diff --git a/.github/ISSUE_TEMPLATE/Feature_request.md b/.github/ISSUE_TEMPLATE/Feature_request.md deleted file mode 100644 index 3e0bb71a..00000000 --- a/.github/ISSUE_TEMPLATE/Feature_request.md +++ /dev/null @@ -1,10 +0,0 @@ ---- -name: 'Feature Request' -about: 'Suggest a new idea or improvement for Brainpy' -labels: 'enhancement' ---- - -Please: - -- [ ] Check for duplicate requests. -- [ ] Describe your goal, and if possible provide a code snippet with a motivating example. \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 98171646..0c17e794 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -1,5 +1,5 @@ --- -name: 'Bug report' +name: 'Bug Report' about: 'Report a bug to help improve the package' labels: 'bug' --- diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 1583f23a..4d3640ad 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1,5 +1,5 @@ blank_issues_enabled: false contact_links: - name: Question - url: https://github.com/google/jax/discussions + url: https://github.com/PKU-NIP-Lab/BrainPy/discussions about: Please ask questions on the Discussions tab \ No newline at end of file diff --git a/.github/workflows/Linux_CI.yml b/.github/workflows/Linux_CI.yml index 8f3e319d..dfe658f9 100644 --- a/.github/workflows/Linux_CI.yml +++ b/.github/workflows/Linux_CI.yml @@ -5,9 +5,9 @@ name: Linux CI on: push: - branches: [ master, brainpy-2.x, V2.1.0 ] + branches: [ master ] pull_request: - branches: [ master, brainpy-2.x, V2.1.0 ] + branches: [ master ] jobs: @@ -16,7 +16,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.7", "3.8", "3.9"] + python-version: ["3.7", "3.8", "3.9", "3.10"] steps: - uses: actions/checkout@v2 diff --git a/.github/workflows/MacOS_CI.yml b/.github/workflows/MacOS_CI.yml index f1dfd34d..debd1a53 100644 --- a/.github/workflows/MacOS_CI.yml +++ b/.github/workflows/MacOS_CI.yml @@ -5,19 +5,18 @@ name: MacOS CI on: push: - branches: [ master, brainpy-2.x, V2.1.0 ] + branches: [ master ] pull_request: - branches: [ master, brainpy-2.x, V2.1.0 ] + branches: [ master ] jobs: build: - runs-on: ${{ matrix.os }} + runs-on: macos-latest strategy: fail-fast: false matrix: - os: [macos-10.15, macos-11, macos-latest] - python-version: ["3.7", "3.8", "3.9"] + python-version: ["3.7", "3.8", "3.9", "3.10"] steps: - uses: actions/checkout@v2 @@ -39,4 +38,4 @@ jobs: flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest run: | - pytest + pytest brainpy/ diff --git a/.github/workflows/Sync_branches.yml b/.github/workflows/Sync_branches.yml new file mode 100644 index 00000000..76a98f9c --- /dev/null +++ b/.github/workflows/Sync_branches.yml @@ -0,0 +1,18 @@ +name: Sync multiple branches +on: + pull_request: + branches: + - master +jobs: + sync-branch: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@master + + - name: Merge master -> brainpy-2.x + uses: devmasx/merge-branch@v1.3.1 + with: + type: now + from_branch: master + target_branch: brainpy-2.x + github_token: ${{ github.token }} \ No newline at end of file diff --git a/.github/workflows/Windows_CI.yml b/.github/workflows/Windows_CI.yml index 10dea073..bcc46f32 100644 --- a/.github/workflows/Windows_CI.yml +++ b/.github/workflows/Windows_CI.yml @@ -5,9 +5,9 @@ name: Windows CI on: push: - branches: [ master, brainpy-2.x, V2.1.0 ] + branches: [ master ] pull_request: - branches: [ master, brainpy-2.x, V2.1.0 ] + branches: [ master ] jobs: @@ -16,7 +16,7 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.7", "3.8", "3.9"] + python-version: ["3.7", "3.8", "3.9", "3.10"] steps: - uses: actions/checkout@v2 diff --git a/.github/workflows/contributors.yml b/.github/workflows/contributors.yml index b456b4b9..5cbd6145 100644 --- a/.github/workflows/contributors.yml +++ b/.github/workflows/contributors.yml @@ -2,6 +2,8 @@ name: Add contributors on: schedule: - cron: '20 20 * * *' + push: + branches: [ master ] jobs: add-contributors: diff --git a/.gitignore b/.gitignore index 84676411..548a943f 100644 --- a/.gitignore +++ b/.gitignore @@ -17,6 +17,7 @@ BrainModels/ book/ docs/examples docs/apis/jaxsetting.rst +docs/quickstart/data examples/recurrent_neural_network/neurogym develop/iconip_paper develop/benchmark/COBA/results diff --git a/README.md b/README.md index 5bfc879c..906c186d 100644 --- a/README.md +++ b/README.md @@ -28,9 +28,9 @@ BrainPy is a flexible, efficient, and extensible framework for computational neu -## Install +## Installation -BrainPy is based on Python (>=3.6) and can be installed on Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Install the latest version of BrainPy: +BrainPy is based on Python (>=3.7) and can be installed on Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Install the latest version of BrainPy: ```bash $ pip install brain-py -U @@ -54,7 +54,121 @@ import brainpy as bp -**1\. E-I balance network** +### 1. Operator level + +Mathematical operators in BrainPy are the same as those in NumPy. + +```python +>>> import numpy as np +>>> import brainpy.math as bm + +# array creation +>>> np_arr = np.zeros((2, 4)); np_arr +array([[0., 0., 0., 0.], + [0., 0., 0., 0.]]) +>>> bm_arr = bm.zeros((2, 4)); bm_arr +JaxArray([[0., 0., 0., 0.], + [0., 0., 0., 0.]], dtype=float32) + +# in-place updating +>>> np_arr[0] += 1.; np_arr +array([[1., 1., 1., 1.], + [0., 0., 0., 0.]]) +>>> bm_arr[0] += 1.; bm_arr +JaxArray([[1., 1., 1., 1.], + [0., 0., 0., 0.]], dtype=float32) + +# mathematical functions +>>> np.sin(np_arr) +array([[0.84147098, 0.84147098, 0.84147098, 0.84147098], + [0. , 0. , 0. , 0. ]]) +>>> bm.sin(bm_arr) +JaxArray([[0.84147096, 0.84147096, 0.84147096, 0.84147096], + [0. , 0. , 0. , 0. ]], dtype=float32) + +# linear algebra +>>> np.dot(np_arr, np.ones((4, 2))) +array([[4., 4.], + [0., 0.]]) +>>> bm.dot(bm_arr, bm.ones((4, 2))) +JaxArray([[4., 4.], + [0., 0.]], dtype=float32) + +# random number generation +>>> np.random.uniform(-0.1, 0.1, (2, 3)) +array([[-0.02773637, 0.03766689, -0.01363128], + [-0.01946991, -0.06669802, 0.09426067]]) +>>> bm.random.uniform(-0.1, 0.1, (2, 3)) +JaxArray([[-0.03044081, -0.07787752, 0.04346445], + [-0.01366713, -0.0522548 , 0.04372055]], dtype=float32) +``` + + + +### 2. Integrator level + +Numerical methods for ordinary differential equations (ODEs). + +```python +sigma = 10; beta = 8/3; rho = 28 + +@bp.odeint(method='rk4') +def lorenz_system(x, y, z, t): + dx = sigma * (y - x) + dy = x * (rho - z) - y + dz = x * y - beta * z + return dx, dy, dz + +runner = bp.integrators.IntegratorRunner(lorenz_system, dt=0.01) +runner.run(100.) +``` + + + +Numerical methods for stochastic differential equations (SDEs). + +```python +sigma = 10; beta = 8/3; rho = 28 +p=0.1 + +def lorenz_noise(x, y, z, t): + return p*x, p*y, p*z + +@bp.odeint(method='milstein', g=lorenz_noise) +def lorenz_system(x, y, z, t): + dx = sigma * (y - x) + dy = x * (rho - z) - y + dz = x * y - beta * z + return dx, dy, dz + +runner = bp.integrators.IntegratorRunner(lorenz_system, dt=0.01) +runner.run(100.) +``` + + + +Numerical methods for delay differential equations (SDEs). + +```python +xdelay = bm.TimeDelay(bm.zeros(1), delay_len=1., before_t0=1., dt=0.01) + + +@bp.ddeint(method='rk4', state_delays={'x': xdelay}) +def second_order_eq(x, y, t): + dx = y + dy = -y - 2 * x - 0.5 * xdelay(t - 1) + return dx, dy + + +runner = bp.integrators.IntegratorRunner(second_order_eq, dt=0.01) +runner.run(100.) +``` + + + +### 3. Dynamics simulation level + +Building an E-I balance network. ```python class EINet(bp.dyn.Network): @@ -77,9 +191,36 @@ runner = bp.dyn.DSRunner(net) runner(100.) ``` +Simulating a whole brain network by using rate models. + +```python +import numpy as np +class WholeBrainNet(bp.dyn.Network): + def __init__(self, signal_speed=20.): + super(WholeBrainNet, self).__init__() -**2\. Echo state network** + self.fhn = bp.dyn.RateFHN(80, x_ou_sigma=0.01, y_ou_sigma=0.01, name='fhn') + self.syn = bp.dyn.DiffusiveDelayCoupling(self.fhn, self.fhn, + 'x->input', + conn_mat=conn_mat, + delay_mat=delay_mat) + + def update(self, _t, _dt): + self.syn.update(_t, _dt) + self.fhn.update(_t, _dt) + + +net = WholeBrainNet() +runner = bp.dyn.DSRunner(net, monitors=['fhn.x'], inputs=['fhn.input', 0.72]) +runner.run(6e3) +``` + + + +### 4. Dynamics training level + +Training an echo state network. ```python i = bp.nn.Input(3) @@ -88,16 +229,14 @@ o = bp.nn.LinearReadout(3) net = i >> r >> o -# Ridge Regression -trainer = bp.nn.RidgeTrainer(net, beta=1e-5) +trainer = bp.nn.RidgeTrainer(net, beta=1e-5) # Ridge Regression -# FORCE Learning -trainer = bp.nn.FORCELearning(net, alpha=1.) +trainer = bp.nn.FORCELearning(net, alpha=1.) # FORCE Learning ``` -**3. Next generation reservoir computing** +Training a next-generation reservoir computing model. ```python i = bp.nn.Input(3) @@ -111,7 +250,7 @@ trainer = bp.nn.RidgeTrainer(net, beta=1e-5) -**4. Recurrent neural network** +Training an artificial recurrent neural network. ```python i = bp.nn.Input(3) @@ -128,7 +267,9 @@ trainer = bp.nn.BPTT(net, -**5\. Analyzing a low-dimensional FitzHugh–Nagumo neuron model** +### 5. Dynamics analysis level + +Analyzing a low-dimensional FitzHugh–Nagumo neuron model. ```python bp.math.enable_x64() @@ -149,9 +290,10 @@ analyzer.show_figure()

-For **more functions and examples**, please refer to the [documentation](https://brainpy.readthedocs.io/) and [examples](https://brainpy-examples.readthedocs.io/). +### 6. More others +For **more functions and examples**, please refer to the [documentation](https://brainpy.readthedocs.io/) and [examples](https://brainpy-examples.readthedocs.io/). ## License diff --git a/README2.md b/README2.md deleted file mode 100644 index 0c14038b..00000000 --- a/README2.md +++ /dev/null @@ -1,159 +0,0 @@ -

- Header image of BrainPy - brain dynamics programming in Python. -

- - -

- Supported Python Version - LICENSE - Documentation - PyPI version - Linux CI - Linux CI -

- - -:clap::clap: **CHEERS**: A new version of BrainPy (>=2.0.0, long term support) has been released! :clap::clap: - - - -# Why use BrainPy - -``BrainPy`` is an integrative framework for computational neuroscience and brain-inspired computation based on the Just-In-Time (JIT) compilation (built on top of [JAX](https://github.com/google/jax)). Core functions provided in BrainPy includes - -- **JIT compilation** for class objects. -- **Numerical solvers** for ODEs, SDEs, and others. -- **Dynamics simulation tools** for various brain objects, like neurons, synapses, networks, soma, dendrites, channels, and even more. -- **Dynamics analysis tools** for differential equations, including phase plane analysis and bifurcation analysis, and linearization analysis. -- **Seamless integration with deep learning models**. -- And more ...... - -`BrainPy` is designed to effectively satisfy your basic requirements: - -- **Pythonic**: BrainPy is based on Python language and has a Pythonic coding style. -- **Flexible and transparent**: BrainPy endows the users with full data/logic flow control. Users can code any logic they want with BrainPy. -- **Extensible**: BrainPy allows users to extend new functionality just based on Python code. Almost every part of the BrainPy system can be extended to be customized. -- **Efficient**: All codes in BrainPy can be just-in-time compiled (based on [JAX](https://github.com/google/jax)) to run on CPU, GPU, or TPU devices, thus guaranteeing its running efficiency. - - - -# How to use BrainPy - -## Step 1: installation - -``BrainPy`` is based on Python (>=3.6), and the following packages are required to be installed to use ``BrainPy``: `numpy >= 1.15`, `matplotlib >= 3.4`, and `jax >= 0.2.10` ([how to install jax?](https://brainpy.readthedocs.io/en/latest/quickstart/installation.html#dependency-2-jax)) - -``BrainPy`` can be installed on Linux (Ubuntu 16.04 or later), macOS (10.12 or later), and Windows platforms. Use the following instructions to install ``brainpy``: - -```bash -pip install brain-py -U -``` - -*For the full installation details please see documentation: [Quickstart/Installation](https://brainpy.readthedocs.io/en/latest/quickstart/installation.html)* - - - - -## Step 2: useful links - -- **Documentation:** https://brainpy.readthedocs.io/ -- **Bug reports:** https://github.com/PKU-NIP-Lab/BrainPy/issues -- **Examples from papers**: https://brainpy-examples.readthedocs.io/ -- **Canonical brain models**: https://brainmodels.readthedocs.io/ - - - -## Step 3: inspirational examples - -Here we list several examples of BrainPy. For more detailed examples and tutorials please see [**BrainModels**](https://brainmodels.readthedocs.io) or [**BrainPy-Examples**](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/). - - - -### Neuron models - -- [Leaky integrate-and-fire neuron model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.LIF.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/LIF.py) -- [Exponential integrate-and-fire neuron model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.ExpIF.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/ExpIF.py) -- [Quadratic integrate-and-fire neuron model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.QuaIF.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/QuaIF.py) -- [Adaptive Quadratic integrate-and-fire model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.AdQuaIF.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/AdQuaIF.py) -- [Adaptive Exponential integrate-and-fire model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.AdExIF.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/AdExIF.py) -- [Generalized integrate-and-fire model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.GIF.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/GIF.py) -- [Hodgkin–Huxley neuron model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.HH.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/HH.py) -- [Izhikevich neuron model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.Izhikevich.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/Izhikevich.py) -- [Morris-Lecar neuron model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.MorrisLecar.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/MorrisLecar.py) -- [Hindmarsh-Rose bursting neuron model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.neurons.HindmarshRose.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/neurons/HindmarshRose.py) - -See [brainmodels.neurons](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/neurons.html) to find more. - - - -### Synapse models - -- [Voltage jump synapse model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.synapses.VoltageJump.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/synapses/voltage_jump.py) -- [Exponential synapse model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.synapses.ExpCUBA.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/synapses/exponential.py) -- [Alpha synapse model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.synapses.AlphaCUBA.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/synapses/alpha.py) -- [Dual exponential synapse model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.synapses.DualExpCUBA.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/synapses/dual_exp.py) -- [AMPA synapse model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.synapses.AMPA.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/synapses/AMPA.py) -- [GABAA synapse model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.synapses.GABAa.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/synapses/GABAa.py) -- [NMDA synapse model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.synapses.NMDA.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/synapses/NMDA.py) -- [Short-term plasticity model](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/generated/brainmodels.synapses.STP.html), [source code](https://github.com/PKU-NIP-Lab/BrainModels/blob/brainpy-2.x/brainmodels/synapses/STP.py) - -See [brainmodels.synapses](https://brainmodels.readthedocs.io/en/brainpy-2.x/apis/synapses.html) to find more. - - - -### Network models - -- **[CANN]** [*(Si Wu, 2008)* Continuous-attractor Neural Network](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/cann/Wu_2008_CANN.html) -- [*(Vreeswijk & Sompolinsky, 1996)* E/I balanced network](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/ei_nets/Vreeswijk_1996_EI_net.html) -- [*(Sherman & Rinzel, 1992)* Gap junction leads to anti-synchronization](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/gj_nets/Sherman_1992_gj_antisynchrony.html) -- [*(Wang & Buzsáki, 1996)* Gamma Oscillation](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/oscillation_synchronization/Wang_1996_gamma_oscillation.html) -- [*(Brunel & Hakim, 1999)* Fast Global Oscillation](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/oscillation_synchronization/Brunel_Hakim_1999_fast_oscillation.html) -- [*(Diesmann, et, al., 1999)* Synfire Chains](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/oscillation_synchronization/Diesmann_1999_synfire_chains.html) -- **[Working Memory]** [*(Mi, et. al., 2017)* STP for Working Memory Capacity](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/working_memory/Mi_2017_working_memory_capacity.html) -- **[Working Memory]** [*(Bouchacourt & Buschman, 2019)* Flexible Working Memory Model](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/working_memory/Bouchacourt_2019_Flexible_working_memory.html) -- **[Decision Making]** [*(Wang, 2002)* Decision making spiking model](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/decision_making/Wang_2002_decision_making_spiking.html) - - - -### Dynamics training - -- [Train Integrator RNN with BP](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/recurrent_networks/integrator_rnn.html) - -- [*(Sussillo & Abbott, 2009)* FORCE Learning](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/recurrent_networks/Sussillo_Abbott_2009_FORCE_Learning.html) - -- [*(Laje & Buonomano, 2013)* Robust Timing in RNN](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/recurrent_networks/Laje_Buonomano_2013_robust_timing_rnn.html) -- [*(Song, et al., 2016)*: Training excitatory-inhibitory recurrent network](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/recurrent_networks/Song_2016_EI_RNN.html) -- **[Working Memory]** [*(Masse, et al., 2019)*: RNN with STP for Working Memory](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/recurrent_networks/Masse_2019_STP_RNN.html) - - - - -### Low-dimensional dynamics analysis - -- [[1D] Simple systems](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/dynamics_analysis/1d_simple_systems.html) -- [[2D] NaK model analysis](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/dynamics_analysis/2d_NaK_model.html) -- [[3D] Hindmarsh Rose Model](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/dynamics_analysis/3d_hindmarsh_rose_model.html) -- **[Decision Making Model]** [[2D] Decision making rate model](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/decision_making/Wang_2006_decision_making_rate.html) - - - -### High-dimensional dynamics analysis - -- [*(Yang, 2020)*: Dynamical system analysis for RNN](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/recurrent_networks/Yang_2020_RNN_Analysis.html) -- [Continuous-attractor Neural Network](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/dynamics_analysis/highdim_CANN.html) -- [Gap junction-coupled FitzHugh-Nagumo Model](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/dynamics_analysis/highdim_gj_coupled_fhn.html) - - - -# BrainPy 1.x - -If you are using ``brainpy==1.x``, you can find *documentation*, *examples*, and *models* through the following links: - -- **Documentation:** https://brainpy.readthedocs.io/en/brainpy-1.x/ -- **Examples from papers**: https://brainpy-examples.readthedocs.io/en/brainpy-1.x/ -- **Canonical brain models**: https://brainmodels.readthedocs.io/en/brainpy-1.x/ - -The changes from ``brainpy==1.x`` to ``brainpy==2.x`` can be inspected through [API documentation: release notes](https://brainpy.readthedocs.io/en/latest/apis/auto/changelog.html). - - -# Contributors diff --git a/brainpy/__init__.py b/brainpy/__init__.py index 7521db97..de872844 100644 --- a/brainpy/__init__.py +++ b/brainpy/__init__.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- -__version__ = "2.1.0" +__version__ = "2.1.2" try: @@ -15,7 +15,7 @@ except ModuleNotFoundError: # fundamental modules -from . import errors, tools +from . import errors, tools, check # "base" module @@ -37,9 +37,11 @@ from . import integrators from .integrators import ode from .integrators import sde from .integrators import dde +from .integrators import fde from .integrators.ode import odeint from .integrators.sde import sdeint from .integrators.dde import ddeint +from .integrators.fde import fdeint from .integrators.joint_eq import JointEq @@ -59,12 +61,12 @@ from . import running from . import analysis -# "visualization" module, will be remove soon +# "visualization" module, will be removed soon from .visualization import visualize # compatible interface -from .compact import * # compact +from .compat import * # compat # convenient access diff --git a/brainpy/analysis/highdim/slow_points.py b/brainpy/analysis/highdim/slow_points.py index 57c58114..9cec0107 100644 --- a/brainpy/analysis/highdim/slow_points.py +++ b/brainpy/analysis/highdim/slow_points.py @@ -1,17 +1,18 @@ # -*- coding: utf-8 -*- -import inspect import time +import warnings from functools import partial +from jax import vmap import jax.numpy import numpy as np from jax.scipy.optimize import minimize import brainpy.math as bm +from brainpy import optimizers as optim from brainpy.analysis import utils from brainpy.errors import AnalyzerError -from brainpy import optimizers as optim __all__ = [ 'SlowPointFinder', @@ -56,15 +57,15 @@ class SlowPointFinder(object): if f_loss_batch is None: if f_type == 'discrete': self.f_loss = bm.jit(lambda h: bm.mean((h - f_cell(h)) ** 2)) - self.f_loss_batch = bm.jit(lambda h: bm.mean((h - bm.vmap(f_cell, auto_infer=False)(h)) ** 2, axis=1)) + self.f_loss_batch = bm.jit(lambda h: bm.mean((h - vmap(f_cell)(h)) ** 2, axis=1)) if f_type == 'continuous': self.f_loss = bm.jit(lambda h: bm.mean(f_cell(h) ** 2)) - self.f_loss_batch = bm.jit(lambda h: bm.mean((bm.vmap(f_cell, auto_infer=False)(h)) ** 2, axis=1)) + self.f_loss_batch = bm.jit(lambda h: bm.mean((vmap(f_cell)(h)) ** 2, axis=1)) else: self.f_loss_batch = f_loss_batch self.f_loss = bm.jit(lambda h: bm.mean(f_cell(h) ** 2)) - self.f_jacob_batch = bm.jit(bm.vmap(bm.jacobian(f_cell))) + self.f_jacob_batch = bm.jit(vmap(bm.jacobian(f_cell))) # essential variables self._losses = None @@ -87,8 +88,13 @@ class SlowPointFinder(object): """The selected ids of candidate points.""" return self._selected_ids - def find_fps_with_gd_method(self, candidates, tolerance=1e-5, num_batch=100, - num_opt=10000, opt_setting=None): + def find_fps_with_gd_method(self, + candidates, + tolerance=1e-5, + num_batch=100, + num_opt=10000, + optimizer=None, + opt_setting=None): """Optimize fixed points with gradient descent methods. Parameters @@ -104,17 +110,30 @@ class SlowPointFinder(object): Print training information during optimization every so often. opt_setting: optional, dict The optimization settings. + + .. deprecated:: 2.1.2 + Use "optimizer" to set optimization method instead. + + optimizer: optim.Optimizer + The optimizer instance. + + .. versionadded:: 2.1.2 """ # optimization settings if opt_setting is None: - opt_method = optim.Adam - opt_lr = optim.ExponentialDecay(0.2, 1, 0.9999) - opt_setting = {'beta1': 0.9, - 'beta2': 0.999, - 'eps': 1e-8, - 'name': None} + if optimizer is None: + optimizer = optim.Adam(lr=optim.ExponentialDecay(0.2, 1, 0.9999), + beta1=0.9, beta2=0.999, eps=1e-8) + else: + assert isinstance(optimizer, optim.Optimizer), (f'Must be an instance of ' + f'{optim.Optimizer.__name__}, ' + f'while we got {type(optimizer)}') else: + warnings.warn('Please use "optimizer" to set optimization method. ' + '"opt_setting" is deprecated since version 2.1.2. ', + DeprecationWarning) + assert isinstance(opt_setting, dict) assert 'method' in opt_setting assert 'lr' in opt_setting @@ -122,26 +141,25 @@ class SlowPointFinder(object): if isinstance(opt_method, str): assert opt_method in optim.__dict__ opt_method = getattr(optim, opt_method) - assert isinstance(opt_method, type) - if optim.Optimizer not in inspect.getmro(opt_method): - raise ValueError + assert issubclass(opt_method, optim.Optimizer) opt_lr = opt_setting.pop('lr') assert isinstance(opt_lr, (int, float, optim.Scheduler)) opt_setting = opt_setting + optimizer = opt_method(lr=opt_lr, **opt_setting) if self.verbose: - print(f"Optimizing with {opt_method.__name__} to find fixed points:") + print(f"Optimizing with {optimizer.__name__} to find fixed points:") # set up optimization fixed_points = bm.Variable(bm.asarray(candidates)) grad_f = bm.grad(lambda: self.f_loss_batch(fixed_points.value).mean(), grad_vars={'a': fixed_points}, return_value=True) - opt = opt_method(train_vars={'a': fixed_points}, lr=opt_lr, **opt_setting) - dyn_vars = opt.vars() + {'_a': fixed_points} + optimizer.register_vars({'a': fixed_points}) + dyn_vars = optimizer.vars() + {'_a': fixed_points} def train(idx): gradients, loss = grad_f() - opt.update(gradients) + optimizer.update(gradients) return loss @partial(bm.jit, dyn_vars=dyn_vars, static_argnames=('start_i', 'num_batch')) @@ -191,7 +209,7 @@ class SlowPointFinder(object): opt_method = lambda f, x0: minimize(f, x0, method='BFGS') if self.verbose: print(f"Optimizing to find fixed points:") - f_opt = bm.jit(bm.vmap(lambda x0: opt_method(self.f_loss, x0))) + f_opt = bm.jit(vmap(lambda x0: opt_method(self.f_loss, x0))) res = f_opt(bm.as_device_array(candidates)) valid_ids = jax.numpy.where(res.success)[0] self._fixed_points = np.asarray(res.x[valid_ids]) diff --git a/brainpy/analysis/lowdim/lowdim_analyzer.py b/brainpy/analysis/lowdim/lowdim_analyzer.py index a5698246..0d7ac1b6 100644 --- a/brainpy/analysis/lowdim/lowdim_analyzer.py +++ b/brainpy/analysis/lowdim/lowdim_analyzer.py @@ -2,8 +2,8 @@ from functools import partial -import matplotlib.pyplot as plt import numpy as np +from jax import vmap from jax import numpy as jnp from jax.scipy.optimize import minimize @@ -12,6 +12,8 @@ from brainpy import errors, tools from brainpy.analysis import constants as C, utils from brainpy.base.collector import Collector +pyplot = None + __all__ = [ 'LowDimAnalyzer', 'Num1DAnalyzer', @@ -207,7 +209,10 @@ class LowDimAnalyzer(object): self.analyzed_results = tools.DictPlus() def show_figure(self): - plt.show() + global pyplot + if pyplot is None: + from matplotlib import pyplot + pyplot.show() class Num1DAnalyzer(LowDimAnalyzer): @@ -258,7 +263,7 @@ class Num1DAnalyzer(LowDimAnalyzer): @property def F_vmap_fx(self): if C.F_vmap_fx not in self.analyzed_results: - self.analyzed_results[C.F_vmap_fx] = bm.jit(bm.vmap(self.F_fx), device=self.jit_device) + self.analyzed_results[C.F_vmap_fx] = bm.jit(vmap(self.F_fx), device=self.jit_device) return self.analyzed_results[C.F_vmap_fx] @property @@ -285,7 +290,7 @@ class Num1DAnalyzer(LowDimAnalyzer): # --- # "X": a two-dimensional matrix: (num_batch, num_var) # "args": a list of one-dimensional vectors, each has the shape of (num_batch,) - self.analyzed_results[C.F_vmap_fp_aux] = bm.jit(bm.vmap(self.F_fixed_point_aux)) + self.analyzed_results[C.F_vmap_fp_aux] = bm.jit(vmap(self.F_fixed_point_aux)) return self.analyzed_results[C.F_vmap_fp_aux] @property @@ -304,7 +309,7 @@ class Num1DAnalyzer(LowDimAnalyzer): # --- # "X": a two-dimensional matrix: (num_batch, num_var) # "args": a list of one-dimensional vectors, each has the shape of (num_batch,) - self.analyzed_results[C.F_vmap_fp_opt] = bm.jit(bm.vmap(self.F_fixed_point_opt)) + self.analyzed_results[C.F_vmap_fp_opt] = bm.jit(vmap(self.F_fixed_point_opt)) return self.analyzed_results[C.F_vmap_fp_opt] def _get_fixed_points(self, candidates, *args, num_seg=None, tol_aux=1e-7, loss_screen=None): @@ -497,7 +502,7 @@ class Num2DAnalyzer(Num1DAnalyzer): @property def F_vmap_fy(self): if C.F_vmap_fy not in self.analyzed_results: - self.analyzed_results[C.F_vmap_fy] = bm.jit(bm.vmap(self.F_fy), device=self.jit_device) + self.analyzed_results[C.F_vmap_fy] = bm.jit(vmap(self.F_fy), device=self.jit_device) return self.analyzed_results[C.F_vmap_fy] @property @@ -659,7 +664,7 @@ class Num2DAnalyzer(Num1DAnalyzer): if self.F_x_by_y_in_fx is not None: utils.output("I am evaluating fx-nullcline by F_x_by_y_in_fx ...") - vmap_f = bm.jit(bm.vmap(self.F_x_by_y_in_fx), device=self.jit_device) + vmap_f = bm.jit(vmap(self.F_x_by_y_in_fx), device=self.jit_device) for j, pars in enumerate(par_seg): if len(par_seg.arg_id_segments[0]) > 1: utils.output(f"{C.prefix}segment {j} ...") mesh_values = jnp.meshgrid(*((ys,) + pars)) @@ -675,7 +680,7 @@ class Num2DAnalyzer(Num1DAnalyzer): elif self.F_y_by_x_in_fx is not None: utils.output("I am evaluating fx-nullcline by F_y_by_x_in_fx ...") - vmap_f = bm.jit(bm.vmap(self.F_y_by_x_in_fx), device=self.jit_device) + vmap_f = bm.jit(vmap(self.F_y_by_x_in_fx), device=self.jit_device) for j, pars in enumerate(par_seg): if len(par_seg.arg_id_segments[0]) > 1: utils.output(f"{C.prefix}segment {j} ...") mesh_values = jnp.meshgrid(*((xs,) + pars)) @@ -693,9 +698,9 @@ class Num2DAnalyzer(Num1DAnalyzer): utils.output("I am evaluating fx-nullcline by optimization ...") # auxiliary functions f2 = lambda y, x, *pars: self.F_fx(x, y, *pars) - vmap_f2 = bm.jit(bm.vmap(f2), device=self.jit_device) - vmap_brentq_f2 = bm.jit(bm.vmap(utils.jax_brentq(f2)), device=self.jit_device) - vmap_brentq_f1 = bm.jit(bm.vmap(utils.jax_brentq(self.F_fx)), device=self.jit_device) + vmap_f2 = bm.jit(vmap(f2), device=self.jit_device) + vmap_brentq_f2 = bm.jit(vmap(utils.jax_brentq(f2)), device=self.jit_device) + vmap_brentq_f1 = bm.jit(vmap(utils.jax_brentq(self.F_fx)), device=self.jit_device) # num segments for _j, Ps in enumerate(par_seg): @@ -752,7 +757,7 @@ class Num2DAnalyzer(Num1DAnalyzer): if self.F_x_by_y_in_fy is not None: utils.output("I am evaluating fy-nullcline by F_x_by_y_in_fy ...") - vmap_f = bm.jit(bm.vmap(self.F_x_by_y_in_fy), device=self.jit_device) + vmap_f = bm.jit(vmap(self.F_x_by_y_in_fy), device=self.jit_device) for j, pars in enumerate(par_seg): if len(par_seg.arg_id_segments[0]) > 1: utils.output(f"{C.prefix}segment {j} ...") mesh_values = jnp.meshgrid(*((ys,) + pars)) @@ -768,7 +773,7 @@ class Num2DAnalyzer(Num1DAnalyzer): elif self.F_y_by_x_in_fy is not None: utils.output("I am evaluating fy-nullcline by F_y_by_x_in_fy ...") - vmap_f = bm.jit(bm.vmap(self.F_y_by_x_in_fy), device=self.jit_device) + vmap_f = bm.jit(vmap(self.F_y_by_x_in_fy), device=self.jit_device) for j, pars in enumerate(par_seg): if len(par_seg.arg_id_segments[0]) > 1: utils.output(f"{C.prefix}segment {j} ...") mesh_values = jnp.meshgrid(*((xs,) + pars)) @@ -787,9 +792,9 @@ class Num2DAnalyzer(Num1DAnalyzer): # auxiliary functions f2 = lambda y, x, *pars: self.F_fy(x, y, *pars) - vmap_f2 = bm.jit(bm.vmap(f2), device=self.jit_device) - vmap_brentq_f2 = bm.jit(bm.vmap(utils.jax_brentq(f2)), device=self.jit_device) - vmap_brentq_f1 = bm.jit(bm.vmap(utils.jax_brentq(self.F_fy)), device=self.jit_device) + vmap_f2 = bm.jit(vmap(f2), device=self.jit_device) + vmap_brentq_f2 = bm.jit(vmap(utils.jax_brentq(f2)), device=self.jit_device) + vmap_brentq_f1 = bm.jit(vmap(utils.jax_brentq(self.F_fy)), device=self.jit_device) for j, Ps in enumerate(par_seg): if len(par_seg.arg_id_segments[0]) > 1: utils.output(f"{C.prefix}segment {j} ...") @@ -837,7 +842,7 @@ class Num2DAnalyzer(Num1DAnalyzer): xs = self.resolutions[self.x_var].value ys = self.resolutions[self.y_var].value P = tuple(self.resolutions[p].value for p in self.target_par_names) - f_select = bm.jit(bm.vmap(lambda vals, ids: vals[ids], in_axes=(1, 1))) + f_select = bm.jit(vmap(lambda vals, ids: vals[ids], in_axes=(1, 1))) # num seguments if isinstance(num_segments, int): @@ -917,10 +922,10 @@ class Num2DAnalyzer(Num1DAnalyzer): if self.convert_type() == C.x_by_y: num_seg = len(self.resolutions[self.y_var]) - f_vmap = bm.jit(bm.vmap(self.F_y_convert[1])) + f_vmap = bm.jit(vmap(self.F_y_convert[1])) else: num_seg = len(self.resolutions[self.x_var]) - f_vmap = bm.jit(bm.vmap(self.F_x_convert[1])) + f_vmap = bm.jit(vmap(self.F_x_convert[1])) # get the signs signs = jnp.sign(f_vmap(candidates, *args)) signs = signs.reshape((num_seg, -1)) @@ -950,10 +955,10 @@ class Num2DAnalyzer(Num1DAnalyzer): # get another value if self.convert_type() == C.x_by_y: y_values = fps - x_values = bm.jit(bm.vmap(self.F_y_convert[0]))(y_values, *args) + x_values = bm.jit(vmap(self.F_y_convert[0]))(y_values, *args) else: x_values = fps - y_values = bm.jit(bm.vmap(self.F_x_convert[0]))(x_values, *args) + y_values = bm.jit(vmap(self.F_x_convert[0]))(x_values, *args) fps = jnp.stack([x_values, y_values]).T return fps, selected_ids, args diff --git a/brainpy/analysis/lowdim/lowdim_bifurcation.py b/brainpy/analysis/lowdim/lowdim_bifurcation.py index fab83071..58ac8469 100644 --- a/brainpy/analysis/lowdim/lowdim_bifurcation.py +++ b/brainpy/analysis/lowdim/lowdim_bifurcation.py @@ -3,7 +3,7 @@ from functools import partial import jax.numpy as jnp -import matplotlib.pyplot as plt +from jax import vmap import numpy as np import brainpy.math as bm @@ -11,6 +11,8 @@ from brainpy import errors from brainpy.analysis import stability, utils, constants as C from brainpy.analysis.lowdim.lowdim_analyzer import * +pyplot = None + __all__ = [ 'Bifurcation1D', 'Bifurcation2D', @@ -41,12 +43,14 @@ class Bifurcation1D(Num1DAnalyzer): @property def F_vmap_dfxdx(self): if C.F_vmap_dfxdx not in self.analyzed_results: - f = bm.jit(bm.vmap(bm.vector_grad(self.F_fx, argnums=0)), device=self.jit_device) + f = bm.jit(vmap(bm.vector_grad(self.F_fx, argnums=0)), device=self.jit_device) self.analyzed_results[C.F_vmap_dfxdx] = f return self.analyzed_results[C.F_vmap_dfxdx] def plot_bifurcation(self, with_plot=True, show=False, with_return=False, tol_aux=1e-8, loss_screen=None): + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am making bifurcation analysis ...') xs = self.resolutions[self.x_var] @@ -72,21 +76,21 @@ class Bifurcation1D(Num1DAnalyzer): container[fp_type]['x'].append(x) # visualization - plt.figure(self.x_var) + pyplot.figure(self.x_var) for fp_type, points in container.items(): if len(points['x']): plot_style = stability.plot_scheme[fp_type] - plt.plot(points['p'], points['x'], '.', **plot_style, label=fp_type) - plt.xlabel(self.target_par_names[0]) - plt.ylabel(self.x_var) + pyplot.plot(points['p'], points['x'], '.', **plot_style, label=fp_type) + pyplot.xlabel(self.target_par_names[0]) + pyplot.ylabel(self.x_var) scale = (self.lim_scale - 1) / 2 - plt.xlim(*utils.rescale(self.target_pars[self.target_par_names[0]], scale=scale)) - plt.ylim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) + pyplot.xlim(*utils.rescale(self.target_pars[self.target_par_names[0]], scale=scale)) + pyplot.ylim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) - plt.legend() + pyplot.legend() if show: - plt.show() + pyplot.show() elif len(self.target_pars) == 2: container = {c: {'p0': [], 'p1': [], 'x': []} for c in stability.get_1d_stability_types()} @@ -99,7 +103,7 @@ class Bifurcation1D(Num1DAnalyzer): container[fp_type]['x'].append(x) # visualization - fig = plt.figure(self.x_var) + fig = pyplot.figure(self.x_var) ax = fig.add_subplot(projection='3d') for fp_type, points in container.items(): if len(points['x']): @@ -121,7 +125,7 @@ class Bifurcation1D(Num1DAnalyzer): ax.grid(True) ax.legend() if show: - plt.show() + pyplot.show() else: raise errors.BrainPyError(f'Cannot visualize co-dimension {len(self.target_pars)} ' @@ -156,7 +160,7 @@ class Bifurcation2D(Num2DAnalyzer): if C.F_vmap_jacobian not in self.analyzed_results: f1 = lambda xy, *args: jnp.array([self.F_fx(xy[0], xy[1], *args), self.F_fy(xy[0], xy[1], *args)]) - f2 = bm.jit(bm.vmap(bm.jacobian(f1)), device=self.jit_device) + f2 = bm.jit(vmap(bm.jacobian(f1)), device=self.jit_device) self.analyzed_results[C.F_vmap_jacobian] = f2 return self.analyzed_results[C.F_vmap_jacobian] @@ -212,6 +216,8 @@ class Bifurcation2D(Num2DAnalyzer): - parameters: a 2D matrix with the shape of (num_point, num_par) - jacobians: a 3D tensors with the shape of (num_point, 2, 2) """ + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am making bifurcation analysis ...') if self._can_convert_to_one_eq(): @@ -289,21 +295,21 @@ class Bifurcation2D(Num2DAnalyzer): # visualization for var in self.target_var_names: - plt.figure(var) + pyplot.figure(var) for fp_type, points in container.items(): if len(points['p']): plot_style = stability.plot_scheme[fp_type] - plt.plot(points['p'], points[var], '.', **plot_style, label=fp_type) - plt.xlabel(self.target_par_names[0]) - plt.ylabel(var) + pyplot.plot(points['p'], points[var], '.', **plot_style, label=fp_type) + pyplot.xlabel(self.target_par_names[0]) + pyplot.ylabel(var) scale = (self.lim_scale - 1) / 2 - plt.xlim(*utils.rescale(self.target_pars[self.target_par_names[0]], scale=scale)) - plt.ylim(*utils.rescale(self.target_vars[var], scale=scale)) + pyplot.xlim(*utils.rescale(self.target_pars[self.target_par_names[0]], scale=scale)) + pyplot.ylim(*utils.rescale(self.target_vars[var], scale=scale)) - plt.legend() + pyplot.legend() if show: - plt.show() + pyplot.show() # bifurcation analysis of co-dimension 2 elif len(self.target_pars) == 2: @@ -320,7 +326,7 @@ class Bifurcation2D(Num2DAnalyzer): # visualization for var in self.target_var_names: - fig = plt.figure(var) + fig = pyplot.figure(var) ax = fig.add_subplot(projection='3d') for fp_type, points in container.items(): if len(points['p0']): @@ -340,7 +346,7 @@ class Bifurcation2D(Num2DAnalyzer): ax.grid(True) ax.legend() if show: - plt.show() + pyplot.show() else: raise ValueError('Unknown length of parameters.') @@ -350,6 +356,8 @@ class Bifurcation2D(Num2DAnalyzer): def plot_limit_cycle_by_sim(self, duration=100, with_plot=True, with_return=False, plot_style=None, tol=0.001, show=False, dt=None, offset=1.): + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am plotting the limit cycle ...') if self._fixed_points is None: utils.output('No fixed points found, you may call "plot_bifurcation(with_plot=True)" first.') @@ -386,31 +394,33 @@ class Bifurcation2D(Num2DAnalyzer): # visualization if with_plot: if plot_style is None: plot_style = dict() - fmt = plot_style.pop('fmt', '.') + fmt = plot_style.pop('fmt', '*') if len(self.target_par_names) == 2: - for i, var in enumerate(self.target_var_names): - plt.figure(var) - plt.plot(ps_limit_cycle[0], ps_limit_cycle[1], vs_limit_cycle[i]['max'], - **plot_style, label='limit cycle (max)') - plt.plot(ps_limit_cycle[0], ps_limit_cycle[1], vs_limit_cycle[i]['min'], - **plot_style, label='limit cycle (min)') - plt.legend() + if len(ps_limit_cycle[0]): + for i, var in enumerate(self.target_var_names): + pyplot.figure(var) + pyplot.plot(ps_limit_cycle[0], ps_limit_cycle[1], vs_limit_cycle[i]['max'], + **plot_style, label='limit cycle (max)') + pyplot.plot(ps_limit_cycle[0], ps_limit_cycle[1], vs_limit_cycle[i]['min'], + **plot_style, label='limit cycle (min)') + pyplot.legend() elif len(self.target_par_names) == 1: - for i, var in enumerate(self.target_var_names): - plt.figure(var) - plt.plot(ps_limit_cycle[0], vs_limit_cycle[i]['max'], fmt, - **plot_style, label='limit cycle (max)') - plt.plot(ps_limit_cycle[0], vs_limit_cycle[i]['min'], fmt, - **plot_style, label='limit cycle (min)') - plt.legend() + if len(ps_limit_cycle[0]): + for i, var in enumerate(self.target_var_names): + pyplot.figure(var) + pyplot.plot(ps_limit_cycle[0], vs_limit_cycle[i]['max'], fmt, + **plot_style, label='limit cycle (max)') + pyplot.plot(ps_limit_cycle[0], vs_limit_cycle[i]['min'], fmt, + **plot_style, label='limit cycle (min)') + pyplot.legend() else: raise errors.AnalyzerError if show: - plt.show() + pyplot.show() if with_return: return vs_limit_cycle, ps_limit_cycle @@ -437,6 +447,8 @@ class FastSlow1D(Bifurcation1D): def plot_trajectory(self, initials, duration, plot_durations=None, dt=None, show=False, with_plot=True, with_return=False): + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am plotting the trajectory ...') # check the initial values @@ -470,14 +482,14 @@ class FastSlow1D(Bifurcation1D): end = int(plot_durations[i][1] / dt) p1_var = self.target_par_names[0] if len(self.target_par_names) == 1: - lines = plt.plot(mon_res[self.x_var][start: end, i], - mon_res[p1_var][start: end, i], label=legend) + lines = pyplot.plot(mon_res[self.x_var][start: end, i], + mon_res[p1_var][start: end, i], label=legend) elif len(self.target_par_names) == 2: p2_var = self.target_par_names[1] - lines = plt.plot(mon_res[self.x_var][start: end, i], - mon_res[p1_var][start: end, i], - mon_res[p2_var][start: end, i], - label=legend) + lines = pyplot.plot(mon_res[self.x_var][start: end, i], + mon_res[p1_var][start: end, i], + mon_res[p2_var][start: end, i], + label=legend) else: raise ValueError utils.add_arrow(lines[0]) @@ -488,10 +500,10 @@ class FastSlow1D(Bifurcation1D): # scale = (self.lim_scale - 1.) / 2 # plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) # plt.ylim(*utils.rescale(self.target_vars[self.target_par_names[0]], scale=scale)) - plt.legend() + pyplot.legend() if show: - plt.show() + pyplot.show() if with_return: return mon_res @@ -517,6 +529,8 @@ class FastSlow2D(Bifurcation2D): def plot_trajectory(self, initials, duration, plot_durations=None, dt=None, show=False, with_plot=True, with_return=False): + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am plotting the trajectory ...') # check the initial values @@ -548,25 +562,25 @@ class FastSlow2D(Bifurcation2D): end = int(plot_durations[i][1] / dt) # visualization - plt.figure(self.x_var) - lines = plt.plot(mon_res[self.target_par_names[0]][start: end, i], - mon_res[self.x_var][start: end, i], - label=legend) + pyplot.figure(self.x_var) + lines = pyplot.plot(mon_res[self.target_par_names[0]][start: end, i], + mon_res[self.x_var][start: end, i], + label=legend) utils.add_arrow(lines[0]) - plt.figure(self.y_var) - lines = plt.plot(mon_res[self.target_par_names[0]][start: end, i], - mon_res[self.y_var][start: end, i], - label=legend) + pyplot.figure(self.y_var) + lines = pyplot.plot(mon_res[self.target_par_names[0]][start: end, i], + mon_res[self.y_var][start: end, i], + label=legend) utils.add_arrow(lines[0]) - plt.figure(self.x_var) - plt.legend() - plt.figure(self.y_var) - plt.legend() + pyplot.figure(self.x_var) + pyplot.legend() + pyplot.figure(self.y_var) + pyplot.legend() if show: - plt.show() + pyplot.show() if with_return: return mon_res diff --git a/brainpy/analysis/lowdim/lowdim_phase_plane.py b/brainpy/analysis/lowdim/lowdim_phase_plane.py index ab3af243..693d93f7 100644 --- a/brainpy/analysis/lowdim/lowdim_phase_plane.py +++ b/brainpy/analysis/lowdim/lowdim_phase_plane.py @@ -1,14 +1,16 @@ # -*- coding: utf-8 -*- import jax.numpy as jnp -import matplotlib.pyplot as plt import numpy as np +from jax import vmap import brainpy.math as bm from brainpy import errors, math from brainpy.analysis import stability, constants as C, utils from brainpy.analysis.lowdim.lowdim_analyzer import * +pyplot = None + __all__ = [ 'PhasePlane1D', 'PhasePlane2D', @@ -62,6 +64,8 @@ class PhasePlane1D(Num1DAnalyzer): def plot_vector_field(self, show=False, with_plot=True, with_return=False): """Plot the vector filed.""" + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am creating the vector field ...') # Nullcline of the x variable @@ -72,19 +76,21 @@ class PhasePlane1D(Num1DAnalyzer): if with_plot: label = f"d{self.x_var}dt" x_style = dict(color='lightcoral', alpha=.7, linewidth=4) - plt.plot(np.asarray(self.resolutions[self.x_var]), y_val, **x_style, label=label) - plt.axhline(0) - plt.xlabel(self.x_var) - plt.ylabel(label) - plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=(self.lim_scale - 1.) / 2)) - plt.legend() - if show: plt.show() + pyplot.plot(np.asarray(self.resolutions[self.x_var]), y_val, **x_style, label=label) + pyplot.axhline(0) + pyplot.xlabel(self.x_var) + pyplot.ylabel(label) + pyplot.xlim(*utils.rescale(self.target_vars[self.x_var], scale=(self.lim_scale - 1.) / 2)) + pyplot.legend() + if show: pyplot.show() # return if with_return: return y_val def plot_fixed_point(self, show=False, with_plot=True, with_return=False): """Plot the fixed point.""" + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am searching fixed points ...') # fixed points and stability analysis @@ -102,10 +108,10 @@ class PhasePlane1D(Num1DAnalyzer): for fp_type, points in container.items(): if len(points): plot_style = stability.plot_scheme[fp_type] - plt.plot(points, [0] * len(points), '.', markersize=20, **plot_style, label=fp_type) - plt.legend() + pyplot.plot(points, [0] * len(points), '.', markersize=20, **plot_style, label=fp_type) + pyplot.legend() if show: - plt.show() + pyplot.show() # return if with_return: @@ -153,7 +159,7 @@ class PhasePlane2D(Num2DAnalyzer): @property def F_vmap_brentq_fy(self): if C.F_vmap_brentq_fy not in self.analyzed_results: - f_opt = bm.jit(bm.vmap(utils.jax_brentq(self.F_fy))) + f_opt = bm.jit(vmap(utils.jax_brentq(self.F_fy))) self.analyzed_results[C.F_vmap_brentq_fy] = f_opt return self.analyzed_results[C.F_vmap_brentq_fy] @@ -178,6 +184,8 @@ class PhasePlane2D(Num2DAnalyzer): "units", "angles", "scale". More settings please check https://matplotlib.org/api/_as_gen/matplotlib.pyplot.quiver.html. """ + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am creating the vector field ...') # get vector fields @@ -197,7 +205,7 @@ class PhasePlane2D(Num2DAnalyzer): speed = np.sqrt(dx ** 2 + dy ** 2) dx = dx / speed dy = dy / speed - plt.quiver(X, Y, dx, dy, **plot_style) + pyplot.quiver(X, Y, dx, dy, **plot_style) elif plot_method == 'streamplot': if plot_style is None: plot_style = dict(arrowsize=1.2, density=1, color='thistle') @@ -207,15 +215,15 @@ class PhasePlane2D(Num2DAnalyzer): min_width, max_width = 0.5, 5.5 speed = np.nan_to_num(np.sqrt(dx ** 2 + dy ** 2)) linewidth = min_width + max_width * (speed / speed.max()) - plt.streamplot(X, Y, dx, dy, linewidth=linewidth, **plot_style) + pyplot.streamplot(X, Y, dx, dy, linewidth=linewidth, **plot_style) else: raise errors.AnalyzerError(f'Unknown plot_method "{plot_method}", ' f'only supports "quiver" and "streamplot".') - plt.xlabel(self.x_var) - plt.ylabel(self.y_var) + pyplot.xlabel(self.x_var) + pyplot.ylabel(self.y_var) if show: - plt.show() + pyplot.show() if with_return: # return vector fields return dx, dy @@ -224,6 +232,8 @@ class PhasePlane2D(Num2DAnalyzer): y_style=None, x_style=None, show=False, coords=None, tol_nullcline=1e-7): """Plot the nullcline.""" + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am computing fx-nullcline ...') if coords is None: @@ -240,7 +250,7 @@ class PhasePlane2D(Num2DAnalyzer): if x_style is None: x_style = dict(color='cornflowerblue', alpha=.7, ) fmt = x_style.pop('fmt', '.') - plt.plot(x_values_in_fx, y_values_in_fx, fmt, **x_style, label=f"{self.x_var} nullcline") + pyplot.plot(x_values_in_fx, y_values_in_fx, fmt, **x_style, label=f"{self.x_var} nullcline") # Nullcline of the y variable utils.output('I am computing fy-nullcline ...') @@ -252,17 +262,17 @@ class PhasePlane2D(Num2DAnalyzer): if y_style is None: y_style = dict(color='lightcoral', alpha=.7, ) fmt = y_style.pop('fmt', '.') - plt.plot(x_values_in_fy, y_values_in_fy, fmt, **y_style, label=f"{self.y_var} nullcline") + pyplot.plot(x_values_in_fy, y_values_in_fy, fmt, **y_style, label=f"{self.y_var} nullcline") if with_plot: - plt.xlabel(self.x_var) - plt.ylabel(self.y_var) + pyplot.xlabel(self.x_var) + pyplot.ylabel(self.y_var) scale = (self.lim_scale - 1.) / 2 - plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) - plt.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale)) - plt.legend() + pyplot.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) + pyplot.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale)) + pyplot.legend() if show: - plt.show() + pyplot.show() if with_return: return {self.x_var: (x_values_in_fx, y_values_in_fx), @@ -273,6 +283,8 @@ class PhasePlane2D(Num2DAnalyzer): select_candidates='fx-nullcline', num_rank=100, ): """Plot the fixed point and analyze its stability. """ + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am searching fixed points ...') if self._can_convert_to_one_eq(): @@ -338,10 +350,10 @@ class PhasePlane2D(Num2DAnalyzer): for fp_type, points in container.items(): if len(points['x']): plot_style = stability.plot_scheme[fp_type] - plt.plot(points['x'], points['y'], '.', markersize=20, **plot_style, label=fp_type) - plt.legend() + pyplot.plot(points['x'], points['y'], '.', markersize=20, **plot_style, label=fp_type) + pyplot.legend() if show: - plt.show() + pyplot.show() if with_return: return fixed_points @@ -377,7 +389,8 @@ class PhasePlane2D(Num2DAnalyzer): show : bool Whether show or not. """ - + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am plotting the trajectory ...') if axes not in ['v-v', 't-v']: @@ -413,28 +426,31 @@ class PhasePlane2D(Num2DAnalyzer): start = int(plot_durations[i][0] / dt) end = int(plot_durations[i][1] / dt) if axes == 'v-v': - lines = plt.plot(mon_res[self.x_var][start: end, i], mon_res[self.y_var][start: end, i], - label=legend, **kwargs) + lines = pyplot.plot(mon_res[self.x_var][start: end, i], + mon_res[self.y_var][start: end, i], + label=legend, **kwargs) utils.add_arrow(lines[0]) else: - plt.plot(mon_res.ts[start: end], mon_res[self.x_var][start: end, i], - label=legend + f', {self.x_var}', **kwargs) - plt.plot(mon_res.ts[start: end], mon_res[self.y_var][start: end, i], - label=legend + f', {self.y_var}', **kwargs) + pyplot.plot(mon_res.ts[start: end], + mon_res[self.x_var][start: end, i], + label=legend + f', {self.x_var}', **kwargs) + pyplot.plot(mon_res.ts[start: end], + mon_res[self.y_var][start: end, i], + label=legend + f', {self.y_var}', **kwargs) # visualization of others if axes == 'v-v': - plt.xlabel(self.x_var) - plt.ylabel(self.y_var) + pyplot.xlabel(self.x_var) + pyplot.ylabel(self.y_var) scale = (self.lim_scale - 1.) / 2 - plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) - plt.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale)) - plt.legend() + pyplot.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) + pyplot.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale)) + pyplot.legend() else: - plt.legend(title='Initial values') + pyplot.legend(title='Initial values') if show: - plt.show() + pyplot.show() if with_return: return mon_res @@ -462,6 +478,8 @@ class PhasePlane2D(Num2DAnalyzer): show : bool Whether show or not. """ + global pyplot + if pyplot is None: from matplotlib import pyplot utils.output('I am plotting the limit cycle ...') # 1. format the initial values @@ -487,18 +505,18 @@ class PhasePlane2D(Num2DAnalyzer): x_cycle = x_data[max_index[0]: max_index[1]] y_cycle = y_data[max_index[0]: max_index[1]] # 5.5 visualization - lines = plt.plot(x_cycle, y_cycle, label='limit cycle') + lines = pyplot.plot(x_cycle, y_cycle, label='limit cycle') utils.add_arrow(lines[0]) else: utils.output(f'No limit cycle found for initial value {initial}') # 6. visualization - plt.xlabel(self.x_var) - plt.ylabel(self.y_var) + pyplot.xlabel(self.x_var) + pyplot.ylabel(self.y_var) scale = (self.lim_scale - 1.) / 2 - plt.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) - plt.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale)) - plt.legend() + pyplot.xlim(*utils.rescale(self.target_vars[self.x_var], scale=scale)) + pyplot.ylim(*utils.rescale(self.target_vars[self.y_var], scale=scale)) + pyplot.legend() if show: - plt.show() + pyplot.show() diff --git a/brainpy/analysis/utils/measurement.py b/brainpy/analysis/utils/measurement.py index 3cf4e76b..24d7d9dd 100644 --- a/brainpy/analysis/utils/measurement.py +++ b/brainpy/analysis/utils/measurement.py @@ -2,6 +2,7 @@ import jax.numpy as jnp import numpy as np +from brainpy.tools.others import numba_jit __all__ = [ @@ -10,7 +11,7 @@ __all__ = [ ] -# @tools.numba_jit +@numba_jit def _f1(arr, grad, tol): condition = np.logical_and(grad[:-1] * grad[1:] <= 0, grad[:-1] >= 0) indexes = np.where(condition)[0] @@ -19,7 +20,8 @@ def _f1(arr, grad, tol): length = np.max(data) - np.min(data) a = arr[indexes[-2]] b = arr[indexes[-1]] - if np.abs(a - b) <= tol * length: + # TODO: how to choose length threshold, 1e-3? + if length > 1e-3 and np.abs(a - b) <= tol * length: return indexes[-2:] return np.array([-1, -1]) diff --git a/brainpy/analysis/utils/model.py b/brainpy/analysis/utils/model.py index d499394d..2a3ab2b1 100644 --- a/brainpy/analysis/utils/model.py +++ b/brainpy/analysis/utils/model.py @@ -49,8 +49,7 @@ def model_transform(model): new_model = [] for intg in model: if isinstance(intg.f, JointEq): - new_model.extend([type(intg)(eq, var_type=intg.var_type, dt=intg.dt, dyn_var=intg.dyn_var) - for eq in intg.f.eqs]) + new_model.extend([type(intg)(eq, var_type=intg.var_type, dt=intg.dt) for eq in intg.f.eqs]) else: new_model.append(intg) diff --git a/brainpy/analysis/utils/optimization.py b/brainpy/analysis/utils/optimization.py index c1a5a618..f24fc11b 100644 --- a/brainpy/analysis/utils/optimization.py +++ b/brainpy/analysis/utils/optimization.py @@ -4,7 +4,7 @@ import jax.lax import jax.numpy as jnp import numpy as np -from jax import grad, jit +from jax import grad, jit, vmap from jax.flatten_util import ravel_pytree import brainpy.math as bm @@ -197,7 +197,7 @@ def brentq_candidates(vmap_f, *values, args=()): def brentq_roots(f, starts, ends, *vmap_args, args=()): in_axes = (0, 0, tuple([0] * len(vmap_args)) + tuple([None] * len(args))) - vmap_f_opt = bm.jit(bm.vmap(jax_brentq(f), in_axes=in_axes)) + vmap_f_opt = bm.jit(vmap(jax_brentq(f), in_axes=in_axes)) all_args = vmap_args + args if len(all_args): res = vmap_f_opt(starts, ends, all_args) @@ -397,7 +397,7 @@ def roots_of_1d_by_x(f, candidates, args=()): return fps starts = candidates[candidate_ids] ends = candidates[candidate_ids + 1] - f_opt = bm.jit(bm.vmap(jax_brentq(f), in_axes=(0, 0, None))) + f_opt = bm.jit(vmap(jax_brentq(f), in_axes=(0, 0, None))) res = f_opt(starts, ends, args) valid_idx = jnp.where(res['status'] == ECONVERGED)[0] fps2 = res['root'][valid_idx] @@ -406,7 +406,7 @@ def roots_of_1d_by_x(f, candidates, args=()): def roots_of_1d_by_xy(f, starts, ends, args): f = f_without_jaxarray_return(f) - f_opt = bm.jit(bm.vmap(jax_brentq(f))) + f_opt = bm.jit(vmap(jax_brentq(f))) res = f_opt(starts, ends, (args,)) valid_idx = jnp.where(res['status'] == ECONVERGED)[0] xs = res['root'][valid_idx] diff --git a/brainpy/analysis/utils/others.py b/brainpy/analysis/utils/others.py index 446ebe89..5266ca23 100644 --- a/brainpy/analysis/utils/others.py +++ b/brainpy/analysis/utils/others.py @@ -1,6 +1,7 @@ # -*- coding: utf-8 -*- import jax.numpy as jnp +from jax import vmap import numpy as np import brainpy.math as bm @@ -76,7 +77,7 @@ def get_sign(f, xs, ys): def get_sign2(f, *xyz, args=()): in_axes = tuple(range(len(xyz))) + tuple([None] * len(args)) - f = bm.jit(bm.vmap(f_without_jaxarray_return(f), in_axes=in_axes)) + f = bm.jit(vmap(f_without_jaxarray_return(f), in_axes=in_axes)) xyz = tuple((v.value if isinstance(v, bm.JaxArray) else v) for v in xyz) XYZ = jnp.meshgrid(*xyz) XYZ = tuple(jnp.moveaxis(v, 1, 0).flatten() for v in XYZ) diff --git a/brainpy/check.py b/brainpy/check.py new file mode 100644 index 00000000..55fc5a9d --- /dev/null +++ b/brainpy/check.py @@ -0,0 +1,27 @@ +# -*- coding: utf-8 -*- + + +__all__ = [ + 'is_checking', + 'turn_on', + 'turn_off', +] + +_check = True + + +def is_checking(): + """Whether the checking is turn on.""" + return _check + + +def turn_on(): + """Turn on the checking.""" + global _check + _check = True + + +def turn_off(): + """Turn off the checking.""" + global _check + _check = False diff --git a/brainpy/compact/models.py b/brainpy/compact/models.py deleted file mode 100644 index ac2db882..00000000 --- a/brainpy/compact/models.py +++ /dev/null @@ -1,12 +0,0 @@ -# -*- coding: utf-8 -*- - -from brainpy.dyn import LIF, AdExIF, Izhikevich, ExpCOBA, ExpCUBA, DeltaSynapse - -__all__ = [ - 'LIF', - 'AdExIF', - 'Izhikevich', - 'ExpCOBA', - 'ExpCUBA', - 'DeltaSynapse', -] diff --git a/brainpy/compact/runners.py b/brainpy/compact/runners.py deleted file mode 100644 index d533e888..00000000 --- a/brainpy/compact/runners.py +++ /dev/null @@ -1,11 +0,0 @@ -# -*- coding: utf-8 -*- - -from brainpy.integrators.runner import IntegratorRunner -from brainpy.dyn.runners import DSRunner, StructRunner, ReportRunner - -__all__ = [ - 'IntegratorRunner', - 'DSRunner', - 'StructRunner', - 'ReportRunner' -] diff --git a/brainpy/compact/__init__.py b/brainpy/compat/__init__.py similarity index 100% rename from brainpy/compact/__init__.py rename to brainpy/compat/__init__.py diff --git a/brainpy/compact/brainobjects.py b/brainpy/compat/brainobjects.py similarity index 77% rename from brainpy/compact/brainobjects.py rename to brainpy/compat/brainobjects.py index 6b5b1459..d2f0fbe2 100644 --- a/brainpy/compact/brainobjects.py +++ b/brainpy/compat/brainobjects.py @@ -15,6 +15,11 @@ __all__ = [ class DynamicalSystem(dyn.DynamicalSystem): + """Dynamical System. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.DynamicalSystem" instead. + """ def __init__(self, *args, **kwargs): warnings.warn('Please use "brainpy.dyn.DynamicalSystem" instead. ' '"brainpy.DynamicalSystem" is deprecated since ' @@ -23,6 +28,11 @@ class DynamicalSystem(dyn.DynamicalSystem): class Container(dyn.Container): + """Container. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.Container" instead. + """ def __init__(self, *args, **kwargs): warnings.warn('Please use "brainpy.dyn.Container" instead. ' '"brainpy.Container" is deprecated since ' @@ -31,6 +41,11 @@ class Container(dyn.Container): class Network(dyn.Network): + """Network. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.Network" instead. + """ def __init__(self, *args, **kwargs): warnings.warn('Please use "brainpy.dyn.Network" instead. ' '"brainpy.Network" is deprecated since ' @@ -39,6 +54,11 @@ class Network(dyn.Network): class ConstantDelay(dyn.ConstantDelay): + """Constant Delay. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.ConstantDelay" instead. + """ def __init__(self, *args, **kwargs): warnings.warn('Please use "brainpy.dyn.ConstantDelay" instead. ' '"brainpy.ConstantDelay" is deprecated since ' @@ -47,6 +67,11 @@ class ConstantDelay(dyn.ConstantDelay): class NeuGroup(dyn.NeuGroup): + """Neuron group. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.NeuGroup" instead. + """ def __init__(self, *args, **kwargs): warnings.warn('Please use "brainpy.dyn.NeuGroup" instead. ' '"brainpy.NeuGroup" is deprecated since ' @@ -55,6 +80,11 @@ class NeuGroup(dyn.NeuGroup): class TwoEndConn(dyn.TwoEndConn): + """Two-end synaptic connection. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.TwoEndConn" instead. + """ def __init__(self, *args, **kwargs): warnings.warn('Please use "brainpy.dyn.TwoEndConn" instead. ' '"brainpy.TwoEndConn" is deprecated since ' diff --git a/brainpy/compact/integrators.py b/brainpy/compat/integrators.py similarity index 71% rename from brainpy/compact/integrators.py rename to brainpy/compat/integrators.py index 29d10b65..3980ad44 100644 --- a/brainpy/compact/integrators.py +++ b/brainpy/compat/integrators.py @@ -13,6 +13,11 @@ __all__ = [ def set_default_odeint(method): + """Set default ode integrator. + + .. deprecated:: 2.1.0 + Please use "brainpy.ode.set_default_odeint" instead. + """ warnings.warn('Please use "brainpy.ode.set_default_odeint" instead. ' '"brainpy.set_default_odeint" is deprecated since ' 'version 2.1.0', DeprecationWarning) @@ -20,6 +25,11 @@ def set_default_odeint(method): def get_default_odeint(): + """Get default ode integrator. + + .. deprecated:: 2.1.0 + Please use "brainpy.ode.get_default_odeint" instead. + """ warnings.warn('Please use "brainpy.ode.get_default_odeint" instead. ' '"brainpy.get_default_odeint" is deprecated since ' 'version 2.1.0', DeprecationWarning) @@ -27,6 +37,11 @@ def get_default_odeint(): def set_default_sdeint(method): + """Set default sde integrator. + + .. deprecated:: 2.1.0 + Please use "brainpy.ode.set_default_sdeint" instead. + """ warnings.warn('Please use "brainpy.sde.set_default_sdeint" instead. ' '"brainpy.set_default_sdeint" is deprecated since ' 'version 2.1.0', DeprecationWarning) @@ -34,6 +49,11 @@ def set_default_sdeint(method): def get_default_sdeint(): + """Get default sde integrator. + + .. deprecated:: 2.1.0 + Please use "brainpy.ode.get_default_sdeint" instead. + """ warnings.warn('Please use "brainpy.sde.get_default_sdeint" instead. ' '"brainpy.get_default_sdeint" is deprecated since ' 'version 2.1.0', DeprecationWarning) diff --git a/brainpy/compact/layers.py b/brainpy/compat/layers.py similarity index 92% rename from brainpy/compact/layers.py rename to brainpy/compat/layers.py index 167394fd..23a17727 100644 --- a/brainpy/compact/layers.py +++ b/brainpy/compat/layers.py @@ -23,7 +23,10 @@ def _check_args(args): class Module(Base): - """Basic module class.""" + """Basic module class. + + .. deprecated:: 2.1.0 + """ @staticmethod def get_param(param, size): @@ -47,7 +50,7 @@ class Module(Base): def __init__(self, name=None): # initialize parameters warnings.warn('Please use "brainpy.rnns.Module" instead. ' '"brainpy.layers.Module" is deprecated since ' - 'version 2.0.3.', DeprecationWarning) + 'version 2.1.0.', DeprecationWarning) super(Module, self).__init__(name=name) def __call__(self, *args, **kwargs): # initialize variables diff --git a/brainpy/compat/models.py b/brainpy/compat/models.py new file mode 100644 index 00000000..4aec16d2 --- /dev/null +++ b/brainpy/compat/models.py @@ -0,0 +1,98 @@ +# -*- coding: utf-8 -*- + +import warnings + +from brainpy.dyn import neurons, synapses + +__all__ = [ + 'LIF', + 'AdExIF', + 'Izhikevich', + 'ExpCOBA', + 'ExpCUBA', + 'DeltaSynapse', +] + + +class LIF(neurons.LIF): + """LIF neuron model. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.LIF" instead. + """ + + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.LIF" instead. ' + '"brainpy.models.LIF" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(LIF, self).__init__(*args, **kwargs) + + +class AdExIF(neurons.AdExIF): + """AdExIF neuron model. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.AdExIF" instead. + """ + + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.AdExIF" instead. ' + '"brainpy.models.AdExIF" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(AdExIF, self).__init__(*args, **kwargs) + + +class Izhikevich(neurons.Izhikevich): + """Izhikevich neuron model. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.Izhikevich" instead. + """ + + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.Izhikevich" instead. ' + '"brainpy.models.Izhikevich" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(Izhikevich, self).__init__(*args, **kwargs) + + +class ExpCOBA(synapses.ExpCOBA): + """ExpCOBA synapse model. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.ExpCOBA" instead. + """ + + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.ExpCOBA" instead. ' + '"brainpy.models.ExpCOBA" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(ExpCOBA, self).__init__(*args, **kwargs) + + +class ExpCUBA(synapses.ExpCUBA): + """ExpCUBA synapse model. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.ExpCUBA" instead. + """ + + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.ExpCUBA" instead. ' + '"brainpy.models.ExpCUBA" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(ExpCUBA, self).__init__(*args, **kwargs) + + +class DeltaSynapse(synapses.DeltaSynapse): + """Delta synapse model. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.DeltaSynapse" instead. + """ + + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.DeltaSynapse" instead. ' + '"brainpy.models.DeltaSynapse" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(DeltaSynapse, self).__init__(*args, **kwargs) diff --git a/brainpy/compact/monitor.py b/brainpy/compat/monitor.py similarity index 77% rename from brainpy/compact/monitor.py rename to brainpy/compat/monitor.py index d4dbe4b6..c21cf0da 100644 --- a/brainpy/compact/monitor.py +++ b/brainpy/compat/monitor.py @@ -9,8 +9,13 @@ __all__ = [ class Monitor(monitor.Monitor): + """Monitor class. + + .. deprecated:: 2.1.0 + Please use "brainpy.running.Monitor" instead. + """ def __init__(self, *args, **kwargs): - super(Monitor, self).__init__(*args, **kwargs) warnings.warn('Please use "brainpy.running.Monitor" instead. ' - '"brainpy.Monitor" is deprecated since version 2.0.3.', + '"brainpy.Monitor" is deprecated since version 2.1.0.', DeprecationWarning) + super(Monitor, self).__init__(*args, **kwargs) diff --git a/brainpy/compat/runners.py b/brainpy/compat/runners.py new file mode 100644 index 00000000..83b38423 --- /dev/null +++ b/brainpy/compat/runners.py @@ -0,0 +1,65 @@ +# -*- coding: utf-8 -*- + +import warnings + +from brainpy.dyn import runners as dyn_runner +from brainpy.integrators import runner as intg_runner + +__all__ = [ + 'IntegratorRunner', + 'DSRunner', + 'StructRunner', + 'ReportRunner' +] + + +class IntegratorRunner(intg_runner.IntegratorRunner): + """Integrator runner class. + + .. deprecated:: 2.1.0 + Please use "brainpy.integrators.IntegratorRunner" instead. + """ + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.integrators.IntegratorRunner" instead. ' + '"brainpy.IntegratorRunner" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(IntegratorRunner, self).__init__(*args, **kwargs) + + +class DSRunner(dyn_runner.DSRunner): + """Dynamical system runner class. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.DSRunner" instead. + """ + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.DSRunner" instead. ' + '"brainpy.DSRunner" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(DSRunner, self).__init__(*args, **kwargs) + + +class StructRunner(dyn_runner.StructRunner): + """Dynamical system runner class. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.StructRunner" instead. + """ + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.StructRunner" instead. ' + '"brainpy.StructRunner" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(StructRunner, self).__init__(*args, **kwargs) + + +class ReportRunner(dyn_runner.ReportRunner): + """Dynamical system runner class. + + .. deprecated:: 2.1.0 + Please use "brainpy.dyn.ReportRunner" instead. + """ + def __init__(self, *args, **kwargs): + warnings.warn('Please use "brainpy.dyn.ReportRunner" instead. ' + '"brainpy.ReportRunner" is deprecated since ' + 'version 2.1.0', DeprecationWarning) + super(ReportRunner, self).__init__(*args, **kwargs) diff --git a/brainpy/connect/tests/test_regular_conn.py b/brainpy/connect/tests/test_regular_conn.py index f2f46467..f6d9e79a 100644 --- a/brainpy/connect/tests/test_regular_conn.py +++ b/brainpy/connect/tests/test_regular_conn.py @@ -14,7 +14,7 @@ def test_one2one(): num = bp.tools.size2num(size) actual_mat = bp.math.zeros((num, num), dtype=bp.math.bool_) - actual_mat = bp.math.fill_diagonal(actual_mat, True) + bp.math.fill_diagonal(actual_mat, True) assert bp.math.array_equal(actual_mat, conn_mat) assert bp.math.array_equal(pre_ids, bp.math.arange(num)) @@ -42,7 +42,7 @@ def test_all2all(): print(mat) actual_mat = bp.math.ones((num, num), dtype=bp.math.bool_) if not has_self: - actual_mat = bp.math.fill_diagonal(actual_mat, False) + bp.math.fill_diagonal(actual_mat, False) assert bp.math.array_equal(actual_mat, mat) diff --git a/brainpy/datasets/chaotic_systems.py b/brainpy/datasets/chaotic_systems.py index 89687ada..98885a68 100644 --- a/brainpy/datasets/chaotic_systems.py +++ b/brainpy/datasets/chaotic_systems.py @@ -167,8 +167,8 @@ def mackey_glass_series(duration, dt=0.1, beta=2., gamma=1., tau=2., n=9.65, assert isinstance(inits, (bm.ndarray, jnp.ndarray)) rng = bm.random.RandomState(seed) - xdelay = bm.FixedLenDelay(inits.shape, tau, dt=dt) - xdelay.data = inits + 0.2 * (rng.random((xdelay.num_delay_steps,) + inits.shape) - 0.5) + xdelay = bm.TimeDelay(inits, tau, dt=dt) + xdelay.data = inits + 0.2 * (rng.random((xdelay.num_delay_step,) + inits.shape) - 0.5) @ddeint(method=method, state_delays={'x': xdelay}) def mg_eq(x, t): diff --git a/brainpy/dyn/neurons/IF_models.py b/brainpy/dyn/neurons/IF_models.py deleted file mode 100644 index 6fd0d904..00000000 --- a/brainpy/dyn/neurons/IF_models.py +++ /dev/null @@ -1,752 +0,0 @@ -# -*- coding: utf-8 -*- - -import brainpy.math as bm -from brainpy.integrators.joint_eq import JointEq -from brainpy.integrators.ode import odeint -from brainpy.dyn.base import NeuGroup - -__all__ = [ - 'LIF', - 'ExpIF', - 'AdExIF', - 'QuaIF', - 'AdQuaIF', - 'GIF', -] - - -class LIF(NeuGroup): - r"""Leaky integrate-and-fire neuron model. - - **Model Descriptions** - - The formal equations of a LIF model [1]_ is given by: - - .. math:: - - \tau \frac{dV}{dt} = - (V(t) - V_{rest}) + I(t) \\ - \text{after} \quad V(t) \gt V_{th}, V(t) = V_{reset} \quad - \text{last} \quad \tau_{ref} \quad \text{ms} - - where :math:`V` is the membrane potential, :math:`V_{rest}` is the resting - membrane potential, :math:`V_{reset}` is the reset membrane potential, - :math:`V_{th}` is the spike threshold, :math:`\tau` is the time constant, - :math:`\tau_{ref}` is the refractory time period, - and :math:`I` is the time-variant synaptic inputs. - - **Model Examples** - - - `(Brette, Romain. 2004) LIF phase locking `_ - - **Model Parameters** - - ============= ============== ======== ========================================= - **Parameter** **Init Value** **Unit** **Explanation** - ------------- -------------- -------- ----------------------------------------- - V_rest 0 mV Resting membrane potential. - V_reset -5 mV Reset potential after spike. - V_th 20 mV Threshold potential of spike. - tau 10 ms Membrane time constant. Compute by R * C. - tau_ref 5 ms Refractory period length.(ms) - ============= ============== ======== ========================================= - - **Neuron Variables** - - ================== ================= ========================================================= - **Variables name** **Initial Value** **Explanation** - ------------------ ----------------- --------------------------------------------------------- - V 0 Membrane potential. - input 0 External and synaptic input current. - spike False Flag to mark whether the neuron is spiking. - refractory False Flag to mark whether the neuron is in refractory period. - t_last_spike -1e7 Last spike time stamp. - ================== ================= ========================================================= - - **References** - - .. [1] Abbott, Larry F. "Lapicque’s introduction of the integrate-and-fire model - neuron (1907)." Brain research bulletin 50, no. 5-6 (1999): 303-304. - """ - - def __init__(self, size, V_rest=0., V_reset=-5., V_th=20., tau=10., - tau_ref=1., method='exp_auto', name=None): - # initialization - super(LIF, self).__init__(size=size, name=name) - - # parameters - self.V_rest = V_rest - self.V_reset = V_reset - self.V_th = V_th - self.tau = tau - self.tau_ref = tau_ref - - # variables - self.V = bm.Variable(bm.zeros(self.num)) - self.input = bm.Variable(bm.zeros(self.num)) - self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) - self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) - - # integral - self.integral = odeint(method=method, f=self.derivative) - - def derivative(self, V, t, I_ext): - dvdt = (-V + self.V_rest + I_ext) / self.tau - return dvdt - - def update(self, _t, _dt): - refractory = (_t - self.t_last_spike) <= self.tau_ref - V = self.integral(self.V, _t, self.input, dt=_dt) - V = bm.where(refractory, self.V, V) - spike = V >= self.V_th - self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) - self.V.value = bm.where(spike, self.V_reset, V) - self.refractory.value = bm.logical_or(refractory, spike) - self.spike.value = spike - self.input[:] = 0. - - -class ExpIF(NeuGroup): - r"""Exponential integrate-and-fire neuron model. - - **Model Descriptions** - - In the exponential integrate-and-fire model [1]_, the differential - equation for the membrane potential is given by - - .. math:: - - \tau\frac{d V}{d t}= - (V-V_{rest}) + \Delta_T e^{\frac{V-V_T}{\Delta_T}} + RI(t), \\ - \text{after} \, V(t) \gt V_{th}, V(t) = V_{reset} \, \text{last} \, \tau_{ref} \, \text{ms} - - This equation has an exponential nonlinearity with "sharpness" parameter :math:`\Delta_{T}` - and "threshold" :math:`\vartheta_{rh}`. - - The moment when the membrane potential reaches the numerical threshold :math:`V_{th}` - defines the firing time :math:`t^{(f)}`. After firing, the membrane potential is reset to - :math:`V_{rest}` and integration restarts at time :math:`t^{(f)}+\tau_{\rm ref}`, - where :math:`\tau_{\rm ref}` is an absolute refractory time. - If the numerical threshold is chosen sufficiently high, :math:`V_{th}\gg v+\Delta_T`, - its exact value does not play any role. The reason is that the upswing of the action - potential for :math:`v\gg v +\Delta_{T}` is so rapid, that it goes to infinity in - an incredibly short time. The threshold :math:`V_{th}` is introduced mainly for numerical - convenience. For a formal mathematical analysis of the model, the threshold can be pushed - to infinity. - - The model was first introduced by Nicolas Fourcaud-Trocmé, David Hansel, Carl van Vreeswijk - and Nicolas Brunel [1]_. The exponential nonlinearity was later confirmed by Badel et al. [3]_. - It is one of the prominent examples of a precise theoretical prediction in computational - neuroscience that was later confirmed by experimental neuroscience. - - Two important remarks: - - - (i) The right-hand side of the above equation contains a nonlinearity - that can be directly extracted from experimental data [3]_. In this sense the exponential - nonlinearity is not an arbitrary choice but directly supported by experimental evidence. - - (ii) Even though it is a nonlinear model, it is simple enough to calculate the firing - rate for constant input, and the linear response to fluctuations, even in the presence - of input noise [4]_. - - **Model Examples** - - .. plot:: - :include-source: True - - >>> import brainpy as bp - >>> group = bp.dyn.ExpIF(1) - >>> runner = bp.dyn.DSRunner(group, monitors=['V'], inputs=('input', 10.)) - >>> runner.run(300., ) - >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V', show=True) - - - **Model Parameters** - - ============= ============== ======== =================================================== - **Parameter** **Init Value** **Unit** **Explanation** - ------------- -------------- -------- --------------------------------------------------- - V_rest -65 mV Resting potential. - V_reset -68 mV Reset potential after spike. - V_th -30 mV Threshold potential of spike. - V_T -59.9 mV Threshold potential of generating action potential. - delta_T 3.48 \ Spike slope factor. - R 1 \ Membrane resistance. - tau 10 \ Membrane time constant. Compute by R * C. - tau_ref 1.7 \ Refractory period length. - ============= ============== ======== =================================================== - - **Model Variables** - - ================== ================= ========================================================= - **Variables name** **Initial Value** **Explanation** - ------------------ ----------------- --------------------------------------------------------- - V 0 Membrane potential. - input 0 External and synaptic input current. - spike False Flag to mark whether the neuron is spiking. - refractory False Flag to mark whether the neuron is in refractory period. - t_last_spike -1e7 Last spike time stamp. - ================== ================= ========================================================= - - **References** - - .. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation - mechanisms determine the neuronal response to fluctuating - inputs." Journal of Neuroscience 23.37 (2003): 11628-11640. - .. [2] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). - Neuronal dynamics: From single neurons to networks and models - of cognition. Cambridge University Press. - .. [3] Badel, Laurent, Sandrine Lefort, Romain Brette, Carl CH Petersen, - Wulfram Gerstner, and Magnus JE Richardson. "Dynamic IV curves - are reliable predictors of naturalistic pyramidal-neuron voltage - traces." Journal of Neurophysiology 99, no. 2 (2008): 656-666. - .. [4] Richardson, Magnus JE. "Firing-rate response of linear and nonlinear - integrate-and-fire neurons to modulated current-based and - conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919. - .. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire - """ - - def __init__(self, size, V_rest=-65., V_reset=-68., V_th=-30., V_T=-59.9, delta_T=3.48, - R=1., tau=10., tau_ref=1.7, method='exp_auto', name=None): - # initialize - super(ExpIF, self).__init__(size=size, name=name) - - # parameters - self.V_rest = V_rest - self.V_reset = V_reset - self.V_th = V_th - self.V_T = V_T - self.delta_T = delta_T - self.R = R - self.tau = tau - self.tau_ref = tau_ref - - # variables - self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) - # variables - self.V = bm.Variable(bm.zeros(self.num)) - self.input = bm.Variable(bm.zeros(self.num)) - self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) - - # integral - self.integral = odeint(method=method, f=self.derivative) - - def derivative(self, V, t, I_ext): - exp_v = self.delta_T * bm.exp((V - self.V_T) / self.delta_T) - dvdt = (- (V - self.V_rest) + exp_v + self.R * I_ext) / self.tau - return dvdt - - def update(self, _t, _dt): - refractory = (_t - self.t_last_spike) <= self.tau_ref - V = self.integral(self.V, _t, self.input, dt=_dt) - V = bm.where(refractory, self.V, V) - spike = self.V_th <= V - self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) - self.V.value = bm.where(spike, self.V_reset, V) - self.refractory.value = bm.logical_or(refractory, spike) - self.spike.value = spike - self.input[:] = 0. - - -class AdExIF(NeuGroup): - r"""Adaptive exponential integrate-and-fire neuron model. - - **Model Descriptions** - - The **adaptive exponential integrate-and-fire model**, also called AdEx, is a - spiking neuron model with two variables [1]_ [2]_. - - .. math:: - - \begin{aligned} - \tau_m\frac{d V}{d t} &= - (V-V_{rest}) + \Delta_T e^{\frac{V-V_T}{\Delta_T}} - Rw + RI(t), \\ - \tau_w \frac{d w}{d t} &=a(V-V_{rest}) - w - \end{aligned} - - once the membrane potential reaches the spike threshold, - - .. math:: - - V \rightarrow V_{reset}, \\ - w \rightarrow w+b. - - The first equation describes the dynamics of the membrane potential and includes - an activation term with an exponential voltage dependence. Voltage is coupled to - a second equation which describes adaptation. Both variables are reset if an action - potential has been triggered. The combination of adaptation and exponential voltage - dependence gives rise to the name Adaptive Exponential Integrate-and-Fire model. - - The adaptive exponential integrate-and-fire model is capable of describing known - neuronal firing patterns, e.g., adapting, bursting, delayed spike initiation, - initial bursting, fast spiking, and regular spiking. - - **Model Examples** - - - `Examples for different firing patterns `_ - - **Model Parameters** - - ============= ============== ======== ======================================================================================================================== - **Parameter** **Init Value** **Unit** **Explanation** - ------------- -------------- -------- ------------------------------------------------------------------------------------------------------------------------ - V_rest -65 mV Resting potential. - V_reset -68 mV Reset potential after spike. - V_th -30 mV Threshold potential of spike and reset. - V_T -59.9 mV Threshold potential of generating action potential. - delta_T 3.48 \ Spike slope factor. - a 1 \ The sensitivity of the recovery variable :math:`u` to the sub-threshold fluctuations of the membrane potential :math:`v` - b 1 \ The increment of :math:`w` produced by a spike. - R 1 \ Membrane resistance. - tau 10 ms Membrane time constant. Compute by R * C. - tau_w 30 ms Time constant of the adaptation current. - ============= ============== ======== ======================================================================================================================== - - **Model Variables** - - ================== ================= ========================================================= - **Variables name** **Initial Value** **Explanation** - ------------------ ----------------- --------------------------------------------------------- - V 0 Membrane potential. - w 0 Adaptation current. - input 0 External and synaptic input current. - spike False Flag to mark whether the neuron is spiking. - t_last_spike -1e7 Last spike time stamp. - ================== ================= ========================================================= - - **References** - - .. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation - mechanisms determine the neuronal response to fluctuating - inputs." Journal of Neuroscience 23.37 (2003): 11628-11640. - .. [2] http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model - """ - - def __init__(self, size, V_rest=-65., V_reset=-68., V_th=-30., V_T=-59.9, delta_T=3.48, a=1., - b=1., tau=10., tau_w=30., R=1., method='exp_auto', name=None): - super(AdExIF, self).__init__(size=size, name=name) - - # parameters - self.V_rest = V_rest - self.V_reset = V_reset - self.V_th = V_th - self.V_T = V_T - self.delta_T = delta_T - self.a = a - self.b = b - self.tau = tau - self.tau_w = tau_w - self.R = R - - # variables - self.w = bm.Variable(bm.zeros(self.num)) - self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.V = bm.Variable(bm.zeros(self.num)) - self.input = bm.Variable(bm.zeros(self.num)) - self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) - - # functions - self.integral = odeint(method=method, f=self.derivative) - - def dV(self, V, t, w, I_ext): - dVdt = (- V + self.V_rest + self.delta_T * bm.exp((V - self.V_T) / self.delta_T) - - self.R * w + self.R * I_ext) / self.tau - return dVdt - - def dw(self, w, t, V): - dwdt = (self.a * (V - self.V_rest) - w) / self.tau_w - return dwdt - - @property - def derivative(self): - return JointEq([self.dV, self.dw]) - - def update(self, _t, _dt): - V, w = self.integral(self.V, self.w, _t, self.input, dt=_dt) - spike = V >= self.V_th - self.t_last_spike[:] = bm.where(spike, _t, self.t_last_spike) - self.V.value = bm.where(spike, self.V_reset, V) - self.w.value = bm.where(spike, w + self.b, w) - self.spike.value = spike - self.input[:] = 0. - - -class QuaIF(NeuGroup): - r"""Quadratic Integrate-and-Fire neuron model. - - **Model Descriptions** - - In contrast to physiologically accurate but computationally expensive - neuron models like the Hodgkin–Huxley model, the QIF model [1]_ seeks only - to produce **action potential-like patterns** and ignores subtleties - like gating variables, which play an important role in generating action - potentials in a real neuron. However, the QIF model is incredibly easy - to implement and compute, and relatively straightforward to study and - understand, thus has found ubiquitous use in computational neuroscience. - - .. math:: - - \tau \frac{d V}{d t}=c(V-V_{rest})(V-V_c) + RI(t) - - where the parameters are taken to be :math:`c` =0.07, and :math:`V_c = -50 mV` (Latham et al., 2000). - - **Model Examples** - - .. plot:: - :include-source: True - - >>> import brainpy as bp - >>> - >>> group = bp.dyn.QuaIF(1,) - >>> - >>> runner = bp.dyn.DSRunner(group, monitors=['V'], inputs=('input', 20.)) - >>> runner.run(duration=200.) - >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True) - - - **Model Parameters** - - ============= ============== ======== ======================================================================================================================== - **Parameter** **Init Value** **Unit** **Explanation** - ------------- -------------- -------- ------------------------------------------------------------------------------------------------------------------------ - V_rest -65 mV Resting potential. - V_reset -68 mV Reset potential after spike. - V_th -30 mV Threshold potential of spike and reset. - V_c -50 mV Critical voltage for spike initiation. Must be larger than V_rest. - c .07 \ Coefficient describes membrane potential update. Larger than 0. - R 1 \ Membrane resistance. - tau 10 ms Membrane time constant. Compute by R * C. - tau_ref 0 ms Refractory period length. - ============= ============== ======== ======================================================================================================================== - - **Model Variables** - - ================== ================= ========================================================= - **Variables name** **Initial Value** **Explanation** - ------------------ ----------------- --------------------------------------------------------- - V 0 Membrane potential. - input 0 External and synaptic input current. - spike False Flag to mark whether the neuron is spiking. - refractory False Flag to mark whether the neuron is in refractory period. - t_last_spike -1e7 Last spike time stamp. - ================== ================= ========================================================= - - **References** - - .. [1] P. E. Latham, B.J. Richmond, P. Nelson and S. Nirenberg - (2000) Intrinsic dynamics in neuronal networks. I. Theory. - J. Neurophysiology 83, pp. 808–827. - """ - - def __init__(self, size, V_rest=-65., V_reset=-68., V_th=-30., V_c=-50.0, c=.07, - R=1., tau=10., tau_ref=0., method='exp_auto', name=None): - # initialization - super(QuaIF, self).__init__(size=size, name=name) - - # parameters - self.V_rest = V_rest - self.V_reset = V_reset - self.V_th = V_th - self.V_c = V_c - self.c = c - self.R = R - self.tau = tau - self.tau_ref = tau_ref - - # variables - self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) - # variables - self.V = bm.Variable(bm.zeros(self.num)) - self.input = bm.Variable(bm.zeros(self.num)) - self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) - - # integral - self.integral = odeint(method=method, f=self.derivative) - - def derivative(self, V, t, I_ext): - dVdt = (self.c * (V - self.V_rest) * (V - self.V_c) + self.R * I_ext) / self.tau - return dVdt - - def update(self, _t, _dt, **kwargs): - refractory = (_t - self.t_last_spike) <= self.tau_ref - V = self.integral(self.V, _t, self.input, dt=_dt) - V = bm.where(refractory, self.V, V) - spike = self.V_th <= V - self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) - self.V.value = bm.where(spike, self.V_reset, V) - self.refractory.value = bm.logical_or(refractory, spike) - self.spike.value = spike - self.input[:] = 0. - - -class AdQuaIF(NeuGroup): - r"""Adaptive quadratic integrate-and-fire neuron model. - - **Model Descriptions** - - The adaptive quadratic integrate-and-fire neuron model [1]_ is given by: - - .. math:: - - \begin{aligned} - \tau_m \frac{d V}{d t}&=c(V-V_{rest})(V-V_c) - w + I(t), \\ - \tau_w \frac{d w}{d t}&=a(V-V_{rest}) - w, - \end{aligned} - - once the membrane potential reaches the spike threshold, - - .. math:: - - V \rightarrow V_{reset}, \\ - w \rightarrow w+b. - - **Model Examples** - - .. plot:: - :include-source: True - - >>> import brainpy as bp - >>> group = bp.dyn.AdQuaIF(1, ) - >>> runner = bp.dyn.DSRunner(group, monitors=['V', 'w'], inputs=('input', 30.)) - >>> runner.run(300) - >>> fig, gs = bp.visualize.get_figure(2, 1, 3, 8) - >>> fig.add_subplot(gs[0, 0]) - >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V') - >>> fig.add_subplot(gs[1, 0]) - >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.w, ylabel='w', show=True) - - **Model Parameters** - - ============= ============== ======== ======================================================= - **Parameter** **Init Value** **Unit** **Explanation** - ------------- -------------- -------- ------------------------------------------------------- - V_rest -65 mV Resting potential. - V_reset -68 mV Reset potential after spike. - V_th -30 mV Threshold potential of spike and reset. - V_c -50 mV Critical voltage for spike initiation. Must be larger - than :math:`V_{rest}`. - a 1 \ The sensitivity of the recovery variable :math:`u` to - the sub-threshold fluctuations of the membrane - potential :math:`v` - b .1 \ The increment of :math:`w` produced by a spike. - c .07 \ Coefficient describes membrane potential update. - Larger than 0. - tau 10 ms Membrane time constant. - tau_w 10 ms Time constant of the adaptation current. - ============= ============== ======== ======================================================= - - **Model Variables** - - ================== ================= ========================================================== - **Variables name** **Initial Value** **Explanation** - ------------------ ----------------- ---------------------------------------------------------- - V 0 Membrane potential. - w 0 Adaptation current. - input 0 External and synaptic input current. - spike False Flag to mark whether the neuron is spiking. - t_last_spike -1e7 Last spike time stamp. - ================== ================= ========================================================== - - **References** - - .. [1] Izhikevich, E. M. (2004). Which model to use for cortical spiking - neurons?. IEEE transactions on neural networks, 15(5), 1063-1070. - .. [2] Touboul, Jonathan. "Bifurcation analysis of a general class of - nonlinear integrate-and-fire neurons." SIAM Journal on Applied - Mathematics 68, no. 4 (2008): 1045-1079. - """ - - def __init__(self, size, V_rest=-65., V_reset=-68., V_th=-30., V_c=-50.0, a=1., b=.1, - c=.07, tau=10., tau_w=10., method='exp_auto', name=None): - super(AdQuaIF, self).__init__(size=size, name=name) - - # parameters - self.V_rest = V_rest - self.V_reset = V_reset - self.V_th = V_th - self.V_c = V_c - self.c = c - self.a = a - self.b = b - self.tau = tau - self.tau_w = tau_w - - # variables - self.V = bm.Variable(bm.zeros(self.num)) - self.w = bm.Variable(bm.zeros(self.num)) - self.input = bm.Variable(bm.zeros(self.num)) - self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) - self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) - - # integral - self.integral = odeint(method=method, f=self.derivative) - - def dV(self, V, t, w, I_ext): - dVdt = (self.c * (V - self.V_rest) * (V - self.V_c) - w + I_ext) / self.tau - return dVdt - - def dw(self, w, t, V): - dwdt = (self.a * (V - self.V_rest) - w) / self.tau_w - return dwdt - - @property - def derivative(self): - return JointEq([self.dV, self.dw]) - - def update(self, _t, _dt): - V, w = self.integral(self.V, self.w, _t, self.input, dt=_dt) - spike = self.V_th <= V - self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) - self.V.value = bm.where(spike, self.V_reset, V) - self.w.value = bm.where(spike, w + self.b, w) - self.spike.value = spike - self.input[:] = 0. - - -class GIF(NeuGroup): - r"""Generalized Integrate-and-Fire model. - - **Model Descriptions** - - The generalized integrate-and-fire model [1]_ is given by - - .. math:: - - &\frac{d I_j}{d t} = - k_j I_j - - &\frac{d V}{d t} = ( - (V - V_{rest}) + R\sum_{j}I_j + RI) / \tau - - &\frac{d V_{th}}{d t} = a(V - V_{rest}) - b(V_{th} - V_{th\infty}) - - When :math:`V` meet :math:`V_{th}`, Generalized IF neuron fires: - - .. math:: - - &I_j \leftarrow R_j I_j + A_j - - &V \leftarrow V_{reset} - - &V_{th} \leftarrow max(V_{th_{reset}}, V_{th}) - - Note that :math:`I_j` refers to arbitrary number of internal currents. - - **Model Examples** - - - `Detailed examples to reproduce different firing patterns `_ - - **Model Parameters** - - ============= ============== ======== ==================================================================== - **Parameter** **Init Value** **Unit** **Explanation** - ------------- -------------- -------- -------------------------------------------------------------------- - V_rest -70 mV Resting potential. - V_reset -70 mV Reset potential after spike. - V_th_inf -50 mV Target value of threshold potential :math:`V_{th}` updating. - V_th_reset -60 mV Free parameter, should be larger than :math:`V_{reset}`. - R 20 \ Membrane resistance. - tau 20 ms Membrane time constant. Compute by :math:`R * C`. - a 0 \ Coefficient describes the dependence of - :math:`V_{th}` on membrane potential. - b 0.01 \ Coefficient describes :math:`V_{th}` update. - k1 0.2 \ Constant pf :math:`I1`. - k2 0.02 \ Constant of :math:`I2`. - R1 0 \ Free parameter. - Describes dependence of :math:`I_1` reset value on - :math:`I_1` value before spiking. - R2 1 \ Free parameter. - Describes dependence of :math:`I_2` reset value on - :math:`I_2` value before spiking. - A1 0 \ Free parameter. - A2 0 \ Free parameter. - ============= ============== ======== ==================================================================== - - **Model Variables** - - ================== ================= ========================================================= - **Variables name** **Initial Value** **Explanation** - ------------------ ----------------- --------------------------------------------------------- - V -70 Membrane potential. - input 0 External and synaptic input current. - spike False Flag to mark whether the neuron is spiking. - V_th -50 Spiking threshold potential. - I1 0 Internal current 1. - I2 0 Internal current 2. - t_last_spike -1e7 Last spike time stamp. - ================== ================= ========================================================= - - **References** - - .. [1] Mihalaş, Ştefan, and Ernst Niebur. "A generalized linear - integrate-and-fire neural model produces diverse spiking - behaviors." Neural computation 21.3 (2009): 704-718. - .. [2] Teeter, Corinne, Ramakrishnan Iyer, Vilas Menon, Nathan - Gouwens, David Feng, Jim Berg, Aaron Szafer et al. "Generalized - leaky integrate-and-fire models classify multiple neuron types." - Nature communications 9, no. 1 (2018): 1-15. - """ - - def __init__(self, size, V_rest=-70., V_reset=-70., V_th_inf=-50., V_th_reset=-60., - R=20., tau=20., a=0., b=0.01, k1=0.2, k2=0.02, R1=0., R2=1., A1=0., - A2=0., method='exp_auto', name=None): - # initialization - super(GIF, self).__init__(size=size, name=name) - - # params - self.V_rest = V_rest - self.V_reset = V_reset - self.V_th_inf = V_th_inf - self.V_th_reset = V_th_reset - self.R = R - self.tau = tau - self.a = a - self.b = b - self.k1 = k1 - self.k2 = k2 - self.R1 = R1 - self.R2 = R2 - self.A1 = A1 - self.A2 = A2 - - # variables - self.I1 = bm.Variable(bm.zeros(self.num)) - self.I2 = bm.Variable(bm.zeros(self.num)) - self.V_th = bm.Variable(bm.ones(self.num) * -50.) - self.V = bm.Variable(bm.zeros(self.num)) - self.input = bm.Variable(bm.zeros(self.num)) - self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) - - # integral - self.integral = odeint(method=method, f=self.derivative) - - def dI1(self, I1, t): - return - self.k1 * I1 - - def dI2(self, I2, t): - return - self.k2 * I2 - - def dVth(self, V_th, t, V): - return self.a * (V - self.V_rest) - self.b * (V_th - self.V_th_inf) - - def dV(self, V, t, I1, I2, I_ext): - return (- (V - self.V_rest) + self.R * I_ext + self.R * I1 + self.R * I2) / self.tau - - @property - def derivative(self): - return JointEq([self.dI1, self.dI2, self.dVth, self.dV]) - - def update(self, _t, _dt): - I1, I2, V_th, V = self.integral(self.I1, self.I2, self.V_th, self.V, _t, self.input, dt=_dt) - spike = self.V_th <= V - V = bm.where(spike, self.V_reset, V) - I1 = bm.where(spike, self.R1 * I1 + self.A1, I1) - I2 = bm.where(spike, self.R2 * I2 + self.A2, I2) - reset_th = bm.logical_and(V_th < self.V_th_reset, spike) - V_th = bm.where(reset_th, self.V_th_reset, V_th) - self.spike.value = spike - self.I1.value = I1 - self.I2.value = I2 - self.V_th.value = V_th - self.V.value = V - self.input[:] = 0. diff --git a/brainpy/dyn/neurons/__init__.py b/brainpy/dyn/neurons/__init__.py index 62e268a3..3824555f 100644 --- a/brainpy/dyn/neurons/__init__.py +++ b/brainpy/dyn/neurons/__init__.py @@ -1,7 +1,8 @@ # -*- coding: utf-8 -*- from .biological_models import * -from .IF_models import * +from .fractional_models import * from .input_models import * +from .noise_models import * from .rate_models import * from .reduced_models import * diff --git a/brainpy/dyn/neurons/biological_models.py b/brainpy/dyn/neurons/biological_models.py index ce1e5a58..35e68c18 100644 --- a/brainpy/dyn/neurons/biological_models.py +++ b/brainpy/dyn/neurons/biological_models.py @@ -1,9 +1,14 @@ # -*- coding: utf-8 -*- +from typing import Union, Callable + import brainpy.math as bm +from brainpy.dyn.base import NeuGroup +from brainpy.initialize import OneInit, Uniform, Initializer, init_param from brainpy.integrators.joint_eq import JointEq from brainpy.integrators.ode import odeint -from brainpy.dyn.base import NeuGroup +from brainpy.tools.checking import check_initializer +from brainpy.types import Shape, Parameter, Tensor __all__ = [ 'HH', @@ -178,8 +183,24 @@ class HH(NeuGroup): The Journal of Mathematical Neuroscience 6, no. 1 (2016): 1-92. """ - def __init__(self, size, ENa=50., gNa=120., EK=-77., gK=36., EL=-54.387, gL=0.03, - V_th=20., C=1.0, method='exp_auto', name=None): + def __init__( + self, + size: Shape, + ENa: Parameter = 50., + gNa: Parameter = 120., + EK: Parameter = -77., + gK: Parameter = 36., + EL: Parameter = -54.387, + gL: Parameter = 0.03, + V_th: Parameter = 20., + C: Parameter = 1.0, + V_initializer: Union[Initializer, Callable, Tensor] = Uniform(-70, -60.), + m_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.5), + h_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.6), + n_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.32), + method: str = 'exp_auto', + name: str = None + ): # initialization super(HH, self).__init__(size=size, name=name) @@ -194,10 +215,14 @@ class HH(NeuGroup): self.V_th = V_th # variables - self.m = bm.Variable(0.5 * bm.ones(self.num)) - self.h = bm.Variable(0.6 * bm.ones(self.num)) - self.n = bm.Variable(0.32 * bm.ones(self.num)) - self.V = bm.Variable(bm.zeros(self.num)) + check_initializer(m_initializer, 'm_initializer', allow_none=False) + check_initializer(h_initializer, 'h_initializer', allow_none=False) + check_initializer(n_initializer, 'n_initializer', allow_none=False) + check_initializer(V_initializer, 'V_initializer', allow_none=False) + self.m = bm.Variable(init_param(m_initializer, (self.num,))) + self.h = bm.Variable(init_param(h_initializer, (self.num,))) + self.n = bm.Variable(init_param(n_initializer, (self.num,))) + self.V = bm.Variable(init_param(V_initializer, (self.num,))) self.input = bm.Variable(bm.zeros(self.num)) self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) @@ -334,11 +359,29 @@ class MorrisLecar(NeuGroup): .. [3] https://en.wikipedia.org/wiki/Morris%E2%80%93Lecar_model """ - def __init__(self, size, V_Ca=130., g_Ca=4.4, V_K=-84., g_K=8., V_leak=-60., - g_leak=2., C=20., V1=-1.2, V2=18., V3=2., V4=30., phi=0.04, - V_th=10., method='exp_auto', name=None): + def __init__( + self, + size: Shape, + V_Ca: Parameter = 130., + g_Ca: Parameter = 4.4, + V_K: Parameter = -84., + g_K: Parameter = 8., + V_leak: Parameter = -60., + g_leak: Parameter = 2., + C: Parameter = 20., + V1: Parameter = -1.2, + V2: Parameter = 18., + V3: Parameter = 2., + V4: Parameter = 30., + phi: Parameter = 0.04, + V_th: Parameter = 10., + W_initializer: Union[Callable, Initializer, Tensor] = OneInit(0.02), + V_initializer: Union[Callable, Initializer, Tensor] = Uniform(-70., -60.), + method: str = 'exp_auto', + name: str = None + ): # initialization - super(MorrisLecar, self).__init__(size=size, name=name) + super(MorrisLecar, self).__init__(size=size, name=name) # params self.V_Ca = V_Ca @@ -356,8 +399,10 @@ class MorrisLecar(NeuGroup): self.V_th = V_th # vars - self.W = bm.Variable(bm.ones(self.num) * 0.02) - self.V = bm.Variable(bm.zeros(self.num)) + check_initializer(V_initializer, 'V_initializer', allow_none=False) + check_initializer(W_initializer, 'W_initializer', allow_none=False) + self.W = bm.Variable(init_param(W_initializer, (self.num,))) + self.V = bm.Variable(init_param(V_initializer, (self.num,))) self.input = bm.Variable(bm.zeros(self.num)) self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) diff --git a/brainpy/dyn/neurons/fractional_models.py b/brainpy/dyn/neurons/fractional_models.py new file mode 100644 index 00000000..fad1d6ea --- /dev/null +++ b/brainpy/dyn/neurons/fractional_models.py @@ -0,0 +1,294 @@ +# -*- coding: utf-8 -*- + +from typing import Union, Sequence, Callable + +import brainpy.math as bm +from brainpy.dyn.base import NeuGroup +from brainpy.initialize import ZeroInit, OneInit, Initializer, init_param +from brainpy.integrators.fde import CaputoL1Schema +from brainpy.integrators.fde import GLShortMemory +from brainpy.integrators.joint_eq import JointEq +from brainpy.tools.checking import check_float, check_integer +from brainpy.tools.checking import check_initializer +from brainpy.types import Parameter, Shape, Tensor + +__all__ = [ + 'FractionalNeuron', + 'FractionalFHR', + 'FractionalIzhikevich', +] + + +class FractionalNeuron(NeuGroup): + """Fractional-order neuron model.""" + pass + + +class FractionalFHR(FractionalNeuron): + r"""The fractional-order FH-R model [1]_. + + FitzHugh and Rinzel introduced FH-R model (1976, in an unpublished article), + which is the modification of the classical FHN neuron model. The fractional-order + FH-R model is described as + + .. math:: + + \begin{array}{rcl} + \frac{{d}^{\alpha }v}{d{t}^{\alpha }} & = & v-{v}^{3}/3-w+y+I={f}_{1}(v,w,y),\\ + \frac{{d}^{\alpha }w}{d{t}^{\alpha }} & = & \delta (a+v-bw)={f}_{2}(v,w,y),\\ + \frac{{d}^{\alpha }y}{d{t}^{\alpha }} & = & \mu (c-v-dy)={f}_{3}(v,w,y), + \end{array} + + where :math:`v, w` and :math:`y` represent the membrane voltage, recovery variable + and slow modulation of the current respectively. + :math:`I` measures the constant magnitude of external stimulus current, and :math:`\alpha` + is the fractional exponent which ranges in the interval :math:`(0 < \alpha \le 1)`. + :math:`a, b, c, d, \delta` and :math:`\mu` are the system parameters. + + The system reduces to the original classical order system when :math:`\alpha=1`. + + :math:`\mu` indicates a small parameter that determines the pace of the slow system + variable :math:`y`. The fast subsystem (:math:`v-w`) presents a relaxation oscillator + in the phase plane where :math:`\delta` is a small parameter. + :math:`v` is expressed in mV (millivolt) scale. Time :math:`t` is in ms (millisecond) scale. + It exhibits tonic spiking or quiescent state depending on the parameter sets for a fixed + value of :math:`I`. The parameter :math:`a` in the 2D FHN model corresponds to the + parameter :math:`c` of the FH-R neuron model. If we decrease the value of :math:`a`, + it causes longer intervals between two burstings, however there exists :math:`a` + relatively fixed time of bursting duration. With the increasing of :math:`a`, the + interburst intervals become shorter and periodic bursting changes to tonic spiking. + + Examples + -------- + + - [(Mondal, et, al., 2019): Fractional-order FitzHugh-Rinzel bursting neuron model](https://brainpy-examples.readthedocs.io/en/latest/neurons/2019_Fractional_order_FHR_model.html) + + + Parameters + ---------- + size: int, sequence of int + The size of the neuron group. + alpha: float, tensor + The fractional order. + num_memory: int + The total number of the short memory. + + References + ---------- + .. [1] Mondal, A., Sharma, S.K., Upadhyay, R.K. *et al.* Firing activities of a fractional-order FitzHugh-Rinzel bursting neuron model and its coupled dynamics. *Sci Rep* **9,** 15721 (2019). https://doi.org/10.1038/s41598-019-52061-4 + """ + + def __init__( + self, + size: Shape, + alpha: Union[float, Sequence[float]], + num_memory: int = 1000, + a: Parameter = 0.7, + b: Parameter = 0.8, + c: Parameter = -0.775, + d: Parameter = 1., + delta: Parameter = 0.08, + mu: Parameter = 0.0001, + Vth: Parameter = 1.8, + V_initializer: Union[Initializer, Callable, Tensor] = OneInit(2.5), + w_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + y_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + name: str = None + ): + super(FractionalFHR, self).__init__(size, name=name) + + # fractional order + self.alpha = alpha + check_integer(num_memory, 'num_memory', allow_none=False) + + # parameters + self.a = a + self.b = b + self.c = c + self.d = d + self.delta = delta + self.mu = mu + self.Vth = Vth + + # variables + check_initializer(V_initializer, 'V_initializer', allow_none=False) + check_initializer(w_initializer, 'w_initializer', allow_none=False) + check_initializer(y_initializer, 'y_initializer', allow_none=False) + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.w = bm.Variable(init_param(w_initializer, (self.num,))) + self.y = bm.Variable(init_param(y_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + + # integral function + self.integral = GLShortMemory(self.derivative, + alpha=alpha, + num_memory=num_memory, + inits=[self.V, self.w, self.y]) + + def dV(self, V, t, w, y): + return V - V ** 3 / 3 - w + y + self.input + + def dw(self, w, t, V): + return self.delta * (self.a + V - self.b * w) + + def dy(self, y, t, V): + return self.mu * (self.c - V - self.d * y) + + @property + def derivative(self): + return JointEq([self.dV, self.dw, self.dy]) + + def update(self, _t, _dt): + V, w, y = self.integral(self.V, self.w, self.y, _t, _dt) + self.spike.value = bm.logical_and(V >= self.Vth, self.V < self.Vth) + self.t_last_spike.value = bm.where(self.spike, _t, self.t_last_spike) + self.V.value = V + self.w.value = w + self.y.value = y + self.input[:] = 0. + + def set_init(self, values: dict): + for k, v in values.items(): + if k not in self.integral.inits: + raise ValueError(f'Variable "{k}" is not defined in this model.') + variable = getattr(self, k) + variable[:] = v + self.integral.inits[k][:] = v + + +class FractionalIzhikevich(FractionalNeuron): + r"""Fractional-order Izhikevich model [10]_. + + The fractional-order Izhikevich model is given by + + .. math:: + + \begin{aligned} + &\tau \frac{d^{\alpha} v}{d t^{\alpha}}=\mathrm{f} v^{2}+g v+h-u+R I \\ + &\tau \frac{d^{\alpha} u}{d t^{\alpha}}=a(b v-u) + \end{aligned} + + where :math:`\alpha` is the fractional order (exponent) such that :math:`0<\alpha\le1`. + It is a commensurate system that reduces to classical Izhikevich model at :math:`\alpha=1`. + + The time :math:`t` is in ms; and the system variable :math:`v` expressed in mV + corresponds to membrane voltage. Moreover, :math:`u` expressed in mV is the + recovery variable that corresponds to the activation of K+ ionic current and + inactivation of Na+ ionic current. + + The parameters :math:`f, g, h` are fixed constants (should not be changed) such + that :math:`f=0.04` (mV)−1, :math:`g=5, h=140` mV; and :math:`a` and :math:`b` are + dimensionless parameters. The time constant :math:`\tau=1` ms; the resistance + :math:`R=1` Ω; and :math:`I` expressed in mA measures the injected (applied) + dc stimulus current to the system. + + When the membrane voltage reaches the spike peak :math:`v_{peak}`, the two variables + are rest as follow: + + .. math:: + + \text { if } v \geq v_{\text {peak }} \text { then }\left\{\begin{array}{l} + v \leftarrow c \\ + u \leftarrow u+d + \end{array}\right. + + we used :math:`v_{peak}=30` mV, and :math:`c` and :math:`d` are parameters expressed + in mV. When the spike reaches its peak value, the membrane voltage :math:`v` and the + recovery variable :math:`u` are reset according to the above condition. + + Examples + -------- + + - [(Teka, et. al, 2018): Fractional-order Izhikevich neuron model](https://brainpy-examples.readthedocs.io/en/latest/neurons/2018_Fractional_Izhikevich_model.html) + + + References + ---------- + .. [10] Teka, Wondimu W., Ranjit Kumar Upadhyay, and Argha Mondal. "Spiking and + bursting patterns of fractional-order Izhikevich model." Communications + in Nonlinear Science and Numerical Simulation 56 (2018): 161-176. + + """ + + def __init__( + self, + size: Shape, + alpha: Union[float, Sequence[float]], + num_step: int, + a: Parameter = 0.02, + b: Parameter = 0.20, + c: Parameter = -65., + d: Parameter = 8., + f: Parameter = 0.04, + g: Parameter = 5., + h: Parameter = 140., + tau: Parameter = 1., + R: Parameter = 1., + V_th: Parameter = 30., + V_initializer: Union[Initializer, Callable, Tensor] = OneInit(-65.), + u_initializer: Union[Initializer, Callable, Tensor] = OneInit(0.20 * -65.), + name: str = None + ): + # initialization + super(FractionalIzhikevich, self).__init__(size=size, name=name) + + # params + self.alpha = alpha + check_float(alpha, 'alpha', min_bound=0., max_bound=1., allow_none=False, allow_int=True) + self.a = a + self.b = b + self.c = c + self.d = d + self.f = f + self.g = g + self.h = h + self.tau = tau + self.R = R + self.V_th = V_th + + # variables + check_initializer(V_initializer, 'V_initializer', allow_none=False) + check_initializer(u_initializer, 'u_initializer', allow_none=False) + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.u = bm.Variable(init_param(u_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + + # functions + check_integer(num_step, 'num_step', allow_none=False) + self.integral = CaputoL1Schema(f=self.derivative, + alpha=alpha, + num_step=num_step, + inits=[self.V, self.u]) + + def dV(self, V, t, u, I_ext): + dVdt = self.f * V * V + self.g * V + self.h - u + self.R * I_ext + return dVdt / self.tau + + def du(self, u, t, V): + dudt = self.a * (self.b * V - u) + return dudt / self.tau + + @property + def derivative(self): + return JointEq([self.dV, self.du]) + + def update(self, _t, _dt): + V, u = self.integral(self.V, self.u, t=_t, I_ext=self.input, dt=_dt) + spikes = V >= self.V_th + self.t_last_spike.value = bm.where(spikes, _t, self.t_last_spike) + self.V.value = bm.where(spikes, self.c, V) + self.u.value = bm.where(spikes, u + self.d, u) + self.spike.value = spikes + self.input[:] = 0. + + def set_init(self, values: dict): + for k, v in values.items(): + if k not in self.integral.inits: + raise ValueError(f'Variable "{k}" is not defined in this model.') + variable = getattr(self, k) + variable[:] = v + self.integral.inits[k][:] = v diff --git a/brainpy/dyn/neurons/noise_models.py b/brainpy/dyn/neurons/noise_models.py new file mode 100644 index 00000000..6d1dc13d --- /dev/null +++ b/brainpy/dyn/neurons/noise_models.py @@ -0,0 +1,72 @@ +# -*- coding: utf-8 -*- + +import brainpy.math as bm +from brainpy.dyn.base import NeuGroup +from brainpy.integrators.sde import sdeint +from brainpy.types import Parameter, Shape + +__all__ = [ + 'OUProcess', +] + + +class OUProcess(NeuGroup): + r"""The Ornstein–Uhlenbeck process. + + The Ornstein–Uhlenbeck process :math:`x_{t}` is defined by the following + stochastic differential equation: + + .. math:: + + \tau dx_{t}=-\theta \,x_{t}\,dt+\sigma \,dW_{t} + + where :math:`\theta >0` and :math:`\sigma >0` are parameters and :math:`W_{t}` + denotes the Wiener process. + + Parameters + ---------- + size: int, sequence of int + The model size. + mean: Parameter + The noise mean value. + sigma: Parameter + The noise amplitude. + tau: Parameter + The decay time constant. + method: str + The numerical integration method for stochastic differential equation. + name: str + The model name. + """ + + def __init__( + self, + size: Shape, + mean: Parameter, + sigma: Parameter, + tau: Parameter, + method: str = 'euler', + name: str = None + ): + super(OUProcess, self).