quick-graph-3d.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "notebookRunGroups": {
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    }
   },
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "12"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "634\n",
      "    level_0 level_1  level_2  level_3 mouse_list  week_list  frame_list  \\\n",
      "0    CSC009  week_0        0      690     CSC009          0         690   \n",
      "1    CSC009  week_0        0      691     CSC009          0         691   \n",
      "2    CSC009  week_0        0      692     CSC009          0         692   \n",
      "3    CSC009  week_0        0      693     CSC009          0         693   \n",
      "4    CSC009  week_0        0      694     CSC009          0         694   \n",
      "..      ...     ...      ...      ...        ...        ...         ...   \n",
      "261  CSC009  week_0        0      951     CSC009          0         951   \n",
      "262  CSC009  week_0        0      952     CSC009          0         952   \n",
      "263  CSC009  week_0        0      953     CSC009          0         953   \n",
      "264  CSC009  week_0        0      954     CSC009          0         954   \n",
      "265  CSC009  week_0        0      955     CSC009          0         955   \n",
      "\n",
      "       timestamps  trial_num  trial_idx trial_type  principal component 1  \\\n",
      "0    23453.433039          0          1    Sucrose               3.595670   \n",
      "1    23487.423521          0          1    Sucrose               3.561247   \n",
      "2    23521.414004          0          1    Sucrose               3.526656   \n",
      "3    23555.404487          0          1    Sucrose               3.525219   \n",
      "4    23589.394969          0          1    Sucrose               3.525467   \n",
      "..            ...        ...        ...        ...                    ...   \n",
      "261  32324.949014          0          1    Sucrose               3.090119   \n",
      "262  32358.939497          0          1    Sucrose               3.173356   \n",
      "263  32392.929980          0          1    Sucrose               3.248464   \n",
      "264  32426.920462          0          1    Sucrose               3.223640   \n",
      "265  32460.910945          0          1    Sucrose               3.154311   \n",
      "\n",
      "     principal component 2  principal component 3  \n",
      "0                 0.325973               0.471295  \n",
      "1                 0.353116               0.481619  \n",
      "2                 0.378534               0.495137  \n",
      "3                 0.381569               0.497520  \n",
      "4                 0.380936               0.498655  \n",
      "..                     ...                    ...  \n",
      "261               0.074992               0.244556  \n",
      "262               0.108843               0.272311  \n",
      "263               0.105401               0.270343  \n",
      "264               0.230028               0.432070  \n",
      "265               0.149456               0.367276  \n",
      "\n",
      "[266 rows x 14 columns]\n",
      "     level_0 level_1  level_2  level_3 mouse_list  week_list  frame_list  \\\n",
      "1330  CSC009  week_0        5     4428     CSC009          0        4428   \n",
      "1331  CSC009  week_0        5     4429     CSC009          0        4429   \n",
      "1332  CSC009  week_0        5     4430     CSC009          0        4430   \n",
      "1333  CSC009  week_0        5     4431     CSC009          0        4431   \n",
      "1334  CSC009  week_0        5     4432     CSC009          0        4432   \n",
      "...      ...     ...      ...      ...        ...        ...         ...   \n",
      "1591  CSC009  week_0        5     4689     CSC009          0        4689   \n",
      "1592  CSC009  week_0        5     4690     CSC009          0        4690   \n",
      "1593  CSC009  week_0        5     4691     CSC009          0        4691   \n",
      "1594  CSC009  week_0        5     4692     CSC009          0        4692   \n",
      "1595  CSC009  week_0        5     4693     CSC009          0        4693   \n",
      "\n",
      "         timestamps  trial_num  trial_idx trial_type  principal component 1  \\\n",
      "1330  150509.857240          5          1    Sucrose               3.531305   \n",
      "1331  150543.847723          5          1    Sucrose               3.495275   \n",
      "1332  150577.838205          5          1    Sucrose               3.484006   \n",
      "1333  150611.828688          5          1    Sucrose               3.444696   \n",
      "1334  150645.819171          5          1    Sucrose               3.407941   \n",
      "...             ...        ...        ...        ...                    ...   \n",
      "1591  159381.373215          5          1    Sucrose               3.209516   \n",
      "1592  159415.363698          5          1    Sucrose               3.243762   \n",
      "1593  159449.354181          5          1    Sucrose               3.262153   \n",
      "1594  159483.344663          5          1    Sucrose               3.191238   \n",
      "1595  159517.335146          5          1    Sucrose               3.240507   \n",
      "\n",
      "      principal component 2  principal component 3  \n",
      "1330               0.319690               0.421491  \n",
      "1331               0.324178               0.406049  \n",
      "1332               0.346745               0.418253  \n",
      "1333               0.365149               0.408349  \n",
      "1334               0.331463               0.454490  \n",
      "...                     ...                    ...  \n",
      "1591               0.431802               0.490216  \n",
      "1592               0.329137               0.446623  \n",
      "1593               0.267675               0.394637  \n",
      "1594               0.379206               0.448423  \n",
      "1595               0.393432               0.466846  \n",
      "\n",
      "[266 rows x 14 columns]\n"
     ]
    }
   ],
   "source": [
    "with pd.HDFStore(\n",
    "        \"/Users/annieehler/Projects/Jupyter_Notebooks/python_outputs/exprs.h5\"\n",
    ") as data_file:\n",
    "    data = data_file.get('/SLEAP_data_by_trial/D3_PCA_trial_data')\n",
    "\n",
    "    data = data.dropna(axis=0, thresh=2, subset=[\"principal component 1\", \"principal component 2\"])\n",
    "\n",
    "    weeks = [0, 1, 5, 6]\n",
    "\n",
    "    trials = [\"Sucrose\", \"Airpuff\"]\n",
    "\n",
    "    new_data = []\n",
    "\n",
    "    for week in weeks:\n",
    "        df = data[(data.week_list == week)]\n",
    "        for trial in trials:\n",
    "            df1 = df[(df.trial_type == trial)]\n",
    "            mice = df1.mouse_list.unique().tolist()\n",
    "            for mouse in mice:\n",
    "                df2 = df1[(df1.mouse_list == mouse)]\n",
    "                ind_trials = df2.trial_num.unique().tolist()\n",
    "                for ind_trial in ind_trials:\n",
    "                    df3 = df2[(df2.trial_num == ind_trial)]\n",
    "                    new_data.append(df3)\n",
    "\n",
    "\n",
    "print(len(new_data))\n",
    "print(new_data[0])\n",
    "print(new_data[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "12"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "634\n",
      "    mouse_list  week_list  frame_list    timestamps  trial_num  trial_idx  \\\n",
      "0       CSC009          0         690  23453.433039          0          1   \n",
      "1       CSC009          0         691  23487.423521          0          1   \n",
      "2       CSC009          0         692  23521.414004          0          1   \n",
      "3       CSC009          0         693  23555.404487          0          1   \n",
      "4       CSC009          0         694  23589.394969          0          1   \n",
      "..         ...        ...         ...           ...        ...        ...   \n",
      "261     CSC009          0         951  32324.949014          0          1   \n",
      "262     CSC009          0         952  32358.939497          0          1   \n",
      "263     CSC009          0         953  32392.929980          0          1   \n",
      "264     CSC009          0         954  32426.920462          0          1   \n",
      "265     CSC009          0         955  32460.910945          0          1   \n",
      "\n",
      "    trial_type  principal component 1  principal component 2  \\\n",
      "0      Sucrose               3.595670               0.325973   \n",
      "1      Sucrose               3.561247               0.353116   \n",
      "2      Sucrose               3.526656               0.378534   \n",
      "3      Sucrose               3.525219               0.381569   \n",
      "4      Sucrose               3.525467               0.380936   \n",
      "..         ...                    ...                    ...   \n",
      "261    Sucrose               3.090119               0.074992   \n",
      "262    Sucrose               3.173356               0.108843   \n",
      "263    Sucrose               3.248464               0.105401   \n",
      "264    Sucrose               3.223640               0.230028   \n",
      "265    Sucrose               3.154311               0.149456   \n",
