savu.data.experiment_collection.Experiment._set_initial_datasets() - Code Metrics - Inspection of "mpi_log / run_log improvments" - DiamondLightSource/Savu - Measure and Improve Code Quality continuously with Scrutinizer

Test Failed

Pull Request — master (#827)

unknown

created 2021-09-09 11:40 UTC

Experiment._set_initial_datasets() A

↳ Parent: savu.data.experiment_collection

Complexity

Conditions

Size

Total Lines	2
Code Lines	2

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	1
eloc	2
nop	1
dl	0
loc	2
rs	10
c	0
b	0
f	0

# -*- coding: utf-8 -*-
# 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:: experiment
   :platform: Unix
   :synopsis: Contains information specific to the entire experiment.

.. moduleauthor:: Nicola Wadeson <[email protected]>
"""

import os
import copy
import h5py
import logging
from mpi4py import MPI

from savu.data.meta_data import MetaData
from savu.data.plugin_list import PluginList
from savu.data.data_structures.data import Data
from savu.core.checkpointing import Checkpointing
from savu.plugins.savers.utils.hdf5_utils import Hdf5Utils
import savu.plugins.loaders.utils.yaml_utils as yaml


class Experiment(object):
    """
    One instance of this class is created at the beginning of the
    processing chain and remains until the end.  It holds the current data
    object and a dictionary containing all metadata.
    """

    def __init__(self, options):
        self.meta_data = MetaData(options)
        self.__set_system_params()
        self.checkpoint = Checkpointing(self)
        self.__meta_data_setup(options["process_file"])
        self.collection = {}
        self.index = {"in_data": {}, "out_data": {}}
        self.initial_datasets = None
        self.plugin = None
        self._transport = None
        self._barrier_count = 0
        self._dataset_names_complete = False

    def get(self, entry):
        """ Get the meta data dictionary. """
        return self.meta_data.get(entry)

    def __meta_data_setup(self, process_file):
        self.meta_data.plugin_list = PluginList()
        try:
            rtype = self.meta_data.get('run_type')
            if rtype == 'test':
                self.meta_data.plugin_list.plugin_list = \
                    self.meta_data.get('plugin_list')
            else:
                raise Exception('the run_type is unknown in Experiment class')
        except KeyError:
            template = self.meta_data.get('template')
            self.meta_data.plugin_list._populate_plugin_list(process_file,
                                                             template=template)
        self.meta_data.set("nPlugin", 0) # initialise

    def create_data_object(self, dtype, name, override=True):
        """ Create a data object.

        Plugin developers should apply this method in loaders only.

        :params str dtype: either "in_data" or "out_data".
        """
        if name not in list(self.index[dtype].keys()) or override:
            self.index[dtype][name] = Data(name, self)
            data_obj = self.index[dtype][name]
            data_obj._set_transport_data(self.meta_data.get('transport'))
        return self.index[dtype][name]

    def _setup(self, transport):
        self._set_nxs_file()
        self._set_process_list_path()
        self._set_transport(transport)
        self.collection = {'plugin_dict': [], 'datasets': []}

        self._barrier()
        self._check_checkpoint()
        self._barrier()

    def _finalise_setup(self, plugin_list):
        checkpoint = self.meta_data.get('checkpoint')
        self._set_dataset_names_complete()
        # save the plugin list - one process, first time only
        if self.meta_data.get('process') == \
                len(self.meta_data.get('processes'))-1 and not checkpoint:
            # Save original process list
            plugin_list._save_plugin_list(self.meta_data.get('process_list_path'))
            # links the input data to the nexus file
            plugin_list._save_plugin_list(self.meta_data.get('nxs_filename'))
            self._add_input_data_to_nxs_file(self._get_transport())
        self._set_dataset_names_complete()
        self._save_command_log()

    def _save_command_log(self):
        """Save the original Savu run command and a
        modified Savu run command to a log file for reproducibility
        """
        folder = self.meta_data.get('out_path')
        log_folder = os.path.join(folder, "run_log")
        filename = os.path.join(log_folder, "run_command.txt")
        modified_command = self._get_modified_command()
        if not os.path.isfile(filename):
            # Only write savu command if savu_mpi command has not been saved
            with open(filename, 'w') as command_log:
                command_log.write(f"# Original Savu run command\n")
                command_log.write(f"{self.meta_data.get('command')}\n")
                command_log.write(f"# A modified Savu command to use to "
                                  f"reproduce the  obtained result\n")
                command_log.write(f"{modified_command}\n")

