index.ipynb

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    "import os\n",
    "\n",
    "# Ignore numpy warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "#%matplotlib notebook\n",
    "\n",
    "# Use seaborn settings.\n",
    "import seaborn as sns\n",
    "sns.set(\n",
    "        #context=\"paper\",\n",
    "        context=\"talk\",\n",
    "        #context=\"poster\",\n",
    "        style='darkgrid',\n",
    "        #style=\"dark\",\n",
    "        #palette='deep',\n",
    "        font='sans-serif', \n",
    "        #font_scale=1.0, \n",
    "        #color_codes=False, \n",
    "        rc={'figure.figsize': (12, 8.0)}, # width and height in inches.\n",
    "        #rc={'figure.figsize': (12, 12.0)}, # width and height in inches.\n",
    ")\n",
    "\n",
    "import IPython\n",
    "\n",
    "def _embed(src, width=\"800\", height=\"400\"):\n",
    "    return IPython.display.IFrame(src=src, width=str(width), height=str(height))\n",
    "\n",
    "from IPython.core.magic import (register_line_magic, register_cell_magic,\n",
    "                                register_line_cell_magic)\n",
    "\n",
    "@register_line_magic\n",
    "def embed(line):\n",
    "    \"my line magic\"\n",
    "    #return line\n",
    "    return _embed(line)\n",
    "\n",
    "# We delete these to avoid name conflicts for automagic to work\n",
    "del embed\n",
    "#del lcmagic\n",
    "\n",
    "# Theme\n",
    "# import jtplot module in notebook\n",
    "#from jupyterthemes import jtplot\n",
    "\n",
    "# choose which theme to inherit plotting style from\n",
    "# onedork | grade3 | oceans16 | chesterish | monokai | solarizedl | solarizedd\n",
    "#jtplot.style(theme='monokai')\n",
    "\n",
    "# set \"context\" (paper, notebook, talk, poster)\n",
    "# scale font-size of ticklabels, legend, etc.\n",
    "# remove spines from x and y axes and make grid dashed\n",
    "#jtplot.style(context='talk', fscale=1.4, spines=False, gridlines='--')\n",
    "\n",
    "# turn on X- and Y-axis tick marks (default=False)\n",
    "# turn off the axis grid lines (default=True)\n",
    "# and set the default figure size\n",
    "#jtplot.style(ticks=True, grid=False, figsize=(6, 4.5))\n",
    "\n",
    "# reset default matplotlib rcParams\n",
    "#jtplot.reset()\n",
    "\n",
    "from abipy import abilab\n",
    "import abipy.data as abidata\n",
    "\n",
    "si_pseudo = os.path.join(abidata.pseudo_dir, \"14si.pspnc\")"
   ]
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   "source": [
    "# New features of AbiPy v0.7\n",
    "\n",
    "### M. Giantomassi and the AbiPy group\n",
    "\n",
    "9th international ABINIT developer workshop<br> \n",
    "<small>20-22nd May 2019 - Louvain-la-Neuve, Belgium</small>\n",
    "\n",
    "<!-- link rel=\"stylesheet\" href=\"reveal.js/css/theme/sky.css\" id=\"theme\" -->\n",
    "<img src=\"./assets/intro_logos.png\" width=\"85%\" heigh=\"15%\" align=\"center\">\n",
    "<hr> \n",
    "\n",
    "* These slides have been generated using [jupyter](https://jupyter.org), [nbconvert](https://github.com/jupyter/nbconvert) and [revealjs](https://revealjs.com/)\n",
    "* The notebook can be downloaded from this [github repo](https://github.com/gmatteo/abipy_intro_abidev2019)\n",
    "* To install and configure the software, follow these [installation instructions](https://github.com/abinit/abipy#getting-abipy)\n",
    "\n",
    "Use the Space key to navigate through all slides."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<img src=\"./assets/abipy_logo.jpg\" width=\"55%\" align=\"center\">\n",
    "\n",
    "## What is AbiPy?\n",
    "\n",
    "#### Python package for:\n",
    "\n",
    "   * Generating ABINIT input files automatically\n",
    "   * Post-processing output results (*netcdf* and *text* files)\n",
    "   * Interfacing ABINIT with external tools (e.g. [Vesta](http://jp-minerals.org/vesta/en/))\n",
    "   * Creating and executing workflows (band structures, DFPT, $GW$, BSE…)\n",
    "\n",
    "#### Why python?\n",
    "\n",
    "   * Easy to use and to learn\n",
    "   * Great support for science ([numpy](http://www.numpy.org/), [scipy](https://www.scipy.org/), [pandas](http://pandas.pydata.org/), [matplotlib](https://matplotlib.org/) …)\n",
    "   * Interactive environments ([ipython](http://ipython.org/), [jupyter notebooks](https://jupyter.org/), GUIs)\n",
    "   * More powerful and flexible than Fortran for implementing the high-level logic needed in modern ab-initio workflows\n",
    "   * [pymatgen](https://github.com/materialsproject) ecosystem and the [materials project database](https://www.materialsproject.org/)…"
   ]
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   "source": [
    "## How to install AbiPy\n",
    "\n",
    "<!-- From the most simple to the most complex one, you have three options:-->\n",
    "\n",
    "Using [pip](https://pypi.org/project/pip/) and python wheels:\n",
    "\n",
    "        pip install abipy --user\n",
    "\n",
    "Using [conda](https://conda.io/en/latest/) (*recommended*):\n",
    "\n",
    "        conda install abipy --channel abinit \n",
    "\n",
    "From the [github repository](https://github.com/abinit/abipy) (*develop mode*):\n",
    "\n",
    "        git clone https://github.com/abinit/abipy.git\n",
    "        cd abipy \n",
    "        python setup.py develop\n",
    "    \n",
    "<!--\n",
    "https://pypi.org/project/abipy/\n",
    "https://anaconda.org/abinit/abipy\n",
    "-->\n",
    "\n",
    "<hr>\n",
    "\n",
    "For further info see http://abinit.github.io/abipy/installation.html"
   ]
  },
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   "source": [
    "## AbiPy documentation with galleries of matplotlib examples and workflows\n",
    "\n",
    "<!--\n",
    "* http://abinit.github.io/abipy/index.html\n",
    "\n",
    "* Jupyter notebooks \n",
    "* Gallery of matplotlib examples and flows\n",
    "* abitutorial github repo with additional example\n",
    "\n",
    "<img src=\"./assets/abipy_doc_homepage.png\" width=\"100%\" align=\"center\">\n",
    "-->"
   ]
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    "%embed https://abinit.github.io/abipy/index.html"
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   },
   "source": [
    "##  Jupyter notebooks with [examples and lessons](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/index.ipynb) inspired by the official tutorials\n",
    "\n",
    "<!-- img src=\"./assets/abipy_notebooks.png\" width=\"100%\" align=\"center\" -->"
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    "%embed https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/index.ipynb"
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   "source": [
    "<img src=\"./assets/Data-Analysis.jpg\" width=\"95%\" align=\"center\">\n",
    "\n",
    "* Since AbiPy is not restricted to high-throughput, we'll show how to use the <u>terminal</u> to analyze calculations\n",
    "\n",
    "* Well, a python script would be much more flexible but the goal here is to show \n",
    "  that one can replace *grep*, *vim*, *gnuplot* with AbiPy\n",
    "\n",
    "<div class=\"alert alert-warning\">\n",
    "No perl scripts were harmed in the making of this notebook\n",
    "</div>"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Command line interface\n",
    "\n",
    "* *abiopen.py* ➝ Open output files inside *ipython* or print/visualize file\n",
    "* *abistruct.py* ➝ Operate on crystalline structures read from file\n",
    "* *abicomp.py* ➝ Compare multiple files (i.e. convergence studies)\n",
    "* *abiview.py* ➝ Quick visualization of output files\n",
    "* *abinp.py* ➝ Generate input files for typical calculations\n",
    "    \n",
    "### Documentation \n",
    "\n",
    "* `abistruct.py --help` for manpage\n",
    "* `abistruct.py COMMAND --help` for help about `COMMAND`\n",
    "<hr>\n",
    "HTML documentation available at http://abinit.github.io/abipy/scripts/index.html\n",
    "\n",
    "### Examples\n",
    "<p></p>\n",
    "\n",
    "```shell\n",
    "abistruct.py spglib si_scf_GSR.nc\n",
    "abistruct.py convert si_scf_GSR.nc -f cif\n",
    "abiopen.py si_scf_GSR.nc --print\n",
    "```\n",
    "\n",
    "### and many more..."
