Dockerfile for pyublas - Moved to https://github.com/parsa/octoviz

.dockerignore

*.bat
*.ipynb

.gitignore

.ipynb_checkpoints/

README.md

Octoviz

Generate VTU from an Octo-tiger .silo file

Files

• Dockerfile - Dockerfile of the image containing the notebook with Silo, VTK, HDF5 and h5py
• build-image.bat - Helper script that builds the notebook Docker image
• start-container.bat - Helper script that runs a container from the notebook image
• notebooks/octoviz_silo.ipynb - Jupyter notebook that attempts to read Octo-tiger .silo files using Silo
• notebooks/octoviz_hdf5.ipynb - Jupyter notebook that reads Octo-tiger .silo files as HDF5 files
• octoviz_hdf5.py - Condensed and cleaned up version of the HDF5 notebook as a script

build-image.bat

docker build -t prsam/octoviz .
docker push prsam/octoviz

Dockerfile

FROM debian

RUN apt-get update && \
    apt-get install --yes \
        libboost-python-dev \
        python-dev \
        python-setuptools \
        python-pip \
        python-vtk6 \
        libhdf5-dev \
        curl

RUN pip install --no-cache-dir numpy && \
    pip install --no-cache-dir pandas && \
    pip install --no-cache-dir matplotlib && \
    pip install --no-cache-dir h5py && \
    pip install --no-cache-dir jupyterlab && \
    pip install --no-cache-dir line-profiler && \
    pip install --no-cache-dir psutil && \
    pip install --no-cache-dir memory_profiler

RUN mkdir /work && \
    cd /tmp && \
    curl https://wci.llnl.gov/content/assets/docs/simulation/computer-codes/silo/silo-4.10.2/silo-4.10.2-bsd.tar.gz | \
        tar xz && \
    ( \
        cd silo-4.10.2-bsd && \
        ./configure --prefix=/silo --enable-pythonmodule=yes --enable-fortran=no --with-hdf5=/usr/include/hdf5/serial,/usr/lib/x86_64-linux-gnu/hdf5/serial && \
        make -j && \
        make -j install \
    ) && \
    rm -rf silo-4.10.2-bsd.tar.gz silo-4.10.2-bsd && \
    apt-get purge && apt-get clean && rm -rf /var/lib/apt/lists/*

COPY octoviz_hdf5.py /usr/local/bin/octoviz

ENV PYTHONPATH "${PYTHONPATH}:/silo/lib"
WORKDIR /repos
CMD /usr/local/bin/jupyter-lab --ip=0.0.0.0 --no-browser --allow-root --NotebookApp.token='' --NotebookApp.password=''

LICENSE_1_0.txt

Boost Software License - Version 1.0 - August 17th, 2003

Permission is hereby granted, free of charge, to any person or organization
obtaining a copy of the software and accompanying documentation covered by
this license (the "Software") to use, reproduce, display, distribute,
execute, and transmit the Software, and to prepare derivative works of the
Software, and to permit third-parties to whom the Software is furnished to
do so, all subject to the following:

The copyright notices in the Software and this entire statement, including
the above license grant, this restriction and the following disclaimer,
must be included in all copies of the Software, in whole or in part, and
all derivative works of the Software, unless such copies or derivative
works are solely in the form of machine-executable object code generated by
a source language processor.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT
SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE
FOR ANY DAMAGES OR OTHER LIABILITY, WHETHER IN CONTRACT, TORT OR OTHERWISE,
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.

octoviz_hdf5.ipynb

octoviz_hdf5.ipynb

octoviz_hdf5.py

#!/usr/bin/env python
# Copyright (c) 2018 Parsa Amini
# Copyright (c) 2018 Patrick Diehl
#
# Distributed under the Boost Software License, Version 1.0. (See accompanying
# file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)

# ## Synopsis
# ```
# usage: octoviz_hdf5.py [-h] hdf5_file vtu_file
# 
# Generate VTU from an Octo-tiger Silo file
# 
# positional arguments:
#   hdf5_file   Octo-tiger .silo file to read
#   vtu_file    Path to write the .vtu file
# 
# optional arguments:
#   -h, --help  show this help message and exit
# ```

from __future__ import print_function
import argparse
import os
import sys


def silo_to_vtu(hdf5_file, vtu_file):
    print('Importing h5py, numpy, pandas, and vtk...')
    try:
        import h5py
        import numpy as np
        import pandas as pd
        import vtk
    except ImportError:
        print(
            'h5py, numpy, pandas, and vtk modules are required to run this script.',
            file=sys.stderr
        )
        sys.exit(1)

    db = h5py.File(hdf5_file, 'r')
    print('File {} opened.'.format(hdf5_file))

    # ## 1. Field Variables
    # `n_species` Tells me how many $\rho$ fractions there are
    rho_fractions = db['n_species'].value[0]
    print('n_species: {}'.format(rho_fractions))
    rhos = ['rho_' + str(x) for x in range(1, rho_fractions + 1)]

    groups = []
    datasets = []
    for k, v in db.iteritems():
        if isinstance(v, h5py.Group):
            groups.append(v)
        elif isinstance(v, h5py.Dataset):
            datasets.append(v)
        elif isinstance(v, h5py.Datatype):
            pass
        else:
            raise Exception('Bad key: ' + k)
    print('Number of directories: {}'.format((len(groups) - 2)))

    # Symbol | SILO variable name | Description
    # --- | --- | ---
    # $\rho$ | `rho_i` | $i$-th density fraction
    # $s_i$ | `sx, sy, sz` | inertial frame momentum
    # $z_i$ | `zx, zy, zz` | inertial fram spin momentum relative to cell center
    # $g_i$ | `gx, gy, gz` | gravitational acceleration
    # $\phi$ | `phi` | gravitational potential
    # $E$ | `egas` | kinetic + internal energy density
    # $\tau$ | `tau` | entropy tracer
    print('Reading directories...')
    col = []