__init__(size=size, name=name) + + # parameters + self.mean = mean + self.sigma = sigma + self.tau = tau + + # variables + self.x = bm.Variable(bm.ones(self.num) * mean) + + # integral functions + self.integral = sdeint(f=self.df, g=self.dg, method=method) + + def df(self, x, t): + f_x_ou = (self.mean - x) / self.tau + return f_x_ou + + def dg(self, x, t): + return self.sigma + + def update(self, _t, _dt): + self.x.value = self.integral(self.x, _t, _dt) diff --git a/brainpy/dyn/neurons/rate_models.py b/brainpy/dyn/neurons/rate_models.py index 12385f7d..fd1f1cfe 100644 --- a/brainpy/dyn/neurons/rate_models.py +++ b/brainpy/dyn/neurons/rate_models.py @@ -1,145 +1,155 @@ # -*- coding: utf-8 -*- +from typing import Union, Callable + +import numpy as np +from jax.experimental.host_callback import id_tap + import brainpy.math as bm +from brainpy import check from brainpy.dyn.base import NeuGroup +from brainpy.initialize import Initializer, Uniform +from brainpy.initialize import init_param from brainpy.integrators.dde import ddeint from brainpy.integrators.joint_eq import JointEq from brainpy.integrators.ode import odeint -from brainpy.types import Parameter, Shape +from brainpy.tools.checking import check_float, check_initializer +from brainpy.types import Parameter, Shape, Tensor +from .noise_models import OUProcess __all__ = [ - 'FHN', + 'RateGroup', + 'RateFHN', 'FeedbackFHN', - 'MeanFieldQIF', + 'RateQIF', + 'StuartLandauOscillator', + 'WilsonCowanModel', ] -class FHN(NeuGroup): - r"""FitzHugh-Nagumo neuron model. - - **Model Descriptions** - - The FitzHugh–Nagumo model (FHN), named after Richard FitzHugh (1922–2007) - who suggested the system in 1961 [1]_ and J. Nagumo et al. who created the - equivalent circuit the following year, describes a prototype of an excitable - system (e.g., a neuron). +class RateGroup(NeuGroup): + def update(self, _t, _dt): + raise NotImplementedError - The motivation for the FitzHugh-Nagumo model was to isolate conceptually - the essentially mathematical properties of excitation and propagation from - the electrochemical properties of sodium and potassium ion flow. The model - consists of - - a *voltage-like variable* having cubic nonlinearity that allows regenerative - self-excitation via a positive feedback, and - - a *recovery variable* having a linear dynamics that provides a slower negative feedback. +class RateFHN(NeuGroup): + r"""FitzHugh-Nagumo system used in [1]_. .. math:: - \begin{aligned} - {\dot {v}} &=v-{\frac {v^{3}}{3}}-w+RI_{\rm {ext}}, \\ - \tau {\dot {w}}&=v+a-bw. - \end{aligned} - - The FHN Model is an example of a relaxation oscillator - because, if the external stimulus :math:`I_{\text{ext}}` - exceeds a certain threshold value, the system will exhibit - a characteristic excursion in phase space, before the - variables :math:`v` and :math:`w` relax back to their rest values. - This behaviour is typical for spike generations (a short, - nonlinear elevation of membrane voltage :math:`v`, - diminished over time by a slower, linear recovery variable - :math:`w`) in a neuron after stimulation by an external - input current. - - **Model Examples** - - .. plot:: - :include-source: True - - >>> import brainpy as bp - >>> fhn = bp.dyn.FHN(1) - >>> runner = bp.dyn.DSRunner(fhn, inputs=('input', 1.), monitors=['V', 'w']) - >>> runner.run(100.) - >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w') - >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, legend='V', show=True) - - **Model Parameters** - - ============= ============== ======== ======================== - **Parameter** **Init Value** **Unit** **Explanation** - ------------- -------------- -------- ------------------------ - a 1 \ Positive constant - b 1 \ Positive constant - tau 10 ms Membrane time constant. - V_th 1.8 mV Threshold potential of spike. - ============= ============== ======== ======================== - - **Model Variables** + \frac{dx}{dt} = -\alpha V^3 + \beta V^2 + \gamma V - w + I_{ext}\\ + \tau \frac{dy}{dt} = (V - \delta - \epsilon w) - ================== ================= ========================================================= - **Variables name** **Initial Value** **Explanation** - ------------------ ----------------- --------------------------------------------------------- - V 0 Membrane potential. - w 0 A recovery variable which represents - the combined effects of sodium channel - de-inactivation and potassium channel - deactivation. - input 0 External and synaptic input current. - spike False Flag to mark whether the neuron is spiking. - t_last_spike -1e7 Last spike time stamp. - ================== ================= ========================================================= + Parameters + ---------- + size: Shape + The model size. + x_ou_mean: Parameter + The noise mean of the :math:`x` variable, [mV/ms] + y_ou_mean: Parameter + The noise mean of the :math:`y` variable, [mV/ms]. + x_ou_sigma: Parameter + The noise intensity of the :math:`x` variable, [mV/ms/sqrt(ms)]. + y_ou_sigma: Parameter + The noise intensity of the :math:`y` variable, [mV/ms/sqrt(ms)]. + x_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`x` variable, [ms]. + y_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`y` variable, [ms]. - **References** - .. [1] FitzHugh, Richard. "Impulses and physiological states in theoretical models of nerve membrane." Biophysical journal 1.6 (1961): 445-466. - .. [2] https://en.wikipedia.org/wiki/FitzHugh%E2%80%93Nagumo_model - .. [3] http://www.scholarpedia.org/article/FitzHugh-Nagumo_model + References + ---------- + .. [1] Kostova, T., Ravindran, R., & Schonbek, M. (2004). FitzHugh–Nagumo + revisited: Types of bifurcations, periodical forcing and stability + regions by a Lyapunov functional. International journal of + bifurcation and chaos, 14(03), 913-925. """ - def __init__(self, - size: Shape, - a: Parameter = 0.7, - b: Parameter = 0.8, - tau: Parameter = 12.5, - Vth: Parameter = 1.8, - method: str = 'exp_auto', - name: str = None): - # initialization - super(FHN, self).__init__(size=size, name=name) - - # parameters - self.a = a - self.b = b + def __init__( + self, + size: Shape, + + # fhn parameters + alpha: Parameter = 3.0, + beta: Parameter = 4.0, + gamma: Parameter = -1.5, + delta: Parameter = 0.0, + epsilon: Parameter = 0.5, + tau: Parameter = 20.0, + + # noise parameters + x_ou_mean: Parameter = 0.0, + x_ou_sigma: Parameter = 0.0, + x_ou_tau: Parameter = 5.0, + y_ou_mean: Parameter = 0.0, + y_ou_sigma: Parameter = 0.0, + y_ou_tau: Parameter = 5.0, + + # other parameters + x_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05), + y_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05), + method: str = None, + sde_method: str = None, + name: str = None, + ): + super(RateFHN, self).__init__(size=size, name=name) + + # model parameters + self.alpha = alpha + self.beta = beta + self.gamma = gamma + self.delta = delta + self.epsilon = epsilon self.tau = tau - self.Vth = Vth + + # noise parameters + self.x_ou_mean = x_ou_mean # mV/ms, OU process + self.y_ou_mean = y_ou_mean # mV/ms, OU process + self.x_ou_sigma = x_ou_sigma # mV/ms/sqrt(ms), noise intensity + self.y_ou_sigma = y_ou_sigma # mV/ms/sqrt(ms), noise intensity + self.x_ou_tau = x_ou_tau # ms, timescale of the Ornstein-Uhlenbeck noise process + self.y_ou_tau = y_ou_tau # ms, timescale of the Ornstein-Uhlenbeck noise process # variables - self.w = bm.Variable(bm.zeros(self.num)) - self.V = bm.Variable(bm.zeros(self.num)) + check_initializer(x_initializer, 'x_initializer') + check_initializer(y_initializer, 'y_initializer') + self.x = bm.Variable(init_param(x_initializer, (self.num,))) + self.y = bm.Variable(init_param(x_initializer, (self.num,))) self.input = bm.Variable(bm.zeros(self.num)) - self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) - # integral - self.integral = odeint(method=method, f=self.derivative) + # noise variables + self.x_ou = self.y_ou = None + if bm.any(self.x_ou_mean > 0.) or bm.any(self.x_ou_sigma > 0.): + self.x_ou = OUProcess(self.num, + self.x_ou_mean, self.x_ou_sigma, self.x_ou_tau, + method=sde_method) + if bm.any(self.y_ou_mean > 0.) or bm.any(self.y_ou_sigma > 0.): + self.y_ou = OUProcess(self.num, + self.y_ou_mean, self.y_ou_sigma, self.y_ou_tau, + method=sde_method) - def dV(self, V, t, w, I_ext): - return V - V * V * V / 3 - w + I_ext + # integral functions + self.integral = odeint(f=JointEq([self.dx, self.dy]), method=method) - def dw(self, w, t, V): - return (V + self.a - self.b * w) / self.tau + def dx(self, x, t, y, x_ext): + return - self.alpha * x ** 3 + self.beta * x ** 2 + self.gamma * x - y + x_ext - @property - def derivative(self): - return JointEq([self.dV, self.dw]) + def dy(self, y, t, x, y_ext=0.): + return (x - self.delta - self.epsilon * y) / self.tau + y_ext def update(self, _t, _dt): - V, w = self.integral(self.V, self.w, _t, self.input, dt=_dt) - self.spike.value = bm.logical_and(V >= self.Vth, self.V < self.Vth) - self.t_last_spike.value = bm.where(self.spike, _t, self.t_last_spike) - self.V.value = V - self.w.value = w + if self.x_ou is not None: + self.input += self.x_ou.x + self.x_ou.update(_t, _dt) + y_ext = 0. + if self.y_ou is not None: + y_ext = self.y_ou.x + self.y_ou.update(_t, _dt) + x, y = self.integral(self.x, self.y, _t, x_ext=self.input, y_ext=y_ext, dt=_dt) + self.x.value = x + self.y.value = y self.input[:] = 0. @@ -151,8 +161,8 @@ class FeedbackFHN(NeuGroup): .. math:: \begin{aligned} - \frac{dv}{dt} &= v(t) - \frac{v^3(t)}{3} - w(t) + \mu[v(t-\mathrm{delay}) - v_0] \\ - \frac{dw}{dt} &= [v(t) + a - b w(t)] / \tau + \frac{dx}{dt} &= x(t) - \frac{x^3(t)}{3} - y(t) + \mu[x(t-\mathrm{delay}) - x_0] \\ + \frac{dy}{dt} &= [x(t) + a - b y(t)] / \tau \end{aligned} @@ -160,10 +170,10 @@ class FeedbackFHN(NeuGroup): >>> import brainpy as bp >>> fhn = bp.dyn.FeedbackFHN(1, delay=10.) - >>> runner = bp.dyn.DSRunner(fhn, inputs=('input', 1.), monitors=['V', 'w']) + >>> runner = bp.dyn.DSRunner(fhn, inputs=('input', 1.), monitors=['x', 'y']) >>> runner.run(100.) - >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w') - >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, legend='V', show=True) + >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.y, legend='y') + >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.x, legend='x', show=True) **Model Parameters** @@ -181,6 +191,23 @@ class FeedbackFHN(NeuGroup): when negative, it is a inhibitory feedback. ============= ============== ======== ======================== + Parameters + ---------- + x_ou_mean: Parameter + The noise mean of the :math:`x` variable, [mV/ms] + y_ou_mean: Parameter + The noise mean of the :math:`y` variable, [mV/ms]. + x_ou_sigma: Parameter + The noise intensity of the :math:`x` variable, [mV/ms/sqrt(ms)]. + y_ou_sigma: Parameter + The noise intensity of the :math:`y` variable, [mV/ms/sqrt(ms)]. + x_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`x` variable, [ms]. + y_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`y` variable, [ms]. + + + References ---------- .. [4] Plant, Richard E. (1981). *A FitzHugh Differential-Difference @@ -189,61 +216,109 @@ class FeedbackFHN(NeuGroup): """ - def __init__(self, - size: Shape, - a: Parameter = 0.7, - b: Parameter = 0.8, - delay: Parameter = 10., - tau: Parameter = 12.5, - mu: Parameter = 1.6886, - v0: Parameter = -1, - Vth: Parameter = 1.8, - method: str = 'rk4', - name: str = None): + def __init__( + self, + size: Shape, + + # model parameters + a: Parameter = 0.7, + b: Parameter = 0.8, + delay: Parameter = 10., + tau: Parameter = 12.5, + mu: Parameter = 1.6886, + x0: Parameter = -1, + + # noise parameters + x_ou_mean: Parameter = 0.0, + x_ou_sigma: Parameter = 0.0, + x_ou_tau: Parameter = 5.0, + y_ou_mean: Parameter = 0.0, + y_ou_sigma: Parameter = 0.0, + y_ou_tau: Parameter = 5.0, + + # other parameters + x_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05), + y_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05), + method: str = 'rk4', + sde_method: str = None, + name: str = None, + dt: float = None + ): super(FeedbackFHN, self).__init__(size=size, name=name) + # dt + self.dt = bm.get_dt() if dt is None else dt + check_float(self.dt, 'dt', allow_none=False, min_bound=0., allow_int=False) + # parameters self.a = a self.b = b self.delay = delay self.tau = tau self.mu = mu # feedback strength - self.v0 = v0 # resting potential - self.Vth = Vth + self.v0 = x0 # resting potential + + # noise parameters + self.x_ou_mean = x_ou_mean + self.y_ou_mean = y_ou_mean + self.x_ou_sigma = x_ou_sigma + self.y_ou_sigma = y_ou_sigma + self.x_ou_tau = x_ou_tau + self.y_ou_tau = y_ou_tau # variables - self.w = bm.Variable(bm.zeros(self.num)) - self.V = bm.Variable(bm.zeros(self.num)) - self.Vdelay = bm.FixedLenDelay(self.num, self.delay) + check_initializer(x_initializer, 'x_initializer') + check_initializer(y_initializer, 'y_initializer') + self.x = bm.Variable(init_param(x_initializer, (self.num,))) + self.y = bm.Variable(init_param(x_initializer, (self.num,))) + self.x_delay = bm.TimeDelay(self.x, self.delay, dt=self.dt, interp_method='round') self.input = bm.Variable(bm.zeros(self.num)) - self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + + # noise variables + self.x_ou = self.y_ou = None + if bm.any(self.x_ou_mean > 0.) or bm.any(self.x_ou_sigma > 0.): + self.x_ou = OUProcess(self.num, + self.x_ou_mean, self.x_ou_sigma, self.x_ou_tau, + method=sde_method) + if bm.any(self.y_ou_mean > 0.) or bm.any(self.y_ou_sigma > 0.): + self.y_ou = OUProcess(self.num, + self.y_ou_mean, self.y_ou_sigma, self.y_ou_tau, + method=sde_method) # integral - self.integral = ddeint(method=method, f=self.derivative, - state_delays={'V': self.Vdelay}) + self.integral = ddeint(method=method, + f=JointEq([self.dx, self.dy]), + state_delays={'V': self.x_delay}) - def dV(self, V, t, w, Vdelay): - return (V - V * V * V / 3 - w + self.input + - self.mu * (Vdelay(t - self.delay) - self.v0)) + def dx(self, x, t, y, x_ext): + return x - x * x * x / 3 - y + x_ext + self.mu * (self.x_delay(t - self.delay) - self.v0) - def dw(self, w, t, V): - return (V + self.a - self.b * w) / self.tau + def dy(self, y, t, x, y_ext): + return (x + self.a - self.b * y + y_ext) / self.tau - @property - def derivative(self): - return JointEq([self.dV, self.dw]) + def _check_dt(self, dt, *args): + if np.absolute(dt - self.dt) > 1e-6: + raise ValueError(f'The "dt" {dt} used in model running is ' + f'not consistent with the "dt" {self.dt} ' + f'used in model definition.') def update(self, _t, _dt): - V, w = self.integral(self.V, self.w, _t, Vdelay=self.Vdelay, dt=_dt) - self.spike.value = bm.logical_and(V >= self.Vth, self.V < self.Vth) - self.t_last_spike.value = bm.where(self.spike, _t, self.t_last_spike) - self.V.value = V - self.w.value = w + if check.is_checking(): + id_tap(self._check_dt, _dt) + if self.x_ou is not None: + self.input += self.x_ou.x + self.x_ou.update(_t, _dt) + y_ext = 0. + if self.y_ou is not None: + y_ext = self.y_ou.x + self.y_ou.update(_t, _dt) + x, y = self.integral(self.x, self.y, _t, x_ext=self.input, y_ext=y_ext, dt=_dt) + self.x.value = x + self.y.value = y self.input[:] = 0. -class MeanFieldQIF(NeuGroup): +class RateQIF(NeuGroup): r"""A mean-field model of a quadratic integrate-and-fire neuron population. **Model Descriptions** @@ -282,6 +357,21 @@ class MeanFieldQIF(NeuGroup): J 15 \ the strength of the recurrent coupling inside the population ============= ============== ======== ======================== + Parameters + ---------- + x_ou_mean: Parameter + The noise mean of the :math:`x` variable, [mV/ms] + y_ou_mean: Parameter + The noise mean of the :math:`y` variable, [mV/ms]. + x_ou_sigma: Parameter + The noise intensity of the :math:`x` variable, [mV/ms/sqrt(ms)]. + y_ou_sigma: Parameter + The noise intensity of the :math:`y` variable, [mV/ms/sqrt(ms)]. + x_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`x` variable, [ms]. + y_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`y` variable, [ms]. + References ---------- @@ -294,15 +384,32 @@ class MeanFieldQIF(NeuGroup): """ - def __init__(self, - size: Shape, - tau: Parameter = 1., - eta: Parameter = -5.0, - delta: Parameter = 1.0, - J: Parameter = 15., - method: str = 'exp_auto', - name: str = None): - super(MeanFieldQIF, self).__init__(size=size, name=name) + def __init__( + self, + size: Shape, + + # model parameters + tau: Parameter = 1., + eta: Parameter = -5.0, + delta: Parameter = 1.0, + J: Parameter = 15., + + # noise parameters + x_ou_mean: Parameter = 0.0, + x_ou_sigma: Parameter = 0.0, + x_ou_tau: Parameter = 5.0, + y_ou_mean: Parameter = 0.0, + y_ou_sigma: Parameter = 0.0, + y_ou_tau: Parameter = 5.0, + + # other parameters + x_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05), + y_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.05), + method: str = 'exp_auto', + name: str = None, + sde_method: str = None, + ): + super(RateQIF, self).__init__(size=size, name=name) # parameters self.tau = tau # @@ -310,54 +417,309 @@ class MeanFieldQIF(NeuGroup): self.delta = delta # the half-width at half maximum of the Lorenzian distribution over the neural excitability self.J = J # the strength of the recurrent coupling inside the population + # noise parameters + self.x_ou_mean = x_ou_mean + self.y_ou_mean = y_ou_mean + self.x_ou_sigma = x_ou_sigma + self.y_ou_sigma = y_ou_sigma + self.x_ou_tau = x_ou_tau + self.y_ou_tau = y_ou_tau + # variables - self.r = bm.Variable(bm.ones(1)) - self.V = bm.Variable(bm.ones(1)) - self.input = bm.Variable(bm.zeros(1)) + check_initializer(x_initializer, 'x_initializer') + check_initializer(y_initializer, 'y_initializer') + self.x = bm.Variable(init_param(x_initializer, (self.num,))) + self.y = bm.Variable(init_param(x_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + + # noise variables + self.x_ou = self.y_ou = None + if bm.any(self.x_ou_mean > 0.) or bm.any(self.x_ou_sigma > 0.): + self.x_ou = OUProcess(self.num, + self.x_ou_mean, self.x_ou_sigma, self.x_ou_tau, + method=sde_method) + if bm.any(self.y_ou_mean > 0.) or bm.any(self.y_ou_sigma > 0.): + self.y_ou = OUProcess(self.num, + self.y_ou_mean, self.y_ou_sigma, self.y_ou_tau, + method=sde_method) # functions - self.integral = odeint(self.derivative, method=method) + self.integral = odeint(JointEq([self.dx, self.dy]), method=method) + + def dy(self, y, t, x, y_ext): + return (self.delta / (bm.pi * self.tau) + 2. * x * y + y_ext) / self.tau + + def dx(self, x, t, y, x_ext): + return (x ** 2 + self.eta + x_ext + self.J * y * self.tau - + (bm.pi * y * self.tau) ** 2) / self.tau + + def update(self, _t, _dt): + if self.x_ou is not None: + self.input += self.x_ou.x + self.x_ou.update(_t, _dt) + y_ext = 0. + if self.y_ou is not None: + y_ext = self.y_ou.x + self.y_ou.update(_t, _dt) + x, y = self.integral(self.x, self.y, t=_t, x_ext=self.input, y_ext=y_ext, dt=_dt) + self.x.value = x + self.y.value = y + self.input[:] = 0. + + +class StuartLandauOscillator(RateGroup): + r""" + Stuart-Landau model with Hopf bifurcation. + + .. math:: + + \frac{dx}{dt} = (a - x^2 - y^2) * x - w*y + I^x_{ext} \\ + \frac{dy}{dt} = (a - x^2 - y^2) * y + w*x + I^y_{ext} + + Parameters + ---------- + x_ou_mean: Parameter + The noise mean of the :math:`x` variable, [mV/ms] + y_ou_mean: Parameter + The noise mean of the :math:`y` variable, [mV/ms]. + x_ou_sigma: Parameter + The noise intensity of the :math:`x` variable, [mV/ms/sqrt(ms)]. + y_ou_sigma: Parameter + The noise intensity of the :math:`y` variable, [mV/ms/sqrt(ms)]. + x_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`x` variable, [ms]. + y_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`y` variable, [ms]. + + """ + + def __init__( + self, + size: Shape, + + # model parameters + a=0.25, + w=0.2, + + # noise parameters + x_ou_mean: Parameter = 0.0, + x_ou_sigma: Parameter = 0.0, + x_ou_tau: Parameter = 5.0, + y_ou_mean: Parameter = 0.0, + y_ou_sigma: Parameter = 0.0, + y_ou_tau: Parameter = 5.0, + + # other parameters + x_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.5), + y_initializer: Union[Initializer, Callable, Tensor] = Uniform(0, 0.5), + method: str = None, + sde_method: str = None, + name: str = None, + ): + super(StuartLandauOscillator, self).__init__(size=size, + name=name) + + # model parameters + self.a = a + self.w = w + + # noise parameters + self.x_ou_mean = x_ou_mean + self.y_ou_mean = y_ou_mean + self.x_ou_sigma = x_ou_sigma + self.y_ou_sigma = y_ou_sigma + self.x_ou_tau = x_ou_tau + self.y_ou_tau = y_ou_tau + + # variables + check_initializer(x_initializer, 'x_initializer') + check_initializer(y_initializer, 'y_initializer') + self.x = bm.Variable(init_param(x_initializer, (self.num,))) + self.y = bm.Variable(init_param(x_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + + # noise variables + self.x_ou = self.y_ou = None + if bm.any(self.x_ou_mean > 0.) or bm.any(self.x_ou_sigma > 0.): + self.x_ou = OUProcess(self.num, + self.x_ou_mean, self.x_ou_sigma, self.x_ou_tau, + method=sde_method) + if bm.any(self.y_ou_mean > 0.) or bm.any(self.y_ou_sigma > 0.): + self.y_ou = OUProcess(self.num, + self.y_ou_mean, self.y_ou_sigma, self.y_ou_tau, + method=sde_method) - def dr(self, r, t, v): - return (self.delta / (bm.pi * self.tau) + 2. * r * v) / self.tau + # integral functions + self.integral = odeint(f=JointEq([self.dx, self.dy]), method=method) - def dV(self, v, t, r): - return (v ** 2 + self.eta + self.input + self.J * r * self.tau - - (bm.pi * r * self.tau) ** 2) / self.tau + def dx(self, x, t, y, x_ext, a, w): + return (a - x * x - y * y) * x - w * y + x_ext - @property - def derivative(self): - return JointEq([self.dV, self.dr]) + def dy(self, y, t, x, y_ext, a, w): + return (a - x * x - y * y) * y - w * y + y_ext def update(self, _t, _dt): - self.V.value, self.r.value = self.integral(self.V, self.r, _t, _dt) - self.integral[:] = 0. + if self.x_ou is not None: + self.input += self.x_ou.x + self.x_ou.update(_t, _dt) + y_ext = 0. + if self.y_ou is not None: + y_ext = self.y_ou.x + self.y_ou.update(_t, _dt) + x, y = self.integral(self.x, self.y, _t, x_ext=self.input, + y_ext=y_ext, a=self.a, w=self.w, dt=_dt) + self.x.value = x + self.y.value = y + self.input[:] = 0. +class WilsonCowanModel(RateGroup): + """Wilson-Cowan population model. -class VanDerPolOscillator(NeuGroup): - pass + Parameters + ---------- + x_ou_mean: Parameter + The noise mean of the :math:`x` variable, [mV/ms] + y_ou_mean: Parameter + The noise mean of the :math:`y` variable, [mV/ms]. + x_ou_sigma: Parameter + The noise intensity of the :math:`x` variable, [mV/ms/sqrt(ms)]. + y_ou_sigma: Parameter + The noise intensity of the :math:`y` variable, [mV/ms/sqrt(ms)]. + x_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`x` variable, [ms]. + y_ou_tau: Parameter + The timescale of the Ornstein-Uhlenbeck noise process of :math:`y` variable, [ms]. -class ThetaNeuron(NeuGroup): - pass + """ -class MeanFieldQIFWithSFA(NeuGroup): - pass + def __init__( + self, + size: Shape, + + # Excitatory parameters + E_tau=1., # excitatory time constant + E_a=1.2, # excitatory gain + E_theta=2.8, # excitatory firing threshold + + # Inhibitory parameters + I_tau=1., # inhibitory time constant + I_a=1., # inhibitory gain + I_theta=4.0, # inhibitory firing threshold + + # connection parameters + wEE=12., # local E-E coupling + wIE=4., # local E-I coupling + wEI=13., # local I-E coupling + wII=11., # local I-I coupling + + # Refractory parameter + r=1, + + # state initializer + x_initializer: Union[Initializer, Callable, Tensor] = Uniform(max_val=0.05), + y_initializer: Union[Initializer, Callable, Tensor] = Uniform(max_val=0.05), + + # noise parameters + x_ou_mean: Parameter = 0.0, + x_ou_sigma: Parameter = 0.0, + x_ou_tau: Parameter = 5.0, + y_ou_mean: Parameter = 0.0, + y_ou_sigma: Parameter = 0.0, + y_ou_tau: Parameter = 5.0, + + # other parameters + sde_method: str = None, + method: str = 'exp_euler_auto', + name: str = None, + ): + super(WilsonCowanModel, self).__init__(size=size, name=name) + + # model parameters + self.E_tau = E_tau + self.E_a = E_a + self.E_theta = E_theta + self.I_tau = I_tau + self.I_a = I_a + self.I_theta = I_theta + self.wEE = wEE + self.wIE = wIE + self.wEI = wEI + self.wII = wII + self.r = r + + # noise parameters + self.x_ou_mean = x_ou_mean + self.y_ou_mean = y_ou_mean + self.x_ou_sigma = x_ou_sigma + self.y_ou_sigma = y_ou_sigma + self.x_ou_tau = x_ou_tau + self.y_ou_tau = y_ou_tau + # variables + check_initializer(x_initializer, 'x_initializer') + check_initializer(y_initializer, 'y_initializer') + self.x = bm.Variable(init_param(x_initializer, (self.num,))) + self.y = bm.Variable(init_param(x_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + + # noise variables + self.x_ou = self.y_ou = None + if bm.any(self.x_ou_mean > 0.) or bm.any(self.x_ou_sigma > 0.): + self.x_ou = OUProcess(self.num, + self.x_ou_mean, self.x_ou_sigma, self.x_ou_tau, + method=sde_method) + if bm.any(self.y_ou_mean > 0.) or bm.any(self.y_ou_sigma > 0.): + self.y_ou = OUProcess(self.num, + self.y_ou_mean, self.y_ou_sigma, self.y_ou_tau, + method=sde_method) + + # functions + self.integral = odeint(f=JointEq([self.dx, self.dy]), method=method) -class JansenRitModel(NeuGroup): + # functions + def F(self, x, a, theta): + return 1 / (1 + bm.exp(-a * (x - theta))) - 1 / (1 + bm.exp(a * theta)) + + def dx(self, x, t, y, x_ext): + x = self.wEE * x - self.wIE * y + x_ext + return (-x + (1 - self.r * x) * self.F(x, self.E_a, self.E_theta)) / self.E_tau + + def dy(self, y, t, x, y_ext): + x = self.wEI * x - self.wII * y + y_ext + return (-y + (1 - self.r * y) * self.F(x, self.I_a, self.I_theta)) / self.I_tau + + def update(self, _t, _dt): + if self.x_ou is not None: + self.input += self.x_ou.x + self.x_ou.update(_t, _dt) + y_ext = 0. + if self.y_ou is not None: + y_ext = self.y_ou.x + self.y_ou.update(_t, _dt) + x, y = self.integral(self.x, self.y, _t, x_ext=self.input, y_ext=y_ext, dt=_dt) + self.x.value = x + self.y.value = y + self.input[:] = 0. + + +class JansenRitModel(RateGroup): pass -class WilsonCowanModel(NeuGroup): +class KuramotoOscillator(RateGroup): pass -class StuartLandauOscillator(NeuGroup): + +class ThetaNeuron(RateGroup): pass -class KuramotoOscillator(NeuGroup): +class RateQIFWithSFA(RateGroup): pass + +class VanDerPolOscillator(RateGroup): + pass diff --git a/brainpy/dyn/neurons/reduced_models.py b/brainpy/dyn/neurons/reduced_models.py index c395e4ba..8d2a6036 100644 --- a/brainpy/dyn/neurons/reduced_models.py +++ b/brainpy/dyn/neurons/reduced_models.py @@ -1,16 +1,861 @@ # -*- coding: utf-8 -*- +from typing import Union, Callable + import brainpy.math as bm +from brainpy.dyn.base import NeuGroup +from brainpy.initialize import ZeroInit, OneInit, Initializer, init_param from brainpy.integrators.joint_eq import JointEq from brainpy.integrators.ode import odeint -from brainpy.dyn.base import NeuGroup +from brainpy.tools.checking import check_initializer +from brainpy.types import Shape, Parameter, Tensor __all__ = [ + 'LIF', + 'ExpIF', + 'AdExIF', + 'QuaIF', + 'AdQuaIF', + 'GIF', 'Izhikevich', 'HindmarshRose', + 'FHN', ] +class LIF(NeuGroup): + r"""Leaky integrate-and-fire neuron model. + + **Model Descriptions** + + The formal equations of a LIF model [1]_ is given by: + + .. math:: + + \tau \frac{dV}{dt} = - (V(t) - V_{rest}) + I(t) \\ + \text{after} \quad V(t) \gt V_{th}, V(t) = V_{reset} \quad + \text{last} \quad \tau_{ref} \quad \text{ms} + + where :math:`V` is the membrane potential, :math:`V_{rest}` is the resting + membrane potential, :math:`V_{reset}` is the reset membrane potential, + :math:`V_{th}` is the spike threshold, :math:`\tau` is the time constant, + :math:`\tau_{ref}` is the refractory time period, + and :math:`I` is the time-variant synaptic inputs. + + **Model Examples** + + - `(Brette, Romain. 2004) LIF phase locking `_ + + **Model Parameters** + + ============= ============== ======== ========================================= + **Parameter** **Init Value** **Unit** **Explanation** + ------------- -------------- -------- ----------------------------------------- + V_rest 0 mV Resting membrane potential. + V_reset -5 mV Reset potential after spike. + V_th 20 mV Threshold potential of spike. + tau 10 ms Membrane time constant. Compute by R * C. + tau_ref 5 ms Refractory period length.(ms) + ============= ============== ======== ========================================= + + **Neuron Variables** + + ================== ================= ========================================================= + **Variables name** **Initial Value** **Explanation** + ------------------ ----------------- --------------------------------------------------------- + V 0 Membrane potential. + input 0 External and synaptic input current. + spike False Flag to mark whether the neuron is spiking. + refractory False Flag to mark whether the neuron is in refractory period. + t_last_spike -1e7 Last spike time stamp. + ================== ================= ========================================================= + + **References** + + .. [1] Abbott, Larry F. "Lapicque’s introduction of the integrate-and-fire model + neuron (1907)." Brain research bulletin 50, no. 5-6 (1999): 303-304. + """ + + def __init__( + self, + size: Shape, + V_rest: Parameter = 0., + V_reset: Parameter = -5., + V_th: Parameter = 20., + tau: Parameter = 10., + tau_ref: Parameter = 1., + V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + method: str = 'exp_auto', + name: str = None + ): + # initialization + super(LIF, self).__init__(size=size, name=name) + + # parameters + self.V_rest = V_rest + self.V_reset = V_reset + self.V_th = V_th + self.tau = tau + self.tau_ref = tau_ref + + # variables + check_initializer(V_initializer, 'V_initializer') + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) + + # integral + self.integral = odeint(method=method, f=self.derivative) + + def derivative(self, V, t, I_ext): + dvdt = (-V + self.V_rest + I_ext) / self.tau + return dvdt + + def update(self, _t, _dt): + refractory = (_t - self.t_last_spike) <= self.tau_ref + V = self.integral(self.V, _t, self.input, dt=_dt) + V = bm.where(refractory, self.V, V) + spike = V >= self.V_th + self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) + self.V.value = bm.where(spike, self.V_reset, V) + self.refractory.value = bm.logical_or(refractory, spike) + self.spike.value = spike + self.input[:] = 0. + + +class ExpIF(NeuGroup): + r"""Exponential integrate-and-fire neuron model. + + **Model Descriptions** + + In the exponential integrate-and-fire model [1]_, the differential + equation for the membrane potential is given by + + .. math:: + + \tau\frac{d V}{d t}= - (V-V_{rest}) + \Delta_T e^{\frac{V-V_T}{\Delta_T}} + RI(t), \\ + \text{after} \, V(t) \gt V_{th}, V(t) = V_{reset} \, \text{last} \, \tau_{ref} \, \text{ms} + + This equation has an exponential nonlinearity with "sharpness" parameter :math:`\Delta_{T}` + and "threshold" :math:`\vartheta_{rh}`. + + The moment when the membrane potential reaches the numerical threshold :math:`V_{th}` + defines the firing time :math:`t^{(f)}`. After firing, the membrane potential is reset to + :math:`V_{rest}` and integration restarts at time :math:`t^{(f)}+\tau_{\rm ref}`, + where :math:`\tau_{\rm ref}` is an absolute refractory time. + If the numerical threshold is chosen sufficiently high, :math:`V_{th}\gg v+\Delta_T`, + its exact value does not play any role. The reason is that the upswing of the action + potential for :math:`v\gg v +\Delta_{T}` is so rapid, that it goes to infinity in + an incredibly short time. The threshold :math:`V_{th}` is introduced mainly for numerical + convenience. For a formal mathematical analysis of the model, the threshold can be pushed + to infinity. + + The model was first introduced by Nicolas Fourcaud-Trocmé, David Hansel, Carl van Vreeswijk + and Nicolas Brunel [1]_. The exponential nonlinearity was later confirmed by Badel et al. [3]_. + It is one of the prominent examples of a precise theoretical prediction in computational + neuroscience that was later confirmed by experimental neuroscience. + + Two important remarks: + + - (i) The right-hand side of the above equation contains a nonlinearity + that can be directly extracted from experimental data [3]_. In this sense the exponential + nonlinearity is not an arbitrary choice but directly supported by experimental evidence. + - (ii) Even though it is a nonlinear model, it is simple enough to calculate the firing + rate for constant input, and the linear response to fluctuations, even in the presence + of input noise [4]_. + + **Model Examples** + + .. plot:: + :include-source: True + + >>> import brainpy as bp + >>> group = bp.dyn.ExpIF(1) + >>> runner = bp.dyn.DSRunner(group, monitors=['V'], inputs=('input', 10.)) + >>> runner.run(300., ) + >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V', show=True) + + + **Model Parameters** + + ============= ============== ======== =================================================== + **Parameter** **Init Value** **Unit** **Explanation** + ------------- -------------- -------- --------------------------------------------------- + V_rest -65 mV Resting potential. + V_reset -68 mV Reset potential after spike. + V_th -30 mV Threshold potential of spike. + V_T -59.9 mV Threshold potential of generating action potential. + delta_T 3.48 \ Spike slope factor. + R 1 \ Membrane resistance. + tau 10 \ Membrane time constant. Compute by R * C. + tau_ref 1.7 \ Refractory period length. + ============= ============== ======== =================================================== + + **Model Variables** + + ================== ================= ========================================================= + **Variables name** **Initial Value** **Explanation** + ------------------ ----------------- --------------------------------------------------------- + V 0 Membrane potential. + input 0 External and synaptic input current. + spike False Flag to mark whether the neuron is spiking. + refractory False Flag to mark whether the neuron is in refractory period. + t_last_spike -1e7 Last spike time stamp. + ================== ================= ========================================================= + + **References** + + .. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation + mechanisms determine the neuronal response to fluctuating + inputs." Journal of Neuroscience 23.37 (2003): 11628-11640. + .. [2] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). + Neuronal dynamics: From single neurons to networks and models + of cognition. Cambridge University Press. + .. [3] Badel, Laurent, Sandrine Lefort, Romain Brette, Carl CH Petersen, + Wulfram Gerstner, and Magnus JE Richardson. "Dynamic IV curves + are reliable predictors of naturalistic pyramidal-neuron voltage + traces." Journal of Neurophysiology 99, no. 2 (2008): 656-666. + .. [4] Richardson, Magnus JE. "Firing-rate response of linear and nonlinear + integrate-and-fire neurons to modulated current-based and + conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919. + .. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire + """ + + def __init__( + self, + size: Shape, + V_rest: Parameter = -65., + V_reset: Parameter = -68., + V_th: Parameter = -30., + V_T: Parameter = -59.9, + delta_T: Parameter = 3.48, + R: Parameter = 1., + tau: Parameter = 10., + tau_ref: Parameter = 1.7, + V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + method: str = 'exp_auto', + name: str = None + ): + # initialize + super(ExpIF, self).__init__(size=size, name=name) + + # parameters + self.V_rest = V_rest + self.V_reset = V_reset + self.V_th = V_th + self.V_T = V_T + self.delta_T = delta_T + self.R = R + self.tau = tau + self.tau_ref = tau_ref + + # variables + check_initializer(V_initializer, 'V_initializer') + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + + # integral + self.integral = odeint(method=method, f=self.derivative) + + def derivative(self, V, t, I_ext): + exp_v = self.delta_T * bm.exp((V - self.V_T) / self.delta_T) + dvdt = (- (V - self.V_rest) + exp_v + self.R * I_ext) / self.tau + return dvdt + + def update(self, _t, _dt): + refractory = (_t - self.t_last_spike) <= self.tau_ref + V = self.integral(self.V, _t, self.input, dt=_dt) + V = bm.where(refractory, self.V, V) + spike = self.V_th <= V + self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) + self.V.value = bm.where(spike, self.V_reset, V) + self.refractory.value = bm.logical_or(refractory, spike) + self.spike.value = spike + self.input[:] = 0. + + +class AdExIF(NeuGroup): + r"""Adaptive exponential integrate-and-fire neuron model. + + **Model Descriptions** + + The **adaptive exponential integrate-and-fire model**, also called AdEx, is a + spiking neuron model with two variables [1]_ [2]_. + + .. math:: + + \begin{aligned} + \tau_m\frac{d V}{d t} &= - (V-V_{rest}) + \Delta_T e^{\frac{V-V_T}{\Delta_T}} - Rw + RI(t), \\ + \tau_w \frac{d w}{d t} &=a(V-V_{rest}) - w + \end{aligned} + + once the membrane potential reaches the spike threshold, + + .. math:: + + V \rightarrow V_{reset}, \\ + w \rightarrow w+b. + + The first equation describes the dynamics of the membrane potential and includes + an activation term with an exponential voltage dependence. Voltage is coupled to + a second equation which describes adaptation. Both variables are reset if an action + potential has been triggered. The combination of adaptation and exponential voltage + dependence gives rise to the name Adaptive Exponential Integrate-and-Fire model. + + The adaptive exponential integrate-and-fire model is capable of describing known + neuronal firing patterns, e.g., adapting, bursting, delayed spike initiation, + initial bursting, fast spiking, and regular spiking. + + **Model Examples** + + - `Examples for different firing patterns `_ + + **Model Parameters** + + ============= ============== ======== ======================================================================================================================== + **Parameter** **Init Value** **Unit** **Explanation** + ------------- -------------- -------- ------------------------------------------------------------------------------------------------------------------------ + V_rest -65 mV Resting potential. + V_reset -68 mV Reset potential after spike. + V_th -30 mV Threshold potential of spike and reset. + V_T -59.9 mV Threshold potential of generating action potential. + delta_T 3.48 \ Spike slope factor. + a 1 \ The sensitivity of the recovery variable :math:`u` to the sub-threshold fluctuations of the membrane potential :math:`v` + b 1 \ The increment of :math:`w` produced by a spike. + R 1 \ Membrane resistance. + tau 10 ms Membrane time constant. Compute by R * C. + tau_w 30 ms Time constant of the adaptation current. + ============= ============== ======== ======================================================================================================================== + + **Model Variables** + + ================== ================= ========================================================= + **Variables name** **Initial Value** **Explanation** + ------------------ ----------------- --------------------------------------------------------- + V 0 Membrane potential. + w 0 Adaptation current. + input 0 External and synaptic input current. + spike False Flag to mark whether the neuron is spiking. + t_last_spike -1e7 Last spike time stamp. + ================== ================= ========================================================= + + **References** + + .. [1] Fourcaud-Trocmé, Nicolas, et al. "How spike generation + mechanisms determine the neuronal response to fluctuating + inputs." Journal of Neuroscience 23.37 (2003): 11628-11640. + .. [2] http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model + """ + + def __init__( + self, + size: Shape, + V_rest: Parameter = -65., + V_reset: Parameter = -68., + V_th: Parameter = -30., + V_T: Parameter = -59.9, + delta_T: Parameter = 3.48, + a: Parameter = 1., + b: Parameter = 1., + tau: Parameter = 10., + tau_w: Parameter = 30., + R: Parameter = 1., + V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + w_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + method: str = 'exp_auto', + name: str = None + ): + super(AdExIF, self).__init__(size=size, name=name) + + # parameters + self.V_rest = V_rest + self.V_reset = V_reset + self.V_th = V_th + self.V_T = V_T + self.delta_T = delta_T + self.a = a + self.b = b + self.tau = tau + self.tau_w = tau_w + self.R = R + + # variables + check_initializer(V_initializer, 'V_initializer') + check_initializer(w_initializer, 'w_initializer') + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.w = bm.Variable(init_param(w_initializer, (self.num,))) + self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + + # functions + self.integral = odeint(method=method, f=self.derivative) + + def dV(self, V, t, w, I_ext): + dVdt = (- V + self.V_rest + self.delta_T * bm.exp((V - self.V_T) / self.delta_T) - + self.R * w + self.R * I_ext) / self.tau + return dVdt + + def dw(self, w, t, V): + dwdt = (self.a * (V - self.V_rest) - w) / self.tau_w + return dwdt + + @property + def derivative(self): + return JointEq([self.dV, self.dw]) + + def update(self, _t, _dt): + V, w = self.integral(self.V, self.w, _t, self.input, dt=_dt) + spike = V >= self.V_th + self.t_last_spike[:] = bm.where(spike, _t, self.t_last_spike) + self.V.value = bm.where(spike, self.V_reset, V) + self.w.value = bm.where(spike, w + self.b, w) + self.spike.value = spike + self.input[:] = 0. + + +class QuaIF(NeuGroup): + r"""Quadratic Integrate-and-Fire neuron model. + + **Model Descriptions** + + In contrast to physiologically accurate but computationally expensive + neuron models like the Hodgkin–Huxley model, the QIF model [1]_ seeks only + to produce **action potential-like patterns** and ignores subtleties + like gating variables, which play an important role in generating action + potentials in a real neuron. However, the QIF model is incredibly easy + to implement and compute, and relatively straightforward to study and + understand, thus has found ubiquitous use in computational neuroscience. + + .. math:: + + \tau \frac{d V}{d t}=c(V-V_{rest})(V-V_c) + RI(t) + + where the parameters are taken to be :math:`c` =0.07, and :math:`V_c = -50 mV` (Latham et al., 2000). + + **Model Examples** + + .. plot:: + :include-source: True + + >>> import brainpy as bp + >>> + >>> group = bp.dyn.QuaIF(1,) + >>> + >>> runner = bp.dyn.DSRunner(group, monitors=['V'], inputs=('input', 20.)) + >>> runner.run(duration=200.) + >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, show=True) + + + **Model Parameters** + + ============= ============== ======== ======================================================================================================================== + **Parameter** **Init Value** **Unit** **Explanation** + ------------- -------------- -------- ------------------------------------------------------------------------------------------------------------------------ + V_rest -65 mV Resting potential. + V_reset -68 mV Reset potential after spike. + V_th -30 mV Threshold potential of spike and reset. + V_c -50 mV Critical voltage for spike initiation. Must be larger than V_rest. + c .07 \ Coefficient describes membrane potential update. Larger than 0. + R 1 \ Membrane resistance. + tau 10 ms Membrane time constant. Compute by R * C. + tau_ref 0 ms Refractory period length. + ============= ============== ======== ======================================================================================================================== + + **Model Variables** + + ================== ================= ========================================================= + **Variables name** **Initial Value** **Explanation** + ------------------ ----------------- --------------------------------------------------------- + V 0 Membrane potential. + input 0 External and synaptic input current. + spike False Flag to mark whether the neuron is spiking. + refractory False Flag to mark whether the neuron is in refractory period. + t_last_spike -1e7 Last spike time stamp. + ================== ================= ========================================================= + + **References** + + .. [1] P. E. Latham, B.J. Richmond, P. Nelson and S. Nirenberg + (2000) Intrinsic dynamics in neuronal networks. I. Theory. + J. Neurophysiology 83, pp. 808–827. + """ + + def __init__( + self, + size: Shape, + V_rest: Parameter = -65., + V_reset: Parameter = -68., + V_th: Parameter = -30., + V_c: Parameter = -50.0, + c: Parameter = .07, + R: Parameter = 1., + tau: Parameter = 10., + tau_ref: Parameter = 0., + V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + method: str = 'exp_auto', + name: str = None + ): + # initialization + super(QuaIF, self).__init__(size=size, name=name) + + # parameters + self.V_rest = V_rest + self.V_reset = V_reset + self.V_th = V_th + self.V_c = V_c + self.c = c + self.R = R + self.tau = tau + self.tau_ref = tau_ref + + # variables + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + + # integral + self.integral = odeint(method=method, f=self.derivative) + + def derivative(self, V, t, I_ext): + dVdt = (self.c * (V - self.V_rest) * (V - self.V_c) + self.R * I_ext) / self.tau + return dVdt + + def update(self, _t, _dt, **kwargs): + refractory = (_t - self.t_last_spike) <= self.tau_ref + V = self.integral(self.V, _t, self.input, dt=_dt) + V = bm.where(refractory, self.V, V) + spike = self.V_th <= V + self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) + self.V.value = bm.where(spike, self.V_reset, V) + self.refractory.value = bm.logical_or(refractory, spike) + self.spike.value = spike + self.input[:] = 0. + + +class AdQuaIF(NeuGroup): + r"""Adaptive quadratic integrate-and-fire neuron model. + + **Model Descriptions** + + The adaptive quadratic integrate-and-fire neuron model [1]_ is given by: + + .. math:: + + \begin{aligned} + \tau_m \frac{d V}{d t}&=c(V-V_{rest})(V-V_c) - w + I(t), \\ + \tau_w \frac{d w}{d t}&=a(V-V_{rest}) - w, + \end{aligned} + + once the membrane potential reaches the spike threshold, + + .. math:: + + V \rightarrow V_{reset}, \\ + w \rightarrow w+b. + + **Model Examples** + + .. plot:: + :include-source: True + + >>> import brainpy as bp + >>> group = bp.dyn.AdQuaIF(1, ) + >>> runner = bp.dyn.DSRunner(group, monitors=['V', 'w'], inputs=('input', 30.)) + >>> runner.run(300) + >>> fig, gs = bp.visualize.get_figure(2, 1, 3, 8) + >>> fig.add_subplot(gs[0, 0]) + >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, ylabel='V') + >>> fig.add_subplot(gs[1, 0]) + >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.w, ylabel='w', show=True) + + **Model Parameters** + + ============= ============== ======== ======================================================= + **Parameter** **Init Value** **Unit** **Explanation** + ------------- -------------- -------- ------------------------------------------------------- + V_rest -65 mV Resting potential. + V_reset -68 mV Reset potential after spike. + V_th -30 mV Threshold potential of spike and reset. + V_c -50 mV Critical voltage for spike initiation. Must be larger + than :math:`V_{rest}`. + a 1 \ The sensitivity of the recovery variable :math:`u` to + the sub-threshold fluctuations of the membrane + potential :math:`v` + b .1 \ The increment of :math:`w` produced by a spike. + c .07 \ Coefficient describes membrane potential update. + Larger than 0. + tau 10 ms Membrane time constant. + tau_w 10 ms Time constant of the adaptation current. + ============= ============== ======== ======================================================= + + **Model Variables** + + ================== ================= ========================================================== + **Variables name** **Initial Value** **Explanation** + ------------------ ----------------- ---------------------------------------------------------- + V 0 Membrane potential. + w 0 Adaptation current. + input 0 External and synaptic input current. + spike False Flag to mark whether the neuron is spiking. + t_last_spike -1e7 Last spike time stamp. + ================== ================= ========================================================== + + **References** + + .. [1] Izhikevich, E. M. (2004). Which model to use for cortical spiking + neurons?. IEEE transactions on neural networks, 15(5), 1063-1070. + .. [2] Touboul, Jonathan. "Bifurcation analysis of a general class of + nonlinear integrate-and-fire neurons." SIAM Journal on Applied + Mathematics 68, no. 4 (2008): 1045-1079. + """ + + def __init__( + self, + size: Shape, + V_rest: Parameter = -65., + V_reset: Parameter = -68., + V_th: Parameter = -30., + V_c: Parameter = -50.0, + a: Parameter = 1., + b: Parameter = .1, + c: Parameter = .07, + tau: Parameter = 10., + tau_w: Parameter = 10., + V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + w_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + method: str = 'exp_auto', + name: str = None + ): + super(AdQuaIF, self).__init__(size=size, name=name) + + # parameters + self.V_rest = V_rest + self.V_reset = V_reset + self.V_th = V_th + self.V_c = V_c + self.c = c + self.a = a + self.b = b + self.tau = tau + self.tau_w = tau_w + + # variables + check_initializer(V_initializer, 'V_initializer') + check_initializer(w_initializer, 'w_initializer') + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.w = bm.Variable(init_param(w_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) + + # integral + self.integral = odeint(method=method, f=self.derivative) + + def dV(self, V, t, w, I_ext): + dVdt = (self.c * (V - self.V_rest) * (V - self.V_c) - w + I_ext) / self.tau + return dVdt + + def dw(self, w, t, V): + dwdt = (self.a * (V - self.V_rest) - w) / self.tau_w + return dwdt + + @property + def derivative(self): + return JointEq([self.dV, self.dw]) + + def update(self, _t, _dt): + V, w = self.integral(self.V, self.w, _t, self.input, dt=_dt) + spike = self.V_th <= V + self.t_last_spike.value = bm.where(spike, _t, self.t_last_spike) + self.V.value = bm.where(spike, self.V_reset, V) + self.w.value = bm.where(spike, w + self.b, w) + self.spike.value = spike + self.input[:] = 0. + + +class GIF(NeuGroup): + r"""Generalized Integrate-and-Fire model. + + **Model Descriptions** + + The generalized integrate-and-fire model [1]_ is given by + + .. math:: + + &\frac{d I_j}{d t} = - k_j I_j + + &\frac{d V}{d t} = ( - (V - V_{rest}) + R\sum_{j}I_j + RI) / \tau + + &\frac{d V_{th}}{d t} = a(V - V_{rest}) - b(V_{th} - V_{th\infty}) + + When :math:`V` meet :math:`V_{th}`, Generalized IF neuron fires: + + .. math:: + + &I_j \leftarrow R_j I_j + A_j + + &V \leftarrow V_{reset} + + &V_{th} \leftarrow max(V_{th_{reset}}, V_{th}) + + Note that :math:`I_j` refers to arbitrary number of internal currents. + + **Model Examples** + + - `Detailed examples to reproduce different firing patterns `_ + + **Model Parameters** + + ============= ============== ======== ==================================================================== + **Parameter** **Init Value** **Unit** **Explanation** + ------------- -------------- -------- -------------------------------------------------------------------- + V_rest -70 mV Resting potential. + V_reset -70 mV Reset potential after spike. + V_th_inf -50 mV Target value of threshold potential :math:`V_{th}` updating. + V_th_reset -60 mV Free parameter, should be larger than :math:`V_{reset}`. + R 20 \ Membrane resistance. + tau 20 ms Membrane time constant. Compute by :math:`R * C`. + a 0 \ Coefficient describes the dependence of + :math:`V_{th}` on membrane potential. + b 0.01 \ Coefficient describes :math:`V_{th}` update. + k1 0.2 \ Constant pf :math:`I1`. + k2 0.02 \ Constant of :math:`I2`. + R1 0 \ Free parameter. + Describes dependence of :math:`I_1` reset value on + :math:`I_1` value before spiking. + R2 1 \ Free parameter. + Describes dependence of :math:`I_2` reset value on + :math:`I_2` value before spiking. + A1 0 \ Free parameter. + A2 0 \ Free parameter. + ============= ============== ======== ==================================================================== + + **Model Variables** + + ================== ================= ========================================================= + **Variables name** **Initial Value** **Explanation** + ------------------ ----------------- --------------------------------------------------------- + V -70 Membrane potential. + input 0 External and synaptic input current. + spike False Flag to mark whether the neuron is spiking. + V_th -50 Spiking threshold potential. + I1 0 Internal current 1. + I2 0 Internal current 2. + t_last_spike -1e7 Last spike time stamp. + ================== ================= ========================================================= + + **References** + + .. [1] Mihalaş, Ştefan, and Ernst Niebur. "A generalized linear + integrate-and-fire neural model produces diverse spiking + behaviors." Neural computation 21.3 (2009): 704-718. + .. [2] Teeter, Corinne, Ramakrishnan Iyer, Vilas Menon, Nathan + Gouwens, David Feng, Jim Berg, Aaron Szafer et al. "Generalized + leaky integrate-and-fire models classify multiple neuron types." + Nature communications 9, no. 1 (2018): 1-15. + """ + + def __init__( + self, + size: Shape, + V_rest: Parameter = -70., + V_reset: Parameter = -70., + V_th_inf: Parameter = -50., + V_th_reset: Parameter = -60., + R: Parameter = 20., + tau: Parameter = 20., + a: Parameter = 0., + b: Parameter = 0.01, + k1: Parameter = 0.2, + k2: Parameter = 0.02, + R1: Parameter = 0., + R2: Parameter = 1., + A1: Parameter = 0., + A2: Parameter = 0., + V_initializer: Union[Initializer, Callable, Tensor] = OneInit(-70.), + I1_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + I2_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + Vth_initializer: Union[Initializer, Callable, Tensor] = OneInit(-50.), + method: str = 'exp_auto', + name: str = None + ): + # initialization + super(GIF, self).__init__(size=size, name=name) + + # params + self.V_rest = V_rest + self.V_reset = V_reset + self.V_th_inf = V_th_inf + self.V_th_reset = V_th_reset + self.R = R + self.tau = tau + self.a = a + self.b = b + self.k1 = k1 + self.k2 = k2 + self.R1 = R1 + self.R2 = R2 + self.A1 = A1 + self.A2 = A2 + + # variables + check_initializer(V_initializer, 'V_initializer') + check_initializer(I1_initializer, 'I1_initializer') + check_initializer(I2_initializer, 'I2_initializer') + check_initializer(Vth_initializer, 'Vth_initializer') + self.I1 = bm.Variable(init_param(I1_initializer, (self.num,))) + self.I2 = bm.Variable(init_param(I2_initializer, (self.num,))) + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.V_th = bm.Variable(init_param(Vth_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + + # integral + self.integral = odeint(method=method, f=self.derivative) + + def dI1(self, I1, t): + return - self.k1 * I1 + + def dI2(self, I2, t): + return - self.k2 * I2 + + def dVth(self, V_th, t, V): + return self.a * (V - self.V_rest) - self.b * (V_th - self.V_th_inf) + + def dV(self, V, t, I1, I2, I_ext): + return (- (V - self.V_rest) + self.R * I_ext + self.R * I1 + self.R * I2) / self.tau + + @property + def derivative(self): + return JointEq([self.dI1, self.dI2, self.dVth, self.dV]) + + def update(self, _t, _dt): + I1, I2, V_th, V = self.integral(self.I1, self.I2, self.V_th, self.V, _t, self.input, dt=_dt) + spike = self.V_th <= V + V = bm.where(spike, self.V_reset, V) + I1 = bm.where(spike, self.R1 * I1 + self.A1, I1) + I2 = bm.where(spike, self.R2 * I2 + self.A2, I2) + reset_th = bm.logical_and(V_th < self.V_th_reset, spike) + V_th = bm.where(reset_th, self.V_th_reset, V_th) + self.spike.value = spike + self.I1.value = I1 + self.I2.value = I2 + self.V_th.value = V_th + self.V.value = V + self.input[:] = 0. + + class Izhikevich(NeuGroup): r"""The Izhikevich neuron model. @@ -79,8 +924,20 @@ class Izhikevich(NeuGroup): IEEE transactions on neural networks 15.5 (2004): 1063-1070. """ - def __init__(self, size, a=0.02, b=0.20, c=-65., d=8., tau_ref=0., - V_th=30., method='exp_auto', name=None): + def __init__( + self, + size: Shape, + a: Parameter = 0.02, + b: Parameter = 0.20, + c: Parameter = -65., + d: Parameter = 8., + tau_ref: Parameter = 0., + V_th: Parameter = 30., + V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + u_initializer: Union[Initializer, Callable, Tensor] = OneInit(), + method: str = 'exp_auto', + name: str = None + ): # initialization super(Izhikevich, self).__init__(size=size, name=name) @@ -93,11 +950,13 @@ class Izhikevich(NeuGroup): self.tau_ref = tau_ref # variables - self.u = bm.Variable(bm.ones(self.num)) - self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) - self.V = bm.Variable(bm.zeros(self.num)) + check_initializer(V_initializer, 'V_initializer') + check_initializer(u_initializer, 'u_initializer') + self.u = bm.Variable(init_param(u_initializer, (self.num,))) + self.V = bm.Variable(init_param(V_initializer, (self.num,))) self.input = bm.Variable(bm.zeros(self.num)) self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.refractory = bm.Variable(bm.zeros(self.num, dtype=bool)) self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) # functions @@ -157,7 +1016,7 @@ class HindmarshRose(NeuGroup): >>> import matplotlib.pyplot as plt >>> >>> bp.math.set_dt(dt=0.01) - >>> bp.set_default_odeint('rk4') + >>> bp.ode.set_default_odeint('rk4') >>> >>> types = ['quiescence', 'spiking', 'bursting', 'irregular_spiking', 'irregular_bursting'] >>> bs = bp.math.array([1.0, 3.5, 2.5, 2.95, 2.8]) @@ -222,8 +1081,23 @@ class HindmarshRose(NeuGroup): 033128. """ - def __init__(self, size, a=1., b=3., c=1., d=5., r=0.01, s=4., V_rest=-1.6, - V_th=1.0, method='exp_auto', name=None): + def __init__( + self, + size: Shape, + a: Parameter = 1., + b: Parameter = 3., + c: Parameter = 1., + d: Parameter = 5., + r: Parameter = 0.01, + s: Parameter = 4., + V_rest: Parameter = -1.6, + V_th: Parameter = 1.0, + V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + y_initializer: Union[Initializer, Callable, Tensor] = OneInit(-10.), + z_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + method: str = 'exp_auto', + name: str = None + ): # initialization super(HindmarshRose, self).__init__(size=size, name=name) @@ -238,9 +1112,12 @@ class HindmarshRose(NeuGroup): self.V_rest = V_rest # variables - self.z = bm.Variable(bm.zeros(self.num)) - self.y = bm.Variable(bm.ones(self.num) * -10.) - self.V = bm.Variable(bm.zeros(self.num)) + check_initializer(V_initializer, 'V_initializer') + check_initializer(y_initializer, 'y_initializer') + check_initializer(z_initializer, 'z_initializer') + self.z = bm.Variable(init_param(V_initializer, (self.num,))) + self.y = bm.Variable(init_param(y_initializer, (self.num,))) + self.V = bm.Variable(init_param(z_initializer, (self.num,))) self.input = bm.Variable(bm.zeros(self.num)) self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) @@ -269,3 +1146,138 @@ class HindmarshRose(NeuGroup): self.y.value = y self.z.value = z self.input[:] = 0. + + +class FHN(NeuGroup): + r"""FitzHugh-Nagumo neuron model. + + **Model Descriptions** + + The FitzHugh–Nagumo model (FHN), named after Richard FitzHugh (1922–2007) + who suggested the system in 1961 [1]_ and J. Nagumo et al. who created the + equivalent circuit the following year, describes a prototype of an excitable + system (e.g., a neuron). + + The motivation for the FitzHugh-Nagumo model was to isolate conceptually + the essentially mathematical properties of excitation and propagation from + the electrochemical properties of sodium and potassium ion flow. The model + consists of + + - a *voltage-like variable* having cubic nonlinearity that allows regenerative + self-excitation via a positive feedback, and + - a *recovery variable* having a linear dynamics that provides a slower negative feedback. + + .. math:: + + \begin{aligned} + {\dot {v}} &=v-{\frac {v^{3}}{3}}-w+RI_{\rm {ext}}, \\ + \tau {\dot {w}}&=v+a-bw. + \end{aligned} + + The FHN Model is an example of a relaxation oscillator + because, if the external stimulus :math:`I_{\text{ext}}` + exceeds a certain threshold value, the system will exhibit + a characteristic excursion in phase space, before the + variables :math:`v` and :math:`w` relax back to their rest values. + This behaviour is typical for spike generations (a short, + nonlinear elevation of membrane voltage :math:`v`, + diminished over time by a slower, linear recovery variable + :math:`w`) in a neuron after stimulation by an external + input current. + + **Model Examples** + + .. plot:: + :include-source: True + + >>> import brainpy as bp + >>> fhn = bp.dyn.FHN(1) + >>> runner = bp.dyn.DSRunner(fhn, inputs=('input', 1.), monitors=['V', 'w']) + >>> runner.run(100.) + >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.w, legend='w') + >>> bp.visualize.line_plot(runner.mon.ts, runner.mon.V, legend='V', show=True) + + **Model Parameters** + + ============= ============== ======== ======================== + **Parameter** **Init Value** **Unit** **Explanation** + ------------- -------------- -------- ------------------------ + a 1 \ Positive constant + b 1 \ Positive constant + tau 10 ms Membrane time constant. + V_th 1.8 mV Threshold potential of spike. + ============= ============== ======== ======================== + + **Model Variables** + + ================== ================= ========================================================= + **Variables name** **Initial Value** **Explanation** + ------------------ ----------------- --------------------------------------------------------- + V 0 Membrane potential. + w 0 A recovery variable which represents + the combined effects of sodium channel + de-inactivation and potassium channel + deactivation. + input 0 External and synaptic input current. + spike False Flag to mark whether the neuron is spiking. + t_last_spike -1e7 Last spike time stamp. + ================== ================= ========================================================= + + **References** + + .. [1] FitzHugh, Richard. "Impulses and physiological states in theoretical models of nerve membrane." Biophysical journal 1.6 (1961): 445-466. + .. [2] https://en.wikipedia.org/wiki/FitzHugh%E2%80%93Nagumo_model + .. [3] http://www.scholarpedia.org/article/FitzHugh-Nagumo_model + + """ + + def __init__( + self, + size: Shape, + a: Parameter = 0.7, + b: Parameter = 0.8, + tau: Parameter = 12.5, + Vth: Parameter = 1.8, + V_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + w_initializer: Union[Initializer, Callable, Tensor] = ZeroInit(), + method: str = 'exp_auto', + name: str = None + ): + # initialization + super(FHN, self).__init__(size=size, name=name) + + # parameters + self.a = a + self.b = b + self.tau = tau + self.Vth = Vth + + # variables + check_initializer(V_initializer, 'V_initializer') + check_initializer(w_initializer, 'w_initializer') + self.w = bm.Variable(init_param(w_initializer, (self.num,))) + self.V = bm.Variable(init_param(V_initializer, (self.num,))) + self.input = bm.Variable(bm.zeros(self.num)) + self.spike = bm.Variable(bm.zeros(self.num, dtype=bool)) + self.t_last_spike = bm.Variable(bm.ones(self.num) * -1e7) + + # integral + self.integral = odeint(method=method, f=self.derivative) + + def dV(self, V, t, w, I_ext): + return V - V * V * V / 3 - w + I_ext + + def dw(self, w, t, V): + return (V + self.a - self.b * w) / self.tau + + @property + def derivative(self): + return JointEq([self.dV, self.dw]) + + def update(self, _t, _dt): + V, w = self.integral(self.V, self.w, _t, self.input, dt=_dt) + self.spike.value = bm.logical_and(V >= self.Vth, self.V < self.Vth) + self.t_last_spike.value = bm.where(self.spike, _t, self.t_last_spike) + self.V.value = V + self.w.value = w + self.input[:] = 0. diff --git a/brainpy/dyn/runners/ds_runner.py b/brainpy/dyn/runners.py similarity index 88% rename from brainpy/dyn/runners/ds_runner.py rename to brainpy/dyn/runners.py index 64eea723..941c1495 100644 --- a/brainpy/dyn/runners/ds_runner.py +++ b/brainpy/dyn/runners.py @@ -7,6 +7,7 @@ import numpy as np import tqdm.auto from jax.experimental.host_callback import id_tap +from brainpy.base.base import TensorCollector from brainpy import math as bm from brainpy.dyn import utils from brainpy.dyn.base import DynamicalSystem @@ -74,7 +75,6 @@ class DSRunner(Runner): self.dyn_vars.update({'_i': self._i}) else: self._i = None - self.dyn_vars.update(self.target.vars().unique()) # run function self._run_func = self.build_run_function() @@ -159,29 +159,33 @@ class DSRunner(Runner): return_with_idx[key] = (data, bm.asarray(idx)) def func(_t, _dt): - res = {k: (v.flatten() if bm.ndim(v) > 1 else v) for k, v in return_without_idx.items()} + res = {k: (v.flatten() if bm.ndim(v) > 1 else v.value) + for k, v in return_without_idx.items()} res.update({k: (v.flatten()[idx] if bm.ndim(v) > 1 else v[idx]) for k, (v, idx) in return_with_idx.items()}) return res return func - def _run_one_step(self, t_and_dt): - _t, _dt = t_and_dt[0], t_and_dt[1] - self._input_step(_t=_t, _dt=_dt) - self.target.update(_t=_t, _dt=_dt) + def _run_one_step(self, _t): + self._input_step(_t=_t, _dt=self.dt) + self.target.update(_t=_t, _dt=self.dt) if self.progress_bar: id_tap(lambda *args: self._pbar.update(), ()) - return self._monitor_step(_t=_t, _dt=_dt) + return self._monitor_step(_t=_t, _dt=self.dt) def build_run_function(self): if self.jit: - f_run = bm.make_loop(self._run_one_step, dyn_vars=self.dyn_vars, has_return=True) + dyn_vars = TensorCollector() + dyn_vars.update(self.dyn_vars) + dyn_vars.update(self.target.vars().unique()) + f_run = bm.make_loop(self._run_one_step, + dyn_vars=dyn_vars, + has_return=True) else: - def f_run(t_and_dt): - all_t, all_dt = t_and_dt + def f_run(all_t): for i in range(all_t.shape[0]): - mon = self._run_one_step((all_t[i], all_dt[i])) + mon = self._run_one_step(all_t[i]) for k, v in mon.items(): self.mon.item_contents[k].append(v) return None, {} @@ -212,8 +216,7 @@ class DSRunner(Runner): start_t = float(self._start_t) end_t = float(start_t + duration) # times - times = bm.arange(start_t, end_t, self.dt) - time_steps = bm.ones_like(times) * self.dt + times = np.arange(start_t, end_t, self.dt) # build monitor for key in self.mon.item_contents.keys(): self.mon.item_contents[key] = [] # reshape the monitor items @@ -223,7 +226,7 @@ class DSRunner(Runner): self._pbar.set_description(f"Running a duration of {round(float(duration), 3)} ({times.size} steps)", refresh=True) t0 = time.time() - _, hists = self._run_func([times.value, time_steps.value]) + _, hists = self._run_func(times) running_time = time.time() - t0 if self.progress_bar: self._pbar.close() @@ -277,23 +280,24 @@ class ReportRunner(DSRunner): # Build the update function if jit: - self._update_step = bm.jit(self.target.update, dyn_vars=self.dyn_vars) + dyn_vars = TensorCollector() + dyn_vars.update(self.dyn_vars) + dyn_vars.update(self.target.vars().unique()) + self._update_step = bm.jit(self.target.update, dyn_vars=dyn_vars) else: self._update_step = self.target.update - def _run_one_step(self, t_and_dt): - _t, _dt = t_and_dt[0], t_and_dt[1] - self._input_step(_t=_t, _dt=_dt) - self._update_step(_t=_t, _dt=_dt) + def _run_one_step(self, _t): + self._input_step(_t, self.dt) + self._update_step(_t, self.dt) if self.progress_bar: self._pbar.update() - return self._monitor_step(_t=_t, _dt=_dt) + return self._monitor_step(_t, self.dt) def build_run_function(self): - def f_run(t_and_dt): - all_t, all_dt = t_and_dt + def f_run(all_t): for i in range(all_t.shape[0]): - mon = self._run_one_step((all_t[i], all_dt[i])) + mon = self._run_one_step(all_t[i]) for k, v in mon.items(): self.mon.item_contents[k].append(v) return None, {} diff --git a/brainpy/dyn/runners/__init__.py b/brainpy/dyn/runners/__init__.py deleted file mode 100644 index f816b147..00000000 --- a/brainpy/dyn/runners/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -# -*- coding: utf-8 -*- - -from .ds_runner import * diff --git a/brainpy/dyn/synapses/__init__.py b/brainpy/dyn/synapses/__init__.py index 74d93ede..3953e6ef 100644 --- a/brainpy/dyn/synapses/__init__.py +++ b/brainpy/dyn/synapses/__init__.py @@ -3,3 +3,5 @@ from .abstract_models import * from .biological_models import * from .learning_rules import * +from .delay_coupling import * + diff --git a/brainpy/dyn/synapses/abstract_models.py b/brainpy/dyn/synapses/abstract_models.py index 10a28d97..39e54bd3 100644 --- a/brainpy/dyn/synapses/abstract_models.py +++ b/brainpy/dyn/synapses/abstract_models.py @@ -1,9 +1,10 @@ # -*- coding: utf-8 -*- import brainpy.math as bm +from brainpy.dyn.base import NeuGroup +from brainpy.dyn.base import TwoEndConn, ConstantDelay from brainpy.integrators.joint_eq import JointEq from brainpy.integrators.ode import odeint -from brainpy.dyn.base import TwoEndConn, ConstantDelay __all__ = [ 'DeltaSynapse', @@ -67,8 +68,17 @@ class DeltaSynapse(TwoEndConn): """ - def __init__(self, pre, post, conn, delay=0., post_has_ref=False, w=1., - post_key='V', name=None): + def __init__( + self, + pre: NeuGroup, + post: NeuGroup, + conn, + delay=0., + post_has_ref=False, + w=1., + post_key='V', + name=None + ): super(DeltaSynapse, self).__init__(pre=pre, post=post, conn=conn, name=name) self.check_pre_attrs('spike') self.check_post_attrs(post_key) @@ -193,8 +203,17 @@ class ExpCUBA(TwoEndConn): Cambridge: Cambridge UP, 2011. 172-95. Print. """ - def __init__(self, pre, post, conn, g_max=1., delay=0., tau=8.0, - method='exp_auto', name=None): + def __init__( + self, + pre: NeuGroup, + post: NeuGroup, + conn, + g_max=1., + delay=0., + tau=8.0, + method='exp_auto', + name=None + ): super(ExpCUBA, self).__init__(pre=pre, post=post, conn=conn, name=name) self.check_pre_attrs('spike') self.check_post_attrs('input', 'V') diff --git a/brainpy/dyn/synapses/delay_coupling.py b/brainpy/dyn/synapses/delay_coupling.py new file mode 100644 index 00000000..bee34631 --- /dev/null +++ b/brainpy/dyn/synapses/delay_coupling.py @@ -0,0 +1,206 @@ +# -*- coding: utf-8 -*- + +from typing import Optional, Union, Sequence, Dict, List + +from jax import vmap + +import brainpy.math as bm +from brainpy.dyn.base import TwoEndConn +from brainpy.initialize import Initializer, ZeroInit +from brainpy.tools.checking import check_sequence +from brainpy.types import Tensor + +__all__ = [ + 'DelayCoupling', + 'DiffusiveDelayCoupling', + 'AdditiveDelayCoupling', +] + + +class DelayCoupling(TwoEndConn): + """ + Delay coupling base class. + + coupling: str + The way of coupling. + gc: float + The global coupling strength. + signal_speed: float + Signal transmission speed between areas. + sc_mat: optional, tensor + Structural connectivity matrix. Adjacency matrix of coupling strengths, + will be normalized to 1. If not given, then a single node simulation + will be assumed. Default None + fl_mat: optional, tensor + Fiber length matrix. Will be used for computing the + delay matrix together with the signal transmission + speed parameter `signal_speed`. Default None. + + + """ + + """Global delay variables. Useful when the same target + variable is used in multiple mappings.""" + global_delay_vars: Dict[str, bm.LengthDelay] = dict() + + def __init__( + self, + pre, + post, + from_to: Union[str, Sequence[str]], + conn_mat: Tensor, + delay_mat: Optional[Tensor] = None, + delay_initializer: Initializer = ZeroInit(), + domain: str = 'local', + name: str = None + ): + super(DelayCoupling, self).__init__(pre, post, name=name) + + # local delay variables + self.local_delay_vars: Dict[str, bm.LengthDelay] = dict() + + # domain + if domain not in ['global', 'local']: + raise ValueError('"domain" must be a string in ["global", "local"]. ' + f'Bug we got {domain}.') + self.domain = domain + + # pairs of (source, destination) + self.source_target_pairs: Dict[str, List[bm.Variable]] = dict() + source_vars = {} + if isinstance(from_to, str): + from_to = [from_to] + check_sequence(from_to, 'from_to', elem_type=str, allow_none=False) + for pair in from_to: + splits = [v.strip() for v in pair.split('->')] + if len(splits) != 2: + raise ValueError('The (source, target) pair in "from_to" ' + 'should be defined as "a -> b".') + if not hasattr(self.pre, splits[0]): + raise ValueError(f'"{splits[0]}" is not defined in pre-synaptic group {self.pre.name}') + if not hasattr(self.post, splits[1]): + raise ValueError(f'"{splits[1]}" is not defined in post-synaptic group {self.post.name}') + source = f'{self.pre.name}.{splits[0]}' + target = getattr(self.post, splits[1]) + if splits[0] not in self.source_target_pairs: + self.source_target_pairs[source] = [target] + source_vars[source] = getattr(self.pre, splits[0]) + if not isinstance(source_vars[source], bm.Variable): + raise ValueError(f'The target variable {source} for delay should ' + f'be an instance of brainpy.math.Variable, while ' + f'we got {type(source_vars[source])}') + else: + if target in self.source_target_pairs: + raise ValueError(f'{pair} has been defined twice in {from_to}.') + self.source_target_pairs[source].append(target) + + # Connection matrix + conn_mat = bm.asarray(conn_mat) + required_shape = (self.post.num, self.pre.num) + if conn_mat.shape != required_shape: + raise ValueError(f'we expect the structural connection matrix has the shape of ' + f'(post.num, pre.num), i.e., {required_shape}, ' + f'while we got {conn_mat.shape}.') + self.conn_mat = bm.asarray(conn_mat) + bm.fill_diagonal(self.conn_mat, 0) + + # Delay matrix + if delay_mat is None: + self.delay_mat = bm.zeros(required_shape, dtype=bm.int_) + else: + if delay_mat.shape != required_shape: + raise ValueError(f'we expect the fiber length matrix has the shape of ' + f'(post.num, pre.num), i.e., {required_shape}. ' + f'While we got {delay_mat.shape}.') + self.delay_mat = bm.asarray(delay_mat, dtype=bm.int_) + + # delay variables + num_delay_step = int(self.delay_mat.max()) + for var in self.source_target_pairs.keys(): + if domain == 'local': + variable = source_vars[var] + shape = (num_delay_step,) + variable.shape + delay_data = delay_initializer(shape, dtype=variable.dtype) + self.local_delay_vars[var] = bm.LengthDelay(variable, num_delay_step, delay_data) + else: + if var not in self.global_delay_vars: + variable = source_vars[var] + shape = (num_delay_step,) + variable.shape + delay_data = delay_initializer(shape, dtype=variable.dtype) + self.global_delay_vars[var] = bm.LengthDelay(variable, num_delay_step, delay_data) + # save into local delay vars when first seen "var", + # for later update current value! + self.local_delay_vars[var] = self.global_delay_vars[var] + else: + if self.global_delay_vars[var].delay_len < num_delay_step: + variable = source_vars[var] + shape = (num_delay_step,) + variable.shape + delay_data = delay_initializer(shape, dtype=variable.dtype) + self.global_delay_vars[var].init(variable, num_delay_step, delay_data) + + self.register_implicit_nodes(self.local_delay_vars) + self.register_implicit_nodes(self.global_delay_vars) + + def update(self, _t, _dt): + raise NotImplementedError('Must implement the update() function by users.') + + +class DiffusiveDelayCoupling(DelayCoupling): + def update(self, _t, _dt): + for source, targets in self.source_target_pairs.items(): + # delay variable + if self.domain == 'local': + delay_var: bm.LengthDelay = self.local_delay_vars[source] + elif self.domain == 'global': + delay_var: bm.LengthDelay = self.global_delay_vars[source] + else: + raise ValueError(f'Unknown domain: {self.domain}') + + # current data + name, var = source.split('.') + assert name == self.pre.name + variable = getattr(self.pre, var) + + # delays + f = vmap(lambda i: delay_var(self.delay_mat[i], bm.arange(self.pre.num))) # (pre.num,) + delays = f(bm.arange(self.post.num).value) + diffusive = delays - bm.expand_dims(variable, axis=1) # (post.num, pre.num) + diffusive = (self.conn_mat * diffusive).sum(axis=1) + + # output to target variable + for target in targets: + target.value += diffusive + + # update + if source in self.local_delay_vars: + delay_var.update(variable) + + +class AdditiveDelayCoupling(DelayCoupling): + def update(self, _t, _dt): + for source, targets in self.source_target_pairs.items(): + # delay variable + if self.domain == 'local': + delay_var: bm.LengthDelay = self.local_delay_vars[source] + elif self.domain == 'global': + delay_var: bm.LengthDelay = self.global_delay_vars[source] + else: + raise ValueError(f'Unknown domain: {self.domain}') + + # current data + name, var = source.split('.') + assert name == self.pre.name + variable = getattr(self.pre, var) + + # delay function + f = vmap(lambda i: delay_var(self.delay_mat[i], bm.arange(self.pre.num))) # (pre.num,) + delays = f(bm.arange(self.post.num)) # (post.num, pre.num) + additive = (self.conn_mat * delays).sum(axis=1) + + # output to target variable + for target in targets: + target.value += additive + + # update + if source in self.local_delay_vars: + delay_var.update(variable) diff --git a/brainpy/errors.py b/brainpy/errors.py index e4421185..90ee3d90 100644 --- a/brainpy/errors.py +++ b/brainpy/errors.py @@ -101,7 +101,8 @@ class JaxTracerError(MathError): else: raise ValueError - msg += 'While there are changed variables which are not wrapped into "dyn_vars". Please check!' + # msg += 'While there are changed variables which are not wrapped into "dyn_vars". Please check!' + msg = 'While there are changed variables which are not wrapped into "dyn_vars". Please check!' super(JaxTracerError, self).__init__(msg) diff --git a/brainpy/initialize/__init__.py b/brainpy/initialize/__init__.py index 3998d9ae..47d87c37 100644 --- a/brainpy/initialize/__init__.py +++ b/brainpy/initialize/__init__.py @@ -6,6 +6,7 @@ You can access them through ``brainpy.init.XXX``. """ from .base import * +from .generic import * from .random_inits import * from .regular_inits import * from .decay_inits import * diff --git a/brainpy/initialize/base.py b/brainpy/initialize/base.py index 00782f32..c63524e1 100644 --- a/brainpy/initialize/base.py +++ b/brainpy/initialize/base.py @@ -13,7 +13,7 @@ class Initializer(abc.ABC): """Base Initialization Class.""" @abc.abstractmethod - def __call__(self, shape): + def __call__(self, shape, dtype=None): raise NotImplementedError @@ -21,7 +21,7 @@ class InterLayerInitializer(Initializer): """The superclass of Initializers that initialize the weights between two layers.""" @abc.abstractmethod - def __call__(self, shape): + def __call__(self, shape, dtype=None): raise NotImplementedError @@ -29,5 +29,5 @@ class IntraLayerInitializer(Initializer): """The superclass of Initializers that initialize the weights within a layer.""" @abc.abstractmethod - def __call__(self, shape): + def __call__(self, shape, dtype=None): raise NotImplementedError diff --git a/brainpy/initialize/generic.py b/brainpy/initialize/generic.py new file mode 100644 index 00000000..62f5240d --- /dev/null +++ b/brainpy/initialize/generic.py @@ -0,0 +1,46 @@ +# -*- coding: utf-8 -*- + +from typing import Union, Callable + +import jax.numpy as jnp +import numpy as onp + +import brainpy.math as bm +from brainpy.tools.others import to_size +from brainpy.types import Shape +from .base import Initializer + +__all__ = [ + 'init_param', +] + + +def init_param(param: Union[Callable, Initializer, bm.ndarray, jnp.ndarray], + size: Shape): + """Initialize parameters. + + Parameters + ---------- + param: callable, Initializer, bm.ndarray, jnp.ndarray + The initialization of the parameter. + - If it is None, the created parameter will be None. + - If it is a callable function :math:`f`, the ``f(size)`` will be returned. + - If it is an instance of :py:class:`brainpy.init.Initializer``, the ``f(size)`` will be returned. + - If it is a tensor, then this function check whether ``tensor.shape`` is equal to the given ``size``. + size: int, sequence of int + The shape of the parameter. + """ + size = to_size(size) + if param is None: + return None + elif callable(param): + param = param(size) + elif isinstance(param, (onp.ndarray, jnp.ndarray)): + param = bm.asarray(param) + elif isinstance(param, (bm.JaxArray,)): + param = param + else: + raise ValueError(f'Unknown param type {type(param)}: {param}') + assert param.shape == size, f'"param.shape" is not the required size {size}' + return param + diff --git a/brainpy/initialize/random_inits.py b/brainpy/initialize/random_inits.py index 9ea1ad48..821c35ac 100644 --- a/brainpy/initialize/random_inits.py +++ b/brainpy/initialize/random_inits.py @@ -40,7 +40,7 @@ class Normal(InterLayerInitializer): def __init__(self, scale=1., seed=None): super(Normal, self).__init__() self.scale = scale - self.rng = bm.random.RandomState(seed=seed) + self.rng = np.random.RandomState(seed=seed) def __call__(self, shape, dtype=None): shape = [tools.size2num(d) for d in shape] @@ -64,7 +64,7 @@ class Uniform(InterLayerInitializer): super(Uniform, self).__init__() self.min_val = min_val self.max_val = max_val - self.rng = bm.random.RandomState(seed=seed) + self.rng = np.random.RandomState(seed=seed) def __call__(self, shape, dtype=None): shape = [tools.size2num(d) for d in shape] @@ -79,7 +79,7 @@ class VarianceScaling(InterLayerInitializer): self.in_axis = in_axis self.out_axis = out_axis self.distribution = distribution - self.rng = bm.random.RandomState(seed=seed) + self.rng = np.random.RandomState(seed=seed) def __call__(self, shape, dtype=None): shape = [tools.size2num(d) for d in shape] @@ -94,18 +94,17 @@ class VarianceScaling(InterLayerInitializer): raise ValueError("invalid mode for variance scaling initializer: {}".format(self.mode)) variance = bm.array(self.scale / denominator, dtype=dtype) if self.distribution == "truncated_normal": + from scipy.stats import truncnorm # constant is stddev of standard normal truncated to (-2, 2) stddev = bm.sqrt(variance) / bm.array(.87962566103423978, dtype) - res = self.rng.truncated_normal(-2, 2, shape) * stddev - return bm.asarray(res, dtype=dtype) + res = truncnorm(-2, 2).rvs(shape) * stddev elif self.distribution == "normal": res = self.rng.normal(size=shape) * bm.sqrt(variance) - return bm.asarray(res, dtype=dtype) elif self.distribution == "uniform": res = self.rng.uniform(low=-1, high=1, size=shape) * bm.sqrt(3 * variance) - return bm.asarray(res, dtype=dtype) else: raise ValueError("invalid distribution for variance scaling initializer") + return bm.asarray(res, dtype=dtype) class KaimingUniform(VarianceScaling): @@ -180,7 +179,7 @@ class Orthogonal(InterLayerInitializer): super(Orthogonal, self).__init__() self.scale = scale self.axis = axis - self.rng = bm.random.RandomState(seed=seed) + self.rng = np.random.RandomState(seed=seed) def __call__(self, shape, dtype=None): shape = [tools.size2num(d) for d in shape] diff --git a/brainpy/inputs/__init__.py b/brainpy/inputs/__init__.py index 0876618c..792e10e8 100644 --- a/brainpy/inputs/__init__.py +++ b/brainpy/inputs/__init__.py @@ -6,203 +6,5 @@ This module provides various methods to form current inputs. You can access them through ``brainpy.inputs.XXX``. """ -import numpy as np - -from brainpy import math as bm - -__all__ = [ - 'section_input', - 'constant_input', 'constant_current', - 'spike_input', 'spike_current', - 'ramp_input', 'ramp_current', -] - - -def section_input(values, durations, dt=None, return_length=False): - """Format an input current with different sections. - - For example: - - If you want to get an input where the size is 0 bwteen 0-100 ms, - and the size is 1. between 100-200 ms. - - >>> section_input(values=[0, 1], - >>> durations=[100, 100]) - - Parameters - ---------- - values : list, np.ndarray - The current values for each period duration. - durations : list, np.ndarray - The duration for each period. - dt : float - Default is None. - return_length : bool - Return the final duration length. - - Returns - ------- - current_and_duration : tuple - (The formatted current, total duration) - """ - assert len(durations) == len(values), f'"values" and "durations" must be the same length, while ' \ - f'we got {len(values)} != {len(durations)}.' - - dt = bm.get_dt() if dt is None else dt - - # get input current shape, and duration - I_duration = sum(durations) - I_shape = () - for val in values: - shape = bm.shape(val) - if len(shape) > len(I_shape): - I_shape = shape - - # get the current - start = 0 - I_current = bm.zeros((int(np.ceil(I_duration / dt)),) + I_shape, dtype=bm.float_) - for c_size, duration in zip(values, durations): - length = int(duration / dt) - I_current[start: start + length] = c_size - start += length - - if return_length: - return I_current, I_duration - else: - return I_current - - -def constant_input(I_and_duration, dt=None): - """Format constant input in durations. - - For example: - - If you want to get an input where the size is 0 bwteen 0-100 ms, - and the size is 1. between 100-200 ms. - - >>> import brainpy.math as bm - >>> constant_input([(0, 100), (1, 100)]) - >>> constant_input([(bm.zeros(100), 100), (bm.random.rand(100), 100)]) - - Parameters - ---------- - I_and_duration : list - This parameter receives the current size and the current - duration pairs, like `[(Isize1, duration1), (Isize2, duration2)]`. - dt : float - Default is None. - - Returns - ------- - current_and_duration : tuple - (The formatted current, total duration) - """ - dt = bm.get_dt() if dt is None else dt - - # get input current dimension, shape, and duration - I_duration = 0. - I_shape = () - for I in I_and_duration: - I_duration += I[1] - shape = bm.shape(I[0]) - if len(shape) > len(I_shape): - I_shape = shape - - # get the current - start = 0 - I_current = bm.zeros((int(np.ceil(I_duration / dt)),) + I_shape, dtype=bm.float_) - for c_size, duration in I_and_duration: - length = int(duration / dt) - I_current[start: start + length] = c_size - start += length - return I_current, I_duration - - -constant_current = constant_input - - -def spike_input(sp_times, sp_lens, sp_sizes, duration, dt=None): - """Format current input like a series of short-time spikes. - - For example: - - If you want to generate a spike train at 10 ms, 20 ms, 30 ms, 200 ms, 300 ms, - and each spike lasts 1 ms and the spike current is 0.5, then you can use the - following funtions: - - >>> spike_input(sp_times=[10, 20, 30, 200, 300], - >>> sp_lens=1., # can be a list to specify the spike length at each point - >>> sp_sizes=0.5, # can be a list to specify the current size at each point - >>> duration=400.) - - Parameters - ---------- - sp_times : list, tuple - The spike time-points. Must be an iterable object. - sp_lens : int, float, list, tuple - The length of each point-current, mimicking the spike durations. - sp_sizes : int, float, list, tuple - The current sizes. - duration : int, float - The total current duration. - dt : float - The default is None. - - Returns - ------- - current : bm.ndarray - The formatted input current. - """ - dt = bm.get_dt() if dt is None else dt - assert isinstance(sp_times, (list, tuple)) - if isinstance(sp_lens, (float, int)): - sp_lens = [sp_lens] * len(sp_times) - if isinstance(sp_sizes, (float, int)): - sp_sizes = [sp_sizes] * len(sp_times) - - current = bm.zeros(int(np.ceil(duration / dt)), dtype=bm.float_) - for time, dur, size in zip(sp_times, sp_lens, sp_sizes): - pp = int(time / dt) - p_len = int(dur / dt) - current[pp: pp + p_len] = size - return current - - -spike_current = spike_input - - -def ramp_input(c_start, c_end, duration, t_start=0, t_end=None, dt=None): - """Get the gradually changed input current. - - Parameters - ---------- - c_start : float - The minimum (or maximum) current size. - c_end : float - The maximum (or minimum) current size. - duration : int, float - The total duration. - t_start : float - The ramped current start time-point. - t_end : float - The ramped current end time-point. Default is the None. - dt : float, int, optional - The numerical precision. - - Returns - ------- - current : bm.ndarray - The formatted current - """ - dt = bm.get_dt() if dt is None else dt - t_end = duration if t_end is None else t_end - - current = bm.zeros(int(np.ceil(duration / dt)), dtype=bm.float_) - p1 = int(np.ceil(t_start / dt)) - p2 = int(np.ceil(t_end / dt)) - current[p1: p2] = bm.array(bm.linspace(c_start, c_end, p2 - p1), dtype=bm.float_) - return current - - -ramp_current = ramp_input +from .currents import * diff --git a/brainpy/inputs/currents.py b/brainpy/inputs/currents.py new file mode 100644 index 00000000..094b6f91 --- /dev/null +++ b/brainpy/inputs/currents.py @@ -0,0 +1,386 @@ +# -*- coding: utf-8 -*- + +import numpy as np + +from brainpy import math as bm +from brainpy.tools.checking import check_float, check_integer + +__all__ = [ + 'section_input', + 'constant_input', 'constant_current', + 'spike_input', 'spike_current', + 'ramp_input', 'ramp_current', + 'wiener_process', + 'ou_process', + 'sinusoidal_input', + 'square_input', +] + + +def section_input(values, durations, dt=None, return_length=False): + """Format an input current with different sections. + + For example: + + If you want to get an input where the size is 0 bwteen 0-100 ms, + and the size is 1. between 100-200 ms. + + >>> section_input(values=[0, 1], + >>> durations=[100, 100]) + + Parameters + ---------- + values : list, np.ndarray + The current values for each period duration. + durations : list, np.ndarray + The duration for each period. + dt : float + Default is None. + return_length : bool + Return the final duration length. + + Returns + ------- + current_and_duration : tuple + (The formatted current, total duration) + """ + assert len(durations) == len(values), f'"values" and "durations" must be the same length, while ' \ + f'we got {len(values)} != {len(durations)}.' + + dt = bm.get_dt() if dt is None else dt + + # get input current shape, and duration + I_duration = sum(durations) + I_shape = () + for val in values: + shape = bm.shape(val) + if len(shape) > len(I_shape): + I_shape = shape + + # get the current + start = 0 + I_current = bm.zeros((int(np.ceil(I_duration / dt)),) + I_shape, dtype=bm.float_) + for c_size, duration in zip(values, durations): + length = int(duration / dt) + I_current[start: start + length] = c_size + start += length + + if return_length: + return I_current, I_duration + else: + return I_current + + +def constant_input(I_and_duration, dt=None): + """Format constant input in durations. + + For example: + + If you want to get an input where the size is 0 bwteen 0-100 ms, + and the size is 1. between 100-200 ms. + + >>> import brainpy.math as bm + >>> constant_input([(0, 100), (1, 100)]) + >>> constant_input([(bm.zeros(100), 100), (bm.random.rand(100), 100)]) + + Parameters + ---------- + I_and_duration : list + This parameter receives the current size and the current + duration pairs, like `[(Isize1, duration1), (Isize2, duration2)]`. + dt : float + Default is None. + + Returns + ------- + current_and_duration : tuple + (The formatted current, total duration) + """ + dt = bm.get_dt() if dt is None else dt + + # get input current dimension, shape, and duration + I_duration = 0. + I_shape = () + for I in I_and_duration: + I_duration += I[1] + shape = bm.shape(I[0]) + if len(shape) > len(I_shape): + I_shape = shape + + # get the current + start = 0 + I_current = bm.zeros((int(np.ceil(I_duration / dt)),) + I_shape, dtype=bm.float_) + for c_size, duration in I_and_duration: + length = int(duration / dt) + I_current[start: start + length] = c_size + start += length + return I_current, I_duration + + +constant_current = constant_input + + +def spike_input(sp_times, sp_lens, sp_sizes, duration, dt=None): + """Format current input like a series of short-time spikes. + + For example: + + If you want to generate a spike train at 10 ms, 20 ms, 30 ms, 200 ms, 300 ms, + and each spike lasts 1 ms and the spike current is 0.5, then you can use the + following funtions: + + >>> spike_input(sp_times=[10, 20, 30, 200, 300], + >>> sp_lens=1., # can be a list to specify the spike length at each point + >>> sp_sizes=0.5, # can be a list to specify the current size at each point + >>> duration=400.) + + Parameters + ---------- + sp_times : list, tuple + The spike time-points. Must be an iterable object. + sp_lens : int, float, list, tuple + The length of each point-current, mimicking the spike durations. + sp_sizes : int, float, list, tuple + The current sizes. + duration : int, float + The total current duration. + dt : float + The default is None. + + Returns + ------- + current : bm.ndarray + The formatted input current. + """ + dt = bm.get_dt() if dt is None else dt + assert isinstance(sp_times, (list, tuple)) + if isinstance(sp_lens, (float, int)): + sp_lens = [sp_lens] * len(sp_times) + if isinstance(sp_sizes, (float, int)): + sp_sizes = [sp_sizes] * len(sp_times) + + current = bm.zeros(int(np.ceil(duration / dt)), dtype=bm.float_) + for time, dur, size in zip(sp_times, sp_lens, sp_sizes): + pp = int(time / dt) + p_len = int(dur / dt) + current[pp: pp + p_len] = size + return current + + +spike_current = spike_input + + +def ramp_input(c_start, c_end, duration, t_start=0, t_end=None, dt=None): + """Get the gradually changed input current. + + Parameters + ---------- + c_start : float + The minimum (or maximum) current size. + c_end : float + The maximum (or minimum) current size. + duration : int, float + The total duration. + t_start : float + The ramped current start time-point. + t_end : float + The ramped current end time-point. Default is the None. + dt : float, int, optional + The numerical precision. + + Returns + ------- + current : bm.ndarray + The formatted current + """ + dt = bm.get_dt() if dt is None else dt + t_end = duration if t_end is None else t_end + + current = bm.zeros(int(np.ceil(duration / dt)), dtype=bm.float_) + p1 = int(np.ceil(t_start / dt)) + p2 = int(np.ceil(t_end / dt)) + current[p1: p2] = bm.array(bm.linspace(c_start, c_end, p2 - p1), dtype=bm.float_) + return current + + +ramp_current = ramp_input + + +def wiener_process(duration, dt=None, n=1, t_start=0., t_end=None, seed=None): + """Stimulus sampled from a Wiener process, i.e. + drawn from standard normal distribution N(0, sqrt(dt)). + + Parameters + ---------- + duration: float + The input duration. + dt: float + The numerical precision. + n: int + The variable number. + t_start: float + The start time. + t_end: float + The end time. + seed: int + The noise seed. + """ + dt = bm.get_dt() if dt is None else dt + check_float(dt, 'dt', allow_none=False, min_bound=0.) + check_integer(n, 'n', allow_none=False, min_bound=0) + rng = bm.random.RandomState(seed) + t_end = duration if t_end is None else t_end + i_start = int(t_start / dt) + i_end = int(t_end / dt) + noises = rng.standard_normal((i_end - i_start, n)) * bm.sqrt(dt) + currents = bm.zeros((int(duration / dt), n)) + currents[i_start: i_end] = noises + return currents + + +def ou_process(mean, sigma, tau, duration, dt=None, n=1, t_start=0., t_end=None, seed=None): + r"""Ornstein–Uhlenbeck input. + + .. math:: + + dX = (mu - X)/\tau * dt + \sigma*dW + + Parameters + ---------- + mean: float + Drift of the OU process. + sigma: float + Standard deviation of the Wiener process, i.e. strength of the noise. + tau: float + Timescale of the OU process, in ms. + duration: float + The input duration. + dt: float + The numerical precision. + n: int + The variable number. + t_start: float + The start time. + t_end: float + The end time. + + """ + dt = bm.get_dt() if dt is None else dt + dt_sqrt = bm.sqrt(dt) + check_float(dt, 'dt', allow_none=False, min_bound=0.) + check_integer(n, 'n', allow_none=False, min_bound=0) + rng = bm.random.RandomState(seed) + x = bm.Variable(bm.ones(n) * mean) + + def _f(t): + x.value = x + dt * ((mean - x) / tau) + sigma * dt_sqrt * rng.standard_normal(n) + + f = bm.make_loop(_f, dyn_vars=[x, rng], out_vars=x) + noises = f(bm.arange(t_start, t_end, dt)) + + t_end = duration if t_end is None else t_end + i_start = int(t_start / dt) + i_end = int(t_end / dt) + currents = bm.zeros((int(duration / dt), n)) + currents[i_start: i_end] = noises + return currents + + +def sinusoidal_input(amplitude, frequency, duration, dt=None, t_start=0., t_end=None, dc_bias=False): + """Sinusoidal input. + + Parameters + ---------- + amplitude: float + Amplitude of the sinusoid. + frequency: float + Frequency of the sinus oscillation, in Hz + duration: float + The input duration. + t_start: float + The start time. + t_end: float + The end time. + dt: float + The numerical precision. + dc_bias: bool + Whether the sinusoid oscillates around 0 (False), or + has a positive DC bias, thus non-negative (True). + """ + dt = bm.get_dt() if dt is None else dt + check_float(dt, 'dt', allow_none=False, min_bound=0.) + if t_end is None: + t_end = duration + times = bm.arange(0, t_end-t_start, dt) + start_i = int(t_start/dt) + end_i = int(t_end/dt) + sin_inputs = amplitude * bm.sin(2 * bm.pi * times * (frequency / 1000.0)) + if dc_bias: + sin_inputs += amplitude + currents = bm.zeros(int(duration / dt)) + currents[start_i:end_i] = sin_inputs + return currents + + +def _square(t, duty=0.5): + t, w = np.asarray(t), np.asarray(duty) + w = np.asarray(w + (t - t)) + t = np.asarray(t + (w - w)) + if t.dtype.char in ['fFdD']: + ytype = t.dtype.char + else: + ytype = 'd' + + y = np.zeros(t.shape, ytype) + + # width must be between 0 and 1 inclusive + mask1 = (w > 1) | (w < 0) + np.place(y, mask1, np.nan) + + # on the interval 0 to duty*2*pi function is 1 + tmod = np.mod(t, 2 * np.pi) + mask2 = (1 - mask1) & (tmod < w * 2 * np.pi) + np.place(y, mask2, 1) + + # on the interval duty*2*pi to 2*pi function is + # (pi*(w+1)-tmod) / (pi*(1-w)) + mask3 = (1 - mask1) & (1 - mask2) + np.place(y, mask3, -1) + return y + + +def square_input(amplitude, frequency, duration, dt=None, dc_bias=False, t_start=None, t_end=None): + """Oscillatory square input. + + Parameters + ---------- + amplitude: float + Amplitude of the square oscillation. + frequency: float + Frequency of the square oscillation, in Hz. + duration: float + The input duration. + t_start: float + The start time. + t_end: float + The end time. + dt: float + The numerical precision. + dc_bias: bool + Whether the sinusoid oscillates around 0 (False), or + has a positive DC bias, thus non-negative (True). + """ + dt = bm.get_dt() if dt is None else dt + check_float(dt, 'dt', allow_none=False, min_bound=0.) + if t_end is None: + t_end = duration + times = bm.arange(0, t_end - t_start, dt) + currents = bm.zeros(int(duration / dt)) + start_i = int(t_start/dt) + end_i = int(t_end/dt) + sin_inputs = amplitude * _square(2 * bm.pi * times * (frequency / 1000.0)) + if dc_bias: + sin_inputs += amplitude + currents[start_i:end_i] = sin_inputs + return currents + diff --git a/brainpy/integrators/__init__.py b/brainpy/integrators/__init__.py index 0fd5d169..d6040866 100644 --- a/brainpy/integrators/__init__.py +++ b/brainpy/integrators/__init__.py @@ -7,6 +7,7 @@ including: - ordinary differential equations (ODEs) - stochastic differential equations (SDEs) - delay differential equations (DDEs) +- fractional differential equations (FDEs) Details please see the following. """ @@ -41,6 +42,14 @@ from .dde.generic import (ddeint, set_default_ddeint, register_dde_integrator) -# others +# FDE tools from . import fde +from .fde.base import FDEIntegrator +from .fde.generic import (fdeint, + get_default_fdeint, + set_default_fdeint, + register_fde_integrator) + + +# PDE tools from . import pde diff --git a/brainpy/integrators/base.py b/brainpy/integrators/base.py index c606ce81..7c526aa4 100644 --- a/brainpy/integrators/base.py +++ b/brainpy/integrators/base.py @@ -34,12 +34,13 @@ class Integrator(AbstractIntegrator): self._dt = dt check_float(dt, 'dt', allow_none=False, allow_int=True) self._variables = variables # variables - self._parameters = parameters # parameters - self._arguments = list(arguments) + [f'{DT}={self.dt}'] # arguments + self._parameters = parameters # parameters + self._arguments = list(arguments) + [f'{DT}={self.dt}'] # arguments self._integral = None # integral function @property def dt(self): + """The numerical integration precision.""" return self._dt @dt.setter @@ -48,6 +49,7 @@ class Integrator(AbstractIntegrator): @property def variables(self): + """The variables defined in the differential equation.""" return self._variables @variables.setter @@ -56,6 +58,7 @@ class Integrator(AbstractIntegrator): @property def parameters(self): + """The parameters defined in the differential equation.""" return self._parameters @parameters.setter @@ -64,6 +67,7 @@ class Integrator(AbstractIntegrator): @property def arguments(self): + """All arguments when calling the numer integrator of the differential equation.""" return self._arguments @arguments.setter @@ -72,6 +76,7 @@ class Integrator(AbstractIntegrator): @property def integral(self): + """The integral function.""" return self._integral @integral.setter @@ -79,6 +84,7 @@ class Integrator(AbstractIntegrator): self.set_integral(f) def set_integral(self, f): + """Set the integral function.""" if not callable(f): raise ValueError(f'integral function must be a callable function, ' f'but we got {type(f)}: {f}') diff --git a/brainpy/integrators/dde/base.py b/brainpy/integrators/dde/base.py index 413f6a48..10ba9dd1 100644 --- a/brainpy/integrators/dde/base.py +++ b/brainpy/integrators/dde/base.py @@ -25,7 +25,7 @@ class DDEIntegrator(Integrator): dt: Union[float, int] = None, name: str = None, show_code: bool = False, - state_delays: Dict[str, bm.FixedLenDelay] = None, + state_delays: Dict[str, bm.TimeDelay] = None, neutral_delays: Dict[str, bm.NeutralDelay] = None, ): dt = bm.get_dt() if dt is None else dt @@ -59,7 +59,9 @@ class DDEIntegrator(Integrator): # delays self._state_delays = dict() if state_delays is not None: - check_dict_data(state_delays, key_type=str, val_type=bm.FixedLenDelay) + check_dict_data(state_delays, + key_type=str, + val_type=(bm.TimeDelay, bm.LengthDelay)) for key, delay in state_delays.items(): if key not in self.variables: raise DiffEqError(f'"{key}" is not defined in the variables: {self.variables}') @@ -67,7 +69,9 @@ class DDEIntegrator(Integrator): self.register_implicit_nodes(self._state_delays) self._neutral_delays = dict() if neutral_delays is not None: - check_dict_data(neutral_delays, key_type=str, val_type=bm.NeutralDelay) + check_dict_data(neutral_delays, + key_type=str, + val_type=bm.NeutralDelay) for key, delay in neutral_delays.items(): if key not in self.variables: raise DiffEqError(f'"{key}" is not defined in the variables: {self.variables}') @@ -111,11 +115,19 @@ class DDEIntegrator(Integrator): else: new_dvars = {k: new_dvars[i] for i, k in enumerate(self.variables)} for key, delay in self.neutral_delays.items(): - delay.update(kwargs['t'] + dt, new_dvars[key]) + if isinstance(delay, bm.LengthDelay): + delay.update(new_dvars[key]) + elif isinstance(delay, bm.TimeDelay): + delay.update(kwargs['t'] + dt, new_dvars[key]) + raise ValueError('Unknown delay variable.') # update state delay variables for key, delay in self.state_delays.items(): - delay.update(kwargs['t'] + dt, dict_vars[key]) + if isinstance(delay, bm.LengthDelay): + delay.update(dict_vars[key]) + elif isinstance(delay, bm.TimeDelay): + delay.update(kwargs['t'] + dt, dict_vars[key]) + raise ValueError('Unknown delay variable.') return new_vars diff --git a/brainpy/integrators/dde/explicit_rk.py b/brainpy/integrators/dde/explicit_rk.py index 8ba7bcf9..f01f7cc8 100644 --- a/brainpy/integrators/dde/explicit_rk.py +++ b/brainpy/integrators/dde/explicit_rk.py @@ -4,6 +4,7 @@ from brainpy.integrators.constants import F, DT from brainpy.integrators.dde.base import DDEIntegrator from brainpy.integrators.ode import common from brainpy.integrators.utils import compile_code, check_kws +from brainpy.integrators.dde.generic import register_dde_integrator __all__ = [ 'ExplicitRKIntegrator', @@ -47,8 +48,6 @@ class ExplicitRKIntegrator(DDEIntegrator): def integral(*vars, **kwargs): pass - - self.build() def build(self): @@ -72,24 +71,36 @@ class Euler(ExplicitRKIntegrator): C = [0] +register_dde_integrator('euler', Euler) + + class MidPoint(ExplicitRKIntegrator): A = [(), (0.5,)] B = [0, 1] C = [0, 0.5] +register_dde_integrator('midpoint', MidPoint) + + class Heun2(ExplicitRKIntegrator): A = [(), (1,)] B = [0.5, 0.5] C = [0, 1] +register_dde_integrator('heun2', Heun2) + + class Ralston2(ExplicitRKIntegrator): A = [(), ('2/3',)] B = [0.25, 0.75] C = [0, '2/3'] +register_dde_integrator('ralston2', Ralston2) + + class RK2(ExplicitRKIntegrator): def __init__(self, f, beta=2 / 3, var_type=None, dt=None, name=None, show_code=False): self.A = [(), (beta,)] @@ -98,43 +109,67 @@ class RK2(ExplicitRKIntegrator): super(RK2, self).__init__(f=f, var_type=var_type, dt=dt, name=name, show_code=show_code) +register_dde_integrator('rk2', RK2) + + class RK3(ExplicitRKIntegrator): A = [(), (0.5,), (-1, 2)] B = ['1/6', '2/3', '1/6'] C = [0, 0.5, 1] +register_dde_integrator('rk3', RK3) + + class Heun3(ExplicitRKIntegrator): A = [(), ('1/3',), (0, '2/3')] B = [0.25, 0, 0.75] C = [0, '1/3', '2/3'] +register_dde_integrator('heun3', Heun3) + + class Ralston3(ExplicitRKIntegrator): A = [(), (0.5,), (0, 0.75)] B = ['2/9', '1/3', '4/9'] C = [0, 0.5, 0.75] +register_dde_integrator('ralston3', Ralston3) + + class SSPRK3(ExplicitRKIntegrator): A = [(), (1,), (0.25, 0.25)] B = ['1/6', '1/6', '2/3'] C = [0, 1, 0.5] +register_dde_integrator('ssprk3', SSPRK3) + + class RK4(ExplicitRKIntegrator): A = [(), (0.5,), (0., 0.5), (0., 0., 1)] B = ['1/6', '1/3', '1/3', '1/6'] C = [0, 0.5, 0.5, 1] +register_dde_integrator('rk4', RK4) + + class Ralston4(ExplicitRKIntegrator): A = [(), (.4,), (.29697761, .15875964), (.21810040, -3.05096516, 3.83286476)] B = [.17476028, -.55148066, 1.20553560, .17118478] C = [0, .4, .45573725, 1] +register_dde_integrator('ralston4', Ralston4) + + class RK4Rule38(ExplicitRKIntegrator): A = [(), ('1/3',), ('-1/3', '1'), (1, -1, 1)] B = [0.125, 0.375, 0.375, 0.125] C = [0, '1/3', '2/3', 1] + + +register_dde_integrator('rk4_38rule', RK4Rule38) diff --git a/brainpy/integrators/dde/generic.py b/brainpy/integrators/dde/generic.py index 8eb5a0ec..29087725 100644 --- a/brainpy/integrators/dde/generic.py +++ b/brainpy/integrators/dde/generic.py @@ -1,7 +1,6 @@ # -*- coding: utf-8 -*- from .base import DDEIntegrator -from .explicit_rk import * __all__ = [ 'ddeint', @@ -12,19 +11,6 @@ __all__ = [ ] name2method = { - # explicit RK - 'euler': Euler, 'Euler': Euler, - 'midpoint': MidPoint, 'MidPoint': MidPoint, - 'heun2': Heun2, 'Heun2': Heun2, - 'ralston2': Ralston2, 'Ralston2': Ralston2, - 'rk2': RK2, 'RK2': RK2, - 'rk3': RK3, 'RK3': RK3, - 'heun3': Heun3, 'Heun3': Heun3, - 'ralston3': Ralston3, 'Ralston3': Ralston3, - 'ssprk3': SSPRK3, 'SSPRK3': SSPRK3, - 'rk4': RK4, 'RK4': RK4, - 'ralston4': Ralston4, 'Ralston4': Ralston4, - 'rk4_38rule': RK4Rule38, 'RK4Rule38': RK4Rule38, } @@ -132,7 +118,7 @@ def register_dde_integrator(name, integrator): """ if name in name2method: raise ValueError(f'"{name}" has been registered in DDE integrators.') - if DDEIntegrator not in integrator.__bases__: + if not issubclass(integrator, DDEIntegrator): raise ValueError(f'"integrator" must be an instance of {DDEIntegrator.__name__}') name2method[name] = integrator diff --git a/brainpy/integrators/fde/Caputo.py b/brainpy/integrators/fde/Caputo.py new file mode 100644 index 00000000..ff62f7b4 --- /dev/null +++ b/brainpy/integrators/fde/Caputo.py @@ -0,0 +1,401 @@ +# -*- coding: utf-8 -*- + +""" +This module provides numerical methods for integrating Caputo fractional derivative equations. + +""" + +import jax.numpy as jnp +from jax.experimental.host_callback import id_tap + +from brainpy import check +import brainpy.math as bm +from brainpy.errors import UnsupportedError +from brainpy.integrators.constants import DT +from brainpy.integrators.utils import check_inits, format_args +from brainpy.tools.checking import check_integer +from .base import FDEIntegrator +from .generic import register_fde_integrator + +__all__ = [ + 'CaputoEuler', + 'CaputoL1Schema', +] + + +class CaputoEuler(FDEIntegrator): + r"""One-step Euler method for Caputo fractional differential equations. + + Given a fractional initial value problem, + + .. math:: + + D_{*}^{\alpha} y(t)=f(t, y(t)), \quad y^{(k)}(0)=y_{0}^{(k)}, \quad k=0,1, \ldots,\lceil\alpha\rceil-1 + + where the :math:`y_0^{(k)}` ay be arbitrary real numbers and where :math:`\alpha>0`. + :math:`D_{*}^{\alpha}` denotes the differential operator in the sense of Caputo, defined + by + + .. math:: + + D_{*}^{\alpha} z(t)=J^{n-\alpha} D^{n} z(t) + + where :math:`n:=\lceil\alpha\rceil` is the smallest integer :math:`\geqslant \alpha`, + Here :math:`D^n` is the usual differential operator of (integer) order :math:`n`, + and for :math:`\mu > 0`, :math:`J^{\mu}` is the Riemann–Liouville integral operator + of order :math:`\mu`, defined by + + .. math:: + + J^{\mu} z(t)=\frac{1}{\Gamma(\mu)} \int_{0}^{t}(t-u)^{\mu-1} z(u) \mathrm{d} u + + The one-step Euler method for fractional differential equation is defined as + + .. math:: + + y_{k+1} = y_0 + \frac{1}{\Gamma(\alpha)} \sum_{j=0}^{k} b_{j, k+1} f\left(t_{j}, y_{j}\right). + + where + + .. math:: + + b_{j, k+1}=\frac{h^{\alpha}}{\alpha}\left((k+1-j)^{\alpha}-(k-j)^{\alpha}\right). + + + Examples + -------- + + >>> import brainpy as bp + >>> + >>> a, b, c = 10, 28, 8 / 3 + >>> def lorenz(x, y, z, t): + >>> dx = a * (y - x) + >>> dy = x * (b - z) - y + >>> dz = x * y - c * z + >>> return dx, dy, dz + >>> + >>> duration = 30. + >>> dt = 0.005 + >>> inits = [1., 0., 1.] + >>> f = bp.fde.CaputoEuler(lorenz, alpha=0.97, num_step=int(duration / dt), inits=inits) + >>> runner = bp.integrators.IntegratorRunner(f, monitors=list('xyz'), dt=dt, inits=inits) + >>> runner.run(duration) + >>> + >>> import matplotlib.pyplot as plt + >>> plt.plot(runner.mon.x.flatten(), runner.mon.z.flatten()) + >>> plt.show() + + + Parameters + ---------- + f : callable + The derivative function. + alpha: int, float, jnp.ndarray, bm.ndarray, sequence + The fractional-order of the derivative function. Should be in the range of ``(0., 1.)