   ]
  },
  {
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   "metadata": {
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   },
   "source": [
    "#### Print results stored in FILE  (more than 45 file extensions supported)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================= File Info =================================\r\n",
      "Name: si_scf_GSR.nc\r\n",
      "Directory: /Users/gmatteo/talks/abipy_intro_abidev2019\r\n",
      "Size: 14.83 kb\r\n",
      "Access Time: Sun May 19 20:26:25 2019\r\n",
      "Modification Time: Sun May 12 04:20:35 2019\r\n",
      "Change Time: Sun May 12 04:20:35 2019\r\n",
      "\r\n",
      "================================= Structure =================================\r\n",
      "Full Formula (Si2)\r\n",
      "Reduced Formula: Si\r\n",
      "abc   :   3.866975   3.866975   3.866975\r\n",
      "angles:  60.000000  60.000000  60.000000\r\n",
      "Sites (2)\r\n",
      "  #  SP       a     b     c  cartesian_forces\r\n",
      "---  ----  ----  ----  ----  -----------------------------------------------------------\r\n",
      "  0  Si    0     0     0     [-5.89948302e-27 -1.93366148e-27  2.91016902e-27] eV ang^-1\r\n",
      "  1  Si    0.25  0.25  0.25  [ 5.89948302e-27  1.93366148e-27 -2.91016902e-27] eV ang^-1\r\n",
      "\r\n",
      "Abinit Spacegroup: spgid: 227, num_spatial_symmetries: 48, has_timerev: True, symmorphic: True\r\n",
      "\r\n",
      "Stress tensor (Cartesian coordinates in GPa):\r\n",
      "[[5.21161758e+00 7.86452261e-11 0.00000000e+00]\r",
      "\r\n",
      " [7.86452261e-11 5.21161758e+00 0.00000000e+00]\r\n",
      " [0.00000000e+00 0.00000000e+00 5.21161758e+00]]\r\n",
      "\r\n",
      "Pressure: -5.212 (GPa)\r\n",
      "Energy: -241.23646832 (eV)\r\n",
      "\r\n",
      "============================== Electronic Bands ==============================\r\n",
      "Number of electrons: 8.0, Fermi level: 5.598 (eV)\r\n",
      "nsppol: 1, nkpt: 29, mband: 8, nspinor: 1, nspden: 1\r\n",
      "smearing scheme: none, tsmear_eV: 0.272, occopt: 1\r\n",
      "Direct gap:\r\n",
      "    Energy: 2.532 (eV)\r\n",
      "    Initial state: spin=0, kpt=[+0.000, +0.000, +0.000], weight: 0.002, band=3, eig=5.598, occ=2.000\r\n",
      "    Final state:   spin=0, kpt=[+0.000, +0.000, +0.000], weight: 0.002, band=4, eig=8.130, occ=0.000\r\n",
      "Fundamental gap:\r\n",
      "    Energy: 0.562 (eV)\r\n",
      "    Initial state: spin=0, kpt=[+0.000, +0.000, +0.000], weight: 0.002, band=3, eig=5.598, occ=2.000\r\n",
      "    Final state:   spin=0, kpt=[+0.375, +0.375, +0.000], weight: 0.012, band=4, eig=6.161, occ=0.000\r\n",
      "Bandwidth: 11.856 (eV)\r\n",
      "Valence maximum located at:\r\n",
      "    spin=0, kpt=[+0.000, +0.000, +0.000], weight: 0.002, band=3, eig=5.598, occ=2.000\r\n",
      "Conduction minimum located at:\r\n",
      "    spin=0, kpt=[+0.375, +0.375, +0.000], weight: 0.012, band=4, eig=6.161, occ=0.000\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!abiopen.py si_scf_GSR.nc --print"
   ]
  },
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   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   "source": [
    "#### To produce a predefined set of matplotlib figures, use:\n",
    "<p></p>\n",
    "\n",
    "```shell\n",
    "abiopen.py mgb2_kpath_FATBANDS.nc --expose --seaborn\n",
    "```    \n",
    "\n",
    "<img src=\"assets/mgb2_fatbands.png\" width=\"100%\" align=\"center\" alt=\"abiopen_expose\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Replace *--expose* with *--notebook* to generate a jupyter notebook with predefined python code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### *abiopen.py* supports output files (note *abo* extension):"
   ]
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     "text": [
      "ndtset: 3, completed: True\r\n",
      "Full Formula (C2)\r\n",
      "Reduced Formula: C\r\n",
      "abc   :   2.508336   2.508336   2.508336\r\n",
      "angles:  60.000000  60.000000  60.000000\r\n",
      "Sites (2)\r\n",
      "  #  SP       a     b     c\r\n",
      "---  ----  ----  ----  ----\r\n",
      "  0  C     0     0     0\r\n",
      "  1  C     0.25  0.25  0.25\r\n",
      "\r\n",
      "Abinit Spacegroup: spgid: 227, num_spatial_symmetries: 48, has_timerev: True, symmorphic: True\r\n",
      "\r\n",
      "========================= Dimensions of calculation =========================\r\n",
      "         intxc  ionmov  iscf  lmnmax  lnmax  mgfft  mpssoang  mqgrid  natom  \\\r\n",
      "dataset                                                                       \r\n",
      "1            0     0.0     7       1      1     24         2    3001      2   \r\n",
      "2            0     NaN     7       1      1     24         2    3001      2   \r\n",
      "3            0     NaN     7       1      1     24         2    3001      2   \r\n",
      "\r\n",
      "         nloc_mem  nspden  nspinor  nsppol  nsym  n1xccc  ntypat  occopt  \\\r\n",
      "dataset                                                                    \r\n",
      "1               1       1        1       1    48    2501       1       1   \r\n",
      "2               1       1        1       1    48    2501       1       1   \r\n",
      "3               1       1        1       1    48    2501       1       1   \r\n",
      "\r\n",
      "         xclevel  mband  mffmem  mkmem  mpw   nfft  nkpt  mem_per_proc_mb  \\\r\n",
      "dataset                                                                     \r\n",
      "1              1      4       1      8  609  13824     8            4.791   \r\n",
      "2              1      4       1     64  609  13824    64           11.529   \r\n",
      "3              1      4       1     64  609  13824    64           12.285   \r\n",
      "\r\n",
      "         wfk_size_mb  denpot_size_mb spg_symbol  spg_number  \\\r\n",
      "dataset                                                       \r\n",
      "1              0.299           0.107      Fd-3m         227   \r\n",
      "2              2.381           0.107      Fd-3m         227   \r\n",
      "3              2.381           0.107      Fd-3m         227   \r\n",
      "\r\n",
      "                                bravais  \r\n",
      "dataset                                  \r\n",
      "1        Bravais cF (face-center cubic)  \r\n",
      "2        Bravais cF (face-center cubic)  \r\n",
      "3        Bravais cF (face-center cubic)  \r\n",
      " \r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!abiopen.py gs_dfpt.abo -p "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### and log files as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Events found in /Users/gmatteo/talks/abipy_intro_abidev2019/run.log\r\n",
      "\r\n",
      "[1] \u001b[34m<AbinitComment at m_dtfil.F90:1082>\u001b[0m\r\n",
      "    Output file: run.abo already exists.\r\n",
      "    \r\n",
      "[2] \u001b[34m<AbinitComment at m_dtfil.F90:1106>\u001b[0m\r\n",
      "    Renaming old run.abo to run.abo0001\r\n",
      "    \r\n",
      "[3] \u001b[35m<AbinitWarning at m_mpinfo.F90:2295>\u001b[0m\r\n",
      "    nkpt*nsppol (29) is not a multiple of nproc_kpt (2)\r\n",
      "    The k-point parallelisation is not efficient.\r\n",
      "    \r\n",
      "[4] \u001b[35m<AbinitWarning at m_mpinfo.F90:2295>\u001b[0m\r\n",