    for g in groups[2:]:
        gf = pd.DataFrame({
            #'group': g.name,
            'E': db[g['egas'].attrs['silo'][1]].value.flatten(),
            'g_x': db[g['gx'].attrs['silo'][1]].value.flatten(),
            'g_y': db[g['gy'].attrs['silo'][1]].value.flatten(),
            'g_z': db[g['gz'].attrs['silo'][1]].value.flatten(),
            'phi': db[g['phi'].attrs['silo'][1]].value.flatten(),
            's_x': db[g['sx'].attrs['silo'][1]].value.flatten(),
            's_y': db[g['sy'].attrs['silo'][1]].value.flatten(),
            's_z': db[g['sz'].attrs['silo'][1]].value.flatten(),
            'tau': db[g['tau'].attrs['silo'][1]].value.flatten(),
            'z_x': db[g['zx'].attrs['silo'][1]].value.flatten(),
            'z_y': db[g['zy'].attrs['silo'][1]].value.flatten(),
            'z_z': db[g['zz'].attrs['silo'][1]].value.flatten(),
        })
        for i in range(rho_fractions):
            gf[rhos[i]] = db[g[rhos[i]].attrs['silo'][1]].value.flatten()

        gm = np.vstack((
                db[g['quadmesh'].attrs['silo'][0]].value,
                db[g['quadmesh'].attrs['silo'][1]].value,
                db[g['quadmesh'].attrs['silo'][2]].value,
        ))
        gp = .5 * (gm[:, :-1] + gm[:, 1:])

        gf['z'] = gp[2].repeat(gp.shape[1] ** 2)
        gf['y'] = (np.tile(gp[1], gp.shape[1])).repeat(gp.shape[1])
        gf['x'] = np.tile(gp[0], gp.shape[1] ** 2)

        col.append(gf)

    df = pd.concat(col).reset_index(drop=True)
    
    # ## 2. Other variables

    # Symbol | SILO variable name | Description
    # --- | --- | ---
    # $\mu_i$ | `A[i-1]` | atomic mass number of $i$-th fraction
    # $Z_i$ | `Z[i-1]` | electron number of $i$-th fraction
    # $\omega$ | `omega` | rotation frequency of the grid
    atomic_mass = np.array(db['atomic_mass'].value)
    atomic_number = np.array(db['atomic_number'].value)
    omega = db['omega'].value[0]
    
    CYCLE = db['cycle'].value
    TIME = db['dtime'].value

    # ## 3. Derived Expressions

    # total mass density
    # \rho := \Sigma_{i=1}^N \rho_i
    print('Calculating mass density...')
    df['rho'] = 0

    for rho in rhos:
        df.rho += df[rho]

    # x - velocity relative to grid
    # v_x := \frac{s_x}{\rho} + y \Omega
    # v_x := \frac{s_{x_{ijk}}}{\rho_{ijk}} + \frac{1}{2}(y_j + y_{j+1}) \Omega
    print('Calculating v_x velocity relative to grid...')
    df['v_x'] = (df.s_x / df.rho) + df.y * omega

    # y - velocity relative to grid
    # v_y := \frac{s_y}{\rho} - x \Omega
    # v_y := \frac{s_{y_{ijk}}}{\rho_{ijk}} - \frac{1}{2}(x_i + x_{i+1}) \Omega
    print('Calculating v_y velocity relative to grid...')
    df['v_y'] = (df.s_y / df.rho) + df.x * omega

    # z - velocity relative to grid
    # v_z  := \frac{s_z}{\rho}
    print('Calculating v_z velocity relative to grid...')
    df['v_z'] = df.s_z / df.rho

    # \gamma = \frac{5}{3}
    gamma = 5. / 3.

    # internal gas energy density
    # e    :=  \begin{cases}
    #        E - \frac{1}{2} \frac{s^2}{\rho} & \text{if }
    #            E - \frac{1}{2} \frac{s^2}{\rho} > 0.001 E \\
    #        \tau^\gamma & \text{else} \\
    #      \end{cases}
    print('Calculating internal gas energy density...')
    df['e'] = df.E - .5 * ((df.s_x ** 2 + df.s_y **2 + df.s_z ** 2) / df.rho)
    df.e = np.where(df.e > .001 * df.E, df.e, df.tau ** gamma)

    # $m_H$ (mass of hydrogen) = $1.6733 \times 10^{24}$
    mH = 1.6733e-24

    # number density of ith fraction (ions + electrons)
    # n_i := \frac{\rho_i}{\mu_i m_H} \left ( 1 + Z_i \right )
    print('Calculating number density of ith fraction...')
    for i in range(len(atomic_mass)):
        df['n_i' + str(i + 1)] = (df.rho / (atomic_mass[i] * mH)) * (1. + atomic_number[i])

    # total number density
    # n := \Sigma_{i=1}^N  n_i
    print('Calculating total number density...')
    df['n'] = 0
    for i in range(len(atomic_mass)):
        df.n += df['n_i' + str(i + 1)]

    # $K_b := 1.380658 \times 10^{-16}$
    K_b = 1.380658e-16
    
    # temperature
    # T := \frac{1}{\gamma-1} \frac{e}{n}
    df['T'] = ((1. / (gamma - 1.)) * (df.e / df.n)) / K_b

    # pressure
    # P := \left(\gamma-1\right) e
    print('Calculating pressure...')
    df['P'] = (gamma - 1.) * df.e

    db.close()

    print('Number of particles: {}'.format(df.shape[0]))

    # 3. Write to VTU file

    # Add points and fields
    points = vtk.vtkPoints()
    points.SetNumberOfPoints(df.shape[0])

    points.SetDataTypeToDouble()

    for idx, x_i, y_i, z_i in zip(range(df.shape[0]), df.x, df.y, df.z):
        points.InsertPoint(idx, x_i, y_i, z_i)

    # Configure the grid
    grid = vtk.vtkUnstructuredGrid()
    grid.SetPoints(points)
    
    field_data = grid.GetFieldData()
    
    atomic_mass_array = vtk.vtkDoubleArray()
    atomic_mass_array.SetName('atomic_mass')
    atomic_mass_array.SetNumberOfComponents(len(atomic_mass))
    atomic_mass_array.SetNumberOfTuples(1)
    atomic_mass_array.SetTuple(0, atomic_mass)

    field_data.AddArray(atomic_mass_array)
    
    atomic_number_array = vtk.vtkDoubleArray()
    atomic_number_array.SetName('atomic_number')
    atomic_number_array.SetNumberOfComponents(len(atomic_number))
    atomic_number_array.SetNumberOfTuples(1)
    atomic_number_array.SetTuple(0, atomic_number)

    field_data.AddArray(atomic_number_array)
    
    omega_array = vtk.vtkDoubleArray()
    omega_array.SetName('omega')
    omega_array.SetNumberOfComponents(1)
    omega_array.SetNumberOfTuples(1)
    omega_array.SetTuple1(0, omega)

    field_data.AddArray(omega_array)
    