      "\n",
      "     principal component 3  \n",
      "0                 0.471295  \n",
      "1                 0.481619  \n",
      "2                 0.495137  \n",
      "3                 0.497520  \n",
      "4                 0.498655  \n",
      "..                     ...  \n",
      "261               0.244556  \n",
      "262               0.272311  \n",
      "263               0.270343  \n",
      "264               0.432070  \n",
      "265               0.367276  \n",
      "\n",
      "[266 rows x 10 columns]\n",
      "     mouse_list  week_list  frame_list     timestamps  trial_num  trial_idx  \\\n",
      "1330     CSC009          0        4428  150509.857240          5          1   \n",
      "1331     CSC009          0        4429  150543.847723          5          1   \n",
      "1332     CSC009          0        4430  150577.838205          5          1   \n",
      "1333     CSC009          0        4431  150611.828688          5          1   \n",
      "1334     CSC009          0        4432  150645.819171          5          1   \n",
      "...         ...        ...         ...            ...        ...        ...   \n",
      "1591     CSC009          0        4689  159381.373215          5          1   \n",
      "1592     CSC009          0        4690  159415.363698          5          1   \n",
      "1593     CSC009          0        4691  159449.354181          5          1   \n",
      "1594     CSC009          0        4692  159483.344663          5          1   \n",
      "1595     CSC009          0        4693  159517.335146          5          1   \n",
      "\n",
      "     trial_type  principal component 1  principal component 2  \\\n",
      "1330    Sucrose               3.531305               0.319690   \n",
      "1331    Sucrose               3.495275               0.324178   \n",
      "1332    Sucrose               3.484006               0.346745   \n",
      "1333    Sucrose               3.444696               0.365149   \n",
      "1334    Sucrose               3.407941               0.331463   \n",
      "...         ...                    ...                    ...   \n",
      "1591    Sucrose               3.209516               0.431802   \n",
      "1592    Sucrose               3.243762               0.329137   \n",
      "1593    Sucrose               3.262153               0.267675   \n",
      "1594    Sucrose               3.191238               0.379206   \n",
      "1595    Sucrose               3.240507               0.393432   \n",
      "\n",
      "      principal component 3  \n",
      "1330               0.421491  \n",
      "1331               0.406049  \n",
      "1332               0.418253  \n",
      "1333               0.408349  \n",
      "1334               0.454490  \n",
      "...                     ...  \n",
      "1591               0.490216  \n",
      "1592               0.446623  \n",
      "1593               0.394637  \n",
      "1594               0.448423  \n",
      "1595               0.466846  \n",
      "\n",
      "[266 rows x 10 columns]\n"
     ]
    }
   ],
   "source": [
    "drop_data = []\n",
    "\n",
    "for data in new_data:\n",
    "    data = data.drop([\"level_0\", \"level_1\", \"level_2\", \"level_3\"], axis=1)\n",
    "    drop_data.append(data)\n",
    "\n",
    "print(len(drop_data))\n",
    "print(drop_data[0])\n",
    "print(drop_data[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "12"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "634\n",
      "    mouse_list  week_list  frame_list    timestamps  trial_num  trial_idx  \\\n",
      "0       CSC009          0         690  23453.433039          0          1   \n",
      "1       CSC009          0         691  23487.423521          0          1   \n",
      "2       CSC009          0         692  23521.414004          0          1   \n",
      "3       CSC009          0         693  23555.404487          0          1   \n",
      "4       CSC009          0         694  23589.394969          0          1   \n",
      "..         ...        ...         ...           ...        ...        ...   \n",
      "261     CSC009          0         951  32324.949014          0          1   \n",
      "262     CSC009          0         952  32358.939497          0          1   \n",
      "263     CSC009          0         953  32392.929980          0          1   \n",
      "264     CSC009          0         954  32426.920462          0          1   \n",
      "265     CSC009          0         955  32460.910945          0          1   \n",
      "\n",
      "    trial_type  principal component 1  principal component 2  \\\n",
      "0      Sucrose               3.595670               0.325973   \n",
      "1      Sucrose               3.561247               0.353116   \n",
      "2      Sucrose               3.526656               0.378534   \n",
      "3      Sucrose               3.525219               0.381569   \n",
      "4      Sucrose               3.525467               0.380936   \n",
      "..         ...                    ...                    ...   \n",
      "261    Sucrose               3.090119               0.074992   \n",
      "262    Sucrose               3.173356               0.108843   \n",
      "263    Sucrose               3.248464               0.105401   \n",
      "264    Sucrose               3.223640               0.230028   \n",
      "265    Sucrose               3.154311               0.149456   \n",
      "\n",
      "     principal component 3  frame_trial_idx  \n",
      "0                 0.471295                0  \n",
      "1                 0.481619                1  \n",
      "2                 0.495137                2  \n",
      "3                 0.497520                3  \n",
      "4                 0.498655                4  \n",
      "..                     ...              ...  \n",
      "261               0.244556              261  \n",
      "262               0.272311              262  \n",
      "263               0.270343              263  \n",
      "264               0.432070              264  \n",
      "265               0.367276              265  \n",
      "\n",
      "[266 rows x 11 columns]\n",
      "     mouse_list  week_list  frame_list     timestamps  trial_num  trial_idx  \\\n",
      "1330     CSC009          0        4428  150509.857240          5          1   \n",
      "1331     CSC009          0        4429  150543.847723          5          1   \n",
      "1332     CSC009          0        4430  150577.838205          5          1   \n",
      "1333     CSC009          0        4431  150611.828688          5          1   \n",
      "1334     CSC009          0        4432  150645.819171          5          1   \n",
      "...         ...        ...         ...            ...        ...        ...   \n",
      "1591     CSC009          0        4689  159381.373215          5          1   \n",
      "1592     CSC009          0        4690  159415.363698          5          1   \n",
      "1593     CSC009          0        4691  159449.354181          5          1   \n",
      "1594     CSC009          0        4692  159483.344663          5          1   \n",
      "1595     CSC009          0        4693  159517.335146          5          1   \n",
      "\n",
      "     trial_type  principal component 1  principal component 2  \\\n",
      "1330    Sucrose               3.531305               0.319690   \n",
      "1331    Sucrose               3.495275               0.324178   \n",
      "1332    Sucrose               3.484006               0.346745   \n",
      "1333    Sucrose               3.444696               0.365149   \n",
      "1334    Sucrose               3.407941               0.331463   \n",
      "...         ...                    ...                    ...   \n",
      "1591    Sucrose               3.209516               0.431802   \n",
      "1592    Sucrose               3.243762               0.329137   \n",
      "1593    Sucrose               3.262153               0.267675   \n",
      "1594    Sucrose               3.191238               0.379206   \n",
      "1595    Sucrose               3.240507               0.393432   \n",
      "\n",
      "      principal component 3  frame_trial_idx  \n",
      "1330               0.421491                0  \n",
      "1331               0.406049                1  \n",
      "1332               0.418253                2  \n",
      "1333               0.408349                3  \n",
      "1334               0.454490                4  \n",
      "...                     ...              ...  \n",
      "1591               0.490216              261  \n",
      "1592               0.446623              262  \n",
      "1593               0.394637              263  \n",
      "1594               0.448423              264  \n",
      "1595               0.466846              265  \n",
      "\n",
      "[266 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "new_data = []\n",
    "\n",
    "for data in drop_data:\n",
    "    min_frame = data.frame_list.min()\n",
    "    data[\"frame_trial_idx\"] = data.frame_list - min_frame\n",
    "    new_data.append(data)\n",
    "\n",
    "print(len(new_data))\n",
    "print(new_data[0])\n",
