image_analysis_snippets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Image analysis packages\n",
    "\n",
    "import numpy as np\n",
    "from skimage.util import crop\n",
    "import os\n",
    "import sys\n",
    "import argparse\n",
    "import tifffile\n",
    "from skimage import io\n",
    "from skimage import exposure\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "Unable to open file (Bad superblock version number)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-935cb695ef77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m# Read H5 file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/joshtitlow/Downloads/2019_04_26_lothar_josh/20190426_164730_batch.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# Get list of datasets within the H5 file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python2.7/site-packages/h5py/_hl/files.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, **kwds)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m                 \u001b[0mfapl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fapl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdriver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m                 \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_fid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muserblock_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mswmr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mswmr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    274\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m//anaconda/lib/python2.7/site-packages/h5py/_hl/files.pyc\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m     90\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mswmr\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m             \u001b[0mflags\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_SWMR_READ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_RDWR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper (/Users/ilan/minonda/conda-bld/work/h5py/_objects.c:2696)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper (/Users/ilan/minonda/conda-bld/work/h5py/_objects.c:2654)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.open (/Users/ilan/minonda/conda-bld/work/h5py/h5f.c:1942)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: Unable to open file (Bad superblock version number)"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tifffile\n",
    "import os, sys\n",
    "import argparse\n",
    "\n",
    "\n",
    "# Read H5 file\n",
    "image = ('/Users/joshtitlow/Downloads/2019_04_26_lothar_josh/20190426_164730_batch.h5')\n",
    "f = h5py.File(image, 'r')\n",
    "\n",
    "# Get list of datasets within the H5 file\n",
    "datasetNames = [n for n in f.keys()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 file \"mytestfile.hdf5\" (mode r+)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'merged.raw'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-5da265537540>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# working with .raw files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfromfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'merged.raw'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'merged.raw'"
     ]
    }
   ],
   "source": [
    "# working with .raw files\n",
    "\n",
    "# get file\n",
    "im = np.fromfile('merged.raw', dtype = np.uint16)\n",
    "\n",
    "# change shape\n",
    "im = im.reshape(10000, 1024, 414)\n",
    "\n",
    "# delete first rows in 0 axis\n",
    "im = np.delete(im,np.s_[0:69],axis=0)\n",
    "\n",
    "# save file to .raw\n",
    "im.astype('uint16').tofile('merged2.raw')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 1024, 1024)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# working with .tif files\n",
    "\n",
    "im = tifffile.imread('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20190109_20180109/20180109_200nm_tet_beads1.1547048975113.tif')\n",
    "im.shape\n",
    "\n",
    "#tifffile.imsave('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20190109_20180109/ch2_beads.tif',im, imagej = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 1024, 414)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ch2 crop\n",
    "crop_amt = ((0, 0), (0, 0), (610, 0))\n",
    "ch2 = crop(im,crop_amt)\n",
    "ch2.shape\n",
    "#tifffile.imsave('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20190109_20180109/ch2_beads.tif',im, imagej = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 1024, 414)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ch1 crop\n",
    "crop_amt = ((0, 0), (0, 0), (0, 610))\n",
    "ch1 = crop(im,crop_amt)\n",
    "ch1.shape\n",
    "#tifffile.imsave('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20190109_20180109/ch1_beads.tif',im, imagej = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1021, 1024, 414)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get smFISH data from ONI file\n",
