{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "9b5faaf3-017f-4ac9-b2da-913bbee54ebc", "metadata": {}, "outputs": [], "source": [ "# Import external toolboxes, functions\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from matplotlib import pyplot as plt\n", "from matplotlib.colors import to_rgba, LinearSegmentedColormap\n", "\n", "from scipy.stats import pearsonr, sem, wilcoxon, mode, mannwhitneyu, chi2_contingency, friedmanchisquare\n", "from pycircstat import vtest\n", "\n", "from sklearn.decomposition import PCA\n", "from sklearn.cluster import KMeans" ] }, { "cell_type": "code", "execution_count": 2, "id": "0dc97195-ace2-439d-8630-53091a19be38", "metadata": {}, "outputs": [], "source": [ "# Import custom functions\n", "from parameters import * # all-caps variables come from here\n", "from util import *\n", "from hht import HHT" ] }, { "cell_type": "markdown", "id": "0aae3608-8617-421d-a75c-137de25aacc7", "metadata": {}, "source": [ "### Define some plotting functions" ] }, { "cell_type": "markdown", "id": "a2f38a40-29ed-438e-90c8-82a9907331ad", "metadata": {}, "source": [ "### Figure S1A1\n", "Pupil size distributions for all experiments." ] }, { "cell_type": "code", "execution_count": 3, "id": "11d8fdb9-61b0-4d7b-82fd-85852ccea165", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: pupil_spontaneous.pkl\n", "Loading: pupil_sparsenoise.pkl\n", "Loading: pupil_natmov.pkl\n", "Loading: pupil_natmov_opto.pkl\n", "Loading: pupil_dark.pkl\n", "Gray screen: 5 sessions 3\n", "Sparse noise: 10 sessions 8\n", "Nat. movie: 2 sessions 2\n", "Nat. movie: 7 sessions 6\n", "Dark: 5 sessions 2\n" ] } ], "source": [ "# Load pupil size data for all experimental conditions\n", "conditions = ['spontaneous', 'sparsenoise', 'natmov', 'natmov_opto', 'dark']\n", "labels = ['Gray screen', 'Sparse noise', 'Nat. movie', 'Nat. movie', 'Dark']\n", "df_pupil = load_data('pupil', conditions)\n", "\n", "# Print out some metadata\n", "for label, condition in zip(labels, conditions):\n", " df = df_pupil.query('condition == @condition')\n", " print(f\"{label}: {len(df)} sessions {len(df.groupby('m'))}\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "3fe7ae41-1381-4ae1-b49c-286176538cd2", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "# Combine movie experiments with and without optogenetic stimulation \n", "df_pupil.loc[df_pupil['condition'] == 'natmov_opto', 'condition'] = 'natmov'\n", "\n", "# Get mean pupil area to sort distributions within each condition\n", "df_pupil['pupil_area_mean'] = df_pupil['pupil_area'].apply(lambda x: np.nanmean(x))\n", "\n", "# Set a starting x-porunion\n", "p0 = 1\n", "\n", "# Loop over experimental condition\n", "conditions = ['spontaneous', 'sparsenoise', 'natmov', 'dark']\n", "labels = ['Gray screen', 'Sparse noise', 'Nat. movie', 'Dark']\n", "for condition, label in zip(conditions, labels):\n", " df_condition = df_pupil.query('condition == @condition')\n", " \n", " # Sort experiments by mean area\n", " df_condition = df_condition.sort_values('pupil_area_mean')\n", " \n", " # Plot distributions\n", " porunions = np.arange(p0, p0 + len(df_condition))\n", " distributions = [row['pupil_area'] for idx, row in df_condition.iterrows()]\n", " violins = ax.violinplot(distributions, porunions, showextrema=False)\n", " \n", " # Change color of violins\n", " for violin in violins['bodies']:\n", " violin.set_facecolor('none')\n", " violin.set_edgecolor(COLORS[condition])\n", " violin.set_linewidth(1)\n", " violin.set_alpha(1)\n", " \n", " # Increment porunion counter\n", " p0 += len(df_condition)\n", " \n", " # Plot a dummy data point for legend\n", " ax.scatter(-1, -1, c=COLORS[condition], s=10, label=label)\n", " \n", "# Format x-axis\n", "ax.set_xticks([])\n", "ax.set_xlim(left=0, right=len(df_pupil) + 1)\n", "ax.set_xlabel('Recording session')\n", "\n", "# Format y-axis\n", "ax.set_yticks([0, .25, .5, .75])\n", "ax.set_yticklabels([\"0\", \"25\", \"50\", \"75\"])\n", "ax.set_ylim(bottom=0, top=0.75)\n", "ax.set_ylabel('Pupil size\\n(% eye area)')\n", "\n", "ax.legend(frameon=False, ncol=2, loc=(0.05, 0.75))\n", "clip_axes_to_ticks(spines=['left'])\n", "set_plotsize(w=6, h=2)\n", "fig.savefig(FIGUREPATH + 'pupil_area_distribution' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "8822f8e7-3887-43f4-aca1-311b0f83e3c2", "metadata": {}, "source": [ "### Figure S1A2\n", "Power spectral density (PSD) across gray screen and dark conditions." ] }, { "cell_type": "code", "execution_count": 5, "id": "24da71eb-57a1-4101-a416-e087a74a019a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: hht_spontaneous.pkl\n", "Loading: hht_dark.pkl\n" ] } ], "source": [ "# Load data from gray screen and dark experiments\n", "conditions = ['spontaneous', 'dark']\n", "labels = ['Gray screen', 'Dark']\n", "df_hht = load_data('hht', ['spontaneous', 'dark'])\n", "\n", "# Interpolate and normalize\n", "freqs = np.logspace(-3, 0, 1000) # set frequency space\n", "df_hht['hht_psd'] = df_hht.apply(lambda x: interpolate_and_normalize(x['hht_psd'], x['psd_freq'], freqs), axis='columns')" ] }, { "cell_type": "code", "execution_count": 6, "id": "bef98933-7b6a-4f21-9579-b68cfa6e1a3f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "for condition, label in zip(conditions, labels):\n", " # Plot PSD for the given condition\n", " df = df_hht.query('condition == @condition')\n", " psd = np.row_stack(df['hht_psd'].values).mean(axis=0) # mean across experiments\n", " ax.plot(np.log10(freqs), psd, color=COLORS[condition], lw=1, label=label)\n", "\n", " # Plot SE with thin dashed lines\n", " sem_plus = psd + sem(np.row_stack(df['hht_psd'].values), axis=0)\n", " sem_minus = psd - sem(np.row_stack(df['hht_psd'].values), axis=0)\n", " ax.plot(np.log10(freqs), sem_minus, color=COLORS[condition], lw=0.5, ls='--', alpha=0.5)\n", " ax.plot(np.log10(freqs), sem_plus, color=COLORS[condition], lw=0.5, ls='--', alpha=0.5)\n", "\n", "# Format x-axis\n", "ax.set_xticks(FREQUENCYTICKS)\n", "ax.set_xticklabels(FREQUENCYTICKLABELS)\n", "ax.set_xlim(left=-3.1) # allow for a little gap between x and y axes\n", "ax.set_xlabel('Frequency (Hz)')\n", "\n", "# Format y-axis\n", "ax.set_yticks([0, 0.5, 1])\n", "ax.set_ylim(bottom=ax.get_yticks().min() - 0.01) # allow for a little gap between x and y axes\n", "ax.set_ylabel('Power (norm.)')\n", "\n", "ax.legend(frameon=False, loc=(0.1, 0.75))\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=2, h=2)\n", "fig.savefig(FIGUREPATH + 'psd_spontaneous_dark' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "0d4aa895-b20d-4e2e-aa7a-073a89552077", "metadata": {}, "source": [ "### Figure S1B\n", "Quantification of running data." ] }, { "cell_type": "code", "execution_count": 7, "id": "f737ea65-ad41-4c2c-818f-21ea40f6c1c4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: ball_spontaneous.pkl\n", "Loading: ball_sparsenoise.pkl\n" ] } ], "source": [ "# Load data from gray screen and sparse noise experiments\n", "conditions = ['spontaneous', 'sparsenoise']\n", "df_run = load_data('ball', conditions).set_index(['m', 's', 'e'])" ] }, { "cell_type": "markdown", "id": "3bc479bb-d899-41ca-957c-23c485b56eee", "metadata": {}, "source": [ "#### Figure S1B1\n", "Proportion of time spent running." ] }, { "cell_type": "code", "execution_count": 8, "id": "44da783d-b670-4fb6-92c3-22390d2c8e82", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "min: 0.11\n", "max: 0.47\n", "median: 0.27\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get proportion of total recording time spent running\n", "df_run['run_prop'] = df_run.apply(lambda x: np.diff(x['run_bouts'], axis=1).sum() / (x['run_tpts'].max() - x['run_tpts'].min()), axis='columns')\n", "df_run['sit_prop'] = df_run.apply(lambda x: np.diff(x['sit_bouts'], axis=1).sum() / (x['run_tpts'].max() - x['run_tpts'].min()), axis='columns')\n", "print(f\"min: {df_run['run_prop'].min():.2f}\")\n", "print(f\"max: {df_run['run_prop'].max():.2f}\")\n", "print(f\"median: {df_run['run_prop'].median():.2f}\")\n", "\n", "# Make violin plot\n", "dists = [df_run['run_prop'], df_run['sit_prop']]\n", "colors = [COLORS['run'], COLORS['sit']]\n", "ax = violin_plot(dists, colors) \n", "\n", "# Format axes\n", "ax.set_xticklabels(['Loc.', 'Sit'])\n", "ax.set_yticks([0, 0.5, 1])\n", "ax.set_ylabel('Proportion')\n", "\n", "clip_axes_to_ticks(ax=ax, spines=['left'])\n", "set_plotsize(w=1.5, h=2)\n", "fig = ax.get_figure()\n", "fig.savefig(FIGUREPATH + 'run_prop' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "feaafaf8-3867-40eb-babf-9db1f8743da5", "metadata": {}, "source": [ "#### Figure S1B2\n", "Duration of periods of running and sitting." ] }, { "cell_type": "code", "execution_count": 10, "id": "4f6fc0fc-5af9-4ea1-aa46-359cd9194c7a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get lengths of all periods of running or sitting\n", "df_run['run_bout_lengths'] = df_run['run_bouts'].apply(lambda x: x[:, 1] - x[:, 0])\n", "df_run['sit_bout_lengths'] = df_run['sit_bouts'].apply(lambda x: x[:, 1] - x[:, 0])\n", "bout_lengths = np.concatenate(df_run['run_bout_lengths'].values)\n", "bout_intervals = np.concatenate(df_run['sit_bout_lengths'].values)\n", "\n", "# Make violin plot\n", "dists = [np.log10(bout_lengths), np.log10(bout_intervals)]\n", "colors = [COLORS['run'], COLORS['sit']]\n", "ax = violin_plot(dists, colors, logscale=True) \n", "\n", "# Format axes\n", "ax.set_xticklabels(['Loc.', 'Sit'])\n", "ax.tick_params(bottom=False)\n", "ax.set_yticks(np.linspace(0, 3, 4))\n", "ax.set_yticklabels(\"$10^{%d}$\" % tick for tick in np.linspace(0, 3, 4))\n", "ax.set_ylim(bottom=0)\n", "ax.set_ylabel('Duration (s)')\n", "\n", "clip_axes_to_ticks(ax=ax, spines=['left'])\n", "set_plotsize(w=1.5, h=2)\n", "fig = ax.get_figure()\n", "fig.savefig(FIGUREPATH + 'run_bout_lengths' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "0f8d7793-cbb6-41a0-9de9-74c84539f95e", "metadata": {}, "source": [ "#### Figure S1B3\n", "Variance of locomotion speed during and in-between bouts." ] }, { "cell_type": "code", "execution_count": 11, "id": "89085a6e-4369-4b7d-b8f2-1d08f6e47531", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get locomotion speed data for each state (excluding onsets anf offsets)\n", "kwargs = {'data':'run_speed', 'trange':'middle', 'concatenate':True}\n", "df_run['run_speed_run'] = df_run.apply(take_data_in_bouts, bouts='run', **kwargs, axis='columns')\n", "df_run['run_speed_sit'] = df_run.apply(take_data_in_bouts, bouts='sit', **kwargs, axis='columns')\n", "# Get locomotion speed variance\n", "var_run = df_run['run_speed_run'].apply(np.var)\n", "var_sit = df_run['run_speed_sit'].apply(np.var)\n", "\n", "# Make violin plot\n", "dists = [var_run, var_sit]\n", "colors = [COLORS['run'], COLORS['sit']]\n", "ax = violin_plot(dists, colors) \n", "\n", "# Format axes\n", "ax.set_xticklabels(['Loc.', 'Sit'])\n", "ax.tick_params(bottom=False)\n", "ax.set_yticks([0, 20, 40])\n", "ax.set_ylabel('Speed variance (cm/s)$^2$')\n", "\n", "clip_axes_to_ticks(ax=ax, spines=['left'])\n", "set_plotsize(w=1.5, h=2)\n", "fig = ax.get_figure()\n", "fig.savefig(FIGUREPATH + 'run_speed_variance.svg')" ] }, { "cell_type": "markdown", "id": "cb73a669-83ab-4227-9d60-a41237c258e7", "metadata": {}, "source": [ "### Figure S1B4\n", "Pupil size change at locomotion bout onset and offsets." ] }, { "cell_type": "code", "execution_count": 12, "id": "f633eb6d-b10b-4d0b-ba96-8e7cddf832e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: ball_spontaneous.pkl\n", "Loading: ball_sparsenoise.pkl\n", "Loading: pupil_spontaneous.pkl\n", "Loading: pupil_sparsenoise.pkl\n" ] } ], "source": [ "conditions = ['spontaneous', 'sparsenoise']\n", "df_run = load_data('ball', conditions)\n", "df_pupil = load_data('pupil', conditions)\n", "df = pd.merge(df_run, df_pupil, on=['m', 's', 'e']).set_index(['m', 's', 'e'])\n", "\n", "dt = 0.1\n", "df['pupil_tpts'], df['pupil_area'] = zip(*df.apply(lambda x: resample_timeseries(x['pupil_area'], x['pupil_tpts'], dt=dt), axis='columns'))\n", "df['run_times'] = df.apply(lambda x: x['run_bouts'][:, 0], axis='columns')\n", "df['sit_times'] = df.apply(lambda x: x['run_bouts'][:, 1], axis='columns')" ] }, { "cell_type": "code", "execution_count": 13, "id": "dc6ff26f-2629-4191-93f6-14d84f2907ea", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pre = {'run': 2, 'sit': 2}\n", "post = {'run': 2, 'sit': 4}\n", "baseline = {'run': [-2, 0], 'sit': [-2, 0]}\n", "\n", "for state in ['run', 'sit']:\n", " fig, ax = plt.subplots()\n", "\n", " tpts = np.arange(-1 * pre[state], post[state], dt)\n", " kwargs = {'data':'pupil_area', 'events':state, 'pre':pre[state], 'post':post[state]}\n", " responses = np.row_stack(df.apply(apply_get_responses, **kwargs, axis='columns'))\n", " if state == 'run':\n", " bi0, bi1 = tpts.searchsorted(baseline[state])\n", " baselines = responses[:, bi0:bi1].mean(axis=1)\n", " responses = ((responses.T - baselines) / baselines * 100).T\n", " \n", " mean = np.nanmean(responses, axis=0)\n", " err = sem(responses, nan_policy='omit', axis=0)\n", " \n", " ax.plot(tpts, mean, color='black')\n", " ax.plot(tpts, mean + err, ls='--', lw=0.5, color='black', alpha=0.5)\n", " ax.plot(tpts, mean - err, ls='--', lw=0.5, color='black', alpha=0.5)\n", " \n", " if state == 'run':\n", " ax.plot([0, post[state]], [10, 10], lw=5, color=COLORS['run'], alpha=0.5)\n", " if state == 'sit':\n", " ax.plot([-1 * pre[state], 0], [10, 10], lw=5, color=COLORS['run'], alpha=0.5)\n", " \n", " ax.set_xticks([-1 * pre[state], 0, post[state]])\n", " ax.set_xlabel('Lag (s)')\n", " ax.set_yticks([0, 5, 10])\n", " ax.set_ylim(bottom=-0.25, top=10)\n", " ax.set_ylabel('Pupil area\\n(% change)')\n", " \n", " clip_axes_to_ticks(ax=ax)\n", " set_plotsize(w=2, h=2)\n", " fig.savefig(FIGUREPATH + f'{state}_triggered_pupil_area' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "07650dd6-63a9-4f57-b13c-8d3f61781f05", "metadata": {}, "source": [ "### Figure S1B5\n", "Pupil size PSD during gray screen experiments and darkness experiments." ] }, { "cell_type": "code", "execution_count": 14, "id": "3dc84b75-f11a-4f35-a893-f83d98359fe8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: hht_spontaneous.pkl\n", "Loading: hht_dark.pkl\n" ] } ], "source": [ "# Load data from gray screen and dark experiments\n", "conditions = ['spontaneous', 'dark']\n", "labels = ['Gray screen', 'Dark']\n", "df_hht = load_data('hht', ['spontaneous', 'dark'])\n", "\n", "# Set a common frequency space\n", "freqs = np.logspace(-3, 0, 1000)\n", "\n", "# Interpolate\n", "df_hht['run_psd'] = df_hht.apply(lambda x: interpolate(x['run_psd'], x['psd_freq'], freqs), axis='columns')\n", "df_hht['sit_psd'] = df_hht.apply(lambda x: interpolate(x['sit_psd'], x['psd_freq'], freqs), axis='columns')" ] }, { "cell_type": "code", "execution_count": 15, "id": "ee1f6277-4a60-423d-bd25-f6f67c21e94e", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for state, label in zip(['run', 'sit'], ['Loc.', 'Sit']):\n", " # Plot PSD for the given condition\n", " psd = np.row_stack(df_hht['%s_psd' % state].values).mean(axis=0) # mean across experiments\n", " ax.plot(np.log10(freqs), psd, color=COLORS[state], lw=1, label=label)\n", "\n", " # Plot SE with thin dashed lines\n", " sem_plus = psd + sem(np.row_stack(df_hht['%s_psd' % state].values), axis=0)\n", " sem_minus = psd - sem(np.row_stack(df_hht['%s_psd' % state].values), axis=0)\n", " ax.plot(np.log10(freqs), sem_minus, color=COLORS[state], lw=0.5, ls='--', alpha=0.5)\n", " ax.plot(np.log10(freqs), sem_plus, color=COLORS[state], lw=0.5, ls='--', alpha=0.5)\n", "\n", "# Format x-axis\n", "ax.set_xticks(FREQUENCYTICKS)\n", "ax.set_xticklabels(FREQUENCYTICKLABELS)\n", "ax.set_xlim(left=-3.1) # allow for a little gap between x and y axes\n", "ax.set_xlabel('Frequency (Hz)')\n", "\n", "# Format y-axis\n", "ax.set_yticks([0, 0.015, 0.03])\n", "ax.set_ylim(top=0.03) # allow for a little gap between x and y axes\n", "ax.set_ylabel('Power (a.u.)')\n", "\n", "ax.legend(frameon=False, loc=(0.3, 0.8))\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=2, h=2)\n", "fig.savefig(FIGUREPATH + 'psd_run_sit' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "6814f312-34df-4815-8146-cc0b992cf383", "metadata": {}, "source": [ "### Figure S1C\n", "Firing rates across pupil sizes in darkness." ] }, { "cell_type": "code", "execution_count": 16, "id": "7cc62141-c4c1-4a5b-9a5c-09579efc7540", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: sizetuning_dark_spk.pkl\n", "Monotonic increasing: 34/89 (38.2)\n" ] }, { "data": { "image/png": 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5x2rl4cjVPpPTIgGAtl66eysCwPFbT7hlSMsu4GpfUMJvWfm52XO1LxJABjGotYpi1KhRWL58OQYOHIipU6ciLS0N0dHRmDhxIuzt+b5MTdDUojia/FhpuTb8djubq313fxf1ldRw8R7fArEQZr+3k436SlVgaqJ/A9vSTP8yKKPWKgqJRILDhw9j3LhxiIiIgKOjIyZMmIDo6OhqGb/kyV9VXhOEIVFrFQVQceT3yJEj+hn8dRcMFS4ZmXlF3EPZWvB9jeYCPElvZj3nai/E3r+fqx1Xew+JNbcMxZwLw+6ci8JiUTPtnFqAuZNnldcE8Sp2lqaws6y5z+2aK5mBo+li5j+a8S9mhvjw7Z+vPHaHW4YdYzpwtT+ZyrfOAgAWnPP7knIptwxmprptb9px+E9UB2RRiARtjxK1CbIoREJTiyL5IZ+DDgCYm/Lp+1Ehjbhl2H5Zu8Cwr9PYmd8zs3V9Pkc6Y2P+sz3Piku5+6jkaaHyvxl9QBaFSJBFQdQmyKIQCU0tiq+HtOIeq5Rzbh3sXZdbBt51jlABzikcvsnnk8Lrfg1U5BjVhc+6NVUo+3LPDV5xBIMUhUhoalGUS/m3BQ/d4DsvMmvnNW4Z9nzaiat9m5gkbhkuz+ELLvvTCf5F3fYCKN1KHKxqzgInTT1EghyuiNoEWRRioaHDlRXnsWQAmNXHn689twTAD0dTudp/IsCCakYu35kVJxsLbhl4Ha5e5WmhcAujvJBFIRLkcEXUJsiiEAlNFzPXnU7nHot3LitE0Jh6Ej7XY15rAACSONdqBrSpxy1DmK8zdx+VLBvaSqv630aqr6MrZFGIBG2PErUJsihEQlOLIu3JC+6x3mnBFwpPiNPVvTjjavBubQL8n8PjZ8rTAGpDboFuTlIuSo7ZC7newQspCpHQ1KJ4N7A+91htPB252gtxevTafcWwg9rQsbETtwy8FNawoDGn7vCffxEKmnqIhKYWBUEYAmRRiISmFoUPZwwFADBStfeqIaXl/E5fPNGjAeBCuurs4poS7scXqevzrVe4Zejb0l2ndl5KonMtTkzhFUcwyKIQCXK4ImoTZFGIhYYOVykPVUeGOnUwAdfP/45mbTuiQ49+KuvxWiWNBdgeff6SzzmoJWeAYAAoKeM787J2RFtuGYSMK7FGS3k8Jgg2tAKkKETCLuAfeHnvqty1Npw6mIAf5owHABzbHQcAVSoLghATUhQCoCxFqW2zEMAIKE6/AkuvANj6K08RMGPHVaXlOfvls4+t27oHewq8ldaNjuBz4d5+KZOrPQB04Tz9uep4GrcMn4Qo/3w0xcmW39cl0JMvbcHWi/zfhRiQohARW/8QlQpCHeSwRdQkSFEIgLOd7oeJHj17qbRcm+3VRM6cm0JEAr/Emdcjr4D/ANS5u3w7Jw0584IAQH4RX/7SdwM9uGUQA4NTFPHx8fi///s/XLx4Efn5+fDx8cHnn3+OYcOGydX773//i6+++goZGRlo1qwZvvrqK4SHh4siU0+OALnrTyvfDdHGouA9q+HtxL+YuWBgC672TWfu55YhnPOcRX4Rv7K69YgvbcELARIli4HBbY9+8803sLW1xbJly7Br1y507doV7733Hr7//ntZnc2bN2PUqFH48MMPsX//fjRr1gx9+vTBtWv8AVqqC9peJWoSRkyIzCvVSHZ2Npyc5N1933vvPZw+fRp3794FAPj4+KBjx45Ys2YNAEAqlSIgIAABAQHYsGGDYLIY/W8Vc/5e3UOW/XxC+SLew83T5XZNLBq0gOuwhUrrdq3iSZp25gCybpyHm39beAf3VFqnqIT/KRbEmXvUUYBoTi85t0dDBAjHxzt9seFI5lT59yjGT9rgLIrXlQQAtG7dGg8ePAAApKWl4datWxgyZIjsvrGxMQYPHoz9+/nN2+ri9e1UbbdXgQolcWLVbNw+uQsnVs1G2pkDQolHvGEY3BqFMk6fPo2mTSuCkyYnJwMAfH195er4+fkhNzcXT548wVtv8T85XmXvn1k6t23qomJ9wKU3shwskXv7Iuo0CYRbG9XrK6oSHeecPil3fen0Sfzl2FqhnhBJiFw5kwxbcKYcAAATznD7Vub80cbMakCiYzEweEVx+PBh7Ny5UzbNyMurWH13dHSUqyeRSGT3NVUURsocJKoRtzbhVSoIddAWq+HAm5tFbAxaUaSnp+O9995Dv379MGLECL3J8fu0MJ3bLtx3k3v8W4+Ux7TQdIv1j4yn3DKYmej/ac5j2QHCRLiq56hbouPRoYoxQ2tSPIqarcaqIDc3F7169YKnpyc2btwoK6+0HPLz5eMjVFoalfc1gTFW5aumQxYFIRQGaVEUFhaiT58+KCkpwZ49e2Bt/bcWr1ybSE5Ohqfn3wFtk5OTUadOHcHXJwCg9ZcHdW5rL2LuBk0tCj93e+6xCjiDvvi68cswsA2fs1JXX/6/Da+6/E5blVgKEKFdKAxOUZSVlWHw4MG4ffs2Tp06BWdn+a1Bb29vNG3aFPHx8ejZs2I7UCqVIj4+Hr169RJFJp4trU+68IepV3VeRFOL4qmO4dtepWMT1RGqLh/dg9Q/zqBxq2C07tpHaR0hEiHdzS7gal8ng9/i8nXlV3g1EYNTFGPGjMG+ffvw3XffIScnBzk5ObJ7rVu3hoWFBaKjo/H+++/Dy8sLHTt2xLp163D79m1s2rRJj5JXPzUhytblo3uwadHnAIBziVsBQKWyIGouBqcoDh6sMPM/++wzhXt3796Fl5cXhg0bhhcvXmDx4sWIiYlBs2bNsGfPHjRv3lwUmXhiKQiR/Tp9UW+l5ZsDnuG99/6eFv08bQSGDVOs+03SLW4Zlh++rbQ8Z7/8tGzXvoP4nfkq1Av25nPYAgB/zulLoCdfNnQAyMrXLUCvu6NiuoPrD/jikAqJwSmK9PR0jer9+9//xr///W9xhanhVJ5/OXz4MMLDwxXOw1QHtKBaOzA4RVET+fGfbXRuO3rDRe7xey8/WcVdD6DlcGx4AmxQUU+I06Oq0HT684zz1CUA/HqBL5aDEFG4U3VMv7B1VAeFsom/8sfwFAqD3R4lDAOyKGoHZFHoGSFcfoU4Hi0WmloUT54rj8uhDcoiWWtDiA//9mjrBo7cfVSy6v1Areo3pJiZtZf0HL4tPYA/gY+dJf+fgSplpalF0cLDgVsGJ1u+bORtGvAvZgqZRIhX8QkJTT0IUakJW7QEP2RRCMCHv5zVue3QtvwpBZdxbm8WiZhKT1OLwkoAL8TsF3zTl19+u8stg64xLYQIwycmZFEQokKRumoHZFEIwPp/va1z23+u1t0aqWTGO35c7XV1EnqVJQdUpL/TMBHSwyoyiWecT8LjlAtw9glC/bbdVdaz5XClB4C+AbqlA3wVXZ3XlCVpnr2z5oRuJEVBiApvIqSM80k4vzYaAPDXqYpcJ1UpC0IcRIuZefLkSeTm5qJfv4rsVtnZ2Rg/fjxu3LiB8PBwLFq0CGZm4p2crA4qA9t8fVD3ZLKcYRwAANcfPONqf/J2NrcMVcWTyP3zKF6kXYatd2vUadlVeR0VB9Ny9i/Hiz//dgO3bdkDdXuNV1p3XFhjLSRW5J9vN+BqDwAOOp4Gtjbnf2YbZMzMKVOmyEW9/uyzz3D48GEEBwcjNjYWc+fOFWtoooZRp2VXNOg/UaWSqIo3xWGrtFyK0nK+4MBiItrUIyUlRaYMCgsLsWPHDqxZswaRkZFo27YtFixYgAULFog1fLUysXtTndvuuvKAe3xTTj8KVwe+eJdA1cmWNeGsiuQ92myvnk3LUXnv3vkkPE6+AGffIDRQMXXhjZAFABJrPkVWv47i4bCagGiKoqSkBJaWFX+Av//+O8rKytC7d8XJxaZNmyIri/9LqQ2UC/AUUWW2a8qWcxncMnRuqjoehSZEdVGeN/SKtDt+fWXq8U7P7ghQUVdV6oOCG8eRvXsJACD91B6kPSmAjZJUj0LkHn3G6SVbLq2ZVoVoisLX1xeJiYkIDQ3Fxo0b0b59e9jZ2QEAHjx4gDp1+I8VE7WfgNCKh8udK2fQKCBYdq0NxX9dUbhWpigI1YimKObMmYPBgwfjl19+QX5+PhISEmT3EhMT0bq1Yth4Q+XPzKc6t+3Vwo17/AGcIeCaOPOnFOTdYk19rPrUpVNAGJwCKgIY39fhpKum6xzd/fnTFvBG0+bJY7uLa+SqEU1R9O3bFzdv3sTly5fRokULWd4NAGjfvj1atmwp1tAEIQc5ffEjqh+Ft7c3vL0V55NRUVFiDlvt8ES4WncqnXt83shMQoSF3/nHfa72oT58CYarREOnr+Qsvm1mAMgr5FsvKhMgdqgYiKooiouLceLECWRmZqK4WN40NTIywujRo8UcniAAAOZOnnJOX+ZOnlXUJpQhmqL47bffMGjQIDx58kTpfVIUFQhxxDvhD74tVqkADjrWnAl8Eq+Jtwum6RZr5lPV6x85V47gedpl2Hm3Rt0A1QmfdI2r8Z9uTQAApsZ/r3H8cDRVp77EQDRFMX78eHh7e+PgwYPw9/c3eC9MwnCx9AyQ8+609AzQqn3OlSO4G1+RST77YiIAVKksdOE/3RR9cd4IRZGSkoLt27cjIEC7L+VNI9zXhbsPD4luaewqeVDFk1RTmnMmERIiSlduoYo+wpvgUnNX3L58Gk1at0cbFekCVEYST5GPa/ok5SKk3h2V1h3Qmj8tYSW9Wmi3C5Ms2MiKiKYoWrZsiYcPH4rVfa1BiJDsTzjjMAghw4uXfAuiQiwkGleVzbx+MDzqB6MIwO+p2p1t0caN/NDNR1r1XRXn0/ME64sX0c56rFy5EsuWLcPx48fFGoIgqgXaXhXRoujevTsKCwsRFhYGc3NzmVfmqzx+/Jh7nPv378PHxwcFBQV4/vw5bG0rnIcYY1i4cCFWrlyJ7OxstG3bFsuXL0erVq24xxSSJi6Kn4u2POU02x05zycAQLN6fGcUwn35t0eTOJ/mF/9S8QTXcHsVEMZ5rZIOjepqVf83wUZWRDRFMXbsWNmxVzGZPHkybG1tUVAgH6R20aJFiImJwZIlS+Dr64tvvvkG3bp1w7Vr1+Dqyu+BR7w50PaqiIoiOjparK5lnDhxAomJiZgxYwYmT54sKy8uLsaiRYswffp0jBs3DkCFN6iXlxdWrFiBefPmCSrHfY7FwHpKUslpi7LoSNpwJeMptwxSTkehZvX4o3DHX+A/3KYMbU6wXrvPv9ZSyZGb/Ba3UIgeM7OkpAQXL15EUlISLl68iJISYaIwl5eX49NPP8WcOXPg5CT/Qzl16hSePXuGIUOGyMpsbGwQERGB/fv3CzI+8ebwpsTEqApRPTO/+uorLFy4EM+ePZNF3XFwcFCwAHThp59+wsuXLzF27Fhs3LhR7l5ycjJMTEzQpEkTuXI/Pz/ExcVxjasMHqvg2n3+HYernH2YChBmS9dUepVsv8znAg4AdpZ8vjqqtmi1sSjaNRTuVLSnk3bb3hcEG1kR0RTFt99+i+nTp2PUqFEYOnQoXFxc8OjRI8TFxWH69OmwsLDA+PHKQ5qpIycnB7Nnz8aGDRuUOnLl5eXB1tYWJiby3oISiQSFhYUoKSmBubn6p0J1rLEIEbWM9+TmnSf8SYhecp4XaevF/wPLyufzB1EVElAbi4L3rMervCytObEpRFMUP/zwA6ZNm4b58+fLynx8fNClSxc4Ojpi+fLlOiuKmTNnIjg4GO+8845Q4hKESiiJkYiKIiMjA127Ko+RGBoaiq+//lqnfq9fv441a9bgxIkTePr0KYCKUHsAkJ+fDxMTE0gkErx48QLl5eVyVkVeXh6sra01siYA9UFKhbA4hDhnwXtexE2AUHhZT3mtGr6pCyBeIiNtLAohM8PzTimFRLTFzAYNGuDgwYNK7yUlJaFBA90iHt++fRulpaVo3749JBIJJBIJxo4dCwDw8PDAp59+Cl9fX5SXlyM1Vd5XPjk5Gb6+vjqNS7y5kMOVyIfCxo8fj9zcXLz77rtwcXHB48ePER8fj9jYWHz33Xc69dupUyccPXpUriwxMRGLFy/Gvn374O3tDU9PT9jb2yM+Ph6zZs0CUGF17N69W5RYGE1m7tO57aiQRtzj8wZ0nRvRjFsG3kXZyVuvqK+kBm8nPmenG6rcyLVwuOLdqn4V3hO5QiKaohg3bhwsLCzwxRdfYM2aNTAyMgJjDO7u7vjpp58wcuRInfp1cnJCaGioXFl6ejoAoHPnzjLPzGnTpiEmJgYSiUTmcCWVSvHpp5/yvC3iDYQcrkRSFKWlpTh37hx69+6NkSNHIjMzE1lZWXBzc4OHh0e17CZMmzYNUqkUCxcuRE5ODoKCgpCUlAQXF/7Tmq9ze77ui6rfH1F+YlEbXOz51hhm7riqvpIazDhTBvi68p0+BYCXZeLsEmizmDkkSHX80sN7tuPSmZNoE9wZ4X0Gqh336yHanbx+J0ar6lohiqIwMTFBWFgY9u/fD3d3d9SvXx/16/Nn7VbFiBEjMGLECLkyIyMjzJw5EzNnzhRtXOLNgDeeBVChJOZ9XhGoad/WTQCgkbKoKYiiKIyNjdGkSZM35ph5VYln1NGlyVvc4/PGWZzc04dbBt6DZUeS+Y9n6xpdqhKVu1zNh+J2fQfcv34e9Zq1RZP2qvOnjlh7Xml5zv4dctffrt+B/3vy98MzfZFiGoI5O69rIHX1INoaxfz58zF16lS0aNECLVq0EGuYGsEZDkXxmZLIRtqSdIPvR3ZdgG0477f4FhKFmI4mXuN7MCVXle3MIxiOHsEoAPCHDmdj1G2xek3bCwCQWNfMSHCiKYp58+YhJycHrVq1Qr169eDi4qLwx3Du3DmxhieIGoWhO22JpiiaN2+O5s2bi9V9jeKjTg11blsmQErB7v7CL9Bqy72cQq72Tnb8B606cW5N7uIMUgwAk+KVb/Nq6rSVpyqcn54RTVGsXbtWrK4JwuAwdKctUU+PvinwLKKpjKqkBbyBaf0E2JpcuP8mV/veLflTKz56xreY6Wgl4vqAhk5byhY1NR5isc5N1SKaong1FoQqfv31V7GGJ4gahaE7bYmmKJQl/snLy0NycjLq1q0LHx/+LbmaQiOOFX8bc/6vgNd9ukyq/3USS1N+d2VLzgTBcefFiZAFaL6YGbzgsGgy8CCaonj9PEYlGRkZGDBgACZMmCDW0ARR4zD0KFnVvkZRv359TJ8+HVOmTEFERER1Dy8KBS/LdG7rKsARb16LwJzzSQwAFpwWwb1cvl0TgN/x7G1v/uA5qtI7qrMolK1NtP5S+elrfaCXxUwTExNkZmbqY2hRsLHQ/WM8lsIfQJV3MfOCAAuqVzP5pj9CxOXg3VosF2AKpgpdLIriNyHC1Y0bNxTKSkpKcPPmTcyePRtt27YVa2iCqHHQ9qgKmjdvrtQtlzGGoKAgrF69Wqyhq51h/z2jc9uvBrXkHr9+Hb7co8Wc8S4BYDRnXI3cAn5PxSLO9zEh7g9uGVSiRUyLSub1185h8V1hs1DIUa2LmZaWlvDw8EC9esIlciUIQ4C2R1UQEhIiVtc1jmHtdAvrBwC3H1dxEElDHj3ji1fZzJ0/+c5NziTD4X78bugJf/CF/O/qw5/W8P/OKJ9S6HLWY9mhW9zyCIWoCYBevnyJlStX4l//+hd69OiB27crgrTExcXh5k0+Tz6CMCRoe1QFt27dQvfu3ZGfn4/AwEAcO3YMz59XPD1PnjyJvXv3Yv369WINX60Ec2yrOdvxb4++LOObmwuxPtDEhe+YeYYA26MeEr70jL+nKs/rIQS6WBTOdhZiiaM1ogbXbdCgAXbv3g1bW1u5EPkhISGYOnWqWENXOzw/diG2R3mVjYkxfywI3lB4vDE1ACD7Bd9ZjycvxDv6rYtFoUvcC7EQTVGcPHkS8fHxcHR0RHm5/BPPxcUFWVlZYg1NEDUOIeJRvLhxHEXpV2DlFQBb/+pdAxRNUVhaWqKoSHkylPv378PR0VGsoQ2KBpxbmwBgasz3NN/9J38chsbOfFOPOAEykX8W3kR9pSrYePYetwyqpqF/te2AM6/E3WzetgM81UxZX3U0LbhxHNm7lwAAXvx5EIwBNtWoLERTFN27d8eCBQvQrVs3WQh9IyMjvHz5Et9//z2lAyTeKDzb9QAAPEq+CBffQNm1pjz/I1HhulYoiiVLlqBjx45o3LgxunfvDiMjI3z55Ze4fv06SkpKsH37drGGNihyBFhIfMFx1gTgO/1aCe9ipLr0jZowdxdfMFozAbK6n7ubq/rmW0HAW0F4BOBRVfWUoYPDlpCItj1av359XLlyBaNGjUJ6ejoaNWqErKwsDB48GBcvXoSrq6tYQxNEreN1B63qdtgS9VCYRCJBTEwMYmKEzUxSVlaGpUuX4pdffsG9e/fw1ltvYfDgwVi2bJmsDmMMCxcuxMqVK5GdnY22bdti+fLlaNWqlaCy8MK7Ug8AOZyr9bce8Tt98cbMfF7MZxUB/MmW72YXcMtgy3FAsCr0HZxX0HcVFhamcV0jIyMcPqxbkI4RI0bgyJEjmDt3Lnx9fZGRkaFwCG3RokWIiYnBkiVLZCkFu3XrhmvXrpE1Qxgc+nbYElRR1K1bV22drKwsnDp1Suc8DomJiYiLi8OVK1fg7++vtE5xcTEWLVqE6dOnY9y4cQCA9u3bw8vLCytWrMC8ecKentl9RfddA19XO+7xJdZ8T5d3A1WnwdMU3j3/YG/1fzvq+HI33xpFn5bu3DLs4vhbqIpaZVHEx8ervHfv3j0sXrwYe/bsgZOTk84RrtasWYOwsDCVSgIATp06hWfPnsnF7bSxsUFERAT2798vuKLg+bE3cRFAUXBOX24/esEtA2+A3r1/8vvVDOJUeFO2/sktgxCBiJRRqywKZaSmpmLhwoXYsGEDnJ2dsXDhQnzyySewstLN3fbs2bPo27cvxo0bh/Xr16OsrAz/+Mc/sGLFCri7VzwRkpOTYWJigiZN5PfV/fz8EBcXx/2eCEJoKg+0qVonqVUWxatcv34d8+fPR3x8POrXr4/vvvsOH3/8sZwrty48fPgQsbGxCAgIwJYtW/D8+XNMmTIFAwYMwJkzZ2BkZIS8vDzY2trCxEQ+PJtEIkFhYSFKSko0kkPT6RGPVbDzMt+JRwA4n67lVttrCJF0Juupcuc6TbnP2V4IhIjLkfpYN+vst9sV50xUZWSvdRbFxYsXMX/+fCQkJKBJkyZYvXo13n//fYUfra4wxsAYQ0JCgmxNxM3NDSEhIThy5AjCw8MFGYcgahL6jpAlqKLo1asXDh48iBYtWmDLli0YPHiwkN0DqLAKvL295RZOO3XqBHNzc9y4cQPh4eGQSCR48eIFysvL5RRUXl4erK2tNbZq1DkBVVocx28ppibQlP6t+YP4NK/HF08iK5//aS7hzGZ+gzOeBQB8yxm/4WURf4zKMF++mBb7VSVark0OVwcOHABjDJmZmRg7diycnZ2rfOmCn5+f0h8wYwzG/zvz4Ovri/LycqSmpsrVSU5Ohq+vr07jEoQ+qVUOV3PnzhWyO6X06dMHc+fORXZ2NpycKpLSnjhxAqWlpQgICAAAdOjQAfb29oiPj8esWbMAAIWFhdi9ezeioqIEl4ln10OIJMUWnOH2eZP7Apqv56hCiHD97wfz/XiKSvjXKHijkauiVi1mVoeiiIqKwvLlyxEREYEZM2bg+fPnmDp1Krp164ZOnToBqDi5Om3aNMTExEAikcgcrqRSKT799FPRZSQIoal1i5liY29vjyNHjmD8+PGIjIyEubk5+vXrJ+e+DQDTpk2DVCrFwoULkZOTg6CgICQlJcHFhT824+tk5eses9LFnn/f3dmeLxISb+IcAEh7wueLwRv4BgCcbPk+h/t5/Gs1Z+7mcPehDH1bFEZMiGN7byiV5raUI3EMr8kO8CcAEiDAFcrK+f6MVG0LakN6Dt9ZjUwBFMW6U+k6tasM0Pz4+d8PnW8P3Zb9PzfpJzy/tEd2bdemD+p0HyXXx1+L+wAQ5iTu6xicRUEQtZH33laM5P6qoqhV26NvKhN+vaJz26FB9bnHd3fkm7541rXhloF3IfDRc76UAwC/G/nhm/zxS104p4EqqU3bowRBiEOt2h59U/mAY1tOiNOjPEmSAWHcp58X862TXL73lFuGLAc+q4T3PQC6u3CrQ9+LmWRREIQBQNujtYBAT4nObfMEiJl54jbfEe3r9/ndp0+n8W0LCpHspsp4lRrQsr4jtwxC7N4oQ98WBSkKPfPnfX5PPt54EkJMPSzN+A79CRFkmHen+cwdfh8IBnG8DfRtUdDUgyAMALIo3nDebqh73tJKvOryJRESwlzmXYy8+Fcetww25nxWjakA3qFtvfi/T2WQRUEQhFrI4eoNR4hM4o+fcybn5WwP8J838RFgm5h3a5I3KzwAXBLAMlIKOVwRBKEOcrh6wzER4EQW79YibzpAAPCQ6BYsuZLkLP4kRBIbM672zwQI2ylEWkJl6HsxkywKgjAA9L2YSRaFAPjM2q9z25R5vbjHP3hdRZxFDRnYhj8BEG9ejp7N+OOE8B4zF8KqeSZAakRl6NuiIEUhAMcnd9W57WMBTk3yZjNvOlN3RVfJrfl8Co830xgA2HGeeXHjPIULAIENdPfSrQp9WxQ09SAIA4AsiloATxo5IbbknO34noS/jAjiluHHY6nqK1WBEGdebj7kmzo85AhpWMmqE2k6tZv+jl+V9/VtUZCiIIgagNe0vVXeJ4erNxwh3KdNObfkcgv44zC4cgYJFsKicLTi2x6FAJG+7Cz5flI3VS2oksMVQRDqIIerNxwh5sXGnOerrTgPUwGAOeeBKiGikfMely8SIEmxjbk4Pyl9L2YapEWxZcsWtGnTBra2tqhXrx4+/PBDPHjwQK4OYwwLFixA/fr1YWVlhS5duuCPP/7Qj8AEwQktZmrJrl27MGzYMIwdOxZLlixBVlYWZs2ahd69e+PixYuy/KOLFi1CTEwMlixZIssU1q1bN1y7dg2urq56fhd/09SF/zAUL1IBEgDxJhHqypncF+BPtixEFO4lB1K4+1CGvi0Kg1MUmzZtQps2bbBixQpZmb29Pfr164eUlBT4+fmhuLgYixYtwvTp0zFu3DgAQPv27eHl5YUVK1Zg3rx5+hJfgfxC/oXEghI+h6tkzm1FALDldHYSwuHq9J1srvYlnEmMAH7Ff+me8tOn+rYoDG7qUVpaCgcHB7kyR0dHAH9nSDp16hSePXuGIUOGyOrY2NggIiIC+/fzeyESRHVD26Na8vHHH6N///5Yv349+vfvj4cPH2LWrFkICwuDv78/ACA5ORkmJiZo0qSJXFs/Pz/ExcXpQ2yVlAuQ/o03XD+vNQDwx4Koz3n6FAC2cS4MhzZ9i1uGfdd0O3dzcoriMQA53wraHtWO3r17IzY2FlFRUXBwcICPjw/Ky8uxbds2WZ28vDzY2trCxER+NV8ikaCwsBAlJZrN74yMjKp8EUR1QdujWnL06FGMGjUKn332GXr16oVHjx4hOjoaAwYMwKFDhxSUQ3VQVq6701QdG/655lnOUPlCRMC+xxnT4rdU/pgYLzhPbgoRP9vCVJxnLy1masmkSZPQt29fLF68WFbWqlUr+Pr6IiEhAQMHDoREIsGLFy9QXl4upzjy8vJgbW0Nc3PNfpzqskKTVUFUF/pezDQ4RZGcnIxhw4bJlfn4+MDKygp37twBAPj6+qK8vBypqanw8fGRa+vr6yu4TDzRmws4j4gDQKsGjlztk2484pbhn0qycWsDbwIhAGjnxXfE+0YWfyKkeo78ay3KIItCSzw9PXHp0iW5sps3b6KoqAheXl4AgA4dOsDe3h7x8fGYNWsWAKCwsBC7d+9GVFSU4DLxmP5ve9flHv9uNl/AFjMBwtRfyXzK1f6BAEmIeKc/hS/5PTMfPeP3tFUGWRRaMmrUKEyYMAHu7u6yNYovv/wSXl5eeOeddwAAlpaWmDZtGmJiYiCRSGQOV1KpFJ9++qme3wFBaA9ZFFoyfvx4mJubY+XKlfjpp5/g6OiITp06YeHChbCx+fv037Rp0yCVSrFw4ULk5OQgKCgISUlJcHHhD7n2OjxWwcW/+PJlAsCDp3xPMSGe5rxWzQUBwtzzRrjKFOBzcLIV50lPFoWWGBkZYfTo0Rg9erTaejNnzsTMmTOrSTKCEA9yuHrDKRXAbTjnBV8Cn4w8/q3JJ5wy2FtyxpIAkFPAJ4MAmROQJ0BsD6WQwxVBEOogh6s3nGABdj0KS/hW6xu+Zcstw40HfFuLG8/ym9K8SYieCxBqXyzfGn0vZpJFQRAGAC1m6sCWLVvw1Vdf4datW3BwcEB4eDgWLVoEd3d3WZ2nT59i4sSJ2LlzJ0pKStC5c2d8//33aNy4sR4lV+RZMf+ctnV9R672Qrgu1+V0Ra/DmQ4Q4F/n2HI+g1uGbM61GlXo26IwOEWhaeCaoUOH4tq1a/juu+/g4OCAefPmITw8HFevXoW9vb2gMv2eqnschI6NnbjH592a5J02AEAJZ9oBAWLn4PitJ1ztrQUICXhTAO9OZZBFoSWaBK45ffo0Dh48iEOHDiE8PBwA8Pbbb6Nhw4b4+eef8fnnn+tLfILQCbIotESTwDV//PEHzMzMEBoaKqvj4uKCgIAA7N27V3BFMWvnNZ3b/qtTQ+7xJdZ8TxchzOWIAHf1lapACKumqw9fOD2eRE6VnL7Df2ZFGfq2KMAMjD179jBTU1O2bt06lp+fz1JSUljnzp1ZWFiYrM4333zDLC0tFdp26dKFubi4aDwWKqbv9KKXQb3EwOAUBWOMbdiwgVlYWMg+mA4dOrC8vDzZ/V27djEA7M8//5SVFRYWsjp16jAzMzONx9H3F04veunyEgOj//0gDIajR4+ib9++GDNmjFzgGldXV1ngmpKSEvj6+sLV1RVr166Fvb09pk2bho0bN8LU1BTFxeKc8COI2orBKYo2bdrAz88PGzdulJWlpKTA19cX27Ztw8CBAwEA586dw7Bhw5CWVpE0tlOnTmjSpAmOHDmC9PR0fYhOEAaLwTlcJScno1WrVnJlrweuAYB27dohNTUVycnJSE1NxcmTJ/H48WMEBwdXs8QEYfgY3K6HJoFrKjEyMpJFuLp9+zYOHTqE3bt3V5eoBFFrMDhFoUngGgCIiYmBr68vnJyccPXqVcTExCAyMhLdu3fXo/QEYZgYnKLQNHBNTk4Oxo4di+zsCq9JW1tbeHh4KATcJQhCPQa3mKkpeXl5aNasGfz9/TF16lTcuXMHkyZNwoQJE2pUSkGCMAQMzqLQlJ9++glFRUXYvn077O3t0b17dzx79gzR0dGYMmWK4Oc9CKI2Y3C7Hpqyf/9+9OzZU04hREZGoqioCMePH9ejZARheNRaiyI5ORlhYWFyZQ0aNIC1tTWSk5MRERGhtg9K8EMYImKsJtRaiyIvL092WOxVJBIJ8vLyql8ggjBgaq1FIQTqNHOlxZGYmKjzGD169FBavnnzZvzzn/+UXW/cuFEhQ9rrcuhKeTl/4ptVq1apvHfhwgWkpKTAx8cHQUFBSusMGDCAWwapVPccsADkds10pbSULxDRvXv3dG6r6rMVglqrKCQSCfLz8xXK8/LyIJHwpZ6rDiqVwuHDhxEeHq5SSdR0Lly4gLVr1wIATp06BUDcP2hCHGqtovD19UVycrJcWUZGBgoLC0XJPyoGw4YNM1gFUUlKSorCtT4Uxc6dO/Hbb7+hU6dO6N+/f7WPb+jUWkXRq1cvLFmyBM+fP4ednR0AIC4uDlZWVggJCRF0rHr16uncNjMzk3v83Fy+bGOenp7cMowZM0ZpuUQikVkSADBu3Dilyu/IkSPcMrz+YKjkVatmy5YtuHjxolJlZWnJH7jGz8+Pq/2rcV9rErVWUYwaNQrLly/HwIEDMXXqVKSlpSE6OhoTJ04kH4pqpCZMoWqKVcNLQkICTp06hQ4dOqBfv37VOnatVRQJCQl4+PAhHj58iEOHDsnKxdDYDRo00LmtEEqL1yW98ig+D1UtJDZp0gRNmjQBUPF0V8ajR4+4ZfD391daHhYWJmfVhIWFKa0rhAz379/nan/58mWl5a9aRXFxcfjjjz+qVdnVWkVRyZEjR2Bl9XdiGG9vbz1KQ+iDygDLly5dQps2bWTXhoS+raJaryjatm0LW1v+TFiEYRMeHm6QCqISMzOzKq/FptYriuqgcrFUXyjbBtYGnsXYSm7cuMHVvkOHDtwy8D4Q2rVrxy3Dtm3buPtQxuv+Gbz+GtpSaz0zK2nUqBFMTU3h4+NTpVMQQdRkKgMwqboWm1p7zPzAgQM4f/482rVrh/LycmzZsgXr16/HN998gwkTJmjUB531IAwRMX7StVZRKGPo0KE4dOgQnjx5Iks9WBWkKAhDRIyf9Bu1RvHuu+/i119/RXp6uka7H5qe9XiDdK0o0OcoDGI+2Gr9GsWrVH6QZCkQhHa8UYpi69atcHJyEsRlmSDeJGrt1GPQoEFo164dWrZsifLycsTFxSEuLg7Lly/XaH2CIIi/qbWKwsfHB2vWrEFGRgYYY/D398f69evxwQcf6Fs0gjA43qhdD6GhRThhoM9RGMT8HMkGJwhCLaQoCIJQC009CIJQC1kUBEGohRQFQRBqIUVBEIRaSFEQBKEWUhQEQaiFFAVBEGohRUEQhFpIUejAjRs3EB4eDmtra7i7u2POnDmC5O+sjejyWaWnp8PIyEjhFRkZWU1SGxapqan45JNP0LJlS5iYmCA0NFTwMWrtoTCxyMvLQ7du3eDv74+EhATcuXMHkyZNglQqxbx58/QtXo2C97NaunQpOnbsKLt2cnISU1yD5fr169i3bx+Cg4PFC7rLCK1YsGABc3R0ZPn5+bKyxYsXMysrK7kyQvfP6u7duwwA2717d3WIafCUl5fL/j9o0CAWEhIi+Bg09dCS/fv3o2fPnnIZviIjI1FUVITjx4/rUbKaB31W1UN1xFchRaElycnJCtnQGzRoAGtra5VJct9UeD+rjz76CCYmJnBzc8PEiRNRVFQklqiEGmiNQkvy8vLg6OioUC6RSJCXl1f9AtVgdP2sLCwsMHbsWPTo0QP29vY4duwYFi9ejDt37iAhIUFEiQlVkKIgahxubm5YsWKF7Do0NBQuLi4YM2YMrly5goCAAD1K92ZCUw8tkUgkSlP45eXlQSKR6EGimouQn9W7774LALh48aIgshHaQYpCS3x9fRXm1xkZGSgsLFSYj7/pCPlZUaoF/UKKQkt69eqFAwcO4Pnz57KyuLg4WFlZISQkRI+S1TyE/Ky2bt0KAAgMDBRURkJDBN9wreXk5uYyV1dX1q1bN5aUlMRWrVrFbGxs2MyZM/UtWo1D08+qUaNG7OOPP5Zdz507l02cOJFt27aNJSUlsdmzZzNLS0s2cODA6n4LBkFBQQGLj49n8fHxLDg4mPn7+8uuCwoKBBmDFIUOXL9+nXXt2pVZWloyV1dXNmvWLFZWVqZvsWokmnxWnp6ebPjw4bLrzZs3s8DAQGZvb8/MzMxYo0aN2OzZs1lxcXE1S28YVDqoKXvdvXtXkDEoZiZBEGqhNQqCINRCioIgCLWQoiAIQi2kKAiCUAspCoIg1EKKgiAItZCiIAhCLaQoCIJQCykKgiDUQoqCIAi1kKIgCEItpCgIglALKYoaQHR0tFyiG3d3dwwaNAh37twRZbzQ0FBZxKjK8XlzZowYMQJBQUG8omlM37598cUXX1TbeEKzdOlShIeH61sMjaGYmTUEBwcHJCYmAgDS0tIwe/ZshIeH4/r167CxsRF0rB9//BFmZmaC9jl79uxqi5J99uxZHDlyBLGxsdUynhh88sknmD9/Po4dOyZKZi+hIUVRQzA1NUVwcDAAIDg4GA0aNEDnzp2xb98+DB48WNCx/P39Be0PABo1aiR4n6pYvnw5+vXrhzp16lTLeEVFRbCyshK0Tzs7OwwaNAjff/+9QSgKmnrUUCpDvqWnpwOoiBX5amRqQHHKEBsbCyMjI5w/fx6dO3eGlZUVmjZtih07dsi1e33qoQmZmZkYMmQInJ2dYWVlhUaNGmH27Nmy+69PPby8vJTmD42OjpbVuXbtGnr37g07OzvY2dlh8ODBePjwYZVyPH/+HDt27FCQv/I9bdq0CY0bN4a9vT169eqFzMxMuXrZ2dkYPnw46tatC2tra4SGhuLChQtydby8vDBp0iTExMTAw8NDlsDIy8sLn3/+ORYtWgQ3Nzc4ODhg0qRJYIxh3759aNasGezs7NC/f3+NUjcMGjQIe/bsQW5urtq6+oYsihpKpYJwdXXVuu3QoUMxZswYzJgxA6tXr8bgwYNx8eJFrjD3H374IYqKivDzzz/D0dERaWlpVSbx2bFjB16+fCm7Pnr0KGbMmIGmTZsCqEis27FjRwQFBWHDhg0oKyvD7NmzERERgXPnzqkMonvq1CkUFRWhQ4cOCvfOnj2LBw8e4Ouvv0ZRURE+++wzREVFYd++fbI6/fv3R2pqKpYuXQonJycsWbIEXbt2xeXLl9G4cWNZvU2bNqFZs2b48ccfUVZWJivfsmUL2rVrh7Vr1+LixYuYNWsWpFIpTpw4gZiYGBQVFWHcuHGYPn06fvrppyo/0/bt26O0tBQnT55Ev379qqyrdwSJk0VwMXfuXFa3bl1WWlrKSktLWUpKCgsNDWV2dnbswYMHjDHGALDvv/9eabtK1q5dywCw+fPny8rKy8uZj48PGzp0qKwsJCSEDRo0SGU/yrCxsWG7du1SeX/48OEsMDBQ6b309HTm5OTERowYISt7//33WdOmTdnLly9lZbdu3WLGxsZsz549KseZP38+c3JyUigPCQlh9vb2LDc3V1a2bNkyBoAVFhYyxhjbv38/A8COHTsmq/PixQvm5OTEoqKiZGWenp7M1dWVFRUVyY3h6enJGjVqJBfKr23btszExISlpaXJyiZPnsycnZ1VvofX+5wxY4ZGdfUJTT1qCDk5OTAzM4OZmRl8fHyQlpaGuLg4uLm5ad3XgAEDZP83NjZGv379cO7cOS75WrVqhenTpyM2Nhb37t3TuF1RUREGDBgAT09PrFy5UlZ+6NAhDBgwAMbGxigrK0NZWRkaNmwILy8vhanAqzx8+FDlDk3btm3l8oVUrsXcv38fAHDu3Dk4OzvLRQC3sbFBnz598Ntvv8n1FR4eDktLS4UxQkNDYWJiIrtu3LgxvLy80LBhQ7myJ0+eoKSkROX7qMTJyUntdKsmQIqihuDg4IDz58/jwoULyMzMRHp6Onr16qVTX87OzgrXWVlZXPLFxcUhKCgIEyZMgKenJ1q1aoXDhw+rbRcVFYWMjAxs27ZN7oeXnZ2NxYsXy5Rj5SstLQ0ZGRkq+ysuLoaFhYXSe6+nLzQ3N5e1AYCsrCyFzwYAXFxcFNYJXFxcNB5DWRljTCNFYWFhIZOvJkNrFDUEU1PTKv0QLCwsFP7wVC2YPX78GHXr1pW71sUyeZV69eohNjYWUqkU586dQ3R0NPr27Yt79+7JjfUq3377LTZv3ozExER4enrK3atTpw4GDBiAkSNHKrSryqejTp06ePr0qU7vwc3NDY8fP1Yof/TokcIOSnUlGnr69Gm17d7wQBaFgeDh4YGbN2/KrqVSqcon+qu7HFKpFAkJCWjXrp0gchgbGyM4OBhz585FYWEh/vrrL6X1jh49ismTJ2PBggXo1q2bwv1KH5HAwEAEBQXJvby8vFSO7+PjgwcPHsgtlGrK22+/jcePH+PEiROyssLCQuzduxedOnXSuj9epFIp7t27J1vgrcmQRWEgDBgwAD/88ANat24Nb29vrF69Gs+ePVNad/Xq1TA3N0fz5s2xevVqpKamYvPmzTqPnZ+fj549e+LDDz9E06ZN8fLlS3z99ddwdXWFn5+f0vpDhgxB8+bN0aVLF5w5c0Z2z8PDAx4eHoiOjka7du3Qu3dvfPzxx3BycsL9+/eRlJSEESNGqPQt6NixI0pLS3H16lWtPUF79uyJDh06YOjQoVi0aBHq1q2LpUuXoqioCJMnT9aqL20xNTXFnDlzMGfOHFlZSkoKXrx4gY4dO4o6thCQojAQ5s6di8ePH2PWrFkwNzfHuHHj0KxZM/zwww8Kdbds2YIJEyZg1qxZqF+/PuLi4tC6dWudx7a0tESLFi3w3XffISMjA9bW1ggODsbBgweVOiLl5eUhOzsb2dnZaN++vcL7iI6ORtOmTXHmzBnMmjULUVFRKCoqQr169RAeHi63Tfk6TZs2RfPmzbF//36dXMZ37tyJSZMm4T//+Q+Ki4vRrl07HDlypMoxhaC8vBxSqVSuLDExEd7e3lzfTXVBCYBqEbGxsfjoo4/w/Plz2Nra6lsc0Vi2bBl++eUXXLt2Td+icNG+fXv07t0bs2bN0rcoaqE1CsLgiIqKwpMnT3Do0CF9i6IzZ8+eRXJyMsaNG6dvUTSCFAVhcNjY2GDdunUoKCjQtyg6k5ubi3Xr1ilsrdZUaOpBEIRayKIgCEItpCgIglALKQqCINRCioIgCLWQoiAIQi2kKAiCUAspCoIg1EKKgiAItZCiIAhCLaQoCIJQCykKgiDUQoqCIAi1kKIgCEItpCgIglALKQqCINRCioIgCLWQoiAIQi3/D89DkLHSHtlyAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load pupil size tuning data for tonic spikes from dark experiments\n", "conditions = ['dark']\n", "df = load_data('sizetuning', conditions, spk_type='spk').set_index(['m', 's', 'e', 'u'])\n", "\n", "# Get graded colormap for tonic spikes\n", "toniccmap = LinearSegmentedColormap.from_list(\"toniccmap\", ['white', np.array(to_rgba(COLORS['tonicspk']))])\n", "# Plot rate heatmap\n", "fig = pupil_area_rate_heatmap(df, toniccmap, max_rate='high')\n", "\n", "set_plotsize(w=2.25, h=3)\n", "fig.savefig(FIGUREPATH + 'pupil_size_spk_tuning_dark' + FIGSAVEFORMAT)" ] }, { "cell_type": "code", "execution_count": 17, "id": "f4cb6e42-c9e5-4ae7-a81c-b571529963bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: sizetuning_dark_burst.pkl\n", "Monotonic decreasing: 34/44 (77.3)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Load pupil size tuning data for bursts from dark experiments\n", "conditions = ['dark']\n", "df = load_data('sizetuning', conditions, spk_type='burst').set_index(['m', 's', 'e', 'u'])\n", "\n", "# Get graded colormap for tonic spikes\n", "burstcmap = LinearSegmentedColormap.from_list(\"burstcmap\", ['white', np.array(to_rgba(COLORS['burst']))])\n", "# Plot rate heatmap with example\n", "fig = pupil_area_rate_heatmap(df, burstcmap, max_rate='low', example=FIG1BEXAMPLEKEY)\n", "# Change label from 'Spikes' to 'Bursts'\n", "fig.axes[1].set_xlabel('Bursts')\n", "\n", "set_plotsize(w=3, h=4)\n", "fig.savefig(FIGUREPATH + 'pupil_size_burst_tuning_dark' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "76137bdb-19d7-494d-9b8a-0e8ae1953dc5", "metadata": {}, "source": [ "### Figure S1C2\n", "Fano factor across pupil size bins in light and darkness." ] }, { "cell_type": "code", "execution_count": 20, "id": "bfa83a9c-552c-45d2-9169-31ccae0ee63a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: sizetuning_spontaneous_spk.pkl\n", "Loading: sizetuning_sparsenoise_spk.pkl\n", "Loading: sizetuning_dark_spk.pkl\n", "FF light > 1: w=1.205e+04, p=5.236e-26\n", "FF dark > 1: w=4.353e+03, p=6.412e-16\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_53774/4088828529.py:10: RuntimeWarning: invalid value encountered in divide\n", " df_light['area_fano'] = df_light.apply(lambda x: x['area_var'] / x['area_means'], axis='columns')\n", "/tmp/ipykernel_53774/4088828529.py:12: RuntimeWarning: invalid value encountered in divide\n", " df_dark['area_fano'] = df_dark.apply(lambda x: x['area_var'] / x['area_means'], axis='columns')\n" ] } ], "source": [ "# Load data from experiments with monitor on\n", "conditions = ['spontaneous', 'sparsenoise']\n", "df_light = load_data('sizetuning', conditions, spk_type='spk').set_index(['m', 's', 'e', 'u'])\n", "\n", "# Load data from experiments in darkness\n", "conditions = ['dark']\n", "df_dark = load_data('sizetuning', conditions, spk_type='spk').set_index(['m', 's', 'e', 'u'])\n", "\n", "# Compute Fano factor for each pupil size bin\n", "df_light['area_fano'] = df_light.apply(lambda x: x['area_var'] / x['area_means'], axis='columns')\n", "fano_light_matrix = np.row_stack(df_light['area_fano'])\n", "df_dark['area_fano'] = df_dark.apply(lambda x: x['area_var'] / x['area_means'], axis='columns')\n", "fano_dark_matrix = np.row_stack(df_dark['area_fano'])\n", "\n", "# Test for Fano factor > 1\n", "print(\"FF light > 1: w=%.3e, p=%.3e\" % wilcoxon(np.nanmean(fano_light_matrix, axis=1) - 1, alternative='greater'))\n", "print(\"FF dark > 1: w=%.3e, p=%.3e\" % wilcoxon(np.nanmean(fano_dark_matrix, axis=1) - 1, alternative='greater'))" ] }, { "cell_type": "code", "execution_count": 21, "id": "54358e95-baa4-4e1d-ac06-6c03beb7e202", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Take mean Fano factor across pupil sizes\n", "fano_light = np.nanmean(fano_light_matrix, axis=1)\n", "fano_dark = np.nanmean(fano_dark_matrix, axis=1)\n", "\n", "# Plot Fano factor distributions\n", "dists = [np.log10(fano_light), np.log10(fano_dark)] # log-transform fano factors\n", "colors = [toniccmap(0.99), toniccmap(0.5)]\n", "ax = violin_plot(dists, colors)\n", "ax.axhline(0, color='gray', ls='--', lw=1) # add line a FF = 1\n", "\n", "# Format axes\n", "ax.set_xticklabels(['Light', 'Dark'])\n", "ax.set_yticks([-1, 0, 1,])\n", "ax.set_ylabel('log$_{10}$(Fano factor)')\n", "\n", "clip_axes_to_ticks(ax=ax, spines=['left'])\n", "set_plotsize(w=1.5, h=2)\n", "fig = ax.get_figure()\n", "fig.savefig(FIGUREPATH + 'pupil_size_fano_dists' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "33b4ad01-1d7a-41d1-8622-ada437895268", "metadata": {}, "source": [ "### Figure S1D1\n", "Schematic diagram for joint phase distributions." ] }, { "cell_type": "code", "execution_count": 22, "id": "8b218780-1b8b-4828-bcc7-ea6d7e9f21a6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_53774/3976392503.py:4: RuntimeWarning: covariance is not symmetric positive-semidefinite.\n", " x, y = np.random.multivariate_normal([-1, 0], [[2, 4], [4, 2]], 5000).T\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "# Sample from some 2D distribution\n", "x, y = np.random.multivariate_normal([-1, 0], [[2, 4], [4, 2]], 5000).T\n", "\n", "# Plot 2D histogram as heatmap\n", "H, _, _ = np.histogram2d(x, y, bins=np.linspace(-5, 5, 17))\n", "mat = ax.imshow(H)\n", "\n", "# Add a colorbar\n", "cax = plt.colorbar(mat, ax=ax, label='Probability', shrink=0.75)\n", "cax.ax.set_yticks([])\n", "\n", "# Format x-axis\n", "ax.set_xticks([-0.5, 8, 15.5])\n", "ax.set_xticklabels(['-'+u'\\u03C0', 0, u'\\u03C0'])\n", "ax.set_xlabel(\"CPD$_1$ phase\")\n", "\n", "# Format y-axis\n", "ax.set_yticks([-0.5, 8, 15.5])\n", "ax.set_yticklabels(['-'+u'\\u03C0', 0, u'\\u03C0'])\n", "ax.set_ylabel(\"CPD$_2$ phase\")\n", "\n", "set_plotsize(w=1.5, h=1.5)\n", "fig.savefig(FIGUREPATH + 'imf_sync_example' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "e3a442f2-6ed4-4b9b-bb1d-fa2b28562bf0", "metadata": {}, "source": [ "### Figure S1D2\n", "Phase couping analysis for the example IMFs from Figure 1D." ] }, { "cell_type": "code", "execution_count": 26, "id": "ef10d682-5aae-4fb7-85e5-b038fdd98da5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: pupil_spontaneous.pkl\n", "Loading: pupil_sparsenoise.pkl\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/crombie/code/imftuning_code/util.py:661: RuntimeWarning: divide by zero encountered in log\n", " return np.sum(np.where(p != 0, p * np.log(p / q), 0))\n", "/home/crombie/code/imftuning_code/util.py:661: RuntimeWarning: invalid value encountered in multiply\n", " return np.sum(np.where(p != 0, p * np.log(p / q), 0))\n" ] } ], "source": [ "# Load pupil size data from gray screen and sparse noise experiments\n", "conditions = ['spontaneous', 'sparsenoise']\n", "df_pupil = load_data('pupil', conditions).set_index(['m', 's', 'e'])\n", "\n", "# Get pupil size data for example experiment (same as Figure 1B)\n", "key = FIG1BEXAMPLEKEY\n", "idx = key2idx(key)[:-1]\n", "pupil_area = df_pupil.loc[idx]['pupil_area']\n", "pupil_tpts = df_pupil.loc[idx]['pupil_tpts']\n", "pupil_fs = 1 / np.diff(pupil_tpts).mean()\n", "\n", "# Apply Hilbert-Huang transform to example pupil size trace\n", "hht = HHT(pupil_area, pupil_fs) # instantiate transform object\n", "hht.emd() # empirical mode decomposition\n", "hht.hsa() # Hilbert spectral analysis\n", "hht.check_number_of_phasebin_visits(remove_invalid=True) # remove IMFs with fewer than 4 complete cycles\n", "\n", "# Perform phase coupling analysis on joint phase distributions for all IMF pairs\n", "# DD: joint phase distributions, masks: regions in joint distribution with p <= 0.05\n", "# (i.e. the combination of phases that occur more than would be expected by chance)\n", "# ranges: time ranges in which the two IMFs pass through significant in phase-phase space\n", "DD, _, DD_p, DD_masks, DD_ranges = hht.phase_synchrony()" ] }, { "cell_type": "code", "execution_count": 27, "id": "6d4bde57-8916-413b-821f-dae29ebcb12a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(DD.shape[0], DD.shape[1])\n", "\n", "# Loop over joint phase distributions of all IMF pairs\n", "for i in range(DD.shape[0]):\n", " for j in range(DD.shape[1]):\n", " # Set IMF label\n", " if i == 0: # Top\n", " axs[i, j].xaxis.set_label_position('top')\n", " axs[i, j].set_xlabel('%.3f s$^{-1}$' % hht.characteristic_frequency[j], fontsize=LABELFONTSIZE)\n", " if j == (len(DD) - 1): # Right\n", " axs[i, j].yaxis.set_label_position('right')\n", " axs[i, j].set_ylabel('%.3f s$^{-1}$' % hht.characteristic_frequency[i], rotation=270, labelpad=10, fontsize=LABELFONTSIZE)\n", " # Plot only the upper triangle\n", " if i < j:\n", " # Remove ticks and labels\n", " axs[i, j].tick_params(left=False)\n", " axs[i, j].set_yticklabels([])\n", " axs[i, j].matshow(DD[i, j], interpolation='spline16')\n", " axs[i, j].contour(DD_masks[i, j], levels=[0.5], linewidths=1, colors='white')\n", " axs[i, j].set_xticks([])\n", " axs[i, j].text(-0.225, 0.05, f'p = {DD_p[i, j]:.3f}', rotation=90, fontsize=LABELFONTSIZE, transform=axs[i, j].transAxes)\n", " if DD_p[i, j] <= 0.05:\n", " for spine in axs[i, j].spines.values():\n", " spine.set_edgecolor('magenta')\n", " spine.set_linewidth(2)\n", " else:\n", " axs[i, j].remove()\n", "fig.subplots_adjust(wspace=0.4)\n", "set_plotsize(w=8, h=8)\n", "fig.savefig(FIGUREPATH + 'imf_phase_coupling' + FIGSAVEFORMAT)" ] }, { "cell_type": "code", "execution_count": 28, "id": "e4800234-76b9-42ad-b4d3-cdf7c1e1e449", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: hht_spontaneous.pkl\n", "Loading: hht_sparsenoise.pkl\n", "Coupled CPDs: 23.0\n" ] } ], "source": [ "# Load HHT data from gray screen and sparse noise experiments\n", "conditions = ['spontaneous', 'sparsenoise']\n", "df_hht = load_data('hht', conditions).set_index(['m', 's', 'e'])\n", "\n", "# Collect IMF pair frequencies and correlations for all decompositions\n", "x_freqs = np.array([])\n", "y_freqs = np.array([]) \n", "klds = np.array([])\n", "sig = np.array([], dtype='bool') # whether joint distribution has p <= 0.05\n", "# Loop over all decompositions\n", "for idx, row in df_hht.iterrows():\n", " # Get Kullback-Leibler divergence comparing joint phase distribution to uniform for all IMF pairs in decomposition\n", " # expressed as z-score relative to the KLDs of 1000 joint distibutions from cycle-shuffled IMFs\n", " kld = row['sync_kld']\n", " p = row['sync_p'] # p-value of actual KLD relative to shuffle distribution\n", " # Get IMF frequencies\n", " freq = row['frequency']\n", " # Get index grid\n", " x_inds, y_inds = np.meshgrid(np.arange(len(kld)), np.arange(len(kld)))\n", " # Use grid indices to get frequencies for IMF pairs (then flatten arrays and concatenate to group)\n", " x_freqs = np.append(x_freqs, freq[x_inds].ravel())\n", " y_freqs = np.append(y_freqs, freq[y_inds].ravel())\n", " # Flatten correlation matrix and concatenate to group data\n", " klds = np.append(klds, kld.ravel())\n", " sig = np.append(sig, p.ravel() <= 0.05)\n", "# Log-transform frequencies\n", "x_freqs = np.log10(x_freqs)\n", "y_freqs = np.log10(y_freqs)\n", "\n", "print(f\"Coupled CPDs: {sig.mean() * 100:.1f}\")" ] }, { "cell_type": "code", "execution_count": 29, "id": "a997fb87-e1b0-4e9f-9702-89f321e0e9e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No significant coupling: 354 IMFs\n", "Significant coupling: 106 IMFs\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make the same plot for IMF pairs with and without significant phase coupling\n", "for label, imf_pairs_to_plot in zip(['ns', 'sig'], [~sig, sig]):\n", " fig, ax = plt.subplots()\n", " # Freq-freq scatter plot of IMF pairs, KLD z-score is indicated by color\n", " im = ax.scatter(x_freqs[imf_pairs_to_plot], y_freqs[imf_pairs_to_plot], c=klds[imf_pairs_to_plot], \n", " cmap='viridis', vmin=-2, vmax=20, s=5, alpha=0.5)\n", "\n", " # Set frequency ticks and labels\n", " freqticks = np.arange(-3, 1)\n", " freqticklabels = ['10$^{%d}$' % tick for tick in freqticks]\n", " ax.set_xticks(freqticks)\n", " ax.set_xticklabels(freqticklabels)\n", " ax.set_xlim(right=0.3)\n", " ax.invert_xaxis()\n", " ax.set_yticks(freqticks)\n", " ax.set_yticklabels(freqticklabels)\n", " ax.set_ylim(top=0.3, bottom=-3.2)\n", " ax.set_xlabel('Inverse timescale (s$^{-1}$)', labelpad=6)\n", " ax.set_ylabel('Inverse timescale (s$^{-1}$)')\n", " \n", " # Make colorbar for KLD z-scores\n", " bbox = ax.get_position()\n", " cax = fig.add_axes([bbox.x1, bbox.y0 + 0.1, 0.02, bbox.height - 0.2])\n", " cbar = fig.colorbar(im, cax=cax, ticks=[-2, 2, 10, 20])\n", " cbar.ax.set_ylabel('$z$($\\Delta$ KLD)')\n", "\n", " # Make title and print number of IMF pairs plotted\n", " if label == 'ns':\n", " print(f'No significant coupling: {sum(imf_pairs_to_plot)} IMFs')\n", " ax.set_title('No significant coupling', fontsize=LABELFONTSIZE)\n", " elif label == 'sig':\n", " print(f'Significant coupling: {sum(imf_pairs_to_plot)} IMFs')\n", " ax.set_title('Significant coupling', fontsize=LABELFONTSIZE)\n", " \n", " clip_axes_to_ticks(ax=ax, spines=['left', 'bottom'])\n", " fig.subplots_adjust(left=0.1, right=0.8)\n", " set_plotsize(w=2, h=2, ax=ax)\n", " fig.savefig(FIGUREPATH + f'imf_phase_coupling_scatter_{label}' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "6f1a30f4-cc2f-4a3f-a98c-b0a83c8c4fb1", "metadata": {}, "source": [ "### Fig S2\n", "Firing rate distributions and correlograms." ] }, { "cell_type": "code", "execution_count": 30, "id": "799840ae-89d2-405c-a323-b7dfd7c31073", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: spikes_spontaneous.pkl\n", "Loading: spikes_sparsenoise.pkl\n", "Loading: pupil_spontaneous.pkl\n", "Loading: pupil_sparsenoise.pkl\n" ] } ], "source": [ "# Load spike data from gray screen and sparse noise experiments\n", "conditions = ['spontaneous', 'sparsenoise']\n", "df_spikes = load_data('spikes', conditions)\n", "# Load pupil data as well and merge dataframes so that we only look at neurons that have accompanying eye tracking data\n", "df_pupil = load_data('pupil', conditions)\n", "df_spikes = pd.merge(df_spikes, df_pupil).set_index(['m', 's', 'e', 'u'])\n", "df_spikes = filter_units(df_spikes, MINRATE) # remove units with suspiciously low firing rates" ] }, { "cell_type": "markdown", "id": "fbac7734-f62a-4fe4-8719-20daa3db1e87", "metadata": {}, "source": [ "#### Figure S2A1\n", "Mean firing rate and mean tonic spike rate distributions." ] }, { "cell_type": "code", "execution_count": 31, "id": "23ef987a-b999-46fa-8efb-372368c7aec2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean spk rate: 4.49 (N = 156)\n", "Mean tonicspk rate: 4.11 (N = 156)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get mean overall firing rate ('spk'), and mean rate only including tonic spikes (i.e. after eliminating spikes that are part of a burst)\n", "df_spikes['length'] = df_spikes['spk_tinfo'].apply(lambda x: x[1] - x[0])\n", "for spk_type in ['spk', 'tonicspk']:\n", " df_spikes[f'{spk_type}_rate'] = df_spikes.apply(lambda x: len(x[f'{spk_type}_times']) / x['length'], axis='columns')\n", "\n", "# Get histogram bins (log rate)\n", "rate_bins = np.linspace(-2, 2, 1000)\n", "\n", "# Plot rate histograms for each spike type\n", "fig, ax = plt.subplots()\n", "for spk_type in ['spk', 'tonicspk']:\n", " # Pull mean rates from data frame, log transform\n", " rates = np.log10(df_spikes[f'{spk_type}_rate'])\n", " print(f\"Mean {spk_type} rate: {10 ** rates.mean():.2f} (N = {len(rates)})\")\n", " # Plot cumulative histogram\n", " cumulative_histogram(rates, rate_bins, color=COLORS[spk_type], ax=ax)\n", " # Add marker for mean rate to top of plot\n", " ax.plot(rates.mean(), 1, marker=7, color=COLORS[spk_type], fillstyle='none')\n", "\n", "# Format axes\n", "ax.set_xticks([-2, 0, 2])\n", "ax.set_xticklabels(['$10^{%d}$' % tick for tick in [-2, 0, 2]])\n", "ax.set_xlabel(\"Spike rate (s$^{-1}$)\")\n", "ax.set_yticks([0, 0.5, 1])\n", "ax.set_ylabel('Proportion')\n", "\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=2, h=2)\n", "fig.savefig(FIGUREPATH + 'rate_distributions' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "0d4293a1-e4cc-417b-9a27-e257a3ac56b2", "metadata": {}, "source": [ "#### Figure S1A2\n", "Burst ratio distribution." ] }, { "cell_type": "code", "execution_count": 32, "id": "751394d9-04bc-4563-9747-8663c5d8e704", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Median burst ratio: 3.03e-02 (3.26e-05, 6.65e-01)\n", "N = 145\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get burst ratio (proportion of all spikes that are part of a burst)\n", "df_spikes['burst_ratio'] = df_spikes.apply(lambda x: len(x['burstspk_times']) / len(x['spk_times']), axis='columns')\n", "ratios = df_spikes.query('burst_ratio > 0')['burst_ratio'] # only include neurons that have bursts\n", "ratio_bins = np.linspace(0, 0.5, 1000)\n", "\n", "# Plot burst ratio distribution as cumulative distribution\n", "fig, ax = plt.subplots()\n", "cumulative_histogram(ratios, ratio_bins, color=COLORS['burst'], ax=ax) \n", "\n", "# Add marker for median burst ratio at top of plot\n", "print(f\"Median burst ratio: {np.median(ratios):.2e} ({ratios.min():.2e}, {ratios.max():.2e})\")\n", "ax.scatter(np.median(ratios), 0.95, marker=7, s=10, ec=COLORS['burst'], fc='none')\n", "print(f'N = {len(ratios)}')\n", "\n", "# Format axes\n", "ax.set_xticks([0, 0.25, 0.5])\n", "ax.set_xlabel('Burst ratio')\n", "ax.set_yticks([0, 0.5, 1])\n", "ax.set_ylabel('Proportion')\n", "\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=2, h=2)\n", "fig.savefig(FIGUREPATH + 'burst_ratio_distribution' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "3afff8d0-6c0c-4390-835e-d1666b209a0f", "metadata": {}, "source": [ "#### Figure S2B1\n", "Mean auto-correlogram of tonic spiking." ] }, { "cell_type": "code", "execution_count": 33, "id": "9a1207eb-017b-4fb0-bc61-4721c30aea5c", "metadata": {}, "outputs": [], "source": [ "# Get auto-correlogram for tonic spikes\n", "df_spikes['tonicspk_acg'] = df_spikes['tonicspk_times'].apply(correlogram)" ] }, { "cell_type": "code", "execution_count": 34, "id": "b3875894-cfeb-4799-9491-d04b8489daa9", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/crombie/code/imftuning_code/util.py:574: RuntimeWarning: divide by zero encountered in divide\n", " ccg /= len(ts1)\n", "/home/crombie/code/imftuning_code/util.py:574: RuntimeWarning: invalid value encountered in divide\n", " ccg /= len(ts1)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get mean ACG and SE from dataframe\n", "tonicspk_acg = df_spikes['tonicspk_acg'].values.mean(axis=0)\n", "tonicspk_sem = sem(df_spikes['tonicspk_acg'].values, axis=0)\n", "tpts, _ = correlogram(np.array([]), return_tpts=True) # get lags\n", "\n", "# Plot mean ACG and SE\n", "fig, ax = plt.subplots()\n", "ax.plot(tpts[tpts >= 0], tonicspk_acg[tpts >= 0], color=COLORS['tonicspk'])\n", "ax.plot(tpts[tpts >= 0], tonicspk_acg[tpts >= 0] - tonicspk_sem[tpts >= 0], color=COLORS['tonicspk'], lw=0.5, ls='--')\n", "ax.plot(tpts[tpts >= 0], tonicspk_acg[tpts >= 0] + tonicspk_sem[tpts >= 0], color=COLORS['tonicspk'], lw=0.5, ls='--')\n", "\n", "# Add dashed line at 300ms (peak prior to this value indicates presence of short timescale structure)\n", "ax.axvline(0.3, ls='--', color='gray')\n", "\n", "# Format axes\n", "ax.set_xticks([0, 0.5, 1])\n", "ax.set_xlabel(\"Lag (s)\")\n", "ax.set_yticks([0.1, 0.15, 0.2])\n", "ax.set_ylim(bottom=0.1)\n", "ax.set_ylabel(\"P(spike)\")\n", "\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=2, h=2)\n", "fig.savefig(FIGUREPATH + 'tonicspk_acg' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "238decea-0e76-4aaf-a3c8-b335c26b3df7", "metadata": {}, "source": [ "#### Figure S2B1\n", "Mean auto-correlogram of bursts." ] }, { "cell_type": "code", "execution_count": 35, "id": "d964a680-d8bf-4eaf-88f0-2c45a5d2fe28", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_53774/3371122251.py:3: 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", " df_bursts['burst_acg'] = df_bursts['burst_times'].apply(correlogram)\n" ] } ], "source": [ "# Get auto-correlogram for bursts\n", "df_bursts = df_spikes.query('burst_times.str.len() > 0') # only consider neurons with bursts\n", "df_bursts['burst_acg'] = df_bursts['burst_times'].apply(correlogram)" ] }, { "cell_type": "code", "execution_count": 36, "id": "80df32b0-7ca5-406d-a54d-344ffa70b195", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/crombie/code/imftuning_code/util.py:574: RuntimeWarning: divide by zero encountered in divide\n", " ccg /= len(ts1)\n", "/home/crombie/code/imftuning_code/util.py:574: RuntimeWarning: invalid value encountered in divide\n", " ccg /= len(ts1)\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get mean ACG and SE from dataframe\n", "burst_acg = df_bursts['burst_acg'].values.mean(axis=0)\n", "burst_sem = sem(df_bursts['burst_acg'].values, axis=0)\n", "tpts, _ = correlogram(np.array([]), return_tpts=True) # get lags\n", "\n", "# Plot mean ACG and SE\n", "fig, ax = plt.subplots()\n", "ax.plot(tpts[tpts >= 0], burst_acg[tpts >= 0], color=COLORS['burst'])\n", "ax.plot(tpts[tpts >= 0], burst_acg[tpts >= 0] - burst_sem[tpts >= 0], color=COLORS['burst'], lw=0.5, ls='--')\n", "ax.plot(tpts[tpts >= 0], burst_acg[tpts >= 0] + burst_sem[tpts >= 0], color=COLORS['burst'], lw=0.5, ls='--')\n", "\n", "# Add dashed line at 300ms (peak prior to this value indicates presence of short timescale structure)\n", "ax.axvline(0.3, ls='--', color='gray')\n", "\n", "# Format axes\n", "ax.set_xticks([0, 0.5, 1])\n", "ax.set_xlabel(\"Lag (s)\")\n", "ax.set_yticks([0, 0.004, 0.008])\n", "ax.set_ylabel(\"P(burst)\")\n", "\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=2, h=2)\n", "fig.savefig(FIGUREPATH + 'burst_acg' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "0b297139-0a23-493a-9d3b-ab1880fb04e2", "metadata": {}, "source": [ "#### Figure S2C\n", "Mean burst cross-correlogram" ] }, { "cell_type": "code", "execution_count": 37, "id": "09506540-6352-46fe-af50-7c6aabf3076d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/crombie/code/imftuning_code/util.py:574: RuntimeWarning: divide by zero encountered in divide\n", " ccg /= len(ts1)\n", "/home/crombie/code/imftuning_code/util.py:574: RuntimeWarning: invalid value encountered in divide\n", " ccg /= len(ts1)\n" ] } ], "source": [ "ccgs = []\n", "# Loop over recordings\n", "for idx, group in df_spikes.groupby(['m', 's', 'e']):\n", " # Loop over unit pairs\n", " for u1 in range(len(group)):\n", " for u2 in range(len(group)):\n", " if u1 >= u2: continue\n", " # Compute cross-correlogram\n", " bt1 = group.iloc[u1]['burst_times']\n", " bt2 = group.iloc[u2]['burst_times']\n", " tpts, ccg = correlogram(bt1, bt2, tau_max=0.5, return_tpts=True)\n", " ccgs.append(ccg[tpts >= 0])" ] }, { "cell_type": "code", "execution_count": 38, "id": "0c9565fe-093b-4c0f-a691-b80494b6a71a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get mean CCG and SE\n", "mean = np.nanmean(np.row_stack(ccgs), axis=0)\n", "se = sem(np.row_stack(ccgs), axis=0, nan_policy='omit')\n", "\n", "# Plot mean CCG and SE\n", "fig, ax = plt.subplots()\n", "ax.plot(tpts[tpts >= 0], mean, color=COLORS['burst'])\n", "ax.plot(tpts[tpts >= 0], mean - se, color=COLORS['burst'], lw=0.5, ls='--')\n", "ax.plot(tpts[tpts >= 0], mean + se, color=COLORS['burst'], lw=0.5, ls='--')\n", "\n", "# Format axes\n", "ax.set_xticks([0, 0.25, 0.5])\n", "ax.set_xlabel(\"Lag (s)\")\n", "ax.set_yticks([0.0025, 0.005, 0.0075])\n", "ax.set_ylabel(\"P(burst)\")\n", "\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=2, h=2)\n", "fig.savefig(FIGUREPATH + 'burst_ccg' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "dd7ab157-d57f-486c-92ba-4afc1228d2a9", "metadata": {}, "source": [ "### Figure S3A\n", "Phase couplng measured in darkness." ] }, { "cell_type": "code", "execution_count": 39, "id": "4b87bb64-f4f5-4a3b-8ace-983d5ac80f91", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: phasetuning_spontaneous.pkl\n", "Loading: phasetuning_sparsenoise.pkl\n", "Loading: phasetuning_dark.pkl\n", "Neurons with significant coupling: 0.968 (91/94)\n", "Tonicspk prop. significant: 0.968 (91/94)\n", "Tonicspk num. CPDs per neuron: 3.41, 1.46\n", "Burst prop. significant: 0.648 (35/54)\n", "Burst num. CPDs per neuron: 1.85, 1.73\n" ] } ], "source": [ "# Load spike data from dark experiments\n", "df = load_data('phasetuning', ['spontaneous', 'sparsenoise'])\n", "df_dark = load_data('phasetuning', ['dark'])\n", "\n", "# Assign significance based on p-value from shuffled permutation test\n", "df['tonicspk_sig'] = df['tonicspk_p'] <= 0.05\n", "df['burst_sig'] = df['burst_p'] <= 0.05\n", "df_dark['tonicspk_sig'] = df_dark['tonicspk_p'] <= 0.05\n", "df_dark['burst_sig'] = df_dark['burst_p'] <= 0.05\n", "\n", "# Print a summary of coupling across the population of neurons\n", "coupling_summary(df_dark)" ] }, { "cell_type": "markdown", "id": "7d2bb07b-812f-45e9-af7e-e4d22a0386d0", "metadata": {}, "source": [ "#### Figure S3A1" ] }, { "cell_type": "code", "execution_count": 40, "id": "76d5b122-2f8c-4aee-b99a-32f0e950550c", "metadata": {}, "outputs": [ { "data": { "image/png": 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fgYPEBzVFvmcHz32zg84hbrzYq2GN+707MoPHvzvMmkGT0cvklJatLtQtW6Ia8wT/2xrJc7+c4MvtV2q19K+KxJXr2BbYlsVNHuK3JgNY0aA7SSvX3na99wqLC/iQIUN4//33iYmJKb8mCAJZWVl8/vnnPProo5Zust7w14U0tts1IGz7NlqcO4P4+Tz+73SeRX641+MpNTB985fkX4xE1aQJxRs38vaxn3EqO46z7dAe/+8WErhsKcv9OvFAcx/kUgk+Tmo6ywsRAduuXQlY8iOqJo0B0CUmAuZThpxzF0AqxXnsE9i7OtN91hsIwPZhAcwd1RqFrOY/u3m7rjKynT8fz3yKhst+4lzzbpzoNhTv77/nqV9Os+FsCs62ClYdT2T80mOYTLd3yhGTq8XOqON8SgGnEvJwFHUkF+lvq857icWX6J9++il9+vShSZMmREREADB58mSioqIIDg5m1qxZlm6y3pBdpMPTUVXu7RQR6IzeKFKg1aOqxb71ZrRIuUScUcuoxk/gLnFG1sKGL/+eh21BDthUPHZq7O3Ar4fj6RjsQlKehqjUfPoBUhcXZO7uSB3M59miKFJqMPLSb6cQU+141Wjk5xHPMfCD1yiY9SEIAq4tm9W6ryU6I/4uNhQfPUrS5OdohhRZqZYr46I532AEe995AC9HFTGZRfT+4m8upRXU6Iy9KtYGdGLq9m+YiYhBJqdjzHHmdn6KB2+5xnuLxWdwZ2dnDh8+zPz58wkMDKRv374EBwfzySefcODAAezt7S3dZL0hItCZ+OwSFuyJ4nRiHrM3R9LA3RY32+osv2uPIIqolTI+eawNo9oH8PHjZWfSJtMN937yaAsyC0tpP3snjy44iFezRghA7q+/EjNoMIU7dgCg8PFhwe5oTiXm8cJnryJ06Ez7c3+T9ejDlEZFsaLNULovOM63e6JrZUvQt7En3+6J5sob75AS0Y2RD7xP9P/9hCQpgYfiDmOvMs9RTjZmE1Od4cYx1IbjLg14q/NEgj3saeyi5KOOT7PHu3LDnrqA1dClhtwtQ5e1J5N474/zFOuMhHna8e3YiGpNO7UXL5I643108fEow8Lw+fgjFEFB1bZhyM0l9uGhKIKDse3cmbw1a5D7+BCw5MdKtcWlBiNRGUXYKmQEOKtIeeUVig8dRhkaijYyEsfBg/Ge9QHP/HSMQFcb3h/cFIDfftnG4d3HOe0UxIThndEZTHz212XeG9SEsR1rpmgzGE18siWSwdMe57J3GKHaLBzdnDFJpWwWvNjeaxRNfBw5m5iHzmhi05Rut7Xa6TR7B3kaPY5qBSZRRKM3h74698EDt1znvcTiAp6RkUFxcTHBZUEDRFHk+++/5+LFi/Tp04fBgwdbsrm7xt0ScDDbkxfrDNgrZdUezxhyc4l5aBA2bdviMHAgeStXoktIoMHGDUhUqmrb0MXHk/H5F+hTU1E1b4bH1KlI7Wp2FCcajWQvWUJp5GXUrVvhPHo0giDw3h/nORaXw8rJnTCZRAbO3UdavpYgV1sWPRlBQw97Zv55gdisYpY+075Wz+RK5y4YCwpQNW2KqaAAXWwsF/s+xlS7fwJPvNynIa/2a1Srev/N/7ZG8t3fMQgCCAKYRBjayocvHm91W/XeKywu4AMHDqRhw4bMnTsXgPfee485c+bQsGFDoqKi+OGHH3j66act2eRd4W4KeE0p3LOH5Ckv0+j4MQSFAkNuLlc7dSZo1UrUzZvfvIJbJGfpUtI/+dQcFUYux+ejD3F8+GGyikp5/LtDpOVr0RlMmEQRLwcVBpOIwSSyZUpX3lt/AalE4NuxEbVqM7J5C0T9dcouiYQNAR34se1jtApw4kR8LgajyP7pvfB1vvXzcb3RxLTVZ/jjVAoAvRq5M39MG2xqaS583yBaGE9PT3HdunWiKIqi0WgU3dzcxE8//VQURVGcMWOG2LJlS0s3eVfAfJx7r7tRgeKjR8WLjcLFxJdfEePGjBUTp7wsXmwULmqjo+9Ym9roaPFieGMxd+060WQwiFmLfxQvNWsu6jMzRVEUxUKNTvxuT5QYOH2juP1CqrjvSqbY4M2NYujbm8Uen+0SQ9/ZLJ6Iz6lVm8biYvFio3Ax5vERoubKFVEbFyeeb9pM/L3zQPF8Up4oiqJ4MCpTDJy+UVx7Mski4yzU6sW8Ep1F6rqXWFzJlp+fj6urKwAnTpwgJyeHMWPMRgK9e/cmKuq/F/juTqFs3BhBraZw+3YMOTkUbt+O1NkZeUDNI5bWFl1cHIJKhdMjQxGkUpxHjkDU69EnJaHVG3l15Rlmb4kE4H9/XaaZrwMLn4hAREQplfDL+Pa0Cah5VFR9cjIxDw8FQHvmDPFjnyD5lVcRREiyc+dAdBb5JXoORmcD4KCyzGmDnVKGo7rmXnz3KxYXcD8/Py5evAjApk2bCA8Px9fXFzALv+ome0MrNUdz+gyiXo/bCy9g074drs8/hzE3F93Vq3esTUVQEKJWS/6ffyKaTOSuXAlyOXI/PxbsieZMYh7rX+hCjzA3ojOLeWTBAZ7/9SQGo0hSrobXV50lvRaBH9Jmz0Hm4YF9v35InJwQtVoMmZkIJiMHA9swe3MkLWdtY96uKNQKCe2DXW9rfKIoMn93FB1m76DtR9uZs+VSuY99XcTiG4tnnnmGN954gx07drBp0ybmXBe/6/DhwzRu3NjSTf7ncXliLFJ7e3RJyWR/M/+OtqVs0ACP6W+QMv1NUqa/CVIp3h9+iMzNjfPJsTzUwpuW/k58OzCIneO+xCvpKnk2jgTOfA/f3t0Z9f1hvth2mf8Nb1mj9nRxcTiPGIHjo4+S9NJLlBw5gjEvD/XU6ZyKcqtwr1ZvwmC8PWH87WgCc3de5c0Hw1HJpczefAlbhYwpfUJvq957hcUF/K233sLX15djx44xb948nnnmmfLPcnJymDBhgqWb/M9iE9EGhb8/CeOewa5XTwo2b0HVogXK0Dv7Y3R9+mnse/dGn5SEIigIuY8PYI5rdig6m7wSHTkvvYRtSSGL2o1ghCQV01tTkf+xjs4hbpxPvjFyTFUoQ0LI37gRESg5fBhBIUdQqsj64QccOr3Au6O78EgbX37YH8MnWy6z8ngik3pUHX9PFEXis0vQGU00cLO9IUDEzksZjGznz7gu5lOgzMJSdlxKr7MCfkf8wZ988knmzZvH+PHjKxzzLFy4kKeeeupONPmfRKJWE7D0J5QhIRTvP4BNRAQB3y9CkFX+3i7U6onLKr5tYxAARUAAtp07lws3wMt9QtEbTTw4cz2l584xr83j5LbrxnvBD2FycubKll2sP51c46iqAJ7vvIOpuJiM2bNBFFG3aEnIX1sxyuQ8GHeEIDcbZFIJ/mWac62harPeUoORZ5edoOfne+j/f3t5eP6BG3zTbZUyEnM15acliTkl2NZVDTp30NDFYDCQkJCAVnvjfqtJk7oXe/p+PCarDT8fjmfWhgvojSLu9koWPRFB6xoouwxGE3N3RbHtQhq2Shkv9Aqhd3jVDkPFpQb2n43H/8nBfNLhSY4HtEJaVMDi7Z/wbYtHEPo9wIIxbWpljGLS6bjcrj3uU17C9emnEaRSIsc+zeo8FYubDcbXSU1SngaAQ2/2xtvpxtBTAHN3XuXnw/Esebodjmo5z/16gkAXW+aP+Se6zPnkfB799iD2ShkSQSCnWMfSZ9rTNdSt0jrvdyz+atLr9UyZMoWlS5eWB3r4N0ZrSttqicksIqOwlDBPe1xsbz/K5/nkfN5ff54PHm5GzzB3/m/7FSb9fIL903vf1PHjs78u8/vRBCb3DCElT8OEpcdZMakT7YIqzyxiq5TRq3Uw/xfWkzdO/I6jfQoFZ88Tb+PEoOdH8mjnhrX2rZYoFNj36EH+ylUoAwMpjYlFPHmMJq9+CNGQlKdBEOCzYS2qFG6AiykF9G/iWb6CGNHWnyUH4irco9UbMZlMqBVSJIKAqUi0uLPP3cTiAj5r1iw2btzI4sWLGTNmDPPnz8fW1pZffvmF6OjoOhXl9F4wZ8slvvvb7Ilnr5Sx8IkIujSsevbQRkaS9PIrGLKykPt4479wIYqyU4trnEvOx8dJzRNl5qEv9w1l7alk0gu05Y4tVfHH6WSmPxjOmDIf7oQcDRvOpFQp4GAO3fxt+EAeGd4DWXQkSZ368papMd5bzuK9+Cu8i7NQNQrHY+prSB1rtlz3/vgjUt95l+TXpyGxt8N71gc0Hv4ovYp1JOdp8He2uWlwSn8XNTsvZZBTrMNGIWXHhTR6Z10k59dk1C1aom7ejB8PxNK3sRcLnzAb4rz7xzkW7Yuhb5O66eZscQFfuXIlM2fO5PHHH2fMmDG0b9+eiIgInnzySZ566inWr1/PwDvgqF8f2H81i8X7YvlpXDs6h7jx8aaLvPjbSY6907fSaKGG3FxiHx+BIIrIfX3RxcQSO+RhQg8eQKL8x0HF29Ecsul8cj7NfB3ZeSkDhVSCaw1igMskEopLzWGPRVGkuNSA7Cb+6fYqKa52CpbYBhDUuy2fbb2M3KBl5q5vSBUVGPr2xvf4XhInTSbw118QpDdfrkvt7fGb+/UN151tFeXpim7Gi71C2R+VTcc5O1Eg8snuuYRkx5P+h/lz99enUipriZfjP0e5LrZKSg0FNar/fsTiAp6YmEhYWBhSqRSVSkVubm75Z2PGjGH06NF89913lm62XnA1o5AgN1t6NjLn0RrbMZClh+LJKdbh4XCj/UDeqtWg0yFS5o8tipiKiynctRvHB/9xjugR5s5DLbx5ZMEB3O2UpBVomfNo8xqZX47rEsSnWyOJzSomKVfDxZQC5jxauRms0SQy888L/HokHpMIq08koS87thqgS8Y2Lwsatka/fy/KVmEUbtlKaVQ0qkZht/C0ao+jjZx1z3fmUHQ2ig2rccmOx3HoUBwefIC0Dz8k84svGfTjBqZtuIyHvQqVXML3e2N4tV/d1KDDHRBwb29v8vLyAAgODmbv3r307dsXgOjoaEs3V69o4G5HfHYxh6Kz6RDswsrjiTiq5VXuww1Z5sg5Hm+/jcsTY0n/+GNyf/kVY35ehfvyNXqe6BhAjzB3dAYTzXwdaeLjUEmNNzK+azDONgq2XUzD3U7J6uc6EeZZucvvj/tj+eN0MgvGRGCrlDJt1VkKS/UUlxpp7q5CajIgZKRxLjyCwPMnzIXE29fo1waVXEqvcA+SFl6lUCbDe85sAFzGTyD9gw8Y4FhK0eAmLDsUj1EUebF3QyZ0bXBX+2hJLC7gPXv2ZN++fQwePJhnn32WadOmERUVhVKpZMWKFYwaNcrSTdYbuoe6MaZDIKO+P4xUIqCUSZg/pk2VwfxVZUZDmV9+SdHOnZScPAmA8jp30d2RGbz420mKdUYEAd4Z2LjGwg3m04NhEX4Mi/C76b1HYrN5tLUvDzQzB40Y3zWYz/4yB0gs0ZUlVzAZUBp0iIayZX+Ne2JZlGFhFG7dSszgIehTUhBLzPnTsxd8y9gvv+CJTkH3qGeWxeIC/vHHH5OVlQXAK6+8giiKrF69Go1Gw0svvcSMGTMs3WS9QRAEZg5pysj2/ua8Yl72lS7Nr6FuabYGE5RKNKdOIbG1wVRYVG7oUlxq4KXfTzG2YyBT+oTy14U0pq46Q8cGrrU6i64prrZKziXnU2owIpdIOJWYi0ouoVe4O8bYDExSGRr/YBpkxGLTrh0Ff/6JpAb77zuB49CHyZo3D911vhFSVxc0J0+S/eOPuE2efE/6ZWmsAR9qyP16Dp7z8y+kz5kDJhOCXI737I9xLPO5v5peSL//20tEgDNX0gvxdVaTlq/l/SFNeKT1zWfk2pKYU8Kj3x5EIZWglElIL9AyvmswC/ZEMy7Cmz5fTyOrVMStd0/cju9D7uVFwNKfEO5BqufMBQvImjsPZDIk9vYIAhhzcnEcNgxjXh7+87+56326E9RdEx0rgNkO3b53L3TJySgCg5B7epR/5manQAAyCrX8b3gLfjkST2RaIXaKO+Ml5e9iw6YpXdl8NhWDSaR/Ey8CXG0IdLVl5fFEYoe9wcQrf+GZGoOqdy/cXppyT4QboDTqOn2QyYSxLPGi5sQJbLt0uSd9uhPcEUOXr7/+mrVr15KUlFSpJZs1Nrplkfv6Iv/X2TdATokeEUjN1/LayjNo9EaUMgklesMd64uHvYqny+y4r1FxD9//jrVdGwS1eesj8/bGmJ5efl00GnF7/rl71S2Lc0cym3z33XcMGjSIXr16oVDUr3zLdYlriew/G94CW6UMtULK+KXH61WC+1tFFdaIAokEQ1m452sE/PA9Mtfbczm9n7D4N71q1So++eQTpk6daumqrdQSTwcVo9oH8M66c7QKcOZqRiHNfR3pFup+r7t2z5EolWAyoWrWDHlAAMUHD2IqKEBWz7KVWnwDJIoiLVrU3TCz9Y2HmnshInAwOpvsIh2DW3jXKvFAfUXU65F5eqJPT6Nwyxbknp7mvXhezV1Z6wIW/6afffZZfv/9d0tXa+UW0OiMTP7lJDKp+QRAJZPwwcaLRKbVXdNLS6EMC8OQlYX3Bx8Qduwo6jatzckcXKu2sa+LWGSJvmDBgvJ/e3l58euvv9KrVy/69euHk5NThXsFQeC55+qPEuN+JiariKJSAz3D3Hm5byh/XUhn4d/R7L2SSbhXzY1d6iO2HdrjNmkiSc+/AIDEwQG/uXOr9KWvq1jkHLw2yfEEQaiT7qL36zl4dZxKyOGRBYeY2j+Ml3qHciQmmxGLDjOld0Ne61/z+OFGk0hSbglqhRQP+/oVU08bFYUuNg6bNq3rlXLtGhZ5XZkqSXlj5d4T4GILwJfbr7D0YBw5xTokgjlFUk1JzdfwzE/HuZRqXtaPah/Ax0ObIZHUzXzZ15O7fDnpH89G1OuR+/jgt2A+qvDwe90ti2LVttRjXO2UvNwnFAlgo5CikEroHe5RKy369DXnUMkl7Jrag1/Gd+DP08ksP5Z484L3OZpz50ib9SEeb06nwebNqJo2JemFFxHr4OqyOu7IhkOn0/HTTz9x9OhRUlNT8fb2pkOHDjz11FPWc/GbsO9qJjP/vEBGQSkRQc78b1iLau3Rb8ar/cJoHeDEhZQCfJ3UDG7pU6vZ93xyPjOHNKWBux0N3O3oGe7BueQ84M7FXr8baM6cRREYiEtZzH73l6cQM2gwhuxs5B4eNyldd7D4DH7p0iVCQ0N54YUXOH/+PFKplPPnz/PCCy/QsGHD8pjpVm4kOrOICUuP0ynElf8Nb0F2kY6JP5+47X1/z0YevNCrIUNb+yKtgXCn5Wv5esdVPtkSiaNazu7IDIwmkayiUk4n5OHjWHVYpLqCzNMDfWoqpWUuzMUHDiAoFDWOMFNXsPgMPnHiRBwdHdm3bx8B12XYSEhIYNCgQUyePJm9e/dautl6wcGoLDwdVHz4cDMEQcDfxYZB8/aTU6zD1e7WUwjrkpIovXIFuY/PTfeYyXkaHv7mAI5qGW52SuKyiknN17D3SiYlOiOhnnY80zW42jrqAva9e2PbpQuxjw5D5uWJPjEJ71kfVIiEUx+wuIAfP36c33//vYJwAwQEBPDBBx8wevRoSzdZb7BVysjX6Mkr0eNsqyA+uwRBALXi1l0q8zdsJOXttxGkUkStFteJE/F47dUq7//pQCwe9kr+eKELCpmE2Zsvse1CGi/3DcVGIaNHmHutIqKKoljrIIt3A0EqxW/u1xTt2YMhIwNVixaomza9192yOBYX8KCgoEodTAC0Wu0Ngm/lHx5o5sWivTEMmrefJj4O/H0lk8k9Qm45s6WxsJDUd94xhxueMIHi/QdInDgR+969ULdqVWmZAo2BAFc1Z5Ly0OiM+Dqp0epNtXYv3XM5g3fWnSetQEsLP0e+HtGaANdbz/p5JxCkUuz79Kn0s1KDEVHktnKN3w9YXMCv2aEHBwfTocM/uZsPHz7Me++9x+eff27pJusNNgoZqyZ3YunBONILSvlseAuGtPS5ecEqMKSnI+p0OD78MIIgYNetK1JXV3QJCVUKeOcQV15ecZqt59O5tl3v36R29tlxWcVM+vkEozsE0LORBwv3RPPM0mP89Ur3GukA7iUGo4kZf15g+dEETCIMauHN54+1rLOCbnEB/+ijjygoKKBz5854eHjg4eFBRkYGGRkZuLq6Mnv2bGbPnl1+/9GjRy3dhTqNvUrOi70tE+RP7u2NxNaW3J9/xu255yjcuRNjdjbKhg2rLJOUp8FOKUMmEdDqjTjayMksqjy+fVUcjc3B2UbBjEFNzLoEZzW9v/iblDzNTcM032sW7Yth45kUFo6NQCmX8sbqM3z212XeG1T3knXAHRDwZs2a0axZM0tX+5+hqNTAskNx5mOyQGcGtfC+5T2sxNYWny8+J/m1qWR//wMIAh7TpqGqJrNMbFYxfRp78NWIVhhNIhvOpjB7c2St2nVQm3UJaQVavB3VXEkvRBDAQWXZQBMmk8jm86nEZhYT5mVP/yaet73fPxqbw2Nt/enf1LxqebpzMJvOpViiu/cEiwv4kiVLLF3lfwaNzsjjCw+RV6KjsbcDvx1J4HJaIa8PqLlZ6b+x79mThjt3oIuLQ+7lhdzbu9r7Qz3s+GrHVRq/txWd0YSTjQIvBxWf/3UZG6WUxyL8cbevXtPcO9yT5r6ODJ63n3AvB47G5vBcj5CbJiaoDaIo8vrqM2w+l0q4lwPzdkcxsp0/sx6ufnIp2refnGXLEPV6HB4aiNPw4RVeCi62Cs4l5aMzmJBJBE4l5OJqW3c16/XLsr6Os/VCKkm5JeyZ1gsXWwUbzqTw8vJTPN/r1hVtADJnZ2TONTNPdbNToNUby6Od5pXoyjOBZBaVsuxgPOtf7IJnNcY3CpmEZePbs