    "print(new_data[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "12"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mouse_list</th>\n",
       "      <th>week_list</th>\n",
       "      <th>frame_list</th>\n",
       "      <th>timestamps</th>\n",
       "      <th>trial_num</th>\n",
       "      <th>trial_idx</th>\n",
       "      <th>trial_type</th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>principal component 3</th>\n",
       "      <th>frame_trial_idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>223259</th>\n",
       "      <td>CSE008</td>\n",
       "      <td>0</td>\n",
       "      <td>408</td>\n",
       "      <td>1.386812e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sucrose</td>\n",
       "      <td>2.540067</td>\n",
       "      <td>-4.256426</td>\n",
       "      <td>0.950106</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223260</th>\n",
       "      <td>CSE008</td>\n",
       "      <td>0</td>\n",
       "      <td>409</td>\n",
       "      <td>1.390211e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sucrose</td>\n",
       "      <td>2.541198</td>\n",
       "      <td>-4.271615</td>\n",
       "      <td>0.940855</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223261</th>\n",
       "      <td>CSE008</td>\n",
       "      <td>0</td>\n",
       "      <td>410</td>\n",
       "      <td>1.393610e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sucrose</td>\n",
       "      <td>2.541825</td>\n",
       "      <td>-4.274457</td>\n",
       "      <td>0.939087</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223262</th>\n",
       "      <td>CSE008</td>\n",
       "      <td>0</td>\n",
       "      <td>411</td>\n",
       "      <td>1.397009e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sucrose</td>\n",
       "      <td>2.541997</td>\n",
       "      <td>-4.260828</td>\n",
       "      <td>0.948314</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223263</th>\n",
       "      <td>CSE008</td>\n",
       "      <td>0</td>\n",
       "      <td>412</td>\n",
       "      <td>1.400408e+04</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Sucrose</td>\n",
       "      <td>2.511451</td>\n",
       "      <td>-4.224019</td>\n",
       "      <td>0.964444</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427886</th>\n",
       "      <td>CSE020</td>\n",
       "      <td>6</td>\n",
       "      <td>39251</td>\n",
       "      <td>1.334160e+06</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>Airpuff</td>\n",
       "      <td>-3.297897</td>\n",
       "      <td>-3.252484</td>\n",
       "      <td>1.588278</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427887</th>\n",
       "      <td>CSE020</td>\n",
       "      <td>6</td>\n",
       "      <td>39252</td>\n",
       "      <td>1.334194e+06</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>Airpuff</td>\n",
       "      <td>-3.330289</td>\n",
       "      <td>-3.289866</td>\n",
       "      <td>1.562665</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427888</th>\n",
       "      <td>CSE020</td>\n",
       "      <td>6</td>\n",
       "      <td>39253</td>\n",
       "      <td>1.334228e+06</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>Airpuff</td>\n",
       "      <td>-3.673116</td>\n",
       "      <td>-3.420215</td>\n",
       "      <td>1.737303</td>\n",
       "      <td>263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427889</th>\n",
       "      <td>CSE020</td>\n",
       "      <td>6</td>\n",
       "      <td>39254</td>\n",
       "      <td>1.334262e+06</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>Airpuff</td>\n",
       "      <td>-3.466275</td>\n",
       "      <td>-3.469853</td>\n",
       "      <td>1.531822</td>\n",
       "      <td>264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427890</th>\n",
       "      <td>CSE020</td>\n",
       "      <td>6</td>\n",
       "      <td>39255</td>\n",
       "      <td>1.334296e+06</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>Airpuff</td>\n",
       "      <td>-3.707314</td>\n",
       "      <td>-3.591396</td>\n",
       "      <td>1.840738</td>\n",
       "      <td>265</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78923 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mouse_list  week_list  frame_list    timestamps  trial_num  trial_idx  \\\n",
       "223259     CSE008          0         408  1.386812e+04          0          1   \n",
       "223260     CSE008          0         409  1.390211e+04          0          1   \n",
       "223261     CSE008          0         410  1.393610e+04          0          1   \n",
       "223262     CSE008          0         411  1.397009e+04          0          1   \n",
       "223263     CSE008          0         412  1.400408e+04          0          1   \n",
       "...           ...        ...         ...           ...        ...        ...   \n",
       "427886     CSE020          6       39251  1.334160e+06         51          0   \n",
       "427887     CSE020          6       39252  1.334194e+06         51          0   \n",
       "427888     CSE020          6       39253  1.334228e+06         51          0   \n",
       "427889     CSE020          6       39254  1.334262e+06         51          0   \n",
       "427890     CSE020          6       39255  1.334296e+06         51          0   \n",
       "\n",
       "       trial_type  principal component 1  principal component 2  \\\n",
       "223259    Sucrose               2.540067              -4.256426   \n",
       "223260    Sucrose               2.541198              -4.271615   \n",
       "223261    Sucrose               2.541825              -4.274457   \n",
       "223262    Sucrose               2.541997              -4.260828   \n",
       "223263    Sucrose               2.511451              -4.224019   \n",
       "...           ...                    ...                    ...   \n",
       "427886    Airpuff              -3.297897              -3.252484   \n",
       "427887    Airpuff              -3.330289              -3.289866   \n",
       "427888    Airpuff              -3.673116              -3.420215   \n",
       "427889    Airpuff              -3.466275              -3.469853   \n",
       "427890    Airpuff              -3.707314              -3.591396   \n",
       "\n",
       "        principal component 3  frame_trial_idx  \n",
       "223259               0.950106                0  \n",
       "223260               0.940855                1  \n",
       "223261               0.939087                2  \n",
       "223262               0.948314                3  \n",
       "223263               0.964444                4  \n",
       "...                       ...              ...  \n",
       "427886               1.588278              261  \n",
       "427887               1.562665              262  \n",
       "427888               1.737303              263  \n",
       "427889               1.531822              264  \n",
       "427890               1.840738              265  \n",
       "\n",
       "[78923 rows x 11 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.concat(new_data)\n",
    "\n",
    "cse_array = ['CSE008', 'CSE020']\n",
    "csc_array = ['CSC009', 'CSC013']\n",
    "\n",
    "cse = data.mouse_list.isin(cse_array)\n",
    "csc = data.mouse_list.isin(csc_array)\n",
    "\n",
    "data_cse = data[cse]\n",
    "data_csc = data[csc]\n",
    "\n",
    "data_cse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">principal component 1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">principal component 2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">principal component 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_list</th>\n",
       "      <th>trial_type</th>\n",
       "      <th>frame_trial_idx</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Sucrose</th>\n",
       "      <th>0</th>\n",
       "      <td>2.753560</td>\n",
       "      <td>0.123063</td>\n",
       "      <td>-3.895515</td>\n",
       "      <td>0.221214</td>\n",
       "      <td>0.139938</td>\n",
       "      <td>0.370257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.760678</td>\n",
       "      <td>0.126934</td>\n",
       "      <td>-3.896811</td>\n",
       "      <td>0.226895</td>\n",
       "      <td>0.125316</td>\n",
       "      <td>0.390973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.764755</td>\n",
       "      <td>0.124772</td>\n",
       "      <td>-3.903945</td>\n",
       "      <td>0.237884</td>\n",
       "      <td>0.113211</td>\n",
       "      <td>0.402907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.761017</td>\n",
       "      <td>0.116240</td>\n",
       "      <td>-3.908762</td>\n",
       "      <td>0.225905</td>\n",
       "      <td>0.129432</td>\n",
       "      <td>0.391394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.751739</td>\n",
       "      <td>0.119524</td>\n",
       "      <td>-3.913665</td>\n",
       "      <td>0.216947</td>\n",
       "      <td>0.135930</td>\n",
       "      <td>0.390913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">6</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Airpuff</th>\n",
       "      <th>261</th>\n",
       "      <td>-2.058643</td>\n",
       "      <td>1.473447</td>\n",
       "      <td>-3.922854</td>\n",
       "      <td>0.703326</td>\n",
