============================== Save JSON Model ================================\u001B[0m\n", + "\u001B[32mModel saved in: saved/neuralODE_msd.json\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m========================== Export Python Torch Model ===========================\u001B[0m\n", + "\u001B[32mModel exported in: saved/neuralODE_msd.py\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m=============================== Load JSON Model ================================\u001B[0m\n", + "\u001B[32mModel loaded from: saved/neuralODE_msd.json\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[33m[neuralizeModel] Closed loop on x with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {},\n", + " 'Functions': {'FNeuralODE5': {'atol': 1e-09,\n", + " 'code': 'def FNeuralODE5(state, *weights):\\n'\n", + " ' from '\n", + " 'nnodely.support.odeint.adjoint import '\n", + " 'odeint_adjoint as odeint\\n'\n", + " ' def ode_func_torch(t, state, '\n", + " 'weight_fir):\\n'\n", + " ' import torch\\n'\n", + " ' import torch.nn.functional as '\n", + " 'F\\n'\n", + " ' # state: (B, W, 2)\\n'\n", + " ' # B = batch size\\n'\n", + " ' # W = window length\\n'\n", + " ' # 2 channels = '\n", + " '[position_window, force_window]\\n'\n", + " ' #\\n'\n", + " ' # weight_fir: FIR weights '\n", + " 'applied independently to each channel\\n'\n", + " ' \\n'\n", + " ' # Apply two independent FIR '\n", + " 'filters (grouped convolution):\\n'\n", + " ' # this produces one-step '\n", + " 'predictions for each channel\\n'\n", + " ' # (B, W, 2) -> (B, 2, W) -> '\n", + " 'conv -> (B, 2, 1)\\n'\n", + " ' out = '\n", + " 'F.conv1d(state.transpose(1, 2), '\n", + " 'weight_fir, groups=2)\\n'\n", + " ' \\n'\n", + " ' # Restore time dimension '\n", + " 'ordering\\n'\n", + " ' # (B, 2, 1) -> (B, 1, 2)\\n'\n", + " ' out = out.transpose(1, 2)\\n'\n", + " ' \\n'\n", + " ' # Combine the two FIR '\n", + " 'contributions:\\n'\n", + " ' # next_velocity = '\n", + " 'free_response + forced_response\\n'\n", + " ' out[:, :, 0] = out[:, :, 0] + '\n", + " 'out[:, :, -1]\\n'\n", + " ' \\n'\n", + " ' # Set the force prediction to '\n", + " 'zero, for the intermediate time steps '\n", + " 'done in the integration process (if a '\n", + " 'variable step integrator, such as '\n", + " 'Dopri5, is used)\\n'\n", + " ' out[:, :, -1] = 0.0\\n'\n", + " ' \\n'\n", + " ' # Shift the sliding window '\n", + " 'forward by one step\\n'\n", + " ' # drop the oldest sample and '\n", + " 'append the new prediction\\n'\n", + " ' # resulting shape: (B, W, 2)\\n'\n", + " ' out = torch.cat((state[:, 1:, '\n", + " ':], out), dim=1)\\n'\n", + " ' \\n'\n", + " ' return out\\n'\n", + " ' \\n'\n", + " ' ans = odeint(lambda t, y: '\n", + " 'ode_func_torch(t, y, *weights), state, '\n", + " 't=torch.tensor([0.0, 0.01]), '\n", + " 'rtol=1e-07, atol=1e-09, '\n", + " \"method='dopri5', \"\n", + " 'adjoint_params=list(weights))\\n'\n", + " ' return ans[-1]\\n',\n", + " 'dt': 0.01,\n", + " 'method': 'dopri5',\n", + " 'name': 'FNeuralODE5',\n", + " 'rtol': 1e-07}},\n", + " 'Info': {'SampleTime': 0.01,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [10, 1],\n", + " 'ntot': 11,\n", + " 'num_parameters': 20},\n", + " 'Inputs': {'F': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'x': {'closedLoop': 'TimePart8',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [10, 0],\n", + " 'ntot': 10,\n", + " 'tw': [-0.1, 0]},\n", + " 'x_t': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]}},\n", + " 'Minimizers': {'x[t]': {'A': 'SamplePart10', 'B': 'TimePart8', 'loss': 'mse'}},\n", + " 'Models': 'msd',\n", + " 'Outputs': {'x_n': 'TimePart8'},\n", + " 'Parameters': {'weight_fir': {'dim': [2, 1, 10],\n", + " 'init_fun': {'code': 'def '\n", + " 'init_random_range(indexes, '\n", + " 'params_size, '\n", + " \"dict_param={'min_value': \"\n", + " \"0.0, 'max_value': 1.0}):\\n\"\n", + " ' import numpy as np\\n'\n", + " ' min_val = '\n", + " \"dict_param.get('min_value', \"\n", + " '0.0)\\n'\n", + " ' max_val = '\n", + " \"dict_param.get('max_value', \"\n", + " '1.0)\\n'\n", + " ' return '\n", + " 'np.random.uniform(low=min_val, '\n", + " 'high=max_val)\\n',\n", + " 'name': 'init_random_range',\n", + " 'params': {'max_value': 0.1,\n", + " 'min_value': -0.1}},\n", + " 'values': [[[-0.025091975927352905,\n", + " 0.0901428610086441,\n", + " 0.04639878869056702,\n", + " 0.01973169669508934,\n", + " -0.06879626959562302,\n", + " -0.06880109757184982,\n", + " -0.0883832797408104,\n", + " 0.07323522865772247,\n", + " 0.02022300288081169,\n", + " 0.04161451384425163]],\n", + " [[-0.09588310122489929,\n", + " 0.09398197382688522,\n", + " 0.06648852676153183,\n", + " -0.0575321763753891,\n", + " -0.06363500654697418,\n", + " -0.06331909447908401,\n", + " -0.03915155306458473,\n", + " 0.004951286129653454,\n", + " -0.013610996305942535,\n", + " -0.04175417125225067]]]}},\n", + " 'Relations': {'Concatenate4': ['Concatenate', ['TimePart1', 'TimePart3']],\n", + " 'NeuralODE5': ['NeuralODE',\n", + " ['Concatenate4', 'weight_fir'],\n", + " 'FNeuralODE5'],\n", + " 'SamplePart10': ['SamplePart', ['x_t'], -1, [0, 1]],\n", + " 'Select7': ['Select', ['NeuralODE5'], 2, 0],\n", + " 'TimePart1': ['TimePart', ['x'], -1, [-0.1, 0]],\n", + " 'TimePart3': ['TimePart', ['F'], -1, [-0.1, 0]],\n", + " 'TimePart8': ['TimePart', ['Select7'], 0.1, [0.09, 0.1]]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m========================== Import Python Torch Model ===========================\u001B[0m\n", + "\u001B[32mModel imported from: saved/neuralODE_msd.py\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[33m[neuralizeModel] Closed loop on x with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {},\n", + " 'Functions': {'FNeuralODE5': {'atol': 1e-09,\n", + " 'code': 'def FNeuralODE5(state, *weights):\\n'\n", + " ' from '\n", + " 'nnodely.support.odeint.adjoint import '\n", + " 'odeint_adjoint as odeint\\n'\n", + " ' def ode_func_torch(t, state, '\n", + " 'weight_fir):\\n'\n", + " ' import torch\\n'\n", + " ' import torch.nn.functional as '\n", + " 'F\\n'\n", + " ' # state: (B, W, 2)\\n'\n", + " ' # B = batch size\\n'\n", + " ' # W = window length\\n'\n", + " ' # 2 channels = '\n", + " '[position_window, force_window]\\n'\n", + " ' #\\n'\n", + " ' # weight_fir: FIR weights '\n", + " 'applied independently to each channel\\n'\n", + " ' \\n'\n", + " ' # Apply two independent FIR '\n", + " 'filters (grouped convolution):\\n'\n", + " ' # this produces one-step '\n", + " 'predictions for each channel\\n'\n", + " ' # (B, W, 2) -> (B, 2, W) -> '\n", + " 'conv -> (B, 2, 1)\\n'\n", + " ' out = '\n", + " 'F.conv1d(state.transpose(1, 2), '\n", + " 'weight_fir, groups=2)\\n'\n", + " ' \\n'\n", + " ' # Restore time dimension '\n", + " 'ordering\\n'\n", + " ' # (B, 2, 1) -> (B, 1, 2)\\n'\n", + " ' out = out.transpose(1, 2)\\n'\n", + " ' \\n'\n", + " ' # Combine the two FIR '\n", + " 'contributions:\\n'\n", + " ' # next_velocity = '\n", + " 'free_response + forced_response\\n'\n", + " ' out[:, :, 0] = out[:, :, 0] + '\n", + " 'out[:, :, -1]\\n'\n", + " ' \\n'\n", + " ' # Set the force prediction to '\n", + " 'zero, for the intermediate time steps '\n", + " 'done in the integration process (if a '\n", + " 'variable step integrator, such as '\n", + " 'Dopri5, is used)\\n'\n", + " ' out[:, :, -1] = 0.0\\n'\n", + " ' \\n'\n", + " ' # Shift the sliding window '\n", + " 'forward by one step\\n'\n", + " ' # drop the oldest sample and '\n", + " 'append the new prediction\\n'\n", + " ' # resulting shape: (B, W, 2)\\n'\n", + " ' out = torch.cat((state[:, 1:, '\n", + " ':], out), dim=1)\\n'\n", + " ' \\n'\n", + " ' return out\\n'\n", + " ' \\n'\n", + " ' ans = odeint(lambda t, y: '\n", + " 'ode_func_torch(t, y, *weights), state, '\n", + " 't=torch.tensor([0.0, 0.01]), '\n", + " 'rtol=1e-07, atol=1e-09, '\n", + " \"method='dopri5', \"\n", + " 'adjoint_params=list(weights))\\n'\n", + " ' return ans[-1]\\n',\n", + " 'dt': 0.01,\n", + " 'method': 'dopri5',\n", + " 'name': 'FNeuralODE5',\n", + " 'rtol': 1e-07}},\n", + " 'Info': {'SampleTime': 0.01,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [10, 1],\n", + " 'ntot': 11,\n", + " 'num_parameters': 20},\n", + " 'Inputs': {'F': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'x': {'closedLoop': 'TimePart8',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [10, 0],\n", + " 'ntot': 10,\n", + " 'tw': [-0.1, 0]},\n", + " 'x_t': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]}},\n", + " 'Minimizers': {'x[t]': {'A': 'SamplePart10', 'B': 'TimePart8', 'loss': 'mse'}},\n", + " 'Models': 'msd',\n", + " 'Outputs': {'x_n': 'TimePart8'},\n", + " 'Parameters': {'weight_fir': {'dim': [2, 1, 10],\n", + " 'init_fun': {'code': 'def '\n", + " 'init_random_range(indexes, '\n", + " 'params_size, '\n", + " \"dict_param={'min_value': \"\n", + " \"0.0, 'max_value': 1.0}):\\n\"\n", + " ' import numpy as np\\n'\n", + " ' min_val = '\n", + " \"dict_param.get('min_value', \"\n", + " '0.0)\\n'\n", + " ' max_val = '\n", + " \"dict_param.get('max_value', \"\n", + " '1.0)\\n'\n", + " ' return '\n", + " 'np.random.uniform(low=min_val, '\n", + " 'high=max_val)\\n',\n", + " 'name': 'init_random_range',\n", + " 'params': {'max_value': 0.1,\n", + " 'min_value': -0.1}},\n", + " 'values': [[[-0.025091975927352905,\n", + " 0.0901428610086441,\n", + " 0.04639878869056702,\n", + " 0.01973169669508934,\n", + " -0.06879626959562302,\n", + " -0.06880109757184982,\n", + " -0.0883832797408104,\n", + " 0.07323522865772247,\n", + " 0.02022300288081169,\n", + " 0.04161451384425163]],\n", + " [[-0.09588310122489929,\n", + " 0.09398197382688522,\n", + " 0.06648852676153183,\n", + " -0.0575321763753891,\n", + " -0.06363500654697418,\n", + " -0.06331909447908401,\n", + " -0.03915155306458473,\n", + " 0.004951286129653454,\n", + " -0.013610996305942535,\n", + " -0.04175417125225067]]]}},\n", + " 'Relations': {'Concatenate4': ['Concatenate', ['TimePart1', 'TimePart3']],\n", + " 'NeuralODE5': ['NeuralODE',\n", + " ['Concatenate4', 'weight_fir'],\n", + " 'FNeuralODE5'],\n", + " 'SamplePart10': ['SamplePart', ['x_t'], -1, [0, 1]],\n", + " 'Select7': ['Select', ['NeuralODE5'], 2, 0],\n", + " 'TimePart1': ['TimePart', ['x'], -1, [-0.1, 0]],\n", + " 'TimePart3': ['TimePart', ['F'], -1, [-0.1, 0]],\n", + " 'TimePart8': ['TimePart', ['Select7'], 0.1, [0.09, 0.1]]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m======================== nnodely Model Train Parameters ========================\u001B[0m\n", + "\u001B[32mmodels: ['msd']\u001B[0m\n", + "\u001B[32mnum of epochs: 20\u001B[0m\n", + "\u001B[32mupdate per epochs: 1088\u001B[0m\n", + "\u001B[34mβ””>len(train_indexes)//(batch_size+step)\u001B[0m\n", + "\u001B[32mshuffle data: True\u001B[0m\n", + "\u001B[32mprediction samples: 0\u001B[0m\n", + "\u001B[32mstep: 0\u001B[0m\n", + "\u001B[32mclosed loop: {}\u001B[0m\n", + "\u001B[32mconnect: {}\u001B[0m\n", + "\u001B[32mtrain dataset: simulations_train\u001B[0m\n", + "\u001B[32m\t- batch size: 128\u001B[0m\n", + "\u001B[32m\t- num of samples: 139370\u001B[0m\n", + "\u001B[32m\t- num of first samples: 139370\u001B[0m\n", + "\u001B[32mvalidation dataset: simulations_val\u001B[0m\n", + "\u001B[32m\t- batch size: 128\u001B[0m\n", + "\u001B[32m\t- num of samples: 39820\u001B[0m\n", + "\u001B[32m\t- num of first samples: 39820\u001B[0m\n", + "\u001B[32mtest dataset: simulations_test\u001B[0m\n", + "\u001B[32m\t- num of samples: 19910\u001B[0m\n", + "\u001B[32m\t- num of first samples: 19910\u001B[0m\n", + "\u001B[32mminimizers: {'x[t]': {'A': 'SamplePart10',\n", + " 'B': 'TimePart8',\n", + " 'loss': 'mse'}}\u001B[0m\n", + "\u001B[32moptimizer: Adam\u001B[0m\n", + "\u001B[32moptimizer defaults: {'lr': 0.001}\u001B[0m\n", + "\u001B[32moptimizer params: [{'params': 'weight_fir'}]\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m================= nnodely Training =================\u001B[0m\n", + "\u001B[32m| Epoch |\u001B[0m\u001B[32m x[t] |\u001B[0m\u001B[32m Total |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m val |\u001B[0m\n", + "\u001B[32m|--------------------------------------------------|\u001B[0m\n", + "\u001B[32m| 1/20 |\u001B[0m\u001B[32m2.946e-05|\u001B[0m\u001B[32m3.003e-05|\u001B[0m\u001B[32m2.946e-05|\u001B[0m\u001B[32m3.003e-05|\u001B[0m\n", + "\u001B[32m| 2/20 |\u001B[0m\u001B[32m2.333e-05|\u001B[0m\u001B[32m2.359e-05|\u001B[0m\u001B[32m2.333e-05|\u001B[0m\u001B[32m2.359e-05|\u001B[0m\n", + "\u001B[32m| 3/20 |\u001B[0m\u001B[32m1.811e-05|\u001B[0m\u001B[32m1.807e-05|\u001B[0m\u001B[32m1.811e-05|\u001B[0m\u001B[32m1.807e-05|\u001B[0m\n", + "\u001B[32m| 4/20 |\u001B[0m\u001B[32m1.361e-05|\u001B[0m\u001B[32m1.329e-05|\u001B[0m\u001B[32m1.361e-05|\u001B[0m\u001B[32m1.329e-05|\u001B[0m\n", + "\u001B[32m| 5/20 |\u001B[0m\u001B[32m9.822e-06|\u001B[0m\u001B[32m9.341e-06|\u001B[0m\u001B[32m9.822e-06|\u001B[0m\u001B[32m9.341e-06|\u001B[0m\n", + "\u001B[32m| 6/20 |\u001B[0m\u001B[32m6.726e-06|\u001B[0m\u001B[32m6.203e-06|\u001B[0m\u001B[32m6.726e-06|\u001B[0m\u001B[32m6.203e-06|\u001B[0m\n", + "\u001B[32m| 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|\u001B[0m\u001B[32m1.724e-09|\u001B[0m\u001B[32m1.689e-10|\u001B[0m\u001B[32m1.724e-09|\u001B[0m\u001B[32m1.689e-10|\u001B[0m\n", + "\u001B[32m| 14/20 |\u001B[0m\u001B[32m1.613e-10|\u001B[0m\u001B[32m 2.83e-11|\u001B[0m\u001B[32m1.613e-10|\u001B[0m\u001B[32m 2.83e-11|\u001B[0m\n", + "\u001B[32m| 15/20 |\u001B[0m\u001B[32m4.815e-11|\u001B[0m\u001B[32m6.983e-12|\u001B[0m\u001B[32m4.815e-11|\u001B[0m\u001B[32m6.983e-12|\u001B[0m\n", + "\u001B[32m| 16/20 |\u001B[0m\u001B[32m1.415e-11|\u001B[0m\u001B[32m1.566e-12|\u001B[0m\u001B[32m1.415e-11|\u001B[0m\u001B[32m1.566e-12|\u001B[0m\n", + "\u001B[32m| 17/20 |\u001B[0m\u001B[32m1.537e-11|\u001B[0m\u001B[32m2.816e-12|\u001B[0m\u001B[32m1.537e-11|\u001B[0m\u001B[32m2.816e-12|\u001B[0m\n", + "\u001B[32m| 18/20 |\u001B[0m\u001B[32m2.932e-11|\u001B[0m\u001B[32m3.224e-12|\u001B[0m\u001B[32m2.932e-11|\u001B[0m\u001B[32m3.224e-12|\u001B[0m\n", + "\u001B[32m| 19/20 |\u001B[0m\u001B[32m2.718e-11|\u001B[0m\u001B[32m7.241e-11|\u001B[0m\u001B[32m2.718e-11|\u001B[0m\u001B[32m7.241e-11|\u001B[0m\n", + "\u001B[32m| 20/20 |\u001B[0m\u001B[32m2.717e-11|\u001B[0m\u001B[32m1.739e-12|\u001B[0m\u001B[32m2.717e-11|\u001B[0m\u001B[32m1.739e-12|\u001B[0m\n", + "\u001B[32m|--------------------------------------------------|\u001B[0m\n", + "\u001B[1;32m============================ nnodely Training Time =============================\u001B[0m\n", + "\u001B[32mTotal time of Training: 685.4026439189911\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[34mThe selected model is the LAST model of the training.\u001B[0m\n", + "\u001B[1;32m=============================== Save JSON Model ================================\u001B[0m\n", + "\u001B[32mModel saved in: saved/neuralODE_msd.json\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m========================== Export Python Torch Model ===========================\u001B[0m\n", + "\u001B[32mModel exported in: saved/neuralODE_msd.py\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 6 + }, + { + "cell_type": "markdown", + "id": "d29d6d28", + "metadata": {}, + "source": [ + "## Closed-Loop Evaluation\n" + ] + }, + { + "cell_type": "code", + "id": "5dd1d858", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-27T15:39:16.793767Z", + "start_time": "2026-02-27T15:39:15.286428Z" + } + }, + "source": [ + "# Recursive plot using getSamples\n", + "msd.importPythonModel(name = model_name)\n", + "msd.neuralizeModel(sample_time=0.01)\n", + "samples = msd.getSamples(dataset='simulations', window=2000-10, index=0)\n", + "result = msd(samples, sampled=True, prediction_samples=2000-10)\n", + "\n", + "#region Plotting\n", + "t = np.arange(len(result['x_n'])) * 0.01\n", + "plt.figure(figsize=(12, 7))\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot(t, result['x_n'], label=r'Estimated $x$', linewidth=2)\n", + "plt.plot(t, np.array(samples['x_t']).squeeze(-1).squeeze(-1), label=r'True $x$', linestyle='--', linewidth=2)\n", + "plt.title('Position estimation')\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel(r'$x$ [m]')\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.subplot(2, 1, 2)\n", + "plt.plot(t, np.array(samples['F'])[:, 0, 0], label=r'Applied force $F$')\n", + "plt.title('Applied force')\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel('Force [N]')\n", + "plt.grid()\n", + "plt.legend()\n", + "plt.tight_layout()\n", + "#endregion\n", + "\n", + "plt.show()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m=============================== Load JSON Model ================================\u001B[0m\n", + "\u001B[32mModel loaded from: saved/neuralODE_msd.json\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[33m[neuralizeModel] Closed loop on x with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {},\n", + " 'Functions': {'FNeuralODE5': {'atol': 1e-09,\n", + " 'code': 'def FNeuralODE5(state, *weights):\\n'\n", + " ' from '\n", + " 'nnodely.support.odeint.adjoint import '\n", + " 'odeint_adjoint as odeint\\n'\n", + " ' def ode_func_torch(t, state, '\n", + " 'weight_fir):\\n'\n", + " ' import torch\\n'\n", + " ' import torch.nn.functional as '\n", + " 'F\\n'\n", + " ' # state: (B, W, 2)\\n'\n", + " ' # B = batch size\\n'\n", + " ' # W = window length\\n'\n", + " ' # 2 channels = '\n", + " '[position_window, force_window]\\n'\n", + " ' #\\n'\n", + " ' # weight_fir: FIR weights '\n", + " 'applied independently to each channel\\n'\n", + " ' \\n'\n", + " ' # Apply two independent FIR '\n", + " 'filters (grouped convolution):\\n'\n", + " ' # this produces one-step '\n", + " 'predictions for each channel\\n'\n", + " ' # (B, W, 2) -> (B, 2, W) -> '\n", + " 'conv -> (B, 2, 1)\\n'\n", + " ' out = '\n", + " 'F.conv1d(state.transpose(1, 2), '\n", + " 'weight_fir, groups=2)\\n'\n", + " ' \\n'\n", + " ' # Restore time dimension '\n", + " 'ordering\\n'\n", + " ' # (B, 2, 1) -> (B, 1, 2)\\n'\n", + " ' out = out.transpose(1, 2)\\n'\n", + " ' \\n'\n", + " ' # Combine the two FIR '\n", + " 'contributions:\\n'\n", + " ' # next_velocity = '\n", + " 'free_response + forced_response\\n'\n", + " ' out[:, :, 0] = out[:, :, 0] + '\n", + " 'out[:, :, -1]\\n'\n", + " ' \\n'\n", + " ' # Set the force prediction to '\n", + " 'zero, for the intermediate time steps '\n", + " 'done in the integration process (if a '\n", + " 'variable step integrator, such as '\n", + " 'Dopri5, is used)\\n'\n", + " ' out[:, :, -1] = 0.0\\n'\n", + " ' \\n'\n", + " ' # Shift the sliding window '\n", + " 'forward by one step\\n'\n", + " ' # drop the oldest sample and '\n", + " 'append the new prediction\\n'\n", + " ' # resulting shape: (B, W, 2)\\n'\n", + " ' out = torch.cat((state[:, 1:, '\n", + " ':], out), dim=1)\\n'\n", + " ' \\n'\n", + " ' return out\\n'\n", + " ' \\n'\n", + " ' ans = odeint(lambda t, y: '\n", + " 'ode_func_torch(t, y, *weights), state, '\n", + " 't=torch.tensor([0.0, 0.01]), '\n", + " 'rtol=1e-07, atol=1e-09, '\n", + " \"method='dopri5', \"\n", + " 'adjoint_params=list(weights))\\n'\n", + " ' return ans[-1]\\n',\n", + " 'dt': 0.01,\n", + " 'method': 'dopri5',\n", + " 'name': 'FNeuralODE5',\n", + " 'rtol': 1e-07}},\n", + " 'Info': {'SampleTime': 0.01,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [10, 1],\n", + " 'ntot': 11,\n", + " 'num_parameters': 20},\n", + " 'Inputs': {'F': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'x': {'closedLoop': 'TimePart8',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [10, 0],\n", + " 'ntot': 10,\n", + " 'tw': [-0.1, 0]},\n", + " 'x_t': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]}},\n", + " 'Minimizers': {'x[t]': {'A': 'SamplePart10', 'B': 'TimePart8', 'loss': 'mse'}},\n", + " 'Models': 'msd',\n", + " 'Outputs': {'x_n': 'TimePart8'},\n", + " 'Parameters': {'weight_fir': {'dim': [2, 1, 10],\n", + " 'init_fun': {'code': 'def '\n", + " 'init_random_range(indexes, '\n", + " 'params_size, '\n", + " \"dict_param={'min_value': \"\n", + " \"0.0, 'max_value': 1.0}):\\n\"\n", + " ' import numpy as np\\n'\n", + " ' min_val = '\n", + " \"dict_param.get('min_value', \"\n", + " '0.0)\\n'\n", + " ' max_val = '\n", + " \"dict_param.get('max_value', \"\n", + " '1.0)\\n'\n", + " ' return '\n", + " 'np.random.uniform(low=min_val, '\n", + " 'high=max_val)\\n',\n", + " 'name': 'init_random_range',\n", + " 'params': {'max_value': 0.1,\n", + " 'min_value': -0.1}},\n", + " 'values': [[[-5.0214056968688965,\n", + " -4.025257587432861,\n", + " -3.0458261966705322,\n", + " -1.9209206104278564,\n", + " -0.7650308012962341,\n", + " 0.5171442031860352,\n", + " 1.7535150051116943,\n", + " 3.087076187133789,\n", + " 4.083190441131592,\n", + " 5.219380855560303]],\n", + " [[0.00026014805189333856,\n", + " 0.0007117472123354673,\n", + " 0.0013745456235483289,\n", + " 0.002709923079237342,\n", + " 0.003882204182446003,\n", + " 0.005919742863625288,\n", + " 0.0070823198184370995,\n", + " 0.008177743293344975,\n", + " 0.009529106318950653,\n", + " 0.008549299091100693]]]}},\n", + " 'Relations': {'Concatenate4': ['Concatenate', ['TimePart1', 'TimePart3']],\n", + " 'NeuralODE5': ['NeuralODE',\n", + " ['Concatenate4', 'weight_fir'],\n", + " 'FNeuralODE5'],\n", + " 'SamplePart10': ['SamplePart', ['x_t'], -1, [0, 1]],\n", + " 'Select7': ['Select', ['NeuralODE5'], 2, 0],\n", + " 'TimePart1': ['TimePart', ['x'], -1, [-0.1, 0]],\n", + " 'TimePart3': ['TimePart', ['F'], -1, [-0.1, 0]],\n", + " 'TimePart8': ['TimePart', ['Select7'], 0.1, [0.09, 0.1]]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m========================== Import Python Torch Model ===========================\u001B[0m\n", + "\u001B[32mModel imported from: saved/neuralODE_msd.py\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[33m[neuralizeModel] Closed loop on x with sample in the future.\u001B[0m\n", + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {},\n", + " 'Functions': {'FNeuralODE5': {'atol': 1e-09,\n", + " 'code': 'def FNeuralODE5(state, *weights):\\n'\n", + " ' from '\n", + " 'nnodely.support.odeint.adjoint import '\n", + " 'odeint_adjoint as odeint\\n'\n", + " ' def ode_func_torch(t, state, '\n", + " 'weight_fir):\\n'\n", + " ' import torch\\n'\n", + " ' import torch.nn.functional as '\n", + " 'F\\n'\n", + " ' # state: (B, W, 2)\\n'\n", + " ' # B = batch size\\n'\n", + " ' # W = window length\\n'\n", + " ' # 2 channels = '\n", + " '[position_window, force_window]\\n'\n", + " ' #\\n'\n", + " ' # weight_fir: FIR weights '\n", + " 'applied independently to each channel\\n'\n", + " ' \\n'\n", + " ' # Apply two independent FIR '\n", + " 'filters (grouped convolution):\\n'\n", + " ' # this produces one-step '\n", + " 'predictions for each channel\\n'\n", + " ' # (B, W, 2) -> (B, 2, W) -> '\n", + " 'conv -> (B, 2, 1)\\n'\n", + " ' out = '\n", + " 'F.conv1d(state.transpose(1, 2), '\n", + " 'weight_fir, groups=2)\\n'\n", + " ' \\n'\n", + " ' # Restore time dimension '\n", + " 'ordering\\n'\n", + " ' # (B, 2, 1) -> (B, 1, 2)\\n'\n", + " ' out = out.transpose(1, 2)\\n'\n", + " ' \\n'\n", + " ' # Combine the two FIR '\n", + " 'contributions:\\n'\n", + " ' # next_velocity = '\n", + " 'free_response + forced_response\\n'\n", + " ' out[:, :, 0] = out[:, :, 0] + '\n", + " 'out[:, :, -1]\\n'\n", + " ' \\n'\n", + " ' # Set the force prediction to '\n", + " 'zero, for the intermediate time steps '\n", + " 'done in the integration process (if a '\n", + " 'variable step integrator, such as '\n", + " 'Dopri5, is used)\\n'\n", + " ' out[:, :, -1] = 0.0\\n'\n", + " ' \\n'\n", + " ' # Shift the sliding window '\n", + " 'forward by one step\\n'\n", + " ' # drop the oldest sample and '\n", + " 'append the new prediction\\n'\n", + " ' # resulting shape: (B, W, 2)\\n'\n", + " ' out = torch.cat((state[:, 1:, '\n", + " ':], out), dim=1)\\n'\n", + " ' \\n'\n", + " ' return out\\n'\n", + " ' \\n'\n", + " ' ans = odeint(lambda t, y: '\n", + " 'ode_func_torch(t, y, *weights), state, '\n", + " 't=torch.tensor([0.0, 0.01]), '\n", + " 'rtol=1e-07, atol=1e-09, '\n", + " \"method='dopri5', \"\n", + " 'adjoint_params=list(weights))\\n'\n", + " ' return ans[-1]\\n',\n", + " 'dt': 0.01,\n", + " 'method': 'dopri5',\n", + " 'name': 'FNeuralODE5',\n", + " 'rtol': 1e-07}},\n", + " 'Info': {'SampleTime': 0.01,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [10, 1],\n", + " 'ntot': 11,\n", + " 'num_parameters': 20},\n", + " 'Inputs': {'F': {'dim': 1, 'ns': [10, 0], 'ntot': 10, 'tw': [-0.1, 0]},\n", + " 'x': {'closedLoop': 'TimePart8',\n", + " 'dim': 1,\n", + " 'local': 1,\n", + " 'ns': [10, 0],\n", + " 'ntot': 10,\n", + " 'tw': [-0.1, 0]},\n", + " 'x_t': {'dim': 1, 'ns': [0, 1], 'ntot': 1, 'sw': [0, 1]}},\n", + " 'Minimizers': {'x[t]': {'A': 'SamplePart10', 'B': 'TimePart8', 'loss': 'mse'}},\n", + " 'Models': 'msd',\n", + " 'Outputs': {'x_n': 'TimePart8'},\n", + " 'Parameters': {'weight_fir': {'dim': [2, 1, 10],\n", + " 'init_fun': {'code': 'def '\n", + " 'init_random_range(indexes, '\n", + " 'params_size, '\n", + " \"dict_param={'min_value': \"\n", + " \"0.0, 'max_value': 1.0}):\\n\"\n", + " ' import numpy as np\\n'\n", + " ' min_val = '\n", + " \"dict_param.get('min_value', \"\n", + " '0.0)\\n'\n", + " ' max_val = '\n", + " \"dict_param.get('max_value', \"\n", + " '1.0)\\n'\n", + " ' return '\n", + " 'np.random.uniform(low=min_val, '\n", + " 'high=max_val)\\n',\n", + " 'name': 'init_random_range',\n", + " 'params': {'max_value': 0.1,\n", + " 'min_value': -0.1}},\n", + " 'values': [[[-5.0214056968688965,\n", + " -4.025257587432861,\n", + " -3.0458261966705322,\n", + " -1.9209206104278564,\n", + " -0.7650308012962341,\n", + " 0.5171442031860352,\n", + " 1.7535150051116943,\n", + " 3.087076187133789,\n", + " 4.083190441131592,\n", + " 5.219380855560303]],\n", + " [[0.00026014805189333856,\n", + " 0.0007117472123354673,\n", + " 0.0013745456235483289,\n", + " 0.002709923079237342,\n", + " 0.003882204182446003,\n", + " 0.005919742863625288,\n", + " 0.0070823198184370995,\n", + " 0.008177743293344975,\n", + " 0.009529106318950653,\n", + " 0.008549299091100693]]]}},\n", + " 'Relations': {'Concatenate4': ['Concatenate', ['TimePart1', 'TimePart3']],\n", + " 'NeuralODE5': ['NeuralODE',\n", + " ['Concatenate4', 'weight_fir'],\n", + " 'FNeuralODE5'],\n", + " 'SamplePart10': ['SamplePart', ['x_t'], -1, [0, 1]],\n", + " 'Select7': ['Select', ['NeuralODE5'], 2, 0],\n", + " 'TimePart1': ['TimePart', ['x'], -1, [-0.1, 0]],\n", + " 'TimePart3': ['TimePart', ['F'], -1, [-0.1, 0]],\n", + " 'TimePart8': ['TimePart', ['Select7'], 0.1, [0.09, 0.1]]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": "", + "id": "439c02826528ca8" + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/case-studies/neuralODE/saved/neuralODE_msd.json b/case-studies/neuralODE/saved/neuralODE_msd.json new file mode 100644 index 00000000..47ab6487 --- /dev/null +++ b/case-studies/neuralODE/saved/neuralODE_msd.json @@ -0,0 +1,57 @@ +{"Constants": {}, + "Functions": {"FNeuralODE5": {"atol": 1e-09, + "code": "def FNeuralODE5(state, *weights):\n from nnodely.support.odeint.adjoint import odeint_adjoint as odeint\n def ode_func_torch(t, state, weight_fir):\n import torch\n import torch.nn.functional as F\n # state: (B, W, 2)\n # B = batch size\n # W = window length\n # 2 channels = [position_window, force_window]\n #\n # weight_fir: FIR weights applied independently to each channel\n \n # Apply two independent FIR filters (grouped convolution):\n # this produces one-step predictions for each channel\n # (B, W, 2) -> (B, 2, W) -> conv -> (B, 2, 1)\n out = F.conv1d(state.transpose(1, 2), weight_fir, groups=2)\n \n # Restore time dimension ordering\n # (B, 2, 1) -> (B, 1, 2)\n out = out.transpose(1, 2)\n \n # Combine the two FIR contributions:\n # next_velocity = free_response + forced_response\n out[:, :, 0] = out[:, :, 0] + out[:, :, -1]\n \n # Set the force prediction to zero, for the intermediate time steps done in the integration process (if a variable step integrator, such as Dopri5, is used)\n out[:, :, -1] = 0.0\n \n # Shift the sliding window forward by one step\n # drop the oldest sample and append the new prediction\n # resulting shape: (B, W, 2)\n out = torch.cat((state[:, 1:, :], out), dim=1)\n \n return out\n \n ans = odeint(lambda t, y: ode_func_torch(t, y, *weights), state, t=torch.tensor([0.0, 0.01]), rtol=1e-07, atol=1e-09, method='dopri5', adjoint_params=list(weights))\n return ans[-1]\n", + "dt": 0.01, + "method": "dopri5", + "name": "FNeuralODE5", + "rtol": 1e-07}}, + "Info": {"SampleTime": 0.01, + "nnodely_version": "1.5.2", + "ns": [10, 1], + "ntot": 11, + "num_parameters": 20}, + "Inputs": {"F": {"dim": 1, "ns": [10, 0], "ntot": 10, "tw": [-0.1, 0]}, + "x": {"closedLoop": "TimePart8", + "dim": 1, + "local": 1, + "ns": [10, 0], + "ntot": 10, + "tw": [-0.1, 0]}, + "x_t": {"dim": 1, "ns": [0, 1], "ntot": 1, "sw": [0, 1]}}, + "Minimizers": {"x[t]": {"A": "SamplePart10", "B": "TimePart8", "loss": "mse"}}, + "Models": "msd", + "Outputs": {"x_n": "TimePart8"}, + "Parameters": {"weight_fir": {"dim": [2, 1, 10], + "init_fun": {"code": "def init_random_range(indexes, params_size, dict_param={'min_value': 0.0, 'max_value': 1.0}):\n import numpy as np\n min_val = dict_param.get('min_value', 0.0)\n max_val = dict_param.get('max_value', 1.0)\n return np.random.uniform(low=min_val, high=max_val)\n", + "name": "init_random_range", + "params": {"max_value": 0.1, + "min_value": -0.1}}, + "values": [[[-5.0214056968688965, + -4.025257587432861, + -3.0458261966705322, + -1.9209206104278564, + -0.7650308012962341, + 0.5171442031860352, + 1.7535150051116943, + 3.087076187133789, + 4.083190441131592, + 5.219380855560303]], + [[0.00026014805189333856, + 0.0007117472123354673, + 0.0013745456235483289, + 0.002709923079237342, + 0.003882204182446003, + 0.005919742863625288, + 0.0070823198184370995, + 0.008177743293344975, + 0.009529106318950653, + 0.008549299091100693]]]}}, + "Relations": {"Concatenate4": ["Concatenate", ["TimePart1", "TimePart3"]], + "NeuralODE5": ["NeuralODE", + ["Concatenate4", "weight_fir"], + "FNeuralODE5"], + "SamplePart10": ["SamplePart", ["x_t"], -1, [0, 1]], + "Select7": ["Select", ["NeuralODE5"], 2, 0], + "TimePart1": ["TimePart", ["x"], -1, [-0.1, 0]], + "TimePart3": ["TimePart", ["F"], -1, [-0.1, 0]], + "TimePart8": ["TimePart", ["Select7"], 0.1, [0.09, 0.1]]}} \ No newline at end of file diff --git a/case-studies/neuralODE/saved/neuralODE_msd.py b/case-studies/neuralODE/saved/neuralODE_msd.py new file mode 100644 index 00000000..d87c5470 --- /dev/null +++ b/case-studies/neuralODE/saved/neuralODE_msd.py @@ -0,0 +1,111 @@ +import torch + +def nnodely_basic_model_update_state(data_in, rel): + data_out = data_in.clone() + max_dim = min(rel.size(1), data_in.size(1)) + data_out[:, -max_dim:, :] = rel[:, -max_dim:, :] + return data_out + +def nnodely_basic_model_timeshift(data_in): + return torch.cat((data_in[:, 1:, :], data_in[:, :1, :]), dim=1) + +def nnodely_layers_neuralODE_FNeuralODE5(state, *weights): + from nnodely.support.odeint.adjoint import odeint_adjoint as odeint + def ode_func_torch(t, state, weight_fir): + import torch + import torch.nn.functional as F + # state: (B, W, 2) + # B = batch size + # W = window length + # 2 channels = [position_window, force_window] + # + # weight_fir: FIR weights applied independently to each channel + + # Apply two independent FIR filters (grouped convolution): + # this produces one-step predictions for each channel + # (B, W, 2) -> (B, 2, W) -> conv -> (B, 2, 1) + out = F.conv1d(state.transpose(1, 2), weight_fir, groups=2) + + # Restore time dimension ordering + # (B, 2, 1) -> (B, 1, 2) + out = out.transpose(1, 2) + + # Combine the two FIR contributions: + # next_velocity = free_response + forced_response + out[:, :, 0] = out[:, :, 0] + out[:, :, -1] + + # Set the force prediction to zero, for the intermediate time steps done in the integration process (if a variable step integrator, such as Dopri5, is used) + out[:, :, -1] = 0.0 + + # Shift the sliding window forward by one step + # drop the oldest sample and append the new prediction + # resulting shape: (B, W, 2) + out = torch.cat((state[:, 1:, :], out), dim=1) + + return out + + ans = odeint(lambda t, y: ode_func_torch(t, y, *weights), state, t=torch.tensor([0.0, 0.01]), rtol=1e-07, atol=1e-09, method='dopri5', adjoint_params=list(weights)) + return ans[-1] + +class TracerModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.all_parameters = {} + self.all_constants = {} + self.all_parameters["weight_fir"] = torch.nn.Parameter(torch.tensor([[[-5.0214056968688965, -4.025257587432861, -3.0458261966705322, -1.9209206104278564, -0.7650308012962341, 0.5171442031860352, 1.7535150051116943, 3.087076187133789, 4.083190441131592, 5.219380855560303]], [[0.00026014805189333856, 0.0007117472123354673, 0.0013745456235483289, 0.002709923079237342, 0.003882204182446003, 0.005919742863625288, 0.0070823198184370995, 0.008177743293344975, 0.009529106318950653, 0.008549299091100693]]]), requires_grad=True) + self.all_constants["SamplePart10"] = torch.tensor([[1.0]], requires_grad=True) + self.all_constants["Select7"] = torch.tensor([1.0, 0.0], requires_grad=True) + self.all_constants["TimePart1"] = torch.tensor([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]], requires_grad=True) + self.all_constants["TimePart3"] = torch.tensor([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]], requires_grad=True) + self.all_constants["TimePart8"] = torch.tensor([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]], requires_grad=True) + self.all_parameters = torch.nn.ParameterDict(self.all_parameters) + self.all_constants = torch.nn.ParameterDict(self.all_constants) + + def update(self, closed_loop={}, connect={}, disconnect=False): + pass + def forward(self, kwargs): + getitem = kwargs['x_t'] + relation_forward_sample_part10_w = self.all_constants.SamplePart10 + einsum = torch.functional.einsum('bij,ki->bkj', getitem, relation_forward_sample_part10_w); getitem = relation_forward_sample_part10_w = None + getitem_1 = kwargs['x'] + relation_forward_time_part1_w = self.all_constants.TimePart1 + einsum_1 = torch.functional.einsum('bij,ki->bkj', getitem_1, relation_forward_time_part1_w); getitem_1 = relation_forward_time_part1_w = None + getitem_2 = kwargs['F']; kwargs = None + relation_forward_time_part3_w = self.all_constants.TimePart3 + einsum_2 = torch.functional.einsum('bij,ki->bkj', getitem_2, relation_forward_time_part3_w); getitem_2 = relation_forward_time_part3_w = None + cat = torch.cat((einsum_1, einsum_2), dim = 2); einsum_1 = einsum_2 = None + all_parameters_weight_fir = self.all_parameters.weight_fir + fneural_ode5 = nnodely_layers_neuralODE_FNeuralODE5(cat, all_parameters_weight_fir); cat = all_parameters_weight_fir = None + relation_forward_select7_w = self.all_constants.Select7 + einsum_3 = torch.functional.einsum('ijk,k->ij', fneural_ode5, relation_forward_select7_w); fneural_ode5 = relation_forward_select7_w = None + unsqueeze = einsum_3.unsqueeze(2); einsum_3 = None + relation_forward_time_part8_w = self.all_constants.TimePart8 + einsum_4 = torch.functional.einsum('bij,ki->bkj', unsqueeze, relation_forward_time_part8_w); unsqueeze = relation_forward_time_part8_w = None + return ({'x_n': einsum_4}, {'SamplePart10': einsum, 'TimePart8': einsum_4}, {'x': einsum_4}, {}) + +class RecurrentModel(torch.nn.Module): + def __init__(self): + super().__init__() + self.Cell = TracerModel() + self.inputs = ['F', 'x_t', ] + self.states = dict() + + def forward(self, kwargs, n_samples = None): + n_samples = n_samples if n_samples else min([kwargs[key].size(0) for key in self.inputs]) + self.states['x'] = kwargs['x'] + results = {'x_n':[], } + X = dict() + for idx in range(n_samples): + for key in self.inputs: + X[key] = kwargs[key][idx] + for key, value in self.states.items(): + X[key] = value + out, _, closed_loop, connect = self.Cell(X) + for key, value in results.items(): + results[key].append(out[key]) + for key, val in closed_loop.items(): + self.states[key] = nnodely_basic_model_timeshift(self.states[key]) + self.states[key] = nnodely_basic_model_update_state(self.states[key], val) + for key, val in connect.items(): + self.states[key] = nnodely_basic_model_timeshift(val) + return results diff --git a/case-studies/pinn/pinn_Burgers_equation.ipynb b/case-studies/pinn/pinn_Burgers_equation.ipynb new file mode 100644 index 00000000..9a320310 --- /dev/null +++ b/case-studies/pinn/pinn_Burgers_equation.ipynb @@ -0,0 +1,1970 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f13cea85edf7a5bb", + "metadata": {}, + "source": [ + "# **Solution of the Burger's equation using Physics-informed neural network via nnodely**\n", + "\n", + "This tutorial implements the physics-informed neural network from appendix A of the paper titled:\n", + "\n", + "*Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations*\n", + "\n", + "(available at https://doi.org/10.1016/j.jcp.2018.10.045)\n", + "\n", + "This NN solves the Burger's equation" + ] + }, + { + "cell_type": "code", + "id": "19b3470f12bc42b4", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-25T17:39:07.630833Z", + "start_time": "2026-02-25T17:39:06.884462Z" + } + }, + "source": [ + "from nnodely import *\n", + "import numpy as np" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ">>>>>>>>>>>>>>>>>>>>>>>>>>>>>>-- nnodely_v1.5.2 --<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\n" + ] + } + ], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "3078475152d0ccab", + "metadata": {}, + "source": [ + "Creation of the PINN network of the burger's equation" + ] + }, + { + "cell_type": "code", + "id": "f6f28e7b27c45c9e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-25T17:39:07.653727Z", + "start_time": "2026-02-25T17:39:07.631468Z" + } + }, + "source": [ + "t = Input('t')\n", + "x = Input('x')\n", + "x_last = x.last()\n", + "t_last = t.last()\n", + "\n", + "xt = Concatenate(x_last,t_last)\n", + "for hidden in range(4):\n", + " xt = Tanh(Linear(20, b = True, b_init = 'init_constant', b_init_params = {'value':0})(xt))\n", + "u = Linear(1, b = True, b_init = 'init_constant', b_init_params = {'value':0})(xt)\n", + "\n", + "f = Differentiate(u,t_last) + Differentiate(u,x_last) * u - (0.01 / np.pi) * Differentiate(Differentiate(u,x_last),x_last)\n", + "\n", + "U = Output('U',u)\n", + "F = Output('F',f)" + ], + "outputs": [], + "execution_count": 2 + }, + { + "cell_type": "markdown", + "id": "7c6ae3382dee26b6", + "metadata": {}, + "source": [ + "Added two additional inputs to manage the boundary conditions" + ] + }, + { + "cell_type": "code", + "id": "3997a16202edbedd", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-25T17:39:07.658442Z", + "start_time": "2026-02-25T17:39:07.654224Z" + } + }, + "source": [ + "u_target = Input('u_target').last()\n", + "bound_cond = Input('b').last()" + ], + "outputs": [], + "execution_count": 3 + }, + { + "cell_type": "markdown", + "id": "6a7f6a52bf2b578a", + "metadata": {}, + "source": [ + "Creation of the Modely model of the PINN network" + ] + }, + { + "cell_type": "code", + "id": "bca4c69105a806fb", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-25T17:39:07.703055Z", + "start_time": "2026-02-25T17:39:07.658898Z" + } + }, + "source": [ + "pinn = Modely(visualizer=TextVisualizer(), seed=42)\n", + "pinn.addModel('pinn',[U,F])\n", + "pinn.addMinimize('errorU',u * bound_cond, u_target * bound_cond)\n", + "pinn.addMinimize('errorF',f, bound_cond * 0)\n", + "pinn.neuralizeModel()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m================================ nnodely Model =================================\u001B[0m\n", + "\u001B[32m{'Constants': {'Constant18': {'dim': 1, 'values': [0.0031830989755690098]},\n", + " 'Constant23': {'dim': 1, 'values': [0.0]}},\n", + " 'Functions': {},\n", + " 'Info': {'SampleTime': 1,\n", + " 'nnodely_version': '1.5.2',\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'num_parameters': 1341},\n", + " 'Inputs': {'b': {'dim': 1, 'ns': [1, 0], 'ntot': 1, 'sw': [-1, 0]},\n", + " 't': {'dim': 1,\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'sw': [-1, 0],\n", + " 'type': 'derivate'},\n", + " 'u_target': {'dim': 1, 'ns': [1, 0], 'ntot': 1, 'sw': [-1, 0]},\n", + " 'x': {'dim': 1,\n", + " 'ns': [1, 0],\n", + " 'ntot': 1,\n", + " 'sw': [-1, 0],\n", + " 'type': 'derivate'}},\n", + " 'Minimizers': {'errorF': {'A': 'Sub22', 'B': 'Mul30', 'loss': 'mse'},\n", + " 'errorU': {'A': 'Mul27', 'B': 'Mul28', 'loss': 'mse'}},\n", + " 'Models': 'pinn',\n", + " 'Outputs': {'F': 'Sub22', 'U': 'Linear13'},\n", + " 'Parameters': {'PLinear12W': {'dim': [20, 20],\n", + " 'values': [[0.6442350149154663,\n", + " 0.01178830862045288,\n", + " 0.14224576950073242,\n", + " 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0.355430543422699,\n", + " 0.5253631472587585,\n", + " 0.8000994920730591,\n", + " 0.21456867456436157,\n", + " 0.7503269910812378,\n", + " 0.3208093047142029,\n", + " 0.8020546436309814,\n", + " 0.4762613773345947,\n", + " 0.06195652484893799,\n", + " 0.22487705945968628,\n", + " 0.1381239891052246,\n", + " 0.7479812502861023,\n", + " 0.1647258996963501,\n", + " 0.4583408832550049,\n", + " 0.6078779101371765,\n", + " 0.22580265998840332]]},\n", + " 'PLinear9b': {'dim': 20,\n", + " 'init_fun': {'name': 'init_constant',\n", + " 'params': {'value': 0}},\n", + " 'values': [0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0,\n", + " 0.0]}},\n", + " 'Relations': {'Add17': ['Add', ['Differentiate14', 'Mul16']],\n", + " 'Concatenate4': ['Concatenate', ['SamplePart1', 'SamplePart3']],\n", + " 'Differentiate14': ['Differentiate',\n", + " ['Linear13', 'SamplePart3']],\n", + " 'Differentiate15': ['Differentiate',\n", + " ['Linear13', 'SamplePart1']],\n", + " 'Differentiate18': ['Differentiate',\n", + " ['Linear13', 'SamplePart1']],\n", + " 'Differentiate19': ['Differentiate',\n", + " ['Differentiate18', 'SamplePart1']],\n", + " 'Linear11': ['Linear',\n", + " ['Tanh10'],\n", + " 'PLinear12W',\n", + " 'PLinear12b',\n", + " 0],\n", + " 'Linear13': ['Linear',\n", + " ['Tanh12'],\n", + " 'PLinear15W',\n", + " 'PLinear15b',\n", + " 0],\n", + " 'Linear5': ['Linear',\n", + " ['Concatenate4'],\n", + " 'PLinear3W',\n", + " 'PLinear3b',\n", + " 0],\n", + " 'Linear7': ['Linear', ['Tanh6'], 'PLinear6W', 'PLinear6b', 0],\n", + " 'Linear9': ['Linear', ['Tanh8'], 'PLinear9W', 'PLinear9b', 0],\n", + " 'Mul16': ['Mul', ['Differentiate15', 'Linear13']],\n", + " 'Mul21': ['Mul', ['Constant18', 'Differentiate19']],\n", + " 'Mul27': ['Mul', ['Linear13', 'SamplePart26']],\n", + " 'Mul28': ['Mul', ['SamplePart24', 'SamplePart26']],\n", + " 'Mul30': ['Mul', ['SamplePart26', 'Constant23']],\n", + " 'SamplePart1': ['SamplePart', ['x'], -1, [-1, 0]],\n", + " 'SamplePart24': ['SamplePart', ['u_target'], -1, [-1, 0]],\n", + " 'SamplePart26': ['SamplePart', ['b'], -1, [-1, 0]],\n", + " 'SamplePart3': ['SamplePart', ['t'], -1, [-1, 0]],\n", + " 'Sub22': ['Sub', ['Add17', 'Mul21']],\n", + " 'Tanh10': ['Tanh', ['Linear9']],\n", + " 'Tanh12': ['Tanh', ['Linear11']],\n", + " 'Tanh6': ['Tanh', ['Linear5']],\n", + " 'Tanh8': ['Tanh', ['Linear7']]}}\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n" + ] + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "d9a3abc5d551eac9", + "metadata": {}, + "source": [ + "Dataset Creation" + ] + }, + { + "cell_type": "code", + "id": "b5e35e8f7b0e19a2", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-25T17:39:07.716985Z", + "start_time": "2026-02-25T17:39:07.704197Z" + } + }, + "source": [ + "import torch\n", + "# Create boundary conditions\n", + "Nu = 100\n", + "tt_0 = torch.zeros(Nu//2, dtype=torch.float32)\n", + "xx_0 = 2 * torch.rand(Nu//2, dtype=torch.float32) - 1\n", + "uu_0 = -torch.sin(torch.pi * xx_0)\n", + "b_0 = torch.ones(Nu//2, dtype=torch.float32)\n", + "#\n", + "tt_1 = torch.rand(Nu//4, dtype=torch.float32)\n", + "xx_1 = torch.ones(Nu//4, dtype=torch.float32)\n", + "uu_1 = torch.zeros(Nu//4, dtype=torch.float32)\n", + "b_1 = torch.ones(Nu//4, dtype=torch.float32)\n", + "#\n", + "tt_2 = torch.rand(Nu//4, dtype=torch.float32)\n", + "xx_2 = -torch.ones(Nu//4, dtype=torch.float32)\n", + "uu_2 = torch.zeros(Nu//4, dtype=torch.float32)\n", + "b_2 = torch.ones(Nu//4, dtype=torch.float32)\n", + "# Internal points\n", + "Nf = 10000\n", + "tt_3 = torch.rand(Nf, dtype=torch.float32)\n", + "xx_3 = 2 * torch.rand(Nf, dtype=torch.float32) - 1\n", + "uu_3 = torch.zeros(Nf, dtype=torch.float32)\n", + "b_3 = torch.zeros(Nf, dtype=torch.float32)\n", + "\n", + "data = {'x':torch.cat((xx_0, xx_1, xx_2, xx_3)),\n", + " 't':torch.cat((tt_0, tt_1, tt_2, tt_3)),\n", + " 'u_target':torch.cat((uu_0, uu_1, uu_2, uu_3)),\n", + " 'b':torch.cat((b_0, b_1, b_2, b_3))}" + ], + "outputs": [], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "id": "4cc9f8976cde1b3f", + "metadata": {}, + "source": [ + "Load the dataset and train the model" + ] + }, + { + "cell_type": "code", + "id": "4a20e7b55167eb6c", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-25T17:50:42.873840Z", + "start_time": "2026-02-25T17:39:07.718011Z" + } + }, + "source": [ + "from nnodely.support import earlystopping\n", + "pinn.loadData('dataset2',data)\n", + "pinn.trainModel(train_dataset='dataset2', train_batch_size=128, num_of_epochs=5000, lr=0.0005, \n", + " minimize_gain={'errorU':1,'errorF':0.0005}, \n", + " early_stopping=earlystopping.early_stop_patience, \n", + " early_stopping_params={'patience':500, 'error':'errorU'}, \n", + " select_model=earlystopping.select_best_model)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[1;32m============================ nnodely Model Dataset =============================\u001B[0m\n", + "\u001B[32mDataset Name: dataset2\u001B[0m\n", + "\u001B[32mNumber of files: 1\u001B[0m\n", + "\u001B[32mTotal number of samples: 10100\u001B[0m\n", + "\u001B[32mShape of b: (10100, 1, 1)\u001B[0m\n", + "\u001B[32mShape of x: (10100, 1, 1)\u001B[0m\n", + "\u001B[32mShape of t: (10100, 1, 1)\u001B[0m\n", + "\u001B[32mShape of u_target: (10100, 1, 1)\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[33m[_setup_recurrent_variables] The value of the prediction_samples=0 but the network has no recurrent variables.