/xoAqkFWsZ0CCiPtGopziXns/ZkMn++2IUWfk4cjM5i9PdHeLpzEA3c7SotU3z4MImTJuH48MNIHRxIn/UhYqkOl7Fjyu95uU8ojy44SO8v9iCXSsgsLGX5xI4W7fvdxCrg9xGFWoM5U0fZTBfsZotJhBKd8bYEvDYcic3hweZevNwnjBKdgWeXHadEZ2T1c53R6o08/M0BfjoYx/QHqj9PV8mlN4RusiQ5xTrkUoGmPmbDlNb+zuXXG1RhiZu3Zi32ffviUxYLXWJvT96qVRUEPNDVls0vd2PzuVSMJpEBTb3ue71BdVgF/D6ic4gbH226xAcbLtIpxJUFe6Jp6eeIqwUSENYUZxsFZxLzCXS1QSWXUlRqwEltbl8llxLsZku+Rn/X+lMVTX0cUcqkzFh/nkfb+PHrkXicbeSEVpGUoTZ4OqjK84PXdaxhk2vI3XIX/ftKJu+vP096QSltg5z5/LGW1S6HLU16gZah8w8gkwq42Cg4m5SPm72SOY80JzlPw6yNF5k/ujUPNKt+L29pTBoN6bNnU/T3XiT29rhPmcKFhhG8suIU6QWl+DqpmTuqdbWecsWHD5PwzPjyJXrub7/hMX16hRm8vmEV8BpSF/3Bb5WsolLWnUymRGekYwMXlh6KY8fFDFRyCVP6hDKh291N5XM+OZ+U6W/iFnUOx+dewC4jiZwlPxH48zLUbdqg0RtRy6U10qAX7dtHzrKfzUq2gQ/i9Nhj96WlnaWwCngNqesCfjgmmxnrz5Oar6WVvxOfDa+YRfNmmEwigkCNhSGzsJRvdl0lNV9LS38nJnZvgLyKFEzVcSAqi6eXHGXVn2+zvMeTbHJryrrnOyOf9hKqpk3xfGNarev8L2GRPXhwcHCt3oIxMTGWaNZKDUnMKeGZn44xuIUPr/R1Z9HeGMYvPcaGF7vW2HW0Ni6mhVo9jy08iEoupX2wC4v3x3I5rZC5o1rXuu9f7bjCI619sdljx+sRLsTpHVi06wqTsrOQqOu+V9udxiICPmzYsAoCvnz5ckpKSujXr1+5Jdv27duxtbVl5MiRlmiy3pKQXcIX2y+XG7q81KchStntmUkeis7GXiVjzqPNkUgEQj3s6Pd/e0kt0OLrdKOQnE7M42xSHl4OKvo29qx19JZdkRnkFOs4+FYf7JQyBjb3ZuSiw7w3qMlNz9D/TYHGQEMPO9wmTSL90095qkk7bDJSMRqKcHpseK3q+i9iEQG/3r589uzZhISEsGnTJmxtbcuvFxUVMWjQIBwc7kywv4sXL/LSSy9x6NAhnJycmDBhAu+//z7Se5Tc7lbIKipl+MKDBLna0jbImeXHEonLLuab0W1uq15bpYwirYGcEh1udkoScsyZNG0r8VL7+XA8768/T5CrLcl5Gno18mDBmDa1EnK9UUQhM+cnA7BXmX9mhlsI7dWzkTvf/R2D5+DeyJ6TEL1xOy1bRxA87Xnk9SyG+R1BtDA+Pj7ixo0bK/1sw4YNopeXl6WbFHNyckRvb2+xT58+4rZt28Rvv/1WtLGxEd955x2LtYE5063F6quM34/Ei20/2i5q9QZRFEXxwNVMMXD6RjGvRHdb9Wp0BnHQ3H1ip9k7xInLjomN3t0szvzz/A33FWn1YsO3N4nLDsWJoiiKl9MKxNB3Nos7LqbVqr30Ao3YetY28ZklR8Uf98eIvT/fLT628KBoMplq3fdSvVF8a+1Zscl7W8QWM/8Sv9p+5Zbq+a9i8XPwgoIC0q/L9XQ9aWlpFBUVWbpJFi5ciEajYe3atTg4ONCvXz8KCgqYOXMmb7zxxh1bNVgakwgSAQxGkSJt6T+z5m3q9VRyKb9P7MjSg3Gk5mv48OFmDK8k33duiQ69UeRobDbLjybg5aDCxVZBekHtnE087FX8/mxHPtp0kV+PJNDK37nc8aS2KGQSZj/SnNmPNK91WSt3QIs+evRo/vrrL7777juGDBmCQqFAp9Oxfv16Jk+ezIABA/jtt98s2STdu3fHx8eH5cuXl19LSEggMDCQP//8k8Fl6XRvh7uhRc8o0NLriz1odEZMIkglAh2DXfj12TtjKrn5XCo/H4rHKIoMj/DjkVY+NJu5DalEYEyHAHZGZhCTWcwv4zvQNdTtjvTByp3F4jP4t99+y9NPP83jjz+OIAjY29tTWFiIKIoMGTKEb7/91tJNEhkZSe/evStcCwgIwMbGhsjIyBoJ+P1wFppWoKW41IiHvRK90YRMKpCYW4LJJFo8TPFf51N54deTOKrNZrFvrjlLfrGOUoMJdzsl3++LRSmTYKeUklZQeQCPe0GpwYhcIil/HqIootWbbivqTX3G4gLu6OjIunXruHDhAseOHSM9PR0vLy/atWtHk2rcFG+H3NzcGzKoADg7O5Obm3tH2rwTHI/LQS4VyCisuCTOKi6tVcKBlDwNr686w+lEsyb8o6HN6Nyw4gw8d9dVRMzhoASgQKtn8YFYAOaNak2Qmy1KmUCvL/7mFo6vLU52USkv/X6Kg9HZqOVSpvYPo6mPI6+uOE1agRY/Z7MlW5uAmsd8r4rzyfmsOJaIURQZ2sqX9sF1N53RHbNFb9q0KU3rUIyrmy2978YMfzwuF71RZFr/MCb3bEivz/eQkFOCvBZtbzqbwltrzyGTShjXOYiMwtLyaCqBrv+caiTkaACQSQQEwbz/zynW8VALb1747SRhnnbEZZeYY6k38rT4WGvL66vOkFOs47dnOxCTWcyM9edRyqQ80saXYW18+fVwAhOWHmfPtJ635Xd+KiGXEYsOExHgjEouYdT3h/nhqbb0alQ3QynfkXdzRkYG06dPp0+fPjRq1IgLFy4A8PXXX3Po0CGLt+fs7Ex+/o1ZIXNzc3GuoZvk/YDRJCIAX2y/Qvh7W8qPs1JruERefjSBl34/RYHWQIFGx/w90aw9mYRCJuFwTPYNbQGk5mtJyTPXbxLhnYGN0RlMHIvLLXMVLeV0Up7FxnirHIrJ5rV+YXQOcWNsx0Ca+jhSajDy4cPNiAh04eNHmpNTrONKWuFttfPD/lh6hLnz27MdWDKuPY+39WPhnmgLjeLuY3EBP3r0KKGhoaxZs4agoCCioqIoLTUvOVNTU/niiy8s3STh4eFERlaMOpKYmEhJSQnhdSgVTbi3AyLmoAMdg13L45c1cLOtvmAZi/bFMLKdPwBBrrY093GghZ8TBRoDOcUVPcCupZsymEQMZcIuiiKL/o5BazBip5TiaqdAROC9P85baIS3jouNgjNlL5oCrZ70Ai0mES6mmFMqnU40f+Zym553Wp0RLwdV+YrN00GFVl93s51YfIn+6quv0qtXL9auXYvJZKoQ4aV9+/YW16ADPPjgg3z22WcUFhZib292F1yxYgVqtZoePXpYvL07hbu9EoVUIKtIx76orPLrGr0JVQ1+t0VaA6tPJAEQlVkMgIB5GW5znRKqqNSA1mAWaqkAxrLdiQgcjMlCFM1pj3yc1DiqFVy+zVnRErzzUBOmLD/F3itZpBdosVVKGRTszePfHSLc254LKQU80TGwymAP1WEsKkJz4gSCXM4DYZ68s+kKvs5qVDIJP+yL5fleIXdgRHcHiwv4yZMnWb9+PRKJ5IZ9raurKxkZGZZuksmTJzN37lweffRRpk+fTkxMDDNnzuS1116rM2fgAAqpBHuVnPljWpOcp0VA5LWVZ8vze98MuVHPc8dX0jnlHDqZnBWhvfm7cQ8KSw3lVmUAf1/OLP+38bqvSBTNWU0MJpHUfC0J2SXoTSLyW1jnJedp+GLbZdLyzWGTh7T0Qa2QEeRqc0v6jIdaeOPnrGZ/VBb2KhlDW/tip5Cx6VyqOYpr9wYMaFp7yzZdYiLxTz2FMSsb0WQiIiyMtya9z5IjCRhNIs90DWZyd6uAl+Po6EhmZmaln8XExODpaXmFjbOzMzt37uTFF19k8ODBODk58eqrrzJz5kyLt3UnGdDUi7m7rjJrwyVaBzix4UwKI9v5Y19DpdHIE+tom3GJ75o/jEtpAZPPrSdX5cCJwJZcby1zvcmoVAKCCGUTOgVaA2COInMNVS2OoHKKdaw9mcS8XVH4OatpF+TMor0xLPzb7GAU4GLDlpe7YqusvSKspb8TLf2dKlwbfBthpQHSZ89B4eeP/6ZNmEpKiB8zloGntjDuzem3Ve/9gsUFfMiQIbz//vt06tSJwMBAwKyBzsrK4vPPP+fRRx+1dJMANGnShF27dt2Ruu8WjjZy1j7XmW92R5FRUMrTnYMIcLVl49kUejXywFZZ/dfVKuk8y8L6sSsgAoCGeUl0ybjEcf8W2F+3xu9y3ZGZ8Trz8GuvAKmk4vXi0prtQZPzNAxbcJASnYGiUgMJOSI6g4lrC7mnOgWy9FA8E38+wa8T7o84Z7qEBJxHjECiViNRq7Ht3BldYt3PnnoNiyvZPv30UxwcHGjSpAndu3cHzEvoRo0aoVarmTVrlqWbrFd4OKiY9XAzpvQJZdnheOZsvsRba84x5BtzjrLqKJIo8C42a8slJiOeJTnkSxToTSJNfP4JZeRmp0Qpu3GZLJOAnVKKyQSNvOxxVMvNS/saGu/N23kVT0cV7zzUGEe1nA7BLkRnFtHc1xw37bmeDWnu68CFMsXY/YAyLJT8P/5An5ZGaVQUhbt2oQyzTFz6+wGLC7izszOHDx9m/vz5BAYG0rdvX4KDg/nkk084cOBAuRLMSvV8tOki7YJcOPx2Hw681RuJIDB/d1S1ZX4N78+jUX/z0cFFzP37a3yKstkY0hVXOyXH4ioa/FSWqUMiCIxqH4AIXE4rpFCrp9Rgws+5ZkEHMwtLae3vRN/GnsilEq6mFyEC51Py6dLQDZVcIC6rBPubrETuJl5vv41oNBLVsxcxgwajbNAAt0mT7nW3LIZFn7RWq2XIkCG8/fbbjB8/nvHjx1uy+v8UKXkawr0cGPbtQRRSCV6OKlLzNdWW2e/bkreUdnROOU+Uox9bgjqQ7+SGXG+soGQDs0vnvxFFkawi3XV/m/9frDOg0RmRSwVk1Zi1tQ1yYeHf0fQIc+edgY15c+05bBVSikqNHIrOovWHO0A0W8rdL8jc3QlevYrSqCgEmQxFSAiC5D4w3bMQFhVwlUrFsWPHMBrr7rnhvSY1X8PcnVFkFZWy9UIaEQFOlOiNHI/P5bG2lScAPB6XQ1y22SjmnFsI59yu0/rqRfydzYEbrsdUdvYtocyDTTQbupxPzkcqETCaRETMy/bsYh2NZ2xFLhV4vmdDXukbWqkm/NluwcRlFTPup2MA9G3swdcjW3M2KY8f9sUiCBAR4MKVjEIkEoHuYVXEN77LCHI5qsaN73U37gh3RMn2xx9/0KeKrI1WqiavRMewBQdxs1eWb3tPJOQBIBUE8kpuDFc8Z/Mlvt8XU22klFWTO92goDOWTc8mzIJN2f9LdAaMJhFbhRSFTEJuWZvLJ3YkOVfDW2vP0cDdlodb+d7Qjkwq4aU+DfF0UKI3mTOS2ipldApxo2MDV6auPMNXO6/QwN2Oq+mXmdwjpFZZW/ZfzeLvKxnYq+SM7hCA223kTP+vYHEBHzBgANOmTSM1NZWBAwfi6XljvqiBAwdautl6wbYL6eiMJlZM7ETEh9vxcVSRkq9l85SuPP7dYWIyK/rSX0jJ57u9MbzaLxQQ+L/tV26oUyKAk82NVjJyqQS90YitQopEgMJSI1KpgM5gXn0V64wUX3dU1rGBKwA7I9M5HJNTqYBHZxYx7NuDuNoqkEklLNobS9tAZ3o28qBNgBPrTifz5wtdae7nyLYLaUwsy0DqU0nYqH+z8lgi09eepUuIG8l5GpYfTWD9i11rHQLqv4bFBXzs2LEArF27lrVr197wuSAI1iV8FehNJuRSCXKpgI1SSmq+2Ub8821XKNYZ8LB3rHB/cq4GiQD/t/1qlXVWZVJip5RR8i8hVsmk5GvM5+Ce9kpkEkjON5sZi6JIic5IZFohA5t5k1lYSnKehkAXG5zLzEMX7I6mkac93z0RwdBv9iMR4GRCLmcS82jp74StQkYzX7PhUYdg8wsju0hXIwH/fNtlXu/fiBd6NUSrN/Lg1/v4/WgCU/rUH433ncDiAh4bG2vpKv8z9A734H9bLzP5l5P4OqnJLtJhr5SRXVSKSialX5OK++jsYl358roqqjKSkUgEBP45ARMwz/YSidl2Nb2wlOtd0B/8eh85xTrUCilONnI6zdmJwSSilEn4akQrHmzuTb5GTyMve3ZfziQu5x+FoB6RI7E5KGUSPtkayeAWPvx4IBZXWwUN3GtmZ1+oNRBSdq9KLsXXSU2h9t5nWLnfsbgW/dlnn+Xtt9+mZ8+elqz6P4G3o5rfn+3InC2XKNAYkEkFCksNnEnKJ9jNlrEdAircfyQmB6CCoP4bEbF8Br2ea3pi83JaIKOgFEEAf2cbrmYUIRX+0aJLBRja2hdbhZRW/k4MXXCQ6Q80ol2QCzsuZvDKitN0aOBKt1A3Pt0ayf6r/1gyXntHiMCk7sEsPZTAd3/H4OWg4vun2t7UeOca3cPceH3laaavOYtMIpBdrOe5nnXXhPRuYdHzAKsW/fZp4uPAz+M70NLfkSBXW1ZP7sTSce3ILdGx5GBchXt1Zc95UHMvfhrXjn+brDuqZdgp5YxdfISUvIpHbNcinTb3daSZjyMSwRz8YWp/85LXKJoVcADtgpyZ3COEJzoFkVZgntk/33aFRxYc5KdDsZQaTMRlF/NEx0Ce7BREbFZJeTsKqVD+8rmaXoSNQopKLkGtkODvXPO45sWlRop0JvI1BrLLPOMqO8u3UhGLH/hd06JbuT3OJOXzZOcg2ga50KORBw828y53l7zGg03N+cE2nEvj6SXHKjiO+DqryNcYaBfkjFouZf913mkAEUEueDqqSMnXkJBTgo+Tmtb+TszacKn8nms/jiOx/xjJKKQCeqNISz9HJnVvQJCLedlsp5AhkQi8+WA44Z7/LLsN1+0htlxIN+sVRIjNKqHrp7sxGGsWSnl/VBZquYTVkzvxxoAwAN7941yNyv6XsWrR71O8HFTsvZLJqHb+lOiNnIjPoWvDiufGg1p689XOK0SXuYZeT3KuWUGXW6xDU4mhy2v9wjgSk018dgkSQcDFVsHbA5vQ47PdAFyY2R+1QkbT97ei0f8jhKUGE3KjAf/t65CXZBNk78WloI4U6czKubwSHabrVHv/tqdZ9EQb+jf15vO/IvlmdzRrTiYxol3FrUdVNPSwY8v5NGyVZs1/VmHtor3+F7Fq0e9T3h3UmDE/HKHjnJ3mQIj2Sl74l19ybomenGIdvo4qEASS8zTIpQJtApw5m5SP3mjiYEw2ga62Nxi6uNoqeLytP4v3x2ISRYa29sHXSY1MIqAzijT/YFtZIPiK/SrUlDLz8GJCi9KI9gyhV+RWmmfHYjJ2ITVfw/BvD5FVVLXg7biYwW9HE1GXLa9zb2Jffw0BOJdcQFq+hrwSPSYRQjxq7/v9X8OqRb9PaerjyNrnOrP8WCIquZRxXYJw/td59qmEXPI1enQGE818HUnO06A3itirZPg5q7maUUSQqw2rKzF0WXUiiS+2XeGVfqEopBK+3H4FB5Wc5r6OnEjIq6Cdv94vxSkmEq/MKMb3e5M0W1dCAxOZ+/fXlCQlMPeUDFc7BYGuNhyMrhgi6horTyRhp5RRVGqe8YdVEp+9MmwUUop1RjKLzPtvQcAiARbrOxYX8GsuolZuj/jsYp5YfJT0Ai1GUWTf1Ux+Ht8Bu+sENTlPg0kEtVzKqYRcVHIJWr2Jy2mFqOTmZez7Q5pWauiy5VwqI9r583zPhoDZD3zL+TQaedoTk1VcbsFmo5Biq/hneV+Uk49BIiVLbT6TT7M1RxzVFRSSUaCmTYAzV9Kr9xa7JtwAeSV63GsQMdZoEgn3tCMmqxilXIpXWWhpK9VzR6zqS0tL+fbbbxk/fjz9+/fn6lWzIcaKFSu4dOnSTUpbAZj55wU8HZTMGNyEGYOakFGgZcG/vMlsy5a5WcU6dEZzfHAwx1dPydMwuUdIldFAVXIpGYXa8qg7mYVaVHIJjjZyPB1UXP7oAeI+eYhR7QNwtv3HWmwX7pTIlEw//itdk88w/fivpNq4cNDoQJtAZ9afTq52XBIBVNfpA64Z89wMlVxKZHoROqNIodbA1czi246/9l/A4jP4lStX6NevH/n5+URERLBnzx4KC80xvfbt28emTZtYtmyZpZutN+gMJtafTuZYfC46vZGUPC1agxGDUeRyWsWZcW/UP+fNEuEfm/KIAGd8nNR8tzeGPo09iAi8Ma73hG4NGLXoME8sPopSJmH35QwWP92Oxl4OrD+dzID/24uLrYIzSfksHBtRXi5DouKdzhN55fQqXju7hmg7T97t/Cy9jRLe796AmMxi1pw0x4UTALVcoET/z3rfJFJh5i3V/zObV0ee5kajltXHk3iubAVyqxRo9eSUWdMpZPXHi+waFhfwKVOmEBAQwIYNG7Czs0Oh+Oct26NHD6ZPrx+hcO4EeqOJp348yoWUfDSlBowijGrvT+/GHjwy/2D5+e81rgVDFKDCnlmrN/JAMy8yi0pZfSKpUgGPCHRm9XOdWH4sEZNJ5PdnO9KhzN58w0tdWXMiGY3OwIzBTWl1nZFMgIsNh5x8mdLzlQr1BbnaIpdK+OLxlpTo9Gw5n44IFYT7GoIgIBdE9CZIzqt51hQHlZSZQ5qRW6Ljw42XyCi8vYwri/fF8PHmS5hEUMok9GvsyYt9GhLuVXfi+N0Miwv4vn37WLVqFU5OTjdoyz09PUlNTbV0k/WGrefTOJuUx1+vdmfUosPkluj5vx1X+b8dV/FyUOHpUNGxws/Jhsi0ohus2C6lFTJl+SlkEgmeDlXvb1v4OdHCz+mG6x72qiqtxNoGObPieNIN15v5/mMnHxHgzJbzlSeghIpn47VJOVSgNfLayjPlf9dkia5PT0eflIQiIACZ+z/HjMdis/lw0yVkEvPLsdRgYsuFNHZEprN6cucK46nLWHxNolKp0GgqD0yQnJxcaYohK2ZyinV4Oqjwc7ahXZALTjZmO/LpDzSiRGeg7b9m4jCvyo+JOjRwoUeYO0WlBiydkKWqFN/G64Q2q4ZHX2B2aqkOURT5fm9MpZ95OVZvCZe7ciVRvfsQP2YsUb37kL9hQ/lnq08kIQBhng481SmQl3qFYDSJNPd15McD9eckyOIC3q9fP2bPnl0h04ggCJSWljJv3jyrkUs1tA1yJj6nhG92XWVoa18Kyvadn269TL8mnjzTNbjC/VfSK0/FHJ1RhMEo0i3UDWOZpcn55HweX3iIbv/bxUu/n7ppfLequBZVxs9ZTQs/RzzKBPR6oS41mN8CX41oxbsPhSOrJnFiRmH1/fh+XwxfVuIGC+b9c1XoEhNJ+2AWnu+8TdjpU7i/8jKpb7+Dvixs97XkhRq9ES9Hdbkxj4e9Co2u/thpWFzAP/vsMzIzM2nYsCFPPPEEgiAwa9YsmjdvTkpKCh9//LGlm6w3NPVx5IvHWvLd3zE8vfgwQ09t5Icdn/Dtrs9x2PMXxbqKCqlrs7NUANvrlrq9G3kwqUcIZxLzaObrSFq+ltHfH8bVTsFzPRpyJa2QicuO31IqZGWZ5j4pV8OF5PzyRInXh3K6Fp/8tZWnWbQ3BkNZSqbyfl/3by/H6o/I1p1KKd8u2Cll+Dqpyl8qOkPVx2S6GPOsn/Prr1xp2468P9Yj6vXoExIAymPPxWYV8+nWSH7YH4uNQspfF9J4oFnt46vfr1hcwP39/Tlz5gyTJ08mLi6OkJAQUlNTeeyxxzhx4gReXvXn4d0Jhrb25cz7/ZmecYBHYg/Q/LlnaDx8MMN3L2Xt179UuLeZj1kZZBSp4Nf985EEHv/uEH0ae/JU5yD2Xs1EKZcyb1RrHm3jy/+NaMXx+NxbSgsc7m0OmikADup/XFEbuv0TmLFjA1cmdW+ASYSsIh0KqYQR7f4xaLn+tWJ7kz24QipQVGpAgvn8PDlPW/5SaepTtTJM4uQERiPRJRI+7zaB+DK/domzS1lZR4JdKwaT1BtNPN8zpNJgFnUViyjZZs2axYQJE/Dx8SEhIQFvb28+/PBDPvzwQ0tU/59DIhEIvXSEzEfG0Hr8OAA2nbyE09F9wLjy+1Ry89cnCGUuo6JZeD4c0pR+Tb3KZ0elTEKR1kDER9vJ1xjKvbgUt5AXWCybNEUoN4YBylMhXeOtgY15tI0fqfnm4JFfbDPnjhMw5/u69nK5ml5ARFDV6XnHdQnmtZWnQTAnaBABuRT0RpBWExyxKDsXI+CfGsXU1H/sBzRZ2ahCGrD6RCKx2SUVyuiNIvN2R9G3sSct/uVeW1exyAz+wQcfkJxsNnAIDg7m1KlTlqj2P41UpST6SiIpeRoupeRTnJ6FnX3FGSegbAYSRXPI42si9ulfl3l4/n6WHYoDINjNBo3eiEQQ6BPuQWq+FjulFEd17bOLFJZZofVv4sGk7g3oHGI+WjNUon1r5GVPz0YeeDmqymfdziEudA5xpbGnWUGYUlC9w0iPy/tZv+8zftv0Pi+dWoXcqOdaLsDK2rzG1aRspECJTMlJjzC0UvNYo8vCXm06mwJQrh9wszV/Loow6ZcTN30OdQWLCLi7uzsXL14EzFrPu5FLu77T4IVJdD+1jQ2PPs2pEU8QnhlN+9cqxut2KPPp9nRQEuRmW+4x9lLvhjzTJZgPNlxk6/lULqYU4uWgYliEH2qFlIndG1BUaqyxFdn1KGUSJAKcSswnKVfD+WSzMvVm33iXEHM2lQPROaw9lcylMgVhx+CqZ+/CnTtJmzWL1B4P8n2zwbRPv8SHyTsIK3s5VOeoknomstxZxsNeWe40k3ba/Du9psAwmET8ndV42JtXNbYK6S0rIO9HLLJEHzZsGOPGjWPq1KkIgsCAAQOQyaqu+k4kIKxv+D/2CHluznRftwGJUkHA03NQN21a4Z4SnQmpBDIKSkm/biZsF+xCmwBnziXns+NSBv2aeJKn0fFUpyD8XWz441QyEsGcKqm2BLralhvV7L6cgX2Z44iPU9XJEfZczmDn5YwKmUwBfBxVtC+LzVYZmdt2csK/Bd/YtiUtQIvMZGTsxW3EN3gImUQoN82tjHypEgHYHNwJCSKywPYMjT1ArtS8bRnUwoe9V8w+8om5GsB8OlCsM5Yr8eoDFhHwb775ht69e3Pp0iVmzJjBsGHD8POrmZeQlapx6tUTp149q/zcUS3DaIIuIa682i+MMT8codRgws1WgcFoIjFXQxMve5YciMVgNNHjs9008rTnSkYRU/s3wqGGSQ2jMorYdzUTW4WMLqFuqGQSMsuW3CU6I45qOc62lde1+ngSr68+U+GozFYhpYWfI/NGt6k2kcKFXD1uJbnseqUrrT7eiVdJNiUyJSaTCZP4j8KvUvoPpHDzrwyP+ptMG2fcS3LIU9hi188czvuh5t588OeFCsrJa3w9olUNnkrdwCICLggCw4YNA2Dnzp1MnTqV8PBwS1RtpRryNQbkUoHj8TkMX3io3Injpd9PoTOKpOSVEJ9dXCGe+uW0QuaOasWgljXTFO+/msX4pcfwcVJToNEj3SagNZjo29idJj6OHI3N4XBMDmcS8yqdjedsuYS7vZJ9b/Rk+uqzrD+TSqnBxNG4XJYdiue1fmFVtn0qoh+Dj+0hbdQIPi2E0JQrfNF2NDZKGfkaAy0rscK7hpeHE+P7vclbx37BqySHkx5hzGn3BN85mqPNbL+YjiiKNPVxICVXg9EkUlBq4Jfx7el0XXLGuo7FTVV3795d/m9RFElNTcXDw6PaJbuViiTmlJBRWEpDD7tqFWHejir0RpEfn25HoKsNZxLzeG3lGRp52eNmp6Sxlz0vLT9NI0973h/ShNXHk1h7KpnDMdk1FvAPN15kWIQfHw9tRmGpge7/M3+/Hwxpjq+z2vxy+fYQafmVK8s0eiMt/Ry5kFLIn2dTCXG3JbtYxyePtmDyLycY1d4f7yos0ppGNGafZxP6Xj5KOBDn6E1UYFNa+TuTXVTK1YzKDX0AUgu0FCrt+LTvC0ikEgwGEyWlRrKLzf1Mydeg0ZvQG4242yuJzjJHxbkVvcT9zB2Rus2bN/PBBx9w+vRpjEYjR48epU2bNkycOJHu3buXR32xciNfbLvMvF3mYx0HlYxFT7YtTzrwb5r5OjKuSxDPLjuOt6OKlDwN0waYY4cDrDxuNup4tlswnUPcaOrtwNpTySTkVp/j7Hoyi0rpEOyCIAg4qOSEe9lzOCaHRxYcoEeYO9summ3Om/tWPJMWRZH1p1OwUUg5FJPDN7uuIBUEojOL6R7mRqeyMWUV6qoU8N6xR0hKOcM3bR4jX6rmmavbWZa/G98nv2Lg3H3lUWEqQ1u29C7SGXG3l1NQtoopKUuFrDOYEIGr6cUVzuWnrzmLm72ySjfbuobFDV2WLVvGkCFDCA8PZ9GiRZiuO8oIDQ1l8eLFlm6y3nAwOosFe6L5/sm2XJw1gEEtfXjxt5MV7Lz/zYxBTfhlfAde7NWQVZM7lws3QLeypeaba88x+ZfjdCubfR9oWnNjo7aBzny7J5pTCblsPJvCqYQ8mvk6kFuiY1dkBkWlBka3DyDYvaJd/CdbI5m+5iwt/JyQSgR2X87CYBJxtpHzWt8wPtp0EWcbOcHVxEUvOXECtYc7L51cxbvHliEr1ZB98AgPzz9AXomeUe2rjuXWNsgc7cVogvwSXblyr5W/+Xp2WVipfz/Zvo09WX3iRmeauorFZ/CPP/6YadOmMWfOHIxGI+PG/WOY0bRpUz7//HNLN1lvuJxWSLCbbXmCg6c7B/HbkQSyi0rxqMIrTBAEOoW40inkxlne28mGxyP8WHkiia1l3l1NfRxqHOQQYM6jzXnu15M8suAgEgEmdg/h9f5h/HkmhbisYhp5OTCwecUXRlGpgUV7Y1gwug0PNvcmp6iUHp/vYVALbzacSWHogoN42Cv54am2FSLU/BtDRgb65GT8vluIIjAQ42MjMer0dA9148nOQdWauTqpzZ5mAqC7TnV/rb20Ks7fdQZTuTlufcDiAh4fH0+/fv0q/UylUlFQcP8kf7/fCHKzJT67mONxOUQEOrP2ZDL2Kll5aqCasPFsClvOpaGUS3imSzCfDm9B78aenE/Ox9dZzfAIP6TVOH/8G1c7JSsndSK/RI9SLkEll1Kg1XM5rZC47GIQBPo09qgQo1yjMyKK/xjiONsqcFTLaerjyIcPNyNPo8fFRlHu8FEVUicnJDY2pL75FhI7O6TaEuQ2Nrw18OaZQFPzzWmdEMHZRk52kQ4Rc7SbAFcbMgsr36bsvZrJLxM61Pj53O9YXMD9/f05deoUvXv3vuGz48eP07Dh7UXgqM/0DHPn8bb+DF94CKVMgiDAN6PaIK+hSemvR+KZsf4CQ1v5kllUyvCFB1n7XBceaOZ12w4U187MdQYTT/xwhDyNnm6hbqw8lsiphFyWjmtfLrBudgpa+jny1tpzPNcjhP1RWWQVldI5xBWZVFLjrKDKoCA0p0/j8tRTIBEo3rcfY35ejcpKBAGTCMMjfJnSO4zZWy6x9XwairLsEBpd5Wfocx5pRucQqxa9SsaPH88HH3yAp6cnQ4cOBcwKl507d/K///2PGTNmWLrJeoMgCHw0tBkj2wWQXqCliY9DjRLzXWPxvlhe7hPKlD6hiKLIqO8P89vReD4a2rxW/fjtSALf7LqKpiwyzPuDm5bP0MfjcjifUsCht3rjYa/i6c6F9P1yL1cyCgn3csBkErmaUcSrfUOZsyWSl1ecxs1WweKn2tHAvXZhjl2eGUfR33+T+X//h0RpTqkc8MMPNSorAlIJbDmXxuoTyeWZXPRl+gyVvPKXZkPPas7W6yAWF/Dp06eTmJjIU089hVRq/lF07twZo9HIpEmTmDJliqWbrFfsvpzBjPUXyCgoZUxpNGOKI1HbqnEZOwZ1q1bVltUZTeXmq9e03npD1Qq6yth2IY13/zjH1P6N8HZU8cmWSCSCwMePNC9vQyKAvdI8o18zljEYRTQ6I88uO16eRUUiQCNPey6nF3IuOZ8utTxfltrZEfjbrxTv349Jo8WmfTvkHv9otxNzSvhgw0Vis4oI87Tn/cFNy/flTXwcUMqkDGvjR/dG7my7kM7W86mElsVS7xDsypmkAlRyCS42clLKjvlCavkSut8RxFtxCq4B0dHR7Ny5k6ysLFxcXOjduzdhYVUbNdzvXLOvv0OPC4CojEIGfr2fJzoF0jf+KPbffMbJRh3p5aOkaN9+gn5eVq2Qf/ZXJMsOxfNCr4ZkFJTy08FYlj3Tga6hNResN9ecJa9Ez8InzIEWVxxL4P+2X+Xw22YLsEKtnge+2keAiw0Dm3ux+kQSWr2JDS915asdV/jjVDKTe4Tw0aaLBLra0sTHge6h7kxbfYbT7/evsfXczSguNfDA13vxdlAzsLkX606nUFJqYOOUrihl5onl7yuZTPn9JPkaAy62ChaMaUOHYBdis4op1BoYt+QoOdcZAQ1u6c28UW0s0r/7BYtnF3V0dGTFihUMHTqUkBBr9sfacDA6G28nFe8+1JjYR99B2yCEsJgzFGeqUPj7k7t8xQ0CrjlzhpS330GXkMCw0FCchj7HqpNJqOVS5o9uUyvhBrBVyriQUoDBaEImlZCQU4Kt8h8Fmr1Kzm/PduCDDRdZciCORl72zBzSFIVMQmRaIQOaeaGUS3C3VzGinT8rjyeikEowidDlk1081SmI1/qF3VTBdjNOJeSRkqdly8vdsVPKGNTSh7Yf7eBSamF5kMjUPA2FWrP3W75GT1JuCd/vjWFnpNkXooGbLXZqGYUaA819HflsWIvb6tP9iEUFXKVSWa3WbgN7lYycYh3ZxTr0iUlQVESuvRcucgFdTAwSdcX9uCE7m4SJk7Dr0R3PN6aRu3wFPZfMZvymjUhsqnb+qI6nOwex9mQSg785gIe9kn1XM/m/f9lmB7ra8uPT7W4oG+hqw94rmQxs5kVavoYlB+IAWHcqGRdbOVP7NeLjzZdwsVXcEH7qZmh0RmZtvMieyxnYq2QMbuGNSRQpKTWYM6WUCfI1m/f47GLeWnsOpVxCkKstMZnFTFt1FkcbOY9F+KEzmth4JhVHGzlPdAxkxfFEXl15hm+vCxFdH7C4JE6aNIm5c+cyYMAA5HLLLMf+KzzYzJsf98fx0Nx9LC4pAQR03fvg6C4n56ef0KenVbhfc/YsolaLz+zZCDIZ6latuNKhI6XR0aib106xdg0vXQErrv6G9uxZNI4uvPDKG7SvYYSTV/qEcSQmh1HfH0EQBFLyNYhlx1RLn2lPqIc9h2OzWXk8kU4hrjT2rnl44nf/OM/B6Cxe7hNKdGYRX26/SoiHHY99d4geYe7svJRBpwb/1PnXhTREQKc3kV1Uiq4sFnuhRs+ltAI0OiNGUUQuFXitfyM6N3Rj5KLD5JXoKs0EU1exuIDn5eVx/vx5goKC6NOnzw3ZRQVB4NNPP7V0s/UClVzK8okd+e1IAvwqwejlQfOYk5QkyZE4OiJRVTTskDo4IOp06BISUTYIpjTaHIdMYndriiLRZCLxuedRKBR4ffYJxfsPkP/hm+jahqMIuLlxjKONnLXPd+ZYXA4Go0ibACcm/XyCxNwSBs87UH6fXCrw0Nx9zBjUhKe71Gwm33o+lU+Ht2BQCx8AziXnE+7lgFImISarmKGtfXihV8PyM/5irdkk1QRkFv3j3y0IAkueasfWC2m8/+eFctPV+orFBXzNmjUoleZzzn379t3wuVXAqycqo4jtl9Kx921Mi4RzmNzdEAqLMOXn4zr+mQr3qlu3xq5HD+JHjULVsgWa4ydwfOQRFEFBt9S2ISOD0kuXaLDhT5Shodj360fR3r2UHD1aIwEH80uqW6g5/vjOS+lkFZeSnKfF10lVnuRgRDt/Wvg68ebaswxs4Y1HDXKTqeTScp93vdFETrEOB7W8Sm80QfhHGSovy2lu/reELp/uRiIxB4PQGU18uf0KK44l8GAzr3o1e4M1u+h9RVJuCWN/OEL3MHf0M+Zw6qP3aBp5CbVKjuvkybhNnFjhfkEiwW/eXPLWrEEXF4/DAw/iOPThW46oc22Pr0tMRBkaijEnB1Nh4S2tCObtvMrXO6+ilEtRySUUlxqRS+CRNn7EZ5cwbUA4b6w5S1q+tkYC/nyvhszZfIlTCbnEZ5eQXaRjZDv/Ku+/FgbZWS1FaxCxVUjJ0xgo1RsZ2T4ApVzCmhNJuNkpORSdxdBWvrxajetqXcViAq7RaNi8eTNxcXF4e3uXL8+t1Jw9lzNxUMuZO6o1UonAyUXzGbrgIKfe61eluaogl+M8cmT530X7D1C4fTsSlQrnUSNrNZtLHR1xefppkl99DduuXdFeuIAiKAi7nj1rNY4SnYGvdl7l88dasPFMKqV6IycT8zCY4HBMDk18HJi/OwpbhZRA16qdTa5nfNdgvB1V7I7MoF2QC+O6BFVrBNQ+2IXv9sZQVGpCbxLLs6mM7RTIlvNp6I0mhrb25d2HmtTLnGTXsIiAx8TE0LdvX+Li4sqvOTg4sHLlSvr372+JJv4TKGQStHojWr0RW6UMzYEDvHJyFYWfHEU9djSqRo2qLZ+/YSMp06djExGBsSCfvLVrCVq5AmVwzTXWHtPfQNWkMZrTZ1C3bInLmNE37P1vRlGpAaNJpLG3A408HXj8u4PoDCbUcvOxW0JOCXZKGd+MaVOrwI8Dm3szsLl3je7tHe5BuyBnjsXlAuaIqT3C3PhgSFNmPdysVuOpy1jE0GX48OGcPn2apUuXEhERQWxsLM8//zxxcXH1Zsl+Nwxd8jV6Bs3bh61CxtC8S3Rb/jVpDZsTYiegjbxM0O+/oWpctaNF1IAHMObnY8rLM1+QSJC5uxO0amUFC7BrGPPzKdqzB9EkYtetKzI3y9hgm0wiA+fuw0EtZ0rvUDadS2HFsUQeae3Ls90aIAgCvs7qaj3JLNWPlccTuZRaQAt/Jx5t7fufCwhqEQH39fXliy++YOR1S8UrV67QuHFjkpKS8Pau2Vv3fuZuCDhARqGWBbuj6fP5qzhoi1AUmGcgqasrdt274TNnTpVlI1u2QubhgaBUoov6Jxa4oFIRumc30uvywulTU4kfMxZTcTGU2S0E/rwMZYMGt9X/nF9/I+enJRhK9ewIbMeXPt2RSKXlS+SIQGeWjGtnMYs2K9Vjkc1HamoqDf71wwgJCUEURdLS0qooZaUyPOxVzBzSFI+SHNRKGQ02bybw998xFRaivXip+sISCfrsbHRRUcR4hyIC0S7+mLRaMhd8W+HWzHnfIHF1xe+7hfjPn48yNJSMz7+4rb7nrVtH+kcfoU9MQsxIp8+JzXyYe5CGHnYcebsP+6f3IqdYx5fbKs81djc4FpfDC7+d5Nllx9lYFhv9ekRR5KsdV4j4cDstP9jGrA0XMRirjt56v2Mx7cJ/belzpxElUkpz83nz7e/530dLMen0CKrq3SyVoaGgN9tWB6deRVQoKG3dARMCeZk5Fe7VJydjzMggfuQo4kaORBcTgz4pCX16OgnjJ3C5XXuiBw2i+MjRGvc5e9H3CEolQWtW02DLZqTOzoQf2c7glj7lWVMHNPUiqppYaneS43E5jFp0GKNRxMVGwcvLT7PyWGKFe345HM/Cv6N5tV8Y7z7UmDUnk8pDaNVFLLYJqioWep8+fW64bo2LfnNS3QMQ0tMYl3IIoyAlV2lHlk8o1anLpM7OoNcjYo5kIuh0ND+1CwMiV1r14Po4tyatFkNmJj5ffI7E1o6kl15CsLEh8bnnEORyvD/8kOID+0mcPJkGf65H4V/1kVR5ncXFSF2c0cXHg9GE3McHeeRltl1IY2zHQAxGE7si02lfTbKDO8lvRxLo2ciDr0a2wmAScbNX8NPBOB6/7rhtV2QGI9sFMLZjIADpBVq2XUyvs0doFhHw999/3xLVWLmOH1oO5a1NXyDNKkUOGOxd+L1Zf7pXU6b08mUc3pjO55svMj56J1JNCcbcXH5sOZQ+bSraWEts1MgDAkiZ+joA8sBABKmU0ovXGboM6E/Rvv2UHDlSIwFXNm1K8e7dpLw+DSQSMBhQNgihsNRAmw+3I4oi4V4OTO1X/WnAnUJvNBGVUUTjGVsRRQhytSFfo+fVFadpE+jMmPYB2KnkxGYVYzKJCALEZBXfcWXgncQq4PcZhVo9yw7F0zD2HCaDAcc+vTFpSzHt20eDzPhqy0qdnSndvJFRmfnEy51wlio56N+ahF5D6NO4ok2CMjgYQ2YmwRs3IEilpLz5FnIPd3TR0egSk8yGLrm5ZkMX25qdVUsdHMwpgUwm83+A0smBZ7oE8/OhOARBYEK34FqFoLIkdioZcdnF9GrkjoNKxvozqShlAoIAn2y+xNX0Qp7rEcKwbw/Q9//+Ri4RiMksZtn4uhvCqe6+muohGp2Rx787TIFGz8fxx/g1rC+7vQZir5IxNr6I/imngCeqLC/38aFo507sgWu2Z81aNeTF8e1vMOZwe+klSsY9Q+yQh0EiQeHri+fXXyH38SH5tdew69YVzfkLKAIDsevVq2b9P3MGFArcn38eQS4ja9H3FEZeZeafFxjXJQi9UeT1VWdQy6U8WMPz7JthMokUag04qGU31QPllejp2tCNAq2BqIwi5BIBb0c1Xz7eih3N0pmw7DgTuzfAz9mGqxlFCICPk5pgt5q94O5HrAJ+H7HlfCrJuSX8Pa0XuYcd6BnswIrCUka296frJXts1dXPfEV/7yn/97WfeuDviyhwlKB65eUK98qcnQlasRzNyZOIJhM2rVsjsbHBfdo0DDm5aM+dQ+7ri++nn9TY0EXArGy1adcWbXQ0psJCpEYji88vpfOUr5F7eVFqMLLqRNItCbix7CRBam+HsnFjDkRl8/LyU2QX6/B0UPLN6Da0qyYVsZudksTcElZP7szK44nM2RxZbibr4WBWYM7bFYVJFDn2Tl+UcglPLj7Kh5suMn903QwEYRXw+4iiUgNONgqcbOQwehSBM2cxxT+Ohwvt0R0+iNOypTeUEUWRkiNHzYotQ+WeUdkLF2LbuRO27dtXuC5RKrHt1KnCteSZH5CzbQexniH4XI5GM248AStXEZlvwFYppZGnfZUzpaptBLr1fxI/eoz5giCQ5uKLvFRL4uTnCF67Bp1B5FZiPWgjI4kb/yxitjkclLxPP55zfoBh7QMZ1saPnw/HMWHpcfZO61VlUsXne4Yw4Ku9NJmxFQCTCEq5hJXHE/lhXwztg11IzdPQq5EHeSU6DCaRAU092Xg2tfYdvk+wCvh9RJeGbny08SLPLjuOn3MTSns+RdvYEyhljnj/tASbNjfOIulz5pD762/I/21MJJGU74MB8v9YX0HARVEke+FCcn79DUwmHB8Zisu4cRStXo0gSGiUcN58X2EuX036gO+Dzcv0AU09+WZ05ZFelb6+YDCYDWcMBhBFbJo1Zbq8JQt3fsbMH3axLraUH55qW+tnE/XaGxxT+7Dg8ek4Z6fxyb4F9GjqyIxZDyKRCMx6uBkrjydxNaOQtlXM4odjsynRGRnQ1Au5VMKuyAwi0wqJyrhCmwBnPhrajK92XOGXw/H8sN9sgamSS+p0lpN6IeDV7b0CAwMr2MjfzwS72tIm0JkdlzLMS2yHJjSZ8Rh+nYIqvV9z4QK5y34mYOlSbDu051L4dWasZcIt2NsjFhZiyMysUDZv+XKyvluEx7TXERQKMv73GcVas9+0TDQhsbPDVFICJhMDz2/jxZ/mkJKnYfT3h/n5UHylEVk0Z8/hOHw4ErWavOXLcR4zBuXBg8x8pAfshCyjhMVPtaNXeO0FxhQfS+GoVzny9kOk5Gv4e9gWfArSOZ2UR5sAZ47Fmc/5XasJybzjUgaPtPbls8daAvDDvhiWH0tkx2s9yu+5Fm75GqJovlZXqRcCfujQITp16sTUqVMZPnw4APPmzWPXrl2sW7fuHveu5uy4lM6phDw2T+lW7nE1Z0skI9oFVOrxZEhPR1CpsGlvDp+kbtUKzenTFe4RCwvNn7VpXeF64e7dOA0fjssY83LakJFB9mbz0hVBwPOtt8jbsoWS/ftRGvU4quU4quV0bujG1YzCSvsvdXJCFxuD/3ffUXLiBLm//45Ercb7qw9xevIJ5j93Y6z8mpJk50HX6COgH4NbRhJNsuO42q4VoxYdpqGHHVfSC3mmS3C1CjE7hYz4HPMRmEQikJhTgu2/jsCis4p5olMAQ1v5YRRFDkZlselc3V2i1ws/uY4dOwIQFBREx44d6dixI97e3iiVSlq3bn2T0mYEQaj2v7tBcp4GfxcbmviYww71a+JJic5IXomu0vuVYY3AaCRr/gJ0iYkoKotaKwhI7O1xfvzxCpcltrboExPLbev1CYkobMqUaaJI7CefEXXZbOVlFMxebgnZJRyJySGoChdPt0kT0V2NIm7YcLPBjcGATdsIvN5/H8+33rqVR1LOpoETMJ45zeVWrYl9+GFOuTWk+cQnmDuqNYNb+vDdExG8N6j6jCfjuwVzPrmAoQsOMPaHI/x8OJ6XrsvlBhDoYsO+q1n4OasJ9bBjV2QGAS63Ft/ufqDOzeCiKGI0/qNMEgShPP56Xae5ryMxmUWsPpFEl4auzN8dhbejqsplp8LPF5/PPiPl7bfJ+uYbJI6OBPy4GEXDhmR9uxDNmTPIfXzwfH0qMteKuctcJ0wgfuwTxI8chaBQUHL6NP4LF5L04ouIGg2qwjx8CvMAWN+sPz+9/xcGk0i3UDee7hJUeX+Cgghet5b8jZvAaMC+Xz+UFspkM+2FITwvc0QWc5ViuYrOAzozq11grV6+YZ72/PliF1YcS0RnNPFK39Ab9uuv9gtj1KLDtJ+9A0EQ8HRQ8tXIVhYZw73gjsVFv1Ps2bOHXtedy/bo0YM9e/YgCALz5s3jxRdfBOD1119n9erVdWb/fY1Fe6P5ZEskJhHc7ZX88GRbWpaFAa4Kk0aDITsbmYcHEkXNjUhKr14lb90fYDTiMGgQ6ubN0Fy4QMIzz2DKLwBBwOXZCagmv8TppDxsFVJaBzjXKreZJdEbTcRnF2OvkuNZRTJGS6DVGzkSm4PJJNI2yBn7Ouz5Vudm8IiICI4dO1b+t7393U81c7eW7PFAq3fvSlPwdhVL6KlTzf9ZuS3u1Txa5wTc3t6etm1vfswikdQL9YIVK7dFnRPwmuLi4kJqaipbt26ld+/eKGqxdK0pdWx3A9y9wBWWpq73+15Rb6e5IUOG4O7uzoMPPkhOTs7NC1ixUg+pc0q2+4G6OptA3e27td+3Rr2dwa1YsWIVcCtW6jVWAbdipR5jFXArVuoxVgG3YqUeY9WiW7FSj7HO4Fas1GOsAm7FSj3GKuBWrNRjrAJuxUo9xirgVqzUY6wCbsVKPcYq4Fas1GOsAm7FSj3GKuBWrNRjrAJ+l+jRowctW7akefPmDB8+nIKCgnvdpRqRmJhInz59aNy4MU2bNuWNN96oMz7Zzz33HL6+vvc8qkp1nD9/njZt2hAaGsqQIUMoLKw85vytYhXwu8Sff/7JmTNnOHfuHH5+fnz22Wf3uks1QiaT8emnn3Lp0iVOnTrFoUOHWLt27b3uVo0YNWoUJ0+evNfdqJbJkyfz0UcfcfXqVcLDw/nf//5n0fr/swIeFRXFpEmTaNGiBVKplJ49e1Z638WLF+nTpw82Njb4+PgwY8aMCnHZa4qjoyMAJpMJjUZzW7PK3ey7t7d3eZBLhUJB69atSUpKuu/7DdC9e3c8PT1vfmMtsdQ40tPTiY2NZeDAgQCMHz+eNWvWWLSv9Tbo4s24cOECmzdvpmPHjuj1+krvyc3NpW/fvjRp0oT169cTHR3N1KlTMZlMfPTRR7Vuc+DAgRw7doymTZvyxRdf1Km+A+Tk5PDHH3+wbdu2OtVvS2OpcSQlJeHn51deJiAggMTERMt2VvyPYjQay/89bNgwsUePHjfcM3v2bNHJyUnMz88vv/bpp5+KarW6wrX+/fuLTZs2veG/559//oY6DQaDOG3aNPHTTz+tU30vLS0Ve/XqJX7++ed1qt+iKIqW/plbahzHjh0T27dvX/55SUmJaGdnZ9G+/mcF/Hqq+pK6desmjhgxosK1+Ph4ERD//PPPW27v/PnzYtOmTW+5/PXcjb4bDAZx2LBh4quvvno7Xa3A3Xzmd3Ieu51xpKamij4+PuWfR0ZGiuHh4Rbt3392D14TIiMjCQ8Pr3AtICAAGxsbIiMja1xPbm4u6enp5X+vWbOGZs2aWayflWGpvgNMmjQJe3v729pW1BRL9vteUpNxeHl5ERQUxObNmwFYvHgxjz76qEX7YRXwasjNzcXJyemG687OzuTm5taqnkGDBtGiRQuaN2/O+fPn+frrry3Y08rbtETfDxw4wOLFizl+/DitW7emVatWzJ0714I9rYil+g0wYcKE8j2un58fEyZMsEQXa0RNx/Htt9/yzjvvEBoaysWLF3njjTcs2o//rJLtbtKgQYMK+dTqEl26dKkz597/5ocffrjXXbgpLVq04NSpU3esfusMXg3Ozs7k5+ffcD03NxdnZ+d70KOaU1f7Xlf7/W/ul3FYBbwawsPDb9j3JSYmUlJScsP+6n6jrva9rvb739wv47AKeDU8+OCD/PXXXxXMB1esWIFaraZHjx73sGc3p672va72+9/cN+OwqE6+DlFcXCyuWrVKXLVqldixY0exSZMm5X8XFxeLoiiKOTk5opeXl9i3b19x+/bt4nfffSfa2tqK77zzjrXv/6F+/5u6NI7/rIDHxsaKQKX/xcbGlt934cIFsVevXqJKpRK9vLzEd999VzQYDPeu42Ld7Xtd7fe/qUvjsMZFt2KlHmPdg1uxUo+xCrgVK/UYq4BbsVKPsQq4FSv1GKuAW7FSj7EKuBUr9RirgFuxUo+xCrgVK/UYq4BbsVKPsQq4FSv1GKuAW7FSj7EKuBUr9RirgFuxUo+xCrgVK/UYq4BbsXIXudsJEa3+4Fas3EX27t1Lo0aN8PLyuivRav+TM/jMmTNxc3O71924a6xcuZKffvqpwrWnn366PKlgfcKS4xJFkVatWrF06VKL1Ac3T4j44osvMn78eIu1958U8P8alQn4e++9d8M1KxVZuXIlOTk5jB49+q61+frrr/Prr78SFRVlkfqsiQ/uA4xGI0ajEYVCcdfaDAkJuWtt1VXmzp3LE088gVwur3GZAQMGkJycfMP1Hj16MH/+/JuWDwoKomvXrnz77beWSRV1VyPA3Se8//77oqura/nfTz31lBgRESFu27ZNbN68uWhjYyN26dJFPH/+vCiKorhkyRJRLpeLubm5Feo5f/68CIjbt28vv7Z3716xe/fuolqtFl1cXMQJEyaIBQUFFcpda2/dunVikyZNRJlMJu7du1c8f/68OGDAANHZ2Vm0sbERw8PDxW+++aZC2ZrU/++2+FdgwPfff7+8D5X1a+PGjWLjxo1FtVotDhw4UMzOzhavXr0q9uzZU7SxsREjIiLEM2fO1KpfNRnb33//Lfbs2VO0tbUVHRwcxB49eognT54URVEUDx48KA4ePFj08vISbWxsxJYtW4q//PJLpeP997hq+8xEURSvXr0qAuXt12YcNaE60VuwYIHo6elZIYvprWJdopeRkJDAtGnTeOedd/j999/JyMhgxIgRiKLI0KFDEQSBdevWVSizYsUKPD096dWrF2DO49W3b1+8vLxYvXo1X331FZs3b2bcuHE3tBcXF8cbb7zBW2+9xZYtWwgODmbw4MFIpVJ++eUX/vzzT1566aUKcbVrU/813nvvPXr16kXr1q05dOgQhw4dqjZHV0JCAjNmzOCjjz5i0aJFHDx4kIkTJzJy5EhGjhzJ6tWrMRgMjBw5slxJVJN+3Wxse/bsoU+fPsjlcpYuXcqKFSvo1q1b+WwYHx9Ply5dWLx4MRs2bGDYsGGMGzeO33//vbqv9ZaeGcDOnTuxtbWlZcuWFa7fbByWoHPnzqSnp3Pu3Lnbr+y2XxF1kMpmcKlUKl65cqX82rp160RAvHTpkiiKojhkyBBxwIABFeoJCwsTX3jhhfK/u3btKvbs2bPCPTt37hQB8dy5cxXaA8RTp06VX8vMzBQB8ezZs1X2u6b1/5vKUtxWNYNLpVIxKiqq/Nq0adNEQFy6dGn5tU2bNomAePHixRr1qyZj69ixoxgRESGaTKYq77mGyWQS9Xq9OHHiRLFXr17VjutWn9mzzz4rtm3btsK1mozjZowfP1709fUVAdHX11ccP378Dffo9XpRKpWKixYtuuV2rmGdwcsICgoiNDS0/O8mTZoAkJSUBMCIESPYuXMn2dnZAJw+fZorV64wYsQIAEpKSjh06BCPP/44BoOh/L+uXbsil8s5ceJEhfZ8fX1p1apV+d8uLi74+/szefJkVqxYQUZGRoX7a1v/7TyH6/fnDRs2BKB37943XEtOTq5Rv242tuLiYo4cOcJTTz1V5flwbm4uU6ZMITAwELlcjlwuZ9GiRVy5cqXKsdzOM0tLS7vhpOVm46gJP/zwA0lJSYiiSFJSUqUJEmUyGU5OTqSlpdW6/n9jFfAy/p3q9ZrCS6vVAjBkyBDkcjlr1qwBzMtzPz8/unbtCph/gEajkeeff778ByiXy1Eqlej1ehITEyvU/++jEolEwrZt2/Dy8uKZZ57By8uLbt26lWeerG39ln4O11+//tnUpF81GZsoinh7e1fZr6effpoVK1Ywbdo0tm3bxrFjx3jmmWfKv5/KuJ1nptVqUSqVFa7dbByWRKlUVju2mmLVotcQOzs7HnroIVasWMHEiRNZuXIljz32WPmM4+TkhCAIzJw5k4EDB95Q3sfHp8Lflc1U4eHhrFmzBr1ez759+5g+fToPPfQQSUlJta7/blHTflU3NmdnZyQSCampqZW2odVq2bhxI/Pnz2fy5Mnl100mk0X6VhkuLi6VzqDVjUMisdx8mZeXh4uLy23XYxXwWjBy5EhGjBjBhg0biImJYeTIkeWf2dra0rFjRy5fvsyMGTNuqx25XE7v3r157bXXGD16dPmXfav1KxQKi8wGlVHbcVc1tg4dOrBs2TJefPHFG15+paWlmEymCjNqYWEhf/75Z7Umn7fznTRq1IhDhw7VehyWIDMzk5KSEsLCwm67LquA14KBAwdiY2PDpEmTCA4Opn379hU+/9///kefPn2QSCQMHz4ce3t7EhIS2LRpEx9//HG1X9jZs2d5/fXXGTFiBA0aNCA3N5dPP/2Uli1blv9wbrX+8PBw1q9fzx9//IGfn5/FZ/ub9Uur1d50bJ988gl9+/blwQcfZOLEidja2nLo0CHatm3LoEGDaNeuHbNmzcLBwQGJRMInn3yCo6MjBQUFt9W3qp5Zly5dmDVrFpmZmbi7uwM1+44swfHjxxEEgc6dO99+ZbetpquDVHUOfj3XEsxt2LChwvUxY8aIgPjmm29WWvfhw4fFAQMGiPb29qKNjY3YuHFj8dVXXxXz8vKqbS89PV0cO3asGBwcLCqVStHT01McOXKkGB8fX+v6/01mZqY4dOhQ0dnZuUbn4NezZMkSERALCwurfTbV9aumY9uzZ4/YrVs3Ua1Wi46OjmLPnj3LTxquXr0q9u7dW7SxsRH9/f3FTz/99Ibvsaox3MozKy0tFV1cXMRly5aVX6vpOG6XKVOm3KD5v1WsziZWrFTByy+/TFRUFJs2bbprbRqNRgIDA/nkk08YO3bsbddn1aJbsVIF06ZNY/fu3dUexVmaVatWoVarK+h3bgergFuxUgV+fn78+OOPVWr37wSiKLJ48WJkMsuox6xLdCtW6jHWGdyKlXqMVcCtWKnHWAXcipV6jFXArVipx1gF3IqVeoxVwK1YqcdYBdyKlXqMVcCtWKnHWAXcipV6jFXArVipx1gF3IqVeoxVwK1YqcdYBdyKlXqMVcCtWKnHWAXcipV6jFXArVipx1gF3IqVeoxVwK1YqcdYBdyKlXrM/wMhnuZimyjCTQAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make