       "      <td>0.583855</td>\n",
       "      <td>0.907997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>-2.040160</td>\n",
       "      <td>1.464365</td>\n",
       "      <td>-3.921137</td>\n",
       "      <td>0.704833</td>\n",
       "      <td>0.587255</td>\n",
       "      <td>0.907812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>-2.049378</td>\n",
       "      <td>1.480530</td>\n",
       "      <td>-3.920336</td>\n",
       "      <td>0.687708</td>\n",
       "      <td>0.577456</td>\n",
       "      <td>0.937651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>-2.031307</td>\n",
       "      <td>1.458036</td>\n",
       "      <td>-3.898150</td>\n",
       "      <td>0.692669</td>\n",
       "      <td>0.603043</td>\n",
       "      <td>0.911271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>-1.963858</td>\n",
       "      <td>1.504595</td>\n",
       "      <td>-3.952273</td>\n",
       "      <td>0.679860</td>\n",
       "      <td>0.595137</td>\n",
       "      <td>0.966923</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2128 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     principal component 1            \\\n",
       "                                                      mean       std   \n",
       "week_list trial_type frame_trial_idx                                   \n",
       "0         Sucrose    0                            2.753560  0.123063   \n",
       "                     1                            2.760678  0.126934   \n",
       "                     2                            2.764755  0.124772   \n",
       "                     3                            2.761017  0.116240   \n",
       "                     4                            2.751739  0.119524   \n",
       "...                                                    ...       ...   \n",
       "6         Airpuff    261                         -2.058643  1.473447   \n",
       "                     262                         -2.040160  1.464365   \n",
       "                     263                         -2.049378  1.480530   \n",
       "                     264                         -2.031307  1.458036   \n",
       "                     265                         -1.963858  1.504595   \n",
       "\n",
       "                                     principal component 2            \\\n",
       "                                                      mean       std   \n",
       "week_list trial_type frame_trial_idx                                   \n",
       "0         Sucrose    0                           -3.895515  0.221214   \n",
       "                     1                           -3.896811  0.226895   \n",
       "                     2                           -3.903945  0.237884   \n",
       "                     3                           -3.908762  0.225905   \n",
       "                     4                           -3.913665  0.216947   \n",
       "...                                                    ...       ...   \n",
       "6         Airpuff    261                         -3.922854  0.703326   \n",
       "                     262                         -3.921137  0.704833   \n",
       "                     263                         -3.920336  0.687708   \n",
       "                     264                         -3.898150  0.692669   \n",
       "                     265                         -3.952273  0.679860   \n",
       "\n",
       "                                     principal component 3            \n",
       "                                                      mean       std  \n",
       "week_list trial_type frame_trial_idx                                  \n",
       "0         Sucrose    0                            0.139938  0.370257  \n",
       "                     1                            0.125316  0.390973  \n",
       "                     2                            0.113211  0.402907  \n",
       "                     3                            0.129432  0.391394  \n",
       "                     4                            0.135930  0.390913  \n",
       "...                                                    ...       ...  \n",
       "6         Airpuff    261                          0.583855  0.907997  \n",
       "                     262                          0.587255  0.907812  \n",
       "                     263                          0.577456  0.937651  \n",
       "                     264                          0.603043  0.911271  \n",
       "                     265                          0.595137  0.966923  \n",
       "\n",
       "[2128 rows x 6 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_and_std = []\n",
    "\n",
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "        df = data_cse[(data_cse.week_list == week) & (data_cse.trial_type == trial)]\n",
    "        mean_and_std.append(df.groupby(by=[\"week_list\", \"trial_type\", \"frame_trial_idx\"]).agg({\"principal component 1\": ['mean', 'std'], \"principal component 2\": ['mean', 'std'], \"principal component 3\": ['mean', 'std']}))\n",
    "\n",
    "mean_and_std_df_cse = pd.concat(mean_and_std)\n",
    "\n",
    "mean_and_std_df_cse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">principal component 1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">principal component 2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">principal component 3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>week_list</th>\n",
       "      <th>trial_type</th>\n",
       "      <th>frame_trial_idx</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Sucrose</th>\n",
       "      <th>0</th>\n",
       "      <td>5.576109</td>\n",
       "      <td>2.225894</td>\n",
       "      <td>-0.198892</td>\n",
       "      <td>0.724169</td>\n",
       "      <td>0.418532</td>\n",
       "      <td>0.215903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.575006</td>\n",
       "      <td>2.231695</td>\n",
       "      <td>-0.192652</td>\n",
       "      <td>0.722054</td>\n",
       "      <td>0.424535</td>\n",
       "      <td>0.218844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.572107</td>\n",
       "      <td>2.235099</td>\n",
       "      <td>-0.201647</td>\n",
       "      <td>0.721197</td>\n",
       "      <td>0.429973</td>\n",
       "      <td>0.223661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.567700</td>\n",
       "      <td>2.233908</td>\n",
       "      <td>-0.204420</td>\n",
       "      <td>0.717615</td>\n",
       "      <td>0.431129</td>\n",
       "      <td>0.220495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.560069</td>\n",
       "      <td>2.230233</td>\n",
       "      <td>-0.209984</td>\n",
       "      <td>0.724693</td>\n",
       "      <td>0.431423</td>\n",
       "      <td>0.222140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">6</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">Airpuff</th>\n",
       "      <th>261</th>\n",
       "      <td>-5.916056</td>\n",
       "      <td>0.249466</td>\n",
       "      <td>-4.637531</td>\n",
       "      <td>1.804084</td>\n",
       "      <td>-0.522574</td>\n",
       "      <td>0.572717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>-5.922579</td>\n",
       "      <td>0.277868</td>\n",
       "      <td>-4.640701</td>\n",
       "      <td>1.818310</td>\n",
       "      <td>-0.522794</td>\n",
       "      <td>0.551607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>-5.913747</td>\n",
       "      <td>0.263841</td>\n",
       "      <td>-4.636400</td>\n",
       "      <td>1.824412</td>\n",
       "      <td>-0.530834</td>\n",
       "      <td>0.565855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>-5.910616</td>\n",
       "      <td>0.251326</td>\n",
       "      <td>-4.625415</td>\n",
       "      <td>1.829485</td>\n",
       "      <td>-0.523953</td>\n",
       "      <td>0.563039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>-5.908105</td>\n",
       "      <td>0.275319</td>\n",
       "      <td>-4.630557</td>\n",
       "      <td>1.824173</td>\n",
       "      <td>-0.483907</td>\n",
       "      <td>0.591246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2128 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     principal component 1            \\\n",
       "                                                      mean       std   \n",
       "week_list trial_type frame_trial_idx                                   \n",
       "0         Sucrose    0                            5.576109  2.225894   \n",
       "                     1                            5.575006  2.231695   \n",
       "                     2                            5.572107  2.235099   \n",
       "                     3                            5.567700  2.233908   \n",
       "                     4                            5.560069  2.230233   \n",
       "...                                                    ...       ...   \n",
       "6         Airpuff    261                         -5.916056  0.249466   \n",
       "                     262                         -5.922579  0.277868   \n",
       "                     263                         -5.913747  0.263841   \n",
       "                     264                         -5.910616  0.251326   \n",
