# #
# This file is distributed as part of the WannierBerri code #
# under the terms of the GNU General Public License. See the #
# file `LICENSE' in the root directory of the WannierBerri #
# distribution, or http://www.gnu.org/copyleft/gpl.txt #
# #
# The WannierBerri code is hosted on GitHub: #
# https://github.com/stepan-tsirkin/wannier-berri #
# written by #
# Stepan Tsirkin, University of Zurich #
# #
#------------------------------------------------------------
#
# The purpose of this module is to provide Result classes for
# different types of calculations.
# child classes can be defined specifically in each module
import numpy as np
from .__result import Result
# A class to contain results or a calculation:
# For any calculation there should be a class with the samemethods implemented
# a class for data defined for a set of Fermi levels
#Data is stored in an array data, where first dimension indexes the Fermi level
[docs]class ResultDict(Result):
'''Stores a dictionary of instances of the class Result.'''
def __init__(self, results):
'''
Initialize instance with a dictionary of results with string keys and values of type Result.
'''
self.results = results
# multiplication by a number
def __mul__(self, number):
return ResultDict({k: v * number for k, v in self.results.items()})
def __truediv__(self, number):
return ResultDict({k: v / number for k, v in self.results.items()})
# +
def __add__(self, other):
if other == 0:
return self
results = {k: self.results[k] + other.results[k] for k in self.results if k in other.results}
return ResultDict(results)
# -
def __sub__(self, other):
return self + (-1) * other
# writing to a text file
def savedata(self, prefix, suffix, i_iter):
for k, v in self.results.items():
v.savedata(k, prefix, suffix, i_iter)
# how result transforms under symmetry operations
def transform(self, sym):
results = {k: self.results[k].transform(sym) for k in self.results}
return ResultDict(results)
# a list of numbers, by each of those the refinement points will be selected
@property
def max(self):
return np.array([x for v in self.results.values() for x in v.max])