      "    nkpt*nsppol (29) is not a multiple of nproc_kpt (2)\r\n",
      "    The k-point parallelisation is not efficient.\r\n",
      "    \r\n",
      "[5] \u001b[34m<AbinitComment at m_xgScalapack.F90:237>\u001b[0m\r\n",
      "    xgScalapack in auto mode\r\n",
      "    \r\n",
      "[6] \u001b[35m<AbinitWarning at m_phonons.F90:3215>\u001b[0m\r\n",
      "    ph_nqpath <= 0 or ph_ndivsm <= 0. Phonon bands won't be produced. returning\r\n",
      "    \r\n",
      "[7] \u001b[34m<AbinitComment at m_xgScalapack.F90:237>\u001b[0m\r\n",
      "    xgScalapack in auto mode\r\n",
      "    \r\n",
      "[8] \u001b[35m<AbinitWarning at m_phonons.F90:3215>\u001b[0m\r\n",
      "    ph_nqpath <= 0 or ph_ndivsm <= 0. Phonon bands won't be produced. returning\r\n",
      "    \r\n",
      "num_errors: 0, num_warnings: 4, num_comments: 4, completed: True\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!abiopen.py run.log -p "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### If you are still using perl scripts to extract data from the main output file, check this out:\n",
    "<p></p>\n",
    "\n",
    "```sh\n",
    "abiopen.py gs_dfpt.abo --expose --seaborn\n",
    "```\n",
    "\n",
    "<center>\n",
    "<img src=\"assets/abiopen_abo.png\" width=\"75%\" align=\"center\">\n",
    "</center>\n",
    "\n",
    "#### The introduction of Yaml documents in the main output (*cfr.* Theo's talk) will facilitate the implementation of new post-processing tools..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"assets/abistruct.png\" width=\"35%\" align=\"center\">\n",
    "\n",
    "#### It works with any file providing a structure object (*.nc*, .abi, *.cif* …) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Convert structure from netcdf format to CIF (*abivars*, *xsf*, *poscar*,  *qe*,  *siesta*,  *wannier90*,  *json*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# generated using pymatgen\r\n",
      "data_Si\r\n",
      "_symmetry_space_group_name_H-M   'P 1'\r\n",
      "_cell_length_a   3.86697464\r\n",
      "_cell_length_b   3.86697464\r\n",
      "_cell_length_c   3.86697464\r\n",
      "_cell_angle_alpha   60.00000000\r\n",
      "_cell_angle_beta   60.00000000\r\n",
      "_cell_angle_gamma   60.00000000\r\n",
      "_symmetry_Int_Tables_number   1\r\n",
      "_chemical_formula_structural   Si\r\n",
      "_chemical_formula_sum   Si2\r\n",
      "_cell_volume   40.88829228\r\n",
      "_cell_formula_units_Z   2\r\n",
      "loop_\r\n",
      " _symmetry_equiv_pos_site_id\r\n",
      " _symmetry_equiv_pos_as_xyz\r\n",
      "  1  'x, y, z'\r\n",
      "loop_\r\n",
      " _atom_site_type_symbol\r\n",
      " _atom_site_label\r\n",
      " _atom_site_symmetry_multiplicity\r\n",
      " _atom_site_fract_x\r\n",
      " _atom_site_fract_y\r\n",
      " _atom_site_fract_z\r\n",
      " _atom_site_occupancy\r\n",
      "  Si  Si1  1  0.000000  0.000000  0.000000  1\r\n",
      "  Si  Si2  1  0.250000  0.250000  0.250000  1\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!abistruct.py convert si_scf_GSR.nc -f cif"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Are you still using *cut3d* to export the structure in a format that other applications can understand?\n",
    "<p></p>\n",
    "\n",
    "\n",
    "```sh\n",
    "abistruct.py visualize run_si02.abi\n",
    "```\n",
    "\n",
    "<img src=\"assets/sio2_vesta.png\" width=\"75%\" align=\"center\">\n",
    "\n",
    "#### Supports also *ovito*, *xcrysden*, *vtk*, *mayavi*, *matplotlib*  (Use *--application* option)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Read structure from FILE and print ABINIT variables for NSCF along k-path:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Abinit Structure\r\n",
      " natom 2\r\n",
      " ntypat 1\r\n",
      " typat 1 1\r\n",
      " znucl 14\r\n",
      " xred\r\n",
      "    0.0000000000    0.0000000000    0.0000000000\r\n",
      "    0.2500000000    0.2500000000    0.2500000000\r\n",
      " acell    1.0    1.0    1.0\r\n",
      " rprim\r\n",
      "    6.3285005521    0.0000000000    3.6537614973\r\n",
      "    2.1095001840    5.9665675402    3.6537614973\r\n",
      "    0.0000000000    0.0000000000    7.3075229946\r\n",
      "\r\n",
      "# K-path in reduced coordinates:\r\n",
      "# tolwfr 1e-20 iscf -2 getden ??\r\n",
      " ndivsm 10\r\n",
      " kptopt -11\r\n",
      " kptbounds\r\n",
      "    +0.00000  +0.00000  +0.00000  # $\\Gamma$\r\n",
      "    +0.50000  +0.00000  +0.50000  # X\r\n",
      "    +0.50000  +0.25000  +0.75000  # W\r\n",
      "    +0.37500  +0.37500  +0.75000  # K\r\n",
      "    +0.00000  +0.00000  +0.00000  # $\\Gamma$\r\n",
      "    +0.50000  +0.50000  +0.50000  # L\r\n",
      "    +0.62500  +0.25000  +0.62500  # U\r\n",
      "    +0.50000  +0.25000  +0.75000  # W\r\n",
      "    +0.50000  +0.50000  +0.50000  # L\r\n",
      "    +0.37500  +0.37500  +0.75000  # K\r\n",
      "    +0.62500  +0.25000  +0.62500  # U\r\n",
      "    +0.50000  +0.00000  +0.50000  # X\r\n"
     ]
    }
   ],
   "source": [
    "!abistruct.py kpath si_scf_GSR.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Read structure from FILE, find little group of k-point, print irreps character table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "================================= Structure =================================\r\n",
      "Full Formula (Si2)\r\n",
      "Reduced Formula: Si\r\n",
      "abc   :   3.866975   3.866975   3.866975\r\n",
      "angles:  60.000000  60.000000  60.000000\r\n",
      "\r\n",
      "Spglib space group info (magnetic symmetries not taken into account).\r\n",
      "Spacegroup: Fd-3m (227), Hall: F 4d 2 3 -1d, Abinit spg_number: 227\r\n",
      "Crystal_system: cubic, Lattice_type: cubic, Point_group: m-3m\r\n",
      "\r\n",
      "  Idx  Symbol    Reduced_Coords              Wyckoff      EqIdx\r\n",
      "-----  --------  --------------------------  ---------  -------\r\n",
      "    0  Si        +0.00000 +0.00000 +0.00000  (2a)             0\r\n",
      "    1  Si        +0.25000 +0.25000 +0.25000  (2a)             0\r\n",
      "\r\n",
      "\r\n",
      "================================ Little Group ================================\r\n",
      "Kpoint-group: m-3m, Oh (221), Kpoint: [0.0, 0.0, 0.0], Symmorphic: True\r\n",
      " \r\n",
      "Oh     1+ [1]   2+ [3]   3+ [8]   2+ [6]   4+ [6]  -2- [1]  -2+ [3]  -6- [8]  -2+ [6]  -4+ [6]\r\n",
      "Irrep                                                                                         \r\n",
      "A1g    (1+0j)   (1+0j)   (1+0j)   (1+0j)   (1+0j)   (1+0j)   (1+0j)   (1+0j)   (1+0j)   (1+0j)\r\n",
      "A1u    (1+0j)   (1+0j)   (1+0j)   (1+0j)   (1+0j)  (-1+0j)  (-1+0j)  (-1+0j)  (-1+0j)  (-1+0j)\r\n",
      "A2g    (1+0j)   (1+0j)   (1+0j)  (-1+0j)  (-1+0j)   (1+0j)   (1+0j)   (1+0j)  (-1+0j)  (-1+0j)\r\n",
      "A2u    (1+0j)   (1+0j)   (1+0j)  (-1+0j)  (-1+0j)  (-1+0j)  (-1+0j)  (-1+0j)   (1+0j)   (1+0j)\r\n",
      "Eg     (2+0j)   (2+0j)  (-1+0j)       0j       0j   (2+0j)   (2+0j)  (-1+0j)       0j       0j\r\n",
      "Eu     (2+0j)   (2+0j)  (-1+0j)       0j       0j  (-2+0j)  (-2+0j)   (1+0j)       0j       0j\r\n",
      "T1g    (3+0j)  (-1+0j)       0j  (-1+0j)   (1+0j)   (3+0j)  (-1+0j)       0j  (-1+0j)   (1+0j)\r\n",
      "T1u    (3+0j)  (-1+0j)       0j  (-1+0j)   (1+0j)  (-3+0j)   (1+0j)       0j   (1+0j)  (-1+0j)\r\n",
