{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2D Image\n",
    "\n",
    "**Suhas Somnath**\n",
    "\n",
    "10/12/2018\n",
    "\n",
    "**This example illustrates how a 2D grayscale image would be represented in the Universal Spectroscopy and\n",
    "Imaging Data (USID) schema and stored in a Hierarchical Data Format (HDF5) file, also referred to as the h5USID file.**\n",
    "\n",
    "This document is intended as a supplement to the explanation about the [USID model](../../usid_model.html)\n",
    "\n",
    "Please consider downloading this document as a Jupyter notebook using the button at the bottom of this document.\n",
    "\n",
    "Prerequisites:\n",
    "--------------\n",
    "We recommend that you read about the [USID model](../../usid_model.htm)\n",
    "\n",
    "We will be making use of the ``pyUSID`` package at multiple places to illustrate the central point. While it is\n",
    "recommended / a bonus, it is not absolutely necessary that the reader understands how the specific ``pyUSID`` functions\n",
    "work or why they were used in order to understand the data representation itself.\n",
    "Examples about these functions can be found in other documentation on pyUSID and the reader is encouraged to read the\n",
    "supplementary documents.\n",
    "\n",
    "### Import all necessary packages\n",
    "The main packages necessary for this example are ``h5py``, ``PIL`` (pillow), ``matplotlib``, and ``sidpy``, in addition to ``pyUSID``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import subprocess\n",
    "import sys\n",
    "import os\n",
    "from warnings import warn\n",
    "import h5py\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "def install(package):\n",
    "    subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
    "\n",
    "\n",
    "try:\n",
    "    # This package is not part of anaconda and may need to be installed.\n",
    "    import wget\n",
    "except ImportError:\n",
    "    warn('wget not found.  Will install with pip.')\n",
    "    import pip\n",
    "    install('wget')\n",
    "    import wget\n",
    "\n",
    "# Finally import pyUSID.\n",
    "try:\n",
    "    import pyUSID as usid\n",
    "    import sidpy\n",
    "except ImportError:\n",
    "    warn('pyUSID not found.  Will install with pip.')\n",
    "    import pip\n",
    "    install('pyUSID')\n",
    "    import sidpy\n",
    "    import pyUSID as usid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source image\n",
    "-------------\n",
    "For this example, we will be working with a simple grayscale image.\n",
    "\n",
    "### Download from GitHub\n",
    "As mentioned earlier, this image is available on the USID repository and can be accessed directly as well.\n",
    "Here, we will simply download the file using ``wget``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "image_path = 'temp.tif'\n",
    "url = 'https://raw.githubusercontent.com/pycroscopy/USID/master/data/simulated_STEM_Image.tif'\n",
    "if os.path.exists(image_path):\n",
    "    os.remove(image_path)\n",
    "_ = wget.download(url, image_path, bar=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More about this image\n",
    "Lets visualize this image and learn about its original shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "img_obj = Image.open(image_path)\n",
    "# Convert to grayscale:\n",
    "img_obj = img_obj.convert(mode=\"L\")\n",
    "orig_image = np.asarray(img_obj)\n",
    "\n",
    "fig, axis = plt.subplots()\n",
    "usid.plot_utils.plot_map(axis, orig_image, num_ticks=5)\n",
    "axis.set_title('Image of shape: {}'.format(orig_image.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "h5USID File\n",
    "-----------\n",
    "### Download from GitHub\n",
    "Similarly the corresponding h5USID dataset is also available on the USID repository.\n",
    "Here, we will simply download the file using ``wget``:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "h5_path = 'temp.h5'\n",
    "url = 'https://raw.githubusercontent.com/pycroscopy/USID/master/data/simulated_STEM_Image.h5'\n",
    "if os.path.exists(h5_path):\n",
    "    os.remove(h5_path)\n",
    "_ = wget.download(url, h5_path, bar=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open the file\n",
    "Let us open the file and look at its contents using\n",
    "[sidpy.hdf_utils.print_tree()](https://pycroscopy.github.io/sidpy/notebooks/03_hdf5/hdf_utils_read.html#print_tree())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "h5_file = h5py.File(h5_path, mode='r')\n",
    "usid.hdf_utils.print_tree(h5_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Access the ``Main`` Dataset containing the image of interest\n",
    "------------------------------------------------------------\n",
