# Copyright 2019 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:: get_data_statistics
:platform: Unix
:synopsis: A plugin to calculate global statistics (max, min, sum, mean) of the input data
.. moduleauthor:: Daniil Kazantsev <scientificsoftware@diamond.ac.uk>
"""
from savu.plugins.plugin import Plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
import numpy as np
[docs]@register_plugin
class GetDataStatistics(Plugin, CpuPlugin):
def __init__(self):
super(GetDataStatistics, self).__init__("GetDataStatistics")
[docs] def setup(self):
in_dataset, out_dataset = self.get_datasets()
in_pData, out_pData = self.get_plugin_datasets()
pattern_type=self.parameters['pattern']
in_pData[0].plugin_data_setup(pattern_type, 'single')
out_dataset[0].create_dataset(in_dataset[0])
out_pData[0].plugin_data_setup(pattern_type, 'single')
fullData = in_dataset[0]
slice_dirs = list(in_dataset[0].get_slice_dimensions())
self.new_shape = (np.prod(np.array(fullData.get_shape())[slice_dirs]), 4)
out_dataset[1].create_dataset(shape=self.new_shape,
axis_labels=['stattype', 'value'],
remove=True,
transport='hdf5')
out_dataset[1].add_pattern("METADATA", core_dims=(1,), slice_dims=(0,))
out_pData[1].plugin_data_setup('METADATA', self.get_max_frames())
[docs] def process_frames(self, data):
data_temp = data[0]
indices = np.where(np.isnan(data_temp))
data_temp[indices] = 0.0
# collecting values for each slice
slice_values = [np.max(data_temp), np.min(data_temp), np.sum(data_temp), np.mean(data_temp)]
return [data_temp, np.array([slice_values])]
[docs] def post_process(self):
data, slice_values = self.get_out_datasets()
all_slice_values = slice_values.data[...]
max_stat = np.max(all_slice_values[:,0])
min_stat = np.max(all_slice_values[:,1])
sum_stat = np.sum(all_slice_values[:,2])
mean_stat = np.sum(all_slice_values[:,3])
data.meta_data.set(['stats', 'max', 'pattern'], max_stat)
data.meta_data.set(['stats', 'min', 'pattern'], min_stat)
data.meta_data.set(['stats', 'sum', 'pattern'], sum_stat)
data.meta_data.set(['stats', 'mean', 'pattern'], mean_stat)
[docs] def nOutput_datasets(self):
return 2
[docs] def get_max_frames(self):
return 'single'