# MIT License
#
# Copyright (c) 2019 Tuomas Halvari, Juha Harviainen, Juha Mylläri, Antti Röyskö, Juuso Silvennoinen
#
# 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
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# SOFTWARE.
from .node import LeafNode, get_node_data, assign
[docs]class Array(LeafNode):
"""An Array node represents a data array of any dimension (>= 0).
One or more filters (error sources) can be added to the node.
The filters are applied in the order in which they are added.
You can optionally provide the constructor with a reshape parameter.
In that case the filters attached to the node operate on data
reshaped to the desired shape. The final shape of the data is
unaffected.
Constructor Args:
reshape (tuple, optional): The data shape required by the
node's filters if different from the actual shape of
the data
"""
def __init__(self, reshape=None):
super().__init__()
self.reshape = reshape
[docs] def apply_filters(self, node_data, random_state, named_dims):
"""Apply filters to data contained in this array.
Args:
node_data (numpy.ndarray): Data to be modified as a Numpy array.
random_state (mtrand.RandomState): An instance of numpy.random.RandomState.
named_dims (dict): Named dimensions.
"""
for f in self.filters:
if self.reshape:
original_shape = node_data.shape
temp_data = node_data.reshape(self.reshape)
f.apply(temp_data, random_state, named_dims)
node_data[...] = temp_data.reshape(original_shape)
else:
f.apply(node_data, random_state, named_dims)
[docs] def process(self, data, random_state, index_tuple=(), named_dims={}):
"""Apply all filters in this node.
Args:
data (numpy.ndarray): Data to be modified as a Numpy array.
random_state (mtrand.RandomState): An instance of numpy.random.RandomState
index_tuple (tuple, optional): The index of the node. Defaults to ().
named_dims (dict, optional): Named dimensions. Defaults to {}.
"""
node_data, is_list, is_scalar, is_tuple = get_node_data(data, index_tuple)
if is_list:
self.apply_filters(node_data, random_state, named_dims)
assign(data, index_tuple, list(node_data))
elif is_scalar:
self.apply_filters(node_data, random_state, named_dims)
assign(data, index_tuple, node_data[()])
elif is_tuple:
self.apply_filters(node_data, random_state, named_dims)
assign(data, index_tuple, tuple(node_data))
else:
self.apply_filters(node_data, random_state, named_dims)