    def _get_modified_command(self):
        """Modify the input Savu run command, and replace the path to the
        process list
        :returns modified Savu run command string
        """
        pl_path = self.meta_data.get('process_file')
        new_pl_path = self.meta_data.get('process_list_path')
        input_command = self.meta_data.get('command')
        updated_command = input_command.replace(pl_path, new_pl_path)
        return updated_command

    def _set_process_list_path(self):
        """Create the path the process list should be saved to"""
        log_folder = os.path.join(self.meta_data.get('out_path'), "run_log")
        plname = os.path.basename(self.meta_data.get('process_file'))
        filename = os.path.join(log_folder, plname if plname
            else "process_list.nxs")
        self.meta_data.set('process_list_path', filename)

    def _set_process_list_path(self):
        """Create the path the process list should be saved to"""
        log_folder = os.path.join(self.meta_data.get('out_path'),"run_log")
        plname = os.path.basename(self.meta_data.get('process_file'))
        filename = os.path.join(log_folder, plname if plname
            else "process_list.nxs")
        self.meta_data.set('process_list_path', filename)

    def _set_initial_datasets(self):
        self.initial_datasets = copy.deepcopy(self.index['in_data'])

    def _set_transport(self, transport):
        self._transport = transport

    def _get_transport(self):
        return self._transport

    def __set_system_params(self):
        sys_file = self.meta_data.get('system_params')
        import sys
        if sys_file is None:
            # look in conda environment to see which version is being used
            savu_path = sys.modules['savu'].__path__[0]
            sys_files = os.path.join(
                os.path.dirname(savu_path), 'system_files')
            subdirs = os.listdir(sys_files)
            sys_folder = 'dls' if len(subdirs) > 1 else subdirs[0]
            fname = 'system_parameters.yml'
            sys_file = os.path.join(sys_files, sys_folder, fname)
        logging.info('Using the system parameters file: %s', sys_file)
        self.meta_data.set('system_params', yaml.read_yaml(sys_file))

    def _check_checkpoint(self):
        # if checkpointing has been set but the nxs file doesn't contain an
        # entry then remove checkpointing (as the previous run didn't get far
        # enough to require it).
        if self.meta_data.get('checkpoint'):
            with h5py.File(self.meta_data.get('nxs_filename'), 'r') as f:
                if 'entry' not in f:
                    self.meta_data.set('checkpoint', None)

    def _add_input_data_to_nxs_file(self, transport):
        # save the loaded data to file
        h5 = Hdf5Utils(self)
        for name, data in self.index['in_data'].items():
            self.meta_data.set(['link_type', name], 'input_data')
            self.meta_data.set(['group_name', name], name)
            self.meta_data.set(['filename', name], data.backing_file)
            transport._populate_nexus_file(data)
            h5._link_datafile_to_nexus_file(data)

    def _set_dataset_names_complete(self):
        """ Missing in/out_datasets fields have been populated
        """
        self._dataset_names_complete = True

    def _get_dataset_names_complete(self):
        return self._dataset_names_complete

    def _reset_datasets(self):
        self.index['in_data'] = self.initial_datasets
        # clear out dataset dictionaries
        for data_dict in self.collection['datasets']:
            for data in data_dict.values():
                data.meta_data._set_dictionary({})

    def _get_collection(self):
        return self.collection

    def _set_experiment_for_current_plugin(self, count):
        datasets_list = self.meta_data.plugin_list._get_datasets_list()[count:]
        exp_coll = self._get_collection()
        self.index['out_data'] = exp_coll['datasets'][count]
        if datasets_list:
            self._get_current_and_next_patterns(datasets_list)
        self.meta_data.set('nPlugin', count)