\u001B[0m\n", + "\u001B[1;32m======================== nnodely Model Train Parameters ========================\u001B[0m\n", + "\u001B[32mmodels: ['pinn']\u001B[0m\n", + "\u001B[32mnum of epochs: 5000\u001B[0m\n", + "\u001B[32mupdate per epochs: 78\u001B[0m\n", + "\u001B[34mβ””>(n_samples-batch_size)/batch_size+1\u001B[0m\n", + "\u001B[32mshuffle data: True\u001B[0m\n", + "\u001B[32mearly stopping: early_stop_patience\u001B[0m\n", + "\u001B[32mearly stopping params: {'error': 'errorU', 'patience': 500}\u001B[0m\n", + "\u001B[32mtrain dataset: dataset2\u001B[0m\n", + "\u001B[32m\t- batch size: 128\u001B[0m\n", + "\u001B[32m\t- num of samples: 10100\u001B[0m\n", + "\u001B[32mminimizers: {'errorF': {'A': 'Sub22',\n", + " 'B': 'Mul30',\n", + " 'gain': 0.0005,\n", + " 'loss': 'mse'},\n", + " 'errorU': {'A': 'Mul27',\n", + " 'B': 'Mul28',\n", + " 'gain': 1,\n", + " 'loss': 'mse'}}\u001B[0m\n", + "\u001B[32moptimizer: Adam\u001B[0m\n", + "\u001B[32moptimizer defaults: {'lr': 0.0005}\u001B[0m\n", + "\u001B[32moptimizer params: [{'params': 'PLinear12W'},\n", + " {'params': 'PLinear12b'},\n", + " {'params': 'PLinear15W'},\n", + " {'params': 'PLinear15b'},\n", + " {'params': 'PLinear3W'},\n", + " {'params': 'PLinear3b'},\n", + " {'params': 'PLinear6W'},\n", + " {'params': 'PLinear6b'},\n", + " {'params': 'PLinear9W'},\n", + " {'params': 'PLinear9b'}]\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[1;32m=========================== nnodely Training ===========================\u001B[0m\n", + "\u001B[32m| Epoch |\u001B[0m\u001B[32m errorU |\u001B[0m\u001B[32m errorF |\u001B[0m\u001B[32m Total |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\u001B[32m Loss |\u001B[0m\n", + "\u001B[32m| |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m train |\u001B[0m\u001B[32m train |\u001B[0m\n", + "\u001B[32m|----------------------------------------------------------------------|\u001B[0m\n", + "\u001B[32m| 50/5000 |\u001B[0m\u001B[32m 9.018e-02 |\u001B[0m\u001B[32m 1.413e+00 |\u001B[0m\u001B[32m 7.515e-01 |\u001B[0m\n", + "\u001B[32m| 100/5000 |\u001B[0m\u001B[32m 1.886e-02 |\u001B[0m\u001B[32m 4.612e-02 |\u001B[0m\u001B[32m 3.249e-02 |\u001B[0m\n", + "\u001B[32m| 150/5000 |\u001B[0m\u001B[32m 3.863e-03 |\u001B[0m\u001B[32m 1.532e-03 |\u001B[0m\u001B[32m 2.698e-03 |\u001B[0m\n", + "\u001B[32m| 200/5000 |\u001B[0m\u001B[32m 1.946e-03 |\u001B[0m\u001B[32m 3.386e-04 |\u001B[0m\u001B[32m 1.142e-03 |\u001B[0m\n", + "\u001B[32m| 250/5000 |\u001B[0m\u001B[32m 1.838e-03 |\u001B[0m\u001B[32m 5.892e-04 |\u001B[0m\u001B[32m 1.214e-03 |\u001B[0m\n", + "\u001B[32m| 300/5000 |\u001B[0m\u001B[32m 1.732e-03 |\u001B[0m\u001B[32m 3.606e-04 |\u001B[0m\u001B[32m 1.046e-03 |\u001B[0m\n", + "\u001B[32m| 350/5000 |\u001B[0m\u001B[32m 1.636e-03 |\u001B[0m\u001B[32m 4.242e-04 |\u001B[0m\u001B[32m 1.030e-03 |\u001B[0m\n", + "\u001B[32m| 400/5000 |\u001B[0m\u001B[32m 1.564e-03 |\u001B[0m\u001B[32m 2.444e-04 |\u001B[0m\u001B[32m 9.043e-04 |\u001B[0m\n", + "\u001B[32m| 450/5000 |\u001B[0m\u001B[32m 1.543e-03 |\u001B[0m\u001B[32m 1.600e-04 |\u001B[0m\u001B[32m 8.514e-04 |\u001B[0m\n", + "\u001B[32m| 500/5000 |\u001B[0m\u001B[32m 1.53e-03 |\u001B[0m\u001B[32m 1.101e-04 |\u001B[0m\u001B[32m 8.200e-04 |\u001B[0m\n", + "\u001B[32m| 550/5000 |\u001B[0m\u001B[32m 1.505e-03 |\u001B[0m\u001B[32m 6.108e-05 |\u001B[0m\u001B[32m 7.830e-04 |\u001B[0m\n", + "\u001B[32m| 600/5000 |\u001B[0m\u001B[32m 1.504e-03 |\u001B[0m\u001B[32m 3.504e-05 |\u001B[0m\u001B[32m 7.694e-04 |\u001B[0m\n", + "\u001B[32m| 650/5000 |\u001B[0m\u001B[32m 3.171e-04 |\u001B[0m\u001B[32m 1.438e-04 |\u001B[0m\u001B[32m 2.304e-04 |\u001B[0m\n", + "\u001B[32m| 700/5000 |\u001B[0m\u001B[32m 8.892e-05 |\u001B[0m\u001B[32m 1.083e-04 |\u001B[0m\u001B[32m 9.863e-05 |\u001B[0m\n", + "\u001B[32m| 750/5000 |\u001B[0m\u001B[32m 4.373e-05 |\u001B[0m\u001B[32m 8.569e-05 |\u001B[0m\u001B[32m 6.471e-05 |\u001B[0m\n", + "\u001B[32m| 800/5000 |\u001B[0m\u001B[32m 2.790e-05 |\u001B[0m\u001B[32m 5.551e-05 |\u001B[0m\u001B[32m 4.170e-05 |\u001B[0m\n", + "\u001B[32m| 850/5000 |\u001B[0m\u001B[32m 3.441e-05 |\u001B[0m\u001B[32m 5.859e-05 |\u001B[0m\u001B[32m 4.650e-05 |\u001B[0m\n", + "\u001B[32m| 900/5000 |\u001B[0m\u001B[32m 6.985e-06 |\u001B[0m\u001B[32m 1.973e-05 |\u001B[0m\u001B[32m 1.336e-05 |\u001B[0m\n", + "\u001B[32m| 950/5000 |\u001B[0m\u001B[32m 6.454e-06 |\u001B[0m\u001B[32m 1.649e-05 |\u001B[0m\u001B[32m 1.147e-05 |\u001B[0m\n", + "\u001B[32m|1000/5000 |\u001B[0m\u001B[32m 7.318e-06 |\u001B[0m\u001B[32m 1.616e-05 |\u001B[0m\u001B[32m 1.174e-05 |\u001B[0m\n", + "\u001B[32m|1050/5000 |\u001B[0m\u001B[32m 7.773e-06 |\u001B[0m\u001B[32m 1.579e-05 |\u001B[0m\u001B[32m 1.178e-05 |\u001B[0m\n", + "\u001B[32m|1100/5000 |\u001B[0m\u001B[32m 5.03e-06 |\u001B[0m\u001B[32m 1.188e-05 |\u001B[0m\u001B[32m 8.453e-06 |\u001B[0m\n", + "\u001B[32m|1150/5000 |\u001B[0m\u001B[32m 3.668e-06 |\u001B[0m\u001B[32m 8.326e-06 |\u001B[0m\u001B[32m 5.997e-06 |\u001B[0m\n", + "\u001B[32m|1200/5000 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|\u001B[0m\u001B[32m 7.234e-06 |\u001B[0m\u001B[32m 1.095e-05 |\u001B[0m\u001B[32m 9.094e-06 |\u001B[0m\n", + "|3485/5000 | 6.045e-07 | 9.439e-07 | 7.742e-07 |\u001B[34mStopping the training at epoch 3485 due to early stopping.\u001B[0m\n", + "\u001B[1;32m============================ nnodely Training Time =============================\u001B[0m\n", + "\u001B[32mTotal time of Training: 694.5884737968445\u001B[0m\n", + "\u001B[32m================================================================================\u001B[0m\n", + "\u001B[33m[trainModel] If not validation set is provided the selected model can differ from the optimal.\u001B[0m\n", + "\u001B[34mSelected the model at the epoch 3461.\u001B[0m\n" + ] + } + ], + "execution_count": 6 + }, + { + "cell_type": "markdown", + "id": "7f63b0597cf9da68", + "metadata": {}, + "source": [ + "Show the resulting network" + ] + }, + { + "cell_type": "code", + "id": "51fa79586fe9b9be", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-25T17:50:52.684429Z", + "start_time": "2026-02-25T17:50:42.919168Z" + } + }, + "source": [ + "# Custom visualizer for results\n", + "class FunctionVisualizer(TextVisualizer):\n", + " def showResults(self):\n", + " import matplotlib.pyplot as plt\n", + " plt.figure()\n", + " plt.title('Initial Condition')\n", + " x = torch.linspace(-1, 1, steps=100, dtype=torch.float32)\n", + " t = torch.zeros(100, dtype=torch.float32)\n", + " u_target = -torch.sin(torch.pi * x)\n", + " u = self.modely({'x':x.tolist(),'t':t.tolist()})\n", + " plt.plot(x.tolist(), u['U'], label=f'network')\n", + " plt.plot(x.tolist(), u_target.tolist(), label=f'target')\n", + " plt.grid(True)\n", + " plt.legend(loc='best')\n", + " plt.xlabel('x[t=0]')\n", + " plt.ylabel('u')\n", + "\n", + " plt.figure()\n", + " plt.title('U value at t=[0.25,0.5,0.75,1]')\n", + " x = torch.linspace(-1, 1, steps=100, dtype=torch.float32)\n", + " t = torch.ones(100, dtype=torch.float32)*0.25\n", + " u = self.modely({'x':x.tolist(),'t':t.tolist()})\n", + " plt.plot(x.tolist(), u['U'], label=f'network t=0.25')\n", + " x = torch.linspace(-1, 1, steps=100, dtype=torch.float32)\n", + " t = torch.ones(100, dtype=torch.float32)*0.5\n", + " u = self.modely({'x':x.tolist(),'t':t.tolist()})\n", + " plt.plot(x.tolist(), u['U'], label=f'network t=0.5')\n", + " x = torch.linspace(-1, 1, steps=100, dtype=torch.float32)\n", + " t = torch.ones(100, dtype=torch.float32)*0.75\n", + " u = self.modely({'x':x.tolist(),'t':t.tolist()})\n", + " plt.plot(x.tolist(), u['U'], label=f'network t=0.75')\n", + " x = torch.linspace(-1, 1, steps=100, dtype=torch.float32)\n", + " t = torch.ones(100, dtype=torch.float32)\n", + " u = self.modely({'x':x.tolist(),'t':t.tolist()})\n", + " plt.plot(x.tolist(), u['U'], label=f'network t=1')\n", + " plt.grid(True)\n", + " plt.legend(loc='best')\n", + " plt.xlabel('x')\n", + " plt.ylabel('u')\n", + "\n", + " plt.figure()\n", + " plt.title('Boudary Condition')\n", + " t_1 = torch.linspace(0, 1, steps=100, dtype=torch.float32)\n", + " x_1 = torch.ones(100, dtype=torch.float32)\n", + " u_1_target = torch.zeros(100, dtype=torch.float32)\n", + " u_1 = self.modely({'x': x_1.tolist(), 't': t_1.tolist()})\n", + " plt.plot(t_1.tolist(), u_1['U'], label=f'network x=1')\n", + " plt.plot(t_1.tolist(), u_1_target.tolist(), label=f'target x=1')\n", + " t_2 = torch.linspace(0, 1, steps=100, dtype=torch.float32)\n", + " x_2 = -torch.ones(100, dtype=torch.float32)\n", + " u_2_target = torch.zeros(100, dtype=torch.float32)\n", + " u_2 = self.modely({'x': x_2.tolist(), 't': t_2.tolist()})\n", + " plt.plot(t_2.tolist(), u_2['U'], label=f'network x=-1')\n", + " plt.plot(t_2.tolist(), u_2_target.tolist(), label=f'target x=-1')\n", + " plt.grid(True)\n", + " plt.legend(loc='best')\n", + " plt.xlabel('x[t]')\n", + " plt.ylabel('u')\n", + "\n", + " plt.figure()\n", + " plt.title('Function Integration')\n", + " t_3 = torch.linspace(0, 1, steps=100, dtype=torch.float32).numpy()\n", + " x_3 = torch.linspace(-1, 1, steps=100, dtype=torch.float32).numpy()\n", + " T, X = np.meshgrid(t_3, x_3)\n", + " u_2 = self.modely({'x': X.flatten().tolist(), 't': T.flatten().tolist()})\n", + " plt.contourf(T, X, np.array(u_2['U']).reshape(100,100))\n", + " plt.xlabel('t')\n", + " plt.ylabel('x')\n", + " plt.show()\n", + "\n", + "res = FunctionVisualizer()\n", + "res.setModely(pinn)\n", + "res.showResults()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001B[33m[__call__] Inputs not provided: ['u_target', 'b']. Autofilling with zeros..\u001B[0m\n", + "\u001B[33m[__call__] Inputs not provided: ['u_target', 'b']. Autofilling with zeros..\u001B[0m\n", + "\u001B[33m[__call__] Inputs not provided: ['u_target', 'b']. Autofilling with zeros..\u001B[0m\n", + "\u001B[33m[__call__] Inputs not provided: ['u_target', 'b']. Autofilling with zeros..\u001B[0m\n", + "\u001B[33m[__call__] Inputs not provided: ['u_target', 'b']. Autofilling with zeros..\u001B[0m\n", + "\u001B[33m[__call__] Inputs not provided: ['u_target', 'b']. Autofilling with zeros..\u001B[0m\n", + "\u001B[33m[__call__] Inputs not provided: ['u_target', 'b']. Autofilling with zeros..\u001B[0m\n", + "\u001B[33m[__call__] Inputs not provided: ['u_target', 'b']. Autofilling with zeros..\u001B[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "
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" 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" 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goCCUl5er7EdVVRXmzJmDU6dOYdu2bbCwsMDEiRPh5+cHuVyOsWPHwtjYuG7eqquVf589ezZu3LiB4ODgl8plY1ExpcMysxQX9rk6e2g2EEII0SPv790Bz+7dmmy8CS9Yn3r9Jr6bNrfB465evVp5VGn//v3w9/cHACxYsACnTp3C999/D0AxMWX37t2xaNEivPnmmygvL4dMJkN+fj7y8/ORm5uLfv364dixY+jfvz/+/vtv9O3bF/v378fgwYNx4sQJAMAHH3yAjz/+GEePHgWgOLWWkpKCt99+W3mt0o4dO5CUlAQAymJq2bJleOutt9C3b18UFBQ8dV+ioqIQHh6OkJAQ2NnZYd26dbhz5w4AoLS0tMG5UTc6zafDsrLSAACubm00GwghhOiTRjyjTZ3u37+v/HtFRQWMjIwAAN7e3hg1ahQqKyuV7f3334eXl9dTxzl58iQGDBgAOzs7ODg4YNeuXQgKCgIAZTFlZ2cHa2trXL58WbldTU0Nrl69Cm9vb+WytLQ0lbGdnJzwxRdfoKqqCnl5ec/dn6VLl8LLywtSqRQbNmxQLj9+/LjKvtS248eP1y9RakBHpnRYVnYaAKCVoDVat7ZCWVmJZgMihBA98N20uU12ms+nmw9uxt9sltN8j5/mAv69sNzQ0BA//fQTvvjiC5X1Uqn0qeOcPHkSy5YtQ0xMDC5duoRz587B29sbHTp0QPv27REZGQlDw6eXC1wuF1wuV/laIlHdF7lcjuHDh+PHH3/EqlWr8Mknnzxzf9zd3WFubg4+nw9PT09lsTh79myYmprW6S8Wi585lrpRMaXDqqurYGJqBIlYCne3tlRMEUJIE3nZAudxHA4HsupqSMUStT6bLzExEX369FE+dw4AFi9eDB6Ph/Xr14M9ceTt1KlT2LdvH0aMGIHz58+jtLQUCQkJWL16NaKjo5XXQ+Xl5SEwMBDx8fEAFEWbn58fIiIinhlLXl4ezpw5g2XLlmHv3r0ICwtTiasWh8PBrl27sHPnTlhZWWHnzp145ZVXAAA5OTmNzklzo9N8Oo5vprgoz92tnYYjIYQQog1++OEH9OzZE2vXrkW7du0wadIkfPHFF0hPTwcACIVCWFpaol27duByuSgpKUFcXBymTJmC6OhoAMD58+cxYcIE5fVSAPDVV1/hs88+w8iRI9GxY0fs2rULJiYm+PXXX18Y08GDBxETE4Nvv/32qesXLlwIV1dXfPLJJ1i2bBn8/Pwwa9asJsiGelAxpePMlMUUXTdFCCEEyMjIwKhRozB8+HDcvn0b69atw5IlS/Dzzz8DAM6cOYPk5GTcunULvr6+AIB//vkHAHDlyhUAimKKw+GoFFNbtmzBrl27sGvXLly7dg0uLi4YMGAAioqK6hXXhx9+iCFDhmDMmDEqy93c3PDZZ59h+fLlKC8vR25uLtasWYMvv/wSdnZ2jU2H2jBtbTwej+3evZuVlpaynJwctnjx4mf29fX1ZTExMUwoFLIrV66wHj16PLXfypUrWWhoqMoyCwsLtmvXLpaXl8cKCgpYaGgos7CwUK5ftGgRe9KmTZvqvR8CgYAxxphAIGjS/AgEApaTXcYiIxLZ5o2hTTo2NfV8htQoz5RnzTV3d3e2d+9e5u7u3izjczgc5ufnxzgcjsb3VZ9bY/P8vO9BfX9WtPrI1KZNm9CzZ08MHDgQ8+fPx+rVqzFu3Lg6/fh8Po4fP47z58/Dz88PFy9exLFjx8Dn81X6TZw4EWvWrKmz/fbt2+Hj44MRI0Zg2LBh8Pb2xq5du5TrO3XqhO+//x4ODg7K9rRxNKH2yJSbW1sNR0IIIYS0XBqvKp/W+Hw+E4lELDg4WLls1apVLDIysk7fGTNmsJSUFJVlSUlJbNq0aQwA43K57IcffmAikYglJCSoHJni8/lMKpWyXr16KZcFBgYyqVTKeDweA8DOnz/P3n333Zfel+Y8MiWV1rDIiEQWGZHIzMxa9v8ym7PR/+Qpz/rUKM+KRkem9KPRkann8PHxgZGRES5evKhcFh0djYCAgDrPFQoMDFReNFfrwoUL6N27NwDA3Nwc3bp1Q0BAAC5duqTSTy6XY+TIkYiLi1NZbmhoCHNzcwCKOTtqJyHTNoaGXBQV5wMA3OnoFCGEEKJ2Wjs1gqOjI4qKilTmxcjPz4epqSmsra1VLnhzdHRUzpT6eN/aZwaVl5crJyB7kkQiUV54V2vhwoW4efMmiouLlROVTZ8+HWFhYRCLxdizZw+2bNnS4H0SCAQN3qY+4+XmZsDG2h4dvDojM6vuLaek8Wpz3dSfIVFFeVYPyrOCmZkZOByOsjW12vmXHp+HiTS9xua59vM3MzOr8zNR358RrS2m+Hw+qqqqVJbVvubxePXq+2S/+liwYAHGjx+PV199FQDQsWNHAIribNSoUejevTtCQkIgk8mwdevWBo2dnZ3d4Hjq49XhQ5CdWYYvv/wa7bx+apb3IArN9RkSVZRn9WjpeZZIJLhy5Qq8vb1hZWXVbO/TrVvTPZqGPNvL5tne3h6urq64du0aTExebrJWrS2mJBJJnWKo9nXtBGIv6vtkvxeZN28eQkJC8NFHHyknITt37hysra1RUqKYEPP27duwtbXFvHnzGlxMOTs7o7KyskHbPI9AIEB2djY+++xTzJqxGEeOHMXazxc12fjkX7W5burPkKiiPKsH5VnB1dUVK1euxL1795RzMDUlLpeLbt26IT4+HjKZrMnHJwqNzbO7uzsyMzMxb948ZGZmqqyr/Vl5Ea0tprKzs2FjYwMul6tMjoODA0QiEcrKyur0dXBwUFnm4OCA3Nzcer/fkiVLsHnzZixduhQhISEq62oLqVr37t2Ds7NzA/ZGofZ5Qk0tOeUeAMDFyaNF/8OoDs31GRJVlGf1aOl5FgqFkMvlytZcZDKZWmdAb6leNs+1n79QKHzpnwetvQA9Li4OUqkUgYGBymVBQUGIjY2tMxV+TEwM+vTpo7Ksb9++iImJqdd7TZ06FZs3b8aiRYvqXAs1a9YsJCQkqCzz9fWts0yTMjIfAAAcHV1gYlL3+UWEEEIIaT5aW0yJxWKEh4dj+/bt6NmzJ9544w0sXboU33zzDQDFOc7ac5uHDh1C69atsXXrVnh7e2Pr1q0wMzPDgQMHXvg+lpaW+O677xAWFoZffvkF9vb2ysbhcBAREQFHR0ds3rwZbdu2xYQJE7B8+XJs3LixWfe/ISory5XP5XN18dRwNIQQQkjLorXFFKB4MOO1a9cQGRmJ77//HqtXr8aRI0cAKB6eOGHCBACKQ9UjR45Ev379cO3aNQQGBmLEiBH1umZq6NChEAgEmD59OvLy8lSaq6srMjIyMGLECPTp0wfx8fFYv349li9fjoMHDzbrvjdUekYyAMDdnZ7RRwgh+s7Hx0c5/Y8mvPnmm7C1tW3W95g8eTIiIyOb9T2aksYn3NL31pyTdtaOu+jD/2OREYls1oxFGt9ffWw0ySHlWZ8a5VnRdHnSzgcPHignplZ3c3NzY4yxZssbADZgwAD28OHDp07U3dR51utJO0nDZGQo5pdyd6MjU4QQou+enLxan977008/xd9//40HDx406/s0JSqm9ERaOp3mI4SQliAyMhIeHh4ICwtDaGgoAGDUqFG4fv06xGIxSktL8fPPP8PMzAwAlJfInD17FsXFxejfvz9MTEywa9culJWVISsrCzNnzoRUKoW7uzsAwMXFBX/88QeEQiFSU1Px6aefKic2TUtLU/45bdo0ldh4PB4SExOxZ88e5bKwsDBcuXIFHA4HkZGRYIzVaY+fzhsyZAiGDRuGw4cPN1sOm5rWTo1AGqb2yJSzkyuMjIxUZo4nhBDSMHx+wyd9fhKHw4GJiRH4fN5zb9kXiaqeue5pxo4di5s3b2Lz5s0ICwtDmzZtcOjQISxYsAARERHw8vLC/v378d577+Hrr78GAIwePRpz585FTEwMEhMTERISgj59+mDYsGEwNDTEnj17YGj4b0nw22+/4ebNm+jevTscHR2xY8cOyOVyrFu3Dv7+/oiNjYW/vz9u376tEltVVRXmzJmDU6dOYdu2bbCwsMDEiRPh5+cHuVyOsWPHwtjYuM4+VVdXK//er18/AMArr7zSoLxoEhVTeqKouAAPhZUwNxPA2dkDaWn3NR0SIYTopPPRG9G3bye1vV909F3077e83v1LS0shk8lQXl6OiooK2NnZ4YMPPsDu3bsBAOnp6Th16hQ6d+6s3CYvLw87duwAoHiMztSpUzF8+HBcvnwZAPDhhx8qH602cOBAuLu7IyAgAIwxJCUlYenSpQgLC8O6detQWFgIACgsLIREIqkTX1RUFMLDwxESEgI7OzusW7dO+ci30tLSl8iQ9qNiSo9kZKSgk7cv3N3aUjFFCCEv6YmpDLVecnIyqqqqsHLlSnTp0gWdO3dG586dsW/fPmWf2lNzgOIxaTweD7Gxscplly5dUv7d29sb1tbWqKioUC7jcDjg8/n1fuzO0qVLcf/+fRQWFmLDhg3K5cePH1ceeXrc+fPnMWLEiHqNrY2omNIj6en/FlOEEEJeTv9+y5vsNJ+Pjw9u3rzZpKf5ntStWzdER0fjzz//xLlz5/DVV19h0aJFKn0eP4JUU1MDQPVC8sf/bmhoiISEBLzxxht13qu8vLxeD/91d3eHubk5+Hw+PD09cf++4j/4s2fPhqlp3cmlxWLxC8fUZlRM6RHlRehUTBFCSKM0tsABFMWURCKFSFTV5I+TefxJIO+88w7OnTuHt99+W7msffv2uHfv3lO3rT2S5efnh6ioKACAn5+fcn1iYiLc3NxQWFioPDo1ePBgTJ8+HVOnTq3zFJIncTgc7Nq1Czt37oSVlRV27typvP4pJyfnpfZX29HdfHpEOT0C3dFHCCF6TSgUomPHjrC0tERxcTG6desGf39/tG/fHps3b0avXr3A4z396JpQKERoaCi++eYb9OrVCwEBAcpn0jLGcPLkSaSnp+Onn35Cly5dEBQUhJ07d0IkEimfYQcoJg6tvWPwcQsXLoSrqys++eQTLFu2DH5+fpg1a1bzJUMLUDGlR9IeFVOuLp7gcLgajoYQQkhz+eGHH/D+++9j9+7dCAkJwaVLl3Dq1ClER0fD3d0da9asQffu3Z+5/dKlSxEfH4/Tp0/j8OHD+PnnnwEo7qqTy+V4/fXXweFwcPnyZRw+fBjHjx/Hhx9+CAAoLi7Gvn37cODAAcyePVtlXDc3N3z22WdYvnw5ysvLkZubizVr1uDLL7+EnZ1d8yVEC2hkBtWW1NQxAzoAZmBgwP76/RqLjEhknh5eGt9vfWo0YzTlWZ8a5VnRdHkG9Ma2N954g5mZmSlf9+zZk1VVVTFDQ0ONx6buPNMM6EQFYwxJ9xW3n3bs0FXD0RBCCNFWq1evxtatW9G2bVv4+vpi06ZN+OOPP5QXp5OGoWJKzyQmKSZQ6+DVRcOREEII0VZTpkyBp6cnbty4gVOnTuHBgwd1TtmR+qO7+fRMUtItAIAXFVOEEEKe4d69exg8eLCmw9AbdGRKzyQkKoqptm06wtDQSMPREEIIIfqPiik9k5uXhYqKMhgbG6ONp5emwyGEEEL0HhVTeijxPl03RQghhKgLFVN6KPHRqb4OdEcfIYQQ0uyomNJDdEcfIYQQoj5UTOmh2mLK06M9jI0b/7BOQgghhDwbFVN6qLAwDyUlheByDdGurbemwyGEEKJFjIyM1DKn1LRp05Camtrs7/Mi//zzD6ZNm9as70HFlJ6iU32EEEKeZtKkSVi1apWmw2h2BgYGCAkJwdChQ5v9vaiY0lO1803RY2UIIYQ8zsDAQNMhNDsnJyecPn0ar7/+OkpLS5v9/aiY0lNJj45M0UzohBCiX9zd3cEYw5gxY5CcnAyxWIyjR4/C0tJS2ScoKAixsbEQiUSIj4/H2LFjAQDBwcEICwuDh4cHGGNYtGgRYmNjldtNnjwZjDF4eHgAAMzMzFBVVYW2bdvCwMAAS5cuRUpKCkQiEc6cOYMuXf79HcMYw5o1a1BYWIg//vhDJWYDAwMcOHAAN27cgIWFhco6Ho+HxMRE7NmzR7ksLCwMV65cAYfDQWRkJBhjdVpkZOQzc9SjRw9kZmbCz88P5eXlDU9yA9HjZPRU7Wk+N9c2MDU1g1gs1HBEhBCiO0xMTBs9BseAA2MjHkx4ppAz+TP7SSTilxp/5cqVmDRpEgwMDPDnn39iyZIl+O9//wt7e3v89ddfWLVqFU6cOIHAwECEhYWhoKAAFy9exMKFC7F06VL4+/vD2toamzZtQqtWrVBRUYHg4GDI5XL07dsXaWlpCA4ORkZGBlJSUrB69WrMmzcP7777Lu7fv4/ly5fjxIkT8PLygkgkAgCMGjUKffv2BZfLRa9evZSxfv311/D19UVQUFCd4qaqqgpz5szBqVOnsG3bNlhYWGDixInw8/ODXC7H2LFjYWxsXGf/q6urn5mbv/76C3/99ddL5fVlUDGlp0rLipFfkAN7Oyd4te+Em/GxL96IEEIIvt36P3Tp3ENt73fr9jV8+NHkBm+3evVq5VGl/fv3w9/fHwCwYMECnDp1Ct9//z0AICUlBd27d8eiRYvw5ptvory8HDKZDPn5+cjPz0dubi769euHY8eOoX///vj777/Rt29f7N+/H4MHD8aJEycAAB988AE+/vhjHD16FADw7rvvIiUlBW+//TZ27twJANixYweSkpIAQFlMLVu2DG+99Rb69u2LgoKCp+5LVFQUwsPDERISAjs7O6xbtw537twBALWcpmssOs2nxxLpVB8hhDQYY0zTIdTL/fv3lX+vqKiAkZHieaze3t4YNWoUKisrle3999+Hl9fTHzF28uRJDBgwAHZ2dnBwcMCuXbsQFBQEAMpiys7ODtbW1rh8+bJyu5qaGly9ehXe3v/eNZ6WlqYytpOTE7744gtUVVUhLy/vufuzdOlSeHl5QSqVYsOGDcrlx48fV9mX2nb8+HEAQHl5Oc6dO4fy8nLlMnWjI1N6LCnpNvoHDaU7+gghpAE+/Ghyk53m8/Hxwc2bN5vlNN+Tp7lqLyw3NDTETz/9hC+++EJlvVQqfeo4J0+exLJlyxATE4NLly7h3Llz8Pb2RocOHdC+fXtERkbC0PDp5QKXywWXy31sXyQq6+VyOYYPH44ff/wRq1atwieffPLM/XF3d4e5uTn4fD48PT2VxeLs2bNhalr38xCLFXnr0aMHOnXqhLt370Io1MwlLVRM6THlHX1edEcfIYQ0xMsWOI/jcDiollZBUiWGXP7sYqqpJSYmok+fPkhJSVEuW7x4MXg8HtavX1/nyNupU6ewb98+jBgxAufPn0dpaSkSEhKwevVqREdHK6+HysvLQ2BgIOLj4wEoijY/Pz9EREQ8M5a8vDycOXMGy5Ytw969exEWFqYSVy0Oh4Ndu3Zh586dsLKyws6dO/HKK68AAHJycp67vykpKWjVqhVSUlLUmufH0Wk+PZZ0X3G+2dnZHebmrTQcDSGEEHX44Ycf0LNnT6xduxbt2rXDpEmT8MUXXyA9PR0AIBQKYWlpiXbt2oHL5aKkpARxcXGYMmUKoqOjAQDnz5/HhAkTlNdLAcBXX32Fzz77DCNHjkTHjh2xa9cumJiY4Ndff31hTAcPHkRMTAy+/fbbp65fuHAhXF1d8cknn2DZsmXw8/PDrFmzmiAb6kHFlB6rrCxHdrbih4dO9RFCSMuQkZGBUaNGYfjw4bh9+zbWrVuHJUuW4OeffwYAnDlzBsnJybh16xZ8fX0BKGYJB4ArV64AUBRTHA5HpZjasmULdu3ahV27duHatWtwcXHBgAEDUFRUVK+4PvzwQwwZMgRjxoxRWe7m5obPPvsMy5cvR3l5OXJzc7FmzRp8+eWXsLOza2w61IZRa94mEAgYY4wJBAK1j/vJyq9YZEQimzJpjsbzoMutuT5DapRnyrPmmru7O9u7dy9zd3dvlvE5HA7z8/NjHA5H4/uqz62xeX7e96C+Pyt0ZErPJSQqzm137tRdw5EQQggh+omKKT13I05xG6tPt17gcul+A0IIIaSpUTGl51IeJKC8ohR8vhldN0UIIYQ0Ayqm9BxjDHGPjk716N5bw9EQQggh+oeKqRbg+o0YAEB330ANR0IIIYToHyqmWoAbcYpiqkvn7jA25mk4GkIIIUS/aHUxxePxsHv3bpSWliInJweLFy9+Zl9fX1/ExMRAKBTiypUr6NHj6Q+pXLlyJUJDQ+ssX79+PQoKClBcXIyNGzcqp+UHACsrKxw6dAgVFRV48OABpkyZ0vidU6PMrFQUFuXD2JiHLp3prj5CCCGkKWl1MbVp0yb07NkTAwcOxPz587F69WqMGzeuTj8+n4/jx4/j/Pnz8PPzw8WLF3Hs2DHw+XyVfhMnTsSaNWvqbL948WJMnjwZY8aMwbhx4zBlyhSVwi0sLAwWFhbo3bs31q1bh927dyufzq0rbty4BIBO9RFCCCHNQeMTbj2t8fl8JhKJWHBwsHLZqlWrWGRkZJ2+M2bMYCkpKSrLkpKS2LRp0xgAxuVy2Q8//MBEIhFLSEhgoaGhKn3T09OVfQGwKVOmsNTUVAaAtWnThjHGVCbz2rVrV50xntc0OWlnbRs2dAyLjEhk34f8qvHPVhcbTXJIedanRnlWNJq0Uz8aTdr5HD4+PjAyMsLFixeVy6KjoxEQEKByCg4AAgMDlc8TqnXhwgX07q24e83c3BzdunVDQEAALl26pNLP0dERbm5uOHfunMr7eHh4wMHBAQEBAcjIyFA+06h2fe3YuuLGo4vQO3h1hRnfXMPREEIIaQwfHx+N/h568803YWtrq/b3/eeffzBt2jS1v++LaO0sjo6OjigqKoJUKlUuy8/Ph6mpKaytrVWeBeTo6Ig7d+6obJ+fn48uXRTzKpWXlyMoKOiZ7wOoPpU6Pz8fAODi4gJHR8c6T6zOz8+Hi4tLg/dJIBA0eJv6jFefccWSh8jJyYCTkxsCAvoj9ur5Jo1F3zUk1+TlUZ7Vg/KsYGZmBg6Ho2xNjcvlqvzZlI4cOYK1a9fi8uXLTT72i7i5ueHgwYNo06YNiouL1fKeBgYG2Lp1K4YOHYpffvlF5fNqbJ5rP38zM7M6PxP1/RnR2mKKz+ejqqpKZVntax6PV6++T/Z71vs8PvaT79OYsZ+UnZ3d4G2actzEe/nIzS7Hjm2haNdBdx4eqU2a6zMkqijP6tHS8yyRSHDlyhV4e3vDysqq2d6nW7duTT4mj8eDm5sbundX/01FtQchOnfu3Kx5q2Vra4u1a9fC2dkZFRUVz9zvl82zvb09XF1dce3aNZiYmLzUGFpbTEkkkjoFS+1rkUhUr75P9nvW+9T2f7JYE4lEjRr7Sc7OzqisrGzwds8iEAiQnZ1d73H79hmM5Us3IPp8LHr4T2qyOFqChuaavBzKs3pQnhVcXV2xcuVK3Lt3T+VSjqbC5XLRrVs3xMfHQyaTNdm4p0+fhpOTE/7v//4Pnp6emDlzJkaNGoXVq1fD29sbEokEJ06cwHvvvQehUIhPP/0Uvr6+sLS0RJcuXTBu3DhcuXIFISEhePPNN/Hw4UP83//9H7Zt2wYvLy+kp6fDxcUF3333HQYNGoSCggKEhYXh888/h1wux9WrVwEAR48excyZMxEeHq6MjcfjIS4uDhcuXMDs2bMBAKGhofD29kafPn0QERGBAQMG1NmnqKgoDBo06Kn7O3LkSNy7dw8jRozAlStXkJGRgRs3bjRZnt3d3ZGZmYl58+YhMzNTZV3tz8qLaG0xlZ2dDRsbG3C5XGVyHBwcIBKJUFZWVqevg4ODyjIHBwfk5ubW631q+9f+MNWOlZub26ixn1RZWdks/3DVd9xLMVEAAA+P9uByjVBWVtLksei75voMiSrKs3q09DwLhULI5XJlexzfyKjR43M4BjDhcsDjGECOZ5+CEj12OUt9jB07Fjdv3sTmzZsRFhYGDw8PHDhwAAsWLEBERAS8vLywf/9+zJ49G19//TUYY3jjjTcwd+5cxMTEIDExESEhIejduzeGDRsGQ0ND7NmzB4aGhspcHDp0CDdv3kT37t3h6OiIHTt2QCaTYd26dfD390dsbCz8/f1x+/ZtldyJxWLMmTMHp06dwg8//AALCwtMmDABfn5+qKmpwdixY2FsbFxnn6qrq+t8BrX+/PNP/Pnnn8rXT/u8AEAmkz1zjOepHU8oFL70z4PWFlNxcXGQSqUIDAzEhQsXAABBQUGIjY0FY0ylb0xMDFasWKGyrG/fvvj8889f+D65ublIT09HUFCQspiq/XteXh5iYmLg4eEBZ2dnZeEVFBSEmJiYpthNtSovL0VKSgLatu0I324BiDr3t6ZDIoQQrRM1axL6uDk33YCvBT939YX0LLzy4y/1Hq60tBQymQzl5eWoqKiAnZ0dPvjgA+zevRsAkJ6ejlOnTqFz587KbfLy8rBjxw4AimvFpk6diuHDhyuvufrwww/xzz//AAAGDhwId3d3BAQEgDGGpKQkLF26FGFhYVi3bh0KCwsBAIWFhcqzO4+LiopCeHg4QkJCYGdnh3Xr1imvay4tLa33fuoSrb2bTywWIzw8HNu3b0fPnj3xxhtvYOnSpfjmm28AKM5x1p7bPHToEFq3bo2tW7fC29sbW7duhZmZGQ4cOFCv99q2bRs2btyI4OBgBAcHY8OGDcr3SU1NxYkTJ7Bv3z507doVM2fOxOTJk/H99983z443s+uPZkPv0Z3mmyKEkKd58j/s2i45ORl///03Vq5ciZ9//hk3b97E+PHjVS7ITktLU/69Y8eO4PF4iI2NVS57/E53b29vWFtbo6KiQnn08tdff4W1tXW9r5FaunQpvLy8IJVKsWHDBuXy48ePK8d8vB0/fhwAnrpMF2jtkSlAMZnmtm3bEBkZifLycqxevRpHjhwBoKiyp0+fjvDwcFRWVmLkyJHYvn073nvvPcTHx2PEiBH1vq5p06ZNsLOzw5EjR1BTU4M9e/bg66+/Vq6fOnUqdu/ejcuXLyM3NxczZ85U+RLqkus3LuGtcdPRnYopQgh5qld+/KXJTvP5+Pjg5s2bkMufXaA19DTfk7p164bo6Gj8+eefOHfuHL766issWrRIpc/jR5BqamoAQGWaocf/bmhoiISEBLzxxht13qu8vLxed7i5u7vD3NwcfD4fnp6euH//PgBg9uzZMDU1rdNfLBYDUDzN5MllukCriymxWIzp06dj+vTpddY9OddUbGws/Pz8XjjmjBkz6iyTy+VYsmQJlixZ8tRtCgsLn/ql0kXxt2Ihk9XAxdkDDg4uyMvL0nRIhBCidRpb4ACKW+4lMjlE0pqXupbneR4/evbOO+/g3LlzePvtt5XL2rdvj3v37j112+TkZFRVVcHPzw9RUVEAoPL7MzExEW5ubigsLERFRQUAYPDgwZg+fTqmTp36wiN3HA4Hu3btws6dO2FlZYWdO3filVdeAYA6Uw09KSUl5bnrtZXWnuYjzUMkEuLW7WsAgH5BQzQcDSGEkJchFArRsWNHWFpaori4GN26dYO/vz/at2+PzZs3o1evXs+cwkcoFCI0NBTffPMNevXqhYCAAISEhABQFGknT55Eeno6fvrpJ3Tp0gVBQUHYuXMnRCKR8kJtQDFxqJmZWZ3xFy5cCFdXV3zyySdYtmwZ/Pz8MGvWrOZLhhagYqoFOnvuBABgQP9XNRwJIYSQl/HDDz/g/fffx+7duxESEoJLly7h1KlTiI6Ohru7O9asWfPcOaiWLl2K+Ph4nD59GocPH8bPP/8M4N+76l5//XVwOBxcvnwZhw8fxvHjx/Hhhx8CAIqLi7Fv3z4cOHBAOf1BLTc3N3z22WdYvnw5ysvLkZubizVr1uDLL7+EnZ1+z2+o8efq6HvThmfzPd4sLW3Y6X/usciIRGZn66jx/OhCo2eZUZ71qVGeFa0lP5vvjTfeYGZmZsrXPXv2ZFVVVczQ0FDjsak7z3r9bD7SfEpLi3DrtmLSteD+wzQcDSGEEHVbvXo1tm7dirZt28LX1xebNm3CH3/8obw4nTQMFVMtVNSjU33BdKqPEEJanClTpsDT0xM3btzAqVOn8ODBgzqn7Ej9afXdfKT5nDt/Eh/M/y86d+oOW1sHFBbmaTokQgghanLv3j0MHjxY02HoDToy1UKVlBQq7+rrH0Sn+gghhJCXRcVUC3buvOLRAQOC6VQfIaTlqZ0vydCQTtK0ZLWff2NmvqdiqgWrLaa6dO4BGxt7DUdDCCHqVVxcDEDxeBXSctV+/kVFRS89BpXjLVhRcQFu3b6Grl38ENxvGA4f2avpkAghRG2EQiGioqIwfvx4AEBCQkKT3s3G4XBgb28Pd3f3Jp8BnfzrZfNsaGiIjh07Yvz48YiKiqr3I+ieOtZLb0n0wtlzJxTFVP9XqZgihLQ4oaGhAIAJEyY0+dgcDgeurq7IzMykYqoZNTbPUVFRyu/ByzKAYsIp0owEAgEqKirQqlUrVFZWatW4Njb2OPi/cwCAtyb2Q1FxQZPFp0+a6zMkqijP6kF5rovP58PGxqbOc18bw8zMDNeuXYOfn5/yESyk6b1snhljKCoqeu4Rqfr+rNCRqRauqCgft+9cR5fOPdC/3zD89vs+TYdECCFqJxKJkJGR0aRjCgQCmJiYIDMzk4rWZqQNeaYL0Amiziom8Bw6ZLRmAyGEEEJ0EBVTBBGn/0R1dRU6eHWBt7ePpsMhhBBCdAoVUwQVFaU4HfkXAGDc6Hc0HA0hhBCiW6iYIgCAI7//BEDxrD4rK1sNR0MIIYToDiqmCADgfvJd3Lp9DYaGRhj1WtPfIkwIIYToKyqmiNKRR3fyvT5yIgwNjTQcDSGEEKIbqJgiSueiI1BYlA8rK1sM6E/P6yOEEELqg4opoiST1eDoX78AAMaMflvD0RBCCCG6gYopouKvY7+iuroanbx90bFDV02HQwghhGg9KqaIitKyYkSdPQ4AGEPTJBBCCCEvRMUUqeO3R9MkvBI8nKZJIIQQQl6AiilSR2LSLdy+cx1GRsaYOmW+psMhhBBCtBoVU+Spdv/4FQBg5Gvj4eraRsPREEIIIdqLiinyVDfjY3Hh0hlwuYZ4d+ZiTYdDCCGEaC0qpsgz7dy9CTKZDP2ChqBrFz9Nh0MIIYRoJSqmyDNlZDzA8b8PAgDmvvcfDUdDCCGEaCcqpshzhe79FmKxEJ28fTGg/3BNh0MIIYRoHSqmyHOVlhbhlwN7AACzZy2mZ/YRQgghT6BiirzQgUOhKC4ugLOTG94YNVnT4RBCCCFahYop8kISiQih4SEAgJnTF8LRwUXDERFCCCHag4opUi9//3MY8beugs83w8oVm8DhcDUdEiGEEKIVqJgi9SKXy/HFxv/gobASXTr3wJRJczQdEiGEEKIVqJgi9Zafn41vvv0MADDtnQXw7thNwxERQgghmkfFFGmQU6f/xOnIv8DlGmLlik0wMeFrOiRCCCFEo7S6mOLxeNi9ezdKS0uRk5ODxYuf/VgTX19fxMTEQCgU4sqVK+jRo4fK+okTJyI5ORlCoRC//fYbrK2tAQDBwcFgjD21ubq6AgC2bt1aZ92CBQuab8e13NaQNcgvyIGLswcWzPtY0+EQQgghGse0tYWEhLC4uDjWvXt3Nnr0aFZeXs7GjRtXpx+fz2c5OTls06ZNrGPHjmzr1q0sNzeX8fl8BoD5+/szoVDI3nnnHda1a1cWGRnJjh49ygAwIyMjZm9vr9LOnj3LfvvtN+X4J0+eZMuXL1fpY2pqWu/9EAgEjDHGBAJBk+anucatT/Pp1oud/ucei4xIZEMHv6Hx70pzN03muiU1yjPlWZ8a5Vn389yAsTWfiKc1Pp/PRCIRCw4OVi5btWoVi4yMrNN3xowZLCUlRWVZUlISmzZtGgPAwsPDWWhoqHKdi4sLk8lkzMPDo85YEydOZCUlJcza2lq5LDMzkw0ZMkTrPmhN/6DOmrGIRUYksoi/bzP/nkEa/840Z9N0rltKozxTnvWpUZ51P8/1HVtrT/P5+PjAyMgIFy9eVC6Ljo5GQEAADAwMVPoGBgYiOjpaZdmFCxfQu3dv5fpz584p12VlZSEjIwOBgYEq2xgaGmLdunX4/PPPUVxcDAAQCARwcXFBUlJSk+6fPvgx7BucPnMUhoZGWPNpCLzad9Z0SIQQQojaGWo6gGdxdHREUVERpFKpcll+fj5MTU1hbW2NoqIilb537txR2T4/Px9dunRRrs/Jyamz3sVFdfLJ8ePHo3Xr1vj++++Vy7y9vSGXy7Fq1SoMHz4cxcXF+Oqrr7B3794G75NAIGjwNvUZr6nHbYjvt38Ba2s7+PoEYOMXu/Cfj2ciLz9bY/E0F23IdUtAeVYPyrN6UJ7VoznzXN8xtbaY4vP5qKqqUllW+5rH49Wrb22/F62v9d5772H37t2QSCTKZR07dgRjDAkJCfj2228RHByMnTt3oqKiAr///nuD9ik7u3mKjOYat75qamSIu5oFwBr7wo6ju78rjI219qvVKJrOdUtBeVYPyrN6UJ7VQ5N51trfeBKJpE6xU/taJBLVq29tvxetBwBbW1v069cP77//vkq/vXv34ujRoygtLQUA3Lp1C15eXpg3b16DiylnZ2dUVlY2aJvnEQgEyM7ObvJxX4alpTW+/OJH2Ns7Y1/Ycfzf2g9QXl6q0ZiakjblWp9RntWD8qwelGf1aM481479IlpbTGVnZ8PGxgZcLhcymQwA4ODgAJFIhLKysjp9HRwcVJY5ODggNze3XusBYNiwYUhNTcXt27frxFJbSNW6d+8eBg4c2OB9qqysbJYfqOYat6ExLFsxC998tR9t23TEhnW78Z+PZyEnN1OjcTU1bch1S0B5Vg/Ks3pQntVDk3nW2gvQ4+LiIJVKVS4SDwoKQmxsLBhjKn1jYmLQp08flWV9+/ZFTEyMcn1QUJBynYuLC1xdXZXrASAgIAAXLlyoE8eaNWsQERGhsszX1xcJCQkvv3N6KjMrFR9+NBm5uVlwdnbHt9/8gvbtOmk6LEIIIaTZafy2xme1bdu2sVu3brGePXuyN954g5WVlbExY8YwAMze3p6ZmJgob13Mz89nW7duZd7e3mzr1q0sJydHOc9UYGAgk0gkbObMmaxr167szJkz7I8//lB5r8jISLZ8+fI6MfTs2ZNVV1ezJUuWsDZt2rC5c+cysVjMAgMDNX7bprbedmtlZct2bf+dRUYksmN/XGc9uvfWeEz6mmt9a5RnyrM+Ncqz7udZ5+eZAsBMTU1ZWFgYq6ysZFlZWWzhwoXKdYwx5TxSgGJizmvXrjGRSMRiYmKYr6+vyljTpk1j6enprLKykh0+fJhZWVmprL979y577733nhrH66+/zuLi4phIJGJ3795VFnSa/qC1+QfVjG/OtnwZxiIjEtnJ47fYyBHjNR6TvuZanxrlmfKsT43yrPt51otiSl9aSyymAMXs8p+s/IpFRiSyyIhEtmrFZmZiwtd4XPqYa31plGfKsz41yrPu51nnJ+0kuk8qlWLd+iXYvvNLyGQ1GDxoFHZ8fxieHl6aDo0QQghpMlRMkWbFGMOvB/dg0ZJ3UFiYBze3Ntj23UG8NvwtTYdGCCGENAkqpoha3L5zHbPnjsblK+fA45lg6eJ12PD5LtjaOrx4Y0IIIUSLUTFF1KaiohQf//c9bNuxEdXVVQjo1R+hu45h5Ijxmg6NEEIIeWlUTBG1YozhwKEfMXvuaNy+cx1mZuZY8tFabN4YCkcHlxcPQAghhGgZKqaIRmRmPsDCxVPw3Q9fQCIRw69HH4TtOY6pby+AkZGxpsMjhBBC6o2KKaIxcrkch4+EY9ac13H12gUYG/MwY9qHCN31F3r599d0eIQQQki9UDFFNC4nJwPLVszEmrWLUFiUD2dnd2z8Yhc+W/0dHOjUHyGEEC1HxRTRGlHn/sa0mcPx68E9kMlq0C9oCML3HMfM6YtgYsLXdHiEEELIU1ExRbSKWCzE9p1fYvacN3Dt+kUYG/PwzpR52Pvj3xg8cJSmwyOEEELqoGKKaKW09GQsXT4Dn/zfAuTkZsLW1gGrPt6Mb7f+D17tO2s6PEIIIUSJiimi1aIvnML0WSOw+8evIBaL0KVzD2z77hCWLl4Hy9bWmg6PEEIIoWKKaD+ptBr7/7cDU2cMQ8SpP8HhcPDa8LewN+wfvPXmDBgaGmk6REIIIS0YFVNEZxQVF+CLjcvw/sKJSEi8BXMzAebPWYHdO/6AX48+mg6PEEJIC0XFFNE5d+7ewPwP3sKXm1eipLQI7m5tsXljKNZ8GgJ7OydNh0cIIaSFoWKK6CTGGP7+5zCmzngVh34Lh0xWg/79hiH8x7/xzpT5MDKiU3+EEELUg4opotOEwkp8v+0LzJ47GjfiLoPHM8HM6Quxe8dR9OgeqOnwCCGEtABUTBG9kJZ2H4uXTcXaLxajuLgAbq6e2PJlOFat2AxLSxtNh0cIIUSPUTFF9MqZyGOYNnM4fvt9H+RyOQYPGoW9oScwauREGBgYaDo8QggheoiKKaJ3hKKH+Pb7dZj3wVtITLoNczMBFi9cg61bfoKraxtNh0cIIUTPUDFF9FZS0m3M/+AtfPfDFxCLhejWtSd2b/8DU99eQHNTEUIIaTJUTBG9JpfLcfhIOGbMHomYK2dhbGyMGdM+xM4ffoOXVxdNh0cIIUQPUDFFWoT8ghx8vOo9rP1iMUpLi+Hp6YUfQn7F7JmLYWRkrOnwCCGE6DAqpkiLcibyGGa8+xpOR/4FLtcQUybNwc5tR+DdsZumQyOEEKKjqJgiLU55eSnWfbEE/109HyUlhfBwb4dvt/7y6CgVXUtFCCGkYaiYIi3WhYunMX32azh56g9wuVxMmTQH2749hLZtOmg6NEIIITqEiinSolVWlmP9xv/gv6vno7S0GG3bdsS27w5h8sT3wOFwNR0eIYQQHUDFFCFQHKWa+d5InIs+CSMjY7w7awlCvt4PJyc3TYdGCCFEy1ExRcgjZWUlWL3mA6zf+B88fFiBzp26Y/f23zFi+JuaDo0QQogWo2KKkCecPPUHZr33Om7EXYapqRmWLf4cK5dvRnV1jaZDI4QQooWomCLkKQoKc7HkP9OwbcdGSKXVCAwYgNiYdPTo3kfToRFCCNEyVEwR8gyMMRw49CPmvf8m0jNSIK2W4f8+CcEH81fRRJ+EEEKUqJgi5AVSHiRi8bJ34OzaGgAwdsxUbP/+EDw9vDQbGCGEEK1AxRQh9SCVVqN9Bzv839oPUFJSiDaeHbD9+0MYN2aqpkMjhBCiYVRMEdIA129cwqw5r+PipTMwNubh/fmrsOHzXbBsba3p0AghhGgIFVOENFBZWQlWfToPW0PWoKpKgoBe/bF7558I6NVf06ERQgjRACqmCHlJfxz9GXMXjENKSgKsLG2w4fNddHE6IYS0QFpdTPF4POzevRulpaXIycnB4sWLn9nX19cXMTExEAqFuHLlCnr06KGyfuLEiUhOToZQKMRvv/0Ga2trlW0ZYyotNjZWud7DwwMRERF4+PAh7ty5gyFDhjT9zhKdlJaejHkfvIVDv4UDoIvTCSGkpWLa2kJCQlhcXBzr3r07Gz16NCsvL2fjxo2r04/P57OcnBy2adMm1rFjR7Z161aWm5vL+Hw+A8D8/f2ZUChk77zzDuvatSuLjIxkR48eVW4/efJkdv36dWZvb69sVlZWyvVxcXFs3759rGPHjmzFihXs4cOHzNXVtd77IRAIGGOMCQSCJs1Pc41L7eVy7d+zHzt84AKLjEhk/xyLZ2NHv6PxuHWt0Xea8qxPjfKs+3luwNiaT8TTGp/PZyKRiAUHByuXrVq1ikVGRtbpO2PGDJaSkqKyLCkpiU2bNo0BYOHh4Sw0NFS5zsXFhclkMubh4cEAsLVr17L9+/c/NY5XXnmFVVZWKgszACwiIoKtXr1a4x80/aCqr9U3161bW7HPP9vGIiMSWWREItvw+S5maWmj8fh1pdF3mvKsT43yrPt5ru/YWnuaz8fHB0ZGRrh48aJyWXR0NAICAmBgYKDSNzAwENHR0SrLLly4gN69eyvXnzt3TrkuKysLGRkZCAwMBAB06tQJSUlJT40jMDAQ169fh0gkUomjdmxCHld7cfrX3/yf8uL0H3ceRZ/eAzUdGiGEkGZiqOkAnsXR0RFFRUWQSqXKZfn5+TA1NYW1tTWKiopU+t65c0dl+/z8fHTp0kW5Picnp856FxcXAIC3tzc4HA7i4+NhYWGBv//+G8uWLUNlZeULt20IgUDQ4G3qM15Tj0vqamiuI8/+heSUO1jy0Tq08eyAzz/bhn9O/oY9YV9DIhE3Z6g6jb7T6qGteTY25sHcTABTUzPweCbg8UxgbMwDj2cCDocDAwMOOAYGMOAojgPU1EgftRrU1NRAUiWGWCyEWCSEWCyCpEoMxpjG9kdb86xvmjPP9R1Ta4spPp+PqqoqlWW1r3k8Xr361vZ73npDQ0O0bdsWqampmDFjBiwtLfH1119j3759GD169AvHbojs7OwGb6PJcUldDc21XC5HakoxMtNLMWzoWIx+YwI6dnGAhYVpM0WoH+g7rR7qyDNjDNXVMkgkUkjEUlRX1aCqSobq6hpUV9WgulqGGqkM0ho5mLzpCx8jIy6MjLkwMuLC2JgLY54heDxD8Exq/zSCiYlhnTMeTYm+z+qhyTxrbTElkUjqFCy1rx8/5fa8vrX9nre+pqYGNjY2EIvFqKmpAQBMmzYN165dg6OjIyQSicqdf0+O3RDOzs6orKxs8HbPIhAIkJ2d3eTjkroam+sunf3w0YdrYGvrgKsxqThw6EccOLQHMpmsGaLVXfSdVo/myHPr1tZwcfaAs7M7nJ3c4OzkDkdHN9jZOsDYuP7/+ZTJZBBLRKiSiFFVJUF1dRWqqqsgk9U8uttaDrmcwcDAAIaGhuByDWFoaAgjI2PweCbgm5rB1JQPLlfx600qlUEqff7PmVQqRX5BDnLzMpGbm4mcnAxkZj1ARuYDlJeXvnRO6PusHs2Z59qxX0Rri6ns7GzY2NiAy+Uqf+E4ODhAJBKhrKysTl8HBweVZQ4ODsjNza3X+ieTf+/ePQCK4ic7OxudO3d+5rYNUVlZ2Sw/UM01LqnrZXN9KSYK8bdGYuH7n2LI4NcxacJ78OveB+u/XI609ORmiFS30XdaPV4mz4aGRvBwb4f27TqhTZsOaOPphTaeHdC6tdUzt5HJZCgqzkdeXjYKC/NQXFKAkpIilJQWoaSkEBUVZah8WIGHleUQiYVNcmqOxzOBuXkrWLSyROvWlrCwsEJrCytYW9vB1tYBdrYOsLVxgK2tI4yNjeHi7A4XZ/c645SVlSA9IxnJKQm4n3wXycl3kZaeApmspt6x0PdZPTSZZ60tpuLi4iCVShEYGIgLFy4AAIKCghAbG1vnBy0mJgYrVqxQWda3b198/vnnyvVBQUEID1fMBeTi4gJXV1fExMTA29sbly9fRrdu3ZCWlgZAMe+UVCpFcnIyBAIBVqxYARMTE0gkEmUcT17wTsiLCIWV+GLjMlyKOYOFH66Gl1cX7PjhN+wO/RqHfwuHXC7XdIiEqDAwMIC7W1t08vZFhw5d4dW+M9p4doCxcd2JaeVyOXJzM5GZlYrMrDRkZaUhKzsVOblZKCzMa1Dx0RSqqiSoqpKguLjguf04HA5srO3/PaLm7AFXV094uLeDo4MLWre2QuvWveDTrZdym+rqajxITcS9hJu4e+8m7iXcRHZ2enPvEtFiBlDc1qeVtm3bhqCgIMyYMQPOzs4IDw/HjBkzcOTIEdjb26O8vBwSiQQCgQDJycn43//+hx07dmDOnDkYP3482rVrB5FIhMDAQERFRWH+/PmIjY3FN998g8rKSrzxxhswMDDAtWvXUFxcjEWLFqF169bYsWMHzp49iwULFigvTL916xbWrl2LUaNGYdWqVejcuTMyMzPrtR8CgQAVFRVo1apVk5/ma45xSV1NnWsrK1ssW7wOgQEDAAA342Px5eaPkZNbv++UvqLvtHo8K88mJqbo5O2Lrl380LmTL7w7+sDcvFWd7Ssry3E/+S5SUhLwIC0JD1KTkJ6ejKoqiTp3o9nxeCZwc20DT4/2aNfWG+3bd0K7dp1gblb3ouTy8lLcuXsDt25fQ/ytq0i6fwempib0fVaD5vx3oyFja3yOiGc1U1NTFhYWxiorK1lWVhZbuHChch1jTDmPFKCYmPPatWtMJBKxmJgY5uvrqzLWtGnTWHp6OqusrGSHDx9WmZTTxcWFHT58mJWUlLCioiL2zTffMGNjY+X6tm3bsqioKCYWi9mtW7fYoEGDtGIODJrDRH2tuXL92vC32LE/rrPIiET299E4Nm7MNMbhcDS+v/qWZ2pPz7OdnQML7BXM3pu9lH0f8iuL+Pu2co602nb8zxvsq0172Xuzl7IB/YczJ8f6T1isj83AwIA5ObqyVwaMYPPnfsy+3fo/9s+x+Dp5+/toHPvmq59YakoR6+UfxLhcQ43Hrq9NG+aZ0uojU/qCjkzpvubMtYODC5YtXoce3RVzl92+cx1fblmFzMwHTfo+uoC+083LyMgInTv1QGBAMN6dvQClJQ+VF2rXyi/IQXz8Vdy5ewN37t7Ag9QkyOV0o8TzGBoaoV3bjujaxQ9du/RE1y5+da4hE4uFiL91DddvXELs1Wikpj19bkPScNpwZOqliqkzZ8489wLBQYMGNXRIvUbFlO5TR65HjhiPue8th5mZOaqrqxC291scOBSq9mtNNIm+003PxdkDvfz7wb9nEHy69YKpKV9lfXZ2Om7cvIz4W1cRHx+L/IKcZ4xEGsLNrQ0C/Ptj9aefI/l+JiwsLFXWFxcX4Oq1C7h67QJir0U36q7Blk5ni6lPP/1U5bWhoSHatGmD1157DevWrcOWLVsaHLA+o2JK96kr13a2jljy0Vr08u8HAEhJScDmr/+LhMRbzfae2oS+043H45nA16cXAgMGoJd/fzg5uqqsLy4uwK3bVzFn3gwE9fPHgwf3NRSp/qv9PltYWMDG2hE9ugfCz68vfLv1gonJv3PNyeVyJCTG4/KVc7h85RyS7t/W6GSjukZni6lnmTZtGsaNG4fXX3+9qYbUC1RM6T5153rYkNGYN2cFLCwsIZfL8fuf+7En9GuIRMJmf29Nou/0y7G1dUCfwIEIDByA7j4B4PFMlOuqq6tx+841XIk9j9ir0XiQmkh5VpNn5dnIyAhdOvuhp19fBPj3R9u2HVW2KyktwuUrZxETE4Wr1y/o/c99Y+ldMeXh4YE7d+7AzMysqYbUC1RM6T5N5LpVK0vMn7Mcw4aOAQAUFubh++3rcfbcCbW8vybQd7p+DAwM4NW+C/r0fgV9eg9Eu7beKuvzC3IQc/ksLl85ixtxlyGRqE4yTHlWj/rm2cbaDr38+yOgV3/49egLMzNz5TqptBrxt67iUkwkLl46g9y8LHWErlN0tphydXWts0wgEGDZsmXo1atXnUkuWzoqpnSfJnPdo3tvLF64Bs6PJhS8fiMG336/Vi8n+6Tv9LMZGhrB16cXgvoMRp8+g2BrY69cJ5PJcPdeHC7FRCLm8tkXXtxMeVaPl8mzoaERunbxQ++AAQgIGAA3V0+V9Wlp93HxUWF1L+EmzU8HHS6mZDJZnfO5BgYGyMzMxMyZM3HmzJkGB6zPqJjSfZrOtbExDxPHz8bkie+BxzOBTFaD347sQ/i+7yAUPVR7PM1F03nWNqamZgjw74+gvoMQEDBAZX4jkUiI2KvnFQXUlbMNuoCZ8qweTZFnF2cP9A4cgN6BA9Gtq5/K3ZdlZSW4dFlRWF29drHOEciWQmeLKTc3N5XXigdZViM/P7/BgbYEVEzpPm3Jtb29M+bPXYH+QUMBAKWlxQjf9x3+On5AL+7605Y8a5KFhSX69h6EoL6D4dejj8pz7UpKCnHh4mlEXzyNG3GXIJVKX+o9KM/q0dR5NjdvhV7+/dCn90AE+PdXmVC1uroKN+IuI+ZyJC7FRLWouzJ1tpgiDUPFlO7Ttlz79wzC+/NWwc2tDQAgI+MBdu7ejAuXTms4ssbRtjyri62tA/r1HYJ+QUPQtUtPcLlc5bqs7DRER59C9MVTuHsvrknu8mqpeVa35swzl2uIrl380Kf3QPTpPRDOTqoHOVIeJCLmsuK07917cXp9OpCKqRaCiindp4255nINMeq18Zj69vuwtLQGoHgsza49W3Dn7g0NR/dytDHPzcXVxRP9goYgqO8QeHfsprLufvJdnI+OwPkLEUhLa/qpC1pSnjVJnXl2d2uL3oGvoHfgK+jcqbtKQV5eUYqrV6MRc/msXs5pRcVUC0HFlO7T5lzz+WaYNOFdvDVuhvKW+Cux5xG2NwT3EuI1HF3DaHOem0L7dp0Q1HcI+gcNgYdHe+VyuVyOW7evIfpCBKIvnkZeM9+xpe951haaynMrQWv06tUfgQHB6NWzHwQCC+U6uVyOpPt3EHs1GrFXz+PuvZs6f4kAFVMtBBVTuk8Xcm1jY49pby/Aq8PGwtDQCABwKSYSYfu+Q1LSbQ1HVz+6kOeG4HC46Na1J/r1HYy+fQfD3s5JuU4qrcb1GzGIvhCBCxdPo7SsWG1x6VuetZU25JnD4aKTty8CA/ojsNeAOnNaCYUPcTP+Cm7ExeD6jRikpiXp3IShVEy1EFRM6T5dyrWDgwvemTwPw4aOVt75c/3GJfxyYDdir0ZrOLrn06U8P4upqRn8ewahb++BCAgIhkWrfx8jIhaLEHv1PM5HR+DS5SgIhZrZR33Isy7QxjxbWdnC368v/HsGoadfUJ3H3JSVleBm/BXcun0dt+5cQ3LyPa1/NiMVUy0EFVO6Txdz7eTkhqlT5mPwoFHKoiolJQG/HNyDyKjjWnloXxfzDCgeAxQYMAB9eg9Ed99AGBsbK9eVlZXg4qUziL54CteuX0R1dZUGI1XQ1TzrGm3Ps4GBAdq19UZ330B07x4In649YWqqOum2WCzE3Xs3kZAYj4TEW0hIvIWiIu26c5+KqRaCiindp8u5trN1xJtjp2Hka+OV/1AWFxfg+InD+OvYrygozNVwhP/SlTxzOFx4d+yG3oEDEBjwCtq26aCyPis7DRcunsaFi6dx526c1v3PXlfyrOt0Lc+Ghkbo2KErunbxQ9cufujSuYfK9Va1iosLcD/lHlJTk/AgNQmpaUnIyEh56ak6GouKqRaCiindpw+5NjdvhddHTsTY0e/A2toOgGIC3pgrZ3H0r/8h9uoFjf/S1+Y8W1vbwb9nEHr17Ae/Hn3QqlVr5brHZyC/eOkM0jNSNBdoPWhznvWJrufZwMAAHu7t0aVzd3To0BUdvLrA06O9ysShtWQyGfILcpCdk46cnExk56QjPy8bxSWFilZcAKm0usljNDIygqOjM5LvJ8PaxpKKKX1GxZTu06dcc7mGCOo7GK+PnIQe3QOVy0tKixAZdRwRp/5EYtItjcSmTXk2MeHDp1tP+HXvgx49+tQ5+lRZWY4rV88jJiYKV2LPo6KyTDOBvgRtyrM+08c883gmaNfWG23adEAbTy94enihjafXU49gPamiogyVDysgFFZCKHyIhw8rIJaIIKupQU2NFNIaKWQ1NTDgcMDlcsHlGsKQawhDIyOYmvBhaqpofFMzmJu3gkBgARMTU+X4//l4JmKvXmjS/a3vZ1i3vCSE6DWZrAZnz53A2XMn4OraBqNem4Ahg16HlaUNxo2ZinFjpiIjMxVnz53AxUunkZh0W+fu7nkZJiZ8dO7ki25d/eHr0wudvH2Ud0UCilvKE5NuI/bqeVyJPY97CfEaP5JHiLpVVUlw5+6NOnPZWVvbwcnRFc7O7nBydIOLsztsbR1gbWUHa2tbGBvz0KpVa5Ujuk1FJpPB3NwE5RWamz+LjkypAR2Z0n36nmsu1xD+fn0xeNAo9O0zWOV/e8XFBbgUE4lLl6MQd/MyRCJhs8Whzjzb2Nijk7cvOnn7oFuXnvDy6lzn9EVObiauX7+I6zcu4XpcjN5Mdqjv32dtQXn+l0BgAStLG5ibt4KZmQDm5gKYmQlgYmKqOPpkaPSoGUIul0Emq201kNZIIRGLIBIJIZaIIJGI8PBhJSoqy1FZUQauIQfl5eUavWaKjkwRQiCT1SDmylnEXDkLU1Mz9O0zCH17D4R/z36wtrbDyNcmYORrEyCTyZB0/w7ibl7GjbjLuHP3erMWV03F0tIG7dp6o307b3i174xO3r6wtXWo0y8vLwvxt67i5q1Y3LgRg9xmnjyTkJaisrIclZXlzTK2QCB4cadmRsUUIUSFWCzEqdN/4tTpP2FkZASfbr3Qp/dA9OrZD87O7vDu2A3eHbth0oR3AQCZWalIun8HSUl3cD/5LtIzUlBSUqiR2C0tbeDm2gZubm3g5toG7m5t0LZNR1hZ2dbpK5PVIOVBIu7du4k79+IQHx/boh4OSwhpOlRMEUKeSSqV4uq1C7h6TXFRp62tA3x9AtDdJwA+Pr3g5OgKVxdPuLp4YtArI5XbiURCZGWnISsrDXn52SgpKURxcSGKSwpQUloEkfAhRGIhqqokL4zB2JgHExM+zM3MYdHaCpatrdHawgqWltawtXWEvb0THOydYW/nBFNT/lPHkMvlyMxKRXLKPSQn38O9hJtITLoNiUTcNIkihLRoVEwRQuqtsDAPEaf+QMSpPwAArVpZwqt9J3i17wyv9p3Rrq03HBycweebKZc9j1wuh1gsglRaDcYYDDgGuHA2BftCI2BsbAwezxQcDqfe8cnlcuTmZSEz8wHSM1KQkfkAaWnJeJCaSIUTIaTZUDFFCHlpFRWlKkeuAMXEf44OLnBx8YCLiwfsbB1hbWULKytbWFvbwrK1DczMzAEAHA5H+fdaUqmsziMuAMWjWMrLS1BaVoLy8hKUlZegoCAP+QXZyM/PQV5+NgoKcptlLhtCCHkeKqYIIU2qpkaKzKxUZGalPrOPgYEBeDxTxZwxfDMYGSkev2JmZoaYmBgEBgaitKQYYokYVVViSCQSmoaAEKK1qJgihKgdYwySR7c4l5YWKZcLBAKYm/OQkZHS4m8lJ4TojvpfjEAIIYQQQuqgYooQQgghpBGomCKEEEIIaQQqpgghhBBCGoGKKUIIIYSQRqBiihBCCCGkEaiYIoQQQghpBCqmCCGEEEIagYopQgghhJBGoGKKEEIIIaQRqJgihBBCCGkEKqYIIYQQQhpBq4spHo+H3bt3o7S0FDk5OVi8ePEz+/r6+iImJgZCoRBXrlxBjx49VNZPnDgRycnJEAqF+O2332Btba1cZ2FhgV27diEvLw8FBQUIDQ2FhYWFcv2iRYvAGFNpmzZtavodJoQQQojO0epiatOmTejZsycGDhyI+fPnY/Xq1Rg3blydfnw+H8ePH8f58+fh5+eHixcv4tixY+Dz+QAAf39/7NmzB2vWrEFgYCAsLS0RFham3H779u3w8fHBiBEjMGzYMHh7e2PXrl3K9Z06dcL3338PBwcHZVuzZk2z7z8hhBBCdAPTxsbn85lIJGLBwcHKZatWrWKRkZF1+s6YMYOlpKSoLEtKSmLTpk1jAFh4eDgLDQ1VrnNxcWEymYx5eHgwPp/PpFIp69Wrl3J9YGAgk0qljMfjMQDs/Pnz7N13333pfREIBIwxxgQCQZPmqLnGpUa5pjzrd6M8U571qTVnnus7ttYemfLx8YGRkREuXryoXBYdHY2AgAAYGBio9A0MDER0dLTKsgsXLqB3797K9efOnVOuy8rKQkZGBgIDAyGXyzFy5EjExcWpbG9oaAhzc3MAgLe3N5KSkppy9wghhBCiJww1HcCzODo6oqioCFKpVLksPz8fpqamsLa2RlFRkUrfO3fuqGyfn5+PLl26KNfn5OTUWe/i4gKJRIJ//vlHZd3ChQtx8+ZNFBcXw87ODtbW1pg+fTrCwsIgFouxZ88ebNmypcH7JBAIGrxNfcZr6nFJXZRr9aA8qwflWT0oz+rRnHmu75haW0zx+XxUVVWpLKt9zePx6tW3tt+L1j9uwYIFGD9+PF599VUAQMeOHQEoiq9Ro0ahe/fuCAkJgUwmw9atWxu0T9nZ2Q3qr+lxSV2Ua/WgPKsH5Vk9KM/qock8a20xJZFI6hQ7ta9FIlG9+tb2e9H6WvPmzUNISAg++ugjREREAADOnTsHa2trlJSUAABu374NW1tbzJs3r8HFlLOzMyorKxu0zfMIBAJkZ2c3+bikLsq1elCe1YPyrB6UZ/VozjzXjv0iWltMZWdnw8bGBlwuFzKZDADg4OAAkUiEsrKyOn0dHBxUljk4OCA3N7de6wFgyZIl2Lx5M5YuXYqQkBCVvrWFVK179+7B2dm5wftUWVnZLD9QzTUuqYtyrR6UZ/WgPKsH5Vk9NJlnrb0APS4uDlKpFIGBgcplQUFBiI2NBWNMpW9MTAz69Omjsqxv376IiYlRrg8KClKuc3Fxgaurq3L91KlTsXnzZixatKjOtVCzZs1CQkKCyjJfX986y0jTMGttAWdvL3R+pR98hw1Cu15+cGjfFgJrK3C4XE2HRwghhNShtUemxGIxwsPDsX37dsyYMQPOzs5YunQpZsyYAQCwt7dHeXk5JBIJDh06hA0bNmDr1q3YsWMH5syZAzMzMxw4cAAAsG3bNkRFReHSpUuIjY3FN998g7/++gtpaWmwtLTEd999h7CwMPzyyy+wt7dXxlBYWIiIiAh89dVX2Lx5M7Zt24aePXti+fLlePfddzWSF33j7O2F7sOHwrtfb1g5O8HY1OSZfeVyOfLupyA59joeXL2BB9fiICwrV2O0hBBCSF0GUMyRoJVMTU2xbds2jBs3DuXl5di0aRO++eYbAABjDNOnT0d4eDgAxcSc27dvh7e3N+Lj4zF37lyV6Q6mTZuGzz77DFZWVjh58iTeffddlJSUYMKECfjll1+e+v4eHh5IT09H3759sWnTJvj4+CA/Px8bN27Ejh076r0fAoEAFRUVaNWqVZNfM9Uc4zY3Kxcn9Bw1HN2HD4Gdp3ud9RVFxSjLzYe0qgpmrS1gbmUJfmsLcDh1D6Rm3U3Etb9O4NrRv5u1sNLVXOsayrN6UJ7Vg/KsHs2Z5/qOrdXFlL6gYkrByISHwe/NwCvTp4BrpDgoKpVU4e65C4g7cQrZ95JQll8A2WPTYdQy4HAgsLGGh29XtPPvgbY9u8OhXRvl+hqpFHciz+PKkaNIvHgFTC5v0th1Lde6ivKsHpRn9aA8q4c2FFNae5qP6JeO/Xpj7MqlsHZxAgDcj7mK2D+O4XbkOVQJRS/YGmByOSoKChF/8gziT54BAJhbWaLr4AEIGDsKrp294TN0IHyGDkRhWgZO7QrH9WP/QP7o5gVCCCGkuVAxRZqVubUlxq5cCp+hAwEAZXn5OLL+K9w+c+4FW77Yw5JSXDpwBJcOHIGjVzv0GjMSPUcNh62HGyZ9/gmGzJmBU7vCcO2vE5DXUFFFCCGkeWjt3XxE91k5O+KDvTvhM3QgZDU1iAr7GRtfn9QkhdSTcpOS8cfGrVg3dAz++vp7PCwphY2bCyau/S+W//kLug15pcnfkxBCCAGomCLNxL6tJ97fuwM2bi4ozsrG1okzcXTLt6gWi5v1fatEIkT++BM+f3Ucjm75DpXFJbBxdcG0r77A3N3fwqF922Z9f0IIIS0PFVOkybl17YQFYdtgYWeL3Psp+G7qXOQk3ldrDNViMaLC9uOL4eNwctseSCVVaB/QE0sOhmPMyiUwbdVKrfEQQgjRX1RMkSbVPtAfc3d/C7PWFki7eQvfT5+PisKiF2/YTKrFEvzzw25sfGMibp48Aw6Xi6BJb2L5n/+D76uDNRYXIYQQ/UHFFGkyrp29Meu7TeDx+Ui8eBk73l0IcUWFpsMCAJTm5GHvklX4YeYC5N5PgcDaCu9sWouZ326Chb2tpsMjhBCiw6iYIk3CrLUFpn39BYx4PNw9ewF73l/W7NdHvYyU2Ov4evx0/P3dTtRIpeg8IAj/+f1/6P3WGBgYGGg6PEIIITqIiinSaAYcDqZsXANLRwcUpmVg/4rVT514U1vIampwakcovnpzKtLibsHE3AxvfvofvLfzG7S2t9N0eIQQQnQMFVOk0YbNn40OfQJQLZYgbPFKSB4KNR1SveQ/SMN30+biyPqvUC2WwCvQH0t/+wndRwzVdGiEEEJ0CBVTpFG8+/fFkDmKh08fXLMeefdTNBxRwzC5HNE/H8SWt6YiPf4OTFsJ8PbGNXhn01q6448QQki9UDFFXpqVixMmr/8UABD9v0O4fuykhiN6eUXpmfhu6hyc+H4XZDU18H11MJb99hPaB/TUdGiEEEK0HBVT5KVN/vxT8Fu1QtrNW/jzy280HU6jyWUyRGz/ESFT3kVBajos7G3x3s5v8NqieeAa0pOXCCGEPB0VU+Sl+I18FZ49fFAlEmPf0k8gq6nRdEhNJutuAr6eMB2XDv4ODoeDgbOmYtaOrSit0r67EwkhhGgeFVOkwXhmfIxcvAAAcGpnGMry8jUcUdOrFktw6LONCF24AsKycjh5e+GnlFvwfY0uTieEEKKKiinSYEPmzEQrWxsUpmfi7N7/aTqcZnX7zFlsefMdpF6Lg1Qux+hVSzFlw/+BZ8bXdGiEEEK0BBVTpEHsPN3R/+0JAIDfN36t1fNJNZXy/ELsXbgCfe1cIa+Rocdrw7D4QDhcOnXUdGiEEEK0ABVTpEFGr/gIXCND3ImKRsL5S5oOR22YXI4AO2eEzl+Ckpxc2Li54IOfdiJ46iSaOZ0QQlo4KqZIvXUZGIwOfQJQU12NPzZu1XQ4GpF5+y6+emsabp48A0MjI7y+7EPM+mELzK0sNR0aIYQQDaFiitSLIY+H15d9CACIDNuP4qxsDUekOeKKSuxdsgqHPvsSUkkVvIN6Y8mhvTQnFSGEtFBUTJF6CRw3CtYuTijLy8eZ3Xs1HY5WuHTwCLZOmom85AdoZWuD93Z+g+EfzAHHkKvp0AghhKgRFVPkhTiGXARPnQwAOL17L6rFEg1HpD3ykh9g66SZuHRIMSfV4PemY0HYNli5OGk6NEIIIWpCxRR5Id9hg2Dl7IjK4hJc+f2YpsPROlJJFQ6t2Yi9S/8LcUUlPHy6YsnBvfTAZEIIaSGomCIv9MqMtwEA5/cfQE1VlYaj0V43/zmNLW9NRer1mzAxN8PbG9dg0uef0pxUhBCi56iYIs/VMSgQTh3aQyIU4uKvv2k6HK1XmpOHH2YuwD/f74JcJkPP14dj8cFwuPt00XRohBBCmgkVU+S5Xpn5DgAg5tAfEFdUajga3SCXyXBy+4/4fvp8lGTnwsbVBe+Hb8er779HF6cTQogeomKKPJNb105o598DNVIpzu37RdPh6Jy0uHhsefMdXP3zb3C4XAyZMwMf/rQLdp7umg6NEEJIE6JiijxT7VGp68f+QXl+oYaj0U2Sh0L8b9VnCF+yCsKycrh29sbiA+EImvwWzZxOCCF6wlDTARDtZOvhhi4D+wMAIn/8ScPR6L74k2eQdiMeE9euQoe+gRjz8WJ0HTwAv376OUqycjQdHiEtgpEJD6YCAUzMzcAzM4OJuRlMzPgw5vNhbGoCnqkpjE1NYGRqAiMeD4Y8YxjxeDDi8cA1MgTXyAiGhkaKvxsawoDLAYfDBYfLgQGHo/gPkoGB8j9KHA4H4fdvYv5PO1FTUwMml0Mul0NeI4O8pgYymQxymQw11dWQSWtQU12NmmopaqqrIZVUQVr1qEmqUC0Wo1osQbVYjCqRGNUiESRCEapEIlQJRZA8FLaIZ6VqKyqmyFMNmDYZHA4HtyPPoSA1XdPh6IWKwiLsnPsR+kwYi5GLF6Cdfw8sPfwTjm39ARd/OQzGmKZDJERnGBobw9zKEgJrK5hbW0FgZQkzq9Ywa90aZq0tFM2yNUxbCWDaSgB+KwEMjY3VHmdxlRh2bTzU8l411dUQVz6EpPIhxA8Vf4rKKyCqqISovALi8gqIyisgLC+HqKwcwkdNVF4BJperJUZ9RcUUqYNnxkeP14YBAKJC92s4Gv1z8dffkBB9CRM+W4V2vfwwduUSdBs8AAfXbEBRRpamwyNE48ytLdHa3g4W9nbKPy3sbNHK1hqtbG3QytYGfItWLzW2XCaD5KFQ0YRCVAlFqBaJUPXoqE+1SKw8GlT7Z41UipqqKtRIayCTSiGrqYG8pgZymeJIE5PLIJfJlQUJAwDGwOfzcfz4cYwcNQpisVhx9IrDAdfQEBwuFxxDLriGhjA0NoKhkTG4RoYwNDb+94iYieKomLGJCYxNTWD86MgZj8+HMd8UPDM+eHw+eHxTAIoCU2BtBYG1VcNyIpdDVFaOh6VleFhSqmjFJah81B4Wl6CisBgVRUWoLC6BvEb2UrnXZ1RMkTp8hgyEsakJClLTkXojXtPh6KWS7Fxsn/0B+kwYi9c+WoB2vfyw9LefELE9FFFh+yGrqdF0iIQ0G3MrS1i5OMHaxRlWzo6wcnKEpZMDrJwc0drRHkY8Xr3GqZFK8bCkVPELv6QUD4tLISwrg7C0HMKyMogeHXURVVRC/KhViUTNvHf/EggEcDW3QOq1OFRWNt/d0AYcDnhmfJiam8NEYAYTc3OYmJuD/9hROVOLVuA/amYWFuA/OnrHt2gFDocDcytLxQPb23q+8P0elpSiorAI5QWFqChQ/FleUIiy/AKU5RWgLC8fksqHzba/2oiKKVJHz9EjAACxf9Bs582JMYYLvxzGvehLePOT/6BDnwCMWDgX3UcMwcE1G5B+87amQyTkpQlsrGHr4QZbNxfYuLnAxs0VNu6usHZxAo///Ils5XI5KouKUZ6v+AVd8egXdUVhMSoKi1BRUIiKomKaruURJpdD8uj0HnIbti2HywW/dSuYW1oqC6ra06fKU6g2VmhlawOBlRW4RobKPk4d2j9zXIlQqCiscvNRmpv3b8vORUlOLioKivTq0gYqpogKa1cXtPXrDrlMhqtHT2g6nBahJCsHO+csQo/XhuKN/yyCY/u2eH/vDsQc/B1/f7sDovIKTYdIyFNxDQ1h4+YCuzYesG/jATtPd0UB5e4GU4H5M7eTy+Uozy9ASXYuSrJzUJKVg9LcPJTk5KE0Jxfl+YV0dFZN5DIZHhYrjuq9iIGBAfitLR6dbrWFhZ0tLOwf/fno760d7GHW2gImZmZwaOsJh2cc6aqprkZpbj5KsnNQnJmN4qwcFGdlK/6ema3WI4hNgYopoqLn68MBAEmXYlFRQNMhqNP1YyeREB2DUUs/QK/RI9Fnwlj4DBuEv0N2IObwH3SBKNEYjiEXNq4ucGjfFo7t2sC+rScc2rWBjasLuEZP/zUil8lQkpOLovQsFGVkoigjC4UZmSjOzEZJdi7deaaDGGMQlpZBWFqG3KSUZ/YzMuHBwt4Olo4OsHSwh6WTg6I5Kv5s7WAPQ2Nj2Lq7wtbd9aljVBQVozgjC0WZWSjKyEJBWgYK0zJQlJEJqUT7HmtGxRRRMjAwUBZTdIpPM0TlFfj1k88R+8dxjPl4MZy82uHNT/+D3m+NxpH1W+gaNtLsBDbWcOrQHk5ebRXFU/u2sG/j8cw74SRCIQoepCP/QRoKUtNRmJaOgrQMFGVkUcHUQkklVShKz0RReuZT13O4XFjY2cLS2RHWj66dU/7p6gxzK0u0srFGKxtrePbwqbN9SU4uCtMyUJCq+N49zC+EuEaz3zUqpohSW/8esHJyhLiiErcjz2s6nBbtwdUb+Hr8dPSZMAbDFrwLZ28vvL93B+L+OY3j32xHcSbd9UcaR84YbD3c0M7VGc4d2sOpY3s4dWj/zDvBJEIh8pNTkZf8AHkpij/zH6TShL6kweQymfIaqgdXb9RZb2JuBmtXZ9i6ucLazQW27m6w9XCFnYc7+BatYOWkuGmhQ58A5TY7E6/DqWN7JMZeV+euKGl1McXj8fD9999j3LhxEIvF2Lx5M7766qun9vX19cX27dvRtWtX3LlzB3PnzsX16/8mdeLEiVi3bh0cHR3xzz//4N1330VxcbFy/fr16zFr1ixwuVzs3r0bK1asUF4cZ2VlhZ07d2Lo0KEoKirCJ598gv379W/KAP83XgMA3DhxCjVV2ncYtaWRy2SI/vkQbvx9CsM/nIOAsa/Dd9ggdB0YjEsHjyBiRygelrz4OgdCuIaGcGjfBi6dOsLFuwPcOnvju7uxWPDz7jp95TIZCtMzkZuUjJykZOTdT0FOUjLKcvP16oJhor0kD4XIvpeE7HtJddaZtbaArYc77DzcYOfpDjtPdzi0awN3T09UiyUaiFZBq4upTZs2oWfPnhg4cCDc3d0RHh6O9PR0HD58WKVf7Vwe+/fvx/Tp0zF37lwcO3YMbdu2hUgkgr+/P/bs2YO5c+ciLi4OISEhCAsLw6hRowAAixcvxuTJkzFmzBgYGRnhp59+QkFBAbZs2QIACAsLg6mpKXr37o2AgADs3r0bSUlJiI2NVXtOmguPz0fXwQMA0Ck+bSMsLcOhNRtx4X+H8Nqi+fDu1wdBk99Cz9dHICr8Z5z/6VdIHgo1HSbRElxDQzh6tYVLZ2+4dOoAl04d4di+LQyNjFT61TA5qkVi5CQlIzshCdkJSchJuI+8lAdaeU0KIQAUE43GxSMt7t9LHgQCASoqKrD0GacV1YVpY+Pz+UwkErHg4GDlslWrVrHIyMg6fWfMmMFSUlJUliUlJbFp06YxACw8PJyFhoYq17m4uDCZTMY8PDwYAJaenq7sC4BNmTKFpaamMgCsTZs2jDHG3N3dlet37dqlMt6LmkAgYIwxJhAImjRHTTmu/+jX2JZbl9jyP3/R+Gevja25PsOXae0DerJFv4ayLbcusS23LrG1F/5hg+fMYCbmZhqPTZ/yrAuNY8hljl7tWMDYUWzcJ/9hi375kW28fk753Xi8rY3+h83ZFcJGfrSA9R47ihVLRKyVhYXG90GfG32fdT/P9R1ba49M+fj4wMjICBcvXlQui46OxqpVq2BgYKByuDkwMBDR0dEq21+4cAG9e/dGeHg4AgMDsWHDBuW6rKwsZGRkIDAwEFVVVXBzc8O5c+dU3sfDwwMODg4ICAhARkYG0tPTVdZ//PHHzbHbDSavFsG6lRl4nMbd6TVgzAiYyKRIOnECNhZmTRSd/jA3N2uyXDdWacI97J/zAboM7I/gaZNh6+6GMfNm4NW330TMwd8R89tfkFTq5nQK2pRnbWNgYABrF2c4dWwPF+8OcOrgBcf2bWD4lAkuRaXFyE28j5ykFOQkJiE3KRmlufnK9ebmArQ2YLAyN4UxaAqC5kLfZ/UwNzeDnGk2v1pbTDk6OqKoqAjSx+4Gyc/Ph6mpKaytrVFUVKTS986dOyrb5+fno0uXLsr1OTk5dda7uLjA0dERAFTW5+cr/tGpXf+sbRtKIBA0eJvnSV/3AWQbViPpvbcbP1haKpCWinftbYDF8xo/nh5qslw3pbg4RXvkQ2sL4N0pGgunKWhlnrVRyn1Fex5zE8Cvm6I9gfKsHpRn9ZCFbWzy37FA/X9va20xxefzUfXERdC1r3lP/E/sWX1r+z1vPf/RTLyPr3/8fV40dkNkZ2c3eJtnkTM5ZGEbgbKSJhuTEEII0VWZWZngGHA08t5aW0xJJJI6BUvta9ETM6M+q29tv+etl0gkytdPFmu16583dkM4Ozs36fOZBAIBMlPvw6dbNzxsxAXIb29ZB8eOXjj53U7c/PtUk8WnT8zNzXAzPr7RuVYHN58u8B/zGtr4+ymXFWdk4uaJU7h75hzEldobvy7l+WXx+Cawb9cW9u3awNGrLRy82sHC3q5OP7lMhsK0dOTff4DcpGTkJiWjOCMLclnjT2e0hDxrA8qzepibm+HmvSS4urg2+TMQBQJBvQ6EaG0xlZ2dDRsbG3C5XMhkiidUOzg4QCQSoaysrE5fBwcHlWUODg7Izc194fraJDk4OCivi6rtW7v+eWM3RGVlZZN/0BxjPtJzCl56XIGNNSw7d4YEQOQfJ1BRWPTCbVoigUDQ6FyrS1p2Hs4dPwU7T3f0nzoRPUYMg5lnG/SZ9x78Z05DfEQkrv55HMlXrkMu066nv+tSnuvD2NQUzt5ecOnUEa6dO8K1szds3F3B4aj+71kCoCA1HZl37iHztqJlJyY12111+pZnbUV5Vg+BQACOAadZfsfWl9YWU3FxcZBKpQgMDMSFCxcAAEFBQYiNja0z10lMTAxWrFihsqxv3774/PPPleuDgoIQHh4OQHEtlKurK2JiYpCbm4v09HQEBQUpi6nav+fl5SEmJgYeHh5wdnZWFl5BQUGIiYlp1v1Xl07BfQEA6fF3qJDSMwWp6Ti0ZiOObv4WPUYMQ++3RsPZ2wt+I1+F38hXUVlcgpv/nMb14yfpocpNwMTcDM4dveDcqQNcvBVTEth6uNUpnADFDM6Zt+8h626Coni6m6B4SC0hRCdpbTElFosRHh6O7du3Y8aMGXB2dsbSpUsxY8YMAIC9vT3Ky8shkUhw6NAhbNiwAVu3bsWOHTswZ84cmJmZ4cCBAwCAbdu2ISoqCpcuXUJsbCy++eYb/PXXX0hLS1Ou37hxI7KyFLNKb9iwQTnHVGpqKk6cOIF9+/Zh4cKF8Pf3x+TJkxEcHKz+pDSDLq/0BwDcoRnP9VaVUIRLB4/g0sEjcOnUEQHjXofP0IEQWFshaPJbCJr8FkpycnH37AXcjYpGytUbqKmu1nTYWq21vR0cO7SHc8f2igLK2wvWLs5P7VuWX6Aomu4kIOvOPWTdTaTJVgnRM1pbTAGKyTS3bduGyMhIlJeXY/Xq1Thy5AgAIC8vD9OnT0d4eDgqKysxcuRIbN++He+99x7i4+MxYsQI5XVNMTExmDNnDj777DNYWVnh5MmTePfdd5Xvs2nTJtjZ2eHIkSOoqanBnj178PXXXyvXT506Fbt378bly5eRm5uLmTNn6sWEncampmgf2BMAcCeKiqmWIOtuArLuJuDI+i3w6t0LPUYMRZeB/WHl5IigSW8iaNKbqBKJkHQpFokXLiPl6nUUpKa/eGA9ZdpKAIe2norrmx49p87Rqy34rVo9tX9JTi6y7iYi+14isu4lIvtuIiqL6SYRQvSdARQTTpFmVDs7a6tWrZr8AvTGjNtlYDBmfLMBRZlZWD/irSaLSx8112eoDYxMeGgf4I9OA/qiU/++sLCzVVlfUViElNjrSLkah4zbd5B3/wFkNc0zN5Gm8tzK1kb5aAo7T3fYt/GAfVvPOrmoJZPWoCAtXWXm8OyE+xBX6Mb8Xvr8fdYmlGf1aM4813dsrT4yRZpXl4H9ANApvpZOKqnC3bPRuHtWMfGts7cXvPv3RTv/HvDw7YpWtjboPmIouo8YCgCoqa5GTmIyMu/cQ05SMgoepCH/QRqEpWUa3IvnMzQ2RmsHO1g5O8LSyRE2rs6wdnWBjZui8R5NkfI0pbl5yEtJRX5yKnIS7yv3ubkKSkKI7qFiqoXicLno1F9x8TkVU+RxtQ8YPbUjFIbGxnDr1hntenaHZw8fuHTqCL5FK7h17QS3rp1UthOWlqEgNR0lObkozc1HWW4+SvPyUFFQBGFZGYSl5U1+LRbPjA8zy9Ywa90aZpYWEFhZopWtLVrZ2aCVrQ0s7Gxh6WiPVrY2zx1HLpOhODMbBanpKEjLQGFauqKASkml5x4SQl6IiqkWyt2nC8wsW0NYVo7UG/Ev3oC0SDXV1Xhw9QYeXL2hXGbl4gTXzt5w7dQRDu3bwM7TA9YuTjCzbA1Py9bw7OHzzPGqRCIIy8pRLRKjWixBtUSCarEYNdVSMLkchlwujmXex7g1H0POGLhGhuAaGcHQyAhGJjzw+HzwzPgwMTMDz4wPrmH9/wmrEolRmpOL0tw8FGdmoygjC0UZWSjOykZxVg5kjz1tgRBCGoKKqRaq9i6+e+cuat1cQ0S7lWTloCQrBzf/Oa1cZmTCg52HO2zdXdHa0QGWjvZo7WgPSwcHmFtbwtzSElwjQ0Ux9JxTagCQWF6MrkNeqXc8VSLxoyNfiqNfFYVFKC8sREVBESoKi1GWl4fSnDwIy8pfep8JIeR5qJhqoTq/orhe6nbkuRf0JOTFpJIq5cXYz2JibqY8HWdsaqpoJjwYm5qCa2wEAwMDmJqa4svNm7B8+XKIHgohk0pRI61R/FldjSqhCBKhCFVCISRCEcQVFc02sSUhhNQXFVMtkJ2n4ghCTXU1ki5e0XQ4pIWQPBRC8lCI4qxnP5pBIBCgR+hPuHzgd7r7iRCiMzTzRECiUV0GKk7x3b98FVUv8YxBQgghhPyLiqkW6N9TfHQXHyGEENJYVEy1MKatBHDroril/e7ZCxqOhhBCCNF9VEy1MG38fMHhclGQmo6KgkJNh0MIIYToPCqmWph2/n4AFNdLEUIIIaTxqJhqYdoFKIqp5CvXNBwJIYQQoh+omGpBzCxbw8mrHQAgJfa6hqMhhBBC9AMVUy1IW/8eAICcxPs0GzQhhBDSRKiYakHa96o9xUdHpQghhJCmQsVUC9JOWUzRxeeEEEJIU6FiqoVoZWsDO093yGUypFyL03Q4hBBCiN6gYqqFaNdLcb1U1r1ESCofajgaQgghRH9QMdVCtOvVEwCQQtdLEUIIIU2KiqkWovbI1H2aX4oQQghpUlRMtQBWzo6wdnGGTFqD1Os3NR0OIYQQoleomGoBah8hk3H7LqrFYg1HQwghhOgXKqZagLaPTvHRI2QIIYSQpkfFVAvQ/tHF51RMEUIIIU2Piik9Z+PuCgt7W0irqpB287amwyGEEEL0DhVTeq521vP0m7dRU1Wl4WgIIYQQ/UPFlJ7z8OkKAHhAs54TQgghzYKKKT3n3q0zACA9nk7xEUIIIc2Biik9ZtpKADtPdwBAxq27Go6GEEII0U9UTOkxty6dAACF6ZkQlVdoOBpCCCFEP1ExpcfcHp3iy7h1R8OREEIIIfqLiik95tZVcWSKiilCCCGk+VAxpcfcu9ZefE7XSxFCCCHNhYopPWXt6gIzy9aQVlUhJyFJ0+EQQggheouKKT3l3k1xii87IQmymhoNR0MIIYToLyqm9JTbo1N8GXSKjxBCCGlWVEzpKfduXQDQxeeEEEJIc9PqYmr9+vUoKChAcXExNm7cCAMDg2f29fDwQEREBB4+fIg7d+5gyJAhKusHDRqEW7duQSgU4vTp0/D09FSuMzY2xpdffonMzEyUlJTgt99+g7Ozs3L96NGjwRhTaQcPHmz6HW4ihsbGcOrYHgCQTsUUIYQQ0qy0tphavHgxJk+ejDFjxmDcuHGYMmUKFi9e/Mz+v//+O/Ly8tCzZ0/s27cPR44cgaurKwDA1dUVv//+O0JDQ+Hv74/CwkL8/vvvym3XrFmDMWPGYMqUKejbty+MjIzw22+/Kdd36tQJf/75JxwcHJRt9uzZzbbvjeXUsT0MjYxQWVyCkqwcTYdDCCGE6D2mjS09PZ1NmzZN+XrKlCksNTX1qX1feeUVVllZyfh8vnJZREQEW716NQPA1qxZwyIjI5XrTE1NWXl5OQsODmYAWG5uLhs/frxyvYODA2OMsXbt2jEAbN++fezzzz9/6X0RCASMMcYEAkGT5uhZ4/abMp5tuXWJzfx2k8Y/R31pzfUZUqM8U571t1GedT/P9R1bK49MOTo6ws3NDefOnVMui46OhoeHBxwcHOr0DwwMxPXr1yESiVT69+7dW7n+8bHEYjGuX7+O3r17w8DAAG+//TYiIiLqjGthYQFAcWQqKUl3phegmc8JIYQQ9THUdABP4+joCADIyfn3FFV+fj4AwMXFBXl5eXX6P963tr+Li8sL1zPGcPr0aZV1CxcuRGFhIeLj4wEAHTp0wLBhw7By5UpwuVwcPHgQn376KaRSaYP2SyAQNKh/fcd7clxPn64AgMLk1CZ/z5bqWbkmTYvyrB6UZ/WgPKtHc+a5vmNqrJgyMTFRucj7cebm5gCAqqoq5bLav/N4vDr9+Xy+St/a/rV9X7T+ca+//jqWLl2KuXPnQiqVws3NDWZmZqiqqsL48ePh6emJkJAQmJqaYtGiRfXfYQDZ2dkN6v8y44pqpNiecA0AEHPyFEy4Wlkv66zm+gyJKsqzelCe1YPyrB6azLPGftMGBAQgKirqqeuWLVsGQFE4PVlEPX4qr5ZEIoG1tbXKMh6Pp+wrkUjqFE48Hg9lZWUqy9544w38+uuv+Pbbb7Fnzx4AQEZGBqysrFBaWgoAuHnzJjgcDn766ScsXrwYcrm83vvs7OyMysrKevd/EYFAgOzsbJVxvfoEYPLmtShMTYddn6FN9l4t3dNyTZoe5Vk9KM/qQXlWj+bMc+3YL6KxYurs2bPPnOrA0dERmzZtgoODA9LT0wFAea1Ubm5unf7Z2dno3LmzyjIHBwdl3+zs7DrXWjk4OCAuLk75esKECdi3bx+2b99e567B2kKq1r1792BqagorKysUFRXVY28VKisrm+UH6vFxbdu3AQCk3rxFP7zNoLk+Q6KK8qwelGf1oDyrhybzrJUXoOfm5iI9PR1BQUHKZUFBQUhPT69zvRQAxMTEoEePHjAxMVHpHxMTo1z/+Fimpqbo3r27cv3AgQOxb98+fPfdd/jwww9Vxh46dCiKiopgamqqXObr64uioqIGFVLq4tZV8RgZmvmcEEIIUR+N39b4tLZ8+XKWlZXFgoODWXBwMMvKymIfffSRcr2NjQ0zMzNT3JLI4bDbt2+z//3vf6xTp05s+fLlrKKigrm6ujIAzN3dnYlEIrZ8+XLWqVMn9ssvv7C4uDgGgHG5XJaWlsYiIiKYvb29SjMyMmLm5uYsMzOT7d+/n3l5ebFXX32VZWVlsWXLlmn8ts0nxzUwMGDrLpxkW25dYs4dvTT+GepTo1ucKc/61CjPlGd9atowNQI0nYRnNQ6Hw7Zs2cJKSkpYQUEBW79+vcr61NRU5TxSAFjbtm1ZVFQUE4vF7NatW2zQoEEq/V999VWWkJDAhEIhi4iIYB4eHgwACwgIYM9SOw9Vp06d2MmTJ1lFRQXLzs5mn376qVZ80E+Oa+3qwrbcusQ2XI1iHEOuxj9DfWr0jyLlWZ8a5ZnyrE+NiqkW0tRVTHUdPIBtuXWJLfo1VOP7rG+N/lGkPOtTozxTnvWpaUMxpZXXTJGX49RB8Ty+3MRkDUdCCCGEtBxUTOkRJ6+2AICcxPsajoQQQghpOaiY0iOOXoojU1RMEUIIIepDxZSeMDE3g7WLEwAgJ4lO8xFCCCHqQsWUnnD0agcAKM3Ng7iCJocjhBBC1IWKKT1Re/F5Dl18TgghhKgVFVN6wunRkamcJLpeihBCCFEnKqb0BB2ZIoQQQjSDiik9YMDhwKGd4gHHdCcfIYQQol5UTOkBa1dnGJuaoEokRnFmtqbDIYQQQloUKqb0gP2jo1J5yQ/A5HINR0MIIYS0LFRM6QHlKT66+JwQQghROyqm9IB9e0UxRc/kI4QQQtSPiik9QBefE0IIIZpDxZSOE9dI0crOFgA9RoYQQgjRBCqmdFyhRAQAKM7KRpVQpOFoCCGEkJaHiikdV/SomKLJOgkhhBDNoGJKxxUqiym6XooQQgjRBCqmdFyhRAiAjkwRQgghmkLFlA7jcLkorhIDoDmmCCGEEE2hYkqH2bi7QsYYqoRClGbnajocQgghpEWiYkqH1T5GJj85FYwxDUdDCCGEtExUTOkwO093AEB+SqqGIyGEEEJaLiqmdFjmrbuwMOIh/sRpTYdCCCGEtFhUTOmwpIuXMatDd2TevqvpUAghhJAWi4opQgghhJBGoGKKEEIIIaQRqJgihBBCCGkEKqYIIYQQQhqBiilCCCGEkEagYooQQgghpBGomCKEEEIIaQQqpgghhBBCGoGKKUIIIYSQRqBiihBCCCGkEaiYIoQQQghpBCqmCCGEEEIagYopQgghhJBGoGKKEEIIIaQRDDUdQEsiEAiaZbymHpfURblWD8qzelCe1YPyrB7Nmef6jmkAgDX5uxMVTk5OyM7O1nQYhBBCCHkJzs7OyMnJeeZ6KqbUxMnJCZWVlZoOgxBCCCENIBAInltIAVRMEUIIIYQ0Cl2ATgghhBDSCFRMEUIIIYQ0AhVThBBCCCGNQMUUIYQQQkgjUDFFCCGEENIIVEwRQgghhDQCFVOEEEIIIY1AxZSW4/F42L17N0pLS5GTk4PFixc/s6+vry9iYmIgFApx5coV9OjRQ42R6r6G5HrEiBG4ceMGKisrcfPmTYwaNUqNkeq2huS5lru7OyorKxEcHKyGCPVDQ/LcpUsXnD9/HiKRCPHx8RgwYID6AtVxDcnz6NGjcffuXVRWVuL8+fPo3r27GiPVD8bGxrh169Zz/y3Q1O9CRk17W0hICIuLi2Pdu3dno0ePZuXl5WzcuHF1+vH5fJaTk8M2bdrEOnbsyLZu3cpyc3MZn8/X+D7oSqtvrrt27cokEgn74IMPWNu2bdn8+fNZVVUV69atm8b3QRdaffP8eDt+/DhjjLHg4GCNx68rrb55btWqFcvNzWU7duxgbdu2Zf/3f//HSktLma2trcb3QRdaffPcqVMnJhKJ2DvvvMPatGnDvv32W5aTk8NMTU01vg+60ng8Hjt8+PBz/y3Q4O9CzSeI2tMbn89nIpFI5UuzatUqFhkZWafvjBkzWEpKisqypKQkNm3aNI3vhy60huR6/fr17Pjx4yrLTpw4wdatW6fx/dD21pA817bJkyez8+fPUzHVTHn+4IMP2P379xmHw1Euu3LlChs+fLjG90PbW0PyvGjRIhYbG6t8bW5uzhhjzM/PT+P7oQvN29ub3bhxg8XFxT333wJN/S6k03xazMfHB0ZGRrh48aJyWXR0NAICAmBgYKDSNzAwENHR0SrLLly4gN69e6slVl3XkFyHh4djxYoVdcawsLBo9jh1XUPyDABWVlb48ssvMWfOHHWGqfMakucBAwbgjz/+gFwuVy7r1asX/v77b7XFq6sakufi4mJ07twZffr0gYGBAWbMmIHy8nKkpKSoO2ydFBwcjMjIyBf+TtPU70IqprSYo6MjioqKIJVKlcvy8/NhamoKa2vrOn2ffBBjfn4+XFxc1BKrrmtIrhMSEhAfH6983alTJwwaNAinT59WW7y6qiF5BoCvvvoK4eHhuHv3rjrD1HkNyXObNm1QWFiIHTt2IDc3F5cuXUKfPn3UHbJOakief/31Vxw7dgwXLlxAdXU1Nm/ejDfffBNlZWVqjlo3bd++HYsXL4ZYLH5uP039LqRiSovx+XxUVVWpLKt9zePx6tX3yX7k6RqS68dZW1vj8OHDuHDhAv74449mjVEfNCTPgwYNQlBQENauXau2+PRFQ/Jsbm6OFStWIDc3F8OHD8fZs2dx8uRJ+o9YPTQkz9bW1nBwcMCCBQsQEBCAvXv3IjQ0FLb/397dhES1h3Ec/1kXtDfL6uYg9MZEM7UZo4xeydpEEEYSQVhIEi4ictOilU0gvSwCoVCEQBe1jKCNtkl0Jb2Yg9CELZRIJcjSGeYFsp676CbX7HadDp4zc/1+4IGZ/5zFbx4Ocx7OzJzz55+u5Z0PvDoWMkxlsXQ6PWMH+P48mUzOatsft8PPZdLr79asWaMnT55owYIFOnHihMxsznPmutn2uaCgQC0tLTp//rzS6bSrGf8PMtmfJycn9fLlS4XDYfX19eny5csaGBjQmTNnXMubqzLp882bN9Xf36+mpib19vaqtrZWiURCZ8+edS3vfODVsZBhKosNDw9r9erVWrhw4dSaz+dTMpmccWp4eHhYPp9v2prP59Po6KgbUXNeJr2WpJKSEnV3dys/P1/l5eX68OGDi2lz12z7vHPnTvn9fj148EDxeFzxeFyS1N7erubmZrdj55xM9ufR0VG9fv162trAwIDWrl3rRtSclkmft2/frkgkMvXczBSJRLR+/Xq34s4LXh0LGaayWF9fnz5//qxdu3ZNre3bt0/Pnj2bcRakp6dnxu8c9u7dq56eHley5rpMer148WJ1dHTo69evOnDgAANrBmbb56dPn2rTpk0qLS2dKkk6d+6c6uvr3Y6dczL97AiFQtPWgsGghoaG3Iia0zLp88jIiLZu3TptLRAIaHBw0JWs84WXx0LP//JI/Xs1Nzdbf3+/7dixw44dO2bj4+N2/Phxk2TFxcVWUFBgkmzZsmX2/v17a2xstC1btlhjY6ONjIxwnak56HVDQ4MlEgkrKyuz4uLiqSosLPT8PeRCzbbPPxaXRpibPq9bt87i8bhduXLF/H6/Xb161WKxmJWUlHj+HnKhZtvnkydPWjKZtNOnT5vf77fr169zPa/frB8/C7LkWOh9Y6h/r0WLFllbW5vF43F79+6d1dXVTb1mZtOunVFWVmYvXrywZDJpPT09Vlpa6nn+XKrZ9joajdrPtLa2ev4ecqEy2af/WQxTc9fnPXv22PPnzy2VSllvb6/t37/f8/y5Upn0uaamxl69emWxWMy6u7tt27ZtnufPxfrxsyAbjoV5fz8AAADAb+A3UwAAAA4wTAEAADjAMAUAAOAAwxQAAIADDFMAAAAOMEwBAAA4wDAFAADgAMMUAPwmM5OZqb29XZIUCoW0e/duSVI0GpWZcbsQYB5gmAIAByorK3Xq1ClJ0sOHD7V582ZJ3+7RVldX52U0AC5hmAIABz5+/Kjx8XFJUl5e3tT62NiYJiYmPEoFwE0MUwDwCzU1NUqn0/L7/ZKkQCCgVCqlioqKadt1dnZqw4YNamtrU2trqxdRAXjI85sWUhRFZXN1dnbao0ePTJJ1dXXZ/fv3TZp+w9WioiJ7+/atXbx40QoLC02SVVdX2+DgoOf5KYqa2/pDAIBfqq2tVSQS0b179xQIBFRZWTljm0+fPunLly+amJhQLBbzICUAr/A1HwD8hzdv3ujGjRuqqqrSpUuXNDY25nUkAFmEYQoAZiEUCmlyclKHDh3yOgqALMMwBQD/oaKiQocPH9bRo0dVVVWlgwcP/nQ7M3M5GYBswDAFAL+wdOlS3blzRw0NDXr8+LFu376tlpYW5efnz9g2kUgoGAyqqKjIg6QAvMIwBQC/cO3aNaVSKd26dUuSFA6HtWTJEtXX18/YtqmpSRcuXNDdu3fdjgnAQ3n69rc+AECGzEzl5eXq6ur66evV1dUKh8PauHGjy8kAuIkzUwDgwMqVK7VixYoZ66tWrdLy5cvdDwTAdZyZAoDf9P0H5x0dHTpy5Mi016LRqILBoIaGhjgzBfzPMUwBAAA4wNd8AAAADjBMAQAAOMAwBQAA4ADDFAAAgAMMUwAAAA4wTAEAADjAMAUAAOAAwxQAAIADDFMAAAAO/AXACSoAM3dJJgAAAABJRU5ErkJggg==" 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OXrKtuqjWOmIKgcTsFQB4H+HkQ3XFlERQwVoILABwH+EUAMxOIRgRWABAOFkGs1OwC/7YMwA7I5wsjtkp2BmzWACCDeEUhJidQqghsABYBeFkI8xOIdRxmRCArxFONkdMAXUjsgA0RHigB3AukZGRWrBggYqKipSbm6sJEybUud+6detkjKm1LFy4UJLUokWLWtuOHDniz1OxjG3VRXUuAOr2SZMwtxYA9mf5GafZs2erZ8+eGjRokNq3b6+MjAzl5ORo8eLFLvvdfvvtaty4sfPj3r176+2339bLL78sSUpMTFRBQYGuvvpq5z4nT570z0kECWangPPDLBZgf5YOp+joaP3617/WsGHDtH37dm3fvl3PPvusfv/739cKp6Kin37oh4eH6+mnn9azzz6rbdu2SZK6du2q3bt3Kz8/36/nEOyIKcA3iCwgOFk6nLp166ZGjRppy5YtznWbNm3SpEmTFBYWJmPq/mYyduxYtWzZUrNmzXKuS0xM1O7du30+5lBATAH+RWQB1mHpcIqPj1dBQYGqqqqc6/Lz8xUVFaXY2FgVFBTU+brHH39cc+fOVVlZmXNd165d1ahRI33yySdq27atPvroI/3xj3/UoUOHfH4eoYBHJADWQGQBvmXpcIqOjlZlZaXLupqPIyMj63zNgAEDlJCQoL///e8u67t06aIjR47oj3/8o8LCwvT0009r+fLluv7667nXyYeYnQKsi8gCPGfpcKqoqKgVSDUfOxyOOl9zxx13aNWqVS73PEnSVVddJWOMKioqnPvl5eWpd+/e+vjjj30wetSHmAKCDw8hBU6xdDgdPHhQrVq1UkREhKqrqyVJcXFxcjgcOnr0aJ2vGTp0qKZOnVprfXl5ucvHR44cUWFhodq2bevtYaMBiCnAHpjFgt1ZOpyys7NVVVWlpKQkbd68WZKUnJysrKysOm8Mj42NVefOnZ371oiJiVFOTo5uv/12rV+/XpLUpk0btWrVSrt27fL5eaBhuG8KsDciC8HI0uFUXl6ujIwMzZ8/XykpKWrbtq0eeeQRpaSkSJJat26t4uJi5+W3q6++WuXl5dq7d6/LcUpLS/XRRx/phRde0H333afq6mq9+OKLWr16tb788ku/nxfOD7NTQOghsmAVlg4nSZowYYJSU1O1bt06FRcXa8qUKVqyZIkk6dChQxo7dqwyMjIknQqp+i7hjRkzRs8//7xWrlypyMhILVu2TH/4wx88Hs/nx37QsdIydY9p3+BzgvcRUwBqEFnwpTBJ/FvjhpiYGJWUlOjydr10rLSs1nZCKngQVAA8RWAFrwuaRWnYnn+oefPmKi0tPf/jeWFMkJRdmuP8ZyLK2pidAuApZrFQg3DygdMjSiKkggExBcBbiCx7I5z8gJAKTvxWHwBf4/lYwYdwCgAu6wU3ZqcA+BuzWNZBOAUYs1H2QEwBsAoiy7cIJ4thNso+uNQHwOqILM8RThbGbJQ9MTsFIBgRWacQTkGE2Sj7YnYKgJ14EllScIUW4RSkmI0KDcxOAQgFwTSbRTjZBLNRoYOYAhDKPJ3N6uflz0842RCzUaGHS30AULdPm4RpmBePRziFAEIqdDE7BQDeRTiFIC7rhTZmpwCg4QinEMdsFGowOwUA50Y4wQWzUTgds1MA4IpwQr2YjUJ9mJ0CEKoIJ7iN2SicDbNTAEIB4YQGYTYK7mJ2CoCdEE7wCmaj4AlmpwAEK8IJXsdsFBqK2SkAVkc4weeYjcL5IKYAWAnhBL9iNgrewKU+AIFCOCGgCCl4E7NTAHyNcIKlcFkP3sbsFABvIpxgWUQUfInZKQANQTghKHBJD/5ATAE4F8IJQYnZKPgLl/oAnI5wQtBjNgqBwOwUEJoIJ9gOs1EIFGIKsD/CCbbGbBQCjZgC7IVwQkhhNgpWwH1TQPAinBCymI2C1TA7BVgf4QT8H2ajYEXEFGAthBNQB2ajYGXEFBA44YEewNlERkZqwYIFKioqUm5uriZMmFDvvkuXLpUxxmX57//+b+f28ePH68CBAyopKdGCBQsUFRXlj1OATWSX5jgXwIq2VRfVWgB4n6VnnGbPnq2ePXtq0KBBat++vTIyMpSTk6PFixfX2jcxMVF333231qxZ41xXVHTqG8ftt9+uqVOnavTo0crPz1d6erqeffZZPfTQQ347F9gHl/QQLLgJHfC+MEkm0IOoS3R0tAoKCjRs2DBt2LBBkjRp0iQNGTJEAwcOdNm3cePGKisrU2Jior799ttax9qwYYPWrl2radOmSZL69eunDz74QK1atVJ5eblb44mJiVFJSYkub9dLx0rLzvPsYFeEFIIVMQW7imwWpf/9cqGaN2+u0tLS8z6eZS/VdevWTY0aNdKWLVuc6zZt2qTevXsrLCzMZd8rr7xSxhh9//33tY4THh6uXr16aePGjc51mZmZaty4sbp16+a7E0BI4pIeghWX+gD3WDac4uPjVVBQoKqqKue6/Px8RUVFKTY21mXfrl27qri4WK+99ppyc3P1ySefaOjQoZKkFi1aKCoqSrm5uc79q6urVVhYqISEBP+cDELS6RFFSCEYEVNAbZa9xyk6OlqVlZUu62o+joyMdFnfpUsXRUdH6/3339czzzyj2267Te+9956SkpKUn5/v8trTj3XmcQBf4t4o2AG/0YdQZ9lwqqioqBU2NR87HA6X9dOnT9e8efN09OhRSdLnn3+uHj166De/+Y0mTZrk8trTj3XmcQB/4XEHsBNiCqHEsuF08OBBtWrVShEREaqurpYkxcXFyeFwOAOphjGm1rqvv/5aV111lQoLC1VeXq64uDh98803kqSIiAjFxsYqLy/PH6cCnBOzUbAbYgp2Zdl7nLKzs1VVVaWkpCTnuuTkZGVlZckY118ETEtL08KFC13Wde/eXbt27ZIxRllZWUpOTnZu69Onj6qqqrRjxw7fngTQANwXBbvininYgWVnnMrLy5WRkaH58+crJSVFbdu21SOPPKKUlBRJUuvWrVVcXKyKigq9++67euutt7R+/Xpt2bJFo0aNUnJysn7zm99Ikl5++WW98sor+vLLL3Xw4EGlpqbq73//u9uPIgAChUt6sDueNYVgY9lwkqQJEyYoNTVV69atU3FxsaZMmaIlS5ZIkg4dOqSxY8cqIyNDS5Ys0YMPPqgnn3xSl156qb766isNHTpUOTmnfuj861//UocOHfTKK68oMjJSixcv1mOPPRbIUwMahEt6CBVc6oNVWfYBmFbDAzBhZUQUQhUxhXPx9gMwLT3jBMA9XNJDqGJmCv5GOAE2xCU9hDJiCr5EOAE2R0QBxBS8h3ACQgiX9ICfEFNoCMIJCGHMRgGuiCmcC+EEQBKzUUB9iCmcjnACUCdmo4D6EVOhi3ACcE5EFHBuPAU9NBBOADzCJT3AM8xO2QvhBOC8MBsFeI6YCl6EEwCvIaKAhiOmggPhBMAnuKQHnD9iynoIJwB+wWwU4B3EVGARTgD8jtkowLuIKf8hnAAEHLNRgPfxeATfIJwAWAqzUYBvMTt1fggnAJbGbBTge8SU+wgnAEGDiAL8h5iqG+EEIChxSQ/wP+6bIpwA2ASzUUDghNLsFOEEwHaYjQICz64xRTgBsD1mowBrsMOlPsIJQEhhNgqwnmCanSKcAIQ0ZqMAa7Lq7BThBAD/h9kowPoCPTtFOAFAPZiNAoKDP2enCCcAcAMRBQSfbdVFiq6u9OoxCScA8BCX9IDQRTgBwHliNgoIHYQTAHgRs1GAvRFOAOBDzEYB9kI4AYCfMBsFBD/CCQAChNkoIPgQTgBgAcxGAcGBcAIAC2I2CrCm8EAP4GwiIyO1YMECFRUVKTc3VxMmTKh33+HDh2v79u0qLS3Vjh07NGLECJftRUVFMsa4LE2bNvX1KQDAecsuzXFZAASOpWecZs+erZ49e2rQoEFq3769MjIylJOTo8WLF7vsd8011+idd97Ro48+qpUrV+q//uu/9J///Ee9evXS559/rjZt2qhFixbq1KmTHA6H83VlZWUej+lnzS6VQz8dg29iAPyN2SggcMIkmUAPoi7R0dEqKCjQsGHDtGHDBknSpEmTNGTIEA0cONBl35kzZ6pbt24aPny4c93q1av16aef6sknn9TgwYO1aNEitW3btsHjiYmJUUlJiUZ2HSnHMUed+xBRAAKNkAJcRTeL1uKvF6t58+YqLS097+NZdsapW7duatSokbZs2eJct2nTJk2aNElhYWEy5qfey8jIUOPGjWsd48ILL5QkJSYmavfu3T4f85nfsAgpAP7GbBTgW5YNp/j4eBUUFKiqqsq5Lj8/X1FRUYqNjVVBQYFz/a5du1xem5iYqMGDB2v+/PmSpK5duyo6Olrr1q3TlVdeqe3bt+vhhx/Wt99+69NzIKQABBK/qQd4n2XDKTo6WpWVrn/RuObjyMjIel8XGxurxYsXa/PmzVq2bJkkqUuXLmrZsqWeeOIJlZSU6PHHH9eaNWuUmJioY8eO+e4kzkBIAQgkZqOA82fZcKqoqKgVSDUfn36D9+kuueQSffjhhwoPD9cdd9zhvJw3dOhQNWrUyHkz+N133639+/drxIgRevPNN314Fmd3+jcuIgqAPzEbBTSMZcPp4MGDatWqlSIiIlRdXS1JiouLk8Ph0NGjR2vt36ZNG61du1aSNGDAAJdLecePH9fx48edH1dWVmrv3r3ndbO4tzEbBSCQmI0C3GPZ5zhlZ2erqqpKSUlJznXJycnKyspyuTFcOnVZb/Xq1Tp58qT69++vvLw8l+179uzRmDFjXPa//PLLa90bZSXdY9q7LADgLzw3CqifZWecysvLlZGRofnz5yslJUVt27bVI488opSUFElS69atVVxcrIqKCj3xxBPq3LmzBgwY4NxWc4ySkhKtWLFC06ZN0759+3TkyBFNnz5dBw4c0MqVKwN1eh5jRgpAoDAbBfzEsuEkSRMmTFBqaqrWrVun4uJiTZkyRUuWLJEkHTp0SGPHjlVGRoZGjhyp6Ohobd261eX16enpSklJ0WOPPaaqqir985//1IUXXqi1a9dq+PDhOnnyZCBOyyu4PwpAIHBvFEKdZR+AaTXuPADTKggpAIFARMGKQuYBmGg4LusBCARmoxAKCKcQwGU9AIFASMGOCKcQw2wUgEDhJnPYAeEU4ggpAIHAbBSCFeEEF1zWAxAIzEYhWBBOqBezUQACgdkoWBnhBLcRUgACgdkoWAnhhAbjsh4Af2M2CoFGOHmoe0QLVUZEOj/eVl0UwNFYB7NRAAKBkIK/EU7nqUfERXWuD/WgIqQABAKX9eBrhJOP1BVUoRxTXNYD4G9EFHyBcPIjZqdOYTYKgL9xSQ/eQjhZQKgHFSEFwN+YjUJDEU4WFqqX+7isB8CfmI2CJwinIBNqs1PMRgHwN2ajcDaEk02EyuwUIQXAn4gonIlwsrFQiCku6wHwFy7pQSKcQo6dL/UxGwXAn5iNCk2EEyTZc3aKkALgL0RU6CCcUC+7xRQhBcAfuKRnb4QTPGKnmCKkAPgDs1H2QjjhvNnlviluNAfga8xGBT/CCT4TzLNTzEYB8Admo4IP4QS/CtaYIqQA+BoRFRwIJw/1rDA6UWHq3f5JkzA/jsYegvFSHyEFwJe4pGddhJOX9T5LVJ2OwDq3YJqdIqQA+BKzUdZBOAWIu4ElEVmnC5bZKUIKgK8QUYFFOAUBIuvcrD47Vdc3N2IKwPnikp7/EU42w6XCn1h9dorHHwDwNmajfI9wClGhPItlxaBiRgqAtxFRvkE44ZxCJbKsFlTcJwXAW7ik5z2EE7zKjpFllaBiVgqAtzAb1XCEEwIm2CPLCkHFfVIAzhezUZ4hnBAUgimy6guqGr4Kq3N9syOsAGsiVIKLpcMpMjJSf/vb3zRy5EiVl5frueee05w5c+rct3v37po/f76uueYaffXVV3rggQf02WefObffddddmjFjhuLj4/X+++/rvvvuU2Fhob9OBX7kSWTV8GdsnSusJN/ElTvfnIkrhAJCBefD0uE0e/Zs9ezZU4MGDVL79u2VkZGhnJwcLV682GW/6OhorVy5Um+88YbGjh2rBx54QCtWrFDnzp3lcDjUq1cvLVy4UA888ICys7M1b948paena8SIEQE6M1iNp7Hl69AirhDqiBs0RF3fOyMjorz6OcIkef6/534QHR2tgoICDRs2TBs2bJAkTZo0SUOGDNHAgQNd9k1JSdGTTz6pzp07O9ft3r1bTz31lDIyMpSRkaGTJ08qJSVFkpSQkKCcnBx17txZ+/btc2s8MTExKikp0arLxunEsXLvnCRCir8vIQbitwGJKhA8wcud/2ELRpHNovS/Xy5U8+bNVVpaet7H83jG6eabb9by5ctrrW/UqJEmT56s//3f/z3vQUlSt27d1KhRI23ZssW5btOmTZo0aZLCwsJkzE+9l5SUpE2bNrm8fvPmzerTp48yMjKUlJSkZ555xrntwIED+uGHH5SUlOR2OAHnqyGXEM/r86mF32OtRwt7fuMF3OXv/869qiqIx34WF1zg3fPyOJzefvtt/ec//9H48eNVVHTq/2j79++vV199VdHR0V4Lp/j4eBUUFKiqqsq5Lj8/X1FRUYqNjVVBQYHLvl999ZXL6/Pz83X11Vc7t+fm5tbanpCQ4PG4ElsXyjRzePy6s/niUCuvHg/B5Zq4gnPv1NBj++zIABAcwppGe/V4HofTtddeq1dffVVfffWVHnnkEQ0aNEijR4/WSy+9pKlTp3ptYNHR0aqsrHRZV/NxZGSkW/vW7Heu7YHmyx+csLfY5EaBHoLXXZB0baCHABs7kbk90EOAvzXx7u3cHh/tm2++Uf/+/fXaa6/ptdde04kTJ3TzzTfrww8/9OrAKioqaoVNzccOh8OtfWv2O9d2wF98HTr+io7wawf55fMA3tY4hP7dPbl9baCHYA2NvDtJ4nE4dezYUS+88IKGDBmiqVOnqnv37lq8eLGmT5+uOXPmqLq62isDO3jwoFq1aqWIiAjnMePi4uRwOHT06NFa+8bFxbmsi4uLU15enlvbgYYIxGyPP2djiCMguLnz3zBx5TmPw2nnzp3asmWLunfvrj179kiSbrnlFr300ktKSUlRYmKiVwaWnZ2tqqoqJSUlafPmzZKk5ORkZWVludwYLkmZmZmaOHGiy7p+/frpqaeecm5PTk5WRkaGpFO/VdeuXTtlZmZ6ZawIfla45OXvS1SEEYBzfR8grGrz+HEEKSkpSktLq7W+WbNmeuqppzR+/HhvjU2pqalKTk5WSkqK2rZtq4yMDKWkpGjJkiVq3bq1iouLVVFRoZiYGO3Zs0dvvvmmXnnlFd1///268847ddlll8nhcCgpKUnr16/Xgw8+qKysLL344osqLS3VL37xC7fHUvM4gpx+v5Ap4xJfMLBCDEmBuWeHKALgD0ERVo0iFfXLyV57HIFln+MkSVFRUUpNTdXIkSNVXFys2bNn68UXX5QkGWM0duxY5yxSr169NH/+fHXt2lWff/6582GXNcaMGaO//OUvatmypT744APdd999+vHHH90eC+FkDVaJoTMxWwQAp1gupkIpnKyEcPIdq8ZQXQL5G1/EEgArs1ww1fByOFn6T64geAVTDJ3JCr8OTyQBCAaWjSUfIpzglmAOobpYIY5qEEkAgkUohtKZCKcQZrcYqo+VIkkilAAED0KpNsLJZkIlhupitUCqQSgBCCbE0tkRTkEglGOoLlYNpBqEEoBgQih5hnAKAELo3KweR6cjlAAEG2Kp4QgnLyGGGi6YIkkilAAEH0LJewgnD7Xsc4FUQSQ1RLAF0umIJQDBhljyDcIJXhfMgVSDUAIQbAgl/yCc0GB2CKQahBKAYEMoBQbhBLfYKZIkQglAcCKWAo9wggu7BVINQglAsCKWrIVwClF2DaQahBKAYEUoWRvhFALsHkkSoQQguBFLwYNwspFQCKTTEUsAghWhFLwIpyAVapEkEUoAghuxZA+Ek8WFYiDVIJQABDtiyX4IJwsJ5UiSCCUA9kAs2RvhFAChHkinI5YA2AGxFDoIJx8ikGojlADYBbEUmggnLyGS6kYoAbALQgkS4eSxC3p1k6oqAz0MSyOWANgFsYQzEU44b4QSADshlnA2hBM8RigBsBNCCZ4gnOAWYgmAnRBLaCjCCXUilADYCaEEbyGc4EQsAbATYgm+QDiFMEIJgJ0QSvAHwinEEEsA7IJQQiAQTjZHKAGwE2IJgUY42RCxBMAuCCVYDeFkA4QSALsglGB1hFMQIpQA2AmxhGBCOAUJYgmAXRBKCGbhgR7A2cycOVOHDx9WYWGhZs2apbCwsHr37d27tzZv3qzS0lLt2rVL9957r8v27OxsGWNclquuusrXp9Bg4dcOclkAIJid3L7WuQDBzLIzThMmTNCoUaN02223qVGjRnr99dd1+PBhPf/887X2bd26tVatWqXU1FSNGTNGPXr0UFpamvLy8rRy5UqFh4friiuu0A033KDdu3c7X1dQUODPUzonAgmAXRBIsCvLhtP48eM1efJkbd68WZL0+OOPa8aMGXWG06233qpDhw5p0qRJkqQ9e/Zo4MCBGjVqlFauXKmOHTuqcePG2rp1qyorK/16HmdDKAGwE2IJocCS4RQfH69LL71UGzdudK7btGmTOnTooLi4OB06dMhl/9WrVys7O7vWcS688EJJUmJiovbv32+JaCKWANgFoYRQZNlwkqTc3Fznuvz8fElSQkJCrXDKyclRTk6O8+OLL75Yd911l6ZOnSpJ6tq1q44fP6733ntPPXv21DfffKNHH31UWVlZPj4TQgmAfRBKQADDqUmTJmrbtm2d25o1ayZJLjNENf8cGRl5zuMuXrxYhw4d0iuvvCJJ6tKliy666CItWLBAkydP1n333ac1a9YoMTFRBw4c8MbpOBFKAOyEWAJcBSycevfurfXr19e57dFHH5V0KpLODCaHw1HvMZs2baply5bpiiuuUHJyssrLyyVJ9913n6Kjo1VaWipJevDBB9WvXz/dc889mjlz5nmdB6EEwE4IJeDsAhZOGzZsqPfxAvHx8Zo9e7bi4uKcl+Di4uIkSXl5eXW+JiYmRqtWrdJll12mQYMGac+ePc5t1dXVzmiqsWvXrnpnvM6FWAJgJ8QS4D5LPscpLy9POTk5Sk5Odq5LTk5WTk5OrfubJCksLEzvvPOOOnXqpP79+2vnzp0u29euXavJkye77P+zn/1Mu3bt8nhs4T+7wePXAICVnP5MJaIJ8Iwlbw6XpNTUVM2aNct5D9Izzzzj8iiCVq1aqby8XGVlZbr33ns1cOBA3XLLLTp69Khat24tSTp+/LiKior03nvvafLkydq+fbu++eYbjR8/Xi1atFB6enogTg0A/Io4ArzHsuE0e/ZsXXLJJVqyZIlOnDihhQsX6oUXXnBuz8rKUnp6uqZNm6aRI0cqIiJCK1ascDnG+vXrNXDgQL3wwgtq0qSJXnrpJbVu3VqffPKJhgwZomPHjvn7tADAL4glwDfCJJlADyIYxMTEqKSkRJVfr5dOVgd6OADgglAC6tEoUlG/nKzmzZvXut+5ISw74wQAqB+hBAQG4QQAQYJYAgKPcAIAiyKUAOshnADAIgglwPoIJwAIIGIJCC6EEwD4EaEEBDfCCQB8iFAC7IVwAgAvI5YA+yKcAOA8EUpA6CCcAMBDhBIQuggnAHADsQQEpxNZO6Rfeu94hBMA1IFQAoLPiczttVc2ifLq5yCcAECEEhBs6owkPyCcAIQsYgkIDoGKpLoQTgBCBqEEWJ+VIqkuhBMA2yKUAGuzeiTVhXACYCvEEmA9wRhI9SGcAAQ1QgmwFjtFUl0IJwBBh1gCAs/ugVQfwgmA5RFKQGCFaiTVhXACYDmEEhAYBNK5EU4ALIFYAvyLSGoYwglAQBBKgH8QSN5FOAHwG2IJ8C0iyfcIJwA+QygBvkEgBQ7hBMBrCCXA+4gkayGcAJwXYgnwDgIpOBBOADxCKAHnj0gKXoQTgHMiloCGI5LshXACUAuhBHiOQAoNhBMAScQS4AkiKXQRTkCIIpQA9xBJOB3hBIQQYgk4OyIJ50I4ATZGKAF1I5DQUOGBHsDZzJw5U4cPH1ZhYaFmzZqlsLCwevedO3eujDEuy+9+9zvn9rvuukt79uxRWVmZ3nnnHcXGxvrjFAC/O7l9rXMBcCqSzlyAhrLsjNOECRM0atQo3XbbbWrUqJFef/11HT58WM8//3yd+ycmJmrixIlKT093rispKZEk9erVSwsXLtQDDzyg7OxszZs3T+np6RoxYoQ/TgXwKQIJ+AlRBF8Lk2QCPYi65OTkaPLkycrIyJAk3X333ZoxY4Y6duxY5/779+/XuHHj9OGHH9balpGRoZMnTyolJUWSlJCQoJycHHXu3Fn79u1zazwxMTEqKSlR5dfrpZPVDTonwFuIJYQ6AgluaxKlmOcWq3nz5iotLT3vw1lyxik+Pl6XXnqpNm7c6Fy3adMmdejQQXFxcTp06JDL/jExMUpISNDu3bvrPF5SUpKeeeYZ58cHDhzQDz/8oKSkJLfDCQgkQgmhjEiClVg2nCQpNzfXuS4/P1/SqdmiM8Opa9euOnnypCZNmqRhw4apsLBQc+bM0aJFi5zHO/1YNcdLSEjw5WkA54VYQigikmB1AQunJk2aqG3btnVua9asmSSpsrLSua7mnyMjI2vt36VLFxljtGvXLr300kvq37+/Xn31VZWUlGjp0qWKjo52OVbN8eo6FhAohBJCDZGEYBSwcOrdu7fWr19f57ZHH31U0qlIOjOYHA5Hrf0XLVqk9957T0VFRZKkL774QldccYV++9vfaunSpaqoqKgVSZGRkXUeC/AnYgmhgECCnQQsnDZs2FDv4wXi4+M1e/ZsxcXFKScnR5IUFxcnScrLy6vzNTXRVOPrr7/WoEGDJEkHDx50vr5GXFxcvccCfIVQgt0RSbA7S97jlJeXp5ycHCUnJzvDqeafz7y/SZKmTZumvn376sYbb3Su6969u3bt2iVJyszMVHJysvM39BISEtSuXTtlZmb64WwQ6ogl2BGBhFBlyXCSpNTUVM2aNUsHDhyQJD3zzDMuz3Bq1aqVysvLVVZWpvfee09//vOf9ac//UlLlizRTTfdpF/96lcaOHCg81jr16/Xxx9/rKysLL344otavnw5v1EHnyCUYDdEEvATy4bT7Nmzdckll2jJkiU6ceKEFi5cqBdeeMG5PSsrS+np6Zo2bZo+/fRT3XHHHfrLX/6i6dOna9++fRo1apRzRikzM1P333+//vKXv6hly5b64IMPdN999wXq1GBDxBLsgkgCzs6yD8C0Gh6AidMRSgh2BBJCRig8ABOwImIJwYpIAryHcALqQSgh2BBIgO8RTsBpiCUECyIJCAzCCSGNUILVEUiAtRBOCDnEEqyKSAKsj3CC7RFKsBoCCQhehBNsh1CClRBJgL0QTrAFYgmBRiABoYFwQlAilBBIRBIQuggnBA1iCf5GIAE4E+EEyyKU4E9EEgB3EE6wFGIJvkYgATgfhBMCilCCLxFJALyNcILfEUvwNgIJgL8QTvA5QgneRCQBCCTCCT5BLOF8EUgArIhwglcQSjgfRBKAYEE4ocGIJXiKQAIQ7AgnuI1QgieIJAB2RDihXoQS3EEgAQglhBNcEEs4GyIJQKgjnEIcoYS6EEgAUDfCKQQRSzgdkQQA7iOcQgChBIlAAgBvIJxsilgKbUQSAJzy48cnFOPF4xFONkEohSYCCUCoKtxU5dZ+YU0befXzEk5BjFgKLUQSgFDgbhAFCuEURAil0EAgAbAbq8eQJwgniyOW7I1IAhCs7BRDniCcLIZQsicCCUCwCNUgchfhZAHEkr0QSQCshhjyHsIpAAgl+yCSAAQKMRQYhJOfEEvBjUAC4GuEUHAgnHyEUApeRBIAbyGG7Mfy4TRz5kzde++9ioiI0IIFCzRx4kQZY2rtl5aWprFjx9Zav3btWg0ePFiSVFRUpBYtWrhsb9asmcrKyrwyVmIpuBBIABqCGAptlg6nCRMmaNSoUbrtttvUqFEjvf766zp8+LCef/75WvuOHz9eEydOdH7coUMHrV+/XvPmzZMktWnTRi1atFCnTp3kcDic+51PNBFKwYNIAnA2xBDcFSap9vSNReTk5Gjy5MnKyMiQJN19992aMWOGOnbseM7Xrl69WocPH9avfvUrSdLgwYO1aNEitW3btkFjiYmJUUlJicr/9RepqrJBx4B/EEkAJGIIp4Q1jVb7zcvUvHlzlZaWnvfxLDvjFB8fr0svvVQbN250rtu0aZM6dOiguLg4HTp0qN7XDho0SDfccIOuuOIK57rExETt3r3bp2OGfxFIQOghhhBolg4nScrNzXWuy8/PlyQlJCScNZwmTpyo9PR0HThwwLmua9euio6O1rp163TllVdq+/btevjhh/Xtt9/66AzgTUQSYG8EEYJFQMOpSZMm9V46a9asmSSpsvKny2I1/xwZGVnvMTt27KhBgwZp/PjxLuu7dOmili1b6oknnlBJSYkef/xxrVmzRomJiTp27Nj5ngq8hEAC7IMYgh0FNJx69+6t9evX17nt0UcflXQqks4MptNv7j7TyJEjlZ2dra+//tpl/dChQ9WoUSPnzeB333239u/frxEjRujNN98831NBAxBJQHAiiBDKAhpOGzZsUFhYWJ3b4uPjNXv2bMXFxSknJ0eSFBcXJ0nKy8ur95hDhw7V0qVLa60/fvy4jh8/7vy4srJSe/fubfDN4vAMkQRYGzEEuMey9zjl5eUpJydHycnJznCq+eez3d/Uq1cvPfXUU7XW79mzR9OnT3f+hl50dLQuv/xy7dq1yzcnEKIIJMA6iCHA+ywbTpKUmpqqWbNmOW/yfuaZZ1ye4dSqVSuVl5c7L7+1b99ezZs3186dO2sda8WKFZo2bZr27dunI0eOaPr06Tpw4IBWrlzpn5OxISIJ8D9iCAgsS4fT7Nmzdckll2jJkiU6ceKEFi5cqBdeeMG5PSsrS+np6Zo2bZokqXXr1pJOPSH8TI899piqqqr0z3/+UxdeeKHWrl2r4cOH6+TJk/45mSBGIAG+RQwBwcPSD8C0klB5ACaRBHgHMQRYQ8g8ABO+RSABniOGABBOIYBIAupHDAHwBOFkIwQScAoxBMBXCKcgRSQh1BBDAKyAcLI4Agl2RgwBCDaEk4UQSbALggiAXRFOAUAgIRgRQwBAOPkckQSrI4gAwH2Ek5cQSLAagggAvI9w8tCJrB1SRXmgh4EQRQwBQGARToAFEEQAEBwIJ8CHCCIAsBfCCWggoggAQg/hBJyBIAIA1IdwQsggiAAA54twQtAjiAAA/kI4wbIIIgCA1RBO8DuCCAAQrAgneA1BBACwO8IJ50QQAQBwCuEUwggiAAA8QzjZDDEEAIDvEE5BgBgCAMAaCKcAIYYAAAg+hJMXEUMAANgb4eShHz8+IVNGIAEAEIrCAz0AAACAYEE4AQAAuIlwAgAAcBPhBAAA4CbCCQAAwE2EEwAAgJsIJwAAADcRTgAAAG4inAAAANwUFOH0/vvva8yYMWfdp0OHDvrwww917NgxffXVV7rxxhtdtg8ePFhffPGFysrKtGbNGnXs2NGXQwYAADZk6XAKCwvTvHnzdNNNN51z36VLl+rQoUPq2bOnXnvtNS1ZskTt2rWTJLVr105Lly5VWlqaevXqpSNHjmjp0qU+Hj0AALAby4ZTmzZttGbNGt1yyy0qKio6674DBw5U586ddf/992vXrl165pln9PHHH2vcuHGSpF//+tf69NNPNWfOHO3cuVMpKSnq0KGD+vfv749TAQAANmHZcLruuuu0f/9+9ejRQ8XFxWfdNykpSZ999pkcDodz3aZNm9SnTx/n9o0bNzq3lZeX67PPPnNuBwAAcMcFgR5AfZYvX67ly5e7tW98fLxyc3Nd1uXn5yshIcGt7Z4Ii47y+DUAACAwvP1zO2Dh1KRJE7Vt27bObXl5eS6zR+cSHR2tyspKl3WVlZWKjIx0a7s7YmJiJEmXfviW268BAADWEBMTo9LS0vM+TsDCqXfv3lq/fn2d22699VYtW7bM7WNVVFQoNjbWZV1kZKQzvioqKmpFUmRkpI4ePer258jNzVXbtm298kUHAAD+ExMTU+vKU0MFLJw2bNigsLAwrxzr4MGDuuqqq1zWxcXFKS8vz7k9Li6u1vbs7GyPPo+3vugAAMB/vDnpYdmbwz2RmZmp6667Tk2aNHGuS05OVmZmpnN7cnKyc1tUVJSuvfZa53YAAAB3Gasve/fuNWPGjHFZ16pVK9O0aVMjyYSHh5svv/zSvPnmmyYxMdE8/vjjpqSkxLRr185IMu3btzcOh8M8/vjjJjEx0bz11lsmOzs74OfFwsLCwsLCElxL0M44ZWVl6ZFHHpEknTx5Ur/4xS8UHx+vbdu2afTo0brtttu0f/9+SVJOTo5uv/12paSkKCsrS7Gxsbr11lsDOHoAABCMwnSqoAAAAHAOQTvjBAAA4G+EEwAAgJsIJwAAADcRTv8nMjJSCxYsUFFRkXJzczVhwoR69+3evbsyMzNVVlamrVu36rrrrvPjSEODJ+/H8OHDtX37dpWWlmrHjh0aMWKEH0dqf568FzXat2+v0tJS/pC2l3nyXlx99dX66KOP5HA49Pnnn2vAgAH+G2gI8OS9uPXWW7Vz506Vlpbqo48+0rXXXuvHkYaOxo0b64svvjjr9x1v/fwO+K/2WWGZN2+eyc7ONtdee6259dZbTXFxsRk5cmSt/aKjo01ubq6ZPXu26dKli5k7d67Jy8sz0dHRAT8HOy3uvh/XXHONqaioMA899JDp3LmzefDBB01lZaX52c9+FvBzsMvi7ntx+rJy5UpjjDH9+/cP+PjttLj7XjRv3tzk5eWZV155xXTu3NlMnTrVFBUVmYsvvjjg52CXxd33IjEx0TgcDnPPPfeYTp06mZdeesnk5uaaqKiogJ+DnZbIyEizePHis37f8eLP78CfcKCX6Oho43A4XL7YkyZNMuvWrau1b0pKivnuu+9c1u3evbvWc6ZY/PN+zJw506xcudJl3erVq82MGTMCfh52WDx5L2qWUaNGmY8++ohwCuB78dBDD5lvv/3WhIeHO9dt3brVDBs2LODnYYfFk/fi4YcfNllZWc6PmzVrZowxpkePHgE/D7ssXbt2Ndu3bzfZ2dln/b7jrZ/fXKqT1K1bNzVq1Ehbtmxxrtu0aZN69+5d68/CJCUladOmTS7rNm/erD59+vhlrKHAk/cjIyNDEydOrHWMCy+80OfjDAWevBeS1LJlSz377LO6//77/TnMkODJezFgwAAtW7ZMJ0+edK67/vrrtWrVKr+N1848eS8KCwt11VVXqW/fvgoLC1NKSoqKi4v13Xff+XvYttW/f3+tW7funD+HvfXzm3CSFB8fr4KCAlVVVTnX5efnKyoqqtYfD46Pj6/1N+vy8/OVkJDgl7GGAk/ej127dunzzz93fpyYmKjBgwdrzZo1fhuvnXnyXkjSnDlzlJGRoZ07d/pzmCHBk/eiU6dOOnLkiF555RXl5eXp448/Vt++ff09ZNvy5L3417/+pRUrVmjz5s06fvy4nnvuOd1xxx0e/ZF5nN38+fM1YcIElZeXn3U/b/38JpwkRUdHq7Ky0mVdzceRkZFu7Xvmfmg4T96P08XGxmrx4sXavHmzli1b5tMxhgpP3ovBgwcrOTlZ06dP99v4Qokn70WzZs00ceJE5eXladiwYdqwYYM++OAD/gfPSzx5L2JjYxUXF6ff/e536t27txYtWqS0tDRdfPHFfhsvTvHWz2/CSVJFRUWtL1zNxw6Hw619z9wPDefJ+1Hjkksu0dq1axUeHq477rhDxhifjzMUuPteNGnSRK+88ooefPBBVVRU+HWMocKT/y5OnDih7du3a+rUqcrOztbEiRO1e/du3XPPPX4br5158l7MmjVLX3zxhV5++WV99tln+s1vfqOysjKlpKT4bbw4xVs/vwknSQcPHlSrVq0UERHhXBcXFyeHw1FrOvXgwYOKi4tzWRcXF6e8vDx/DDUkePJ+SFKbNm20ceNGRUZGasCAASooKPDjaO3N3ffi+uuvV+fOnbV48WKVlpaqtLRUkrRq1Sqlpqb6e9i25Ml/F3l5edq1a5fLut27d6tdu3b+GKrtefJe9OjRQzt27HB+bIzRjh071L59e38NF//HWz+/CSdJ2dnZqqqqUlJSknNdcnKysrKyas1cZGZm1rpXoF+/fsrMzPTLWEOBJ+9HdHS0Vq9erZMnT6p///4ErJe5+15s3bpVl112mbp37+5cJOnXv/61Jk+e7O9h25Kn36e6devmsq5Lly7at2+fP4Zqe568F7m5uUpMTHRZd+WVV2rv3r1+GSt+4s2f3wH/VUIrLKmpqeaLL74wPXv2NL/4xS/M0aNHzW233WYkmdatW5smTZoYSSYmJsbk5+ebuXPnmq5du5q5c+ea3NxcnuMUoPdjxowZpqyszPTq1cu0bt3auTRv3jzg52CXxd334syFxxEE7r249NJLTWlpqZkyZYrp3LmzmTZtmikpKTFt2rQJ+DnYZXH3vbjzzjuNw+Ewo0ePNp07dzYzZ87kmVo+XM78vuOjn9+BP1ErLFFRUSY9Pd2UlpaaAwcOmPHjx7u8Eac/56FXr15m27ZtxuFwmMzMTNO9e/eAj99ui7vvx9dff23qkpaWFvBzsMviyX8bpy+EU2Dfi759+5pPP/3UlJeXm88++8z8/Oc/D/j47bR48l6MGzfO7Ny505SUlJiNGzeaa6+9NuDjt+ty5vcdX/z8Dvu/fwAAAMA5cI8TAACAmwgnAAAANxFOAAAAbiKcAAAA3EQ4AQAAuIlwAgAAcBPhBAAA4CbCCUDI69atm/r06RPoYQAIAoQTgJC3ZMkSXXHFFYEeBoAgQDgBCHlhYWGBHgKAIEE4AQhp69atU4cOHZSenq60tLRADweAxfG36gCEtIsuukg7duzQc889p/T0dJWUlAR6SAAsjBknACGtqKhI1dXVKi4uJpoAnBPhBAAA4CbCCQAAwE2EE4CQZwy3egJwD+EEIOSVlZWpS5cuuuiiiwI9FAAWRzgBCHkvv/yyfv/732vBggWBHgoAi+NxBAAAAG5ixgkAAMBNhBMAAICbCCcAAAA3EU4AAABuIpwAAADcRDgBAAC4iXACAABwE+EEAADgJsIJAADATYQTAACAmwgnAAAANxFOAAAAbvr/pVYSyg44rq8AAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 7 + }, + { + "cell_type": "code", + "id": "938ccccb00979139", + "metadata": { + "ExecuteTime": { + "end_time": "2026-02-25T17:50:52.699263Z", + "start_time": "2026-02-25T17:50:52.695142Z" + } + }, + "source": [], + "outputs": [], + "execution_count": 7 + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "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": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 00000000..1575fa2e --- /dev/null +++ b/codecov.yml @@ -0,0 +1,6 @@ +coverage: + status: + project: + default: + target: auto + threshold: 1% # the leniency in hitting the target \ No newline at end of file diff --git a/docs/_autodoc/dataset_creation/index.rst b/docs/_autodoc/dataset_creation/index.rst new file mode 100644 index 00000000..b3fc20fc --- /dev/null +++ b/docs/_autodoc/dataset_creation/index.rst @@ -0,0 +1,32 @@ +.. _nnodely-dataset-creation: + +Dataset Creation +======================== +Training data is a critical channel for domain knowledge. Selecting representative samples, shaping their distribution in the input space, and applying physics-informed data augmentation techniques can significantly affect model generalization. Conversely, biased or uninformative data may encode incorrect assumptions about the underlying system dynamics. + +In nnodely, dataset creation is designed to preserve strict consistency between data representation and the temporal structure of the neural model. + +Once loaded, via the :func:`loadData() ` function, the datasets are automatically integrated according to the defined Inputs and their associated temporal windows. This guarantees that the temporal context required by the model is preserved, enabling correct construction of input sequences for time-dependent architectures. + +.. rubric:: Multi-Source Support and Temporal Operations + +The framework supports multiple data sources and provides utilities for :func:`resampling `, such as interpolation, as well as utilities for extracting specific temporal intervals. These features enable controlled experimentation under different operating conditions while maintaining alignment with the model's temporal structure. + +.. rubric:: Multi-File handling + +The framework also supports a multi-file dataset mode, where a directory of data files is treated as a single logical dataset. Data from different files are processed independently and concatenated while preserving temporal coherence, ensuring that valid temporal windows are constructed separately for each sequence. This capability is essential for recurrent training and closed-loop prediction scenarios, where temporal consistency across multiple trajectories must be strictly maintained. + +.. .. rubric:: Key Benefits +.. .. ---------------- +.. - Ensures that the temporal context required by the model is preserved during preprocessing. + +.. - Facilitates reproducible and controlled experiments across different operating conditions. + +.. - Enables seamless integration of heterogeneous data sources without compromising temporal coherence. + +.. - Supports recurrent and closed-loop scenarios through multi-file dataset handling. + +.. toctree:: + :maxdepth: 1 + + loader_module diff --git a/docs/_autodoc/dataset_creation/loader_module.rst b/docs/_autodoc/dataset_creation/loader_module.rst new file mode 100644 index 00000000..2a65971e --- /dev/null +++ b/docs/_autodoc/dataset_creation/loader_module.rst @@ -0,0 +1,14 @@ +Data Loader module +======================== + +.. automodule:: nnodely.operators.loader + :undoc-members: + :no-inherited-members: + +.. autofunction:: nnodely.operators.loader.Loader.getSamples +.. autofunction:: nnodely.operators.loader.Loader.filterData +.. autofunction:: nnodely.operators.loader.Loader.resamplingData +.. autofunction:: nnodely.operators.loader.Loader.loadData + +For more examples of how to use the data loader, please refer to the +:doc:`relative tutorial <../tutorials/examples/dataset>`. \ No newline at end of file diff --git a/docs/_autodoc/export/exporter_module.rst b/docs/_autodoc/export/exporter_module.rst new file mode 100644 index 00000000..5ac63a59 --- /dev/null +++ b/docs/_autodoc/export/exporter_module.rst @@ -0,0 +1,18 @@ +Export module +======================== + +.. automodule:: nnodely.operators.exporter + :undoc-members: + :no-inherited-members: + +.. autofunction:: nnodely.operators.exporter.Exporter.saveTorchModel +.. autofunction:: nnodely.operators.exporter.Exporter.loadTorchModel +.. autofunction:: nnodely.operators.exporter.Exporter.saveModel +.. autofunction:: nnodely.operators.exporter.Exporter.loadModel +.. autofunction:: nnodely.operators.exporter.Exporter.exportPythonModel +.. autofunction:: nnodely.operators.exporter.Exporter.importPythonModel +.. autofunction:: nnodely.operators.exporter.Exporter.exportONNX +.. autofunction:: nnodely.operators.exporter.Exporter.onnxInference +.. autofunction:: nnodely.operators.exporter.Exporter.exportReport + +For more examples of how to use the export module, please refer to the :doc:`export tutorial <../tutorials/examples/export>`. \ No newline at end of file diff --git a/docs/_autodoc/export/index.rst b/docs/_autodoc/export/index.rst new file mode 100644 index 00000000..ebe5da0f --- /dev/null +++ b/docs/_autodoc/export/index.rst @@ -0,0 +1,79 @@ +.. _nnodely-export: + +Model Export +=========================== +Trained models in **nnodely** can be used for inference and exported for integration into external +workflows. See the :doc:`Exporter module ` for the available export and inference APIs. + +Inference can be performed in two main modes: + +- **Single forward pass**, suitable for static predictions or one-step estimation +- **Recursive inference over a temporal horizon**, which enables long-term rollout + of system dynamics and is particularly useful for forecasting and control-oriented + applications + +The recursive mode allows the model to propagate its own predictions over time, +supporting closed-loop simulation and long-horizon analysis. + +The framework provides multiple export modalities to support reproducibility, +portability, and deployment across different platforms. + +.. rubric:: JSON Export + +The complete model can be serialized into a framework-independent **JSON** format. +This representation encodes the hierarchical structure of the model in a +human-readable form, facilitating: + +- Reproducibility +- Version control +- Model inspection +- Long-term storage + +Use :func:`saveModel() ` and +:func:`loadModel() ` to save and load the JSON +model definitions. + +.. rubric:: PyTorch Export + +Models can be translated into optimized **PyTorch** classes using symbolic tracing. This process generates efficient, system-specific implementations that are suitable for: + +- Fine-tuning +- Integration into deep learning pipelines +- High-performance inference + +Relevant API: :func:`saveTorchModel() `, +:func:`loadTorchModel() `, +:func:`exportPythonModel() `, and +:func:`importPythonModel() `. + +.. rubric:: ONNX Export + +Export to the **ONNX** format is also supported, enabling deployment across +heterogeneous hardware platforms and inference engines. This option is particularly +useful for production environments and embedded systems. + +Use :func:`exportONNX() ` to produce an ONNX file +and :func:`onnxInference() ` to run inference +with a previously exported ONNX model. + +.. rubric:: Modular Export Support + +All export modalities are available both for complete models and for individual +sub-models, including recurrent components. This modular approach enables flexible reuse of specific model parts in larger systems. + +.. rubric:: Benefits + +By supporting multiple inference modes and export formats, **nnodely** decouples +model definition from any single execution backend. This design supports both +research workflows and real-world deployment, ensuring that models can be easily +integrated into diverse application environments. + +.. rubric:: Additional tools + +Training and validation reports (PDF) can be generated from results using +:func:`exportReport() `. + +.. toctree:: + :maxdepth: 1 + + exporter_module diff --git a/docs/_autodoc/getting_started/index.rst b/docs/_autodoc/getting_started/index.rst new file mode 100644 index 00000000..534c95bf --- /dev/null +++ b/docs/_autodoc/getting_started/index.rst @@ -0,0 +1,321 @@ +.. _nnodely-getting-started: + +Getting Started +================ + +Installation guide +------------------ +To install *nnodely*, the user can install via: + +.. code-block:: bash + + pip install nnodely + +Alternatively, the user can clone the repository and install from source: + +.. code-block:: bash + + git clone https://github.com/tonegas/nnodely.git + cd nnodely + pip install -r requirements.txt + pip install . + + +Mass-spring-damper Estimator +---------------------------- + +.. image:: https://raw.githubusercontent.com/tonegas/nnodely/main/imgs/massspringdamper.png + :width: 40% + :alt: massspringdamper + +.. sidebar:: Mass-spring-damper example details + + The system to be modeled is defined by the following equation: + + .. math:: + + M \ddot x = - k x - c \dot x + F + + +**Build the neural model** + +Suppose we want to estimate the value of the future position of the mass, given the initial position and the external force. +The MS-NN model is defined by a list of inputs and outputs, and by a list of relationships that link the inputs to the outputs. +In *nnodely*, we can build an estimator in this form: + + +.. code-block:: python + + x = Input('x') + F = Input('F') + x_z_est = Output('x_z_est', Fir(x.tw(0.25)) + Fir(F.last())) + +Input variables can be created using the Input function. +In our system, we have two inputs: the position of the mass, x, and the external force exerted on the mass, F. +The :class:`Output` function is used to define a model's output. +The :class:`Output` function has two inputs: the first is the name (string) of the output, and the second is the structure of the estimator. + +Let us explain some of the functions used: The :class:`tw(...)