{ "cells": [ { "cell_type": "markdown", "id": "ce1a86b1", "metadata": {}, "source": [ "# Example 1: Gene expression model" ] }, { "cell_type": "markdown", "id": "098e3327", "metadata": {}, "source": [ "The DynGPT package can be applied to solve a given state transition network and infer the associated model parameters. In this section, we will demonstrate the basic use of DynGPT using the gene expression model as an illustrative example." ] }, { "cell_type": "markdown", "id": "84dff04a", "metadata": {}, "source": [ "\"Schematic\n" ] }, { "cell_type": "markdown", "id": "9a57cfc3", "metadata": {}, "source": [ "In gene expression model, the STN can be described as:
\n", "(1) The system state is $s={{\\left( {{s}_{\\text{OFF}}},{{s}_{\\text{ON}}},{{s}_{\\text{M}}},{{s}_{\\text{P}}} \\right)}^{\\text{T}}}\\in {{\\left\\{ 0,1 \\right\\}}^{2}}\\times {{\\mathbb{N}}^{2}},$ where ${{s}_{\\text{OFF}}}$ and ${{s}_{\\text{ON}}}$ indicate whether the promoter is in the inactive or active state, ${{s}_{\\text{M}}}$ and ${{s}_{\\text{P}}}$ are the number of mature RNA and protein molecules, respectively.The system consists of seven events, each associated with a corresponding state transition vectors:
\n", "(2)\tEvent sets are $\\mathcal{R}=\\left\\{ \\left. {{\\mathcal{R}}_{1}},\\ldots ,{{\\mathcal{R}}_{7}} \\right\\} \\right.$ where ${{\\mathcal{R}}_{1}}$ represents promoter binding, ${{\\mathcal{R}}_{2}}$ represents promoter unbinding, ${{\\mathcal{R}}_{3}}$ represents active transcription, ${{\\mathcal{R}}_{4}}$ represents leaky transcription, ${{\\mathcal{R}}_{5}}$ represents degradation of mature RNA, \n", "${{\\mathcal{R}}_{6}}$ represents protein production and ${{\\mathcal{R}}_{7}}$ represents protein degradation.
\n", "(3) State change vectors are: $\\left\\{ {{\\Delta }^{\\left( k \\right)}} \\right\\},k=1,2,\\ldots ,7,$ where ${{\\Delta }^{\\left( k \\right)}}=\\left( \\Delta _{\\text{OFF}}^{\\left( k \\right)},\\Delta _{\\text{ON}}^{\\left( k \\right)},\\Delta _{\\text{M}}^{\\left( k \\right)},\\Delta _{\\text{P}}^{\\left( k \\right)} \\right)$ represents the state change vector corresponding to $k\\text{-th}$ event with \n", "$$\\begin{align}\n", " & \\Delta _{i}^{\\left( 1 \\right)}=-{{\\delta }_{i,\\text{OFF}}}+{{\\delta }_{i,\\text{ON}}},\\text{ } \\\\ \n", " & \\Delta _{i}^{\\left( 2 \\right)}={{\\delta }_{i,\\text{OFF}}}-{{\\delta }_{i,\\text{ON}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 3 \\right)}={{\\delta }_{i,\\text{M}}},\\text{ } \\\\ \n", " & \\Delta _{i}^{\\left( 4 \\right)}={{\\delta }_{i,\\text{M}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 5 \\right)}=-{{\\delta }_{i,\\text{M}}}, \\\\ \n", " & \\Delta _{i}^{\\left( 6 \\right)}={{\\delta }_{i,\\text{P}}},\\text{ } \\\\ \n", " & \\Delta _{i}^{\\left( 7 \\right)}=-{{\\delta }_{i,\\text{P}}}, \\\\ \n", "\\end{align}$$\n", "where $i\\in \\left\\{ \\text{OFF},\\text{ON},\\text{M},\\text{P} \\right\\}$ and ${{\\delta }_{i,j}}$ is the Kronecker delta.
\n", "(4) The corresponding occurrence propensities of events depend on the current promoter state, mature RNA and protein counts are: ${{\\lambda }_{1}}={{k}_{\\text{on}}}{{s}_{\\text{OFF}}}$, ${{\\lambda }_{2}}={{k}_{\\text{off}}}{{s}_{\\text{ON}}}$, ${{\\lambda }_{3}}=k_{\\text{syn}}^{\\text{on}}{{s}_{\\text{ON}}}$, ${{\\lambda }_{4}}=k_{\\text{syn}}^{\\text{off}}{{s}_{\\text{OFF}}}$, ${{\\lambda }_{5}}=k_{\\text{syn}}^{\\text{M}}{{s}_{\\text{M}}}$, ${{\\lambda }_{6}}=k_{\\text{deg}}^{\\text{M}}{{s}_{\\text{M}}}$, ${{\\lambda }_{7}}=k_{\\text{deg}}^{\\text{P}}{{s}_{\\text{P}}}$.
\n", "In this STN, the parameters are ${{\\theta }_{\\text{dyn}}}=\\left\\{ {{k}_{\\text{on}}},{{k}_{\\text{off}}},k_{s\\text{yn}}^{\\text{off}},k_{\\text{syn}}^{\\text{on}},k_{\\text{syn}}^{\\text{M}},k_{\\text{deg}}^{\\text{M}},k_{\\text{deg}}^{\\text{P}} \\right\\}$." ] }, { "cell_type": "markdown", "id": "b99c7072", "metadata": {}, "source": [ "### 1. Construct configuration file" ] }, { "cell_type": "markdown", "id": "fbcb707b", "metadata": {}, "source": [ "To facilitate data generation and subsequent neural network training, we need create a configuration file for the gene expression model that encapsulates its key properties. The key propertie of the model primarily consists of the following components:
\n", "(1) State-related configuration: This includes the names of the states. In central dogma model, the state names (`state_names`) are: (\"ON\", \"OFF\", \"mRNA\", \"Protein\")
\n", "(2) Event-related configuration: The occurrence rate functions governing event dynamics, and state transition vectors associated with each event's occurrence. In central dogma model, the state transition vectors (`state_transition`) are: $\\Delta _{i}^{\\left( 1 \\right)}$, $\\Delta _{i}^{\\left( 2 \\right)}$, $\\Delta _{i}^{\\left( 3 \\right)}$, $\\Delta _{i}^{\\left( 4 \\right)}$, $\\Delta _{i}^{\\left( 5 \\right)}$, $\\Delta _{i}^{\\left( 6 \\right)}$ and $\\Delta _{i}^{\\left( 7 \\right)}$. Occurrence rates for each event (`event_rates`) are: ${{\\lambda }_{1}}={{k}_{\\text{on}}}{{s}_{\\text{OFF}}}$, ${{\\lambda }_{2}}={{k}_{\\text{off}}}{{s}_{\\text{ON}}}$, ${{\\lambda }_{3}}=k_{\\text{syn}}^{\\text{on}}{{s}_{\\text{ON}}}$, ${{\\lambda }_{4}}=k_{\\text{syn}}^{\\text{off}}{{s}_{\\text{OFF}}}$, ${{\\lambda }_{5}}=k_{\\text{syn}}^{\\text{M}}{{s}_{\\text{M}}}$, ${{\\lambda }_{6}}=k_{\\text{deg}}^{\\text{M}}{{s}_{\\text{M}}}$, ${{\\lambda }_{7}}=k_{\\text{deg}}^{\\text{P}}{{s}_{\\text{P}}}$. Specifically, in certain events, specific states actively participate and drive the occurrence of the event without changing their own identity. Such a state is referred to as an `drive_state` in the context of the event. In ${{R}_{3}}$ $({{R}_{4}})$, ON (OFF) can be described as drive state, as its presence is essential for initiating the mRNA transciption while remaining unchanged in the event. In ${{R}_{6}}$, mRNA can be described as a drive state, as it provides the genetic instructions for protein synthesis while remaining unchanged in the event.
\n", "(3) Parameter-related configuration: The parameter names and the parameter ranges. In central dogma model, the parameter names (`param_names`) are: [\"kon\", \"koff\", \"ksynon\", \"ksynoff\", \"konM\", \"kdegM\", \"kdegP\"]. Upper bounds of parameters (`upper_bound_val`) are: [0.1, 0.1, 10, 10, 2, 0.5, 0.4]. Lower bounds of parameters (`lower_bound_val`) are: [0.01, 0.01, 0.01, 0.01, 0.01, 0.2, 0.2].\n", "\n", "Additionally, we need to specify several hyperparameters for performing SSA simulations on the model, including the model name (`model_name`), the initial values (`initial_val`), the maximum simulation time (`t_end`), the number of parameter sets to simulate for the training (`train_data_size`) and validation datasets (`valid_data_size`), and the directory for storing the resulting simulation data (`dataset_dir`). Specifically, in the central dogma model, ON and OFF are mutually exclusive, such that their sum is always equal to 1. Consequently, it suffices to record the state value of ON, mRNA, and Protein. Accordingly, we define a subset of state indices corresponding to the state names to extract, referred to as the `minimal_state_indices`." ] }, { "cell_type": "markdown", "id": "cb61a2a5", "metadata": {}, "source": [ "Hence, the following configuration files can be defined for gene expression central dogma model. And we use Python's json package to store this configuration for subsequent simulation and neural network training." ] }, { "cell_type": "code", "execution_count": null, "id": "926c8b00", "metadata": {}, "outputs": [], "source": [ "import json\n", "config_file =\n", "{\n", " \"state_names\": [\"ON\", \"OFF\", \"mRNA\", \"Protein\"],\n", " \"events\": [\n", " {\"state_transition\": {\"OFF\": -1, \"ON\": 1}, \"event_rates\": \"kon*OFF\"},\n", " {\"state_transition\": {\"ON\": -1, \"OFF\": 1}, \"event_rates\": \"koff*ON\"},\n", " {\"state_transition\": {\"mRNA\": 1}, \"event_rates\": \"ksynon*ON\", \"drive_state\": \"ON\"},\n", " {\"state_transition\": {\"mRNA\": 1}, \"event_rates\": \"ksynoff*OFF\", \"drive_state\": \"OFF\"},\n", " {\"state_transition\": {\"Protein\": 