    TIME_array = vtk.vtkDoubleArray()
    TIME_array.SetName('TIME')
    TIME_array.SetNumberOfComponents(1)
    TIME_array.SetNumberOfTuples(1)
    TIME_array.SetTuple1(0, TIME)

    field_data.AddArray(TIME_array)
    
    CYCLE_array = vtk.vtkDoubleArray()
    CYCLE_array.SetName('CYCLE')
    CYCLE_array.SetNumberOfComponents(1)
    CYCLE_array.SetNumberOfTuples(1)
    CYCLE_array.SetTuple1(0, CYCLE)

    field_data.AddArray(CYCLE_array)
    
    data_out = grid.GetPointData()
    
    keys = list(set(df.keys()).difference({'x', 'y', 'z', 'group'}))
    keys.sort()
    
    for k in keys:
        print('Adding key \'{}\'...'.format(k))
        dis_array = vtk.vtkDoubleArray()
        dis_array.SetName(k)
        dis_array.SetNumberOfComponents(1)
        dis_array.SetNumberOfTuples(df.shape[0])
        
        for idx, val in zip(range(df.shape[0]), df[k]):
            dis_array.SetTuple1(idx, val)
        
        data_out.AddArray(dis_array)

    # Configure the VTU writer
    writer = vtk.vtkXMLUnstructuredGridWriter()
    writer.SetFileName(vtu_file)

    writer.SetInputData(grid)

    # Writer Data modes:
    # 1. Binary (with compression), saves space
    writer.SetDataModeToBinary()
    # 2. ASCII - Plain text, human readable
    # writer.SetDataModeToAscii()

    print('Writing VTU file {}...'.format(vtu_file))
    writer.Write()


# Determine if a path is actually a readable file
def existing_file(path):
    if not os.path.isfile(path) and os.access(path, os.R_OK):
        raise argparse.ArgumentTypeError('Path {} does not exist'.format(path))

    return path


def main():
    parser = argparse.ArgumentParser(
        description='Generate VTU from an Octo-tiger Silo file')
    parser.add_argument(
        'hdf5_file', type=existing_file, help='Octo-tiger .silo file to read')
    parser.add_argument(
        'vtu_file', type=str, help='Path to write the .vtu file')

    args = parser.parse_args()

    silo_to_vtu(args.hdf5_file, args.vtu_file)


if __name__ == '__main__':
    main()

