{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import pathlib\n",
    "from pathlib import Path\n",
    "import math\n",
    "import time\n",
    "from functools import partial\n",
    "from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# import h5py\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy.linalg import svd, norm, lstsq, solve\n",
    "from scipy.io import savemat, loadmat\n",
    "import sklearn\n",
    "from sklearn.linear_model import Ridge, RidgeCV, LogisticRegression, LogisticRegressionCV, LinearRegression, ElasticNet, ElasticNetCV, MultiTaskElasticNet, MultiTaskElasticNetCV\n",
    "from sklearn.cross_decomposition import CCA\n",
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import dask\n",
    "# import statsmodels.api as sm\n",
    "# import statsmodels.formula.api as smf\n",
    "from scipy.stats import kde\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "# mpl.use(\"pdf\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load libraries and set plot parameters\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.gridspec as gridspec\n",
    "import seaborn as sns\n",
    "\n",
    "# from IPython.display import set_matplotlib_formats\n",
    "# set_matplotlib_formats('pdf', 'png')\n",
    "plt.rcParams['savefig.dpi'] = 75\n",
    "plt.rcParams['figure.autolayout'] = False\n",
    "plt.rcParams['figure.figsize'] = 10, 6\n",
    "plt.rcParams['axes.labelsize'] = 18\n",
    "plt.rcParams['axes.titlesize'] = 20\n",
    "plt.rcParams['font.size'] = 16\n",
    "plt.rcParams['lines.linewidth'] = 2.0\n",
    "plt.rcParams['lines.markersize'] = 8\n",
    "plt.rcParams['legend.fontsize'] = 14\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['savefig.bbox'] = 'standard'  # 'tight'\n",
    "# plt.rcParams['text.usetex'] = True\n",
    "# plt.rcParams['font.family'] = 'serif'\n",
    "# plt.rcParams['font.serif'] = 'cm'\n",
    "# plt.rcParams['text.latex.preamble'] = \"\\\\usepackage{subdepth}, \\\\usepackage{type1cm}\"\n",
    "\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "from mpl_toolkits.axisartist.axislines import SubplotZero\n",
    "import mpl_toolkits.axisartist as AA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sys.path.append(os.path.expanduser('~/popcp'))\n",
    "from analysis import design_matrix, cp_per_dim, cp_cum_dim, spike_cp_cum_dim, spike_cp_per_dim\n",
    "import recipe\n",
    "import stats\n",
    "import toolkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <span>StrVector with 14 elements.</span>\n",
       "    <table>\n",
       "      <tbody>\n",
       "      <tr>\n",
       "      \n",
       "      <td>\n",
       "        'tidyr'\n",
       "      </td>\n",
       "      \n",
       "      <td>\n",
       "        'ggplot2'\n",
       "      </td>\n",
       "      \n",
       "      <td>\n",
       "        'boot'\n",
       "      </td>\n",
       "      \n",
       "      <td>\n",
       "        'glmnet'\n",
       "      </td>\n",
       "      \n",
       "      <td>\n",
       "        ...\n",
       "      </td>\n",
       "      \n",
       "      <td>\n",
       "        'utils'\n",
       "      </td>\n",
       "      \n",
       "      <td>\n",
       "        'datasets'\n",
       "      </td>\n",
       "      \n",
       "      <td>\n",
       "        'methods'\n",
       "      </td>\n",
       "      \n",
       "      <td>\n",
       "        'base'\n",
       "      </td>\n",
       "      \n",
       "      </tr>\n",
       "      </tbody>\n",
       "    </table>\n",
       "    "
      ],
      "text/plain": [
       "R object with classes: ('character',) mapped to:\n",
       "<StrVector - Python:0x7f2b3c0af708 / R:0xf3b3150>\n",
       "['tidyr', 'ggplot2', 'boot', 'glmnet', ..., 'utils', 'datasets', 'methods', 'base']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rpy2\n",
    "from rpy2.robjects.packages import importr\n",
    "base = importr('base')\n",
    "utils = importr('utils')\n",
    "# utils.install_packages(\"readr\")\n",
    "# utils.install_packages(\"glmnet\")\n",
    "# utils.install_packages(\"tidyverse\")\n",
    "rpy2.robjects.r(\"\"\"\n",
    "library(glmnet)\n",
    "library(boot)\n",
    "library(ggplot2)\n",
    "library(tidyr)\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "motion on: 100\n",
      "pulses: [ 100  250  400  550  700  850 1000]\n"
     ]
    }
   ],
   "source": [
    "# constants\n",
    "zdim = 4\n",
    "eps = 1e-16\n",
    "binwidth = 1/1000  # 1ms\n",
    "suffix = \"\"\n",
    "num_pulses = 7\n",
    "pulse_duration = 150  # 150ms\n",
    "motionon_idx = 200 if suffix == \"_v2\" else 100 # fixed at certain bin for the trials started 100ms/200ms before motion on\n",
    "print(f\"motion on: {motionon_idx}\")\n",
    "pulses_idx = motionon_idx + pulse_duration * np.arange(num_pulses)  # 7 pulses, each last for 150ms\n",
    "print(f\"pulses: {pulses_idx}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# noise correlation\n",
    "def rebin(x, binwidth=100, axis=1):\n",
    "    length = x.shape[axis]\n",
    "    num_bins = length // binwidth\n",
    "    bins = np.array_split(x, num_bins, axis=axis)\n",
    "    binned = np.concatenate([np.sum(b, axis=axis, keepdims=True) for b in bins], axis=axis)\n",
    "    return binned\n",
    "    \n",
    "def noise_corr(cooked, binwidth=100, seed=0):\n",
    "    np.random.seed(seed)\n",
    "        \n",
    "    y = cooked['y']\n",
    "    mu = cooked['mu']\n",
    "    a = cooked['a']\n",
    "    b = cooked['b']\n",
    "    mask_frozen_good = cooked['mask_frozen_good']    \n",
    "    \n",
    "    num_units = y.shape[-1]\n",
    "    \n",
    "    frozen_y = y[mask_frozen_good, ...]\n",
    "    frozen_mu = mu[mask_frozen_good, ...]\n",
    "    \n",
    "    frozen_rate = np.exp(frozen_mu @ a + b)\n",
    "    \n",
    "    sim = np.random.poisson(frozen_rate)\n",
    "    \n",
    "    binned_y = rebin(frozen_y)\n",
    "    corr_y = np.corrcoef(np.reshape(binned_y, (-1, binned_y.shape[-1])), rowvar=False)\n",
    "    \n",
    "    binned_sim = rebin(sim)\n",
    "    corr_sim = np.corrcoef(np.reshape(binned_sim, (-1, binned_y.shape[-1])), rowvar=False)\n",
    "    \n",
    "    upper_idx = np.triu_indices(num_units)\n",
    "    \n",
    "    corr = np.copy(corr_y)\n",
    "    corr[upper_idx] = corr_sim[upper_idx]\n",
    "    \n",
    "    return corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_dishes = pathlib.Path('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sessions\n",
      " ['n20150305a', 'n20150305b', 'n20150306c', 'n20150324a', 'n20160906', 'p20140304', 'p20140305', 'l20190807', 'l20190814', 'l20190816', 'l20190829', 'l20190905', 'l20190917', 'l20190919']\n"
     ]
    }
   ],
   "source": [
    "with open('merged.txt', 'r') as f:\n",
    "    exnames = f.read().splitlines() \n",
    "print('Sessions\\n', exnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/yuan/popcp/result/dishes/n20150305a.npy\n",
      "/home/yuan/popcp/result/dishes/n20150305b.npy\n",
      "/home/yuan/popcp/result/dishes/n20150306c.npy\n",
      "/home/yuan/popcp/result/dishes/n20150324a.npy\n",
      "/home/yuan/popcp/result/dishes/n20160906.npy\n",
      "/home/yuan/popcp/result/dishes/p20140304.npy\n",
      "/home/yuan/popcp/result/dishes/p20140305.npy\n",
      "/home/yuan/popcp/result/dishes/l20190807.npy\n",
      "/home/yuan/popcp/result/dishes/l20190814.npy\n",
      "/home/yuan/popcp/result/dishes/l20190816.npy\n",
      "/home/yuan/popcp/result/dishes/l20190829.npy\n",
      "/home/yuan/popcp/result/dishes/l20190905.npy\n",
      "/home/yuan/popcp/result/dishes/l20190917.npy\n",
      "/home/yuan/popcp/result/dishes/l20190919.npy\n",
      "14 sessions\n",
      " ['n20150305a', 'n20150305b', 'n20150306c', 'n20150324a', 'n20160906', 'p20140304', 'p20140305', 'l20190807', 'l20190814', 'l20190816', 'l20190829', 'l20190905', 'l20190917', 'l20190919']\n"
     ]
    }
   ],
   "source": [
    "dishes = []\n",
    "for s in exnames:\n",
    "    path_to_dish = path_to_dishes / f'{s}.npy'\n",
    "    if path_to_dish.exists():\n",
    "        print(path_to_dish)\n",
    "        dishes.append(np.load(path_to_dish)[()])\n",
    "print(f'{len(exnames)} sessions\\n', exnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(622, 1484, 4)\n",
      "(702, 1484, 4)\n",
      "(711, 1484, 4)\n",
      "(668, 1484, 4)\n",
      "(780, 1600, 4)\n",
      "(833, 1484, 4)\n",
      "(1000, 1484, 4)\n",
      "(895, 1600, 4)\n",
      "(1088, 1600, 4)\n",
      "(1208, 1600, 4)\n",
      "(1400, 1600, 4)\n",
      "(1125, 1600, 4)\n",
      "(1203, 1600, 4)\n",
      "(1101, 1600, 4)\n"
     ]
    }
   ],
   "source": [
    "# a quick fix to exname\n",
    "for dish in dishes:\n",
    "    if not isinstance(dish['exname'], str):\n",
    "        dish['exname'] = dish['exname'][()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "rsquareds = [s['rsquared'] for s in dishes] \n",
    "stim_rsq_order = np.argsort(rsquareds)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "nancy_corrs = [noise_corr(s) for s in dishes if np.any(s['mask_frozen']) and s['exname'].startswith('n')]\n",
    "pat_corrs = [noise_corr(s) for s in dishes if np.any(s['mask_frozen']) and s['exname'].startswith('p')]\n",
    "leo_corrs = [noise_corr(s) for s in dishes if np.any(s['mask_frozen']) and s['exname'].startswith('l')]\n",
    "\n",
    "corrs = nancy_corrs + pat_corrs + leo_corrs\n",
    "\n",
    "nancy_corr = sp.linalg.block_diag(*nancy_corrs)\n",
    "pat_corr = sp.linalg.block_diag(*pat_corrs)\n",
    "leo_corr = sp.linalg.block_diag(*leo_corrs)\n",
    "\n",
    "big_corr = sp.linalg.block_diag(*corrs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x864 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nancy_size = sum([dish['y'].shape[-1] for dish in dishes if np.any(dish['mask_frozen']) and dish['exname'].startswith('n')])\n",
    "pat_size = sum([dish['y'].shape[-1] for dish in dishes if np.any(dish['mask_frozen']) and dish['exname'].startswith('p')])\n",
    "leo_size = sum([dish['y'].shape[-1] for dish in dishes if np.any(dish['mask_frozen']) and dish['exname'].startswith('l')])\n",
    "\n",
    "dish = dishes[-3]  # pick a (not) random session\n",
    "\n",
    "# rotate by pulse\n",
    "mu = np.copy(dish['mu'])\n",
    "a = np.copy(dish['a'])\n",
    "pulse_weight = dish['pulse_weight']\n",
    "u, s, v = np.linalg.svd(pulse_weight, full_matrices=False)\n",
    "mu = mu @ v.T\n",
    "a = v @ a\n",
    "\n",
    "fig2 = plt.figure(2, figsize=(25, 12))\n",
    "spec = gridspec.GridSpec(ncols=7, nrows=2)\n",
    "\n",
    "#\n",
    "ax = fig2.add_subplot(spec[0, :3])\n",
    "ax.spy(dish['y'][0, ...].T, aspect=\"auto\", marker=\"|\", color=\"k\", markersize=10)\n",
    "sns.despine(left=True, bottom=True, ax=ax)\n",
    "# ax.tick_params(axis=\"x\", bottom=True, labelbottom=True, top=False, labeltop=False)\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "# ax.set_xticklabels([0])\n",
    "# ax.set_xlabel(\"time (ms)\")\n",
    "#\n",
    "ax = fig2.add_subplot(spec[1, :3])\n",
    "z_colors = [\"C0\", \"C1\", \"C2\", \"C3\"]\n",
    "for z, c in zip(dish['mu'][0, :, :].T, z_colors):\n",
    "    sns.lineplot(x=np.arange(dish['mu'].shape[1]) - 100, y=z, ax=ax, color=c)\n",
    "sns.despine(ax=ax, left=True)\n",
    "ax.set_yticks([])\n",
    "ax.locator_params(axis='x', nbins=4)\n",
    "ax.set_xlabel(\"time (ms)\")\n",
    "\n",
    "#\n",
    "ax = fig2.add_subplot(spec[1, 3:5])\n",
    "vm = 1\n",
    "sns.heatmap(a.T, square=False, cmap='bwr', vmin=-vm, vmax=vm, ax=ax)\n",
    "# ax.set_axis_off()\n",
    "for i, c in enumerate(z_colors):\n",
    "    pdim = mpatches.Rectangle((0 + i + 0.1, dish['y'].shape[-1] + 1), 0.8, 0.3, ec=c, fc=c)\n",
    "    ax.add_patch(pdim)\n",
    "sns.despine(left=True, bottom=True, trim=True, ax=ax)\n",
    "ax.set_ylim(dish['y'].shape[-1] + 2, 0)\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel(\"Loading matrix\")\n",
    "    \n",
    "#\n",
    "ax = fig2.add_subplot(spec[0, 3:5])\n",
    "vm = 1.\n",
    "# ax.imshow(corr, cmap='bwr', vmin=-vm, vmax=vm, )\n",
    "sns.heatmap(nancy_corr, square=True, cmap='bwr', vmin=-vm, vmax=vm, ax=ax)\n",
    "ax.xaxis.set_label_position('bottom')\n",
    "sns.despine(left=True, bottom=True, trim=True, ax=ax)\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel(\"model\")\n",
    "ax.set_ylabel(\"data\")\n",
    "ax.set_title(\"Monkey N\")\n",
    "\n",
    "ax = fig2.add_subplot(spec[0, 5:])\n",
    "vm = 1.\n",
    "# ax.imshow(corr, cmap='bwr', vmin=-vm, vmax=vm, )\n",
    "sns.heatmap(pat_corr, square=True, cmap='bwr', vmin=-vm, vmax=vm, ax=ax)\n",
    "ax.xaxis.set_label_position('bottom')\n",
    "sns.despine(left=True, bottom=True, trim=True, ax=ax)\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel(\"model\")\n",
    "ax.set_ylabel(\"data\")\n",
    "ax.set_title(\"Monkey P\")\n",
    "\n",
    "ax = fig2.add_subplot(spec[1, 5:])\n",
    "vm = 1.\n",
    "# ax.imshow(corr, cmap='bwr', vmin=-vm, vmax=vm, )\n",
    "sns.heatmap(leo_corr, square=True, cmap='bwr', vmin=-vm, vmax=vm, ax=ax)\n",
    "ax.xaxis.set_label_position('bottom')\n",
    "sns.despine(left=True, bottom=True, trim=True, ax=ax)\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks([])\n",
