# 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:: data_threshold
:platform: Unix
:synopsis: module to threshold the data (less, lessequal, equal, greater, greaterequal) than the given value
.. 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 DataThreshold(Plugin, CpuPlugin):
def __init__(self):
super(DataThreshold, self).__init__("DataThreshold")
[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')
[docs] def process_frames(self, data):
if (self.parameters['inequality_condition'] == 'less'):
indeces = np.where(data[0] < self.parameters['given_value'])
elif (self.parameters['inequality_condition'] == 'lessequal'):
indeces = np.where(data[0] <= self.parameters['given_value'])
elif (self.parameters['inequality_condition'] == 'greater'):
indeces = np.where(data[0] > self.parameters['given_value'])
elif (self.parameters['inequality_condition'] == 'greaterequal'):
indeces = np.where(data[0] >= self.parameters['given_value'])
else:
indeces = np.where(data[0] == self.parameters['given_value'])
corr_data = np.copy(data[0])
corr_data[indeces] = self.parameters['new_value']
return corr_data
[docs] def nOutput_datasets(self):
return 1
[docs] def get_max_frames(self):
return 'single'