    "im = tifffile.imread('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20181228_20181228_rRNA_PAINT_ATTO655_1in20/20181228_rRNA_PAINT_ATTO655_1in20__LP80_50ms.1546002133942_0.tif')\n",
    "crop_amt = ((0, 0), (0, 0), (0, 610))\n",
    "ch1 = crop(im,crop_amt)\n",
    "ch1.shape\n",
    "#tifffile.imsave('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20190109_20180109/ch1_beads.tif',im, imagej = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "tifffile.imsave('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20181228_20181228_rRNA_PAINT_ATTO655_1in20/20181228_rRNA_PAINT_ATTO655_1in20__LP80_50ms.1546002133942_0_ch1.tif',ch1, imagej = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "total size of new array must be unchanged",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-84-e4dcb0ea1d84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#merge = ()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmerge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mch1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mch2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmerge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1024\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m414\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mmerge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: total size of new array must be unchanged"
     ]
    }
   ],
   "source": [
    "# Merge images\n",
    "#merge = ()\n",
    "merge = np.append(ch1,ch2)\n",
    "merge = merge.reshape(2, 300, 1024, 414)\n",
    "merge.shape\n",
    "\n",
    "# Change data structure to make image-j happy\n",
    "print \"changing format from:\"\n",
    "print (merge.shape)\n",
    "merge = np.rollaxis(merge,0,1)\n",
    "print(merge.shape)\n",
    "            \n",
    "#tifffile.imsave('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20190109_20180109/ch1_ch2_beads_huygens.tif',merge, imagej = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JlFKtse894niyZ4Y2G6f226HThxrj7KXn58CmBX5YHmYuJw2As7ye1+hbpAQo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\nD+BkKI7+HZHaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111a48c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show images\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "f = plt.figure()\n",
    "f.add_subplot(1,2,1)\n",
    "plt.imshow(ch1[10])\n",
    "f.add_subplot(1,2, 2)\n",
    "plt.imshow(ch2[10])\n",
    "plt.show(block=True)\n",
    "\n",
    "#log_img = exposure.adjust_log(dset, 1)\n",
    "#gamma_img = exposure.adjust_gamma(dset, 1)\n",
    "#plt.imshow(dset[0, 15,0], cmap='gray')\n",
    "#plt.imshow(log_img[0, 15,0], cmap='gray')\n",
    "#plt.imshow(gamma_img[0, 15,0], cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11500b110>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4NqZtvaAkGeRhJEPzATbY398frPjlclkrGiAsCJ0Tg5h8WF+btGT4wkhrHbvi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115d03710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.imshow(ch1[0])\n",
    "log_img = exposure.adjust_log(ch1[75], 50)\n",
    "plt.imshow(log_img, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-27fcb1e6a773>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtifffile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Users/joshtitlow/tmp/525390.ome.tiff'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object is not callable"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tifffile\n",
    "\n",
    "im = tifffile.imread('/Users/joshtitlow/tmp/525390.ome.tiff')\n",
    "im.shape()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 33, 512, 512)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im = np.asarray(im)\n",
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "xmlExample = str(im.tag[270])\n",
    "#source = io.StringIO(xmlExample.strip())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "ename": "XMLSyntaxError",
     "evalue": "Start tag expected, '<' not found, line 1, column 1",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<string>\"\u001b[0;36m, line \u001b[0;32munknown\u001b[0m\n\u001b[0;31mXMLSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Start tag expected, '<' not found, line 1, column 1\n"
     ]
    }
   ],
   "source": [
    "elem = etree.XML(xml_str, parser=parser)\n",
    "elem "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Element 'p' at 0x10c626390>\n",
      "405.00000000000006 561.0 488.00000000000006\n"
     ]
    }
   ],
   "source": [
    "import xml.etree.ElementTree as ET  \n",
    "tree = ET.parse('/Users/joshtitlow/Desktop/ome.xml')\n",
    "root = tree.getroot()\n",
    "print root\n",
    "#print(root[0][0][1][0].attrib)\n",
    "\n",
    "for key, value in root[0][0][1][0].attrib.items():\n",
    "    #print key,':', value\n",
    "    if key == 'excitationwavelength':\n",
    "        ch1 = value\n",
    "for key, value in root[0][0][1][1].attrib.items():\n",
    "    #print key,':', value\n",
    "    if key == 'excitationwavelength':\n",
    "        ch2 = value\n",
    "for key, value in root[0][0][1][2].attrib.items():\n",