    "ax.set_xlabel(\"model\")\n",
    "ax.set_ylabel(\"data\")\n",
    "ax.set_title(\"Monkey L\")\n",
    "# ax.set_ylim(80, 0)\n",
    "\n",
    "# big_size = nancy_size + pat_size + leo_size\n",
    "\n",
    "# pnancy = mpatches.Rectangle((nancy_size, 0), 0.1, big_size, ec=\"gray\", fc=\"gray\", linestyle='--')\n",
    "# pleo = mpatches.Rectangle((nancy_size + pat_size, 0.1), 0.1, big_size, ec=\"gray\", fc=\"gray\", linestyle='--')\n",
    "\n",
    "# ax.add_patch(pnancy)\n",
    "# # ax.add_patch(ppat)\n",
    "# ax.add_patch(pleo)\n",
    "\n",
    "# ax.annotate(\"Monkey N\", xy=(nancy_size/2, big_size), xycoords='data', \n",
    "#             ha=\"center\", va=\"top\",\n",
    "#             size=18,\n",
    "#             arrowprops=dict(arrowstyle=\"-[\",\n",
    "#                             patchB=pnancy,\n",
    "#                             shrinkA=5,\n",
    "#                             shrinkB=5,\n",
    "#                             fc=\"k\", ec=\"k\",\n",
    "#                             connectionstyle=\"arc3, rad=-0.05\",\n",
    "#                             ),\n",
    "#             bbox=dict(boxstyle=\"square\", fc=\"w\", ec=\"w\"),\n",
    "#            )\n",
    "\n",
    "# ax.annotate(\"Monkey P\", xy=(nancy_size + pat_size/2, big_size), xycoords='data', \n",
    "#             ha=\"center\", va=\"top\",\n",
    "#             size=18,\n",
    "#             arrowprops=dict(arrowstyle=\"-[\",\n",
    "#                             patchB=pleo,\n",
    "#                             shrinkA=5,\n",
    "#                             shrinkB=5,\n",
    "#                             fc=\"k\", ec=\"k\",\n",
    "#                             connectionstyle=\"arc3, rad=-0.05\",\n",
    "#                             ),\n",
    "#             bbox=dict(boxstyle=\"square\", fc=\"w\", ec=\"w\"),\n",
    "#            )\n",
    "\n",
    "# ax.annotate(\"Monkey L\", xy=(nancy_size + pat_size + leo_size/2, big_size), xycoords='data', \n",
    "#             ha=\"center\", va=\"top\",\n",
    "#             size=18,\n",
    "# #             arrowprops=dict(arrowstyle=\"-[\",\n",
    "# #                             patchB=pleo,\n",
    "# #                             shrinkA=5,\n",
    "# #                             shrinkB=5,\n",
    "# #                             fc=\"k\", ec=\"k\",\n",
    "# #                             connectionstyle=\"arc3, rad=-0.05\",\n",
    "# #                             ),\n",
    "#             bbox=dict(boxstyle=\"square\", fc=\"w\", ec=\"w\"),\n",
    "#            )\n",
    "\n",
    "plt.savefig(\"figure/example_and_corr.pdf\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pulse-trigger average\n",
    "def pulse_singular_value_spectrum(cooked):\n",
    "    pulse_weight = cooked['pulse_weight']\n",
    "    u, s, v = svd(pulse_weight, full_matrices=False)\n",
    "    pulse_sval = s\n",
    "    return pulse_sval\n",
    "\n",
    "\n",
    "def pulse_singular_value_spectrum2(cooked):\n",
    "    \"\"\"pulse-triggered average\"\"\"\n",
    "    mask_good = cooked['mask_good']\n",
    "    coh = np.sum(cooked['coh'], axis=-1)[mask_good]\n",
    "    mu = cooked['mu']\n",
    "    pulse_triggered_average = np.mean(mu[:, motionon_idx:motionon_idx + 1200, :] * coh[:, None, None], axis=0)\n",
    "    \n",
    "    u, s, v = svd(pulse_triggered_average, full_matrices=False)\n",
    "    pulse_sval = s\n",
    "    return pulse_sval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "pulse_svals = [pulse_singular_value_spectrum(s) for s in dishes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6  3 10 11  1  4  8  5 12  0  2  9  7 13] ['p20140305' 'n20150324a' 'l20190829' 'l20190905' 'n20150305b' 'n20160906'\n",
      " 'l20190814' 'p20140304' 'l20190917' 'n20150305a' 'n20150306c' 'l20190816'\n",
      " 'l20190807' 'l20190919']\n"
     ]
    }
   ],
   "source": [
    "print(stim_rsq_order, np.array(exnames)[stim_rsq_order])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def smooth_1d(x, sigma=10):\n",
    "    from scipy.ndimage import gaussian_filter1d\n",
    "    assert x.ndim == 1\n",
    "    y = gaussian_filter1d(x, sigma=sigma, mode='constant', cval=0.0)\n",
    "    return y\n",
    "\n",
    "\n",
    "def smooth(x, sigma=10):\n",
    "    return np.stack([smooth_1d(row, sigma) for row in x.T]).T\n",
    "\n",
    "\n",
    "fig3 = plt.figure(3, figsize=(12, 8))\n",
    "spec = gridspec.GridSpec(nrows=9, ncols=9)\n",
    "\n",
    "#\n",
    "npulse = 7\n",
    "nbasis = 4\n",
    "duration = 100\n",
    "interbasis = 50 \n",
    "bfunc = 'cosine'\n",
    "\n",
    "highlight_colors = [\"C0\", \"C3\", \"C2\"]\n",
    "\n",
    "# Pat\n",
    "dish = dishes[stim_rsq_order[0]]\n",
    "\n",
    "T = dish['mu'].shape[1]\n",
    "pulse_weight = dish['pulse_weight']\n",
    "u, s, v = np.linalg.svd(pulse_weight, full_matrices=False)\n",
    "\n",
    "pulse_designs = []\n",
    "for k in range(npulse):\n",
    "    d4fig = np.zeros((T, nbasis * npulse))\n",
    "    d4fig[:, nbasis * k:nbasis * (k + 1)] = design_matrix(10,\n",
    "                                                           duration,\n",
    "                                                           pulses_idx[k],\n",
    "                                                           length=T,\n",
    "                                                           nbasis=nbasis,\n",
    "                                                           interval=interbasis,\n",
    "                                                           bfunc=bfunc\n",
    "                                                         )\n",
    "    pulse_designs.append(d4fig)\n",
    "\n",
    "z4fig = np.concatenate([d4fig @ pulse_weight @ v.T for d4fig in pulse_designs], axis=-1)\n",
    "z4fig = smooth(z4fig, sigma=40)\n",
    "\n",
    "sign = np.sign((pulse_weight @ v.T)[0, 0])\n",
    "\n",
    "colors = sns.color_palette(\"inferno\", 7)\n",
    "\n",
    "ymax = np.max(np.abs(z4fig))\n",
    "\n",
    "for k in range(zdim):\n",
    "    ax = fig3.add_subplot(spec[k, :4])\n",
    "    for j in range(npulse):\n",
    "        s = 80 + j * 150\n",
    "        t = np.arange(z4fig.shape[0] - s) + s\n",
    "        r = z4fig[s:, k::4][:, j] * sign\n",
    "        ax.plot(t, r, color=colors[j], label=f'Pulse {j + 1}', zorder=j)\n",
    "    \n",
    "    sns.despine(left=True, bottom=True, trim=True, ax=ax)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_ylabel(f'Factor {k + 1}', fontsize=10)\n",
    "    ax.set_ylim([0, ymax * 1.1])\n",
    "    ax.set_xlim([0, 1500])\n",
    "    \n",
    "ax.set_axis_on()\n",
    "ax.set_xlabel(\"Monkey P\", fontsize=12, color=highlight_colors[0])\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks(np.arange(motionon_idx, motionon_idx + 1050, 150))\n",
    "ax.set_xticklabels([f\"P. {k + 1}\" for k in range(npulse)], fontsize=12)\n",
    "for xtick, color in zip(ax.get_xticklabels(), colors):\n",
    "    xtick.set_color(color)\n",
    "\n",
    "# ax.legend(frameon=False, bbox_to_anchor=(1.2, 3), loc='upper right', fontsize=12)\n",
    "\n",
    "\n",
    "# Nancy\n",
    "dish = dishes[stim_rsq_order[1]]\n",
    "\n",
    "T = dish['mu'].shape[1]\n",
    "pulse_weight = dish['pulse_weight']\n",
    "u, s, v = np.linalg.svd(pulse_weight, full_matrices=False)\n",
    "\n",
    "pulse_designs = []\n",
    "for k in range(npulse):\n",
    "    d4fig = np.zeros((T, nbasis * npulse))\n",
    "    d4fig[:, nbasis * k:nbasis * (k + 1)] = design_matrix(3,\n",
    "                                                           duration,\n",
    "                                                           pulses_idx[k],\n",
    "                                                           length=T,\n",
    "                                                           nbasis=nbasis,\n",
    "                                                           interval=interbasis,\n",
    "                                                           bfunc=bfunc\n",
    "                                                         )\n",
    "    pulse_designs.append(d4fig)\n",
    "\n",
    "z4fig = np.concatenate([d4fig @ pulse_weight @ v.T for d4fig in pulse_designs], axis=-1)\n",
    "z4fig = smooth(z4fig, sigma=40)\n",
    "\n",
    "sign = np.sign((pulse_weight @ v.T)[0, 0])\n",
    "\n",
    "ymax = np.max(np.abs(z4fig))\n",
    "\n",
    "for k in range(zdim):\n",
    "    ax = fig3.add_subplot(spec[k + 5, :4])\n",
    "    for j in range(npulse):\n",
    "        s = 80 + j * 150\n",
    "        t = np.arange(z4fig.shape[0] - s) + s\n",
    "        r = z4fig[s:, k::4][:, j] * sign\n",
    "        ax.plot(t, r, color=colors[j], label=f'Pulse {j + 1}', zorder=j)\n",
    "    \n",
    "    sns.despine(left=True, bottom=True, trim=True, ax=ax)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_ylabel(f'Factor {k + 1}', fontsize=10)\n",
    "    ax.set_ylim([0, ymax * 1.1])\n",
    "    ax.set_xlim([0, 1500])\n",
    "    \n",
    "ax.set_axis_on()\n",
    "ax.set_xlabel(\"Monkey N\", fontsize=12, color=highlight_colors[1])\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks(np.arange(motionon_idx, motionon_idx + 1050, 150))\n",
    "ax.set_xticklabels([f\"P. {k + 1}\" for k in range(npulse)], fontsize=12)\n",
    "for xtick, color in zip(ax.get_xticklabels(), colors):\n",
    "    xtick.set_color(color)\n",
    "    \n",
    "\n",
    "# Leo\n",
    "dish = dishes[stim_rsq_order[2]]\n",
    "\n",
    "T = dish['mu'].shape[1]\n",
    "pulse_weight = dish['pulse_weight']\n",
    "u, s, v = np.linalg.svd(pulse_weight, full_matrices=False)\n",
    "\n",
    "pulse_designs = []\n",
    "for k in range(npulse):\n",
    "    d4fig = np.zeros((T, nbasis * npulse))\n",
    "    d4fig[:, nbasis * k:nbasis * (k + 1)] = design_matrix(3,\n",
    "                                                           duration,\n",
    "                                                           pulses_idx[k],\n",
    "                                                           length=T,\n",
    "                                                           nbasis=nbasis,\n",
    "                                                           interval=interbasis,\n",
    "                                                           bfunc=bfunc\n",
    "                                                         )\n",
    "    pulse_designs.append(d4fig)\n",
    "\n",
    "z4fig = np.concatenate([d4fig @ pulse_weight @ v.T for d4fig in pulse_designs], axis=-1)\n",
    "z4fig = smooth(z4fig, sigma=40)\n",
    "\n",
    "sign = np.sign((pulse_weight @ v.T)[0, 0])\n",
    "\n",
    "ymax = np.max(np.abs(z4fig))\n",
    "\n",
    "for k in range(zdim):\n",
    "    ax = fig3.add_subplot(spec[k, 5:])\n",
    "    for j in range(npulse):\n",
    "        s = 80 + j * 150\n",
    "        t = np.arange(z4fig.shape[0] - s) + s\n",
    "        r = z4fig[s:, k::4][:, j] * sign\n",
    "        ax.plot(t, r, color=colors[j], label=f'Pulse {j + 1}', zorder=j)\n",
    "    \n",
    "    sns.despine(left=True, bottom=True, trim=True, ax=ax)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([])\n",
    "    ax.set_ylabel(f'Factor {k + 1}', fontsize=10)\n",
    "    ax.set_ylim([0, ymax * 1.1])\n",
    "    ax.set_xlim([0, 1500])\n",
    "    \n",
    "ax.set_axis_on()\n",
    "ax.set_xlabel(\"Monkey L\", fontsize=12, color=highlight_colors[2])\n",
    "ax.set_yticks([])\n",
    "ax.set_xticks(np.arange(motionon_idx, motionon_idx + 1050, 150))\n",
    "ax.set_xticklabels([f\"P. {k + 1}\" for k in range(npulse)], fontsize=12)\n",
    "for xtick, color in zip(ax.get_xticklabels(), colors):\n",
    "    xtick.set_color(color)\n",
    "\n",
    "\n",
    "###\n",
    "session_markers = ['o'] * 5 + ['^'] * 2 + ['D'] * 7\n",
    "np.random.seed(0)\n",
    "ax = fig3.add_subplot(spec[5:, 5:])\n",
    "lines = []\n",
    "alpha = 0.5  # sessions[i]['rsquared'] * 10\n",
    "\n",
    "for i, sv in enumerate(pulse_svals):\n",
    "    alpha = 0.5\n",
    "    color = \"k\"\n",
    "    zorder = 1\n",
    "    if stim_rsq_order[0] == i:\n",
    "        color = highlight_colors[0]\n",
    "        alpha = 0.9\n",
    "        zorder = 5\n",
    "    if stim_rsq_order[1] == i:\n",
    "        color = highlight_colors[1]\n",
    "        alpha = 0.9\n",
    "        zorder = 5\n",
    "    if stim_rsq_order[2] == i:\n",
    "        color = highlight_colors[2]\n",
    "        alpha = 0.9\n",
    "        zorder = 5\n",
    "\n",
    "    lines.append(ax.plot(range(1, 5) + np.random.randn(4) * 0.08, 100 * sv**2 / np.sum(sv**2), \n",
    "             ls='', marker=session_markers[i], markersize=10, color=color, alpha=alpha, zorder=zorder))\n",
    "ax.set_xticks([1, 2, 3, 4])\n",
    "ax.set_xlabel(\"stimulus-oriented factor\")\n",
    "ax.set_ylabel(\"% power explained\")\n",
    "ax.legend([lines[0][0], lines[5][0], lines[-1][0]], ['Monkey N', 'Monkey P', 'Monkey L'], frameon=False)\n",
    "sns.despine(trim=True, ax=ax)\n",
    "plt.savefig(\"figure/STA_and_power.pdf\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6,  3, 10, 11,  1,  4,  8,  5, 12,  0,  2,  9,  7, 13])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stim_rsq_order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "windows [   0  100  200  300  400  500  600  700  800  900 1000 1100 1200 1300\n",
