2D Devolved Data Visualization.ipynb

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   "source": [
    "# 2D Visualization of 3D Data\n",
    "## Zachary Neronha, Wong Lab\n",
    "### 26 October 2018, created for the purpose of simple viewing of data in two dimensions "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Workflow Overview\n",
    "- Data should first be converted from Imaris files to the XLS folders in the convert folder tab\n",
    "- This code allows for the user to easily visualize and restrict the data of interst\n",
    "- Data must then be reexported to CSV for reading by the R codes for TDA to be performed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages and creater helper functions to streamline analysis later on"
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {},
   "outputs": [
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.cm as cm\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "%matplotlib inline\n",
    "from __future__ import division\n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.plotly as py\n",
    "import plotly\n",
    "plotly.tools.set_credentials_file(username='zjneronha', api_key='PMrKGUGAet6kUZ5Rcf5E')\n",
    "import brewer2mpl as cbrew"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create helper functions\n",
    "\n",
    "#function 1: return the cells within a certain location\n",
    "def findRange(xPts,yPts,xRange,yRange):\n",
    "    cellList = []\n",
    "    for ii in range(0,np.size(xPts,0)):\n",
    "        qLog = np.greater(xPts[ii,:],xRange[0]) & np.less(xPts[ii,:],xRange[1])\n",
    "        pLog = np.greater(yPts[ii,:],yRange[0]) & np.less(yPts[ii,:],yRange[1])\n",
    "        qLog = sum(qLog)\n",
    "        pLog = sum(pLog)\n",
    "\n",
    "        if qLog > 0 and pLog >0:\n",
    "            cellList.append(ii)\n",
    "    \n",
    "    return cellList\n",
    "\n",
    "#function 2: pull and export only the cells of interest (list the cells you want)\n",
    "def selExport(xPts,yPts,zPts,MyCells,pathname):\n",
    "    selX = xPts[MyCells,:]\n",
    "    selY = yPts[MyCells,:]\n",
    "    selZ = zPts[MyCells,:]\n",
    "    \n",
    "    ptsSZE = sum(sum(np.logical_not(np.isnan(selX))))\n",
    "    selPTS = np.empty((ptsSZE,3))\n",
    "    loopcounter = 0\n",
    "    \n",
    "    #convert back to direct matrix format and export\n",
    "    for ii in range(0,np.size(selX,0)):\n",
    "        for jj in range(0,np.size(selX,1)):\n",
    "            if np.isnan(selX[ii,jj])==0:\n",
    "                selPTS[loopcounter,:] = [selX[ii,jj],selY[ii,jj],selZ[ii,jj]]\n",
    "                loopcounter = loopcounter + 1\n",
    "                \n",
    "    dTPS = pd.DataFrame(selPTS)\n",
    "    dTPS.to_csv(pathname,index=False,header = False)\n",
    "    \n",
    "    print(\"Feature Complete; Cluster AllTemp Pts File Saved!\")\n",
    "\n",
    "#function 3: pull out and export all points at one time frame (all cells from one frame)\n",
    "def temExport(xPts,yPts,zPts,Frame,pathname):\n",
    "    selX = xPts[:,Frame]\n",
    "    selY = yPts[:,Frame]\n",
    "    selZ = zPts[:,Frame]\n",
    "    \n",
    "    ptsSZE = sum(np.logical_not(np.isnan(selX)))\n",
    "    selPTS = np.empty((ptsSZE,3))\n",
    "    loopcounter = 0\n",
    "    \n",
    "    #convert back to direct matrix format and export\n",
    "    for ii in range(0,np.size(selX,0)):\n",
    "        if np.isnan(selX[ii])==0:\n",
    "            selPTS[loopcounter,:] = [selX[ii],selY[ii],selZ[ii]]\n",
    "            loopcounter = loopcounter + 1\n",
    "                \n",
    "    dTPS = pd.DataFrame(selPTS)\n",
    "    dTPS.to_csv(pathname,index=False,header = False)\n",
    "    \n",
    "    print(\"Feature Complete; Cluster SingTemp Pts File Saved!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import position data, calculate velocity data\n",
    "- Load data for a set well at a specified imaging interval (fixed based on microscopy data)\n",