    "    #print key,':', value\n",
    "    if key == 'excitationwavelength':\n",
    "        ch3 = value\n",
    "print ch1, ch2, ch3        \n",
    "#for elem in root:  \n",
    "#    print ('these are root names:',elem.text)\n",
    "#    print ('these are root attributes:',elem.attrib)\n",
    "#    for subelem in elem:\n",
    "#        print('these are subroot names:',subelem.text)\n",
    "#        print('these are subroot attributes:',subelem.attrib)\n",
    "        #for subsubelem in subelem:\n",
    "            #print('these are subsubroot elements:',subsubelem.attrib)\n",
    "            #for subsubsubelem in subsubelem:\n",
    "                #print('these are subsubsubroot elements:',subsubsubelem.attrib)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TiffFile '382793.ome.tiff'  31.55 MiB  BigEndian  OME  63 Pages\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'TiffFile' object has no attribute 'tag'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-230-f2ee817cc7e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtifffile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTiffFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/382793.ome.tiff'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m270\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m//anaconda/lib/python2.7/site-packages/tifffile/tifffile.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   2521\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2522\u001b[0m         raise AttributeError(\"'%s' object has no attribute '%s'\" %\n\u001b[0;32m-> 2523\u001b[0;31m                              (self.__class__.__name__, name))\n\u001b[0m\u001b[1;32m   2524\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2525\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__enter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'TiffFile' object has no attribute 'tag'"
     ]
    }
   ],
   "source": [
    "import tifffile\n",
    "im = tifffile.TiffFile('/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/382793.ome.tiff')\n",
    "print im\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "ename": "ParseError",
     "evalue": "XML or text declaration not at start of entity: line 2, column 4",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<string>\"\u001b[0;36m, line \u001b[0;32munknown\u001b[0m\n\u001b[0;31mParseError\u001b[0m\u001b[0;31m:\u001b[0m XML or text declaration not at start of entity: line 2, column 4\n"
     ]
    }
   ],
   "source": [
    "from lxml import etree, html\n",
    "\n",
    "xml_doc = html.fromstring(str(im.tag[270]))\n",
    "tree = ET.parse('/Users/joshtitlow/Desktop/ome.xml').lstrip()\n",
    "\n",
    "#print (etree.tostring(xml_doc, encoding='unicode', pretty_print=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(u'<?xml version=\"1.0\" encoding=\"UTF-8\"?><!-- Warning: this comment is an OME-XML metadata block, which contains crucial dimensional parameters and other important metadata. Please edit cautiously (if at all), and back up the original data before doing so. For more information, see the OME-TIFF web site: https://docs.openmicroscopy.org/latest/ome-model/ome-tiff/. --><OME xmlns=\"http://www.openmicroscopy.org/Schemas/OME/2016-06\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" Creator=\"OME Bio-Formats 5.9.2\" UUID=\"urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594\" xsi:schemaLocation=\"http://www.openmicroscopy.org/Schemas/OME/2016-06 http://www.openmicroscopy.org/Schemas/OME/2016-06/ome.xsd\"><Image ID=\"urn:lsid:export.openmicroscopy.org:Image:7637023b-3f61-416e-97c6-be8ea199b758_382793:15525066\" Name=\"2017oct17_001430(kat80)_20x_YFP_DLG_syp_test1overview2%lasers.czi\"><ImagingEnvironment/><Pixels DimensionOrder=\"XYZCT\" ID=\"urn:lsid:export.openmicroscopy.org:Pixels:7637023b-3f61-416e-97c6-be8ea199b758_382793:15525069\" PhysicalSizeX=\"1.037832280124744\" PhysicalSizeXUnit=\"\\xc2\\xb5m\" PhysicalSizeY=\"1.037832280124744\" PhysicalSizeYUnit=\"\\xc2\\xb5m\" PhysicalSizeZ=\"3.0\" PhysicalSizeZUnit=\"\\xc2\\xb5m\" SizeC=\"3\" SizeT=\"1\" SizeX=\"512\" SizeY=\"512\" SizeZ=\"21\" Type=\"uint16\"><Channel AcquisitionMode=\"LaserScanningConfocalMicroscopy\" Color=\"65535\" EmissionWavelength=\"446.12408800000003\" EmissionWavelengthUnit=\"nm\" ExcitationWavelength=\"405.00000000000006\" ExcitationWavelengthUnit=\"nm\" ID=\"urn:lsid:export.openmicroscopy.org:Channel:7637023b-3f61-416e-97c6-be8ea199b758_820492:15525066\" IlluminationType=\"Epifluorescence\" Name=\"ChS1-T1\" SamplesPerPixel=\"1\"><LightPath/></Channel><Channel AcquisitionMode=\"LaserScanningConfocalMicroscopy\" Color=\"-16776961\" EmissionWavelength=\"602.0153655\" EmissionWavelengthUnit=\"nm\" ExcitationWavelength=\"561.0\" ExcitationWavelengthUnit=\"nm\" ID=\"urn:lsid:export.openmicroscopy.org:Channel:7637023b-3f61-416e-97c6-be8ea199b758_820493:15525066\" IlluminationType=\"Epifluorescence\" Name=\"ChS3-T2\" SamplesPerPixel=\"1\"><LightPath/></Channel><Channel