      " 1400]\n",
      "Time: 100 to None\n",
      "average individual CP 0.49484242939522455 WilcoxonResult(statistic=7183.0, pvalue=0.06661059025804571) Ttest_1sampResult(statistic=-1.3048246063933304, pvalue=0.19359100449816843)\n",
      "Analysis of Deviance Table\n",
      "\n",
      "Model: binomial, link: logit\n",
      "\n",
      "Response: choice\n",
      "\n",
      "Terms added sequentially (first to last)\n",
      "\n",
      "\n",
      "        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    \n",
      "NULL                    10486      14493              \n",
      "stim     1    60.24     10485      14433 8.398e-15 ***\n",
      "nonstim  1   243.43     10484      14189 < 2.2e-16 ***\n",
      "pop      1   258.08     10483      13931 < 2.2e-16 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Analysis of Deviance Table\n",
      "\n",
      "Model: binomial, link: logit\n",
      "\n",
      "Response: choice\n",
      "\n",
      "Terms added sequentially (first to last)\n",
      "\n",
      "\n",
      "        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    \n",
      "NULL                    10486      14493              \n",
      "stim     1    60.24     10485      14433 8.398e-15 ***\n",
      "pop      1   498.66     10484      13934 < 2.2e-16 ***\n",
      "nonstim  1     2.85     10483      13931   0.09144 .  \n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "t1 = 100\n",
    "t2 = None\n",
    "\n",
    "# np.random.seed(20180713)\n",
    "def remove_signal(r, s, t1, t2, regular=True):\n",
    "    y = np.sum(r[:, t1:t2, :], axis=1)\n",
    "    if regular:\n",
    "        regress = RidgeCV(fit_intercept=True, alphas=np.logspace(-2, 4, 100))\n",
    "        regress.fit(s, y)\n",
    "    else:\n",
    "        regress = LinearRegression(fit_intercept=True)\n",
    "        regress.fit(s, y)\n",
    "    residual = y - regress.predict(s)\n",
    "    return residual, regress\n",
    "\n",
    "window = 100\n",
    "step = 100\n",
    "penalty = \"l2\"\n",
    "windows = np.arange(0, 1450, step)\n",
    "print(\"windows\", windows)\n",
    "\n",
    "nfolds = 3\n",
    "# scoring = 'accuracy'\n",
    "scoring = 'roc_auc'\n",
    "Cs = np.logspace(-15, 4, num=100)\n",
    "regular = False\n",
    "refit = True\n",
    "penalty = \"l2\"\n",
    "pulse_penalty = np.logspace(-2, 5, 100)\n",
    "\n",
    "print(f\"Time: {t1} to {t2}\")\n",
    "\n",
    "align = (7, 10)\n",
    "\n",
    "choices = []\n",
    "pop_outputs = []\n",
    "pop_resids = []\n",
    "pop_models = []\n",
    "pop_slides = []\n",
    "pop_prefs = []\n",
    "\n",
    "nonstim_outputs = []\n",
    "nonstim_models = []\n",
    "nonstim_slides = []\n",
    "\n",
    "stim_outputs = []\n",
    "stim_models = []\n",
    "stim_slides = []\n",
    "\n",
    "latent_slides = []\n",
    "latent_models = []\n",
    "latent_resids = []\n",
    "latent_outputs = []\n",
    "\n",
    "individual_cps = []\n",
    "individual_temporal_cps = []\n",
    "\n",
    "for dish in dishes:\n",
    "    coh = dish['good_coherences']\n",
    "    cohsum = np.sum(coh, axis=1, keepdims=True)\n",
    "    \n",
    "    mask = dish['mask_good_weak']\n",
    "    targchosen = dish['good_targchosen'][mask]\n",
    "    choice = (targchosen == 2) + 0\n",
    "    choices.append(choice)\n",
    "    \n",
    "    Y = np.concatenate([np.sum(dish['y'] [:, w:w+window, :], axis=1, keepdims=True) for w in windows], axis=1)\n",
    "    Y = np.reshape(Y, (Y.shape[0], -1))\n",
    "        \n",
    "    regress = RidgeCV(fit_intercept=True, alphas=pulse_penalty)\n",
    "    regress.fit(coh, Y)\n",
    "    coef = regress.coef_.T\n",
    "    coef = np.mean(np.reshape(np.mean(coef, axis=0), (-1, dish['y'].shape[-1])), axis=0)\n",
    "    pop_pref = np.ones_like(coef)\n",
    "    pop_pref[coef < 0] = 0\n",
    "    \n",
    "    residual = Y - regress.predict(coh)\n",
    "    pop_resid = np.reshape(residual, (residual.shape[0], len(windows), -1))\n",
    "    pop_resid = pop_resid[mask, ...]\n",
    "    pop_prefs.append(pop_pref)\n",
    "    pop_resids.append(pop_resid)\n",
    "    pop_resid_sum = np.sum(pop_resid[:, 1:, :], axis=1)\n",
    "    pop_resid_align = np.sum(pop_resid[:, align[0]:align[1], :], axis=1)\n",
    "    pop_resid_sum2 = pop_resid_sum\n",
    "#     pop_resid_sum2 = np.sum(pop_resid[:, 1:6, :], axis=1)\n",
    "    for spk, pref in zip(pop_resid_sum2.T, pop_pref):\n",
    "        individual_cps.append(stats.cp(choice, spk, pos=pref, method=\"ranksum\"))\n",
    "\n",
    "    for spk, pref in zip(pop_resid.T, pop_pref):\n",
    "        individual_temporal_cps.append([stats.cp(choice, w, pos=pref, method=\"ranksum\") for w in spk])\n",
    "        \n",
    "    # latent\n",
    "    mu = np.copy(dish['mu'])\n",
    "    pulse_weight = dish['pulse_weight']\n",
    "    u, s, v = np.linalg.svd(pulse_weight, full_matrices=False)\n",
    "    mu = mu @ v.T\n",
    "        \n",
    "    Z = np.concatenate([np.sum(mu[:, w:w+window, :], axis=1, keepdims=True) for w in windows], axis=1)\n",
    "    Z = np.reshape(Z, (Z.shape[0], -1))\n",
    "    \n",
    "    regress = RidgeCV(fit_intercept=True, alphas=np.logspace(-2, 5, 100))\n",
    "    regress.fit(coh, Z)\n",
    "    residual = Z - regress.predict(coh)\n",
    "    latent_resid = np.reshape(residual, (residual.shape[0], len(windows), -1))\n",
    "    latent_resid = latent_resid[mask, ...]\n",
    "    latent_resids.append(latent_resid)\n",
    "    \n",
    "    stim_resid = latent_resid[:, :, [0]]\n",
    "    nonstim_resid = latent_resid[:,:, [1, 2, 3]]\n",
    "    \n",
    "    stim_resid_sum = np.sum(stim_resid[:, 1:, :], axis=1)\n",
    "    stim_resid_align = np.sum(stim_resid[:, align[0]:align[1], :], axis=1)\n",
    "\n",
    "    nonstim_resid_sum = np.sum(nonstim_resid[:, 1:, :], axis=1)\n",
    "    nonstim_resid_align = np.sum(nonstim_resid[:, align[0]:align[1], :], axis=1)\n",
    "    \n",
    "    latent_resid_sum = np.sum(latent_resid[:, 1:, :], axis=1)\n",
    "    latent_resid_align = np.sum(latent_resid[:, align[0]:align[1], :], axis=1)\n",
    "    \n",
    "    output, model = recipe.logitcv(pop_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)    \n",
    "    pop_slides.append(\n",
    "        np.array([model.predict_proba(pop_resid[:, i, :])[:, 1] for i in range(len(windows))]))\n",
    "    #             sp.special.expit(pop_resid[:, i, :] @ model.coef_.T)[:, 0]\n",
    "    pop_models.append(model)\n",
    "    pop_outputs.append(model.predict_proba(pop_resid_sum)[:, 1])\n",
    "\n",
    "    output, model =  recipe.logitcv(nonstim_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)\n",
    "    nonstim_slides.append(\n",
    "        np.array([model.predict_proba(nonstim_resid[:, i, :])[:, 1] for i in range(len(windows))]))  #  sp.special.expit(nonstim_resid[:, i, :] @ model.coef_.T)[:, 0]\n",
    "    nonstim_models.append(model)\n",
    "    nonstim_outputs.append(model.predict_proba(nonstim_resid_sum)[:, 1])\n",
    "    \n",
    "    output, model =  recipe.logitcv(stim_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)\n",
    "    stim_slides.append(\n",
    "        np.array([model.predict_proba(stim_resid[:, i, :])[:, 1] for i in range(len(windows))]))  # sp.special.expit(stim_resid[:, i, :] @ model.coef_.T)[:, 0] \n",
    "    stim_models.append(model)\n",
    "    stim_outputs.append(model.predict_proba(stim_resid_sum)[:, 1])\n",
    "    \n",
    "    output, model =  recipe.logitcv(latent_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)\n",
    "    latent_slides.append(\n",
    "        np.array([model.predict_proba(latent_resid[:, i, :])[:, 1] for i in range(len(windows))]))  # sp.special.expit(stim_resid[:, i, :] @ model.coef_.T)[:, 0] \n",
    "    latent_models.append(model)\n",
    "    latent_outputs.append(model.predict_proba(latent_resid_sum)[:, 1])\n",
    "    \n",
    "    \n",
    "print(\"average individual CP\", np.mean(individual_cps), sp.stats.wilcoxon(np.array(individual_cps) - 0.5), sp.stats.ttest_1samp(individual_cps, 0.5))\n",
    "\n",
    "all_choices = np.concatenate(choices)\n",
    "all_pop_outputs = np.concatenate(pop_outputs)\n",
    "all_nonstim_outputs = np.concatenate(nonstim_outputs)\n",
    "all_stim_outputs = np.concatenate(stim_outputs)\n",
    "all_latent_outputs = np.concatenate(latent_outputs)\n",
    "\n",
    "all_pop_slides = np.concatenate(pop_slides, axis=1).T\n",
    "all_nonstim_slides = np.concatenate(nonstim_slides, axis=1).T\n",
    "all_stim_slides = np.concatenate(stim_slides, axis=1).T\n",
    "all_latent_slides = np.concatenate(latent_slides, axis=1).T\n",
    "\n",
    "individual_temporal_cps = np.stack(individual_temporal_cps)\n",
    "individual_temporal_cp = individual_temporal_cps.mean(axis=0)\n",
    "individual_temporal_cp_q5, individual_temporal_cp_q95 = np.percentile(individual_temporal_cps, q=[5, 95], axis=0)\n",
    "\n",
    "pd.DataFrame(np.column_stack([all_choices, all_stim_outputs, all_nonstim_outputs, all_pop_outputs]), \n",
    "             columns=[\"choice\", \"stim\", \"nonstim\", \"pop\"]).to_csv(\"pooled_outputs.csv\", index=False)\n",
    "\n",
    "pd.DataFrame(np.column_stack([all_choices, all_stim_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_stim_slides.shape[1])]).to_csv(\"all_stim_slides.csv\", index=False)\n",
    "pd.DataFrame(np.column_stack([all_choices, all_nonstim_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_nonstim_slides.shape[1])]).to_csv(\"all_nonstim_slides.csv\", index=False)\n",
    "pd.DataFrame(np.column_stack([all_choices, all_pop_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_pop_slides.shape[1])]).to_csv(\"all_pop_slides.csv\", index=False)\n",
    "pd.DataFrame(np.column_stack([all_choices, all_latent_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_latent_slides.shape[1])]).to_csv(\"all_latent_slides.csv\", index=False)\n",
    "\n",
    "stim_cps = []\n",
    "nonstim_cps = []\n",
    "latent_cps = []\n",
    "pop_cps = []\n",
    "for choice, stim_output, nonstim_output, pop_output, latent_output in zip(choices, stim_outputs, nonstim_outputs, pop_outputs, latent_outputs):\n",
    "    stim_cps.append(stats.cp(choice, stim_output, method=\"ranksum\"))\n",
    "    nonstim_cps.append(stats.cp(choice, nonstim_output, method=\"ranksum\"))\n",
    "    pop_cps.append(stats.cp(choice, pop_output, method=\"ranksum\"))\n",
    "    latent_cps.append(stats.cp(choice, latent_output, method=\"ranksum\"))\n",
    "\n",
    "\n",
    "# append pooling sessions\n",
    "stim_cps.append(stats.cp(all_choices, all_stim_outputs, method=\"ranksum\"))\n",
    "nonstim_cps.append(stats.cp(all_choices, all_nonstim_outputs, method=\"ranksum\"))\n",
    "pop_cps.append(stats.cp(all_choices, all_pop_outputs, method=\"ranksum\"))\n",
    "latent_cps.append(stats.cp(all_choices, all_latent_outputs, method=\"ranksum\"))\n",
    "\n",
    "df_cps = pd.DataFrame({\n",
    "    \"session\": exnames + [\"all\"], \n",
    "                        \"stim.\": np.array(stim_cps), \n",
    "                        \"nonstim.\": np.array(nonstim_cps), \n",
    "                        \"pop.\": np.array(pop_cps),\n",
    "                        \"latent\": np.array(latent_cps),    \n",
    "                      })\n",
    "\n",
    "df_cps_long = df_cps.melt(id_vars=[\"session\"], value_vars=[\"stim.\", \"nonstim.\", \"pop.\", \"latent\"], var_name=\"dimension\", value_name=\"CP\")\n",
    "\n",
    "print(rpy2.robjects.r('''\n",
    "mt <- read.csv(\"pooled_outputs.csv\")\n",
    "anova(glm(choice ~ stim + nonstim + pop, data=mt, family=binomial()), test=\"LRT\")\n",
    "'''))\n",
    "print(rpy2.robjects.r('''\n",
    "anova(glm(choice ~ stim + pop + nonstim, data=mt, family=binomial()), test=\"LRT\")\n",
    "'''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>session</th>\n",
       "      <th>dimension</th>\n",
       "      <th>CP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>all</td>\n",
       "      <td>stim.</td>\n",
       "      <td>0.546377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>all</td>\n",
       "      <td>nonstim.</td>\n",
       "      <td>0.591264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>all</td>\n",
       "      <td>pop.</td>\n",
       "      <td>0.627135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>all</td>\n",
       "      <td>latent</td>\n",
       "      <td>0.620520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   session dimension        CP\n",
       "14     all     stim.  0.546377\n",
       "29     all  nonstim.  0.591264\n",
       "44     all      pop.  0.627135\n",
       "59     all    latent  0.620520"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cps_long[df_cps_long.session == \"all\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbins=100\n",
    "# xi, yi = np.mgrid[all_stim_outputs.min():all_stim_outputs.max():nbins*1j, \n",
    "#                   all_nonstim_outputs.min():all_nonstim_outputs.max():nbins*1j]\n",
    "# k = kde.gaussian_kde([all_stim_outputs, all_nonstim_outputs])\n",
    "# zi = k(np.vstack([xi.flatten(), yi.flatten()]))\n",