    "- Also pull out velocity data based on positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "currentwell = 'a08'\n",
    "calcVel = 0\n",
    "interval = (1/3) #hours between imaging \n",
    "\n",
    "#import data from csv\n",
    "xlsZ = pd.ExcelFile(\"WholeWell022519/2D_Devolved/\"+currentwell+\"_combineddata.xls\")\n",
    "dxx = pd.read_excel(xlsZ,'xStore')\n",
    "dyy = pd.read_excel(xlsZ,'yStore')\n",
    "dzz = pd.read_excel(xlsZ,'zStore')\n",
    "\n",
    "# conver to an array\n",
    "xDat = np.array(dxx)\n",
    "yDat = np.array(dyy)\n",
    "zDat = np.array(dzz)\n",
    "\n",
    "if calcVel == 1:\n",
    "    #Initialize and fill velocity arrays \n",
    "    xVel = np.empty((np.size(xDat,0),np.size(xDat,1)))\n",
    "    yVel = np.empty((np.size(xDat,0),np.size(xDat,1)))\n",
    "    zVel = np.empty((np.size(xDat,0),np.size(xDat,1)))\n",
    "    xVel[:] = np.nan\n",
    "    yVel[:] = np.nan\n",
    "    zVel[:] = np.nan\n",
    "\n",
    "    #fill sequentially by time points\n",
    "    for k in range(0,np.size(xDat,1)-1):\n",
    "        xVel[:,k] = xDat[:,k+1]-xDat[:,k]\n",
    "        yVel[:,k] = yDat[:,k+1]-yDat[:,k]\n",
    "        zVel[:,k] = zDat[:,k+1]-zDat[:,k]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the data temporally\n",
    "- Note each color corresponds to a distinct cell track over the time window\n",
    "- This block of code plots all specified cells over the window in a devoloved 2D setting for easy identification of ranges of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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   "source": [
    "pltdata = []\n",
    "for i in range(0,np.size(xDat,0)):\n",
    "    # Create a trace\n",
    "    trace = go.Scatter(\n",
    "        name = 'Cell ' + repr(i),\n",
    "        x = xDat[i,:],\n",
    "        y = yDat[i,:],   \n",
    "        )\n",
    "    pltdata.append(trace)\n",
    "data = [pltdata]\n",
    "\n",
    "py.iplot(pltdata, filename='basic-line1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Range Restriction\n",
    "- This block of code allows the user to specify a range in X and Y and pulls out the cells of interest in that area\n",
    "- This is useful if one wishes to only extract and export one feature of interest for further processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4, 20, 21, 37, 40, 43, 48, 49, 52, 62, 65, 72, 74, 82, 95, 103, 107, 113, 117, 122, 135, 136, 146, 147, 150, 157, 159, 172, 183]\n"
     ]
    }
   ],
   "source": [
    "xRan = [540,575]\n",
    "yRan = [330,370]\n",
    "celL = findRange(xDat,yDat,xRan,yRan)\n",
    "print(celL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Frequency Estimation\n",
    "- The following block of code allows the user to plot the x and y positions respectivly across time\n",
    "- This allows for the extraction of frequency information, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false
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   "outputs": [],
   "source": [
    "#X and Y temporal plots, frequency estimation, etc.\n",
    "xred = xDat[celL,:]\n",
    "yred = yDat[celL,:]\n",
    "trL = np.multiply(xred,0)+1\n",
    "for k in range(0,np.size(trL,1)):\n",
    "    trL[:,k] = np.multiply(trL[:,k],k)\n",
    "\n",
    "trL = np.multiply(trL,interval)"
   ]
  },
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   "source": [
    "#plot sequentially\n",
    "pltdata = []\n",
    "for i in range(0,np.size(xred,0)):\n",
    "    # Create a trace\n",
    "    trace = go.Scatter(\n",
    "        name = 'Cell ' + repr(i),\n",
    "        x = trL[i,:],\n",
    "        y = xred[i,:], \n",
    "        )\n",
    "    pltdata.append(trace)\n",
    "data = [pltdata]\n",
    "\n",
    "py.iplot(pltdata, filename='XFREQ')\n"
   ]
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   "execution_count": 80,
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   "source": [
    "#plot Y frequency\n",
    "pltdata = []\n",
    "for i in range(0,np.size(xred,0)):\n",
    "    # Create a trace\n",
    "    trace = go.Scatter(\n",
    "        name = 'Cell ' + repr(i),\n",
