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# -*- coding: utf-8 -*-
# CCP in Tomographic Imaging (CCPi) Core Imaging Library (CIL).
# Copyright 2017 UKRI-STFC
# Copyright 2017 University of Manchester
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy
import sys
from datetime import timedelta, datetime
import warnings
from functools import reduce
from numbers import Number
from ccpi.framework import DataContainer
class VectorData(DataContainer):
'''DataContainer to contain 1D array'''
def __init__(self, array=None, **kwargs):
self.geometry = kwargs.get('geometry', None)
self.dtype = kwargs.get('dtype', numpy.float32)
if self.geometry is None:
if array is None:
raise ValueError('Please specify either a geometry or an array')
else:
if len(array.shape) > 1:
raise ValueError('Incompatible size: expected 1D got {}'.format(array.shape))
out = array
self.geometry = VectorGeometry(array.shape[0])
self.length = self.geometry.length
else:
self.length = self.geometry.length
if array is None:
out = numpy.zeros((self.length,), dtype=self.dtype)
else:
if self.length == array.shape[0]:
out = array
else:
raise ValueError('Incompatible size: expecting {} got {}'.format((self.length,), array.shape))
deep_copy = True
super(VectorData, self).__init__(out, deep_copy, None)
class VectorGeometry(object):
'''Geometry describing VectorData to contain 1D array'''
RANDOM = 'random'
RANDOM_INT = 'random_int'
def __init__(self,
length):
self.length = length
self.shape = (length, )
def clone(self):
'''returns a copy of VectorGeometry'''
return VectorGeometry(self.length)
def allocate(self, value=0, **kwargs):
'''allocates an VectorData according to the size expressed in the instance'''
self.dtype = kwargs.get('dtype', numpy.float32)
out = VectorData(geometry=self, dtype=self.dtype)
if isinstance(value, Number):
if value != 0:
out += value
else:
if value == VectorGeometry.RANDOM:
seed = kwargs.get('seed', None)
if seed is not None:
numpy.random.seed(seed)
out.fill(numpy.random.random_sample(self.shape))
elif value == VectorGeometry.RANDOM_INT:
seed = kwargs.get('seed', None)
if seed is not None:
numpy.random.seed(seed)
max_value = kwargs.get('max_value', 100)
out.fill(numpy.random.randint(max_value,size=self.shape))
else:
raise ValueError('Value {} unknown'.format(value))
return out
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