opencv-test/2016-06-15-opencv-RGBmasks-xcorr.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os, sys, subprocess, datetime, time\n",
    "from timeit import default_timer as timer\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import h5py\n",
    "import pandas as pd\n",
    "from skimage.draw import polygon\n",
    "from skimage import data\n",
    "from skimage.util import random_noise\n",
    "import tifffile\n",
    "#from skimage.feature import match_template\n",
    "#import scipy.io as spio\n",
    "#from scipy import ndimage as ndi\n",
    "#from scipy import signal as signal\n",
    "#from joblib import Parallel, delayed\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def importFilelist(filepath):\n",
    "    #import text file of filenames as pandas dataframe\n",
    "    files = pd.read_table(filepath,names=['filename','matfilename', 'age.g'],sep=' ',header=None)\n",
    "    return files\n",
    "\n",
    "\n",
    "def writeData(filepath,data):\n",
    "    #write results dataframe by appending to file\n",
    "    #filepath = 'dXcorrObs.txt'\n",
    "    if os.path.isfile(filepath):\n",
    "        writeHeader=False\n",
    "    else:\n",
    "        writeHeader=True\n",
    "\n",
    "    with open(filepath, 'a') as f:\n",
    "        data.to_csv(f, index=False, sep='\\t', header=writeHeader)  #make if logic to not append with header if file already exists\n",
    "\n",
    "\n",
    "\n",
    "def getA3binaryArray(f,region):\n",
    "    #read in domainData into A3 binary array\n",
    "    sz = np.int64(region['domainData']['CC/ImageSize'][:,0])\n",
    "    A3 = np.zeros(sz,dtype='uint8')\n",
    "    for i in np.arange(region['domainData']['CC/PixelIdxList'].shape[0]):\n",
    "        pxind = np.array(f[region['domainData']['CC/PixelIdxList'][i,0]], dtype='int64')\n",
    "        A3.T.flat[pxind[0,:]] = 255\n",
    "    return A3\n",
    "\n",
    "\n",
    "def getHemisphereMasks(f,region,A3):\n",
    "    #read region['coords'] and region['name'] to get hemisphere masks\n",
    "    sz = A3.shape\n",
    "    leftMask = np.zeros((sz[0],sz[1]),dtype=bool)\n",
    "    rightMask = np.zeros((sz[0],sz[1]),dtype=bool)\n",
    "    bothMasks = np.zeros((sz[0],sz[1]),dtype=bool)\n",
    "    for i in np.arange(region['name'].len()):  #this is length 33\n",
    "        currentString = f[region['name'][i,0]][...].flatten().tostring().decode('UTF-16')\n",
    "        if (currentString == 'cortex.L') or (currentString == 'cortex.R'):\n",
    "            #print(i,currentString)\n",
    "            x = np.array(f[region['coords'][i,0]],dtype='int64')[0,:]\n",
    "            y = np.array(f[region['coords'][i,0]],dtype='int64')[1,:]\n",
    "            x = np.append(x,x[0]) #close the polygon coords\n",
    "            y = np.append(y,y[0]) #close the polygon coords\n",
    "            rr, cc = polygon(y,x,bothMasks.shape)\n",
    "            if currentString == 'cortex.L':\n",
    "                x_L = x\n",
    "                y_L = y\n",
    "                leftMask[rr,cc] = True\n",
    "            elif currentString == 'cortex.R':\n",
    "                x_R = x\n",
    "                y_R = y\n",
    "                rightMask[rr,cc] = True\n",
    "            bothMasks[rr,cc] = True\n",
    "    return leftMask, rightMask\n",
    "\n",
    "\n",
    "\n",
    "def getHemisphereCoords(f,region):\n",
    "    for i in np.arange(region['name'].len()):  #this is length 33\n",
    "            currentString = f[region['name'][i,0]][...].flatten().tostring().decode('UTF-16')\n",
    "            if (currentString == 'cortex.L') or (currentString == 'cortex.R'):\n",
    "                #print(i,currentString)\n",
    "                x = np.array(f[region['coords'][i,0]],dtype='int32')[0,:]\n",