octoviz_silo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2018 Parsa Amini  \n",
    "Copyright (c) 2018 Patrick Diehl\n",
    "\n",
    "Distributed under the Boost Software License, Version 1.0. (See accompanying\n",
    "file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%%bash\n",
    "pip install line-profiler\n",
    "pip install psutil\n",
    "pip install memory_profiler\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%load_ext line_profiler\n",
    "%load_ext memory_profiler\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Silo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Silo.Open(filename [, DB_READ|DB_APPEND]])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = Silo.Open(\"/repos/test.silo\", Silo.DB_APPEND)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvar = 38\n",
      "var_names = (X, Z, _fileinfo, _hdf5libinfo, _silolibinfo, atomic_mass, atomic_number, cgs_time, code_to_cm, code_to_g, code_to_s, cycle, dtime, eos, epoch, gravity, hostname, hydro, leaf_count, locality_count, n_species, node_count, node_list, node_positions, omega, output_frequency, problem, radiation, refinement_floor, rotational_time, step_count, steps_elapsed, thread_count, time, time_elapsed, timestamp, version, xscale)\n",
      "ndir = 512\n",
      "dir_names = (100, 1000007, 100225, 10025, 10040007, 101, 1011116, 101116, 10116, 101334, 10134, 10151116, 1016, 102, 10207, 1022225, 102225, 10261, 10262225, 103, 10316, 1033334, 103334, 10334, 1034, 10370, 10373334, 104, 10400007, 1043, 10443, 104443, 1044443, 10444443, 10461, 104661, 105, 10511116, 1051116, 1052, 10552, 105552, 1055552, 10555552, 10570, 105770, 106, 1061, 10622225, 1062225, 10625, 10643, 10661, 106661, 1066661, 10666661, 1070, 10733334, 1073334, 10734, 10752, 10770, 107770, 1077770, 10777770, 110, 1100007, 110007, 11007, 110225, 11025, 11040007, 1107, 111, 1111116, 111334, 11134, 11151116, 112, 11207, 1122225, 112225, 11225, 1125, 11261, 11262225, 113, 11316, 1133334, 113334, 11370, 11373334, 114, 11400007, 1140007, 1143, 11443, 114443, 1144443, 11444443, 11461, 114661, 115, 11511116, 1152, 11552, 115552, 1155552, 11555552, 11570, 115770, 1161, 11622225, 1162225, 11625, 11643, 11661, 116661, 1166661, 11666661, 117, 1170, 11733334, 1173334, 11734, 11752, 11770, 117770, 1177770, 11777770, 120, 1200007, 120007, 12007, 120225, 12025, 12040007, 121, 1211116, 121116, 12116, 121334, 12134, 12151116, 1216, 122, 12207, 1222225, 12261, 12262225, 123, 12316, 1233334, 123334, 12334, 1234, 12370, 12373334, 124, 12400007, 1240007, 1243, 12443, 124443, 1244443, 12444443, 12461, 124661, 12511116, 1251116, 1252, 12552, 125552, 1255552, 12555552, 12570, 125770, 126, 12622225, 12625, 12643, 12661, 126661, 1266661, 12666661, 127, 1270, 12733334, 1273334, 12734, 12752, 12770, 127770, 1277770, 12777770, 130, 1300007, 130007, 13007, 130225, 13025, 13040007, 1307, 131, 1311116, 131116, 13116, 131334, 13134, 13151116, 132, 13207, 1322225, 132225, 13225, 1325, 13261, 13262225, 133, 13316, 1333334, 13370, 13373334, 13400007, 1340007, 1343, 13443, 134443, 1344443, 13444443, 13461, 134661, 135, 13511116, 1351116, 1352, 13552, 135552, 1355552, 13555552, 13570, 135770, 136, 1361, 13622225, 1362225, 13625, 13643, 13661, 136661, 1366661, 13666661, 137, 13733334, 13734, 13752, 13770, 137770, 1377770, 13777770, 140, 14007, 140225, 14025, 14040007, 1407, 141, 1411116, 141116, 14116, 141334, 14134, 14151116, 1416, 142, 14207, 1422225, 142225, 14225, 1425, 14261, 14262225, 14316, 1433334, 143334, 14334, 1434, 14370, 14373334, 144, 14400007, 1440007, 14444443, 14461, 144661, 145, 14511116, 1451116, 1452, 14552, 145552, 1455552, 14555552, 14570, 145770, 146, 14622225, 1462225, 14625, 14643, 146661, 1466661, 14666661, 147, 1470, 14733334, 1473334, 14734, 14752, 14770, 147770, 1477770, 14777770, 150, 1500007, 150007, 15007, 150225, 15025, 15040007, 1507, 151, 15116, 151334, 15134, 15151116, 1516, 15207, 1522225, 152225, 15225, 1525, 15261, 15262225, 153, 15316, 1533334, 153334, 15334, 1534, 15370, 15373334, 154, 15400007, 1540007, 1543, 15443, 154443, 1544443, 15444443, 15461, 154661, 155, 15511116, 1551116, 15555552, 15570, 155770, 156, 1561, 15622225, 1562225, 15625, 15643, 15661, 156661, 1566661, 15666661, 157, 15733334, 1573334, 15734, 15752, 157770, 1577770, 15777770, 160, 1600007, 160007, 16007, 160225, 16025, 16040007, 1607, 1611116, 161116, 16116, 161334, 16134, 16151116, 1616, 162, 16207, 16225, 16261, 16262225, 163, 16316, 1633334, 163334, 16334, 1634, 16370, 16373334, 164, 16400007, 1640007, 16443, 164443, 1644443, 16444443, 16461, 164661, 165, 16511116, 1651116, 1652, 16552, 165552, 1655552, 16555552, 16570, 165770, 166, 16622225, 1662225, 16625, 16643, 16666661, 167, 1670, 16733334, 1673334, 16734, 16752, 16770, 167770, 1677770, 16777770, 1700007, 170007, 17007, 170225, 17025, 17040007, 1707, 171, 1711116, 171116, 17116, 171334, 17134, 17151116, 1716, 172, 17207, 1722225, 172225, 17225, 1725, 17261, 17262225, 173, 17316, 17334, 17370, 17373334, 174, 17400007, 1740007, 1743, 17443, 174443, 1744443, 17444443, 17461, 174661, 175, 17511116, 1751116, 17552, 175552, 1755552, 17555552, 17570, 175770, 176, 1761, 17622225, 1762225, 17625, 17643, 17661, 176661, 1766661, 17666661, 177, 17733334, 1773334, 17734, 17752, 17777770)\n",
      "nmultimesh = 1\n",
      "multimesh_names = (quadmesh)\n",
      "nmultivar = 18\n",
      "multivar_names = (egas, gx, gy, gz, phi, rho_1, rho_2, rho_3, rho_4, rho_5, roche_geometry, sx, sy, sz, tau, zx, zy, zz)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "toc = db.GetToc()\n",
    "print(toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_names = ['/' + dn + '/' for dn in toc.dir_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_names = toc.var_names\n",
    "multivar_names = toc.multivar_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Field Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`n_species` Tells me how many $\\rho$ fractions there are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.SetDir('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.GetVar('n_species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rho_fractions = db.GetVar(\"n_species\")\n",
    "rhos = ['rho_' + str(x) for x in range(1, rho_fractions + 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.SetDir('/100')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'align1': 0.5,\n",
       " 'align2': 0.5,\n",
       " 'align3': 0.5,\n",
       " 'centering': 111,\n",
       " 'conserved': 1,\n",
       " 'cycle': 75,\n",
       " 'dims1': 8,\n",
       " 'dims2': 8,\n",
       " 'dims3': 8,\n",
       " 'dtime': 365159.3096867017,\n",
       " 'guihide': 1,\n",
       " 'max_index1': 7,\n",
       " 'max_index2': 7,\n",
       " 'max_index3': 7,\n",
       " 'meshid': 'quadmesh',\n",
       " 'min_index1': 0,\n",
       " 'min_index2': 0,\n",
       " 'min_index3': 0,\n",
       " 'name': 'egas',\n",
       " 'ndims': 3,\n",
       " 'nels': 512,\n",
       " 'nvals': 1,\n",
       " 'time': 365159.3125,\n",
       " 'type': 'quadvar',\n",
       " 'use_specmf': -1000,\n",
       " 'value0': '/.silo/#000157',\n",
       " 'zones1': 7,\n",
       " 'zones2': 7,\n",
       " 'zones3': 7}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.GetVarInfo('egas')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbol | SILO variable name | Description\n",