    def _get_current_and_next_patterns(self, datasets_lists):
        """ Get the current and next patterns associated with a dataset
        throughout the processing chain.
        """
        current_datasets = datasets_lists[0]
        patterns_list = {}
        for current_data in current_datasets['out_datasets']:
            current_name = current_data['name']
            current_pattern = current_data['pattern']
            next_pattern = self.__find_next_pattern(datasets_lists[1:],
                                                    current_name)
            patterns_list[current_name] = \
                {'current': current_pattern, 'next': next_pattern}
        self.meta_data.set('current_and_next', patterns_list)

    def __find_next_pattern(self, datasets_lists, current_name):
        next_pattern = []
        for next_data_list in datasets_lists:
            for next_data in next_data_list['in_datasets']:
                if next_data['name'] == current_name:
                    next_pattern = next_data['pattern']
                    return next_pattern
        return next_pattern

    def _set_nxs_file(self):
        folder = self.meta_data.get('out_path')
        fname = self.meta_data.get('datafile_name') + '_processed.nxs'
        filename = os.path.join(folder, fname)
        self.meta_data.set('nxs_filename', filename)

        if self.meta_data.get('process') == 1:
            if self.meta_data.get('bllog'):
                log_folder_name = self.meta_data.get('bllog')
                with open(log_folder_name, 'a') as log_folder:
                    log_folder.write(os.path.abspath(filename) + '\n')

        self._create_nxs_entry()

    def _create_nxs_entry(self):  # what if the file already exists?!
        logging.debug("Testing nexus file")
        if self.meta_data.get('process') == len(
                self.meta_data.get('processes')) - 1 and not self.checkpoint:
            with h5py.File(self.meta_data.get('nxs_filename'), 'w') as nxs_file:
                entry_group = nxs_file.create_group('entry')
                entry_group.attrs['NX_class'] = 'NXentry'

    def _clear_data_objects(self):
        self.index["out_data"] = {}
        self.index["in_data"] = {}

    def _merge_out_data_to_in(self, plugin_dict):
        out_data = self.index['out_data'].copy()
        for key, data in out_data.items():
            if data.remove is False:
                self.index['in_data'][key] = data
        self.collection['datasets'].append(out_data)
        self.collection['plugin_dict'].append(plugin_dict)
        self.index["out_data"] = {}

    def _finalise_experiment_for_current_plugin(self):
        finalise = {'remove': [], 'keep': []}
        # populate nexus file with out_dataset information and determine which
        # datasets to remove from the framework.

        for key, data in self.index['out_data'].items():
            if data.remove is True:
                finalise['remove'].append(data)
            else:
                finalise['keep'].append(data)

        # find in datasets to replace
        finalise['replace'] = []
        for out_name in list(self.index['out_data'].keys()):
            if out_name in list(self.index['in_data'].keys()):
                finalise['replace'].append(self.index['in_data'][out_name])

        
        return finalise

    def _reorganise_datasets(self, finalise):
        # unreplicate replicated in_datasets
        self.__unreplicate_data()

        # delete all datasets for removal
        for data in finalise['remove']:
            del self.index["out_data"][data.data_info.get('name')]

        # Add remaining output datasets to input datasets
        for name, data in self.index['out_data'].items():
            data.get_preview().set_preview([])
            self.index["in_data"][name] = copy.deepcopy(data)
        self.index['out_data'] = {}

    def __unreplicate_data(self):
        in_data_list = self.index['in_data']
        from savu.data.data_structures.data_types.replicate import Replicate
        for in_data in list(in_data_list.values()):
            if isinstance(in_data.data, Replicate):
                in_data.data = in_data.data._reset()

    def _set_all_datasets(self, name):
        data_names = []
        for key in list(self.index["in_data"].keys()):
            if 'itr_clone' not in key:
                data_names.append(key)
        return data_names

    def _barrier(self, communicator=MPI.COMM_WORLD, msg=''):
        comm_dict = {'comm': communicator}
        if self.meta_data.get('mpi') is True:
            logging.debug("Barrier %d: %d processes expected: %s",
                          self._barrier_count, communicator.size, msg)
            comm_dict['comm'].barrier()
        self._barrier_count += 1

    def log(self, log_tag, log_level=logging.DEBUG):
        """
        Log the contents of the experiment at the specified level
        """
        logging.log(log_level, "Experimental Parameters for %s", log_tag)
        for key, value in self.index["in_data"].items():
            logging.log(log_level, "in data (%s) shape = %s", key,
                        value.get_shape())
        for key, value in self.index["in_data"].items():
            logging.log(log_level, "out data (%s) shape = %s", key,
                        value.get_shape())