    "                y = np.array(f[region['coords'][i,0]],dtype='int32')[1,:]\n",
    "                x = np.append(x,x[0]) #close the polygon coords\n",
    "                y = np.append(y,y[0]) #close the polygon coords\n",
    "                if currentString == 'cortex.L':\n",
    "                    xyPtsL = np.stack((x,y),axis=-1)\n",
    "                elif currentString == 'cortex.R':\n",
    "                    xyPtsR = np.stack((x,y),axis=-1)\n",
    "    return xyPtsL, xyPtsR\n",
    "\n",
    "\n",
    "def stridemask3d(mask,z):\n",
    "    #make a 2D image mask into a 3D repeated image mask using numpy stride_tricks to fake the 3rd dimension\n",
    "    mask3d = np.lib.stride_tricks.as_strided(\n",
    "                    mask,                              # input array\n",
    "                    (mask.shape[0], mask.shape[1],z),  # output dimensions\n",
    "                    (mask.strides[0], mask.strides[1],0) # stride length in bytes\n",
    "                )\n",
    "    return mask3d\n",
    "\n",
    "\n",
    "def cvblur(image,template):\n",
    "    #fast gaussian blur with cv2\n",
    "    image = cv2.GaussianBlur(image,(0,0),3)\n",
    "    template = cv2.GaussianBlur(template,(0,0),3)\n",
    "    return image, template\n",
    "\n",
    "\n",
    "def cvcorr(image,template):\n",
    "    #faster xcorr with cv2\n",
    "    image = image.astype('float32')\n",
    "    template = template.astype('float32') #cv2.filter2D needs a float as input\n",
    "    c = cv2.filter2D(image,-1,template)\n",
    "    corrValue = c[c.shape[0]/2,c.shape[1]/2]\n",
    "    return corrValue, c\n",
    "\n",
    "\n",
    "def getA3hemipshereArrays(A3,leftMask,rightMask):\n",
    "    #calculate lateral column shift for a flipped right hemisphere for a common coord system\n",
    "    rightMask_flip = np.fliplr(rightMask)\n",
    "    IND_L = np.nonzero(leftMask) #dim2 of the tuple is the col indices\n",
    "    IND_Rflip = np.nonzero(rightMask_flip) #dim2 of the tuple is the col indices\n",
    "    dx = np.amax(IND_L[1]) - np.amax(IND_Rflip[1]) #number of pixesl to shift the flipped right hemisphere column indices by\n",
    "\n",
    "    #get 3d mask arrays, new binary data arrays, and a flipped R hemi array\n",
    "    leftMask3d = stridemask3d(leftMask,A3.shape[2])\n",
    "    rightMask3d = stridemask3d(rightMask,A3.shape[2])\n",
    "\n",
    "    A3left = np.logical_and(A3, leftMask3d)\n",
    "    A3left = A3left.view(np.uint8)  #cast to uint8 inplace and multiply by 255 inplace\n",
    "    A3left *= 255\n",
    "    #np.place(A3left, A3left>0, 255)\n",
    "    \n",
    "    A3right = np.logical_and(A3, rightMask3d)\n",
    "    A3right = np.fliplr(A3right)\n",
    "    \n",
    "    #make a new shifted right hemisphere array\n",
    "    IND_R3d = np.nonzero(A3right) #dim2 of the tuple is the col indices\n",
    "    INDnew = IND_R3d[1] + dx #add shift to column indices\n",
    "    #A3right_shift = np.zeros_like(A3)\n",
    "    #A3right = np.zeros_like(A3)\n",
    "    A3right = A3right.view(np.uint8) #cast to uint8 inplace and multiply by 0 inplace\n",
    "    #np.putmask(A3right, A3right>0, A3right*0)\n",
    "    #np.place(A3right, A3right>0, 0)\n",
    "    A3right *= 0\n",
    "    A3right[IND_R3d[0],INDnew,IND_R3d[2]] = 255\n",
    "    \n",
    "    return A3left, A3right\n",
    "\n",
    "def getCorr(A3left,A3right,i):\n",
    "    image = A3left[:,:,i]\n",
    "    template = A3right[:,:,i]\n",
    "    image,template = cvblur(image,template)\n",
    "    autoCorr = cvcorr(image,image)\n",
    "    obsCorr = cvcorr(image,template)\n",
    "    fr = i + 1\n",
    "    return fr, autoCorr, obsCorr\n",
    "\n",
    "\n",
    "def cvmatch(image,template):\n",
    "    #faster xcorr with cv2\n",
    "    c = cv2.matchTemplate(image,template,cv2.TM_CCORR_NORMED)\n",