       "                     265                         -5.908105  0.275319   \n",
       "\n",
       "                                     principal component 2            \\\n",
       "                                                      mean       std   \n",
       "week_list trial_type frame_trial_idx                                   \n",
       "0         Sucrose    0                           -0.198892  0.724169   \n",
       "                     1                           -0.192652  0.722054   \n",
       "                     2                           -0.201647  0.721197   \n",
       "                     3                           -0.204420  0.717615   \n",
       "                     4                           -0.209984  0.724693   \n",
       "...                                                    ...       ...   \n",
       "6         Airpuff    261                         -4.637531  1.804084   \n",
       "                     262                         -4.640701  1.818310   \n",
       "                     263                         -4.636400  1.824412   \n",
       "                     264                         -4.625415  1.829485   \n",
       "                     265                         -4.630557  1.824173   \n",
       "\n",
       "                                     principal component 3            \n",
       "                                                      mean       std  \n",
       "week_list trial_type frame_trial_idx                                  \n",
       "0         Sucrose    0                            0.418532  0.215903  \n",
       "                     1                            0.424535  0.218844  \n",
       "                     2                            0.429973  0.223661  \n",
       "                     3                            0.431129  0.220495  \n",
       "                     4                            0.431423  0.222140  \n",
       "...                                                    ...       ...  \n",
       "6         Airpuff    261                         -0.522574  0.572717  \n",
       "                     262                         -0.522794  0.551607  \n",
       "                     263                         -0.530834  0.565855  \n",
       "                     264                         -0.523953  0.563039  \n",
       "                     265                         -0.483907  0.591246  \n",
       "\n",
       "[2128 rows x 6 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_and_std = []\n",
    "\n",
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "        df = data_csc[(data_csc.week_list == week) & (data_csc.trial_type == trial)]\n",
    "        mean_and_std.append(df.groupby(by=[\"week_list\", \"trial_type\", \"frame_trial_idx\"]).agg({\"principal component 1\": ['mean', 'std'], \"principal component 2\": ['mean', 'std'], \"principal component 3\": ['mean', 'std']}))\n",
    "\n",
    "mean_and_std_df_csc = pd.concat(mean_and_std)\n",
    "\n",
    "mean_and_std_df_csc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "88 171 177 177\n"
     ]
    }
   ],
   "source": [
    "#fig.add_vline\n",
    "trail_start = round((33.990482664853836 ** -1) * 3000)\n",
    "\n",
    "cs_start = round((33.990482664853836 ** -1) * 5800)\n",
    "\n",
    "cs_end = round((33.990482664853836 ** -1) * 6000)\n",
    "\n",
    "trial_end = round((33.990482664853836 ** -1) * 6000)\n",
    "\n",
    "print(trail_start, cs_start, cs_end, trial_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 18 [88, 95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 171, 177] ['Trial start (2991ms)', '3229ms', '3399ms', '3569ms', '3739ms', '3909ms', '4079ms', '4249ms', '4419ms', '4589ms', '4759ms', '4929ms', '5099ms', '5269ms', '5438ms', '5608ms', 'CS Start (5812ms)', 'Trial end (6016ms)']\n",
      "3 3 [88, 171, 177] ['Trial start (2991ms)', 'CS Start (5812ms)', 'Trial end (6016ms)']\n",
      "{88: 'Trial start (2991ms)', 171: 'CS Start (5812ms)', 177: 'Trial end (6016ms)'}\n"
     ]
    }
   ],
   "source": [
    "events = [trail_start, cs_start, trial_end]\n",
    "tickvals = [i for i in range(trail_start, trial_end+1, 1)]\n",
    "ticklabels = []\n",
    "ticks = []\n",
    "eventlabels = []\n",
    "for i in tickvals:\n",
    "    if not i in events:\n",
    "        if i % 5 == 0 and i != 90 and i != 170 and i != 175:\n",
    "            string = str(round(i*33.990482664853836))\n",
    "            ticklabels.append(string + \"ms\")\n",
    "            ticks.append(i)\n",
    "    elif i == 88:\n",
    "        string = str(round(i*33.990482664853836))\n",
    "        ticklabels.append(f\"Trial start ({string}ms)\")\n",
    "        eventlabels.append(f\"Trial start ({string}ms)\")\n",
    "        ticks.append(i)\n",
    "    elif i == 171: \n",
    "        string = str(round(i*33.990482664853836))\n",
    "        ticklabels.append(f\"CS Start ({string}ms)\")\n",
    "        eventlabels.append(f\"CS Start ({string}ms)\")\n",
    "        ticks.append(i)\n",
    "    elif i == 177:\n",
    "        string = str(round(i*33.990482664853836))\n",
    "        ticklabels.append(f\"Trial end ({string}ms)\")\n",
    "        eventlabels.append(f\"Trial end ({string}ms)\")\n",
    "        ticks.append(i)\n",
    "print(len(ticks), len(ticklabels), ticks, ticklabels)\n",
    "print(len(events), len(eventlabels), events, eventlabels)\n",
    "\n",
    "\n",
    "eventdict = {}\n",
    "for i in range(0, len(events)):\n",
    "    eventdict[events[i]] = eventlabels[i]\n",
    "\n",
    "print(eventdict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1383335075.py:6: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1383335075.py:7: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1383335075.py:8: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1383335075.py:10: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1383335075.py:25: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter3d(\n",
    "                x=mean_and_std_df_cse.loc[(week, trial), (\"principal component 1\", \"mean\")],\n",
    "                y=mean_and_std_df_cse.loc[(week, trial), (\"principal component 2\", \"mean\")],\n",
    "                z=mean_and_std_df_cse.loc[(week, trial), (\"principal component 3\", \"mean\")],\n",
    "                line=dict(\n",
    "                    color=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                    colorscale=[[0, 'rgb(48, 112, 173)'], [1, 'rgb(192, 125, 165)']],\n",
    "                    cmin=trail_start,\n",
    "                    cmid=cs_start,\n",
    "                    cmax=trial_end,\n",
    "                    colorbar=dict(\n",
    "                        lenmode='pixels',\n",
    "                        len=400,\n",
    "                        orientation='v',\n",
    "                        tickmode='array',\n",
    "                        tickvals=ticks,\n",
    "                        ticktext=ticklabels\n",
    "                    ),\n",
    "                ),\n",
    "                marker=dict(\n",
    "                    color=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                    colorscale=[[0, 'rgb(48, 112, 173)'], [1, 'rgb(192, 125, 165)']],\n",
    "                    cmin=trail_start,\n",
    "                    cmid=cs_start,\n",
    "                    cmax=trial_end,\n",
    "                    colorbar=dict(\n",
    "                        lenmode='pixels',\n",
    "                        len=400,\n",
    "                        orientation='v',\n",
    "                        tickmode='array',\n",
    "                        tickvals=events,\n",
    "                        ticktext=eventlabels\n",
    "                    ),\n",
    "                    showscale=False,\n",
    "                    size=3,\n",
    "                    \n",
    "                ),\n",
    "                mode='lines+markers'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title=(f\"Average of PCA Coordinates for Trial Type {trial} on Week {week} for CSE Mice\"),\n",
    "            scene = dict(\n",
    "                xaxis = dict(title=\"principal component 1\", type=\"linear\", ticks=\"outside\"),\n",
    "                yaxis = dict(title=\"principal component 2\", type=\"linear\", ticks=\"outside\"),\n",
    "                zaxis = dict(title=\"principal component 3\", type=\"linear\", ticks=\"outside\"),\n",
    "                annotations=[dict(\n",
    "                    x=mean_and_std_df_cse.loc[(week, trial, event), (\"principal component 1\", \"mean\")], \n",
    "                    y=mean_and_std_df_cse.loc[(week, trial, event), (\"principal component 2\", \"mean\")],\n",
    "                    z=mean_and_std_df_cse.loc[(week, trial, event), (\"principal component 3\", \"mean\")],\n",
    "                    text=eventdict[event],\n",
    "                    showarrow=True,\n",
    "                    arrowhead=1\n",
    "                ) for event in events],\n",
    "            ),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        today = date.today().strftime(\"%Y%m%d\")\n",
    "        \n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/CSE/3D/pdfs/{today}_3D_CSE_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/CSE/3D/vectorized/{today}_3D_CSE_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.svg\")\n",
