scipy_test_import_matlab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4851c9f2",
   "metadata": {},
   "source": [
    "Import the SciPy input output package to get the function that imports the `.mat` file, and import the NumPy package to manipulate the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e8848e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ce2ac8",
   "metadata": {},
   "source": [
    "Import the first MatLab file from the `Robot.zip` file from the [data repository](https://data.mendeley.com/datasets/gpsjkbp6h4/1) for the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "906cdaa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = sio.loadmat(\"data_m22_day1.mat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f6d2f0",
   "metadata": {},
   "source": [
    "Based on the printout, this is a MatLab 5.0 `.mat` file. The `.loadmat` command loads a dictionary, and the `'dataMouse'` has the data we want. As described from the Mendeley data [page](https://data.mendeley.com/datasets/gpsjkbp6h4/1) there are five arrays:\n",
    "> Each .zip file refers to a different mouse group. Groups are defined accordingly to the paper. A .zip file contains a .mat file (matlab) associated to each mouse and each day of recording. The .mat files are \"structure arrays\" containing the following fields:\n",
    "- force: vector (time x 1), force applied by the mouse to the handle;\n",
    "- status: vector (time x 1), discrete codification of the current phase of the passive extension and active retraction cycle, see Table S1 in the Supporting Information of the paper;\n",
    "- index: vector (pixels x 1), position of pixels in the 200x200 matrix of the recorded calcium;\n",
    "- calcium: matrix (time x pixels), calcium fluorescence;\n",
    "- day: number, relative day from lesion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e04d976",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'__header__': b'MATLAB 5.0 MAT-file, Platform: MACI64, Created on: Thu Oct 15 10:48:37 2020', '__version__': '1.0', '__globals__': [], 'dataMouse': array([[(array([[-0.02927687],\n",
      "               [-0.02794236],\n",
      "               [-0.03791983],\n",
      "               ...,\n",
      "               [-0.01703624],\n",
      "               [-0.01767177],\n",
      "               [-0.022116  ]]), array([[0],\n",
      "               [0],\n",
      "               [0],\n",
      "               ...,\n",
      "               [3],\n",
      "               [3],\n",
      "               [3]], dtype=uint8), array([[20868],\n",
      "               [20869],\n",
      "               [20870],\n",
      "               ...,\n",
      "               [27926],\n",
      "               [27927],\n",
      "               [27928]], dtype=uint16), array([[ 5394,  4969,  4707, ..., 14570, 14234, 14019],\n",
      "               [ 5554,  5123,  4820, ..., 14965, 14879, 14484],\n",
      "               [ 5530,  5170,  4878, ..., 14723, 14656, 14287],\n",
      "               ...,\n",
      "               [ 5219,  4859,  4665, ..., 14472, 14265, 13924],\n",
      "               [ 5332,  4982,  4747, ..., 14344, 14128, 13875],\n",
      "               [ 5260,  4929,  4850, ..., 14577, 14157, 13942]], dtype=uint16), array([[-14]], dtype=int16))]],\n",
      "      dtype=[('force', 'O'), ('status', 'O'), ('index', 'O'), ('calcium', 'O'), ('day', 'O')])}\n"
     ]
    }
   ],
   "source": [
    "print(test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3ea6432",
   "metadata": {},
   "source": [
    "In the following cell, a new variable holds only the data. The labels for the arrays are accessible through `.dtype`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1bbd0d4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[(array([[-0.02927687],\n",
      "         [-0.02794236],\n",
      "         [-0.03791983],\n",
      "         ...,\n",
      "         [-0.01703624],\n",
      "         [-0.01767177],\n",
      "         [-0.022116  ]]), array([[0],\n",
      "         [0],\n",
      "         [0],\n",
      "         ...,\n",
      "         [3],\n",
      "         [3],\n",
      "         [3]], dtype=uint8), array([[20868],\n",
      "         [20869],\n",
      "         [20870],\n",
      "         ...,\n",
      "         [27926],\n",
      "         [27927],\n",
      "         [27928]], dtype=uint16), array([[ 5394,  4969,  4707, ..., 14570, 14234, 14019],\n",
      "         [ 5554,  5123,  4820, ..., 14965, 14879, 14484],\n",
      "         [ 5530,  5170,  4878, ..., 14723, 14656, 14287],\n",
      "         ...,\n",
      "         [ 5219,  4859,  4665, ..., 14472, 14265, 13924],\n",
      "         [ 5332,  4982,  4747, ..., 14344, 14128, 13875],\n",
      "         [ 5260,  4929,  4850, ..., 14577, 14157, 13942]], dtype=uint16), array([[-14]], dtype=int16))]]\n",
      "[('force', 'O'), ('status', 'O'), ('index', 'O'), ('calcium', 'O'), ('day', 'O')]\n"
     ]
    }
   ],
   "source": [
    "test_data = test[\"dataMouse\"]\n",
    "print(test_data)\n",
    "print(test_data.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e8baeb6",
   "metadata": {},
   "source": [
    "Use the labels from the `dtype` attribute of the data to select it from the overall data structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a67b71fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[array([[-0.02927687],\n",
      "         [-0.02794236],\n",
      "         [-0.03791983],\n",
      "         ...,\n",
      "         [-0.01703624],\n",
      "         [-0.01767177],\n",
      "         [-0.022116  ]])]]\n",
      "[array([[-0.02927687],\n",
      "        [-0.02794236],\n",
      "        [-0.03791983],\n",
      "        ...,\n",
      "        [-0.01703624],\n",
      "        [-0.01767177],\n",
      "        [-0.022116  ]])]\n",