      "T2g    (3+0j)  (-1+0j)       0j   (1+0j)  (-1+0j)   (3+0j)  (-1+0j)       0j   (1+0j)  (-1+0j)\r\n",
      "T2u    (3+0j)  (-1+0j)       0j   (1+0j)  (-1+0j)  (-3+0j)   (1+0j)       0j  (-1+0j)   (1+0j)\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!abistruct.py lgk si_scf_GSR.nc -k 0 0 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Connect to the materials project database to find similar structures.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "# Found 1 structures in Materials Project database (use `verbose` to get further info)\r\n",
      "\r\n",
      " \r\n",
      "########################## abivars input for mp-149 ##########################\r\n",
      "# Full Formula (Si2)\r\n",
      "# Reduced Formula: Si\r\n",
      "# abc   :   3.866975   3.866975   3.866975\r\n",
      "# angles:  60.000000  60.000000  60.000000\r\n",
      "# \r\n",
      "# Spglib space group info (magnetic symmetries not taken into account).\r\n",
      "# Spacegroup: Fd-3m (227), Hall: F 4d 2 3 -1d, Abinit spg_number: None\r\n",
      "# Crystal_system: cubic, Lattice_type: cubic, Point_group: m-3m\r\n",
      "# \r\n",
      "#   Idx  Symbol    Reduced_Coords              Wyckoff      EqIdx\r\n",
      "# -----  --------  --------------------------  ---------  -------\r\n",
      "#     0  Si        +0.87500 +0.87500 +0.87500  (2a)             0\r\n",
      "#     1  Si        +0.12500 +0.12500 +0.12500  (2a)             0\r\n",
      "\r\n",
      " natom 2\r\n",
      " ntypat 1\r\n",
      " typat 1 1\r\n",
      " znucl 14\r\n",
      " xred\r\n",
      "    0.8750000000    0.8750000000    0.8750000000\r\n",
      "    0.1250000000    0.1250000000    0.1250000000\r\n",
      " acell    1.0    1.0    1.0\r\n",
      " rprim\r\n",
      "    6.3285005334    0.0000000000    3.6537614829\r\n",
      "    2.1095001778    5.9665675247    3.6537614829\r\n",
      "    0.0000000000    0.0000000000    7.3075229659\r\n",
      "\r\n",
      "\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!abistruct.py mp_match si_scf_GSR.nc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### To search by chemical system or formula (no FILE required)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "# Found 3 structures in Materials Project database (use `verbose` to get further info)\n",
      "\n",
      "           pretty_formula  e_above_hull  energy_per_atom  \\\n",
      "mp-1185301            LiF      0.007565        -4.837577   \n",
      "mp-1009009            LiF      0.273111        -4.572031   \n",
      "mp-1138               LiF      0.000000        -4.845142   \n",
      "\n",
      "            formation_energy_per_atom  nsites     volume spacegroup.symbol  \\\n",
      "mp-1185301                  -3.173315       4  42.223600            P6_3mc   \n",
      "mp-1009009                  -2.907769       2  16.768040             Pm-3m   \n",
      "mp-1138                     -3.180880       2  17.022154             Fm-3m   \n",
      "\n",
      "            spacegroup.number  band_gap  total_magnetization material_id  \\\n",
      "mp-1185301                186    7.4778         0.000000e+00  mp-1185301   \n",
      "mp-1009009                221    7.5593         0.000000e+00  mp-1009009   \n",
      "mp-1138                   225    8.7161         9.000000e-07     mp-1138   \n",
      "\n",
      "                   a         b         c  alpha  beta       gamma  \n",
      "mp-1185301  3.139544  3.139544  4.946425   90.0  90.0  120.000008  \n",
      "mp-1009009  2.559533  2.559533  2.559533   90.0  90.0   90.000000  \n",
      "mp-1138     2.887419  2.887419  2.887419   60.0  60.0   60.000000  \n",
      " \n",
      " \n",
      "######################## abivars input for mp-1185301 ########################\n",
      "# Full Formula (Li2 F2)\n",
      "# Reduced Formula: LiF\n",
      "# abc   :   3.139544   3.139544   4.946425\n",
      "# angles:  90.000000  90.000000 120.000008\n",
      "# \n",
      "# Spglib space group info (magnetic symmetries not taken into account).\n",
      "# Spacegroup: P6_3mc (186), Hall: P 6c -2c, Abinit spg_number: None\n",
      "# Crystal_system: hexagonal, Lattice_type: hexagonal, Point_group: 6mm\n",
      "# \n",
      "#   Idx  Symbol    Reduced_Coords              Wyckoff      EqIdx\n",
      "# -----  --------  --------------------------  ---------  -------\n",
      "#     0  Li        +0.00000 +0.00000 +0.38019  (2b)             0\n",
      "#     1  Li        +0.33333 +0.66667 +0.88019  (2b)             0\n",
      "#     2  F         +0.00000 +0.00000 +0.99481  (2b)             2\n",
      "#     3  F         +0.33333 +0.66667 +0.49481  (2b)             2\n",
      "\n",
      " natom 4\n",
      " ntypat 2\n",
      " typat\n",
      " 1 1 2\n",
      " 2\n",
      " znucl 3 9\n",
      " xred\n",
      "    0.0000000000    0.0000000000    0.3801910000\n",
      "    0.3333333333    0.6666666667    0.8801900000\n",
      "    0.0000000000    0.0000000000    0.9948070000\n",
      "    0.3333333333    0.6666666667    0.4948100000\n",
      " acell    1.0    1.0    1.0\n",
      " rprim\n",
      "    5.9328791503    0.0000000000    0.0000000000\n",
      "   -2.9664403365    5.1380236222    0.0000000000\n",
      "    0.0000000000    0.0000000000    9.3473877186\n",
      "\n",
      "\n",
      "\n",
      " \n",
      "######################## abivars input for mp-1009009 ########################\n",
      "# Full Formula (Li1 F1)\n",
      "# Reduced Formula: LiF\n",
      "# abc   :   2.559533   2.559533   2.559533\n",
      "# angles:  90.000000  90.000000  90.000000\n",
      "# \n",
      "# Spglib space group info (magnetic symmetries not taken into account).\n",
      "# Spacegroup: Pm-3m (221), Hall: -P 4 2 3, Abinit spg_number: None\n",
      "# Crystal_system: cubic, Lattice_type: cubic, Point_group: m-3m\n",
      "# \n",
      "#   Idx  Symbol    Reduced_Coords              Wyckoff      EqIdx\n",
      "# -----  --------  --------------------------  ---------  -------\n",
      "#     0  Li        +0.00000 +0.00000 +0.00000  (1a)             0\n",
      "#     1  F         +0.50000 +0.50000 +0.50000  (1b)             1\n",
      "\n",
      " natom 2\n",
      " ntypat 2\n",
      " typat 1 2\n",
      " znucl 3 9\n",
      " xred\n",
      "    0.0000000000    0.0000000000    0.0000000000\n",
      "    0.5000000000    0.5000000000    0.5000000000\n",
      " acell    1.0    1.0    1.0\n",
      " rprim\n",
      "    4.8368167948    0.0000000000    0.0000000000\n",
      "    0.0000000000    4.8368167948    0.0000000000\n",
      "    0.0000000000    0.0000000000    4.8368167948\n",
      "\n",
      "\n",
      "\n",
      " \n",
      "######################### abivars input for mp-1138 #########################\n",
      "# Full Formula (Li1 F1)\n",
      "# Reduced Formula: LiF\n",
      "# abc   :   2.887419   2.887419   2.887419\n",
      "# angles:  60.000000  60.000000  60.000000\n",
      "# \n",
      "# Spglib space group info (magnetic symmetries not taken into account).\n",
      "# Spacegroup: Fm-3m (225), Hall: -F 4 2 3, Abinit spg_number: None\n",
      "# Crystal_system: cubic, Lattice_type: cubic, Point_group: m-3m\n",
      "# \n",
      "#   Idx  Symbol    Reduced_Coords              Wyckoff      EqIdx\n",
      "# -----  --------  --------------------------  ---------  -------\n",