    "        fig.write_html(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/CSE/3D/html/{today}_3D_CSE_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.html\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/2802035262.py:6: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/2802035262.py:7: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/2802035262.py:8: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/2802035262.py:10: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/2802035262.py:25: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter3d(\n",
    "                x=mean_and_std_df_csc.loc[(week, trial), (\"principal component 1\", \"mean\")],\n",
    "                y=mean_and_std_df_csc.loc[(week, trial), (\"principal component 2\", \"mean\")],\n",
    "                z=mean_and_std_df_csc.loc[(week, trial), (\"principal component 3\", \"mean\")],\n",
    "                line=dict(\n",
    "                    color=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                    colorscale=[[0, 'rgb(48, 112, 173)'], [1, 'rgb(192, 125, 165)']],\n",
    "                    cmin=trail_start,\n",
    "                    cmid=cs_start,\n",
    "                    cmax=trial_end,\n",
    "                    colorbar=dict(\n",
    "                        lenmode='pixels',\n",
    "                        len=400,\n",
    "                        orientation='v',\n",
    "                        tickmode='array',\n",
    "                        tickvals=ticks,\n",
    "                        ticktext=ticklabels\n",
    "                    ),\n",
    "                ),\n",
    "                marker=dict(\n",
    "                    color=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                    colorscale=[[0, 'rgb(48, 112, 173)'], [1, 'rgb(192, 125, 165)']],\n",
    "                    cmin=trail_start,\n",
    "                    cmid=cs_start,\n",
    "                    cmax=trial_end,\n",
    "                    colorbar=dict(\n",
    "                        lenmode='pixels',\n",
    "                        len=400,\n",
    "                        orientation='v',\n",
    "                        tickmode='array',\n",
    "                        tickvals=events,\n",
    "                        ticktext=eventlabels\n",
    "                    ),\n",
    "                    showscale=False,\n",
    "                    size=3,\n",
    "                ),\n",
    "                mode='lines+markers'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title=(f\"Average of PCA Coordinates for Trial Type {trial} on Week {week} for CSC Mice\"),\n",
    "            scene = dict(\n",
    "                xaxis = dict(title=\"principal component 1\", type=\"linear\", ticks=\"outside\"),\n",
    "                yaxis = dict(title=\"principal component 2\", type=\"linear\", ticks=\"outside\"),\n",
    "                zaxis = dict(title=\"principal component 3\", type=\"linear\", ticks=\"outside\"),\n",
    "                annotations=[dict(\n",
    "                    x=mean_and_std_df_csc.loc[(week, trial, event), (\"principal component 1\", \"mean\")], \n",
    "                    y=mean_and_std_df_csc.loc[(week, trial, event), (\"principal component 2\", \"mean\")],\n",
    "                    z=mean_and_std_df_csc.loc[(week, trial, event), (\"principal component 3\", \"mean\")],\n",
    "                    text=eventdict[event],\n",
    "                    showarrow=True,\n",
    "                    arrowhead=1\n",
    "                ) for event in events],\n",
    "            ),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        today = date.today().strftime(\"%Y%m%d\")\n",
    "        \n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/CSC/3D/pdfs/{today}_3D_CSC_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/CSC/3D/vectorized/{today}_3D_CSC_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.svg\")\n",
    "        fig.write_html(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/CSC/3D/html/{today}_3D_CSC_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "1"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:6: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:7: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:8: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:10: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:25: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:46: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:47: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:48: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:50: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n",
      "/var/folders/70/0hjh5llj0ys9mr1pvl2b1w9c0000gn/T/ipykernel_73141/1233450566.py:65: PerformanceWarning:\n",
      "\n",
      "indexing past lexsort depth may impact performance.\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Average of PCA Coordinates for Trial Type Airpuff on Week 6"
        },
        "width": 1066
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter3d(\n",
    "                x=mean_and_std_df_cse.loc[(week, trial), (\"principal component 1\", \"mean\")],\n",
    "                y=mean_and_std_df_cse.loc[(week, trial), (\"principal component 2\", \"mean\")],\n",
    "                z=mean_and_std_df_cse.loc[(week, trial), (\"principal component 3\", \"mean\")],\n",
    "                line=dict(\n",
    "                    color=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                    colorscale=[[0, 'rgb(48, 112, 173)'], [1, 'rgb(192, 125, 165)']],\n",
    "                    cmin=trail_start,\n",
    "                    cmid=cs_start,\n",
    "                    cmax=trial_end,\n",
    "                    colorbar=dict(\n",
    "                        lenmode='pixels',\n",
    "                        len=400,\n",
    "                        orientation='v',\n",
    "                        tickmode='array',\n",
    "                        tickvals=ticks,\n",
    "                        ticktext=ticklabels\n",
    "                    ),\n",
    "                ),\n",
    "                marker=dict(\n",
    "                    color=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                    colorscale=[[0, 'rgb(48, 112, 173)'], [1, 'rgb(192, 125, 165)']],\n",
    "                    cmin=trail_start,\n",
    "                    cmid=cs_start,\n",
    "                    cmax=trial_end,\n",
    "                    colorbar=dict(\n",
    "                        lenmode='pixels',\n",
    "                        len=400,\n",
    "                        orientation='v',\n",
    "                        tickmode='array',\n",
    "                        tickvals=events,\n",
    "                        ticktext=eventlabels\n",
    "                    ),\n",
    "                    showscale=False,\n",
    "                    size=3,\n",
    "                    \n",
    "                ),\n",
    "                mode='lines+markers',\n",
    "                name=\"CSE\"\n",
    "            ),\n",
    "            go.Scatter3d(\n",
    "                x=mean_and_std_df_csc.loc[(week, trial), (\"principal component 1\", \"mean\")],\n",
    "                y=mean_and_std_df_csc.loc[(week, trial), (\"principal component 2\", \"mean\")],\n",
    "                z=mean_and_std_df_csc.loc[(week, trial), (\"principal component 3\", \"mean\")],\n",
    "                line=dict(\n",
    "                    color=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                    colorscale=[[0, 'rgb(48, 112, 173)'], [1, 'rgb(192, 125, 165)']],\n",
    "                    cmin=trail_start,\n",
    "                    cmid=cs_start,\n",
    "                    cmax=trial_end,\n",
    "                    colorbar=dict(\n",
    "                        lenmode='pixels',\n",
    "                        len=400,\n",
    "                        orientation='v',\n",
    "                        tickmode='array',\n",
    "                        tickvals=ticks,\n",
    "                        ticktext=ticklabels\n",
    "                    ),\n",
    "                ),\n",
    "                marker=dict(\n",
    "                    color=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                    colorscale=[[0, 'rgb(48, 112, 173)'], [1, 'rgb(192, 125, 165)']],\n",
    "                    cmin=trail_start,\n",
    "                    cmid=cs_start,\n",
    "                    cmax=trial_end,\n",
    "                    colorbar=dict(\n",
    "                        lenmode='pixels',\n",
    "                        len=400,\n",
    "                        orientation='v',\n",
    "                        tickmode='array',\n",
    "                        tickvals=events,\n",
    "                        ticktext=eventlabels\n",
    "                    ),\n",
    "                    showscale=False,\n",
    "                    size=3,\n",
    "                    \n",
    "                ),\n",
    "                mode='lines+markers',\n",
    "                name = \"CSC\"\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        annotate = []\n",
    "        for event in events:\n",
    "            annotate.append(dict(\n",