    "--- | --- | ---\n",
    "$\\rho$ | `rho_i` | $i$-th density fraction\n",
    "$s_i$ | `sx, sy, sz` | inertial frame momentum\n",
    "$z_i$ | `zx, zy, zz` | inertial fram spin momentum relative to cell center\n",
    "$g_i$ | `gx, gy, gz` | gravitational acceleration\n",
    "$\\phi$ | `phi` | gravitational potential\n",
    "$E$ | `egas` | kinetic + internal energy density\n",
    "$\\tau$ | `tau` | entropy tracer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timer unit: 1e-06 s\n",
       "\n",
       "Total time: 62.4071 s\n",
       "File: <ipython-input-11-fd52f1aeaac6>\n",
       "Function: get_multivars at line 1\n",
       "\n",
       "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
       "==============================================================\n",
       "     1                                           def get_multivars(db):\n",
       "     2         1         28.0     28.0      0.0      multivars = []\n",
       "     3         1         13.0     13.0      0.0      quadmeshes = []\n",
       "     4       513       5601.0     10.9      0.0      for dir_name in dir_names:\n",
       "     5       512      74791.0    146.1      0.1          db.SetDir(dir_name)\n",
       "     6                                           \n",
       "     7       512     199577.0    389.8      0.3          dir_multivars = {}\n",
       "     8       512       6830.0     13.3      0.0          dir_multivars['dir'] = dir_name\n",
       "     9      9728     130040.0     13.4      0.2          for multivar_name in multivar_names:\n",
       "    10      9216     111413.0     12.1      0.2              dir_multivars[multivar_name] = db.GetVar(\n",
       "    11      9216   43990468.0   4773.3     70.5                  db.GetVarInfo(multivar_name)['value0'])\n",
       "    12       512    4507336.0   8803.4      7.2          multivars.append(pd.DataFrame(dir_multivars))\n",
       "    13                                                   \n",
       "    14       512       7073.0     13.8      0.0          dir_quadmesh = pd.DataFrame({\n",
       "    15       512       5430.0     10.6      0.0              'dir': dir_name,\n",
       "    16       512    3528794.0   6892.2      5.7              'x': db.GetVar(db.GetVarInfo('quadmesh')['coord0']),\n",
       "    17       512    3332191.0   6508.2      5.3              'y': db.GetVar(db.GetVarInfo('quadmesh')['coord1']),\n",
       "    18       512    4329427.0   8455.9      6.9              'z': db.GetVar(db.GetVarInfo('quadmesh')['coord2'])\n",
       "    19                                                   })\n",
       "    20       512       6754.0     13.2      0.0          quadmeshes.append(dir_quadmesh)\n",
       "    21                                           \n",
       "    22         1    2171327.0 2171327.0      3.5      return pd.concat(quadmeshes), pd.concat(multivars)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_multivars(db):\n",
    "    multivars = []\n",
    "    quadmeshes = []\n",
    "    for dir_name in dir_names:\n",
    "        db.SetDir(dir_name)\n",
    "\n",
    "        dir_multivars = {}\n",
    "        dir_multivars['dir'] = dir_name\n",
    "        for multivar_name in multivar_names:\n",
    "            dir_multivars[multivar_name] = db.GetVar(\n",
    "                db.GetVarInfo(multivar_name)['value0'])\n",
    "        multivars.append(pd.DataFrame(dir_multivars))\n",
    "        \n",
    "        dir_quadmesh = pd.DataFrame({\n",
    "            'dir': dir_name,\n",
    "            'x': db.GetVar(db.GetVarInfo('quadmesh')['coord0']),\n",
    "            'y': db.GetVar(db.GetVarInfo('quadmesh')['coord1']),\n",
    "            'z': db.GetVar(db.GetVarInfo('quadmesh')['coord2'])\n",
    "        })\n",
    "        quadmeshes.append(dir_quadmesh)\n",
    "\n",
    "    return pd.concat(quadmeshes), pd.concat(multivars)\n",
    "\n",
    "%lprun -f get_multivars quadmeshes, multivars = get_multivars(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Other variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbol | SILO variable name | Description\n",
    "--- | --- | ---\n",
    "$\\mu_i$ | `A[i-1]` | atomic mass number of $i$-th fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.SetDir('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13.714, 1.081, 1.081, 1.081, 1.0)\n"
     ]
    }
   ],
   "source": [
    "atomic_mass = db.GetVar(\"atomic_mass\")\n",
    "print(atomic_mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbol | SILO variable name | Description\n",
    "--- | --- | ---\n",
    "$Z_i$ | `Z[i-1]` | electron number of $i$-th fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6.857, 1.098, 1.098, 1.098, 1.0)\n"
     ]
    }
   ],
   "source": [
    "atomic_number = db.GetVar(\"atomic_number\")\n",
    "print(atomic_number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbol | SILO variable name | Description\n",
    "--- | --- | ---\n",
    "$\\omega$ | `omega` | rotation frequency of the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.17951974157e-05\n"
     ]
    }
   ],
   "source": [
    "omega = db.GetVar(\"omega\")\n",
    "print(omega)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Derived Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "total mass density\n",
    "$$\n",
    "\\rho := \\Sigma_{i=1}^N \\rho_i \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['rho'] = multivars[rhos[0]]\n",
    "for rho_index in rhos[1:]:\n",
    "    multivars['rho'] += multivars[rho_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x - velocity relative to grid\n",
    "$$\n",
    "v_x  := \\frac{s_x}{\\rho} + y \\Omega \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What to do with $y$?\n",
    "```python\n",
    "multivars['vx'] = multivars['sx'] / multivars['rho'] + quadmesh['coord1'] * omega\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y - velocity relative to grid\n",
    "$$\n",
    "v_y  := \\frac{s_y}{\\rho} - x \\Omega \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What to do with $x$?\n",
    "```python\n",
    "multivars['vx'] = multivars['sy'] / multivars['rho'] + quadmesh['coord0'] * omega\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "z - velocity relative to grid\n",
    "$$\n",
    "v_z  := \\frac{s_z}{\\rho} \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['vz'] = multivars.sz / multivars.rho"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "s^2  :=  {s_x}^2 + {s_y}^2 + {s_z}^2\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['s2'] = multivars.sx ** 2 + multivars.sy ** 2 + multivars.sz ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\gamma = \\frac{5}{3}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = 5. / 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "internal gas energy density\n",
    "$$\n",
    "e    :=  \\begin{cases}\n",
    "       E - \\frac{1}{2} \\frac{s^2}{\\rho} & \\text{if } E - \\frac{1}{2} \\frac{s^2}{\\rho} > 0.001 E \\\\\n",
    "       \\tau^\\gamma & \\text{else} \\\\\n",
    "     \\end{cases} \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['e'] = multivars.egas - .5 * multivars.s2 / multivars.rho\n",