phase-frequency scatter plot\n", "fig, ax = plt.subplots()\n", "ax = phase_coupling_scatter(df_dark, ax=ax)\n", "\n", "set_plotsize(w=2, h=2)\n", "clip_axes_to_ticks(ax=ax)\n", "fig.savefig(FIGUREPATH + 'phase_coupling_scatter_dark' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "a5d7cfa7-dbce-43e1-a33c-7d3393e15a79", "metadata": {}, "source": [ "#### Figure S3A2\n", "Burst-tonic phase difference distribution for phase coupling measured in darkness." ] }, { "cell_type": "code", "execution_count": 41, "id": "90781f30-ed62-4f54-a09f-fc5bccab368e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dark mean: 2.522758801418326\n", "V 2.58e+01, p 5.12e-05\n", "N = 88\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bins = np.linspace(0, 2 * np.pi, 9)\n", "fig, ax = plt.subplots(subplot_kw={'polar':True})\n", "\n", "# Plot the circular distribution for burst-tonic spike phase differences with the display monitor on\n", "df_sig_light = df.query('(tonicspk_sig == True) & (burst_sig == True)')\n", "phase_diff_light = (df_sig_light['tonicspk_phase'] - df_sig_light['burst_phase']) % (2 * np.pi)\n", "ax, counts = plot_circhist(phase_diff_light, bins=bins, ax=ax, color='gray', ls='--')\n", "\n", "# Plot the circular distribution for burst-tonic spike phase differences in darkness\n", "df_sig_dark = df_dark.query('(tonicspk_sig == True) & (burst_sig == True)')\n", "phase_diff_dark = (df_sig_dark['tonicspk_phase'] - df_sig_dark['burst_phase']) % (2 * np.pi)\n", "ax, counts = plot_circhist(phase_diff_dark, bins=bins, ax=ax, color='black')\n", "\n", "# V-test for non-uniformity and mean of pi\n", "p, v = vtest(phase_diff_dark, np.pi)\n", "print(f'Dark mean: {circmean_angle(phase_diff_dark)}')\n", "print(f\"V {v:.2e}, p {p:.2e}\")\n", "print(f\"N = {len(phase_diff_dark)}\")\n", "\n", "# Format axes\n", "ax.set_xlabel('$\\Delta$ Phase preference' + '\\nTonic spike - burst')\n", "ax.set_yticks([0.25, 0.5])\n", "ax.set_yticklabels([])\n", "ax.set_ylim(top=0.5)\n", "\n", "set_plotsize(w=1.5, h=1.5)\n", "fig.savefig(FIGUREPATH + 'phase_diff_circhist_dark' + FIGSAVEFORMAT)" ] }, { "cell_type": "code", "execution_count": 43, "id": "c907a05b-ffb1-4312-8e23-ab5b1c563572", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tonicspk light median: 0.0081, N: 682\n", "Tonicspk dark median: 0.0058, N: 321\n", "Light vs dark: U=1.222e+05, p=2.945e-03\n", "Burst light median: 0.0528, N: 320\n", "Burst dark median: 0.0386, N: 100\n", "Light vs dark: U=1.806e+04, p=5.227e-02\n", "Burst vs tonic: W=5.800e+01, p=2.666e-15\n", "N = 88 CPD-neuron pairs\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot median coupling strengths and SE across timescale bins\n", "fig, ax = plt.subplots()\n", "ax = coupling_strength_line_plot(df_dark, agg=np.median, err=se_median, ax=ax)\n", "ax = coupling_strength_line_plot(df, agg=np.median, err=se_median, ax=ax, alpha=0.5)\n", "\n", "# For each spike type compare overall coupling strengths between conditions\n", "for spk_type in ['tonicspk', 'burst']:\n", " strengths_light = df.query(f'{spk_type}_sig == True')[f'{spk_type}_strength']\n", " strengths_dark = df_dark.query(f'{spk_type}_sig == True')[f'{spk_type}_strength']\n", " print(f'{spk_type.capitalize()} light median: {strengths_light.median():.4f}, N: {len(strengths_light)}')\n", " print(f'{spk_type.capitalize()} dark median: {strengths_dark.median():.4f}, N: {len(strengths_dark)}')\n", " U, p = mannwhitneyu(strengths_light, strengths_dark)\n", " print(f\"Light vs dark: U={U:.3e}, p={p:.3e}\")\n", "\n", "# Compare burst and tonic spike strengths for coupling in darkness\n", "df_sig = df_dark.query('(tonicspk_sig == True) & (burst_sig == True)')\n", "W, p = wilcoxon(df_sig['burst_strength'], df_sig['tonicspk_strength'])\n", "print(f\"Burst vs tonic: W={W:.3e}, p={p:.3e}\")\n", "print(f\"N = {len(df_sig)} CPD-neuron pairs\")\n", "\n", "# Format y-axis\n", "yticks = [-4, -2, 0]\n", "ax.set_yticks(yticks)\n", "ax.set_yticklabels(['10$^{%d}$' % tick for tick in yticks])\n", "ax.set_ylim(bottom=-4.1)\n", "\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=3, h=2)\n", "fig.savefig(FIGUREPATH + 'coupling_strength_dark' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "e5f5057f-3685-4214-8fdf-975e40988a08", "metadata": {}, "source": [ "### Figure S3B\n", "Phase coupling measured after removal of phase combinations where IMFs are coupled (from analysis in Figure S1D)" ] }, { "cell_type": "code", "execution_count": 44, "id": "946dc426-f5ee-473e-8269-85aba56ffe0f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: phasetuning_spontaneous.pkl\n", "Loading: phasetuning_sparsenoise.pkl\n", "Loading: phasetuning_spontaneous_desync.pkl\n", "Loading: phasetuning_sparsenoise_desync.pkl\n", "Neurons with significant coupling: 0.986 (146/148)\n", "Tonicspk prop. significant: 0.980 (145/148)\n", "Tonicspk num. CPDs per neuron: 3.80, 1.57\n", "Burst prop. significant: 0.742 (89/120)\n", "Burst num. CPDs per neuron: 2.08, 1.71\n" ] } ], "source": [ "# Load spike data from gray screen and sparsenoise experiments\n", "# 'desync': coupling measured only after removal of periods in which\n", "# the IMF couples to other IMFs in the recording ('desync')\n", "conditions = ['spontaneous', 'sparsenoise']\n", "df = load_data('phasetuning', conditions)\n", "df_desync = load_data('phasetuning', conditions, subsample='desync')\n", "\n", "# Assign significance based on p-value from shuffled permutation test\n", "df['tonicspk_sig'] = df['tonicspk_p'] <= 0.05\n", "df['burst_sig'] = df['burst_p'] <= 0.05\n", "df_desync['tonicspk_sig'] = df_desync['tonicspk_p'] <= 0.05\n", "df_desync['burst_sig'] = df_desync['burst_p'] <= 0.05\n", "\n", "coupling_summary(df_desync)" ] }, { "cell_type": "markdown", "id": "f8a62764-2184-401f-bb3e-464fc8d63532", "metadata": {}, "source": [ "#### Figure S3B1" ] }, { "cell_type": "code", "execution_count": 45, "id": "5dc84041-7656-4717-a304-3304e1a1787a", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Make phase-frequency scatter plot\n", "fig, ax = plt.subplots()\n", "ax = phase_coupling_scatter(df_desync, ax=ax)\n", "set_plotsize(w=2, h=2)\n", "\n", "clip_axes_to_ticks(ax=ax)\n", "fig.savefig(FIGUREPATH + 'phase_coupling_scatter_desync' + FIGSAVEFORMAT)" ] }, { "cell_type": "code", "execution_count": 46, "id": "1741cd06-c1bc-4a3d-8e10-8b7d16452669", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Desync mean: 2.661021493637446\n", "V 1.091e+02, p 0.000e+00\n", "N = 203\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bins = np.linspace(0, 2 * np.pi, 9)\n", "fig, ax = plt.subplots(subplot_kw={'polar':True})\n", "\n", "# Plot the circular distribution for burst-tonic spike phase differences\n", "df_sig = df.query('(tonicspk_sig == True) & (burst_sig == True)')\n", "phase_diff = (df_sig['tonicspk_phase'] - df_sig['burst_phase']) % (2 * np.pi)\n", "ax, counts = plot_circhist(phase_diff, bins=bins, ax=ax, color='gray', ls='--')\n", "\n", "# Plot the circular distribution for burst-tonic spike phase differences in 'desync' periods\n", "df_sig_desync = df_desync.query('(tonicspk_sig == True) & (burst_sig == True)')\n", "phase_diff_desync = (df_sig_desync['tonicspk_phase'] - df_sig_desync['burst_phase']) % (2 * np.pi)\n", "ax, counts = plot_circhist(phase_diff_desync, bins=bins, ax=ax, color='black')\n", "\n", "# V-test for non-uniformity and mean of pi\n", "p, v = vtest(phase_diff_desync, np.pi)\n", "print(f'Desync mean: {circmean_angle(phase_diff_desync)}')\n", "print(f\"V {v:.3e}, p {p:.3e}\")\n", "print(f\"N = {len(phase_diff_desync)}\")\n", "\n", "# Format axes\n", "ax.set_xlabel(r'$\\Delta$ Phase preference' + '\\nTonic spike - burst')\n", "ax.set_yticks([0.25, 0.5])\n", "ax.set_yticklabels([])\n", "\n", "set_plotsize(w=1.5, h=1.5)\n", "fig.savefig(FIGUREPATH + 'phase_diff_circhist_desync' + FIGSAVEFORMAT)" ] }, { "cell_type": "code", "execution_count": 48, "id": "20117da9-7f7e-4923-a29e-d541c198135b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tonicspk median: 0.0081, N: 682\n", "Tonicspk desync median: 0.0089, N: 563\n", "All periods vs desync: U=1.860e+05, p=3.419e-01\n", "Burst median: 0.0528, N: 320\n", "Burst desync median: 0.0583, N: 250\n", "All periods vs desync: U=3.885e+04, p=5.568e-01\n", "Burst vs tonic: W=1.699e+03, p=5.336e-25\n", "N = 203 CPD-neuron pairs\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot median coupling strengths and SE across timescale bins\n", "fig, ax = plt.subplots()\n", "ax = coupling_strength_line_plot(df_desync, agg=np.median, err=se_median, ax=ax)\n", "ax = coupling_strength_line_plot(df, agg=np.median, err=se_median, ax=ax, alpha=0.5)\n", "\n", "# For each spike type compare overall coupling strengths between conditions\n", "for spk_type in ['tonicspk', 'burst']:\n", " strengths = df.query(f'{spk_type}_sig == True')[f'{spk_type}_strength']\n", " strengths_desync = df_desync.query(f'{spk_type}_sig == True')[f'{spk_type}_strength']\n", " print(f'{spk_type.capitalize()} median: {strengths.median():.4f}, N: {len(strengths)}')\n", " print(f'{spk_type.capitalize()} desync median: {strengths_desync.median():.4f}, N: {len(strengths_desync)}')\n", " U, p = mannwhitneyu(strengths, strengths_desync)\n", " print(f\"All periods vs desync: U={U:.3e}, p={p:.3e}\")\n", "\n", "# Compare burst and tonic spike strengths for coupling in 'desync' periods\n", "df_sig = df_desync.query('(tonicspk_sig == True) & (burst_sig == True)')\n", "W, p = wilcoxon(df_sig['burst_strength'], df_sig['tonicspk_strength'])\n", "print(f\"Burst vs tonic: W={W:.3e}, p={p:.3e}\")\n", "print(f\"N = {len(df_sig)} CPD-neuron pairs\")\n", "\n", "# Format y-axis\n", "yticks = [-4, -2, 0]\n", "ax.set_yticks(yticks)\n", "ax.set_yticklabels(['10$^{%d}$' % tick for tick in yticks])\n", "ax.set_ylim(bottom=-4.1)\n", "\n", "clip_axes_to_ticks(ax=ax)\n", "set_plotsize(w=3, h=2)\n", "fig.savefig(FIGUREPATH + 'coupling_strength_desync' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "2cb83ebf-a6ed-4eb4-86a9-64e60374a76d", "metadata": {}, "source": [ "### Figure S3C2\n", "Distance (index) between CPD with strongest coupling and CPD with second strongest coupling for each neuron." ] }, { "cell_type": "code", "execution_count": 49, "id": "fbf67dff-e583-43c0-84a3-37cb7d3b76d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tonicspk mean no. timescales: 3.80\n", "Burst mean no. timescales: 2.08\n" ] } ], "source": [ "# First, print the mean number of IMFs to which a neuron was coupled for each spike type\n", "for spk_type in ['tonicspk', 'burst']:\n", " # Remove neurons where no coupling analysis was done, group data into neurons, count number of significant entries\n", " n_imfs = df_desync.dropna(subset=f'{spk_type}_p').groupby(['m', 's', 'e', 'u']).apply(lambda unit: unit[f'{spk_type}_sig'].sum())\n", " print(f\"{spk_type.capitalize()} mean no. timescales: {n_imfs.mean():.2f}\")" ] }, { "cell_type": "code", "execution_count": 50, "id": "dd25965d-e31b-49ce-b0bb-57a8bd009c69", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for spk_type in ['tonicspk', 'burst']:\n", " fig, ax = plt.subplots()\n", " # Use 'desync' coupling as adjacent IMFs tend to have phase relationships that could\n", " # drive strong coupling to adjacent timescales\n", " units = df_desync.query(f'{spk_type}_p == {spk_type}_p').groupby(['m', 's', 'e', 'u'])\n", " # Collect indices of IMFs with stongest and second strongest coupling for each neuron\n", " i1s, i2s = [], []\n", " for idx, unit in units:\n", " # Only consider neuron if it has significatn coupling with at least 2 IMFs for the given spike type\n", " if (unit[f'{spk_type}_sig']).sum() < 2:\n", " continue\n", " i1s.append(unit[f'{spk_type}_strength'].argsort().iloc[-1])\n", " i2s.append(unit[f'{spk_type}_strength'].argsort().iloc[-2])\n", "\n", " # Get index difference between two strongest IMFs for each unit\n", " diffs = np.abs(np.array(i1s) - np.array(i2s))\n", "\n", " # Plot distribution of differences as cumulative histogram\n", " ax, counts = cumulative_hist(diffs, np.linspace(0.5, 5.5, 6), ax=ax, color=COLORS[spk_type])\n", " \n", " # Label for the proportion of neurons where the difference is 1\n", " # (i.e. two strongest IMFs are adjacent in freq space)\n", " ax.scatter(1, np.sum(counts[:1]) + 0.1, s=10, marker='v', color='black')\n", " ax.text(1, np.sum(counts[:1]) + 0.2, f'{100 * np.sum(counts[:1]):.0f}%', ha='center', fontsize=LABELFONTSIZE)\n", " \n", " # Format axes\n", " ax.set_xticks([1, 2, 3, 4, 5])\n", " ax.set_xlim(left=0.25)\n", " ax.set_xlabel('$1^{st}$ CPD - $2^{nd}$ CPD')\n", " ax.set_yticks([0, 0.5, 1])\n", " ax.set_ylabel('Prop. Neurons')\n", " \n", " clip_axes_to_ticks(ax=ax, ext={'bottom':[-0.5, 0.5]})\n", " set_plotsize(w=1.5, h=1.5)\n", " fig.savefig(FIGUREPATH + f'preferred_timescale_{spk_type}' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "abc42de9-6ebf-4665-a1ce-a118205dd1ce", "metadata": {}, "source": [ "### Figure S3C1\n", "Correlation between the frequency of switches between locomotor states and the most common strongest coupling CPD in an experiment." ] }, { "cell_type": "code", "execution_count": 51, "id": "d0ebfc9b-270a-494d-af0d-cc1b50175bdb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: ball_spontaneous.pkl\n", "Loading: ball_sparsenoise.pkl\n" ] } ], "source": [ "df_run = load_data('ball', conditions).set_index(['m', 's', 'e'])\n", "# State switches marked by onset and offset times of locomotion bouts, unravel and take difference gives dt, 1/dt gives frequency,\n", "# log transform to match IMF frequency\n", "state_variability = df_run.apply(lambda x: np.log10(1 / np.diff(x['run_bouts'].ravel()).mean()), axis='columns')" ] }, { "cell_type": "code", "execution_count": 52, "id": "43dabaa9-497d-4aad-9ba9-93f21aa8b20e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tonicspk correlation: r=0.712, p=4.290e-03\n", "burst correlation: r=-0.298, p=3.007e-01\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for spk_type in ['tonicspk', 'burst']:\n", " # Get IMF with strongest coupling for each neuron\n", " units = df.groupby(['m', 's', 'e', 'u'])\n", " pref_timescale = units.apply(lambda x: x['freq'].iloc[np.argmax(x[f'{spk_type}_strength'])])\n", " # Get the distribution of strongest coupling timescales for each recording session\n", " sessions = pref_timescale.groupby(['m', 's', 'e'])\n", " modes = np.log10(sessions.apply(lambda x: mode(x)[0])) # mode of each distribution (in 1/s), log-transformed\n", " df_state = pd.merge(state_variability.rename('switch'), modes.rename('freq'), on=['m', 's', 'e'])\n", " r, p = pearsonr(df_state['switch'], df_state['freq'])\n", " print(f'{spk_type} correlation: r={r:.3f}, p={p:.3e}')\n", "\n", " # Scatter preferred timescale mode vs frequency of state switches\n", " fig, ax = plt.subplots()\n", " ax.scatter(df_state['switch'], df_state['freq'], s=5, fc='none', ec=COLORS[spk_type])\n", "\n", " # Format axes\n", " ax.set_xticks(np.log10(FREQUENCYBINS)[2::2])\n", " ax.set_xticklabels(FREQUENCYTICKLABELS[1:])\n", " ax.set_xlim(left=-2.1)\n", " ax.set_xlabel('State-switching\\nfrequency (Hz)')\n", " ax.set_yticks(np.log10(FREQUENCYBINS)[::2])\n", " ax.set_yticklabels(FREQUENCYTICKLABELS)\n", " ax.set_ylabel(\"Mean timescale\\npreference (Hz)\")\n", " ax.set_ylim(bottom=-3.1)\n", " \n", " clip_axes_to_ticks(ax=ax)\n", " set_plotsize(w=1.5, h=1.5, ax=ax)\n", " fig.savefig(FIGUREPATH + f'timescale_prefs_{spk_type}_behav_corr' + FIGSAVEFORMAT)" ] }, { "cell_type": "markdown", "id": "c034e9b9-cef4-4105-baaa-108a2c104295", "metadata": {}, "source": [ "### Figure S3D1\n", "Difference in coupling phase for all neuron-CPD pairs across interleaved data partitions to help show stability of coupling." ] }, { "cell_type": "code", "execution_count": 53, "id": "6b24e507-9356-49c4-bb4f-407a5bd5ecdf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: phasetuning_spontaneous_split1.pkl\n", "Loading: phasetuning_sparsenoise_split1.pkl\n", "Loading: phasetuning_spontaneous_split2.pkl\n", "Loading: phasetuning_sparsenoise_split2.pkl\n", "split1 tonicspk significant: 0.949 (148/156)\n", "split1 burst significant: 0.746 (91/122)\n", "split2 tonicspk significant: 0.968 (151/156)\n", "split2 burst significant: 0.750 (90/120)\n" ] } ], "source": [ "conditions = ['spontaneous', 'sparsenoise']\n", "subsamples = ['split1', 'split2']\n", "\n", "dfs = {}\n", "for sub in subsamples:\n", " df = load_data('phasetuning', conditions, subsample=sub)\n", " df['tonicspk_sig'] = df['tonicspk_p'] <= 0.05\n", " df['burst_sig'] = df['burst_p'] <= 0.05\n", " df = df.set_index(['m', 's', 'e', 'u', 'imf']).sort_index()\n", " dfs[sub] = df\n", "suffixes = ['_' + sub for sub in subsamples]\n", "df = pd.merge(dfs['split1'], dfs['split2'], left_index=True, right_index=True, suffixes=suffixes)\n", "\n", "freqbins = np.logspace(-3, 0, 7)\n", "df['freq_bin'] = np.digitize(df['freq_split1'], bins=freqbins).clip(1, len(freqbins) - 1) - 1\n", "\n", "for sub in subsamples:\n", " for spk_type in ['tonicspk', 'burst']:\n", " n_units = len(df.dropna(subset=f'{spk_type}_p_{sub}').reset_index(level='imf').index.unique())\n", " n_sig = len(df.query(f'{spk_type}_sig_{sub} == True').reset_index(level='imf').index.unique())\n", " print(f\"{sub} {spk_type} significant: {(n_sig / n_units):.3f} ({n_sig}/{n_units})\")" ] }, { "cell_type": "code", "execution_count": 54, "id": "d2945415-94a9-49f0-8164-94f211e5f44f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N IMFs tonicspk: 0.768\n", "tonicspk diff: 0.13\n", "0.9391480730223124\n", "N IMFs burst: 0.812\n", "burst diff: -0.10\n", "0.9529411764705882\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def compare_phase_preferences(df, conds, spk_type):\n", " df_sig = df.query(f'({spk_type}_sig_{conds[0]} == True) & ({spk_type}_sig_{conds[1]} == True)')\n", " angle_diffs_sig = (df_sig[f'{spk_type}_phase_{conds[0]}'] - df_sig[f'{spk_type}_phase_{conds[1]}']) % (2 * np.pi)\n", " ax, counts = plot_circhist(angle_diffs_sig, color=COLORS[spk_type])\n", " print(f'{spk_type} diff: {circmean_angle(angle_diffs_sig):.2f}')\n", " df_ns = df.query(f'({spk_type}_sig_{conds[0]} == False) | ({spk_type}_sig_{conds[1]} == False)')\n", " angle_diffs_ns = (df_ns[f'{spk_type}_phase_{conds[0]}'] - df_ns[f'{spk_type}_phase_{conds[1]}']) % (2 * np.pi)\n", " ax, counts = plot_circhist(angle_diffs_ns, ax=ax, color='black', alpha=0.5)\n", " ax.set_xlabel(r'$\\Delta$ Phase preference')\n", " return angle_diffs_sig, ax\n", "\n", "for spk_type in ['tonicspk', 'burst']:\n", " # Proportion of unit-IMF pairs that have significant coupling in both partitions\n", " n_both = len(df.query(f'{spk_type}_sig_split1 == {spk_type}_sig_split2'))\n", " n_tot = len(df)\n", " print(f'N IMFs {spk_type}: {(n_both / n_tot):.3f}')\n", "\n", " angle_diffs, ax = compare_phase_preferences(df, subsamples, spk_type)\n", " print(((angle_diffs < (np.pi / 2)) | (angle_diffs > (3 * np.pi / 2))).mean())\n", " ax.set_yticks([0.25, 0.5])\n", " ax.set_yticklabels([])\n", " set_plotsize(w=1.5, h=1.5)\n", " ax.get_figure().savefig(FIGUREPATH + f'coupling_phase_splits_{spk_type}.svg')" ] }, { "cell_type": "markdown", "id": "4c0fdb3b-4596-419e-bc29-35883f3878a4", "metadata": {}, "source": [ "### Figure S3D2\n", "Difference in CPD preference (CPD with the strongest coupling) across interleaved partitions for all neurons." ] }, { "cell_type": "code", "execution_count": 55, "id": "a2787efc-eea1-4165-b8b1-3cc2759eac02", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def compare_timescale_preferences_diff(df, conds, spk_type):\n", " df_sig = df.query(f'({spk_type}_sig_{conds[0]} == True) | ({spk_type}_sig_{conds[1]} == True)')\n", " units = df_sig.groupby(['m', 's', 'e', 'u'])\n", " def _get_pref_timescale_diff(series):\n", " pref_cond0 = np.argmax(series[f'{spk_type}_strength_{conds[0]}'])\n", " pref_cond1 = np.argmax(series[f'{spk_type}_strength_{conds[1]}'])\n", " return np.abs(pref_cond0 - pref_cond1)\n", " diff = units.apply(_get_pref_timescale_diff)\n", " bins = np.linspace(0, 6, 7) - 0.5\n", " ax, counts = cumulative_hist(diff, bins, color=COLORS[spk_type])\n", " ax.set_xticks(bins[:-1] + 