1}, \"event_rates\": \"ksynM*mRNA\", \"drive_state\": \"mRNA\"},\n", " {\"state_transition\": {\"mRNA\": -1}, \"event_rates\": \"kdegM*mRNA\"},\n", " {\"state_transition\": {\"Protein\": -1}, \"event_rates\": \"kdegP*Protein\"}\n", " ],\n", " \"parameters\": {\n", " \"param_names\": [\"kon\", \"koff\", \"ksynon\", \"ksynoff\", \"ksynM\", \"kdegM\", \"kdegP\"],\n", " \"upper_bound_val\": [0.1, 0.1, 10, 10, 2, 0.5, 0.4],\n", " \"lower_bound_val\": [0.01, 0.01, 0.01, 0.01, 0.01, 0.2, 0.2]\n", " },\n", " \"model_name\": \"central_dogma_model\",\n", " \"initial_val\": [1, 0, 0, 0],\n", " \"t_end\": 6000,\n", " \"train_data_size\": 10000,\n", " \"valid_data_size\": 1000,\n", " \"dataset_dir\": \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/data/ts_txl/\",\n", " \"minimal_state_indices\": [1, 3, 4]\n", "}\n", "config_path_dynmodel = \"your_path_config\"\n", "with open(config_path_dynmodel, \"w\") as file:\n", " json.dump(config_file, file, indent=4)" ] }, { "cell_type": "markdown", "id": "b7ca67e6", "metadata": {}, "source": [ "### 2. Generate datasets" ] }, { "cell_type": "markdown", "id": "ebd6f06c", "metadata": {}, "source": [ "After generating the configuration file, we construct and simulate the state transition network for gene expression model to obtain the dataset required for training the neural network addressing the model. The simulation is implemented in Julia, which provides higher efficiency for stochastic simulation algorithms (SSA) compared to Python. To generate the training data, execute the following command in the terminal:" ] }, { "cell_type": "code", "execution_count": null, "id": "f1e1f555", "metadata": {}, "outputs": [], "source": [ "julia simulation.jl /path/to/config.json" ] }, { "cell_type": "markdown", "id": "597a08ee", "metadata": {}, "source": [ "### 3. Train DynGPT-Solver" ] }, { "cell_type": "code", "execution_count": 1, "id": "88774620", "metadata": {}, "outputs": [], "source": [ "# import packages\n", "import sys\n", "import os\n", "import numpy as np\n", "import json\n", "import matplotlib.pyplot as plt \n", "from matplotlib.gridspec import GridSpec\n", "import logging\n", "import warnings\n", "root_dir = \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/\"\n", "sys.path.append(root_dir)\n", "os.chdir(root_dir)\n", "import dyngpt as dg\n", "logging.getLogger().setLevel(logging.ERROR)\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "markdown", "id": "dc47acbc", "metadata": {}, "source": [ "To train a neural network for addressing the gene expression model and inferring its parameters, a basic configuration file must first be defined. This file includes four key components: (1) the information of the gene expression model, (2) the neural network architecture, (3) the directory for saving outputs and visualizations, and (4) the default parameter settings for the neural network. Subsequent configurations for training, sampling, and inference build upon this foundation by incorporating additional task-specific parameters as needed. The relationships between the configurations of each subsequent stage are presented as follows:" ] }, { "cell_type": "markdown", "id": "8190bdd3", "metadata": {}, "source": [ "\"Schematic" ] }, { "cell_type": "markdown", "id": "06cc429f", "metadata": {}, "source": [ "Firstly, the basic configuration file must include detailed information about the dynamic model, which has already been stored in the previous configuration file." ] }, { "cell_type": "code", "execution_count": 2, "id": "d07c9342", "metadata": {}, "outputs": [], "source": [ "config_basic = {}\n", "config_path_dynmodel = \"your_path_config\"\n", "model_name = \"central_dogma_model\"\n", "config_path_dynmodel = \"config/{}_config.json\".format(model_name)\n", "config_basic = dg.tl.update_dynmodel_config(config_basic,config_path_dynmodel)" ] }, { "cell_type": "markdown", "id": "8fb1d480", "metadata": {}, "source": [ "Next, the architecture of the neural network is defined by specifying the relevant structural details within the configuration." ] }, { "cell_type": "code", "execution_count": 3, "id": "fd5a6fb3", "metadata": {}, "outputs": [], "source": [ "config_basic[\"num_encoder_layers\"] = 1 # transformer n_layer\n", "config_basic[\"n_head\"] = 8 # transformer n_head\n", "config_basic[\"feed_forward_dimension\"] = 1024 # transformer feed_forward_dimension" ] }, { "cell_type": "markdown", "id": "523716a2", "metadata": {}, "source": [ "Additionally, we should designate a directory for saving output files and visualizations." ] }, { "cell_type": "code", "execution_count": 4, "id": "f91f5cfd", "metadata": {}, "outputs": [], "source": [ "config_basic[\"dataset_dir\"]=\"your/path/to/saveresult/\"\n", "config_basic[\"figure_dir\"]=\"your/path/to/savefigure/\"\n", "config_basic[\"result_dir\"]=\"result/{}/\".format(config_basic[\"model\"])\n", "config_basic[\"figure_dir\"]=\"figure/{}/\".format(config_basic[\"model\"])" ] }, { "cell_type": "markdown", "id": "382ce42e", "metadata": {}, "source": [ "Finally, execute the folowing code to append default configuration parameters." ] }, { "cell_type": "code", "execution_count": 5, "id": "8e1954d8", "metadata": {}, "outputs": [], "source": [ "config_basic = dg.tl.update_default_config(config_basic)" ] }, { "cell_type": "markdown", "id": "36396aaf", "metadata": {}, "source": [ "After defining the configuration file, we proceed to train the neural network designed to solve the gene expression model. The training process is divided into two stages: the first stage involves pretraining the neural network, while the second stage focuses on fine-tuning it. Additionally, for each stage, we will visualize the corresponding loss function curves to evaluate the training progress." ] }, { "cell_type": "markdown", "id": "6e8bd71b", "metadata": {}, "source": [ "For model pretraining, the configuration file is extended from the basic configuration. We should specify the number of epochs (`epochs_pretrain`) and the training dataset path (`train_dataset_path`) in the configuration file." ] }, { "cell_type": "code", "execution_count": null, "id": "972d37f3", "metadata": {}, "outputs": [], "source": [ "config_pretrain = config_basic\n", "config_pretrain.epochs_pretrain = 1 # 1000\n", "config_pretrain.train_dataset_path = \"your/path/to/train_dataset\"\n", "config_pretrain.train_dataset_path = \"data/ts_txl/ts_txl_train_stable.json\"\n", "result_pre_train = dg.solver.pre_training(config_pretrain)" ] }, { "cell_type": "markdown", "id": "c433755d", "metadata": {}, "source": [ "Visualize the loss values during the pretraining phase of the model." ] }, { "cell_type": "code", "execution_count": 6, "id": "52e1ceab", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_pre_train = np.load(\"tutorials/result/arl/config6/train_loss/arl_epoch500.npz\")\n", "dg.pl.plot_loss(result_pre_train[\"loss_mean\"])" ] }, { "cell_type": "markdown", "id": "5b87a25f", "metadata": {}, "source": [ "During the fine-tuning phase, the configuration file is also extended from the basic configuration, specifying the number of epochs (`epochs_fine_tune`), the training dataset path (`train_dataset_path`), and the pretrained network weights (`weight_nn_pretrain_path`)." ] }, { "cell_type": "code", "execution_count": null, "id": "2676a651", "metadata": {}, "outputs": [], "source": [ "config_fine_tune = config_basic\n", "config_fine_tune.epochs_fine_tune = 1 # 1000\n", "config_fine_tune.train_dataset_path = \"your/path/to/train_dataset\"\n", "config_fine_tune.train_dataset_path = \"data/ts_txl/ts_txl_train_stable.json\"\n", "config_fine_tune.weight_nn_pretrain_path = result_pre_train[\"weight_nn_pretrain_path\"]\n", "result_fine_tune = dg.solver.fine_tune_training(config_fine_tune)" ] }, { "cell_type": "markdown", "id": "633a8d3a", "metadata": {}, "source": [ "Visualize the loss values during the fine tunning phase of the model." ] }, { "cell_type": "code", "execution_count": 7, "id": "f0231303", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_fine_tune = np.load(\"result/central_dogma_model/train_loss/fine_tune_central_dogma_model_epoch1000.npz\")\n", "dg.pl.plot_loss(result_fine_tune[\"loss_mean\"])" ] }, { "cell_type": "markdown", "id": "9b0e45db", "metadata": {}, "source": [ "After the neural network training converges, we valid the performance of the neural network model in solving gene expression model from comparative analysis of the samples generated by the neural network and those obtained through simulations in terms of their mean, standard deviation, and overall distribution. \n", "When using the trained model for sampling, the configuration file is also extended from the basic configuration, specifying the validation dataset path (`valid_dataset_path`) and incorporating the fine-tuned network weights (`weight_nn_fine_tune_path`)." ] }, { "cell_type": "code", "execution_count": null, "id": "cfb0e9ec", "metadata": {}, "outputs": [], "source": [ "config_sampling = config_basic\n", "valid_dataset_path = \"your/valid/datasetpath/\"\n", "valid_dataset_path = \"data/ts_txl/ts_txl_valid_stable.json\"\n", "# config_sampling.weight_nn_fine_tune_path = result_fine_tune[\"weight_nn_fine_tune_path\"]\n", "config_sampling.weight_nn_fine_tune_path = \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/dyngpt/_weights/ts_txl_final.pt\"\n", "config_sampling.valid_dataset_path = valid_dataset_path\n", "result_valid_set = dg.tl.compute_sampling_stats(config_sampling)" ] }, { "cell_type": "markdown", "id": "67973826", "metadata": {}, "source": [ "We visualize the performance of the neural network model on the validation set. This includes a comparative analysis of the samples generated by the neural network and those obtained through simulations in terms of their mean, standard deviation, and overall distribution." ] }, { "cell_type": "code", "execution_count": 9, "id": "e81c72bd", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dg.pl.plot_model_comparison_stats(result_valid_set,state_index=[1,2])" ] }, { "cell_type": "code", "execution_count": null, "id": "e5bace80", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_valid_set[\"state_name\"]=['mRNA', 'Protein', 'Protein']\n", "dg.pl.plot_distribution_comparison_2d(result_valid_set,data_index = [12,15,16],width_mm=150)" ] }, { "cell_type": "markdown", "id": "0f09266c", "metadata": {}, "source": [ "### 4. Infer the parameters with DynGPT-Inferrer" ] }, { "cell_type": "markdown", "id": "2b5e257f", "metadata": {}, "source": [ "In this section, we employ the trained model to infer the corresponding stochastic dynamics from the observed data." ] }, { "cell_type": "code", "execution_count": 15, "id": "677263da", "metadata": {}, "outputs": [], "source": [ "observed_dataset_path = \"your/observed/datasetpath/\"\n", "observed_dataset_path = \"data/ts_txl/ts_txl_valid_stable.json\"\n", "observed_data,true_params = dg.tl.load_synthetic_data(observed_dataset_path,params_number=100) " ] }, { "cell_type": "markdown", "id": "6cd00cbf", "metadata": {}, "source": [ "For the inference process, the configuration file is also extended from the basic configuration, defining the following hyperparameters: the trained neural network parameters path (`weight_nn_fine_tune_path`), the loss function (`inference_loss_func`), the learning rate (`inference_lr`), the maximum number of iterations (`iteration_steps`), the initial number of parameters sampled from the prior distribution (`random_r0_number`), and the threshold for parameter acceptance (`param_acceptance_threshold`)." ] }, { "cell_type": "code", "execution_count": 16, "id": "1b8dcd3a", "metadata": {}, "outputs": [], "source": [ "config_infer = config_basic\n", "# config_infer.weight_nn_fine_tune_path = result_fine_tune[\"weight_nn_fine_tune_path\"]\n", "config_infer.weight_nn_fine_tune_path = \"/GPUFS/sysu_jjzhang_1/hzw/academicCode/DynGPTTest/DynGPT/dyngpt/_weights/ts_txl_final.pt\"\n", "config_infer.inference_loss_func = \"Likelihood\"\n", "config_infer.inference_lr = 0.0001\n", "config_infer.iteration_steps = 80\n", "config_infer.random_r0_number = 2\n", "config_infer.param_acceptance_threshold = 0.5 \n", "result_infer = dg.inferrer.inferring_dynamics(observed_data,config_infer,new_model=True,synthetic_flag=True)" ] }, { "cell_type": "markdown", "id": "0a72f3e1", "metadata": {}, "source": [ "We visualize the performance of the neural network model on the observed dataset. This includes a comparative analysis of the samples generated by the estimated parameters of gene expression model and those obtained through simulations in terms of their mean and overall distribution." ] }, { "cell_type": "code", "execution_count": 17, "id": "ed692055", "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAACoCAYAAACIRDHKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAA9hAAAPYQGoP6dpAAB+f0lEQVR4nO3dd1xT5/4H8E8GJCB7IwjIVNkORFzFhdoKVrC1S7nVar31tnZdO+zttrW317a37a/a1trb2lpbV62juLdVUBBxMSTsGWaAhIzn90dKSiBAAtl53q9XXzUn5yTfhCfne57zLAYhhICiKIqiKIqiKIqidIRp6AAoiqIoiqIoiqIo80YrnhRFURRFURRFUZRO0YonRVEURVEURVEUpVO04klRFEVRFEVRFEXpFK14UhRFURRFURRFUTpFK54URVEURVEURVGUTtGKJ0VRFEVRFEVRFKVTtOJJURRFURRFURRF6RSteFIURVEURVEURVE6RSueFEVRFqSgoAAJCQkIDQ3FhAkTcOPGjV77yGQyPPfccxgzZgyioqKQmJiIwsJCA0RLURRFUZS5YBBCiKGD6Mvq1avxxRdfGDoMitIaWqYpQ5sxYwaWLl2K9PR07Nq1Cxs3bkRmZqbSPvv27cN7772Hc+fOwcrKCu+88w5yc3Px888/K+0XHh6OoKAgfYavE0VFRSor4OaAnnMoc0PLNGVOzCWPAurlUraeYhmUiooKQ4dAUVpFyzRlSLW1tcjKysKRI0cAAKmpqVizZg0KCwsRHBys2I/BYEAkEkEoFILNZqOlpQW+vr69Xi8oKAj79+/XW/y6kpycbOgQdIaecyhzQ8s0ZU7MJY8C6uVSo654UhRFUdpTVlYGb29vsNnyUz+DwYCfnx9KS0uVKp4LFizAyZMn4eXlBXt7e/j4+OD06dOGCpuiKIqiKDNAx3hSGpO2t0JUVgBpe6uhQ6EoSgeysrKQl5eHiooKVFZWYubMmXjyyScVz2dkZOC5555DbW2tAaOkKPMlEElQVCeAQCQxdCgURVFaQyuelEak7a3oLMoGg3SgsyibVj4pyoSMGDECVVVVkEjkF7OEEJSWlsLPz09pv++++w4zZsyAk5MTmEwmli1bhpMnTyqeT0pKwqZNm+Dh4aHX+CnKEghEElwurkdtUzsuF9fTyidFUWZDL11tt23bhscffxx79+7FwoULUVtbi6VLl6KoqAgcDgf/93//h2nTpqn9ejKZDDU1NYqLJ6o3NpsNT09PMJnavbcg4VeD5egEAGA5OkHaUA2Wrb1GryGTyXD69GlMnTpV0eXPktHyrB5dlWltqK6uRm1tLaKiogwdSr88PDwwduxYbN++Henp6di9ezd8fX2VutkCQGBgIA4dOoQXXngB1tbWOHDgACIiIvQaK3/rW5A01kDaUAOWiycAgO3sCdfl/9JrHOaKnncGRx/noZoWIWxY8te3YTFR0yKEnbud0j6EEFy4cAGxsbGwtbXVWSympKWlBc3NzYYOw6QYU15tampCfn4+4uLiDB2K2fj0XBH47Z3gt3XCdZg1AMDV1hr/mGK4yYx0ftXP4/Hw1VdfIT4+XrHtpZdeQnx8PH7//XdkZmbi/vvvR3FxMaysrNR6zZqaGtjb28POzm7gnS2UQCBATU0NvL29tfq6bFcvdBZlyyudzU2wDhqp8Ws0NDTA1dWVVjr/RMuzenRVprWBx+MZfaWzy5YtW5Ceno4NGzbAwcEB27ZtAwCsWLECycnJSE5OxlNPPYVbt24hOjoaVlZW8PLywubNm/UaZ1cFs+bDp+D5wud6fW9LQM87g6OP85CnAxclfAFsWEx0SGXwdOD22qe9vR1sNptWOv9UW1sLBoMBX19fMBgMQ4djMowprxYXFyM0NNTQYZiVrgrmG0du4Y05ow0cjZxOr/xlMhlWrFiBTz/9FM8//7xi+88//6xYE27ChAkYPnw4Tp8+jVmzZqn1uhKJhCbLAdjZ2aGxsVHrr8uytYd1UCykDdWwDhqpUWunVCrFgQMHMGvWLJO5SNcHWp7Vo6syPRTl5eUoKChAYmKioUNRW1hYGC5evNhr+9dff634N4fDwVdffaXPsCg9o+edwdHHeciOw0bcSDfUtAjh6cCFHYcNgUiCmhYhPOw5uHD6BGJjYzFx4kSdxmFKRCIRRowYYegwTI4x5FU+n4/Lly9j3rx5Bo2D0g+dVjw3bdqEyZMnY9y4cYptfD4fYrEYXl5eim0BAQEoLS1VPM7IyEBGRgaduMJIsWztNe5eSwjBvn37MGbMGAwbNkxHkVGU/lRWVuL06dNIS0szdCgURZkZOw5b0b22a8ynDYuJk8eOYFTAcDq+mjILjY2NOHDgABYtWmToUCg90VnFMy8vD7t378aZM2c0PjYpKQlJSUlmvbaaJZFIJGhtbcXs2bPh4OBg6HAoasjq6+vh7OyMxYsXw9ra2tDhUBRlxmpahOAygLaWRgSNiYH3cFrppExfQ0MD7OzskJaWRhskLIjORhOfPXsWPB4PISEhCAgIwB9//IGVK1fi559/BpvNRnV1tWJfHo/Xa1ZFc8VgMBAZGYno6GiMGTNGMb4KAE6dOgUGg4G3335bsS0vLw8BAQFKr9Ha2go7OzssX75cX2EPmlgsxq5du9DY2EgrnWbK0sp0YWEhjh07BisrK1rppCgtCQgIQFhYGGJiYjBmzBh8/rlm43pzcnLw008/qbVvZWUlpk6dOpgwDcLdzhq5f5xEM78OhGOrcswnZXxome5bVVUVDh48CKlUSiudFkZnFc/Vq1ejqqoKPB4PPB4P8fHx+PLLL7F69WosXrxYMVFFZmYmKioqMH36dF2FYnTOnj2La9euYceOHVi1ahWqqqoUz3l5eeHTTz9FfX19n8fv3LkT48aNw549eyAQCPQR8qBdv34dEydORGBgoKFDoXTIUsq0VCrFnTt3kJaWRifHoigt27lzJ3JycnD48GG88soryM3NVTwnk8kgk8n6PFaTi/Thw4fj7NmzQ45XX6rLeBgfNQYxsTGIG+kGOw4995gKWqZ7I4Qg88pVjLtnHqRM9SYVpcyHQeZP3rhxIy5cuICQkBCkp6dj+/btas9oa4wYDAbeffddTJw4EQEBAdi3bx/ee+89jB8/HiEhITh16pTK46Kjo+Hs7Izy8nLFNk9PTzz22GNKLUQ9bd26FevWrcO0adOwc+dObX8crRCJRDh27BhiY2MxcqTmM99ShkXLdG+3bt0Cj8fDvffeSyudFKVD/v7+CAsLw8MPP4zU1FQkJSUhIiICVVVV+P777xEVFYWoqCjce++9qKioQG1tLf71r3/h5MmTiImJwZNPPglAfmN7xowZGD9+PGJjY/HLL78AkPeycnJyUrwfg8HAhg0bEBcXh5EjRyr12jAkqVSKI0eOICgoCONjoxHkbkcrnSaKlmm50tJSZF7NgV3wODQLZXSdWguktzNY9wtVT09PHDlyRF9vrSBtb4WEXw22q5fGk+MMxM7ODpcuXcLx48eRkpKCzz77DFlZWfjll1/w4osvIjMzs9cxp0+fhpubG6Kjo5W2v/rqqxg1ahTWrl3b65ibN2+irKwMSUlJkEgkeP/9942ue6JQKMSuXbtwzz330GnNdYyWaf3Iy8tDfn4+UlJSDB0KRRlU1+yqXbOt6sL169dx+/ZtLFiwACdPnkR2djY8PT2Rl5eHF198EVeuXIGPjw/effddrFixAocPH8Zbb72Fffv2Yd++fQDkawKuXLkShw4dgre3N+rr6zF27FgkJCSofE8Oh4PLly/j9u3bmDBhAh577DGD3mCSSCTYs2cPoqOjwWKxDBaHJaBlWj94PB4uXLiAsdPnQtour2z2tU4tZb4s5taZtL1Vsf5kZ1E2rINitXqh/uCDDwIAxo8fj7a2NixZsgQAEBcXh4KCAqV9p06dio6ODhQXF2PXrl29xom5uLhg7dq1WL9+PV5++WWl57Zu3YqlS5eCxWJh/vz5WLVqFW7duoXRo41jfR6hUAgGg4E5c+bQWfd0jJZp/Whvb4erqysWLlxoFItsU5ShdJ9dtYQv0Hq3zwcffBA2NjawtbXFN998g7y8PNjY2MDT0xMAcPLkScydOxc+Pj4AgL///e946623IJVKe73WhQsXcPfu3V5LNNy5c0fl0I9HHnkEADBq1CjFPBS+vr5a+2yaEIvFkMlkmDJlCoYPH26QGCwFLdP60d7eDltbW6SlpaGTMFH553fe1zq1lPmymIqnhF8NlqMTAIDl6ARpQ7VWL9K5XPkPp+vOZPfHEolyN4KzZ8/CyckJ3377LdLT05GQkKA4CXVZu3YtQkNDMXfuXMU2sViM77//HlZWVvjxxx8ByH/MW7duxYcffqi1zzJYbW1t2L17NxYsWEArnXpAy7TuZWdno6amRilmirJUNS1C2LDkN1900VKxc+dOxMTEKB7n5eX1u9Zofz1qCCEIDw/HhQsXej3H4/F6bes6vwGqz3H60tnZid27d2PatGmKygilO7RM615BQQGuXbuG1NRUMBgMWAO91qmlLIfF3L5nu3pB2twEAJA2N4Hl4tX/AXqQnp6OmTNnYsOGDb2es7W1xWuvvYZ//etfim379+9HYGAgKioqFJM2/fHHH/j+++8hFov1GbpKx48fx7x58+Ds7GzoUHTi6aefRkBAABgMBnJychTbu89cFxMTo7cxirRM61Z1dTUqKyuRlJRk0Dgoylh4OnDRIZVPhmKIlorExET8/vvvqKysBABs3rwZM2fOBIvFgoODA5qbmxX7JiQkoLi4GMeOHVNsy8nJQWdnp15j1tTp06eRkJBAK516Qsu0bgkEAuTm5mLhwoVKlWo7DpuOWbZQFlPxZNnawzooFmDaaL1L4lBs3LgR27ZtQ0VFRa/nli9frtRlcevWrYquE11Gjx4NHx8f/PbbbzqPtS8tLS3Iy8vDggUL4O7ubrA4dC0tLQ3nzp2Dv79/r+e6Zq7LyclRdFHVNVqmdefq1atwdHTE/Pnz6ThlivqTHYeNuJFu8HCyNcjsqhEREfj3v/+NuXPnIioqCmfPnsVXX30FAJg5cyZEIhGioqLw5JNPwtnZGQcPHsSGDRsUSz299NJL/c4iakhCoRBXr17FrFmzVOYYSjdomdadW7duQSwWY9GiRXRCPkrBokoCy9ZeJxfnhBDFv+3s7JQe+/r6Ki0P0f05AAgJCUFLSwsAwMfHR6kljc1m486dO4rHhw4dUvn+V69eHVL8Q9HU1IRff/0VCxYsMPsL9GnTphk6hF5omda+c+fOoaOjA1wu1+zLNEVpyo7D1slEIKq6Cr7xxhu9tj322GN47LHHem13dHTs1QVx7NixOHHiRK99AwIC0NTUpHjc8xzW39JPutDe3o7du3dj5syZ9JxjALRMa19ubi7u3r2LsLAwWqYpJRq3eAoEAuTn56OsrEwX8VAmRCqVorW1FSkpKXBxcTF0OAa1dOlSREZGYvny5airqzN0ONQgSaVSODs7Y9asWTRZUkNCcyWlDqlUivb2dsyePZtOJESZBalUChaLheTkZDohH9WLWiVCJpNh27ZtiI+Ph4eHBxITExETEwN3d3esXLlSqQWDsgz19fXYuXMnfHx8lNaOskRnzpxBbm4url69Cjc3NyxbtqzXPhkZGXjuuedQW1trgAiND5FKIesUgqiYuc8QCCE4ceIEbty4gfDwcFrppAaF5kpKEwKBAD/++CMcHBzg5WX4Mfr6IhKJsGbNGoSEhCAyMhKPPvooAPkkNAkJCQgNDcWECRNw48YNA0dKaerKlSu4ePEiwsPDaaWTUkmtUjFp0iRcv34d//3vf9Ha2oqKigrw+XzcuHEDU6ZMwRNPPIGffvpJ17FSRkIkEuHw4cP0btaf/Pz8AABWVlZYu3Ytzp4922ufpKQkbNq0ic72C3mlk4jaASIDEbUbReXz8uXL4HA4iIqKMnQolAmjudKyCUQSFNUJIBBJBtwuk8nw66+/Yt68eb2WnzJ3L730EhgMBvLz83H9+nXFDOarVq3CypUrkZ+fj3Xr1iE9Pd2wgVIauX37NmprazF58mRDh0IZMbXGeP72228qL5g9PDywdOlSLF26lLbkWIjq6mpYW1vjoYceooPFIV9CRiwWK1p9d+zYgdjYWMMGZeSIVAx0LYjOYoFIxWAYaIF0Qghu376NcePG0fJMDRnNlZarr/UgVW2XdAjQ3t6OBx980OLOO21tbdi6dSvKy8sVPUu8vLxQW1uLrKwsHDlyBACQmpqKNWvWoLCwEMHBwYYMmVLD7du3ERQURMd0UgNSq7nqmWeeGXAffbbksNlspclNqN4EAoHWE1plZSWOHj0KLpdrcckSkN+N9fX1RXl5OZKSkhAcHIyamhokJiYiKioKkZGROH36NL777juNXtfSyjODZQV0tXJKpfLHatB2mSaE4NChQ2hra7PI8kxp32Bz5WCWahpqt0RLO+9oS1/nIVXrQaraXlhWjV9//RW2trYWed4pKiqCi4sLNmzYgPHjx2Pq1Kk4fvw4ysrK4O3trfhOGAwG/Pz8UFpaqtbrcjgc1NXV9ZpUh+qfNvLqmTNnUFlZCTabTSud1IDUKm23b9/WdRwa8fT0RE1NDRobGw0ditFis9nw9PTU6mtWVFRg8eLFSosSW5ItW7ao3J6dnT2k17XE8kykUhCJCAw2BwxWk1rHaLtMt7S0wNfXF9HR0Vp7TcqyDTZXpqWl4Z///CemTJnS67meC9B36eqWmJ6ejl27diE9PR2ZmZlqv6clnncGQyojEElk4LCZYDEZfZ6HPB24KOELYMNiKq0H2XM7q60OCxcuhKOjo74/ilGQSCQoKSnBmDFj8P777yM7OxuzZ8/GwYMHBzw2IyMDGRkZKnsNeHh4oKWlBeXl5boI22wNNa+KRCJwuVzExcVpMSrKnKlV8TS2OxhMJhPe3t6GDsOgpO2tkPCrwXb10vn6jSUlJSgqKsKMGTN0+j6WipZn/ZLJZPjtt98wffp0WumktGqwuVLTpZq00S2RnncG1r2bbEeHrN91HrvWg6xpEcLTgavYr2v7HV4FavJzMSN5gT4/gtHx8/MDk8lUrN8cGxuLkSNHoqSkBFVVVZBIJGCz2SCEoLS0VDGHAiCfKyEpKQnJyckqX9vBwQEODg56+RyWjhCC48ePIywsjFY6KY2o1dU2NzcXLi4uvf5zdna2+GU0DEHa3orOomwwSAc6i7IhbW/V2XtVVFTgwoULKu/Em4ubN2/iyJEjOHv2rNI6WJR5+u233xAcHGzxszFT2qeLXKlqqSZ1uiXSmbSHrq/us32x47AR5G7Xq3Iqbm/FjUunMXum+d68VTePurm5YebMmcjIyAAAFBcXo7i4GJMnT8bYsWOxfft2AMDu3bvh6+trceM7+5qgyticPHkSw4YNw4gRIwwdit71NTSip61btyIkJARBQUF44oknIBaL9RekEVOrxTMsLKzPhd4p/ZPwq8FydAIAsBydIG2o1kmrZ3V1NVxdXZGWlgYrK/XG4ZmK1tZWfPjhh/jqq69ga2sLT09PCIVCFBUVIS4uDuvWrcPMmTMNHSalRRKJBA0NDZg1axaGDRtm6HAoM6TtXHnmzBn4+flBLBZj/fr1WLZsmdqvP1DrEDWwvrrPaqKurg4ODg5YvHgxbGxsdBCl4Qw2j27evBnLly/HunXrwGQysWXLFvj4+GDLli1IT0/Hhg0b4ODggG3bthngUxlOXxNUGRNCCGpqajBx4kSLzaP9DY3oUlxcjNdeew1Xr16Fp6cnUlJS8OWXX+Kpp57SY6TGSa0SzeFw4O/vr+tYKDWxXb3QWZQtr3Q2N8E6aKTW3yM/Px85OTlYtGiRWU6AkJiYiKVLlyI7O1tpfINMJsPZs2exefNmFBYWYtWqVQaMktIWiUSCPXv2IDY21uKXtCkoKMCyZctQX18PR0dHfPvttwgPD++13/Xr1/GPf/wDNTU1AIB3330XixYt0ne4JkXbubLnUk2hoaEAgBEjRgzYLZEaur66z6qroqICJ0+eRFpamtlVOoHB59HAwECcPHmy1+uFhYXh4sWLOo/bWKlqYbdztzNwVH+RyWQ4ePAgfH19LWrd2Z7UGRqxa9cuJCcnK76nJ598Ehs2bKAVT6hZ8aSzhBkXlq09rINiIW2ohnXQSK23dkqlUty9exepqalgGWiZC107f/48OBxOr+1MJhPTp0/H9OnTIRKJDBAZpQt37tzBuHHjEBQUZOhQDE6dSWna29uRkpKC7777DlOmTIFUKkVDQ4OBIjYd2syV/S3V5OHhoeiWmJ6ebrHdEvXBjsMe1MU/IQQ3btxAWlqa2U7IR/OodmmjhV2XysrK4OfnR+dGUENpaanSTciAgAC1Z2g2d2qN8czMzMTnn3+Op59+Gv/73/90HROlBpatPax9Q7Re6czLy0PR7RtIDA8CRO1afW1jMmfOnAH3UZVQKdPS2dmJw4cPY8yYMWZZ6WxubsaaNWtw3333AZCPs9qxY0ef+3dNSvPoo48CkE9KU1ZWhsLCQqX9fvzxR8THxyu6ErFYLLi7u+voU5iPwebKwSzVtGXLFmzZsgWhoaF4//33La5bojHj8Xi4du0a5syZY7aVToDmUW3ramH3cLI1qm62UqkUhw8fxvDhw8220qlpLtUGSx2Hr1bF8x//+Ae+//572NjYYNOmTXj33Xd1HRdlALm5uSi4cxs+kga9TFxkSC0tLYYOgdIxkUiEX375BeHh4UY3M7e2rFq1Cl5eXuDxeACAkSNHYuPGjX3ur+5aeTdv3gSHw8F9992HmJgYLF26VDGxDdW3webKLVu2oLy8HBKJBDU1NSgsLERgYCCys7ORm5uL69ev49dff0VAQIDimK5uifn5+cjKykJkZKSOPhWlibt37+KPP/5Q2X3d3NA8qn19TVBlKFKpFHv37kVAQIDZzfXRnaa5dCB+fn4oKSlRPObxeL2GQiQlJWHTpk0WN/xHrYrn6dOncebMGWzcuBGnTp3C3r17dR0XpWcCgQBeXl5IigyGlbN89sWuiYvMkUwmQ2trK1paWlT+R5k2oVA+++ScOXPMetxbfn4+1q9fr7ggsLGx0Up3T4lEgmPHjmHLli3Izs6Gj48PVq9erXjeUu/UDoTmSssmEAjg6OholhPyqULzqHmTSCTo7OzE1KlTMXr0aEOHo1PazqWpqanYv38/qqurQQjB5s2bsWTJEm2Fa9LUqnhyuVxYW1sDAJydnSGTydR68Tlz5iAqKgoxMTGYOnUqsrOzAcgnt0hISEBoaCgmTJiAGzduDDJ8ShsyMzNx/vx5uNrZgLTUQlIvr2xKmvhguZjnAPLr16/DyclJ6T9nZ2fF/ynT1d7ejl9++QUtLS1m3z2067zcpaOjo99k2X1SGgB9Tkrj5+eHxMRE+Pj4gMFg4NFHH8Uff/yheN5S79QOZLC5kjJ9t2/fRkZGBlxcXMxyQj5VaB41X2KxGLt27UJtba3SxFHmSpNcqmpoBACsWLEC+/fvByCfQOvNN9/E5MmTERwcDHd3dzpZ5Z/UOjvW1tbiv//9b5+Pn376aZXH/fzzz4qJEfbu3Yv09HRcu3ZNrcktKP2oqalBQ0MD5syZg87yQlh5eEEm6oCkvhpMB0+dLNNiDKKjoxU3QijzcubMGcyZM8fsK52AfFbJd999F0KhEMeOHcNHH33U78yz6k5K88ADD2Dr1q1oaWmBg4MDDh06ZLZje7RpsLmSMm0CgQC3b9/GwoULzbZbvyo0j5qvixcvYuLEiRazooUmuXTLli0qt3/99ddKj5944gk88cQTWo/V1KlV8Zw9e7bSyaX74/5Ost0XaG9ubgaDwVBMbnHkyBEA8uboNWvWoLCwkM7Ip2eZmZmIiIhAUlISAOVlWohQBKvhgQaOUHcs6eLAUrS2tqKgoABz5841dCh68/bbb+Pf//43HBwc8Morr2DhwoVYt25dv8f0tVbeihUrkJycjOTkZPj5+eGVV15BQkICmEwmfHx88OWXX+rjI5m0weZKynRdv34dPj4+WLhwoaFD0Ttaps2PSCRCdna2WkuGmJPB5FJqcNSqeA5ltrylS5cq1ms6dOhQv5Nb0Iqn/pw+fRoymUxpxj1dL9NiTLy9vQ0dAqVFLS0t2Lt3r2JGOkvBZrPx8ssv4+WXX1b7mL7Wyut5t/axxx7DY489NuQYLQmdWdayZGdno6yszCImElKF5lHzIhQKsWvXLtxzzz2GDkXvBpNLqcFRa4ynWCxWWsPtyJEj2L9/P/bv34/GxsZ+j/3uu+9QVlaGd955R+27B3TiCt0hhEAsFsPLywuJiYm97ljqapkWY/PDDz/gxRdfRHJyMt5880261pgJk0gkEAqFWLBgAVxdXQ0djl6tWLECfD5f8bi+vp6OIzGgoeRKyrRIJBLY2tpiwYIFYDLVupQyOzSPmo+uiYRmzJgBX19fQ4ejdzSX6o9aZ8v169fjiy++UDx+4okn8NFHH+G9997DBx98oNYbLVu2DCdPnoSvr++Ak1vQiSt0gxCCo0eP4vbt2wgLCzN0OAa1evVq3L17F/PmzcOFCxfoXS4T1dDQgB07dsDFxQUuLi6GDkfvrly5olTZdnNzo+PlDUgbuZIyfpcuXcKlS5cQFhZm0d1NaR41D21tbdixYwfYbDaGDx9u6HAMguZS/VGrq+2RI0dw4cIFxWMXFxecPHkSMpmsz37gTU1NaG9vVxTiffv2wdXVVe3JLSjtu3z5MhwdHel6bwCuXbuGGzdugMFgID09HVOmTDF0SJSGJBIJDh06hJSUFIuZRbKnrht4XQgh6OzsNFA01GByJWVabt++jZaWFsyaNcvQoRgczaOmjxCCAwcOICkpCba2toYOx2BoLtUfta7WmEwmbGxsFI+XLVum2N5X14rm5mYsXrwYHR0dYDKZcHd3x4EDB8BgMPqc3ILSDUII8vLyMGHCBIvtEtQTl8tV3KnuXrYp01BbWwuZTIaHH37Yost0fHw81qxZgxdffBGEEHz44YeIj483dFgWazC5kjIdeXl5GDVqlMW3dHahedS0tbS0oKGhAYsXL7boPArQXKpPalU8m5qalB6vXbtW8e/ufaK78/f3x+XLl1U+19fkFpT2yWQyHDhwACNGjLD4E0t35eXleO655xSPy8rKlB5v2rTJEGFRaqiursaRI0eQmppq8WX6P//5D9auXYsJEyaAwWAgOTkZH330kaHDsliDyZWU8SOE4OTJk2CxWBbbu0IVmkdNV1NTE3799VeLHqPcHc2l+qPWGXTcuHH49ttvkZ6errT9u+++w9ixY3URFzUE0vZWSPjVYLl4ol0KBAUFWeyse3156qmn+n1MGSdCCGpqapCWlmbR3YK6ODg44JtvvjF0GNSfaK40P10T8jk5OdG/YQ80jw6eQCRBTYsQng5c2HH0ezODEIKqqiqkpKQoLXtoyWgu1R+1Svt7772HKVOm4MiRI5g4cSIA+eD6EydO4OzZszoNkNKMtL0VnUXZkFlbYd/BQ0hMmkcrnSq8/vrrhg6B0lB5eTmuX7+OefPmGToUo3Lp0iUUFRUpjVFZunSpASOyXDRXmhdCCDIyMhAeHk4rnSrQPDo4ApEEl4vrYcNiooQvQNxIN71VPvl8Ps6cOYP7779fL+9nSmgu1Q+1SnpQUBCys7Px+eef48iRIwCA2NhY/Oc//6HrOBkZCb8aDC4Hh89cQXh4GOwaSyD19Db75VE01djYiIqKCkRERAAAtm7dira2NgDAokWLLHI6cWNWU1ODM2fOIDU11dChGJXVq1cjIyMDMTExYLFYAORrI9NkaRg0V5qX48ePw9XVFSNGjDB0KEaJ5tHBqWkRwoYl795qw2KipkUIO3e7AY8baitpa2srDh48SCudKtBcqj9ql1wvLy+8/fbbuoyF0kBXd1q2q5dypdLRDRXXL2P2tDjYcKzl+zZU04pnD6+++iqio6MVCfPdd9