    "multivars['e'] = np.where(multivars.e > .001 * multivars.egas, multivars.e, multivars.tau ** gamma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$m_H$ (mass of hydrogen) = $1.6733 \\times 10^{24}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mH = 1.6733e-24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number density of ith fraction (ions + electrons)\n",
    "$$\n",
    "n_i := \\frac{\\rho_i}{\\mu_i m_H} \\left ( 1 + Z_i \\right ) \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(atomic_mass)):\n",
    "    multivars['ni_' + str(i + 1)] = (multivars.rho / (atomic_mass[i] * mH)) * (1. + atomic_number[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "total number density\n",
    "$$\n",
    "n := \\Sigma_{i=1}^N  n_i\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['n'] = multivars.ni_1 + multivars.ni_2 + multivars.ni_3 + multivars.ni_4 + multivars.ni_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\gamma := \\frac{5}{3}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "temperature\n",
    "$$\n",
    "T := \\frac{1}{\\gamma-1} \\frac{e}{n}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['T'] = (1. / (gamma - 1.)) * (multivars.e / multivars.n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pressure\n",
    "$$\n",
    "P := \\left(\\gamma-1\\right) e\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['P'] = (gamma - 1.) * multivars.e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.Close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Zone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Silo.Create(filename , info [, DB_PDB|DB_HDF5 [, DB_CLOBBER|DB_NOCLOBBER]])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "db2 = Silo.Create('/repos/test2.silo', 'Octo-tiger', Silo.DB_HDF5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(100):\n",
    "    db2.MkDir(str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "db2.Write('q', (1, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvar = 4\n",
      "var_names = (_fileinfo, _hdf5libinfo, _silolibinfo, q)\n",
      "ndir = 100\n",
      "dir_names = (0, 1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 6, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 7, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 8, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 9, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(db2.GetToc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "db2.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "db3 = Silo.Open('/repos/test2.silo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvar = 4\n",
      "var_names = (_fileinfo, _hdf5libinfo, _silolibinfo, q)\n",
      "ndir = 100\n",
      "dir_names = (0, 1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 6, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 7, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 8, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 9, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(db3.GetToc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "db3.Close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "db2 = Silo.Create(\"/repos/test.silo\", \"Octo-tiger\", Silo.DB_HDF5)\n",
    "db2.Write(\"q\", 3)\n",
    "db.SetDir('/')\n",
    "db.MkDir('silo')\n",
    "db.GetVarInfo('phi')\n",
    "db2.WriteObject(\"w\", db.GetVarInfo('phi'))\n",
    "db2.Close()\n",
    "db3 = Silo.Open(\"/repos/test.silo\")\n",
    "db3.GetVar(\"q\")\n",
    "db3.GetVarInfo('w')\n",
    "db3.Close()\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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 },
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octoviz_silo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) 2018 Parsa Amini  \n",
    "Copyright (c) 2018 Patrick Diehl\n",
    "\n",
    "Distributed under the Boost Software License, Version 1.0. (See accompanying\n",
    "file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%%bash\n",
    "pip install line-profiler\n",
    "pip install psutil\n",
    "pip install memory_profiler\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "%load_ext line_profiler\n",
    "%load_ext memory_profiler\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import Silo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Silo.Open(filename [, DB_READ|DB_APPEND]])`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = Silo.Open(\"/repos/test.silo\", Silo.DB_APPEND)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvar = 38\n",
      "var_names = (X, Z, _fileinfo, _hdf5libinfo, _silolibinfo, atomic_mass, atomic_number, cgs_time, code_to_cm, code_to_g, code_to_s, cycle, dtime, eos, epoch, gravity, hostname, hydro, leaf_count, locality_count, n_species, node_count, node_list, node_positions, omega, output_frequency, problem, radiation, refinement_floor, rotational_time, step_count, steps_elapsed, thread_count, time, time_elapsed, timestamp, version, xscale)\n",
      "ndir = 512\n",
      "dir_names = (100, 1000007, 100225, 10025, 10040007, 101, 1011116, 101116, 10116, 101334, 10134, 10151116, 1016, 102, 10207, 1022225, 102225, 10261, 10262225, 103, 10316, 1033334, 103334, 10334, 1034, 10370, 10373334, 104, 10400007, 1043, 10443, 104443, 1044443, 10444443, 10461, 104661, 105, 10511116, 1051116, 1052, 10552, 105552, 1055552, 10555552, 10570, 105770, 106, 1061, 10622225, 1062225, 10625, 10643, 10661, 106661, 1066661, 10666661, 1070, 10733334, 1073334, 10734, 10752, 10770, 107770, 1077770, 10777770, 110, 1100007, 110007, 11007, 110225, 11025, 11040007, 1107, 111, 1111116, 111334, 11134, 11151116, 112, 11207, 1122225, 112225, 11225, 1125, 11261, 11262225, 113, 11316, 1133334, 113334, 11370, 11373334, 114, 11400007, 1140007, 1143, 11443, 114443, 1144443, 11444443, 11461, 114661, 115, 11511116, 1152, 11552, 115552, 1155552, 11555552, 11570, 115770, 1161, 11622225, 1162225, 11625, 11643, 11661, 116661, 1166661, 11666661, 117, 1170, 11733334, 1173334, 11734, 11752, 11770, 117770, 1177770, 11777770, 120, 1200007, 120007, 12007, 120225, 12025, 12040007, 121, 1211116, 121116, 12116, 121334, 12134, 12151116, 1216, 122, 12207, 1222225, 12261, 12262225, 123, 12316, 1233334, 123334, 12334, 1234, 12370, 12373334, 124, 12400007, 1240007, 1243, 12443, 124443, 1244443, 12444443, 12461, 124661, 12511116, 1251116, 1252, 12552, 125552, 1255552, 12555552, 12570, 125770, 126, 12622225, 12625, 12643, 12661, 126661, 1266661, 12666661, 127, 1270, 12733334, 1273334, 12734, 12752, 12770, 127770, 1277770, 12777770, 130, 1300007, 130007, 13007, 130225, 13025, 13040007, 1307, 131, 1311116, 131116, 13116, 131334, 13134, 13151116, 132, 13207, 1322225, 132225, 13225, 1325, 13261, 13262225, 133, 13316, 1333334, 13370, 13373334, 13400007, 1340007, 1343, 13443, 134443, 1344443, 13444443, 13461, 134661, 135, 13511116, 1351116, 1352, 13552, 135552, 1355552, 13555552, 13570, 135770, 136, 1361, 13622225, 1362225, 13625, 13643, 13661, 136661, 1366661, 13666661, 137, 13733334, 13734, 13752, 13770, 137770, 1377770, 13777770, 140, 14007, 140225, 14025, 14040007, 1407, 141, 1411116, 141116, 14116, 141334, 14134, 14151116, 1416, 142, 14207, 1422225, 142225, 14225, 1425, 14261, 14262225, 14316, 1433334, 143334, 14334, 1434, 14370, 14373334, 144, 14400007, 1440007, 14444443, 14461, 144661, 145, 14511116, 1451116, 1452, 14552, 145552, 1455552, 14555552, 14570, 145770, 146, 14622225, 1462225, 14625, 14643, 146661, 1466661, 14666661, 147, 1470, 