From 13d40f66c7fd0aab8063374ad85545f45006e8b9 Mon Sep 17 00:00:00 2001 From: leon Date: Thu, 21 Sep 2023 16:48:34 +0200 Subject: [PATCH] edit notebooks --- notebooks/bump.ipynb | 1 + org/bump.org | 1087 ++++++++++++++++++++++++++++++++++++ org/doc.org | 44 ++ src/analysis/plot_utils.py | 2 +- src/model/rate_model.py | 20 +- 5 files changed, 1147 insertions(+), 7 deletions(-) create mode 100644 notebooks/bump.ipynb create mode 100644 org/bump.org diff --git a/notebooks/bump.ipynb b/notebooks/bump.ipynb new file mode 100644 index 0000000..ac9fa77 --- /dev/null +++ b/notebooks/bump.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{},"source":"Continuous Bump Attractor Model\n===============================\n\n"},{"cell_type":"markdown","metadata":{},"source":["## notebook settings\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"The autoreload extension is already loaded. To reload it, use:\n %reload_ext autoreload\nPython exe\n/home/leon/mambaforge/envs/dual_data/bin/python"}],"source":["%load_ext autoreload\n%autoreload 2\n%reload_ext autoreload\n\n%run ../notebooks/setup.py\n%matplotlib inline\n%config InlineBackend.figure_format = 'png'"]},{"cell_type":"markdown","metadata":{},"source":["## Continuous Bump Attractor Model\n\n"]},{"cell_type":"markdown","metadata":{},"source":["### imports\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import sys\nsys.path.insert(0, '/home/leon/tmp/rnn_numba') # put here the path to the repo\nfrom src.model.rate_model import Network"]},{"cell_type":"markdown","metadata":{},"source":["### Single trial\n\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Simulation\n\n"]},{"cell_type":"markdown","metadata":{},"source":["To run a simulation, first we need to define a network model.\nThe class Network takes three mandatory arguments:\n\n1. The name of the configuration file that defines the model,\n eg 'configbump.yml', these files are in ../conf/ and well detailed.\n\n2. The name of the output file that will contain the simulation data.\n eg 'bump'. Simulation results will be saved as a data frame to '../data/simul/bump.h5'.\n\n3. The path to the root of this repository.\n\nOne can also pass extra arguments to Network, basically any parameter that is in the config file so that it will be overwritten.\n\nHere is an example:\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\nSaving data to /home/leon/tmp/rnn_numba/data/simul/bump.h5\nJab [[-2.75]]\nTuning, KAPPA [0.4]\nAsymmetry, SIGMA [0.]\nIext [14.]"}],"source":["REPO_ROOT = \"/home/leon/tmp/rnn_numba\"\nmodel = Network('config_bump.yml', 'bump', REPO_ROOT, VERBOSE=1, NUM_THREADS=116)\n# Here for example, we are adding two extra parameters"]},{"cell_type":"markdown","metadata":{},"source":["Then one just runs the model with\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"#+begin_example\n Generating matrix Cij\n all to all connectivity\n with cosine structure\n Saving matrix to /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Parameters:\n N 1000 Na [1000] K 1.0 Ka [1.]\n Iext [14.] Jab [-2.75]\n Tuning, KAPPA [0.4]\n Asymmetry, SIGMA [0.]\n MF Rates: [5.09090909]\n Transfert Func Sigmoid\n Running simulation\n times (s) 0.5 rates (Hz) [2.18]\n times (s) 1.0 rates (Hz) [2.17]\n STIM ON\n times (s) 1.5 rates (Hz) [6.25]\n STIM OFF\n times (s) 2.0 rates (Hz) [5.87]\n times (s) 2.5 rates (Hz) [5.85]\n times (s) 3.0 rates (Hz) [5.87]\n times (s) 3.5 rates (Hz) [5.88]\n times (s) 4.0 rates (Hz) [5.87]\n saving data to /home/leon/tmp/rnn_numba/data/simul/bump.h5\n Elapsed (with compilation) = 7.382569322013296s\n#+end_example"}],"source":["model.run()"]},{"cell_type":"markdown","metadata":{},"source":["#### Analysis\n\n"]},{"cell_type":"markdown","metadata":{},"source":["Most of the code for the analysis of the results of the simulations comes from\n../src/analysis/plotutils.py and ../src/analysis/decode.py But here we will\nreimplement some of it.\n\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Imports\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pandas as pd\nfrom src.analysis.decode import decode_bump, circcvl"]},{"cell_type":"markdown","metadata":{},"source":["##### Load data\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"rates ff h_E neurons time\n0 1.750975 6.669991 -6.010844 0 0.499\n1 2.879797 1.936005 -6.011033 1 0.499\n2 3.251747 -0.653561 -6.011222 2 0.499\n3 0.755492 -1.949484 -6.011411 3 0.499\n4 1.402129 3.749234 -6.011600 4 0.499"}],"source":["df = pd.read_hdf(REPO_ROOT + \"/data/simul/bump.h5\", mode=\"r\") \nprint(df.head())"]},{"cell_type":"markdown","metadata":{},"source":["The dataframe from a simulation contains 5 cols corresponding to\nthe rates of the neurons, the total feedforward input (ff), the net recurrent input (hE), the neuron id, and the time step.\n\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Rates\n\n"]},{"cell_type":"markdown","metadata":{},"source":["###### Raster plot\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots()\npt = pd.pivot_table(df, values=\"rates\", index=[\"neurons\"], columns=\"time\")\n\nsns.heatmap(pt, cmap=\"jet\", ax=ax, vmax=15, vmin=0)\nax.set_yticks([0, 500, 1000], [0, 500, 1000])\nax.set_xticks([0, 2, 4, 6, 8], [0, 1, 2, 3, 4])\nax.set_title('Excitatory')\n\nplt.show()"]},{"cell_type":"markdown","metadata":{},"source":["###### Rates Distribution\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["mean_df = df.groupby(\"neurons\").mean()\nmean_df[mean_df.rates<.01] = np.nan\n\nsns.histplot(mean_df, x=mean_df.rates, kde=True, color='r')\nplt.xlabel(\"Rates (au)\")\nplt.show()"]},{"cell_type":"markdown","metadata":{},"source":["##### Tuning\n\n"]},{"cell_type":"markdown","metadata":{},"source":["###### Fourier moments and phase\n\n"]},{"cell_type":"markdown","metadata":{},"source":["Here we use the functions from ../src/analysis/decode.py to decode the location and amplitude of the bump.\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"time m0 m1 phase\n0 0.499 2.181534 0.032431 2.262011\n1 0.999 2.167945 0.083064 2.123314\n2 1.499 6.249701 7.102196 3.164585\n3 1.999 5.867596 5.310970 3.190126\n4 2.499 5.853998 5.470547 3.245525"}],"source":["data = df.groupby(['time'])['rates'].apply(decode_bump).reset_index()\ndata[['m0', 'm1', 'phase']] = pd.DataFrame(data['rates'].tolist(), index=data.index)\ndata = data.drop(columns=['rates'])\n\nprint(data.head())"]},{"cell_type":"markdown","metadata":{},"source":["This new dataframe contains 4 cols: the time step, the mean activity (m0), the amplitude of the bump (m1, this is the first fourier moment of the population vec)\nand the location or phase of the center of the bump.\n\nWe can look at the time course of these observables\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(1, 3, figsize=[2*width, height])\n\nsns.lineplot(data=data, x='time', y='m0', legend=False, lw=2, ax=ax[0])\nax[0].set_xlabel('Time (s)')\nax[0].set_ylabel('$\\mathcal{F}_0 (Hz)$')\nax[1].set_xticks([0, 1, 2, 3, 4])\n\nsns.lineplot(x=data['time'], y=data['m1']/data['m0'], legend=False, lw=2, ax=ax[1])\nax[1].set_xlabel('Time (s)')\nax[1].set_ylabel('$\\mathcal{F}_1 / \\mathcal{F}_0$')\nax[1].set_xticks([0, 1, 2, 3, 4])\n\nsns.lineplot(x=data['time'], y=data['phase']*180/np.pi, legend=False, lw=2, ax=ax[2])\nax[2].set_xlabel('Time (s)')\nax[2].set_ylabel('$\\phi$ (°)')\nax[2].set_xticks([0, 1, 2, 3, 4])\nax[2].set_yticks([0, 90, 180, 270, 360])\nplt.show()"]},{"cell_type":"markdown","metadata":{},"source":["###### Spatial profile\n\n"]},{"cell_type":"markdown","metadata":{},"source":["We can alternatively look at the shape of the bump at different epochs, using circcvl from ../src/analysis/decode.py\nHere, during stimulation and during the delay period:\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["# Stimulus presentation\ndf_stim = df[df.time < 1.5]\ndf_stim = df_stim[df_stim.time >= 1]\n\nmean_stim = df_stim.groupby(\"neurons\").mean()\narray = mean_stim[[\"rates\"]].to_numpy()\n\nX_stim = circcvl(array[:, 0], windowSize=10)\nm0, m1, phase = decode_bump(X_stim)\n\nX_stim = np.roll(X_stim, int((phase / 2.0 / np.pi - 0.5) * X_stim.shape[0]))"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["df_delay = df[df.time >= 1.5]\n\nmean_delay = df_delay.groupby(\"neurons\").mean()\narray = mean_delay[[\"rates\"]].to_numpy()\n\nX_delay = circcvl(array[:, 0], windowSize=10)\nm0, m1, phase = decode_bump(X_delay)\n\nX_delay = np.roll(X_delay, int((phase / 2.0 / np.pi - 0.5) * X_delay.shape[0]))"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["theta = np.linspace(-180, 180, X_stim.shape[0])\nfig, ax = plt.subplots(1, 2, figsize=[2*width, height])\n\nax[0].plot(theta, X_stim)\nax[0].set_xlabel(\"Prefered Location (°)\")\nax[0].set_ylabel(\"Rate (Hz)\")\nax[0].set_xticks([-180, -90, 0, 90, 180])\nax[0].set_title('Stimulation')\nax[0].set_ylim([0, 15])\n\nax[1].plot(theta, X_delay)\nax[1].set_xlabel(\"Prefered Location (°)\")\nax[1].set_ylabel(\"Rate (Hz)\") \nax[1].set_xticks([-180, -90, 0, 90, 180])\nax[1].set_title('Delay')\nax[1].set_ylim([0, 15])\nplt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Multiple trials\n\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Simulations\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"#+begin_example\n ##########################################\n trial 25\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_25.h5\n Generating matrix Cij\n Saving matrix to /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.199274960090406s\n ##########################################\n trial 26\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_26.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.282144900993444s\n ##########################################\n trial 27\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_27.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.1860768320038915s\n ##########################################\n trial 28\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_28.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.1425280519761145s\n ##########################################\n trial 29\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_29.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.261533895973116s\n ##########################################\n trial 30\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_30.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.245834131957963s\n ##########################################\n trial 31\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_31.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.186810823041014s\n ##########################################\n trial 32\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_32.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.218402197002433s\n ##########################################\n trial 33\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_33.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.241861543036066s\n ##########################################\n trial 34\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_34.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.160159121034667s\n ##########################################\n trial 35\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_35.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.191513100988232s\n ##########################################\n trial 36\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_36.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.1625350209651515s\n ##########################################\n trial 37\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_37.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.261842316947877s\n ##########################################\n trial 38\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_38.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.175270576030016s\n ##########################################\n trial 39\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_39.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.177886882913299s\n ##########################################\n trial 40\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_40.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.220661935978569s\n ##########################################\n trial 41\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_41.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.259679448092356s\n ##########################################\n trial 42\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_42.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.217046734993346s\n ##########################################\n trial 43\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_43.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.18391912302468s\n ##########################################\n trial 44\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_44.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.2021880720276386s\n ##########################################\n trial 45\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_45.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.22361149196513s\n ##########################################\n trial 46\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_46.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.195027380017564s\n ##########################################\n trial 47\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_47.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.219246768974699s\n ##########################################\n trial 48\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_48.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.189989945967682s\n ##########################################\n trial 49\n ##########################################\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_49.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.227059144992381s\n#+end_example"}],"source":["ini_list = np.arange(25, 50)\n\nREPO_ROOT = \"/home/leon/tmp/rnn_numba\"\n\nIF_LOAD_MAT = 0\nIF_SAVE_MAT = 1\n\nfor ini in ini_list:\n print('##########################################')\n print(\"trial\", ini)\n print('##########################################')\n\n model = Network('config_bump.yml', 'bump_ini_%d' % ini, REPO_ROOT,\n IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT)\n\n model.run()\n\n IF_LOAD_MAT = 1\n IF_SAVE_MAT = 0"]},{"cell_type":"markdown","metadata":{},"source":["#### Analysis\n\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Imports\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pandas as pd\nfrom src.analysis.decode import decode_bump"]},{"cell_type":"markdown","metadata":{},"source":["##### Load data\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"rates ff h_E neurons time trial\n0 3.048436 -3.519403 -5.976190 0 0.499 0\n1 2.469401 -0.738135 -5.975781 1 0.499 0\n2 1.352286 3.643990 -5.975371 2 0.499 0\n3 1.230724 -6.516477 -5.974960 3 0.499 0\n4 1.657853 0.255504 -5.974548 4 0.499 0"}],"source":["ini_list = np.arange(0, 50)\n\ndf_list = []\n\nfor ini in ini_list:\n df_i = pd.read_hdf(REPO_ROOT + \"/data/simul/bump_ini_%d.h5\" % ini, mode=\"r\")\n df_i['trial'] = ini\n df_list.append(df_i)\n\ndf = pd.concat(df_list, ignore_index=True)\nprint(df.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"time trial m0 m1 phase\n0 0.499 0 2.152970 0.053400 4.523900\n1 0.499 1 2.181172 0.077984 3.345704\n2 0.499 2 2.188593 0.065588 1.236631\n3 0.499 3 2.171368 0.053285 0.990382\n4 0.499 4 2.170371 0.002783 4.552415"}],"source":["data = df.groupby(['time', 'trial'])['rates'].apply(decode_bump).reset_index()\ndata[['m0', 'm1', 'phase']] = pd.DataFrame(data['rates'].tolist(), index=data.index)\ndata = data.drop(columns=['rates'])\nprint(data.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"time trial m0 m1 phase\n350 3.999 0 5.906027 5.449286 2.892502\n351 3.999 1 5.891126 5.420843 3.135631\n352 3.999 2 5.874590 5.361630 3.339823\n353 3.999 3 5.891533 5.479044 2.938857\n354 3.999 4 5.886632 5.519670 3.141416"}],"source":["end_point = data[data.time == data.time.iloc[-1]]\nprint(end_point.head())"]},{"cell_type":"markdown","metadata":{},"source":["##### Phases\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABHsAAAFACAYAAAAs1HAnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5xU1dnA8d+dvr13OktHBKQqgoCIiKJiD7ao8TUajZpEE6MhmkRNYhIVNFGjKLGBDQUVFBWkN+kLyy7be9+dPnPL+8edvdtYmiCynO/ns+wt59577swus/PMc54jaZqmIQiCIAiCIAiCIAiCIHQJplPdAUEQBEEQBEEQBEEQBOHEEcEeQRAEQRAEQRAEQRCELkQEewRBEARBEARBEARBELoQEewRBEEQBEEQBEEQBEHoQkSwRxAEQRAEQRAEQRAEoQsRwR5BEARBEARBEARBEIQuRAR7BEEQBEEQBEEQBEEQuhAR7BEEQRAEQRAEQRAEQehCLKe6A8IPx+l0kp2dbawPGDCAqKioU9gjQRAEQRCEQxN/twiCIAjC8Tsjgj2VlZX873//Y/Xq1ZSUlACQkpLChAkTuOaaaxgwYMARz5GTk8O7777L+vXrqaioQFVVMjIyOP/887n11ltJS0s77PGqqvLRRx+xZMkSsrOz8Xg8JCUlMXLkSK6//npGjx59Qu71cLKzs5kzZ46x/tZbbzFq1KiTfl1BEARBEIRjJf5uEQRBEITj1+WDPStXruThhx/G5XK12Z6fn09+fj7vvPMOd911F/fee2+n53jxxRd54YUXkGW5zfaDBw9y8OBB3n//ff75z38yadKkQx7vdDq5++672bx5c5vtZWVllJWV8emnn3Lrrbfy29/+9jjvUhAEQRAEQRAEQRAEQdelgz3bt2/n/vvvJxgMYjabufbaa5k4cSKRkZFkZWXxyiuvUFNTw/z584mIiOC2227rcI758+czb948AOLi4rjtttsYMWIEsiyzfPlyFi9ejMvl4r777uPDDz+kb9++bY7XNI3777/fCPRMmDCBG264gcTERPbt28crr7xCaWkpCxYsID4+njvvvPPkPzCCIAiCIAiCIAiCIHRZXTrY88QTTxAMBgF4/vnnufDCC419Y8aM4bLLLuPyyy+nurqaefPmcdVVVxETE2O02bdvH//+978ByMjI4I033qB79+7G/vHjxzN48GD+8Ic/4PP5eO6553j++efb9GHp0qWsXbsWgNmzZ/PUU08Z+4YPH86MGTOYM2cOubm5zJ8/n1mzZpGamnriHwxBEARBEARBEARBEM4IXXY2rj179pCVlQXA9OnT2wR6miUkJHD77bcD4PF4WLVqVZv9zz//PLIsI0kSzz77bJtAT7PrrruO/v37A/D111/j8/na7F+wYAEAkZGRPPzwwx2Oj42N5fHHHwfA7/ezcOHCY7xTQRAEQRAEQRAEQRCEFl022BMIBLjwwgvp0aMH06ZN67Rdnz59jOXy8nJjub6+njVr1gB6sGjYsGGdnuP222/n2muv5bbbbsPj8Rjbi4uLjYDT5MmTiY2NPeTxo0aNonfv3gAsX778yDcnCIIgCIIgCIIgCILQiS47jGvkyJGMHDnyiO1KS0uN5eTkZGN5/fr1xhCwSy+99LDnuOKKK7jiiis6bN+2bZuxPG7cuMOeY8yYMeTn51NaWkpRURE9evQ4Yt8FQRAEQRAEQRAEQRDa67KZPUejrq6O1157DYDw8HAmT55s7Nu/f7+x3DqrR1VVKisrycvLw+12H/b8ubm5xnKvXr0O27b1ELGcnJyj6r8gCIIgCIIgCIIgCEJ7XTazpzN+v5+SkhK++uorFi5cSHV1NZIk8dhjjxEXF2e0aw64WK1WkpOTqampYd68eXz++ec0NjYCYDabGT16NPfeey+jRo3qcK2KigpjOT09/bD9SktLO+RxgiAIgiAIgiAIgiAIx+KMCvbs3r2bq6++us221NRU/vjHP7bJ6gFoaGgA9MLKO3bs4K677jK2NVMUhY0bN7Jp0yYeeuihDlO3NweFACIiIg7bt/DwcGPZ6XR22q6srIyysrLDnqsz2dnZx3WccHJomkZ1eQ05e/LI2ZPHwX35pPdK446HbsJqPaN+NQVBEARBEARBEIQT6Ix6R3moIEl1dTWLFi0iKSmJoUOHGtubh2j5/X7uuusuGhsbuemmm7j++uvp0aMHdXV1fP755zz//PN4PB7++te/kpqayiWXXGKcIxAIGMsOh+OwfWu9v/Vx7X3wwQfMnz//yDcr/Og01TvJ2ZtH7t58cvYeJGdPHk31emAvqMg0+p1IayQiosO5+d7rTnFvBUEQBEEQBEEQhNPVGRXs6dWrFy+99BLx8fFUVVXx6aef8tlnn/HNN9+wceNG5s2bx/nnnw+A1+sF9CnZPR4Pf/7zn7nmmmuMc6WmpvLTn/6UESNGcOONNxIMBnn66aeZOnUqdrsd0Id5NZMk6bB90zTNWDaZzuhSSkdNlWUCjU7scTFIP7LHzO/1k5ddaGTt5OzNo7Kk6pBtNU3DGXCjoaFpGm/99wOuvHkmUTGRP3CvBUEQBEEQBEEQhK7gjAr2DBgwgAEDBhjrF154IRMmTOCRRx7B6/Xy61//mq+++orIyMg2mTbjx49vE+hpbfjw4Vx99dW88847VFZWsn79emNIWOuhWT6fD5vN1mnf/H6/sXy4dkKL1f/+kJ0bDjBu2tmM++llp6wfiqxQnFdqBHVy9uRRdLAEVVEPe1x4mJ3k2Cg8Xjc1ZfXGdrfPwyt/W8iDf7n7ZHddEARBEARBOAOpmobbLwMQYbdgOsIH04IgnH7OqGDPoVx11VWsXr2aFStW0NDQwIoVK7jqqquIjGzJqrjooosOe44pU6bwzjvvALBjxw4j2NO6To/X6yU6OrrTc3g8HmM5JibmsP0dP3784W+qE9nZ2TzxxBPHdeyP0ZovdhOUFT5/fyODZ4wjOjXppF9T0zSqymrIDQV1mmvt+H2dD70DsNmtZGQkkRwVSQwSSZHhRIXZ8csy81eu6tD+0yUrufGea0lOTzxJdyIIgiAIgiCcqdx+mf+uyQfgjvN7E+WwnuIeCYJwop3xwR7QgzkrVqwAYN++fQAkJbUEDlJTUw97fOuZturrWzI0MjIyjOXy8nJSUlI6PUd5ebmxfLh26enpR5zZ60xhd9gIuryoqsaKfy/lmsdvO/JBx6ilzk5LcKepofMC2gAmk0T3Phl075ZCUmQYEQGZcEXDfIihZt9m5+AJ1WgalJFKfmUtPjmIXw7w/OMv8eeXfn/C70kQBEEQBEEQBEHo2rpssMfpdFJUVERJSQkXXXTRYWvmxMbGGsvBYBDQh3x98cUXQNtZtQ6ldUHl1tk7/fr1M5aLiooYPnx4p+coLi42ljMzMw97PUE3YPwAvvx4LVF2B7u35DK5uIrE7snHfT6/18/B/QXk7Mkjd08eOVn5ndbZaS05PZHMIX3o1SuNhDAHYd4A7pJqVFkBvwJIYGr5+bPYzETE2nEpAbbkFwBgNZu5avwIvjtQzGe79gCw9tst5O0voM/AXsd9T4IgCIIgCIIgCMKZp8sGe5544gk++eQTAJYsWcKgQYM6bVtUVGQsN2fxtA7MbNu2jSuvvLLT43Nycozlbt26GcvDhw9HkiQ0TWPr1q3MmjWr03Ns3rwZgLS0tDbnEDr3xerNHKytwCRJpEXFsfS5D/jpMz8/qmMVWaHoYKmesXMMdXaiYiPpN6QP/Yb2oc+AniSEOfCX11KzvwhfQRUy0CHvR4KwKDsRsXYiYhzYwixIksSbb36BouqFuSef1Z+EmAjOHdiHNQdycfp8yKrMM4++yIvv/+3YHxxBEARBEARBEAThjNVlgz2jR482gj3vv/8+jz322CHbqarK+++/b6xPmDAB0IsyJyUlUV1dzeeff879999PYuKh66d89NFHgD771pQpU4ztaWlpDB8+nO3bt7NixQoeeuihNrWAmm3dupX8fH3M7PTp04/jbs9MAade50jVNEqb6vjgi2+xPhfPDfdcjdnSMhNac52dnD0HjSLKB/cVEDhSnR2Hjb6DetFvSB8yh/Qhc0hvomxWarOLqckuou6LrTR0Ehxqzt6JiHEQHmPHZNaHcFmjowhPS2VvURXbswoAiI0MZ8owvXB4eKSVS4YNYdHmbQDs3bOf79btZOR5Z3+vx0oQBEEQBEEQBEE4c3TZYM8ll1zCP//5T+rr61m0aBEXXnhhh8LGmqbx5JNPsnfvXgDOO+88zjrrLEAP3Nx+++08/fTTuFwufv3rX/PCCy+0KboM8MYbb7BhwwYApk2bRnJy22FEN910E9u3b6ehoYG5c+fy97//vc3U6o2NjcydOxcAq9XKjTfeeGIfiC7s0gvG89mX6ymsrwUgqCr875UP+GblBqZecj4SkLs3n5y9eTgbXIc9l8kk0aNvNzKH6lk7/Yb0pUffDDRVpS63lJrsInLe/BJffSf1ejrJ3sFkwpGUQHhaKuGpKVgiwpGDMv95eF7LfZwzFJvFTNBkQ1IVzu6bwdf7DlDtdKJoKv/8439YuOKFNj83giAIgiAIgiAIgtCZLhvsiYyM5PHHH+f+++8nGAxy2223cc011zBp0iQSExPJz8/n3XffZfv27YA+fOvJJ59sc45bbrmFVatWsXHjRjZs2MCVV17JLbfcwqBBg3A6nXzyyScsW7YMgPj4eP7whz906MfMmTP58MMPWbt2LcuWLaOiooKbb76ZlJQUsrOzeemllygtLQXg3nvvpXv37if5kek6Bk88m+K9RUTbw8irrcYd1KevL8kv440XFmGSTNjMFkxSxyBJSkaSnrETCu70GdgLR5gdTdNwVzVQk13E9m93Up9XhnaM2Tsmu43w1BTC0lIIS07GZG37a7bk3c8pyNWHDvZKTWRk3+64nAG8oRnZIqNtXDbiLF77dj0ARcUlfPXxt0y78oIT8bAJgiAIgiAIgiAIXVyXDfaAPiTq73//O4899hgej4dFixaxaNGiDu2GDh3Kc88912HWLZPJxEsvvcTDDz/M8uXLKSwsPOTU5b169eKFF14gISHhkP147rnnuOuuu9iyZQtbt25l69atHdrceuut3Hnnncd5p2emPXmFfHMgG73qjYTdbCOoBlE1fYuqqfjkAA67nbNGDmLoOQPpN6QvmUN6ExPfUkhb9gepyy0hL7uImuziw2bvhEfZCW+fvUPL8KywtBTs8XGdFgRvrG/i1XlvGetXjBmGomh4vbKxzeOR6d89mR4J8RTV1qFqGv/+2wIumHkeVpuYFlMQBEEQBEEQBEE4vC4d7AG49NJLGT16NG+99RbffvstRUVFBAIB4uLiGDZsGDNnzuTiiy/udIiMw+HgueeeY/369XzwwQd899131NTUEBUVRc+ePbn00ku54oorOgzvai0yMpKFCxeyZMkSPvnkE/bv34/T6SQuLo4RI0YwZ84cxo0bd7Iegi7r60/WhAI9OrPJhFmyYzZJBCUFn1+vyePz+9mxbS+9B/dk4PB+hEeG4aqso2Z/ETXZRdTnlx8he8dBRKyd8OiW7B1jeFaqHuCxRoQfVZ9fm/82zkZ9SNnoAb3pkRhHQ72P1jeiyip+v8Ll5wxj3herAKiuq+OD15dx/Z2dFwoXBEEQBEEQBEEQBDgDgj0AKSkpPPjggzz44IPHfY5zzz2Xc88997iPN5lMzJ49m9mzZx/3OYS2Lp8+nBWfb8PXFCDMaiPcakNVAUnCYrPQf8Zw3l+4FFeTm2AgyOJXP+bTd1Zw3qC+DE5KxHyIAJ/UqvZOeKwDm6Mle+dIw7OOJC+nkCXvfAaA3WblkhGDCfgVggE90OSIjUT2+pH9QXxemfTEWAanp5FVVo6GxsJ/L+KyGy4iIqrzwKIgCIIgCIIgCIIgnBHBHqFrcvgl+sckokZJuL16Fo9kk6hv9CIHZKwNAZ547E4+fGc5G3bsR1E13B4fX2zby+bwMM4f0Id+qUnYHBbCYw6RvcPRD886Ek3TmP/0f1FCGURTzx5ITLiD2hqv0WbwFRPw1jvZ9/E6QB/ONXPkUPaXV6BqGk0eFwuefYdfPHbH8T5kgiAIgiAIgiAIwhlABHuE09a6tQdoaPIQFWbDYdZr2UgyWMwmZEVl5+pdmEsrGZuaysCJMaw9kM/+skoAGjxelm7fS++MBK65+BxSesTqJzWZcCQmGAGeox2edSTrV21h81q9GHhifAyTBmficQdRFX38VmL/bqSe1QdN1SjZvB9neS1yUCEmMoxzevVkS34BAB8vWs71P7uSxNRD14cSBEEQBEEQBEEQBDGXs3DaUk16lo3TGyC0iKpqpMS1DHPKq2hA0zQS46P4yUWjuf+GKfTrmWzszy+t5W+vfsGrS7cid+9Jj0svJvX88URn9j5hgZ5gIMi8p/9rrF86YhBmJNzuIACSycTgK89HkiRMZhNDr5potPV4ZKafPQiryQyAN+jjxacWnJB+CYIgCIIgCIIgCF2TCPYIp63U1HhjWdZaCiyH2SzYrXpwpMHlx5booNewZFJ6xzJoYDq/+uk07rt9BunpicYxm7fu55d3Ps1Lz/yPxvqmE9rP999cSklBGQD9+3RjWM8MXM6AUZS51/lnEZXSci/xfdLpNnogAJqqYTGbOX9AprF/1ZfrKMwpPqF9FARBEARBEARBELoOEewRTls2k0aTz4esqniDLVOX+wIKo8/pbqxv216CBjiSk4g/eyjdZkxj5gM/5b+fzePex+4gNj4GAFlWWPLW5/z0kvtY/NrHBEKzeX0f9bUNvP7CuwBIksRlZw0gGNBn2wKwR4XR76LRAHgrq/HXNQAw8NLxWMLsAPh9ChOHZBJm1YeqBZQg/5r70vfumyAIgiAIgiAIgtA1iWCPcNraU1KJ0++jxuXE6fUb213uAOlpUSQl6MO5Ght9VFoSQ8Oz+hjDs8wWM5dedxELPn+en9x1FXaHDQC308Or/3yL2y+9n6+WfouqHnpa9qPxynNv4nZ5AJgwcjAZCbE4m1r6OmDmeKxhdur3HaBk5bcULf8aT3kl9qhwBlwy1mgnBzWmDR1orO/YvpvdW7KOu1+CIAiCIAiCIAhC1yWCPcJpKz7aAYCiaTR4vSiaYuyrrvFyzogMY33VO9/gdXk7nAMgPCKMW35xHa99+jzTr5xszLhVVV7D3343n3uv/x07Nu055v7l7M9j2XtfABAW7mDagN54PTJKqChzbM8Uuo0aiLeqhvLNO9m5o4x9WZVUbNiGEgzSc/wQYrol6fcoa4zs25OYsDAAZFXhn3/49/cKRAmCIAiCIAiCIAhdkwj2CKeta2+/GIddn1DOJwepdLqMfeUVbhITIujVIw4AT5OHr/67DE95OZ7ycrwVFXgrK/FWVeGtqsJXXU2kWeXu+6/luVd/z8ixQ4xz5Wbl8/DtT/D7n/2J3B37CDqdBF0ugm43stuN7PEge73IXi+Kz4fi9yP7/Tz/55eNYMwl44cTYbPjdoWGhkkwZPZEFL+fsjUb2bi+kKysKnZsL2PPd4XUfLcLyWRi6FWTIFR8OuBTmDliqNGvvPxC1izfcDIfYkEQBEEQBEEQBOE0JKZeF05bEVaVa68ezcK39IBHldNFrCOMcJuNmopGNC2FEcPSKS5tQFE0tqzcQd8MOzGxYYc9bxRw7/XnsndETxYv3URRaS0AWzfsZtvGPUwcO4ArLj6H2JjOZ+vauD2H7Vv0bKDk+GjGdU/D5QqghYoyJ2RE4963g9pGD3u2l1FS0mgcu2tnOWlp+4jsnkFsz1R6jB1M0cYsNA0yk5NJjY6moqkJRVOZ/+SrnDttLFar+FUWBEEQBEEQBEEQdOIdonDaqsmrorGwkbjIMOpDQ7Ty6+roFhODJEls3VVGmMNMbJyDsgonBDWWLtnB4LPS25xHa7Oitdl+6aQRZOeXs2lnLi6PH03TWL1xP+u25jB8YA9GDOyJ1Wppcw5ZVnjt41XG+ogePdiXV4PHo0+1bjJJeKNMFH/bSEOth4MHa0EDVVMxSybCbDY2bCgiLmUrvWdNZ8DM8ZTvziPo9hEMqlw68iz+u2odABXV1Sx7azlX3nrpCXxkBUEQBEEQBEEQhNOZCPYIp60lS7ZSWt6ApmmYJAlV0wgqCqWNjYRZbdR+V9DhmKKSOopK6o75WimOeOy4qPe6UDUNWVbYuief7XsLSQiPItoebtT6qXTV0eTWizJH2sKoKPdSWeFrc76souo2666Alz2VB7GYLIztrhdi3rm5gKhuu0gZdw4DZ45n9+JvAEiNjqFPUiJ51TVoaLw2722mXz2V8MjDZywJgiAIgiAIgiAIZwZRs0c4bZnt+tTkkiQRbrUZ2wOKQlBROjvsuJgkifiwKHrGJhPjiDC2K5pKlbuRosZq3AEfATlIpbve2J8RlWgEgQ6npLEKVdMIKEG2l+USVGSysirJ27gXd2k53ccMIrZXqn5NReOSVrV7GlxNvP3vD07g3QqCIAiCIAiCIAinM5HZI5y2bnt0Dl//+0MURSW3qJ7y2kacfn1ac58cJDEigrEXDUeprUHTNHZnV+N26hk2o2aMpsegHtAqENM6JNMmQNO6TWixpqqOL5Z9S9buAwAEFJkyZx22cCtqaCjY6OGDGZeairMpYIwVy5w2Cn9lOfu2F1JVpReUtkbYqC9yGtdwBbzsqihgZHpfNmwoJCFtC32vuJihV01k7T/fA00jyhbGsG4Z7CopRQPeW/gxs2+ZSXxy3Il4aAVBEARBEARBEITTmAj2CKethMzuDBrSg0CjC6vJjMvlR1ZVvMEgqqZR63EjuQOcNawHis9PfGwEX63KBaBwex5X3H05NoftCFfp3Iw5F7Hnu/288sz/2L8rB0VVqG/Uh29ZrRamDuxNuGIi0qxnIKUNz6Rb/0S25hykqc6Pw2LFarfiSTShta0cRHFjFckRsXSXEtm2IZ/o7jtJPXc0vc4bSsHa3aDB1KED2VNahqppuANeXvn7Qh7++y+P+34EQRAEQRAEQRCErkEM4xJOW5IkkTCoDwDd06OxmE3EhoVhNuk/1u5AgPUb9hLVtwcAaSlR9BqgF2duqmlk/Udrv3cfho4cyLNv/ZlHnrm/TehUUiRe/Gg1K3ccwB+UMdssdB/Rm4PrdvHdtlKj3cU/n8mqbzca69H2SGN5d2Ue7oCPnJwaDqzbg6ukjP4zxmKP0mvzOMw2xvbpbbT/4tPVFOe1nFsQBEEQBEEQBEE4M4lgj3Daqi2qJC+7nIBfxmI2kxgXgUkyERfWMiV6SV0De3bkGeOvzh6QiCkUDFqz+FucdU3fux+SJBGQg/hCQ8jMZjMWkwVFVdlWUMwbazdRQJCSzdtZu7YAVdWzeMZecS57d+zH7ddnEnNY7AzrP5AwqwPQh4Z9V5qLoqps2lRM8bdbMJkkBl52nnHt8/r1xWY2A+BXAsx74r/f+34EQRAEQRAEQRCE05sI9ginrb/e+0/++uybLFu/H03TSE/Us2LsFguJUS0ZMov+9zWmxAQAosLMDDtPn+0q4Avw1cKV37sfPq+Pf/99gbF+z61Xcu7A3phCASZfUOajZd/ypxeXUVxTh6Zp9Bzai3OvmsB77yw1jhvYP5PHX/sdPRPSMEn6r2att4kDNaV4PAE2rz1I9dYdZJzTn/i+eoaS1Wxh4sB+xjm2bNpO1vbs731PgiAIgiAIgiAIwulLBHuE01ZVg56Vs6e4jKoaFz0yYo0CymFmC5E2vR6P1x/kwyUb0UKFkwf3icURoWfPfPfFNsoPln2vfrz96odUVdQAMObc4fSWJMb27sXNE8YwIDXZaOf2+8muKSOrtpQhM89h7XurKG2oBUBC4u7f3kZcYiy//usvSIlMNI7LqS2hxuOksLCevat34S4pY+hVE5FCGUojuvcgInSvQVXm2T/8x7hXQRAEQRAEQRAE4cwjgj3CaWvkucMAUDWNbftLsVpNxEeHh7ZBWkyMUb9nz64CdhbUAWDyeTl35mgANE1j+SufHXdwpLK8mrde0ac9N1vMXDVxNK4mP6qqER3m4PpLJ3LjRWOItocZxzS43fz+7id5/oWFqJoKQFpSMudMOFu/r4nDmX3DJcTYo4z721GWi1+W2bKlhIJVmwiPjaT3RP3+LWYzFw4ZZJz/QE4e61duOa77EQRBEARBELq+gKzS6A2KDwgFoQsTwR7htDXrtpnGck5FFS5ngPSkluFbkiTRIzbWWF++cheVtfoU530zIohLjQcgb2ce2Zv2H1cf/vPM6/h9eq2ey2dfiKmiDo87qF/fJIHmoyi3iaEp3RmUlEFScoJxbJ27pV7QFdfPQJIk4wX3lt/cwLCBA7GZrQC4gz52VeTh98tsWJ1L5Zbt9Js+GkdMBACD0tKIC9cDXbKmMP/PryAH5eO6J0EQBEEQBKHr+nhHKec9/TWvry/gfxsLKar1nOouCYJwEohgj3Da6tWvBz17ZwBQ7/FyoKCGHhkxhEZy4Zdloh0OEiP0gIgsK3y0ci+BoIK3rJKpP5lsnGv5fz9HkZVjuv7u7/bx5bLVAMTERjNlYB9cTX5jf1RyBBs3FqIoGpIkMeXyiby+Yh6333s9EhpBVQ/GmCUzS976nKnDruT6KbdRUVKBxWLh9y/+mm6xqcb9lDbVUNRQTXm5k51f78RbUcXgyycAemDroqGDjWuXVlay4r2vj+l+BEEQBEEQhK5tT2kjDy7eiU/Ws8vrPUF+/d5OFFVk+AhCVyOCPcJpbcYN04zl/aWVqArEROr1eGRFRVZV0mNiCLfpGTI1dS6+XHcAgNQYMz2H9ASgtrSGLZ9uOurrqqrKc3952Vi/6ZbLceWVE/DrASOzzcz+A5W4XAEA0jLTmfHzS7FYLVgbgpha/eY5rHYanE5cHi8FxeXcc/2v8bi9JKcn8uBffk5CeJzRdk9lAU0+L9u/KyPvqw0kDsggsX93AHonJpIeG6P3T1N55dn/4fX4jvqeBEEQBEEQhK5t3tc5HQI7+yqcrMmpPkU9EgThZBHBHuG0NmnGuVgs+tTjhXV1VFW6SEtsGcrllxVMkkSv+HjMZv3Hffu+UrIOVtKUU8D02y422n791ld4nd6juu6Kj79h/54cAPr068nQyAhcTQFjf73XR3m5PmQsLCqMa35/A1a7lbKcUnZ+u4tqV6PRdnjvHgTVoLFeUlHDr2/+Haqqcu70sVx2+TTCQ9OxB1WZ7eW5+P1B1n17kKpNOxg8+3xMZpOe3TNkiHGe2sYG3vvvx0f3QAqCIAiCIAhdWqMnyFf7qg6577Pd5T9wbwRBONlEsEc4bTWW1VC0ZjdjJo4AICAr5JbXkJIQYbQJqgqapmEzW+iX3DLD1aer9lFX20SkKcjZU4YD4HV6WfXuN0e8rsft5T//eMNYv+2nV1KTW4ai6J+SeBSZnNzQLFuSxOyHryU2ORaA1f9byYHqchRNzwDqHp9A75Q42tu2cz//+N2/ALjrj7cxpHd/zKHp2Ou9TvbXlFJd5WLrl9vB46LPZP0xSIuNITM5CQANjXde/ZD6moYj3pMgCIIgCILQtX21vxK5k+FaG/Jqf+DeCIJwsolgj3DaWvvKZ6x8cwUxrpaMmvyaWrxumegwfSpyWVFRVH1McpwjjP699anQ/QGZJSv3ULvnABfeMg2rXR/mtemTDdSW1hz2uv97+T1qq/WZvSZMHUtsXZNRlNkXkMktrDPaTrnlQvqOyNT7tuMgB7fnUtLYkiZ74dmD2JSTb6xHh7UEqt7/4Es+ePVDrDYrc196iLTolmncc2tLqXY1sXtXBdkr1tPrvKGExemzd104eJBR58fpc/P6v9450kMpCIIgCIIgdHFf7T90Vg9AcZ2XikYx/F8QupIzIthTU1PDvHnzuO666xgzZgxDhw5l3Lhx3HTTTbz++ut4PB0r0M+bN48BAwYc89dvf/vbQ/ZBVVU++OADbrrpJqMPkydP5le/+hVbtohpso/HeyvX8NGOHWzbd5CYaH3oVkVjEzX1bpLiwo12/lDhZVlVGdojjbhYfV9JZSNffb0La8DPebP1QseKrLDiteWdXrOsuIJFr30EgNVq4carLqImvwpNA0VVKahsJBjUg0sDxw/m3KvPB/Qp3lctXElZUwOugP7z5rBaSU+IpaBK/yQlc2Bv/v7fxwm3tUzT/szTr7Bl9VbSe6Xxyz/cSYxDD+hoaGyvyMXjD7D+21yqtm5n8BX6PcRFRDCsezfjHJ99vJLSQpGaKwiCIAiCcKbSNI0t+XWHbbO3rPGw+wVBOL10+WDPypUrufjii5k/fz47duygsbGRYDBIfX09mzdv5qmnnuLSSy9l3759J+R6Vqu1wzan08ktt9zCI488wubNm40+lJWVsWzZMm666SaefvrpE3L9M4nTr898lV9bTaJdD5BoQEFtHVFhDqNdQNGDPUFZpaHKzdWzRmIy6bkva7fls/XTNUy4ZiKRocyYfeuzyN+Vd8hrvvj3BQQCehbP1TfPwrs7D79PHypWVNmE26PvS8hI4PIHr0SS9Otkr8+iPLeUvNoK41xj+/chR2mp1XPZ1dMZPnYov/3zvcaU66qm8Zu7n6A4r5ipsycx/cJJxj5v0M/O8jzq671sXL6D8EgLyYN7ATBpQH/MoSrQ3qCff/95wfE8xIIgCIIgCEIXUFznpcrpb7MtzGpus36g0vVDdkkQhJOsSwd7Nm/ezP3334/T6cRqtTJnzhxeeeUV3nvvPZ5//nkmT9an3i4tLeW2226jvLwl++H6669nyZIlR/yaP3++EeBJSkrinnvuadMHTdO4//772bx5MwATJkzghRdeYNGiRfzxj38kIyMDTdNYsGABL7/8MsLRGzd5FKAHeIJyS9Akv7aWoF8hIjQ0S1b1WbkAgkEFu2Zm2gWDjPaLFq3BVV3Phbe0zOy1/JXPUEPHNNu+eTerVqwDID4xlkvGj6CurAGAqgYPdU166qvVYeW6x+ZgD9cDTqqi8M0bX1Dv8VDvbTLOd9tvbmPVuu8AsFgtXDxL/3mcPnsKt9xxLRaT/gLs9Qe4+/rf4Gpycf/f7qZ/Rm9jmFa5q5b8+ir276ti76drGDBjNCaLmQi7nbG9exvX2rBuK/t35hzjIywIgiAIgiB0BVsK2mb1xIdb6ZsU0WbbgUrnD9klQRBOsi4b7NE0jccff5xgMIjVauXVV1/lD3/4AxMnTmTYsGFMnz6d//znP9x3330A1NXV8cwzzxjHJyUlMWjQoMN+9e3blxdffJFgMIjJZOIf//gHqampbfqxdOlS1q5dC8Ds2bN59dVXufDCCxk+fDg33HADH374IZmZek2X+fPnU1FRgXB0rrjlUmO50tlEfLj+guX0+al1u4mNbMnu8csyAEFFpaKokYkT+tM3VL/H6fbz8uOvMXzqCFL7pAFQllvGzq93GMcritJmqvU77p1D6ZqdyLKK0xOgtKrlxfGKB68isXuSsb7x3W+or6inoL6aoKr3Y8CAPtQFgtTXNgAwceo4YuNjjGNu//WNTJ16HqZQUeaqugbuu+EhLFYLf3rldyRFJhhts6oKaPB62LAmj/q9++l74TkAjMvsg91i0e9fCfD83JfRtEMX5RMEQRAEQRC6rq2FbYM9I3rEkRBpb7NNBHsEoWvpssGeHTt2kJubC+hZOmPHjj1ku7vvvpv+/fsD8MUXXxyyfk9n5s+fT1ZWFgC33XbbIa+xYIE+fCYyMpKHH364w/7Y2Fgef/xxAPx+PwsXLjzq65/pMgf1plvPdABqPS7iw1vq9OTX1BJhsxnrAbl5KJdCXZWLoE/mmstHEO7Qs3+y9hTy5eKvmfGzS4xjVr7+BQGfXvz50w++JHe/Xki5/+C+DEtOpKnWTSCokFfWQHMI5dyrz2fQeS3Tn5du2sOGJetx+f1UuRuM7Tf+37UsfX+FsX7ZNdPb3JskSTz67K85a2B/I4tnb3Yef7n/aXr278E9v/4pEVZ96JqsKnxXmktDo5d1n35Hcq8EwhNjsFssTOzfzzjnvuwcNq/67qgfX0EQBEEQBKFr2FJQ32Z9RI9YEiJsbbblVbvFB4OC0IV02WBP66LHU6dO7bSdJEmcd955AAQCAfLyDl2rpb39+/fz6quvAtCjRw/uvffeDm2Ki4uNYNDkyZOJjY095LlGjRpF79CQm+XLOy8OLLQlSRIXXjbRWA8oQaNOTVFdPSYJwmx6Zous6rNyKaqGqmhUFjcSExPOVVeMNo5/94WPMIfbGDhOH+LVVNvE2g/W4HK6eflf/zPa/eLXt5C/ageKqpFX1oCs6MO9ep/dmyk3XwjomWUFX21m7f++JBBQKG9qwOV3AxAW5mDEqKGsX6UP7UtKSWTshJEd7s9mt/G3N54gIznF2PbZ8jW8Of8tLr35YiadOw6zpA/1avS72FdVTE5ODbuWrWXQTD3wOKJnDyLt+qc2QVVm/hOvoIQCX4IgCIIgCELX1+AJkFvVth7PyB5xxIS1rTXqDSrUuQMIgtA1dNlgz7Bhw7jrrru48sorjUBKZ1pHsP1+/2FatvjTn/6EHBoa9Oijj+JwODq02bZtm7E8bty4w55vzJgxgF4/qKio6Kj6IMCUSyYYy2WNDaRERgN6cKe4voHosJb01OZZuYKKQkWxXjtnYN9ExgzrDugzcb3w6H+ZNGcKJrP+q7H2vW955R8LaajTZyeYMuN8ImuceFwBSqqcuH16raDohCiuevg6TGYTSlDmwIdfU7RuF0VlTXiDAcqd9Wih/J9LrryQrz5fixIKEl161YVImkbtjl3U781CVVqCMbHx0fzrzSeJCY80ts1/9n+sW7GOR+b/il7JGcb2vPpyKl0NbFqfj6e0mNRhfTCbTEwdNNBoU1RezsoPV3+vx1wQBEEQBEE4fewsaTvLls1iYmBqFJF2CyapbduSeu8P2DNBEE6mLhvsGTduHA888ABPP/10hzo67W3atMlYzsjIOExL3cqVK9m6dSsA5513HpMmTTpku+ZhZAC9evU67Dm7d+9uLOfkiEK6R6t77wz6De4DQIPPQ2xYy7TlBbW17YI9obo9skpjjRuvK4DJYmb6hWeTmqjPxFVeWMGKxV8z5lI9M6bR5eKjRZ8DeqbNHT+/loIN+6ht9FLdoA/5M1vMXPvYHMJjIgi4vGS99Rl12YUUlzciKypVThdN/pYx0Fded0mbIVwzZ19EQ9Z+3EXFOA/mU71pC2qorwA9+mTwt5fn4rDo96IBv7v/aSpKKvjLS78nzhEd2q6xo/wgdU1uvl22ne7De2G2WRiUnkZChB4sUjSFl/7xBj7v0QU1BUEQBEEQhNPbruKGNutD06OxWkyYTBKRdkubfSLYIwhdR5cN9hyt1atXG9Ou9+/f/4iBIYB58+YZy80Fng+ldbHl9PT0w54zLS3tkMe1V1ZWxtatW4/rKzs7+4j3djqaOvN8Y7nJ5yHcqo8/rnK6CKoKdos+1Kl5KFdQ1qdKryjRs3tsdokrpw3FGmq36uO1RKbH44h0kFtbZszKdcNtV1K7dg+NTT4KK1s+IZn5i1mk98vAU13PnjeW4iqrwR+QKalwElBkKpwN+GQ9JbbfgD4EgzKFeSUAjBxzFmlJsTTm5uOp9+Bt8OKtrKZ642bUYMsMY8PHncXDj9+DxaS/IAdkmbtv+A3J3ZO44+dzsJv1e/bJAXaU5VNQUM/uFRvpc8FwJEnioqEts4/VNNbz4WtLT+AzIAiCcGaTWwXoBUEQfmx2ljS0WR/WLdZYjm43lKu4/ujrlwqC8ONmOXKTrquuro65c+ca67fffvsRj1m/fj379+8H9KFXw4cP77RtY2NLQCAiIqLTdgDhrYoLO52dV8L/4IMPmD9//hH7eSaZPGMC//n7GwBUBdx0j47lYG0NoBdqTo6MprpJf+EKyApmmwlZVakqc9N7YCJmq5nklDguPn8AS7/Rayy99exihk8YQu0uva5OuMPBZTMnseWFTzhYWk/zyL+RF49i+LSRNOaXkf3h1yh+PahTUu1BVTVqXC6a/C1jpK+8/hKWvf+FsX7ZNdOp3Lwdb72H5irP3novUEvVhk0kjRuDOVRo+pLrLqLgQCH/W/gRqqZS73RxzzUP8vrn/2Hjqq2s3b4VDY1Kdx15tRXYN5jJGJBOZEocvYBucXGU1NejaipvvfIBM2+4iJj46JPxlAiCIJxRhg8fTp8+fbjpppu45pprTnV3OvXYY4+xePFi7rrrLh544IHDtlVVlY8++oglS5aQnZ2Nx+MhKSmJkSNHcv311zN69OjDHi8Iwo+DpmkdhnGd3b1lBthohxVoyeYpEcEeQegyztjMHrfbzc9//nPKy8sBPXAza9asIx7XPLsWwB133HHYtoFAS4GzQ9X0aa31/tbHCUeWnJbIsHMGA1DX2ERsVKQxg1V+TS3RjlZDuZSWoVxN1U24nAEkScJsMzFsQBpDMvViyB6Xl4+XrjSO6xmVxLqXlpJbWk9A1jN90jPTuPiumVTtPMC+RSuMQA9RkZSWN6KoKjVuF85QsMdqtTBp2rms/OxbAMIjwhmWlkDjwRIj0AOgqRreei++mnqq1m9EaVVH6q7f386kCaORQneYW1jK7//vj/zp1UdIj0022u2rLqSqycm3y3bSc6w+I9e0IS3ZPU1eFwuffff4HnBBEAShDVmWycnJ+VFn0H755ZcsXrz4qNo6nU5uueUWHnnkETZv3kxjYyPBYJCysjKWLVvGTTfdxNNPP32SeywIwolQ0eSj2tl2+P7ZbTJ7xDAuQeiqzshgj9Pp5I477mDHjh0ApKam8s9//hOT6fAPx8GDB1mzZg0AAwYM6LRWTzOz2WwsS5J0mJZti0QfqR9CR1NaDeWy94k3atR4g0HqvR5soSFaQUVF1TSCoWLNtfX6d4vdgsli5pJJg4iNDsMZcOP26J9sRNnDSImMZc++cprc+otlWISDax79CWXrdnDw07Voqv78xfXrTqUXVEWl1u2m0edG0fTg0OTpE9i2cScet/4iev64s2jac8DotyMxFluMXjtIUzU89R58tQ1UrtuI7PMB+s/GE//5PQP6tBQdX71mG+/8ZxF/fvERImx6hpiiqXxXlkNxSQP7vt1B6rA+pMbEMCA0TFFDY9mHX1JeXHlingBBEAThuNXX1xtDhk+G1atXHzGTp5mmadx///1s3qxntk6YMIEXXniBRYsW8cc//pGMjAw0TWPBggW8/PLLJ63PgiCcGDuL22b1RDks9EpoGXGgZ/a0EMEeQeg6zrhhXFVVVdx5551GnZ7ExERee+01kpKSjnjssmXLjKDM7Nmzj9i+9dAsn8+HLTQc51BazwJ2uHZXXXUV48ePP+K1DyU7O5snnnjiuI79MSrZdoCCtbvpf9EoJk0fz/N/eQVVUdm5K5sxZ/Xnm43bAThYXUO/pBRqnHrwxi/LmCQrqqpRllNFj549kUwSFocVu6Iy/fz+/O3tPOM6wwcORHIqyKqKSZKwSBJXP3IdlWu3U7sv32iXOmowYX17sO/Nl9A0jRqPu80QriuuncFrL7xjrI/OaPmZs0U56HnpVLSgTNHy1fhqG0ADb4MXNKhau4Hkc8diCQ/HZrfx3LtPc/P0u6isrwPgtVc/IHNgH+bMuZJXX38XRVNw+j3srSzEvsXCZX1TsThsTBk0gAMVlWhouINeXvrLAv74n9+elOdHEARBODrz58/ngw8+YODAgbz77onNunz99dd55plnCLaqA3c4S5cuZe3atYD+t85TTz1l7Bs+fDgzZsxgzpw55ObmMn/+fGbNmnVU9Q4FQTg1OtbricHUagquKEfbt4OVjb4foluCIPwAzqgUkv3793PNNdcYgZ7U1FQWLlxI3759j+r4L7/8EtCzdGbMmHHE9q3r9Hi9h4+Sezwt42NjYmI6bZeens6oUaOO62vAgAFH7PPpJPvzTdQeLGPLguWEO+ycM/5sACrLqjn3molYQ5lVFU1NWFq9qAVaTcHuqmkiYNKzgCyh2Qi2ZOejhrJxIqxh1FbUo4Q+cVU1jXOvPBf3rv0tgR5Jote0sfS+aByr3/oagHqvB7ffhyeoP+9pGSmkpiezc+teANITY+mTlgiALdJGytjhmC0WLGEOes6cTFiKvq854OOrb6Jy3UaCbjegT8k+/92/EmlvmX3sDw//g0mzxnNWZsvzXNBQQXF9Deu+2EO34b2JDQ9nRI+Wmd/WrtlCzt6WwJYgCIJweIqinJTz+nw+du7cecLOV1BQwF133cVTTz1FMBhsk218OM3D1SMjI3n44Yc77I+NjeXxxx8H9A+qFi5ceML6LAjCiberXbCn9RAugIh2s3E5/TKegCg6LwhdwRkT7Fm9ejU33HCDMdNVnz59ePvtt4860FNQUGBMiT5q1ChSUlKOeEzradybawN1pvX+ozm3AOHRetqp7Auw7+NVTDh3sLFv3VfrOGfUQEAP0JQ2NGIx6z/uAUUJDeXSAzhl+XoxZ5PFREWjk2+26QW4zSYT8WExeP1BatwuI6uraOs+nCVVoWPMZM4cT+LgHuRtzSJvWw6aplHr9Ri1egAuu3Iqn7y9zFg//6x+SJKEI9pBWGIM4elpqIqMpmmYbTZ6zphEREbLJ6W+Rh/+eidVazcQDBXw7tG3O0+/+BhWs/4iLasqv7jpdzz6/IMkRsQax+6qyKOwrJ7cXTlEpcZxfv9MLKGhgj7Zz7w/vNRmGKEgCILQuZEjR3L11Vczd+5cFi9ezJ49e051lzp46623uPTSS/nmm28AyMzMNAI0h1NcXExWlj5RweTJk4mNjT1ku1GjRtG7tz6cePny5Sem04IgnHCqqrGrXXHmYe2DPbaOAz2qmvwdtgmCcPo5I4I9H330EXfffbeRPTNy5EjeeeedNsGYI/nqq6+M5aPJ6gHo16+fsVxUVHTYtsXFxcZyZmbmUffrTJYxMMmohVS05SBDeyditeqfXK75ZhvTLxlmtC1rbCDM2vJiFpAVgoo+BXvxzkIkhw1N01i0cgtqKPAxffxQHFY9oOQNBnH69bTW/MIaXJ4AZruF7uN7Y8ZFU+5+Vr2uz7Ll9PtxeX00hoI9kiQxLjOZTz/QCz6bTRLnDcskLC4Mi8OCJdxK4/5dNOzdQeOBvcgeNyaLhe4XTSCqVzejz74mH74GF5XrNhJo1KeNHz1pJL/+3Z2YJf1X2eX18evbH+P3Tz9gTMfuV4LsKD/Iju2l2GJshNvtjOvbxzjvnn0H2LbmxH2aLAiC0JX5/X727t3L4sWLmTt3bpvZt9avX8+LL77IN998Q2Xl0ddEa56982izb45k9+7dBINBbDYb//d//8eHH35Ijx49jnjctm3bjOVx48Ydtu2YMWMAKC0tPeLfOIIgnBoFtW6cvrZZOq1n4gKwWUxE2Nr+31PZJIZyCUJX0OWDPR9++CG/+93vkGX9P7oZM2bwxhtvdPppVWe2bNliLI8dO/aojhk+fLgRjNi6deth2zYXQkxLS6Nbt26HbSvoYnqmkdQ7FgBVVqnNrmbkCD1Q1tjoxuny0L27Phyqye8joLS82PllGU0DRdXwewI0NfrZnlXIrgN60C0+OoK+sckkR0QZx9R7PcZxB0rq6TkhE0eMXpepcH8llcX1eju/B6/sQ1b1640Y0od9mw7Q4NKDjcP79SAlIx6z1YzJasES0TITm+r30XRwP/76WkxmM92mjCemX6+Wfjv9+BpcVK3fiL+hAYArbp3F1VfPMGboKq2sYfEbHzJr5kXGtmp3Aweqy9i4ej8JmWmM7dMbh0UPZAWUIPMefxlFOXnFQQVBELoKq9WKpmltvkAvbJyfn8+8efO4++67ueCCCxg3bhy33XYbf/vb31i2bBkHDx7skEkZCASMvxGO9W+Tztjtdq655hqWL1/Ogw8+iN1uP/JBQG5urrHcq1evw7bt3r1lSHBz5rMgCD8u7bN6kqPspEZ3nCE4Kart/xGVTpHZIwhdQZcu0LxlyxYeffRR4w+rG2+8kUcfffSIM2MdSvPMXVFRUUc99CstLY3hw4ezfft2VqxYwUMPPURkZGSHdlu3biU/X6//Mn369GPu25kqZsBgetY0Ul2wDU3VqNhfxQUXT2TTZn3q2027Srj46qm88q9FAFS7nMQ6IlE1jUAoqycoK1jMJsqKPby5bL1x7otGDiE7u5YIm51YRxgNPi+aBrVuF6nRMVRUOqnzmOnZMxVVUdnyjT5LmycQoN7locnXMoTr3P69+XZHy3S80yYNx2TR46xhGSnYovTaTmrAj+L3gabhLs5H8XoIS+tG+sQxmK1W6rL0P6YDrgBoULluIynjxmBPiOf+v9xDUX4pG7buAGDr9ix69+1B3/Qe5JYVAnCgppjE8GjSDpYSGxnOpAH9WLFXT9cvKivj6yWrmHbVlBP+PAmCIHQl27dvJzs7mz179rBnzx727t1LVlYWkiR1COQ0NjayYcMGNmzYYGxzOBz069ePzMxMIiMj2bBhAxUVFUiSxNChQ09IH+fOnXtcM3s2D3UHvUbg4aSlpR3yuPbKysooKys75r4AP+qp7AXhdLCjuKHN+rBusYd8H5QcZaegtqV+aJXI7BGELqHLBntcLhe/+c1vjEKKV111FY899thxnauyspL6ej1r46yzzjqmYNFNN93E9u3baWhoYO7cufz9739v8wdYY2Mjc+fOBfRPC2+88cbj6uOZKunsoaR8l09FTi2qohIfNBMeEYbH7WXtqm3c+ZufsmD+B8hBmQpnE8mRMfiCesZNQFGwqfqvwIoV6ymr0Ge26tstGWdFSwHOod1T2F9VRU2jh4CiUOd2kxgZydfvb+bnz9/DntW7qCtvAMBlUlFUBVdAL6QcGWanX3oy/136LQDx8dEM76dnbtniYok/62zj50lTVTxlxfjrqgHw1VQi+7xE9uhDyvgRmGxWanbowZmAO4CmaVRu2ETyuNE4EhP56+tPcMv0u8gv1f+ofu/95fzilzfz3xfexenXp4DfXp5L1J4wJk8exNk9urPhYB5NPh9BVeblv/+PiTMnYHd0PhucIAjCmc5isTBkyBCGDBnCddddB8DAgXqNuPPOO4/hw4eTlZXF/v37D1mvz+v1snv3bnbv3t1h39VXX31C+ng8gR5oGU4GbSeZOJTWM446Q7XkDuWDDz5g/vz5x9UfQRC+n47FmQ89CUxSVNtsHzGMSxC6hi4b7HnzzTeNP7KSkpK49tprjVm4DictLa1DGnVBQYGxfDRj3lubOXMmH374IWvXrmXZsmVUVFRw8803k5KSQnZ2Ni+99BKlpaUA3HvvvW3SooUjs0ZG0ee8wVTlr0OVVSq+y2HshBF8s2I9bqeHrJ0HGD9lFGtWbCSoKHgCfkySPi7ZL8vYZQuegJ+t+XrquiRB35hUgn59SFN0hJ3+vePp0SOa99fsIxAI4gr4cfitVOSVs+2LrWz+QJ+iNiDLVFTX4gy4af5s99yh/diaXYCi6lsmjR6EOVQoOnbQwDaBQ8lkIqJbT8xhYXhKiwEN2dVEU+4+IntlkjzqLEw2K1Wb9fo6QU8QTdWo2rCZpLGjCEtO5t8f/JOfXHgndS69ps+Lz/+PW396La+++g6yquAKeNldUUjMtjBGnNWTqYMH8tF3OwCoaqjjo9eWcv3dV52050sQBKEr6927N/fee6+x3tjYSFZWFvv27TO+8vPzO8zoZTKZuP3227nwwgt/6C63EQgEjGWHo+NQj9Za7299nCAIPw5BRWVvWVObbcO6xx6ybXK7YVxVYhiXIHQJXTbY8+677xrL1dXVxqdvR/LUU08xe/bsNttafzLXOm35aD333HPcddddbNmyha1btx6yfs+tt97KnXfeecznFiB+8EDS+mVTuq8KTdXoExvDN6F9X326hsuuvog1KzYCUO12khoVi6bpRZo1TWNbUQGB0B/eA9LTwa8Hg2xWM5ndY7GHWTjrimlETRjJS4+/DkCtx43dYmHFfz/HFFSQJIlAhAW1UmszhGvS2f157cvNLesj9KLdjqREHIkJh7wfR0IyZnsYrsKDaIqMGvDrAZ/uvUkcNhCz1UL5Or2IpuyT8WleqjZuIWn0OcSlpfLCu3/jp1fehy8YQNU03n7zQ84ddQ7fhupCFTdVkl0WQ0pyJANSU0mOiqLK6UTRFN5+5QMu+clFRMdGHbJvgiAIQkf33XcfBw4cIDU1tc32mJgYxo8fz/jx441tfr+fAwcOkJ+fT0NDA5GRkYwePfpH8WFP6wLRR8pibj1k7XgziQRBOHkOVDrxy23rMXaW2ZPYvmaPyOwRhC6hSwZ76urqjjjV+bFwu93Gcvs/5I5GZGQkCxcuZMmSJXzyySfs378fp9NJXFwcI0aMYM6cOUec9ULoqDS/nK1ff8eEmePpM+lsKnK/QgmqhNd7iYqOxNnkYsOqLfzqiZ+TmBJPTWUdtW4XGdGxyBpoQI3bSU6NXmvAYjKTYo0HQhk+6bHExDiISYzAajdz3oxx7N6Uxfrlm9E0jWqXC4vJhNVsxmSSKC6rwS/78Sv6J5y905KIHz6EogVLARjYJ4OM5DhAz+o5HGtkFNH9BuEqOIji84Cq4io8SFhKOrED+2KyWSldtQk0DdkvozV4qd6yjcRzRtBnYC+eevZ3/ObePyOrCr5AkJyDB0mLTaK8QR8itqcyn7jsCM4f258Lhwzi7Y16IKjB6+KtZ9/l53/82Ql/vgRBELqqu++++6jb2u12zjrrLM4666yT2KPj03pols/nw2brfFiv39/yyf/h2l111VVtgl3HIjs7myeeeOK4jhWEM93udsWZeyaEExt+6N/VDpk9Yup1QegSumSwJz4+/oQW9ZszZw5z5sz5XucwmUzMnj27Q9aQcPxeefx1ig4Us/3bnTz239+QPnAPxbvLMUsmhvTqxsZd+/H7AmxYtZUps85n8SsfowGNPh8RNgeappFVVWIMueodl4I9NENVz9QYYmMcxKVGYLGZqc/KIbpPD279zU/I3ZNPVUk1AUWmweslLjwca1w4/vKAMd06wOw5l/HNxl3G+pQxeoAnLC0VW+yhP1lpzWyzE505AHdxIYFGvZ6Qt7IM2eshuldvTBdaKPl6PZqiogQUfPVearZ8h6YonDt9PHffdyPznl2IhkZNQxN9e2ZQ72rCJ/sJKEG2lx0kbm84wzJT6ZmQQGFtLaqm8skHK7nqzitITk86MU+UIAiCcFpoXafH6/USHR3daVuPp6WYa0xM569p6enpRyz2LAjCiZdV3nYI19CMzn9PO8zGJTJ7BKFLEHm3wmkrPFKvF1CUU8LBrEIyp47CYtdT0LvZwox2X3+6hqmzJhrrdV4XaBq1Xif1oSFXYVYbPWP14EZybDiJMWEkd4/BHvoExF9bj7eqlrDIMO750x2YQnV3Gn1ePIEAObnFqJqK069ngdkdNqZfczFfLF0FgMNmZdzZ+rTwsQP7H/U9SiYzET16E5aaYWwLNjXQdHA/4amJ9Jg+Ecmix2yVoIK3wUvtdztxFhQy5xc3cOmMC4zjDhaW0q9vT0yh1Pw6bxO7Soooq3UytVWmkSvg4aUnXjvqPgqCIAhdQ0ZGy2vNkTKkW+9PSUk5aX0SBOH47GsX7Bmc1nnwtn1mjzug4PLLJ6VfgiD8cESwRzhtTZh5rrG8eslaonr1oNtQ/dPDjNhYYiL0dPQt63YQERXOkHP0gEaTz0dQC1JQ3zJV7ICEDEySCbvFTHpiJBHRdmK7xWAJa0l3bdinT33eZ3Avhp8zyNhe6WwiIMu4Ax5UTR8bfeGMSWxetx23S//kc/zwfoTZbUR074Y16tjq4UiSRFhyGpG9MpFMejBL8Xlpys3CFhVGz0suwBRKoVdlFW+9l9rtu2g6mM8jz/2G4YNagkv7c/PpntxSdyq3rpSdB0sJDw9ncKgelYbGt2u2cDAr/5j6KQiCIJze+vXrZywXFRUdtm1xcbGxnJmZedL6JAjCsVNVjX3lbWfJO1ywJyGi4/CuGlGkWRBOeyLYI5y2Rk0ZQURUKKDzzXe4nR4yLxqLLcyCJEn0DRVAVmSFb7/cyIWXt2T3FDZU45ODAMQ6IkiKiMFkkkiIDiMoq/SdPAJ7dCQWh0Uv4AM4C0sJuj00VNYTKK0nwmZD0zRkVS/u3HoI1+XXXszS978w1qeMHQQmEzEDWv6QPla26FiiMwdhsusZTZqi4Mw/gMkk03PmBZjD9O2qouJt8FK3aw/O3DyeW/x30hMTjfOU11QTZddT9VVNZWfFQbIOVnJeZqaR9eOVfbzwh5eOu6+CIAhnopUrV/LAAw9w5ZVXctlll3HzzTfz+9//njfeeIMtW7bgcrmOfJJTaPjw4UZh5kNNJtHa5lDR/7S0NLp163bS+yYIwtErqfd2yMwZdJhgT7jdQoTN3GZbtUsEewThdHfSavY0NTWxdetWdu7cSUFBAeXl5TidTgKBAA6Hg8jISLp160bv3r0ZPnw455xzDmFhYUc+sSCE2Ow2zr1kHF8u+ho5ILP+801Mu24KPYb3IHdDHgNSU/iuUP/k8etP1/Dkv3/PS0+9gcfjpdLZ8mlH7zi96HZ8hAOzyURQ1UgbOwxfVSX1u/dgDbMS9ARA02jYf5CNa3JQFZWMmBhya6sJqhBUgniD+vjmbj3TSUlN4rtNer2e9ORYBvRKI7JnDyytil8eD7PDQXTmQNxF+QSdeuE9T1kxtrgEel1yAYXLv0V2e9AUDW+9F/bsI1ZRePmjZ7nhojtxej1oQFCVsZjMyKqCJ+jju5KDJMScxciePdhaUAjArqwctn27nXMmjvhefRYEQTgTPPzww3zyyScdtm/ZsqXNerdu3Rg0aBCDBw82vicl/ThqpKWlpTF8+HC2b9/OihUreOihh4iMjOzQbuvWreTn69mf06dP/6G7KQjCEWSVty3OHB9hIyXa3klrXXK0g/yalklpqkVmjyCc9k5osCcQCPDZZ5/x0UcfsW3bNpTQdNad2bWrpXit2WxmwoQJzJo1i2nTpmG1Wk9k14QuatLl5/Hloq8BWLVkLdOum0Lf6edSvKuYZC2K2PAwGjxedm7Zi8vp5vzp41jy3udGUebUyDgibA7MJgk1NI2s1xPE6/ISkZFOU04uFkXVgz1AwaY97PlmPwBhDivRUQ58dS6aWmX1XDb7Ij79aKWxPmXMYEwWCzH9Tkyau8lsIbJXJt7KUnxV+lC0QH0tis9HzxnnU/zlegKNTjRVD/hoWdnEDshk/htP8rMbfk1AkVFUFZvFihzQf0fLnDVszy9iXP9e7CguQVYUfIqfF5/4L698MU9MqysIgnAY77//Ph9//PFRtS0pKaGkpIQvv/zS2JaQkGAEfh544IGT1c2jctNNN7F9+3YaGhqYO3cuf//739u8BjQ2NjJ37lwArFYrN95446nqqiAInchqN4RrUFqUkbXXmaRIuwj2CEIXc0LewblcLl544QXOP/98fve737F582ZkWUbTtKP+kmWZ1atX86tf/YopU6bw8ssvt5nyXBAOJb1XGgNG6EOjKooqyd6egyM+jl5j+iJJEgNS9aKRmqax6vN1pCVHE1D04VsSEj1jkwGQVZWArKCoes2d/E37kUwmInv3wmQ2YbZb0DSNPTtKjGsnpIRTVedC07Q2wZ5EaxSffqD/EW8ySUwcNZCoPr0xOw7/icqxkCSJ8NRuRPToA5L+a6x43XjKCuk2ZSz2+Fjjvr0NXhqyc0m2BPnDX+7HFGofVBQc1pY+7asuJLe0ljG9ehnb8svK+Pqj1Ses34IgCF3R+++/D+j/N/fs2ZNnn32WTz/9lHfeeYcnn3ySW265hdGjRxMVFXXIv4Fqa2tZs2YNL7/88im+E5g5cyYTJkwAYNmyZdx0002sWLGCHTt2sGjRIq688kpyc3MBuPfee+nevfup7K4gCIeQVda2OPOg1M6HcDVrPyOXCPYIwunve2X2aJrG22+/zfPPP09TUxOaphn7+vbty8CBAxkwYAB9+vQhNjaWyMhIIiIi8Pv9eDweysvLKSkpISsri927dxvFAKurq/nXv/7FggUL+PnPf86NN94oMguETk26fALZ2/XiyauWrGXgyP70nTaewq35DEhNYVNeAQCfL1pBoNXwLYfFbmT4BBQFVdXw+oNEOGzkbdzHWZeMIaJ7N5wHD2K2B6kobqCiUg/qOBwWas36r48n6DPq9kTawvlw4adUVdcAMGJQLxISYonO7HNS7t0eG4/Z7sBVkIsaDKDJQTxlBaSdO4zKzXvxVtWChp7hcyCPkX178dNbr+S1BR+gAbKiGMO5gqrMd2W5TOl/FmFWK95gkIAS5JVn/sfES8/DZu9YvE8QBEGAnBz9NcjhcLBw4cI2s1ONGNF2KGxxcTFZWVlkZWWxb98+srKyqKmp+UH7eyTPPfccd911F1u2bGHr1q2HrN9z6623cuedd56C3gmCcCQ5Ve0ze0SwRxDORMcd7MnOzuZ3v/sd+/btQ9M0JEni3HPP5dJLL2XChAkkJycf8RzDhg1rs15SUsKqVatYunQpO3fupL6+nqeeeooPP/yQP/3pT5x11lnH212hCzvnguFExkbianCxbdV2muqdRMdF0XfCQHyf7yQpKpJqp4uD+SUomh6UMUkm7GYbnqAPmzkSVdOQVRWLaiIgK9QVVtFQVktsegLWuEQaCveTta/KuObACYNY+OYKALyaz9geFxZNWWOtsT5lzCCiM/tiOonDEi1h4UT3G4Sr8CCyW59W3ldVSuLZfanba8Zdpvfb1+CD3AJmXzyKvOxCvlm/FUmS0DQ9y0lDo8HnYkdxIWd368nGfP2T28qGOj565ROu+8XVJ+0eBEEQTmeKoiBJEmPHjj3iNOTdu3ene/fubWrdVFdXG8GfH4PIyEgWLlzIkiVL+OSTT9i/fz9Op5O4uDhGjBjBnDlzGDdu3KnupiAIh+CXFYrrPG22ZSZ3rL3VXvtgT5XT10lLQRBOF8cV7Hnttdd49tlnCQQChIeH85Of/IQbbriBjIyM79WZbt26ceONN3LjjTeSl5fHwoUL+fjjj9m/fz833HADv/zlL/nZz372va4hdD1Wm5UJl4xj+dsrUWSFdZ9tZMacafSePJb89dkMSE2hqslpBHoAwi0OJEmi1uMmxh6BJEn4ZQW7xYI/qGA1m8nfuI/Mc3pTsyuHygontaEXzshIG7k5BaiKiqIqNHqcoe0RhFscVLj0YE9kmJ3RIwYQ2bvXSX8MTBYrUX364ykrxl9bDUCwsZbYfqlIFguuojIAfI0+tIOF3P/zWZSWVnKgsBhJkjBJJuPxKWgoJzE8imhHGE0+L7Iq885rS5h58wwioyNO+r0IgiCcblJTUyksLDyqD7oOJSkpiUmTJjFp0qQT3LMWY8eOJTs7+6jbm0wmZs+ezezZs09anwRBOPGKaj2oWtttfZKO/PdbUmS7zB4xG5cgnPaOa2zU3/72N1RV5eabb2blypX8+te//t6Bnvb69OnDH//4R1auXMnNN9+MJEn885//PKHXELqOSZdPMJZXf7wWVVWRAzIRUQ76p6Sgohr7E8Ki6JWmz8DlCwZxB/QXs4Aso4Zq9ngDQbK/2UHRF2tQZZms/S1ZPX0z49m85QAAHsVnHHPlDTOxxzmMdhGWcGL69cNkaTuV5ckiSSYiMnoSntHTmC5e8bqJ6h5NVO+WaXH9TX7chWX85dFbSIjW03pNJpNRy0fVNHZVFtAjpmV2mHqPk4V/e+sHuQ9BEITTzbBhw9A07Uc3HEsQhDPPweq2NU+TouxEOY6cYS6GcQlC13NcwZ4LLriATz/9lEceeYT4+PgT3ac2EhISeOSRR/jss8+YPHnySb2WcPpK6Z7MoHMGAFBVUs2OlVvZ/fpSCAYwmSVUrSXYM3Jwf4aktgQyXEEvAIqmIat6sUxF1WisdeFyBigtbaKpSX/Bi4sPp6SmkUBQQdM0AhbZOM/l10zHS0vKq8Pk4N2FX5/U+z4UR0ISUX36I1n0xD0tGCQ8yUFM35Yimn6Xn0BFNX/7w22EWfVaPCbJRPM8DT7ZT0FdFXFh+idBiqaw7JOvqSqt/kHvRRAE4XRw2WWXAbBt2zaCweAp7o0gCGeyg9WuNut9Eo8uK7t9sKfGFUBtnyIkCMJp5biCPf/5z3/o2bPnie7LYXXv3p0XX3zxB72mcHq54MrzjeXPXv6EoEsfdrV8915juwkTqlmjZ0Is9lAwpN7jMWbh8suyUbTZF5QpyqvjQH6DcfywURls2l0IQEAJUFdXr28fORi/L0BBfikADosNu8XGui+2sGtDy/V/KNaIKGIyB2MOCwdAQsMRbyWmb0uGT8AdwO7xMPeBGzCbzPpwLlNLFlKVux6bqeWTIFfAw8t/fOWHuwlBEITTxPnnn8+oUaNoamrinXfeOdXdEQThDKRqGk5fkOyKtsWZ+yRF4PQFja/WE+q01j7Yo6ga9Z7ASeuvIAgnn5jiSugyRpw/jIioMADyy+vx+IJ8l19Bfk1LNopJMrFjbw4mSaJ/mp7do2oajT49MORXZEySZLQvqXDibNDTYXsM7Ul5vRNX6IXPZG+pAXTFtTP4aOESY71XYkuBzhcefYXGurZTYP4QTDYb0X0HYovVs+8kSSIswU5MZsuQy6AnSLcIO3ffMB0JvXZP83AugOLGKqLtesBI1VTWbNjOwb15P+yNCIIgnAb+8pe/EBUVxT/+8Q+2b99+qrsjCMIZxu2X+e+afDbn17XZ3i0unP+uyTe+OsvViY+w0epPYEDU7RGE050I9ghdgqaqFK/aSmZyjL6uaWRVNPD55p1Gm/iwSCRJosnro6y+kRH90o19Tf7QUC5VIygrSJJ+Dl+wZZjWpJun8e2mg4Ae+Kho0LN6wsIdTJg6jpUr1gNgs1r45SO34gjNwOVxefnPHxcYtX1+SJLJRET33oSltWT0hMU7iOmbZqwHvUHGZGZw2cRzgObhXPqrvawqOH0e41MgT9DHC4/+5we8A0EQhB+/P/zhD6xdu5Z7770XgNtuu43Fixef4l4JgnAmap+N0ysh/KiOs5pNxIfb2mwTdXsE4fT2vYI9wWCQmpoaZFk+cmNBOEmUQJDsD76mYksWA3u01OJZ+90+Gv16xk5SXCznnzXA2Le/opJe6fH0ytTr2LgDfnyyXmfBGwxit1qQWwVnuvXPoLKylopyPcDjiJDwBfT2kyePZf2yb3B79Ho9540ezIgZ53Le5HOMLKE9m7JYseiHr98DoYyepFSievdDMuvDtMISwonJTKW5SI/sl7n6guGM7NcbSZIwtxrO5Q56sZpC9X/Q2H0gj22rtv3g9yEIgvBjtXjxYv785z/z5JNPEggE8Hq9zJ07l4svvpiXX36ZXbt2iVo+giCcdP6ggl9u++Fiz6MM9oAo0iwIXc1xBXvq6ur4zW9+w6hRo4xx6r/97W9paGg4wd0ThMMLuDzsffMz6nOKAIiJcpA5oLueeeOqN9o9+Mefk5mYgNmk/8hnl1cSM6AXE6eMNNo0ePXhWn5ZQdVUgkrLi6WrvI6P3/jcWHfLHmN54pC+LH3/C2P9yptmIUkSs34+i5TQbFcAi+Z/SMH+ohN168fMGhVDdOYgzHZ9xrCw+AhiM1NbZu4KKNx77WQyEuI7BHycfjdaqMi1V/bx7ydeOyWZSoIgCD9WmqYZWZCSJKFpGoWFhfzrX//iuuuuY8SIEVxxxRU88sgjvPnmm3z33Xd4vd5T3GtBELqSJl/bD+AlCdJiwo76eBHsEYSu5ZiDPQ0NDVx//fUsW7aM5ORkpk2bRlJSEkuWLOGGG26gqemHr00inJk81fXsfn0p7opaAMx2KwOvu4gYh406bxOKptfUGTdhJFF1XmySmd6JCfqxgQBbN+ymlxYwAkCNoeFKsqrS5G1JgTWbJGqb3GTvzAUgNjmagnK9DlB6YixhPh+79utFm9NS4hlz4XkAxKcnMOWaC4h26MEVRVZ44dFX8LpbZuz6oZntDqIzB2GNjgXAERdBXP9UpNBjgKLx2E9nEuUI04dzhQJBGhpBRTbeyBRUlLNy0Ven4hYEQRB+dP70pz8xZ84czjnnHCIiIoz/K5sDQJqmIcsy2dnZfPTRR/zlL38x2s+YMYNf/epXvPrqq2zYsOEU34kgCKezJl/bDMKUKAc2y9G/3UuKFMEeQehKjjnYM2/ePIqKirj88sv5/PPPef755/n888+5/PLLyc/PZ968eSejn4LQQd7n6wg06dk49phIht58Kdmb9lGcW0G9tyXoeNXs6ZRuzQZgUHpL4eT1W/bhsJoZ3EffJqsqTr8eiPEGWl4srWYzZa2y1qLSW7J1Lhg+kLW7co31S6+aZgRIACbdMJmMlERsoeFTFcVVLPzHu9/73r8PyWwmsmdfHMl63R57TDhxA9KQzPp/B3aLhUdvmYnVbMEstWT3yKqCrOifGPmUAAuee4dgUAzhFARBuOaaa3jsscd466232Lp1K19++SXz5s3j7rvvZsqUKaSn6zXiWgd/NE1DVVUKCgr47LPPeOaZZ7j99ttP8Z0IgnA6a/S2DfZ0jz/6rB6ApOh2wR5RoFkQTmvHHOz58ssvsVqtzJ07F0to6mqLxWKsf/HFF0c4gyCcGNYI/QUsIi2RobdcSk1pNSvf/pacunJjpoE4RxRbP1pnHDOsTwY2ix7AyKmspqbCyeQZ4439DT49eBRU9KygcLsVRVOp9ejDtqxmE7t2ZQFgNpmYMKwfa3YeAMAkSVx2w6Vt+hgWGcaUGy8kKTLKKHq89tMNrFu+6UQ+FMdMkiTCUzOI7NkXTCZsUQ7iB6VjCj02yXFR/PKqqZhNJiyhej0AQVVGUfXHprKpnvefFwVIBUEQ2uvevTvTpk3jvvvu48UXX+Trr79my5YtLFy4kEceeYQrr7ySQYMGYbVaOwSABEEQjpfT2/ZDuG5xR1+vB0RmjyB0NZYjN2mrvr6ehIQEwsLaRorDw8NJSEigrq6ukyMF4cTqf+VkPNX1hCfH465z8v7Ti6l2Oal0NQBglkzEh8WwP7eU3iP6YbWaiY62M7RXGt/llhCQZXZkl3L7bZcR/+431FXX4/T7kFUFMKMBUQ4bWeWVxjWtZqgp13/Gx4wcQEWjk9pQdtGIs/uTkpZEe6MvGcOmpRvwH5SpcbsAeP2vb5M5tA8p3Tq2/yHZYuKIttlxFeZiBeIHpVOXXYYaUBjSJ53rLxjD299swiSZUEM1ewJyEIfVREAJ8t5bn3HZHbOIjIk8pfchCILwYxcVFcWYMWMYM2aMsU1RFHJzc9m/f7/xJQiCcLzaD+PqHneMmT2iZo8gdCnHnNnTrVs3qqqqKC0tbbO9pKSEqqoqevToccI6JwiHI5lMRKQkoCoqi+e+jrPJR3ZNibF/2JBBmE0mvIEgFQ0uIqNtmMwmxg7tbbTZX1bBgc/WM+WyCca2Bm+o+LJZQkOjtKER0DN3aj2NRrsZF47n2x0HjPUJQ/qgqR0/lTVbzFx8xyVE2u1E2PQXUZ/Hx4uP/Rf5RzAMyhIWTnTmYCyRUVjCbCQMysBs1+PA08YOZtLQ/m2mY9fQCMgBNE2jzutiwR9fPZXdFwRBOG2ZzWYGDBjA5ZdfzsMPP8yCBQtOdZcEQTiNNbYL9hxzZk+7YE+VCPYIwmntmIM9V199Naqq8sADD1BWVgZAaWkpDz74oLFfEH5IX8z/kJL8akqddTj9+swm/Qb1YcqoYUabwpp6bDYz1jALQwf0JCoqAoC86hoKdhczdmQ/o22D142maXg9fmotGmoorT4hPIzSJj2rJ8Jup2eUg8178gCICndwdo8UXMVlh+xj/zED6DO8LwkREVhCxZDzsgp4/6WPT/CjcXxMFgtRvftjT0zGbLcSPygDS5gVgFtmnkv/9NQ2s3MpmqrX8FFlvvh6I5XFVaeq64Ig/AA0TUMJBAm43PhqG3CXV+MsLKUhp4C6vTlUb8+ictNOytZsofir9RR+tpr8pV9Tl5V75JMLgiAI35umaYcYxnVsmT3J7YI9jd4gfln53n0TBOHUOOZhXD/96U/Ztm0bX3/9NVOnTiU2NpaGhgY0TePCCy/k1ltvPQndFISO8vfk88WC5ZRllaChkVvbEmi5487rqFu5gzCbBW9ApqzWicsXIKNHAj1nXMAFBVUsXfQFsqpysLKa7jtzSIyPpaauAb8i45ODaEBRaEp3CQhqfqOeQp+4FD5auplg6AXw3KF9sZjN1O/LIapnRoe+SpLEjDsv4cV75pMUGUV5k54h9On/vmDIqEGcNW7wyX2wjoIkSUSk98DiCMddWkj8oAzqs8sJuv08NGc6v/3Ph1Q7m4yaPUEliNlkosnv5t8Pz+ePbz9xiu9AEIRD0VQVNSij+AMogSBqIBj63m49tL+zNsfDU1ZFbL9emKzH/OfGaeVf//oXAwYMYNCgQfTq1atNoX5BEIQfQpNXJqCobbZ1jz/Wmj2ODttqXAEyYo8taCQIwo/DMf/1ZTKZeOGFF3jvvfdYunQpVVVV9OvXj8suu4yrr75a/IEj/GDe/esiqktrkIAKdx2B0ExRF0w/D1NeJSagV3Ic+0qq0YCDNU7O/83NWMIcTLnkfJYu0ouJZ1dU0n1XAjGSjZrQueu9biJtYfi8evpqclQkeyrzjWtnJqaxfm/LEK5JIwcA4K2oxlfXgCM+tkN/U3unMXL6OWxbvpW4sHDqQ8PFXnp8AX958zFiEqI7HHMq2OMTMdsdOAsPEjcwnYYD5eD08ditM/ndSx/h8nuN+j3+YADJKrFp934ObM+m/4gBp7j3gtC1aIqK0j4oEwiiBAKtgjTBw7ZRT+Fw0Zh+Pbt8oAfgpZdeMv7+cTgc9O/f3wj+DBo0iP79+xMefmxvugRBEI5FRZOvzbokQWqMA1/w6DNzosMs2MymNkGjaqdfBHsE4TR1XH+BSZLEtddey7XXXnui+/ODeeyxx1i8eDF33XUXDzzwwGHber1eFi1axJdffklOTg4ej4fo6GgGDx7M5ZdfziWXXILZbD7sOVRV5aOPPmLJkiVkZ2fj8XhISkpi5MiRXH/99YwePfpE3t4ZIeDSgyVeOUBRYzUANpuVqy6ZTNkX2wDomxrP/lCwJ6ugBsmqD00aNmowiSkJ1FTWUlhbR3F1I4kRkeTXVaNqGo0+D4rSUn8n0mGlwasXYk6PjUdRVeo8erHlAQN60adXKoHQ9JQN+3JIPe/Qz+fUm6axe9UuojUNnxzEGwzSWNfEy396nV/98xeYTMc8svKksEREEtNvkB7wGQD1uZXEAA/fMJ0n/rcMvxwAQvV7lCDuoJcXH/k3z37+7CnttyAciqZpaKoKqv5d/9LQtI7baL1fVfVsvtbrrdtpLds6nLt5m9Z+2xHOrahtMmq0U5g+L5nNmG1WTDYrZrsVk83Wsm58b7XNbmu3z4rJ0vUDPc2aMz+9Xi+7du1i165dxj5JkujevTsDBw5k0KBBxveUlJRT1V1BELqYKmfbYE9ipB2r2XRMwR5JkkiKslPa4DW2iSLNgnD6OnP+Cmvlyy+/ZPHio5syuqCggLvuuov8/Pw222tra1mzZg1r1qxh8eLFzJ8/n5iYmEOew+l0cvfdd7N58+Y228vKyigrK+PTTz/l1ltv5be//e3x3dAZqqihnJ2F+ZhNJiRMmE1mrr15FtXftvyBnZwYQc/UWAoqGmioaWTX+j2MmHg2JpOJyTPO473XP0HVNIrqa+kWk0B6QjwlNbWomoZPCWIxmemd2Y2yypaC5GP69SG3uGWGruH9+2OPiyLg9oMGTQeLSDxnGBZH23HPAFHxUZx/7SS+WvgliRGRVHpcBAJBdm3Yy+dvfcGM66diCgWkTjWT1UZ0nwG4SwuJQ6Ixr4qewP9dMpEXl61CVvVsAUVVCCoy+4pLWb9kNedeMenUdlz4UdE0DU1WUGUZTVFD3xVUWf/SZAVVUdBkWV8P7TO+d7K/eVmTFdT2QZTWwRdNgzNwOmuTxYLJ3i7w0i5YY7bZDhvIMR3hQ4zWFFnB7/PjdfvwNbjxefwEA0H6Du6F1fbj+D/tZPrNb35DVlYWe/fupbCwsMMU6pqmUVRURFFREV988YWxPSYmxgj8NGcCDRggMiQFQTh27YMy7evvHK1EEewRhC7jjAv2rF69+oiZPM08Hg933HEHxcXFAIwePZrrr7+etLQ0CgoKeO2118jNzWXz5s384he/YOHChR2GsWmaxv33328EeiZMmMANN9xAYmIi+/bt45VXXqG0tJQFCxYQHx/PnXfeeWJvuAurVFwElCCEPrAwm8zU7c0lJToZm8WCzW7G7rAwasxACj7ZCMCqj9cyYuLZAEydeT7vvf4JABVNjXSLSWB4ZjolNbUAyKqCxWQm2mxhdbke7LGZLaTYY/m6MUu/pmRCKnFhik/BUtOI7A2iqSqNB/JIGDbokP0+b/YEtn6+mcbqRuLsDipDtTDe+/fHpDn89OiTitkRhtnhwOwIw+IIwxwWhtnuQDqGN18ngmQyEdGtF5awcJBAMkuMOasPJTUNLNnwXctwLjmAW/Ly6j/fYfzlE8Vwzh+x5oCL2hw8MYIphw7IdNgfWj90QEbpcKymqkfulNCGZDFjav1ltWCymJGsFsxWi77e+stm1bc3B2qsFiSzWf89DH1Jkp41KJlMqKpGwB/E7wvi9vrxNXnx+xrxevz4fQF8Xj8+rx+/14/PG8Dr8enLHj9ejw+fx4/P48Pn1Ze9bh/BTmr6pPdK5R+L/oTJ/OPIWjxZbr/9dmPZ4/GQlZVlfO3du5f8/HxkueNwuoaGBjZt2sSmTZsA/VP1rKysH6zfgiB0He1nzjreYE9SpJh+XRC6iuMK9vzpT3/ivvvu6zST5WRwOp0899xzPProo8d9jtdff51nnnmGYPDoCk2++eabRqBn1qxZ/O1vfzPexJ5zzjlcdtll/N///R/r169n8+bNrFixgosvvrjNOZYuXcratWsBmD17Nk899ZSxb/jw4cyYMYM5c+aQm5vL/PnzmTVrFqmpqcd9j2eS+395Ew/95hlkRY/2KKrCu9+sJcxqY1Sv3lw8agjg4PybLuabTQeoraxjz8YsasprSUxLoP+QviQmxlFTU0+d14XJpJFsDSMmOoLGJjeqpmI2SeQWFOH368OW+ienUVRXizf0xqZ7bAImTKx6cw0TLuqL7K0HoH5fDvFDByAdYliW1W5l2q3Tef/viwmz2khLTqC8qhZFUXn531/RrXs8MTHhxMRGEBMbTmxsuL4eE05UfBSWsHA9GBQWFgoGOTDZHSctwCJJEo7EFMz2MP0NpNnElReMoKSqni25eWjon2D7ZD+FNdV8+I93uOrXPzkpfTnTNAdelGAQNSCjBvX6Ky1fHdeVQ+5rWe5SwRdJQjJJoaBGaD20LIXW9WbN26SW46S23439zdtovY1DX4PW2zo5L4e7VnOfDrEvRNM0gkEFvz+Izx/A7/Loy74gfr+MPxS08QdC676gvs0vh9oE27QP+IMEgvIPluxUXVxNMBDEHnZ8bzpOR+Hh4YwaNYpRo0YZ2wKBAPv372fnzp2sXbuWtWvXoigKkiR1yAISBEE4HjXtgjIp0R2LLR+N9tOvV7t8nbQUBOHH7riCPW+99Raffvopd911F3PmzMF6EoedBAIB3nnnHV566SXq6+uPK9hTUFDA008/zTfffAOA2WxGUY48fnX16tXG8m9/+9sOb6ZtNhsPPfQQV1xxBQBfffVVh2DPggULAIiMjOThhx/ucI3Y2Fgef/xx5syZg9/vZ+HChTz00EPHdH9nqqrCEsIsYcgmGVmRCYaGFXmDAdbkZLM2J5vRZw3kjnPP4vzLzmXJf5ehaRrffrKO2f83Czkgk2CPoAY9QNMYcGMyJWFvNcW4SZLIr2sZsjU4tRtbi/KM9b6Jer2FyqJqyit7EGezoARkFK8fZ0EJ0X16HLLvZ10wjI2frKckuwRbUCMxMYqaGidut5/s/eWd3rPFYiI6plUAqPl7XATxSbHEpyYQn5qILTLCyAqSrNYTEgiyRkUT028IJqsNk9nEL66ZzKOvNFJUo5e1VjUVp9/Fe4u/5NJ7ZmMPO74/Mk5XJywwI8tGUd2TFZhRVf3NZfsfi+/1cyJJevaJWc9GkSyhbBSTCcls0r+b9OBMS+CjuQ/Nb3ZVfciVphrtCQVzmo+VpNC25uUj3KeqqqiqhqKoKIqKqqgoqoasqG32qYqKoigozeuqiqpoKGrzPrXVPs3YZpxbVfWhTEGFgD+APyATCMoEAkH8wSDBgExAVggGZYKyTDCo6N/llu+yrKCoqv5waKEhcKGHRiL0ONAqQBRabt53dO2MVqHYUquA2DEwSRJmkwmzScIsmTBJ+gQO5tB2KfS9T1IciGl7sdlsDBs2jGHDhnHTTTdRUlLCk08+yddff01MTAw33XQTxcXF5OTknOquCoJwmuqQ2RN9nJk97YM9IrNHEE5bxxXsmTNnDm+//TZ//etfeeONN/jZz37GFVdccUJnmmhsbGTx4sX873//o7q6Gk3TuP7664/5PG+99RZPPfWUkc2TmZnJrbfeelRBo5rQm9jo6GgSEhIO2aZ3797GcnV1dZt9xcXFRjr25MmTiY2NPeQ5Ro0aRe/evcnPz2f58uUi2HOUvvh6B5IkYTVbmTl8JEXlDRQ3VlLpqkPV9HyTzbv3s3nOg/Tq0x3NoxDviGbNsvXMun0m2z7dRIKl5We2oL6OQd40mlwtL2oNXheNPr0wc/fUBMKtNoob6gBITk5g8szzyVq9G4Aty3dw4eyhENCDTnV7szsN9phMJqbfcQmv/uZlJEkixuogvKeD8vKGTodDAMiySl2ti7paV6dtJAkio8JaAkJxEcQlxhCfHEd8agIJaYkkdEshIi4Gk/nY/gsw2+3EZA4KDSkz8YdbL+P+ee/i8utju4OqTHljLa899AI/n/erYzr3yaBpmj7ldIdZikLLwSCyL4Di9enf/X4Uf6hNcyZMaEgSYLxRRqIlPqG/K0fT9xxa+6BKxw1GfxVVCwUA9OBBQFYIKqoeDFD07YFQYCAoq0bbYOt2HbY1t9O/moM9h2PcK62CQlJLz1sHB1oWpY4BpFb3d6Rjmh9WDT1Io2mhYE1oXdX0gseKpukBF01DI1Srh1bBEU0/R0v6SqtMmdATp+/S9GNoaa/Rej10Tlqdn9A1JP1787bW5z4ROvyM8D2DcUd5zSMFg9oGlkJbjHatn0t9S5Wzif/r4kO4jke3bt148cUX+de//sVLL73EunXrWLBgAQ7HmRUkFwThxOlYs+cEZfaIYI8gnLaOK9jz2GOPccEFF/Doo49SXl7On/70J5555hlmzpzJJZdcwqhRo44r28fn87F27Vo+/fRTvvnmG/x+P5qmkZiYyNy5c5k2bdoxn3P37t0Eg0FsNhs//elPueeee9ixY8dRHZucnExBQQFNTU1UV1eTlJTUoU1eXkuWR/vhV9u2bTOWx40bd9hrjRkzhvz8fEpLSykqKqJHj0MHCYQWqZHh5AJTRgxgZEYGniaVaHsE/eK7Y4kKsrO4iNpGJwAFefpwvBKpgsSmOD57ewUHV+wi0u4g0u7A5fdRXt/AjsJSTJKESTKhaipN/pagyrRxQ8nb3xLQG5iSzuS7ZlLwXS4ep5dgUGHHhhKGD09GU1T8tQ14a+oIS4w/ZP+TE21kDk4jN6scxa8ycea5XHTHTNxNHuqq6qmvbjC+t16uq6zH7fR0+rhoGjibvDibvBRT22k7R5iV2NgIYuOj9GBQShzxKfEkpCeRkJ5MQmoCkTERHd5gSmYzkT37YgkLx2Q28cRtl/Obl95DUfWAiCvg4dNVW7i6oIKkXsc+JFHTNDRF6RiYMb63Ct4Egyj+AIrXr39vbiM3B2qOPTNG0zT8QRlvIIjXH9C/BwKoiooGRpBBzxrRlxVFD0YoKnqGh7GuIit65occ2iaHlmVFadknqwRV/bumacf9pl7TNFRND4q0/a7qAZRW2+DQAYXm80DrwEjLuhEkad7Sav1Qx3VsF9qmGUe1O6dwtNo8f1InAaLm4OShjmneFvp5a/380urb9+Gs8+DxeLCHiyDGoTzwwAPk5eWxcuVK/vGPf/D73//+VHdJEITTVPvMnpTjzOxpX+un2iWCPYJwujruAs3nn38+y5cvZ968eSxcuBCPx8P777/P+++/T1hYGKNGjWLgwIH079+fPn36EBMTQ1RUFOHh4QQCAdxuNxUVFZSUlLBv3z527tzJ9u3bjQwcTdOwWq1cf/31/PKXvyQyMvK4+mm327nmmmv4+c9/TkZGxjEdO3XqVKOw8j/+8Q+efvrpNvsVReGZZ54x1i+55JI2+3Nzc43lXr16HfZa3bt3N5ZzcnJEsOco3DDpHC4dMQjNr9HU4KdfRjx7C6uxWayEa+G89s8H2HGwhI8+38CO7fsAUDSVSnctjz72V9KjE+ifmMHZZw9k3eYdAORUVhFmdWA3W/AE/XiC+jhlSZIY1b8Xn32727h+T3s01QdKmfHgbD54/C0AyotqyciIJDFef2NTt3s/GZPP7dD3QFMj7sJ8zrtwIHnZlaiKysalmxk76zxiU+KIjImgR79und673xegvjn4U91AfVU9dVUN1FXUGkGhxnrXYWtB+LxBKrwNVJQ3AMWHbGOxmomJiyQ+MYa4UGZQfGoCCclxxCXHEtmjN31NJh6smcbf31sO6G8YK911/PWuv/HYiw+gBGW0UHZNhwyboKwHafyBNtk0R1tQRNM0grJqZLz4AkGcXj9unx+Pz4/L68ftC+Dy+fXAjT+IL6h/BYIyAVkmqOgZM7Kqf1e0H19Nm9Z5FIdyuDBJZwGdIx0n/Pi1ef60wzyfR/k0d5Zx1n67hHTY/dAuC0kTBdsP56GHHmLlypW89dZbXHfddWRmZp7qLgmCcJpRVY1aV6DNtuQTVbPH6f9eH0QJgnDqfK/ZuMLCwnjooYeYM2cOL7zwAp988gmyLOPxeIxpyY9F8xtTm83GrFmzuOuuu+jWrfM3vEdj7ty5mA5RJPdo3HDDDXz11Vds3ryZjz76iPLycq699lrS0tIoLi7mjTfeYO/evQBcf/31TJw4sc3xFRUVxnJ6evphr5WWlnbI44TOxfXtgbznIJVVevZOSmwENU4PlXVuPL4gW9YdZOplZzNx9EDyyur4eOVWli35CjX0c1bWVEtZUy3p/lS8QR8Oix1P0E+Y1cGAtGS2FuYZGRBJ4TFs2HTQyBTqFhtPhN3B7qUbuejh6+g/dgAHNmUDsGtbKROn9MFilnAVlSF7vFjCw4x+KwE/TTl625j4cEZNHcrmL3YhB2WWvvAJI6aN1GtfWMyYzCbjq2W9ZXtyWiKp3ZIxN7ezmI1jNU3D2eiivrqRuqo6akurQ8GgOuqrG2ioddJQ7yIY7LyehhxUqK1qpLaqEbKKDtlGkiAyMow+Cank1eo/u7IqszZnLz+77vdYTWYsJhOSphdo0VoNnWkebqfXSlGRVT3YIqt6NoqqqigdMlX0oTxq83lC27ty4EJr9e+xH3t6PS5HG3DotH07zfffup0+Uql9AOMwa0c1Pu9Ij/TRBjDbna91dlToNCfrOe1wXq2T7e32H06YxYHd8sPOIni66d69O/379+fAgQN8+OGHYii3IAjHrNYdQGn3QVnKCZqNyxdUcfllohwnr0arIAgnxwmZej0jI4Mnn3ySBx54gMWLF/Pxxx9TVHToN4aHk5mZyaxZs7jqqqs6rZFzrI430AN6VtArr7zCq6++yoIFC9i4cSMbN25s0yYpKYmHHnqIWbNmdTi+sbHRWI6IiDjstVrXO3I6nZ22Kysro6ys7GhvoY3s7OzjOu7HqsdF51G4Pd94cxQeYWX0kHQ+W5eLqmpsWpPD0HN6kpIeS5/0eB64+SIGJKfx9jsrqHbX69O2A2WleoDCG/Rgt9gJt9oYlJHMxoKWxyvGEcn6vQeN9YHJevCuIruYqpxSpt13JUU/+xc+j59AQGbvzgrOHpkGmkZ9Vg5Jo4YBoKkqTQf2o8n6ta3RsUy9YwR7NubiafJwYEs2B7ac2OfJZDJhsoQCRqFAkcNiJT0+kW6JSWgaeoFrWf8KBIMEgzKBgL7sD8jIhxkOpWnQ1ORBVcyYkAio+r15ZIX9VYWdHnc0NUk6ZBSc9JoloX+lluWW0jJHN2vO982uOKpznQStszXaVmTBiHZIHVvTtk5P22es7fMlhWryaK3qvrS+Rtvzt+9H26UOnQ8FQcD4t1UdJaMGTxfTMmwu9G+rQFD7fe2HyrX8LLcUgT4Zj5FX9lGYW8Sg0UNP+Lm7ku7du5Odnc26detOdVcEQTgNVTa1nTFLAhIiT0yBZtCze0SwRxBOPyck2NMsKSmJe+65h3vuuYfc3Fw2bdrErl27yM/Pp6ysDJfLRSAQwG63ExkZSUZGBr1792b48OGMHTv2iEOdToXc3Fz27duHx3PoGik1NTV8/vnnDBkyhL59+7bZFwi0pFMeqehi6/2tj2vvgw8+YP78+UfT9S6vNreUpko9oCaZICLShskkMX7K2axbuQNN1fhiWRa3/GIKakB/ETx7cDrfRCWRGplIk99FRM9odny3B9DfFPlkPwUNpSzaGqDG1QhIWExmZFWjzKnXv4mICKNXQqJ+jKqx59NNTLl/Nhfdcxmf/P19AMrLm0iviiYpOYKG7IMkjBiCyWzGVZiH7NbrAJnsdqIz+2OyWrnwlov4ZN6Sk/I4qaqKGjj6oUkWwIKFMIsFLA4I0zNw9Myb0KxDqoor4KPO00i910mT33XMw58O9cbyiMGUdrs7Cwa1n53I+C61zEp0qOOO6CTGmjoGmtpsNf5p34Wj6Xtnj+upSMluewdtc5a01tuPJe5wmLY/9hDPkYboHYqRrdSuUPKJ+Pls/bPSoZh1y8qhg0haq2BS6Dm0mi2k90j+/h37kVu/fj2DBg0iLi7uuI5vamoCOO4PcwRBOLNVOdsGe8JtZsym43tRcFjNRDksOH1yq/P76ZN0fCU1BEE4dU5osKe1zMxMMjMzmTNnzsm6xEm3atUqfvnLX+Lz+YiPj+e+++5j6tSpxMbGUlZWxieffMIrr7zC119/zXfffcerr77K0KEtn16azS2p60d6U9X6D+zvk410Jsn5couxHBkK9EgmiZm/uJKc7FKqiqspOlBKVp6bUef1w1Newv4tRUTa7bj8fmIcUdx6/aUkzr2HX976ByrrW4ov59e0LNskK/VepzH86+JZU7CXB1GCCqoGpbvyqC2spPeYgfQe1ov8XQUA7NlZzsQpfQCZptwC7HER+KpC07hLJqL7DcQUKmQ+asZoohOjqSuvQ5WV0FTQij69s6xP/dyyrugBHFlt087Y1/5YtZPt7c8Zmm5aO8RMTSZJQpWgyeekyt1AlasBV8B72OenfVjiZGQNtD9nm6DGYS5nBH7oJDjUySxE7a8n0fbNdvsMl+bMEmPF6FgoS0hq2dI8vI3DvLE+5K1p7fe2tGrbruO2NoEGqZPt7dtLHfd1ttb2v73DZei0D3IdpiedZBF12Cq1biFhatchCZAkU8sppfb9a3ftw2Y3tQQRm2emkiQJEyZMkmRMQ26S9HVzq+9msxmLpO8zd/J/v9oqCwf0p7z189jx96Clbeul9tk8rQM2bYpwN5+zeaay1sW0W51Qa/dzCqC260uE1QHSSftT40fjtttuQ5IkUlNTGTRoEIMHD2bw4MEMGTKElJSUwx5bX19vDAlvrlsoCIJwLKqa2hZRjrB/v/93k6LsbYI9YkYuQTg9df2/wI5TVVUVDzzwAD6fj7i4OBYtWtSmaHKvXr247777GDt2LLfffjsNDQ3ce++9LF++HLtdT39sPTTL5/Nhs9k6vZ7f3/Kf6OHaCS2aZ1qyR9hxhOmBNckkUb83h6sfuIoXH/wPAJ++8hnDJjyMPyyFg3vKiXE4cIUe77Wfb2GMs4l4SwxahIYr4MYn+41aPaAPQ/DKPswmMxaThYsvPo/a7SUUhIZbaRrsWbaJSffMYsavr+G/P3uWgD+IP6CQtauSs0akUbszi6ie8cabw6g+fbFGtHxCIkkSA8YMPNkP2VFR1ZbAUXZWLhu+3cLG9dvYtT2LYFA+5DE2s4XUyDgafX5AQkVBVmWa3xBL6DOcmUz6G2ANDZNkonVQQw29uWyuxaPX5tHfThp1eoz171dE2ZhWu/Ub0x97CsjJdgbff0tgr81aKICj/342B21agjV6cMZiMoVm8JOQpOZlQkEfxRiuJqGgAYomoWitQkWyfuHm/xuaAyuqGvo9aBWMaTPJu6YZbdvMdGZMJQ+tp5JvP8SrTQYObYM9LYlVnWfstKkk1C5w1LxXAhLD4wh08v9GV1RRUUFFRQXffPONsS0uLq5NAGjgwIH07NkTk8lEYWEhc+fOxe12I0lSm8kaBEEQjlbliQ72RNrJq3Yb6yLYIwinJxHs6cSSJUuMoVv33Xdfp7NjjR07ljlz5vD6669TVlbGV199ZczK1bpOj9frJTo6utPrtR4mFhMT02m7q666ivHjxx/TvTTLzs7miSeeOK5jf4xG3DSd4g27qduxT39DJYHJLFG1LYsBN1zC6Omj2LJiKz63j4///QlWWQ8QhFmtRMdE0NToJie7nNoGvUZSuDUMRVOxSBZcQbfx5qqZoiooqsI/n/w3My+7yJiZQNU0ir/LoaG0htiMRKbccTHLX1gKQGlZE916xBAHyG4/1gg7YSlpOBJ/nMMaamvq2bhmK+u+3cyGNVupra47ZDsJiaTIaLrFJJARk0hSZDTbigpxB5unejfTL6Eb/175LFarGZMkoakqaCqaGvqSFZRgADkQQPb4kD0+gj4/si+of/cGCHj8BN1+fG4/fk8Qn8dHwCfj98m4PX6avF6aPD7cfj+egF+fJl0O4JdlAnKQgCITUPTAkzHjlqYXg/6+ASOh62g/XKnVDuF7qnTXIpm7foHmsWPHkp2dTUNDQ4d99fX1rF+/nvXr1xvbTCYTVqu1zQc9ANOnTz/ZXRUEoQuqbDeMK8L+/f7f7TAjl5h+XRBOSyLY04ldu3YZy1OnTj1s22nTpvH6668DsGPHDiPY03qq9/Ly8sOmcpeXlxvLh2uXnp5+xJm9zhT26HACFVWYQmOS4wf0xFlQiqaqFH+9iUvvnMneDVl4mjx899V2YsIc2CxmYpJjGT7pPN7/98eomkZJiR7Q6NUjmX0FRXgCXj3rRIKpgwazLienzZCl7/Yc4Ls9B4gNj+DstJ4MTu1OpMPGns82M+FnlzDggrPZ8+V3lBwoBWDnjnImTu6Nt8ZJeGoSET16/eCPVWeCgSA7tu1h/bdbWLd6M/v35nTaNtLmICMmgW4xCaRFx2O3WLFZzVgtJg5W1lDubDDaRtsjePDJnxEWEdbp+b4PTVXRFBlNUVCbvwdlgh4PQZcXX6MLf5ObgMtHwOXF6/Thd/vxewME/TJBv0IgIOPxBfD4/XgC+rTsnkAAT6AlcOSTg/iDQfxKEFXTOhk4BK3HaWnG2Ky2g2vajzAzMilCKRDtawsZy7QdZnToLJTQcrtt7esWtd5Gqz7ofeoY8DD+1TpsoSWLo3mLFLp7yXg4mvvTsdiShNS+jpDUMtTKJLW9z9b3377PRpZKcxaL1np7qyyx0LGqphrLbTLHOmTLtGSZtW5/cgYk/ji1HpYG7X6emoerGY3b/AQbw9oyohOxyF0/s+eNN94A9NfyrKws9u3bZ3wdqg6Poigoij4TYvPj2r9/f26//fYfrtOCIHQZHYZx2b7/MK7WRGaPIJyeRLCnE60zbaKiog7btvXMYa1n0urXr5+xXFRUxPDhwzs9R3FxsbGcmZl5LF09Y9XtL8BTpQdqwpPj6TVjAtlvf0ag0Ym7vBp/aQWX3jmTxc+8B4DL5ycuIozzfzKF3iMzWfLfT/F5WoI4F188AvMaWL1pJwBhdhuTzzmLTXn5OCwOZFVGAoKq/salweNm9cEs1udnMzitG6M8Ls6+/FyikmO55KFrWfDz5wkGFXw+mf17qxl8VgqOlAykU1iTSdM0CvNL2PDtFtZ9u5ktG3bg9Ry69o7NaiEtKo60yHi6xSQQ7Qg33pRER9mJjwujqd5HdZObXeUlKJr+xsUkSfRLT2fY5FEn7T4kkwnJZAMrtP7synEUk/i1DhTJAT+y20vQ7UP2eJG9PoIeH7LXj+wLoPgCBN0+PC4/iqygaaCGhtSoaGhqaF0NBQzU0PCb0LqqYtRB0reHhsm1bqdqqEqo+LWiIstKy7KqGdPSK6qmH6tpKKGC2Yqmn7t5vXlqen3aen27pqkoxnb9HBqEhiNJ+mxtkj7MzmwMVWoZmmQMUWqua6SBJjUHeiQjeKKqoeuGrqMYwZGTQwKj70DboIy+4aQFZdrUt2k1bKl1NqCx3Bw8Osy2lho6LT1ufrzNbZ6H0PNiMoWeO70WkPn/2bvv+KjK7PHjn6lpk95Io3eQ3gRsSFNQEdYOwtoWdXXtsrsqq7s/db9bLIu7666uir0hKiiKohTpvQcCCem9TSbT5/7+uMlNJiEBIRXO+/Xy5Z25z9x5Zi7J3Jw5zzn1zpdBb8Cg879fp/3nH8Cpv0xNBzVLK2v2N9uavt79Jwke1v4bADDp9ZhbKejbESUkJJCQkOD3JVFlZaVfACg1NZX09HStGUN0dDQzZ87k17/+NUFB5897JYRoOQ0LNFtaoGZPfRLsEaJzkmBPE+p31MjMzKR//6brqRQUFGjb9QM/w4YNQ1fTrnn79u0nbc9ea+vWrYB6oZicnHw2Uz9vFOw4qG0nXzISg8lEyqQxHPvsewByN+xk+C0zWPfJOvIzCvAqCgSaGHzJEPQGPf1H9WXL2l0AWMJDmHTrLLbUy2zpEhVBRaAbp8eDTqfDYg4hLMBCXKgFd6iHPYfUVuxun5c9OSfYk3OCn25I5+7f3snFl1/IuFljWf+xmraflVVOcko4pfsOk3jp+LZ6iwCwVlaxdeNOLXsnNzu/ybG9e3cjISiUcG8gMcHhfgVj9XodsTEh9OgdRUmxg9J8K3anhy3pGTg8dRcBkYGh/O5/j7fqazobfoGiwCACwiJ+9jFq/0CnNqCh1Nv21fwZ7/P57Vf/f7L71Psb3qctd/N6UXxeqCm07XWpxcE9bjc+txev24PX48PjcuN1efG4vHjd3poxXnweLx6Pt66gt0epKeztw+dVt7212x4vXo+Cx+PF6/b5jfOdpHC3//vRuBCwT1Fqls/5cHu9eGqCWvX/a9jl7XQDNAqoj/nZZ+/s+RVjbhAwaVyouYn7GgZb6hd2rrfdEmqLLPu/uY3ugBZ4N+vVz0aHDnNIAAHBzXejPNeFhYUxbtw4xo0bp92nKAoVFRUYjUYsFulwI4Q4Ow1br7dEzZ76JNgjROckwZ4mjBkzhhUrVgDw+eefNxvs+fLLL7Xt0aNHa9sJCQkMGzaMXbt28c033/DYY4+d9KJu+/btpKenA7Je/+cwBKidrMJ7JRPWXV3aFpqSQGT/npQdPo7P5SZn3U6C9XW5H6VlVopzionrGofHV1ezJSzMQmBoKIdST2j3VdncbNh9WLsdaAxQv2nHyC0TxpDy19/y7L3PsS8zA7dP/SPp8PEMfnPn70lMiueqSSOxRAZQVeZEUWD3rlzCIgPxut0YarpwtQav18uBvalsWreNjeu2sXfXQW25QENRMZFcOHEkPWNjUDKt2MtcjcaYTQaSksPo2TsKXXAIR/bmYyux4nZ72Z2dTYWzut63+EYunjiCqC6nkWLTidUW3a234qrDUxQFxedF8Xjw+bzodHo1y0yvrwsqnCLS4vP68Lg8eF1uXA4X1WVV6n/lVdgrbNgrq3Fa7ThsDlw2By67E7fdhdfjrbcUrMESttqn1TKPFNy1wSGfryZA5L/t9voHi6Am08cvU6leVpLfNlqmUl3WC9pttG01MGjQ69Eb9BgM6vtlMKiPqStYXLvEqybjqyZzqzZQVvv/80X9lX0KCuU2J26vj3O/as/Po9PpiIiIaO9pCCHOAV6f0igYIzV7hBAgwZ4mzZgxgxdffJHS0lLeeustxowZw2WXXdZo3Jdffsmnn34KQM+ePZkwYYLf/nnz5rFr1y7Ky8tZvHgxf/nLX/xaq1dUVLB48WIATCYTc+fObcVXdW7pddUlVBeUYEmO9/sGPOmiEVRm5OB1ODm4YT/WogqCzCbsLjc+r4+PX/yUm393E3u3HNAeU55fxrqV60g7ng2oyxkcTg87dqcBYAkIwGxQAzRljmoyMksYptOz8M5b2P31Fg7kZ7EnJ4Nyu7r8LzengFff/gqz2UjPiC4MiEsBLBw5WETU7gPEjh7Wou9Ffl4hG9duY9P6bWzesIOK8sqTjjOZTQwfdQHjLx7NqJGDsO47waENqdhySxqNDQ0x061nJInJYQRGReAMDGfX51vwujx4vT4yS8vILCvF5a1rFRxrCedX/3d3i7420TJ0Oh06gxEMxjP+w1tvBGOA2i0wBIhMij2tx7kdLhwVNuyVNuwV1ep27e1yG47a+yttKD7/7lDqds3/69UJqv2/Twva6hrU1/EPxLSYk8ZN61q8o9OrawsNte3d6yoaqfvrty9XtJJGWp5N/c5XtUvEGtZMUqh3DP/3RVsMdpLgmnZf7cRqp0S9IE3D59YeV7P8TKl3X/0lc/UOUH+u8V0iMAe0XnBbCCHOdyU2Jw2Tb1u6Zk9JlROvT8Gg7yxfcQkhQII9TbJYLPy///f/uPfee/F6vdx9993MmDGDK664gri4OAoLC/n6669ZuXIliqIQGBjI888/j9Ho/5bOmDGDZcuWsWHDBlasWEF+fj633nor8fHxpKam8uqrr5KToxbyve+++6Tt6s9gDAwgrFvjYtXGoECSLhpB+qqfOHpcDWKEmE2YQoOoLKnk2O5jvPn8u7hdau0dk96ATqfjfy+/ox3DbDDj9Xm0P5LGDOhDdnY55XY7To+b1MwiCnbso/uoYRxavZMRyT0ZmdKTE2VF7MpO53hxIQAul4fDhdkcLswmITSSgWUpJHSPIHrkEL+g389ltzvYsWUPG9dtY9O6bRw7mtHk2O69ujL+4tGMv2g0I8cOwVlYzOb317L+hZV4PP4ZB3odREUF07tvDBFRQZjDLUSPHErqhsOkfvkToP5xXVHtZHdONm6fR6szEmQK4Ka7riUg0HzGr0ucm0yBZkyBZkLjI5sdp/gUnDY79gpbTUCoGntFlRYIqn+/q7o2Zf30fo7qZxPVBYVoFCDS6uk0GHcm6j9WC4D4HUvnd7tROW1dg/93QnqngtfjQ38edOSqVVpayieffML27dspLFQ/CyIjI0lJSWH48OGMGzeOhISEdp6lEOJc0bA4s04HQeaWzezxKVBqczW6XwjRsUmwpxmTJk3i5ZdfZtGiRVRVVbFixQptaVd9sbGxvPDCCwwdOvSkx3nppZdYuHAh27ZtY/v27Wzfvr3RmAULFnDXXXe1+Gs4X0X278nmTzfgcKgBneSe8Yy+aRL//e3r+BSFrevUWj0Gg57AADMet4eDaRmAWpg4MiyMknotdOcv+iUfPP02OzLVQtoFFZUUZBQS1TubkMgQbGU2FAV6RsfRKyaeoJQg9hZk8+3aXdhsarZPnrWMPGsZW184yk0FRcz9zQIiIsNP6/UoisLR1ONa9s6OrXtxORsvuQIIDbMwdsJIxl+iBngSk7vgdTk5sXUfK556k4LsskZ/vJqMerokhNK7bzQBgSZMoSHEjh6KITSM9f9eQVGa2k3G5/PhcHnZeiIDl9frl9WTFBHNlb+84rRejxAno9PrCAwNJjA0GJKbzxryuj04KquxV9jwumvTbeoKJtdmnqh3+y8bo1FGSv3MFy0lqC57RlHrGbmcbjx2F26nC5fDhdvhxu1U/+9xuhvfrvnP46z7Oels9AZ9zX8G9MZ62wZ9zW1D3Rij/7bRZKTPhIGYzqPMno8++ojnnnsOh8PRaN/mzZv5+OOP0el0jB07lgULFnDJJZe0wyyFEOeSRvV6zEatecGZig4JQK/DL2OoyOqUYI8QnYwEe05hypQpjBw5kvfff59169aRnp6OzWYjNDSUPn36MGnSJK6//npCQkKaPIbFYmHp0qUsX76cL774gsOHD2O1WomMjGT48OHccsstfoUbxdlz2Z0cOVzXzr57XCDdesQz9JIh/LR6q1ZsdsL0cZgNBr767Ds8NXV3Lr5oBDpTMD9+p2ay9OrTnf6jBjF0UE92ZWXjUxTKHdVk5JbTtaCSxF5RHN1uA+qWa7jynTzy1G949P8sfPHJKt59/ROystSAidVp5z9vLOOt91Zw5awp3LxgNv0GNu7AVlZazqb129m0fhub1m2nsKD4pK9Vr9czeGh/xl8yhvEXjWbwsP4YjUYURcFZXs62d79i3w8HsVY0/uMjJMhESrcIUrpHYDDoMVmCiRk5hNDuyRQdzWHdi+/iqLDVjFZwexQO5edT4bD7BXpCA4L59XMLzypbSYifw2AyEhIdRkh0WHtP5ZQUn4LL4cRZ5cBV7cRpc9T9V+3EWWXHVe3EYXPgcbpPEWAxYDhZcKXh2AaBGIPxZEGZkwRtGjyupYpEnw/efvttnn322VN2oVMUhc2bN7N582YuueQSnnvuOb+mEEII8XMUNGy7fpb1egAMeh3RlgC/WkBSt0eIzue8DPaMHTuW1NTU0x4fFRXFvffey7333nvGz6nX65k9ezazZ88+42OI07f1803YrWpL8YR4C2GWALLWbGbmXVfy7VcbtHHDxw4mIjyQd979VLvvF7+cw3v/W67dDgxUv8UYdvUE4jbsIt9aiU9R2HssjxEFXUnsG8vR7WrGjzHAiM+ldjI6vu0YQ68Zz82/nMON86/lv7//F5+vXEN2hRq0cTpdfPbhSj77cCUjxgzh5gWziYqJYuParWxav42D+440+UdDl8Q4dWnWxWMYO2EE4RF1f/D6PB5Kj6ez/ZOfSNuThdvlX2REp4PI8CB69o4iOi4EnU6HMTiImBGDCevZDXSQ+t0udny8FqWmsKzRpMfp9FBYaSWtuKimg5KaNaVDx4Ce3Rk64YIzOldCnOt0eh0BwYHnfVeqc1lmZiZ//vOftd/Z/fr1Y9asWfTu3Ruz2YzVaiUvL489e/awfv16KioqAFi7di3XX389b775JklJSe35EoQQnVTDtutnW6+nVmzDYI905BKi02m1YI/dbufgwYNUVVWh0+lITEwkOTmZwEC52BWty1ZexdblastzvUHP4BHdweXEXljKti/X46npTBVgMLLhk3VMnTkIq0tdamU2mAgPDWfXtn3a8TLTcrBWVNF1dD96xcWSb1WLH+eVV1BeXEXimP6ERgVjLa3GWe3GZFQ7NR1evYMBU0ZgDg5Er9dz1/+7G3OhnezCUg4WZHG0JBeXRw2Y7Ny6l51b9zb5mgIDAxg1bhjjLxnDhItH071X10bfuHuqbeTsPsS25dvIzShptFTLaNATFxdC774xBFvUujqGoABihg8mvHd3dHo9HqebzW99S8aWui5klvAAqiqdWKtdbM/KBMDlrVtCFhFk4Td/+/Xpnh4hhDjnLF26FI/Hg06n4/rrr2fx4sUnzXScN28eLpeLzz77jBdeeIHy8nKysrK47bbb+PjjjwkL6/iZakKIjqVxZk8LBXtCA6AuSb5RUEkI0fG1eLDnyJEjvPjii6xbt65Ru2eDwcDgwYOZMGECs2fPlm+xRKvY+PE6XA41GDFs6kj6Xz2GtE++RVEUVi1bp42zBASSn1HAG2/WtVuPCgrnrX98gMOufnAa9Qa8Hi/rVm9mxi8mM3zcQLZnnMDp9WBzOUlNLyLxWC6JfWJI3aIGQkKiQ7CVVOO2u9j70TcMmXURpvBIdHo90x64lk//+AHjuvVjZHIvfClB/LRvP+nHMhu9jr4DemnZO8NHDSYgsPE6acXnw1FazOHvd7BnzSEqSqsbjQkKNJKUFEa3XlGYTGpqryHATPSwQYT37aEVTq0sKGPdK19QnlO3XKxL13BK863Yqt3syMrE5fXg8XnxKmrGj0Fv4JLLxpDYQ4qNCiHOX5s2bQIgOjqa3//+980uaTWbzdxwww1MmTKFhQsXsnfvXjIzM3nmmWf461//2lZTFkKcIwob1uxpgWVcAPFh/tedeeUS7BGis2nRYM+3337LY489htPpPOnyE4/Hw549e9izZw///ve/mTx5MosWLZKuFKLFlBeUsXOVWgDbFGBiwvWXYIkKJXpwH3Z+t5W8YjUrJ6l7FyhzovgUth06pj0+KjiMzZt2arcNOvVHZM3K9cz4xWT6XTKUhK82klGmdvnafSyH0XkpJPSN14I9Hk/dv/3j20/QdUAsARHhBCemkHhBL3r2iyPtcAEmgxFDgYf3Pv0ne/cfZuXy7/D5fIybOJILJ44mNj66ydfpdTmpPJHNrpVbOLIjE2dNIepaOh2EhwbSvUcE8Ylh6GpaZerNJqKHDCCify/09TrHZe1OY+NrX+O2q0Eyo9lAj4FxZKcWUmF1cbykmGJbFYqi+GX1RAeHctuTC07z7AghxLkpLy8PnU7H6NGjMZtPryNhVFQUb775Jtdddx3Hjh1j5cqV3HnnnfTr16+VZyuEOJcUNFzG1UKZPYkRQX63c8vtLXJcIUTbabFgT0ZGBo899hgOh8NveUlMTAxGo5Hy8nIcDocWBPJ6vXz77besW7eOP/7xj8ycObOlpiLOY+vf/wGfR80oG33VOCxRoQAkTBjOtiWfaeOmzxxPRUEFKz/7EYdHDV50TU5CcSrYnOqHWa9+3cENeVkF7N66n9KiMhIv6EHP2Bgt2FNQWUl5WTVRHi/R3WMpySiiqsRG8uAUCg7n4LK7yTqYT/ehBiqPHsIUFs7E26aSu/gDqu1uvF6FFc9/yA1/voMLLxrd7GtTFAW3tYK8/UfYuXI32WlFWqHpWgaDjtgYCz16RRIWEaj9LOpNRqIG9ydyYG/0prrOOD6fj73LN7J/5RbtPktUCL2HxHJsey7lFS7Kq+0cys8HwOPz4qv5GTYbTFy34CpCIyw/7yQJIcQ5xu1WC9aHhob+rMcFBwfzzDPPcMsttwCwfPlyHn/88RafnxDi3NWw9XpL1expGOzJkWCPEJ1OiwV73njjDS3QExoayv33389VV11FeHhda+mcnBy2b9/OmjVrWLNmDW63G7vdzmOPPUZ5eTlz585tqemI81DRiQL2/6jWvQm0BDH22gnavvycYo5mFgEQFhJAvKua3oNjefP9Mm3MRRNG83294s1X/WIa1RXVvPPqJ/h8Pn5Y9RNz5s2kR78Uwk6coNLpwOX1su1AFvHdoug1fjAlGT8AEBwdCeQAkL47l5TBCRgMetyVFUAFQ8d2ZfPaYygK5B/P5+D3uxh4+fCTvi6fx4OjqJCjG/eyd00qpYXWRmMCA4wkJoXTrUc4AYF1wRyd0UDUwL5EDuqLIcD/22ZnlZ0N/1lJ3oG6ZWwJfeLp3j+SQ+szKC134PR42ZaZgYKiBpt8dR24EiOiueaOq5o9J0IIcT6IioqisLCQgoKCn/3YkSNH0qtXL44fP87WrVtbYXZCiHOVx+ujuEGXLEsLZfYkS7BHiE7vZ/02ePXVV+nfvz8DBgwgLi7Ob9/GjWpBXJPJxNtvv33SNOSkpCSSkpK45pprKCoq4oUXXmDZsmX4fD6ee+45unfvzsSJE8/i5Yjz2dp3vqe2KvGFcyYSaKn7kPry7VXa9ugBKSgOJ8UZ+RRaywEw6Y2UphZidas1b3TABYP7ERJh4Z1XPwHUpVxz5s2k74RBJGzaT6VTzXY5lF3IJYVWeo6woNPpUBSFvIMnSBrak5w9x3FUOSku9JHQLRCfS80iSh7QheSjxWTlqB1Zfnj9G3qO7e83Z0+1jcqsbPZ/t5tDWzNwVNcFWmrnGGoJoFvvKLp0sWAw1NWI0Bn0RPTvTdQF/TGepNZPSUYB6/75BbYSdVmbTq+j/4TexMSbOfjDcYpLq3F7fOzJycZe8411/ayeYFMgty+6FVOAqdGxhRDifNO/f38KCgrYtWsXDofjZzej6N+/P8eOHSMzs3H9NiGEaEqJzUWDJO8zqtkTbG78mIaZPVaHh0qHm7BAufYTorP4WcGeF154QVsWEhkZyYABA7TgT0FBATqdjgkTJpzWevPY2FieffZZJkyYwOOPP47H4+Gpp55i1apVp73eXYha2YcyObo1FYDQ6DBGzhir7SsrLmf915sBCA4JYviAZADWbj+E16cWGo63RJKdV4CrNhU/IISdP+7h1kdvomffbhw/coKDe46Qm5VPdJyZ5MhwjpYU4FMUSmw2cnKtxKYdp8uAFPIOZmItqmDo1ReSs+c4AEfWHaLfnxbgKivGnp+DOTyI/sPiKS62YXd68Lg9fP3Xj5n11FxcZSUUph5n9+r9ZBzKx+dtsFRLryMqOphefWMIjwhAR72uXHodEX17ET2kP8Zg/w/pWmnr97H1ne+15W6BocEMndqfIKOHAz+kUVxcjcvlI6usjNyKcgAUxedXq6dPtxQmzBh3RudKCCHONZMmTWLt2rVYrVbee+89brvttp/1+JCQEEDtZCqEEKeroEFxZr0Ogkw/P9jTsMMrQJfwxkHrvHIHYV0k2CNEZ9F0u4gmKIq6nKOsrIyNGzfyv//9j0cffRRXTcbCkSNH+POf/8wXX3xBWlraSQs11zdjxgxtfXpeXh7Lli07g5chzmeKovDj0u+02xNvvNQv4+TrD77HWxPYmHLdZXQZ3BNFUVi//4g2JiU6nkJb3ZKuiMBQNn+zDUe1g0kzLtLu//ajr9F7bERFW4gOVmvVKCjsPJpFaVYpib1jtbGlWUUkDOwGQFVxBSe2HSEorgsRg4YSnJBMaEIkAy+Ip/bzNfNAJj8u+YhPn/mAZS+s5vj+PL9AT4DJQLfuUVwytQ+jxqUQERFYF+jR6Qjv04Oes68gftzwkwZ6vG4Pm5euZvOb32qBnpieXbjwF8MIDvByeH06JUXVOFxebC4ne3Oz695jFGpnEhYQwt3P3HnSCwMhhDgfXX311URGRgLw97//nS1btpziEf5qM3osFqmBJoQ4fQ3r9QSbjWd1feZTFKwON1aHG7fXR4zF/wv4tEIrVodby/QWQnRsPyvYc8899zBp0iQSExO1oE/tf7XLV3Jzc3nzzTd5/PHHueqqqxg+fDg33HADf/jDH/joo4/Yt2+fFhiqNW/ePJKT1WyLb7/9tuVenTgvHNtxlKyDat2ZqMRohlw+TNtnt9lZ/YlaR8dgNDDtukvRearJKi0ju1gN7vTr040pN15OqV2thWM2mrCYg3BUO9iyejuTrqxbWvjDKnW5YnLvGBLC6upRHS8qpTTPSmiAE71R/bFK33KYQVeO0cbs/2oLik9BbzASnJhMl4kTiE0IpUtcXUHPvevTKMqt0G7rAEuwmUFDunDZjH4MvCAOs0GHUi9nN6xnV3pcO50uE0ZhsoSc9D2ylVby7Z8/JG3tXu2+PpcOYdT0/uhcNo5syKCk0Ea104vX62PbiQztg9ykN1DtVi8m9DodY8YMod/wPs2eEyGEOJ8EBQXx+OOPoygKHo+HO+64gw8//PC0Hpuens727dvR6XT06SO/W4UQp69xJ66za7tuc3p4bX269l9Sg6Vcn+zI5rX16dicniaOIIToSH7WMq77779f266srOTQoUMcOnSI1NRUli9frgV86nM4HOzdu5e9e+v+yDQYDPTo0YOBAwf6/Zednc2RI0cQ4nQpPh9r367L6rlk7uXoDXUfdGs+30B1lZoWf9EV4zBZy7E5XWw8clwbc2GPFEqr6wIsMUHh2rciPy5fzyXXTGTAkD4c2nuUzOxisnKK6XvRBezelE6A0YTT48bqdJBdWEF8biXxPePIO5KPvcKGz6cQ2yeJoqM5VOaVkrnzKN1G9QXAFBJMWM9uDHS4KV1bjdPl1eZg0OuIiAim14BYYhND8TncKG4v9X+6LN2SiRk+iICIsGbfo7yDJ9jw6kqcNe+DwWRk9NxJRFncOMvKSNucRXF+FVV2Nz6fwoH8PKxONbhj0OlxeOq+NYoICuXOp355WudGCCHOJ7NmzWL//v288847eDwe/vCHP/Dhhx8yf/58Lr/88pNm7ezatYtFixbh8XjQ6XRMmTKlHWZ+cmvXruWuu+46rbETJ07k9ddfb+UZCSEaKqhsneLMtRIjgtiTXXeNbHVIkEeIzuSMfyOEhYUxduxYxo5Va6P8+OOPlJeXM2HCBCZPnsyhQ4c4cOAAR48ebZTJ4/F4SEtLIy0tjS+++MJvX1lZGa+99hp9+/alX79+xMfHn+kUxXng4Pr9FGao3U+69Eqk3/iB2j6Px8NX79Vlis246XIqj6bidLnZsFut72M2GhnVqyt/+ehrbVwXSyRewOP1ciI1i/SD6Ywf2ZtDe48CsGlXOnfPuorQsO+JDbGQXaFmCO3PyicpLoL4fjHk1cQsj28+xAUzx7HmhU/VMSs203VkHy2YFH1BfyrTMhg0OJ4D+9TXEZcQTs8BMYRHBuKuduOt9v/5CUlJIGbYYAKjI5p9bxRF4eDX29i9bIMWhLXEhHPRwhlQnouzrJzj23MoyqnEanPh9SoUVFaSUVqiHSPQaKLCWQWAUW/gylmXE5cce9LnE0KI890TTzyByWTijTfeQKfTcejQIRYtWoRer6dnz54kJSUREhKC3W4nLS2NrKws7bGJiYnceOON7Th7f4cPH27vKQghTqGwQc2elmq7XqthkeZKh7uJkUKIjqjFfiN0796dXbt2UVxczE033aTd7/V6SUtL49ChQxw8eFDLBqqqqjrpcbxeL3/729+02+Hh4fTr10/7b86cOS01ZdHJed0e1r23Rrt96a2T/dYpb/5uO8X5pQCMmDiEMJ+DSrebjXuOUu1QAyhjB/Umr6yS3EJ1XHJCF4JMATg9HjyomTbfvfM1l17Sm//VZK79tPkw9+h0pAzqSl5xmRbsOVFSSrXNhVEHRpMBj9vLie1HGDfvcqK6xVN6ooCyrCJy9qaTPLQnAObwUIK6xBIHhEUEEhAaSGCICbfNhcvaYB12YjwxwwcRFBt9yvfGZXey6X+ryNqZpt2XeEEPxv9yKrZjqbgrK8jcm0/BiXIqq5x4vQoOt5ud2XWdYCICQiioV8coLjSSG34jP39CCNGcxx9/nJEjR/Lss8+Sm5sL1F0LpaWl+Y2tDcRHR0fz73//u0M1qDh48CAAMTExvPbaa82OlVpDQrSPwgbXiiEtntnjX6RZMnuE6Fxa7DfCZZddxq5duzhy5AirV6/WUpENBoMWqJk1a5Y2PisriwMHDmhBoN27d2O1WhstBauoqGDr1q1s3boVnU4nwR6h2b16J+X5ajCi+9Ce9BjWS9unKApfvv2NdvvKGyZhPZYOwPebD2j3X3fbHD57va4o+M13zGLP57tQyqqw13ye7dh4mOnTBjJ4QFf2HTxBXnYBh/cdZeCk4ezfeIiwgCAqnXZcXi+ZxWWEHAkgLiWc3OOluKqd5OzP4IKZY1n7iprFtn/FZpKG9PDL7snOLyIwyAQeL86KuuVcAEHxMcQMH0xwl9PLqCnPLWHtks+xFtQFai64+kIGXzGa8v17cFdWknOokNy0EqpqMnp8isL2zBN4arqTBZsCcHpduH3qmxBgNHHT3XOwhJ28JpAQQog6kydP5uKLL2blypUsW7aMXbt24fE0/iPJaDRy5ZVX8sgjjxAXF9cOM23aoUOHABg4cCADBgxo59kIIU6mYTeus63Z01DDmj0S7BGic2mxYM+NN97Ia6+9htVq5be//S1xcXEMHTq0yfEpKSmkpKQwffp0AB588EG+/vprDAYDd9xxB6mpqaSmpmrfignR0J7VO7XtS+ZN9tu3f9shMlLVLJWeA7uTZNFTVeQlr7ic/Wlq2nz3nikMnzSO3//2RQACTSYuiI6g28Kree/59zEbjLi8HlwuD7t2pHP5zIvZd/BtANas3MDCR+cTGGAi1hJKpVOth3Mwt4DusVHEJoRCTVmg45sOcendVxGeGE1FbgnFx/MoOJxFlwFdATVjxxRqwW31z3YLjIlUgzyJ8afdWeHEtlQ2vfENHqeaZmsODmD8HVeSODCFsr17cFsrKThWQtbBQqpsbtweHz4FjhQWUGavBtQ6PWEBwaSX5WnH7RrXhek3Tz7pcwohhGjMbDZz7bXXcu2112K329m7dy+5ublUVFRgNptJTExkxIgRhIU1X3etPVRVVWkdwgYOHHiK0UKI9tKwZk/LZ/b4B3tsTg9en3TiEqKzaLHfCGFhYTz66KM8+eST2Gw25s2bx0MPPcTcuXMxGpt/msLCQtatWweo6cIPPPCAtq+qqorDhw9rwR8hagVHh2I9lMG4K8aQ2CfJb1/9rJ4Z10+i6oR60fr9lrqsnquvm84P32zA4VSXdI3q243q49n0nXU5vYb25MiuNFxe9RuMbdsyePRfj7DkhffxuD388PUGFj42n/ju8VTYHJwoK8bj85FXXoHD7caaZ8UUYMDt9JK1+xgel5vBM8fy03++AmDfis1asEen0xF1QX8KNm4HICAynJjhgwlJSTjtII/P62PXJ+s49O0O7b7IlFguvudqQiJDKN27G4/VSnFWOem787DZ3bg9Xrw+hRKbjSNFhdrj4kLCKa6uwKuoWT4hpkDu/N18jKaWvYAQQojOymq1EhoaeuqBNYKCgrQah53B4cOHtSxryeoRomPyeH2U2BoEe1q5Zo8C0olLiE7kZ7VeP5XrrruOm2++GUVRcLvd/PnPf2bKlCksWbKE/fv346tZIlLfhg0bmDdvHjabDZ1Ox8iRI/32WywWRo0axS233MIzzzzTktMVnVxmdgGltmq2/LSPipJK7f4TR7PYs2k/ALGJMfRNCAGfD6/Xxw871IKTBoOemXOm8uUndQWcJwxUl4Fl/7iVqTMGYzYZMejUH5GcE0UU5RQz5qLhAJQUlbFvxyH6ThhIgNFARGAwoH4IHs0vxlHlIjJOrWHgdXnI3JlGt9H9CI2PBKDgcBZFaTnac4f36U7ipReSNHki3a6egqVr4mkHeuwVNr7728d+gZ4eFw5k2m9vUgM9e9RAT1melWPbc3A4vThdXjxeBZfHw47ME9rjooIseBUfZQ41y0gHDOrfh1GTRpzWXIQQ4lyXlZXFxIkTGT9+PLfffjsbNmxo7ym1uNp6PQCDBg1qx5kIIZpSXOWiQRPkFl/GFRlsItjsf8wKuxRpFqKzaNFgD8BTTz3FwoULURQFRVHIz8/nlVde4brrrmPkyJFcddVVzJs3j5tuuolx48Zx5513aqnCer2e+fPnt/SUxDnKEqEGU0rySvjHI//CXtNafMU7dQGcK35xCY4cNaiy+2gWJSVq+8iJl42juqqa3dvUoFD3XikMHq5+e+kqt6KvqGTcJX0xG+q+Ifnhs3VcfuVF2u3vV66n/6ThGAx64ix1afiH89SuWr5633wc33wIvV7PoCtGa/ftW7FF29bpdIR2T8aSfPrZPABFabl89cw7FKZmA6A36Bl9y+WMv306ep1C6Z5deKqsVBZVcXRLFk6nl2qHG69P/fnclZ2FsyZ7KchoJiwgiCJbuXb8sIAQ7lr8y581JyGEOJf93//9H06nk9LSUioqKhg9evSpH9TJ1NbrCQ0Nxev18txzzzFjxgyGDBnCiBEjuPbaa1myZAmVlZWnOJIQorXkN6jXY9TrCDK1bLBHp9PRNSrY774K6cglRKfR4sEegAceeIC3336bPn36aEEfRVG0VqPbt29n9+7dlJeXA3XdKB5++GGGDBnSGlMS56CbHr6eqJpMmey0HP71u/+Sn13IT6vUIEpIWDDDetV1rvpx7zFte9b1V7Di09Xa7auvm07K5eOgJqhhzS1n9LgeJCTHaGO2fLudYaMHERgUAMC6bzaiNxmIiAkjPCiIIJPaRaXC7qDYasPr8GCuWTudsz8DR5WdHhcOJCRKTf3P3ZdO6YmCM3rtiqKQumY3q//vQ+zlahZOUISFKY/dQL9Jw1Dc7ppATxVVZdWkbszE5fJgs7vx+RQUBdJLiimssgKg1+mIt4RT6bRjddlr7tNz0aVj6DWoxxnNUQghzjUFBQWsWbMGnU5HQEAAL774IgEBAe09rRZXm9njdruZOXMmb775JmlpaTidTmw2GwcPHuQf//gHU6dOZdu2be08WyHOT/kV/sGeuNCAVvlyrlGwp1qCPUJ0Fq1WhGP06NF8+eWXbNiwgU8//ZRNmzZRXl7u12kL1D9ae/TowcMPP8zkyVIAVpy+yNgI7v/bPfzl3hexVdg4siuNvz24BK9X7WZ1+czxeEuKAfVbiI1b9gEQExvF2ItG8X+LlwBgMBqYPmsSpiATlsRwqnLKQQF7YTXXPXQdLzz4Ci6vB6/Hy6ZV2xh/2WjWfLWByooqtm/cQ/fhvSgpKCcyMBi7W63/cyS/kPF9emAy6XE5QfH6yNiWSv/LhjHwitFse1dtGb9/5RYuvufqn/W6PU43W9/5juMb69Ls4/omc9HCmQSFh+B1uSjbvQtPtQ17pZPDG07gdnmpsqmBHp8CFXY7B/PrCjDHh4TjVRQK62X1RAWHMv/xW37eSRFCiHPYypUr8Xq96HQ65s2bR3Jy8hkfKyMjgyeffJL+/fszaNAgLr74YqKiolpwtmfG5XJx7Jj65YjD4SA0NJQFCxYwduxYwsLCSE9P59NPP2Xr1q2UlZVx++2389577zF48OCTHi83N/eMm21IrUYhmlZobRDsCQtsYuTZaRjsqZRlXEJ0Gq1ecXXixIlMnDgRgKNHj5Kenk5ubi6KomCxWBg8eDD9+/eXZSLijETHhfPrP/+KFx5YgtPuJDNdXbJlNBkZOzgBHDYANh/Px+tRg0Az50xl+6ZdFBeWAjDxsrFERoRStn8PYYnh2Iur8Do92IsrSB6iZ9iEwWxdtxuAVe+u5obHrmPNV2qNhjVfrWfhvbew85udxFpCybOWowBphcWM7dWN+sm0x346QP/LhtFr4mD2rdiCo8JG5s6jlOeWEJEYzemwFpWz7pUvKMsq0u4bMHUkw+dchN5owOt0UrpnF97qapw2F4fWZ+B2erHaXPgUBa9PweP1sj3zBLVh14jAEEICAimsqsDhUYNVJr2BWTdfQUzC6c1LCCHOBzt21NVGu+GGG87qWN27d2fIkCG8/vrr6HQ65syZw5/+9KezneJZO3r0KG63+sdc9+7def311/2CWkOHDmXWrFn87W9/4z//+Q9Op5PHHnuMFStWoNc3Thj/9NNPWbJkSZvNX4jzRcPMntjQ1sky7Boty7iE6KxaZRlXU/r06cPUqVNZsGABv/zlL7nuuusYMGCABHrEGUlb+ROb/+8dqvcd4fYn5+HyebV93XomYK4J9OgDA1n1XV19nGuum+5XmHnmnClY047gczrQ6fVED6i7qM3dsJPrf30tppqOcjZrNfaiKixhIQBs+H4rIXHhBIUEEGw2E1ZTqNnl8XKipAyDXofBoP6YFR7NpaqkEqPZxMCpNYXIFTiwsm5uzcnZe5yvn3lHC/QYA0xctHAmI2+4tC7Qs1sN9Ljsbg6tz8DlcGOtdtUEedTwzt7cHKprMpACDCZigkOpdrn8avXER0Rz7Z1Xnda8hBDifFGbadK7d29SUlLO+nj33HMPFosFRVFYtWoVLpfrrI95tvr378/q1at54403GgV66nvooYcYPlxtWnDs2DF+/PHHNpylEKJhzZ741gr2NFzGJZk9QnQabRrsEaIlVWar7cJLUzNx7ElFX68DQVFGMbv3qkWLsz160o+pRcCHj76AsPBQ1n+/GYDo2EiG9Y7HVVEGgM5oJH7saCL79wTA53JTue8w46fXtcxd8cYqJl6u3nZUO9j043YS+yRhNhiICKr7QEwrLEan02E21v2YpdVkCPW5dCjmEDXdNmPrYayF5U2+TsWnsPfzjfzw8me4qtUWm6HxkUz//c10G90PoCbQsxOvvRq308PhDSdw2FxUVbvxeHx4fWqgJ6usjJwK9bl06EgIjVC7b9mtuGuCZYFGM7fefwPBocGNJyOEEOex8vJydDod3bp1a5HjhYSEMGfOHABsNhvbt29vkeOeDYPBQNeuXRk/fnyzy9R0Op1fdtPGjRvbYnpCiBqFlf5t11sts6dBsMfh9mGV7B4hOoVWX8YlRGvpfeV4Drz/LR67k717jlFlrQbUJUgGvZ6vvz1AaFQo607U1baZdcMVrPp8jbaka9oVE3EW1NWuCevdD0NAAEkXjaAyIwevw0n50RNcMXsCG77ehNfrw2q1YTHUfaCuWbmeuTfM4Nju40QGBpOrN+D2eckqLsPmdBFoMmKv+Tw+vvEgQ2ddhCnQTP8pI9i7fCOKT+HA11sZN39qo9fotDn46b9fkbsvXbsvZXhvLrxtOuZgdQ5eh0PN6HHY8bq9pG7MpLrSQbXDg9vjw6eAokCV08m+3GztOHGWcAKMRiocdkrsdR1VeiYnc9mcS87m1AghxDnJblcL2LdkbZ1Zs2bx1ltvAbB582bGjx/fYsdubQMGDNC2c2o6XzY0Z84cLrzwwjM6fmpqKs8888wZPVaIc13DzJ64sMBGS7taQlJkEDqgftXV7DI7iRHypaAQHZ0Ee0SnFZYSz7A7rmH/e9+wa+0e7f4L+iWTnlGMosBHH21hT8FRAEIswVw+/SJu/8WD2thLhtZ9OxvStTvm8AgAjEGBJF00gszVmwAo2rSb0ZePZPO3ateRQ+sPEhEVRnlpJVvW7eCRZ+5Br9cRaDIRERRMkc2KAqSXljA4IQGDQYfXq1BRaKXo4DHiBvWm/+XDOfTNdtx2F8d/OsAFV40jJKquhXtpZiHr/vkFVUVqu3idTsew2RMZeMVobemjGujZidfhwOf1cWRzNrYyO06XF6fLqxZk9il4fT62Z57AW1MgPSwgiIjAIOweD8XVlfhq7reYg7jjd/MxGlu2dacQQpwLgoKCsNlsLbrcqn///oSHh1NZWcnhw4db7LhtITCwriBsU+9JYmIiiYmJbTUlIc4bBRWNl3G1RrAnwGigS3ggefWOnV1mZ4w0axWiw5NlXKLTclfZcBYVQt8USirVrJ74CAvjenahdze1sHBhZSkOh5pWM3XmZaSnZXH86AkABvbvRlIXtXV7QFQ0QV38L0Yj+/fEktxFfS6rjdGDu2r7HG43sWER6j63h40/biMmKQaz0UBEUIg2LjW3EEVRMJvqgidH1uxA8fkwBwfS97JhAPi8Pg6uqkvfP77xIN88+74W6AmwBDHpoTkMunKMFujx2O11gR6fQtq2HCqLqnC5vVQ7PCg1BZkBDubnYXWqH9Img4E4Sxhenw+720m5Q23drkPHsCEDGDbxgp9/MoQQ4jwQHx8PQGZmZosdU6fTMXDgQBRFISMjo8WOe6YOHjzIt99+y/vvv69lMjWlpKRE246JiWntqQkhaticHqxOj999rbWMCyA5MsjvdnZZdas9lxCi5Uhmz2kqKyvj7bff5ocffiAzMxOXy0ViYiLjx49n7ty59OrVq9nH+3w+PvvsM5YvX05qairV1dXExsYyYsQIbrzxRkaPHt1Gr+TcUbBxG87SMpZ/UlfgeHivRPR6PaN6JeBw+Ti457i2b9LkCXz5yTfa7ckXDQHAEBRMaM8+jQqF63Q6UiaN4fC7K1C8PnR5+fQa0I1jh06omTAVdd9irlm5nmsnXUxhVhEWcwAh5gBsLidlVXbKXXbCTUHYHeqHcu6RAqpzcwhJTmHA1JEc/m4nXpeHtHX7GDh9FAe+2sqRH+oylaK6x3PJPVcTEl2X9VMb6PE5nSiKwvFd+ZTlVuLx+KiqdqModQWZ8ysryChVL8h1QGJoJEa9gSqnk8Kqcu2Y4YEh3P77+VIwXQghmtCrVy+OHTvGwYMHqa6uJji4ZZYx1C4LKysra5HjnY3//ve/fPXVV4Dajau5JVj1u5MNGTKk1ecmhFAVVDbO4IkLa81gTzDbMup+P2WXNR8IFkJ0DJLZcxo2bNjAtGnTeOWVVzh48CBVVVW4XC4yMjJ47733uPrqq7X19idjtVqZP38+v/vd79i6dSsVFRW43W5yc3NZsWIF8+bN4/nnn2/DV3RuMFmCyS2sIC1D7U4VEx3GqDE9QAcGvZ5eXSxUudQPoyBjAF//dxXffvEjAIEBJiaOHYDOYCCsb390hpMvWwqICCN+TE2mi6IwsGtdnQa9zkCgyQzAzs37SB7dG0DN7gmsy+45WliEXq/DWNOVy1bpJHfHAXweD4GhwfS5ZCgAXreHFU8t9Qv09L74AqYtutE/0FNd7RfoObGviJITpXh9PqzVLr+MHrvLxe6cujo9MSFhBJsCcHo82NwObG71YsGg0zPpiovo3r8ue0kIIYS/2i9mnE4nK1eubLHjWiwWgFNm0rSFcePGadvLly9vcpzdbueDDz4AwGQyMXVq47pzQojW0bBeT2igkWBz632H3zCzJ6u0/X9XCSFOTYI9p7Bjxw4WLlxIRYW6nGbixIm89NJLfPTRR/z1r39lyJAheDwenn32Wf7xj380eryiKDzwwANs3bpVe/wrr7zChx9+yB/+8AeSkpJQFIU33niD//znP2362jq72DEj2HwwX7s9flhXQkLNxPeOwhgUwI7jGXVjQyI4cjQdm01NO504biDBQQGE9uqLMTCo4aH9xI0YSGBUOAApYQGEWNQaBR7FR5hZ/VbX5/OxY9t+QsKCCTAaCQsMRF+TIXMosxBjoB6zue7HLSe1EFtmzXKyaaPQ19TIcddUctYbDYxbMJVx86diMNV9eKuBnl1aoCcntZSCtCJ8PgWrzY2ioBVk9ikKO7MzcXvVYtQh5kAiA0PwoeD0ePyyeqJDwrnlN784zXdeCCHOT1OnTsVQ8+XAP//5T2w2W4sct6pKXU4bFNT851FbuPLKK4mIiADgiy++4Lvvvms0xu128/jjj2tFmW+++WZiY2PbcppCnNcaduKKDwtsYmTLSJFlXEJ0ShLsaYbH42HRokW43Wp7wXvvvZfXX3+d6dOnM3ToUK666iref/99pk+fDsC//vUvDh486HeML7/8kg0bNgAwe/ZsXn/9dSZPnsywYcO46aabWLZsGb17qxkhS5YsIT8/H3F6Sosr2LnrGADBQSZGDE5B8SkYTUb6zr6YbelqMMWg15EUEUOJvUJ77OSLhhKclEJA5Kk7qugNBpInqa3WjQY9/bvW1SUIMdd9uH6/cj0pg7uh1+kIMBoJD1QDQW6Pl5yKCgLqfeOSe7yUqqxMvE4nwZEWek0cXHfMqFCm/fZGel/kXzvHY7OpGT0u9QM+P72SnIP5KIpCVbVLLcasqAWZAY4UFlBarX4YG/UGEkMjMOh12F0uKp3VOL3qv2uzwcicBTOJim+57jJCCHEuio+PZ+rUqSiKQn5+Pr/97W9RFOXUDzyFY8fUz7LaIEt7Cg0NZfHixeh0Onw+H/fffz+LFy/mp59+Ys+ePXz88cfMnj2bb75Rl0UPGTKEBx988BRHFUK0pIaZPV1aOdiTHOm/ZDWvwoHH62vV5xRCnD0J9jTjxx9/1Iowjh8/nvvvv7/RGKPRyLPPPktERARer5e//OUvfvvfeOMNQE3Rfvzxxxs9PiIigqeffhpQ08KXLl3a0i/jnPXV+9/hq/mgGT+mt1YEWfF6+fbtT7FWqymmF6QkM3pAorakK8BgIu1QKUGJyaf9XJbEOKIH9wFgUI+6by/1Oj2BRhMAB3YdJv6C7upzGI1EBNV9MO45nkt4TAgmo/oj57S7Kc2rpCpdrSk07NoJdB3Zh14TBnHFU3OJ7t7F7/nVQM8ufDXdToqyqsjcnY2iKNjsbjxeBUVR8NV87hZXVXG0qFB7fEJoBCaDAbfPh9vnpbi6LvCVGBXLVQuuPO33QgghzmcPPfSQ1oVq9erVPProo2fVnSsvL48jR46g0+no06dPS03zrFx55ZU8//zzBAUF4fV6+eCDD7jtttu4/vrreeKJJzhy5AigZiu//vrrHSIjSYjzScOuW62d2dNwGZfHp/h15xJCdEwS7GnGpk2btO1bb721yXEhISFads/mzZu17hRZWVlaps9ll13W5Dd2o0aNokcPtX/hqlWrWmLq54U9m/YDYDIZmTCqJ8ZAE4ZAtTjdmq0HtHEzr53M0XoZU9HB4ezYeIQv/vvz6i0kTBiOMTiQiNAgkuPUZV0+RSEsoF73rYwTGAx6zAYDQUYzAQY1myezsAyHy05AoEkbm32kGHt+Hu6qKgIsQVx8z9VceNt0AkP9vz1xV1WpGT1u9Y+J0gI7x7epWUt2pweX26fW6VHUZYNOj4ddOXWdYqKDQwkxB6CgdhErrbbi8alLu4KMAcx/6CaCQlr3IkEIIc4VKSkpfhk9K1eu5KabbtICID/XkiVL8NVE6seMGdNi8zxbs2bN4uuvv+bOO+9kwIABhISEYDKZSEhIYNq0afzzn//k9ddfJyws7NQHE0K0qEJrw2BP6xVnBogINmE2+P/ZeKJElnIJ0dFJsKcZtWvRAYYOHdrs2Npv43w+H7t37wb8u1TUL3h4MrUXeDk5OS3a0vVcNmRkf8xmE5Mn9MMSEoDOoCfx8otwR0ezM1UNhkSHWxjSLYqdmWqRYp1OR1SQemG66p3VrPlk7Wk/nzHATPIlanHOC3rHa/cH11vK9d2X64jrHo9Br8dk8G/Dvi8jn8RedY/LzyjD5/VhPZbW5HO6q6oo3bMLX81SwopSN0c3pgPgdHlwONWgjU9RUHxqds+e3CwcbrXzV5DJTFxIKHqdDrvbjdvnpdRu1Y7fp0c3Lpo5/rTfAyGEEHDDDTdwxx13aAGfAwcOMHv2bJ544glOnDhx2sf53//+x6effgqA2WzmqquuapX5nqmEhAQeeeQRli9fzs6dO9m/fz8//vgjL7/8Mpdffnl7T0+I81bDzJ4u4a37pZ1OpyM82OR3X0ZJy9QsE0K0Hgn2NKO2Vg9wyvaqRmNdPZaMjAwA0tLq/ojv3r17s49PSUnRto8ePfozZnn+Cix1cGHXbjhyXeRklGHpmoI5NJSNR7O1C/BLR/Rn7740SivU4pcDuyYyfmBdx6mPX/6Ubd/tOOnxTya8d1fCuifRMymKoAD1Q0+v0xNsUr9RyTiWRXgfdQlW/bo9ALuP5WDWewgIUP+t+LwKucdLcZWV4iwtbfRcbquV0t27UGr+HVZZvRxZnwaKgtvtxWZXAzo+X93yreMlxRRUqsEcg05PclgUOp0Or8+H2+ul2FaBgvrehJqDuX3RPAzGk3ciE0II0bRHHnmEBx98EJ1Oh06nw+Px8Omnn3LFFVdwyy238NZbb7F3716cTv9CqlVVVaxZs4YFCxZoS791Oh033HAD0dHR7fFShBCdTE65fzeshPDWX0oZEeQf7DkhwR4hOrzW69F3DoiMjNS28/Pzmw3Y5OXladtFRUXaY2olJiY2+1wJCQl+z9WU3NxccnNzmz1WU1JTU8/ocR1VTNcYSk4U4vMp7NqYBdEJjB3s4/OP65bCTRo3mA++qluOd811k+lmDMbu8rA/vRBFgTf+9DYh4SEMHN3/lM+p0+lIvnQ0Vdn5DOwZx45DOSiKQkRIKNXl6gX9zv2HCQYCjAZMBgOhAYFYnQ6s1U6OZeWT2D+F9D3qN78Z+wtI7hOD9Vga5sjR6Go6eLmtVkr37ELxqAEdu13H4R/T8Hl9eLw+quxqAEhRFGrL45VXV3OooO7fTrwlAqNeDeRUu904PW4qnOoHsw4do8cM5YILB53BOy+EEALgV7/6FSNHjmTRokVkZ6sZpD6fj507d7Jz505A/dwIDg7GYrFgt9uxWq3aFxK1/x8wYACPPPJI+7wIIUSn4vL4KLT6B5GTItog2NMgsye9WJZxCdHRSbCnGcOGDePLL78E4Ntvv+Wuu+5qcuyaNWu07eqaDki17dpBrevTnPqZQ1artclxn376KUuWLGl+4ueJEFc14RYzFVVqLZtdX25l144DZGWoy++GD+lDbLc4th/KACAsJIj+oSYsSbFcHnEh9o/Xcyy3FJ/Xx78ef5WH/vEbegzqfsrnNYdZ6DJuKIPLbew4pD5XRFgYueXFAGxau4OZw0Zgtzow6vVEBIZgdarptrvTcpk/bjAZe3UoioK13EFVqR0L4CjIJ6hLAu7KSkr37tYCPU63gYM/HMbr9uDzqQWZlZr6PBgMKC4Pbq+XXblZ2h8OkYEhRFtC8PkUHG43PkWh0FauvYaIQAvzH7tZCy4JIYQ4M6NGjeKLL77g3//+Nx988AGVlZUoioJOp/6eVxSFqqoqrb16rdrfv6NGjeLll1/GbDa3x/SFEJ1MfoWDhk0A2yTYE+T/O0qWcQnR8ckyrmZMnz5d67jx6quv+i3Lqm/p0qV+hRk9NX+k1+/OUXucptTffzZdPc4nisdNfEwwsVF1H3A/rt+ibU+9eChrNx/UWkNeNKwPRoMBR34BwYEe5tw+neQYtX6P2+3l5Qf+QU7q6dVLih3Wny49EunaJQIAu9VOVLhatNnuclLoUoM7AUY1s8egU3/UDmcWUHw8m9gedbV7jmxXvw22ph/HWVZG6Z66QI8bMwd/OILH4dI6b3lrOm/pzSY8Lg+KonCgIJcqh/otT6DRRGJ4BF6vD19NwWaby0G1W52TQadn+qxJdO1z+t3IhBBCNC04OJiHHnqIH3/8kUWLFjFs2DD0+uYvseLj43nyySd58803iYqKaqOZCiE6u+xy/4waS4CRsKDW//6+YWZPZkk1Xp/SxGghREcgmT3NiImJ4Z577uHvf/87VVVV3Hzzzdx3331MmzaNyMhIsrKyeO+993jnnXeIj4+noKAAAJNJ/WVoMNTVQjlVBoVSL0R/qgtEoeo380IOLf8JnU6H2WTgRG456aVqu/EAo5H+yYk8++oybfzsu25An5+Hz+3GU1WNzuBk7m+u5n9/X05hWRV2h5sX7/8HD/ztbpIG92z2uXV6PSmXj2XwrjQy88sBSEqIp7Qmm+tQWjrDErpiNhrRudxEBAVTUl2F16ewLy2Xi6+4mMLj6pKr0oIqbOV2QiKgbM8u7Tm8hkAOrj6Mq8qBoihUOz24PWrgyhBoxmlTgzs5leVklZYBoNfpSAyNRFHUf3PVLlejrJ7Y0Aiuu2fWGb/vQgghTi44OJgFCxawYMECqqqq2L59Ozk5OZSUlFBZWYnFYiE2NpYRI0YwYMCA9p6uEKITOl7knyWYGBHYJpnaDYM9Lq+PvAo7yZHN1zUVQrQfCfacwl133UV+fj7vvfceFRUV/OlPf+JPf/qT35jk5GSef/555s6dC9Qtyaq/NMvhcDSbol2/gGNz4+bMmcOFF154Rq8lNTWVZ5555owe2xFFDejDuF7dObRsLexJo9RTjremUnGv6C58+f5WMjLVANygof0ZPGEk7iob+Ru24CqvQPF68ebnMO+e6bz+j5WUV9qxVjtZ8tir3L34VrqObb6eTXBcNGOuGMeP249R7XBTmV+JXq/H5/NRVm2l3G4n1hKiLuUKCqGkWv1w3p2Wy6XVVsxBAbjsTtweHyf25TPwoh51Bw8I5tDqIzjKbSiKgsPtxVnTeUtvMmqBHqvDwb56XePiQsIJCw7E7VELMru9XiqcNlxetcaP2WDi+jtmERkb0SLnQAghxMlZLBYuvfTS9p6GEOIck9egE1dbFGcGCDIZMBv0uLw+7b6M4moJ9gjRgUmw5xR0Oh2LFy9m/PjxvPrqq+zfv1/LwomNjWXWrFksXLjQr017TEwM4F+nx263ExYW1uTz1Nb5AQivWQ50MomJiacs9nw+MZhNDLrhckKTYnn52++1+/vGJnKkoK5o9lVzpgBgsoSQNPliirbvpiojCwB9eQlz51/Cm//7gSqbkzKrnf89+y633H0VvaeNa/bbkqQJwxkyYC2bd6Wj1+lITowjMztfbXFebSPYbMZsMBBoNBFkNGH3uMkrrST9cAbdRvbm6IYDABTnVGItrSY0KhhdUCgHfziKrVjNEnJ7fdhrOm+h0+Gpye7x+nzsyc/GUxPgCg8IJjEyHIdTHevwuPEpPoptdbWjUuLiuXLu1LN6z4UQQgghRPvIK/cP9iRFtk2wR6fTERFs8isOnV5iY2KfmDZ5fiHEzyfrhU7TlClT+OSTT9i6dStfffUV69evZ/369TzyyCNYLBaOHTumjU1OVmuhJCUlaffV79Z1MvX3x8fHNzNSNKTT6XDGWMiqaV+eGBFBt9hIbUmXQa+HE5W4HWotJL3RSNzYkcSMHAp6NZAT5HNywy9GExiopqjml1bx8WtfceD9b/E4m66hZDCbmDL/Su22Uu3RtsscVVTa1SLNABFBdcG/XWk5REbXfTg7XT5yj5ZjjksgdUM6lbklAGrnLVtd5y1jcCBet/ocaWWFlFWpQUKzwUhCeAROl0d9PzwevD6FkupKvIoaDAo2BTD/oZsICAr4eW+wEEIIIYToEBpm9iRGNF8XtCWFN2y/XixFmoXoyCTY8zOFhYXRq1cv4uLi/DI+du2qq7UycOBAAPr06aPdl5nZfOHfrKwsbbt3794tNd3zxievf6Rtjx/Yh2JHBS6vGhTpHhlL3p4Mli1+G2tNtoxOpyO8T0+SJl2EIUj9kIyymJg94wKMRrXWUmZBBatXbGb3a19gL6ts8rl7jBlE794JAOh9Bu3x5fYqfIqCzeXCoNcTHhhM7b+YvcfzcOQXEhqrZnF5vD5Ks0rZ/flOyk6oS898Ph9WmxpoUhQFS3wk9gr1Q7XYbuVInhrM0gFdLBGEBJoBHV6fD6fHg9vrocxet657QN9eTLhi3Bm9v0IIIYQQov3lVdj9brdFJ65aDev2SEcuITo2CfY0IysrixdffJEnn3yS3bt3NzlOURS+/15dQtS1a1dSUlIAtXV7bUBo+/btzT7X1q1bAUhISNAyg8TpqczL5ZtvfgLAZDJy+x/vZl9xkba/b6waiCk5Ucgnv3+T/CPZ2r7AmGiSp11GYKyagprUJZyrpw3UzltqVgnbth1h938/pzyj6eysqfOmA6DX6YmqWb7nVXxUOquxuz3oUDOMwgLVD2S7083B1BMkDUzRjuFye7HmqdlJiqJgrXZrrTUju8ZRnqvuc3hc7KgXPIwNCScxOgyHU+3M5fJ6UIDi6goU1AOEBQTzy8fmojfIj7wQQgghRGekKMpJMnvaMtjjX1c0XTJ7hOjQ5C+/Zrjdbv71r3/x0UcfsXz58ibHffXVV1rNnlmzZmn3JyQkMGzYMAC++eYbqqqqTvJoNRCUnp4OwLRp01pk7ucLT7WNbz9agbVK/Zbj0ktH4zUHcDhdzZSKCglhdN9kTEb1n7q9oprlz7xH6rp92jGMgYEkXjaB8H5qRlWv7jFMv6yftn/PsXxSj+Wx/+2vyNtx+KTzGH7ZCMIjLADovHVd2Moc6jl3uNVATERg/aVcuYQG1411utUCzIqiYHN48HrVQE1kSixFGTXZPorCvoIcXDVjLeZAEiPCqKrJAPL4fDg9XhweF5VOdYmXXqfjwomjGDRGOr8IIYQQQnRWpTYXTo/P7742zexpsIwrq9Qu7deF6MAk2NOMnj170rdvXwCWLVvG0aNHG41JTU3l6aefBiA6OlrryFVr3rx5AJSXl7N48WJ8Pv9f0BUVFSxevBhQW7Y3fLxoms/joeLIYb5ZU5c1NfvWa/lq2XdaEe0Z115OSFgQ3RJDCQ401jzOy/f/XMGm935AqfmA0un1xAy/gPjxo9EZDQwekMDF4+rar285lE1WQQVpKzZw7OuNKA3Oo8Fo4JLZFwMQZAzAZFCDOJXOanyKD5+ioCgKIea6fWk5RZSkZxOZrGYVeb1KzRIsHy6XGswJjYugvKCcmgQdsm2l5JdZATDpDXSxRBBgNqGgBomcXjWoVFhVrs0tIjCUeQ/dcLZvtxBCCCGEaEdZZf5LuHQ6iAttu1qMJ2u/nltub2K0EKK9SbDnFB5++GFAbY0+d+5cXnvtNbZt28amTZv4y1/+wg033EBFRQUGg4HnnnuuUSetGTNmMHHiRABWrFjBvHnz+Oabb9i9ezcffvgh1157LWlpaQDcd9992hIwcWpVJ9LJz8ln577jACSldGHkuKGs+PRbAPR6Pdf96jpG3zOb0PhIkrtYCA+tSz/d9cVmVv39U61wM4ClazLJUy7FFGphzIiujByiLqlTFNiw7wRF5TZytx5k/7urcNud1Hfx1RPQ6XXodDpCAtQ6QF6fjwqHmmHjqQksRQQGa8fcfSSTqJi6lpU2u4fqarUgc2BYMD50uKrV56lWHOzJqOv61iU0gqTYMKqqa7J6FB8erw+b24Hdoz7GqDcw87rJJPWUDm5CCCGEEJ3ZiQY1csICTRjbcIl+bfv1+qRujxAdlwR7TuHSSy/ld7/7HQaDgfLycv7yl78wd+5cFixYwGuvvYbdbic0NJSXX36ZSy655KTHeOmllxg9ejSgLtm6//77ueGGG3jqqae05V8LFizgrrvuarPXdS7wVFfx7dpdWhbP1b+Yzq6t+8jLUQsXj71oJPEJsYTERTLqntnE9O9GfHQwcVF16a7p24+y7Km3sRbVtSc3h4eRPPVSLCmJXDaxN/37xAHg9Sn8uDuDiioH5cdz2fPa51QXl2uPi4qLZOj4CwAI1Nd9y2KnLpjk8foID6wL7uxKy8Hkqq7bX5OaazAbiUiJo7ymK5dP72NTWro2LiY4lKTocEoqqmvm5qPa6UJRFIpsdXOKC4tk9q+u+TlvqxBCCCGE6IAyS6r9bjfsjtXaatuv15fRYE5CiI5Dgj2nYf78+Xz88cfMmjWLxMRETCYTwcHBDBw4kHvuuYdvvvmGyZMnN/l4i8XC0qVLee6557jwwguJjIzEaDQSGxvL1KlTeeutt/jtb3/r191LnFpIt56sXr8XUD98Zs6Zyhcff6Ptv+oXU7VtU1AAQ2+dTo9JI4kMDyQ53oK+pu16SaZauDkvta5ws95kIn7CWKKHDeaKyQPolhwJgMvjZc3udGwOF/bSSna/9gVlaXWPu3SWmsUVaDRjrlmuVWytQEEN4iio2TYhZjUYVFJZTX5ZJRZLXcaRTqejx7gBZO05XnMb9ufnUO1UM36CTQHEhYah1+lQFHX5lhcfClDuqNK6kAUYTNxw17WER4Wd5TsthBBCCCHaW8PASlsHe6Bx3Z4MKdIsRIdlbO8JdBaDBg3iz3/+8xk/Xq/XM3v2bGbPnt2Cszq/ff3WSvLz1cyXkSMGERwcxI81XbnCI8O4aNJYv/E6vZ5eU8dgSYjm4Mc/0NWoJ6egCrfHh72yms//+B6X3jmd/pcMUcfrdEQO6EtAZATX6o28/9EWCoqqqHa4+XHvCSYP70EAsP+9b+g5dSyJYwcxeOxAouOjKCkoJdAYgMtbjdfrw+V1E2BQAzxen0J4QDA2l7rUandaDldOm8jBDakA9Js8nP3f7NDmXeSu5ERhGQAGnZ54SzgpcWFkF1pr5gnVDjden4+S6roW8d0Sk5h2U9NBSCGEEEII0XlklvoHVhpm2bSFhh25JNgjRMclmT2i0/p65Vpte1BIGP995O+4XGr2y/RrJmEOMJ/0cfEX9GLU3dcSHh/RqHDzmn+tZNO7P/gV0g7uEkevq6Zw09yLiAhTl4CVV9rZcDAbj9cHisLxbzZz9Mv1gMLFV08AIMRct1ys2F5JkKkuthpiDtDWWO9Pz8ekuLj0nqu4+O4ZpP10UCscTaCXzakntMd1CY2gR5dI8orVLl+KomBzqcvESqor8SrqvENMgcx/8EYCAk/+HgghhBBCiM7lRAfI7AlvtIxLgj1CdFQS7BGdVoFd/cALCTAzODmRjfvq2qIPi4/zq6fTUGhCNGPunUNM3xSSu1iIqNfJYNeXm1n1t09x1SvAbAoJpu81U1lw75UE13yw5hdVsiUtD19NYKZg1xH2Lf2KsZOGo9frCTCYtM5bRVWVBJoM1C3U0xFWU7vH5fGya89RopMi2ffVduyV6usyB+pZd+iYVpMoKshCXHgobo9Xa3NpMOpwe324vR7KHVbt6IMG9mPctNE/8x0VQgghhBAdUbXLQ6HVvzlIR1jGJe3Xhei4JNgjOq0F997MgMF9eWzRr3CEBZFTWg5AclQEhqxitr38Efve/prSo1l1mTL1mEICGfbLGXS7aCjxMcHERddl4mTsSGPZ4repLCzX7tMbDPS/8hLuXHQDZpMaxMnIKmFPVhHU1P+pzCzg2Aff0jMpEp1OR7BJ7cqlKFBYVUlYUKB2vGBDXYBp55EcNrz+DYXH8tS5mfXsL8ilwuYA1BpAMcGhpMSGUVqp3mcwQGmV2u6yyFZe252d8IAQfvnYzej18uMthBBCCHEuyCxtXAi5XYI90n5diE5D/hoUndaMKybw75ce5vJpYzhUXZfVMqZPT2279GgW+97+mm1LPiJnywG8NUWOa+kNevrMGM/A6yYRHRVCcpe6ws2lmUV8+sRb5B3O8nvMgEljuOOpudq4g0fzOVJQir4mAORxuOgZrxZ0tpjqAki5lWWEmE0Ya4IwgUYTgSb1AzOzsIzU/RnqnPQ6KpVqUrPUrmJ6nY4ulgj6do3mRL7aNUxRFFxeLwB2txOry66NnXjZWPqP6HtG76kQQgghhOh4Gi7hCgkwYGrDtuu1gkwGLAH+ZV9lKZcQHZMEe0SnZU07ij0vl7LUw6xavgYAs9nEL/9wBz2mjCEgwqKNtRdXkLbyJzb99R3Svt6EvbTS71gJI/oy8lfXENVFreNjMqo/GrWFmw//uNdv/JBLR7Lgd7dot7ftySKjoBRDzeMSo0KxBJkxGYyY9OoHYqndhsPjJiJYze7R6XSEmOoyfbLK1CLMQeF6fthzVLs/3hJBYnQo1mqnWiMICAkyYbXXtFqvrmsbHxUUxi0PXH8mb6cQQgghhOigTjQIqLRHVg+o169do4L97pMizUJ0TBLsEZ2WKULNntmy6yhWm5rZcuHIvlCYTZDRRv9pQ+g9fRTh3bpoj/E63eRs2sfWlz5g/3vfUHYsR6uJE5Ycx+h75xDbJ8m/cLPXx5p/r2TjO2u0ws2uyiqSAhQuHtVDO/b6nSfYf7yQnPwq0o6XERkYrAZ0zHUBndzKMgKMRq0te1hA/cyfcrr1CGP1niPa2ueIwGCiQkJIjAmluCZF1mTUU1SpfqhWuezY3er6bZPewFU3TiOhW3xLvcVCCCGEEKID6Aht12t1i/YP9qQXN15iJoRof9J6XXRalu49CIyL44dXPtfum3KR2jZd8XlxlZdiAhL7RxDbK5Ky7ApKj+WjeH2gQMnhE5QcPkFwXCRJ4wYTP6QPAaHBjLj9Ko6s+An95gMUltgprymGt3vFFoqO5ZKUEk7p8VxsNheKTaFLmIX8SrU71q4jufSOiyE0MIDYUAvZZeVYzEGUO9T9ORVl9IqOJzw4gCJrNUa9gWBTANVuJzani09/2ktJTSAnwGAkOjiMQT1iST1Ror3G2qLMiqJQXC+rJz4imll3zGzV91wIIYQQQrS9tMIqv9uRwe3XcTWlYWaPLOMSokOSYI/otHQ6HaUVdrZtOwhAQlIcE66ZhqeiHGdZGYqnrj6PyaQQ1yOMqKRgKnKtlGWW4barLcurC8s4+sV60ldvJWFkfxLHDKLPzAkoJjO21dtwe7zY7B4Acg5lkXMoC4Neh06n1uxJCA/D7fVRYqtGAY4VlTAgKY7YqBDiK0PJL7diNphwed1UOu1UOR1a63WP10d4QBDVNdk5eTXLy3To6BIaSY+ECIrLq7XlWxGhAWTUFI0ud1Th8qrzCjSauWnhbMIiQ1v3TRdCCCGEEG2uYbAnOqT9gj3dG2T2HCuqamKkEKI9SbBHdGpfLf9OW4Y18xfTCI7vAvFdUBQFT5UVZ1kprrJSPFXqh5DRbCS6eyRRXSOwFlopPVGGtdiG0+XFWe4g69ONON9fj8vt044LatFkX72OXl6fgkEPer2eiMRoeo7rz7Ydh8k8notPUUgvKWX8pT2ITArmw5W7sZgDKbWrwaecyjL6xSYQGmimvNpBiDkQg06PV/Fpx4+zhBEdFkJMRDD70tRCzSajnlJrdc3zeymprqs71CM5mSnXT2qld1kIIYQQQrSXUpuLUpvL777Idgz29Iy1+N3OLK3G4fYSWNOsRAjRMUiwR3RaPp+PLz/+FlCzfGbMnqzt0+l0mELDMIWGQdfu+FwuqouLKD6SSXF6HhVFVVSWVFNZUo3T7m7qKTR6nQ6DUYfH66M2BuTT6ZgwfwqDpwwH4BKHixcfXMLx/RlU2918/OUebrp2OJHhQXh8Xkrtasew3Moy+sZ0Idhsosrhwq34CA0IotyhpsCGBgQRERTCBT1j2XO0QJtDuMVMWp46psxu1YJDFnMQtz54I6aA9lu7LYQQQgghWkfDrB6DTkd4YPtd9/WI8c/sURR1joOTwttpRkKIk5Fgj+i0dm3dR05WHgCjxw8nIUktTKwoCtXlVZScKKQ0q4jSzCJKMgspzy1BqZed0xyzWU+A2UiA2UBggIGAAAOBocHYSqvILbRR7fCgeH2se30VFfmlXHjLZZgDzdz751/xl3teJP9EAeUVdj77ej9DBiSydvMxAo1mHB4XNpeTSoedyJAQNYik1xEVZMHt9aDX64kLCaN/txhyCq24PWpAJyY8SFu+5fK6tcCRDhgyZABjp4xq2TdXCCGEEEJ0CEcLrX63I4JN6PW6dpoNBJuNpEQFkVVq1+6TYI8QHY8Ee0Sn9eUn32rbowb1Z+Pb31OaWUhJZhHOKnszj6wTGBZMVHI0IRYTRpcdMz4Cg4x4XF7sNjded93SKrfNjslsILmLhcJSO+WVap2dPSu3UpZbwtT7riEkLIT7/3YPf7nnBcoKy8kvqMQUaMKgV7tyOTxqCm5OZRnhQcGEBJqx2p0Y9XoSw6IAiIsOJTTIRFpWKQBmox67263V7SmzW6kNWYUHWljw6M1a/SAhhBBCCHFuaZjZE9WOS7hq9YkL9Qv2HCmwNjNaCNEeJNgjOq3dm/cCYDYYse3KZv+evCbH6g16IpKiiUqJI7pbLNEpcUSmxKDYbBTt2I8ttxDMdT8O5shAYvqFofh8lGWVUVlgRYcOnQ4w6omPDibAZKCgpg1m5q5jLHtqKVc8+gui4iO5769389d7X6LaWk3WiRLCIiy4PF5KUOvs5FaWMSAukWCzkSqHC4NBj85oICQ4gMHdo9l6MFebS0KMhT3p6nIuh9upLfcy6PRcMnk8fYb0atH3VQghhBBCdBwdMtgTb2HN4ULt9tFCKdIsREcjwR7RafWPS6K63MaI5O4Y9XUF4YIiQohOiSWqaxzRXeOI6hpLRGI0BqM6RlEUqrLyKPhxM9X5RX7HNIVZiB0+kIh+PdAbDHjsduLKSrHl5FFw4ATlWeXg9qLTQURYACaTntxCGz6fQml2MZ/87g2mPzSbpEHduffPd/HiA6/gdrmxlldj0OsJMgZg9zhxeNyU2m10CQsD1JpAoZZAJg5MYE9aIS63F4C4iGCO5ZVp8y6217VajwoJ46bfzGnNt1gIIYQQQrSzYx0x2BPn3wH2qGT2CNHhSLBHdFp3P3EHB1fvJCg8hKiuNcGdlFiCwkNOOl5RFKwncinasR97YYnfPnN4KLEjBxHRpzs6vV673xgUhDEoieDEJGJGDMdRUkLBzsMU7M/AXmHHEmyiW2IoOQVVuNw+nDYnX/zpfcbMHMrAScO4/clb+M8flqJ41OCNxRyI3aMu/8qtKCMmxIIl0ESVw421oppjOeXkFqkfliajHqNJR5VDXfpl9zixudTHmvRGrrn5CuKT41r2TRVCCCGEEB2Ky+tfc7I9267X6hMnHbmE6Ogk2CM6rW4jetNtRO9TjlMUhcr0bIp27MdRXOa3LyAynNiRgwjv1dUvyHMyOoOBoLg4uk+Po+vUiZQdPUH2T3soPVZA14RQcotsVNs9KAps+XIP+YezGTm5L9dcP5bP3t+EQacn2ByIrroCBcizljPIl0xIgJkqh9oR7EhmXRCqR0I4O9LUpWk+xUdhVd3cE6Njueb2Gaf7VgkhhBBCiE5q5pAE3tyYAcDYHlFEWwLad0JA7wbBHp8Cx4tsDEwMa6cZCSEakmCPOGcpPh+Vx7Mo3LEfZ2mF377A6AhiRw4mrGfKGRU31uv1RPfrQXS/HjjKrRz9aiP6vcf9CjefOFpCaf4Ohl7YjcsuH8Ca7w6i1+kJNgVicztweT0U26x0jYpqdPzYyGCyiyvx1nQPs7sdOL0eAIKMAdx8zxwsYSfPYBJCCCGEEOeOp2YOZELvGGxODxf3ieGtTSfae0qEBBhJiggip9y/SLMEe4ToOCTYI845is9HRdoJinYcwFle6bcvMDaKuJGDCe2e1GIdrAIjQrng5ml0v6yE3W+sJCCnTCvcbLW62PrjMbomhXFB/0R2H8omxByEze0A1ELNSeERhASYsDnV7B6jQU90eBBHc9UsH0XxkWety+rp1a0rl82+uEXmLoQQQgghOja9XseUgfEAWGuywTuCfl1C/YI9B3IrmDU8qR1nJISoT4I94pyheH2UH02naOcBXBX+heyC4qOJG3kBlq4JrdamPDQhmgsfvpEDH63BvOMoOTWFm10uH+knKuiREEpBlAVPsQ8dOhQU8qzlONwewoMDtGBP35Qodh/P145rddnwKmrb9VBzEPMfuhGT2dQqr0EIIYQQQojTcUFSuF9Hrj3ZFc2MFkK0NQn2iE7P5/VSfvg4RbsO4rba/PYFJ8QRN2owIUnxrRbkqc8YYGbILdMITYzB+PUWrXCz16dwIsfK0K4JFJTbCDEHUuWy4/X5yKssZ3BSAv17xRFkNlBQXEl1TeBHh488a3nNto7hIy9g5GXDW/11CCGEEEII0ZwhyeF+tw/kVOD1KRj0rX/NLYQ4NQn2iE7L5/VSdjCNol2H8Niq/faFJMWrQZ7E+Dafl06vo+flowhNiMb83ndk55Rjs6v1dgpL7PSMjaLSYafKpaa9Hi8tokdsDEmGIAJMOg5n1RVpLqqu+4YkPDCEWx++sU2CVkIIIYQQQjTngiT/YI/N5eV4URV94kObeIQQoi1JsEd0Wjk/bKbiqH+BOktKAnGjBhPcJbadZlUndmAPxt43h8Clq8g8mk9ZTeHmYGMAIaYA9Do9PsWH1VlNWkEx0aGB7MkoxqeoRZl1OoWSanU5mkGn5/IrLqLXoB7t9nqEEEIIIYSoFRcWSJewQPIrHdp9e7MrJNgjRAfRfK9pITowV4VV2w7tlkTPOdPoPvOyDhHoqWWJj2LMr+cw4MJ+xMcEA2DU64kKCSHEHAiAAhTZrOzMyKOwQs1QMuh0ZFcUa8eJsURww69nt/n8hRBCCCGEaErDpVy7ssqaGCmEaGuS2SM6rZQpE6lMzyYkKZ6gmMj2nk6TTEEBDJ1/BZaEbRxatZXcQhuxlhByKoKwOmu6djmrsTnrWql7FQ9VLvVbErPByLW3XklsYky7zF8IIYQQQoiTGd41km8PFmi3Nx0raWa0EKItSbCnDfl8Pj777DOWL19Oamoq1dXVxMbGMmLECG688UZGjx7d3lPsVMxhFmKG9m/vaZwWnV5P7+ljCU2MZvd735OZXUZEYDCFtnK8Pi82twOPz4tRbyDAaOBwUZ722KSYOK6af0U7zl4IIYQQQojGxveK9rt9rMhGfoWDLuGB7TQjIUQtCfa0EavVyj333MPWrVv97s/NzSU3N5eVK1eyYMECFi1a1E4zFG0hfkhvxsdGEPTm1xRX2citLKXCqXYQq3LaiQ4OpcpVjcurFnQONgVwy6+vIzg0uD2nLYQQQgghRCODk8IJDTRidXi0+zYeK2b2iOR2nJUQAqRmT5tQFIUHHnhAC/RMnDiRV155hQ8//JA//OEPJCUloSgKb7zxBv/5z3/aebaitYUmxDDu/l9w6ZThhAXWBXGcXifxYRYyyoq0+/r07M4lsy5qj2kKIYQQQgjRLINex7ie/tk9q/bnt9NshBD1SbCnDXz55Zds2LABgNmzZ/P6668zefJkhg0bxk033cSyZcvo3bs3AEuWLCE/X35BnuvMIUGMXTiLEcP7YdIbACh3VLM/P1vrxhUWEMz8h27CaDS051SFEEIIIYTQBJv9r02nDIj3u/1jahEVdndbTkkIcRIS7GkDb7zxBgAWi4XHH3+80f6IiAiefvppAJxOJ0uXLm3T+Yn2oTfomfWb6wgxB2n3Fdkq1X06HaNGD2XYRUPaa3pCCCGEEEI0otPp/G5PG9wFs6Huz0qX18cXu3PaelpCiAYk2NPKsrKyOHjwIACXXXYZERERJx03atQoevToAcCqVavaanqinXXv35W+/Xs1uj880MK8h29s9GEqhBBCCCFER+BTFKwON3odTOzj3zX2nz8eo7DSgdXh1rLWhRBtS4I9rWzHjh3a9rhx45odO2bMGABycnLIzMxs1XmJjuPKGydjNpi020a9gakzL6F7/67tOCshhBBCCCGaZnN6eG19Oq+tTyciyOS3L6/CwS2vbeGVNWnYnJ4mjiCEaE3SjauVpaWladvdu3dvdmxKSoq2ffToUbp2lT/2zwdjp4wi8rkwCipKAIgNjeD6e69t51kJIYQ43/l8Pj777DOWL19Oamoq1dXVxMbGMmLECG688UZGjx7d3lMUQnQQyZFBjOkRxdb0Uu2+o4VVHC+ycTCvklsv7M7kgfHNHEEI0dIks6eV1S+2nJiY2OzYhISEkz5OnNsCgwO5YvYkooPDiA2JYM6Cq4iKj2rvaQkhhDiPWa1W5s+fz+9+9zu2bt1KRUUFbreb3NxcVqxYwbx583j++efbe5pCiA5Cp9Px7LWDCTL5F2/2KgrrjhZzx9LtvL4hvZ1mJ8T5STJ7WllFRYW2HRIS0uzY4OC6NtxWq/WkY3Jzc8nNzT2juaSmpp7R40Tru/aOqyjMLsISHsKMW6e193SEEEKcxxRF4YEHHmDr1q0ATJw4kZtuuomYmBgOHTrEf//7X3JycnjjjTeIiorirrvuaucZCyE6gp6xFv49byQL396O3e1rtP/LPbncPrFHO8xMiPOTBHtamcvl0rYDAwObHVt/f/3H1ffpp5+yZMmSlpmc6DDCokJ59OXftPc0hBBCCL788ks2bNgAwOzZs3nuuee0fcOGDeOKK67glltuIS0tjSVLlnD11VfTpUuX9pquEKIDuaRvLJ8sHM9vPtzN8aIqfPVqM4/oGtl+ExPiPCTLuFqZwVCXyniqzkpKvUr1er2cGiGEEEK0vTfeeAMAi8XC448/3mh/REQETz/9NABOp5OlS5e26fyEEB1b1+hgZlyQwF0X92TqwHhuHJ3C49P7s+iK/u09NSHOKxJRaGX1l2Y5HI5mxzqdTm3bbDa32pyEEEIIIU4mKyuLgwcPAnDZZZcRERFx0nGjRo2iRw91OcaqVavaanpCiE4kwGhgQEIYv58xgLsv7YXZKH96CtGWZBlXK6tfp8dutxMWFtbk2Orqam07PDz8pGPmzJnDhRdeeEZzSU1N5ZlnnjmjxwohhBDi3Ldjxw5te9y4cc2OHTNmDOnp6eTk5JCZmSldRIUQQogORII9rSwpKUnbzsvLIz6+6ZaDeXl52nZT4xITE0/Z1UsIIYQQ4kykpaVp2927d292bEpKirZ99OhRCfYIIYQQHYjk0rWyPn36aNuZmZnNjs3KytK2e/fu3WpzEkIIIYQ4mfz8fG37VF8uJSQknPRxQgghhGh/ktnTyoYNG4ZOp0NRFLZv387VV1/d5NjaFqcJCQkkJye3+FzqLxMDacUuhBCic+nXrx+hoaHtPY1zWkVFhbZdfyn6ydSvS2i1Wk86Jjc3l9zc3DOay549e/xuy3WLEGB3eck7VvcztSOyEt1Z3t8Sx2ju/t0hZQSZ65rWCHE+6AjXLBLsaWUJCQkMGzaMXbt28c033/DYY49hsVgajdu+fTvp6ekATJs2rVXmUj9zCJD6PUIIITqVd999l1GjRrX3NM5pLpdL2w4MDGx2bP399R9X36effsqSJUtaZG5y3SJEY5+3wP0tcYwzuV+Ic1lHuGaRZVxtYN68eQCUl5ezePFifD6f3/6KigoWL14MgMlkYu7cua0yj/rf1gkhhBBCNGQw1H37rtPpmh2rKIq2rdfLJaUQQghRq6SkpL2nIJk9bWHGjBksW7aMDRs2sGLFCvLz87n11luJj48nNTWVV199lZycHADuu+8+v4KHLUmCPUIIIYRoTv2lWQ6HA7PZ3ORYp9OpbTc3TgghhDjfFBcXt/cUJNjTVl566SUWLlzItm3b2L59O9u3b280ZsGCBdx1112tNofhw4fz5ptvarcfe+wxhg4d2mrPJ05famqqX3r6U089Rb9+/dpxRqKWnJuOS85Nx9Va50bOb+urX6fHbrcTFhbW5Nj6tQDDw8NPOmbOnDlceOGFZzSXrVu38tJLL2m35bql45Lfx52LnK/OQ85V59HwXLVWAsfPIcGeNmKxWFi6dCnLly/niy++4PDhw1itViIjIxk+fDi33HIL48aNa9U5xMTE+N0eOnRou68jFCfXr18/OTcdlJybjkvOTccl56bzSEpK0rbz8vKIj49vcmxeXp623dS4xMTEU3b1Ol1y3dJ5yM985yLnq/OQc9V51M+UbS8S7GlDer2e2bNnM3v27PaeihBCCCFEI3369NG2MzMzGTZsWJNj6zd+6N27d2tOSwghhBA/k1TTE0IIIYQQAAwbNkwrzHyyJef1bd26FVA7jyYnJ7f63IQQQghx+iTYI4QQQgghADVwU5vN880331BVVXXScdu3byc9PR2AadOmtdX0hBBCCHGaJNgjhBBCCCE08+bNA6C8vJzFixfj8/n89ldUVLB48WIATCYTc+fObfM5CiGEEKJ5UrNHCCGEEEJoZsyYwbJly9iwYQMrVqwgPz+fW2+9lfj4eFJTU3n11VfJyckB4L777usQHUeEEEII4U+CPUIIIYQQws9LL73EwoUL2bZtG9u3bz9p/Z4FCxZw1113tcPshBBCCHEqEuwRQgghhBB+LBYLS5cuZfny5XzxxRccPnwYq9VKZGQkw4cP55ZbbmHcuHHtPU0hhBBCNEGCPUIIIYQQohG9Xs/s2bOZPXt2e09FCCGEED+TFGgWQgghhBBCCCGEOIdIsEcIIYQQQgghhBDiHCLBHiGEEEIIIYQQQohziAR7hBBCCCGEEEIIIc4hEuwRQgghhBBCCCGEOIdIN67zSGJiIr/+9a/9bouOQc5NxyXnpuOSc9NxybkRLUH+HXUecq46FzlfnYecq86jI54rnaIoSntPQgghhBBCCCGEEEK0DFnGJYQQQgghhBBCCHEOkWCPEEIIIYQQQgghxDlEgj1CCCGEEEIIIYQQ5xAp0Hwe8Pl8fPbZZyxfvpzU1FSqq6uJjY1lxIgR3HjjjYwePbq9pyjqefLJJ/noo49YuHAhDz74YHtP57xVXFzM+++/z4YNG0hPT6e6uhqLxUKfPn24/PLLuf766wkODm7vaZ6XCgoKePvtt1m7di3Z2dkAxMfHM3HiRK677jr69evXzjMU9VVWVjJjxgwKCwu56qqr+Otf/9reUxIdnFy3dAxlZWW8/fbb/PDDD2RmZuJyuUhMTGT8+PHMnTuXXr16Nft4OY+t7+dcM9rtdj788ENWr17N0aNHqa6uJiwsjIEDB3LNNddw5ZVXYjAYmj2GnNPTcybXkP/4xz9YsmTJz36ua6+9lueff77R/XKuTk9LXFMePXqUDz74gI0bN5Kfn4/P5yMpKYmLLrqIBQsWkJCQ0OzjW/NcSYHmc5zVauWee+5h69atJ92v0+lYsGABixYtauOZiZNZvXq1VsVdgj3t57vvvmPRokVYrdYmxyQlJfHKK68wYMCANpyZ+O6773j88cepqqo66X6j0cjChQu577772nhmoimPPvooX3zxBYAEe8QpyXVLx7BhwwYeeughKioqTrrfaDTy2GOPMX/+/JPul/PY+n7ONWNGRgYLFy4kPT29yTFjxoxhyZIlhIeHn3S/nNPTc6bXkGca7Ln++uv54x//6HefnKvT0xLXlP/85z955ZVX8Hg8J91vsVj4+9//ziWXXHLS/a19riTYcw5TFIU77riDDRs2ADBx4kRuuukmYmJiOHToEP/973/JyckB4OGHH+auu+5qz+me99auXcu9996L2+0GJNjTXrZu3cptt92G2+3GZDJx/fXXc+mllxIREUFeXh6fffYZP/zwAwBRUVEsW7bslBF70TJ27drFvHnzcLvdGAwGrr/+ei6++GIsFgsHDx7kv//9L8XFxQA8/vjj3Hbbbe08Y/Hdd99x7733arcl2COaI9ctHcOOHTuYP3++dj1S+w13QkICmZmZLF26lL179wLw61//utEfQnIeW9/PuWasrq7m6quvJisrC4DRo0dz4403kpCQQEZGBv/73/9IS0sD1IDP0qVL0el0fseQc3p6zuYasqioSLuGaU52djYPPvggbreb2NhYPvnkE7p06aLtl3N1elrimnLJkiX84x//ACAyMpLbbruN4cOH4/F4WLVqFR999BE+n4/AwECWLVvWKBuyTc6VIs5Zn3/+udK3b1+lb9++yqJFixrtLysrU6688kqlb9++ygUXXKDk5eW1wyyFoijKG2+8oQwaNEg7X3379lX+/ve/t/e0zjs+n0/7mRg0aJCyefPmk45bsmSJdp4eeuihNp7l+WvWrFna+7569epG+4uLi5UJEyYoffv2VYYNG6aUl5e3wyxFrZKSEmX8+PF+v9cefvjh9p6W6MDkuqX9ud1uZfLkydp5eOmll0465v7771f69u2rDBgwQDlw4IDffjmPrevnXjO++uqr2rhHHnlE8fl8fvudTqeyYMECbczXX3/d6BhyTk+tLa4hnU6ndi3Uv3//kz6HnKvTc7bXlAcPHlQGDhyo9O3bV7nsssuUzMzMRsf44IMPtOe47777Gu1vi3MlBZrPYW+88Qagpo89/vjjjfZHRETw9NNPA+B0Olm6dGmbzk/UpdU+99xzWmRZtJ/du3dr327deOONjB079qTj7rnnHvr27QvAt99+S3V1dZvN8Xy1f/9+Dh48CMC0adOYPHlyozHR0dHcfvvtgPpN5o8//tiWUxQNPP300xQXFxMVFdXeUxGdhFy3tL8ff/yRzMxMAMaPH8/999/faIzRaOTZZ58lIiICr9fLX/7yF7/9ch5bx5leM65du1bbXrRoUaOsHbPZzGOPPabd/v777xsdQ87pqbXFNeSSJUu0a6HbbrvtpM8h5+rUWuKa8uWXX8bj8aDT6XjxxRdJSUlpdIwbbrhBO9dr1qzB4XD47W+LcyXBnnNUVlaW9o/4sssuIyIi4qTjRo0aRY8ePQBYtWpVW01PAO+++y4zZ87U0jl79+6t/UCL9rFt2zZt+/LLL29ynE6nY8KECQC4XC6OHz/e6nM737lcLiZPnkzXrl2ZMmVKk+N69uypbefl5bXF1MRJfPXVV6xatQq9Xs8TTzzR3tMRnYBct3QMmzZt0rZvvfXWJseFhIQwffp0ADZv3kxJSQkg57G1nM01Y+1SlLCwMKKjo086pvZcgLqcqD45p6enta8hDx8+zOuvvw5A165dT1pHRs7V6Tnba8qysjLWr18PqMGiIUOGNHmM22+/neuvv57bbrvNL7DXVudKunGdo3bs2KFtjxs3rtmxY8aMIT09nZycHDIzM+natWtrT08A+/btw+12Yzab+eUvf8m9997L7t2723ta57UhQ4awcOFCCgoK/C58TkapV+7M6XS29tTOeyNGjGDEiBGnHFe7thkgLi6uNackmlBcXMwzzzwDwIIFCxg6dGg7z0h0BnLd0jHU/x16qp/dPn36AGonmd27d3P55ZfLeWwlZ3PNGBcXR0ZGBpWVlRQVFREbG9toTP2AQ/36LyA/m6erta8h//jHP2pFgJ944gkCAwMbjZFzdXrO9ppy48aNWr2smTNnNnuMWbNmMWvWrEb3t9W5kmDPOao2jRCge/fuzY6tn3Z29OjR8+qHvT0FBARw3XXXcffdd5OUlNTe0xGov2xP9Qu31pYtW7RtOX8dQ2lpKf/73/8ACA4O5rLLLmvnGZ2fFi9eTFlZGT169OCBBx5o9C2xECcj1y0dQ+0fMECj1tANGY11f0ZkZGQAch5by9lcM15++eVap5+//e1vjdp0e71ev8L5V155pd9+OaenpzWvIb/77ju2b98OwIQJE5rs7CTnquU0d015+PBhbbt+Vo/P56OoqAibzUZ8fDwhISFNHr+tzpUEe85R+fn52nZiYmKzY+t3Eqr/ONG6Fi9ejF4vKyk7o7Vr13Lo0CEA+vbt2+hbMNF2nE4n2dnZfP/99yxdupSioiJ0Oh1PPvkkkZGR7T29887y5cv57rvv0Ov1PPfccwQEBLT3lEQnIdctHUP935v5+fnN/hFSf1lDbVBXzmPrOJtrxptuuonvv/+erVu38tlnn5GXl8f1119PQkICWVlZvPXWWxw4cABQa81cfPHFfo+Xc9qyzuQasrbjE3DSOlq15FydndO9pjx69CgAJpOJuLg4iouL+cc//sHXX39NRUUFAAaDgdGjR3PfffcxatSoRs/VVudKgj3nqNp/aECzUUXw/+bGarW22pyEPwn0dE6lpaUsXrxYu11bvE20vX379vGLX/zC774uXbrwhz/8QbJ62kFBQQH/7//9P0BdvjV8+PB2npHoTOS6pWMYNmwYX375JaAWj22u1e+aNWu07dpaFHIeW8fZXDMGBATw3//+l9dff5033niDzZs3s3nzZr8xsbGxPPbYY1x99dWNHi/ntOWcyTXkxo0btUySMWPGMGzYsCbHyrk6cz/nmrK8vBxQCyvv3r2bhQsXavfV8nq9bN68mS1btvDYY481at3eVudK/to8R7lcLm37ZGs666u/v/7jhBD+bDYbd999t/Zt5pgxY056YSTaRm5ubqP7ioqK+PDDD9m/f387zOj89sQTT1BZWUn37t35zW9+097TEZ2MXLd0DNOnT9fe31dffdVvqUF9S5cu5ciRI9rt2loich47prS0NA4dOtRk56fi4mK+/vprjh071mifnNOWcabXkLUdmwDuuOOOZsfKuTpzP+ea0mazAWom0MKFC6moqGDevHmsXLmSffv2sXbtWhYtWkRwcDCKovDnP/+Zr776yu8YbXWuJNhzjqrfjrFhi8WG6hcJk2wTIU7OarVyxx13aAURu3Tpwt///nf5mWlH3bt359VXX+Xjjz/mlVde4corr8Tr9fLDDz8wd+5crVOCaH0ff/wx69at05ZvnerCRYiG5LqlY4iJieGee+4BoKqqiptvvpm3336bwsJC3G43x48f509/+hPPPvss8fHx2uNMJhMg57Ej+vHHH7nllltYvXo14eHh/OEPf2D9+vXs27ePb775hnvvvReTycSaNWu4+eabG/1hK+f07J3pNeSxY8e0a5l+/fo1WaunlpyrM/dzrintdjugZjSWl5fzxz/+kSeeeILevXtjNpvp0qULv/zlL3njjTe0343PP/+8XzHutjpXcmbPUfXTvRwOR7Nj6//DM5vNrTYnITqrwsJC5s2bx86dOwH1Yvh///vfSTtaiLbTr18/Lr30UoYMGcLkyZN54YUXePbZZwH1g/iRRx6hqqqqnWd57svNzdUKfs6fP/+0OlwI0ZBct3Qcd911FzfffDOgLjX405/+xEUXXcTgwYO54oorePvtt0lKSuJvf/ub9pja8yfnsWMpLCzkwQcfxOFwEBkZyYcffshNN91EXFwcZrOZ7t27c//99/Paa69hMpkoLy/nvvvu8zs3ck7PztlcQ65YsUL7Q3/27NmnHC/n6sz9nGvK+l9oXXjhhVx33XUnPeawYcO0pWEFBQVs3LhR29dW50qCPeeo+mv/aqOPTamf0hkeHt5qcxKiMzp8+DDXXXedVkyvS5cuLF26lF69erXzzMTJzJkzh2nTpgHqmupvvvmmnWd0blMUhd///vdUVVXRvXt3Hnjggfaekuik5Lql49DpdCxevJglS5ZwwQUX+H3rHBsby5133snnn39OWFiYdn9MTAwg57GjWb58ufY+33///U128Rk7diy33HILoAbwv//+e22fnNMzd7bXkKtXrwbUn8krrrjilOPlXLWspq4pLRaLNmbq1KnNHmPSpEnadm1mF7TduZICzeeo+m388vLy/FJtG6rfTaG5cUKcb9auXcsDDzyg/ZLt2bMnr732mrRa7+CmTp2qfSDXXmCJ1vHhhx9q31TdeuutpKenNxpTWFiobVdWVmrnJCYmRrLjhEauWzqeKVOmMGXKFCorKykqKiI0NJTY2Fgt+FO/vktycjIg57Gj2bt3r7Z9+eWXNzt2ypQpvPnmm4D6R2ltC3Y5p2fmbK8hMzIytK5Po0aNOq33U85VyzvZNWX9a5dTdVOr32mrrKxM226rcyXBnnNUnz59tO3MzMxmK7dnZWVp2717927NaQnRaXz22Wc88cQTWtHJESNG8K9//YuIiIj2ndh5ymq1kpmZSXZ2NlOnTm12fXP9c+R2u9tgduev+t9SPfPMM6ccv3btWtauXQvAr3/9a+67777WmproZOS6peMKCwvzy+KptWvXLm174MCBgJzHjqZ+RkBoaGizY6Ojo7Xt+h1/5Jz+fC1xDVk/u+p0snpAztXpOttryn79+vHtt98C/l21TqZ+QeX6v0fb6lzJMq5z1LBhw7R/uNu3b2927NatWwFISEjQvpkR4ny2bNkyfvvb32of0ldccQVvvfWWBHra0TPPPMPs2bO5//77tRakTcnMzNS2T/WNixCiY5Drlo4hKyuLF198kSeffNIvmNuQoijaH6Ndu3YlJSUFkPPY0URGRmrb9T8bT6agoEDbrh/4kXP687TUNeS2bdu07bFjx57WY+RcnZ6zvaasH5jZsWNHs4+vzc4C/N7ntjpXEuw5RyUkJGj/EL/55psmi5Ru375dS7uvXZMoxPls27ZtPPHEE1pBvLlz5/LCCy9I8bp2Nnr0aG37k08+aXKcz+fz2z9x4sRWndf57vnnnyc1NbXZ/+p/O3nVVVdp90tWj6hPrls6Brfbzb/+9S8++ugjli9f3uS4r776ipycHABmzZql3S/nsWMZM2aMtv355583O/bLL7/Utut/5so5PX0teQ1ZG2wNDQ097Ro/cq5Oz9leU1544YXaUq6vv/6a4uLiJo/x2WefAWr3rfr1e9rqXEmw5xw2b948QC0otXjxYnw+n9/+iooKFi9eDKgtM+fOndvmcxSiI6mqquLRRx/F6/UCamG2J5988pQtEUXru/LKK7VvKD/88EM2bdrUaIyiKDz77LMcOHAAgAkTJnDBBRe06TyFEGdOrlvaX8+ePenbty+gZijU/1a6VmpqKk8//TSgZoA0PA9yHjuOGTNmEBUVBcBbb73FDz/8cNJxX375JZ9++img/huYMGGC3345p6fWkteQBQUFWn2XhkXST0XO1amd7TWlwWDg9ttvB9Tz/sgjj2Cz2Rod46233tKOPWXKFOLi4vz2t8W5kpo957AZM2awbNkyNmzYwIoVG8c0lwAAF0JJREFUK8jPz+fWW28lPj6e1NRUXn31Ve1bmfvuu09LwRXifPXOO+9oRdBiY2O5/vrrT6vAb0JCgizxamUWi4Wnn36aBx54ALfbzW233cZ1113HJZdcQkxMDOnp6XzwwQdaDYkuXbpoLTOFEJ2DXLd0DA8//DC/+tWvcDqdzJ07lzvvvJOhQ4fi8XjYsGED7777Lna7HYPBwHPPPdeoO4ycx47DYrHw//7f/+Pee+/F6/Vy9913M2PGDK644gri4uIoLCzk66+/ZuXKlSiKQmBgIM8//zxGo/+fiHJOT60lryEzMjK07aY6qDVFztWptcQ15fz58/nxxx/ZvHkzmzZt4tprr2X+/PkMGDAAq9XKF198wYoVKwCIioriqaeeajSPtjhXOqU2z0yck6qqqli4cKHfus+GFixYwKJFiyR7oQPYsmULt956KwALFy7kwQcfbOcZnV8uvfRSv4r3p+u5555j9uzZrTAj0dCKFSt48skn/YpONjR48GBeeuml824NekeVnZ2tdYG56qqr+Otf/9rOMxIdmVy3dAxvvfUWf/7zn7UshYZCQ0N5/vnnmTx58kn3y3lsfT/nmnH16tUsWrSoyaUioAYoXnjhBb8lLvXJOW1eS15DLl++nMcffxyABx98kIULF/6sY8q5Oj1ne03pcDh4/PHHWbVqVZOP7969O6+88kqThZVb+1xJZs85zmKxsHTpUpYvX84XX3zB4cOHsVqtREZGMnz4cG655RbGjRvX3tMUot2Vlpae0Ye0aFszZ85k9OjRvPvuu6xbt47MzExcLheRkZEMGTKEGTNmMH36dPR6WaUsRGck1y0dw/z58xk1ahRLly5l69atFBUVYTKZ6N69O5deeilz5871K+LbkJzHjmXKlCmMHDmS999/n3Xr1pGeno7NZiM0NJQ+ffowadIkrr/+ekJCQpo8hpzTprX0NWT9JUFn0mhCztXpOdtrysDAQF566SU2btzIp59+ys6dOykuLiY0NJRu3boxc+ZMZs2a1a4/V5LZI4QQQgghhBBCCHEOka8+hRBCCCGEEEIIIc4hEuwRQgghhBBCCCGEOIdIsEcIIYQQQgghhBDiHCLBHiGEEEIIIYQQQohziAR7hBBCCCGEEEIIIc4hEuwRQgghhBBCCCGEOIdIsEcIIYQQQgghhBDiHCLBHiGEEEIIIYQQQohziAR7hBBCCCGEEEIIIc4hEuwRQgghhBBCCCGEOIcY23sCQohz05YtW7j11ltb5FjXXnstzz//PPPmzWPr1q0AfP/99yQnJ7fI8TurP/7xj7zzzjvcfPPNLF68uMWOa7PZmDZtGqWlpbz33nsMGzasxY4thBBCCCGEaH2S2SOEEJ3Q+vXreffddwkPD+f+++9v0WOHhITw8MMP4/V6efTRR7HZbC16fCGEEEIIIUTrksweIUSr6Nq1K4899liT+/fv389XX30FQEpKCjfddFOTY/v06dPi8+vMqqureeKJJ1AUhYULFxIZGdniz3HNNdewdOlSDh48yAsvvMATTzzR4s8hhBBCiNPz8ssv88orrwDwwAMPcPfdd7fzjIQQHZ0Ee4QQrSIhIYHbb7+9yf3Lli3Tgj2nGlvr7bffbrH5dWZLliwhPz+fLl26MHfu3FZ5Dr1ez4MPPsidd97Je++9x5w5cxgwYECrPJcQQgghmnfo0CFtu70+j71eL2+++SaffPIJWVlZREZGMmXKFO67775W+eJJCHF2ZBmXEEJ0ItnZ2SxduhSAO++8E7PZ3GrPdfHFFzNkyBC8Xi/PP/98qz2PEEIIIZp3+PBhbbs1gz1//OMf6devH08//bTf/T6fj/vuu4//+7//Iz09naioKMrKynj33Xe5/vrrKSkpaXSshQsX0q9fP1577bVWm68QomkS7BFCiE7kn//8J263m+DgYGbNmtXqz3fzzTcDsHnzZq04thBCCCHaTkVFBbm5uQBERkYSHx/fKs/TXD3ATz75hO+//57BgwezevVq1q1bx5YtW7jmmmvIzMzk2WefbXS8RYsWYTKZePHFF/2CVUKItiHBHiFEpzFv3jz69etHv379yM7O9tu3bNkybd++ffsA+Omnn/j1r3/NxRdfzAUXXMCkSZN48MEH2bNnj99jjx8/ztNPP82UKVMYMmQIY8aM4dZbb2XlypWnNa/y8nL+/e9////27j0oqvL/A/ibBQQkCkFER+gL6iAVE454SUQLXRzNSw6W4iiaikWGq6A4oqkjYumUCTFeBmomBUTGGwZqOCEmmqEm5T3B0dxEEZRFAuTm/v7Y4eks191ll1/i+/XXs3uePZ9z/uLwOc/z+WDmzJnw9fWFl5cXRo4ciblz52LXrl2orq42yv0/fPgQP/zwAwBgwoQJeOmll9qcn5ubixUrViAgIADe3t7w9vaGv78/Fi1ahLS0NNTU1LQbc/z48bCzswMAJCQkdPwmiIiISC+dsYWrvXqAGRkZAIDY2Fi4uroC0DR02LhxI1xdXXH8+HHU1tZq/cbNzQ0zZ85EXV0doqKi0NDQYJJrJ6KWsWYPEXU5DQ0NWLVqFQ4cOKD1/b1793Dv3j0cP34cGzduxNSpU3Hw4EGsX78eT58+FfNqamqQl5eHvLw8nDt3rtlSZqmMjAxER0fjyZMnWt+XlpaitLQUv/76KxITE7F161YMHTq0Q/e1b98+1NXVAQAmTZrU6rzq6mqEh4cjJyen2bGioiIUFRUhOzsb27Ztw/bt2+Hl5dXquWxsbDB27Fikp6fj9OnTUCqV4iGPiIiITE+6KsbT09MkMdqrB/jw4UPY29s3ewawtLTEa6+9BqVSCZVKhV69emkd/+STT7B//35cu3YNe/bsQXBwsEmun4ia48oeIupyNm/ejAMHDkAmk2HMmDEICwvDwoUL8eqrrwIA6uvrsX79euzZswerVq3C06dP4evri7CwMLG/vNHevXtbTJoAQEpKCpYvXy4SPQMHDsT8+fMRERGB4OBgEa+kpATz5s3DmTNnOnRf6enpAIDu3btjyJAhrc7bsGGDuGZbW1tMmjQJixcvhkKhQGBgIGxtbQEAxcXFWLBgAcrLy9uM6+fnBwBQq9U4fPhwh+6BiIiI9GPqej261APs1asXVCoVlEql1vd1dXW4fv06rKysYG9v3+x3Dg4OYkt4fHw8KioqjH79RNQyruwhoi7n4sWLcHJyQkJCAl5//XXx/aJFixAYGIjbt2+jqqoK69evh42NDeLj4zFq1Cgxb8mSJYiMjERmZiYAzT51f39/rRhXrlzBF198AQCwtrbGxo0bm622iYqKQmJiImJjY1FXV4fly5fjyJEjcHBw0PueCgoKcPfuXQCAj49Pq4WZ79+/j0OHDgEAnJyckJqa2uwtXGRkJObMmYOCggKoVCqkpqYiNDS01di+vr5inJ2djbCwML2vn4iIiHRTXl6OtLQ0nDhxAnfu3IFKpRLHVq9ejW+++Qa+vr6YN28e3N3dOxxPl3qAkydPxrlz5xAeHo6tW7fC1dUVlZWViI6OhlKpxJQpU1p9Npk5cya+++47lJeXY9euXXyOIOokXNlDRF1STEyMVqIH0KyImTNnjtZ3y5Yt00r0AJq245GRkeLzlStXmp0/Li5ObKnasGFDi9uqzM3NERoaiunTpwMAHj9+jOTkZIPuJy8vT4zbWsJ9+fJlPHv2DICmrk9LW64cHBywevVq8fnq1attxnZ0dISTkxMATd2A9lYCERERkWH27dsHuVyOLVu2ID8/H2VlZVCr1eJ4bW0tlEol0tLSMHXqVGRlZXUonq71AN9//32MHTsWly9fRkBAAEaPHo3hw4cjPT0dbm5uiIqKajWGi4sLRowYAQDYvXu31tZ5IjIdJnuIqMtxc3PDO++80+IxaaLEwsIC06ZNa3Fe79698fLLLwPQJGmkiouLkZubCwBwdXXFlClT2ryeTz/9VIwN3QZ16dIlMfbw8Gh1nrm5uRhfvnwZ9fX1Lc4bNmwYDh8+jPz8fMTHx7cbv3Frm1qtFgWwiYiIyHiio6Px2Wefie3hHh4eGDlypDjet29f+Pv7w9LSEgDw9OlTREZGdqjTla71AGUyGeLj47FixQq4u7vj8ePH6NGjB2bPno20tLR2Vy03nru8vBxHjx41+HqJSHdM9hBRl9NWweGePXuKsbu7O7p3797q3MbaNk27S5w/f168ZWu6eqglzs7O6Nu3LwDNvvji4uJ2f9PU7du3xbhfv36tzhs0aJB4CMzPz0dQUBAOHTqE0tJSrXnm5ubw9PRs8/6lpDHv3Lmjx5UTERFRe2JjY5GSkgJA8/wRFxeHjIwMyOVyMScgIAA7d+7E/v37xQupmpoaxMbGGhxX13qAgObZYcGCBTh27BiuXLmC3NxcrFmzpsVaPU011v8DILabE5FpsWYPEXU5TTtBSMlk/+a4Gx+UdJkrVVhYKMZZWVlaBZ11UVRUBGdnZ71+c//+fTF+5ZVXWp3n6OiIhQsXYvv27QA0q3tWrlwJMzMzeHp6ws/PD6NHj8bgwYNhYaH7nwBpzKKiIr2unYiIiFp3/vx57NixA4Cmu9W3336LwYMHA2i57bqnpycUCgViYmIAAKdOnUJlZaV4SaUrXesBGoOzszP69++PW7du4bfffoNKpdIpSUREhuPKHiLqcmxsbHSaJ93ypI+O1qxp2qZdF9LuFXZ2dm3OVSgUWLZsGaytrcV3arUa169fR2JiIoKDg+Hn54fo6GitJFJbpDH/+ecfPa+eiIiIWqJWqxEdHS0+h4eHi0QPoF1XT7qaePLkyWLc0NCglRTSla71AI2lMVnV0NCACxcumDwe0YuOK3uIqMsxMzMz6fkbGhrEeOzYsfDx8dHr925ubnrHlG4la2/rlZmZGT766CNMnz4dP/74I7Kzs3Hu3DmtgohlZWVISUnBwYMHm3Uja4k0ZtNtbURERGSY06dP4+bNmwA09QKDg4PFsfr6ehQUFAAArKys0L9/f3HM3t4eTk5OKCkpAWDYixhd6wEaizTGpUuXtLaoEZHxMdlDRKQn6favAQMGYMGCBSaPaW1tjaqqKgCaZIsuS63t7e0RFBSEoKAg1NbWIj8/H2fPnsXJkyfFG8Dq6mpEREQgJyen1Q4cjTEbWVlZdfBuiIiICAAOHDggxoGBgVp/3wsLC8Xf34EDBzZbkSz9bMiLLl3rARqLNFnF+n9EpsdtXEREepK2M5e+FWuLSqXSap2qL+k+/MrKSr1/361bNwwfPhxLly5Feno6kpOTRXLnyZMnyMnJafP30phtJYWIiIhIN2q1GmfPnhWf/f39tY63VK9H+luVSiU+9+nTR+/4utYDNBbW/yPqXEz2EBHpSdqt4sKFC+1213r06BH8/Pzg7e2NCRMmNGvlrgsXFxcxbiteQkICZs2ahbfeegsXL15sdd7QoUMxceJE8fnBgwdtxpcel14LERERGaagoEAkbKytrZt1+Lx27ZoYNz127949sT3b3NzcoL/N+tQDNAZpDGlsIjINJnuIiPTUv39/vPnmmwCAuro6bNq0qc35cXFxqKurQ01NDWxtbeHg4KB3THd3dzH++++/W51XUlKCCxcuoKysDBkZGW2e89GjR2LcXncwaUzptRAREZFhpNuoXFxcmnXJlK7saZrskRY49vLyareeX0v0qQdoDNIYdXV1Jo9H9KJjsoeIyAAKhULsjz969CjWrVunVQAZ0BRyTkxMRFpamvhu8eLFBsXz9vYWY+mbvqamTZsmxqmpqdi/f3+L28eysrJw4sQJAJqHr/YKNDfGtLCwgJeXl17XTkRERM1Jt1H16NFD61hjF01As3KnaQHlxr/hADBy5EiD4ku7dnZG84WamhoxZv0/ItNjgWYiIgOMGjUKoaGh2LFjBwBg7969yM7OxpgxY9CnTx+UlJTgzJkzWgUIZ8+ejbffftugeMOHDxfjP/74o9V5np6emDFjBtLS0qBWq7F69WokJSVh8ODB6N27N6qqqpCfn6/VblWhUDR7yJR68OCB6Pbh5eXFmj1ERERG8OzZMzGur6/XOqZUKkWHrX79+mklZh4+fCiSPTKZDIGBgQbFt7W1Fc0fKisrTf73vTEWwPp/RJ2ByR4iIgMtXboUjo6O2LJlC6qrq1FSUqK1iqeRubk5QkJCEB4ebnAsd3d3DBgwAIWFhcjPz0dVVVWrS67XrFmD2tpaHDp0CABw48YN3Lhxo9m8bt26ISwsDPPmzWsz9unTp8U4ICDA4HsgIiKif0kTHrdv34ZarRarhq9evSqONS3OHBsbK7ZByeVyrcYR+nBxcREvc4qLi9vd0t1R0pVMrP9HZHrcxkVE1AHBwcH46aefoFAo4OPjA0dHR1hYWMDW1haenp6YO3cuMjIyEBERYVBbVKkpU6YA0OxzP3PmTKvzLC0tsWnTJqSkpOCDDz6Ah4cHbG1tYWFhAQcHB3h5eWHRokU4evQoPv7443bj/vzzzwA0bw8nT57coXsgIiIiDem2aJVKhdzcXPFZWq/njTfeEOMjR46Idu02NjZYuXKlwfF1rQdoLKz/R9S5zNQd6QVMRESdprS0FP7+/qitrcX48eMRFxdn8pgVFRXw9fVFbW0t5HI5tm3bZvKYREREL4Jnz57h3XffFYWa//e//yEpKQnOzs4ICQkRyZ/du3dj2LBhSE5OxqZNm1BfXw8zMzNs3rwZ7733nsHx9+7di3Xr1gEAFi5ciOXLl3f8ptqwbNkyZGZmAgASExMxevRok8YjetFxZQ8R0XOiZ8+eYnVPTk4OysrKTB4zMzNTFG2cP3++yeMRERG9KGQymVaC5a+//sKECROwdOlS/P777+L75ORkyOVyxMTEoL6+HjKZDFFRUR1K9AC61wM0lkuXLgHQNHvw8fExeTyiFx2TPUREz5HQ0FBYWlqipqYG+/btM3m85ORkAJpOH3wwIyIiMi65XI7IyEjIZJp/yyorK3Hs2DFUVFSIOcePHxdboHr37o2dO3di7ty5HY7dWA8QgKgHaCpKpRJ3794FoHmmsLW1NVksItJgsoeI6Dni6uqKoKAgAJpl3dI2psaWk5ODwsJCmJmZISIiwmRxiIiIXmQhISFITU3FxIkT4ezsDAuLf3voyGQyODo6YsSIEVi7di2ysrIM7uzZEl3rAXbUyZMnm8UkItNisoeI6DmzZMkSODk5oaSkBKmpqSaJoVarER8fDwCYMWOGVhFJIiIiMq5Bgwbh66+/xqlTpxASEiK+Dw8Pxy+//ILvv/8es2bN0mrBbgzTpk1Dt27dAEDU0zGFjIwMAICTkxPGjRtnsjhE9C8me4iInjN2dnZYu3YtAGDnzp0oLy83eozMzExcvXoVffr0MXnBRiIiIvrXn3/+KcYDBw40aazOqAd469YtURNo1qxZIrlERKbFZA8R0XNo3LhxCAoKQllZmdG7clVVVeHLL7+Eubk5vvrqK9jZ2Rn1/ERERNS6mzdvirGpkz2A6esBJiUlAQDs7e0RHBxs9PMTUcvYep2IiIiIiOg/oKKiAkOGDAGgSY7k5eV1StyYmBgkJSXByckJ2dnZsLKyMsp5S0tLIZfLUV1djaioKHz44YdGOS8RtY8re4iIiIiIiP4DpFu4PDw8Oi2uqeoBJiQkoLq6Gh4eHpg9e7bRzktE7WOyh4iIiIiI6D+gM+v1SJmiHqBSqURqaiosLCwQExOj1WWMiEyPyR4iIiIiIqL/gP+vZA9g/HqAn3/+OWpra6FQKODt7W2EKyQifbBmDxERERERERFRF8KVPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXQiTPUREREREREREXcj/AW9tGSsm8x+PAAAAAElFTkSuQmCC","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(1, 2, figsize=[2*width, height])\n\nsns.lineplot(data=data, x='time', y=data['phase']*180/np.pi, legend=False, lw=2, ax=ax[0], hue='trial')\nax[0].set_xlabel('Time (s)')\nax[0].set_ylabel('$\\phi$ (°)')\nax[0].set_xticks([0, 1, 2, 3, 4])\nax[0].set_yticks([0, 90, 180, 270, 360])\n\nsns.histplot(data=end_point, x=end_point['phase']*180/np.pi, legend=False, ax=ax[1], bins='auto', kde=True)\nax[1].set_xlabel('$\\phi$ (°)')\nax[1].set_ylabel('$Count$')\nax[1].set_xticks([0, 90, 180, 270, 360])\nplt.show()"]},{"cell_type":"markdown","metadata":{},"source":["##### Accuracy / Precision Errors\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"#+begin_example\n time trial m0 m1 phase accuracy precision\n 350 3.999 0 5.906027 5.449286 2.892502 -0.249091 -0.243759\n 351 3.999 1 5.891126 5.420843 3.135631 -0.005961 -0.000629\n 352 3.999 2 5.874590 5.361630 3.339823 0.198231 0.203562\n 353 3.999 3 5.891533 5.479044 2.938857 -0.202736 -0.197404\n 354 3.999 4 5.886632 5.519670 3.141416 -0.000176 0.005156\n /tmp/ipykernel_3718977/1857574883.py:4: SettingWithCopyWarning: \n A value is trying to be set on a copy of a slice from a DataFrame.\n Try using .loc[row_indexer,col_indexer] = value instead\n\n See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n end_point['accuracy'] = end_point.phase - stim_phase\n /tmp/ipykernel_3718977/1857574883.py:5: SettingWithCopyWarning: \n A value is trying to be set on a copy of a slice from a DataFrame.\n Try using .loc[row_indexer,col_indexer] = value instead\n\n See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n end_point['precision'] = end_point.phase - circmean(end_point.phase)\n#+end_example"}],"source":["from scipy.stats import circmean\nstim_phase = np.pi\n\nend_point['accuracy'] = end_point.phase - stim_phase\nend_point['precision'] = end_point.phase - circmean(end_point.phase)\nprint(end_point.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(1, 2, figsize=[2*width, height])\n\nsns.histplot(data=end_point, x=end_point['accuracy']*180/np.pi, legend=False, lw=2, ax=ax[0], kde=True)\nax[0].set_xlabel('$\\phi - \\phi_{stim}$ (°)')\nax[0].set_ylabel('Count')\nax[0].set_xlim([-25, 25])\n\nsns.histplot(data=end_point, x=end_point['precision']*180/np.pi, legend=False, ax=ax[1], bins='auto', kde=True)\nax[1].set_xlabel('$\\phi - <\\phi>_{trials}$ (°)')\nax[1].set_ylabel('$Count$')\nax[1].set_xlim([-25, 25])\n\nplt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Parameter Space\n\n"]},{"cell_type":"markdown","metadata":{},"source":["#### Changing J0\n\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Simulation\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"[2. 2.2 2.4 2.6 2.8 3. 3.2 3.4 3.6 3.8 4. ]"}],"source":["REPO_ROOT = \"/home/leon/tmp/rnn_numba\" \nJ0_list = np.linspace(2, 4, 11)\nprint(J0_list)"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"#+begin_example\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.0.h5\n Generating matrix Cij\n Saving matrix to /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.148258017026819s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.2.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.156889496953227s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.4.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.209725192980841s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.6.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.21836295700632s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.8.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.296212543034926s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.253062576986849s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.2.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.352824991103262s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.4.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.316471469006501s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.6.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.391232304042205s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.8.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.313245614059269s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_4.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.380521073937416s\n#+end_example"}],"source":["IF_LOAD_MAT = 0\nIF_SAVE_MAT = 1\n\nfor J0 in J0_list:\n model = Network('config_bump.yml', 'bump_J0_%.1f' % J0, REPO_ROOT,\n IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT,\n Jab=[-J0], VERBOSE=0)\n\n model.run()\n\n IF_LOAD_MAT = 1\n IF_SAVE_MAT = 0"]},{"cell_type":"markdown","metadata":{},"source":["##### Analysis\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"rates ff h_E neurons time J0\n0 2.979453 9.873323 -5.351787 0 0.499 2.0\n1 2.830152 -4.640124 -5.351531 1 0.499 2.0\n2 3.098595 2.158386 -5.351274 2 0.499 2.0\n3 3.415840 -6.115636 -5.351014 3 0.499 2.0\n4 3.625322 0.626738 -5.350751 4 0.499 2.0"}],"source":["J0_list = np.linspace(2, 4, 11)\n\ndf_list = []\n\nfor i in range(J0_list.shape[0]):\n df_i = pd.read_hdf(REPO_ROOT + \"/data/simul/bump_J0_%.1f.h5\" % J0_list[i], mode=\"r\")\n df_i['J0'] = J0_list[i]\n df_list.append(df_i)\n\ndf = pd.concat(df_list, ignore_index=True)\nprint(df.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"time J0 m0 m1 phase\n0 0.499 2.0 2.643238 0.074680 4.044520\n1 0.499 2.2 2.506782 0.028911 3.321685\n2 0.499 2.4 2.372550 0.021852 6.135836\n3 0.499 2.6 2.273263 0.004630 5.104311\n4 0.499 2.8 2.154610 0.054661 5.969938"}],"source":["res = df.groupby(['time', 'J0'])['rates'].apply(decode_bump).reset_index()\nres[['m0', 'm1', 'phase']] = pd.DataFrame(res['rates'].tolist(), index=res.index)\nres = res.drop(columns=['rates'])\nprint(res.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"J0 m0 m1 phase\n77 2.0 7.223276 0.059838 5.009779\n78 2.2 6.785306 0.235569 5.189480\n79 2.4 6.319345 2.347832 3.364468\n80 2.6 6.067178 4.456472 2.924240\n81 2.8 5.822750 5.580686 3.089648"}],"source":["last = res[res.time==res.time.iloc[-1]]\nlast = last.drop(columns=['time'])\nprint(last.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["sns.lineplot(last, x='J0', y=last['m1']/last['m0'])\nplt.xlabel('Recurrent Strength $J_0$')\nplt.ylabel('$\\mathcal{F}_1$ (Hz)')\nplt.show()"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":[""]},{"cell_type":"markdown","metadata":{},"source":["#### Changing J1\n\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Simulation\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"[0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]"}],"source":["REPO_ROOT = \"/home/leon/tmp/rnn_numba\" \nJ1_list = np.linspace(0, 1, 11)\nprint(J1_list)"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"#+begin_example\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.0.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.366484464961104s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.1.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.422051499015652s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.2.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.4423628660151735s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.3.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.4173392209922895s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.4.