    "        x = trL[i,:],\n",
    "        y = yred[i,:], \n",
    "        )\n",
    "    pltdata.append(trace)\n",
    "data = [pltdata]\n",
    "\n",
    "py.iplot(pltdata, filename='YFREQ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exporting\n",
    "- The following block of code exports the xyz position data for selected wells using a specified path\n",
    "- Only exports data for specified cells (this is linked to the above block of code that allows the user to pull out the cells of interest in a specific location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Complete; File Saved!\n"
     ]
    }
   ],
   "source": [
    "pathnameS = \"WholeWell022519/2D_Devolved/TDAready_w\"+repr(currentwell)+\".csv\"\n",
    "selExport(xDat,yDat,zDat,celL,pathnameS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Breakup analysis for all points in one frame\n",
    "- Allows user to look at the position of cells in only one time point\n",
    "- Look specifically at plotting\n",
    "- Then export to a csv for TDA in R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting in one frame in an interactive environment \n",
    "- Specify frame of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define frame of interest\n",
    "FOI = 100\n",
    "\n",
    "plt_data = []\n",
    "trace = go.Scatter3d(\n",
    "            name = 'SyntheticSphere'+repr(i),\n",
    "            x = xDat[:,FOI],\n",
    "            y = yDat[:,FOI],\n",
    "            z = zDat[:,FOI],\n",
    "            mode = 'markers',\n",
    "            marker = dict(\n",
    "                color = 1,\n",
    "                size = 8,\n",
    "                symbol = 'circle',\n",
    "                line = dict(\n",
    "                    color = 'rgb(180, 180, 180)',\n",
    "                    width = 1.0\n",
    "                ),\n",
    "                opacity = 0.4\n",
    "            )\n",
    "        )\n",
    "\n",
    "plt_data.append(trace)\n",
    "\n",
    "layout = go.Layout(margin = dict(l = 0, r = 0, b = 0, t = 0))\n",
    "\n",
    "fig = go.Figure(data=plt_data, layout=layout)\n",
    "\n",
    "iplot(fig)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export\n",
    "- Block export of the positions of all cells at only a single time point to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Complete; Cluster SingTemp Pts File Saved!\n"
     ]
    }
   ],
   "source": [
    "pathnameF = \"SelectedFeatures/TemBreak_w\"+repr(currentwell)+\"t\"+repr(FOI)+\".csv\"\n",
    "temExport(xDat,yDat,zDat,FOI,pathnameF) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export at Scale\n",
    "- Export here is streamlined for an entire folder's worth of xls files\n",
    "- Exports all cells at that particular timepoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WholeWell022519/3D_Top/a07_combineddata.xls\n",
      "Feature Complete; Cluster SingTemp Pts File Saved!\n",
      "WholeWell022519/3D_Top/g09_combineddata.xls\n",
      "Feature Complete; Cluster SingTemp Pts File Saved!\n",
      "WholeWell022519/3D_Top/b11_combineddata.xls\n",
      "Feature Complete; Cluster SingTemp Pts File Saved!\n"
     ]
    }
   ],
   "source": [
    "import glob\n",
    "import os\n",
    "readpath = glob.glob(\"WholeWell022519/3D_Top/*.xls\")\n",
    "FrameS = 0\n",
    "for path in readpath:\n",
    "    print(path)\n",
    "    filename = os.path.basename(path)\n",
    "    wellname = filename.split(\"_\")[0]\n",
    "    #import data from csv\n",
    "    xlsZ = pd.ExcelFile(path,engine='xlrd')\n",
    "    dxx = pd.read_excel(xlsZ,'xStore')\n",
    "    dyy = pd.read_excel(xlsZ,'yStore')\n",
    "    dzz = pd.read_excel(xlsZ,'zStore')\n",
    "    xDat = np.array(dxx)\n",
    "    yDat = np.array(dyy)\n",
    "    zDat = np.array(dzz)\n",
    "    \n",
    "    expPath = \"WholeWell022519/3D_Top/TDAready_\"+wellname+\".csv\"\n",
    "    temExport(xDat,yDat,zDat,FrameS,expPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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