14733334, 1473334, 14734, 14752, 14770, 147770, 1477770, 14777770, 150, 1500007, 150007, 15007, 150225, 15025, 15040007, 1507, 151, 15116, 151334, 15134, 15151116, 1516, 15207, 1522225, 152225, 15225, 1525, 15261, 15262225, 153, 15316, 1533334, 153334, 15334, 1534, 15370, 15373334, 154, 15400007, 1540007, 1543, 15443, 154443, 1544443, 15444443, 15461, 154661, 155, 15511116, 1551116, 15555552, 15570, 155770, 156, 1561, 15622225, 1562225, 15625, 15643, 15661, 156661, 1566661, 15666661, 157, 15733334, 1573334, 15734, 15752, 157770, 1577770, 15777770, 160, 1600007, 160007, 16007, 160225, 16025, 16040007, 1607, 1611116, 161116, 16116, 161334, 16134, 16151116, 1616, 162, 16207, 16225, 16261, 16262225, 163, 16316, 1633334, 163334, 16334, 1634, 16370, 16373334, 164, 16400007, 1640007, 16443, 164443, 1644443, 16444443, 16461, 164661, 165, 16511116, 1651116, 1652, 16552, 165552, 1655552, 16555552, 16570, 165770, 166, 16622225, 1662225, 16625, 16643, 16666661, 167, 1670, 16733334, 1673334, 16734, 16752, 16770, 167770, 1677770, 16777770, 1700007, 170007, 17007, 170225, 17025, 17040007, 1707, 171, 1711116, 171116, 17116, 171334, 17134, 17151116, 1716, 172, 17207, 1722225, 172225, 17225, 1725, 17261, 17262225, 173, 17316, 17334, 17370, 17373334, 174, 17400007, 1740007, 1743, 17443, 174443, 1744443, 17444443, 17461, 174661, 175, 17511116, 1751116, 17552, 175552, 1755552, 17555552, 17570, 175770, 176, 1761, 17622225, 1762225, 17625, 17643, 17661, 176661, 1766661, 17666661, 177, 17733334, 1773334, 17734, 17752, 17777770)\n",
      "nmultimesh = 1\n",
      "multimesh_names = (quadmesh)\n",
      "nmultivar = 18\n",
      "multivar_names = (egas, gx, gy, gz, phi, rho_1, rho_2, rho_3, rho_4, rho_5, roche_geometry, sx, sy, sz, tau, zx, zy, zz)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "toc = db.GetToc()\n",
    "print(toc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_names = ['/' + dn + '/' for dn in toc.dir_names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_names = toc.var_names\n",
    "multivar_names = toc.multivar_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Field Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`n_species` Tells me how many $\\rho$ fractions there are"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.SetDir('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.GetVar('n_species')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "rho_fractions = db.GetVar(\"n_species\")\n",
    "rhos = ['rho_' + str(x) for x in range(1, rho_fractions + 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.SetDir('/100')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'align1': 0.5,\n",
       " 'align2': 0.5,\n",
       " 'align3': 0.5,\n",
       " 'centering': 111,\n",
       " 'conserved': 1,\n",
       " 'cycle': 75,\n",
       " 'dims1': 8,\n",
       " 'dims2': 8,\n",
       " 'dims3': 8,\n",
       " 'dtime': 365159.3096867017,\n",
       " 'guihide': 1,\n",
       " 'max_index1': 7,\n",
       " 'max_index2': 7,\n",
       " 'max_index3': 7,\n",
       " 'meshid': 'quadmesh',\n",
       " 'min_index1': 0,\n",
       " 'min_index2': 0,\n",
       " 'min_index3': 0,\n",
       " 'name': 'egas',\n",
       " 'ndims': 3,\n",
       " 'nels': 512,\n",
       " 'nvals': 1,\n",
       " 'time': 365159.3125,\n",
       " 'type': 'quadvar',\n",
       " 'use_specmf': -1000,\n",
       " 'value0': '/.silo/#000157',\n",
       " 'zones1': 7,\n",
       " 'zones2': 7,\n",
       " 'zones3': 7}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.GetVarInfo('egas')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbol | SILO variable name | Description\n",
    "--- | --- | ---\n",
    "$\\rho$ | `rho_i` | $i$-th density fraction\n",
    "$s_i$ | `sx, sy, sz` | inertial frame momentum\n",
    "$z_i$ | `zx, zy, zz` | inertial fram spin momentum relative to cell center\n",
    "$g_i$ | `gx, gy, gz` | gravitational acceleration\n",
    "$\\phi$ | `phi` | gravitational potential\n",
    "$E$ | `egas` | kinetic + internal energy density\n",
    "$\\tau$ | `tau` | entropy tracer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timer unit: 1e-06 s\n",
       "\n",
       "Total time: 62.4071 s\n",
       "File: <ipython-input-11-fd52f1aeaac6>\n",
       "Function: get_multivars at line 1\n",
       "\n",
       "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
       "==============================================================\n",
       "     1                                           def get_multivars(db):\n",
       "     2         1         28.0     28.0      0.0      multivars = []\n",
       "     3         1         13.0     13.0      0.0      quadmeshes = []\n",
       "     4       513       5601.0     10.9      0.0      for dir_name in dir_names:\n",
       "     5       512      74791.0    146.1      0.1          db.SetDir(dir_name)\n",
       "     6                                           \n",
       "     7       512     199577.0    389.8      0.3          dir_multivars = {}\n",
       "     8       512       6830.0     13.3      0.0          dir_multivars['dir'] = dir_name\n",
       "     9      9728     130040.0     13.4      0.2          for multivar_name in multivar_names:\n",
       "    10      9216     111413.0     12.1      0.2              dir_multivars[multivar_name] = db.GetVar(\n",
       "    11      9216   43990468.0   4773.3     70.5                  db.GetVarInfo(multivar_name)['value0'])\n",
       "    12       512    4507336.0   8803.4      7.2          multivars.append(pd.DataFrame(dir_multivars))\n",
       "    13                                                   \n",
       "    14       512       7073.0     13.8      0.0          dir_quadmesh = pd.DataFrame({\n",
       "    15       512       5430.0     10.6      0.0              'dir': dir_name,\n",
       "    16       512    3528794.0   6892.2      5.7              'x': db.GetVar(db.GetVarInfo('quadmesh')['coord0']),\n",
       "    17       512    3332191.0   6508.2      5.3              'y': db.GetVar(db.GetVarInfo('quadmesh')['coord1']),\n",
       "    18       512    4329427.0   8455.9      6.9              'z': db.GetVar(db.GetVarInfo('quadmesh')['coord2'])\n",
       "    19                                                   })\n",
       "    20       512       6754.0     13.2      0.0          quadmeshes.append(dir_quadmesh)\n",
       "    21                                           \n",
       "    22         1    2171327.0 2171327.0      3.5      return pd.concat(quadmeshes), pd.concat(multivars)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_multivars(db):\n",
    "    multivars = []\n",
    "    quadmeshes = []\n",
    "    for dir_name in dir_names:\n",
    "        db.SetDir(dir_name)\n",
    "\n",
    "        dir_multivars = {}\n",
    "        dir_multivars['dir'] = dir_name\n",
    "        for multivar_name in multivar_names:\n",
    "            dir_multivars[multivar_name] = db.GetVar(\n",
    "                db.GetVarInfo(multivar_name)['value0'])\n",
    "        multivars.append(pd.DataFrame(dir_multivars))\n",
    "        \n",
    "        dir_quadmesh = pd.DataFrame({\n",
    "            'dir': dir_name,\n",
    "            'x': db.GetVar(db.GetVarInfo('quadmesh')['coord0']),\n",
    "            'y': db.GetVar(db.GetVarInfo('quadmesh')['coord1']),\n",
