########################
 Astrophysics Example 1
########################

In this example we will use BoxKit to work with three dimensional
astrophysics data. After performing the required installation steps we
are read to import BoxKit into Python environment

.. code:: ipython3

   import boxkit

Next we read and parse dataset information from a HDF5 file,
``25m_3d_32km_hdf5_plt_cnt_1000``, that contains Flash-X simulation
output

.. code:: ipython3

   dset = boxkit.read_dataset("25m_3d_32km_hdf5_plt_cnt_1000", source="flash")

We can probe into the information for this dataset by using a simple
print statement

.. code:: ipython3

   print(dset)

.. parsed-literal::

   Dataset:
    - type         : <class 'boxkit.library._dataset.Dataset'>
    - file         : <HDF5 file "25m_3d_32km_hdf5_plt_cnt_1000" (mode r)>
    - keys         : ['dens', 'temp', 'velx', 'vely', 'ye  ', 'fe54', 'fe56', 'o16 ', 'si28', 'entr', 'enuc', 'cr48', 'cr56', 'fe52', 'ni56', 'pres', 'c12 ', 's32 ', 'gpot', 'gamc', 'dtbn', 'velz']
    - dtype    : [<class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>, <class 'h5pickle.Dataset'>]
    - bound(z-y-x) : [-10000000000.0, 10000000000.0] x [-10000000000.0, 10000000000.0] x [-10000000000.0, 10000000000.0]
    - shape(z-y-x) : 8 x 8 x 8
    - guard(z-y-x) : 0 x 0 x 0
    - nblocks      : 90200
    - dtype        : {'dens': <class 'h5pickle.Dataset'>, 'temp': <class 'h5pickle.Dataset'>, 'velx': <class 'h5pickle.Dataset'>, 'vely': <class 'h5pickle.Dataset'>, 'ye  ': <class 'h5pickle.Dataset'>, 'fe54': <class 'h5pickle.Dataset'>, 'fe56': <class 'h5pickle.Dataset'>, 'o16 ': <class 'h5pickle.Dataset'>, 'si28': <class 'h5pickle.Dataset'>, 'entr': <class 'h5pickle.Dataset'>, 'enuc': <class 'h5pickle.Dataset'>, 'cr48': <class 'h5pickle.Dataset'>, 'cr56': <class 'h5pickle.Dataset'>, 'fe52': <class 'h5pickle.Dataset'>, 'ni56': <class 'h5pickle.Dataset'>, 'pres': <class 'h5pickle.Dataset'>, 'c12 ': <class 'h5pickle.Dataset'>, 's32 ': <class 'h5pickle.Dataset'>, 'gpot': <class 'h5pickle.Dataset'>, 'gamc': <class 'h5pickle.Dataset'>, 'dtbn': <class 'h5pickle.Dataset'>, 'velz': <class 'h5pickle.Dataset'>}

The dataset contains 90200 blocks each of size 8x8x8, along with
variables listed in ``keys``. We can now use this dataset to create a
slice at location ``y = 0.1``

.. code:: ipython3

   yloc = 0.1
   dset_slice = boxkit.create_slice(dset, ymin=yloc, ymax=yloc)

.. code:: ipython3

   print(dset_slice)

.. parsed-literal::

   Region:
    - type          : <class 'boxkit.library._slice.Slice'>
    - bound (z-y-x) : [-10000000000.0, 10000000000.0] x [0.0, 1666666624.0] x [-10000000000.0, 10000000000.0]

Now we can loop over blocks in this slice and extract data from right
indices

.. code:: ipython3

   # import required libraries
   import numpy
   import matplotlib.pyplot as pyplot

   # create a pyplot figure object
   pyplot.figure()

   # Name of the contour variable
   cvar = "temp"

   # Initialize min/max values
   min_cval = 1e10
   max_cval = -1e10

   # loop over blocks for blocklist included in the slice
   for block in dset_slice.blocklist:

       # Get y-index closest to the probe location
       yindex = (numpy.abs(block.yrange("center") - yloc)).argmin()

       # Create a mesh grid in x-z plane
       xmesh, zmesh = numpy.meshgrid(block.xrange("center"), block.yrange("center"))

       # plot contour
       pyplot.contourf(xmesh,zmesh,block[cvar][:,yindex,:])

       min_cval = min(numpy.min(block[cvar][:,yindex,:]), min_cval)
       max_cval = max(numpy.max(block[cvar][:,yindex,:]), max_cval)

   #pyplot.colorbar()
   #pyplot.clim(min_cval,max_cval)

.. image:: output_10_0.png