AcquisitionMode=\"LaserScanningConfocalMicroscopy\" Color=\"16711935\" 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FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"7\" IFD=\"28\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"8\" IFD=\"29\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"9\" IFD=\"30\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"10\" IFD=\"31\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"11\" IFD=\"32\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"12\" IFD=\"33\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"13\" IFD=\"34\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"14\" IFD=\"35\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"15\" IFD=\"36\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"16\" IFD=\"37\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"17\" IFD=\"38\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"18\" IFD=\"39\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"19\" IFD=\"40\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"1\" FirstT=\"0\" FirstZ=\"20\" IFD=\"41\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"0\" IFD=\"42\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"1\" IFD=\"43\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"2\" IFD=\"44\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"3\" IFD=\"45\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"4\" IFD=\"46\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"5\" IFD=\"47\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"6\" IFD=\"48\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"7\" IFD=\"49\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"8\" IFD=\"50\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"2\" FirstT=\"0\" FirstZ=\"9\" IFD=\"51\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData 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TheZ=\"18\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"20\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"5\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"12\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"2\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"7\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"17\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"18\"/></Pixels></Image></OME>',)"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.tag[270]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "256 (512,)\n",
      "257 (512,)\n",
      "258 (16,)\n",
      "259 (1,)\n",
      "324 (448, 33216, 65984, 98752, 131520, 164288, 197056, 229824, 262592, 295360, 328128, 360896, 393664, 426432, 459200, 491968)\n",
      "325 (32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768, 32768)\n",
      "262 (1,)\n",
      "322 (128,)\n",
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TheZ=\"13\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"15\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"19\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"14\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"10\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"11\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"4\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"16\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"0\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"19\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"1\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"3\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"6\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"4\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"7\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"14\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"11\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"9\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"0\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"19\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"13\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"20\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"14\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"4\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"16\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"1\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"20\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"10\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"7\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"3\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"17\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"12\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"18\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"1\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"3\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"9\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"2\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"9\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"8\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"6\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"6\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"18\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"20\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"5\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"12\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"2\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"7\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"17\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"18\"/></Pixels></Image></OME>',)\n",