    "            'z': db.GetVar(db.GetVarInfo('quadmesh')['coord2'])\n",
    "        })\n",
    "        quadmeshes.append(dir_quadmesh)\n",
    "\n",
    "    return pd.concat(quadmeshes), pd.concat(multivars)\n",
    "\n",
    "%lprun -f get_multivars quadmeshes, multivars = get_multivars(db)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Other variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbol | SILO variable name | Description\n",
    "--- | --- | ---\n",
    "$\\mu_i$ | `A[i-1]` | atomic mass number of $i$-th fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.SetDir('/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13.714, 1.081, 1.081, 1.081, 1.0)\n"
     ]
    }
   ],
   "source": [
    "atomic_mass = db.GetVar(\"atomic_mass\")\n",
    "print(atomic_mass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbol | SILO variable name | Description\n",
    "--- | --- | ---\n",
    "$Z_i$ | `Z[i-1]` | electron number of $i$-th fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6.857, 1.098, 1.098, 1.098, 1.0)\n"
     ]
    }
   ],
   "source": [
    "atomic_number = db.GetVar(\"atomic_number\")\n",
    "print(atomic_number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Symbol | SILO variable name | Description\n",
    "--- | --- | ---\n",
    "$\\omega$ | `omega` | rotation frequency of the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.17951974157e-05\n"
     ]
    }
   ],
   "source": [
    "omega = db.GetVar(\"omega\")\n",
    "print(omega)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Derived Expressions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "total mass density\n",
    "$$\n",
    "\\rho := \\Sigma_{i=1}^N \\rho_i \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['rho'] = multivars[rhos[0]]\n",
    "for rho_index in rhos[1:]:\n",
    "    multivars['rho'] += multivars[rho_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x - velocity relative to grid\n",
    "$$\n",
    "v_x  := \\frac{s_x}{\\rho} + y \\Omega \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What to do with $y$?\n",
    "```python\n",
    "multivars['vx'] = multivars['sx'] / multivars['rho'] + quadmesh['coord1'] * omega\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "y - velocity relative to grid\n",
    "$$\n",
    "v_y  := \\frac{s_y}{\\rho} - x \\Omega \n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What to do with $x$?\n",
    "```python\n",
    "multivars['vx'] = multivars['sy'] / multivars['rho'] + quadmesh['coord0'] * omega\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "z - velocity relative to grid\n",
    "$$\n",
    "v_z  := \\frac{s_z}{\\rho} \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['vz'] = multivars.sz / multivars.rho"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "s^2  :=  {s_x}^2 + {s_y}^2 + {s_z}^2\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['s2'] = multivars.sx ** 2 + multivars.sy ** 2 + multivars.sz ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "\\gamma = \\frac{5}{3}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = 5. / 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "internal gas energy density\n",
    "$$\n",
    "e    :=  \\begin{cases}\n",
    "       E - \\frac{1}{2} \\frac{s^2}{\\rho} & \\text{if } E - \\frac{1}{2} \\frac{s^2}{\\rho} > 0.001 E \\\\\n",
    "       \\tau^\\gamma & \\text{else} \\\\\n",
    "     \\end{cases} \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['e'] = multivars.egas - .5 * multivars.s2 / multivars.rho\n",
    "multivars['e'] = np.where(multivars.e > .001 * multivars.egas, multivars.e, multivars.tau ** gamma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$m_H$ (mass of hydrogen) = $1.6733 \\times 10^{24}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mH = 1.6733e-24"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "number density of ith fraction (ions + electrons)\n",
    "$$\n",
    "n_i := \\frac{\\rho_i}{\\mu_i m_H} \\left ( 1 + Z_i \\right ) \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(atomic_mass)):\n",
    "    multivars['ni_' + str(i + 1)] = (multivars.rho / (atomic_mass[i] * mH)) * (1. + atomic_number[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "total number density\n",
    "$$\n",
    "n := \\Sigma_{i=1}^N  n_i\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['n'] = multivars.ni_1 + multivars.ni_2 + multivars.ni_3 + multivars.ni_4 + multivars.ni_5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\gamma := \\frac{5}{3}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "temperature\n",
    "$$\n",
    "T := \\frac{1}{\\gamma-1} \\frac{e}{n}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['T'] = (1. / (gamma - 1.)) * (multivars.e / multivars.n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pressure\n",
    "$$\n",
    "P := \\left(\\gamma-1\\right) e\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "multivars['P'] = (gamma - 1.) * multivars.e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.Close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Zone"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "Silo.Create(filename , info [, DB_PDB|DB_HDF5 [, DB_CLOBBER|DB_NOCLOBBER]])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "db2 = Silo.Create('/repos/test2.silo', 'Octo-tiger', Silo.DB_HDF5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(100):\n",
    "    db2.MkDir(str(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "db2.Write('q', (1, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvar = 4\n",
      "var_names = (_fileinfo, _hdf5libinfo, _silolibinfo, q)\n",
      "ndir = 100\n",
      "dir_names = (0, 1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 6, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 7, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 8, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 9, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(db2.GetToc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "db2.Close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "db3 = Silo.Open('/repos/test2.silo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvar = 4\n",
      "var_names = (_fileinfo, _hdf5libinfo, _silolibinfo, q)\n",
      "ndir = 100\n",
      "dir_names = (0, 1, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 6, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 7, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 8, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 9, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(db3.GetToc())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "db3.Close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "db2 = Silo.Create(\"/repos/test.silo\", \"Octo-tiger\", Silo.DB_HDF5)\n",
    "db2.Write(\"q\", 3)\n",
    "db.SetDir('/')\n",
    "db.MkDir('silo')\n",
    "db.GetVarInfo('phi')\n",
    "db2.WriteObject(\"w\", db.GetVarInfo('phi'))\n",
    "db2.Close()\n",
    "db3 = Silo.Open(\"/repos/test.silo\")\n",
    "db3.GetVar(\"q\")\n",
    "db3.GetVarInfo('w')\n",
    "db3.Close()\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
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 },
 "nbformat": 4,
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start-container.bat

docker run -p 8888:8888 -it --rm -v C:\Repos:/repos prsam/octoviz