jarvis.analysis.diffraction.xrd
===============================

.. py:module:: jarvis.analysis.diffraction.xrd

.. autoapi-nested-parse::

   Module to predict X-ray diffraction.



Classes
-------

.. autoapisummary::

   jarvis.analysis.diffraction.xrd.XRD


Functions
---------

.. autoapisummary::

   jarvis.analysis.diffraction.xrd.baseline_als
   jarvis.analysis.diffraction.xrd.recast_array
   jarvis.analysis.diffraction.xrd.sharpen_peaks
   jarvis.analysis.diffraction.xrd.processed
   jarvis.analysis.diffraction.xrd.smooth_xrd


Module Contents
---------------

.. py:class:: XRD(wavelength=1.54184, thetas=[0, 180], two_theta_array=[], dhkl_array=[], intensity_array=[], scaling_factor=100, two_theta_tol=1e-05, intensity_tol=0.5, max_index=5)

   Bases: :py:obj:`object`


   Constryct an XRD class.


   .. py:attribute:: wavelength
      :value: 1.54184



   .. py:attribute:: min2theta


   .. py:attribute:: max2theta


   .. py:attribute:: thetas
      :value: [0, 180]



   .. py:attribute:: two_theta_array
      :value: []



   .. py:attribute:: dhkl_array
      :value: []



   .. py:attribute:: intensity_array
      :value: []



   .. py:attribute:: two_theta_tol
      :value: 1e-05



   .. py:attribute:: intensity_tol
      :value: 0.5



   .. py:attribute:: max_index
      :value: 5



   .. py:attribute:: scaling_factor
      :value: 100



   .. py:method:: simulate(atoms=None)

      Simulate XRD pattern.

      Forked from https://github.com/qzhu2017/XRD.



   .. py:method:: get_unique_families(hkls)

      Return unique families of Miller indices.

      Families must be permutations of each other.
      Args:
          hkls ([h, k, l]): List of Miller indices.
      Returns:
          {hkl: multiplicity}: A dict with unique hkl and multiplicity.



.. py:function:: baseline_als(y, lam, p, niter=10)

   ALS baseline correction to remove broad background trends.


.. py:function:: recast_array(x_original, y_original, x_new, tol=0.1)

   Recast original spectrum onto a new grid, accumulating close values.


.. py:function:: sharpen_peaks(y, sigma=0.5)

   Sharpen peaks using a narrow Gaussian filter.


.. py:function:: processed(x, y, x_range=[0, 90], intvl=0.1, sigma=0.05, recast=True, tol=0.1, background_subs=True)

   Process the spectrum: background removal and peak sharpening.


.. py:function:: smooth_xrd(atoms=None, thetas=[0, 90], intvl=0.5)