jarvis.analysis.structure.neighbors
===================================

.. py:module:: jarvis.analysis.structure.neighbors

.. autoapi-nested-parse::

   This module provides classes to specify atomic structure.



Classes
-------

.. autoapisummary::

   jarvis.analysis.structure.neighbors.NeighborsAnalysis


Functions
---------

.. autoapisummary::

   jarvis.analysis.structure.neighbors.check_array
   jarvis.analysis.structure.neighbors.special_arange
   jarvis.analysis.structure.neighbors.calc_structure_data


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

.. py:function:: check_array(arr, type_=None, shape=None)

   Check type and shape of np.ndarray.

   Follwing arguments are needed.
   Args:
     x: array to check

     type_: the type of the array (int, float), None to not check

     shape: the shape of the array, None to not check, -1 to ignore a
       dimension



.. py:function:: special_arange(dims)

   Multiple dimensional arange.

   This could be extended to any dimension.
   Args:

     dims: sequence of ints of length 3

   Returns:
     an array of shape (np.prod(dims), 3) with each index having a
     different arrangement of arange.
   This function implements the equivalent of a multidimensional for
   loop, which feels like multidimensional arange (see the test)

   >>> dim0, dim1, dim2 = 2, 4, 3
   >>> arr_test = np.zeros((dim0 * dim1 * dim2, 3), dtype=int)
   >>> counter = 0
   >>> for i in np.arange(dim0):
   ...     for j in np.arange(dim1):
   ...         for k in np.arange(dim2):
   ...             arr_test[counter, 0] = i
   ...             arr_test[counter, 1] = j
   ...             arr_test[counter, 2] = k
   ...             counter += 1

   >>> arr_actual = special_arange((dim0, dim1, dim2))
   >>> print(arr_actual)
   [[0 0 0]
    [0 0 1]
    [0 0 2]
    [0 1 0]
    [0 1 1]
    [0 1 2]
    [0 2 0]
    [0 2 1]
    [0 2 2]
    [0 3 0]
    [0 3 1]
    [0 3 2]
    [1 0 0]
    [1 0 1]
    [1 0 2]
    [1 1 0]
    [1 1 1]
    [1 1 2]
    [1 2 0]
    [1 2 1]
    [1 2 2]
    [1 3 0]
    [1 3 1]
    [1 3 2]]
   >>> assert np.all(arr_actual == arr_test)



.. py:function:: calc_structure_data(coords, box, all_symbs, c_size)

   Calcuate a dictionary of structure data.

   Args:

     coords: the coordinates for each element

     box: the lattic matrix

     all_symbs: the elements

     c_size: the c size

   Returns:
     a set of structure data

   >>> coords = np.array([[0, 0, 0], [0.25, 0.2, 0.25]])
   >>> lat = [[2.715, 2.715, 0], [0, 2.715, 2.715], [2.715, 0, 2.715]]
   >>> box = np.array(lat)
   >>> elements = ["Si", "Si"]
   >>> c_size = 10.0
   >>> data = calc_structure_data(coords, box, elements, c_size)
   >>> assert len(data['coords']) == 128
   >>> assert np.allclose(data['coords'][9], [0.    , 0.5   , 0.25  ])
   >>> assert np.all(data['dim'] == [4, 4, 4])
   >>> assert len(data['new_symbs']) == 128



.. py:class:: NeighborsAnalysis(atoms=None, max_n=500, rcut1=None, max_cut=10.0, rcut2=None, verbose=False)

   Bases: :py:obj:`object`


   Get neighbor informations (RDF,ADF,DDF) for Atoms object.


   .. py:attribute:: _atoms
      :value: None



   .. py:attribute:: max_n
      :value: 500



   .. py:attribute:: max_cut
      :value: 10.0



   .. py:attribute:: nb_warn
      :value: ''



   .. py:attribute:: rcut1
      :value: None



   .. py:attribute:: rcut2
      :value: None



   .. py:attribute:: verbose
      :value: False



   .. py:method:: get_structure_data(c_size=10.0)

      Provide non-repetitive structure information.



   .. py:method:: nbor_list(rcut=10.0, c_size=12.0)

      Generate neighbor info.



   .. py:method:: get_rdf(plot=False)

      Calculate radial distribution function.



   .. py:method:: get_dist_cutoffs()

      Get different distance cut-offs.

      Args:

          s: Structure object

      Returns:
             rcut: max-cutoff to ensure all the element-combinations
             are included, used in calculating angluar distribution
             upto first neighbor

             rcut1: decide first cut-off based on total RDF and a buffer
             (previously used in dihedrals, but not used now in the code)

             rcut2: second neighbor cut-off

             rcut_dihed: specialized cut-off for dihedrals to avaoid large
             bonds such as N-N, uses average bond-distance and standard
             deviations



   .. py:method:: ang_dist(nbor_info={}, plot=False)

      Get  angular distribution function upto first neighbor.

      Args:
          struct_info: struct information

          max_n: maximum number of neigbors

          c_size: max. cell size

          plot: whether to plot distributions


      Retruns:

          ang_hist1: Angular distribution upto first cut-off

          ang_bins1: angle bins



   .. py:method:: ang_dist_first(plot=False)

      Get angular distribution upto first neighbor.



   .. py:method:: ang_dist_second(plot=False)

      Get angular distribution upto second neighbor.



   .. py:method:: get_ddf(plot=False)

      Get dihedral distribution upto first neighbor.



   .. py:property:: get_all_distributions

      Get all distributions.



   .. py:method:: atomwise_radial_dist(rcut=10.0, c_size=0)

      Get pair/radial distribution for each atom.



   .. py:method:: atomwise_angle_dist(rcut=None, nbins=180, c_size=0)

      Get angle distribution for each atom.