    "                    x=mean_and_std_df_cse.loc[(week, trial, event), (\"principal component 1\", \"mean\")], \n",
    "                    y=mean_and_std_df_cse.loc[(week, trial, event), (\"principal component 2\", \"mean\")],\n",
    "                    z=mean_and_std_df_cse.loc[(week, trial, event), (\"principal component 3\", \"mean\")],\n",
    "                    text=(eventdict[event]+\" CSE\"),\n",
    "                    showarrow=True,\n",
    "                    arrowhead=1\n",
    "                ))\n",
    "            annotate.append(dict(\n",
    "                    x=mean_and_std_df_csc.loc[(week, trial, event), (\"principal component 1\", \"mean\")], \n",
    "                    y=mean_and_std_df_csc.loc[(week, trial, event), (\"principal component 2\", \"mean\")],\n",
    "                    z=mean_and_std_df_csc.loc[(week, trial, event), (\"principal component 3\", \"mean\")],\n",
    "                    text=(eventdict[event]+\" CSC\"),\n",
    "                    showarrow=True,\n",
    "                    arrowhead=1\n",
    "                ))\n",
    "\n",
    "        fig.update_layout(\n",
    "            title=(f\"Average of PCA Coordinates for Trial Type {trial} on Week {week}\"),\n",
    "            scene = dict(\n",
    "                xaxis = dict(title=\"principal component 1\", type=\"linear\", ticks=\"outside\"),\n",
    "                yaxis = dict(title=\"principal component 2\", type=\"linear\", ticks=\"outside\"),\n",
    "                zaxis = dict(title=\"principal component 3\", type=\"linear\", ticks=\"outside\"),\n",
    "                annotations=annotate\n",
    "            ),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        today = date.today().strftime(\"%Y%m%d\")\n",
    "        \n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/comparisons/3D/pdfs/{today}_3D_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/comparisons/3D/vectorized/{today}_3D_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.svg\")\n",
    "        fig.write_html(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/averaged-by-mouse/comparisons/3D/html/{today}_3D_mouse_by_mouse_average_of_pca_coords_cs-{trial}_week-{week}.html\")\n",
    "        fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "2"
    }
   },
   "outputs": [],
   "source": [
    "diff = data_cse[[\"timestamps\", \"principal component 1\", \"principal component 2\"]].diff()\n",
    "locational_vector = np.sqrt((diff[\"principal component 1\"] ** 2 ) + (diff[\"principal component 2\"] ** 2))\n",
    "velocity = locational_vector / diff[\"timestamps\"]\n",
    "acceleration = velocity / diff[\"timestamps\"]\n",
    "\n",
    "data_cse[\"locational_vector\"] = locational_vector\n",
    "data_cse[\"velocity\"] = velocity\n",
    "data_cse[\"acceleration\"] = acceleration\n",
    "\n",
    "data_cse.dropna(axis=0, subset=[\"locational_vector\", \"velocity\", \"acceleration\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "2"
    }
   },
   "outputs": [],
   "source": [
    "diff = data_csc[[\"timestamps\", \"principal component 1\", \"principal component 2\"]].diff()\n",
    "locational_vector = np.sqrt((diff[\"principal component 1\"] ** 2 ) + (diff[\"principal component 2\"] ** 2))\n",
    "velocity = locational_vector / diff[\"timestamps\"]\n",
    "acceleration = velocity / diff[\"timestamps\"]\n",
    "\n",
    "data_csc[\"locational_vector\"] = locational_vector\n",
    "data_csc[\"velocity\"] = velocity\n",
    "data_csc[\"acceleration\"] = acceleration\n",
    "\n",
    "data_csc.dropna(axis=0, subset=[\"locational_vector\", \"velocity\", \"acceleration\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "2"
    }
   },
   "outputs": [],
   "source": [
    "mean_and_std = []\n",
    "\n",
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "        df = data_cse[(data_cse.week_list == week) & (data_cse.trial_type == trial)]\n",
    "        mean_and_std.append(df.groupby(by=[\"week_list\", \"trial_type\", \"frame_trial_idx\"]).agg({\"locational_vector\": ['mean', 'std'], \"velocity\": ['mean', 'std'], \"acceleration\": ['mean', 'std']}))\n",
    "\n",
    "mean_and_std_df_cse = pd.concat(mean_and_std)\n",
    "\n",
    "mean_and_std_df_cse[\"locational_vector\", \"upper_std\"] = mean_and_std_df_cse[\"locational_vector\", \"mean\"]+(mean_and_std_df_cse[\"locational_vector\", \"std\"] * 2)\n",
    "mean_and_std_df_cse[\"locational_vector\", \"lower_std\"] = mean_and_std_df_cse[\"locational_vector\", \"mean\"]-(mean_and_std_df_cse[\"locational_vector\", \"std\"] * 2)\n",
    "mean_and_std_df_cse[\"velocity\", \"upper_std\"] = mean_and_std_df_cse[\"velocity\", \"mean\"]+(mean_and_std_df_cse[\"velocity\", \"std\"] * 2)\n",
    "mean_and_std_df_cse[\"velocity\", \"lower_std\"] = mean_and_std_df_cse[\"velocity\", \"mean\"]-(mean_and_std_df_cse[\"velocity\", \"std\"] * 2)\n",
    "mean_and_std_df_cse[\"acceleration\", \"upper_std\"] = mean_and_std_df_cse[\"acceleration\", \"mean\"]+(mean_and_std_df_cse[\"acceleration\", \"std\"] * 2)\n",
    "mean_and_std_df_cse[\"acceleration\", \"lower_std\"] = mean_and_std_df_cse[\"acceleration\", \"mean\"]-(mean_and_std_df_cse[\"acceleration\", \"std\"] * 2)\n",
    "mean_and_std_df_cse = mean_and_std_df_cse.sort_index(axis=1)\n",
    "mean_and_std_df_cse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "2"
    }
   },
   "outputs": [],
   "source": [
    "mean_and_std = []\n",
    "\n",
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "        df = data_csc[(data_csc.week_list == week) & (data_csc.trial_type == trial)]\n",
    "        mean_and_std.append(df.groupby(by=[\"week_list\", \"trial_type\", \"frame_trial_idx\"]).agg({\"locational_vector\": ['mean', 'std'], \"velocity\": ['mean', 'std'], \"acceleration\": ['mean', 'std']}))\n",
    "\n",
    "mean_and_std_df_csc = pd.concat(mean_and_std)\n",
    "\n",
    "mean_and_std_df_csc[\"locational_vector\", \"upper_std\"] = mean_and_std_df_csc[\"locational_vector\", \"mean\"]+(mean_and_std_df_csc[\"locational_vector\", \"std\"] * 2)\n",
    "mean_and_std_df_csc[\"locational_vector\", \"lower_std\"] = mean_and_std_df_csc[\"locational_vector\", \"mean\"]-(mean_and_std_df_csc[\"locational_vector\", \"std\"] * 2)\n",
    "mean_and_std_df_csc[\"velocity\", \"upper_std\"] = mean_and_std_df_csc[\"velocity\", \"mean\"]+(mean_and_std_df_csc[\"velocity\", \"std\"] * 2)\n",
    "mean_and_std_df_csc[\"velocity\", \"lower_std\"] = mean_and_std_df_csc[\"velocity\", \"mean\"]-(mean_and_std_df_csc[\"velocity\", \"std\"] * 2)\n",
    "mean_and_std_df_csc[\"acceleration\", \"upper_std\"] = mean_and_std_df_csc[\"acceleration\", \"mean\"]+(mean_and_std_df_csc[\"acceleration\", \"std\"] * 2)\n",
    "mean_and_std_df_csc[\"acceleration\", \"lower_std\"] = mean_and_std_df_csc[\"acceleration\", \"mean\"]-(mean_and_std_df_csc[\"acceleration\", \"std\"] * 2)\n",
    "mean_and_std_df_csc = mean_and_std_df_csc.sort_index(axis=1)\n",
    "mean_and_std_df_csc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "2"
    }
   },
   "outputs": [],
   "source": [
    "today = date.today().strftime(\"%Y%m%d\")\n",
    "\n",
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_cse.loc[(week, trial), (\"locational_vector\", \"mean\")],\n",
    "                line=dict(color='rgb(0,0,0)'),\n",
    "                mode='lines'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"locational_vector\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"locational_vector\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(255, 0, 0, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Vector Path for Trial Type {trial} on Week {week} for CSE Mice\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10),\n",
    "            yaxis = dict(title=\"vector length\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "        \n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSE_mouse_by_mouse_average_of_pca_locational_vectors_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSE_mouse_by_mouse_average_of_pca_locational_vectors_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_cse.loc[(week, trial), (\"velocity\", \"mean\")],\n",
    "                line=dict(color='rgb(0,0,0)'),\n",
    "                mode='lines'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"velocity\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"velocity\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(255, 0, 0, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Velocity for Trial Type {trial} on Week {week} for CSE Mice\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10), \n",
    "            yaxis = dict(title=\"velocity\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )   \n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSE_mouse_by_mouse_average_of_pca_velocity_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSE_mouse_by_mouse_average_of_pca_velocity_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_cse.loc[(week, trial), (\"acceleration\", \"mean\")],\n",
    "                line=dict(color='rgb(0,0,0)'),\n",
    "                mode='lines'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"acceleration\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"acceleration\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(255, 0, 0, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Acceleration for Trial Type {trial} on Week {week} for CSE\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10),\n",
