# 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:: hdf5_saver
:platform: Unix
:synopsis: A class to save data to a hdf5 output file.
.. moduleauthor:: Nicola Wadeson <scientificsoftware@diamond.ac.uk>
"""
import logging
import os
import copy
from savu.plugins.savers.utils.hdf5_utils import Hdf5Utils
from savu.plugins.savers.base_saver import BaseSaver
from savu.plugins.driver.cpu_plugin import CpuPlugin
from savu.plugins.utils import register_plugin
from savu.data.chunking import Chunking
[docs]@register_plugin
class Hdf5Saver(BaseSaver, CpuPlugin):
def __init__(self, name='Hdf5Saver'):
super(Hdf5Saver, self).__init__(name)
self.in_data = None
self.out_data = None
self.data_name = None
self.filename = None
self.group_name = None
[docs] def pre_process(self):
# Create the hdf5 output file
self.hdf5 = Hdf5Utils(self.exp)
self.in_data = self.get_in_datasets()[0]
self.data_name = self.in_data.get_name()
current_pattern = self.__set_current_pattern()
pattern_idx = {'current': current_pattern, 'next': []}
self.filename = self.__get_file_name()
self.group_name = self._get_group_name(self.data_name)
logging.debug("creating the backing file %s", self.filename)
self.backing_file = self.hdf5._open_backing_h5(self.filename, 'w')
group = self.backing_file.create_group(self.group_name)
group.attrs['NX_class'] = 'NXdata'
group.attrs['signal'] = 'data'
self.exp._barrier()
shape = self.in_data.get_shape()
chunking = Chunking(self.exp, pattern_idx)
dtype = self.in_data.data.dtype
chunks = chunking._calculate_chunking(shape, dtype)
self.exp._barrier()
self.out_data = self.hdf5.create_dataset_nofill(
group, "data", shape, dtype, chunks=chunks)
[docs] def process_frames(self, data):
self.out_data[self.get_current_slice_list()[0]] = data[0]
[docs] def post_process(self):
self._link_datafile_to_nexus_file(self.data_name, self.filename,
self.group_name + '/data')
self.backing_file.close()
def __set_current_pattern(self):
pattern = copy.deepcopy(self.in_data._get_plugin_data().get_pattern())
pattern[list(pattern.keys())[0]]['max_frames'] = self.get_max_frames()
return pattern
[docs] def get_pattern(self):
if self.parameters['pattern'] != 'optimum':
return self.parameters['pattern']
previous_pattern = self.get_in_datasets()[0].get_previous_pattern()
if previous_pattern:
return list(previous_pattern.keys())[0]
return list(self.get_in_datasets()[0].get_data_patterns().keys())[0]
def __get_file_name(self):
nPlugin = self.exp.meta_data.get('nPlugin')
plugin_dict = \
self.exp._get_experiment_collection()['plugin_dict'][nPlugin]
fname = self.data_name + '_p' + str(nPlugin) + '_' + \
plugin_dict['id'].split('.')[-1] + '.h5'
out_path = self.exp.meta_data.get('out_path')
return os.path.join(out_path, fname)