DM3_to_H5_with_alignment.ipynb

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   "source": [
    "%matplotlib qt4\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DM3 Translator\n",
    "* This file is used to translate the raw DM3 data file into standard H5 file. \n",
    "\n",
    "* The data will be initially processed and stored in the same file for future analysis. \n",
    " - The process includes: ZLP alignment, Thickness computing, ZLP substraction. \n",
    " - Please be aware that the data is limited to low-loss data so far. <br>\n",
    " - If the raw data is core-loss signal, please include additional low-loss data for the ZLP alignment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Software:\n",
    "* Standard distribution (include numpy, scipy, matplotlib, sci-kit learn).\n",
    "* **hyperspy** : can load and process DM3 data directly. (import hyperspy.api, not hyperspy)\n",
    "* **pycroscopy** : mainly used here for plotting purposes only. \n",
    "* **hyperspy_tools** : to extract EELS zlp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "WARNING:hyperspy_gui_traitsui:The module://ipykernel.pylab.backend_inline matplotlib backend is not supported by the installed traitsui version and the ETS toolkit has been set to null. To set the ETS toolkit independently from the matplotlib backend, set it before importing matplotlib.\n",
      "WARNING:hyperspy_gui_traitsui:The traitsui GUI elements are not available.\n"
     ]
    }
   ],
   "source": [
    "#Import packages\n",
    "\n",
    "# Ensure that this code works on both python 2 and python 3\n",
    "from __future__ import division, print_function, absolute_import, unicode_literals\n",
    "\n",
    "# basic numeric computation:\n",
    "import numpy as np\n",
    "\n",
    "# basic system operation:\n",
    "import os\n",
    "\n",
    "# The package used for creating and manipulating HDF5 files:\n",
    "import h5py\n",
    "import hyperspy.api as hs\n",
    "\n",
    "# To extract EELS ZLP, put 'eels.py' from hyperspy_tools in the python path and then import here:\n",
    "from eels import extract_ZLP\n",
    "\n",
    "# Plotting and visualization:\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "# multivariate analysis:\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import NMF\n",
    "\n",
    "# finally import pycroscopy:\n",
    "import pycroscopy as px"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load DM3 data:\n",
    "* Use **hyperspy** to read DM3 data.\n",
    "* Reshape the data as h5 file required.\n",
    "## Tips:\n",
    "* The 'np.reshape' will reshape the matrix acoording to the C memory order, i.e. the most rapidly changing index comes last: (k,j,i)\n",
    "* The h5 file for pycroscopy requires the 3D EELS data (i,j,k) to be flattened as 2D (i*j, k) in the C memory order as well, i.e. the order of row in the flattened data is [0,0],[0,1]...[0,N-1],[1,0],[1,1] ..."
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n",
      "WARNING:hyperspy.signal:<EELSSpectrum, title: EELS Spectrum Image, dimensions: (22, 22|2048)> data is replaced by its optimized copy\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(22, 22, 2048)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "text/plain": [
       "├── Acquisition_instrument\n",
       "│   └── TEM\n",
       "│       ├── Detector\n",
       "│       │   └── EELS\n",
       "│       │       ├── aperture_size = 5.0\n",
       "│       │       ├── collection_angle = 52.083333333333336\n",
       "│       │       ├── frame_number = 1\n",
       "│       │       └── spectrometer = GIF Abruzzi\n",
       "│       ├── Stage\n",
       "│       │   ├── tilt_alpha = 1.7100000381469727\n",
       "│       │   ├── x = -0.055094000000000004\n",
       "│       │   ├── y = 0.069595796875\n",
       "│       │   └── z = -0.051299\n",
       "│       ├── acquisition_mode = STEM\n",
       "│       ├── beam_current = 0.0\n",
       "│       ├── beam_energy = 200.0\n",
       "│       ├── camera_length = 20.0\n",
       "│       ├── convergence_angle = 4.5\n",
       "│       ├── magnification = 15000000.0\n",
       "│       └── microscope = []\n",
       "├── General\n",
       "│   ├── original_filename = EELS Spectrum Image.dm4\n",
       "│   └── title = EELS Spectrum Image\n",
       "└── Signal\n",
       "    ├── Noise_properties\n",
       "    │   └── Variance_linear_model\n",
       "    │       ├── gain_factor = 1.0\n",
       "    │       └── gain_offset = 0.0\n",
