Brunton brains (#141)

* Fixed indexing error in brains module Small indexing error when concatenating the electrode positions to plot them. * Update to color electrodes by R2 Added code to color electrodes by R2. * Simplify colors for grid location Realized I already had the color array I needed for grid location coloring. * Generalize coloring by any column in electrode table Switched out radio button for dropdown menu that automatically populates using all the column names in the electrodes table. Handles different data types and prints error message if column is selected that does not have a valid data type for coloring. * Dynamically show legend of color bar Show legend if visualizing a categorical value and color if visualizing a continuous variable. * Notebook for binder testing / edits during mtg w/ Ben * Setup postbuild for streaming nwb on binder * Revert "Setup postbuild for streaming nwb on binder" This reverts commit b3a80d0. * Generalize electrode coloring to include 'bytes' datatype * Fix widget control layout * Fix layout for dashboard Commented this line out because it was causing formatting issues in the Brunton dashboard * Decrease fig size for Brunton dashboard Decreasing electrical series trace fig size to fit in Brunton dashboard better * Make traces same color Update SeparateTraces widget to have same color across traces Co-authored-by: Michael Scheid <[email protected]> Co-authored-by: Ben Dichter <[email protected]>
NeurodataWithoutBorders · Apr 9, 2021 · fdb9016 · fdb9016
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diff --git a/brains_widget_BruntonLab.ipynb b/brains_widget_BruntonLab.ipynb
@@ -0,0 +1,294 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "cd C:\\Users\\micha\\Dropbox\\Catalyst Neuro\\repos\\nwb-jupyter-widgets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pynwb import NWBHDF5IO\n",
+    "from nwbwidgets.brains import HumanElectrodesPlotlyWidget\n",
+    "from nwbwidgets.utils.timeseries import get_timeseries_tt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from datetime import datetime\n",
+    "from ndx_events import LabeledEvents, AnnotatedEventsTable, Events\n",
+    "\n",
+    "io = NWBHDF5IO(r'C:\\Users\\micha\\Desktop\\Brunton Lab Data\\H5\\subj_01_day_3.nwb', mode='r')\n",
+    "nwb = io.read()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ffeb489c5533482aa5df02ba564f494e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HumanElectrodesPlotlyWidget(children=(FigureWidget({\n",
+       "    'data': [{'color': 'lightgray',\n",
+       "              'hoveri…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "HumanElectrodesPlotlyWidget(nwb.electrodes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, n in enumerate([0]):\n",
+    "    print(i, n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([x for x in range(3)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = np.array([1, 1, 1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[b == 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['x',\n",
+       " 'y',\n",
+       " 'z',\n",
+       " 'imp',\n",
+       " 'location',\n",
+       " 'filtering',\n",
+       " 'group',\n",
+       " 'group_name',\n",
+       " 'standard_deviation',\n",
+       " 'kurtosis',\n",
+       " 'median_deviation',\n",
+       " 'good',\n",
+       " 'low_freq_R2',\n",
+       " 'high_freq_R2']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(nwb.electrodes.colnames)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nwb.electrodes['good'][:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.bool_"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(nwb.electrodes['good'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(nwb.electrodes['x'][0], np.float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.float64"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(nwb.electrodes['z'][0])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}