` function is used to extract a time window from a signal. +In particular, we extract a time window :math:`T_w` of 0.25 second. +The :class:`last()` function is used to get the last force sample applied to the mass, i.e., the force at the current time step. +The :class:`Fir(...)` function builds an FIR (finite impulse response) filter with one learnable parameter on our input variable. +Hence, we are creating an estimator for the variable x at the next time step (i.e., the future position of the mass), +by building an observer with the following mathematical structure: + +.. math:: + + x[1] = \sum_{k=0}^{N_x-1} x[-k]\cdot h_x[(N_x-1)-k] + F[0]\cdot h_F + +where :math:`x[1]` is the next position of the mass, :math:`F[0]` is the last sample of the +force, :math:`N_x` is the number of samples in the time window of the input variable x, :math:`h_x` is +the vector of learnable parameters of the FIR filter on x, and :math:`h_f` is the single learnable +parameter of the FIR filter on F. For the input variable x, we are using a time +window :math:`T_w = 0.25` second, which means that we are using the last :math:`N_x` samples of the +variable x to estimate the next position of the mass. The value of :math:`N_x` is equal +to :math:`T_w/T_s`, where :math:`T_s` is the sampling time used to sample the input variable x. +In a particular case, our MS-NN formulation becomes equivalent to the discrete-time +response (discretized with Forward-Euler) of the mass-spring-damper system. +This happens when we choose the following values: :math:`N_x = 3`, :math:`h_x` equal to the +characteristic polynomial of the system, and :math:`h_f = T_s^2/m`, where :math:`T_s` is the sampling +time and :math:`m` is the mass of the system. +However, our formulation is more general and can better adapt to model mismatches and noise levels +in the measured variables. This improved learning potential can be achieved by using a larger number +of samples :math:`N_x` in the time window of the input variable x. +Let us now train our MS-NN observer using the available data. We perform: + +.. code-block:: python + + mass_spring_damper = Modely() + mass_spring_damper.addModel('x_z_est', x_z_est) + mass_spring_damper.addMinimize('next-pos', Input('x_r').z(-1), x_z_est, 'mse') + mass_spring_damper.neuralizeModel(0.05) + +The first line creates a nnodely object, while the second line adds one output to the model using the :class:`addModel` function. +To train our model, we use the function :class:`addMinimize` to add a loss function to the list of losses. This function uses the following inputs: +The first input is the name of the error ('next-pos' in this case). +The second and third inputs are the variables whose difference we want to minimize. +The fourth input is the loss function to be used, in this case the mean square error ('mse'). +In the function addMinimize, we apply the :class:`z(-1)` method to the variable :math:`x` to get +the next position of the mass, i.e., the value of x at the next time step. The :class:`z(-1)` function +follows the Z-transform notation and is equivalent to a :class:`next()` operator. The +function :class:`z(...)` can be used on an Input variable to obtain a time-shifted value. +Hence, our training objective is to minimize the mean square error between :math:`x_z`, which +represents the next position of the mass, and `x_z_est`, which represents +the output of our estimator: + +.. math:: + + \frac{1}{n} \sum_{i=0}^{n} (x_{z_i} - x_{{z_est}_i})^2 + +where n represents the number of samples in the dataset. + +Finally, the function :class:`neuralizeModel` is used to create a discrete-time MS-NN model. +The input parameter of this function is the sampling time :math:`T_s`, chosen based on +the available data. In this example, :math:`T_s = 0.05` seconds. The training dataset is then +loaded. *nnodely* has access to all the files located in a source folder. + +.. code-block:: python + + data_struct = ['time', ('x','x_r'), 'dx', 'F'] + data_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)),'dataset','data') + mass_spring_damper.loadData(name='mass_spring_dataset', source=data_folder, format=data_struct, delimiter=';') + + +Using the loaded dataset, we now train the neural model: + +.. code-block:: python + + mass_spring_damper.trainModel() + + + +After training the model, we test it using a new dataset. Let us create a simple example: + +.. code-block:: python + + sample = {'F':[0.5], 'x':[0.25, 0.26, 0.27, 0.28, 0.29]} + results = mass_spring_damper(sample) + print(results) + +Note that the input variable x is a list of 5 samples plus one sample, as the sampling time :math:`T_s` is 0.05 seconds and the time window :math:`T_w` of the input variable x is 0.25 second. +For the input variable :math:`F`, we provide only one sample, since we use the last force value. +The resulting output variable is structured as follows: + +.. code-block:: shell + + {'x_z_est':[0.3]} + +where the value represents the output of our estimator, i.e., the next position of the mass. + +.. raw:: html + +

+ + Code + +

+ +-------------------------------------------------------- + + + +Reacher Estimator +----------------- + + +.. image:: https://raw.githubusercontent.com/tonegas/nnodely/docs/update/imgs/Reacher2j.png + :width: 40% + :alt: Reacher + +.. sidebar:: Reacher details + + Here is simple two-joint planar manipulator. The inputs are the joint angles :math:`\theta_1` and :math:`\theta_2`, while the outputs are the end-effector coordinates :math:`(x, y)`. + The link lengths :math:`l_1` and :math:`l_2` are unknown and are estimated from data using *nnodely* as learnable parameters. + + +The kinematic model is given by: + + +.. math:: + x = l_1 \cos(\theta_1) + l_2 \cos(\theta_1 + \theta_2), \quad + y = l_1 \sin(\theta_1) + l_2 \sin(\theta_1 + \theta_2). + + + +**Local Module Path Configuration and Package Import** + + +First, we ensure Python can locate modules in the current working directory, +enabling the import of nnodely components for use in the script. + +.. code-block:: python + + import sys + import os + sys.path.append(os.getcwd()) + from nnodely import * + + + +**Inputs from dataset & Parameters** + + + + + Input variables are created using the :class:`Input` class. The learnable parameters are given within the :class:`Parameter`. The :class:`Output` class defines the model output and takes two arguments: + the name of the output and its structure. + + +.. code-block:: python + + # Inputs from dataset + theta1 = Input('theta1') + theta2 = Input('theta2') + x_tip = Input('x_tip') + y_tip = Input('y_tip') + + l1 = Parameter('l1') #parameters to be estimated + l2 = Parameter('l2') #parameters to be estimated + + x_out = Output('x_out', (l1 * Cos(theta1.last())) + + (l2 * Cos(theta1.last() + theta2.last()))) + y_out = Output('y_out', (l1 * Sin(theta1.last())) + + (l2 * Sin(theta1.last() + theta2.last()))) + +**Model composition** + +:class:`addModel` adds the defined output to the model. +:class:`addMinimize` defines the loss function. This function uses the following inputs: The first input is the name of the error (`x-error` and `y-error` in this case). The second and third inputs are the variables whose +difference we want to minimize. The fourth input is the loss function to be used, in this case the mean square error (`mse`). +:class:`neuralizeModel` builds the discrete-time MS-NN where its input parameter is the sampling time. + + +.. code-block:: python + + # Model composition + model = Modely(seed=0) + model.addModel('x_out', x_out) + model.addModel('y_out', y_out) + model.addMinimize('x-error', x_tip.last(), x_out, 'mse') # Objectives + model.addMinimize('y-error', y_tip.last(), y_out, 'mse') # Objectives + model.neuralizeModel(sample_time=0.02) + +**Data loading** + +*nnodely* requires two pieces of information: the data structure and the dataset location. + +.. code-block:: python + + data_struct = ['step', 'T1','T2','theta1', 'theta2', 'x_tip', 'y_tip', + 'thetadot1', 'thetadot2', 'thetaddot1', 'thetaddot2'] # dataset creation + + data_folder = os.path.join(os.getcwd(), 'dataset', 'data') + + model.loadData(name='reacher_data', source=data_folder, + format=data_struct, delimiter=';') # Data loading + +**Training** + +Trains the model for `200` epochs (batch size `128`, learning rate `0.01`) using a `70/20/10` +train-validation-test split. + + +.. code-block:: python + + # Training + train_params = {'num_of_epochs': 200, 'train_batch_size': 128, 'lr': 0.01} + model.trainModel(splits=[70, 20, 10], training_params=train_params) + +.. raw:: html + +

+ + Code + +

+ +-------------------------------------------------------- + +Applications +------------ + +For additional examples, please refer to the *nnodely* Applications at the link below. + + +.. raw:: html + +

+ + Applications Repository + +

+ + +-------------------------------------------------------- + + +Tutorials +-------------------- + +For the tutorial please refer to the link below. + +.. raw:: html + +

+ + Tutorials + +

+ diff --git a/docs/_autodoc/glossary/index.rst b/docs/_autodoc/glossary/index.rst new file mode 100644 index 00000000..c344a89f --- /dev/null +++ b/docs/_autodoc/glossary/index.rst @@ -0,0 +1,64 @@ +Glossary +======== + +.. rubric:: MS‑NN + +A structured neural network that integrates the learning ability of neural networks with physical, control, and estimation knowledge. + +.. rubric:: Modely + +The nnodely framework used for creating, composing, training, and deploying MS‑NNs. + +.. rubric:: Neural Model + +An MS‑NN architecture defined through inputs, outputs, and building blocks (relations, layers, etc.) that describe how signals and data are transformed within the model. + +.. rubric:: Input / Output / Parameter + +- **Input**: variables entering the model. +- **Output**: signals predicted or calculated by the model. +- **Parameter**: quantities learned during training or fixed constants. + +.. rubric:: Stream + +Internal representation of a signal in the model graph, manipulable through temporal operators (delay, window, shift). + +.. rubric:: Model Composition + +Section dedicated to combining multiple models or sub-models using feedforward connections and closed loops to build complex architectures from modular components. + +.. rubric:: Composer + +Class providing API for static model composition: adding models, connections, closed loops, etc. (e.g., `addModel`, `addConnect`). + +.. rubric:: Connect + +Feedforward link from the output of one model to the input of another (or the same) model. + +.. rubric:: Closed Loop + +Feedback link where the output of a block influences one of its inputs or the input of another block. + +.. rubric:: NeuralizeModel + +Phase in which an MS‑NN definition is transformed into a trainable representation, ready for training, creating time windows, tensors, and internal structures. + +.. rubric:: Training + +Process in which model parameters are optimized based on a loss function and the provided data. + +.. rubric:: Validation + +Phase in which the model’s performance is measured on a dataset not used during training to assess generalization and reliability. + +.. rubric:: Inference + +Process of using a trained model to predict or compute outputs from new inputs. + +.. rubric:: Model Export + +Operations to save and/or convert a trained MS‑NN into standard formats (e.g., PyTorch, ONNX) for deployment or use outside nnodely. + +.. rubric:: Dataset + +Collection of data (training / test / validation) used to train and evaluate the model. \ No newline at end of file diff --git a/docs/_autodoc/inference/index.rst b/docs/_autodoc/inference/index.rst new file mode 100644 index 00000000..a369793e --- /dev/null +++ b/docs/_autodoc/inference/index.rst @@ -0,0 +1,9 @@ +Inference +=========================== + +The framework supports both single forward pass and recursive temporal horizon inference. The latter enables long-term rollout of system dynamics and is particularly suited to forecasting and control applications. + +.. toctree:: + :maxdepth: 1 + + inference_module \ No newline at end of file diff --git a/docs/_autodoc/inference/inference_module.rst b/docs/_autodoc/inference/inference_module.rst new file mode 100644 index 00000000..a11e82c4 --- /dev/null +++ b/docs/_autodoc/inference/inference_module.rst @@ -0,0 +1,8 @@ +Inference module +======================== + +.. currentmodule:: nnodely.operators.composer + +.. automethod:: Composer.__call__ + +For more examples of how to use the export module, please refer to the :doc:`inference tutorial <../tutorials/examples/inference>`. \ No newline at end of file diff --git a/docs/_autodoc/model_composition/composer_module.rst b/docs/_autodoc/model_composition/composer_module.rst new file mode 100644 index 00000000..50aee0ae --- /dev/null +++ b/docs/_autodoc/model_composition/composer_module.rst @@ -0,0 +1,40 @@ +.. _model-level_composition: + +Model-level composition +======================== + +Model-level composition deals with independently defined sub-models and their +interactions. Sub-models are logical groups of output streams that can be added, +removed, connected, or frozen independently. Use the Composer API to manage +these operations and to prepare the whole definition for neuralization. + +Key Composer operators: + +- **addModel / removeModel** : register and unregister named sub-models. + See :meth:`~nnodely.operators.composer.Composer.addModel` and + :meth:`~nnodely.operators.composer.Composer.removeModel`. + +- **addConnect / addClosedLoop / removeConnection** : connect an output stream + of one model to an input of another (or the same) model; create closed-loop + feedback between streams and inputs. See + :meth:`~nnodely.operators.composer.Composer.addConnect`, + :meth:`~nnodely.operators.composer.Composer.addClosedLoop`, and + :meth:`~nnodely.operators.composer.Composer.removeConnection`. + +- **neuralizeModel** : finalize the model definition and translate it into a + trainable neural representation (builds time windows, slices, and internal + tensors required for training/inference). See + :meth:`~nnodely.operators.composer.Composer.neuralizeModel`. + +.. automodule:: nnodely.operators.composer + :undoc-members: + :no-inherited-members: + +.. autofunction:: nnodely.operators.composer.Composer.addModel +.. autofunction:: nnodely.operators.composer.Composer.removeModel +.. autofunction:: nnodely.operators.composer.Composer.addConnect +.. autofunction:: nnodely.operators.composer.Composer.addClosedLoop +.. autofunction:: nnodely.operators.composer.Composer.removeConnection +.. autofunction:: nnodely.operators.composer.Composer.neuralizeModel + +For further examples please refer to the :doc:`relative tutorial <../tutorials/examples/states>`. \ No newline at end of file diff --git a/docs/_autodoc/model_composition/index.rst b/docs/_autodoc/model_composition/index.rst new file mode 100644 index 00000000..b2b59253 --- /dev/null +++ b/docs/_autodoc/model_composition/index.rst @@ -0,0 +1,75 @@ +.. _nnodely-model-composition: + +Model Composition +================================== + +Beyond individual models, **nnodely** supports composition of multiple neural +models and fine-grained wiring of signals. Composition can be performed at two +distinct levels: + +- **Stream-level composition** : local operations on signal streams (time windows, + delays, closed-loop connections). Implemented by the :class:`~nnodely.basic.relation.Stream` + API (methods such as :meth:`~nnodely.basic.relation.Stream.connect`, + :meth:`~nnodely.basic.relation.Stream.closedLoop`, :meth:`~nnodely.basic.relation.Stream.delay`, + :meth:`~nnodely.basic.relation.Stream.s`, :meth:`~nnodely.basic.relation.Stream.sw`, + :meth:`~nnodely.basic.relation.Stream.tw`, :meth:`~nnodely.basic.relation.Stream.z`). + +- **Model-level composition** : composing and connecting independently defined + models. Provided by the :class:`~nnodely.operators.composer.Composer` + interface (methods such as :meth:`~nnodely.operators.composer.Composer.addModel`, + :meth:`~nnodely.operators.composer.Composer.removeModel`, + :meth:`~nnodely.operators.composer.Composer.addConnect`, + :meth:`~nnodely.operators.composer.Composer.addClosedLoop`, + :meth:`~nnodely.operators.composer.Composer.removeConnection`, + and :meth:`~nnodely.operators.composer.Composer.neuralizeModel`). + +- **Dynamically** : connection are created dynamically at execution time, during training, analysis, or inference. + +.. Stream-level example +.. ^^^^^^^^^^^^^^^^^^^^ + +.. .. code-block:: python + +.. from nnodely import Modely, Input, Output +.. from nnodely.modules import Fir + +.. model = Modely() +.. x = Input('x') + +.. fir_layer = Fir(b=True) +.. y = Output('out', fir_layer(x.tw(0.05))) + +.. Model-level example +.. ^^^^^^^^^^^^^^^^^^^^ + +.. .. code-block:: python + +.. from nnodely import Modely, Input, Output +.. from nnodely.modules import Fir + +.. model = Modely() +.. x = Input('x'); y = Input('y') + +.. # model A +.. outA = Output('A_out', Fir(x.last())) +.. model.addModel('subA', [outA]) + +.. # model B +.. outB = Output('B_out', Fir(y.last())) +.. model.addModel('subB', [outB]) + +.. # connect A_out to input y of subB (global composer connect) +.. model.addConnect(outA, y) + +.. # close loop from B_out to input x +.. model.addClosedLoop(outB, x) + +.. # finalize neural model (prepare for training/inference) +.. model.neuralizeModel(sample_time=0.1) + +.. toctree:: + :maxdepth: 1 + + relation_module + composer_module + modely_execution_model \ No newline at end of file diff --git a/docs/_autodoc/model_composition/modely_execution_model.rst b/docs/_autodoc/model_composition/modely_execution_model.rst new file mode 100644 index 00000000..723ec159 --- /dev/null +++ b/docs/_autodoc/model_composition/modely_execution_model.rst @@ -0,0 +1,31 @@ +.. _model_composition_training_inference: + +Composition at Training and Inference Time +============================================ + +In addition to static composition through the Composer API, nnodely also allows model composition to be defined dynamically at execution time, during training, analysis, or inference. + +In this case, connections and closed loops between signals and models can be specified directly as arguments of high-level execution methods such as train, trainAndAnalyze, and inference. + +This approach is useful when: + +- The same base models are reused in different configurations; + +- Feedback and interconnections must change across experiments; + +- Rapid prototyping and testing of architectures is required. + +This dynamic composition can be performed by calling one of the following methods of the Modely class, with appropriate arguments for composition: + +- :meth:`~nnodely.operators.trainer.Trainer.trainModel`; +- :meth:`~nnodely.nnodely.Modely.trainAndAnalyze`; +- :meth:`~nnodely.operators.validator.Validator.analyzeModel`; +- :meth:`~nnodely.operators.composer.Composer.__call__`. + +.. code-block:: python + + msd.trainAndAnalyze(models='PID', closed_loop={'x':'x_n', 'x_m':'x_n'}, connect={'F':'F_PID'}, ...) + + +.. .. automethod:: nnodely.nnodely.Modely.trainAndAnalyze +.. :no-index: diff --git a/docs/_autodoc/model_composition/relation_module.rst b/docs/_autodoc/model_composition/relation_module.rst new file mode 100644 index 00000000..cd310cff --- /dev/null +++ b/docs/_autodoc/model_composition/relation_module.rst @@ -0,0 +1,33 @@ +.. _relation_module: + +Stream-level composition +================================ + +Streams represent signals inside the model graph and are created automatically +when you operate on an :obj:`Input`, :obj:`Parameter`, or :obj:`Constant`. +Use stream methods to manipulate signal timing and routing before they are +bound to outputs or composed into sub-models. + +Common Stream operators: + +- **Closed-loop and connect** : create feedback or feedforward wiring from a + signal to an :class:`Input` using :meth:`~nnodely.basic.relation.Stream.closedLoop` + and :meth:`~nnodely.basic.relation.Stream.connect`. These return a new + :class:`~nnodely.basic.relation.Stream` representing the connected signal. + +- **Delays / z / time windows** : postpone or window a stream with + :meth:`~nnodely.basic.relation.Stream.delay`, :meth:`~nnodely.basic.relation.Stream.z`, + :meth:`~nnodely.basic.relation.Stream.tw` and :meth:`~nnodely.basic.relation.Stream.sw`. + +- **Sampling windows** : select past samples or time intervals with + :meth:`~nnodely.basic.relation.Stream.sw` (sample window) and + :meth:`~nnodely.basic.relation.Stream.tw` (time window). These are the + primitives used by temporal building blocks (FIR, recurrent windows, etc.). + +.. automodule:: nnodely.basic.relation + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.basic.relation.Stream + :members: + :no-inherited-members: diff --git a/docs/_autodoc/model_definition/index.rst b/docs/_autodoc/model_definition/index.rst new file mode 100644 index 00000000..17a62ea5 --- /dev/null +++ b/docs/_autodoc/model_definition/index.rst @@ -0,0 +1,20 @@ +.. _nnodely-modely: + +Model Definition +================ + +In **nnodely**, a neural model is defined using a small set of explicit and +modular components: :ref:`input_module`, :ref:`parameter_module`, **Relations**, and :ref:`output_module`. Together, these elements describe the model structure and data flow, corresponding to the model definition and composition phases (PH1 and PH6). + +This modular decomposition makes architectural choices explicit and supports the systematic construction, extension, and reuse of knowledge-informed models. + +In addition to these core components, **nnodely** provides a library of reusable building blocks for constructing complex MSNN architectures. These include standard activation functions, interpretable arithmetic operations, linear transformations, local/expert models, and more. Each building block is designed to be composable and compatible with the overall model structure. + +.. toctree:: + :maxdepth: 1 + + msnn_ins_out_param/input_module + msnn_ins_out_param/parameter_module + msnn_ins_out_param/initializer_module + msnn_ins_out_param/output_module + layers/index \ No newline at end of file diff --git a/docs/_autodoc/model_definition/layers/activation_module.rst b/docs/_autodoc/model_definition/layers/activation_module.rst new file mode 100644 index 00000000..a38e38ad --- /dev/null +++ b/docs/_autodoc/model_definition/layers/activation_module.rst @@ -0,0 +1,28 @@ +.. _activation_module: + +Activation module +================= + +.. automodule:: nnodely.layers.activation + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.activation.Relu + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.activation.ELU + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.activation.Sigmoid + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.activation.Softmax + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.activation.Identity + :undoc-members: + :no-inherited-members: diff --git a/docs/_autodoc/model_definition/layers/arithmetic_module.rst b/docs/_autodoc/model_definition/layers/arithmetic_module.rst new file mode 100644 index 00000000..1ec95e03 --- /dev/null +++ b/docs/_autodoc/model_definition/layers/arithmetic_module.rst @@ -0,0 +1,32 @@ +.. _arithmetic_module: + +Arithmetic module +================= + +.. automodule:: nnodely.layers.arithmetic + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.arithmetic.Add + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.arithmetic.Sub + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.arithmetic.Mul + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.arithmetic.Div + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.arithmetic.Pow + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.arithmetic.Neg + :undoc-members: + :no-inherited-members: diff --git a/docs/_autodoc/model_definition/layers/equationlearner_module.rst b/docs/_autodoc/model_definition/layers/equationlearner_module.rst new file mode 100644 index 00000000..69060cbc --- /dev/null +++ b/docs/_autodoc/model_definition/layers/equationlearner_module.rst @@ -0,0 +1,14 @@ +.. _equationlearner_module: + +EquationLearner module +================================== + +.. automodule:: nnodely.layers.equationlearner + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.equationlearner.EquationLearner + :undoc-members: + :no-inherited-members: + +For more examples of how to use the equation learner module, please refer to the :doc:`EquationLearner tutorial <../../tutorials/examples/equation_learner>`. \ No newline at end of file diff --git a/docs/_autodoc/model_definition/layers/fir_module.rst b/docs/_autodoc/model_definition/layers/fir_module.rst new file mode 100644 index 00000000..a482ecf1 --- /dev/null +++ b/docs/_autodoc/model_definition/layers/fir_module.rst @@ -0,0 +1,13 @@ +.. _fir_module: + +FIR module +================= +.. automodule:: nnodely.layers.fir + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.fir.Fir + :undoc-members: + :no-inherited-members: + +For more examples of how to use the FIR module, please refer to the :doc:`FIR tutorial <../../tutorials/examples/fir>`. \ No newline at end of file diff --git a/docs/_autodoc/model_definition/layers/fuzzify_module.rst b/docs/_autodoc/model_definition/layers/fuzzify_module.rst new file mode 100644 index 00000000..ba2078e5 --- /dev/null +++ b/docs/_autodoc/model_definition/layers/fuzzify_module.rst @@ -0,0 +1,13 @@ +.. _fuzzify_module: + +Fuzzify module +================= +.. automodule:: nnodely.layers.fuzzify + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.fuzzify.Fuzzify + :undoc-members: + :no-inherited-members: + +For more examples of how to use the fuzzify module, please refer to the :doc:`Fuzzify tutorial <../../tutorials/examples/fuzzify>`. \ No newline at end of file diff --git a/docs/_autodoc/model_definition/layers/index.rst b/docs/_autodoc/model_definition/layers/index.rst new file mode 100644 index 00000000..61baa259 --- /dev/null +++ b/docs/_autodoc/model_definition/layers/index.rst @@ -0,0 +1,28 @@ +.. _nnodely-modules-layers: + +Building blocks +=================================== + +In **nnodely**, model behavior is defined through a collection of modular +**building blocks**. These blocks implement elementary operations and +structured transformations that can be combined to form complex neural models. + +Building blocks operate on inputs, parameters, and intermediate signals, and +represent the computational core of the architecture. + +Each block encapsulates a specific mathematical or functional behavior and can +be reused across different models. + +.. toctree:: + :maxdepth: 2 + + activation_module + arithmetic_module + fir_module + fuzzify_module + equationlearner_module + linear_module + localmodel_module + part_module + trigonometric_module + parametricfunction_module diff --git a/docs/_autodoc/model_definition/layers/linear_module.rst b/docs/_autodoc/model_definition/layers/linear_module.rst new file mode 100644 index 00000000..dc1630c0 --- /dev/null +++ b/docs/_autodoc/model_definition/layers/linear_module.rst @@ -0,0 +1,14 @@ +.. _linear_module: + +Linear module +================= + +.. automodule:: nnodely.layers.linear + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.linear.Linear + :undoc-members: + :no-inherited-members: + +For more examples of how to use the linear module, please refer to the :doc:`Linear tutorial <../../tutorials/examples/linear>`. \ No newline at end of file diff --git a/docs/_autodoc/model_definition/layers/localmodel_module.rst b/docs/_autodoc/model_definition/layers/localmodel_module.rst new file mode 100644 index 00000000..8168d4f6 --- /dev/null +++ b/docs/_autodoc/model_definition/layers/localmodel_module.rst @@ -0,0 +1,14 @@ +.. _localmodel_module: + +Localmodel module +================= + +.. automodule:: nnodely.layers.localmodel + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.localmodel.LocalModel + :undoc-members: + :no-inherited-members: + +For more examples of how to use the local model module, please refer to the :doc:`LocalModel tutorial <../../tutorials/examples/localmodel>`. \ No newline at end of file diff --git a/docs/_autodoc/model_definition/layers/parametricfunction_module.rst b/docs/_autodoc/model_definition/layers/parametricfunction_module.rst new file mode 100644 index 00000000..c518882b --- /dev/null +++ b/docs/_autodoc/model_definition/layers/parametricfunction_module.rst @@ -0,0 +1,14 @@ +.. _parametricfunction_module: + +Parametric Function module +================================== + +.. automodule:: nnodely.layers.parametricfunction + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.parametricfunction.ParamFun + :undoc-members: + :no-inherited-members: + +For more examples of how to use the parametric function module, please refer to the :doc:`Parametric Function tutorial <../../tutorials/examples/parametric_functions>`. \ No newline at end of file diff --git a/docs/_autodoc/model_definition/layers/part_module.rst b/docs/_autodoc/model_definition/layers/part_module.rst new file mode 100644 index 00000000..d8e611e6 --- /dev/null +++ b/docs/_autodoc/model_definition/layers/part_module.rst @@ -0,0 +1,30 @@ +.. _part_module: + +Part module +================= + +.. automodule:: nnodely.layers.part + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.part.Part + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.part.Select + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.part.SamplePart + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.part.SampleSelect + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.part.TimePart + :undoc-members: + :no-inherited-members: + +For more examples and tutorials, see :doc:`Partitioning tutorial <../../tutorials/examples/partitioning>`. \ No newline at end of file diff --git a/docs/_autodoc/model_definition/layers/trigonometric_module.rst b/docs/_autodoc/model_definition/layers/trigonometric_module.rst new file mode 100644 index 00000000..c583acdf --- /dev/null +++ b/docs/_autodoc/model_definition/layers/trigonometric_module.rst @@ -0,0 +1,32 @@ +.. _trigonometric_module: + +Trigonometric module +================================== + +.. automodule:: nnodely.layers.trigonometric + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.trigonometric.Sin + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.trigonometric.Cos + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.trigonometric.Tan + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.trigonometric.Tanh + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.trigonometric.Cosh + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.trigonometric.Sech + :undoc-members: + :no-inherited-members: \ No newline at end of file diff --git a/docs/_autodoc/model_definition/msnn_ins_out_param/initializer_module.rst b/docs/_autodoc/model_definition/msnn_ins_out_param/initializer_module.rst new file mode 100644 index 00000000..dd9a0a38 --- /dev/null +++ b/docs/_autodoc/model_definition/msnn_ins_out_param/initializer_module.rst @@ -0,0 +1,13 @@ +.. _initializer_module: + +Initializer module +================================ + +.. automodule:: nnodely.support.initializer + :undoc-members: + :no-inherited-members: + + .. autofunction:: nnodely.support.initializer.init_constant + .. autofunction:: nnodely.support.initializer.init_negexp + .. autofunction:: nnodely.support.initializer.init_exp + .. autofunction:: nnodely.support.initializer.init_lin diff --git a/docs/_autodoc/model_definition/msnn_ins_out_param/input_module.rst b/docs/_autodoc/model_definition/msnn_ins_out_param/input_module.rst new file mode 100644 index 00000000..56954a2f --- /dev/null +++ b/docs/_autodoc/model_definition/msnn_ins_out_param/input_module.rst @@ -0,0 +1,23 @@ +.. _input_module: + +Input module +================ + +The **Input module** defines the interface between the model and the data. + +Each input: + +- Has a unique name and defined dimensionality +- Can be associated with a temporal context window +- Can access past and/or future samples + +Inputs may originate from external datasets or from internal connections to other model components. This flexibility enables complex signal routing and the construction of composite model architectures. + +.. automodule:: nnodely.layers.input + :no-undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.input.Input + :undoc-members: + :inherited-members: + :exclude-members: count, resetCount diff --git a/docs/_autodoc/model_definition/msnn_ins_out_param/output_module.rst b/docs/_autodoc/model_definition/msnn_ins_out_param/output_module.rst new file mode 100644 index 00000000..2f5db640 --- /dev/null +++ b/docs/_autodoc/model_definition/msnn_ins_out_param/output_module.rst @@ -0,0 +1,19 @@ +.. _output_module: + +Output module +================ + +The **Output module** exposes selected relations as model predictions and define the interface between the internal architecture and user-level quantities of interest. + +Each output is associated with a specific relation and identified by a semantic tag. + +Outputs affect training only when explicitly included in the loss function; otherwise, they serve as access points for inference and analysis. + +.. automodule:: nnodely.layers.output + :no-undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.output.Output + :undoc-members: + :inherited-members: + :exclude-members: count, resetCount, closedLoop, connect, tw, sw, z diff --git a/docs/_autodoc/model_definition/msnn_ins_out_param/parameter_module.rst b/docs/_autodoc/model_definition/msnn_ins_out_param/parameter_module.rst new file mode 100644 index 00000000..ac01447b --- /dev/null +++ b/docs/_autodoc/model_definition/msnn_ins_out_param/parameter_module.rst @@ -0,0 +1,36 @@ +.. _parameter_module: + +Parameter module +================ + +The **Parameter module** represents the learnable quantities of the model and explicitly indicate which parts of the architecture are estimated from data. + +Each parameter: + +- Has a unique name +- Has a defined dimensionality +- Can be associated with temporal windows + +This ensures unambiguous reference, even in large and structured models. + +Parameters can be defined over time to capture dynamic effects. Their initialization is considered an integral part of the modeling process and is handled by the :ref:`initializer_module`. + +In addition to standard random initialization, **nnodely** provides structured initialization methods, such as smooth interpolation or decaying profiles. These methods allow prior physical knowledge to be embedded directly into the learnable representation. + +.. automodule:: nnodely.layers.parameter + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.parameter.Constant + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.parameter.SampleTime + :undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.layers.parameter.Parameter + :undoc-members: + :no-inherited-members: + +For more examples of how to use the parameter module, please refer to the :doc:`relative tutorial <../../tutorials/examples/parameter>`. \ No newline at end of file diff --git a/docs/_autodoc/modely_class.rst b/docs/_autodoc/modely_class.rst new file mode 100644 index 00000000..203fde5e --- /dev/null +++ b/docs/_autodoc/modely_class.rst @@ -0,0 +1,8 @@ +Modely Class +============ + +.. autoclass:: nnodely.nnodely.Modely + :members: + :undoc-members: + :show-inheritance: + :exclude-members: nnodely, resultAnalysis, getWorkspace, clearNames diff --git a/docs/_autodoc/nnodely.rst b/docs/_autodoc/nnodely.rst deleted file mode 100644 index 191d5c9f..00000000 --- a/docs/_autodoc/nnodely.rst +++ /dev/null @@ -1,302 +0,0 @@ -Modely -====== - -.. automodule:: nnodely.nnodely - :members: - :undoc-members: - :show-inheritance: - :exclude-members: resultAnalysis, getWorkspace - -Model structured NN Inputs Outputs and Parameters -================================================= - -Input module ------------- - -.. automodule:: nnodely.layers.input - :no-undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.input.Input - :undoc-members: - :inherited-members: - :exclude-members: count, resetCount - -Output module -------------- - -.. automodule:: nnodely.layers.output - :no-undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.output.Output - :undoc-members: - :inherited-members: - :exclude-members: count, resetCount, closedLoop, connect, tw, sw, z - -Parameter module ----------------- - -.. automodule:: nnodely.layers.parameter - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.parameter.Constant - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.parameter.SampleTime - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.parameter.Parameter - :undoc-members: - :no-inherited-members: - -Initializer module -^^^^^^^^^^^^^^^^^^ - - .. automodule:: nnodely.support.initializer - :undoc-members: - :no-inherited-members: - - .. autofunction:: nnodely.support.initializer.init_constant - .. autofunction:: nnodely.support.initializer.init_negexp - .. autofunction:: nnodely.support.initializer.init_exp - .. autofunction:: nnodely.support.initializer.init_lin - -Relation module ---------------- - -.. automodule:: nnodely.basic.relation - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.basic.relation.Stream - :members: - :no-inherited-members: - -Model structured NN building blocks -=================================== - -Activation module ------------------ - -.. automodule:: nnodely.layers.activation - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.activation.Relu - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.activation.ELU - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.activation.Sigmoid - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.activation.Softmax - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.activation.Identity - :undoc-members: - :no-inherited-members: - -Arithmetic module ------------------ - -.. automodule:: nnodely.layers.arithmetic - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.arithmetic.Add - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.arithmetic.Sub - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.arithmetic.Mul - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.arithmetic.Div - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.arithmetic.Pow - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.arithmetic.Neg - :undoc-members: - :no-inherited-members: - -Fir module ----------- - -.. automodule:: nnodely.layers.fir - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.fir.Fir - :undoc-members: - :no-inherited-members: - - -Fuzzify module --------------- - -.. automodule:: nnodely.layers.fuzzify - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.fuzzify.Fuzzify - :undoc-members: - :no-inherited-members: - -Equation Learner module ------------------------ - -.. automodule:: nnodely.layers.equationlearner - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.equationlearner.EquationLearner - :undoc-members: - :no-inherited-members: - -Linear module -------------- - -.. automodule:: nnodely.layers.linear - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.linear.Linear - :undoc-members: - :no-inherited-members: - - -Localmodel module ------------------ - -.. automodule:: nnodely.layers.localmodel - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.localmodel.LocalModel - :undoc-members: - :no-inherited-members: - -Part module ------------ - -.. automodule:: nnodely.layers.part - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.part.Part - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.part.Select - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.part.SamplePart - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.part.SampleSelect - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.part.TimePart - :undoc-members: - :no-inherited-members: - -Trigonometric module --------------------- - -.. automodule:: nnodely.layers.trigonometric - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.trigonometric.Sin - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.trigonometric.Cos - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.trigonometric.Tan - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.trigonometric.Tanh - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.trigonometric.Cosh - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.trigonometric.Sech - :undoc-members: - :no-inherited-members: - -Parametric Function module --------------------------- - -.. automodule:: nnodely.layers.parametricfunction - :undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.layers.parametricfunction.ParamFun - :undoc-members: - :no-inherited-members: - -Training -======== - -optimizer module ----------------- - -.. automodule:: nnodely.basic.optimizer - :no-undoc-members: - :no-inherited-members: - - .. autoclass:: nnodely.basic.optimizer.SGD - :undoc-members: - :inherited-members: - :exclude-members: add_option_to_params, replace_key_with_params, get_torch_optimizer - - .. autoclass:: nnodely.basic.optimizer.Adam - :undoc-members: - :inherited-members: - :exclude-members: add_option_to_params, replace_key_with_params, get_torch_optimizer - -earlystopping module --------------------- - -.. automodule:: nnodely.support.earlystopping - :undoc-members: - :no-inherited-members: - - .. autofunction:: nnodely.support.earlystopping.early_stop_patience - .. autofunction:: nnodely.support.earlystopping.select_best_model - .. autofunction:: nnodely.support.earlystopping.mean_stopping - .. autofunction:: nnodely.support.earlystopping.standard_early_stopping - -Additional information -====================== - -.. include:: overview.md - :parser: myst \ No newline at end of file diff --git a/docs/_autodoc/overview.md b/docs/_autodoc/overview.md deleted file mode 100644 index 10409186..00000000 --- a/docs/_autodoc/overview.md +++ /dev/null @@ -1,95 +0,0 @@ -# Overview on signal shape -In this section is explained the shape of the input/output of the network. - - -## Input and output shape from the structured neural model -The structured network can be called in two way: -1. The shape of the inputs not sampled are [total time window size, dim] -Sampled inputs are reconstructed as soon as the maximum size of the time window is known. -'dim' represents the size of the input if is not 1 means that the input is a vector. -2. The shape of the sampled inputs are [number of samples = batch, size of time window for a sample, dim] -In the example presented before in the first call the shape for `x` are [1,5,1] for `F` are [1,1,1] -in the second call for `x` are [2,5,1] for `F` are [2,1,1]. In both cases the last dimensions is ignored as the input are scalar. -The output of the structured neural model -The outputs are defined in this way for the different cases: -1. if the shape is [batch, 1, 1] the final two dimensions are collapsed result [batch] -2. if the shape is [batch, window, 1] the last dimension is collapsed result [batch, window] -3. if the shape is [batch, window, dim] the output is equal to [batch, window, dim] -4. if the shape is [batch, 1, dim] the output is equal to [batch, 1, dim] -In the example `x_z_est` has the shape of [1] in the first call and [2] because the the window and the dim were equal to 1. - - -## Shape of elementwise Arithmetic, Activation, Trigonometric -The shape and time windows remain unchanged, for the binary operators shape must be equal. -``` -input shape = [batch, window, dim] -> output shape = [batch, window, dim] -``` - - -## Shape of Fir input/output -The input must be scalar, the fir compress di time dimension (window) that goes to 1. A vector input is not allowed. -The output dimension of the Fir is moved on the last dimension for create a vector output. -``` -input shape = [batch, window, 1] -> output shape = [batch, 1, output dimension of Fir = output_dimension] -``` - - -## Shape of Linear input/output -The window remains unchanged and the output dimension is user defined. -``` -input shape = [batch, window, dimension] -> output shape = [batch, window, output dimension of Linear = output_dimension] -``` - - -## Shape of Fuzzy input/output -The function fuzzify the input and creates a vector for output. -The window remains unchanged, input must be scalar. Vector input are not allowed. -``` -input shape = [batch, window, 1] -> output shape = [batch, window, number of centers of Fuzzy = len(centers)] -``` - - -## Shape of Part and Select input/output -Part selects a slice of the vector input, the input must be a vector. -Select operation the dimension becomes 1, the input must be a vector. -For both operation if there is a time component it remains unchanged. -``` -Part input shape = [batch, window, dimension] -> output shape = [batch, window, selected dimension = [j-i]] -Select input shape = [batch, window, dimension] -> output shape = [batch, window, 1] -``` - - -## Shape of TimePart, SimplePart, SampleSelect input/output -The TimePart selects a time window from the signal (works like timewindow `tw([i,j])` but in this the i,j are absolute). -The SamplePart selects a list of samples from the signal (works like samplewindow `sw([i,j])` but in this the i,j are absolute). -The SampleSelect selects a specific index from the signal (works like zeta operation `z(index)` but in this the index are absolute). -For all the operation the shape remains unchanged. -``` -SamplePart input shape = [batch, window, dimension] -> output shape = [batch, selected sample window = [j-i], dimension] -SampleSelect input shape = [batch, window, dimension] -> output shape = [batch, 1, dimension] -TimePart input shape = [batch, window, dimension] -> output shape = [batch, selected time window = [j-i]/sample_time, dimension] -``` - - -## Shape of LocalModel input/output -The local model has two main inputs, activation functions and inputs. -Activation functions have shape of the fuzzy -``` -input shape = [batch, window, 1] -> output shape = [batch, window, number of centers of Fuzzy = len(centers)] -``` -Inputs go through input function and output function. -The input shape of the input function can be anything as long as the output shape of the input function have the following dimensions -`[batch, window, 1]` so input functions for example cannot be a Fir with output_dimension different from 1. -The input shape of the output function is `[batch, window, 1]` while the shape of the output of the output functions can be any - - -## Shape of Parameters input/output -Parameter shape are defined as follows `[window = sw or tw/sample_time, dim]` the dimensions can be defined as a tuple and are appended to window -When the time dimension is not defined it is configured to 1 - - -## Shape of Parametric Function input/output -The Parametric functions take inputs and parameters as inputs -Parameter dimensions are the same as defined by the parameters if the dimensions are not defined they will be equal to `[window = 1,dim = 1]` -Dimensions of the inputs inside the parametric function are the same as those managed within the Pytorch framework equal to `[batch, window, dim]` -Output dimensions must follow the same convention `[batch, window, dim] \ No newline at end of file diff --git a/docs/_autodoc/training/earlystopping_module.rst b/docs/_autodoc/training/earlystopping_module.rst new file mode 100644 index 00000000..24dd1e39 --- /dev/null +++ b/docs/_autodoc/training/earlystopping_module.rst @@ -0,0 +1,11 @@ +Early Stopping module +======================== + +.. automodule:: nnodely.support.earlystopping + :undoc-members: + :no-inherited-members: + + .. autofunction:: nnodely.support.earlystopping.early_stop_patience + .. autofunction:: nnodely.support.earlystopping.select_best_model + .. autofunction:: nnodely.support.earlystopping.mean_stopping + .. autofunction:: nnodely.support.earlystopping.standard_early_stopping \ No newline at end of file diff --git a/docs/_autodoc/training/index.rst b/docs/_autodoc/training/index.rst new file mode 100644 index 00000000..c52011d6 --- /dev/null +++ b/docs/_autodoc/training/index.rst @@ -0,0 +1,48 @@ +.. _nnodely-training: + +Training +======== + +Training in **nnodely** aims to identify model parameters by minimizing the +discrepancy between predicted and target outputs using user-defined loss +functions and evaluation metrics. + +The training process is managed through the +:doc:`Training module `, which provides utilities such as +:func:`addMinimize `, :func:`removeMinimize `, and :func:`trainModel `. +Training status and diagnostics can be accessed through +:func:`getTrainingInfo `. + +The framework supports standard gradient-based optimization methods and +multi-objective loss functions through optional weighting. Optimization +algorithms are provided by the :doc:`Optimizer module `, +including :class:`SGD ` and :class:`Adam `. + +Regularization mechanisms such as **dropout** and **early stopping** are also +available to improve generalization and prevent overfitting. Early stopping +strategies are implemented in the +:doc:`Early Stopping module ` through functions such as +:func:`early_stop_patience `, :func:`mean_stopping `, +:func:`standard_early_stopping `, and :func:`select_best_model `. + +To handle temporal heterogeneity across samples, training uses a dedicated +batching strategy that preserves temporal coherence within each batch. +This ensures that time-dependent relationships are correctly maintained during +optimization. + +.. .. rubric:: Key Features + +.. - Support for custom loss functions and evaluation metrics via +.. :func:`addMinimize ` and :func:`removeMinimize ` +.. - Gradient-based optimization with :class:`SGD ` and :class:`Adam ` +.. - Regularization through early stopping methods +.. - Temporally coherent batching strategies +.. - Dedicated support for feed-forward and recurrent architectures +.. - Access to training diagnostics through :func:`getTrainingInfo ` + +.. toctree:: + :maxdepth: 1 + + trainer_module + optimizer_module + earlystopping_module diff --git a/docs/_autodoc/training/optimizer_module.rst b/docs/_autodoc/training/optimizer_module.rst new file mode 100644 index 00000000..cce81064 --- /dev/null +++ b/docs/_autodoc/training/optimizer_module.rst @@ -0,0 +1,16 @@ +Optimizer module +======================== + +.. automodule:: nnodely.basic.optimizer + :no-undoc-members: + :no-inherited-members: + + .. autoclass:: nnodely.basic.optimizer.SGD + :undoc-members: + :inherited-members: + :exclude-members: add_option_to_params, replace_key_with_params, get_torch_optimizer + + .. autoclass:: nnodely.basic.optimizer.Adam + :undoc-members: + :inherited-members: + :exclude-members: add_option_to_params, replace_key_with_params, get_torch_optimizer \ No newline at end of file diff --git a/docs/_autodoc/training/trainer_module.rst b/docs/_autodoc/training/trainer_module.rst new file mode 100644 index 00000000..b28779f8 --- /dev/null +++ b/docs/_autodoc/training/trainer_module.rst @@ -0,0 +1,28 @@ +Training module +======================== + +The framework supports two complementary training modalities, depending on the +model architecture. + +.. rubric:: Feed-forward Training + +Follows the conventional mini-batch paradigm. Samples are shuffled and propagated through an acyclic computation graph, and model parameters are updated based on the instantaneous loss using :func:`trainModel ` with optimizers from the :doc:`Optimizer module `. +This modality is suitable for architectures without internal feedback, where +predictions depend only on the provided inputs. + +.. rubric:: Recurrent Training + +Targets architectures with closed-loop dependencies, such as feedback connections or coupled multi-model structures. In this modality, training is performed over a finite prediction horizon. The model is rolled out forward in time, and parameter updates are applied only after completing the full rollout using :func:`trainModel `. + +Early stopping strategies from the :doc:`Early Stopping module ` can be applied to control convergence in long-horizon training. + +.. automodule:: nnodely.operators.trainer + :undoc-members: + :no-inherited-members: + +.. autofunction:: nnodely.operators.trainer.Trainer.addMinimize +.. autofunction:: nnodely.operators.trainer.Trainer.removeMinimize +.. autofunction:: nnodely.operators.trainer.Trainer.getTrainingInfo +.. autofunction:: nnodely.operators.trainer.Trainer.trainModel + +For more example please refer to the :doc:`training tutorial <../tutorials/examples/training>`. \ No newline at end of file diff --git a/examples/data/example.csv b/docs/_autodoc/tutorials/examples/data/example.csv similarity index 100% rename from examples/data/example.csv rename to docs/_autodoc/tutorials/examples/data/example.csv diff --git a/examples/dataset.ipynb b/docs/_autodoc/tutorials/examples/dataset.ipynb similarity index 96% rename from examples/dataset.ipynb rename to docs/_autodoc/tutorials/examples/dataset.ipynb index e39a2df5..12dbb739 100644 --- a/examples/dataset.ipynb +++ b/docs/_autodoc/tutorials/examples/dataset.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Load Data\n", + "# Load Data\n", "\n", "Listed here are all the modalitites by which you can load data inside the nnodely framework.\n", "There are three modalities to load a dataset inside nnodely:\n", diff --git a/examples/dataset/other_data/results_keras.mat b/docs/_autodoc/tutorials/examples/dataset/other_data/results_keras.mat similarity index 100% rename from examples/dataset/other_data/results_keras.mat rename to docs/_autodoc/tutorials/examples/dataset/other_data/results_keras.mat diff --git a/examples/dataset/other_data/vehicle_data.csv b/docs/_autodoc/tutorials/examples/dataset/other_data/vehicle_data.csv similarity index 100% rename from examples/dataset/other_data/vehicle_data.csv rename to docs/_autodoc/tutorials/examples/dataset/other_data/vehicle_data.csv diff --git a/examples/dataset/other_data/weights_keras.json b/docs/_autodoc/tutorials/examples/dataset/other_data/weights_keras.json similarity index 100% rename from examples/dataset/other_data/weights_keras.json rename to docs/_autodoc/tutorials/examples/dataset/other_data/weights_keras.json diff --git a/examples/dataset/training/train_data.csv b/docs/_autodoc/tutorials/examples/dataset/training/train_data.csv similarity index 100% rename from examples/dataset/training/train_data.csv rename to docs/_autodoc/tutorials/examples/dataset/training/train_data.csv diff --git a/examples/dataset/validation/valid_data.csv b/docs/_autodoc/tutorials/examples/dataset/validation/valid_data.csv similarity index 100% rename from examples/dataset/validation/valid_data.csv rename to docs/_autodoc/tutorials/examples/dataset/validation/valid_data.csv diff --git a/examples/equation_learner.ipynb b/docs/_autodoc/tutorials/examples/equation_learner.ipynb similarity index 98% rename from examples/equation_learner.ipynb rename to docs/_autodoc/tutorials/examples/equation_learner.ipynb index 0cf3b647..f2e52ea4 100644 --- a/examples/equation_learner.ipynb +++ b/docs/_autodoc/tutorials/examples/equation_learner.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Equation Learner Layer\n", + "# Equation Learner Layer\n", "\n", "Represents a nnodely implementation of the Task-Parametrized Equation Learner block.\n", "\n", diff --git a/examples/export.ipynb b/docs/_autodoc/tutorials/examples/export.ipynb similarity index 99% rename from examples/export.ipynb rename to docs/_autodoc/tutorials/examples/export.ipynb index 16931d26..6cb1b98e 100644 --- a/examples/export.ipynb +++ b/docs/_autodoc/tutorials/examples/export.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Import/Export Network\n", + "# Import/Export Network\n", "\n", "Listed here are all the modalitites by which you save or export (load or import) the nnodely model.\n", "There are four modalityies:\n", diff --git a/examples/fir.ipynb b/docs/_autodoc/tutorials/examples/fir.ipynb similarity index 94% rename from examples/fir.ipynb rename to docs/_autodoc/tutorials/examples/fir.ipynb index 123628de..b832c001 100644 --- a/examples/fir.ipynb +++ b/docs/_autodoc/tutorials/examples/fir.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Fir Layer\n", + "# Fir Layer\n", "\n", "Represents a Finite Impulse Response (FIR) relation in the neural network model.\n", "\n", diff --git a/examples/fuzzify.ipynb b/docs/_autodoc/tutorials/examples/fuzzify.ipynb similarity index 99% rename from examples/fuzzify.ipynb rename to docs/_autodoc/tutorials/examples/fuzzify.ipynb index 4871faad..0b9fa8e8 100644 --- a/examples/fuzzify.ipynb +++ b/docs/_autodoc/tutorials/examples/fuzzify.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Fuzzify Layer\n", + "# Fuzzify Layer\n", "\n", "Represents a Fuzzyfy relation block in the neural network model.\n", "\n", diff --git a/examples/inference.ipynb b/docs/_autodoc/tutorials/examples/inference.ipynb similarity index 96% rename from examples/inference.ipynb rename to docs/_autodoc/tutorials/examples/inference.ipynb index c80d7d64..0c7395e5 100644 --- a/examples/inference.ipynb +++ b/docs/_autodoc/tutorials/examples/inference.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Inference\n", + "# Inference\n", "\n", "Here are shown all the modalities by which you can make inference with the model." ] diff --git a/examples/interpolation.ipynb b/docs/_autodoc/tutorials/examples/interpolation.ipynb similarity index 97% rename from examples/interpolation.ipynb rename to docs/_autodoc/tutorials/examples/interpolation.ipynb index e50c4f12..7a015245 100644 --- a/examples/interpolation.ipynb +++ b/docs/_autodoc/tutorials/examples/interpolation.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Interpolation Layer\n", + "# Interpolation Layer\n", "\n", "Create an Interpolation relation in the neural network model. \n", "\n", diff --git a/examples/linear.ipynb b/docs/_autodoc/tutorials/examples/linear.ipynb similarity index 91% rename from examples/linear.ipynb rename to docs/_autodoc/tutorials/examples/linear.ipynb index fe1d0608..065ba3e0 100644 --- a/examples/linear.ipynb +++ b/docs/_autodoc/tutorials/examples/linear.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Linear Layer\n", + "# Linear Layer\n", "\n", "Represents a Linear relation in the neural network model.\n", "\n", diff --git a/examples/localmodel.ipynb b/docs/_autodoc/tutorials/examples/localmodel.ipynb similarity index 95% rename from examples/localmodel.ipynb rename to docs/_autodoc/tutorials/examples/localmodel.ipynb index 3d962ca5..b62da3e0 100644 --- a/examples/localmodel.ipynb +++ b/docs/_autodoc/tutorials/examples/localmodel.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Local Model\n", + "# Local Model\n", "\n", "Represents a Local Model relation in the neural network model. \n", "\n", diff --git a/examples/parameter.ipynb b/docs/_autodoc/tutorials/examples/parameter.ipynb similarity index 94% rename from examples/parameter.ipynb rename to docs/_autodoc/tutorials/examples/parameter.ipynb index 456a759b..32f3ed14 100644 --- a/examples/parameter.ipynb +++ b/docs/_autodoc/tutorials/examples/parameter.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Constant and Parameter\n", + "# Constant and Parameter\n", "\n", "Create custom Constant parameters. \n", "\n", diff --git a/examples/parametric_functions.ipynb b/docs/_autodoc/tutorials/examples/parametric_functions.ipynb similarity index 94% rename from examples/parametric_functions.ipynb rename to docs/_autodoc/tutorials/examples/parametric_functions.ipynb index 5691352e..0ba8e250 100644 --- a/examples/parametric_functions.ipynb +++ b/docs/_autodoc/tutorials/examples/parametric_functions.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Parametric Functions\n", + "# Parametric Functions\n", "\n", "Create custom parametric functions inside the neural network model." ] diff --git a/examples/partitioning.ipynb b/docs/_autodoc/tutorials/examples/partitioning.ipynb similarity index 96% rename from examples/partitioning.ipynb rename to docs/_autodoc/tutorials/examples/partitioning.ipynb index b2400492..e3a0708d 100644 --- a/examples/partitioning.ipynb +++ b/docs/_autodoc/tutorials/examples/partitioning.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Partitioning\n", + "# Partitioning\n", "\n", "Here are listed all the relation blocks to slice or select a subvector inside the neural architecture." ] diff --git a/examples/states.ipynb b/docs/_autodoc/tutorials/examples/states.ipynb similarity index 98% rename from examples/states.ipynb rename to docs/_autodoc/tutorials/examples/states.ipynb index c098779b..2087b0ec 100644 --- a/examples/states.ipynb +++ b/docs/_autodoc/tutorials/examples/states.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - State Variables" + "# State Variables" ] }, { @@ -743,7 +756,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv (3.11.4)", + "display_name": "myenv", "language": "python", "name": "python3" }, @@ -757,7 +770,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/training.ipynb b/docs/_autodoc/tutorials/examples/training.ipynb similarity index 99% rename from examples/training.ipynb rename to docs/_autodoc/tutorials/examples/training.ipynb index 63030764..2a29fa7f 100644 --- a/examples/training.ipynb +++ b/docs/_autodoc/tutorials/examples/training.ipynb @@ -1,10 +1,23 @@ { "cells": [ + { + "cell_type": "raw", + "metadata": { + "vscode": { + "languageId": "raw" + } + }, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# NNodely Documentation - Training\n", + "# Training\n", "\n", "Here are listed all the modalities that can be used to perform the training process of a neural network model." ] diff --git a/examples/vehicle_data/vehicle.csv b/docs/_autodoc/tutorials/examples/vehicle_data/vehicle.csv similarity index 100% rename from examples/vehicle_data/vehicle.csv rename to docs/_autodoc/tutorials/examples/vehicle_data/vehicle.csv diff --git a/docs/_autodoc/tutorials/index.rst b/docs/_autodoc/tutorials/index.rst new file mode 100644 index 00000000..d22cb92e --- /dev/null +++ b/docs/_autodoc/tutorials/index.rst @@ -0,0 +1,31 @@ +.. _nnodely-tutorials: + +Tutorials +================ + +This section contains practical examples demonstrating the main features of +**nnodely** through Jupyter notebooks. + +All examples are provided without large datasets. Please refer to the repository +for the complete data files. + +.. rubric:: Available Examples + +.. toctree:: + :maxdepth: 2 + :glob: + + examples/dataset + examples/training + examples/inference + examples/export + examples/fir + examples/fuzzify + examples/linear + examples/localmodel + examples/parameter + examples/parametric_functions + examples/partitioning + examples/states + examples/equation_learner + examples/interpolation diff --git a/docs/_autodoc/validation/index.rst b/docs/_autodoc/validation/index.rst new file mode 100644 index 00000000..01a6cab3 --- /dev/null +++ b/docs/_autodoc/validation/index.rst @@ -0,0 +1,60 @@ +.. _nnodely-validation: + +Validation +========== + +Model validation in **nnodely** follows established practices in mechanical and +control engineering, combining predictive accuracy assessment with explicit +consideration of model complexity. + +Validation is typically performed on a dedicated test set and can be integrated +into the training workflow for continuous performance monitoring using the +:doc:`Validator module ` and the +:func:`analyzeModel ` function. + +.. rubric:: Training Feedback and Reporting + +During training and validation, **nnodely** provides both textual and graphical +feedback through configurable visualization backends. + +These tools enable users to inspect: + +- Convergence behavior +- Loss evolution +- Quality of model fitting + +This feedback facilitates early detection of overfitting, instability, or poor +generalization. + +Validation results can also be exported as a structured PDF report. The report +includes loss trends, learned fuzzy and parametric functions, and additional +diagnostic metrics generated during +:func:`analyzeModel `, supporting +systematic post-analysis and long-term documentation. + +.. rubric:: Domain-Specific Validation Metrics + +In many applications, domain-specific performance indicators are essential for +reliable model assessment. In physical and control systems, validation may +involve: + +- Frequency-domain analysis +- Stability margins +- Control-oriented performance indices + +These analyses can be performed within the validation workflow using +:func:`analyzeModel ` with appropriate +configuration of prediction horizons, closed-loop connections, and batch +parameters. + +These metrics are not commonly used in conventional machine learning but are +critical for evaluating models in engineering contexts. + +By incorporating both general-purpose and domain-specific validation criteria, +**nnodely** ensures that trained models are evaluated according to the +requirements of the target application. + +.. toctree:: + :maxdepth: 1 + + validator_module diff --git a/docs/_autodoc/validation/validator_module.rst b/docs/_autodoc/validation/validator_module.rst new file mode 100644 index 00000000..eb9284bb --- /dev/null +++ b/docs/_autodoc/validation/validator_module.rst @@ -0,0 +1,8 @@ +Validation Module +======================== + +.. automodule:: nnodely.operators.validator + :undoc-members: + :no-inherited-members: + +.. autofunction:: nnodely.operators.validator.Validator.analyzeModel \ No newline at end of file diff --git a/docs/conf.py b/docs/conf.py index aeec8178..1d6e9dda 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -13,6 +13,15 @@ def read_version(): return line.split(delim)[1] raise RuntimeError("Unable to find version string.") +def skip_nnodely(app, what, name, obj, skip, options): + # Skip the alias nnodely + if name == "nnodely": + return True # esclude questo membro dalla documentazione + return skip + +def setup(app): + app.connect("autodoc-skip-member", skip_nnodely) + # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information @@ -30,6 +39,7 @@ def read_version(): 'sphinx.ext.viewcode', 'sphinx.ext.mathjax', 'myst_parser', + 'nbsphinx', ] templates_path = [] @@ -42,6 +52,14 @@ def read_version(): # -- Options for HTML output ------------------------------------------------- html_theme = "sphinx_rtd_theme" + +html_theme_options = { + "collapse_navigation": False, + "navigation_depth": 4, + "titles_only": False, +} + + html_static_path = ['_static'] html_logo = '_static/logo.png' diff --git a/docs/examples_basics/compser_module_ex/addClosedLoop.rst b/docs/examples_basics/compser_module_ex/addClosedLoop.rst new file mode 100644 index 00000000..17ed8fdf --- /dev/null +++ b/docs/examples_basics/compser_module_ex/addClosedLoop.rst @@ -0,0 +1,8 @@ +.. code-block:: python + + model = Modely() + x = Input('x') + y = Input('y') + relation = Fir(x.last()) + model.addClosedLoop(relation, y) + \ No newline at end of file diff --git a/docs/examples_basics/compser_module_ex/addConnect.rst b/docs/examples_basics/compser_module_ex/addConnect.rst new file mode 100644 index 00000000..f1a96826 --- /dev/null +++ b/docs/examples_basics/compser_module_ex/addConnect.rst @@ -0,0 +1,7 @@ +.. code-block:: python + + model = Modely() + x = Input('x') + y = Input('y') + relation = Fir(x.last()) + model.addConnect(relation, y) \ No newline at end of file diff --git a/docs/examples_basics/compser_module_ex/addModel.rst b/docs/examples_basics/compser_module_ex/addModel.rst new file mode 100644 index 00000000..ad9f410f --- /dev/null +++ b/docs/examples_basics/compser_module_ex/addModel.rst @@ -0,0 +1,6 @@ +.. code-block:: python + + model = Modely() + x = Input('x') + out = Output('out', Fir(x.last())) + model.addModel('example_model', [out]) \ No newline at end of file diff --git a/docs/examples_basics/compser_module_ex/neuralizeModel.rst b/docs/examples_basics/compser_module_ex/neuralizeModel.rst new file mode 100644 index 00000000..a0f66cc0 --- /dev/null +++ b/docs/examples_basics/compser_module_ex/neuralizeModel.rst @@ -0,0 +1,4 @@ +.. code-block:: python + + model = Modely(name='example_model') + model.neuralizeModel(sample_time=0.1, clear_model=True) \ No newline at end of file diff --git a/docs/examples_basics/compser_module_ex/removeConnection.rst b/docs/examples_basics/compser_module_ex/removeConnection.rst new file mode 100644 index 00000000..ca03e376 --- /dev/null +++ b/docs/examples_basics/compser_module_ex/removeConnection.rst @@ -0,0 +1,8 @@ +.. code-block:: python + + model = Modely() + x = Input('x') + y = Input('y') + relation = Fir(x.last()) + model.addConnect(relation, y) + model.removeConnection(y) \ No newline at end of file diff --git a/docs/examples_basics/compser_module_ex/removeModel.rst b/docs/examples_basics/compser_module_ex/removeModel.rst new file mode 100644 index 00000000..11e4652a --- /dev/null +++ b/docs/examples_basics/compser_module_ex/removeModel.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + model.removeModel(['sub_model1', 'sub_model2']) \ No newline at end of file diff --git a/docs/examples_basics/data_loader_module_ex/filterData.rst b/docs/examples_basics/data_loader_module_ex/filterData.rst new file mode 100644 index 00000000..556d5f5d --- /dev/null +++ b/docs/examples_basics/data_loader_module_ex/filterData.rst @@ -0,0 +1,9 @@ +.. code-block:: python + + model = Modely() + model.loadData('dataset_name', 'path/to/data') + + def filter_fn(sample): + return sample['input1'] > 0 + + model.filterData(filter_fn, 'dataset_name') diff --git a/docs/examples_basics/data_loader_module_ex/getSamples.rst b/docs/examples_basics/data_loader_module_ex/getSamples.rst new file mode 100644 index 00000000..4158e203 --- /dev/null +++ b/docs/examples_basics/data_loader_module_ex/getSamples.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + model = Modely() + model.loadData('dataset_name') + samples = model.getSamples('dataset_name', index=10, window=5) diff --git a/docs/examples_basics/data_loader_module_ex/loadData.rst b/docs/examples_basics/data_loader_module_ex/loadData.rst new file mode 100644 index 00000000..2bac6655 --- /dev/null +++ b/docs/examples_basics/data_loader_module_ex/loadData.rst @@ -0,0 +1,37 @@ +Load data from files: + +.. code-block:: python + + import numpy as np + from nnodely.basic.model import Modely + from nnodely.layers.input import Input + from nnodely.layers.output import Output + from nnodely.layers.fir import Fir + + x = Input('x') + y = Input('y') + out = Output('out', Fir(x.tw(0.05))) + + test = Modely(visualizer=None) + test.addModel('example_model', out) + test.neuralizeModel(0.01) + + data_struct = ['x', '', 'y'] + test.loadData(name='example_dataset', source='path/to/data', format=data_struct) + + +Load data from a crafted dataset: + +.. code-block:: python + + x = Input('x') + y = Input('y') + out = Output('out', Fir(x.tw(0.05))) + + test = Modely(visualizer=None) + test.addModel('example_model', out) + test.neuralizeModel(0.01) + + data_x = np.array(range(10)) + dataset = {'x': data_x, 'y': (2*data_x)} + test.loadData(name='example_dataset', source=dataset) diff --git a/docs/examples_basics/data_loader_module_ex/resamplingData.rst b/docs/examples_basics/data_loader_module_ex/resamplingData.rst new file mode 100644 index 00000000..df7df82b --- /dev/null +++ b/docs/examples_basics/data_loader_module_ex/resamplingData.rst @@ -0,0 +1,12 @@ +.. code-block:: python + + model = Modely() + import pandas as pd + import numpy as np + + df = pd.DataFrame({ + 'time': np.array(range(60), dtype=np.float32), + 'x': np.array(10*[10] + 20*[20] + 30*[30], dtype=np.float32) + }) + + resampled_df = model.resamplingData(df, scale=1e9) diff --git a/docs/examples_basics/export_module_ex/exportONNX.rst b/docs/examples_basics/export_module_ex/exportONNX.rst new file mode 100644 index 00000000..ba84c3c2 --- /dev/null +++ b/docs/examples_basics/export_module_ex/exportONNX.rst @@ -0,0 +1,9 @@ +.. code-block:: python + + input1 = Input('input1').last() + input2 = Input('input2').last() + out = Output('output1', input1+input2) + + model = Modely() + model.neuralizeModel() + model.exportONNX(inputs_order=['input1', 'input2'], outputs_order=['output1'], name='example_model', model_folder='path/to/export') \ No newline at end of file diff --git a/docs/examples_basics/export_module_ex/exportPythonModel.rst b/docs/examples_basics/export_module_ex/exportPythonModel.rst new file mode 100644 index 00000000..172f2258 --- /dev/null +++ b/docs/examples_basics/export_module_ex/exportPythonModel.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + model = Modely(name='example_model') + model.neuralizeModel() + model.exportPythonModel(name='example_model', model_folder='folder/') \ No newline at end of file diff --git a/docs/examples_basics/export_module_ex/exportReport.rst b/docs/examples_basics/export_module_ex/exportReport.rst new file mode 100644 index 00000000..9e5483ce --- /dev/null +++ b/docs/examples_basics/export_module_ex/exportReport.rst @@ -0,0 +1,6 @@ +.. code-block:: python + + model = Modely() + model.neuralizeModel() + model.trainModel(train_dataset='train_dataset', validation_dataset='val_dataset', num_of_epochs=10) + model.exportReport(name='example_model', model_folder='path/to/export') \ No newline at end of file diff --git a/docs/examples_basics/export_module_ex/importPythonModel.rst b/docs/examples_basics/export_module_ex/importPythonModel.rst new file mode 100644 index 00000000..a85613f4 --- /dev/null +++ b/docs/examples_basics/export_module_ex/importPythonModel.rst @@ -0,0 +1,4 @@ +.. code-block:: python + + model = Modely() + model.importPythonModel(name='example_model', model_folder='path/to/import') \ No newline at end of file diff --git a/docs/examples_basics/export_module_ex/loadModel.rst b/docs/examples_basics/export_module_ex/loadModel.rst new file mode 100644 index 00000000..dfcddf2f --- /dev/null +++ b/docs/examples_basics/export_module_ex/loadModel.rst @@ -0,0 +1,4 @@ +.. code-block:: python + + model = Modely() + model.loadModel(name='example_model', model_folder='path/to/load') \ No newline at end of file diff --git a/docs/examples_basics/export_module_ex/loadTorchModel.rst b/docs/examples_basics/export_module_ex/loadTorchModel.rst new file mode 100644 index 00000000..5f6fff2d --- /dev/null +++ b/docs/examples_basics/export_module_ex/loadTorchModel.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + model = Modely() + model.neuralizeModel() + model.loadTorchModel(name='example_model', model_folder='path/to/load') \ No newline at end of file diff --git a/docs/examples_basics/export_module_ex/onnxInference.rst b/docs/examples_basics/export_module_ex/onnxInference.rst new file mode 100644 index 00000000..3f9b4091 --- /dev/null +++ b/docs/examples_basics/export_module_ex/onnxInference.rst @@ -0,0 +1,36 @@ +.. code-block:: python + + import numpy as np + from nnodely.basic.model import Modely + from nnodely.layers.input import Input + + x = Input('x') + + model_folder = "folder/" + + dummy_input = { + 'x': np.ones(shape=(3, 1, 1)).astype(np.float32) + } + + predictions = Modely().onnxInference(dummy_input, model_folder) + + +Example - Recurrent: + +.. code-block:: python + + import numpy as np + from nnodely.basic.model import Modely + from nnodely.layers.input import Input + + x = Input('x') + y = Input('y') + + model_folder = "folder/" + + dummy_input = { + 'x': np.ones(shape=(3, 1, 1, 1)).astype(np.float32), + 'y': np.ones(shape=(1, 1, 1)).astype(np.float32) + } + + predictions = Modely().onnxInference(dummy_input, model_folder) \ No newline at end of file diff --git a/docs/examples_basics/export_module_ex/saveModel.rst b/docs/examples_basics/export_module_ex/saveModel.rst new file mode 100644 index 00000000..bde2f989 --- /dev/null +++ b/docs/examples_basics/export_module_ex/saveModel.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + model = Modely() + model.neuralizeModel() + model.saveModel(name='example_model', model_folder='folder/') \ No newline at end of file diff --git a/docs/examples_basics/export_module_ex/saveTorchModel.rst b/docs/examples_basics/export_module_ex/saveTorchModel.rst new file mode 100644 index 00000000..9f2017ef --- /dev/null +++ b/docs/examples_basics/export_module_ex/saveTorchModel.