parameter_recovery_data_make_binned.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import os\n",
    "import numpy as np\n",
    "import boundary_functions as bf\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bin_simulator_output(out = [0, 0],\n",
    "                         bin_dt = 0.04,\n",
    "                         nbins = 0): # ['v', 'a', 'w', 'ndt', 'angle']\n",
    "        \n",
    "        # Generate bins\n",
    "        if nbins == 0:\n",
    "            nbins = int(out[2]['max_t'] / bin_dt)\n",
    "            bins = np.zeros(nbins + 1)\n",
    "            bins[:nbins] = np.linspace(0, out[2]['max_t'], nbins)\n",
    "            bins[nbins] = np.inf\n",
    "        else:  \n",
    "            bins = np.zeros(nbins + 1)\n",
    "            bins[:nbins] = np.linspace(0, out[2]['max_t'], nbins)\n",
    "            bins[nbins] = np.inf\n",
    "\n",
    "        cnt = 0\n",
    "        counts = np.zeros( (nbins, len(out[2]['possible_choices']) ) )\n",
    "\n",
    "        for choice in out[2]['possible_choices']:\n",
    "            counts[:, cnt] = np.histogram(out[0][out[1] == choice], bins = bins)[0] / out[2]['n_samples']\n",
    "            cnt += 1\n",
    "        return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pickle.load(open('/Users/afengler/OneDrive/project_nn_likelihoods/data/kde/ddm_elife/parameter_recovery_data_binned_0_nbins_0_n_4096/elife_ddm_nchoices_2_parameter_recovery_binned_0_nbins_0_nreps_1_n_4096.pickle', 'rb'))\n",
    "# data[2]['possible_choices'] = [-1, 1]\n",
    "n_str = '4096'\n",
    "model_str = 'ddm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pickle.load(open('/Users/afengler/OneDrive/project_nn_likelihoods/data/kde/' + model_str + \\\n",
    "                        '_elife/parameter_recovery_data_binned_0_nbins_0_n_' + n_str + \\\n",
    "                        '/elife_' + model_str + \\\n",
    "                        '_nchoices_2_parameter_recovery_binned_0_nbins_0_nreps_1_n_' + n_str + '.pickle', 'rb'))\n",
    "data[2]['possible_choices'] = [-1, 1]\n",
    "\n",
    "# change max_t to 10s because that's what CNN was trained\n",
    "data[2]['max_t'] = 10.0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "500\n",
      "600\n",
      "700\n",
      "800\n",
      "900\n"
     ]
    }
   ],
   "source": [
    "data_binned_256 = []\n",
    "data_binned_256.append(data[0])\n",
    "data_binned_512 = []\n",
    "data_binned_512.append(data[0])\n",
    "\n",
    "binned_tmp_256 = np.zeros((1, 1000, 256, 2))\n",
    "binned_tmp_512 = np.zeros((1, 1000, 512, 2))\n",
    "\n",
    "\n",
    "for i in range(1000):\n",
    "    out = []\n",
    "    out.append(data[1][0, i, :, 0])\n",
    "    out.append(data[1][0, i, :, 1])\n",
    "    out.append(data[2])\n",
    "    \n",
    "    x_256 = bin_simulator_output(out = out,\n",
    "                                 nbins = 256)\n",
    "    x_512 = bin_simulator_output(out = out,\n",
    "                                 nbins = 512)\n",
    "    \n",
    "    binned_tmp_256[0, i, :, :] = x_256\n",
    "    binned_tmp_512[0, i, :, :] = x_512\n",
    "    \n",
    "    if i % 100 == 0:\n",
    "        print(i)\n",
    "\n",
    "data_binned_256.append(binned_tmp_256)\n",
    "data_binned_512.append(binned_tmp_512)\n",
    "    \n",
    "data_binned_256.append(data[2])\n",
    "data_binned_512.append(data[2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(data_binned_256, open('/Users/afengler/OneDrive/project_nn_likelihoods/data/kde/' + model_str + '_elife/parameter_recovery_data_binned_1_nbins_256_n_' + n_str + \\\n",
    "                                  '/elife_' + model_str + '_nchoices_2_parameter_recovery_binned_1_nbins_256_nreps_1_n_' + n_str + '.pickle', 'wb'), \n",
    "            protocol = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(data_binned_512, open('/Users/afengler/OneDrive/project_nn_likelihoods/data/kde/' + model_str + '_elife/parameter_recovery_data_binned_1_nbins_512_n_' + n_str + \\\n",
    "                                  '/elife_' + model_str + '_nchoices_2_parameter_recovery_binned_1_nbins_512_nreps_1_n_' + n_str + '.pickle', 'wb'), \n",
    "            protocol = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SOME TESTS\n",
    "import cddm_data_simulation as cds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11c1a3a90>]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 100\n",
    "    \n",
    "out = cds.ddm_flexbound(v = data_binned_512[0][i, 0], \n",
    "                        a = data_binned_512[0][i, 1],\n",
    "                        w = data_binned_512[0][i, 2],\n",
    "                        ndt = data_binned_512[0][i, 3], \n",
    "                        delta_t = 0.001, \n",
    "                        s = 1,  #np.sqrt(2),\n",
    "                        max_t = 10,\n",
    "                        boundary_fun = bf.constant,\n",
    "                        n_samples = 20000)\n",
    "\n",
    "binned = bin_simulator_output(out = out,\n",
    "                              nbins = 512)\n",
    "\n",
    "\n",
    "    \n",
    "plt.plot(binned[:, 0], color = 'black', alpha = 0.5)\n",
    "plt.plot(binned[:, 1], color = 'red', alpha = 0.5)\n",
    "plt.plot(data_binned_512[1][0][i][:, 0], color = 'black')\n",
    "plt.plot(data_binned_512[1][0][i][:, 1], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1196c0c10>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(binned[:, 0], color = 'black', alpha = 0.5)\n",
    "plt.plot(binned[:, 1], color = 'red', alpha = 0.5)\n",
    "plt.plot(data_binned_512[1][0][0][:, 0], color = 'black')\n",