    "\n",
    "colors1 = sns.color_palette(\"Blues\", 3)\n",
    "colors2 = sns.color_palette(\"Reds\", 3)\n",
    "\n",
    "\n",
    "def hhist(box, outputs, choices):\n",
    "    hist, bin_edges = np.histogram(outputs, density=True)\n",
    "    hist1, _ = np.histogram(outputs[choices == 0], bins=bin_edges, density=True)\n",
    "    hist2, _ = np.histogram(outputs[choices == 1], bins=bin_edges, density=True)\n",
    "    dx = np.arange(len(hist1) + len(hist2))\n",
    "    axh = fig.add_axes(box, frameon=False)\n",
    "    axh.bar(dx[::2], hist1, color=colors1[-1])\n",
    "    axh.bar(dx[1::2], hist2, color=colors2[-1])\n",
    "    axh.spines['top'].set_visible(False)\n",
    "    axh.spines['right'].set_visible(False)\n",
    "    # axh.spines['bottom'].set_visible(False)\n",
    "    axh.spines['left'].set_visible(False)\n",
    "    # ax.get_xaxis().set_visible(False)\n",
    "    # ax.get_yaxis().set_visible(False)\n",
    "    axh.set_xticks([])\n",
    "    axh.set_yticks([])\n",
    "    \n",
    "\n",
    "def vhist(box, outputs, choices):\n",
    "    hist, bin_edges = np.histogram(outputs, density=True)\n",
    "    hist1, _ = np.histogram(outputs[choices == 0], bins=bin_edges, density=True)\n",
    "    hist2, _ = np.histogram(outputs[choices == 1], bins=bin_edges, density=True)\n",
    "    dx = np.arange(len(hist1) + len(hist2))\n",
    "    axh = fig.add_axes(box, frameon=False)\n",
    "    axh.barh(dx[::2], hist1, color=colors1[-1])\n",
    "    axh.barh(dx[1::2], hist2, color=colors2[-1])\n",
    "    axh.spines['top'].set_visible(False)\n",
    "    axh.spines['right'].set_visible(False)\n",
    "    axh.spines['bottom'].set_visible(False)\n",
    "#     axh.spines['left'].set_visible(False)\n",
    "    # ax.get_xaxis().set_visible(False)\n",
    "    # ax.get_yaxis().set_visible(False)\n",
    "    axh.set_xticks([])\n",
    "    axh.set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x432 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10 * 3, 6))\n",
    "\n",
    "\n",
    "# Nancy\n",
    "ax = fig.add_subplot(1, 3, 1)\n",
    "\n",
    "xlim = (-5.5e-9, 5.5e-9)\n",
    "ylim = (-0.55, 0.55)\n",
    "\n",
    "monkey_choices = np.concatenate(choices[:5])\n",
    "monkey_nonstim_outputs = np.concatenate(nonstim_outputs[:5])\n",
    "monkey_stim_outputs = np.concatenate(stim_outputs[:5])\n",
    "xi, yi = np.mgrid[(monkey_stim_outputs.min() - 5e-10):(monkey_stim_outputs.max() + 5e-10):nbins*1j, \n",
    "                  (monkey_nonstim_outputs.min() - 1e-1):(monkey_nonstim_outputs.max() + 1e-1):nbins*1j]\n",
    "\n",
    "hhist([0.3, 0.13, 0.05, 0.1], monkey_stim_outputs, monkey_choices)\n",
    "vhist([0.13, 0.12, 0.6/30, 1.5/6], monkey_nonstim_outputs, monkey_choices)\n",
    "\n",
    "k = kde.gaussian_kde([monkey_stim_outputs[monkey_choices == 0], monkey_nonstim_outputs[monkey_choices == 0]])\n",
    "zi = k(np.vstack([xi.flatten(), yi.flatten()]))\n",
    "# zi = np.log(100 + zi)\n",
    "\n",
    "levels = np.percentile(zi, [50, 90, 99])\n",
    "ax.contour(xi - .5, yi - .5, zi.reshape(xi.shape), colors=colors1, levels=levels, zorder=2, linewidths=3)\n",
    "\n",
    "k = kde.gaussian_kde([monkey_stim_outputs[monkey_choices == 1], monkey_nonstim_outputs[monkey_choices == 1]])\n",
    "zi = k(np.vstack([xi.flatten(), yi.flatten()]))\n",
    "# zi = np.log(100 + zi)\n",
    "\n",
    "levels = np.percentile(zi, [50, 90, 99])\n",
    "ax.contour(xi - .5, yi - .5, zi.reshape(xi.shape), colors=colors2, levels=levels, zorder=2, linewidths=3)\n",
    "\n",
    "ax.arrow(xlim[0], 0, xlim[1]-xlim[0], 0, width=0.001 * (ylim[1]-ylim[0]), head_width=(ylim[1]-ylim[0]) * 0.02, head_length=(xlim[1]-xlim[0]) * 0.02, length_includes_head = True, zorder=1)\n",
    "ax.arrow(0, ylim[0], 0, ylim[1]-ylim[0], width=0.001 * (xlim[1]-xlim[0]), head_width=(xlim[1]-xlim[0]) * 0.02, head_length=(ylim[1]-ylim[0]) * 0.02, length_includes_head = True, zorder=1)\n",
    "\n",
    "ax.set_xlim(*xlim)\n",
    "ax.set_ylim(*ylim)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "# ax.get_xaxis().set_visible(False)\n",
    "# ax.get_yaxis().set_visible(False)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "ax.set_xlabel(f'stim. (CP={stats.cp(monkey_choices, monkey_stim_outputs, method=\"ranksum\"):.2f})')\n",
    "ax.set_ylabel(f'nonstim. (CP={stats.cp(monkey_choices, monkey_nonstim_outputs, method=\"ranksum\"):.2f})')\n",
    "ax.set_title('Monkey N')\n",
    "\n",
    "\n",
    "# Pat\n",
    "ax = fig.add_subplot(1, 3, 2)\n",
    "\n",
    "xlim = (-1.1e-9, 1.1e-9)\n",
    "ylim = (-0.006, 0.006)\n",
    "\n",
    "monkey_choices = np.concatenate(choices[5:7])\n",
    "monkey_nonstim_outputs = np.concatenate(nonstim_outputs[5:7])\n",
    "monkey_stim_outputs = np.concatenate(stim_outputs[5:7])\n",
    "xi, yi = np.mgrid[(monkey_stim_outputs.min() - 1e-10):(monkey_stim_outputs.max() + 1e-10):nbins*1j, \n",
    "                  (monkey_nonstim_outputs.min() - 1e-3):(monkey_nonstim_outputs.max() + 1e-3):nbins*1j]\n",
    "\n",
    "hhist([0.57, 0.13, 0.05, 0.1], monkey_stim_outputs, monkey_choices)\n",
    "vhist([0.405, 0.12, 0.6/30, 1.5/6], monkey_nonstim_outputs, monkey_choices)\n",
    "\n",
    "k = kde.gaussian_kde([monkey_stim_outputs[monkey_choices == 0], monkey_nonstim_outputs[monkey_choices == 0]])\n",
    "zi = k(np.vstack([xi.flatten(), yi.flatten()]))\n",
    "# zi = np.log(100 + zi)\n",
    "\n",
    "levels = np.percentile(zi, [50, 90, 99])\n",
    "ax.contour(xi - .5, yi - .5, zi.reshape(xi.shape), colors=colors1, levels=levels, zorder=2, linewidths=3)\n",
    "\n",
    "k = kde.gaussian_kde([monkey_stim_outputs[monkey_choices == 1], monkey_nonstim_outputs[monkey_choices == 1]])\n",
    "zi = k(np.vstack([xi.flatten(), yi.flatten()]))\n",
    "# zi = np.log(100 + zi)\n",
    "\n",
    "levels = np.percentile(zi, [50, 90, 99])\n",
    "ax.contour(xi - .5, yi - .5, zi.reshape(xi.shape), colors=colors2, levels=levels, zorder=2, linewidths=3)\n",
    "\n",
    "ax.arrow(xlim[0], 0, xlim[1]-xlim[0], 0, width=0.001 * (ylim[1]-ylim[0]), head_width=(ylim[1]-ylim[0]) * 0.02, head_length=(xlim[1]-xlim[0]) * 0.02, length_includes_head = True, zorder=1)\n",
    "ax.arrow(0, ylim[0], 0, ylim[1]-ylim[0], width=0.001 * (xlim[1]-xlim[0]), head_width=(xlim[1]-xlim[0]) * 0.02, head_length=(ylim[1]-ylim[0]) * 0.02, length_includes_head = True, zorder=1)\n",
    "\n",
    "ax.set_xlim(*xlim)\n",
    "ax.set_ylim(*ylim)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "# ax.get_xaxis().set_visible(False)\n",
    "# ax.get_yaxis().set_visible(False)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "ax.set_xlabel(f'stim. (CP={stats.cp(monkey_choices, monkey_stim_outputs, method=\"ranksum\"):.2f})')\n",
    "ax.set_ylabel(f'nonstim. (CP={stats.cp(monkey_choices, monkey_nonstim_outputs, method=\"ranksum\"):.2f})')\n",
    "ax.set_title('Monkey P')\n",
    "\n",
    "\n",
    "# Leo\n",
    "ax = fig.add_subplot(1, 3, 3)\n",
    "xlim = (-1.5e-8, 1.5e-8)\n",
    "ylim = (-0.65, 0.65)\n",
    "\n",
    "monkey_choices = np.concatenate(choices[7:])\n",
    "monkey_nonstim_outputs = np.concatenate(nonstim_outputs[7:])\n",
    "monkey_stim_outputs = np.concatenate(stim_outputs[7:])\n",
    "xi, yi = np.mgrid[(monkey_stim_outputs.min() - 2e-10):(monkey_stim_outputs.max() + 2e-10):nbins*1j, \n",
    "                  (monkey_nonstim_outputs.min() - 1e-1):(monkey_nonstim_outputs.max() + 1e-1):nbins*1j]\n",
    "\n",
    "hhist([0.84, 0.13, 0.05, 0.1], monkey_stim_outputs, monkey_choices)\n",
    "vhist([0.68, 0.12, 0.6/30, 1.5/6], monkey_nonstim_outputs, monkey_choices)\n",
    "\n",
    "k = kde.gaussian_kde([monkey_stim_outputs[monkey_choices == 0], monkey_nonstim_outputs[monkey_choices == 0]])\n",
    "zi = k(np.vstack([xi.flatten(), yi.flatten()]))\n",
    "# zi = np.log(100 + zi)\n",
    "\n",
    "levels = np.percentile(zi, [50, 90, 99])\n",
    "ax.contour(xi - .5, yi - .5, zi.reshape(xi.shape), colors=colors1, levels=levels, zorder=2, linewidths=3)\n",
    "\n",
    "k = kde.gaussian_kde([monkey_stim_outputs[monkey_choices == 1], monkey_nonstim_outputs[monkey_choices == 1]])\n",
    "zi = k(np.vstack([xi.flatten(), yi.flatten()]))\n",
    "# zi = np.log(100 + zi)\n",
    "\n",
    "levels = np.percentile(zi, [50, 90, 99])\n",
    "ax.contour(xi - .5, yi - .5, zi.reshape(xi.shape), colors=colors2, levels=levels, zorder=2, linewidths=3)\n",
    "\n",
    "ax.arrow(xlim[0], 0, xlim[1]-xlim[0], 0, width=0.001 * (ylim[1]-ylim[0]), head_width=(ylim[1]-ylim[0]) * 0.02, head_length=(xlim[1]-xlim[0]) * 0.02, length_includes_head = True, zorder=1)\n",
    "ax.arrow(0, ylim[0], 0, ylim[1]-ylim[0], width=0.001 * (xlim[1]-xlim[0]), head_width=(xlim[1]-xlim[0]) * 0.02, head_length=(ylim[1]-ylim[0]) * 0.02, length_includes_head = True, zorder=1)\n",
    "\n",
    "ax.set_xlim(*xlim)\n",
    "ax.set_ylim(*ylim)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "# ax.get_xaxis().set_visible(False)\n",
    "# ax.get_yaxis().set_visible(False)\n",
    "ax.set_xticks([])\n",
    "ax.set_yticks([])\n",
    "\n",
    "ax.set_xlabel(f'stim. (CP={stats.cp(monkey_choices, monkey_stim_outputs, method=\"ranksum\"):.2f})')\n",
    "ax.set_ylabel(f'nonstim. (CP={stats.cp(monkey_choices, monkey_nonstim_outputs, method=\"ranksum\"):.2f})')\n",
    "ax.set_title('Monkey L')\n",
    "\n",
    "\n",
    "plt.savefig('contour.pdf', bbox_inches='tight', dpi=200)\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49499998531171846"
      ]
     },
     "execution_count": 632,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monkey_stim_outputs.min() * 0.99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.499999985163352,\n",
       " 0.5000000139404797,\n",
       " 0.0008828151984729008,\n",
       " 0.9999336056143047)"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_stim_outputs.min(), all_stim_outputs.max(), all_nonstim_outputs.min(), all_nonstim_outputs.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.5039006321021472, 0.5000000000871788)"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(all_nonstim_outputs), np.mean(all_stim_outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.49484242939522455,\n",
       " Ttest_1sampResult(statistic=-1.3048246063933304, pvalue=0.19359100449816843),\n",
       " WilcoxonResult(statistic=7183.0, pvalue=0.06661059025804571))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.array(individual_cps)), sp.stats.ttest_1samp(np.array(individual_cps), 0.5), sp.stats.wilcoxon(np.array(individual_cps) - 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 4 CP\n",
    "fig, axs = plt.subplots(1, 2, figsize=(16, 10))\n",
    "\n",
    "sns.barplot(x=\"dimension\", y=\"CP\", data=df_cps_long[df_cps_long.session == \"all\"], ci=None, palette=\"Set1\", ax=axs[0])\n",
    "sns.despine()\n",
    "axs[0].set_ylim((0.5, 0.56))\n",
    "axs[0].set_yticks([0.5, 0.55])\n",
    "axs[0].set_ylabel(\"CP\", fontsize=16)\n",
    "axs[0].set_xlabel(\"\")\n",
    "\n",
    "capprops = dict(linestyle='-', linewidth=2, color='0.3')\n",
    "whiskerprops = dict(linestyle='-', linewidth=1, color='0.3')\n",
    "boxprops = dict(linestyle='-', linewidth=2, edgecolor='0.3')\n",
    "medianprops = dict(linestyle='-', linewidth=2, color='0.3')\n",
    "meanprops = dict(linestyle='-', linewidth=5, color='C1')\n",
    "\n",
    "sns.boxplot(y=individual_cps, width=0.2, color=\"w\", whis=[5, 95],\n",
    "            boxprops=boxprops,\n",
    "            capprops=capprops,\n",
    "            whiskerprops=whiskerprops,\n",
    "            medianprops=medianprops,\n",
    "            meanprops=meanprops, meanline=True, showmeans=True,\n",
    "            showfliers=False, notch=False, ax=axs[1])\n",
    "sns.swarmplot(y=individual_cps, color=\".5\", s=5)\n",
    "sns.despine()\n",
    "axs[0].set_yticks([0.4, 0.5, 0.6])\n",
    "axs[0].set_ylabel(\"CP\", fontsize=16)\n",
    "\n",
    "# plt.savefig(\"fig4(CP).pdf\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bcp(sample):\n",
    "    choice, windows = sample\n",
    "    return [stats.cp(choice, w, pos=1, method=\"ranksum\") for w in windows.T]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average individual CP 0.49744053638402336 WilcoxonResult(statistic=7518.0, pvalue=0.17030422543028723) Ttest_1sampResult(statistic=-0.6573416333468163, pvalue=0.5117871817064579)\n",
      "Analysis of Deviance Table\n",
      "\n",
      "Model: binomial, link: logit\n",
      "\n",
      "Response: choice\n",
      "\n",
      "Terms added sequentially (first to last)\n",
      "\n",
      "\n",
      "        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    \n",
      "NULL                    10486      14493              \n",
      "stim     1   10.867     10485      14482 0.0009787 ***\n",
      "nonstim  1   65.438     10484      14417 5.998e-16 ***\n",
      "pop      1  175.826     10483      14241 < 2.2e-16 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Analysis of Deviance Table\n",
      "\n",
      "Model: binomial, link: logit\n",
      "\n",
      "Response: choice\n",
      "\n",
      "Terms added sequentially (first to last)\n",
      "\n",
      "\n",
      "        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    \n",