1			# -- coding: utf-8 --
2			# Copyright 2014 Diamond Light Source Ltd.
3			#
4			# Licensed under the Apache License, Version 2.0 (the "License");
5			# you may not use this file except in compliance with the License.
6			# You may obtain a copy of the License at
7			#
8			# http://www.apache.org/licenses/LICENSE-2.0
9			#
10			# Unless required by applicable law or agreed to in writing, software
11			# distributed under the License is distributed on an "AS IS" BASIS,
12			# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13			# See the License for the specific language governing permissions and
14			# limitations under the License.
15
16			"""
17			.. module:: experiment
18			:platform: Unix
19			:synopsis: Contains information specific to the entire experiment.
20
21			.. moduleauthor:: Nicola Wadeson <[email protected]>
22			"""
23
24			import os
25			import copy
26			import h5py
27			import logging
28			from mpi4py import MPI
29
30			from savu.data.meta_data import MetaData
31			from savu.data.plugin_list import PluginList
32			from savu.data.data_structures.data import Data
33			from savu.core.checkpointing import Checkpointing
34			from savu.plugins.savers.utils.hdf5_utils import Hdf5Utils
35			import savu.plugins.loaders.utils.yaml_utils as yaml
36
37
38			class Experiment(object):
39			"""
40			One instance of this class is created at the beginning of the
41			processing chain and remains until the end. It holds the current data
42			object and a dictionary containing all metadata.
43			"""
44
45			def __init__(self, options):
46			self.meta_data = MetaData(options)
47			self.__set_system_params()
48			self.checkpoint = Checkpointing(self)
49			self.__meta_data_setup(options["process_file"])
50			self.collection = {}
51			self.index = {"in_data": {}, "out_data": {}}
52			self.initial_datasets = None
53			self.plugin = None
54			self._transport = None
55			self._barrier_count = 0
56			self._dataset_names_complete = False
57
58			def get(self, entry):
59			""" Get the meta data dictionary. """
60			return self.meta_data.get(entry)
61
62			def __meta_data_setup(self, process_file):
63			self.meta_data.plugin_list = PluginList()
64			try:
65			rtype = self.meta_data.get('run_type')
66			if rtype == 'test':
67			self.meta_data.plugin_list.plugin_list = \
68			self.meta_data.get('plugin_list')
69			else:
70			raise Exception('the run_type is unknown in Experiment class')
71			except KeyError:
72			template = self.meta_data.get('template')
73			self.meta_data.plugin_list._populate_plugin_list(process_file,
74			template=template)
75			self.meta_data.set("nPlugin", 0) # initialise
76
77			def create_data_object(self, dtype, name, override=True):
78			""" Create a data object.
79
80			Plugin developers should apply this method in loaders only.
81
82			:params str dtype: either "in_data" or "out_data".
83			"""
84			if name not in list(self.index[dtype].keys()) or override:
85			self.index[dtype][name] = Data(name, self)
86			data_obj = self.index[dtype][name]
87			data_obj._set_transport_data(self.meta_data.get('transport'))
88			return self.index[dtype][name]
89
90			def _setup(self, transport):
91			self._set_nxs_file()
92			self._set_process_list_path()
93			self._set_transport(transport)
94			self.collection = {'plugin_dict': [], 'datasets': []}
95
96			self._barrier()
97			self._check_checkpoint()
98			self._barrier()
99
100			def _finalise_setup(self, plugin_list):
101			checkpoint = self.meta_data.get('checkpoint')
102			self._set_dataset_names_complete()
103			# save the plugin list - one process, first time only
104			if self.meta_data.get('process') == \
105			len(self.meta_data.get('processes'))-1 and not checkpoint:
106			# Save original process list
107			plugin_list._save_plugin_list(self.meta_data.get('process_list_path'))
108			# links the input data to the nexus file
109			plugin_list._save_plugin_list(self.meta_data.get('nxs_filename'))
110			self._add_input_data_to_nxs_file(self._get_transport())
111			self._set_dataset_names_complete()
112			self._save_command_log()
113
114			def _save_command_log(self):
115			"""Save the original Savu run command and a
116			modified Savu run command to a log file for reproducibility