``. + num_step: int + The total time step of the simulation. + inits: sequence + A sequence of the initial values for variables. + dt: float, int + The numerical precision. + name: str + The integrator name. + + References + ---------- + .. [1] Li, Changpin, and Fanhai Zeng. "The finite difference methods for fractional + ordinary differential equations." Numerical Functional Analysis and + Optimization 34.2 (2013): 149-179. + .. [2] Diethelm, Kai, Neville J. Ford, and Alan D. Freed. "Detailed error analysis + for a fractional Adams method." Numerical algorithms 36.1 (2004): 31-52. + """ + + def __init__(self, f, alpha, num_step, inits, dt=None, name=None): + super(CaputoEuler, self).__init__(f=f, alpha=alpha, dt=dt, name=name) + + # fractional order + if not jnp.all(jnp.logical_and(self.alpha < 1, self.alpha > 0)): + raise UnsupportedError(f'Only support the fractional order in (0, 1), ' + f'but we got {self.alpha}.') + + # memory length + check_integer(num_step, 'num_step', min_bound=1, allow_none=False) + self.num_step = num_step + + # initial values + self.inits = check_inits(inits, self.variables) + + # coefficients + from scipy.special import rgamma + rgamma_alpha = bm.asarray(rgamma(bm.as_numpy(self.alpha))) + ranges = bm.asarray([bm.arange(num_step + 1) for _ in self.variables]).T + coef = rgamma_alpha * bm.diff(bm.power(ranges, self.alpha), axis=0) + self.coef = bm.flip(coef, axis=0) + + # variable states + self.f_states = {v: bm.Variable(bm.zeros((num_step,) + self.inits[v].shape)) + for v in self.variables} + self.register_implicit_vars(self.f_states) + self.idx = bm.Variable(bm.asarray([1], dtype=bm.int32)) + + self.set_integral(self._integral_func) + + def _check_step(self, args, transform): + dt, t = args + if self.num_step * dt < t: + raise ValueError(f'The maximum number of step is {self.num_step}, ' + f'however, the current time {t} require a time ' + f'step number {t / dt}.') + + def _integral_func(self, *args, **kwargs): + # format arguments + all_args = format_args(args, kwargs, self.arguments) + dt = all_args.pop(DT, self.dt) + if check.is_checking(): + id_tap(self._check_step, (dt, all_args['t'])) + + # derivative values + devs = self.f(**all_args) + if len(self.variables) == 1: + if not isinstance(devs, (bm.ndarray, jnp.ndarray)): + raise ValueError('Derivative values must be a tensor when there ' + 'is only one variable in the equation.') + devs = {self.variables[0]: devs} + else: + if not isinstance(devs, (tuple, list)): + raise ValueError('Derivative values must be a list/tuple of tensors ' + 'when there are multiple variables in the equation.') + devs = {var: devs[i] for i, var in enumerate(self.variables)} + + # function states + for key in self.variables: + self.f_states[key][self.idx[0]] = devs[key] + + # integral results + integrals = [] + idx = ((self.num_step - 1 - self.idx) + bm.arange(self.num_step)) % self.num_step + for i, key in enumerate(self.variables): + integral = self.inits[key] + self.coef[idx, i] @ self.f_states[key] + integrals.append(integral * (dt ** self.alpha[i] / self.alpha[i])) + self.idx.value = (self.idx + 1) % self.num_step + + # return integrals + if len(self.variables) == 1: + return integrals[0] + else: + return integrals + + +register_fde_integrator(name='CaputoEuler', integrator=CaputoEuler) + + +class CaputoABM(FDEIntegrator): + """Adams-Bashforth-Moulton (ABM) Method for Caputo fractional differential equations. + + + """ + pass + + +class CaputoL1Schema(FDEIntegrator): + r"""The L1 scheme method for the numerical approximation of the Caputo + fractional-order derivative equations [3]_. + + For the fractional order :math:`0<\alpha<1`, let the fractional derivative of variable + :math:`x(t)` be + + .. math:: + + \frac{d^{\alpha} x}{d t^{\alpha}}=F(x, t) + + The Caputo definition of the fractional derivative for variable :math:`x` is + + .. math:: + + \frac{d^{\alpha} x}{d t^{\alpha}}=\frac{1}{\Gamma(1-\alpha)} \int_{0}^{t} \frac{x^{\prime}(u)}{(t-u)^{\alpha}} d u + + where :math:`\Gamma` is the Gamma function. + + The fractional-order derivative is capable of integrating the activity of the + function over all past activities weighted by a function that follows a power-law. + Using one of the numerical methods, the L1 scheme method [3]_, the numerical + approximation of the fractional-order derivative of :math:`x` is + + .. math:: + + \frac{d^{\alpha} \chi}{d t^{\alpha}} \approx \frac{(d t)^{-\alpha}}{\Gamma(2-\alpha)}\left[\sum_{k=0}^{N-1}\left[x\left(t_{k+1}\right)- + \mathrm{x}\left(t_{k}\right)\right]\left[(N-k)^{1-\alpha}-(N-1-k)^{1-\alpha}\right]\right] + + Therefore, the numerical solution of original system is given by + + .. math:: + + x\left(t_{N}\right) \approx d t^{\alpha} \Gamma(2-\alpha) F(x, t)+x\left(t_{N-1}\right)- + \left[\sum_{k=0}^{N-2}\left[x\left(t_{k+1}\right)-x\left(t_{k}\right)\right]\left[(N-k)^{1-\alpha}-(N-1-k)^{1-\alpha}\right]\right] + + Hence, the solution of the fractional-order derivative can be described as the + difference between the *Markov term* and the *memory trace*. The *Markov term* + weighted by the gamma function is + + .. math:: + + \text { Markov term }=d t^{\alpha} \Gamma(2-\alpha) F(x, t)+x\left(t_{N-1}\right) + + The memory trace (:math:`x`-memory trace since it is related to variable :math:`x`) is + + .. math:: + + \text { Memory trace }=\sum_{k=0}^{N-2}\left[x\left(t_{k+1}\right)-x\left(t_{k}\right)\right]\left[(N-k)^{1-\alpha}-(N-(k+1))^{1-\alpha}\right] + + The memory trace integrates all the past activity and captures the long-term + history of the system. For :math:`\alpha=1`, the memory trace is 0 for any + time :math:`t`. When the fractional order :math:`\alpha` is decreased from 1, + the memory trace non-linearly increases from 0, and its dynamics strongly + depends on time. Thus, the fractional order dynamics strongly deviates + from the first order dynamics. + + + Examples + -------- + + >>> import brainpy as bp + >>> + >>> a, b, c = 10, 28, 8 / 3 + >>> def lorenz(x, y, z, t): + >>> dx = a * (y - x) + >>> dy = x * (b - z) - y + >>> dz = x * y - c * z + >>> return dx, dy, dz + >>> + >>> duration = 30. + >>> dt = 0.005 + >>> inits = [1., 0., 1.] + >>> f = bp.fde.CaputoL1Schema(lorenz, alpha=0.99, num_step=int(duration / dt), inits=inits) + >>> runner = bp.integrators.IntegratorRunner(f, monitors=list('xz'), dt=dt, inits=inits) + >>> runner.run(duration) + >>> + >>> import matplotlib.pyplot as plt + >>> plt.plot(runner.mon.x.flatten(), runner.mon.z.flatten()) + >>> plt.show() + + + Parameters + ---------- + f : callable + The derivative function. + alpha: int, float, jnp.ndarray, bm.ndarray, sequence + The fractional-order of the derivative function. Should be in the range of ``(0., 1.]``. + num_step: int + The total time step of the simulation. + inits: sequence + A sequence of the initial values for variables. + dt: float, int + The numerical precision. + name: str + The integrator name. + + References + ---------- + .. [3] Oldham, K., & Spanier, J. (1974). The fractional calculus theory + and applications of differentiation and integration to arbitrary + order. Elsevier. + """ + + def __init__(self, f, alpha, num_step, inits, dt=None, name=None): + super(CaputoL1Schema, self).__init__(f=f, alpha=alpha, dt=dt, name=name) + + # fractional order + if not jnp.all(jnp.logical_and(self.alpha <= 1, self.alpha > 0)): + raise UnsupportedError(f'Only support the fractional order in (0, 1), ' + f'but we got {self.alpha}.') + from scipy.special import gamma + self.gamma_alpha = bm.asarray(gamma(bm.as_numpy(2 - self.alpha))) + + # memory length + check_integer(num_step, 'num_step', min_bound=1, allow_none=False) + self.num_step = num_step + + # initial values + inits = check_inits(inits, self.variables) + self.inits = {v: bm.Variable(inits[v]) for v in self.variables} + self.register_implicit_vars(self.inits) + + # coefficients + ranges = bm.asarray([bm.arange(1, num_step + 2) for _ in self.variables]).T + coef = bm.diff(bm.power(ranges, 1 - self.alpha), axis=0) + self.coef = bm.flip(coef, axis=0) + + # variable states + self.diff_states = {v + "_diff": bm.Variable(bm.zeros((num_step,) + self.inits[v].shape)) + for v in self.variables} + self.register_implicit_vars(self.diff_states) + self.idx = bm.Variable(bm.asarray([self.num_step - 1], dtype=bm.int32)) + + # integral function + self.set_integral(self._integral_func) + + def hists(self, var=None, numpy=True): + if var is None: + hists_ = {k: bm.vstack([self.inits[k], self.diff_states[k + '_diff']]) + for k in self.variables} + hists_ = {k: bm.cumsum(v, axis=0) for k, v in hists_.items()} + if numpy: + hists_ = {k: v.numpy() for k, v in hists_} + return hists_ + else: + assert var in self.variables, (f'"{var}" is not defined in equation ' + f'variables: {self.variables}') + hists_ = bm.vstack([self.inits[var], self.diff_states[var + '_diff']]) + hists_ = bm.cumsum(hists_, axis=0) + if numpy: + hists_ = hists_.numpy() + return hists_ + + def _check_step(self, args, transform): + dt, t = args + if self.num_step * dt < t: + raise ValueError(f'The maximum number of step is {self.num_step}, ' + f'however, the current time {t} require a time ' + f'step number {t / dt}.') + + def _integral_func(self, *args, **kwargs): + # format arguments + all_args = format_args(args, kwargs, self.arguments) + dt = all_args.pop(DT, self.dt) + if check.is_checking(): + id_tap(self._check_step, (dt, all_args['t'])) + + # derivative values + devs = self.f(**all_args) + if len(self.variables) == 1: + if not isinstance(devs, (bm.ndarray, jnp.ndarray)): + raise ValueError('Derivative values must be a tensor when there ' + 'is only one variable in the equation.') + devs = {self.variables[0]: devs} + else: + if not isinstance(devs, (tuple, list)): + raise ValueError('Derivative values must be a list/tuple of tensors ' + 'when there are multiple variables in the equation.') + devs = {var: devs[i] for i, var in enumerate(self.variables)} + + # integral results + integrals = [] + idx = ((self.num_step - 1 - self.idx) + bm.arange(self.num_step)) % self.num_step + for i, key in enumerate(self.variables): + self.diff_states[key + '_diff'][self.idx[0]] = all_args[key] - self.inits[key] + self.inits[key].value = all_args[key] + markov_term = dt ** self.alpha[i] * self.gamma_alpha[i] * devs[key] + all_args[key] + memory_trace = self.coef[idx, i] @ self.diff_states[key + '_diff'] + integral = markov_term - memory_trace + integrals.append(integral) + self.idx.value = (self.idx + 1) % self.num_step + + # return integrals + if len(self.variables) == 1: + return integrals[0] + else: + return integrals + + +register_fde_integrator(name='CaputoL1', integrator=CaputoL1Schema) +register_fde_integrator(name='CaputoL1Schema', integrator=CaputoL1Schema) diff --git a/brainpy/integrators/fde/GL.py b/brainpy/integrators/fde/GL.py new file mode 100644 index 00000000..fa234237 --- /dev/null +++ b/brainpy/integrators/fde/GL.py @@ -0,0 +1,190 @@ +# -*- coding: utf-8 -*- + +""" +This module provides numerical solvers for Grünwald–Letnikov derivative FDEs. +""" + +import jax.numpy as jnp + +import brainpy.math as bm +from brainpy.errors import UnsupportedError +from brainpy.integrators.constants import DT +from brainpy.tools.checking import check_integer +from .base import FDEIntegrator +from brainpy.integrators.utils import check_inits, format_args + +__all__ = [ + 'GLShortMemory' +] + + +class GLShortMemory(FDEIntegrator): + r"""Efficient Computation of the Short-Memory Principle in Grünwald-Letnikov Method [1]_. + + According to the explicit numerical approximation of Grünwald-Letnikov, the + fractional-order derivative :math:`q` for a discrete function :math:`f(t_K)` + can be described as follows: + + .. math:: + + {{}_{k-\frac{L_{m}}{h}}D_{t_{k}}^{q}}f(t_{k})\approx h^{-q} + \sum\limits_{j=0}^{k}C_{j}^{q}f(t_{k-j}) + + where :math:`L_{m}` is the memory lenght, :math:`h` is the integration step size, + and :math:`C_{j}^{q}` are the binomial coefficients which are calculated recursively with + + .. math:: + + C_{0}^{q}=1,\ C_{j}^{q}=\left(1- \frac{1+q}{j}\right)C_{j-1}^{q},\ j=1,2, \ldots k. + + Then, the numerical solution for a fractional-order differential equation (FODE) expressed + in the form + + .. math:: + + D_{t_{k}}^{q}x(t_{k})=f(x(t_{k})) + + can be obtained by + + .. math:: + + x(t_{k})=f(x(t_{k-1}))h^{q}- \sum\limits_{j=1}^{k}C_{j}^{q}x(t_{k-j}). + + for :math:`0 < q < 1`. The above expression requires infinity memory length + for numerical solution since the summation term depends on the discritized + time :math:`t_k`. This implies relatively high simulation times. + + To reduce the computational time, the upper bound of summation needs to be modified by + :math:`k=v`, where + + .. math:: + + v=\begin{cases} k, & k\leq M,\\ L_{m}, & k > M. \end{cases} + + This is known as the short-memory principle, where :math:`M` + is the memory window with a width defined by :math:`M=\frac{L_{m}}{h}`. + As was reported in [2]_, the accuracy increases by increaing the width of memory window. + + Examples + -------- + + >>> import brainpy as bp + >>> + >>> a, b, c = 10, 28, 8 / 3 + >>> def lorenz(x, y, z, t): + >>> dx = a * (y - x) + >>> dy = x * (b - z) - y + >>> dz = x * y - c * z + >>> return dx, dy, dz + >>> + >>> integral = bp.fde.GLShortMemory(lorenz, + >>> alpha=0.96, + >>> num_memory=500, + >>> inits=[1., 0., 1.]) + >>> runner = bp.integrators.IntegratorRunner(integral, + >>> monitors=list('xyz'), + >>> inits=[1., 0., 1.], + >>> dt=0.005) + >>> runner.run(100.) + >>> + >>> import matplotlib.pyplot as plt + >>> plt.plot(runner.mon.x.flatten(), runner.mon.z.flatten()) + >>> plt.show() + + + Parameters + ---------- + f : callable + The derivative function. + alpha: int, float, jnp.ndarray, bm.ndarray, sequence + The fractional-order of the derivative function. Should be in the range of ``(0., 1.)``. + num_memory: int + The length of the short memory. + inits: sequence + A sequence of the initial values for variables. + dt: float, int + The numerical precision. + name: str + The integrator name. + + References + ---------- + .. [1] Clemente-López, D., et al. "Efficient computation of the + Grünwald-Letnikov method for arm-based implementations of + fractional-order chaotic systems." 2019 8th International + Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2019. + .. [2] M. F. Tolba, A. M. AbdelAty, N. S. Soliman, L. A. Said, A. H. + Madian, A. T. Azar, et al., "FPGA implementation of two fractional + order chaotic systems", International Journal of Electronics and + Communications, vol. 78, pp. 162-172, 2017. + """ + + def __init__(self, f, alpha, num_memory, inits, dt=None, name=None): + super(GLShortMemory, self).__init__(f=f, alpha=alpha, dt=dt, name=name) + + # fractional order + if not jnp.all(jnp.logical_and(self.alpha <= 1, self.alpha > 0)): + raise UnsupportedError(f'Only support the fractional order in (0, 1), ' + f'but we got {self.alpha}.') + + # memory length + check_integer(num_memory, 'num_memory', min_bound=1, allow_none=False) + self.num_memory = num_memory + + # initial values + self.inits = check_inits(inits, self.variables) + + # delays + self.delays = {} + for key, val in self.inits.items(): + delay = bm.Variable(bm.zeros((self.num_memory,) + val.shape, dtype=bm.float_)) + delay[0] = val + self.delays[key] = delay + self._idx = bm.Variable(bm.asarray([1], dtype=bm.int32)) + self.register_implicit_vars(self.delays) + + # binomial coefficients + bc = (1 - (1 + self.alpha.reshape((-1, 1))) / jnp.arange(1, num_memory + 1)) + bc = jnp.cumprod(jnp.vstack([jnp.ones_like(self.alpha), bc.T]), axis=0) + self._binomial_coef = jnp.flip(bc[1:], axis=0) + + # integral function + self.set_integral(self._integral_func) + + @property + def binomial_coef(self): + return bm.as_numpy(jnp.flip(self._binomial_coef, axis=0)) + + def _integral_func(self, *args, **kwargs): + # format arguments + all_args = format_args(args, kwargs, self.arguments) + dt = all_args.pop(DT, self.dt) + + # derivative values + devs = self.f(**all_args) + if len(self.variables) == 1: + if not isinstance(devs, (bm.ndarray, jnp.ndarray)): + raise ValueError('Derivative values must be a tensor when there ' + 'is only one variable in the equation.') + devs = {self.variables[0]: devs} + else: + if not isinstance(devs, (tuple, list)): + raise ValueError('Derivative values must be a list/tuple of tensors ' + 'when there are multiple variables in the equation.') + devs = {var: devs[i] for i, var in enumerate(self.variables)} + + # integral results + integrals = [] + idx = (self._idx + bm.arange(self.num_memory)) % self.num_memory + for i, var in enumerate(self.variables): + summation = self._binomial_coef[:, i] @ self.delays[var][idx] + integral = (dt ** self.alpha[i]) * devs[var] - summation + self.delays[var][self._idx[0]] = integral + integrals.append(integral) + self._idx.value = (self._idx + 1) % self.num_memory + + # return integrals + if len(self.variables) == 1: + return integrals[0] + else: + return integrals diff --git a/brainpy/integrators/fde/RL.py b/brainpy/integrators/fde/RL.py deleted file mode 100644 index c76eafc6..00000000 --- a/brainpy/integrators/fde/RL.py +++ /dev/null @@ -1,95 +0,0 @@ -# -*- coding: utf-8 -*- - -import jax.numpy as jnp -from jax import vmap -from jax.lax import cond - -from brainpy.math.special import Gamma -from brainpy.tools.checking import check_float - -__all__ = [ - 'RL', -] - - -def RLcoeffs(index_k, index_j, alpha): - """Calculates coefficients for the RL differintegral operator. - - see Baleanu, D., Diethelm, K., Scalas, E., and Trujillo, J.J. (2012). Fractional - Calculus: Models and Numerical Methods. World Scientific. - """ - - def f1(x): - k, j = x - return ((k - 1) ** (1 - alpha) - - (k + alpha - 1) * k ** -alpha) - - def f2(x): - k, j = x - return cond(k == j, lambda _: 1., f3, x) - - def f3(x): - k, j = x - return ((k - j + 1) ** (1 - alpha) + - (k - j - 1) ** (1 - alpha) - - 2 * (k - j) ** (1 - alpha)) - - return cond(index_j == 0, f1, f2, (index_k, index_j)) - - -def RLmatrix(alpha, N): - """ Define the coefficient matrix for the RL algorithm. """ - ij = jnp.tril_indices(N, -1) - coeff = vmap(RLcoeffs, in_axes=(0, 0, None))(ij[0], ij[1], alpha) - mat = jnp.zeros((N, N)).at[ij].set(coeff) - diagonal = jnp.arange(N) - mat = mat.at[diagonal, diagonal].set(1.) - return mat / Gamma(2 - alpha) - - -def RL(alpha, f, domain_start=0.0, domain_end=1.0, dt=0.01): - """ Calculate the RL algorithm using a trapezoid rule over - an array of function values. - - Examples - -------- - - >>> RL_sqrt = RL(0.5, lambda x: x ** 0.5) - >>> RL_poly = RL(0.5, lambda x: x**2 - 1, 0., 1., 100) - - Parameters - ---------- - alpha : float - The order of the differintegral to be computed. - f : function - This is the function that is to be differintegrated. - domain_start : float, int - The left-endpoint of the function domain. Default value is 0. - domain_end : float, int - The right-endpoint of the function domain; the point at which the - differintegral is being evaluated. Default value is 1. - dt : float, int - The number of points in the domain. Default value is 100. - - Returns - ------- - RL : float 1d-array - Each element of the array is the RL differintegral evaluated at the - corresponding function array index. - """ - # checking - assert domain_start < domain_end, ('"domain_start" should be lower than "domain_end", ' - f'while we got {domain_start} >= {domain_end}') - check_float(alpha, 'alpha', allow_none=False) - check_float(domain_start, 'domain_start', allow_none=False) - check_float(domain_end, 'domain_start', allow_none=False) - check_float(dt, 'dt', allow_none=False) - # computing - points = jnp.arange(domain_start, domain_end, dt) - f_values = vmap(f)(points) - # Calculate the RL differintegral. - D = RLmatrix(alpha, points.shape[0]) - RL = dt ** -alpha * jnp.dot(D, f_values) - return RL - - diff --git a/brainpy/integrators/fde/__init__.py b/brainpy/integrators/fde/__init__.py index 40a96afc..df31e4f3 100644 --- a/brainpy/integrators/fde/__init__.py +++ b/brainpy/integrators/fde/__init__.py @@ -1 +1,8 @@ # -*- coding: utf-8 -*- + +from .base import * +from .generic import * +from .GL import * +from .Caputo import * + + diff --git a/brainpy/integrators/fde/base.py b/brainpy/integrators/fde/base.py index 5b8dc22e..f3207128 100644 --- a/brainpy/integrators/fde/base.py +++ b/brainpy/integrators/fde/base.py @@ -1,8 +1,82 @@ # -*- coding: utf-8 -*- -from ..base import Integrator +import abc +from typing import Union, Callable + +import jax.numpy as jnp +import brainpy.math as bm +from brainpy.integrators.base import Integrator +from brainpy.integrators.utils import get_args +from brainpy.errors import UnsupportedError + + +__all__ = [ + 'FDEIntegrator' +] class FDEIntegrator(Integrator): - pass + """Numerical integrator for fractional differential equations (FEDs). + + Parameters + ---------- + f : callable + The derivative function. + alpha: int, float, jnp.ndarray, bm.ndarray, sequence + The fractional-order of the derivative function. + dt: float, int + The numerical precision. + name: str + The integrator name. + """ + + """The fraction order for each variable.""" + alpha: jnp.ndarray + + """The numerical integration precision.""" + dt: Union[float, int] + + """The fraction derivative function.""" + f: Callable + + def __init__(self, f, alpha, dt=None, name=None): + dt = bm.get_dt() if dt is None else dt + parses = get_args(f) + variables = parses[0] # variable names, (before 't') + parameters = parses[1] # parameter names, (after 't') + arguments = parses[2] # function arguments + + # super initialization + super(FDEIntegrator, self).__init__(name=name, + variables=variables, + parameters=parameters, + arguments=arguments, + dt=dt) + + # derivative function + self.f = f + + # fractional-order + if isinstance(alpha, (int, float)): + alpha = jnp.ones(len(self.variables)) * alpha + elif isinstance(alpha, (jnp.ndarray, bm.ndarray)): + alpha = bm.as_device_array(alpha) + elif isinstance(alpha, (list, tuple)): + for a in alpha: + assert isinstance(a, (float, int)), (f'Must be a tuple/list of int/float, ' + f'but we got {type(a)}: {a}') + alpha = jnp.asarray(alpha) + else: + raise UnsupportedError(f'Do not support {type(alpha)}, please ' + f'set fractional-order as number/tuple/list/tensor.') + if len(alpha) != len(self.variables): + raise ValueError(f'There are {len(self.variables)} variables, ' + f'while we only got {len(alpha)} fractional-order ' + f'settings: {alpha}') + self.alpha = alpha + + @abc.abstractmethod + def build(self): + raise NotImplementedError('Must implement how to build your step function.') + diff --git a/brainpy/integrators/fde/generic.py b/brainpy/integrators/fde/generic.py new file mode 100644 index 00000000..07d6b17d --- /dev/null +++ b/brainpy/integrators/fde/generic.py @@ -0,0 +1,92 @@ +# -*- coding: utf-8 -*- + +from .base import FDEIntegrator + +__all__ = [ + 'fdeint', + 'set_default_fdeint', + 'get_default_fdeint', + 'register_fde_integrator', + 'get_supported_methods', +] + +name2method = { +} + +_DEFAULT_DDE_METHOD = 'CaputoL1' + + +def fdeint(f=None, method='CaputoL1', **kwargs): + """Numerical integration for FDEs. + + Parameters + ---------- + f : callable, function + The derivative function. + method : str + The shortcut name of the numerical integrator. + + Returns + ------- + integral : FDEIntegrator + The numerical solver of `f`. + """ + method = _DEFAULT_DDE_METHOD if method is None else method + if method not in name2method: + raise ValueError(f'Unknown FDE numerical method "{method}". Currently ' + f'BrainPy only support: {list(name2method.keys())}') + + if f is None: + return lambda f: name2method[method](f, **kwargs) + else: + return name2method[method](f, **kwargs) + + +def set_default_fdeint(method): + """Set the default ODE numerical integrator method for differential equations. + + Parameters + ---------- + method : str, callable + Numerical integrator method. + """ + if not isinstance(method, str): + raise ValueError(f'Only support string, not {type(method)}.') + if method not in name2method: + raise ValueError(f'Unsupported ODE_INT numerical method: {method}.') + + global _DEFAULT_DDE_METHOD + _DEFAULT_ODE_METHOD = method + + +def get_default_fdeint(): + """Get the default ODE numerical integrator method. + + Returns + ------- + method : str + The default numerical integrator method. + """ + return _DEFAULT_DDE_METHOD + + +def register_fde_integrator(name, integrator): + """Register a new ODE integrator. + + Parameters + ---------- + name: ste + The integrator name. + integrator: type + The integrator. + """ + if name in name2method: + raise ValueError(f'"{name}" has been registered in ODE integrators.') + if not issubclass(integrator, FDEIntegrator): + raise ValueError(f'"integrator" must be an instance of {FDEIntegrator.__name__}') + name2method[name] = integrator + + +def get_supported_methods(): + """Get all supported numerical methods for DDEs.""" + return list(name2method.keys()) diff --git a/brainpy/integrators/fde/tests/test_Caputo.py b/brainpy/integrators/fde/tests/test_Caputo.py new file mode 100644 index 00000000..9db813a2 --- /dev/null +++ b/brainpy/integrators/fde/tests/test_Caputo.py @@ -0,0 +1,33 @@ +# -*- coding: utf-8 -*- + + +import unittest + +import numpy as np + +import brainpy as bp + + +class TestCaputoL1(unittest.TestCase): + def test1(self): + bp.math.enable_x64() + alpha = 0.9 + intg = bp.fde.CaputoL1Schema(lambda a, t: a, + alpha=alpha, + num_step=10, + inits=[1., ]) + for N in [2, 3, 4, 5, 6, 7, 8]: + diff = np.random.rand(N - 1, 1) + memory_trace = 0 + for i in range(N - 1): + c = (N - i) ** (1 - alpha) - (N - i - 1) ** (1 - alpha) + memory_trace += c * diff[i] + + intg.idx[0] = N - 1 + intg.diff_states['a_diff'][:N - 1] = bp.math.asarray(diff) + idx = ((intg.num_step - intg.idx) + np.arange(intg.num_step)) % intg.num_step + memory_trace2 = intg.coef[idx, 0] @ intg.diff_states['a_diff'] + + print() + print(memory_trace[0], ) + print(memory_trace2[0], bp.math.array_equal(memory_trace[0], memory_trace2[0])) diff --git a/brainpy/integrators/fde/tests/test_GL.py b/brainpy/integrators/fde/tests/test_GL.py new file mode 100644 index 00000000..fed98ac6 --- /dev/null +++ b/brainpy/integrators/fde/tests/test_GL.py @@ -0,0 +1,32 @@ +# -*- coding: utf-8 -*- + + +import unittest +import matplotlib.pyplot as plt +import brainpy as bp + + +class TestGLShortMemory(unittest.TestCase): + def test_lorenz(self): + a, b, c = 10, 28, 8 / 3 + + def lorenz(x, y, z, t): + dx = a * (y - x) + dy = x * (b - z) - y + dz = x * y - c * z + return dx, dy, dz + + integral = bp.fde.GLShortMemory(lorenz, + alpha=0.99, + num_memory=500, + inits=[1., 0., 1.]) + runner = bp.integrators.IntegratorRunner(integral, + monitors=list('xyz'), + inits=[1., 0., 1.], + dt=0.005) + runner.run(100.) + + plt.plot(runner.mon.x.flatten(), runner.mon.z.flatten()) + plt.show(block=False) + + diff --git a/brainpy/integrators/fde/tests/test_RL.py b/brainpy/integrators/fde/tests/test_RL.py deleted file mode 100644 index b032073b..00000000 --- a/brainpy/integrators/fde/tests/test_RL.py +++ /dev/null @@ -1,16 +0,0 @@ -# -*- coding: utf-8 -*- - -import unittest -from brainpy.integrators.fde.RL import RLmatrix -import brainpy.math as bm - - -class TestRLAlgorithm(unittest.TestCase): - def test_RL_matrix_shape(self): - bm.enable_x64() - print() - print(RLmatrix(0.4, 5)) - self.assertTrue(RLmatrix(0.4, 10).shape == (10, 10)) - self.assertTrue(RLmatrix(1.2, 5).shape == (5, 5)) - - diff --git a/brainpy/integrators/joint_eq.py b/brainpy/integrators/joint_eq.py index 59c0ebdb..b98f6c9d 100644 --- a/brainpy/integrators/joint_eq.py +++ b/brainpy/integrators/joint_eq.py @@ -153,7 +153,7 @@ class JointEq(object): for par in args[len(vars) + 1:]: if (par not in vars_in_eqs) and (par not in all_arg_pars) and (par not in all_kwarg_pars): all_arg_pars.append(par) - for key, value in kwargs.values(): + for key, value in kwargs.items(): if key in all_kwarg_pars and value != all_kwarg_pars[key]: raise errors.DiffEqError(f'We got two different default value of "{key}": ' f'{all_kwarg_pars[key]} != {value}') diff --git a/brainpy/integrators/ode/adaptive_rk.py b/brainpy/integrators/ode/adaptive_rk.py index d6a60626..40462bcb 100644 --- a/brainpy/integrators/ode/adaptive_rk.py +++ b/brainpy/integrators/ode/adaptive_rk.py @@ -58,6 +58,7 @@ from brainpy import errors from brainpy.integrators import constants as C, utils from brainpy.integrators.ode import common from brainpy.integrators.ode.base import ODEIntegrator +from .generic import register_ode_integrator __all__ = [ 'AdaptiveRKIntegrator', @@ -239,6 +240,9 @@ class RKF12(AdaptiveRKIntegrator): C = [0, 0.5, 1] +register_ode_integrator('rkf12', RKF12) + + class RKF45(AdaptiveRKIntegrator): r"""The Runge–Kutta–Fehlberg method for ODEs. @@ -285,6 +289,9 @@ class RKF45(AdaptiveRKIntegrator): C = [0, 0.25, 0.375, '12/13', 1, '1/3'] +register_ode_integrator('rkf45', RKF45) + + class DormandPrince(AdaptiveRKIntegrator): r"""The Dormand–Prince method for ODEs. @@ -336,6 +343,9 @@ class DormandPrince(AdaptiveRKIntegrator): C = [0, 0.2, 0.3, 0.8, '8/9', 1, 1] +register_ode_integrator('rkdp', DormandPrince) + + class CashKarp(AdaptiveRKIntegrator): r"""The Cash–Karp method for ODEs. @@ -384,6 +394,9 @@ class CashKarp(AdaptiveRKIntegrator): C = [0, 0.2, 0.3, 0.6, 1, 0.875] +register_ode_integrator('ck', CashKarp) + + class BogackiShampine(AdaptiveRKIntegrator): r"""The Bogacki–Shampine method for ODEs. @@ -427,6 +440,9 @@ class BogackiShampine(AdaptiveRKIntegrator): C = [0, 0.5, 0.75, 1] +register_ode_integrator('bs', BogackiShampine) + + class HeunEuler(AdaptiveRKIntegrator): r"""The Heun–Euler method for ODEs. @@ -457,6 +473,9 @@ class HeunEuler(AdaptiveRKIntegrator): C = [0, 1] +register_ode_integrator('heun_euler', HeunEuler) + + class DOP853(AdaptiveRKIntegrator): # def DOP853(f=None, tol=None, adaptive=None, dt=None, show_code=None, each_var_is_scalar=None): r"""The DOP853 method for ODEs. @@ -473,3 +492,23 @@ class DOP853(AdaptiveRKIntegrator): .. [2] http://www.unige.ch/~hairer/software.html """ pass + + +class BoSh3(AdaptiveRKIntegrator): + """ + Bogacki--Shampine's 3/2 method. + + 3rd order explicit Runge--Kutta method. Has an embedded 2nd order method for + adaptive step sizing. + + """ + A = [(), + (0.5,), + (0.0, 0.75), + ('2/9', '1/3', '4/9')] + B1 = ['2/9', '1/3', '4/9', 0.0] + B2 = ['-5/72', 1 / 12, '1/9', '-1/8'] + C = [0., 0.5, 0.75, 1.0] + + +register_ode_integrator('BoSh3', BoSh3) diff --git a/brainpy/integrators/ode/base.py b/brainpy/integrators/ode/base.py index 1003c12a..a487186b 100644 --- a/brainpy/integrators/ode/base.py +++ b/brainpy/integrators/ode/base.py @@ -21,6 +21,17 @@ def f_names(f): class ODEIntegrator(Integrator): """Numerical Integrator for Ordinary Differential Equations (ODEs). + + Parameters + ---------- + f : callable + The derivative function. + var_type: str + The type for each variable. + dt: float, int + The numerical precision. + name: str + The integrator name. """ def __init__(self, f, var_type=None, dt=None, name=None, show_code=False): @@ -29,7 +40,7 @@ class ODEIntegrator(Integrator): parses = utils.get_args(f) variables = parses[0] # variable names, (before 't') parameters = parses[1] # parameter names, (after 't') - arguments = parses[2] # function arguments + arguments = parses[2] # function arguments # super initialization super(ODEIntegrator, self).__init__(name=name, diff --git a/brainpy/integrators/ode/explicit_rk.py b/brainpy/integrators/ode/explicit_rk.py index 3d71a0ea..ee54b900 100644 --- a/brainpy/integrators/ode/explicit_rk.py +++ b/brainpy/integrators/ode/explicit_rk.py @@ -70,6 +70,7 @@ More details please see references [2]_ [3]_ [4]_. from brainpy.integrators import constants as C, utils from brainpy.integrators.ode import common from brainpy.integrators.ode.base import ODEIntegrator +from .generic import register_ode_integrator __all__ = [ 'ExplicitRKIntegrator', @@ -247,6 +248,9 @@ class Euler(ExplicitRKIntegrator): C = [0] +register_ode_integrator('euler', Euler) + + class MidPoint(ExplicitRKIntegrator): r"""Explicit midpoint method for ODEs. @@ -341,6 +345,9 @@ class MidPoint(ExplicitRKIntegrator): C = [0, 0.5] +register_ode_integrator('midpoint', MidPoint) + + class Heun2(ExplicitRKIntegrator): r"""Heun's method for ODEs. @@ -406,6 +413,9 @@ class Heun2(ExplicitRKIntegrator): C = [0, 1] +register_ode_integrator('heun2', Heun2) + + class Ralston2(ExplicitRKIntegrator): r"""Ralston's method for ODEs. @@ -437,6 +447,9 @@ class Ralston2(ExplicitRKIntegrator): C = [0, '2/3'] +register_ode_integrator('ralston2', Ralston2) + + class RK2(ExplicitRKIntegrator): r"""Generic second order Runge-Kutta method for ODEs. @@ -560,6 +573,9 @@ class RK2(ExplicitRKIntegrator): super(RK2, self).__init__(f=f, var_type=var_type, dt=dt, name=name, show_code=show_code) +register_ode_integrator('rk2', RK2) + + class RK3(ExplicitRKIntegrator): r"""Classical third-order Runge-Kutta method for ODEs. @@ -598,6 +614,9 @@ class RK3(ExplicitRKIntegrator): C = [0, 0.5, 1] +register_ode_integrator('rk3', RK3) + + class Heun3(ExplicitRKIntegrator): r"""Heun's third-order method for ODEs. @@ -622,6 +641,9 @@ class Heun3(ExplicitRKIntegrator): C = [0, '1/3', '2/3'] +register_ode_integrator('heun3', Heun3) + + class Ralston3(ExplicitRKIntegrator): r"""Ralston's third-order method for ODEs. @@ -651,6 +673,9 @@ class Ralston3(ExplicitRKIntegrator): C = [0, 0.5, 0.75] +register_ode_integrator('ralston3', Ralston3) + + class SSPRK3(ExplicitRKIntegrator): r"""Third-order Strong Stability Preserving Runge-Kutta (SSPRK3). @@ -674,6 +699,9 @@ class SSPRK3(ExplicitRKIntegrator): C = [0, 1, 0.5] +register_ode_integrator('ssprk3', SSPRK3) + + class RK4(ExplicitRKIntegrator): r"""Classical fourth-order Runge-Kutta method for ODEs. @@ -741,6 +769,9 @@ class RK4(ExplicitRKIntegrator): C = [0, 0.5, 0.5, 1] +register_ode_integrator('rk4', RK4) + + class Ralston4(ExplicitRKIntegrator): r"""Ralston's fourth-order method for ODEs. @@ -772,6 +803,9 @@ class Ralston4(ExplicitRKIntegrator): C = [0, .4, .45573725, 1] +register_ode_integrator('ralston4', Ralston4) + + class RK4Rule38(ExplicitRKIntegrator): r"""3/8-rule fourth-order method for ODEs. @@ -811,3 +845,6 @@ class RK4Rule38(ExplicitRKIntegrator): A = [(), ('1/3',), ('-1/3', '1'), (1, -1, 1)] B = [0.125, 0.375, 0.375, 0.125] C = [0, '1/3', '2/3', 1] + + +register_ode_integrator('rk4_38rule', RK4Rule38) diff --git a/brainpy/integrators/ode/exponential.py b/brainpy/integrators/ode/exponential.py index 5042fb80..7a96b0be 100644 --- a/brainpy/integrators/ode/exponential.py +++ b/brainpy/integrators/ode/exponential.py @@ -113,6 +113,7 @@ from brainpy.base.collector import Collector from brainpy.integrators import constants as C, utils, joint_eq from brainpy.integrators.analysis_by_ast import separate_variables from brainpy.integrators.ode.base import ODEIntegrator +from .generic import register_ode_integrator try: import sympy @@ -506,6 +507,10 @@ class ExponentialEuler(ODEIntegrator): return s_df_part +register_ode_integrator('exponential_euler', ExponentialEuler) +register_ode_integrator('exp_euler', ExponentialEuler) + + class ExpEulerAuto(ODEIntegrator): """Exponential Euler method using automatic differentiation. @@ -762,3 +767,7 @@ class ExpEulerAuto(ODEIntegrator): return args[0] + dt * phi * derivative return [(integral, vars, pars), ] + + +register_ode_integrator('exp_euler_auto', ExpEulerAuto) +register_ode_integrator('exp_auto', ExpEulerAuto) diff --git a/brainpy/integrators/ode/generic.py b/brainpy/integrators/ode/generic.py index 9e1afeb3..50d3014e 100644 --- a/brainpy/integrators/ode/generic.py +++ b/brainpy/integrators/ode/generic.py @@ -1,9 +1,6 @@ # -*- coding: utf-8 -*- from .base import ODEIntegrator -from .adaptive_rk import * -from .explicit_rk import * -from .exponential import * __all__ = [ 'odeint', @@ -14,31 +11,6 @@ __all__ = [ ] name2method = { - # explicit RK - 'euler': Euler, 'Euler': Euler, - 'midpoint': MidPoint, 'MidPoint': MidPoint, - 'heun2': Heun2, 'Heun2': Heun2, - 'ralston2': Ralston2, 'Ralston2': Ralston2, - 'rk2': RK2, 'RK2': RK2, - 'rk3': RK3, 'RK3': RK3, - 'heun3': Heun3, 'Heun3': Heun3, - 'ralston3': Ralston3, 'Ralston3': Ralston3, - 'ssprk3': SSPRK3, 'SSPRK3': SSPRK3, - 'rk4': RK4, 'RK4': RK4, - 'ralston4': Ralston4, 'Ralston4': Ralston4, - 'rk4_38rule': RK4Rule38, 'RK4Rule38': RK4Rule38, - - # adaptive RK - 'rkf12': RKF12, 'RKF12': RKF12, - 'rkf45': RKF45, 'RKF45': RKF45, - 'rkdp': DormandPrince, 'dp': DormandPrince, 'DormandPrince': DormandPrince, - 'ck': CashKarp, 'CashKarp': CashKarp, - 'bs': BogackiShampine, 'BogackiShampine': BogackiShampine, - 'heun_euler': HeunEuler, 'HeunEuler': HeunEuler, - - # exponential integrators - 'exponential_euler': ExponentialEuler, 'exp_euler': ExponentialEuler, 'ExponentialEuler': ExponentialEuler, - 'exp_euler_auto': ExpEulerAuto, 'exp_auto': ExpEulerAuto, 'ExpEulerAuto': ExpEulerAuto, } _DEFAULT_DDE_METHOD = 'euler' @@ -134,7 +106,7 @@ def register_ode_integrator(name, integrator): """ if name in name2method: raise ValueError(f'"{name}" has been registered in ODE integrators.') - if ODEIntegrator not in integrator.__bases__: + if not issubclass(integrator, ODEIntegrator): raise ValueError(f'"integrator" must be an instance of {ODEIntegrator.__name__}') name2method[name] = integrator diff --git a/brainpy/integrators/runner.py b/brainpy/integrators/runner.py index dcba7428..f38ca8f5 100644 --- a/brainpy/integrators/runner.py +++ b/brainpy/integrators/runner.py @@ -93,7 +93,7 @@ class IntegratorRunner(Runner): >>> dt = 0.01; beta=2.; gamma=1.; tau=2.; n=9.65 >>> mg_eq = lambda x, t, xdelay: (beta * xdelay(t - tau) / (1 + xdelay(t - tau) ** n) >>> - gamma * x) - >>> xdelay = bm.FixedLenDelay(1, delay_len=tau, dt=dt, before_t0=lambda t: 1.2) + >>> xdelay = bm.TimeDelay(bm.asarray([1.2]), delay_len=tau, dt=dt, before_t0=lambda t: 1.2) >>> integral = bp.ddeint(mg_eq, method='rk4', state_delays={'x': xdelay}) >>> runner = bp.integrators.IntegratorRunner( >>> integral, diff --git a/brainpy/integrators/sde/generic.py b/brainpy/integrators/sde/generic.py index 05ffd9c2..36259d29 100644 --- a/brainpy/integrators/sde/generic.py +++ b/brainpy/integrators/sde/generic.py @@ -1,8 +1,6 @@ # -*- coding: utf-8 -*- from .base import SDEIntegrator -from .normal import * -from .srk_scalar import * __all__ = [ 'sdeint', @@ -13,15 +11,6 @@ __all__ = [ ] name2method = { - 'euler': Euler, 'Euler': Euler, - 'heun': Heun, 'Heun': Heun, - 'milstein': Milstein, 'Milstein': Milstein, - 'exponential_euler': ExponentialEuler, 'exp_euler': ExponentialEuler, 'ExponentialEuler': ExponentialEuler, - - # RK methods - 'srk1w1': SRK1W1, 'SRK1W1': SRK1W1, - 'srk2w1': SRK2W1, 'SRK2W1': SRK2W1, - 'klpl': KlPl, 'KlPl': KlPl, } _DEFAULT_SDE_METHOD = 'euler' @@ -98,7 +87,7 @@ def register_sde_integrator(name, integrator): """ if name in name2method: raise ValueError(f'"{name}" has been registered in SDE integrators.') - if SDEIntegrator not in integrator.__bases__: + if not issubclass(integrator, SDEIntegrator): raise ValueError(f'"integrator" must be an instance of {SDEIntegrator.__name__}') name2method[name] = integrator diff --git a/brainpy/integrators/sde/normal.py b/brainpy/integrators/sde/normal.py index d9e9ef73..ff296c05 100644 --- a/brainpy/integrators/sde/normal.py +++ b/brainpy/integrators/sde/normal.py @@ -6,6 +6,7 @@ from brainpy import errors, math from brainpy.integrators import constants, utils from brainpy.integrators.analysis_by_ast import separate_variables from brainpy.integrators.sde.base import SDEIntegrator +from .generic import register_sde_integrator try: import sympy @@ -142,6 +143,9 @@ class Euler(SDEIntegrator): func_name=self.func_name) +register_sde_integrator('euler', Euler) + + class Heun(Euler): def __init__(self, f, g, dt=None, name=None, show_code=False, var_type=None, intg_type=None, wiener_type=None): @@ -154,6 +158,9 @@ class Heun(Euler): self.build() +register_sde_integrator('heun', Heun) + + class Milstein(SDEIntegrator): def __init__(self, f, g, dt=None, name=None, show_code=False, var_type=None, intg_type=None, wiener_type=None): @@ -238,6 +245,9 @@ class Milstein(SDEIntegrator): func_name=self.func_name) +register_sde_integrator('milstein', Milstein) + + class ExponentialEuler(SDEIntegrator): r"""First order, explicit exponential Euler method. @@ -399,3 +409,7 @@ class ExponentialEuler(SDEIntegrator): if hasattr(self.derivative[constants.F], '__self__'): host = self.derivative[constants.F].__self__ self.integral = self.integral.__get__(host, host.__class__) + + +register_sde_integrator('exponential_euler', ExponentialEuler) +register_sde_integrator('exp_euler', ExponentialEuler) diff --git a/brainpy/integrators/sde/srk_scalar.py b/brainpy/integrators/sde/srk_scalar.py index c95164df..47535ed6 100644 --- a/brainpy/integrators/sde/srk_scalar.py +++ b/brainpy/integrators/sde/srk_scalar.py @@ -2,6 +2,7 @@ from brainpy.integrators import constants, utils from brainpy.integrators.sde.base import SDEIntegrator +from .generic import register_sde_integrator __all__ = [ 'SRK1W1', @@ -175,6 +176,9 @@ class SRK1W1(SDEIntegrator): func_name=self.func_name) +register_sde_integrator('srk1w1', SRK1W1) + + class SRK2W1(SDEIntegrator): r"""Order 1.5 Strong SRK Methods for SDEs with Scalar Noise. @@ -315,6 +319,9 @@ class SRK2W1(SDEIntegrator): func_name=self.func_name) +register_sde_integrator('srk2w1', SRK2W1) + + class KlPl(SDEIntegrator): def __init__(self, f, g, dt=None, name=None, show_code=False, var_type=None, intg_type=None, wiener_type=None): @@ -354,7 +361,7 @@ class KlPl(SDEIntegrator): self.code_lines.append(f' {var}_g1 = -{var}_I1 + {var}_I11/dt_sqrt + {var}_I10/{constants.DT}') self.code_lines.append(f' {var}_g2 = {var}_I11 / dt_sqrt') self.code_lines.append(f' {var}_new = {var} + {constants.DT} * {var}_f_H0s1 + ' - f'{var}_g1 * {var}_g_H1s1 + {var}_g2 * {var}_g_H1s2') + f'{var}_g1 * {var}_g_H1s1 + {var}_g2 * {var}_g_H1s2') self.code_lines.append(' ') # returns @@ -367,3 +374,6 @@ class KlPl(SDEIntegrator): code_lines=self.code_lines, show_code=self.show_code, func_name=self.func_name) + + +register_sde_integrator('klpl', KlPl) diff --git a/brainpy/integrators/utils.py b/brainpy/integrators/utils.py index b9e9a8ae..abbf3a29 100644 --- a/brainpy/integrators/utils.py +++ b/brainpy/integrators/utils.py @@ -4,12 +4,17 @@ import inspect from pprint import pprint +import brainpy.math as bm +from brainpy.errors import UnsupportedError + from brainpy import errors __all__ = [ 'get_args', 'check_kws', 'compile_code', + 'check_inits', + 'format_args', ] @@ -103,3 +108,35 @@ def compile_code(code_lines, code_scope, func_name, show_code=False): exec(compile(code, '', 'exec'), code_scope) new_f = code_scope[func_name] return new_f + + +def check_inits(inits, variables): + if isinstance(inits, (tuple, list)): + assert len(inits) == len(variables), (f'Then number of variables is {len(variables)}, ' + f'however we only got {len(inits)} initial values.') + inits = {v: inits[i] for i, v in enumerate(variables)} + elif isinstance(inits, dict): + assert len(inits) == len(variables), (f'Then number of variables is {len(variables)}, ' + f'however we only got {len(inits)} initial values.') + else: + raise UnsupportedError('Only supports dict/sequence of data for initial values. ' + f'But we got {type(inits)}: {inits}') + for key in list(inits.keys()): + if key not in variables: + raise ValueError(f'"{key}" is not defined in variables: {variables}') + val = inits[key] + if isinstance(val, (float, int)): + inits[key] = bm.asarray([val], dtype=bm.float_) + return inits + + +def format_args(args, kwargs, arguments): + all_args = dict() + for i, arg in enumerate(args): + all_args[arguments[i]] = arg + for key, arg in kwargs.items(): + if key in all_args: + raise ValueError(f'{key} has been provided in *args, ' + f'but we detect it again in **kwargs.') + all_args[key] = arg + return all_args diff --git a/brainpy/losses/__init__.py b/brainpy/losses/__init__.py index d59997a5..8dadd449 100644 --- a/brainpy/losses/__init__.py +++ b/brainpy/losses/__init__.py @@ -41,7 +41,7 @@ def _return(outputs, reduction): def cross_entropy_loss(logits, targets, weight=None, reduction='mean'): - """This criterion combines ``LogSoftmax`` and `NLLLoss`` in one single class. + r"""This criterion combines ``LogSoftmax`` and `NLLLoss`` in one single class. It is useful when training a classification problem with `C` classes. If provided, the optional argument :attr:`weight` should be a 1D `Tensor` @@ -120,7 +120,7 @@ def cross_entropy_loss(logits, targets, weight=None, reduction='mean'): def cross_entropy_sparse(logits, labels): - """Computes the softmax cross-entropy loss. + r"""Computes the softmax cross-entropy loss. Args: logits: (batch, ..., #class) tensor of logits. @@ -155,7 +155,7 @@ def cross_entropy_sigmoid(logits, labels): def l1_loos(logits, targets, reduction='sum'): - """Creates a criterion that measures the mean absolute error (MAE) between each element in + r"""Creates a criterion that measures the mean absolute error (MAE) between each element in the logits :math:`x` and targets :math:`y`. It is useful in regression problems. The unreduced (i.e. with :attr:`reduction` set to ``'none'``) loss can be described as: @@ -207,7 +207,7 @@ def l1_loos(logits, targets, reduction='sum'): def l2_loss(predicts, targets): - """Computes the L2 loss. + r"""Computes the L2 loss. The 0.5 term is standard in "Pattern Recognition and Machine Learning" by Bishop [1]_, but not "The Elements of Statistical Learning" by Tibshirani. @@ -246,7 +246,7 @@ def l2_norm(x): def mean_absolute_error(x, y, axis=None): - """Computes the mean absolute error between x and y. + r"""Computes the mean absolute error between x and y. Args: x: a tensor of shape (d0, .. dN-1). @@ -261,7 +261,7 @@ def mean_absolute_error(x, y, axis=None): def mean_squared_error(predicts, targets, axis=None): - """Computes the mean squared error between x and y. + r"""Computes the mean squared error between x and y. Args: predicts: a tensor of shape (d0, .. dN-1). @@ -276,7 +276,7 @@ def mean_squared_error(predicts, targets, axis=None): def mean_squared_log_error(y_true, y_pred, axis=None): - """Computes the mean squared logarithmic error between y_true and y_pred. + r"""Computes the mean squared logarithmic error between y_true and y_pred. Args: y_true: a tensor of shape (d0, .. dN-1). @@ -291,7 +291,7 @@ def mean_squared_log_error(y_true, y_pred, axis=None): def huber_loss(predicts, targets, delta: float = 1.0): - """Huber loss. + r"""Huber loss. Huber loss is similar to L2 loss close to zero, L1 loss away from zero. If gradient descent is applied to the `huber loss`, it is equivalent to @@ -353,7 +353,7 @@ def multiclass_logistic_loss(label: int, logits: jn.ndarray) -> float: def smooth_labels(labels, alpha: float) -> jn.ndarray: - """Apply label smoothing. + r"""Apply label smoothing. Label smoothing is often used in combination with a cross-entropy loss. Smoothed labels favour small logit gaps, and it has been shown that this can provide better model calibration by preventing overconfident predictions. @@ -411,7 +411,7 @@ def softmax_cross_entropy(logits, labels): def log_cosh(predicts, targets=None, ): - """Calculates the log-cosh loss for a set of predictions. + r"""Calculates the log-cosh loss for a set of predictions. log(cosh(x)) is approximately `(x**2) / 2` for small x and `abs(x) - log(2)` for large x. It is a twice differentiable alternative to the Huber loss. diff --git a/brainpy/math/__init__.py b/brainpy/math/__init__.py index 60f50ac2..4d5619f0 100644 --- a/brainpy/math/__init__.py +++ b/brainpy/math/__init__.py @@ -46,7 +46,7 @@ from . import random from .autograd import * from .controls import * from .jit import * -from .parallels import * +# from .parallels import * # settings from . import setting @@ -56,8 +56,7 @@ from .function import * # functions from .activations import * from . import activations -from .compact import * -from . import special +from .compat import * def get_dint(): diff --git a/brainpy/math/autograd.py b/brainpy/math/autograd.py index 39ef6ee8..0e8364c6 100644 --- a/brainpy/math/autograd.py +++ b/brainpy/math/autograd.py @@ -1,8 +1,7 @@ # -*- coding: utf-8 -*- -from typing import Union, Callable, Dict, Sequence - from functools import partial +from typing import Union, Callable, Dict, Sequence import jax import numpy as np @@ -41,7 +40,7 @@ def _make_cls_call_func(grad_func, grad_tree, grad_vars, dyn_vars, except UnexpectedTracerError as e: for v, d in zip(grad_vars, old_grad_vs): v.value = d for v, d in zip(dyn_vars, old_dyn_vs): v.value = d - raise errors.JaxTracerError(variables=dyn_vars+grad_vars) from e + raise errors.JaxTracerError(variables=dyn_vars + grad_vars) from e for v, d in zip(grad_vars, new_grad_vs): v.value = d for v, d in zip(dyn_vars, new_dyn_vs): v.value = d diff --git a/brainpy/math/compact/__init__.py b/brainpy/math/compat/__init__.py similarity index 51% rename from brainpy/math/compact/__init__.py rename to brainpy/math/compat/__init__.py index 8559e955..727f41ea 100644 --- a/brainpy/math/compact/__init__.py +++ b/brainpy/math/compat/__init__.py @@ -1,8 +1,10 @@ # -*- coding: utf-8 -*- __all__ = [ - 'optimizers', 'losses' + 'optimizers', 'losses', + 'FixedLenDelay', ] from . import optimizers, losses +from .delay_vars import * diff --git a/brainpy/math/compat/delay_vars.py b/brainpy/math/compat/delay_vars.py new file mode 100644 index 00000000..93321538 --- /dev/null +++ b/brainpy/math/compat/delay_vars.py @@ -0,0 +1,45 @@ +# -*- coding: utf-8 -*- + +import warnings +from typing import Union, Callable + +import jax.numpy as jnp + +from brainpy.math.jaxarray import ndarray +from brainpy.math.numpy_ops import zeros +from brainpy.math.delay_vars import TimeDelay + + +__all__ = [ + 'FixedLenDelay' +] + + +def FixedLenDelay(shape, + delay_len: Union[float, int], + before_t0: Union[Callable, ndarray, jnp.ndarray, float, int] = None, + t0: Union[float, int] = 0., + dt: Union[float, int] = None, + name: str = None, + interp_method='linear_interp', ): + """Delay variable which has a fixed delay length. + + .. deprecated:: 2.1.2 + Please use "brainpy.math.TimeDelay" instead. + + See Also + -------- + TimeDelay + + """ + warnings.warn('Please use "brainpy.math.TimeDelay" instead. ' + '"brainpy.math.FixedLenDelay" is deprecated since version 2.1.2. ', + DeprecationWarning) + return TimeDelay(inits=zeros(shape), + delay_len=delay_len, + before_t0=before_t0, + t0=t0, + dt=dt, + name=name, + interp_method=interp_method) + diff --git a/brainpy/math/compact/losses.py b/brainpy/math/compat/losses.py similarity index 72% rename from brainpy/math/compact/losses.py rename to brainpy/math/compat/losses.py index 934c1f86..f2de660b 100644 --- a/brainpy/math/compact/losses.py +++ b/brainpy/math/compat/losses.py @@ -17,6 +17,11 @@ __all__ = [ def cross_entropy_loss(*args, **kwargs): + """Cross entropy loss. + + .. deprecated:: 2.1.0 + Please use "brainpy.losses.cross_entropy_loss" instead. + """ warnings.warn('Please use "brainpy.losses.XXX" instead. ' '"brainpy.math.losses.XXX" is deprecated since version 2.0.3. ', DeprecationWarning) @@ -24,6 +29,11 @@ def cross_entropy_loss(*args, **kwargs): def l1_loos(*args, **kwargs): + """L1 loss. + + .. deprecated:: 2.1.0 + Please use "brainpy.losses.l1_loss" instead. + """ warnings.warn('Please use "brainpy.losses.XXX" instead. ' '"brainpy.math.losses.XXX" is deprecated since version 2.0.3. ', DeprecationWarning) @@ -31,6 +41,11 @@ def l1_loos(*args, **kwargs): def l2_loss(*args, **kwargs): + """L2 loss. + + .. deprecated:: 2.1.0 + Please use "brainpy.losses.l2_loss" instead. + """ warnings.warn('Please use "brainpy.losses.XXX" instead. ' '"brainpy.math.losses.XXX" is deprecated since version 2.0.3. ', DeprecationWarning) @@ -38,6 +53,11 @@ def l2_loss(*args, **kwargs): def l2_norm(*args, **kwargs): + """L2 normal. + + .. deprecated:: 2.1.0 + Please use "brainpy.losses.l2_norm" instead. + """ warnings.warn('Please use "brainpy.losses.XXX" instead. ' '"brainpy.math.losses.XXX" is deprecated since version 2.0.3. ', DeprecationWarning) @@ -45,6 +65,11 @@ def l2_norm(*args, **kwargs): def huber_loss(*args, **kwargs): + """Huber loss. + + .. deprecated:: 2.1.0 + Please use "brainpy.losses.huber_loss" instead. + """ warnings.warn('Please use "brainpy.losses.XXX" instead. ' '"brainpy.math.losses.XXX" is deprecated since version 2.0.3. ', DeprecationWarning) @@ -52,6 +77,11 @@ def huber_loss(*args, **kwargs): def mean_absolute_error(*args, **kwargs): + """mean absolute error loss. + + .. deprecated:: 2.1.0 + Please use "brainpy.losses.mean_absolute_error" instead. + """ warnings.warn('Please use "brainpy.losses.XXX" instead. ' '"brainpy.math.losses.XXX" is deprecated since version 2.0.3. ', DeprecationWarning) @@ -59,6 +89,11 @@ def mean_absolute_error(*args, **kwargs): def mean_squared_error(*args, **kwargs): + """Mean squared error loss. + + .. deprecated:: 2.1.0 + Please use "brainpy.losses.mean_squared_error" instead. + """ warnings.warn('Please use "brainpy.losses.XXX" instead. ' '"brainpy.math.losses.XXX" is deprecated since version 2.0.3. ', DeprecationWarning) @@ -66,6 +101,11 @@ def mean_squared_error(*args, **kwargs): def mean_squared_log_error(*args, **kwargs): + """Mean squared log error loss. + + .. deprecated:: 2.1.0 + Please use "brainpy.losses.mean_squared_log_error" instead. + """ warnings.warn('Please use "brainpy.losses.XXX" instead. ' '"brainpy.math.losses.XXX" is deprecated since version 2.0.3. ', DeprecationWarning) diff --git a/brainpy/math/compact/optimizers.py b/brainpy/math/compat/optimizers.py similarity index 74% rename from brainpy/math/compact/optimizers.py rename to brainpy/math/compat/optimizers.py index eb8dfb5e..d12d29fe 100644 --- a/brainpy/math/compact/optimizers.py +++ b/brainpy/math/compat/optimizers.py @@ -22,6 +22,11 @@ __all__ = [ def SGD(*args, **kwargs): + """SGD optimizer. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.SGD" instead. + """ warnings.warn('Please use "brainpy.optim.SGD" instead. ' '"brainpy.math.optimizers.SGD" is ' 'deprecated since version 2.0.3. ', @@ -30,6 +35,11 @@ def SGD(*args, **kwargs): def Momentum(*args, **kwargs): + """Momentum optimizer. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.Momentum" instead. + """ warnings.warn('Please use "brainpy.optim.Momentum" instead. ' '"brainpy.math.optimizers.Momentum" is ' 'deprecated since version 2.0.3. ', @@ -38,6 +48,11 @@ def Momentum(*args, **kwargs): def MomentumNesterov(*args, **kwargs): + """MomentumNesterov optimizer. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.MomentumNesterov" instead. + """ warnings.warn('Please use "brainpy.optim.MomentumNesterov" instead. ' '"brainpy.math.optimizers.MomentumNesterov" is ' 'deprecated since version 2.0.3. ', @@ -46,6 +61,11 @@ def MomentumNesterov(*args, **kwargs): def Adagrad(*args, **kwargs): + """Adagrad optimizer. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.Adagrad" instead. + """ warnings.warn('Please use "brainpy.optim.Adagrad" instead. ' '"brainpy.math.optimizers.Adagrad" is ' 'deprecated since version 2.0.3. ', @@ -54,6 +74,11 @@ def Adagrad(*args, **kwargs): def Adadelta(*args, **kwargs): + """Adadelta optimizer. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.Adadelta" instead. + """ warnings.warn('Please use "brainpy.optim.Adadelta" instead. ' '"brainpy.math.optimizers.Adadelta" is ' 'deprecated since version 2.0.3. ', @@ -62,6 +87,11 @@ def Adadelta(*args, **kwargs): def RMSProp(*args, **kwargs): + """RMSProp optimizer. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.RMSProp" instead. + """ warnings.warn('Please use "brainpy.optim.RMSProp" instead. ' '"brainpy.math.optimizers.RMSProp" is ' 'deprecated since version 2.0.3. ', @@ -70,6 +100,11 @@ def RMSProp(*args, **kwargs): def Adam(*args, **kwargs): + """Adam optimizer. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.Adam" instead. + """ warnings.warn('Please use "brainpy.optim.Adam" instead. ' '"brainpy.math.optimizers.Adam" is ' 'deprecated since version 2.0.3. ', @@ -78,6 +113,11 @@ def Adam(*args, **kwargs): def Constant(*args, **kwargs): + """Constant scheduler. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.Constant" instead. + """ warnings.warn('Please use "brainpy.optim.Constant" instead. ' '"brainpy.math.optimizers.Constant" is ' 'deprecated since version 2.0.3. ', @@ -86,6 +126,11 @@ def Constant(*args, **kwargs): def ExponentialDecay(*args, **kwargs): + """ExponentialDecay scheduler. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.ExponentialDecay" instead. + """ warnings.warn('Please use "brainpy.optim.ExponentialDecay" instead. ' '"brainpy.math.optimizers.ExponentialDecay" is ' 'deprecated since version 2.0.3. ', @@ -94,6 +139,11 @@ def ExponentialDecay(*args, **kwargs): def InverseTimeDecay(*args, **kwargs): + """InverseTimeDecay scheduler. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.InverseTimeDecay" instead. + """ warnings.warn('Please use "brainpy.optim.InverseTimeDecay" instead. ' '"brainpy.math.optimizers.InverseTimeDecay" is ' 'deprecated since version 2.0.3. ', @@ -102,6 +152,11 @@ def InverseTimeDecay(*args, **kwargs): def PolynomialDecay(*args, **kwargs): + """PolynomialDecay scheduler. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.PolynomialDecay" instead. + """ warnings.warn('Please use "brainpy.optim.PolynomialDecay" instead. ' '"brainpy.math.optimizers.PolynomialDecay" is ' 'deprecated since version 2.0.3. ', @@ -110,6 +165,11 @@ def PolynomialDecay(*args, **kwargs): def PiecewiseConstant(*args, **kwargs): + """PiecewiseConstant scheduler. + + .. deprecated:: 2.1.0 + Please use "brainpy.optim.PiecewiseConstant" instead. + """ warnings.warn('Please use "brainpy.optim.PiecewiseConstant" instead. ' '"brainpy.math.optimizers.PiecewiseConstant" is ' 'deprecated since version 2.0.3. ', diff --git a/brainpy/math/delay_vars.py b/brainpy/math/delay_vars.py index 3eb799b5..a18a7c5b 100644 --- a/brainpy/math/delay_vars.py +++ b/brainpy/math/delay_vars.py @@ -1,41 +1,47 @@ # -*- coding: utf-8 -*- - -from typing import Union, Callable, Tuple +from typing import Union, Callable import jax.numpy as jnp +import numpy as np from jax import vmap from jax.experimental.host_callback import id_tap from jax.lax import cond -from brainpy import math as bm +from brainpy import check from brainpy.base.base import Base -from brainpy.tools.checking import check_float -from brainpy.tools.others import to_size +from brainpy.errors import UnsupportedError +from brainpy.math import numpy_ops as ops +from brainpy.math.jaxarray import ndarray, Variable +from brainpy.math.setting import get_dt +from brainpy.tools.checking import check_float, check_integer __all__ = [ 'AbstractDelay', - 'FixedLenDelay', + 'TimeDelay', 'NeutralDelay', + 'LengthDelay', ] class AbstractDelay(Base): - def update(self, time, value): + def update(self, *args, **kwargs): raise NotImplementedError _FUNC_BEFORE = 'function' _DATA_BEFORE = 'data' +_INTERP_LINEAR = 'linear_interp' +_INTERP_ROUND = 'round' -class FixedLenDelay(AbstractDelay): - """Delay variable which has a fixed delay length. +class TimeDelay(AbstractDelay): + """Delay variable which has a fixed delay time length. For example, we create a delay variable which has a maximum delay length of 1 ms >>> import brainpy.math as bm - >>> delay = bm.FixedLenDelay(bm.zeros(3), delay_len=1., dt=0.1) + >>> delay = bm.TimeDelay(bm.zeros(3), delay_len=1., dt=0.1) >>> delay(-0.5) [-0. -0. -0.] @@ -43,13 +49,13 @@ class FixedLenDelay(AbstractDelay): 1. the one-dimensional delay data - >>> delay = bm.FixedLenDelay(3, delay_len=1., dt=0.1, before_t0=lambda t: t) + >>> delay = bm.TimeDelay(bm.zeros(3), delay_len=1., dt=0.1, before_t0=lambda t: t) >>> delay(-0.2) [-0.2 -0.2 -0.2] 2. the two-dimensional delay data - >>> delay = bm.FixedLenDelay((3, 2), delay_len=1., dt=0.1, before_t0=lambda t: t) + >>> delay = bm.TimeDelay(bm.zeros((3, 2)), delay_len=1., dt=0.1, before_t0=lambda t: t) >>> delay(-0.6) [[-0.6 -0.6] [-0.6 -0.6] @@ -57,8 +63,8 @@ class FixedLenDelay(AbstractDelay): 3. the three-dimensional delay data - >>> delay = bm.FixedLenDelay((3, 2, 1), delay_len=1., dt=0.1, before_t0=lambda t: t) - >>> delay(-0.6) + >>> delay = bm.TimeDelay(bm.zeros((3, 2, 1)), delay_len=1., dt=0.1, before_t0=lambda t: t) + >>> delay(-0.8) [[[-0.8] [-0.8]] [[-0.8] @@ -68,8 +74,8 @@ class FixedLenDelay(AbstractDelay): Parameters ---------- - shape: int, sequence of int - The delay data shape. + inits: int, sequence of int + The initial delay data. t0: float, int The zero time. delay_len: float, int @@ -83,155 +89,225 @@ class FixedLenDelay(AbstractDelay): of :math:`(num_delay, ...)`, where the longest delay data is aranged in the first index. name: str + The delay instance name. + interp_method: str + The way to deal with the delay at the time which is not integer times of the time step. + For exameple, if the time step ``dt=0.1``, the time delay length ``delay_len=1.``, + when users require the delay data at ``t-0.53``, we can deal this situation with + the following methods: + + - ``"linear_interp"``: using linear interpolation to get the delay value + at the required time (default). + - ``"round"``: round the time to make it is the integer times of the time step. For + the above situation, we will use the time at ``t-0.5`` to approximate the delay data + at ``t-0.53``. + + .. versionadded:: 2.1.1 + + See Also + -------- + LengthDelay """ def __init__( self, - shape: Union[int, Tuple[int, ...]], + inits: Union[ndarray, jnp.ndarray], delay_len: Union[float, int], - before_t0: Union[Callable, bm.ndarray, jnp.ndarray, float, int] = None, + before_t0: Union[Callable, ndarray, jnp.ndarray, float, int] = None, t0: Union[float, int] = 0., dt: Union[float, int] = None, name: str = None, - dtype=None, + interp_method='linear_interp', ): - super(FixedLenDelay, self).__init__(name=name) + super(TimeDelay, self).__init__(name=name) # shape - self.shape = to_size(shape) - self.dtype = dtype + assert isinstance(inits, (ndarray, np.ndarray)), (f'Must be an instance of brainpy.math.ndarray ' + f'or jax.numpy.ndarray. But we got {type(inits)}') + self.shape = inits.shape # delay_len self.t0 = t0 - self._dt = bm.get_dt() if dt is None else dt + self.dt = get_dt() if dt is None else dt check_float(delay_len, 'delay_len', allow_none=False, allow_int=True, min_bound=0.) - self._delay_len = delay_len - self.delay_len = delay_len + self._dt - self.num_delay_steps = int(bm.ceil(self.delay_len / self._dt).value) + self.delay_len = delay_len + self.num_delay_step = int(ops.ceil(self.delay_len / self.dt).value) + 1 + + # interp method + if interp_method not in [_INTERP_LINEAR, _INTERP_ROUND]: + raise UnsupportedError(f'Un-supported interpolation method {interp_method}, ' + f'we only support: {[_INTERP_LINEAR, _INTERP_ROUND]}') + self.interp_method = interp_method - # other variables - self._idx = bm.Variable(bm.asarray([0])) + # time variables + self.idx = ops.Variable(ops.asarray([0])) check_float(t0, 't0', allow_none=False, allow_int=True, ) - self._current_time = bm.Variable(bm.asarray([t0])) + self.current_time = Variable(ops.asarray([t0])) # delay data - self._data = bm.Variable(bm.zeros((self.num_delay_steps,) + self.shape, dtype=dtype)) + self.data = Variable(ops.zeros((self.num_delay_step,) + self.shape, + dtype=inits.dtype)) if before_t0 is None: self._before_type = _DATA_BEFORE elif callable(before_t0): - self._before_t0 = lambda t: jnp.asarray(bm.broadcast_to(before_t0(t), self.shape).value, - dtype=self.dtype) + self._before_t0 = lambda t: jnp.asarray(ops.broadcast_to(before_t0(t), self.shape).value, + dtype=inits.dtype) self._before_type = _FUNC_BEFORE - elif isinstance(before_t0, (bm.ndarray, jnp.ndarray, float, int)): + elif isinstance(before_t0, (ndarray, jnp.ndarray, float, int)): self._before_type = _DATA_BEFORE - try: - self._data[:] = before_t0 - except: - raise ValueError(f'Cannot set delay data by using "before_t0". ' - f'The delay data has the shape of ' - f'{((self.num_delay_steps,) + self.shape)}, while ' - f'we got "before_t0" of {bm.asarray(before_t0).shape}. ' - f'They are not compatible. Note that the delay length ' - f'{self._delay_len} will automatically add a dt {self.dt} ' - f'to {self.delay_len}.') + self.data[:-1] = before_t0 else: - raise ValueError(f'"before_t0" does not support {type(before_t0)}: before_t0') - - @property - def idx(self): - return self._idx - - @idx.setter - def idx(self, value): - raise ValueError('Cannot set "idx" by users.') - - @property - def dt(self): - return self._dt - - @dt.setter - def dt(self, value): - raise ValueError('Cannot set "dt" by users.') - - @property - def data(self): - return self._data - - @data.setter - def data(self, value): - self._data[:] = value + raise ValueError(f'"before_t0" does not support {type(before_t0)}') + # set initial data + self.data[-1] = inits - @property - def current_time(self): - return self._current_time[0] + # interpolation function + self.f = jnp.interp + for dim in range(1, len(self.shape) + 1, 1): + self.f = vmap(self.f, in_axes=(None, None, dim), out_axes=dim - 1) def _check_time(self, times, transforms): prev_time, current_time = times - current_time = bm.as_device_array(current_time) - prev_time = bm.as_device_array(prev_time) - if prev_time > current_time: + current_time = current_time[0] + if prev_time > current_time + 1e-6: raise ValueError(f'\n' f'!!! Error in {self.__class__.__name__}: \n' f'The request time should be less than the ' f'current time {current_time}. But we ' f'got {prev_time} > {current_time}') - lower_time = jnp.asarray(current_time - self.delay_len) - if prev_time < lower_time: + lower_time = current_time - self.delay_len + if prev_time < lower_time - self.dt: raise ValueError(f'\n' f'!!! Error in {self.__class__.__name__}: \n' f'The request time of the variable should be in ' f'[{lower_time}, {current_time}], but we got {prev_time}') - def __call__(self, prev_time): + def __call__(self, time, indices=None): # check - id_tap(self._check_time, (prev_time, self.current_time)) + if check.is_checking(): + id_tap(self._check_time, (time, self.current_time)) if self._before_type == _FUNC_BEFORE: - return cond(prev_time < self.t0, + return cond(time < self.t0, self._before_t0, - self._fn1, - prev_time) + self._after_t0, + time) else: - return self._fn1(prev_time) - - def _fn1(self, prev_time): - diff = self.delay_len - (self.current_time - prev_time) - if isinstance(diff, bm.ndarray): diff = diff.value - req_num_step = jnp.asarray(diff / self._dt, dtype=bm.get_dint()) - extra = diff - req_num_step * self._dt - return cond(extra == 0., self._true_fn, self._false_fn, (req_num_step, extra)) + return self._after_t0(time) + + def _after_t0(self, prev_time): + diff = self.delay_len - (self.current_time[0] - prev_time) + if isinstance(diff, ndarray): + diff = diff.value + if self.interp_method == _INTERP_LINEAR: + req_num_step = jnp.asarray(diff / self.dt, dtype=ops.int32) + extra = diff - req_num_step * self.dt + return cond(extra == 0., self._true_fn, self._false_fn, (req_num_step, extra)) + elif self.interp_method == _INTERP_ROUND: + req_num_step = jnp.asarray(jnp.round(diff / self.dt), dtype=ops.int32) + return self._true_fn([req_num_step, 0.]) + else: + raise UnsupportedError(f'Un-supported interpolation method {self.interp_method}, ' + f'we only support: {[_INTERP_LINEAR, _INTERP_ROUND]}') def _true_fn(self, div_mod): req_num_step, extra = div_mod - return self._data[self.idx[0] + req_num_step] + return self.data[self.idx[0] + req_num_step] def _false_fn(self, div_mod): req_num_step, extra = div_mod - f = jnp.interp - for dim in range(1, len(self.shape) + 1, 1): - f = vmap(f, in_axes=(None, None, dim), out_axes=dim - 1) idx = jnp.asarray([self.idx[0] + req_num_step, self.idx[0] + req_num_step + 1]) - idx %= self.num_delay_steps - return f(extra, jnp.asarray([0., self._dt]), self._data[idx]) + idx %= self.num_delay_step + return self.f(extra, jnp.asarray([0., self.dt]), self.data[idx]) def update(self, time, value): - self._data[self._idx[0]] = value - # check_float(time, 'time', allow_none=False, allow_int=True) - self._current_time[0] = time - self._idx.value = (self._idx + 1) % self.num_delay_steps + self.data[self.idx[0]] = value + self.current_time[0] = time + self.idx.value = (self.idx + 1) % self.num_delay_step + + +class NeutralDelay(TimeDelay): + pass -class VariedLenDelay(AbstractDelay): - """Delay variable which has a functional delay +class LengthDelay(AbstractDelay): + """Delay variable which has a fixed delay length. + Parameters + ---------- + inits: int, sequence of int + The initial delay data. + delay_len: int + The maximum delay length. + delay_data: Tensor + The delay data. + name: str + The delay object name. + + See Also + -------- + TimeDelay """ - def update(self, time, value): - pass + def __init__( + self, + inits: Union[ndarray, jnp.ndarray], + delay_len: int, + delay_data: Union[ndarray, jnp.ndarray, float, int] = None, + name: str = None, + ): + super(LengthDelay, self).__init__(name=name) + self.init(inits, delay_len, delay_data) - def __init__(self): - super(VariedLenDelay, self).__init__() + def init(self, inits, delay_len, delay_data): + assert isinstance(inits, (ndarray, np.ndarray)), (f'Must be an instance of brainpy.math.ndarray ' + f'or jax.numpy.ndarray. But we got {type(inits)}') + self.shape = inits.shape + # delay_len + check_integer(delay_len, 'delay_len', allow_none=False, min_bound=0) + self.delay_len = delay_len + self.num_delay_step = delay_len + 1 -class NeutralDelay(FixedLenDelay): - pass + # time variables + self.idx = Variable(ops.asarray([0], dtype=ops.int32)) + + # delay data + self.data = Variable(ops.zeros((self.num_delay_step,) + self.shape, + dtype=inits.dtype)) + if delay_data is None: + pass + elif isinstance(delay_data, (ndarray, jnp.ndarray, float, int)): + self.data[:-1] = delay_data + else: + raise ValueError(f'"delay_data" does not support {type(delay_data)}') + + def _check_delay(self, delay_len, transforms): + if isinstance(delay_len, ndarray): + delay_len = delay_len.value + if np.any(delay_len >= self.num_delay_step): + raise ValueError(f'\n' + f'!!! Error in {self.__class__.__name__}: \n' + f'The request delay length should be less than the ' + f'maximum delay {self.delay_len}. But we ' + f'got {delay_len}') + + def __call__(self, delay_len, indices=None): + # check + if check.is_checking(): + id_tap(self._check_delay, delay_len) + # the delay length + delay_idx = (self.idx[0] - delay_len - 1) % self.num_delay_step + if delay_idx.dtype not in [ops.int32, ops.int64]: + raise ValueError(f'"delay_len" must be integer, but we got {delay_len}') + # the delay data + if indices is None: + return self.data[delay_idx] + else: + return self.data[delay_idx, indices] + + def update(self, value): + if ops.shape(value) != self.shape: + raise ValueError(f'value shape should be {self.shape}, but we got {ops.shape(value)}') + self.data[self.idx[0]] = value + self.idx.value = (self.idx + 1) % self.num_delay_step diff --git a/brainpy/math/numpy_ops.py b/brainpy/math/numpy_ops.py index f041297f..a7d1baa3 100644 --- a/brainpy/math/numpy_ops.py +++ b/brainpy/math/numpy_ops.py @@ -79,7 +79,7 @@ __all__ = [ 'setxor1d', 'tensordot', 'trim_zeros', 'union1d', 'unravel_index', 'unwrap', 'take_along_axis', # others - 'clip_by_norm', 'as_device_array', 'as_variable', 'as_jaxarray', 'as_numpy', + 'clip_by_norm', 'as_device_array', 'as_variable', 'as_numpy', ] _min = min @@ -89,6 +89,10 @@ _max = max # others # ------ +# def as_jax_array(tensor): +# return asarray(tensor) + + def as_device_array(tensor): if isinstance(tensor, JaxArray): return tensor.value @@ -111,10 +115,6 @@ def as_variable(tensor): return Variable(asarray(tensor)) -def as_jaxarray(tensor): - return asarray(tensor) - - def _remove_jaxarray(obj): if isinstance(obj, JaxArray): return obj.value @@ -1507,10 +1507,10 @@ def vander(x, N=None, increasing=False): def fill_diagonal(a, val): - a = _remove_jaxarray(a) - assert a.ndim >= 2 + assert isinstance(a, JaxArray), f'Must be a JaxArray, but got {type(a)}' + assert a.ndim >= 2, f'Only support tensor has dimension >= 2, but got {a.shape}' i, j = jnp.diag_indices(_min(a.shape[-2:])) - return JaxArray(a.at[..., i, j].set(val)) + a._value = a.value.at[..., i, j].set(val) # indexing funcs diff --git a/brainpy/math/parallels.py b/brainpy/math/parallels.py index 080b560f..84d86dc6 100644 --- a/brainpy/math/parallels.py +++ b/brainpy/math/parallels.py @@ -27,6 +27,7 @@ from brainpy import errors from brainpy.base.base import Base from brainpy.base.collector import TensorCollector from brainpy.math.random import RandomState +from brainpy.math.jaxarray import JaxArray from brainpy.tools.codes import change_func_name __all__ = [ @@ -35,29 +36,31 @@ __all__ = [ ] -def _make_vmap(func, dyn_vars, rand_vars, in_axes, out_axes, - batch_idx, axis_name, reduce_func, f_name=None): +def _make_vmap(func, nonbatched_vars, batched_vars, in_axes, out_axes, + batch_idx, axis_name, f_name=None): @functools.partial(jax.vmap, in_axes=in_axes, out_axes=out_axes, axis_name=axis_name) - def vmapped_func(dyn_data, rand_data, *args, **kwargs): - dyn_vars.assign(dyn_data) - rand_vars.assign(rand_data) + def vmapped_func(nonbatched_data, batched_data, *args, **kwargs): + nonbatched_vars.assign(nonbatched_data) + batched_vars.assign(batched_data) out = func(*args, **kwargs) - dyn_changes = dyn_vars.dict() - rand_changes = rand_vars.dict() - return out, dyn_changes, rand_changes + nonbatched_changes = nonbatched_vars.dict() + batched_changes = batched_vars.dict() + return nonbatched_changes, batched_changes, out def call(*args, **kwargs): - dyn_data = dyn_vars.dict() n = args[batch_idx[0]].shape[batch_idx[1]] - rand_data = {key: val.split_keys(n) for key, val in rand_vars.items()} + nonbatched_data = nonbatched_vars.dict() + batched_data = {key: val.split_keys(n) for key, val in batched_vars.items()} try: - out, dyn_changes, rand_changes = vmapped_func(dyn_data, rand_data, *args, **kwargs) + out, dyn_changes, rand_changes = vmapped_func(nonbatched_data, batched_data, *args, **kwargs) except UnexpectedTracerError as e: - dyn_vars.assign(dyn_data) - rand_vars.assign(rand_data) - raise errors.JaxTracerError(variables=dyn_vars) from e - for key, v in dyn_changes.items(): dyn_vars[key] = reduce_func(v) - for key, v in rand_changes.items(): rand_vars[key] = reduce_func(v) + nonbatched_vars.assign(nonbatched_data) + batched_vars.assign(batched_data) + raise errors.JaxTracerError() from e + # for key, v in dyn_changes.items(): + # dyn_vars[key] = reduce_func(v) + # for key, v in rand_changes.items(): + # rand_vars[key] = reduce_func(v) return out return change_func_name(name=f_name, f=call) if f_name else call @@ -77,7 +80,7 @@ def vmap(func, dyn_vars=None, batched_vars=None, ---------- func : Base, function, callable The function or the module to compile. - dyn_vars : dict + dyn_vars : dict, sequence batched_vars : dict in_axes : optional, int, sequence of int Specify which input array axes to map over. If each positional argument to @@ -207,13 +210,19 @@ def vmap(func, dyn_vars=None, batched_vars=None, axis_name=axis_name) else: + if isinstance(dyn_vars, JaxArray): + dyn_vars = [dyn_vars] + if isinstance(dyn_vars, (tuple, list)): + dyn_vars = {f'_vmap_v{i}': v for i, v in enumerate(dyn_vars)} + assert isinstance(dyn_vars, dict) + # dynamical variables - dyn_vars, rand_vars = TensorCollector(), TensorCollector() + _dyn_vars, _rand_vars = TensorCollector(), TensorCollector() for key, val in dyn_vars.items(): if isinstance(val, RandomState): - rand_vars[key] = val + _rand_vars[key] = val else: - dyn_vars[key] = val + _dyn_vars[key] = val # in axes if in_axes is None: @@ -249,13 +258,12 @@ def vmap(func, dyn_vars=None, batched_vars=None, # jit function return _make_vmap(func=func, - dyn_vars=dyn_vars, - rand_vars=rand_vars, + nonbatched_vars=_dyn_vars, + batched_vars=_rand_vars, in_axes=in_axes, out_axes=out_axes, axis_name=axis_name, - batch_idx=batch_idx, - reduce_func=reduce_func) + batch_idx=batch_idx) else: raise errors.BrainPyError(f'Only support instance of {Base.__name__}, or a callable ' diff --git a/brainpy/math/special.py b/brainpy/math/special.py deleted file mode 100644 index 741a41c1..00000000 --- a/brainpy/math/special.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- - -import jax.numpy as jnp - -from brainpy.tools.checking import check_integer - -__all__ = [ - 'poch', - 'Gamma', - 'Beta', -] - - -def poch(a, n): - """ Returns the Pochhammer symbol (a)_n. """ - # First, check if 'a' is a real number (this is currently only working for reals). - assert not isinstance(a, complex), "a must be real: %r" % a - check_integer(n, allow_none=False, min_bound=0) - # Compute the Pochhammer symbol. - return 1.0 if n == 0 else jnp.prod(jnp.arange(n) + a) - - -def Gamma(z): - """ Paul Godfrey's Gamma function implementation valid for z complex. - This is converted from Godfrey's Gamma.m Matlab file available at - https://www.mathworks.com/matlabcentral/fileexchange/3572-gamma. - 15 significant digits of accuracy for real z and 13 - significant digits for other values. - """ - zz = z - - # Find negative real parts of z and make them positive. - if isinstance(z, (complex, jnp.complex64, jnp.complex128)): - Z = [z.real, z.imag] - if Z[0] < 0: - Z[0] = -Z[0] - z = jnp.asarray(Z) - z = z.astype(complex) - - g = 607 / 128. - c = jnp.asarray([0.99999999999999709182, 57.156235665862923517, -59.597960355475491248, - 14.136097974741747174, -0.49191381609762019978, .33994649984811888699e-4, - .46523628927048575665e-4, -.98374475304879564677e-4, .15808870322491248884e-3, - -.21026444172410488319e-3, .21743961811521264320e-3, -.16431810653676389022e-3, - .84418223983852743293e-4, -.26190838401581408670e-4, .36899182659531622704e-5]) - if z == 0 or z == 1: - return 1. - if ((jnp.round(zz) == zz) - and (zz.imag == 0) - and (zz.real <= 0)): # Adjust for negative poles. - return jnp.inf - z = z - 1 - zh = z + 0.5 - zgh = zh + g - zp = zgh ** (zh * 0.5) # Trick for avoiding floating-point overflow above z = 141. - idx = jnp.arange(len(c) - 1, 0, -1) - ss = jnp.sum(c[idx] / (z + idx)) - sq2pi = 2.5066282746310005024157652848110 - f = (sq2pi * (c[0] + ss)) * ((zp * jnp.exp(-zgh)) * zp) - if isinstance(zz, (complex, jnp.complex64, jnp.complex128)): - return f.astype(complex) - elif isinstance(zz, int) and zz >= 0: - f = jnp.round(f) - return f.astype(int) - else: - return f - - -def Beta(x, y): - """ Beta function using Godfrey's Gamma function. """ - return Gamma(x) * Gamma(y) / Gamma(x + y) diff --git a/brainpy/math/tests/test_delay_vars.py b/brainpy/math/tests/test_delay_vars.py index 6473d8cc..93eb58f6 100644 --- a/brainpy/math/tests/test_delay_vars.py +++ b/brainpy/math/tests/test_delay_vars.py @@ -5,50 +5,66 @@ import unittest import brainpy.math as bm -class TestFixedLenDelay(unittest.TestCase): +class TestTimeDelay(unittest.TestCase): def test_dim1(self): + bm.enable_x64() + + # linear interp t0 = 0. - before_t0 = bm.repeat(bm.arange(11).reshape((-1, 1)), 10, axis=1) - delay = bm.FixedLenDelay(10, delay_len=1., t0=t0, dt=0.1, before_t0=before_t0) - self.assertTrue(bm.array_equal(delay(t0 - 0.1), bm.ones(10) * 10)) - self.assertTrue(bm.array_equal(delay(t0 - 0.15), bm.ones(10) * 9.5)) + before_t0 = bm.repeat(bm.arange(10).reshape((-1, 1)), 10, axis=1) + delay = bm.TimeDelay(bm.zeros(10), delay_len=1., t0=t0, dt=0.1, before_t0=before_t0) + print(delay(t0 - 0.1)) + print(delay(t0 - 0.15)) + self.assertTrue(bm.array_equal(delay(t0 - 0.1), bm.ones(10) * 9.)) + self.assertTrue(bm.array_equal(delay(t0 - 0.15), bm.ones(10) * 8.5)) + print() + print(delay(t0 - 0.23)) + print(delay(t0 - 0.23) - bm.ones(10) * 8.7) # self.assertTrue(bm.array_equal(delay(t0 - 0.23), bm.ones(10) * 8.7)) + # round interp + delay = bm.TimeDelay(bm.zeros(10), delay_len=1., t0=t0, dt=0.1, before_t0=before_t0, + interp_method='round') + self.assertTrue(bm.array_equal(delay(t0 - 0.1), bm.ones(10) * 9)) + print(delay(t0 - 0.15)) + self.assertTrue(bm.array_equal(delay(t0 - 0.15), bm.ones(10) * 8)) + self.assertTrue(bm.array_equal(delay(t0 - 0.2), bm.ones(10) * 8)) + def test_dim2(self): t0 = 0. - before_t0 = bm.repeat(bm.arange(11).reshape((-1, 1)), 10, axis=1) - before_t0 = bm.repeat(before_t0.reshape((11, 10, 1)), 5, axis=2) - delay = bm.FixedLenDelay((10, 5), delay_len=1., t0=t0, dt=0.1, before_t0=before_t0) - self.assertTrue(bm.array_equal(delay(t0 - 0.1), bm.ones((10, 5)) * 10)) - self.assertTrue(bm.array_equal(delay(t0 - 0.15), bm.ones((10, 5)) * 9.5)) + before_t0 = bm.repeat(bm.arange(10).reshape((-1, 1)), 10, axis=1) + before_t0 = bm.repeat(before_t0.reshape((10, 10, 1)), 5, axis=2) + delay = bm.TimeDelay(bm.zeros((10, 5)), delay_len=1., t0=t0, dt=0.1, before_t0=before_t0) + self.assertTrue(bm.array_equal(delay(t0 - 0.1), bm.ones((10, 5)) * 9)) + self.assertTrue(bm.array_equal(delay(t0 - 0.15), bm.ones((10, 5)) * 8.5)) # self.assertTrue(bm.array_equal(delay(t0 - 0.23), bm.ones((10, 5)) * 8.7)) def test_dim3(self): t0 = 0. - before_t0 = bm.repeat(bm.arange(11).reshape((-1, 1)), 10, axis=1) - before_t0 = bm.repeat(before_t0.reshape((11, 10, 1)), 5, axis=2) - before_t0 = bm.repeat(before_t0.reshape((11, 10, 5, 1)), 3, axis=3) - delay = bm.FixedLenDelay((10, 5, 3), delay_len=1., t0=t0, dt=0.1, before_t0=before_t0) - self.assertTrue(bm.array_equal(delay(t0 - 0.1), bm.ones((10, 5, 3)) * 10)) - self.assertTrue(bm.array_equal(delay(t0 - 0.15), bm.ones((10, 5, 3)) * 9.5)) + before_t0 = bm.repeat(bm.arange(10).reshape((-1, 1)), 10, axis=1) + before_t0 = bm.repeat(before_t0.reshape((10, 10, 1)), 5, axis=2) + before_t0 = bm.repeat(before_t0.reshape((10, 10, 5, 1)), 3, axis=3) + delay = bm.TimeDelay(bm.zeros((10, 5, 3)), delay_len=1., t0=t0, dt=0.1, before_t0=before_t0) + self.assertTrue(bm.array_equal(delay(t0 - 0.1), bm.ones((10, 5, 3)) * 9)) + self.assertTrue(bm.array_equal(delay(t0 - 0.15), bm.ones((10, 5, 3)) * 8.5)) # self.assertTrue(bm.array_equal(delay(t0 - 0.23), bm.ones((10, 5, 3)) * 8.7)) def test1(self): print() - delay = bm.FixedLenDelay(3, delay_len=1., dt=0.1, before_t0=lambda t: t) + delay = bm.TimeDelay(bm.zeros(3), delay_len=1., dt=0.1, before_t0=lambda t: t) print(delay(-0.2)) - delay = bm.FixedLenDelay((3, 2), delay_len=1., dt=0.1, before_t0=lambda t: t) + delay = bm.TimeDelay(bm.zeros((3, 2)), delay_len=1., dt=0.1, before_t0=lambda t: t) print(delay(-0.6)) - delay = bm.FixedLenDelay((3, 2, 1), delay_len=1., dt=0.1, before_t0=lambda t: t) + delay = bm.TimeDelay(bm.zeros((3, 2, 1)), delay_len=1., dt=0.1, before_t0=lambda t: t) print(delay(-0.8)) def test_current_time2(self): print() - delay = bm.FixedLenDelay(3, delay_len=1., dt=0.1, before_t0=lambda t: t) + delay = bm.TimeDelay(bm.zeros(3), delay_len=1., dt=0.1, before_t0=lambda t: t) print(delay(0.)) - before_t0 = bm.repeat(bm.arange(11).reshape((-1, 1)), 10, axis=1) - before_t0 = bm.repeat(before_t0.reshape((11, 10, 1)), 5, axis=2) - delay = bm.FixedLenDelay((10, 5), delay_len=1., dt=0.1, before_t0=before_t0) + before_t0 = bm.repeat(bm.arange(10).reshape((-1, 1)), 10, axis=1) + before_t0 = bm.repeat(before_t0.reshape((10, 10, 1)), 5, axis=2) + delay = bm.TimeDelay(bm.zeros((10, 5)), delay_len=1., dt=0.1, before_t0=before_t0) print(delay(0.)) # def test_prev_time_beyond_boundary(self): @@ -56,3 +72,42 @@ class TestFixedLenDelay(unittest.TestCase): # delay = bm.FixedLenDelay(3, delay_len=1., dt=0.1, before_t0=lambda t: t) # delay(-1.2) + +class TestLengthDelay(unittest.TestCase): + def test1(self): + dim = 3 + delay = bm.LengthDelay(bm.zeros(dim), 10) + print(delay(1)) + self.assertTrue(bm.array_equal(delay(1), bm.zeros(dim))) + + delay = bm.jit(delay) + print(delay(1)) + self.assertTrue(bm.array_equal(delay(1), bm.zeros(dim))) + + def test2(self): + dim = 3 + delay = bm.LengthDelay(bm.zeros(dim), 10, delay_data=bm.arange(1, 11).reshape((10, 1))) + print(delay(0)) + self.assertTrue(bm.array_equal(delay(0), bm.zeros(dim))) + print(delay(1)) + self.assertTrue(bm.array_equal(delay(1), bm.ones(dim) * 10)) + + delay = bm.jit(delay) + print(delay(0)) + self.assertTrue(bm.array_equal(delay(0), bm.zeros(dim))) + print(delay(1)) + self.assertTrue(bm.array_equal(delay(1), bm.ones(dim) * 10)) + + def test3(self): + dim = 3 + delay = bm.LengthDelay(bm.zeros(dim), 10, delay_data=bm.arange(1, 11).reshape((10, 1))) + print(delay(bm.asarray([1, 2, 3]), + bm.arange(3))) + # self.assertTrue(bm.array_equal(delay(0), bm.zeros(dim))) + + delay = bm.jit(delay) + print(delay(bm.asarray([1, 2, 3]), + bm.arange(3))) + # self.assertTrue(bm.array_equal(delay(1), bm.ones(dim) * 10)) + + diff --git a/brainpy/measure/__init__.py b/brainpy/measure/__init__.py index 065156ae..31078b53 100644 --- a/brainpy/measure/__init__.py +++ b/brainpy/measure/__init__.py @@ -5,207 +5,6 @@ This module aims to provide commonly used analysis methods for simulated neurona You can access them through ``brainpy.measure.XXX``. """ +from .correlation import * +from .firings import * -import numpy as np - -from brainpy import tools, math - -__all__ = [ - 'cross_correlation', - 'voltage_fluctuation', - 'raster_plot', - 'firing_rate', -] - - -# @tools.numba_jit -def _cc(states, i, j): - sqrt_ij = np.sqrt(np.sum(states[i]) * np.sum(states[j])) - k = 0. if sqrt_ij == 0. else np.sum(states[i] * states[j]) / sqrt_ij - return k - - -def cross_correlation(spikes, bin, dt=None): - r"""Calculate cross correlation index between neurons. - - The coherence [1]_ between two neurons i and j is measured by their - cross-correlation of spike trains at zero time lag within a time bin - of :math:`\Delta t = \tau`. More specifically, suppose that a long - time interval T is divided into small bins of :math:`\Delta t` and - that two spike trains are given by :math:`X(l)=` 0 or 1, :math:`Y(l)=` 0 - or 1, :math:`l=1,2, \ldots, K(T / K=\tau)`. Thus, we define a coherence - measure for the pair as: - - .. math:: - - \kappa_{i j}(\tau)=\frac{\sum_{l=1}^{K} X(l) Y(l)} - {\sqrt{\sum_{l=1}^{K} X(l) \sum_{l=1}^{K} Y(l)}} - - The population coherence measure :math:`\kappa(\tau)` is defined by the - average of :math:`\kappa_{i j}(\tau)` over many pairs of neurons in the - network. - - Parameters - ---------- - spikes : - The history of spike states of the neuron group. - It can be easily get via `StateMonitor(neu, ['spike'])`. - bin : float, int - The time bin to normalize spike states. - dt : float, optional - The time precision. - - Returns - ------- - cc_index : float - The cross correlation value which represents the synchronization index. - - References - ---------- - .. [1] Wang, Xiao-Jing, and György Buzsáki. "Gamma oscillation by synaptic - inhibition in a hippocampal interneuronal network model." Journal of - neuroscience 16.20 (1996): 6402-6413. - """ - spikes = np.asarray(spikes) - dt = math.get_dt() if dt is None else dt - bin_size = int(bin / dt) - num_hist, num_neu = spikes.shape - num_bin = int(np.ceil(num_hist / bin_size)) - if num_bin * bin_size != num_hist: - spikes = np.append(spikes, np.zeros((num_bin * bin_size - num_hist, num_neu)), axis=0) - states = spikes.T.reshape((num_neu, num_bin, bin_size)) - states = (np.sum(states, axis=2) > 0.).astype(np.float_) - all_k = [] - for i in range(num_neu): - for j in range(i + 1, num_neu): - all_k.append(_cc(states, i, j)) - return np.mean(all_k) - - -# @tools.numba_jit -def _var(neu_signal): - return np.mean(neu_signal * neu_signal) - np.mean(neu_signal) ** 2 - - -def voltage_fluctuation(potentials): - r"""Calculate neuronal synchronization via voltage variance. - - The method comes from [1]_ [2]_ [3]_. - - First, average over the membrane potential :math:`V` - - .. math:: - - V(t) = \frac{1}{N} \sum_{i=1}^{N} V_i(t) - - The variance of the time fluctuations of :math:`V(t)` is - - .. math:: - - \sigma_V^2 = \left\langle \left[ V(t) \right]^2 \right\rangle_t - - \left[ \left\langle V(t) \right\rangle_t \right]^2 - - where :math:`\left\langle \ldots \right\rangle_t = (1 / T_m) \int_0^{T_m} dt \, \ldots` - denotes time-averaging over a large time, :math:`\tau_m`. After normalization - of :math:`\sigma_V` to the average over the population of the single cell - membrane potentials - - .. math:: - - \sigma_{V_i}^2 = \left\langle\left[ V_i(t) \right]^2 \right\rangle_t - - \left[ \left\langle V_i(t) \right\rangle_t \right]^2 - - one defines a synchrony measure, :math:`\chi (N)`, for the activity of a system - of :math:`N` neurons by: - - .. math:: - - \chi^2 \left( N \right) = \frac{\sigma_V^2}{ \frac{1}{N} \sum_{i=1}^N - \sigma_{V_i}^2} - - Parameters - ---------- - potentials : - The membrane potential matrix of the neuron group. - - Returns - ------- - sync_index : float - The synchronization index. - - References - ---------- - .. [1] Golomb, D. and Rinzel J. (1993) Dynamics of globally coupled - inhibitory neurons with heterogeneity. Phys. Rev. reversal_potential 48:4810-4814. - .. [2] Golomb D. and Rinzel J. (1994) Clustering in globally coupled - inhibitory neurons. Physica D 72:259-282. - .. [3] David Golomb (2007) Neuronal synchrony measures. Scholarpedia, 2(1):1347. - """ - - potentials = np.asarray(potentials) - num_hist, num_neu = potentials.shape - avg = np.mean(potentials, axis=1) - avg_var = np.mean(avg * avg) - np.mean(avg) ** 2 - neu_vars = [] - for i in range(num_neu): - neu_vars.append(_var(potentials[:, i])) - var_mean = np.mean(neu_vars) - return avg_var / var_mean if var_mean != 0. else 1. - - -def raster_plot(sp_matrix, times): - """Get spike raster plot which displays the spiking activity - of a group of neurons over time. - - Parameters - ---------- - sp_matrix : bnp.ndarray - The matrix which record spiking activities. - times : bnp.ndarray - The time steps. - - Returns - ------- - raster_plot : tuple - Include (neuron index, spike time). - """ - sp_matrix = np.asarray(sp_matrix) - times = np.asarray(times) - elements = np.where(sp_matrix > 0.) - index = elements[1] - time = times[elements[0]] - return index, time - - -def firing_rate(sp_matrix, width, dt=None): - r"""Calculate the mean firing rate over in a neuron group. - - This method is adopted from Brian2. - - The firing rate in trial :math:`k` is the spike count :math:`n_{k}^{sp}` - in an interval of duration :math:`T` divided by :math:`T`: - - .. math:: - - v_k = {n_k^{sp} \over T} - - Parameters - ---------- - sp_matrix : math.JaxArray, np.ndarray - The spike matrix which record spiking activities. - width : int, float - The width of the ``window`` in millisecond. - dt : float, optional - The sample rate. - - Returns - ------- - rate : numpy.ndarray - The population rate in Hz, smoothed with the given window. - """ - sp_matrix = np.asarray(sp_matrix) - rate = np.sum(sp_matrix, axis=1) / sp_matrix.shape[1] - dt = math.get_dt() if dt is None else dt - width1 = int(width / 2 / dt) * 2 + 1 - window = np.ones(width1) * 1000 / width - return np.convolve(rate, window, mode='same') diff --git a/brainpy/measure/correlation.py b/brainpy/measure/correlation.py new file mode 100644 index 00000000..7d228ac5 --- /dev/null +++ b/brainpy/measure/correlation.py @@ -0,0 +1,270 @@ +# -*- coding: utf-8 -*- + +from functools import partial + +import numpy as np +from jax import vmap, jit, lax, numpy as jnp + +from brainpy import math as bm + +__all__ = [ + 'cross_correlation', + 'voltage_fluctuation', + 'matrix_correlation', + 'weighted_correlation', + 'functional_connectivity', + 'functional_connectivity_dynamics', +] + + +@jit +@partial(vmap, in_axes=(None, 0, 0)) +def _cc(states, i, j): + sqrt_ij = jnp.sqrt(jnp.sum(states[i]) * jnp.sum(states[j])) + return lax.cond(sqrt_ij == 0., + lambda _: 0., + lambda ij: jnp.sum(states[i] * states[j]) / sqrt_ij, + (i, j)) + + +def cross_correlation(spikes, bin, dt=None): + r"""Calculate cross correlation index between neurons. + + The coherence [1]_ between two neurons i and j is measured by their + cross-correlation of spike trains at zero time lag within a time bin + of :math:`\Delta t = \tau`. More specifically, suppose that a long + time interval T is divided into small bins of :math:`\Delta t` and + that two spike trains are given by :math:`X(l)=` 0 or 1, :math:`Y(l)=` 0 + or 1, :math:`l=1,2, \ldots, K(T / K=\tau)`. Thus, we define a coherence + measure for the pair as: + + .. math:: + + \kappa_{i j}(\tau)=\frac{\sum_{l=1}^{K} X(l) Y(l)} + {\sqrt{\sum_{l=1}^{K} X(l) \sum_{l=1}^{K} Y(l)}} + + The population coherence measure :math:`\kappa(\tau)` is defined by the + average of :math:`\kappa_{i j}(\tau)` over many pairs of neurons in the + network. + + Parameters + ---------- + spikes : + The history of spike states of the neuron group. + It can be easily get via `StateMonitor(neu, ['spike'])`. + bin : float, int + The time bin to normalize spike states. + dt : float, optional + The time precision. + + Returns + ------- + cc_index : float + The cross correlation value which represents the synchronization index. + + References + ---------- + .. [1] Wang, Xiao-Jing, and György Buzsáki. "Gamma oscillation by synaptic + inhibition in a hippocampal interneuronal network model." Journal of + neuroscience 16.20 (1996): 6402-6413. + """ + spikes = bm.asarray(spikes) + dt = bm.get_dt() if dt is None else dt + bin_size = int(bin / dt) + num_hist, num_neu = spikes.shape + num_bin = int(np.ceil(num_hist / bin_size)) + if num_bin * bin_size != num_hist: + spikes = bm.append(spikes, bm.zeros((num_bin * bin_size - num_hist, num_neu)), axis=0) + states = spikes.T.reshape((num_neu, num_bin, bin_size)) + states = bm.asarray(bm.sum(states, axis=2) > 0., dtype=jnp.float_) + indices = jnp.tril_indices(4, k=-1) + return jnp.mean(_cc(states.value, *indices)) + + +@partial(vmap, in_axes=(None, 0)) +def _var(neu_signal, i): + neu_signal = neu_signal[:, i] + return jnp.mean(neu_signal * neu_signal) - jnp.mean(neu_signal) ** 2 + + +@jit +def voltage_fluctuation(potentials): + r"""Calculate neuronal synchronization via voltage variance. + + The method comes from [1]_ [2]_ [3]_. + + First, average over the membrane potential :math:`V` + + .. math:: + + V(t) = \frac{1}{N} \sum_{i=1}^{N} V_i(t) + + The variance of the time fluctuations of :math:`V(t)` is + + .. math:: + + \sigma_V^2 = \left\langle \left[ V(t) \right]^2 \right\rangle_t - + \left[ \left\langle V(t) \right\rangle_t \right]^2 + + where :math:`\left\langle \ldots \right\rangle_t = (1 / T_m) \int_0^{T_m} dt \, \ldots` + denotes time-averaging over a large time, :math:`\tau_m`. After normalization + of :math:`\sigma_V` to the average over the population of the single cell + membrane potentials + + .. math:: + + \sigma_{V_i}^2 = \left\langle\left[ V_i(t) \right]^2 \right\rangle_t - + \left[ \left\langle V_i(t) \right\rangle_t \right]^2 + + one defines a synchrony measure, :math:`\chi (N)`, for the activity of a system + of :math:`N` neurons by: + + .. math:: + + \chi^2 \left( N \right) = \frac{\sigma_V^2}{ \frac{1}{N} \sum_{i=1}^N + \sigma_{V_i}^2} + + Parameters + ---------- + potentials : + The membrane potential matrix of the neuron group. + + Returns + ------- + sync_index : float + The synchronization index. + + References + ---------- + .. [1] Golomb, D. and Rinzel J. (1993) Dynamics of globally coupled + inhibitory neurons with heterogeneity. Phys. Rev. reversal_potential 48:4810-4814. + .. [2] Golomb D. and Rinzel J. (1994) Clustering in globally coupled + inhibitory neurons. Physica D 72:259-282. + .. [3] David Golomb (2007) Neuronal synchrony measures. Scholarpedia, 2(1):1347. + """ + + potentials = bm.as_device_array(potentials) + num_hist, num_neu = potentials.shape + var_mean = jnp.mean(_var(potentials, jnp.arange(num_neu))) + avg = jnp.mean(potentials, axis=1) + avg_var = jnp.mean(avg * avg) - jnp.mean(avg) ** 2 + return lax.cond(var_mean != 0., + lambda _: avg_var / var_mean, + lambda _: 1., + ()) + + +def matrix_correlation(x, y): + """Pearson correlation of the lower triagonal of two matrices. + + The triangular matrix is offset by k = 1 in order to ignore the diagonal line + + Parameters + ---------- + x: tensor + First matrix. + y: tensor + Second matrix + + Returns + ------- + coef: tensor + Correlation coefficient + """ + x = bm.as_numpy(x) + y = bm.as_numpy(y) + if x.ndim != 2: + raise ValueError(f'Only support 2d tensor, but we got a tensor ' + f'with the shape of {x.shape}') + if y.ndim != 2: + raise ValueError(f'Only support 2d tensor, but we got a tensor ' + f'with the shape of {y.shape}') + x = x[np.triu_indices_from(x, k=1)] + y = y[np.triu_indices_from(y, k=1)] + cc = np.corrcoef(x, y)[0, 1] + return cc + + +def functional_connectivity(activities): + """Functional connectivity matrix of timeseries activities. + + Parameters + ---------- + activities: tensor + The multidimensional tensor with the shape of ``(num_time, num_sample)``. + + Returns + ------- + connectivity_matrix: tensor + ``num_sample x num_sample`` functional connectivity matrix. + """ + activities = bm.as_numpy(activities) + if activities.ndim != 2: + raise ValueError('Only support 2d tensor with shape of "(num_time, num_sample)". ' + f'But we got a tensor with the shape of {activities.shape}') + fc = np.corrcoef(activities.T) + return np.nan_to_num(fc) + + +@jit +def functional_connectivity_dynamics(activities, window_size=30, step_size=5): + """Computes functional connectivity dynamics (FCD) matrix. + + Parameters + ---------- + activities: tensor + The time series with shape of ``(num_time, num_sample)``. + window_size: int + Size of each rolling window in time steps, defaults to 30. + step_size: int + Step size between each rolling window, defaults to 5. + + Returns + ------- + fcd_matrix: tensor + FCD matrix. + """ + pass + + +def _weighted_mean(x, w): + """Weighted Mean""" + return jnp.sum(x * w) / jnp.sum(w) + + +def _weighted_cov(x, y, w): + """Weighted Covariance""" + return jnp.sum(w * (x - _weighted_mean(x, w)) * (y - _weighted_mean(y, w))) / jnp.sum(w) + + +@jit +def weighted_correlation(x, y, w): + """Weighted Pearson correlation of two data series. + + Parameters + ---------- + x: tensor + The data series 1. + y: tensor + The data series 2. + w: tensor + Weight vector, must have same length as x and y. + + Returns + ------- + corr: tensor + Weighted correlation coefficient. + """ + x = bm.as_device_array(x) + y = bm.as_device_array(y) + w = bm.as_device_array(w) + if x.ndim != 1: + raise ValueError(f'Only support 1d tensor, but we got a tensor ' + f'with the shape of {x.shape}') + if y.ndim != 1: + raise ValueError(f'Only support 1d tensor, but we got a tensor ' + f'with the shape of {y.shape}') + if w.ndim != 1: + raise ValueError(f'Only support 1d tensor, but we got a tensor ' + f'with the shape of {w.shape}') + return _weighted_cov(x, y, w) / jnp.sqrt(_weighted_cov(x, x, w) * _weighted_cov(y, y, w)) diff --git a/brainpy/measure/firings.py b/brainpy/measure/firings.py new file mode 100644 index 00000000..7bca1a0c --- /dev/null +++ b/brainpy/measure/firings.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- + +import numpy as np +from jax import jit + +from brainpy import math as bm + +__all__ = [ + 'raster_plot', + 'firing_rate', +] + + +def raster_plot(sp_matrix, times): + """Get spike raster plot which displays the spiking activity + of a group of neurons over time. + + Parameters + ---------- + sp_matrix : bnp.ndarray + The matrix which record spiking activities. + times : bnp.ndarray + The time steps. + + Returns + ------- + raster_plot : tuple + Include (neuron index, spike time). + """ + sp_matrix = np.asarray(sp_matrix) + times = np.asarray(times) + elements = np.where(sp_matrix > 0.) + index = elements[1] + time = times[elements[0]] + return index, time + + +@jit +def _firing_rate(sp_matrix, window): + sp_matrix = bm.asarray(sp_matrix) + rate = bm.sum(sp_matrix, axis=1) / sp_matrix.shape[1] + return bm.convolve(rate, window, mode='same') + + +def firing_rate(sp_matrix, width, dt=None, numpy=True): + r"""Calculate the mean firing rate over in a neuron group. + + This method is adopted from Brian2. + + The firing rate in trial :math:`k` is the spike count :math:`n_{k}^{sp}` + in an interval of duration :math:`T` divided by :math:`T`: + + .. math:: + + v_k = {n_k^{sp} \over T} + + Parameters + ---------- + sp_matrix : math.JaxArray, np.ndarray + The spike matrix which record spiking activities. + width : int, float + The width of the ``window`` in millisecond. + dt : float, optional + The sample rate. + + Returns + ------- + rate : numpy.ndarray + The population rate in Hz, smoothed with the given window. + """ + dt = bm.get_dt() if (dt is None) else dt + width1 = int(width / 2 / dt) * 2 + 1 + window = bm.ones(width1) * 1000 / width + fr = _firing_rate(sp_matrix, window) + return fr.numpy() if numpy else fr + diff --git a/brainpy/measure/tests/test_correlation.py b/brainpy/measure/tests/test_correlation.py new file mode 100644 index 00000000..d1378ffd --- /dev/null +++ b/brainpy/measure/tests/test_correlation.py @@ -0,0 +1,59 @@ +# -*- coding: utf-8 -*- + + +import unittest +import brainpy as bp + + +class TestCrossCorrelation(unittest.TestCase): + def test_cc(self): + spikes = bp.math.ones((1000, 10)) + cc1 = bp.measure.cross_correlation(spikes, 1.) + self.assertTrue(cc1 == 1.) + + spikes = bp.math.zeros((1000, 10)) + cc2 = bp.measure.cross_correlation(spikes, 1.) + self.assertTrue(cc2 == 0.) + + def test_cc2(self): + spikes = bp.math.random.randint(0, 2, (1000, 10)) + print(bp.measure.cross_correlation(spikes, 1.)) + print(bp.measure.cross_correlation(spikes, 0.5)) + + def test_cc3(self): + spikes = bp.math.random.random((1000, 100)) < 0.8 + print(bp.measure.cross_correlation(spikes, 1.)) + print(bp.measure.cross_correlation(spikes, 0.5)) + + def test_cc4(self): + spikes = bp.math.random.random((1000, 100)) < 0.2 + print(bp.measure.cross_correlation(spikes, 1.)) + print(bp.measure.cross_correlation(spikes, 0.5)) + + def test_cc5(self): + spikes = bp.math.random.random((1000, 100)) < 0.05 + print(bp.measure.cross_correlation(spikes, 1.)) + print(bp.measure.cross_correlation(spikes, 0.5)) + + +class TestVoltageFluctuation(unittest.TestCase): + def test_vf1(self): + voltages = bp.math.random.normal(0, 10, size=(1000, 100)) + print(bp.measure.voltage_fluctuation(voltages)) + + voltages = bp.math.ones((1000, 100)) + print(bp.measure.voltage_fluctuation(voltages)) + + +class TestFunctionalConnectivity(unittest.TestCase): + def test_cf1(self): + act = bp.math.random.random((10000, 3)) + print(bp.measure.functional_connectivity(act)) + + +class TestMatrixCorrelation(unittest.TestCase): + def test_mc(self): + A = bp.math.random.random((100, 100)) + B = bp.math.random.random((100, 100)) + print(bp.measure.matrix_correlation(A, B)) + diff --git a/brainpy/measure/tests/test_firings.py b/brainpy/measure/tests/test_firings.py new file mode 100644 index 00000000..5b703349 --- /dev/null +++ b/brainpy/measure/tests/test_firings.py @@ -0,0 +1,22 @@ +# -*- coding: utf-8 -*- + + +import unittest +import brainpy as bp + + +class TestFiringRate(unittest.TestCase): + def test_fr1(self): + spikes = bp.math.ones((1000, 10)) + print(bp.measure.firing_rate(spikes, 1.)) + + def test_fr2(self): + spikes = bp.math.random.random((1000, 10)) < 0.2 + print(bp.measure.firing_rate(spikes, 1.)) + print(bp.measure.firing_rate(spikes, 10.)) + + def test_fr3(self): + spikes = bp.math.random.random((1000, 10)) < 0.02 + print(bp.measure.firing_rate(spikes, 1.)) + print(bp.measure.firing_rate(spikes, 5.)) + diff --git a/brainpy/nn/base.py b/brainpy/nn/base.py index 39260dbd..36a3c8d5 100644 --- a/brainpy/nn/base.py +++ b/brainpy/nn/base.py @@ -12,7 +12,7 @@ This module provide basic Node class for whole ``brainpy.nn`` system. This means ``brainpy.nn.Network`` is only used to pack element nodes. It will be never be an element node. - ``brainpy.nn.FrozenNetwork``: The whole network which can be represented as a basic - elementary node when composing a larger network. TODO + elementary node when composing a larger network (TODO). """ from copy import copy, deepcopy @@ -48,6 +48,16 @@ __all__ = [ NODE_STATES = ['inputs', 'feedbacks', 'state', 'output'] +SUPPORTED_LAYOUTS = ['shell_layout', + 'multipartite_layout', + 'spring_layout', + 'spiral_layout', + 'spectral_layout', + 'random_layout', + 'planar_layout', + 'kamada_kawai_layout', + 'circular_layout'] + def not_implemented(fun: Callable) -> Callable: """Marks the given module method is not implemented. @@ -92,8 +102,10 @@ class Node(Base): self._is_ff_initialized = False self._is_fb_initialized = False self._is_state_initialized = False + self._is_fb_state_initialized = False self._trainable = trainable self._state = None # the state of the current node + self._fb_output = None # the feedback output of the current node # data pass function if self.data_pass_type not in DATA_PASS_FUNC: raise ValueError(f'Unsupported data pass type {self.data_pass_type}. ' @@ -111,12 +123,9 @@ class Node(Base): name = type(self).