      "NULL                    10486      14493              \n",
      "stim     1   10.867     10485      14482 0.0009787 ***\n",
      "pop      1  189.728     10484      14292 < 2.2e-16 ***\n",
      "nonstim  1   51.536     10483      14241 7.031e-13 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "205.65123083486534\n",
      "pop. R2:  0.005141781126325129\n",
      "394.420605943766\n",
      "nonstim. R2:  0.0026210965598415514\n",
      "1000.0\n",
      "stim. R2:  0.0008536109776419165\n",
      "average individual CP 0.49744053638402336 WilcoxonResult(statistic=7518.0, pvalue=0.17030422543028723) Ttest_1sampResult(statistic=-0.6573416333468163, pvalue=0.5117871817064579)\n",
      "Analysis of Deviance Table\n",
      "\n",
      "Model: binomial, link: logit\n",
      "\n",
      "Response: choice\n",
      "\n",
      "Terms added sequentially (first to last)\n",
      "\n",
      "\n",
      "        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    \n",
      "NULL                    10486      14493              \n",
      "stim     1    60.24     10485      14433 8.398e-15 ***\n",
      "nonstim  1   243.43     10484      14189 < 2.2e-16 ***\n",
      "pop      1   264.16     10483      13925 < 2.2e-16 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Analysis of Deviance Table\n",
      "\n",
      "Model: binomial, link: logit\n",
      "\n",
      "Response: choice\n",
      "\n",
      "Terms added sequentially (first to last)\n",
      "\n",
      "\n",
      "        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    \n",
      "NULL                    10486      14493              \n",
      "stim     1    60.24     10485      14433 8.398e-15 ***\n",
      "pop      1   494.74     10484      13938 < 2.2e-16 ***\n",
      "nonstim  1    12.85     10483      13925 0.0003379 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "67.34150657750828\n",
      "pop. R2:  0.014529433869807762\n",
      "97.70099572992257\n",
      "nonstim. R2:  0.010155719061063273\n",
      "327.4549162877732\n",
      "stim. R2:  0.0031519431439659407\n",
      "average individual CP 0.49744053638402336 WilcoxonResult(statistic=7518.0, pvalue=0.17030422543028723) Ttest_1sampResult(statistic=-0.6573416333468163, pvalue=0.5117871817064579)\n",
      "Analysis of Deviance Table\n",
      "\n",
      "Model: binomial, link: logit\n",
      "\n",
      "Response: choice\n",
      "\n",
      "Terms added sequentially (first to last)\n",
      "\n",
      "\n",
      "        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    \n",
      "NULL                    10486      14493              \n",
      "stim     1    29.32     10485      14464 6.126e-08 ***\n",
      "nonstim  1   192.16     10484      14272 < 2.2e-16 ***\n",
      "pop      1   335.58     10483      13936 < 2.2e-16 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "Analysis of Deviance Table\n",
      "\n",
      "Model: binomial, link: logit\n",
      "\n",
      "Response: choice\n",
      "\n",
      "Terms added sequentially (first to last)\n",
      "\n",
      "\n",
      "        Df Deviance Resid. Df Resid. Dev  Pr(>Chi)    \n",
      "NULL                    10486      14493              \n",
      "stim     1    29.32     10485      14464 6.126e-08 ***\n",
      "pop      1   510.44     10484      13953 < 2.2e-16 ***\n",
      "nonstim  1    17.30     10483      13936 3.187e-05 ***\n",
      "---\n",
      "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
      "\n",
      "67.34150657750828\n",
      "pop. R2:  0.014985413149529614\n",
      "81.11308307896873\n",
      "nonstim. R2:  0.012033462950179707\n",
      "475.0810162102798\n",
      "stim. R2:  0.0022882059620301654\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nonstim_spatial = []\n",
    "pop_spatial = []\n",
    "\n",
    "color_seq = sns.color_palette(\"inferno\", 3)[::-1]\n",
    "\n",
    "fig, axes = plt.subplots(1, 1, figsize=(12, 5))\n",
    "axes = [axes]\n",
    "for i, align in enumerate([(3, 6),  (7, 10), (11, 14)]):\n",
    "    choices = []\n",
    "    pop_outputs = []\n",
    "    pop_resids = []\n",
    "    pop_models = []\n",
    "    pop_slides = []\n",
    "    pop_prefs = []\n",
    "\n",
    "    nonstim_outputs = []\n",
    "    latent_resids = []\n",
    "    nonstim_models = []\n",
    "    nonstim_slides = []\n",
    "\n",
    "    stim_outputs = []\n",
    "    stim_models = []\n",
    "    stim_slides = []\n",
    "\n",
    "    individual_cps = []\n",
    "    individual_temporal_cps = []\n",
    "\n",
    "    for dish in dishes:\n",
    "        coh = dish['good_coherences']\n",
    "        cohsum = np.sum(coh, axis=1, keepdims=True)\n",
    "\n",
    "        mask = dish['mask_good_weak']\n",
    "        targchosen = dish['good_targchosen'][mask]\n",
    "        choice = (targchosen == 2) + 0\n",
    "        choices.append(choice)\n",
    "\n",
    "        Y = np.concatenate([np.sum(dish['y'] [:, w:w+window, :], axis=1, keepdims=True) for w in windows], axis=1)\n",
    "        Y = np.reshape(Y, (Y.shape[0], -1))\n",
    "        Y = np.log1p(Y)\n",
    "        \n",
    "        regress = RidgeCV(fit_intercept=True, alphas=pulse_penalty)\n",
    "        regress.fit(coh, Y)\n",
    "        coef = regress.coef_.T\n",
    "        coef = np.mean(np.reshape(np.mean(coef, axis=0), (-1, dish['y'].shape[-1])), axis=0)\n",
    "        pop_pref = np.ones_like(coef)\n",
    "        pop_pref[coef < 0] = 0\n",
    "\n",
    "        residual = Y - regress.predict(coh)\n",
    "        pop_resid = np.reshape(residual, (residual.shape[0], len(windows), -1))\n",
    "        pop_resid = pop_resid[mask, ...]\n",
    "        pop_prefs.append(pop_pref)\n",
    "        pop_resids.append(pop_resid)\n",
    "        pop_resid_sum = np.sum(pop_resid[:, 1:, :], axis=1)\n",
    "        pop_resid_align = np.sum(pop_resid[:, align[0]:align[1], :], axis=1)\n",
    "        pop_resid_sum2 = pop_resid_sum\n",
    "    #     pop_resid_sum2 = np.sum(pop_resid[:, 1:6, :], axis=1)\n",
    "        for spk, pref in zip(pop_resid_sum2.T, pop_pref):\n",
    "            individual_cps.append(stats.cp(choice, spk, pos=pref, method=\"ranksum\"))\n",
    "\n",
    "        for spk, pref in zip(pop_resid.T, pop_pref):\n",
    "            individual_temporal_cps.append([stats.cp(choice, w, pos=pref, method=\"ranksum\") for w in spk])\n",
    "\n",
    "        # latent\n",
    "        mu = np.copy(dish['mu'])\n",
    "        pulse_weight = dish['pulse_weight']\n",
    "        u, s, v = np.linalg.svd(pulse_weight, full_matrices=False)\n",
    "        mu = mu @ v.T\n",
    "\n",
    "        Z = np.concatenate([np.sum(mu[:, w:w+window, :], axis=1, keepdims=True) for w in windows], axis=1)\n",
    "        Z = np.reshape(Z, (Z.shape[0], -1))\n",
    "#         Z = np.exp(Z + np.mean(dish['b']))\n",
    "\n",
    "        regress = RidgeCV(fit_intercept=True, alphas=np.logspace(-2, 5, 100))\n",
    "        regress.fit(coh, Z)\n",
    "        residual = Z - regress.predict(coh)\n",
    "        latent_resid = np.reshape(residual, (residual.shape[0], len(windows), -1))\n",
    "        latent_resid = latent_resid[mask, ...]\n",
    "        latent_resids.append(latent_resid)\n",
    "\n",
    "        stim_resid = latent_resid[:, :, [0]]\n",
    "        nonstim_resid = latent_resid[:,:, [1, 2, 3]]\n",
    "        stim_resid_sum = np.sum(stim_resid[:, 1:, :], axis=1)\n",
    "        stim_resid_align = np.sum(stim_resid[:, align[0]:align[1], :], axis=1)\n",
    "\n",
    "        nonstim_resid_sum = np.sum(nonstim_resid[:, 1:, :], axis=1)\n",
    "        nonstim_resid_align = np.sum(nonstim_resid[:, align[0]:align[1], :], axis=1)\n",
    "\n",
    "        output, model = recipe.logitcv(pop_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)    \n",
    "        pop_slides.append(\n",
    "            np.array([model.predict_proba(pop_resid[:, i, :])[:, 1] for i in range(len(windows))]))\n",
    "        #             sp.special.expit(pop_resid[:, i, :] @ model.coef_.T)[:, 0]\n",
    "        pop_models.append(model)\n",
    "        pop_outputs.append(model.predict_proba(pop_resid_sum)[:, 1])\n",
    "        \n",
    "        pop_spatial.append(model.coef_)  # collect spatial weights\n",
    "\n",
    "        output, model =  recipe.logitcv(nonstim_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)\n",
    "        nonstim_slides.append(\n",
    "            np.array([model.predict_proba(nonstim_resid[:, i, :])[:, 1] for i in range(len(windows))]))  #  sp.special.expit(nonstim_resid[:, i, :] @ model.coef_.T)[:, 0]\n",
    "        nonstim_models.append(model)\n",
    "        nonstim_outputs.append(model.predict_proba(nonstim_resid_sum)[:, 1])\n",
    "        \n",
    "        nonstim_spatial.append(model.coef_)  # collect spatial weights\n",
    "\n",
    "        output, model =  recipe.logitcv(stim_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)\n",
    "        stim_slides.append(\n",
    "            np.array([model.predict_proba(stim_resid[:, i, :])[:, 1] for i in range(len(windows))]))  # sp.special.expit(stim_resid[:, i, :] @ model.coef_.T)[:, 0] \n",
    "        stim_models.append(model)\n",
    "        stim_outputs.append(model.predict_proba(stim_resid_sum)[:, 1])\n",
    "\n",
    "    print(\"average individual CP\", np.mean(individual_cps), sp.stats.wilcoxon(np.array(individual_cps) - 0.5), sp.stats.ttest_1samp(individual_cps, 0.5))\n",
    "\n",
    "    all_choices = np.concatenate(choices)\n",
    "    all_pop_outputs = np.concatenate(pop_outputs)\n",
    "    all_nonstim_outputs = np.concatenate(nonstim_outputs)\n",
    "    all_stim_outputs = np.concatenate(stim_outputs)\n",
    "\n",
    "    all_pop_slides = np.concatenate(pop_slides, axis=1).T\n",
    "    all_nonstim_slides = np.concatenate(nonstim_slides, axis=1).T\n",
    "    all_stim_slides = np.concatenate(stim_slides, axis=1).T\n",
    "\n",
    "    individual_temporal_cps = np.stack(individual_temporal_cps)\n",
    "    individual_temporal_cp = individual_temporal_cps.mean(axis=0)\n",
    "    individual_temporal_cp_q5, individual_temporal_cp_q95 = np.percentile(individual_temporal_cps, q=[5, 95], axis=0)\n",
    "\n",
    "    pd.DataFrame(np.column_stack([all_choices, all_stim_outputs, all_nonstim_outputs, all_pop_outputs]), \n",
    "                 columns=[\"choice\", \"stim\", \"nonstim\", \"pop\"]).to_csv(\"pooled_outputs.csv\", index=False)\n",
    "\n",
    "    pd.DataFrame(np.column_stack([all_choices, all_stim_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_stim_slides.shape[1])]).to_csv(\"all_stim_slides.csv\", index=False)\n",
    "    pd.DataFrame(np.column_stack([all_choices, all_nonstim_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_nonstim_slides.shape[1])]).to_csv(\"all_nonstim_slides.csv\", index=False)\n",
    "    pd.DataFrame(np.column_stack([all_choices, all_pop_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_pop_slides.shape[1])]).to_csv(\"all_pop_slides.csv\", index=False)\n",
    "\n",
    "    stim_cps = []\n",
    "    nonstim_cps = []\n",
    "    pop_cps = []\n",
    "    for choice, stim_output, nonstim_output, pop_output in zip(choices, stim_outputs, nonstim_outputs, pop_outputs):\n",
    "        stim_cps.append(stats.cp(choice, stim_output, method=\"ranksum\"))\n",
    "        nonstim_cps.append(stats.cp(choice, nonstim_output, method=\"ranksum\"))\n",
    "        pop_cps.append(stats.cp(choice, pop_output, method=\"ranksum\"))\n",
    "\n",
    "    # append pooling sessions\n",
    "    stim_cps.append(stats.cp(all_choices, all_stim_outputs, method=\"ranksum\"))\n",
    "    nonstim_cps.append(stats.cp(all_choices, all_nonstim_outputs, method=\"ranksum\"))\n",
    "    pop_cps.append(stats.cp(all_choices, all_pop_outputs, method=\"ranksum\"))\n",
    "\n",
    "    df_cps = pd.DataFrame({\"session\": exnames + [\"all\"], \n",
    "                            \"stim.\": np.array(stim_cps), \n",
    "                            \"nonstim.\": np.array(nonstim_cps), \n",
    "                            \"pop.\": np.array(pop_cps)})\n",
    "\n",
    "    df_cps_long = df_cps.melt(id_vars=[\"session\"], value_vars=[\"stim.\", \"nonstim.\", \"pop.\"], var_name=\"dimension\", value_name=\"CP\")\n",
    "\n",
    "    print(rpy2.robjects.r('''\n",
    "    mt <- read.csv(\"pooled_outputs.csv\")\n",
    "    anova(glm(choice ~ stim + nonstim + pop, data=mt, family=binomial()), test=\"LRT\")\n",
    "    '''))\n",
    "    print(rpy2.robjects.r('''\n",
    "    anova(glm(choice ~ stim + pop + nonstim, data=mt, family=binomial()), test=\"LRT\")\n",
    "    '''))\n",
    "\n",
    "    alphas = np.logspace(-1, 3, 100)\n",
    "    X = (all_choices[:, None] + 0) * 2 - 1\n",
    "    fit_intercept = False\n",
    "    temp_cv = None\n",
    "    B = 1000\n",
    "\n",
    "    # pop\n",
    "    slides = all_pop_slides\n",
    "    pop_cps = bcp((all_choices, slides))\n",
    "\n",
    "#     slides = sp.stats.zscore(all_pop_slides)\n",
    "    slides = all_pop_slides - 0.5\n",
    "    if temp_cv == 0:\n",
    "        regress = LinearRegression(fit_intercept=fit_intercept)\n",
    "    else:\n",
    "        regress = RidgeCV(fit_intercept=fit_intercept, alphas=alphas, cv=temp_cv)\n",