    "plt.plot(data_binned_512[1][0][0][:, 1], color = 'red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 512, 2)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_binned_512[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.29013616,  0.5732122 ,  0.56510556,  0.6202364 ],\n",
       "       [ 2.3652787 ,  0.7475329 ,  0.6085406 ,  0.45740852],\n",
       "       [-1.9124724 ,  1.0613471 ,  0.5251666 ,  0.22333424],\n",
       "       ...,\n",
       "       [ 1.2240385 ,  1.0580128 ,  0.4856123 ,  1.7803314 ],\n",
       "       [-2.2178957 ,  1.6264052 ,  0.27447334,  1.5300837 ],\n",
       "       [-0.02114056,  0.7334838 ,  0.39837652,  0.87968767]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_str = '1024'\n",
    "model_str = 'ddm'\n",
    "\n",
    "data_ = pickle.load(open('/Users/afengler/OneDrive/project_nn_likelihoods/data/kde/' + model_str + \\\n",
    "                        '_elife/parameter_recovery_data_binned_1_nbins_512_n_' + n_str + \\\n",
    "                        '/elife_' + model_str + \\\n",
    "                        '_nchoices_2_parameter_recovery_binned_1_nbins_512_nreps_1_n_' + n_str + '.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 1.3078684 ,  1.5692139 ,  0.40353978,  0.7989374 ],\n",
       "        [ 0.15081045,  2.1154125 ,  0.5016084 ,  1.4090556 ],\n",
       "        [-2.0069778 ,  1.6092589 ,  0.712587  ,  1.4454311 ],\n",
       "        ...,\n",
       "        [ 2.4429796 ,  1.2634765 ,  0.7085911 ,  1.6392365 ],\n",
       "        [ 2.1372757 ,  1.6593502 ,  0.32406518,  1.9461266 ],\n",
       "        [ 0.7318187 ,  1.8007475 ,  0.7009304 ,  1.5478374 ]],\n",
       "       dtype=float32),\n",
       " array([[[[0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ]],\n",
       " \n",
       "         [[0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.00097656],\n",
       "          [0.        , 0.00097656],\n",
       "          [0.03710938, 0.0703125 ]],\n",
       " \n",
       "         [[0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ]],\n",
       " \n",
       "         [[0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ]],\n",
       " \n",
       "         [[0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.        ],\n",
       "          [0.        , 0.        ],\n",
       "          [0.00097656, 0.00195312]]]]),\n",
       " {'s': 1.0,\n",
       "  'delta_t': 0.001,\n",
       "  'max_t': 10.0,\n",
       "  'n_samples': 1024,\n",
       "  'print_info': False,\n",
       "  'boundary_multiplicative': True,\n",
       "  'possible_choices': [-1, 1]}]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cddm_data_simulation as cds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = cds.test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_arr = np.concatenate([out[0], out[1]], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(20000, 2)"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "out_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ],
   "source": [
    "type(out) == tuple\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "source": [
    "type(out_arr) == np.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(20000, 1)"
      ]
     },
     "metadata": {},
     "execution_count": 33
    }
   ],
   "source": [
    "out[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(20000, 1)"
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "np.expand_dims(out_arr[:, 0], axis = 1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "          ...,\n",
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       " \n",
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       "          ...,\n",
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       "          ...,\n",
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       "          ...,\n",
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       "          [ 2.1599724 ,  1.        ]]]], dtype=float32), {'s': 1.0,\n",
       "  'delta_t': 0.001,\n",
       "  'max_t': 20,\n",
       "  'binned_max_t': 20.0,\n",
       "  'n_samples': 1000,\n",
       "  'print_info': False,\n",
       "  'boundary': <function boundary_functions.constant(t=0)>,\n",
       "  'boundary_multiplicative': True,\n",
       "  'possible_choices': [-1, 1]})"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "pickle.load(open('/Users/afengler/OneDrive/project_nn_likelihoods/data/tests/models/test/parameter_recovery_data_binned_0_nbins_0_n_1000/test_nchoices_2_parameter_recovery_binned_0_nbins_0_nreps_1_n_1000.pickle', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "binned256 = pickle.load(open('/Users/afengler/OneDrive/project_nn_likelihoods/data/tests/models/test/parameter_recovery_data_binned_1_nbins_256_n_1000/test_nchoices_2_parameter_recovery_binned_1_nbins_256_nreps_1_n_1000.pickle', 'rb'))"
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   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
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   "source": [
    "binned256[1][0,0,:100, :]"
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  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "binned512 = pickle.load(open('/Users/afengler/OneDrive/project_nn_likelihoods/data/tests/models/test/parameter_recovery_data_binned_1_nbins_512_n_1000/test_nchoices_2_parameter_recovery_binned_1_nbins_512_nreps_1_n_1000.pickle', 'rb'))"
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     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "source": [
    "binned512[1][0,0,:100, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'1.17.4'"
      ]
     },
     "metadata": {},
     "execution_count": 49
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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