    "    corrValue = c[c.shape[0]/2,c.shape[1]/2]\n",
    "    return corrValue\n",
    "\n",
    "def cvmatchN(image,template):\n",
    "    #faster xcorr with cv2\n",
    "    c = cv2.matchTemplate(image,template,cv2.TM_CCOEFF_NORMED)\n",
    "    corrValue = c[c.shape[0]/2,c.shape[1]/2]\n",
    "    return corrValue\n",
    "\n",
    "\n",
    "def playMovie(A3,newMinMax=False):\n",
    "    #play movie in opencv\n",
    "    cv2.startWindowThread()\n",
    "    cv2.namedWindow(\"raw\", cv2.WINDOW_NORMAL) #Create a resizable window\n",
    "    i = 0\n",
    "    \n",
    "    if newMinMax:\n",
    "        #im = cv2.normalize(im, None, newMinMax[0], newMinMax[1], cv2.NORM_MINMAX)\n",
    "        #im = cv2.normalize(im, None, newMinMax[0], newMinMax[1], cv2.NORM_L2)\n",
    "        #im = cv2.equalizeHist(im)\n",
    "        #im,cdf = histeq(im)\n",
    "        cv2.normalize(A3,A3,newMinMax[0],newMinMax[1])\n",
    "    \n",
    "    while True:\n",
    "        #im = np.uint8(A3[:,:,i] * 255)\n",
    "        im = A3[:,:,i]\n",
    "        im = cv2.applyColorMap(im, cv2.COLORMAP_HOT)\n",
    "        im = cv2.GaussianBlur(im,(0,0),3)\n",
    "        #th,bw = cv2.threshold(im,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n",
    "        #bw = cv2.adaptiveThreshold(im,255,cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY,5,0)\n",
    "        cv2.imshow('raw',im)\n",
    "        k = cv2.waitKey(10) \n",
    "        if k == 27: #if esc is pressed\n",
    "            break\n",
    "        if k == ord('b'):\n",
    "            i -= 1000\n",
    "        elif k == ord('f'):\n",
    "            i += 1000\n",
    "        else:\n",
    "            i += 1\n",
    "        if (i > (A3.shape[2]-1)) or (i < 0) :\n",
    "            i = 0\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "def histeq(im, nbr_bins=256):\n",
    "    \"\"\"histogram equalization of a grayscale image\"\"\"\n",
    "    #get image histo\n",
    "    imhist,bins = np.histogram(im.flatten(),nbr_bins,normed=True)\n",
    "    cdf = imhist.cumsum() #cumulative distribution function\n",
    "    cdf = 255 * cdf / cdf[-1] #normalize\n",
    "    #use linear interp of cdf to find new pixel values\n",
    "    im2 = np.interp(im.flatten(),bins[:-1],cdf)\n",
    "    return im2.reshape(im.shape), cdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def playMovieRGB(A3,pts=False,filename=False):\n",
    "    #play movie in opencv\n",
    "    #input is a uint8 numpy rgb array with A3.ndim = 4 and shape (x,y,z,3)\n",
    "    #pts should be a list of numpy.ndarrays 'list((xyPts1,xyPts2))', since cv2.polylines() can be used to draw multiple lines if a list of arrays is passed to it.\n",
    "    cv2.startWindowThread()\n",
    "    cv2.namedWindow(\"raw\", cv2.WINDOW_NORMAL) #Create a resizable window\n",
    "    i = 0\n",
    "    toggleNext = True\n",
    "    tf = True\n",
    "    #if isinstance(pts, (list, np.ndarray)):\n",
    "    if isinstance(pts, (list)):\n",
    "        drawCoords = True\n",
    "    else:\n",
    "        drawCoords = False\n",
    "    \n",
    "    if filename == False:\n",
    "        import datetime\n",
    "        filename = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d-%H%M%S')\n",
    "    \n",
    "    while True:\n",
    "        im = A3[:,:,i,:]\n",
    "        img = cv2.cvtColor(im, cv2.COLOR_RGB2BGR) #convert numpy RGB input to BGR colorspace for cv2\n",
    "        img = cv2.GaussianBlur(img,(0,0),3)\n",
    "        corrValue = cvmatch(img[:,:,2],img[:,:,1])\n",
    "        cv2.putText(img, str(i), (5,25), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (155,155,155)) #draw frame text\n",
    "        cv2.putText(img, '{0:.4f}'.format(corrValue), (img.shape[1]-150,25), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (155,155,155)) #draw frame text\n",