    "    regress.fit(X, slides)\n",
    "    alpha_min = regress.alpha_\n",
    "    pop_weights = np.squeeze(regress.coef_)\n",
    "    print(regress.alpha_)\n",
    "    pop_r2 = 1 - np.mean((slides - regress.predict(X)) ** 2) / np.mean(slides ** 2)\n",
    "    print(\"pop. R2: \", pop_r2)\n",
    "#     pop_weights_B = np.stack(dask.compute(*[dask.delayed(f)(sklearn.utils.resample(X, slides), alpha=alpha_min) for _ in range(B)]))\n",
    "#     pop_q5, pop_q95 = np.percentile(pop_weights_B, [5, 95], axis=0)\n",
    "    \n",
    "    # nonstim\n",
    "    slides = all_nonstim_slides\n",
    "    nonstim_cps = bcp((all_choices, slides))\n",
    "\n",
    "#     slides = sp.stats.zscore(all_nonstim_slides)\n",
    "    slides = all_nonstim_slides - 0.5\n",
    "    if temp_cv == 0:\n",
    "        regress = LinearRegression(fit_intercept=fit_intercept)\n",
    "    else:\n",
    "        regress = RidgeCV(fit_intercept=fit_intercept, alphas=alphas, cv=temp_cv)\n",
    "    regress.fit(X, slides)\n",
    "    alpha_min = regress.alpha_\n",
    "    nonstim_weights = np.squeeze(regress.coef_)\n",
    "    print(regress.alpha_)\n",
    "    nonstim_r2 = 1 - np.mean((slides - regress.predict(X)) ** 2) / np.mean(slides ** 2)\n",
    "    print(\"nonstim. R2: \", nonstim_r2)\n",
    "#     nonstim_weights_B = np.stack(dask.compute(*[dask.delayed(f)(sklearn.utils.resample(X, slides), alpha=alpha_min) for _ in range(B)]))\n",
    "#     nonstim_q5, nonstim_q95 = np.percentile(nonstim_weights_B, [5, 95], axis=0)\n",
    "\n",
    "    # stim\n",
    "    slides = all_stim_slides\n",
    "    stim_cps = bcp((all_choices, slides))\n",
    "\n",
    "    slides = all_stim_slides - 0.5\n",
    "    if temp_cv == 0:\n",
    "        regress = LinearRegression(fit_intercept=fit_intercept)\n",
    "    else:\n",
    "        regress = RidgeCV(fit_intercept=fit_intercept, alphas=alphas, cv=temp_cv)\n",
    "    regress.fit(X, slides)\n",
    "    alpha_min = regress.alpha_\n",
    "    stim_weights = np.squeeze(regress.coef_)\n",
    "    print(regress.alpha_)\n",
    "    stim_r2 = 1 - np.mean((slides - regress.predict(X)) ** 2) / np.mean(slides ** 2)\n",
    "    print(\"stim. R2: \", stim_r2)\n",
    "#     stim_weights_B = np.stack(dask.compute(*[dask.delayed(f)(sklearn.utils.resample(X, slides), alpha=alpha_min) for _ in range(B)]))            \n",
    "#     stim_q5, stim_q95 = np.percentile(stim_weights_B, [5, 95], axis=0)\n",
    "\n",
    "    ax = axes[0]\n",
    "    sns.lineplot(x=windows - 50, y=nonstim_cps, label=\"nonstim.\", color=color_seq[i], ax=ax, legend=False, lw=3)\n",
    "#     ax.plot(windows[align[0]:align[1]] - 100, nonstim_cps[align[0]:align[1]], color=color_seq[i], lw=5)\n",
    "    ax.plot([windows[align[0]]  - 100 , windows[align[1]]  - 100], [0.49, 0.49], color=color_seq[i], lw=5)\n",
    "    sns.despine(ax=ax)\n",
    "\n",
    "#     ax = axes[1]\n",
    "#     sns.lineplot(x=windows - 50, y=pop_cps, label=\"pop.\", color=color_seq[i], ax=ax, legend=False, lw=3)\n",
    "#     ax.plot(windows[align[0]:align[1]] - 100, nonstim_cps[align[0]:align[1]], color=color_seq[i], lw=5)\n",
    "#     ax.plot([windows[align[0]]  - 100 , windows[align[1]]  - 100], [0.49, 0.49], color=color_seq[i], lw=5)\n",
    "#     sns.despine(ax=ax)\n",
    "\n",
    "axes[0].set_xlabel(\"time (ms) from motion onset\")\n",
    "axes[0].set_xlim(-100)\n",
    "axes[0].set_ylim([0.45, 0.65])\n",
    "# axes[0].set_title(\"Non-stimulus\")\n",
    "axes[0].plot([-100, 1400], [0.5, 0.5], ls='--', color='0.5')\n",
    "axes[0].set_yticks([0.5, 0.6])\n",
    "axes[0].set_xticks([0, 1000])\n",
    "# axes[1].set_xlabel(\"time (ms)\")\n",
    "# axes[1].set_xlim(-100)\n",
    "# axes[1].set_ylim([0.48, 0.58])\n",
    "# axes[1].set_title(\"Population\")\n",
    "# axes[1].plot([-100, 1400], [0.5, 0.5], ls='--', color='0.5')\n",
    "\n",
    "# axes[1].legend(frameon=False)\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figure/cp_time_course.pdf\")\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average individual CP 0.49744053638402336 WilcoxonResult(statistic=7518.0, pvalue=0.17030422543028723) Ttest_1sampResult(statistic=-0.6573416333468163, pvalue=0.5117871817064579)\n",
      "205.65123083486534\n",
      "pop. R2:  0.005141781126325129\n",
      "394.420605943766\n",
      "nonstim. R2:  0.0026210965598415514\n",
      "1000.0\n",
      "stim. R2:  0.0008536109776419165\n",
      "average individual CP 0.49744053638402336 WilcoxonResult(statistic=7518.0, pvalue=0.17030422543028723) Ttest_1sampResult(statistic=-0.6573416333468163, pvalue=0.5117871817064579)\n",
      "67.34150657750828\n",
      "pop. R2:  0.014529433869807762\n",
      "97.70099572992257\n",
      "nonstim. R2:  0.010155719061063273\n",
      "327.4549162877732\n",
      "stim. R2:  0.0031519431439659407\n",
      "average individual CP 0.49744053638402336 WilcoxonResult(statistic=7518.0, pvalue=0.17030422543028723) Ttest_1sampResult(statistic=-0.6573416333468163, pvalue=0.5117871817064579)\n",
      "67.34150657750828\n",
      "pop. R2:  0.014985413149529614\n",
      "81.11308307896873\n",
      "nonstim. R2:  0.012033462950179707\n",
      "475.0810162102798\n",
      "stim. R2:  0.0022882059620301654\n"
     ]
    },
    {
     "data": {
      "image/png": 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HM0++7FM+K01nsxoayUrnRho6LxwLuSGqLlRVg1UtVMXm7QtFg3QBxLogLAEAsE7yiSklzpxX4ttnlTh9XrnRiYd6nFBXey38VLvHPTphqFwua3JkplYJupuYUxEav5fUbDK94ucJRoLq2tGurh1t6tzR7qzv7HD3taujv02hyOMzsUSxUFJqMl0XolLztuv2JVKaHp3WbDKlTPrhp0vPpfLKpfKauD+5rPOC4YATouqDVltUsTZnX6w16tzc7XjdfcEwH3/ROH5bAABYI6VsTpNnryhx6pwSp89p9upAQ+eFu9sV2VbfTa4WisK9XVt+OudsKqeJ+5OeFaHxe0lN3J9UqbDy6clbuuLq2ukEn0oY6t7ZUV1v6YpTnagTCPrV2t2s1u7mZZ1XLpU1Mz6t4VOXNXzqokbeva7krWFlC1KuZJQr+ZQr+5xl5VZ2bg87GqSQKyo5PK3k8PSyzw1Fg7VA1eZUrGpBK6qm1qizr3VuAIu3RRWKbu3/e1g+xiwBALBKbLms2Wu3nXB06qwmz15ROV9Y8Hh/LKqOE0+r/fmjatrdr2hfj8K9nVs6DFlrNTU6s2D3uPG7Sc0kUit+nkDIr87t7dUwVAlE1TC0vY0PthuomM5o8t0rSp45r8SZ855fFFgrFcpG2ZJPpUiTgnv3yNe/Q+ruVsEXeqCqVdmeSaRWJUw/jGA4UA1V9WGqPnDVwlZdpastqnBTiHC+uTB1OAAAay07Mu6Go3NKnDmvQnLhb7qN36+Wpw+o44Vj6nzhmJqf2r8lr1tTLpeVHJrW8K0xjdwc1/DNMQ3fGnfWb40pl8qv+DmaO2Ju+KlVheq7ybV0x+Xz+Vbh1WA95CenlXz7ohueLihzd2jR40Nd7dWL43aceFqRbd3V+6y1yqXzToBKpjU7WekemHEC1WS6up6amreczGzYDIf+oH9Od8D5YSruBq1YW1TNHXH1PdGt1p5mAtbaISwBALDaiqm0km9frHatS98eXPT4pt396njhuDpeOKb25w4rEGtap5auTLlcVuL+pIZuVgLRuEZujTnLgXHlMwtXzJbiD/jUub19wTA0Z0IFPJKyw2NKnLlQDU/58eSix0d39Kr9xFHnGk/PH1GovfWhntdaq+xsrhqcZqfS1fXUoiErrdnJzLpXtJpaouo/2KPtB3vVf6DXWR7sVe+eTvm34BctmwxhCQCAlSoXS5q5fF0TbvVo+sI12dLCH5iCbc3qOHlMHS84t0hv1zq2dnlKxZIm7k86laEbc6tDowMTKuQebuB+U0tU3bvciRLmjRnq2tmhtp5m+fxUheCozgz59gUlT59X8jsXVZxZvKtm/MDu6sVx2555SoGm6Lq0M5fO1wUrJ0zNTqaVrqtczU7WBbCpWgB72P9PXvxBv7bt66oFqAO92n6wR/0HehVtfjSug7YOCEsAACyXtVaZu8Nu5eiskm9fVHF24ZnXfKGg2o4fqlaP4gd2y2yi7mHFQknjdxMarnSXq1SKbo1r9PbEQ39THm9v0rYnurVtb5d69znLbfu61buvS80dMboO4aHZUkkzVweUOH1eyTPnnbF/uYW7dhq/Xy1H9jtfUpw8qpanD8gX2HxzmOUzec26wSk9lal1F/Sobk2OTmvw2shDTefe0d+q/gP1lagebT/Qq/a+Vv5fzkVYAgCgEYWpGSVOn6+OPcoOjy16fPzgnmr1qO34oQ2fqruYL2r09oQTiG6NafhGrcvc2J3EQ4/RaOmKq3dvl/qe6K4LRF3q3duleHtslV8F4K2cL2jqwlUlzpxX8vQFTV+6tuiFmf1NEbU9e9ip7p48ptjeHVsyJFhrlRx2QtPg1RHdvzqiwWsjun91VInB5U21LknR5rBniOrd2/W4XhyYsAQAgJdyvqDJ8+851zs6dU4z793SYnMYh3s6q93qOk4cVajj4cZLrEQ+W9DowIRTGbpVqxIN3xzX+L2k7ENeyLatt0W9bgjatq/bvXWpd0+XmlrXvmsTsFzFVEaT715yKk9vX9DstduLHh/qalfHyaPqOHlM7SeeVqSnc51aunYyM1kNXh/V4NURDV4b1f2rwxq8NqqhG2PLrhb7Az717u1yg1SP+g86YWr7gd5H/W8AYQkAAMn5hjZ14061cpR897LK2YW7t/ibImp/9og6XnS61jXt7l+Xb6Zz6bwThG7VJlVwKkVjSgxO6WHfszv6W9W71wlB2/Z2ud3nutW7t0uROBMpYGvLJ6eUPHOhWh1eqjLctGd7teq0lSZdaUSpWNLIwEQ1RDmVqBHdf29E6enMsh+vrbfFHRNVC1H9B3rVub1tS1br5iEsAQAeX7nxZPV6R4nT55WfWKTbis+o5fB+t3p0XK2rOOahXCprJpHS9PhsdVlZn3HXk8NTGhkYV2Jw6qGewxijzu1t6t3XpW1uKOrd16W+fd3q2dOlcBPXG8LjwVqrzP2R6myVybcvqDi98GQRxu9Ty+EDbnjavOOdVspaq6mxGacrX32Qem9E4/cWn4nQSzgWUv/+nursfJWufdv2dSsY3jI/P8ISAODxUcpklXznUrV6lLp5d9Hjozu2VbvWtT//tILNjY3ByaXzmpmY1fSEG4AmZjU9MS8ATaSc/eOzSk1mHroiVM/4jLp3driByJ1MwV327OlUKBJc8XMAj5rqZBFueJo8e0W2sPCsdI/KeKflyKZyGroxpvvvDc8JUUM3RlXML69Ln/EZ9e7pnDPNef+BXm1/slfxtk1XwSMsAQAeXbZU0sx7t5Q4dU4Tp85p6twV2eLCb+yBlnh13ELHC8cU7e9RuVxWeirjhJtxJ/TMuCGofr0SjmYmZpVLr/yCqwvx+X3q2d1R7TJXXynq2d35uA7CBlZNKZvT5NkrTnfc0+c1c/XWoseHOtucvxsvHH9kxjs1qlwqa/ROYk6Iqkw0MZtceIbQhbR2x/Xzn/mEnnh21xq09qEQlgAAj45yvqCZawOavnxDk9+5qMSZ8w90rylZKVvyKVv0KWeC8vXvkOnrU7m1Q1kb0GwyPbcb3ETqoWeKW454e5OaO+Nq6YyppSuu5s64mivrHXG1djuzznXt7FAgyIUmgfWST045F5k+7c6EOdTAeCf3C5dHbbxTo6y1mplIOWOh3Bn6Kl37xu4kFq2k//bFX1FHf9s6tnZRhCUAwNZULpaUunVPM5eva/ryDU1fuq6Z63c0mTYazoQ0VfArW/Q7wajuli+v/fWN/EG/WjpjTvjpiqulPvi4gai5bn9zR0z+AAEI2Oyq451On1fy9DnPL2Tq1cY7ORXrR3W803Lk0nkN3xjTfXdiier4qOuj8gd8+t07/+tm6tZIWAIAbH62XFb67pCmL93QzJUbzvLqLRWyeY1ngxrJhDTs3nKl1Q8d0eawmjvibsBxQ5AbhJo7Yw+sR1sim+nNHsAaqV0c95wSp85r6twVlfOFBY+vjndyK0+Pw3inRpVLZU2Ozqijb/0vu7AIwhKA9WELKckfkfHx7flmYUt5KZuUclOyuUmZSLvUtn/D37ittcoOj2v68nXNXLqh6cvXNX3lpkqpjHIlo9G6YDSWDalkl9de4zNq7ohVg01zV1wtHW6lpyuu5g6nAlTp/tbcGWNiBAANqYx3Sp4+r8Tpc5q5OrDo9dke5/FOWwRhCcDqs7YsO3FJ9v63ZAe/pfLgN6XpO86doWYp3CoTbpPCrXXrzraJuMtwa21f5dhQC2FrHlvMSbmklJ2UzU1K2aS7nJRySdnspJSblM0mJXe/dY9X0WPwbaRTpu+kfH0vyvS/JNP7vEwovqavITeR1Mzlm04ounRD01duqJCcliTNFvwazoSqlaNELqCl3rvi7U168sV92vfsTrX2tMzpDtfcGVO8rUk+/9p3xQOA/OS0e30nxjttUYQlACtnixnZ4berwcgOftv5YL7qjBRuWThM1a07oWt+2GqWMZvvQ7ItZpyQk52UclNzQk59+KmGnPrwU1z+BQSXxfhkuo/K9L0o0/eSfH0vSq17Hrr6VJie1fTlG5q5fMMZZ3T5hnKjE5KkspWSucCcLnWp4tJ9+3v2dOrQS/v05Ev7dOilfeo70COfb/P9OwNA5v6IJk6dW/Z4p/aTx1b12m4rYctllfMFlXN5lbK5ecv8IvtzKuUWu9/ZfuH3/50CsehGv8wKwhKA5bOZcScYuZUjO/IdqbxwH21Jki8glUuSNvLviakLVW7YirRV1+cGsHlhLNImBeOeIcFa64SWSshpoKpTqQIpNymVchvws5Bk/LWfQahFdvqOlJ1Y+rymHpm+F+uqT8/KBB58Yytlspp+75ZmLl2vBqPMveHq/cWyNJYNVStHI5nQkpMvGJ/RnqPbq8Ho4Iv7Nlv/dgBoiC2VNHPNvb7Tcsc7nTyq2L6d1fcka63KlSCSy6uczc1bLrS/LsA0cl4up3Juiff7Ffquv/yUwp3MhreqCEvA2rHWSlM3Vb7/TdnBb6o8+JaUeG/pE5t6ZPrfJ9/2l2X63yfTfVzy+aXcdHWMjFNFmZTNTc3bN+UGjHn789Nr/4IXY3y1ABVqli3l6gLP2l1XZ/E2+aVIuxP23GqacbedINjuLtvqjnPvDzXPCX/WWmnyusqD35Ydekt26JTs+EUtGXB9QdmO40rpGc3O9GlmOKCZ60NKDdxzykWubNE3p0vdeDao8hLvQ+GmkA6c3FMNR/uf361oc2QlPzEA2JRK2Zymzr1XvTjuUuOdAs0xyZhqSHpUvPxn/7ei/T0b3YwKwhKAuWypIDt21q0cfVN28FtSenTpEzuelK//JZn+l+Xb/rLUum/VJwqw5ZITmOoDVDbpHbbmr2cnpcLsqrZn1fiCyww8rdV9C1W7VovNTckOn5Ed+rbKg2/JDp+WzUwrPR3XTKJNM4lWzSZalZpqka2rClkrzbjjjSoBaTK/9CQJrT3N1WD05Ev7tPvp7VxTCMBjKT857Vzf6dTZhsY7rSdfOChfOCx/OCRfpLIMzdt2l+EF9i9wXqS/Z1N0N3QRloDHnc1Nux+E3S51Q6e9B/7X8wVlep9zg9FLMn0vyTR1r0+DV8CWiwuHqfpQtVDwKizct1z+kBtu2qvd9qqBpi78OGFoXvgJNG34DHQLqUzZXZuA4bpmrt56oBtG2UoTuaCG07XKUaaBKbz7D/ZWg9GTL+1T757OTfuzAICN5Fzf6ZwSp88rcfq8itNzvwB0AkxI/nDYWUYqy9C87XAtwITDC9+/wH5fKCjz+IwLbegNadNEOwArZ2fuOcHIrRrZ8QuSLS9+UrhVpv8l+fpflul/WWbbc55jVDY74wtI0U4p2tnYX795bKkg5SvdBCelQNQJP5H2LfnzmG/OlN3uGKOZKzdVnH0wPBfKRqOZoIYzYQ1nQhrNBFW0i795+n0l7ekb18Fdwzq4a0QHngqq7cBzMv3tMn0Bma5WghIALCC6vVfbt7+i7T/0imyppHxiSiYYcELM4xVgNh0qS8AWVZvC+5vuTHXfqk3hvZiW3fL1v88Za7T9ZZnOpzblLHKPA2utbLGocr6ocj6vcqHo9E8vuNv5ecu6+22+4MxYVCh4n1P/WLm8UrfvV6fsni/tjjeqVI4mckHZJSJnNObTwQN5Hegb0MGey9rXP6ZQsLTwCYEmmW3P100e8aJMtGslPz4AAFaCyhLwKKlN4f1NZ7zR0Led7mOLqUwN3f8+p1td/0syzTvWp8FblLVWs9dvqzA54wYRN5DkPW7zgorNF1Vyl9XAslDocbcXG+C7Nq9PmszXpvAeyUU0nVs6LHdub5sz3mjnU33V6xnZ9Kjb3fOU7NC3ZYfPSKXs3AcopmXvfV323tdVrXW27a8Fp74XZToPc60tAMCmQmUJ2KRsesytGL0lO/hN2ZF3lp7CO9Ak0/eCO1Pd+2S2vSATblmfBm9x5XxBw3/zdd35oy8odfPuRjdnxUplKVv2KVv0KVvyaTwb0mghopFsWJnc4n/3jTHaebivNt7oxb3q2tnR8HM7E4mcc4KTO3mEZhr4mYaaZbadlOl7wQn2204648EAAFh9TPAAbBXOtM433Iu+Otc4UvLq0ic29VRnqHOm8D4m4196VjLUFKZmdO/PvqR7f/JF5SfW4mK7izN+n9MfPRiUP+wsfaFxj3SYAAAgAElEQVS6WzAoBQPKlfy18FOUMjkpk7fKZMtKZ8tKZ0pKp4tKpQpKzeSVyxYbbkMwEtT+53ZVJ2I4+MIexVpX98rydnZQduiUE/6Hvi07+k5DU7Kbjqdk+l9wu++9JHUcpNsoAGA1EJbwaLDZpJSZkIyR83ttnGviVLaNmXefe7/q9ntt15+v+Y/nHL9WA9Kdb97flb3/LTcgvdXgFN6H5k3hvZdB8w8pfW9Yd//4Cxr8wldVzs69cKy/KaLmQ0/IHwrK5wYYfygo44aXOWGmLtT4wkvfL59fmUxRqZm8ZqZzSk9nNT0xq5mJlHNLuOuJlGbc/ampzKq+9uaOWDUYHXppn/Ye36FAaH17ZdtiTnb0Xdmht6rXflJqeOkTw21O9bT3WZnmnTLxfineLxPfLkU6+P8AAGgUYQlbiy3lZRPvyY5flB2/4N4uSrP3N7ppqxS+VNvOTUnFJT4A+4Iyvc/LbHfHGvW/xID4VTB57j3d+fQbGvvaqQfGC4W7O7Tzx35A/T/0YQWbY0s+VrlcVno6Ww01M4lKyHGDjsf6bDKt9fi76/P71NzRpObOuJo7Yure3VntVtd/oGfThQprrTRzV+Whb8tWq09nJbvIpBHz+SNSvE8mvn1OiKquN2+XmnqpvgIAJMISNivnQ9E92fHzbjByw1HyqlRuvOvQIyfcVjeF9/u27BTem5EtlTT2tdO6/enPa/rCtQfujx/YrV0f+4h6P/yy5PPr7qUhJYan5lZ6vKo+ibTKpSWmZl8FxhjF25vU3BlTc0fMCUCdMcXbY2rpjLn7nX3OLa6mloh8W3yqWVtIy458R3bwLSdEDZ2SMiu9cKNxuq/G+6VmJ0g5YWreeii+Kq8BALBpEZaw8Wxuqq5S5C4nLi09i1s9f0iK9TsVGVuWZN2KgHW2q+sLLRs5x+OYtdayx5nCe7szU53pPMRYjFVWTGc09IWv6u5n/lKZ+yMP3N/50jPa9U8+Kv/ePTr/d+/pnTcv6dzfXdFMYpEL1K6CWGu0Gmqc8BNTvCOmlrogNCcUtTVVZ557nFlrpambTre95FVpdtAZCzU7KM0OSnnvqdEfSqilrjLV92CVKt4vNXXzfxYAti7CEtaPLRVkk1cf7ELXyAxY9Vp2y3Qdkel6Wqbrafm6jkht+zes24xdbsCqrC91jj8iE+3ckNf0OMiNJ3X3s3+t+5/7korTc4OPCQbU+73fJfvMSb13OaF33rykG2/feeiucdHmcK2qUx922mNzKj3VUNQeUyDI9NhrweZnagFq5r6UqluvhKr0qJz/o6vAF3S6/cXcLn5eVapYn0wgvDrPBwBYTYQlrD5rrTR7f273ufGLsokrS09rXS/cVheK3GXnYaa5xorM3rijO59+Q8N/83XZ4tyxLqWmmFKHnte9bEwX/v6GpsZmF3yctt4W7TrSXw04zR3zglBHLQAFw1yubiuxpYKUGnarUffrKlP351apSrmlH6xR0W63OlUXotwugKoErVDLphtHBgCPOMISVsbmZ2THL82pFNnxi1Iu2fiD+IIyHU/WhSInGCm+nQ8GWBXWWiVPn9ftT39eibfO1u2XErmAhgJdGjKdun1zUrbs/ffO+IwOnNijZ185rGe+97B2P92/5cf74OFZa6XsRDU42RknSClVqVINOdvL+Vu4lFCz87ey87Bz63KWivXxtxIA1gZhCY2x5aKUvK7y+MU5ky5oemB5D9S8o9p9rlotaj8g4w+tSbvxeCsXChp585u68+k3NHv9tiQpXzK6