      "#     0  Li        +0.00000 +0.00000 +0.00000  (1a)             0\n",
      "#     1  F         +0.50000 +0.50000 +0.50000  (1b)             1\n",
      "\n",
      " natom 2\n",
      " ntypat 2\n",
      " typat 1 2\n",
      " znucl 3 9\n",
      " xred\n",
      "    0.0000000000    0.0000000000    0.0000000000\n",
      "    0.5000000000    0.5000000000    0.5000000000\n",
      " acell    1.0    1.0    1.0\n",
      " rprim\n",
      "    4.7254082787    0.0000000000    2.7282157318\n",
      "    1.5751360851    4.4551576322    2.7282157281\n",
      "    0.0000000000    0.0000000000    5.4564314785\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!abistruct.py mp_search LiF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"assets/abiview.png\" width=\"35%\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Want to know what's happening in the structural relaxation?\n",
    "<p></p>\n",
    "\n",
    "```sh\n",
    "abiview.py hist sic_relax_HIST.nc\n",
    "```\n",
    "\n",
    "<center>\n",
    "<img src=\"assets/abiview_hist.png\" width=\"60%\" align=\"center\">\n",
    "</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Want a GUI? \n",
    "\n",
    "Use *--appname* for app in [avogadro, ovito, v_sim, vesta, xcrysden, mpl, mayavi, vtk]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Need to call *anaddb* to compute and visualize ph-bands and DOS from DDB?\n",
    "<p></p>\n",
    "\n",
    "```sh\n",
    "abiview.py ddb ZnSe_hex_qpt_DDB --seaborn\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "<img src=\"assets/abiview_ddb.png\" width=\"80%\" align=\"center\">\n",
    "\n",
    "#### Add --phononwebsite to visualize data on the [phononwebsite](http://henriquemiranda.github.io/phononwebsite/) by Henrique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Need to get a quick look at the Fermi surface?\n",
    "<p></p>\n",
    "\n",
    "```\n",
    "abiview.py fs mgb2_kmesh181818_FATBANDS.nc\n",
    "```\n",
    "<p></p>\n",
    "<center>\n",
    "<img src=\"assets/mgb2_fs.png\" width=\"50%\" align=\"center\">\n",
    "</center>\n",
    "\n",
    "#### Use *-a xsf* for Xcrysden or *-a mayavi* for Mayavi backend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"assets/abicomp.png\" width=\"30%\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## Abipy Robots\n",
    "\n",
    "* High-level interface to operate on <u>multiple files</u> with the same file extension\n",
    "\n",
    "* Useful for:\n",
    "\n",
    "     * convergence studies\n",
    "     * producing multiple plots\n",
    "     * building [Pandas dataframes](https://pandas.pydata.org/) (data in tabular format powered by python)\n",
    "\n",
    "* Each *Robot* is associated to a file extension, *e.g.* \n",
    "\n",
    "     * GSR.nc ➝ *GsrRobot*\n",
    "     * DDB ➝ *DdbRobot*\n",
    "     \n",
    "* *Robots* can be constructed from:\n",
    "\n",
    "    1. List of filenames\n",
    "    2. Directories and regular expressions\n",
    "    \n",
    "* Command line interface provided by the *abicomp.py* script:\n",
    "\n",
    "* To generate notebook to compare multiple *GSR* files, use:\n",
    "\n",
    "            abicomp.py gsr out1_GSR.nc  out2_GSR.nc --notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### To compare multiple structures:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spglib options: symprec= 0.001 angle_tolerance= 5.0\n",
      "Lattice parameters:\n",
      "                  formula  natom  alpha  beta  gamma         a         b  \\\n",
      "AlAs.cif          Al1 As1      2   60.0  60.0   60.0  3.970101  3.970101   \n",
      "si.cif                Si2      2   60.0  60.0   60.0  3.866975  3.866975   \n",
      "si_nscf_GSR.nc        Si2      2   60.0  60.0   60.0  3.866975  3.866975   \n",
      "ZnSe_hex_qpt_DDB  Zn2 Se2      4   90.0  90.0  120.0  4.050187  4.050187   \n",
      "\n",
      "                         c     volume abispg_num spglib_symb  spglib_num  \\\n",
      "AlAs.cif          3.970101  44.247584       None       F-43m         216   \n",
      "si.cif            3.866975  40.888292       None       Fd-3m         227   \n",
      "si_nscf_GSR.nc    3.866975  40.888292        227       Fd-3m         227   \n",
      "ZnSe_hex_qpt_DDB  6.652328  94.504936          0      P6_3mc         186   \n",
      "\n",
      "                 spglib_lattice_type  \n",
      "AlAs.cif                       cubic  \n",
      "si.cif                         cubic  \n",
      "si_nscf_GSR.nc                 cubic  \n",
      "ZnSe_hex_qpt_DDB           hexagonal  \n",
      " \n"
     ]
    }
   ],
   "source": [
    "!abicomp.py structure *.cif si_nscf_GSR.nc `find . -name *_DDB`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Note shell wildcard characters and Unix `find` inside backticks (bash rocks!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "outputs": [],
   "source": [
    "#plotter = abilab.ElectronBandsPlotter()\n",
    "#plotter.add_ebands(label=\"BZ sampling\", bands=\"si_scf_GSR.nc\")\n",
    "#plotter.add_ebands(label=\"k-path\", bands=\"si_nscf_GSR.nc\")\n",
    "#plotter.gridplot(with_gaps=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Using *ElectronBandsPlotter* to visualize multiple band structures:\n",
    "<p></p>\n",
    "\n",
    "```sh\n",
    "abicomp.py ebands *_GSR.nc\n",
    "```\n",
    "\n",
    "<p></p>\n",
    "\n",
    "<img src=\"assets/abicomp_ebands.png\" width=\"85%\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Pandas dataframe printed to terminal:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "```text\n",
    "               nsppol  nspinor  nspden  nkpt  nband  nelect    fermie  \\\n",
    "si_nscf_GSR.nc       1        1       1    14      8     8.0  5.598453\n",
    "si_scf_GSR.nc        1        1       1    29      8     8.0  5.598453\n",
    "\n",
    "               formula  natom  alpha  beta  gamma         a         b  \\\n",
    "si_nscf_GSR.nc     Si2      2   60.0  60.0   60.0  3.866975  3.866975\n",
    "si_scf_GSR.nc      Si2      2   60.0  60.0   60.0  3.866975  3.866975\n",
    "\n",
    "                       c     volume abispg_num spglib_symb  spglib_num  \\\n",
    "si_nscf_GSR.nc  3.866975  40.888292        227       Fd-3m         227\n",
    "si_scf_GSR.nc   3.866975  40.888292        227       Fd-3m         227\n",
    "\n",
    "               spglib_lattice_type scheme  occopt  tsmear_ev  bandwidth_spin0  \\\n",
    "si_nscf_GSR.nc               cubic   none       1   0.272114        11.855854\n",
    "si_scf_GSR.nc                cubic   none       1   0.272114        11.855874\n",
    "\n",
    "                fundgap_spin0  dirgap_spin0  gap_type  \\\n",
    "si_nscf_GSR.nc       0.524340      2.531828  indirect\n",
    "si_scf_GSR.nc        0.562277      2.531798  indirect\n",
    "\n",
    "                                   fundgap_kstart              fundgap_kend  \\\n",
    "si_nscf_GSR.nc  [+0.000, +0.000, +0.000] $\\Gamma$  [+0.000, +0.429, +0.429]\n",
    "si_scf_GSR.nc            [+0.000, +0.000, +0.000]  [+0.375, +0.375, +0.000]\n",
    "\n",
    "                                    dirgap_kstart  \\\n",
    "si_nscf_GSR.nc  [+0.000, +0.000, +0.000] $\\Gamma$\n",
    "si_scf_GSR.nc            [+0.000, +0.000, +0.000]\n",
    "\n",
    "                                      dirgap_kend\n",
    "si_nscf_GSR.nc  [+0.000, +0.000, +0.000] $\\Gamma$\n",