      "339 (1,)\n",
      "277 (1,)\n",
      "282 ((9635468, 1000),)\n",
      "283 ((9635468, 1000),)\n",
      "284 (1,)\n",
      "296 (3,)\n",
      "323 (128,)\n",
      "305 (u'OME Bio-Formats 5.9.2',)\n"
     ]
    }
   ],
   "source": [
    "for t in im.tag.keys():\n",
    "    print t, im.tag[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "from PIL import Image\n",
    "im = Image.open('/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/382793.ome.tiff')\n",
    "imageDesc = im.tag[270]\n",
    "#imfinal = imageDesc.replace(\"u'<?xml version=\"1.0\" encoding=\"UTF-8\"??><!-- Warning: this comment is an OME-XML metadata block, which contains crucial dimensional parameters and other important metadata. Please edit cautiously (if at all), and back up the original data before doing so. For more information, see the OME-TIFF web site: https://docs.openmicroscopy.org/latest/ome-model/ome-tiff/. -->'\", \"\")\n",
    "\n",
    "#imfinal\n",
    "\n",
    "#import xml.etree.ElementTree as ET\n",
    "#root = ET.fromstring(imageDesc)\n",
    "#root.tag\n",
    "\n",
    "#for child in root:\n",
    "#    print child.tag, child.attrib\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(u'<?xml version=\"1.0\" encoding=\"UTF-8\"?><!-- Warning: this comment is an OME-XML metadata block, which contains crucial dimensional parameters and other important metadata. Please edit cautiously (if at all), and back up the original data before doing so. For more information, see the OME-TIFF web site: https://docs.openmicroscopy.org/latest/ome-model/ome-tiff/. --><OME xmlns=\"http://www.openmicroscopy.org/Schemas/OME/2016-06\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" Creator=\"OME Bio-Formats 5.9.2\" UUID=\"urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594\" xsi:schemaLocation=\"http://www.openmicroscopy.org/Schemas/OME/2016-06 http://www.openmicroscopy.org/Schemas/OME/2016-06/ome.xsd\"><Image ID=\"urn:lsid:export.openmicroscopy.org:Image:7637023b-3f61-416e-97c6-be8ea199b758_382793:15525066\" Name=\"2017oct17_001430(kat80)_20x_YFP_DLG_syp_test1overview2%lasers.czi\"><ImagingEnvironment/><Pixels DimensionOrder=\"XYZCT\" ID=\"urn:lsid:export.openmicroscopy.org:Pixels:7637023b-3f61-416e-97c6-be8ea199b758_382793:15525069\" PhysicalSizeX=\"1.037832280124744\" PhysicalSizeXUnit=\"\\xc2\\xb5m\" PhysicalSizeY=\"1.037832280124744\" PhysicalSizeYUnit=\"\\xc2\\xb5m\" PhysicalSizeZ=\"3.0\" PhysicalSizeZUnit=\"\\xc2\\xb5m\" SizeC=\"3\" SizeT=\"1\" SizeX=\"512\" SizeY=\"512\" SizeZ=\"21\" Type=\"uint16\"><Channel AcquisitionMode=\"LaserScanningConfocalMicroscopy\" Color=\"65535\" EmissionWavelength=\"446.12408800000003\" EmissionWavelengthUnit=\"nm\" ExcitationWavelength=\"405.00000000000006\" ExcitationWavelengthUnit=\"nm\" ID=\"urn:lsid:export.openmicroscopy.org:Channel:7637023b-3f61-416e-97c6-be8ea199b758_820492:15525066\" IlluminationType=\"Epifluorescence\" Name=\"ChS1-T1\" SamplesPerPixel=\"1\"><LightPath/></Channel><Channel AcquisitionMode=\"LaserScanningConfocalMicroscopy\" Color=\"-16776961\" EmissionWavelength=\"602.0153655\" EmissionWavelengthUnit=\"nm\" ExcitationWavelength=\"561.0\" ExcitationWavelengthUnit=\"nm\" ID=\"urn:lsid:export.openmicroscopy.org:Channel:7637023b-3f61-416e-97c6-be8ea199b758_820493:15525066\" IlluminationType=\"Epifluorescence\" Name=\"ChS3-T2\" SamplesPerPixel=\"1\"><LightPath/></Channel><Channel AcquisitionMode=\"LaserScanningConfocalMicroscopy\" Color=\"16711935\" EmissionWavelength=\"521.8427085000001\" EmissionWavelengthUnit=\"nm\" ExcitationWavelength=\"488.00000000000006\" ExcitationWavelengthUnit=\"nm\" ID=\"urn:lsid:export.openmicroscopy.org:Channel:7637023b-3f61-416e-97c6-be8ea199b758_820494:15525066\" IlluminationType=\"Epifluorescence\" Name=\"ChS2-T3\" SamplesPerPixel=\"1\"><LightPath/></Channel><TiffData FirstC=\"0\" FirstT=\"0\" FirstZ=\"0\" IFD=\"0\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"0\" FirstT=\"0\" FirstZ=\"1\" IFD=\"1\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"0\" FirstT=\"0\" FirstZ=\"2\" IFD=\"2\" PlaneCount=\"1\"><UUID FileName=\"__omero_export__1259410826999208.ome.tiff\">urn:uuid:40948692-11fa-4f67-8677-c3f6277ee594</UUID></TiffData><TiffData FirstC=\"0\" FirstT=\"0\" FirstZ=\"3\" 