    "            yaxis = dict(title=\"acceleration\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSE_mouse_by_mouse_average_of_pca_acceleration_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSE_mouse_by_mouse_average_of_pca_acceleration_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "2"
    }
   },
   "outputs": [],
   "source": [
    "today = date.today().strftime(\"%Y%m%d\")\n",
    "\n",
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_csc.loc[(week, trial), (\"locational_vector\", \"mean\")],\n",
    "                line=dict(color='rgb(0,0,0)'),\n",
    "                mode='lines'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"locational_vector\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"locational_vector\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(255, 0, 0, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Vector Path for Trial Type {trial} on Week {week} for CSC Mice\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10),\n",
    "            yaxis = dict(title=\"vector length\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "        \n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSC_mouse_by_mouse_average_of_pca_locational_vectors_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSC_mouse_by_mouse_average_of_pca_locational_vectors_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_csc.loc[(week, trial), (\"velocity\", \"mean\")],\n",
    "                line=dict(color='rgb(0,0,0)'),\n",
    "                mode='lines'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"velocity\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"velocity\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(255, 0, 0, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Velocity for Trial Type {trial} on Week {week} for CSC Mice\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10), \n",
    "            yaxis = dict(title=\"velocity\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )   \n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSC_mouse_by_mouse_average_of_pca_velocity_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSC_mouse_by_mouse_average_of_pca_velocity_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_csc.loc[(week, trial), (\"acceleration\", \"mean\")],\n",
    "                line=dict(color='rgb(0,0,0)'),\n",
    "                mode='lines'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"acceleration\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"acceleration\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(255, 0, 0, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Acceleration for Trial Type {trial} on Week {week} for CSC Mice\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10),\n",
    "            yaxis = dict(title=\"acceleration\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSC_mouse_by_mouse_average_of_pca_acceleration_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_CSC_mouse_by_mouse_average_of_pca_acceleration_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "2"
    }
   },
   "outputs": [],
   "source": [
    "today = date.today().strftime(\"%Y%m%d\")\n",
    "\n",
    "for week in weeks:\n",
    "    for trial in trials:\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_csc.loc[(week, trial), (\"locational_vector\", \"mean\")],\n",
    "                line=dict(color='rgb(48,112,173)'),\n",
    "                mode='lines',\n",
    "                name=\"CSC\"\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_cse.loc[(week, trial), (\"locational_vector\", \"mean\")],\n",
    "                line=dict(color='rgb(192,125,165)'),\n",
    "                mode='lines',\n",
    "                name=\"CSE\"\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"locational_vector\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"locational_vector\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval CSC',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(48, 112, 173, 0.2)'\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"locational_vector\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"locational_vector\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval CSE',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(192, 125, 165, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Vector Path for Trial Type {trial} on Week {week}\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10),\n",
    "            yaxis = dict(title=\"vector length\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "        \n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_mouse_by_mouse_average_of_pca_locational_vectors_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_mouse_by_mouse_average_of_pca_locational_vectors_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_csc.loc[(week, trial), (\"velocity\", \"mean\")],\n",
    "                line=dict(color='rgb(48,112,173)'),\n",
    "                mode='lines',\n",
    "                name=\"CSC\"\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_cse.loc[(week, trial), (\"velocity\", \"mean\")],\n",
    "                line=dict(color='rgb(192, 125, 165)'),\n",
    "                mode='lines',\n",
    "                name=\"CSE\"\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"velocity\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"velocity\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval CSC',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(48, 112, 173, 0.2)'\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"velocity\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"velocity\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval CSE',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(192, 125, 165, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Velocity for Trial Type {trial} on Week {week} for\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10), \n",
    "            yaxis = dict(title=\"velocity\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )   \n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_mouse_by_mouse_average_of_pca_velocity_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_mouse_by_mouse_average_of_pca_velocity_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n",
    "\n",
    "        fig = go.Figure([\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_csc.loc[(week, trial), (\"acceleration\", \"mean\")],\n",
    "                line=dict(color='rgb(48, 112, 173)'),\n",
    "                mode='lines',\n",
    "                name=\"CSC\"\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x=mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y=mean_and_std_df_cse.loc[(week, trial), (\"acceleration\", \"mean\")],\n",
    "                line=dict(color='rgb(192, 125, 165)'),\n",
    "                mode='lines',\n",
    "                name=\"CSE\"\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.add_traces([\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"acceleration\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_csc.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_csc.loc[(week, trial), (\"acceleration\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval CSC',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(48, 112, 173, 0.2)'\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"acceleration\", \"upper_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                showlegend = False\n",
    "            ),\n",
    "            go.Scatter(\n",
    "                x = mean_and_std_df_cse.loc[(week, trial)].index,\n",
    "                y = mean_and_std_df_cse.loc[(week, trial), (\"acceleration\", \"lower_std\")],\n",
    "                mode = 'lines', \n",
    "                line_color = 'rgba(0,0,0,0)',\n",
    "                hoverinfo=\"skip\",\n",
    "                name = '95% confidence interval CSE',\n",
    "                fill='tonexty', \n",
    "                fillcolor = 'rgba(192, 125, 165, 0.2)'\n",
    "            )\n",
    "        ])\n",
    "\n",
    "        fig.update_layout(\n",
    "            title = (f\"Average of PCA Acceleration for Trial Type {trial} on Week {week}\"),\n",
    "            xaxis = dict(title=\"frame\", type=\"linear\", ticks=\"outside\", tick0=0, dtick=10),\n",
    "            yaxis = dict(title=\"acceleration\", type=\"linear\", ticks=\"outside\"),\n",
    "            height = 600,\n",
    "            width = 1066\n",
    "        )\n",
    "\n",
    "        fig.add_vline(x=trail_start, line_width=1, line_dash=\"dot\", annotation_text=\"trial start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_start, line_width=1, line_dash=\"dot\", annotation_text=\"cs start\", annotation_textangle=90)\n",
    "        fig.add_vline(x=cs_end, line_width=1, line_dash=\"dot\", annotation_text=\"trial & cs end\", annotation_textangle=90)\n",
    "\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_mouse_by_mouse_average_of_pca_acceleration_cs-{trial}_week-{week}.pdf\")\n",
    "        fig.write_image(f\"/Volumes/specialk_cs/2p/analysis/behavior/trajectories/by-trial/{today}_mouse_by_mouse_average_of_pca_acceleration_cs-{trial}_week-{week}.svg\")\n",
    "        fig.show()\n"
   ]
  }
 ],
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