/F9OnT0d7ejvz8fHz22WcGi63Pv62R0nW8lZWVcHNzQ1paGqytrfX+/sbs2LFjuHnzJrhcrqFDof5Ec6Xpk8lkqKysxOTJk+mkOf0w5jxqzDwduCjhC2DDYqJDKoOnw8Dn76G2ktbV1cHe3h4PPPAAzRcq0FyqPxrdMmlqasKWLVt6NUXTftH61dWdluXohM6ibFgHxYJlaw+xWIy9hzIQGxYKq9ZGgOMJaXMTrINGGjpko3PhwgX897//VTx2dHRUzK5syCnh+/rbGquhxKtOhbGwsBBZWVlITU2FlZWVVt/fHHh7e4PD4Rg6DKoHmitNV0tHJ37dvx9hwUG0xW4AxppHjZ0dh424kW4atV4OtpUUAKqqqnDs2DGkpqbSuRH6QHOp/mhU8UxLS4O7uzsmTZqkaIqmdKO/i3IJvxosRycAAMvRSdGiWVBQgPj4eAQEBEDa3gppQzWsg0Za1IW4uhgMhtLshN0Xvu7o6DBESAD6/tsaq8HGq06FkRCC4uJipKWl9TmTpKl9X9o2ceJEpKWl4cEHH1S6U5ucnGzAqCiaK02TQCTB8aybsHMdDsEwbwhEEr1P/GJKjDWPmgI7DlvtiiMwuFbSLrdv38bixYtpa14/aC7VH43OqF13TSjdGuiinO3qpXhe2twEme9wHDlwAPfee69iTS2Wrb1FXYBrqqWlBYQQxff1wAMPAJB3sWpsbDRYXD3/tsbeWj3YeAeqMN66dQsymQyzZ8/Wyfubi6ysLADAF198odjWNRU8ZTg0V5oeiUSC/ft/g19EHFhsee8KTVqVLJGx5lFzNJhW0tLSUlRVVSExMVEPEZo2mkv1R6OKZ1BQEJqamuj0yzo20EU5y9Ye1kGxkDZUg/gOx96DvyMxMZEuaK2Be+65Bxs2bMCrr76qtH3jxo2YPn26gaJS/tuaQmv1YOPtr8J448YN5Ofn495ZiRCVFfTbFdfUvi9tO3nypKFDoFSgudK0SKVS7NmzB+HhkeAzWLABNG5VskTGmkfNlSatpCUlJbhw4QKdkE9NNJfqj0YVT1tbW4wdOxZz585VaoqmiwRrlzqtOCxbe4jAAovFwvz58+Hi4mKASE3Xe++9h2nTpuHgwYOKZQ8uX76M+vp6nDlzRuUxTz/9NPbv34+SkhJkZ2cjJiYGAFBQUIBly5ahvr4ejo6O+Pbbb4e0hI2ptVYPJt6+KozNzc3wcLJHwJgASIqzwXZyHXDspql9X9omFotRXFwMoVCo2BYVFWXAiCiaK02HWCyGWCzGjBkz4ObmZtD1FU3NYPIopXvNzc1wcnJCWlqayrkRKNVoLtUPpiY7jx49GsuWLYOnpyccHR0V/1Ha1XVRDqaN0gW3tL0VorICSNtb0dbWhl27dqG1tZVWOgfBw8MD2dnZWL58OTo7O9HZ2Ym//e1vyM7Ohqenp8pj0tLScO7cOfj7+yttX7VqFVauXIn8/HysW7eu1+LxlGosW3tY+4YoyvfVq1dx9tRJ2DXwwGirA9vJVb7fn63+VG8HDhyAn58foqKikJiYiJiYGKSkpBg6LItHc6Vp6OzsxK5du1BfXw83NzcA8lalIHc7WulUw2DyKKVb+fn5yMjIgIODA610aoDmUv3R6Mz62muvgcnUqK5KDVLPVpye4z7Pl7Vi3rx5imRJac7GxgbLly/H8uXL1dp/2rRpvbbV1tYiKytLsWZfamoq1qxZg8LCQgQHB2s1XnPW2NiImpoazIodAyaEkIk6IKmvBtvNyyLHbqrrtddewx9//IGFCxciOzsb27dvx7Vr1wwdlsWjudI0XL58GQkJCfDz8zN0KCZL0zxK6Y5QKMStW7ewaNEiOvRKQzSX6o9GmTEgIAAbNmxAfX29ruKh+tA17rNF0I5rVQ24JyoU7u7uhg7L5PF4PKxevRpz5szBjBkzFP+pq6ysDN7e3oqZ/RgMBvz8/FBaWqqrkI1K91b4wcrMzASLxcK8efNg5eYNaXMTmBwbEBkg6yQWt0SKJphMJvz9/RVLdjz66KM4ceKEgaOiaK40bh0dHTh//jymTJnSqwcLpbnB5tFt27aBwWBg3759AOQ3cufOnYuQkBBERESYfXddgUiCojoBBCJJv9vUcePGDTQ2NiIlJaXPWeCpvtFcqj8alc6jR4/iiy++QHh4OObMmYM1a9Yo+vVTuiNtbwURtYNfWYIjt8qQFB0MlouXocMyCw888ABmzpyJNWvW6HTZg4yMDGRkZKC2tlZn76Fv2lhD89y5c+jo6IC9vfy47mM/uWMm0QrnALq6Uvn6+mLv3r0ICAigs0kaAU1z5WDGj2t7bLmlaG9vx+7duzFz5kxDh2I2BpNHeTwevvrqK8THxyu2vfTSS4iPj8fvv/+OzMxM3H///SguLjbLLqMCkQSXi+thw2KihC9A3Eh577We29Tp8p2bm4u7d+/SGViHwJxy6YZjt9EiFCtta+wQ46UD15W2OXCt8MqsUfoMDYCGFc+wsDB8/PHH2LBhA7Zv344HHngAHh4eePbZZ/HQQw/Rpn0d6Lq4l9raQcpiYd7EsXALjaAX5FoiFArx3nvvDfr4ESNGoKqqChKJBGw2G4QQlJaW9uq6lZSUhKSkJLNKDENZQ5MQArFYDG9vbwQGBiqdOyx9siBNPPPMM2hsbMQ777yDJUuWoKmpCR9//LGhw7J4mubKtLQ0/POf/8SUKVOUtneNH09PT8euXbuQnp6OzMzMAZ+jVOvs7IRMJsOcOXPoGEQt0jSPymQyrFixAp9++imef/55xfaff/4ZhYWFAIAJEyZg+PDhOH36NGbNmqX1mA2tpkUIG5a806ENi4maFqHi3923DTSLbWdnJ+zs7JCcnEy79w+BOeXSFqEYqaGuA+63O5+vh2h607g9nhCCI0eOYOfOnbCzs8NDDz2EHTt24JdffsHevXt1EaNFk/Cr0URYOHL8DyyePQlsKzt6Ua5FERERKiuK6vLw8MDYsWOxfft2pKenY/fu3fD19bWI8Z2DXUOTEIITJ07AyckJ48aN03GU5m327NlwdnbGuHHjUFBQAAAm3b2z9tMXIBU0K22TNtWh6r0nlLax7Bzh8Y8P9RmaxjTJlZqOH3dwcKBjyzXU2tqKvXv3IjU1lVY6tUzTPLpp0yZMnjxZ6fzP5/MhFovh5fVXb66AgACzHbbi6cBFCV8AGxZTaekeVdv6kpWVhdbWVrpOpxaYWy41ZhpVPN977z18+eWXCA8Px7p16zBnzhwAwHPPPYeQkBCdBGjxHN1w7ODvWHDPRDAErWAF0YsKbaqrq0N0dDQmTZqktOzBnj17eu27atUqHDx4ENXV1UhKSoK9vT0KCwuxZcsWpKenY8OGDXBwcMC2bdv0+REMZrBraF67dg02Nja00qkFc+bMwdWrVwfc1p0mXTQJIZg5cyauXr2KpqYmbYauklTQDIdHnxtwv5btxr0siTZyZX/jxx0dHft8jlY85bovizLMmoXDhw9j/vz5GDZsmKFDMzua5NG8vDzs3r17UOM3zWnIih2HjbiRbkpL9whEEng6cEEARLr2P7Py3bt3UV9fj6SkJP0FbcYGk0upwdGo4llZWYmMjAyEhob2eu6nn37SWlCUXHV1NWQyGR56bBlkjTVguXjR1k4te/TRR/Hoo4+qte+WLVtUbg8LC8PFixe1GZbJ6N4tVtreCgm/GmxX1eWUEIKcnBxER0fTLkFD1NnZCaFQCKlUitbWVhBCAMjXb2tra+v3WE26aH700UcICgqiyVdDxpArzekiXRMCkQQ8vgDVzR0YxmbhTmkV/IYRLF68mA4H0hFN8ujZs2fB4/EUN2Cqq6uxcuVKvPnmm2Cz2aiurla0evJ4PKVWVHMbsmLHYSu60nYf89khlSHAte8utrm5uRg9ejRGjhxJy/QQDSWXUoOjUcVzw4YNuHPnDrKysjBq1CjY2f31wzD31ouBLqq19R7iyrsgBKhn2eLU+T+QmpoKtq0tMMxBJ+9p6VJSUuDk5GToMExe11hkBpeD9ivXwRkzCdau3ornCSE4ePAgvLy8aKVTC9577z28+eabYDAYSutDOjg4KI2Z6kmT5X9u3LiBffv2Ydu2bfjll19080HMlDZyZX/jxx0cHAYcW25uF+nq6Lp47+iUwMXGGu2tzSjMPAWfOffSC3Qd0iSPrl69GqtXr1Y8vueee7B27VosXLgQly5dwubNm/HGG28gMzMTFRUVmD59uo6iNi6qxnyqGt955swZxe+elumhG2wupQZPrStAmUyGZ555Bm5ubpg1axZmzpwJV1dXPPvss4q7A+ZMcVFNOuQT/Qxy6Yi+lp6Qtreio+AaRPmXwbRmQNZSjdq8TCy6dy5sbW218RGoPoSEhOCJJ55Abm6uoUPRG20sgdKThF8NBpcDWWszOP7BkJTkKV6fEAKhUIiRI0di/PjxWntPS/b6669DJpNh5cqVkMlkiv+amprw2muv9Xmcusv/iMViPPHEE9iyZYtOZ3s2N9rMld3HjwNQGj/e33OWrOvifZgVG/x2EdpaGhESl4ggHw9Dh2bWtJVHN27ciAsXLiAkJATp6enYvn27Wc5oq4qnAxcdUhkAqBzfSQiBRCKBvb09ZsyYQSudWjLYXEoNnloVz88++wxZWVnIzc1FU1MTmpubkZubi6ysLHz22We6jtHgVM3eqam+Kq9d20lrDdgu7iitrseJwlqM9nWDVXuTFj8FpUphYSHCw8OxePFiTJ06FT///DOkUqmhw9IZbd1E6Ynt6gVxdQXYbvIuUmw3T0gb5F3F9+3bh9bWVrrUgw688sor6OzsBACcP38en332GVpbh/43ffPNN7Fo0SKMHj1a5fMZGRl47rnnLK4b50AGmytXrVoFX19flJeXIykpSVGB3LJlC7Zs2YLQ0FC8//77SuPH+3vOUnVdvIsFTeBlnUZgaCimhgeotSQFNXhDyaOnTp3CwoULAQCenp44cuQICgoKcOPGDYuaNKdrzKeHk22vZVQIITh27Bh4PB5iY2MNGKX50lUupXpTq+L5ww8/4IcffkBYWJhiW1hYGL7//nvFHdeehEIhFi5ciNDQUERHR2P27NmKabJNbZFgtqsXpM1NAABpc9Og1tDsq/LatZ1p74iKogJcyivA1JFuIDLQtTr1wNHREWvXrsWdO3fw6quv4oUXXoCfnx/effdds+zfr42bKKqwbO3BGTMJkvoaAH/9Tg79th+Brg5wtbPRyvtQylJSUiCTyVBRUYElS5bg/PnzePzxx/vcv3v3TQB9Lv9z+vRpfPrppwgICMCUKVPQ0tKCgIAA1NXVAZB349y0aRM8PGhLUneDyZWAvBJZXl4OiUSCmpoaRa7sGj+en5+PrKwsREZGKr1uX89ZKjsOG6PdrHE39yIeuH8BIoY70UqnHlhaHtWEQCRBUZ0AApFkwH3tOGwEufeeVOjs2bOwt7e3+B4NuqRpLqUGT62KZ1NTEwICAnptDwgIQHNzc+8D/rRy5UrcuXMH165dQ0pKClasWAHgr0WCCwoKsG3bNjz88MMQi8V9vo6hdc3eCaYNrINiBzXGs2flFTZ2EJUVgGFrB0kTH5XN7XC0YmKuNxfWw5zBHTOJTiSkJy0tLfj444/xzDPPIDw8HJ9++qli5lpzo42bKH2xdvUGZ1Q8wLQB0z8SZeUVmDLCAaMD3LXauqoruuiCrA9cLhcHDx7EqlWrsGPHDuTn5/e5r7pdNM+ePYuSkhLweDycO3cODg4O4PF4cHd31+lnMXWDzZVU/9S9eK+uroajLRd/e+wReLg49rsvpV2WlEfV1TXmuLapHZeL69WqfHZHCAGPx8PEiRMRHx+voyipLprkUmrw1Kp4dp8Yoae+pibncrmYP3++oh96fHw8eDweAPkiwU8++SQA5UWCjRnL1h7WviGDqgx2TUzEGh4CMG3AGh4CaWUBGKQD0soCFFa34tLVPFi7ecI+PBZsR1da6dSTVatWISQkBIWFhdi/fz8OHz6MRYsW4dNPPwWfb5jFdXVJGzdRBnp9hmcA9h0+gvb6ati6ucm3a7F1VRd01QVZ10QiEUQiEY4ePap2t7S+umiuWLEC+/fv12W4Zm8wuZLqn7oX7+Xl5Th27BiYTCasra31HKVls7Q8qi5VEwapSyaT4bfffkNjYyM4HI6uQqT+NJhcSg2OWn1QysrK8NxzvddWI4SgvLxcrTf65JNPkJKSotYiwaY+DXz3GXABoLMoW37hXVkF66BYpe6OTAdH1ORXYsHESFjb2kHa3ATroJEGjN6yhIaGIj8/X2k2sy4nTpwwQES6130JFF0oLS3F+PHjEeDt8VfZN/JyraoLsinc/HnooYfg5eWF0NBQJCQkoKqqasAJyfpa/ufrr79WuX9AQIBe1vA0B9rIlZQyHl+Ajk4JmFZs2LD7nu3z7t27WLx4Mb1INwBLzKPq8HTgooQvUCyR0nPCoP7U1dUhICAAUVFROoyQ6jKYXEoNjloVz6eeeqrP5/7+978PePyGDRtQWFiI48ePo6OjY8D9TXka+K6WE5ajEzqLsgGuY68LWrarFzqLsnGHL4CsvQ0z5t0rP7ahGtZBI3td8OpjKRdLNX/+fBw9ehQAEBERgVGjRime8/b27uswCr3LpUgkwu+//4777rtPMROqdVBsn+XamHT9Jk2hktzd+vXrsWbNGjg4OIDBYMDe3h67du0ydFgWa6i50tIJRBJ5xZLLhkAogR2XjermDrjYWKOhoxNCKRORPS7ei4uLUVNTg2nTphkoaormUdW6JgyqaREqKp1FdQJ4OnD7HHsslUpx8OBBzJ49G56envoM16LRXKo/alU8X3/99UG/wYcffog9e/bg2LFjsLW1ha2t7YCLBJuyni0nsk4iH0vX7YKWZWuPgk4bFFeW4L7kZMUFuaoL854VWV10j7REQqEQDz74II4fP47g4GAQQlBYWIg5c+bgp59+onfNB9CzXDL8IrD7wGFMnTpVafkNXbeuaktXF2RTqCQDQEFBAUJCQvpcvsDHx0fPEVHA0HKlpevqUmvDYuJmeyfsrdloEIrhbSc/F7vYWMPBjqN0wV5cXIxLly4hNTXVUGFbNJpHB2bHYcPO3U6pfJfwBb1mrgXkPSP27t2L8PBw2NjQCfn0geZS/VOr4nnp0iVMnDixz+c7OjpQXFyMMWPGKG3ftGkTduzYgWPHjiktLrx48WKzXSS4d8uJfOrr7he0DQ0N8A8OReTY8QOuxWSqXQCN3QcffABAPi6oq2w2NjZi+fLl2LhxI/71r38ZMDrj171cdnJtQeoqce+996q9iLgxMpVKMgA8++yzOHDgAFJSUno9x2AwcPfuXQNERQ02V1LK4+HsrdmoFgjhzLVGs0gCRw4bHVIZIl3+Gifb0NAANzc3pKWlKdalpfTLkvJoV2t8f62V/VE13rN7l3GJRIL29nbMmjXLpPOoqaG5VP/UmlzoP//5D2bPno1t27bh5s2b4PP5qKiowIkTJ/DPf/4TkyZNQk1NjdIx5eXleP7559HU1ITExETExMQoErI5LxKsavKW7hMTXb58GZcuXYKjo6NaCwDrchZSS7Z3715s3bpV6QTv7OyMLVu2YO/evYYLTA8GM3trz2O6ymW7UIS9xy6gnW1r0clS3zPiHjhwAIC8xafnfzRRGs5gciUl17UGp0giQ5NQDH8nW3TKZAjzdui1tuGtW7dw4sQJDBs2jFY6DchS8qims9OqmoW5q3wD6DXeUywWY9euXaivr7foPGoINJfqn1pn7J9//hmZmZnYsmUL3n33XZSXl2PYsGGIiopCamoqzp8/32vGPl9fXxBCVL5e1yLB5qTneDdVLSeNjY1obm7G3Llz1X5dU+sCaCpEIhHc/pxxtTt3d3cIherPPGdq1Om63bMs93WMdVAsLh4/ijnz7sPwgEADfSLDM0R3+O6TsaliLkMXTM1gciUl1zUeLqe8EZ7sv1o+K5o6EOPrrKh0CoVCFBUV4f777weTqda9c0pHLCWPDtRa2V33LrUFNS2I9XeBpz2313jP7q2mOTk5mDhxIkaONI25BcwJzaX6p/atwgkTJmDChAm6jMVkqXPhefHiRYwZMwazZ8/W+PVNqQugqehv/IQ5z2Q2UNdtVWVZ1THtUiA3NxczFiwywKcwLoboDj9u3DhFjwk+n6/oMSIWi+Hq6mqyM4KbA5or5QbTNdGOw0aMrzMuF9eDSYDWTgnc2ExcLq5H3Eg33L1zE25ubrjvvvt0HD2lDkvJo5rMTtu9kurIYeN8YR3mjPGWj/X8c7xnF5FIhFNnzyE4eiLcNZjxltKewebSgoICLFu2DPX19XB0dMS3336L8PBwpX1OnTqFefPmISwsTLHt4sWLFj9+l/ZR0YK+Ljy7Wo4u3C4Gw8oaDg4Ohg2UUigqKsKiRb0rTYQQs+5eMdDsrarKcs9jOtzdcGDvXtx7770G+ATGxxAz4tbV1QEA1q1bh+DgYCxfvhwA8M0336CoqEjn709R/VFnIpW+dLUMXeLVw81Wvh6nDYuJ85cy0dlcj4iICF2GTmnAUvJof62VPXk6cFFQ0wJHDhsNHZ1w4VqpbCEVCoXY+fMvcAyKQm1Tu8a/E0o7BptLV61ahZUrVyI9PR27du1Ceno6MjMze+0XFhaGnJwcncRuqmgJ14KeF56s4e4QFl6DpLEaUtth8BLWIGjsLLXGdFL68cknn/T53MKFC/UXiJ6p6rrdvWutqkpU92NkvsPBZlljwYIFcHZ2NvTHMQqG7A6fkZGBjRs3Kh6vWLECsbGxeO+99/QWA0X1pEnXxL6IxTI0yCRwsbFGc3sHRvp4YXjceBTz2wc9wQulXZaUR3u2Vva3X6y/C84X1sGFawUZA71aSEUiEQAgfMIUiNnyrveD/Z1Q2qFJLq2trUVWVpZiyGBqairWrFmDwsJCBAcH6y1mU0XP3FrQ/cKTNdwd0soCMB0ccermXbg5DMPYcTGQlOSBZWNHu8waiWXLlhk6BIPp3nW7Z9da1vAQgOsIWSdR6jLOsrVHs1CM3/b/hiVLltBp8nswVHf4zs5O3LlzR9GVJz8/X3FRQ1GGoknXRFVqWoRw5LAhkshw6/pV2FkBPnNmDboVldINS86j/fG052LOGG+VLaRtbW3YvXs3UlJSMCpwhKJMD+Z3QmmPJrm0rKwM3t7eionNGAwG/Pz8UFpa2qviWVRUhLFjx4LFYuFvf/sbXc8ZtOKpFd1bjLq6KuYWlMDJzhZjx8UAANhunnQpFCPy888/44EHHujz+crKSvB4PCQkJOgxKv3r3rWWweVAUpInL6t/zqTcRSaT4ejRo7j//vtppdOIvP/++5g8eTKio6MByMfdfvPNNwaOirJ0mnRNVHk8l41rAhEYzTUgYiHmzb9XK62olHbRPNq3vlpIjxw5grlz58LR0REAhvQ7obRHF7l07NixKC8vh6OjI8rLyzF//ny4ubkpfjMZGRnIyMiwuDkZNCrlEokEu3fvRlFRESSSv6aJNqe1mjTVs8UITt7IOn8J48dFQurAhriuClbu3nob+0WpJzMzE2+99RZSU1MxceJEeHp6QigU4s6dO/j999/B4/GwZcsWQ4epc9271oqrK8Dxl9+t6z5Wuba2Fm1tbXjggQdod3Ejk5ycjFu3buGPP/4AAEyaNEnlLJOUftFcqXzhre5EQwKRBCV8Acoa2iGtLsKw4YG4d2w47LlWYDAYQ2pFpbSP5lH1tbS0oKKiAgsXLlTKo+p24aV0S5NcOmLECFRVVUEikYDNZoMQgtLS0l4z4Haf18XX1xcPPfQQzp49q6h4JiUlISkpCcnJyTr6VMZJo4rnkiVLUF1djbi4OLBYLF3FZLR6LjMBKLcYEWtr/H70GPz8fUEErbAOjAHLxo4uhWKE/v3vf+PZZ5/FN998g02bNqG8vBy2traIiorC0qVLce+991pEJat7N3HOmEmQVhYoje+srq7G0aNHsWjRIov4PkyRu7s7FixYYOgwqG4sPVd2p+5EQ137cZkMlOZdBsfKGr62XLSJpACG3opKaR/No+ppamrCr7/+igULFtDvw4ipm0s9PDwwduxYbN++Henp6di9ezd8fX17dbOtqqqCp6cnmEwmWltbceDAAcXkRZZMozP39evXcfv2bZP84aiqNGp6vKolU7pajBj2jmirKENYaCBC/LzlB3UIwHL1VhpPN5QYKO0aPnw41q9fj/Xr1xs6FL1RVQaVxnx2u1HC4A5DS3kV0tLSLH76b4rShCnnSm1Tt4tsTYsQXCYDMqkUbq7u4Hj692rZpK1DxscS86gmZDIZmpqakJKSAicnpz73G8zyQ5ThbNmyBenp6diwYQMcHBywbds2APJJiZKTk5GcnIzdu3fjiy++AJvNhkQiweLFi/G3v/3NwJEbnkale8SIEejs7DS5MV7aWOC9ryVTWLb2YAVEYd++fUiIm4DATr78PXt0rTXEIvOUZRnoxoaqMghA6Ziu/8rKypB16XfMnxgDNpH0ei2KovpmqrlSF9SZaEggkqC9U4y8rHPw9vGHg28gvBxtEOBqRy/CKaOiSQWxvr4ex44dw4MPPtjvTaihLD9EGUZYWBguXrzYa/vXX3+t+PeaNWuwZs0afYZlEjQq2cHBwbjnnntw//33g8v9K3k8/fTTWg9Mm4a6wLu0vRVE1A5JUw2sPLyUKpXS9lYcPngQEVHR8A0Lh7S9VWXXWkMsMk9ZDnVubPQsg+LKuyAdzb2O4fP5OHPqJO4d4w0G6YCoMAuc4PG0vJqI3bt3IzU11dBhWDRTzZW6MFAX2a6L7sqbV8Cxc0RgaAj8XYbRC2/K6GhSQezo6MChQ4dw//33D9jzgU6cZZxoLtUNjc7sIpEIo0aNwq1btxTbTKEr0VAWeO+6oGdwOZA01UMqEoI7agJYtvYQNjeAl3UWs+LGgNnWCml7a5/LKhhikXnKcgx0Y6OTX4XOyrtgMGWw9vSRz1rLdex1TEV9I9zd3ZESFwFrG/npge3kCnHlXbCCo/X8qajBePbZZ2myNDBTzZW60t9EQ1VN7WirKUfA6Fiw2Gxw2Sxa6aSMktrdxmtqwOVy8dBDD8HKymrA1x3q8kPmxJi6HNNcqhsa/VW7+jCbmqEs8C7hV8srnfwaWLm4ge3mBUnFbUhZHOzZswfjQrzBaKoDw96x31ZMQy4yT5m//m5sCMsLISm/Bc5wf3RW8NBZz4fNqPEAoHRMGRPIzruKRYsWAUwWJPXV8vJeXw2mgycA1d156dhl40IIMXQIFs9Uc6Wu9WwxGu/vgmvnjkMyzBVubLbFX3RTxk2dCmJlZSWOHz+O1NRUtSqdAJ04q4uxdTmmuVQ3mJoeUFlZiSNHjmD//v2K/0wBy9Ye1r4hGl8Ys129IK6uANOaC7abl3ybkyvK8m9g4vhY+DA7AQDi2krApv+uEYONgdKNffv2IS4uDnZ2drCzs0N8fDz27dtn6LAGpevGBpg2St1spe2t6Cy8Auvh/gAAa58AyFpqex1jFRiDmoZmRbK09gkEkQGS+moQGWA1PPCv1n/Sgc6ibHm3chXbKMOy5JY1Y2KquVKXerYYFZRWYvSoUNyXmAAPJ1vEjZQvX1BUJ4BARMeWmwJzyqMCkaTfstdVQewqq6oqRaWlpVi8eDFsbW01em87DhtB7pY9pllVi7Ih0VyqGxqV8G+++QZvvfUWGhoaEBISgmvXriE+Pt6s16Bh2dqDM2YSOguyIKmvhsTBFb+fvoSFDzwMaXUJiGSYvEJaXw1ZYy3g6m3okCk1HDp0CKtXr8Ybb7yBiRMnghCCy5cv4+9//zs4HA7mzZun8WsGBASAw+EoZoB9+eWX8eCDD2o79D6p6uYt4VfD2icA4upyWHn5QlIvf9x9cqw7vDK0V+dj6tSpSq/FHTNJvp+LvCVTVFbQq2suIaBjlw3gv//9r8rthBAIBAI9R0P1ZIm5sj9d3efsuGyUSGXggCD3j5NIW7gA7k4BAAA7dzuja/Gg+qeLPGoo6pa9vmZWLikpAY/Hw/Tp0/URrlkyRJdjmkv1T6Mz+kcffYTs7GzMmDEDV65cwZkzZ/Dtt9/qKDTjwbKxA9vND6J2AfaduIwZM2fD2t4JwpoSsLpaQd28IOukzfKm4pNPPsFPP/2klCRiY2MxZswYvP3224NOmDt37kRMTIyWohw6tqsXRIXlYNjaoiM/Fxz/EEiaGsBmcCFtb0V+STnu3Lmj8oK4Z0W2r+68moxdpt1ytSM7O7vP51JSUvQYCaWKpeZKVZQu6KUyjBnuiMP792HyhFi4OymfA+gkK6ZFV3nUEFS2tjlwB+z+KhBJkJd/FwXXMvHgA4v1GbLZMUSXY5pL9U+jv6q1tTWcnZ0hkci7IUybNg1r167VRVxGo6sroYhjA4moAykLF8LJQ96qaTU8EKLCLLCdXCFp4oMTPN7A0VLqKi4uVnlncurUqSgpKTFARLrBsrUHw8YF4poCkE4RRGVFYDu6gmnNQGX2OQwPjUVYSgqYzIF73fc1Tlndsct0SSHt+fLLL/scPzRQ+S0oKMCyZctQX18PR0dHfPvttwgPD1fa58SJE3jppZcgEAjAYDBw77334v3331ernFDazZV99aRQ5+9oSF2tnEKxRHFBb02kKK2qQ1rKfbC37/3bp5OsmJbB5FGhUIglS5bg5s2bsLGxgYeHB7744gsEBwejtrYWS5cuRVFRETgcDv7v//4P06ZN0/XHANC77Nlx2b1aQAEoVYoEIglO5hTA0c4OXjHT0EmYsNZLtOZL32v1DiWXUoOj0VUEh8MBIQShoaH4+OOPsXfvXrNvihZX3oXQmot9pzIhsubCtvOvz8uytZdXNpk2dLkJE2Nn1/eJbdiwYYN+3aVLlyIyMhLLly9HXV3doF9Hmxg2w8C0tgE3JAKkvQUsByfk3CnG1dJacIUtGlUmVI1TVnfssqqZd6nBefTRR1VuLy0tRWJiYr/Hrlq1CitXrkR+fj7WrVuH9PT0Xvs4Ozvjp59+ws2bN3HlyhVcuHAB3333nTZCtwjazpU7d+5ETk4OcnJyFN331fk7GkpXK2dtUzvKGtoh6JRAIhYj63QGeJV1YFjbqDxOnTF0lPEYbB5duXIl7ty5g2vXriElJQUrVqwAALz00kuIj49HQUEBtm3bhocffhhisVjrcavSs+wJhBKlFtCShjZcLq5HWb0AR25WoaZViCu5N1B+IxMsFht2HI7BxyRSmhtKLqUGR6Oz+jvvvIOWlhZ88MEHePLJJ9HU1IT/+7//01VsBidtb4W0qQbZRRWYEx8FJ4jBcvFS2qev5VMo49bU1ITffvtN5axlzc3Ng3rNM2fOwM/PD2KxGOvXr8eyZctw6NAhAEBGRgYyMjJQW1s7pLgHQ1pbDrazG2StzbCNSkBTyV3wG5sxOzwA7D/HJOujC6wulhSy1K67IpEIq1evxhdffKHYVlZWhsTERDz77LN9HldbW4usrCwcOXIEAJCamoo1a9agsLAQwcHBiv1iY2MV/+ZyuYiJiQGPx9P+BzFTus6V6v4dDaV7t0VHDhvtUhmK7uRhZPhYeHgN77cLrb5bPKjBG0we5XK5mD9/vuJxfHw8PvzwQwDAzz//jMLCQgDAhAkTMHz4cJw+fRqzZs3Satx9LdmhVPZ6tIDaEwImAdrEEnjbcXDlbi3aK0oRMnEmmCwWbaE3UYPNpdTgaVTxnDFjBgDA0dERR48e1UlAxoRfUoQbVc1IGDsGstZmMB08Leri1pz5+flh06ZNfT432NcEACsrK6xduxahoaGK55KSkpCUlGSQyUWs/MMgzDkJbkgELt8oRJCPJ6bbOYETPBYsW3utdoHtryKo7SWFLLnr7s6dOzFv3jy8+uqrePfdd1FeXo7ExEQ8/fTT+Mc//tHncWVlZfD29gabLT/1MxgM+Pn5obS0tM8KS3V1NXbt2oUDBw7o5LOYI23nyqVLl4IQgri4OLz//vuD+jvqQ89JhGxYTPBb29DEuwGv0LFw5NAlU8yJNvLoJ598gpSUFPD5fIjFYnh5/XVzPyAgAKWlpYrH2riBq8kkQt3HGwJAfk0rvO04qC4phDXXBrGTE+GpxjhQyngNNpcas8YOMX7Iqxlwvw6pYeal0ehXUlZWhtWrV6O8vFzR7efkyZN93hV4+umnsX//fpSUlCA7O1sx6Yqxj00BgMbGRhz64yqSRvuAybEBEYpgNTwQgOW2spiTU6dOafX12traIBaL4eTkBADYsWOHUquRIVm7ekMWFodz505DYm0LByICw84V0g4BJPxqEFG7VmamVaciqM0eAqq67lrK75HD4eDXX3/FzJkzAQC7du3CU089hWeeeUar79PS0oIFCxbgn//8J8aP/2sMuyFb8E2BprmyP6p6Urz99tsDHqfvv1HPSYTCfZxQVFWPgkvHMWbsJLBYDNgPs0akq2UvGWFOhppHN2zYgMLCQhw/fhwdHR0D7q+NG7iaTGDVs/V9crA7jpy5BCG/CoHjpykqm7pqoe+rZZbSHn3lUn1ytrFCaqjrgPvtzufrIZreNBrjuWrVKixZskTRrSIiIgLffPNNn/unpaXh3Llz8Pf37/U6xjg2RdreCmHhNdRdOQNZeT6S5yXBIypBaW1Eum6hebp79y4+/vjjQbfq1NTUIDExEVFRUYiMjMTp06eNZkycpK0FbY0NCPDxxeSwkYBYAhaHAfHdHBBhA6RNNZA0yU9A0uamXt3J1X4fPY/hZLt6QdrcBGBocZui3NxcFBcX4/3338eXX36JhIQEzJw5E7m5ucjNze3zuBEjRqCqqkox6Q0hBKWlpSpbJ1pbWzF37lykpKTgueeeU3ouKSkJmzZtgoeHh3Y/mJnQNFf2p2dPirNnz6r1d9T336jnBX0lvxkVTSJETJgKBxd3DGOzYGPFphfQZkyTPPrhhx9iz549OHz4MGxtbeHq6go2m43q6r/yBo/HG3QPpC491+b0dOCiQyoDgH5b31Wt6WnPkiEhIhD3JM3HxCAPxQRDulh3tvs46cvF9b1eX1fva2kGm0upwdOo4llbW4tHH31UMRkJm81WdPVRZdq0afD19e31GllZWYoBvampqSgrK1P06zeErgpnx41zaGhvxW8XssBmScCtzQcAsFy8IOFXK1o66QQppm/WrFnIyckBIF/offz48cjIyMALL7yAjRs3avx6gYGByM7ORm5uLq5fv45ff/0VAQEB2g1aA9L2VojKCiCqr8SxvT/jVv4t+Pp7Q8ovh5WHvIJm5ekDWWszrDy8wLR1kS8HxHUc9HvquyLY1XW3+40hS5GSkoKUlBQsX74cdnZ2OHXqlGLbwoUL+zzOw8MDY8eOxfbt2wEAu3fvhq+vb6/umQKBAHPnzsXcuXOxfv16XX4Us6RpruxLW1sbmpqaFI+7elKo+3fUp+4X9M2CVlw8egD2LIJOa/kkM80iCe1ia2YGm0c3bdqEHTt24OjRo4peQgCwePFibN68GQCQmZmJioqKIa2LqaryNtAEVgKRBDcqm3CuoFbpuMzMTJw/fx5B/iMQ4umgqHT2VzkcCpXLu/TzuajBGWwupQZPo0zIZrOVBpE3NjaqHFTeH2MbmyIsL4SksgDWXj4Ak4kzV27g/jlTYNXWBLabF8SVd0E6mhXdB1nDQyCtrNLqBCmU/lVUVCi6fv/444+YPn069u7di8bGRkyfPh3r1q0zbIAqqNvFW9reqljm5/rps7DhsBE3Qd7t19p7BCT1NWC7eUJcUwGWk4u8kjg8BNLKgiGNl9T2GE5139OSKpxdiouLB33sli1bkJ6ejg0bNsDBwQHbtm0DAKxYsQLJyclITk7GJ598gsuXL6OtrQ179uwBIL8ofPXVV7USv7nTRq4E5D0pUlNTIZVKQQhBYGCgoidFX39HQ+k+Jq7i+nnMTJqHG3wphjGYqBKIMDnYnbZ2mpnB5NHy8nI8//zzCAwMVMwayuFwcOnSJWzcuBGPPfYYQkJCYG1tje3bt/e51IU6+upW21f32O7dxaUyGUQSGWzYTNws5KGBz0dSUpJar68N/S0tRNe71Z6h5FJqcDTKAosXL8aqVavQ0tKCr7/+Gps3b1ZMg61N/Y1N0eb4yk5+FTrvXAY3JALV/CZ0dLKwICoAaK4H01m+NifDxgUMLgeS+mow7R2BDoHeL64p7etaEw8ALly4oJhlz9nZeVAtE7qm7kQ60vZWdORdgJWnFy7fKMTYiNGQVJdBXF0OKy9fyDrawfaPADoEsAqMAToEYPl4aW28pKVWBE1JWFgYLl682Gv7119/rfj3q6++SiuZQ6CtXNnVk0KVvv6OhiTpEEBYW4Gk+fchk8eHtx0bzSIJJge7w9Oetnaam8HkUV9f3z5vwnh6eipmatYGTdeF7V6hc7GxRkNHJ+7euY1ZUyZgwpggtHVKlcZc6nLd2Z6TG3W/aUPXu6VMmUZdbZ9//nkkJiZi/PjxOHLkCJ577jmsWbNGozccytgUbY6vlLa3Qnj9Aqz9glBWWIiTWTfgImkDg+sIxjAXgMGVr83p4gFpUwPYbl6QNjUANnZqr1tIGS8mk4ny8nIIBAKcPn1aqTtPe3u7ASNTra8u3tL2VnQUXEP7zUtoy/sDosIssJwccej4eVixWZA11oE1zB4sZ1cIeflg+4yCtas3rH1DFP9n2dpb9HhJitI2beRKU9PQ0IBff/0VPj4+qG0VKS2nIhDK8z0dl2ZejD2ParoubPfu4vXtnSi7eRUMcQc6ZUy0dUo17rarjfiD3HtPxkXXu6VMmcal9aGHHsJDDz006DfsPjYlPT1do7Ep2mqV6arAst1cIeLXQsSyxjw/O9iPDAfXVzkOCb8aVp4+AORj4tAx+EXAKePxyiuvIDY2Fmw2G4mJiYqlTy5cuGDQsZl9UbUGZie/CqKCLLCsOWC7eaGzohgMF09IWdYIGTkCfow2yNoZ4AYHAAC4AaF9ll9DdJOlKHM21FxpCrpm3XQbZgWBQID7778fDg4OYIskvVpk1F3GgjIdppBHB5p1tufMsXEj3cDjC9DYLoKnuzscfUbCy9FG4267ukbXu6VMlVpn/Z4zGvbU1zpOq1atwsGDB1FdXY2kpCTY29ujsLBw0GNTBrMAvaquuRJ+NRhcDkqLS5FbWoukEE9wx8+Ctau3Vt6TMn6LFi1CQkICampqEBUVpdgeEBCAL7/80oCRqdazYggAopsXwRpmB7abvHWS7e2P/YdOYHxcLAJd7cC09QPT2UMxdnOg8ku7yVLU0Aw2V5oagUgCHl+A6uYOyARNOJTzB9IfexT2XPl4PFXdBPMqm9DRKQHTig0bNh2XZg5MLY/2pOpmyDBrFirzLsPL0xuOUZF/dXOl3VspSivUqnh+/PHHmDBhAubOnauYpU8dW7ZsUbl9sGNTNG2V6WtcHMPWDnVFecgqb8D8cH9wQ8eprHQO5j0p0+Hl5aW0WDUADB8+3EDRDKx7xVBUVgArLx9I+DWQ1FeD7eaFY2cvISRwBDykneAEJyiW/5HZOELWSSxu5lfK9Eib6tD87fsD7icTNOshGs0NNleaEoFIgnMFtWjq6MRwWxauXb2AqIRZqG0VKSqegHKLjEAkQXVzh2LcnFDKRCS9cDcLppZHu7dwqmrFrCnIhYODA8ZGRyod19+YS4qi1KfWL+f48eP45ptv8OOPP+KBBx7A448/jqCgIF3HppImrTJ9dc3lFRXBY7gvFvmPBIvFHLD7LG0JooyNvCW+CmxXT7TdzQfv+g1Mj40GCwz52ORua852tXZSlLFjObnD4dH+Ww0BoGW7cbYcGlOu1LauC/aGdiGkMhmGdbaiupOFsdPnQwRGvy1ANS1CDGOzAMgnbXGw49ALd0rvulo4mQS4Vt6IWD9ndEhlsGEx0S6RoqWKh4kTJ4LFYqk8nnZvpaihU+uWbGJiIr7//ntcuXIFfn5+eOSRR5CYmIhLly7pOr4h6T5hiri2GjJhO27l5iCnqBSs9jawWEw6kQplkrpa4sVSK/xe3AjbkHCwJFKwfUYpdSmna85SlP6Yaq4ciEAkwR9361Db1I7KRiGkLXyU3MiEi90wiJjMAcdrdp+0pUMqg7/LMH2FTlEKNS1CMAnQJpbAhWuFP+7Ww8uRCzcHLppuXwKkYpWVTjopFkVpj0Z9gRwcHBQLq96+fRu3b9/WVVxD1jW2kzU8BJLWdoBBwLAC6grzsPC++bANHWeRC89T5oNpYwd+RyfGx4xBkK8X2G6eSq33dKZaijIMU8qV6ijhCxQtlu62ViiprET0lDmAtTXi/F0HbL2ks3BS+jBQBdHTgYsGoRjDrNhoE0sQ4GSLO1UtgLgDYSHBCBkT2ev4rlbS7rPZUhQ1eGpVPKVSKfbu3Yv77rsPs2fPBovFwtWrV7Fs2TJdxzco3ZddkZTdRGfxdeS3SJB9pxjx46OA5jq6JApl0kQiEX75aQecuVYYYSu/IOxZuexqFaU3WChKP0wtV6qLAGjo6AS/uhx52ZfgOXIMGBwO4gPd1a5E9rU0BEVpgzoVRDsOG7F+zihrboeLjTU6OsUozzmDgloBAkJGqTxe1ThQiqIGT60M4OPjAz8/P/ztb3/D5MmTAQB1dXWoq6sDAKXZzIxB9y6GbBd33OZVoLiIh3tnTYGkvgacUfGKfVXNektRxkwmk+GXnT8hzssOVh18SIVCMG1d5K37fHl32q6yTMcnU5T+mFquVFeAqx1u5BehOj8XvrHTMczGCiEe9vKLcDrRCmUE+lrupKd6gRDtnVIUN7aBn3cefgEhYFhxwftzxtqex3vS2WwpSqvUyhZcLhd1dXX44IMPwGAwQAhRPMdgMHD37l2dBTgYbFcviAqzwHZyRUVhIUYGj8ToMVYQ5l2G9ci/Zirra9ZbijJWHR0dEAqFmBc7GjbiJvlSKvXVkIk6QP5cNoWWZYoyDFPLlQPpmlAI7U2YFRuGiqAAMFksuNtzcaOiia7JSRmNnhVEOy4bRXUCpRloa1qFqGoSItxjGK6XVCFqwlRYc20AACKpTDHRUPcKJp3NlqK0S61fEI/H03EY2sWytQfDxgWX/shEQ5sQc4OC0Fl+F9a+gWDb2youzPua9ZaijFF7ezt2796N2bNnw8mGA6bjn+t3unlBVFUFjrd8SSBalinKMEwtV/anq+tiSyUPFbxCPLpkMaJGyFuQiup6tw7hz+Up6MU5ZQjdK4h2XLbKGyMFta1wsWYg99wR2I0Ig4Cw4AJ5N3JPDqvPCiadzZaitMc8FxoDIHbyQoeMhTmTx6GztAgs5+Fgu7gD+OvCnE6+QpmS69evY86cOfDy8oLV8EBImvgAAEkTH5zgKFqWKYpSW38TsQhEEuSUNwISCZpqqxAzeSbq28SK53vOUmvHZdMJWCiD6xpHLBBKVI7LDPGwR1FRIfxHRcPGbTg6xDI0dHSiXSwFl82i45ApSg/M7tclbW/FuZMnMCoqGjOTUyFtqIb1uHAAUFrT0Dpo5F9LUlTeBcPG0cCRU5Rqra2tuHz5MmbOnKnYxrK1Byd4PKQN1X+t22ljJy/vf5ZtiqIoVbpaM1V1le16rrG0AEImBwHRkyAiUBrb1rN1qaC2Fcw/exX3N76OovRB1bhMoVCIq+dOImnaPbhW0QRxqwjD7TmoEnTAxoqNAFdaXinz4MC1wu58vtK2xg4xnG2seu1nCGZV8ZS2t+LkgT1gcLiwbywBXF1h7RuieN46KLbXhbm0QwBxVRGsvHzo2DjK6LS0tChmyeyp58RBdCIhiqLU0d9ELCV8ARp4d9BUX4MxE6ahXiTBRBVjOO04bMCBq6jANnZKALAhY4BOwEIZVM9xmWwiwa5du3DPPffAy8EGbSIpOGCgqUMMLpuFYA972spJmY1XZo3qte2NI7fwxpzRBoimN7P5pRFC0FRWjFGhgfBwkbde9hzn1vPCXNreCnFJHjj+wZDUV4Np70jHxlFGQyAQwNraGikpKXBycjJ0OBRFmYm+ZuqsaRXidlktYOeMMYGjUN8hhoO1fLzcMBWVz+4VWDdba0iZDMT4OtOLeMrgum6MlNY2wsGWgzFxU+HkLh+C0lX+Pe056JDKaGsnRemRWWQHQgiOHDkCZ7thiHSQ9/fp6k7bHwm/GlZungD+nKClpBC248boPF6KGgifz8eBAwewePFiWumkLAbLzhEt2zcpbZM21YHl5N5rP2rwVM3UKRBJ8GvGSXCJGH4RcShpboe3nQ04bHnFUlX32Z4V2Dg/OrstZRxqWoW4fKcCvKyT8IqcBHcXV1wurld0K6cz1VKUYZjFr+327dtwdnZGXFwcpO2tao9zY7t6KcZ9SuprwBkzibZ2UgZHCMHZs2dx//33w9bW1tDhUJTeePzjw17baj58Cp4vfG6AaMxbz5k6bxaWgC0RwWnUOHDYTHDZLDSLJPBgW/e5fiG9gKeMkUAkwfnCOjQXXMOo8VNg5+iCho5OuNhYK26g0JlqKcowTDpLEEJw/vx5JCQkgMmU35XVZJxb1+RC0oZqcEbF00onZXC1tbXg8/lYuHChoUOhKMrMCUQSVDd3oDL/OsZERkHAGobmjk40dHTCmWsNAJAyGf22ZNILeMrY3K2sg6iyCG5j4mBna43aNhEcOVZok0jp+GOKMjCTrXjKZDL89ttv8Pf3V1Q6B4NOyEIZi6qqKhw7dgyLFi0ydCgURZmZmlYhCmpbEeJhj2HWbJTwBSjlt6HuzlXImCyM5XIRN5ILHl+A6uYOcNjy7rN0zCZlSpqampB1KgMuo+PR1NGJDrEUBAREBgR70UmEKMrQTPIXKJPJIJFIEBERgaCgIEOHQ1FDJpFIIBaLkZaWBhsbG0OHQ1GUGalpFeLy3Xq42Vrj0t16AIC9FQuNbR2wdnCH/8hA1LQIEeRuh4jhTghwtaPdZymT09QmRGFVA6bNmQdeiwyebBZqBCI4ca3A7+iESCJDTasQAqGElm2KMpDBNxUaiFQqxd69e1FbW0srnZRZKCsrw549ezBixAha6aR0rqCgAAkJCQgNDcWECRNw48YNlftt3boVISEhCAoKwhNPPAGxWKznSCltKahthZutvOusgzUb5Y1tuJF1FrZtdbDx8EGjUIwOsQQCkQSAvPts0J/j4CjKFJRUVuN/23+AiGmDwkYp2GAAADztOGgUdoLLZqJTJMHlu/UoqxfgcnG9orxTFKU/JlfxPH78OCIiIuDr62voUChqyFpbW3H27FmkpKSAwWAYOhzKAqxatQorV65Efn4+1q1bh/T09F77FBcX47XXXsPZs2dRWFiImpoafPnll3qNk7/1LdR8+BSkDTWo+fAp1Hz4FPhb39JrDOYixMMetW3yFp9mkRjOLSWwc3SB0/CR8BjGQWNHJ1rbOunFOGWSxGIxMn7/HdGTZoLJZMGRw0ZtmwgAUN/eCQZhwMVGfuPFzdYabWKJYv3angQiCYrqBPR3QPXLVG7gfnquCG8cuQV+W6d8Lc8jt/DpuSK9xtCTydzOFIvFKCgowKxZs4Y0ppOijAWPx4OLiwuWLFlCyzSlF7W1tcjKysKRI0cAAKmpqVizZg0KCwsRHBys2G/Xrl1ITk6Gl5d83bsnn3wSGzZswFNPPaW3WF2X/0tv72XuPO25cLPnoJTfCk5zFfzCosBkMtHQ0Yk2MWBrxQIAxcU4nSyIMhXV1dVgMBhY8tDDyCppACCvbDpyrFAlEGGUlz2qmoUQdEpgZ81GfXsn7K3ZKmdqFogkuFxcDxsWEyV8gWLpFYrqqesGbnp6Onbt2oX09HRkZmYq7dN1A/fq1avw9PRESkoKvvzyS73m0X9MMb6eoQa72lX3bgEAdHZ2YteuXeByufQCnTJampTpgoICXLp0CTY2NrRMU3pTVlYGb29vsNnyiykGgwE/Pz+UlpYq7VdaWgp/f3/F44CAgF77UIanzjlHIiO4UdmE+pYO1F2/AEGnGEwmE9WtQnRKZeiQyMD68xzU17IpFKUvmuTRyspKHD16FPb29nCwsUbcSDdImQzYW7PhwGXDhWuFUn47bFlMSAngYMdBXKAbRrjZqaxU1rQIYcOS/xb6ahGlqK4buI8++igA+Q3csrIyFBYWKu3X/QYug8HAk08+iR07dhgiZKNisCtedbp7AfIlU/h8PhISEhAYGKjfIClKA5qU6ebmZqSmpsLKykq/QVKUFmRkZOC5555DbW2toUOxaOqcc5raO9EsEEEqaoe7z0jYDw9EpUAEWys2rFlMuNlYI8jDHh5OtrSFhzI4TfJobW0tFi9erFjv2o7DRoyvM2R/jlppEIrh+Gd5duSwwWWz4GnP7XP8sqcDFx1SGQB6E4bqG72BOzQGqXiqe7dAJpNh586dcHV1VfrjUZSxUbdMt7a24vz58xg/frzipEVR+jJixAhUVVVBIpGPXyKEoLS0FH5+fkr7+fn5oaSkRPGYx+Mp7ZOUlIRNmzbBw8NDP4FTvah7zmGC4Pr5Y3C2sYbDcH842lhjSrA7rKyYcLGxhowB+LsMo5MJUQanbpnu6OjAkSNHEBMTAy5XuXJox2EjbqQbPJxsMTnYXaOKZPdj6U0YStcs9QauQSqe6t4tqKysxNSpU2FtbW2IMClKbeqWaYFAgISEBEOESFHw8PDA2LFjsX37dgDA7t274evrqzS+E5Bf8O3fvx/V1dUghGDz5s1YsmSJIUKm+qDuOaelsR7eI0MhtbaB/59dDD3tufQCmzI66pbphoYGJCYm9vk6XbMyD6ac0xmdqYHQG7hDY5S/rIyMDGRkZKCiogKrV682dDg6V1tbazEFT53P6uPjgy+++EJPEelHV5kuLS3FwoULDR2O0THn34CxlectW7YgPT0dGzZsgIODA7Zt2wYAWLFiBZKTk5GcnIzAwEC8+eabmDx5MgDgnnvuwapVq3q9VlFREZKTk3Uarz7KRlGRYWf504Wuc05VRTk+ef91cNgssJmWO3O2OZ9jVDG28442dJXp8vJypKWlGTqcAZlKmTOVOI2lTHe/gZuent7vDdwpU6bgjTfegKenZ583cM0ljwJq5lJiADU1NcTe3p6IxWJCCCEymYx4enqSgoICpf2effZZQ4Snd5byOQkx389Ky/TQ0O+F6gstG6rRc45m6Pdg/MytTNM4zdft27dJfHw8CQkJIePGjSO5ubmEEEKWL19Ofv31V8V+X375JQkMDCSBgYHk8ccfJ52dnQaJ15j+xgbpaqtud6+kpCRDhKd3lvI5AfP9rLRMDw39Xqi+0LKhGj3naIZ+D8bP3Mo0jdN8hYWF4eLFi8jPz0dWVhYiIyMBAF9//bVS6+UTTzyBoqIiFBUVYevWrQabUNKY/sYMQggxxBvfuXMH6enp4PP5iu5eXX84ijJFtExTFKVP9JxDmRtapinKvBlsOZW+7hYAmq3jZEqEQiEWLlyI0NBQREdHY/bs2YrZ2mprazF37lyEhIQgIiICZ86cMXC02rFt2zYwGAzs27cPgPl+TsAyy7Smnn76aQQEBIDBYCAnJ0exnX4/FCCfTCQyMhLR0dEYM2aMYvwpAJw6dQoMBgNvv/22YlteXh4CAgKUXqO1tRV2dnZYvny5vsI2GHrOkRvMecWSvh9TYoplur9ru3vuuQcjR45ETEwMYmJi8NFHHxkszoCAAISFhSli2blzJwDj/V4p5b/ZmDFj8Pnnn2t0fE5ODn766Se19u2a0FXnDN3XV5XExESybds2Qgghv/zyCxk/frxhA9KSjo4OcvDgQSKTyQghhHz66adk+vTphBBC/va3v5HXX3+dEELI5cuXiY+Pj8H6gmtLcXExmTRpEomPjyd79+4lhJjn51SHuZZpTZ0+fZqUlZURf39/kp2drdhOvx+KEEIAkMbGRkIIITk5OcTKyopUVlYSQgg5efIk8fLyIu7u7qSuro4QQsj169eJv7+/0mt89dVXZNq0acTJyYm0trbqM3yjYkm/qcGcVyzp+zEXxvo36+/abvr06YrrH0Pr+fvoYqzfK6X8N+PxeMTBwYFcu3ZN8bxUKiVSqbTP47dt20ZSUlJ0HKVmjK7iqe7gcnOQmZmpuGgaNmwYqaqqUjw3YcIEcvToUQNFNnRSqZTMnDmTZGVlKZ14ze1zqsOSyrS6up9M6fdj3gCQd955h8TFxRF/f3+yd+9esmHDBjJu3DgSHBxMTp48qbRvV8WTEEI8PDzI5cuXCSHyimd0dDR57rnnyNNPP00IUV3xjI+PJwcPHiTJycnk66+/1vXHM0qW+ptS97xiqd+PKTOlv1n3aztjr3ia0vdqiXr+zSZMmEDCw8PJokWLyJw5c8jo0aNJeXk5+e6770hkZCSJjIwk8+fPJ+Xl5aSmpoaMGDGCODg4kOjoaLJq1SpCiLzRJzExkYwbN47ExMSQn3/+mRAibyxydHRUvBcA8u6775IJEyaQgIAA8s0332jlMxmsq21f1F3HyRx88sknSElJAZ/Ph1gshpeXl+K5gIAAk/7MmzZtwuTJkzFu3DjFNnP8nOqwpDI9GPT7MX92dna4dOkStm7dikcffRTe3t7IysrChg0b8OKLL6o85vTp03Bzc0N0dLTS9ldffRU7duxAcXFxr2Nu3ryJsrIyJCUlYfny5di6datOPo+xo7+p/r8D+v2YHlP6m3Vd23V56aWXEBkZiQcffBB37941YGTA0qVLERkZieXLl6Ours6kvldLd/36ddy+fRvR0dG4ePEivvvuO9y8eRONjY148cUXcfjwYeTm5iIhIQErVqyAh4cH3nrrLSQmJiInJwebN29GU1MTVq5ciR9++AFZWVk4evQonn/+eVRUVKh8Tw6Hg8uXL+Pw4cN4+umnFWuXDoVRruNpCTZs2IDCwkIcP34cHR0dhg5Hq/Ly8rB7926zGr9JUdTgPfjggwCA8ePHo62tTbGWWVxcHAoKCpT2nTp1Kjo6OlBcXIxdu3bB2tpa6XkXFxesXbsW69evx8svv6z03NatW7F06VKwWCzMnz8fq1atwq1btzB69GgdfjqKoii57td2APD9999jxIgRIITg888/x3333YebN28aJLYzZ87Az88PYrEY69evx7Jly5TGzFPG6cEHH4SNjQ1sbW3xzTffIC8vDzY2NvD09AQAnDx5EnPnzoWPjw8A4O9//zveeustSKXSXq914cIF3L17F/PmzVPafufOHQQGBvba/5FHHgEAjBo1Cmw2G9XV1fD19R3S5zG6iueIESNQVVUFiUQCNpsNQghKS0vh5+dn6NC05sMPP8SePXtw7Ngx2NrawtbWVvEH7WoN5PF4JvuZz549Cx6Ph5CQEABAdXU1Vq5ciTfffNOsPqe6LKFMDwX9fswfl8sFALBYrF6Pe95BPXv2LJycnPDtt98iPT0dCQkJigTbZe3atQgNDcXcuXMV28RiMb7//ntYWVnhxx9/BAC0t7dj69at+PDDD3X22YwR/U31/x04ODhY/PdjakyhTPe8tgPkcQPylsQ1a9bghRdeAJ/Ph6urq97j6/qurKysFOdQU/heLd3OnTsRExOjeJyXlwc7O7s+92cwGH0+RwhBeHg4Lly40Os5Ho/Xa1tXrgZU5+vBMLqutuqu42SqNm3ahB07duDo0aNwcnJSbF+8eDE2b94MAMjMzERFRQWmT59uoCiHZvXq1aiqqgKPxwOPx0N8fDy+/PJLrF692qw+p7rMvUwPFf1+KFXS09Mxc+ZMbNiwoddztra2eO211/Cvf/1LsW3//v0IDAxERUWF4tzzxx9/4Pvvv4dYLNZn6AZHf1P9fwf0+zE9xv43U3VtJ5FIUFNTo9hn9+7d8PT0NEils62tDU1NTYrHO3bsQGxsrNF/r9TAEhMT8fvvv6OyshIAsHnzZsycORMsFgsODg5obm5W7JuQkIDi4mIcO3ZMsS0nJwednZ36C1grI0W17Pbt2yQ+Pp6EhISQcePGkdzcXEOHpBVlZWUEAAkMDCTR0dEkOjqaxMXFEUIIqa6uJrNnzybBwcFkzJgx5MSJEwaOVnu6D64358/ZH3Mt05pauXIl8fHxISwWi3h4eJCgoCBCCP1+zBm6TRjU2tpKuqedsrIyMmzYMJX7EkJIfn4+sbe3J+Xl5YrJhbqIxWISGhqqmMRj3rx55L///W+v94+NjSW7d+/W6mcyBZb0mxrMecWSvh9zYax/s76u7QQCARk3bhyJiIggUVFRZMaMGSQnJ8cgMRYVFZGYmBgSGRlJIiIiSHJyMikuLiaEGO/3SqmeEOr1118nzzzzjNI2VZMLEUJIU1MTmTRpEomMjFRMLnTlyhWSmJhIoqKiyOjRo0lSUhLp6OhQOblQ93zs6uqqKDNDwfjzxSmKoiiKoiiKoihKJ4yuqy1FURRFURRFURRlXmjFk6IoiqIoiqIoitIpWvGkKIqitGrPnj0YN24cYmJiMGrUKMyYMQMymUzr7zN//nzcuXNH669LURRFUYZkrnnU7CqeAQEB8PDwUJrF8OTJk2AwGFi7dq1eY1m8eDEuXrwIQL74cXJyMiIjIxEZGYmYmBicOHECgHwGqn//+99afe9vv/0WCxcuHHC/nJwc/PTTT0rbYmJi0NraOqT3nzp1qsoF3int6u/EdM8992DkyJGIiYlBcHAwZs+ejYMHD/b7ei+88IKiPDQ1NeHRRx9FREQEoqKiEBERoVimYv/+/Xj22We1+llOnTqlNGV4X3g8nmJm5C7aOHEuXrxY5RTjlGaqqqqwcuVK7NmzBzk5Obh9+zY+/PDDfqd4H6xDhw4hLCxM669r6WgelaN51DLQPCpH86jxMOs8OuTpiYyMv78/GTduHNm1a5di2yOPPELGjx/faxYoXbp06RKZMWOG4vG9995LNm3apHhcV1dHSkpKdPb+27ZtIykpKVrbT1N79+4ljz32mNZfl/pLZWUlcXV1JTweT7HtypUrRCaTEUKUZxMmhJCTJ08ST0/PPmf4LC8vJ6NHj1Yc/9RTT5G1a9cqHre0tJD8/HwdfRrSa9bSoe6nqezsbDJ16lStv66lyc7OJh4eHqSlpaXXc/7+/uSFF14gY8eOJUFBQeSDDz5QPJefn0/mz59Pxo8fTyIjI8mnn36qeO7ChQtk8uTJJCoqikRGRpJ9+/YpXq9rxr+qqiqyePFiMmHCBBIREUFeffVVQgghUqmUPPXUU2TUqFEkKiqKjB07lnR0dOjwGzB9NI/K0Txq/mge1S6aR7XDnPOoWVY8P/vsMzJ//nxCiHwq4aCgILJ+/XqlhPnvf/+bTJgwgcTGxpKkpCTFSefYsWMkPj6exMTEkDFjxpCvv/5accyyZcvIypUryYwZM0hISAi5//77iUgkUhnH448/rnRsdHQ02blzp8p9u0+NvG3bNjJz5kyyZMkSMnr0aDJp0iRy48YNsnDhQjJq1Cgye/Zs0tra2us4Qgj59NNPybJlyxSv05UIq6qqyD333EPGjh1LxowZQ5566ikilUpJTU0NGTFiBHFwcCDR0dGKqZbRbQrlzMxMxVTMEyZMIOfOnSOEEMW0y//6178Uhf/gwYOKWDo7O4mbmxtpamrq609FDVF/JyZCeidMQgj54osvyPjx41Xu/9Zbb5H169crHqekpJCNGzeq3Ld7+Tp58iQJDw8nTz75pGKq9mvXrpFly5aRiIgIEhcXp5jau+cF2m+//UamT5+ueJ2uRCgWi8mcOXPIuHHjyJgxY8hDDz1EBAIBIYSQsLAwwuVySXR0NFmwYAEhRPnEWVBQQGbOnEkiIyNJdHS00ncAgLz77rtkwoQJJCAggHzzzTdKn2vUqFHk5s2bKj8zpR6pVEoWLVpEnJ2dycKFC8kHH3yg+Pv7+/uTxx57jMhkMlJXV0dGjBhBzp8/TyQSCRk3bhy5desWIYSQtrY2EhkZSS5fvkz4fD7x8PAgZ86cUbw+n89XvF7X333OnDnk1KlThBB5+UlKSiI///wzuXr1Khk1ahSRSqWEEHlO6Po3pRrNo8sUr0PzqHmjeZTmUWNkznnU7LraAsDkyZPB4/FQWVmJHTt2YPHixWCxWIrnf/zxR9y5cwcXL17E1atX8cgjj+Dvf/87AGDs2LE4d+4csrOzcfbsWbz11lsoLy9XHJuTk4PffvsNt27dQk1NDXbv3q0yhlOnTmHixImKx+vWrcPy5csxefJkPP/88zhz5kyf8WdmZmLjxo24efMmgoKCsGDBAmzevBm3bt2CtbU1/ve//2n0fTg5OeG3337DlStXkJubCx6Ph59//hkeHh546623kJiYiJycnF7dLjo7O7Fo0SK8/vrryM3NxaZNm5CamgqBQAAAaG5uRlRUFK5cuYLPPvtMqcuIlZUVIiMjcfbsWY1ipdQXFRWFKVOmwN/fH/fffz/+/e9/o6Kiot9jJk6ciBs3bqh8rmeZfeaZZ7Bx40aMGzcOa9aswYEDB/p83du3b2PFihXIzc3FwoULMWPGDLz00ku4fv06xo8fj48//lijz8ZisfDjjz8iKysLeXl5cHR0xKeffgpA3qUuLCwMOTk52L9/f69jH3nkESxevBi5ubn45ZdfsHz5cpSUlCie53A4uHz5Mg4fPoynn34aEolE8dykSZNw/PhxjWKllDGZTOzevRsXLlzA3Llzcf78eYSHh6OwsBAAsHz5cjAYDLi5uWHRokU4duwY7ty5gxs3bmDJkiWIiYlBQkICWltbcfPmTVy8eBFhYWGYOnWq4vVdXFyU3rOtrQ3Hjx/HM888g5iYGIwfPx6FhYW4c+cOAgMDIZFI8Pjjj+N///sfxGIxmEyzTH1aRfOoMppHzRPNozSPGiNzzqPsoX01xuuxxx7Dt99+i3379uGHH37ADz/8oHhu3759yMzMxLhx4wAAUqlU8Ryfz8fy5cuRn58PNpsNPp+PvLw8+Pr6AgDuv/9+2NraAgDi4uJQVFSk8v3Ly8vh6empePzQQw9h7ty5OHnyJM6fP4+UlBS88sorePHFF3sdO2nSJPj5+QEAxo8fD7FYrHitCRMmoKCgQKPvQiaTYd26dTh37hwIIaitrUVERASWLFnS73F37twBk8lEUlISAGDKlCnw9PRETk4OfH19weVysWjRIkXMPb8LLy8vpYsNSru6Tky3b9/G6dOncfjwYbz77rvIyspCcHCwymNIP8v29iyziYmJKC0txenTp3HhwgWsWrUKCxcuxOeff97r2ODgYMXvafz48QgODsaoUaMAyH8ne/fu1eizEULw0Ucf4eDBg5BIJGhubkZCQsKAx7W2tuLq1as4f/48ACAkJARTpkzB2bNn4e/vD0CeUAFg1KhRYLPZqK6uVvy+aZnVnlGjRmHUqFFYtWoV5s6dq/LiBgAYDAYIIXBxcUFOTk6v5wcaTwX8Va7/+OMPcLncXs/n5eXh9OnTOHnyJF5++WWcOXOmz98I9ReaR/9C86h5onm0N5pHjYc55lGzve27dOlS/Pe//wWXy0VISIjSc4QQvPzyy8jJyUFOTg6uX7+O69evAwCefPJJTJkyBdevX0dOTg5CQ0MhFAoVx3b/Y7BYLKW7PN3Z2toqHQcAzs7OWLRoEf7zn//giy++wPfff6/y2J7v0dd7stlspWTf8/26bNq0CbW1tbh06RJyc3Px8MMP97nvQLoPbOZwOIrHLBZLKZaueGxsbAb1PpT6uk5K+/btQ3x8fJ8nJkDeChAREaHyOVVldtiwYZg/fz7eeecd7N69W29l9scff8SJEydw+vRpXL9+HS+88IJWyqyqWLv/hmmZHbqKigrFBQsANDY2ori4GEFBQQDkE7YAQENDA/bu3YuZM2ciLCwMDg4O2LZtm+K4wsJCNDQ0ICEhAQUFBYpWH5lMhoaGBqX3tLOzQ2JiIt5//33FtsrKSpSXl6Ourg5tbW2YM2cONmzYgICAANy8eVNXH9+s0Dz6F5pHzRvNo/2jeVS/zDmPmm3Fc/jw4XjvvfewcePGXs8tXLgQmzdvVnzpYrEY2dnZAOR/XH9/fzAYDJw5cwbXrl0b1PtHRUUpzQ524MABtLe3A5An7OzsbEUBGqzg4GBkZWVBKpWivb29z+5KjY2N8PLyApfLRXV1NX755RfFcw4ODmhublZ5XFhYGGQyGY4ePQoAuHDhAqqrq9WaMQ0Abt26hejoaM0+FKW2gU5MPZ09exZvvPEG1q1bp/L5nmX2yJEjaGxsVDy+cuWKVspsbm4uOjo6IJFIFLP79dTY2Ag3Nzc4ODigtbVVcZIF+i+z9vb2GDt2rOLEW1hYiHPnzmHatGlqxUfL7NBJJBK89dZbCA0NRUxMDKZOnYply5YhJSUFAODu7o5x48YhLi4Oa9asQUJCAthsNg4cOIA9e/YgKioK4eHhWL58OTo6OuDs7Iy9e/fipZdeQlRUFMaOHatU7rv88MMPKCwsREREBCIjI7Fo0SLw+XyUlZVh9uzZihklIyIiMG/ePH1/LSaJ5tG/0Dxqnmge7Y3mUcMz5zxqtl1tAeBvf/ubyu2PPPII+Hw+EhMTAUDRbzk2Nhbvv/8+/v73v+Ptt99GTEyMUl99TaSlpSEjIwOzZs0CAJw+fRovvvgi2Gw2CCEICwvDZ599NrgP9qdFixbhl19+wejRo+Hr64vY2FhFUu7umWeeQVpaGsLDwzF8+HBFTAAwc+ZMfPjhh4iKikJCQoLS+BRra2vs2bMHTz/9NJ5//nlwuVzs2rULdnZ2qK+v7zc2Ho8HqVRKTz461HViKi4uhq2tLSQSidKJCQCeffZZvPHGG2hra4O/vz+++uor3HfffSpfLy0tDd999x1WrFgBALh+/Tqef/55EELAZDLh7e2N7du3Dynm+Ph4zJ8/HxEREfD29sbkyZNx6dKlXvstXboUv/76K8LCwuDu7o6pU6cqxpd0nVAjIiIQGBjY6870Dz/8gCeffBKfffYZGAwGvv76a0WXu/60tbXh+vXrSr8PSnP+/v7IyMjo8/mHH34YH3zwQa/tQUFB+O2331QeEx8frzJJ8ng8xb89PDz6LJ9XrlwZIGqqLzSPytE8ap5oHqV51BiZcx5lkP46q1ODJhAIkJCQgIsXL2LYsGGGDkfvXnrpJQQHBytOvpTxk8lkiIuLw759+xRjNSzJ5s2bUV5ejnfeecfQoZitgIAA7Nu3T+3WHsqy0TxK86ipoXmU5lFdM/U8arZdbQ3Nzs4OH330kcUu/jx8+HA8/vjjhg6D0gCTycSWLVuU7n5ZEiaTiZdfftnQYZg1Ho9nssmS0j+aR2keNTU0j9I8qmumnkdpiydFURRFURRFURSlU7TFk6IoiqIoiqIoitIpWvGkKIqiKIqiKIqidIpWPCmKoiiKoiiKoiidohVPiqIoiqIoiqIoSqdoxZOiKIqiKIqiKIrSKVrxpCiKoiiKoiiKonSKVjwpiqIoiqIoiqIonaIVT4qiKIqiKIqiKEqn/h9wXIdaxnTSIwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_dict = dg.tl.compute_kl_stats(result_infer)\n", "dg.pl.plot_model_comparison_stats(result_dict,state_index=[1,2])" ] }, { "cell_type": "markdown", "id": "72f17c67", "metadata": {}, "source": [ "We visualize the distribution obtained from the observational data with the distribution corresponding to the inferred dynamical parameters." ] }, { "cell_type": "code", "execution_count": 28, "id": "6d2a4402", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dg.pl.plot_distribution_comparison_nd(result_infer,species_index=[1,2],data_index=[4],width_mm=120)" ] }, { "cell_type": "markdown", "id": "7c8af0bf", "metadata": {}, "source": [ "We visualize the posterior distribution of the inferred dynamical model parameters." ] }, { "cell_type": "code", "execution_count": 25, "id": "fb96bda6", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "result_infer[\"param_names\"] = np.array([r\"$k_{on}$\", \"$k_{off}$\",r\"$k_{on}^{syn}$\", r\"$k_{off}^{syn}$\", r\"$k_{M}^{syn}$\", r\"$k_{M}^{deg}$\", r\"$k_{P}^{deg}$\"])\n", "dg.pl.plot_param_posterior_dist(result_infer,param_indexes=[45,54])" ] } ], "metadata": { "celltoolbar": "原始单元格格式", "kernelspec": { "display_name": "Python [conda env:.conda-hzw_dyngpt_dev]", "language": "python", "name": "conda-env-.conda-hzw_dyngpt_dev-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": "3.8.13" } }, "nbformat": 4, "nbformat_minor": 5 }