      "[[-0.02927687]\n",
      " [-0.02794236]\n",
      " [-0.03791983]\n",
      " ...\n",
      " [-0.01703624]\n",
      " [-0.01767177]\n",
      " [-0.022116  ]]\n",
      "[-0.02927687]\n"
     ]
    }
   ],
   "source": [
    "test_data_force = test_data[\"force\"]\n",
    "print(test_data_force)\n",
    "print(test_data_force[0])\n",
    "print(test_data_force[0][0])\n",
    "print(test_data_force[0][0][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b994c9",
   "metadata": {},
   "source": [
    "Flatten the first three arrays so that it's easier to slice them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b044804f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((10000,), (10000,), (2196,), (10000, 2196), array([[-14]], dtype=int16))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "force = test['dataMouse'][0][0][0].flatten()\n",
    "status = test['dataMouse'][0][0][1].flatten()\n",
    "index = test['dataMouse'][0][0][2].flatten()\n",
    "calcium = test['dataMouse'][0][0][3] \n",
    "day = test['dataMouse'][0][0][4]\n",
    "\n",
    "force.shape, status.shape, index.shape, calcium.shape, day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a5acc5",
   "metadata": {},
   "source": [
    "#### Interpreting our slices / Slicing tips"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8802f25a",
   "metadata": {},
   "source": [
    "In the calcium array, each row represents the calcium fluorescence of **each** pixel at **one time point**. This is evident by the shape of array, made out of 2196 data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1aa0eda",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2196,)\n"
     ]
    }
   ],
   "source": [
    "print(calcium[0].shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bde551",
   "metadata": {},
   "source": [
    "Slicing like this gives you the calcium fluorescence of all time points in **one pixel**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "94edb615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcium[:, 0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff6ff61",
   "metadata": {},
   "source": [
    "Slicing like this gives you the calcium fluorescence of all pixels in the first 5000 time points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b8a685bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 2196)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcium[: 5000, :].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17732714",
   "metadata": {},
   "source": [
    "Slicing like this gives you the calcium fluorescence of all pixels at the **last** time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3a03812e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2196,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcium[-1, :].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f0a8f5",
   "metadata": {},
   "source": [
    "Slicing like this gives you the calcium fluorescence of each pixel of every **fifth** time point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "28b32850",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 2196)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calcium[::5, :].shape ## [start:end:interval]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64caf7fe",
   "metadata": {},
   "source": [
    "Using negative numbers as the interval flips the array around."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3937c0b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       " array([0, 2, 4, 6, 8]),\n",
       " array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(10)\n",
    "a, a[::2], a[::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4bbcfa1",
   "metadata": {},
   "source": [
    "#### Examining the index array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f6be8b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "for i in index:\n",
    "    print (i)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "7e653766",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc7e4e69",
   "metadata": {},
   "source": [
    "Running a `for` loop to print every element of the index reveals that some pixels in the 200x200 matrix have been skipped. Here is an example: the indices between -528 and -668 (non-inclusive) are skipped."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9a1a88a",
   "metadata": {},
   "source": [
    "Here, another data set from another mouse from another day is loaded. While there remains the same number of pixels, the number of time points is not 10,000 as seen from the other data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7c7175a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3524,), (3524,), (2196,), (3524, 2196), array([[27]], dtype=uint8))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test2 = sio.loadmat(\"data_m25_day20.mat\")\n",
    "force2 = test2['dataMouse'][0][0][0].flatten()\n",
    "status2 = test2['dataMouse'][0][0][1].flatten()\n",
    "index2 = test2['dataMouse'][0][0][2].flatten()\n",
    "calcium2 = test2['dataMouse'][0][0][3] \n",
    "day2 = test2['dataMouse'][0][0][4]\n",
    "\n",
    "force2.shape, status2.shape, index2.shape, calcium2.shape, day2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c20224b0",
   "metadata": {},
   "source": [
    "The `np.isin` function is used to check whether the indices of these two data sets match. The fact that no \"false\" is returned in the resulting Boolean array means that the indices do match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6f86dad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "test_bool = np.isin(index, index2)\n",
    "for i in range(len(test_bool)):\n",
    "    if test_bool[i] == False:\n",
    "        print(\"false\")\n",
    "    else:\n",
    "        print(\"matches\")"
   ]
  }
 ],
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