    "si_scf_GSR.nc            [+0.000, +0.000, +0.000]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Post-processing the DFPT results available in the MP database\n",
    "\n",
    "* More than 1500 DFPT calculations done with abiflows ([Petretto et al.](https://www.nature.com/articles/sdata201865))\n",
    "* Results available on the materials project website (including the *DDB* files) \n",
    "\n",
    "<hr>\n",
    "\n",
    "Let's assume we want to reuse the raw data for our research work:\n",
    "\n",
    "   * Handling 1500 tabs in the web browser is not feasible\n",
    "   * We need a <u>programmatic</u> interface to automate stuff. \n",
    "\n",
    "With python we can easily connect the different parts of the puzzle:\n",
    "\n",
    "* REST API to get the raw data (*DDB*) from the MP database\n",
    "* Computation of phonons, thermodinamical properties, Born effective charges, dielectric tensor, IR spectrum with ABINIT\n",
    "* Post-processing with AbiPy\n",
    "\n",
    "For a more comprehensive discussion see this \n",
    "[abitutorial](https://nbviewer.jupyter.org/github/abinit/abitutorials/blob/master/abitutorials/ddb.ipynb)\n",
    "\n",
    "<!--\n",
    "The results for the rocksalt phase of MgO are available at https://materialsproject.org/materials/mp-1009129/\n",
    "-->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Bash is handy but python is more flexible\n",
    "\n",
    "##### Let's use the *DdbRobot* to compare phonons obtained with different ${\\bf k}$-meshes and smearing values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "run_control": {
     "marked": true
    },
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "paths = [\n",
    "    \"mgb2_888k_0.01tsmear_DDB\",\n",
    "    \"mgb2_888k_0.04tsmear_DDB\",\n",
    "    \"mgb2_121212k_0.01tsmear_DDB\",\n",
    "    \"mgb2_121212k_0.04tsmear_DDB\",\n",
    "]\n",
    "\n",
    "paths = [os.path.join(abidata.dirpath, \"refs\", \"mgb2_phonons_nkpt_tsmear\", f) \n",
    "         for f in paths]\n",
    "\n",
    "robot = abilab.DdbRobot()\n",
    "for i, path in enumerate(paths):\n",
    "    robot.add_file(path, path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol start=\"0\">\n",
       "<li>nkpt: 256, tsmear: 0.01</li>\n",
       "<li>nkpt: 256, tsmear: 0.04</li>\n",
       "<li>nkpt: 864, tsmear: 0.01</li>\n",
       "<li>nkpt: 864, tsmear: 0.04</li>\n",
       "</ol>"
      ],
      "text/plain": [
       "Label                    Relpath\n",
       "-----------------------  ------------------------------------------------------------------------------------------\n",
       "nkpt: 256, tsmear: 0.01  ../../git_repos/abipy/abipy/data/refs/mgb2_phonons_nkpt_tsmear/mgb2_888k_0.01tsmear_DDB\n",
       "nkpt: 256, tsmear: 0.04  ../../git_repos/abipy/abipy/data/refs/mgb2_phonons_nkpt_tsmear/mgb2_888k_0.04tsmear_DDB\n",
       "nkpt: 864, tsmear: 0.01  ../../git_repos/abipy/abipy/data/refs/mgb2_phonons_nkpt_tsmear/mgb2_121212k_0.01tsmear_DDB\n",
       "nkpt: 864, tsmear: 0.04  ../../git_repos/abipy/abipy/data/refs/mgb2_phonons_nkpt_tsmear/mgb2_121212k_0.04tsmear_DDB"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define function to change labels:\n",
    "func = lambda ddb: \"nkpt: %s, tsmear: %.2f\" % (\n",
    "    ddb.header[\"nkpt\"], ddb.header[\"tsmear\"])\n",
    "\n",
    "robot.remap_labels(func)\n",
    "robot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<!-- We are usually interested in the convergence behavior with respect to one or two parameters of \n",
    "the calculations.-->\n",
    "\n",
    "#### Now we can build a dataframe with the most important parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nkpt</th>\n",
       "      <th>nsppol</th>\n",
       "      <th>ecut</th>\n",
       "      <th>tsmear</th>\n",
       "      <th>occopt</th>\n",
       "      <th>ixc</th>\n",
       "      <th>nband</th>\n",
       "      <th>usepaw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nkpt: 256, tsmear: 0.01</th>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nkpt: 256, tsmear: 0.04</th>\n",
       "      <td>256</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nkpt: 864, tsmear: 0.01</th>\n",
       "      <td>864</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nkpt: 864, tsmear: 0.04</th>\n",
       "      <td>864</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         nkpt  nsppol  ecut  tsmear  occopt  ixc  nband  \\\n",
       "nkpt: 256, tsmear: 0.01   256       1  35.0    0.01       4    1      8   \n",
       "nkpt: 256, tsmear: 0.04   256       1  35.0    0.04       4    1      8   \n",
       "nkpt: 864, tsmear: 0.01   864       1  35.0    0.01       4    1      8   \n",
       "nkpt: 864, tsmear: 0.04   864       1  35.0    0.04       4    1      8   \n",
       "\n",
       "                         usepaw  \n",
       "nkpt: 256, tsmear: 0.01       0  \n",
       "nkpt: 256, tsmear: 0.04       0  \n",
       "nkpt: 864, tsmear: 0.01       0  \n",
       "nkpt: 864, tsmear: 0.04       0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "robot.get_params_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### and check that all DDBs have been computed with the same crystalline structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>formula</th>\n",
       "      <th>natom</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta</th>\n",
       "      <th>gamma</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>volume</th>\n",
       "      <th>abispg_num</th>\n",
       "      <th>spglib_symb</th>\n",
       "      <th>spglib_num</th>\n",
       "      <th>spglib_lattice_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nkpt: 256, tsmear: 0.01</th>\n",
       "      <td>Mg1 B2</td>\n",
       "      <td>3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3.086</td>\n",
       "      <td>3.086</td>\n",
       "      <td>3.523</td>\n",
       "      <td>29.055953</td>\n",
       "      <td>0</td>\n",
       "      <td>P6/mmm</td>\n",
       "      <td>191</td>\n",
       "      <td>hexagonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nkpt: 256, tsmear: 0.04</th>\n",
       "      <td>Mg1 B2</td>\n",
       "      <td>3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3.086</td>\n",
       "      <td>3.086</td>\n",
       "      <td>3.523</td>\n",
       "      <td>29.055953</td>\n",
       "      <td>0</td>\n",
       "      <td>P6/mmm</td>\n",
       "      <td>191</td>\n",
       "      <td>hexagonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nkpt: 864, tsmear: 0.01</th>\n",
       "      <td>Mg1 B2</td>\n",
       "      <td>3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3.086</td>\n",
       "      <td>3.086</td>\n",
       "      <td>3.523</td>\n",
       "      <td>29.055953</td>\n",
       "      <td>0</td>\n",
       "      <td>P6/mmm</td>\n",
       "      <td>191</td>\n",
       "      <td>hexagonal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nkpt: 864, tsmear: 0.04</th>\n",
       "      <td>Mg1 B2</td>\n",
       "      <td>3</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>3.086</td>\n",
       "      <td>3.086</td>\n",
       "      <td>3.523</td>\n",
       "      <td>29.055953</td>\n",
       "      <td>0</td>\n",
       "      <td>P6/mmm</td>\n",
       "      <td>191</td>\n",
       "      <td>hexagonal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        formula  natom  alpha  beta  gamma      a      b  \\\n",