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.33573145698756s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.5.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.341766959987581s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.6.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.347771545988508s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.7.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.189968526014127s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.8.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.179180574021302s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.9.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.288797255954705s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_1.0.h5\n Generating matrix Cij\n Running simulation\n Elapsed (with compilation) = 7.284084509010427s\n#+end_example"}],"source":["IF_LOAD_MAT = 0\nIF_SAVE_MAT = 0\n\nfor J1 in J1_list:\n model = Network('config_bump.yml', 'bump_J1_%.1f' % J1, REPO_ROOT,\n IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT,\n KAPPA=[J1], VERBOSE=0)\n\n model.run()\n\n IF_LOAD_MAT = 0\n IF_SAVE_MAT = 0"]},{"cell_type":"markdown","metadata":{},"source":["##### Analysis\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"rates ff h_E neurons time J1\n0 2.596293 10.528442 -5.977084 0 0.499 0.0\n1 1.005097 4.559610 -5.977084 1 0.499 0.0\n2 3.421589 -1.393433 -5.977084 2 0.499 0.0\n3 2.504058 -1.709201 -5.977084 3 0.499 0.0\n4 2.531308 -2.055951 -5.977084 4 0.499 0.0"}],"source":["J1_list = np.linspace(0, 1, 11)\n\ndf_list = []\n\nfor i in range(J1_list.shape[0]):\n df_i = pd.read_hdf(REPO_ROOT + \"/data/simul/bump_J1_%.1f.h5\" % J1_list[i], mode=\"r\")\n df_i['J1'] = J1_list[i]\n df_list.append(df_i)\n\ndf = pd.concat(df_list, ignore_index=True)\nprint(df.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"time J1 m0 m1 phase\n0 0.499 0.0 2.176421 0.030178 0.875617\n1 0.499 0.1 2.179004 0.034184 3.006940\n2 0.499 0.2 2.198129 0.047730 2.877824\n3 0.499 0.3 2.168901 0.060924 5.164094\n4 0.499 0.4 2.186594 0.116088 1.851756"}],"source":["res = df.groupby(['time', 'J1'])['rates'].apply(decode_bump).reset_index()\nres[['m0', 'm1', 'phase']] = pd.DataFrame(res['rates'].tolist(), index=res.index)\nres = res.drop(columns=['rates'])\nprint(res.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"J1 m0 m1 phase\n77 0.0 5.765645 0.012321 0.674993\n78 0.1 5.749736 0.028280 2.303137\n79 0.2 5.710444 0.017764 1.827065\n80 0.3 5.739739 0.139635 0.915704\n81 0.4 5.875741 5.332170 2.996620"}],"source":["last = res[res.time==res.time.iloc[-1]]\nlast = last.drop(columns=['time'])\nprint(last.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["sns.lineplot(last, x='J1', y=last['m1']/last['m0'])\nplt.xlabel('Tuning Strength $J_1$')\nplt.ylabel('$\\mathcal{F}_1$ (Hz)')\nplt.show()"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":[""]},{"cell_type":"markdown","metadata":{},"source":["#### Changing I0\n\n"]},{"cell_type":"markdown","metadata":{},"source":["##### Simulation\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"[10. 12. 14. 16. 18. 20. 22. 24. 26. 28. 30.]"}],"source":["REPO_ROOT = \"/home/leon/tmp/rnn_numba\" \nI0_list = np.linspace(10, 30, 11)\nprint(I0_list)"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"#+begin_example\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_10.0.h5\n Generating matrix Cij\n Saving matrix to /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.1810109979705885s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_12.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.145081017049961s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_14.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.189407243044116s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_16.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.206623823032714s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_18.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.2632918279850855s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_20.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.173060012981296s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_22.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.135047971038148s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_24.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.1645337590016425s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_26.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.200579374912195s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_28.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.172602592967451s\n Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml\n Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_30.0.h5\n Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy\n Running simulation\n Elapsed (with compilation) = 7.21300628897734s\n#+end_example"}],"source":["IF_LOAD_MAT = 0\nIF_SAVE_MAT = 1\n\nfor I0 in I0_list:\n model = Network('config_bump.yml', 'bump_I0_%.1f' % I0, REPO_ROOT,\n IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT,\n Iext=[I0], VERBOSE=0)\n\n model.run()\n\n IF_LOAD_MAT = 1\n IF_SAVE_MAT = 0"]},{"cell_type":"markdown","metadata":{},"source":["##### Analysis\n\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"rates ff h_E neurons time I0\n0 2.991737 2.570595 -6.026989 0 0.499 10.0\n1 1.881618 0.980764 -6.026621 1 0.499 10.0\n2 2.940181 -5.795781 -6.026254 2 0.499 10.0\n3 1.170019 -0.076887 -6.025889 3 0.499 10.0\n4 2.863903 2.739512 -6.025525 4 0.499 10.0"}],"source":["I0_list = np.linspace(10, 30, 11)\n\ndf_list = []\n\nfor i in range(I0_list.shape[0]):\n df_i = pd.read_hdf(REPO_ROOT + \"/data/simul/bump_I0_%.1f.h5\" % I0_list[i], mode=\"r\")\n df_i['I0'] = I0_list[i]\n df_list.append(df_i)\n\ndf = pd.concat(df_list, ignore_index=True)\nprint(df.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"time I0 m0 m1 phase\n0 0.499 10.0 2.201509 0.059715 4.983395\n1 0.499 12.0 2.158195 0.189081 1.364071\n2 0.499 14.0 2.171295 0.075219 3.018876\n3 0.499 16.0 2.177301 0.077118 1.571473\n4 0.499 18.0 2.169490 0.118072 4.750044"}],"source":["res = df.groupby(['time', 'I0'])['rates'].apply(decode_bump).reset_index()\nres[['m0', 'm1', 'phase']] = pd.DataFrame(res['rates'].tolist(), index=res.index)\nres = res.drop(columns=['rates'])\nprint(res.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"I0 m0 m1 phase\n77 10.0 4.748553 3.372240 2.832190\n78 12.0 5.299185 4.570933 2.924747\n79 14.0 5.893788 5.414350 2.964203\n80 16.0 6.393171 5.944921 3.220780\n81 18.0 6.918446 6.382643 3.213330"}],"source":["last = res[res.time==res.time.iloc[-1]]\nlast = last.drop(columns=['time'])\nprint(last.head())"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":""},"metadata":{},"output_type":"display_data"}],"source":["sns.lineplot(last, x='I0', y=last['m1']/last['m0'])\nplt.ylabel('$\\mathcal{F}_1$')\nplt.xlabel('FF Input (Hz)')\nplt.show()"]}],"metadata":{"org":null,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.5.2"}},"nbformat":4,"nbformat_minor":0} diff --git a/org/bump.org b/org/bump.org new file mode 100644 index 0000000..2085129 --- /dev/null +++ b/org/bump.org @@ -0,0 +1,1087 @@ +#+STARTUP: fold +#+TITLE: Continuous Bump Attractor Model +#+PROPERTY: header-args:ipython :results both :exports both :async yes :session dual_data :kernel dual_data + +* notebook settings + +#+begin_src ipython + %load_ext autoreload + %autoreload 2 + %reload_ext autoreload + + %run ../notebooks/setup.py + %matplotlib inline + %config InlineBackend.figure_format = 'png' +#+end_src + +#+RESULTS: +: The autoreload extension is already loaded. To reload it, use: +: %reload_ext autoreload +: Python exe +: /home/leon/mambaforge/envs/dual_data/bin/python + +* Continuous Bump Attractor Model +** imports +#+begin_src ipython + import sys + sys.path.insert(0, '/home/leon/tmp/rnn_numba') # put here the path to the repo + from src.model.rate_model import Network +#+end_src + +#+RESULTS: +** Single trial +*** Simulation +To run a simulation, first we need to define a network model. +The class Network takes three mandatory arguments: + + 1. The name of the configuration file that defines the model, + eg 'config_bump.yml', these files are in ../conf/ and well detailed. + + 2. The name of the output file that will contain the simulation data. + eg 'bump'. Simulation results will be saved as a data frame to '../data/simul/bump.h5'. + + 3. The path to the root of this repository. + +One can also pass extra arguments to Network, basically any parameter that is in the config file so that it will be overwritten. + +Here is an example: + +#+begin_src ipython + REPO_ROOT = "/home/leon/tmp/rnn_numba" + model = Network('config_bump.yml', 'bump', REPO_ROOT, VERBOSE=1, NUM_THREADS=116) + # Here for example, we are adding two extra parameters +#+end_src + +#+RESULTS: +: Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml +: Saving data to /home/leon/tmp/rnn_numba/data/simul/bump.h5 +: Jab [[-2.75]] +: Tuning, KAPPA [0.4] +: Asymmetry, SIGMA [0.] +: Iext [14.] + +Then one just runs the model with +#+begin_src ipython + model.run() +#+end_src + +#+RESULTS: +#+begin_example + Generating matrix Cij + all to all connectivity + with cosine structure + Saving matrix to /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Parameters: + N 1000 Na [1000] K 1.0 Ka [1.] + Iext [14.] Jab [-2.75] + Tuning, KAPPA [0.4] + Asymmetry, SIGMA [0.] + MF Rates: [5.09090909] + Transfert Func Sigmoid + Running simulation + times (s) 0.5 rates (Hz) [2.18] + times (s) 1.0 rates (Hz) [2.17] + STIM ON + times (s) 1.5 rates (Hz) [6.25] + STIM OFF + times (s) 2.0 rates (Hz) [5.87] + times (s) 2.5 rates (Hz) [5.85] + times (s) 3.0 rates (Hz) [5.87] + times (s) 3.5 rates (Hz) [5.88] + times (s) 4.0 rates (Hz) [5.87] + saving data to /home/leon/tmp/rnn_numba/data/simul/bump.h5 + Elapsed (with compilation) = 7.382569322013296s +#+end_example + +*** Analysis + +Most of the code for the analysis of the results of the simulations comes from +../src/analysis/plot_utils.py and ../src/analysis/decode.py But here we will +reimplement some of it. + +**** Imports +#+begin_src ipython + import pandas as pd + from src.analysis.decode import decode_bump, circcvl +#+end_src + +#+RESULTS: + +**** Load data + +#+begin_src ipython + df = pd.read_hdf(REPO_ROOT + "/data/simul/bump.h5", mode="r") + print(df.head()) +#+end_src + +#+RESULTS: +: rates ff h_E neurons time +: 0 1.750975 6.669991 -6.010844 0 0.499 +: 1 2.879797 1.936005 -6.011033 1 0.499 +: 2 3.251747 -0.653561 -6.011222 2 0.499 +: 3 0.755492 -1.949484 -6.011411 3 0.499 +: 4 1.402129 3.749234 -6.011600 4 0.499 + +The dataframe from a simulation contains 5 cols corresponding to +the rates of the neurons, the total feedforward input (ff), the net recurrent input (h_E), the neuron id, and the time step. + +**** Rates +***** Raster plot + +#+begin_src ipython + fig, ax = plt.subplots() + pt = pd.pivot_table(df, values="rates", index=["neurons"], columns="time") + + sns.heatmap(pt, cmap="jet", ax=ax, vmax=15, vmin=0) + ax.set_yticks([0, 500, 1000], [0, 500, 1000]) + ax.set_xticks([0, 2, 4, 6, 8], [0, 1, 2, 3, 4]) + ax.set_title('Excitatory') + + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/e9ac09572e875b34d2d7586a3e0cd73067ed6407.png]] + +***** Rates Distribution + +#+begin_src ipython + mean_df = df.groupby("neurons").mean() + mean_df[mean_df.rates<.01] = np.nan + + sns.histplot(mean_df, x=mean_df.rates, kde=True, color='r') + plt.xlabel("Rates (au)") + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/0e683531b04e9530611f77de655cc2e1397503c1.png]] + +**** Tuning +***** Fourier moments and phase +Here we use the functions from ../src/analysis/decode.py to decode the location and amplitude of the bump. + +#+begin_src ipython + data = df.groupby(['time'])['rates'].apply(decode_bump).reset_index() + data[['m0', 'm1', 'phase']] = pd.DataFrame(data['rates'].tolist(), index=data.index) + data = data.drop(columns=['rates']) + + print(data.head()) +#+end_src + +#+RESULTS: +: time m0 m1 phase +: 0 0.499 2.181534 0.032431 2.262011 +: 1 0.999 2.167945 0.083064 2.123314 +: 2 1.499 6.249701 7.102196 3.164585 +: 3 1.999 5.867596 5.310970 3.190126 +: 4 2.499 5.853998 5.470547 3.245525 + +This new dataframe contains 4 cols: the time step, the mean activity (m0), the amplitude of the bump (m1, this is the first fourier moment of the population vec) +and the location or phase of the center of the bump. + +We can look at the time course of these observables + +#+begin_src ipython + fig, ax = plt.subplots(1, 3, figsize=[2*width, height]) + + sns.lineplot(data=data, x='time', y='m0', legend=False, lw=2, ax=ax[0]) + ax[0].set_xlabel('Time (s)') + ax[0].set_ylabel('$\mathcal{F}_0 (Hz)$') + ax[1].set_xticks([0, 1, 2, 3, 4]) + + sns.lineplot(x=data['time'], y=data['m1']/data['m0'], legend=False, lw=2, ax=ax[1]) + ax[1].set_xlabel('Time (s)') + ax[1].set_ylabel('$\mathcal{F}_1 / \mathcal{F}_0$') + ax[1].set_xticks([0, 1, 2, 3, 4]) + + sns.lineplot(x=data['time'], y=data['phase']*180/np.pi, legend=False, lw=2, ax=ax[2]) + ax[2].set_xlabel('Time (s)') + ax[2].set_ylabel('$\phi$ (°)') + ax[2].set_xticks([0, 1, 2, 3, 4]) + ax[2].set_yticks([0, 90, 180, 270, 360]) + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/b48294c86d83e809032468368fa73451ef96f117.png]] + +***** Spatial profile +We can alternatively look at the shape of the bump at different epochs, using circcvl from ../src/analysis/decode.py +Here, during stimulation and during the delay period: + +#+begin_src ipython + + # Stimulus presentation + df_stim = df[df.time < 1.5] + df_stim = df_stim[df_stim.time >= 1] + + mean_stim = df_stim.groupby("neurons").mean() + array = mean_stim[["rates"]].to_numpy() + + X_stim = circcvl(array[:, 0], windowSize=10) + m0, m1, phase = decode_bump(X_stim) + + X_stim = np.roll(X_stim, int((phase / 2.0 / np.pi - 0.5) * X_stim.shape[0])) +#+end_src + +#+RESULTS: + +#+begin_src ipython + df_delay = df[df.time >= 1.5] + + mean_delay = df_delay.groupby("neurons").mean() + array = mean_delay[["rates"]].to_numpy() + + X_delay = circcvl(array[:, 0], windowSize=10) + m0, m1, phase = decode_bump(X_delay) + + X_delay = np.roll(X_delay, int((phase / 2.0 / np.pi - 0.5) * X_delay.shape[0])) +#+end_src + +#+RESULTS: + +#+begin_src ipython + + theta = np.linspace(-180, 180, X_stim.shape[0]) + fig, ax = plt.subplots(1, 2, figsize=[2*width, height]) + + ax[0].plot(theta, X_stim) + ax[0].set_xlabel("Prefered Location (°)") + ax[0].set_ylabel("Rate (Hz)") + ax[0].set_xticks([-180, -90, 0, 90, 180]) + ax[0].set_title('Stimulation') + ax[0].set_ylim([0, 15]) + + ax[1].plot(theta, X_delay) + ax[1].set_xlabel("Prefered Location (°)") + ax[1].set_ylabel("Rate (Hz)") + ax[1].set_xticks([-180, -90, 0, 90, 180]) + ax[1].set_title('Delay') + ax[1].set_ylim([0, 15]) + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/961f82129f5a0f204e79c201222275400d6a685d.png]] + +** Multiple trials +*** Simulations +#+begin_src ipython + ini_list = np.arange(25, 50) + + REPO_ROOT = "/home/leon/tmp/rnn_numba" + + IF_LOAD_MAT = 0 + IF_SAVE_MAT = 1 + + for ini in ini_list: + print('##########################################') + print("trial", ini) + print('##########################################') + + model = Network('config_bump.yml', 'bump_ini_%d' % ini, REPO_ROOT, + IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT) + + model.run() + + IF_LOAD_MAT = 1 + IF_SAVE_MAT = 0 +#+end_src + +#+results: +#+begin_example + ########################################## + trial 25 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_25.h5 + Generating matrix Cij + Saving matrix to /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.199274960090406s + ########################################## + trial 26 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_26.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.282144900993444s + ########################################## + trial 27 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_27.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.1860768320038915s + ########################################## + trial 28 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_28.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.1425280519761145s + ########################################## + trial 29 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_29.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.261533895973116s + ########################################## + trial 30 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_30.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.245834131957963s + ########################################## + trial 31 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_31.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.186810823041014s + ########################################## + trial 32 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_32.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.218402197002433s + ########################################## + trial 33 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_33.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.241861543036066s + ########################################## + trial 34 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_34.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.160159121034667s + ########################################## + trial 35 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_35.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.191513100988232s + ########################################## + trial 36 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_36.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.1625350209651515s + ########################################## + trial 37 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_37.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.261842316947877s + ########################################## + trial 38 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_38.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.175270576030016s + ########################################## + trial 39 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_39.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.177886882913299s + ########################################## + trial 40 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_40.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.220661935978569s + ########################################## + trial 41 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_41.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.259679448092356s + ########################################## + trial 42 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_42.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.217046734993346s + ########################################## + trial 43 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_43.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.18391912302468s + ########################################## + trial 44 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_44.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.2021880720276386s + ########################################## + trial 45 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_45.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.22361149196513s + ########################################## + trial 46 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_46.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.195027380017564s + ########################################## + trial 47 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_47.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.219246768974699s + ########################################## + trial 48 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_48.