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PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"4\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"7\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"14\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"11\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"9\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"0\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"19\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"13\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"20\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"14\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"4\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"16\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"1\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"20\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"10\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"7\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"3\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"17\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"12\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"18\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"1\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"3\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"9\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"2\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"9\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"8\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"6\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"6\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"18\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"20\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"1\" TheT=\"0\" TheZ=\"5\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"12\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"2\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"7\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"0\" TheT=\"0\" TheZ=\"17\"/><Plane DeltaT=\"2489.145529364\" DeltaTUnit=\"s\" PositionX=\"-6702.47\" PositionXUnit=\"\\xc2\\xb5m\" PositionY=\"-3083.39\" PositionYUnit=\"\\xc2\\xb5m\" PositionZ=\"175.107\" PositionZUnit=\"\\xc2\\xb5m\" TheC=\"2\" TheT=\"0\" TheZ=\"18\"/></Pixels></Image></OME>',)"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.tag[270]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/382793.ome.tiff (3, 21, 512, 512)\n",
      "/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/390478.ome.tiff (4, 20, 512, 512)\n",
      "/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/390479.ome.tiff\n",
      "/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/390480.ome.tiff (4, 19, 512, 512)\n",
      "/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/390483.ome.tiff (3, 25, 512, 512)\n",
      "/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/390484.ome.tiff\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import tifffile\n",
    "indir = '/Volumes/bioc1301/src/OMERO_scripts/OMERO_Figure_screen_dataset/CNS_figures/davisgroup/'\n",
    "infiles =  os.listdir(indir)\n",
    "for file in infiles:\n",
    "    try:\n",
    "        if file.endswith('.tiff'):\n",
    "            file = os.path.join(indir,file)\n",
    "            im = tifffile.imread(file)\n",
    "            print file, im.shape\n",
    "    except: \n",
    "        print file\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 37, 512, 512)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# working with .tif files\n",
    "\n",
    "im = tifffile.imread('/Volumes/bioc1301/tmp/smFISH_data/20171013_CS_2colorsmFISH_msp670_570_DAPI_HRP405_p1s3r.ome.tiff')\n",
    "im.shape\n",
    "\n",
    "#tifffile.imsave('/Volumes/bioc1301/data/NanoImager/DEFAULT_USER/20190109_20180109/ch2_beads.tif',im, imagej = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.68109566544510924, 0.0)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tifffile\n",
    "from scipy import stats\n",
    "\n",
    "# specify image file\n",
    "infile = ('/Volumes/bioc1301/tmp/smFISH_data/20171013_CS_2colorsmFISH_msp670_570_DAPI_HRP405_p1s3r.ome.tiff')\n",
    "\n",
    "# read image file\n",
    "im = tifffile.imread(infile)\n",
    "\n",
    "# load channels into a flattened array\n",
    "ch1 = im[0].flatten()\n",
    "ch2 = im[1].flatten()\n",
    "\n",
    "# Perform pearson correlation (returns coefficient and p-value)\n",
    "stats.pearsonr(ch1, ch2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ch1 = im[0].flatten()\n",
    "ch2 = im[1].flatten()\n",
    "\n",
    "#ch1 = np.array([0, 0, 0, 1, 1, 1, 1])\n",
    "#ch2 = b = np.arange(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3991 3980 4504 ..., 5598 5761 5780]\n",
      "[ 7049  8227  8032 ..., 11284 10316  8582]\n"
     ]
    }
   ],
   "source": [
    "print(ch1)\n",
    "print(ch2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data format: (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.75068466561840519, 0.0)\n",
      "data format: (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.76199767756664705, 0.0)\n",
      "data format: (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.74781335454502318, 0.0)\n",