       "    ├── binned = True\n",
       "    ├── quantity = Intensity (Counts)\n",
       "    └── signal_type = EELS"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folder_path=''\n",
    "file_name='EELS Spectrum Image.dm3'\n",
    "\n",
    "folder_path = 'D:/Books/research/IMAGE/20180110_TMD/'\n",
    "folder_name = '150_3'\n",
    "file_name = folder_path + folder_name + '/EELS Spectrum Image.dm4'\n",
    "raw = hs.load(file_name,signal_type='EELS')\n",
    "#raw = hs.load(folder_path+file_name,signal_type='EELS')\n",
    "data = raw.data\n",
    "np.shape(data)\n",
    "raw.metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "count = 0\n",
    "data_shape = np.shape(data)\n",
    "ind_mat = np.zeros(data_shape)\n",
    "for (ind1,ind2,ind3), val in np.ndenumerate(data):\n",
    "    ind_mat[ind1,ind2,ind3] = count\n",
    "    count += 1\n",
    "#print(np.shape(data))\n",
    "\n",
    "h5_data = np.reshape(data,(-1,data_shape[2]))\n",
    "h5_ind = np.reshape(ind_mat,(-1,data_shape[2]))\n",
    "##present the ordering:\n",
    "#make a function to transfer count of the memory order to 3D position.\n",
    "#def getindex(number):\n",
    "#    id1 = number//(data_shape[2]*data_shape[1])\n",
    "#    res1 = number%(data_shape[2]*data_shape[1])\n",
    "#    id2 = res1//data_shape[2]\n",
    "#    id3 = res1 - id2*data_shape[2]\n",
    "#    return (id1,id2,id3)\n",
    "\n",
    "#for i in range(30):\n",
    "#    print(getindex(h5_ind[i,0]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Ancillary Data and Translate into H5 file:\n",
    "* Create the new file path.\n",
    "* Specify the spectroscopic axis: minimum of the axis, and dispersion\n",
    "* Translate the data into h5 file using **NumpyTranslator**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The h5 file will be saved in the following path:\n",
      "D:/Books/research/IMAGE/20180110_TMD/150_3/EELS Spectrum Image.h5\n",
      "├── TagGroup0\n",
      "│   ├── Origin = 0.0\n",
      "│   ├── Scale = 0.6330757737159729\n",
      "│   └── Units = nm\n",
      "├── TagGroup1\n",
      "│   ├── Origin = 0.0\n",
      "│   ├── Scale = 0.6330757737159729\n",
      "│   └── Units = nm\n",
      "└── TagGroup2\n",
      "    ├── Origin = 200.0\n",
      "    ├── Scale = 0.05000000074505806\n",
      "    └── Units = eV\n",
      "\n",
      "beam_energy : 200.0\n",
      "convergence_angle : 4.5\n",
      "EELS_collection_angle : 52.083333333333336\n",
      "num_rows : 22\n",
      "num_cols : 22\n",
      "spec_length : 2048\n",
      "spec_axis : [-10.00000015  -9.95000015  -9.90000015 ...  92.25000137  92.30000138\n",
      "  92.35000138]\n",
      "spec_pixel_size : 0.05000000074505806\n",
      "spec_unit : eV\n",
      "spatial_pixel_size : 0.6330757737159729\n",
      "spatial_unit : nm\n"
     ]
    }
   ],
   "source": [
    "h5_path = os.path.join(folder_path, file_name[:-4] + '.h5')\n",
    "print('The h5 file will be saved in the following path:')\n",
    "print(h5_path)\n",
    "\n",
    "image_cal = raw.original_metadata.ImageList.TagGroup0.ImageData.Calibrations.Dimension\n",
    "pos_cal = image_cal.TagGroup0\n",
    "spec_cal = image_cal.TagGroup2\n",
    "print(image_cal)\n",
    "\n",
    "# Generate the x / spectroscopic axis:\n",
    "pos_pixel_size = pos_cal.Scale\n",
    "pos_units = pos_cal.Units\n",
    "\n",
    "\n",
    "spec_pixel_size = spec_cal.Scale\n",
    "spec_min = (0-spec_cal.Origin)*spec_cal.Scale\n",
    "spec_units = spec_cal.Units\n",
    "spec_vec = np.linspace(spec_min, spec_min+spec_pixel_size*(data_shape[2]-1), data_shape[2])\n",
    "\n",
    "\n",
    "# Generate attributes dictionary:\n",
    "parm_dict=dict()\n",
    "tem = raw.metadata.Acquisition_instrument.TEM\n",
    "parm_dict['beam_energy'] = tem.beam_energy\n",
    "parm_dict['convergence_angle'] = tem.convergence_angle\n",
    "parm_dict['EELS_collection_angle']=tem.Detector.EELS.collection_angle\n",
    "parm_dict['num_rows']=data_shape[0]\n",
    "parm_dict['num_cols']=data_shape[1]\n",
    "parm_dict['spec_length']=data_shape[2]\n",
    "parm_dict['spec_axis']=spec_vec\n",
    "parm_dict['spec_pixel_size']=spec_pixel_size\n",
    "parm_dict['spec_unit']=spec_units\n",
    "parm_dict['spatial_pixel_size']=pos_pixel_size\n",
    "parm_dict['spatial_unit']=pos_units\n",
    "for key in parm_dict:\n",
    "    print(key, ':', parm_dict[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tran = px.io.NumpyTranslator()\n",