__name__ prefix = ' ' * (len(name) + 1) line1 = (f"{name}(name={self.name}, " - f"trainable={self.trainable}, " f"forwards={self.feedforward_shapes}, " f"feedbacks={self.feedback_shapes}, \n") - line2 = (f"{prefix}output={self.output_shape}, " - f"support_feedback={self.support_feedback}, " - f"data_pass_type={self.data_pass_type})") + line2 = f"{prefix}output={self.output_shape}" return line1 + line2 def __call__(self, *args, **kwargs) -> Tensor: @@ -194,7 +203,7 @@ class Node(Base): @property def state(self) -> Optional[Tensor]: """Node current internal state.""" - if self.is_ff_initialized: + if self._is_ff_initialized: return self._state return None @@ -209,9 +218,9 @@ class Node(Base): This method allows the maximum flexibility to change the node state. It can set a new data (same shape, same dtype) - to the state. It can also set the data with another batch size. - - We highly recommend the user to use this function. + to the state. It can also set a new data with the different + shape. We highly recommend the user to use this function. + instead of using ``self.state.value``. """ if self.state is None: if self.output_shape is not None: @@ -225,31 +234,52 @@ class Node(Base): self.state._value = bm.as_device_array(state) @property - def trainable(self) -> bool: - """Returns if the Node can be trained.""" - return self._trainable + def fb_output(self) -> Optional[Tensor]: + return self._fb_output - @property - def is_ff_initialized(self) -> bool: - return self._is_ff_initialized + @fb_output.setter + def fb_output(self, value: Tensor): + raise NotImplementedError('Please use "set_fb_output()" to reset the node feedback state, ' + 'or use "self.fb_output.value" to change the state content.') - @is_ff_initialized.setter - def is_ff_initialized(self, value: bool): - assert isinstance(value, bool) - self._is_ff_initialized = value + def set_fb_output(self, state: Tensor): + """ + Safely set the feedback state of the node. - @property - def is_fb_initialized(self) -> bool: - return self._is_fb_initialized + This method allows the maximum flexibility to change the + node state. It can set a new data (same shape, same dtype) + to the state. It can also set a new data with the different + shape. We highly recommend the user to use this function. + instead of using ``self.fb_output.value``. + """ + if self.fb_output is None: + if self.output_shape is not None: + check_batch_shape(self.output_shape, state.shape) + self._fb_output = bm.Variable(state) if not isinstance(state, bm.Variable) else state + else: + check_batch_shape(self.fb_output.shape, state.shape) + if self.fb_output.dtype != state.dtype: + raise MathError('Cannot set the feedback state, because the dtype is ' + f'not consistent: {self.fb_output.dtype} != {state.dtype}') + self.fb_output._value = bm.as_device_array(state) - @is_fb_initialized.setter - def is_fb_initialized(self, value: bool): - assert isinstance(value, bool) - self._is_fb_initialized = value + @property + def trainable(self) -> bool: + """Returns if the Node can be trained.""" + return self._trainable @property - def is_state_initialized(self): - return self._is_state_initialized + def is_initialized(self) -> bool: + if self._is_ff_initialized and self._is_state_initialized: + if self.feedback_shapes is not None: + if self._is_fb_initialized and self._is_fb_state_initialized: + return True + else: + return False + else: + return True + else: + return False @trainable.setter def trainable(self, value: bool): @@ -268,7 +298,7 @@ class Node(Base): self.set_feedforward_shapes(size) def set_feedforward_shapes(self, feedforward_shapes: Dict): - if not self.is_ff_initialized: + if not self._is_ff_initialized: check_dict_data(feedforward_shapes, key_type=(Node, str), val_type=(list, tuple), @@ -278,11 +308,11 @@ class Node(Base): if self.feedforward_shapes is not None: for key, size in self._feedforward_shapes.items(): if key not in feedforward_shapes: - raise ValueError(f"Impossible to reset the input data of {self.name}. " + raise ValueError(f"Impossible to reset the input shape of {self.name}. " f"Because this Node has the input dimension {size} from {key}. " f"While we do not find it in the given feedforward_shapes") if not check_batch_shape(size, feedforward_shapes[key], mode='bool'): - raise ValueError(f"Impossible to reset the input data of {self.name}. " + raise ValueError(f"Impossible to reset the input shape of {self.name}. " f"Because this Node has the input dimension {size} from {key}. " f"While the give shape is {feedforward_shapes[key]}") @@ -296,7 +326,7 @@ class Node(Base): self.set_feedback_shapes(size) def set_feedback_shapes(self, fb_shapes: Dict): - if not self.is_fb_initialized: + if not self._is_fb_initialized: check_dict_data(fb_shapes, key_type=(Node, str), val_type=(tuple, list), name='fb_shapes') self._feedback_shapes = fb_shapes else: @@ -321,14 +351,21 @@ class Node(Base): self.set_output_shape(size) @property - def support_feedback(self): - if hasattr(self.init_fb, 'not_implemented'): - if self.init_fb.not_implemented: + def is_feedback_input_supported(self): + if hasattr(self.init_fb_conn, 'not_implemented'): + if self.init_fb_conn.not_implemented: return False return True + @property + def is_feedback_supported(self): + if self.fb_output is None: + return False + else: + return True + def set_output_shape(self, shape: Sequence[int]): - if not self.is_ff_initialized: + if not self._is_ff_initialized: if not isinstance(shape, (tuple, list)): raise ValueError(f'Must be a sequence of int, but got {shape}') self._output_shape = tuple(shape) @@ -368,84 +405,88 @@ class Node(Base): new_obj.name = self.unique_name(name or (self.name + '_copy')) return new_obj - def _ff_init(self): - if not self.is_ff_initialized: + def _init_ff_conn(self): + if not self._is_ff_initialized: try: - self.init_ff() + self.init_ff_conn() except Exception as e: raise ModelBuildError(f'{self.name} initialization failed.') from e self._is_ff_initialized = True + if self.output_shape is None: + raise ValueError(f'Please set the output shape when implementing ' + f'"init_ff()" of the node {self.name}') - def _fb_init(self): - if not self.is_fb_initialized: + def _init_fb_conn(self): + if not self._is_fb_initialized: try: - self.init_fb() + self.init_fb_conn() except Exception as e: raise ModelBuildError(f"{self.name} initialization failed.") from e self._is_fb_initialized = True @not_implemented - def init_fb(self): + def init_fb_conn(self): + """Initialize the feedback connections. + This function will be called only once.""" raise ValueError(f'This node \n\n{self} \n\ndoes not support feedback connection.') - def init_ff(self): + def init_ff_conn(self): + """Initialize the feedforward connections. + This function will be called only once.""" raise NotImplementedError('Please implement the feedforward initialization.') - def init_state(self, num_batch=1): + def _init_state(self, num_batch=1): + state = self.init_state(num_batch) + if state is not None: + self.set_state(state) + + def _init_fb_output(self, num_batch=1): + output = self.init_fb_output(num_batch) + if output is not None: + self.set_fb_output(output) + + def init_state(self, num_batch=1) -> Optional[Tensor]: + """Set the initial node state. + + This function can be called multiple times.""" pass - def initialize(self, - ff: Optional[Union[Tensor, Dict[Any, Tensor]]] = None, - fb: Optional[Union[Tensor, Dict[Any, Tensor]]] = None, - num_batch: int = None): + def init_fb_output(self, num_batch=1) -> Optional[Tensor]: + """Set the initial node feedback state. + + This function can be called multiple times. However, + it is only triggered when the node has feedback connections. """ - Initialize the whole network. This function must be called before applying JIT. + return bm.zeros((num_batch,) + self.output_shape[1:], dtype=bm.float_) - This function is useful, because it is independent from the __call__ function. - We can use this function before we applying JIT to __call__ function. + def initialize(self, num_batch: int): """ + Initialize the node. This function must be called before applying JIT. - # feedforward initialization - if not self.is_ff_initialized: - # feedforward data - if ff is None: - if self._feedforward_shapes is None: - raise ValueError('Cannot initialize this node, because we detect ' - 'both "feedforward_shapes"and "ff" inputs are None. ') - in_sizes = self._feedforward_shapes - if num_batch is None: - raise ValueError('"num_batch" cannot be None when "ff" is not provided.') - check_integer(num_batch, 'num_batch', min_bound=0, allow_none=False) - else: - if isinstance(ff, (bm.ndarray, jnp.ndarray)): - ff = {self.name: ff} - assert isinstance(ff, dict), f'"ff" must be a dict or a tensor, got {type(ff)}: {ff}' - assert self.name in ff, f'Cannot find input for this node \n\n{self} \n\nwhen given "ff" {ff}' - batch_sizes = [v.shape[0] for v in ff.values()] - if set(batch_sizes) != 1: - raise ValueError('Batch sizes must be consistent, but we got multiple ' - f'batch sizes {set(batch_sizes)} for the given input: \n' - f'{ff}') - in_sizes = {k: (None,) + v.shape[1:] for k, v in ff.items()} - if (num_batch is not None) and (num_batch != batch_sizes[0]): - raise ValueError(f'The provided "num_batch" {num_batch} is consistent with the ' - f'batch size of the provided data {batch_sizes[0]}') - - # initialize feedforward - self.set_feedforward_shapes(in_sizes) - self._ff_init() - self.init_state(num_batch) - self._is_state_initialized = True + This function is useful, because it is independent of the __call__ function. + We can use this function before we apply JIT to __call__ function. + """ - # feedback initialization - if fb is not None: - if not self.is_fb_initialized: # initialize feedback - assert isinstance(fb, dict), f'"fb" must be a dict, got {type(fb)}' - fb_sizes = {k: (None,) + v.shape[1:] for k, v in fb.items()} - self.set_feedback_shapes(fb_sizes) - self._fb_init() - else: - self._is_fb_initialized = True + # feedforward initialization + if self.feedforward_shapes is None: + raise ValueError('Cannot initialize this node, because we detect ' + 'both "feedforward_shapes" is None. ' + 'Two ways can solve this problem:\n\n' + '1. Connecting an instance of "brainpy.nn.Input()" to this node. \n' + '2. Providing the "input_shape" when initialize the node.') + check_integer(num_batch, 'num_batch', min_bound=0, allow_none=False) + self._init_ff_conn() + + # initialize state + self._init_state(num_batch) + self._is_state_initialized = True + + if self.feedback_shapes is not None: + # feedback initialization + self._init_fb_conn() + # initialize feedback state + self._init_fb_output(num_batch) + self._is_fb_state_initialized = True def _check_inputs(self, ff, fb=None): # check feedforward inputs @@ -477,9 +518,8 @@ class Node(Base): forced_feedbacks: Dict[str, Tensor] = None, monitors=None, **kwargs) -> Union[Tensor, Tuple[Tensor, Dict]]: - # # initialization - # self.initialize(ff, fb) - if not (self.is_ff_initialized and self.is_fb_initialized and self.is_state_initialized): + # checking + if not self.is_initialized: raise ValueError('Please initialize the Node first by calling "initialize()" function.') # initialize the forced data @@ -511,6 +551,7 @@ class Node(Base): assert self.state is not None, (f'{self} \n\nhas no state, while ' f'the user try to monitor its state.') state_monitors[key] = None + # calling ff, fb = self._check_inputs(ff, fb=fb) if 'inputs' in state_monitors: @@ -528,7 +569,7 @@ class Node(Base): else: return output - def forward(self, ff, fb=None, **kwargs): + def forward(self, ff, fb=None, **shared_kwargs): """The feedforward computation function of a node. Parameters @@ -537,7 +578,7 @@ class Node(Base): The feedforward inputs. fb: optional, tensor, dict, sequence The feedback inputs. - **kwargs + **shared_kwargs Other parameters. Returns @@ -547,12 +588,12 @@ class Node(Base): """ raise NotImplementedError - def feedback(self, **kwargs): + def feedback(self, ff_output, **shared_kwargs): """The feedback computation function of a node. Parameters ---------- - **kwargs + **shared_kwargs Other global parameters. Returns @@ -560,12 +601,17 @@ class Node(Base): Tensor A feedback output tensor value. """ - return self.state + return ff_output class RecurrentNode(Node): """ Basic class for recurrent node. + + The supports for the recurrent node are: + + - Self-connection when using ``plot_node_graph()`` function + - Set trainable state with ``state_trainable=True``. """ def __init__(self, @@ -617,19 +663,6 @@ class RecurrentNode(Node): else: self.state._value = bm.as_device_array(state) - def __repr__(self): - name = type(self).__name__ - prefix = ' ' * (len(name) + 1) - - line1 = (f"{name}(name={self.name}, recurrent=True, " - f"trainable={self.trainable}, \n") - line2 = (f"{prefix}forwards={self.feedforward_shapes}, " - f"feedbacks={self.feedback_shapes}, \n") - line3 = (f"{prefix}output={self.output_shape}, " - f"support_feedback={self.support_feedback}, " - f"data_pass_type={self.data_pass_type})") - return line1 + line2 + line3 - class Network(Node): """Basic Network class for neural network building in BrainPy.""" @@ -806,8 +839,8 @@ class Network(Node): def replace_graph(self, nodes: Sequence[Node], - ff_edges: Sequence[Tuple[Node, Node]], - fb_edges: Sequence[Tuple[Node, Node]] = None) -> "Network": + ff_edges: Sequence[Tuple[Node, ...]], + fb_edges: Sequence[Tuple[Node, ...]] = None) -> "Network": if fb_edges is None: fb_edges = tuple() # assign nodes and edges @@ -817,16 +850,45 @@ class Network(Node): self._network_init() return self - def init_ff(self): + def set_output_shape(self, shape: Dict[str, Sequence[int]]): + # check shape + if not isinstance(shape, dict): + raise ValueError(f'Must be a dict of , but got {type(shape)}: {shape}') + for key, val in shape.items(): + if not isinstance(val, (tuple, list)): + raise ValueError(f'Must be a sequence of int, but got {val} for key "{key}"') + # for s in val: + # if not (isinstance(s, int) or (s is None)): + # raise ValueError(f'Must be a sequence of int, but got {val}') + + if not self._is_ff_initialized: + if len(self.exit_nodes) == 1: + self._output_shape = tuple(shape.values())[0] + else: + self._output_shape = shape + else: + for val in shape.values(): + check_batch_shape(val, self.output_shape) + + def init_ff_conn(self): + """Initialize the feedforward connections of the network. + This function will be called only once.""" # input shapes of entry nodes for node in self.entry_nodes: + # set ff shapes if node.feedforward_shapes is None: if self.feedforward_shapes is None: raise ValueError('Cannot find the input size. ' 'Cannot initialize the network.') else: node.set_feedforward_shapes({node.name: self._feedforward_shapes[node.name]}) - node._ff_init() + # set fb shapes + if node in self.fb_senders: + fb_shapes = {node: node.output_shape for node in self.fb_senders.get(node, [])} + if None not in fb_shapes.values(): + node.set_feedback_shapes(fb_shapes) + # init ff conn + node._init_ff_conn() # initialize the data children_queue = [] @@ -840,49 +902,79 @@ class Network(Node): children_queue.append(child) while len(children_queue): node = children_queue.pop(0) - # initialize input and output sizes + # set ff shapes parent_sizes = {p: p.output_shape for p in self.ff_senders.get(node, [])} node.set_feedforward_shapes(parent_sizes) - node._ff_init() + if node in self.fb_senders: + # set fb shapes + fb_shapes = {node: node.output_shape for node in self.fb_senders.get(node, [])} + if None not in fb_shapes.values(): + node.set_feedback_shapes(fb_shapes) + # init ff conn + node._init_ff_conn() # append children for child in self.ff_receivers.get(node, []): ff_senders[child].remove(node) if len(ff_senders.get(child, [])) == 0: children_queue.append(child) - def init_fb(self): + # set output shape + out_sizes = {node: node.output_shape for node in self.exit_nodes} + self.set_output_shape(out_sizes) + + def init_fb_conn(self): + """Initialize the feedback connections of the network. + This function will be called only once.""" for receiver, senders in self.fb_senders.items(): fb_sizes = {node: node.output_shape for node in senders} + if None in fb_sizes.values(): + none_size_nodes = [repr(n) for n, v in fb_sizes.items() if v is None] + none_size_nodes = "\n".join(none_size_nodes) + raise ValueError(f'Output shapes of nodes \n\n' + f'{none_size_nodes}\n\n' + f'have not been initialized, ' + f'leading us cannot initialize the ' + f'feedback connection of node \n\n' + f'{receiver}') receiver.set_feedback_shapes(fb_sizes) - receiver._fb_init() + receiver._init_fb_conn() - def init_state(self, num_batch=1): - """Initialize the states of all children nodes.""" + def _init_state(self, num_batch=1): + """Initialize the states of all children nodes. + This function can be called multiple times.""" for node in self.lnodes: - node.init_state(num_batch) + node._init_state(num_batch) + + def _init_fb_output(self, num_batch=1): + """Initialize the node feedback state. - def initialize(self, - ff: Optional[Union[Tensor, Dict[Any, Tensor]]] = None, - fb: Optional[Union[Tensor, Dict[Any, Tensor]]] = None, - num_batch: int = None): + This function can be called multiple times. However, + it is only triggered when the node has feedback connections. + """ + for node in self.feedback_nodes: + node._init_fb_output(num_batch) + + def initialize(self, num_batch: int): """ Initialize the whole network. This function must be called before applying JIT. - This function is useful, because it is independent from the __call__ function. - We can use this function before we applying JIT to __call__ function. + This function is useful, because it is independent of the __call__ function. + We can use this function before we apply JIT to __call__ function. """ - # feedforward initialization - if not self.is_ff_initialized: + # set feedforward shapes + if not self._is_ff_initialized: # check input and output nodes - assert len(self.entry_nodes) > 0, (f"We found this network \n\n" - f"{self} " - f"\n\nhas no input nodes.") - assert len(self.exit_nodes) > 0, (f"We found this network \n\n" - f"{self} " - f"\n\nhas no output nodes.") - - # check whether has a feedforward path for each feedback pair + if len(self.entry_nodes) <= 0: + raise ValueError(f"We found this network \n\n" + f"{self} " + f"\n\nhas no input nodes.") + if len(self.exit_nodes) <= 0: + raise ValueError(f"We found this network \n\n" + f"{self} " + f"\n\nhas no output nodes.") + + # check whether it has a feedforward path for each feedback pair ff_edges = [(a.name, b.name) for a, b in self.ff_edges] for node, receiver in self.fb_edges: if not detect_path(receiver.name, node.name, ff_edges): @@ -895,49 +987,42 @@ class Network(Node): f'feedforward connection between them. ') # feedforward checking - if ff is None: - in_sizes = dict() - for node in self.entry_nodes: - if node._feedforward_shapes is None: - raise ValueError('Cannot initialize this node, because we detect ' - 'both "feedforward_shapes" and "ff" inputs are None. ' - 'Maybe you need a brainpy.nn.Input instance ' - 'to instruct the input size.') - in_sizes.update(node._feedforward_shapes) - if num_batch is None: - raise ValueError('"num_batch" cannot be None when "ff" is not provided.') - check_integer(num_batch, 'num_batch', min_bound=0, allow_none=False) - - else: - if isinstance(ff, (bm.ndarray, jnp.ndarray)): - ff = {self.entry_nodes[0].name: ff} - assert isinstance(ff, dict), f'ff must be a dict or a tensor, got {type(ff)}: {ff}' - for n in self.entry_nodes: - if n.name not in ff: - raise ValueError(f'Cannot find the input of the node {n}') - batch_sizes = [v.shape[0] for v in ff.values()] - if len(set(batch_sizes)) != 1: - raise ValueError('Batch sizes must be consistent, but we got multiple ' - f'batch sizes {set(batch_sizes)} for the given input: \n' - f'{ff}') - in_sizes = {k: (None,) + v.shape[1:] for k, v in ff.items()} - if (num_batch is not None) and (num_batch != batch_sizes[0]): - raise ValueError(f'The provided "num_batch" {num_batch} is consistent with the ' - f'batch size of the provided data {batch_sizes[0]}') - - # initialize feedforward + in_sizes = dict() + for node in self.entry_nodes: + if node.feedforward_shapes is None: + raise ValueError('Cannot initialize this node, because we detect ' + '"feedforward_shapes" is None. ' + 'Maybe you need a brainpy.nn.Input instance ' + 'to instruct the input size.') + in_sizes.update(node._feedforward_shapes) self.set_feedforward_shapes(in_sizes) - self._ff_init() - self.init_state(num_batch) - self._is_state_initialized = True + + # feedforward initialization + if self.feedforward_shapes is None: + raise ValueError('Cannot initialize this node, because we detect ' + 'both "feedforward_shapes" is None. ') + check_integer(num_batch, 'num_batch', min_bound=1, allow_none=False) + self._init_ff_conn() + + # initialize state + self._init_state(num_batch) + self._is_state_initialized = True + + # set feedback shapes + if not self._is_fb_initialized: + if len(self.fb_senders) > 0: + fb_sizes = dict() + for sender in self.fb_senders.keys(): + fb_sizes[sender] = sender.output_shape + self.set_feedback_shapes(fb_sizes) # feedback initialization - if len(self.fb_senders): - # initialize feedback - if not self.is_fb_initialized: - self._fb_init() - else: - self.is_fb_initialized = True + if self.feedback_shapes is not None: + self._init_fb_conn() + + # initialize feedback state + self._init_fb_output(num_batch) + self._is_fb_state_initialized = True def _check_inputs(self, ff, fb=None): # feedforward inputs @@ -986,8 +1071,7 @@ class Network(Node): monitors: Optional[Sequence[str]] = None, **kwargs): # initialization - # self.initialize(ff, fb) - if not (self.is_ff_initialized and self.is_fb_initialized and self.is_state_initialized): + if not self.is_initialized: raise ValueError('Please initialize the Network first by calling "initialize()" function.') # initialize the forced data @@ -1038,7 +1122,7 @@ class Network(Node): forced_states: Dict[str, Tensor] = None, forced_feedbacks: Dict[str, Tensor] = None, monitors: Dict = None, - **kwargs): + **shared_kwargs): """The main computation function of a network. Parameters @@ -1053,7 +1137,7 @@ class Network(Node): The fixed feedback for the nodes in the network. monitors: optional, sequence Can be used to monitor the state or the attribute of a node in the network. - **kwargs + **shared_kwargs Other parameters which will be parsed into every node. Returns @@ -1077,10 +1161,11 @@ class Network(Node): parent_outputs = {} for i, node in enumerate(self._entry_nodes): ff_ = {node.name: ff[i]} - fb_ = {p: (forced_feedbacks[p.name] if (p.name in forced_feedbacks) else p.feedback()) + fb_ = {p: (forced_feedbacks[p.name] if (p.name in forced_feedbacks) else p.fb_output) for p in self.fb_senders.get(node, [])} self._call_a_node(node, ff_, fb_, monitors, forced_states, - parent_outputs, children_queue, ff_senders, **kwargs) + parent_outputs, children_queue, ff_senders, + **shared_kwargs) runned_nodes.add(node.name) # run the model @@ -1088,23 +1173,23 @@ class Network(Node): node = children_queue.pop(0) # get feedforward and feedback inputs ff = {p: parent_outputs[p] for p in self.ff_senders.get(node, [])} - fb = {p: (forced_feedbacks[p.name] if (p.name in forced_feedbacks) else p.feedback()) + fb = {p: (forced_feedbacks[p.name] if (p.name in forced_feedbacks) else p.fb_output) for p in self.fb_senders.get(node, [])} # call the node self._call_a_node(node, ff, fb, monitors, forced_states, parent_outputs, children_queue, ff_senders, - **kwargs) - - # #- remove unnecessary parent outputs -# - # needed_parents = [] - # runned_nodes.add(node.name) - # for child in (all_nodes - runned_nodes): - # for parent in self.ff_senders[self.implicit_nodes[child]]: - # needed_parents.append(parent.name) - # for parent in list(parent_outputs.keys()): - # _name = parent.name - # if _name not in needed_parents and _name not in output_nodes: - # parent_outputs.pop(parent) + **shared_kwargs) + + # - remove unnecessary parent outputs - # + needed_parents = [] + runned_nodes.add(node.name) + for child in (all_nodes - runned_nodes): + for parent in self.ff_senders[self.implicit_nodes[child]]: + needed_parents.append(parent.name) + for parent in list(parent_outputs.keys()): + _name = parent.name + if _name not in needed_parents and _name not in output_nodes: + parent_outputs.pop(parent) # returns if len(self.exit_nodes) > 1: @@ -1114,7 +1199,8 @@ class Network(Node): return state, monitors def _call_a_node(self, node, ff, fb, monitors, forced_states, - parent_outputs, children_queue, ff_senders, **kwargs): + parent_outputs, children_queue, ff_senders, + **shared_kwargs): ff = node.data_pass_func(ff) if f'{node.name}.inputs' in monitors: monitors[f'{node.name}.inputs'] = ff @@ -1123,12 +1209,17 @@ class Network(Node): fb = node.data_pass_func(fb) if f'{node.name}.feedbacks' in monitors: monitors[f'{node.name}.feedbacks'] = fb - parent_outputs[node] = node.forward(ff, fb, **kwargs) + parent_outputs[node] = node.forward(ff, fb, **shared_kwargs) else: - parent_outputs[node] = node.forward(ff, **kwargs) - if node.name in forced_states: # forced state + parent_outputs[node] = node.forward(ff, **shared_kwargs) + # get the feedback state + if node in self.fb_receivers: + node.set_fb_output(node.feedback(parent_outputs[node], **shared_kwargs)) + # forced state + if node.name in forced_states: node.state.value = forced_states[node.name] - parent_outputs[node] = forced_states[node.name] + # parent_outputs[node] = forced_states[node.name] + # monitor the values if f'{node.name}.state' in monitors: monitors[f'{node.name}.state'] = node.state.value if f'{node.name}.output' in monitors: @@ -1143,7 +1234,7 @@ class Network(Node): fig_size: tuple = (10, 10), node_size: int = 2000, arrow_size: int = 20, - layout='spectral_layout'): + layout='shell_layout'): """Plot the node graph based on NetworkX package Parameters @@ -1155,7 +1246,17 @@ class Network(Node): arrow_size:int, default to 20 The size of the arrow layout: str - The graph layout. More please see networkx Graph Layout. + The graph layout. The supported layouts are: + + - "shell_layout" + - "multipartite_layout" + - "spring_layout" + - "spiral_layout" + - "spectral_layout" + - "random_layout" + - "planar_layout" + - "kamada_kawai_layout" + - "circular_layout" """ try: import networkx as nx @@ -1204,15 +1305,8 @@ class Network(Node): G.add_edges_from(fb_edges) G.add_edges_from(rec_edges) - assert layout in ['shell_layout', - 'multipartite_layout', - 'spring_layout', - 'spiral_layout', - 'spectral_layout', - 'random_layout', - 'planar_layout', - 'kamada_kawai_layout', - 'circular_layout'] + if layout not in SUPPORTED_LAYOUTS: + raise UnsupportedError(f'Only support layouts: {SUPPORTED_LAYOUTS}') layout = getattr(nx, layout)(G) plt.figure(figsize=fig_size) @@ -1252,10 +1346,12 @@ class Network(Node): proxie = [] labels = [] if len(nodes_trainable): - proxie.append(Line2D([], [], color='white', marker='o', markerfacecolor=trainable_color)) + proxie.append(Line2D([], [], color='white', marker='o', + markerfacecolor=trainable_color)) labels.append('Trainable') if len(nodes_untrainable): - proxie.append(Line2D([], [], color='white', marker='o', markerfacecolor=untrainable_color)) + proxie.append(Line2D([], [], color='white', marker='o', + markerfacecolor=untrainable_color)) labels.append('Untrainable') if len(ff_edges): proxie.append(Line2D([], [], color=ff_color, linewidth=2)) @@ -1267,8 +1363,7 @@ class Network(Node): proxie.append(Line2D([], [], color=rec_color, linewidth=2)) labels.append('Recurrent') - plt.legend(proxie, labels, scatterpoints=1, markerscale=2, - loc='best') + plt.legend(proxie, labels, scatterpoints=1, markerscale=2, loc='best') plt.tight_layout() plt.show() diff --git a/brainpy/nn/nodes/ANN/conv.py b/brainpy/nn/nodes/ANN/conv.py index 63c590fe..85c378a6 100644 --- a/brainpy/nn/nodes/ANN/conv.py +++ b/brainpy/nn/nodes/ANN/conv.py @@ -4,9 +4,8 @@ import jax.lax import brainpy.math as bm -from brainpy.initialize import XavierNormal, ZeroInit +from brainpy.initialize import XavierNormal, ZeroInit, init_param from brainpy.nn.base import Node -from brainpy.nn.utils import init_param __all__ = [ 'Conv2D', @@ -81,7 +80,7 @@ class Conv2D(Node): self.padding = padding self.groups = groups - def init_ff(self): + def init_ff_conn(self): assert self.num_input % self.groups == 0, '"nin" should be divisible by groups' size = _check_tuple(self.kernel_size) + (self.num_input // self.groups, self.num_output) self.w = init_param(self.w_init, size) @@ -90,7 +89,7 @@ class Conv2D(Node): self.w = bm.TrainVar(self.w) self.b = bm.TrainVar(self.b) - def forward(self, ff, **kwargs): + def forward(self, ff, **shared_kwargs): x = ff[0] nin = self.w.value.shape[2] * self.groups assert x.shape[1] == nin, (f'Attempting to convolve an input with {x.shape[1]} input channels ' diff --git a/brainpy/nn/nodes/ANN/dropout.py b/brainpy/nn/nodes/ANN/dropout.py index 6a735066..bbf4e24c 100644 --- a/brainpy/nn/nodes/ANN/dropout.py +++ b/brainpy/nn/nodes/ANN/dropout.py @@ -44,11 +44,11 @@ class Dropout(Node): self.prob = prob self.rng = bm.random.RandomState(seed=seed) - def init_ff(self): + def init_ff_conn(self): self.set_output_shape(self.feedforward_shapes) - def forward(self, ff, **kwargs): - if kwargs.get('train', True): + def forward(self, ff, **shared_kwargs): + if shared_kwargs.get('train', True): keep_mask = self.rng.bernoulli(self.prob, ff.shape) return bm.where(keep_mask, ff / self.prob, 0.) else: diff --git a/brainpy/nn/nodes/ANN/rnn_cells.py b/brainpy/nn/nodes/ANN/rnn_cells.py index 9fdae486..3d5683b1 100644 --- a/brainpy/nn/nodes/ANN/rnn_cells.py +++ b/brainpy/nn/nodes/ANN/rnn_cells.py @@ -1,14 +1,20 @@ # -*- coding: utf-8 -*- +from typing import Union, Callable + import brainpy.math as bm -from brainpy.initialize import (XavierNormal, ZeroInit, - Uniform, Orthogonal) +from brainpy.initialize import (XavierNormal, + ZeroInit, + Uniform, + Orthogonal, + init_param, + Initializer) from brainpy.nn.base import RecurrentNode -from brainpy.nn.utils import init_param from brainpy.tools.checking import (check_integer, check_initializer, check_shape_consistency) +from brainpy.types import Tensor __all__ = [ 'VanillaRNN', @@ -33,19 +39,21 @@ class VanillaRNN(RecurrentNode): def __init__( self, num_unit: int, - state_initializer=Uniform(), - wi_initializer=XavierNormal(), - wh_initializer=XavierNormal(), - bias_initializer=ZeroInit(), - activation='relu', - trainable=True, + state_initializer: Union[Tensor, Callable, Initializer] = Uniform(), + wi_initializer: Union[Tensor, Callable, Initializer] = XavierNormal(), + wh_initializer: Union[Tensor, Callable, Initializer] = XavierNormal(), + bias_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(), + activation: str = 'relu', + trainable: bool = True, **kwargs ): super(VanillaRNN, self).__init__(trainable=trainable, **kwargs) self.num_unit = num_unit check_integer(num_unit, 'num_unit', min_bound=1, allow_none=False) + self.set_output_shape((None, self.num_unit)) + # initializers self._state_initializer = state_initializer self._wi_initializer = wi_initializer self._wh_initializer = wh_initializer @@ -55,23 +63,23 @@ class VanillaRNN(RecurrentNode): check_initializer(state_initializer, 'state_initializer', allow_none=False) check_initializer(bias_initializer, 'bias_initializer', allow_none=True) + # activation function self.activation = bm.activations.get(activation) - def init_ff(self): + def init_ff_conn(self): unique_size, free_sizes = check_shape_consistency(self.feedforward_shapes, -1, True) assert len(unique_size) == 1, 'Only support data with or without batch size.' - num_input = sum(free_sizes) - self.set_output_shape(unique_size + (self.num_unit,)) # weights + num_input = sum(free_sizes) self.Wff = init_param(self._wi_initializer, (num_input, self.num_unit)) self.Wrec = init_param(self._wh_initializer, (self.num_unit, self.num_unit)) - self.bff = init_param(self._bias_initializer, (self.num_unit,)) + self.bias = init_param(self._bias_initializer, (self.num_unit,)) if self.trainable: self.Wff = bm.TrainVar(self.Wff) self.Wrec = bm.TrainVar(self.Wrec) - self.bff = None if (self.bff is None) else bm.TrainVar(self.bff) + self.bias = None if (self.bias is None) else bm.TrainVar(self.bias) - def init_fb(self): + def init_fb_conn(self): unique_size, free_sizes = check_shape_consistency(self.feedback_shapes, -1, True) assert len(unique_size) == 1, 'Only support data with or without batch size.' num_feedback = sum(free_sizes) @@ -80,16 +88,15 @@ class VanillaRNN(RecurrentNode): if self.trainable: self.Wfb = bm.TrainVar(self.Wfb) - def init_state(self, num_batch): - state = init_param(self._state_initializer, (num_batch, self.num_unit)) - self.set_state(state) + def init_state(self, num_batch=1): + return init_param(self._state_initializer, (num_batch, self.num_unit)) - def forward(self, ff, fb=None, **kwargs): + def forward(self, ff, fb=None, **shared_kwargs): ff = bm.concatenate(ff, axis=-1) h = ff @ self.Wff h += self.state.value @ self.Wrec - if self.bff is not None: - h += self.bff + if self.bias is not None: + h += self.bias if fb is not None: fb = bm.concatenate(fb, axis=-1) h += fb @ self.Wfb @@ -98,8 +105,7 @@ class VanillaRNN(RecurrentNode): class GRU(RecurrentNode): - r""" - Gated Recurrent Unit. + r"""Gated Recurrent Unit. The implementation is based on (Chung, et al., 2014) [1]_ with biases. @@ -130,17 +136,18 @@ class GRU(RecurrentNode): def __init__( self, num_unit: int, - wi_initializer=Orthogonal(), - wh_initializer=Orthogonal(), - bias_initializer=ZeroInit(), - state_initializer=ZeroInit(), - trainable=True, + wi_initializer: Union[Tensor, Callable, Initializer] = Orthogonal(), + wh_initializer: Union[Tensor, Callable, Initializer] = Orthogonal(), + bias_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(), + state_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(), + trainable: bool = True, **kwargs ): super(GRU, self).__init__(trainable=trainable, **kwargs) self.num_unit = num_unit check_integer(num_unit, 'num_unit', min_bound=1, allow_none=False) + self.set_output_shape((None, self.num_unit)) self._wi_initializer = wi_initializer self._wh_initializer = wh_initializer @@ -151,30 +158,39 @@ class GRU(RecurrentNode): check_initializer(state_initializer, 'state_initializer', allow_none=False) check_initializer(bias_initializer, 'bias_initializer', allow_none=True) - def init_ff(self): + def init_ff_conn(self): # data shape unique_size, free_sizes = check_shape_consistency(self.feedforward_shapes, -1, True) assert len(unique_size) == 1, 'Only support data with or without batch size.' - num_input = sum(free_sizes) - self.set_output_shape(unique_size + (self.num_unit,)) + # weights - self.i_weight = init_param(self._wi_initializer, (num_input, self.num_unit * 3)) - self.h_weight = init_param(self._wh_initializer, (self.num_unit, self.num_unit * 3)) + num_input = sum(free_sizes) + self.Wi_ff = init_param(self._wi_initializer, (num_input, self.num_unit * 3)) + self.Wh = init_param(self._wh_initializer, (self.num_unit, self.num_unit * 3)) self.bias = init_param(self._bias_initializer, (self.num_unit * 3,)) if self.trainable: - self.i_weight = bm.TrainVar(self.i_weight) - self.h_weight = bm.TrainVar(self.h_weight) + self.Wi_ff = bm.TrainVar(self.Wi_ff) + self.Wh = bm.TrainVar(self.Wh) self.bias = bm.TrainVar(self.bias) if (self.bias is not None) else None - def init_state(self, num_batch): - state = init_param(self._state_initializer, (num_batch, self.num_unit)) - self.set_state(state) + def init_fb_conn(self): + unique_size, free_sizes = check_shape_consistency(self.feedback_shapes, -1, True) + assert len(unique_size) == 1, 'Only support data with or without batch size.' + num_feedback = sum(free_sizes) + # weights + self.Wi_fb = init_param(self._wi_initializer, (num_feedback, self.num_unit * 3)) + if self.trainable: + self.Wi_fb = bm.TrainVar(self.Wi_fb) - def forward(self, ff, fb=None, **kwargs): - ff = bm.concatenate(ff, axis=-1) - gates_x = bm.matmul(ff, self.i_weight) + def init_state(self, num_batch=1): + return init_param(self._state_initializer, (num_batch, self.num_unit)) + + def forward(self, ff, fb=None, **shared_kwargs): + gates_x = bm.matmul(bm.concatenate(ff, axis=-1), self.Wi_ff) + if fb is not None: + gates_x += bm.matmul(bm.concatenate(fb, axis=-1), self.Wi_fb) zr_x, a_x = bm.split(gates_x, indices_or_sections=[2 * self.num_unit], axis=-1) - w_h_z, w_h_a = bm.split(self.h_weight, indices_or_sections=[2 * self.num_unit], axis=-1) + w_h_z, w_h_a = bm.split(self.Wh, indices_or_sections=[2 * self.num_unit], axis=-1) zr_h = bm.matmul(self.state, w_h_z) zr = zr_x + zr_h has_bias = (self.bias is not None) @@ -235,48 +251,62 @@ class LSTM(RecurrentNode): def __init__( self, num_unit: int, - weight_initializer=Orthogonal(), - bias_initializer=ZeroInit(), - state_initializer=ZeroInit(), - trainable=True, + wi_initializer: Union[Tensor, Callable, Initializer] = XavierNormal(), + wh_initializer: Union[Tensor, Callable, Initializer] = XavierNormal(), + bias_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(), + state_initializer: Union[Tensor, Callable, Initializer] = ZeroInit(), + trainable: bool = True, **kwargs ): super(LSTM, self).__init__(trainable=trainable, **kwargs) self.num_unit = num_unit check_integer(num_unit, 'num_unit', min_bound=1, allow_none=False) + self.set_output_shape((None, self.num_unit,)) self._state_initializer = state_initializer - self._weight_initializer = weight_initializer + self._wi_initializer = wi_initializer + self._wh_initializer = wh_initializer self._bias_initializer = bias_initializer - check_initializer(weight_initializer, 'weight_initializer', allow_none=False) + check_initializer(wi_initializer, 'wi_initializer', allow_none=False) + check_initializer(wh_initializer, 'wh_initializer', allow_none=False) check_initializer(bias_initializer, 'bias_initializer', allow_none=True) check_initializer(state_initializer, 'state_initializer', allow_none=False) - def init_ff(self): + def init_ff_conn(self): # data shape unique_size, free_sizes = check_shape_consistency(self.feedforward_shapes, -1, True) assert len(unique_size) == 1, 'Only support data with or without batch size.' - num_input = sum(free_sizes) - self.set_output_shape(unique_size + (self.num_unit,)) # weights - self.weight = init_param(self._weight_initializer, (num_input + self.num_unit, self.num_unit * 4)) + num_input = sum(free_sizes) + self.Wi_ff = init_param(self._wi_initializer, (num_input, self.num_unit * 4)) + self.Wh = init_param(self._wh_initializer, (self.num_unit, self.num_unit * 4)) self.bias = init_param(self._bias_initializer, (self.num_unit * 4,)) if self.trainable: - self.weight = bm.TrainVar(self.weight) + self.Wi_ff = bm.TrainVar(self.Wi_ff) + self.Wh = bm.TrainVar(self.Wh) self.bias = None if (self.bias is None) else bm.TrainVar(self.bias) - def init_state(self, num_batch): - hc = init_param(self._state_initializer, (num_batch * 2, self.num_unit)) - self.set_state(hc) + def init_fb_conn(self): + unique_size, free_sizes = check_shape_consistency(self.feedback_shapes, -1, True) + assert len(unique_size) == 1, 'Only support data with or without batch size.' + num_feedback = sum(free_sizes) + # weights + self.Wi_fb = init_param(self._wi_initializer, (num_feedback, self.num_unit * 4)) + if self.trainable: + self.Wi_fb = bm.TrainVar(self.Wi_fb) + + def init_state(self, num_batch=1): + return init_param(self._state_initializer, (num_batch * 2, self.num_unit)) - def forward(self, ff, fb=None, **kwargs): + def forward(self, ff, fb=None, **shared_kwargs): h, c = bm.split(self.state, 2) - xh = bm.concatenate(tuple(ff) + (h,), axis=-1) - if self.bias is None: - gated = xh @ self.weight - else: - gated = xh @ self.weight + self.bias + gated = bm.concatenate(ff, axis=-1) @ self.Wi_ff + if fb is not None: + gated += bm.concatenate(fb, axis=-1) @ self.Wi_fb + if self.bias is not None: + gated += self.bias + gated += h @ self.Wh i, g, f, o = bm.split(gated, indices_or_sections=4, axis=-1) c = bm.sigmoid(f + 1.) * c + bm.sigmoid(i) * bm.tanh(g) h = bm.sigmoid(o) * bm.tanh(c) @@ -291,7 +321,7 @@ class LSTM(RecurrentNode): @h.setter def h(self, value): if self.state is None: - raise ValueError('Cannot set "h" state. Because it is not initialized.') + raise ValueError('Cannot set "h" state. Because the state is not initialized.') self.state[:self.state.shape[0] // 2, :] = value @property @@ -302,7 +332,7 @@ class LSTM(RecurrentNode): @c.setter def c(self, value): if self.state is None: - raise ValueError('Cannot set "c" state. Because it is not initialized.') + raise ValueError('Cannot set "c" state. Because the state is not initialized.') self.state[self.state.shape[0] // 2:, :] = value diff --git a/brainpy/nn/nodes/RC/linear_readout.py b/brainpy/nn/nodes/RC/linear_readout.py index 5b9350d5..40cae1db 100644 --- a/brainpy/nn/nodes/RC/linear_readout.py +++ b/brainpy/nn/nodes/RC/linear_readout.py @@ -39,12 +39,12 @@ class LinearReadout(Dense): super(LinearReadout, self).__init__(num_unit=num_unit, weight_initializer=weight_initializer, bias_initializer=bias_initializer, **kwargs) def init_state(self, num_batch=1): - state = bm.Variable(bm.zeros((num_batch,) + self.output_shape[1:], dtype=bm.float_)) - self.set_state(state) + return bm.zeros((num_batch,) + self.output_shape[1:], dtype=bm.float_) - def forward(self, ff, fb=None, **kwargs): - self.state.value = super(LinearReadout, self).forward(ff, fb=fb, **kwargs) - return self.state + def forward(self, ff, fb=None, **shared_kwargs): + h = super(LinearReadout, self).forward(ff, fb=fb, **shared_kwargs) + self.state.value = h + return h def __force_init__(self, train_pars: Optional[Dict] = None): if train_pars is None: train_pars = dict() @@ -76,4 +76,4 @@ class LinearReadout(Dense): # update the weights e = bm.atleast_2d(self.state - target) # (1, num_output) dw = bm.dot(-c * k, e) # (num_hidden, num_output) - self.weights += dw + self.Wff += dw diff --git a/brainpy/nn/nodes/RC/nvar.py b/brainpy/nn/nodes/RC/nvar.py index dc2a97d4..142b8455 100644 --- a/brainpy/nn/nodes/RC/nvar.py +++ b/brainpy/nn/nodes/RC/nvar.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- from itertools import combinations_with_replacement -from typing import Union +from typing import Union, Sequence import numpy as np @@ -9,7 +9,8 @@ import brainpy.math as bm from brainpy.nn.base import RecurrentNode from brainpy.tools.checking import (check_shape_consistency, check_float, - check_integer) + check_integer, + check_sequence) __all__ = [ 'NVAR' @@ -46,7 +47,7 @@ class NVAR(RecurrentNode): ---------- delay: int The number of delay step. - order: int + order: int, sequence of int The nonlinear order. stride: int The stride to sample linear part vector in the delays. @@ -63,59 +64,67 @@ class NVAR(RecurrentNode): def __init__(self, delay: int, - order: int, + order: Union[int, Sequence[int]], stride: int = 1, constant: Union[float, int] = None, **kwargs): super(NVAR, self).__init__(**kwargs) - self.delay = delay + if not isinstance(order, (tuple, list)): + order = [order] self.order = order - self.stride = stride - self.constant = constant + check_sequence(order, 'order', elem_type=int, allow_none=False) + self.delay = delay check_integer(delay, 'delay', allow_none=False) - check_integer(order, 'order', allow_none=False) + self.stride = stride check_integer(stride, 'stride', allow_none=False) + self.constant = constant check_float(constant, 'constant', allow_none=True, allow_int=True) - def init_ff(self): + self.comb_ids = [] + # delay variables + self.num_delay = self.delay * self.stride + self.idx = bm.Variable(bm.array([0], dtype=bm.uint32)) + self.store = None + + def init_ff_conn(self): + """Initialize feedforward connections.""" # input dimension batch_size, free_size = check_shape_consistency(self.feedforward_shapes, -1, True) self.input_dim = sum(free_size) assert batch_size == (None,), f'batch_size must be None, but got {batch_size}' - # linear dimension linear_dim = self.delay * self.input_dim - # for each monomial created in the non linear part, indices + # for each monomial created in the non-linear part, indices # of the n components involved, n being the order of the # monomials. Precompute them to improve efficiency. - idx = np.array(list(combinations_with_replacement(np.arange(linear_dim), self.order))) - self.comb_ids = bm.asarray(idx) - # number of non linear components is (d + n - 1)! / (d - 1)! n! + for order in self.order: + idx = np.array(list(combinations_with_replacement(np.arange(linear_dim), order))) + self.comb_ids.append(bm.asarray(idx)) + # number of non-linear components is (d + n - 1)! / (d - 1)! n! # i.e. number of all unique monomials of order n made from the # linear components. - nonlinear_dim = len(self.comb_ids) + nonlinear_dim = sum([len(ids) for ids in self.comb_ids]) # output dimension - output_dim = int(linear_dim + nonlinear_dim) + self.output_dim = int(linear_dim + nonlinear_dim) if self.constant is not None: - output_dim += 1 - self.set_output_shape((None, output_dim)) - - # delay variables - self.num_delay = self.delay * self.stride - self.idx = bm.Variable(bm.array([0], dtype=bm.uint32)) - self.store = None + self.output_dim += 1 + self.set_output_shape((None, self.output_dim)) def init_state(self, num_batch=1): - # to store the k*s last inputs, k being the delay and s the strides + """Initialize the node state which depends on batch size.""" + # To store the last inputs. + # Note, the batch axis is not in the first dimension, so we + # manually handle the state of NVAR, rather return it. state = bm.zeros((self.num_delay, num_batch, self.input_dim), dtype=bm.float_) if self.store is None: self.store = bm.Variable(state) else: self.store.value = state - def forward(self, ff, fb=None, **kwargs): - # 1. store the current input + def forward(self, ff, fb=None, **shared_kwargs): + all_parts = [] + # 1. Store the current input ff = bm.concatenate(ff, axis=-1) self.store[self.idx[0]] = ff self.idx.value = (self.idx + 1) % self.num_delay @@ -124,12 +133,15 @@ class NVAR(RecurrentNode): select_ids = (self.idx[0] + bm.arange(self.num_delay)[::self.stride]) % self.num_delay linear_parts = bm.moveaxis(self.store[select_ids], 0, 1) # (num_batch, num_time, num_feature) linear_parts = bm.reshape(linear_parts, (linear_parts.shape[0], -1)) + # 3. constant + if self.constant is not None: + constant = bm.broadcast_to(self.constant, linear_parts.shape[:-1] + (1,)) + all_parts.append(constant) + all_parts.append(linear_parts) # 3. Nonlinear part: # select monomial terms and compute them - nonlinear_parts = bm.prod(linear_parts[:, self.comb_ids], axis=2) - if self.constant is None: - return bm.concatenate([linear_parts, nonlinear_parts], axis=-1) - else: - constant = bm.broadcast_to(self.constant, linear_parts.shape[:-1] + (1,)) - return bm.concatenate([constant, linear_parts, nonlinear_parts], axis=-1) + for ids in self.comb_ids: + all_parts.append(bm.prod(linear_parts[:, ids], axis=2)) + # 4. Return all parts + return bm.concatenate(all_parts, axis=-1) diff --git a/brainpy/nn/nodes/RC/reservoir.py b/brainpy/nn/nodes/RC/reservoir.py index 7add8a95..78233142 100644 --- a/brainpy/nn/nodes/RC/reservoir.py +++ b/brainpy/nn/nodes/RC/reservoir.py @@ -3,9 +3,8 @@ from typing import Optional, Union, Callable import brainpy.math as bm -from brainpy.initialize import Normal, ZeroInit, Initializer +from brainpy.initialize import Normal, ZeroInit, Initializer, init_param from brainpy.nn.base import RecurrentNode -from brainpy.nn.utils import init_param from brainpy.tools.checking import (check_shape_consistency, check_float, check_initializer, @@ -158,7 +157,7 @@ class Reservoir(RecurrentNode): self.noise_type = noise_type check_string(noise_type, 'noise_type', ['normal', 'uniform']) - def init_ff(self): + def init_ff_conn(self): """Initialize feedforward connections, weights, and variables.""" unique_shape, free_shapes = check_shape_consistency(self.feedforward_shapes, -1, True) self.set_output_shape(unique_shape + (self.num_unit,)) @@ -197,10 +196,9 @@ class Reservoir(RecurrentNode): def init_state(self, num_batch=1): # initialize internal state - state = bm.Variable(bm.zeros((num_batch, self.num_unit), dtype=bm.float_)) - self.set_state(state) + return bm.zeros((num_batch, self.num_unit), dtype=bm.float_) - def init_fb(self): + def init_fb_conn(self): """Initialize feedback connections, weights, and variables.""" if self.feedback_shapes is not None: unique_shape, free_shapes = check_shape_consistency(self.feedback_shapes, -1, True) @@ -215,7 +213,7 @@ class Reservoir(RecurrentNode): if self.trainable: self.Wfb = bm.TrainVar(self.Wfb) - def forward(self, ff, fb=None, **kwargs): + def forward(self, ff, fb=None, **shared_kwargs): """Feedforward output.""" # inputs x = bm.concatenate(ff, axis=-1) diff --git a/brainpy/nn/nodes/base/activation.py b/brainpy/nn/nodes/base/activation.py index 4a699c04..7af429bf 100644 --- a/brainpy/nn/nodes/base/activation.py +++ b/brainpy/nn/nodes/base/activation.py @@ -37,8 +37,8 @@ class Activation(Node): self._fun_setting = dict() if (fun_setting is None) else fun_setting assert isinstance(self._fun_setting, dict), '"fun_setting" must be a dict.' - def init_ff(self): + def init_ff_conn(self): self.set_output_shape(self.feedforward_shapes) - def forward(self, ff, **kwargs): + def forward(self, ff, **shared_kwargs): return self._activation(ff, **self._fun_setting) diff --git a/brainpy/nn/nodes/base/dense.py b/brainpy/nn/nodes/base/dense.py index 0719cb0c..1d28aa30 100644 --- a/brainpy/nn/nodes/base/dense.py +++ b/brainpy/nn/nodes/base/dense.py @@ -7,8 +7,7 @@ import jax.numpy as jnp from brainpy import math as bm from brainpy.errors import UnsupportedError, MathError -from brainpy.initialize import XavierNormal, ZeroInit, Initializer -from brainpy.nn import utils +from brainpy.initialize import XavierNormal, ZeroInit, Initializer, init_param from brainpy.nn.base import Node from brainpy.tools.checking import (check_shape_consistency, check_initializer) @@ -49,37 +48,60 @@ class Dense(Node): **kwargs ): super(Dense, self).__init__(trainable=trainable, **kwargs) + + # shape self.num_unit = num_unit if num_unit < 0: raise ValueError(f'Received an invalid value for `num_unit`, expected ' f'a positive integer. Received: num_unit={num_unit}') + + # weight initializer self.weight_initializer = weight_initializer self.bias_initializer = bias_initializer check_initializer(weight_initializer, 'weight_initializer') check_initializer(bias_initializer, 'bias_initializer', allow_none=True) - def init_ff(self): + # weights + self.Wff = None + self.bias = None + self.Wfb = None + + def init_ff_conn(self): # shapes - in_sizes = [size[1:] for size in self.feedforward_shapes] # remove batch size - unique_shape, free_shapes = check_shape_consistency(in_sizes, -1, True) - weight_shape = (sum(free_shapes), self.num_unit) - bias_shape = (self.num_unit,) - # set output size - self.set_output_shape((None, ) + unique_shape + (self.num_unit,)) + other_size, free_shapes = check_shape_consistency(self.feedforward_shapes, -1, True) + self._other_size = other_size + # set output size # TODO + self.set_output_shape(other_size + (self.num_unit,)) + # initialize feedforward weights - self.weights = utils.init_param(self.weight_initializer, weight_shape) - self.bias = utils.init_param(self.bias_initializer, bias_shape) + self.Wff = init_param(self.weight_initializer, (sum(free_shapes), self.num_unit)) + self.bias = init_param(self.bias_initializer, (self.num_unit,)) if self.trainable: - self.weights = bm.TrainVar(self.weights) - if self.bias is not None: - self.bias = bm.TrainVar(self.bias) + self.Wff = bm.TrainVar(self.Wff) + self.bias = bm.TrainVar(self.bias) if (self.bias is not None) else None - def forward(self, ff: Sequence[Tensor], **kwargs): - ff = bm.concatenate(ff, axis=-1) - if self.bias is None: - return ff @ self.weights + def init_fb_conn(self): + other_size, free_shapes = check_shape_consistency(self.feedback_shapes, -1, True) + if self._other_size != other_size: + raise ValueError(f'The feedback shape {other_size} is not consistent ' + f'with the feedforward shape {self._other_size}') + + # initialize feedforward weights + weight_shapes = (sum(free_shapes), self.num_unit) + if self.trainable: + self.Wfb = bm.TrainVar(init_param(self.weight_initializer, weight_shapes)) else: - return ff @ self.weights + self.bias + self.Wfb = init_param(self.weight_initializer, weight_shapes) + + def forward(self, ff: Sequence[Tensor], fb=None, **shared_kwargs): + ff = bm.concatenate(ff, axis=-1) + res = ff @ self.Wff + if fb is not None: + fb = bm.concatenate(fb, axis=-1) + res += fb @ self.Wfb + if self.bias is not None: + res += self.bias + return res def __ridge_train__(self, ffs: Sequence[Tensor], @@ -93,6 +115,7 @@ class Dense(Node): Also, the element in ``ffs`` should have the same shape. """ + assert self.Wfb is None, 'Currently ridge learning do not support feedback connections.' # parameters if train_pars is None: train_pars = dict() @@ -119,9 +142,9 @@ class Dense(Node): W = bm.linalg.pinv(temp) @ (ffs.T @ targets) # assign trained weights if self.bias is None: - self.weights.value = W + self.Wff.value = W else: - self.weights.value = W[:-1] + self.Wff.value = W[:-1] self.bias.value = W[-1] def __force_init__(self, *args, **kwargs): diff --git a/brainpy/nn/nodes/base/io.py b/brainpy/nn/nodes/base/io.py index 853be5db..2b7e72c2 100644 --- a/brainpy/nn/nodes/base/io.py +++ b/brainpy/nn/nodes/base/io.py @@ -21,10 +21,10 @@ class Input(Node): name: str = None): super(Input, self).__init__(name=name, input_shape=input_shape) self.set_feedforward_shapes({self.name: (None,) + to_size(input_shape)}) - self._ff_init() + self._init_ff_conn() - def init_ff(self): + def init_ff_conn(self): self.set_output_shape(self.feedforward_shapes) - def forward(self, ff, **kwargs): + def forward(self, ff, **shared_kwargs): return ff diff --git a/brainpy/nn/nodes/base/ops.py b/brainpy/nn/nodes/base/ops.py index 93914fdb..32871451 100644 --- a/brainpy/nn/nodes/base/ops.py +++ b/brainpy/nn/nodes/base/ops.py @@ -23,13 +23,13 @@ class Concat(Node): super(Concat, self).__init__(**kwargs) self.axis = axis - def init_ff(self): + def init_ff_conn(self): unique_shape, free_shapes = check_shape_consistency(self.feedforward_shapes, self.axis) out_size = list(unique_shape) out_size.insert(self.axis, sum(free_shapes)) self.set_output_shape(out_size) - def forward(self, ff, **kwargs): + def forward(self, ff, **shared_kwargs): return bm.concatenate(ff, axis=self.axis) @@ -45,11 +45,11 @@ class Select(Node): if isinstance(index, int): self.index = bm.asarray([index]).value - def init_ff(self): + def init_ff_conn(self): out_size = bm.zeros(self.feedforward_shapes[1:])[self.index].shape self.set_output_shape((None, ) + out_size) - def forward(self, ff, **kwargs): + def forward(self, ff, **shared_kwargs): return ff[..., self.index] @@ -69,7 +69,7 @@ class Reshape(Node): self.shape = tools.to_size(shape) assert (None not in self.shape), 'Batch size can not be defined in the reshaped size.' - def init_ff(self): + def init_ff_conn(self): in_size = self.feedforward_shapes[1:] if -1 in self.shape: assert self.shape.count(-1) == 1, f'Cannot set shape with multiple -1. But got {self.shape}' @@ -84,7 +84,7 @@ class Reshape(Node): out_size = self.shape self.set_output_shape((None, ) + out_size) - def forward(self, ff, **kwargs): + def forward(self, ff, **shared_kwargs): return bm.reshape(ff, self.shape) @@ -97,11 +97,11 @@ class Summation(Node): def __init__(self, **kwargs): super(Summation, self).__init__(**kwargs) - def init_ff(self): + def init_ff_conn(self): unique_shape, _ = check_shape_consistency(self.feedforward_shapes, None, True) self.set_output_shape(list(unique_shape)) - def forward(self, ff, **kwargs): + def forward(self, ff, **shared_kwargs): res = ff[0] for v in ff[1:]: res = res + v diff --git a/brainpy/nn/operations.py b/brainpy/nn/operations.py index 25919511..acb18e39 100644 --- a/brainpy/nn/operations.py +++ b/brainpy/nn/operations.py @@ -365,9 +365,9 @@ def fb_connect( all_nodes, all_ff_edges, all_fb_edges, fb_senders, fb_receivers = _retrieve_nodes_and_edges(senders, receivers) - # detect whether the node implement its own "init_fb()" function + # detect whether the node implement its own "init_fb_conn()" function for node in fb_receivers: - if not node.support_feedback: + if not node.is_feedback_input_supported: raise ValueError(f'Establish a feedback connection to \n' f'{node}\n' f'is not allowed. Because this node does not ' diff --git a/brainpy/nn/runners/back_propagation.py b/brainpy/nn/runners/back_propagation.py index a419d363..46bdd790 100644 --- a/brainpy/nn/runners/back_propagation.py +++ b/brainpy/nn/runners/back_propagation.py @@ -12,7 +12,7 @@ import brainpy.math as bm import brainpy.optimizers as optim from brainpy.errors import UnsupportedError from brainpy.nn.base import Node, Network -from brainpy.nn.utils import check_rnn_data_batch_size +from brainpy.nn.utils import check_rnn_data_batch_size, serialize_kwargs from brainpy.running.runner import Runner from brainpy.tools.checking import check_dict_data, check_float from brainpy.types import Tensor @@ -70,10 +70,14 @@ class BPTT(RNNTrainer): self.loss_fun = loss self._train_losses = None self._test_losses = None - self._f_loss_public = None - self._f_train = None - self._f_grad = None - self._mapping_type = None # target/output mapping types + + # target/output mapping types + self._mapping_type = None + + # functions + self._f_loss = dict() + self._f_train = dict() + self._f_grad = dict() # training parameters self.max_grad_norm = max_grad_norm # gradient clipping @@ -81,9 +85,7 @@ class BPTT(RNNTrainer): self.metrics = metrics # initialize the optimizer - if not (self.target.is_ff_initialized and - self.target.is_fb_initialized and - self.target.is_state_initialized): + if not self.target.is_initialized: raise ValueError('Please initialize the target model first by calling "initialize()" function.') self.optimizer.register_vars(self.target.vars().subset(bm.TrainVar).unique()) @@ -93,7 +95,7 @@ class BPTT(RNNTrainer): forced_states: Dict[str, Tensor] = None, forced_feedbacks: Dict[str, Tensor] = None, reset=True, - shared_pars: Dict = None, + shared_kwargs: Dict = None, **kwargs ): """Predict a series of input data with the given target model. @@ -115,6 +117,8 @@ class BPTT(RNNTrainer): The fixed feedback states. Similar with ``xs``, each tensor in ``forced_states`` must be a tensor with the shape of `(num_sample, num_time, num_feature)`. Default None. + shared_kwargs: dict + Shared keyword arguments for the given target model. reset: bool Whether reset the model states. Default True. @@ -141,8 +145,8 @@ class BPTT(RNNTrainer): num_train: int = 100, num_report: int = 100, reset: bool = True, - shared_args: Dict = None, - # currently unsupported features + shared_kwargs: Dict = None, + # current unsupported features forced_states: Dict[str, Tensor] = None, forced_feedbacks: Dict[str, Tensor] = None, ): @@ -179,6 +183,8 @@ class BPTT(RNNTrainer): The forced node states. forced_feedbacks: optional, dict The forced node feedbacks. + shared_kwargs: dict + The shared keyword arguments for the target models. """ # check forced states/feedbacks assert forced_states is None, (f'Currently {self.__class__.__name__} does ' @@ -200,8 +206,9 @@ class BPTT(RNNTrainer): batch_size = check_rnn_data_batch_size(x) if batch_size != num_batch: raise ValueError(f'"num_batch" is set to {num_batch}, but we got {batch_size}.') - if reset: self.target.init_state(batch_size) - loss = self.f_train(x, y) + if reset: + self.target.initialize(batch_size) + loss = self.f_train(shared_kwargs)(x, y) all_train_losses.append(loss) train_i += 1 if train_i % num_report == 0: @@ -219,34 +226,35 @@ class BPTT(RNNTrainer): raise ValueError(f'"num_batch" is set to {num_batch}, ' f'but we got {batch_size}.') if reset: - self.target.init_state(batch_size) - loss = self.f_loss(x, y) + self.target.initialize(batch_size) + loss = self.f_loss(shared_kwargs)(x, y) all_test_losses.append(loss) self._train_losses = bm.asarray(all_train_losses) self._test_losses = bm.asarray(all_test_losses) - @property - def f_grad(self): - if self._f_grad is None: - self._f_grad = self._get_f_grad() - return self._f_grad - - @property - def f_loss(self): - if self._f_loss_public is None: - self._f_loss_public = self._get_f_loss() - if self.jit: - dyn_vars = self.target.vars() - dyn_vars.update(self.dyn_vars) - self._f_loss_public = bm.jit(self._f_loss_public, dyn_vars=dyn_vars) - return self._f_loss_public - - @property - def f_train(self): - if self._f_train is None: - self._f_train = self._get_f_train() - return self._f_train + def f_grad(self, shared_kwargs=None) -> Callable: + shared_kwargs_str = serialize_kwargs(shared_kwargs) + if shared_kwargs_str not in self._f_grad: + self._f_grad[shared_kwargs_str] = self._make_f_grad(shared_kwargs) + return self._f_grad[shared_kwargs_str] + + def f_loss(self, shared_kwargs=None) -> Callable: + shared_kwargs_str = serialize_kwargs(shared_kwargs) + if shared_kwargs_str not in self._f_loss: + self._f_loss[shared_kwargs_str] = self._make_f_loss(shared_kwargs) + if self.jit: + dyn_vars = self.target.vars() + dyn_vars.update(self.dyn_vars) + self._f_loss[shared_kwargs_str] = bm.jit(self._f_loss[shared_kwargs_str], + dyn_vars=dyn_vars) + return self._f_loss[shared_kwargs_str] + + def f_train(self, shared_kwargs=None) -> Callable: + shared_kwargs_str = serialize_kwargs(shared_kwargs) + if shared_kwargs_str not in self._f_train: + self._f_train[shared_kwargs_str] = self._make_f_train(shared_kwargs) + return self._f_train[shared_kwargs_str] @property def train_losses(self): @@ -263,12 +271,17 @@ class BPTT(RNNTrainer): """Mapping type for the output and the target.""" return self._mapping_type - def _get_f_loss(self): + def _make_f_loss(self, shared_kwargs: Dict = None): + if shared_kwargs is None: + shared_kwargs = dict() + assert isinstance(shared_kwargs, dict), (f'Only supports dict for "shared_kwargs". ' + f'But got {type(shared_kwargs)}: {shared_kwargs}') + def loss_fun(inputs, targets): inputs = self._format_xs(inputs) targets = self._format_ys(targets) inputs = {k: bm.moveaxis(v, 0, 1) for k, v in inputs.items()} - outputs, _ = self._predict(xs=inputs) + outputs, _ = self._predict(xs=inputs, shared_kwargs=shared_kwargs) outputs = self._format_ys(outputs) loss = 0. for key, output in outputs.items(): @@ -279,8 +292,8 @@ class BPTT(RNNTrainer): return loss_fun - def _get_f_grad(self): - _f_loss_internal = self._get_f_loss() + def _make_f_grad(self, shared_kwargs: Dict = None): + _f_loss_internal = self._make_f_loss(shared_kwargs) dyn_vars = self.target.vars() dyn_vars.update(self.dyn_vars) tran_vars = dyn_vars.subset(bm.TrainVar) @@ -289,11 +302,16 @@ class BPTT(RNNTrainer): grad_vars=tran_vars.unique(), return_value=True) - def _get_f_train(self): + def _make_f_train(self, shared_kwargs: Dict = None): + if shared_kwargs is None: + shared_kwargs = dict() + assert isinstance(shared_kwargs, dict), (f'Only supports dict for "shared_kwargs". ' + f'But got {type(shared_kwargs)}: {shared_kwargs}') + def train_func(inputs, targets): inputs = self._format_xs(inputs) targets = self._format_ys(targets) - grads, loss = self.f_grad(inputs, targets) + grads, loss = self.f_grad(shared_kwargs)(inputs, targets) if self.max_grad_norm is not None: check_float(self.max_grad_norm, 'max_grad_norm', min_bound=0.) grads = bm.clip_by_norm(grads, self.max_grad_norm) diff --git a/brainpy/nn/runners/ridge_regression.py b/brainpy/nn/runners/ridge_regression.py index ee8a0e64..0f371588 100644 --- a/brainpy/nn/runners/ridge_regression.py +++ b/brainpy/nn/runners/ridge_regression.py @@ -8,6 +8,7 @@ from jax.experimental.host_callback import id_tap import brainpy.math as bm from brainpy.nn.base import Node, Network +from brainpy.nn.utils import serialize_kwargs from brainpy.tools.checking import check_dict_data from brainpy.types import Tensor from .rnn_trainer import RNNTrainer @@ -40,7 +41,7 @@ class RidgeTrainer(RNNTrainer): The target model. beta: float The regularization coefficient. - **kwargs: dict + **kwarg Other common parameters for :py:class:`brainpy.nn.RNNTrainer``. """ @@ -58,7 +59,7 @@ class RidgeTrainer(RNNTrainer): # train parameters self.train_pars = dict(beta=beta) # training function - self._f_train = None + self._f_train = dict() def fit( self, @@ -67,7 +68,7 @@ class RidgeTrainer(RNNTrainer): forced_states: Dict[str, Tensor] = None, forced_feedbacks: Dict[str, Tensor] = None, reset=False, - shared_args: Dict = None, + shared_kwargs: Dict = None, ): # checking training and testing data if not isinstance(train_data, (list, tuple)): @@ -132,7 +133,7 @@ class RidgeTrainer(RNNTrainer): for node in self.train_nodes: monitor_data[f'{node.name}.inputs'] = self.mon.item_contents.get(f'{node.name}.inputs', None) monitor_data[f'{node.name}.feedbacks'] = self.mon.item_contents.get(f'{node.name}.feedbacks', None) - self.f_train(monitor_data, ys) + self.f_train(shared_kwargs)(monitor_data, ys) # close the progress bar if self.progress_bar: @@ -144,22 +145,24 @@ class RidgeTrainer(RNNTrainer): if self.true_numpy_mon_after_run: self.mon.numpy() - @property - def f_train(self): - if self._f_train is None: - self._f_train = self._make_fit_func() - return self._f_train + def f_train(self, shared_kwargs: Dict = None): + shared_kwargs_str = serialize_kwargs(shared_kwargs) + if shared_kwargs_str not in self._f_train: + self._f_train[shared_kwargs_str] = self._make_fit_func(shared_kwargs) + return self._f_train[shared_kwargs_str] + + def _make_fit_func(self, shared_kwargs): + shared_kwargs = dict() if shared_kwargs is None else shared_kwargs - def _make_fit_func(self): def train_func(monitor_data: Dict[str, Tensor], target_data: Dict[str, Tensor]): for node in self.train_nodes: ff = monitor_data[f'{node.name}.inputs'] fb = monitor_data.get(f'{node.name}.feedbacks', None) targets = target_data[node.name] if fb is None: - node.__ridge_train__(ff, targets, train_pars=self.train_pars) + node.__ridge_train__(ff, targets, train_pars=self.train_pars, **shared_kwargs) else: - node.__ridge_train__(ff, targets, fb, train_pars=self.train_pars) + node.__ridge_train__(ff, targets, fb, train_pars=self.train_pars, **shared_kwargs) if self.progress_bar: id_tap(lambda *args: self._pbar.update(), ()) diff --git a/brainpy/nn/runners/rnn_runner.py b/brainpy/nn/runners/rnn_runner.py index 31bf741b..1a42adaa 100644 --- a/brainpy/nn/runners/rnn_runner.py +++ b/brainpy/nn/runners/rnn_runner.py @@ -11,7 +11,8 @@ from brainpy import math as bm from brainpy.errors import UnsupportedError from brainpy.nn.base import Node, Network from brainpy.nn.utils import (check_rnn_data_time_step, - check_rnn_data_batch_size) + check_rnn_data_batch_size, + serialize_kwargs) from brainpy.running.runner import Runner from brainpy.tools.checking import check_dict_data from brainpy.types import Tensor @@ -45,14 +46,14 @@ class RNNRunner(Runner): assert isinstance(self.target, Node), '"target" must be an instance of brainpy.nn.Node.' # function for prediction - self._predict_func = None + self._predict_func = dict() def predict(self, xs: Union[Tensor, Dict[str, Tensor]], forced_states: Dict[str, Tensor] = None, forced_feedbacks: Dict[str, Tensor] = None, reset=False, - shared_args: Dict = None, + shared_kwargs: Dict = None, progress_bar=True): """Predict a series of input data with the given target model. @@ -76,7 +77,7 @@ class RNNRunner(Runner): reset: bool Whether reset the model states. progress_bar: bool - shared_args: optional, dict + shared_kwargs: optional, dict The shared arguments across different layers. Returns @@ -87,11 +88,13 @@ class RNNRunner(Runner): # format input data xs, num_step, num_batch = self._check_xs(xs) # get forced data - iter_forced_states, fixed_forced_states = self._check_forced_states(forced_states, num_step, num_batch) - iter_forced_feedbacks, fixed_forced_feedbacks = self._check_forced_feedbacks(forced_feedbacks, num_step, num_batch) + iter_forced_states, fixed_forced_states = \ + self._check_forced_states(forced_states, num_step, num_batch) + iter_forced_feedbacks, fixed_forced_feedbacks = \ + self._check_forced_feedbacks(forced_feedbacks, num_step, num_batch) # reset the model states if reset: - self.target.init_state(num_batch) + self.target.initialize(num_batch) # init monitor for key in self.mon.item_contents.keys(): self.mon.item_contents[key] = [] # reshape the monitor items @@ -104,9 +107,8 @@ class RNNRunner(Runner): # prediction outputs, hists = self._predict(xs=xs, iter_forced_states=iter_forced_states, - fixed_forced_states=fixed_forced_states, iter_forced_feedbacks=iter_forced_feedbacks, - fixed_forced_feedbacks=fixed_forced_feedbacks) + shared_kwargs=shared_kwargs) # close the progress bar if self.progress_bar and progress_bar: self._pbar.close() @@ -121,46 +123,46 @@ class RNNRunner(Runner): self, xs: Dict[str, Tensor], iter_forced_states: Dict[str, Tensor] = None, - fixed_forced_states: Dict[str, Tensor] = None, iter_forced_feedbacks: Dict[str, Tensor] = None, - fixed_forced_feedbacks: Dict[str, Tensor] = None, - shared_args: Dict = None, + shared_kwargs: Dict = None, ): - """ + """Predict the output according to the inputs. Parameters ---------- xs: dict Each tensor should have the shape of `(num_time, num_batch, num_feature)`. iter_forced_states: dict - fixed_forced_states: dict iter_forced_feedbacks: dict - fixed_forced_feedbacks: dict - shared_args: dict + shared_kwargs: dict Returns ------- - + outputs, hists + A tuple of pair of (outputs, hists). """ - # check run function - if self._predict_func is None: - self._predict_func = self._make_run_func() + _predict_func = self._get_predict_func(shared_kwargs) # rune the model iter_forced_states = dict() if iter_forced_states is None else iter_forced_states - fixed_forced_states = dict() if fixed_forced_states is None else fixed_forced_states iter_forced_feedbacks = dict() if iter_forced_feedbacks is None else iter_forced_feedbacks - fixed_forced_feedbacks = dict() if fixed_forced_feedbacks is None else fixed_forced_feedbacks - outputs, hists = self._predict_func([xs, iter_forced_states, iter_forced_feedbacks]) # TODO: fixed? + outputs, hists = _predict_func([xs, iter_forced_states, iter_forced_feedbacks]) # TODO: fixed? f1 = lambda x: bm.moveaxis(x, 0, 1) f2 = lambda x: isinstance(x, bm.JaxArray) outputs = tree_map(f1, outputs, is_leaf=f2) hists = tree_map(f1, hists, is_leaf=f2) return outputs, hists - def _make_run_func(self, shared_args=None): - if shared_args is None: - shared_args = dict() - assert isinstance(shared_args, dict), f'"shared_args" must be a dict, but got {type(shared_args)}' + def _get_predict_func(self, shared_kwargs: Dict = None): + shared_kwargs_str = serialize_kwargs(shared_kwargs) + if shared_kwargs_str not in self._predict_func: + self._predict_func[shared_kwargs_str] = self._make_run_func(shared_kwargs) + return self._predict_func[shared_kwargs_str] + + def _make_run_func(self, shared_kwargs: Dict = None): + if shared_kwargs is None: + shared_kwargs = dict() + assert isinstance(shared_kwargs, dict), (f'"shared_kwargs" must be a dict, ' + f'but got {type(shared_kwargs)}') def _step_func(a_input): xs, forced_states, forced_feedbacks = a_input @@ -169,7 +171,7 @@ class RNNRunner(Runner): forced_states=forced_states, forced_feedbacks=forced_feedbacks, monitors=monitors, - **shared_args) + **shared_kwargs) if self.progress_bar and (self._pbar is not None): id_tap(lambda *args: self._pbar.update(), ()) return outs diff --git a/brainpy/nn/runners/rnn_trainer.py b/brainpy/nn/runners/rnn_trainer.py index 24200ace..f1ad1aca 100644 --- a/brainpy/nn/runners/rnn_trainer.py +++ b/brainpy/nn/runners/rnn_trainer.py @@ -27,7 +27,7 @@ class RNNTrainer(RNNRunner): forced_states: Dict[str, Tensor] = None, forced_feedbacks: Dict[str, Tensor] = None, reset: bool = False, - shared_args: Dict = None): # need to be implemented by subclass + shared_kwargs: Dict = None): # need to be implemented by subclass raise NotImplementedError('Must implement the fit function. ') def _get_trainable_nodes(self): diff --git a/brainpy/nn/utils.py b/brainpy/nn/utils.py index 997aac2e..a767088d 100644 --- a/brainpy/nn/utils.py +++ b/brainpy/nn/utils.py @@ -1,13 +1,13 @@ # -*- coding: utf-8 -*- -from typing import Union, Sequence, Dict, Any, Callable +import warnings +from typing import Union, Sequence, Dict, Any, Callable, Optional import jax.numpy as jnp -import numpy as onp import brainpy.math as bm -from brainpy.initialize import Initializer -from brainpy.tools.others import to_size +from brainpy.initialize import Initializer, init_param as true_init_param +from brainpy.tools.checking import check_dict_data from brainpy.types import Tensor, Shape __all__ = [ @@ -15,6 +15,7 @@ __all__ = [ 'init_param', 'check_rnn_data_batch_size', 'check_rnn_data_time_step', + 'serialize_kwargs', ] @@ -37,6 +38,9 @@ def init_param(param: Union[Callable, Initializer, bm.ndarray, jnp.ndarray], size: Shape): """Initialize parameters. + .. deprecated:: 2.1.2 + Please use "brainpy.init.init_param" instead. + Parameters ---------- param: callable, Initializer, bm.ndarray, jnp.ndarray @@ -48,19 +52,10 @@ def init_param(param: Union[Callable, Initializer, bm.ndarray, jnp.ndarray], size: int, sequence of int The shape of the parameter. """ - size = to_size(size) - if param is None: - return None - elif callable(param): - param = param(size) - elif isinstance(param, (onp.ndarray, jnp.ndarray)): - param = bm.asarray(param) - elif isinstance(param, (bm.JaxArray,)): - param = param - else: - raise ValueError(f'Unknown param type {type(param)}: {param}') - assert param.shape == size, f'"param.shape" is not the required size {size}' - return param + warnings.warn('Please use "brainpy.init.init_param" instead. ' + '"brainpy.nn.init_param" is deprecated since version 2.1.2. ', + DeprecationWarning) + return true_init_param(param, size) def check_rnn_data_batch_size(data: Dict, num_batch=None): @@ -93,3 +88,14 @@ def check_rnn_data_time_step(data: Dict, num_step=None): if (num_step is not None) and time_step != num_step: raise ValueError(f'Time step is not consistent with the expected {time_step} != {num_step}') return time_step + + +def serialize_kwargs(shared_kwargs: Optional[Dict]): + """Serialize kwargs.""" + shared_kwargs = dict() if shared_kwargs is None else shared_kwargs + check_dict_data(shared_kwargs, + key_type=str, + val_type=(bool, float, int, complex), + name='shared_kwargs') + shared_kwargs = {key: shared_kwargs[key] for key in sorted(shared_kwargs.keys())} + return str(shared_kwargs) diff --git a/brainpy/optimizers/optimizer.py b/brainpy/optimizers/optimizer.py index 399a6fe0..3b2365a4 100644 --- a/brainpy/optimizers/optimizer.py +++ b/brainpy/optimizers/optimizer.py @@ -12,7 +12,6 @@ from brainpy.math.jaxarray import Variable from .scheduler import make_schedule, Scheduler __all__ = [ - # optimizers 'Optimizer', 'SGD', 'Momentum', @@ -21,6 +20,7 @@ __all__ = [ 'Adadelta', 'RMSProp', 'Adam', + 'LARS', ] @@ -393,3 +393,57 @@ class Adam(Optimizer): # Bias correction. p.value -= lr * m.value / (jnp.sqrt(v.value) + self.eps) self.lr.update() + + +class LARS(Optimizer): + """Layer-wise adaptive rate scaling (LARS) optimizer. + + Parameters + ---------- + momentum: float + coefficient used for the moving average of the gradient. + weight_decay: float + weight decay coefficient. + tc: float + trust coefficient eta ( < 1) for trust ratio computation. + eps: float + epsilon used for trust ratio computation. + """ + def __init__(self, + lr: Union[float, int, Scheduler], + train_vars: Dict[str, Variable]=None, + momentum: float = 0.9, + weight_decay: float = 1e-4, + tc: float = 1e-3, + eps: float = 1e-5, + name:str=None): + super(LARS, self).__init__(lr=lr, train_vars=train_vars, name=name) + + self.momentum = momentum + self.weight_decay = weight_decay + self.tc = tc + self.eps = eps + + def register_vars(self, train_vars: Optional[Dict[str, Variable]] = None): + train_vars = dict() if train_vars is None else train_vars + if not isinstance(train_vars, dict): + raise MathError('"train_vars" must be a dict of Variable.') + self.vars_to_train.update(train_vars) + ms = dict((k + '_m', Variable(ops.zeros_like(x))) for k, x in train_vars.items()) + self.register_implicit_vars(ms) + + def update(self, grads: dict): + self.check_grads(grads) + lr = self.lr() + for k, p in self.vars_to_train.items(): + g = grads[k] + m = self.implicit_vars[k + '_m'] + p_norm = jnp.linalg.norm(ops.as_device_array(p)) + g_norm = jnp.linalg.norm(ops.as_device_array(g)) + trust_ratio = self.tc * p_norm / (g_norm + self.weight_decay * p_norm + self.eps) + local_lr = lr * jnp.maximum(jnp.logical_or(p_norm == 0, g_norm == 0), trust_ratio) + m.value = self.momentum * m.value + local_lr * (g + self.weight_decay * p.value) + p.value -= m.value + self.lr.update() + + diff --git a/brainpy/tools/checking.py b/brainpy/tools/checking.py index add5080b..60a25512 100644 --- a/brainpy/tools/checking.py +++ b/brainpy/tools/checking.py @@ -20,6 +20,7 @@ __all__ = [ 'check_float', 'check_integer', 'check_string', + 'check_sequence', ] @@ -209,6 +210,25 @@ def check_connector(connector: Union[Callable, conn.Connector, Tensor], f'tensor or callable function. While we got {type(connector)}') +def check_sequence(value: Sequence, + name=None, + elem_type=None, + allow_none=True): + if name is None: name = '' + if value is None: + if allow_none: + return + else: + raise ValueError(f'{name} must be a sequence, but got None') + if not isinstance(value, (tuple, list)): + raise ValueError(f'{name} should be a sequence, but we got a {type(value)}') + if elem_type is not None: + for v in value: + if not isinstance(v, elem_type): + raise ValueError(f'Elements in {name} should be {elem_type}, ' + f'but we got {type(elem_type)}: {v}') + + def check_float(value: float, name=None, min_bound=None, max_bound=None, allow_none=False, allow_int=True): """Check float type. diff --git a/brainpy/tools/others/__init__.py b/brainpy/tools/others/__init__.py index 381d7993..30fe2ea5 100644 --- a/brainpy/tools/others/__init__.py +++ b/brainpy/tools/others/__init__.py @@ -3,3 +3,4 @@ from .ast2code import * from .dicts import * from .others import * +from .numba_jit import * diff --git a/brainpy/tools/others/numba_jit.py b/brainpy/tools/others/numba_jit.py new file mode 100644 index 00000000..062eadfd --- /dev/null +++ b/brainpy/tools/others/numba_jit.py @@ -0,0 +1,22 @@ +# -*- coding: utf-8 -*- + +try: + from numba import njit +except (ImportError, ModuleNotFoundError): + njit = None + + +__all__ = [ + 'numba_jit' +] + + +def numba_jit(f=None, **kwargs): + if f is None: + return lambda f: (f if (njit is None) else njit(f, **kwargs)) + else: + if njit is None: + return f + else: + return njit(f) + diff --git a/changelog.rst b/changelog.rst index ed8df32c..18a046e3 100644 --- a/changelog.rst +++ b/changelog.rst @@ -6,6 +6,28 @@ brainpy 2.x (LTS) ***************** +Version 2.1.1 (2022.03.18) +========================== + +This release continues to update the functionality of BrainPy. Core changes include + +- numerical solvers for fractional differential equations +- more standard ``brainpy.nn`` interfaces + + +New Features +~~~~~~~~~~~~ + +- Numerical solvers for fractional differential equations + - ``brainpy.fde.CaputoEuler`` + - ``brainpy.fde.CaputoL1Schema`` + - ``brainpy.fde.GLShortMemory`` +- Fractional neuron models + - ``brainpy.dyn.FractionalFHR`` + - ``brainpy.dyn.FractionalIzhikevich`` +- support ``shared_kwargs`` in `RNNTrainer` and `RNNRunner` + + Version 2.1.0 (2022.03.14) ========================== diff --git a/docs/__init__.py b/docs/__init__.py new file mode 100644 index 00000000..40a96afc --- /dev/null +++ b/docs/__init__.py @@ -0,0 +1 @@ +# -*- coding: utf-8 -*- diff --git a/docs/_static/rnn_training_mapping.png b/docs/_static/rnn_training_mapping.png new file mode 100644 index 0000000000000000000000000000000000000000..95d3bf40fa6000fe971cfdbffe0bb687e1503734 GIT binary patch literal 26540 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zW_8S4J8Q-sx`gi*-A}>@ZTuhZ@zDR00rj3jBfV2fu@6fR01x;Qlv{gQX(bBn|0~|v k^8fe>nyY!;LOJ+>moXnUd}WL7p!wPQ{K>)-*M9xK0AMZIWdHyG literal 0 HcmV?d00001 diff --git a/docs/apis/compat.rst b/docs/apis/compat.rst new file mode 100644 index 00000000..03d41492 --- /dev/null +++ b/docs/apis/compat.rst @@ -0,0 +1,16 @@ +``brainpy.compat`` module +=========================== + +.. currentmodule:: brainpy.compat +.. automodule:: brainpy.compat + + +.. toctree:: + :maxdepth: 1 + + auto/compat/brainobjects + auto/compat/integrators + auto/compat/layers + auto/compat/models + auto/compat/runners + auto/compat/monitor diff --git a/docs/apis/integrators.rst b/docs/apis/integrators.rst index bd499f2b..cf6fb02b 100644 --- a/docs/apis/integrators.rst +++ b/docs/apis/integrators.rst @@ -12,4 +12,5 @@ integrators/ODE integrators/SDE integrators/DDE + integrators/FDE diff --git a/docs/apis/integrators/FDE.rst b/docs/apis/integrators/FDE.rst new file mode 100644 index 00000000..0c116455 --- /dev/null +++ b/docs/apis/integrators/FDE.rst @@ -0,0 +1,15 @@ +Numerical Methods for FDEs +========================== + +.. currentmodule:: brainpy.integrators.sde +.. automodule:: brainpy.integrators.sde + + +.. toctree:: + :maxdepth: 2 + + ../auto/integrators/fde_base + ../auto/integrators/fde_generic + ../auto/integrators/fde_Caputo + ../auto/integrators/fde_GL + diff --git a/docs/apis/math_compat.rst b/docs/apis/math_compat.rst new file mode 100644 index 00000000..2f2983c1 --- /dev/null +++ b/docs/apis/math_compat.rst @@ -0,0 +1,12 @@ +``brainpy.math.compat`` module +=============================== + +.. currentmodule:: brainpy.math.compat +.. automodule:: brainpy.math.compat + + +.. toctree:: + :maxdepth: 1 + + auto/math_compat/optimizers + auto/math_compat/losses diff --git a/docs/auto_generater.py b/docs/auto_generater.py index 7c8bf39c..811322af 100644 --- a/docs/auto_generater.py +++ b/docs/auto_generater.py @@ -6,14 +6,13 @@ import os from brainpy.math import (activations, autograd, controls, function, jit, operators, parallels, setting, delay_vars, - compact) - + compat) block_list = ['test', 'register_pytree_node'] for module in [jit, autograd, function, controls, activations, operators, parallels, setting, - delay_vars, compact]: + delay_vars, compat]: for k in dir(module): if (not k.startswith('_')) and (not inspect.ismodule(getattr(module, k))): block_list.append(k) @@ -230,20 +229,26 @@ def generate_dyn_docs(path='apis/auto/dyn/'): filename=os.path.join(path, 'base.rst'), header='Base Class') - module_and_name = [('biological_models', 'Biological Models'), - ('IF_models', 'Integrate-and-Fire Models'), - ('input_models', 'Input Models'), - ('rate_models', 'Rate Models'), - ('reduced_models', 'Reduced Models'), ] + module_and_name = [ + ('biological_models', 'Biological Models'), + ('fractional_models', 'Fractional-order Models'), + ('input_models', 'Input Models'), + ('noise_models', 'Noise Models'), + ('rate_models', 'Rate Models'), + ('reduced_models', 'Reduced Models'), + ] write_submodules(module_name='brainpy.dyn.neurons', filename=os.path.join(path, 'neurons.rst'), header='Neuron Models', submodule_names=[a[0] for a in module_and_name], section_names=[a[1] for a in module_and_name]) - module_and_name = [('biological_models', 'Biological Models'), - ('abstract_models', 'Abstract Models'), - ('learning_rules', 'Learning Rules'), ] + module_and_name = [ + ('biological_models', 'Biological Models'), + ('abstract_models', 'Abstract Models'), + ('delay_coupling', 'Delay Coupling Models'), + ('learning_rules', 'Learning Rule Models'), + ] write_submodules(module_name='brainpy.dyn.synapses', filename=os.path.join(path, 'synapses.rst'), header='Synapse Models', @@ -325,6 +330,20 @@ def generate_integrators_doc(path='apis/auto/integrators/'): filename=os.path.join(path, 'dde_explicit_rk.rst'), header='Explicit Runge-Kutta Methods') + # FDE + write_module(module_name='brainpy.integrators.fde.base', + filename=os.path.join(path, 'fde_base.rst'), + header='Base Integrator') + write_module(module_name='brainpy.integrators.fde.generic', + filename=os.path.join(path, 'fde_generic.rst'), + header='Generic Functions') + write_module(module_name='brainpy.integrators.fde.Caputo', + filename=os.path.join(path, 'fde_Caputo.rst'), + header='Methods for Caputo Fractional Derivative') + write_module(module_name='brainpy.integrators.fde.GL', + filename=os.path.join(path, 'fde_GL.rst'), + header='Methods for Riemann-Liouville Fractional Derivative') + # Others write_module(module_name='brainpy.integrators.joint_eq', filename=os.path.join(path, 'joint_eq.rst'), @@ -444,8 +463,6 @@ def generate_nn_docs(path='apis/auto/nn/'): header='Nodes: reservoir computing') - - def generate_optimizers_docs(path='apis/auto/'): if not os.path.exists(path): os.makedirs(path) @@ -491,3 +508,39 @@ def generate_tools_docs(path='apis/auto/tools/'): filename=os.path.join(path, 'others.rst'), header='Other Tools') + +def generate_compact_docs(path='apis/auto/compat/'): + if not os.path.exists(path): + os.makedirs(path) + + write_module(module_name='brainpy.compat.brainobjects', + filename=os.path.join(path, 'brainobjects.rst'), + header='Brain Objects') + write_module(module_name='brainpy.compat.integrators', + filename=os.path.join(path, 'integrators.rst'), + header='Integrators') + write_module(module_name='brainpy.compat.layers', + filename=os.path.join(path, 'layers.rst'), + header='Layers') + write_module(module_name='brainpy.compat.models', + filename=os.path.join(path, 'models.rst'), + header='Models') + write_module(module_name='brainpy.compat.monitor', + filename=os.path.join(path, 'monitor.rst'), + header='Monitor') + write_module(module_name='brainpy.compat.runners', + filename=os.path.join(path, 'runners.rst'), + header='Runners') + + +def generate_math_compact_docs(path='apis/auto/math_compat/'): + if not os.path.exists(path): + os.makedirs(path) + + write_module(module_name='brainpy.math.compat.optimizers', + filename=os.path.join(path, 'optimizers.rst'), + header='Optimizers') + + write_module(module_name='brainpy.math.compat.losses', + filename=os.path.join(path, 'losses.rst'), + header='Losses') diff --git a/docs/conf.py b/docs/conf.py index bc245596..9b123c3e 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -35,6 +35,9 @@ auto_generater.generate_optimizers_docs() auto_generater.generate_measure_docs() auto_generater.generate_datasets_docs() auto_generater.generate_tools_docs() +auto_generater.generate_compact_docs() +auto_generater.generate_math_compact_docs() + import shutil diff --git a/docs/index.rst b/docs/index.rst index 7a0371b2..deccd712 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -7,7 +7,8 @@ high-performance Brain Dynamics Programming (BDP). Among its key ingredients, Br - **JIT compilation** and **automatic differentiation** for class objects. - **Numerical methods** for ordinary differential equations (ODEs), stochastic differential equations (SDEs), - delay differential equations (DDEs), etc. + delay differential equations (DDEs), + fractional differential equations (FDEs), etc. - **Dynamics simulation** tools for various brain objects, like neurons, synapses, networks, soma, dendrites, channels, and even more. - **Dynamics training** tools with various machine learning algorithms, @@ -37,6 +38,7 @@ The code of BrainPy is open-sourced at GitHub: quickstart/installation quickstart/simulation + quickstart/rate_model quickstart/training quickstart/analysis @@ -58,11 +60,13 @@ The code of BrainPy is open-sourced at GitHub: tutorial_toolbox/ode_numerical_solvers tutorial_toolbox/sde_numerical_solvers tutorial_toolbox/dde_numerical_solvers + tutorial_toolbox/fde_numerical_solvers tutorial_toolbox/joint_equations tutorial_toolbox/synaptic_connections tutorial_toolbox/synaptic_weights tutorial_toolbox/optimizers tutorial_toolbox/runners + tutorial_toolbox/inputs tutorial_toolbox/monitors tutorial_toolbox/saving_and_loading @@ -97,6 +101,8 @@ The code of BrainPy is open-sourced at GitHub: apis/auto/measure.rst apis/auto/running.rst apis/tools.rst + apis/compat.rst + apis/math_compat.rst apis/auto/changelog-brainpy.rst apis/auto/changelog-brainpylib.rst diff --git a/docs/quickstart/analysis.ipynb b/docs/quickstart/analysis.ipynb index e3b35012..3c74def7 100644 --- a/docs/quickstart/analysis.ipynb +++ b/docs/quickstart/analysis.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 1, "id": "993ca509", "metadata": {}, "outputs": [], @@ -56,20 +56,24 @@ }, { "cell_type": "markdown", - "source": [ - "Here, we demonstrate how to perform a bifurcation analysis through a one-dimensional neuron model." - ], + "id": "b600c817", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Here, we demonstrate how to perform a bifurcation analysis through a one-dimensional neuron model." + ] }, { - "cell_type": "code", - "execution_count": null, - "outputs": [], + "cell_type": "markdown", + "id": "59fed6d5", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "source": [ "Let's try to analyze how the external input influences the dynamics of the Exponential Integrate-and-Fire (ExpIF) model. The ExpIF model is a one-variable neuron model whose dynamics is defined by:\n", "\n", @@ -77,13 +81,7 @@ "\\tau {\\dot {V}}= - (V - V_\\mathrm{rest}) + \\Delta_T \\exp(\\frac{V - V_T}{\\Delta_T}) + RI \\\\\n", "\\mathrm{if}\\, \\, V > \\theta, \\quad V \\gets V_\\mathrm{reset}\n", "$$" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", @@ -93,36 +91,28 @@ "We can analyze the change of ${\\dot {V}}$ with respect to $V$. First, let's generate an ExpIF model using pre-defined modules in ``brainpy.dyn``:" ] }, - { - "cell_type": "markdown", - "id": "5d5e66ad", - "metadata": {}, - "source": [ - "expif = bp.dyn.ExpIF(1, delta_T=1.)" - ] - }, { "cell_type": "code", - "execution_count": 7, - "id": "d94df42f", + "execution_count": 2, + "id": "8d6b11cb", "metadata": {}, "outputs": [], "source": [ - "The default value of other parameters can be accessed directly by their names:" + "expif = bp.dyn.ExpIF(1, delta_T=1.)" ] }, { "cell_type": "markdown", - "id": "d7cb929d", + "id": "a818b78c", "metadata": {}, "source": [ - "expif.V_rest, expif.V_T, expif.R, expif.tau" + "The default value of other parameters can be accessed directly by their names:" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "31e1ee06", + "execution_count": 3, + "id": "040b7004", "metadata": {}, "outputs": [ { @@ -131,33 +121,27 @@ "(-65.0, -59.9, 1.0, 10.0)" ] }, - "execution_count": 8, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "After defining the model, we can use it for bifurcation analysis." + "expif.V_rest, expif.V_T, expif.R, expif.tau" ] }, { "cell_type": "markdown", - "id": "88acb8ac", + "id": "09f5722a", "metadata": {}, "source": [ - "bif = bp.analysis.Bifurcation1D(\n", - " model=expif,\n", - " target_vars={'V': [-70., -55.]},\n", - " target_pars={'I_ext': [0., 6.]},\n", - " resolutions=0.01\n", - ")\n", - "bif.plot_bifurcation(show=True)" + "After defining the model, we can use it for bifurcation analysis." ] }, { "cell_type": "code", - "execution_count": 12, - "id": "aa6d013a", + "execution_count": 4, + "id": "358060fb", "metadata": {}, "outputs": [ { @@ -180,6 +164,20 @@ "output_type": "display_data" } ], + "source": [ + "bif = bp.analysis.Bifurcation1D(\n", + " model=expif,\n", + " target_vars={'V': [-70., -55.]},\n", + " target_pars={'I_ext': [0., 6.]},\n", + " resolutions=0.01\n", + ")\n", + "bif.plot_bifurcation(show=True)" + ] + }, + { + "cell_type": "markdown", + "id": "4f03e723", + "metadata": {}, "source": [ "In the ``Bifurcation1D`` analyzer, ``model`` refers to the modelto be analyzed (essentially the analyzer will access the derivative function in the model), ``target_vars`` denotes the target variables, ``target_pars`` denotes the changing parameters, and ``resolution`` determines the resolutioin of the analysis." ] @@ -223,46 +221,28 @@ "Users can easily define a FHN model which is also provided by BrainPy:" ] }, - { - "cell_type": "markdown", - "id": "d5d3c97e", - "metadata": {}, - "source": [ - "fhn = bp.dyn.FHN(1)" - ] - }, { "cell_type": "code", - "execution_count": 13, - "id": "7caae875", + "execution_count": 5, + "id": "e6b176c7", "metadata": {}, "outputs": [], "source": [ - "Because there are two variables, $v$ and $w$, in the FHN model, we shall use 2-D phase plane analysis to visualize how these two variables change over time." + "fhn = bp.dyn.FHN(1)" ] }, { "cell_type": "markdown", - "id": "cc02c0ef", + "id": "b13e4ee9", "metadata": {}, "source": [ - "analyzer = bp.analysis.PhasePlane2D(\n", - " model=fhn,\n", - " target_vars={'V': [-3, 3], 'w': [-3., 3.]},\n", - " pars_update={'I_ext': 0.8}, \n", - " resolutions=0.01,\n", - ")\n", - "analyzer.plot_nullcline()\n", - "analyzer.plot_vector_field()\n", - "analyzer.plot_fixed_point()\n", - "analyzer.plot_trajectory({'V': [-2.8], 'w': [-1.8]}, duration=100.)\n", - "analyzer.show_figure()" + "Because there are two variables, $v$ and $w$, in the FHN model, we shall use 2-D phase plane analysis to visualize how these two variables change over time." ] }, { "cell_type": "code", - "execution_count": 11, - "id": "bdbb963c", + "execution_count": 6, + "id": "78078951", "metadata": {}, "outputs": [ { @@ -279,7 +259,7 @@ "\tThere are 866 candidates\n", "I am trying to filter out duplicate fixed points ...\n", "\tFound 1 fixed points.\n", - "\t#1 V=-0.2738719079019268, w=0.5329731347793121 is a unstable node.\n", + "\t#1 V=-0.2738719079879798, w=0.5329731346879486 is a unstable node.\n", "I am plotting the trajectory ...\n" ] }, @@ -296,6 +276,24 @@ "output_type": "display_data" } ], + "source": [ + "analyzer = bp.analysis.PhasePlane2D(\n", + " model=fhn,\n", + " target_vars={'V': [-3, 3], 'w': [-3., 3.]},\n", + " pars_update={'I_ext': 0.8}, \n", + " resolutions=0.01,\n", + ")\n", + "analyzer.plot_nullcline()\n", + "analyzer.plot_vector_field()\n", + "analyzer.plot_fixed_point()\n", + "analyzer.plot_trajectory({'V': [-2.8], 'w': [-1.8]}, duration=100.)\n", + "analyzer.show_figure()" + ] + }, + { + "cell_type": "markdown", + "id": "247760da", + "metadata": {}, "source": [ "In the ``PhasePlane2D`` analyzer, the parameters ``model``, ``target_vars``, and ``resolution`` is the same as those in ``Bifurcation1D``. ``pars_update`` specifies the parameters to be updated during analysis. After defining the analyzer, users can visualize the nullcline, vector field, fixed points and the trajectory in the image. The phase plane gives users intuitive interpretation of the changes of $v$ and $w$ guided by the vector field (violet arrows)." ] @@ -314,42 +312,34 @@ }, { "cell_type": "markdown", - "source": [ - "- For more details about how to perform bifurcation analysis and phase plane analysis, please see the tutorial of [Low-dimensional Analyzers](../tutorial_analysis/lowdim_analysis.ipynb).\n", - "- A good example of phase plane analysis and bifurcation analysis is the decision-making model, please see the tutorial in [Analysis of a Decision-making Model](../tutorial_analysis/decision_making_model.ipynb)\n", - "- If you want to how to analyze the slow points (or fixed points) of your high-dimensional dynamical models, please see the tutorial of [High-dimensional Analyzers](../tutorial_analysis/highdim_analysis.ipynb)" - ], + "id": "315c47ff", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "- For more details about how to perform bifurcation analysis and phase plane analysis, please see the tutorial of [Low-dimensional Analyzers](../tutorial_analysis/lowdim_analysis.ipynb).\n", + "- A good example of phase plane analysis and bifurcation analysis is the decision-making model, please see the tutorial in [Analysis of a Decision-making Model](../tutorial_analysis/decision_making_model.ipynb)\n", + "- If you want to how to analyze the slow points (or fixed points) of your high-dimensional dynamical models, please see the tutorial of [High-dimensional Analyzers](../tutorial_analysis/highdim_analysis.ipynb)" + ] }, { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [], + "id": "77fc3778", "metadata": { - "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8955c420", - "metadata": {}, + }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -363,9 +353,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/quickstart/dynamics_intro.ipynb b/docs/quickstart/dynamics_intro.ipynb deleted file mode 100644 index 17b1255e..00000000 --- a/docs/quickstart/dynamics_intro.ipynb +++ /dev/null @@ -1,523 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Dynamics Programming Introduction" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "@[Chaoming Wang](https://github.com/chaoming0625)\n", - "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> What I cannot create, I do not understand. --- Richard Feynman" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The brain is a complex dynamical system. To simulate the dynamics of the brain, one of the most important things is to model the dynamically changed states of each component. Mathematically, the dynamics of a system can be expressed as\n", - "\n", - "$$\n", - "\\dot{X} = f(X, t)\n", - "$$\n", - "\n", - "where $X$ is the state of the system, $t$ is the time, and $f$ is a function describes the time dependence of the system state. \n", - "\n", - "Simulation of such dynamical systems is called **dynamic modeling**. BrainPy provides users with various tools and convenient interface for neurodynamic modeling, including **dynamic building**, **dynamic simulation**, **dynamic analysis** and **dynamic training**. This section helps users to get familiar with the basic structure and common operations of neurodynamic modeling in BrainPy." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2021-03-25T03:02:48.939126Z", - "start_time": "2021-03-25T03:02:47.073698Z" - } - }, - "outputs": [], - "source": [ - "import brainpy as bp\n", - "import brainpy.math as bm\n", - "\n", - "bp.math.set_platform('cpu')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dynamical System Building" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In BrainPy, [``brainpy.DynamicalSystem``](../apis/auto/building/generated/brainpy.building.brainobjects.DynamicalSystem.rst) is used to define dynamic brain objects. Various children classes are implemented to build different elements, such as [brainpy.NeuGroup](../apis/auto/building/generated/brainpy.building.brainobjects.NeuGroup.rst) for neuron group modeling, [brainpy.TwoEndConn](../apis/auto/building/generated/brainpy.building.brainobjects.TwoEndConn.rst) for synaptic computation, [brainpy.Network](../apis/auto/building/generated/brainpy.building.brainobjects.Network.rst) for network modeling, etc. Arbitrary composition of these objects is also an instance of ``brainpy.DynamicalSystem``. Therefore, ``brainpy.DynamicalSystem`` is the universal language to define dynamical models in BrainPy. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "According to the definition of a dynamical system, any subclass of ``brainpy.DynamicalSystem`` must implement the updating rule in the *update* function (``def update(self, _t, _dt)``), and dynamically changed variables should be defined in the system." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class YourDynamicalSystem(bp.DynamicalSystem):\n", - " \n", - " def __init__(self):\n", - " # define dynamically changed variables\n", - " pass\n", - " \n", - " def update(self, _t, _dt):\n", - " # update the variables\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here, we illustrate how to build a dynamcial system by using the well known [FitzHugh–Nagumo neuron model](https://brainmodels.readthedocs.io/en/latest/apis/generated/brainmodels.neurons.FHN.html), whose dynamics is given by: \n", - "\n", - "$$\n", - "{\\dot {v}}=v-{\\frac {v^{3}}{3}}-w+I, \\\\\n", - "\\tau {\\dot {w}}=v+a-bw.\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This model contains two differential equations. In BrainPy, the numerical integration of ordinary differential equations can be accomplished with [brainpy.odeint](../apis/integrators/generated/brainpy.integrators.odeint.rst) (please see [Numerical Integrator](../tutorial_intg/index.rst) for more details). \n", - "\n", - "The above two differential equations as Python functions can be defined as:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def dV(V, t, w, Iext=0.): \n", - " return V - V * V * V / 3 - w + Iext\n", - " \n", - "def dw(w, t, V, a=0.7, b=0.8): \n", - " return (V + a - b * w) / self.tau" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "where ``t`` is the time variable, **arguments before ``t`` are variables, and arguments after ``t`` are parameters**.\n", - "\n", - "Thereafter, the numerical solvers for the two equations can be defined as:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "int_V = bp.odeint(dV, method='euler')\n", - "\n", - "int_w = bp.odeint(dw, method='euler')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "where the ``method`` defines the numerical integration method to use (all implemented methods can be referred to in [Numerical Solvers for ODEs](../tutorial_intg/ode_numerical_solvers.ipynb)). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The FitzHugh–Nagumo neuron model can be defined as a Python class, in which the parameters, variables, and integral functions are defined in the constructor ``__init__()``, and the updating rule from the current time $\\mathrm{\\_t}$ to the next time $\\mathrm{\\_t + \\_dt}$ can be defined in the update function ``update()``." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "class FitzHughNagumoModel(bp.DynamicalSystem):\n", - " def __init__(self, num, method='exp_auto'):\n", - " super(FitzHughNagumoModel, self).__init__()\n", - "\n", - " # parameters\n", - " self.a = 0.7\n", - " self.b = 0.8\n", - " self.tau = 12.5\n", - "\n", - " # variables\n", - " self.V = bm.Variable(bm.zeros(num))\n", - " self.w = bm.Variable(bm.zeros(num))\n", - " self.Iext = bm.Variable(bm.zeros(num)) # to receive the external input\n", - "\n", - " # functions\n", - " def dV(V, t, w, Iext=0.): \n", - " return V - V * V * V / 3 - w + Iext\n", - " def dw(w, t, V, a=0.7, b=0.8): \n", - " return (V + a - b * w) / self.tau\n", - " self.int_V = bp.odeint(dV, method=method)\n", - " self.int_w = bp.odeint(dw, method=method)\n", - "\n", - " def update(self, _t, _dt):\n", - " self.V.value = self.int_V(self.V, _t, self.w, self.Iext, _dt)\n", - " self.w.value = self.int_w(self.w, _t, self.V, self.a, self.b, _dt)\n", - " self.Iext[:] = 0." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# instantiation\n", - "fhn = FitzHughNagumoModel(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In BrainPy, any dynamical model can be defined as a Python class. More advanced usage of dynamical system building can be obtained in [Dynamics Building](../tutorial_building/index.rst)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dynamical System Simulation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dynamics simulation in BrainPy is highly efficient. It can deploy models to CPUs or GPUs. To switch the backend device, you can use ``brainpy.math.set_platform(\"cpu\" or \"gpu\")`` at the top of your script. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Runners are used for dynamic simulation. They can [**monitor**](../tutorial_simulation/monitors_and_inputs.ipynb) variable trajectories and give [**inputs**](../tutorial_simulation/monitors_and_inputs.ipynb) to target variables during simulation. Currently, BrainPy provides several [runners](../apis/auto/simulation/runner.rst) to satisfy different simulation requirements. Here, we use ``brainpy.StructRunner`` to run the above instance ``fhn``. During simulation, we monitor variables ``V`` and ``w``, and give inputs to ``Iext`` variable. " - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dcefc71b42c64080916703e550f1b365", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/1000 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "bp.visualize.line_plot(runner.mon.ts, runner.mon['V'], legend='V')\n", - "bp.visualize.line_plot(runner.mon.ts, runner.mon['w'], legend='w', show=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For more details, please refer to the tutorials in [Dynamics Simulation](../tutorial_simulation/index.rst)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dynamical System Analysis" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In BrainPy, the defined model can not only be used for simulation, but also to perform automatic dynamics analysis. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "BrainPy provides rich interfaces to support analysis, incluing\n", - "\n", - "- phase plane analysis, bifurcation analysis, and fast-slow bifurcation analysis for [low-dimensional systems](../tutorial_analysis/lowdim_analysis.ipynb);\n", - "- linearization analysis and fixed/slow point finding for [high-dimensional systems](../tutorial_analysis/highdim_analysis.ipynb). " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the above FitzHugh-Nagumo model, it is a two variable model. We can use [brainpy.analysis.PhasePlane2D](../apis/auto/analysis/generated/brainpy.analysis.lowdim.PhasePlane2D.rst) to make phase plane analysis. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "bp.math.enable_x64()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "I am computing fx-nullcline ...\n", - "I am evaluating fx-nullcline by optimization ...\n", - "I am computing fy-nullcline ...\n", - "I am evaluating fy-nullcline by optimization ...\n", - "I am creating the vector field ...\n", - "I am searching fixed points ...\n", - "I am trying to find fixed points by optimization ...\n", - "\tThere are 866 candidates\n", - "I am trying to filter out duplicate fixed points ...\n", - "\tFound 1 fixed points.\n", - "\t#1 V=-0.2738719079019268, w=0.5329731347793121 is a unstable node.\n", - "I am plotting the trajectory ...\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "analyzer = bp.analysis.PhasePlane2D(\n", - " fhn,\n", - " target_vars={'V': [-3, 3], 'w': [-3., 3.]},\n", - " pars_update={'Iext': 0.8}, \n", - " resolutions=0.01,\n", - ")\n", - "analyzer.plot_nullcline()\n", - "analyzer.plot_vector_field()\n", - "analyzer.plot_fixed_point()\n", - "analyzer.plot_trajectory({'V': [-2.8], 'w': [-1.8]}, duration=100.)\n", - "analyzer.show_figure()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To find more tools for dynamics analysis, you can refer to the tutorials in [Dynamics Analysis](../tutorial_analysis/index.rst) and examples in [BrainPy-Examples](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/dynamics_analysis/index.html)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dynamical System Training" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In recent years, we saw the revolution that training a dynamical system from data or tasks has provided important insights to understand brain functions. To support this, BrainPy porvides various interfaces to help users train dynamical systems. \n", - "\n", - "Examples of using FORCE learning algorithm, back-propagation algorithm or others to train recurrent neural networks can be found in [BrainPy-Examples](https://brainpy-examples.readthedocs.io/en/brainpy-2.x/recurrent_networks/index.html). " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "hide_input": false, - "jupytext": { - "encoding": "# -*- coding: utf-8 -*-" - }, - "kernelspec": { - "display_name": "Python [conda env:root] *", - "language": "python", - "name": "conda-root-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.8" - }, - "latex_envs": { - "LaTeX_envs_menu_present": true, - "autoclose": false, - "autocomplete": true, - "bibliofile": "biblio.bib", - "cite_by": "apalike", - "current_citInitial": 1, - "eqLabelWithNumbers": true, - "eqNumInitial": 1, - "hotkeys": { - "equation": "Ctrl-E", - "itemize": "Ctrl-I" - }, - "labels_anchors": false, - "latex_user_defs": false, - "report_style_numbering": false, - "user_envs_cfg": false - }, - "toc": { - "base_numbering": 1, - "nav_menu": { - "height": "411px", - "width": "316px" - }, - "number_sections": false, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "243.068px" - }, - "toc_section_display": true, - "toc_window_display": true - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/quickstart/rate_model.ipynb b/docs/quickstart/rate_model.ipynb index 9fa48e0c..1433e9af 100644 --- a/docs/quickstart/rate_model.ipynb +++ b/docs/quickstart/rate_model.ipynb @@ -5,23 +5,800 @@ "id": "16ac58ee", "metadata": {}, "source": [ - "# Simulating a Rate Network Model" + "# Simulating a Whole-brain Neural Mass Model" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "39953757", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "@[Chaoming Wang](https://github.com/chaoming0625)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Whole-brain modeling is the grand challenge of computational neuroscience. Simulating a whole-brain models with spiking neurons is still nearly impossible for normal users. However, by using rate-based neural mass models, in which each brain region is approximated to several simple variables, we can build an abstract whole-brain model. In recent years, whole-brain models can be used to address a wide range of problems. In this section, we are going to talk about how to simulate a whole-brain neural mass model with BrainPy." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import brainpy as bp\n", + "import brainpy.math as bm\n", + "\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['image.cmap'] = 'plasma'" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Neural mass model" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "A neural mass models is a low-dimensional population model of spiking neural networks. It aims to describe the coarse grained activity of large populations of neurons and synapses. Mathematically, it is a dynamical system of non-linear ODEs. A classical neural mass model is the two dimensional [Wilson–Cowan model](https://en.wikipedia.org/wiki/Wilson%E2%80%93Cowan_model). This model tracks the activity of an excitatory population of neurons coupled to an inhibitory population. With the augmentation of such models by more realistic forms of synaptic and network interaction they have proved especially successful in providing fits to neuro-imaging data." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Here, let's try the Wilson-Cowan model." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" + ] + }, + { + "data": { + "text/plain": " 0%| | 0/100 [00:00", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "wc = bp.dyn.WilsonCowanModel(2,\n", + " wEE=16, wIE=15., wEI=12., wII=3.,\n", + " E_a=1.5, I_a=1.5, E_theta=3., I_theta=3.,\n", + " method='exp_euler_auto')\n", + "wc.x[:] = [-0.2, 1.]\n", + "wc.y[:] = [0.0, 1.]\n", + "\n", + "runner = bp.dyn.DSRunner(wc, monitors=['x', 'y'], inputs=['input', -0.5])\n", + "runner.run(10.)\n", + "\n", + "bp.visualize.line_plot(runner.mon.ts, runner.mon.x,\n", + " plot_ids=[0, 1], legend='e', show=True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "We can see this model at least has two stable states." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "**Bifurcation diagram**\n", + "\n", + "With the automatic analysis module in BrainPy, we can easily inspect the bifurcation digram of the model. Bifurcation diagrams can give us an overview of how different parameters of the model affect its dynamics (the details of the automatic analysis support of BrainPy please see the introduction in [Analyzing a Dynamical Model](./analysis.ipynb) and tutorials in [Dynamics Analysis](../tutorial_analysis/index.rst)). In this case, we make ``x_ext`` as a bifurcation parameter, and try to see how the system behavior changes with the change of ``x_ext``." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I am making bifurcation analysis ...\n", + "I am filtering out fixed point candidates with auxiliary function ...\n", + "I am trying to find fixed points by optimization ...\n", + "\tThere are 40000 candidates\n", + "I am trying to filter out duplicate fixed points ...\n", + "\tFound 579 fixed points.\n", + "I am plotting the limit cycle ...\n", + "C:\\Users\\adadu\\miniconda3\\lib\\site-packages\\jax\\_src\\numpy\\lax_numpy.py:3610: UserWarning: Explicitly requested dtype requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", + " lax._check_user_dtype_supported(dtype, \"asarray\")\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bf = bp.analysis.Bifurcation2D(\n", + " wc,\n", + " target_vars={'x': [-0.2, 1.], 'y': [-0.2, 1.]},\n", + " target_pars={'x_ext': [-2, 2]},\n", + " pars_update={'y_ext': 0.},\n", + " resolutions={'x_ext': 0.01}\n", + ")\n", + "bf.plot_bifurcation()\n", + "bf.plot_limit_cycle_by_sim(duration=500)\n", + "bf.show_figure()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Similarly, simulating and analyzing a rate-based FitzHugh-Nagumo model is also a piece of cake by using BrainPy." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "I am making bifurcation analysis ...\n", + "I am filtering out fixed point candidates with auxiliary function ...\n", + "I am trying to find fixed points by optimization ...\n", + "\tThere are 20000 candidates\n", + "I am trying to filter out duplicate fixed points ...\n", + "\tFound 200 fixed points.\n", + "I am plotting the limit cycle ...\n", + "C:\\Users\\adadu\\miniconda3\\lib\\site-packages\\jax\\_src\\numpy\\lax_numpy.py:3610: UserWarning: Explicitly requested dtype requested in asarray is not available, and will be truncated to dtype float32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.\n", + " lax._check_user_dtype_supported(dtype, \"asarray\")\n" + ] + }, + { + "data": { + "text/plain": "