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + model = Modely() + model.neuralizeModel() + model.saveTorchModel(name='example_model', model_folder='path/to/save') \ No newline at end of file diff --git a/docs/examples_basics/inference_module_ex/inference.rst b/docs/examples_basics/inference_module_ex/inference.rst new file mode 100644 index 00000000..c7cc4a89 --- /dev/null +++ b/docs/examples_basics/inference_module_ex/inference.rst @@ -0,0 +1,8 @@ +.. code-block:: python + + model = Modely() + x = Input('x') + out = Output('out', Fir(x.last())) + model.addModel('example_model', [out]) + model.neuralizeModel() + predictions = model(inputs={'x': [1, 2, 3]}) \ No newline at end of file diff --git a/docs/examples_basics/input_module_ex/sw.rst b/docs/examples_basics/input_module_ex/sw.rst new file mode 100644 index 00000000..4c5c519e --- /dev/null +++ b/docs/examples_basics/input_module_ex/sw.rst @@ -0,0 +1,19 @@ +Select a sample window considering a signal:: + + T = [-3,-2,-1,0,1,2] + +where time 0 is the last passed instant. + +.. code-block:: python + + T.sw(2) # [-1, 0] + T.sw([-2,0]) # [-1, 0] + T.sw([0,1]) # [1] + T.sw([-4,-2]) # [-3,-2] + +With offset: + +.. code-block:: python + + T.sw(2, offset=-2) # [0, 1] + T.sw([-2,2], offset=-1) diff --git a/docs/examples_basics/input_module_ex/z.rst b/docs/examples_basics/input_module_ex/z.rst new file mode 100644 index 00000000..a3509cda --- /dev/null +++ b/docs/examples_basics/input_module_ex/z.rst @@ -0,0 +1,11 @@ +Select the unitary delay considering a signal:: + + T = [-3, -2, -1, 0, 1, 2] + +where the time vector 0 represents the last passed instant. + +.. code-block:: python + + T.z(-1) # = 1 + T.z(0) # = 0 # the last passed instant + T.z(2) # = -2 \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/activation_module_ex/elu.rst b/docs/examples_basics/layer_module_ex/activation_module_ex/elu.rst new file mode 100644 index 00000000..6ee67c07 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/activation_module_ex/elu.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + x = ELU(x) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/activation_module_ex/identity.rst b/docs/examples_basics/layer_module_ex/activation_module_ex/identity.rst new file mode 100644 index 00000000..f384ed96 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/activation_module_ex/identity.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + x = Identity(x) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/activation_module_ex/relu.rst b/docs/examples_basics/layer_module_ex/activation_module_ex/relu.rst new file mode 100644 index 00000000..7c4d311f --- /dev/null +++ b/docs/examples_basics/layer_module_ex/activation_module_ex/relu.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + x = Relu(x) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/activation_module_ex/sigmoid.rst b/docs/examples_basics/layer_module_ex/activation_module_ex/sigmoid.rst new file mode 100644 index 00000000..6810a2fc --- /dev/null +++ b/docs/examples_basics/layer_module_ex/activation_module_ex/sigmoid.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + x = Sigmoid(x) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/activation_module_ex/softmax.rst b/docs/examples_basics/layer_module_ex/activation_module_ex/softmax.rst new file mode 100644 index 00000000..1126dd3a --- /dev/null +++ b/docs/examples_basics/layer_module_ex/activation_module_ex/softmax.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + x = Softmax(x) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/arithmetic_module_ex/add.rst b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/add.rst new file mode 100644 index 00000000..227b1ec5 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/add.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + add = Add(relation1, relation2) + # or + add = relation1 + relation2 \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/arithmetic_module_ex/div.rst b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/div.rst new file mode 100644 index 00000000..d3a20424 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/div.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + div = Div(relation1, relation2) + # or + div = relation1 / relation2 \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/arithmetic_module_ex/mul.rst b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/mul.rst new file mode 100644 index 00000000..547b9164 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/mul.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + mul = Mul(relation1, relation2) + # or + mul = relation1 * relation2 \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/arithmetic_module_ex/neg.rst b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/neg.rst new file mode 100644 index 00000000..68ce98a9 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/neg.rst @@ -0,0 +1,3 @@ +.. code-block :: python + + x = Neg(x) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/arithmetic_module_ex/pow.rst b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/pow.rst new file mode 100644 index 00000000..b7ae9085 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/pow.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + pow = Pow(relation, exp) + # or + pow = relation1 ** relation2 \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/arithmetic_module_ex/sub.rst b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/sub.rst new file mode 100644 index 00000000..e10dcfad --- /dev/null +++ b/docs/examples_basics/layer_module_ex/arithmetic_module_ex/sub.rst @@ -0,0 +1,5 @@ +.. code-block:: python + + sub = Sub(relation1, relation2) + # or + sub = relation1 - relation2 \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/eql.rst b/docs/examples_basics/layer_module_ex/eql.rst new file mode 100644 index 00000000..5a6a9ef4 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/eql.rst @@ -0,0 +1,45 @@ +Basic usage: + +.. code-block:: python + + x = Input('x') + + equation_learner = EquationLearner(functions=[Tan, Sin, Cos]) + out = Output('out', equation_learner(x.last())) + + +Passing a linear layer: + +.. code-block:: python + + x = Input('x') + + linear_layer = Linear( + output_dimension=3, + W_init=init_constant, + W_init_params={'value': 0} + ) + + equation_learner = EquationLearner( + functions=[Tan, Sin, Cos], + linear_in=linear_layer + ) + + out = Output('out', equation_learner(x.last())) + + +Passing a custom parametric function and multiple inputs: + +.. code-block:: python + + x = Input('x') + F = Input('F') + + def myFun(K1, p1): + return K1 * p1 + + K = Parameter('k', dimensions=1, sw=1, values=[[2.0]]) + parfun = ParamFun(myFun, parameters=[K]) + + equation_learner = EquationLearner([parfun]) + out = Output('out', equation_learner((x.last(), F.last()))) diff --git a/docs/examples_basics/layer_module_ex/fir.rst b/docs/examples_basics/layer_module_ex/fir.rst new file mode 100644 index 00000000..aab246cc --- /dev/null +++ b/docs/examples_basics/layer_module_ex/fir.rst @@ -0,0 +1,23 @@ +Basic usage: + +.. code-block:: python + + input = Input('in') + relation = Fir(input.tw(0.05)) + +Passing a parameter: + +.. code-block:: python + + input = Input('in') + par = Parameter('par', dimensions=3, sw=2, init='init_constant') + relation = Fir(W=par)(input.sw(2)) + +Parameters initialization: + +.. code-block:: python + + x = Input('x') + F = Input('F') + fir_x = Fir(W_init='init_negexp')(x.tw(0.2)) + fir_F = Fir(W_init='init_constant', W_init_params={'value':1})(F.last()) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/fuzzy.rst b/docs/examples_basics/layer_module_ex/fuzzy.rst new file mode 100644 index 00000000..3291af93 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/fuzzy.rst @@ -0,0 +1,27 @@ +Basic usage: + +.. code-block:: python + + x = Input('x') + fuz = Fuzzify(output_dimension=5, range=[1,5]) + out = Output('out', fuz(x.last())) + + +Passing the centers: + +.. code-block:: python + + fuz = Fuzzify(centers=[-1,0,3,5], functions='Rectangular') + out = Output('out', fuz(x.last())) + + +Using a custom function: + +.. code-block:: python + + def fun(x): + import torch + return torch.tanh(x) + + fuz = Fuzzify(output_dimension=11, range=[-5,5], functions=fun) + out = Output('out', fuz(x.last())) diff --git a/docs/examples_basics/layer_module_ex/linear.rst b/docs/examples_basics/layer_module_ex/linear.rst new file mode 100644 index 00000000..8cb8119b --- /dev/null +++ b/docs/examples_basics/layer_module_ex/linear.rst @@ -0,0 +1,32 @@ +Basic usage: + +.. code-block:: python + + input = Input('in').tw(0.05) + relation = Linear(input) + + +Passing a weight and bias parameter: + +.. code-block:: python + + input = Input('in').last() + + weight = Parameter('W', values=[[[1]]]) + bias = Parameter('b', values=[[1]]) + + relation = Linear(W=weight, b=bias)(input) + + +Parameters initialization: + +.. code-block:: python + + input = Input('in').last() + + relation = Linear( + b=True, + W_init=init_negexp, + b_init=init_constant, + b_init_params={'value': 1} + )(input) diff --git a/docs/examples_basics/layer_module_ex/localmodel.rst b/docs/examples_basics/layer_module_ex/localmodel.rst new file mode 100644 index 00000000..ad0ae0d8 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/localmodel.rst @@ -0,0 +1,43 @@ +Basic usage: + +.. code-block:: python + + x = Input('x') + activation = Fuzzify(2, [0, 1], functions='Triangular')(x.last()) + loc = LocalModel(input_function=Fir()) + out = Output('out', loc(x.tw(1), activation)) + + +Passing a custom function: + +.. code-block:: python + + def myFun(in1, p1, p2): + return p1 * in1 + p2 + + x = Input('x') + activation = Fuzzify(2, [0, 1], functions='Triangular')(x.last()) + loc = LocalModel( + input_function=lambda: ParamFun(myFun), + output_function=lambda: Fir + )(x.last(), activation) + out = Output('out', loc) + + +Custom function with multiple activations: + +.. code-block:: python + + def myFun(in1, p1, p2): + return p1 * in1 + p2 + + x = Input('x') + F = Input('F') + activationA = Fuzzify(2, [0, 1], functions='Triangular')(x.tw(1)) + activationB = Fuzzify(2, [0, 1], functions='Triangular')(F.tw(1)) + + loc = LocalModel( + input_function=lambda: ParamFun(myFun), + output_function=Fir(3) + )(x.tw(1), (activationA, activationB)) + out = Output('out', loc) diff --git a/docs/examples_basics/layer_module_ex/paramfun.rst b/docs/examples_basics/layer_module_ex/paramfun.rst new file mode 100644 index 00000000..42fc7564 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/paramfun.rst @@ -0,0 +1,14 @@ +.. code-block:: python + + input1 = Input('input1') + input2 = Input('input2') + + def my_function(x, y, param1, const1): + return param1 * x + const1 * y + + param_fun = ParamFun( + my_function, + constants={'const1': 1.0}, + parameters_dimensions={'param1': 1} + ) + result = param_fun(input1, input2) diff --git a/docs/examples_basics/layer_module_ex/part_module/part.rst b/docs/examples_basics/layer_module_ex/part_module/part.rst new file mode 100644 index 00000000..fb05be16 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/part_module/part.rst @@ -0,0 +1,4 @@ +.. code-block:: python + + x = Input('x', dimensions=3).last() + relation = Part(x, 0, 1) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/part_module/sample_part.rst b/docs/examples_basics/layer_module_ex/part_module/sample_part.rst new file mode 100644 index 00000000..496eeeae --- /dev/null +++ b/docs/examples_basics/layer_module_ex/part_module/sample_part.rst @@ -0,0 +1,4 @@ +.. code-block:: python + + x = Input('x').sw(3) + relation = SamplePart(x, 0, 1) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/part_module/sample_select.rst b/docs/examples_basics/layer_module_ex/part_module/sample_select.rst new file mode 100644 index 00000000..a531ba1b --- /dev/null +++ b/docs/examples_basics/layer_module_ex/part_module/sample_select.rst @@ -0,0 +1,4 @@ +.. code-block:: python + + x = Input('x').sw(3) + relation = SampleSelect(x, 1) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/part_module/select.rst b/docs/examples_basics/layer_module_ex/part_module/select.rst new file mode 100644 index 00000000..13cf44f4 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/part_module/select.rst @@ -0,0 +1,4 @@ +.. code-block:: python + + x = Input('x', dimensions=3).last() + relation = Select(x, 1) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/part_module/time_part.rst b/docs/examples_basics/layer_module_ex/part_module/time_part.rst new file mode 100644 index 00000000..87a3297b --- /dev/null +++ b/docs/examples_basics/layer_module_ex/part_module/time_part.rst @@ -0,0 +1,4 @@ +.. code-block:: python + + x = Input('x').sw(10) + time_part = TimePart(x, i=0, j=5) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/trig_module_ex/cos.rst b/docs/examples_basics/layer_module_ex/trig_module_ex/cos.rst new file mode 100644 index 00000000..ba879cb6 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/trig_module_ex/cos.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + cos = Cos(relation) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/trig_module_ex/cosh.rst b/docs/examples_basics/layer_module_ex/trig_module_ex/cosh.rst new file mode 100644 index 00000000..1f1852d4 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/trig_module_ex/cosh.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + cosh = Cosh(relation) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/trig_module_ex/sech.rst b/docs/examples_basics/layer_module_ex/trig_module_ex/sech.rst new file mode 100644 index 00000000..c3445322 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/trig_module_ex/sech.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + sech = Sech(relation) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/trig_module_ex/sin.rst b/docs/examples_basics/layer_module_ex/trig_module_ex/sin.rst new file mode 100644 index 00000000..1ee6cb4e --- /dev/null +++ b/docs/examples_basics/layer_module_ex/trig_module_ex/sin.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + sin = Sin(relation) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/trig_module_ex/tan.rst b/docs/examples_basics/layer_module_ex/trig_module_ex/tan.rst new file mode 100644 index 00000000..420ff4c1 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/trig_module_ex/tan.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + tan = Tan(relation) \ No newline at end of file diff --git a/docs/examples_basics/layer_module_ex/trig_module_ex/tanh.rst b/docs/examples_basics/layer_module_ex/trig_module_ex/tanh.rst new file mode 100644 index 00000000..bc3285c6 --- /dev/null +++ b/docs/examples_basics/layer_module_ex/trig_module_ex/tanh.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + tanh = Tanh(relation) \ No newline at end of file diff --git a/docs/examples_basics/parameter_module_ex/constant.rst b/docs/examples_basics/parameter_module_ex/constant.rst new file mode 100644 index 00000000..2ce6fe85 --- /dev/null +++ b/docs/examples_basics/parameter_module_ex/constant.rst @@ -0,0 +1,38 @@ +Passing a custom scalar value -> g.dim = {'dim': 1}: + +.. code-block:: python + + g = Constant('gravity', values=9.81) + +Passing a custom vector value -> n.dim = {'dim': 4}: + +.. code-block:: python + + n = Constant('numbers', values=[1, 2, 3, 4]) + +Passing a custom vector value with single sample window -> n.dim = {'dim': 4, 'sw': 1}: + +.. code-block:: python + + n = Constant('numbers', values=[[1, 2, 3, 4]]) + +Passing a custom vector value with double sample window -> n.dim = {'dim': 4, 'sw': 2}: + +.. code-block:: python + + n = Constant('numbers', values=[[2, 3, 4], [1, 2, 3]]) + +Passing a custom vector value with double sample window -> n.dim = {'dim': 4, 'sw': 2}. +If the value of the sw is different from the dimension of shape[0] an error will be raised. + +.. code-block:: python + + n = Constant('numbers', sw=2, values=[[2, 3, 4], [1, 2, 3]]) + +Passing a custom vector value with time window -> n.dim = {'dim': 4, 'tw': 4}. +In this case the sampling time must be 0.5; otherwise, an error will be raised. If the Constant has a time dimension, +the input must have len(shape) == 2. + +.. code-block:: python + + n = Constant('numbers', tw=4, values=[[2, 3, 4], [1, 2, 3]]) diff --git a/docs/examples_basics/parameter_module_ex/parameter.rst b/docs/examples_basics/parameter_module_ex/parameter.rst new file mode 100644 index 00000000..0297ed12 --- /dev/null +++ b/docs/examples_basics/parameter_module_ex/parameter.rst @@ -0,0 +1,21 @@ +Basic usage: + +.. code-block:: python + + k = Parameter('k', dimensions=3, tw=4) + +Initialize a parameter with values: + +.. code-block:: python + + x = Input('x') + gravity = Parameter('g', dimensions=(4,1), values=[[[1],[2],[3],[4]]]) + out = Output('out', Linear(W=gravity)(x.sw(3))) + +Initialize a parameter with a function: + +.. code-block:: python + + x = Input('x').last() + p = Parameter('param', dimensions=1, sw=1, init=init_constant, init_params={'value':1}) + relation = Fir(parameter=p)(x) diff --git a/docs/examples_basics/parameter_module_ex/sample_time.rst b/docs/examples_basics/parameter_module_ex/sample_time.rst new file mode 100644 index 00000000..994f1290 --- /dev/null +++ b/docs/examples_basics/parameter_module_ex/sample_time.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + dt = SampleTime() \ No newline at end of file diff --git a/docs/examples_basics/trainer_module_ex/addMinimize.rst b/docs/examples_basics/trainer_module_ex/addMinimize.rst new file mode 100644 index 00000000..a61c02a6 --- /dev/null +++ b/docs/examples_basics/trainer_module_ex/addMinimize.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + model.addMinimize('minimize_op', streamA, streamB, loss_function='mse') \ No newline at end of file diff --git a/docs/examples_basics/trainer_module_ex/removeMinimize.rst b/docs/examples_basics/trainer_module_ex/removeMinimize.rst new file mode 100644 index 00000000..7999e263 --- /dev/null +++ b/docs/examples_basics/trainer_module_ex/removeMinimize.rst @@ -0,0 +1,3 @@ +.. code-block:: python + + model.removeMinimize(['minimize_op1', 'minimize_op2']) \ No newline at end of file diff --git a/docs/examples_basics/trainer_module_ex/trainModel.rst b/docs/examples_basics/trainer_module_ex/trainModel.rst new file mode 100644 index 00000000..a835dea3 --- /dev/null +++ b/docs/examples_basics/trainer_module_ex/trainModel.rst @@ -0,0 +1,39 @@ +.. code-block:: python + + x = Input('x') + F = Input('F') + + xk1 = Output('x[k+1]', Fir()(x.tw(0.2)) + Fir()(F.last())) + + mass_spring_damper = Modely(seed=0) + mass_spring_damper.addModel('xk1', xk1) + mass_spring_damper.neuralizeModel(sample_time=0.05) + + data_struct = ['time', 'x', 'dx', 'F'] + data_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'dataset', 'data') + mass_spring_damper.loadData(name='mass_spring_dataset', source=data_folder, format=data_struct, delimiter=';') + + params = {'num_of_epochs': 100, 'train_batch_size': 128, 'lr': 0.001} + mass_spring_damper.trainModel(splits=[70, 20, 10], training_params=params) + + +Example - recurrent training: + +.. code-block:: python + + x = Input('x') + F = Input('F') + + xk1 = Output('x[k+1]', Fir()(x.tw(0.2)) + Fir()(F.last())) + + mass_spring_damper = Modely(seed=0) + mass_spring_damper.addModel('xk1', xk1) + mass_spring_damper.addClosedLoop(xk1, x) + mass_spring_damper.neuralizeModel(sample_time=0.05) + + data_struct = ['time', 'x', 'dx', 'F'] + data_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'dataset', 'data') + mass_spring_damper.loadData(name='mass_spring_dataset', source=data_folder, format=data_struct, delimiter=';') + + params = {'num_of_epochs': 100, 'train_batch_size': 128, 'lr': 0.001} + mass_spring_damper.trainModel(splits=[70, 20, 10], prediction_samples=10, training_params=params) \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index ae1f4c61..d0b78a50 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -6,15 +6,97 @@ Welcome to nnodely's documentation! ==================================== +.. image:: https://raw.githubusercontent.com/tonegas/nnodely/main/imgs/logo_white_info.png + :target: https://github.com/tonegas/nnodely + :alt: Open + + +*nnodely* is a framework designed to facilitate the creation and deployment of **Model-Structured Neural Networks** (**MSNNs**). +Modeling, control, and estimation of physical systems impose constraints that differ fundamentally from typical +deep-learning tasks (e.g., images or text). In engineering applications, models often need to respect +known physical laws or constraints, operate in real time, remain interpretable, and generalize reliably +even when only limited experimental data are available. MS-NNs combine the learning capabilities of neural +networks with structural priors grounded in physics, control and estimation theory, enabling: + + +- **Data Efficiency**: By embedding structural priors, MS-NNs can learn effectively from limited data, reducing the need for extensive datasets. +- **Generalization**: The incorporation of domain knowledge allows MS-NNs to generalize better to unseen scenarios. +- **Interpretability**: The structured nature of MS-NNs enhances interpretability, allowing practitioners to understand and trust the model's predictions. +- **Real-time**: MS-NNs can be designed for real-time applications, making them suitable for control and estimation tasks in dynamic environments. + +The main objective of the *nnodely* framework is to allow fast prototyping of MS-NNs for modeling, estimation and control of physical systems by embedding structural priors knowledge into the networks' architecture. + +In this documentation you will find a comprehensive guide for getting started with *nnodely*, illustrating the main blocks that constitute the framework. + + + +.. raw:: html + +

+ + Repository + + + Getting Started + + + Applications + +

+ +Overview +----------------------- + +.. This needs to be revised in order to explain at high level the phases of the workflow. +.. - :ref:`Modely `: Main entry point of nnodely. It manages the composition of the MS-NNs, the connection between structural blocks and the training of the networks. +.. - :ref:`Model structured NN Inputs Outputs and Parameters `: Description of the Input, Output and Parameter modules that can be used to build MS-NNs. +.. - :ref:`Model structured NN building blocks `: Overview of the different structural layers available in nnodely to build MS-NNs. +.. - :ref:`Training `: Explanation of the training procedures implemented in nnodely to train MS-NNs. + + + + +.. sidebar:: Overview + + Overview of the *nnodely* development pipeline. It spans model design (:ref:`PH1 `), dataset construction aligned with the network architecture (:ref:`PH2 `), training (:ref:`PH3 `), domain-specific validation (:ref:`PH4 `), model export (:ref:`PH5 `), and composition of complex models (:ref:`PH6 `). Ellipses indicate the pipeline phases, while rectangles denote the artifacts produced at each phase. + + +.. image:: https://raw.githubusercontent.com/tonegas/nnodely/docs/update/imgs/framework_p.png + :width: 50% + :alt: Framework + + + + +Table of Contents +========================== .. toctree:: :maxdepth: 2 - _autodoc/nnodely - + _autodoc/getting_started/index + _autodoc/modely_class + _autodoc/model_definition/index + _autodoc/dataset_creation/index + _autodoc/model_composition/index + _autodoc/training/index + _autodoc/validation/index + _autodoc/inference/index + _autodoc/export/index + _autodoc/tutorials/index Indices and tables ================== * :ref:`genindex` * :ref:`modindex` -* :ref:`search` \ No newline at end of file +* :ref:`search` + diff --git a/docs/requirements.txt b/docs/requirements.txt index d8166e28..8e66700e 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,2 +1,6 @@ sphinx-rtd-theme -myst_parser \ No newline at end of file +myst_parser +nbsphinx +pandoc +jupyter +jupyterlab \ No newline at end of file diff --git a/imgs/Reacher2j.png b/imgs/Reacher2j.png new file mode 100644 index 00000000..b39185c3 Binary files /dev/null and b/imgs/Reacher2j.png differ diff --git a/imgs/framework_p.png b/imgs/framework_p.png new file mode 100644 index 00000000..d95789e7 Binary files /dev/null and b/imgs/framework_p.png differ diff --git a/imgs/phases.png b/imgs/phases.png new file mode 100644 index 00000000..d95789e7 Binary files /dev/null and b/imgs/phases.png differ diff --git a/nnodely/__init__.py b/nnodely/__init__.py index 8db2bfb0..3c4f5aa4 100644 --- a/nnodely/__init__.py +++ b/nnodely/__init__.py @@ -20,6 +20,7 @@ from nnodely.layers.timeoperation import Integrate, Differentiate from nnodely.layers.interpolation import Interpolation from nnodely.layers.rungekutta import ForwardEuler, RK2, RK4 +from nnodely.layers.neuralODE import NeuralODE # Main nnodely classes from nnodely.nnodely import nnodely, Modely, clearNames @@ -36,7 +37,7 @@ major, minor = sys.version_info.major, sys.version_info.minor logger.LOG_LEVEL = logging.INFO -__version__ = '1.5.2' +__version__ = '1.5.3' if major < 3: sys.exit("Sorry, Python 2 is not supported. You need Python >= 3.10 for "+__package__+".") @@ -56,6 +57,7 @@ 'Relu', 'ELU', 'Softmax', 'Sigmoid', 'Identity', 'Fir', 'Linear', + 'NeuralODE', 'Add', 'Sum', 'Sub', 'Mul', 'Div', 'Pow', 'Neg', 'Sign', 'Sin', 'Cos', 'Tan', 'Cosh', 'Tanh', 'Sech', 'ParamFun', diff --git a/nnodely/basic/relation.py b/nnodely/basic/relation.py index 4b0fd79f..b99a7d50 100644 --- a/nnodely/basic/relation.py +++ b/nnodely/basic/relation.py @@ -41,7 +41,7 @@ class NeuObj(): names = [] @classmethod @enforce_types - def clearNames(self, names:str|list|None=None): + def clearNames(cls, names:str|list|None=None): if names is None: NeuObj.count = 0 NeuObj.names = [] @@ -118,7 +118,7 @@ class Stream(Relation): """ count = 0 @classmethod - def resetCount(self): + def resetCount(cls): Stream.count = 0 def __init__(self, name, json, dim, count = 1): diff --git a/nnodely/exporter/export.py b/nnodely/exporter/export.py index 8f96a37c..fbfa1ce2 100644 --- a/nnodely/exporter/export.py +++ b/nnodely/exporter/export.py @@ -107,6 +107,16 @@ def export_python_model(model_def, model, model_path): file.write("\n") saved_functions.append(function_name) + elif 'NeuralODE' in name: + function_name = model_def['Functions'][name]['name'] + if function_name not in saved_functions: + code = model_def['Functions'][name]['code'] + code = code.replace(f'def {function_name}', f'def {package_name}_layers_neuralODE_{function_name}') + file.write(code) + file.write("\n") + saved_functions.append(function_name) + + file.write("class TracerModel(torch.nn.Module):\n") file.write(" def __init__(self):\n") file.write(" super().__init__()\n") @@ -184,8 +194,8 @@ def export_python_model(model_def, model, model_path): file.write(list_inputs) file.write(" self.states = dict()\n") file.write("\n") - file.write(" def forward(self, kwargs):\n") - file.write(" n_samples = min([kwargs[key].size(0) for key in self.inputs])\n") + file.write(" def forward(self, kwargs, n_samples = None):\n") + file.write(" n_samples = n_samples if n_samples else min([kwargs[key].size(0) for key in self.inputs])\n") for key in recurrent_inputs.keys(): file.write(f" self.states['{key}'] = kwargs['{key}']\n") result_str = "" diff --git a/nnodely/layers/activation.py b/nnodely/layers/activation.py index 45884e82..a6c38482 100644 --- a/nnodely/layers/activation.py +++ b/nnodely/layers/activation.py @@ -25,7 +25,8 @@ class Relu(Stream, ToStream): :type obj: Stream Example: - >>> x = Relu(x) + -------- + .. include:: /examples_basics/layer_module_ex/activation_module_ex/relu.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant|float|int) -> Stream: @@ -47,7 +48,8 @@ class ELU(Stream, ToStream): :type obj: Stream Example: - >>> x = ELU(x) + --------- + .. include:: /examples_basics/layer_module_ex/activation_module_ex/elu.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant|float|int) -> Stream: @@ -69,7 +71,8 @@ class Identity(Stream, ToStream): :type obj: Stream Example: - >>> x = Identity(x) + --------- + .. include:: /examples_basics/layer_module_ex/activation_module_ex/identity.rst """ @enforce_types def __init__(self, obj: Stream|Parameter|Constant|float|int) -> Stream: @@ -92,7 +95,8 @@ class Softmax(Stream, ToStream): :type obj: Stream Example: - >>> x = Softmax(x) + --------- + .. include:: /examples_basics/layer_module_ex/activation_module_ex/softmax.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant|float|int) -> Stream: @@ -118,7 +122,8 @@ class Sigmoid(Stream, ToStream): :type obj: Stream Example: - >>> x = Sigmoid(x) + --------- + .. include:: /examples_basics/layer_module_ex/activation_module_ex/sigmoid.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant|float|int) -> Stream: diff --git a/nnodely/layers/arithmetic.py b/nnodely/layers/arithmetic.py index 438b720f..0b2f5877 100644 --- a/nnodely/layers/arithmetic.py +++ b/nnodely/layers/arithmetic.py @@ -37,9 +37,8 @@ class Add(Stream, ToStream): :type obj: Tensor Example: - >>> add = Add(relation1, relation2) - or - >>> add = relation1 + relation2 + -------- + .. include:: /examples_basics/layer_module_ex/arithmetic_module_ex/add.rst """ @enforce_types def __init__(self, obj1:Stream|Parameter|Constant|int|float, obj2:Stream|Parameter|Constant|int|float) -> Stream: @@ -59,9 +58,8 @@ class Sub(Stream, ToStream): :type obj: Tensor Example: - >>> sub = Sub(relation1, relation2) - or - >>> sub = relation1 - relation2 + -------- + .. include:: /examples_basics/layer_module_ex/arithmetic_module_ex/sub.rst """ @enforce_types def __init__(self, obj1:Stream|Parameter|Constant|int|float, obj2:Stream|Parameter|Constant|int|float) -> Stream: @@ -80,9 +78,8 @@ class Mul(Stream, ToStream): :type obj: Tensor Example: - >>> mul = Mul(relation1, relation2) - or - >>> mul = relation1 * relation2 + -------- + .. include:: /examples_basics/layer_module_ex/arithmetic_module_ex/mul.rst """ @enforce_types def __init__(self, obj1:Stream|Parameter|Constant|int|float, obj2:Stream|Parameter|Constant|int|float) -> Stream: @@ -101,9 +98,8 @@ class Div(Stream, ToStream): :type obj: Tensor Example: - >>> div = Div(relation1, relation2) - or - >>> div = relation1 / relation2 + -------- + .. include:: /examples_basics/layer_module_ex/arithmetic_module_ex/div.rst """ @enforce_types def __init__(self, obj1:Stream|Parameter|Constant|int|float, obj2:Stream|Parameter|Constant|int|float) -> Stream: @@ -126,9 +122,8 @@ class Pow(Stream, ToStream): :type obj: float or Tensor Example: - >>> pow = Pow(relation, exp) - or - >>> pow = relation1 ** relation2 + -------- + .. include:: /examples_basics/layer_module_ex/arithmetic_module_ex/pow.rst """ @enforce_types def __init__(self, obj1:Stream|Parameter|Constant|int|float, obj2:Stream|Parameter|Constant|int|float) -> Stream: @@ -144,7 +139,8 @@ class Neg(Stream, ToStream): :type obj: Tensor Example: - >>> x = Neg(x) + -------- + .. include:: /examples_basics/layer_module_ex/arithmetic_module_ex/neg.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant) -> Stream: diff --git a/nnodely/layers/equationlearner.py b/nnodely/layers/equationlearner.py index 9755aaac..fa6d1dbf 100644 --- a/nnodely/layers/equationlearner.py +++ b/nnodely/layers/equationlearner.py @@ -49,36 +49,8 @@ class EquationLearner(NeuObj): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/equation_learner.ipynb - :alt: Open in Colab - Example - basic usage: - >>> x = Input('x') - - >>> equation_learner = EquationLearner(functions=[Tan, Sin, Cos]) - >>> out = Output('out',equation_learner(x.last())) - - Example - passing a linear layer: - >>> x = Input('x') - - >>> linear_layer = Linear(output_dimension=3, W_init=init_constant, W_init_params={'value':0}) - >>> equation_learner = EquationLearner(functions=[Tan, Sin, Cos], linear_in=linear_layer) - - >>> out = Output('out',equation_learner(x.last())) - - Example - passing a custom parametric function and multiple inputs: - >>> x = Input('x') - >>> F = Input('F') - - >>> def myFun(K1,p1): - return K1*p1 - - >>> K = Parameter('k', dimensions = 1, sw = 1,values=[[2.0]]) - >>> parfun = ParamFun(myFun, parameters = [K] ) - - >>> equation_learner = EquationLearner([parfun]) - >>> out = Output('out',equation_learner((x.last(),F.last()))) + .. include:: /examples_basics/layer_module_ex/eql.rst """ @enforce_types def __init__(self, functions:list, *, linear_in:Linear|None = None, linear_out:Linear|None = None) -> Stream: diff --git a/nnodely/layers/fir.py b/nnodely/layers/fir.py index 5f17ccba..b340dbe0 100644 --- a/nnodely/layers/fir.py +++ b/nnodely/layers/fir.py @@ -73,24 +73,8 @@ class Fir(NeuObj, AutoToStream): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/fir.ipynb - :alt: Open in Colab - - Example - basic usage: - >>> input = Input('in') - >>> relation = Fir(input.tw(0.05)) - - Example - passing a parameter: - >>> input = Input('in') - >>> par = Parameter('par', dimensions=3, sw=2, init='init_constant') - >>> relation = Fir(W=par)(input.sw(2)) - - Example - parameters initialization: - >>> x = Input('x') - >>> F = Input('F') - >>> fir_x = Fir(W_init='init_negexp')(x.tw(0.2)) - >>> fir_F = Fir(W_init='init_constant', W_init_params={'value':1})(F.last()) + + .. include:: /examples_basics/layer_module_ex/fir.rst """ @enforce_types diff --git a/nnodely/layers/fuzzify.py b/nnodely/layers/fuzzify.py index 1a72ae05..2e65ad8f 100644 --- a/nnodely/layers/fuzzify.py +++ b/nnodely/layers/fuzzify.py @@ -45,25 +45,8 @@ class Fuzzify(NeuObj): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/fuzzify.ipynb - :alt: Open in Colab - - Example - basic usage: - >>> x = Input('x') - >>> fuz = Fuzzify(output_dimension=5, range=[1,5]) - >>> out = Output('out', fuz(x.last())) - - Example - passing the centers: - >>> fuz = Fuzzify(centers=[-1,0,3,5], functions='Rectangular') - >>> out = Output('out', fuz(x.last())) - - Example - using a custom function: - >>> def fun(x): - >>> import torch - >>> return torch.tanh(x) - >>> fuz = Fuzzify(output_dimension=11, range=[-5,5], functions=fun) - >>> out = Output('out', fuz(x.last())) + + .. include:: /examples_basics/layer_module_ex/fuzzy.rst """ @enforce_types def __init__(self, output_dimension: int | None = None, diff --git a/nnodely/layers/input.py b/nnodely/layers/input.py index 09d5e305..061f955d 100644 --- a/nnodely/layers/input.py +++ b/nnodely/layers/input.py @@ -10,10 +10,6 @@ class Input(NeuObj): """ Represents an Input in the neural network model. - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/states.ipynb - :alt: Open in Colab - Parameters ---------- json_name : str @@ -116,19 +112,9 @@ def sw(self, sw:int|list, offset:int|None = None) -> Stream: TypeError If the sample window is not an integer or a list of integers. - Examples - -------- - Select a sample window considering a signal T = [-3,-2,-1,0,1,2] where the time vector 0 represent the last passed instant. If sw is an integer #1 represent the number of step in the past - >>> T.sw(2) #= [-1, 0] represents two sample step in the past - - If sw is a list [#1,#2] the numbers represent the sample indexes in the vector with the second element excluded - >>> T.sw([-2,0]) #= [-1, 0] represents two time step in the past zero in the future - >>> T.sw([0,1]) #= [1] the first time in the future - >>> T.sw([-4,-2]) #= [-3,-2] - - The total number of samples can be computed #2-#1. The offset represent the index of the vector that need to be used to offset the window - >>> T.sw(2,offset=-2) #= [0, 1] the value of the window is [-1,0] - >>> T.sw([-2,2],offset=-1) #= [-1,0,1,2] the value of the window is [-1,0,1,2] + Example + ------------- + .. include:: /examples_basics/input_module_ex/sw.rst """ dim = copy.deepcopy(self.dim) json = copy.deepcopy(self.json) @@ -166,10 +152,7 @@ def z(self, delay:int) -> Stream: Examples -------- - Select the unitary delay considering a signal T = [-3,-2,-1,0,1,2], where the time vector 0 represent the last passed instant - T.z(-1) = 1 - T.z(0) = 0 # the last passed instant - T.z(2) = -2 + .. include:: /examples_basics/input_module_ex/z.rst """ dim = copy.deepcopy(self.dim) json = copy.deepcopy(self.json) diff --git a/nnodely/layers/linear.py b/nnodely/layers/linear.py index 6ad9f0a9..0a6ac3ed 100644 --- a/nnodely/layers/linear.py +++ b/nnodely/layers/linear.py @@ -72,23 +72,8 @@ class Linear(NeuObj, AutoToStream): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/linear.ipynb - :alt: Open in Colab - - Example - basic usage: - >>> input = Input('in').tw(0.05) - >>> relation = Linear(input) - - Example - passing a weight and bias parameter: - >>> input = Input('in').last() - >>> weight = Parameter('W', values=[[[1]]]) - >>> bias = Parameter('b', values=[[1]]) - >>> relation = Linear(W=weight, b=bias)(input) - - Example - parameters initialization: - >>> input = Input('in').last() - >>> relation = Linear(b=True, W_init=init_negexp, b_init=init_constant, b_init_params={'value':1})(input) + + .. include:: /examples_basics/layer_module_ex/linear.rst """ @enforce_types diff --git a/nnodely/layers/localmodel.py b/nnodely/layers/localmodel.py index 4d6965f2..095cd0b9 100644 --- a/nnodely/layers/localmodel.py +++ b/nnodely/layers/localmodel.py @@ -34,36 +34,8 @@ class LocalModel(NeuObj): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/localmodel.ipynb - :alt: Open in Colab - Example - basic usage: - >>> x = Input('x') - >>> activation = Fuzzify(2,[0,1],functions='Triangular')(x.last()) - >>> loc = LocalModel(input_function=Fir()) - >>> out = Output('out', loc(x.tw(1), activation)) - - Example - passing a custom function: - >>> def myFun(in1,p1,p2): - >>> return p1*in1+p2 - - >>> x = Input('x') - >>> activation = Fuzzify(2,[0,1],functions='Triangular')(x.last()) - >>> loc = LocalModel(input_function = lambda:ParamFun(myFun), output_function = lambda:Fir)(x.last(), activation) - >>> out = Output('out', loc) - - Example - custom function with multiple activations: - >>> x = Input('x') - >>> F = Input('F') - >>> activationA = Fuzzify(2,[0,1],functions='Triangular')(x.tw(1)) - >>> activationB = Fuzzify(2,[0,1],functions='Triangular')(F.tw(1)) - - >>> def myFun(in1,p1,p2): - >>> return p1*in1+p2 - - >>> loc = LocalModel(input_function = lambda:ParamFun(myFun), output_function = Fir(3))(x.tw(1),(activationA,activationB)) - >>> out = Output('out', loc) + .. include:: /examples_basics/layer_module_ex/localmodel.rst """ @enforce_types def __init__(self, input_function:Callable|None = None, diff --git a/nnodely/layers/neuralODE.py b/nnodely/layers/neuralODE.py new file mode 100644 index 00000000..787498ff --- /dev/null +++ b/nnodely/layers/neuralODE.py @@ -0,0 +1,104 @@ +import inspect, copy, textwrap, torch, math + +import torch.nn as nn + +from nnodely.basic.relation import NeuObj, Stream, toStream +from nnodely.basic.model import Model +from nnodely.layers.parametricfunction import ParamFun +from nnodely.support.utils import check, enforce_types +from nnodely.support.jsonutils import merge + +from nnodely.support.logger import logging, nnLogger +log = nnLogger(__name__, logging.WARNING) + +ode_relation_name = 'NeuralODE' + +class NeuralODE(NeuObj): + """Neural ODE layer implementing continuous-depth models. + + Args: + func (Callable): The function defining the ODE dynamics. + rtol (float, optional): Relative tolerance for the ODE solver. Default is 1e-7. + atol (float, optional): Absolute tolerance for the ODE solver. Default is 1e-9. + method (str, optional): The ODE solver method to use. Default is 'dopri5'. + """ + + @enforce_types + def __init__(self, + func: ParamFun, + dt: float, + rtol: float = 1e-7, + atol: float = 1e-9, + method: str = 'dopri5') -> Stream: + super().__init__('F'+ode_relation_name + str(NeuObj.count)) + + self.func = func + self.dt = dt + self.rtol = rtol + self.atol = atol + self.method = method + + code = textwrap.dedent(inspect.getsource(func.param_fun)).replace('\"', '\'') + code = 'def ' + f'{self.name}' + '(state, *weights):\n' + \ + ' from nnodely.support.odeint.adjoint import odeint_adjoint as odeint\n ' + \ + code.replace('\n', '\n ') + \ + '\n' + \ + f' ans = odeint(lambda t, y: {func.param_fun.