      "data format: (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.72028028623932339, 0.0)\n",
      "data format: (3, 512, 512)\n",
      "Pearson coef, pvalue: (0.17330807423529637, 0.0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Mo9FIRUUFX/nKV1R/TpEJpK17huW8C4VCochUysvLpxlufD4fXq+XrVu3atsTf0+cOEFx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11606dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import os\n",
    "import tifffile\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "\n",
    "# specify directory of images\n",
    "indir = ('/Volumes/bioc1301/tmp/smFISH_data/')\n",
    "infiles = os.listdir(indir)\n",
    "\n",
    "# setup a figure and start counting\n",
    "fig = plt.figure(figsize = (200,200))\n",
    "n = 1\n",
    "\n",
    "# Convert .ome.tiff to .tiff \n",
    "for file in infiles: \n",
    "    if file.endswith('.tif'):\n",
    "        if not file.startswith('._'):\n",
    "            # read image file and show data structure\n",
    "            im = tifffile.imread(os.path.join(indir, file))\n",
    "            print 'data format:', im.shape\n",
    "\n",
    "            # load channels into a flattened array\n",
    "            ch1 = im[0].flatten()\n",
    "            ch2 = im[1].flatten()\n",
    "\n",
    "            # Perform pearson correlation analysis (returns coefficient and p-value)\n",
    "            pearson = stats.pearsonr(ch1, ch2)\n",
    "            print 'Pearson coef, pvalue:', pearson\n",
    "\n",
    "            # plot the data\n",
    "            #ax = plt.subplot(111)\n",
    "            ax = fig.add_subplot(50,50,n)\n",
    "            ax.scatter(ch1,ch2, c='black', alpha=0.5)\n",
    "\n",
    "            ax.set_xlabel('Ch1', fontsize=10)\n",
    "            ax.set_ylabel('Ch2', fontsize=10)\n",
    "            ax.spines['right'].set_visible(False)\n",
    "            ax.spines['top'].set_visible(False)\n",
    "            \n",
    "            # iterate\n",
    "            n+=1\n",
    "\n",
    "# show the data\n",
    "plt.show(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1664593567\n",
      "668336871\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = sum(ch1)\n",
    "print c1\n",
    "c2 = sum(ch2)\n",
    "print c2\n",
    "c1c2 = c1+c2\n",
    "c1c2/c1\n",
    "#float(c1+c2/c1)\n",
    "\n",
    "#sum(c1,c2)/sum(c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-74-d83ed26c2b78>, line 13)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-74-d83ed26c2b78>\"\u001b[0;36m, line \u001b[0;32m13\u001b[0m\n\u001b[0;31m    return (np.multiply(a,b,dtype=np.uint16).\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "from numpy.core.umath_tests import inner1d\n",
    "# inner1d computes inner product on last dimension\n",
    "# and broadcasts the rest\n",
    "\n",
    "R,G = 0,1\n",
    "channelPairs = [(R,G), (R,B), (G,B)]\n",
    "\n",
    "# safely perform dot product on uint8 arrays\n",
    "# note the trailing \".\" to call sum\n",
    "def safedot(a, b):\n",
    "return (np.multiply(a,b,dtype=np.uint16).\n",
    "sum(dtype=np.float64))\n",
    "\n",
    "# Compute colocalization coefficients on\n",
    "# the image array\n",
    "def ccc(ia):\n",
    "    # means, sumSqMeanErrors are 1x3; others Nx3\n",
    "    # indicator is dtype bool; others float64\n",
    "    sumSqs = \\\n",
    "    inner1d(ia.T, ia.T).astype(np.float64)\n",
    "\n",
    "    sums = \\\n",
    "    ia.sum(axis=0, dtype=np.float64)\n",
    "    means = sums / ia.shape[0]\n",
    "    meanErrors = ia - means\n",
    "\n",
    "    sqMeanErrors = meanErrors**2\n",
    "    sumSqMeanErrors = sqMeanErrors.sum(axis=0)\n",
    "    del sqMeanErrors\n",
    "\n",
    "    indicator = ia>0\n",
    "\n",
    "    # dict of channelPairs -> respective dot product\n",
    "    crossDot = {(c1,c2) : safedot(ia[:,c1], ia[:,c2])\n",
    "    for c1,c2 in channelPairs}\n",
    "\n",
    "    # dict of channelPairs -> sum of c1, when c2 > 0\n",
    "    # factored out of loop for readability\n",
    "    sumIf = {(c1,c2) :\n",
    "    ia[:,c1][indicator[:,c2]].sum()\n",
    "    for c1,c2 in channelPairs}\n",
    "\n",
    "    results = {}\n",
    "    for c1, c2 in channelPairs:\n",
    "    k1 = crossDot[(c1,c2)] / sumSqs[c1]\n",
    "    k2 = crossDot[(c1,c2)] / sumSqs[c2]\n",
    "\n",
    "    results[(c1,c2)] = {\n",
    "    \"Pearson\" :\n",
    "    (np.dot(meanErrors[:,c1],\n",
    "    meanErrors[:,c2]) /\n",
    "    np.sqrt(sumSqMeanErrors[c1] *\n",
    "    sumSqMeanErrors[c2])),\n",
    "\n",
    "    \"Manders\" : math.sqrt(k1*k2),\n",
    "\n",
    "    \"Coloc(m)1\" : sumIf[(c1,c2)] / sums[c1],\n",
    "    \"Coloc(m)2\" : sumIf[(c2,c1)] / sums[c2],\n",
    "    \"Overlap(k)1\" : k1,\n",
    "    \"Overlap(k)2\" : k2}\n",
    "\n",
    "return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "!conda install --yes --prefix {sys.prefix} scikit-image\n",
    "\n",
    "#conda install -c conda-forge scikit-image"
   ]
  }
 ],
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