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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fhn = bp.dyn.RateFHN(1, method='exp_auto')\n", + "\n", + "bf = bp.analysis.Bifurcation2D(\n", + " fhn,\n", + " target_vars={'x': [-2, 2], 'y': [-2, 2]},\n", + " target_pars={'x_ext': [0, 2]},\n", + " pars_update={'y_ext': 0.},\n", + " resolutions={'x_ext': 0.01}\n", + ")\n", + "bf.plot_bifurcation()\n", + "bf.plot_limit_cycle_by_sim(duration=500)\n", + "bf.show_figure()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "In this model, we find that when the external input ``x_ext`` has the value in [0.72, 1.4], the model will generate limit cycles. We can verify this by simulation." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/1000 [00:00", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "runner = bp.dyn.DSRunner(fhn, monitors=['x', 'y'], inputs=['input', 1.0])\n", + "runner.run(100.)\n", + "\n", + "bp.visualize.line_plot(runner.mon.ts, runner.mon.x, legend='x')\n", + "bp.visualize.line_plot(runner.mon.ts, runner.mon.y, legend='y', show=True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Whole-brain model" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "A rate-based whole-brain model is a network model which consists of coupled brain regions. Each brain region is represented by a neural mass model which is connected to other brain regions according to the underlying network structure of the brain, also known as the connectome. In order to illustrate how to use BrainPy's support for whole-brain modeling, here we provide a processed data in the following link:\n", + "\n", + "- A processed data from ConnectomeDB of the Human Connectome Project (HCP): [https://share.weiyun.com/wkPpARKy](https://share.weiyun.com/wkPpARKy)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Please download the dataset and place it in your favorite ``PATH``." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "PATH = './data/hcp.npz'" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "In genral, a dataset for whole-brain modeling consists of the following parts:\n", + "\n", + "1\\. A structural connectivity matrix which captures the synaptic connection strengths between brain areas. It often derived from DTI tractography of the whole brain. The connectome is then typically parcellated in a preferred atlas (for example the AAL2 atlas) and the number of axonal fibers connecting each brain area with every other area is counted. This number serves as an indication of the synaptic coupling strengths between the areas of the brain.\n", + "\n", + "2\\. A delay matrix which calculated from the average length of the axonal fibers connecting each brain area with another.\n", + "\n", + "3\\. A set of functional data that can act as a target for model optimization. Resting-state fMRI offers an easy and fairly unbiased way for calibrating whole-brain models. EEG data could be used as well." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Now, let's load the dataset." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [ + "data = bm.load(PATH)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "source": [ + "# The structural connectivity matrix\n", + "\n", + "data['Cmat'].shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "(80, 80)" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "(80, 80)" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The fiber length matrix\n", + "\n", + "data['Dmat'].shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "(7, 80, 80)" + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The functional data for 7 subjects\n", + "\n", + "data['FCs'].shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Let's have a look what the data looks like." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(15,5))\n", + "fig.subplots_adjust(wspace=0.28)\n", + "\n", + "im = axs[0].imshow(data['Cmat'])\n", + "axs[0].set_title(\"Connection matrix\")\n", + "fig.colorbar(im, ax=axs[0],fraction=0.046, pad=0.04)\n", + "im = axs[1].imshow(data['Dmat'], cmap='inferno')\n", + "axs[1].set_title(\"Fiber length matrix\")\n", + "fig.colorbar(im, ax=axs[1],fraction=0.046, pad=0.04)\n", + "im = axs[2].imshow(data['FCs'][0], cmap='inferno')\n", + "axs[2].set_title(\"Empirical FC of subject 1\")\n", + "fig.colorbar(im, ax=axs[2],fraction=0.046, pad=0.04)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Let's first get the delay matrix according to the fiber length matrix, the signal transmission speed between areas, and the numerical integration step ``dt``. Here, we assume the axonal transmission speed is 20 and the simulation time step ``dt=0.1`` ms." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [], + "source": [ + "sigal_speed = 20.\n", + "\n", + "# the number of the delay steps\n", + "delay_mat = data['Dmat'] / sigal_speed / bm.get_dt()\n", + "delay_mat = bm.asarray(delay_mat, dtype=bm.int_)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The connectivity matrix can be directly obtained through the structural connectivity matrix, which times a global coupling strength parameter ``gc``. b" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, "outputs": [], - "source": [] + "source": [ + "gc = 1.\n", + "\n", + "conn_mat = bm.asarray(data['Cmat'] * gc)\n", + "\n", + "# It is necessary to exclude the self-connections\n", + "bm.fill_diagonal(conn_mat, 0)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "We now are ready to intantiate a whole-brain model with the neural mass model and the dataset the processed before." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "class WholeBrainNet(bp.dyn.Network):\n", + " def __init__(self, Cmat, Dmat):\n", + " super(WholeBrainNet, self).__init__()\n", + "\n", + " self.fhn = bp.dyn.RateFHN(80, x_ou_sigma=0.01, y_ou_sigma=0.01,\n", + " name='fhn', method='exp_auto')\n", + " self.syn = bp.dyn.DiffusiveDelayCoupling(self.fhn, self.fhn,\n", + " 'x->input',\n", + " conn_mat=Cmat,\n", + " delay_mat=Dmat,\n", + " delay_initializer=bp.init.Uniform(0, 0.05))\n", + "\n", + " def update(self, _t, _dt):\n", + " self.syn.update(_t, _dt)\n", + " self.fhn.update(_t, _dt)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/60000 [00:00", + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 2, figsize=(12, 4))\n", + "fc = bp.measure.functional_connectivity(runner.mon['fhn.x'])\n", + "ax = axs[0].imshow(fc)\n", + "plt.colorbar(ax, ax=axs[0])\n", + "axs[1].plot(runner.mon.ts, runner.mon['fhn.x'][:, ::5], alpha=0.8)\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "We can compute the element-wise Pearson correlation of the functional connectivity matrices of the simulated data to the empirical data to estimate how well the model captures the inter-areal functional correlations found in empirical resting-state recordings." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 17, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Correlation per subject: ['0.62', '0.49', '0.61', '0.5', '0.56', '0.5', '0.47']\n", + "Mean FC/FC correlation: 0.54\n" + ] + } + ], + "source": [ + "scores = [bp.measure.matrix_correlation(fc, fcemp)\n", + " for fcemp in data['FCs']]\n", + "print(\"Correlation per subject:\", [f\"{s:.2}\" for s in scores])\n", + "print(\"Mean FC/FC correlation: {:.2f}\".format(bm.mean(bm.asarray(scores))))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "name": "python3", "language": "python", - "name": "python3" + "display_name": "Python 3" }, "language_info": { "codemirror_mode": { @@ -38,4 +815,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/quickstart/simulation.ipynb b/docs/quickstart/simulation.ipynb index e0ea9c06..b1e84081 100644 --- a/docs/quickstart/simulation.ipynb +++ b/docs/quickstart/simulation.ipynb @@ -170,7 +170,7 @@ "id": "43ec39f4", "metadata": {}, "source": [ - "After build a SNN, we can use it for dynamic simulation. To run a simulation, we need first wrap the network model into a runner. Currently BrainPy provides ``DSRunner``, ``StructRunner``, ``ReportRunner`` in ``brainpy.dyn``. They receive inputs with the same structure, and will be expanded in [Running a Simulation](../tutorial_simulation/runner.ipynb). Here we use ``DSRunner`` as an example:" + "After build a SNN, we can use it for dynamic simulation. To run a simulation, we need first wrap the network model into a **runner**. Currently BrainPy provides ``DSRunner`` and ``ReportRunner`` in ``brainpy.dyn``, which will be expanded in the [Runners](../tutorial_simulation/runner.ipynb) tutorial. Here we use ``DSRunner`` as an example:" ] }, { @@ -191,7 +191,7 @@ "id": "11473917", "metadata": {}, "source": [ - "To make dynamic simulation more applicable and powerful, users can [**monitor**](../tutorial_simulation/monitors_and_inputs.ipynb) variable trajectories and give [**inputs**](../tutorial_simulation/monitors_and_inputs.ipynb) to target neuron groups. Here we monitor the ``spike`` variable in the ``E`` and ``I`` LIF model, which refers to the spking status of the neuron group, and give a constant input to both neuron groups. The time interval of numerical integration ``dt`` (with the default value of 0.1) can also be specified.\n", + "To make dynamic simulation more applicable and powerful, users can [**monitor**](../tutorial_toolbox/monitors.ipynb) variable trajectories and give [**inputs**](../tutorial_toolbox/inputs.ipynb) to target neuron groups. Here we monitor the ``spike`` variable in the ``E`` and ``I`` LIF model, which refers to the spking status of the neuron group, and give a constant input to both neuron groups. The time interval of numerical integration ``dt`` (with the default value of 0.1) can also be specified.\n", "\n", "After creating the runner, we can run a simulation by calling the runner:" ] @@ -290,9 +290,9 @@ ], "metadata": { "kernelspec": { - "display_name": "brainpy", + "display_name": "Python [conda env:root] *", "language": "python", - "name": "brainpy" + "name": "conda-root-py" }, "language_info": { "codemirror_mode": { @@ -304,7 +304,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.11" + "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/docs/quickstart/training.ipynb b/docs/quickstart/training.ipynb index b7876b53..0e8e5730 100644 --- a/docs/quickstart/training.ipynb +++ b/docs/quickstart/training.ipynb @@ -1164,7 +1164,7 @@ } ], "source": [ - "model.init_state(1)\n", + "model.initialize(1)\n", "x, y = build_inputs_and_targets(batch_size=1)\n", "predicts = trainer.predict(x)" ] diff --git a/docs/tutorial_basics/tensors_and_variables.ipynb b/docs/tutorial_basics/tensors_and_variables.ipynb index 80375268..5d72c603 100644 --- a/docs/tutorial_basics/tensors_and_variables.ipynb +++ b/docs/tutorial_basics/tensors_and_variables.ipynb @@ -22,7 +22,7 @@ "id": "b39bc3a4", "metadata": {}, "source": [ - "In this section ,we will briefly intrduce two basic and important data structures: tensors and vriables. They form the foundation for mathmatical operations of brain dynamics programming (BDP) in BrainPy." + "In this section ,we will briefly introduce two basic and important data structures: tensors and variables. They form the foundation for mathematical operations of brain dynamics programming (BDP) in BrainPy." ] }, { @@ -90,7 +90,7 @@ "source": [ "import brainpy.math as bm\n", "\n", - "bm.set_platform('cpu')" + "# bm.set_platform('cpu')" ] }, { @@ -175,7 +175,7 @@ "id": "6be487b0", "metadata": {}, "source": [ - "Below we will give a few examples of tensor operations that are commonly used in brain dynamics programming. For more details about tensor operations, please refer to the [tensor tutorial](../tutorial_basics/tensors.ipynb)." + "Below we will give a few examples of tensor operations that are commonly used in brain dynamics programming. For more details about tensor operations, please refer to the [tensor tutorial](tensors.ipynb)." ] }, { @@ -194,11 +194,11 @@ "outputs": [], "source": [ "t2 = bm.arange(4)\n", - "# t2: JaxArray(DeviceArray([0, 1, 2, 3], dtype=int32))\n", + "# t2: JaxArray([0, 1, 2, 3], dtype=int32)\n", "\n", "t3 = bm.ones((2, 4)) * 1.5\n", - "# t3: JaxArray(DeviceArray([[1.5, 1.5, 1.5, 1.5],\n", - "# [1.5, 1.5, 1.5, 1.5]], dtype=float32))" + "# t3: JaxArray([[1.5, 1.5, 1.5, 1.5],\n", + "# [1.5, 1.5, 1.5, 1.5]], dtype=float32)" ] }, { @@ -258,18 +258,18 @@ "source": [ "# algebraic operations\n", "t2 + t3[0]\n", - "# JaxArray(DeviceArray([1.5, 2.5, 3.5, 4.5], dtype=float32))\n", + "# JaxArray([1.5, 2.5, 3.5, 4.5], dtype=float32)\n", "\n", "t3[0] / t1[0, 1]\n", "# DeviceArray([1.5 , 0.75 , 0.5 , 0.375], dtype=float32)\n", "\n", "# broadcasting\n", "t2 + 3\n", - "# JaxArray(DeviceArray([3, 4, 5, 6], dtype=int32))\n", + "# JaxArray([3, 4, 5, 6], dtype=int32)\n", "\n", "t2 + t3\n", - "# JaxArray(DeviceArray([[1.5, 2.5, 3.5, 4.5],\n", - "# [1.5, 2.5, 3.5, 4.5]], dtype=float32))" + "# JaxArray([[1.5, 2.5, 3.5, 4.5],\n", + "# [1.5, 2.5, 3.5, 4.5]], dtype=float32)" ] }, { @@ -292,14 +292,14 @@ "source": [ "# some functions\n", "bm.dot(t2, t3.T)\n", - "# JaxArray(DeviceArray([9., 9.], dtype=float32))\n", + "# JaxArray([9., 9.], dtype=float32)\n", "\n", "bm.max(t1, axis=2)\n", - "# JaxArray(DeviceArray([[3, 4, 7],\n", - "# [0, 1, 2]], dtype=int32))\n", + "# JaxArray([[3, 4, 7],\n", + "# [0, 1, 2]], dtype=int32)\n", "\n", "t3.flatten()\n", - "# JaxArray(DeviceArray([1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5], dtype=float32))" + "# JaxArray([1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5], dtype=float32)" ] }, { @@ -716,4 +716,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/tutorial_simulation/network_models.ipynb b/docs/tutorial_simulation/network_models.ipynb index 470bfe05..d830cd5c 100644 --- a/docs/tutorial_simulation/network_models.ipynb +++ b/docs/tutorial_simulation/network_models.ipynb @@ -342,7 +342,7 @@ "id": "84449872", "metadata": {}, "source": [ - " All elements are passed as ``**kwargs`` argument can be accessed by the provided keys. This will affect the following dynamics simualtion and will be discussed in greater detail in [tutorial of Monitors and Inputs](../tutorial_simulation/monitors_and_inputs.ipynb)." + "All elements are passed as ``**kwargs`` argument can be accessed by the provided keys. This will affect the following dynamics simualtion and will be discussed in greater detail in tutorial of [Runners](../tutorial_toolbox/runners.ipynb)." ] }, { @@ -466,13 +466,21 @@ "id": "ee0ef0f9", "metadata": {}, "source": [ - "Above are some simulation examples showing the possible application of network models. The detailed description of dynamics simulation is covered in [Dynamics Simulation](../tutorial_simulation/index.rst), where the use of monitors and inputs will be expatiated." + "Above are some simulation examples showing the possible application of network models. The detailed description of dynamics simulation is covered in the toolboxes, where the use of [runners](../tutorial_toolbox/runners.ipynb), [monitors](../tutorial_toolbox/monitors.ipynb), and [inputs](../tutorial_toolbox/inputs.ipynb) will be expatiated." ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d31c4afc", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -486,7 +494,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/docs/tutorial_simulation/neuron_models.ipynb b/docs/tutorial_simulation/neuron_models.ipynb index 70c1c851..9f82aba1 100644 --- a/docs/tutorial_simulation/neuron_models.ipynb +++ b/docs/tutorial_simulation/neuron_models.ipynb @@ -395,7 +395,7 @@ "id": "f9d2604b", "metadata": {}, "source": [ - "The details of the model simulation will be expanded in the [Dynamics Simulation](../tutorial_simulation/index.rst) section. In brief, running any dynamical system instance should be accomplished with a runner, such like `brianpy.StructRunner` and `brainpy.ReportRunner`. In the runner, the variables want to monitor and the input crrents try to specify can be provided when initializing the runner. The details please see the tutorial of [Monitors and Inputs](../tutorial_simulation/monitors_and_inputs.ipynb). " + "The details of the model simulation will be expanded in the [Runners](../tutorial_toolbox/runners.ipynb) section. In brief, running any dynamical system instance should be accomplished with a runner, such like `brianpy.DSRunner` and `brainpy.ReportRunner`. The variables to be monitored and the input crrents to be applied in the simulation can be provided when initializing the runner. The details are accessible in [Monitors](../tutorial_toolbox/monitors.ipynb) and [Inputs](../tutorial_toolbox/inputs.ipynb). " ] }, { @@ -512,7 +512,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -526,7 +526,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, @@ -562,4 +562,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} diff --git a/docs/tutorial_simulation/synapse_models.ipynb b/docs/tutorial_simulation/synapse_models.ipynb index 7e081b03..8ae2b03c 100644 --- a/docs/tutorial_simulation/synapse_models.ipynb +++ b/docs/tutorial_simulation/synapse_models.ipynb @@ -169,7 +169,7 @@ "1. Constructor function ``__init__()``, in which three key arguments are needed. \n", " - `pre`: the pre-synaptic neural group. It should be an instance of `brainpy.dyn.NeuGroup`.\n", " - `post`: the post-synaptic neural group. It should be an instance of `brainpy.dyn.NeuGroup`.\n", - " - `conn` (optional): the connection type between these two groups. BrainPy has provided abundant connection types that are described in details in the [Synaptic Connections](./synaptic_connections.ipynb).\n", + " - `conn` (optional): the connection type between these two groups. BrainPy has provided abundant connection types that are described in details in the [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb).\n", "2. Update function ``update(_t, _dt)`` describes the updating rule from the current time $\\mathrm{\\_t}$ to the next time $\\mathrm{\\_t + \\_dt}$. " ] }, @@ -385,7 +385,7 @@ "id": "44fa4941", "metadata": {}, "source": [ - "More details of the connection structures please see the tutorial of [Synaptic Connections](./synaptic_connections.ipynb)." + "More details of the connection structures please see the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)." ] }, { @@ -953,7 +953,7 @@ "\n", "Imaging you want to connect 10,000 pre-synaptic neurons to 10,000 post-synaptic neurons with a 10% random connection probability. Using matrix, you need $10^8$ floats to save the synaptic state, and at each update step, you need do computation on $10^8$ floats. Actually, the number of synapses you really connect is only $10^7$. See, there is a huge memory waste and computing resource inefficiency. Moreover, at the given time $\\mathrm{\\_t}$, the number of pre-synaptic neurons in the spiking state is small on average. This means we have made many useless computations when defining synaptic computations with matrix-based connections (zeros dot connection matrix results in zeros).\n", "\n", - "Therefore, we need new ways to define synapse models. Specifically, we use vectors to store the connected neuron indices, like the ``pre_ids`` and ``post_ids`` (see [Synaptic Connections](./synaptic_connections.ipynb)). " + "Therefore, we need new ways to define synapse models. Specifically, we use vectors to store the connected neuron indices, like the ``pre_ids`` and ``post_ids`` (see [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)). " ] }, { @@ -961,7 +961,7 @@ "id": "b67256b8", "metadata": {}, "source": [ - "In the below, we assume you have learned the synaptic connection types detailed in the tutorial of [Synaptic Connections](./synaptic_connections.ipynb)." + "In the below, we assume you have learned the synaptic connection types detailed in the tutorial of [Synaptic Connections](../tutorial_toolbox/synaptic_connections.ipynb)." ] }, { @@ -1233,7 +1233,7 @@ "encoding": "# -*- coding: utf-8 -*-" }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -1247,7 +1247,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/docs/tutorial_toolbox/dde_numerical_solvers.ipynb b/docs/tutorial_toolbox/dde_numerical_solvers.ipynb index fd98f859..202b1098 100644 --- a/docs/tutorial_toolbox/dde_numerical_solvers.ipynb +++ b/docs/tutorial_toolbox/dde_numerical_solvers.ipynb @@ -6,7 +6,7 @@ "collapsed": true }, "source": [ - "# Numerical Solvers for DDEs" + "# Numerical Solvers for Delay Differential Equations" ] }, { @@ -84,7 +84,7 @@ "BrainPy provides several kinds of delay variables: \n", "\n", "\n", - "- [brainpy.math.FixedLenDelay](../apis/auto/math/generated/brainpy.math.delay_vars.FixedLenDelay.rst)\n", + "- [brainpy.math.TimeDelay](../apis/auto/math/generated/brainpy.math.delay_vars.TimeDelay.rst)\n", "- [brainpy.math.NeutralDelay](../apis/auto/math/generated/brainpy.math.delay_vars.NeutralDelay.rst)" ] }, @@ -92,7 +92,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "All of these can be used for defining delay differential equations. ``brainpy.math.FixedLenDelay`` can be used to define delay variables which depend on states, and ``brainpy.math.NeutralDelay`` is used to define delay variables which depend on the derivative. " + "All of these can be used for defining delay differential equations. ``brainpy.math.TimeDelay`` can be used to define delay variables which depend on states, and ``brainpy.math.NeutralDelay`` is used to define delay variables which depend on the derivative." ] }, { @@ -109,7 +109,7 @@ } ], "source": [ - "d = bm.FixedLenDelay(shape=1, delay_len=10, dt=1, t0=0, before_t0=lambda t: t)" + "d = bm.TimeDelay(bm.zeros(1), delay_len=10, dt=1, t0=0, before_t0=lambda t: t)" ] }, { @@ -119,9 +119,7 @@ "outputs": [ { "data": { - "text/plain": [ - "DeviceArray([0.], dtype=float32)" - ] + "text/plain": "DeviceArray([0.], dtype=float32)" }, "execution_count": 3, "metadata": {}, @@ -141,9 +139,7 @@ "outputs": [ { "data": { - "text/plain": [ - "DeviceArray([-0.5], dtype=float32)" - ] + "text/plain": "DeviceArray([-0.5], dtype=float32)" }, "execution_count": 4, "metadata": {}, @@ -167,26 +163,7 @@ "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR:absl:Outside call . at 0x000001F4686F41F0> threw exception \n", - "!!! Error in FixedLenDelay: \n", - "The request time should be less than the current time 0. But we got 0.10000000149011612 > 0.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "!!! Error in FixedLenDelay: \n", - "The request time should be less than the current time 0. But we got 0.10000000149011612 > 0\n" - ] - } - ], + "outputs": [], "source": [ "try:\n", " d(0.1)\n", @@ -210,39 +187,14 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "['euler',\n", - " 'Euler',\n", - " 'midpoint',\n", - " 'MidPoint',\n", - " 'heun2',\n", - " 'Heun2',\n", - " 'ralston2',\n", - " 'Ralston2',\n", - " 'rk2',\n", - " 'RK2',\n", - " 'rk3',\n", - " 'RK3',\n", - " 'heun3',\n", - " 'Heun3',\n", - " 'ralston3',\n", - " 'Ralston3',\n", - " 'ssprk3',\n", - " 'SSPRK3',\n", - " 'rk4',\n", - " 'RK4',\n", - " 'ralston4',\n", - " 'Ralston4',\n", - " 'rk4_38rule',\n", - " 'RK4Rule38']" - ] + "text/plain": "['euler',\n 'midpoint',\n 'heun2',\n 'ralston2',\n 'rk2',\n 'rk3',\n 'heun3',\n 'ralston3',\n 'ssprk3',\n 'rk4',\n 'ralston4',\n 'rk4_38rule']" }, - "execution_count": 3, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -291,14 +243,14 @@ "def equation(x, t, xdelay):\n", " return -xdelay(t-1)\n", "\n", - "case1_delay = bm.FixedLenDelay((1,), 1., before_t0=-1.)\n", - "case2_delay = bm.FixedLenDelay((1,), 1., before_t0=0.)\n", - "case3_delay = bm.FixedLenDelay((1,), 1., before_t0=1.)" + "case1_delay = bm.TimeDelay(bm.zeros((1,)), 1., before_t0=-1., interp_method='round')\n", + "case2_delay = bm.TimeDelay(bm.zeros((1,)), 1., before_t0=0., interp_method='round')\n", + "case3_delay = bm.TimeDelay(bm.zeros((1,)), 1., before_t0=1., interp_method='round')" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -313,72 +265,65 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "scrolled": false }, "outputs": [ { "data": { + "text/plain": " 0%| | 0/200 [00:00" - ] + "text/plain": "
", + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -428,78 +373,70 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "def eq(x, t, xdelay): \n", " return -xdelay(t-2)\n", "\n", - "delay1 = bm.FixedLenDelay(1, 2., before_t0=lambda t: bm.exp(-t)-1, dt=0.01)\n", - "delay2 = bm.FixedLenDelay(1, 2., before_t0=lambda t: bm.exp(t)-1, dt=0.01)\n", - "delay3 = bm.FixedLenDelay(1, 2., before_t0=lambda t: bm.exp(-t)-1, dt=0.01)\n", - "delay4 = bm.FixedLenDelay(1, 2., before_t0=lambda t: bm.exp(t)-1, dt=0.01)" + "delay1 = bm.TimeDelay(bm.zeros(1), 2., before_t0=lambda t: bm.exp(-t)-1, dt=0.01, interp_method='round')\n", + "delay2 = bm.TimeDelay(bm.zeros(1), 2., before_t0=lambda t: bm.exp(t)-1, dt=0.01, interp_method='round')\n", + "delay3 = bm.TimeDelay(bm.zeros(1), 2., before_t0=lambda t: bm.exp(-t)-1, dt=0.01, interp_method='round')\n", + "delay4 = bm.TimeDelay(bm.zeros(1), 2., before_t0=lambda t: bm.exp(t)-1, dt=0.01, interp_method='round')" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "data": { + "text/plain": " 0%| | 0/400 [00:00" - ] + "text/plain": "
", + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -570,19 +505,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { + "text/plain": " 0%| | 0/1000 [00:00" - ] + "text/plain": "
", + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -649,21 +580,19 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "lines_to_next_cell": 2 }, "outputs": [ { "data": { + "text/plain": " 0%| | 0/300 [00:00" - ] + "text/plain": "
", + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -746,19 +673,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { + "text/plain": " 0%| | 0/1600 [00:00" - ] + "text/plain": "
", + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -826,7 +749,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -848,43 +771,39 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ - "delay1 = bm.FixedLenDelay(1, 30., before_t0=-1, dt=0.01)\n", - "delay2 = bm.FixedLenDelay(1, 30., before_t0=1, dt=0.01)" + "delay1 = bm.TimeDelay(bm.ones(1), 30., before_t0=-1, dt=0.01)\n", + "delay2 = bm.TimeDelay(-bm.ones(1), 30., before_t0=1, dt=0.01)" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { + "text/plain": " 0%| | 0/3000 [00:00" - ] + "text/plain": "
", + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -948,9 +865,9 @@ "notebook_metadata_filter": "-all" }, "kernelspec": { - "display_name": "Python 3", + "name": "brainpy", "language": "python", - "name": "python3" + "display_name": "brainpy" }, "language_info": { "codemirror_mode": { @@ -998,4 +915,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/docs/tutorial_toolbox/fde_numerical_solvers.ipynb b/docs/tutorial_toolbox/fde_numerical_solvers.ipynb new file mode 100644 index 00000000..745dc41d --- /dev/null +++ b/docs/tutorial_toolbox/fde_numerical_solvers.ipynb @@ -0,0 +1,702 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Numerical Solvers for Fractional Differential Equations" + ] + }, + { + "cell_type": "markdown", + "source": [ + "@[Chaoming Wang](mailto:adaduo@outlook.com)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 33, + "outputs": [], + "source": [ + "import brainpy as bp\n", + "import brainpy.math as bm\n", + "\n", + "import matplotlib.pyplot as plt" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Factional differential equations have several definitions. It can be defined in a variety of different ways that do often do not all lead to the same result even for smooth functions. In neuroscience, we usually use the following two definitions:\n", + "\n", + "- Riemann–Liouville fractional derivative\n", + "- Caputo fractional derivative\n", + "\n", + "See [Fractional calculus - Wikipedia](https://en.wikipedia.org/wiki/Fractional_calculus) for more details." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Methods for Caputo FDEs" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "For a given fractional differential equation\n", + "\n", + "$$\n", + "\\frac{d^{\\alpha} x}{d t^{\\alpha}}=F(x, t)\n", + "$$\n", + "\n", + "where the fractional order $0<\\alpha\\le 1$. BrainPy provides two kinds of methods:\n", + "\n", + "- Euler method - ``brainpy.fde.CaputoEuler``\n", + "- L1 schema integration - ``brainpy.fde.CaputoL1Schema``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### ``brainpy.fde.CaputoEuler``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "``brainpy.fed.CaputoEuler`` provides one-step Euler method for integrating Caputo fractional differential equations.\n", + "\n", + "Given a fractional-order Qi chaotic system\n", + "\n", + "$$\n", + "\\left\\{\\begin{array}{l}\n", + "D^{\\alpha} x_{1}=a\\left(x_{1}-x_{2}\\right)+x_{2} x_{3} \\\\\n", + "D^{\\alpha} x_{2}=c x_{1}-x_{2}-x_{1} x_{3} \\\\\n", + "D^{\\alpha} x_{3}=x_{1} x_{2}-b x_{3}\n", + "\\end{array}\\right.\n", + "$$\n", + "\n", + "we can solve the equation system by:\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [], + "source": [ + "a, b, c = 35, 8/3, 80\n", + "\n", + "def qi_system(x, y, z, t):\n", + " dx = -a*x + a*y + y*z\n", + " dy = c*x - y - x*z\n", + " dz = -b*z + x*y\n", + " return dx, dy, dz" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/50000 [00:00" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 8))\n", + "plt.plot(runner.mon.x, runner.mon.y)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 8))\n", + "plt.plot(runner.mon.x, runner.mon.z)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### ``brainpy.fde.CaputoL1Schema``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "``brainpy.fed.CaputoL1Schema`` is another commonly used method to integrate Caputo derivative equations. Let's try it with a fractional-order Lorenz system, which is given by:\n", + "\n", + "$$\n", + "\\left\\{\\begin{array}{l}\n", + "D^{\\alpha} x=a\\left(y-x\\right) \\\\\n", + "D^{\\alpha} y= x * (b - z) - y \\\\\n", + "D^{\\alpha} z =x * y - c * z\n", + "\\end{array}\\right.\n", + "$$\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [], + "source": [ + "a, b, c = 10, 28, 8 / 3\n", + "\n", + "def lorenz_system(x, y, z, t):\n", + " dx = a * (y - x)\n", + " dy = x * (b - z) - y\n", + " dz = x * y - c * z\n", + " return dx, dy, dz" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 39, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/50000 [00:00", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 8))\n", + "plt.plot(runner.mon.x, runner.mon.y)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 41, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 8))\n", + "plt.plot(runner.mon.x, runner.mon.z)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Methods for Riemann–Liouville FDEs" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Grünwald-Letnikov FDE is another commonly-used type in neuroscience. Here, we provide a efficient computation method according to the short-memory principle in Grünwald-Letnikov method." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### ``brainpy.fde.GLShortMemory``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "``brainpy.fde.GLShortMemory`` is highly efficient, because it does not require infinity memory length for numerical solution. Due to the decay property of the coefficients, ``brainpy.fde.GLShortMemory`` implements a limited memory length to reduce the computational time. Specifically, it only relies on the memory window of ``num_memory`` length. With the increasing width of memory window, the accuracy of numerical approximation will increase." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Here, we demonstrate it by using a fractional-order Chua system, which is defined as\n", + "\n", + "$$\n", + "\\left\\{\\begin{array}{l}\n", + "D^{\\alpha_{1}} x=a\\{y- (1+m_1) x-0.5*(m_0-m_1)*(|x+1|-|x-1|)\\} \\\\\n", + "D^{\\alpha_{2}} y=x-y+z \\\\\n", + "D^{\\alpha_{3}} z=-b y-c z\n", + "\\end{array}\\right.\n", + "$$" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 42, + "outputs": [], + "source": [ + "a, b, c = 10.725, 10.593, 0.268\n", + "m0, m1 = -1.1726, -0.7872\n", + "\n", + "def chua_system(x, y, z, t):\n", + " f = m1*x+0.5*(m0-m1)*(abs(x+1)-abs(x-1))\n", + " dx = a*(y-x-f)\n", + " dy = x - y + z\n", + " dz = -b*y - c*z\n", + " return dx, dy, dz" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/200000 [00:00", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 8))\n", + "plt.plot(runner.mon.x, runner.mon.z)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 45, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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MBhCvEoHBYIDJANotTnSOck6nPxRCDqbEKjE1ToHCOAUyI2VnPHOjs8uFKj29+l2tt9KtQO1dqDPYYHMNLmIYDPhX93mIkPIRLuUjQsqHRkqv+EfI+NBI+JCPgfU0RVGw+0MvLYHKhRtW/32Lv6Jhc3kC4ZgGmwttZicajXaY7GP//znWqMRcRMhocUDP1XAh5rEg4LIh4DDpahWXnsmR8On5G/qaDRGXParKis9HwWh3Q291Qm9xot3qhN7qCtzX+++3mh1otzpDas/s/v3QyQWIVgoRpRAgWuG/VgoRKeePSzhqKPh8FJpMdpS3WlHmFz9H6jpRpe86I/szEnhsJm6dl4C5KWrkRMnO2M+SQCAQziaIyCFMehqNdnxztAlP/VQ6oowZhZADnUIAnw9weX2obLeO+VxNvEqEwlha0BTGKZHgF1ITjc9HoaHTjlOtFpS1WlDZbvWLmq4hTRfCJbzACSp9OX2yqh3hSarPR7e6ddroWZzOLhc6/dUFQ5cLtR02nGwyock0fm2BvwY0Uh4yI2WIVggQJuZBIeJCKeRCIeLQVTQhF3Ihd9jVFrfXh3aLE83+VsVmkwMtJjta/Jk53Y8PteDAYAAaCT8gegIiSClAvEoEjYQ/4S1xbv93QUmzGSebzChptmBnhX5C92E0XD8zFgvTwjElVgEJn3Omd4dAIBAmHUTkECYlZocbPxQ348UtFSNyWopXiaAQcuCjAL3VOeZuTbFhQsxKDMPMRBVmJCgnPCCQoii0mp20mGmx+Feo6VXqwVrLtDI+4lWiwCVBLUJsmAg6uWBYlSavj0KbxREwGui+3WS042i9EeVt1rH4mIRxRCXmYVq8ArFhIoRLeAiX8BEu5QVuh5ql5PNRMNhcaDY6glod67uvDUO3O/I5TMSF0b+TcSoR4sNEiFeLEBcmgkrMnbAFA4qi0GZx4mRA+Jixt6oDeuvw580mmjV5kViZrcWM+DDIhET0EAgEAhE5hEmDy+PD9rJ2vL27ZkQrqqkaCWQCDrwUhcp266jtonsSLuFhdpIKMxPDMCsxDFEK4Zi99lD4fBRqDbS17olGE443mXC80TxgWxaXzUSiWoxUjRiJajES1GL/yaMQQi47pPd0uL1oNNrR2GlHk9GORqMdFW1WbC5tO+fyTQhDkx8jR160HFoZH1qZAJFyASLldLvdUPMiFEXB0OUKiJ6GTjudm+O/rjPYBp0REvPYiFMJERcmQoJKRGdChdO/2xPVAmpzeXCyyYxjDSYUNxixv9ow6auPFxXocH5OJKbGKyHmhfZ3TyAQCOcSROQQzjgVbRa8v7cOb++uGfZzs3UyyIUceH0UTjabx0zYcFlMTItXYkGqGgtS1UhUiydkNdnno1Cl70JxoxHHG80objThZJO53/R4FpOBuDAhUiMkSNFIkKqRICVCglilMCRDA7PDjRp/K1tthw0VbVb8fLJ1XK2OCecuOVEyTI9XIkYpRKRcAK1MAJ1cAKmAPejfjtvrQ0OnPfC7WK3vQk0Hfd1otA/YWspgADFKIZLUYiRpxEgOlyA5nA7EnYiTerPDjRONZhQ3GlHUYMLOCv2YLqyMNdfPjMXSjAhMiVWEXKUjEAiEsxkicghnBLvLiw3FtIHAcFdEUzRiRMoF8PooFDeaxuzEQicX+EVNOGYlhkE0QSdKx+qNOFxrxOG6Thyp64S5H1tgHpuJdK0UWTopsiJlyNLJkBQ+9Eq22+tDbYcN5a0WVLRZUaXvwuaS1n7fYzLDZTMh5XPAZtKOZz6KgtdHVwm8FEWHs1IABfrkl8VkgMlggKIo+Pw5R915R935NRNFd6Wjex9+raRFSDA3WYU4lQixSpFfDPEHFeQ97aK7hU9lWxfK2iyD/t1HyvhI0tCiJzVCgvQIKZI141/5MdpcKG40oajBhKP1Rvx8snVc32803L4gEUvSw5EbJZ8ULo8EAoEw1hCRQ5hQSprNeHdPDT7aXz+s50XK+MiOksFHASebzGg0js2MTX6MHOdlRmBxWjiSwse3WkNRFGo7bNhfbcDhuk4crutEeVtfE4Tu/JwsXfdFiiT14HbTHq8PNX4xU95mRXGjaVKeYAk4LMgEHLCYtABhMBh+QUILFK9frHh9VCAbhvDrYG1eJNK0UsSFCZGgFiM2TDig2UV3dlN5qxUVbfTvfHmrFeVtVuitzn6fw2IyEK8S9QralSBCyh+3v3uKolBvsAf+3g/UdKJkElpaR8r4+O3CJMxPUSNaOXGtuAQCgTCeEJFDGHe8Pgo/n2zFkz+Voqo9dNtWEZeF2UkqcNlM1BtsONZgGvW+sJkMzEwMw7LMCCzL0EAjHT/DgG5Rs7eqw38x9BsuGq0UoCBGEbikaSWDBoJanR6UNJv98zlmfH644YxXCCQ8NsLEXL94AcAAWAx/NQUUupxetFuccHnJPA9heMgEHKzNi0RqhBQJatosQy3mDShMjDYXKtpowVPWakFpswUlLQO3ssqFHKRFSJCulSI9QopMnRQpmsH/BkeD3eVFUYMRh+vo6u1kXIxYkRWBiwuiMHOCKtoEAoEwHhCRQxg3TDY3PjlYh39uKB3W8wpi5IgLE8Fkd2NHuX7UJ8ZcNhMLUtRYnhWBxWmacXUequ3owp7KgUUNl8VEbrQMBbG0oMmPkQ/qzNbZ5UJRowknmkw4Vm/ETyfOzAmRSsyFWsIHi0mLFwaDAQqAw+VFs8k+qdrfGAxAxGVDwGWBx2aCy2aCx2bR1ywmeBwmuD2uuWwmOCxmoMWNwQCY/hwlpv+zdt/utjmmKDpEtLtVzuvrbqHr+Rh9cXl9cHr8F7f39G2PF053j9seHzF1GCYLU9UojFMiOVyMtAgpohSCfq2ou90IS1pox7TSZgtKms2o0nf1a3rA9beHZuukyPZXVMdL+FAUbft+sNaA/dWd2F7WPmaV6rHirsXJWJKuQWakdMKtvgkEAmGkEJFDGHMq2614dVsVPjkYekuaSszF/JRwsJjAgZpOVI8yqI/FZGB2kgoX5EZiWaYG0nHKkehyerCnsgPbytqxvbwdtR22oH/nspjIi5ZjRoISMxLCkB8z8NCvy+NDaYsZR+uNOFDTiW+PNY3LPg9EjFIInVwABoOuvvkougJTZ7BNaNuYhM8OBGTKhRzIhVzIBRzIhRzIBByIeWwIeWyIeSyIuGyIePSle16Homh3uC6XFzaXBzanFza3F3aXJyAk3F4Kbq8Pbq8PLq8Pbk+v+15aqAAABf+8D3X6Nv04ArdZTIDNZILNotvvOEwmWCwG2ExGr8cZ4HFo8cXnsPwXJgT+2xwW87RY8lHwF8Vgd3vR5fSgy+mB1en1X3tgtrthtLthtLlgsrthsrthtLnhGUmg1DnEkvRwzEgIow05IiQIl/Rf+XG4vahoo7NySpotONlswokmMyz9iHYum4n0CAmyo2TjLnz0VicO1hiwr9qAPZUdKG2xjPl7jJRp8UrcMCsOc5JVJJ+HQCBMaojIIYwZRQ1GPL2xDNvK2kN+TmGsAulaKdotTmwubR0yVHAopsQqAnkRKjFvVK/VHxRFu7htL9NjW1kbDtV2Bu0zm8lAfowcMxPChhQ1bWYHDtR0BlpW6gy2frcbS4RcFjIjpRBw2f6hfApGmxtV7V3j5qrGYTGgEvOglvDoazEPKgnXf03fDxPzoBTRYZWdXa5AgKjZf+JusrkDJ/HdF0OXC41GO2yuX5cbnITHhk4hgFbGh1LEg4TPhpjHhlTADghCLpsJBoOB7tN6i8MDg82FDqsTeqsTHVYX9FYn9P7r/k7qz0UuK4xCdpQcGVoJ0iKk/bZi+XwU6vyW7cWNJhQ30LbtAwmfbJ0M+dFy5Psrs1rZ2M/4mB1uHKrtxP5qA/ZWdeBInXFMX3+kMBnA/cvTsCxDgwS1+EzvDoFAIARBRA5hVFAUhT2VHXjs+xKcDHGgls9hYmWWFnIhF/uqO3CiaXSDuNFKAS6dEo0L83XjMjTr8viwt6oDP59sxaaSVjT3coOLVgowP0WN+SnhmJkYNqB9bUOnLXCS8unBhjHfz95kaKWIkPEDJ7qNRjuq2rvGdC6GzWRAI+VDJxdAK+/OUKGvtTI+IuUCSPls6K0uNJnsaDE56JNrixP6Lhd9bXWixeSY9Lkj5wIaKQ+JajEiZHyo/cJTLeFByueAyaRFkdvrQ4fVhTaLA80mB1rNp6/PhlDM4VIYq8CSDLoVKzNSBqWI22ebnsLnuN89bSDho5HykB9NC578GAWydbIxt2x2uL04XNeJvZUd2FGhnzSi56J8HS4qiMK0eCW4bOLYRiAQzixE5BBGBEXRZgIPf30creb+3Yx6o5MLsDI7Am4vhe+KmkZ1wsRjM7EyW4tLC6MwIz5szPvETXY3tp5qw8aTrdh2qj2oXUvAYWFWYhjmpagxL0WNuDBhvyu3dR027KrUY19VB746On6tZ2IeG1NiFVAIOfBSdIWopNk8ZnMyKjEXMUohYsNE/mv6EqUQQiXmwWx3o85Ahzw2m+xoNjnQYnIEbvcWhYSzA6WIi8xIKXRyATRSPiLldOWIzWQADLo61Or//20x29HQSV8MXWe/EAoTcXH19Bhk6mSBn0Hvv3Gfj0JNRxeO1htxpM6II/WdKGm29JnxYTEZSIuQID9GjoIYBabGKRGl6Pt6o8Hq9OBAtQG7K/XYUa6fFO1tiWoRfrsgCYvTwyEX9hWOBAKBMN4QkUMYFhRFYWtZOx78vChkcZMZKcXidA3qOrrwfXHzqFrSsnUyXD41GufnRkImGNt+cKPNhZ9OtOC7ombsqewImmtQiXlYmhGOpRkazEpU9Zu3YbK7sadSj+3leny4r25M960bNpOBWUkqqMU8uL0+VOmtKGuxjro6I+axkRgupsMU1WLEhQkRE0YLGx6biSajHXUGG2o7bIGU+u7bFmLz/KtmSqwCyeHiQPVOLuSAwWDA66PQanagodOGeoMdDUZaCE/mwMzBiJDycdnUaORHy5ETJUNYP+2wdpcXxY0mHKnrxBG/e1qbpe/3ZISUj6nxSkyNo0VPqkYypgs1hi4X9lZ1YHelHptL2ibFQsNfVqVjZbYWkXLBmd4VAoHwK4GIHELI7K7Q409fFvcZrh+IuckqzEgIQ1GDERtPto7Y5pjLYmJ1jhbXzYpDXrR8ZC8yAGaHGz+faMV3RU3YWaEPEmBJ4WIszdBgaYYGeVHyPichbq8PR+uN2FHWju+KmlE1SrOE/kjVSJAXLQeHzUBVexeKGkyjMgEIE3GRFC5GskZMJ8WHS5AULoZGyoPV6UFVexcq2qyobO++dKG2o2vUs1KTCTaTETAxkAk4kPBpMwMRjwUhl55vEXBZ4LKYAae17iwfRq/bpwNHaYe1ns5q3QYCbr+7WrcZgtXhCWQAWRy0eUCnzXXOzhalaMTIi5YjRilEtFIIpYgLiqIrQd0BnzV6+rrjLKsC5UTJcEFuJPKi5ciM7NuWRlEUmk0OHK034nBtJw7WduJ4o6mPMYSEz0ZhrAJT45WYFqdEdpRswIygkdBssmN3RQd2lLePa1U5VO5cmITzcyORohnfbDICgfDrhogcwpAcrTfioS+KQ5q5YTCAldla5EfLsa2sHTvK9SN+X51cgKumx+CKqdH9rpqOFIfbi00lrfj6aBO2lbUH2famRUiwKluLVTnafgdpTTY3tpa1YVNJ27i4n81OCkNyuAQmuxsHagxo6ByZlSyTASSoxcjQSpGulSIjUop0rQThEj7sLi/KWmkL3dIWC8rbLKhos4ZcmTuTxCiFiFOJEKMUIFIugELIhULIgZTPgZhPu6yJ/W5rQg7rnLC7pSgKdreXFkY9BJLV4UGXi75uszhRb7Ch1kBX186W2RkJn42ZCWFIDBcjXiWCWsIDA3RVtEZvQ01HF6r0Xahqs541FcML83WYmRCGKXEKJKhEfU7i7S4vjtR34kB1Jw7WGnCotrOPwOWymciLkmN6ghIzE8NQEKPot3o8Enw+CsebTNh2qh3bytpxsLZzTF53pFw5LRoXF0QhP0YB1jnw90ogECYPROQQBqSh04b/+74EPxxvGXLbbnEzLU6Jb4414dAoDpxT4xS4aU4ClqSHgz1G9qwUReFYgwnrD9Xjm6NNQfMqiWoRVudEYnWOFskaSZ/nVuu7sLmkFd8VNeNovXFM9qebOUkqZEfJUNdhw75qw4Bp7YPBYTGQrpUiJ0qGzEgZMrR0mCGfw0SzyeG3xzWjpIUWNjX6LkwWh2EmA8iOkiNbJ0W0QohwKQ9qMR9qCQ/hEh5kAs45IVTOJBRFwer0wGij7aU7bS50dDnRYnKivNWC0hYLTrX2nSU500TK+JiREIYEtQjxKjFEPBY8Xgr1nTaUt1lR0WpFWZtl0re/JahFuKwwGoWxin4rNB6vDyebzThQ04kD1QYcqDH0qWhx2UxMiVFgVmIYZiaGITdaPmbW1Sa7G7sr9NhW1o4Nxc1nNPNqVbYW186MxdQ4JRE8BAJh1BCRQ+iD2eHGf3+pwCvbqkLaflW2FnOSVfj2WBN2V3aM6D0ZDGBZhga3zEvElFjFiF6jP1pMDnx5pBHrD9Wjsv10O5lWxsfafB0uyI1EWoQkaLW12yZ6Q3EzPthXN6YnUVPjFJgSq0SdoQu7KztG9NpxYULkRsuRFy1HbrQcGVop+BwW9FYnihqMOFZvwrEGI4oaTGd8CDxbJ0NhnAJxYSLo5HT1RaegHddIm8rkxO31wdDlQrvFiXarE21mB8parThab8TReuOkEEOL08KRppUgRSOBUsSF10eh3kCLnzK/cJvM4ufG2fGYkaBEYZyyj5sbRVGo1ncFnBh3V3b0mesRclmYGkdXeWYlhiEzUjYmooCiKFS0WbGtrB1bT7VjZ8XIK/GjZWV2BK6bGUcED4FAGDFE5BACeH0UPj5Qhz9/eTyk7ZdlaLA8KwLfFTVjS2nbiN6Ty2bi4gIdbp6bgMQxylnw+ihsL2vH+3tr8cuptkDVgs9hYnlmBC6ZEo2ZiWFBB85uYfN9UTNe2V41ZidyGikPi9M1YADYWaEPeZ6pGx6bifwYOabFKVEQq0BulBwKERcOtxfH/CedRQ0mHK03npGUdDGPjXkpKuRGyRGnEiFeRTuwjVVrDWFyQlEUzA7PaQc9owMnm004UN2JU61nxtmLwQCWpGuQFiFBskYCmYADp9uL8kDYpxnVk6iK2ZO1eZGYn6rGjIQwaGXBg/kURaGyvQt7qjqwp1KPPZUd6Owl4CR8NmYkhGFesgpzk9WIU4nGZL+sTg92lLXT7blFTUGtvRPJ6hwtrp0Ri0IieAgEwjAgIocAAChuMOG3Hx5CvWHoE+WCGDmunxU3qrkUHpuJa2bE4tZ5CQiX8kf0Gr3psDrx6cEGfLi/NuhzTI1T4JIpUViZre2T0F3aYsY3R5vwzu4adIUw+C3hsYecDZiVGIZsnQwHaztxpK5zWCdVEj4bU+OUmBqnxLR4JbJ1MnDZTBhtLhys6cSBGrqdpbjRNKFmAFk6KRal0SeQ8SoR4sJEY579QTi3cHq8aOykXfnqDTYUNZiwo1yPFvPEOn3FKIWYmRCGjEgp4lUisJkM1HfaUNJswclmM0qazCOe92H5s4V6GwmMllU5WixN12BGQhgiZMHfjz4fhVOtFuyupEXPvipDn/2PUQoxL4UWPLMSw/p8740Er4/C0fpObCppw88nW1HRZh31a46ENXmRuGpaDKbGKUkrK4FAGBQicn7lmGxu/Oun0pAsj+PChLhjYRKq9F14Y2f1iFb1AuJmfgLCJaMXNxRF4XBdJ97dU4sfilsCVspSPhuXTInG1TNi+lSI2iwOfHO0CW/tqgmp+qGV8Ye0YF2ZHQGZgIMd5fphmQUIuSzMSAjD7CQVZiaEITVCAhaTgTazA3uqOrDf36Nf1joxJxTxKhHOy4xARqQUqRpa0JBQP8JYQ1EUbS7QYUNVO90Kt6W0bcRGGyNheWYEMiOlSNNKIeKx0G5xorjBhKJGE040mkJa9JgoVmZH4LzMCEyP7yt6PF4fTjSZsbNCj+1l7ThU2xkkuthMBgpiFAHRk62TjYk4qDfYsLmkFZtL20ZlMDMarpoeg2tnxCJdS475BAKhL0Tk/EqhKApfH23C3Z8cHXJbKZ+Nu5akgMtm4vlN5SMajuexmbh6eixuWzA24sbj9eGnE614bUdVkBlAbpQMV8+Ixfk5kUGVBrvLi40nW/DZwYaQ+swT1SI0Gu1wuPsXclw2E0szNHC6fdhTqQ/5hIjFZCA3SoY5yWrMSVIhL1oOLpsJi8ONfVUG7KzQY1eFHuUTsEo6PV6JBanhyPYHHir6SXr/NeLzUXB6fHB6vAHrZ7f3tCW0j6Lg89tFe30UKP+1l6IACmAyGX7badp++rQF9Wn7aRaTAS6bCR6bBR6HCR6b6besJivTPh+FRqMd1XraznxnhX7E7bDDZVFaOHKj5MiOkkLC56Chk65AFTeYcKLJDLt7ZMKHxWSM6SzT5YXRWJQejpmJYZD2qtJYnR7srezA9nLa3bK6l7W9QsjBnGQ1FqaqsSA1vM9M0EiwONzYXqbHTyda8M04uE6Gwt1LknHJlChEKYRn5P0JBMLkg4icXyEtJgfuW38spNW3ywujsSxTg6c3loVkId0bBgO4pCAKf1iW0qfXfCRYnR58eqAeb+6qDqz6ctlMrMmNxLUzY5ETJQ/avrjBhA/31+Gj/UNXqpLDxfD6qAHzbpj+nn+X14fdlR0hV7I0Uh4WpWmwIFUdOCnxeH04Um/EjnJa1Iz3QHeqRoI1+afzPMY6SPVM4/VRMNpc6LS5YOhyw2R3w+p0w+rwwNLTernXfZvLA4fbd1rUuH2jDlYdDbTw8YsfNhM8DhMCDitgjd1tjy3msYLsskU8NiQ8NmRCDuQCDuRCLmQCzjk3v2BxuFHeZsWpFgt2VejxfXHziPO3QmVldgRyouTIipRBwGWivNWKw3WdOFxnHHbLFpfFBJMJ+CiM6XzL7xclYW6Kms7U6uW6Vm+w+e3827G7oiOotY3BAApiFFiUFo5FaeF9TFhGgtPjxe6KDvx4vAVfHW2E8wzM8Ty6Ngurs7Vk4YZA+JVDRM6vCIqi8NmhBty/vmjIbXOiZPjD0hT8dKIVHx+oG9GJxPwUNR5ckTYmrQSGLhfe2FmF9/bUBixOFUIOrp0Zh2tnxEItOZ2jY3G48c2xJryxo3rIgM4MrRQcFgPHGkwDbjMjQQkAOFxnDPnEJDdKhkVpGixOD0dmpBQMBgNGmwvbytqxpbQNW0+1w2QfP/enS6ZEYVZiGKbEKhCjFJ6VFQKH24s2sxOtFgdazQ60mBxotzhh6KLFTKfNjc4uFww2F0x297ic7LKYDPDZTLBZzB7VmOAKDYvJAIMBsBj0tY+iqxFeqkfVx3+/u+rj8YeEDlQpHCukfDbk/iwhmZALuYADhZAWQWoJDyoxD2oJF2oxHyoJF0Iue1z3Z7zosDpR1kobDPx8shV7qkbm8hgKGikPc5PVmBKrQHK4GBanB0frjDhc14mj9UZYhmHBzGAADGBMzRASVCJcPysOc5NViO+V0+P2+nCs3oitp+jvod4LV5EyPhalh2NxmgYzE8NGbSDi8fpwoKYTP52gBc9EO97Fq0T4w9IULM3QEDMUAuFXCBE5vxKaTXbc/fFR7Ks2DLqdhMfGAyvSwGMz8cQPpSNKIM/QSvHQynTMSVaNdHcDtFuceG1HFd7fWxsIzItXiXDTnHhcXBAV1JJW3GDCB/tq8fGB+kFfU8JnIz1CCqvTM2B1Ki1CAiGXhZPN5pBORLlsJuYlq7AsIwIL0tQIl/BBURTK26zYXNKGLaWtOFQ7PBOC4XDTnHjMSVahMFYxJkPG443b60Oz0YH6ThsaOm1o6LSjyehAm1/QtJqdIxKBMgEHShEXUgEHUv7p6oeYT1c6xHw2xDxO4L6AywKf46+asJmB293XY5XTNBAURcHtpQKtcU6PD0736dsOtxd2txddTg+6nB5YHB50Ob10CKj/sZ4BoSa7Gyabe8SD9CIuCyoJD2pxtwDiQSPlQSsTQCvjQyunr8+GE0afj87UOdFkxt6qDnxxuBHWcQoUnZeixtRYBfJjFBDxWDjVYsHhuk4crO1EVfvgCy09YTAADpOu9oyVAL50ShTOy4zArKSwPiK22WTHL6Xt2FLaip0V+qD35HOYmJ2owqL0cCxJ10AzSoMYn4/CsQYjfjzRgm+ONg055zjWLM3Q4PYFiciPlp+Viz4EAmH4EJHzK+CnEy249b1DQ263JD0ct81PxLObyrCrYvgroTIBB/cvT8UVU2NG3SbTanbglW1V+HB/beDAm6WT4s6FyViWoQkMznq8Pmw82YpXt1cNGdQ5J0kFj8+HvVX9Cz2FkAOFkAuT3R0k7rgsJtw+X59KAY/NxIJUNVZma7EoLRwSPgcUReFEE52x88Pxlj798GPFdTNjMTdZjWnxyknbemZ2uFHV3oVqvRU1eptf0NjR2GlHs8kekuDjsZmIkPGhkfChkfGhFvMQJuZCKeJCIey+5kAhoqsU4y1KzhbcXh9Mdrc/ANRFX9tP3+7ockFvdUJvdaLdQl8P56RaIeT0ED78wO1IuQAxSiEipPxJ63zVZnbgRJMZh2o7sf5Qw7i4vSWoRZiTpMKUWDojqtFox/5qA/ZXG1DSYg656shlM8FjMeGjqDExQgiX8HD7gkQsTA3vYzPtcHuxp7IDm0tbsaWkDU29REhetBznZUbgvEwNEkZp909RFI43mvFtURO+OtLYJwdovLl3aQoumxo9auFGIBAmN0TknMPYXV7847uTQ86jhIm4+OsFmTDb3Xh8Q8mwD6YMBnDF1Gjcd17aqIdYO7tc+N/WCryzpzbQGpYXLcfvFydhYWp4YAXOZHPj4wN1eHZT2aAnZ8nhYsSGidBqdqC4sf+WNCmfDRaTEZQ90d2GBAT3zvM5TCxMDcfKbC0WpoVDzGPD56NwtMGIH4+3YENx87g4RC1MVeP83EjMS1FDJeYN/YQJwuujUGewobLNiiq9FVXtXajSd6GqvWtIgwoum4kohQDRCiGiFHRQqEbKh0bKQ4SUj3Apn4SGThCU/yS6W/D0vG4xOdBidqDJSGfi2EL4fuCy/P+3SiFi/JfA7TAhxLzJ1RZnsrlR3GjCnio9Pt5fP6IK9mBI+WwszYjAjAQl0rVStJod2F9Di57iBlPIFtQcFgMMMOCjqDGxrb5mRgzOy4zAtHgleOzT1TmKolDaYsGW0jZsKmnFkTpj0PNSNGK/4IkItOOOFJ+PwpH6Tnx7rBlfHmkc1zbe3kTK+PjzqgwsyQgP+vwEAuHcgIicc5SSZjOueHXvkAeMVTla3D4/EU/8UDqidOtsnQyPrc1CbrR8hHtKY3d58eauary8rTLQ0z4lVoG7FidjbrIqcBCt67DhtR1VeG9v7aCvtyxDAwYDOFTbCb114BMWBgOBVVW23/HK46Xg8fkClQYGg86+uTA/CsuzIgInaKUtZnx5pBHfHm3qs+o5WjgsBm6fn4gFfrenyTBA3mF14lSLBSUtFpxqMaO0xYKyVsugIlMt4SHBHxIaraTFTJRCiGiFACoxb9Ku9hP6p78Q0MBtE12la+i0D3kCrhRxEaMUIkElQoJahES1GAlqMWLDJkeQLEVRaDI5cKzeiO1l7UO2wA4XLouJ1TlaTE9QIlsnh6HLhX3VHdhVocexBlPIJiQCDgtsFgM+3+grPbnRclw9LQaL08MR1mshpdXswMaTrdh4ogV7KjuC/n91ckGgwjPasE6vj8L+agO+LWrC+kMNExo+ujYvEjfOiUe2TkYWVgiEcwQics5BPj/UgHs/OzboNmIeG/9YkwkGA3j4qxMh9ar3FAQ8NhP3LkvBjbPjR9Ui5PH68MnBejy/qTzQspAWIcEDK9KwIEUdONiUtVrw0tZKfHmkcdDPtCxTg3aLE7sq9CHPvyhFXLCYDDhc3qBZhlSNBBcW6LAmLzLgDNdqduDro4348kgTSkbgNjcYKRoxrpsZh2UZmjELSB0J3Sd4RfVGFDWacLzRhNIWC9oHaCnhc5hIUImRoBb5T1rp23EqUR97W8K5j9dHodl0OgS0zmBDneH0fcMgVRIGA4hSCGjR0/07pRYhSS2GWsI7oyefHq8P5W20s9pnBxuGbI8dDnwOE8syIjAzMQz5MXI0dtqxs0KP3RUdONVqCek1uCwmpAI2fBQG/RmHAo/NxH3npWJphgaxYcFtbSabG1tOteKn463YWtYWtMgRJuJiWaYGq3MiMSMhbFSCx+31YU9lB74+2oTPDzeM+HVGwj/WZGJtvo58fxEIZzlE5JxDuDw+/OO7E3h/7+DtaYWxCjx2YRZe3V6FLw4PLBq6EXFZoIBAm8q0OCX+dUkO4nv1dA+X3RV6/O3bE4GgyyiFAPcuS8GaXF1ghf9YvRH/+aUCP59sHfB18mPkSIuQoKTZEvKJB5fFhExIH8AMXa7AyqmUz8ZFBVG4tDAKGVq6DcPh9mJDcTO+ONyIXZX6MXXxmh6vxBXTorEoVRPYn4mm3eJEUYMRxxpMKG4worjR1G/1i8EAYpVCpEZIkBYhRVqEBGlaKWKUwklRaSKcHVgcbtQb7KgzdKGyvQuV7XSbY2W7dVBnMpmAg9QICVI1Ev/voATJGskZnUnTW504VNuJLSVt+OTg2FV7wiU8LM3QYE6SCskaCU40mbCrQo9dFR0hBRgD9Pc2k8mA0+MbdUXktwsSsSJLiyxdcGua3eXF9vJ2/HSiBZtOtgacLwFAJeZiRZYWq3O0mBqnHFXVtjvn7IvDjdhW1j6qzzIcFqaqcdeSFORGkeoOgXA2QkTOOUKLyYHr39w/6KofkwHcvSQF81PUuPuTo0MOxTMYdCtC91CygMPCQyvTcPX02FEdsOoNNvxzQwl+ON4CgB5i/v3iZFw1PSbQF32othPP/lw2aAvdiqwIKEVc7K7sCHnAXyvjg8tmBoayu5kap8CV02KwMlsbaJcpbTHjo311+PJIY9DBe7QUxMixbnY8lqSHT7hlr89HobLdigM1nThYY8CBWgPqDX1PmthMBlIjJMiJkiNbJ0NGpBQpGvFZazFMmPxQFAW91YWqdisq27tQ1W5FlZ4WP/UG24CV2UgZH6kREqT4hU+KRoKkcPEZmbGwu7w41mDEznI9Xt9ZNWYOaZmRUixIVWN2kgpqMQ/7qg3YXtaOXRWhBRGzmQxI/LOHg7XvhsJV02OwKluL6fHKoCq+2+vD3qqOgOlKz+/XcAkPK7O1OD9Xi/xoxaiOH20WB7452oQvjzTiRNPYVtMHg1R3CISzDyJyzgGON5qw+sWdg24TJuLihSvzUdluxaPfnYTbO/h/pVrCg5TPRqXf/jRbJ8PzV+SNylXH4fbipa2VeHlbJZweH5gM4NoZsbhnaQrkQtqw4ESTCU9vLBs04fzKadHgc1j45mhTyAPCiWoRWEwGGjvtgZMCKZ+Ni6dE4cppMUjRSAAANpcH3xU146P9dX2GbUdDjFKIW+YlYNUEB9S5vT4UNZhoQVPTiUO1hiCDBYAWs0lqMXKi5MiJkiEnSoZ0rXRSzEZMFB6vDxYHbdFsd3thc9HXDrcXdpfPHxpK2znbXT54fD64vRS8Ph88Pso/xxV830dRYDAYYPqzUBj+HB0G/I8xEMja4bKZASet7ttcFhNcNisQECrksiDkdgeAsgIhoEIO65yfbXK4vahst6Ks1YLSFgtO+S8D2RBzWAykaCTIipQhSydFpk6G9AhpkOX8ROD1UShtMWNHuR6vba8aM0ODeSlqzEtWYU6yCoYuOn9r26l2lLaE1tqmkfLAYjBg8duSj5SLC6JwYb4OMxL6Cp5dFXp8V9SMn060BL1HpIyPVTlarM6JRM4oKyRlrRZ8eaQRnx1sGNLoZKxYmqHBHQuTSHWHQDgLICLnLGfTyVbc/O7BQbcpiJHjmcvy8J9fKrD+0NC9zfkxcrSaHGgyOcBgALfNT8Q9S1LAZY989mZ3pR4PfVGMmg4bADpg828XZCItgv6/qmiz4tmfy/B9cfOAr3HNjBjw2Sx8eaQxpJMFPoeJuDARXF4fqvVdgTazRLUIN86Jx4X5ukBlorajC2/vrsH6gw0jzhjpjz8sTcFFBTpEKYRj9pqD0Z3Ns7Ncj10Veuyt6uiz0svnMJEfrcDUOAUK45TIj5GfFdk6oWJzeaC3uKDvckJvcUJvdaHD6gyEhprtHpgdbpjt9MVkd4+JPe+ZRMj1ix4uKxD6KRdyIBdwgu4rhFzI/I8rRVzIBJyz+kTNZHf3ED5mlLVYUdpi7rfyymQASeFiZEXKkKmTIStSioxI6YT+7nt9FE40mbCjXI/Xd1T1WXAYCRFSPs7L1GBBWjjiw0TYV92BrafasbNcH9J3mYTPhoDDgsvrG1VY54X5OlxcENVH8Dg9Xuwo0+P74mb8fLI1aP4zXiXC2jwd1uZH9pn9GQ4+H4W9VR1Yf6gBXwwytznWPH5RNtbm6SZcPBMIhNAgIucshaIovLWrBv/47uSg262bFYcbZ8fjzo8Oo6ihfwvlboRcFuYkqQItEOESHp67Ig+zEkce6mmyufH4DyUBdyKNlIdHVmdiZXYEGAwG2swOPL2xbNB+9nWz4sBlM7H+UENIA7VKERdJajGMdldg3gcA5qeoceOceMxNUoHJZICiKOyp6sCbO2uwubR1zGZtlmZocP3MOMxKDJuQFfY2iwPby2hRs7NC38cgQCHkYFq8ElPjlCiMUyIzUgrOWZgn4/NRaLc6A1bGTUY6QLTZZEeL2YEOK539EorF8UAIOCwI/QGhQi4rEBYq5LIg4NAXnj8olMVkgM1kgM1igMVkBm6zmfR9pt+ogwL990rfpuCj6Md9FAXKbwXcPTcRuHjpa6f/tjMoFNR/7fKMOlyWw2IEQj/V/uvAfcnpxyPOkgBQgP5ZN3TacaLJhOONZhxvos0zBpozS1SLkR8tR16MHHnRcqRqJBOWt9Rdad16qg0vbqkYk9dcmKrGwrRwzE1Wo93ixC+n2rC5pDXou3AgumcVWQzGqPKDBhI8DrcXW0+14/viZmw62Qq7+/Tf6pRYBS7M12F1jjZQ2R8JZocb3xxtwmcH63FsiGPeWHHltBjcPj8RMWETs5hFIBBCg4icsxCKovDPDSV4bUf1gNuwmQw8tjYLCWoxbn//0JCVj7QICaKVwsCA//R4Jf5zVQHUkpFnsvxQ3IxHvjkROOm+ZkYM7l+eBimfA4fbi9d3VOHfG8sGfP66WXFQirh4d09NSH3kGikPsWEidFidgTY7LouJi6focNOcBCSF0612To8XXx9twps7q0Nu7wiFv6xKx0UFUaPOChqK7sDRzSVt2FLa2udAzmMzMS1eiTlJKsxOUiFDKz1r2pmsTg9qO7pQ22FDTUcXavX0daPRjlazY8g2y254bCZUYh5UEh7UYi5UYh4U/sqFTMCBlM+BVMDucZsDCZ99Vok/iqLgcPvQ5fKgy+mB1S+ATP7Qz0AYqN2FTpsbJv9to//2cCuWCiEHkXIBtDIBdHI+tHI6AFQnF0ArF0Aj4U3aMFaKotBmceJ442nhc6LR1K/1u4DDQnaUDPnRcuTHyJEXrUCEbGLcDh1uLw7WdOLbY01jYmQQrxJhcVo4lmRoEC7hBXJvDtR0DmlTzWYyoFMIwGIwUDWKUOMrpkbj4ilRmBITPIvT5fQEzAR6umFyWAwsTA3HRQU6LEwbXX5NaYsZnx5owCcH6iakWpuoFuGv52dijn8hjUAgnFmIyDnL8PooPPRF8aAHQCmfjZevmYJ2qxP3fVYEl3fw4dcVWRHo6HJhf7UBAHDLvATcf17qiE9YTHY3/vr1cXx1tAkAnf79xEU5mBavhM9H4duiJjz63ckBhcvFBVFI10rwwb66kAwFusMG6/x2tQB9onL19BjcPDchcIJic3nw4b46vLajCq3msenf7g4qXZASPq4HNYfbi53lejqNvLStz/5n62SYl0KLmoIYxaRedacoCo1GO8pb6RmL8jYravRdqOmwDdlXz2QAGinff7J9+lor49Oixi9sRFzWWd2GNRE4PV7orS7oLXToZ7uVbu9r7xUG2mYJrTrGZNCtU9FKIWLDuoM/RYj13x/N6vx40W5x4li9EUfrjThS34mielO/4i9CykdBrBxT4+iKaLpWOiGOgm1mB3aU6/He3tpRW1bz2EyszonEskwNcqJk2FdlwM8lrdh2qn3ICAEmA4GW2+7v2JFw+4JEXJivC8xAdtNqPm0mcLKHNb+Uz8aqnEhcVKBDYaxixH/TTo8Xm0624dOD9RPmzvbX8zNwyZSoc6oVmEA42yAi5yzC5fHh9x8dwY8nWgbcJjZMiDfXTcXGE63414+lg74em8nAjXPi8UtpG8rbrBByWfj3pblYma0d8T7uqezAvZ8eRZPJASYD+O2CJNy5KAl8Dgsnm8z4y1fFODzAQP/itHAsSg/H+kMNgaF/ob/Xub+TrCiFADlRMlS0WQOtGFI+G+tmxWHd7PhARcVkc+OdPTV4a1f1mPTAA7T5wY2z45Hc62A9lnS3dmwobsbmktaglcju1sLF6eFYmBp+RnN1BqOzy4XjTSac8geHlrVaUd5qGXRVVSniIjZMiLgwUeA6SiFApFyA8ElcLThX6RkA2t0iGNQyaLKjxTR0lU3KZyM2TIQYvwCKV9EhoEnh4jNqA92TbvfBI/VGHKmjxc+pFnOftkAxj42CWAWmxSkwNU6J3Gj5uC8s+HwUTjabsfFkK17YXD7q11uSrsGyTA3mJatR1mrBppJW/HyydUAzh55EyvhgMhlo6AzNzro//rQiDRf0yCDr5lSLBV8cacDXR5qCWubiVSJcWhiFSwqiRvV912S049OD9Xh/b+2oneZC4eKCKPxuURLiRhm5QCAQhg8ROWcJTo8Xv3n3ELYPsgqVGSnFW+um4oUt5UNm5YSJuLhlXgJe21EFvdUFjZSHN66fiiydbET75/L48PTPp/Dq9ipQFC22nrksD1NiFbA6PXju5zK8vrP/9roYpRD3LE3GppI2fF9EGw8IOCykREhQ19HVR5ioJTxMi1OizmBDcSPdqiXhsXHz3ATcOCcusHJmtLnwyvYqvLu7ZsxaFe5anIx1s+LGzSHN7vJi66k2fF/cjC2lbUHiTivjY1mGBovSNZger5x01Rq9tbsdiG4JKm40DZjpwWYykKAWIVkjQUq4BInhIsT5T4Ank0UrnSRPO1BZnbTDmtPjg9Ptg93thdXpDriy0du4YXN54XTT8zQ+ivLP3iBw2+ejZ3PYTHquh8NiQsBlQcBhQsBhgd9j/kfIY0PKZ0PK5/i3YQWu+T1un+mcIp+Pgr7LiYZOO+o6bKjtsKHW0IV6A327bYAg2W7UEh4S1SIkhYsDwidRLYZWxj/jFTmby4OiBhMO1XZif7UBh2s7+1R7uCwmsqNkmBqnxLR4WviM9wq+2eHG9rJ2vLWrBodqO0f1WtPilFiWqcHSDA0MXS5sKG7GhuKWITN5GAwgPkwELpuJmo6uEVlmqyU83Ls0BSuytUFi1+ujsK+qA18cacSG4ubAdyGLycDCVDUuK4zGwrTwEbeYerw+bC5tw/t7a7GjfOCogrEiLUKCf6zJwtS4kVekCATC8CAi5yzA7fXhtvcOYXMvW2UWkxHoq54er8TL10zBI9+cwLfHmgZ9veRwMa6bGYvHvi+B0+NDulaKN9cV9llRC5Umox13fHg4UH25Ymo0Hl6dASGXhR+Pt+AvXx0fcCboL6vSYXPR1tJ2txdMBnB+biS6nB5sKgn+vDIBB4vTw9FgsGN/Dd1ax+cwcf2sONw2LzEgPKxOD97YUY3Xd1SNmVPaQyvTcNX0WIh5Y58T4/NR2FdtwBeHG7ChuDlIkOnkAqzMjsCKbC3youSTps/b5fHhZLMZB2sMOFzXiSN1xgFXgOPChEjXSpGsoYMcUzRixKlEEzr/4vb60GlzwdDlgsHqQkOnHZW98ljORXRyAaKVAkQrhIhWChGnEkEj4SFMzEWYiAeZgDMhv1N2lxd1BhtqO7r81zZU6a2obOsadMBdxGUhMVyMVA0dPJvuD6Ad77m3wei2hT5QTduy768x9DH7YDEZyNbJMDspDLMSVZgSO74tpF4fhaP1nfj2WDPe3l0zqtfK1smwMluLldkRMNnd2FDcgh+ON6O2Y/A2NTaTEZh7HOms45L0cFw3Mw6zk1RBwt3q9OD7oiZ8erAhSNCpxDxcXKDDpYXRgfceCTX6Lny0vw4f7KsbsnVvLHjhynyszIogVWkCYZwhImeS4/H68LuPjgSCM7vhspmBFOsl6Ro8fVku7v30GDaVtA76etPjlVidG4m/f3MCHh+FhalqvHhVwYhP3reVtePuj4+g0+aGhM/GU5fkYnlWBNrMDjz05fEB9+eaGTEojFXi+c3lgbmbwlgF1uRF4pmfy/pUby7K18FLUfiuqBleHwUOi4Erp8XgzoVJgdYFh9uL9/bU4qVtlSG5sIXCo2uzcOmUqHE5Qalst+LLw4348khj0IqpTi7AqhwtVmZrJ00Wg8nmxsFaAw7WduJQbSeO1Rvh7JWizmAACSoRsnQyZOtkyIykQ0THuxXJ6vSgyWhHo9GO4w0mHGswoajBOGT1gNA/qRoJMiPpbJlUjQQRMj4iZPxxEfgAYHG4UdXehYo2KyrbrYHr2g4bPAMMx2ukPKRFSJGmlSBDK0VahBQJ6okVzt1QFIU6gw37qw04UGPAvmpDH0HAZTFRECvH7EQVZiWFISdKPq77Wm+wYVNJK5766dSo3AazdTKsytFiVbYWZocbPxS3YENx85CLAjw2E7FhQnh9VMAEZrjcsTARl06J7tPmVdFmwacHG/DF4YagdrMpsQpcXhiN1bnaEYcWO9xe/HC8Ge/vrRt1dSwU/rQiDVdOj5lU1WsC4VyCiJxJjM9H4d7PjuHLXr7/Ag4LDo8XFAUsz4zAk5fm4I4PDg9Zcl+Vo8XUWAX+/t1JUBSwNi8ST12aO6KDrc9H4bnN5XhxSzkoCsjSSfG/q6YgWinA10ebcO9nx/p170lUi/CXVRn4/HADvvO3pqklPNx3Xiqq2rvw8rbKoO3nJqsQrRTiu2NNgeyL8zI1eGhleiBXweej8MWRRvz7p1Ojsj3tyZ9XpuPambFjLm7sLi++LWrqEzYq4bGxOleLiwqiRjVgO1bYXB4cqOnE7go9dld24HiTqY/FtkLIwZRYBQpiFZgSo0CmTjZula5WiwPV+i4crOnE7ko99lYZxvx9CKEj4bORH0PPpOREyRGjFCJSLhhVllZvXB4f6gw2lPtzcEpbzChptgw4+N4dAJoTJUOWToYcnRypEZIx3adQaei0YU9lB/ZUdmBXpb6PUYiQy8K0eCXmJqsxP0WNRLVo3P7mLQ43tpS24aWtlaNyk8yJkmFVNr340uXy4Ltjzfj6WCPqDYO3tEl4bOgUAlAUcKp1+O8v5bPxl9UZWJWthajH94vb68OW0jZ8eqAev5xqC8xNSXhsXFSgw9UzYvsYHAyHkmYz3ttbiw/3Dd76PRZcMyMGty9Igk4+sm4KAoHQP0TkTGIe/6EEr2yrCnpMzGPD5s/HWJqhwXOX5+G29w8NKXDWzYpDjFIYyNW5enoMHl2TNaJWFZvLg3s+OYqfTtBVmqumx+CR1RkwO9z485fHAzbUvXlwRRqUIi7+uaEERpsbTAZww+x4XDE1Gpe/ujeo+qKR8nBxQRR+OtESWAlM10rx8Or0oNyeAzUGPPrdySEzgELlrsXJuHlu/Jj301e0WfD+3jp8frghkP7NYjIwL1mFiwqisDRDc0ZnbHw+CkWNdF7H7ooOHKnv7DNInqAWoTBWgcJYJabEKZCgGtsTM7fXh9oOG47WG7HxRAs2DvB7NFlQCDmQC7lQ+EM2pQJ6bobLYoLLZgauOYH7jMCCgsdHwe31+S/0bY//2uX1weH2wuzwBMJKu+2gTfaxMc4Yb4RcFmYl0q1aGZFSJKhEUEt4Y/L7YnV6cKrFgpJmM0pbzChtpkVQf21GXBYTaVoJsnUy5ETJkK2TI1kjntCKD0VRqNZ3YXdlB3ZX6rGnsqNPpVonF2B+Ki14ZiWGjds8j8PtxY5yPd7dUzOqOZTcKBkuyNPh/BwtGox2fHO0Cd8VNQ05yB+lEEAj5cPicIeU29ObtXmRuHpGbJ+FoFazA58fbsAnB+qDqmjT4pW4ZkYslmdGjFjsGm0ufHygHu/srgnJlGE0LExV48+r0pEUPn6GNgTCrwkiciYp7+6pwSNfnwh6TMJnw+H2wu2lsDgtHC9elY+7Pj46oKjo5vYFiYhSCPDnL48DAG6dn4AHl6eN6ISj2WTHze8cxIkmM7gsJv55UTYumRKFH4+34A+fHu23NWJWYhh+tygZr2yvxNZTtHFCulaKJy/OQavZgZvfPRi0/a3zEtBmcQYqWGEiLv54XiouK4wO9GnXG2x44sfSgFHBaLmsMAr3nZc2qlyg3ni8Pvx4ogXv7anFvurTlYcohQBXTY/BJVOiEC45c65oFofbb0vdhq2n2vqcoOjkAsxKDMPsJBVmJoZBM4YOboYuF47VG/H10caA1fhEIuSykBpBzwglayTQyfmBvBwpn87MkfDZZ1XPPEXRoaK0CYIbVidthmCyu1HbYUNluxVV7VaUt1kDQvtMsSBVjcXpGuRGyRCvEo36pL5nAGhRgwnFjfR1f6KQx2YiI1KK/GgFCmLlmBKrGPE84kjw+SiUtliwq0KP7eXt2FdlCLL5ZzMZmBKrCIieDK10XKo8bq8P+6sNeG9P7aCOnUMxL0WNtXmRWJyu8f9NN+GnEy1Dzraka6XgsZk43mgasC1xMB5ZnYGLC6IgE57+3fH5KOys0OP9vbXYVNIaqO6oxFxcVhiNK6fFIFo5ssBOj9eHn0604q1d1Tg4zFY2JgPgsJjwUVRIWV/ZOhkeW5uF3Gj5iPaVQCDQEJEzCdl4ogW3vHco6DEJnw231weH24cZCUq8tW4aHvyiCF8PcYJ41+JkxCiF+OP6Y6AoWkA8uGJkAqeowYib3zmINosTYSIuXrl2CrJ0Mjz2/ckB3dweXZsFEZeFv359AhanB1w2M1Atue29Q/jl1Gm3uLgwIdbk6fDe3tpAVefq6XSAaPdch8vjw6vbK/Hiloo+MyHdpGjEIa8SZutkePyi7BG7yvWHxeHGJwfq8daumsCsDZNBz05dPSMWc89gUFyLyYEfjjdjU0kr9lcbgg64Eh4bc5JVmJusxuykMMQohWNycmXocmF3pR4f7K3DnqqOUb/eYPA5TBTGKlEQq0B6hCSQo6MUcc8qwTLROD1eGLpcaLc40WxyoLTZ4hcKEzvbFBsmxPKsCCxMDUd6hDToBHa4UBSFeoMdRY1GFDfQoud4Y/85OJEyPvL9bZdTYhXIiJROWLXH5vJgX5UB28rasb2svc+8i1rCw4IUNZZkaDA3WTXieZPB8PkoHKrrxDu7awJtxCNhTV4k1ubpMDVeie1l7fjqSCN+OdU26Ik9j81EsoY2DTjeaB5wu4FYnaPFjXPikR8tD/q+ajbZ8dH+eny8vy7wO8xgAAtTw3HtjFjMT1GP+Hu4uMGEt3ZV44tereRDwWDQn9dHITBTOxg6uQBPXZKDmYlhZ7yFmUA4GyEiZ5JR2mLG8ud2BD0m5rHBAGBxepAWIcGnt83EMxvLhnTRuXdpCpI1Yvz2g8PwUXTL2l/PzxjRl+XOcj1uee8gbC4vUjUSvH59IexuL3734ZF++6yzdTI8ujYLb+ysDri9FcTI8eQluVBLeMj9+8ag7f+wNAX7qw3YWUG3UKRqJPjnRVmYEqsMbLO/2oCHvixGRVv/AobHZuKKqdF4Z09tSJ/phSvzcX6OdswOHo1GO97aWY2PD9QHVjHDRFxcPT0GV06PmdDV4p60mBx+S9jmPiuQCSoRFqaFY3FaOArjlKOeX/B4fShtseDTg/V4N8T/h+EyNU6BeclqZOlkiA0TIkohPCNzF79GXB4f2iwONBkdONVixr5qA/ZWGYYMcR0tmZFSXJAbiekJYUjRiEd8ou/zUajp6EJRgwmH62gTjdIWS5/5QT6HiRydHAWxCkyPV6IwTjFhoY51HTZsK2/HtlPt2F2pD6qOc9lMzE4Mw5IMDRanaQJBx2NJt3XzO3tqAi3Jw0Uh5GBNng5r8iIRGybCN0cbsf5ww5AiRicXIEohgMnuHtH80KNrs7A2LzLo/8rt9WHTyVa8v68WuypOL7LEq0S4fmYsLimMHvEsYZvFgQ/21uHt3TXDbiXl+b+zBlqs673tC1fmY2m6ZtI4bBIIZwNE5EwiTHY35j/1C4w9+rU5LAZkAg70Vhd0cgE+v30WfjjejL9/e3LQ17p1fgKWZUTgqtf2wunx4Yqp0fjnhdkj+oLcUNyMuz4+AreXwpwkFV66pgA/HG/B/euL+t3+D0tTUBCjwP3rj6HJ5ACLycBdi5Px2wWJ2Fmhx7q3DgRt/+eV6XhhczksTg/4HCbuXpKCm+bEB1ZSO7tceOKHUnxysH7AfXz+ijzct74opNWxq6fH4IEVaWPmaFPVbsV/f6nEV0cbAydLSeFi3DwnHmvzdWdk1qbD6sQ3x5rwXVFzH5egKbEKrMiKwKK0cCSoR267CtCi5ki9Ea9sq+xj+T0awkRcrM7RYmaiCikaMaKVwjPinEUYHl4fhTaLAw2ddpxqsWBbWfuQ7bSj4aICHdbk6ZAbJYNcODJb6S6nB8cajDhc24nDdUYcrusM+g4G6Epslk6G6fFKzEgIQ2GcckICTF0eHw7WGLCppA2bSlr7mC5k62RYkq7BkozwcWlrc3l82FWhxyvbK0ds9pGoFuGywmhcWKBDh9WFzw814KujjYPO77D8LXs8NnNEs0OXTInCullxfSr0Ve1WfLCvDp8erA+0bEp4bFw2NRrrZsWNuJXN4fZi/aEGvL6jCjVDWG33ZjhiBwBevDIfK7O1Zzwbi0A4GyAiZ5Lg81G44e0D2NYr7DM2TIjaDhvEPDa+/O0s1HTYcMt7B/s4XfXkymkxuGVeAi5+aTcMXS4sSQ/HK9cWjuhL8cN9dfjzV8WgKGBVthZPXpKDx38o6bc9Tcpn4811U7GzQo/nN9Oua3FhQjx3RT5yo2S4b30R1h9qCGy/OkcLh9sXsJmeEqvAvy/NRXwPy9CNJ1rw0JfFAx4Q71iYiLxoBX7Ta66nP1RiHl66pgBT45RDbhsKFW0W/GdLBb451hTo/Z6VGIbfzE0YVSvESHF5fNhS2or1hxqx9VRbUJ97YawCK7O1WJEdMaqKks9HobjRhP/+UjFmxgBr8yKxJEODVI1kwvNzCBMHRVHo6HKhWt+F4gYTtvrbs8aaVdlarMmLRF6MfEQzbz4fhSp9Fw7XdeLgAJbQDAaQoZViRkIYpscrMS1eOWKRFSoURaGizYqfS1qx6WQrjtQbg44DkTI+lmVGYEVWBArjlGN+Euxw00HFT/10asS20EvSNbisMArzUtTYWa7H+kMN2FzaOmg7W4SUj6RwMTptLpxoGl47m1bGx0Mr07E8KyLoe6XL6cHnhxvw9q6aQHtgd0vxDbPjMSNBOSLB6PVR2HiiBS9vq8SxYZrhiLgscNlMmB2efp1Je/PClflYRcQOgTAoRORMEl7cXI6nfy4LeiwpXBxozXrtukJEKQS46H+7YXcPnHuwKkeLR9dk4eKXdqNa34WcKBk+vmXGiNo7epofXDU9Br9flIw7Pjzcb37AwlQ1Hl6dgb99ezJw4nJ5YTQeOT8DHi+FeU/9ElTO//2iJLy/rw6GLhe4LCbuWZqCW+YlBL6wTXY3/v7tCXxxuP+eZxaTgd0PLsL/fV+Cb4YIPwWA3y5IxF1LksFjj76qUtluxXObyvFdUVPgJGNxWjh+tzgZeWdgUPRkkxmfHKjDN8eaglybcqJkWJunw8ps7ajaWrpnjB77vmTU+7omLxLnZUYgWydDlEJA+swJAOiKYE2HDSeaTNh4ohXfF4+NoUg3lxdG45LCKGTrZCOqrLaYHNhX3YG9VR3YV2XoMzfDYAA5OhnmJKswJ0mNKbGKcW+hbLc48UtpG34uacWO8nY43KcrASoxD8uzNFiZpcW0eOWYz6MZbS58W9SMh786PqLnh4m4uHhKFC6dEoUwMQ9fHWnER/vrUD5AKzJA/4xzo+QQ89iBtubh8MdlKbhyWgzCxKfNZXw+CtvK2/HmzuqgilG6VoobZsdhTV7kiI4ZFEUHPL+6vQpbSodX4Zby2eCymTDZ3SGZFBCxQyAMDBE5k4DDdZ246H+7gx6LCxOiyeiAy+vDPUtSsG52HC74z85BU6cLYuT44OYZuO39Q9hW1g6dXIAv75g1otXMD/fV4aEviwHQrW/nZUbg1vcO9Un2BoD7l6diRkIY7vzgMJpMDvA5TPzf2mxcPCUKR+o6cWGvz3bltBh8tJ+uBKVrpXj28lykRZz+P9tZrsd9648NaNf59KW5WJCqxpTHNg35OcQ8uro0LX701Zs2swPPbirHpwfrAyttyzI0+P3i5DE1LggFp8eLH4/Tzm0952zCJTxcWKDDJQVRSB5FRkRFmwWPflfSp7I4HOJVIlw3MxYzE8OQEi4hveSEYeP0eFGtp2dovi9qHtXvY0/iVSKsmxWHeSlqxIUN32Cj1ezwzyN1YF9VR5/KhoDDwowEJeYkqzE3WYXkcPG4CnqH24ud5Xr8cLwFP59sCWSKAbSgWJYZgZXZEZiREDbmldJqfRc+PlDXJ+4gVPJj5Li8MBoX5EWipNmMD/bV4fui5kHbt3RyATIipeiwOnG4R95YKJyfG4lb5yX0+c4ub7Xgrd01+OJwQ0AwqiU83Dg7HldNjxlxe+KpFgte3V6Fzw83DL1xDyR8NrgsJswOInYIhJFCRM4ZpsvpwZx/bQlagVeJuQAY0FudWJKuwSvXTsFt7x8atLc9SiHAV3fMxrt7avHC5nLwOUx8cftsZEQO/2fx6YF63P85PW/zm7nxyI9R4LcfHO532w9vno4Gox1//rIYbi+FeJUIL11TgFSNBO/uqcVfvwm2wc6MlAZaDtbNisOfVqYFVspcHh/+9WMp3thZ3e97xatE+ObO2ajR23D+f3YO+Tkuytfhb2syRz17Y3G48cq2KryxszpQRVuSrsEflqaM6Oc7GhqNdnywtxafHKhHh9+Bjs1kYFmmBpcVRmNOkmrEq7bHG0340xfFKG4cWebQjAQlrpoei6lxE2vJS/j1obc6Udxowo/FLYPO6g2HK6fF4Mpp0cjQSof9N9RicmBnhR47y9uxs6KjjxGDRsrD7CQV5qfQttDj2drm8viwu1KPH4pb8NPJlqD5IrmQg2UZGpyfG4mZCWFjWuHx+SgcqDHgpW2nowKGg5jHxsX+EE+NhI8vjzTgw/11gzplctlMzEtWgcNi4ofjw7PB1skFeGhlOs7L1AT9HLpzcd7eVRMIlxbz2LhqegxunB0/4qp4s8mOV7ZVDWkY1BsJj67sWByeIKvxgXj+ijycnxNJFpUIBBCRc8Z5YH1Rn4N0WoQEpS0WREj5+PHuufj4QD2e+KF0wNcQ89j4/PZZaDTacOPb9GzKs5fn4sL8qGHvz4biZtzx4WFQFHDD7Djo5IJ+25QSVCK8sW5q0Are8swIPHlpDnhsJh756kTQ5+p2r7E6PZDw2Xjqkhwsz9IG/r3eYMOdHx3BsXpjv/v1/BV5WJOnw5dHGnDPJ8eG/Bz/vjQXl0wZ/ufviddH4eMDdXh6Y1nA0npKrAIPrkgbs7meUClpNuPV7VX45lhToIqkkfJw1bRYXDEtesQZNieaTPjdR0dQNYIe+2ilALfOS8S8ZDViwkY2sEsgjBVmhxsnm8zYeKIVb+7qf6FkOKzK0eKa6bHIj5EPq8WNougcnJ3ldA7O/mpDUFWCyaC/R2hXQw1SNONX5XF7fdhXZcCG48346XhLYGEEoFvaVuf455d62S+PFrvLix+ON+PBz4tDOjHvzbQ4Ja6eEYPlWRE43mjCh/vq8V1R06DVnSydFJEyAQ7WdgYFS4fC387PwGVTo4Paul0eH7491oRXtlcGhBaHxcCaPB1unZcw4kp5m8WB13dU4+3dNSEZ5XQj5rEh5rFhsrsHbVnv5o3rC7EoLZy0BBN+1UwakfPSSy/hpZdeQk1NDQAgMzMTjzzyCFasWBHS889GkbOzXI9r3tgX9FhOlAxFDSYwGMCHN8+AhM/G2v/uGjQs7X9XF6AwToHlz+2AocuF62bG4h9rsoa9P/urDbjmjX1weXy4clo0uCxmv3bMC1PVeOLiHPzlq+OB6tLvFyfj7sXJ0Hc5cfv7fed2GAyAouik7P9cVRDkYvPTiRbc99mxoBaLnhz8yxKEibj498ZT+O8vlYN+BqWIi/dumobMyNG1jx2u68Rfvz4RqGokqkW4f3kalmVoJuygQVEU9lR24OXtVUED2jMSlFg3Kw5L0jUjWontsDrxpy+KR2QccMPsOKzN0yFbJyMrhYRJT4fViUO1nVh/qGHURhmL0sJx89x4FMYOz2rd4fbiUG0ntpe3Y2tpex/LfZ1cgIVpaixO02BmYti4uTF6fRT2VxvwXVETNhQ3B3UPxIYJsSY3Ehfk6ZAUPjrHxd5Utlvxzu6aEVnKK0VcXFoYhaumxUAu4OKzQ7Q1fW+Xud7PKYxVwOLwDDuX63eLknDdzLigUGiKorD1VDte3lYZFOq8JD0ct85PHPGCl6HLhTd2VuHNnTUhiZZuVGIuxDw2WsyOoDms/pALOXj12sIxadcmEM5GJo3I+fbbb8FisZCUlAQAeOedd/DUU0/hyJEjyMzMHPL5Z5vIcbi9mPOvLUGuYbFhQhhtbpjsbtyxMBG/W5SM81/cOegwZnf2zU3vHMSW0jaka6X46o5Zwx6WrGiz4OKX9sBkd2NhqhpcNrPfjIR1s+Jw2/xE3PTOAZxoMoPLZuLJi3OwNl+Hk01m3Pj2gUCJvzeXFUbh0bVZgX3z+ig8+VPpgL3cdyxMxB+XpcJHAXd8cHjIVO75KWq8cEX+qAIE9VYn/vVDKT7zu8BJ+GzcuzQF18yInbAwSYqisKNcj2d+LsNRf2WLyQBWZmtx67xEZEcNX8B5fRRe3laJp346Nazn8TlMPLA8DcsyI6CTkxY0wtkN7ZxmxY5yPV7dXjXg3F8oXDE1GtfOjEV6hHRYgr+h04ZfTrVjS0krdld2BFUn+BwmZiWqsCxDg6UZmqAh+bHE5fFhZ0U7vj7ahI0nWoNOsjMjpVibp8P5uZFjmsPj8tBOmo9+d3JEP/f5KWqsmx2HuUkqbCtrxzt7agd15+OymJifqoaExx52aOelU6Jw24JEJPay2D9S14lXtlXhp5MtAdOZaXFK/G5xEuYkqUa0AGayufH27hq8vqOq36DagQiX8CDkstBkcgxZEUqLkODpy3JHvfhHIJxtTBqR0x9KpRJPPfUUbrrppiG3PdtEzjMbT+GFLRVBj2XppDjeaEZahATf3Dln0PkUAMiNluOzW2di/aEGPPRlMbhsJr69cw5SI4ZXRu+wOnHBf3ah0WhHhlYKmYDT7wrY/ctTsTo7Ete8sQ91BhtUYi5eubYQU2IV2F2hxy3vHQqEYPaExWTg4VXpuH5WXOAgYHa4cddHR/DLAL3b39w5GzlRcri9Pqx+YWe/gaM9WTcrDg+vzhjx0CVFUfj8cCP+8e2JQEXpkilReGB5WtCq3nizu1KPZ38uw4EauhLG5zBxWWE0bp6TMKKWsHqDDde+sW9Y2Q0qMRcPrkjHskzNmGUJEQiTlQ6rE3uqOvDO7prA391I+OOyFKzJ0w0ra8Xu8mJ3pR5bStvwS2kbmnqc/DMZwNQ4JZZnReC8zAhEjtMiQ5fTg00lrfj6aBO2l7UHugaYDGBOshqXFUZhSbpmTCtM9QYb3t1Tg9d2DL+tMEFNm0ZcXBCFFrMD7+2pxfpDDf0ee7qZmRCG2DAhvjzSGHIeDQDMTVbh7iXJQaHUAJ238/rOaqw/2BBox8uLluOuxclYkKoekdixONx4d08tXt5WGcjwCQWtjA8em4mGTvugHR8AMC9Fjb9fkBkU00AgnMtMSpHj9Xrx2Wef4frrr8eRI0eQkZHRZxun0wmn8/Rwp9lsRnR09Fkhcqr1XVj4761BjxXGKnCwthMMBvDF7bPgo4CLX9rd/wuAdu/58e654LCYWPLMNthcXvxlVTpunpswrH3xeH24/q392FXRAbWEhzARt9+k6ScvyUFOlAzXvbEfbRYnYpRCvHfTNMSGifD10Ub88bNj/TrAyIUc/PeqAsxOUgUeq2q34uZ3Dw44B3Lsr8sgE3Dg9vow519b0GoePE39r+dn4IbZ8cP63D1pNNrx0BfFAeemzEgp/rEmC1NiFSN+zeFyrN6IJ34oDYhLLpuJa2fE4rb5icMWWRRF4YN9dfjLMOxd+RwmHl6dgZVZWihE45v3QSBMZqxODw7XduLjA3XYUDy8YfZu0iIkeGB5GmYnqUJubeue5dl0shU/nWzB8cbgTJicKBnOy4zA8qyIPhWGscLQ5cL3xc34+khjkGujTMDBmrxIXDolGlm6sQsddXt9+PlkKx78vGjAduWBkPDZuLwwGtfPioNcyMEXhxvxzp6aQecLk8PFyI2Wo6jBOKihQW/SIiR4eHUGZiWGBX32FpMDr2yvxIf76gLiKVsnw52LkrA0XTOill6zw403dlTjtR1VsLlCb2PTyQXgspmo7ejCUDE7FxXo8KcV6RO6gEcgnAkmlcgpLi7GzJkz4XA4IBaL8eGHH2LlypX9bvu3v/0Nf//73/s8fjaInNveOxTUehUp48NHAS1mB26YHYc/r0zH+f/ZhZLmgYPPHl2bRZ8E+1+rMFaBT2+dOewv1cc3lOCV7XS7mJTP7vdA8/p1hVCIuLjhrf0wOzxIi5Dg3RunIVzKx+s7qgbMT4lWCvDODdOQ0OOAvLeqA7e8e7Df97l0ShT+dXEOmEwGXB4fZj2xpY9LUW9eu64QSzM0w/nIASiKwof76/D4hlJYnR5w2Uz8YWkKbp4TP2Gtac0mO5788RS+9LdTcFgMXDktBncsTBq2mYDH68PDXx/HR/tDd5u6a3EyrpgWTdzQCIQBsLu82F9jwOs7qoKyVIbD3UuScfnU4f2d1Rts2HiyFT8db8GBWkNQ8GdyuBgrsrW4IFeLpPCRW8UPRo2+C+sPNeDzww1B7WVpERJcWhiNtXmRY9pOd6rFgpe3VQa+C4fDsgwNbpwTj2lxSmwra8er26sGnccJE3ExP0WNji7XsGzJtTI+Hlub1Wegv93ixOs7qvDe3tqAMEmLkOB3i5KxIitiRGKns8uFV7ZX4c1d1cMyKEgKF4PNZPS7WNmbe5ak4Nb5CeM2B0YgnGkmlchxuVyoq6uD0WjE559/jtdffx3btm07pyo5h2o7+1RopsUpsb/GgHAJD7/8cQE+PViPv397csDXmJOkwns3TcPWU+244e0DYDEZ+P73c4KyZkJhQ3HzgNbQ3bx74zSIeGxc98Y+dLm8mBKrwJvXT4VMyMF/tpTj3xvL+n1elk6KN9dNDcro2VDcjLs/Ptqv287Tl+biYr8bmtdHYdYTm4es4Hx483TM6lEhGg4dVifuW18UCGqbEqvAk5fkjNsKaW/sLi9e3laJV7ZXBoZHLyrQ4Q9LUxClGF5bmsnuxnVv7h/Qma43c5JUuHNREqbHjyzVm0D4NWO0ubCjXI+Xtlbi5CALUQMxKzEM9y5LQX60IuST33aLE5tKWvHj8RbsrtQHVc3TIiS4IC8S5+dEDqtVLlS8Pgo7K/T47GA9Np5sDZxws5kMLE4Px5XTYjAvWT1mRiRmhxufHWzAo98NfAwciMxIKW6dn4iVWREoabbgtR1V+L64OeBI2Rsxj40VWREAEJjDDAUem4lnL8/D8sxgAdNtJvDO7tpA+1xSuBj3LEkZsdhptzjx0tbKYTkGspgMpGslsLm8ITlnPnt5Ltbk6oiZDOGcY1KJnN4sWbIEiYmJeOWVV4bc9myYyaEoChe9tBtHeoSXZWilqO+0weLw4MlLcrAgVY1F/942YH8xn8PEpj/Mh1rCw9JntqPOYMMt8xLw0Mr0Ye1Lo9GO5c9tH7T3972bpkHC5+Da1/fB4vRgVmIYXr++EAIOC89vLsdzm8oB0AeKnvs7N1mFl66ZErCNBoB3dtfgb9+eQH+/QV/fMRu50XIA9M/o0pf3BLVK9Mfnt8/s0ycdKtvL2nHvZ8fQbnGCy6YH69fNipuwELVfTrXh4a+Oo6HTDgCYGqfAw6szkBMlH9brGG0urHphJxqN9pC2f2hlGi6fOvJQOwKB0Jdmkx3fFzUPWNEeDLWEh8fWZmFhanjIbW0muxtbSlvx3bFmbC9vDxI8edFynJ8bidU52hHbyg/63jY3vjnWiM8ONaCo4XSeVrRSgKumxeLSwiioxqi64/NR2FWpx1+/OTFsi/topQC/mZuAS6dEo6PLibd21eDj/XXoGqD9i8dm4oLcSEj4nGHbjz9zWS4uyI0Mqv6bbG68uasab+2qDnQtZGil+ON5KViYOjJb52aTHS9uqcCH++pCfo6Qy0JSuBgdVteQxwkRl4U3103F9ISwYe8bgTBZmdQiZ/HixYiOjsbbb7895LZng8jZXaHHVa8HW0ZPj1diX7UB6VopvvvdHPz92xODWm3+cVkK7lyUjLd2VePv354MVH9EPQTFUHh9FK56bW+QHWZv3r9pOuRCDq56bS/MDg+mxSvx9g1TIeCw8PTGMvznF9o0YUGqOij4bUVWBJ6/Ij9wwKYoCs9uKscLm8v7fZ99Dy0OOhj/6YuiIduteoqi4eD2+vDUT6fwqr89LzlcjBeuzEe6dmJ+X9osDvzj25P4rqgZAN1D/edV6ViRFTGsg57J7saa/+wMyUxAK+Pj0TVZWJgWTpKwCYRxxu314WBNJ17dXjmgqcpg/OvibKzKiQxaIBoMo82FH4+34NuiJuyp7AjMYjAY9LFlTZ4OK7O147KwUdpixsf76/H54YbAYhmHxcDyLC2unh4zppXiqnYrnt9cjq+PNg3reQohB9fPisP1M+PAZDLw0f46vLWresAuATaTgRXZWoRLeIOa/vTH81fkYXVOZND3bPd8zRs7qwMLgQUxcvxxWeqIuxCq2q146qdTwwo/VYq4iFII0NhpD8pK6o9pcUr865IcYk5AOCeYNCLnoYcewooVKxAdHQ2LxYKPP/4YTzzxBH788UcsXbp0yOefDSLnylf3BvUJF8TIcbzRDJfXh/dvmo4YpRCLnt46oENKXJgQP90zD24vhflP/oKOLhf+78IsXD09dlj78cq2Sjw+SLjoG9cXIjZMiEte3gOjzY3CWAXe8betvbC5HM/8TLeo3bU4Gc/3EC+rc7R49vI8cFinBc5g2TbFf1sGSQ/3rvf21uLhIYblP75lBmaMYKWp3eLEnR8eDgi7a2fE4s+r0iekF7m3cxuTAdw4Ox73LE0Zljh1ery47JW9IbWlLUhV408r0ofttEcgEMaOJqMdG4pHVuUZbuW1zeLAD8Ut+OZYU1BOGY/NxLLMCFxcoMPcZPWYL3bYXV58W9SED/bVBX03JapFuHp6LC4uiBqVrX9PDF0uvLunJtBFECoCDguXT43GTXPioZHy8eWRBvxvayVqB1goYjCAxWkaxKuEw3KA47KYeP6KPJzXq42ts8uFl7dV4p09NYH25FmJYfjjeakoiBmZwc2h2k488UPJsBwBdXIBImR8lDabB6xqdbNuVhzuXZYSdIwmEM42Jo3Iuemmm7B582Y0NzdDJpMhJycHDzzwQEgCB5j8IudIXScu/F/wLM7spDDsqujA1DjaNOCeT47iq0FWqt64vhCL0zV4flM5nt1UhniVCBvvmRcQFaFQre/Cec9tH3CQ8elLczEnWYWL/rcbjUY7cqPleN/ftvbenho8/PUJAMDDqzPw0taKQM7P0gwNXrq6IFCypygK//rxFF7e1r/AKX10eZDA6O/n05vXryvEkhGYDByp68Tt7x9Gi9kBMY+Nf1+ag+VZ2mG/zkgwdLnw5y+LA6tu2ToZHr8oG1m60PMKKIrCPzeUhHSwXZMXiT8sTUFsGFmFIxAmEw63F9vL2vF/G0oGPLkeiEdWZ+CyqdEhV3gaOm34rqgZXxxuCHIR00h5WJuvwyUFUUjWjP0CyPFGEz7YV4evjzYGBvAFHBYuKtDhhtlxY2aS4PR48fXRJty/vmhYz2MxGViTF4k7FyYhRinE98XN+M+WikGz6JZmaJCulQ7YjdAfEj4bz16Wh8Xpwa1pbWYH/vtLBT7cXxdoM1ycFo4/npc6oo4CiqKwqaQN//qxFBWDfIbe5ETJIOCwsL/G0G8LeU+evjQXF+aTeR3C2cmkETmjZbKLnDs/PBxoUwLoOYzjjWbY3V68c+M0RCkEWPLMtgG/cLqFkM3lxawntsBkd+PFK/Nxfm5kyPtAURSufWM/dlb07xD055XpuGJaNC57ZS9Kms2IV4nw+e2zoBRx8c2xJtz18RFQFF3BcXi8gRBPnVyA7fcvDKwQDiZwwiU87HpwUZAwazU7MP2fmwfd9ycvzsFlU6ND/qzdfHawHn/+8jhcXh8S1CK8em3hmCd6D8TWU224b30R2i1OsJkM3LM0BbfOSxiWc1so5hAAHUx495KUMQ3vIxAI44PXR+FQbSde3FI+bMe2f12cjQtydRBwh65CUxSF4kYTPj/UgK+PNcFocwf+LSdKhkumROGC3EjIhWNrG29xuPHV0SZ8sLc2yOVrbrIKN86Jx/wxMiroDk7+w6fHhnTi7AmDAazJjcSdi5KQoBJj48lW/OeX8j623T1ZkRWB7CgZnvwx9EBlnVyAxy/Kxtzk4KDQhk4bXthcjs8PN8Lro8BgABcXROHeZSkjcrr0eH347FADnv25DG2W0H4OHBYD0+KV6LC6hnRiU0t4eGvd1GEtzhEIkwEiciaAdosTU/9vU9BjMxPCsKeqA9k6Gb65czYe+rJ40FmU7jat7lmceJUIm/4wf1itB18dacTdnxzt99+unxmLh1dn4Ia3D2BHuR4qMQ9f3D4LMWFC7CzXY91b++HxUbhuZizW5utwUY+qS9ljK4KGZl/aWol//di3HU4l5mHfQ4uD9tnro5D40IZB9/vmOfH4y+q+DnuD0XsWaFmGBk9fljshpXevj8LTG0/hf1tpkZcULsZzl+cN6wChtzpR+NimIbdbmqHBw6syRhQUSiAQzjwUReFUqwVv7qzGpwdDd/jisph44cp8LEkPD2nhxOXxYUtpG9YfasDWU22Btmgum4lV2VpcOS0GU+MUY+q4SFEU9lUb8ObOavxc0hpYxEtQibBuNh3oOZyW3cE4XNeJv359AsWNpqE37sHqHC1+tygZKRoxtpa1479bKgY0vmEwgJXZWqRpJHj65/6dRfsjWyfDP9ZkIr9Xa1pVuxVP/1yG7/0LoHwOEzfNicdt8xNHdKyyuTx4ZVsVXtpa2a+LaX+oxFyka6Uob7WixewYdNuLCnR4eFUGyVIjnDUQkTMB/G9rRdDqT4pGjE6bG+0WJ168Mh8zEsIw+19bBmwhm5uswns3TYfH68OCf29FQ6cdj63NwjUzQp/Fsbk8KHxsU7/hYnOTVXhr3VT8c0Mp3txVDSGXhU9umYnsKBkq261Y+99dsDg8OD83En9akYZZT2wJPPfIw0uDvvA+OVCHBz4v7vMeEh4bRx5Z2udgfM8nRwfNRZifosab66YOS8y5PD786YtifH6YPmG4Y2Ei7l2aOiHl9g6rE3d9fDRQLbtuZiweWhn67I/PR+H/NpQMOfSapZPiyYtzkRE5uX7XCQTC6Khos+LlbZVYPwxL4wytFE9dmoPMyNAWUvRWJ74+2oT1hxqC8tiSwsW4cloMLi7QjXl1p95gwzu7a/DJgXpY/EP4Ej4bV0yNxg2z4xEpH5usrpJmM/65oWTYFbIVWRG4c1ESMrRS7KnqwDMbywYVO6uytUgOl+DZTaGLnfMyNfjTinTE9RrqP1LXiX9uOD1fEybi4u4lybhiWsyw2tG7aTTa8cQPpfj2WOhGDUnhYmhlfOyvNgSCTQfi0bVZuGpaDDGzIUx6iMgZZyiKQuFjm4IcTeanqLGtrB0qMQ+7H1yE//xSMWi/70e/mYGZiWH48XgLbnv/EJQiLnY/uGhYQ/Pdczy9SVCJ8OVvZ2NTSSvu/ewYAODla6ZgeVYEjDYXLvzfblTruwL5OLOe2BwYWHzmslxcVBAVeK2fTrTg9vcP9UlbZjMZKHl0eZ8v60O1Blz80p6gfanSn7YKVUt42HTP/GENrdpcHtz63iHsKNeDxWTg0TVZuGp6TMjPHw1FDUbc9t4hNJkcEHJZeOLiHFwwjHbCynYrFj+9bcjtXr+usE+vN4FAOLforvC8tLVyWK5iV0+PwV2LkxEegoU0RVE41mDCR/vq8M2xJtjd9Hf7eFZ3rE4PPj/UgLd316Da/33PZjJwQV4kbpufiJQxmhWq0XfhqY2nAlWSUDkvU4N7l6UiOVyM7eV6PLPxFI419F8dYjMZuLQwGtFKwbDa2K6bGYvfL04OstumKAobT7biiR9KAz+XBJUID6xIw7IMzYj+Dw7UGPCPb08Oq7o1I0EJJoOB3ZUDh6kCtGPbezdNC1lYEwhnAiJyxpmj9Uas/e+uwH0Bh4WUCAmO1Rtx58Ik3LM0ZdDgy8xI2lqawWDgprcPYHNpG26bn4gHV6SFvA8DtT5xWAz8ePc8dDk9uOTlPXB5fPj9oiT8YVkqPF4frn9rP3ZVdEAnF+CrO2bj79+eCMwVpWjE2HjP/MBrHW804ZKXdwecY3rS22QAoKstKX/5IXA/QS3qk4Ww/raZKIwLPQvH4nDjxrcP4EBNJ4RcFv57VQEWpoWH/PzR8PPJVvz+oyOwu71IUIvw8jVTQj5Y+3wU/vVTaWDGaSBIOjWB8OuEoiicaDLjuU1l2FTSFvLznrw4B2vzdSFl8Jgdbnx9tAkf7qsLqu4kh4tx/aw4XFSgg5A7Nq1lAP29t62sHa9urwpyHV2cFo7bFiRi6jC++wej2WTHMxvLhhX2yWAAF+bpcM/SFEQpBNhc0oanfy4L+rn0RMBh4fpZcZAK2MMSO/cuTcFNc+ODfq5urw8f7a/Dc5vKYfAvjk6LV+Jv52eOqHLv81FYf7gBT/54KuS5JZmAg3kpahxvNAUE10CsmxWH+85LHbO2QwJhLCEiZ5x59LuTQa1HMxLoXByKAnY+sBDlbVbc8NaBAZ//7OW5uDA/Cm0WB2Y+vgVeH4XN985Hojr04fnLX9nTbybO81fkYWFaOFa9sAP1BjsWp4XjtesKwWQy8O+fTuE/v1RAyGXh89tnYXNJK/698XQl6Od75gXcedosDqz5zy40m/r28/ZuZ+vmilf3YG/VwDk93XlAoWKyu3H9m/txtN4ICZ+Nd26cNmJrzuHy9q5q/P27k6AoYF6KGv+9Kj/kfupmkx0zH98y6DYZWileuqaAOKYRCAT4fBR2V3bggc+LQg4CzouW45nLcpEQwnFjoOqOlM/G5VOjcd3MOEQrx3YG8Fi9Ea9sr8QPx1sCcztTYhW4dV4ClqRrxqTVuNlkx9Mby4bVBshhMXDF1Bj8blESVGIefjzRgmd/LhvQjU0h5ODG2fEwO9zDsp7+18XZuGRKdFD7l8XhxsvbKvH6jmo4PT4wGcBV02Nw79LUEc3EWBxu/OeXiiEX03qSpZMiVinCppLWIVvYRup+SiCMJ0TkjCM+H4X0R34M+nKYl6LG9rJ2TI1T4LPbZuG3HxzChuL+Q73CRFzs+dNicNlMvLq9Ev/cUIqCGDm++O3skPehyWgPmqHp5spp0fjnhdm46+Oj+OZYE6IUAnz/+7mQCTjYVtaOdW/tB0UBL16ZD6WIi2ve2Bc4+FyUr8Mzl+cBoK08r3h1L47UGfu8x/b7FvY7EF/easHSZ7cH7udGyYLaAbJ0Unz129khu5CZ7G5c8/o+FDeaIBNw8P5N05EdNf4ldIqi8NRPpw0GrpwWjX+syQq5h3rTyVbc/O7BQbd5dE0mrp4eS+w7CQRCH7qcHnx9tAkPfdl3DnIg/rEmE5cVRodUETY73Fh/sAHv7KkJ2F4zGMCSdA1umBWHmYlhY9rKVq3vwqvbq/D5oYbA4HxSuBh3LEzE+TmRw3KmHIhGox1P/Vg6aFxDb/gcJtbNisdt8xMg4XPw9dFGPL2xbECRqZMLsG5WHIoaTSHPxUQpBHj28rw+FaxGox3/3FASaLuTCTi4d1kKrpoWM6KfR0WbFQ9/dTyoejYYLCYDi9LCYbK5sb9m4IVJAJiTpMK/L80lLp+ESQMROePIiSYTVr2wM3BfJuAgWinA8UYz/rEmE2vydCh87OeAX35vbpwdj0fOp13F1v53F47WG4dtOBD34Pd9HkvVSPD1nbPxXVEz/vjZMbCYDHx660xMiVWgxeTAyhd2wNDlwjUzYnDX4hSseH5HoMzNYjLwy70LAuLlT18U4aP99WAxGfD2GMb55JYZmD5AaGfPferOCurJ97+fE3Kfr93lxXVv7sOBmk4oRVy8f9P0CRnGpygKj31/2iDgvvNS8dsFiSEd8D1eHx7++vigbnqZkVK8fM2UMV8xJRAI5yaNRjte31GFt3bVhLR9brQcz4ZY3fH5KGwta8Nbu2qCBvpTNGLcMDseF+brxrSNts3iwFu7avD+3lpYHLRJQbxKhDsXJmFN3tiInXqDDf/cUBLIMAsFCZ+N2+Yn4qY58QCA9/bU4sUt5TD797E32ToZrpsZi/f31g4419ObVdla/HlVeh8jhr1VHfjbNycCds9pERL89fxMzEwcfjg2RVH45lgTHv2uJOQWNo2UhxkJYdhfbei3a6Mn/1iTiWvI4hxhEkBEzjjS20p5RoISe6sMYDCA/Q8twe5KPe76+OiAz+8+2W+z0DkyFAXse2gxNCEMlALAtrJ2XP/m/j6Pf/e7OZAJODjvue2wuby477xU3LEwCRRF4bo392NHuR6ZkVKsv20WfvPuwaBcnfNzI/HilfkAgG+PNeF3Hx3p8/p3LU7GPUtT+t2nLw434A+fHgvcl/LZQQeIW+cl4E8r00P6fG6vD7e+dwhbStsg4bPxyS0zJ0zg/PWbE3h3Ty0Autpy7cy4kJ5rtLmw5JltgRDV/rhrcTJ+vziZONcQCIRhQ1EU9lcbcM8nR9E0xMloN89fkYfzcyJDOimtaLPi3T01WH+oIeDWqRJzsW5WHK6ZETumrmwWhxvv7qnF6zuq0OnP+IkLE+KOhUm4MF83JmKnRt+Fh78+Piw3tkgZH/ctT8WaXB3MDjf+t7USb++uGdAhdVWOFiuyIvDHz471O7faH3cvScat8xKD8pA8Xh8+OlCPpzeeCmQercrR4i+r0keUr2Oyu/HMxlN4x38sC4U5SSooRVx8V9TUx2SoJ/EqEd5aN7WPkxyBMJEQkTOO9J6FmZuswo5yPXKj5fj6jtm448PDAzq/pEVI8OPd8wCctmXOjZLh6zvnhPTeTo8XqX/5sc/jdy9Jxl2LkwNiZlqcEh/dMgMsJgMf7KvFn788Dj6HiQ2/n4stpW147PuSoOd/dcds5EXLUddhw6oXdgSsQLtJChfj53vm9VvRoCgK8X86nYlzUYEOXxw+bR8t4bOx+8FFIc2zUBSFez89hi+ONILHZuL9m6eP2aDqUDz+Qwle2VYFBgN4/MJsXDEtNPe2qnYrFg3hnvbhb6ZjVqJqLHaTQCD8yjHaXHh7dw2e2zSwe2dPrpsZi3uXpUImGPo72Oxw49MD9XhrV02gbUvIZeGKqTG4aW48dGNkCQ3QbXnv7qnFazuqAsP4MUoh7lyYhAsLdCOyWe7N8UYT7vnk6IDzNv2RrZPhz6vSMSMhDA2dNjyzsQxfHm3sN9Sby2bi5jnxiFYK8acvQm8v/M9V+ViVrQ06pnZ2ufDMz2X4YF8tfBQg4rJw77JUXDczdkTCr7jBhL98VRxytUnCZ+OC3EgcrOnEqdbBg0QfXp2BdbPiyKId4YxARM440Z/ImJOkws4KPX6/OBl3LkxCwaM/w+rsv8x995Jk3L2Erobc/v4h/HC8BX9YmoLfLw5tGL8/y+gMrRRf3zkbXx9twh8/OwYum4kf75qLBLUY9QZboLLzyOoMLEwLx/LntsPp8SFaKUC9wR6YB3J7fbjk5T04Vm+EXMgJStHuz0mtv33ispl9Vr2GUxF5cXM5nv65DGwmA69eNwWL0iZm4LF7Ngqgh0UvnxqawNlT2YErX9s74L+na6V458apCJeQXmYCgTC2UBSFg7WdeODzoj4ulv2RoBLh5WtDc4h0e334vqgZr2yvCriPsZgMXJAbiVvmJSBdO3bH4y6nB+/vrcWr26sCsQzRSgHuWZKCNXm6MTmR3lHejlvePRQwXAiFpRka/GlFGhLUYpxoMuHR704OaKyjlvDwu0VJqOuw4fUh8tC6KYxV4ImLc5AUHtxaeLLJjL98VYzD/pnYLJ0U/7c2G7nR8pD3vRuvj8KH+2rxrx9PDXhe0pvp8UrEKIX46mjjgG33AC1I37ph6rAMkwiEsYCInHHiWL0Ra3pYRyeFi6G3OmG0ufH57TNBUcAlL+8Z8Pnf3jkH2VEyUBSFqf+3GXqrM2RL5dqOLsx/amu/r6mV87H46W0w2d14YHkabl+QCIqicM0b+7CrogPT4pT48DfTccWre3GwthNzklRoMTtQ0WbFExfRVYv//lKBp346BQGHFXQg+OGuuQMe0HpXca6YGo2PD5yeSYlRCrH53vkhrcj9eLwZt71/GADw+EXZuDLESspo+epII+7+5CgA4MEVabhtfmJIz+vONxqIiwp0+OeF2cQamkAgjDtGmwsvba3EK9tDc9l6c10hFqYOnctFURR2lOvxyvbKoDnLhalq/H5xMvLH0O3S5vLgg711eGV7ZaD1N0Ujxn3npWHJGGSI+XwUvi1qGrSdvDdsJgPXzIjFPUtSIBWw8ePxFjz2fcmA5gS5UTLcviARL2+rwtF6Y0jv8btFSbhjYVLQscLno/DJwXo8vqEEZocHDAZw3YxY3HteKqQhunz2pMlox1++Oo4tpaFZlQs4LFxYoMOJJjOODfE5/rQiDTfPTSBVHcKEMRxtMPp68K+IYw3GoPsqMRdGmxs8NhM5UfJ+LZ270Uh5yNLR/xm1HTborU5wWUxk6UIbxn/ih9I+j101PQbZUTL8+6dTMNndyNBK8Zu59PDkd0XN2FXRAR6biacuzcEH++pwsLYTIi4L18yIQUWbFVw2EytztKhos+B5f+tDT4Fz6xArdu/26PmV8tn4vji4Te/2BYkhCZySZjPu+YSe6Vk3K27CBM6xeiPu/7wIAHDznHjcOi8hpOd9eaRhUIHzwPI0PH1pLhE4BAJhQpALufjTynRU/N8K/O/qgiG3v/Htg4j/0wa8t6cGHu/A8yQMBgPzUtT44OYZ+PbOOVidowWTAfxyqh0X/m83rn1jHw7VDu7OFSpCLhu/mZeAHfcvwgPL0yDls1HWasVv3j2Ii1/ajb0hOocNBJPJwJo8HU49thx/WRXajKjHR+Ht3TVY+PRWfHqwHudlRmDzvfNx79IUCPr5fj/WYMLtHxxGtk6G164rDOk9XtxSgbSHf8TWU6cFCJPJwJXTYrDljwtwYb4OFAW8s6cWS57ehu+KmjDctelIuQBvXF+I56/IgyKEIG6724sP99WBAeD6mbH9ftZuHv+hFAv+/QtqO4auJhIIEw0ROcOg98pM98pFtk4GDos56Jfw3GR1YCXqgN+yMSdKFtKJ8MEaQx+3GJmAg/uWpeJ4owmfHKSrJ/9Ykwk2i4kupwf/55+7uX1BIsQ8Np7eSIeZPbAiLdCjuzRdAxGXjfvXF8Hl9fXpt35g+eDhpH/95kTg9spsbcAxBwBUYh4uKtAN+dm6nB7c8eFh2N1ezE1WhXzwGS3tFidufe8QXB4flqRr8NDK9JBWCj/cVxcQZP3x5CU5uD1ERzYCgUAYS9gsJlZma1HzxCr8ePdcTI0bvNLy8NcnkPTnH/D4hhLYXIO3M2VHyfCfqwqw5d4FuKwwCmwmAzvK9bj4pT24+vW92DdKEdKNgMvC7QsSseP+RfjtgkTwOUwcrjPiilf34ro39+N4Y2gzJgPBY7Nw89wEHH54KS6dEhXScwxdLjzweTEu/N8unGqx4HeLk7Hlj/OxNi+yz7YUBby3txYPfl6EJy/OwZ9DNN1Z99YB3PbeIbT0MJZQiXl49vI8vH/TdMSrRGizOHHnh0fwm3cPotUcmgFFNwwGLfI2/WE+1vSz3/1xtN6ITw824LqZsZg2SMdJvcGO+U9txcf764YtwAiE8YSInGFwsik4GbnbUSUvWg6fj8LRfnJluul5sClppof6Qumx7bY17s0fl6VALuTgUX9g5fm5kYG2txe2lKPF7ECMUojb5ifiqZ9OwezwIEMrxdXTY/GLv2S9LFODj/bX4XCdESIuK6gEv/WPCwZ15TlS1xl0v6bXKs4Ns+PAYw8t4B75+gSq2rsQIeXj+Svyx8RZZyi8Pgq/++gwWswOJKpFePby3JAciL480jBodsXL10zBZYXRY7mrBAKBMCLSIqT47LZZOPrI0oA98kC8sr0KGY/8hDs+OAyjbWCXSACIU4nw5CW5+OWPC3DltGiwmQzsqujA5a/uxeWv7MHuitAdzQZDJuTg/uVp2H7fQlw7IxZsJgPby9qx+sWduOeTo2g2hRaaOhBKERdPXZqLH++ei9QQ5pQAulKz9n+78ODnReCymHjuinx8fvtMZPbjANrR5cL9nxfh55OtePfGaciPkQ/5+j+eaMGMxzfj3T018PWwOZuTrMIPd83FXYuTwWExsKmkDUue2YZPD9YPW1SEiXl4/op8vHF9ISJCcHW1u714ZXsVeBwmbpoTD+4gx+gHvyjGVa/tC9nCmkAYb4jICRGKogJe9gDAZNDDmQCQpZOhodPex5WsJ1NiT6+ClLfRr5OiGXpgb1NJW58KUoxSiCumxWBzSRv2VRvA5zDx4Aq66tJktAcyFR5ZnYGyVktQpafF7EBpiwVMBpAfrcAzP9OmAT0tQq+cFj2kReRv3j3drnXtjNg+A5mhrJB9eaQBnx9uAJMBvOAPKJ0IXttRhb1VBoi4LLx6XWFIzm8/n2wdtILz5rpCLM+KGMvdJBAIhFEjF3Lx8OoMlD66HH/1Z7QNxPfFzcj7x8+46rW9AcezgYhWCvH4RTnYet8CXD09BhwWA/uqDbjq9X246rW9Ic+kDEW4lI9H12Zh872nKxBfHmnEwn9vxTMbT6ErxIH6gUiLkOLHu+filWunhLQ9RQEfH6jHwn9vxbt7apAXrcDXd8zGI6szIOax+2y/v8aAG94+gKlxypDf45GvT+Cil3ajqv20Kxyfw8I9S1Pw3e/mIidKBovDg/vXF+G6N/cPOCM0GIvTNdj4h3m4JMRq1o5yPdYfasCt8xP6FXXd7KnqQOFjm7C5pHXY+0QgjDVE5IRI7wyUuDARGjvpL5ZEtRgnmwcuocuFHCSqT4uG8lb6iyspfPDVI4qi8OKWvjahf1iaAjaTgec20wJl3azT1p4vbC6Hy+PD9HglFqeH48kfT4GigLV5dKVnlz83ID9GgXf21MDQ5UKkjB/0Jfm3CzIH3a8upydopcbTy1h/SXo4wodYIWozO/DXr+l2t7uXpGBa/MRYRR9vNAVa9/56fmZIzjAHawz4zbsHB/z3/11dMGFOcAQCgTAS+BwWbpgdj6p/rsRLQ8zt7K7sQMGjP+PyV/YMuSofpRDi/y7Mxrb7FuK6mbHgspjYXdmBtf/dhVvfO4jyIeyIQyU2TITnr8jHN3fOxrQ4JRxuH17YUoGF/96Kzw7WB1U+hguDwcB5mREofXQ57jsvNaTnmB2egBgpb7Pixjnx2PSH+ViVre312nT3wKvbq/B/35fgrRum4uYhKmsA3Sq26OlteGVbZdDcVGqEBF/cPgsPrkgDl83EjnI9lj2zDe/vrR32z0DK5+Dfl+bilWunICyERUaT3Y0Xt1QgSiHAdTNjBzUbuOmdg7h//bEh2yAJhPGEiJwQqe+0Bd2XCzkBu8sEtSioytObtAhJYEbD4faixd9LmzBEtWRbWTuKenncs5kMnJ8biZ9PtuJ4oxkiLgu3+Afmq9qt+OxQAwDg/uWp2FtlwM4K/f+zd97hUZRdFD+zPb33QkiBQIBQQ++9CSJFioogXUGxIyIqguWzoCigCErvvXdCCYEE0gvpvfe+2fL9MTuT7dkkC7b39zw+kt3Z2dmFzMx5773ngM+l8PYY+sT9SNFm5mghxJ/30gEAUqVy928v9262zexMZC77ZwGXo7Fi93wP/StDcrkcH52MQWW9BN3crbB8mGGOZm1FIpXhnSORaJTKMaazE2b0bn4FK7usFgv/1C1w/jcjEBPULmoEAoHwd4XDoTC+qwvSNk3A4SX99d6ohqaVoveGq5ix7R4Km5kBcbU2wWdT6IrLCz3dwaGAS7EFGPtDMN45EolstWtoa+nmbo1DS/ph69ye8LQ1RWFVA949GoXJW+602ZxAxOdixXBf3P1gBPp5G7bwFplVjsk/3cFXFxNgbcrHz3N74o9X+8DT1hQAXfnhcShwORQyS2vx6q6HqG6Q4OSKgVorP+psupCAcZtvIyG/qV2ex+Vg6VAfnF85GD09rVEjlmLtyRjM3RGKrNKWf89jA5xx6a0hGN3ZsMW6S7EFuBxbgPfHddR7H3M4LBvdP7uicuwEwrOEiBwDKa5SXc0SK1ZWHC2EMBPykFWqu1ys7INfpNiPgMeBdTMuJz/fSNZ47NuZgeBQwOZrdIXnlQFebJvXT9eTIZXJMdLfET09bdiKxYt9POGhOOE+VswN3UkuhkQmR2cXSxRUNn22UZ0c9R4TAHx+No7989x+nmyOAkCvWg3t6KD39eei83AlrgB8LoWvp3d7JnM4AO0Gl5BfBWtTPjZN69qsOUBNgwSLdoejoq5R6/MrR/oZXOonEAiEvxMURSGovS1SNk7AmdcHwUKk+4b7YXoZgjZew5zf7qOiVvv5kMHD1hTfzgzEpTeHYGyAE2Ry4Gh4Nkb87xY+PRPb7MyPocc+vqsLrqwegjUT/GEh5CE2txIv/nofbxx4rDK83xrcrE1wcHF/7DDQIU0ik2PrzRSM/SEYd5KKMayjIy6/NQRvjPAFn0tBIpNDLpdDxKevdQcfZmHx7jD8b0Yg1k3S30IIAMmF1Rj3w238cPUJ2yYP0PcWR5YOwLpJnSHicxCSWoIJm2/j+KPsFs/q2JsL8etLvfC/GYGwMEB85VfW48sLCRjZyVGvyZBYIsO4H27jADElIPwFEJFjICVq/clcxQ2ysxXdlqVvlcpPqS2tsIo++TpaCPXeZMfkVOBhepnG4xO7uiAkpQSxuZUwFXCxaDBdxckpr8NpRYXlzVEdEJJSgrCMMgh5HLw+whcAUCeW4oliHohxQlO2fTy1YmCzN/7VDRLUiJtspqVq5fGhHRz0rk7ViiXYcJY2Ulg+zBf+zs8m/6iwsp6dP3pvrD/szIV6t5fL5VhzIlpFwCkzqZsL3hplWIgrgUAg/J3p6m6F6PVjcfaNQbA31922dC+lBIGfXcaqg4+bbUPyc7LA9pd648TyAejvbQexVIZdd9Mx9Jub2HknTeVmvbUIeVwsHuKDm+8Ow7x+nuBQdKfByG9vYsft1Da/x6jOToj7bCzbLdEcGSW1mPd7KFYfikCtWIq3x3TEmTcGoZu7FWRy2qzIVMCFiM9BYVUDlu4NR3hGGU6uGAgnS/3XJAD44WoSJv90B8mFTbM6XA6FBYPa4+KqIejpaY2qBglWH47E6wcet1hQUhSF6b3cceHNwehrQAu5TA78djsN2aV1+HC8v15x9OHxaCzZE46qev0imUAwJkTkGEiJel+yQgzYK26W9Q3+tbMzZf9cqKiaOFroP6HtDknXeMzeXAAel4OdCmOBF3q6w0ZRxdl5Jw1SmRwDfOzQ1d0Kv96mQ+Fm9fGAk2I+JrW4GsoLKYEe1qxgsTblG+T2puzlDwCValWOoR30V3G23UpFfmU9PGxNsOwZtakBtONcdYMEge5WeLFP8w5oxx/l4FRErtbnvOxM8b8ZgcQmmkAg/Kvo4maFsLWjcW7lILha6Z6rPBWRi87rLuGriwkQS/QLiR6eNti/qC92LwiCv7MFKuoa8dnZOIz9PhhX4wqMsrpvZy7Ehqldcfr1QeihaN/acC4ek36802Zra1MBD2smdMLlt4bAw9ak+RcAOP44B6O/u4WLMXnwd7ZUmaGpFUshkcphZcIHh6I7G17d9QDrJwdg07Suze47Ib8Ko767hT/vqTqwedmb4fCS/nh7dAdwORTOReVh3A+3cSep5W537jam2L+oH1aP7gBDMj4fpJfil5spWDXKD9313EdcjitA1/WXEZ3dNhtwAsFQiMgxkFql6gUANChCM5lVr5Jq3SsmTkpD+NUKJxhLE92tauW1Yq032J9MDkBGSQ2uJdCuJfMHegGghwEPPsgEACwe4o2kgircTCwCRQELBjYNOKYVq9o8VyutqOxd2Ffn8SijfML0tjfTqDb10eOln1teh1+DUwAAa8Z3emZhmVmltTj0kHaYWzOhU7N20enFNfj4VIzO53e80ocEfRIIhH8tAa5WuPfhSJxfOVhvZX7rzRR0WHsBe0LS9YoVJlT03MrB2Ph8V9ibC5BaXIPXdodh3u+hOivmLaWLmxWOLR2Ar17oChtTPhILqjDr1/t461AE20XRWjo4WeDWO8Px+RT9xjwMJTViLN37CCsPPEZ1gwRLh/rgwqrB6NXOBhKZHBV1jTAX8mAp4qGsthHL9j3Cw7RSXF09BO2bmdcF6Jy6eb+HqrTm8bgcvDHSD8eXDYC3vRnyK+sx7/dQfHYmDvWNUj1704TLobBypB8OL+mvkaGnjYq6Rmw4F49OLpZ4bVB76FsDnLzlDnbdTSPta4SnDhE5BqK+WsX8blqK+GiUylCn5wSiXLVpUOxHn9f88Uc57HbK9Gpng733MyCXA8M6OrDOYKciclAjlqKDkzmGdnDAzrtpAIAxnZ1UrKDTlUROVzcrpBQ1/dzFzUrn8ShzUCEWAGCAr51KBUvE58DfWbdj3E/Xk1DfKENQe9tnare85XoyGqVyDPK1R19vO73byuVyvH8sSkPUMnw9vZvKjBWBQCD8W+nsaomYT8fi8JL+erf7+FQs2n94HiEp+qsmXA6FOX09ceOdYVg2zAcCHgd3k0sw8cfb+OhEdLPzPobA4VCY1Yd+jzl9PUFRtOX0qG9blyujvu+X+nvh9nvD9VYslDkdmYvR3wfjWnwBfBzMcXhJf3aGprJeggaJDC5WInAougL08u8PsGFqF4PE1L2UEvTbdE3FDAiguzTOrhyEef08AQA776Zh6s93VdrcDKW3ly3OrxyM8QZesw88yMSD9FJ8Na2b3tbHT8/EYfm+R8R9jfBUISLHQMRqvb3MKoWQz2HnW7TB41CwUcqgYcSLUE8l4GREjtbH7c2FOPGYPpnN7dsOAH1TfuABLTxmB3miVixlq0DKVRwAyFRyXVF21NmzMEjnsSijbiUq4Kp+hm5u1jpNBLLLanEkTOH8NrbjM2v1Kq5uwInH9Pf51ujmZ2iOhGUjNK1U63OD/ewNTsgmEAiEfwtB7W2RtmkCfm0m52X2b/fR6eOLzea2WIj4eH+cP66tHoqJ3VwgkwP7QjMx/NubONxGO2gGa1MBNj7fFSeXD0QXN0tUKnJlXvr9ATJL2ub05mFrihPLB+Dr6d0M2r6oqgEL/wzD24cjUd0gwYJB7XHpzSHo1c4GDRIZ8irq4WghgpUJH7kV9Zi7IxSpxTW4+OZgg/b/xoHHWH04QiUzyFTAw4apXbFrfh/YmwuQkF+F57bcwfFH2S3+vFamfPwytye+eL4LhLzmbxujsiuw8UI83hvnj17tbHRudyEmHwO+vK4yG0wgGBMicgxEprb6www0inhcvYN0ZkKeSnuUVEa/jqejZSq1qFrDNhqg53ruJhejuLoBtmYCDFM4mMXkVCI+rxICHgfP93DDuag81IqlaG9vppE9czis6eSmbPs8yNde5/ErE5ur2lLA46p+Bh9H3SX2rTdTIFHMDPXW09JmbA4+yIRYKkOgu5VKIKs2SmvE2HghXufzhjiyEQgEwr8RiqIwJsAZKRsn4Es9syN1jVIM/PI6lu4Jbzao08PWFD/P6YkDi/rBz9EcpTVivHc0CjO3hxithS3Qwxonlw/Eh+P9IeRxcCe5GGN/CMaO26kaxjktgaIozOztgdA1Iw0a0geAY4+yMfb7YNxLKUY7OzMcWtwPbyty7/Ir6yGTy+GlmOHddTcdb+x/jHMrB2Hp0ObnV48/ysGo725pfG/D/R1xfuVgDPCxQ61YitWHI/HukZbn11AUhbl92+HU6wMNaqcrr23E+8eiMMDHDvMHeOndbug3N3FDbd6XQDAGROQYiHp2DFPZ4XAo6DtPqq96MG1quoY1dQ27u1mb4JhiBea5QFfwFfs5Gk5XccYFOMPaVIDDYfTPM3q7G3RD/sYIX4Nv3NVD3erUWrq87bW3cRVXN7BVnJUjn50jmVQmx75QelbpFT0nWYafbySjXEe7xIfj/eFuY6r1OQKBQPivwOVQeDHIEwmfj9PoFlDmYmw+Aj65hD33M5ptEevvY4fzqwbjw/H+MBVwEZZRhkk/3cFnZ+KM4sbF43KwZKgPLr05BP28bVHXSBsTTPvlbpszXJwsRTi4uB++MbCqk19JV2q+vpgAOUDP0CynZ2iq6iVIL6mFt4MZrEz4SCqsxrRf7sHD1gSnXx/Y7L7zKuoxfvNtRVt703fuaCnCnoV98dYo2kjgSHg2pmy5iyetCGr1d7bEqdcHYmxA85k6cjkdbZFRUoMvnu8CM4HuDpZXdz3E5qtJRqniEQgMROQYiLpYYSykG6Uy6JMIQr7q60wUv+S6hgAvxuRrfdxUwMW1eHqlg/Gkl8nkuKDY/vkebsgqrUVYRhk4FO28poyu9oFX9Vyk1ElUCzwtVbOnZLJ41Dn0MIutpvRrZibGmDxML0VeRT0sRTxM7KY/sDO7rBZ7QjJ0Pm+ISCIQCIT/CiI+F+smd0bIhyP0OnN+fDIG7T883+w8CF8hRK6uHorxXZwhlcmx824aRn9Hz7MYAy97MxxY1A+bpnWFhYiHyOwKTP7pDn65mdzmqs6M3h4Ifnc4/AyY2ZTLgV9upmD61nvIKKlBN3fVGZrUohqYCrhob2+GBokMH52IwdabKbj/4UiDOi/WnozB6/sfo1JJIHI5FFaN8sO+1/rB0UKIpMJqPLflDo6Ft7x9zVLEx7Z5vfDheH+D3NduJBZh680UfPmC/pnW768+wSu7HhCbaYLRICLHQNTdtJjTYXP2mXyO6lfM7KdeoilycsrrkKhjZSUquwJ1jVK4WonQVWES8CizDIVVDbAQ8TDA1w6XYmnBE9TeVsXRDQBOPtac87E25bNBooaQrtY3qz4kaqdlyFAilWHffVo8vNTfy+D3MgbMMOa4Ls4alTh1fryWpDF3xfD9rEDipkYgEAhacLEywakVA3F8+QC924367haW7Alr1uXL1doEW+f1wp8LgtDOzhT5lfVY+GcYVh18jNIa4wSJzg7yxNXVQzGqkxMapXJ8fTERM7eHtHk2xNPOFBffHIJ3x3Y0aPvI7Ao2vNOEz8WGqV2xc35v2JoJkFdRj8LKegS6W4HPpXAhJh8vbL2Ht0Z3wNcvNF81OhedhxH/u6lh18xUzQb72aO+UYa3j0Ri3amYZu9l1KEoCkuG+mDfa/30GgwwZJfV4f1jUVgx3AfjAnSbGNxOKsagr24gq7Rtc1MEAkBEjsHYmKpaPjOtWg0SmcoQvzrqN86MdXRZjeZKxfUE3T2phVX00P+ozk5se9n5aFrUjOrkBCGPy1aBtJ1ALsTkaTz26XOGWWEyFFWpGg+UqVVylA0WGG49KUJuRT1sTPmY1Ew1xZjIZHJW9E3q5qp324LKetacQBvPBepOcyYQCAQC0NPTBqkbJ+CrF3TP61yKLYD/xxdxIVrzeqTO0A4OuLhqCBYP8QaHolu5R313C6cjc41iPexkKcJvL/fCN9O7wVzIQ3hGGcZvvo39oZlt2j+XQ2HFcF+ceX2Q3nsDhhrFnMxbh2jjgBH+Tji/cjCCvGxRI5YiMrsCHZ0t4GQpRE55HWZuD0FprRhXVw9pdt/F1WJM3nIHR8KyVB63Nxfiz1eDsErRPr47JANzfruPwsqW22z397HDuZWD9RoMMNSKpXjrUCQCXC31tq5X1DVi8Nc3EJau3QSIQDAUInIMxEEtvJMxHqiqb4SlSHfmjboVtJMFXWHR5tl/U4vI6eFprfLzqE5NfbBMMOfYACcUVTUgPJPOrBmjJnLyK+oRk6PZd6y8L0MoVBI5Ij5HY6jU0kQzT4GZMZraw+2ZVkMSC6pQXC2GqYDbbIvcn/fS0SjVflHbMLWLQRcqAoFA+K/D2DdHrBuNIXqCoZfte4Tun11utjJjIuBizYROOL58IDo6WaC0RoyVBx5j0e5wFLTihlwdps3swqrB6NveFrViKdaciMbCP8PanKvT1d0KMevHYloPwxbJTkbkYsrPd5FcWAVnKxH2L+qL14f7gqJogyE+l4MubpaQyuT48kIC/nfpCcLWjjLoOv7u0SisPRmtUq3hcCi8NboDfn+lNyyEPHYOKjyj5cLCyZI+3pm9DXMf/fbKE6QX1+C7mYEw0XNfMH1bSKvc4AgEBiJyDERd5HAVbWjF1WJYiHg6g68a1ErzTpZC9nWNSlUeqUyuYV1sZcJHF9em/Bohj4O+3rSLS255HVKLa8ChgP4+9riXUgy5HAhwtYSrWnCXtgqRCZ8LMz0hb+rUiaUq2TF8LkfDcIGr9iXUiiW4Ekf3Uk/p/myrIXeT6dDSPl62EOixvGyQSLFfEaSqjenEMppAIBBahLWpALsXBOHYMt35OuW1jej5+RUc0HP+ZejuYY0zbwzCm6P8wOdSuBpfgDHfB+NslHajnpbiYWuKA4v6Ye3EThDwOLieUIgJm28j+ElRm/ZrIuDiu1nd8f2sQIO2Ty6sxnNb7uJURA54XA7eGdsRuxcEwc5MgOyyOqQV1WBIBwfwuRQuxuZj1vYQfDC+o0H733s/EzO2h2iIw5GdnHD6jUHo4GSOwqoGvPjrfey9r3s+VRdCHhdfvdANH0/qbNCczunIXPx5Lx2/zO0JVyuRzu1WH47EVxcTiCEBoVUQkWMgDuaqv4RMObuoqgEcDgULHYKhukGiMtBoYypgHdaUk4oT86tQrVYZ6e9tp1JF6Olpw86WMDfx3dytYWXCx71kOoRtoJahRG0i58MJ/jo+qXbU+6gbGmUawk7dpe1afCHqGqXwtDVFoLthYaPG4qGizD3AR38V50ZCoU5Htem93MksDoFAILSSXu1skfTFeKwe3UHnNh8ej0bHtRc02qHVEfA4eHNUB5x9YzC6ulmhoq4Rr+9/jLcORagM2LcWDofCa4O9ceb1QfB3tkBxtRgv73yAry4mqCxItobne7jj6uqhsDNgBrZWLMWqgxFYdyoGDRIpBvs5sJWmGrEUwU+K0M/bDo4WQqQU1WDKlrsQ8bg4v7L5TJ3IrHL033QND9QWVNvbm+HE8oGY2NUFjVI51p6MwbpTMZC08HNTFIWFg9pj16tBsBA1v4gamV2BD45H4avp3fS2u229mYIle8ObneciENQhIsdAXKxVRQ5T1WBK2vbmQo3XAIBMDpWSPIdDoZ3CBz+5qMltJkxLibivt62KkGCqOACddAw0ZdzcS6VFT3+1m3qZTI4HaZop1MM6OGo9Xl2ozxaJpTKNyo16ltANhbga39X5mefLxCmyAhiTBl0ce6R7Fufl/u2MekwEAoHwX4PP5WDlSD/cfm+4TgvhBokMfb64alAFoaOzBY4vH4A3RviCQwEnHudg/A+3EZqqeZ1rDR2dLXByxUDM7Us7nW29mYJZ20OQXda2QXhfR3PceX+EwYHSu0MyMHP7feRV1MHRUoS9r/VlLbtvJxXDxUqEAFdL1IilWLbvEU5F5CB87Si46KmKAPQ9ycztITj8UHVOx0zIw5Y5PfD+OH9QFP3+C/8Ma5WAHNrBASdXGJanU1DZgGV7H2H5MB9M7Kp7bvdKXAGmb7unYXhEIOiDiBwDYXJpGJjqTGFVA+rEUrjrsE+mt1EtD3dwsgAAJBc0iZzwjDKN1wV6WMNCad5HOdyTCfPs7WWDvIo6ZJXWgcuh0EctaDOpsBqV9RKYql1cPGxVW9qaQ5vzioWJ6ixSdX1TJUomkyM4iRZeQ/X0Zj8NquobkVVKW2Z3crHUu91NPQFkzQkkAoFAIBiGh60potePxbpJnXVus/ZkDHp+fqXZG2s+l4O3x3TEkaX94WlripzyOrz42318eaHtVReAdkH94vmu+HlOT1gIeXiUWY4Jm2+zZjatxUTAxTczAvHdTMPa1yKzyjH5p7sIzygDn8vBusmd8d3MQAh5HERmV6CyvhHDFcHg24NT8faRSFx8c4hBC3TvHYvCpvPxKm1gFEVh2TAfbJ3bCyZ8Lm49KcL0rfda5XTm42COk8sHop9380Gp1Q0SLNkTjuH+jlg8xFvndjE5lRj53S3kVWiPxCAQ1CEipwUwJxOArmQIeRzI5UBacQ08bHSLhkK1MryfE+0TrxzEFZeragzA41Do7GKp4vLC3HRX1DUirZi2uuzmbs2aCvg5msNcrW2OqRB1V8sxaGllRV3kAYC1msgpr2u6MMXnV6K4ugEmfK5BrivGJL2YPiHbmwtho6c94F5KCRqlcraypsxoJRc7AoFAILQdDofCgkHtcfu94Tpbt0prxOi2/jLuKBbJ9NGrnS3OrxqMmb3dIZcD226lYOb2EJ25cC1lYjcXnF81GIEe1qisp2/Ev76Y0KZMHQCY1tMdZ98YZNC2xdUNmP3rfRxV5NlM6+mOY8sGwM3aBFmldXiQVoppPdwg4nNwM7EIM7bdw6LB3tg6t2ez+94enIole8M1TITGdXHGkaX94WQpxJOCakz9+W6rDAmsTPn4c0EQngvU73AKABKZHO8ciYSVCR+fTQnQOddTXN2A/puutyrIlPDfg4icFtDZVbUqIFGc6FKKqnUGYQJARrGq976/M13JiVEImwaJlBUtDB2cLCDic1VO1kxVJyaH9r13tzGBrZkA0Yqfu2ipPDzKKGe3bQsmWtoMzNV6bpUtpZme377ets1m1BgbZpXH1Vp/2f6WYqjUWov19bx+pFWNQCAQngYetqYIWzsKn0zWXdWZ93solu0Nb7YyYy7k4evpgdg2rycsRDw8zizHxB9v43qCcQJEPWxNcWRJf7ZV7JebKXj1j4cor21bZk8XNyuErx1l0LyqWCrDO0ci8cW5OEhlcnRxs8Lp1weiv7cdasRSnIjIwfguLnC0oEXJlJ/vwsFCiHMrmxdSV+IK8MLWexrVkS5uVji1YhACXC1RUiPG7N9Ccd4A6291hDwufpjVHUuH+hi0/TeXEpGQX4Vf5vaCiK/7FnXM98Eas0UEgjpE5LSArm7WKj8zqzlJhdXwcdCd4ptSpCpgenrSlY3E/EpU1TcitaiGFUwMjBDSNowZrzZvEsuIHFfN1qzEAnpbddv/WrFEY1t9qLe7AQBfbaklp6zpJBmpaKfr4fFsqzgAWPcY9UBUdUIUc03ZWkrxvZ9x9YlAIBD+S1AUhVcHtsfV1UN1bnMhJh9+H10wqF1qXBcXnF85GN3crVBe24gFf4Rh04V4o7SvCXh0q9jmF7tDxOcg+EkRJm+5o9GB0VLszIU4umwA5g/wMmj7326nYcEfD1FZ3wg7cyF2LwzC7CBPyOX0bFJ3D2v4O9NW23N+C0VifhVC14zU2I9QzXE0Ib8KY74PZu8tGJytRDiytD/GdHaCWCLDiv2PsCckvcWfk8Oh8MF4f3w2JUCnE60y+0MzcSYyF3sW9oW1qe6IjpnbQ3BDT8s5gUBETgvoqZZZwxCVXa5R5VEmRclgAAAcLUXwsDWBTA48zixHUmG1xmuYgb2GxqYTNNO6lqqo+jDCKiGfLtuqz5/IZHIkK/atXr5nXmMofC5HY2hUvWKvnBYdqUhZDvR49nMtjFuaPiebitqmlr8SLVkNLbHXJhAIBELr8HU0R+KGcZjV20PnNoO/voHTkc3bRXvYmuLI0v6saNh+K7XVIZfamNLdDceXDYSHLd0qNm0rbffcFvhcDtY/F4BvpnczaPtbT4owY2sIcsvrwOdysPH5LlgzgTYLuBxXACsTPgb52kMslWH14UjsC81E3Gdj0VExCwzQRg/qts1V9RKM33ybXfxjMBXwsHVeL8zrR4upj0/F4tvLia0KTH25vxe2zeulIbK0cS46Dz/fSMbuBUFs9IY2Xt31EBe1hJ0TCAAROS3CUUdlICKrHK5WIp0rDk8KqjROCL3b0cN4YemlWl1bvBQiR9mxjHFpS1fcnHvZm0EskbFlZnUnk6yyWtQ3yiDgcZCoJmpiW7EC5ax2UqxTs3NMU8zCVNU3CYhAd+sWv09bYQJY9dk/My1+nlraDPXl6hAIBALBuAh5XHw1vRv2LAzSuc3KA4+xZE9Ys7bGQh4X658LwNa5tGnAw/QyPLflLttd0FY6u1rizOuDMKSDA+obZVh1MALftfKmX5kZvT1wYFE/g7ZNLKjCtF/uISG/EhRFYfEQH2yd2xMiPgehaaUoqmpgQ0h/vJaEz8/G4+zKQXixT5OQzK2oh7eDpvvZ7N/ua7SlcTkUPp/ShbUC/+l6Mj48Ht1ii2kAGBvgjL2v9dUZu6HMzcQibDgbj13zg7TOzjIs3fsIJx6T0FCCJuRuroVoSxcur21EekktAnRUc4qrxcguU62k9PemrZ5vPSlCrpYhSS87+uSjbFqQXkKLCEbktLc3Q055HWRyOtxTPbA0VdEm521vplGteNiKXlb1kFHlyg3Q1EbXNPgv0Dv4/7RokNDiS99qUUI+faxd3DT/zob4aWYNEQgEAuHpMtjPAeFrR2lcyxguxRbA96PmM3UAYHxXF5x+YxB8Hc2RX1mPGdtDcCzcODfC1qYC7Jrfh50z+fF6MlYdjGhzjkt/HztcXT3UoEpHfmU9ZmwNwT1FZt64Li44tLg/7M2FSCyoQmhaKRYMbA+KAg48yMTyfY+w/rkAfDi+KSMvtagGPlqEzvJ9j7BbrS2NoiisHOmHjc93BYcCDj7MwtK9j1r1mft42eLA4n6wNeD+4EF6Kd4/FoVfX+rNtvFr461Dkdgf2nywLOG/BRE5LWR0Z+35Mg/TS9HLU/ccR4TaKtIwhVNbZHYFohStXco4WQkhlclVBFBcXiUapTLkKUrvnramrNDwtDXVcAPLU4SNullrmg7cTy1p8cqTq5Xqfp4UVKt48ueU16GoqgFpJU0i7K9E36djerzVPxPw11SfCAQCgUDPqdz/cCReHeilc5s+X1zFo0zN2AV16JDLARjViZ4peftIJDacjWtVBUIdrmLO5KsXuoLHoXA6MhfzdoSipLp5AaYPX0dz3PtghEp7mS6qGiR4ZdcDnHxMt8wFeljj5IoB8HagF0BPRuRg+TAfCHgcXIkrwLwdoZjVxwObX+zO7iOlqAZeWqok607Faq1Qzenria3zekHA4+BqfAFe+zMMdeKWC50ublY4vKSf3lY0huicCry+/xG2zOmJHjrGBgBgzYlo/H4nrcXHQvj3QkROCxmiI/PldlIx+nnbaX0OoGdvlHG0FLHGAeoih6IAW1MBSqobVAwJ4nIrUVYjhlwOcCjA1kyAfIWQ0eYklq9oY7NUsnpePMQbAh4HhVUNGo5uzdFByyqKr6Oq4UJkVnlTO53dXyNyTAR0GVyfuUKWorIm1OLe0sUAtxsCgUAgPB24HAqfTA7Ajpd769xm2i/3DAoPtRDx8etLvbByhC8AYMedNLz6x0NUtSLkUhuz+nhi94IgWIp4CMsow/O/3GNnYVuLnbkQp14fiNGdNTtH1GmUyvHmoQjsuJ0KAHC3od3gurlbobRGjD/upmPZUB9YKI5vxrYQ9Pe2w96Ffdl9pJfUal0M/fF6Mjaej9cQOmMDnLF7QRBMBVzcSS7GK7seoLqhZWZGAODraIGjSwcYlNuXVFiNZXvD8cOs7npjKT4/G4c/7hKhQ6AhIqeFuGhZ+QeAO0lFCPSwhkBLngwAhKZppjEP99deFbI1FYDH5ajkzgBAXG4FiqvptjNbMwG4HApliiF7bW1hTCVH+Zg6u1iyFacbiUVa318X2krF6s41D9JLWXczFy0nzWcB4wRXq2d1iRGHHC1WLw7mza8sEQgEAuHpMqqzE269O0zn82tPxmDlgccqgZba4HAorB7TEb/M7QkTPhe3k4oxc/t99jrQVgb42uP48oHwtDVFZmktpm+7p9G90VJEfC62zeulMkejjw3n4vHjtSTI5XLYmQuxf1E/DPChLaa33kzBwkHt4WQpRFJhNWZuD4G3gxnOvN5kMZ1TXqe1++K322n45HSsxnfcz9sOexYGwULIw4O0Urz8e2izIa7a8LA1xdGlA7TOB6mTVFiNRbvD8P3M7nodUNefiTNIABP+/RCR0woWDW6v8VhZbSNSiqo1QjcZYnMrNfqIJ3Z10bqtnTktWCoUIodxao7Pq2Jd0uzM6BtxxqvfRkvWCzOHw+U23chbmfLZ1aGWpjd31CJyCipVP9OtxCLWIEGfu9nTxFbxXZRU684xqGqgv1ttgZ8WIuKsRiAQCH8H2tmZIe6zseimo8J+OjIXfb64alDL1ISuLji0pB/szYWIz6vE87/c1TDlaS2+juY4sXwAAj2sUV7biDm/3Tco0FQfXA6FTdO6YtkwwzJmvrvyBF9eSIBcLoe5kIddr/bB+C7OEEtl+PFaEl7u7wV3GxOkl9Ri5vYQWJvyce3tJgvvtOIarQP+u0MysOZEtEYIaq92tti3qC+sTPh4lFmOeTtCW5Uf5GQpwoFF/QxqcX9SUI3Fe8Lw/azu6OOlW+isPRmDww+zWnwshH8XROS0gkndtKf3Xo0rwIhO2qszAHAnWbVy0tHZQmvfLWNfzFghd3WzgouVCGKpDNcTaE94K4WTGxPAqW2Aj0kxVu4/tjbhY2wXZwD0HJEhA5wM9uZCDTeytOIaFTGTWFCFOIUBgSFDhU8DxiBBm6EDQ3U9/d0IuJoi51mHlxIIBAJBN6YCHk4uH6gzT6akRoxO6y6iTEscgDrd3K1xYjldOcirqMf0bffY4f22YmcuxP7X+mKwnz1qxVK8+seDVgVoKkNRFN4f569iGKCP7cGp+PhUDGQyOYQ8LrbM6YmZvd0hkwPfXk7EnL6e8LIzRXZZHWZuDwEF4OY7w9jXZ5TUwt3GBFy1HLyDD7PwzpFIjXmmbu7W2L+oL2zNBIjKrsDcHaGoqG15RYcROtrmg9RJyK/Cot1h+G6mfqHz3rEoHH9EXNf+yxCR0wp0rSidjc7T20N7S0t72JgAze1FiptsRqRYiPisG9tNRfAVY49c00CvXqln2ABAjWImRXmux8qEDzdrE3Rzt4Jc3vJqTlB7W43H3GzUXdfooX5t1aVnATOflFNep9NcoV6RP6StklNe17YkawKBQCAYFw6HajZPpsfnV7RGMqjjYWuK48sGoI+XDarq6eH9izEtuxbqwkzIw45XemNiVxc0SuVYsf+RUVy/lgz1wVcvdDVo2733M/H+sSjIZHJwORS+nNYNs4M8IJMD31xKxKw+nvB1NEdeRT1m/XofEpkMt98bzr4+u6wOzpYiDZvnE49z8N7RKI3WtQBXKxxc3A/25gLE5la2ekbH2UqEA4v76bWLZkjIr8LyfY/w85yeOjMMAWD14UiciyI5Ov9ViMhpBRRFYclQb43HU4tq0NAo02rJCADXEgpZe2OGid00W9aYYXhGnHA5FPr50CJHfc6GmYnha7GcrG3QtFJmMmAmKd73aAstNRmRo1y90dUW9lflzbha06tQtWIp+32pw6xSaUtfbkl1i0AgEAjPjhm9PXBose48mUFf3UCcATlw1qYC7FnYFxO6OrNixFhZK0IeFz/O7oE5fekAzTUnojUsmVvDrD6e+N+MQIO2PRKejY9OxkAul4PDofDF1K6Yqziery8lYHovd/g7W6CoqgFzfguFTC7H3Q9GsK/PKa9DO3tTjfy/449z8PGpGI0FxA5OFtj7Wl9Ym/IRkVWOhX88bJXrmouVCQ4s6meQGUF0TgXePBSBbS/10msvvWL/I6NV6wj/LIjIaSXPK4K21DkblYsxAc5an6uql2j06DpZaLqiMaJEpiRyBvmqZreoCyE+R/OvUizVrFbwFNs938MdPA6FiKzyFvUkD1QcR5lS321OeR04WsQCT0sr2LNAxOfCT+H6piv0lDlebUYRmaXNrwQSCAQC4a+hr7cdLqwarPP5CT/exv1UTbMfdUR8Ln6a3RPTe7lDKpNj9eFIow2sczkUvpjaBYuH0Aui607FGkXoTO/lbrDQOfAgE+tPx7JCZ8PULnipXzvI5cBXFxPwYh8PdHSyQKFC6ADA/Q9Hsq+PyamEv7MFTNSCtfeFZuKLc5qua/7Olti9gDYjCE0rxZK94RoLu4bgam2CfQv76cxLUuZeSgk+PhmDP14N0tvqNmdHKGJyNOM6CP9uiMhpJf7OluBruYk/8TgHzwVqn9kBoFE2lWlpp2IeYgQMh6Lgam3CWk4DAFchXJhttAkKplqh3EPLaCEHCyFGKuaHDrVgOM/N2gQBrpaQyVXd1ry0DAzytCmfZ0RnRTBrbK72k5qIr3vuJjyj+fwFAoFAIPx1dHKxVGmxUufFX+/jdlLzDqJcDoWvX+iGV/rTN/9rT8bg1+AUoxwjRVH4cLw/2/mx7lQs/ryX3ub9tkTo/BmSgQ0KQUJRFD6bEoCXFZ/183PxeHWgF7wVweLzdoSCw4HK93o/tRQDfe01uh523EnD91eeaLxfN3dr7Hq1D0z4XAQ/KcIb+x+3KpfI086UFkwGGAFdii3A91eeYM/CvnC21Fw4Zpj00x2NEHPCvxsictrAF89r9sfmVdQjr6JORZAoczmuQCW/RdvEiLotM8NYpfkdZhvmvKPuegI02SPrOr+82McTAHA0PIud/zEEZu7IXKlfN7VI88TRKG1Z2Kgx6ab4/nUJFsYUQdvITlg6ETkEAoHwd8fD1hRha0fpbI1+6fcHuPWkeaHDzPswLmYbzycYLVSSoih8MM4fS4fS+/7k9LMXOr/fScN3CkFCURTWTw7AtB5ukMrkWHc6Fq+P8IWbtQnSimswb0cozIQ8XF3d5Lp2Nb5A6+Ltj9eTtQrC3l622PFKbwh4HFyOK8AnimpSS+nkYomd8/tApCXPTp1DYVnY/yATe18Lgo2ixU7b66b+fBeFVcaxDif8/SEipw1M1uGyduBBFl7oqb2drbpBolLNEWo5Odc10uVd5heUKfeO69LUBsfMwTBl5HqJppJhVl6UAy8bGpu2G9LBAe3tzVBZL2lRNWdMZ/o4onMqVMJAHdVKy211lWkLAxRtdQ/TS1HfqFkuZ8rg2i6OpF2NQCAQ/hnYmwsRtnaUzhX/V3Y+wPWEgmb3w7iYrRzpB4AOlTRW6xq9744qQscY8z/Te7nj8ykBBm370/VkVlxxOBS+nt4Nozs7QSyR4eOTMfhgvD+cLIV4UlCNhX8+hJu1Cc6vbGoJPBWRi9cGacZnbDyfgFMRORqPD/S1x48vdgdF0e1tv9xsXXWsj5ctts7tZVBnyNabKQhLL8Pv8/tAyOOgvlGm8e+irLYRL+140Kp5IcI/DyJy2oCJgIvpvdw1Hr+eUIig9nY6V5cOKgkKU4Hmifl+aqniOdVQS1/HpvawB+n0NqwQ0nIjzwgg5QUU5YBMLofCa4rMn9/vpBlcUu7kYgE/R3M0SGToq+S2Vqg2sG+slbDW4OdoDkcLIeobZXiUqVmZsVcEftaIJRr9xgBatepEIBAIhGePpYiPux+MYFfw1VnwR5hBrWsA8NYoP7a9bO3JGBwJM07WCiN0Xh3oBQB450gUrsU3L76a46X+XnhzlJ9B264/E8susvK4HPw0uwcbGLruVAw2TO0KKxM+HmeWY+XBx+jobIHjywewr99xJ03re606GKF1sH9cFxesn0yLsG8uJbbY6IhhuL8jvnpBt6ueMmtPxqBOLMUPs2iBVVUv0fh3kVhQhbcORTQbIkv450NEThtZNVLzF14qk+N0ZK7O2ZzwjDI8KaCH/bkcSutNtkQqg4lCACkLk15qKb/MbIm2VQlLEf2Lrdz+ptwqBwAv9HSHnZkAOeV1OGdg5YWiKMzsTacw6xrsZ/irBv0oqsms4Xp8ocbzTOhYalGN1gCyHD0ZOwQCgUD4e2Ep4uPWe8N1Dqu/9PsDPNay4KUO017GZPK8fywKZ6NyjXKMFEXh44md8byiVWz5vkd4kFba5v2uGumHV/q3a3Y7uRx461AE7qXQgkTE5+LXl3sj0N0KZbWN+OJcHL6Z3g0CHgdX4gqw/nQsenhYY/tLvdh9/HA1Set9z8s7HyAhX/N+4JUBXqxo/OBYFIINaB/Uxgu93LFyhG+z20lkcizdGw5fR3M2W6i8rlEjt+9ibD6+vZLYqmMh/HMgIqeNeNiaonc7zTCqffczMENLlYdBuQxuJtQUOacjc2GueLy6oSlYa6FSufhJQRUsTWghU1GnGb5laUKLJOXVCnXvehGfy64s/XA1yeBqztQebuAq3Nn0hZRtuZ5s0P6eBkzo6bnoPI0VGx8Hus0uubAanVwsNV4bktK8Mw+BQCAQ/j5Yivi4/vZQna1rz/9yD8mFzbuJUhSFTyZ3ZrNlVh+KRKgBbm2GwLSKjfR3RINEhoV/PER8XvOW180fb4Be0yMGsVSGJbvDWVdVcyEPO17pA3cbE6SX1OLX4FR8/UI3UBSw534GtgenYmyAM9ZMaLrOb76WhCVDVGM0JDI5Xtn5APlaYhveH+uPqd1dIZHJsWLfIyQXVrfqc741ugMbf6GPqnoJFvz5ENN6urO22VX1jSrRFwDw840UEhb6L4eIHCOw/jnNntiqBgmisisQ5KUZngkAh8OyUKpIZ7bWEpr57eUnsFBUYpRzaDo4Nc3AHHyQBXtz+rVF1ZrZLkwlR7ltTlsGzPyB7WFrJkBacQ2OGfgL72AhxEh/2p0tQ88My8XYfK2rO8+CoR0cYC7kIa+iHo+zVFfwmFkiWuRo+uv/9BeKMwKBQCC0DgsRH7fe1e26Nuq7YIMq9RRFYcPUrhgb4ASxVIZFu8OQVGB43II++FwOfp7bE0FetqhqkOC1P8PanM/G4VD4ZkY3vcGYDFUNEiz88yFKFPcNDhZC7JrfBxYiHsIyynAtoRBrJ3YGAHx5IQHno/OweIgPXujZtHC7PTgVc/p6quy3oLIBS/aGa8zB0sIukA5fbZBg8e4wVNRqLsw2B0VR+N+MQPQw4DNmldZh6Z5wfDypM4Z2cECjVA6xVAYrE9XWtdWHIxGe0fZqGuHvCRE5RqCLm5XG0D0A7Lybhrn9PLW8AqhvlGFPCF3N0fbanPI6nFX0ztaKpWwFxkZJEO1/kMG2qxVrETl25pr7TdKygmIu5GG5wlVm89UkrYP62ligqCodC8/W6/Ly3WVNm8lngYjPZZ3gjj9SHYz0cTCDCZ+L6gaJVjvpzNJarY51BAKBQPh7Y2smUAm2VGfgl9c1uhq0weVQ2PxiD/T0tEZlvQTzdz1EYaVxnLlEfC5+e7k3a9+8eE+YwddeXQh5XOx4pY9B+TLZZXVYtu8RxArTIj8nC2yfRw/4n4nMRXmtGAsG0tf4tw9HIj6vEt9M74YA16bOh7SiGgzt4KCy38iscqw5Hq0x1yrgcbB1Xi+4WomQWlyDNw62zlqa+d7cbZoPCw3LKMOm8/H4cXYPeNmZoqpeAiGPoxH/MfvXUOK49i+FiBwj8dvLvTUey6uoR2VdI3wcNGc+AODPkHTUiaVaRQ4A7LrbNLjPrPJYmwpY17T6RhmuJ9DzJsVVYo3XuylOAsVKlSBdMzLz+rWDi5UIuRX12HnXMMOAvu1t0dXNCg0SGXLKdK+MXY4rMCiY7Wkwoze98nTycY7KRY3H5aC7hzUA7fbbAMnLIRAIhH8qbtYmuPLWEJ3P995wxaCFLBGfFg7tFWJkwZ8P2yxGGKxM+fh9fh922P/do1FtNr2xNRPgwKJ+Bm37IK0U607FsO85wNcem6bR0Rg/XU9GHy8bDPazR12jFIt2h6G8rhFHlzYZEYSkliDQ3Qrt1EI4jz/OwY7bmvcR9uZC/PZKb4j4HAQ/KcKXFxJa9RntzYXYNq+XVndadf4MycC1+AL8+nJvmAm4KKxqgLuN6vGKpTK8vq91oovw94aIHCMR6GENPyU7ZYYtN5LZxGN1SmvEOPQwE046wquq6ptuypnVIy6HgqtV0wrGzUR6iC+3ok7j5OhuTW+nXJp/nFmu9b1EfC7eHduRPubrycirMKycz7iz7Q5Jx96FfXVu+8mp2L/EyaS/tx18HMxQI5bixGPVag5j4hCZXc6aFChjLPtQAoFAIDx7/JwsdN7w1zfKsGRPmEH7sTUT4I9X+8DWTICYnEp8qKVS0Vra25thm1IFxRit0r6O5nqvx8ocfJilktszo7cHW8F592gU3hzVAe3sTJFdVocV+x6Bx6Vw5/2mdsAfryeznSDKfHE+XqvJQICrFb6d0R0A7dZ2OrJ1pg5d3KywUUtWoTY+PB4NsUSGb2fSHSdpxTUqrf8A7Vj79SViRPBvg4gcI/LL3J4ajxVUNqCsthHeOqo5W26kaJ3JmdJddYBQee5FfdUEoAURM+PD4MqInLJatjyrr0T/fA839G5ng1qxFBvPG7bCMqGrCzxtTVFSI0ZkdrlWoQfQlo2GzvsYE4qiMK8f7Tqz+166itDq72MHAAh+Usz+WZnTkblGW7EjEAgEwrOnv4+dzpvhq/GF2Glg1EE7OzNsmdMDXA6FE49zsOtuulGPccPULgCA768+abUDmTKD/OxVzAL0seFcvIrz3IcT/BHU3hbVDRK8dzQS38/qDjMBFyGpJfj6YgLcbUyxf1GTiHr/WLTWlvWVBx9rXTCd2M2FFUYfHotCalHrjAhe6OWOeTpGApRpkMiwdG84+nvbY8Vw+n2zSus07st+DU79S/P9CMbnqYqcTZs2oU+fPrCwsICjoyOmTp2KxMR/r1L2c7LQajSw/VYKXh2oGaIF0LM02k5onramKkInRWmWxtOWFjnq4VjpJTUqP3vY0iKnsl6C3u2ajktXiZ6iKHw6JQAcCjgTmcvaTOqDz+Wwvvnbb6VgyxxNocfw7tEoDSH2LHihlzssRDwkFVbjclw++3gfL1uYC3korm7Q2TJ41Qg5BgQCgUD465jT1xMv67BY/uxsHOKaiUJgGOBjjzUTOgGgKxXGdOF8McgTs4NoJ7BVBx8j1wgxBosGe2NcgHOz20lkcry+/zHKa+nrM5/Lwc9zesLZUoSUohr8FpzKVkF+u52Ga/EFGOBjz3Z/AMCnp2M1rKXLaxux8oD2NrDVozsgqL0tasRSrNj/uNULiusmBRhkRJBdVoc1J6KxenRH9Pe2Q12jFA2NMjYzj2H5vkfILCGB4P8WnqrIuXXrFlasWIH79+/jypUrkEgkGDNmDGpqapp/8T+UH2f30HisrLYR2WW18HfWdPEC6L5WddJLavHRxE7sz9uDU9k/eyoqOcM6OsJaKeQqtUj1ezUV8Fih42TZ9Ius7+QZ4GqFuX3pi8Ga49EGpQJP6e4GX0dzVNZLcC4ql/XE18aGs3HN7s/YWIr4bObBT9eT2TYDAY+DIR3oNrXM0lqts1M/XE16ZsdJIBAIhKfDukmd0dFJ+zV4wo+3UVlvmNvXgoFebM7NyoOPWYcyY/DJ5M7o4maJstpGrNjfZArQWiiKwtczusHVSntLvDI55XV4+3Ak2+3gYCHE1nk9wedSuBCTj9KaRjZu4u0jkcirqMPyYT5s21dVgwR1jVKN1u+H6WX4/qqm+RATRmpnJkB8XiU+a+W9gYDHwS9ze6rcC+niXHQejoRl4YcXu8NWkQ/oZq353axspSkC4e/HUxU5Fy9exPz58xEQEIDAwEDs2rULmZmZCA8Pf5pv+5fibCXS2p+660462zZlCE/yq+BoIcKoTk7sY8xsDZPxklNepzLv80SLvWVHJ9oJRbl4k9RMTsA7YzvC2VKE9JJa/O9y85U3LofC6tEdANCrPPP66v6cxx/nGKUU31JeHdgepgIuYnMrWbMGABjpT3+/56LzMLGrpv9+cmE1orLLn9VhEggEAuEpwONysGdhkM7nJ/14x6A5G4qisPH5rvBzNEdRVYNRzAIYRHwuts7tBUsRD48zyw26/jaHpYiPna/2MWjbawmF2HGnaUG1h6cNW6357GwspvdyR1c3K7ZCI5XJcUTJiODX4FTM69dOw6b55xspuKXluu9kKcL3s7qDooD9oZmtDl11sTLBl9O6GbTtp2fiUFXfyFamIrVEfURkleNHEiPxr+CZzuRUVNDOXra22rNjGhoaUFlZqfLfP5FVozTTgMVSGW4kFGJYRwctr9AkpagaYokM6yZ1Zh9762AEZDI5OivCK5MLqzAnqKkf9TctbiYdnWlBRCl1tinf5GvDyoSPTS/QPcw776bhYXrzHvLjuzijdzsb1DVK8fWlRKxVqkKps3hPmMGrZsbC1kyAlxTtCl9dTGBXacYEOEHI4yC1qAbt7LTPTWlziSEQCATCPwtHS5FOoZNZWqsygK8PEwEXP87uAQGPg+sJhQa/zhA8bE3x7czuAIDfbqcapSXO39kSn03RzPPTxjeXElXa914b5I3Bfvaob5Th7cOR+HZmICyEPDxML8NP15NhZcLH8eVNQmfp3nB8M11TcLxzJBJlWtrVh3RwwIphvgCAtSdjWm3RPa6Ls0ZujzbqGqV440AE+nvbsYvEiQVV6OKmGgr+47UkPEgj+Tn/dJ6ZyJHL5Vi9ejUGDRqELl26aN1m06ZNsLKyYv/z8PB4VodnVIQ8LnZosZS+llCIAT52UBul0YpEJkdKUTXbbgbQ7h+7Q9LhbmMCSxEPjVI5csrrMFVpdkddPPg707+46cVNrWx772c2+/7DOzpiRi93yOXAe0ejUCvWnylAURTWPxcASjHP46vDgACgXW0+PhnT7DEYm+VDfWFtyseTgmocDqNNECxEfIxV9CxHZpdrnak6HZnb6sFIAoFAIPx9GOznwA6fq7P+TJxBQaEA0MnFEmvG04P9Gy8kaO2kaC2jOzthdpAH5HJaHBhjUfClfu00Mm200SiVY/XhCDRI6FZ1DofCtzMDYWcmQEJ+FQ4+yMIXCpvpn28kIyanAj09bVS+0z9D0rFMraOlqKoBH5/Sft1fNcoPXdwsUV7biPePtb4y9vHEzjojO5SJz6vEj9eS8M6YjghwtURFXSO4FKXR8rZsbziqnvGCLMG4PDOR8/rrryMqKgoHDhzQuc2HH36IiooK9r+srKxndXhGZ1RnJ7hZa4ZV7QvNxMzehom3+LxKUBSl0uP65cUEpBbXoLMikCsutxIblJxjPjwerbIPZiAvNrdSJTzLkJPI2kmd4WIlQlpxDT45Fdvs9l3crDBbUVn68kKCTkc5ADgVkYuTapbOTxsrUz5WjqCrbN9dSWSd5l7oRWfpnI7MxbSeblpf+z2ZzSEQCIR/BatGdoCdmaarKQAM+uq6wXEHrwzwwgh/R4glMrx3NMqoAdJrJ3aGp60pcsrrsP5089ff5qAoCt9M7wauAausCflV2Kx0zXO0ELHuabvupcHNWoSJXV0gkTUJotWjm0wI7iaXoIeHtcYc8tmoPJzRYhnN53Lw3czuEHA5uJFYhMNhrbv3Yyps6qZM2th2KwXxeZX4dmYg+FwKkdkV6OVpo7JNSY241Vk+hL8Hz0TkvPHGGzh9+jRu3LgBd3d3ndsJhUJYWlqq/PdP5oRSCZcho6QWFEWpGAHoIjKrHADQ1d2Kfay+UYa3DkWgg2KAMjK7HOZCHvv8uag8pClVbdysTeBoIYREqc0NoJ1GmsPKhI/vZ3UHhwKOhGfjxOPmLaDfGdMRNqZ8JORXaRghqPPmoQhklT5bF5N5/dqhvb0ZiqvF+P4KPQw5yNcebtYmKK9thFgqg6WIp/G6M5G5iM/7Z7ZPEggEAqEJAY+D3Tra1uRyGByIzcznWAh5iMgqx+6QdKMdo5mQh+9mBoJDAccf5eBmov42c0NwtBRhixZzJG1su5WCR0q20sP9HTFd0d3x7tEorJ3UCfbmAjwpqMYPV5PA5VC48c4wdvvFe8Lx6XOaLXIfn4pBYZVmS1oHJwu8PYae7f3sTFyr7w0CXK2wYrhvs9vJ5MC7RyPR3t4Mb46i3/dBeimC2qt2c+wLzTSqix7h2fJURY5cLsfrr7+O48eP4/r162jfXruN8r8VR0sR632vzKGHmZjaQ3vFQJmwDPoE09WNFjl2ZgJYm/IRlV2Bq3G0tfHDNHob5V7Uz87EspUaiqLY0EuBUjqwNkc3bfTztsNKhS3kRydimm3bsjUT4JPJhvX+AsArOx880ywaAY+DTybTc0677qYhMqscXA7Fuq/tu5+J+Trsvr+6mGC0AVMCgUAg/HUEuFppWB4zbDgXb3DcgbOVCO8r2ta+uZSI7DLjLdz19rJlgzk/PhVjkNtpc4zv6mKQrbRMTjusNiq5jH08sTMcLIRILarBnpAMbJhKd5Fsv5WC6OwKtLc3U7GV3nIjWeVngLaV1lWZem2wN/p42aBGLMW6UzGtvt6uGO6r00lPmScF1fjpWjKWDPFGoIc1quolkEhlGovQbx+OaLZln/D35KmKnBUrVmDv3r3Yv38/LCwskJ+fj/z8fNTVtd3//Z/C3L6eEPJUv2aZHLiVWITBfvY6XkUTn1eJmgYJK1LKasVYrxAQuRX0SkhiQRXKa8Xo590UZnkjsQhno5oCrZjX1ygFgW67lWLwZ3hjhB/6eduiVizF8n2Pmj3RTunuipH+jgbtO7W4BmtPtv5k1hqGdXTElO6ukMmBDxQn8Zl9PGAq4CKxoEpnoOnNxCJci2/7ahqBQCAQ/nqWD/eBgKv9NmjpXsNdYOcEeSLIi75Grj9t3JiEt0Z3gKuVCFmldfjpunHaptdrqbBoIyG/Cn8ohZ5amfLxhWLhdntwKjxsTTA5kL6Wrj0VA5lMjmVDm2ZxbicVo4+XLTq5qHblnI/O11qZ4nIobJrWDXwuhRuJRbgYk6+xjSEIeBx8Pb2bQfPPW2+lIKmwGt/O6AYBl4NHmeUq91MAfb/17WVNG2zC35+nKnK2bt2KiooKDBs2DC4uLux/hw4deppv+7eCoihcVyrhMiQoLKKVW83UkclpK0MnSxG87c0gkwMWIh6WDFHNoXmYXoZ+aiXWT8/EssFefdvbsduJ+PRfeWpRjcHCgsuhsPnFHuzg4btHI/W+lqIofKEo4TMsHKS7inc0PBt7Q5s3QzAmH0/qDGtTPuLzKrH9VgqsTPjsrNT+0Ex29UydT8/GPtPKE4FAIBCeDkIeF9tf6qX1uQdppYhQtIw3B4dD4Yvnu4DLoXA1vgD3kpsP0jYUMyGPFSW/BqciyQgGB85WInyupctEG99ffaJixjAmwBkTu7pAKpPj09NxWDuxE8yFPERmlePgwyxwOBSuvDWE3X7m9hB8rsXZbd0p7ddSX0dzViitPxPLzs62lEAPaywarDuzj0Eqk+OTU7HwcTDHUkXGX2hqKfp5q95T/X4nzeDQWMLfh6ferqbtv/nz5z/Nt/3b4WZtotVS8cTjbEzoqr9szNg391WsLNxPLcE7Yzuy1RkAuPWkEI6WIpVB/+JqMb44Fw8ACHC1hJ2ZANUNEhUTA+XZneZwshRh67xe4HEonI3Kw9ZmKkHOViJ8PLnJ/vr3O2kq/brqfHwyxiCramNhby5k7bl/uJqEyKxyLB7iDQGXg5DUEnRTmoNSJqu0rkVVMAKBQCD8fRnu76izq2Lqz3cNXgz0c7LAPEXb+Gdn44xqQjAmwBmjOjlBIpNj4/l4o+xzbpAnAnVc55SpFUvx2RnV9rKPJnaCCZ+LB+mluJ9awubkfXUxASXVDfBzssALPZvmryOzKzTsnTNLa/HLDe1ZNMuH+6KdnSkKKhvwbRuyglaN8oOLAUGoD9JLcTIiB8uH+8LT1hT5lfWwNRPAhM9V2e6T08+264TQdp5pTs5/mRm9PVQG/wG6UnMvpUSrbTEDk9HCrCrcTy0FX5EUzLD3fibkcjlbYmVmeI6EZyP4SRE4HIo9ifOVSvMXWlgKDmpvi08VKzLfXErE9YQCvdvP6OWO4Uq5QMXVDdg2T/uqGQDM2BbSIuHVVp7v4YaJ3WiHmFUHH8PKhI8Xg5qqOUuHarcZ/eFqEjEhIBAIhH8J+tq37rSgKvPmqA6wFPGQkF+FY+HNG/W0hI8mdgKPQ7dxGaNSxOFQ+Fgph08XFAVcii1QyYxxtTZhLaM3no/HC73c0cmFtmL+5hItSj5Vqt58fjYOiwZ7a1g0b72VgswSzRkmEZ+Lz6fQlabdIRlIaWWEg6mAh4/0ZPYp88W5BIilMva4L8UWYLzaIvTD9DKcimhdYCnhr4GInGfI4aX9NR7LLqsDn6fpz85Q3SBBYVU9K2BicitQWiOGq7UJflPK4vnweDSGKTzwy+vEeFkRfPnu0UiU14oxRPFcltJQJHMyaglz+7bD3L6ekMuBlQci9JZvKYrCZiUxNmNbCMYGOOltXZuw+bbBA59thaIobJzaFa5WIqSX1GL96VgsH+YLAY+DB+mlCHC1hKmAq/W17xyJVBnIJBAIBMI/Ex8Hc0zTYQb00u8PDF69tzET4A1FTMGP15OMeo1ob2+GuYpqyMYL8QbbXOujt5dts90kzEffeD5e5Xt4bbA3PGxNUFDZgN+CU9mw0cNhWUgurIK5kIcfZnVnt//uyhN8MM5fZd+NUjm+0VGpGdLBAaM6OUIqk2PT+dbbOE/s6oL+ajM22iiubsDmq0kY3tER4wKcIZXJkVNWpzGju/5MLMnO+QdBRM4zxFzI02orfTe5BH30VHMm/3QHTpYidHKxhFwOdmBvdGcndpuDD7NQ3SCBgMdBVmkdpvdyh7eDGQoqG7DmRDQG+zmAooCYnEoEelizr2PmdlrCJ5MD0M/bFtUNErz6xwO94WmWIj5eHejF/rznfgbWTuzE5veoU9coxaztIc9s7sXKlI/vZnUHpbDJvp1UhJf60QLxx2tJrLOcOrG5lfjlBmlbIxAIhH8Dq0ZpP9cDwK0nRQbvZ16/drA3FyK7rA4njJwFt3KkH8yFPMTkVOJsdF7zLzCA99WEhzY4FD0ffD66qftDxOfiowl0lWTn3TS0tzfD6M5OkMmBry/SwmWKUlD5mchcDPS1RwcnVdFwJjKXjctQ54Pxndg5p9baODNB5YbkA+0OSUdWaS0+mtgJAi4HoWmlGOir2spYXtuILTra7Ah/P4jIecb08LTBWwpPdmXuJBXrbFsrqGzAjYRCjOpEO5YpO3y9M6ZpXx+diGEzXu4ml+CHWd3B41A4H52P4CdF6K2Y41EOBb0U23L3EgGPg+0v9UYHJ3MUVDbglZ0P9Iql98Y2nUTXnYrFw/QyHFnSX2dgV1JhNZbtDYfkGVVK+nnb4c2R9Pf40ckYDO/oCBtTPpIKq8HjUPDV4bb2/dUnKjkCBAKBQPhn0s7OTGWORJn5ux4avB8TAZc1B/r5RrJRr2N25kIsZvZ9Pdko1Zx2dmZ4RdH5oQvmbb65lKDyecYGOCPQ3Qq1Yil+uZGC98Z2BIcCLscVIDyjFBRF4eDifuz2qw9H4MPxmu1j6lUiBl9Hc8xRBIxvPN/66lVHZwvM7K07o5GhUSrHt5cT4WFrilcG0N9JSEoJBvqqVoK230pFfoVm1g/h7wcROX8BK0f6or29mcpjdY1SZJfVwtlS+5DcW4cj4O9Mz/TcelIEsYQ+0Qzr6Kiyj+JqWmxciy9AN3drvKlYnfrkdCwCXOlZHeVfzs/OtM7u0sqEjz9eDYKzpQjJhdVYvDtcZ/XFRMBly+wAsHxfOIqqGxD5yRid+7+RWIS3j0Qa5SRuCG+M8MWoTnRy9fvHovCqwl3tp+vJKkJSnaV7wlFRS0rXBAKB8E9nwSAvnc8l5Bs+hzm3nydszQTIKKnFlTj9s6st5ZX+XjAX8pBYUIVrCcaJNFg6TPv8qTJmAi7SS2pV4ikoisLbY+gcnL2hGTAX8ViXUqaa08/bDrZmAgD0TIu7jQkG+KiKhtC0UgQnaZ8zWjWKrl5F51TgSnzrv8uVI/004jy0cTIiF7G5FVgx3BeWIvp77uikGUz/o5HsvAlPFyJy/gIoisL5lYM1Hs+tqIeVifbZnPLaRmy7lQJLEQ/VDRJ2CDDA1VKlMsMQllGGvIo6LBvmiz5eNqhukOB0JD0w9yizDG7W9GtqxFJUtrK/1NXaBH8s6AMLIQ8P0kvxxoHHOnuQZwc1iZziajGW7gkHl0PhwUcjde7/VEQuPnpGGTocDoXvZnVHe3sz5JTX4U5yMXwczFBR14ibiUU6+7ULqxrwTjOW2gQCgUD4+xPgaqWzlXrDWcNdzUwFPLYC8WdIuhGOrAkrUz5eUlRettxINsq1x8XKhG3T1kWNIh/v5xuqFaTBfvYIam8LsUSGn28kY9UoP/C5FELTShGmcEzd91pfdvt3j0axbmzK/HQtSetnsTcXsmHdP+rYxhBcrEzY/TTHVxcTYW3aNF91LjoXU5Va7wDanOhZGiURWgcROX8RJgIubr07TOPxRD0e+NE5Faispz3jL8TQqykURWFiNxcAQJCXrYrgOfwwG1wOhZ9m94SdmYAd6JfLge5KJ/KTbegb9ne2xK8v94aAx8GVuAK8eShCa3m+i5sVurg1rYZEZlfg7SORsDcT4t4HI3Tu/8CDTJ2lbGNjKeJj+0u9YCbg4kFaKSSKE/nBh1kY7u+oM9PoSlwBfr+T9tSPj0AgEAhPl8U6slXuJBc3G4StzJy+nuByKNxPLUViftuzbZRZMLA9hDwOIrPK8Siz3Cj7XGZANUfE5yCpsBqXlapTFEWxouVIWDYEXA7b9vfLTXputZOLJZwshQDo2R5nK5GGGUBYRhlC07THSCwc1B5mAi5icyvbFMi9bJgPLES6swkZgp8UITyjDC8PaAcXKxEKKhvgZCXSaLFvi7014dlARM5fSDs7M+xQckhrCeej89iqyaSu9ApDVE65ikXz91efQCyRwdlKhB9n91BJ/80qbXJZW3dK1QO/pfT3scP2eb3A51I4F5WH945GaW0zmxPUtFLEoYBzUXn46mICXK1NtAo+ht9up2HThYRnInQ6OFlgy9ye4HIoZChZW/7vciKbq6ONDefiEdyC4VQCgUAg/P0Y0clR53MXYw0f9ne1NsEYhTnQ/tCMNh+XMg4WQkzqRl/39903zr5drU00qhXqMBEU6jl5fdvbItDdCg0SGXaHZGDJUB9wKOB6QiHrwLpV6d7k87NxeGOkr8b+t1zXPtBvYybAy0w153rrqznWpgK8amA1Z+vNZAh5XDZK4kxELp5X6+g4G5WH5ELjCliCcSEi5y9mVGcndm6mJZTVNrL+/V3cLOFpa4r6RhnSimvw+ytNwum5LXfQKJVhoK892zsLAFHZFSpBYMqipzUM93fEljm0ODj+OAdrTkRrCJ1pPd1go7DKHtmJPvlvD07F7pB0tLMzU0lJVufX4FR8cjr2mQid4R0dsUEtDTqjpBYpRdWYpKiaaePlnQ9a7edPIBAIhL8eIY+L2Yq8NHU+OhHTon3N6kPv51x0ntGNdOb1o9vhzkbnocxIsQvzB+qOdwCAqnoJBFy6ghSdXcE+TlEUFikMEfbcz4CzpQgTutLXyh23UwEAPT2bAswvxRagk7OlSqg5QFfLYnIqoI3XBrWHiM9BVHaFSmZPS3llgBdE/OZvfa/GFyI+rxKz+njAwUKI3Ip6uFiJwOeqVnO230pt9bEQnj5E5PwNeHNUB4N83NXZrlhNUW5ZOxWRg5GdnNiqTUJ+FVYfjoRUJseyoT4Y6a+0SkU1/bIyJ6K2MDbAGT/M6g4ORbd4fXQyRkXoiPhctu+3uLoB746lRdf607G4HJsPPycLXHxTc1aJYXdIBt4/pr1KZGxmB3myYWcMv91OxeRAV9gphii1MWv7fWJEQCAQCP9gJgdqr2jUiqUtykgZ6GsPWzMBiqvFuNdKC2RddPewRoCrJcQSGY49Mk7waHcPa5WICW142ZsCAPaqVZDGBTjDw9YEpTVinI7MYfPwlEXYrvl92O33hWZggRZRtVvHDJOduRDTFG1wu+5q38YQ7MyFeLGPZ/MbAth6MwUifpNb3qnIXA0HviPh2cjVE6NB+GshIudvwl6lwTxDuZ9aipLqBgDACz3pMuqNxCIUVtbj25mB7HZnInPx7tFIAMB3SuFcyt70f4ZkQGoE8TA50BX/mxEIDkXP07x9JFJlBeul/l4Q8Dh4nFmOft62mB3kAZkceP3AY9xLKYa/syWuvT1U5/4Ph2VjlY65H2PzzpiOKuVpmRz49HQs1k3W3bZWXN2A13Y/RIPk2eT8EAgEAsG46Mutu63DBUwbfC6HDdtkjH+MBUVRbKXojBH3/XIzBgRPCuhuhVOROSoLejwuB3P70q89+DBLRYQdCc8CAAxVhJIDwP8uP8Hozk4ajrKnInJ1VqYY44DLcfnILmt998lrg9vrjLBQ5mxULrJKazGnryesTPjIKKnVcMYFgB23yUzu3xUicv4mcDkUYj8d2+LX9dpwFVKZHL6OFujpaQ2pTI7jj3MwvosLm5kDAMcf5eCjk9GwEPJwdbX2trCrbbBnVGZaT3dsfrEHeBwKJx7nYMX+R+xNv4OFEM93p4XDLzdS8PmULhjVyQliiQyv/RmG8Iwy+DiYI/jd4Tr3fyYyFy/vfNCiIdDWQFEUvpneDWMDmkJXcyvqcTmugO3T1cbD9DK8eTDimdlfEwgEAsF48LkcjAtw1vrcrrstu6Fl2rZuJhYZ/ZowvosLOBRt5JNZ0raWc4ZxXZwhaMZq2dveDPWNMpyMUDUteqGnO3gcCo8zy/GkoJrt3Nh7PxMymRwcDoU3RjTN4oSll7JOcQwNEhkOh2Vpfd8OThYY5GsPmRzYE9L6WSR3G1OM76q79ZxBJgf2hWbCVMDDi4oWxuCkItWOGNAOei2p8BGeHUTk/I0wE/IQvnZUi1/3xTnafYxZ1Tn8MAtCHoct7QJQVFay8N6xKLS3N9dakl6yJ7zVx67O5EBXbJ3XCwIuB5diC7B4dzgrSpYOo4cSryUUIi6vElvm9MBgP3vUiqWYv+sBYnIq4GlnipAPdbuu3UspwdgfglnHuKcFj8vBj7N7qKxAnYvKg5edKYLa617tuxCTj/Vnns0MEYFAIBCMy+AO9loff5jesgDo3u1sYSrgori6AfEtyNoxBAcLIQb40Md5Jso41RwzIQ8TumgXeAymQi4AzeqUg4UQoxTztgcfZuK57q6wEPKQWVqLsAz6e1O2cV53Ohaz+ngod84rXpul89r5iuL1xx5l64ysMIR5fQ1rWTscloX6Rile6tcOHIoOWh/sp/pvQyqTt8mllvD0ICLnb4aduRC339NdxdDGzrtp2B6ciondXGEq4CK1uAZhGWXsygOPQ2HNhE7gcigcDc/GqoOP8b1SO5syGSXG830f3dkJO+f3gQmfi1tPivDS76EorxWjvb0ZpirawDZfTYKIz8X2l3qhj5cNquoleHnnAyQVVMHFygRhekRfZmkten5+pc2mCc0h5NHH18+7SdR8cDway4f5wNpUe64RQM8Q6XKLIRAIBMLflx4eNjqfK6pqMHg/Ah4H/RQzty1pdTMUplJ03UjBoADY67MuYnIqQVFAeEaZRtvYzD704urZqDwIeVyMCWDa9WgRYGcuZHP6kgurYSrgYkRH1cpIWnENorK1GxAM6+gAe3N6zqktjqZB7W3h52iudxuKAkprxDgfnQd3G1OMVXyWJ4XV8He2UNl2z/0Msqj5N4SInL8hHramWsNC9fHlhQRciM5j3b8OPMiEv7MluntYQyKTo1Eqx5bZPcDnUjgblYeN5xMwRKk6wbDhnOGBZ4YwyM8euxcGwULEQ1hGGV7Yeg9ZpbV4Y4QfW82Jyi6HqYCH3+f3QTd3K5TWiDH7t/tIzK+CvbkQcZ/pb+Mb/PUNFaeXp4GIz8WOV/qgj1fThW/+rofY9HxXva/79soTo5g6EAgEAuHZ0cFJ9w3wo8yWVXMG+dIr//dTjWs+AABDO9LX8YisclTUGadlapCvfbN5Ml3daHfWM5GqttqDfB1gIeKhqKoB4RllmKKwpT4fnc9WXtZO7MRufyWuAM/31BRVJ3RURvhcDqYoWt6PP2p99YSiKMxtppoj4tEVq/2hmQDAtt+dicjVEIJPCqpbXOUjPH2IyPmb0tnVEvtbaEbw7tEodojvbGQeiqsbMEfxS7z3fgZGd3bCry/1hpDHwdX4AtxN1lxVuhJXgEoj95b28bLF0aUD4GIlQkpRDaZtvYeaBgl7kvjhahIAOozzz1eDEOBqieJqMV78NQQxORUwFfCQ/MV4dHSy0Pkek7fcwbkowzMMWoO5kIfdC/qyJ3cAWLH/ET59LkDv6zaci8dOEhZKIBAI/xh4XA6663Aai1Ay7TEEJnw7JqfC6Kv9btYm8HYwg1QmR4iRHNx4XI5Ki7Y2rEzoLoYrcfkqjwt4HIxW5AOdj87DAB872JvTYeSMw9xwpZmWz87EYVQnJ1iohW2ficzV2Y42TSGKrsQVtMnNdGoPNw1LaHUoig4qzSypRT9vO7hZm6CqQQJTAVdjdungg8xWHwvh6UBEzt+YAb722P5Sr+Y3VGLLDbo9SiyVYX9oJp5TWB7nlNfhYmw+hvs7Ytf8PjAVcHW6qa0+FNHWQ9ego7MFTiwfCH9nCxRVNWDW9hB097AGl0PhekIhQhUrXDZmAux/rR8CPaxRVtuIOb/dR0RWOXhcDi6+OVjDvlGZFfsf4bvLiU914N9EwMWRpf3hakWLSXowMYNd4dHFZ2fj8EcLB1YJBAKB8NehzUkLAK7Ht6w1rLOLJbgcCsXVYuRX1hvj0FQYrKgU3UsxXjvcCH/doahAU+tdRFY5ymtVZ2MndKE7Si7F5oPLodiWtesKcyMRn4vOLpYAgJIaMeRysMKIoaRGjDAdlZEAVyt0dLKAWCrD9cTWGyZZmwr0irm6RikbgHoqIgccDsU62V6NL2Tb1xjORuc9dUMkQssgIudvztgA52ZXVJRRvr/fcz8DHIrCXMUN+O+KasIAX3vsWdiXXYlR52p8IVKfQqils5UIh5f2xwAfO9SIpfj0TBwrtDaej2fFiZUpH3sXBqF3OxtU1kswb0coHqaXgqIofDszEJ9N0V05+fF6Ml7Z9QC1YonRj59BxOfiltLc1JOCasTkVrAtCbpYf4YIHQKBQPin4GFrqvXxxIKWpdyL+Fx2/iM+z7jmAwDQW2F5HWnEtm1D7js6OJlDJtecNRrkZw8hj4O8inqkFFVjmGJfNxKL2ErWiuFNLmv300rYgHBlrifoFjBjFK6nV+PaNov0XHf980dChcg5GZEDuVyOF3rRC623k4o0viOxRIYrRnKpJRgHInL+AWyd17NVryuqasD56Dy81K8dBFw6m4bpJe7VzgbHlvVXCbZUdjgZ8e2tpxJwZSni449XgzCth5tKJSkyu0LFHcZCxMefC4LQz9sW1Q0SvPR7KK7G0SePl/t74cjS/jrf43ZSMXp9fvWpGhLwuRykbJzA/vw4sxyFVfXw1rHyx7D+TBy+v/KEDCgSCATC3xx3xYC8MWCqQsayelammzvdQh2fVwmxxDgZcnbmQvg46L+etbOjn1c3ABDxuWzW0J2kYgzwtQefSyGztBZpxbS50SAlh7L9oZkY0sFeo3VMn5kC4+J260lRm3LpRnVyhKmAq3cbAY+DlKIaxOVVop2dGXp4WkMuB6rqG2GrFg5+OoK4rP2dICLnH4CpgMcm7raU945Gwd5cgOcUw3/KsyG+jhY4v6rJ4ED9vvvFX+8jv8L4pXUBj4NvZwbi/XH+KsJqzfFo1Dc2nazMhDzsmh+EEf6OqG+UYfGeMLbntY+XLULXjNT5HnWNUgz++gauPcVVFS6HUhE6TwqqUdUg0eu4BgCbryXho5MxROgQCATC3xhLE93D9y09fzNVocxS4y8eetqawlLEg1giw5MWVpn00audboc5ZbQZMQzwpR3l7qaUwFzIY0UPU/VR7iS5ElcACxEffdvbqewjpagG6cXaHV+7ulnB0UKI6gYJ7qeWGnSc2jAV8PR2YVQ1SNhjvaZoU2Ta1K7FF6rk6AF0J4yxDCAIbYeInH8ILw/wAteAhF51xFIZ9tzPwMJB7QHQ+S3KNtFOliLc+0B7Hk1maS3m7LiPwirjCx2KorBsmA92vNyb/Vw1YinWHI9W2c5EQNs3z+jlDpmctm7+8VoS5HI5nCxFSPh8HGtHqY2Ff4Zh0/l4SNrgp68P9RDXoqoGVBpwgtsfmolFu8N0zkURCAQC4a/FXKh7waq2hbMX7jb0dSqn3PiVHIqi4K+YcUkxYqt573a6s+AAWpzQ71mjYQAwUJHfE5paArlczgoYZUG0aHB79s+FVfXo76MqcgDdjnQcDoXhCutpbSZKLaG5+SPm/oFZNGVEzv3UEjanSJnbSa23tiYYFyJy/iG4WZtorBgYyrpTsQBof3mpTI5tt1JUnne1NtFZKUotqsHc30JRXG14LkBLGNnJCReVqknHH+doWC7zuRx8Pb0bXlf08H535Qk+OhkDqUwOEZ+LO+8Px9KhPjrfY3twKsZ8H9yibIOWYCbkqVSVDNUtV+MLMemnOyrVKwKBQCD8PTAR6L5Fqmnh3Ke1Kd3W9LRW+dsxlSIjtsN1drVsdhumDS8iu1zl8U4ulhBwOaisl9CZdu2sAaiKnGFK+TgRmeVaA7b12TL386G3D22jNffwZkQOTzGXE5ldgcLKerS3N0MHJ3NIZHLUN0phqWa33VJjCsLTg4icfxALBrZvfiMdjN98G4P96CG5o+HZyFGbt1k8xBtCnvZ/DkmF1U+tdQ0A/Jws8Ojj0ezPG87FY92pGJU+W4qi8M7YjvhsSgAoiq6EvPbnQ1TVN4KiKHww3h8HF/fT+R6pxTXo88VV3Gvjio8unCxFuLCqZdlGAN1D7f/xRZQ8JRFJIBAIhNYhkepesaLQss4K5ka4qv7pmOJ4su1wxhM5Pg7mKi3l2uiiiFSIyVE1PRDwOOjkQsc+ROdUINDDGhQFZJXWsQuOzCwRAFyNL0BXNysIuKr3IeEZulvRmOpQTG4lqhta/706WYrQyUW3oCuqamArcTcV80cj/OlF5/uppRio1u52I7GQdGn8TSAi5x9Er3Y2KieFlrLxfDy4HAqNUjl+Vavm2JkLMbevbhvk5MJqzNwe8tSG+W3NBDi5YiD78+6QDMzcpvl+L/f3wi9zekLE5+BGYhGm/XKPXbnq522Hx0piSRtzdoTi87NxRhvOVKaTiyUOL9FtiKCPXhuuIrnQ+I52BAKBQGgdjfpETgu7x5lwzbbcjOvD3ZZphzPezI+JgMuKJ12IFIujqUWaszOMAIrOroCliA8fB9phLiaXFkQWoqZ2wMNh2RDxuejipio20ktqdXZhuFqbwN3GBFKZHI8y2hbE2VdRRdL198pRPPEgjRZdAxUzRyEpxSomCgBQVtuISLXKFuGvgYicfxAURelty2oOqUzOri4ceJiFQjW//mXDfGDCp11GRHzNfxqZpbWYsS3kqd2Md/ewVsmbicyuwKSf7mjYSI7v6oLDS/rDyVKIpMJqTP3lLnvisTETIHXjBEzvpTtP5/c7aejx2WWj9i4zBLW3xc75vVv12lHf3WpzbzGBQCAQjIO+ljQ+t2W3T4xPQcsnaw2DaYczdpi3l51+hzVGBmq7njLVEeaegbHRVhZEHZzMtb5GmYR83bbbTGBrbG7brLm1tcopw8zlMPcavdvZgs+lkFtRr1UIMtsR/lqIyPmHMTbAGd7N2Doaglgiw6/BqrMvDhZCvDrQCwDgaqV9mD+/sh6ztocgro0nFF28M6ajiq11RV0jFvxBmwcoV1+6uVvj1IpB6OpmhdIaMebuuI/DYVkA6IHE/80IxP5FfXW+T41YipHf3sL+0Eyju5yN8HfC5he7t3ilDwDm7gjFb2p/LwQCgUB49hTqmeNUn8NoDmaBkdMKAyFDYI6nss64lSInS6He5xsVN//aRE47O9UWOqaSo7xQqpyPU1HXCH9nC439PCnQvSDJiKK4NuYP9VY4yem6HeByKXAo+rPkVdTBRMBFD0/6NVmldfCwVb1nIiLn7wEROf8wuBwKy7RUc3p6Wrd4XzvupKFArZqzZIgPLEQ8pBbXINBD+z5LasR48dcQhKUb/5fYypSPD8b7AwB4HAojFQOB24NTMW3rXZUTqbOVCIeX9MeErs5olMrx3tEorD8dy4qhAT72iFo/BuZC3RejNSeiMfWXe0Z3kJvS3Q2fPac7tFQfX5yPx7Rf7j6VljoCgUAgGIZ6t4MyVAtXsSQKkcN7aiKHbv0ydiXH0UKk93nG4rmqXoI6Nce5draKbKDSWshkcvg40j8rX8cDlVrwU4uq0cFJU+QkF+q2xWbMEdoasupoKWLnbrQ52eaW18NbIdLCFGYIQUwIa1Y5enqq2m0/TC8lczl/A4jI+QcytYebhm3yo8xyHNIzeK+LvhuvqVQyrEz5rNNaaY3uVazKegnm7gjFxZj8Fr9nc7zQ0x1929tCIpOjQSLDtnm9YG3KR0xOJSb9eAcHHjRVX0wEXGyZ3RMrR/oBAP64l47Zv91nxZuliI+YT8di7cROOt8vMqscQV9cwylForGxeKm/l9731cejzHJ0WHvhqdh3EwgEAqF5MozoVFZWKwbQ1FZmbJgbc2PfWDtY6K/kRGZXQKCYy1F3YXW1FoHLodAgkaG4ugEeNnRlJ6+iaW6IEQ4AkF5SAx9H1fY1AEjSU8lhKj+pRdVsVam1MPsSaTFhksrk4CqELVM16qoQaFE5FeihtihcVS9BYr7xMosIrYOInH8gfC4HS4ZqWj5vuZGMtE0TtLxCPzO3h6icHF4d2B52ZgJkldZhaAcH9nE/R3MVcdUgkWHZvnD8eS+9xe+pDw6HwpcvdIOQx8Gd5GJU1jXi0ptDMNDXDnWNUnx4PBpL94ajrEbMbr96dAfseLk3LEQ8hGeUYeKPd1T89V8b7I3b7w3X+76rDkZg2lbjVnVeG+ytInR0OdjpIuiLazpzAggEAoHw9NDVAqWtpao5Sqrp65W9+dMROYy24bSmT1oPJgJus9s4mNNCSF3k8Lgc2CjCsUtrxaxgKq4Ss9s4WzVVirJK62BnJtBwWMvT4+zqZCGCgMeBTI42O8B2VPy96tKJUsUiKFM1YoygnhRUsTlFysTmVmg8Rni2EJHzD2Vmbw/Ym6uusNxOKsajzDIkfD6uRft6mF6GhX+GoUbh+mIm5OH1EXQmjfIvaVJhNX6f3xvDOjYJH7kc+OR0LDZdiIfMiCtI7e3N8PaYDgCAz8/FAQD2LOiLNRP8wedSuBRbgHGbg3EjscmPflRnJ5x5fRD8nS1QXN3Azrcw1RkPW1OkbJyA+QO8dL7v40y6qnPysfGqOspCp0EiQz9v/QOO6rz4631sOh9v9NkhAoFAIGinvlGq02RnjCIMsiUwi2d2ZvorI62FuT4YuxtOxG9e5NiY0UKmXEsGkJWJ4rnaRvaepa5Ryt5vWCi1kyfmV4GiKBXhAwAFlfU67y84HAruisXXrLK2Vd46OtNCRabjWsssUjIzyc6WItibCyGVyUFBsxWxrXNChLZDRM4/FBGfqzXA84erSRDxudg0rWuL9hf8pAgztoWwZeS5fduhvb0ZiqvFGKxkj7j6UCR2vtIHb43qoHIy3X4rFasPR6hk27SVBQPbo5u7FarqJVh7MgYUBSwe4oMTywfCx8EMBZUNeHXXQ7x7JJINWPOyN8OJ5QPxfA83SGVyfHE+Hkv2hKNc0SrA5VBY/1wArq4eqve93zwUgfGbbxvNMltZ6NxPLcXM3u7NrugpP789OBXtPzyPKiP3WxMIBAJBk4iscp3PBbYiyoFxFGOG8Y0NY01tKmiZIUJzaGvdUoepvDRqmSNtEjlimAl5rIMrU9lSnm1iXNScLVVFjkQmR0mNGLpwU8zSZJe2zT7bR2HqxONQWo2DGEe9wqoGlNWIQVEUW9XLLK1lg1EZnpZBE8FwiMj5B/NS/3ZwtNCs5oSll2J6L3d4tfBkGpdXiTHfByMiqxwCHgcfKgwAHqSVsisUcXmVSCyowqpRfti/qB9clFZcTkbkYu5voUYLtuRxOfh6ejfwuRSuxBXgbFQeANp7/+wbg7FwUHtQFHAkPBtjv2+q6pgIuPhuZiA+nxIAPpfC5bgCjN98WyUV2dfRHKkbJ+CV/rqzgRLyqzD46xv4+UZym3t9AVrorJvUGQCdCTDS3wlz+3rq3L64WqxxQey6/jJCUkj7GoFAIDxN9Nn5927Xsmo80OQo5qtl5sQYMDM/TFXFWOiqaijD3PyLtVwnzRWGCNUN9AIo0/6mbUE0XTEDZWmiKdRK9YgcxhyhWM8csSG4W9PX2xqxFLZaZqfyK+rZey6masQIm7TiGo2/27i8StKB8RdDRM4/GBGfizcUbWXKfHflCfhcDt4a3aHF+6yql2Dqz3dxJjIXozs7oW97WzRIZKwXPQCM33wbcrkc/bztcH7lYIzp3GQBGZZRhkFf3dDra98S/J0tsXwY/RnXnYph3W5MBFx8PKkzDi/pDy87U+RX1uPVXQ/x3tFIVNY3gqIovNTfCyeWD0R7ezPkVdRj9m/38f2VJ6zfPYdD4dMpXXDjnWF6j+GbS4nw++gCwtsYNgYACwa1x9fTu4FDAYfCslBWK8aOl3Xn6mSU1MLVSqQiJmf/dh+rD0WQkyeBQCA8JYKTdIscK9OWCYn6RinSS+hKjp8W9zBjUFZDV/ltjGxs0GCAyycjcrQtBjIFEeZ6xedSOvfLmCZoq0bV6sksYqpFFVra5VqCpQmPdWPVdnXNr6yHo8JSO0tRNVIWOX5qIqeqXqIzyJTwbCAi5x/OrD6eGk5r91JKEPykCJO7ubKl1CVDvLXaIurijQOP8cPVJKyd2BkURYsX5crQ8Uc5AOjwze0v9cLnU7uwz9U1SjHuh9u4Glegsd/WsGK4Lzq7WKKsthHvHYtSubnv42WLC6uGYMFAuqpzOCwbY74LxuVY2vWNrvoMwgs93SGTA5uvJWHOb6HIVUqFbm9vhrRNE/DOGP2i8IWt97B8Xzgqatt2Ip3Z2wO/zO0JPpfC+eh8/BmSjps6hBaHAnIr6lFe24gZSgGnxx/noP2H58kJlEAgEIxMbnkdInW0q83srTtoWheRWeVolMrhaCGEq5V+S+bWkqO4pjlZGnf/9Y3Nt