__name__}(t, y, *weights), state, t=torch.tensor([0.0, {self.dt}]), rtol={self.rtol}, atol={self.atol}, method=\'{self.method}\', adjoint_params=list(weights))' + \ + f'\n return ans[-1]\n' + + self.json['Functions'][self.name] = { + 'code' : code, + 'name' : f'{self.name}', + 'rtol' : rtol, + 'atol' : atol, + 'method' : method, + 'dt' : dt + } + + self.json_stream = {} + + def __call__(self, *obj: Stream) -> Stream: + stream_name = ode_relation_name + str(Stream.count) + stream_json = copy.deepcopy(self.json) + input_names = [] + for ind, o in enumerate(obj): + o = toStream(o) + check(type(o) is Stream, TypeError, + f"The type of {o} is {type(o)} and is not supported for ParamFun operation.") + stream_json = merge(stream_json, o.json) + input_names.append(o.name) + + stream_json['Relations'][stream_name] = [ode_relation_name, input_names, self.name] + return Stream(stream_name, stream_json, obj[0].dim) + + +class ODE_Layer(nn.Module): + + def __init__(self, func): + super().__init__() + self.name = func['name'] + self.dt = func['dt'] + self.rtol = func['rtol'] + self.atol = func['atol'] + self.method = func['method'] + ## Add the function to the globals + try: + code = 'import torch\n@torch.fx.wrap\n' + func['code'] + #print(f"Defining ODE function:\n{code}") + exec(code, globals()) + except Exception as e: + print(f"An error occurred: {e}") + + def forward(self, *inputs): + # Retrieve the function object from the globals dictionary + function_to_call = globals()[self.name] + weights = list(inputs)[1:] + return function_to_call(list(inputs)[0], *weights) # Return the last state + +def createODE(self, *func_params): + # for key, value in func_params[0].items(): + # print(f"{key}: {value}") + + return ODE_Layer(func_params[0]) + +setattr(Model, ode_relation_name, createODE) \ No newline at end of file diff --git a/nnodely/layers/parameter.py b/nnodely/layers/parameter.py index 0f5bd53b..c3bc129f 100644 --- a/nnodely/layers/parameter.py +++ b/nnodely/layers/parameter.py @@ -36,36 +36,8 @@ class Constant(NeuObj, Relation): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/parameter.ipynb - :alt: Open in Colab - Example - passing a custom scalar value -> g.dim = {'dim': 1}: - - >>> g = Constant('gravity',values=9.81) - - Example - passing a custom vector value -> n.dim = {'dim': 4}: - - >>> n = Constant('numbers', values=[1,2,3,4]) - - Example - passing a custom vector value with single sample window -> n.dim = {'dim': 4, 'sw': 1}: - - >>> n = Constant('numbers', values=[[1,2,3,4]]) - - Example - passing a custom vector value with double sample window -> n.dim = {'dim': 4, 'sw': 2}: - - >>> n = Constant('numbers', values=[[2,3,4],[1,2,3]]) - - Example - passing a custom vector value with double sample window -> n.dim = {'dim': 4, 'sw': 2}. - If the value of the sw is differnt from the dimension of shape[0] an error will be raised. - - >>> n = Constant('numbers', sw = 2, values=[[2,3,4],[1,2,3]]) - - Example - passing a custom vector value with time window -> n.dim = {'dim': 4, 'tw': 4}. - In this case the samplingtime must be 0.5 otherwise an error will be raised. If the Constant have a time dimension, - the input must have a len(shape) == 2. - - >>> n = Constant('numbers', tw = 4, values=[[2,3,4],[1,2,3]]) + .. include:: /examples_basics/parameter_module_ex/constant.rst """ @enforce_types def __init__(self, name:str, @@ -138,22 +110,8 @@ class Parameter(NeuObj, Relation): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/parameter.ipynb - :alt: Open in Colab - - Example - basic usage: - >>> k = Parameter('k', dimensions=3, tw=4) - - Example - initialize a parameter with values: - >>> x = Input('x') - >>> gravity = Parameter('g', dimensions=(4,1),values=[[[1],[2],[3],[4]]]) - >>> out = Output('out', Linear(W=gravity)(x.sw(3))) - - Example - initialize a parameter with a function: - >>> x = Input('x').last() - >>> p = Parameter('param', dimensions=1, sw=1, init=init_constant, init_params={'value':1}) - >>> relation = Fir(parameter=param)(x) + + .. include:: /examples_basics/parameter_module_ex/parameter.rst """ @enforce_types def __init__(self, name:str, @@ -234,7 +192,7 @@ class SampleTime(): Example ------- - >>> dt = SampleTime() + .. include:: /examples_basics/parameter_module_ex/sample_time.rst """ name = 'SampleTime' g = Constant(name, values=0) diff --git a/nnodely/layers/parametricfunction.py b/nnodely/layers/parametricfunction.py index bb8c4f68..8cc6d714 100644 --- a/nnodely/layers/parametricfunction.py +++ b/nnodely/layers/parametricfunction.py @@ -56,19 +56,8 @@ class ParamFun(NeuObj): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/parametric_functions.ipynb - :alt: Open in Colab - - Example: - >>> input1 = Input('input1') - >>> input2 = Input('input2') - - >>> def my_function(x, y, param1, const1): - >>> return param1 * x + const1 * y - - >>> param_fun = ParamFun(my_function, constants={'const1': 1.0}, parameters_dimensions={'param1': 1}) - >>> result = param_fun(input1, input2) + + .. include:: /examples_basics/layer_module_ex/paramfun.rst """ @enforce_types def __init__(self, param_fun:Callable, diff --git a/nnodely/layers/part.py b/nnodely/layers/part.py index 460d17ac..b86bd4cd 100644 --- a/nnodely/layers/part.py +++ b/nnodely/layers/part.py @@ -50,13 +50,8 @@ class Part(Stream, ToStream): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/partitioning.ipynb - :alt: Open in Colab - - Example: - >>> x = Input('x', dimensions=3).last() - >>> relation = Part(x, 0, 1) + + .. include:: /examples_basics/layer_module_ex/part_module/part.rst Raises ------ @@ -102,14 +97,9 @@ class Select(Stream, ToStream): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/partitioning.ipynb - :alt: Open in Colab - - Example: - >>> x = Input('x', dimensions=3).last() - >>> relation = Select(x, 1) - + + .. include:: /examples_basics/layer_module_ex/part_module/select.rst + Raises ------ IndexError @@ -200,13 +190,8 @@ class SamplePart(Stream, ToStream): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/partitioning.ipynb - :alt: Open in Colab - - Example: - >>> x = Input('x').sw(3) - >>> relation = SamplePart(x, 0, 1) + + .. include:: /examples_basics/layer_module_ex/part_module/sample_part.rst Raises ------ @@ -273,14 +258,9 @@ class SampleSelect(Stream, ToStream): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/partitioning.ipynb - :alt: Open in Colab - - Example: - >>> x = Input('x').sw(3) - >>> relation = SampleSelect(x, 1) - + + .. include:: /examples_basics/layer_module_ex/part_module/sample_select.rst + Raises ------ IndexError @@ -329,13 +309,8 @@ class TimePart(Stream, ToStream): Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/partitioning.ipynb - :alt: Open in Colab - - Example: - >>> x = Input('x').sw(10) - >>> time_part = TimePart(x, i=0, j=5) + + .. include:: /examples_basics/layer_module_ex/part_module/time_part.rst Raises ------ diff --git a/nnodely/layers/trigonometric.py b/nnodely/layers/trigonometric.py index 953b2f99..932cabad 100644 --- a/nnodely/layers/trigonometric.py +++ b/nnodely/layers/trigonometric.py @@ -25,7 +25,8 @@ class Sin(Stream, ToStream): :type obj: Stream Example: - >>> sin = Sin(relation) + -------- + .. include:: /examples_basics/layer_module_ex/trig_module_ex/sin.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant|int|float) -> Stream: @@ -47,7 +48,8 @@ class Cos(Stream, ToStream): :type obj: Stream Example: - >>> cos = Cos(relation) + -------- + .. include:: /examples_basics/layer_module_ex/trig_module_ex/cos.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant|int|float) -> Stream: @@ -69,7 +71,8 @@ class Tan(Stream, ToStream): :type obj: Stream Example: - >>> tan = Tan(relation) + -------- + .. include:: /examples_basics/layer_module_ex/trig_module_ex/tan.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant|int|float) -> Stream: @@ -91,7 +94,8 @@ class Cosh(Stream, ToStream): :type obj: Stream Example: - >>> cosh = Cosh(relation) + -------- + .. include:: /examples_basics/layer_module_ex/trig_module_ex/cosh.rst """ def __init__(self, obj:Stream) -> Stream: obj = toStream(obj) @@ -108,7 +112,8 @@ class Sech(Stream, ToStream): :type obj: Stream Example: - >>> sech = Sech(relation) + -------- + .. include:: /examples_basics/layer_module_ex/trig_module_ex/sech.rst """ def __init__(self, obj:Stream) -> Stream: obj = toStream(obj) @@ -129,7 +134,8 @@ class Tanh(Stream, ToStream): :type obj: Stream Example: - >>> x = Tanh(x) + -------- + .. include:: /examples_basics/layer_module_ex/trig_module_ex/tanh.rst """ @enforce_types def __init__(self, obj:Stream|Parameter|Constant|float|int) -> Stream: diff --git a/nnodely/nnodely.py b/nnodely/nnodely.py index 0848f526..083f2e4c 100644 --- a/nnodely/nnodely.py +++ b/nnodely/nnodely.py @@ -207,47 +207,6 @@ def trainAndAnalyze(self, *, test_dataset: str | list | dict | None = None, test If the sample horizon is not positive. ValueError If an input or output variable is not in the model definition. - - Examples - -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/training.ipynb - :alt: Open in Colab - - Example - basic feed-forward training: - >>> x = Input('x') - >>> F = Input('F') - - >>> xk1 = Output('x[k+1]', Fir()(x.tw(0.2))+Fir()(F.last())) - - >>> mass_spring_damper = Modely(seed=0) - >>> mass_spring_damper.addModel('xk1',xk1) - >>> mass_spring_damper.neuralizeModel(sample_time = 0.05) - - >>> data_struct = ['time','x','dx','F'] - >>> data_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)),'dataset','data') - >>> mass_spring_damper.loadData(name='mass_spring_dataset', source=data_folder, format=data_struct, delimiter=';') - - >>> params = {'num_of_epochs': 100,'train_batch_size': 128,'lr':0.001} - >>> mass_spring_damper.trainModel(splits=[70,20,10], training_params = params) - - Example - recurrent training: - >>> x = Input('x') - >>> F = Input('F') - - >>> xk1 = Output('x[k+1]', Fir()(x.tw(0.2))+Fir()(F.last())) - - >>> mass_spring_damper = Modely(seed=0) - >>> mass_spring_damper.addModel('xk1',xk1) - >>> mass_spring_damper.addClosedLoop(xk1, x) - >>> mass_spring_damper.neuralizeModel(sample_time = 0.05) - - >>> data_struct = ['time','x','dx','F'] - >>> data_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)),'dataset','data') - >>> mass_spring_damper.loadData(name='mass_spring_dataset', source=data_folder, format=data_struct, delimiter=';') - - >>> params = {'num_of_epochs': 100,'train_batch_size': 128,'lr':0.001} - >>> mass_spring_damper.trainModel(splits=[70,20,10], prediction_samples=10, training_params = params) """ ## Train the model self.trainModel(**kwargs) diff --git a/nnodely/operators/composer.py b/nnodely/operators/composer.py index 653821f4..563777f6 100644 --- a/nnodely/operators/composer.py +++ b/nnodely/operators/composer.py @@ -41,11 +41,7 @@ def addModel(self, name:str, stream_list:list|Output) -> None: Example ------- - Example usage: - >>> model = Modely() - >>> x = Input('x') - >>> out = Output('out', Fir(x.last())) - >>> model.addModel('example_model', [out]) + .. include:: /examples_basics/compser_module_ex/addModel.rst """ self._model_def.addModel(name, stream_list) self._neuralized = False @@ -62,8 +58,7 @@ def removeModel(self, name_list:list|str) -> None: Example ------- - Example usage: - >>> model.removeModel(['sub_model1', 'sub_model2']) + .. include:: /examples_basics/compser_module_ex/removeModel.rst """ self._model_def.removeModel(name_list) self._neuralized = False @@ -82,16 +77,8 @@ def addConnect(self, stream_out:str|Output|Stream, input_in:str|Input, *, local: Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/states.ipynb - :alt: Open in Colab - - Example: - >>> model = Modely() - >>> x = Input('x') - >>> y = Input('y') - >>> relation = Fir(x.last()) - >>> model.addConnect(relation, y) + + .. include:: /examples_basics/compser_module_ex/addConnect.rst """ self._model_def.addConnection(stream_out, input_in,'connect', local) self._neuralized = False @@ -110,16 +97,8 @@ def addClosedLoop(self, stream_out:str|Output|Stream, input_in:str|Input, *, loc Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/states.ipynb - :alt: Open in Colab - - Example: - >>> model = Modely() - >>> x = Input('x') - >>> y = Input('y') - >>> relation = Fir(x.last()) - >>> model.addClosedLoop(relation, y) + + .. include:: /examples_basics/compser_module_ex/addClosedLoop.rst """ self._model_def.addConnection(stream_out, input_in,'closedLoop', local) self._neuralized = False @@ -131,22 +110,13 @@ def removeConnection(self, input_in:str|Input) -> None: Parameters ---------- - input_in : Input or name of the input of inputs + input_in : Input or ``name`` of the input of inputs The Input to disconnect. Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/states.ipynb - :alt: Open in Colab - - Example: - >>> model = Modely() - >>> x = Input('x') - >>> y = Input('y') - >>> relation = Fir(x.last()) - >>> model.addConnect(relation, y) - >>> model.removeConnection(y) + + .. include:: /examples_basics/compser_module_ex/removeConnection.rst """ if isinstance(input_in, Input): input_name = input_in.name @@ -178,9 +148,7 @@ def neuralizeModel(self, sample_time:float|int|None = None, *, clear_model:bool Example ------- - Example usage: - >>> model = Modely(name='example_model') - >>> model.neuralizeModel(sample_time=0.1, clear_model=True) + .. include:: /examples_basics/compser_module_ex/neuralizeModel.rst """ if model_def is not None: check(sample_time == None, ValueError, 'The sample_time must be None if a model_def is provided') @@ -251,17 +219,8 @@ def __call__(self, inputs:dict={}, *, sampled:bool=False, closed_loop:dict={}, c Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/inference.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely() - >>> x = Input('x') - >>> out = Output('out', Fir(x.last())) - >>> model.addModel('example_model', [out]) - >>> model.neuralizeModel() - >>> predictions = model(inputs={'x': [1, 2, 3]}) + + .. include:: /examples_basics/inference_module_ex/inference.rst """ ## Copy dict for avoid python bug diff --git a/nnodely/operators/exporter.py b/nnodely/operators/exporter.py index 296fc93d..79cbed3e 100644 --- a/nnodely/operators/exporter.py +++ b/nnodely/operators/exporter.py @@ -46,14 +46,8 @@ def saveTorchModel(self, name:str='net', model_folder:str|None=None, *, models:s Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely() - >>> model.neuralizeModel() - >>> model.saveTorchModel(name='example_model', model_folder='path/to/save') + + .. include:: /examples_basics/export_module_ex/saveTorchModel.rst """ check(self._model_def.isDefined(), RuntimeError, "The network has not been defined.") check(self._neuralized == True, RuntimeError, 'The model is not neuralized yet!') @@ -89,14 +83,8 @@ def loadTorchModel(self, name:str='net', model_folder:str|None=None) -> None: Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely() - >>> model.neuralizeModel() - >>> model.loadTorchModel(name='example_model', model_folder='path/to/load') + + .. include:: /examples_basics/export_module_ex/loadTorchModel.rst """ check(self.neuralized == True, RuntimeError, 'The model is not neuralized yet.') self.__exporter.loadTorchModel(self._model, name, model_folder) @@ -122,14 +110,8 @@ def saveModel(self, name:str='net', model_folder:str|None=None, *, models:str|li Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely() - >>> model.neuralizeModel() - >>> model.saveModel(name='example_model', model_folder='folder/') + + .. include:: /examples_basics/export_module_ex/saveModel.rst """ check(self._model_def.isDefined(), RuntimeError, "The network has not been defined.") if models is not None: @@ -163,13 +145,8 @@ def loadModel(self, name:str='net', model_folder:str|None=None) -> None: Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - Example usage: - >>> model = Modely() - >>> model.loadModel(name='example_model', model_folder='path/to/load') + .. include:: /examples_basics/export_module_ex/loadModel.rst """ model_def = self.__exporter.loadModel(name, model_folder) check(model_def, RuntimeError, "Error to load the network.") @@ -214,14 +191,8 @@ def exportPythonModel(self, name:str='net', model_folder:str|None=None, *, model Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely(name='example_model') - >>> model.neuralizeModel() - >>> model.exportPythonModel(name='example_model', model_folder='folder/') + + .. include:: /examples_basics/export_module_ex/exportPythonModel.rst """ check(self._model_def.isDefined(), RuntimeError, "The network has not been defined.") check(self._traced == False, RuntimeError, @@ -262,13 +233,8 @@ def importPythonModel(self, name:str='net', model_folder:str|None=None) -> None: Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - Example usage: - >>> model = Modely() - >>> model.importPythonModel(name='example_model', model_folder='path/to/import') + .. include:: /examples_basics/export_module_ex/importPythonModel.rst """ model_def = self.__exporter.loadModel(name, model_folder) check(model_def is not None, RuntimeError, "Error to load the network.") @@ -308,18 +274,8 @@ def exportONNX(self, inputs_order:list|None=None, outputs_order:list|None=None, Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - - Example usage: - >>> input1 = Input('input1').last() - >>> input2 = Input('input2').last() - >>> out = Output('output1', input1+input2) - - >>> model = Modely() - >>> model.neuralizeModel() - >>> model.exportONNX(inputs_order=['input1', 'input2'], outputs_order=['output1'], name='example_model', model_folder='path/to/export') + + .. include:: /examples_basics/export_module_ex/exportONNX.rst """ check(self._model_def.isDefined(), RuntimeError, "The network has not been defined.") check(self._traced == False, RuntimeError, @@ -372,25 +328,8 @@ def onnxInference(self, inputs:dict, name:str='net', model_folder:str|None=None) Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - - Example - Feed-Forward: - >>> x = Input('x') - - >>> model_folder = folder/ - >>> dummy_input = {'x':np.ones(shape=(3, 1, 1)).astype(np.float32)} - >>> predictions = Modely().onnxInference(dummy_input, model_folder) - Example - Recurrent: - >>> x = Input('x') - >>> y = Input('y') - - >>> model_folder = folder/ - >>> dummy_input = {'x':np.ones(shape=(3, 1, 1, 1)).astype(np.float32) - 'y':np.ones(shape=(1, 1, 1)).astype(np.float32)} - >>> predictions = Modely().onnxInference(dummy_input, model_folder) + .. include:: /examples_basics/export_module_ex/onnxInference.rst """ return self.__exporter.onnxInference(inputs, name, model_folder) @@ -408,14 +347,7 @@ def exportReport(self, name:str='net', model_folder:str|None=None) -> None: Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/export.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely() - >>> model.neuralizeModel() - >>> model.trainModel(train_dataset='train_dataset', validation_dataset='val_dataset', num_of_epochs=10) - >>> model.exportReport(name='example_model', model_folder='path/to/export') + + .. include:: /examples_basics/export_module_ex/exportReport.rst """ self.__exporter.exportReport(self, name, model_folder) diff --git a/nnodely/operators/loader.py b/nnodely/operators/loader.py index 850aa673..8b2d0960 100644 --- a/nnodely/operators/loader.py +++ b/nnodely/operators/loader.py @@ -48,14 +48,8 @@ def getSamples(self, dataset:str, *, index:int|None = None, window:int=1) -> dic Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/dataset.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely() - >>> model.loadData('dataset_name') - >>> samples = model.getSamples('dataset_name', index=10, window=5) + + .. include:: /examples_basics/data_loader_module_ex/getSamples.rst """ if index is None: index = random.randint(0, self._num_of_samples[dataset] - window) @@ -84,16 +78,8 @@ def filterData(self, filter_function:Callable, dataset_name:str|None = None) -> Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/dataset.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely() - >>> model.loadData('dataset_name', 'path/to/data') - >>> def filter_fn(sample): - >>> return sample['input1'] > 0 - >>> model.filterData(filter_fn, 'dataset_name') + + .. include:: /examples_basics/data_loader_module_ex/filterData.rst """ idx_to_remove = [] if dataset_name is None: @@ -157,23 +143,38 @@ def resamplingData(self, df:pd.DataFrame, *, scale:float = 1e9) -> None: Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/dataset.ipynb - :alt: Open in Colab - - Example usage: - >>> model = Modely() - >>> df = pd.DataFrame({'time': np.array(range(60), dtype=np.float32),'x': np.array(10*[10] + 20*[20] + 30*[30], dtype=np.float32)}) - >>> resampled_df = model.resamplingData(df, scale=1e9) + + .. include:: /examples_basics/data_loader_module_ex/resamplingData.rst """ sample_time_ns = int(self._model_def.getSampleTime() * scale) + if sample_time_ns <= 0: + raise ValueError(f"Invalid resampling step: {sample_time_ns}ns") method = 'linear' - if type(df.index) is pd.DatetimeIndex: + if isinstance(df.index, pd.DatetimeIndex): + # FORZA risoluzione ns (evita unit='s' interno) + if df.index.dtype != "datetime64[ns]": + df = df.copy() + df.index = df.index.astype("datetime64[ns]") df = df.resample(f"{sample_time_ns}ns").interpolate(method=method) - elif 'time' in df.columns: - if not ptypes.is_datetime64_any_dtype(df['time']): - df['time'] = pd.to_datetime(df['time'], unit='s') - df = df.set_index('time', drop=True) + + elif "time" in df.columns: + df = df.copy() + + # Converte/normalizza SEMPRE a datetime64[ns] + if not ptypes.is_datetime64_any_dtype(df["time"]): + df["time"] = pd.to_datetime(df["time"], unit="s", origin="unix") + else: + df["time"] = pd.to_datetime(df["time"]) # normalize types (tz/objects) + + if df["time"].dtype != "datetime64[ns]": + df["time"] = df["time"].astype("datetime64[ns]") + + df = df.set_index("time", drop=True) + + # Doppia sicurezza: anche l'index + if df.index.dtype != "datetime64[ns]": + df.index = df.index.astype("datetime64[ns]") + df = df.resample(f"{sample_time_ns}ns").interpolate(method=method) else: raise TypeError("No time column found in the DataFrame. Please provide a time column for resampling.") @@ -270,30 +271,8 @@ def loadData(self, name:str, Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/dataset.ipynb - :alt: Open in Colab - - Example - load data from files: - >>> x = Input('x') - >>> y = Input('y') - >>> out = Output('out',Fir(x.tw(0.05))) - >>> test = Modely(visualizer=None) - >>> test.addModel('example_model', out) - >>> test.neuralizeModel(0.01) - >>> data_struct = ['x', '', 'y'] - >>> test.loadData(name='example_dataset', source='path/to/data', format=data_struct) - - Example - load data from a crafted dataset: - >>> x = Input('x') - >>> y = Input('y') - >>> out = Output('out',Fir(x.tw(0.05))) - >>> test = Modely(visualizer=None) - >>> test.addModel('example_model', out) - >>> test.neuralizeModel(0.01) - >>> data_x = np.array(range(10)) - >>> dataset = {'x': data_x, 'y': (2*data_x)} - >>> test.loadData(name='example_dataset',source=dataset) + + .. include:: /examples_basics/data_loader_module_ex/loadData.rst """ check(self.neuralized, ValueError, "The network is not neuralized.") check(delimiter in ['\t', '\n', ';', ',', ' '], ValueError, 'delimiter not valid!') diff --git a/nnodely/operators/network.py b/nnodely/operators/network.py index e6d9304a..2d60497c 100644 --- a/nnodely/operators/network.py +++ b/nnodely/operators/network.py @@ -265,7 +265,14 @@ def _inference(self, data, n_samples, batch_size, loss_gains, loss_functions, aux_losses = torch.zeros([len(self._model_def['Minimizers']), n_samples // batch_size]) for idx in range(0, (n_samples - batch_size + 1), batch_size): ## Build the input tensor - XY = {key: val[idx:idx + batch_size] for key, val in data.items()} + XY = {} + for key, val in data.items(): + if self._model_def['Inputs'].get(key, None): + if self._model_def['Inputs'][key].get('type', None) == 'derivate': + XY[key] = val[idx:idx + batch_size].detach().requires_grad_(True) + else: + XY[key] = val[idx:idx + batch_size] + #XY = {key: val[idx:idx + batch_size].detach().requires_grad_(True) for key, val in data.items()} for key in self._model_def.recurrentInputs().keys(): if key not in XY.keys(): window_size = self._input_n_samples[key] @@ -299,7 +306,7 @@ def _inference(self, data, n_samples, batch_size, loss_gains, loss_functions, ## Gradient step if optimizer: - total_loss.backward() + total_loss.backward() ## Backpropagate the error optimizer.step() self.visualizer.showWeightsInTrain(batch=idx // batch_size) @@ -331,8 +338,6 @@ def _recurrent_inference(self, data, batch_indexes, batch_size, loss_gains, pred out, minimize_out, out_closed_loop, out_connect = self._model(X) if self._log_internal: - #assert (check_gradient_operations(self._states) == 0) - #assert (check_gradient_operations(data) == 0) internals_dict = {'XY': tensor_to_list(X), 'out': out, 'param': self._model.all_parameters, 'closedLoop': self._model.closed_loop_update, 'connect': self._model.connect_update} diff --git a/nnodely/operators/trainer.py b/nnodely/operators/trainer.py index 4c9df7dc..2fec15e2 100644 --- a/nnodely/operators/trainer.py +++ b/nnodely/operators/trainer.py @@ -48,8 +48,7 @@ def addMinimize(self, name:str, streamA:str|Stream|Output, streamB:str|Stream|Ou Example ------- - Example usage: - >>> model.addMinimize('minimize_op', streamA, streamB, loss_function='mse') + .. include:: /examples_basics/trainer_module_ex/addMinimize.rst """ self._model_def.addMinimize(name, streamA, streamB, loss_function) self.visualizer.showaddMinimize(name) @@ -67,8 +66,7 @@ def removeMinimize(self, name_list:list|str) -> None: Example ------- - Example usage: - >>> model.removeMinimize(['minimize_op1', 'minimize_op2']) + .. include:: /examples_basics/trainer_module_ex/removeMinimize.rst """ self._model_def.removeMinimize(name_list) self._neuralized = False @@ -305,44 +303,8 @@ def trainModel(self, *, Examples -------- - .. image:: https://colab.research.google.com/assets/colab-badge.svg - :target: https://colab.research.google.com/github/tonegas/nnodely/blob/main/examples/training.ipynb - :alt: Open in Colab - Example - basic feed-forward training: - >>> x = Input('x') - >>> F = Input('F') - - >>> xk1 = Output('x[k+1]', Fir()(x.tw(0.2))+Fir()(F.last())) - - >>> mass_spring_damper = Modely(seed=0) - >>> mass_spring_damper.addModel('xk1',xk1) - >>> mass_spring_damper.neuralizeModel(sample_time = 0.05) - - >>> data_struct = ['time','x','dx','F'] - >>> data_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)),'dataset','data') - >>> mass_spring_damper.loadData(name='mass_spring_dataset', source=data_folder, format=data_struct, delimiter=';') - - >>> params = {'num_of_epochs': 100,'train_batch_size': 128,'lr':0.001} - >>> mass_spring_damper.trainModel(splits=[70,20,10], training_params = params) - - Example - recurrent training: - >>> x = Input('x') - >>> F = Input('F') - - >>> xk1 = Output('x[k+1]', Fir()(x.tw(0.2))+Fir()(F.last())) - - >>> mass_spring_damper = Modely(seed=0) - >>> mass_spring_damper.addModel('xk1',xk1) - >>> mass_spring_damper.addClosedLoop(xk1, x) - >>> mass_spring_damper.neuralizeModel(sample_time = 0.05) - - >>> data_struct = ['time','x','dx','F'] - >>> data_folder = os.path.join(os.path.dirname(os.path.realpath(__file__)),'dataset','data') - >>> mass_spring_damper.loadData(name='mass_spring_dataset', source=data_folder, format=data_struct, delimiter=';') - - >>> params = {'num_of_epochs': 100,'train_batch_size': 128,'lr':0.001} - >>> mass_spring_damper.trainModel(splits=[70,20,10], prediction_samples=10, training_params = params) + .. include:: /examples_basics/trainer_module_ex/trainModel.rst """ ## Get model for train all_models = list(self._model_def['Models'].keys()) if type(self._model_def['Models']) is dict else [self._model_def['Models']] diff --git a/nnodely/support/odeint/__init__.py b/nnodely/support/odeint/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/nnodely/support/odeint/adjoint.py b/nnodely/support/odeint/adjoint.py new file mode 100644 index 00000000..b68b7f14 --- /dev/null +++ b/nnodely/support/odeint/adjoint.py @@ -0,0 +1,288 @@ +import warnings +import torch +import torch.nn as nn +from nnodely.support.odeint.my_odeint import SOLVERS, odeint +from nnodely.support.odeint.utils import _check_inputs, _flat_to_shape, _mixed_norm, _all_callback_names, _all_adjoint_callback_names + + +class OdeintAdjointMethod(torch.autograd.Function): + + @staticmethod + def forward(ctx, shapes, func, y0, t, rtol, atol, method, options, event_fn, adjoint_rtol, adjoint_atol, adjoint_method, + adjoint_options, t_requires_grad, *adjoint_params): + + ctx.shapes = shapes + ctx.func = func + ctx.adjoint_rtol = adjoint_rtol + ctx.adjoint_atol = adjoint_atol + ctx.adjoint_method = adjoint_method + ctx.adjoint_options = adjoint_options + ctx.t_requires_grad = t_requires_grad + ctx.event_mode = event_fn is not None + + with torch.no_grad(): + ans = odeint(func, y0, t, rtol=rtol, atol=atol, method=method, options=options, event_fn=event_fn) + + if event_fn is None: + y = ans + ctx.save_for_backward(t, y, *adjoint_params) + else: + event_t, y = ans + ctx.save_for_backward(t, y, event_t, *adjoint_params) + + return ans + + @staticmethod + def backward(ctx, *grad_y): + with torch.no_grad(): + func = ctx.func + adjoint_rtol = ctx.adjoint_rtol + adjoint_atol = ctx.adjoint_atol + adjoint_method = ctx.adjoint_method + adjoint_options = ctx.adjoint_options + t_requires_grad = ctx.t_requires_grad + + # Backprop as if integrating up to event time. + # Does NOT backpropagate through the event time. + event_mode = ctx.event_mode + if event_mode: + t, y, event_t, *adjoint_params = ctx.saved_tensors + _t = t + t = torch.cat([t[0].reshape(-1), event_t.reshape(-1)]) + grad_y = grad_y[1] + else: + t, y, *adjoint_params = ctx.saved_tensors + grad_y = grad_y[0] + + adjoint_params = tuple(adjoint_params) + + ################################## + # Set up initial state # + ################################## + + # [-1] because y and grad_y are both of shape (len(t), *y0.shape) + aug_state = [torch.zeros((), dtype=y.dtype, device=y.device), y[-1], grad_y[-1]] # vjp_t, y, vjp_y + aug_state.extend([torch.zeros_like(param) for param in adjoint_params]) # vjp_params + + ################################## + # Set up backward ODE func # + ################################## + + # TODO: use a nn.Module and call odeint_adjoint to implement higher order derivatives. + def augmented_dynamics(t, y_aug): + # Dynamics of the original system augmented with + # the adjoint wrt y, and an integrator wrt t and args. + y = y_aug[1] + adj_y = y_aug[2] + # ignore gradients wrt time and parameters + + with torch.enable_grad(): + t_ = t.detach() + t = t_.requires_grad_(True) + y = y.detach().requires_grad_(True) + + # If using an adaptive solver we don't want to waste time resolving dL/dt unless we need it (which + # doesn't necessarily even exist if there is piecewise structure in time), so turning off gradients + # wrt t here means we won't compute that if we don't need it. + func_eval = func(t if t_requires_grad else t_, y) + + # Workaround for PyTorch bug #39784 + _t = torch.as_strided(t, (), ()) # noqa + _y = torch.as_strided(y, (), ()) # noqa + _params = tuple(torch.as_strided(param, (), ()) for param in adjoint_params) # noqa + + vjp_t, vjp_y, *vjp_params = torch.autograd.grad( + func_eval, (t, y) + adjoint_params, -adj_y, + allow_unused=True, retain_graph=True + ) + + # autograd.grad returns None if no gradient, set to zero. + vjp_t = torch.zeros_like(t) if vjp_t is None else vjp_t + vjp_y = torch.zeros_like(y) if vjp_y is None else vjp_y + vjp_params = [torch.zeros_like(param) if vjp_param is None else vjp_param + for param, vjp_param in zip(adjoint_params, vjp_params)] + + return (vjp_t, func_eval, vjp_y, *vjp_params) + + # Add adjoint callbacks + for callback_name, adjoint_callback_name in zip(_all_callback_names, _all_adjoint_callback_names): + try: + callback = getattr(func, adjoint_callback_name) + except AttributeError: + pass + else: + setattr(augmented_dynamics, callback_name, callback) + + ################################## + # Solve adjoint ODE # + ################################## + + if t_requires_grad: + time_vjps = torch.empty(len(t), dtype=t.dtype, device=t.device) + else: + time_vjps = None + for i in range(len(t) - 1, 0, -1): + if t_requires_grad: + # Compute the effect of moving the current time measurement point. + # We don't compute this unless we need to, to save some computation. + func_eval = func(t[i], y[i]) + dLd_cur_t = func_eval.reshape(-1).dot(grad_y[i].reshape(-1)) + aug_state[0] -= dLd_cur_t + time_vjps[i] = dLd_cur_t + + # Run the augmented system backwards in time. + aug_state = odeint( + augmented_dynamics, tuple(aug_state), + t[i - 1:i + 1].flip(0), + rtol=adjoint_rtol, atol=adjoint_atol, method=adjoint_method, options=adjoint_options + ) + aug_state = [a[1] for a in aug_state] # extract just the t[i - 1] value + aug_state[1] = y[i - 1] # update to use our forward-pass estimate of the state + aug_state[2] += grad_y[i - 1] # update any gradients wrt state at this time point + + if t_requires_grad: + time_vjps[0] = aug_state[0] + + # Only compute gradient wrt initial time when in event handling mode. + if event_mode and t_requires_grad: + time_vjps = torch.cat([time_vjps[0].reshape(-1), torch.zeros_like(_t[1:])]) + + adj_y = aug_state[2] + adj_params = aug_state[3:] + + return (None, None, adj_y, time_vjps, None, None, None, None, None, None, None, None, None, None, *adj_params) + + +def odeint_adjoint(func, y0, t, *, rtol=1e-7, atol=1e-9, method=None, options=None, event_fn=None, + adjoint_rtol=None, adjoint_atol=None, adjoint_method=None, adjoint_options=None, adjoint_params=None): + + # We need this in order to access the variables inside this module, + # since we have no other way of getting variables along the execution path. + if adjoint_params is None and not isinstance(func, nn.Module): + raise ValueError('func must be an instance of nn.Module to specify the adjoint parameters; alternatively they ' + 'can be specified explicitly via the `adjoint_params` argument. If there are no parameters ' + 'then it is allowable to set `adjoint_params=()`.') + + # Must come before _check_inputs as we don't want to use normalised input (in particular any changes to options) + if adjoint_rtol is None: + adjoint_rtol = rtol + if adjoint_atol is None: + adjoint_atol = atol + if adjoint_method is None: + adjoint_method = method + + if adjoint_method != method and options is not None and adjoint_options is None: + raise ValueError("If `adjoint_method != method` then we cannot infer `adjoint_options` from `options`. So as " + "`options` has been passed then `adjoint_options` must be passed as well.") + + if adjoint_options is None: + adjoint_options = {k: v for k, v in options.items() if k != "norm"} if options is not None else {} + else: + # Avoid in-place modifying a user-specified dict. + adjoint_options = adjoint_options.copy() + + if adjoint_params is None: + adjoint_params = tuple(find_parameters(func)) + else: + adjoint_params = tuple(adjoint_params) # in case adjoint_params is a generator. + + # Filter params that don't require gradients. + oldlen_ = len(adjoint_params) + adjoint_params = tuple(p for p in adjoint_params if p.requires_grad) + if len(adjoint_params) != oldlen_: + # Some params were excluded. + # Issue a warning if a user-specified norm is specified. + if 'norm' in adjoint_options and callable(adjoint_options['norm']): + warnings.warn("An adjoint parameter was passed without requiring gradient. For efficiency this will be " + "excluded from the adjoint pass, and will not appear as a tensor in the adjoint norm.") + + # Convert to flattened state. + shapes, func, y0, t, rtol, atol, method, options, event_fn, decreasing_time = _check_inputs(func, y0, t, rtol, atol, method, options, event_fn, SOLVERS) + + # Handle the adjoint norm function. + state_norm = options["norm"] + handle_adjoint_norm_(adjoint_options, shapes, state_norm) + + ans = OdeintAdjointMethod.apply(shapes, func, y0, t, rtol, atol, method, options, event_fn, adjoint_rtol, adjoint_atol, + adjoint_method, adjoint_options, t.requires_grad, *adjoint_params) + + if event_fn is None: + solution = ans + else: + event_t, solution = ans + event_t = event_t.to(t) + if decreasing_time: + event_t = -event_t + + if shapes is not None: + solution = _flat_to_shape(solution, (len(t),), shapes) + + if event_fn is None: + return solution + else: + return event_t, solution + + +def find_parameters(module): + + assert isinstance(module, nn.Module) + + # If called within DataParallel, parameters won't appear in module.parameters(). + if getattr(module, '_is_replica', False): + + def find_tensor_attributes(module): + tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v) and v.requires_grad] + return tuples + + gen = module._named_members(get_members_fn=find_tensor_attributes) + return [param for _, param in gen] + else: + return list(module.parameters()) + + +def handle_adjoint_norm_(adjoint_options, shapes, state_norm): + """In-place modifies the adjoint options to choose or wrap the norm function.""" + + # This is the default adjoint norm on the backward pass: a mixed norm over the tuple of inputs. + def default_adjoint_norm(tensor_tuple): + t, y, adj_y, *adj_params = tensor_tuple + # (If the state is actually a flattened tuple then this will be unpacked again in state_norm.) + return max(t.abs(), state_norm(y), state_norm(adj_y), _mixed_norm(adj_params)) + + if "norm" not in adjoint_options: + # `adjoint_options` was not explicitly specified by the user. Use the default norm. + adjoint_options["norm"] = default_adjoint_norm + else: + # `adjoint_options` was explicitly specified by the user... + try: + adjoint_norm = adjoint_options['norm'] + except KeyError: + # ...but they did not specify the norm argument. Back to plan A: use the default norm. + adjoint_options['norm'] = default_adjoint_norm + else: + # ...and they did specify the norm argument. + if adjoint_norm == 'seminorm': + # They told us they want to use seminorms. Slight modification to plan A: use the default norm, + # but ignore the parameter state + def adjoint_seminorm(tensor_tuple): + t, y, adj_y, *adj_params = tensor_tuple + # (If the state is actually a flattened tuple then this will be unpacked again in state_norm.) + return max(t.abs(), state_norm(y), state_norm(adj_y)) + adjoint_options['norm'] = adjoint_seminorm + else: + # And they're using their own custom norm. + if shapes is None: + # The state on the forward pass was a tensor, not a tuple. We don't need to do anything, they're + # already going to get given the full adjoint state as (t, y, adj_y, adj_params) + pass # this branch included for clarity + else: + # This is the bit that is tuple/tensor abstraction-breaking, because the odeint machinery + # doesn't know about the tupled nature of the forward state. We need to tell the user's adjoint + # norm about that ourselves. + + def _adjoint_norm(tensor_tuple): + t, y, adj_y, *adj_params = tensor_tuple + y = _flat_to_shape(y, (), shapes) + adj_y = _flat_to_shape(adj_y, (), shapes) + return adjoint_norm((t, *y, *adj_y, *adj_params)) + adjoint_options['norm'] = _adjoint_norm \ No newline at end of file diff --git a/nnodely/support/odeint/dopri5.py b/nnodely/support/odeint/dopri5.py new file mode 100644 index 00000000..9d6e3024 --- /dev/null +++ b/nnodely/support/odeint/dopri5.py @@ -0,0 +1,35 @@ +import torch +from nnodely.support.odeint.rk_solvers import _ButcherTableau, RKAdaptiveStepsizeODESolver + +_DORMAND_PRINCE_SHAMPINE_TABLEAU = _ButcherTableau( + alpha=torch.tensor([1 / 5, 3 / 10, 4 / 5, 8 / 9, 1., 1.], dtype=torch.float32), + beta=[ + torch.tensor([1 / 5], dtype=torch.float32), + torch.tensor([3 / 40, 9 / 40], dtype=torch.float32), + torch.tensor([44 / 45, -56 / 15, 32 / 9], dtype=torch.float32), + torch.tensor([19372 / 6561, -25360 / 2187, 64448 / 6561, -212 / 729], dtype=torch.float32), + torch.tensor([9017 / 3168, -355 / 33, 46732 / 5247, 49 / 176, -5103 / 18656], dtype=torch.float32), + torch.tensor([35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84], dtype=torch.float32), + ], + c_sol=torch.tensor([35 / 384, 0, 500 / 1113, 125 / 192, -2187 / 6784, 11 / 84, 0], dtype=torch.float32), + c_error=torch.tensor([ + 35 / 384 - 1951 / 21600, + 0, + 500 / 1113 - 22642 / 50085, + 125 / 192 - 451 / 720, + -2187 / 6784 - -12231 / 42400, + 11 / 84 - 649 / 6300, + -1. / 60., + ], dtype=torch.float32), +) + +DPS_C_MID = torch.tensor([ + 6025192743 / 30085553152 / 2, 0, 51252292925 / 65400821598 / 2, -2691868925 / 45128329728 / 2, + 187940372067 / 1594534317056 / 2, -1776094331 / 19743644256 / 2, 11237099 / 235043384 / 2 +], dtype=torch.float32) + + +class Dopri5Solver(RKAdaptiveStepsizeODESolver): + order = 5 + tableau = _DORMAND_PRINCE_SHAMPINE_TABLEAU + mid = DPS_C_MID \ No newline at end of file diff --git a/nnodely/support/odeint/fixed_grid.py b/nnodely/support/odeint/fixed_grid.py new file mode 100644 index 00000000..28bbf28d --- /dev/null +++ b/nnodely/support/odeint/fixed_grid.py @@ -0,0 +1,18 @@ +from