0.5)\n", " ax.set_xlabel(r'$\\Delta$ CPD preference')\n", " ax.set_ylabel('Prop. Neurons')\n", " ax.scatter(1, np.sum(counts[:2]) + 0.1, s=10, marker='v', color='black')\n", " ax.text(1, np.sum(counts[:2]) + 0.2, f'{100 * np.sum(counts[:2]):.0f}%', ha='center', fontsize=LABELFONTSIZE)\n", " return ax\n", "\n", "for spk_type in ['tonicspk', 'burst']:\n", " ax = compare_timescale_preferences_diff(df, subsamples, spk_type)\n", " clip_axes_to_ticks(ax=ax, ext={'bottom':[-0.5, 0.5]})\n", " set_plotsize(w=1.5, h=1.5)\n", " ax.get_figure().savefig(FIGUREPATH + f'best_frequecy_splits_{spk_type}.svg')" ] }, { "cell_type": "markdown", "id": "4df11387-2755-432b-b8d2-9210624255ac", "metadata": {}, "source": [ "### Figure S3E\n", "Neurons with strong monotonic size tuning are those most affected by size-matching." ] }, { "cell_type": "code", "execution_count": 56, "id": "883d7f8f-b573-4343-9b71-13367c489733", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: phasetuning_spontaneous.pkl\n", "Loading: phasetuning_sparsenoise.pkl\n", "Loading: phasetuning_spontaneous_sizematched.pkl\n", "Loading: phasetuning_sparsenoise_sizematched.pkl\n" ] } ], "source": [ "# Load phase coupling data from gray screen and sparse noise experiments\n", "conditions = ['spontaneous', 'sparsenoise']\n", "df_all = load_data('phasetuning', conditions)\n", "# Coupling data measured after sub-sampling for pupil size matching\n", "df_sizematched = load_data('phasetuning', conditions, subsample='sizematched')\n", "\n", "# Assign significance based on p-value from shuffled permutation test\n", "df_all['tonicspk_sig'] = df_all['tonicspk_p'] <= 0.05\n", "df_all['burst_sig'] = df_all['burst_p'] <= 0.05\n", "df_sizematched['tonicspk_sig'] = df_sizematched['tonicspk_p'] <= 0.05\n", "df_sizematched['burst_sig'] = df_sizematched['burst_p'] <= 0.05\n", "\n", "# Combine control and sub-sampled data\n", "df_all = df_all.set_index(['m', 's', 'e', 'u', 'imf']).sort_index()\n", "df_sizematched = df_sizematched.set_index(['m', 's', 'e', 'u', 'imf']).sort_index()\n", "suffixes = ['_all', '_sizematched']\n", "df_phasetuning = pd.merge(df_all, df_sizematched, left_index=True, right_index=True, suffixes=suffixes)" ] }, { "cell_type": "code", "execution_count": 57, "id": "f399c4a6-a290-4eec-b196-973e2d2029ce", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: sizetuning_spontaneous_burst.pkl\n", "Loading: sizetuning_sparsenoise_burst.pkl\n", "Chi2ContingencyResult(statistic=9.482519661991745, pvalue=0.008727643900112143, dof=2, expected_freq=array([[17.78666667, 5.02666667, 6.18666667],\n", " [28.21333333, 7.97333333, 9.81333333]]))\n", "[[12 6 11]\n", " [34 7 5]]\n", "[46 13 16]\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def preferred_timescale_shift(series, spk_type, conds):\n", " pref0 = np.argmax(series[f'{spk_type}_strength_{conds[0]}'])\n", " pref1 = np.argmax(series[f'{spk_type}_strength_{conds[1]}'])\n", " return np.abs(pref0 - pref1) != 0 \n", "\n", "units = df_phasetuning.groupby(['m', 's', 'e', 'u'])\n", "units = units.filter(lambda x: x['burst_sig_all'].any() & x['burst_sig_sizematched'].any()).groupby(['m', 's', 'e', 'u'])\n", "timescale_shift = units.apply(lambda x: preferred_timescale_shift(x, 'burst', ['all', 'sizematched']))\n", "\n", "df_sizetuning = load_data('sizetuning', conditions, spk_type='burst').set_index(['m', 's', 'e', 'u'])\n", "df_sizetuning['tuning_max'] = df_sizetuning['area_means'].apply(np.argmax)\n", "df_sizetuning['mono'] = df_sizetuning.apply(lambda x: x['tuning_max'] == 0, axis='columns')\n", "\n", "df = pd.merge(df_sizetuning, timescale_shift.rename('shift'), on=['m', 's', 'e', 'u'])\n", "df_mono = df.query('(area_p <= 0.05) & (mono == True)')\n", "df_other = df.query('(area_p <= 0.05) & (mono == False)')\n", "df_ns = df.query('(area_p > 0.05)')\n", "counts = np.array([\n", " [(~df_mono['shift']).sum(), (~df_other['shift']).sum(), (~df_ns['shift']).sum()],\n", " [df_mono['shift'].sum(), df_other['shift'].sum(), df_ns['shift'].sum()]\n", " ])\n", "\n", "fig, ax = plt.subplots()\n", "ax.bar([0, 1, 2], counts[0] / counts.sum(axis=0), width=0.8, color=COLORS['burst'])\n", "ax.bar([0, 1, 2], counts[1] / counts.sum(axis=0), bottom=(counts[0] / counts.sum(axis=0)), width=0.8, color=COLORS['burst'], alpha=0.5)\n", "for j, c in enumerate(counts.T):\n", " ax.text(j, 0.85, f'{c[1] / c.sum() * 100:.0f}%', ha='center', fontsize=LABELFONTSIZE)\n", "ax.set_xticks([0, 1, 2])\n", "ax.set_xticklabels(['Mono.', 'Other', 'None'], rotation=-15)\n", "ax.set_xlabel('Pupil size modulation')\n", "ax.set_yticks([0, .5, 1])\n", "ax.set_ylabel('Prop. Neurons')\n", "\n", "clip_axes_to_ticks(ax=ax, ext={'bottom':[-0.4, 0.4]})\n", "set_plotsize(w=2, h=1.5, ax=ax)\n", "fig.savefig(FIGUREPATH + 'size_tuning_comparison_timescale_burst.svg')\n", "\n", "print(chi2_contingency(counts))\n", "print(counts)\n", "print(counts.sum(axis=0))" ] }, { "cell_type": "code", "execution_count": 59, "id": "c78c3c0e-fc7f-4ffa-920e-01badf558509", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: sizetuning_spontaneous_tonicspk.pkl\n", "Loading: sizetuning_sparsenoise_tonicspk.pkl\n", "[[26 22 5]\n", " [37 43 6]]\n", "[63 65 11]\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "units = df_phasetuning.groupby(['m', 's', 'e', 'u'])\n", "units = units.filter(lambda x: x['tonicspk_sig_all'].any() & x['tonicspk_sig_sizematched'].any()).groupby(['m', 's', 'e', 'u'])\n", "timescale_shift = units.apply(lambda x: preferred_timescale_shift(x, 'tonicspk', ['all', 'sizematched']))\n", "\n", "df_sizetuning = load_data('sizetuning', conditions, spk_type='tonicspk').set_index(['m', 's', 'e', 'u'])\n", "df_sizetuning['tuning_max'] = df_sizetuning['area_means'].apply(np.argmax)\n", "df_sizetuning['mono'] = df_sizetuning.apply(lambda x: x['tuning_max'] == 9, axis='columns')\n", "\n", "df = pd.merge(df_sizetuning, timescale_shift.rename('shift'), on=['m', 's', 'e', 'u'])\n", "df_mono = df.query('(area_p <= 0.05) & (mono == True)')\n", "df_other = df.query('(area_p <= 0.05) & (mono == False)')\n", "df_ns = df.query('(area_p > 0.05)')\n", "counts = np.array([\n", " [(~df_mono['shift']).sum(), (~df_other['shift']).sum(), (~df_ns['shift']).sum()],\n", " [df_mono['shift'].sum(), df_other['shift'].sum(), df_ns['shift'].sum()]\n", " ])\n", "\n", "fig, ax = plt.subplots()\n", "ax.bar([0, 1, 2], counts[0] / counts.sum(axis=0), width=0.8, color=COLORS['tonicspk'])\n", "ax.bar([0, 1, 2], counts[1] / counts.sum(axis=0), bottom=(counts[0] / counts.sum(axis=0)), width=0.8, color=COLORS['tonicspk'], alpha=0.5)\n", "for j, c in enumerate(counts.T):\n", " ax.text(j, 0.85, f'{c[1] / c.sum() * 100:.0f}%', ha='center', fontsize=LABELFONTSIZE)\n", "ax.set_xticks([0, 1, 2])\n", "ax.set_xticklabels(['Mono.', 'Other', 'None'], rotation=-15)\n", "ax.set_xlabel('Pupil size modulation')\n", "ax.set_yticks([0, .5, 1])\n", "ax.set_ylabel('Prop. Neurons')\n", "\n", "clip_axes_to_ticks(ax=ax, ext={'bottom':[-0.4, 0.4]})\n", "set_plotsize(w=2, h=1.5, ax=ax)\n", "fig.savefig(FIGUREPATH + 'size_tuning_comparison_timescale_tonicspk.svg')\n", "\n", "chi2_contingency(counts)\n", "print(counts)\n", "print(counts.sum(axis=0))" ] }, { "cell_type": "markdown", "id": "c6f90945-ad92-4a65-8f1d-0b5dc8747138", "metadata": {}, "source": [ "### Figure S4A3 \n", "Burst ratios during quiescence and various time periods relative to locomotion." ] }, { "cell_type": "code", "execution_count": 60, "id": "d4f3fd02-2e53-4501-aff7-53c4ffbb6b5b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: spikes_spontaneous.pkl\n", "Loading: spikes_sparsenoise.pkl\n", "Loading: ball_spontaneous.pkl\n", "Loading: ball_sparsenoise.pkl\n" ] } ], "source": [ "df_spikes = load_data('spikes', ['spontaneous', 'sparsenoise'])\n", "df_spikes = filter_units(df_spikes, MINRATE)\n", "df_run = load_data('ball', ['spontaneous', 'sparsenoise'])\n", "df = pd.merge(df_run, df_spikes, on=['m', 's', 'e']).set_index(['m', 's', 'e', 'u'])" ] }, { "cell_type": "code", "execution_count": 61, "id": "e0cf3496-7724-423c-b926-c5d7f16dde67", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "FriedmanchisquareResult(statistic=21.95538461538432, pvalue=6.664040651685976e-05)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def take_events_in_bouts(series, data, bouts, trange=None, dt=2):\n", " header, _ = data.split('_')\n", " events_in_bouts = []\n", " for t0, t1 in series['%s_bouts' % bouts]:\n", " if trange == 'start':\n", " t1 = t0 + dt\n", " elif trange == 'end':\n", " t0 = t1 - dt\n", " elif trange == 'middle':\n", " t0 = t0 + dt\n", " t1 = t1 - dt\n", " if (t1 - t0) <= 0:\n", " continue\n", " events_in_bouts.append(series[data][(series[data] >= t0) & (series[data] <= t1)])\n", " return np.concatenate(events_in_bouts)\n", "\n", "# TODO: just use list with state_trange\n", "states = {'sit0':'middle', 'run0':'start', 'run1':'middle', 'run2':'end'}\n", "brs = {}\n", "means = {}\n", "errs = {}\n", "for state, trange in states.items():\n", " df['burstspks_%s' % state] = df.apply(take_events_in_bouts, data='burstspk_times', bouts=state[:-1], trange=trange, axis='columns')\n", " df['spks_%s' % state] = df.apply(take_events_in_bouts, data='spk_times', bouts=state[:-1], trange=trange, axis='columns')\n", " df['burst_%s' % state] = df.apply(take_events_in_bouts, data='burst_times', bouts=state[:-1], trange=trange, axis='columns')\n", " br = df.query('spks_%s.str.len() > 0' % state).apply(lambda x: len(x['burstspks_%s' % state]) / len(x['spks_%s' % state]), axis='columns')\n", " brs[state] = br\n", " means[state] = br.median()\n", " errs[state] = se_median(br)\n", "df_brs = pd.concat([v.rename(k) for k, v in brs.items()], axis=1).dropna()\n", "friedmanchisquare(*df_brs.values.T)" ] }, { "cell_type": "code", "execution_count": 62, "id": "adf768ee-27a1-46d8-8a27-b4b4b564224f", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for pos, (state, _) in enumerate(states.items()):\n", " ax.bar(pos, means[state], yerr=errs[state], color=COLORS[state[:-1]])\n", "ax.set_xticks(np.arange(len(states)))\n", "ax.set_xticklabels(['Sit Middle', 'Loc. Onset (2s)', 'Loc. Middle', 'Loc. Offset (2s)'], rotation=300, ha='left')\n", "ax.set_yticks([0, 0.02, 0.04])\n", "ax.set_ylabel('Burst ratio')\n", "clip_axes_to_ticks(ax=ax, ext={'bottom':[-0.5, 0.5]})\n", "set_plotsize(w=2, h=2)\n", "fig.savefig(FIGUREPATH + 'burstratio_run_sit.svg')" ] }, { "cell_type": "markdown", "id": "e82ccfbb-500a-46f6-bdfa-2e146498c4b1", "metadata": {}, "source": [ "### Figure S4A, S5C1\n", "Run-, sit-, and saccade-triggered spiking." ] }, { "cell_type": "code", "execution_count": 63, "id": "2009d934-d666-4559-bf2d-8c318ff8ac28", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: responses_spontaneous.pkl\n", "Loading: responses_sparsenoise.pkl\n" ] } ], "source": [ "conditions = ['spontaneous', 'sparsenoise']\n", "df = load_data('responses', conditions).dropna()" ] }, { "cell_type": "code", "execution_count": 64, "id": "8a0992bd-b3a6-4dee-81b1-bc2814d7b945", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sit tonicspk: 0.917 (111 / 121)\n", "Sit burst: 0.818 (99 / 121)\n", "Run tonicspk: 0.893 (108 / 121)\n", "Run burst: 0.793 (96 / 121)\n", "Saccade tonicspk: 0.975 (118 / 121)\n", "Saccade burst: 0.744 (90 / 121)\n" ] }, { "data": { "image/png": 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for event in ['sit', 'run', 'saccade']:\n", " \n", " fig, ax = plt.subplots()\n", " \n", " for spk_type in ['tonicspk', 'burst']:\n", " df_sig = df.query(f'{event}_{spk_type}_sig == True')\n", " df_resp = df_sig[f'{event}_{spk_type}_response']\n", " responses = np.row_stack(df_resp.apply(lambda x: x / np.abs(x).max()))\n", " mean = responses.mean(axis=0)\n", " err = sem(responses, axis=0) * 1.96\n", " tpts = df_sig[f'{event}_{spk_type}_tpts'].iloc[0]\n", " ax.plot(tpts, mean, color=COLORS[spk_type])\n", " ax.plot(tpts, mean - err, lw=0.5, ls='--', color=COLORS[spk_type])\n", " ax.plot(tpts, mean + err, lw=0.5, ls='--', color=COLORS[spk_type])\n", " print(f'{event.capitalize()} {spk_type}: {len(df_sig)/len(df):.3f} ({len(df_sig)} / {len(df)})')\n", " \n", " ax.axvline(BEHAVEXCLUSIONS[event][0], ymax=0.9, ls='--', lw=1, color='gray')\n", " ax.axvline(BEHAVEXCLUSIONS[event][1], ymax=0.9, ls='--', lw=1, color='gray')\n", " if event == 'sit':\n", " ax.plot([-5, -0.25], [0.5, 0.5], lw=3, alpha=0.5, color=COLORS['run'])\n", " ax.plot([0.25, 5], [0.5, 0.5], lw=3, alpha=0.5, color=COLORS['sit'])\n", " elif event == 'run':\n", " ax.plot([-5, -0.25], [0.5, 0.5], lw=3, alpha=0.5, color=COLORS['sit'])\n", " ax.plot([0.25, 5], [0.5, 0.5], lw=3, alpha=0.5, color=COLORS['run'])\n", " elif event == 'saccade':\n", " ax.scatter([0, 0], [0.55, 0.55], lw=1, s=7, marker=7, fc='none', ec=COLORS['saccade'])\n", " \n", "\n", " ax.set_xticks([tpts.min(), 0, tpts.max()])\n", " ax.set_xlabel('Lag (s)')\n", " ax.set_yticks([-0.5, 0, 0.5])\n", " ax.set_ylim(bottom=-0.55)\n", " ax.set_ylabel('Response (norm.)')\n", "\n", " clip_axes_to_ticks(ax=ax)\n", " set_plotsize(w=2, h=2)\n", " \n", " fig.savefig(FIGUREPATH + f'mean_{event}_response.svg')" ] }, { "cell_type": "markdown", "id": "55ae235e-e797-4a13-a9d0-9771ed3733a7", "metadata": {}, "source": [ "### Figure S5C3\n", "Peri-saccadic modulation index for each spike type" ] }, { "cell_type": "code", "execution_count": 65, "id": "8dff8caa-031c-4fd0-af42-fcc2008fc0ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tonicspk AUCs > 0: 105/118(88.98)\n", "0.20354414330917253\n", "WilcoxonResult(statistic=543.0, pvalue=1.598717146350466e-15)\n", "burst AUCs > 0: 33/90(36.67)\n", "-0.06178675404301504\n", "WilcoxonResult(statistic=1367.0, pvalue=0.0061791483476051675)\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for trigger in ['saccade']:\n", " fig, ax = plt.subplots()\n", "\n", " for spk_type in ['tonicspk', 'burst']:\n", " i0, i1 = df['%s_%s_tpts' % (trigger, spk_type)].iloc[0].searchsorted(BEHAVEXCLUSIONS[trigger])\n", " df['%s_%s_auc' % (trigger, spk_type)] = np.row_stack(df['%s_%s_response' % (trigger, spk_type)].apply(lambda x: x[i0:i1] / np.abs(x).max())).mean(axis=1)\n", " df_sig = df.query('%s_%s_sig == True' % (trigger, spk_type))\n", "\n", " aucs = df_sig['%s_%s_auc' % (trigger, spk_type)]\n", " print(\"%s AUCs > 0: %d/%d(%.2f)\" % (spk_type, (aucs > 0).sum(), len(aucs), (aucs > 0).mean() * 100))\n", " print(aucs.mean())\n", " weights = np.ones_like(aucs) / len(aucs)\n", " vals, bins = np.histogram(aucs, bins=np.linspace(-0.75, 0.75, 1001), weights=weights)\n", " xvals = bins[:-1] + np.diff(bins).mean() / 2\n", " ax.plot(xvals, np.cumsum(vals), color=COLORS[spk_type])\n", "\n", " ax.scatter(np.median(aucs), 1, marker=7, fc='none', ec=COLORS[spk_type])\n", " ax.axvline(0, ls='--', lw=1, color='gray')\n", " # print(wilcoxon(aucs, alternative='greater'))\n", " print(wilcoxon(aucs))\n", "\n", " ax.set_xticks([-0.75, 0, 0.75])\n", " ax.set_xlabel('Modulation strength')\n", " ax.set_yticks([0, 0.5, 1])\n", " ax.set_ylabel('Proportion')\n", "\n", " clip_axes_to_ticks(ax=ax)\n", " set_plotsize(w=2, h=2)\n", "\n", " fig.savefig(FIGUREPATH + '%s_modulation.svg' % trigger)" ] }, { "cell_type": "markdown", "id": "b4b7fb2c-9caa-4812-af63-83c049f60a85", "metadata": {}, "source": [ "### Figure S5D\n", "Correlation of saccade modulation index and CPD phase coupling for each neuron." ] }, { "cell_type": "code", "execution_count": 66, "id": "7eede009-6606-4a61-b31f-9a5d8a0d0646", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: phasetuning_spontaneous.pkl\n", "Loading: phasetuning_sparsenoise.pkl\n" ] } ], "source": [ "conditions = ['spontaneous', 'sparsenoise']\n", "df_phasetuning = load_data('phasetuning', conditions)\n", "\n", "df_phasetuning['tonicspk_sig'] = df_phasetuning['tonicspk_p'] <= 0.05\n", "df_phasetuning['burst_sig'] = df_phasetuning['burst_p'] <= 0.05\n", "# TODO: replace with sort_data function from util\n", "df_phasetuning['freq_bin'] = np.digitize(df_phasetuning['freq'], bins=FREQUENCYBINS) - 1\n", "\n", "df = pd.merge(df, df_phasetuning)" ] }, { "cell_type": "code", "execution_count": 67, "id": "fb9443e0-a228-4b28-9100-99ddc068e7d0", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for spk_type in ['tonicspk', 'burst']:\n", " fig, ax = plt.subplots()\n", " for xpos, (freqbin, group) in zip(FREQUENCYXPOS, df.groupby('freq_bin')):\n", " group = group.query(f'({spk_type}_sig == True) & (saccade_{spk_type}_sig == True)')\n", " if len(group) <= 5:\n", " continue\n", " r, p = pearsonr(group[f'saccade_{spk_type}_auc'], group[f'{spk_type}_strength'])\n", " ax.bar(xpos, r, width=0.5, color=COLORS[spk_type])\n", " ax.text(xpos, 0.5, p2stars(p), fontsize=LABELFONTSIZE)\n", "\n", " ax.set_xticks(FREQUENCYTICKS)\n", " ax.set_xticklabels(FREQUENCYTICKLABELS)\n", " ax.set_xlabel('Timescale (Hz)')\n", " ax.set_yticks([-0.5, 0, 0.5])\n", " ax.set_ylim(bottom=-0.55)\n", " ax.set_ylabel('Correlation')\n", "\n", " clip_axes_to_ticks(ax=ax)\n", " set_plotsize(w=2, h=2)\n", " \n", " fig.savefig(FIGUREPATH + f'phasetuning_saccade_response_correlation_{spk_type}.svg')" ] }, { "cell_type": "markdown", "id": "aa143dea-7e64-43b2-9304-195b9411a689", "metadata": {}, "source": [ "### Figure S4B\n", "Correlation between CPDs and running speed." ] }, { "cell_type": "code", "execution_count": 68, "id": "a0162f9b-5f3a-47f3-87f2-8d64d1b67d65", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: imfcorr_spontaneous.pkl\n", "Loading: imfcorr_sparsenoise.pkl\n", "IMFs significantly correlated: 55.3\n", "52 94\n" ] } ], "source": [ "conditions = ['spontaneous', 'sparsenoise']\n", "df = load_data('imfcorr', conditions)\n", "\n", "df['freq_bin'] = np.digitize(df['freq'], bins=FREQUENCYBINS).clip(1, len(FREQUENCYBINS) - 1) - 1\n", "\n", "lags = np.linspace(-30, 30, 1000)\n", "df['xcorr_tpts'] = df['xcorr_lags']\n", "df['xcorr'] = df.apply(lambda x: interpolate(x['xcorr'], x['xcorr_lags'], lags), axis='columns')\n", "\n", "print(\"IMFs significantly correlated: %.1f\" % (100 * df['xcorr_sig'].mean()))\n", "print(df['xcorr_sig'].sum(), len(df))" ] }, { "cell_type": "code", "execution_count": 69, "id": "753eb098-1fbb-42a9-89f6-3ed9da17e599", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "sorted_xcorrs = sort_data(df['xcorr'], df['freq'], bins=FREQUENCYBINS)\n", "xcorr_mat = np.row_stack([np.row_stack(xcorrs).mean(axis=0) for xcorrs in sorted_xcorrs]) \n", "xcorr_max = np.abs(xcorr_mat).max()\n", "mat = ax.matshow(xcorr_mat, aspect='auto', vmin=-xcorr_max, vmax=xcorr_max, cmap='RdBu')\n", "\n", "for sig, color, alpha in zip([False, True], ['gray', 'black'], [0.5, 1]):\n", " df_sig = df.query(f'xcorr_sig == {sig}')\n", " sorted_peaks = sort_data(df_sig['xcorr_peak'], df_sig['freq'], bins=FREQUENCYBINS)\n", " for i, peaks in enumerate(sorted_peaks):\n", " ax.scatter(lags.searchsorted(peaks), np.ones(len(peaks)) * i, s=5, fc='none', ec=color, alpha=0.5)\n", " \n", "lagticks = [-30, 0, 30]\n", "ax.set_xticks(lags.searchsorted(lagticks))\n", "ax.set_xticklabels(lagticks)\n", "ax.xaxis.tick_bottom()\n", "ax.set_xlabel(\"Lag (s)\")\n", "ax.set_yticks(np.arange(len(FREQUENCYBINS))[::2] - 0.5)\n", "ax.set_yticklabels(FREQUENCYTICKLABELS)\n", "ax.invert_yaxis()\n", "ax.set_ylabel(\"Timescale (Hz)\")\n", "\n", "fig.subplots_adjust(bottom=0.3, right=0.7)\n", "bbox = ax.get_position()\n", "\n", "cax = fig.add_axes([bbox.x0 + 0.15, bbox.y1 + 0.05, 0.3, 0.03])\n", "cbar = plt.colorbar(mat, cax=cax, orientation='horizontal', label='Correlation')\n", "cbar.set_ticks([-0.1, 0, 0.1])\n", "cax.xaxis.set_ticks_position('top')\n", "cax.xaxis.set_label_position('top')\n", "\n", "ax_prop = fig.add_axes([bbox.x1 + 0.05, bbox.y0, 0.25, bbox.height])\n", "sig = sort_data(df['xcorr_sig'], df['freq'], bins=FREQUENCYBINS)\n", "ax_prop.plot([s.mean() for s in sig], np.arange(len(FREQUENCYBINS))[:-1] + 0.5, color='black')\n", "ax_prop.set_xticks([0, 1])\n", "ax_prop.set_xlim(left=0, right=1)\n", "ax_prop.set_xlabel('Proportion')\n", "ax_prop.set_ylim(bottom=0, top=len(FREQUENCYBINS) - 1)\n", "ax_prop.yaxis.set_visible(False)\n", "clip_axes_to_ticks(ax=ax_prop, spines=['bottom'])\n", "\n", "set_plotsize(w=3, h=2, ax=ax)\n", "fig.savefig(FIGUREPATH + \"imf_run_correlation.svg\")" ] }, { "cell_type": "code", "execution_count": 70, "id": "4d49b1a3-1c46-430d-a563-ec947d86f19b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: phasetuning_spontaneous.pkl\n", "Loading: phasetuning_sparsenoise.pkl\n" ] } ], "source": [ "df_phasetuning = load_data('phasetuning', ['spontaneous', 'sparsenoise'])\n", "df_phasetuning['freq'] = np.round(df_phasetuning['freq'], decimals=4)\n", "df_phasetuning['tonicspk_sig'] = df_phasetuning['tonicspk_p'] <= 0.05\n", "df_phasetuning['burst_sig'] = df_phasetuning['burst_p'] <= 0.05\n", "df = pd.merge(df_phasetuning, df, on=['m', 's', 'e', 'imf'])" ] }, { "cell_type": "code", "execution_count": 71, "id": "b7955397-d846-49a5-9c24-a1d9f10af8e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chi2: burst tuning - IMF run speed correlation\n", "Significant tuning for uncorrelated IMFs: 0.503 (186/370)\n", "Significant tuning for correlated IMFs: 0.382 (128/335)\n", "chi2 = 9.87e+00, p = 1.68e-03\n", "MannwhitneyuResult(statistic=8683.0, pvalue=4.625688574517385e-05)\n", "Strength uncorrelated = 0.0678\n", "Strength correlated = 0.0326 \n", "\n", "Chi2: tonicspk tuning - IMF run speed correlation\n", "Significant tuning for uncorrelated IMFs: 0.759 (341/449)\n", "Significant tuning for correlated IMFs: 0.735 (305/415)\n", "chi2 = 5.64e-01, p = 4.53e-01\n", "MannwhitneyuResult(statistic=46645.0, pvalue=0.023684398941296883)\n", "Strength uncorrelated = 0.0102\n", "Strength correlated = 0.0064 \n", "\n" ] } ], "source": [ "# Loop over spike types\n", "for spk_type in ['burst', 'tonicspk']:\n", " # Restrict to entries where tuning was evaluated\n", " df_valid = df.dropna(subset=f'{spk_type}_strength')\n", "\n", " # Build contingency table\n", " rxc_table = np.array([\n", " [len(df_valid.query(f'({spk_type}_sig == False) & (xcorr_sig == False)')), len(df_valid.query(f'({spk_type}_sig == False) & (xcorr_sig == True)'))],\n", " [len(df_valid.query(f'({spk_type}_sig == True) & (xcorr_sig == False)')), len(df_valid.query(f'({spk_type}_sig == True) & (xcorr_sig == True)'))]\n", " ])\n", " # Perform chi2 contingency test\n", " print(f\"Chi2: {spk_type} tuning - IMF run speed correlation\")\n", " chi2, p, dof, expected = chi2_contingency(rxc_table)\n", " propsig = rxc_table[1, 0] / rxc_table[:, 0].sum()\n", " print(f\"Significant tuning for uncorrelated IMFs: {propsig:.3f} ({rxc_table[1, 0]}/{rxc_table[:, 0].sum()})\")\n", " propsig = rxc_table[1, 1] / rxc_table[:, 1].sum()\n", " print(f\"Significant tuning for correlated IMFs: {propsig:.3f} ({rxc_table[1, 1]}/{rxc_table[:, 1].sum()})\")\n", " print(f\"chi2 = {chi2:.2e}, p = {p:.2e}\")\n", "\n", " # Compare coupling strengths\n", " strength_sig = df_valid.query(f'({spk_type}_sig == True) & (xcorr_sig == True)')[f'{spk_type}_strength']\n", " strength_ns = df_valid.query(f'({spk_type}_sig == True) & (xcorr_sig == False)')[f'{spk_type}_strength']\n", " print(mannwhitneyu(strength_sig, strength_ns))\n", " print(f\"Strength