nw7rTiqs+7m4UtmF/761dMV1/ENP6dlXDuvYB59UvH3p2e+AeraQrhs3dd9zXanhlY2BDLfVhaenZDqPOOvMjAkAK0VYwlzWWqf7ycRF2TG3UjRxQXbiyvK6nIRa6qpER2S6jjpv5pG2tWs84CrMpHT/c2/q3mf/StnRpJL5gO7ORnQ3FdZwJiS7wN8+Y4z2PbdLz77iBKS9z+ykeoQ1Z8tFKT1SV6WqdfWzbtc/zQ5KxfTyHripp1aF6nzK+Vvc+ZRMuHVtXggAPHoaCkt0tn9E2UJKduJSXShyApKyE40/iC8g037wgbFFat5Jt5CHZK2tTjXtDzDIfzkyg6O6+5m/1O3PfVl3JozupsK6m+pVqrjwzzHe3qRjHzykZ185rOMfekotXUwHjfVlfAF3rNL2BY+x1krpUdmJy87f7YlLThfoiUsLz/CXHpVNj8re/erc/fHtbhXKDU+dh52ZNoNUTgHgYVBZ2iJsMSNlJmQzE1I24Swz43XrE7LZhJQZl82MSzP3tawZn+L9tSpRpWLU/uSmmMWpVCypkCuqVCypVCirWCipVCg528Vydb22v3ZMuTT3+PpjSgVnu1wsq1h09xdK7nr5geeoP8Z5rAePmfMchbnrpVJZpaITlIyRura3qe/gNvUf6FHfEz3uslsd29uoeNSZvHBV7/z25/Tul6/o7mxYw+mQyot8GbT3+A4988phPfvKYe1/fjdTbmPLqk6oUxeenNvlpS9qPYeRWve6Fai6INV+cFP8jQeADUI3vM1qTvCpCzm19Uooqh2z7C4aCwnGnTfL+nFFXUdkIh0Lt9dalYplFbIFFXJF5bMF5bOF6nahsu3eV8gVlc/ka/flispnCirkCipkiyrknH2FTEH5+n3uMfnKdta5bz0u+rmZhKJBbdvXrb79Perf36O+/c563/4exduaNrp56yI7m9W3PvnX+vanv6HrdzKaLSxcBG9qierYB5/UM68c1jMfekptvcyoiEebtWVpamBeFery8mclNX6p/YBM51PydR6uduVT2xNORQwAHm2EpfXwYPBxw0113a38ZMdlM4nVDT4eplMR3bjfo9lMVIVQnwqhfhWC21QMdqng61TBRp2g4gaSQraofDZfDSjOdi3Y5N0Qs9AsYlg+I7vguJqltHTF1fdEt/rcalTf/m717+9V796uLT+F9fDNMb39V+d06jPf0vVLoyqVF/4Z7TrS78xc98phHTi5h+sWAXKnRZ+8ITtxWeWJi7WufJPXlzfJhD80d2Y+96bW3VyEF8CjhLC0XLXgM+6EnKzb1a26PuEEnuy4G44Saxp85vAFpEincyHTaKcU6VQp2KE7w626cTOia1esrl9Oa/TecrpmbBVWfmPlM5LPWPmkeevz76ttmyXur982DTxeI9vG3Q6EgwpGQwpGwwpEggrGIs56U0S52azuX7ijxFRJU/mAcyv4NZUPKFta/gd/4zPq3tUxpztfJVB19Lduym59+WxBl79xXe+8eUnvfPGCRm4nFjw2FDQ68v4ndPKHT+j4h55S53YmEwEaZYtZ96Lhl+ZUo5Y942mgyR0HVZlQwplcgktBANiiCEuNKPzpa7LJq+scfIJSpMOZ+jXa6a47Ici4QcjZ7pCJuMeEmjU5Mq1rpwd07fSArp4e0M1376qQXUaXixUwPqNQOKBAyK9gwKdA0KeAX/L7pICR/KYsv8ry2ZJ85ZJ8pYJ8paJMsaCAccKO31j5fbZuW/O2K/drzrFGzhif1eYLBeWLhOSPROSPhuWPhOWPRh7cFwnLFw0/sM8fjbj7K9u1Y3zhkMwSAcWWy8rcG9b0pRuavnJD05dvaOa9W0qn8rUAVX8rBFSyy/9BhJtCbrc+tztfJVDt71asdX279Y3entC7b17SO29e0sWvX1M+s/Dvb3ukpCMntuv9r7+io68+o0Boa1fOgM3G5mdlE1eq46CcyYAuObPzLUe4tTYrX2Vq886nnIvxEqIAbF6EpUbkf/85KXHl4R/AF3RCTsSt+EQ7nfE/0U4nDNUHocoxoeYl30AKuaIGzt+rhqNrpwc0fm/pCyAGwwHtPb5TvXu7FIoEFYwEFAoH5PdJfln5VZJPZflKRTfM5OUr5GWKeSmblclnZbJZ2UxayqRl02n5Cnn5Nuj9zgT8CjTHFYhF54QRJ5C4ASUaWTTo+KNh+eYEIudc3yacjc6WSkrdHtTMZSc8TV+5odlrAyrnCrJWmi363fDkr4aoyUJAswW/Gvw/P0drd1zbqtWoSre+HvXsWZ1ufYVcUVfeuuEGpMsavDqy4LEBU1Z/U177+oN63099QEd+8h8oEIuuuA0AlsdmJ92Z+S7WZugbvyRlxpb3QP6wFOuVaep1lrFtdeu9UmybTNM2Zxp0JpoAsP4IS40ofObDsoPfdDYqwccNObVKT60KND8INRJ8GjF+L6nrZ5yK0bXTA7p19q6K+dKS53Xt7NDBk3u0/8Ru7eiPKD49qpmzl5QbTagwm1JxJqXibFrawH9nf1NUwZaYAvGYAs2xOeuB5piCzXEFmpucZbxJgRZnGWyJOxWax/ybyXKxqNSte7UAdfmGZq/fli3Wfj+KZWm6MLcSNelWo3Kl5XfBMz6jnt2dc7rz9dd161vs32T8XlLv/u1lvfvmJZ3/2nvKpfILHtsaLGpnPKudsZwOPrdd+/7pD6r7Ay9uyiALPO5sZXrz8UtzgpRyUyt/8HC7TGxbLUg19da268KWIh2P/XsCgFVDWGqEnbzhzAgU7XRmiluHP8L5TF63zt5zgtGZAV0/M6DE4NJvNqFoUPue2aUDJ/fowMnd2rWnVeWBW0qcOqfkmfPKJ1bhDcuD8fsVaIkp2OyGHDfsBOeEncp6bM66P9bEB981UM4XNHvjjtN1z61ApW7elfWYOTBbMvO68wU1ragm01KptPz//+GmkPqe6Na2ymx9T3Qr3hHTpb+/rnffvKS7l4cWPNdvrPqbctoZcwJSS7isru8+oV0f+4jajh/iQxCwxdSmN69UoNwQlbwq5WdW/wl9QacSVa1M1SpVJrZNatrm3tcrE6AyDWBRhKXNwFqrsTuJWne6MwMaOH9fpcLSVaPevV1OMDqxRwdf2KO+Xa2aOf+eEqfOKXH6nNK3G+9X7m+K1qo3zU0KNMcVrFZx5oac+tATbI7JFwnzIXYLKGVzmr02oOnLNzV9xQlRqYH7C1YV53TrK4WUjrZpxkaVmClpciKzasXIllBJO5oy2hXLqa8pp4BP8oWD6vuBD2jXj7+mpl39q/NEADYVm5+V0iOyqREpNSKbGpZNDzvr6REpNezclx5d3mx9jQq3uhWqSnVqW13Vqm472sUsf9gQ1pal9JhsakiaHZq7LKRk2g/IdB+T6XmGiVTWBmFpI2RTOd189+6ccDQ1uvS3a+FYSPuf210NRwdO7lG8NaypC9eUOH1OydPnNX3pumfloCLQElfHyaPqOHlU8QN7FGxxKz7xGNWdx1QxndHM1YFqF76ZKzeUvrNw5ad6XlmaUVT5zj6lom2aLoU0kSxo5O6kUpOLz7gYCPq1c1tQ2/Kj2tGUUWuwVJ2gI9jeoh3/8FXt+K+/V6H21tV4iQC2OFsuOdcVTLlBKjXihqxasHIC17CUn179Bhi/1NTt2e3PNNV1DYz1yQQfj2vdYWWsLTszKFfDz7CUGpKdH4hSI5Jd+stzSc7kXz3H3fB0XL7u48510nx8vlsBwtJas9Zq5Na4rp4aqI43unNxsKGLqPYf6NH+E3t00A1HOw/3yfiMUjfvVitHk+9cUimTW/AxfKGg2o4fUvvJY+p44aiaD+5dcgY2oDib0vSVm5pxK1DTl28oOzja0Ln+poj8e/Yo39Gn2XCLpnJ+jY+klRyeUm93WN2p+2oduq6gb+7flabd27Xrx1/Ttle/W/4IA7kBPBxbSEvpUffD5/CcIFULWc5S5eLqNyDcKhPrl+J9MrE+Zxnvk4n3O2Eq3u9UrvzB1X9ubDhrrZRNzAk/c0JQatjdN7y8C0Q/rEBUputpN0Qdd5ZdR+iC2jjC0mrLzGR14507unbKqRhdOzOgmYnUkudFmyPaf2J3tWK0//ndau6ISZKyoxNKnj7vBKQz55WfmFz4gYxR88E96njhmDpOHlPrsSf54IlVUZiacQNUbRa+3MhEQ+cGmmMKxJqUHX5wpqy2545o98c+os6XnyXIA1g31pbdD7W1LoD1QcpWwlVqRMotPdPs8hinUlUNUH1z1hXvd4JWtItuVZuEtdaZqMQj+MytBA1LpYW/xH4okY5a8I5tc7qHxvskf8QZBzh6VnbsXONVVeN3LipdCU/dx2R6jjkTlGE+wtJKlMtlDV0fc0KRG47uXh6SLS/989pxaNuc7nTbD/bK53c+KBZTaSXfvqiEG5DSt+8v+liRvm4nHL1wXO3PH1GorWVVXh+wlNxEUjNXbmr6ciVEXW9oEhHj96nnQ+/Tro99RC2HnliHlgLAw7PFbK1alR6RnR12x1TVj61yugSuarXAH3LHTdUClInXqlbV9VDz6j3nY8Za60w0khqWnVcJUsrpHldZV3HxLubLFm6r/vuaeF8tOM8PRoFIA6+jLE0NyI6dU3n0Xdmxs7Kj55x2N6pll0z3MfkqIYpxUBJhaXnSUxlde/u2053u1ICuv31bqcmlL1Iba4063ele2KP9J/Zo//O75lzos1wsaurCNSVPn1Pi1HlNX7q2xLijmDpOHK1Wj6Lbe1fl9QErZa1VbizhBqgb1SpUYcoZk+dvimr7D31YO3/s+xXZ1r3BrQWA1eWMQxmv+8A9WK042NnB2ofwdGPdmhsWjDthKt43pwtgNUzF+50P3f7Q6j7vBrLWOhWcYtYJMe66LWXdfVnJXbeV9dyUE2qr/yZuCCos3QNoWULNc4NPrE8mvs1ZVoPRtnUZ32ZTI7Jj56rVp/LYWSl5XVKDn+0jndUxUM44qGNS+8HHaRzU6oUlY8xOSb8l6RX3gf9W0s9Za+80cO5CT/Cstfbdpc5fy7CUzxb0uz//J7p2ekD33xvRUj8L4zPa+VSfc12jk854o21PdMtX173IWqvUrXtzxx2lsws+pi8UVOuxQ+p4wQlIzQf2yPgfm19SbHHWWmWHxpQbSyi+f5cCMQY/A3i82VLBrVANSrODtQ/us06ocrpzDa3O9anqRbvqKhgLdAFs6ml45j8nsNSHk5xUzLjhZG6QqQaWYkYq5qSSG2LqQo1zXKbusZzjbXW99hyr3tWtEYEmt8K3bU4lqBaM3ErQJq/02fys7PgF2WoF6qxzPbTSwtc8nGPOOCg3SHU9/aiOg1qdsGSMaZJ0VlJO0i/Kiau/LqlJ0jFr7aKR3Q1Lvyfpd+bddc5au2TpZi3DkrVWP3P4l5Qc9u4H2twZq3alO3Byj554dpeizQ+WS7OjE0qeuaDEqbNKnG5w3NHJY2o/eVRtxw8x7ggAgMeMzc/WdQ8brIap6rgZd31Vg4Mv4ExAEe+XAtFadaYSUuqDzEYElrXgj9RV4+pD0Nwucgo1P7Jd0mypIJu44lagnCqUHTvXeGA3Ppn2J6sVKGc81CMxDqqhf/BAA8e8LmmfpCettdclyRhzTtI1SZ+Q9JsNPMZ9a+1bjTRoPRljdODkHp1645x8fp92P90/Jxz17vUefFlMpZX8ziUlTp9zxh0NNDju6OQxtZ94mnFHAAA85kwoLoX2y7TvX/AYZ+KB5KJhys4ONn6tqnLRuYjw7OKfWzYFf8gJdP6wFIhI/ohMIOzui0iBsLsv6qwH43XjgWqBSOG2RzYENcr4gzLdR6Xuo5J+QpL7uzU1IDt2VuVKBWr0rPc4KFuWTVyWTVyWrvxxbX/zzuo05qbnmEz3M1Lzjkfu591IZenLkiLW2vfP2/81SbLWfs8S51tJ/5O19hcfpoFrPWbp+tu3VcgWtPeZnYrEvCs85WJR0xeuVSdlaGTcUfvzT6vjhePqOHlU0e29j9wvDgAA2BxsuehMUuEZpupmc8smlvfA1aDiLgNRmbrw4uyLyFTCS12QcfZFq6FGATfYzDvfVNfd893n40LBG8OmR2vjoEbPqjx2TkpeU+PjoDpq3fe6j8vXU7keVCP1mXW3at3whiX9hbX2E/P2/7akH7XWLjqS2w1LCUlxSSVJb0n6ZWvt1xtpYH9/v/3EJz7hed9rr72m559/XpL09ttv6wtf+MKCj/PLv/zL1fVPfvKTGhrynkHkueee02uvvabUrXu6+vVv6Y2Byws+5jPfuK74dFYmGNDt9x/SnQW6c/b19enjH/94dftXfuVXFnzMtXpNH/nIRyRJg4OD+tSnPrXgY77++uvq7++XJL3xxhv6zne+43kcr4nXVMFr4jV54TXxmip4TZv3Nf2Pr3+fE0wCEX3qP/+thka9w9RWek2P4r/TRr+m13/qY844qLGz+vU/H17wMV+NfFHPhs5Kkt7JH9cXs69u2Gtq0Kp1w+uQ5HURgoSk9gbO/0NJX5A0KGm3pH8t6e+MMa9Ya7/qdYIx5uOSPi45/0jrKfmdi/r733lD+fGkZlsi0vsXLo9v+/7v0f6XT6rt+CH91Ztf0p0FftEAAAA2G1//S7UNf0PfYeMxZEJxmf6XpP6XpD9fOADq0ZwEoqHKUl7Sb1prf2He/l+X9AvW2mXV1YwxzZIuSLprrf2upY5fy254xXRGN/6fP1Ly9Hmlbt1b9NjItsq4o6POuKP21jVpEwAAALDVWGul6duyo++qXJ3S/Kw0O+gc4A8r+C/HNlOXvFWrLCXlXUFaqOK0KGvtjDHmLyX9s+Weu9r8kbBGvvT3KkzOPHBfoDmm9hNPq+OkE5CiO7Yx7ggAAADwYIyRWvfItO6R78APVffb9JgzDmp2cDMFpYY10uKLko547D8s6dIKnnvDr4ZrfD61nziq0b/9pkwwoLZjT1YnZWh+ci/XOwIAAABWwDR1y+z+0EY346E1EpY+L+l/M8bss9belCRjzB5J75f0C4uc58kY0yLpNUmnlnvuWtj146+p/yMf5HpHAAAAAOZoZMxSTM5FaTOqXZT21yQ1y7ko7ax73G5JNyT9qrX2V919/0rSk5K+otoED5V9H2pkRry1njocAAAAwGNndcYsWWtTxpgPSvotSX/gPvCXJf1cJSjVPaFfUv3E+O9J+mH31ippWtI3JP0za+2mqCwBAAAAgJeGRllZa+9I+pEljhnQvIRmrX1D0hsP2zgAAAAA2ChcHhkAAAAAPBCWAAAAAMADYQkAAAAAPGy9K0MBayD/W00b3QRAof8uvdFNeGhffulHN7oJgD701mc3ugkP7R+3/+xGNwGPuT9O/p8b3YRNicoSAAAAAHggLAEAAACAB8ISAAAAAHggLAEAAACAB2Ot3eg2LOrEiRP2zJkzG90MAAAAAI8O08hBVJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8EJYAAAAAwANhCQAAAAA8GGvtRrdhUcaYMUm3N7odq6hL0vhGNwIAAGxKfE4A1se4tfbVpQ7a9GHpUWOMOWOtPbHR7QAAAJsPnxOAzYVueAAAAADggbAEAAAAAB4IS+vvkxvdAAAAsGnxOQHYRBizBAAAAAAeqCwBAAAAgAfCEgAAAAB4ICytA2PMTmPMnxhjpowx08aYPzPG7NrodgEAgJUzxuwwxvx7Y8y3jDFpY4w1xuzxOC5ijPkNY8yQMSbjHv9feRznM8b8D8aYAWNM1hhz1hjzIws89+vGmCvGmJwx5j1jzE+v/isEHl+EpTVmjGmS9HeSDkn6SUn/VNIBSV8xxsQ2sm0AAGBV7Jf0Y5KSkr6+yHH/n6TXJf2SpNckDUn6G2PMM/OO+zVJ/0bSf5D0/ZLekvRZY8wP1B9kjHld0u9I+lNJr0r6rKTfNsb8ixW+HgAuJnhYY8aYn5X0m5KetNZed/ftlXRN0s9ba39zI9sHAABWxhjjs9aW3fV/LulTkvZaawfqjjku6V1J/6219nfdfQFJFyW9Z639qLuvR9JdSf/WWvvLded/WVK3tfZY3bmDkv7aWvuTdcf9R0kfldRnrS2s3asGHg9UltbeRyW9VQlKkmStvSXpG5J+cMNaBQAAVkUlKC3ho5IKkj5Td15R0h9L+j5jTNjd/X2SQpL+cN75fyjpqPuFqyS9T1K3x3F/IKlT0nct5zUA8EZYWntHJF3w2H9R0uF1bgsAANgYRyTdstam5+2/KCcc7a87LifpusdxUu2zwxF3Of8zxvzjAKwAYWntdcjpwzxfQlL7OrcFAABsjMU+D1Turywn7YPjJLyOk8djzj8OwAoQlgAAAADAA2Fp7SXlXUFa6BsmAADw6Fns84BUqwglJbUZY0wDx8njMecfB2AFCEtr76Jq/YrrHZZ0aZ3bAgAANsZFSXvdS4rUOywpr9oYpYuSwpKe8DhOqn12qIxNmv8ZY/5xAFaAsLT2Pi/pJWPMvsoO90J173fvAwAAj743JAUl/Whlhzv99z+S9CVrbc7d/UU5s+b9k3nn/4SkC+6MupL0LUnjCxyXkDPrLoAVCmx0Ax4Dn5L0LyX9hTHmFyVZORebuyvnQnIAAGCLM8b8Q3f1eXf5/caYMUlj1tqvWWvfMcZ8RtL/YYwJSrol6V/o/2/vzqOtKuswjn8fcUJlKQmkrRyXZGppLm1VuhIUzRY5pIm2KhOH1BwpUyO11OWUipoVDpninKWAkiapiKLmFIqWZqTiABikogKiKL/+eN+Lm80+957LPXF9pPsAAAqSSURBVIeLl+ez1l6H8+537/3bE3e/5x02bEShwBMRMyRdAAyV9A4wkVSg2ok0/HhLvvmSTiG9hHYqcHfOcxBwdES838z9NVte+KW0S4Gk9YELgV0AAfcAQ4ovqzMzM7OPL0m1Hqjui4j+OU934EzgO8BawCTgxIgYX1pXN2Ao8ANgHeA54PSIuLliu4cBxwEbAC8DF0bE8AbskpnhwpKZmZmZmVkl91kyMzMzMzOr4MKSmZmZmZlZBReWzMzMzMzMKriwZGZmZmZmVsGFJTMzMzMzswouLJmZmZmZmVVwYcnMujxJ/SWFpMGdHUtrJN0k6cFO2nZ3SdMk/aKdy/WSdE1eNiSNb1KIXZ6kEa28q8fMzDqBC0tm1iVI+oKkUyVt2NmxLAlJ2wP7Aid3xvYj4l3gHOB4Seu2Y9FhwH7ApcD+pBduWg2SBksa0tlxdAWSvinp1M6Ow8y6Nr+U1sy6hFxrdBWwY0SML81bAVgZmB8RHy796NomaSzQJyK27sQYVgdeAy6NiOPrXGYa8HhE7NHU4LqIXPO2YURsWDFvJaBbRMxb2nF9HEkaARwQEersWMys63LNkpl1eRGxICLmLcMFpU2AXYBrOjOOiJgDjAQGS1qlzsXWAd6oJ6OkHksa2/IgIua7oGRmtmxxYcnMPvZyU5yr8td7c9+ZyL88V/ZZKqZJOkLSc5LmSXpa0m45z+cl3SnpbUmvS7o4//pf3n5fSddKmi7pfUlTJJ2Xa2rqsQ8g4I6KdU+RNF7SVpLuljRb0gxJwyStKGlVSedLmprjv1/SZqV1rJqbKD4naa6kWXk/z6uI5S9AL2DH1gLO64sc9wGFYz44z4/cB2eApAckzQbGFJbfMB+z/0p6T9Lzks6StFrVdiRtLumifIznSrpH0qY5z96SJkp6Nx+vQ+s45o2+BnaQdJekt3IcEyUdXMozBegHbFA4XiGpf55f2WdJ0paSRuXtz5P0jKQTJHUr5RuR17empEvydTJP0oOSvlTPMcnrae+52TTPfzXnnyRpYMV6vy/p0Xz9zZH0gqTrJfUu5WvzflKqoTsg/7t4LAfXu59mZvVYsbMDMDNrgJHAusChwFnAszn9+TqWPRLoCVwBzAOOAUZJGgT8DrgRGA18DTgamAGc0bKwpG2AccAs4DJgKrBVXs/2kvpFxPw2YuiXl/93jfmfBu4CbgJuzrH8GPgA2ALoTupv1Av4CTBa0mYRsSAv/1vgIFLN1QWk//v7AjtVbOtv+bM/cGcrMY8E/gNcC0wALs/pDxXybAt8i3Qcr25JlLQB8CiwJjAcmJy3N5R0zAZExAel7V0NzCad397AccBYSacA5wKXAFcCBwOXSXomIh5oJf6ijl4DuwOjSE0YhwHvAN8GrpC0cUSclLMOAc4mnacfFbb/LDVI2ha4D5hPOo+vAbsDvyRdZ9+tWGwsMBM4HVibdK3cLmmjiHintQPRgXMzHzif1Nx1COka/ExETMnr3T/nmwD8HHgXWA8YCPTJ8bbnfjqT9IPvV0l95VoUrz8zs46LCE+ePHn62E/AYCCA/hXz+ud5gyvSpgJrFtK3zOkLgL1L6/k7ML2UNgn4F9CjlL5XeZutxP4SMLHGvCl5PYMqYlkA3Eruf5rTj8n5dy2kvQHc0Y5jOR8YU2feAEbUSA9g54p51+d5A0vp5+X0gwtpp+a0MTX2821gvUJ6b1KB58Y6Yu/wNQB0y+dvFvCpQvrKwIPAh0DfQvp4YEqNeEakP8uLpD1IKhRvWUgT8Mcc44Dy8sDw0joG5fTD6jgmS3Ju/lw6N1/M6WcX0kbmc7ViG9uv+36qOl6ePHny1OjJzfDMbHk3IiLeavkSEU+RHuqmRcTIUt4HgHUkrQGpiRbpwfoGYBWlYbR7SeqV884h1Ua0pTet9/uZGhF/qohFwK8joth0a0L+7FtIewvYQtLn6oiFHEufOvO2ZlJE3F1MUBpsYw/giYgoNzs8m1RA2atiXRfX2M/bIuKVlsSImAk8x6L735YlvgaAbYD1gSsjYlphHe+TarxWAPZsRywLSeoDbEfax6cK6w4+GnWw6lhdWPo+Ln+2ekw6cG5+VTw3EfEYqRawfA2uBnxDUuWADA28n8zMGsaFJTNb3r1QkfYm8GKNdEhNmwBa+gadRmpGVJxmAKsDn6wjhpa+P7W0Fkt5XjlGSM2iegJP5/4nV0jaMz8cV1GOqaOqmhX2BtYA/lmeERFvANOBjSuWK5+nWvvfMm/tivRaOnINbJQ/F9ufQlrV/tSjtXU/Syq8tHmsIuL1/M+2jkmjzg3A66XtnUWqgRsNzJR0i6RDtOigH426n8zMGsZ9lsxseVdrhLzWRs5T6XMYtfv3vFkjvWgm8IlW5rcWS615CwtfEXGr0vunBpL6R+1M6tszQdLOuRakqGeOqaPmNmAdLdp7ntoznHRHroFlTtQe9bFZMddzDU6WtDkwIE/9SP3BTpO0Q0Q8T+PuJzOzhnFhycy6is54adzk/PlhublZO/0D2EHSCvHRoAwNlWsGrgOuy82gzgFOIDURW9jELxeqVswxNcNM0gAIW5RnSOpJGqjjySZtuxlaalUW2x9g81IeaN912lKzVbXuz5Jah1TV6iyppp6biHiPNOLjHXmdA4HbSQNQHEn77ye/KNLMms7N8Mysq5idP1uroWm0J0iFisMlLdY8SWlo73riGQ/04KOH64aR1E3SWsW03L/kify1HN+X8+d9jY4lb3sBabCGrSV9vTT7p6S/S6Oase0mmQi8DBwoaZ2WxDy8+PGkB/pbC/lnAz1r9dspiogZpNHddi/2N8vLDs1fG3asmnlucr+json5s+UabO/9NDunL8173syWM65ZMrOu4jFSH46T8q/gc4AXI+KRZm0wIiIPiTwOeErSlaT+HqsBmwB7kx5qR7SxqltIQ0EPpPE1Oj2A6ZJuIz2MziD1hfkhqUnTmFL+gcD/gHsbHEfRz0gv4R0taThpCPIdgP2A+ykMM76si4gPJR1FKkQ8JulyUu3MfqSC51kRMbmwyMPAbsBvJD1EasI2LheMqhxLKrhOkNQydPhuwK7ADRFxT4N3qVnn5q+SZpEG5ngFWIuPRrC8FpbofnoYOAoYLul20iiOj0REVV8zM7Ml4sKSmXUJEfGypIOAE0nv3FmJ9GDXtMJS3u6TkrYmPcTtARxOelieQnqoa/NhNiJelDSW9L6Ycxsc4lzgIlI/kZ1JHfinA7eRhnZeOIJbfunn3sAluclUU0TES/klqacD3yM9OL9KGnHtjFj8PT7LtIgYI2kAcDKpNmll0gAMh0TE70vZLyQNkrAP6VpZgfQC4MrCUkQ8Lmk70qAHR5AGOXiBdJ0Pa8K+NOvcXALsCxxGqkl6nVR4PzoiFhbM23k/3QhsTXqn1SDSsTyQ6oE5zMyWiBYdidXMzDqDpK+Qmlzt0sH+Tx2J4VjSkNR9I2J6Z8RgZma2LHFhycxsGSHpD8D6EbFdJ2y7O6nG4tKIOG1pb9/MzGxZ5MKSmZmZmZlZBY+GZ2ZmZmZmVsGFJTMzMzMzswouLJmZmZmZmVVwYcnMzMzMzKyCC0tmZmZmZmYVXFgyMzMzMzOr4MKSmZmZmZlZhf8DTPkFL1li5VsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# nonstim_spatial = []\n",
    "# pop_spatial = []\n",
    "\n",
    "# color_seq = sns.color_palette(\"inferno\", 3)[::-1]\n",
    "\n",
    "# fig, axes = plt.subplots(1, 1, figsize=(12, 5))\n",
    "# axes = [axes]\n",
    "# for i, align in enumerate([(3, 6),  (7, 10), (11, 14)]):\n",
    "#     choices = []\n",
    "#     pop_outputs = []\n",
    "#     pop_resids = []\n",
    "#     pop_models = []\n",
    "#     pop_slides = []\n",
    "#     pop_prefs = []\n",
    "\n",
    "#     nonstim_outputs = []\n",
    "#     latent_resids = []\n",
    "#     nonstim_models = []\n",
    "#     nonstim_slides = []\n",
    "\n",
    "#     stim_outputs = []\n",
    "#     stim_models = []\n",
    "#     stim_slides = []\n",
    "\n",
    "#     individual_cps = []\n",
    "#     individual_temporal_cps = []\n",
    "\n",
    "#     for dish in dishes:\n",