    "        if drawCoords:\n",
    "            #cv2.polylines(img, pts, False,(255, 255, 255), thickness=1, lineType=cv2.CV_AA) #lineType for OpenCV 2.4.13\n",
    "            cv2.polylines(img, pts, False,(255, 255, 255), thickness=1, lineType=cv2.LINE_AA) #lineType for OpenCV 3.x\n",
    "        cv2.imshow('raw',img)\n",
    "        k = cv2.waitKey(10) \n",
    "        if k == 27: #if esc is pressed\n",
    "            break\n",
    "        elif (k == ord(' ')) and (toggleNext == True): #if space is pressed\n",
    "                tf = False\n",
    "        elif (k == ord(' ')) and (toggleNext == False): #if space is pressed\n",
    "                tf = True\n",
    "        toggleNext = tf #toggle the switch\n",
    "        if k == ord('b') and toggleNext:\n",
    "            i -= 100\n",
    "        elif k == ord('f') and toggleNext:\n",
    "            i += 100\n",
    "        elif k == ord('>') and (toggleNext == False):\n",
    "            i += 1\n",
    "        elif k == ord('<') and (toggleNext == False):\n",
    "            i -= 1\n",
    "        elif k == ord('s') and (toggleNext == False):\n",
    "            #params = list()\n",
    "            #params.append(cv.CV_IMWRITE_PNG_COMPRESSION)\n",
    "            #params.append(8)\n",
    "            #cv2.imwrite('image{0}.png'.format(i), img, params)\n",
    "            cv2.imwrite(filename + '-fr' + str(i) + '.png', img)\n",
    "        elif toggleNext:\n",
    "            i += 1\n",
    "        \n",
    "        if (i > (A3.shape[2]-1)) or (i < 0) :\n",
    "            i = 0\n",
    "\n",
    "    cv2.destroyAllWindows()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.7707633420359343\n",
      "0.030280801933258772\n",
      "10.70011685800273\n",
      "2.7615016559138894\n",
      "(540, 640, 3000, 3)\n"
     ]
    }
   ],
   "source": [
    "#matfilename = '/Users/ackman/Data/2photon/120518i/2015-03-13-114620_sigma3_thresh1/120518_07_20150321-153601_d2r.mat'\n",
    "#matfilename = '/Users/ackman/Data/2photon/140219/2015-03-13-114620_sigma3_thresh1/140219_07_20150313-122433_d2r.mat'\n",
    "#matfilename = '/Users/ackman/Data/2photon/140328/2015-03-13-114620_sigma3_thresh1/140328_05_20150313-125415_d2r.mat'\n",
    "matfilename = '/Users/ackman/Data/2photon/140331/2015-03-13-114620_sigma3_thresh1/140331_05_20150314-090750_d2r.mat'\n",
    "#matfilename = '/Users/ackman/Data/2photon/140509/2015-03-13-114620_sigma3_thresh1/140509_22_20150313-193050_d2r.mat'\n",
    "#matfilename = '/Users/ackman/Data/2photon/141125/2015-03-13-114620_sigma3_thresh1/141125_05_20150313-140653_d2r.mat'\n",
    "\n",
    "f = h5py.File(matfilename,'r') \n",
    "region = f.get('region')\n",
    "t0=timer()\n",
    "A3 = getA3binaryArray(f,region); print(timer()-t0)\n",
    "t0=timer()\n",
    "leftMask, rightMask = getHemisphereMasks(f,region,A3); print(timer()-t0)\n",
    "t0=timer()\n",
    "A3left, A3right_shift = getA3hemipshereArrays(A3,leftMask,rightMask); print(timer()-t0)\n",
    "\n",
    "t0 = timer()\n",
    "A4 = np.zeros((A3left.shape[0],A3left.shape[1],A3left.shape[2],3),dtype='uint8')\n",
    "A4[:,:,:,0] = A3left\n",
    "A4[:,:,:,1] = A3right_shift\n",
    "print(timer() - t0)\n",
    "print(A4.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fn = os.path.splitext(os.path.basename(matfilename))[0]\n",
    "xyPtsL,xyPtsR = getHemisphereCoords(f,region)\n",
    "#pts = np.stack((xyPtsL,xyPtsR),axis=2)\n",
    "playMovieRGB(A4,list((xyPtsL,xyPtsR)),fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables already gone...\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    del(region, A3, leftMask, rightMask, A3left, A3right_shift, A4, images, image)\n",
    "except:\n",
