# 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:: basic_operations
:platform: Unix
:synopsis: Plugin to perform basic mathematical operations on datasets.
.. moduleauthor:: Nicola Wadeson <scientificsoftware@diamond.ac.uk>
"""
from savu.plugins.plugin import Plugin
from savu.plugins.utils import register_plugin
from savu.plugins.driver.cpu_plugin import CpuPlugin
[docs]@register_plugin
class BasicOperations(Plugin, CpuPlugin):
def __init__(self):
super(BasicOperations, self).__init__("BasicOperations")
[docs] def pre_process(self):
self.operations = self._amend_ops(self._set_data_mappings())
self.out_data = self._set_out_data_names()
[docs] def process_frames(self, data):
# creates an 'environment' that will store the variables created
# inside the exec statement
exec_environment = {'data': data}
for i in range(len(self.operations)):
# runs the exec with no builtins, and only 'data' available as a
#variable initially
exec(f"{self.out_data[i]} = {self.operations[i]}",
{"builtins": None}, exec_environment)
# Find the result from each exec. Does list comprehension on the
# results instead of just exec_environment.items to keep the order the
# same as in out_data
return [exec_environment[out] for out in self.out_data]
[docs] def setup(self):
"""
Initial setup of all datasets required as input and output to the \
plugin. This method is called before the process method in the \
plugin chain.
"""
in_datasets, out_datasets = self.get_datasets()
in_pData, out_pData = self.get_plugin_datasets()
pattern = self.parameters['pattern']
for pData in in_pData:
pData.plugin_data_setup(pattern, self.get_max_frames())
# making the assumption that the basic operations do not change the
# shape of the data for now.
copy_datasets = self._get_associated_datasets()
for i in range(len(out_datasets)):
out_datasets[i].create_dataset(in_datasets[copy_datasets[i]])
out_pData[i].plugin_data_setup(pattern, self.get_max_frames())
[docs] def nOutput_datasets(self):
return 'var'
[docs] def get_max_frames(self):
return 'multiple'
def _set_data_mappings(self):
"""
Maps the input datasets names to the data array passed to process
frames.
"""
mapping_dict = {}
in_datasets = self.get_in_datasets()
for i in range(len(in_datasets)):
mapping_dict[in_datasets[i].get_name()] = 'data[' + str(i) + ']'
return mapping_dict
def _set_out_data_names(self):
out_datasets = self.get_out_datasets()
return [out_datasets[i].get_name() for i in range(len(out_datasets))]
def _amend_ops(self, mappings_dict):
"""
Replaces the dataset names in the operations with the data array.
"""
operations = self.parameters['operations']
new_ops = []
for op in operations:
for key, value in mappings_dict.items():
op = op.replace(key, value)
new_ops.append(op)
return new_ops
def _get_associated_datasets(self):
operations = self.parameters['operations']
in_datasets = self.get_in_datasets()
data_names = [d.get_name() for d in in_datasets]
index = []
for op in operations:
names = [d for d in data_names if op.find(d) > -1]
index.append(data_names.index(names[0]))
return index