    "Clearly, this file contains a single `Measurement` which has a single [Channel](../../usid_model.html#channels).\n",
    "We can access the [Main Dataset](../../usid_model.html#main-datasets) where all the information is located in\n",
    "multiple ways. Given that this file contains just a single ``Main Dataset`` we can conveniently use the\n",
    "[pyUSID.hdf_utils.get_all_main()](https://pycroscopy.github.io/sidpy/notebooks/03_hdf5/hdf_utils_read.html#get_all_main()) function.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "h5_main = usid.hdf_utils.get_all_main(h5_file)[-1]\n",
    "print(h5_main)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, ``h5_main`` is a [USIDataset](https://pycroscopy.github.io/pyUSID/notebooks/user_guide/usi_dataset.html), which can be thought of as a supercharged\n",
    "HDF5 Dataset that is not only aware of the contents of the plain ``Raw_Data`` dataset but also its links to the\n",
    "[Ancillary Datasets](../../usid_model.html#ancillary-datasets) that make it a ``Main Dataset``.\n",
    "\n",
    "Understanding Dimensionality\n",
    "----------------------------\n",
    "What is more is that the above print statement shows that this ``Main Dataset`` has two ``Position Dimensions`` -\n",
    "``X`` and ``Y`` each of size ``256`` and a single ``Spectroscopic Dimension`` - ``None`` which was never varied\n",
    "at each of the locations in the dataset.\n",
    "Therefore, this dataset is really just a simple 2D dataset with both dimensions being position dimensions.\n",
    "In other words, this dataset is just a simple 2D spatial / locations map.\n",
    "\n",
    "The original shape of the source image was ``(256, 256)``. However, recall that USID requires all position dimensions\n",
    "to be flattened along the first axis and all spectroscopic dimensions to be flattened along the second axis of the\n",
    "``Main Dataset``. In other words, this means that the image needs to be represented as a ``(256 * 256, 1)`` in USID.\n",
    "In USID, this shape needs to (explicitly) include a spectroscopic axis to state that a single data point was recorded\n",
    "at each location.\n",
    "\n",
    "Visualize the ``Main`` Dataset\n",
    "--------------------------\n",
    "Now lets visualize the contents within this ``Main Dataset`` using the ``USIDataset's`` built-in\n",
    "[visualize()](../user_guide/usi_dataset.html#Interactive-Visualization) function. Clearly, this dataset is indeed\n",
    "a simple 2D image\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "usid.plot_utils.use_nice_plot_params()\n",
    "h5_main.visualize(num_ticks=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ancillary Datasets\n",
    "------------------\n",
    "As mentioned in the documentation on USID, ``Ancillary Datasets`` are required to complete the information for any\n",
    "dataset. Specifically, these datasets need to provide information about the values against which measurements were\n",
    "acquired, in addition to explaining the original dimensionality (2 in this case) of the original dataset. Let's look\n",
    "at the ancillary datasets and see what sort of information they provide. We can access the ``Ancillary Datasets``\n",
    "linked to the ``Main Dataset`` (``h5_main``) just like a property of the object.\n",
    "\n",
    "Ancillary Position Datasets\n",
    "---------------------------\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "print('Position Indices:')\n",
    "print('-------------------------')\n",
    "print(h5_main.h5_pos_inds)\n",
    "print('\\nPosition Values:')\n",
    "print('-------------------------')\n",
    "print(h5_main.h5_pos_vals)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall from the USID definition that the shape of the Position Ancillary datasets is ``(N, P)`` where ``N`` is the\n",
    "number of Position dimensions and the ``P`` is the number of locations over which data was recorded. Here, we have\n",
    "two position dimensions. Therefore ``N`` is ``2``. ``P`` matches with the first axis of the shape of ``h5_main``\n",
    "which is ``65536``. Generally, there is no need to remember these rules or construct these ancillary datasets\n",
    "manually since pyUSID has several functions that automatically simplify this process.\n",
    "\n",
    "### Visualize the contents of the Position Ancillary Datasets\n",
    "Notice below that there are two sets of lines, one for each dimension. The blue lines on the left-hand column\n",
    "appear solid simply because this dimension (``X`` or columns) varies much faster than the other dimension (``Y`` or\n",
    "rows). The first few rows of the dataset are visualized on the right-hand column.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "fig, all_axes = plt.subplots(ncols=2, nrows=2, figsize=(8, 8))\n",
    "\n",