    "    print('Variables already gone...')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ackman/anaconda/envs/py27/lib/python2.7/site-packages/tifffile/tifffile.py:1974: UserWarning: tags are not ordered by code\n",
      "  warnings.warn(\"tags are not ordered by code\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.4423279762\n",
      "(540, 640, 3000)\n",
      "float64\n",
      "(345600, 3000)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS4AAAEACAYAAAAN5psFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6H8ACuMExit5+vsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e3af810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t0 = timer()\n",
    "#fn = '/Users/ackman/Desktop/140509_21.tif'\n",
    "fn = '/Users/ackman/Data/2photon/120518i/120518_07_fr1-300.tif'\n",
    "with tifffile.TiffFile(fn) as tif:\n",
    "    A = tif.asarray()\n",
    "del(tif)\n",
    "A = np.rollaxis(A, 0, 3)\n",
    "A = np.float64(A)\n",
    "print(timer()-t0)\n",
    "print(A.shape)\n",
    "print(A.dtype)\n",
    "sz = A.shape\n",
    "\n",
    "plt.imshow(A[:,:,0])\n",
    "A = np.reshape(A, (A.size/sz[2], sz[2]))\n",
    "print(A.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This takes just 0.32 s to read in array at its ** default uint16.**\n",
    "And takes 0.78 s to read in array and cast as float64.\n",
    "\n",
    "Matlab takes 2.11 s to read in same array into a float64.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.2841491699\n"
     ]
    }
   ],
   "source": [
    "t0=timer()\n",
    "Amean = np.mean(A,axis=1)\n",
    "Amean2D = np.lib.stride_tricks.as_strided(Amean, (Amean.shape[0],A.shape[1]),(Amean.strides[0],0))\n",
    "A /= Amean2D\n",
    "A -= 1\n",
    "print(timer()-t0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "A += abs(np.amin(A))\n",
    "A /= np.amax(A)\n",
    "A = np.reshape(A,sz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without stride tricks, the above F/F0-1 sequence was slower than matlab (2s vs 1s). With stride tricks we get 0.44 s, a big improvement. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55.4744589329\n",
      "22.1628820896\n",
      "75.272135973\n"
     ]
    }
   ],
   "source": [
    "t0=timer()\n",
    "meanA = np.mean(A) \n",
    "stdA = np.std(A)\n",
    "newMin = meanA - 2*stdA\n",
    "newMax = meanA + 5*stdA\n",
    "#playMovie(A2,(newMin,newMax))\n",
    "print(timer()-t0)\n",
    "\n",
    "t0=timer()\n",
    "A -= newMin\n",
    "A *= 255.0/(newMax-newMin)\n",
    "print(timer()-t0)\n",
    "\n",
    "t0=timer()\n",
    "#np.clip(A, 0, 255, out=A)\n",
    "A.clip(0, 255) #faster than the documented np.clip(A,min,max,out=A) which should be inplace. Possible version/memory leak issue\n",
    "print(timer()-t0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A2[logical]: 1.28217601776\n",
    "\n",
    "np.clip: 1.14894986153\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "playMovie(np.uint8(A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del(A,Amean,Amean2D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable                Type                          Data/Info\n",
      "---------------------------------------------------------------\n",
      "cv2                     module                        <module 'cv2' from '/User<...>.7/site-packages/cv2.so'>\n",
      "cvblur                  function                      <function cvblur at 0x11e318488>\n",
      "cvcorr                  function                      <function cvcorr at 0x11e318de8>\n",
      "cvmatch                 function                      <function cvmatch at 0x11e318140>\n",
      "cvmatchN                function                      <function cvmatchN at 0x11e318050>\n",
      "data                    module                        <module 'skimage.data' fr<...>image/data/__init__.pyc'>\n",
