{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to the openPMD-viewer API\n",
    "\n",
    "This notebook explains how to use the openPMD-viewer API, in order to access and plot the data stored in a set of openPMD files. You can run this notebook locally by downloading it from [this link](https://github.com/openPMD/openPMD-viewer/blob/dev/docs/source/tutorials/1_Introduction-to-the-API.ipynb).\n",
    "\n",
    "Note that, even though this document is from a Jupyter notebook, the openPMD-viewer API does not require a Jupyter notebook, and can be run in a normal Python environment.\n",
    "It can typically be used to write Python scripts, which perform pre-determined data analysis operations.\n",
    "\n",
    "## (optional) Preparing this notebook to run it locally\n",
    "\n",
    "If you choose to run this notebook on your local machine, you will need to download the openPMD data files which will then be visualized. To do so, and execute the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "def download_if_absent( dataset_name ):\n",
    "    \"Function that downloads and decompress a chosen dataset\"\n",
    "    if os.path.exists( dataset_name ) is False:\n",
    "        import wget, tarfile\n",
    "        tar_name = \"%s.tar.gz\" %dataset_name\n",
    "        url = \"https://github.com/openPMD/openPMD-example-datasets/raw/draft/%s\" %tar_name\n",
    "        wget.download( url, tar_name )\n",
    "        with tarfile.open( tar_name ) as tar_file:\n",
    "            tar_file.extractall()\n",
    "        os.remove( tar_name )\n",
    "\n",
    "download_if_absent( 'example-2d' )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In addition, we choose here to incorporate the plots inside the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the API\n",
    "\n",
    "In order to start using the API:\n",
    "\n",
    "- Load the class `OpenPMDTimeSeries` from the module `openpmd_viewer`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openpmd_viewer import OpenPMDTimeSeries"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a time series object by pointing to the folder which contains the corresponding openPMD data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts = OpenPMDTimeSeries('./example-2d/hdf5/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting iterations and time values\n",
    "\n",
    "One can extract all available iterations and corresponding time values from the time series into a numpy array object by doing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "it = ts.iterations\n",
    "time = ts.t"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the API for the fields\n",
    "\n",
    "### Accessing the field data\n",
    "\n",
    "The list of available fields is accessible through `ts.avail_fields`.\n",
    "\n",
    "The fields can be read with the method `get_field`. \n",
    "\n",
    "The user can either require a time (in seconds) or an iteration (an integer). When giving a time, it is not needed to provide the exact time of an available iteration, as the time of the closest available value will be used instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One example\n",
    "rho, info_rho = ts.get_field( iteration=300, field='rho' )\n",
    "# Another example\n",
    "Ex, info_Ex = ts.get_field( t=100.e-15,  field='E', coord='x' )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `get_field` returns two quantities:\n",
    "- A 2D array containing the values of the requested field.\n",
    "- An object containing metainformation about the extent of the grid (e.g `info_rho.z` and `info_rho.x` respectively return an array of the z and x positions).\n",
    "\n",
    "These two objects can then be used in a Python environment to perform numerical analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the field data \n",
    "\n",
    "A user could directly plot the extracted array `Ex` by using e.g. matplotlib's `imshow`.\n",
    "\n",
    "However, for convenience, openPMD-viewer can also directly plot the data, if the argument `plot = True` is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111db1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ex, info_Ex = ts.get_field( t=100.e-15,  field='E', coord='x', plot=True )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing or inconsistent argument"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If an argument is missing or inconsistent, an exception will be raised and a helper message will be printed."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional documentation\n",
    "\n",
    "Additional documentation on `get_field` can be obtained by reading its docstring. This can be done for instance by executing the following command in an IPython environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.get_field?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, additional documentation about the meta-information object which is returned by get_field can be obtained by adding `?` after any metainformation object. For instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "info_rho?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API for the particles\n",
    "\n",
    "### Accessing the particle data\n",
    "\n",
    "The particle quantities can be read by using the method `get_particle`. \n",
    "\n",
    "Again, the user can give either a physical time, or an iteration. Additionally, the user can request several particle quantities simultaneously, by passing a list of requested quantities (`var_list`). The method `get_particle` then returns a list of 1darray (one per requested quantity in `var_list`, returned in the same order). These 1darrays have one element per macroparticle.\n",
    "\n",
    "Finally, the user also needs to specify the name of the considered species. The list of available species is accessible through `ts.avail_species`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hydrogen1+', 'electrons']\n"
     ]
    }
   ],
   "source": [
    "# Look for the available species\n",
    "print(ts.avail_species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One example: extracting several quantities\n",
    "x, y, z = ts.get_particle( var_list=['x','y', 'z'], iteration=300, species='Hydrogen1+') \n",
    "# Another example: extracting 1 quantity \n",
    "# (notice the comma after z, so that z is a 1darray, not a list)\n",
    "z, = ts.get_particle( var_list=['z'], t=150.e-15, species='electrons')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the particle data\n",
    "\n",
    "The openPMD-viewer provides plotting support when the argument `plot = True` is set:\n",
    "\n",
    "- When only one quantity is requested, a histogram of this quantity is plotted. (This histogram takes into account the weight of the macroparticles.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b1d42e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ux = ts.get_particle( var_list=['ux'], iteration=400, species='electrons', plot=True )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- When two quantities are requested, a 2d histogram is plotted. (It also takes into account the weight of the macroparticles.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b502a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z, uz = ts.get_particle( var_list=['z','uz'], iteration=400, species='electrons', plot=True, vmax=3e12 )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing or inconsistent arguments\n",
    "\n",
    "As for the fields, if an argument is missing or inconsistent, an exception will be raised and an helper message will be printed."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Additional documentation\n",
    "\n",
    "As for the `get_field` method, the documentation on `get_particle` can be obtained by reading its docstring. This can be done for instance by executing the following command in an IPython environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "ts.get_particle?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Continue this tutorial\n",
    "\n",
    "- To learn more about mesh geometries and the corresponding API, look at the notebook `2_Specific-field-geometries`.\n",
    "\n",
    "- To learn about the openPMD-viewer GUI, look at the notebook `3_Introduction-to-the-GUI`."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}