    "for axes, h5_pos_dset, dset_name in zip(all_axes,\n",
    "                                        [h5_main.h5_pos_inds, h5_main.h5_pos_vals],\n",
    "                                        ['Position Indices', 'Position Values']):\n",
    "    axes[0].plot(h5_pos_dset[()])\n",
    "    axes[0].set_title('Full dataset')\n",
    "    axes[1].set_title('First 1024 rows only')\n",
    "    axes[1].plot(h5_pos_dset[:1024])\n",
    "    for axis in axes.flat:\n",
    "        axis.set_xlabel('Row in ' + dset_name)\n",
    "        axis.set_ylabel(dset_name)\n",
    "        axis.legend(h5_main.pos_dim_labels)\n",
    "\n",
    "for axis in all_axes[1]:\n",
    "    axis.legend(h5_main.pos_dim_descriptors)\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making sense of the visualization\n",
    "Given that the columns vary faster\n",
    "than the rows means that the contents of each row of the image have been stored end-to-end in the ``Main Dataset``\n",
    "as opposed to on top of each other as in the original 2D image.\n",
    "\n",
    "### Attributes associated with the Position Indices Dataset\n",
    "Just looking at the shape and values of the Position ancillary datasets does not provide all the information.\n",
    "Recall that the ancillary datasets need to have some mandatory attributes like ``labels`` and ``units`` that\n",
    "describe the quantity and units for each of the dimensions:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "for key, val in sidpy.hdf_utils.get_attributes(h5_main.h5_pos_inds).items():\n",
    "    print('{} : {}'.format(key, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ancillary Spectroscopic Datasets\n",
    "--------------------------------\n",
    "Given that this is a 2D image where data was not acquired as a function of some independent parameter at each\n",
    "location, the spectroscopic datasets will contain bare minimum information\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "print('Spectroscopic Indices:')\n",
    "print('-------------------------')\n",
    "print(h5_main.h5_spec_inds)\n",
    "print('containing:')\n",
    "print(h5_main.h5_spec_inds[()])\n",
    "print('\\nSpectroscopic Values:')\n",
    "print('-------------------------')\n",
    "print(h5_main.h5_spec_vals)\n",
    "print('containing:')\n",
    "print(h5_main.h5_spec_vals[()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that as explained above, these spectroscopic datasets only contain a single value for the `fake` spectroscopic\n",
    "axis over which the data was collected. Regardless of the fact that a single data poing was collected at each\n",
    "location, the Spectroscopic datasets should be two dimensional in shape (one element in each axis) according the\n",
    "USID rules.\n",
    "\n",
    "Regardless of how uninformative the spectroscopic Datasets seem for this specific example, they are still necessary\n",
    "for the ``Raw_Data`` dataset to be a ``Main Dataset``.\n",
    "\n",
    "### Attributes within the Spectroscopic Indices Dataset\n",
    "Again, the attributes of Spectroscopic Datasets show mandatory information about the Spectroscopic dimensions such as\n",
    "the quantity (``labels``) and ``units``:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "for key, val in sidpy.hdf_utils.get_attributes(h5_main.h5_spec_inds).items():\n",
    "    print('{} : {}'.format(key, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Attempting to visualize the first few rows of the image manually\n",
    "----------------------------------------------------------------\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "print(h5_main.pos_dim_labels)\n",
    "print(h5_main.pos_dim_sizes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "rows_to_read = 50\n",
    "num_cols = h5_main.pos_dim_sizes[1]\n",
    "first_few_rows_1D = h5_main[: rows_to_read * num_cols, :]\n",
    "print(first_few_rows_1D.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "first_few_rows_2D = np.reshape(first_few_rows_1D, (rows_to_read, num_cols))\n",
    "print(first_few_rows_2D.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "fig, axis = plt.subplots(figsize=(4, 1))\n",
    "axis.imshow(first_few_rows_2D, origin='lower')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clean up\n",
    "--------\n",
    "Finally lets close the HDF5 file.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "h5_file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will even delete the HDF5 file in addition to the source 2D image. Please comment out the following lines if\n",
    "you want to look at the HDF5 file using software like HDFView or work with the 2D image\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "os.remove(h5_path)\n",
    "os.remove(image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}