      "datetime                module                        <module 'datetime' from '<...>lib-dynload/datetime.so'>\n",
      "f                       File                          <HDF5 file \"140331_05_201<...>090750_d2r.mat\" (mode r)>\n",
      "fn                      str                           140331_05_20150314-090750_d2r\n",
      "getA3binaryArray        function                      <function getA3binaryArray at 0x11e21f578>\n",
      "getA3hemipshereArrays   function                      <function getA3hemipshereArrays at 0x11e318f50>\n",
      "getCorr                 function                      <function getCorr at 0x11e318230>\n",
      "getHemisphereCoords     function                      <function getHemisphereCoords at 0x11e318500>\n",
      "getHemisphereMasks      function                      <function getHemisphereMasks at 0x11e21f0c8>\n",
      "h5py                    module                        <module 'h5py' from '/Use<...>kages/h5py/__init__.pyc'>\n",
      "histeq                  function                      <function histeq at 0x11b996ed8>\n",
      "importFilelist          function                      <function importFilelist at 0x11e21fc80>\n",
      "matfilename             str                           /Users/ackman/Data/2photo<...>5_20150314-090750_d2r.mat\n",
      "meanA                   float64                       0.133782654165\n",
      "newMax                  float64                       0.226439697864\n",
      "newMin                  float64                       0.0967198366862\n",
      "np                      module                        <module 'numpy' from '/Us<...>ages/numpy/__init__.pyc'>\n",
      "os                      module                        <module 'os' from '/Users<...>27/lib/python2.7/os.pyc'>\n",
      "pd                      module                        <module 'pandas' from '/U<...>ges/pandas/__init__.pyc'>\n",
      "playMovie               function                      <function playMovie at 0x11b996f50>\n",
      "playMovieRGB            function                      <function playMovieRGB at 0x11b996e60>\n",
      "plt                     module                        <module 'matplotlib.pyplo<...>s/matplotlib/pyplot.pyc'>\n",
      "polygon                 builtin_function_or_method    <built-in function polygon>\n",
      "pts                     ndarray                       20x2x2: 80 elems, type `int32`, 320 bytes\n",
      "random_noise            function                      <function random_noise at 0x11643d7d0>\n",
      "stdA                    float64                       0.0185314087397\n",
      "stridemask3d            function                      <function stridemask3d at 0x11e318ed8>\n",
      "subprocess              module                        <module 'subprocess' from<...>ython2.7/subprocess.pyc'>\n",
      "sys                     module                        <module 'sys' (built-in)>\n",
      "sz                      tuple                         n=3\n",
      "t0                      float                         1466189975.43\n",
      "tifffile                module                        <module 'tifffile' from '<...>s/tifffile/__init__.pyc'>\n",
      "time                    module                        <module 'time' from '/Use<...>2.7/lib-dynload/time.so'>\n",
      "timer                   builtin_function_or_method    <built-in function time>\n",
      "writeData               function                      <function writeData at 0x11e21f398>\n",
      "xyPtsL                  ndarray                       20x2: 40 elems, type `int32`, 160 bytes\n",
      "xyPtsR                  ndarray                       20x2: 40 elems, type `int32`, 160 bytes\n"
     ]
    }
   ],
   "source": [
    "%whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}