    "#         coh = dish['good_coherences']\n",
    "#         cohsum = np.sum(coh, axis=1, keepdims=True)\n",
    "\n",
    "#         mask = dish['mask_good_weak']\n",
    "#         targchosen = dish['good_targchosen'][mask]\n",
    "#         choice = (targchosen == 2) + 0\n",
    "#         choices.append(choice)\n",
    "\n",
    "#         Y = np.concatenate([np.sum(dish['y'] [:, w:w+window, :], axis=1, keepdims=True) for w in windows], axis=1)\n",
    "#         Y = np.reshape(Y, (Y.shape[0], -1))\n",
    "#         Y = np.log1p(Y)\n",
    "        \n",
    "#         regress = RidgeCV(fit_intercept=True, alphas=pulse_penalty)\n",
    "#         regress.fit(coh, Y)\n",
    "#         coef = regress.coef_.T\n",
    "#         coef = np.mean(np.reshape(np.mean(coef, axis=0), (-1, dish['y'].shape[-1])), axis=0)\n",
    "#         pop_pref = np.ones_like(coef)\n",
    "#         pop_pref[coef < 0] = 0\n",
    "\n",
    "#         residual = Y - regress.predict(coh)\n",
    "#         pop_resid = np.reshape(residual, (residual.shape[0], len(windows), -1))\n",
    "#         pop_resid = pop_resid[mask, ...]\n",
    "#         pop_prefs.append(pop_pref)\n",
    "#         pop_resids.append(pop_resid)\n",
    "#         pop_resid_sum = np.sum(pop_resid[:, 1:, :], axis=1)\n",
    "#         pop_resid_align = np.sum(pop_resid[:, align[0]:align[1], :], axis=1)\n",
    "#         pop_resid_sum2 = pop_resid_sum\n",
    "#     #     pop_resid_sum2 = np.sum(pop_resid[:, 1:6, :], axis=1)\n",
    "#         for spk, pref in zip(pop_resid_sum2.T, pop_pref):\n",
    "#             individual_cps.append(stats.cp(choice, spk, pos=pref, method=\"ranksum\"))\n",
    "\n",
    "#         for spk, pref in zip(pop_resid.T, pop_pref):\n",
    "#             individual_temporal_cps.append([stats.cp(choice, w, pos=pref, method=\"ranksum\") for w in spk])\n",
    "\n",
    "#         # latent\n",
    "#         mu = np.copy(dish['mu'])\n",
    "#         pulse_weight = dish['pulse_weight']\n",
    "#         u, s, v = np.linalg.svd(pulse_weight, full_matrices=False)\n",
    "#         mu = mu @ v.T\n",
    "\n",
    "#         Z = np.concatenate([np.sum(mu[:, w:w+window, :], axis=1, keepdims=True) for w in windows], axis=1)\n",
    "#         Z = np.reshape(Z, (Z.shape[0], -1))\n",
    "# #         Z = np.exp(Z + np.mean(dish['b']))\n",
    "\n",
    "#         regress = RidgeCV(fit_intercept=True, alphas=np.logspace(-2, 5, 100))\n",
    "#         regress.fit(coh, Z)\n",
    "#         residual = Z - regress.predict(coh)\n",
    "#         latent_resid = np.reshape(residual, (residual.shape[0], len(windows), -1))\n",
    "#         latent_resid = latent_resid[mask, ...]\n",
    "#         latent_resids.append(latent_resid)\n",
    "\n",
    "#         stim_resid = latent_resid[:, :, [0]]\n",
    "#         nonstim_resid = latent_resid[:,:, [1, 2, 3]]\n",
    "#         stim_resid_sum = np.sum(stim_resid[:, 1:, :], axis=1)\n",
    "#         stim_resid_align = np.sum(stim_resid[:, align[0]:align[1], :], axis=1)\n",
    "\n",
    "#         nonstim_resid_sum = np.sum(nonstim_resid[:, 1:, :], axis=1)\n",
    "#         nonstim_resid_align = np.sum(nonstim_resid[:, align[0]:align[1], :], axis=1)\n",
    "\n",
    "#         output, model = recipe.logitcv(pop_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)    \n",
    "#         pop_slides.append(\n",
    "#             np.array([model.predict_proba(pop_resid[:, i, :])[:, 1] for i in range(len(windows))]))\n",
    "#         #             sp.special.expit(pop_resid[:, i, :] @ model.coef_.T)[:, 0]\n",
    "#         pop_models.append(model)\n",
    "#         pop_outputs.append(model.predict_proba(pop_resid_sum)[:, 1])\n",
    "        \n",
    "#         pop_spatial.append(model.coef_)  # collect spatial weights\n",
    "\n",
    "#         output, model =  recipe.logitcv(nonstim_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)\n",
    "#         nonstim_slides.append(\n",
    "#             np.array([model.predict_proba(nonstim_resid[:, i, :])[:, 1] for i in range(len(windows))]))  #  sp.special.expit(nonstim_resid[:, i, :] @ model.coef_.T)[:, 0]\n",
    "#         nonstim_models.append(model)\n",
    "#         nonstim_outputs.append(model.predict_proba(nonstim_resid_sum)[:, 1])\n",
    "        \n",
    "#         nonstim_spatial.append(model.coef_)  # collect spatial weights\n",
    "\n",
    "#         output, model =  recipe.logitcv(stim_resid_align, choice, class_weight='balanced', penalty=penalty, Cs=Cs, cv=nfolds, scoring=scoring, refit=refit)\n",
    "#         stim_slides.append(\n",
    "#             np.array([model.predict_proba(stim_resid[:, i, :])[:, 1] for i in range(len(windows))]))  # sp.special.expit(stim_resid[:, i, :] @ model.coef_.T)[:, 0] \n",
    "#         stim_models.append(model)\n",
    "#         stim_outputs.append(model.predict_proba(stim_resid_sum)[:, 1])\n",
    "\n",
    "#     print(\"average individual CP\", np.mean(individual_cps), sp.stats.wilcoxon(np.array(individual_cps) - 0.5), sp.stats.ttest_1samp(individual_cps, 0.5))\n",
    "\n",
    "#     all_choices = np.concatenate(choices)\n",
    "#     all_pop_outputs = np.concatenate(pop_outputs)\n",
    "#     all_nonstim_outputs = np.concatenate(nonstim_outputs)\n",
    "#     all_stim_outputs = np.concatenate(stim_outputs)\n",
    "\n",
    "#     all_pop_slides = np.concatenate(pop_slides, axis=1).T\n",
    "#     all_nonstim_slides = np.concatenate(nonstim_slides, axis=1).T\n",
    "#     all_stim_slides = np.concatenate(stim_slides, axis=1).T\n",
    "\n",
    "#     individual_temporal_cps = np.stack(individual_temporal_cps)\n",
    "#     individual_temporal_cp = individual_temporal_cps.mean(axis=0)\n",
    "#     individual_temporal_cp_q5, individual_temporal_cp_q95 = np.percentile(individual_temporal_cps, q=[5, 95], axis=0)\n",
    "\n",
    "#     pd.DataFrame(np.column_stack([all_choices, all_stim_outputs, all_nonstim_outputs, all_pop_outputs]), \n",
    "#                  columns=[\"choice\", \"stim\", \"nonstim\", \"pop\"]).to_csv(\"pooled_outputs.csv\", index=False)\n",
    "\n",
    "#     pd.DataFrame(np.column_stack([all_choices, all_stim_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_stim_slides.shape[1])]).to_csv(\"all_stim_slides.csv\", index=False)\n",
    "#     pd.DataFrame(np.column_stack([all_choices, all_nonstim_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_nonstim_slides.shape[1])]).to_csv(\"all_nonstim_slides.csv\", index=False)\n",
    "#     pd.DataFrame(np.column_stack([all_choices, all_pop_slides]), columns=[\"choice\"] + [f\"w{i + 1}\" for i in range(all_pop_slides.shape[1])]).to_csv(\"all_pop_slides.csv\", index=False)\n",
    "\n",
    "#     stim_cps = []\n",
    "#     nonstim_cps = []\n",
    "#     pop_cps = []\n",
    "#     for choice, stim_output, nonstim_output, pop_output in zip(choices, stim_outputs, nonstim_outputs, pop_outputs):\n",
    "#         stim_cps.append(stats.cp(choice, stim_output, method=\"ranksum\"))\n",
    "#         nonstim_cps.append(stats.cp(choice, nonstim_output, method=\"ranksum\"))\n",
    "#         pop_cps.append(stats.cp(choice, pop_output, method=\"ranksum\"))\n",
    "\n",
    "#     # append pooling sessions\n",
    "#     stim_cps.append(stats.cp(all_choices, all_stim_outputs, method=\"ranksum\"))\n",
    "#     nonstim_cps.append(stats.cp(all_choices, all_nonstim_outputs, method=\"ranksum\"))\n",
    "#     pop_cps.append(stats.cp(all_choices, all_pop_outputs, method=\"ranksum\"))\n",
    "\n",
    "#     df_cps = pd.DataFrame({\"session\": exnames + [\"all\"], \n",
    "#                             \"stim.\": np.array(stim_cps), \n",
    "#                             \"nonstim.\": np.array(nonstim_cps), \n",
    "#                             \"pop.\": np.array(pop_cps)})\n",
    "\n",
    "#     df_cps_long = df_cps.melt(id_vars=[\"session\"], value_vars=[\"stim.\", \"nonstim.\", \"pop.\"], var_name=\"dimension\", value_name=\"CP\")\n",
    "\n",
    "# #     print(rpy2.robjects.r('''\n",
    "# #     mt <- read.csv(\"pooled_outputs.csv\")\n",
    "# #     anova(glm(choice ~ stim + nonstim + pop, data=mt, family=binomial()), test=\"LRT\")\n",
    "# #     '''))\n",
    "# #     print(rpy2.robjects.r('''\n",
    "# #     anova(glm(choice ~ stim + pop + nonstim, data=mt, family=binomial()), test=\"LRT\")\n",
    "# #     '''))\n",
    "\n",
    "#     alphas = np.logspace(-1, 3, 100)\n",
    "#     X = (all_choices[:, None] + 0) * 2 - 1\n",
    "#     fit_intercept = False\n",
    "#     temp_cv = None\n",
    "#     B = 1000\n",
    "\n",
    "#     # pop\n",
    "#     slides = all_pop_slides\n",
    "#     pop_cps = bcp((all_choices, slides))\n",
    "\n",
    "# #     slides = sp.stats.zscore(all_pop_slides)\n",
    "#     slides = all_pop_slides - 0.5\n",
    "#     if temp_cv == 0:\n",
    "#         regress = LinearRegression(fit_intercept=fit_intercept)\n",
    "#     else:\n",
    "#         regress = RidgeCV(fit_intercept=fit_intercept, alphas=alphas, cv=temp_cv)\n",
    "#     regress.fit(X, slides)\n",
    "#     alpha_min = regress.alpha_\n",
    "#     pop_weights = np.squeeze(regress.coef_)\n",
    "#     print(regress.alpha_)\n",
    "#     pop_r2 = 1 - np.mean((slides - regress.predict(X)) ** 2) / np.mean(slides ** 2)\n",
    "#     print(\"pop. R2: \", pop_r2)\n",
    "# #     pop_weights_B = np.stack(dask.compute(*[dask.delayed(f)(sklearn.utils.resample(X, slides), alpha=alpha_min) for _ in range(B)]))\n",
    "# #     pop_q5, pop_q95 = np.percentile(pop_weights_B, [5, 95], axis=0)\n",
    "    \n",
    "#     # nonstim\n",
    "#     slides = all_nonstim_slides\n",
    "#     nonstim_cps = bcp((all_choices, slides))\n",
    "\n",
    "# #     slides = sp.stats.zscore(all_nonstim_slides)\n",
    "#     slides = all_nonstim_slides - 0.5\n",
    "#     if temp_cv == 0:\n",
    "#         regress = LinearRegression(fit_intercept=fit_intercept)\n",
    "#     else:\n",
    "#         regress = RidgeCV(fit_intercept=fit_intercept, alphas=alphas, cv=temp_cv)\n",
    "#     regress.fit(X, slides)\n",
    "#     alpha_min = regress.alpha_\n",
    "#     nonstim_weights = np.squeeze(regress.coef_)\n",
    "#     print(regress.alpha_)\n",
    "#     nonstim_r2 = 1 - np.mean((slides - regress.predict(X)) ** 2) / np.mean(slides ** 2)\n",
    "#     print(\"nonstim. R2: \", nonstim_r2)\n",
    "# #     nonstim_weights_B = np.stack(dask.compute(*[dask.delayed(f)(sklearn.utils.resample(X, slides), alpha=alpha_min) for _ in range(B)]))\n",
    "# #     nonstim_q5, nonstim_q95 = np.percentile(nonstim_weights_B, [5, 95], axis=0)\n",
    "\n",
    "#     # stim\n",
    "#     slides = all_stim_slides\n",
    "#     stim_cps = bcp((all_choices, slides))\n",
    "\n",
    "#     slides = all_stim_slides - 0.5\n",
    "#     if temp_cv == 0:\n",
    "#         regress = LinearRegression(fit_intercept=fit_intercept)\n",
    "#     else:\n",
    "#         regress = RidgeCV(fit_intercept=fit_intercept, alphas=alphas, cv=temp_cv)\n",
    "#     regress.fit(X, slides)\n",
    "#     alpha_min = regress.alpha_\n",
    "#     stim_weights = np.squeeze(regress.coef_)\n",
    "#     print(regress.alpha_)\n",
    "#     stim_r2 = 1 - np.mean((slides - regress.predict(X)) ** 2) / np.mean(slides ** 2)\n",
    "#     print(\"stim. R2: \", stim_r2)\n",
    "# #     stim_weights_B = np.stack(dask.compute(*[dask.delayed(f)(sklearn.utils.resample(X, slides), alpha=alpha_min) for _ in range(B)]))            \n",
    "# #     stim_q5, stim_q95 = np.percentile(stim_weights_B, [5, 95], axis=0)\n",
    "\n",
    "#     ax = axes[0]\n",
    "#     sns.lineplot(x=windows - 50, y=stim_cps, label=\"nonstim.\", color=color_seq[i], ax=ax, legend=False, lw=3)\n",
    "# #     ax.plot(windows[align[0]:align[1]] - 100, nonstim_cps[align[0]:align[1]], color=color_seq[i], lw=5)\n",
    "#     ax.plot([windows[align[0]]  - 100 , windows[align[1]]  - 100], [0.49, 0.49], color=color_seq[i], lw=5)\n",
    "#     sns.despine(ax=ax)\n",
    "\n",
    "# #     ax = axes[1]\n",
    "# #     sns.lineplot(x=windows - 50, y=pop_cps, label=\"pop.\", color=color_seq[i], ax=ax, legend=False, lw=3)\n",
    "# #     ax.plot(windows[align[0]:align[1]] - 100, nonstim_cps[align[0]:align[1]], color=color_seq[i], lw=5)\n",
    "# #     ax.plot([windows[align[0]]  - 100 , windows[align[1]]  - 100], [0.49, 0.49], color=color_seq[i], lw=5)\n",
    "# #     sns.despine(ax=ax)\n",
    "\n",
    "# axes[0].set_xlabel(\"time (ms) from motion onset\")\n",
    "# axes[0].set_xlim(-100)\n",
    "# axes[0].set_ylim([0.45, 0.65])\n",
    "# axes[0].set_title(\"Stim.\")\n",
    "# axes[0].plot([-100, 1400], [0.5, 0.5], ls='--', color='0.5')\n",
    "# axes[0].set_yticks([0.5, 0.6])\n",
    "# axes[0].set_xticks([0, 1000])\n",
    "# # axes[1].set_xlabel(\"time (ms)\")\n",
    "# # axes[1].set_xlim(-100)\n",
    "# # axes[1].set_ylim([0.48, 0.58])\n",
    "# # axes[1].set_title(\"Population\")\n",
    "# # axes[1].plot([-100, 1400], [0.5, 0.5], ls='--', color='0.5')\n",
    "\n",
    "# # axes[1].legend(frameon=False)\n",
    "# plt.tight_layout()\n",
    "# # plt.savefig(\"figure/cp_time_course.pdf\")\n",
    "# plt.show()\n",
    "# plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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