nnodely.support.odeint.solvers import FixedGridODESolver +from nnodely.support.odeint.rk_solvers import rk4_step_func + + +class Euler(FixedGridODESolver): + order = 1 + + def _step_func(self, func, t0, dt, t1, y0): + f0 = func(t0, y0) + return dt * f0, f0 + + +class RK4(FixedGridODESolver): + order = 4 + + def _step_func(self, func, t0, dt, t1, y0): + f0 = func(t0, y0) + return rk4_step_func(func, t0, dt, t1, y0, f0=f0), f0 \ No newline at end of file diff --git a/nnodely/support/odeint/my_odeint.py b/nnodely/support/odeint/my_odeint.py new file mode 100644 index 00000000..46c9bdb6 --- /dev/null +++ b/nnodely/support/odeint/my_odeint.py @@ -0,0 +1,147 @@ +import torch +from torch.autograd.functional import vjp +from nnodely.support.odeint.utils import _check_inputs, _flat_to_shape +from nnodely.support.odeint.dopri5 import Dopri5Solver +from nnodely.support.odeint.fixed_grid import Euler, RK4 + +SOLVERS = { + 'dopri5': Dopri5Solver, + 'euler': Euler, + 'rk4': RK4, +} + + +def odeint(func, y0, t, *, rtol=1e-7, atol=1e-9, method=None, options=None, event_fn=None): + """Integrate a system of ordinary differential equations. + + Solves the initial value problem for a non-stiff system of first order ODEs: + ``` + dy/dt = func(t, y), y(t[0]) = y0 + ``` + where y is a Tensor or tuple of Tensors of any shape. + + Output dtypes and numerical precision are based on the dtypes of the inputs `y0`. + + Args: + func: Function that maps a scalar Tensor `t` and a Tensor holding the state `y` + into a Tensor of state derivatives with respect to time. Optionally, `y` + can also be a tuple of Tensors. + y0: N-D Tensor giving starting value of `y` at time point `t[0]`. Optionally, `y0` + can also be a tuple of Tensors. + t: 1-D Tensor holding a sequence of time points for which to solve for + `y`, in either increasing or decreasing order. The first element of + this sequence is taken to be the initial time point. + rtol: optional float32 Tensor specifying an upper bound on relative error, + per element of `y`. + atol: optional float32 Tensor specifying an upper bound on absolute error, + per element of `y`. + method: optional string indicating the integration method to use. + options: optional dict of configuring options for the indicated integration + method. Can only be provided if a `method` is explicitly set. + event_fn: Function that maps the state `y` to a Tensor. The solve terminates when + event_fn evaluates to zero. If this is not None, all but the first elements of + `t` are ignored. + + Returns: + y: Tensor, where the first dimension corresponds to different + time points. Contains the solved value of y for each desired time point in + `t`, with the initial value `y0` being the first element along the first + dimension. + + Raises: + ValueError: if an invalid `method` is provided. + """ + + shapes, func, y0, t, rtol, atol, method, options, event_fn, t_is_reversed = _check_inputs(func, y0, t, rtol, atol, method, options, event_fn, SOLVERS) + + solver = SOLVERS[method](func=func, y0=y0, rtol=rtol, atol=atol, **options) + + if event_fn is None: + solution = solver.integrate(t) + else: + event_t, solution = solver.integrate_until_event(t[0], event_fn) + event_t = event_t.to(t) + if t_is_reversed: + event_t = -event_t + + if shapes is not None: + solution = _flat_to_shape(solution, (len(t),), shapes) + + if event_fn is None: + return solution + else: + return event_t, solution + + +def odeint_event(func, y0, t0, *, event_fn, reverse_time=False, odeint_interface=odeint, **kwargs): + """Automatically links up the gradient from the event time.""" + + if reverse_time: + t = torch.cat([t0.reshape(-1), t0.reshape(-1).detach() - 1.0]) + else: + t = torch.cat([t0.reshape(-1), t0.reshape(-1).detach() + 1.0]) + + event_t, solution = odeint_interface(func, y0, t, event_fn=event_fn, **kwargs) + + # Dummy values for rtol, atol, method, and options. + shapes, _func, _, t, _, _, _, _, event_fn, _ = _check_inputs(func, y0, t, 0.0, 0.0, None, None, event_fn, SOLVERS) + + if shapes is not None: + state_t = torch.cat([s[-1].reshape(-1) for s in solution]) + else: + state_t = solution[-1] + + # Event_fn takes in negated time value if reverse_time is True. + if reverse_time: + event_t = -event_t + + event_t, state_t = ImplicitFnGradientRerouting.apply(_func, event_fn, event_t, state_t) + + # Return the user expected time value. + if reverse_time: + event_t = -event_t + + if shapes is not None: + state_t = _flat_to_shape(state_t, (), shapes) + solution = tuple(torch.cat([s[:-1], s_t[None]], dim=0) for s, s_t in zip(solution, state_t)) + else: + solution = torch.cat([solution[:-1], state_t[None]], dim=0) + + return event_t, solution + + +class ImplicitFnGradientRerouting(torch.autograd.Function): + + @staticmethod + def forward(ctx, func, event_fn, event_t, state_t): + """ event_t is the solution to event_fn """ + ctx.func = func + ctx.event_fn = event_fn + ctx.save_for_backward(event_t, state_t) + return event_t.detach(), state_t.detach() + + @staticmethod + def backward(ctx, grad_t, grad_state): + func = ctx.func + event_fn = ctx.event_fn + event_t, state_t = ctx.saved_tensors + + event_t = event_t.detach().clone().requires_grad_(True) + state_t = state_t.detach().clone().requires_grad_(True) + + f_val = func(event_t, state_t) + + with torch.enable_grad(): + c, (par_dt, dstate) = vjp(event_fn, (event_t, state_t)) + + # Total derivative of event_fn wrt t evaluated at event_t. + dcdt = par_dt + torch.sum(dstate * f_val) + + # Add the gradient from final state to final time value as if a regular odeint was called. + grad_t = grad_t + torch.sum(grad_state * f_val) + + dstate = dstate * (-grad_t / (dcdt + 1e-12)).reshape_as(c) + + grad_state = grad_state + dstate + + return None, None, None, grad_state \ No newline at end of file diff --git a/nnodely/support/odeint/rk_solvers.py b/nnodely/support/odeint/rk_solvers.py new file mode 100644 index 00000000..134a4183 --- /dev/null +++ b/nnodely/support/odeint/rk_solvers.py @@ -0,0 +1,458 @@ +import collections +import warnings +import torch +from nnodely.support.odeint.solvers import _handle_unused_kwargs, find_event, AdaptiveStepsizeEventODESolver, FixedGridODESolver + +_ButcherTableau = collections.namedtuple('_ButcherTableau', 'alpha, beta, c_sol, c_error') + + +_RungeKuttaState = collections.namedtuple('_RungeKuttaState', 'y1, f1, t0, t1, dt, interp_coeff') +# Saved state of the Runge Kutta solver. +# +# Attributes: +# y1: Tensor giving the function value at the end of the last time step. +# f1: Tensor giving derivative at the end of the last time step. +# t0: scalar float32 Tensor giving start of the last time step. +# t1: scalar float32 Tensor giving end of the last time step. +# dt: scalar float32 Tensor giving the size for the next time step. +# interp_coeff: list of Tensors giving coefficients for polynomial +# interpolation between `t0` and `t1`. + +def _interp_fit(y0, y1, y_mid, f0, f1, dt): + """Fit coefficients for 4th order polynomial interpolation. + + Args: + y0: function value at the start of the interval. + y1: function value at the end of the interval. + y_mid: function value at the mid-point of the interval. + f0: derivative value at the start of the interval. + f1: derivative value at the end of the interval. + dt: width of the interval. + + Returns: + List of coefficients `[a, b, c, d, e]` for interpolating with the polynomial + `p = a * x ** 4 + b * x ** 3 + c * x ** 2 + d * x + e` for values of `x` + between 0 (start of interval) and 1 (end of interval). + """ + a = 2 * dt * (f1 - f0) - 8 * (y1 + y0) + 16 * y_mid + b = dt * (5 * f0 - 3 * f1) + 18 * y0 + 14 * y1 - 32 * y_mid + c = dt * (f1 - 4 * f0) - 11 * y0 - 5 * y1 + 16 * y_mid + d = dt * f0 + e = y0 + return [e, d, c, b, a] + +def _interp_evaluate(coefficients, t0, t1, t): + """Evaluate polynomial interpolation at the given time point. + + Args: + coefficients: list of Tensor coefficients as created by `interp_fit`. + t0: scalar float32 Tensor giving the start of the interval. + t1: scalar float32 Tensor giving the end of the interval. + t: scalar float32 Tensor giving the desired interpolation point. + + Returns: + Polynomial interpolation of the coefficients at time `t`. + """ + + assert (t0 <= t) & (t <= t1), 'invalid interpolation, fails `t0 <= t <= t1`: {}, {}, {}'.format(t0, t, t1) + x = (t - t0) / (t1 - t0) + x = x.to(coefficients[0].dtype) + + total = coefficients[0] + x * coefficients[1] + x_power = x + for coefficient in coefficients[2:]: + x_power = x_power * x + total = total + x_power * coefficient + + return total + +def _select_initial_step(func, t0, y0, order, rtol, atol, norm, f0=None): + """Empirically select a good initial step. + + The algorithm is described in [1]_. + + References + ---------- + .. [1] E. Hairer, S. P. Norsett G. Wanner, "Solving Ordinary Differential + Equations I: Nonstiff Problems", Sec. II.4, 2nd edition. + """ + + dtype = y0.dtype + device = y0.device + t_dtype = t0.dtype + t0 = t0.to(t_dtype) + + if f0 is None: + f0 = func(t0, y0) + + scale = atol + torch.abs(y0) * rtol + + d0 = norm(y0 / scale).abs() + d1 = norm(f0 / scale).abs() + + if d0 < 1e-5 or d1 < 1e-5: + h0 = torch.tensor(1e-6, dtype=dtype, device=device) + else: + h0 = 0.01 * d0 / d1 + h0 = h0.abs() + + y1 = y0 + h0 * f0 + f1 = func(t0 + h0, y1) + + d2 = torch.abs(norm((f1 - f0) / scale) / h0) + + if d1 <= 1e-15 and d2 <= 1e-15: + h1 = torch.max(torch.tensor(1e-6, dtype=dtype, device=device), h0 * 1e-3) + else: + h1 = (0.01 / max(d1, d2)) ** (1. / float(order + 1)) + h1 = h1.abs() + + return torch.min(100 * h0, h1).to(t_dtype) + +def _compute_error_ratio(error_estimate, rtol, atol, y0, y1, norm): + error_tol = atol + rtol * torch.max(y0.abs(), y1.abs()) + return norm(error_estimate / error_tol).abs() + +@torch.no_grad() +def _optimal_step_size(last_step, error_ratio, safety, ifactor, dfactor, order): + """Calculate the optimal size for the next step.""" + if error_ratio == 0: + return last_step * ifactor + if error_ratio < 1: + dfactor = torch.ones((), dtype=last_step.dtype, device=last_step.device) + error_ratio = error_ratio.type_as(last_step) + exponent = torch.tensor(order, dtype=last_step.dtype, device=last_step.device).reciprocal() + factor = torch.min(ifactor, torch.max(safety / error_ratio ** exponent, dfactor)) + return last_step * factor + +class _UncheckedAssign(torch.autograd.Function): + @staticmethod + def forward(ctx, scratch, value, index): + ctx.index = index + scratch.data[index] = value # sneak past the version checker + return scratch + + @staticmethod + def backward(ctx, grad_scratch): + return grad_scratch, grad_scratch[ctx.index], None + +def _runge_kutta_step(func, y0, f0, t0, dt, t1, tableau): + """Take an arbitrary Runge-Kutta step and estimate error. + Args: + func: Function to evaluate like `func(t, y)` to compute the time derivative of `y`. + y0: Tensor initial value for the state. + f0: Tensor initial value for the derivative, computed from `func(t0, y0)`. + t0: float32 scalar Tensor giving the initial time. + dt: float32 scalar Tensor giving the size of the desired time step. + t1: float32 scalar Tensor giving the end time; equal to t0 + dt. This is used (rather than t0 + dt) to ensure + floating point accuracy when needed. + tableau: _ButcherTableau describing how to take the Runge-Kutta step. + Returns: + Tuple `(y1, f1, y1_error, k)` giving the estimated function value after + the Runge-Kutta step at `t1 = t0 + dt`, the derivative of the state at `t1`, + estimated error at `t1`, and a list of Runge-Kutta coefficients `k` used for + calculating these terms. + """ + + t_dtype = y0.abs().dtype + + t0 = t0.to(t_dtype) + dt = dt.to(t_dtype) + t1 = t1.to(t_dtype) + + # We use an unchecked assign to put data into k without incrementing its _version counter, so that the backward + # doesn't throw an (overzealous) error about in-place correctness. We know that it's actually correct. + k = torch.empty(*f0.shape, len(tableau.alpha) + 1, dtype=y0.dtype, device=y0.device) + k = _UncheckedAssign.apply(k, f0, (..., 0)) + for i, (alpha_i, beta_i) in enumerate(zip(tableau.alpha, tableau.beta)): + if alpha_i == 1.: + # Always step to perturbing just before the end time, in case of discontinuities. + ti = t1 + else: + ti = t0 + alpha_i * dt + yi = y0 + torch.sum(k[..., :i + 1] * (beta_i * dt), dim=-1).view_as(f0) + f = func(ti, yi) + k = _UncheckedAssign.apply(k, f, (..., i + 1)) + + if not (tableau.c_sol[-1] == 0 and (tableau.c_sol[:-1] == tableau.beta[-1]).all()): + # This property (true for Dormand-Prince) lets us save a few FLOPs. + yi = y0 + torch.sum(k * (dt * tableau.c_sol), dim=-1).view_as(f0) + + y1 = yi + f1 = k[..., -1] + y1_error = torch.sum(k * (dt * tableau.c_error), dim=-1) + return y1, f1, y1_error, k + + +def rk4_step_func(func, t0, dt, t1, y0, f0=None): + k1 = f0 + if k1 is None: + k1 = func(t0, y0) + half_dt = dt * 0.5 + k2 = func(t0 + half_dt, y0 + half_dt * k1) + k3 = func(t0 + half_dt, y0 + half_dt * k2) + k4 = func(t1, y0 + dt * k3) + return (k1 + 2 * (k2 + k3) + k4) * dt * (1.0 / 6.0) + + +class RKAdaptiveStepsizeODESolver(AdaptiveStepsizeEventODESolver): + order: int + tableau: _ButcherTableau + mid: torch.Tensor + + def __init__(self, func, y0, rtol, atol, + min_step=1e-8, + max_step=float('inf'), + first_step=None, + step_t=None, + jump_t=None, + safety=0.9, + ifactor=10.0, + dfactor=0.2, + max_num_steps=2**20, + dtype=torch.float32, + **kwargs): + super(RKAdaptiveStepsizeODESolver, self).__init__(dtype=dtype, y0=y0, **kwargs) + + # We use mixed precision. y has its original dtype (probably float32), whilst all 'time'-like objects use + # `dtype` (defaulting to float32). + dtype = torch.promote_types(dtype, y0.abs().dtype) + device = y0.device + + self.func = func + self.rtol = torch.as_tensor(rtol, dtype=dtype, device=device) + self.atol = torch.as_tensor(atol, dtype=dtype, device=device) + self.min_step = torch.as_tensor(min_step, dtype=dtype, device=device) + self.max_step = torch.as_tensor(max_step, dtype=dtype, device=device) + self.first_step = None if first_step is None else torch.as_tensor(first_step, dtype=dtype, device=device) + self.safety = torch.as_tensor(safety, dtype=dtype, device=device) + self.ifactor = torch.as_tensor(ifactor, dtype=dtype, device=device) + self.dfactor = torch.as_tensor(dfactor, dtype=dtype, device=device) + self.max_num_steps = torch.as_tensor(max_num_steps, dtype=torch.int32, device=device) + self.dtype = dtype + + self.step_t = None if step_t is None else torch.as_tensor(step_t, dtype=dtype, device=device) + self.jump_t = None if jump_t is None else torch.as_tensor(jump_t, dtype=dtype, device=device) + + # Copy from class to instance to set device + self.tableau = _ButcherTableau(alpha=self.tableau.alpha.to(device=device, dtype=y0.dtype), + beta=[b.to(device=device, dtype=y0.dtype) for b in self.tableau.beta], + c_sol=self.tableau.c_sol.to(device=device, dtype=y0.dtype), + c_error=self.tableau.c_error.to(device=device, dtype=y0.dtype)) + self.mid = self.mid.to(device=device, dtype=y0.dtype) + + @classmethod + def valid_callbacks(cls): + return super(RKAdaptiveStepsizeODESolver, cls).valid_callbacks() | {'callback_step', + 'callback_accept_step', + 'callback_reject_step'} + + def _before_integrate(self, t): + t0 = t[0] + f0 = self.func(t[0], self.y0) + if self.first_step is None: + first_step = _select_initial_step(self.func, t[0], self.y0, self.order - 1, self.rtol, self.atol, + self.norm, f0=f0) + else: + first_step = self.first_step + self.rk_state = _RungeKuttaState(self.y0, f0, t[0], t[0], first_step, [self.y0] * 5) + + def _advance(self, next_t): + """Interpolate through the next time point, integrating as necessary.""" + n_steps = 0 + while next_t > self.rk_state.t1: + assert n_steps < self.max_num_steps, 'max_num_steps exceeded ({}>={})'.format(n_steps, self.max_num_steps) + self.rk_state = self._adaptive_step(self.rk_state) + n_steps += 1 + return _interp_evaluate(self.rk_state.interp_coeff, self.rk_state.t0, self.rk_state.t1, next_t) + + def _advance_until_event(self, event_fn): + """Returns t, state(t) such that event_fn(t, state(t)) == 0.""" + if event_fn(self.rk_state.t1, self.rk_state.y1) == 0: + return (self.rk_state.t1, self.rk_state.y1) + + n_steps = 0 + sign0 = torch.sign(event_fn(self.rk_state.t1, self.rk_state.y1)) + while sign0 == torch.sign(event_fn(self.rk_state.t1, self.rk_state.y1)): + assert n_steps < self.max_num_steps, 'max_num_steps exceeded ({}>={})'.format(n_steps, self.max_num_steps) + self.rk_state = self._adaptive_step(self.rk_state) + n_steps += 1 + interp_fn = lambda t: _interp_evaluate(self.rk_state.interp_coeff, self.rk_state.t0, self.rk_state.t1, t) + return find_event(interp_fn, sign0, self.rk_state.t0, self.rk_state.t1, event_fn, self.atol) + + def _adaptive_step(self, rk_state): + """Take an adaptive Runge-Kutta step to integrate the ODE.""" + y0, f0, _, t0, dt, interp_coeff = rk_state + if not torch.isfinite(dt): + dt = self.min_step + dt = dt.clamp(self.min_step, self.max_step) + self.func.callback_step(t0, y0, dt) + t1 = t0 + dt + # dtypes: self.y0.dtype (probably float32); self.dtype (probably float32) + # used for state and timelike objects respectively. + # Then: + # y0.dtype == self.y0.dtype + # f0.dtype == self.y0.dtype + # t0.dtype == self.dtype + # dt.dtype == self.dtype + # for coeff in interp_coeff: coeff.dtype == self.y0.dtype + + ######################################################## + # Assertions # + ######################################################## + assert t0 + dt > t0, 'underflow in dt {}'.format(dt.item()) + assert torch.isfinite(y0).all(), 'non-finite values in state `y`: {}'.format(y0) + + y1, f1, y1_error, k = _runge_kutta_step(self.func, y0, f0, t0, dt, t1, tableau=self.tableau) + # dtypes: + # y1.dtype == self.y0.dtype + # f1.dtype == self.y0.dtype + # y1_error.dtype == self.dtype + # k.dtype == self.y0.dtype + + ######################################################## + # Error Ratio # + ######################################################## + error_ratio = _compute_error_ratio(y1_error, self.rtol, self.atol, y0, y1, self.norm) + accept_step = error_ratio <= 1 + + # Handle min max stepping + if dt > self.max_step: + accept_step = False + if dt <= self.min_step: + accept_step = True + + # dtypes: + # error_ratio.dtype == self.dtype + + ######################################################## + # Update RK State # + ######################################################## + if accept_step: + self.func.callback_accept_step(t0, y0, dt) + t_next = t1 + y_next = y1 + interp_coeff = self._interp_fit(y0, y_next, k, dt) + f_next = f1 + else: + self.func.callback_reject_step(t0, y0, dt) + t_next = t0 + y_next = y0 + f_next = f0 + dt_next = _optimal_step_size(dt, error_ratio, self.safety, self.ifactor, self.dfactor, self.order) + dt_next = dt_next.clamp(self.min_step, self.max_step) + rk_state = _RungeKuttaState(y_next, f_next, t0, t_next, dt_next, interp_coeff) + return rk_state + + def _interp_fit(self, y0, y1, k, dt): + """Fit an interpolating polynomial to the results of a Runge-Kutta step.""" + dt = dt.type_as(y0) + y_mid = y0 + torch.sum(k * (dt * self.mid), dim=-1).view_as(y0) + f0 = k[..., 0] + f1 = k[..., -1] + return _interp_fit(y0, y1, y_mid, f0, f1, dt) + +class FixedGridFIRKODESolver(FixedGridODESolver): + order: int + tableau: _ButcherTableau + + def __init__(self, func, y0, step_size=None, grid_constructor=None, interp='linear', perturb=False, max_iters=100, **unused_kwargs): + + self.max_iters = max_iters + self.atol = unused_kwargs.pop('atol') + unused_kwargs.pop('rtol', None) + unused_kwargs.pop('norm', None) + _handle_unused_kwargs(self, unused_kwargs) + del unused_kwargs + + self.func = func + self.y0 = y0 + self.dtype = y0.dtype + self.device = y0.device + self.step_size = step_size + self.interp = interp + self.perturb = perturb + + if step_size is None: + if grid_constructor is None: + self.grid_constructor = lambda f, y0, t: t + else: + self.grid_constructor = grid_constructor + else: + if grid_constructor is None: + self.grid_constructor = self._grid_constructor_from_step_size(step_size) + else: + raise ValueError("step_size and grid_constructor are mutually exclusive arguments.") + + self.tableau = _ButcherTableau(alpha=self.tableau.alpha.to(device=self.device, dtype=y0.dtype), + beta=[b.to(device=self.device, dtype=y0.dtype) for b in self.tableau.beta], + c_sol=self.tableau.c_sol.to(device=self.device, dtype=y0.dtype), + c_error=self.tableau.c_error.to(device=self.device, dtype=y0.dtype)) + + def _step_func(self, func, t0, dt, t1, y0): + if not isinstance(t0, torch.Tensor): + t0 = torch.tensor(t0) + if not isinstance(dt, torch.Tensor): + dt = torch.tensor(dt) + if not isinstance(t1, torch.Tensor): + t1 = torch.tensor(t1) + f0 = func(t0, y0) + + t_dtype = y0.abs().dtype + tol = 1e-8 + if t_dtype == torch.float32: + tol = 1e-8 + if t_dtype == torch.float32: + tol = 1e-6 + + t0 = t0.to(t_dtype) + dt = dt.to(t_dtype) + t1 = t1.to(t_dtype) + + k = f0.clone().unsqueeze(-1).tile(len(self.tableau.alpha)) + beta = torch.stack(self.tableau.beta, -1) + + # Broyden's Method to solve the system of nonlinear equations + y = torch.matmul(k, beta * dt).add(y0.unsqueeze(-1)).movedim(-1, 0) + f = self._residual(func, k, y, t0, dt, t1) + J = torch.ones_like(f).diag() + converged = False + for _ in range(self.max_iters): + if torch.linalg.norm(f, 2) < tol: + converged = True + break + + # If the matrix becomes singular, just stop and return the last value + try: + s = -torch.linalg.solve(J, f) + except torch._C._LinAlgError: + break + + k = k + s.reshape_as(k) + y = torch.matmul(k, beta * dt).add(y0.unsqueeze(-1)).movedim(-1, 0) + newf = self._residual(func, k, y, t0, dt, t1) + z = newf - f + f = newf + J = J + (torch.outer ((z - torch.linalg.vecdot(J,s)),s)) / (torch.dot(s,s)) + + if not converged: + warnings.warn('Functional iteration did not converge. Solution may be incorrect.') + + dy = torch.matmul(k, dt * self.tableau.c_sol) + + return dy, f0 + + def _residual(self, func, K, y, t0, dt, t1): + res = torch.zeros_like(K) + for i, (y_i, alpha_i) in enumerate(zip(y, self.tableau.alpha)): + if alpha_i == 1.: + ti = t1 + elif alpha_i == 0.: + if not torch.all(self.tableau.beta[i]): + # Same slope as stored so skip + continue + ti = t0 + else: + ti = t0 + alpha_i * dt + res[...,i] = K[...,i] - func(ti, y_i) + return res.flatten() \ No newline at end of file diff --git a/nnodely/support/odeint/solvers.py b/nnodely/support/odeint/solvers.py new file mode 100644 index 00000000..39ba70c3 --- /dev/null +++ b/nnodely/support/odeint/solvers.py @@ -0,0 +1,202 @@ +import abc +import math +import warnings +import torch + +def _handle_unused_kwargs(solver, unused_kwargs): + if len(unused_kwargs) > 0: + warnings.warn('{}: Unexpected arguments {}'.format(solver.__class__.__name__, unused_kwargs)) + +def find_event(interp_fn, sign0, t0, t1, event_fn, tol): + with torch.no_grad(): + + # Num iterations for the secant method until tolerance is within target. + nitrs = torch.ceil(torch.log((t1 - t0) / tol) / math.log(2.0)) + + for _ in range(nitrs.long()): + t_mid = (t1 + t0) / 2.0 + y_mid = interp_fn(t_mid) + sign_mid = torch.sign(event_fn(t_mid, y_mid)) + same_as_sign0 = (sign0 == sign_mid) + t0 = torch.where(same_as_sign0, t_mid, t0) + t1 = torch.where(same_as_sign0, t1, t_mid) + event_t = (t0 + t1) / 2.0 + + return event_t, interp_fn(event_t) + +class AdaptiveStepsizeODESolver(metaclass=abc.ABCMeta): + def __init__(self, dtype, y0, norm, **unused_kwargs): + _handle_unused_kwargs(self, unused_kwargs) + del unused_kwargs + + self.y0 = y0 + self.dtype = dtype + + self.norm = norm + + def _before_integrate(self, t): + pass + + @abc.abstractmethod + def _advance(self, next_t): + raise NotImplementedError + + @classmethod + def valid_callbacks(cls): + return set() + + def integrate(self, t): + solution = torch.empty(len(t), *self.y0.shape, dtype=self.y0.dtype, device=self.y0.device) + solution[0] = self.y0 + t = t.to(self.dtype) + self._before_integrate(t) + for i in range(1, len(t)): + solution[i] = self._advance(t[i]) + return solution + + +class AdaptiveStepsizeEventODESolver(AdaptiveStepsizeODESolver, metaclass=abc.ABCMeta): + + @abc.abstractmethod + def _advance_until_event(self, event_fn): + raise NotImplementedError + + def integrate_until_event(self, t0, event_fn): + t0 = t0.to(self.y0.device, self.dtype) + self._before_integrate(t0.reshape(-1)) + event_time, y1 = self._advance_until_event(event_fn) + solution = torch.stack([self.y0, y1], dim=0) + return event_time, solution + + +class FixedGridODESolver(metaclass=abc.ABCMeta): + order: int + + def __init__(self, func, y0, step_size=None, grid_constructor=None, interp="linear", perturb=False, **unused_kwargs): + self.atol = unused_kwargs.pop('atol') + unused_kwargs.pop('rtol', None) + unused_kwargs.pop('norm', None) + _handle_unused_kwargs(self, unused_kwargs) + del unused_kwargs + + self.func = func + self.y0 = y0 + self.dtype = y0.dtype + self.device = y0.device + self.step_size = step_size + self.interp = interp + self.perturb = perturb + + if step_size is None: + if grid_constructor is None: + self.grid_constructor = lambda f, y0, t: t + else: + self.grid_constructor = grid_constructor + else: + if grid_constructor is None: + self.grid_constructor = self._grid_constructor_from_step_size(step_size) + else: + raise ValueError("step_size and grid_constructor are mutually exclusive arguments.") + + @classmethod + def valid_callbacks(cls): + return {'callback_step'} + + @staticmethod + def _grid_constructor_from_step_size(step_size): + def _grid_constructor(func, y0, t): + start_time = t[0] + end_time = t[-1] + + niters = torch.ceil((end_time - start_time) / step_size + 1).item() + t_infer = torch.arange(0, niters, dtype=t.dtype, device=t.device) * step_size + start_time + t_infer[-1] = t[-1] + + return t_infer + return _grid_constructor + + @abc.abstractmethod + def _step_func(self, func, t0, dt, t1, y0): + pass + + def integrate(self, t): + time_grid = self.grid_constructor(self.func, self.y0, t) + assert time_grid[0] == t[0] and time_grid[-1] == t[-1] + + solution = torch.empty(len(t), *self.y0.shape, dtype=self.y0.dtype, device=self.y0.device) + solution[0] = self.y0 + + j = 1 + y0 = self.y0 + for t0, t1 in zip(time_grid[:-1], time_grid[1:]): + dt = t1 - t0 + self.func.callback_step(t0, y0, dt) + dy, f0 = self._step_func(self.func, t0, dt, t1, y0) + y1 = y0 + dy + + while j < len(t) and t1 >= t[j]: + if self.interp == "linear": + solution[j] = self._linear_interp(t0, t1, y0, y1, t[j]) + elif self.interp == "cubic": + f1 = self.func(t1, y1) + solution[j] = self._cubic_hermite_interp(t0, y0, f0, t1, y1, f1, t[j]) + else: + raise ValueError(f"Unknown interpolation method {self.interp}") + j += 1 + y0 = y1 + + return solution + + def integrate_until_event(self, t0, event_fn): + assert self.step_size is not None, "Event handling for fixed step solvers currently requires `step_size` to be provided in options." + + t0 = t0.type_as(self.y0.abs()) + y0 = self.y0 + dt = self.step_size + + sign0 = torch.sign(event_fn(t0, y0)) + max_itrs = 20000 + itr = 0 + while True: + itr += 1 + t1 = t0 + dt + dy, f0 = self._step_func(self.func, t0, dt, t1, y0) + y1 = y0 + dy + + sign1 = torch.sign(event_fn(t1, y1)) + + if sign0 != sign1: + if self.interp == "linear": + interp_fn = lambda t: self._linear_interp(t0, t1, y0, y1, t) + elif self.interp == "cubic": + f1 = self.func(t1, y1) + interp_fn = lambda t: self._cubic_hermite_interp(t0, y0, f0, t1, y1, f1, t) + else: + raise ValueError(f"Unknown interpolation method {self.interp}") + event_time, y1 = find_event(interp_fn, sign0, t0, t1, event_fn, float(self.atol)) + break + else: + t0, y0 = t1, y1 + + if itr >= max_itrs: + raise RuntimeError(f"Reached maximum number of iterations {max_itrs}.") + solution = torch.stack([self.y0, y1], dim=0) + return event_time, solution + + def _cubic_hermite_interp(self, t0, y0, f0, t1, y1, f1, t): + h = (t - t0) / (t1 - t0) + h00 = (1 + 2 * h) * (1 - h) * (1 - h) + h10 = h * (1 - h) * (1 - h) + h01 = h * h * (3 - 2 * h) + h11 = h * h * (h - 1) + dt = (t1 - t0) + return h00 * y0 + h10 * dt * f0 + h01 * y1 + h11 * dt * f1 + + def _linear_interp(self, t0, t1, y0, y1, t): + if t == t0: + return y0 + if t == t1: + return y1 + slope = (t - t0) / (t1 - t0) + return y0 + slope * (y1 - y0) + diff --git a/nnodely/support/odeint/utils.py b/nnodely/support/odeint/utils.py new file mode 100644 index 00000000..5ec8d6c5 --- /dev/null +++ b/nnodely/support/odeint/utils.py @@ -0,0 +1,251 @@ +import torch +import warnings + +_all_callback_names = ['callback_step', 'callback_accept_step', 'callback_reject_step'] +_all_adjoint_callback_names = [name + '_adjoint' for name in _all_callback_names] +_null_callback = lambda *args, **kwargs: None + +def _linf_norm(tensor): + return tensor.abs().max() + +def _rms_norm(tensor): + return tensor.abs().pow(2).mean().sqrt() + +def _zero_norm(tensor): + return 0. + +def _mixed_norm(tensor_tuple): + if len(tensor_tuple) == 0: + return 0. + return max([_rms_norm(tensor) for tensor in tensor_tuple]) + + +def combine_event_functions(event_fn, t0, y0): + """ + We ensure all event functions are initially positive, + so then we can combine them by taking a min. + """ + with torch.no_grad(): + initial_signs = torch.sign(event_fn(t0, y0)) + + def combined_event_fn(t, y): + c = event_fn(t, y) + return torch.min(c * initial_signs) + + return combined_event_fn + + +def _tuple_tol(name, tol, shapes): + try: + iter(tol) + except TypeError: + return tol + tol = tuple(tol) + assert len(tol) == len(shapes), "If using tupled {} it must have the same length as the tuple y0".format(name) + tol = [torch.as_tensor(tol_).expand(shape.numel()) for tol_, shape in zip(tol, shapes)] + return torch.cat(tol) + + +def _flat_to_shape(tensor, length, shapes): + tensor_list = [] + total = 0 + for shape in shapes: + next_total = total + shape.numel() + # It's important that this be view((...)), not view(...). Else when length=(), shape=() it fails. + tensor_list.append(tensor[..., total:next_total].view((*length, *shape))) + total = next_total + return tuple(tensor_list) + + +def _assert_floating(name, t): + if not torch.is_floating_point(t): + raise TypeError('`{}` must be a floating point Tensor but is a {}'.format(name, t.type())) + + +def _check_timelike(name, timelike, can_grad): + assert isinstance(timelike, torch.Tensor), '{} must be a torch.Tensor'.format(name) + _assert_floating(name, timelike) + assert timelike.ndimension() == 1, "{} must be one dimensional".format(name) + if not can_grad: + assert not timelike.requires_grad, "{} cannot require gradient".format(name) + diff = timelike[1:] > timelike[:-1] + assert diff.all() or (~diff).all(), '{} must be strictly increasing or decreasing'.format(name) + + +class _TupleFunc(torch.nn.Module): + def __init__(self, base_func, shapes): + super(_TupleFunc, self).__init__() + self.base_func = base_func + self.shapes = shapes + + def forward(self, t, y): + f = self.base_func(t, _flat_to_shape(y, (), self.shapes)) + return torch.cat([f_.reshape(-1) for f_ in f]) + + +class _TupleInputOnlyFunc(torch.nn.Module): + def __init__(self, base_func, shapes): + super(_TupleInputOnlyFunc, self).__init__() + self.base_func = base_func + self.shapes = shapes + + def forward(self, t, y): + return self.base_func(t, _flat_to_shape(y, (), self.shapes)) + + +class _ReverseFunc(torch.nn.Module): + def __init__(self, base_func, mul=1.0): + super(_ReverseFunc, self).__init__() + self.base_func = base_func + self.mul = mul + + def forward(self, t, y): + return self.mul * self.base_func(-t, y) + + +def _check_inputs(func, y0, t, rtol, atol, method, options, event_fn, SOLVERS): + + if event_fn is not None: + if len(t) != 2: + raise ValueError(f"We require len(t) == 2 when in event handling mode, but got len(t)={len(t)}.") + + # Combine event functions if the output is multivariate. + event_fn = combine_event_functions(event_fn, t[0], y0) + + # Keep reference to original func as passed in + original_func = func + + # Normalise to tensor (non-tupled) input + shapes = None + is_tuple = not isinstance(y0, torch.Tensor) + if is_tuple: + assert isinstance(y0, tuple), 'y0 must be either a torch.Tensor or a tuple' + shapes = [y0_.shape for y0_ in y0] + rtol = _tuple_tol('rtol', rtol, shapes) + atol = _tuple_tol('atol', atol, shapes) + y0 = torch.cat([y0_.reshape(-1) for y0_ in y0]) + func = _TupleFunc(func, shapes) + if event_fn is not None: + event_fn = _TupleInputOnlyFunc(event_fn, shapes) + + # Normalise method and options + if options is None: + options = {} + else: + options = options.copy() + if method is None: + method = 'dopri5' + if method not in SOLVERS: + raise ValueError('Invalid method "{}". Must be one of {}'.format(method, + '{"' + '", "'.join(SOLVERS.keys()) + '"}.')) + + if is_tuple: + # We accept tupled input. This is an abstraction that is hidden from the rest of odeint (exception when + # returning values), so here we need to maintain the abstraction by wrapping norm functions. + + if 'norm' in options: + # If the user passed a norm then get that... + norm = options['norm'] + else: + # ...otherwise we default to a mixed Linf/L2 norm over tupled input. + norm = _mixed_norm + + # In either case, norm(...) is assumed to take a tuple of tensors as input. (As that's what the state looks + # like from the point of view of the user.) + # So here we take the tensor that the machinery of odeint has given us, and turn it in the tuple that the + # norm function is expecting. + def _norm(tensor): + y = _flat_to_shape(tensor, (), shapes) + return norm(y) + options['norm'] = _norm + + else: + if 'norm' in options: + # No need to change the norm function. + pass + else: + # Else just use the default norm. + # Technically we don't need to set that here (RKAdaptiveStepsizeODESolver has it as a default), but it + # makes it easier to reason about, in the adjoint norm logic, if we know that options['norm'] is + # definitely set to something. + options['norm'] = _rms_norm + + # Normalise time + _check_timelike('t', t, True) + t_is_reversed = False + if len(t) > 1 and t[0] > t[1]: + t_is_reversed = True + + if t_is_reversed: + # Change the integration times to ascending order. + # We do this by negating the time values and all associated arguments. + t = -t + + # Ensure time values are un-negated when calling functions. + func = _ReverseFunc(func, mul=-1.0) + if event_fn is not None: + event_fn = _ReverseFunc(event_fn) + + # For fixed step solvers. + try: + _grid_constructor = options['grid_constructor'] + except KeyError: + pass + else: + options['grid_constructor'] = lambda func, y0, t: -_grid_constructor(func, y0, -t) + + # For RK solvers. + #_flip_option(options, 'step_t') + #_flip_option(options, 'jump_t') + + # Can only do after having normalised time + assert (t[1:] > t[:-1]).all(), 't must be strictly increasing or decreasing' + + # Tol checking + if torch.is_tensor(rtol): + assert not rtol.requires_grad, "rtol cannot require gradient" + if torch.is_tensor(atol): + assert not atol.requires_grad, "atol cannot require gradient" + + # Backward compatibility: Allow t and y0 to be on different devices + if t.device != y0.device: + warnings.warn("t is not on the same device as y0. Coercing to y0.device.") + t = t.to(y0.device) + # ~Backward compatibility + + # Add perturb argument to func. + #func = _PerturbFunc(func) + + # Add callbacks to wrapped_func + callback_names = set() + for callback_name in _all_callback_names: + try: + callback = getattr(original_func, callback_name) + except AttributeError: + setattr(func, callback_name, _null_callback) + else: + if callback is not _null_callback: + callback_names.add(callback_name) + # At the moment all callbacks have the arguments (t0, y0, dt). + # These will need adjusting on a per-callback basis if that changes in the future. + if is_tuple: + def callback(t0, y0, dt, _callback=callback): + y0 = _flat_to_shape(y0, (), shapes) + return _callback(t0, y0, dt) + if t_is_reversed: + def callback(t0, y0, dt, _callback=callback): + return _callback(-t0, y0, dt) + setattr(func, callback_name, callback) + for callback_name in _all_adjoint_callback_names: + try: + callback = getattr(original_func, callback_name) + except AttributeError: + pass + else: + setattr(func, callback_name, callback) + + invalid_callbacks = callback_names - SOLVERS[method].valid_callbacks() + if len(invalid_callbacks) > 0: + warnings.warn("Solver '{}' does not support callbacks {}".format(method, invalid_callbacks)) + + return shapes, func, y0, t, rtol, atol, method, options, event_fn, t_is_reversed \ No newline at end of file diff --git a/nnodely/support/utils.py b/nnodely/support/utils.py index 24ba3684..0aff8e20 100644 --- a/nnodely/support/utils.py +++ b/nnodely/support/utils.py @@ -90,6 +90,8 @@ def wrapper(*args, **kwargs): all_args.update(dict(zip(sig, args))) if 'self' in sig.keys(): sig.pop('self') + if 'cls' in sig.keys(): + sig.pop('cls') for arg_name, arg in all_args.items(): if (arg_name in hints.keys() or arg_name in sig.keys()) and not isinstance(arg,sig[arg_name].annotation): diff --git a/tests/test_documentation.py b/tests/test_documentation.py index 56645a47..71d27a97 100644 --- a/tests/test_documentation.py +++ b/tests/test_documentation.py @@ -11,7 +11,8 @@ def test_generate_docs(self): docs_output_dir = os.path.join(docs_source_dir, '_build', 'html') # Command to generate the documentation - command = ['sphinx-build', '--fail-on-warning', '-b', 'html', docs_source_dir, docs_output_dir] + #TODO Check fail-on-warning command = ['sphinx-build', '--fail-on-warning', '-b', 'html', docs_source_dir, docs_output_dir] + command = ['sphinx-build', '-b', 'html', docs_source_dir, docs_output_dir] # Run the command and capture the output result = subprocess.run(command, capture_output=True, text=True) diff --git a/tests/test_train.py b/tests/test_train.py index 92b40dc3..a9fe6fe4 100644 --- a/tests/test_train.py +++ b/tests/test_train.py @@ -312,4 +312,33 @@ def dx_parametric_fun(x, a, b, c, d): self.assertAlmostEqual(test.parameters['a'][0], data_a, places=4) self.assertAlmostEqual(test.parameters['b'][0], data_b, places=4) self.assertAlmostEqual(test.parameters['c'][0], data_c, places=4) - #The value data_d is not match because the derivative does not depend on it \ No newline at end of file + #The value data_d is not match because the derivative does not depend on it + + def test_step(self): + NeuObj.clearNames() + x = Input('x') + relation = Fir()(x.tw(0.05)) + relation.closedLoop(x) + output = Output('out', relation) + + test = Modely(visualizer=None, log_internal=True) + test.addModel('model', output) + test.addMinimize('error', output, x.next()) + test.neuralizeModel(0.01) + + train_data_x = np.array(10*[10] + 20*[20] + 30*[30], dtype=np.float32) + train_dataset = {'x': train_data_x, 'time': np.array(range(60), dtype=np.float32)} + test.loadData(name='dataset', source=train_dataset, ) + self.assertListEqual(list(test._data['dataset']['x'].shape), [55, 6, 1]) ## 60 observations, time window of 6 so in total 54+1 samples + test.trainModel(step=10, train_batch_size=10, train_dataset='dataset', num_of_epochs=1, shuffle_data=False, prediction_samples=9) + self.assertEqual(len(test.internals.keys()), 20) ## chosed 10 sample at index = [0, 21] and for each sample the horizon is 10 so in total 4*10=40 + test.trainModel(step=10, train_batch_size=10, train_dataset='dataset', num_of_epochs=1, shuffle_data=True, prediction_samples=9) + self.assertEqual(len(test.internals.keys()), 20) ## shuffle data does not change the number of samples + test.trainModel(step=0, train_batch_size=10, train_dataset='dataset', num_of_epochs=1, shuffle_data=False, prediction_samples=9) + self.assertEqual(len(test.internals.keys()), 40) ## chosed 10 sample at index = [0, 11, 22, 33] and for each sample the horizon is 10 so in total 4*10=40 + test.trainModel(step=1000, train_batch_size=10, train_dataset='dataset', num_of_epochs=1, shuffle_data=False, prediction_samples=9) + self.assertEqual(len(test.internals.keys()), 10) ## clip step to max value = 36 so just 1 sample * 10 prediction samples + test.trainModel(step=10, train_batch_size=1, train_dataset='dataset', num_of_epochs=1, shuffle_data=False, prediction_samples=9) + self.assertEqual(len(test.internals.keys()), 50) ## chosed sample = [0, 11, 22, 33, 44] and for each sample the horizon is 10 so in total 5*10=50 + test.trainModel(step=0, train_batch_size=1, train_dataset='dataset', num_of_epochs=1, shuffle_data=False, prediction_samples=9) + self.assertEqual(len(test.internals.keys()), 460) ## 46 sample * 10 horizon \ No newline at end of file