6AzFRxtAalM2xdz1dYniBjMtMQ+qNtTK8OInMo2ihyKouBqTX9/ulzqhDy6EsVWchyaRI42Z7hMI7W8E1oHETn/cAQ8DlaN8tN4/MsLCQDocE0A+DMkHSEfjMDLetqz1Nl8LQnbbqVgQhcXAKqrK28fiWRXViiKwkv92uHim4NV2ude2x2Gby8ntrniIOBx8MOL3SHgcXAzsQj7QjNVnjcRcLFucmccWtxU1Vm8JxyLd4cht7wOZkIevp0ZiB9mdYe5kIcH6aUYv/k2zkTmsvugKAqvj/DDo49HqwxCqnM+Oh+Bn13G3vsZbbLqHNfFBb+/0gcmfC5uJxXj7SORuP/hSA3ByrxFXaMUR8KzNUwT+nxxFTtuk/BQAoFAMBanla4N6szs7dHi/T1UZMr18bJtcb6OoTB5NepzIW2lqr75cFFGgJhpqcCwn1ZxLWM+vr7LJ1PtUaZWj8hh2tsqDTjW5mDMEXQ5oTKPM/O6nrb04m92WR3cbTRHBIjI+WshIudfwLQebvBWH3jLq8SZqFyM7OSInp7WqG+U4etLifhsShfsf62vwfs+F52H20lF7D4H+tqxz310IkZlW39nSwS/N1zlRvyn68kY+OV1g1aD9NHByQLvj6NnhL44F49ULenKQe3pqs7yYT7gcehZnNHf3cLOO2mQyuSY2sMN51YOQqC7FSrqGvHGgcd4ff8j1ooaAGzNBIj+dCx2ze+j93jWnoyBz5rzKnM+LWVIBwfsfS0Ilgrb65d+D8WBRf30rhT+cS8dwzs6YLpSVWfDuXh4fXCOmBIQCARCG5HL5TgSlqXz+R5qoY+GcDeZvk708Wr5aw0lTdEO52VnXJFTaEC3QI1iwVOb3TQjTpjn6hvpCo6JHtc2iRYFxNFzt8rMDBvD4ZUJVRXytb8hs2bLGCcwC7vVDRK2oqQMETl/LUTk/AvgcTl4U8v8zTeXEiGWyvCxYtj9aHg2IrLKMcDXHg8+Gmnw/pVXRxLymsKtTjzOYVePGER8LtY/F6ASTJpbUQ//jy/iSUHbgrFeHeDFZuW8dThSa7nbRMDFe+P8cW7lYPRqZ4MasRSfnY3D1J/vIjq7Au3szHB02QCsGukHLofC2ag8jPkhGNfiVVvrhvs7InHDOEzt7qr3mGb9eh8vbL2H7FZaV/ZqZ4tDS/rDyVKIpMJqTN92Dy/392JNH7RxI7EIISkl2Pxid5XHu66/jBOPs1t1HAQCgUCg3S9Timq0Pjezt3uL2r4BoKxGjAeKSs4If6dmtm4dEqmMvTZ3cDKusQETrK0LZ0sR2yZmIdKs5DBzMowAqFeIHlM9+TvaxApXj8phqmOGmCQ0B1MVkunoppMonmCMHsyFPIgUgoiiNC28s8va5vhGaBtE5PxLmNTVBV3cVMOossvqsO9+Jnp42mBaTzcAwPrTsZDJ5HC0EOHnOT1b/D4lNWL09LRmfx63OVjrdn297RDz6ViVOZ4x3wfjm0sJLX5PBg6Hwv9mBMJSxENkVjl+vpGsc9uOzhY4sqQ/Nk3rCksRD9E5FZjy8x2sPx2L2gYp3hrdASeWD4CvozmKqhqw8M8wvHc0UqUaIuRx8cOLPXDvgxF6jytcYbaw6Xy83uFIXXRyscSJ5QPR0ckChVUNmLU9BP4ulvj9ld5skrQ6OeV1eOdIJNZP7ozJgU1C7K1DkQhYd1Fv/zKBQCAQtPP7nTSdzy0d6tPi/V1LKIRUJoe/swU8n5J99JOCatQ1SmEh5MHHwbgiR3l+VRtDOtijWFHVcLEy0XieaXezNOFDJpOjtlG3yGFaxbW5tPH1iEt27scIXjxMJUeiQ+UwM0XlitlciqLYkNOymqbAUwZjuc0SWgcROf8SOBwKH0/srPH4T9eTUFnfiPfH+cNUwEVEVjlORtCmARO6OqNXu5aXzx9llrN/rm+UsUP+6pgLebj57nCVzJ6fb6TA64NzrXZBcbEyYU0OfrqerDfLgMOhMDvIE9feHoYp3V0hkyvavb69iQMPMhHgSpsSLBrcZFow7ofbCH5SpLIfV2sTpH85Ebte1d/Ctj04FZ3XXcL+0EzWwc1QXK1NcHhpfwzwsUONWIoFfzxEcXUDzrw+CB62mhcOAGiUyrH+TBxkcjmOLO3PPl4jlqLTuovYF5rRomMgEAiE/zJJBVW4Gq/dMEfE58C7FQLilOJ6O7YVAaKGwlwHu3lYgWPENFC5XI4kHYGoDMozLDZqrnNyuRwlCltnG1M+SmrEdHAmRbeGq+OiGPrXZu4j1NfeJqXVDU/LLE9LYdrqeDoqR8zCY2ltU5s78x0UV4s1wl715fsQnj5E5PyL6OtthwldVU+kZbWN+PVWKpwsRXhdYTf95YUEVDdIQFEUPlIEVLaFxXvC9c7czA7yxMOPRqk8FvjpZRwLb11r1ZTubpgc6AqpTI5VBx83O4viYCHE5hd7YO/CvvBzNEdpjRgfHo/Gc1vuICanAh9N7IyDi/rBw9YEOeV1eHnnA6w+FKHhyz+8oyNSNk7A68M1bbuVWXMiGr4fXcCl2PwWmS5YmfDxx6tBmKYIMn3/WDTOReXi5PKBCGqvO5fhXFQe3j8ahbNvDFIJbv3oRAy8PjhHVpIIBALBAL65lKjzuV9f0m31r4us0lrcVlhRK89RGhsm06dXK/J79FFU1cBWLHTBtO+5WptomCoUVTegvlEGDkUvUDKtb/bmQvAUFRHla2RXN2sATa1gymgTRQxMEKq5HtMgQ2E+g3ILmsrziv8rd0sw71srlmi07DGzO4S/BiJy/mV8OL6TRovTtlspyCmvw8JB7dHOzhSFVQ1sq1dPTxu23al3OxvYm7fOY/+DY1F6n3ewECL9y4kqJ/q3j0Ri4JfXUdyKm/ANU7vAzdoEGSW1WHMixiAxMcjPHudXDca6SZ1hIeIhNrcS07eF4M2Dj9HOzgwXVw3B/AFeoCjaonnUd7dwKiJHZd9cDoV3xnZE5Cdj0LuZuQcfXwABAABJREFUKtiSPeHouPYiwhT92IYg4HHw7cxAVkj9eD0Z68/EYccrvfFiH92uPqnFNZj00x0M7eCAS28OUXmu14arrNsegUAgEDR5lFmGy3piD5QXkAzl4MNM9rUetk+nVa1RKkOwwhxoeEcHo+47Ib/5OVrmpl9bmxzjQOZiZQIBj8OKHCfLpmpHmZKICnC11HiMQVs4J0OTyGl7ECpHzfKaq6ZyGFME5ZY6xkShViyFhUj1GPSFmBKePkTk/MvwsDXFa4Paqzwmkcmx6Xw8hDwu1ipa2n6/ncaaBqyZ4A8TPhdhGWVYO7Ez1kzQPfSui5MRuUg04IT4vxmBOL58APtzTnkdem+4isMPs1pc9fhpTg/wOBTORObi0EPdbjjK8LkcLBjUHjffGYbZQR6gKPrYR3x7EzvvpOH9cf44tmwAOjpZoLRGjFUHIzB/10MNYwErEz6OLhuA2+8NZ51dtCGWyjB9WwhGfnvTYOMFiqKF1JfTurKf76UdoXhrdAdsmNpF7+DrhnPx2HAuDg8+GqliF77tFt0mmNRG8wcCgUD4tyGVybFWzS1UmZ3ze7fY+rmmQcLGHcwO8mzT8ekjPKMMVfUS2Jjy0c3d2uj7bo58hXDp7KIZcppRomqznKowdGin5ACnPPPjYWsKmUzOZv4wcDmUVlMDhup6RuTobmkzFI7i71muZnmtfCyAas4Pk+tTJ5bCUu046xqlRnF9I7QOInL+hSwf7sv2iDKcjcpDaGoJRnVyxGA/e4ilMmw4Fw+AXmVhWtk2no/HnL7tsHKE/pYsbYz9IdigX+aenjaIXj8G3g5NJ7r3jkVhxLe3kJBfafD79fS0wTtj6Ryg9WdiW+TeZmcuxKZp3XB6xSD09LRGrViKb688wbD/3UByQTVOvT4Qb4/uAAGXg1tPijDm+2D8fidNY9bGw9YUyRsnqAg3baQU1WDM98GYvvUekpvpcWZ4McgTexb2hbUpH5HZFXhuyx10c7fCkaX94awn8O12UjGCvriG4R0dEbpG1UVv9PfBGPdDMBokxJiAQCAQAGDv/QzE5Wm/9gh5HAzv6NjifR54kIny2kZ42Zk+1Xmcs1F0ps9wf8cWO781x4O05rsQ4hWubp1cLDWei8ulv1M/heMbc432UwrNVL4e+jiYoaCqHmKJ6nXW3cZE76wRE4qtr6XNUJhKDSNuKKi+L5OTI5c3BYYyczy1YilMtQitBknLZnQJxoOInH8h5kIe3lPc/Cvz6Zk4yOTAJ5M7g8uhcDW+gB2yf21we3gpWtl+upaElSP90FnLSas5enx+xSChYyHi49rqoSqmBGnFNRj3w218fjbO4MyXxYO9MaSDA+obZVix71GLXcW6ulvh2LIB2Pxid7jbmKCgsgHvHYvClC130cXNChfeHIyg9raoFUvx+dk4TPrpjtYTf09PG6R/ORHb5ul3rAvLKMOo725h1vYQpBVrtylVpr+PHU6tGAhfR3MUVDZgxrYQ5JTV4ezKQejnrb//+tU/HmLdqRgkfD6OtREH6BaEjmsvYqceFyECgUD4L5BXUYdPTsfqfP7MG4NaXMWpb5Ti12A6pHnZMB+jiw8GsUSGs1F5AIDne7gZfd+Ps/RXcmYHeSJeIQ67ultpPB+VUwEAbIXpiULQdHBqqvrcUjL68bQ1Zas/yrRrJvsnr4Ku/LhYazfpaQl1CodUEY8WK+p/9cqGBIzIaWpxk2u0twEgi4p/IUTk/Et5oZc729/KEJdXicNhWfB1tGBbmT47G4dGqQxCHhfrJtM3wjvvpiG9pBafTw1o8ftW1DVi1He3mrWdBOi2rNlBnri6eqhKiNbvd9Iw+OsbGvMw2uBwKHw3MxCOFnTOzKdndF+s9B3HlO5uuPb2UKyd2AlWJnwkFlTh1T8e4uOTMVg7sRM2Pt8V1qZ8JORXYeb2EKw+FIHCKs38gHFdXJC6cQI+mazpdKdMaFophv/vJmb/eh8ZJfrFTjs7MxxfPgDDOzqgQSLDGwceY3dIBvYs7IslQ7z1vvZSbAH8P76ITi4WSPh8HHyUqmefnY0jLWwEAuE/i1Qmxxv7H+t8fkp3V5UbckPZcTsVhVUNcLM2wfM9np7hwPWEQpTXNsLRQogBPi2fGdLH/dQSNrhTFw7mAkhkcrjbmMDdRnXmSCqTI5YVOVZokEhZQeTv3PSdnovOY//M43K0Lv61a2aeKbeCvha7arGwbik1agul6gJVuU2Nz2XyeeifKVBaK07NfY+EpwcROf9SuBwK6yZp3mh/cykRFXWNeHNUB9iaCZBcWI09IbTV8Ah/J4z0d0SjVI5Pz8Sip6cNZiiMAjq5WOqdPVEmtbgGA768zlpnNoevozlC16jOkJTXNmLVwQjM/u1+szfh9uZC/DCrOygKOPgwC6cjcw16X3WEPC5eG+yN4HeHY/EQbwi4HNxLKcFzW+7ifmoJfn+lDzvHc/xxDkb+75bWFjYOh8KrA9sj+YvxeFtLSKsyIaklGPrNTcz57T5SinS3sVmK+NjxSh8sGkzPW/14LQmrDj7GqlF++GVuT5jpCVYDgDm/haL3hqs49fognF85WOW50d8HY8Lm26hpaHnGD4FAIPxT+eVGMsL0zJ18Oa1bi/dZWFmPX26mAADeG9dRZ9aZMdgdkg4AmNaz5SGlzXE5Tns0hDLVDbQg6O9tp/FcfF4lasRSmAm48HEwR0xOBcQSGezMBGhv37TYxrSmOSryZaIVwkgZ5e3VaZBIWfMixoK6LTDdIEywKEdHFU/A47AVPmYtlkNpGhUwx0j4ayAi51+MNkvp0hoxfryWBCsTPt4ZQ7e0fX/1CWsz/PGkzhBwObidVIzLcQV4f7w/LEU8xOdV4pPJnbFtXi+D33/VwQi8vv8RyrXYQaoj4nPx2ZQu2P5SLxUbyPuppRjzQzDWn47Vu58BvvZ4Q+FItuZ4NGuq0BqsTPlYM6ETrr09FFO7085zpyNzMXN7CKQyObbM7olu7laoapDg87NxmPjjHYSmlmjsh8fl4I2Rfkj6YjyWD9MfIncvpQQjv72FST/dRoyWkzxAC9ePJnbG1y90A59L4Xx0Pp7/+R46u1ji1OuDtPZEK7vlVTdI0OWTSzgZkYPkL8bjXaWWxri8SgR8cglfnItjS/AEAoHwb+VeSjG+vfJE5/PHlvVnZy1awpcXElArlqKHpzWeUwpqNjbxeZW4l1ICLofCS0oLhMZAJpPjih6nOYZ7KbR1dX8fTZFzR2Fr3c/bDlwOhbB0Wkz2bGfDigPljo+FCsMkbdc/ba1wDGnFNZDLAQsRD3ZGmMlRz/BTlyxMXo+Q23T7zHSccDjaKzm6hBLh6UNEzr+cD8d3goCr+tf8+500JBdWYVYfDwS4WqKqXoL/XaZP9l72Zlg0hD7ZfH42DuZCHnsz/M2lRPT2skHC5+MMfv+zUXnoveEq7iiyAppjbIAzbrwzDKM6NQ16yhUhnkO/uYldd9NUysXKrBzphyAvW1Q3SPDGgcdtXj3xsDXFDy/2wNk3BmGEvyOkMjkOh2XjzUOP0cXNCqtG+sHalG5tm/XrfSzfF45MLf3EfC4H743zx5MN47GsGbETk1OJST/dQe8NVxCaWqK1XW9mHw/sX9QP9uZCJBZU4bktd5BVWosTywfg1YFeKtsWV4vR0clCxbLz1+BU+H50AR62poj5dKzKKtlvt9Pgs+Z8q6thBAKB8Hcno6QGc34L1fn8vH6ercqcuZFYiOOPc0BRwCeTA1o8y9MSdtymZyrHBTjDzQizKMrcTytBQaX+aIelQ32QkF8FHofCCH9NY4bbCltrxno7VDHLqhy98FApXmGwnwPEEhkS8lQ7NygKeueDGVfXjk4WRvm+mTZ0VuCq7ZK5n1Ku0DVKm6o+TDCpMk9rJovQPETk/MvxsDXFwsHtNR7/7Gw8OIoTMUD7+cfm0isoK4b7wsVKhOyyOmy7lYI5fdshwNUSlfUSfHUhASI+F+lfTsSGqV0MOgaJTI55v4di/elYg4wBHCyE+O3l3vhyWleVNqyKukZ8eiYOY38IxrX4Ag0BwONysHl2d1ib8hGdU4GvLugOdmsJXdyssHN+HxxbNgCDfO3RKJVjf2gmtt5KwQh/R4wLcAaHAs5H52PUd7ew8Xy8xmoQQJ8U3x/nj8QN45oNFC2uFmPWr/fR/sPzuBZfoGHm0MfLFudW0s5wlfUSLPjzIX4NTsXHEztj5/zeKi4ziQVVKKtpVGkHBICVBx4j6IureG9sR1x8c7DGc14fnNNZVSIQCIR/IlX1jXjx1/s6n+dzKXz6nGHXNvX9fnQ8GgCwYGB7dPewbu0hNktKUTVOPKbDtBc1M5fZGg4bEMnAXH/7+9jBWi3DpqZBgoeKys3gDg6oE0vZwNKhSlk+f9xLZ//c0dkCUdnlKvkzAJ2/Y6Yn5DOpgG7z9mvF7JQ2ChXijqm+qAsU5nMrzxEzrd7mIh7qtASjG9rqTzA+ROT8B1ihxVI6+EkRbiQWIqi9LSYHukIuBz49HQe5XA5TAQ8fTewEANh6MwW55XX4XCFojoRnIzyDXn2Z169di6o6f9xLR9+NVw2ypaQoCi8GeeLCqiEaoZupRTVY+GcYXt75QCObx8XKBP+bHgiANlC4akDJ3VB6tbPB3tf64sCifujjZQOxRIbjj3IQnFSEQX4O8He2gFgqw6/BqRj2zQ3sDknXWnUS8rh4Z2xHJH8xHh+Mbz6TaOGfYfBecx5772egXukE6mQpwsHF/TGvnyfkcuC7K0+weE8YenvZ4uKqwRjk2zSIKpbKsDskA/297TB/gBf7eK1YimX7HuHNgxHY/lIv/Di7h8p7T/rpDgZ+eZ11ryEQCIR/KvWNUry88wHyKjRNYxhC14xq8cq7XC7HJ6dikVtRD09bU7w9Rv8sZlv54WoSZHJgVCdHo4upitpGnI9pfh6HaUcb10XTHvtqfAHEEhm87EzhbW+GkNRiNEhkcLM2QUeFGJHL5XicWQ5AMcvCoXA3WbPtu6entd7jYMwMOjpphpG2FKlMzs73NDmnqRkPKB63MVNtBQcACyFP5RrNoM/+mvB0ISLnP4AuS+nPz8ZDLJHhw/H+EPE5eJBeytpRTuzqgv7edmiQyPDZ2Tj09LTBrN4eAICPT8ayw/ZMVee7mYEGHUtlvQQzt4dg3akYgwbdPe1McWhJf7w/zp91MmG4nVSM8ZuD8cGxKOQrXbRGdXbCgoF09eqdo5EGOb21hP4+dji8pD92LwhCoLsVasVSBD8pQmpxDTxtTWEq4KKsthHrTsVinI6qE0BXnpYO9UHapgn4enrzA65rT8bA/+OL+OxMHFtSF/A42DC1K76e3g0CHgdX4wsxZctdlNc1YveCIHww3l/lgh2SWoJjj7Lx7tiOKjM8CflVWLInHNtvpWDbvJ5sfzRAB7b233QdC/54yM5uEQgEwj+JRqkMy/aGszfW2ji3clCrslaOhGXj+OMccCg68NpUoLvy0FZicipwRtFOvHq05nW9rRwJz9LIqVFnyVBvxOZWQsDlYHwXF43nmfuISd1cQVEUrsYXAgBG+DuyLWUpRU1zsx8pQsrvpmi2tQ/01e0aJ5fL8SiTrhgFGkHsFVc3QCanRRezQKmuT5j7EBul6hUTeWEu5GntVnma5hME/ZBv/j+CNkvptOIa/HEvDa7WJlg2lG6f2nQ+HnViKSiKwqdTAsDjULgSV4AbCYV4b1xHWJnwEZdXiT33M1T2Na2nO44s7W/w8ewOyUCPz68YNKvD5VBYNswHZ94YhEC1AUSZnHZUG/rNDWy6EI+KWvpk88F4f3Rzt1K4tD3WcEBrKxRFYUgHB5xcMRC75vdBr3Z0ZSeztBa1Sie5FEXVadav99kKmLZ9zeztgbRNE7Bzfu9m33vn3TQEfXENM7eHsKtYM3t74NjSAXCzNkFacQ2m/nwXF2LysXSoD44tG4B2dk0WnFX1EnxzKRHeDmZYreb+FptbiaV7HyE0rQQbn++Krm5N3/f1hEL02nAVHxyLQqWBOUYEAoHwV9MolWHVwce4kVikc5ud83sjwFX3gLsuEvOrsO50DADg7TEdEdS+5bM8hiKTybHuFP1ezwW6orNry7Ps9CGWyNhZH32UVtMmQOO6OGuIwsr6RtxSfM+TAl0glshwQWETPbqzE7vd0fBs9s8Tu7qgukGCx5maTnfaTA0YUotrUFbbCCGP06q/O3UYwyJ3G1O2oqM+58OEgdqaNbWrVSm1q9WIVRdvKQowf4qil6AfInL+I3A5lEogJMPmq0koqmrAkqHecLM2QW5FPbbeou0vOzhZYIFiRX/9mViYCXl4bxy9cvTt5ScorFQt+ffxssXcvp4GH5NYIsO830MNvmn2d7bEsWUD8OF4f/ZEw9AgkWH7rVQM/vo6tt5MgUwux0+ze8BCyMPD9DL8cDXJ4ONqCRRFYbi/I44u7Y8Di/phoK/2E/KDtFK8sDUEr/35EAn52pO1KYrCCH8npH85EWffGKRhGKFtn+M334b3h+dwOTYfnV0tcfr1gRjoa4dasRQr9j/CpvPx6OJqibNvDNIIizsXlYf9oZn4Zno3jFQbHI3JqcSaE9GoEUuweIi3SiDawYdZ6Lb+Mv53KbHF4asEAoHwLKlvlGLx7jCcj9bdgvXZlACM8HfS+bwuSqobsGh3GOobZRjSwQHLhuo3lmkrR8Oz8SizHGYCLtZM6GT0/Z+OzEV+pe5WPoA2EjivEC0vBnloPH8qIhdiqQx+jubo6GSB4CdFKKtthIOFEAOUBMs2xX0GADhbiXA9oZAd4Gfo4GQORwvdttDhCvvvQHdro1RLmIweV2sRiqoUIkdjK/oRO6URgBKF6LM1E6BMzQXWXMgj7Wp/IUTk/Ifo522H8Wr9szViKb65RJsJMHM422+lILuMdglbOdIPTpZCZJTUYvutVMzu44lAD2tUN0iw4Vy8xnu8N9ZfxbbYEJibZkMsK3lcDpYM9cGFVYMR5KW6YsbnUrQ5wsUEDP3mBu6llGDD8/Qs0c83k1m3l6cBRVHo72OHfa/1w/HlAzREA8PV+EKM33wbbx2K0OrExtDFzQpPvhiPB2tGalSv1JHJgcV7wuGz5jz+uJeO72Z2x5Kh9DDq9uBUvLLrARqlcnw/qzu+nxWoYuaQX1mPd49GoZ2dGb6bGagxu5VaVINfg1PhbCnCuADVfztbbiSj07qL2HE7tdn2BgKBQHjW1IoleHnnA70VnHfHdsTL/b1avO/6RikW7wlHZmktPG1N8cOs7k/1ZrakugFfXkwAALw5qgOcrdqeCaOMRCrDLzeTm93Oz9ECNWIpvB3MNPJx5HI59im6PGYHeYKiKJxQ5OU9F+gKnmLhTjkAmwm0vqRlDmhkJ/3Ck5nv7ak2t9ta0hTHxaEoyOT0jE21Ulu9vbmAXdx1t6Ed7eobpazRkJOliBU8DJYiPgh/HUTk/MdYM0HTUvpwWDaisssxvosz+nnbokEiw6bz9MnUXMjDWkW/7C83k5FdVocvpnYBh6JXfdTbzaxM+ez26tWW5li0OwwL/nho0AyNt4M5Di7uh8+nBLA37XI54GFrAntzIQoqG/Dh8WhsvpYECxEPcjnw1qEIjerT06Cnpw1+n98H51YOwsSuLlB3tZTLgROPczDyu5tYdyqGna/RhqOlCKdeH4T4z8bhNaU5GV38dD0ZfTdew+0nxZg/wAumAi7uJpdg0o+3EZlVjud7uOPcysEawmnn3TRsu5WCn2b3wGwtq3N5FfW4GJsPa1O+Slo1AGw4F48Oay/gz3vpWocuCQQC4VlTWFmP8Ztv6zW6eWtUB6xoxulSGzKZHO8djUJ4RhksRDzsnN+nVbM8hiKXy/Hh8WiU1ojh72yB+WpRAcbgSHg2Uov058vZmwtwLpqeB1o6xEejlSs8owwJ+VUQ8Tl4oac7iqsbcCWWXrxU7iT4+UaTmHp1YHvUN0pxI7FQ4/3UF9aUkcvlCH5Ci1ddHRQthfn8jOmAhYin0n7uZmOKLMUCsIcN3QLOuLGJ+BxYingoqVETOSZE5PyVEJHzH0OXpfSnZ+IA0JbSHAo4F52HkBTa6WRSNxcM8GFMCGLRxc2KXfladypGI49mSndXDPSlt+/RjDOKOtcTCjHgy+vYcTu12TkaDofCS/29cOmtIRjawQESmRxZpXUQ8jgY4e8IG1M+UotqUFVPr8QUV4ux8uDjZxZ2GeBqhZ/n9sS11UMxt6+nhuhrlMqxOyQDQ76+gQ1n4/SKHRMBF2sndUbqxgnY/GL3Zt87Lq8Sf9xLZ0/QuRX1mLEtBPtDM9HOzhRHlg5gqz0MTwqq8fLOUHjamuHAon7wdlBNmRZwOSivbURCfpXWVrpPTsfC/+OL2HU3jYgdAoHwlxGXW4mgjdeQoada/t64jlg1yq/F+5bL5fj4VAxOR+aCx6GwbV4v+Dq23dlLH0fDs3E5rgB8LoVvZwaC30wrc0upE0vxvZ5gVIbne7ihoLIBTpZCTOmhGXS6O4Su4kzu5gorUz4OPcyCWCpDoLsVuijmO+VyOm+OgWlVq1VrfXaxEqGbni6G+LwqFFY1wITPNdocFDPjypj1qIs4HwczZJfRi7BMJadAcd12shShukGi0dVgZULmcf5KiMj5D7J8mI9GW1J4RhlOR+aik4sl5val81Q+PUO7qFEUhc+mdAGfS7ukXI0rwOoxHeBgIURqcQ1+C05V2RdFUfh8ShcIuBw8zixXGTY0lA3n4jH0m5uIzCpvdlt3G1P88Wof/DynJxwthMgpr8P1hEL08LTBy/3bwVLUdJK5n1qKiT/e1sideZp4O5jji+e74t4HI/DWqA4aqcz1jTLsuJOGIV/fwOfNiB0Oh8KU7m5I/3Iirr89VEOI6EMslWHNiWi8ezQKMrkcH47vhL0L+8LBounfQqNUjq8uJuD7K0/w28u9sWqkH+smw+HQQW6MVbYuPj0TB/+P6TY2MrNDIBCeJVfiCjDhx9t6t/lmejcsH9byCo5cLseGc/HYF5oJigK+nRmo1/3LGKQX17CLkKtHdzTKgL06vwanorBKv3Mmn0vhkqIq89ogbwh5XJXnM0pqcDaKrvK8MsALEqkMexWta68oxRYw1tMA8OlzdE7fIS25PGMDnPWGe958Qld+BvjYaRxLayirEbMCRsTXvj9bUwErxlwVAayMs6uTRdMcjzLOlsZtKyS0DCJy/oNYiPh4d6ymj/+m8wmoFUuwenQHWJnwkZBfhQOKk4+vozkWDqJX/j89GwsBl4O1ihmen64nI6tUdcXM28Ecy4bRQ5iRWeUqNoyMeUFz5JTXYcrPd/HxyZhmjQkoisLEbi649vZQzB/gBQ5FV4VOPM7BkqE+WDWyacUuIb8K3mvO40J03jMVO3bmQqwa5Ye7H4zApmld4aMmUOobZfj9ThoGf3UDn56Jbba1ztvBHNffHoaEz8dhaQsGXo+GZ8P/44t4nFmGQX72uLhqMIYrBbQBwIP0UkzZcheetqY4v3IwerezQX2jDGEZZRDyuVg3qbPOuSOGDefi0WndRWy/lWKQXTiBQCC0FolUhk3n47Fod5je7fYsDMKM3potuc0hl8vx1cVE/H6Hdh/7alo3TOnu1syr2katWIIle8JR3SBBUHtbLH4KwZ/pxTX46Xrzxjzz+rVDZmkt7M2FmKPFYGjbrVTI5MCwjg7o4maFS7EFyKuoh725ABO7NdlMz9/1kP3zrD4eyC2vQ7CWeVl1oxx1ritsqYepXbtaS2wuXcXxsjNlZ4bUzQwYzeVqJWKFUKbi3sfd1gRZZZqt9s5WJkY5PkLreKoiJzg4GJMnT4arK+2VfvLkyaf5doQWML2XBzq7qNpP5lfWY+vNFNiYCVhr4W8vJ6Jc4RbyxghfuFiJkFVah19upuC5QFe2je2T07EaWTDLhvmgvb0ZCqsaMEppgPDri4lI+NywGRMA2HM/A93WX8ax8OxmRYmFiI/1zwXg9OuD0M3dirVLvplYiH2v9VU9vn2PMOHH289c7Ij4XMwO8sSVt4bi91d6o69aqb1BIsOuu+kI2ngN60/HoqAZsSPic/HBeH+kfzkRhxb3Mzhd+flf7sHrg3OIyCrHry/3xrpJnVXa0KobJHj7SCS+u/IE21/qhQ1Tu8BCyENkVjk2no9HB2cLnF85GPP6eULE130q2XQhAQGfXMKP15LYf0sEAoFgLAoq6/HclrvYrtZVoM6lN4dgsF/Lb4qlMjnWnoxhHcE+fS4AM/u0XCi1BLlcjvePRSOxoAoOFkJsmd2jxSGlhrzHx6diIGnm+ufnaI5TEXSV5s1RfjATqrZg5VfU45jCEnrFcF/I5XJ27mZO33ZspSWvoo5tFx/h7wgRn4tj4dlQj5Hr4GSut1Utt7wOYRlloChgdGfdczstITqnAgDQzs4MqQqXNfVvm3F/66g0l8rM8Xjbm2ks9gKAs6VQ4zHCs+OpipyamhoEBgZiy5YtT/NtCK2Ay6GwbrKmpfS2WynIKq3F3L6e6OhkgfLaRrZX10zIY22ot91KQUZJLdvGdj2hEJfV3NFEfC4+n0K7m12JL1AROp+cisXaSZ2R/MV4g4/57SORGPXdLURnVzS7bRc3K5xYPhCfTwmgb8yzKzDv91BMDnRVqSol5Fdh2b5HGPX9LRwOaz4EzZhwOBRGdnLCoSX9cX7lYMwO8tAQC3/cS0ffjdew5kQ06+Gvj77edkjeOAER60YbbOe98M8w+H10AXF5lfhmRjeNCtOFmHyM33wbHramuPr2UIwLcIZEJsfWmylYti8c47u4IOSDkXh3bEc46Tmhf3flCbp/dgWfnYkzekArgUD4b3IxJh99N15DXJ52a36GiHWjVW5ODUUskeHNQxFsi9oXz3dRab96WmwPTsUZxdzPL3N7wvEptD2dicrDbQOy6nq1s0FpjRje9maYpUXc/XQ9CWKpDEFetujjZYtr8YWIy6uEmYCLV5W+q7UnYtg/f/pcABqlMux/kKmxvxm9PPS2qjEW1n3a2RrNZY5pjaco2hzI2VKEPKWQcRGfgwbF/UEHpX9HacXVAAAvezPWlEAZY7vgEVrGUxU548ePx4YNGzBt2jSDtm9oaEBlZaXKf4SnhzZL6UapHF9eSACPy8EnChG0NzQTiflVAIDxXZwx2M8eYokM68/EwsfBjC2hf3o6FrVqQViD/Owxpbsr5HIgv7LpxvZQWBaeFFTh/+ydZXRUZ9uFr9FM3N1dkBCCuxUtLdDSlpZ6+9bd7a3RUnd3d4XSFncnEDSEuLvb+Pl+nJlDJpNAgADt9861VtYkk9EwnPPcz73vvZUKOYXPzWLDfRN79Zrza9uY/dYm7v9pL3Wtx9YQKyzGBKvvHc/ctFAEAZbuLafzppWrWoGHRkl+TRv3/7SPCS+u5dPNBWd8liQlxIPF8way/aEpPDormQgfF5vff7O9mAkvreOmrzI4UHb8Is/LRc0zcwdQsHgmP9wwEuceNMad+SmjlDu+y7RJorZS3aLjyk928M7aXF69eBDvX55OkIeGorp2LvtoO4uWZXHpsAg23j+JVy9OZdAx0qc/2VzAqOfWcPcPmRypajnu63LgwIGDrjRrDdz+7R5u/CrjmLebNSCYvGdn4uVy4u5nTe0GrvlsJ0v3lqNSyHjjkjRpZvV08ntmGc/9JTqcPnZuCkOj+mawvjNVzVopWPRYnD8ohJ93i12a+6cn2Zke5NW08p1F1n7P1AQEQeANi/zt8pFReFtmUJvaDaw+LErM1Ao54T4u/Lm/wqaQAFDKZcw5jlRt6V6xq3RuavAxb9dbBEFgR6Howmc1CHJ1UtDRyUAnJdiDvBqxoOnsMFpoMbeI9nOltN5+8y7YIVc7q/yjZnIWL16Mp6en9BUefnrbwQ7goRn2ltLL9lewLb+OUXF+TO8XhMks8NQfohxNJpPx5Hn9UClkrMuuYeWhKm6dGE+Ytxgk+vpqe23vI7OScXdScqCsmYUjjnYXpr66QZKJRfi6UPjcLJ6x5Nocjx92lZK+aBWfbi44rgtbgLuGVy8exM83jWRAqG0LvE1v4pOrhvLQjCT83Z0ob9Ly5NJDjH5+DW+uzqGp/fghpX2Jp4uK68bGsO7eCXxy1RDGJ9hKK/46UMm5b25iyivr2ZxbaycR7IpMJmNYtA9ZT08n66npNrNJJ8PnW4s4982NhHg6s/LucVwxMhKZDH7eXcrkV9bz5/4K5gwK5bdbRrP01jFcNCSsRyvxX3aXMfXVDVz3+U52FvZs8+rAgQMHnVmXXc3AJ1awxLLY7Yn3Fqbz9mWDT0rmVVDbxtx3N7MptxYXtYIPrhjC7FR7R7G+ZktuLff+uBeAq0dHccXIvi+qBEHgvp/20diL81tlkxaDSWBCoj/T+tmbCL3w92FMZoEpyQEMj/FldVY1+0qbcFYpuK6Tk+sTSw9K3/96yygEQZDmmzozOzXExgynK0V1bewtbUIugxn9+6bIyalupb5Nj0Yll+R08i6dpAGhntJmb3yAWOQ0tOmpt1hGR/m6Ulhnv0EY5dd7cyAHfc8/qsh56KGHaGpqkr5KSuwdNxz0LRG+LlzTzWzMU0sPYTILPDIrGbVSzubcOslZJcbf7Wj3xuL6YnVJ+XhjATldducD3DXcM1Wc8VmSWc64Tgv3z7cW2tz2suGRFCyeabe474knlx4i7amVbMntTcvdh99vGc3zFwywyTS48L2tTEwKYOP9E3lmbn8ifFyob9Pz8sojjH5+DYv/zDruXExfI5fLmJQUyOfXDGPtvRO4ZrTtv1FudSuXfbSd6If+ZMne8l7ZYjurFdx1TgKFz81i9T3jT9je20peTRtz39nMZ5sLeXx2P366cRQJgW7Ut+m58/tMrvx0JyX17QwI8+SFC1PZ/vBkHp2VTJSvS7ePtyqrmvnvbeWCd7ew6lDVGZ2PcuDAwb+HqmYt136202Z4vSd2PDyZ6f1Pbl5jS24tc97eTH5NGyGeGn66cRQTE49ttNIX7Ctt5IYvMzCYBGYOCOKxWSnHlG2dLF9uK5IyZo7F1aOj2F5Qj5NSzlPn9bd7LTsK6ll+sAq5DB6YnoTBZObZv8SQ8KtGR0kurq06I7/uKZPu1y/Ekx0F9ezrRnre9VzXFasT2+g4v2MWQyfC9nwxLiMt3JvdxY0AdoWxp7OKpg4DaqWchECxyLFKJCN8XNCoFORWt9rcx89NjacjJ+es8o8qcpycnPDw8LD5cnD6uWWivaX0oYpmftxVQriPC/8ZKxY0i//KkjJxbpkYR6iXM2WNHby/IY/JyYGckxKI0Szw5NJDdh2GhSMi6RfiQbPWiHunocUnlx6ioUt4lkwm4/NrhpHTy3mdFp2RSz/azsXvb7U7yHRFLpdx8dAI1t47wca9ZeqrG3h11RHOHRjCmnvG8/olg0gKcqdVZ+T9DfmMeX4Nd3+f2SupWF8T7efKf2encPjp6bx+ySDJn9/K7d/uIfbhP3n6j0M26czHItbfjV9vHk3+szP5+MohJ/yajGaBl1ceYe47mwlwd+KP28Zy79QE1Eo5G47UMPXVDXy4Qcw68nJRc93YGNbcM4EvrhnWo6V4RlED132xiymvrOfLbUV20kcHDhz8b2IyC3xumU+0Sp564sbxseQ/O/OkZljMZoE3V+ew8OPtNHUYGBTuxW+3jiYl5PSvRQ6UNbHwo+20WJzUXrloEPI+NhoAsZD67+8Hj3u7AaGe/GYpTG6fHE9El00qg8nMY7+JcreLh0YQH+jON9uLya9pw9dVzc0Tjjp+PrHk6PP9fNNIAN5el2f3nMOifBhwDMMBg8nMjxaDg0uH9W7mtDdsswTG+ripqW/T46yylaoBkjlD/xAPyXXtYLm4HugX4kFxfbs0s2Ml2tHFOev8o4ocB2cHd41K6rR05sXl2TRrDdw0IZYAdyeK6tr5bHMhAC5qJQ/PFC2k31ufR3ljB4/NSkGtlLMpt9bOhECpkLNoTn9klqDRzrs1N3zZvaZaZZnX2fnIlF69j+0F9Ux5ZT0P/bL/uPM6ns4qXr14kGSDDfD++nwmvLiWL7cVMaN/MH/dMZaPrxzCsCgfDCaBX/aUce6bm1jwwTZWZ535joNGpeD8QaFsemASa+4Zz4XpYTa//3hTAf0fX86QRavsumk9YTU/KHxuFllPTcdVfWJ5A/tKmxj7wlru+j6T68fF8PcdYxkR40OHwcQzf2Yx553NUmEol8sYl+DPh1cMYfODk7hzSny3TnD5tW089tsBhj+zmsV/ZVHmMClw4OB/lo05NaT8928eX3L8hfm6eyfw4IykkyoO6lp1XPnpDl5eeQSzABemh/Hdf0YQ4H76B8cPljdx2UfbadYaSY/05pOrhvaY1XIqNLTpuemr3b26rYezkoZ2AynBHlw/1t66+pNNBWRXteDjqub+aYk0dRh4bZVoUnTnOQm4a8QORm2rjp8yjoZ/pkf6sKuwvttO0jVjoo75mlZnVVPTosPPzYkpJ5G/1x0ms8DmLkqQaD9XmyBZbxeVFCqeFuEtXX/IYjvdL8RDkrJ1fRwHZxdHkeMAgIuGhNsM0wHUtel5a00urk5K7p+eBIiZONbAq5kDghgW7YPWYOa5vw4T4evC9RYN7qJlh+xS79MivLlkqLj7sjm3VlpQ7yisZ09xQ4+vzd/dicLnZkk7QMfj2x3FpC9axTvrcu1eQ1euGxtjo3luaDfw5NJDTH11PX8fqGRSUgA/3DiS328ZzXmpISjkMrbm13Ht52LH4attRWcl8DLG342X5qdyZNEMXrxwoM3valt1nPPqBqIeXMabq3N6XYw5qxUcfGo67142+IRfz7L9FSQ++jcLPtzGNaOjeer8fng6qzhQ1sz5b29m8V9ZNv8WoV7O3DklgexFM/jimmF2NtogdujeX5/P6OfWcMvXu8koqj/uDJIDBw7+f3CkqoV572zm8o932O2Qd+WuKQnkPzvzpOcftuTWMuuNTWzMqUWjkvPihQN5aX7qaSk0upJR1MClHx7tHH129VDculg09wVms8Cd32f2atPoipGRbM6tQ62Q8+rFg+zyYkob2nltlTh/+9CMJLxd1by68ggN7QbiAtxY0MmB7apPd0jf/33nWEB02uxKQqAbU49jB211Yps/JMzOAOFk2VPcQGO7AU9nFTrLOaqrVG1YtA97SxsBbEx1rNk6KSEe3ZroRPu59clrdHDynNYip7W1lczMTDIzMwEoKCggMzOT4mJ7y0AHZxeFXMYjnboaVj7eVEBBbRvz0kIZGOZJq87IKyuzAVFW9vjsFGQyWLK3nJ2F9dw8IY4gDzFL56ON9pkF909LxMdVTXZVCxd06kTMfWfLcedK0iN9KHxuFk/P6Z05wQt/Z5P02N/8tqfsmAv9h2cmEx9w9GDk5+ZEYV07N329mwvf20pGUQOp4V68sSCNjfdP5IZxMbhrlOTXtvHobwcY+dxqXlqeLSUfn0nUSjnzh4RLDnVdXc1eXnmEmIf/ZOwLa3rd3ZkxQOxidZbF9WQe0JWqZh3/+TKD//5+kBZLgKvJLPD++nymvbaBLXm2O2YKS3fn+xtGkvHoFG6b1H0K+bL9FVzw7lbmvL2Z3zPLzqjVtwMHDs4cZY0d3PV9JlNf3SDNR/REiKeGHQ9P5o4p8SfVvenQm3hiyUEu/Wg7lc1aYv1d+f2WMScVFnoyrM2uZuFHYoGTFuHFF9cOkzogfc1LK7JZ34s5nMlJAVLn5b5piXa224IgZgZ1GEwMi/LhwvQwMksapfnaJ2b3Q2kpQApq2zhQJhYCkb4uJAV5sDWvji15dXbPe8fkhGP+G+ZWt7DhSA0yGVzShxlFayzyx9FxvuywyNa6vozUcC+pa2OdZW3XGyW3tX4hnhyutHcDTg4+cctyB33LaS1ydu3aRVpaGmlpaQDcfffdpKWl8d///vd0Pq2Dk2RsvL9derDJLPDMskPI5TL+a8nI+W5nSSctqqd0wHly6UGcVQoemil2fd5em0dFk+2ukbermgctXaFfdpfZJCFbg9aOx+UjIil8bhY3jO9d+vOd32cyeNFKu5a0FY1KwcsXpUq7N4/OSub2yfE4qxRkFDVwwbtbuPnrDApr2wjxcuahmclsfWgyj89OIdzHmcZ2A2+tzWX082u4+esMtubVnZWOQ4SvC7/dMpqCxTPtXOpK6juk7s6Lyw9LjjA9kRzswZJbxzAyxhcQA0ofmpHEhvsmMiW5dwO4XevKorp2Lv1wOw/8tK9b1zpfNyfumZpIweKZfH7NsG4fc29pE3d8l0n/J5bz9trc474PBw4c/Dsob+zgvh/3Mvq5NTZD6j3x7fUj2PLQ5JPOj8koqmfmGxv5bEshAAtHRLDk1jEnlaVzMvy2p4zrP99Fh8HE+AR/vr5uOB6nqcD5bkcx73QzA9MdpQ0dtOtNDI/26daU6NsdJazLrkGtlPPM3P6YzAIP/bIfQYC5aaGMifcDxGJo4kvrpPv9cMNIzGaBxRZjgs4kBLrZxVl05aONohPb1JRAIn37TgZmLXLcnVQ0a434uKpp7LA9PynlMoxmgWBPDaFe4uZfZkkjZgGCPTUEuDuxt8R+XvdMzHI5ODantciZMGECgiDYfX322Wen82kdnAKPzEy2a9WuyqpmW34dQ6J8OHdgMIIguq9ZF/L3TE2ULKJ/yijlvNQQhkR602EwsfjPw3bPcWF6GKmWrpBTp5bzi8uzjztL05mHZiRTsHimTaHUE43tBi77aDvnvLKeTEvoV2cGhnlJjnHP/JnFtaOjWXffBC4eEo5MBn/ur2TKK+t59Lf9VDVrcXNScvXoaNbdO5H3Fg5mWLQPJrPAn/srWfDhNqa9toEvtxb22gigL5HJZFw2XCwEtzw4yU6G+PbaPAY/vZLoh5bx464SqePSFR9XNV9cO4wrLXK+xX8d5oONeby3MJ3C52ax/r4JjInzO+HX9/2uElKfWsHiP7O67bDJZDLGJ/hT+Nwsdj06hf6h9icKvdHMi8uzGfz0SkfejgMH/2LKGju4/6e9jHpujTRUfizun55I3rMzGRnre1LP16I18MSSg8x/bysFtW0EeWj44pphLJozANfTIBPriiAIvLE6hzu/z8RoFpgzKISPrhyCi/r0PPfGnBoe/GV/r247JTmQ7KoW/NzUvLEgzW4tUFzXzqJloqPq/dMSiQ9055PNBWRVNOPlorJRg3Sew5mbFkqgh4Zf95R166h2++Rjd+KqW7T8slssfLubDzpZyhs7OFzZgkx21Fig6zyOp7OK2lZxM21UrJ/kMJdRKErs0yO9qW3V28kA/dyczsg8l4NjIxP+wSL35uZmPD09aWpqcjitnUEe/W0/X22zlRSmhnvx282jKGvsYPLL69EZzby3MF2y6PxoYz6LlmXh56Zm7b0TKKprZ/ZbmxAE0U0lPdJ25iKjqJ4L3t2KTAb3nJPASytEje74BP8ed/GPhdksMPutTZJG9ngMi/Zh0Zz+khUkgNZgYuYbG8mvaeOCwWG8fFEqAIcrm1n852Gp1e+klHPVqChuHB8rBZ1Zb/fF1iJ+3V0mObO4OSm5YHAol4+MJC7g7LWuDSYzX28r4gmL5XdXhkX7cOXIKCYnB3SrQ/94UwGLlh1CEGBSUgBvLkiTFgSCIPDi8uxe7xR2xc/NiUdmJTEu3h9ft+4tQbUGE2+tyeWttbk9Po5SLuO9helMSgo4La5EDhw46DsOlDXx8ops1mYfX0IFcNGQMJ48rz/OJ2iOYkUQBJbtr+CppYeotsyVzksL5fHz+p0xm98OvYn7ftrLH/sqALhuTDQPz0w+bcerrIpm5r6zGa3h+PLehSMi+GpbMXIZfHXdcEbF2m5gmcwCCz7Yxo7CeoZH+/Dt9SPIq2ll1pub0BvNPH/BAC62zNy26Yz0e3y5dN/sRdMxmsTOjvVvbyU1zJNfbx59zL/ByyuyeXNNLmkRXvxy06g+s9W2rluGRnlT16onv7aNYVE+UjAoiLk9hbVt7C9r4pWLUpk3WJTZX/HJDjYcqeHJ8/oR6uXMdV/ssnnscQn+fHESaxkHx+dEagNHkePAjtpWHRNfXEdLly7E25cOZtbAYF5ans1ba3OJ8XdlxZ3jUCrk6I1mpr+2gfzaNm6fHM/d5yTwwE/7+H5XCemR3vx040i7A9Nt3+5h6d5yhkf7sKekUZqzWHnXOOIDT64gMJjMpD65gvZemgHM6B/EwzOTCfcR7TEziuq58L2tCAJ8fs0wm7yebfl1vLQ8m11F4g6Om5OS68fGcO3YaJtB0WatgZ8zSvlyaxH5tUfDwUbF+nLZ8EjOSQm0G+Q8k+RWt/D0H1k96rPnDArhvEEhjI7zw0l5dEHx94EK7vguE53RzIBQTz67eqhNUbKnuIGbv95tl2B9olw+IpK5g0NJDfOy20nUGU38nFHGw78ee2fyvNQQnp034LQM8Dpw4ODkEASBdUdqeGrpIQpq7YMTu2NCoj+vXTwILxf18W/cA/k1rTyx9JDk6BXl68LTc/ozNr53eWx9QWWTlv98uYt9pU0o5TKentOfBX1og9yV/JpWLnp/q9SFOBazU0NYfqASvcnMfdMSuWWi/WzkqyuP8PrqHFzVCv6+cxyBHhrmvrOZg+XNjE/w57Orh0rn+Es+2Mq2fLFQ+OSqIUxKCpQKla78eONIhkbZG89YadYaGPv8Wpo6DLx72WBmDOibAFCAOW9vJrOkkatGRfHZlkJUChkDQj1tZsH+e24KT1s2+LY/PJlADw0ms8CgJ1fQojPyx21jWH6w0u693TQhlgcs0nwHfYujyHFwyry9NpcXl2fbXBfl68LKu8ejNZgY/+I66tv0LJ43QDpQ/7W/gpu+3o2LWsG6+yYgCDD+xbVoDWbevzydaf1sNbelDe1SV+jucxIkxxVXi8vXqaA1mEh67O9e337BsAjuPicBf3cnnlx6kE83FxLl68Lyu8bZLPQFQWBddg0vLs+WgsB8LJkAC0dE2nRBzGaBzXm1fLG1SLSctvxP83VVc0F6GJcMDSfG/+y5r7TrjSzJLOfhX/fbzc9YmTMohOn9gxiX4I+LWsnu4gau+3wX9W164gLc+Ora4QR5Hm3J17bquO2bPWzNtx8sPRlCPDXcMSWecQn+BHseNUIwmMwsySznrbW5x10svX3pYGYOCDotoXoOHDg4Po3ten7KKGXRMvt5jJ6YkOjPS/NT7TLcToT6Nj1vrM7hq21FGM0CaoWcmyfGcuP42DPinGZlY04Nd36XSV2bHm8XFe8uTGdEzMnJ7XpDaUM7F723lfJebDgNDPOkrKGDujY956QE8v7CdLuuysacGq74ZAeCAK9dPIg5aaG8uPwwb6/Nw8tFxYo7x0mzUdY5VhClXnsfn0pBbRvTXttgZxgzvV8Q712efszX9/qqHF5ddYRYf1dW3DXebuPrZCmpb2fsC2uRycQA0o83FTA4wosD5c3S65TJYNGc/jzy6wFi/V1Zfc8EQOyQzXh9I65qBXsfn8rVn+1kY47tzO87lw1mZh8WZA6O4ihyHJwy7Xoj415YR22XGZmnz+/H5SOj+HhTAU//cYhADyfW3TsRZ7UCQRCY884W9pY0cvmISJ6e07/brk9nrLs7kb4uBHpoJHeTT68e2icJ0yda7Fw1KoqrRkUx//2t1LToetzVMpsF/jxQwSsrjkjdmiAPDbdNjmN+eni3lpvfbC/mx4xSyYIbRJnYgmHhzOgffEZPup0RBIHMkka+3FYk6Z67olbKmZQYwPT+QUT7uXLjVxlUNGmJ8HHh6+uGS50wAKNJtBT/aJM4KKpRyTGZBQwm8VDjrlFKmQMnyth4Py4dFsHIWF+8XNSYzAJ/HajgrTW5HO4mp6AzQ6O8eXx2P/qFeDgKHgcOTjOCILCnpJH31+ex/GDV8e9g4dyBwTwzZwCeLicvIdMZTXy+pZA31+RKx5pJSQE8dm7KGc0uMZkFXl91hDfX5iIIkBTkzgeXD7EL1uxLqlu0XPTeVgo7zZUci7gAN3KrW+kX4sGPN460mw2qbNIy642N1LXpuXR4BM/OHcCuwnouen8rZgGb7krX8+3OR6bg56bm0g+32218qRQyVt41/pi2303tBsa8sIYWrZE3F6QxOzWkt3+G4/L++jwW/3WYETE+tOqMHChrZkycH5s6GRSlhnuRHOTOdztLuHJkJE+eLxr6WNc/Y+P9+OSqoQx6cgVtXdQj1q6Pg77HUeQ46BO+2Fpol4zs56Zm3X0TUSlkTHppPWWNHTwwPYmbLOnG2/LruOSDbSjlMlbdPR5fN7XU9Xlmbn8uGx5p83htOiPjXxSLqbumJPDqqqP++XnPzuyzXRud0UTKf5cf16baireLioZ2AxqVnNX3TJAcVbpiNJn5ZXcZr606Iu2ahXo5c/PE2G6LHYPJzNrD1Xy3s4R12dVSB8VDo2Te4DAuGRZOUtDZ+6w3aw0sySznu53FkvVnV1QKGZG+ruRWi/aZwZ4afrhhpE2hA/DVtiIeX3IQk1nAy0WF2SzQrDXiqlbwxoI0UsO9+GhjQa9d9brj3IHBzB8SzpBIbzbn1vLW2txuB1u7cvXoKM4dGExauLdjfseBgz6kukXL73vKeebP3ndtAG6eEMstE+NOafjfYDLzc0Ypb67JlQbBk4M9eHRWMqNPwiTlVKhu0XLHt5nS4n7BsHAen93vtG5mVTR1cNmH221k0sdiWLQPOwrqCfRw4rdbRtt0y0H8e1764TZ2FjaQEuzBLzePok1n5Nw3N1HRpGXe4FBeuWiQdPvOMrVFc/qzcEQkP+wq4f6f9tk99w3jY3hohn1sRWdeWZHNG2tySQh04+87xvXpsXr2m5vYX9bEtWPELo5CLmN4tI+NvfUdk+P5ensxta06vrx2mCRvvPrTHazNruHhmUkMjfJh7jtbbB47zNuZTQ9M6rPX6sAWR5HjoE/QG81MfmUdJfW2riH3nJPAbZPj+TmjlHt+3IuHRsnG+ydJO2/WA8C5A4N569LBfLq5gCeXHsLf3Yn1902w2yn6fEshjy85SKCHEzP6B0uWni/PT7XJ0ukLdEYTqU+u6NUgppURMT58959jB5HqjCa+2S7adFo7NSGeGm6aGMdFQ8JsJG9WKpo6+GFnKT/sKrFxZkkN9+LCwaHMTg05JR36qXKovJkfdpXw654ymjq6d2DrzNfXDWdUrK9Nl2T9kRpu+Xo3rTojIZ4a3DRKjlS1IpfB4nlHB1WNJjOrsqq58auMU3rNFw0Jw12jYnNu7XE7O2DJGUoPY3r/IEbE+PZZwJwDB/9LaA0mVhyq4vVVR8ir6d0C28obC9I4d0DwKS1gjSYzv+wp4801OdL5KsDdiXunJXLB4LA+2yzrLX/tr+CR3w5Q36bHRa3g2bkDmJMWelqfs6S+nUs/2mZ3vu6JCYn+rMuuQaOS8+MNoxgQ5mnze0EQePjXA3y7oxh3JyVLbxtDuI8LV326g405tcT4u7Lk1jHS3OPm3Fou+2g7cFRyXtuqY/LL6+3OH6Fezqy8e9wxHeXq2/SMe2EtrTpjn8/iZFe2MO21DSjlMi4bHsHnW4sYFu3D/tImyTQIxHPUQ7/sx81Jye7HzkGtlKMzmhj05Eo6DCb+umMs67JreP5vWxfZ8weF8PolaX32eh3Y4ihyHPQZv+wu5e4f9tpc5+msYtMDE3FRK5n5+kayq1q4eUIs91uG7LIqmpn5xkYEAZbdPob4AHemvLKe4vp2Hp6ZxH/Gxdo8ns5okrpCXbs5uc/MsJO49QU6o4nRz63p1VAmiDaR71+eflx9uNZg4tsdxby7Lk9ykQn21HDThFguGhLe7S6eySywKbeW73YUs/JQlWRlqVbImZQUwAXpYUxI9D9rC3CtwcTKQ1X8sKuETbm1HOuIYe1ITU4OYFi0D05KBVkVzVz+8XZqW/VE+7kS4eMimR7cNy2RmyfESoWRIAj8uKuU+3+23/k73XholExODmRav0BpBsmBAwfdozOa2Jxby+dbinoVMtmZpCB3Xr8k7ZQzafRGM0v2lvPWmhxJnuXnpubG8bFcNjzypJ3YTpamDtGe2przkxTkzluXDiYu4PTOXhbUtnHph9t6bfoyo38Qfx2oRCmX8f7l6UxODrS7zWebC3hi6SFkMvj4StE84LVVR3htVQ4alZzfbzmaKdTUYSD1yRXSfTP/ew6ezipu+WY3f+6vtHtsqxnBsXj89wN8vrWIlGAP/rhtTJ92cRb9cYiPNhUwNSWQ/No2cqtbmZwUwGpLZg6IVtIzBwTx9to8Zg0I5u3LBgOwNa+OBR9uw8/NiZ2PTOaqT3faff6fOr8fV4yM6rPX68AWR5HjoM8wmQVmvL6BI1WtNtdbuzkrDlbyny8zcFUr2PzgJKnzcPu3e1iyt5wZ/YN4d2E6P+4q4b6f9uHnpmbj/ZPsTj7Wlra3i4qLhoTz/oZ84GjL+3RhNgtc+tE2qcV+PC4fEcltk+OO63+vNZj4fmcJ767Lo7JZPPEEejhx0/hYLhkW0aNkobZVx297yvhld5lkbACiucF5qSFcmB52VmdKShva+SmjlF/3lNlkCXSHq1rB2Hh/JicHEOnryu3f7qGyWUuMnyvDon34bmcJALdPiuPuqYk29z1S1cINX2ZQUNuGi1rBnVPiKapr5+vtxd091QmjVsgJ8tRQXN/9e9Co5IyL92davyAmJgXg43r2OmoOHPxT0BpMbDhSw5fbiuwGrXvDwzOTuGJk1ClLtpq1Br7bUcwnmwql46uPq5obx8ewcETkWdmg2JhTw30/7qOyWYtcJrpr3TE54bQ7aR4oa+KqT3f0esNuXloov1iKMKuJQFc2HKnhqk93YBaQNibXW64TBGyslAVBIPqhP6X7vnVpGucODOl2gxR6ZzaQW93KtNc2YDILfHPdcEb1odRQbzQzcvFq6tr03DtVjK9QK+WMiPGV3PcAbpkYy+qsag5XtvDqxanMTRPfr9VwYc6gEF6cn9rtPM4ft42hf6htZ8xB3+Eochz0KX/ur+Dmr3fbXOfprGLjAxNxd1Iy841NZFU0S9bRIC5Sp722AUGA5XeOI8bflUkvi9K3R2clc12XQC+jyczU1zaQX9PGbZPibOwYjyyacdpPFIIg8N/fD/LltqJe3X52agj3TU087gCpzmjih12lvLs2V5rZCXB34obxsSwYFn7Mk3FWRTM/Z5TyW2a5jQFEQqAbFwwOY44lYO1sIAgCu4sb+W1PGUv3ldPYfmw5m0wGPi5q6trEE3G/EA+mpgRJXbvOc11WmtoN3PLNbmkQ9Ok5/bl8RCRms8D+sibe35DX7S5hXyOXQVqEN5OSApicHEBioLvDuMDB/wy1rTrWZdfwyaYCm42X3jI82odn5w0gtg+cJCuaOvh0cyHfbi+WIg783Z24dkw0l4+IPCNhnl2pa9XxzJ9ZkmlLlK8LL180iPRI79P+3BuO1HD9F7vQGXsnv74wPUwK6XzyvH5cOSrK7jZHqlq44N0ttGiNXDA4jJfmDySvpo2572ymRWtkwbAIFs8bIN3+mWWH+HCjaDIzOs6Xr68bQWlDOzNe22gXQ+GuUbLyrvE2jpzdcd3nO1mVVc2U5AA+unJor95bb/n7QAU3frUbf3cnZvQP4outRYyN92NXYYONVO29henc+FUGchlkPHqOlIlnneV5eX4q0f6uzOsyj+PmpCTzv+ecFgWKAxFHkeOgTzGbBaa+tkEaNLdi7eZYiyB3jZLND07CQyPO5tz8dQZ/7q+UZnO+31nMAz/vx8/NiY33T7Tr5vy2p4w7v8/E11XN1aOjpIDQ5+YN4JLTmCfQFWubvjeMjvPlv+f2O67sQmc08VNGKe+szZPmb7xcVFw1KoorR0bZhIp2xWgyszGnlp92l7LyUJVkbymXwYgYX85LFW2ez9b8jt5oZv2RGm76KkOS2vWWUC9n6e/x0vxULuwyg2U0mXli6UEpnPbBGUncON62GDJZip4PN+azzBKydzoJ9XJmUlIAk5ICGBnre9Zc8Rw4OB1YNxFWHqo6ZvjusfB2UfH6JWmMifM7ZZmRIAhsL6jnq21F/H2gUjrGxAW48Z+xMZyfFtLtzOPpRhAEfsoo5dk/s2hoNyCTwRUjInlgRtIZ6ST9lFHKvT/ad0p64oLBYfy8Wyxw7pqSwB1T4u1uU97YwQXvbqGiScuQSG++vn447ToTc97ZTFFdO0OjvPnquuHS33tPcYPN0H3OMzOQy2Qs+HCb5JTamc4doJ7YklvLpR9tRyGXsfzOcX0u9bvms52sOVzNNaOj+WVPKY3tBmYNCGbZ/qPnjihfF+YPCefF5dmMivXlm+tHAKLT3IjFq5HJYMfDU/hme7GNvB44LYWZA1scRY6DPufXPaXc9b39bM7WhyahUSqY9toGcqpbpcIHxMH1mW9sRCYTAz4jfV2Z8OI6yho7eHx2ClePjrZ5PKPJzISX1lHaIHZ7Omcq5D8784y7YK06VGWXYtwTCYFuLJ438Li7d3qjmV92l/Le+jxJQ+6iVrBgWATXjY22c7fpSlOHgWX7Kvhld6kUSgqi49m4eH9mp4ZwTkrgWdnRBHjk1/2nJCm7eUIsN02IxV1z1D5WEAReWpHN22tFF7aediA73/5IVStfbC3sM3lbT2hUcsbE+TEpKZBJSQHH3aF04OCfSEl9O1vyavlmRwl7SxpP+nFev2QQM/oH90nnvUVr4Nc9ZXy5tYicThtsw6J8uGF8DBMTA86aM2JudQuP/nZAkjknBbmzeN4A0iJOf/dGEATeXJMr5cr1hnmDQ6VO0x2T47lzSrxdN7qxXc+F720lt7qVuAA3frxhJK5OSq74ZDvb8usJ83bm91tGSwHQTe0GUp86Ooez+cFJhHo58+66PLtBfIBzUgL54PL0Y3bBDSYz576xieyqFhvL5r6itKGdcS+sxSwgZfMFeWgI93FmZ+HR8+ktE2NZc7iGrIpmm03WL7cV8dhvBxgc4cUvN4+WwkQ7899zU7hmjO3axkHf4ihyHPQ5RpOZSS+vt5thsC44f88s447vMvF2UbH1ocnS7vb1X+xi5aEqLhkaznMXDJQOEuE+zqy7d6Kd643VaS3M25np/YKkrJWPrxzS7XDkmSCnqoVzXt3Qq9t6uah445I0xsb7HfNgbs13eXddHgfLRQmISiFjzqBQbhgf26vdq5L6dpbsLWfp3nIbJzGNSs7kpEBmp4YwIdH/jHYadEYT57+1mcOVLYyM8WVikj9/7KuwsXWWyTimeQHA4AgvRsb6MiLGlyGRPjirFdLQq0wG7y9MZ2qXcNmeEASBssYOlu2r4MON+b3Wrp8MKcEeTE4WuzypYV4Oe2oH/0gqm7Rsza9l6d4K1nQatj5R3JyULJ43gHNSAvvkOCPKYBv4KaOU3zPLabfMOjirFMxJC2XhiAj6hZy9WYfGdj2vrcrhy21FmMwCGpWcu6YkcM2Y6DNiDNOuN3LfT/tOqGPducC5c0o8d05JsLtNh97EZR9tY3dxI8GeGn6+aRTBnhoe+mU/3+0swc1Jyc83jZIUC0aTmbhH/pLub+3Q7CioZ8GH2+yiGrxdVKy4azz+7sc27vlgQx7P/nkYbxcVa+6ZcEyFw8mw+K8s3l+fz+g4X+QyGRtzapkzKITfMsttX8fl6fznywyUchm7Hp0iqSSu+GQHG47UcP/0RC4ZGkH6opV257Lld447ZUMNB8fGUeQ4OC18u6OYh37Zb3NdhI8La++dAMD4F9dS2tDB4nkDWGDZ+dhZWM/897aiVsrZ+uAkXNRKRj63msZ2A+8tTGd6f9uFaofexOjn10i5Oo/8ekD6XeFzs07vGzwGR6pamNrLQsfK65cMYuaA4GOe/ARBYENOLe+uy5V2BWUymJYSxE0TYkkN9+rVc+VUtbB0bzlL9pbbhMC5OymZ2i+IcwcGMyrO94zIOqwSBpkMlt02lpQQD4rq2li2v4I/9lZ0q+sfG+/X4yCzSiFjULgXI2N8+WNfBfm1bXholPx95zhCesgvOh5ag4ntBfV8ubWQVVknv8g7HnMGhTC1XxBj4/1sulMOHJwpjCYz2VUtZBQ18PPuslPq1IBYyD86K5nhMb59Zs1c1tjBr7tL+Xl3GQWdMl7iAtxYODyCeelhkgz6bGA0mfnaIk2yzh9OSQ7k8dkpdvlgp4uyxg6u/3zXCc1FzU4NYelecQHfWWXRGZ3RxA1fZrAuuwZPZxU/3jiShEB3Xl15hNdX59i4q1mZ985mdhc3AjBzQBDvXJZObauOWW9spKpZZ/cc71w2mJnHsYAub+xgyivradebeOGCgVw0NLzX77M3aA0mRiwW1x6Pz07hqT8OIQi2RgwAg8K9mJDoz2urcpiY6M+nVw8DxM7i4KdXYjAJrLp7HAfKmrnz+0yb57A6rjlmNk8vjiLHwWlBaxBtl63D41asHvYfbcxn0bIs4gPcWHHXOGQyGYIgcP7bm9lX2sS9UxO4dVI8L/x9mHfW5TEsyocfbrTPn3ll5RHeWJ3DiBgfvF3U/HVAHC5fedc44gPP3g6JNejszinxZBQ19Npd6L5piVw+MvK4J+ndxQ28uy6PlYeOpoOPivXl+nExjI/371VXQBAEDpQ1s3Sf2OHpbCnq7qRkUnIAM/oHMT4h4LTaq97y9W6W7a+wC4sDyK9pZdm+Cj7aVNBj/s6lwyPQ6k1sza87pi3qd/8ZwaBwrz7ZRT6ZQvZEGRPnx00TYhkZ4+vo8jjocwRBoLpFx4GyJjbn1vHltkIMplM/xV87JpqrRkX16YK+qd3AikOV/LqnjK35ddKOuItawYz+wVyYHsaIGJ+zumAUBIF12TU8+2eWJJlLDHTnsXNTGBN/5sJFt+fXceNXGTQcx+ClM1OSA1mVJZ5LujN2AVE+ffPXu1mVVYVGJeera4czJMpHUlyAvcPpO+tyeeHvbOnngsUzMQtwxSfb2ZxbZ/ccC4aFs3jewOO+3hu/zODvg5UMifTmhxtG9vnx0ToTHObtzPmDQnh7bR4jY3wpaWintOFottDTc/rz2eYC8mrabGaI/thXzq3f7CHaz5U194znzu8z+b1LB8iRj3NmcBQ5Dk4b1gKkM2kRXvx682iatQZGPruaNr2JL64ZxrgEMR3YaigQ4O7EpgcmUd+mZ8zzazCahW6tFssbOxjz/BrMwlGHE8DGq/5sYA0/jfBxYd29E5DLZZK8rjdcMDiMO6fEH3ehkFPVwnvr8/k9s8xmyPa6MdHMSQvt9YLebBbIKG5g6d5y/j5QKeX2gChpm5AQwIwBokVyX++SWrs5aqWcnY9MwdO5+8e3DoF2R1qEF9P6BZEY5E5Vk5at+XVszauzeR8gdnr6hXgyNMqb9EgfhkR5HzfPqCcEQeCLrUU8ufQgZkF8DZOTAvh2h21ga18yPz2M0XF+xPi7EuPvJoXrOXBwLARBoLShg4PlTewsbOCHXSW0aI3Hv2MvGBrlze2T4xke7dunzpZNHQZWHapi2f4KNubU2BRgI2N8uSA9jBn9g87aTGFntufX8eLybGn20dtFxd1TE1kwNPyMOWcJgsDHmwps5lN7w+g4Xzbn1iGXwbNzuzfuMZjM3PrNbpYfrMJJKeeTq4YyOs6PP/dXcMs3uxEEe3mb1RTAyqGnpuGiVkpdn64kBLrx+y1jjruhtvZwNVd/thOFXMay28eQFNS36z1BECQX2HunJvDZlkJqW/UsGBbOtztKpNuplXI+u3ool364HbVSTsajU6QO/B3f7eH3zHL+My6GB6YnMWTRSrui83R0oBzY4yhyHJw2qlu0jH5ujd3uoLVYeWLJQT7bUsikpAA+uUp0GNEbzYx5fg3VLTrevnQwswYGc+s3u/ljXwWXj4jk6Tn2w4XXfb6LVVlVXD06ik83F0rXH356+llzs2rXGxm6aBVtehO/3TKaQZ2kZAfKmjj3zU29epwBoZ48Pae/zf27o7Shnc82F/LdzhJaLVacvq5qFo6IZOGIyOPqmztjNgvsKWngr/2V/H2w0mbnSq2QMzrOl+n9gzgnJahPMmEEQWDyK+vJr2njvYWDmd6/e6lCSX07Y19Yi1wGP944kuf/ymZHob0rT0KgG9P6BTGtXxDOagWTX15/zOeP9nMlPdJbKnxi/V1PaEf47wOV3PrNboxmwSbHx7qwvOO7PZJc43QxINSTCYn+9AvxJMLHhXAfZ4fk7X8QQRCoadWRU9VKdmULa7OrTyqj5lgkB3tw+6Q4xib493mR3diuZ83hapbtq2BDl8ImMdCdWQODmZsWesZkX8djf2kTL67IljJTnJRyrhwVxS0T4vB0OXP//xrb9dz74z6pG9MbXNQKkoLc2V3ciFop541L0uwk4SDK7+74LpNl+ytQK+R8eOUQxif4szm3lqs/3YneZOay4REsmtNfOm7m1bTaHHc33DeRCF8XKSuvKxqVnKW3jjmu+qJZa2D6qxsob9Lyn3ExPDwzudfvt7fsKKjnove3olHJuX9aEk/9cYggDw39QjxsAkBnDQgmwMOJTzcXSjI8EFUsQxatolVn5KcbRyKTybjg3S12z7P1oUnHNQ9ycOo4ihwHp5W7vs+UEp2tLBwRwaI5A6QDoVwGWx+aLOW4WAO0JiT689nVw9iYU8PlH+/AQ6NkxyNT7AqXtdnVXP3pTrxcVDw4PYkHLbNAby5IY3ZqyJl5o91w01cZ/HWg0iYTqDMdehOjnlvda1nBewvTOScl8Jja9hatge93lvDp5kKpm6BWypk7KJRrx0aTcIISPkEQOFjezF8HKvjrQCX5NUc18HIZDIn0YXJyAJOTA0+4OOiMteC9alQUT5zXr8fbTX9tA4crW3jnssFMSQ5k0FMraNebuHJkJPm1bWzNq7Oxpg721BDkqWGPpchYedc49pc1sauogYzCBrKrWuyew8dVTVq4F6nWrzDP41puWy1a5TL49ebRdvNRxXXtXP/FLrKrWpDJ4JGZyewva2LJ3vLjGiucKj6uatGYIdqHGH83wr1dCPR0OitWug76Bq3BRHF9O4W1beTWtLLhSE2vQ4pPlPEJ/lw1OooR0b59LlsVBIG8mlZWZ1WzOquaXUX1dJ5DTwh0Y9aAEGYNDCIu4J8zoH2grIm31uTy90FRHq2Uy7hkWDi3TYo/43lke4obuPWbPSfUPR4R40N1i478mjbcnZR8eOUQRsT42t1ObzRz1/digaNSyPjg8iFMTApgW34dV326A63BzIz+Qbx16WDpvFTfpmfw0yulx/jq2uGMiffjcGUz897ZIhlEdOb5CwZw8dDjRz88+PM+vttZQqSvC3/dMfa02G9bN00XDAvnYHkz+0qbWDgigm+2F9t8Nt+/PJ2HftlPfZueT68aysSkAEDc9LrxqwxCPDVsemASz/99WAost5IS7MGfd4zt89fuwJ4TqQ3Ofk/Ywb+OhSMi7Yqc3/aU8/DMZGL93RgS6c2uogZ+3l3KzRPiAFGq9fbaPDYcqaGqWcuoWD9CPDWUN2lZcaiK87oULmPj/PBzU1Pbqser0+7Z838fPqtFzuTkQP46UMmaw1XdFjnOagV7/jsVQRB4e22ulPXTE1Yp3t3nJHDFyMhuF97uGhXXjY3hqlFRLD9YxYcb88ksaeT7XSV8v6uEcQn+XDcm+riOblZkMhn9Qz3pH+rJfdOSyKlq4e8Dlfx1oJJDFc3sKKxnR2E9i/86TJSvC5OTA5mcHMDQKJ8TchCyOswU1rUd83YDwzw5XNlCdmULMwcEkxLswa6iBgZHevPk+f1pajewJruK5QeqWH+khoomrc2czoXvbeXeqQncPCGWMG8XmtoN7C5uYFdRPbsKG8gsaaS+Tc/qw9U2u3ZRvi6WgkcsfPqFeNgU2xemh7HhSA1L9pbz8sojfHHNMJvXHeHrwg83jOTKT3eQWdLIV9uK+OuOcZIm2+rqtjWvjmX7K1iXXUNfUd+mZ9m+ih5dljQqOYPCvUiL8CYx0J1gTw0hXs4EeDgKobOF0WSmqkVHRWMH5U1acqta2JJXZ2MFfzrwd3fi8hGRzE4NIcrX5bTMuGgNJnYVNrD6cBVrDldTVGfrwpkU5M60fkHMGhh8wpsyp5tdhfW8tTZX+v8pk8HcQaHcOSXhuGHPfY3ZLPDRpnye/dPegvlYzE8PY2VWFY3tBkI8NXx05VBSQuwXfx16Ezd9LZoMqBQy3rksnYlJAewsrOeaz3aiNZjFoftLBkkFjtZgsilwFs3pz5h4P+padVz3+a5uC5x5aaFcNOT4sq0NR2r4bqcoF3vhgoGnpcA5UtXCqqwqZDJIj/Th2x0lkgSzc4ET6uWM0SRQ36YnwN2JsZ1mrpbuE2dvZg0MRiZDKoQ7Mzk5oM9fu4NTx1HkODhhBkd4EePvatMBaNUZ+WNfBRcNCeeiIeHsKmrgx12l3DQ+FplMRkyn4ueX3WXcNCGWC9PDeGNNLr/sLrUrcpQKOTP6B/PltiJWHqomyteFwjpxQLBdbzwjYWvdMc5y4DtU3kybztijdlwmk3HrpHhunRTfq4H2V1Ye4ZWVRxgT58cjs5JJDrY/QSkVcmYNDGbWwGAyiur5aGMByw9WsuFIDRuO1JAQ6MaVo6KYmxZ6Qn+f+EB34gPduW1yPCX17azOqmL14Wq25ddRWNfOx5sK+HhTAR4aJeMTA5iSHMCEhIDjSjcCPUQ5Xd1xLJsD3MVdUqsJgVWG12z52dNFxdy0MOamhaE1iGYEa7Kq+XJbkXS/x34/CL8fJDHQnYlJAUxODuCuKQkoFXL0RjMHy5vILGlkb0kje0ubKKhto7CuncK6dml4VCmXkRTsTmqYFwNCPekX4sntk+NYsrecDUdqqG3V2c36eLqo+PjKIZz75iYK69r5cGM+t1scjGQyGWHeLswfIgbLWREEgZoWHTsLG1iVVcWqQ1V2yeCnitZgZlt+/XE7AaFeziQGuZMQ6E6Mnyv+Hk74uznh5+aEr5v6jNji/tsxmwUaOwzUtuqobdFR26anrKGD/WWN7C1pOm2zXN0R6OHERUPCmZoSRHyg22mT9prMAofKm9mUW8vm3Fp2Ftajs4QUgyiBHRnrK9mph3n/M6RoVgRBYGNOLW+tzZVCK+UyOC81hFsmxp0Vg5vShnbu+WEv27sJ0TwWlw2P4IddJRhMAqlhnnx4xRACuuk8tWgNXPv5LnYU1KNRyXn/clGitru4gas+2UG73sTYeD/eW5gubYSYzAJJj/0tPcbCEREsHBGJ3mjmpq9328ierfQP9eDZeQOOW0w3aw08+PM+AK4aFcXwbrpOfcH768WOy7SUIEmCOKN/EMsP2soAFwwL5xdLWOq8wWHS3FWbzshqi2RwdmoIhytb7Ip4gElJjiLnn4ijyHFwwshkMi4aEs5zf9nuNv2eWcZFQ8KZOTCYJ5YepKC2jT0ljQy2BKRdmB7GrqIG/thXzk0TYjlvUAhvrMllS24dLVqD3bzB7NQQvtxWxIqDlTw9p79k17j2cA2zBh7bjvJ0EeChIdhTQ0WTlgNlTb06MCcEulP43Cx0RhOXfLBNkll1x6bcWma8vhGAty5NY1q/oG4XmumRPqRH+lBc186nWwr4YWcJR6paeeTXAzz312EuGhLO5SMiifJzPaH3F+7jwlWjo7lqdDQtWgObcmpZlVXN2uxq6tv0LLXk8ijkMoZEejMhMYBxCX6kBHvYndSsPwscW7tlVeoZzWbL/cSfu2YtAGhUCiYmBjAxMYCC2jY25daSEOiGp7OKjCJRqpZd1cJ76/PwdFYxJt6P8fH+jIn3swmfbWzXs6+0yVL0NJJZ0khtq54DZc0cKDtq0arsJCO86/tMbpkYR0qIh41Rg6+bE/dPT+Su7/fy3Y5ibpsUd8wTvEwmI8BDIxWsnWnTGSmobWNnYT2/7C5jf1lTD4/SN5Q1dlDW2NHrrBRPZxURPi5E+LqIc0LeLvi4qnDXqHBRK3BzUuLipMRNrcTFSfGvKpJMZoF2vZFmrZGmdgNNHQaateJlXaue4vp2SurbKaxr63Zxd6YZG+/H+YNCGR7tQ5i382l1ITObRQna9oJ6tuTVsiWvTrJSthLo4cS4eH8mJwcyNt7vH2Ee0BWd0cTSvRV8sqlAsmJWKWRcmB7GjeNjifQ9seNlXyAIAr/sLuPR3w7QYbDvihyL81JDpMDjWQODeXl+areFbUObnis/3cG+0ibcnZR8cvVQhkb5kFnSyJUf76BNb2JkjC8fXD5Eur/ZLJC+6GgHJzXMk0VzBiAIAo/+tl8qDjvj46rm/U6PcSyeXZZFeZOWCB8X7p+eeELvu7eUN3bwe6aoOpk7OJRbvt4NQIiXMzWdDGyUchkTkwJ4dZVonnBhepj0u9WHq9EazET4uDAg1JPXVtkbLPi6qkkN8zot78HBqfHPOwo5+FcwLy2UF5dn2yxEt+bVUdOiw9/diSnJgSzZW87yA5VSkXNOSiAP/7qfg+XNlDa0E+vvRoyfK/m1baw/UsO5A227OUMivfFxVVPfZitZ+2ZH0VkrckCUV1U0adnfyyLHipNSwa83jwZgdVYV136+65i3v/WbPQDcMD6G68fGdOsYFuHrwuOz+3HnlAR+yijly62FNt2XCYn+XDkqqtcW1J1x16iYMSCYGQOCMZkFMksaWJVVzeqsKo5UiQue7QX1PP+32H0ZF+/PuAQ/xsb74+OqpsFiNe7udOyOj9Utzd9N3H20StH83Y+tg69sFm/333P7MSbej8Z2PeuP1LDmcDXrsmto6jDYSLriAtwYG+/HuAR/hkf7MC7BX3IAtErL9pY0sbe0kYPlTRwsb7ZZyG3MqZUGviN8XEgJ9qBfiAdJwR70C/FEJoPyJi01Lbpud1J7g6uTUpISdi7K6lp1/JRRymurck54IdSXNHUY2F/W1CfFl0Iuw1WtwMVSELk5KaVCyVmtRK2Qo1bKUStk0gLeuo6Xy2RYP81Gs4DJLGAwmdEaTLTrTXRYL6XvjTR3GM/q3+5kcFLKmZISyJTkAIZE+hDi5dxn2TTHQm80i3NuhfXsLGwgo6jebs7QzUnJiBhfxsT5Mibej1h/t39sPkhtq46vtxXz5bYialvF441GJWfBsAj+My7mrA2L17fpeeTX/VJMQm9JDHTHw1nJEksGzu2T47lzcny3x/iS+nau/mwnudWt+Liq+eKaYfQP9WRbfh3XfraTNr2JYdE+fHzVEGk+SxAEpr22QTr+uaiPnrteX53DD7tK7Z5HIZfx1qVphPYiu2zFwcqjMrULT49MDeCjjQUYzQIjY3zJKGrAaBYYFuVDRheJ6LR+QWzKqcVkFkiL8LIJ47bmDM1ODUYmk7G8G6nahMQARyTAPxRHkePgpAjw0DAq1tfG5ccsiFrVy0dEMq1fEEv2lvP3wUoenJGETCbD182JIVE+7CioZ+WhKq4eHc3UfkG8tz6P5Qer7IocuVzG6Dg/lu4tZ3eng1J3XvxnEmt35FR2cycnB1L43CzadEamvrrhmJKW99fn8/76fJKC3Fk0pz/pkd52iwlPZxXXjonm6lFRrM+p4YsthazNrmGd5SvK14XLR0ZxYXpYj3bOx0Ihl0ndowemJ1Fc187a7Go2HKlha75Y3P68u5Sfd5cik8HAUE/2looL4eMFdh4sF3dUo/xc0BlNHLL8nBjk1uN9Gtr05NWIuRXxgeLtvFzUnD8olPMHhWI0mcksaWRDTi0bc2rYW9JIbnUrudWtfLq5ELVCzpAob8ZaCrPkIA/CvF0I83aRCmirk9rYF9YC4OemxkmpoKyxg+L6dorr27vVZt/+3R4mJQWQEOhOYpA7QR6aU178+bo5ccP4WP4zLoaD5c18vqWQ3zPL0ZvE7tfYeD+eOK8fKrmcQxXN7ClpYE9RI3tKGvokJ+V0YDILNGvFzsn/MoEeTgyP9mVsvB+Dwr0I93E5ow6S1gJ/X2kT+0qb2FMszrF1lp/B0TmvkTF+jIn3ZWCY1z++U7e/tIkvtxXyW2Y5esv7CfLQcOWoKBYMCz+u+cjpQhAEluwt54klB08o+wZEWdW67Bqyq1pwUSt48cLUHjf99pc2cc3nO6lp0RHkoeHLa4cRH+jOuuxqbvgyA53RzMgYXz66cohNoXHZR9ulXCCAfY9PRS6X8cPOkm47GQAPz0xmVOzxs4OqmrU8YJGp/WdcTLfmCH1BY7ue73aKXa5Lh0dIQeaj4/x4dZXtrOylwyN47HcxF2h++lFpcbPWwHrLrNbs1BAKats4XGlvbOOYx/nn4ihyHJw0U/sF2VmZLttXzuUjIpmQ6I9aKaeorp2sihZpCHJqSqBNkTMlOYD31uexObcWQRDsFoNj48UiZ1NuLdeMjuaTzQWAKLPpzY7R6SDM8rzlfaC1d3VSsvnBSQAs2VvO7d/u6fG2hytbuPC9rQA8OCOJBcMi7AoWuVwmybkKa9v4clsRP+wqobCunaf/OMRLy7OZOziUK0dGScYAJ0OErwtXjoriylFR6IwmMgobWH+khvVHajhc2SIVOAA/7y6lXW9kVJwfo2J9ifE76thW36bnQLl42xExvmzJrUNnNOPnpibWv+ciZ/nBSgRB3NHszvlIqZAzJMqHIVE+3H1OAk3tBrbk1bIhp5YNR2ooa+xgS14dW/LqeP5vsYAZFevHyFhfRsb4EmkZ0LaaJrg7Kdn0wCQ0KgUNbXqyKpo5WN7MoYpmsitbbFLIu87CuDspSQhyJyHQTexe+rsS4+dGmLfzCedtWE0jXpyfyn3TE/lgfT5fbC1iY04tc97ezIdXDGF6/6BubWNB3KGva9NR3tghvv7yZrIqWzhc0Wy3qHVw6rioFfQP9STNYgIRF+BGkKfmrGYhVbdo2V/axN7SJvaVNrK/tMku4BnEXJghUT4MixKzp/qFePZpZs7polVnZElmOd/sKLKRnqaGe3HtmGhm9O9eAnymKGvs4LHfDvRaItqZK0ZG8u2OYgwmgRh/Vz64PL1Hh7q1h6u55ZvdtOtNJAW58+nVQwn2dOav/RXc/t0eDCaByUkBvH3ZYJui+o7v9rAl7+hGYvai6SgVctZlV/PQr/u7fa5LhoZzzeio475+s1ng7h8yaWg30C/Eg3unnh6ZGsDHmwpo15tICfaguL6dVp2RxEB3cmtabW4XY9m0zK9pw1Wt4LxBRzdb/9xXgd5kJj7AjcRAd95bb+uoBqLc8UwGwzo4MRxFjoOTZlpKIP/9/YCNXe6uwgZadUbcnJSMifNjzeFqNuTUSEXOhER/Fi3LIqOoAb3RzMAwLzQqOfWWnfmuB+zh0T4AHChv5rbJ8VKRs7+08awVOVYZlVX20FeclxrCeakhNGsNnPvGJorr7YcbrTz312Ge++swA8M8efK8fgwK97IrEKP8XHns3BTuPieB3zLL+GJLEdlVLXyzvZhvthczPNqHS4dHML1/0Ck5bjkpFWIBE+fHQzOTqWrWsnRvuU2A3V8W9zYQd1JHxfoyMtaX7MoWBEEcVg300PD472Kw6rkDQ3rsfpjNgpSddEF6aK9eo6fLUemdIAgU1LZZ5Gc1bM2ro7ZVz5K95ZL8I9hTw7BoH8mUYO7goyGs3q5q6f2CuCt7xSc72JhTi0Yl56bxcRypauFIVQv5tW206IxkFDXYSSRUChkRPi7EWGSbMf6uRPuJRZCvq/q43Z8Adw2PnpvC5SMjufP7TPYUN3Ld57tYfc/4Hi1v1Uo5wZ7OBHs6kx7pc8zHN5jMNHUYaGzXU1LfQWFdG0V14lxKUX07RXVt/9gu0elE/DcTC9Vof1fiA9wI8tDg46bG3Un5j5FsaQ0mcqvFbJ3Dlc0crmzhcGWLzSyCFaVcRmKQOwPDRHv1IVEnni11ttlf2sQ3O4pZkllGm8XxS62QM2NAEFeMjCI90vusvj6zWeDLbUU8+2fWCW8ohHo5kxzswRdbRbOVGf2DeOHCgT3mZn2zvZjHfj+AySwwJs6PdxYOxkOj4ueMUu77aS9mQZzhee3iQTYF3z0/7JWOeSCGfTopFewvbeLmr3d3Oyc5Js6Ppzvl6RyLjzblszm3Do1KzuuXpJ22ormxXS+dI24YH8PTf4jnotmpwdLcjZWFIyL5erv4d507ONRmA+LnTkYEMpmMZfvL6cqoWL8+D9N20Hc4ihwHJ02Ah4a0cC+bUESjWWBbXh1TUgIZFevLGotL143jYwGI9XeT5mz2lzWSHulDWrg3W/Pr2F5Qb1fkRPi44K5R0qI1opIfPSBmFDX0GDB5urHqlrWG07Pz7aFRseH+icDRvKCe2FfaxNx3xFCyR2clc8mwCLtdYlcnJZcNj+TSYRFsy6/ni62FrDhUJc3U+LiquTA9jAXDIog+QaOC7gj00EgnzsRAd567YAAbc2rZmldHRlEDlc1aftlTxi+dbMgPlDXz8opsSf51ybCe7Ud/2l1KdlULbk7KXtmUdsXq9hfjL7rR6Y1mdhc3sCWvjm15dewpaaCiSWtzsv9hVwntluHckbG+NhK8TzcXsjGnFqVcxp+3jyWmUwdKZzRRUNvGkapWjlS2kF/bSn5NGwW1beiMZvJq2sirsbfY9tAoifR1JdzHmXDLgL8YCOpCqJezzeIg0teVb68fwfz3trK/rMnGuv1UUCnk+Fmc1k40z0QQBHRGMy1aI206I6068VIsmgw0tOtpaDfQqjPQpjPRqjPSqjXSphdvpzeZ0RnM6Ixm9EYzBpMZsyDQzRoLmUxcpCvkMpRyORqVHCelAme1AmeVAo1KjquTEneNCg+NeOnprMLXVY2PqxovF5V0W3eNCneNEiel/F+zwO/Qi5+x/NpWCmraRPONSrHA7m5RKpNBnL8bA8O8GBjmycAwT5KDPc5ayPKpUN2iZUlmOb/uKZNkrwAx/q5cOiyCeYPD+iTc+FQ5UNbEY78fOKbpTE9cPiKS9UdqWJVVhUIu475pidwwLqbbz6fJLPD834f5wJLhcsHgMBbPG4BKIePddXk8/7doFnTRkDAWzxtoM991+7d7pE0egH1PTMVFrSSnqoUrPtnerVV0fIAb7ywc3KvO2IGyJl5cng3A47P72cy99DUfbSygVWckOdiDVp2R2lYdIZ4a6tr0Nv8n3J2UjE/055k/xSJo4YhI6XdFdW3sLGxALoO5aaHk17TadAat9NQ1d/DPwFHkODglxsT72yW/b8ypYUpKICNjRa3tzoJ6jCYzSoW4cBgW5cPfByvZll9PeqQPQ6PEImdPcSOXDY+0eSyZTEb/EE+25tfZyMN+yyznkVkpp/39dYeTZYGpNZ7+IeaJiQEUPjfLJsCtJxYty2LRsixSw71YdH5/+ofaOp7JZDJRjhXrS0VTB9/tKOH7nSVUNmv5YEM+H2zIZ1SsL5cOj2BqStBJ77IZTWY+21IIiMVKWoQ3aRHe3D45Hq3BREZRA1vyanl7bZ7N/d5ckyt9//W2YoZF+zAs2semK1HdouV5i6vf7ZPj+kRTr1bKGRHjK2rDzxEXjff9tJc/OmXQaA1mfsoo5acMcWcv0teF4dE+FNW1S5avD89MtilwQOxyJQV5kBTkAalHrzebBcqbOsTFqaXoyatppaC2jbLGDpq1xh4H/OUysRsWbil6InxcCPbUkGWRzHV1vTobyGQyNCoFGpVCsgR3cPLojCbKG7UU1bXZfGbya1op75QZ1RUvFxWJge4kB3uQGCTOiCUGuv8jnc96S4fexIpDlfy6p4yNlmFxONq1WTAsguHRPv+IIrWxXc+Ly7MlB7QT5bLhEXy7oxijWSDUy5k3FqT12JFq6jBw+7d7WG+xSb59cjx3TYnHLMB/fz8oWe5fNyaah2cm2wzKW0Ourex9fCoeGhXFde0s/Hh7t3NDfm5OfHLV0F51MVq0Bm77VpTITesXyCVDT3xzqrc0tOn51KL4uH1SnFTYXTIsgg+7BHhePDScP/ZWYDILDIn0Fo/TFn7eLW7CjY7zI8hTw+vdzCLJZKKhkoN/Lv/eI52DfwQjYnx4Y7XtddZFX3KQB57OKpo6DGRVtDAgzBOAwZFe/H2wUhowT7JkwuR0k1QPYkL21vw6CjqFSnYnuThTGE1HT6pnCrVSztuXDeZtxL/TOcfI3dlb0sjstzYBcOeUeK4cGYV3l93MYE9n7jongdsmxbE2u4Zvthex7kiNNKfi56bmwvRwFgwLP2Fb1d8zyymobcPbRWWTDwOiBfToOD+Sgz34fmcJta16xsT5cbiyxUb+9+W2IumkHOHjwlDLXMDba3Opa9OTFOTOlaOiTuh19Zale8ulYvKG8THcPimenYX1bM0XOz37y5ooqmu3y0rYUVCPAAyN8iYl2OOY8zZyuUwyOhgb72/zO63BRFFdu2RZXFzfTmmD9ecOOgwmypu0lDdpu83U+GBDPt/vLCHYU7Q7D/J0JsRTQ5CnhmBPMRDUz80JL2eVwxHoH4LWYKKySUtpQwelDe02lyUN7VS36GxkwV3xclER4yfKHeMD3UgKcicpyINAD6d/xGL/VDGYzGzNqxPNbA5U0topVyotwot5aaGcOzDE7jh3tjCZBb7fWcILyw+f1KbD1JRAmrUGG3voZ+cO6NE0Jq+mles/30V+bRsalZwXL0xldmoIHXoTt3+3h5WHxDDMR2elcO2YaJv7Xvf5TlZlHZ0P2v/EVNw1KiqaOrj0o21UNdufazUqOR9dOYRwn+PnHwmCwP0/7aOgto0QTw3PzRt4Wj+TH27Mp80yi2MSBArr2vF0ViGXYZNHJpeJnZtLPtgG2HZxzGZBysy5MD3MYhRhG4AOMDTKp1vXUwf/HBxFjoNTYnCEN2qlXHKuAcipbqVDb8JZraB/qAebc+vIqmiWihxr4nW2paix/nykqhWzWbBbeIV6i9KgsoYO+od6dNsyPpNYT7Au6rMj74i35O4IgsDX24t59LcDPd72tVU5khvOJ1cNYXxCgI1EQamQc05KIOekBFLa0M73O8XuTnWLjvfW5/He+jzGxvtx6bAIpqQEHleW0KYz8vIKUZLwn3GxPQ5YP7n0ILWteuID3Hh6Tn/mvrMZEEPhhkWLDnw7C+vJqmiWnMys+mgAsyDwzfZihkb5kBTkfsID/N1hNgu8svIIb60VO0pXjIzkwemiM+CExAAmJIoOOlXNWia/vN5moQWis6BVbueiVpAW4cWQSB+GRvmQFuHV691zjUoh7bp3RRAEalv1lDS0U1zXzgcb8m1MD6w0dYj5Lt05AVlRymX4uqnxdxeLHn83J/zcj176uVnkXM6ipOvfKGc6mwiCQIvOSEObntpWPdXNWqqatVS16Khq1lLdbLls0UlBuMfCWaUgzNvZZnYr1jIb9E9Z3PcleqOZLXm1/Lm/ghWHqmyKhXAfZ+YOCmVOWqhd9/Rsk1FUzxNLDp20zfqN42P5dkcxTR0GnFUKnjyvH/OHhPVYGKzNrub2b/fQojUS4qnhgyuG0D/Uk7pWHdd+vovMkkbUSjmvXTyImQOOSrwFQeDyj3ewKfeoedCBJ6fh5qSktlXHwo+2d+sgqpDLeOeywQwK9+rV+/lkcyF/HahEpZDx9mWDT+tntb5Nz+cWFcHtk+N5daXoonbFyEjJstrKtH5BZFe1UNmsxcdVzYwBR2VnOwvrKW3owM1JydSUILIqWrqVFU/v55Cq/dNxFDkOTgmNSkG/EA8brbHJLHCoopl0S/t3c26dzULMungrrG1DZzQR5euCSiGjw2CiollrZygQ6iXuFpU1djAo3EsqcrQG01lZeLVoxZPt2ZZ8yGQyFo6IZOGISNr1Rm7/do/NjlxXrvlMzOUZEunNS/NT7YJCw7xduGdqIrdPjmd1VjXf7ChmY06NlA/j7+7EhelhXDA4rEc99csrjlDepCXcx5mreui0/LK7lN8zy5HLYNGc/tz+7R4a2w0MCPXkkVnJqBRy6WTcrDWQUdTAnd9l2iwEj1S18uTSQ4BYUAwM82SwRRaXFuF1wrtrTR1i+rZVsnHzhFjunZpot7DYWVjPvT/upVVnRCmX8fjsFOYPCWd/WRM7C+vZVdjArsJ6mrVGNufWSXbnCrmMxEB30iK8GBTuRVqEFzF+bifcSZHJZHi5qNia38HHnQINAz2ceGl+KmPj/WnWGqhs0lLe2CFeNmmpbOqgoklLRZOW2lYdje0GjGaBqmZdtzu13eGklOPtIhY8ns4qvFxUeLuo8bT87O6kxNVJiZvly7XzzxolLirFv65zZDILtOnFeaEWrZFWncFyefS6pg4D9e16Gtr01LfpaWjXU98mGjYYuxsi6gGNSm7p7jlbvlyky3BvZ3x6YUbxb0drMFkKm0pWHKy0sRf3dVUzrX8Qc9NCGdKNjf7ZpqC2jRf+PnzCmTdWpqYEYhYE3lsvynj7h3rw2sVpPR5rzWaBt9fm8sqqIwiCeFx/d2E6/u5O5FS1cO3nuyiub8fLRcWHVwxhaJSPzX0nvbyOwk7d6INPTsPVSUlDm54rPt7R7aIe4KX5A5mU1DuJ1q7CehZb5l0enZVCWsTpNX+wdnH6hXhgFgSyq1pw1yjxc3OitKEDmQypI3rNmGjeWC1uAF40JNzGfMe6oTZrQDDOaoXNrFJnHPM4/3wcRY6DUyY52MNuoPJgeRPpkd4kW6RohyuPFjlBHhpc1Qra9CZKGzqI9XcjwF1DmWVR1rXICfAQF6y1rTpi/I7uRDW2GwjyPPNFTnmjqIEP9jy5wMfTgYtayUdXDgXE4nHCS+t6vO2uogbp97dNiuOmCbE2GQkqhVyyIS6pb+fbHcX8sKuUmhYd767L4911eaRFeHFhehjnDgyRJBQ7Cur5bIuohV40Z4Bk0NCZA2VNUl7BrZPi+X5nCfvLmvB2UfFuNwOsHhoVje16qbC8eUIsExID2FkomibsLhLd/LraNof7OItFj8W6NznYo8cZo4yiBu74bg+lDR2oFDKenTvATmbXoTfxyspsPtpUgCBAiKeG1y5JY5jF/W9olI+0iDCbBXKqWy1FTz27ihoobejgUIVoOW2VoLhrlAwK97L58j1GcVbdouW7HSV8ta1IClB1VSu4bmwM142NlpyWPDQqPDQqqUPaHVYr6ZoWHbWtOmpb9NS0Hv3Zemk1CjCaRSOBymatFMJ6MriqFbg4iYP94pcCJ9XR79VdrlfJxSBQhcVYQCYDhcz6vczyvVj8mc0CJos5gfS9JSjULIjdP5NZQG80ozOa0BnF8NDuLnVGM+06o+TSdSq4qhV4u6oJ9NAQ6OFEgLuGQA8NAe5OR6/z0OCh+ec4s51Jqlu0rD1czeqsajbl1toMuPu5OTGjfxAzBgQxPNr3jAShnih1rTreWJ3D5xbns5PhtklxfLO9mLo2PUq5jNsmxXPzxNgeO+f1bXru+j5Tmr9ZMCycJ8/rj1opZ83hKm7/NpNWnZFwH2c+vWqYTaFkMJlJePQvG/njoaem4aJWUt+m57KPtkvzfV3577kpzE0L69V7qm3Vces3ezCaBc4dGMwVIyOPf6dToLpFy2cWR7XOXZyrRkXxlUX67KxS0K43MSDUE28XNRtzapHJ4NJhEdLjdOhN/LlfLFQvsEjVlnZT5KSGeR43A87B2cdR5Dg4ZayFTGcKasVdIKtbV0n90ba3TCYj0FNDfk0bVc1aYv3dCPRwoqyxg5oW+wWUVfLUpjPaLJy1Zym9vKRB3P2K6IUe+WwQ5ecqydnWZldLHZzueHNNrjTw/8KFA7lwcJjNbnu4jwv3T0/irnMSWJ1VxU8ZpazNrmFPcSN7iht5cukhpvULYlKSP88sO4xZEB19xif42z1XdbNWCqCbmOhPm87IL3vKkMvgrUsHE+Zt//f8YVcJD/y8D0GABcMiuG+a2F0ZFu3DLRPFnfbc6lb2FDeIr6mkgZzqVkrqOyip75Ac0pyUcgaEejIwzIsBYR4MCPUi3MeZD9bn89rqHExmgXAfZ964JM1mt1EQBP46UMmiPw5JA97z08N4bHZKjwO3cosdb2KQu6TzrmjqILO4kT0ljWQWN7KvrJEWrVHqklmJ8HGROj2Dwr0I9XZm7eFqluwtZ2teneQu5u/uxGXDI7h8ROQxC6Oe6GwlfTwEQaBVZ6SxXSx4Gjv0lu8tl5ZCqLNTWpuuk6ua3iQNh7fpTX1SOJxpVAoZ7hoVbk5K3DVKm0sPZxU+Fqc2bxfbS4fMzx5BEDhY3syaw9WszqqyydQCsSs5vV8QMwYEMzTK5x9Z2IC4GP5kcwFvr83t1nmsN8wZFILeZJaOwUlB7rw0P5X+oZ493md3cQO3fr2b8iYtGpWcp8/vz/wh4QiWLtDzfx9GEMT4hXcXptu4y3XoTST/92+bx8t5ZgYqhZy6Vh2XfbS9R4nrrRPjuKbLPE9PmMwCd36XSWWzllh/V5674PTO4QC8tSaXDoOJQeFeCILA4UrRgTPcx4Wc6lbUSrl0HLp2TLS0ITc5KZAI36PnnuUHK6UCcUikN7uLG7sN657m6OL8K3AUOQ5OmaRuZgeKLW1wa1emslmLySxIJ6wgj6NFDoiZH4C0Q90Za5HTojWiUR3d2eo4S0WOtYDrzdDl2UQmkzEpKZDC52ZJjmeds2u6cv9P+7j/JzGJ+qMrhjClk2uM2N0JZnr/YGpadPyeWcaPu0Qr56V7y212urqTqTW1G7jikx2UNXYQ5etCQpA771uC1Z67YCCj4+zD1D7fUsjjS8TcnMuGR/D0+fZZDIpOBcUllt24Zq2BvSWNlkKsgT0ljTS2G9hV1MCuLlk1nXlzwWAGdlpc5FS18PiSg1IwXqiXM0+d34/JySfuphPs6UzwAGdmWGR4BpOZ7MoWMksaySwRX2deTZs0f9STPEKtlPPMnP6cPyj0jAUzymQyi7WyivBjR+t0iyAIaA3mo8WP3mjppli+OnVPdEaTZB2tM4rFUddOjNnSoTELYBIEBEHAbAa5HOSWLo9cJrN8LxadCsvPcrnsaBdJpZC+11i+10jXiRbU7hqlxVLaUaicChVNHWzKqWVzbi2bcuvsMsYGhnkyOSmQyckB9Avx+Ed3tLQGE9/uKOaddXmnZIBz26Q4vtxWRGO7AbkMbpoQy+2T43v8rAmCmA+2+K8sDCaBaD9X3l04mKQgD7QGEw//sl+y5b90eARPzO5nc4xoajeQ+tQK6edIXxfW3jMBuVxGbauOyz7cLs3JduXS4RHcMzWh1+/theWH2ZRbi7NKwbsL0097+G1RXRvfWDrk909PZJElF+eqUVFS59zNSexUhXhqGBPvJykKrhkTZfNYP2aIszsXWDb8uuvigGMe59+Co8hxcMqEd7MDb02K93d3QimXYTQLVLdopZ3jAIutrPUk4aYRP4rd7Yg5WQobndGMslNWTncZEKcbo8nMwXJx5/FYu23/NJQKuUXWFIPWYOLJpQf5dkdJj7e/7ouj3Z/v/jNCtFe24O/uxHVjY7h2TDQHy5s5981NNved/dYmGzmbWiHn2s93criyBX93J0bG+kkFzqOzku2ybkxmgWf/zOLjTeJO21Wjonh8dkqvFz4eGhVj4/0l1zJr+Oee4kZ2FtbbDaBamfP2ZlzVCgI9NOTXHtWjK+Qybp0Yx43jY7uV4J0MKoWc/qGe9A/1ZOGISLQGE79nlrFoWRYtWmOP99MbzTz8634+31rIAMv9B4R6khjk/o9diMtkMjGHRu2wk/5foUVrYFt+PZtzxcDdrvMdzioFY+L9mJIcwMTEAAJ6CK/9J6EzmvhhVylvr8k9Jcnm7ZPi2FnYIHVvkoM9eG7eAFKPMcjf0KbnwV/2sfxgFSDOijx3wQDcNSoqm7Tc+FUGmSWNKCxzgpePiLQ5XhbXtTPuxbXSz2Pj/fjimmHIZDJqWnRc+uE2cqpbu33ueYNDWdTNBlNP/LanTDq+P3/hwGPKZvuKV1YewWgWGJfgT6vWyKGKZlzVCpKC3XlrbS5qhRyDSTRHunZsDD9nlNJhMJEU5M7ITue20oZ2aVPrgsFhmMyCTZSAlYRAt3+c4YWD7nEUOQ5Omc6FjJVqyzCzQi7Dz82JymYttS16qcixDu1bixpr9oyum4BNazGjUshsXNw6d3XOFEeqWtEazLg7KYk+QWvlfwoalYLF8wayeN5AGtv1XP/FLnYW9tzhsFpsKuUyvrhmGCNifJFbZiJ2Fh6dgzl3YDAdehPrjhyVsz3y61HnN1e1goGhnny7Q9xZu3NKPNeNjbF5rna9kTu/y2TFIfFkft+0RG6eEHtKO7symYxoP1dyqlvZnHdUGpYU5M7ctFAqmrTsL2sio6iBNr3JpsAB8fO3KquK0oYOkoNFa96kYPdTtg41mMxsyqll6d5yVhyqsnFri/ZzZfbAYNIivOkwmNhf1sSBsib2lTbR1GHgQFmzxYBDLNhUChkJge4MDPOkX4gY7pgU9O/OQ3Hw76GuVcfOwgZ2FoquiAfLm202oeQyGBDmxdg4P0bH+TE40usfW5R3xWAy83NGKW+uye1WttRbhkX7MCjci/c25KM3mtGo5Nx9TgLXjI4+pjvk5txa7v4hk6pmHSqFjEdmJnPlqChkMhmbc2u5/ds91LXp8XRW8c5lg+264tvy66RjOIiy38XzBgBQ2aRl4cfbye2hwDkvNYQXL0zttWHIvtJGHvhZVAPcNCGW81JDenW/U+FgeZMkS75/WqL0/FeOipK6O96uKqqadXg6q5g/JIwZr20E4OrRUTbnll92lyEIMCrWl3AfFzbl1Np1HYGzFkTu4MRxnAEdnDIKuYxgL43N3E2LzojBZEalkOPqJJ7MOi/irDp1raWosZ7wugvYtJ4sFXIZuk5Fztk4SWYUi8XAwHDPf51TVHd4uaj58cZRgLiLdf5bm6lr03d7W6NZ4NKPtgPiv8X5g0L4xRKY9sD0JG6aEAuIA6C/7ynn080FNkGFbXoTqw+L7m8PzUjihvGxNo9fUt/OTV9ncKCsGbVSzkvzU/vkJJlT1cKTSw9JVqnBnhoWzekvyc60BhNfbC3kSGWLTY4CIBlkHCxvtklUB3EoOjnYXSookoI8iPF3PeYMhskssD2/jqX7yvnrQKWNLW6Ip4bZqSHMTg2xk+xY3eYEQaC0oUMKCj1guWxsN3R6jUc7VZG+LiQHeYivMdidlGAPwryd/9FyIAf/bKyfQWtBs6OgvlsnrihfF8bE+zEmzo+RMX54uhw/NPKfhNZg4oddJXywIb9bK+UT4eGZSXy/U3wsgPEJ/iya0/+Ykme90czLK7L5YGM+ggCx/q68fkka/UM9MZsF3lmXy8srsjELYjfovYWD7TLNvtlezMO/7pd+fmJ2CleNFudqCmvbWPhx9zbRADMHBPHKRam9nomqbjk6czkpKYB7pyb26n6nyovLxciC2akhVDRpOVjejItawZAob95Zl4dSLsPSxOHKkZFszqmlrLEDH1c15w8KlR7HbBaksOcL00Vzhd8z7bNxAGY45nH+NTiKHAd9go+L2qbIAdH9zN/dycY4wIq1C2M1D1AqxANpdxI0gyV8UymXd3mMM1/krM8W3WxGxdrPkPzbCfN2IeOxc4DjO7SZzIJU4AD4uKqoa9Xh6ya6R12YHsbSfeU9prH/fbASN41Scmdbl13Nnd9n0thuwNtieTok6iQGQDrR1GHg9VU5fL61EJNZQK2U85+xMdw8UXST0xlN/JRRyltrcqmwvM64ADfum5bI1JRAybGrtKGDrMpmDle0cLiymcOVLRTWtVHbqmNjjs7GOEAuE80D4gLciA1wI87fjbgAN5q1RtYermbZ/gobHb+fmxPnDgxmdmowaeHexy2cZTIZ4T4uhPu42BU+1oLnYHkzWRXNVLfopNBSa34PiNr0pCB3qfCJD3AnLsDNZkD5RDGZBRvDAb3JjMksYDQLGE0CRrP4s1wmQ6WQo1KIl1Y3NS9nNe4a5f+LjYP/bzS06dlb2sjekib2lTayt7SR2lb7jZCEQDeGRvkwLNqHIVE+di6Z/xaaOgx8ta2ITzYV9Ljh01vum5bI3pJGnv3zMAB+bmoeOzeF81JDjrnRkFfTyp3fZUpZO5cOj+CxWSk4qxU0tRu4+4dMacNofnoYT8/pb3c+fPjX/VInA+Cra4czJl48b2VVNHP5xzu67VIAnJMSyOuXpPU6f0xnNHHTV7upaBKNBl67ZNAZMYzYll/HuuwalHIZd5+TwK3f7AbgipFRfLNd3OyxbsA6KeVcMSqKm78Sb3PpsAibv9mOwnqK69txc1Iyo38wWoPJ5rhpJcrXpds5ZAf/TBxFjoM+wb0bp6mmDj3+7k6SZKZNf7RAUVgO8GaLj6W12NF0M0zd0C6eaDydVRzpNBjpfYZ3BnVGMcMBYEKivXvY/yesDm0gnnAnv7z+mLd/4Of9gLhjeOnwCJuTa9ewWIVcdtSdbckh9Kajv0sN8+SdhemntEAymsx8v6uEV1YckRYpU1MCeXRWChG+LmgNJj7fUsh76/Ok4ibEU8Od5yRwweAwm5OzXC4jwteFCF8XpnUaNG3TGTlS1cLhyhYOVzSTZbls1hoprGunsK79mJlFIGrzFwyPIMhDc8pyPGvhM6NT2F9dq47DlS1kVTSTVSFe5la30qozdmvC4OOqFoszf1c8LUWHl4sKlVxOfbueulYddZYsmPo2Pc0dBlp1Jtp0xj4xAZHLsOTvqAn21BDuLf7do/1cSQ33IsTz1P5ODo5PY7uerIoWDpY3sbe0ib0ljRTXt9vdTimXMSDMk2FRYkEzJNL7Xx9IWt2i5eNNBXy9rdgu6PdEWTAsAneNktdX56A3mlHIZVw5Moo7psRLlvvdYTYLfLW9iMV/HqbDYMLLRcXzFwyUjj0Hypq46esMSuo7UCvlPH1+Py4eGmHzGCazwMzXN9qYCKy9d4LkdJpR1MDVn+6wySDqzKSkAN66NO24wc9WBEHg8d8PklHUgLtGyYdXDOnRebIvEQSB5/8Wi8dLhoVzpKqFg+XiLM74BH/eW59H58PFRUPCqWzSsqOwHqVcxuVdLK1/3CV2cc4dKGbj/H2gstv5yOn9gx3HoX8RjiLHQZ/grrH/KOmNR2VmcLSgAdBZFrZqhW1Hx6mb7ky9ZdfQ101tIxnqi5T7E2FHQT3tehMB7k6kdGOb/f+VWH83qeD5dHOBFMLZE50LHBBlF05KOV9cM4zhMb6SnO39DXl2O8JDo3xO2hpcEASWH6zkheXZ5FvkM3EBbjw+O4Wx8f506E18vKmA99fnSS5+gR5O3Dg+lgVddvWOh6uT0hI8ams3vTW/jtdW5rCj06xST7yxJpc31uTirFIQ6etClK8rUX6uRPm6EOnrSpSfC4HumpPubvi6OTE6zslGo28wmcmqaGb5wUr+PlBpIzOqb9Ozo0CUH50sKoUMV0sOjlIuRyGXoZTLUCpEdzNBEF+D3mTGYDJjMAl06E10GEyYBWhoN9DQbrA4GNbZPHaQh4YpKQEsHBFJUtD/zv+/04HJLJpxiAVws1QMV/TQeY2xFJqpYZ4MDPciJdjj/401dlZFM59uLuCX3WUnFN7aHYPCvZidGsLHG/OlLvaoWF+eOK/fcQfwS+rbuf+nfWzNFz/3o+N8eeWiQQR6aBAEgc+2FLL4z8PoTWbCfZx597J0O/ObZq2BgU+ssLlu73+nSlLBjTk1/OeLjB43JSYnBfD2ZYNPSAr+8aYCvttZglwGby5IO2MD+SsOVbGnuBFnlYLbJsVz1ac7AcssjmXuM9rXlfzaNuQyuH5sDK+tFrNzZg0MJrCT2UWbzshfB0SDgflDRKlaT65qDqnavwtHkeOgT+huwNla1FglaPJOux/WnX2rxeXR2Rz7wqXeshvv7aKWws/OBkssw42TkwP/J3dyVhysZPFf4s7ZxER/bp8cz9x3tvTqvjqjmYd+2c/lIyOZkBhAYpB7t3/DjzYV8NGmAgZHeHFhejizBgYfc+fTyrb8Op776zCZJY2A2JW4bVIcC0dEYjCZ+WBDHh9syJeKqhBPDTdNjGN+etgpL9ZK6ttZuq+cpXsrbEL0nJRyJiUFMDs1hCGR3pQ0dJBX3UpuTSu51eJXaUM7HQaT2BHqJp9Co5IT6eMqFkF+roR5OxPq5UyYtwuh3s49WrNqDSaK6toprGujsLZN7C7VtlFU10ZFs9YmCPBUcVYpSAnxICHQjQgfVwLcnfBxVRPg4USQhwaV5f905+d0UspRK+RSAaczmmjqMNDUbqC+TU95UwfFdR0U17eTXSV2oiqbtXy1rZhvd5Tw7mWDmeqwcD0uOqOJwtp28mpayatuJb+2jdzqVo5UtdjMN3Ym3MeZ5CAPS1HjxYAwz179H/w3YTILrDlczSebCqSi4lRw1yh5ZGYyP+wq4ek/xE2gUC9nHjs3mWn9go55vjCbBb7eUcziP7No15twVil4cEYSl4+IRC6XUdeq476f9rHGIk+bkhzAy/MH2c04Ha5sZrploB7Ec+v+J6ZKBcuyfRXc9X2mTee8MzP6B/H6JWknZE2//GAlz/wp2jU/PDOZCYkBvb7vqWA0mXnB0sW5ZkwUe4obyKpoxs1JyZSUQC58VzwvWTdYZw4QuzN/7BULmatH2+b9LNtfQbveRIyfK4MjvGnVGVmVVWX3vKFezgwM+/e4qjpwFDkO+ojuDuHWIse6O9bZ/lnXpcjpXMh0xepoE9Rp58Wjm87R6URrMPHXAVGfOzct9Di3/v/Hr3tKuffHfZjMAlNTAnljQRoalUIKHX18yUG+OE7id35tG08uPWTXCfrtltEkB7uzJquanzJKWXekht3FjewubuTJpQeZ1i+IC9PDGB3nZ6fzPlTezAvLD7POMivlolZw3dgYrh8bjUwm48ON+Xy0sUD6fIV5O3PLxDguGBx2SjkzNS06lu0r5/e95ewpbpSuV8pljEvwZ3ZqMFOSA21knAEeGtIjvW0eR280U9bYIRUj1sKkqK6dkvp2tAYz2VUtPeZXuKoVlq7IiVUt7k5KIv1cCPUSA0FDvDSEeDkT5KGRFkX17Xqqm7XSPIZ1PqArHQYTGUUNZBwjg+hEUSvleGiUKOQyzIJYFFmdGK1ud44iR8RgMlPeKBaEJfUdFNS2klfTRl5NKyX17fTUnHBWKUi0zGalBLuTFOxBYpD7GZEanS1atAZ+3FXKZ1sKu5XhnQwvXjiQVVlVPGjJXXFWKfjPuJheWc6XNrTzwM/72JwrFlrDonx4cf5AyUBgc24td32fSXWLDrVSziMzk7liZKRd0fRTRin3/rhX+nnOoBBevXiQdLuPNuYfMyNtzqAQXpqfekLqiP2lTdz5XSaCIOaYXdvLoNC+4KeMUvJq2vB2UfGfcbFc/P5WQIwb+G5HMWYB4gPcyKsRXeNuHB/L19uL0JvMUtCyzeNZpGoXpIchk8lYcbCy202A6f2PXbA6+OfhKHIcnDasO7dGy85R5wVqQ5eixjqMHeBhb8trzdwJ9jpa5Ew/wy3jlRaL31AvMQX5f4kvthby39/FUM55g0N54YKB0slQazDx8K/7bUwITpQ5b2+mf6gHcwaFcv/0JBbPG8DvmeX8mFHCkapWluwtZ8necoI8NMwbHMoF6WGoFXJeWXmE3zJFy0+lXMalwyO4bVI8Tio5n20u5OPNBZJ7WaSvC7dMjGNuWmivteZdaeowsPxgJUsyy9mSVystHmUyGBHty3mDQpjeL+iEZhPUSjnRfq6iXr6LGZHRZKa8UUtBndiBySxu5O+DlTZZUm0nmLTu7aIi1NsZdycVRrOZulY9DW0GdhbWU92io65V1+Oi+EyiN5q7HW638sOuUjbn1hHm7UyMv/j3i/J1JcbflXAfl3+NPXFv0BvNVLdoqWzSUtbYQYmlmLEGx1Y0dRzz38zdSUlsgBux/m7EBrgS4+dGYpA7kT4u/zNGDwfKmvhmRzG/7yk74f8zPbF43gAOlDXx4C/7LaYacPHQcO6aknDc3B+TWeDr7UU8/9dh2vQmNCo5D0xP4sqRUcjlMgwmMy+vOML7G/IQLAv2NxakkdxFJi0IAnd8l2kTHvzy/FQusLiDmcwCi5Yd4tPNhT2+louGhLF43sATMgooa+zgms930mEwMS7BnyfP63fGFv8dehOvrhJlZ7dOimdzbi2HK1twd1Iyc0Aw570l5rZpVArMgpgJFB/oJsnZunZxCmvb2FFYj1wmZuMAPYYxO6Rq/z4cRY6DPqG7c6yLZRerqUNcaHaWPNR1mrMBcegT6DYssLDOfsdtZKyv3XWnkx92iU4t5w8K+Z9ZGAiCwOurc3htVQ4g7pL999wU6f2XN3Zw41cZ7Cu13eHXqOQsnjeAuWlhNLUbuOzjbZZMl545mvsi7jbOGhDMf8bF4uWsYmNODb/vLaeyWcs76/J4Z12ezX1np4ZwzzkJeLuo+WRzAZ9uLpCGamP8XLl1UhznpYac1AxXh97E6sNVLMksZ112jY3UY1C4F+elhnDuwOA+DzOsbtZyoLzJ8ncRHdNOJaPDinXm5UQJ93Em2s+NGMvMkLerGg9nFc4qBRqVAo1KjpNSvNQoFTip5MhlMpo7DFQ0aalv19PYrqe+zUB9m46qZh2VTVoaO/Q0thtobDec8LB3WWMHZY0dbO8yQ6SQy4jzd6NfiAf9Qj3pF+JBSojHP65DoTOaqG/TU9cqGjlUNWupatZS2SwWNNbLYxV7VpyUciIs5hOSu5+lqPF3c/qf3H1u1xtZurecb7YXs7e0+y7kyfDM3P5UNmlZ9MchqWCakhzAA9OTiO9F8GVWRTMP/bJfktYOifTmxfmpkjFAXk0rd3+fKb3mzs5qndEaTAx4YrlNF3f5neNItDh/aQ0m7vo+U1IgdMfCERE8dV7/EzqntWgNXPvZTmpadCQGuvP2pb13YesLPt1SQFWzjlAvZy4bHsH5b20G4Oox0Xy/sxijWSApyJ2carH7feP4WP7cX0Ftq44gD41dofLzbrGLMzbenyBPDXWtto6ZVgLcnRgc8b+1wfn/AUeR46BPMHaj87XO6ViLHK9OGuLaNrFz4+fmhNZgkhZege62i0VBECiwtJw7O3TF+Z85C8fc6hY25tQik4muOf8L6C0zNNYTwO2T4rjrnARpsbSjoJ6bv86wW4BF+Ljw3sJ0UkLEHcc2vREf1xMPzVy2v4Jl+48mTc9NC+XXPd13ixrb9dzxfSY5VS1SlyMuwI3bJsVx7sCQE7YyNZjMbMqtZUlmOSsOVtrs/MYHuHH+IDHLpmsmxcmiNZg4UNbE7uIGdhc1sqekgarm7q1do/1cSQn2kOyWlXIZ7XoTBbVtFNS2SbK8vqakvoOS+g42WGbivFxUeLuo8dAo8XBWiV8aFZ7OKjyclZ2+V+GhURLhI+b1OKsVuKgVJ91NA/Hfp6ZFR0WT+JryLe+9oLaVgpo22vQmSeL3S6fPTGKgO0OjvRka5cPQKB9C+sjiWGsw0aw10KI10txhoFlrpEVroLnDSLPWQHOHOGdU1yY61FkLm66ZTMdCrZAT6OlEsKezWMx4uxDhe/R7f/f/zUKmO7IqmvlmezG/7ik7ZZe0ziyeN4DqZh3P/3VY2kRJDfPkoZnJjIg5/qZbh97Ea6uP8NHGAkxmATcnJfdPT+Sy4ZGiLNMsmgs8//dhdEYzns4qnr9gQLfBk7nVrUx5xdbxct8TU6VCvrFdz3Wf77JzUOzM9WOjeXhm8gl9bowmM7d+s4fDlS34uzvxydVDu3VWPV00tOl517LJde+0BFZlVZFd1YK7Rsn5g0KY+bo4k+ThrEJrMNM/1INRsb6c/7ZYCF0+MtLm2GMyC/zcJRvnzwOV3UZZTOsX9D+zwfn/CUeR46BP6M5q0VWtRBAESTJkLXIEQaCm2VrkqCU5mmgfa3vALKnvoFlrRK2Q28wEJJ5Bn/rPt4izJlOSA48Z3vb/haYOAzd+mcHW/DoUchlPn9+fS4eLxZ0gCHy1vZgnlxy0cyKamOjPaxen4emiQhAEftxVytN/HKJFZ8RJKee+aYlcMzoaueWE/t6GPF74O7tXr6mnAgew23W755wEbpkYd0InJEEQOFDWzM+7S1myt9ymWAj1cua8QSGclxpCUg+GCSfyPGWNHeLMUVEDe4obOFTRbDdTI5eJhVqIlzNKuRylXIZCLqO0oZ01h6uPadkc5KGRJFyRvi6iSYGXM2Hezvi4qu1ef6vOSFWzlupmncUeWketpbtQ16aTOg11bXoa2vUIAlL35WRRK+RSwWO9dFErLZcKNEqxEFIpLXk6CrklX0e8rvPPoV4awrycGWvJAKlq0nKooplDFc0UdeoCWwufr7bZuv8NjRILH7VSjtEkYDCb0RvNaA0myfmtw2BGqzfRbjDSoTehNZjpMJho1RltNl9OFIVcho+rGl9XNf7uolFDsKeGQE8NQR4agiyX3f27OThKXauOJXvL+WV3WY+zYyfLCxcMpKZVx/N/H5Y+8wmBbtw1JaHXMxrrj9Tw6G/7pSy5Gf2DeHx2P4I8xU29kvp27vtpL9vyxa7k2Hg/nr9gYLeF+JfbinjstwPSz2kRXvx04yhpM6ekvp2rPt3RbUCrlfunJ3LT+NgT+kwJgsATSw+y/kgNGpWcj68ccsazkN5Zl0uL1khysAezB4Yww1LUXDsmmh92laAzmsUujmWG8YZxsewubmRfaRNqpZxLhobbPN6WvFrKm7R4aJSckyKGQy9xBID+v8JR5DjoE7orclycFDR3GKXFsHX+pq5N3MWUycQAytWWPJEYf1e7g671hJUU7G6zmD2VofEToVlrkLoZV4+KOiPPeTYpqW/nms92klPdiqtawduXDZYcc1p1Rh75dT+/Z9rqlRWWILabxscil8uobNLy4C/7JDOAtAgvXpqfSmwna1G5XMbNE+K4eUIcIC4CrvxkR5+8h5dXHmFtdjUXpodzbmrwMWVKlU1afsss4+eMUnKqW6Xr/dzUzBoQzHmDQhkc4XXSC0xBEK16t+XXs72gju359VQ229v0+rk5EebtjEohFjOCIBo1HKlq7eZRRXlSfKAYNhrt5yYVNdF+rt06HR4LNyclbv6ivEkQBNr0JksGjqUjoTXSqjXSojXS2KGntKGD0oYOqpu11LbqeiWn6oreZEbfYZa6vGeTnYUN7Cw8NdMEmUycfXHXHO1eid+LXS0fVzU+rmr83NT4uDrh6yYWNh4alWN3+CTRGU2syarm591lrMuuPmX75668enEqtS16Xlh+WPqMx/i5cseU+F53iCubtDz7Z5Y04xHiqeGp8/szxbKgFgTB4siWRavOiLNKwcOzklk4PMLumGMyC8x7Z7ON9G7RnP4sHHE072VXYT03fJnRY4ipXAbPzB1wUoqEDzfm89W2YmQyeO3iNAaGeZ3wY5wKpQ3t0objA9MT+fNAJTnVrXholFwwOIzpr20AIMhTw+HKFsJ9nJnRP4g7v88ERHMFXzdbVYE1G+f8QaFoVArKGju6PRZ4u6gYFn1q4dQOzg6OIsdBn9CstV2s+LqqUSnklDS0SD9brXoLa8UdphBPZzQqheSAEtuNv/6+skZAlJpYZz+sOy5ngi+3FtGuN5EQ6HbG54DONHtLGrn2812SdvmTq4ZKsrOsimZu+Xo3+bW2u4Nh3s68sSCNwRHeCILATxmlPLn0IC1aI2qlnHvOSeC6sTHHXRCMT/CXsni+3l7EI78eOObtj4fVne3R3/Zz7sAQG3e2Dr2J5Qcr+Xl3KZtzjxoIOCnlTO0XxLzBoYyN8zspnbkgCOTVtLItv55t+XVsL6iXTDWsKOUygjw1qBRHOzT1bXpJo9+VCB8xYTsp6KgDVpSv6wnJ8Dr0JsqbOqhoFGc9rMGeda223ZraVl2P1sL/NoI9xU5IZ5ttmUyGSi5DZzSzu7jBxsShK/7uTswbHEq4twvOKrHj5KxWiN+rjnagPJxVuKmVjmLlDGA2C+wpaeDXPWUs3VvR50Wys0rBmwvSyK5qYdEfWVKxEOHjwh2T4zl/UO9m+3RGE59sKuTNNTm0603IZXDVqGjunpogfR6rm7U8+Mt+yRp6SKQ3L81PJcrPXgZb06Jj6DOrbK5bfc94m3PmL7tLefDn/T1aRKsVcl6/ZJBNYHBv+T2zjGf/FC2bH56RfMaNfwBeWXkEvcnMyBhfxsb7M/VVUa533dgYft5dSpveRGKgu7SeuG5MDDWtOmkm6apRtoYDViMZOCpV6ykbZ2pK0BnP5XPQNziKHAd9QkO77c6RNWirtEGUi3SWeVkXyjH+4sH8iKW13F2Rk9nJntfKnEFnxsK5VWfkw435ANw8Ie7/tVzkj33l3PvjXrQGsd3/6dVDCfZ0RhAEvttZwhNLDtotfs8dGMyz8wbgoVFR1azl4V/2s9pywk4N9+Ll+QOJC+i9rPBAWRPP/31Y6ti5qhVcMiyCjzcVnPT7MgtI7mw9MTTKmwsGhzFz4LG7Pj1R0dTBxpxaNubUsjWv1q6zoVbIcbfYISvlMkyCQGmDvYmAXAbxAe4MCPNkYJgn/UI8SQxy7zELx4ogCDS0GyiyWE+XNrRT0aSloklLeWMHFU3aE14MqhQy3DWqowuyFq2UZfVvwfo3cFUrCPZyJthTQ4inhkhfV6L9XHhkVjJRvq5oVHLyatpYl13NXwcqJSvsmhYdH27IZ3yCPxcPjWBycsApzRI5ODkEQWBvaRPL9pWzbF+FFLLZl/QP9eChGcmSZbN1XirM25lbJ8ZxQXpYr//t12ZX89TSQ5ZAWxgc4cVT5/eXgjut3ZtnlmVJUux7pva8GbTmcBXXfLZL+tlVrSDjsXOkTUOzWeClFdl2hiydcVUr+PCKIYzqFAzcWzbn1kr21NeMjua6sWfOKtpKVkWzJFl+cEYSS/eWk1fThqeziouGhDPN0sWJ8XflrwOVeLuomD8kjLfX5mIyCwyP9pE27Kz8sa8cndFMQqCblH2zJLP788T0AQ6p2r8VR5Hj4JQxmMxSgryVo1pjcTFnU+RYtMJRlsFta4emf6jtQUhrMEkZJJ0zDUadoY7Kl1uLaGw3EO3nyrkDT3z369+A2SzwysojvLU2FxA7Km9dmoa7RtWjPM1ZpeDJ8/sx37L79eueUp5YcoimDgNqhZw7z4nnP2Njer3zVVzXzksrsqVCRKWQcdnwSGanBvPdjhKUclmfS1GsfH7NMMYn+J/Qfdr1RrYX1LPxSC0bc2psZG5WFJYOjcpS1HQnH4nxd2VgqCcDw7wYGOZJSogHLuqeD8kNbXpyqlvFPJ16MeDTWth0JxftSufFvp+bE76uanylSzVyuYx6S0enrLGD3OpWcqpbejRB6Iyvqxo/Nyf83Y9+ebuocdcocdcoRUmckxI3jRKNSoFKLkepkEmdLKVcjtFsFjN/jIIoZzOaadcbaWw30NRhoLFDvKxu1pJV2cKBsqZuB4S70qY3SeGr3RHkoSHS14VoP1empgQyb3AoedVtZBTVs7e0ibXZNazNrsHPTc0F6WFcMjRCcsJycHoQBIGD5c0stRQ23W0K9AXnDgzmylFRLNtXwTWf7ZQ2cuID3Lh5YiznDgzpdXFTVNfG038cYpVFfu3n5sRDM5KYmxYqdfkKa9t46Jf9UgDpgFBPXpqf2u2MqdkscN0Xu6ROD8B/xsXw8Mxk6ed2vZG7vs9k+UH74EorPq5qPrt66EnJyw6VN3PDlxkYTAKzBgbz6KwTMyroK174+zCCALMGBtMvxIO7LBK068dG81tmGU0dBmL8XKV1whUjo5DLZHyzXZy/u3p0lN1jWqVq89PDkclk5Fa3cKjC3gXUXaNkdOyJF4cO/hk4ihwHp0xVNwnqgZa8m6J6saAJ9z46oHiwXCxqkoM9qG/TSwemrgfhjKIG9CYzQR4aG5vYE8khOVna9Ue7OLdOjPt/2apu1YknyJWHxBPk9WOjeXBGMgq5rEd5WkqwB29emkasvxvVLVoe+fWAdP8BoZ68fFEqCb2wUQWobdXx1ppcvt5eJA3enz8ohDlpoSzJLGf+e1slKdnYeD9umxRvo4veWVjP/Pe2ntLfwDoH5O/uxN3nJDA82odoP9vZMEEQOFzZwrrsGjbm1LCrsKFHSYgVk1nAZBawljbOKgWDwr0YEuVNeqQ3aRHePabI17XqOFLVSm51CzmWdPrc6tbjzr4Ee2oko4EQT41U0AR7OhPspbHpUlW3aDlosadelVXFptzaY0q3jkedxZigp9DSviDEIj8L8XJmdKwvC4aGE+Hrgo+rmjadifyaVkth1sqh8uZuZ58646SUozOaRavmZq2dFbW7kxJfV7VUoNa26nl/fT7vr89nUlIA142JZmSs7//rDu+ZxGQW2F3cwKpDVSw/WNltdEBf8eisZFLDvfh2RzELPtgmbaKkhnly88Q4zkkO7LX8sFVn5L11eXywMR+90YxSLuPq0VHcPjlech4zmsx8srmAV1YeQWswo1HJueecRK4eHdXtuaWqWcvwZ1fbXPfDDSNtjn8VTR1c9/kuDpb3bM8f6uXMF9cO61YlcTxKG0QDg1adkeHRPrw8P/WsSDK35tWxNrsGpVzGvVMTWbK3nPzaNrxcVFwyLILpr4nmA4MivPhldxkalZwrRkayJLOchnYDoV7OTEm2lbjnVreQWdKIQi5jjiXc29rF6bqpNiU58IzNADvoexxFjoNTpqIb+YDVXje7Ulz0xAeKB1lBEKTOzcAwT/Za5hBi/F3tFn2bc0XZUv9QD2nBYtXOnm4+31JEfZueSF8Xzh8Uckae80xSVNfG9V/s4khVK2qlnMVzB3BBehiCIPDtjuJu5WnXjI7mgRmJqBVyfs8s4/ElB2lsN6BSyLhzSgI3jOtd96ZVZ+Sjjfl8uCFfsmcel+DP3LQQVmVVc81nO6WieVJSALdOius2n2BolI80x9PYrmfQUytP+u9R06LjIUtiOYgdLYNJHIyvbtHZzdUcj0APJ4ZE+pAe6c2QKG+Sgz3sdoRNZoGC2lYOlDVzsFzMwjlc2XJMG+hQLzH8MspXdE6L9BVza8J9XCT5ipWGNj17SxtZurecb3cU9ziM/G+hvElLeZOW3d1IWE8GpVzGsHg/UkI8iPJ1pbJJS25NKzlVLeTXtImSpR7+2dccrpZ22B+ZmcwVoyL/XwWQnik69CY25NSw6lAVaw5Xn9bPqLtGyRsL0jAYxYJj0bIs6XejYn25ZWIco06gaDWazHy/q4RXVx6RNiDGxPnxxHkpNjLdg+VNPPjzfslEZ1SsL4vnDejRgv6HXSXc/9M+m+sOPDnNRraaWdLIf77YZaeg6Ey/EA8+vWroSWV4NbbruerTnVS36EgIdOODK4bYHV/OBIIg8Nzf4izQgmERhHs7c/Wn4sbU9WNjWLZPzL8J9XKmyrJGmJ8ejo+rmk+3FAJwxchIu/PSjxbb6ImJ/vi7OyEIAr9blASKLkXO2Zg/ctB3OIocB6dMeTchhXEWp6bDFWKRY01qLqnvkGRNCYHurLB0AQaFe9k9xuY8saXfebF9cRcLyNNBfZuedyzyrdsnxf+/6+Jszq3l5q9309RhIMDdiQ+uGMKgcK8e5Wk+rmpemj+QSUmB1LToePS3/ZI8ol+IBy/NT7VL4u4OvdHMdzuLeWN1jrQoGBjmyYXpYewoqOfuH/ZKxc3UlEBumxTPAItW+liPueJQJd/vLEEmw66jeLKst+TB9BY/NydGxvoyKtaXkTG+RPq62CyWDCYz+0ubOFDedLSgqWjp1gpaJoNwbxfiA9yID3S3XLoRF+Amydmsrm2bc2v5ZXcp2/LrTirk83+ZNr1JmqWycuP4WJ6bNwAnpYKC2jayq1o4UilaT2dXttjIZq0882cWz/wpLphvnxzPqFhfUsO87MIbHYhUNWtZl13NykNVbMypPe1GF7MGBHPThFh2Fzfw5JKDUodIIZcxc0Aw146J7vb80xOCILAuu4Zn/8ySpKpRvi48OCOZaf0Cpf/37Xojb67J5YMN+ZjMAh4aJY/OSmH+kLBuCym90cysNzbayF9vnhDL/dOTbG73464SHvntwDGty8cn+PPOZYNP2GkRRJn4dZ/vIre6lWBPDZ9fM6zHrvPp5u8DlewtacRFreC2yXH8lllOYV073i4qLhseIeXiTEj05+vtxchlcN3YaLYX1JNV0YyzSsElQ22d5IwmM7/sFud7LkwX1xP7SpsoqmtHKZdh7nQScVErTljO7OCfhaPIcXDK5HWjdY8LcKO0oYMWnRGVQkaMn9jJ6WwJrVbK2W7RJnfdqa9u1rKvtBGwzUFJO4GT0cnyxuocWnRGUoI9mJt2ZkwOzgSCIIbNLVqWhckskBruxQeXpxPooelRnjY6zpdXLxpEgIeGP/aV89hvB2hoN6CUy7htUjw3T4w9rmbdbBb4Y38FL6/IlnJLonxdmD8knCNVLTy+5KBUnMwcEMRtk+KPWzRVNHXw7fZivt1ZYtNlGRPnxyXDwjknJVDaWe9Le+queLuouHmCOJjs00lGWdmkZU9xA3tKGtlT3MC+0qZuF3POKgXJwe70D/WkX4gHKcGexAW4SQvkgto2Vh6q5Kc/SyUdv4PTx3vr83hvve0At5NSzqwBwdw7LZG0cC9KGzo4UNbExtxaKRzVyhurc3hjdQ4gSp8GR4ryxPRIb4I9z2ymyD8FndHEzoIGNuTUsOFIDYcrT5+ksTMvzU8lNcyTn3aXcumH26QATw+NkgXDI7hyZNQJB8IeLG/i2T+z2Jwr/l/0clFxx+R4LhseaSNpWnGwkieXHqKs8WguzpPn9euxq9JduOdfd4y1OQ4aTGaeWZbFZ5YORU9c/H/snXd4m+XZxX/asi3vvfeKV+LsvRcQAoGw9yizFCijFNoyCoUWKKWUlr03gUAgjAQSsvdwbMfxivfeQ9Z+vz9e6Y1l2Zl2CHw616VLw1qW5fd5zn2f+5xx0fz1/MyTMsmw2gTu/HAfuyrb8dYqefPaCT/b99ZstfGP78QctRumJxDgqebfP4r/W7+Zkcj3BY3UdRoI9tZI+UWLs8KJDfTib3YnuPNzI/EdkL23oaSZ5m4jAV5q5qSJ8QiOeVAPtcJpvnFWavDP0sFyY/jgJjlunDIGDl6rlXKiAzz54aBY7U8K8ZYWgJ0Vou49J8oPvcnCnirRyWjaANeXtQebEARIC/OWFsWMCJ8R76ocbunl3W2iF/+DZ6f/amxhjRYrf1qZz8f2YctlYyJ5YlkWGqV8UHmaUi7j9wtSuWlGAu16E7e9t4evD9QDYlfu6eXZZEQcvcsCsKmkhSe/PUh+ragbD9KJtrxNXQaeXVMsDY4vzAjlznkpRyU3giCwtayVt7dWsuZgo/TYYG8NF4+L5uLx0YOGtfa3p+7Um8l59Pvj+ciOC+16s1Ml/2jw0SrJivIlM0I0GciI8CU+yAu5TAyq/Dqvnv+sKxu0W+DGzwejxcZne2v5bJBA2pRQHTNTgtlV2S6ZpDiwv6aT/TWdvLG5AhBninJj/ZkYH8CkhECSQnS/ynkeR5fxp2KR1GwrbztqeO1wYunoCG6ZlUhNWx/vbq/k3k+PdIfjAj25blo8F+RGnXCHo6Zdz3NrS1ixpwZBEB0Tr50ax62zk5y6HNVteh5ZVSCZD0T6efCXJaNYkDG45EkQBJ75/ojxi+MxP94z00n+2NJj5Lb39rjMjQ3E3fNT+O2ck3MCFQSBR1YV8G1BA2qFnFeuGndaQ7cH4uNd1ZS39BLopebG6fF8treWylY9AV5qLp8Uw9IXNgOiecQ7W8U1+6YZCdS06/m+ULSGHizb7kg2TgRqpVwswuWJJMdLrXQiOXPSTl9chRsjAzfJceOUMZDkJASJGR6Ors2ofhvXbfZq9KSEQHYcbsNsFYj08yA20Hlz6jhIKRVHDtb3D2jbjwSe+qYIi01gdmowU0/CbvNMRFO3gVve3cPuynbkMvjjWelcPy2eXpOV+z/a5yJPiw7w4PlLxjAmxp/VB+r508p8WntNKOUybpudxG2zk445iDmYHfQ52RH0ma28vumwpHmemxbCXfNTJHvVwdBtMPPZnlre2Vbp5JA1IT6AqybHsjAj7KhVS0EQKKzv4psDDXyTX3/Mz2skcOWkWJbkRBAX5Mm6oibe3VZ5ygGUbvz8KG7sGTKwdSDqOg3U5dXzVZ74HQzSaZiUEMBku8RxoOHFLwWCIFDVpmdbeauUDzXYnOZI4p8X5zA+LoCVe2u5/s1dUgcFRNOSqyfHMSct5ISLVs3dRv6zrpT3t1dJZiNLciK4b2GqU0HFaLHyyoZy/v1jKUaLDZVCxo3TE7h9TtKQjontvSbGPOY8R/jIuRlcPWBjfqCmk5ve2XVU62ylXMaTF2Sf0szq/34q5+2tlchk8OzFOUxK+Ply4fQmC8+tFbs2v52ThFalkLo4N81IYP2hZg7bzQcMZisWm8CUxECyo/x46tsibIJYOE0eYILT3mtirb34utwuVTtQ20ljlxEvtQKLzbnb7paq/fLhJjlunBJMFpsU7ulAln3Dusu+iRsbK0rR2npNUldmYkIAL9llIdOSgpwW926DmS12OYCjAwCMeBjntvJWvi1oQC6DB/rZdP6Ssb+6g5vf3U19pwFvrZIXLstlZkrwkPK0c3MiePz8TMxWgdvf3yNtyNLCvHl6ec5RyQiIMq2nvz8kVTxVCpkU3vrZ3hrJRW1GSjB3zUtmzCCGAg6UNHbz1tYKPt9TKxkUeKoVLMuN5IpJsaSFHV3SVtrUw5f7avlyf92IOjUdD97ZVsk79g6hG/+/EeajpV0vBq9+1Y/0hPpomJQgEp5pyUFE+bt2Jc8EnAmkBsR5lUsnxFDT3sd72yu579M86fji5ynmp1w2IWbQcM1joVNv5qUNZbyxuULqQk1JDOTehakux6xNJS38+Yt86Vg6JTGQR5dmkhQytKPZit01/N6ePePA5j/MIXKAfO6zPTU88NmBo84t6TRK/ntFLtOTT35D/tmeGp6yD/j/6exRnJP985rtvLbxMM3dRmICPLlsYiyf7amhuq2PIJ2aKybFsuzFLQBcNC6a9+zH1d/MSMBksfHJrmoArpwc6/K8X+yrxWwVRGmwPTfH4Q4a6quV4i1ANDwK9taM6O/pxsjDTXLcOCWUNfe4ZJhkR/thttrYWy2SnPFx4qKw47BIXFJCdQTpNFKVf0qSM3lZd6hZso52uKolhehGNIjPbLXx5y/yAdHF5XhtkM9krNhdwwOfH8BksZEQ7MWrV40jPshrUHmap1rBo0szuSA3ku8KGnlo5QFaekwo5DJunZXIb+ckH7V702u08NIG0THNsSmYnhyEp1rB2oNN0pDslMRA7p6fwri4gEGfRxAENpW28OrGw07D/0khOq6cFMuy3EjJlnUw1Hf2sWp/HV/sqzuqtWr/33tGcjDzR4UyJy0Efy+1ZBm92D7U+muHWiHHx0OJt1bllGtz5LoKb40SrUqORqlA0+9caz9X2/83HdKgoRoSgoDTYG9/2AQBmyBgtNgwmm3oTVb6zFb6TJZ+l8XzXqOVLoOYm9NlP3X2mSUyfCZjKGvrxi4jX+yrkzqrCcFezEgOZkZKEJMSAo+aoTSSMJitFNR1sqeygz1V7eypaj+u7KSRwMKMUG6amUiIt4YVu2u57NVtUhYbwJgYP66YGMvZ2eEnNUvRa7Tw5pYK/vdTmSRbGh3tx70LU106+9Vtev72zUFWHxBVB8HeGh46O51zcyKG7MgZzFZmP73eiRQuy43k6Qud7ZktVhtPrC7i9c1HD0IO89Hy+jXjXYIuTwQbS5olN7cbp8dz3bTTH/bZH609Rl7aIMY33LMwFYB//yjK+W6emcjm0hYONXaj0yjxUivpNVlJDhFlo9/mN9DSYyLYWyPN2/SHw1Vteb+Ol4PkKAd0+WaluD7ejV8e3CTHjVOCwwK6P0ZH+VFY14XBbMPXQyV59G+1u6VNTgikuk1PUUM3chkuFagv7Nr3/lkkjy3NHKHfQMQbmw9T3NhDgJeae+0H1l8qzFYbT6w+KM0CzEsP4dmLRyOXybhzEHlaZqQPz18yBn9PNb/7cJ80hJkSquPp5TlHDZGz2gRW7Knh6e8OSXam6eE+RPpp2V7eJiWHT4gL4K75KUN24wxmK1/uq+O1TYelrBWZDOanh3LNlLij5pF06E18k9/AF/tq2X647ZgOa0E6DfPSQ1iQEcqUxCC0KgVtvSY2lrawsbiZjSUtx8xYOdPhrVES5qsl1EdLkE4M/QzwUhOkUxPgpcFbq0Quk2G1CbT0GKnr6KO+00BDp4H6zj6KG3tO2Db7dEMplxHmqyXK34OsSF+i/D0J99Xi46FEo1QgIGC2CnTqzbT0GmnpFrsnrfbLrb1GWntNw+bIN9wob+6lvLmXN7dUoFbIGRfnz4yUYKYnBzEq3GdEpG2CIFDfaWBvlUhodle2U1jXdcxcqJFEQrAX9ywQScb6Q008+30xm8tapL+bTqNkSU4EV0yKOa45wcFgMFt5f3sVL64vlZwfU0O9uWdhKvPSQ5w+616jhf/2y8WRy8TwybsXpDhlUQ3EtvJWLnl5m9Ntq26f5uIg2dxt5I4P9h7TaCQz0ofXrh5P6ElYRDtQUNfJLe/uwWITWJITwQOLf34FwwvrSukxWsiM9OGcrHA+2lVNTXsfQToNl0+M5ZJXxM/wikmxfLFP3CtcNSUOmUzGBzvFLs7ysVEuRdGihi4K6rpQKWScO1o0FKpq1XOosRuFXMbAXOFZqW6p2q8Bp4XkvPjii/zjH/+gvr6ejIwMnnvuOaZPn346XtqNEcZ+uwOaA2qFnNQwb0maMy7WH7lchiAIrDskVuanJAVJ1tET4gOcXKlae4xSBb9/XsikhMEr/8OB+s4+Sf/7h0Vp+HmOfNjoSKGtVzQJcCyQd8xN5s65yRxq7B5UnnbDtHjuXZTKT4ea+ePn+bT0GJHLxIrZ7+YlHzX7Y3NpC3/9+iAH7SnR4b5a4gK9KGnqkW5LD/fh/kWpzEwJHnRT1tJj5N1tlby7rVLaXHipFSwfF821U+OGzJKwWG2sP9TMJ7ur+bGoSZKpDIVwXy1nZ4WzOCuMMdH+yGRQUNfFSz+V82NRI3m1nU6bXY1SzoT4AGYkBzM9JQgvtZIX15fywY7qo77O6YZMJso9pyUFkRSiQy6X0Wu0UNrUw/7qDg7UdtHSc2aTlZOFxSZQ095HTXsf2zj6QPZAhPtqGRXhS1KwjkCdGm+tEkEQCwRN3SLpEwmfgcYug0u3+nTDZLWxpayVLWWtPPmNSNRnJAcxOy2EGSnBJ2XxKwiC5BR3oLaT/LouCmo7z4g8pZgAT36/IIW56aEUN3bz6e4a7l+R5zQUPiUxkOXjoliUEX7Sdt16k4X3t1fxv5/Kpf+T2EBP7p6fwjnZESj6VfdtNoEv9tfy5DdFUidrSmIgf14y6qjSWatN4Ia3dkrrH4hzqitvm+rSHd9d2cat7+05Zqds/qhQ/nXJ6FPq7lW36bnmjZ30GC1MSgjg6eXZP7vRTnWbXjL++cOidCw2gRfsXZxbZiWSX9fJ/uoONEo5KaE6/vdTL94aJcvGRFLdpmdjifgZDxY18Y294zYzJVjaczhmf5NDdBT3CzL20SpPyFbcjTMXI05yPvroI+68805efPFFpk6dyksvvcTixYspLCwkJibm2E/gxhmNfdWdTtdHRfigVsrZYg/ydMiSypp7qGrTo1bImZYUxHVv7gRg/ihn15mvD9RjsQkEeKklknPRELkCw4W/fn0QvclKbozfaQsbHQnk13Zy0zu7qe3ow0ut4NmLR7NgVCgf7qx2kacFeql5+qIcxkT78YcVB/jc3j1LChG7N0c7wJc29fC31Qf5wR6G6K1REhfkRUuPUSJXjk3KkuyIQRfO0qYeXt1Yzmd7ayUpW4SvlmumxnHx+JghN22lTT18sruaz/bUHrPTEOKt4ayscJbkhDMm2h+T1cbWslb+9EU+PxY1ucwRpIV5S5XyaH9PXtpQLrqmrT7qy/ysEARcsl5OB+Qy0f7aQ61Aq1LgoRJla47ukNUmoFTIUMjlyGUgl8mQy0CGTOqwGC02TBYrNkHcCNoEAZtNwGITMFtt9BgtxySvJ4t6O4EZaAE9EFqVnNwYf6L8PQjy1uCpVmCy2GjsMrKzou1nc8Jr6TFKjm9KuYzxcQHMTQ9hTloICYOk2xvMVkqbeihp6qaovpv8uk7ya7vo7DtzspUi/Ty4e34K8zNCaeoy8MW+Op75vtjpM4708+DCsVFcODZqUCfF40Wv0cI72yp5ZUO5ROoi/Ty4bXYSy8e5dgH2VXfwyKoCyUEvOsCDh84exYJRoUddm4oaulj0nLPs9aUrx7JwgNuaw97/8a8PHpNQ3zg9nj8sTnciYCcKMexzB83dRlJDvXnpynFnRJjtM98fwmwVmJ4cxLTkIN7dVkltRx8h3hounxjD3R/vA+C80ZF8bZ9lu2Cs6Jb3v5/KEAQx9mCw4th3BSKhWZQZLt3mMCHQKOVOnZzpycG/uny8/68YcZLz7LPPcv3113PDDTcA8Nxzz/Hdd9/x3//+l7/97W8j/fJujCD0JguHGpznHiYmBGCy2KTN7vRkUcf8g91Sc3JiIEaLTbKSXjDK2aLRsdnu38W5Y27yyPwCiEOjX+fVI5fBY+dl/uyVrJPFF/tquX9FHgazjbhAT16+ahyRfh7c9dE+Vg6Qp01LCuLZi3M4UNPJgn9uoKlb7N78ZkYid85LHlLL3tln5rm1xby9tRKrTUAhlxEb4InBbJWc9IJ0au6Ym8wl42MGneHZX93Bi+tL+b6wUeqc5ET5cv30BBZnDu6S1m0w81VePZ/sqj5m2n2QTsNZWWGckx3BuFh/OvrMrCls4L/ry9lc2uJkZeuhUjAtOYh56SFMTgjiy/21PP19MS/b9eC/dgTpNET5eyCTQbfBQofeTJfBfNSQQQdsghimeabMwCjlMrw04jyRSiFHhjjj02O00NlnPmmyZDDb2FHRxo6KYX27wwqLTWBreStby1v569eilXm4Xcbn66GivLmXitZeFznOmYDkEB03z0xkXnoovSYLq/bXcclL2yisP7KueKgULMwI5aJx0UxKCDylY3S3wczbWyt5dWO5FJ4bE+DJ7bOTOD830uX409hl4Klvi6TwSE+1gtvnJHHd1PijzvzYbAK/+2gfq/Y7H3sPPLzAZaaw12jhD58dcLnvQCjkMh5dmsHlE10H6k8EjrDPsuZewn21vHnd+J8t7LM/DtZ3SWvV/YvSMFqs/GfdkS5Oa69JCqGemx7CTe/uBuCqybFYrDY+thsOXDrBtXh+uKWXooZulHIZ89LFWZsug1lyuBzYCZw0wiZHbpw+jCjJMZlM7N69mz/84Q9Oty9YsIAtW7a43N9oNGI0HqnOdnUde3DYjZ8PB2o6XRbOyQmB7K5sR2+yEqRTS/bRDpIzNz2EHw42YhNEKVP/alxFSy97qzpckutHymXIYLbyJ7vZwFWT405az/1zwmoT+Pu3RdKg5syUYJ6/ZAz1XX0seWGTk1uMXAb3LkzjsgkxPPpVISv2iEOYCcFePL08xyWQ1QGbTeCT3dX8/dtDTlVPQRAk+ZtOo+Q3MxK4flq8SwaFIAhsKWvlxfWlUogeiJKLm2YkMDbW36UaKggCe6o6eG97JasP1GMwD73x9lQrWJwZzrLcSCYlBNKuN/FdQQPP/1DC1vJWKU8HxM3fnLQQ5qWL5PqRVQXS4On/N7T0GI8pZfP1UOGlVuCpUYrnaiUeagUapRyVQjyplTLpskohR62QobQTjWPBKohdG4tVwGS1YbbaMFvE20z2240Wq2Q80Gu00GcnV3rTkU6PxSbQaTcfOBHIZRDgpUalkGOxCSM2h5QSqiPa3xOZTEZNu57DLb1Hdcw6VTg6VWci5o8K5ZLx0UxNCqLLYOa7gkZufHsXOyqOSA6VchmzUoM5d3Qk89JDTtl0oUNv4q0tlby2qVwKBY0P8uK22UksHR3hQm56jBZe+qmMVzaWS8eeC3KjuG9R6jFnYEoau5n/zw1Otw1mDQ1iZ/qWd3e7xDA4oFbKMVlseGuUvHiKDmrgGvb51nU/X9jnQDiygs7OCicz0pd3tlVS32kg1EfDpRNieG5tCVabwOSEQHZWiLOXM1KCSQjWse5QE41dYsDn/FGu2TaOLs7kxEBJjr6tTFwbEoK8qGnvc7r/pPiRk8e7cXoxoiSnpaUFq9VKaKjzly40NJSGhgaX+//tb3/jkUceGcm35MYwYlu5qw5+XFyAVH2ZnhyMXC6jQ29iV6V439mpITy4UiQWCzOcvxcf2Ssx/QnOUxdkjcRbB+Cfa4s53NJLiLeGu+anjNjrjBQ69CZ++8FeSap0y6xE7lmQyqe7q/nTygKnYWFH9o3RYuOs5zdS29GHTAY3Tk/g7vkpQ1Yl91S18/CXBeTViJ0aH7vbVn1nHzZ7KN4Vk2K5bXYigTpnu02bTeD7wkb+u76U/fbHK+QyMbBvZqJLhgGIVc2V+2p5d1uVNNczGOQymJoUxAW5USzICKXHaOG7/AZe+LGU7Ydbnch3RoQPCzPCmJwYyI9FTfx3fRnvba86vg/5JKDTKMmMFMM+w3y0tPQY2VfdccwgvzMRFqsNL42GCD8PIvw8iPL3IMJPS4SvB2G+WkK8tSc9DzEcMFls9Jms6M0Weu1dG8epQ3/kcme/y+16E229Jtr1ZmwC0izYsaBRinK8kwm2PFqeTqSfB4E6tfQ/9mvENVPiWJITwZhoP+o6+/g2v4H//VTGrsp2J0e+CXEBLB0dyeLMMPy9Tn02srajj1c3lvPRzmr09q5jYrAXv52TzDnZ4S6SJLPVxoc7qnhubYlU0MmN8ePPSzKOOaNhswnc+2meVDxyYO+f5g/6u6w+UM+9n+wfshvqIDhR/h68cc34QY+XJwJBEHh0QNjnmeIiWtrUw2p72PTtc5Kw2QRe2ygW7m6emYggwAc7xGP2FZNieWjlAQCumiR2tRxmRUuywweV3X2bL+43+8sEN9sl9bGBnk7zUgFe6qPaf7vxy8JpMR4YrEo7mI71gQce4O6775aud3V1ER3tOkDmxpmBreXOcwBjYvzQaZSSzt0hVVt7sAmbILrVaFUK6eCy1O5wAjj52/fH+WNGZkZmf3UHr9i7H4+fn3VGtOtPBEUNXfzm7d1UtenxUCn4x/Js5qSFcO8n+12S2c/JDufhczN4eUM5r2wsRxBEicazF+UMaeXc1G3gqW8OSQu2Ui5Dp1WiN1npsgftnZ0Vzv2L0ogZEORqsdr4Yl8d//2pTArv1CjlXDI+mhtnJAzamStq6OLdbZWs3FtHj9Hi8nMHUkO9uWBsJEtHR6JWyPn6QD3XvLFTquw5kB3ly+LMcDIjffjv+jKeXVMMa4Z82pOGQi4jLcybnGg/gnQaDrf0sl3KD/nlkZqB6DVZKWnqGbLSDCKpC/HWEGw/hXhr7efi9SCdBn8vFf6e6pOy9T0a1Eo5aqUcX078/9dstdHWa6K522jvaplo7TlyuaXHSGOX2BHpNlhGrPNS29HnFF75a0B6uA9XTY5lRkowkX4elDX38G1+Aw9/WSBJWx0YHe3HWVlhLMmJGLauQlFDFy//VM6X++ukGZdR4T7cMiuRs7LCXeZZBEEsyDz1TZHUnY4P8uL+RakszAg75kxoaVMP8579yem2h85O54bpCS73NVtt/P3bIl7ZOLQ9tFIuw2SxMSbGj5evHDcseS0vbSjnra3iUP/PHfY5EC+uL0UQxC5fergPawsbqWjV46NVctG4aD7fW0tnn5noAA8EBNr1ZkJ9NMxKDUZvskhGRuf221M40NBpYF+1qBDpL4/fZN+HDPwuTIgL+EWG8roxOEaU5AQFBaFQKFy6Nk1NTS7dHQCNRoNG4w5f+iXAYLa6zEdMTgikudso5ZM4Wutf54k628VZYXydV4fVJpAT5Ut8v5C2NYWNLhXVJTkRR81mOVmYLDbuX5GHTRDDLwdrb5/JWH2gnns+2Y/eZCXK34OXrxyHQi7j3Bc2S6QCRGLx2NJMsqJ8ueLV7VIQ6yXjo3nonFHoNK7//iaLjbe2VPCvH0oksqFSyJAho8OuYc+J9uNPZ6e7ECSL1caX++v494+lHLZvFLy1Sq6aHMu1U+MJGtDpMVlsfJNfzztbK9lV2T7k7+utUbJ0TASXjI8hKUTH2oONPPh5Pj8VO7uqjYnx46zMcIK81fx5ZYEUbjecCNKpGR8XQGKwjoYuA7sr2ymo6zquTJ7+CPPR0mu0SBbbv2T0GC30GC0uzn0nggAvNQFeavw9Vfh5qgnwVOPnpcLPo99t9vt4aUTZnKdacUrZWSqFnFAf7XFZ8PYYLdS06ylv7qWsqYey5h7KW0SL56OR8v9P+M2MBOalhzImxg+5TMbeqnbe3VbJ2sJGJ5Isl8H4uAAWZ4axICOMCL/hITaCILDjcBv/+6nMqTI/JTGQm2cmMj05aNDN656qdv62+qA0nxHopeZ385K5dELMMb9fNpvAgysPuLgu7n5onktnG6Cpy8Dt7+91kub1R7ivlvpO0c3vnOxwnl6eMyyFgZV7RVc4EMnXzx322R9VrXop1uD22UkAUj7QpRNi8NIoJce1qyfHscKedbMsNwqlvdClN1mJCfAkN8bP5fkdRdfsKD9C7P/r9Z19lDX3IvIb5+/ExBF0cnXj9GNESY5arWbs2LGsWbOG888/X7p9zZo1LF26dCRf2o0Rxp7KdpcB5alJQfxgdytxpAV36E2SnOqc7Aju/VRMeV46oOLy/g7XNPiHzh4Zz/4X15dS1NBNgJeavywZNSKvMRKw2QSeWXOI/6wrA0QXmRcuzWXtwUYe+OyAkytPWpg3z186hnVFTSx9YTMmq41ALzVPXpA9JKnbWtbKQysPUNbsvFkViYRApJ8H9y1KdXFMs9oEvtxfy79/KJU2uv6eKm6ckcAVk2JdsiPaek28v72St7dWStk6g2FcrD8Xj49mcVY4+6s7eHNLBd/mNzhtKjMifDhvdCQ6rZIHPjsguR8NF6L8PRgfF4CXRkFeTSd5NZ18k+8qtR0KMhl4qZUuG2FHDo9CLiMmwJO4QE/ig3TEB3kSF+RFkE7Dzoo2Hl1V+LPbF58OtPWanMxGRhI6jRJfDzHo1EGUrDYBs03AYp8DsthsWGyCPZTUQp/ZOmJOb79ULLEXiCYnBBLsraFTb+ankmbu/WQ/64ubpaIIiIWSKYlBLMoMY/6oUJeCx6nAahNYU9jISxvKpP9/uQwWZ4Zz08yEIXO+Klp6+ft3RVKYp1Yl54ZpCdw0M+GogcMOHG7pZfbT651u+8PiNG6emTjo/XccbuO29/cMOfeVEOQlHT9/OyeJu+alDIsRzqaSFmndvWFa/KDdpZ8T//2pDKtNYEZKMDnRfhys72JLWSsKuYyrpsSRX9tJYX0XaoWc6cnBPLFaNNdwBHo6CNLS0YOHsP5kt5WemXwkzNUxG5od5UfRAPOksbGDz6a68cvEiMvV7r77bq688krGjRvH5MmTefnll6mqquLmm28e6Zd2YwQxMKhMpRBtTF+162gX2q2hvy9oxGITSAvzRqWQsbeqA7kMzsk5YuNY0dLrNJAOogzlVELOhkJRQ5c0M/TwuRmDVtvORPQYLdz54T7J8vLG6fHcOS+Fh78skFKcHbh6cixXTo7jj58fYId9DmReeihPXpA16OaitcfI46sPSg5CA+GlVnDr7CSun+bsKGS1CXyVV8e/fiiRDA78PFX8ZkYCV0+OczEgKGns5vXNFXy2p2ZI6Y+/p4pluVFcMj4alULOx7uqmfvMeqfciEg/D84bE0GAl4bHvio84S7K0RAT4MmE+AAsVhu7q9qpbuujpn3wz2UgVAoZMpnMifwLgvi3k8kg2t+TlFBvUsN09nNv4oO8hrRuFSU/cbT3ikYKK/bUSNVmN04ejs6TGyeGtDBvbp6ZyKSEQMJ8tQiCwKHGblbsqeHHoiZ2V7Y7mXz4aJXMShVtrWenhQy7JLjLYObjndW8tbWC6jZR7qdWylk+NoobpycQFzR4xlZDp4Hnfyzh453VWGwCMpm4Yb5rfspxyeUEQeChlfkuc307HpxLiLfrmiUIAq9tOszfvikSrdXlMpfCRVygJ+UtvagUMv62LHvYogwK6jq5+d3dmK1iZ+iPZ/38YZ/90dhl4NPdYhfst3PsXZxNYhdnUWYYkX4e/He9uF4vyAjlhyLRtGh8nD8JwTrae01Sp2Zg4RTENWqTvcg6I+WIacMueyctJVTHvn6B5iqFjNSwM2NOyY3hwYiTnIsvvpjW1lYeffRR6uvryczMZPXq1cTGnpoNohs/LxxzNQ7MSQvBaLFKZGVhpkhyVtmlaudkh/OlveIyNSnIaTFwDBT2x5vXjh/292yx2rjv0zzMVoH5o0JZkh1+7AedAahp13PDW7soauhGrZTz5LIsxscFcNFLW502+N4aJU9flEOPwcJ5/9lMj9GCl1rBX5ZksHyQrCGbTeDjXdX87ZuiQV2pZDK4aGw09yxMddKE22wCXx+o57m1xVLXx89TxY3TE7h6SpyTDE4QBDaUtPDapsNHzSTJjfHj6ilxzEoJ4YeiRh5ame80qO/roeLs7HDiA714fPVBqZt1qvD1UDExPgCbANsPt1LVpj/p7BNHx8tDpSAjwofMSF9GRfiQFuZNUojuhB2iOvvMVLb2UtWmp8tgJiPCF6tNOKaNthtujASKGrq586N90nVfD5XLcSMlVMectFDmpIWQG+M3IlkjFS29vLmlgk92VUtD+36eKi6fGMM1U+KHnF9p6zXx3/WlvLW1UipEzEoN5g+L044a5tkfg3Vv7p6fwm/nJA3aReg1WrhvRZ6U6aJVyZ2cIn09VHiqFVS06vH1UPHSlWOHbVamodPAtfawz4nxATxzUc4ZF5Hw7rZKzFaBcbH+jI8LoL3XxBd2K+3rpsZjMFulTs3F46N5+MsCAJaPE2e1fyxqkoqog5kF5NV00NlnxntAuOeeKrFYpJA7fz/TwnzOiLwgN4YPp8V44NZbb+XWW289HS/lxmlAc7fRZaM1Jy2E9YeaMVltxAd5kRyio63XxJYykfScnR3BtW/sAJwrLgaz1aUTAeJ8z3DjtU2HyavpxFur5K/nZf4ihgt3VbRx0zu7ae01EaTT8PJVY+nsM3PWvzY6zXNkR/ny5LJs/vdTGV/aF4lxsf48e9FoF2MAEDtaD36ez+4hZmFyov149NwMcvotDIIgsLGkhae+LZLIla+Hihunx3P1lDgniYfZauPLfXW8tKFsSFcptVLOuTkRXD05DgGBj3ZW89Dn+dLvJZOJltjTk4N5b3sl7w+DI5pSLmNsrD8BXmp2V7bT1G2UhlZPBl5qBRmRvmRG+JIV5UNWpC/xQbrjDupr7TFS0aqnsrWXilY9VfbzytZeKcfDDTfORPQnOIsywnjw7PRTCug8Ghw29G9sPswPRU2SyUhyiI7rpsVz3ujIIV3+ug1mXt14mNc2HZY6eOPj/Ll3YRoTjtMqeMjuzR/nSnMeA1Ha1MPN7+6mtKkHpVyGAE4EZ0piIAV1XdR3GogJ8OSNa8eTOEiQ68mgz2Tlhrd30tRtJDlEx8tXnRlhn/1hMFulz/P6afEArNwnhkOPCvdhbKw/n++todtgIcrfg0AvDWXNvaiVchbbi6gO+/8FA8JVHXBI5acmBkmEu8tglmbEBh6mMyN/eTESbhwdp4XkuPHrwo9FrpvC2akhPPpVISC2lWUyGd/mN2C1CWRG+tDYZaCiVY+XWiEdoABW7a9z0eI/sDht2AlIeXOP6LAF/OmcUSMihRtufLKrmj9+fgCzVSAjwoeXrxrHp7tq+OfaYqf7XTU5lnOyI7j5XdFtTSGXcde8ZG6ZleSy2dabLPzrhxJe23h40FmPQC819y9K48KxUU5Vv/3VHTz1bZFEWnUaJTdOT+DaaXFOMzd9Jisf76rm5Q3lQzpGRfhquXxSLOfmRLChpJn7VuQ52UVH+XuwdHQEBXVdrD/UzPpDR0+lPxYCvNSMifajpKmHqjb9KVk5J4foGBvrT26MP7mxfiQE6Y5ZHRUEgZYeEyWN3ZQ09VBsPy9t6jnmHEqwt4bYAE8i/UUL5whfLRF+HoT7ehDp54GPhxKZTEafycqGkma+K2hgbWGjlAXihhunA98WNPCtPYtkSU4E/7gwe1gG5vtMVr7cX8vrmyo41Ngt3T4nLYRrp8YxLWlwMwEQN9HvbK3kxfWlUsEgI8KHexemMjMl+LjXmMG6N3fNS+GOuYN3bwC+za/nnk/y6DFaBu14LcuNZNX+OsxWgTExfrx61bhhk07bbAK//2Qf+bVdBHipef2aMyPscyC+3Ceu/ZF+HswfFYogCHxoN3C4ZILYqflkl1gAXT42mm/zxW7YzJRgvLUqDGbxmAcwP33wOVOHXHtq0pGi6f7qDgRBjFUYuEZluUnOrw5ukuPGCeP7AmeSkxnpg6+nStqMLrJXVT7fKx6gzsmO4KOd4sHr3NER0qyGIAi8vrnC5fmvmRo3rO/XZhO4f0UeRouN6clB0sDimQqrTeCpb4t42W5xvTgzjL8syeCPnx/gx6Im6X4qhYynl+dQ32ngsle2YbGJ5gDPXzqasbGuFcqNJc088NkBl+AzECtaV02O4655Kfh6HlkQy5t7ePr7Q9Jw7lC5OJ16M+9sq+CNzRVSvsRATIgL4LppcSQG63h/RxVnPb+RbvtmXK2UsygjDF8PFe9sqzxlOVpamDcBXmq2lLXS1mvih36f2/HCS61gdIwfY2P8yY31Z0y0v9NnMxgMZiuHGropqOuisL6T4oYeipu6nYawByLCV0tsoBdxQZ7ieaB4HhPg6TLXNBQ81AoWZoSxMCMMs9XGrop21h1q4seiJifHPRAdnBKCvfD1UCEIouRjJIMp3fj/hVX761hl7yYDfHrz5CGt6odCWXMP722r4tPd1RJh91QruHBsFNdMiSPhKB0Ps9XGx7uqef6HEmmWLyHYi9/PT2VxZthxS7ZOpntjsdr4x/eHeOkn8dgdG+hJZauz/PXCsVF8alcvnJUVxrMXjR5Wa/Xnfihh9YEGVAoZ/7ti7Ih1104F4tovzt5cPSUWpULOvuoODjV2o1HKWZoTSVO3gW322d9luZFcbVeCnJ0lysy3lrWiN1kJ89GSGekqN7TaBPbaZWn910OHOcWYaH+paOdAerh7HufXBjfJceOE0Gu0sHHgPE5qCFtKW+kxWgj10ZAT5UdFSy87K9qRy2BuWgj/tHdRLhp3JPdoW3mbS+DjpROih72t/u72SnZWtOOpVvDE+VlntEyt22Dmzg/3SZvyO+YksSAjjOUvbZGGa0HcxD98bgYv/Fgq+f2fnRXOE8tcM386+8w8/nUhH+9ylQUCTIgP4JFzM0gPP7JQNHUbeG5tCR/trMZqH849f0wkd81LcVo0m7uNvLqxnPe2Vw06zC2XiQOk109LoK3XxNtbKyQJAYibgMWZ4Xy2p0aS2Z0MZDLIjPClo89EdVufZJd9IvBSKxgfH8CkhEAmJwSSEeFz1JmCboOZwrou8uu6KKjrpLCui5KmHqfh6/7vLzbAk6QQb1JCdSSH6kgO8SYxWDfsYZoqhZzJiYFMTgzkj2elU92mZ92hJtYVNbGlrJX6TjH7xfG+RoX7MDUpiNRQb7y1Sqra9Gwpa3Ui1D8XZDKQy2TIEM8dEnqrTUCGDJkMbIIwIs5nMplI6h2XZciQy8TcN8cRxGwT3dhs9uw3meO+9sunkzzOSAlmVLgPuyranCzZvbVKZqeGMDraj3WHmpz+/04HLvzfVunypROieXRp5qDWzGarjTWFjby7rdJp8xkd4MGVk2K5eHzMUTsSVpvAF/tq+dcPJRKxiPTz4Hfzklk2JvKE5oNOdPYGoKXHyG/f3yuZ8oyO9nMaavdQKZiVGiwRnJtmJnD/wrRhnZP5cn8dz/9QAsAT52cdtxzvdGNbeRtFDd14qhVcPC4GgI92imTyrKxwfD1VvLO1FpsAOVG+9JoslNulanPTQwAkE555o0IG/Zscauim12TFW6N0MhNw7DliAz1d1pyjkWc3fplwkxw3TggbS5pdrKPnpofy1tYKQEwUlstlfGYPkZyeHMy28laMFhupod5Ow3+OSk5/PDDM7i817XqesucD/GFx2hlZ1XKguk3P9W/tpLixB41SztPLc7DYbJzz701O91s+Noq56SHc9t4eWntNaFVyHl6SwcXjo10O9t8XNPDQyvxBrZr9PVU8dPYoluVGSo8zmK28tukwL64rlYZ656aFcO+iVKfh3JYeIy/9VMY72yqddOYOaFVyLhoXzUXjotlc2sIdH+yVpAEymUiMNSo5qw+IyecnA7lMlBdUtOrp7DO7hAweCx4qBePi/EVSkxhIVqTvkLkYFquN4sYe9lS1s7eqg73V7ZKj3EAEeKnJiPBhVIQP6WE+JIfqSAzWDXsQ5vEiOsCTqybHcdXkOPpMVraWt7ChuIUtZS0UN/Y45fyoFDLGRPszMSGAq6fEMTrajw69iUMN3ZS39JJf28n6Q82nzZ1MEMAqpbwK4BQO70xsPNUKFHIZCrkMm00kPiI5sRMlO/lwEBWLVbSJdvwcWb/7IQahnhBJ6Z9Gy/CTrmNhW3krTV0GEkN03Dwzka/y6qhp76PbYOHL/XWnVEQYLnywo9opU2bt3TPw0ij5YEc1H+6oko5TcpkoSbt8Uiwzk4OPSgQGc3kM0qm5fXYSl06MOaGimc0m8NAX+S7zf0fr3gDsrWrn1vf2UN9pwEutIDXM22ludU5aCHqThW/yG1DIZTy6NIPLJw6v+dK+6g7u/US0iv7NjARpOP9MxMf24O+loyPx9RSlZ6v2i3I0RyF0ld2s4ZzsCL7LFwnNjOQgafbTYX40KyVk0NfYXSlK1UbH+DnJtovtskf5gLUySKc+I2V9bpwa3CTHjRPCQKlamI+WlFBvvrNnh5ybE4HNJrDCbkd8wdgoXt4gbmIv6rcJr2ztlSoxDoyL9XfJVDkVCILAA58doNdkZUJcAFcM86IynNhX3cENb+2kpcdEiLeGl64cy7f5Dbxkl6w58MT5WVS29nLzu3sAsaPzwmVjSApxbrO39Bh5+MsCvrIvFANx4dgo/nhWOgFeakD8rL4+UM/fVhdJZCQn2o8Hz0p3qga29hh5eUM5b2+tpM9sdXneAC81V0+OY256CJ/vreXil7Y6OSAtHBXGl/vrTko+BuLmJzPSl6KGbkwWG/trTozYpIf7MDMlmJkpwYyN9R8ybLalxyiSmap29lS1k1fTid7k+vtG+GoZFeFLZqQPGRG+ZET4EO6rPWO7hR5qhd39StSwN3Ub2FrWyqaSFraUtVLb0ceOijYprFAmg9RQb8bF+TMuNoCzs8L596VjkMlkdBvMVLToKW/p4XBLL0X13eysaBtSrjjSGOzv80uEt0bJuDh/0sN9iAvyIiHIi7ggL/w8VKzcV8cjqwokmWd/mCw2ihq6j7uL+fv5KVw1Jc5pY9dtMJNf28WB2g7213RyoKbzpN0Gjwfznt3gdD1Ip+bi8dFcOiGGKP+jF6QcLo//+qFEkmQezcL+WBise/P7+SncfpTujSAIvLe9ikdWFWC2CsQGemKxOjsg3jEniW/yGyhp6sFLreCFy3OZnTr4xvxk0dxt5KZ3dmG02JibFsL9i9KG9fmHE10GM6sPiOvSxeNFQrOhWCychPtqmRgfQFOXgZ32Y9BZ2eH89n1xvZtrn72p7+yjolWPXAYThgjv3Fctrg25MUdyb4wWKxX2Lp9yAHGOH8Jy3I1fNtwkx43jhtlqc9mcLskJ58eiJnpNViL9PMiN8WfbYXGz5K1VEunnQX6tGOR1/pgjrmpvbqlwLnwC/75szLC+309217CxpAWNUs6TF2SdcfaZDnxX0MDvPtyLwWwjI8KH5y4ezeOrDzoN3If5aPnreZm8vKFc2oBePTmWB85Kd+oQCILAl/vrePjLgkHdueKDvHj8/EymJB4JRsur6eDRVYWSxCXMR8sfFqdxbs6R0M+2XpOd3FQMupkM9dFw88xERoX78M62Sp7/sUSSbaWE6siM8OWzvbV8tKva5bHHg4RgL8qbe7EJkHcCxMbPU8X0ZJHUzEgOGrIa29RlYNvhNraXt7KtvNUlEBVEs4XR0X6MiRFPOVF+v5icpaEQ4q1l6ehIlo6ORBAEKltFqZpD8lTVppc2zu9uE6vbwd4asiJ9ybTbZE+ID+DcHOcgPrPVRkOngdqOPmrb+6jt6KO6TU9xUw+FdZ3/r4M108N9SA/3Jsrfk0g/LZF+nkT4iYYSR+v2WW2i1e4/LxrNV3l1rNx3ap2ZZ9YU88yaYnQaJb+bm0xGpNh5dEgdHejQm8ir6eRAbSd5NR3k1XRKcsfhRkuPif+sK2P9oWZW3DJl0M/DZhP4tqCBf60tkcwIhnJ5PB7YbAIPrsx3iTI4VvfGYLby4Of5rLCrFiYlBLCt3NnU5KkLsnj6+2Kau42E+Wh5/ZrxjIo4Prvq44XFauOOD/bS2GUkKUTHvy4dc9zujj8Hvtpfj9FiIzlER06UOOj/tZ30nJUVjlwu40e7g15OtB86tVKS/Tmybrba5YxZkb5DFkYdIZ8Z/T7vwy29WG0C3lrXgOa4QDfJ+TXCTXLcOG7srGhzcYk5Kyuc/64XOzXnjhY3xSt2i12cc7Ij+GKfeHlBRqjUNeg2mCXXFAe8NcrjCmI7XjR1Gfir3e3t7vkpZ6zW9vVNh3ns60IEAWanBvP7Ban85p3dHG45ssmelBDA5RNj+cNnebT0mNBplPzjwmwWZznn/LT3mnhoZb60YPSHSiHjlpmJ3Do7Sdo4NHUZeOrbQ9Ii7aFScNPMBH4zI0HKdOkxWnh5QzmvbSyXOjL9Eennwc0zEwjSaXhzSwWPrCqUfjYlMZC2XhNFDd1D2kgfDeG+Wlp7TZgstiGlYYMhOUTH/FGhzBsVSk6U36ALfmOXONS6rVwkNuUtzs8vk4nPMybanzExfuTG+pMYfPzW0L9EyGQy4uxdg8smijr5pi4Duyvb2WU/FdR20txt5MeiJqeZnSCdWupkOeaNkkJ0R5WHWm0Crb1GmrvFU1O3kbZeE519Zjr0Zlp7jFS391HZ2nvGdml8tEpiAj2J9PMgwEuNj4cKXw8VQToNwd4agu3nAV7qIaWQA9FnslLVpqfU7sBX0tRNaVMP5S29LlLhoRAb6MlDZ49iTloILT1GSu2ufocauvlsb63T8/QYLTxuT5EHsWCRFuZDWrg3mRG+ZEb6Mi0pyClMsanbwL6qDvZUddg7nR2DylZPFgV1XaT96Vvp+pq7ZpAUouO7gkaeW1ssdau8tUpumObq8ni8KG/uYc4zPzndds+CFG6bPXT3BqCqVc/N7+6msL4LuQzOGxPpEqb8wmVjeGDFAbqNFtLCvHnj2vHDusY58OyaYraWt+KpVvC/K3KdcsrORDikao7sNoPZylq7FfTZ9uy6n+yZanNSQ9hU2oJNEI/HkX7i5+cgOZMSB4+asFhtkk10f5m1o+OXHKKjut25QxnhN/x/Gzd+fpzZ/w1unFEYKFUL0qmJD/KSOg5LR0fQa7TwTb5DSxvOze/uBo60pQE+3lXjUkX56KbJw/Y+BUGszHUZLGRH+Uoe/GcSrDaBx74q5M0tFQBcPjGGeemhLvM3106Nw89Dze8+3ItNEOVp/71irEtrfd2hJu77NI/mQWZvxsb68+SyLJJDRUmbxWrjra2V/HNNsfR3WDYmknsXpUqLsMli4/3tlfz7x9JB5UcxAZ7cPDNRXFh/KpM2HUq5jFmpwaw92OTiXHM88FIrkMlk9Bgtx10tlslgbIw/CzJCmT8qbFDZgcFsZfvhNjYWN7OhpNmFdDkG8CclBDIxPoAJ8QH4eapP+P3/2hDio2VxVrhEqPtMVgrru8ivFSv7+bWdlDT10NJj4qfiZmlzAuJnGhPgSXKItzSXFBPgSUyAJyHeGhRyGSHe2kFT4oeCzSZgsFjRm6z0maz0mcVzvcmKwSxet9gErP0MASw2AZtNsN8uGgQoZNjnd+Qo5Didq+QytGoFnioFHmoFHioFWpUCT7V4XatUnFJX2GYTaOk1UtWql8Jnq9r00vXB5ucc0CjlJATrSA4RTxmRPowK9yVIp2btwUbe3FLBtvI2Klv13Pj2LiL9PLh4fDQXj49mapLYvX3ygmysNoFdFW08tDJf2gw60NhlpLHL+W/prVGSESnmQGXaT/PSQ6V8ErPVxqGGbvZUtbOnsp09VR3DKnOb/09nWZtOo+S6afFcPy3+pOYohuzePDj3mN/HLWUt3PreHjr0ZgK91CzJiZCO4yBmlt00I5G7P96H0WJjQlwAr1w9bkTmPdYUNvKivcj41AXZLrLlMw2Vrb3sq+5AIZdxnl3ZsaG4WVKCjIn2w2y1sclujjEzNZgP7X+j/iTbEeY5cQhjhYpWsSDgpVYQ5X+EvNTZpdjRAZ4uLqMhPr/srrwbg8NNctw4LgiCIAVvObB0dCTfFTRgsoqmAmlhPny6uwa9yUp8kBcNnQa6DRYi/TyYapdHWW0Cb25xNRwYzhb+V3n1rClsRKWQ8fcLs0ckdftUoDdZ+N2H+6TP8w+L01DKZVz75k6n+z1ybgbrDzWx7lAFIBoOPLo008mNq9dehR0sKFOrknPvwjSumRIndSAcGxsHKcmJ9uORczMkQwibTWBVXh3PfF886CYlJsCT22YnIpPJeOmnMknW5aVWMC05iO8KGll78MTnbUK8NTR1GwftFg0GlULG9ORgFmWEMSc9hKABsjFBEDjU2M3G4hY2lDSz/XCbU/VaJhNlDJPiA5mUEMj4uIBj2kO7Ic70jI31Z2zsEZ27wWzloJ34FDV0U9J4xDa7slVPZaveZf5OrZQT7e8hkZ5wPw9CfTSEemsJ8dES6qNBp1G6VNPlchmeaqXUaTyTIAgCPUYLbb0mWnqM1HcaaHCcugw0domudk1dRkzWo3c9vLVKkkJ0JAXrxPMQsTsW6e8xZDdxUWY4izLDKWvu4YPtVXy6p4bajj6eXVPMv34oYV56CJdNjGV6UhAKuYyJCYGsuXsmBrOVL/fV8frmw4PO86gVcrqNFraVtznJsXQaJaMifJyCcJflRqFVKahp7xvRWZ4eo4Xnfyjhx6JGVt469YSO8YN1b+5dmMqtsxKPOUv37rZKHv6yAItNICfajzHRfk4E57bZicQGenHHh3ux2gTmpYfywmVjRsR0pKpVz90f7wPEYtiSnIhhf43hxrf22d1JCQESmVxvJ9PzR4n5enur2uk2WvD3VJEV6cs9dhn1JHtAeI/RInXesyL9Bn2dQw0icU8J83YqSNR1iIWzcF8PlyDs4F+49NiNwXHmrRRunJEoqOtyCc46KytMCtg8d7R4gP10t9iKviA3kvftFZhLJ0RLB5o1hY1OVsgAb1w7ftjeZ1uviYe/LADgttlJTq3qMwHN3UZueGsn+2s6USvl/OPCbDaXtjjZO4f6aLh7fgrP/1BKbUcfGqWcx5ZmctF4Z7ec3ZXt3P3xPpccBhATvf9+YY7U1WjtMfLkN0V8Yrcv9fNUcf+iNC4ed+Rvs7GkmSe/KZKctvojxFvDrbMSUSjk/GddmbSB8dEqyYz0ZUtZK98VuIbEHg39Q/KOVr12QCGXMSUxkCU5ESwcFeZCSowWK1vLWll7sJG1hU00dDl3gsJ9tcxIDmZ6ShBTE4Pw93J3aoYDWpWCMTH+jOk34CsFoDaJpKekqZuKFrFTUdvRh8lio6y5d9DZJwc81QpCfbQEeKnx81Dh66nCz0ONn6coCfPzVOGtVaJVKtDaOy4e9u6LVqVAo5RLFtTyfq5q/d+jxSZgttowWwTMNhtmq9gBMlqs9Bit9Bot9Bgt9NpPjtu6DGbaek1Op3a96bhnjeQycaMVE+BJbKAn0fbzmABPYgO8TolwJwbreOicUdyzMJVv8ut5f3sVOyva+a6gke8KGokJ8OSSCdFcmBtFiI8WrUrBReOjWT4uiq1lrby++TA/2GciQJzBmpQQSFqYN9Xteg7UdnKwvoseo4Udh9ukwMXBMCbGj1tnJTErVazC59d2So/ZWdF2yqG1+bVdJD34jXR910PzXAoeDojdmwNO7m5wfN0bi9XGY18V8tbWSkA02AGcCM7/rhhLZWsv932aB4jmLk8uyxqRIpvFauPOj/bSbbCQG+PHA4uH15V0pOAIjHVk6QmCwAY7yZmRIhZCHVK0acnB9BgsksQsN8YPgMK6LgRBnB0N9h78b+2QosUOkMs6OjnhvlqaupzXnKPNX7nxy4Wb5LhxXBjYxfHRKon085QkSefmRFDdpmdbeZso/Ynw4envi1HKZU7ZOIPZRs/q14Y+VTy6qoDWXhOpod7cOitp2J53OFDVqufK17dT2arH31PFsxeN5tVN5WwuPSLrmhAfwKKMMP78RQFGi43YQE9evDyXjIgjScwWq41//VDCf9aVMjCSRauSc5+9eyO3W+l+sLOKv397SCIUF4+L5v7FadKMVGlTN499ddBJnuKAw63IU6XglY2HJaIb4KUm3FdLQV3XCcvSVAoZZqvgMt81GGQymBQfyDk54SzKCHMZ9G/rNbGuqIm1Bxsl2UP/z2JSQqDdeCCIxGDdGet69muDTCYTZ1K8NU4mFyB+f+s7DZJMq7JVT6O909HYJXY6uo0W9CYrh1t6nebThgMOsjNYntFwwEOlIFAn/n+E+mgJ89ES5iueHLeFeGuHdPYbLmhVCs4fE8X5Y6Iobuzm/e1VrNhTQ1Wbnr9/e4hnvi9mdmowy8dFMyctBJVCzpSkIKYkBXG4pZe3tlSwYrfYDVqxpwaNUs65ORE8cm4Go8J9WHuwice+KnQpfvXH3qoOHv2qgFX7/SXTjmumxnHTzERsNrHb6iA9OyraBpXbngjG/XWtdPnrO6ZJx81T6d506s3c9v4eKY/sznnJrDvUzP5+GTif3TqF9YeapYyam2Yk8IfFaSN2vPnv+jL2VHXgrVHy/KVjRvy7NBxo6DRIQZwOmePhll5q2vtQK+RSp2a3XYo2LtafvdXi5bhAT+nY74gKyIz0ZSjU2qVoAx36HIUvL41S6qaqFXJMVht+bvvoXyXcJMeN48L3A0jOstwovj5QjyCIMx/RAZ48t1bs6kxOCJTmdOaPCpUqJAdqOl2qfo8tzRi2heDHokZW7qtDLoO/X5h9Rh34D9Z3cdXrO2juNhId4MHfzs/mT1/kO23gluVG4qNV8ajdMGFOWgjPXTLaaaC2pl3P7z7c59JqB5gQF8DfL8wmzt69KW3q4YHP8thZId53VLgPj52XKUmNOvVm/vVDCW9vrcAyYMPnpVZw/bR4Qn21vLrxsPQ+Q7w1eGuVlDX30nYCVsFqpVySix1PtTs93IcLciM5NyfCpcJW39nH6gMNfJffwK7KNieiF+qjYV66aDowOSHwZ8umcWNoKBVyogPE7sXUIe7Ta7TQ1G2ksctAh95Eh95Mh92QoLPPTGefeFuv0SLO5Zit9JlsGM1W9GbrUQmMTQAXa0c7VAoZSrkctVKOTqPES6PAS6MUL6uV9ssKvLUqArzUBOrU+HuqCfAST/6e6mEPdx0OpISK4cH3L0pjVV4dH+6oYk9VB2sPNrH2YBOBXmrOHxPJReOjSQn1Jj7Ii4fPzeDehal8sa+Ot7dWUNTQzSe7a6RucH94a5QsygwjO8oXuVzGvqoO9lV3UNLUQ3VbH9VtfVJOj1ohZ1SED6Oj/Rgb68/CjDDSw31o6TGeMsnpj7Of3zTkz46newNQ1tzDDW/t4nBLL55qBY8uzeQvX+Q7FVN+/P1MPttTywvrSgG4f1Eat8xKPPVfYAjsr+7gOTuZevS8jGNabZ8pWH9IlDGPjvYj1H5MdxDHcXH+eKqV2GwCe+0kJzfGnx+KGqXLDpQ2iZLKUeFDzx85iHekv7OZgKOwprHvDVQKmZTD5XkG/t+6cepwkxw3jonqNr2UEuzA2dnhknvZ0tFiNo7DMe2c7AiesLv19A88e22Tc+YLwKUTYoblPXYbzPzxs3wAbpyeQE6/0NGfGzsOt3H9WzvpNoguO79fkMp1b+500uXfNjuRvJpOyaHnttmJ3D0/1Ul//21+Pfd9muci8VApZNyzIJUbpycgl8swW2289FMZz/9QiskqDl/+fkEqV02ORamQY7UJfLizime+L3YhKgq5jEsnRJMd6eek0Q/0EoPSylt6j0ta5oCD3ByPK1Sgl5qloyO5YGykU+cKRDe01Qfq+Tqv3inNHUTyNm9UKPPTQ8mM9HF3a34F8NIoidcoTzq7wmy1YbTYEAQBm4B0bhNEMwJBEL/rKoUclUI8V8plv/rvjodaIYX0ljb18MnualbsrqWlx8irmw7z6qbD5ET7cdG4KJbkROCjVbF8XBShPhoe/DzfRQIKovTnzWsnOKXKO477XQYzedWd7K1qZ191B3urO2jrNbGvWiRB/eVeDkxLCuKZi3II9dFS3aZna1krW8pa2FzWOiwk6DczEo5r/mJ3ZTvXv7WTDr2ZSD8Pnrogmyte2+50nx0PzuWNzRWSw+hDZ6dzw/SEU36PQ8FgtnLXR/uw2gTOzg7nvNGRx37QGQJHx39G8pHO7p4B8zZlzT10Gyx4qBSkhXtLQdHp4Udk5xUtohQt7ijHhv6ytP5w5Es5gkBVCrnk3KhxF8R+lXCTHDeOiYFSNQ+VggAvNftrOlHIZZyVFS4FCXprlZgsVnqMFuICPZlit3is7+xzCaa8flr8sOmV//ZNEQ1dBuICPblzXsqwPOdwYE1hI7e/v0dy2Vk+Loob397ldJ/7FqXy4Y5qqtr0eKgUPL08R7LSBHFh++vXhVJOSX8kheh47uLRUus+r6aD+z7Nk8jJrNRgHj8/y8l685FVBYMOGM9JC2FOWggr9tRIr+WtFSvYDV2Gkwp5PBa5USlkzEsP5YLcKGamBjvZ7DZ1G/jmQANf59Wzs7LNqfg+Ps6fs7LCmT8q9BdTyXTj9EEkL2dOJ/dMRFKIjgcWp3PPglR+OtTMx7uq+bGoif3VHeyv7uDBz/MHfVyQToNNEOgxWDDZpYcLn9vA1KRALh4fw8KMUDRKccPoo1UxLTmIafaNrSAIVLf18d6OSl76ybXoBWJ1f9FzGxgXF8D4OH/GxwVw3phIVAoZZc09bC4VSc/WstaTmul5eUM5L9tDlqclBfH6NeNduv5rCxu5/YM9GMw2cqL9eOL8TKfOUICXmg33zebfP5ZIv8dflozi2qkj6+T57x9LKG/pJdRHw+PnZf5iSLkgCBLJmdxPvuoIc3YY3xTWH8m2USnklNi7NkmhRyIgKltFVUHsUXJtHGTGf4BDZo/9dsehQd7v8/Nwk5xfJdwkx41j4vvCBqfrF42L4qv9ImGZlhREkE7Dx7uOdHVW2LsRl02MkYba39pS6SKJumdB6rC8v61lrZK72JMXZJ8xcpGPd1XzwGcHJJedsbH+3GsfSgVx3uWGafG88GMpepOVKH8PXr5ynJPTXEljN7/9YO+gpOSqybE8sDgdD7UCg9nKP9cU88rGcmwC+Huq+MuSDJaOFkMam7uNPLH6IJ/vrXV5nrQwby6bGMOG4hYeWilubBxEtrajb9B09cEgkw2pAnJBTIAnl02M4cKxUU6Dwgazle8KGvhsTy0bS5qdpGhjY/05OyucxVlhI5I34YYb/x+hUsiZZ8+VKqzr4to3d9DYNXjH5PlLx7AkO1yaaVpX1MQ72yrZUNLM5tJWNpe24u+pYlluFJdOiHaxNC5t6uGfa4tZfUBcU+QyWDAqjGnJQTR1GdhR0ca+6g7a9WbWFDZKBTatSs7oaD8mxAcyOSGQi8dHo1LIKazrYnNZC1vKWtle3orxOHOEHNhU2kLKQ6JxQW6MH+/fOImVe2v54+cHsNmzy/54VrqThXVCsBer75jO/34qkwjOo0szuGpy3Am99omiuLG73+tl/qIs7suae2jpMaJRyhljNxDo0JskGXS2PRRUyrEJ1WG22qSfp9jjD4wWK/X2bmJs4NDFrS67LM2n35yNyWKT1BNKuchy1Eo52L/qv+IItP/XcJMcN46K9l6TS4rz4qxw/vj5AUAkNZ16s+Sakh7uw7vbqlAr5Fw4VjQc6DVaeH97pdNzzE4NHhYy0mey8ofPROJw+cQYqe39c+PVjeX89WtRsrd8bBQBXmqe+rZI+nl6uA8zU4J5Zk0xgiDOMf3n8lzJDADgy/113P9pHn1mZ1vlIJ2Gf1yYzey0EEDUaN/98T7JqercnAj+smQUgToNNpvA+zsqeeqbIpeqZ5BOzfXTEqjr6OPRVYVYbAIKuYzEYC+KG3uOOlDcHw5ycyyCo5DLmJ8eymUTY5iWFCQRYEEQ2FnRzordNaw+UE93vwyl0dF+nJMdzllZ4e6wNjfcGAEYzFa+L2xkxe4al8LCQNzxwV5e21jOslxRzuYgR9Vtej7ZVc3Hu2po6DLw2qbDvLbpMONi/blkQgwZET689FMZX+yvQxDEY8Y52RHcOS+ZxAFBzSaLjfy6TnZVtLHjcDu7Ktvo0JslC+vnfyhBrZQzNsafKYmBTE4M5Lqp8eyv6WD5/7ae9Oewp6rDKYD07Oxw7pqXzLxnjxCcrEhfVtwyhQ92VPHcWnEu5pFzR57g2GwCD35+AIu9YLbQPrj/S4HDcCAn2k+ak8yvFbs2cYGeEmFzkJykEG/qOwyYrQIapZxw+wxPa48JQRAVAIFDuGNabYK0hvhoj2xxbf0WKJW9c9ef2FhsAsozoz7qxjDCTXLcOCr6p5qDeFDwUCkob+5Fo5SzICOMz/fUYLLYSAvzJq9abD8vzgqTNuyf7q5x2WA/vTxnWN7fP9cWU9mqJ9xXyx8Wpw3Lc54q/v1DCc/YrbVvnB5PZ5+ZlzYckWZMTw4ixFsr6Y0vnRDDo0szJHmNyWLjidUHB9Wrz0wJ5pmLcgjSaTBbbbzwYykvrCvFahMI9dHwxPlZzE0PBUSrzQdXHpAWGAfkMvE1A73U/Hd9qfS3iQ30pLJV7xKUeSwci9yE+Wi5fGIMF42PlgZOQRwO/XhnNZ/trXGyFY/08+CC3EjOz4066ZkMN9xwY2jYbAK7KgcvLIyN9WdZbiTnZEXg66nCYLayprCRz/bUsKGkhf01neyv6eSxrwqZlRrM+WOimJsewt0LUrljbjI/FTfzwY5q1h1qYldlu8sM3cKMUO6anzKkvb9aKSc3xp/cGH9+M0N8r+UtPew43M72w61sLWulqdvI1vJWtpa3whrX58iM9OGVq8axtayV1QfqTyq76+s8cQbQgUkJAbx7/US+PlDPX+wxBXfNS+HqKXEn/Nwnii/217Kzoh1PtYJHlmaM+OsNNxyxBBn9VArlLUcIjQNlzeJticFeNHWLHZsQH41UEHPMkPp7qoeU6vUvCnppjmxx+99dIV05cqPZanMb1fwK4SY5bhwVA6Vql0+M5as80SVnXnooOo2Sj3aJuQOLM8Oljbtj8NRqE1xso6MDPFysgE8G+6s7eHWjSB6eOD8Lb+3PawEpCALPfF8suezcPjuJooZupyDEs7PD6eozs2JPDTIZ/HFxOjdMj5cO2I1dBm59b4+Le5pCLuP3C1K4eUYicrmM0qZu7vpov2SnuSQngseWZuDnqUZvsvDPNcW8vrnCxWVqbKw/s1OD+XhXjZR1E+Grpa7TMGjezqkgJ8qX66bFc1ZWuETgLFYb6w818/6OKtYdOpLF4aVWcFZWOBeMjWJCXMApJcq74YYbg6OipZfP9tby+QkUFrQqBUtyIliSE0Fzt5FV++v4bG8N+bVdkjubt1bJOdnhnD8mitmpIUyID+DRVYWDOrHVdRjYXdlOhJ+Hk3PkUJDLZSSFeJMUIspqBUGgrLmXreWt/HSoaVACU9mi56HP85maFMQfFqfx8pXjKKjrYsWemkGLR8eDbeVtTpk8V0+O5Y65Ix9TYDBbefo7sWh22+wkab7yl4RCieQcMZRxSNHig47Izho6RWIT5e8hBXqG9nPBa9eLJCfgJDLO+s/fOJaX/svM8ZjjuPHLg5vkuDEk+kxW1h1yzk5ZlBkmpSwvHR1BQV0n+bVdqBTi0aLPbCUpRMf4ONHyce3BRpfN8zvXTTzl92ay2Ljv0zxsApw/JlKSbv1cEASBJ1Yf5JWNIqG7Y04Sm8tancjKJeOj2VfdQVFDN1qVnOcuHsOizCOyg23lrdz+/l5aepz18KE+Gv59aS4T4gOw2QRe33SYp74twmix4euh4rHzMqVwum3lrdz3aZ5L2nigl5orJsVyoLaTp78XF8wgnZqWHhN1na6OSScLuUz8jlw/LZ7cGH+JvNV39vHRzmo+2llNfb/Xc+jrF2aEnTGzVG648WtCZ5+Zr/Pq+WxPjVNXRadRclZWGMtyj7+wEOyt4bpp8Vw3LZ6Sxm4+21vLyr211Hca+GBHtUvQJoh5J9OTgylu7Ob7wgYO1HZy4PNOHl1VyKLMMJaPjWZKYuBxFzZkMhlJITo81Qr+tNLZHCEn2o9yu0PXD0VN/GBXIkT4apluDwK+c14yCrmMD3ZU8cTqosFe4rhwsL4bvcnq1C0YCby7rZLajj7CfLRcN8LGBiOFQ43iTGl6P9vnCjvJcbikGcxWSVUQrNOyqVu0lw7xOVIQdcyHHg85HghFP5LjMDzqM1nx1ijpNlro6DMPS/HVjTMLbpLjxpDYVNriUt0QBGjsMuKjVTIzNZi/2ReJ+aNC+SZfbO1fPjFG2ty+ttE1/PNo1o/Hi1c3lXOosZsALzV/OmfUKT/fqcBmE3h4VQFv29Ow75ibzNd5dU5p7tdMieOb/Hoau4wE6TS8dvU4yeZaEARe31zBE6sPunReZqQE88+LcgjUaWjpMfL7j/dLoZ0zU4L5+4XZhPpo6TVaeOrbIuk99MfysVF4a1X876cyjBYbSrmYAN/Sc+JuaUPBS63gsokxXD0lTnI7Ex11WnhjcwU/HGyUtP7+niouHBvFpRNiSBigx3fDDTdOHWarjY0lzazYXcuag43ScVwuE5PkL8iNZMGoUyssJId6c/+iNO5dkCoWaD7YO2h21jnZ4SzJiSBQp6G1x8hne2r5eFc1JU09fLGvji/21UmdpAvHRhNzlIFyB9YfauKaN3ZK15NCdHzzu+mo7Bb5DkOCzaUtbD/cRl2ngY92VfPRrmpkMsiO8mNGchCzUoOlTLcTxY6KNjL+8h0gHmMfPz9r2LPZ+kxW/mNXBtw1P/kXWQjqNpilfJr+jmiOYpejM+WwB1cr5fh4KKUsIm/NEULjMC9SnESnXy6X4a1V0m2wSO5q3UYLkX4edBsttPWaSBy+XHI3zhC4SY4bQ2LNAKnaZRNjJKnaWVmixfHKfaJbV1KwjtUHGtCq5CwbEwWIcrIdFc6mBZ/ePPmU31d1m15Kln7o7PSTal0PF2w2gQdXHuCDHeLiede8FD7cUeXUHfnNjAQ+2FFFt8FCSqiO168ZLxEBk8XGn1bmS5I/B2Qy+P38FG6dlYRcLmNzaQt3frSP5m7Roeahc0ZxhZ1Mbi5t4f4VedS0OxsFJAR7sSgjjK/y6qXOjp+nig69GRietHd/TxXXTY3nqvzLzXwAAQAASURBVMlx+HqKi5HBbGXV/jpe31zhlK80IT6AyyfGsDAjzK19dsONEUBBXScrdtfy5f5apyJGSqiOC3KjOG9MpNNc3HCgpr2PVzaWDxkO/PCqQh77+iAzU4I5b0wkV0yK5Ybp8eyv6eSTXdV8ub+O2o4+nv+xlOd/LGVSQgDLx0azOCsMT7XrFuWdrRX86YsC6fqd85KdYgMUchlZUb5kRfly88xE+kxWdlS0sbG4mQ0lzRQ39kg22f3x2NIMPtldQ57d1vhE0D8k9Y65ydw5N3lYJLef7K6mXW8mJsCTC3KjTvn5fg44DGz8PFXo+nW9xHXoiPSsf5dGJpNhNDvya44QR6tNJOtHIzmafkRTb7I6rTV+niq6DRYEQST8NuHIrE7rMBb93Dhz4CY5bgwKq01wyceZlx7CnR/uA2Dp6EjWFjbRoTcT5qOVNtFLsiOkze5rm1y7OOPiAk7pfQmCwJ+/yMdgtjE5IZDzx/x8YWiCIPDnL/P5YEc1chn8fkEqr24sp91+8Aa4dmocb22pkHJyXrl6HL52W8vWHiO3vLvHhQj6aJX8+7JcZqYEY7HaePa7Yv6zvhRBgOQQHS9clktqmDd6k4XHvz7Ie9ud83OUchnLx0XR0mPiRXtIna+Hik57YvxwIMxHy40zErh0QrS0EWnuNvLutkre214pbbA8VAouGBvJ1ZPjSA4dOqHajV8PHMGbZqsNi03AYrVhtgpYbYLLbbYBrhWODYfMPhAsXZeJ32tH/o1KIUetkKNS/v8J8hwKRouV1QfqeWtLJfv6bdwDvdScOzqCC3KjyIgY/pBco8XKKxvK+fePpRgtNtQKOddNi+fW2Yn4aFU0dRv4an89K/fVklfTyY9FTfxY1ISXWsGizHDOHxPJo0sz+dM5o/iuoIFPdtWwuaxFclH7y5cFnJ0VzvJxUYyNFaWvj6wq4I3NFdJ7+ODGSUxOPLqjpodawcyUYGamiGX6hk4Dj31d6GQqADgRJ4A/LE6jz2TlX/aC2vHi+R9KpCLc4+dnctmEmJP67C1WG6/alRA3TB++TLnTjfoOseAXMcD2v6PviIkAINk7O0iKww68P2mxcxyO9nGqFHK81Ap6TVa6+sxORVBfDxXV9NFlMOPnqaat1yTN6jiMDtz4dcFNctwYFLsr25026wAGs40ug4UwHy0T4wO45k1RLjBvVAgf7xKrWJdNjAHExOGvDzgvIs9fOuaU39e3+Q2sO9SMWiHnr+f/fGFogiDw6FdiQKejg/PvH0swmI/I+y6dEMNbWyqwCaJJwwuXjZGqSkUNXdzw1i6X7ktKqI6XrxxHXJAXdR193PHBXklHf+mEaP58TgYeagV5NR3c+eE+ylt6nR6fFenL5MRAPt5VTYfejEIu5lk45AKniugAD26blcT5uZFS4N/hll5e+qmMz/bUSgtVuK+Wq6fEccn46F9UnsP/V9hsAj0mC119orSkq89Cl8FMV5+ZLoPFfm6m12ihz2yjz2Shz2xFb7LSZ7JKlw0mK3qz1UV2eTqgVshRKWSolHI8VAo81Ao81Qo81Ur7uQIPlRIvjf1n9ss+WhU+Hkp8PFT4aFX4eqjw8VDhrVGe0QYYdR19vLe9kg93VEtBvSqFjPmjxHDdGSnBIxaIuruynfs+3S9JcqckBvLYeZlOdtAh3lppfqe0qYcv9tXy+d5aatr7WLGnhhV7agj10bB0dCTnjY7knesnUNdpYMXuGj7dLRqjOCRmCUFeLse6H38/86TkrkaLlY12ye+y3EiumRLHCz+W8v2Aot6T35z8vI4DD36eL4WqfvibSScUcbChpJmqNj1+niqW2+MYfonoMohrj2+/zBqL1Satld52m2fTAFLjOIb0X+Md66fRfHSTAB8PlUhyDM7rXoi3FuiivtNApJ+HneSIPzs84Pvlxq8DbpLjxqAYKFW7ZHy0JFU7d3QEDV0GNpaIC4VcJsNksTEq3EdKLn57a6XLRmdJdvgpvadug5mHV4nVtptnJrjkK5wuCILAk98WSRXFW2Ym8uzaYicr5fNGR/DBDrHDsnxsFH9bliVV4tYWNvK7D/dKmmMHFmeG8fTyHLw0SjaVtPDbD/bQrjfjrVHyxLIsluREYLUJ/GddKf9cU+wUrqpSyLh4fDSVrXopzVunUdJjPPFE8MEQ7qvlt3OSWT4uSto4Hazv4sX1ZXydVyfN2+TG+HHdtHgWZoS5E+d/ZhjMVhq7DDR3G2npMdHaa6S1x0Rrj5GWXvG8tcdEa6+Jdr3puINcTxZKuQylQoZKLkepkKFUyJ2GgQUE6T0I9LcmF2+3CgJmi9gBcpDp/jBZbZisgMlKB6dO6mUy8NaI5MfXfgrwUhPopSZQpyHAS02QTk2Al4ZAnXi7j1Y14sSooqWX/6wr5bO9tdIx1mHTfsmEGIK9R2542my18e8fSnhhXSk2Qczs+tM56ZybE3HUglNSiI7fL0jlrnkp7K5q5/O9tXydJ84ovryhnJc3lJMa6s15YyK5cGwUt89OYkdFG5/sEi2uBxKcR87NINL/xF3G+kxWbnpnN10GC7kxfjy5LBuT1eZEcLKjfEkI8mLdoWan4pBjlkchE62MK07QjfKSl7dJl3f8cS4hx5ANrtgtSsHPHxP5i5zFcaDXKK5z/Q0a+m8NHN8bqatr/xo5fmdjP0tonZ0QdR9jXQvUqanvNLiE2sYEiDLxqlY9cUFeHKjtlF7fTXJ+nXCTHDdcIAgC3xU4V7WmJwdzl91V7dycCFbsrkEQYGJ8AFvLWgG41D4jYjBb+Wins4Tq3oWpp9x1eXZNMY1dRmIDPbl19shbdw6Ff64plpKnr58WL0nCQKwmz88IZeU+kRDePDOR+xcd+d3f2VbJX77IH3CQh3sWpHLrrEQAXlxfytPfHcImiHkPL142lphAT2ra9dz98X52HHaWt6WE6hgbG8Dne2rpNVlRK+SYrLZhIThBOg23z07kkgkxUhVtd2Ub/1lX5pShNDcthFtnJzI29tTkiG4cH4wWK7XtfdR1GKjv7KOxy0B9p4GGTvt5l2HIGYmjQauS2zsbKny04gbfU61ALpNJOnirTZSZWW0CVpu4ObEJAnKZzH4/kaDYBPFYYrXf12ixYbTYsNkf7/i547JNEOyPO0J2FHLxdRX21+9/6v8cMpkMhUy8v0wmAzspsgmC/fWOVIblctFpSW434HDcxyFv6ewzY7TYEATELpbB4tJxHQpKuQx/OxEK9dES5qMl1FdLqI9GvGw/BXqpT5gM1XX08fT3h/hiX530u0xKCODqyXHMHxU64nKm6jY9t7+/h/32mZXzx0Ty8LkZThX6Y0EulzE+LoDxcQH8Zcko1h9qZuXeWn442MShxm6e+raIv39XxMT4AM4fE8mfl4zCarNJx1MH/vJlAc+uKWbp6AiWj40mM/L45HhPrD5IUUM3QTo1L14+FrVSLmXegEhwVt46Fblcxie7qrn3UzFo2jGw3n+OR6OUS5KqE8WEJ34AxA7/i5fnuhgWdBnMrLFHD/xSZ3Ec0JvEdchLc3Si5mFfXwz24p9jvemfe+OY6ek2HL2IEe3vSX5tF9UDXEZj7aYWla16UkKdi6SOjB43fl1wkxw3XFDc2ONiQdxlMGOy2EgM9iIjwofb3t8DQEqoN+9sq0SrkrN0tGhjvGp/nYvU7cbpCaf0nvJrO3nLnm/w1/Myf7bB9f+sE4djQZy36Z8BpFXJmZMWImm9/3hWGr+ZIRIXQRD4x3eHnAgRiAf2f10ymgUZYXQbzNz7SR7fFohdtOVjo3jM/ruuPlDP/SvypOFMEMnRuTkRdvvWKuk9GI7Ryj8e+HmquGVmIldNjpMqarsq2njm+2IxgA9xcPPs7AhumZnIqIjBg/3cODkIgkC73kxVm57K1l6q2/RU2U/VbX3UdfYdV+dFq5IT4q0lSCd2H/w8VAiI8jQQq6gKudiNtdoE9CYr7XoT7XozdR0GCuu7huX79EuAViVHp1ER4eeBWiFHZidNcjshknFk3sgqETWRvLX1mug2WLDYBJq7jTR3Gylq6B7ytZRyGSHeGkJ9RSIU6edBlL8Hkf6e9vMjGTJWu238P9cWo7dvAGenBvPbucnkxvifjo+G7eWt3PLeHtp6Tfh6qHj8/EzOyY44pefUKBUszAhjYUYYnXoz3+TX8/neWrYfbpPmcu5fccDpMStvm8qawgZW7K6locvA21sreXtrJWlh3iwfF835YyKHNKLZUtbCO9tE98nnLh5DmK+W/dUd0rET4OUrxyGXy+gzWXnq20MA3L8ojVtmJVLb0SfOFR1sZEtZ60kTnP5Ye7CRlIfE7J2Hl4zi6ilxoplMiehsmhDk5RSg+UuEg3wKAwp7DjiORZ72dUZvJzUOGVtX35E1L8TepWzqNmKzCUMWCqLtHZvqdud9jMPZtaSpm8VZYfb3Jb5+dVsfHXqTW179K4Ob5LjhgoFStQvHRrHaPl9z3uhI9lR1UNmqx0OlkDSvZ2WG46NVIQgCb22tcHr8vPSQU7LWFASBh78swCaIoZfTk38en8f3tlfyj+/Ehe+aKXG8u61SOnB7qBRMTQpk9QHxs3t0aQZXTY4DRK3xHz7L47M9tU7PF+Kt4bWrx5MV5UtpUzc3vbObsuZeVAoZD5+bwWUTYjBbxd99YIBdpJ8Hc9JC+HJ/HZ19ZqmqeKobUrVCzjVT47htVpJkIHGgppOnvz8kWVerFDIuyI3ippmJLsGBbpwYrDaBmnY9pU09R07NPZQ19UiZEUPBQ6Ug0t+DcF8t4b5adBoVVptNkjE6ZKRN3aJsY29VB629xpOWpWmUcry1Srw0SrzUSnQaJTr7dZ1GgUapQKOSo1HI0agUqBVyNCp5v3MFaqUoVRO7PuJ7lNnPHbfJZOLGyNHVETtGzieLnWQ4TAxMFhsGsxWD/dzouG62Yex3XW+y0mO00GO00G2w0GM0S/8zBrMNg9noklN1LKgUMoJ0GiL9PKTfTQZOZMght+vsM9PSY8RiE6jrNBw1o8pHq8Rbq5LcqRz4+4XZLMmOOG0SplX767jro31YbAKZkT68fOU4IoY5kNLXU8UlE0S5XU27ni/31/F3O8noj093V3P+mEjumpfC5rJWPtlVzfeFjRQ1dPPYV4U8+c1BFmSEcfG4aKYlBUmbYJPFxh8/EwnTZRNjmJYchCAIUuYbiE6dYb6ihEw0TzESE+DJ9dPEbJpIPw+unBTLlZNi6TNZ2VjSzHcFjfxQ1Dgshi4Pryrk4VWFAIyJ8QNgZmrwL95QQ23P0DP3k5iqFOLcXJ9ZnJvx91JLYd7dBgtWmyC5ADZ0HfkfCfPVIpeJf8+WHuOQkj9Hx6Z/jANApj2MtLyll6QQsZNT1yHO59R29LGvuoNZqT9v5p4bwws3yXHDBQNd1cbH+fOAfYE4d3QEr2wUpVozUoL4wZ42fdF4cTByb3UH+bVdTo9/6oLsU3o/X+6vY1dlOx4qBX88K+2Unutk8W1+vRQ8d9G4KD7aWY3ZKu4WVQoZ4+L8WXuwCZkMnjg/i0sniAYMPUYLt7y7m40lLU7PlxbmzevXjCfCz4N1h5r47ft76TGKpg4vXpFLbow/Ne16bnt/r4vV6fTkIFQKuVSVlMsYlqriOdnh3LcwTcqpONTQzbNrDknSRdG1LZrb5/wyU7d/TgiCQFO3kcK6LgrruzhY30VpUw/lLb1HTdoO89ESE+BJdIAnIT4aBLs8y1Ftruvoo6a9j7yazuM2l1DIZQTp1ATpNPh7qvH3UuPvqcLPUzwP8FJLl/09xTkTT43iVztjZbba6JVIj4MAiU6E7XozHXpxZqm91yx1uRy3GczijFC9XSZ4LHhrlSQEeaFSyKUumgwZAqL0z2qz0dxtpF1vlqRyA3Hfp3nc92keYT5a4oI8iQ/yIi7Qi9hAL+KDvIgN9By2TvdXeXX87sO92AQ4Ozucpy/MGXFyFeXvyQ3TEgYlOe9uq+LdbVVEB3hw/uhI7pqfwuPnZfHl/lo+3lXDgdpOvs6r5+u8eiL9PLhoXDTLx0Wx9mAjFa16gnQaHlgsriHri5ulTbC3VsnVU+IAUQr6kn2u8dZZiYMW6DzUChZkhLEgIwyL1caOw218V9DA94WNx/U9OBb2VnUA8MbmCh5YnD7s+TunEw6DGoPZeQbV31NFX6eVdr2Z2EAxaNZhlNPcbSTcTjgb+n2eKoWcMB8tdZ0Gqtv7hiQ5GXYyU1DbKclZQXyNCF/x8d0GC/6eKtr1ZrR2m+q9VW6S82uDm+S44YSmLoOkuXags8+MTYDR0X6E+3rwlV2OpVUp6DFaiA30ZGK8OIvx9oCOg4dKcUopwnqTRQocvW12IuG+p39zvaWshTs+2IdNEENP1xQ2SjphmQzGxwWwsaQFuQz+fmEOF44VNdRtvSauen27C+mbmRLMC5eNwVur4s3Nh3n0q0Jsgpgj85/Lcgn21vBjUSN3fbTfaeOqVso5OyucbeWt1HcaJJ//UzWyyo3x46FzRknSl7qOPp7+7hCf76tFsOcInD86kt/NS3YKc3NjcFisNspbep0ITWFdl+SANRBqpZyEIC+SQnTEBXqhVclRyOUICNR3GDjc0su28laXiv5g8PdU2bs7HoT6aAjx1krnwd4aQnw0BHppTipM71RgtQmYLDaMFqv93IbJapPmZQQEbDbnuRzHzA0IKOTyQedylHJRSqa0X1cr5WiVClSK47eUVink+HmqT0qm0mey0qY3SRI16dRjkC432c+NFhvdBouT5HQwBOnUJAR7UT6wCh3pg80GNe16ugwWGrrE2att5W0uzxHhqyU2UPxOJYfqSArWkRSqI1inOe7PZX91B3d/tB+bIEpnn7og+7S5zfXvsNy/KI3fzEhgS1kLn++t5bv8BqrbjuTq5ET5ct6YSN64djwNnQY+3lXNyr211Hb08c+1xfxzbbH0XLfOSpQ6Bv9ae8Qa+i9LMiQSv66oieZuIyHeGpYdxzyMUiFnSlIQU5KCePjcDA7UdvJdQQPfFTRS2nTqcx4OOdtLV45lYUbYKT/f6UagTvy/Ghg+7e+lpq7TQIs9BFQhlxHmo6W2Q5TjOkwCGrsN9JmsErlODNFR12mguLGbsbGDyzXTwrxRymW09pqoszupOZAV5Utdp4F91R3kRPux/lCztIZuP9w6rL+7Gz8/3CTHDSf80G+YHGDp6AjW2rs152SHs/6QmI0T4q2RhnEvGheNTCajudvoYhv90U2TTun9vLiujIYuA1H+HtxwinM9J4P82k5+8/ZuTFYbU5MCOVjf5TRvNCM5mJ+Km1HIZfzz4tGcmyPq1Bu7DFzx6nZKBixyF4+L5vHzMwH408p8qRvjSMxWymU8+/0hae7HgfggLzIjfflyvzh07Gj1nwpCvDU8ePYRZ6Qeo4X/rS/jlY3lUmfo7Kxw7pyX7M64OQrqO/vYV9XBvuoO9lZ3cKCmc9C/jVwGicE60sN9SA/3QadVYrXa6DVZKWns5lBjD98XNA7qHOaAj1ZJfJAXUQHi7EaUnzi/EeXvSaSfh5OD0anCbLXZuxkm2nvFDkZXn5luo4Ueg4Vek73zYbDQazzSBekxWoY1k+lUoVbK8fVQ4W8PI9SqRDtph9zOW6vCW6vEWyNe1mmU4nX77Q4ThsHIgYdaQaTa45idTUEQ6DZaaLRL1Oo7+qjr6BMvd/ZR32GgtqMPo8VGS4/JZUMIkF/bhUIuI9xXS5S/pxMRdLyzhi6xQu2Qwjnm5xzw9VCRbCc+icE6kkO9SQ7REe6rdfr9DGYrt72/B5PVxsKM0NNKcJq6DVIhDUQnTZlMxvTkYKYnB6M/z8KawkZW7q1lQ0kL+2s62V/TyV+/Psj05CBJzvZTcTMf7ax2+gyeW1tMfWcf4+MCnDKFHMdtgE/trmbLcqNOuIMik8nIjvIjO8qPexemUdYs/k9/m1/vUjw8Udz0zm5AdPzbcN9s/H/GEOwTgWjb7JpDExfoRUFdFxWtR8h8lL8oGzvc3EtujD9BOjUtPSYONXZLzq0ZEb5sLGnhQG0nlw7xmlqVgpRQbwrru9hX1eH0/zkxPpDvChrZXNrCjORg1h9qln62q6KdboNZIsJu/PLhJjluOOGHg85StXFxAfz5C1GmtTgrnL9+JWqGs6P8WHuwEbnsiPvLhzuqJAmXA9lRfif9Xqpa9bxsl8Y9dHb6aTcbqG7Tc80bO+kxinajnX1mJ5elOWkh/FgkStSevShHWihr2vVc/up2KgdYjN4yK5H7FqbSZbBw+/t72FjSgkwmVipvmpFAr8nKbe/vc5ELzkwJptdoYdX+Iw5Dp0JwFHIZ10yJ4855yXhrVVhtAh/vrOKZ74uleYQJ8QE8dHb6Kf39fo0wWWwcqO1kZ0Ube6va2Vfd4WJTCuClVpAe7sOoCB+SQ3QoFXKMZislTT0cauhm3aGmISv6WpWcuEAvEoK9JCmSeFmHv6fqlDT6BrOVpi6jNKfT/7y1R5RgtfaYjqtr9EuByWKTuiqnivggL0J9RPtof0+187ndUjrE29U9TSaTia51WtWQBQOH2USdnQD9xr6pBdGCubpNj9Fisx+DBv/7RPiK8kaFwygBsSvWbbBQ3aans8/Mrsp2KXvLAS+1gpQwb4mAbyxupqa9jwhfLf9YnnNa84J+//F+6fKau2a4fN891UqWjo5k6ehIWnqMfLW/js/31bG/uoP1h5pZf6gZT7WCRRlh3DIrkcrWXmn2qctg4ZWNh3ll4xHDmMsmxkhkxmixsqlU3PQ6jHROBYnBOm6ZpeOWWYlUt+n5+oAopTtQe/KEp9toYcxjawB4YHEaN81MPOX3OZJwzDk1dxsxmK3SOu6Y5+xvDz4qwofth9soqOvigrGI38WSFg7Wd0kkJzNSNGLIP8ZnODEhgML6LjaXtXB2v/iKGSlBAGw/3Ma9C1MBUb0Q5qOlocvA5tJWFmX+8jpmbgwON8lxQ0KfySp1bRwwmKwIdqmaTq2UZnCs9ujhmSnBhPlqsVhtvLfd2Tb6yWVZp/R+nlh9EJPFxpTEwNPepu8ymLn+rZ209BhJDfXGQ61gT2mH9PPZqcGShfJTy7JZOjoSgPLmHi5/dbuLLvvBs9K5cUYCDZ0Grn59B4cau/FQKXjuktEszAijqlXPDW/vpLjxSOdHKZexKDOMnRVtNHYZUSlkLiTyRDE+zp9Hl2aSHi4uFNvKW3n4ywLJCSou0JMHzkpnwajQX/zA63DAYLayr7qD7eVt7KhoZXdlu4u5g0IuIzXUm9ExfmRH+qLTKtEbreTXdZJX08mHO6sHnbtRymUkBHuRGuZDWpg3qaHepIZ5E+nncVKbSkEQaO4xUtMuzulUt+ntl/U0dBqobNMfdf7HjWPjcEvvCeVpjAr3IcJPS3A/2WCIXTYY7utBkE4t/Z/JZDICvETClBnpy98vzOa+T/NQKWTcNS+FxZlhNPcYqWzt77Qnuu+Vt/TSoTcPaWbgqVaQEKwTjR2QSc5xBrOVylY9vSYre6s6pFkQB+o6Ddz3SR7p4T6khXszKtyHKH+PETs2WKw2aX5RpZAds4McpNNwzdR4rpkaT3lzDyv31bFyby1VbXo+21vLZ3uPmL28dOVYFDIZH+6sZm2/Yt7726sQBIHLJsRisIhmFUE6NWlhw9u9jg7w5OaZidw8M5GKll6+PlDPV3n1HKzvGvIxUxID2VI2tITqb98U8Td7aOnOB+eNaEbSySJIp5ZmX0qbesiMFOdlEkNEklPU7/eXZmnqRAIzKkIkOfuqOqQ51zF2WXVBXddRuy4zkoN5Y3MFG4qbneZyEoPFzmV9p2iz77jskMP9WNToJjm/IrhJjhsSNpU6D8fPSw+VNvJnZYXx9YF6TFYbKaE6DtaLm+KLxomGAxtKmp1cUABpNuVksLOijW8LGlDIZfxlScZp3XBbrDZuf38vxY09hHhriA30dAqLm5kSzDp7i/uRczMk04WSxm4ufWW7kzuTQi7jqQuyuXBsFKVNPVz9+g5qO/oI8dbw+jXjyYz0ZUtpC7e+v8dJ3hOk0zA+zp9v8hukTIxTITi+HioeOjudC8dGIZPJaOo28LfVRXxu3wT4eqj43dxkrpgU+4secj1VWKw29td0sqG4ma1lreyr7nCRj/l7qhgfF8DYWH+SQnSYrQKFdZ3srGhn5d5ayea3P7y1SrKjfMmM9GVUuA+pYd4kBOlO+LO22gTqOvoob+mlvLmHwy29VLTqqWnXu8xwuPHzo7BenMs6GsJ8tCSH6ojwFaWHkX4eRPh5MCk+kLOywlh9oIHb3t/DReOieGBxOhPiA5gQ75pH1dZrory5h/LmXsqaeyhr7qW8pYeqVj16k3XQ+RAPu6xHo5JLs1At3UYnovRtQYNkaw+iXGpUhA/ZUb52aZYvMQGew3KM7i/pev6SMSf02IRgHXfPT+Guecnsqepg5d5aSQ4MotxrXKw/F46NciI5AB/sqOaDHdXS9eQQ7xFdc+KCvLhtdhK3zU6irLmH1Xn1vLOtkqYB3cYtZa3IZHDphBgqWnqPSnjGP74WgKcuyOLi8TEj9t5PFDKZjNQwb7aVt3GwvksiOWOiRbKSX9sldXiyo8Sf5dV0YrRYmRQfyEs/lbOl/MjeJNLPg/ggLw639LK1rJUFQxRAJyYEoFLIqGnv43BLLwn28HCZTMas1BA+2FHFdwUNzE0P4d1tVZL727f5Dfz1vKz/1+vgrwlukuOGhLUDZFJjY/35x3dilWhxZjj3fCLKCCL9PChubMZHq2ROuuhE8tHOaqfHLjiFcDpBEHjSXp26aFw0qcNcUTvWaz+yqpANxc14qBSMjwtwmjOalx4qLZAPLE6THHnKmntcCI5KIeM/l+WyICOMfdUdXPvGDtr1ZhKCvHjruglEB3jy8a5qHvjsgERkQByaDPBS802+s5X3yWJxZhiPLM0gxFuL1Sbw7tYKnv7+EN0GCzIZXD4xhnsWpP6/zQeoadezobiFjSXNbC5tcXG0CvbWMDE+gInxAcQFedHaY2J3ZTsr99VR1NDlYsnspVaQGelLdpQvWVFidycmwPOEujNGi5Wypl4ONXZR0ihuXA+39HKocejsFTd+mXAYCBwLH++q4eNdNaSE6nj43Ayyo/ykcETA3gUKYFycMwEyW21UtYkkuLSph5LGbooauilt7qHPbD0qCUsL85ZMHoxmG+UtPXQbLWw/3Mb2fqHEPlol2VF+ZEX5kh3pS1aUL5F+J97x2dyv0DYlKeiEHuuATCZjbKw/Y2P9iQ/y4lG7xFouY1Cp3gc3TuKDHVV8k18vFZK2lrfyyKoCLp8YQ1LIyK4/icE6fjs3md/OTaaksZslL2xy6hYLgthtAnEuNjvKlyfsZjyD4f4VB7h/xQHCfLR8d9eMEwprHSlkRPiyrbyNvdUdLLcXRmMDPaWZm4K6TsbGBpAcoiNIp6Glx8ieyg7GxweglMuobhM70478m2lJQRxu6WVjScuQJMdTrWRSQiAbS1r4Jr+B2/oFiJ+THW7/mzfwn8tyeXdbFR16M36eKjr0ZjYUNzNvVOjIfzBujDjcJMcNQAzkWj3ANMBstWETxBRojVLOjgpxUXM0FM7KCkejVNDUbZBkbA781T5cfzL4vrCR3ZXtaFVy7pyXfNLPczJ4c0sF72yrRCaDRZlhUqcDYGFGqDQvc/vsJEkLXdHSy2WvbHMiOGqFnP9dmcuctFB+Km7m5nd202e2kh3lyxvXjCfAS81za4t5rp/DD4gH75YeI1vKWiX3tJNFkE7DY0szWJwl6pHzajr44+cHJLe3rEhf/npeJjl2rfP/F1isNnZWtLOmsJH1h5qcNOEgbtimJQcxLSmYURE+NHYZ2FLawptbKlxyFwBiAjwZF+vPuLgAxsX5kxSsO25CIwii/XBRQxdFDd0U1XdT1NDlJFs8U6HTKAn31RLioyHUW0uwj4ZgnYYQH1GSFeqjJVCnxkutPO1ubv1htoquZm29Rtp6zbT1GmntNdHWY6K110Rzj5H6jj5qO/oGna8601Dc2MNlr2yXro+O9iMh2GEj7UlisGgq4JDfqBRy6bb5/TZuFquNilY9xXbSU9zQzaHGbic5Xv9AU61KTmqYN1qlQpr1MZhtUqbTptIWJzVAgJearEhfRkf7MTbWn9ExflLA6VAorDtCuIZjc95hd6e8YlIMv52TzKe7a6SsMwf+9EU+F9ut8Rf8c4N0+xubK3hjcwWTEgK4fGIsCzPCRry6nxzqzUNnj+KhlfkEeqlJDfN26t58lVcvmTI8ujSD7eVtLmY/DjR0Gch55HsAXr5y7JBk4HRgckIgr206zJZ+3w+ZTMb4uAC+yW9gY0kLY2MDkMlkTEsKZOW+OjaWNDM5MZDR0X7sqmxn3aEmKXtuZkow72yrZE1hI4+cmzHk8fac7HA2lrSwan+dE8mZGB8gESyT1Uaoj4bGLiPRAR506M18ub/OTXJ+JXCTHDcAu77VeKSCnRbmLdkpLs4M59uCBgQBMiJ82FclVsIccyif7amVAggdcDiqnCgsVht//1asUl0/LV4KBDsd2FLawmP2qt/ZWeFOBGdaUhA/FYtWkxeNi+L3C1IA0Zzgsle2OW2O1Eo5L105ltmpIXyb38Dt7+/BYhOYnhzE/64Yi1op5/4VeXy8q8bp9WenBnOgtpOWHtMpE5zzx0TylyWj8PNUYzBb+efaYl7ZUI5NEKVT9y1K47IJMT/r5vN0Qm+ysKG4me8LG/mxqMlJGqiQyxgT7cf05GCmJgVistrYVNLCR7uqOVDT4fR3kMnEQLnxdkIzLtZ/yKyGwdDUZSCvppO8mg67K1THGeNClhyiIzPSl4wIcfg8yV5V/aV/R1QKuTTrcioQBIEeo4XDLb0UN/ZQ0tRNif28uu3nM2rYV93h5BTmgJ+nijHRfiSHepMUrCMxREdSiE4iD0qFnCT7bWdlHRnM7jNZyX1sjWRuMj7On4K6LvQmq4sdvkohIy7QC7VSLoa5ymUYzaI0rq3XxE/FzVKIsEwGKSHe5Mb6kRvjT26sPwlBXk7dHvNRnAVPCsKRYNxQHy23zU5iZkow5/x7k3SX0qYeHl99kKe+PdIdyYz0IczHgx+LGtlW3sa28jaCdGouGhfNpRNipI7CSMCRDxPmq+X9Gydhstj4Kq+Ou/sZMgD8+YsCAJaNiWRSYiD3fZo35HM6TCzmjwrlP5flnnYp1sSEABRymSStjfIXP7/ZaSF8k9/A2oON3DlPXFNnpASzcl8dawobuW9RGgszwthV2c7XefUSyZmeEoS3RklDl4HdVe2Mj3OVbwIszAjjwc/zxc5lU48UAKpUyFmUGca726r4cl8d52RH8Nqmw1jsFdzvCxvo7DOfEV0wN04NbpLjBiDO1PTHzNRgXrU70JyVFca99gOol1pJl0EMrZwYH4AgCC5Stf9dkXvS7+PT3TWUNffi76k6ra4xtR193P6BGHo3PTnIycI0I8KHA7WdGMw2ZqcG8/j5WchkMuo7+7j0lW1O+nW1Us4rV41jZkqwPUhvH1abwDnZ4Tx70WhMVhvXv7WLDcXNTo+ZmRLMT4eapfmPkyU4vh4qHj8/k3OyRWeg3ZVt3PtpnjSvsSQngj+fM+qMHFAdbnQZzHxf0Mg3B+rZWNriNHTv56libloo89JDGBXhw56qdn442MRrm8pd5GoJwV5MTQxialIgkxICj1vWZzBb2V/dwa7KdvZXd5BX03lcsqThhlwmuuVNjA9kYnwA2dHOMic3jg8ymQxvrUqyCD4WBEGgtkMMat1f00FetUhueweZ2RoJdOjNrDvULM0P9seUxECSQ3Sk2d3UHOYqINpi/+fyMVz35i6Uchn3L0ojN8afw629FNR1UVDbSUFdF/l1nXTozS42+R4qBRkRPmhUCuQykWfUdfZR3dbHoUaxW+SYf/H3VDEmRpSWjYnxIyXMW4ox6J+NcrLwsW9S2/plVPXfuIb6aPjd3BQ+2lXtFLqcX9vFvPRQbp2dyPqiJj7cWU1Tt5EX15fx35/KmJUSzFWT45iZEjzsznMOAlDZqsdmE1Ar5SzLjWJZbhSdejP3fLrfyYHTYbDgrVHy+LIsvthb6xIF4cCawkYpd2fT/bOl1xppeGtV5Mb4sbOine8LGrluWjwgOpTKZOLnXdfRR4SfB3PTQ1Er5JIT5eKsMB5ffZAdFW00dRsI8daiUSqYnxHKZ3tqWbW/bkiS4+epZlpyEOsPNfPZnhruW3QkTPzCsdG8u62Krw/U8851E3ht02Gau40EeKlp6zWxcm+tJEd345cL90rnBoBUbXNAJZdjtQkkh+jwUCnYaZeqGe2b8HNHRyCXy9he3uriNrRg1Mm1xR0dB4Db5yQfU9owXDCYrdzy7m7aek0kBns5ud2E+2pp6THS2WcmJ9qP/1yei0ohp0Nv4qrXdjhZSqsVcl69apxYidpby90fiwGiy8ZE8vcLs+kxWrj6jZ1Oi6m/p4rcGH8X2+iTwZTEQJ65KIdwXw/6TFae/v4Qr28+jCCIcyWPn5f5s0oWTgf6TFZ+KGrky311rO9HGkGUlc0fFcr8UaEEeKlZU9jIq5sOs7eq3YlUBnipmZkSzLSkIKYkBR53AG2n3syuyjZ2VrSzs6KN3QO0/yOJhCAv5qSFMCcthNxY/9Nut+6GK2QyGVH+nkT5ezp1Sgai12hhb1UH2w+3sq28lZ0VI/+92VLW6jLEHuqjYVxsAOnh3qSF+TA21p/dle3c8PYu3r1+IpmRviQG6ySrfEEQqOs0kF9r70xWd7K/uoNuo8UlEybEW0NujJ89+FWURx9q7KZdb+bHoibJ4KY//vp1IQ+clX5KhNwRXlzcb5Ytol9mSmOXkeXjorhsYgyHGrq55OWtUg7ac2tL+PePpcxNC+Fvy7IwmG18sKOKTaUtEnmMC/Tk6ilxXDg2atiyVRKDvfCwB22XNveQ0s9hztdTxStXjQNg3aEmrn1jp/SzbqOFOz7YC8Bv5yQR4qPlTyvzh3ydaU+tA+C/l+dKkuaRxFlZ4eysaOervDqJ5ATpNIyN8WdXZTur9tdx08xEfD1UzEwNZk1hI6v213HPwlRGR/uxr7qDr/PquXaq+NglORF8tqeWr/LqefDsdDTKwY95F42LZv2hZj7ZXcNd81Ok0NecKF/Sw33EsOb6LnJj/NhT1YHSTlrf217JVZNj3S6jv3C4SY4b9Bgt7DjsnJpd1SZmvMxND5WkakkhOokAODIEVuxxllwtzgw76crWe9uraOwyEunnwRWTTo87jCAI/PmLfPJqOvH3VOHjoXKau/D1UFHU0E1MgCevXz0OT7USg9nKDW/tcqpgymXw/KVjmJESzKe7a7j30/0I9qTwJy/Ipq3XxJWvbXfSuEf4aokL8hqy6na8UCvk3LswleunxSOXyyis6+KOD/dKbkoX5Ebxp3PSf7XGAg7b2c/31rL2YKOTu1lSiI4l2REsygxDIYev8xr4yxcFLgP86eE+zE0LYXZaCKOj/Y5LotVjtLC9vJWNJS1sK291+tuOFM7OCmdxVhjTk4PdUopfCbw09hmw5MEH7XuNFvZVd7C5tIUNJc0ukrHhQmOXGOY8cMajQ2/mnH9v4pzscO6cl0JCkBdyuQyZTEakn+gG57D4t9kEylt62GsPx91X3UFRQzdN3UYn5zCVQnTccsxryWRQ1tTr1Ol8b3sVH+6sJjPCh4kJYhdyXFzACX3vx8b6I5OJc0yNXQZCfbQo5DKJwIHo5DklMYjUMG9W3DKFOc/8BIib4P01nXxf2Mj3hY3EBHhy6YQY7pqfzDcHGvhoVzUVrXoeWVXI098dYvm4aK6aHCu5eJ0slAo5o6P92FreyvbyVieS0x+zU0OoePJsmruN3PHBXqfQ03/bw6Rzony5cFz0UcnOLe/tAUQp24v2It5I4KyscB79qpA9VR1OkrVluVHsqmzn0901/GaGGPy6JCeCNYWNfL63lrvmp7AsN5J91R18uKOaa6bEieGwSUHSLM33BY0syRk812j+qFCCdBqau42sLWyUCJ1MJuOyCdH86YsCPthRxW9mJLKnqgOjxYZaKae4sYddlUNL4dz4ZcBNctxg64CK3mUTY/jaLtealx7C3+2DmhqlHJPFRlKIjlHhPhjMVr454OwA9sjSjJN6Dwazlf/9VAbA7XOShqzKDDc+3lXNx7tqJEnPdwVHOirj4/zZWdGOTqPktavHEajTSPbSAx16/n5hDosyw/hiX61EcC6dEMPj52XS2G3g8le2Ow24Rwd44KNVSRahAx26jhdR/h68eHku2VF+2GwCr206zFPfFGGy2gjx1vDUBdnMTgs5uSc/w3G4pZdPdlWzYk+N00xUdIAHS7IjOHd0BEq5nFX767j9/T1OpFQplzE1KYgFGaHMTg1xqu4OBYe99KaSFjaVNo9oxX10tB9LR0dwdlb4Cc38uPHrg5dGydSkIKYmBTnJbUB04dtd2c76Q838WNQ0qE30cKH/0PuE+AByBrGQlstlJIV4kxTiLblo9ZnEzKh9duKzq1LM/RpI1hKDvZiaFMjm0iPrkdUm2GfXOnl5QzkymZg9NDE+kKlJgUxMCDxqpyfAS82YaLFCv2JPDbfOEofP56SFSCTn67x6piSKBDM+yItIPw9qO/q4cUYCqaHevLe9ihV7aqhq0/PUt0WoFDIWZ4bz/CVjqOno483Nhylr7uXNLRW8uaWCWanBXDMljhnJJy9lm5UazNbyVlYfaOBK+xzKUAj21vDBbyahN1l4a0ul02yR47MDuGFaPMVNPU5S6f5YU9hI8oOilG37H+cO+zxsqF3ivq28jU92iV0VgHNywnlkVQElTT3k1XSSE+3HglGh+HqoqO3o46fiJpaOjuSJ1Qc51NjNnqoOxsb6o1TIuWR8DP/6oYT3tlcOSXJUCjkXjYvixfVlvL+jyqlrtXRMJE+sLqK4sYdALzU+WiWdfWZRsmYx8cbmw26S8wuHTBBOdns18ujq6sLX15fOzk58fHx+7rfzq8WfVuY75QncMC2eVzcdxt9TxXd3zWDiEz8gCOIwZn5tF3fMTebu+SmsPlDPrfYqkAMVT559Uu/hzc2HeXhVIZF+Hqy7Z9ZpGYzsb9c5f1Sok2TMEcImk8HrV49ndloIgiDwwGcH+HDADNJflozi2qnxrCls5OZ3d2O1CVw+MYa/npdJdVsfl726zUnWlhjshUIuo7ix55QCPuemhfDsRaPx9VTR1G3gnk/ypAVsXnoIT12QTaDu1zV7ozdZ+Dqvnk921UhufyDK/paOjuS8MZFE+3vwVV49K/bUkNdPNqNSyJieHMxZWeHMTw/F1/PYFeH2XhPri5tYW9jEhpJmugfM6wwHJsQHcMWkWBaMCnXLzNwYNrT1mvgmv55Hvix0yXoaCUxNCiQ7yo+cKD9yY/2GNJ8RBIGa9j52VhyRdh6NnI2O9sNLo6Cuw+AijVbKZYyJ8WNaUjDTksXXH9iJ+HhXNfd9mkeYj5Z198zCQ62gtqOPqU/+KN1n/58XSMeDp787xAvrSpmaFMh7N0wCRKK2Kq+O97ZXOcmNk0J0XD4xhjAfLZ/uruHHQ01SwSohyIurp8RxwdioE5bc1bTrmfbUOuQy2PKHuYT5Hj/hsNoEvito4A8r8lzmC0H8OyWHePPmlopjPtcXt00dVvfNL/fXcccHewn10bDp/jnS3+p3H+7li311LB8bxT+W5wDw168KeXXTYeakhfD6NeO555P9fLq7hgtyo3jmIvE+9Z3i39EmwNq7Zwxp913dpmfGP9YhCPD9XTOcumMPrTzAu9uqmJ0aTEqYNy/9VI6PVpw9lstg3T2zJNmjG2cGToQbuEmOG0z52w9Ow/OXTojhgx1VLBsTyZSkIO75ZD8JQV7UdvRhtNhYfcd0RkX48Ju3dzmFZD54Vjo3zkg44dc3mK3M/Mc6GruM/PW8TK6YFDssv9exXvO8/2ymqKGbjAgfCvpZl+bG+LG/phOrTeCPZ6XxmxmiAcJ/15c5VckAfjc3mbvmp7C5tIVr39yJyWJj2ZhInl6eQ32XgYv+t5XajiMEJz3cB6PFSnlzL0q5zMWV7nggl8G9C9O4aUYCcrmMLWUt3PHBXlp6TGiUch46ZxRXTIz5VWmJq1r1vLOtgo92VksLt1wmWoleNC6a6SnBbCppZsWeWtYVNUmfq1IuY0ZKMGdnhTPPXh08Fg639LK2sJE1BxtdZJyninBfLTfPTGRZbuSwafjdcON4YLMJ7K3u4IMdVXy6u+bYDzhFBHqpmZ4cxNhY0UktNdR7yOy0tl4xe2pXRRs7K9rYU9Xhcp8ofw8Sg3WSlLS0qUeSVTug04jZKNOSApmWHERisA6jxcbcZ36itqOPu+encMdcMZbgtvf2SNK838xI4I9npQPihnj20+ux2AQ+uXmySyU/v7aT97ZX8cW+I8G/Oo2SC8dGMSs1mA3FLXyyq1pyK/XWKLlofDTXTo07oUH/i17ayo7Dbdw2O5F7F6Yd+wEDIAgCOyva+e/60kHNJ4K9NSzODOPtrZWDPNoZT12QxUXjok95TTFZbEx58gdaekxOs0C7K9u54L9bUCvkbP7DHIK9NRxu6WX20+uRyeCne2bT0mtk2YvifTbdP1vqcDv2IZdOiOZvy7KHfO1b3t3NN/kNXDg2iqftRArECIjZz6xHEOCd6ydw3Zs7MVsFiehcOSmWx847+UgMN4YfbpLjxnFjYEUrNdQbi81GWXMv/7ksl68P1LH6QAMpoTqKG3uICfDkp3tn0dVnYfzja50qhAcfXXRSbjjvbK3gT18UEO6rZf29s06LVO3PX+Tz9tZKAr3UeGoUkgVsbKAnXX1m2vVmluVG8szyHGQyGWsKG/nNO7ucZGUXjo3iHxdms7e6gyte3Y7+/9g77/Aoyi6Kn9mWTe+994QkhIQQeu9FOggqiAIKCgoKCBZsIIgoigUQqSJIb9J7EhIIARJCeu+9923z/TG7k53sJtmEIPgxv+fhkezOzBbJzNz3nnuOSIpRcl1zWZ0IM3dEIKus+SLsZaWPBrEUWWX1nZaoGevw8durPdHX1RQkSWJHSDo2XUyEjKSOv3V2QKsa7v8aMhmJ0NRS7AvPxA2lFVJ7E23M6uWAaYF2EElkOBiZjaNROShTclDyszXE1EBbTPS30aiblVpcg7Mx1DxCV0p+Ah2M8M4QNwz1svjPWzGz/P9AkiQSCmpwKa4Ql+IK/5V5MoDqkCuKnkB741a7qQ0iKbzXXmzzWC7murAz1oFMRkIqI5FQWK1ix25lIMRAdzNUN4pxKa4IAh4HZ5b0h5eVAdJLaun5G4DZCfj4ZCwO3s1GD3sjHF/cT+3vbk2jGCcf5mFfi/ysIZ7mmNHTHiU1jdh/J4t2tuRyCIz3s8bCgS7wszNs97u6+LgQiw7ch5EOH+Grh0FH0PnpgtTiWvwekqYSW6BgsIe5ivmQOib1sMH3M/w7HfQNAJsuJuK3m2kIcjTGscX96Mcn/3ob0TmV9MIhAMzdHYmQ5BK60Ji2LRz3syqweIgrPpJLN6MyyzF9e4RK8dOSh9kVmPJbOPhcAqGrhjG6Y4v+vI+LcYWYGWQHGUm5vBpq81HVIIaQz0H46uFPbEHP0nWwRQ6Lxpx6mIdlh6Ppn18OssfhqBxwCODeJyMw+LubqG2SwMVMF+mldfSK16HIbKw5EUvvx+MQSP1mXIdfXyKVYfB3N5FX2YCvJvnQPvhPk8txhXRuwEB3M4SmNAeUeVrqI6moBt7WBjj5Tj8I+VwkFFRj2rZwxkB7P1dT7H0jGLkV9Zi6LRyV9WIMcDPDrnlBqGmU4OUdEYwLn4elHmoaJSio6ryFsIelHna93gv2JjqoaRRjxdEYeoZoWqAd1k/x/b+QOzVJpDj5IA+/h6Qz5pgGe5jj9X6OGOBmjptJxfjrbjZCUkro4sdCXwtTAm0xLdBOo0Ivs7QO/zzKxz+PCrrsJq+7nSE+GuOFfq6m/1edNJb/bzJL63AprhAX4wrxUE0X5WnhZqGH/q6UNXuwswljQUImI+Hy8XnG9m8PckFEehke51Wp5Ff52BhQuU4EgdomCR7mVDJs45U59W5/+NsZYuOFROwISQdAdVlvrRwKAY+DwqpGjPjhFmqbJLQcuTVIkkRYain23s5UkavN6etIzcxEZjNmjfq4mOCtQS4Y4mHR6tyOVEZi2Pc3kVVWj5WjPRlhlp0lr7IBO0PS8eedLEjVqAhsDIUMVUdr8LkEoteOgm4nnO+Kqhsx8NsbEEllOPJ2XwQ7U52yszH5WHroIUx1Bbi9ehiEfC4i0sowe+cdCHhUAROTU4WF+6OgL+QhfPUwuhs+fVs4orIqsGiwK1aPbb3rNXN7BCIzy/H2IBeskXfuAGYnade8IMzZFQmCoCIzapsktESf5fmALXJYNGbNiUd0ZgEAzA62x6HIHAQ4GOHDkZ54bdddGOnwIZFSQXjHF/dDT0djvPbHXUa69c65QYw0bU1Rd2J7mpTXiTBqyy2U1oowwM2M8RmUjQbOLh0AZzNdlNY2YdIvtxmSMzcLPRxf3A8SqQxTt4Ujq6we/vZGOLSwN8QSEi//HsG4afa01EdtkwR5lQ2dlqgN97LAj7N6QF/IR1pJLRbui0J6aR0EXA4+n9gNrwT/9+VpNY1iHLybjV1hGbQTk74WD9OD7DCnjyNM9bTwd2Q29oZnMorFge5meLW3I0Z4W7S7wlhRJ8KZmHwcu5+L2LyqNrfVBD0tHlaP9cLMIPt/PWCPheVpUFjViCvxVMFzJ72ccTPsaKqDPs6mqBVJ8Ci3sstDUB1NdTDI3Ry9XahcJ2MdPtzkw/AKUtaPRb1IisiMcoSnlSI8tUzFLVFfi4eeTsbQEXBBkkBSUQ3dUVFgbShEfzczhnRvXj8nfDGRMs85cCcLn556DG0+F2eW9Ie7hgsn+yOyGHI1hZStl5MJriZQtsiKa4CbhR4WDnTGpB62aq99ikVIfSEPoauGdplDZklNE3aFZeDPiEy1uU0CHgcEgKZWikRlHnw2ssNdDkWnbLCHOfa9GQyAueD56XhvLBjoApIkMXVbOB5mV2LRYFesGu2JkVtuIa2kDmvGetFZelfji7BgfxT0tXi4vWZYq/ET1xKKMH9fFPS0qO/TWOl9z9gejnuZFXi9ryNyKxpwLbEYQj4HjWIZ9IU8hH00jHW0fE5gixwWjem/8TrjBn58d2uce1SApcPcUNskwZ7bmfTqjoW+Fu6sGY6aRgl6rrvCuFlP+2Zch+U4JEli8q+3EZNbhWUj3OnE46fJ0kMPcTYmHw4mOgw9d4CDEb2C+durgRjnZw2JVIbXdt3FnfTmuQxTXQFOvdsf5vpaeGXnHTzIroS9iTZOLO4PfSEPc3dFMgbiPS31US+WPNHNwMKBzlg91htc+fzNoj/vo7pRAhtDIX57rSd6dOFg6LOgvE6EXWHp2B+RRQ/2WxkIsWCgM2YFO6CyXoQ9tzPxd2Q2fUE21uHT6eNOZm0PhSospo/ez8GluCK1K5gdYbCHOb6Y6APndl6XheW/TkWdCNcSi3HxcSFCUkoYnRFLAy2M6maFUT6WiMmpxObLyV3++vYm2ujnYobDUUyzl/ufjmB0fYqqGxGSXIKQlFKEppSoyNZczXXhbKaLyIxytcP4yqwY5YElw9whk5GYs/subqeWwc1CD6ff7a9x56K2SYKTD3KxJzyTLq4IAhjpbYkJ/jaIza3Eocgc1MoLITM9Lczr54hXezsybrxlMhLjfw5DQkE1Xu/riC8nde1sSFW9GHvDM7H7dgaqGsRqt9FUWh26aijsTTSbOcouq8eQzTcgI4GzSwbQ8r2/I7Ox+kQszPS0ELpqKLQFXLqA0dPiIeyjobgSX4SVxx7BRFeA0FVDoavFg0xGYsxPIUguqsXyER54f4S72teVyUhM+DkM8QXVDMkbANxOLcWrf9yFgMvBL68E0GoPhTkQ2815fmCLHBaNKKlpQq/1V+mf+7iYILW4FqW1Ivz9Vh+sPv4ImWX1sDPWRm5FA2b1ssfGad1x4kEuPjgSQ+9npidA1KcjO/z69zLLMWN7BAQ8DsJXD4PZU3YCu/i4AIsOPACXQ6CbtQG9km+mpwWJTIbKejFjJe+7S4n49UYavT+XQ+DP+cHo42yKJYce4HxsIQyEPJx4pz9czHTpxxRQtqp4ohmczyZ0w3x5cNqRezn4+GQsJDISgQ5G+H1u0FP/zp4m1Y1i/BGagd1hGfTF3sVcF4sGu2JyD1ukFtdi2600nI8toAsTD0s9LBjggok9bNrt+uVVNuDg3Swcu8+0mO4Mbw1ywXvD3Z8omJCF5b9MXZMEt5JLcPFxIa4nFtO/swCVJzbMywJiqQyPcqtUDAGeBttf64kxvqrhxlIZidi8KtxKKkFISolK2G9LFJbRLVk52hM9HY3x/t8PUVTdhHF+VvhldmCHbKFlMkrKtud2BmP4P8DBCLN7OaC8XoR9Sp1pHQEXrwQ7YOEgF9rCWXHzTRDAyXf6P5VFrdomCf66k4WdoRkorX2yc6XCmKg9lh+OxsmHeRjoboY/5/cGAIilMgz7/iZyyhtoIyOZjMS4raFILKzBosGuWDHKAyN+uIXMsnq6IAWAfx7lY8nBh2q7NMpciS/Cwv1R0BFwEbJqKH0NJUkSL/9+B5EZ5ZjTxxEFVQ24mlAMgTw6Q19L3s3RwJWT5enCFjksGtHSAnqivw3OxORDm8/F+fcHYujmm+ByCJjraaGwupF2Q3n7zyhGnswfc4MwohNSNU1dUboCZZmachAc0GyN7W1tgFPv9oMWj4vriUV4c28U4xiKFvrWayn44Uoy+FwCf87vjd7OJvjqn3jsuZ1Jb2ttKIQ2n0tLyjpq4crnEvh+Zg9M9LeBTEZi06UkOkdoor8NNk3v/p+dv6kXUR3C30PS6dVDHxsDLB3mjlHdLJFQWI2t11IY/8YGuJlhwUBnDPYwb1OWR5IkwtPKsC88k+H81xmWj/DA4iGurAyNhaUFTRIpwtPKcOlxIS7HF6FcyfRDyOdAJgPjnMchgG42BuBxOIhWsmDuCj4Z542BHmbwtNRXe26oqhfjdlopQpJLcCOpWO2Cx9QAW5x4mNfua80f4IzPJnTr1PtMLa7BrrAMHH+QR3fEHEx08Ho/J2jzufjzThYdti3gcjA9yA6LBrnCwVSHLgi8rQ1wZkn/pxbY2SiW4lBkNn67mYaSmicrdv5a0Bv93dQH3AJUN2f4DzchlpKMbY/cy8Gq449gqivArVVDoafFo2VmWjwObq0cirsZZXj/b0rGF7aKKjyUu15vD3bBmrHeal+XJElM+vU2HuVWYeFAZ3wyvvn/Jz0D1Eo3Z+kwN3w4yvOJvheWJ4ctclg04oszcQyv/DE+VrgYV4hBHuaY0N0aq449ohOFuRwCDz4bCQGXg4CvL6NR3HwBS10/tsNuK8q+9W3523cVHxyJxokHeSoytWAnE0RmlkPA5eDs0gHwtNJHbkU9xm8NY7TvX/K3wdZZPXAzqQRv7rsHkgQ2Te+OmUH22HM7A1+ejae3NdMTwMpQiMd51Z0qcHQFXOyYE4QB7maQSGVYfSKW1o2/P9wdy0a4/yfnb6QyEkejcrD5cjK9WuhuoYcPRnpgtI+VSnFDEMCE7jZYNNgFPjZtuxHViyQ4dj9Xxemoo7w1yAUfjPT4zxaQLCz/NlIZiXuZ5ZRT2+PCdgfXB3uY42357/SBO1nYciW5U3OKLdHicTDezxqDPc0x2MNc7fwKSZJ4nFeNqwlF+OlaSqdfa/+bwejratqpYqOkpgl/RmRi/50sWlZnpMPHa70d4Wiqg8P3cuiwaS6HwER/G8wMssfiv+6jsl7cphyrq2gUS3HgTha230pDaa2o/R3a4NdXAjG+u7Xa5xT3IH62hjj9bn9wOATEUhlGyjs1S4a6YcVoT5AkiRnbIxCVVUGHbI/9KRRJRTX0NgDoxUkhn4OQla07rd1IKsYbe+5Bi8dB6CrmdrN+j8Cd9HK80tsBJTVNuBJfRBc5elo8hKwayjqtPWPYIodFIyb9EkanIQPAUE9z3EgqwUdjvJBUWI1T0fkw1RWgrE6EXk7GOLqoH8OZDKAuLEnrxnb4tRU2ksqt6qdFZEY5Zu6IAEEAgQ7NXRwbQyEqG8SoF0np1rhURmLW7xGMNHs3Cz2cWdIfxdVNeOmXMNQ0SvBaHwesm+yH26mlmLs7kpZT6Qi48LUxRGRmObgcosPzH/paPOybH4xAB2M0iqV479BDXI4vApdD4Ntp3TG9p13XfTH/IuFppfj6nwR6pdLBRAfLR7pjor8t8ioa8O2lRJyTp6kTBPBSdxu8N9yt3eK3vI6Se+yPyERFvXpNeXsMcDPDj7N6/KelfywszwMkSUnFLsUV4kJsIcMdsSXd7Qzx9iBXjPG1ApdDoKpejN23M56o+FAmyNEYw7wtMMzLotUuzycnY/HX3ew2j+NtbQAnUx1ceFzIeNxYh48xvlYY72eDPi4mHV7oqxdJcPx+Lv4Iy6CjBgRcDqYG2iLQ0Rj/PCqgw52V4XIIHF3UF4EOxh16vc5QL5LIi510RreuM3w/wx/TWly/SmubMHjTDdSJpPh5dgBe8rcB0GyfrcXj4MaKIbAx0qbl7VwOgSvLByG5qBaLDtyHNp+LGyuGwMpQCJIkMW1bOB5kV7aZb6O83au9HbB+ih/9nOJ+gcsh8NOsHlhy8CFj3zf7O2PtS53r5rF0DWyRw9IuYqkM7kquNU6mOqgTSVFS04Qjb/fFkoMPUFzTBH0hDzWNElr7qnBFUbDlZX9MCejYjbdYKkPfDddRWtuE7a8FYoyv+lWerkAslWHC1jAkFdXAz9aQ4ajlbqGHlOJa9HY2waGFfcDhENh+Kw0bLzQHfgp4HJx+tz8cTXUw5ddwJBXVoKejMQ4t7IPCqkZM/DWMMeSq6Ax1Bn0hD3/O740e9kaobZLgrf1RCE8rg4DHwa+vBHbKve5Zk11Wj3Xn4mnpmIGQh/dHeGBOH0fUNUnw8/VU/HknE2Ip2aHiRmGFevheDhrEqu5AmrD/zWAM8jDv1L4sLCxtQ5Ik4vKrcfZRPv6JKVA79wJQIZ9vD3bFjJ52jA5qWW0T/gjLwLabaWr36wiG2ny85G+NYV4W6OdqxnidA3ey8NnpxyBJqsgY7WuFszH5KscY52fFmLlUxkRXgDG+VpjgZ43eLqYdMuGRykhciS/EjpB02vyGQ1DqgYHu5riWUISLcYUqM52xX4z61wKF65ok2B+RhR0hafT1js8lYKIrQEW9uFWrbnW0LHZ+upqCLVeTYWMoxLUPh0BbwKXmY3bcQWRmOaYE2GLLyz0AAG/uvYfricUY1c0SO+b0xPTtEZT9c6Advp9JBXwqJGdcDoFLywa2ei25m16Gl39Xv92CffdwNaEYo30soSPg4aSSlJHPJXD9wyEamyywdD1skcPSLnH5VRi/NYz+ebyfNc7FFoDPJXDynf6Y8HMYBFwOCIKykTz9bn90tzPEwE03kFvRfLFK/HpMh6U9CgMAMz0tRKwZ9tT0xQDwR2g61p1LgKE2H/UiCcRS6p97XxdTRKSXQcjn4PKywXAw1UF8fjUm/RpGbwMAX7zUDfP6O9NW2+b6Wvhn6QDoafEwbVs4wyq6l5MxorIqOmUwYCDk4cCC3uhuRxU4c3fdxYPsSugKuNj5ehD6ubaubX4eEUtl+CM0Az9dS0ajWAYuh8BrvR2wbIQH9IQ87AvPxNZrKbTT0SAPc6wZ6wVv67Z/zwuqGvDL9VQcvpfTKYnLpB42+Hbaf3eeiYXlv4hMRuJhTgXOROfjXGxBqxKo5SM8MLevo9qh8czSOnx3KQnnYgue+P0M87LAyG6WGNnNEmZ6Wjj3qADLDj+EWErC394I214NxIztEa0WZsoY6/AZXWQzPXnB090GwU4mHTIqiMosx683UhkmBSO7WWK0jxXC00px4gFzbujoor7o5WSi8fGflJpGMfaFZ2JnaLMbm7OZLqwMhGrDWNvip1k9MKmHLRpEUoz44RbyKhsYMy+Pcisx8ZfbAIAzS/qju50RkotqMPanUEhlJA7M7w09IQ+Tf2VuAwAL9kXhakIRhnqaY88bwa2+h4X7o3AlvggjvC3wx+u96MdTi2swaksIZCT1PlcefcSQnU/0t8HW2QEaf1aWroUtcljapWWY52gfS1yKK0IPeyNM72mHT089hpEOH5X1YugKuIj5fBRyKhowdPNNxnEyN47v8GvP2XUXoSmleGeIK1aNaT2460kpqWnCkO+oVniggxEeyFfJjOTuKJX1Ynw0xguLh7iiSSLFpF9uM4qWYV4W2PV6EC48LsQ7fz0AQQB/ze+Nvq6meP/vaJxRWu3rbmeIjJI6OhuhI+hp8XBoYR/42RmiXiTBvN33EJlZDkNtPva/GQz//5hF9IPsCnx8Ipb+Lvu6mOKrST5wt9THnfQyrD39GMlFtQAALyt9fDzOu92OSklNE367mYq/7mZ3aNVQgeKCysLC8myRSGW4k16OszH5OPEwl7GopGC0jyU+Hd+t1dVymYzEpbhCrDuXoFEh0hbBTiYY5WMJIx0B1p2LR2W9GNaGQvzxehBOPsjDH2EZ7R7j1d4OqBdJcSOpmHGjb20oxKQetpgSYAtPK83nTh/nVWHbzTScf1xAL5oNcDPDpB422BWWwbhODXQ3w7IRHujp+PTlawqqGsTYfisNe25n0PO5fV1M4Wyui7CU0g656217NRAEQWDRgfsQcDm4vHwQHQugMFzo6WiMo2/3BYdD0HM87hZ6OP/+QKw8GoNT0fno5WSMI2/3BUEQyCitw6gttyCWktj3ZjAGt3J9SSupxagtIZDKSBxa2Ad9XU3p59aciMWhyGwEOhgh0MFY5d+BsvU1y78LW+SwtIviF1jBEE9z3EwqwfwBzqisF+P4g1xaqjbIwxz73wzGvvBMfH4mjt5nRk87fDfDv0Ovm1tRjwHf3gAAhKwcCgfTp9fy/ezUY/x5JwvOZrrIUNKGK9zUvKz0cXbpAPC5HGy5kszQgpvoCnB5+SCIJDKM+TEE1Y0S2lf/SFQOVh17RG9rb6INmQydutgKeBzse4MaYm0QSfHm3nuISC+DvpCHgwv6/KdOog0iKb69mIh9EZkgSWqF85Px3TAt0BaltSJsOJ9AOxiZ6Arw0RhPTO9p36a0o7ZJgm03U7E7LLPDsjQBj4PLywa1m6PDwsLybGiSSBGaXIoTD3NblYIdW9QXQe10K6rqxfjtZip2hKQ/0fvR5nPp84w2n4sfZ/VARmkdQ8I8wM0MvraGtNulAn0tHoZ7W8BMTwvl9SJciS+ic78AoJu1AaYE2GJiDxvaHro9Uotrsf1WGk49zKM71z0djVHVIEZqcS1j20Ee5lg+wh0B/8KsjoKi6kb8dC0Fh+/lQCqjJMcT/W3gY2OAszEFnQpcHu5lgV3zqK5KQVUDhn9/C/UiKW30U1UvxpDNN1BRL8bnL3XDaB8rDPv+JhrFMjrjDgC+/iceu8Iy4G6hhwvvD2x1Zmrt6cfYH5HFMD8AgOLqRgz+7iYaxFJsnOqHb84nMDKW+rma4q8Fvf+TJkD/dTpSG7DeqC8oLW08FXaRgQ7GeJhNDd1L5CtsvZ2pC0xoCnMIUpE23BHOxhTQx3yaBU56SS0Oyos4LSULYD9bQzzOqwZBABum+oHP5VB5LC10319M9IGJjgAfHIlGdaME/naG+GCkB1KLa/D56eZCT1fAha6Ah7zKBgj5Hft14hDAL7MD0NfVFGKpDG8fuI+I9DLoafGw/83g/1SB8yi3EhN+DsXecKrAmRZoh2sfDsG0QFuceJCH4d/fxImHeSAI4JXeDrj+4WC83Muh1QJHJiNxJCoHQzffxK830jpU4Dib6eL+pyOQvG4sW+CwsDzHaPG4GNHNEr+92hOxX4zCt9P8VLaZvj0CTqvP4btLia0auRjq8LFmnDcyN45HxoZxMBB2Ls9K+TzTIJbi7T/v415GOX55pVmaFJZairj8KiSvG4sR3s1zkjVNEpyKzscfYRkISS7BhO7WWDjQGSO8LcHnEogvqMb68wnou+Ea5uy6i+P3cxlZQ+pws9DD5hn+uLlyCOb2dYSAx8H9rAqVAgcAQpJLMOW3cLyxJxKPcis79fk7iqWBEN9M8cOV5YMw3s8aJAmcjs7H5kvJCHIyxpaX/RHUwQ7TtcRiXJXPcFobamOZ3Eluw/kEVNSJYKjDp93UtlxJhhaPg7cGugAA1p9LQIM8MPq9Ye4w1uEjpbgWh+7lqHklivfl+WexeVU4HdMsB7QwEGLhIOq4O0LSVe53wtPKcEuNMQTL8wVb5LyAyGQk7XIFyAfw5fIhBxMd2hFHoUENdjaBVEbibgZzoN7VvOM3kAqJ19OWDn13KQlSGQk3Cz1Ga19xEZvZ0x4BDsYgSRKfnIxl6G1HeFvipe7WOHQvG3fSy6HN5+KnWQGQykgsOfiQcSH0tTVEYmENeByCYautCRundscoHyuQJImPjj9CSHIJtPlc7Hmj17+6GvckSKQybL2Wgqm/hSOtpA4W+lrY+0YvfD/THxKpDAv3R+HDozGobpTA19YAJ9/pj2+m+Km1d1UQmVGOib+GYdWxRx3KaujlZIz4r0bjxoohjDR0FhaW5x99IR8v93JA5sbxCF01VMVo5dcbaXD9+DyGfHcDmW24thEEgUdfjEbIyqFd8r6uJRZjycGH8FdadApNKUXPdVfw/Qx/ldwcPS0eSmtFOBSZg52hGUgqqsbMIHvMH+CMno7GkJHU/h8ejUHQuiv44HA0ItLK0Jaoxs5YB19N8kXYR0Px9iAXaKuZKRzvZw0uh8CNpBJM/OU2FuyLQpLSte9p4mKuh19fDcSZJf3R380UIqkMe25n4vPTcRjtY4U/5wdjQBuZOS1ZsD8KiYXUPcob/Z3hYamHinoxNl1KAgDM6uWAbtYGqG6U4LtLSVg0xBU2hkLkVTbglxuUIsNQh49lIzwAAD9cTkJFK+5wpnpaeGcoVcBsvJCIOqXC861BLjDTE9BKEFsjbca+35xPgKSDEREs/y5skfMComwcAABWhkKIpDLoC3l0fglBUK4vAh4H3e0MkVxUw2i9EwQ63KZNKapBQkE1+FwCY9UkVXcVj3IrceFxITgEGCegYCcTpBbXQkfAxYejqZPf0fu5jOJNX8jD+im+KKhqxIbzlERh5WhPOJnp4serKYyCKdDBCA/lHbGODsEvHuKKmb3sAQA/XEnGiQd54HII/PZq4L86SPokFFU34pWdd/GDPOdivJ81Li0bhCGeFjgdnYeRW0JwNaEYfC6BlaM9caqdtO6KOhE+PBKDmTsi8DivutXtWuJuoYfHX47G0UX9oCPo3AouCwvL84O9iQ52zg1CxoZx+FHurKUgs6weQzbfhNPqczgalYMmifour4OpDjI3jsfbg1265D0pxy0AQE2jBP5fXcZgD3NsnuFPd6W9rfXx06wemBJgC10BFznlDfjrbjZ2yWc6Fg9xxcKBznA200WjWIYTD/Mwe+cdDNl8E7/eSEVhGzlDFvpCrBnnjdCPhuKtQczPdS62AF9O9MHUQFtwCOBqQhHG/BSCD45EI7dC8xmZJ6G7nRH+WtAHf84Phre8CFl/PgGfnnqM1/o44uQ7/TDC20KjY435MRROq8+hXiTFuslUh+/ve9l4mF0BLofAl5N85I/lID6/Gmtfon7+PSQdaSXUou0rvR3gaanPKJDU8WZ/ZziY6KCougm/3EilH9fT4mGlvGu0/WYaFg50ZuyXXNSsGGF5PmGLnBeQlGL1qzve1gb0TTufQ/3T6GZtAC0elw4nU7BkqFuHX1fRxRnkbq7WPaer+Pk6dZLysTFEZlnzyT2zjFqNeWeIKyz0hahtkmDTxUTGvitGecJCXwufnnqM2iYJAh2M8Ho/J8TkVOL3kGZJmyIktTND8CO7WWKl3EHmUGQ2/X7XT/bFUC/NLgDPmoi0MozfGobIzHLoafGw5WV//PJKALT4HKw4GoP3/45GVYMYvrYGOLt0AN4d6taqJpokSZx6mIfhP9zC8Qe5Gr8HQ20+7n86Alc+GAw9Lba4YWH5f4MgCEwOsEXmxvGIWDMMbhZ6jOdXHnsEz08vYsXRGLUSLgBYM9YbMWtHqTxurMPHR11gfDPih1toFEuxe14v6GnxcC+zAj9fT8UHIz0Q9elI/Dw7AMO8LMAhgPtZFdh2Mw37I7LQ3c4QH470wMtB9tDT4iGrrB7fXUpCv43X8MaeSFx8XNDq9cVMTwsfj/NG5CfDEeBgRD/+6anHyKtowPopfhjnZwWSBE48yMOwzbfw5dk4lNVq3hl/Ega6m+OfpQOwaVp3mOtrIausHosO3MeGC4l4f7gHzr83sNWA0Jb4f3kZM3dEYHIPG5Ak9RklUhl6OZlgZhBlRf3xyVgM87LAUE9ziKUk1p5+DJIkwedy8BVdDGWryPQVCPlcuiP3R2g6Y4Z3Rk97+NkaoqZJgoSCGvRyYqosfriSjMr6J8sQYnl6PFXjgfXr1+PcuXOIjo6GQCBAZWVlh/ZnjQeeDttupuFbpZv73s4muJtRjnn9nJBX2YAr8UUQ8DgQSWR0oNb7fz/E6ehmN7HOmAYM//4m0krqnqrTVUJBNcb+FAqCAEx1tejOVC8nY9zLrICNoRDXVwyBkM/F5ktJjFUbLyt9/LN0AC7HF+Gdvx5AwOXg/PsDYG+ig4k/30ZSEVUcEgTQw94ID7Mroa/F65CjmpeVPo4v7gddLR7uZZZj9u93IJGReG+4Oz4Y6dG1X8ZTgCRJbL+Vju8uJUJGUp9n22s94Wymi+SiGrz71wOkFNeCQwBLh7ljyTC3Ni3CC6oa8NHxWLWhd21xefkgeFhq7lbEwsLy/0GDSIofriRhZ6iq65m1oRArRnlinJ81tAWqki5FfIEyn473xpv9nXHsQS7DUKYzvD3IBX/fy0FVgxhmelrYPS+ItjUurm7EyYd5OHY/FylKBZmNoRBjfK2hI+AiMqOckbNmqivAlABbzAp2UCnwlNkVloGv/4lnPNbfzRRjfK1xIbYA4WllAKjOxMKBLlgw0Bm6/9LCUG2TBDtupeH3kHQ0yYu2qYG2WDXaC1UNYvx4NVklaLU9Ph7nhbcGuaKiToThP9xCeZ0Iq8Z4YoKfDUZuuYUmiQxbZwdgojxc9IPD0TjxMA++tgY4/e4AtbOgJEli3p57uJVcomI9fT+rHNO2UYHiG6b4Yc3JWEZUxLx+Tvhiok8nvh2WzvDcuKt9/vnnMDIyQm5uLnbt2sUWOc8Jil94Bf72RojJqcS30/zw6400ZJfXgyAAkgQ2TeuOmb3s0X/jdYZ7WMaGcR2Sq2WW1mHI5pvgcQg8WDsSBk8pxOzdgw9w7lEB3Cz0GCt7ZnoClNaKsHGqH2YFOyC/krLDblJaKTu4sDcCHYwx/HvKs19RePx4NRk/Xm12XgtyNMaD7Ap0NKZFX4uHc+8NhIOpDgqrGjHh5zCU1jZhQndr/Dw74Ll3aWmSSLH6eCwdjDYt0A7rJvtCW8DF8fu5+ORULBrFMljoa+GnWQEMO051nInJx6cnYxmONe3x3fTumBFk/0Sfg4WF5b+PRCrDudgCvP93tNrn3+zvjDl9HeHcwnxEKiMxe+cdRLaYMQ1dNRT2JjogSRLnYwvx7kFmMdQZtHgc/Dw7AKN8muXZJEniUW4Vjt3PxenoPMb5r6+LKfq5mqKqQYwzMfkoVppJ7Otiitf6OGKUj6XahaNfrqdg8+VklcfH+Vmhj4spjkTl0DJgMz0Blg5zx+xgBwh4/46gJ7+yAZsuJuKUfLFUyOdg8WA3vD3YBanFtfjxajKuJhRrfLybK4bAyUwXx+/n4sOjMdDicXBl+WCcis7DD1eSYaGvhWsfDoa+kI+SmiYM+/4mahol+HqSD+b0dVJ7zPSSWoz+MQRiKYldrwdhuJKxhGKhN8jRGK7mejgc1WxmwOUQuPj+QLizC2//Cs9NkaNg7969WLZsGVvkPCdM/CUMj5T0xWZ6VMfj4ILeeOWPu4xtz703ABb6QvRaf5XxeEfzcRShnP1cTXFwYZ/Ov/k2yCitw7Dvb4IkKVvnnHKqKAt2MkFkZjnsjLVxY8UQ8LkcfHgkhiGNGutrhW2v9VRJXy6tbcLwH27RsgFrQyF4XII+dkfY9mogxvpZo0kixazf7+BhdiW8rPRx4p3nf5akvE6Et/+Mwr1MuR56og9e7e0AGQlsvJBAr6oO8jDHDzP9YdbG4H9Vgxifn35MX+w0YYinOXa/3qtDwXosLCz//5AkiYi0Mnx5Np7utisz0N0Mr/d1wlAvC8YKfmpxLUb8cIux7QhvC+ycG0QvOJEkiaNRuVh1/Mk6PC8H2WPjND+VhaxGsRTXEopx9H4ObiWX0N0BU10BJgfYwsZIG3fSy3AtoYheVLPQ18KsXvaY3dsB1obNg/AkSWLlsUc4dp+6rrmY6SKjrA4kSd2Ezwyyh5uFHv6MyKRl3I6mOlgz1hujfSz/tUW26JxKrPsnnpbA25toY+0EH4zwtkBMbhV+uJKscWf/0MI+6ONigld23kVEehkGeZjj9zk9MfanUGSU1jE6LIoIDAMhD9dXDGn1GrXhQgJ23EqHo6kOLi0bRAdHF1Q1YNjmW2gQS/HZhG748UoyQ8Ux0N0M+98Mfu4XK/8f+M9aSDc1NaG6uprxh6XrUS5wHE11aEmXqIVLiIDHgYelPsOJDaCC2jrK9URqhUZ5ZaSr2S/PZ/Gy0mcUIYohxKVy6VR6SS1OPmwucLgcajC+oKoB225R8rU147yhLeBi3bl4hi7axogqnvQ72Oqf188JY+X+/ZsvJeFhdiUMhDzsmNPzuS9wssvqMeW327iXWQF9IQ973+iF1/o4oqZJgvn77tEFznvD3bF3Xq82C5zHeVWY8HNohwqcK8sHYe8bwWyBw8LCogJBEOjnZoZLywfh3HsD8JJcoqQgNKUUC/ZHYcC317H9VhrtsuVmoYfMjeNpi2IAuJpQDOc15+mbbIIgMLOXPTI3jkfq+rFYMapzkuLDUTlwXnMef93Noi2OAWoWZHx3a+x9IxhhHw3De8PdYWmghbI6ES1BqxdJsGasN94e7AJzfS0U1zRh6/VUDPj2Bt7aH4WQ5BLIZCQIgsCGqX4Y4kkFX5bXi+h5IKmMxKHIbGy+lITRPlZYNcYTZnrNszKzd97B405k2nSGHvZGOLqoL7bODoCVgRA55Q1YuD8Kb+69ByN5APbRRX3Rx6V9A57ZO+/Aec15fDDKAwIuByHJJbgSX4Qv5YXN/ohMxMjncF7r4wgfG8oMQWEqpI6lw9xhIZ8j2qUUAGptqI135S5sO0PSsWAg0/ghNKWUvs9heX54roqcDRs2wNDQkP5jb8/KUrqaxhZ5I4obUhNdAQpauLq4mOmCz+UgLp9Z5MzsoFyoplFMSwM0dVbpKHVNEhyLogoX5c/Yy8kYZXUi2BlrY2ogNaT46400htRsWqAtXMz1sPVaKhrFMvRyMsaE7tYITSnBpbgiejs/W0PE5VMXgo7M4Xha6mPNOGrANSyllC4Kvp/ZA46mz3eOS0pRDaZvD0dWWT3sjLVxYnE/DHQ3R2FVI6ZvC8fNpBII+Rz8+kogPhjp0WYhciQqB1O3hWvcBXu9ryMyNoxjJQAsLCwa4WNjiJ9nByB01VDM6+fEeK6gqhEbLyQi4Osr+PBIDJ0ls2yEB2K/GAWBkgRs7u5I+H1+CfWi5vM8j8vBkmHuyNw4HvFfjcZrfRw6/P4+OfkY3msv4tNTsSpFha2RNj4Y6YHbHw3D73N6YoinOQgCuJ1ahvXnE3D8fi4m97DBp+O90ceFinW4HF+EubsjMez7m9gVloFGsRS/vRoIf3sjVNaL8c25BKyf4osjb/dFoIMRGsRS7AhJx+8h6ZjTxxFvDXKBgMfBnfRyvPRLGFYejUFxdevubl0FQRCY6G+Dax8OxuIhruBzKevrUVtCsPlSEnxtDPH3W32x5WXNwsZnbI+gF2m/PBsPfzsjTO5hAxkJfHT8EcRSGbgcAl9P9gUAHH+QqyJXVKCnxcPH47wBAL9cT0W+kkx/wUAX2Blro7C6EfUiiYoUct25hE6ZEbE8PTpc5HzxxRcgCKLNP1FRUZ16M2vWrEFVVRX9Jyen9QAnls6h/AsLNFssO5rqqGQPuMoHHeNbdHKCHDtmcXwvsxwSGQlHU52ndlN/4mEeapoksDTQYjiqldZSq3Zv9ncGn8tBVlkdTkU3zyPxuQTeG+6O7LJ6HJVrbFeN8QJJAt8orfZo8TjgyrNwDLU1nyficghsnuEPLR6Xskg+Gg0AeLW3g0oOxPNGbG4VZu6IQHFNEzwt9XFicT+4W+ojraQW07aFI7moFpYGWji2qF+bTjkiiQxrTjzCqmOPNL4A3FgxBF9O8mVb/ywsLB3G3kQHX0z0wcPPRuKDkR7QaWFCcPxBLib+chszt0fgUlwhdAQ8JK8fiz1v9KK3qWmSoNvaS/gjNF3l+DoCHtZN9kPmxvEa34grc+BONib8HIYJP4fir7tZqGkU08/xuByM8rHC3jeCEbJyKJYOc4OFvhZKa0XYGZqBDRcSYaIrwPopvpjXzwn6WjxkltXj63/i0XfDdXx/ORlfT/KBi7ku8qsaMXdXJDws9XB8cT/smNMTbhZ6qKwXY8vVZFyKK8RHY7ww0Z9yLjt6PxdDNt/Ez9dSVBZEnwa6Wjx8NMYLF5cNwkB3M4ikMvxyIxUjfriFC7EFmNzDFt/P6Nj3W1rbhI0XE/DZhG4w0uEjsbAGO+X/DwMdjDE7mFqk/fRULMStZNxM6mGDXk7GaBBLsf58Av24kM/Fp+OpAmhPeKZKIZ1RWod94Zkder8sT5cOz+SUlpaitLS0zW2cnJwgFArpn9mZnOeHkOQSzN0dSf/sZaWPxMIaTAmwRYNIiotxzS4nisF7hSuago7O42w4n4AdIel4Ocge307v/uQfQg1jfgxBYmEN/XkAyiAgKqsCelo8RKwZBn0hH2tOxOKQkq/9K70d8M0UP3pGZ5CHOfa/GYx/HuVjycGH9HaBDkZ4kF0JDoEOGQ4sHeaGD+V20YrBRRdzXZxbOlCt+8/zQmxuFV7ZeQc1TRJ0tzPEvjeCYawrQGxuFV7fE4nyOhFczHSxf34w7Ixbd9mrqhdj0YH7iEgv0+h1Ax2McGxRP1aaxsLC0mU0iqU4ej8XO0PSkV2umhnjZKqD+QOcMa2nHYQ8Ll7fE4nQFOZ9zu3Vw1TCIBWU1jZh+eFolX06wswgO7zWx5F2Y1NGLJXhWkIR9oVnMc6l3awNMDPIDjISOBiZTZvtcAgqqFohTQ9yNMaf83tDW8CFRCrD8Qe52Hw5mQ5b7uNiggndbXD8QS4eZlcCoFzfPhpLFUD/xmITSVKdqa/OxtMmR4M9zLFusi++PBuPqwlF7RyBySfjvGGsK8AKuSnBpWWD4GSmy3BkWz3WC4sGu6rdPz6/GhN+DoWMBP5a0Bv95WGmJEnitV13cTu1DKN9LNEkkeFmUvMMkb4WDzdWtj7zw/LkPNWZHDMzM3h5ebX5R7nAYXm+KKhq0cmR37E7mOionPxdzXUhkcqQVfZkQWJ35CflPq5PJ+QyPr8aiYU1EHA5jLDORnlI3IwgO+gL+aisFzFmcQgCWDjQBdll9fTjH470gEQqww9Xml1q9IU81DVRx+rI/IyruS6WDqP03iHJJTgdnQ8OAWyZ2eO5LnBSimowd/dd1DRJ0MvJGH8t6E0XOK/8cQfldSL42Rri6KK+bRY4OeX1mLY9XOMCZ9urgTjxTn+2wGFhYelShHwu5vRxxPUPB+O76d3hYMI8b2WW1eOz03Hot/E6vr+ShM0z/HHtw8GMbfpvvI53Dz6AunVhMz0t7HsjGCtHe6Kzp68jUVR3afKvt3HqYR4j5JTP5WCMrzUOvdUHF5cNxOxgewj5HMQXVOOLs/H4+XoKRnhbYuNUPwx0N4OMZM7eRmVVYNGB+5BIZeBxOXi5lwNurhiCJUPdoCWXq312+jHcLfTw6Xhv2BppI7+qEe//HY1Zv99BUqH6bL2uhCAIjPaxwtUPBuO94e4QcDm4lVyCkVtuwdlMh56DfWuQi0Y5fevPJ2DF0Rj0cjJGk0SGj0/GgiRJGOsKsGYsJR//6WpKq0Gp3WwMMKePIwDg8zNxdNeHIAh8/pIPuBwCl+KKMNTTAjyl/+k1TRJ8f7n14FGWf5enOpOTnZ2N6OhoZGdnQyqVIjo6GtHR0aitVR/axfL0KatjhlYpfjmtDIXIaVHkuJjpIa+ygS6EAOrGvSPUNIoRK9ce93Zu21K4s5yQu6TZGTevsrmY69KzRK/L7SKPROWgUdzcnh7pbQlnM13svp0BGUmtGvnbG+Hso3ykK3WufG0MkVRUAwGPg9oOzOJ8MdEHAh4HjWIpPjv9mHov/Zzgb2/U2Y/61Mkuq8erf9xFRb0Y3e0MsXteL+gL+YjPr8ac3XdR0yhBkKMxDr3VB6ZtrFTF51djym/hrQb0tSR89TDamIGFhYXlacDjcjAjyB7XPhyMTdO7w96E2ZmprBfj1xtpGPDtdfx6IxWXlg3Ce8ObjQnOPSqA85rzauc5OBwC7w51w6GFfWBpQJ0bBW1khLVGdE4llh2ORtC6q/j+chIKW8zKelkZYMPU7ohYPRyrx3rB1kgbFfVibL+Vhk9OPYaxjgA/vtwDs4PtoaVkD30ruQRun1ygpXG6WjysGO2J6yuG0HK1I1G52HIlGdN62mHJUDcI+RzczSjHuK2h+PqfeIas7mmhLeDig5EeuLhsIPq4mKBRLMPO0Ax6DnZ3WAbG+lkh8pPhCHI0budowL1MysUtPK0MR+XOc9N72iHYyQQNYim+PBvf6r4fjPKEqa4AqcW12Hs7k37cw1Ifc/tSBdCfd7LwmrwYUvD3vZx/zciBpW2eapGzdu1aBAQE4PPPP0dtbS0CAgIQEBDQ6ZkdlienvFZ9Mi+fy1EZprc11mYk/wIdd0eLyamCjKQKEJtWWv1PgkQqw+kYyqlLLGsuYKwMhCBJINjZBE5mupDKSOyPyGLsu3CQC6oaxDgin8VZMNAZJElix61mDbahNh/l8sKwIwOFo30sMdCdcrn57WYassrqYWUgpKVrzyOV9SK8vicSxTVN8LDUw743gqEv5CO1uAav7bqLynoxAhyMsPfNYOi14S73KLcSs3feoV372oLHIZC6fuxT+bfBwsLCog4+l4OZQfa4/uEQfDvNj7FABgBiKYkTD/Iw+scQJBRU48/5wYznZ+6IQN8N1xjdFgW9XUxx/r2BGORhTg/Dd6a7U9Mowc/XU9FnwzW8+9cDRGaUM7pIxroCLBrsilsrh2D7az1pM4IzMflYdjga2eX1+HZad5WQab8vLuP7y0kok5+fbY20sXV2AI4v7gt/eyPUiaTYei0Fp6LzsGq0F0b7WEIqI7ErLAPDvr+FUw/z1HazuhoXcz0cWtgH303vDiOd5jlYiYzEogP3YaQtwLHF/bCpAxL4VcceoaSmCQRBYN0UX/A4BK7EF+FKvHopnKE2Hx+Nobo+P15NZpgyLBvhARN5AWSkw4eZnoB+jiSBL8/G/SvfE0vbPNUiZ+/evSBJUuXPkCFDnubLsrRBeT2zyCmvU1/0CHgcGOvwVaRqg+Q37pryWO5G5q9GZ9wVRGaWo6SmCUY6fIZrl8JgYbrcUS00pQS5Fc3Pe1npI8jRGH9HZqNeJIWnpT4GuJkhPK2MIXlzs9BDUlENhHzNf1UEXA4+Hd8NAJVyvTOEKprWvtStzeLgWSKWyrD4wANklNbB1kgbB+ZTErXimka8vvseyutE6G5niL1vtF3g3M+qwKs776Kqof0Vv0Ee5kj9Zhx4nVjtZGFhYXlS+HLp1vUPh2DjVD+1MzdX4oswZ1ckBriZ4fW+zSv2BVWN8Pz0Iv66m6Wyj6meFvbO64UVozw6PMepjnOxBZi5IwLjtobh+P1cxoIbj8vBGF8r/P1WX/yzdAAm9bABl0PgdmoZlh2OxoXHhdg0rTsCHIzofX6+nor+317H56cf03Ktno4mOLm4H358uQesDYXIrWjAV/9QEQpfTfKBk6kOSmqasOxwNF7+/Q4SC59+xAdBEFTn7YPBmBJgSz+eU96Aib+EAaDcXt8a5NLaIVTotf4qRBIZPCz1aRvoL87EMZz0lJne0w495MXfN0omBIbafKwaTS1a7grNwBv9nRn73cuswD+PCjR+XyxPB/bu4gWjZVGjWG1veZ9pbSgEQRAqRY6HpV6HXk/RsvW1NezgO9WMq/GUL72lfvMcmK+tATLL6qHN52Kc3PXr5MM8xn4KG+y/7lImBPMHOIMgCIYvvoDHod3ntPmaz9DMDraHvVzzveVqChrEUgQ6GGGsr1U7ez4bSJLE52fiEJFeBl0BF7vmBcHCQIh6kQQL9kUhr7IBzma62PtGcJvOcrG5VXh9d6RG9tqLh7hi/5vB7W7HwsLC8rQR8DiYFeyAGyuGYN1kX1joq0pxw1JLsS8iC/52zGvZJycfw2n1OZXONYdDYMkwd/y1oA/M1RxPQUfcOhMKqvHh0Rj020hl/rRcTPK1NcRPswJwa+UQvNHfCToCLhIKqrHq+CMUVTUyhuEbxTLsi8jC4O9u4oPD0UgqrAGHQ2BygC2ufzgE7w5ttnZedy4BY3ytsXQYJWGLzCjH+K1h2HA+gZH787Qw1dPClpd7MDpqiYU1GLb5JirqRFg+wgNOpq3Ph7bE49ML2BWWgfeGu8HWSBt5lQ3Yei1V7bYcDoGvJvmAIIBT0fm4qzRjOiPIHn62hqhpkiCjtA6+tswh+H/r+2FpHbbIecFQuKkoUKwwteyqWhlQRUNRC8/8tk7W6lDMxbT85e8KSJKkHVeUZ2UUF41h3hbQ0+KhtkmCS0qucXwudSKPzChHdnk99LR4mOBvjeyyekaYV4C9EWLkw5sV9ZppkQU8Dt6RD0Wml9TSUrg147yfWzvko/dzcfBuNggC2Do7AF5WBiBJEssPR+NRbhWMdfjYM68XTHQFrR4jvaQW8/ZEajSztGGqHy0BYGFhYXleEPA4eK2PI26tHIqPxnjBQKjatY7JVT9rEbTuKsOwRkFfV1Oce28A+rqon0mtahDj1d4OHZrfKa1twsYLifD/8jK+OhuvMjxvZ6yDz1/yQfjqYVg5mgr+zK9qVCshlspInHhISfPe2h+FuPwqaAu4WDnaC5cU1s4SGbbfSsOp6Dx8Ms4bY3ysIJWR2BGSjlE/3kJoSonKcZ8GA93Nkfj1GPrn9NI6DP7uBm4ll+Dbac2ytZ9m9Wj3WF//E49uay9h6TDqev1HaDqSi9QbLHS3M8LsYCoX6fMzcfTiJ5dD4IuJlGrj+INcTA2wY+yXX9WIHSFpmn9Ali6HLXJeMJSLFj6XuunWEXBR2eIm3kJe5LQsijpyo14nX90AqJC2ria1uBbZ5fUQ8Di05SQAelBzlDyH5nJcIcNwYLiXJUx0BTgmH0Ic72cNHQEPxx/kQhmpvAJsGfjVFq8EO8BS/t39HpIOqYzEMC8L9HJ6Os5yT0pqcQ0+Px0HAFgxypOeufo9JB2X4oog4HKwc24QnNr4DoqqGzFnV6SKqYU6vpvenb5YsLCwsDyPaAu4WDzEFaGrhmHRYFfGAH9bbL2WAqfV51QWBy30hTiwoDfeG+4OdZfQv+5mw8ZIiBk97VSfbIfdtzMw4NsbWHroIWJbFGBGOgK8O9QNYR8NxYapfnBRcx5fMtQNY32tQBDA5fgijN8ahrf/jEJ8fjVczPWw/81g/PZqIKwMhMgpb8Bnp+MgkZH4cqIPrA2px+bsisQHh6Nblb93JUI+Fw8+G0n/XN0owaID9/HX3WxMkCs3Nl9OQvxXo7FpWvvzOqtPxAKgZn0+Pfm41TmalaM86dydP+80SxR7OppgaoAtSBI4+ygfE/1tGPttv5XGuD9h+Xdhi5wXjFIl4wFFx0NXi6dycjKSP1eiwfB4a2SWUQWOia6gzS5AZwmRZxIot6m9rPSRVlIHPpfAUC8LAFAZKhzX3RqNYinOx1J62elBdpDJSEaR42ahh7QSyhmspflCa3AISvYGULM4Jx5QErl3hqj34X/WNIqlWHLwIRrEUgx0N8NieV7A3fQybLpEWWB+PrEbgtoo0BrFUry1P0qjk/jmGf6YIZcJsrCwsDzvGOrwsXqsF26tHIrZwQ7gaugg0Puba9hwIYHxGJdD4IORHtj/ZjBM1VwPM8vqcfR+Lt4a5KLx6yhzNiYfL/0Shrm7I3Evk+n+JuRzMTvYAVc+GIytswPgouSS+suNVNSLpPh7YR95Jg5wKa4I47aGYtGf95FYWINxfta49uFgvD3YBTwOgasJRdh4IRGzejlgTh9HEAQVyD3ih1s4+TD3qQ/cm+gKsP21nozHzsTk41oCpcTIKW/Ad5eSMLOXPf5ZOkDj40ZmltOLny0x1hVgpXwG5weljCEA+GisF3QFXDzMroSnlT5D3t4olmHjhUSV47H8O7BFzguM4jykp8VDXYuhO315m764xYpUR8gspVrojh3QynaEyAxKGyuRNp9QFS4svZ1NYSDko0kiRUhycytdwOVgqKc5bqeWok4khbWhEEGOxriXWc4wJjDX00JFvZjhmNIeI7wt6Vmc3bczIZLKEORo3GaR8CzZei0FiYU1MNPTwg8ze4DDIVBZL8LSQw8hlZGY3MMGr7TRdSFJEh+fiG1VvqHM2gndML0Tq5QsLCwszxorQyE2TPXDleWDMM6POVvJ5RCMnBQFO26lw2n1OZUFoIHu5jj//kAEt3Jd+D0kHfP6OdHXYDtjbXzYwiGtLUKSSzBjewRe3hGBsJRSRsHB5RCY6G+Dq8sHY8vL/vTjt5JL8PLvd2Cur4W/FvTGS/Ji52JcIcb+FIrFB+4ju7wea8Z648L7AxHsTNkvb7majEe5lVg/2Q+elvoorxNh+eEYzN0dqRJJ0dWM8bXC5B7NXRN7E200iJvnX/bczsT9rHL42hq2GvipjpXHHqGilY7UrF4O9AzOtxebCxdLAyGWyq3G94Zn4vV+Toz9zsbkq7UdZ3n6sEXOC4zi1KerxUWjmDkcZ6DNh0giQ53S0JxOBwMsFZ0cZ9OOZetoAkmStP+98kVEsQLWz43SP0dmlDM+Qz83U+gL+XR3Z2Q3SxAEFeqlgCBAB39xOiDPm9ffCQBlNX1UPouzsAOuL/8mj/OqsEPu+rZ+ii89a/Xl2XgU1zTBxVwX66f4tSlP3BueiRMtDB3UMTvYAW8OcG53OxYWFpbnGRdzPfz2ak8cXdQX3eUGBFIZCQGPA1sjbbUunP03Ui5mylgaCHFwYe9Wu/y7wjLQ29kEDiY6yK1owI6QdOyZ1wtnlvTX+L3ezSjHa7vuYspv4biWUMQodjgcAlMC7BCzdpTK67659x5sjbRx5O2+mNDdGgQBXHhMFTtLDz2EgMfB3wv7YMNUP+gLeYjJrcLa048xxNMc7w1zg4DHQWhKKUb/GIIDd7Kealfni4k+9LVruJclFg9xZdh1T9sWgUaxFMtGuHdosTXg6yuIz1d1j+NyCHw5yQcAcOx+Lu5nVdDPvdHfCc5muiipaUKjWKqSwfTl2ThaAs/y78EWOS8wipOBgMtRcQDRF/JUHrNvI91eHVnyIsfxKRQ5aSW1KK8TQcjnoEnJTlNxYlIEjyp3cQAq8FMmI3FV3tYe1c2KYWAAAN2sDWjr6+IazeR6dsba9GDp9cQilNWJYKGvheFyydzzhFRGYvWJR5DKSIz3s8ZoH2pl8kp8EU4+zAOHoKRlum1YRcfnV2PDefUteOWwUw9LPWyY6tel75+FhYXlWdLLyQSn3umPH2b6w8pAiHqRFHmVDbA0EKKnozEELWZ49kVkqczq8LgcrBrjhT3zejFyYBRcTShGVYMY/naGqG2SYP6+e7iXWYGMDeNwe/UwGKvZRx3ROZWYvy8K47aG4UJsAWRKN9qGOnxEfjKccUPeKKZMBt7ccw8elvo4sbgfxstnXc7G5GP497fw+Zk4DPe2wNUPBmOMjxUkchOCfx4V4OtJPgh2MkG9SIpPTz3G3N2RdKRDV2OkI8CGKdT1ZV9EJkZ4W+DEO/0Z8nivzy6CIID1k6ntCAL4dlr716RxW0Mx9qdQlSIt0MEYM4MoVcLa04/pwkWLx8XaCZQJwV93s1RmT+Pyq3Hsfk4nPylLZ2GLnBcYHof6388hCEabFwCEPK6KhM3CoGPOaoXVVIFgYyRsZ8uO8zivWn7s5pOzl5U+KurF0OZz6VU2RbdHQV9XUyQV1aC0tgm6Ai56u5jQBgYKDIR8NIpltMOcJkzqYUN3PQ7fo05k03raPZcZMMfv5+JxXjX0hTx8MZFalaptkuCTk9QA5sKBLgh0aD1JulEsxft/P6SD7pQZ4W2JmJxK+udLywZ17ZtnYWFheQ7gcAhMDbTDjRVDsHyEB7T5XGSV1eN+VgV6O5tgZDfV4Oze31zDjltMt62hXhY4995A+pqlTFWDGDG5VejnagoZSTmCfXwyFhb6Wni4dhQefTGKkX/TFgkF1Vj81wO89EsYric2d3Ys9IU4ML83w17axUwXNU0S/HAlGfP3RSHA3ggn3umHwR7mkMhI/HknC4M33cSBO1nYNKM7tr8WCAt9LaSX1uGj47Fwt9TDilEe0FJ0dbaE4Mi9nKfS1RnRzRLTAu1AksCKo4/gaamP0FVDGXNPnp9ehJWhFqYGUgYBu8MykbRuTBtHbf7OnNecV8kEUjjvxeVX42BkNv34UC8LDPOygFhK4m56Ofq5Mh31vruUhOpGzZxaWbqG5+8OjOVfQyY/4RAEGOFiANWWbRmOZaHfsWKlVN4FUT55dhUJBdRJR6x0o61oW3e3MwRf3p2Ky2+eFzHRFcDDQp/WxvZ0MgGfy6ENDBSQciGfgbbmwZ2Te1BBZVX1YoTKj9cZp5ynTV2TBJsvU6YC7w1zp7+zbTdTUVzTBEdTHSxvR/+9+VISUoprVR53MdNldMSi1458bm2zWVhYWLoCbQEX749wx40VQzCNDp8uxd30Mszt64ihnswA7Q0XEuG0+hzj+qqQh7V2zQhPK0M/V1MQBHAoMgdv7r2HmkYxDIR8nHynPxK/HoOB7mYavd+4/Gq8uTcK07aFIzyVulY5mupi35u9oC/v3ruY6+LHl3vA2UwX5XUirDuXgHcOPMBoHyscmN8b/vZGaBBL8fP1VAzedAO5FQ04995Aunvx191sHL2fi68n+yLAwQg1TRKsOv4I8/dFqTjPdQVrX+oGSwMtZJTWYfPlJOhq8XD/s5EMW+7RP4bC0UQXxjp8JBXV4I/QDFz9QLNFuDE/huLVP+7QP5vqaeHDUZQJweZLSQzjps8mdAOfS+BWcgkGuJsxTCRKa0X4+VrKk35clg7AFjkvMAqZFwFCZfaEwyFQ30KuZqKreWgZAJTVPcUip5Dys5cp1WaKRaJuNlQmT0xuJcRKpgQ9HY3B4RB0kRPsRHUropScaMz0BCiQW1AnF6neyKvD2UwX7pb6AIDrSUWQyEh4WurDxbxjwan/BrvDMlBc0wQHEx3M7Ueld+eU12NnKBWC+sk4bwjbCD6Nz6/GnvBMtc8pX7woCUbXO+qxsLCwPI9YGQrx/Ux/HF/cDz42BqhulGB/RBZKapuwZqyXijKg29pLOPeogP5ZyOdi0/Tu+HqyLx3voEx4WhmcTXWhzeciNKUUL++4Q59zhXwu/pzfG0nrxmCYhhLpB9mVeOWPu5j9+x3czyqHj40hdr4eBAGPg6sJxbidWorLywdh07TusDEUorC6ER+fjMWXZ+Pw/nA3bH8tEK7muqioF2PduQRM+e02BriZ4c/5wbAxFCKrrB4fHX+Eng7G+GCkBwRcDq4nFmPUlhDG5+4KDLX52Ci3i959O4O+xt9ePYzeRiojseVqMp1599O1FHA5HLwnz8lpj9upZXBafY6OqHi1twO8rQ1Q1SDGd5eapdvOZrqYP4CaxT18L4eWtinYczuTdm5lefqwRc4LjPLMDaeFOwyXINByRk5boHlngyRJlMntqk074FCmKcnyIkd5BUVhnuBtTRU5im6PAh958ROVRZ0AezmZgCRJRCkND7qY6yGrrGOuMIM9mlfqLssNDEb5qEoVnjX1Igl236aKmQ9HeUCLRxUzW64kQySRob+bqVqJhQKZjMSnp2LVDk++0tuBNnjwtNSn7btZWFhYXiR6OhrjzJIB+GqSD/SFPDzOq8aGC4kY5GGGdZN9Gdu+e/ABnFafo8+pBEFgTh9H/P1WH1ioCd5OL61Dg1gKAyEP8QXVmPpbOCPAUovHxe55vZC8biydE9ceEellmLYtAm/siYShNh+/zA4Ah6BCojdfpmyYb6wcgrUTusFYh4+U4lq8uTcKf97Jwo8vB+DbaX6wMhAit6IB7x58gK3XUqi4gJ6UhOyPsAycjcnHhql+8LM1RFWDGO8efICVR2NQp0F4tKYM9bTAzCDqNVcei0G9SAJzfS38+HIPtduLJDJ8fCIW7wx1o/OD9NqYQ1XQZ8M1/HYzFTwuB1/LTQj+vpfDkGkvGeYGC30tZJXVw0DIZ8xPSWQk1p9LaHlYlqcEW+S8wEjlrY9GiRTcFp0cggBaumJ2RHjUJJFBIj9x66lJjX4SRBIZimqo1RTlWSKFm5u3FVXMtOzEdLM2QEWdCEXyWSEfW0PkVjQw/O4VVqDKOQKtodh2sFyOIJWRCJNL1UZ4P39FzqHIHFTUi+FoqoMJ3SnrzayyOpyOyQdA6YzbkpedjsnDg+xKlce9rQ1w8G6zLvn8+wO79o2zsLCw/IfgcgjM7euEGyuG0Nb5R6Jy8d2lJKyf4suwPgYA14/PM0xyejqa4J+lAxDkqH42srpRAlNdAfIqGzB9WzjupJcxnhfwOPh9bhBS1o/FaA0X3G4klWDsT6G4mlCED+SS5R230vF7SBq0eFy8OcAZN1cOxduDXCDgcnA7tQwTfw3DvcwKHFzYG8tGuEPI5+BeZgVe3XUXJCjnTjM9LaQU12LV8UcY7GGOxUNcQciLqPFbQxnFwZPy6YRusJZ3kTZdpGTZk3rY0N0tIZ/DcFmLSC/Dmeh8rJtCFZ91Igl2zg1q93U2XUyC0+pz8LU1pINA155+TJs66GnxsGacFwDgzztZeK2PI2P/64nFuJFU/OQfmKVd2CLnBUM590WxelQvkoLT4l9Ck0QKokVZ05GRwSZxs45MyOuY9XR7FFU3giTBcLAx1RXQQacO8pNYanENYz9vawN61cvWSBt6WjwkFTK3UXSDNFnRkchIEATVEQKozlFNkwT6Wjz42qoOkT5LpDISu0Ipy+i3B7nSOuHtt9IglZEY4mmO7nZGre4vlsqw5Yp6LbHyiuPOuUGdCrJjYWFh+X/DTE8Lm2f449iivrS06ZOTj1Fc04Tji/sytp27OxIua87R1yALAyEOLuyDuX0d1R0aZXUimOtrobpRgrm7InFGvlilDJ/LwY45QUj8egz81RgbqONIVC5+uJJMX1+/OZ9IRyIYavOxZpw3rn04GC/524AkKSvlcVtDIZOROPfeQEyR3/Qfu5+L9ecS8JK/NYZ7WUAqI/HLjVTcTS/D5un+sDEUIrOsHtO2heO3m6ldYq9sIOTjW7lsbW94JiLSykAQBNZP8YW+Fg+NYhmmBNjiJf/mInPV8Uew0NfCdHnnafOlJCSv06w49PrsIgZ5mENPi7LSPhLV7J42uYctAh2MUC+SIrOsHl5W+ox9v/4nnjFTzPJ0YIucFwxFWKUyDSKpyhxGg0iGlov6kg78QjZJqBM1h4BaffGToMjFMVNyT7GWO7jpa/FgqE21htNL6ujn+VwCtkbadJHjKT/htBygV3QyUjScx/Gw0KcLomZDA+Pn7kY/JLkE+VWNMNLhY2ogZZJQXifC8ftUzs2SoW3rko9E5TAc6BS83tcRt5RWINuSu7GwsLC8iAQ5meDskv74eJwXhHwOwtPK8Oofd7FmrBcWKGWIyUjqxlmR4ybgcfDVJF+sn+KrNnC0pKYJproCiKQyvHfoIXbcSlPrYCbkc3F6yQBErx2p0YysjGSaEa089ghX45tNZexNdPDz7ACcfKcfghyN0SiWYev1VMz54y5GdrPEyXf60Tf4e25nIrGwBhP9baCvxcOD7Ep8cTYO7w5zw3g/a0hkJDZdTMKrf9yh512ehEEe5rQBwspjlCTO2lAbH4/3BkAt7H040oOWmgHAiB9CMNbXCia6AiQV1WBnaDp2zAnC73N6tvt6yw5Ho1Yuu/v2YiIq66nFVoIg8OVEXxAEZb09QW7DrSC9pI52YmV5erBFzguGuhNcnUgCAyHTVKBBrFr41HZAP9so7+QI+dwud9hSZNco2zPryueFbI0pS2mRRIYypXkda0NtcDgEfaPuLNfgpioVOWZ6WnTScUtL7dbwt29eHbufTc329GolyfpZojiZTgmwpf+/HrufA5FUBj9bQwS18Z6lMhLbbqapfe5RXrN7XbjSkCcLCwsLSzM8LgdvDXLFpWWD0M/VFI1iGTZcSMSdjDL8OT+Yse3C/VEYtOkGiqsVQ+6O2P9mML2Ap0xZnYgO6t5wIREbLiS2atVspCNA1KcjEPbR0A6//wX7o3C3hSwuwMEYRxf1xbZXA2FrpI38qka889cDbL6chG+ndcfW2QGwMRQir7IBZ2LyYWusDUsDLdQ0SvDJycfQEXDx5UQf6Ai4uJNejvFbQ2nJ95PwyXhv2BppI7eiAd9domRrs3rZ09/76hOP8GpvR2xUynCbvy8KDvJF4J+vpyC3oh6jfKzww0x/jV+3ol5Mu5cCgJ+dIWb1sgcAXIwrVOkO/Xg1pUvnklhUYYucFwwTNY5X1Q1iFXlWg0gC/RazNCUaBmMCUOkCdSW1jdRJQbmzpGitK0wOSmqZ71WR1aOYx7E2pH7OUepO2BoJ1XYr2qKb3OQAaDZDULi7PS9U1otoa+eX5SdckiRxKJIqfF7p7dDqvgBwNaEIuRWqYW6LBrvioXxGR1+Lx8gsYmFhYWFRxdFUF38t6I1N07rDQG5MMG/PPbw71BWBSpk32eX1CP7mGv6MyIRURqKfmxlOvtOPHpJXRtkJ9feQdKw+rt4gRoGdsQ4yN47HP0sHdOi9v/z7HZVZEoIgMNbPGtc+HIz3h7tDwKPmdcb+FIrY3Eqcerc/Fg9xBY9DILGwBuV1IlrpcPR+LvaFZ+Lbad3RzdoAZXUizNl9Fz9eTX4i+ZqeFg8bpzWHhEbnVIIgCGyc2h3afKqgOnQvG7OCHfBykD29X7R8PqhRLKPNAaYE2HYo1PvAnWw8Vlr8WzHKE3pa1P/nbtaGLSylm7ArLKPTn5Olfdgi5wXDskWgJ0FQrWmxjClFK68TqxQ5BR1oJfPlXZanoTmtbaIsICVKJ0HFwpW+FrXSVdzCi99U3sFSWG5ayO08S5WKIVJ+zJZp1epQ5Ak4yS84IokMGaWUPM7DUr/V/Z4F1xOLIZGR8LLSh5fclOFRbhUySuugI+Bior9Nm/vvvZ2p9nHlmacrHwzusvfLwsLC8v8MQRCY2cseVz8cjPF+1pDKSPx6Iw0NYhlWj/VibPvZ6ThM/vU24vKr4GKuh5Pv9Ed/N9NWjkxd0w9H5WDpoQe0bLw1fG0NkblxPDbP0Lxb8caee/js1GOGOytAqTaWj/TA1eWDMcLbEhIZiZ2hGRj/cxg8LPVw4f2B6ONiArGUZBQw6aV1+PBIDKYG2mJWL3uQJNXhmLcnEmW1mi+stmSguzk9H7TmRCzEUhkcTHWwcjSVb7PhfCKKqhux9qVusFWzQHfhcSHCUkpBEAS+mOgDIV/z2+UJP4fR3TRTPS0sGkxZSh97kIPpgUxL6R230p7oc7K0DVvkvGBYGjK9+vlyxwGplLlqUlbXBG0+l+GwppiF0QSefA5HLCW7POW4tok6ufKV5GoSeZGmKMzqmpgnYAP544oOj7m86FHuTilWWMw10Cw3yYs3J1OqyMkqq4NERkJPiwcbw46Fpj5tFPpu5XmZi3GFAKiEZt02TBayy+oR0UKiAADju1vjakLzip7Vc/aZWVhYWJ53LPSF+PXVQPz2aiCMdfhIKKjGD5eT8e5QV8Z2sXlVGL81DJsuJkIo4GDvG8F4tZUOPElSs7DnYwuxYF+USqi3Oqb3tEPGhnGMrkZb/HknCwM33cDFxwUq13cHUx388XoQ9szrBSdTHZTUNGH54Rh89U88vp3WHT/N6qEimxdJZVh3LgFNEhm+nuwLIZ+D0JRSjN8ahvtZ5egsn473hpH8e1V0TF7v5wR/eyPUNkmw/lwCdLV4KtbeCl7bdRdiqQz2JjpYOsy9Q6/tvOY8ahqpBdk3BzjDQl8LOeUNMNET0PJCAKgTUaGqLE8Htsh5wWgZSCaS36xzW5gDlNY2gSAIGCvJ2zoiV1MuQERd3c2Rn1RlSidXhROctvzk0XKmRiHHU6w+6Wnx0CSRokaNHtZIp/3QU8VQpmIGSNHlsjPW7vIZpCdBIpXR1qTKttaXHlNFzhgfqzb3P/tI1bEHYMr0/lrQ+0nfJgsLC8sLyzg/a1xaPggjvC0gksrw6400BDkaw86Y2WH47WYaXvo5DAkF1Vg32Rcfj/NSezyZvNAJTSnFnF2RqJIHYLYFQRD4dnp3xH05Wm1noyWltU1YdOAB5u6ORFZZncrzQ70scGn5IKwY5QEBjypaRv8YgsKqRlxZPgjz+jmpxFScfJiHAxFZ+PHlHnAx10VhdSNe3nEH+yMyO7VYaqqnhU/GUYYDP15NRnZZPbgcAusmUYYAZ2LyEZ5WiqFeFrQxgJEOn7FQOez7mwCAhQNd4GZBBXzP6+ek0WKm3xeX8Si3EjoCHpbLbbn/jsymZeMK/rqbpfY7ZHly2CLnBUPTFffSGlGHtm+JrtJKRU1j1w7WKYqIJiX3l5YW2C2LHG35sL3CnlPI59DmCPQ+IoUjnGZFioGQRxdzLWVwzwsJBTWoE0mhL+TBT25rnVtRj/TSOnA5BIZ4mre5/1k1tqQAGMOV/d3Muu4Ns7CwsLyAWOgLsXNuEDZN7w49LR6isipQVitSycpJLqrFlN/CsflyEl7v54SfZvVQ62AqIynp2v2sCszaeYcRnN0Wulo83F49DJeXD9Jo+9CUUoz44Ra23UxTkadr8bhYMswdl5YNQl+XZrOFV/+4i2mBdjj5Tn8Va+WkohqsOPoIiwe7YkJ3yn1t7ek4fHwyluH4pinTe9rRr/3JqViQJAk/O0O81puy5l57Og4iiQyfv+QDAyEPlfVizApu7pLllDdg8YH74BDA5y91AwAcuJOFvxb2wdJhbbuSAsDEX27j52spmNHTDq7muqioF4MkmXEeYimJzZeTO/zZWNqHLXJeMBTyqpY0tbjhz62oB0mS9IB+R+FxOXRHRNOTq6YoahDlYkQil9spFntkLYYWFT8qCiMhn6tywlQcj9QwEchIqculcHyzVJNS/SyJkrf6Ax2MwZEvmymsrn1tDaEvbL1rlV/ZgMQWOUIAsHSYG/0993VpXRvOwsLCwqI5BEFgZpA9Li6j5lcaxFJEZVWge4uMG8UMz0s/h8HZTBd73whWm+1GygudhIJqvNKBQgegZkszN45XmRFSh1hK4tuLiXjp5zA8lLuMKuNspouDC3tj0/TuMNTmI76gGpN+DcPZmHwcfrsvlo/wYBRqtU0SrDz2CNaGQqwa4wmCoMKsX9l5p0OKEoD6Tr+Z6kd3k05HUwt3K0Z5wlRXgNTiWuy5nQFzfS18IreZ3nYzDbeV3EIvPC7E/H1R6GFvhCGe5pDISGy8kIAPR3lqpGT4/koyeq2/io/GUN/l3/eyMbOFNPBsTD7i8qvU7c7yBLBFzgtGa/MX+ZUNjJWFOpEUJbVNKp2cjrSMTeQ5Nl1d5CiMAZQbLoq3JZX/paV5gFSNxK3lqpNiJkem4WKRsp2noltloMbi81miSJNWXg1UFDl9nNu2ur6dqt7KU/liqkiKZmFhYWHpGuyMdXBwQR98NMYLXA6BR7lVauVRiq7O/awK/P1WH0YwswLFJS+xsAav7LzT4SH3RYNdkfj1GBXpnDoSC2sw5bdwfHEmjp5HUaAo4K5+MBgT/W0gI4E/wjIw6ZcwDHA3xbn3BqKHvRFjn52hGbibXo6fZgVAX0h1tyb+EobY3I4VA85munhP3nX5+p94VDWIYahDBZsCwE/XUlBQ1YCZQfYIcjRGg1iK7y8l4fS7/elj3EouwbRt4Xi9LyWzuxRXhLvpZejvZoY7a4a3+x4q6sV468/78LY2QKNYhsLqRjiZMnMLf7qqPnCbpfOwRc4LSM8W7W8AyCyrg4u5HvOx0noVW+BKDbS9CkzlRU5pFzuHGGlTx1UuUhQFisJeWsBl/tNWdHa0eJRsTSSVqfRrFJ0dTcs4ntLKk7IM7nkitYTKAfJUkgTEF1QDgMoFpSXhaaqGAwBw4kEe/XfXFv9mWFhYWFieHA6HwOIhrjjydl86g0ZdIKhURuKHK8n48mwcfp4dAFdz9WoNQFHo3O3wNVnI5yLso2E4u0Qzy+m94ZkYtSWEERStwFxfC1tnB2DPvF6wNhQis6weM7ZH4PiDXBxa2AefjvdmXEdvJZfgx6vJ2DKzB1zMdFFQ1Yjp28NxphUpdWu8NcgVbhZ6KKsTYcsVSho2LdAWvZyMUS+S4ut/4kEQBD6bQEnSTjykrnMzeja7oSUX1WLlsRjapXT9+QTIZCSsDIVIXjdWo/eRIL/+no7Ox6QetoznLscXMeynWZ6c5+uOjOVfQTGboUxmWb2K/35maR3cWtzEKm6QNcHOmFql6Gj2THsoOijKNpSKTk1VA1WEabUoNhQGA1ryDk+jWAqdFmGniqJJ026V8uWGlsHxuOo3fgaQJIn0EmqY0VU+MCmTkUgpogofD6u2ra4f5VaqPNbXxRRJRZSE7TnyV2BhYWH5v6SnozHOvz8QY32tGLEJLbmXWYEF+6Mwt68TwximJUlFVEenM4uPfnaGyNgwrlVnN2UKqhrx+u5IrDkRqzZIfKiXBS4uG4RpgXaQkcCOW+mY9GsYejub4vx7A+GvtAiXXlKHBfuj8OEoTwz1NEeTRIb3Dj3ErzdSNb5eC3gcfPGSDwDKHS6psAYEQeCrSb7gcgicjy1EeFop/O2NMDWQKj6+/iceH47yZBRdpbUi+j7oUW4VXWwJeBxkbBiH3u0oJBRIZSSyy1Xvu35kuzldClvkvIB4W6ve3IokMnpmQ0FyUQ3cW2S+XJS7cmmCIj04u6xrixxj+ayP8qpWk7yToihyFN0eBZX1lGROqGRAoC1gFiS0XE3TIkfpLl/RKWr5HT5LSmqaUC+Sgssh6P8XeZUNaBBLIeBy4Gii0+q+9SIJ0ktV3V5GKNlQLx7sqvI8CwsLC0vXYqjNx2+vBmL9FF96oU4dNY0SfH4mDrbG2vCwbL3LnlxUi1d33qWvix2BIAisn+KHcKWZlbY4FJmNMT+GIDxNVf5sqM3H9zP98fucnjDTE8jld7dxJiYfh9/qgw/kjmQK3j34AP3dzLBwoDMA4LtLSXQGjiYMcDfDGB8rSGUkPj/zGCRJwtvaAHP6UCYE6/5JgFRGYtVoL2jzuYjKqkBUVjkWDqRybiz0tTDYg2nWs+liIq3kIAgCh9/uS0vj2uPkwzxM7MHMqbuaUKR2gZGlc7BFzgtIdzsjtY+3NB94nF8FBxMdxnzLP61YCqvDUa43zexia0RruYROOeW5tJY6WStCQM1baJMVc0HGcgldWa0IWjwOI31YUdxoOnbUqOTgpugcNYrbDl/7N1F8J8Y6AtoFTmF1bWMkBI/b+q9/clGt2u9B+d/C7OD2V/NYWFhYWJ4cgiDwam9HnHinH+xNWp+P4RBUNlpVg5gxZ9uSpKIavL7nntouiybYGGkjc+N4LBvRfn5MbkUDXtl5F1+ciVMJEQWAUT5WuLx8MMbL3dR+vJqCV3bewZQAW5xZ0p/xededS0C9SIovXuoGDgH8fS8H8/dFafw5PhnvDS0eB3fSy3EutgAA8N5wd+gLeYgvqMaJB7mwMhTibXmA58YLiZjXzwlmegIU1zRhsIc5I08ov6oRf93NZrzGB6M88fPsAI3ez49XU2hrauXHWLoGtsh5AXG3UL/C07KDEZdXDaLF9hUdmMlxkrdhU4u7tsixMRKCIJgW0gVVVFBpQXUjRBIZTFuc3EtauJ8V1VA5QIq5IaDZQlrahiRAmWqlwUphC4vq54EK+SqdiW6zGYJCotAyjK0leRXqg1+VH9dkEJWFhYWFpevwsTHE2SUDVDoKCmQklYdXVN2EsnZMf2JyKvHW/qgnum4tG+GBB5+N1GjbveGZGLc1VK1xgImuAL++EoitsymTgQfZlRi3NRRZZfW4snww5vVzorf96242jkTlYuvsAGjzuQhJLsGM7REolC/itYW9iQ4WD6FUCOvPJaBeJIGJrgDvycM+v7uUhHqRBG8PcoW1oRC5FQ04HJWDZSOortIvN1Lx8XhvLBna3K35+p94FaOFl/xtcPKdfhp9L6nFtYyfrycWI1puGsTyZLBFzgsIj8tRO5yYXlrHKGhqmiTIKq9XGVDXtAjwtjIAh6BurBU5Ml2BFo+rEmoqI6mhf5KkCh4+l8MoYHIqGiCTkbAwoG7uFR0fZYtshfysXMMWvnLAmiKHp07NKtWzQmESoSzd07TIURSNLVFuoz9PoacsLCwsLwpGOgLsnterVVlUYXUjejubaKRKCE8rw9JDDzWWfKnDRFeAtG/GabRtRmkdJv92G3+EpqtEPQDARH8bnH9vIAIdjFDTKMHSQw+x9vRjrBrjid3zgujt4guqseTgQ2yXS90SCqox9bfbSC+pVTlmSxYNdoWdsTYKqhqx7WYaAGBuP0c4mOiguKYJO26lQ1vAxaoxngAoS+kxvlZwNddFeZ0IO26lYcVoT3wqt5wGqODPJgnz+h/gYMywou4I226mdmo/FiZskfOCMrKbatL947wqhgsXQAWJBTow3dgSNDQf0BZw6TZsVzuGOJupFmmKlOYs+QyQ8jYiiQwF1Y2wNqS2yZV3JKwYRQ71X00d5GqaJLQjm5m8Q9RRD/+niUTuha3sAqeQ+LVmJa6guJXPwa4usbCwsDx7uBwCH4zyxM65QdBXcz6/m1GOef2cNDKIuRJfhFXHHqktOjryfjTt6EhlJNadS8Abe++pvWbam+jgyNt9sXSYGwgCOBKViwlbw2BpIETEGmbR8PruSHw33R8u5rrIr2rEjO0R7d5vCPlcukDZGZqOoupGaPG4WCPPBNoRkobCqkZM8reFl5U+ahol2BWWgdVjqX323M5EaW0TFgx0wXAvC/q4r+y8q9IVszXSxp55vTT6XpS5HF+ENA0KNpa2YYucF5T+bqohjlIZqSJZi0grQ4CDEeOxPyOyNH4dXxvKyS22i4scHxtV9xhFfouiCGupc80sraMtjxXtYVuj5uH70tom6Ao0c0fjcQiQJJUvBDTL4Iq7sGP1pCj+VyqHpiq6cOqsSJWpF6nqmzkEcw6KhYWFheXZMrKbJU6+2482l1Fmb3gmfpjpTwdzt8XJh3n49lLiE70XE10BjrzdF5r679xKLsHYn0IRmqJqNc3jcvDhKE8cXNAHVgZCpJfWYepv4QhNKUXq+rEY49O8UPvG3ntYONAFvrYGKKsTYfbOO4jKLG/ztUf7WKGnozEaxTL8eJWylB7ja4VeTtRjP1xJAodDYMUoqpuz53YG/O0M4W9vhAaxFDtuUR2g3+c2d5fuZ1Vgwb4olbmjoV4W6OOimeuaApIEdoakd2gfFlXYIucFJchR/S9cy/b2nfQyOJvp0sGeAHA4Kkfj1+khL5CiMlVTkJ+EbmqKHMUiVFy++iInsbAG7pbNRY5MRjIcaBrFMoYRQVsorDxzKqiukaVcPlf4HBU5Ckil5B+FJIHLbftzNohUpQvKEjd9YdudIBYWFhaWfwc3C32cfKcfI/RZwfLDMdj7RjB8bVu3lVaw41Y6DtzRfBFTHcHOJiquaG1RWtuEObsi8e3FREjUSOb6upriwvsDaevoVcce4bPTcfhxVg/8MNOf3m7NiVj0cTZFsJMJaholeG3XXdxMKm71dQmCoDs3h+/lIKWIspRWBIQef5CHjNI6DPe2QKCDERrFMvx6I5X+bPsjslBc3Qguh8D21wLp44allmLenkjUtTBCWDnaU+PvRMGJB3nP1cLpfxG2yHlB0RZw4WmpaiWdXV7PSOHNq2xATnkDBrqbMbbTtK3d14XqGEVllavoVZ8EHxvVrJ9cecERl091jVpK7x7lVsLRRAcCLgcNYilyKxpUsmLachxTh0Iap3CSK6pu6rRbTVejkKTVNjV/7wp3NJGkbf21On22jlKXS5OVQRYWFhaWfwdTPS0cWNAbk1pYEgPA5F9vY+usAIz3s273OGtPP8aNxNaLA01YPMQNA9yoe4a2wkmV2XYzDa/viUSZmvweY10Bdr3eCx+M9ABBULbUM3dEINjZBFeWD6K3+yMsAyRIDPYwR6NYhoX7o3BB7qCmjiAnE4zqZgkZCXx7MQkAEOhgjOFeFpDKSPx0NRkEQWDlaKoYOhiZDRczXQQ6GKFJIsM2eTdntI8VQ11yN6Mcc3dHMsyJejqaoJeTahHaFiKpDLtvZ3ZoHxYmbJHzAjM5wFblsbj8apVsnBtJxRjkznRyicrSrDPjZqEHMz0tNIpliM6u7PR7bYmruZ5KN0Hh/JZeWoeqejH87Y0YeuRHuVXgcTl08ROdWwmPFp9VsZKkbFrQFgppnJGOgLatbumU8qxQhKZWNzSfaPWF1GO1jW0XYkJ+26cGWednVFlYWFhYngJCPhc/vtwDy0eodlKGfX8Lq8d6qX1OGRlJ5dE8yRwtl0Pgh5f9YaYnQFpJHd4e7NLu6wLA7dQyTPg5TO3sJ4dD4L3h7tj7RjCMdPh4lFuFCT+HoaCqEQ+VZoHuZVYgLLUUQzzNIZaSWHLoYZuFzqoxXuByCFxNKEJkBiVxWy7v1pyOyUdyUQ36uppioLsZxFISP11LwQcjqa7MX3ezUVjVCIIg8K6S2xqPQ+B+VgXm7IpkuK4tkmfL6WnxGHEMbbH9VpqKcxuL5rBFzgvMaB9LtY+3XMW/HF+IgR7MTs435xM0eg2CINDXlerm3E5VDQPrLFwOgd7OqnNFDiY6IEngTkYZDIR8hltcRmkdKutF6Clv6T/IqoCeFo+x0tQgHxqUahiWo5DGAaClb8lFNR3/QE8BRbelQsktTjGgWtPU9klT4RanTKVSsfQkTjwsLCwsLE8HgiDw/gh3bJrWXWU2ZuCmG5gdbI/fXg1Uv7OcepEUb+6990RSKQt9Ib6Z4gcA+D0kHf3dTHFp2aB29qKy3GZsD8eBO1kg1VyHB3uY45+lA9DdzhCV9WLM2xOJkw/zkPj1GHobqYzEzaQS9LA3glRGYumhh60GmbtZ6GGmPPdm86UkkCQJX1tDjPOzAkkCW65Q8zoKmdrJh3lwNNVBsJMJRBIZ7YI22scKLnKzo/HdrWGkw0dMTiXm742iZ1yHelrAw1IPtU0SLB/hoXEMw6gtIRptx6IKW+S8wLiYq8/LqW2UMKRJd9LLocXlws+2WSLWEZcthZ//lYQna4G3RFE8KaPoQESklQEAXdAoiEgrox+LyqJWbYKdm+eTFLM2mjqsJRRU090fLyuqXa0uA+BZoLDHrqwX0ytBim5Te3kCOmrcepS/kxI1kgIWFhYWlueDmb3sse21niodg+BvrqG/mxn+WTqgzf2La5qw6MD9J5KZj/KxwrRAO5Ak8OHRGNgZayNl/VgI2pGFi6UkPj31GKuPx6qVVtsZU+5rM4PsICOBr/6Jx5dn4xH/1WhG4RCdUwkbQyEkMhJLDj5otdB5f7g7BFwOIjPLEZFO3TssH0FJ4y48LsTjvCoEOBhjgJsZpDISO0PT6RDUw1E5KKttApdD0J2a8LQy7Hqdcr2LzCzH23/eR6NYCg6HwNuDqG12387A1Q8Gq8RhqKOgqpE2R2DpGGyR84KzVI3PfnROJaM4kMpIXEsswrgWet6MUs1CPod7WYBDUAVBTnn9k71hJfqpKXIy5TMy4WlU16iPC3Ob0NRSBMl1sQkFNahqEKOXU3ORQ5IAv52hfGWaJDI8lndzmounrjVZ6Cz6Qj4tu1PMDikuALkVDWpXyRS0d+LVsNHFwsLCwvKMGO1jhX1vBNPOowr8v7wMV3M9hKwc2ub+D7Ir8cWZ+Cd6D59P7AYbQyGyyuqx4UIC+FwOktePxSu9Hdrd93BUDubsuosKNaGmQj4X307rjk/GedNzOgv2ReHUu/0ZC5f58gU9RaFzOU610LEyFOLlXlQ356erKQAAd0t9TPKn5pt+uU51a94ZShUoh+/lwN1SH93tDNEolmG/3HF2coAtrA2FKKlpQmJhDfa+2Qs6Ai5CU0qx5OADiKUyTOxhAxv5Nmdi8hGxZhjD2Kk1fryagm8vPpn73YsIW+S84LQsXADqZNDyJvZMTL7K0OJnpx5r9BrGugL6pHM5vqhzb1QNXlb6jDBPgBqo5xBAclEtcsrrMcjdnNGyD00pgbWhNlzNdSGVkQhNKWGcEAFqFakjKGR4CmebpMLq50ZD6yA3RMgsowpSa0NtcAiqOGsr06fl96rA3kSz9joLCwsLy7Onr6sp/n6rj8qNtPfai7A2EuL+pyPa3P9QZDb+utt5xzUDIR/fzaBc0A7cyaZVFt9M8cPvc3q2u//djHJM/u222llXgiCwcJAL/pgbBF0BF+FpZZi+LRxfT/KlFSTKUIXOQ3oRVJnFQ1zB5xK4m1GOO/JujmLO5lJ8IdJKatHXxRQBctOB3bcz6K7M/ohM1IskEPA4WDjQBQBl/xxgb4w/5gZBi8fB1YRiLDscDQ5BYG4/J3o/ALj/6QiVQlQd226m0fI5Fs1gi5wXHG9r9baSLTNjQpJLIORz0N2uWbIW1oEZm1Hy8NHzbQwAdhSCIDDaRzXU1M6YurG/FFcIY10Betgb0c/llDcgtbgWw72peaTricWwM9ZhzO4o0LSjozhpWxgI4WiqAxkJeoDxWeMlN1l4nEd1mwQ8DhxNKd1wQmHrs0O2rWiFNWmts7CwsLA8P/jaGuLgwt4qhY77JxdgoitA3Jej29z/izNxeJDdeYVCfzczunPzyclYWgI3yscKt1cPa2tXAJQSYcpvt9Xm6QDAcG9LHH+nH2yNtJFZVo+ZOyKweIgrhikFdSoQSWVYuC8Kj3IrGY/bGGnTsznK3ZyR3SxBksDvt9Ipg4EhVOHzZ0QW+rqawsFEBxX1YhyNygUAzAq2h76Qh8yyetxKLkE/NzNsn9MTfC6Bc48K8OXZOLwcZA8tHgeP86rxMKcSBEEg9otRGnyTwE/XUvBHKJufoylskcOCtRO6qTyWWFjDsGCWkcCp6Dy81J1pT6npiW9Cd2twCCosS1OZmyaMUmOeUC5vbV+St6WHejJPdOdjC+jHbiaVQCojMaKb6nE07ehEZpbTnvgKF7prT2jB2VV0tzMCAMYJ3Vc+WxXb4iSvjKu5ntrMIGU3uq60BGdhYWFheXp4WRng0ELVjs6QzTehq8VDyvqxre4rlpJYevAhqho6r1D4aIwXzPS0kF5ah2030+jHbY2023xtBTWNEszbcw9H7qnP6fOyMsDpJf3Rw94IVQ2UIcH0nnYY4qna0akTSTFvzz2klTC7Q+8MdQOfSyAivYwOE1XM2Zx4mIvCqkYM87KAl5U+apskOBqVg4WD5J2b0HRIpDLoCHh0sbQ3PBMAdQ+y5eUeIAgqX+fvezl4SS6F2y/fhiAIpH8zrt3vAQDWnUvAsfu5Gm37osMWOSxqraQBqNzkHr+fh6mBtuApPT7nj7savYaFgZBuHx/vwl/OYCcTlZO2IqcmKqsCRdWNGNtCZnfuUQGCnIxhqM1HeZ0Id9PLMMJbdcVHU0QSGW4mUStMw+THuZ5Q3ObMy7+Fv7zIic2tglRuquAnD4WLbcMiVMjnwk2NMYVyPk5XWoKzsLCwsDxdPK30cWhhH0ZEQlZZPVYdiwGfy0H8V613dPIqG7DmxKNOX9cMtfn4/CVqQfW3G2mMAoPP5SBjwzh0a0VZokAqI7Hq+CP8eiNV7fsw09PCwYW9MdSTyslZeughhnlZqM2nKa8TYc4fd5Ff2UA/ZmukjSny+6E/QjMAULO2wc4mEEtJ7ApLB4dD4M0BzgCogmVqgC1MdAXIrWigFzfn9nUEQQC3kkuQLv+cE7rb4NPx1Of/9mIiDORxDudiC2jpOIdD4I+5Qe19lQCAFUdjcLUL5f//r7BFDgtMdAUqYZ8AkFJcCweT5mDQpKIapJfWMSRidSIpGkSarehP70mtbhx/kEvfcD8pPC4HE/1Vw88sDbRAklRisJuFHiOoK6moBpmldRjnR32OU9F56GFvDAu581hnuPCYkuH1dTGFNp+LwupGhr30s8LDUg8GQh5qmiR0N6eHPXXCv5dZ0Waoazcb1QvO9cRmucD+J0zGZmFhYWH5d/G00sfBhX0YC1ZHonLx550s6Ah4iFjTunzsfGwhDkWq76RowoTu1hjsYQ6RVIZPTz5mFCoEQeD8+wPxel/Hdo/z3aUkfPVPvNrrl46Ah9/nBmF6TztIZSTWno5DD3sjOuJBmfyqRry59x5jhnaBfKbmUnwhsuSzrIvl3ZxDkTmobZJgor8NTHQFyKtsQGhKKd25OSC/Jjqa6mKYXC2iMCUAgPkDnLFwoKJAygRAdcmORDV/p8O8LDTO6VuwPwr3nxOjo+cVtshhAQBGkJWCynoxw0oaoNqvs4LtGY9tuqSZ48dwbwsYavNRUNWIkFa0tZ1hRpCdymNF1dTKyNGoHJAkqVIIHbufi8k9qBWbC7GFEEtlapOiNeVGYjEaxVII+VwMkmcKnX2U3+njdRU8LgcD5AXsrWTqO+9hbwRdARfldSLEF7ReiLU0ZACaw08BqiPGwsLCwvLfwtNKH3vm9WKoNT479RgJBdWwNtTGn/ODW933y7NxSOlkFhxBEFg32RdaPA4i0stwKU61E/HlJF9smt693WPtuZ2JZYej1VpM87kcfDe9O94ZQhUnO0Mz0M3aADZqDHUSC2uw9NBDOgrCw1IfgzzMQZLUawBUDIaLmS5qmyQ4+TAPQj4Xs3opJGkZeLW3AwgCCE0ppeX4r8vNBY7dz6XVJQCwZqw3XvK3oeMqFNsoCj4Oh8AcDQo9BdO2hSNPqRvFwoQtclgAAL3V3NAClA5WORjy4uNCuJjrwdG0ucOz53amRi1sIZ+LaYFUQbJXfvLoCnxsDNW2uQU8DtJL63A/qwIv+dswXNaO3s+Fv70RbAyFqGmS4Ep8EaYGqhZLmlInktIzQIp296mHeV3WsXoSFDJBhaROwOPQGUNtFZsD3FS7ewDgbKar9nEWFhYWlv8GAQ7G2PtGL8ZjY38KRaNYioHu5nQOTEuaJDKsOBpDFwUdxd5EBwvk3YwNFxLUznbODLLH4bf6tHusMzH5WLg/Co1i1WMQBIFVY7zwmXzm+FR0Ptws9WGozVfZ9mZSCdadaw44V3RbjkTloKpBDA6HwGt9qMLjzwjqfue1Po7gcgjcSS9HbZOEnvP9S97NGehuBmd5YaRsuMThENg8ozvjniujtI4x3zytg/ci/TdepwNHWZiwRQ4LAOqEoG71JK+ygVHQSGUk/rqThflyTaqCMzGadS3m9XOitaqpxZ1bDVKHwuNeGS5BVTUH72bDxkib4bRSXifC1YQiTO9JnUwO3MmCt7UB7UbWGf6Wt/GHelEdq6LqJrVWlf82gz2ozx2TW4mCKmrFZ5C88GlL02tvosOQKypQLig7EgrLwsLCwvL8MNDdHL+8EsB4zOuziwCA94a5o7+bahYdAMTkVuH3J3D4WjzEDeb6Wsgqq8efEeplz71dTHH+vYHtHutWcgkW7o9qVTY/f4Az1k/xBUC5xFobChlzxQr2hmfSErIBbmbwtNRHvUhKGx1M62kHbT4XyUW1uJtRDhsjbYyWGx8diszGHHkRdCQqBw0iKQiCoO8vWpoEaPG42PZaT0Ykw99KMkB7Ex30caGKICdT1WuwOrqtvdSm/PxFhS1yWGjUzbYA1MqN8jnhzztZGONjxdD0vv93tEav4WCqg5Fy++Y9XdjNmRpoC/0WPvMN8tWds4/yUVTdSK/EKDhwJwuzezuAy6G88ZMKa/CqBgFlrRGRXobM0jpo8bh4yZ8yOzgS9ewdUKwMhQh2MgFJAmeiqWJ0tI8VCIIKe2sroFWdBefl+OYwtY9PxHb9G2ZhYWFh+VeY0N2G7nYomL/3HjgcAlte7tFqfsuPV1I6LVvT0+Jh5ShPAJQlcrmasE+Amgu9tXJIu8cLTSnF/H33Wu1mvNrbEZumdwdBUPK01kqBtafjEJZSCoIgaLnZoXvZIEkShtp82qRJUZjN6kXdL5x6mIc+LqawM9ZGdaOEvkZODbQFh6AiJRTzPQpMdAX4Y25zJ+3o/VxGR0oxw0wQmoeTu3x8XuNtXxTYIoeFRsjn4qMxXiqPZ5TWwcaoecWhplGCI1E5eK03s2i4Kw/Qag+FM8nxB7koq209kLIj6Av5KrNCAKAj4EIsJbE/IhOD3M0ZnYk76eUoqWnCKLl99P6ITEwNtIO+sP1QrtY4FJkNoPnkdyG2AIXyxOVnyaQAqoA9LS9yLA2E6ONMrdK1NTukKNaUUbbWbmumh4WFhYXl+efN/k6YrXT9vJZYjLCUUljoC/FdK/MxIqkMK4496nT3YFpPO3SzNkBNowQ7QtJa3c7RVBeRnwxv93jhaWWYt+ceHefQkplB9vhhpj84BNqUkb+2i3Jcm9jDBjoCLtJL6ujcu7nyWZnL8YUorxOhv5sZrA2FqG6U4GpCEabKi6CTD/MAUOHbA9xbd5X1tNLHDqVA1Df33qP/PtbXCkI+Bxmldfh0vHe7n19Bt7UXNd72RYAtclgYzG1l4E3SIjPmj7AMzAiyg4Db/E/o5d/vaPQavZ1N0N3OEI1i2RO1vFvyej8nFdvrenkL+8CdbDRKpCqfb0dIOj3kd/wBtZLycpBqsaQpByOzUdMohq+tIXo5GUMiI2nHlWfJeD9r8LkE4guqEZdPWUcrjBZOPshrdaYqwN5Y7bCmcsBqy1A1FhYWFpb/DgRB4MuJvujp2Gy1/Nquu2gUSzHWz5qeM21JTE4lDkd1zm2NyyHw4SgPAMD+8CyUtrHgaaEvxMPPRrZ7zMiMcizYp35GBwCmBNjhx1kBaNkcEfA4jFmdqb+Fg88l6FzAw3LJmre1AXxtDSCWkjgbkw8uh6DnZ47ez8UU+d9DkktQXEMtbioka8cf5KktCEf7WNFKmfC0MjyWRzvoavHoedqyOpFKVEZr1Iuk+OpsvEbbvgiwRQ4LA10tHj4c6aHyeGF1I2yVujmV9WJceFyI1/sxi4Z78gCttiAIgh5q3B+e1WXdHDtjHYzzU+088DgEqhrEOHg3G7ODHRgyuwuxBbA10qaLrl1hGZjb10nlJKgpNY0SWlv7Zn+qY3UwMrvVk+6/hZGOAGN8qe9mnzx8bFx3a+gIuEgprsWddPX/3zgcApPUXOCUZ3Fe0zAriYWFhYXl+UTA42DX68yMFsV8zhcTfWBloLrYBVCZLxWtyM3aY5iXBfztDNEgluL3kLYXPI11BYj6dES7x4xIL8OSgw8hbsUYYaK/DTZO9WM8JpLIYGesTc/qFFY34oMjMbQ65FxsAarqKZvpqQFU0XLiAdWZURQxoSklEPI5CHQwgkxJGj6qmyX0tXjIq2zAw1ZmWM8sGUD//Y2991Att7RW3M9ciiuk5380YfftjOdiHvh5gC1yWFR4vb+T2sdFLU4aO26l4dXejhDym/8ZzdgeodFrDPW0QHfFya0LuznvDXNTKVAUVo3bbqaBIIA3+jWbJshIKphsidxCe39EFgy1+fQKTmfYFZYBkUSGkd0sYWukjfI6Eb0S9CyZJy9IT0Xno7xOBAMhn16hUwxcquOVYIc2i77qRkmnnXZYWFhYWJ4PjHQEKsP+O0PSYajNxxcTu6ndp7JejO8uJ3Xq9agFT3k3JyKTDsVsDTM9Ldz9uH3p2tWEIqw8GtOqlO7lXg4qc0hx+dUMV7NzjwqQUFADLyt9NElktKx7Yg8b8DgEYnKrkFpcAyczXXrm9dyjArqbo5CsCflcDJeHhF+IVR+74GNjAAO5TL6kpgmrj1Ohq8O8LCDgcpBeUgdXNeHcbfHKzrt0YfYiwxY5LCoYCPlYM1Z1NqekponRBamoF+NwVA7dsVBwRYMU3pbdHEVr90lxt9Rv1UChrE6EfeFZeL2fI3SV8n+OPciFi7kevKz0UdskwZ7wDLw3XLVY0pTC6kYcu58LHpeDRXKf/t9upj7zbk6ggzH8bA0hkshom8u5fZ0AAJfji5Bbod6AwN5Eh7bHVMZAaXZJ2X6ThYWFheW/STcbA2xQ6nSsP5+A2iYJRvtYYainudp9DkVm0zKrjjLE0xw97I3QKJZh9+2Mdre3NBAifHXrgaUKTkXn4/Mzca1KsecPcMbyEUzVypH7Ofhyog/988cnY2lp9j/yIsdMTwtD5N/DiQdUITO+O9VxufC4EBP8rMHlEIjLr6ZNfcb6NT+v7v0QBIHZSqZH52MLsS88E/pCPh3UXtUgVmt/3Rb+X13WKN7j/xm2yGFRi8JZpCWNYinj5n93WAYmdLdhFA0L90dp9Is11NMCPeyN0CCWYsuV5Cd9yzTvDXeHGodIAMCOkDRwOQTeUCrMpDISW64m04Gou0IzYKKrhQlP0M356VoyGsVSzAyyg42hEEXVTbQpwbOCIAja+nvX7QzUNkngaaWP/m6mkMpIbL/V+vCnulmt6sbmAc+94ZplJbGwsLCwPN/M6mWPQAcj+mffzy+BIAh8NYkK8mwJSQLfXep8N2exfDHw4N1sjfJebIy0cfWDQe1u9+edLGxr47r23nA3zFO61yFJ4Jcbqdg9r1m297dchXE3oxzF1dRi7ER5kPhFedEyxtcKAHA/qwJNEhmCnSj7Z0V23mAPc+gIuMirbEBsK8XgWF+m1P6bC4lILqrBaB/q2CEppa0WmW3hvObFdlxjixwWtQj5XPw0q4fK441iGfQEzSv4TRIZ/ghLx3vDmcFhv91s/cSigCAI2jXk8L0cJHSRU5eruV6rwZ6V9WL8ciMVbw92gbFSV+rcowI4mupQbi9NEmy9lqJW+qYpRdVN2BeeCS0eF+8Oo4qnbTfTnnlg14Tu1nAx00VlvZiezVk6jPp/d+ReLp2j05LBHubwszVUeVy5m9OVluAsLCwsLM8GgiBwYEFvxmPnYwtgb6JDS7tbciu5BHc0dFhtyQhvSziY6KCqQYzj8u5Ie7hZ6ONgi/eojk0Xk3DukXqZGEEQ+GxCN4zs1jzvUlLThKNRuVg32ZexLUlSszkAMNTTnJKRldYhtbgWlgZCBMlNGy4+LqDnZy7HUaoWIZ+LofI4hvOxhVCHv50hLPS1AAB8LgGRRIblh6Pp4O5HuZXwszNq9/OqY5sG92P/r7BFDkurtDaX0iSVMVzMTj7MQ5CTMSM09LtLSRrJs4KcTDDezxoyElh/LqHLugErR3tCm89V+9yesEyU14nom3sFG84n4hN50XXgThZ4XA5m9OxY8rAyv91MQ1WDGDN62sPOWBvFNU3tDlc+bXhcDpYOpy5SO0PTUdMoRh8XU/R2NoFIKmv1ZEgQBJYMU724KXdzvvonnu3msLCwsPwfoCPg4diivvTP7/z1ACRJYsFAF1gaaKndZ9PFxE5dA7gcAm/KZ4H3hGVobEvdz80Mm6apt7hWZvmRaNzPqmj1tX+a1QP+ds2LeBceF0JHwEWAUjcLAP6RF0v6Qj4dlKro1ihL0kbKuy/3sspp1zhFVMWt5BK174MgCAyU201P6G4DIx0+4vKrcSQqB56W+iBJoJNrrvj2YuJzEWXxLGCLHJZW4XAIHHm7r8rjIomM8ctGksCXZ+OxZizTy32WhpbSH43xgoDLQVhqKa4nFj/JW6axNBDiHXkLvCUiqQzrzyXg1T4OjMThiPQyVNSLMMTTHBIZiW8vJGLFKE/oCNQXS+1R1SDGj1eTIeBx6O9m+620Vrsl/xYT/W3hYk51cxRFzfvy+aiDd7ORXlKrdr+R3pbwstJv89hbrqZ07ZtlYWFhYXkmBDmZwF8pLmDRgfvQFnDxgRoHVoAKl27tJr49ZgTZQ1/IQ3ppHcJSNXcGm9nLHm8PcmlzG5FEhrf2R7UafK0j4OGP13vBzrj5fuDTU4/xyyuBjO3uZ1XQwaUKGdklebdGUcTcz6qAgZAHP1tDkCRwQ35PM8DNDAQBJBRUtzqDPMiDmr9JLKyhO0m/3kils/viC6rRzdqgzc/aGn02XHshFyHZIoelTYKdTdTqQCUtVloe5VahpKYRA9zM6Meicyo1SkR2MNXBG/JVnLWn47pM0rVwkIvajBeAGrSPyqxQKczWn0vA+/KZnotxhUgsrGm1WNKE/RFZSCysxjg/K/RyMkajWIZNFzunXe4quBwCq+Whr3+EZSCnvB79XM0wVF7cfXM+Ue1+HA6Bj9QYUiiz9VpKm0FrLCwsLCz/HQ6/1Yf++6W4ItSLJJgWaAcPS/VuXz92cqFLV4tHu30eVROc2RZrxnnDSUlJoo6yOhEW/3W/VYWJub4Wds/rRc8c1Yuk+ORkrIqb2ym5a9qIbpYgCCA2rwpF1Y2wN9GBs5kuJDIS4WlldMbNbXnBZqqnBV8bqlsUmqy+iFMuhIKdTTCphw1kJBAl70KFppRgkAfzfqyvi2mbn1uZwK+vaLzt/wtskcPSLptn+Gu03aZLSfTwvoKRW0I0Wj14b7g7bI20kVfZgJ+udU03QMjn4pPx6m0vAWqlZpiXBeOkUVDViCvxRbTxwmenH2NOHydGRlBHkMpIrD0VBwBYO8EHBEHJ++52UrvcVYzsZon+bqYQSWT45jzljPbJ+G7gcghcTSiiT8wtGeppoXKSbcnC/VFd/n5ZWFhYWP59hHwufp4dQP/cbe0l8Lgc2vq5JdE5lYhpJQ+mPWbKg7gvxRV22P74xooh7W7zOK8aa08/bvV5D0t9bHm5B/3zzaQSRGVWMOymv/onHlIZCTOlokWRSTNI7oQWmlKCfnI52+20MvoeSOGUFpKivtulXAiFp5bhq4m+MNNrDgEtqm6CrRFz4TY6pxIhK4e2+9kByhE3tJXX/n+FLXJY2sVUTwvfTPFrd7uaRgkO38vG+y1MCDZp4Lqiq8XDV5Mo68Y/QjO6zIRgnJ8Vhnup2h8DQEZpHbZeS8FXE30gUHKM2RmajvF+1rA00EJWWT12haUzbCU7SmRmOU48yIOfnSFm9aJO4mtOxD5TS2nFwCWHoDTEIcklcLPQw5w+lIvaF2fiIJKoz775dLw3YyarJdcTi1uVBbCwsLCw/Ld4qUUsQ3mdCKN9rOBipqt2++VHojv1Oj42BvCy0odIIsPpGM0MCBQQBIHYL0a1u92RqFz83YbT6Tg/a7w9uFn+9u7BB5gRxJzN/eEKdU/TX65cuZ1KLVoqFgBDkksR6GAMIZ+DkpompBRTEnDFzE24UuHTkmBnypntflYFDHX4Knk+LYUSDWIpKupFDJe4tpizK/KFkq2xRQ6LRszqZd/mja2CU9H58LTSh5tFcyt72800jXJwhntbYqyvFaQyEmtOxHaJ7IkgCHw12Zdhca3MjpB01IkkWDy4WZImlpL44mwc3QXadisNTmY6GO9nrfYYmvDVP/Eorm7E6rHeMNfXQnppHX65ntrp43UFXlYGdMdqzYlY1DVJsGyEO8z0BEgprsVvN9W/Pw9LfboYao2Bm2509dtlYWFhYXlGKHcLAr++Ai6HwKLB6qXc6SV1nZo9JQgCM+TdnLMx+R3eX1/Ix9klA9rdbu2ZuDZzfVaO8qRtoAHgm3MJuPbhYPrnX2+k4WZSMW0+cDu1FCRJoo+LKbgcAtnl9SivE6GX/BgKZUSAgxF4HAIlNU3IrVD//QQ6UC5tD7IpidpEfxu6AwQAKcU1KlLBqKwKfP5S66qVlrxIttJPrcjJzMzE/Pnz4ezsDG1tbbi6uuLzzz+HSCR6Wi/J8hThcAhcWjaw/Q1BzdV8NIY5uxG8/ppG+37+kg/0tXiIzqnsMicyWyNtrBjtqfY5qYzER8cf4a1BLnA1b16VepxHBXkN9TSHWEriw6OP8OkEb3oAsKNUNYjx8cnHMBDy8LW8Y7X9VhoSC7umY9VZVozyhJ0xJRP87lISjHQE+ELetfr1RiqSCtXPVK0c7dmuhO/3kBfXtpKFhYXl/wmHFjMvJTVNmBxgCysD9XOv07dFdOp1xvlRA/1RWRWdCgn3szOk8+AAqF3gFElkeP/vh2gQqVdT8Lgc/PZas+nA3/dyVJQN7/8dDVdzPQh4HBRUNSKzrB66Wjx4W1PmPA+yK9BbqSsDUNI/HxsD+nl1BDoaAaDmcuqaJCAIAusnNytpDtzJRk9HE8Y+D7IqQBAEoxBrj5tJXWPy9Lzz1IqcxMREyGQy7NixA3FxcdiyZQu2b9+Ojz/++Gm9JMtTxs1CH6vGqC8WlCmtbcLZmHyV1X5NAj+tDIVYK1+R+OFKUpfJ1ub2dVKxg1TwOK8av4ekY/MMf0aI6E9XUzCvvzP0hTzE5FTiWFQuPh7nrfYYmnA1oQino/Mxxtcao30sIZGR+OBwDJokz062pqvFo9Ot90Vk4l5mOcb7WWOEtyXEUhKrjj+CRKoqW9PV4mH9FF+Vx5X55nxipy5SLCwsLCzPH1GfjqD/3mv9VQh4HMwKtle7bV5lA2qbOm4iZG2oDX97I5AkcCW+qFPvU1niVSeSqpXVpZXU0fOo6jDT08LeN3rRP4/9KZRhQlTVIMYXZ+LgKy9aonOoooXuxGRVooc99fdHuc1dowD6efVFjrWhNmwMhZCRQExuJQCqwFS+n2r5eeLl90mu5np0d6k95u25p9F2/3WeWpEzZswY7NmzB6NGjYKLiwsmTpyIFStW4MSJE63u09TUhOrqasYflueLtwdp5jR2JiYfPeyN4Kz0y/jTtRSNZjWm97TDyG7UTfbyw9FdUgRwOQR+mNmjVTvon6+nQEaSjPa7SCrDtxcS6cDSn66lwNfGsFOpwwo+PxOHgqoGfD3JF8Y6fMQXVGNzJ5Oiu4qB7uaY0dMOJAks+zsa1Y0SrJvsC30tqrj7uRVZ3RBPC0yVu+G0hqYdPBYWFhaW5xszPWY+ToNIipd72aM1JfuCfZ27kVaEaV58rD44UxPivxpN/z29tE7tNn/eyaItntUxxNOCERRaUc9UIl2OL0JKETVvE5NDFTKKIud+dgX85Nk7CvkaAAQ6KuRola2+rqIQilUqjhQZfgCw/04mY/vMsjralXbfG8GtHrclwzbf1Hjb/yr/6kxOVVUVTExMWn1+w4YNMDQ0pP/Y26tfIWB5dnA5hEYuJgDw9bl4FdnawE032g36IggCG6b6wURXgMTCmk5bUrbE2UwXayeo163KSGDZ4WgsGOjCyIKJL6hGQkENxvpaUZ2XI9H4apIvTHQFao/THlUNYrx/KBomugJsmk651u0MzXjmjiefvdQN9iaUbO3jE7GwNNDCOnmn5ufrKYjMKFe73+cTfdqVra08GtPl75eFhYWF5d/n6geD6L+P/SkE1obaGOZlqXbbO+nNYZgdQZE5czejvNMGPToCHna9HkT/3Npc7spjj9p0cvvllWZnuUOROSBaFHQ18m5VtNxRTlHkxOdXQZvPhYtcBq/oyihCR5OKalqdO/aU34MkKUVwCJXCzXPKmfM8JEll6wCU1E4ToyiAKv7+30NC/7UiJy0tDT///DMWLVrU6jZr1qxBVVUV/ScnJ+ffenssHcDZTBdfT25bqgQAlfVi7A7LwHst3NZe3xPZ7r5mSo5u22+lIaSTAWMtebmXPWNlRpmc8gas+yceP8zswXBb2xueiWFeFjDT00JKcS1+vZFKS7w6Q2RmObZeS8HIbpZ4tbcDAODDIzEo68TFoKswEPKxdVYAeBwC52ILcCQqB5N62GJaoB1VAP79EJX1qvN0htp8bJ3do01TiqP3c5+5ZTYLCwsLy5PjZtG8CJhZVg+ZjMT0nq139Lff7Phspqu5Hiz0tSCSyFqdXdGE4d6W9MxQnUiqNv6gtLYJGy+qz4YDAC0eFwfm96Z/bs2YLL6gGiKJDPYm2tDX4kEsJZFRWofutlRREyc3OrA31oGQz4FIIkNWmfoOk4cl9R0nt8gZbMtBTVna/4r8vkIBn9v69bnPhv9vtUWHi5wvvvgCBEG0+ScqipmTkZ+fjzFjxmDGjBlYsGDB/9g76/Aozq6N37Me2Y27uxAhJJDg7lAoUi9tKdTdjfrbr95SFwptKVahxV0DBAKBuLu7bWR9vj9md7Ob1RCH+V1Xrt2dfWZ2NpCZ537OOfcxeGwulwuBQKD1QzMyuSfeGyEufJPjkkubIZMrtOphEgsacSLHdK7tgghX3B3vTaVR7UodkBUHgiDw4YpIOPG5et/ffa0KGVWt2LBYu/bmg4M5eHVhKAiCKkLsFMvUdtDXw9enCnG+sBFvLA5HoLM16oViPLMrdVgbacZ42+H5eVTN1Vt7s5BT0453lo2Bn6MVqttEeP7PNL1RuFgfezzTS8j25vafLva57wENDQ0NzcjjRQ0jn79SKjAjxNlgKvimcyV9vvYTBIFJAVRtycWi/i2QnXlphvq5ocXSHcnluFyqP1sBAKZouJsZQiJToLy5EwRBIFDpfpZfJ0SQcp5U1EAJGgaDQJCzfhGjQpVNUlDXoTUnCHczPCcuVNpUq9j/ZI/LnFROwsaCbXDfvdfhZDda6LPIeeKJJ5CTk2P0JyKiZ5W/uroaM2fOxMSJE/HTTz8N6MnTDB8EQeDPRyaaNfb7M0W4c4K3upMwADz42xWzIhcbloQj3E2A5k4JntpxTW8RfF9xsObimztjDEYfNuzJwlgvO7XLC0A10dqeXK62mn7930zcHe+DMCMXHWOQJOXO0tYtxTd3xcCCzURiQaPaf3+4eHiaP6YGOUIkVeChrVcglSnw9Z0x4LAYOJFbjy8NNGp9bGagySah0e8evan8+WloaGhuRDRbLrz8TwZ4bCZmGehHBwB/XCrr82dMVImcYsPiwxy4LCa+u7vHKS3BX3/JxKu7Mwz2hgOA5Ndma7225uo6rarqbII1REyAEyV4ihp6RIgqUpNXqy1MVHjZW4LLYkAsU6CypaeOOcBZf18iQDeFLUIZQVJBObXp3/epHdcMHne002eR4+joiNDQUKM/PB4VHqyqqsKMGTMwbtw4bNmyBQwG3ZbnRsLGgo1/H5tkchxJAp8cydNpqBn7/nGT9Tk8NhPf3j0O1lwWkkub8bkZDm3mEO/vgJcM2EpLZAo8ui0FrywIg5d9T71JSlkLWrokmBzogG6pHM/9mYpPV0eBr+diZw6NHWI8/EcKfB2s8OFKKv3t21NFOJJ1/cWW/YXBIPD1nTHwtrdERXM3nthxFaGufPyfMnXwqxMFeotBmQwCX90xFj69bEZ7s+DLxEE5bxoaGhqaoYHRa4Gwrl2EBRGuBkYDW86XGBUQ+lAV32dVt5mcJ5hikUaPu4vFzVgwRvdcC+s78HtSqcFjOAt4GKeRkdIhlulEVl76Ox0AEKwRiVG1pihu6FQv8qnEiqF0NSaDgKcdNffQ7KfjaWf4/qrP1CnxpZ7eRjIFCTtLw7XEr/+bYfC90cygqY7q6mrMmDEDXl5e+PTTT9HQ0IDa2lrU1g7fBI5m4InxtjPLVrpBKMafVyqwTsO/HgBu/e68yX39HHtEwHeni/rluKLJQ9P81QWOvals6cabezPx9Z3jtOpzdiRXYFKAI5z5VH3OF8cK8Mnq6Os+h7SKVrz2bwZuiXbH2snU7+b5P9N0Qs9Dia0lBz+tiYUlh4nzhU344GAuVsZ64oHJvsrzS9XbP8fWkoOf7o0zmLYAUIWUb+3JHKxTp6GhoaEZAr64vee+99uFUkwKMJzS1dghwXEzUtQ18Xe0ApfFQKdEjlIDYqAvnH9llvr5SQM9Yr4+Wai39lTFL/eN13q9ttd8BqAiNt72lBipau2Gj4MVGAQlihqU2Ssqs56qVsMNUz2UgqZKQ+Q4WXPBYeqftle0dOlkSnjZW0Kg0dtPLJUbvD9vu1R+Q2ZaDJrIOXr0KAoLC3Hy5El4enrCzc1N/UNzY/Ho9AAtRzJDXC1vhVAkUzfIAoC0yjb8cq7E5L5LotzVRXfP/Zk6IE00CYLAp7dFw9dA9OF0XgMOZdTgw14mAxuPF+CR6QHgsBg4nlOH9MpWg52fzWH31Sr8cq4Ery4KxQQ/e3SIZXjwt8vDakQQ6irAZ0rxtvl8CX5PKsVri8Iw0d8BnRI5HtiSrLdGKsSVr97PEL8llWHX5fJBOW8aGhoamsFnWXSP2cB3p4tgb8UxWjOy83LfjKRYTAZClcfLqu7//d7D1kKdqiaRKfD4TN17dlu3FF+d0N8yAQDsrDiIU1pAA4BYJtex1f6/g7lwt6WymWrausFhMdQ1wHVt2iKnus2IyNEjhBgMAh7KCM8t0e5a47skcrVNtSZHn+1pENopkcPNRn/zVgC4/ceLBt8brQyayLn//vtBkqTeH5obC4Ig8M+jptPWAGDXFSoSonlheG9/tlkOKq8vDsOkAAd0SeRY//sVvX/QfUXAY+PnNXEGU85+PFsMiUyhFYGSyBX47nQRnpwZCIC6wAe7WBvNSTbFBwdzkFjQgO/uHgcvewuUNXXhoa0p122fORAsjHTDC/OCAVBGBCdz6/Hd3ePg70QZEdy/JRntIt2C0oWRbnhtUajOdk1e/ifDaKEnDQ0NDc3IpXfKWlVrt7qORh+JBQ1a9SXmEO6mSvvSX6DfV35b29ND5ttT+l3ftl4sRXmT4fN8Z1lP2v3r/2bingRtJ7PjOXUoU+7f2CGBSCqHi9Lhra6dWhhUCZXaNpFBsyEPpVDqHe1RCSh9TT8bO3TnRK69RE2XRK5VH61JcmnzDTdHp4tkaAYEKy4LZ16cYdbYb04V4Ileqygrvrtg0k+fzWTg27vGqetFHtuWAukAGBEEufDxzd3jDDY0e+O/TEwPccJUDYeVxg4x9qRV484J1AXuld0ZWDvZz6yIlj4UJPD4tmuoaunGlvvHQ8BjIaWsBS/8pd/RbKh4fGYg7pxAOdw9teMaihs78dsDE+DE5yK3VohH/0jRm2u9fqq/Or3NEKt/SNKyvaShoaGhGT1oLuydzK1HlKeNwbEkCRzMqOnT8b2UaV8VLYYjHn2By2Ji+dieCMjzc4N1xkjlJL4/Y9j2eoy79ne04ugukG5KLIaFsq9NXbsIznylyBFSIseZzwNBUJ9laLFWFf3p/b6qrqZTrLsAauhYR57p6W1U0yaCr4NhA4M7frqxojm0yKEZMHwcrPDrA+NNjpPKSWw8UaBlQwkAce8fN1mcaGfFwab74mDFYeJicTPe2ps1ICsP04Od8NbSMXrfkylIPL7tKp6dG6xu7AVQhYpFDR2YGuQIiUyBp3Zew4Yl4Trha3Pplsqx9tfLYDEY+OHeWLAYBPan1+DjI8PnuEYQBN5bNgazQ50hlinw4G+X0SWRY8v942GlrNl5/q80ndUogiCwYXG4lkOdPhZuTERxw/DVH9HQ0NDQXB+afVsOpFdjjLtxt9EjWX2ry1EV2usrqr9ePtVIp/7MgJHR3ykVqDZSL6PJzsvlWoYEAJWa363MwmjtksJZQM0J6tuphVwmg1C7swn1ZEMAUFs+t3Vrv68SOa1dEp25RouBeqKQXouvBAGwDKzqXjLQ+Hu0QoscmgFlRoizWUYELV1S/HWlAmsm+mhtD37jkEnREuzCx8Y7YkAQwPZL5fjuOpqN6eO+Sb64N8FH73vtIhme2nENH6+M0rqwJJc0w4LNxBh3yub6ld3p+ODWCKPNt4zR1CnB/VuSEeLCx4crowBQzVB/NLKyNNiwmAx8fVcMxnrZorVLirs3XYIlh4nv76GE2L60arz8T7pOxInBIPD5bWP1htU1mfXZGaMFmDQ0NDQ0I4/JgT3ZDReLm+HrYAUe2/C08mp5C+qF5ve789LjMNZfWEwGVo7zVL9+b5nu4qZUTuKns8UGj6FpWFTU0Kk1J+htZiQUycBXFv93imXq7QIeJWLaRTLoQ2BA5NhaUttbu6VwtdEWOU1GUvh/vDdW/Ty3VohoL1uDY9/em2XwvdEGLXJoBpxHpwdgTpjp+pTSpi5kVbfrjF3+rWnHtTnhLnhzSTgAyp76n5TK6zvZXry1NBxzwgw7rr3+bya+vH2slkPJ0ew6hLjy1Wl0G08U4MMVUdd9DqVNXbh/y2XMG+OiFoz/dygXO5KHr1jfksPCbw9MQLibAI0dYtz18yX4Oljha2W/ob9TKvH6f5k6ApXHZuLnNXGY4Ku/N4GKyR+eRI2RIkwaGhoampFF715zpU1d8DJgc+wq4IEkgTN5+hty6kMlHgxFKK6XD1b09HLccqFU75gdyeUGndbcbS20Xpdp1PBEe9lqOaAJRVJYspUiR9KTYqYSMe3dfYvkaG7v3eDTWMRrfi/bbGMLsb8a+J2MRmiRQzPgEASBH+6JNat/TEpZCwiC0Ar3plW24dXd6Sb3fWCyHx6e5g8AePmfdIPdjPsCi8nAN3fFYLyvnd738+qE+OJ4Pj5bHa11gd99tQrxfvZwtOYgq7odu69V4r3lEXqPYQ4ZVW14YMtl3DfRV+3c9tq/Gdg3jJ2JbSzZ2PrgBAQ5W6O2XYQ7f76IKC9bfH5bNBgEdVN4W0/6oCWHhc0PjNcJ6fdm4v+dRElj/61CaWhoaGiGnqtlLeqi+t74OlLipy+GMyohIJYpBtSEh8tiqlPPixs68fbScJ0xYpkCf13Rv3jauxFonoYxwsXiJqyM7YkUCUUy9aJot6QnaqOaH3WI9UdyVDU9vb+3qqWFVK4Au5ed9J9XjDvYLdboF5RW0YZAZ2uDYwdiPjUSoEUOzaDAYjKQ1KtDsCGOZdchwMka/o499S47kivws5FwsYqXF4Tilmh3yBQkHv0jBZlVbdd9zip4bCY2rRmPEBf9JgIpZS3480oF3u0V5v4rpRLTg53V/WXO5jfoLWw0l5SyFqz77Qqenh2Eu+Op4v9nd6XiRB/7DQwkDtZcbFsfD39HK1S1duP2H5MQ42WHj1dRec6/JZXhzT1ZOqlr1lwWfl07AdFGClMBYOanp5E9AHahNDQ0NDSDjyp9CgAK6oU6UQ4Vfo7UhDq5DzUffC4LhHItUWggret62bk+Qf3cULr0H5fK9Br/9HaW0ySxoFGrVqmipQsWSpHTpRHJUX0vhYH0fNUiau/PV22XykkdkdPapT8qpOIDjXYY3VK5up+PPtZsTjZ6rNECLXJoBg1rLgsXXzVP6PyVUomEAAd17ioA/O9gDvakVhndj8Eg8MnqKHX/ljWbk1FY33+7SRtLNn5bO0HtVd+bU3kNuFDUpGOV/M/VSswf4woOi4Fj2XXIrRWqm3xeD0nFTXjkjxRsWBKOZWNVYu4qjmcPn9Bx5vOwbX08fB0sUdnSjdU/XkC0pw0+XBEJggC2XizDC3+lQdbL+U7AY2PruniDUTIVi75KRErZjVX8SENDQ3MjkuDXU3NZ0tgJO0u23nHh7gIQBJXSZm4POAaDUNsdD3Q7BWdBj7Xyz4klWnU6KsqaunCusFH3vEyU3Gqmgv1xsUwtaLSPoRQxBkqQVe/Le4kgNoP6fcjkCr0pZ8ZS1nqntylIUqvZeW+Gs4XFQEGLHJpBxdWGh+PPTTc9EJSJQO8GV0/vTMW5At2LjCZcFhM/rolFhAdV/H/Xz5dQNgAdkl1tePhjXTyc+frd0g6k1yCjqh1Pzw7S2r4ntQpLIt3AZhI4kFGDpk4x7hjvdd3ncSa/AY9tu4r/3RqJxZFukMgVeHRbCg5n1l73MfuLm40F/nx4IoJdrFHXLsbtP11EhIcNvrx9LJgMAruvVeGJ7dcglmlfJAU8Nn5fG4/pwU5Gj7/y+yTsTx++1DwaGhoaGtP4amRgFDd2wlKPpTJAuXmp6nXy+tD3RjXH1ycU+svri8LUzw05gf53TXehlYDxk7lY3LNI19IlhVhKLfhpprirvo8hoyWWUsD0di5VHUOmIMFUCh7N4x4zsQCqqmUGqIbnE/0NGwPdfgPYSdMih2bQCXS2xr4nppg1dtulcnXvGRX3/HLJZLNQ1eQ52MUa9UKqMH4gHLv8HK2wfX0CHK05et/fl1aN0qZOPKSsDQKolZn/UqswN9wFLAaBPanVkClI3B53/ULnZG49Htmagv9bGYml0e6Qykk8sf1qn/sODCTOAh52PTQRUZ42aO6U4M6fLsLd1gLf3z0OHCYDh7Nqse63K1qOMgBgwaHMCEzZSz+x/Ro+Pzp89tk0NDQ0NMZx02g22SAUa5nyaCKSyhHsQqWsFdSNjLYBmr3cfk8q0zvmSFatTkRDlWKm2TtPk4vFTZgR0rOQl1hA1bdoihHVMRgG1JtMTr3PYmhP0yXKDAkuiwmx8rw0j3A02/ji5z29HGSteYZrp9MqWo0eazRAixyaISHS0wY7H0owPRBU8dx9vaylV3x3wWS9jb0VB3+si4efsl7k7p8vor7dfLtKQwQ6W2PbugTYW+kXOntSq1HfLtKyn1aQwKHMWkwLdlK7j0kVCqyK1Q2Jm8u5wkY8+OtlvL8sArfGeECmIPHkjmsmU/oGEzsrDrati8cEX3sIxTLcvekS5AoSm+8fDws2E4kFjbj9pySdfwcOi4Gv7ogxKfy+OlmI+26Q3GAaGhqaGw3NTAehSKYTeVDBYjIQpKxzLTKzNxpJkpApj9fbyW0gYGnUtJzJb8BTvbIyAMoR7WRuvfY25cJdmJtAb1+8SyVN6ugNQKW3A9q9aVTNPK24+kWhqn6nt2jsVm7nsRkQK/sKagql5JJmtBmpzemdniaVKbTKBHozEFkxwwktcmiGjAR/B2xaE2dynFxBYkdyBe6K147oLPn6HHJrjRelO/N52LYuHh62Fiht6sJdmy4NiNAJceXjjwfjtYosNfkvtRpt3VLck9BzziRJRWDi/eypFK6rVegUy7AixuO6z+NyaQvWbL6EDUvCsSrWE3IFiWd2peK3YbR85POo+qXZoc6QyBR4bPtV5Na2Y/v6eNhbcZBZ1Y5bv7uA/F4pCiwmAx+ujMQL84ybM5zJb0DwG4cM3jxpaGhoaIaH3lflBqH+ehsukwF3ZdSnzsx7cqdErr7u964nGSg0ndUMRWZ6p4CpHNH4XJZe19C6djEK6nWFnEDjO6iEkpWB9L4upRObRS+RI1KmgFuwmT0RJg39pyBNO9hpNmI/ndeAKYH6vzcAzPn8jNFjjXRokUMzpMwJd8GXt481OU4iV+CvKxVYMU5bECz4MhF5tcbzed1tLbBjfQJcBTwU1nfgjp8uorat/0In3F1gVOjsTatGbZtYy1kFAC4UNSHB3x5sJoFDmbVo7pL0S+ikVbbhrp8v4rm5wbg3wQckCby1NwufHc0z2Uh1sLDgMPHjvbG4J4FygXv/QA72plXjn0cnqSNrK7+/gAu9ijgJgsATs4Kw8Y6xRo8vkSkQ8NpBgzdQGhoaGpqhR9MxDAA6Jfpd0Lhshjrq0dhhXt8bVY8YDpOhtlQeaO7UWEw91StioyKxoEHL5UyoFCjWPBbG+eg30mnUY65ga9mTDaISSlYGWm10G4jkiNSRnB6R09uBLam4Se8xVWg2YZfIFVrn1RupfHQvLtIih2bIWR7jgU9WmW6WKZWT2JdWreXtDgDzvzxrMqLj7WCJXQ8nwMPWAsWNnbjtxyRUthh2HTGXCA8b7HpoIpwMmBEcz6lDfp0QD07RdlQ7X9iEMe424LIYOJ3XgJo2kVbUp6/k1gqx6vsLuHeiD55T2lR/fbIQr/2bOWwRDxaTgfeWReDVhZTj3JbzpfjgYA62PjgBcT52EIpkuG9LMv68rOvlv2ysB3Y9lACBkbA5AIz/33EdoURDQ0NDMzwIRdqpUS2d+lOlnKy5cOSrRI55i1XNSjFkY8kGMRjOA6BqW1R8d7pIZ2EVoERZdk3PnEO12GZvxTHYakLFBL+eRti2ykgOSZJqu2dDEaqmTuq79xYgjcrt9lYctVhU3fEXRlB1rhdNiBw+r/dnkkYd40bz4iItcmiGhdVxXvjYTKFzNLsWC3p1613wZaLJojgfByvsejgB3vaWKG/uwu0/XhyQ/NIQVz7+eniiQXvpC0VNuFLWgnW9hE5qRSu87S1hwWYiqbgJGVXtOmKoL1S3ibDq+wtI8HfAB7dGqhtyPrYtZdisHwmCwMPTA/D1nTHgMCkb7bW/XsYHKyKxOMoNUjmJl/5Jx1t7MiHtZTEd7++A/x6fjAAnKwNHp7hr0yV8fDh3ML8GDQ0NDY0Z9M6sqBfqz5pwFvDU0RjNehVjVCgXJj0NNBgdKCI8BOrnhpw/Na2kq5WmRh62FkYbagLA3RqRIlVdb3OnBBK5AgQBuGhYWWuiSunr/b5KcDjyuWqxqNInC5ULwtk17ep0N0OEuvaIs4K6DkR52hoc+/DWK0aPNZKhRQ7NsHFbnJfZEZ0TuXU6EZ1l357HJRMrFp52VESnp3nlxQHpo+PraIW/Hpmo1cBUk7SKViQWNOLRGQFa1pcF9R2wt+LAksNEWkUrTuTU6YihvtAukuGeXy7B1pKN7+4eBw6LgSNZdbjjp4sGbzZDwdJod+x4KAHOfC7y6zqw+ock3B7nhWfnUFGn35LKcPemSzorev5O1vjv8cmYF+5i9PjfnS5CzLtHdXrx0NDQ0NAMHVm9mjd3iPUvsLkIuGpbZJnCvOt2qXJR0tfB+MJXf3l4WoD6eaSH/obVqkVVuYJUCxB3WwuDi50AwOexMFfjXqZa2KtRps87WnMN9qmpV4oZl15ZIyqRY8Fmqo0HVHOMcDc+nPhckCSQU2N8nvPYzED18ytlLRjrZWtw7NXyVqPHGsnQIodmWFkd54VPV0ebHCeVkziUWYM5Yc5a22//6aLJxphuNhbY+VACgpytUdsuwqofknDNhCW1ObjbWmDXwxMR5ibQ+35enRD/XavCEzMDwdFwcalq7YaAx4aNBRulTV3491pVvxqGSmQKPL79Kiqau7F17QTYWrKRWtGKW7+9gJwa42l9g0msjx32PTkFY71s0dYtxf1bkmHFZeKne2NhzWUhuaQZt3x9DhmV2q55fB4bP9wTq07DM0RLlxSBrx8aENFKQ0NDQ9N3MjRcT9lMQq+pgKuABz6P3dPg0syU6tJGSuR421sOwJkaRlOIGBIH6cr7VF27CFI5CSaDgDOfC4aRPC93GwuwNe79DcpFPZXI0bTf7o2qjthZoC1yVEZKpNqCuqduxsmahzHu1Hwku9q4G+3sUO25lCHrbxXDVe/bX2iRQzPsrIr1NFl4DlCuIcdz6nWcQNb9fgV/XdGt89DEWcDDrocnItrLFq1dUtz18yWcytNfZNgXnPhc7Ho4wWBDrZo2EX49X4r10/xgrVFgWNsuAkFQ+bhNnRLsSC7HQ9P8tcb0BZIE/ncwB3+nVOLPhyeqI1ervr+Ak7nGReBg4iLgYedDCVgV6wmF0pDgQEYNtimtvqvbRFj1wwXsTC7XuogyGASemh2EX+6LM3nxnfP5WXx9omCwvwoNDQ0NjRHkChLNnbqmAsHK1KgeW2Tz7nOZVdQinWZq1WDA0zA1OJRZo/fzqlq70SAUqxuZ+jtaaVlQA9Dpp+dmy1OntgFAZQv1vKSRcl7zMiLeVFEsb/ueKJZIKkeNSuQot6kebS3ZsLFkq0VO7whbb3obHpgqeVL9W4w2aJFDMyJYNtYDv9xn2l4aoHJjY3rZNr74dzq+PJ5vdD97Kw62r4vHtGAndEvlWP/bFfx7rfJ6T1mNQGmhvHysu973hWIZfjxTjNvivLQ89Vu7pBDL5ODzWOiWyrEpsRirYj37lX/8V0olXtudgZ/vi8NEfwd0SuRY99sV/HKuZNhWYnhsJj5ZFYW3loaDqWyO+uyfqfhoZRRmhTpDLFPgld0ZeGZXqtpxRsXsMBccfnoaoj31pxCo+OxYPoLfOASJjE5fo6GhoRkODAVoVKKhQ6S0XjZhMANQ7mIqQRFtJJVqoDmcWYsZIc563ytq6ECuMtITqieDw9POUiuF3ZnP1apZSlWmfeXVUiLHkGmBQkGiUGlBrVnzU9LYCZKkFkdVvXBU0TEfZUpfkDNfPbYv1LSJ4Gcg/R4AXvw7rU/HGynQIodmxDA7zAW7zGwYeq28VefC9+XxAjy985rR/ay4LGxaE4dlY90hU5B4dlcaNiUWX+8pq+GwGPj8trF4dEaA3vdlChKbz5dgWrAj/J00V2YU6JLIwWMzoCCBXy+UItLDBuEGUuDM4UpZC9b8kozXFoXhjvFeUJDAe/uz8dyfaWpbyqGGIAg8MNkPux6irL2LGzpx7y+XMCfMBS8vCFWLn6Vfn0NWrzC7t4Ml/npkEtZPNZ7SJ5EpEPzGIdp9jYaGhmYIMLep53hfymGspYuK8pgjcrKq2yBXkHDic42mdQ0UKmdPmYKEn6P+CEtZUyfyarWjS5oLa5522kYEXnaWWhGVvDohpHKFumdcsAGRU9MuQpdEDhaDgI9Dz7moft/+TlYoUUZ6VNlyfspxXvbUIqkqamQMzayYgroOo05xuSZad4xUaJFDM6KI93fAviemmDU2raIVoa58sJk9cdY9qdWY+/kZo1ELDouBL24bq66Def9ADt7Zl9Vv62UGg8DLC0Lx3rIxBu0Yd1+tgqedJeI0vPXlChIiqQI8NvXneCizFlw2AxN87fUfxAyqWrtx+09JmBbshA1LqAjKv9eqsOL7Cyhv6r+V9vUS52uPg09PxYwQJ4hlCrz2bwby64TYfP94uNnwUNLYiVu/u4CtF8u0/g05LAZeXxyOzffHmbxB3rXpElZ+f0GndwANDQ0NzcDx7clCk2MIAup7mWri7WlnusZG5WYW52M3aPbRmmi6i7na6M+mKGvqwjWlAYEqLUyzybUznwdfjWiIp72FlsiRK0iUNnaioF4lcvQ7s6mO6etopVXTo4ru+Dtao7COeq6qx/F3oo6lSoGraevWcTDtzYKIHtfanJp2BBk4n9EMLXJoRhyRnjY4/tx0s8bm1grhIuBpNegsqO+A36sHjf6BMxgENiwJwysaPV0e+v2KTrrU9XDvRF9sui/OYH3N2fwGCEUyncI/kVQBrtJp5Vp5K2rau9W+99dDl0SOx7ZdRU1rN357YAIcrTnIqWnHkq8TDTY9GwrsrTjYfN94dQTn32tVeGtPJj5YEYnZoc6QyBTY8F8m1v9+Rceff1aoC449Ox0J/sYFYEpZC/xfO4hsE3nJNDQ0NDTXx+5rVSbHjHEXwEZ5f1ZZQnuZkZJ9Oq8BgGFL54EmzK0niuFuIHJ0rbwVZU1dYBCUsQ5AtYZQIZUrtLIlnKx5yKzSzkzYn14DkVQBPo9l0DVO5eQW4a6d0aEyPwhz4yOzV8aDyhXOyZoLLovKDDHVBF0zkiNTkAZ79qgYjengtMihGZEEOlsj6dVZZo2tbOkGkyB0GnQGvX4IrV2GOysTBIFHpgfgm7tiwGUxcCK3Hqu+v6BVKHi9zAp1we7HJhl0hcmrEyK1ohWLo9y0oj5ijYtIRXM3EgsasSrW02ijLlNsOleCjSfy8ct94xHjbYt2kQxrf7uMjccLhi3awWAQeHRGAHY+lAA3Gx5Km7qw7rcrGONhg1cWhoLDZOB4Tj0WfHkWR7JqtfZ1teFh+7oEvLE4zOTnLPoqEY9tSxm1zjA0NDQ0IxFz7fvnh/cs1BVoRCiM0dwpQVplKwBgesjQiJwQ1x5Boepn05skZcuKcHeBuqHmhaKe9GiJTIHy5p5MiYqWLtS2i8BhMtSLmn+nUHXAcT52Bp3Zrilrd8ZpZHyQJKkWP3weS23ioCJCKXIIglCff4uR+Q+ga3xgyM5ahWbUarRAixyaEYubjQXS355n1timTgm6xDJ1PqqKse8eM2mjvCTKHTsfSoCjNRe5tUIs+/a8yUaj5hDswseexycbjDo0dUpwJLMWc8JcwDcQ9ekQy/DP1UrMDnMxuLpkDpdLW/Dgb1fw7Jxg3B3vDZIEvjiej/u2JA9rN+PxvvY4/PQ03BLtDrmCxFcnCnAosxYb7xiLUFc+mjoleHhrCl78K02rszaDQWDdVH8ce3aawb4GKg5m1MLv1YMDYhtOQ0NDQwNsvVhm1jhVg0qZXKF26IoyYSRzKLMGJAmEuwngZiB1bKDRjGKYcn+b4Eu5qSoUJJKKenr1cVgMrYL/5JJmAECMty28lTUzVcpF1DgD6egKBam+V43z7hE5lS3daOqUgM0k1ClqKo3kKuBpLfKqvktrV889Ux/MXiLLVFrgf2ZE7kYatMihGdEIeGwU/G+hwdxVTTolctS1iRHUqwPxwo2J2JNq/I8zxtsO/z0+CaGufDQIxbj9pyQczKjp17kDgJ0VB1sfjMc9Cd5635cpSBzNrkO4u0CrwFATkgSOZdfB3prTLyvNxg4xHvj1MtxtLfDRykjw2AwkFjRi4cZEJBY0XPdx+4uNJRtf3RmDjXeMhYDHQlpFK577Mw23xXnh4en+IAjKNW7hxkRc7NX8NciFj92PTcLTs4NMfs6t313Agi/P0g1EaWhoaPrJO/uyTY4JceGrC/EL6jvQLZXDmsuCv6Px+/m/V6n79fIY/Y6lg4GFho00i2l8sj8zlIouZde0o0VDSDAZhFYkR1VXNCnAUctZFegxY+hNbq0Q7SIZLNhMhGjc71WCKdxNoF6EVSVi9HabVYmctm7jIqc3YqlcnTKvjz8umSdsRxK0yKEZ8bCZDBx5ZhruitcvFDSRyBUoqO/Qieg8vTMVr/yTbnRfTztL/PXIRMwIcYJIqsBj267ikyO5/TYkYDMZeH95JN5fHqFlkqDJpZJmMAjC6ApXZlU7mjoliPe7fkMCuYLEJ0fy8N+1avy8Jg4hLnw0dohx7y/J+PBQrslCxcFk2VgPHH5mGiYFOKBbKse7+7ORXtGGj1dGwdPOApUt3bjjp4t4/d8MtGtEddhMBp6dG4y9T0w2KQJza4UIfP0QDmf2X8DS0NDQ3Iw0dZgX/b9zgpf6+XnlhD/G29ZoA83ypi5cKWsBQQC3RHv070T7gMr4BzDerJTPZSHej4rkHO3ViPxarwwQVZbEpEAHOGikwPF5LB1houJMPrXgOCnAQct0QC2YAh3Vv0sVk3r1DlTVA3dJ+lZj3NghMepkJ5KOvgVCWuTQjAoIgsAHt0big1sjzRpf0dytdVEBgJ2XKxD59hHjFzAeG5vWxGHdFMp57dtTRVj762W1J31/uCfBB38+PNFg2llJYydKGjoxwddeJ4ysokEoxtXyFkwLdoKViSaZxkgqbsJTO67hiVmBuFspHn84U4TbfkxCRfPwua+521rgjwfj8eaScPDYDCQVN2HDnkysivXEbXGeAIBtl8ox7/OzONbrBhPlaYt9T07Bi/NDTH7OI39che8rB9Bo5s2ahoaGhoZi3e9XTI6xYDOxItZT/VplJGCoB42KbcpowZRAR7gOgXW0Cs2oB5tpeGo8PcRJXbvSO9tDX5q7gxUH47ztYKFxv54e7GTwM87k16s/RwVJkkgsoISNl50lqnsZCkwK0G5Grso662spardEBoEJ84HRBi1yaEYVd8V7469HJpo1tqlTohWCBgChSIaA1w4andyymAy8sSQcG+8YCx6bgTP5DVj6zTmTtT3mEONth/1PTcXUIEe97wvFMiSXNlOONAYuNlI5ibP5DfB3sjbavMsULV1SPLnjGpgMAl/ePhZ8HgvXyluxaGMi/k6pHLZifQaDwNopfjj6zHRMCnCASKrAl8cLkFfXgXduGQNfB0vUtouw/vcreHz7Va2aIjaTgcdnBuLUCzMw0d/ByKdQxL1/HI9vv0obE9DQ0NCYgUSmUBfGG2N5jAcEyuL8dpFUnW41w4iRQKdYhu3J5QCA+yf59vtc+0Jte49wMJbRsDCCqjEqqBOisL4DHCbDaDr9vDEuYDIIsBg90+3ZYfqFXrtIiiulVD2OpqtcTo0QjR1iWHKYOtEZFwFXqwEp0FNb09e7WpdEDisT9UijDVrk0Iw6xvva49zLM80a2y2Vg8NkqBt9qYh7/zjOFRhvGrlsrAd2PzoZXvYWKG/uworvLmBfWvV1n7cKeysOfn1gAp4yUkeSXtkGPo+lk3anSUZVG4QiWb8ahwLA70ll+OFMEb64bSxifewgFMvwwl9peGhryrBGOrwdLLFtXTw+XBEJPpeq1Xn/QDbmR7jigcm+YDIIHEivwZzPz+DPyxVaTnF+jlbYvj4eH6+MMpgiqOJAeg38Xj2IQwNQg0VDQ0NzI/PYtqsmxzAZBB6d3tMY+2B6DSRyBQKdrXUm5Jr8daUCQpEM/o5WmGki4jPQaNot93Yu00QlUP5T1vlODXJEp9jw+PljKHe50qYeQ4IZwfq/27GsOsgUJAKdreGjYS99WOkwOinAESd7tX+YFepi0DCgr2t3XRI5rAyYII1WaJFDMyrxtLNE1jvzdSI1+pDIFWgXyXTS1+755RLe2ZdldN9wdwH2Pj4FU4Mc0S2V48kd1/Duvux++8UzGQSemxuMLfePNxixqWzpRkunFMEu1gYtpBs7xMitbUewi7XRgkFT5NYK8ei2FEwLcsJzc4PBZhI4ll2H+V+cxeHMWtMHGCQIgsAdE7xx7LnpmBPmAqmcxI9ninEqtx7PzwvGGHcB2rqleOmfdKz84YJWTwKCIHDbeC8kvTpbnepmjEe3USlslS3Dl65HQ0NDM1IRSeU4nlNnctyKGA+1mxhANcEGgJXjPA1OyEVSOX44UwwAeGCKn9G6ncFA1YMGAOraDfeX4bGZkMoV+PMKZQW9NNpdKwqkuaDKZhKYFEBlbfyjtI4GKEMifexPpxZRl0b1GC6QJIkDyu0TAxx0zHcW6Omlp+rVY8Hp25xAQZJatUk3AjfWt6G5qbDispD1znzcMd7L9GBQ6WuWvepYtpwvhd+rB4yKFjtl5OUR5crU5vMlWP3DhQGpXZkZ6oyDT09Vd4TuTYdYhvy6DnjaWRq0mVaQQH5dBxytuQb78piDVE7ii+P5OJFTh89v67FwfuSPFDy3K7XPTi0DiasNDz+vicU3d8XAmc9FaVMXPj6cB3dbCzww2RdWHCaulbdi6TfnsOG/TK0aKkdrLj5eFY1/H5tk0roUAKZ8dAqzPj3d56JNGhoamhuZhRsTTY7hMBl4clZPlkJBnRDJpc0gCONuaVuTylDbLoK7DQ+rY00vSg00SRriodaAyAlxoYxtTuTUo0EohqM1F36OVlp1vmEamRV3x/uAw2JAoSBRrGEtrY+WTom67mZJtJt6e16dEEUNnerjaJYU83ksvWnZqnYLfG7f6muYDAIKE+Gf0ZbaTYscmlENg0Hgw5VR+PaucWaN75LIdaIiJAkEv3EIRQ0dBvdjMgi8sjAUP6+Jg40FG2mVbVj0VeKARDk8bC2wfX08npkTZDBiU97cBYIAHK31rwABlP9+U4e4XzbTAJBW2Ybn/0rD0mh3PDzNHwyC6mw9/4uzOJ5tehVvsCAIAkui3HHi+el4cIofmAwq2rQzuQKr47ywMMIVJEn1b5j52WmdFLYYbzv899hkfLgi0mQKW3FjJ8LfPILndqUOW8NUGhoampFCdnW7Vg8YQ6yb6qcVxfk5kYrOzA93Ndjzpl0kxXenCwEAz8wJBs+MDI2BRrXQyWYSqGnVL3JmKVPVVOYIq+M8UVjfM28IdeVrWUirWkckarihqZqC9mZPahVkChLhbgIEOPXU+Px3jYriTAtywolc7fvvnDAXvQ08hSJqgY7P61vqGZvJgExu/H4nNfH+SIMWOTQ3BIuj3HDy+elmjTU0Z5392Rn8eKbI6L5zw11w4KkpiPG2hVAkwyN/pODtvVkQywzn5JoDi8nAM3OCscuI+1q7SIaWLikcrDhgGVBDnRI5cmuFcLfh6dQh9QWJTIFPjuQhpawFH6+KVhf7r/v9Cp7YfnVYa3X4PDY2LAnH/ienYLyvHbqlcvx6oRQF9R14dEYAgpyt0dwpwUv/pOPW7y8gpaxZvS+DQaW/XXl9Lu6b6GPys3Zfq4L/awex+VzJYH4lGhoamhELSZJY9JXpKI6LgIvHZwaqX9e3i9ST9PXT/A3u98WxfLR0SeHvZIUV44bONlofs0KdkVer32TI39EKWdVtSCxoBIMA7prgjfTKVvX790/yRY1GbU+gM7Xg+Ov5nvtH7345APX73XaJMly4Q8N2WyJT4O+UCgDAeF87XCxu1tpvlYGIV3OXBADVg64vsMyI5AxxFmG/oUUOzQ2Dv5M1st+dr1N70xf+71Au4t4/ZtRdxdPOEn8+PBEPKy/av14oxarvk1DWZHqVyxTjfe1x6OlpWBSpm2cLUP79TZ0SWHCYOql3mlS3iSCRK/ReUPvClbIWvPZvBpZEuWPtZD8wCGC/sth/OB3YACot4M+HJ+LT1dFwsOKgsL4D358ugouAh9vjvGDFYSKtohUrv0/CY9tStP59bCzZeGdZBI4ra31M8e7+bPi+coA2J6ChobnpMMdsAADeXjpGq3D9q5MFkMgViPWxQ6yPnd59Mqva8NuFUvX+LCP2zYOFZir2nRO8cTKvXu+4AGdrfH+aWghdEuUOL3tL/JbU0yBzvJ4edsUNHTiV19Ns291WN5qVXNKMgvoOWLCZWB7TI/KO59ShsUMCJz5XZ2HRw9ZCb6pah1iGVmW6toeezzKGjQXbqIkCAIPtLUYqtMihuaGw5LBw5Y05eGFe8HUfo7FDgqDXDxm1jGYzGXh1URg23x8HW0s2MqrasGhjIv68XNHvib+NJRvf3jUOH66INNgLRyiSQSxTGBU6IqkCjR1iOPG5But5zEEiU+CbU4U4X9iIt28Zg3A3AVq7pHjhrzSs2Zw8rH11CILAqlhPnHx+Bh6c4gcWg8C5wkb8fbUSU4OcMC/cBQwCOJhRizmfn8H/DmRr1esEOltj031x2PVQgln1OipzgtMGboI0NDQ0NxJpFa04ZEZa9i3R7lgY2VNLUtzQgR3JVBTCUO8yuYLEG/9lQkECS6LcMC3YsL30YHJE4/sl+Dugorlb7zgmQah74zw6IwAtnRL1e/ckeOOtPT1GRiqzm1+VAk6Fu61upsbvFymhtDzGXW27DQDbldGdFTEe+C9V29l1dZynXnOGqhbq3AU8Fvi8vkVyHKy5aOmSGB1jyDhipEKLHJobDoIg8MSsIPz72KR+HWfhxkT838Eco2Nmhbrg4FNTMcHPHp0SOV76Jx2P/nFV6+J3PahcxQ4/Mw3xelaHAOoG0SWRG0xdU9EgFENBknAR9C+qk1cnxFt7szDW2xaPzwwAl8VAYkEj5n1xFt+dLuy341x/sLGkUtiOPTcd88JdIFeQOJxVi6TiJiyP8UC8nz2kchI/J5Zg+qensOV8idb5xvs74L/HJuOrO2PgxDf9e7p/y2X4vnJA3fuBhoaG5kajUyzDsm/PmxznxOfinVvGaG374GAu5AoSs0KdkWCgZ9kPZ4qQWtEKay4LG5aED8g5Xw8bTxSonxc36M/ICHS2xubzJVCQVEpbmJsA7+7PVr//5KwgnNOovQl3E6CuXYSdlyu0jtM7ulLa2KnOELg3wVe9PaemHecKqbQ4Sw5Lqx8ck0FgdZx+wyXVoqOHXd9NiBysOOoo0I0CLXJoblhivO1wdcPcfh3jx7PF8H3lADrEhp223G0tsGN9Al5ZGAo2k8DhrFrM//IszuY3GNzHXLzsLbFjfQLeXBJu0CJaZkZhfKdEjrp2MWws2CaL7o1BktTq0j8pVXhubjDi/ezRLZXj48N5WLDxrMneQ4ONn6MVfloThx3rEzDGXQChSIbdV6tQ3daNW2M8EOhsjdYuKd7Zl41Zn53GPymVamccBoPALdHuOPfyTGxYEg6OGWkTt/2YBN9XDiClrGWwvxoNDQ3NkEGSJMa8dcTkOAYBbLx9rJYt8uHMGhzPqQNLadijj/TKVnxxLB8A8NbScLgI9NeiDgVVrVT0w82GhwtF+u9hHCYDe5TRlOfmBkOuIPHvtSr1+wd7pTKHugnw/ekiSGQKLce1kF7GQD+eLYKCBGaGOCHcvWecqj54YYQbDmVqH3tRpJvBVLS8OiEAIMjZcINSFb1riR2sOWju5wLtSIMWOTQ3NPZWHJT83yI8oVEMeT1EvHXEaD0Gk0HgkekB+PexyQhwskK9UIw1m5Px9t4siKT9MyVgMAisneKHg09PxVgv234dq61bCrmC7HdebW27CP93KBc8NhNPzQqEozUXxQ2duOeXS3h821XUtOkP9w8VEwMcsPeJKfhkVRSc+VxUNHerb0izQp3hxOeisqUbz/+VhvlfnsWhjBp1miGXxcSDU/xw7c25eGmB/jSL3qz8/gJ8XzmAMwMgbGloaGiGm+VmRHAA4IX5IZgU6Kh+3S6S4k1l2tYj0wMQ7KLr9tkpluGZXamQKUgsinQ1WEA/FNQLe4wCNiwJx750/ff5bGX6+q0xHojwsMGfV3oiNBvvGIt39mVrjbe34mB7MpVutlDZy8ZFwIWDRp1sbZsIfyv752gaNlQ0d6nPI9xdgNxaodax10/1M/h9sqvb1fuZoqheO2plwWZCYqQeeTRCixyaGx6CIPDC/BAcfGpqv47z6LarmPv5GaOmBBEeNtj/5FS1c9evF0qx5OtzuFbe/5X+ACdr/P3IRLw4P6Rf0RgFSaW6MYj+FxGeyW/AD2eKsSjSFXeM9wKDAA5k1GD2Z2fww5miYU1hU4X0T784Ay/OD4GAx0JhfQdO5tbDwYqDGSFOsLVko7C+A49uu4ql35zD6bx6tdix4rLw2IxApL89D8/MCTLxaRT3bU6G7ysHsDet2vRgGhoamhHIy3+nI02jOaYh5oa74JFpAVrb3t6ThXqhGH6OVnhilu7iIkmSeOGvNBQ3dMJFwMX/lkcOa53HO3t7xMmkAAekVbQaHMthMfD8vGAIRVK8ujtDvT2/TluEhLry8dPZYkhkCoz3tVNbOYe7aQuPjScKIJWTmOBnjziNXnk/nCmCXEFiSqAjzuRpL5xN8LVHlKetwXPMqqb+3caYIXJOalhSU85qJncZddAih+amIdxdgOx358PH4fobZhbUdyDo9UNG05MsOEy8sywCWx4YD0drLgrrO7Dy+wv4v0M5/Y7qsJgMPD4zEAefmmrQrcZcVGLHUBqcuUjkCvyeVIaz+Q14fGYg4nzs0CWR48NDuVi48SxO5dYPqwubJYeFx2cGIvGlWXhsRgAs2Ezk1gpxOq8BPg5WmBHiBCsOE5mjq1DtAABz1klEQVRV7bh/y2Xc9mMSLhQ1qs9ZwGPjmTnBSH1zLh6fGWDi0yie2nENvq8cULsG0dDQ0IwGvj1ViF1XKkyOi/K0wcY7xmoVv/+TUond16rAIICPV0Xp7Xfz3ekiHMqsBZtJ4Lu7x2mluQ0HBzQyNI7nGDeUWTfFD552lth4vKeGZ3KgAzYlarcYsOay1BGa1xaF4aKy0aim+1phvRC7LlORHk1jhpLGTnUdT5SnDZJLtes+n5xtOCuluVOC0iaqJmeMu2kjnYMZPYYL3g6WqGodPhOhwYIWOTQ3FZYcFs68OBOfro7u13FWfn8B929JNjp5nxnijGPPTsOtMR5QkMCPZ4qx+KvEAanfCHLh46+HJ+K9ZWNg3Q/nNAAQyxQYiIW06jYRvj5ZCC6bgYen+8PRmoOihk488OtlrNmcjFwDvQeGChtLNl5aEIozL83Amok+YDMJpFW04nReAwKcrRHrYwcOi4HLpS246+dLWP1DklZkx9aSgxfnh+Lqhrl4Ss8KpT7e2psF31cO4I3/MiC7wdIAaGhobiw2nyvBJ0fyTI7zsrfAL/eNhyWn595TWN+BDXsyAQDPzgnGeF9dw5xj2XX49Ch1/HeXRSDWR7+pzlChGYH5+s4YvPBXmsGx3vaWeGp2EPJqhdik0TetqL4T4l4ZC1eU9/hbYzwQ5Wmr7m8zKaAnre/DQ3lQkMC8cBet39WnR/MgV5CYEeKE072iOPF+9piikRrYG5WYCnHhw94M8Zit4SAb7MxXp7oZYoIBE6SRDC1yaG5KVsV64vwrs/p1jNN5DfB79aA6PKwPOysOvrh9LDatiYMzn4uihk6s+uEC/ncge0Bqde6d6Itjz03DnDD9XZTNZSADLecLm7ApsQSTAhyxYpwH2EwCiQWNWLQxEa/uztByiRkOnPk8vLssAiefn4GV4zzBZBBIr2xDSlkLvO0tEerKB4fFwJWyFty/5TKWfXseR7NqoVDG8u2tOHhuXggy3p6HlxfoL6rtzR8XyxH4+iEs2ph4wxV20tDQjH6+PVWo5RZmCAcrDn59YIKWC2VrlwTrf7+CLokckwIc8JieGtiUsmY8sf0qSBK4K94bd07wHtDzvx5u+zFJ/Vxf1EmT/90aQbWO2J2utb22XaR3vAWbiZcWhCCjqg1t3VJYc1mIUKaQnS9sxPGcOjAZBF7SuIekV7biQHoNCIJKN8vu1cbi+XkhRlP7VKYJEwP0u9lp0nuBdqy3LTKqjKco3m7A0W0kQ4scmpsWD1sLFH+wSN3U83pZ/NU5rPvtsnoSrI854S449ux0rBznCZIEfk4swcKNibhc2n8LYjcbC/y8Jg7f3jWu380/Bwq5gsTetGoczarDynGemBXqDAUJ7Egux8xPT+O704X9Fnn9xcveEp/dFo0Tz03HbXGeYDEIFNZ3ILdWCE87C4xxF4DHZiC9sg0PbU3Boq8SsT+9Wu3Gxuex8eiMAOS+twDvmhlRy65px7j3jsH3lQPINHFDoaGhoRkKPj6ca1YEx8GKg+3rExDg1OPcJZEp8OgfV1HS2AkPWwtsvCNGp9Yzv06Itb9egVimwKxQZ7zby256ONBsmhnrY4f1v18xOPbWGA9MDXLCj2eLcLW81azjPzU7CG42FmpntOnBTmAxGRBJ5XjjPyridU+8NwKVLmgKBYkNSsOG+eGu+PdqldbxZoQ4GY2kkCSpdjedZIbIKWnUNh2I8rDREVW9GeNhus5npEGLHJqbGgaDwKuLwnD8uWn9Os7xnHr4v3bQaNGijSUbn90Wjc33x8FFwEVJYydW/5CEV3enazWovB4IgsDiKDeceG467k3w6dexBpIOsQw7L1cgr1aIu+O9Eelhgw6xDB8fzsPsz87g32s9Fs7Dha+jFT5eFY1TL8zAnRO8wWYSKG7oRFZ1OzxsLRDuJoAVh6rjeWL7Ncz9/Ay2XypXizQem4k1E31x7c25+HR1NPydrMz63CVfn4PvKwew9WLZsNYs0dDQ3LzcvekivjtdZHKcvVLgaFogKxQkXt2dgaTiJlhxmPjl/jidPmOljZ1Y80sy2rqliPG2xbd3jQPLDHv+webpHdfUz031kHt76Rjk1LSrLa9NEeEhwPqpfiBJEoeUdS+LlI1SfzxTjJLGTjjxuXheoxZn5+UKpCl7BlnzWKhu64kQsRiEyT5CeXVClDZ1gcNimBXJ6V0vymAQEEmNp1QHOpm2pR5pEOQIvru2t7fDxsYGbW1tEAhGn4KkGV1I5Qq89He6lvf99TDO2xZ/PTLJqHNZW7cU/3cwR11g6GjNwYYl4bgl2n1AnGYyKtuwYU8mUo2IruEgwkOASA8bnM5rQI3yIh7iwscL80MwJ8x5RHRTrmrtxg+ni7DrcoXaTtNVwAOHxUBjhxhdEkrcOFhxsGaiL+6d6KOV/0ySJM4XNuHnxOI+WUrH+9njx3tjYWs5vIW4NDQ0Nz5yBYmA1w6aNdZFwMXva+O1BA5Jknh7bxZ+SyoDgwA23ReHWaEuWvsVN3Tgzp8voq5djEBna/z18MRhNxoAAJFUjtANh80a+/vaCZjgZ4/l357XsXI2xIGnpmCMuw3SKlqx7Nvz4LEZuLphLmraRFi4MRESmQJf3xmDpdHuAICmDjFmfXYGbd1S3Jvgg52XyyGV90zNH5rmj9cWhRn9zC+O5WPjiQLMCXPBpvviTJ6j7ysH1M8n+NpjZqgzPjqca3Sf0g8XmzzuUNAXbTD8cpqGZoTAZjLwxe1jsefxyf06ztXyVgS8dtDoBNfGgo0PV0bhz4cnItDZGo0dEjy9MxVrNiejrEl/x+W+EOlpg92PTsKHKyJhZ8nu9/EGisyqduxIroC3vSUWR7lBwGMhr06I9b9fwcrvL6gLJ4cTD1sLvLc8Amdfmon1U/1gxWGitl2E8uYuMBkE3Gx4sLFgo6lTgi+O52PShyew4b9MlCrD/wRBYEqQI35bOwHHnp2GOyeYl8d8qaQZY9+lUtlO5Rl3+aGhoaG5XurbRWYLnEBna+x+bLKOwPn4SB5+SyoDQQCf3RatI3CKGjpwx0+UwAl2scaO9QkjQuAAwJpfks0ax2IQmBbshLf2ZJktcJ6YGah2NtupdE+bF+4KNpOB53alQiJTYFqwE5ZEuan3eXtfNtq6pQh15SO3tl1L4DjxuXjShNENSZLqtDhVTx5j9G6DMS3Y0WATVBX8fhocDRd0JIeGRg9imRxP70jF4axa04ONYM1l4dJrs2Fl5AIhkSnw09kifHWyEBKZAlwWA0/NDsL6qf7g9NPeGQBaOiX45Ggetl8q7/exBpopgY6w4jJxJr9BHSqfFuyEl+aHIMLDtAXmUNDWLcX2S+XYfL5EbZpgyWHCzpIDqVyBeuU2gqByqddO8cN4XzutqFRThxjbLpXjczPTHVSsiPHA/26NhAXHeFEsDQ0NjTkczqzBI39cNWtsnI8dNt0XpxVdJkkSHxzMwc9K2+T/3RqBu+O1U6Svlbfgwd+uoLlTglBXPv5YFz9i6kVbOiWIee+YWWNT35yLo9l1eOnvdJ333Gx4aBCKIdNIt+axGch4ez7YTAbaRVIkfHACXRI5dj2UgKTiJnx5vAACHgtHnp0GNxsLAMD+9Go8sf0amAwCy6LdsbtXJsk3d8VgSZS70fNMr2zFLd+cp9xBX5sDGxMLmwczavDYtp7/AzsfSsB9m5N1XOI02XjHWCwb62H0uENFX7QBLXJoaIyQU9OOhRsT+32cVxeG4uHpxnuslDZ24o3/MnGukFpRCXK2xju3jNHqJt0f0ipa8c6+LLMLJ4cKBkEJGyZB4Ex+g/qmsTjSDU/NDtJaQRxOxDI59lyrxk+JxSis7wAAsJkE7K04kMpJLde0MDcB7pvog2VjPbQEikSmwOGsWvx2obTPVuI/3huL+WNMr9LR0NDQ9IYkSaz/PQXHc+pMDwawOtYT7y2P0HIdkytIvLY7Q91H562l4Xhgsp/Wfsez6/DEjqsQSRWI9LDBrw+Mh8MIETiAdpqWMdZM9MHt472w4rsLOpN/PpeFCA8bJPXKPEh8aSa87Kk+fL+eL8Hb+7IR6GyNT1ZFYdUPSZArSHx1ZwxuUaap1QtFmPfFWbR2SbE02h3Hsmu16mIWRrji+3tiTZ7rq7vTsSO5AsvHuuPLO2JMjg9+/ZA6FdvNhoc3Fofj8e3Ghe/VDXPNsqUeCmiRQ0MzgMjkCry7Pxu/J5X1+1gnnp+u5UzTG5IksSe1Gu/tz0aTctK8KNIVry8Oh4etRb8/nyRJ7EuvwUeHclHV2t3v4w0kHCYDU4McIZErcK6wUW1rvSjSFU/OCkKY28i4BigUJE7m1uOns8VajdosOUwoSBJSOak2U7CxYOP28V64J94H3r2a0ObVCvHHxTJsvdi3/1cx3rb44Z5YuAh4/f8yNDQ0NzzlTV2Y9skps8ayGATeXBqOexN8tKLR3RI5nvszFYcya8EggA9XRuG2XpbCf1wsw5t7MqEgKTewb+8aZzSLYag5mlWLh7ammDX25PPTcdfPl1DbLoKnnQUqW3rul8/MCcKXGg1BAeCeBG+8vzwSALWYNfPT06hq7cbzc4Ox83IFqlq7cUu0O766kxIhCgWJtb9dxum8BoS68sFlMZBW2eO4aWfJxtFnp+sYOfSmQyzDhP8dV0eM4v2Nmw5IZAoEv3FI/fqhaf6oaRNhX1q10f1GSj0OQIscGppBoaSxEzM/Pd3v48T52GHnQwlGHWbauqT4/Fgetl4sg4KkwuCPzQjEQ9P8Tfr5m4NIKscv50rw9ckCk44qQw2PzcB4X3uIpHJcKWtRi535Y1zw1Owgszo5DxUZlW349UIp9qVVq1fG2EwCbCYDcgWpXgEkCGBWiDPWTPLF1EBHrS7hHWIZ/r1WhR/PFGndSM3h+bnBeHRGwIhwK6KhoRl5fHgoFz+cMe2eBgDOfC6+ujMGCb0myrVtIqz//QoyqtrAZhL4+s4YLIjoqSkRy+R4Z1+2OiV6dawnPlgRCfYIui5J5QoEvX7I9EBQEaq/UyqRVd0Of0crFGvYLT83NxgH0muQV6ddo1Pyf4vUovDPyxV46Z90OFpzEOhsjYvFzfBxsMTeJ6bAxoJKJfv2VCE+OZIHLouBKYGOOJGrXYepaUxgDFXEyN/JCieem27SvGfzuRKtfkh/PTIR929ORqfEcEuHqUGO2PpgvMlzGSpokUNDM0goFCR+SizGh4eMu5CYw8erdFfCepNT04639mYhuYSKGHjZW2DD4nDMDXcZECey+nYRPj2ahz+vVPb7WAMNh8VAnI8dpHKFltiZG+6Cp2cHjZiaHQBo7BBjZ3I5/rhYrm4OxyCohnAyDbEDUJ2zbx/vhdWxnnDWiMaQJImUshbsulyBv1L6/u/xzV0xWBzpNiIc6mhoaIaX0sZOzOjDotycMGd8vCpaJyUpvbIV6367gnqhGHaWbPxwT6xWtKCuXYRH/0jB1fJWEAS18PL4zMARdx2a/OFJs7IXwtwEcBVwcSqvAQ5WHIS68XG+kEpLi/Wxw5wwFx0Xsj8ejMeUICqtXCJTYN4XZ1Da1AU+lwWhWAYui4F/H5uMcGUz0KSiJty96SIUJJWSdihTu/b3tjhPfLwq2uS5SuUKzPiEihi9vzwC95jRPkIzXc/fyQqPTg/Ai3pqjjTZ/+SUEXW/pUUODc0g09ghRsIHJ7SKDq+XUy/MgJ+j4d4qqhSzDw7kqCfQ04Kd8OaScHUjsf6SWdWG9w9k42Jx/5uTDjQcJgMx3raQK0iklPeInVmhznh0RgDG+xpukDbUSOUKHFHW3Fwu7am5IQig95WWySAwK9QZd07wwvRgZy3L8Q6xDAfTa7D1YpnJLtS9cbDi4Kc1cYj1sevXd6GhoRl9KBQk7tuSjMQC425ZKjgsBt5YHKaTnkaSJHYkV+CdfVkQyxQIcrbGL/eN10q7TSpqwtM7r6FeKAafx8JXd8RgZqjzgH+n/vLftSo8syvV5DgGAYS6CpBd0w4ui4EnZgbiMw2zmD2PT8ayb89r7RPnY4e/H52kfr3lfAne2ZetNebT1dFYFesJgFpYXPz1OTQIxZgU4ICcmna0aPTJC3K2xp4nJsOSYzrN7++USrzwVxocrbk49/JMk1kevbNRNiwJx+HMGq17ld79NKJUIwFa5NDQDBEXChtx16ZL/T5OhIcAfz8yyehFqlMsw7enCrEpsQQSuQJMBoG7473x9OygASnsJEkSp/Mb8PHhPOSY6Hw8HLAYhFrspFa0QqUvY33s8Mj0AMwOddZKAxtuMqvasO1SGfakVqt76xjCzYaH1XFeuC3OE5522rU7hfUd+CulAj+eKe7zOUR72mDjHTHwNSKiaWhobgz2plXjKY0ml6YY522Lj1ZGIchF29xFKJLitX8z1XUas0Od8eUdY8HnUalWUrkCXxzLx/dnikCSVK+zH++NHZHXmXqhCBP+d0L9Ot7PHpdKjC/mMRkENiwOw9saYuXiq7Nx96aLKGrQbvFw/LlpCHSmfn9tXVJM//QUWjVEy70JPnhveQQAqq7p9p+SkF7ZBi97C3CYDK3jcVkM7H1iillmOzK5AvO/PIuihk68vCAUj84wbmwEALHvHVPX+nKYDOx4KAErv79gdJ+RlqoG0CKHhmZIkcgU2PBfptpxpj88PTsIz8wJMrpqUtrYifcPZON4DpXDy+ey8NjMQDww2XdA6nUUChL70qvx6dE8VDSPLHMCgFptC3MTgEEQyKsVqmthgpyt8fD0ANwS7T4g1tsDRYdYhr2p1diRXG4yKkMQlK32qlhPzAt31XJmk8oVOJ3XgH+vVeJgRt+tzcd52+LjVdEDFv2joaEZGWRVt2HxV+fMHm/BZuKlBSFYM9FXp2l1emUrntpxDaVNVF+wl+aHYP1Uf/UCUmljJ57elYo0ZaPpO8Z74c2l4WZFHoaa3g1Px7gLkFVtfAGPIIC3l47BW3uz1Nv+fWwS9qRW49cLpVpjH50RgJcXhKpfv703S2vMjBAnbFoTBxaTAYWCxBM7ruJgRi1sLdnwtLNAZpX2ufTFpnlncjle2Z0BW0s2zr40EwKecdvotm4pot85qn595wRvkCSpbkhuCFOZJsMBLXJoaIaBiuYuTP3YPAcbU2xfF2/SOvpCUSM+OJijvlB62FrgpQUhWBrlPiARDYlMgR3J5fjqRIF69WekEexiDQ6LgdLGLnSIZQCoqMiDU/xw5wTvEeXsA1BGBTsul2PPtSqjhZ4AYMVhYmGkG1aM80CCn4PWv2m7SIrDmbX4O6VSXa/VF8LcBPj8tugR41hHQ0PTdxo7xJj60Sl0S41fSzSZHuyE95dHqK2OVYhlcnx1ogA/nCmGXEHC3YaHr++KQawPlQ6sUJDYerEMHx3ORZdEDgGPhQ9XRmFRpJu+jxl2SJKE36s9AofDYsCSw9SKsujjtUWh+OBgT83NO7eMgb0VB0/2ipD5Olji8DPT1AuLKWUtWlGRUFc+/npkojr69dnRPHx9shBsJoE4H3sd++negskYXRIZZnxyGvVCMTYsCceDU/xM7rP6hwtaaWnb18Xj/i2X1YuEhhhJrmoqaJFDQzOMHMmqxcNm2lSa4vwrs4xaRysUJP5LrcInR/JQ00bV60R52uD1RWEmrSTNpVMswy/nSvD96aI+3UyHEi97C1hz2ahrF6n71Qh4LNwxwRtrJvropIANN51iGfalVWPH5Qr1iqgx3G14WBbjgRUxHjqpJbVK+8/fkkr77M4GAP6OVvhkdTRdw0NDM0po7BBjwZeJaOwQm72Pj4MlNiwOx+wwZ51MgcyqNjz/Z5raMWxJlBveWxYBO6UJQWF9B175Jx1XlL294v3s8cXtY+E+AG0NBgOxTI6QNw5rbVsS5Yb96TVG9+ttDb0wwhVPzQ7S2ytPcyFSIlNg2sen1DWzrgIe/nlskvrevTWpFBv2UJEhfelys0Od8dOaOJ2omiE2Hi/AF8fz4WVvgePPTQeXZTyDo3cUZ3GUG7zsLE267n20MhK3j/c265yGElrk0NAMMwOZwhbqyse/j0022vW+WyLH5vMl+O5UoTpCMCfMGS/MD0Go68D87TR3SvBzYjE2JRZDKh+Zlw0nPhcOVhw0d0pQL6QmAAwCWBDhirWT/RDrYzeiCigBoLBeiH+uVuHfq1Xqm6QxIj1ssDzGA4sj3eBqo90rp7BeiD2p1fjjYplWMWtf+ODWSNw+3svsGy4NDc3Q0NQhxoKNiWgQmi9uLDlMPDErEA9O8dOZDAtFUnx5vAC/XiiFXEHCwYqD95dHYKEyOiOWyfHz2WJ8daIQErkCVhwmXlkUhrsneI+o+kdNWrskGPvuMa1t+vraaEIQwHNzgvFTYjGEIiojwMfBEv8+Nhnj3jumM/6Byb54a+kY9et39mVhy/lSAFQ9z5FnpqnTgvekUqYHJEndy/PrhND0K+od8TFFWVMn5n1xFmKZwmyb6YUbE7XqbLevj8dDv6eosx8Mkf/+whGV+q2CFjk0NCOEeqEIUz48ZTIkbA63xXni/1ZEGZ18NgjF+PJ4PnZeroBcQYIggGXR7nh2bjB8HAYmr7apQ4yfEouvqxB+qLDiMOHE50JOklp1RVGeNlg72Q+LIt1G3MVbriBxoagR/6RU4nBWrcn+RQQBjPexx5JoNyyIcIUzX9uOuqC+AwfSa/BbUqnJFA1D3BXvjRfnhahXdGloaIae/Doh5n95Vseh0RT3JvjgyVmBWlb1QI9j5/v7s9WLQYuj3PDuLWPUJjYnc+vw7r5slDZ1AaDS3D5YETkgTakHi7KmTkz/5LTWtv9bEYlXd2cY3e/F+SH46Wwx2rqp6ySTQeDSa7MR9/5xnbHhbgL8+/gktWA8kVOHB3+7on7/wFNT1L3cTubW4aHfUyBTkAhx4aOksVNrLuBpZ4Hdj07S+fcxBEmSWLOZcs6bHOiAPx6MN7loV9smQsL/9RgvLBjjCndbC2w+X2J0vzUTffDusgizzmuoGTEi55ZbbkFqairq6+thZ2eHOXPm4KOPPoK7u2nlCdAih+bGIbWiFct7WU9eL28sDsO6qf5GxxQ1dODzo/k4kEGF51kMAreP98JTs4PgYuYF1RSNHWL8dLYYP50duWKHIAAvZapabbsIEmW/Gmc+F/cm+ODOeG84DoAz3UAjFElxKKMWf181r+aGQQDxfg6U4BnjquO2V1gvxKGMWvx+saxPq8CahLsJsGFJOBL87UdcNIyG5kbkelOfV8R44Jk5wVp2zyoyq9rwwcEcXCiiakJ8HSzxzrIITA92AkAZC7y7Pxsnlc0pnfhcvL4oDMvGuo/ov/sz+Q24b3Oy1rY3l4RrNb7Ux3Nzg/Hz2WIINaIaJ5+fjlmfndEZa8lhYt+TUxDgREVpypu6MO2Tnjrcfx6dpE77PZ1Xj4e2pkAiU8DHwRINQrGWy6a9FQd/PzIR/k7mG8HsSa3C0ztTwWExcOSZaWYZAmj2xQGArQ9OwNpfL5vMxsh4e57Z0aWhZsSInC+++AITJ06Em5sbqqqq8MILLwAALlwwblmnghY5NDcSCgWJbcnl2PBf5oAc7+c1cZgb7mJ0TGZVGz45kocz+Q0AKIvK+yf54pHpAQO2Ot8gFOPHM0XYdM74ytBw48zngstmoL1bpl6x4zAZWBDhinsSfDDed+SlsgFAVWs3DqRXY19ajVk9c5gMApMCHLAo0g1zwlzgxNcWPMUNHTiSVYf96dUmnYaMcXe8N56ZE6xzfBoamutHLJPj7b1Z2JHc91TnxZFueGp2kF4L4ormLnx2NA//pVK20Ko+MOun+YPHZqKxQ4xvTxVi28VySOQKsJkE1k72w5Ozg2A9wgxcNFEoSHxzqhCfa/SzAYAHp/jhFxP3pKdmBeL7M0VaE/7dj03Ciu965qhWHKY6BVwzPay5U6KVyrb1wQmYGkQJxePZdXhs21VI5Ao4WnMgFMm0GkJbcZjYvj4B0V62Zn/P2jYRFmw8i9YuKZ6fG4wnZweZ3OdoVi0e0hDJ66f6oV4oxh7l/wFDLBvrjo13xJh9bkPNiBE5vdm7dy+WL18OsVgMNtu0QqRFDs2NiEgqxyv/pKtvNv3ln0cnqh1wDHGpuAmfHMlTF47yuSysm+qPB6b4mrSeNJd6oQi/JJZg07kSyAegSepgweeywOexIJIp1CYFAOXUdne8D24d5zFgv5OBprihA/vTa7A3rRqF9R0mxxMEEOtth3ljXDAv3FWnj0VThxgnc+txNLsOx7Lrrvu8BDwW3lgcjhXjPMBijqw0QBqa0cDVcsqd63pmZLfFeeKR6QF6owLNnRJ8d6oQvyeVqVOllo11xwvzQuBlbwmhSIpNiSXYlFisnsxPC3bCW0vD1RGLkUpbtxTP7UrFCWXUScWsUGd1JMoQD0z2VdfRqPjp3lgtURDuJkBhfQckcgWenBWI5+eFAKCyGDRT2T5ZFYXVcV4AgMOZNXhi+zXIFCSc+Vy0dkvVGQQAJXB+WzsBcX1oYq1QUGlq5wobEeEhwO5HJ5tMt5bJFQh8/ZD6ta0lGx+tjDIrMjiSozjACBU5zc3NePTRR1FVVYVz5/T7uYvFYojFPakU7e3t8PLyokUOzQ1JU4cYCzcmqnOi+8vRZ6ch2MVwEzGSJHE6rwEfH+lp9snnsbB2sh/WTvGDjcXAXNRauyT4PakMP54pMmmTPNzYK6NZHSKZegJgwWbilmh33JPgg0hPm+E8PYOQJIncWiH2pVVjX3q12f2MQlz4asET4SHQilyJpHJcLG7C8Zw6/HGxvF/nF+LCx7NzgzAv3HXEFijT0Aw3bV1SvP5fhknXL31YsJm4Y4IX1k/11+ty1iAU4+fEYvxxsUydJjU50AGvLgxDhIcNuiVybLtUhu9OF6kXeyI9bPDyglBMCTLevmAkcLm0Gc/sTEVVq/a1j8kgTC6yLY5ywwGN3zmHycCzc4Px0eEe6+gEf3vk1grR2iXF/DEu+P7uWDAYBArrOzDn855UtqdnB+HZucEAgH+vVeKFv9IhV5BwtOZCKJJqRXAslQJnfB8EDgD8cq4E7+3PBo/NwP4np5rV6+yuny+qUxIB4P3lEdh8vgTFvZqZ9mbdFD+8sSS8T+c31IwokfPyyy/jm2++QVdXFxISErB//344OOi3tn377bfxzjvv6GynRQ7NjUxJYydmfnp6wI539sWZenOxVSgUJA5k1OCrEwUoUEYD+DwWHpjshwcn+8HGcmDETpdEhh3JFfj+dFGfrE6HAysOE1w2EzK5Au2intzsaE8b3DnBG0ui3UdsygZJksioasORrFoczqzV6chtCDcbHuaGu2BmqDMm+jtoNZIlSRJZ1e04k9+Ao9l1ZtlcGyPMTYBn5wRhTpgLLXpobmpkcgW+PlmIjScMu30Zw8/RCmsm+mBVrKfe1fbaNhF+OFOEHcnl6gn2GHcBXloQimlBjhCKZdiaVIbN50rU/c/8Ha3wwvwQLIxwHZEpu5pI5Qp8daIA354qRF8TBlgMAqFufK0mnNZcFpZGu2mlB94S7Y5LJU2oaxdjrJcttq2LhxWXhUvFTbj9p4vqccvHuuPLO2JAkiS+P1OEjw/nAaBSAWUKUktsWXKY+PWBCZjg1zeBc628Bbf/eBESuQLvLY/AvQk+JvdJKWvGyu+T1K+nBjlivK+9TkqfPnLfWzAgTcUHk0EVOYaEiCaXL19GXFwcAKCxsRHNzc0oKyvDO++8AxsbG+zfv1/vHxIdyaG5mblc2ozVPySZHmgGXBYDZ1+aadRkQKEgcTCTEjv5dUqxw2Xh/sm+eHCKH2wtB6ZmRyyT479rVfj+dJHaqWekwiCgjmgJRTLIlDcpCzYTiyLdcFucJyb4jezC+8J6IY5k1eFwZq1ZNTwA9f9lUoADZoY6Y0aws45I7hDLkFTUhLP5Ddh6sazf5xjkbI2HpwdgabSbyR4PNDSjHalcgc3nSvB/h3JNDzbAjBAn3D/JF9OCnPQuFOTUtGPzuRLsSa1WR6XHetniqdmBmBnijJYuKbacL8GvF0rVNsle9hZ4fEYgVsV6joo009LGTjyzKxWp17Ho4sznQipXaFnrW3GYCHHl42p5z/EenOKHk7n1KGnsRJCzNf58eCLsrDjqon8VE/0dsG1dPEhQFtK/J1HXRQ6LoZWeBlCpYlvuH48Y7771ImsQirH063OobRdh/hgX/HBPrMl7j0SmQPAbh7S2bVoTp64RMsaP98Zi/hjXPp3jcDCoIqexsRGNjY1Gx/j6+oLH051cVVZWwsvLCxcuXMDEiRNNfhZdk0Nzs0GSJPamVWtdTPuDgMfCseemmxQ7h7Nq8dWJAuTWUs3grLks3DfJB+um+A+YQYFcQeJQZg1+Tizpd2RgKLDiMEGCOm/NlANfB0usjvPCinEecLMZuXaqAFDZ0oWjWXU4klWLy6XNZq98+jtZYWaIM2aGOGO8n52OEClt7MTZggaczK3H6byGATnX9VP9cP9kvxFtUUtDYy7dEjm+PJ6PH/vhPulha4FVsZ5YFesJL3vd6LxCQeJUXj1+OVeilZo03tcOT80OwpRARxTUd2DL+VL8e61SbUsf6GyNx2cGYGmU+6gQN3IFiS3nS/Dp0TyT1vr64PNY6BDLdOqdCAJa256dE4w9qVUobuyEh60F/nl0Epz4XHxyJE+rceY4b1tsX58ABUni2V2pOJJF1TNacphaDmoAFTHf+uAEBDobTiXXh0yuwN2bLuFSSTMCnKzw3+OTzaqTmfbxKZQ39ywmvrYoFLuvVqnv7cYo/XBxn85xuBhR6WqaVFRUwNvbG6dOncKMGTNMjqdFDs3Niuqi/v6BnAE5nq0lG0eemWZS7BzNrsXGE4Xqmh1LDhN3TvDGg1P8Bqy7NUmSSClrwabEEhzOqh2QYw42PDY1EZDKe1IQGARVoLs61gtzwp1HfESiqUOMM/kNOJFbj7P5DerVXFNYcpiY6O+ASYGOmBLoiGAXa63VRJlcgYyqNiQVN+F0XoNZltfmkOBvj/sn+WJ2mAvYo2AiRkOTXd2ODw7m4Fyh8YVgUywb647b4rww0d9Bb9SmuVOC3Vcrsf1SOYobqfRUJoNQNz2O8bLFqbx6bDlfqnUuER4CPD4jEPPHjJ5aubxaIV76J33QF8beuWUMNp8vQVlTFzxsLbB9fTwEPDae2nkNiQXav8OdD01ES6cED21N0Wqy2ZsAJyv8/mB8nxduSJLEhj2Z+ONiOay5LPz3+GSz6nC2nC/BO/t6LLNnhTojwMkKPyeadj5NfGmmXiE9EhkRIic5ORnJycmYMmUK7OzsUFxcjDfffBM1NTXIysoCl2vadpQWOTQ3O1K5Al8ez8e3p4pMDzYDSw4Tx56bbvSiq1CQOJZTh69OFKgthlkMAstjPPDIdP8+r0gZo6ypE1vOl+LXC6UDdszBhMkg1M1YNVMSbC3ZWBzphuUxHoj1thvxEwipXIGUshacyq3Hidx6s5zaVDjxuZgc4IDJgY6YHOioI34lMgXSK1uRVESZGKRVmpcyZw7zx7jgngQfTA5wHPG/Y5obn26JHJvPl+CTI3n9Ptb0YCcsiXLDvDGuek1gFAoSScVN2JFcjqNZderUIz6PhTsneOO+Sb7gMBnYfbUSO5LL1anBDAKYG+6CtZP9RnyqrSYiqRw/nCnCt6cKTfZ06S8fr4zCVycLUNnSDS97C+xYn4C2bike3pqCypYeY4MAJyv89cgk5NUK8di2FK3Ut96M97XDj/fGqc1t+sIPZ4rw4aFcEATwwz3mpZAVNXRgtkZvH0drDjYsCcczu1JNOvY9NSsQzymd40YDI0LkZGRk4Omnn0ZaWho6Ozvh5uaGBQsW4I033oCHh4dZx6BFDg0NhUgqx/sHsvvteqXJ6Rdm6FgKa0KSJM4WNOL704W4WNyzOj8v3AWPzAjAuD7mFxujrUuK7cnl2JRYrC6GHelwlNEFqUKhdRPxtLPAsrHuWD7WA0FG3O5GEhXNXTiZW4+TufVIKm7SySk3hr+jlVLwOGCCn4POTV0klSO1ohVXSpuRVNyE84VNBo50fSyJcsPKWE9MCXSkIz40g0q3RI7tyeXYeDxfy6Dkepno74Cl0e5YEOFqcDJc1tSJPanV+OdqJco0ahojPWxwxwQvLI12x9WyFuxMrsDxnDp1HaGAx8IdE7xxb4LPqFmhV3Eytw7v7MvW+r6DgauAh2fnBuGjw3lo7pTAz9EK29bF40RuPd7fn62VphzsYo0/1sXjUEYt3t2fbdTBbXWsJ96/NeK6ovt706rx1I5rAKhmpmun+JncRySVI3TDYa1tn6yKwv8O5qDViBBTUfzBolG1aDQiRM5AQIscGhptOsQyvLo7A/vSBqbHDgAceGoKxrgbt0q+Vt6CH84UqXOPASDezx6PzgjA9GCnAVsdlMgUOJhRg18vlF5XcelII9xNgFtjPLA02h2uNoZTBUcSIqkcl0ubkVjQiMSCRqPpGPoIcrbGBD979U/vuiWFgkRBfQeulDUjpbQFu69VDeTpq8/h1nEeWD7WY8DSLGluPurbRdieXI6fzxYPmB3+3HAXzAlzxsxQZzjz9V8TGjvE2J9WjT1p1bimURRvzWVheYw77hjvDR6biT2pVfgnpRLVbSL1mLFetrhzgheWRLnDaoQ6QhqitLET7+7PNtnjZiBI8LfHrTEeeHNPFsQyBaI8bfD5bdH4+HAejvbqGRbmJsAP94zDx4fzcCDDsN03QQCvLgzF+qn+13VPPJPfgPW/XYFErsCDU/ywwQwrZ5Ik4ffqQa1tz8wJwomcerOMZ86/MmvU1UHSIoeG5ganrVuKF/9K07kY94dt6+IxOdB4f4TCeiF+PFOM/1Kr1CkEYW4CrJvih6XR7iYblPWF9MpW/J5Uhr9TKgfsmMMFQVCrtsvGumNeuOuAmTkMBQ1CMS4UNSpFTwPq2vtmB+5lb4EJvg6Y4GeHCX4O8HWw1JkA1AtFuFrWgpSyFpwrbOqzsDKXqUGOWBDhirlhLnA2Up9Gc/MhlslxOLMWv10o1XLb6i/2VhzMH+OKOWHOmBzoaNCet6lDjBM59TiQUYNzhY1atX+TAx2xbKwHYrxtcSKnDntSq9WpxADlCLlinAduH++FUNfRN1dqF0nx/eki/JJYYtIBbCC4K94bXnaW+PhILkiSql25d6IPXtudgRoNwQhQbQSenxeCN/7L1Cro7w2fy8IXt4/FnHCX6zqnC4WNeODXyxDLFFgc6Yav74wxK7qy+KtErf8Ltyjvw+bcNz9aGYnbx3tf1/kOJ7TIoaG5SRCKpHj+z4EVO5+tjsaKcR5GV6Jq2rrxS2IJtieXq91knPhcrEnwwd0JPteVh2yI5k4J/rxSgZ/Pjp5UNmMwGQQmBThgcaQb5o8ZXYKHJEkU1ncgsaARF4qacLm0GW3dptMhNHHiczHO2xYx3naI8bJFpKcNLDnaK85yBfU5aZWtSK9sxf70GrPSLq6XcDcB5o9xxfwIF4S48EdN3QLN9aGKlOy+VoX0AawZUzEt2AlTlTVroa58g5PViuYuHM2m3A+v9HI/jPayxbJod8T72yO1ohX70qpxqaRZnRrLYhCYFuyEZWPdMX+M64jvbaIPsUyOrUll+OZU4aD+favgshh4dWEorpa3Yq8yG2JFjAdsLNn49UKpTu3KjBAnxPnYYeOJAqN1QWFuAnx/9zij6d/GSC5pxn2bk9EtlWNOmDO+uzvWrAXDN/dkqq2rASqFcXqwE745VWhy3zA3AQ49PfW6zne4oUUODc1NRqdYhjf+y8S/A5j68+SsQDw7J9joalJrlwTbLpXj96RS9Qo/l8XAinEeWDvZb0BrUuQKEqdy6/FbUqmW281oRlPwzBtjOC9/pKJQkMitFeJicRMulTQhuaTZaDGuPpgMAqGufIz1Ugofb1v4OVjp/L8Ty+TIrREivbIV6ZVtOJFbr+7UPphEe9liaqAjpgY5IsbbbkCjlTSDS2uXBKfzGnAsuw7HsusGLUoQ7WmDyYGOmBLkiFgfXct1FTK5AqkVrTib34DjOfXI7hWxjPAQYH64KyI8bJBfJ8SRrFpcq2jVmnxP8LXHLWPdsSjSbdRdL1QoFFSrhE+P5qkL+z1sLVDV2m1iT8MwCBi1yA9wssIrC8Pw+bF85NS0g8UgsCjSDZlVbWqHOk2mBztBLJNr1aPq447xXnj7ljHXLTKvlFICp1Mix7RgJ/y8JtasWp6fzhbhg4M9fZccrblYN9UPH5rZiynv/QUj3hHUELTIoaG5SemSyPDBwZwBNShYGOGKL24fa/Qirqql+eVciVYe8LRgJzw4xQ/TghwHdHW8uKEDf1wsx+bzpq0xRwsqwbMo0g1zw13gaG3agXKkoVCQyK8X4lJxMy6VNOFScfN1Rd9sLNgY62WLKE8bjHG3QYSHAB62Fjr/h6RyBYobOpFT046cmnYklzZr1TAMNnweC7E+doj1tsM4HztEedqY1cuCZuBoF0mRUtqCi8VNOJPfYFY/kP4yNcgRcT72GO9rh7HetjqRSE2qW7txNr8BZ/IbcK6wUcu6nUEAE/zsMTvUBc4CLvLrhDiaVYeCXm6H0V62WDDGFbeMdR919ROaKBQkDmXW4uuTPT3ZXARcMAlCq66oL1hyqPtS7/40mqyI8cD0ECe8tTcLrV1SWHNZCHC2RnolJSCZDELLSMDXwRJNHRIIxYbNJXhsBt5fHolVsZ7Xdd4AcCqvHo/+kQKRVIGJ/g7YfP94WHBMC49/Uirx/F9p6tdsJoE3l47Be/uyzRLy516eCU+70WVGoQktcmhobnJEUjm+OJ6PH89cfyO63njYWuCvRyYaLeQmSRKXS1vwy7liHM2uU69ABjlb4/7Jvlg+1mNAi2FFUjmOZNViR3K5yRW30QRBALHedpg3xgVzw13hd51pEMMNSZIoaezE1fJWpJS14Fp5C/LqhCYtTfVha8lGhLsNxngIEOFugwgPG/jYW+qNNNYLRcipESK3ph35dR04V9j3WqKBwsGKg3B3AUJd+QhzEyDUVQB/J6tRmV401MjkCpQ2dSKjqg0Zle24VtEypCIWoGrKxvvaI8rDBnG+9gh15RttoFnfLsLFkmZcKm7CpZJmHXt2W0u2st8UHywmgcyqNiQWaIsfFoPAxAAHzAun/v5Hi2mJIeQKEvvTq/HNyUK1gONzWZgc6NivXmmhrnyUNXWhW6pf4NhYsPH6ojCkV7VqLfyxmYQ6/SzI2RpVrd1GRVJvojxt8MXtYxHgZLp3jSH2pVXj2V2pkClIzAhxwvd3x5olcA5n1uKRP1K0tr25JByfH8tHhxFRpmLH+gRMDHC47vMeCdAih4aGBgC10v3bhdIBayqqYtdDCYj3N36hLG/qwpYLJfjzcoXamYjPZWFlrCfuSfAxq7lZXyhp7MSuyxVanalvFAKdrZUTHhdEe9qOKrvP3rSLpEiroETP1fJWXCtvMbsxaW+suSyEuwsQ7kaJiBBXPoJc+LDWI6RJkkRtuwiF9R0oqOtAQX0Hkooa1f1ERgJcFgM+DpbwtreCj4Ol8rklfBys4G7LG7XpJSrkChL1QhFKGjupn4ZOlDZ1orixE6WNnUbTjYYCD1sLxPnaIdLDBlGethjjLjC6KEOSJKpau5Fc0ozkkmZcKmlGSa/UJwZBOZ5Fe9nCksNEp1iO84WNOtEaGws2pgQ5Ym6YC2aGOMPGcvRHBKVyBfalVeObU4UobqB+LwIeC8tjPLRqSfpKqCsfXBbDaA+uGSFOWDvZD+8fyEZ+nW4fMBcBF/6O1rhS1mx2Hx4GATwxMxBPzg7ql1391otleHNPJkgSWBrtjs9WR5uVBnsoowaPbruqte2FecH46WyxWZbm7y0bg3sn+l7vaY8YaJFDQ0OjBUlSaQKP9bpA9peXF4Ti4Wn+Rifd7SIp/rxcgT8ulmlNKCcFOGDNRB/MCXMxujLaVyQyBU7k1GHH5QqczW8YsOOOFJz5XMwOc8G8MS6Y6O8w6iMCCgWJwoYOpFa0IqOyDelVbcipae9Tr57eeNpZINSVj2AXSvgEu/AR4GRtcCLR1CFGaVMXyps7UdrYhdKmTpzJbxiSYujBQMBjwZrLgiWXBSsOE5YcFqy42o+WHCZYDAZ6Z5H2/kuWKkh0iWXolMjRJZGhSyJHp+q1mHrd3i01mtoz0mAyCLUpQLALHyEufAQ6W5tcSW/rliKjsg2pFS1IrWhDakUrGju0I4QEQRlZBLvwQRAASQI5Ne06EUwGQaWhTQ92wrRgJ0R72qobDY922kVS7LhUjl8vlKrdymws2Lg3wQffnS68bjFrY8FGlKeN0ZpMKw4Try4Kg1AkwxfH83WuI0wGgWXR7qho6cLl0hazP9vHwRJf3D62X/3h5AoSHxzMwS/nqDTru+O98e6yCLP+3fekVuHpnala215aEIJNiSVm1SauGOeBz28bez2nPeKgRQ4NDY1BrpQ2Y9UPSQN6zKlBjvj+nli9K+gqFAoS5wobsfViGU7k1KlvdK4CHu6K98Yd470G3Na3orkLf6dU4o+LZTeEM1tveGwGJgU4YmaIE2aEOI+6pn+GkMgUyK8TIqOqDemVbcioakVerbBfnc9ZDAJ+jlYIcLKGn5MV/B2t4O9kDX9HK6MOd23dUpQ3UcKnrKkTVa0iFDV0ILnkxkmPvFHxdbBEuLsAPg5W8HOwgp+TFYKd+WZFSVq7JMiuaUdOjRDZ1e1IrWhBUYNugTqLQSDUjQ87Sw64LAZYDAaKGjp0IjUA4O9khQR/B0wKcMCUQEfYWo5O4wBDVDR3Ycv5Uuy6XK6O3jtac3D/JF/svlqlt8DfXO4Y74WdlyuMjlkwxhV3TPDCZ0fz9faIifWxw/wxLvjpbImOODUEgwDWTvbDc/OCjdZemaJTLMPTO6/heA7VA+i5ucF4clagWbWqWy+WYcN/mVrbnpsbjN+TStHYYfq+FuYmwMGnptwwrpG0yKGhoTFJQZ0Qc784O+DH/fexSYgxsdpV2dKFHcnl2JlcoRYfLAaBBRGuuDveBwn+9gN6QVYoSFwpa8E/KZXYdcX4jXI0E+BkhRkhzpgZ4ozxfoZdnkYjYpkcebVCZFa1I7eWMhrIrREOSATBzpKtFjyUALKGn6MVvOwtjE5sSJJEa5cUVa3dqG7t1nrMrRWqU3RoBgcWg8A4Hzt42VnCw5YHd1sL+Dpawc/RCs58rlnXEIlMgfLmTuTWCpUGFtRj734pKjztLODM54LJIECSVEpWfl2H3rqQYBdrxPs5IN6faoxrqPnnaIYkSSQVN2HbxXIcyqxRL14Fu1jjvkm++PBgbr/+RlfFeiKtolWvaFThYWuBDUvCkFfbgW9O6do9O1pz8Mj0AOTXCfHnFfP7roW68vHRyihEe9le7+kDAKpau7HutyvIqWkHh8XAZ6ujsTTa3ax9/+9Qjk5t7WMzAvDHxTKzUtT4XBZS35p3w0QJAVrk0NDQ9IH6dhEe23YVV8rMD92bw/Nzg/HYzECjF1dVA77fk8qQovH5vg6WuH28N1bGegz4xKBbIsfR7Fr8daUS5wpvDCtqfVhymJgU4IAZIc6YEeI0qt10DKGqiVCZDOTUUsKnpKnzuswN9OFgxYGnvSW87CzgZW8JLztLeNlbwMvOEu62FiZz6UmSRIdYhgahGPXKH+q5CA3tYtQJRSio60C9cHiMEUYqgc7W8LG3hKM1F058LhytOXAW8OBhawF3Wws4WHH6VJvW3ClBcUMHiho6UNzQiaKGDhQ1dKK8uUvLWUsTVwFPff1iMwmwmQzUtYv0Ti6tuSxEe9lgrJctxnrZYZy3LRxGoUOiubR1SfH31Upsu1SmJeanBjlidZwXntpxrV/HXxzlhurWbqNGE2wmgbWT/TDOxw4fHc7VWVTgshhYN9UPYW4C/O9AjkHh2hsOi4GnZwfhoWn+/aq9AYCz+Q14euc1tHRJ4WjNwU9r4sxKeSNJEms2J+uk5j0xMxCbz5eYbZRQ8L+F/f4OIw1a5NDQ0PQZkVSOjScK8P3pgS3cD3HhY8sD4426sgFAVnUbtl0qx97UarVLDJNBYHaoM+6c4I1pwU4DvhpV2ybCf6lV2JpU1q8eDaMBP0crdZrMxACHGy5VRpNuiRwF9UIU1nf0/DR0oKzJ8IT2emAQ1ETY1YYHNxsLuAh4cLPhwcWGenQV8OAs4PYpoiaTK9DWLUVLlxStXRK0dEnR0iVBh0iGTrEMHRo/zZ0S1LWLUNnSN3eooULAY8HVhgdHay5sLNgQ8NiwsWQrn7MgsKCeq34crLkQ8FjXFcXtkshQ0dyNypYuVDR3obKlGxUtXeptxla9WQwCTAYBDosBDpMBDosBBkGgrl0EmZ7/L2wmgUBnZW8nL1vEeNsiwMl6VBuCmANJkrha3oodyeXYl1YNsbLexYrDxK3jPBDtaYsX/07v12dMC3YCAeCMiXrK+WNccN8kX2xNKsOhTF2HtuVj3bFuqj+2nC/FP1fNj97MCnXGhiXh/Xa0VChIfH2yEF+eyAdJUj2Qfrgn1qzFJrmCROTbR7T+ptlMAmun+GHL+VKz6xVz31sw6ms29UGLHBoamuuGJEkcz6nH+t+vDPixP1sdjRXjPIxOYjrFMhzIqMGuyxVa0R03Gx5Wx3pidZzXgNeekCSJzKp27E2rwubzpQM6ER6JEATVHXtSgCOmBDoiztfuhrwZ9kYiU6CsqVNL+BTWU+LHHPvV68XBigMXpeBxsKKiEg7WHDhYceFgzYGjNfVob8UZsBRDkiQhlZMQy+SQyBQQyxTqR6lcAQVJQkFS4/Q9KkgSDIIAi0kJADaDASaDes1iEGAxGGAyCbAZBHgcJizYzEFZMZbKFWjsEKOuXYy6dhEVDWsXoa5dhLp2KjJW1y7qU2NYNpMAQ3kNIgGjk0YbCzbC3QQIcxOonfwCnQ2bWNyIVLV249+rlTp1NWFuAtwW54maNhF+Otu/dgVUE1UGLhQ1GR0X7ibAi/NDkFXdhk+P5uu8n+Bvj5cWhCK3RojX/s0w+/N9HSzx5tJwzAp16fO596alU4Jn/0zF6TxKqN05wRtvLQ036xrb1iVF9LtHtbY587mYEeLUp1S70dzs0xS0yKGhoRkQBqtuJ8HfHt/eNc5kOkd+nRC7Lldg99VKtCidrggCmBLoiNvivDA33GXAJ+cKBYnLpc3Ym1aNbZcGrqnqSIbDYmC8rx0mBThicqAjItwFA+p4N9IhSRLNnRKUNnWhrKkTZcpHynGtq08T6P7C57Jgb81RRz4EFiyN5z0RENVzlVOaBZsJC+XjSPq3I0lS7cjWoXRj6xD3RKXalVGr5k6JVuSqpUuC1s6hc20T8FgIdLZGoLM1Apyon3B3AdxseDdMwXZf6BTLcDizFv9crURScZM6/dOCzcTCSFck+Dvg65MFqGjuXwR8vK8d6oVilJmwcne34eHJ2UEgAGzYk6lTdxPrY4fn5wbDisvCs3+mml0PZ8Fm4snZgXhwit+AiILEggY8/2ca6oVicFkMvL88AqvjvMza91p5C2797oLWtmhPGzgLeDiWXWfWMSzYTKS/Pe+GS1HThBY5NDQ0A0pzpwTv78/G7mtVA35sc6I7YpkcR7PqsOtyhVYdDZ/HwpIoN6wc54lYH7sBn4xI5QqcK2zEvtTqQfnuIxUrDhPjfOwQ72ePCX4OiPayuWFXBc1B5bBW1dqFqlYRqlq0jQZGmnMfh8kAj82AJYelFj4cFgNsVVSGyVCmaFHbWMrXLAahtj0moXqkXpCgojukMsojlVORIYnqURUt0njdJZGjUyIbsPqo/mLJYSprqqj+QwHOlNteoLM1HKw4N6WY0UQkleN0Xj32p9fgRE69lplCgr89ZoY4o7VbOiApzQn+9mY1cHbic/HEzEC42vDw3K5UtWubikgPGzw3LxjhbgK8fyAH+9Kqzfp8JoPA7eO98MzsoAFx9RRJ5fj4cB42n6fsoQOcrPD1neMQ7m7e3PW704X4+HCe1raZIU4QimRm18uGuvJx4KmpN5TJgD5okUNDQzMoyBUkDmbU4Ml+FpXqI9rTBt/fE2uydqe8qQt/pVRg99UqrToaHwdLrIjxxIpxHoNipSySynEqtx57Uqv71aV7NMJhMRDjZasWPeN8bPtlp3qj0S2Ro7pNKXxaulGrTqsSo0EoUpsN6KvvoBk4+DyWukbK3cYC3g6W8LSzgLc9JWxoIaMLJWwacDCjBsdz6rTqQHwdLLEo0g18HhtfHs9X1+D0h1BXPnJrhXrfYzIIdaqwnSUbj0wPQJCLNZ77M02nZ1WoKx/PzQ3GpEBHfHokD79eKDX7HOaPccGL80MHrCF1dnU7nt2Virw66nvdm+CD1xaFmey7BFA1eDM+PY3KFu2I2OJIN1wrb0G1mWYJK8Z54LPV0TfF/29a5NDQ0Aw6ZU2duO3HJNS1D7wr1EsLQvDwtACjK1IKBYmLJU3YfbUKBzNqtG7OE/zssWqcJxZGuoLPG/jO4V0SmXpisD+9ZsCPP9JhMQhEetpggq89YrwpJ6mB7nF0o6FQkGjpkqgd1urbRWjsoFK0mjup9CwqZUuK5i7JqG1EOtCwmQTsrTiwt+LC3ooNeyuuusbJ1YZLPSqFDS28zaOtS4rT+fU4ll2HU7n1WtERD1sLzAlzhsCCjavlLThfaLxGxlxYDMKgyOexGRBJKQHlxOfiwSl+8HWwwiN/pOiMjfK0wSsLQzHB1x7fnS7C58d063IMEedjh1cWhiLO1/76vkQvxDI5vjlZiO9PF0GmIOFozcHHq6LMruspbujArM/O6GxfFOmK4zn1ZhsMvLE4DOum+vfp3EcztMihoaEZMrolcmw+X4JPjuSZHtxH+DwWfl87wWTfnS4JlT+++2oVzhc1qtNjeGwG5oW74pZod0wLdhqUYmGRVI7EgkYcyqi5qVLaeuNha4FxPpTgifG2Q7ib4KYqzh5oerusCUUyCMUydIhk6BBLqdcipdOa8lEokkIkVUAkk0OsfBRJ5eoJ5FDCIAAemwkuiwEuiwkumwErDgt8nuqHDWsuC9aq11xqG5/Hgr0VZcpgZ0WNuRlWpwebksZOnMipw7HsOlwpa9EyV3G34WFmqDOseSyUNnbiSJZ59R/9hcNiqCfyvg6WeGhaAKx5LL320+O8bfHOLREIc+PjqxMF+OpkodmfE+djh2fmBGNyoMOA/V9KKWvBy/+ko1DZv2deuAv+d2sknPjm2YZ/e6pQ554Z7GINVxsLnDXhLKfJb2snYHqwk/knfgNAixwaGpohhyRJXKtoxYpehZMDxdxwF3y8Mspod3oAqG7txn+pVfgnpVKrQ7mNBRsLIyjBE+/vMCh5yxKZAknFTTiUUWOyO/eNDpfFQKSHDWK8bTHO2w4x3nZwtaGjPcMBSZKQyBUQSRUQS+UQyxSQKUjIFSQUJPWo/iFJKNTvUWKFwaDcyBgElVJEPSfAYABMggCDQYDHZoLHYoCrFDZUfQ8tToYLkVSO5JJmnCtsxImcOq1rIUBNqMd524HFJFDXLja7sL2/WHKYWlH3Me4CrJ/qj+KGDr3CZVGkK16aHwpPOwu8/m9mn5o5j/elxM2kgIETN0KRFJ8dzcdvSaUgSarR6LvLIrAwwtWsz+gUyzDmrSM62yf6O6C6rdukAYMmx5+bhkBnfp/O/0aAFjk0NDTDilAkxdcnC/ttK2qIlxeEYv1UP6MuUiRJIq2yDXtSq7A/vQYNGs0WnflcLI5ywy3R7hjrZTsokzGZXIHLpS04kVOHLRdufFtqc3DicxHpYaP+iVI6B9HQ0PQPhYJEdk07Egsaca6wAZdLW7TSnVgMAhP87OFhawGCAHJrhUivbBuy87OzZKsdMpkMAgsiXLE0yh3fnipERpXueTw2IwCPzQwEAWD971dMWktrMsHPHs/MDsLEARQ3JEni32tV+OBgLho7qHvJynGe2LAkzOyeY3tSq/D0zlSd7RP9HZBS3mJ2ehoApL89D4JBSMUeDdAih4aGZsSQWdWGJV+fG7Tjb74/zmQOtFxB4lJxE/amVeNQZi3aunvqHbztLbE02g23RHsgxHVwVsVIkkRRQweO59RjZ3I5SvuwWnej48znIsrTBhFK0RPhYQNnPi18aGiMobqmXCppxsXiZpwvbNSxOnez4SHa0xYcFgMkgFO59YPaD6o3HBYDMrkCqvUdeysO7prgDUdrDt7el613ny9vH4tbot2RWyvEsm/P6VhFG4JBAAsiXLF+qr/J9Oa+klXdhrf2ZKldzvwcrfDOLWMwzcw0sS6JDAkfnNBpSBvmJgCLQegVeYZYMc4Dn66KvuEbzxqDFjk0NDQjji6JDL9eKNWxyRwo/J2s8N3d4xDqavxaIZEpcDa/AXvTqnEsu07LJjXAyQqLIt2wIMIV4W6CQUu3aeoQ42RuPQ5l1uJkbv2gfMZoxlXAQ5gbH6FuAoS68hHuJoCfo9WI6v9CQzOUyOQK5NQIcamkCZdLm3G5tEVH1FhxmIjxtoMFhwkGAdS2i5FW0Trk58plMbSc2Cb42WNOmDOOZdfhcqmuHbKrgIft6+PhZW+JHcnleHNPltmfxWMzsDrWC+um+sHHwWpAzl9FU4cYG08U4I+LZVCQ19dT568rFXjx73Sd7THetiiq79ARPsYwZ0HvZoAWOTQ0NCOa/Doh1v56Wcc2c6CYGuSIj1dFwc3GuB11l0SG4zn12JtajbP5DZDIe27MPg6WWBDhioURboj2tBk0wSOSynGxuAkncuqx9WLZoHzGjQCHxUCQszVCXQWUAFI+mmooS0MzGmnqECOtshWpFW1IrWjF1bIWnSgMl8VAhIcNLDlMsBgEOiVUHc5w0LvWxtGagyVR7pDIFdhuoKnyfRN9sGFJOCpbuvH6fxl9cnJz5nNxd7wP7p3oA3sTdZp9pVsixy/nivHDmWL173xJlBteXxxm8p6iol4owoT/ndDZ7m7DA5fNREmjec1KVSS/NptO7VVCixwaGppRgVSuwJGsWjyxfeD77qhYPtYdby0dY9KwoF0kxcmcehzMqMGZ/AatlUgPWwvMH+OKRZGuGOdtN2ipAiRJorSpC6fz6rE3rRrXylsH5XNuJJz4XAQ5WyNI1a1e+ehkzaUL32lGBd0SObKqKTGTWtGKtMpWVDTrLgDxeSwEu/CV5g+ASKpAZlXbiOm/xGIQmBzoCBcBF3+lVOptAsthMbDroQT4O1rj76uVeG+//rQ1Q8T72WPNRF/MG+MC9gBHdmVyBf5OqcTnx/JRr6zhHOMuwOuLwzApwNGsYygUJF7ZnY4/r1TqvBfhIUBpY1efUgbvnOCF/y2PvKnT03pDixwaGppRR2uXBJ8dzR/UaMY9Cd54dWEYrLjG+2l0imU4lUelk53KrddaoXTmc7EgwhULxrhivJ/9gN9oNRFJ5bhU0ozTefXYcr500D7nRkTAYyFQKXgCna0R5MxHoLM1PGwt6AkDzbDRIBQjp6Yd2TXtyK5uR05NO4oaOqBPp/g5WikttKlifaFIprYsHklEe9nC0YqD1IpWNPVKoVPx1KxAPDIjAElFTfjkSJ7BhqD6sOIwsWKcJ+6d6INgl4Gvm1QoSBzNrsWnR/PVv19POwu8OD8ES6Pczb5enMqtxwO/XtbZ7mFrAZIkzW7sqeKfRych1mdg64tuBGiRQ0NDM6oprO/A+t+v9Dmk3xfun+SLF+aHwNqE4BFJ5TiT34DDmbU4nl0HocYqHJ/HwswQZ8wJd8H0YCfYWAyu201FcxdO5zfgaFYtEgsaB/WzblQ4LAZ87C3h42AFXwdL+DhSj74OVnCz4dF1PzQDQodYhqL6DhTWd6CgvgPZNZSg0XR51MTRmmp2SoCAKgBZ1tSlVTM4kvBxsIStJQdF9R0GIxNTAh3x7rIxqG4VYefl8j43To7xtsXqWC/cMtbd5HX6elAoSBzJqsXGEwVq0WVnycYTs4JwT4K32XU3Fc1dmPrxKb3vuQp4qBeK9IpYQ4z3tcPva+NhwTHv8282aJFDQ0NzQyBXkEgsaMD9W3RXxwaSNRN98ML8EJOWnGKZHBcKm3AwowYnc+u1Vi1ZDALx/vaYE+aCOWEu8LK3HNRzlskVyKxux/nCRvxztRLFDYMnCG8W2EwCXnaW8HGgRJCPgyU8bC3gYWcBT1tLCCzoxpQ0PZAkicYOCQrrO1DY0IGi+g4UNVDCpsbAqj1BAG4CSkwTBMAgCBAAGjvEfSpCHw4crbngshioajVcS+lha4H/WxEJBkHgQEY1diT3rV+YozUXK8Z5YHWsJ4IGIWoDUOLmcFYtNh4vQF4dJW74XBYemOyLddP8zbZm7pLIsPL7JOTUtOu852lngbYuqdaimDnseigB8f4OfdrnZoMWOTQ0NDcc3RI5/r5aiQ3/ZQ7q5ywf6443loTD0URBu1xBIrWiBcey63E8p04njSTUlY+54ZTgifSwGfQUKZFUjpSyFpwrbMQfSWV9vrnSmMaay1KLHg9bC7hrPPe0s4CjNXdQmszSDB8SmQLVrd0oa+5CeVMnypu7UNbUhfLmLlQ0d6FTYjjSYmvJBkdDzDAIAgqSRF1731b2hxMWgwCbyTAaUXK05uLlBVRUPLGw0aDRgCHYTAIzQpxxW5wXZoQ4DVoKsESmwL60avx0tlhb3Ezxw4OT/WBjaZ64kckVeOmfdOy+WqXzno0FGwQBtHZJ9expmMmBDvh5TRwsOQMfsbrRoEUODQ3NDU1blxTfnSnEj2cGp9moihkhTnhvWYRZUZmSxk6cyKlT2qQ2a01inPlcTA92wowQZ0wJdDT7Ztof2rqkSCpuwoWiRmy7VE43Ix0CmAwCTtZcuAi4cBbw4MznwkXAU7924fPgLODC3pJD1wWNABQKEo2dYtS2iVDTJlI/1rR1o6ZNhOrWblS3dhsVJARBTZQZDCoiQxCUKQBATXRHiinAQOMq4GH9NH8wCeBicTMOZ9X2aX+CoJpg3hLtjgURrmY31Lwe2kVS7Ewux+ZzpahtpyJsfB4Layf7YW0fxI1CQeLjI3n44UyR3vd7O8yZy/4npyDCw6bP+92s0CKHhobmpqGuXYRPjuTh7xRdN5uBJNDZGh+uiESsj53JlKWWTglO59fjeHY9zuQ3aOWsMxkEYrxsMSOEEj3hboIhmfC2dUlxubQZScVN+D2p1OwmezQDD4tBKGswOHCw5sDOkkM9t+LA3poDe9Vraw7srbiwsWDTESIzIUkSbd1SNHZI0NQhph47xWgUitHY2bOtrl2EunaRWX8HTAYBJkEABNV0UlU3QwAggeua2I5GxvvaYZyPHZgEgUslzUgp0+15Y4qxXra4JdodS6LcBt0SuaatG1vOl2LHpXJ1ZNuJz8UDk31xd7yP2TWUJEnix7PF+PBQrt73BTwWhGKZXjc5Yzw7JxhPzAqk/7b7CC1yaGhobkpKGzvxzr4snMprGNTPYTMJ/N+KKNwa42HyBiWWyXG5pAWn8+pxOr9BJ63N0ZqLacGOmBHijGlBjoO6oqlJp1iGq+UtuFTcjJ2Xy9HYod8ViWb4IQgqVU7AY0NgwQafp3qufOSxILBgQ8Cj3rPgMGHJYcGCzYQFh/qxVD7nshgjtq5IJlegSypHl1iOLokMXRI5uqVydEnk6BLL0NYtRbtIirZu6qe9W3tbe7d0wKInKhHDIIgbNhpjDnPCXMDnsSBTkEgqaryu60Scjx3mj3HF/DGu8HYY3FpFkiRxqaQZW5PKcCSrVv1vF+hsjYem+mNZjLvZhgIkSeK700X45Ij+BtZMBgGSJPuceugi4GL/k1PhxKd7fF0PtMihoaG56Sms78Dr/2bg0hA0x1s72Q/PzA0yq2C1sqULZ/IbcDqvARcKG7Vy+hkEZcc6PdgJUwIdEe1lO6gW1ZqIpHKkVbTiSlkLDmfWIqOqbUg+l2ZoIQiqc7tKAHGYDLCYBFgMBtgsBtgMAiwmVYfBYhBgMRnqMeZKIxKATE5CKlcof3qeyxQkJDLt510SObolcq1mvDTDg6edBVwFPPDYTDR3SpCtp6jeFGwmgUkBjpg/xhVzwp3hzB/8JpYdYhn+vVaFrUmlyK/rWUiK97PHw9P9MSPY2eyIuVxB4qPDufjp7MCnQ+99YjKiPG0H/Lg3E7TIoaGhodGgtLGzzx21r5exXrZ4+5YxGOtla3KsRKbAlbJmnMmjRI+qGFaFFYeJBH8HTAp0xJRARwS7WA/ZKjxJkihp7MTV8lacK2jAf6nVQ/K5NDQ0Q4uXvQUlZBkMFDV0XFfkysaCjalBjpg3xhUzQpzMdijrL9nV7dh1uRz/XK1SpwVbsJlYHuOBexN8EO5u/txRLJPjpb/TsWcQrnWf3xaNW2M8RmwUdTRBixwaGhoaA1Q0d+Gjw7l97tlwvbyxOAz3JPiAxzadIlHT1o0zeQ1ILGzEhcJGtPRy6HG05mJyoAMmBzpicqAjPGwtBuu09dIhliGtohVXy1qwL71aa8WUhoZmdMBkEOAwGeCwGOgUy647HS/K0wYzgp0wPcQJ0Z62Q9ZjqrVLgj2p1fjzSgWyqnsiTf5OVrg3wQcrxnn2qWdZvVCEezcl6ywyDQTrp/rhhfkhZqfI0ZiGFjk0NDQ0ZlDd2o3vThfij4t9szy9XhL87fHG4nCznHQUChLZNVQfnPNFTUguaYJIqp3O4+dohcmBDpgU4IgJfvYmba8HGlW0J6OqDdfKW/HnlYqbpgibhuZmw86SjalBTpgR4oRpwU5Der2RK0icK2zEX1cqcDSrTp3ayGYSmBfuijsneGNyoEOfIiVXSpux6oekQTnfhRGu+GhV1JBFtG4maJFDQ0ND00dauyTYdqncYJHpYPDUrECzm8+JZXJcLWvFhaJGnCtsRFpFq07Ba6CzNSb42SPezx7xfg5wtRn8XPjeyBUkSho7kF7ZhovFTfjzyuC63tHQ0AwOfB4L8X4OmBjggEkBDghx4Q+p9TlJksiqbsfetGrsS6vWarAa5ibAbXGeWDbWA/ZW5pu1KBQkvj9j2Eygv0R72WLzfXFwGOIFp5sJWuTQ0NDQ9AORVI6TufV4bNvVIftMTzsLvLYoDPPCXcxK+2gXSXGpuBnnCxtxsbgJubW6qRY+DpaI97PHBD8HxPvZm9XvZzCQyRUoauhEemUrrpS24N/UKkhkdJE5Dc1IwpLDxHhfe7WoGeNuMyz2xoX1HdibVo39adUobuxUb7exYGP5WHesjvPqc1+ZiuYurP31MgrqByfFNsJDgE1rxg/LwtLNBi1yaGhoaAYIhYJEWmUrnt2VitKmriH73EkBDnhhfghivGzNSsFo7ZIguaQZl0qakVzSjKzqNp1Ij4etBSb42WOCnz1ifewQ6GQ9bE0pSZJEZUs3smvakVHZhv3p1UP6+6Whudlxs+Eh1scOsT52iPOxR6gbf8jcHHtT1tSJQ5m12JtareXoxmUxMCfMBUuj3TEjxMms2kYVCgWJPy6V4c09WYNxygCohqZf3RlD20EPIbTIoaGhoRkkypo6sSmxBFsvlg3p566I8cCTs4Pg52hl1vh2kRQppS24VNKMSyVNyKhs0ykw5vNYiPG2Q6w3NdEZ620Lay5rME7fbIQiKfJqhcipacfFkmYcGCKDCBqaGxkmg0CYGx9xPvYY52OHOB87uA+xcYkmqlS0o1m1OJJVp1X0z2IQmBbshFui3TEn3KXP16T8OiHW/XYF5c2Dt2iyJMoN/1seCRtLuuZmqKFFDg0NDc0QIJLKcSKnHo9vH7q0NhV3TvDCo9MDzW6u1yWR4WpZKy6VNOFKaQvSKlt1TAIYBBDiKsA4b1v1Cq+3veWw256SJInadhEK6jqQXyfE2YJGnM0f3IavNDSjmRAXPiI9bRDlaYNIDxuEuQn6FAUZDGRyBS6XtuBodi2OZtWhqrVb/R6TQSDB3x6LI92xMMIVdn2oswGAtm4p3tqTOehW90/NCsTD0wNgNcyLQTcztMihoaGhGWJIkkRxYye+OJY/ZPbUmtwW54lHpgfA38narPEyuQK5tUJcLW9BShn1U9nSrTPO0ZqDGG87RHvaIMrTFlGeNrC17NsEZLAgSRINQjEK6jtQUCdEcmkzDmXWYuTe1WhoBhYmg0CQszXC3ARqQRPuLoAlZ2RMwuuFIqoPWH4DzhU0oq27xxbfgs3E9GAnzBvjglmhzn2+rsjkCvycWIKPDucO9Gnr8O1d47AgwnVYapRotKFFDg0NDc0w0yGW4WRuPZ7acW1YPn9xpBsemxmAcDeB2ZGY+naRlujJrGrX24Xex8ESUZ62auET4TFyJlUqhCIpypq6UNzYibzadpzMbUDOdXRvp6EZKbjZ8BDiykeoqwBhbnyEuPLh72gNDmt46mj0IZMrcK2iFafz6nE6r0Grjw0A2FqyMSfMBfPHuGJKoCMsOH2LLsnkCuy8XIE3/sscyNM2yP4np/TZ5IBmcKFFDg0NDc0Io7ypC5vOFf9/e3ceHFd5pgv86X1ftEutXTay8IaNHS8kzBiCMQngDMUNOM4ICBkKbkH5wmRCPNls55/c3KpUSApcSd1iAlM3IR6Mh4GKIWYuDHawYmNblk2EN1mLtUst9aJudffpc878cVqyZEmWWu5N7edX1XVap09Lr6o+y+fp75z3w782pPZenjG3VzjxzN8uwl11hXO+uTgkiPhrt7IGzplOL850eqZtDqBWAbcU2rCyzIGV5Ur4qS2ypf3ymJkMByJodQfQNhjAhb4RHG0ZxJlOb7rLIgIAlOeasKjAipp8KxYVWrCowIq6YlvGzKBOJMsyLvaPoKHFjaMtg2hoccMXik46ZkWpA5uWKOvrzGfR0Kgo4c2TnfjnA2cTWfqMvrGuHC9srkWhjZ3SMhFDDhFRBhMlGWc6Pdj9zl/RlKaTa4dJh6furMY31lXEtaaDNyjgTJcSepquKNteX2jKcVq1CosLrVhaYsdSV+xRYs/IE7WJQoKILs8orgwFcWV4FJ91enGs1c3Ob5RQBq0adSV2LMq3oKZACTI1BVZU5pkz9sMBQAk17e4gjra40XDZjYYWNwZHwpOOcZp1+JsbXDQ0JIh49c+tKV237F+eWItNtYVp6zhJc8OQQ0S0gATCUfz50iD+5/87OaXtcyrdu7QIT36pGuuqcuP6j77PFxoPPE2dHnzW5cVwUJj22FKnaTzwLHXZscxlR6nTlPbmBnMVjETRNTyKHm8Ivb4QWgcDONk+jFPtw1O619HNzeUw4tYSO8pzzcojx4SKPDPKcsxp72I4V6Ik40KfHyfah3GyTWlP3+2d/KGGQauetL7OyjLnvO5d6fOF8LP3zuFAY1eiyp/V11a58M9fuZXr2ywgDDlERAuYeySMD5r7sDNFl2fMxKTT4FtfrMI31lXEtZDoWDe05m4f/trtU7Y9XlwZmtrYAADsRi3qiu2oLbZiSZENtbFHvB2WMkVIENEbC0G93hC6PKO42OdHc48PF/qSsxghpZZGrcLqcicq8swocRhRbDei2GFCicOIIrsR+Vb9ggnuEwUjUZzu8OBE+zBOtA+jsX0Y/vDky890GhVWl+eMh5pVFU4YtPHPPsmyjKMtbvz4Pz5Dy0Bg9jck0O+fWo8N1XmctVmAGHKIiLLI4EgYB8/2JHVRu7mqzrfg7zdU4uHbS+O+9Mw7KuBcTyz4xLYX+/wzzoAU2AzjoWdJsRW1RTbcUmRbMJ+CzyYqShgKRjDoj8AdCGNwJIwBfxiXBwJoGRjB5YEA3IFIusu8qeRZ9LHZFxPyLAbkWfXIsxqQb9Ej32ZAscMIm0G7IAPMtQRRwvleP852ecfvuTvX64d4zb9Hi16D1RU5uL0yB1+oUlrLz7fRSL8/hJc/vJSWexP3bF2Gr68ty7gmKRQfhhwioiw24A/jUHMvfvDvqekwNJtlLju2ravA1pWuuBfHC0dFXOofwcW+EZzv8+NCrx/n+/zTtrMeU+o04Zaiqzdm1+RbsajAggKbIStOPmciyzJCggTPaASeoABPUIA39tw7KmDAH0ZPbOaoxzuKAX84rZc/plOeRY/SHBNcDhOKHUY4TLrxh318q4XdqEOuRZ/R98EkgijJaBkYQdMVz3ioae7xIRKd2j3R5TBiTVUu1sbWyqortsXdLGBMOCrizROd+D/vn5vSkCAVHt9YiWc2LUKJI30Ln1JiMeQQEd1EhgIRHLk4gP/1h9PpLmXc4kIrvr6mDFtXueZ1gjESjuJin/9q+Onz43yvH/3+8IzvsRm0qC6woCb/6o3cNQUWVOdbsv4kNh6RqITRiIhwVEQ4KiEcFRESlG1YkKbui0qIRCWIkqy8VxAhiBKikoyoKCMqSbGtDEGUpswEqFSATqOGVq2GTqOCVqOCVq2GXquGRq2CXqM812mU5zqtGjqNesL+2GtaNcx6LSx6DUx6DSx6LUx6DQxadVaH23gNByI41+vHuV4fzvUo2/N9foSEqYHGbtRiZZlTWTi01IHbyp1wOecfCMJREX8804OX/vMiOobS06zj71a58Pw9tajKt6Tl51NyMeQQEd3EQoKIUx3D+N/vncuo1sgmnQZfX1uGh28vw4pSx7yuh/cEIzjf60fLQACXB0aUy7oGA7gyFJxx1kKlAlwOEyrzzKjMU24Cr8y1jD93mOKbfSLKBIFwFK2DAVzqH8HnEwJNn2/6DwLMeg2WlyphZmW5EytLHajMM99QQByNiDjQ2Im9H7WgyzPz7Guy3b+yBP+4uRaL5rgYMi1cDDlERDROkmRc6Pfj3z7txL980prucqZYXeHE360qxX3Li1Fkn1+Xo3BURIc7iJaBkVgACuDy4Aha+kdmvUzGadahMtYBqzLPjIpcMypyLSjLUS51muu6QkSJJssy+nzh2LhWxvNYwL+2y9lEFblmLCm24dZiG+pK7KgrtqEyzzKvrmcTtbsDeP1oe0b8HXnyi9V4/I5KVOZxxuZmwpBDRETX5R4Jo+GyG98/cDYt18rPxV1LCvDAShfuWVo079kWWZbhDkTQOhhAhzuI9qEgrgwF0e4OoGMoiMGR69/Yr1IBRTYjXE4jSnPMytap3OvhcppQ6jTBbsqOG9EpPQRRQrdnFB1DwfHHldi2dSCAQESc8b15Fj0WFVixpNiGuhIb6ortWFKcmOYcoxERB8/24F8b2tK2nte1fvzAUjy0unTBdl6kG8eQQ0REcZEkGZcHR/BOUw9+9f8vpruc6/rS4nzct7wYd9cVosRhvKGAEQhHr55cuoNoHwqgY2gUHe4Auj0hRMSp9zFcy6LXwOU0ocRpQpHNgEK7AUV2IwptxvHnBVYD9FrOCN2MRiMierzK2ko93hB6PKPomhBquj2j120QoVGrUJlrRk2B0mhjUYE19rAkbHHd0YiID8/1443jHfjzpcGEfM9EqMozY9fWZdhYk8f76ggAQw4RESVAMBLFZ10+vH60DX8825PucmZVmWfGPbcW4Z5bi7CmMueGQ4UkKbNA3Z5RdMdOTLtiz7s9IXR7RuNq8Zxr0aPQNhaAlDCUazEg36pHrkV55FkMyLHo5rXuCKVWJCoprb/9EaX990gY/b4Qur3K+kjdnlH0+kLwzLAw7kQGrTp2maRy2eTY86p85dLJRAbkoUAEfzzbg/0nO9F0xZOw75so39lci4duL0VZztzX5qKbB0MOERElxUg4ijOdHvz648s4fGEg3eXM2coyBzYtKcSmJQVYWmJP2KfCIUEcD0C93hD6/cqJbp8vjD5/CP2+MPr9IQhifP/V2gxa5FrHgs9YCDLAadbBbrzaAtlhuvq1zaidd6vfm50kyfCHo/CNKq25PRNac3uCEQyOKEFGeUQw4A/DOzp7eBlj0WtQ4jSNLxzqcpom3P9lRr7VkPCFKYORKI5dHsI7Td34j9NdGdtO/G9qC7Dj7sVYVe7k+KVZMeQQEVHKBMJRNPf48MaxDhxo7Ep3OXErzzXhS4sL8KXF+Vhfk4s8S2JXq5dlGcNBAf3+WPjxhTAQC0PuQARDgQjcIxG4AxEMByNTWjDHw2rQwm7Uwh5bD8Zm0MJsUNoum/VaWAyTt2a9Bma9BhaD8tyk00CvVVo3GzQaGHRKK+dMWhlelmUIooyIKCEsiIiI0nhr60BYRCAcRTASxUhYjG2jCIZFZRuJIhB77h29GmK8o8K8QoBWrUKeVY98qwH5VgMKbQaUOE1wOYwodhhR4jChxJncBUTHWsh/0NyHQ3/tm9Mllum0zGXH9+6rw9qq+S8qSjcvhhwiIkqrkKAs8nmouS/j7/GZTV2xDV+oysXa2GrvLocpaSf9kiTDFxImhZ+hQARDgTDcAeVk3DeqzDj4QkLsa+G6N6cnglatrFNjiAUgvVYJPzqNGmqVCmo1oFGpoFKpoFGroFZB2R97bew5AEiyDEmWIUoyJFn5nSVZhigrAWbi/rEAM7Z2TyQqISJKSNaZi1GnhtOkV2bMTDo4YwuH5tsMsSCjR4HVMP6106RLSQAMhKM43jqEjy8M4PCFAVweDCT9ZybKilIH/mnLEnyBoYYSgCGHiIgyjizLGPCH0dTpxf89fBnH24bSXVJCGHVqrCp3YnVFDlaVO7Gi1IEiu/GG2/XGQxAl+ENXZyd8sW0gHEUgImI0omyDsa/HZjSCkSiCERHBiDK7ERKSHyQSbWwRUaPu6oyUNTaDZY3NWFlj+y1js1oGLZwmHZxmJdA4YmEmXTe3j30ocKx1CEcvDeJoixujQnKDa7J8Y10F/seaMiwvtfPeMko4hhwiIlowwlHlBO9E2zBe/ugSBvzTL2a40JU4jFhe6sBylwO3lthwa4k9Y9fhmXhJWCR69RGOigjHQlBYkGKzLjJEWY7NwsRmamKzMWP7pdhrKmDSzI5apYJGDWUGKDbrM/5cpbp66dy12wy9lG46IUFEuzuIpk4PTl/xoLHDg897fOkuKyEseg1+/OBSfHFxPkqdJrZSp6TLuJATDoexfv16NDU1obGxEatWrZrT+xhyiIhuXuGocnLY0OLG3v+6NONK7tko16LH4kIrbim0YnGh0jK4Ot+CQruBn45ngJAgYsAfxsV+P871+nGh14/zfSM43+vL2Bv8E+Gf7q3FpiWFqC2ysSU6pUU82SAlF0e++OKLcLlcaGpqSsWPIyKiLGDQalBbZENtkQ2P31E1vj8qSugYCuLzHn/GreuRKEOBCI63DuF4a3yX9BXYDCjPMaEsx6ys3TN+A7yybo/NqFy2xU/cAVGS4Q8J6PWF0OMJods7iq7h0fEFYzuGgnNq/5ytvrtlCf62toCBhhaspIec9957D4cOHcJbb72F9957L9k/joiIspxWo0ZNgRU1BVbcv7Jk0mv+kIB2dxB/uezG6w1tuDI0mqYq02PAH8aAP4xTHZ6EfD+dRgWHSQ+bUbmvxWrQwjrhudmgdGQz6jQwatUw6DQw6tTQazTQalTQqpVGBFq1GlqNatJ9SmPXkSiXswFRSUJUlCGIEqKSshVEGSFBREgQMRoRMSooj5FQFP5QFP6wMP7cF1IaMmR6d7FMs6EmF0/cUYVlLgdKnclrqkGUakkNOX19fXjqqafw9ttvw2yefVGncDiMcPjq5Qg+X3Zcs0pERKlhM+qU+15KHfiHO2smveYJRtA6GMDZLi/ePNGJs13eNFW5cAiiPL4+DC1sK8sc+Ob6CixzObC40Jq2JgtEqZK0kCPLMp544gk888wzWLt2Ldra2mZ9z09/+lPs2bMnWSUREdFNzGnWY3WFHqsrcvDYxqpJrwmihF5vCB1DQRy+MIA3T3ZiKBBJT6FEN+Ch1aV48LYSLC6woTTHlNIuf0SZJO7GA7t37541iHz66ac4evQo9u3bh8OHD0Oj0aCtrQ3V1dXXbTww3UxOeXk5Gw8QEVFahQQRvV7lvo2znV68e6Ybn3XxagNKPbUKqN9QiU1LClGRZ0ap08RZGbppJLW72uDgIAYHr3+TZ1VVFbZt24Z333130s2NoihCo9Hgm9/8Jl5//fVZfxa7qxER0UIRjoro94XR4w3hQp8f//l5H/7r/EC6y6IFxmbQ4sFVLtyxKA+lThMqcs3ItejZLIIIGdJCuqOjY9I9Nd3d3diyZQv279+P9evXo6ysbNbvwZBDRETZJiSIGBxRGgR0eUbxaesQPmlx41L/SLpLoySrKbBgU20h1lTmoDTHBJfDiDyrgZeUEc1RRrSQrqiomPS11WoFACxatGhOAYeIiCgbGXUalOWYUZZjxuqKHDyw0jXjsZIkwxcSMBwUMByMYMAfxuc9PjRd8aCp08v7htJMp1FhdUUO1lTmYGmJHUV2I/KteuTbDLAZtJx9IUqjlKyTQ0RERPFTq1VwmvVwmvWohgUAsGVZ8azvk2UZ4aiktFkOCfCHohgJRzEUiODKcBCX+kfQMhBAuztwU68FAyizK7cUWnFLoQ1V+RbkWnSw6JVW2TlmPZxmHUw6ri1EtNCkLORUVVUhSVfGERER0QQqlUpZu0anQYHNkLDvK8syREmGIMqIiBKisbVsBFFCRJSUtW2iymuRqARZliFDWRNHGn8uQ5YBGXJs/9W1ctQqQKtRQa1S1tTRjG3VKqjVyro76gn7DFo1DFqNstWpodeoodVw4Uoi4kwOERERzZFKpVIW+dQAJrCjFxFlLn7cQUREREREWYUhh4iIiIiIsgpDDhERERERZRWGHCIiIiIiyioMOURERERElFUYcoiIiIiIKKsw5BARERERUVZhyCEiIiIioqzCkENERERERFmFIYeIiIiIiLIKQw4REREREWUVhhwiIiIiIsoqDDlERERERJRVGHKIiIiIiCirMOQQEREREVFWYcghIiIiIqKswpBDRERERERZhSGHiIiIiIiyCkMOERERERFlFYYcIiIiIiLKKgw5RERERESUVRhyiIiIiIgoqzDkEBERERFRVmHIISIiIiKirKJNdwHXI8syAMDn86W5EiIiIiIiSqexTDCWEa4no0OO3+8HAJSXl6e5EiIiIiIiygR+vx8Oh+O6x6jkuUShNJEkCd3d3bDZbFCpVOkuh2bg8/lQXl6OK1euwG63p7scWgA4ZiheHDMUD44XihfHzMIgyzL8fj9cLhfU6uvfdZPRMzlqtRplZWXpLoPmyG638w8DxYVjhuLFMUPx4HiheHHMZL7ZZnDGsPEAERERERFlFYYcIiIiIiLKKgw5dMMMBgN27doFg8GQ7lJogeCYoXhxzFA8OF4oXhwz2SejGw8QERERERHFizM5RERERESUVRhyiIiIiIgoqzDkEBERERFRVmHIISIiIiKirMKQQ0REREREWYUhh+I2PDyM+vp6OBwOOBwO1NfXw+PxzPn9Tz/9NFQqFV566aWk1UiZJd4xIwgCvve972HFihWwWCxwuVx47LHH0N3dnbqiKaX27t2L6upqGI1GrFmzBkeOHLnu8R9//DHWrFkDo9GImpoa/PrXv05RpZQp4hkzBw4cwObNm1FQUAC73Y6NGzfiT3/6UwqrpUwQ79+ZMZ988gm0Wi1WrVqV3AIpoRhyKG7bt2/H6dOn8f777+P999/H6dOnUV9fP6f3vv322zh27BhcLleSq6RMEu+YCQaDOHXqFH70ox/h1KlTOHDgAC5cuICtW7emsGpKlX379uH555/HD37wAzQ2NuLOO+/EV77yFXR0dEx7fGtrK7761a/izjvvRGNjI77//e9jx44deOutt1JcOaVLvGPm8OHD2Lx5Mw4ePIiTJ0/irrvuwoMPPojGxsYUV07pEu+YGeP1evHYY4/hy1/+cooqpYSRieLQ3NwsA5D/8pe/jO9raGiQAcjnzp277ns7Ozvl0tJS+bPPPpMrKyvlX/ziF0muljLBjYyZiY4fPy4DkNvb25NRJqXRunXr5GeeeWbSvrq6Onnnzp3THv/iiy/KdXV1k/Y9/fTT8oYNG5JWI2WWeMfMdJYuXSrv2bMn0aVRhprvmHn00UflH/7wh/KuXbvk2267LYkVUqJxJofi0tDQAIfDgfXr14/v27BhAxwOB44ePTrj+yRJQn19Pb773e9i2bJlqSiVMsR8x8y1vF4vVCoVnE5nEqqkdIlEIjh58iTuvffeSfvvvffeGcdHQ0PDlOO3bNmCEydOQBCEpNVKmWE+Y+ZakiTB7/cjNzc3GSVShpnvmPntb3+LlpYW7Nq1K9klUhJo010ALSy9vb0oLCycsr+wsBC9vb0zvu9nP/sZtFotduzYkczyKAPNd8xMFAqFsHPnTmzfvh12uz3RJVIaDQ4OQhRFFBUVTdpfVFQ04/jo7e2d9vhoNIrBwUGUlJQkrV5Kv/mMmWv9/Oc/RyAQwCOPPJKMEinDzGfMXLx4ETt37sSRI0eg1fJ0eSHiTA4BAHbv3g2VSnXdx4kTJwAAKpVqyvtlWZ52PwCcPHkSv/zlL/Haa6/NeAwtPMkcMxMJgoBt27ZBkiTs3bs34b8HZYZrx8Js42O646fbT9kr3jEz5o033sDu3buxb9++aT+Aoew11zEjiiK2b9+OPXv2oLa2NlXlUYIxmhIA4LnnnsO2bduue0xVVRXOnDmDvr6+Ka8NDAxM+YRkzJEjR9Df34+KiorxfaIo4jvf+Q5eeukltLW13VDtlB7JHDNjBEHAI488gtbWVnz44YecxclC+fn50Gg0Uz5N7e/vn3F8FBcXT3u8VqtFXl5e0mqlzDCfMTNm3759+Pa3v40333wT99xzTzLLpAwS75jx+/04ceIEGhsb8dxzzwFQLnGUZRlarRaHDh3C3XffnZLaaf4YcgiA8gcgPz9/1uM2btwIr9eL48ePY926dQCAY8eOwev14o477pj2PfX19VP+M9myZQvq6+vxrW9968aLp7RI5pgBrgacixcv4qOPPuLJa5bS6/VYs2YNPvjgAzz00EPj+z/44AN87Wtfm/Y9GzduxLvvvjtp36FDh7B27VrodLqk1kvpN58xAygzOE8++STeeOMN3H///akolTJEvGPGbrfj7Nmzk/bt3bsXH374Ifbv34/q6uqk10wJkMamB7RA3XffffLKlSvlhoYGuaGhQV6xYoX8wAMPTDpmyZIl8oEDB2b8HuyudnOJd8wIgiBv3bpVLisrk0+fPi339PSMP8LhcDp+BUqiP/zhD7JOp5NfffVVubm5WX7++edli8Uit7W1ybIsyzt37pTr6+vHj798+bJsNpvlF154QW5ubpZfffVVWafTyfv370/Xr0ApFu+Y+f3vfy9rtVr5lVdemfT3xOPxpOtXoBSLd8xci93VFh7O5FDcfve732HHjh3jXUq2bt2Kl19+edIx58+fh9frTUd5lIHiHTOdnZ145513AGDK4msfffQRNm3alPSaKXUeffRRuN1u/OQnP0FPTw+WL1+OgwcPorKyEgDQ09MzaS2L6upqHDx4EC+88AJeeeUVuFwu/OpXv8LDDz+crl+BUizeMfOb3/wG0WgUzz77LJ599tnx/Y8//jhee+21VJdPaRDvmKGFTyXLsbs1iYiIiIiIsgC7qxERERERUVZhyCEiIiIioqzCkENERERERFmFIYeIiIiIiLIKQw4REREREWUVhhwiIiIiIsoqDDlERERERJRVGHKIiIiIiCirMOQQEREREVFWYcghIiIiIqKswpBDRERERERZ5b8BSuGS6cQTSlAAAAAASUVORK5CYII=\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 8))\n", + "plt.plot(runner.mon.y, runner.mon.z)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Actually, the coefficient used in ``brainpy.fde.GLWithMemory`` can be inspected through:" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 46, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "coef = integrator.binomial_coef\n", + "alphas = bm.as_numpy(integrator.alpha)\n", + "\n", + "plt.subplot(211)\n", + "for i in range(3):\n", + " plt.plot(coef[:, i], label=r'$\\alpha$=' + str(alphas[i]))\n", + "plt.legend()\n", + "plt.subplot(212)\n", + "for i in range(3):\n", + " plt.plot(coef[:10, i], label=r'$\\alpha$=' + str(alphas[i]))\n", + "plt.legend()\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "As you see, the coefficients decay very quickly!" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Further reading" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "More examples of how to use numerical solvers of fractional differential equations defined in BrainPy, please see:\n", + "\n", + "- [(Mondal, et, al., 2019): Fractional-order FitzHugh-Rinzel bursting neuron model](https://brainpy-examples.readthedocs.io/en/latest/neurons/2019_Fractional_order_FHR_model.html)\n", + "- [(Teka, et. al, 2018): Fractional-order Izhikevich neuron model](https://brainpy-examples.readthedocs.io/en/latest/neurons/2018_Fractional_Izhikevich_model.html)\n", + "- [Fractional-order Chaos Gallery](https://brainpy-examples.readthedocs.io/en/latest/classical_dynamical_systems/fractional_order_chaos.html)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "name": "brainpy", + "language": "python", + "display_name": "brainpy" + }, + "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": 0 +} \ No newline at end of file diff --git a/docs/tutorial_toolbox/inputs.ipynb b/docs/tutorial_toolbox/inputs.ipynb new file mode 100644 index 00000000..a201c8f0 --- /dev/null +++ b/docs/tutorial_toolbox/inputs.ipynb @@ -0,0 +1,770 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9f5ef59c", + "metadata": {}, + "source": [ + "# Inputs" + ] + }, + { + "cell_type": "markdown", + "id": "95e252ca", + "metadata": {}, + "source": [ + "@[Chaoming Wang](https://github.com/chaoming0625)\n", + "@[Xiaoyu Chen](mailto:c-xy17@tsinghua.org.cn)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "In this section, we are going to talk about stimulus inputs." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "source": [ + "import brainpy as bp\n", + "import brainpy.math as bm" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Inputs in ``brainpy.dyn.DSRunner``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "In brain dynamics simulation, various inpus are usually given to different units of the dynamical system. In BrainPy, `inputs` can be specified to [runners for dynamical systems](runners.ipynb). The aim of ``inputs`` is to mimic the input operations in experiments like Transcranial Magnetic Stimulation (TMS) and patch clamp recording.\n", + "\n", + "``inputs`` should have the format like ``(target, value, [type, operation])``, where \n", + "- ``target`` is the target variable to inject the input.\n", + "- ``value`` is the input value. It can be a scalar, a tensor, or a iterable object/function.\n", + "- ``type`` is the type of the input value. It support two types of input: ``fix`` and ``iter``. The first one means that the data is static; the second one denotes the data can be iterable, no matter whether the input value is a tensor or a function. The `iter` type must be explicitly stated. \n", + "- ``operation`` is the input operation on the target variable. It should be set as one of `{ + , - , * , / , = }`, and if users do not provide this item explicitly, it will be set to '+' by default, which means that the target variable will be updated as ``val = val + input``. " + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Users can also give multiple inputs for different target variables, like:\n", + "\n", + "```python\n", + "\n", + "inputs=[(target1, value1, [type1, op1]), \n", + " (target2, value2, [type2, op2]),\n", + " ... ]\n", + "```" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "id": "f9c7d3ca", + "metadata": {}, + "source": [ + "The mechanism of ``inputs`` is the same as [``monitors``](monitors.ipynb). BrainPy finds the target variables for input operations through [the absolute or relative path](../tutorial_math/base.ipynb). " + ] + }, + { + "cell_type": "markdown", + "id": "3451b77b", + "metadata": {}, + "source": [ + "## Input construction functions " + ] + }, + { + "cell_type": "markdown", + "id": "e377d41a", + "metadata": {}, + "source": [ + "Like electrophysiological experiments, model simulation also needs various kind of inputs. BrainPy provide several convenient input functions to help users construct input currents. " + ] + }, + { + "cell_type": "markdown", + "id": "844fcb78", + "metadata": {}, + "source": [ + "### 1\\. ``brainpy.inputs.section_input()``\n", + "\n", + "[brainpy.inputs.section_input()](../apis/inputs/generated/brainpy.inputs.section_input.rst) is an updated function of previous `brainpy.inputs.constant_input()` (see below).\n", + "\n", + "Sometimes, we need input currents with different values in different periods. For example, if you want to get an input that is 0 in the first 100 ms, 1 in the next 300 ms, and 0 again from the last 100 ms, you can define:" + ] + }, + { + "cell_type": "code", + "id": "a4ff6914", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "current1, duration = bp.inputs.section_input(values=[0, 1., 0.],\n", + " durations=[100, 300, 100],\n", + " return_length=True,\n", + " dt=0.1)" + ], + "execution_count": 2, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "id": "64f9a99c", + "metadata": {}, + "source": [ + "Where `values` receive a list/arrray of the current values in each section and `durations` receives a list/array of the duration of each section. The function returns a tensor as the current, the length of which is `duration`$/\\mathrm{d}t$ (if not specified, $\\mathrm{d}t=0.1 \\mathrm{ms}$). We can visualize the current input by:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "078fbd0d", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def show(current, duration, title):\n", + " ts = np.arange(0, duration, bm.get_dt())\n", + " plt.plot(ts, current)\n", + " plt.title(title)\n", + " plt.xlabel('Time [ms]')\n", + " plt.ylabel('Current Value')\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "579e8b2d", + "metadata": {}, + "source": [ + "show(current1, duration, 'values=[0, 1, 0], durations=[100, 300, 100]')" + ] + }, + { + "cell_type": "markdown", + "id": "54aec8c9", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "### 2\\. ``brainpy.inputs.constant_input()``" + ] + }, + { + "cell_type": "markdown", + "id": "1a18a549", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "[brainpy.inputs.constant_input()](../apis/inputs/generated/brainpy.inputs.constant_input.rst) function helps users to format constant currents in several periods.\n", + "\n", + "We can generate the above input current with `constant_input()` by:" + ] + }, + { + "cell_type": "code", + "id": "6b1eee02", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "current2, duration = bp.inputs.constant_input([(0, 100), (1, 300), (0, 100)])" + ], + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "markdown", + "id": "26708359", + "metadata": {}, + "source": [ + "Where each tuple in the list contains the value and duration of the input in this section." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8ea6dea6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "
