# Copyright 2014 Diamond Light Source Ltd.
#
# 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.
"""
.. module:: base_component_analysis
:platform: Unix
:synopsis: A base class for all component analysis methods
.. moduleauthor:: Mark Basham <scientificsoftware@diamond.ac.uk>
"""
from savu.plugins.plugin import Plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
import sys
import numpy as np
[docs]class BaseComponentAnalysis(Plugin, CpuPlugin):
def __init__(self, name):
super(BaseComponentAnalysis, self).__init__(name)
[docs] def get_max_frames(self):
return self.spectra_length[0]
[docs] def get_plugin_pattern(self):
return self.parameters['chunk']
[docs] def setup(self):
self.exp.log(self.name + " Setting up the component analysis")
# set up the output dataset that is created by the plugin
in_dataset, out_dataset = self.get_datasets()
self.spectra_length = (in_dataset[0].get_shape()[-1],)
other_dims = in_dataset[0].get_shape()[:-1]
num_comps = self.parameters['number_of_components']
self.images_shape = other_dims + (num_comps,)
components_shape = (num_comps,) + self.spectra_length
# copy all required information from in_dataset[0]
out_dataset[0].create_dataset(patterns=in_dataset[0],
axis_labels=in_dataset[0],
shape=self.images_shape)
axis_labels = ['idx.unit', 'spectra.unit']
out_dataset[1].create_dataset(shape=components_shape,
axis_labels=axis_labels)
spectrum = {'core_dims': (1,), 'slice_dims': (0,)}
out_dataset[1].add_pattern("SPECTRUM", **spectrum)
in_pData, out_pData = self.get_plugin_datasets()
plugin_pattern = self.get_plugin_pattern()
# dirs = range(len(out_dataset[0].get_shape()))
# vxz = {'core_dims': (0,1), 'slice_dims': (2,)}
# in_dataset[0].add_pattern("VOLUME_XZ", **vxz)
in_pData[0].plugin_data_setup(plugin_pattern, self.get_max_frames())
out_pData[0].plugin_data_setup(plugin_pattern, num_comps)
out_pData[1].plugin_data_setup("SPECTRUM", num_comps)
self.exp.log(self.name + " End")
[docs] def nOutput_datasets(self):
return 2
[docs] def remove_nan_inf(self, data):
'''
converts the nans to nums and sets the infs to the max float size
not strictly true, but does allow fitting to take place
'''
data = np.clip(data,-sys.float_info.max,sys.float_info.max)
data[np.isnan(data)]=0
data = np.nan_to_num(data)
return data