    "h5_path = tran.translate(h5_path, h5_data, data_shape[0], data_shape[1], \n",
    "                         qty_name='Count', data_unit='', spec_name='Energy Loss',\n",
    "                         spec_unit='eV', spec_val=spec_vec, spatial_unit='um', \n",
    "                         data_type='STS',translator_name='ASC', parms_dict=parm_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process the raw data:\n",
    "We will initially process the raw data from DM3 file for future analysis. Please be aware that the data is limited to low-loss data so far. If the raw data is core-loss signal, please include additional low-loss data for the ZLP alignment.\n",
    " - The initial process includes:\n",
    "  - ZLP alignment (this file is limited to low-loss data)\n",
    "  - Thickness computing\n",
    "  - ZLP substraction\n",
    "\n",
    "**1. Align ZLP with subpixel accuracy on.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Initial ZLP position statistics\n",
      "-------------------------------\n",
      "Summary statistics\n",
      "------------------\n",
      "mean:\t0.000\n",
      "std:\t0.008\n",
      "\n",
      "min:\t-0.049\n",
      "Q1:\t0.001\n",
      "median:\t0.001\n",
      "Q3:\t0.001\n",
      "max:\t0.001\n"
     ]
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LKl3hvs86fBaRUP0Jxe3u/tfAt4DDgZVmdn5lyxIRyUa/zz67+3zgQqKhup+a2c0Vq6pE\nXWOK6iqKSKCgKTnufh/Rzao2APPM7AEzG1KJwkqhqdsiklbwPEV3/wPwKeCPwBeBp8xsbLkLKwf1\nE0UkVKrJ2+7eRrT4wwPAVOAPZjatnIWVQlf5iUhaqa9ocfcd7n4ecAswGngCOLNchZWFuooiEqjk\ny/zc/SaiEzBOdFidOdeoooik1FcovgFs6etN4hMwM4hOwFQNLQghIqH6WiVnQn/fyN1Xm9lfACNL\nrqpEGlMUkbT6uvY5iLtvA7aV8z1LoWmKIhIq10uHKRNFJFSuQ1FEJFQuQ7FwOwIdPotIqJyGYtYV\niEitymUoFmhKjoiEymUoqqMoImnlMhQLNKYoIqFyGYoaUxSRtHIZigXqKIpIqFyGohaEEJG0chmK\nXTSoKCKBchmKuu+ziKSVy1AUEUkrl6FYGFHU0bOIhMplKIqIpJXPUCwsCKFRRREJlMtQ1IQcEUkr\nl6FYoDFFEQmVy1DUZX4iklYuQ7FAHUURCZXLUNRlfiKSVi5DsUBjiiISKpeh2HWZn1JRRALlMhRF\nRNLKZShqRFFE0splKIqIpJXLUOweU8y2DhGpPbkMRRGRtHIZioV5iloQQkRC5TIUdaZFRNLKZyjG\nNKYoIqFyGYrqKIpIWvtnXUAlqaNYm9rb29m4cSOdnZ1ZlyJVpK6ujsbGRoYNG1bRz8llKHph5W2l\nYs1pb29nw4YNjBkzhoaGBl2qKUD0b3rHjh20tbUBVDQYc3n4LLVr48aNjBkzhiFDhigQpYuZMWTI\nEMaMGcPGjRsr+lm5DMXu+z7rH1Wt6ezspKGhIesypEo1NDRUfFgll6EotU09RNmXgfjdyGUo6r7P\nIpJWLkNRpFosW7YMM+P111+v+Gc9+OCD3HbbbRX/nGLWr1+PmbFs2bLg712xYgUtLS3s2bOn/IWl\nkMtQ1I2r5L+iLEPxsMMOY9WqVZx99tnB37tixQoWLlxYNaGYzyk5mr4tMqDq6+s58cQTsy6jLHLZ\nUyzQgL3spaUl6wqKWr16NbNnz2bs2LE0NDQwadIk5s2bx44dO/ba77HHHmP69OkMHz6coUOHMmnS\nJBYtWgTA3Llzufvuu2lra8PMMDOampr2+ZmFw93vfe97XHPNNTQ2NjJkyBC+8IUvsH79+r327ezs\n5MYbb6SpqYnBgwfT1NTEjTfeuNdZ4GKHz3PnzmXs2LE8//zznHLKKQwZMoSJEyeyZMmSrn1aWlpY\nuHAhEE3OLtSepXz2FNVRlGIWLqzKYHzzzTeZOnUqc+fO5cADD+Tll19m0aJFtLa2ct999wHQ2trK\nrFmzmD17NvPnz2fw4MGsXbuW1tZWAObPn8+mTZtYvXo1Dz/8MBD13vqyePFipk6dytKlS9m4cSPz\n5s3js5/9LC+//DJ1dXUAzJkzh/vvv5958+Zx8skns2rVKm655RZaW1u59957e33/9vZ2LrzwQr7x\njW9w0003sXTpUr761a8yadIkZs6cyaWXXspbb73Fj3/8Y5566ikGDRpUyl9lebh7Jl/HH3+8V8qC\nh17y8dc94j/+XWvFPkMq45VXXqncm0Pl3nsfli5d6oCvXbu2X/vv2bPHOzs7/Z577nEz882bN7u7\n+89+9jMHfOvWrfv83jlz5viYMWP69Tnr1q1zwI8++mjfvXt31/annnrKAf/Rj37k7u4vvviiA75g\nwYK9vv/mm292wF944YW93m/p0qV71QP4E0880bWto6PDR44c6ZdddlnXtgULFjjgnZ2d/aq9r98R\nYI2XkE05P3zOugLJXEtL9ItQ+GUoPK6iHmN7ezvXXXcdRx55JPX19dTV1XHRRRfh7qxduxaAqVOn\nUldXx/nnn8/y5cvLdlXH7Nmz2W+/7hiYPn06Y8eOZdWqVQA8+eSTAHzlK1/Z6/sKz1euXNnr+w8Z\nMoSZM2d2Pa+vr2fixIm8+eabZam/EnIdiiK0tETjKYUxlcLjKgrFSy65hCVLlvD1r3+dX//616xe\nvZo77rgDgI6ODgCOOuooHnvsMfbs2cNFF13E6NGjOeGEE/oMpb4ceuihRbcVrjF+7733gOjsctLo\n0aP3en1fDjrooI9sq6+v72pXNcplKHphQYiM6xDpS0dHBw899BDXXnstV111FTNmzKC5ubnopY4z\nZ87k0UcfZcuWLTz++OPU1dVx9tlns3nz5tSfv2HDhqLbxowZA8DBBx8MwLvvvrvXPoXnI0eOTP3Z\n1SqXoShS1IIFWVfwER9++CG7d+/uOqlR0Nsk6Pr6ek4//XS++c1v8sEHH7Bu3bqu7T3PWPdl+fLl\ne80PfPrpp3nrrbeYNm0aADNmzADoOuFT8NOf/hSAU089NejziimcEAqtvVLyefY5/jPrU/tSZTI8\nZH700Ue7DjkLhg8fzhlnnMGJJ57IrbfeymGHHcYhhxzCXXfd1XX4WrBkyRKefPJJzjrrLI444gg2\nb97M4sWLOfzwwznmmGMAmDx5Mu+99x7f//73aW5u5oADDuDYY4/tta7333+fc889l8svv5xNmzZx\n/fXXM3HiRC6++GIApkyZwgUXXEBLSwu7du3ipJNOYtWqVdx8881ccMEFHHfccSX/3UyePBmAW2+9\nlTPPPJNBgwbR3Nxc8vumVspZmlK+Knn2ef6DL/r46x7xZU+vq9hnSGVU9OxzBgpnn4t9TZkyxd2j\nM7ef//znfejQoT5q1Ci/8sor/ZFHHnHAf/vb37q7+zPPPOOzZs3ysWPH+uDBg3306NE+e/Zsf+21\n17o+a9u2bX7++ef7iBEjHPDx48fvs67C2eI77rjDr776aj/kkEO8oaHBzzrrLG9t3XvWxs6dO/2G\nG27wcePG+f777+/jxo3zG264wXfu3PmR9+t59rnY2fAZM2b4jBkzup7v2rXLr7jiCh81apSbmdPH\nLIFKn302z2hSX3Nzs69Zs6Yi7z3/wZe459k3WHTOFC6e1lSRz5DKePXVVzn66KOzLiP31q9fz4QJ\nE7jzzju59NJLsy4nSF+/I2b2nLun7mpqTFFEJCGXodh932cRkTA1caJlzx7ng527+r1/5y5d5yfS\nm6amJrIaOqt2NRGK77R3MP0fngj/Rp19FpFANRGKBgytDyt1eEMdJ0w4uDIFiUhu1UQoHj6igZcW\nfi7rMmSAuLvmmEpRA3HIn8sTLVK76urqqubKBqk+O3bs+MjVP+WmUJSq0tjYSFtbG9u3b9eJAOni\n7mzfvp22tjYaGxsr+lk1cfgs/3UMGzYMgLfffrvi9/eV2lJXV8ehhx7a9TtSKQpFqTrDhg2r+C++\nyL7o8FlEJEGhKCKSoFAUEUlQKIqIJCgURUQSFIoiIgmZLTJrZpuANwK/7RAg/V16qk/e2gP5a5Pa\nU/16tmm8u49K+2aZhWIaZramlBV1q03e2gP5a5PaU/3K3SYdPouIJCgURUQSFIoiIgkKRRGRhFoL\nxR9mXUCZ5a09kL82qT3Vr6xtqqmzzyIilVZrPUURkYpSKIqIJCgURUQSaiIUzewKM1tnZh1m9pyZ\nnZJ1TftiZqea2cNm1mZmbmZze7xuZtZiZm+b2Q4zW2FmU3rsc5CZ3WNmW+Ove8xsxIA2pLuW681s\ntZm1m9kmM/tXMzumxz410yYzu9LM/l/cnnYzW2VmZ9diW4oxs3nx7913E9tqqk1xrd7j693E65Vt\nj7tX9RfwZaATuAw4GvgnYBswLuva9lHvWcC3gdnAdmBuj9evA94H/go4BrgfeBs4MLHPL4GXgZOA\nafHjf82oPY8Bl8S1Hgv8AngXOLgW2wScA5wJHAX8BfCt+PfruFprS5G2nQisA14AvluLP5+4lhbg\nNWB04mvUQLUn0x9iP/+Cfg/c2WPbWmBx1rX1o/ZtyVAEDHgHuCGxrSH+AV8ePz8acGB6Yp+T422T\nqqBNQ4HdwH/PUZveAy6v5bYAw4E/A6cDKwqhWIttikPxpX28VvH2VPXhs5kNBo4HftXjpV8R/Q9Q\nayYQ/a/X1R533wE8SXd7phGF6TOJ73sa+IDqaPOBRMMu/xk/r9k2mdkgMzufKOifoYbbQjRXb7m7\nP9Fje6226ePxENQ6M7vPzD4eb694e6o6FImWBBoEbOixfQPRX0ytKdTcW3tGA5s8/u8NIH68kepo\n8+3AH4FV8fOaa5OZHWtm24APgSXAF939RWqwLQBmdhnRcMD8Ii/XYpt+D8wlGua4LK7hGTMbyQC0\np1ZucdpzhrkV2VZL+mpPsbZl3mYzu43oMORkd9/d4+VaatOfgKnACKJxqbvN7LTE6zXTFjObRDSG\nfYq77+xl15ppk7v/cq8izJ4FWoE5wLOF3Xp8W9naU+09xc1E41c9072Rj/5PUQsKZ9B6a8+7QKOZ\nWeHF+PEoMmyzmX0HuAA43d1bEy/VXJvcfae7v+7ua9z9eqKe79XUYFuIDhUPAV4ys11mtguYAVwR\nP/6PeL9aatNe3H0b0YmSiQzAz6iqQzH+n+854IweL53B3uMFtWId0Q+sqz1mdgBwCt3tWUU0xjUt\n8X3TgI+RUZvN7HbgQqJAfK3HyzXZph72A+qpzbY8SDQrYGriaw1wX/z436i9Nu0lrvcTRCdYKv8z\nyupsWcCZqC8DO4FLic4q3U40iDo+69r2Ue9Qun85twM3xY/Hxa9fB7QDXyKaTnAfxacTvEg0xWJa\n/Dir6RF3xPWezt5TJIYm9qmZNgH/EP8DaiIKk8XAHuDMWmtLL21cwUen5NRMm4B/JOrtTgBOAB6J\n6x8/EO3J/AfYz7+kK4D1RAPjzwGnZl1TL7WeRjRu0fNrWfy6EU05eAfoAFYCx/R4j4OBn8Q/+Pb4\n8YiM2lOsLQ60JPapmTYBy4juDfQh0cD748DnarEtvbSxZyjWVJsSIbcTaAMeACYPVHu0So6ISEJV\njymKiAw0haKISIJCUUQkQaEoIpKgUBQRSVAoiogkKBRFRBIUiiIiCQpFEZGE/w+370bvvnSA1QAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3775f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw.align_zero_loss_peak(subpixel=True)\n",
    "new_axes = raw.axes_manager['Energy loss']\n",
    "new_spec_vec = np.linspace(new_axes.offset, new_axes.offset+new_axes.scale*(new_axes.size-1), new_axes.size)\n",
    "zlp_pos = raw.estimate_zero_loss_peak_centre()\n",
    "zlp_sort = np.sort(zlp_pos,axis=None)\n",
    "plt.plot(zlp_sort)\n",
    "plt.ylabel('ZLP Positions', fontsize=20)\n",
    "plt.gca().set_yticks([])\n",
    "plt.yticks()\n",
    "plt.plot(np.shape(zlp_sort)[0],0,'r+',label='Last point')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n",
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\signal.py:1990: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(navigator().dtype, complex):\n",
      "D:\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\deprecation.py:106: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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P2PDI4zjnmrB60qb6GICk/FrLkORn7kIIrYHCCU25VaNL5s3YpPppYFJSkDbA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\ndc65DHlSdc65DHlSdc65DHlSdc65DP1/2ury/NnCcrMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b3f0f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gvfx8LLComOAjIiQtoic5jgaeL+2jts/xwI3AUODxUpnH8/KqsSxD0jmkwclZ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\ncYI1M2sTJ1gzszb5/wZkwrxZ/TGbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b5c7b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "raw.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Compute the thickness.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22, 22)\n"
     ]
    }
   ],
   "source": [
    "thickness = raw.estimate_thickness(0.01).data\n",
    "print(thickness.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**3. Extract ZLP signal and get the inelastic signal.**<br>\n",
    "\n",
    "* Remember to translate the data into the format pycroscopy required"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\io.py:341: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
      "  if np.issubdtype(dtype, complex):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting initial Gaussian to determine fitting range\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "331d1c3508ba426e88ed81ddb4520004",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mean HWHM of ZLP is 0.42 eV\n",
      "Fitting Signal in range (-10, 6)*HWHM...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f3cd405cbd1641c1885730cf981e10be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Fit results:\n",
      "Components\tParameter\tValue\n",
      "Gaussian\n",
      "\t\tA\t1.17848e+07\n",
      "\t\tcentre\t0.0424637\n",
      "\t\tsigma\t0.383751\n",
      "Lorentzian\n",
      "\t\tA\t4.15509e+06\n",
      "\t\tcentre\t-0.0679982\n",
      "\t\tgamma\t0.333129\n",
      "\n",
      "Calculating inelastic portion of spectrum:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\lib\\site-packages\\hyperspy\\misc\\math_tools.py:61: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  return np.issubdtype(number, np.float)\n"
     ]
    },
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating ZLP portion of spectrum:\n"
     ]
    },
    {
     "data": {
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       "model_id": "4439390a217e4bd9a356d9fe63cf6f3e",
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     "metadata": {},
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    {
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      },
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       "A Jupyter Widget"
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     "metadata": {},
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    },
    {
     "data": {
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Residual\" mean: nan\n",
      "\"Residual\" std dev: nan\n"
     ]
    }
   ],
   "source": [
    "zlp, sig = extract_ZLP(raw,range_factor=(-10, 6),return_inelastic=True,print_output=True)\n",
    "zlp_data = np.reshape(zlp.data,(-1,zlp.data.shape[2]))\n",
    "sig_data = np.reshape(sig.data,(-1,sig.data.shape[2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "├── TagGroup0\n",
       "│   ├── Origin = 0.0\n",
       "│   ├── Scale = 0.6330757737159729\n",
       "│   └── Units = nm\n",
       "├── TagGroup1\n",
       "│   ├── Origin = 0.0\n",
       "│   ├── Scale = 0.6330757737159729\n",
       "│   └── Units = nm\n",
       "└── TagGroup2\n",
       "    ├── Origin = 200.0\n",
       "    ├── Scale = 0.05000000074505806\n",
       "    └── Units = eV"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw.original_metadata.ImageList.TagGroup0.ImageData.Calibrations.Dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found it! (15,11,143)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found it! (15,14,222)\n"
     ]
    }
   ],
   "source": [
    "# Next, impute the missing values\n",
    "test = deepcopy(sig)\n",
    "t = test.data\n",
    "sp = np.shape(test.data)\n",
    "for i in range(sp[0]):\n",
    "    for j in range(sp[1]):\n",
    "        for k in range(sp[2]):\n",
    "            if np.isnan(test.data[i,j,k]):\n",
    "                print('Found nan! ({},{},{})'.format(i,j,k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t[np.isnan(t)]=0\n",
    "sig.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load and write H5 data:\n",
    "* Use **ioHDF5** to read/write H5 data.\n",
    "* Use **MicroDataset** and **MicroDatagroup** to manage the data and build the tree structure.\n",
    "    - Tips: Remember to assign the original `Attibute` data and `Ancillary` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tree structure within the file:\n",
      "/\n",
      "Measurement_000\n",
      "Measurement_000/Channel_000\n",
      "Measurement_000/Channel_000/Position_Indices\n",
      "Measurement_000/Channel_000/Position_Values\n",
      "Measurement_000/Channel_000/Raw_Data\n",
      "Measurement_000/Channel_000/Spectroscopic_Indices\n",
      "Measurement_000/Channel_000/Spectroscopic_Values\n",
      "The dataset will be appended to the H5 file under the group: Measurement_000/Channel_000/Processed\n"
     ]
    }
   ],
   "source": [
    "# Open the file\n",
    "hdf = px.ioHDF5(h5_path)\n",
    "h5_file = hdf.file\n",
    "h5_main = h5_file['Measurement_000/Channel_000/Raw_Data']\n",
    "h5_pos_ind = h5_file['Measurement_000/Channel_000/Position_Indices']\n",
    "h5_pos_val = h5_file['Measurement_000/Channel_000/Position_Values']\n",
    "h5_spec_ind = h5_file['Measurement_000/Channel_000/Spectroscopic_Indices']\n",
    "h5_spec_val = h5_file['Measurement_000/Channel_000/Spectroscopic_Values']\n",
    "\n",
    "# Visualize the tree structure in the file\n",
    "print('Tree structure within the file:')\n",
    "px.hdf_utils.print_tree(h5_file)\n",
    "\n",
    "#new_pos_ind, new_pos_val = px.io.translators.utils.build_ind_val_dsets([data_shape[0], data_shape[1]], is_spectral=False,labels=['Y', 'X'], \n",
    "#                                                                       units=[pos_units, pos_units],verbose=False)\n",
    "\n",
    "new_spec_inds, new_spec_vals = px.io.translators.utils.build_ind_val_dsets([new_axes.size], is_spectral=True,\n",
    "                                                                           labels=['Energy loss'], units=[new_axes.units], \n",
    "                                                                           verbose=False)\n",
    "\n",
    "# Create three main datasets with four ancillary datasets\n",
    "ds_proc = px.MicroDataset('Processed_Data', sig_data, dtype=h5_main.dtype)\n",
    "px.hdf_utils.copy_main_attributes(h5_main, ds_proc)\n",
    "ds_pos_ind = px.MicroDataset('Position_Indices', h5_pos_ind[()], dtype=h5_pos_ind.dtype)\n",
    "px.hdf_utils.copy_main_attributes(h5_pos_ind, ds_pos_ind)\n",
    "ds_pos_val = px.MicroDataset('Position_Values', h5_pos_val[()], dtype=h5_pos_val.dtype)\n",
    "px.hdf_utils.copy_main_attributes(h5_pos_val, ds_pos_val)\n",
    "ds_spec_ind = px.MicroDataset('Spectroscopic_Indices', new_spec_inds[()], dtype=h5_spec_ind.dtype)\n",
    "px.hdf_utils.copy_main_attributes(h5_spec_ind, ds_spec_ind)\n",
    "ds_spec_val = px.MicroDataset('Spectroscopic_Values', np.array([new_spec_vec]), dtype=h5_spec_val.dtype)\n",
    "px.hdf_utils.copy_main_attributes(h5_spec_val, ds_spec_val)\n",
    "\n",
    "ds_zlp = px.MicroDataset('Zero_Loss_Peak', zlp_data, dtype=h5_main.dtype)\n",
    "px.hdf_utils.copy_main_attributes(h5_main, ds_zlp)\n",
    "    \n",
    "ds_thickness = px.MicroDataset('Thickness', thickness, dtype=h5_main.dtype)\n",
    "\n",
    "\n",
    "\n",
    "# Add the datasets to the Group\n",
    "#source_dset_name = h5_main.parent.name.split('/')[-1]\n",
    "subtree_root_path = h5_main.parent.name[1:]\n",
    "\n",
    "h5_grp = px.MicroDataGroup('Processed', subtree_root_path)\n",
    "print('The dataset will be appended to the H5 file under the group:', subtree_root_path+'/Processed')\n",
    "\n",
    "h5_grp.addChildren([ds_proc, ds_zlp, ds_thickness, ds_pos_ind, ds_pos_val, ds_spec_ind, ds_spec_val])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write to H5 file and get reference to access the written objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created group /Measurement_000/Channel_000/Processed\n",
      "Writing attribute: machine_id with value: LakerHoo-PC\n",
      "Writing attribute: timestamp with value: 2018_01_30-15_02_33\n",
      "Wrote attributes to group: Processed \n",
      "\n",
      "Created Dataset /Measurement_000/Channel_000/Processed/Processed_Data\n",
      "Writing attribute: quantity with value: Count\n",
      "Wrote Attributes of Dataset Processed_Data \n",
      "\n",
      "Writing attribute: units with value: \n",
      "Wrote Attributes of Dataset Processed_Data \n",
      "\n",
      "Created Dataset /Measurement_000/Channel_000/Processed/Zero_Loss_Peak\n",
      "Writing attribute: quantity with value: Count\n",
      "Wrote Attributes of Dataset Zero_Loss_Peak \n",
      "\n",
      "Writing attribute: units with value: \n",
      "Wrote Attributes of Dataset Zero_Loss_Peak \n",
      "\n",
      "Created Dataset /Measurement_000/Channel_000/Processed/Thickness\n",
      "Created Dataset /Measurement_000/Channel_000/Processed/Position_Indices\n",
      "Writing attribute: quantity with value: Unknown\n",
      "Wrote Attributes of Dataset Position_Indices \n",
      "\n",
      "Writing attribute: units with value: [b'um' b'um']\n",
      "Wrote Attributes of Dataset Position_Indices \n",
      "\n",
      "Created Dataset /Measurement_000/Channel_000/Processed/Position_Values\n",
      "Writing attribute: quantity with value: Unknown\n",
      "Wrote Attributes of Dataset Position_Values \n",
      "\n",
      "Writing attribute: units with value: [b'um' b'um']\n",
      "Wrote Attributes of Dataset Position_Values \n",
      "\n",
      "Created Dataset /Measurement_000/Channel_000/Processed/Spectroscopic_Indices\n",
      "Writing attribute: quantity with value: Unknown\n",
      "Wrote Attributes of Dataset Spectroscopic_Indices \n",
      "\n",
      "Writing attribute: units with value: [b'eV']\n",
      "Wrote Attributes of Dataset Spectroscopic_Indices \n",
      "\n",
      "Created Dataset /Measurement_000/Channel_000/Processed/Spectroscopic_Values\n",
      "Writing attribute: quantity with value: Unknown\n",
      "Wrote Attributes of Dataset Spectroscopic_Values \n",
      "\n",
      "Writing attribute: units with value: [b'eV']\n",
      "Wrote Attributes of Dataset Spectroscopic_Values \n",
      "\n",
      "Finished writing to h5 file.\n",
      "Right now you got yourself a fancy folder structure. \n",
      "Make sure you do some reference linking to take advantage of the full power of HDF5.\n",
      "/\n",
      "Measurement_000\n",
      "Measurement_000/Channel_000\n",
      "Measurement_000/Channel_000/Position_Indices\n",
      "Measurement_000/Channel_000/Position_Values\n",
      "Measurement_000/Channel_000/Processed\n",
      "Measurement_000/Channel_000/Processed/Position_Indices\n",
      "Measurement_000/Channel_000/Processed/Position_Values\n",
      "Measurement_000/Channel_000/Processed/Processed_Data\n",
      "Measurement_000/Channel_000/Processed/Spectroscopic_Indices\n",
      "Measurement_000/Channel_000/Processed/Spectroscopic_Values\n",
      "Measurement_000/Channel_000/Processed/Thickness\n",
      "Measurement_000/Channel_000/Processed/Zero_Loss_Peak\n",
      "Measurement_000/Channel_000/Raw_Data\n",
      "Measurement_000/Channel_000/Spectroscopic_Indices\n",
      "Measurement_000/Channel_000/Spectroscopic_Values\n"
     ]
    }
   ],
   "source": [
    "h5_clust_refs = hdf.writeData(h5_grp, print_log=True)\n",
    "ref_sig = px.hdf_utils.getH5DsetRefs(['Processed_Data'], h5_clust_refs)[0]\n",
    "ref_zlp = px.hdf_utils.getH5DsetRefs(['Zero_Loss_Peak'], h5_clust_refs)[0]\n",
    "ref_pos_ind = px.hdf_utils.getH5DsetRefs(['Position_Indices'], h5_clust_refs)[0]\n",
    "ref_pos_val = px.hdf_utils.getH5DsetRefs(['Position_Values'], h5_clust_refs)[0]\n",
    "ref_spec_ind = px.hdf_utils.getH5DsetRefs(['Spectroscopic_Indices'], h5_clust_refs)[0]\n",
    "ref_spec_val = px.hdf_utils.getH5DsetRefs(['Spectroscopic_Values'], h5_clust_refs)[0]\n",
    "\n",
    "px.hdf_utils.print_tree(h5_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Link the two new datasets to the four `Ancillary` datasets dataset:\n",
    "* These two datasets will be the `main` dataset after linking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we already got the reference to the spectroscopic values in the first few cells\n",
    "#h5_spec_inds = px.hdf_utils.getAuxData(h5_main, 'Spectroscopic_Indices')[0]\n",
    "\n",
    "px.hdf_utils.checkAndLinkAncillary(ref_sig,\n",
    "                      ['Position_Indices', 'Position_Values'],\n",
    "                      anc_refs=[ref_pos_ind,ref_pos_val])\n",
    "px.hdf_utils.checkAndLinkAncillary(ref_sig,\n",
    "                      ['Spectroscopic_Indices', 'Spectroscopic_Values'],\n",
    "                      anc_refs=[ref_spec_ind, ref_spec_val])\n",
    "\n",
    "px.hdf_utils.checkAndLinkAncillary(ref_zlp,\n",
    "                      ['Position_Indices', 'Position_Values'],\n",
    "                      anc_refs=[ref_pos_ind,ref_pos_val])\n",
    "px.hdf_utils.checkAndLinkAncillary(ref_zlp,\n",
    "                      ['Spectroscopic_Indices', 'Spectroscopic_Values'],\n",
    "                      anc_refs=[ref_spec_ind, ref_spec_val])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Verify the new H5 file:\n",
    "* We will perform a map of certain eV to check this file. (remember to set the check point in EELS)\n",
    "* Then we will plot a EELS signal at certain spatial point. (set the spatial point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/\n",
      "Measurement_000\n",
      "Measurement_000/Channel_000\n",
      "Measurement_000/Channel_000/Position_Indices\n",
      "Measurement_000/Channel_000/Position_Values\n",
      "Measurement_000/Channel_000/Processed\n",
      "Measurement_000/Channel_000/Processed/Position_Indices\n",
      "Measurement_000/Channel_000/Processed/Position_Values\n",
      "Measurement_000/Channel_000/Processed/Processed_Data\n",
      "Measurement_000/Channel_000/Processed/Spectroscopic_Indices\n",
      "Measurement_000/Channel_000/Processed/Spectroscopic_Values\n",
      "Measurement_000/Channel_000/Processed/Thickness\n",
      "Measurement_000/Channel_000/Processed/Zero_Loss_Peak\n",
      "Measurement_000/Channel_000/Raw_Data\n",
      "Measurement_000/Channel_000/Spectroscopic_Indices\n",
      "Measurement_000/Channel_000/Spectroscopic_Values\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.image.AxesImage at 0x19e35080>,\n",
       " <matplotlib.colorbar.Colorbar at 0x19e35048>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Raw signal map at 28.5 eV')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'X')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Y')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19e60710>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Raw EELS at a single pixel: (11.0,11.0)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Energy Loss (eV)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Counts')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.image.AxesImage at 0x19e7b4a8>,\n",
       " <matplotlib.colorbar.Colorbar at 0x19ec05f8>)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Thickness map at 28.5 eV')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'X')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Y')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x19e9c0f0>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Inelastic EELS signal at : (11.0,11.0)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Energy Loss (eV)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Counts')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l3mOPPUrnueGGGyqV9dhjj5XO84IXvKBSWUuWLCmd54EHHqhU1vTp00vn2Wqr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\npfP88pe/rFRWb29v6TxbbbVVpbKqfPZ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sqKwrrrii7jz7779/Q2VJNW/pPKLe3t6Gypo7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XaJXOfBBx+M1dZuu+0WuU7c9xUnJlq0aFGstjbccMPIdZYvXx6rrXKqZNzo7g+Y2QbApUAfYApw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\nRERERESkYSgIFhERERERkYbx/9nqpWPx3Ll1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a500e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with h5py.File(h5_path, mode='r') as h5_file:\n",
    "    # See if a tree has been created within the hdf5 file:\n",
    "    px.hdf_utils.print_tree(h5_file)\n",
    "\n",
    "    h5_proc = h5_file['Measurement_000/Channel_000/Processed/Processed_Data']\n",
    "    h5_thick = h5_file['Measurement_000/Channel_000/Processed/Thickness'] \n",
    "    h5_main = h5_file['Measurement_000/Channel_000/Raw_Data']\n",
    "    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(18, 12))\n",
    "    \n",
    "    ## 1st row for Plot Raw_Data\n",
    "    # set check point in EELS to do the map:\n",
    "    spec_point = 28.5\n",
    "    data_point = (spec_point-spec_min)//spec_pixel_size + 1\n",
    "    #print(data_point)\n",
    "    #h5_main[()].shape\n",
    "    spat_map = np.reshape(h5_main[:, data_point], (data_shape[0], data_shape[1]))\n",
    "    px.plot_utils.plot_map(axes[0,0],spat_map, origin='lower')\n",
    "    axes[0,0].set_title('Raw signal map at ' + str(spec_point) + ' eV')\n",
    "    axes[0,0].set_xlabel('X')\n",
    "    axes[0,0].set_ylabel('Y')  \n",
    "    #set spatial point to check EELS:\n",
    "    spat_point = [np.floor(data_shape[0]/2),np.floor(data_shape[1]/2)]\n",
    "    axes[0,1].plot(np.linspace(spec_min, spec_min+spec_pixel_size*(h5_main.shape[1]-1), h5_main.shape[1]),\n",
    "                 h5_main[spat_point[0]*data_shape[1]+spat_point[1]])\n",
    "    axes[0,1].set_title('Raw EELS at a single pixel:'+' ('+str(spat_point[0])+','+str(spat_point[1])+')')\n",
    "    axes[0,1].set_xlabel('Energy Loss (eV)')\n",
    "    axes[0,1].set_ylabel('Counts');\n",
    "    axes[0,1].set_yticks([])\n",
    "    \n",
    "    ## 2st row for Plot Processed_Data\n",
    "    # set check point in EELS to do the map:\n",
    "    spec_point = 28.5\n",
    "    data_point = (spec_point-spec_min)//spec_pixel_size + 1\n",
    "    #print(data_point)\n",
    "    #h5_proc[()].shape\n",
    "    #spat_map = np.reshape(h5_proc[:, data_point], (data_shape[0], data_shape[1]))\n",
    "    px.plot_utils.plot_map(axes[1,0],h5_thick, origin='lower')\n",
    "    axes[1,0].set_title('Thickness map at ' + str(spec_point) + ' eV')\n",
    "    axes[1,0].set_xlabel('X')\n",
    "    axes[1,0].set_ylabel('Y')\n",
    "    #set spatial point to check EELS:\n",
    "    spat_point = [np.floor(data_shape[0]/2),np.floor(data_shape[1]/2)]\n",
    "    axes[1,1].plot(np.linspace(spec_min, spec_min+spec_pixel_size*(h5_proc.shape[1]-1), h5_proc.shape[1]),\n",
    "                 h5_proc[spat_point[0]*data_shape[1]+spat_point[1]],'r')\n",
    "    axes[1,1].set_title('Inelastic EELS signal at :'+' ('+str(spat_point[0])+','+str(spat_point[1])+')')\n",
    "    axes[1,1].set_xlabel('Energy Loss (eV)')\n",
    "    axes[1,1].set_ylabel('Counts');\n",
    "    axes[1,1].set_yticks([])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}