examples/c - Intensity Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map rr30a loaded in 0.46 seconds.\n"
     ]
    }
   ],
   "source": [
    "from pymapmanager.mmMap import mmMap\n",
    "\n",
    "filePath = 'exampleMaps/rr30a/rr30a.txt'\n",
    "m = mmMap(filePath=filePath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate new analysis by doing algebra on existing annotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mmMap.getMapValues3() took 0.04 seconds\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from pymapmanager.mmUtil import newplotdict\n",
    "\n",
    "my_pd = newplotdict()\n",
    "my_pd['segmentid'] = None # None for all map segments\n",
    "my_pd['xstat'] = 'ubssSum_int2' # background-subtracted spine sum, channel 2\n",
    "my_pd['ystat'] = 'ubsdSum_int2' # background subtracted dendrite sum, channel 2\n",
    "my_pd = m.getMapValues3(my_pd)\n",
    "\n",
    "# make a new matrix to hold the new stat\n",
    "mRows, nCols = my_pd['x'].shape\n",
    "myNewStat = np.zeros([mRows,nCols])\n",
    "myNewStat[:] = 'nan'\n",
    "\n",
    "# do the algebra\n",
    "myNewStat = my_pd['x'] / my_pd['y'] # spine intensity / segment intensity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot our new analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa8AAAFBCAYAAADAJSDzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1104c0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot with matplotlib\n",
    "# %matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set some matplotlib defaults so our plots look better\n",
    "import matplotlib as mpl\n",
    "fontsize = 16\n",
    "mpl.rcParams['xtick.labelsize'] = fontsize \n",
    "mpl.rcParams['ytick.labelsize'] = fontsize \n",
    "\n",
    "x = my_pd['x'] # the original 'background subtracted spine sum'\n",
    "y = myNewStat # our new stat\n",
    "plt.scatter(x, y, marker='.', c='k', s=15)\n",
    "plt.xlabel('Background Subtracted Spine Sum', fontsize=16)\n",
    "plt.ylabel('Background Subtracted Spine Sum / \\nBackground Subtracted Dendrite Sum', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homework\n",
    "\n",
    " - Do the same but limit analysis to one map segment. Hint, use pd['segmentID']=[1] for segment 1.\n",
    " - Do the same but get statistics (mean/sd/se) for each map segment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Nearest neighbor analysis along a dendritic segment\n",
    "\n",
    "This will pull a spine annotation and calculate its autocorrelation with its nearest neighbors along a segment.\n",
    "\n",
    "Autocorrelation of a spine with its neighbor is the autocorrelation at lag 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# See: http://greenteapress.com/thinkdsp/html/thinkdsp006.html\n",
    "\n",
    "def serial_corr(wave, lag=1):\n",
    "    n = len(wave)\n",
    "    y1 = wave[lag:]\n",
    "    y2 = wave[:n-lag]\n",
    "    corr = np.corrcoef(y1, y2)[0, 1]\n",
    "    return corr\n",
    "\n",
    "def autocorr(wave):\n",
    "    lags = range(len(wave)//2)\n",
    "    corrs = [serial_corr(wave, lag) for lag in lags]\n",
    "    return lags, corrs\n",
    "\n",
    "def segmentAnalysis(map, ps):\n",
    "    ps['ystat'] = 'pDist'\n",
    "\n",
    "    result = np.zeros([map.numMapSegments,map.numSessions])\n",
    "    result[:] = 'nan'\n",
    "    \n",
    "    for i in range(map.numMapSegments):\n",
    "        for j in range(map.numSessions):\n",
    "            stackSegment = map._getStackSegmentID(i, j)\n",
    "            if stackSegment is not None:\n",
    "                ps['segmentid'] = [stackSegment]\n",
    "                ps = map.stacks[j].getStackValues3(ps)\n",
    "                # sort by pDist and make ['x'] values follow\n",
    "                sortedIdx = np.argsort(ps['y'])\n",
    "                xSorted = ps['x'][sortedIdx]\n",
    "                ySorted = ps['y'][sortedIdx]\n",
    "                \n",
    "                xSorted = xSorted[~np.isnan(xSorted)]\n",
    "                [lags, corrs] = autocorr(xSorted)\n",
    "                result[i,j] = corrs[1]\n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAADhCAYAAAA+loVTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nktQwS1ySGva/dT7n5CSPCrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116b38590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Map Segment')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ps = newplotdict()\n",
    "ps['xstat'] = 'ubssSum_int2' # spine intensity, channel 2\n",
    "\n",
    "# each value in result[i][j] is the autocorrelation at lag 1 for each map segment (i) at each session (j).\n",
    "result = segmentAnalysis(m, ps)\n",
    "\n",
    "plt.imshow(result)\n",
    "plt.show()\n",
    "plt.xlabel('Session Number', fontsize=16)\n",
    "plt.ylabel('Map Segment', fontsize=16)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See:\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}