random_lab_codeblocks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = ['dan','beth','juan','jordan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jordan\n"
     ]
    }
   ],
   "source": [
    "print(names[np.random.randint(0,len(names))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/Users/danieljdenman/Desktop/all_data_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Unnamed: 0</th>\n",
       "      <th>corrects</th>\n",
       "      <th>rxnTime</th>\n",
       "      <th>image</th>\n",
       "      <th>licks</th>\n",
       "      <th>trialData</th>\n",
       "      <th>trialDur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.266381</td>\n",
       "      <td>132.261384</td>\n",
       "      <td>13.192997</td>\n",
       "      <td>11.926616</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.916996</td>\n",
       "      <td>132.261384</td>\n",
       "      <td>19.593506</td>\n",
       "      <td>18.676510</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.617861</td>\n",
       "      <td>25.074526</td>\n",
       "      <td>25.361158</td>\n",
       "      <td>24.743297</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.251087</td>\n",
       "      <td>111.861498</td>\n",
       "      <td>31.010559</td>\n",
       "      <td>30.293133</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.935515</td>\n",
       "      <td>119.409241</td>\n",
       "      <td>36.360177</td>\n",
       "      <td>35.693043</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  corrects   rxnTime       image      licks  trialData  trialDur\n",
       "0           0       1.0  1.266381  132.261384  13.192997  11.926616       0.0\n",
       "1           1       1.0  0.916996  132.261384  19.593506  18.676510       0.0\n",
       "2           2       1.0  0.617861   25.074526  25.361158  24.743297       0.0\n",
       "3           3      -1.0  1.251087  111.861498  31.010559  30.293133       0.0\n",
       "4           4      -1.0  0.935515  119.409241  36.360177  35.693043       0.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbccc2e09d0>]"
      ]
     },
     "execution_count": 37,
     "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(df.licks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.load('/Users/danieljdenman/github/cheetah_or_elephant/data/user1.87585068940037e-309_2020_7_20-20_56_24/trialDurationData.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: check versions of cheetah_or_elephant on github"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "power = [0.1,0.2,0.5,0.75,1.0,1.25,1.5]\n",
    "output =[0.51,0.714,2.04,2.81,3.65,4.54,5.07]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'nominal gain on Cyclops driver')"
      ]
     },
     "execution_count": 51,
     "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": [
    "fiber_radius = 0.2 #mm\n",
    "plt.plot(np.array(power),np.array(output)/(fiber_radius*fiber_radius*np.pi),'-o')\n",
    "plt.ylabel('mW / mm2')\n",
    "plt.xlabel('nominal gain on Cyclops driver')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import _pickle as pkl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = pkl.load(open('/Users/danieljdenman/Desktop/labels_.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['M310016_2017-06-15_09-44-15_4', 'M310016_2017-06-15_09-11-15_3', 'M310016_2017-06-15_10-25-04_5', 'M310016_2017-06-15_11-51-37_8', 'M310016_2017-06-15_10-52-16_6', 'M310016_2017-06-15_09-55-55_4b', 'M270512_2016-12-16_10-51-44', 'M310016_2017-06-15_08-43-17_2', 'M310016_2017-06-15_08-10-38_1', 'M310016_2017-06-15_11-21-18_7'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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