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.189989945967682s + ########################################## + trial 49 + ########################################## + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_ini_49.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.227059144992381s +#+end_example + +*** Analysis +**** Imports +#+begin_src ipython +import pandas as pd +from src.analysis.decode import decode_bump +#+end_src + +#+RESULTS: + +**** Load data +#+begin_src ipython + ini_list = np.arange(0, 50) + + df_list = [] + + for ini in ini_list: + df_i = pd.read_hdf(REPO_ROOT + "/data/simul/bump_ini_%d.h5" % ini, mode="r") + df_i['trial'] = ini + df_list.append(df_i) + + df = pd.concat(df_list, ignore_index=True) + print(df.head()) +#+end_src + +#+RESULTS: +: rates ff h_E neurons time trial +: 0 3.048436 -3.519403 -5.976190 0 0.499 0 +: 1 2.469401 -0.738135 -5.975781 1 0.499 0 +: 2 1.352286 3.643990 -5.975371 2 0.499 0 +: 3 1.230724 -6.516477 -5.974960 3 0.499 0 +: 4 1.657853 0.255504 -5.974548 4 0.499 0 + +#+begin_src ipython + data = df.groupby(['time', 'trial'])['rates'].apply(decode_bump).reset_index() + data[['m0', 'm1', 'phase']] = pd.DataFrame(data['rates'].tolist(), index=data.index) + data = data.drop(columns=['rates']) + print(data.head()) +#+end_src + +#+RESULTS: +: time trial m0 m1 phase +: 0 0.499 0 2.152970 0.053400 4.523900 +: 1 0.499 1 2.181172 0.077984 3.345704 +: 2 0.499 2 2.188593 0.065588 1.236631 +: 3 0.499 3 2.171368 0.053285 0.990382 +: 4 0.499 4 2.170371 0.002783 4.552415 + +#+begin_src ipython + end_point = data[data.time == data.time.iloc[-1]] + print(end_point.head()) +#+end_src + +#+RESULTS: +: time trial m0 m1 phase +: 350 3.999 0 5.906027 5.449286 2.892502 +: 351 3.999 1 5.891126 5.420843 3.135631 +: 352 3.999 2 5.874590 5.361630 3.339823 +: 353 3.999 3 5.891533 5.479044 2.938857 +: 354 3.999 4 5.886632 5.519670 3.141416 + +**** Phases +#+begin_src ipython + fig, ax = plt.subplots(1, 2, figsize=[2*width, height]) + + sns.lineplot(data=data, x='time', y=data['phase']*180/np.pi, legend=False, lw=2, ax=ax[0], hue='trial') + ax[0].set_xlabel('Time (s)') + ax[0].set_ylabel('$\phi$ (°)') + ax[0].set_xticks([0, 1, 2, 3, 4]) + ax[0].set_yticks([0, 90, 180, 270, 360]) + + sns.histplot(data=end_point, x=end_point['phase']*180/np.pi, legend=False, ax=ax[1], bins='auto', kde=True) + ax[1].set_xlabel('$\phi$ (°)') + ax[1].set_ylabel('$Count$') + ax[1].set_xticks([0, 90, 180, 270, 360]) + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/dceb04689b390134d5d9dcb484018844fa9ba70b.png]] + +**** Accuracy / Precision Errors + +#+begin_src ipython + from scipy.stats import circmean + stim_phase = np.pi + + end_point['accuracy'] = end_point.phase - stim_phase + end_point['precision'] = end_point.phase - circmean(end_point.phase) + print(end_point.head()) +#+end_src + +#+RESULTS: +#+begin_example + time trial m0 m1 phase accuracy precision + 350 3.999 0 5.906027 5.449286 2.892502 -0.249091 -0.243759 + 351 3.999 1 5.891126 5.420843 3.135631 -0.005961 -0.000629 + 352 3.999 2 5.874590 5.361630 3.339823 0.198231 0.203562 + 353 3.999 3 5.891533 5.479044 2.938857 -0.202736 -0.197404 + 354 3.999 4 5.886632 5.519670 3.141416 -0.000176 0.005156 + /tmp/ipykernel_3718977/1857574883.py:4: SettingWithCopyWarning: + A value is trying to be set on a copy of a slice from a DataFrame. + Try using .loc[row_indexer,col_indexer] = value instead + + See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + end_point['accuracy'] = end_point.phase - stim_phase + /tmp/ipykernel_3718977/1857574883.py:5: SettingWithCopyWarning: + A value is trying to be set on a copy of a slice from a DataFrame. + Try using .loc[row_indexer,col_indexer] = value instead + + See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy + end_point['precision'] = end_point.phase - circmean(end_point.phase) +#+end_example + +#+begin_src ipython + fig, ax = plt.subplots(1, 2, figsize=[2*width, height]) + + sns.histplot(data=end_point, x=end_point['accuracy']*180/np.pi, legend=False, lw=2, ax=ax[0], kde=True) + ax[0].set_xlabel('$\phi - \phi_{stim}$ (°)') + ax[0].set_ylabel('Count') + ax[0].set_xlim([-25, 25]) + + sns.histplot(data=end_point, x=end_point['precision']*180/np.pi, legend=False, ax=ax[1], bins='auto', kde=True) + ax[1].set_xlabel('$\phi - <\phi>_{trials}$ (°)') + ax[1].set_ylabel('$Count$') + ax[1].set_xlim([-25, 25]) + + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/071bb99b1983c6ff84603212037f5741ccb664c2.png]] + +** Parameter Space +*** Changing J0 +**** Simulation +#+begin_src ipython + REPO_ROOT = "/home/leon/tmp/rnn_numba" + J0_list = np.linspace(2, 4, 11) + print(J0_list) +#+end_src + +#+RESULTS: +: [2. 2.2 2.4 2.6 2.8 3. 3.2 3.4 3.6 3.8 4. ] + +#+begin_src ipython + IF_LOAD_MAT = 0 + IF_SAVE_MAT = 1 + + for J0 in J0_list: + model = Network('config_bump.yml', 'bump_J0_%.1f' % J0, REPO_ROOT, + IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT, + Jab=[-J0], VERBOSE=0) + + model.run() + + IF_LOAD_MAT = 1 + IF_SAVE_MAT = 0 +#+end_src + +#+RESULTS: +#+begin_example + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.0.h5 + Generating matrix Cij + Saving matrix to /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.148258017026819s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.2.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.156889496953227s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.4.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.209725192980841s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.6.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.21836295700632s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_2.8.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.296212543034926s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.253062576986849s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.2.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.352824991103262s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.4.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.316471469006501s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.6.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.391232304042205s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_3.8.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.313245614059269s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_4.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.380521073937416s +#+end_example + +**** Analysis +#+begin_src ipython + J0_list = np.linspace(2, 4, 11) + + df_list = [] + + for i in range(J0_list.shape[0]): + df_i = pd.read_hdf(REPO_ROOT + "/data/simul/bump_J0_%.1f.h5" % J0_list[i], mode="r") + df_i['J0'] = J0_list[i] + df_list.append(df_i) + + df = pd.concat(df_list, ignore_index=True) + print(df.head()) +#+end_src + +#+RESULTS: +: rates ff h_E neurons time J0 +: 0 2.979453 9.873323 -5.351787 0 0.499 2.0 +: 1 2.830152 -4.640124 -5.351531 1 0.499 2.0 +: 2 3.098595 2.158386 -5.351274 2 0.499 2.0 +: 3 3.415840 -6.115636 -5.351014 3 0.499 2.0 +: 4 3.625322 0.626738 -5.350751 4 0.499 2.0 + + +#+begin_src ipython + res = df.groupby(['time', 'J0'])['rates'].apply(decode_bump).reset_index() + res[['m0', 'm1', 'phase']] = pd.DataFrame(res['rates'].tolist(), index=res.index) + res = res.drop(columns=['rates']) + print(res.head()) +#+end_src + +#+RESULTS: +: time J0 m0 m1 phase +: 0 0.499 2.0 2.643238 0.074680 4.044520 +: 1 0.499 2.2 2.506782 0.028911 3.321685 +: 2 0.499 2.4 2.372550 0.021852 6.135836 +: 3 0.499 2.6 2.273263 0.004630 5.104311 +: 4 0.499 2.8 2.154610 0.054661 5.969938 + +#+begin_src ipython + last = res[res.time==res.time.iloc[-1]] + last = last.drop(columns=['time']) + print(last.head()) +#+end_src + +#+RESULTS: +: J0 m0 m1 phase +: 77 2.0 7.223276 0.059838 5.009779 +: 78 2.2 6.785306 0.235569 5.189480 +: 79 2.4 6.319345 2.347832 3.364468 +: 80 2.6 6.067178 4.456472 2.924240 +: 81 2.8 5.822750 5.580686 3.089648 + +#+begin_src ipython + sns.lineplot(last, x='J0', y=last['m1']/last['m0']) + plt.xlabel('Recurrent Strength $J_0$') + plt.ylabel('$\mathcal{F}_1$ (Hz)') + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/f76613de4bb887e4b31e648704cdd64935475d1f.png]] + +#+begin_src ipython + +#+end_src + +#+RESULTS: + +*** Changing J1 +**** Simulation +#+begin_src ipython + REPO_ROOT = "/home/leon/tmp/rnn_numba" + J1_list = np.linspace(0, 1, 11) + print(J1_list) +#+end_src + +#+RESULTS: +: [0. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ] + +#+begin_src ipython + IF_LOAD_MAT = 0 + IF_SAVE_MAT = 0 + + for J1 in J1_list: + model = Network('config_bump.yml', 'bump_J1_%.1f' % J1, REPO_ROOT, + IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT, + KAPPA=[J1], VERBOSE=0) + + model.run() + + IF_LOAD_MAT = 0 + IF_SAVE_MAT = 0 +#+end_src + +#+RESULTS: +#+begin_example + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.0.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.366484464961104s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.1.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.422051499015652s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.2.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.4423628660151735s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.3.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.4173392209922895s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.4.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.33573145698756s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.5.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.341766959987581s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.6.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.347771545988508s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.7.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.189968526014127s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.8.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.179180574021302s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_0.9.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.288797255954705s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J1_1.0.h5 + Generating matrix Cij + Running simulation + Elapsed (with compilation) = 7.284084509010427s +#+end_example + +**** Analysis +#+begin_src ipython + J1_list = np.linspace(0, 1, 11) + + df_list = [] + + for i in range(J1_list.shape[0]): + df_i = pd.read_hdf(REPO_ROOT + "/data/simul/bump_J1_%.1f.h5" % J1_list[i], mode="r") + df_i['J1'] = J1_list[i] + df_list.append(df_i) + + df = pd.concat(df_list, ignore_index=True) + print(df.head()) +#+end_src + +#+RESULTS: +: rates ff h_E neurons time J1 +: 0 2.596293 10.528442 -5.977084 0 0.499 0.0 +: 1 1.005097 4.559610 -5.977084 1 0.499 0.0 +: 2 3.421589 -1.393433 -5.977084 2 0.499 0.0 +: 3 2.504058 -1.709201 -5.977084 3 0.499 0.0 +: 4 2.531308 -2.055951 -5.977084 4 0.499 0.0 + + +#+begin_src ipython + res = df.groupby(['time', 'J1'])['rates'].apply(decode_bump).reset_index() + res[['m0', 'm1', 'phase']] = pd.DataFrame(res['rates'].tolist(), index=res.index) + res = res.drop(columns=['rates']) + print(res.head()) +#+end_src + +#+RESULTS: +: time J1 m0 m1 phase +: 0 0.499 0.0 2.176421 0.030178 0.875617 +: 1 0.499 0.1 2.179004 0.034184 3.006940 +: 2 0.499 0.2 2.198129 0.047730 2.877824 +: 3 0.499 0.3 2.168901 0.060924 5.164094 +: 4 0.499 0.4 2.186594 0.116088 1.851756 + +#+begin_src ipython + last = res[res.time==res.time.iloc[-1]] + last = last.drop(columns=['time']) + print(last.head()) +#+end_src + +#+RESULTS: +: J1 m0 m1 phase +: 77 0.0 5.765645 0.012321 0.674993 +: 78 0.1 5.749736 0.028280 2.303137 +: 79 0.2 5.710444 0.017764 1.827065 +: 80 0.3 5.739739 0.139635 0.915704 +: 81 0.4 5.875741 5.332170 2.996620 + +#+begin_src ipython + sns.lineplot(last, x='J1', y=last['m1']/last['m0']) + plt.xlabel('Tuning Strength $J_1$') + plt.ylabel('$\mathcal{F}_1$ (Hz)') + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/d1a263b026a88a641435d9f84a25769c34ba1e60.png]] + +#+begin_src ipython + +#+end_src + +#+RESULTS: + +*** Changing I0 +**** Simulation +#+begin_src ipython + REPO_ROOT = "/home/leon/tmp/rnn_numba" + I0_list = np.linspace(10, 30, 11) + print(I0_list) +#+end_src + +#+RESULTS: +: [10. 12. 14. 16. 18. 20. 22. 24. 26. 28. 30.] + +#+begin_src ipython + IF_LOAD_MAT = 0 + IF_SAVE_MAT = 1 + + for I0 in I0_list: + model = Network('config_bump.yml', 'bump_I0_%.1f' % I0, REPO_ROOT, + IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT, + Iext=[I0], VERBOSE=0) + + model.run() + + IF_LOAD_MAT = 1 + IF_SAVE_MAT = 0 + +#+end_src + +#+RESULTS: +#+begin_example + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_10.0.h5 + Generating matrix Cij + Saving matrix to /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.1810109979705885s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_12.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.145081017049961s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_14.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.189407243044116s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_16.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.206623823032714s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_18.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.2632918279850855s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_20.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.173060012981296s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_22.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.135047971038148s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_24.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.1645337590016425s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_26.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.200579374912195s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_28.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.172602592967451s + Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml + Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_I0_30.0.h5 + Loading matrix from /home/leon/tmp/rnn_numba/data/matrix/Cij.npy + Running simulation + Elapsed (with compilation) = 7.21300628897734s +#+end_example + +**** Analysis +#+begin_src ipython + I0_list = np.linspace(10, 30, 11) + + df_list = [] + + for i in range(I0_list.shape[0]): + df_i = pd.read_hdf(REPO_ROOT + "/data/simul/bump_I0_%.1f.h5" % I0_list[i], mode="r") + df_i['I0'] = I0_list[i] + df_list.append(df_i) + + df = pd.concat(df_list, ignore_index=True) + print(df.head()) +#+end_src + +#+RESULTS: +: rates ff h_E neurons time I0 +: 0 2.991737 2.570595 -6.026989 0 0.499 10.0 +: 1 1.881618 0.980764 -6.026621 1 0.499 10.0 +: 2 2.940181 -5.795781 -6.026254 2 0.499 10.0 +: 3 1.170019 -0.076887 -6.025889 3 0.499 10.0 +: 4 2.863903 2.739512 -6.025525 4 0.499 10.0 + +#+begin_src ipython + res = df.groupby(['time', 'I0'])['rates'].apply(decode_bump).reset_index() + res[['m0', 'm1', 'phase']] = pd.DataFrame(res['rates'].tolist(), index=res.index) + res = res.drop(columns=['rates']) + print(res.head()) +#+end_src + +#+RESULTS: +: time I0 m0 m1 phase +: 0 0.499 10.0 2.201509 0.059715 4.983395 +: 1 0.499 12.0 2.158195 0.189081 1.364071 +: 2 0.499 14.0 2.171295 0.075219 3.018876 +: 3 0.499 16.0 2.177301 0.077118 1.571473 +: 4 0.499 18.0 2.169490 0.118072 4.750044 + +#+begin_src ipython + last = res[res.time==res.time.iloc[-1]] + last = last.drop(columns=['time']) + print(last.head()) +#+end_src + +#+RESULTS: +: I0 m0 m1 phase +: 77 10.0 4.748553 3.372240 2.832190 +: 78 12.0 5.299185 4.570933 2.924747 +: 79 14.0 5.893788 5.414350 2.964203 +: 80 16.0 6.393171 5.944921 3.220780 +: 81 18.0 6.918446 6.382643 3.213330 + +#+begin_src ipython + sns.lineplot(last, x='I0', y=last['m1']/last['m0']) + plt.ylabel('$\mathcal{F}_1$') + plt.xlabel('FF Input (Hz)') + plt.show() +#+end_src + +#+RESULTS: +[[file:./.ob-jupyter/f41fed7f7c05c69f9b96639fa5508d179967240b.png]] + diff --git a/org/doc.org b/org/doc.org index 68d406f..43cdd19 100644 --- a/org/doc.org +++ b/org/doc.org @@ -3,6 +3,7 @@ #+PROPERTY: header-args:ipython :results both :exports both :async yes :session dual_data :kernel dual_data * notebook settings + #+begin_src ipython %load_ext autoreload %autoreload 2 @@ -416,6 +417,49 @@ from src.analysis.decode import decode_bump #+RESULTS: [[file:./.ob-jupyter/7ebef37e9d33cbff0298800444b68f12b1c8b0b9.png]] +** Parameter Search +*** Changing J0 + +#+begin_src ipython + REPO_ROOT = "/home/leon/tmp/rnn_numba" + + J0_list = np.linspace(1, 3, 11) + print(J0_list) +#+end_src + +#+RESULTS: +: [1. 1.2 1.4 1.6 1.8 2. 2.2 2.4 2.6 2.8 3. ] + +#+begin_src ipython + + IF_LOAD_MAT = 0 + IF_SAVE_MAT = 1 + + for J0 in J0_list: + print('##########################################') + print("J0", J0) + print('##########################################') + + model = Network('config_bump.yml', 'bump_J0_%.1f' % J0, REPO_ROOT, + IF_LOAD_MAT=IF_LOAD_MAT, IF_SAVE_MAT=IF_SAVE_MAT, + Jab=[-J0]) + + model.run() + + IF_LOAD_MAT = 1 + IF_SAVE_MAT = 0 + +#+end_src + +#+RESULTS: +: ########################################## +: J0 1.0 +: ########################################## +: Loading config from /home/leon/tmp/rnn_numba/conf/config_bump.yml +: Saving data to /home/leon/tmp/rnn_numba/data/simul/bump_J0_1.0.h5 +: Generating matrix Cij + + * Balanced EI Bump Attractor Model ** imports #+begin_src ipython diff --git a/src/analysis/plot_utils.py b/src/analysis/plot_utils.py index 23282ab..1141b10 100644 --- a/src/analysis/plot_utils.py +++ b/src/analysis/plot_utils.py @@ -1180,7 +1180,7 @@ def bump_I0(filename, config, n_sim=250): cim = my_boots_ci(m1_list, np.nanmean, n_samples=1000) cip = my_boots_ci(phase_list, np.nanvar, n_samples=1000) - + # print(len(cim), len(cip)) m1_ci.append(cim) diff --git a/src/model/rate_model.py b/src/model/rate_model.py index f65f583..250eab0 100644 --- a/src/model/rate_model.py +++ b/src/model/rate_model.py @@ -176,7 +176,9 @@ def __init__(self, conf_file, sim_name, repo_root, **kwargs): print("Tuning, KAPPA", self.KAPPA.flatten()) print("Asymmetry, SIGMA", self.SIGMA.flatten()) + # self.KAPPA = self.KAPPA / np.abs(self.Jab) self.SIGMA = self.SIGMA / np.abs(self.Jab) + self.Jab *= self.GAIN if self.IF_NMDA: @@ -368,7 +370,7 @@ def perturb_inputs(self, step): theta, self.I1[i_pop], self.SIGMA0, self.PHI1) def generate_Cij(self): - """ Generate the connectivity matrix Cij according to the specific regulation rules.""" + """Generate the connectivity matrix Cij according to the specific regulation rules.""" np.random.seed(self.SEED) Cij = np.zeros((self.N, self.N), dtype=np.float64) @@ -386,13 +388,17 @@ def generate_Cij(self): self.PHASE, self.VERBOSE, ) + Cij[ self.csumNa[i_post] : self.csumNa[i_post + 1], self.csumNa[j_pre] : self.csumNa[j_pre + 1], ] = Cab - self.Cij = Cij + return Cij + + def Jab_times_Cij(self, Cij): + """Multiply Cij with Jab.""" for i_post in range(self.N_POP): for j_pre in range(self.N_POP): Cij[ @@ -405,9 +411,9 @@ def generate_Cij(self): ] * self.Jab[i_post][j_pre] ) - + return Cij - + def generate_Cij_NMDA(self, Cij): """Modify the connectivity matrix when NMDA receptors are active.""" Cij_NMDA = np.zeros((self.N, self.Na[0]), dtype=np.float64) @@ -507,7 +513,7 @@ def print_activity(self, times): def run(self): """Perform the simulation for a series of steps within the specified duration.""" start = perf_counter() - + if self.IF_LOAD_MAT: print('Loading matrix from', self.MAT_PATH + "Cij.npy") Cij = np.load(self.MAT_PATH + "Cij.npy") @@ -518,10 +524,12 @@ def run(self): if self.IF_SAVE_MAT: print('Saving matrix to', self.MAT_PATH + "Cij.npy") np.save(self.MAT_PATH + "Cij.npy", Cij) + + Cij = self.Jab_times_Cij(Cij) if self.IF_NMDA: Cij_NMDA = np.ascontiguousarray(self.generate_Cij_NMDA(Cij)) - + np.random.seed(None) if self.IF_STP: