{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Write/read SIDpy Dataset via pyNSID\n",
    "\n",
    "*Author: Maxim Ziatdinov*\n",
    "\n",
    "*Date: September 2020*\n",
    "\n",
    "update: \n",
    "- *Gerd Duscher 01/2021 (compatibility to pyNSID version 0.0.2)*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A fast introduction into how to write SIDpy datasets to NSID formatted HDF5 files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start with standard imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ensure python 3 compatibility:\n",
    "from __future__ import (absolute_import, division, print_function,\n",
    "                        unicode_literals)\n",
    "\n",
    "import warnings\n",
    "\n",
    "import h5py\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "\n",
    "# we will also need a sidpy package\n",
    "try:\n",
    "    import sidpy\n",
    "except ModuleNotFoundError:\n",
    "    !pip3 install sidpy\n",
    "    import sidpy\n",
    "\n",
    "import pyNSID\n",
    "\n",
    "warnings.filterwarnings(\"ignore\", module=\"numpy.core.fromnumeric\")\n",
    "warnings.filterwarnings(\"ignore\", module=\"pyNSID.io.nsi_reader\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating sidpy.Dataset object(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a simple sidpy Dataset from a numpy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.60 kB </td> <td> 1.60 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (4, 5, 10) </td> <td> (4, 5, 10) </td></tr>\n",
       "    <tr><th> Count </th><td> 1 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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      "text/plain": [
       "sidpy.Dataset of type UNKNOWN with:\n",
       " dask.array<new, shape=(4, 5, 10), dtype=float64, chunksize=(4, 5, 10), chunktype=numpy.ndarray>\n",
       " data contains: generic (generic)\n",
       " and Dimensions: \n",
       "a:  generic (generic) of size (4,)\n",
       "b:  generic (generic) of size (5,)\n",
       "c:  generic (generic) of size (10,)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = sidpy.Dataset.from_array(np.random.random([4, 5, 10]), name='new')\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also define the dataset attributes..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.data_type = 'SPECTRAL_IMAGE'\n",
    "dataset.units = 'nA'\n",
    "dataset.quantity = 'Current'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and set individual dimensions. In the case of spectroscopic datasets, the first two dimensions are typically spatial units (e.g. nm) and the third one can be energy (e.g. $meV$ or $nm^{-1}$)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset.set_dimension(0, sidpy.Dimension(np.arange(dataset.shape[0]), 'x',\n",
    "                                        units='nm', quantity='Length',\n",
    "                                        dimension_type='spatial'))\n",
    "dataset.set_dimension(1, sidpy.Dimension(np.linspace(-2, 2, num=dataset.shape[1], endpoint=True), 'y', \n",
    "                                        units='nm', quantity='Length',\n",
    "                                        dimension_type='spatial'))\n",
    "dataset.set_dimension(2, sidpy.Dimension(np.sin(np.linspace(0, 2 * np.pi, num=dataset.shape[2])), 'bias',\n",
    "                                        units='mV', quantity='Voltage',\n",
    "                                        dimension_type='spectral'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x:  Length (nm) of size (4,)\n",
      "y:  Length (nm) of size (5,)\n",
      "bias:  Voltage (mV) of size (10,)\n"
     ]
    }
   ],
   "source": [
    "print(dataset.dim_0)\n",
    "print(dataset.dim_1)\n",
    "print(dataset.dim_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing sidpy.Dataset object(s) to HDF5 files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load NSID-formatted h5 file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hf = h5py.File(\"test.hf5\", 'a')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a new channel where we are going to save our sidpy dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel_000\n"
     ]
    }
   ],
   "source": [
    "hf.create_group('Measurement_000/Channel_000')\n",
    "print(*hf[\"Measurement_000\"].keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's write our sidpy dataset into the newly created channel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gduscher\\Anaconda3\\lib\\site-packages\\pyNSID\\io\\hdf_utils.py:351: FutureWarning: validate_h5_dimension may be removed in a future version\n",
      "  warn('validate_h5_dimension may be removed in a future version',\n"
     ]
    }
   ],
   "source": [
    "pyNSID.hdf_io.write_nsid_dataset(dataset, hf['Measurement_000/Channel_000'], main_data_name=\"new_spectrum\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close h5 file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "hf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading sidpy.Dataset object(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load back the file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel_000\n"
     ]
    }
   ],
   "source": [
    "hf = h5py.File(\"test.hf5\", 'r+')\n",
    "print(*hf[\"Measurement_000\"].keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<HDF5 dataset \"new_spectrum\": shape (4, 5, 10), type \"<f8\">"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_hdf5  =  pyNSID.io.hdf_utils.find_dataset(hf,'new_spectrum')[0]\n",
    "dataset_hdf5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "hf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the dataset stored in HDF5 format as a sidpy object (Dataset) using NSIDReader:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dr = pyNSID.NSIDReader('test.hf5')\n",
    "dataset_sid = dr.read()[0]\n",
    "assert isinstance(dataset_sid, sidpy.Dataset)\n",
    "dataset_sid.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sidpy.Dataset of type SPECTRAL_IMAGE with:\n",
      " dask.array<generic, shape=(4, 5, 10), dtype=float64, chunksize=(4, 5, 10), chunktype=numpy.ndarray>\n",
      " data contains: Current (nA)\n",
      " and Dimensions: \n",
      "x:  Length (nm) of size (4,)\n",
      "y:  Length (nm) of size (5,)\n",
      "bias:  Voltage (mV) of size (10,)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.60 kB </td> <td> 1.60 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (4, 5, 10) </td> <td> (4, 5, 10) </td></tr>\n",
       "    <tr><th> Count </th><td> 1 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "  <line x1=\"38\" y1=\"28\" x2=\"38\" y2=\"88\" style=\"stroke-width:2\" />\n",
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       "  <text x=\"98.235294\" y=\"108.235294\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >10</text>\n",
       "  <text x=\"178.235294\" y=\"58.235294\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,178.235294,58.235294)\">5</text>\n",
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       "</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "sidpy.Dataset of type SPECTRAL_IMAGE with:\n",
       " dask.array<generic, shape=(4, 5, 10), dtype=float64, chunksize=(4, 5, 10), chunktype=numpy.ndarray>\n",
       " data contains: Current (nA)\n",
       " and Dimensions: \n",
       "x:  Length (nm) of size (4,)\n",
       "y:  Length (nm) of size (5,)\n",
       "bias:  Voltage (mV) of size (10,)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(dataset_sid)\n",
    "dataset_sid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Close File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_sid.h5_dataset.file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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