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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show(current2, duration, '[(0, 100), (1, 300), (0, 100)]')" + ] + }, + { + "cell_type": "markdown", + "id": "6cc74d90", + "metadata": {}, + "source": [ + "### 3\\. ``brainpy.inputs.spike_input()``" + ] + }, + { + "cell_type": "markdown", + "id": "e862ebad", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "[brainpy.inputs.spike_input()](../apis/inputs/generated/brainpy.inputs.spike_input.rst) constructs an input containing a series of short-time spikes. It receives the following settings:\n", + "\n", + "- `sp_times` : The spike time-points. Must be an iterable object. For example, list, tuple, or arrays.\n", + "- `sp_lens` : The length of each point-current, mimicking the spike durations. It can be a scalar float to specify the unified duration. Or, it can be list/tuple/array of time lengths with the length same with `sp_times`. \n", + "- `sp_sizes` : The current sizes. It can be a scalar value. Or, it can be a list/tuple/array of spike current sizes with the length same with `sp_times`.\n", + "- `duration` : The total current duration.\n", + "- `dt` : The time step precision. The default is None (will be initialized as the default `dt` step). " + ] + }, + { + "cell_type": "markdown", + "id": "067aae19", + "metadata": {}, + "source": [ + "For example, if you want to generate a spike train at 10 ms, 20 ms, 30 ms, 200 ms, 300 ms, where each spike lasts 1 ms and the average value for each spike is 0.5, then you can define the current by:" + ] + }, + { + "cell_type": "code", + "id": "e6ea2868", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "current3 = bp.inputs.spike_input(\n", + " sp_times=[10, 20, 30, 200, 300],\n", + " sp_lens=1., # can be a list to specify the spike length at each point\n", + " sp_sizes=0.5, # can be a list to specify the spike current size at each point\n", + " duration=400.)\n", + "\n", + "show(current3, 400, 'Spike Input Example')" + ], + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "markdown", + "id": "146ebc19", + "metadata": {}, + "source": [ + "### 4\\. ``brainpy.inputs.ramp_input()``" + ] + }, + { + "cell_type": "markdown", + "id": "1eb035a2", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "[brainpy.inputs.ramp_input()](../apis/inputs/generated/brainpy.inputs.ramp_input.rst) mimics a ramp or a step current to the input of the circuit. It receives the following settings:\n", + "\n", + "- `c_start` : The minimum (or maximum) current size.\n", + "- `c_end` : The maximum (or minimum) current size.\n", + "- `duration` : The total duration.\n", + "- `t_start` : The ramped current start time-point.\n", + "- `t_end` : The ramped current end time-point. Default is the None.\n", + "- `dt` : The current precision.\n", + "\n", + "We illustrate the usage of `brainpy.inputs.ramp_input()` by two examples." + ] + }, + { + "cell_type": "markdown", + "id": "68262531", + "metadata": {}, + "source": [ + "In the first example, we increase the current size from 0. to 1. between the start time (0 ms) and the end time (500 ms). " + ] + }, + { + "cell_type": "code", + "id": "ce29ec3c", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "source": [ + "duration = 500\n", + "current4 = bp.inputs.ramp_input(0, 1, duration)\n", + "\n", + "show(current4, duration, r'$c_{start}$=0, $c_{end}$=%d, duration, '\n", + " r'$t_{start}$=0, $t_{end}$=None' % (duration))" + ], + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ] + }, + { + "cell_type": "markdown", + "id": "7f765623", + "metadata": {}, + "source": [ + "In the second example, we increase the current size from 0. to 1. from the 100 ms to 400 ms." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "d0caf6ea", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "duration, t_start, t_end = 500, 100, 400\n", + "current5 = bp.inputs.ramp_input(0, 1, duration, t_start, t_end)\n", + "\n", + "show(current5, duration, r'$c_{start}$=0, $c_{end}$=1, duration=%d, '\n", + " r'$t_{start}$=%d, $t_{end}$=%d' % (duration, t_start, t_end))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### 5\\. ``brainpy.inputs.wiener_process``" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "[brainpy.inputs.wiener_process()](../apis/inputs/generated/brainpy.inputs.wiener_process.rst) is used to generate the basic Wiener process $dW$, i.e. random numbers drawn from $N(0, \\sqrt{dt})$." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "duration = 200\n", + "current6 = bp.inputs.wiener_process(duration, n=2, t_start=10., t_end=180.)\n", + "show(current6, duration, 'Wiener Process')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### ``brainpy.inputs.ou_process``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "[brainpy.inputs.ou_process()](../apis/inputs/generated/brainpy.inputs.ou_process.rst) is used to generate the noise time series from Ornstein-Uhlenback process $\\dot{x} = (\\mu - x)/\\tau \\cdot dt + \\sigma\\cdot dW$." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "duration = 200\n", + "current7 = bp.inputs.ou_process(mean=1., sigma=0.1, tau=10., duration=duration, n=2, t_start=10., t_end=180.)\n", + "show(current7, duration, 'Ornstein-Uhlenbeck Process')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### ``brainpy.inputs.sinusoidal_input``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "[brainpy.inputs.sinusoidal_input()](../apis/inputs/generated/brainpy.inputs.sinusoidal_input.rst) can help to generate sinusoidal inputs." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "duration = 2000\n", + "current8 = bp.inputs.sinusoidal_input(amplitude=1., frequency=2.0, duration=duration, t_start=100., )\n", + "show(current8, duration, 'Sinusoidal Input')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### ``brainpy.inputs.square_input``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "[brainpy.inputs.square_input()](../apis/inputs/generated/brainpy.inputs.square_input.rst) can help to generate oscillatory square inputs." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 12, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "duration = 2000\n", + "current9 = bp.inputs.square_input(amplitude=1., frequency=2.0,\n", + " duration=duration, t_start=100)\n", + "show(current9, duration, 'Square Input')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### More complex inputs" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "id": "5ec7e24c", + "metadata": {}, + "source": [ + "Because the current input is stored as a tensor, a complex input can be realized by the combination of several simple currents." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "64ac8ffa", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show(current1 + current5, 500, 'A Complex Current Input')" + ] + }, + { + "cell_type": "markdown", + "id": "307b4eb1", + "metadata": {}, + "source": [ + "## General properties of input functions" + ] + }, + { + "cell_type": "markdown", + "id": "4601f294", + "metadata": {}, + "source": [ + "**1\\. Every input function receives a ``dt`` specification.**\n", + "\n", + "If ``dt`` is not provided, input functions will use the default ``dt`` in the whole BrainPy system. " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bf9084a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I1.shape: (600,)\n", + "I2.shape: (6000,)\n" + ] + } + ], + "source": [ + "I1 = bp.inputs.section_input(values=[0, 1, 2], durations=[10, 20, 30], dt=0.1)\n", + "I2 = bp.inputs.section_input(values=[0, 1, 2], durations=[10, 20, 30], dt=0.01)\n", + "print('I1.shape: {}'.format(I1.shape))\n", + "print('I2.shape: {}'.format(I2.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "3b0ac63a", + "metadata": {}, + "source": [ + "**2\\. All input functions can automatically broadcast the current shapes if they are heterogenous among different periods.**\n", + "\n", + "For example, during period 1 we give an input with a scalar value, during period 2 we give an input with a vector shape, and during period 3 we give a matrix input value. Input functions will broadcast them to the maximum shape. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fa0679d0", + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": "(5000, 3, 10)" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "current = bp.inputs.section_input(values=[0, bm.ones(10), bm.random.random((3, 10))],\n", + " durations=[100, 300, 100])\n", + "\n", + "current.shape" + ] + } + ], + "metadata": { + "kernelspec": { + "name": "brainpy", + "language": "python", + "display_name": "brainpy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + }, + "latex_envs": { + "LaTeX_envs_menu_present": true, + "autoclose": false, + "autocomplete": true, + "bibliofile": "biblio.bib", + "cite_by": "apalike", + "current_citInitial": 1, + "eqLabelWithNumbers": true, + "eqNumInitial": 1, + "hotkeys": { + "equation": "Ctrl-E", + "itemize": "Ctrl-I" + }, + "labels_anchors": false, + "latex_user_defs": false, + "report_style_numbering": false, + "user_envs_cfg": false + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": {}, + "toc_section_display": true, + "toc_window_display": true + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/docs/tutorial_toolbox/ode_numerical_solvers.ipynb b/docs/tutorial_toolbox/ode_numerical_solvers.ipynb index d9f1b4b3..0a9f25be 100644 --- a/docs/tutorial_toolbox/ode_numerical_solvers.ipynb +++ b/docs/tutorial_toolbox/ode_numerical_solvers.ipynb @@ -5,7 +5,7 @@ "id": "premium-shield", "metadata": {}, "source": [ - "# Numerical Solvers for ODEs" + "# Numerical Solvers for Ordinary Differential Equations" ] }, { @@ -1256,4 +1256,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/tutorial_toolbox/runners.ipynb b/docs/tutorial_toolbox/runners.ipynb index 4b8a222e..bb83397c 100644 --- a/docs/tutorial_toolbox/runners.ipynb +++ b/docs/tutorial_toolbox/runners.ipynb @@ -78,7 +78,7 @@ "In which\n", "- ``target`` specifies the model to be simulated. It must an instance of [brainpy.DynamicalSystem](../apis/auto/simulation/generated/brainpy.simulation.brainobjects.DynamicalSystem.rst). \n", "- ``monitors`` is used to define target variables in the model. During the simulation, the history values of the monitored variables will be recorded. More information can be found in the [Monitors](monitors.ipynb) tutorial.\n", - "- ``inputs`` is used to define the input operations for specific variables. It will be expanded later in this tutorial.\n", + "- ``inputs`` is used to define the input operations for specific variables. It will be expanded in the [Inputs](inputs.ipynb) tutorial.\n", "- ``dyn_vars`` is used to specify all the dynamically changed [variables](../tutorial_math/variables.ipynb) used in the ``target`` model.\n", "- ``jit`` determines whether to use [JIT compilation](../tutorial_math/compilation.ipynb) during the simulation." ] @@ -316,456 +316,6 @@ "bp.visualize.raster_plot(runner.mon.ts, runner.mon['E.spike'], show=True)" ] }, - { - "cell_type": "markdown", - "id": "50aa356e", - "metadata": {}, - "source": [ - "### Inputs for runners" - ] - }, - { - "cell_type": "markdown", - "id": "f9c7d3ca", - "metadata": {}, - "source": [ - "BrainPy provides `inputs` operations for each instance of ``brainpy.Runner``. The aim of ``inputs`` is to mimic the input operations in experiments like Transcranial Magnetic Stimulation (TMS) and patch clamp recording.\n", - "\n", - "``inputs`` should have the format like ``(target, value, [type, operation])``, where \n", - "- ``target`` is the target variable to inject the input.\n", - "- ``value`` is the input value. It can be a scalar, a tensor, or a iterable object/function.\n", - "- ``type`` is the type of the input value. It support two types of input: ``fix`` and ``iter``. The first one means that the data is static; the second one denotes the data can be iterable, no matter whether the input value is a tensor or a function. The `iter` type must be explicitly stated. \n", - "- ``operation`` is the input operation on the target variable. It should be set as one of `{ + , - , * , / , = }`, and if users do not provide this item explicitly, it will be set to '+' by default, which means that the target variable will be updated as ``val = val + input``. " - ] - }, - { - "cell_type": "markdown", - "id": "3451b77b", - "metadata": {}, - "source": [ - "Users can also give multiple inputs for different target variables, like:\n", - "\n", - "```python\n", - "\n", - "inputs=[(target1, value1, [type1, op1]), \n", - " (target2, value2, [type2, op2]),\n", - " ... ]\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "e377d41a", - "metadata": {}, - "source": [ - "The mechanism of ``inputs`` is the same as [``monitors``](monitors.ipynb). BrainPy finds the target variables for input operations through [the absolute or relative path](../tutorial_math/base.ipynb). " - ] - }, - { - "cell_type": "markdown", - "id": "844fcb78", - "metadata": {}, - "source": [ - "### Input construction functions " - ] - }, - { - "cell_type": "markdown", - "id": "a4ff6914", - "metadata": {}, - "source": [ - "Like electrophysiological experiments, model simulation also needs various kind of inputs. BrainPy provide several convenient input functions to help users construct input currents. " - ] - }, - { - "cell_type": "markdown", - "id": "64f9a99c", - "metadata": {}, - "source": [ - "**1\\. ``brainpy.inputs.section_input()``**\n", - "\n", - "[brainpy.inputs.section_input()](../apis/simulation/generated/brainpy.simulation.inputs.section_input.rst) is an updated function of previous `brainpy.inputs.constant_input()` (see below). \n", - "\n", - "Sometimes, we need input currents with different values in different periods. For example, if you want to get an input that is 0 in the first 100 ms, 1 in the next 300 ms, and 0 again from the last 100 ms, you can define:" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "078fbd0d", - "metadata": {}, - "outputs": [], - "source": [ - "current1, duration = bp.inputs.section_input(values=[0, 1., 0.],\n", - " durations=[100, 300, 100],\n", - " return_length=True,\n", - " dt=0.1)" - ] - }, - { - "cell_type": "markdown", - "id": "579e8b2d", - "metadata": {}, - "source": [ - "Where `values` receive a list/arrray of the current values in each section and `durations` receives a list/array of the duration of each section. The function returns a tensor as the current, the length of which is `duration`$/\\mathrm{d}t$ (if not specified, $\\mathrm{d}t=0.1 \\mathrm{ms}$). We can visualize the current input by:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "54aec8c9", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def show(current, duration, title):\n", - " ts = np.arange(0, duration, 0.1)\n", - " plt.plot(ts, current)\n", - " plt.title(title)\n", - " plt.xlabel('Time [ms]')\n", - " plt.ylabel('Current Value')\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "1a18a549", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "show(current1, duration, 'values=[0, 1, 0], durations=[100, 300, 100]')" - ] - }, - { - "cell_type": "markdown", - "id": "6b1eee02", - "metadata": {}, - "source": [ - "**2\\. ``brainpy.inputs.constant_input()``**" - ] - }, - { - "cell_type": "markdown", - "id": "26708359", - "metadata": {}, - "source": [ - "[brainpy.inputs.constant_input()](../apis/simulation/generated/brainpy.simulation.inputs.constant_input.rst) function helps users to format constant currents in several periods.\n", - "\n", - "We can generate the above input current with `constant_input()` by:" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8ea6dea6", - "metadata": {}, - "outputs": [], - "source": [ - "current2, duration = bp.inputs.constant_input([(0, 100), (1, 300), (0, 100)])" - ] - }, - { - "cell_type": "markdown", - "id": "6cc74d90", - "metadata": {}, - "source": [ - "Where each tuple in the list contains the value and duration of the input in this section." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "e862ebad", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ikUGZ0UfbncZrvdD/OeA9EfGIOnxkPCKWAcsA+vv73VmgJI/FYk0W3sErU+foo4PAjML0dGBLyzr9wIo8BCYDp0naERH/OcL3NDOzPbTbIJD0unbzI+LC3bx0NTBb0izg98DpwD+0bOPPISNpOXCpQ6B6Hp/Lmsb7dLXKnBp6RuH5AcCpwC+BjkEQETsknU3WG2gMcH5ErJe0KF/e8bqAmZl1R5lTQ28tTks6CPh6mY1HxCpgVcu8tgEQEW8os00zM6tWmV5DrR4AZlddiJmZ9UaZawSX8Ghvn/2AOcDFdRZlZmbdU+YawacKz3cAt0bEYE31WIXCw3KZWQnDBoGkJwOHRcRVLfOfK2l8RPy29urMzKx2na4RfA64t838B/NlZmY94d6j1eoUBH0RcX3rzIhYA/TVVpGZmXVVpyA4oMOyA6suxMzMeqNTEKyW9JbWmZLOJLsngZmZNUCnXkNvB74r6dU8+oe/HxhHNmKo7eM8Jpc1mffv6gwbBBFxO/BsSc8Hjspnfz8iftyVyszMrCvKDDFxBXBFF2qxmniALmuaTsPW254byRATZmbWIA4CM7PEOQjMzBLnIDCzUcljaVXHQWBmljgHQQLkkVmsYbxHV8tBYGaWOAeBmVniHARmZolzEJiZJc5B0GDhUbmswbx7V8dBYGaWOAdBAjw+lzWN9+lqOQjMzBLnIDAzS5yDwMwscQ6CBnOvCmsy79/VcRCYmSXOQZAAd7CwpvFAitWqNQgkzZO0UdKApMVtlr9a0vX542eSjq2zHjMz21VtQSBpDHAeMB+YA5whaU7LajcDz4uIY4CPAMvqqsfMzNqr84jgRGAgIjZFxHZgBbCguEJE/Cwi7sonrwGm11iPmZm1UWcQTAM2F6YH83nDORP4QbsFkhZKWiNpzbZt2yos0czM6gyCdldz2nb4kvR8siB4T7vlEbEsIvojon/KlCkVlths7l1nTeZ7FldnbI3bHgRmFKanA1taV5J0DPAVYH5E3FljPcmSB2axpvEuXak6jwhWA7MlzZI0DjgdWFlcQdJM4DvAayPiphprMTOzYdR2RBAROySdDVwGjAHOj4j1khbly5cCHwQOAb6Y/9e6IyL666rJzMx2VeepISJiFbCqZd7SwvM3A2+uswYzM+vMnyw2M0ucg6DBPCiXNZn37+o4CMzMEucgSIB72lnTeJ+uloPAzCxxDgIzs8Q5CMzMEucgaDCPxWJN5r27Og4CM7PEOQgS4DHnrGm8T1fLQWBmljgHgZlZ4hwEZmaJcxCYmSXOQdBgHpTLGs37d2UcBAnwrSqtaeTRhirlIDAzS5yDwMwscQ4CM7PEOQjMzBLnIGgwd6qwJvOgitVxEJiZJc5BYGajjntEV8tBYGaWOAeBmVniHARmZolzEDSZBxuyBvPuXR0HgZlZ4hwEDefeFdZE3q+r5SAwM0ucg8DMLHG1BoGkeZI2ShqQtLjNckn6fL78eklPr7MeMzPbVW1BIGkMcB4wH5gDnCFpTstq84HZ+WMh8KW66jEzs/bG1rjtE4GBiNgEIGkFsAC4sbDOAuDCiAjgGkmTJE2NiNuqLuaqm7bx0Utv3P2KDXLn/dt7XYJZbdb9/m5e+Jmrel1GV/39M2bw5uceUfl26wyCacDmwvQg8MwS60wDHhMEkhaSHTEwc+bMERUzYfxYZh82YUSvHa1mA7MPndjrMswq95pnHs5BB+7f6zK6bvKE8bVst84gaNfBq/UjIGXWISKWAcsA+vv7R/QxkhMOP5gTDj9hJC81s33M/KOnMv/oqb0uozHqvFg8CMwoTE8HtoxgHTMzq1GdQbAamC1plqRxwOnAypZ1VgKvy3sPnQTcXcf1ATMzG15tp4YiYoeks4HLgDHA+RGxXtKifPlSYBVwGjAAPAC8sa56zMysvTqvERARq8j+2BfnLS08D+CsOmswM7PO/MliM7PEOQjMzBLnIDAzS5yDwMwscYpRdpsfSduAW0f48snAHRWWMxq4zWlwm9OwN20+PCKmtFsw6oJgb0haExH9va6jm9zmNLjNaairzT41ZGaWOAeBmVniUguCZb0uoAfc5jS4zWmopc1JXSMwM7NdpXZEYGZmLRwEZmaJSyYIJM2TtFHSgKTFva6nKpLOl7RV0g2FeU+UdLmk3+RfDy4se2/+Pdgo6cW9qXrkJM2QdIWkDZLWS3pbPr/JbT5A0i8kXZe3+Zx8fmPbPETSGEm/knRpPt3oNku6RdI6SWslrcnn1d/miGj8g2wY7N8CRwDjgOuAOb2uq6K2/RXwdOCGwrwlwOL8+WLgE/nzOXnbxwOz8u/JmF63YQ/bOxV4ev58InBT3q4mt1nAhPz5/sD/Aic1uc2Ftr8T+Dfg0ny60W0GbgEmt8yrvc2pHBGcCAxExKaI2A6sABb0uKZKRMTVwB9bZi8AvpY//xrwisL8FRHxUETcTHYfiBO7UWdVIuK2iPhl/vxeYAPZfa6b3OaIiPvyyf3zR9DgNgNImg68BPhKYXaj2zyM2tucShBMAzYXpgfzeU11WOR3esu/HprPb9T3QVIfcDzZf8iNbnN+imQtsBW4PCIa32bgc8C/ADsL85re5gB+JOlaSQvzebW3udYb0+xD1GZeiv1mG/N9kDQB+Dbw9oi4R2rXtGzVNvNGXZsj4hHgOEmTgO9KOqrD6qO+zZJeCmyNiGslzS3zkjbzRlWbcydHxBZJhwKXS/p1h3Ura3MqRwSDwIzC9HRgS49q6YbbJU0FyL9uzec34vsgaX+yEPhmRHwnn93oNg+JiD8BVwLzaHabTwZeLukWslO5p0j6Bs1uMxGxJf+6Ffgu2ame2tucShCsBmZLmiVpHHA6sLLHNdVpJfD6/Pnrge8V5p8uabykWcBs4Bc9qG/ElP3r/1VgQ0R8prCoyW2ekh8JIOlA4AXAr2lwmyPivRExPSL6yH5ffxwRr6HBbZb0eEkTh54DLwJuoBtt7vVV8i5ejT+NrIfJb4H39bqeCtt1EXAb8DDZfwhnAocA/w38Jv/6xML678u/BxuB+b2ufwTtfQ7Z4e/1wNr8cVrD23wM8Ku8zTcAH8znN7bNLe2fy6O9hhrbZrJejdflj/VDf6e60WYPMWFmlrhUTg2ZmdkwHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEFgSJB2SD+27VtIfJP0+f36fpC/W8H7LJd0saVEF2/pkXvO7qqjNrFUqYw1Z4iLiTuA4AEkfBu6LiE/V/Lbvjohv7e1GIuLdku6voiCzdnxEYEmTNLdw05MPS/qapB/lNwh5paQl+Y1CfpiPcYSkEyRdlY8QednQODC7eZ/lkr6k7KY6myQ9T9lNhTZIWp6vMyZf74b8Pd9Ra+PNcg4Cs8c6kmwM/AXAN4ArIuJo4EHgJXkYnAu8KiJOAM4HPlZy2wcDpwDvAC4BPgs8DTha0nFkRyzTIuKo/D0vqKpRZp341JDZY/0gIh6WtI7sznY/zOevA/qAvwCOIhsimHyd20pu+5KIiHzbt0fEOgBJ6/NtXwUcIelc4PvAjyppkdluOAjMHushgIjYKenheHQwrp1kvy8C1kfEs0a67XxbDxXm7wTGRsRdko4FXgycBfwd8KYRvI/ZHvGpIbM9sxGYIulZkN0bQdLTqtiwpMnAfhHxbeADZPeiNqudjwjM9kBEbJf0KuDzkg4i+x36HNmwwXtrGnCBpKF/0N5bwTbNdsvDUJvVIO8JdGkV3Ufz7X2Y7nR5tQT51JBZPe4GPlLVB8qA1wD+LIHVwkcEZmaJ8xGBmVniHARmZolzEJiZJc5BYGaWuP8HXYNsW43a1mgAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "show(current2, duration, '[(0, 100), (1, 300), (0, 100)]')" - ] - }, - { - "cell_type": "markdown", - "id": "067aae19", - "metadata": {}, - "source": [ - "**3\\. ``brainpy.inputs.spike_input()``**" - ] - }, - { - "cell_type": "markdown", - "id": "e6ea2868", - "metadata": {}, - "source": [ - "[brainpy.inputs.spike_input()](../apis/simulation/generated/brainpy.simulation.inputs.spike_input.rst) constructs an input containing a series of short-time spikes. It receives the following settings:\n", - "\n", - "- `sp_times` : The spike time-points. Must be an iterable object. For example, list, tuple, or arrays.\n", - "- `sp_lens` : The length of each point-current, mimicking the spike durations. It can be a scalar float to specify the unified duration. Or, it can be list/tuple/array of time lengths with the length same with `sp_times`. \n", - "- `sp_sizes` : The current sizes. It can be a scalar value. Or, it can be a list/tuple/array of spike current sizes with the length same with `sp_times`.\n", - "- `duration` : The total current duration.\n", - "- `dt` : The time step precision. The default is None (will be initialized as the default `dt` step). " - ] - }, - { - "cell_type": "markdown", - "id": "146ebc19", - "metadata": {}, - "source": [ - "For example, if you want to generate a spike train at 10 ms, 20 ms, 30 ms, 200 ms, 300 ms, where each spike lasts 1 ms and the average value for each spike is 0.5, then you can define the current by:" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "1eb035a2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "current3 = bp.inputs.spike_input(\n", - " sp_times=[10, 20, 30, 200, 300],\n", - " sp_lens=1., # can be a list to specify the spike length at each point\n", - " sp_sizes=0.5, # can be a list to specify the spike current size at each point\n", - " duration=400.)\n", - "\n", - "show(current3, 400, 'Spike Input Example')" - ] - }, - { - "cell_type": "markdown", - "id": "68262531", - "metadata": {}, - "source": [ - "**4\\. ``brainpy.inputs.ramp_input()``**" - ] - }, - { - "cell_type": "markdown", - "id": "ce29ec3c", - "metadata": {}, - "source": [ - "[brainpy.inputs.ramp_input()](../apis/simulation/generated/brainpy.simulation.inputs.ramp_input.rst) mimics a ramp or a step current to the input of the circuit. It receives the following settings:\n", - "\n", - "- `c_start` : The minimum (or maximum) current size.\n", - "- `c_end` : The maximum (or minimum) current size.\n", - "- `duration` : The total duration.\n", - "- `t_start` : The ramped current start time-point.\n", - "- `t_end` : The ramped current end time-point. Default is the None.\n", - "- `dt` : The current precision.\n", - "\n", - "We illustrate the usage of `brainpy.inputs.ramp_input()` by two examples." - ] - }, - { - "cell_type": "markdown", - "id": "7435a038", - "metadata": {}, - "source": [ - "In the first example, we increase the current size from 0. to 1. between the start time (0 ms) and the end time (500 ms). " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "a667a133", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "duration = 500\n", - "current4 = bp.inputs.ramp_input(0, 1, duration)\n", - "\n", - "show(current4, duration, r'$c_{start}$=0, $c_{end}$=%d, duration, '\n", - " r'$t_{start}$=0, $t_{end}$=None' % (duration))" - ] - }, - { - "cell_type": "markdown", - "id": "7f765623", - "metadata": {}, - "source": [ - "In the second example, we increase the current size from 0. to 1. from the 100 ms to 400 ms." - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "d0caf6ea", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "duration, t_start, t_end = 500, 100, 400\n", - "current5 = bp.inputs.ramp_input(0, 1, duration, t_start, t_end)\n", - "\n", - "show(current5, duration, r'$c_{start}$=0, $c_{end}$=1, duration=%d, '\n", - " r'$t_{start}$=%d, $t_{end}$=%d' % (duration, t_start, t_end))" - ] - }, - { - "cell_type": "markdown", - "id": "5ec7e24c", - "metadata": {}, - "source": [ - "Because the current input is stored as a tensor, a complex input can be realized by adding several small currents together." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "64ac8ffa", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "show(current1 + current5, 500, 'A Complex Current Input')" - ] - }, - { - "cell_type": "markdown", - "id": "307b4eb1", - "metadata": {}, - "source": [ - "### General property of input functions" - ] - }, - { - "cell_type": "markdown", - "id": "4601f294", - "metadata": {}, - "source": [ - "1\\. Every input function receives a ``dt`` specification. If ``dt`` is not provided, input functions will use the default ``dt`` in the whole BrainPy system. " - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "bf9084a9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "I1.shape: (600,)\n", - "I2.shape: (6000,)\n" - ] - } - ], - "source": [ - "I1 = bp.inputs.section_input(values=[0, 1, 2], durations=[10, 20, 30], dt=0.1)\n", - "I2 = bp.inputs.section_input(values=[0, 1, 2], durations=[10, 20, 30], dt=0.01)\n", - "print('I1.shape: {}'.format(I1.shape))\n", - "print('I2.shape: {}'.format(I2.shape))" - ] - }, - { - "cell_type": "markdown", - "id": "3b0ac63a", - "metadata": {}, - "source": [ - "2\\. All input functions can automatically broadcast the current shapes, if they are heterogenous among different periods. For example, during period 1 we give an input with a scalar value, during period 2 we give an input with a vector shape, and during period 3 we give a matrix input value. Input functions will broadcast them to the maximum shape. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "fa0679d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(5000, 3, 10)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "current = bp.inputs.section_input(values=[0, bm.ones(10), bm.random.random((3, 10))],\n", - " durations=[100, 300, 100])\n", - "\n", - "current.shape" - ] - }, { "cell_type": "markdown", "id": "3551f214", @@ -785,7 +335,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -799,7 +349,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.8.8" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/docs/tutorial_toolbox/sde_numerical_solvers.ipynb b/docs/tutorial_toolbox/sde_numerical_solvers.ipynb index 716fe1a6..e7038617 100644 --- a/docs/tutorial_toolbox/sde_numerical_solvers.ipynb +++ b/docs/tutorial_toolbox/sde_numerical_solvers.ipynb @@ -5,7 +5,7 @@ "id": "premium-shield", "metadata": {}, "source": [ - "# Numerical Solvers for SDEs" + "# Numerical Solvers for Stochastic Differential Equations" ] }, { @@ -520,4 +520,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/tutorial_toolbox/synaptic_connections.ipynb b/docs/tutorial_toolbox/synaptic_connections.ipynb index f4197ad3..397f5012 100644 --- a/docs/tutorial_toolbox/synaptic_connections.ipynb +++ b/docs/tutorial_toolbox/synaptic_connections.ipynb @@ -388,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-03-25T03:02:48.939126Z", @@ -407,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -439,16 +439,27 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "conn = bp.connect.One2One()\n", - "conn(pre_size=size, post_size=size)" + "conn(pre_size=10, post_size=10)" ] }, { @@ -480,7 +491,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" @@ -491,9 +502,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "pre_ids JaxArray(DeviceArray([0, 1, 2, 3, 4], dtype=uint32))\n", - "post_ids JaxArray(DeviceArray([0, 1, 2, 3, 4], dtype=uint32))\n", - "pre2post (JaxArray(DeviceArray([0, 1, 2, 3, 4], dtype=uint32)), JaxArray(DeviceArray([0, 1, 2, 3, 4, 5], dtype=uint32)))\n" + "pre_ids: JaxArray([0, 1, 2, 3, 4], dtype=uint32)\n", + "post_ids: JaxArray([0, 1, 2, 3, 4], dtype=uint32)\n", + "pre2post: (JaxArray([0, 1, 2, 3, 4], dtype=uint32), JaxArray([0, 1, 2, 3, 4, 5], dtype=uint32))\n" ] } ], @@ -502,9 +513,9 @@ "conn = bp.connect.One2One()(pre_size=size, post_size=size)\n", "res = conn.require('pre_ids', 'post_ids', 'pre2post', 'conn_mat')\n", "\n", - "print('pre_ids', res[0])\n", - "print('post_ids', res[1])\n", - "print('pre2post', conn.pre2post)" + "print('pre_ids:', res[0])\n", + "print('post_ids:', res[1])\n", + "print('pre2post:', res[2])" ] }, { @@ -523,9 +534,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "conn = bp.connect.All2All(include_self=False)\n", "conn(pre_size=size, post_size=size)" @@ -546,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" @@ -557,14 +579,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "pre_ids JaxArray(DeviceArray([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4], dtype=uint32))\n", - "post_ids JaxArray(DeviceArray([1, 2, 3, 4, 0, 2, 3, 4, 0, 1, 3, 4, 0, 1, 2, 4, 0, 1, 2, 3], dtype=uint32))\n", - "pre2post (JaxArray(DeviceArray([1, 2, 3, 4, 0, 2, 3, 4, 0, 1, 3, 4, 0, 1, 2, 4, 0, 1, 2, 3], dtype=uint32)), JaxArray(DeviceArray([ 0, 4, 8, 12, 16, 20], dtype=uint32)))\n", - "conn_mat JaxArray(DeviceArray([[False, True, True, True, True],\n", - " [ True, False, True, True, True],\n", - " [ True, True, False, True, True],\n", - " [ True, True, True, False, True],\n", - " [ True, True, True, True, False]], dtype=bool))\n" + "pre_ids: JaxArray([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4], dtype=uint32)\n", + "post_ids: JaxArray([1, 2, 3, 4, 0, 2, 3, 4, 0, 1, 3, 4, 0, 1, 2, 4, 0, 1, 2, 3], dtype=uint32)\n", + "pre2post: (JaxArray([1, 2, 3, 4, 0, 2, 3, 4, 0, 1, 3, 4, 0, 1, 2, 4, 0, 1, 2, 3], dtype=uint32), JaxArray([ 0, 4, 8, 12, 16, 20], dtype=uint32))\n", + "conn_mat: JaxArray([[False, True, True, True, True],\n", + " [ True, False, True, True, True],\n", + " [ True, True, False, True, True],\n", + " [ True, True, True, False, True],\n", + " [ True, True, True, True, False]], dtype=bool)\n" ] } ], @@ -572,10 +594,10 @@ "conn = bp.connect.All2All(include_self=False)(pre_size=size, post_size=size)\n", "res = conn.require('pre_ids', 'post_ids', 'pre2post', 'conn_mat')\n", "\n", - "print('pre_ids', res[0])\n", - "print('post_ids', res[1])\n", - "print('pre2post', conn.pre2post)\n", - "print('conn_mat', conn.conn_mat)" + "print('pre_ids:', res[0])\n", + "print('post_ids:', res[1])\n", + "print('pre2post:', res[2])\n", + "print('conn_mat:', res[3])" ] }, { @@ -592,9 +614,20 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "conn = bp.connect.GridFour(include_self=False)\n", "conn(pre_size=size)" @@ -615,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" @@ -626,10 +659,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "pre_ids JaxArray(DeviceArray([ 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 5, 5,\n", - " 5, 5, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9,\n", - " 9, 10, 10, 10, 10, 11, 11, 11, 12, 12, 13, 13, 13, 14, 14,\n", - " 14, 15, 15], dtype=uint32))\n" + "pre_ids JaxArray([ 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 4, 4, 4, 5, 5,\n", + " 5, 5, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8, 9, 9, 9,\n", + " 9, 10, 10, 10, 10, 11, 11, 11, 12, 12, 13, 13, 13, 14, 14,\n", + " 14, 15, 15], dtype=uint32)\n" ] } ], @@ -643,12 +676,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -659,7 +692,7 @@ ], "source": [ "# Using NetworkX to visualize network connection\n", - "G = nx.from_numpy_matrix(conn.conn_mat)\n", + "G = nx.from_numpy_matrix(res[1])\n", "nx.draw(G, with_labels=True)\n", "plt.show()" ] @@ -678,9 +711,20 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "conn = bp.connect.GridEight(include_self=False)\n", "conn(pre_size=size)" @@ -701,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" @@ -712,12 +756,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "pre_ids JaxArray(DeviceArray([ 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3,\n", - " 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 6,\n", - " 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8,\n", - " 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10,\n", - " 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 13, 13, 13, 13,\n", - " 13, 14, 14, 14, 14, 14, 15, 15, 15], dtype=uint32))\n" + "pre_ids JaxArray([ 0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3,\n", + " 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 6,\n", + " 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8,\n", + " 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10,\n", + " 10, 10, 10, 11, 11, 11, 11, 11, 12, 12, 12, 13, 13, 13, 13,\n", + " 13, 14, 14, 14, 14, 14, 15, 15, 15], dtype=uint32)\n" ] } ], @@ -743,12 +787,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -759,7 +803,7 @@ ], "source": [ "# Using NetworkX to visualize network connection\n", - "G = nx.from_numpy_matrix(conn.conn_mat)\n", + "G = nx.from_numpy_matrix(res[1])\n", "nx.draw(G, with_labels=True)\n", "plt.show()" ] @@ -796,9 +840,20 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "conn = bp.connect.GridN(N=2, include_self=False)\n", "conn(pre_size=size)" @@ -817,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": { "pycharm": { "name": "#%%\n" @@ -832,12 +887,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -848,7 +903,7 @@ ], "source": [ "# Using NetworkX to visualize network connection\n", - "G = nx.from_numpy_matrix(conn.conn_mat)\n", + "G = nx.from_numpy_matrix(res)\n", "nx.draw(G, with_labels=True)\n", "plt.show()" ] @@ -882,19 +937,19 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[False, True, False, False],\n", - " [False, False, False, False],\n", - " [ True, True, False, True],\n", - " [ True, False, True, False]], dtype=bool))" + "JaxArray([[False, True, False, False],\n", + " [False, False, False, False],\n", + " [ True, True, False, True],\n", + " [ True, False, True, False]], dtype=bool)" ] }, - "execution_count": 35, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -929,19 +984,19 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[ True, True, False, False],\n", - " [False, True, True, False],\n", - " [False, False, True, True],\n", - " [ True, False, False, True]], dtype=bool))" + "JaxArray([[ True, False, False, False],\n", + " [False, True, False, False],\n", + " [ True, True, True, True],\n", + " [False, False, True, True]], dtype=bool)" ] }, - "execution_count": 31, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -975,19 +1030,19 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[ True, False, False, True],\n", - " [ True, True, False, False],\n", - " [False, True, True, False],\n", - " [False, False, True, True]], dtype=bool))" + "JaxArray([[ True, False, True, False],\n", + " [False, True, True, False],\n", + " [False, False, True, True],\n", + " [False, False, True, True]], dtype=bool)" ] }, - "execution_count": 38, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1044,35 +1099,35 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[ True, True, False, True, False, False, False, False,\n", - " True, True],\n", - " [ True, True, True, True, False, False, False, False,\n", - " False, True],\n", - " [ True, True, True, True, False, False, False, False,\n", - " False, True],\n", - " [False, True, True, True, True, False, False, False,\n", - " False, False],\n", - " [False, False, False, True, True, True, False, False,\n", - " False, False],\n", - " [False, False, True, False, True, True, True, True,\n", - " False, False],\n", - " [False, False, False, True, False, True, True, True,\n", - " False, False],\n", - " [ True, False, False, False, False, True, True, True,\n", - " False, True],\n", - " [False, True, False, False, False, False, True, True,\n", - " True, True],\n", - " [ True, False, True, False, False, True, False, True,\n", - " True, True]], dtype=bool))" + "JaxArray([[ True, True, False, True, False, False, False, False,\n", + " True, True],\n", + " [ True, True, True, True, False, False, False, False,\n", + " False, True],\n", + " [ True, True, True, True, False, False, False, False,\n", + " False, True],\n", + " [False, True, True, True, True, False, False, False,\n", + " False, False],\n", + " [False, False, False, True, True, True, False, False,\n", + " False, False],\n", + " [False, False, True, False, True, True, True, True,\n", + " False, False],\n", + " [False, False, False, True, False, True, True, True,\n", + " False, False],\n", + " [ True, False, False, False, False, True, True, True,\n", + " False, True],\n", + " [False, True, False, False, False, False, True, True,\n", + " True, True],\n", + " [ True, False, True, False, False, True, False, True,\n", + " True, True]], dtype=bool)" ] }, - "execution_count": 65, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1085,12 +1140,12 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 34, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1101,7 +1156,7 @@ ], "source": [ "# Using NetworkX to visualize network connection\n", - "G = nx.from_numpy_matrix(conn.conn_mat)\n", + "G = nx.from_numpy_matrix(conn.require('conn_mat'))\n", "nx.draw(G, with_labels=True)\n", "plt.show()" ] @@ -1140,35 +1195,35 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[False, True, True, False, False, False, False, False,\n", - " True, True],\n", - " [ True, False, True, True, False, False, False, False,\n", - " False, False],\n", - " [ True, True, False, True, False, False, False, True,\n", - " False, True],\n", - " [False, True, True, False, True, False, False, False,\n", - " True, False],\n", - " [False, False, False, True, False, True, True, False,\n", - " False, True],\n", - " [False, False, False, False, True, False, True, True,\n", - " False, False],\n", - " [False, False, False, False, True, True, False, False,\n", - " True, True],\n", - " [False, False, True, False, False, True, False, False,\n", - " False, True],\n", - " [ True, False, False, True, False, False, True, False,\n", - " False, True],\n", - " [ True, False, True, False, True, False, True, True,\n", - " True, False]], dtype=bool))" + "JaxArray([[False, True, False, False, False, False, True, False,\n", + " True, False],\n", + " [ True, False, True, True, False, True, False, False,\n", + " False, True],\n", + " [False, True, False, True, True, False, False, False,\n", + " False, False],\n", + " [False, True, True, False, True, True, False, False,\n", + " False, False],\n", + " [False, False, True, True, False, True, True, False,\n", + " False, False],\n", + " [False, True, False, True, True, False, False, True,\n", + " False, True],\n", + " [ True, False, False, False, True, False, False, True,\n", + " True, False],\n", + " [False, False, False, False, False, True, True, False,\n", + " True, True],\n", + " [ True, False, False, False, False, False, True, True,\n", + " False, True],\n", + " [False, True, False, False, False, True, False, True,\n", + " True, False]], dtype=bool)" ] }, - "execution_count": 78, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1181,12 +1236,12 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1197,7 +1252,7 @@ ], "source": [ "# Using NetworkX to visualize network connection\n", - "G = nx.from_numpy_matrix(conn.conn_mat)\n", + "G = nx.from_numpy_matrix(conn.require('conn_mat'))\n", "nx.draw(G, with_labels=True)\n", "plt.show()" ] @@ -1224,35 +1279,35 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[False, False, False, False, False, True, True, True,\n", - " False, True],\n", - " [False, False, False, False, False, True, True, True,\n", - " True, True],\n", - " [False, False, False, False, False, True, True, False,\n", - " False, False],\n", - " [False, False, False, False, False, True, False, False,\n", - " False, True],\n", - " [False, False, False, False, False, True, True, True,\n", - " True, False],\n", - " [ True, True, True, True, True, False, True, True,\n", - " True, True],\n", - " [ True, True, True, False, True, True, False, True,\n", - " True, True],\n", - " [ True, True, False, False, True, True, True, False,\n", - " True, False],\n", - " [False, True, False, False, True, True, True, True,\n", - " False, False],\n", - " [ True, True, False, True, False, True, True, False,\n", - " False, False]], dtype=bool))" + "JaxArray([[False, False, False, False, False, True, True, True,\n", + " False, True],\n", + " [False, False, False, False, False, True, True, True,\n", + " True, True],\n", + " [False, False, False, False, False, True, True, False,\n", + " False, False],\n", + " [False, False, False, False, False, True, False, False,\n", + " False, True],\n", + " [False, False, False, False, False, True, True, True,\n", + " True, False],\n", + " [ True, True, True, True, True, False, True, True,\n", + " True, True],\n", + " [ True, True, True, False, True, True, False, True,\n", + " True, True],\n", + " [ True, True, False, False, True, True, True, False,\n", + " True, False],\n", + " [False, True, False, False, True, True, True, True,\n", + " False, False],\n", + " [ True, True, False, True, False, True, True, False,\n", + " False, False]], dtype=bool)" ] }, - "execution_count": 80, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1265,12 +1320,12 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1281,7 +1336,7 @@ ], "source": [ "# Using NetworkX to visualize network connection\n", - "G = nx.from_numpy_matrix(conn.conn_mat)\n", + "G = nx.from_numpy_matrix(conn.require('conn_mat'))\n", "nx.draw(G, with_labels=True)\n", "plt.show()" ] @@ -1310,35 +1365,35 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[False, False, False, False, False, True, False, True,\n", - " False, True],\n", - " [False, False, False, False, False, True, False, True,\n", - " True, False],\n", - " [False, False, False, False, False, True, True, False,\n", - " True, True],\n", - " [False, False, False, False, False, True, False, False,\n", - " False, False],\n", - " [False, False, False, False, False, True, True, True,\n", - " True, False],\n", - " [ True, True, True, True, True, False, True, True,\n", - " True, True],\n", - " [False, False, True, False, True, True, False, True,\n", - " True, False],\n", - " [ True, True, False, False, True, True, True, False,\n", - " False, True],\n", - " [False, True, True, False, True, True, True, False,\n", - " False, True],\n", - " [ True, False, True, False, False, True, False, True,\n", - " True, False]], dtype=bool))" + "JaxArray([[False, False, False, False, False, True, False, True,\n", + " False, True],\n", + " [False, False, False, False, False, True, False, True,\n", + " True, False],\n", + " [False, False, False, False, False, True, True, False,\n", + " True, True],\n", + " [False, False, False, False, False, True, False, False,\n", + " False, False],\n", + " [False, False, False, False, False, True, True, True,\n", + " True, False],\n", + " [ True, True, True, True, True, False, True, True,\n", + " True, True],\n", + " [False, False, True, False, True, True, False, True,\n", + " True, False],\n", + " [ True, True, False, False, True, True, True, False,\n", + " False, True],\n", + " [False, True, True, False, True, True, True, False,\n", + " False, True],\n", + " [ True, False, True, False, False, True, False, True,\n", + " True, False]], dtype=bool)" ] }, - "execution_count": 87, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1351,12 +1406,12 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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TbCKEIDAwEF5eXggPD8fixYvx22+/oX79+mxHEzlafCyqaOWWauEVgbmzDe311bF582apvJEnJRw5OTkYNWoU5OXlce7cOTRo0EBo7zVt2jS0aNECq1evFtp7UDX34sULbNq0CY8ePcLcuXMxd+7cGk9bkeQ7ydDiY9n/1uqs/ti7iqIc7JSTcXX7UvTq1QsPHjzAyJEjsXbtWnpTS6pSSUlJGDRoEGxsbLBv3z4oKNTgIqsa+vLlC6ysrBAVFSWwuYCUYIWHh2Pz5s24fPkypk6dikWLFqFZs2aVvkYa7iRDL25h2UTbVnBzagt5wgdI5eXH4fxYo9PNqS2O/TEVjx8/RkxMDDp06AA+nw8zMzN4eXmhoKBAROkpSfLx40fY2dlhxIgROHjwoFBLDwC2bNmC6dOn09ITY6ampjh27BjCwsLAMAwsLCwwffp0REZGlrv9qeBYjD0cjDsfU1DIY8pco8D9/8duf0jB2MPBYruYPi0+MTCwTUNk+a9FD0N1KCnIQfk/V3sqK8hBSUEO/c10cM7VtuSWRG3btsU///wDOzs7XL16FatXr8bLly9hYmKCU6dO0Su4qBKPHz+Gg4MD1q5dCzc3N6Hf5iY5ORl//vknFi1aJNT3oQSjefPm2LFjByIjI6Gvr4+uXbti1KhRCAkJKdlGmu4kQ4c6xYCHhweSk5Nx+PBhfMstxMWQBIQn5SCbWww1ZUWY6jXESKvKx82fPXsGFxcXdO7cGePHj8eaNWvAMAy2bduGnj17ivDTUOLm/PnzmDt3Lk6fPi2y+98tW7YMXC4Xu3fvFsn7UYKVm5uLw4cPY9u2bTA3N8eY2cuw+VURuP85JVOcHo9vt/ejKCUa8iqNoNFrKlRN7EttI453kqHFx7LMzEwYGRnh+fPnMDQ0rNO+8vLysGzZMly7dg1Hjx5Feno6Vq5ciQ4dOmDz5s0wNjYWUGpKEhBCsGPHDmzfvh1///03LC3LzgUVhoyMDLRp0wavX79G8+bNRfKelHAUFhbi1KlT2PAwFUSvHfCv1XwIw0fi4Vlo2HEgGtoMBTfuHdL+Wge9qbuh2Ph/5wk5HKC/mQ4OTCy73jBb6FAny/bu3QsnJ6c6lx7wY+Kqj48PDh8+jKlTpyI4OBihoaGwt7eHvb095s+fT1dylxF8Ph+///47jh07hqCgIJGVHvDj/n3Ozs609KSAkpISho2ZiHotO5QqPQAo/hYPfm4GGnb6BRw5eai0soRSMzPkvbtfajtxvJMMLT4W5ebmYvfu3fjjjz8Eut/+/fvjzZs3SE1Nhb29PXr37o2PHz+CYRi0bdsWW7ZsAZfLFeh7UuKjoKAAo0ePxps3b/DkyRO0aNFCZO+dnZ0NHx8frFixQmTvSQlXhXeSKXeskKAo7UuZR8XtTjK0+Fi0f/9+9OrVC6ampgLfd+PGjXHmzBmsWrUKTk5O2L9/P3bu3IknT57gyZMnaNu2Lc6dO0fX8JMy6enpcHR0hJKSEm7evAl1dXWRvv/+/fvRt29fGBkZifR9KeGp6E4yipr6kFdthOxnf4HweSiICQE37h0Ir+yRnbjdSYae42NJQUEBDA0NcevWLbRv316o7/X161f8+uuvyMzMhJ+fH4yNjREYGIglS5ZAUVER27ZtQ9euXYWagRK+z58/Y+DAgXB2dsbGjRtFfrPXgoICGBgY4O7du2jXrp1I35sSnl99X+B+eGq5zxWlxiDjzkEUp31BPT0jyKs2AuQVoeVUdqHzPqZNcHRyJ2HHrRZ6xMeSI0eOoEuXLkIvPeDHbUtu3ryJSZMmwd7eHj4+PnBwcMCLFy8we/ZsjB07FiNHjsSnT5+EnoUSjhcvXqBbt26YP38+vL29WbnD+ZEjR2Bra0tLT8pUdieZek0MoDvBG81/PwOdMevBy0yGkl75F9GJ051kaPGxoLCwEJs3b4a7u7vI3pPD4WDOnDl4+vQpTp48iQEDBiApKQmTJk1CREQEOnbsiM6dO2PRokXIyMgQWS6q7v7++++S4ew5c+awkqGoqAhbtmyBm5sbK+9PCY+pbkMocMofGCxKjQHhFYEp5iLrmT94ud/RwMKxzHbidicZWnws8PX1Rbt27WBjI/rLe01MTPD06VN07doVVlZWOHv2LFRVVeHm5ob3798jPz8fpqam2LlzJ4qKikSej6qZQ4cOYfr06bh27RqGDRvGWg4/Pz+0bdsWnTqJx1AWVXcMw8Df3x/7lk4Gj1f+jbPz3gUiYY8LEnZPBPdLGHTGrgdHoeyRHQEw0kpfyImrj57jE7Hi4mIYGxvj1KlTrJ9Xe/nyJVxcXNChQwf4+PiULC31/v17LF26FJGRkdi0aROGDx8u9JU+qJohhMDDwwNnz57FjRs30KZNG9ay8Hi8kqWvevTowVoOSjD4fD7++usvrF+/HsrKyli1ahWuZerV+k4ydB4fhT///BOtWrVivfQAwMbGBiEhIWjSpAnat2+PW7duAQDMzc0REBCA/fv3Y926dejRoweeP3/Oclrqp6KiIkyZMgV37txBUFAQq6UH/FgZRk9Pj5aehOPxeDh9+jQsLCywfft2bN68Gc+fP8eQIUMwx8EIygrytdqvsoI8ZjuI11W+9IhPhH4uJL1//3707t2b7Til3L17F7/++iuGDh2KzZs3Q1VVFcCPzL6+vvDw8ECPHj3g5eWFVq1asRtWhmVlZWHkyJFQUVEpGaZmE8MwaN++PbZu3YoBA8reLJkSfz8Lz9PTE02aNMHq1avh6OhYZpSntneScXNqW7K+sLigR3widOHCBWhqaqJXr15sRynD0dERb968QWZmJjp27Ihnz54BAOTl5fHrr78iIiICJiYmsLa2xvLly5GZmcluYBn09etX9OjRA23atIG/vz/rpQcAV69ehbKyMvr37892FKqGiouLcezYMZiYmODEiRM4ePAgHj9+jL59+5Z7auPnnWSU5DkgVSyA/+87yYhb6QG0+ESGYRh4enrCw8NDbM+Xqaur/1iXb8MGDB06FKtWrUJxcTEAoEGDBlizZg3evn2L9PR0mJiYYO/evSXPU8L17t072NvbY9y4cfDx8RH6LYWqgxACT09PkdztgRKcwsJCHDp0CMbGxjhz5gyOHz+OwMBA9OrVq8r/jxNtW6FT9hO0kP9eozvJiB1CiYS/vz+xsrIiDMOwHaVaEhMTycCBA4m1tTX58OFDmedfv35NHB0diYmJCbly5YrEfC5JdP/+faKtrU1OnTrFdpRSbt26RczNzQmfz2c7ClUNBQUFxMfHhzRv3pwMGDCAPH36tMb7yMrKIhoaGiQhIYGk53DJgYfR5PezoeTXE8/J72dDyYGH0SQ9hyuE9IJFi08EGIYhVlZWxN/fn+0oNcIwDNm/fz/R1NQkO3fuLPMDjmEYEhAQQMzMzIiDgwN5+fIlS0ml1+nTp4m2tja5d+8e21HK6N69u9iVMVVWfn4+2bVrF2nWrBkZPHgwefbsWa33tWvXLjJ69GgBpmMHLT4RCAgIIO3atZPYb8aRkZHE1taW9OnTh8TFxZV5vri4mBw4cIDo6uoSFxeXcrehaoZhGOLt7U2aN29O3rx5w3acMh49ekQMDQ1JcXEx21GoCuTm5pJt27YRPT098ssvv9T5iymfzydt2rQhjx8/FlBC9tDiEzKGYYidnR05c+YM21HqpLi4mHh6ehJtbW3i5+dX7tBmdnY2cXNzI40bNyZ//PEHyc7OZiGp5OPxeGTWrFmkffv2JD4+nu045erfvz85dOgQ2zGocuTk5JBNmzYRHR0dMnLkSPL69WuB7PfGjRukQ4cOUnFagxafkN27d48YGxsTHo/HdhSBCAkJIWZmZmTkyJEkPT293G3i4uKIi4sL0dXVJQcOHKBHBTWQl5dHhg4dSvr06UMyMzPZjlOuFy9eEH19fcLliv+5HFmSlZVV8uV07Nix5O3btwLdv5OTEzl69KhA98kWWnxC5uDgQE6cOMF2DIEqKCggixYtIk2bNiUBAQEVbvfq1Svi4OBAzMzMyN9//y0V3xSFKTU1lXTp0oW4uLiQwsJCtuNUyNnZmezatYvtGNT/+/79O1m7di3R0tIiEydOLPditLqKiooi2traJD8/X+D7ZgMtPiF6/PgxMTAwIEVFRWxHEYrAwEDSsmVLMmPGDJKTk1PuNgzDkCtXrhBjY2Pi6OgosGEXaRMVFUWMjIyIm5ubWH9BePfuHdHR0SF5eXlsR5F53759Ix4eHkRTU5NMnjyZREZGCu29Fi5cSJYvXy60/YsaLT4hGjBgADl48CDbMYQqMzOTTJ48mbRu3ZoEBQVVuF1RURHZu3cvadKkCZk6dSr5+vWrCFOKt+DgYKKrqysRf1bGjx9PvLy82I4h09LS0sjKlStJ48aNybRp00h0dLRQ3y8nJ4doamqS2NhYob6PKNHiE5Lnz5/L1HkQf39/oqOjQ/74449Kh+kyMzPJsmXLSOPGjcnq1atJbm6uCFOKn8uXLxMtLS1y7do1tqNUKSoqimhqapKsrCy2o8ik5ORksnTpUqKhoUFmzJhBYmJiRPK++/fvJ87OziJ5L1GhxSckw4YNI7t372Y7hkglJSWRwYMHk44dO5J3795Vum1MTAwZN24cadq0KTl69KjUXPxTEz4+PkRPT488f/6c7SjVMn36dOLh4cF2DJmTmJhIFi5cSDQ0NMicOXNEOl2IYRhibm5O7t+/L7L3FAVafEIQFhZGdHV1peZEcE0wDEMOHz5MtLS0yLZt26qcuxgcHEy6du1K2rdvT27fvi2ilOzi8/lk+fLlpE2bNkIfphKUuLg4oqGhUeGVvJTgxcfHk3nz5hENDQ2yYMECkpCQIPIM9+7dI+3atRPr8861QYtPCEaPHk22bNnCdgxWffr0iXTt2pU4ODhUeW6AYRjy119/kdatW5OBAwdWebQoybhcLhk/fjyxs7MjaWlpbMeptvnz55PFixezHUMmfPnyhcyaNYtoaGiQxYsXk6SkJNay/PLLL+TAgQOsvb+w0OITsI8fPxJtbe0Kr3KUJTwej3h7exMtLS1y4sSJKr81FhYWkh07dhBtbW3i6upKkpOTRZRUNL5//04cHByIs7OzRI0GJCcnEw0NDZKYmMh2FKn2+fNn8ttvvxENDQ2yfPlykpKSwmqe2NhYoqmpKZXn4endGQRs48aNmD9/Pho0aMB2FNbJy8tj+fLluHv3LrZt24YRI0YgLS2twu3r1auH33//HeHh4WjQoAHMzMywYcMG5OfnizC1cMTHx6Nbt26wsLDAhQsXoKKiwnakatuxYwfGjRsHPT09tqNIpejoaPz666+wsbFBkyZNEBkZCW9vbzRp0oTVXPv27cOkSZNQv359VnMIBdvNK02io6OJpqam2K64wSYul0uWLl1K9PT0qn0FY3R0NBk5ciTR19cnvr6+ErvW6evXr4m+vj7ZunWrxJ0rycjIII0bN5aqS9nFRXh4OJk0aRLR1NQkq1evJhkZGWxHKpGfn0+0tLQk5hx0TdHiE6Dp06cTd3d3tmOItUePHhEDAwMyffr0aq/l+fTpU2Jra0usrKxIYGCgcAMK2J07d4i2tjY5e/Ys21FqZe3atWTKlClsx5Aq79+/J+PHjydaWlpk/fr1YvlF+ciRI2TQoEFsxxAaWnwC8uXLF3rVWzVlZ2eTadOmEQMDg2qv9M4wDDl79ixp1aoVGTJkCPn48aOQU9adr68vadKkCXn48CHbUWolJyeHaGtrk/DwcLajSIU3b96Q0aNHkyZNmhAvLy+xnQ/JMAyxtLQkN2/eZDuK0NDiE5A5c+aQpUuXsh1Doly5coXo6uqSZcuWVXuif0FBAdmyZQvR0tIic+bMIampqUJOWXMMw5ANGzaQli1bkvfv37Mdp9a2bNkiFfdeY1toaCgZPnw40dHRIVu2bBH7C98ePXpEjI2NJfbUQnXQ4hOAxMREoqGhIXVXIYpCSkoK+eWXX0j79u1JWFhYtV+XlpZG5s2bRzQ1NYm3tzcpKCgQYsrqKy4uJq6urqRDhw4SvSxbfn4+0dPTq9H/E6q0Fy9ekKFDhxI9PT2yY8cOiVnfdNSoUVK/+AYtPgFYvHgxmT9/PtsxJBbDMOT48eNES0uLbNq0qUaruERERJBffvmFtGzZkvz555+sXjySk5NDBg0aRPr37y/x9yLcu3cvGTJkCNsxJNI///xDnJycSLNmzciePXskaupKfHw80dDQENthWEGhxVdHqampRENDQ2xvGCpJYmJiSI8ePUj37t3J58+fa/TaBw8eEGtra9K5c2dW7hCdnJxMbGxsyNSpUyX+bhxFRUWkRYsWJDg4mO0oEuXJkyekX79+pEWLFmT//v0SuU6vm5sbmTt3LtsxhI4WXx398ccfZMaMGWzHkBp8Pp9s3bqVaGlpkSNHjtToCI7P55NTp06RFi1akOHDh5OoqCghJv2f8PBwYmBgQFavXi1x0xXKc+zYMdKnTx+2Y0iMBw8ekN69exMDAwNy+PBhsb6XYmUKCgpIkyZNZOJiJlp8dfBzjlNNj06oqr19+5ZYWlqSIUOG1PjcaX5+Ptm4cSPR1NQkCxYsIN++favR69NyuGT/g2iy4GwImXriOVlwNoTsfxBN0nPKfoN/+vQp0dHRIUeOHKnRe4grHo9H2rRpI3WLEgsawzDk7t27pEePHqR169bk2LFjEn+k7+vrS/r168d2DJGgK7fUwZ49ezB06FAYGBiwHUXqtGvXDs+fP0e7du3QoUMHXL58udqvVVFRwcqVK/HhwwcUFRXBxMQE27dvR2FhYaWvC4vPhKvfS3TddB877kbi8utE3A9PxeXXidh5NxL2m+5jxqmXCIvPBAD4+/tj2LBhOH78OKZNm1aHTys+Ll68CC0tLTg4OLAdRSwRQnDr1i1069YNs2fPxvTp0xEeHo6pU6dCUVGR7Xi1RgjBnj17MG/ePLajiASHEELYDiGJcnJyYGhoiKdPn8LY2JjtOFItKCgIkyZNQvfu3bFr1y6oqanV6PUfPnzAsmXL8PHjR3h7e2PkyJHgcDiltjkVHAvPgHBweXxU9jeCwwGUFeRhr5KEm3vdce3aNVhZWdXmY4kdhmHQoUMHeHt7w8nJie04YoUQgoCAAKxbtw45OTnw8PDA6NGjIS8vz3Y0gQgODsaECRMQGRkpNZ+pMvSIr5b27duHvn370tITAXt7e7x+/Rr16tVD+/bt8eDBgxq93szMDNevX8ehQ4fg6emJbt26ITg4uOT5H6X3EQXFlZceABACFBTzcS9DHUsPSU/pAcD169ehoKCAgQMHsh1FbBBCcOXKFdjY2GDlypVYsmQJ3r17h3HjxklVQezZswdz5syRqs9UGXrEVwv5+fkwNDTE3bt30a5dO7bjyJS///4brq6uGDt2LDw9PaGsrFyj1/P5fPj5+cHd3R3dunXD5IWrsOjvOBQU88tsm/fhITKfngE/Ow3y9TWgOeh3KDf/3/9vFUV5nHO1RXt99bp+LNYRQmBra4ulS5di5MiRbMdhHcMwuHTpEtavXw8Oh4NVq1Zh2LBhkJOTvmOF5ORktG3bFjExMVBXV2c7jkjIbPGl5xbi4qsEhCdnI5vLg5qyAkx11TDKWh+aDZQqfe3OnTvx6NEj+Pv7iygt9W/p6emYOXMmwsPD4efnh44dO9Z4H3l5edi+fTsOvGeg2Mr6xxjmvxTEhOLbjd3QHrYc9Zoag5+bAQBQaKhVsg2HA/Q308GBiTZ1+0Bi4O7du5g3bx7ev38vlT/cq4vP5+PixYtYv349VFRUsGrVKgwePLjM0Lg0WbduHRITE3HgwAG2o4iMzBVfWHwmfB5E42Hkj9vjFPKYkueUFeRAADiYaGN2TyNYNlcv83oul4vWrVtL1bkdSUQIwalTp7Bo0SIsXLgQy5Ytg4KCQo32kZ5bCHvveyjil/0rkOy3BPXb90NDy36V7kNJQQ5By3tX+WVJ3PXq1QtTp07FpEmT2I7CCh6Ph3PnzmHDhg1o1KgRVq9ejQEDBkh14QFAUVERDAwMcOvWLZkavZKpr3angmMx9nAw7nxMQSGPKVV6AMD9/8duf0jB2MPBOBUcW2YfJ06cQIcOHWjpsYzD4cDFxQWvXr3CvXv30KNHD0RHR9doHxdfJZT7g40wfBQmRYPJz8LXA78hwWcyMm7vB1Nc9qpQDoCLIQm1/RhiISgoCLGxsRg3bhzbUUSOx+PB19cXZmZm2L9/P3bv3o1//vkHAwcOlPrSA35cmWxiYiJTpQfIUPHV5gIGz4CPpcqvuLgY3t7ecHd3F25YqtpatGiBO3fuYMyYMbCzs8PBgwdR3UGM8OTsMl9+AICflwkwPORHPIXOxE3Qm7obRSmfkRV0rsy2XB6D8KScun4MVnl6emL58uUSfTl+TRUXF+PYsWMwMTHBiRMncPDgQTx+/Bh9+/aVicL7SZamMPxbzcaGJFRYfCY8A8JRUFz6hxy/IAffAnaBGxsKORU1aPScjPrmDiXPFxQz8AwIR3t9dbTXV8epU6dgZGQEOzs7EX8C2cYwDLhcLgoKCpCfn1/uP/X09LBw4UJ4enpi165dGDp0KOTl5St9TVrbEYBO2zLvx1H8MWzZ0HoIFBo0/vHvnX5BVtA5aPQsOxSYzS0W7n8AIQoJCcHr16/x119/sR1FJAoLC+Hr6wsvLy8YGRnh+PHj6NGjB9uxWBESEoKEhAQMGTKE7SgiJxPF5/MgGlxe2av2Mm7vB0deEfrzTqEo5TNSL66FYhMD1NNuWbINl8fHvgfR2Du2AzZu3IjDhw+LMrrY+ncZVVYugvhnYWEhlJSUoKKiAlVV1Ur/2b9/f7x9+xZ79+7F4MGD0blz53K3U1FRwcE3XDz8UlDms8krN4D8vy5iqYqasuQeKW3cuBFLliyp8dWxkobL5eLYsWPw9vaGubk5Tp8+DXt7e7ZjsWrPnj2YPXt2jc+NSwOp/8TpuYV4GJlWZniTKeIiPyIITaf7QK6eCpSbm0PVqAvy3geinsOUku0IAQIj0nDszwvQ1dVFz549RfsBaoAQAi6XK9QSqk0Z/fexxo0bV/maf/9TWVm5xlcaPnv2DC4uLlBUVMSePXvKvUz7TfEnBH+NLHe4s4GFI3JeXYeKoTUgr4Ccl1egatSpzHaKckAbbdUaZRMXHz9+xOPHj+Hr68t2FKEpKCjA4cOHsXnzZnTs2BEXL15E586d2Y7FurS0NFy+fLnG58WlhdQX38VX5V94wMv4Co6cHBQbNyt5TLGJAQrj3pbZlgNgy8VH2OvuXuPxf0GVUXW24XK5JWVU3VIRZRmJUpcuXRAaGoply5bB0tISx44dQ58+fUptM9JaHzvuRpb7+kZdx4JfkI2vh2aAo6CI+qbd0ch+TJntiot5cJ/giKe9u2P48OEYMGAA6tevL5TPJGheXl6YP3++xOStiby8PBw8eBBbt25Fly5dcPXqVXpB2r8cOXIEzs7O0NTUZDsKK6S++Cq6gIEpLgBHqfQ3dTklVTBFZYe+uDwGjLImHj16hFu3btWosLhcLurVq1ejUpHWMhK1+vXrw8fHB0OHDsXkyZMxcuRIeHl5QUVFBQCg1UAJPY21cedjSpkRAY68AjT7z4Zm/9kV7p/DAfq3b4bVIc9w5coVHDx4EFOnTkWvXr0wfPhwDBkyBI0bNxbmR6y1z58/IyAgALt372Y7ikDl5uZi37592L59O7p3744bN27A0tKS7VhihcfjYf/+/bhy5QrbUVgj9cWXzeWV+7icogpIYemSI4X5kKunUu72GrrNoKLyrVQZVXeYj5YRu/r37483b95g9uzZsLa2hp+fH6ytrQEAcxyM8DgqvdyVW6qirCCP2Q5G0NNTx8yZMzFz5kx8//4d169fx6VLlzBv3jx07twZzs7O+OWXX9CsWbOqdyoimzdvxowZM6RmpY7s7Gzs3bsXO3fuRJ8+feiqSpW4cuUKWrRoUauFH6SF1BefmnL5H1GhcTMQho/ijK8lw51FqTFQ/NeFLf/WpqU+fh03DE2bNhVaVkp4GjdujLNnz+LMmTMYOHAg5s2bh5UrV8KyuTrcnEz/f6pL2ZGBitSTB9ycTMssV6ahoQEXFxe4uLggPz8ft27dgr+/Pzw8PGBsbIzhw4fD2dkZbdq0EfAnrL6vX7/i/PnziIiIYC2DoGRmZmL37t3Ys2cPBgwYgEePHsHU1JTtWGJNVqcw/JvUH4qY6qpBSaHsx5SrpwxVEztkPj4NpogLbsIH5Ec/Q33zXmW25TA8fAkLQvv27aGnp4chQ4ZgzZo1uH79OpKSkkTxMSgBGTduHEJCQvD48WN069YNkZGRmGjbCm5ObaGiKP/flcvK4HCAenJAziNfGMulVrqtqqoqnJ2d4efnh5SUFKxfvx4xMTHo0aMHLCwssGrVKrx+/bra8w4FZevWrZgyZQq0tbVF+r6ClJGRgVWrVsHIyAgxMTEICgqCn58fLb0qvHnzBlFRURg+fDjbUVgl9UuWpecWouum++VPVK5iHt9PP5elaly/HuLi4vDy5Uu8evWq5J/KysqwtraGjY1NyT91dHRE8Omo2mIYBvv27cOaNWuwbt06zJo1C2+/ZmHfg2gERqSBW1BQMp8P+N9ydr1MtDHbwQhxr59g2rRpuHHjRo0vmmAYBsHBwbh06RL8/f3BMEzJkaCdnZ1QV8hPS0uDiYkJ3r17J5GjF+np6di+fTsOHjyI4cOHY+XKlTA0NGQ7lsRwdXVF8+bN4eHhwXYUVkl98QGAq9/Lci9gqA4OCPqb61a4EDEhBLGxsaWK8OXLl6hfv36pIrS2tkaTJk3q+EkoQYuIiICLiws0NDRw7NgxNGvWDF+Sv8Fq5BxMmrf8/xcwV4SpXkOMtCq9gPmlS5cwa9Ys3L59G+3bt6/V+xNC8PbtW/j7++PSpUtISUnBsGHD4OzsjN69e6NevXqC+qgAADc3N3z79k3iFiROSUnBtm3bcPToUYwePRorVqxAy5bln5agypeRkYHWrVsjPDxc5r+Yy0TxhcVnYuzh4NpdwKAoh/OudjW69QwhBDExMaWKMCQkBA0bNixVhNbW1hI93CQtiouLsXHjRuzbtw+7d++GhYUFhg8fjvDw8Cpfe/78eSxYsAD37t2DmZlZnbN8+vSp5Ejw48ePcHJyEtg0iczMTBgZGeHFixcwMDCoc1ZRSEpKwpYtW3DixAlMmDABy5YtQ/PmzdmOJZG2bt2KsLAw+Pn5sR2FdTJRfMC/1+qs/gUMihwGq4daYKJtqzq/PyEEnz59KnVkGBISgkaNGpU5MpTVuTVse/HiBVxcXKCnpwcACAwMrNbrTp8+jWXLluH+/fswMTERWJ7ExERcuXIFly5dQnBwcJ2nSXh6eiIiIgInT54UWEZhSUhIwObNm3Hq1ClMnjwZS5culcihWXHB5/NhZGSEc+fO0Qn8kKHiA36WXzi4vMoXquYAYHiFWDvMElO6tRZaHoZhyi3Dxo0blzkyFNf5YNImPz8fQ4YMwbNnz+Dv749+/Sq/LdFPx48fx6pVq/DgwQO0bi34PzP/niZx9+7dGk+TyM3NhaGhIR4+fIi2bcuuTyou4uLi4O3tjbNnz2LatGlYvHgxdHV12Y4l8a5evQpPT088e/aM7ShiQaaKDwDeJGRi990I3HmfCGVlJRTy/vfxf17AoPQtGsOMVbD+9+kiz8cwDKKjo0sNk4aGhkJLS6vUkaGVlRU0NDREnk8WuLu7Iz4+HoGBgRg6dCg2b94MVdWqlyU7ePAgvLy88ODBA7Rq1Upo+f49TeLvv/+u1jSJ7du3459//sGFCxeElqsuYmJi4OXlhb/++guurq5YtGgRPQ0gQH379sXkyZMxceJEtqOIBZkrPgA4e/Ysjp0+jxFLtyA8KQfZ3OKSCxhMFL9j/Iih+Pz5s9gs3MswDCIjI0sdGYaGhkJHR6fUkaGVlZXUTEhm06RJk9C7d28MGzYM8+bNw4sXL+Dn51etIaI9e/Zgx44dePjwoUjORRUXF+PBgwfw9/fH5cuXoaWlBWdnZwwfPhyWlpbgcDglN0++fv262E1ajo6OxsaNG3H16lXMmjULv//+Ox3qF7CPHz+iV69e+PLlC5SUJPuGyYIik8U3bNgwjBgxoty7TY8aNQp2dnZYtGgRC8mqj8/nIzIystSRYVhYGHR1dUsNkVpZWaFRo0Zsx5UoDg4OWLVqFXr37g0AuHDhAubOnYuZM2fC3d29yvvWbd++Hfv378fDhw9Fel6qomkSHA4HHz58QEBAgMiyVCUiIgIbN27E33//jblz52LBggV0BENI5syZA01NTaxbt47tKOKDyJiMjAyipqZGMjMzyzz3/v170qRJE5Kbm8tCsrrj8Xjk3bt3xNfXl8ybN4/Y29uT+vXrkzZt2pBx48aRrVu3kgcPHpCsrCy2o4o1AwMDEhUVVeqxr1+/kgEDBhAbGxvy8ePHKvexceNGYmpqSpKTk4UVs1IMw5CwsDDi7u5OFBUVSePGjYmrqyu5ceMGKSwsZCUTIT/+jo0fP55oaWmR9evXl/v3kBKczMxMoqGhQb5+/cp2FLEic8V37Ngx4uzsXO5zEyZMIBs3bhRxIuEqLi4mb9++JcePHydz584ltra2RFVVlZiYmJDx48eT7du3k4cPH5Ls7Gy2o4oFPp9P6tWrRwoKCso8xzAM2b9/P9HS0iK7du0ifD6/0n2tWbOGtGvXjqSlpQkrbpV8fX2Jg4MDiY6OJlu2bCF2dnZEXV2djB8/nly8eFFkX/LevHlDRo8eTZo0aUK8vLzonzcR2blzJxkzZgzbMcSOzBVfv379yLlz58o8HhkZSbS0tGTiaKi4uJiEhYWRY8eOkdmzZ5MuXboQVVVVYmpqSiZOnEh27NhBHj9+THJyctiOKnKJiYlER0en0m0iIyOJra0t6dOnD4mLi6twO4ZhyMqVK0mHDh3It2/fBB21Sjwej5iYmJA7d+6Uevzr169k3759pG/fvqRhw4Zk2LBh5MSJE0LJGBoaSoYPH050dXXJli1bZPLPFFv4fD4xMjIiT548YTuK2JGp4ktJSSGNGjUieXl5ZZ779ddfyerVq0UfSkwUFRWR169fkyNHjpBZs2aRTp06ERUVFWJmZkZcXFzIrl27yJMnTyR2GLi6goODSadOnarcrri4mGzYsIFoa2uTU6dOEYZhyt2OYRiyePFiYm1tTb5//y7gtJU7f/486dKlS4XZCPkx9H/y5Eni7OxM1NTUSJ8+fYiPj0+dh8ZevHhBhg4dSvT09MiOHTvK/TtHCdeNGzdIx44dK/3/L6tkqvj27dtHxo8fX+bx2NhY0rhxY1a+lYuzwsJCEhISQg4fPkxmzJhBbGxsiIqKCjE3NyeTJ08mu3fvJkFBQVL1Q+38+fNkxIgR1d7+1atXxMzMjIwaNYqkp6eXuw3DMGTevHnE1tZWZEN8DMMQS0tLcvXq1Wq/Ji8vj/j7+5OJEycSDQ0N0qVLF7Jp0yYSGRlZ7X38888/xMnJiejr65M9e/aQ/Pz82sSnBMDJyYkcO3aM7RhiSaaKr3v37uX+IJg1axZZsWIFC4kkT2FhIXn16hU5ePAgcXV1JdbW1kRFRYVYWFiQKVOmkL1795J//vlHYn/gbd26lSxcuLBGrykoKCALFy4kTZs2JQEBAeVuwzAMmTFjBunWrZtIjpqvX79O2rdvX+tv+0VFReT27dtk5syZRFdXl7Rr1454eHiQ0NDQcvf55MkT0q9fP9KiRQuyf/9+wuVy6/oRqDqIiooi2traEvv3UNhkZjpDQkICLC0tkZiYWGouS2JiItq1a4fw8HC6iHQtFRYW4t27d3j58mXJ9Irw8HC0adOm1KT79u3bi83cyIosWLAArVq1wsKFC2v82sDAQEyZMgVOTk7YsmULGjRoUOp5hmEwffp0xMbG4vr169WaFF8bhBB07doVCxYswJgxY+q8v8ruJlFUVARPT0/ExMTgjz/+wKRJkwS+sDZVcwsXLoSysjK8vLzYjiKWZKb4tm/fjvfv3+Po0aOlHl+4cCE4HA62b9/OUjLpxOVy8fbt25IifPXqFSIiImBiYlJq0n379u3FalKts7MzJk6ciBEjRtTq9VlZWZg/fz6CgoJw8uRJ2NnZlXqez+djypQpSE1NxZUrV4TyRSAwMBAzZ87Ehw8fBH6LI0II3rx5g23btsHf3x9cLhfdunXDkiVL0K9fP1p6YiA3NxctW7ZEaGgoWrRowXYc8cTm4aYoderUidy+fbvUYykpKXSOiwgVFBSQZ8+eER8fH/Lrr7+S9u3bExUVFdKxY0fy22+/kQMHDpCXL1+yOkxmZWVFnj9/Xuf9/PXXX0RHR4e4ubmVmTdXXFxMRo8eTQYNGiSUOXW9e/cWyrkdhmHIzZs3ib29PTExMSF+fn4kPDyc1WkSVFn79u2rcMoW9YNMHPF9+vQJ9vb2+Pr1KxQUFEoeX7lyJbKzs+Hj48NiOtlWUFCAsLCwUsuxRUdHw8zMrNSRYbt27URyNKGtrY13794J5H5lycnJ+O233/D161f4+fnB3Ny85Lni4mKMGTMGhBCcP3++ytVgqis4OBhjx45FVFSUwPZJCEFAQADWrVuH3NxceHh4YNSoUWWOJv97N4nevXvD2dm51neToGqOEIJ27dph79696NWrF9txxBertSsiGzZsIHPmzCn12Ldv30jjxo1JbGwsS6moiuTl5ZGgoCCye/duMnnyZGJubk5UVFSIjY0NmTlzJjl8+DAJDQ0lRUVFAn9fJSWlKiem1wTDMOTw4cNES0uLbNu2rdS+CwsLyeDBg8moUaNIcXGxQN5v8ODBxMfHRyD7YhiGXL58mVhZWRELCwty4cKFav+3EdY0Capyd+/eJe3ataNTGKogE0d8FhYW2L9/P7p161by2Jo1axAfH1/mnB8lnvLy8vD69etSR4axsbFo165dqSNDMzOzWh/pREREYPDgwYiKihJw+h+jDpMnT4aioiJOnDhRcvdwLpeLX375BZqamjh58mSdzsmFhYVh4MCBdV5gnWEYXLp0CevXr4ecnBxWrVqFoUOHQk5Orlb7q83dJKja+eWXXzBw4EDMmDGD7Sjije3mFba3b98SfX39Ut9Us7KyiJaWVpn1GCnJkpOTQx4/fkx27NhBJk6cSExNTYmqqirp0qULmT17Njl27BgJCwur9tHU7du3Se/evYWWl8fjEW9vb6KlpUVOnDhR8q08Pz+f9O7dm0yZMqVOR5ujR48mW7ZsqVO+s2fPEnNzc2JjY0OuXbsm8COHwsJCcuvWrWpPk6CqLyYmhmhqatLzq9Ug9Ud8Hh4eKCgowNatW0se8/Lywvv373Hq1CkWk1HCkJOTg9DQ0FJHhvHx8Wjfvn2pqRWmpqalzvcCwNGjR/HkyRMcP35cqBnDwsLg4uICIyMjHDx4ENra2sjLy8PAgQPRtm1bHDhwABwOp0b7jIiIQPfu3fH58+cy0yiqwuPxcO7cOWzYsAHq6upYvXo1+vfvX+MMNVXZNAk7OzuBX5Eq7ZYtWwY+n49t27axHUXsSXXxEUJgbGyMs2fPwtraGsCPITNDQ0MEBgbCzMyM5YSUKGRnZyM0NLTULZwSExNhaWlZapj07NmzkJOTw9q1a4WeqbCwEB4eHjh16hQOHTqEwYMHIycnB/3794e1tTV2795do+KZMmUKDA0NsWrVqmq/hsfj4fTp0/D09ISOjg5Wr16NPn36CL3wykMIwdu3b+Hv749Lly4hJSUFw4YNw/Dhw9GrVy86TaIK+fn5aNmyJZ49ewZDQ0O244g9qS6+V69eYezYsYiMjCz5yyzud6KmRCMrKwshISFlzhm2atUKgwYNKilEY2NjoR55PHr0CJMnT4ajoyO2b98OhmHg6OiI7t27Y9u2baVKKD23EBdfJSA8ORvZXB7UlBVgqqsG2yYEfbp1QXR0dLXuaVdcXAw/Pz94enqiRYsWWLVqFRwcHFgpvIp8+vSp5EgwPDwcAwcOxPDhwzFgwADUr1+f7Xhi58iRI7hy5QquXbvGdhSJINXFt2TJEqioqGD9+vUAflxIYGhoiICAAHTo0IHdcJTY6dmzJwYNGgQAJYWYmpqKjh07ljoyNDY2rvWFHuXJzs7GwoUL8eDBA/j6+sLc3By9e/fGgAEDsHHjRrxJyILPg2g8jEwDABTymJLXKivIoai4GE053+Ezexgsm6tX+D6FhYXw9fWFl5cXjIyM4OHhgR49egjscwgLnSZROUIIOnTogC1btqBfv35sx5EIUlt8DMOgZcuWuHnzZsn8KR8fH9y6dQtXr15lOR0ljoyNjXHt2jWYmJiUPJaRkVFyZPizDNPT09GxY8dS5wyNjIzqXIZXrlzBzJkzMXnyZMybNw8DBw6E2dAZCJNrDS6Pj8r+pnIAKCvKw83JFBNtW5V6jsvl4tixY/D29oa5uTk8PDxgb29fp6xs+f79O65fv45Lly7h3r176NSpE4YPH45ffvlFpHe7FyePHj2Cq6srPnz4INAvZNJMaovvyZMnmDVrFt6+fQsAKCoqgpGRES5evIjOnTuznI4SN4QQqKqq4tu3b1Wuofnt27dSw6QvX77E9+/fYWVlVerIsHXr1jX+QZSamgpXV1fExMRg2OKtOPk2F1Co/vktFUU5uDm1xUTbVigoKMDhw4exefNmWFlZwd3dXar+7P93moSJiQmcnZ1lbprEqFGj0LNnT8ydO5ftKBJDaotv7ty50NPTg5ubG4AfV+ydP38et27dYjkZJY5SU1Nhbm6OtLS0Wr0+PT291FHhq1evkJWVBSsrq1JHhoaGhlWeSyOEwPPAKRz+VB8cxdLrmCafXoHCxAhw5H6cd5RvqIlmrgdLbaOsKIdh9T/Db5cnbG1t4e7uDisrq1p9LklRVFSEBw8e4NKlS7h8+TK0tLTg7OyM4cOHw9LSUqzOXwpSfHw8LC0t8eXLFzRs2JDtOBJDKouPx+OhWbNmCAoKQuvWrcHj8WBqaorjx4+je/fubMejxNDLly/h6uqKkJAQge0zLS2tTBnm5OTA2tq61JGhgYFBmR/Mrn4vcedDCv77lzP59ArUb9cLDS37V/i+hGHQOD8evr91Q/v27QX2eSTFz2kSP68QleZpEm5ubsjJycHu3bvZjiJRFKreRPIEBgaiZcuWaN26NQDg7Nmz0NfXp6VHVSguLk7gK9lra2tjwIABGDBgQMljqampJUV4+vRpLFy4EPn5+aWK0NDMEg8j08qUXnVx5OSQr26AZoYmVW8sheTk5GBvbw97e3ts2bIFb968waVLlzBnzhypmibB5XJx5MgRPH78mO0oEkcqj/imTZsGc3NzLFq0CHw+H+3atcOePXvg6OjIdjRKTO3cuROfP39m5ZtzcnJyqSPDUK4m5CyHgKNQ9nZNyadXoDg9DgCg2LgZ1Hu4QLll2aM6ZQU5LOxrjBk9Wgs9vySRpmkSvr6+OHPmDG7evMl2FIkjdcVXWFiIpk2bIiwsDPr6+rhw4QK2bduGf/75R2rH+am6W7RoEZo2bYolS5awHQW/nwvF5deJ5T5XmBgBRc3m4MgrIu/jI2TcOQC9qbuhqKFXZlvnDs2wY0wHIaeVXJI8TYIQgk6dOmHt2rUlU3Co6pO6a19v374Nc3Nz6Ovrg2EYbNiwAR4eHrT0qEoJY6izpgghSE1NRVxSxRfYKDU1gZySKjgKimhg0QdKzdqi4NPLcrcNeR+OU6dO4d69e3j//j0yMjIgZd9z66Rp06aYNWsWbt++jS9fvmDEiBG4cuUKDAwM4OjoiH379iExsfwvIGwLDg5GZmYmBg4cyHYUiSR15/jOnj2LcePGAQCuX78OeXl5ODk5sZyKEneiKr7c3FzExMSU/Pr8+XOp3yspKUFr8GKgqWX1dsjhABWcDSTcXAQE3EdSUlLJr8LCQujq6kJPT6/k139/r6enhyZNmkjVRSBV0dDQgIuLC1xcXEpNk3B3dxfLaRJ79uzBnDlz6Ly9WpKqoc78/Hw0bdoUkZGR0NbWRpcuXbBixQoMHz6c7WiUmNPV1UVISEidJ0EXFxcjLi6uwnLLzc1Fq1atYGBgAAMDAxgaGpb8u4GBARo1aoQDDz9hx93IUiu0AADDzUVhYgSUW1gAcvI/hjpv7oXelF1Q1NQvtW1F5/jy8/ORnJxcqgx//vr34xkZGdDS0iq3FP9bmnW5BZK4E8dpEklJSTAzM0NMTAzU1dVF/v7SQKqK7/z58zh69Chu3bqFW7duYfHixXjz5g39VkRVisvlolGjRigoKKjyzwohBMnJyRUWW1JSEvT09CosNl1d3Sp/WKbnFqLrpvtlio+fn4XU82tQnJEAcOSgqKkP9e4ToWLQscw+lBTkELS8NzQblL1ApjqKi4uRmppabin++1dKSgpUVVXLLcT/PqampibRpxxEPU2iorVZvwSeRWZKAvbv3y/Q95MlUlV8w4cPx5AhQzBlyhR0794dc+bMKRn2pKiKREdHo1+/fvj8+TOAHwtYV1RssbGxaNCgQYXF1qJFi1rfCPffXP1e4s7HlEqXKasIB0B/cx0cmGhT5xxVIYQgIyOj3FL8b2Hy+fwKS/Hfv7S0tMT+yyohpGSahKDvJhEWn1np2qwFXC7sDRphxRCrStdmpSomNcWXlZWFFi1a4MuXL3j9+jVcXV3x8eNHmTpPQVVPYWEhvnz5UlJmgYGBuH//Plq2bImYmBgUFhZWWGwGBgY1vt9dbYTFZ2Ls4WAUFPNr/FpSXIjRWonYtGy2WBVIbm5upcX481dWVhaaNGlS4dDqv/9dXObh/XeahJOTE5ydnWs8TeJUcCw8A8KrXpuVAygrlL82K1U1qSm+kydP4q+//sKVK1fg6OiICRMmYOrUqWzHoljAMAwSExMrvIAkNTUV+vr6JcWWkZGBlJQUbN26FYaGhtDS0hKLIbkfPwQ/oqCYqXrj/6eiKIcZXXRw3nMuGjVqBF9fXzRp0kSIKQWvqKgIycnJFQ6v/izM1NRUqKmpVTq8+vOXKL6s/FTbaRK1/f/9c21WqvqkpvicnJwwadIktGzZEuPGjUNUVJRAhpwo8UMIwffv3yssti9fvkBDQ6PcIzZDQ0M0a9as1N3X161bh6KiImzYsIHFT1W+U8GxWP/3B3CL+OBUcvT23yOA4uJirF69Gr6+vjh58iT69OkjwtSiwTAM0tPTq3UUKScnV+VFOnp6etDU1BTol57q3k2ioiP8uG0jS/2e8IrQsKMTGvebWfKYiqI8zrnaor2+usBySzupKL709HQYGRnh69evGD16NIYMGYKZM2dW/UJKbBUUFCA2NrbcYvt5Lq6iYmvVqhVUVFSq/V6//fYbOnXqBFdXV2F9nDoZPXMJEjUskKbQBBwA3P+c8yEAeploY7aDUZkffnfv3sXkyZMxZcoUrFmzRia/DBJCkJ2dXelFOj+fy8vLg46OTpUX6ujo6JT68lQdld1NYktwVpXndJkiLhL2TESTUWug3KJdyeMcDtDfTDTndKWFVBTfwYMHERgYiGXLlmHo0KH49OkTlJRqdzUbJRp8Ph8JCQkVFtv379/RokWLcovNwMAAGhoaAvtm3r9/fyxcuLDUmpriIioqCnZ2doiKigKjqIqLIQkIT8pBNrcYasqKMNVriJFW+pVevZmamopJkyYhOzsbZ86cQcuWLUX4CSQLl8sttxz/+1h6ejo0NDSqdRRZ3m2uSk2TuHkPSqM2A/KVfynJfXsPWU/+RNOZR8r82a/rVbyyRiqKr1evXliwYAFOnjyJnj17YsGCBWxHknmEEKSnp1dYbAkJCdDW1q6w2Jo2bSqyCzPatm2LixcvltywWJxMmDABbdu2hbu7e532wzAMtm/fjs2bN2P//v0YMWKEgBLKJj6fX63pHsnJyVBSUqr0Ip3H6co4HZaJ4ip+Eif/+QeUm5tDvfuEMs/RtVlrRuKLLzExEe3atcOdO3cwaNAgfP78ucobiVKCkZeXV+F5tpiYGCgqKlZYbC1bthSLo3JCCBo0aICkpCSoqamxHaeUd+/ewdHREVFRUQK719rz588xbtw49OvXD9u3b6/RkDBVc4QQZGZmVjq8+qVpLzAtrCvdDy8rFV8PTEfTGYegqK5b7jZ0bdbqk/ji27VrF16/fo3CwkJ06NABy5YtYzuS1CguLkZ8fHyFR205OTllLvX/d9E1atSI7Y9QpW/fvsHIyAjfv39nO0oZzs7O6N69OxYtWiTQ/WZlZWHGjBl4//49zp07BzMzM4Hun6qZX31f4H54aqXbZD49C27sa+hO8K5wmz6mTXB0cidBx5NKEr9W59mzZzF9+nSsXLkSBw8erPoFVAlCCFJSUiostv+uQmJgYIDBgweX/LuOjo5YzROrDXFYnLo8L168wIsXL/Dnn38KfN+NGjXCmTNncOzYMfTs2RNeXl6YNm2aWEzhkEVqylX/GM57dx+NbEdWuo2asuxduFRbEl18MTEx+PTpEx49eoR58+YJbDhImmRnZ1dYbP9dhcTAwAC2trYYN26cQFchEWfiWnzu7u5wd3cX2lAkh8PBtGnTYGdnh7Fjx+LOnTs4dOiQRBylSxtTXTUoKSSXWaLuJ27CR/Bzv0HVtFuF+1BWkIOpHv35V10SXXznzp1D37598ffffyM6OprtOKwoKirCly9fyi22/65CYmBgACMjI/Tt21ekq5CIM3EsvkePHiEqKgq//vqr0N/LzMwMz549w5IlS9CxY0ecOXMGXbp0Efr7Uv8z0lofO+5GVvh83rt7UDW2h5xSxdcuEAAjrfQrfJ4qTWKKr7wFW28+S4AhijFz5kypXaWcYRgkJSVVeNT271VIfp5jGz58eMnvtbW16RBWJcSt+AghcHd3x+rVq0W2HJeKigp8fHzg7++PIUOGYMmSJViyZInED2NLCq0GSuhprF3hPD7NAXMrfT2H82MeJ53KUH1iX3yVLdhKjHrjPThoYqCDsPhMiV2w9ecqJOUVW1xcHNTV1UsVW48ePTB58mQYGBhAX1+/xhNpqf+Ji4uDlZUV2zFK3L59G2lpaZg4caLI33v48OGwtrbG+PHjce/ePZw8eRI6OjoizyGL5jgY4XFUeq3WZlVWkMdsByMhpJJeYn1Vp7Qs2MrlchEbG1vhcCQhpMJFkVu1akWnZwiRnZ0dtm7diq5du7IdBYQQdO7cGcuWLcOoUaNYy8Hj8bBmzRocP34cJ06cQN++fVnLIkvoWp2iI7bFJ0l/CPh8Pr5+/VphsWVkZKBFixYVLrElyFVIqJpp1qwZgoOD0bx5c7aj4NKlS1i3bh1evXolFsOM9+/fx6RJk+Di4oJ169ZJ/YVO4kBavuyLO7EsvooWbOVlpuDb7X0o+hoOKCiivklXaDi6giP3v1sPCWPBVkIIvn37VmGxJSQkQEtLq8JiE+UqJFT1FRUVoUGDBsjPz2d9uJjP58PS0hKbNm3CoEGDWM3yb2lpaZg8eTK+f/+OP//8EwYGBmxHknpvEjKx70E0AiPSyqzNSooLoaSsjN6mTcpdm5WqHrEsvopuwplyfjXkVdWhOWAOGG4eUs65o4Flf6jZDC3ZprYLtv57FZLyViNRVFSssNjEZRUSqmZiYmLg4OCAL1++sB0Fp0+fho+PD54+fSp2R/8Mw2Dnzp3w9vaGj48Pq8OwsuRbbmGZtVkfXT2DJSO7Y5zzYLbjSTSxuyoiPbcQDyPTyj3M52WlQM16MDgK9SDfoB5UDKxRnB5XahtCgMCINHzLLSx1ldO/VyEpr9hycnLQsmXLUsXWvXv3kt/T+U3SR1yu6Px5C6FDhw6JXekBgJycHBYtWoQePXpg7NixuHv3Lnbs2EHPPQuZZgOlMmtven2+gWcP79LiqyOxK76LrxIqfE7NZijyPjyCUgsLMNxcFHx+CfXuZa9+4/P5mLXFFw0TnpUUW2JiInR1dUsV26BBg0p+Lw2rkFA1Iy7F5+vri5YtW6J3795sR6mUjY0NQkJCMGvWLHTu3Bnnzp0Ty4W9pVmfPn0wbdo0tmNIPLErvvDk7ApXMFBuboHc17cQv300QBjUb9cHKsZ2ZbbjEQ5SixTg2KVLySokzZs3F9m8KEoyxMfHs35RS2FhIdatW4fz58+zmqO61NTUcOrUKfj6+sLBwQGenp747bffxPJIVRpZW1sjISEBKSkpdKpJHYjdIU42l1fu44QwSDm/Cqom9mix+C/oL/gTDDcXmQ+Ol7t9a1MLuLq6wtHREa1bt6alR5UhDkd8Bw8ehKWlJWxtbVnNURMcDgdTpkzB48eP4ePjgzFjxiAzM5PtWDJBXl4ePXv2xP3799mOItHErvgqWrCVKcgBPzsNDa0Gg6OgCHkVNTRo74iCTy8r2A+99JqqHNvFl5eXBy8vL6xfv561DHVhamqKZ8+eQUdHBx07dkRwcDDbkWRCnz59cO/ePbZjSDSxK74fC7aWjSWv2ggKjXSQExoAwvDBcHOR+/YeFJuUvbyaLthKVQfbxbdnzx706NEDHTp0YC1DXSkrK2PPnj3YsWMHhg0bBm9vbzBM9efeUjVHi6/uxG46Q3puIbpuul/ueb6ilM/IuHsIxakxgJw8lFtYoHG/WZCvr15qOyUFOQQt703XrqMqRAiBmpoa4uPjWVnnNTMzE23atMHjx49hamoq8vcXhri4OEyYMAHKysrw8/ODrm75N0yl6oYQgmbNmuHJkycwNDRkO45EErsjvp8LtpZ3rryejiF0J3ij+cJzaL7gT2g7ryxTenTBVqo6srKywOFwWJumsn37dgwaNEhqSg8AWrRogcDAQNjb28PKygq3bt1iO5JU4nA46N27Nz3qqwOxKz7gx4KtygryVW9YDrpgK1UdP4c52bgaMT09HT4+Pli9erXI31vYFBQUsHbtWpw+fRrTpk3DsmXLUFRUxHYsqePo6EiLrw7Esvgsm6vDzckUKoo1i/djrU5TuowPVSU2z+9t2rQJY8aMkerlv3r16oXQ0FB8+PAB3bt3x+fPn9mOJFV+nuej51NrRyyLDwAm2raCm1NbqCjKlzvs+W8czo81Oukq5VR1sVV8iYmJOHbsGNzd3UX+3qKmra2Na9euYdy4cbC1tcW5c+fYjiQ1mjdvDg0NDbx9+5btKBJJbIsP+FF+51xt0d9MB0oKclD+z9WeygpyUFKQQ38zHZxztaWlR1UbW8Xn6emJqVOnomnTpiJ/bzZwOBz8/vvvuHHjBtzd3fHbb78hPz+f7VhSgV7dWXtid1VnRcpbsNVUryFGWunTC1moGhs/fjycnJxEesPX2NhYWFtbIyIiAlpaWiJ7X3GRk5OD2bNn49WrVzh37hwsLCzYjiTRLl68iOPHj+Pvv/9mO4rEkZjioyhB6tatGzZu3IgePXqI7D2nTp2K5s2bY926dSJ7T3F08uRJLF68GOvXr8eMGTPocme19O3bNxgaGiI9PZ3eK7GGxHqok6KERdRDneHh4bh+/ToWLVoksvcUV5MmTcKTJ09w8OBBjBo1Ct+/f2c7kkTS1NRE69at8fz5c7ajSBxafJTM4fF4SE5ORrNmzUT2nmvWrMGiRYtYmSwvjkxMTBAcHIxmzZqhY8eOCAoKYjuSRKLn+WqHFh8lcxITE9GkSRORDQ+FhYXh4cOHmD9/vkjeT1IoKSlh165d2L17N5ydnbFx40bw+Xy2Y0kUWny1Q4uPkjmivh2Rh4cHVqxYgfr164vsPSXJ0KFD8erVK9y6dQv9+/dHUlIS25EkRvfu3fHq1Svk5eWxHUWi0OKjZI4oz+8FBwfj9evXmDFjhkjeT1Lp6+vj/v376N69O6ysrHDjxg22I0mE+vXrw9raGo8fP2Y7ikShxUfJHFEWn7u7Ozw8PKCsrCyS95Nk8vLyWL16Nc6ePQtXV1csXbqULndWDXS4s+Zo8VEyR1TFFxgYiNjYWEyZMkXo7yVNevbsidDQUERERKBbt2749OkT25HEGi2+mqPFR8kcURQfIQRubm5Ys2YNnWNVC1paWrhy5QomTpwIW1tbnDlzhu1IYqtz58749OkTvn37xnYUiUGLj5I5oii+GzduICsrC+PGjRPq+0gzDoeD+fPn4/bt21i9ejWmTZtGL+Ioh6KiIrp164bAwEC2o0gMWnyUzBF28TEMA3d3d6xfvx7y8rW7vRb1Px07dsSrV6/A4/FgY2ODN2/esB1J7NDhzpqhxUfJlOzsbBQVFaFx48ZCew9/f3/IycnB2dlZaO8haxo2bAhfX1/88ccf6NOnD/bt2we62uL/0OKrGVp8lEyJj48X6g1o+Xw+Vq1ahQ0bNtA1KIXAxcUFQUFBOHr0KEaMGIGMjAy2I4kFCwsLZGZmIj4+nu0oEoEWHyVThD3Mefr0aWhqaqJ///5Cew9Z16ZNGwQFBaFly5bo2LEjnjx5wnYk1snJyaFXr170qK+aaPFRMkWYxVdUVIQ1a9bA09OTHu0JmZKSEnbs2AEfHx+MHDkSGzZskPnlzhwdHWnxVRMtPkqmCLP4jh07BiMjI5He6kjWDR48GK9evcLdu3fRt29fJCYmsh2JNX369MHdu3fpuc9qoMVHyRRhFV9BQQE2bNiADRs2CHzfVOWaNWuGe/fuoVevXrCyspLZG7MaGhpCWVkZHz9+ZDuK2KPFR8kUYRXfgQMHYGNjg86dOwt831TV5OXl4eHhgQsXLmDWrFlYvHixTC53Rq/urB5afJRMiYuLE/idGXJzc7Fp0yasX79eoPulaq579+4IDQ3Fp0+fYG9vj+joaLYjiRQtvuqhxUfJDD6fj69fv0JfX1+g+921axd69+4NCwsLge6Xqh1NTU1cunQJU6dOhZ2dHU6fPs12JJHp3bs3Hj58CB6Px3YUscYh9EwoJSMSExNhZWWF5ORkge3z+/fvMDY2RlBQENq0aSOw/VKC8fr1a4wdOxZ2dnbYs2cPGjRowHYkobOwsMDRo0fpsHsl6BEfJTOEcX5v69atGDZsGC09MdWhQwe8fPkSAGBjY4PXr1+zG0gE6HBn1WjxUTJD0MWXmpqKAwcOwMPDQ2D7pASvQYMGOH78ODw8PNC3b1/s3btXqi/5p8VXNVp8lMwQdPF5e3tj/PjxaNmypcD2SQnPhAkT8M8//+DEiRNwdnaW2tv49OzZE8+ePUNBQQHbUcQWLT5KZgiy+BISEuDr6ws3NzeB7I8SDSMjIwQFBcHIyAgdO3bE48eP2Y4kcGpqarCwsEBQUBDbUcQWLT5KZgiy+DZs2IDp06dDV1dXIPujRKdevXrYunUrDhw4gFGjRmHdunVSt9wZHe6sHC0+SmYIqvg+f/6MixcvYtmyZQJIRbHFyckJISEhePDgAfr06YOvX7+yHUlgaPFVjhYfJTMEVXxr1qzBvHnzoKmpKYBUFJuaNm2KO3fuoG/fvrC2tsb169fZjiQQdnZ2+PDhAzIzM9mOIpboPD5KJuTl5UFLSwv5+fl1unPChw8f4ODggOjoaKipqQkwIcW2p0+fYvz48XB2dsamTZugpKTEdqQ66devH+bMmYNhw4axHUXs0CM+SibEx8ejefPmdb5d0KpVq7BkyRJaelKoa9euCA0NRVxcHOzs7BAVFcV2pDqhw50Vo8VHyQRBDHOGhIQgKCgIc+fOFVAqStw0btwYf/31F3777TfY29vDz8+P7Ui1RouvYrT4KJkgiOLz8PDAH3/8AVVVVQGlosQRh8PBrFmzcO/ePWzcuBGTJ09Gbm4u27FqrGPHjkhKSkJSUhLbUcQOLT5KJtT1rgxBQUF4//49fvvtNwGmosRZ+/bt8fLlSygqKsLa2hqhoaFsR6oReXl5ODg44P79+2xHETu0+CiZUJcjPkII3NzcsGrVKom/4IGqmfr16+PIkSNYu3Yt+vfvj127dknUcmeOjo64e/cu2zHEDi0+SibUpfju3buHxMRETJo0ScCpKEkxduxY/PPPPzh9+jSGDRuG9PR0tiNVy8/zfJJU1qJAi4+SCfHx8bUqvp9He2vXroWCgoIQklGSonXr1njy5AlMTEzQsWNHPHz4kO1IVTI2NgbDMDJ3Q96q0OKjpB7DMCXTGWrq2rVrKCgowOjRo4WQjJI09erVw5YtW3D48GGMHTsWa9asEeubvnI4HHp1Zzlo8VFSLy0tDQ0bNqzx1ZgMw8DDwwPr16+HnBz9q0L9z4ABAxASEoInT56gT58+SEhIYDtShWjxlUX/NlNSr7bn9y5cuABlZWUMHTpUCKkoSaenp4fbt29jwIABsLGxwdWrV9mOVK4+ffogMDAQDMOwHUVs0OKjpF5tio/H42HVqlXYsGFDnVd7oaSXnJwcVq5cCX9/f8yfPx/z588Hl8tlO1YpzZo1g5aWFsLCwtiOIjZo8VFSrzbF5+fnBz09PTg6OgopFSVN7O3tERoaisTERNjZ2SEiIoLtSKXQ4c7SaPFRUq+mxVdYWIi1a9fC09OTHu1R1aahoYELFy5g5syZ6NatG06ePMl2pBK0+EqjxUdJvZoW35EjR2BmZoauXbsKMRUljTgcDmbMmIH79+/D29sbLi4uyMnJYTsWevXqhadPn6KoqIjtKGKBFh8l9WpSfPn5+fD09MT69euFnIqSZhYWFnj58iVUVFRgZWWFV69esZpHQ0MDJiYmCA4OZjWHuKDFR0m9mhTfvn37YGdnB2trayGnoqSdqqoqDh06BE9PTwwcOBA7duxgdQUVOtz5P/RGtJRUKygogLq6OgoKCqqci5ednY02bdrg/v37MDc3F1FCShbExMRg7Nix0NbWxvHjx6GtrS3yDHfu3MHatWvx5MkTkb+3uKFHfJRUS0hIQLNmzao1AX3nzp3o168fLT1K4AwMDPDkyROYm5vDysoKDx48EHmGrl27IiwsTCJvsSRotPgoqVbdYc6MjAzs3r0ba9asEX4oSiYpKipi06ZNOHr0KMaPH49Vq1aJdLkzVVVV2NjY4NGjRyJ7T3FFi4+SatUtvs2bN2PEiBFo3bq1CFJRsqxfv34ICQlBcHAwevXqhbi4OJG9Nz3P9wMtPkqqVaf4kpOTcejQIXh4eIgoFSXrdHV1cfPmTQwePBidOnXC5cuXRfK+tPh+oMVHSbXqFN/GjRsxadIk6OvriygVRf1Y7mz58uW4cuUKFi5ciLlz5wp9ubNOnTohNjYWaWlpQn0fcUeLj5JqVd2HLy4uDqdPn8bKlStFmIqi/sfW1hahoaFITU1Fly5dEB4eLrT3UlBQQPfu3REYGCi095AEtPgoqVbVEd/69esxY8YM6OjoiDAVRZWmrq6Oc+fOYe7cuejevTuOHz8utDl/jo6OuHv3rlD2LSnoPD5KahFCUL9+faSkpKBhw4Zlno+KioK9vT0iIyOhoaHBQkKKKuv9+/cYM2YMLC0tsX//fqipqQl0/+/evcOwYcPw6dMnge5XktAjPkpqffv2DcrKyuWWHgCsWbMGCxYsoKVHiRVzc3M8f/4cDRs2hJWVFV6+fCnw/efl5SE2Nlag+5UktPgoqVXZMOe7d+9w7949LFiwQMSpKKpqqqqqOHDgALy9veHk5ITt27cL7EayHA4HvXv3lumrO2nxUVKrsuLz8PDAsmXLKjwapChxMHLkSDx//hwXLlzA4MGDkZqaKpD9yvq0Blp8lNSqqPhevHiBFy9eYNasWSykoqiaadWqFR49eoQOHTrAysoK9+/fr/M++/Tpg/v377O6aDabaPFRUqui4vPw8ICbmxtUVFRYSEVRNaeoqIiNGzfixIkTcHFxgbu7e52WO2vVqhXq16+P9+/fCzCl5KDFR0mt8orv8ePHiIyMxLRp01hKRVG15+joiJCQELx8+RI9e/bEly9far0vWR7upMVHSa24uDg0b9685PeEELi5uWH16tWoV68ei8koqvZ0dHQQEBCAX375BZ07d4a/v3+t9iPLxUfn8VFSq2nTpnj27FlJ+d26dQu///473r17B3l5eZbTUVTdPX/+HGPHjsWAAQOwbdu2Gg3fp6WloU2bNkhPT4eCgoIQU4ofesRHSaXCwkKkp6dDT08PwI+jPXd3d6xbt46WHiU1OnfujNDQUGRkZKBLly74+PFjtV+rra2NVq1a4cWLF0JMKJ5o8VFS6evXr9DT0yv5Jnv58mXweDyMGDGC5WQUJViNGjXCmTNnsGDBAvTo0QNHjx6t9tWasjrcSYc6KamRnluIi68SEJ6cjZivKYh6H4b5k0ZieMem6G3fCd7e3hg8eDDbMSlKaD58+ICxY8fC3NwcBw4cQKNGjSrdPiAgAFu2bJG5Ratp8VESLyw+Ez4PovEw8setVgp5/1vhQllBDjw+H/KpETi3+ld0aE6XJ6OkW0FBARYvXoxbt27hzJkz6Ny5c4Xb5ubmQk9PDykpKVBVVRVhSnbR4qMk2qngWHgGhIPL46OyP8kcAMqK8nBzMsVE21aiikdRrPH398esWbOwZMkSLF68GHJy5Z/ZsuvVD/YTFoLfUBfZXB7UlBVgqquGUdb60GygJOLUokGLj5JYP0rvIwqKq7+GoYqiHNyc2tLyo2TCly9fMH78eDRs2BC+vr6lbr/1c6Tk7ockcAgBn/O/i76UFeRAADiYaGN2TyNYNlcXfXghosVHSaSw+EyMPRyMgmJ+qcezX11D3tt7KEqLRf22PaE1eGGZ16ooyuOcqy3a66uLKC1FsYfH42HNmjU4fvw4fH194ejoWP2REg6grCB9IyW0+CiJ5Or3Enc+ppT5S5sfEQRwOCiICQEpLiq3+DgcoL+ZDg5MtBFRWopi3/379zFp0iTYuizFO0VjcGV4pIROZ6AkTnpuIR5GppX7TVXVxB6qxnaQU6n45p2EAIERafiWWyjElBQlXnr37o1TAY/wimlZYekVZ3zFly3OSL+2tdTjBcUMPAPC8SYhUwRJhY8WHyVxLr5KqPM+OAAuhtR9PxQlSf4MywDkFSt8PuP2ASjptSn3OS6Pj30PooUVTaRo8VESJzw5u9SUhdrg8hiEJ+UIKBFFib/KRkoAIO/DQ8gp14dyS8tyn5emkRJafJTEyebW/nYspfdTLJD9UJQkqGykhCnMR+bj09DoXfldS6RlpIQWHyVx1JQFs6CumnLFQz4UJW0qGynJfOSHBpb9oKCmXek+pGWkhBYfJXFMddWgpFD+H13C8EF4RQDDBwgDwisCYfhltlNWkIOpXkNhR6UosVHRSElRymdwv4RBrdOwau5H8kdKZOteFJRUGGmtjx13I8t9LuvpWWQ9PVPy+7z3gWjUdRzUu08otR0BMNJKX5gxKUqsVDRSwo17C15WChL2TQUAkCIuQBgkpS+A3tRd5exH8kdKaPFREkergRJ6GmuXO49PvfuEMiX3XxwO0MtEW2qXY6Ko8vwYKUkuM9zZoEN/1G/bo+T32c/9wctKQeP+c8rsQ1pGSuhQJyWR5jgYQVmhdvfVU1aQx2wHIwEnoijxNtK6/BEOOUVlyDfQKPnFUVQGR6Ee5FXL3tlBWkZKaPFREsmyuTrcnEyholizP8I/VqAwpcuVUTLn50gJh1P5durdJ0BryJIyj0vTSAktPkpiTbRtBTentlBRlK/yLzOH82ONTmladomiaoqOlPxA1+qkJN6bhEzsexCNwIg0cPDjkuuffq4y38tEG7MdjOiRHiXz6F1NaPFRUuRbbiEuhiQgPCkH2dxiqCkrwlSvIUZaSe99xSiqNujdGWjxURRFyRxZHimhxUdRFCXDZHGkhBYfRVEUJVPoVZ0URVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTKHFR1EURckUWnwURVGUTPk/Q7nzrA6lsVwAAAAASUVORK5CYII=\n", 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" ] @@ -1367,7 +1422,7 @@ ], "source": [ "# Using NetworkX to visualize network connection\n", - "G = nx.from_numpy_matrix(conn.conn_mat)\n", + "G = nx.from_numpy_matrix(conn.require('conn_mat'))\n", "nx.draw(G, with_labels=True)\n", "plt.show()" ] @@ -1395,35 +1450,35 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[False, False, False, True, False, True, False, True,\n", - " True, False],\n", - " [False, False, False, True, True, False, False, True,\n", - " False, False],\n", - " [False, False, False, True, True, True, True, False,\n", - " True, True],\n", - " [ True, True, True, False, True, True, True, False,\n", - " False, True],\n", - " [False, True, True, True, False, False, False, True,\n", - " True, False],\n", - " [ True, False, True, True, False, False, True, False,\n", - " False, False],\n", - " [False, False, True, True, False, True, False, False,\n", - " False, True],\n", - " [ True, True, False, False, True, False, False, False,\n", - " False, False],\n", - " [ True, False, True, False, True, False, False, False,\n", - " False, False],\n", - " [False, False, True, True, False, False, True, False,\n", - " False, False]], dtype=bool))" + "JaxArray([[False, False, False, True, False, True, False, True,\n", + " True, False],\n", + " [False, False, False, True, True, False, False, True,\n", + " False, False],\n", + " [False, False, False, True, True, True, True, False,\n", + " True, True],\n", + " [ True, True, True, False, True, True, True, False,\n", + " False, True],\n", + " [False, True, True, True, False, False, False, True,\n", + " True, False],\n", + " [ True, False, True, True, False, False, True, False,\n", + " False, False],\n", + " [False, False, True, True, False, True, False, False,\n", + " False, True],\n", + " [ True, True, False, False, True, False, False, False,\n", + " False, False],\n", + " [ True, False, True, False, True, False, False, False,\n", + " False, False],\n", + " [False, False, True, True, False, False, True, False,\n", + " False, False]], dtype=bool)" ] }, - "execution_count": 89, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1436,12 +1491,12 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1452,7 +1507,7 @@ ], "source": [ "# Using NetworkX to visualize network connection\n", - "G = nx.from_numpy_matrix(conn.conn_mat)\n", + "G = nx.from_numpy_matrix(conn.require('conn_mat'))\n", "nx.draw(G, with_labels=True)\n", "plt.show()" ] @@ -1461,20 +1516,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Customize your connections" + "## Encapsulate your existing connections" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "BrainPy also allows users to customize their connections. The following requirements should be satisfied:\n", - "\n", - "- Your connection class should inherit from `brainpy.connect.Connector`. \n", - "- Initialize the `conn_mat` or `pre_ids`+ `post_ids` synaptic structures.\n", - "- Provide `num_pre` and `num_post` information.\n", + "BrainPy also allows users to encapsulate existing connections with convenient class interfaces. Users can provide connection types as:\n", + "- Index projection;\n", + "- Dense matrix;\n", + "- Sparse matrix.\n", "\n", - "In such a way, based on this customized connection class, users can generate any other synaptic structures (such like `pre2post`, `pre2syn`, `pre_slice_syn`, etc.) easily." + "Then users should provide `pre_size` and `post_size` information in order to instantiate the connection. In such a way, based on the following connection classes, users can generate any other synaptic structures (such like `pre2post`, `pre2syn`, `conn_mat`, etc.) easily." ] }, { @@ -1485,7 +1539,7 @@ } }, "source": [ - "### `bo.conn.IJConn`" + "### `bp.conn.IJConn`" ] }, { @@ -1497,7 +1551,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -1509,20 +1563,20 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[ True, False, False],\n", - " [ True, False, False],\n", - " [ True, False, False],\n", - " [False, False, False],\n", - " [False, False, False]], dtype=bool))" + "JaxArray([[ True, False, False],\n", + " [ True, False, False],\n", + " [ True, False, False],\n", + " [False, False, False],\n", + " [False, False, False]], dtype=bool)" ] }, - "execution_count": 98, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -1533,17 +1587,16 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "(JaxArray(DeviceArray([0, 0, 0], dtype=uint32)),\n", - " JaxArray(DeviceArray([0, 1, 2, 3, 3, 3], dtype=uint32)))" + "(JaxArray([0, 0, 0], dtype=uint32), JaxArray([0, 1, 2, 3, 3, 3], dtype=uint32))" ] }, - "execution_count": 99, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1554,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 8, "metadata": { "scrolled": true }, @@ -1562,11 +1615,10 @@ { "data": { "text/plain": [ - "(JaxArray(DeviceArray([0, 1, 2], dtype=uint32)),\n", - " JaxArray(DeviceArray([0, 1, 2, 3, 3, 3], dtype=uint32)))" + "(JaxArray([0, 1, 2], dtype=uint32), JaxArray([0, 1, 2, 3, 3, 3], dtype=uint32))" ] }, - "execution_count": 100, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1599,7 +1651,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 9, "metadata": { "pycharm": { "name": "#%%\n" @@ -1614,7 +1666,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 10, "metadata": { "pycharm": { "name": "#%%\n" @@ -1624,14 +1676,14 @@ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[ True, True, False],\n", - " [ True, True, False],\n", - " [False, True, True],\n", - " [False, False, True],\n", - " [False, True, False]], dtype=bool))" + "JaxArray([[ True, True, False],\n", + " [False, True, True],\n", + " [ True, False, True],\n", + " [False, False, True],\n", + " [ True, False, False]], dtype=bool)" ] }, - "execution_count": 102, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1642,7 +1694,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 11, "metadata": { "pycharm": { "name": "#%%\n" @@ -1652,11 +1704,11 @@ { "data": { "text/plain": [ - "(JaxArray(DeviceArray([0, 1, 0, 1, 1, 2, 2, 1], dtype=uint32)),\n", - " JaxArray(DeviceArray([0, 2, 4, 6, 7, 8], dtype=uint32)))" + "(JaxArray([0, 1, 1, 2, 0, 2, 2, 0], dtype=uint32),\n", + " JaxArray([0, 2, 4, 6, 7, 8], dtype=uint32))" ] }, - "execution_count": 103, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1667,7 +1719,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 12, "metadata": { "pycharm": { "name": "#%%\n" @@ -1677,11 +1729,11 @@ { "data": { "text/plain": [ - "(JaxArray(DeviceArray([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint32)),\n", - " JaxArray(DeviceArray([0, 2, 4, 6, 7, 8], dtype=uint32)))" + "(JaxArray([0, 1, 2, 3, 4, 5, 6, 7], dtype=uint32),\n", + " JaxArray([0, 2, 4, 6, 7, 8], dtype=uint32))" ] }, - "execution_count": 104, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1714,7 +1766,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 13, "metadata": { "pycharm": { "name": "#%%\n" @@ -1732,7 +1784,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" @@ -1742,14 +1794,14 @@ { "data": { "text/plain": [ - "JaxArray(DeviceArray([[ True, True, True],\n", - " [ True, True, True],\n", - " [ True, False, True],\n", - " [ True, False, True],\n", - " [False, False, True]], dtype=bool))" + "JaxArray([[ True, False, True],\n", + " [ True, False, True],\n", + " [ True, False, False],\n", + " [False, True, True],\n", + " [False, True, False]], dtype=bool)" ] }, - "execution_count": 106, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1760,7 +1812,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 15, "metadata": { "pycharm": { "name": "#%%\n" @@ -1770,11 +1822,11 @@ { "data": { "text/plain": [ - "(JaxArray(DeviceArray([0, 1, 2, 0, 1, 2, 0, 2, 0, 2, 2], dtype=uint32)),\n", - " JaxArray(DeviceArray([ 0, 3, 6, 8, 10, 11], dtype=uint32)))" + "(JaxArray([0, 2, 0, 2, 0, 1, 2, 1], dtype=uint32),\n", + " JaxArray([0, 2, 4, 5, 7, 8], dtype=uint32))" ] }, - "execution_count": 108, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1785,7 +1837,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 16, "metadata": { "pycharm": { "name": "#%%\n" @@ -1795,11 +1847,11 @@ { "data": { "text/plain": [ - "(JaxArray(DeviceArray([ 0, 3, 6, 8, 1, 4, 2, 5, 7, 9, 10], dtype=uint32)),\n", - " JaxArray(DeviceArray([ 0, 4, 6, 11], dtype=uint32)))" + "(JaxArray([0, 2, 4, 5, 7, 1, 3, 6], dtype=uint32),\n", + " JaxArray([0, 3, 5, 8], dtype=uint32))" ] }, - "execution_count": 109, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1816,7 +1868,7 @@ } }, "source": [ - "### Using NetworkX to customize connections\n", + "### Using NetworkX to provide connections and pass into `Connector`\n", "\n", "NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.\n", "\n", @@ -1825,7 +1877,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 17, "metadata": { "pycharm": { "name": "#%%\n" @@ -1857,7 +1909,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 18, "metadata": { "pycharm": { "name": "#%%\n" @@ -1869,16 +1921,16 @@ "output_type": "stream", "text": [ "dense adjacency matrix:\n", - "[[0 1 0 1 1]\n", - " [1 0 0 1 0]\n", + "[[0 0 1 1 0]\n", " [0 0 0 1 1]\n", - " [1 1 1 0 0]\n", - " [1 0 1 0 0]]\n" + " [1 0 0 0 1]\n", + " [1 1 0 0 1]\n", + " [0 1 1 1 0]]\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -1913,7 +1965,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 19, "metadata": { "pycharm": { "name": "#%%\n" @@ -1924,11 +1976,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "JaxArray(DeviceArray([[False, True, False, True, True],\n", - " [ True, False, False, True, False],\n", - " [False, False, False, True, True],\n", - " [ True, True, True, False, False],\n", - " [ True, False, True, False, False]], dtype=bool))\n" + "JaxArray([[False, False, True, True, False],\n", + " [False, False, False, True, True],\n", + " [ True, False, False, False, True],\n", + " [ True, True, False, False, True],\n", + " [False, True, True, True, False]], dtype=bool)\n" ] } ], @@ -1938,11 +1990,122 @@ "\n", "print(res)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Customize your connections" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "BrainPy allows users to customize their connections. The following requirements should be satisfied:\n", + "\n", + "- Your connection class should inherit from `brainpy.connect.TwoEndConnector` or `brainpy.connect.OneEndConnector`.\n", + "- `__init__` function should be implemented and essential parameters should be initialized.\n", + "- Users should also overwrite `require()` function to describe how to build your connection. Remember in `require()` users have to call two built-in functions: `self.check(structures)` and `self.make_returns(structures, [csr, mat, ij])`.\n", + "\n", + "Let's take an example to illustrate the details of customization." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "class FixedProb(bp.connect.TwoEndConnector):\n", + " \"\"\"Connect the post-synaptic neurons with fixed probability.\n", + "\n", + " Parameters\n", + " ----------\n", + " prob : float\n", + " The conn probability.\n", + " include_self : bool\n", + " Whether to create (i, i) connection.\n", + " seed : optional, int\n", + " Seed the random generator.\n", + " \"\"\"\n", + "\n", + " def __init__(self, prob, include_self=True, seed=None):\n", + " super(FixedProb, self).__init__()\n", + " assert 0. <= prob <= 1.\n", + " self.prob = prob\n", + " self.include_self = include_self\n", + " self.seed = seed\n", + " self.rng = np.random.RandomState(seed=seed)\n", + "\n", + " def require(self, *structures):\n", + " # please call this function for auto-checking inputs.\n", + " self.check(structures)\n", + "\n", + " ind = []\n", + " count = np.zeros(self.pre_num, dtype=np.uint32)\n", + " \n", + " def _random_prob_conn(rng, pre_i, num_post, prob, include_self):\n", + " p = rng.random(num_post) <= prob\n", + " if (not include_self) and pre_i < num_post:\n", + " p[pre_i] = False\n", + " conn_j = np.asarray(np.where(p)[0], dtype=np.uint32)\n", + " return conn_j\n", + " \n", + " for i in range(self.pre_num):\n", + " posts = _random_prob_conn(self.rng, pre_i=i, num_post=self.post_num,\n", + " prob=self.prob, include_self=self.include_self)\n", + " ind.append(posts)\n", + " count[i] = len(posts)\n", + "\n", + " ind = np.concatenate(ind)\n", + " indptr = np.concatenate(([0], count)).cumsum()\n", + " \n", + " # please use this built-in function to auto-return all data structures you need.\n", + " return self.make_returns(structures, csr=(ind, indptr))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Users can customize the connection in `require()` function. And at last user will call `self.make_returns(structures, [csr, mat, ij])` function to automatically produce the structures in parameters. Notice there are also three optional parameters users can provide:\n", + "- csr: sparse connection, including a index vector and a indptr vector. \n", + "- mat: dense conncetion, including a connection matrix.\n", + "- ij: index projection, including a pre-neuron index vector and a post-neuron index vector.\n", + "\n", + "Then users can initialize the your own connections as below:" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "JaxArray([[False, True, False, True, False],\n", + " [False, False, True, False, True],\n", + " [ True, False, True, False, True],\n", + " [False, False, False, False, True],\n", + " [ True, True, False, False, True]], dtype=bool)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "conn = FixedProb(prob=0.5, include_self=True)(pre_size=5, post_size=5)\n", + "conn.require('conn_mat')" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1956,7 +2119,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.7" }, "latex_envs": { "LaTeX_envs_menu_present": true, @@ -2029,4 +2192,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} diff --git a/docs/tutorial_training/index.rst b/docs/tutorial_training/index.rst index 34006c53..304ca50b 100644 --- a/docs/tutorial_training/index.rst +++ b/docs/tutorial_training/index.rst @@ -9,4 +9,6 @@ and how to customize your nodes or networks. node_specification node_operations + network_training node_customization + training_customization diff --git a/docs/tutorial_training/network_training.ipynb b/docs/tutorial_training/network_training.ipynb new file mode 100644 index 00000000..9433d7a2 --- /dev/null +++ b/docs/tutorial_training/network_training.ipynb @@ -0,0 +1,486 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Network Training" + ] + }, + { + "cell_type": "markdown", + "source": [ + "@[Chaoming Wang](mailto:adaduo@outlook.com)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "To maker your model powerful, you need to train your created network models. In this section, we are going to talk about how to train your network models." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import brainpy as bp\n", + "import brainpy.math as bm\n", + "\n", + "bp.math.set_platform('cpu')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Setup a ``RNNTrainer``" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Once you create a model, setuping a structural trainer is just to instantiating a ``RNNTrainer``." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "source": [ + "model = (\n", + " bp.nn.Input(1)\n", + " >>\n", + " bp.nn.VanillaRNN(100)\n", + " >>\n", + " bp.nn.Dense(1)\n", + ")\n", + "model.initialize(1)\n", + "\n", + "# set up a ridge regression trainer\n", + "trainer = bp.nn.BPTT(model, loss='mean_squared_error')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "execution_count": 2, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "In the next, all you need is to provide your training data to the ``.fit()`` function." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The **training data** feeding into the ``.fit()`` function can be a tuple or a list of ``(X, Y)`` pair, or a callable function which generate ``(x, y)`` data pairs.\n", + "\n", + "- If the providing training data is the ``(X, Y)`` data pair, ``X`` should be the input data which has the shape of `(num_sample, num_time, num_feature)`, ``Y`` should be the target data which has the shape of `(num_sample, num_time, num_feature)` for ``many-to-many`` training data mapping, or a data with the shape of `(num_sample, num_feature)` for ``many-to-final`` training data mapping.\n", + "\n", + "![](../_static/rnn_training_mapping.png)\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "- If the training data is a callable function, it should generate a Python generator which yield the pair of ``(X, Y)`` data for training. For example,\n", + "\n", + "```python\n", + "\n", + "# when calling this function,\n", + "# it will create a Python generator.\n", + "\n", + "def train_data():\n", + " num_data = 10\n", + " for _ in range(num_data):\n", + " # The (X, Y) data pair should be:\n", + " # - \"X\" is a tensor has the shape of\n", + " # \"(num_batch, num_time, num_feature)\"\n", + " # - \"Y\" is a tensor has the shape of\n", + " # \"(num_batch, num_time, num_feature)\"\n", + " # or \"(num_batch, num_feature)\"\n", + " xs = bm.random.rand(1, 20, 2)\n", + " ys = bm.random.random((1, 20, 2))\n", + " yield xs, ys\n", + "```\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "However, all these data constraints can be released when you customize your training procedures. Please see XXXXXX." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "It is worthy to note that before fitting your data by calling ``.fit()`` function, you need to **initialize the model** by specifying the batch size your data are using. Otherwise, an error will cause." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Supported training algorithms" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Currently, BrainPy provides several ways to train recurrent neural networks, including ridge regression, FORCE learning, and back-propagation through time algorithms, etc. The full list of the supported training algorithms please see the [API documentation](../apis/auto/nn/runners.rst). Here we only talk about few of them." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Ridge regression" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Shared parameters" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Sometimes, there are some global parameters which are shared across all nodes. For example, the training or testing phase control parameter ``train=True/False``. Here, we use one simple model to demonstrate how to provide shared parameters when we calling models." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "model = (\n", + " bp.nn.Input(1)\n", + " >>\n", + " bp.nn.VanillaRNN(100)\n", + " >>\n", + " bp.nn.Dropout(0.3)\n", + " >>\n", + " bp.nn.Dense(1)\n", + ")\n", + "model.initialize(3)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "These shared parameters can be provided as two kinds of ways:\n", + "\n", + "- When you are using the instantiated model directly, you can provide them when calling this model." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "JaxArray([[-2.1306934],\n [ 1.4046229],\n [ 1.2039466]], dtype=float32)" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model(bm.random.rand(3, 1), train=True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "JaxArray([[-0.18169183],\n [-0.09682302],\n [-0.09607743]], dtype=float32)" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model(bm.random.rand(3, 1), train=False)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "- When you are using the structural runners like ``brainpy.nn.RNNRunner`` or ``brainpy.nn.BPTT`` trainer, you can warp all shared parameters in an argument ``shared_kwargs``." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [], + "source": [ + "runner = bp.nn.RNNRunner(model)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/10 [00:00input', + conn_mat=conn_mat, + delay_mat=delay_mat, + delay_initializer=bp.init.Uniform(0, 0.05)) + + def update(self, _t, _dt): + self.coupling.update(_t, _dt) + self.fhn.update(_t, _dt) + + +def brain_simulation(): + net = Network() + runner = bp.dyn.DSRunner(net, monitors=['fhn.x'], inputs=['fhn.input', 0.72]) + runner.run(6e3) + + plt.rcParams['image.cmap'] = 'plasma' + fig, axs = plt.subplots(1, 2, figsize=(12, 4)) + fc = bp.measure.functional_connectivity(runner.mon['fhn.x']) + ax = axs[0].imshow(fc) + plt.colorbar(ax, ax=axs[0]) + axs[1].plot(runner.mon.ts, runner.mon['fhn.x'][:, ::5], alpha=0.8) + plt.tight_layout() + plt.show() + + +if __name__ == '__main__': + bifurcation_analysis() + brain_simulation() + diff --git a/examples/simulation/whole_brain_simulation_with_sl_oscillator.py b/examples/simulation/whole_brain_simulation_with_sl_oscillator.py new file mode 100644 index 00000000..b9484ecf --- /dev/null +++ b/examples/simulation/whole_brain_simulation_with_sl_oscillator.py @@ -0,0 +1,67 @@ +# -*- coding: utf-8 -*- + +import matplotlib.pyplot as plt +import numpy as np + +import brainpy as bp +import brainpy.math as bm + +bp.check.turn_off() + + +def bifurcation_analysis(): + model = bp.dyn.StuartLandauOscillator(1, method='exp_auto') + pp = bp.analysis.Bifurcation2D( + model, + target_vars={'x': [-2, 2], 'y': [-2, 2]}, + pars_update={'x_ext': 0., 'y_ext': 0., 'w': 0.2}, + target_pars={'a': [-2, 2]}, + resolutions={'a': 0.01} + ) + pp.plot_bifurcation() + pp.show_figure() + + +class Network(bp.dyn.Network): + def __init__(self): + super(Network, self).__init__() + + # Please download the processed data "hcp.npz" of the + # ConnectomeDB of the Human Connectome Project (HCP) + # from the following link: + # - https://share.weiyun.com/wkPpARKy + hcp = np.load('hcp.npz') + conn_mat = bm.asarray(hcp['Cmat']) + bm.fill_diagonal(conn_mat, 0) + gc = 0.6 # global coupling strength + + self.sl = bp.dyn.StuartLandauOscillator(80, x_ou_sigma=0.14, y_ou_sigma=0.14, + name='sl', method='exp_auto') + self.coupling = bp.dyn.DiffusiveDelayCoupling(self.sl, self.sl, + 'x->input', + conn_mat=conn_mat * gc, + delay_initializer=bp.init.Uniform(0, 0.05)) + + def update(self, _t, _dt): + self.coupling.update(_t, _dt) + self.sl.update(_t, _dt) + + +def simulation(): + net = Network() + runner = bp.dyn.DSRunner(net, monitors=['sl.x']) + runner.run(6e3) + + plt.rcParams['image.cmap'] = 'plasma' + fig, axs = plt.subplots(1, 2, figsize=(12, 4)) + fc = bp.measure.functional_connectivity(runner.mon['sl.x']) + ax = axs[0].imshow(fc) + plt.colorbar(ax, ax=axs[0]) + axs[1].plot(runner.mon.ts, runner.mon['sl.x'][:, ::5], alpha=0.8) + plt.tight_layout() + plt.show() + + +if __name__ == '__main__': + bifurcation_analysis() + simulation() diff --git a/examples/training/Gauthier_2021_ngrc_double_scroll.py b/examples/training/Gauthier_2021_ngrc_double_scroll.py index d01b3a23..81343afc 100644 --- a/examples/training/Gauthier_2021_ngrc_double_scroll.py +++ b/examples/training/Gauthier_2021_ngrc_double_scroll.py @@ -126,13 +126,13 @@ model.initialize(num_batch=1) # -------- # # warm-up -trainer = bp.nn.RidgeTrainer(model, beta=1e-5) +trainer = bp.nn.RidgeTrainer(model, beta=1e-5, jit=True) # training outputs = trainer.predict(X_warmup) print('Warmup NMS: ', bp.losses.mean_squared_error(outputs, Y_warmup)) trainer.fit([X_train, {'readout': dX_train}]) -plot_weights(di.weights.numpy(), di.bias.numpy(), r.comb_ids.numpy()) +plot_weights(di.Wff.numpy(), di.bias.numpy(), r.comb_ids[0].numpy()) # prediction model = bm.jit(model) diff --git a/examples/training/Gauthier_2021_ngrc_lorenz.py b/examples/training/Gauthier_2021_ngrc_lorenz.py index bb0fa81c..66e5cc06 100644 --- a/examples/training/Gauthier_2021_ngrc_lorenz.py +++ b/examples/training/Gauthier_2021_ngrc_lorenz.py @@ -135,7 +135,7 @@ trainer = bp.nn.RidgeTrainer(model, beta=2.5e-6) outputs = trainer.predict(X_warmup) print('Warmup NMS: ', bp.losses.mean_squared_error(outputs, Y_warmup)) trainer.fit([X_train, {'readout': dX_train}]) -plot_weights(di.weights.numpy(), di.bias.numpy(), r.comb_ids.numpy()) +plot_weights(di.Wff.numpy(), di.bias.numpy(), r.comb_ids.numpy()) # prediction model = bm.jit(model) diff --git a/examples/training/Song_2016_EI_RNN.py b/examples/training/Song_2016_EI_RNN.py index 602f1fed..4c2cb29c 100644 --- a/examples/training/Song_2016_EI_RNN.py +++ b/examples/training/Song_2016_EI_RNN.py @@ -128,17 +128,17 @@ class RNN(bp.dyn.DynamicalSystem): self.mask = bm.asarray(mask, dtype=bm.float_) # input weight - self.w_ir = bm.TrainVar(bp.nn.init_param(w_ir, (num_input, num_hidden))) + self.w_ir = bm.TrainVar(bp.init.init_param(w_ir, (num_input, num_hidden))) # recurrent weight bound = 1 / num_hidden ** 0.5 - self.w_rr = bm.TrainVar(bp.nn.init_param(w_rr, (num_hidden, num_hidden))) + self.w_rr = bm.TrainVar(bp.init.init_param(w_rr, (num_hidden, num_hidden))) self.w_rr[:, :self.e_size] /= (self.e_size / self.i_size) self.b_rr = bm.TrainVar(self.rng.uniform(-bound, bound, num_hidden)) # readout weight bound = 1 / self.e_size ** 0.5 - self.w_ro = bm.TrainVar(bp.nn.init_param(w_ro, (self.e_size, num_output))) + self.w_ro = bm.TrainVar(bp.init.init_param(w_ro, (self.e_size, num_output))) self.b_ro = bm.TrainVar(self.rng.uniform(-bound, bound, num_output)) # variables diff --git a/examples/training/echo_state_network.py b/examples/training/echo_state_network.py index 0c1d0930..a3935e63 100644 --- a/examples/training/echo_state_network.py +++ b/examples/training/echo_state_network.py @@ -66,38 +66,38 @@ def ngrc(num_in=10, num_out=30): outputs = trainer.predict(X) print(outputs.shape) print(bp.losses.mean_absolute_error(outputs, Y)) - trainer.fit(X, Y) + trainer.fit([X, Y]) outputs = trainer.predict(X) print(bp.losses.mean_absolute_error(outputs, Y)) -def ngrc_bacth(num_in=10, num_out=30): - bp.base.clear_name_cache() - model = ( - bp.nn.Input(num_in) - >> - bp.nn.NVAR(delay=2, order=2, name='l1') - >> - bp.nn.Dense(num_out, weight_initializer=bp.init.Normal(0.1), trainable=True) - ) - batch_size = 10 - model.initialize(num_batch=batch_size) - - X = bm.random.random((batch_size, 200, num_in)) - Y = bm.random.random((batch_size, 200, num_out)) - trainer = bp.nn.RidgeTrainer(model, beta=1e-6) - outputs = trainer.predict(X) - # print() - # print(trainer.mon['l1.output'].shape) - print(bp.losses.mean_absolute_error(outputs, Y)) - trainer.fit(X, Y) - outputs = trainer.predict(X) - print(bp.losses.mean_absolute_error(outputs, Y)) +# def ngrc_bacth(num_in=10, num_out=30): +# bp.base.clear_name_cache() +# model = ( +# bp.nn.Input(num_in) +# >> +# bp.nn.NVAR(delay=2, order=2, name='l1') +# >> +# bp.nn.Dense(num_out, weight_initializer=bp.init.Normal(0.1), trainable=True) +# ) +# batch_size = 10 +# model.initialize(num_batch=batch_size) +# +# X = bm.random.random((batch_size, 200, num_in)) +# Y = bm.random.random((batch_size, 200, num_out)) +# trainer = bp.nn.RidgeTrainer(model, beta=1e-6) +# outputs = trainer.predict(X) +# # print() +# # print(trainer.mon['l1.output'].shape) +# print(bp.losses.mean_absolute_error(outputs, Y)) +# trainer.fit([X, Y]) +# outputs = trainer.predict(X) +# print(bp.losses.mean_absolute_error(outputs, Y)) if __name__ == '__main__': - # print('ESN') - # esn(10, 30) + print('ESN') + esn(10, 30) print('NGRC') ngrc(10, 30) - ngrc_bacth() + # ngrc_bacth() diff --git a/examples/training/integrator_rnn.py b/examples/training/integrator_rnn.py index 13780f07..5af37c2b 100644 --- a/examples/training/integrator_rnn.py +++ b/examples/training/integrator_rnn.py @@ -65,7 +65,7 @@ trainer.fit(train_data, plt.plot(trainer.train_losses.numpy()) plt.show() -model.init_state(1) +model.initialize(1) x, y = build_inputs_and_targets(batch_size=1) predicts = trainer.predict(x) diff --git a/extensions/build_wheel_in_linux.sh b/extensions/build_wheel_in_linux.sh index c2f3db8e..e5fba27c 100644 --- a/extensions/build_wheel_in_linux.sh +++ b/extensions/build_wheel_in_linux.sh @@ -1,29 +1,36 @@ #docker run -ti -v $(pwd):/io quay.io/pypa/manylinux2010_x86_64 /bin/bash #cd /io/ -version=0.0.3 +version=0.0.3.1 +linux_version=manylinux2010_x86_64 # py36 /opt/python/cp36-cp36m/bin/python -m pip install pybind11 numpy jax jaxlib /opt/python/cp36-cp36m/bin/python setup.py bdist_wheel -auditwheel repair --plat manylinux2010_x86_64 dist/brainpylib-$version-cp36-cp36m-linux_x86_64.whl -#mv wheelhouse/brainpylib-$version-cp36-cp36m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl wheelhouse/brainpylib-$version-cp36-manylinux2010_x86_64.whl +auditwheel repair --plat $linux_version dist/brainpylib-$version-cp36-cp36m-linux_x86_64.whl # py37 /opt/python/cp37-cp37m/bin/python -m pip install pybind11 numpy jax jaxlib /opt/python/cp37-cp37m/bin/python setup.py bdist_wheel -auditwheel repair --plat manylinux2010_x86_64 dist/brainpylib-$version-cp37-cp37m-linux_x86_64.whl -#mv wheelhouse/brainpylib-$version-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl wheelhouse/brainpylib-$version-cp37-manylinux2010_x86_64.whl +auditwheel repair --plat $linux_version dist/brainpylib-$version-cp37-cp37m-linux_x86_64.whl # py38 /opt/python/cp38-cp38/bin/python -m pip install pybind11 numpy jax jaxlib scipy==1.7.1 /opt/python/cp38-cp38/bin/python setup.py bdist_wheel -auditwheel repair --plat manylinux2010_x86_64 dist/brainpylib-$version-cp38-cp38-linux_x86_64.whl -#mv wheelhouse/brainpylib-$version-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl wheelhouse/brainpylib-$version-cp38-manylinux2010_x86_64.whl +auditwheel repair --plat $linux_version dist/brainpylib-$version-cp38-cp38-linux_x86_64.whl # py39 /opt/python/cp39-cp39/bin/python -m pip install pybind11 numpy jax jaxlib scipy==1.7.1 /opt/python/cp39-cp39/bin/python setup.py bdist_wheel -auditwheel repair --plat manylinux2010_x86_64 dist/brainpylib-$version-cp39-cp39-linux_x86_64.whl -#mv wheelhouse/brainpylib-$version-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl wheelhouse/brainpylib-$version-cp39-manylinux2010_x86_64.whl +auditwheel repair --plat $linux_version dist/brainpylib-$version-cp39-cp39-linux_x86_64.whl +#docker run -ti -v $(pwd):/io quay.io/pypa/manylinux2014_x86_64 /bin/bash +#cd /io/ + +linux_version=manylinux2014_x86_64 + + +# py310 +/opt/python/cp310-cp310/bin/python -m pip install pybind11 numpy jax jaxlib scipy==1.7.2 +/opt/python/cp310-cp310/bin/python setup.py bdist_wheel +auditwheel repair --plat $linux_version dist/brainpylib-$version-cp310-cp310-linux_x86_64.whl diff --git a/extensions/build_wheel_in_windows.sh b/extensions/build_wheel_in_windows.sh index 3691a673..e61d8bae 100644 --- a/extensions/build_wheel_in_windows.sh +++ b/extensions/build_wheel_in_windows.sh @@ -16,3 +16,8 @@ conda activate py39 mkdir "build/lib.win-amd64-3.9/brainpylib" cp build/win_dll/* build/lib.win-amd64-3.9/brainpylib python setup.py bdist_wheel + +conda activate py310 +mkdir "build/lib.win-amd64-3.10/brainpylib" +cp build/win_dll/* build/lib.win-amd64-3.10/brainpylib +python setup.py bdist_wheel diff --git a/extensions/setup.py b/extensions/setup.py index 78e1b0b7..7b88af36 100644 --- a/extensions/setup.py +++ b/extensions/setup.py @@ -36,7 +36,7 @@ setup( include_package_data=True, install_requires=["jax", "jaxlib", "pybind11>=2.6, <2.8"], extras_require={"test": "pytest"}, - python_requires='>=3.6', + python_requires='>=3.7', url='https://github.com/PKU-NIP-Lab/BrainPy', ext_modules=ext_modules, cmdclass={"build_ext": build_ext}, diff --git a/extensions/setup_cuda.py b/extensions/setup_cuda.py index f82209d1..da0e0030 100644 --- a/extensions/setup_cuda.py +++ b/extensions/setup_cuda.py @@ -92,7 +92,7 @@ setup( include_package_data=True, install_requires=["jax", "jaxlib"], extras_require={"test": "pytest"}, - python_requires='>=3.6', + python_requires='>=3.7', url='https://github.com/PKU-NIP-Lab/BrainPy', ext_modules=[ Extension("gpu_ops", ['lib/gpu_ops.cc'] + glob.glob("lib/*.cu")), diff --git a/extensions/setup_mac.py b/extensions/setup_mac.py index 9788979c..7dc85900 100644 --- a/extensions/setup_mac.py +++ b/extensions/setup_mac.py @@ -21,7 +21,8 @@ ext_modules = [ Pybind11Extension("brainpylib/cpu_ops", sources=["lib/cpu_ops.cc"] + glob.glob("lib/*_cpu.cc"), cxx_std=11, - extra_link_args=["-rpath", "/Users/ztqakita/opt/miniconda3/lib"], + # extra_link_args=["-rpath", "/Users/ztqakita/miniforge3/lib"], # m1 + extra_link_args=["-rpath", "/Users/ztqakita/miniforge3/lib"], # intel define_macros=[('VERSION_INFO', __version__)]), ] @@ -37,7 +38,7 @@ setup( include_package_data=True, install_requires=["jax", "jaxlib", "pybind11>=2.6, <2.8"], extras_require={"test": "pytest"}, - python_requires='>=3.6', + python_requires='>=3.7', url='https://github.com/PKU-NIP-Lab/BrainPy', ext_modules=ext_modules, cmdclass={"build_ext": build_ext}, diff --git a/requirements-dev.txt b/requirements-dev.txt index 670959ba..bd3ca878 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,5 +1,6 @@ -r requirements.txt +numba matplotlib>=3.4 jaxlib>=0.1.64 sympy>=1.6 diff --git a/requirements-doc.txt b/requirements-doc.txt index 45015005..9bf40750 100644 --- a/requirements-doc.txt +++ b/requirements-doc.txt @@ -5,6 +5,7 @@ jaxlib>=0.1.64 sympy>=1.6 scipy>=1.1.0 brainpylib +numba # document requirements pandoc diff --git a/requirements-win.txt b/requirements-win.txt index 6add3372..7efaf71b 100644 --- a/requirements-win.txt +++ b/requirements-win.txt @@ -1,5 +1,6 @@ numpy>=1.15 tqdm +numba matplotlib>=3.4 sympy>=1.6 scipy>=1.1.0 diff --git a/setup.py b/setup.py index 8b7d5bde..c44a35d0 100644 --- a/setup.py +++ b/setup.py @@ -27,15 +27,15 @@ setup( author='BrainPy Team', author_email='chao.brain@qq.com', packages=find_packages(), - python_requires='>=3.6', + python_requires='>=3.7', install_requires=[ 'numpy>=1.15', 'jax>=0.2.10', 'tqdm', ], extras_require={ - 'cpu': ['jaxlib>=0.1.64', 'brainpylib>=0.02'], - 'cuda': ['jaxlib>=0.1.64', 'brainpylib>=0.02'], + 'cpu': ['jaxlib>=0.1.64', 'brainpylib>=0.03'], + 'cuda': ['jaxlib>=0.1.64', 'brainpylib>=0.03'], }, url='https://github.com/PKU-NIP-Lab/BrainPy', keywords='computational neuroscience, brain-inspired computation, ' @@ -46,10 +46,10 @@ setup( 'Operating System :: OS Independent', 'Programming Language :: Python', 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', + 'Programming Language :: Python :: 3.10', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)', 'Topic :: Scientific/Engineering :: Bio-Informatics',