       "nkpt: 256, tsmear: 0.01  Mg1 B2      3   90.0  90.0  120.0  3.086  3.086   \n",
       "nkpt: 256, tsmear: 0.04  Mg1 B2      3   90.0  90.0  120.0  3.086  3.086   \n",
       "nkpt: 864, tsmear: 0.01  Mg1 B2      3   90.0  90.0  120.0  3.086  3.086   \n",
       "nkpt: 864, tsmear: 0.04  Mg1 B2      3   90.0  90.0  120.0  3.086  3.086   \n",
       "\n",
       "                             c     volume  abispg_num spglib_symb  spglib_num  \\\n",
       "nkpt: 256, tsmear: 0.01  3.523  29.055953           0      P6/mmm         191   \n",
       "nkpt: 256, tsmear: 0.04  3.523  29.055953           0      P6/mmm         191   \n",
       "nkpt: 864, tsmear: 0.01  3.523  29.055953           0      P6/mmm         191   \n",
       "nkpt: 864, tsmear: 0.04  3.523  29.055953           0      P6/mmm         191   \n",
       "\n",
       "                        spglib_lattice_type  \n",
       "nkpt: 256, tsmear: 0.01           hexagonal  \n",
       "nkpt: 256, tsmear: 0.04           hexagonal  \n",
       "nkpt: 864, tsmear: 0.01           hexagonal  \n",
       "nkpt: 864, tsmear: 0.04           hexagonal  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "robot.get_lattice_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Dataframes can be exported to different formats: CSV, $Latex$, JSON, Excel, ...\n",
    "* Use [dataframe.to_clipboard()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_clipboard.html#pandas-dataframe-to-clipboard) \n",
    "to copy to clipboard and paste into spreadsheet editor\n",
    "* Explore your *DataFrames* inside *jupyter* with [qgrid](https://github.com/quantopian/qgrid)\n",
    "* High-level plotting interface provided by [seaborn](https://seaborn.pydata.org/)\n",
    "<p></p>\n",
    "\n",
    "```python\n",
    "energy_table.plot(x=\"nkpt\", y=[\"energy_Ha\", \"ediff_Ha\", \"pressure\"], \n",
    "                  style=\"-o\", subplots=True);\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### To analyze the effect of k-sampling/smearing on the vibrational properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Invoke anaddb and store results\n",
    "r = robot.anaget_phonon_plotters(nqsmall=2)\n",
    "\n",
    "r.phbands_plotter.gridplot_with_hue(\"tsmear\", with_dos=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### Python function to visualize electronic and vibrational properties downloaded from the MP database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plot_electrons_and_phonons(mpids):\n",
    "    # Construct AbiPy ebands from mpids\n",
    "    ebands_list = [abilab.ElectronBands.from_mpid(mpid) for mpid in mpids]\n",
    "\n",
    "    # Get DDB files from the MP website and run anaddb to get phonon bands.\n",
    "    phbands_list = []\n",
    "    for i, mpid in enumerate(mpids):\n",
    "        ddb = abilab.DdbFile.from_mpid(mpid)\n",
    "        # Call anaddb to compute phonon bands\n",
    "        phbst, _ = ddb.anaget_phbst_and_phdos_files(nqsmall=0)\n",
    "        phbands_list.append(phbst.phbands)\n",
    "        phbst.close()\n",
    "\n",
    "    # Build matplotlib grid. The figure has (len(mpids), 2) subplots\n",
    "    nrows, ncols = len(mpids), 2\n",
    "    ax_mat, fig, plt = abilab.get_axarray_fig_plt(None, nrows=nrows, ncols=ncols,\n",
    "                                                  sharex=False, sharey=False, \n",
    "                                                  squeeze=False)\n",
    "    \n",
    "    for i, (ebands, phbands) in enumerate(zip(ebands_list, phbands_list)):\n",
    "        ebands.plot(ax=ax_mat[i, 0], with_gaps=True, ylims=(-5, 10), \n",
    "                    max_phfreq=phbands.maxfreq, show=False)\n",
    "        phbands.plot(ax=ax_mat[i, 1], show=False)\n",
    "        \n",
    "        # Hide labels\n",
    "        ax_mat[i, 1].yaxis.label.set_visible(False)\n",
    "        if i != len(ebands_list) - 1:\n",
    "            for ax in ax_mat[i]: ax.xaxis.label.set_visible(False)\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_electrons_and_phonons(mpids=[\"mp-149\", \"mp-66\"]); # Si and Diamond"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "##### Qualitative analysis of possible e-ph scattering channels with less than 20 lines of code!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"assets/abinp.png\" width=\"30%\" align=\"center\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "## *AbinitInput* object \n",
    "\n",
    "Programmatic interface to generate input files:\n",
    "\n",
    "* *Dict-like* object storing ABINIT variables \n",
    "* Methods to set multiple variables (*e.g.* **k**-path from *structure*)\n",
    "* Factory functions to generate input files with minimal effort\n",
    "\n",
    "Can invoke ABINIT to get important parameters such as:\n",
    "\n",
    "  * list of **k**-points in the IBZ\n",
    "  * list of irreducible DFPT perturbations\n",
    "  * list of possible configurations for MPI jobs (*npkpt*, *npfft*, *npband* …)\n",
    "     \n",
    "#### To build an input, we need a *structure* and a list of *pseudos*:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Input file for band structure calculation + DOS\n",
    "\n",
    "1. GS run to get the density \n",
    "2. NSCF run along high-symmetry **k**-path\n",
    "3. NSCF run with **k**-mesh to compute the DOS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kptopt</th>\n",
       "      <th>iscf</th>\n",
       "      <th>ngkpt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dataset 0</th>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>[8, 8, 8]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset 1</th>\n",
       "      <td>-11</td>\n",
       "      <td>-2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset 2</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>[14, 14, 14]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           kptopt  iscf         ngkpt\n",
       "dataset 0       1  None     [8, 8, 8]\n",
       "dataset 1     -11    -2          None\n",
       "dataset 2       1    -2  [14, 14, 14]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi = abilab.ebands_input(structure=\"si.cif\", \n",
    "                            pseudos=\"14si.pspnc\",\n",
    "                            ecut=8, \n",
    "                            spin_mode=\"unpolarized\", \n",
    "                            smearing=None, \n",
    "                            dos_kppa=5000)\n",
    "\n",
    "multi.get_vars_dataframe(\"kptopt\", \"iscf\", \"ngkpt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### To build an input for SCF+NSCF run with (relaxed) structure from the materials project database:\n",
    "<p></p>\n",
    "\n",
    "        abinp.py ebands mp-149          "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "#### $GW$ calculations with the plasmon-pole model. The calculation consists of:\n",
    "\n",
    "1. GS run to compute the density \n",
    "2. nscf-run to produce a WFK file with *nscf_nband* states\n",
    "3. Input files to compute the screening ($W$) and the self-energy ($\\Sigma^{e-e} = GW$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>optdriver</th>\n",
       "      <th>ngkpt</th>\n",
       "      <th>nband</th>\n",
       "      <th>ecuteps</th>\n",
       "      <th>ecutsigx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dataset 0</th>\n",
