#!/usr/bin/env python
# -*- coding: utf-8 -*-
# The MIT License (MIT)
# Copyright (c) 2020 Daniel Schick
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# 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 AND NONINFRINGEMENT.
# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
# OR OTHER DEALINGS IN THE SOFTWARE.
__all__ = ['Simulation']
__docformat__ = 'restructuredtext'
from tabulate import tabulate
import numpy as np
import os
[docs]
class Simulation:
"""Simulation
Base class for all simulations.
Handles the caching and some displaying option.
Args:
S (Structure): sample to do simulations with.
force_recalc (boolean): force recalculation of results.
Keyword Args:
save_data (boolean): true to save simulation results.
cache_dir (str): path to cached data.
disp_messages (boolean): true to display messages from within the
simulations.
progress_bar (boolean): enable tqdm progress bar.
Attributes:
S (Structure): sample structure to calculate simulations on.
force_recalc (boolean): force recalculation of results.
save_data (boolean): true to save simulation results.
cache_dir (str): path to cached data.
disp_messages (boolean): true to display messages from within the
simulations.
progress_bar (boolean): enable tqdm progress bar.
"""
def __init__(self, S, force_recalc, **kwargs):
self.S = S
self.force_recalc = force_recalc
self.save_data = kwargs.get('save_data', True)
self.cache_dir = kwargs.get('cache_dir', './')
self.disp_messages = kwargs.get('disp_messages', True)
self.progress_bar = kwargs.get('progress_bar', True)
def __str__(self, output=[]):
"""String representation of this class"""
output = [['force recalc', self.force_recalc],
['cache directory', self.cache_dir],
['display messages', self.disp_messages],
['save data', self.save_data],
['progress bar', self.progress_bar]] + output
class_str = 'This is the current structure for the simulations:\n\n'
class_str += self.S.__str__()
class_str += '\n\nDisplay properties:\n\n'
class_str += tabulate(output, headers=['parameter', 'value'], tablefmt="rst",
colalign=('right',), floatfmt=('.2f', '.2f'))
return class_str
[docs]
def disp_message(self, message):
"""disp_message
Wrapper to display messages for that class.
Args:
message (str): message to display.
"""
if self.disp_messages:
print(message)
[docs]
def save(self, full_filename, data, *args):
"""save
Save data to file. The variable name can be handed as variable argument.
Args:
full_filename (str): full file name to data file.
data (ndarray): actual data to save.
*args (str, optional): variable name within the data file.
"""
if len(args) == 1:
var_name = args[0]
else:
var_name = '_data_'
if self.save_data:
np.savez(full_filename, **data)
filename = os.path.basename(full_filename)
self.disp_message('{:s} saved to file:\n\t {:s}'.format(var_name, filename))
[docs]
@staticmethod
def conv_with_function(y, x, handle):
"""conv_with_function
Convolutes the array :math:`y(x)` with a function given by the handle
on the argument array :math:`x`.
Args:
y (ndarray[float]): y data.
x (ndarray[float]): x data.
handle (@lamdba): convolution function.
Returns:
y_conv (ndarray[float]): convoluted data.
"""
dx = np.min(np.diff(x))
x_lin = np.r_[np.min(x):np.max(x):dx]
y_lin = np.interp(x_lin, x, y)
x0 = np.mean(x_lin)
y_handle = handle(x_lin-x0)
temp = np.convolve(y_lin, y_handle/y_handle.sum(), mode="same")
y_conv = np.interp(x, x_lin, temp)
# finally remove NaN entries due to the interpolation
y_conv[np.isnan(y_conv)] = 0
return y_conv
@property
def cache_dir(self):
return self._cache_dir
@cache_dir.setter
def cache_dir(self, cache_dir):
import os.path as path
if path.exists(cache_dir):
self._cache_dir = cache_dir
else:
print('Cache dir does not exist.\nPlease create the path first.')