uncorrelated = {strength_ns.median():.4f}\")\n", " print(f\"Strength correlated = {strength_sig.median():.4f}\", \"\\n\")" ] }, { "cell_type": "markdown", "id": "193bc6cf-80ac-4d87-8859-c3eef56c535e", "metadata": {}, "source": [ "### Figure S4C\n", "Correlation between CPDs and running speed only in locomotion bouts (i.e. fine-grained locomotion speed info)." ] }, { "cell_type": "code", "execution_count": 72, "id": "ef2c2c68-46ec-4064-bbd2-41fad51d6d53", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: imfcorr_spontaneous_run.pkl\n", "Loading: imfcorr_sparsenoise_run.pkl\n", "IMFs significantly correlated: 64.9\n", "61 94\n" ] } ], "source": [ "conditions = ['spontaneous', 'sparsenoise']\n", "df = load_data('imfcorr', conditions, tranges='run')\n", "\n", "df['freq_bin'] = np.digitize(df['freq'], bins=FREQUENCYBINS).clip(1, len(FREQUENCYBINS) - 1) - 1\n", "\n", "lags = np.linspace(-30, 30, 1000)\n", "df['xcorr_tpts'] = df['xcorr_lags']\n", "df['xcorr'] = df.apply(lambda x: interpolate(x['xcorr'], x['xcorr_lags'], lags), axis='columns')\n", "\n", "print(\"IMFs significantly correlated: %.1f\" % (100 * df['xcorr_sig'].mean()))\n", "print(df['xcorr_sig'].sum(), len(df))" ] }, { "cell_type": "code", "execution_count": 73, "id": "5d9c0139-ce5b-402b-9e0b-dbda6b61ea8b", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "sorted_xcorrs = sort_data(df['xcorr'], df['freq'], bins=FREQUENCYBINS)\n", "xcorr_mat = np.row_stack([np.row_stack(xcorrs).mean(axis=0) for xcorrs in sorted_xcorrs]) \n", "xcorr_max = np.abs(xcorr_mat).max()\n", "mat = ax.matshow(xcorr_mat, aspect='auto', vmin=-xcorr_max, vmax=xcorr_max, cmap='RdBu')\n", "\n", "for sig, color, alpha in zip([False, True], ['gray', 'black'], [0.5, 1]):\n", " df_sig = df.query(f'xcorr_sig == {sig}')\n", " sorted_peaks = sort_data(df_sig['xcorr_peak'], df_sig['freq'], bins=FREQUENCYBINS)\n", " for i, peaks in enumerate(sorted_peaks):\n", " ax.scatter(lags.searchsorted(peaks), np.ones(len(peaks)) * i, s=5, fc='none', ec=color, alpha=0.5)\n", " \n", "lagticks = [-30, 0, 30]\n", "ax.set_xticks(lags.searchsorted(lagticks))\n", "ax.set_xticklabels(lagticks)\n", "ax.xaxis.tick_bottom()\n", "ax.set_xlabel(\"Lag (s)\")\n", "ax.set_yticks(np.arange(len(FREQUENCYBINS))[::2] - 0.5)\n", "ax.set_yticklabels(FREQUENCYTICKLABELS)\n", "ax.invert_yaxis()\n", "ax.set_ylabel(\"Timescale (Hz)\")\n", "\n", "fig.subplots_adjust(bottom=0.3, right=0.7)\n", "bbox = ax.get_position()\n", "\n", "cax = fig.add_axes([bbox.x0 + 0.15, bbox.y1 + 0.05, 0.3, 0.03])\n", "cbar = plt.colorbar(mat, cax=cax, orientation='horizontal', label='Correlation')\n", "cbar.set_ticks([-0.1, 0, 0.1])\n", "cax.xaxis.set_ticks_position('top')\n", "cax.xaxis.set_label_position('top')\n", "\n", "ax_prop = fig.add_axes([bbox.x1 + 0.05, bbox.y0, 0.25, bbox.height])\n", "sig = sort_data(df['xcorr_sig'], df['freq'], bins=FREQUENCYBINS)\n", "ax_prop.plot([s.mean() for s in sig], np.arange(len(FREQUENCYBINS))[:-1] + 0.5, color='black')\n", "ax_prop.set_xticks([0, 1])\n", "ax_prop.set_xlim(left=0, right=1)\n", "ax_prop.set_xlabel('Proportion')\n", "ax_prop.set_ylim(bottom=0, top=len(FREQUENCYBINS) - 1)\n", "ax_prop.yaxis.set_visible(False)\n", "clip_axes_to_ticks(ax=ax_prop, spines=['bottom'])\n", "\n", "set_plotsize(w=3, h=2, ax=ax)\n", "fig.savefig(FIGUREPATH + \"imf_run_correlation_run.svg\")" ] }, { "cell_type": "code", "execution_count": 74, "id": "8241d06c-60ba-4508-a042-be9f6b4e9719", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: phasetuning_spontaneous.pkl\n", "Loading: phasetuning_sparsenoise.pkl\n" ] } ], "source": [ "df_phasetuning = load_data('phasetuning', ['spontaneous', 'sparsenoise'])\n", "df_phasetuning['freq'] = np.round(df_phasetuning['freq'], decimals=4)\n", "df_phasetuning['tonicspk_sig'] = df_phasetuning['tonicspk_p'] <= 0.05\n", "df_phasetuning['burst_sig'] = df_phasetuning['burst_p'] <= 0.05\n", "df = pd.merge(df_phasetuning, df, on=['m', 's', 'e', 'imf'])" ] }, { "cell_type": "code", "execution_count": 75, "id": "12a3a559-f15a-45ce-881d-28154c34a765", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chi2: burst tuning - IMF run speed correlation\n", "Significant tuning for uncorrelated IMFs: 0.307 (58/189)\n", "Significant tuning for correlated IMFs: 0.496 (256/516)\n", "chi2 = 1.93e+01, p = 1.12e-05\n", "MannwhitneyuResult(statistic=8140.0, pvalue=0.2517663418863203)\n", "Strength uncorrelated = 0.0414\n", "Strength correlated = 0.0595 \n", "\n", "Chi2: tonicspk tuning - IMF run speed correlation\n", "Significant tuning for uncorrelated IMFs: 0.687 (169/246)\n", "Significant tuning for correlated IMFs: 0.772 (477/618)\n", "chi2 = 6.27e+00, p = 1.23e-02\n", "MannwhitneyuResult(statistic=45918.0, pvalue=0.007115552089052142)\n", "Strength uncorrelated = 0.0054\n", "Strength correlated = 0.0099 \n", "\n" ] } ], "source": [ "# Loop over spike types\n", "for spk_type in ['burst', 'tonicspk']:\n", " # Restrict to entries where tuning was evaluated\n", " df_valid = df.dropna(subset=f'{spk_type}_strength')\n", "\n", " # Build contingency table\n", " rxc_table = np.array([\n", " [len(df_valid.query(f'({spk_type}_sig == False) & (xcorr_sig == False)')), len(df_valid.query(f'({spk_type}_sig == False) & (xcorr_sig == True)'))],\n", " [len(df_valid.query(f'({spk_type}_sig == True) & (xcorr_sig == False)')), len(df_valid.query(f'({spk_type}_sig == True) & (xcorr_sig == True)'))]\n", " ])\n", " # Perform chi2 contingency test\n", " print(f\"Chi2: {spk_type} tuning - IMF run speed correlation\")\n", " chi2, p, dof, expected = chi2_contingency(rxc_table)\n", " propsig = rxc_table[1, 0] / rxc_table[:, 0].sum()\n", " print(f\"Significant tuning for uncorrelated IMFs: {propsig:.3f} ({rxc_table[1, 0]}/{rxc_table[:, 0].sum()})\")\n", " propsig = rxc_table[1, 1] / rxc_table[:, 1].sum()\n", " print(f\"Significant tuning for correlated IMFs: {propsig:.3f} ({rxc_table[1, 1]}/{rxc_table[:, 1].sum()})\")\n", " print(f\"chi2 = {chi2:.2e}, p = {p:.2e}\")\n", "\n", " # Compare coupling strengths\n", " strength_sig = df_valid.query(f'({spk_type}_sig == True) & (xcorr_sig == True)')[f'{spk_type}_strength']\n", " strength_ns = df_valid.query(f'({spk_type}_sig == True) & (xcorr_sig == False)')[f'{spk_type}_strength']\n", " print(mannwhitneyu(strength_sig, strength_ns))\n", " print(f\"Strength uncorrelated = {strength_ns.median():.4f}\")\n", " print(f\"Strength correlated = {strength_sig.median():.4f}\", \"\\n\")" ] }, { "cell_type": "markdown", "id": "5762ac9e-3d8e-48a9-a7e8-aa08d96029c8", "metadata": {}, "source": [ "### Figure S5A\n", "Example saccades." ] }, { "cell_type": "code", "execution_count": 76, "id": "99769af2-a835-44d9-8d14-aa2f96d7929a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: pupil_sparsenoise.pkl\n", "Loading: pupil_spontaneous.pkl\n" ] } ], "source": [ "df_pupil = load_data('pupil', ['sparsenoise', 'spontaneous']).set_index(['m', 's', 'e'])" ] }, { "cell_type": "code", "execution_count": 77, "id": "857ffa60-2249-4689-805b-d0b9a31325e3", "metadata": {}, "outputs": [ { "data": { 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# TODO: move this to parameters\n", "key = {'m': 'PVCre_2019_0002', 's': 7, 'e':9}\n", "t0, t1 = 175, 205\n", "\n", "fig, (ax_pos, ax_area) = plt.subplots(2, sharex=True)\n", "\n", "tpts = df_pupil['pupil_tpts'].loc[key2idx(key)]\n", "i0, i1 = tpts.searchsorted([t0, t1])\n", "\n", "area = df_pupil['pupil_area'].loc[key2idx(key)]\n", "ax_area.plot(tpts[i0:i1], area[i0:i1], color='black')\n", "ax_area.xaxis.set_visible(False)\n", "ax_area.yaxis.set_visible(False)\n", "ax_area.set_frame_on(False)\n", "\n", "for label in ['azimuth', 'elevation']:\n", " pos = df_pupil['pupil_%s' % label].loc[key2idx(key)][i0:i1]\n", " pos = pos - np.nanmedian(pos)\n", " ax_pos.plot(tpts[i0:i1], pos, color=COLORS[label], label=label)\n", "ax_pos.legend(loc=(0, 1.1), ncol=2, frameon=False)\n", "ax_pos.xaxis.set_visible(False)\n", "ax_pos.yaxis.set_visible(False)\n", "ax_pos.set_frame_on(False)\n", "\n", "saccades = df_pupil['saccade_times'].loc[key2idx(key)]\n", "saccades = saccades[(saccades >= t0) & (saccades <= t1)]\n", "for saccade in saccades:\n", " ax_pos.axvline(saccade, lw=1, ls='--', color='gray')\n", " ax_area.axvline(saccade, lw=1, ls='--', color='gray')\n", "\n", "ax_area.plot([t1 - 4, t1 + 1], [area[i0:i1].min(), area[i0:i1].min()], lw=2, color='black')\n", "ax_area.text(t1 - 3, area[i0:i1].min() - 0.007, '5s', fontsize=LABELFONTSIZE)\n", "ax_area.plot([t1 + 1, t1 + 1], [area[i0:i1].min(), area[i0:i1].min() + 0.01], lw=2, color='black')\n", "ax_area.text(t1 + 1.5, area[i0:i1].min() + 0.005, '1%', fontsize=LABELFONTSIZE)\n", "ax_pos.plot([t1 + 1, t1 + 1], [-2.5, 2.5], lw=2, color='black')\n", "ax_pos.text(t1 + 1.5, -1, '5$^\\circ$', fontsize=LABELFONTSIZE)\n", "\n", "fig.subplots_adjust(hspace=0)\n", "set_plotsize(w=4, h=2)\n", "\n", "fig.savefig(FIGUREPATH + 'saccade_example.svg')" ] }, { "cell_type": "markdown", "id": "a9c60d0a-a0e8-4ba3-9eb6-3cb625e091ee", "metadata": {}, "source": [ "### Figure S5B\n", "Saccade-CPD coupling." ] }, { "cell_type": "code", "execution_count": 78, "id": "4e6d4354-b7dc-46b3-b296-43c8cd4e5891", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: phasetuning_sparsenoise_saccades.pkl\n", "Loading: phasetuning_spontaneous_saccades.pkl\n" ] } ], "source": [ "df_saccades = load_data('phasetuning', ['sparsenoise', 'spontaneous'], s='saccades')\n", "df_saccades['saccade_sig'] = df_saccades['saccade_p'] <= 0.05\n", "df_saccades['freq_bin'] = np.digitize(df_saccades['freq'], bins=FREQUENCYBINS).clip(1, len(FREQUENCYBINS) - 1) - 1\n", "saccade_tuning = []\n", "# As a work-around implementation, the same analysis is repeated for all units in an experiment\n", "# so here we take only the first unit from each experiment\n", "for idx, group in df_saccades.groupby(['m', 's', 'e', 'freq']):\n", " saccade_tuning.append(group.iloc[0].drop('u'))\n", "df_saccades = pd.DataFrame(saccade_tuning)" ] }, { "cell_type": "code", "execution_count": 79, "id": "056a328f-d290-4182-b9c7-75e7bb3f7d49", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15 15\n", "3.6666666666666665\n", "-1.0064810840100598\n" ] } ], "source": [ "n_sig = df_saccades.groupby(['m', 's', 'e']).apply(lambda x: any(x['saccade_sig'])).sum()\n", "n_exp = len(df_saccades.groupby(['m', 's', 'e']))\n", "print(n_sig, n_exp)\n", "n_cpds = df_saccades.groupby(['m', 's', 'e']).apply(lambda x: sum(x['saccade_sig'])).mean()\n", "print(n_cpds)\n", "phase = circmean_angle(df_saccades['saccade_phase'])\n", "print(phase)" ] }, { "cell_type": "code", "execution_count": 80, "id": "555214ab-3d9a-4062-b084-2dd258bc2cf7", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "df_sig = df_saccades.query('saccade_sig == True')\n", "freq = np.log10(df_sig['freq'])\n", "phase = df_sig['saccade_phase']\n", "ax.scatter(freq, phase, ec='purple', fc='none', s=5, lw=0.5)\n", "\n", "# adjust x-axis\n", "ax.set_xticks(FREQUENCYTICKS)\n", "ax.set_xticklabels(FREQUENCYTICKLABELS)\n", "ax.set_xlim(left=-3.1)\n", "ax.set_xlabel(\"Timescale (Hz)\")\n", "\n", "# adjust y-axis\n", "ax.set_yticks(PHASETICKS)\n", "ax.set_yticklabels(PHASETICKLABELS)\n", "ax.set_ylim([-np.pi - 0.15, np.pi + 0.15])\n", "ax.set_ylabel(\"Preferred Phase\")\n", "\n", "set_plotsize(w=2, h=2)\n", "clip_axes_to_ticks(ax=ax)\n", "fig.savefig(FIGUREPATH + 'phase_tuning_scatter_saccades.svg')" ] }, { "cell_type": "markdown", "id": "f521125b-dc1a-46cb-9715-dcb5f42d317b", "metadata": {}, "source": [ "### Figure S5C2\n", "Clustering of saccade-triggered spiking." ] }, { "cell_type": "code", "execution_count": 81, "id": "be9b6685-a4bc-4fe7-ae15-97f09560a292", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: responses_spontaneous.pkl\n", "Loading: responses_sparsenoise.pkl\n" ] } ], "source": [ "conditions = ['spontaneous', 'sparsenoise']\n", "df = load_data('responses', conditions).dropna()\n", "\n", "tpts = df['saccade_tonicspk_tpts'].iloc[0]\n", "i0, i1 = tpts.searchsorted([-1, 1])\n", "# Set spiking to zero if neuron doesn't meaningfully (so that noise doesn't influence clustering)\n", "df['saccade_tonicspk_response'].apply(lambda x: x if ((np.abs(x).max() / x.mean()) < 1.1) & ((x.max() - x.min()) < 1) else np.zeros_like(x))\n", "sta = np.row_stack(df['saccade_tonicspk_response'].apply(lambda x: x[i0:i1] / np.abs(x).max()))" ] }, { "cell_type": "code", "execution_count": 82, "id": "0a2fc654-3ff1-48d2-ba78-878650998e33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/crombie/miniconda3/envs/djd/lib/python3.11/site-packages/sklearn/cluster/_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n", " super()._check_params_vs_input(X, default_n_init=10)\n" ] } ], "source": [ "# Note: this cell involves randmoization, and will therefore not always return the same results\n", "# but multiple re-runs will demonstrate that the main conclusions are robust\n", "pca = PCA().fit((sta.T - sta.mean(axis=1)).T)\n", "variance = np.cumsum(pca.explained_variance_ratio_)\n", "n_dims = np.where(variance > 0.8)[0][0]\n", "print(n_dims)\n", "\n", "sta_pcs = pca.transform(sta)\n", "\n", "n_clusters = 3\n", "labels = KMeans(n_clusters=n_clusters).fit_predict(sta_pcs[:, :n_dims])" ] }, { "cell_type": "code", "execution_count": 83, "id": "330da5a3-b5ef-4c56-ab63-60fbe36684aa", "metadata": {}, "outputs": [ { "data": { "image/png": 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50ahYLFMLWvRUxWJRvAvQV90t53nTU7NFpIasrFKTC878lt65UCjIa6ABsW9OQ+L9qRFDPp+H2+0Wuif/O1A3gl7cZDLt1dDIA4hGo1KVH6hgxeo3Dze2CYcLuhvyzp07ceWVV+LFF1/s92/0JuOJkZPL5cRLEBxMIMkDgHhqkjDsdju6u7ulqJVKpYS3TXIH4XK5pHCTTqfR0tIiRmG1WqWn6/V6kUgkhPBhNpslr/Z4PIhEIrDZbEin00LMcDgc4oU9Ho9EAywSqX1ceiuGy+xDA5ACWKFQkHYXjZEFLvV3+XebzSbsOJPJJHzrjo4OTeHParXC5/OJMaXTaelBl38eak98sJDXbDbD6/UiHo8DqFz8IumGz8s24HBCd0P+7Gc/i61bt+LHP/4xDjvssCEXH8YiyrnW5dsS6WHIcAJ6v7iNjY3o6OgA0GvgDQ0NkqPGYjGNQQG9niMQCCCRSEiFmgcAjcbn88FsNsNqtQoxxOl0IpVKSSjucDiEEeb1ehEIBDRVY16TXpT/z3+j0fAw4D2bzWaN93S5XGK8/ML7fD55bxKJBOx2OwqFgkQspJEmk0np+7ISTjaa0+kUDwr0htl+v1/zXqmdgqF6TBYJWfyKxWJywGWzWc3gBymvww3dDfntt9/Go48+ivPOO0/vpz7oYLPZJBd2uVxwOBya9g29Dr2SyWSSUUWSImjs/C/pmeUnPj1vV1eXhPHNzc1C+KAHYctI7QGzYOZwONDV1QWg9xAKh8Pi6dX8nCGxGmXQcwK9YSVHNrPZLBwOh+S85FTTOzNv5lgnaai8J+bChUJBvCMjD3roUqmkiWZ4fTK26HkZpfDQ2peWktPplNdDgk05H324CluVoPvE/cyZM/u9wPEKfvH8fr8Us9Twi39nmMoWEqmZagXWarXKl1PNxcqvx7xY5UWrUQAjJIbu8XgcnZ2d8u/q85LQAWi9sHoA0AurRSBOTjG8ptGS8UVjZrjNsBno9dhqmE2+thoB0BBp6CqFlNe3WCxSPOzq6kI0GkVLS4u01PYnf/V4PBVHQzm8Uh4pDSd0N+QHHngAK1aswF//+le9n/qghMrZVQt/NGwaHYtfpEeSuRUOh4X7S5pjIpHoV0TMZDJobW0FAMkN2ZtWQS/EnJv5HRlI/II7HI5+FWEeOPwvw2aTyaR5HMNhoDeUZXrBthaFCFgrUQ8P9XCw2WxyIDEiYDTgcDjEmNkiUskX5cUvHphsZ+1PT5dc90AgIIcz/z7SI5W6h9Yf//jHcdxxx+GUU06BzWbrV5gBIDO14wlsKQF9XpETQPxZTU2NTBCRmsg/rAb39PQglUpJf5gtqtbWVilSMcQzm81IJpPiuQAIRZL5J0PYzs5O8WRsybDizQqzyrZSja+8JcNClhoC83Ch0XOqiu8DkcvlpDLMdAKAXFdlrAUCAakoM9fnIcCKN422p6dHXgeLjAciAcX7Gy3Q/U6WLFmClStXYtGiReO+2EUiBUcCVZKA6o1JqC+VSujs7JQvq/pFJjuK0j/pdBqxWEz6sqlUCoVCAcFgUIotmUwGDocDiURCcjcau9lshtPpRE9PjxRwWMUOBAJSFFIHG0iQAPp7znKQoJFMJjURAl8XIw5enwcDc2HSUfmesbjF98NisUjrjVRIyiPRsPn/NptN5rfVGemRGpapBnQ35Keeegr33nsvrr76ar2f+qCDWo3mF4keqbu7W1ohnOah1wH6WhgsKrGgw/5vPp9He3u7VIfpPYPBIJxOJ+LxOLLZLLq6umAymSTMpMejjA6HJ0jRbGpqEmYYDxs1qlKnjuj1K4WVatTBfrI6McV8mdI9/C/hdDplPptFMHLD1efnYAkPuHA4LK/VZDKhvr5eCngspLGFxYimmiiVSojFYjI8Ui0hSN2fta6uDpMmTdL7aQ9KqMP1pDOy2EPRANIb6eHIa6baRzAYlCKKWs3N5/NIJBLClaZkEKvdyWRSCkFAb65KppZafKJRAZDeMgBhRjEv5ReeBwenrQaKuBjGAtDwBvg6GPpykol5LqHytFlxVusCPDxYOAP6lE/4OEYBnJ5inSEWi0mbrdpgHYKHb7WguyEvXboUP/jBD+SLO57B4gg9CRlJ5cJ6JFUw3C3vN1OITpXQ4UwwxxjLi0LqRBEZZMwP+cUmCQXoqxAzMlBHB9PptHiyaDSqGbIYCORKA30Vd6DPAGncamuI3Q4aHf+fBSo+Rz6fR3d3N7q6uuQe+HxqBd7tdmtUTFjBN5lMUhxUe8DVAA8LpgvVgu6h9bPPPot//OMfmDRpEo499lgNcR7o/XI//vjjel92VIIeNxqNwmaziTdlG4mFGIZ8DDUB9AtXGQ5Tr4uhJmVvSPQA+gYUMpmMRlSPxBBWxmtqaoQhpeaO7P1ysoqGxGq5OnJIOSGSSgCIUfIwYEGOBwofA0DaUKQ4smDF/FstiqkVfnW4grk7e9y1tbXi0fl+koPOqIQTWEwrytt0ekDldFebrqm7IXd0dOCwww4D0Psmt7e3632Jgwac51XzW3VggoUdoE+oj6j0pSKDKxaLwWq1imCA1WpFfX29GDOfS+2/qhK2Xq9X8upcLiecYqBP9E7V3SoUCojFYiJwkM/nEQqFxGPzNbHIxmiMtFC2fXjg8LXzdVKNk+EzUwlGCSaTCT09PaIwwiEPpgWqyALfY9I1CR5ydrsd9fX1aG9vlxSB11cVTfUAvfFgnG49oKshF4tF/O53v4PP56vYdhpvsNvt6OrqEoYXq6esvKrje9TGAvrC3EpQmU9kfLFwpLZfgF4vQKOiR6Z3o8dlDs9wml6eHtbpdKK9vV3aT4VCQdpbPp9PquXU6VZzQbfbLfxwemVyqdWJKYfDISEyPSvQpwSSy+UQj8flcHK5XJr5Xh40DL85/sniWvmEE1tsLJKRITaUCaehQm0tlh/S1YCuOXKxWMSUKVMMMsj/QSX7s0fMET+ymjggoQ7lDxTisRfN9hMnbPx+v7RfVLCwxi+UGvqSWOH1euV69Nrq+CILcQA0LCwWkehpOKig5rl8bvaded1UKiUhPEHjoUHxmowc+PskYKgVbrX3zXlrklDsdnvFkUa+Zm6u4MGZTCZ1GeopJ/9UG7p6ZKvVismTJxsUzf+DOnLo8XgkN2ZBil9Gn8+nqWgOZMhUzkwkEsJe8vv9YuAkdagVXXodzvOy0MMvK3NDPob9V/abU6mUtLxYDCMDDeg1BFbgu7q65Evr8Xjk9ZEYQmki8sWBPmF6dbSQ89U0YqCPFcYesdqaYn/carUiHo+LEihJKRTHV72iyoMn35sa4By22F+oiwLK9caqBd2r1rfccguWL18u0zvjGTRkei6SH5ibMXRkCKr2WSshGo1KVdZqtSIYDKK2thYej0fC3o6ODk0VVhXbA6CJCgBoqrl8TDqdFkNiKE26JAcfeA1WY+llaaw8FJj7qq0ovk6TySShPQ29p6cH0WgU0WhUDi0auTopxjYTRQX5fnq9Xim28YDJ5/OaKTS+L3y9ZNHxEOLM9v5iuL0xUIVi10svvYQ9e/Zg8uTJmDNnDhoaGvqN3I2XqjXplRwPBCBjbyxMkXnV2dmJYrGoEZtX0d3djba2NpHH8fl8MkrH3jHQS8FUBek5U0yWFwcMeF8MO8m5poft6uqSQ4jjjyxMWa1WJBIJ6d8yR+b/M7wlyNByu92w2+3y2in/S8WSQqGAQCAgeT/ZZGxPsbrN9wDoC8V5X6q2GFlp7Abw/wkyyxghkQhTXnXfV6hrdYaLg12VqvX06dM1fx/vcDqdiMVi0rvll49GosraVJJ3zWQyaGlpkS9cMBgUI2P7yu/3y2GRSCTEWFgtB3oPlvb2dilMcUBe3QBBr5hOp9Hd3Y26ujoJ/Wn0NTU1cg1uUORrKOcgs5CnyuICfZNVvDefzyeyuPT49NyJREJYWH6/X14/0Ld2hukFDymqjvAQIQlHHSShITPS4EHAUVPyt/cFrH8Aw+eNgSoY8urVq/V+yoMeaqjLPjL7qqxAq4qZFIYncYRMJKA3FA6FQojH45oZYPK32VMl0UEtqrW3t6OlpUU2OlDvix4rmUyKEbEHypCfLDAKzPMwUrnSFANgFECvy8OJ7TAqZPI6zHkpIKCC/Gq2yXgvLGwxd2fUEIlERMuMXprvPzdQUrVE9ZZk1qkC/XwP9sUrq20+FvDU3nu1UPXxjQPdBDAWwLYG6YRdXV2SozH/tFgsSCQS4uHovUnFpKAcPTh/h60ntQJO2RlOBqnrWmjcqVQK7e3t8Hq9wodW/51SP6yml0oleDweKab19PSIVwZ6pWxpjC6XSwySRkRVSmpj896p0cWoohL4HAy5eeCprCy1WEVDZpisTjvxkGRNQL0PRhwUPgD61uYMBTw0gb6Wk1rIG4iXrgeqwuBes2YNzjrrLHi9XmHULFy4EGvXrq3G5UY16FVJ5uAMLU95qkeyUKPSB+mNWEGmV2Key/+qrRTmosz9Ojs7JZxnbk1D40gjCR3s4Xq9XpHK5WFD70wlEV7X5/Ohrq4OhxxyCPx+vxgsAAl5mSuyAKa2qBjW0sOX72xWi2o8XJg/qyw4htNA34ihei9A37hkJc62uraGNFYAmrRnMKjemHURtRpfzV6y7h755Zdfxtlnn43p06fjG9/4BhoaGtDa2opVq1bh1FNPxbPPPovPfOYzel921CIWi0nYxrCUX2CuZlGVF6PRqHzRyKkGIML1QN+WBxqfSs2kVySlkl6eGyZY8CKBgoZEYT56LErvqAZDQ1c3WVAdg60udfiBBsDog0UkVpL9fr+049LptFBE8/m8hMbq3LNaBFOr0BQpUN9H3hsNkt6YlFXmzWqhkNVrYP+8ssqrZoSjvkcHlUD97bffjvPOOw8rV67UnEBLly7FhRdeiNtuu23cGDI9IiuiwWAQ8Xhcs1mRi8/Y6uEXi188Mqc49E/qJMM2ainT+/A9t1gssteI+aTD4YDVakVTU5OGeMFCkzpK6PF45KBgvk7eOPvRNBT1mgAkPKdBk29NYoc6zEG1S3pt/h5FBmmcvA4HRUgp5fuirmDl86lhPfvMzJE5wFKeJ9OQ6ZU5UcXr7O2z5r2qfWRGQdUaXyR0f/aNGzfiiiuuqBhGXHnlldi4caPelxy1UHM4s9mMlpYWKSiFQiFR8KAXVlsoTqcTbW1tMrEUCoWk+suClzq+pw7cA73tKubmbB1RnZKsK3pWGgFXujocDjGEXC4nPGuuOgX6hOZYSQe0a1xU6R0W+Ah14kkVwGfEAPQeBlypw9SDfWlGMeqgCL2tqj6SSqUQjUaRSCRE3IAFL5UHzUOmPKxXGWEDkZxIC21tbZX+dzKZFILNcBgxUAWPHAgE8MEHH1T8tw8++KDfNNRYht/vF751TU0NWltbxbg+9rGPiecplUpCO+Qy723btiGVSsFutyMcDmtCPXoRn88nAgDMxfnli0Qi4oVZRDKZerdPUGidXp9hPgtUqgGSPOJ2u+H1esXr1NbWasL+8p6rWpTi3wHIlgiLxSLVe16LbDV6Th40HNlkFZmHE0N61RvabDYZVGGOTnEEjof29PSgq6tLE+6TXMLPgB0FtYLNdbQAZMZcFWDge8FqPDsS1axWE7ofFYsXL8att96K3/3ud/LiMpkMfve73+G2227DRRddpPclRzUcDgeam5tFRB6A5IKxWExaRkCv4VutVvT09IiBm81mNDQ0aAo56pwvVTrY/yUXmy0ProZhwSqVSiEWi2Hnzp2Ix+MyN8xQs7a2VgYS1D61ynXm2hZGXTxg2AYC+sYLmQ/TWzM6YDSiTkERnL8Geg8fcrwJhvmEWmRSr61yzbkKhwdlPB7Hrl27+gkMsg0XjUY1fW0AGjEICv3T8/NgVFVKqb7Cz7ma0N0jr1ixAp2dnfjc5z6Hz33uc7ILCAAuvfRSrFixQu9LHhRQpW3IXVYr0iaTSUJYfsG5kYJFLECrj6V6MXqqaDQqs8b8GavmrFQz7KUnLRQKEqYzb6X0K/vE7CFzSwXVSOipWDyi4auFJc4P8++MGngflWaBWaAjRZTVfqYaTAeY7/L9UHNT1iQY6mYyGfh8PrS2tkrLieOXpHaS002Pyyo/owL+l4ZJnjb75WqxjDUBNeevlnfW3ZCdTiceffRRfPvb38Zbb72FlpYWNDU1Ye7cuZgxY4belztowMOM439A7xeZXyBuaAR6T36GdipjC9AaMotIzK1ZsIpGozKwb7PZEIlE0NjYCLPZLJpWNAj+LgAp6lCmp1QqyXwvvRuH8Vl0oxHzMQwn6elJXKGR8ItPthu9Z3keyQiD90ShPU5PMb9W81q+FwCkkMW1NByFpPHTs1K8X10kwNdFzjXTGg6mUNVUHUTh4cnPmHK9lBkulwzWG1UjhMyYMWNcG64KVj7JEWZRJhKJaPjOhNPplPYPq88URi+vnNIrlkol7Nq1C6tXr8aGDRvw4YcfytrWbDYLv9+P0047DaeffjpmzZqFcDgsVetMJoNoNAq3241sNiuif+xbc1uiOv/b0dGBuro6IVwwsgD6hi5ovCSJcLghEokIEQXoyyvV8JkGy8OIBxPfH4KCAATvURX4czqdstSO/HRqUat0TdYDamtrhSjC1+V2uyUVURcIsJKuHqq8HxKAWI2vZtGraoa8efNm7Ny5s6LA2cKFC6t12VEJtpb4gXMCye12awYgAO3Mr9orVudqi8Uitm/fjk2bNuGtt97CmjVrsH79es2gQiX87W9/w7333gu73Y4vfOELuPHGG/uFxjReFpuobsEWGnnY7FMzRFdzRR4QZPWxMEVZHXp1gn1X1UDVmWTm7OWiA/TaZrNZvDUPHkYPHEqh8eVyOdTV1UnYyzoD90OzvcXCHrdVhMNhMUQWxNiVUFtT5RRThuPlrTq9obshb9q0CZdccgnefffdiqJmbDuMF1C+hkUtDijYbDYN2aFUKolCBwDRZP7v//5vrF+/Hrt370Z7ezs+/PBDbN68ud9Yngqz2YxJkyahqalJ+rVtbW145513JH/92c9+hqeeego333wzTjvtNKngkiHFcLNUKqG2tlY8NI2NhkpSC6vDrCTzZ9ziSJkfrojlvwN9M8ksEAF97R56PnKqCRolXw/zd/ap2f6hV2WIHQqFhBdONROy7gqFAqLRqBxOqlTQ9u3bZeTT6/XK+28ymWSVLSvubNWRHsvPhJFJNaC7IV911VXo6enBk08+iZkzZ45rgXqgT22DOSfbEaQ+0kuzX5vP5/HII4/ghRdewIYNGzQqGgNh6tSpOProo3HMMcdgypQpqK+vh9lsRl1dnXhRh8OBXbt24b//+7/xH//xH3jnnXfQ2tqKG2+8ESeddBLuuece+Hy+flrPJpNJxiKZB7P4VCwWxUjpLWl4XNhOY+V7kMvl4PF4NI9Xjdfj8Wikb8lFZ2uI96aGqmpUwNZaMplEJBIRw6EXpWf3+XyaOWe73S6vJRKJIBKJSAfBbDbL9Rniq3PkTJ1UQX3eC9OH8hFKvaG7IW/YsAGPPfYYzjnnHL2f+qADvZSq30TNKYZ9ZE198MEH+NnPfob/+I//0HgCwmazYfLkyZg6dSoOO+wwHHnkkfJHVdyIRqMS4gaDQRl2iEajMJlM+OQnP4kFCxbgz3/+M5YvX4729na88cYbuPDCC/Hzn/8cM2fO7CeGQA/H6jbDcOb6dXV1iEQiYtBdXV3i6Wg85Fyripckb3DiioUtNaxWf5/cc3pzdcEde8cApKqtLoRTF+Sx+Mb7YRGsVOoVk1fbXdyYYbFYkM1m4fF40NbWBrfbLUUvEl6YcrDVSC9ssVhk4KVa0N2QDz300GER/j4YoGpzsdXCbQP0FA6HAytXrsQVV1yhCZePOOIIfOYzn8GsWbNw+OGHY9q0aaIgWX4NVYSeMrlcN1NXVycFM+ZydXV1+Od//mcsXLgQN998M/7whz9g586dWLRoEb73ve/hjDPOkHYTQ22mBswfaZgsjtntdnR2dopyKNB7+DQ0NEjKQNUOFsH4RVdBcgYA0fmmiiZbdhTX46yz2WyWw4/yPpz1pkIoJ8mYwrDPzjCYwoWslJN9xwOJuTc/I65r5YHCg4eDJbxvssmqHZnqbsj33HMPbr75ZhxzzDGYOnWq3k9/UIHhFPNJhmacUsrn8/j617+OBx54QH5n7ty5+Pa3v42zzz5bvmTcn8QcUJXtYT+VBBNei19eKlvyZ8xTASAYDOKee+7Bxz72MfzoRz9COp3GV7/6VXzuc5/D9ddfL4/nChoOcbDtQg6zOpHFMFOlfAKQMUxyuPn66W3ZK1eF79mCY3tO3XKh9my5oZKHFFt6FOrjYcrOAavYnNFm3SYYDIoHp9dmKpRKpTQpB5+D3ABWtfkYGrEqg8QprWpAd0O+9dZbsWvXLsyYMQNTpkypSMl866239L7sqIT6obJnzNB0z549+OIXv4h169YB6GV1/eu//ivOO+88hEIhANCEl4S6fIwVYKpXkm5JrjSrwRxXDAaDaGxsFGM3mXo3MFxzzTWYOnUqbrvtNiQSCfzmN7/BBx98gAcffFBE8dlvZmWbX3Lej8oCo4GbzWYRLmCBk/xsHmz8L0FmmsvlkhYcZ3qpdkLaKw8AlZJJcgk9LD2yGpJ3dHRo2kZ8LbFYDIFAQApzXV1dEhIz76ayCimonZ2dElWoUQoLYzyUqjmLDFTBkGfNmoVZs2bp/bQHLVTxOIrHv/fee1i8eDF2794NAJg9ezYeeOABTJgwAblcDpFIRBQkKZrOYpG62ZHKId3d3ULiIDebTCh+SRlq8kAB+phVNpsNZ555JqZNm4avfOUr2Lx5M9544w2cffbZuP/+++Xz5BeURhqNRjVtoVKppFHeZJhNhhYAORBoHKqkD0XmGcazlsD8m7PQnLlma4wFLu7J8nq90upiLkwiDN9fFu38fr+0nvg5NTc3o729Xd5zMrLI6uJBxecniYbMMra0VNHBamtb627IDz/8sN5PedCCXxrSDD0eD9asWYPLL79cdKEuvvhiLF26FPX19ZJHMsdlRZTejhXsZDIpk0EkLHCmmGwlDgyos73MH7u7u8VzMZ/L5XKYOnUqnnjiCSxduhRPP/00duzYgfPPPx//9E//hJtuukmE8ejtAYiHpHcFoGlhJZNJDdeYggQM71kRJs2R+TFzWWqMsXDGni+JFywkMc8tlxomT9xqtSIQCODDDz8UzjkZWOxrqxRMelM+F3NdHmAqUcbhcMhhxfeUdRF+XtVe4Tp6NjWPQbBPSRmbZ555BjfccIPwgW+88UbceOONcLlc8Pv9QvNjrkkwFGWYprZ0AEguy98jw4iHQigUgs/n01R+Ve0qoDdf5h7lH//4xzjmmGOwYsUKpNNp/PGPf8TLL7+MRYsWYcGCBZg2bZp4VABiTJw8crvdIkDPfm8sFpPQUm1xMR0gh1vtLfPAAiCqJqygA1pKpro+pnxFKymgLExRFzyVSkk7iR6Xh4fD4UAgEBCSCNOieDwuk1NOpxNer1cOOKBv6oyFR/aTU6mUtByrAV3q4V/60pfwj3/8Y8iPz+VyeOihh/Db3/5Wj8uPWtTU1IjA3d13342rr75avmR33XUXvvnNb8Jut0uoS8ogebxer1e+9PQAJBnQ89TV1YkXYG7HMJbsJZfLBY/HIwYPQKqzBNlXQO8X/8orr8Rf//pXnHvuuQB6q8mPPPIILr/8cpx33nn4