       "      <td>None</td>\n",
       "      <td>[8, 8, 8]</td>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset 1</th>\n",
       "      <td>None</td>\n",
       "      <td>[8, 8, 8]</td>\n",
       "      <td>50</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset 2</th>\n",
       "      <td>3</td>\n",
       "      <td>[8, 8, 8]</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset 3</th>\n",
       "      <td>4</td>\n",
       "      <td>[8, 8, 8]</td>\n",
       "      <td>50</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          optdriver      ngkpt  nband ecuteps ecutsigx\n",
       "dataset 0      None  [8, 8, 8]     14    None     None\n",
       "dataset 1      None  [8, 8, 8]     50    None     None\n",
       "dataset 2         3  [8, 8, 8]     50       2     None\n",
       "dataset 3         4  [8, 8, 8]     50       2        4"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multi = abilab.g0w0_with_ppmodel_inputs(\n",
    "    structure=\"si.cif\", pseudos=\"14si.pspnc\", \n",
    "    kppa=1000, nscf_nband=50, ecuteps=2, ecutsigx=4, ecut=8, \n",
    "    spin_mode=\"unpolarized\")\n",
    "\n",
    "multi.get_vars_dataframe(\"optdriver\", \"ngkpt\", \"nband\", \"ecuteps\", \"ecutsigx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "* *nscf_nband*  ➝ number of bands in $GW$ (occ + empty)\n",
    "* *ecuteps* ➝ planewave cutoff for $W_{G, G'}$ in Hartree \n",
    "* *ecutsigx* ➝ cutoff energy for the exchange part $\\Sigma_x$\n",
    "* *kppa* ➝ k-point sampling (#kpts per reciprocal atom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### *abinp.py* is handy for generating input files quickly but it cannot compete with the flexibility of the python interface:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def make_scf_input(ecut=2, ngkpt=(4, 4, 4)):\n",
    "    \"\"\"\n",
    "    Generate an `AbinitInput` to perform GS calculation for AlAs.\n",
    "\n",
    "    Args:\n",
    "        ecut: Cutoff energy in Ha.\n",
    "        ngkpt: k-mesh divisions\n",
    "\n",
    "    Return:\n",
    "        `AbinitInput` object\n",
    "    \"\"\"\n",
    "    gs_inp = abilab.AbinitInput(structure=\"AlAs.cif\",\n",
    "                                pseudos=[\"13al.pspnc\", \"33as.pspnc\"])\n",
    "\n",
    "    # Set the value of the Abinit variables needed for GS runs.\n",
    "    gs_inp.set_vars(\n",
    "        nband=4,\n",
    "        ecut=ecut,\n",
    "        ngkpt=ngkpt,\n",
    "        nshiftk=4,\n",
    "        shiftk=[0.0, 0.0, 0.5,   # This gives the usual fcc Monkhorst-Pack grid\n",
    "                0.0, 0.5, 0.0,\n",
    "                0.5, 0.0, 0.0,\n",
    "                0.5, 0.5, 0.5],\n",
    "        tolvrs=1.0e-10,\n",
    "    )\n",
    "\n",
    "    return gs_inp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Inside the notebook, one gets the HTML representation with links to the documentation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "############################################################################################<br>#                                       SECTION: basic                                      <br>############################################################################################<br> <a href=\"https://docs.abinit.org/variables/basic#nband\" target=\"_blank\">nband</a> 4<br> <a href=\"https://docs.abinit.org/variables/basic#ecut\" target=\"_blank\">ecut</a> 2<br> <a href=\"https://docs.abinit.org/variables/basic#ngkpt\" target=\"_blank\">ngkpt</a> 4 4 4<br> <a href=\"https://docs.abinit.org/variables/basic#nshiftk\" target=\"_blank\">nshiftk</a> 4<br> <a href=\"https://docs.abinit.org/variables/basic#shiftk\" target=\"_blank\">shiftk</a><br>    0.0    0.0    0.5<br>    0.0    0.5    0.0<br>    0.5    0.0    0.0<br>    0.5    0.5    0.5<br> <a href=\"https://docs.abinit.org/variables/basic#tolvrs\" target=\"_blank\">tolvrs</a> 1e-10<br>############################################################################################<br>#                                         STRUCTURE                                         <br>############################################################################################<br> <a href=\"https://docs.abinit.org/variables/basic#natom\" target=\"_blank\">natom</a> 2<br> <a href=\"https://docs.abinit.org/variables/basic#ntypat\" target=\"_blank\">ntypat</a> 2<br> <a href=\"https://docs.abinit.org/variables/basic#typat\" target=\"_blank\">typat</a> 1 2<br> <a href=\"https://docs.abinit.org/variables/basic#znucl\" target=\"_blank\">znucl</a> 13 33<br> <a href=\"https://docs.abinit.org/variables/basic#xred\" target=\"_blank\">xred</a><br>    0.0000000000    0.0000000000    0.0000000000<br>    0.2500000000    0.2500000000    0.2500000000<br> <a href=\"https://docs.abinit.org/variables/basic#acell\" target=\"_blank\">acell</a>    1.0    1.0    1.0<br> <a href=\"https://docs.abinit.org/variables/basic#rprim\" target=\"_blank\">rprim</a><br>    6.4972715500    0.0000000000    3.7512014784<br>    2.1657571833    6.1256863630    3.7512014784<br>    0.0000000000    0.0000000000    7.5024029568"
      ],
      "text/plain": [
       "<AbinitInput at 120843880824>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_scf_input()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Moreover, from an `AbiniInput` one can easily build more complicate workflows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def build_flow_alas_phonons():\n",
    "    \"\"\"\n",
    "    Build and return a Flow to compute the dynamical matrix on a (2, 2, 2) qmesh\n",
    "    as well as DDK and Born effective charges.\n",
    "    The final DDB with all perturbations will be merged automatically and placed\n",
    "    in the Flow `outdir` directory.\n",
    "    \"\"\"\n",
    "    from abipy import flowtk\n",
    "    scf_input = make_scf_input(ecut=6, ngkpt=(4, 4, 4))\n",
    "    return flowtk.PhononFlow.from_scf_input(\"flow_alas_phonons\", scf_input,\n",
    "                                            ph_ngqpt=(2, 2, 2), with_becs=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#####  Abipy will call Abinit to get the list of DFPT perturbations and…"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
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    "flow_phbands = build_flow_alas_phonons()\n",
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    "## Future developments\n",
    "\n",
    "### Post-processing tools\n",
    "\n",
    "* Support for more *netcdf* files\n",
    "* More post-processing tools for MD calculations\n",
    "* More converters and interfaces for third-party applications\n",
    "* Integrate AbiPy with [jupyterlab](https://github.com/jupyterlab/jupyterlab) to create a flexible graphical enviroment for Abinit exposing (part) \n",
    "  of the python API \n",
    "* Explore web-based technologies for data analysis and visualization ([plotly](https://plot.ly/), [dash](https://plot.ly/products/dash/)) \n",
    "* Develop toolkit to build web apps powered by AbiPy and pymatgen to disseminate scientific results. \n",
    "\n",
    "## Continuous Integration\n",
    "\n",
    "* Use AbiPy programmatic interface to implement:\n",
    "\n",
    "    * Validation of parallel algorithms for np in range(1, N)\n",
    "    * Stress testing\n",
    "    * Benchmarks"
   ]
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