/ve/jz/96U/YtWuXpjVVX18vvV+mBMxNmWZQDZM7kClYEI/HhYbqdDol9TCZTGhoaJDUQBUyYMGMfW0akVoNZ8uLOTVrDmw9AZA1ttQzI5+8rq5OPDcr5Ky+s/ugXovFLHU0kryBaubIuhiy2+3G0UcfjZNPPhn33Xcf1q1b10/ge/v27XjyySdxxRVXoLm5Gffdd59GNncsolQqobW1FV/+8pfx0EMPAej9wjz44IP4whe+IMUmhqk0VlWtIxQKIRAIwOv1Sl+YoZs6Y0tvxCEHegKGzmzFqFsTy9ejUGebxlJbW4uHH34Yzz77LObPny+/19LSglWrVuEb3/gGFixYgLPOOgv33XefGAVDYAAathoPH05Xqfpfao5KUgjZXcFgUEJxVdifz0mCCvNy5v68XxYEqaBC2R3eC/vBNGRGBzRUzm2z16y248o9LP9OUQOOg1ZbYECXZ77//vvx/vvv45RTTsE999yDefPmyexnKBSC1WrF1KlTcckll6ClpQW//OUv8be//Q3HHXecHpcftXj55Zdx7rnn4n/+538AANOmTcNTTz2Fs846S75Q5e0IfvEZbpI0weIMOcUApOqaSCTQ1dUlLRpVoUI9KACIpwcgXpLhLA2LHoZh+AknnIDXXnsNGzZswNKlSzFnzhzNfW/duhUrVqzAlClTsGjRIrz11lsIBAKacUVV46umpkaGHei96elUbTPOQPPwoZEBvbk5hQPZIrPZepfkxWIxtLe3axhjZIuRvRUMBiUNYQ5b3g6jZ04kEojH49ixYwcSiYSE6uxDq2CKQ1aXzWbTLJyrFkylSoToA8TmzZuxfv16tLS0IJPJIBQKYfr06TjuuOOqnvSPFHj6q2/nX/7yF3zqU59CoVDA2WefjZtuugmhUAiNjY0IBoMVQ61SqYTt27eLIPyECRMA9BoDC1ysurKQlUqlRGI3mUxKrzgQCIgkUDmZX/WArIxzQ4QqwcNCVG1tLSKRiEwAWa1WbNq0CX/5y1/w3HPP4b333tM8/5e+9CXcdtttsmmRYbzf79esTWUu3NbWJnulJkyYgF27diGfz8Pn88ma3t27d4uBOxwO2c4RCoWkKs2VOmRyqcwsapvxoOR7ZrFYRMeMK2gY3ZAUkkwmNeKDLL4Fg0HJn2n8H330kaQQhx9++JDldA8EVTHk8YhKhpzNZrF8+XIAwEknnQSfzwefz4empqYBJY+KxSJ27tyJbLZ3R/GECRNQLBYRi8VEvUIlaNAg6c3UnDEcDsPr9WoUNwhqYtG7+3w+af8wTOeXnW0tDkiQIEHPHo/H8e6772LVqlX4/e9/L5Xn+vp63HnnnZg7d65Uq0OhkLRmAEhY29XVhZaWFgC9clFkYtXW1mLChAkwmUzYuXOnhLx2ux27du0CADQ0NKCpqQl79uyRGWXm56roH2eoGcozj3U6nfD7/QiFQohGo6IgwtSGI5hkc7EFRnYZ230Uh6Boos/nw6RJk4Zl3qD6qmDjGGazGV/84hfxqU99SjSxamtr++lZqVAH1DlJE4/HkU6n0dXVhUKhoGF6sQ/LllBtba1I3wKQfczl5zXzc4KkChbN+OUnI4riciwokbFEmuSsWbNw++2347/+679w8sknAwDa2tqwZMkSfPvb3xaVEdYA+PqZApBTXSgUZGCBzLhMJiOvgzRXvm5OO3GiDOg9lMiI27JlC1pbW0X0UFXOLBfp27VrlwgccuSQRTKGyYFAABMmTJCetclkQnt7O+LxuMxg22w2BINBBINBjThhNWEYchWhso7oIcvH9MqhGnIul0NbWxs6OzvR2toq1Wt6S4a/VMlgwWbSpEnCIKPnLde1AvomggBItdZkMiGRSEixLBAISAuN98wWEL0fDTGXy2HixIn49a9/jZ/+9KdC0Xz66adx0UUXoaOjQ5MrcgMjRewof0syDHvp27Ztw+bNm/HOO+/g/fffR3t7u+S9JF2Q9MLKPyvS7Jsz/2faoO6q4ufBijcPXB6WiURCU/hyuVyoq6sTb+1wOKTV53A4UFtbK+w8rnVVFVWrAaOPXEWwD8oBdXXNS6W9QjQMcqrJn2YoqQ4CsGLKXNrpdCIcDqOhoUHTm2Wu1traivr6+n5qJKz05nI5qbLSW7K9xfUwrBZT9YO0SRaw6CmtVisWL16M4447Dtdeey3efPNNbNu2Daeddho+//nP4/rrr0dzc7PkrerrYPFuw4YNWLNmDTZs2ICdO3dqhAiA3milsbERM2fOxLx583DaaafhyCOPlJU5rDQDfZ7XbrcjFotJH5lEFnp7Gnw4HEYgENCEyqFQCIVCQSN9FA6HYbVaZWKKhTqv1yscAm515GddrT7yuMmRN23ahK985StYu3YtAoEAlixZgjvuuEO3amKlHDkej+Pvf/+7cJUbGxtFHoeMIRZfVM400Js7cgMj20/kHgN9SpEdHR0SWtfX1wPoW4hOUojahrLb7fD5fFIMYuW1paVFc7CwB+7z+UTppVAooK6uTgyYoTefhwyq+vp6mSFuaWnBL37xC/zoRz/S0BW/8IUv4IQTTpCceevWrdi4cSPWr1+Pt99+e7/2E/t8Phx//PE49thjMXXqVEycOBHpdBo+nw9erxfhcBjd3d3o7u6WlhV72JyG8nq98rrz+Tw6OzuF/kkmGSvR/LwZwbDCzr1T7I27XC4Z4KgWxoUhRyIRHHnkkZg5cyZuueUWfPDBB7jxxhvxta99Df/2b/+myzUqGXKxWMSOHTvQ1tYGoC+UZXWTHzw9tbpEzWQyiYd0u92YPHmyRnSORsQ2S7FYRDgclmvzsexN53I5CaM5PqjOBLN1xcNFZWHF43GhdXJ+WKU0cjsG808WeAqFArZv3y4FvB/84Ad49tlnh/yeWq1WHHXUUZg+fTqmTZuGww8/HPF4HHv27MHu3buxceNGbNq0qZ+3Jux2O2bMmIHZs2fjmGOOwdy5c6XoRx1xs9kseuNq6kDdNKD30KyvrxcdcQAIhUKaz5wreACILjbldnO5HCZMmKDp4euNqhhyT08PHnroIbz99tvYsWMHfvKTn+Dwww/H448/jqOOOgpHHHGE3pfcK773ve/hrrvuwvbt24W9dNddd2HZsmVoaWnpN+O7P6hkyJzfzWQy6OjokC9cIBCQsI89UA4YkEzBohYAhMNhGV6nN2eeytFFVsQ5kMCcViX9s3XCg0C9d3obUipp7DxUEomEPD+LS+xBk0tNosfkyZPh9/thsViwbds2AL2kjnA4jDVr1uD222/Ha6+9VvF9bGxsxFFHHYWTTz4Z5513HpqamhCPxzU9cY4zcrNDW1sb3nvvPWzcuBFvvvmmVL8robm5GXPnzsXxxx+PE088EY2NjbDZbIhGoxIO80DlVg+OX6qGXC5tS60vfv4ejwctLS2yT6uurg4NDQ0HT2i9efNmnH766YjFYpgzZw5ee+01rFu3Dscccwyuu+46xONxPPLII3peclCccsopaG5uxmOPPSY/++ijjzB58mQ8/fTTQkM8EFQyZBowWyadnZ3Sg2WFlb1LntzM3RgGk97H9g3DXxaJyDhqamqSAg3QW0hqb2+XYfzGxkZhLpEHzXBQnVFmsY0kCVVVk6/R7/dLrqm2xej1GxoaEAwGpQLNFo06n9zW1obt27dj27Zt2LJlC+x2O4444gihPHJckLPWbKup5BGGryaTSbZxdHd3Y9u2bdi4cSPef/99rFu3Du+//77m4FIxZ84cfPaznxUxBXpW6lCzeu1wONDQ0CCfmRpeE+SUs0DG+2MVe8qUKf2EFPSC7s96/fXXY9KkSXjmmWfkjSDmz5+PW265Re9LDor33nsPn/rUpzQ/Y2X3vffe08WQK4HEem55YE6mFo1YaOIfhtts69DwAMhootVqFWYUR/rC4bB4aXpR9n1VgXRWWQlOXKmbEFjt5bgiv7iswHPxXPn2BJIrKJ3DqnMlL1RfX4/6+nrMmDEDsVgMra2tMq6oss9YKWYtgLuVgT4BPra1nE4n8vk8pk+fjjlz5qCpqQnpdBrvvPMOXnvtNfz973/Hli1b8M4770gYvX79eqxfvx5utxv//M//jBtuuAHhcBjt7e0SETHa6OjoQG1trbxv6uFGFhyHW9gHZ/2CPPlqQXdDfv3117Fy5UpRJVTR0NCAPXv26H3JQRGJRCoWGoLB4KASssT+5DYcy1OLQJFIRAyWWsoApBjFDz0UCsFut8tmRnomv98vShRsd4RCIfGu5ffMAYxEIiGzwSpqamrg9/tFSodVbEYTbGlxxpejfCzosKKez+fFc1Ohg22xSn1sAHLAtLe3i3oHi1B8/6gNHQwGJU3h9BF3RrPVxFyfEQYJLz6fD0cffTRmzZqFQw45BBMnTsT777+PZ599Fr/+9a+xZ88eJJNJ/OIXv8DKlStx880345JLLpFDhREB+/fU+ObrZ9QDQKOOqhYZB2Ly6QXd+8hU16+EXbt2jaslbvRkqoKG3++Hz+dDbW2tFLECgQCamprg9/vR0NCAxsZG4WFzzUp7ezu6u7vFMzE8r62tHXAZN3ue9GCtra0DqnKS/cUcm95EXcfC56LwHumJbKVx3K+8V8w2HPu9DMVbW1s1wgsUBaivr5c1Lk6nE6FQCM3NzbLBkkU2Di5wyCQWi8l7o651YTri8XjQ3NyM2tpanHjiibjzzjvx0Ucf4ZlnnsFpp50GoPfQv/XWW3H22Wfjww8/FPHCUqkkgxwk6HR2dkrriSgWi0KNdbvdMqfM2eZqQXdDPv3003HnnXfK4DwAYcg88MADIyJOHwwGNfdDRCIRTd62N5BcMdCfSuAQPvWM/X6/8KwBSGhNg2hubpYNjgwXOcXDcJz5msvl0ki6An2rSdVpIX6Bgd5wsKOjo584Ikn+kUhEFET4RaRkTW1tLSZOnIjGxkZMmDBB7lNdiM71MLx3dV44Fouhs7NTKuAc8WOY7nA40NjYCL/fL71aoE/AkLPDfr9fWl1UUOHjSU3lAchDiYcQDbL8MzrnnHPw6quv4j//8z8xadIkAL3tykWLFuF3v/udRrSBZJW2tjaZPQYgbT1GVKwLcL68q6trr1rkBwrdQ+u7774bJ554Ig477DCcfvrpMJlM+O53v4t3330X2WwWTz75pN6XHBQzZszoR+rfsWMHUqlUVdfamEwmBIPBfmG52+2WkJ6sKFaESU7gZA5z4ubmZpGVUcNlhtfUqeK1XC6XhJ2UplGF/EhZpFGo4M4pHjRqiEuvyplicsJZfOrq6hLjp3geJ4bS6bTkvqr6J187758HF8NktRpMI+Hr9ng8UisIhUJCo2QY29bWJgcLhQgH+qwWLVqEk08+GT/60Y9w7733IpvNYsWKFXj99dexYsUKqTlQtYWHUVNTk0x00XPzc2PBsNL7rCd098iHHHII3nnnHVx99dX48MMPceihh2LPnj1YvHgx1q9fj8bGRr0vOSjOOussvPjiixq96McffxxOpxPz58+v6rUr5dbq+hBOKbGaqfYuI5EIstksfD4f/H4/Jk2aJNsBaTjs+5b3KFUuNWmLLpdLPCMpieqXi4cB55dJHCHpgcMTADRjlnV1dTKSyJWuXV1dIpnj9/ulh82/816tVquIK/CaPMA4RkiDp3fj79H70WhJTlEprvSCTFXUkcpy0PC++c1v4vnnn8eUKVMAAGvWrMHChQvxhz/8QSagKNgAQKr3LICR+cYBl6amJhxyyCH9qtx6YtwQQmbOnIlZs2bhlltuwdatW/H1r38dX/3qV6tKCBkIHBdkxbmcNplIJCR/9Hg8QsCnSmU0GkVXVxcASE45UDGO01Ek9XMbIqvBFFpX17WwL0zvQiYUIwc+Dw8RzuzGYjFhMtErcTczN0yy6rt9+3YR6VM3XASDQYkcdu/erSkWlUq9Cpg7duxAMpmE1+tFbW2tFBTdbjcOPfRQKb51dnYKHdPv9yMajQIA6urq+k2Dlb9fQO+Be/XVV+OJJ56Qf587dy5uvvlmTJ06VWib1BHj4cYIiAfDs88+i82bN+Oee+4Z9Luxv9A9tK6knPDSSy9h06ZNOOWUU3DMMcfofclBEQwG8eqrr+K6667Dueeei0AggK997WtYtmzZsN8LAPEq1D6uBH4x6KXIHeZGAwBS3d5bRZ3CdGxNMc+m6iOnkmjEpIsC2vCcVeVEIiF7k1TSCj0uWzWcoqKAINtgbNmoYTW/K2qUEolEJLzmQAaFBiikR+/LoQsAUvzi2CFfRyAQkAMonU4PaMiklgK9hdsHH3wQxx9/PO688060t7dj3bp1uPjii3HKKafg0ksvxYIFC2S7B42XRJWnn34aq1atkhnpK6+8smqqOLob8sUXXwy/3y/SNvfffz+++tWvSi/1ySefHJF1MjNnzsSf/vSnYb8u20dqP13tv5YbMkkUzBO5q1ddicIcjVI/ewPDVI4Llkq92wNpEDRODgmoOSQLZvzsGIazAMeeMimJ7G9zNpr0T7ZnVD1tht2U9eF1AAhNlUbF4Q/mnWSpsVXFIZJ8Po+PPvoIdXV1Gulapi5Op1NC/4HWnJK5xsjHZrPhnHPOwZw5c3D//fdj1apVKBaLeO211/Daa6+hoaEBkyZNQnNzM7xeL7Zv347t27dj165dmk5COBzGu+++WzVD1j20njBhAu677z4sWrQIQG/OfMkll+Duu+/Gl7/8ZWzYsAFr167V85KjApVCa/ZJAUhoajKZRGXDYrFo2nHFYlFmZ1Wxea5k4WNYYCIZfzB0dXVJmKmGsio8Ho94E94ve8Us4jBC4OvkalXSF2tqarB582Z0dnbC4XDg0EMPlfcgFotpxOw5zK9K6/Aw4RACxy/JGPN6vejp6cGePXukIMfBkFgsJvky5YAdDgf8fr/kpuxZm0wmNDc3D/jedXd3o6urC7lcTop+LNbt2LEDv/jFL/Dkk08OacHeUUcdhcsuuwznnXceDjvssKr1knX3yJ2dnVLQ2rhxI3bv3o2rr74aALB48WI8+uijel9y1IIMK4aYnLlVxdoJqnGoelvc1siVJBSqozENRXmCBRiSJFjh5TWZ65YrT/JLTg/F6zG85f2q7C018qC+Vm1trbwPPNQYGjPs5/UY/vP1s0pOSqla+VW3ODL35vtMI2b7jOA9MrweyJCZDhChUEjGOGfMmIEf//jHuO222/Dggw/if//3f9HW1iakkilTpmDmzJk48sgjce655+LjH/84otGohgRUDehuyA0NDfjwww9x0kkn4YUXXsDkyZNx6KGHAkC/HbdjHWyFMIdjuMbCD+VgGTqSNFEoFFBfXy9qjeosMr9grNoOBg5n8PdZaQb6dlWrO45INOFzq6E5PaM6xphKpYRhRQIMQ3JqO1MfOpfLSXRBrTEAcmiVSiVNr7WmpkbYbVxER841mW1erxeZTEbyaBaYmHqo9QO+Nu6PKp8HV+8ZQL++tM1mkzaZx+PBt771LWGlsXPgcrkkBcjn8zIeSrXTakF3Q168eDFuueUWvPPOO3j44Ydx3XXXyb9t2LABhx9+uN6XHPWgdAxHC1W5Vuak3d3d8m9UHqV2FeVoVAxlioYeCugNnTnYwMiAAxdq6MxpHxXsnwIQLSr2o2ngu3btkn1L7J1yeIBhN1tMbCMxMlB3PNGI1H44o4BoNCocbo5okpfN99LtdgtNtLz+wMF+0ll7eno0lFXyzlUSCaMnroPh50YySmNjowhF0NBZvWaenkwmZXKqWmqauhvy97//ffh8Pqxbtw7XXHMNbr31Vvm39evX4+KLL9b7kqMW6opRv98Pv98vhSwKCwDajRRer1eTFzM3U8EZ46Fcn2CBiwvdLBaLeFJuFKQyZLkBcBqJhwpzbXVogIUvMs7oPTnny3WjVOpIJBLi+ctfJw8VFrQ8Hg927twp+TYJMNwK6ff7RSlTFdkrB983ekvVkIvFomaumXpb/Fz4HgGQnjoAWRBHgXxGBqxx8FDRY1R2b9DdkK1WK5YuXVrx30aC1TWSUEMp5rnkNLOiSvIApXNoSPSK6hpWejA1P9wb+MWjhyGjjKOTwWBQNiGwRTWQKCDzc+agNPra2lpJHWiUFEno6elBNBqV5+drojYYeegANCG1Gg5TzIDvgapjDUBaUPydaDQqh2C5BDAAea7yXdJUD1WFAFVhfXXslLK6nG7jjity1HnIMfphi02dOtMbVdXsYvhSjrGqbV0OdUKHpz9DM2514Mgf9y6Vq1sC/VtUQ0GlTYCs6nL3E8XjgL7iFO+zUm+a4vg0dmqFkRTCvJN0UM5Qq69BLSLRqFgILAenwYDeghPF+5hv0vuR/cWpLbPZjEgkUnEfMdtijEy4/ZGUVSq4MA+nQfLA4IgnFUQYNVBSiQcYC2qsO1Rb21r3ylM8Hsd1112H5uZmod6V/xlPUHuj7F8CEK40x/yo0KFOKx0I1AOU98AvKv9dNSp6C95nJVDTioMgqodxu91itOwfs1rO4hBftyqeoBa41NYdvV35tWlADodDFpxTDIEGmMvlNOopKliFz+fzQtwgdZeDFmSpEXyf1LFUvgbeG+sABA2b1fNqF3l198hXXXUV/uu//gtLlizBzJkzh0WcezSDbCWGswDktKbXo+cu//IeCFQpIfVgUAc2qPDI+6SCJgs5lbxyee+b4JeZbRbVa1NnmpRUChCw6EcDJmGF96len4+lWAUjCxa91BWrHNJguEt9LoKtJFVlNBgMSqGMbTl6VgokVjocCS7BI8GGhyY56NWG7ob84osv4t5778WSJUv0fuqDEjy5OSljtVqRSqVE3JztHQC65VCqJlelJWMs9iSTSY1RMkTml3h/7odhL+eUORfMlTQqVM/Pw47PoToAhrfMYTmkTwYZiSEqP5yGTxF9HjL0/vS+POjsdrvwuTkvzYo+DyaG22yVVQKpqDyIKuXp1YDu/t7tdmPixIl6P+1BDRpTLpdDZ2enMJlYQS1/3IFCNZDy5+QUFB+n0kXZLwX6xOP3F+ylqpsl1Xtg0YjXUL1v+ZefhwHJKxxf5EinShJh+0vNv2OxGNra2oTmSsNV56YBaLx7IBDQ6INRlIFFq0o5PUUU+HqqvYFRhe5XufHGG/Hggw9WdfbyYAO/0C0tLejq6pL+LUM2oI91dKBQVT1Y5CoH89Xy/jSLcOXPsz+gIQN97DKCAyD0dKoxsuqtgn1aGnL5Y8pXw/KwUgdB1OXofr9fGF40TG4FUVl3fr8fLpdLinGkjFIcQe3Ld3d3a+R01eXzqVRKM0JbDegeWu/atQvvvPMOpk+fjtNOO61fPmUymbBixQq9Lztqwb5rZ2enfJkYKpLXTC+gB/aWxxGsBjOPVHucagWWVdf9Ab0j0FdBp/GogoL8d0AbLaigIQN9B4R66KlOQ+0TU3WUnQK2/6iUwvuksbIARnaWWmdIJBKSAtHrcq0N5YX4esk2K5cdrsQk0wu6G/KqVaukn/byyy/3+/fxZMg0VhY/AGjE32OxmDC49JJJpYGwTVMJ9Lzd3d3ST6Vh8N/UncL7c8gwl2SFWDXkcoILv+zsyZZDVSVhUY4yQpUW4rlcLmnrUd6HLDDej6o4olbyAe1eLnK/qXjCyjjfG0YwjKzUKEsFySoHjSFTkNxA75eAeSBXurBtQ69HJUa9rkejGMz4OAyvLi4jKKDI1lChUOi3rmZvs9QEvRRDWxIxmFqQVknq5kD3rPKi1ZzWbDZXNGTmvlTvpAGxTUXuNp+DvXsKIPD5+BlRtcTpdKK+vl7mjwuFgqZoqYLvDyvs1TJgwljiVkWoAvMkg1CPWjXwco3k/cVQwmqCvU1SE1VDZs+WfW5OH6kUTwCyKpa85PL7ZxhMQ2b1npRHFrFKpdKAJBS+LubS6uvie1eJjaZqZXHQQp3JZsSiFvtUaWAKKKiyt4wE1NaY2tqqVLgbLlSlpLZ161Zcc801mD17NiZMmIDZs2fjy1/+MrZu3VqNy41aWK1WNDQ0oK6uTjwQZ3yLxaJMC9FY2PqIx+MyJTWQvlQ5hlLkUqF6wErX4ZeaetqdnZ2IRqOaAhh3NlOQvdLrp9cm75qGw31UxEAHD6veJJeobSnVy5UXV9XNh/T8Kp+aE2VsVxGkqdJwLZbeZee8ljo9xtel/u5IGDFQBY+8fv16nHbaaXA4HDjnnHPQ0NCA1tZW/P73v8ejjz6K1atXj4jcz0iBHzrzLoZvQF9oqIrHq2E28zASRfYWLqvFo6HmtFz/yfFKdX8z749eG+gbOmB0QU/JcNThcGjCbXU+mZK7fB6gz4PtLXpQC13qeCWfX31ceYpCqSL27tUxR4/HI+8ZPTYHP1QmF1tI5XTN8r+PNHQ35Jtuugmf+MQn8Pzzz2sqkKlUCgsXLsRNN900IpI7Iw2e+uokDfuUNGzOtbKQw9Oeo3DluSpBhhIAoU8OBRT+S6VSInhXvi6UM8ScalK9ERe5xeNxUc7kaleCBBHOLQN9q15IytgbGNID6Nd3L29BlRf3aIhcMxONRjU9aNWQa2pqNFNNnKpSRRxUTjhfw1AjpmpDd0N+66238MQTT/RrI7hcLtx0003jaoyRYBgNQLSa1b1YLNgwVyQ9kuEyvaa6g1clMag9yn1hEnHSh19IVRtM7f2SGVXu8ThOyHvkVJDKpyf1lJEFK740Yk6ADYRyGqZ6iA3UglLBe08kEiKEQLF7TmnRC6vEEhbg1NfBEJ+GzxRpIP2v4YTuhux0OkU1sBxdXV1VHeUaraBHYfEFgEjS8v9J0ichH+jLY202m1AF1Q1/3JpIg/N4PPusQsFtEaoSpYpy4ftyWCwW1NXVidhce3u7GDgAGbQvz0U5e82q8kDGQL5zuYAhgL22oFRwnpoSwsyVOSdNkghTB1aiy98HQh1r5N9HOrzW/Rg5++yz8c1vfhNvvPGG5udvvPEGbr311qptPhzNICmBUkflBBCO1gHQTEgRDPP4GHpnehkAGoG+fQHDe+aTQF8PmLuYBivgcAcV7621tVWei4bG16Z6edVzD+SV1Sp3pUOKxr83Q6ZH9/l8suES6But5EHjcrkGfL2MJgDI7DUfdyAMOL2gu0f+4Q9/iH/6p3/C/PnzZXVmW1sb2tracPzxx1dVpHu0ghQ9khJICVShEgmoNqmCX0S2gsorvvs7NcXJK/5/eR48VLBwRhoj17jyC0+vq/Kl1Rw3n8/387jqPLtK+VSxtxYUwecgv5r7sPhzHiqDRYvsg7NlpmqZjTR0N+RwOIw33ngDL7zwAtatW4c9e/agqakJ8+bNw4IFC/S+3KgGi1TqvGulzRJA3xeVbRoWvcqhKmGqgncHAtIW1THC/YHf75cqNqerksmkkCdYGWauy36uKoKggmE10L9iTZS3oMoPSFW3TOWz89CiUe4tTydU8XpKF5F0QqWXkULVrnzmmWfizDPPrNbTHxQgvY9fML/fv1fPyVla9pX39tih6nYNBerzsJq+v89D5hUpmdQHo6YVdygDEI7yQIasVqwrRTHA4C0ole+sKneyH8zq81AMmRxvFiFV1Q+2tkYKVbvySy+9hLfeekvjkU8//fRqXW5UgkvDOfA+WA471OF+vcFDgQfP/rLM1NlqGghzcIbe5OFTcI+62CyGqR5WLXQNdKgN1oJii4l1Cfa+y9tI7BoM9rq5Lkddd0uZJL1EIfYHuhvy7t27cf7552PdunWaHHnp0qU49thj8dRTT2HChAl6X3bUgjpXACqGhuXQY7h/f2C326VocyDXZZhOo1AlgKj3pVax1cONxAygT3KIRJOB3rtyj6xCNViSONSJKLLF1OsPVh+oFF6zv6wHzXZ/oXvV+sorr8SePXvwxhtvoKWlBf/zP/+DlpYWvP7662hpacFVV12l9yVHNdQK9FAMWR3uJ2l/OKAuchtIs2uozwNAQ57gxBeNiDlluQdWw1t1hpiqJpWgDm+Uh8eVuOfl9MpyQx4MqnQSB0EG+/3yeexqQHdD/tOf/oS77roLJ5xwgubnJ554Ir7//e9j9erVel9yVEP1EkMxZHUmt5IwfbWgtsRUWuS+gkQPGjJbayrvGujT4BrIELnNAti7IQPQ/L5qMHwNKttNfR4W+HiADcWQAWhop2pEUOk946AMd11XC1VZGTNQruB0OqXfOF6gDs0Plf3DKSFWsPcWWuoJdXwxnU7vd9GLTDa2aIC+QpFapVbzYlVml9dnzqtGKZWgGifzbvX51PBdvQc1T6bWViWwQ0DlUfLGOWChbpcoB7dXDFR11wu6e+TbbrsNS5cuxa5duzQ/37lzJ5YtW4bbb79d70uOalRa2DYUqFTLSpNF1YAq8UqPORj4RVWXnav9WfW51f1H1OBSw1uG3KSMcnfzYEXCci8L7H2kU70HNU/mahqCBwrFIdS9WyxyJZNJmbEuFAr9yDzcrJlKpYbs8fcHunvkl156CZ2dnZg6dSqOOeYYKXb9v//3/1BXV4dXXnkFr7zyCoDeD/fxxx/X+xZGFfbXkK3W3j3ADFHL87FqQR0m4NB/JVB0oHwBAZexA31Ve6YKDK8Z/tJo1cIaPSUrySrdcyCoYvI0lnKjU0FxAd6D+vwUn6fiZnkBjb1opj2k3dJQKYELaNfQsDVXLehuyB0dHTj88MNlWRsH6Zkzt7e3633JUQt1zcv+hFUcjqfRcD62muBmRIaRlXSmuKlC9T6knqqDBfRafA5Vb4stG7VSDfR6VLLXKuXWA4HkDB4OA8kBA/09OAco2PbjNBnzZ/bB1TlvdXsHFVOy2SwikQiampoA9O30AlCRv60ndDfk8VbM2hv2tdBVDnKgaTTxeFwzWlct0JMCvZVzdbQxnU5rNLdqamrg8XhEGZOSsLxv6ls7nU4RV6DRZbNZjcAd+ejMb2nEQzFk5rnlmmCVvGClPNlms8nmCR42LAByAR7vCdBqolksFpjNZnnt3L0cj8flegfdypiBQMXI8QTmnAei2URKJgAx5mpLDatKHDQ2XptGwh4xxxs5IkjhAYbT6qA+K9RkeQF91WkaiGrIzI+HcnCpeTbvUW2plUPNk0knbWtrE49cU1MDn88ndNhMJoNYLIZoNCoFQRZ1eeDyWqxQs+PA7RrVhO6G/NOf/hR33XWX/P1vf/sbJk6ciHA4jDlz5mDnzp16X3LUguR8n893QB8kZVyBXi/PhejVhKpLFYvF0NXVpSG2+P1+TW6rhtqhUEh6x9T5UrdIMFQFILrPpGoytKbRD3WAQ21Bqf3ngcCDitNa8XhcXq/NZkNDQ4NGBJ/g64lEIhrhRK7DAXrrBC0tLfKah2Pfme6G/MADD2h0kq+//no0Nzfj0UcfRbFYxDe/+U29LzkuUC5EEIvFNLPIeoPhIIXoKDjAtbDqwcScFug1VO535v3SywF9BqdyrjlrzbFM6kXviyFzNrlcUWQgUI+a+mg0xLq6Olk0z4VxPp8PwWBQs2CPnp/rZUhHtdvtyOfz4pW58rXanQfdc+SPPvoI06dPB9Bb2PrrX/+KV199FaeeeipsNhuuu+46vS85bkDSPyuuNCDyuPUspqj7ltjvLVeQBCDTToA2F6TQoLpQTZ0pprysWlzi8zGP3hfZIqCPN83+9WDvB2e6gd6F5aFQSPPeOp1OzewyV8hSEYUVb3V5PACRPiI5hiF2NfkAuhuyytldvXo1XC4XTj75ZAC9Idd4zJX1grotgaOCai+T+fSBGrSq18VCD/PBaDQqUrrcBUyw6EUwLOVKlWAwKCEz+8eqwgdz2sFGF/d230PtEjDfZ6/a7/fD7XYjk8lIkUqNLFnvoGKLzWaT4hx1ylkI5OtVK9Ukj1TLkHUPrY877jj85Cc/wbvvvov7778fZ555ptz81q1b0dzcrPclxx24gVA1HBabyiVr9xXFYlGzV9hqtcqua/47iR+q4kd5LgloPbRq9Ax5y0UWQqGQjD5WkvYZDENl0anGyqIWK+nqTir1fWTBSyWHkGLqcDhEYEBVBmV64HA4EAwGD64+8j333INzzz0Xs2fPxiGHHIKHHnpI/u3xxx/HiSeeqPclxy2ozcxQj8wi7ilyuVz7FJqykEaPqOp12Ww2ZDKZfoMc/KIOVFjyer2yj5mi7zabTdad5nI5jYxRuQjeUKGG/+XC8yqKxSIikYhsu+DKGK6YYUhOo2PlnH11eluG1TTYQCCAVColqqh+vx/5fF7WuVYbplKVqiWdnZ0IhUKaMG/jxo1obGxEXV1dNS45oijXah5uUAmj3NBYeBos3GbRhr870O+xGKXuYRoMO3bskJG/Qw45BGazGdFoFIVCQXqufO6Ojg6k02kEAgFMmjRpyMZMNcxkMikGxlBeRVdXFyKRCEqlErxeL1wul9BK2aNX180A0BTt0um0xlMzquAiOKD3u0+BBaY7LperqvPKVRMWCIVC2LlzJ3bs2IGjjz4abrcbs2fPrtblxj34peG8bHlBjFXkSoaZTCY1X869fenYM90XeL1eoZpSj4xbHynST1IF5X9YhR4qVBF70kDLVVaYerD6zuIW36tisYhgMChDHKyiq4L1lGriYAgjCc5WJxIJOQjC4bC01rjZslpknqo864MPPogJEyZg8uTJOPnkk/H+++8DAC644AL86Ec/qsYlDfwf+GXjuhegj/MbiUTQ3d0tDKxEIqHJqbnzSW/PobZtWCWml2PhyOPxwO/3IxAISDFtqNFNqVSS1+DxeDTkEj4HpXqpOFJbWyuUUq56UavKrEPwuVjs8nq9qK2tlVVA5fPIfH0sOnIBACmp1YLuhnz33Xfj61//Oq644gr86U9/0nwYp5566pgfkhgtUAtiatjPEJQtEzV8DAQCVSnIsLcM9EUI5I2r3GV1Npj581CgRhM2m00OInrlYrGIPXv2yPMFg0Hxnqyiu93ufgMSFOgLBAIIhULSy+c9U32TBk+Z3GQyKYbMMcyhTHEdCHQPrX/yk5/gu9/9Lm6++eZ+b8z06dOxefNmvS9pYC9QC2L8Q5BzzK2R1QI9PfcvkcrJNIBVYlI1aVzc4DgYmJvScLi0jf3raDQq4TOZdmpRz+fziVRv+ZK2oYT4VqsVPp9PM9hCEUN6eu7Kqtb7rLsht7S0YM6cORX/jatDDAwvGBba7Xap7qrebzjgdDplf1IikYDP5xMhO6DXq7KIRn76UDyyGrKq3tJut6Orq0v6ujU1NXA6nQiHwxoj5qYNVtYzmcx+6XpTlpiHEfnbrPrvK7llX6H7J3nYYYfhz3/+c8V/+8tf/oKZM2fqfUkD+wB6u+HeVWSxWISLzHaZOpZJwUEA0nvem940/40sKs4Dd3d3y5pXVvEZXnu9Xs11uE2Do5u8t72J3Q8E5tocJHE4HKIqYrPZDphvPxh0PyK++tWv4stf/jJsNhsWLVoEAGhra8OvfvUr/PCHP8Qvf/lLvS9p4CCBx+ORgY9EIiGV9HQ6LeE2C0pkAHLpnQoaLskrVPlQd0DRMxeLRWkBqR6ePGpCHd3MZDL7tAyPuTjQF2azAEfJpKG0AA8EVekj33333fjud7+r6Wm6XC7ccccd+MY3vqH35UYFRrqPfDCgVCqhtbUV3d3dsFgsmDBhAqxWKzo7OxGLxWA2m1FfXw+PxyN9ZhbtAEi4qqqAkOetFqK4ooZc8UKhALfbrfkuVqrMx2IxYWtV6kEPBB5EAEQhpKenB11dXUgmk9LTruZMctUIIYlEAmvXrkVHRwdCoRCOP/54+UDGIgxDHhqSyaSM+AUCAdTW1qKzs1Ny1MbGRng8HiSTSfFywWBQGFWE2WzWLIpTyUe5XE4omNy0aLFYJKQeKK3IZrPSPlIle/YGLoln/5tbHQHI/HIul4PX60U4HK5anly1RMnr9WLBggW47LLLcOaZZ8Lv92P16tU466yzqnXJAfH444/jggsuQFNTE0wmE379618P+z0Y6IW6cSMajaK1tVWMgGLv3L5IdHd3a4zY6XTKgjhu8RhoX7TX65XcdLACn6rWOdSirBp1lk+GeTweCd8pgVQt6GbI0WgUjz32GO6++26sWrVKk4+sXLkSxx57LD796U9j27Ztel1yyFi1ahU+/PBDnHPOOcN+bQNacAeWWuSi2gY9YDKZlDZUNptFZ2enbHHw+XwaOiQA+b1sNis5MwDNlouhQtX2ViWDKoEtJQAV176ywEeD3p8i2lChi5/fuHEjFixYgNbWVvnZMcccg9///ve47LLLsHbtWhx55JF49NFHcfHFF+txyX3C448/DrPZjO7ubvz7v//7sF/fgBZerxdmsxmRSERGAYPBIIC+xXc0IubAmUwGDQ0NMhChGpDZbJYxQkI9GPYFDodDqtykkFbq/VIQkRiItsr55WoTQnTxyLfddht8Ph/Wrl2LVCqFv//97wiFQpg7dy7+93//F4888gg2btyISy+9dNjbHsDAOZGBkQNppF6vV/JRhqalUkkKYCxe1dTUSCWY+S+9dCwW0xixGtLuK0heYYisao4R5XJLTqdzwNYSc/TysFtv6OKR3377bdx3332YN28egF4G109/+lMcfvjh+MUvfoHLL79cj8sYGGNwu91iJPF4XOiMXV1d/RholM/hsAIr2qpxkDJ5oP1aq9UKr9crB0YikdDwsNW8mMohe8NwLHbTxZBbW1sxZcoUzc/496OPPlqPS4w4RmrL3lgGJ6nUncUApOdaKBQkt1TDWO7HYkFMla3V63PiLDYLZ5WKX0MdER0O6FYLH+jFVKPcHovFsGfPnkEfN2PGDN2vbUBfUO+aY47FYhEmkwnhcFjE9CiPSx0stWJdLhyvJ2w2m+x1pnYZ0KdHNhybP4YK3azsjDPOqGi0n/70p/v9vK2t7YCutXLlSlxxxRWDPk7Pcr/RH64eyndCAX0OgAUiv98vqh6cmhoOT6iueqECS3lIPxqgiyHfcccdejzNkLFkyRIsWbJkWK9pYHgwUARnNpurqrAxFAzHyp79xUFpyAYMGNCienNVowibNm3Cpk2bpGDx9ttvw+PxoK6uDvPnzx/huzNg4MBRNa71aMKyZcvwne98p9/P58+fj9dee234b8iAAZ0xLgx5pDDaCiIGRh5VW/FTlWc1YMDAsGJc5MgjjfEc9Bjjnb2odnRmeGQDBsYADEM2YGAMwDBkAwbGAAxDNmBgDMAwZAMGxgAMQzZgYAzAMGQDBsYADGaXAQNjAIZHNmBgDMAwZAMGxgAMQzZgYAzAMGQDBsYADEM2YGAMwDBkAwbGAAxDNmBgDMAw5GHCeNwIuWnTJnz605+Gy+VCc3Mzli5dWtVFZqMRW7ZswVVXXYWjjjoKFosFp556alWuYxjyMGG8bYSMRCL4zGc+A5PJhD/+8Y9YunQp7rnnnnGnuPruu+/iueeew/Tp0zFt2rTqXahkYFhQKBRKpVKplEgkSgBKDz/88MjeUJVx5513lgKBQCkWi8nPVqxYUXI6nZqfjXXwcy+VSqULL7ywNH/+/Kpcx/DIw4TxthHy+eefxxlnnAGfzyc/u+SSS5BOp/HnP/95BO9seDFcn/v4+nYZGDa89957/XZvTZo0CS6XC++9994I3dXYhWHIBqqCSCSCQCDQ7+fBYBCRSGT4b2iMw1DR3E8YGyENjCYYhryfGImNkAcTgsEgYrFYv59HIhEEg8ERuKOxDSO03k8sWbIEpVJp0D/jFTNmzOiXC+/YsQOpVMqIUqoAw5ANVAVnnXUWXnzxRSQSCfnZ448/DqfTaSzOqwKM0HqYMN42Ql599dW4//77ccEFF+CWW27B1q1bsWzZMnz961/XtKTGOlKpFJ577jkAwK5duxCPx7Fq1SoAwMKFC+FyufS5UFW60wb64Y477igB6PenWgSB0YB33323dNppp5UcDkepsbGx9K1vfauUz+dH+raGFdu2bav4uQMobdu2TbfrGJpdBgyMARg5sgEDYwCGIRswMAZgGLIBA2MAhiEbMDAGYBiyAQNjAIYhGzAwBmAYsgEDYwCGIRswMAZgGLIBA2MAhiEbMDAGYBiyAQNjAIYhGzAwBmAYsgEsW7YMtbW1I3oPpVIJH//4x/Gb3/xmSI9Pp9Oor6/H66+/XuU7OzhgGLKBUYEnnngCXV1duOyyy4b0eKfTia985Sv49re/XeU7OzhgGLKBUYH7778fn/3sZ1FTUzPk3/n85z+Pv/zlL9i4cWMV7+zggGHIBgZFMpnEddddh+nTp8PlcuFjH/sYrr32WsTjcc3jIpEILrnkErjdbjQ3N2PFihW46aabMGXKlL0+/5YtW7BmzRosWrRI8/Onn34ac+bMgdvtRjAYxLx58zTi9occcgjmzp2LRx55RLfXerDCkPoxMChSqRQKhQKWL1+Ouro67NixA8uXL8fixYvx4osvyuM+//nP44033sB9992HxsZG3Hvvvdi8eTMsFsten//VV1+F2+3G0UcfLT/74IMPsGjRItxwww24++67kclksH79enR1dWl+94QTTsArr7yi7ws+GKGb1oiBgxZ33HFHKRwOD/nxuVyu9MYbb5QAlLZv314qlUqljRs3lgCUnnjiCXlcKpUqhcPh0uTJk/f6fFdccUXp2GOP1fxs5cqVpVAoNOi9PPzwwyWLxVJKp9NDvv+xCCO0NjAk/Pa3v8UnPvEJeDwe1NTU4KSTTgIAbN68GUCvmCAAnHvuufI7TqcTn/nMZwZ97paWln5V89mzZyMWi+Fzn/scXnrpJSSTyYq/W1tbi0KhgPb29v16XWMFhiEbGBRPPfUU/uVf/gXHH388Vq5ciTfffBNPPfUUAIgqaEtLC7xeLxwOh+Z36+rqBn3+TCYDu92u+dn06dPxxz/+EVu3bsXChQtRW1uLyy67rJ/B8vd4H+MVhiEbGBQrV67EvHnz8OCDD+Kss87CvHnz+m2LaGxsRCKR6GdQQ/GUoVAI0Wi038/PPvtsvP766+js7MSvfvUrvPLKK/jKV76ieQx/LxQK7duLGmMwDNnAoEin0/085qOPPqr5+7HHHgugt9Ks/t7LL7886PNPnz4d27ZtG/Df/X4/LrvsMpx//vnYtGmT5t8+/PBDhMNhhMPhQa8zlmFUrQ0AALLZrAinq5g/fz5OP/10XHvttVi+fDnmzZuH5557Dq+++qrmcbNmzcK5556La665BolEAo2NjfjhD38Il8s16I7gE088Ed/97nfR3t4uofjPf/5zrF27FmeeeSaam5vxj3/8AytXrsS//Mu/aH737bffxgknnHCAr34MYKSrbQZGHgOJ5wMorV69upTP50s33nhjqa6uruT1eksXXHBB6c033ywBKD3zzDPyPJ2dnaWLLrqo5HK5SvX19aXvfOc7pSVLlpSOPvrovV6/p6enFAqFSo888oj8bM2aNaWFCxeWmpqaSna7vTRlypTSzTffXMpkMvKYXC5XCofDpV//+te6vycHGwyBegNVQz6fx6xZszBv3rxBOdQ33HADtmzZgmeffXbIz//iiy/ioosuwu7du+F2uw/0dg9qGIZsQDesXLkSu3fvxuzZsxGPx/HLX/4SL7zwAtauXYvjjjtur7+7c+dOTJs2DX/7298wbdq0IV3vzDPPxCc/+UksW7ZMh7s/uGHkyAZ0g9vtxsMPP4wtW7agUChg9uzZeOaZZwY1YgCYOHEiHnroIezZs2dIhpxOp3H88cfja1/7mh63ftDD8MgGDIwBGO0nAwbGAAxDNmBgDMAwZAMGxgAMQzZgYAzAMGQDBsYADEM2YGAMwDBkAwbGAAxDNmBgDMAwZAMGxgAMQzZgYAzAMGQDBsYADEM2YGAMwDBkAwbGAAxDNmBgDMAwZAMGxgAMQzZgYAzAMGQDBsYA/j+lbKaRCWkbbAAAAABJRU5ErkJggg==", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sta = np.row_stack(df['saccade_tonicspk_response'].apply(lambda x: x[i0:i1] / np.abs(x).max()))\n", "for label in range(n_clusters):\n", " fig, ax = plt.subplots()\n", " n_units = len(sta[labels == label])\n", " for unit in sta[labels == label]:\n", " ax.plot(tpts[i0:i1], unit, lw=1, color='gray', alpha=0.15)\n", " mean = np.mean(sta[labels == label], axis=0) \n", " ci = sem(sta[labels == label], axis=0) * 1.96\n", " ax.plot(tpts[i0:i1], mean, lw=1, color='black')\n", " ax.text(0.05, 1.05, 'N = %d' % n_units, transform=ax.transAxes, fontsize=LABELFONTSIZE)\n", " ax.set_xticks([-1, 0, 1])\n", " ax.set_xlabel('Lag (s)')\n", " ax.set_yticks([-1, 0, 1])\n", " ax.set_ylabel('Response (norm.)')\n", " \n", " clip_axes_to_ticks(ax=ax)\n", " set_plotsize(w=2, h=2)\n", " # fig.savefig(FIGUREPATH + f'saccade_response_cluster{label}.svg')" ] }, { "cell_type": "code", "execution_count": 84, "id": "dd4cccf4-9467-406f-a093-f3b06a831b8b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading: imfdecoding_natmov.pkl\n", "Loading: imfdecoding_natmov_opto.pkl\n", "Loading: phasetuning_natmov_noopto.pkl\n", "Loading: phasetuning_natmov_opto_noopto.pkl\n" ] } ], "source": [ "conditions = ['natmov', 'natmov_opto']\n", "\n", "df_decoding = load_data('imfdecoding', conditions)\n", "df_decoding.rename({'tonicspk_phase':'tonicspk_score', 'burst_phase':'burst_score'}, inplace=True, axis='columns')\n", "df_tuning = load_data('phasetuning', conditions, tranges='noopto')\n", "\n", "df = pd.merge(df_decoding, df_tuning).set_index(['m', 's', 'e', 'u', 'imf'])\n", "df['tonicspk_sig'] = df['tonicspk_p'] <= 0.05\n", "df['burst_sig'] = df['burst_p'] <= 0.05" ] }, { "cell_type": "code", "execution_count": 85, "id": "ae9f5eba-0e22-4334-8987-faf5ef54d5f8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tonicspk N = 273\n", "burst N = 68\n" ] }, { "data": { "image/png": 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "positions = np.log10(FREQUENCYBINS[:-1]) + 0.25\n", "for spike_type in ['tonicspk', 'burst']:\n", " fig, ax = plt.subplots()\n", " df_sig = df.query('%s_sig == True' % spike_type)\n", " print(f\"{spike_type} N = {len(df_sig)}\")\n", " scores = sort_data(df_sig['%s_score' % spike_type], df_sig['freq'], bins=FREQUENCYBINS)\n", " violins = ax.violinplot(scores, positions, widths=0.2, showextrema=False)\n", " for violin in violins['bodies']:\n", " violin.set_facecolor('none')\n", " violin.set_edgecolor(COLORS[spike_type])\n", " violin.set_linewidth(2)\n", " violin.set_alpha(1)\n", " ax.scatter(positions, [s.mean() for s in scores], marker='_', color='black')\n", " ax.axhline(0.5, ls='--', lw=1, color='gray')\n", " for position, s in zip(positions, scores):\n", " w, p = wilcoxon(s - 0.5)\n", " ax.text(position, 0.9, p2stars(p), rotation=60, fontsize=LABELFONTSIZE)\n", " ax.set_xticks(FREQUENCYTICKS)\n", " ax.set_xticklabels(FREQUENCYTICKLABELS)\n", " ax.set_xlim(left=-3.11)\n", " ax.set_xlabel('Timescale (Hz)')\n", " ax.set_yticks([0, 0.5, 1])\n", " ax.set_ylim(bottom=-0.01)\n", " ax.set_ylabel('Decoding accuracy')\n", " clip_axes_to_ticks(ax=ax)\n", " set_plotsize(w=3, h=2)\n", " fig.savefig(FIGUREPATH + 'imf_decoding_%s.svg' % spike_type)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 5 }