117			"""
118			folder = self.meta_data.get('out_path')
119			log_folder = os.path.join(folder, "run_log")
120			filename = os.path.join(log_folder, "run_command.txt")
121			modified_command = self._get_modified_command()
122			if not os.path.isfile(filename):
123			# Only write savu command if savu_mpi command has not been saved
124			with open(filename, 'w') as command_log:
125			command_log.write(f"# Original Savu run command\n")
126			command_log.write(f"{self.meta_data.get('command')}\n")
127			command_log.write(f"# A modified Savu command to use to "
128			f"reproduce the obtained result\n")
129			command_log.write(f"{modified_command}\n")
130
131			def _get_modified_command(self):
132			"""Modify the input Savu run command, and replace the path to the
133			process list
134			:returns modified Savu run command string
135			"""
136			pl_path = self.meta_data.get('process_file')
137			new_pl_path = self.meta_data.get('process_list_path')
138			input_command = self.meta_data.get('command')
139			updated_command = input_command.replace(pl_path, new_pl_path)
140			return updated_command
141
142			def _set_process_list_path(self):
143			"""Create the path the process list should be saved to"""
144			log_folder = os.path.join(self.meta_data.get('out_path'), "run_log")
145			plname = os.path.basename(self.meta_data.get('process_file'))
146			filename = os.path.join(log_folder, plname if plname
147			else "process_list.nxs")
148			self.meta_data.set('process_list_path', filename)
149
150			def _set_process_list_path(self):
151			"""Create the path the process list should be saved to"""
152			log_folder = os.path.join(self.meta_data.get('out_path'),"run_log")
153			plname = os.path.basename(self.meta_data.get('process_file'))
154			filename = os.path.join(log_folder, plname if plname
155			else "process_list.nxs")
156			self.meta_data.set('process_list_path', filename)
157
158			def _set_initial_datasets(self):
159			self.initial_datasets = copy.deepcopy(self.index['in_data'])
160
161			def _set_transport(self, transport):
162			self._transport = transport
163
164			def _get_transport(self):
165			return self._transport
166
167			def __set_system_params(self):
168			sys_file = self.meta_data.get('system_params')
169			import sys
170			if sys_file is None:
171			# look in conda environment to see which version is being used
172			savu_path = sys.modules['savu'].__path__[0]
173			sys_files = os.path.join(
174			os.path.dirname(savu_path), 'system_files')
175			subdirs = os.listdir(sys_files)
176			sys_folder = 'dls' if len(subdirs) > 1 else subdirs[0]
177			fname = 'system_parameters.yml'
178			sys_file = os.path.join(sys_files, sys_folder, fname)
179			logging.info('Using the system parameters file: %s', sys_file)
180			self.meta_data.set('system_params', yaml.read_yaml(sys_file))
181
182			def _check_checkpoint(self):
183			# if checkpointing has been set but the nxs file doesn't contain an
184			# entry then remove checkpointing (as the previous run didn't get far
185			# enough to require it).
186			if self.meta_data.get('checkpoint'):
187			with h5py.File(self.meta_data.get('nxs_filename'), 'r') as f:
188			if 'entry' not in f:
189			self.meta_data.set('checkpoint', None)
190
191			def _add_input_data_to_nxs_file(self, transport):
192			# save the loaded data to file
193			h5 = Hdf5Utils(self)
194			for name, data in self.index['in_data'].items():
195			self.meta_data.set(['link_type', name], 'input_data')
196			self.meta_data.set(['group_name', name], name)
197			self.meta_data.set(['filename', name], data.backing_file)
198			transport._populate_nexus_file(data)
199			h5._link_datafile_to_nexus_file(data)
200
201			def _set_dataset_names_complete(self):
202			""" Missing in/out_datasets fields have been populated
203			"""
204			self._dataset_names_complete = True
205
206			def _get_dataset_names_complete(self):
207			return self._dataset_names_complete
208
209			def _reset_datasets(self):
210			self.index['in_data'] = self.initial_datasets
211			# clear out dataset dictionaries
212			for data_dict in self.collection['datasets']:
213			for data in data_dict.values():
214			data.meta_data._set_dictionary({})
215
216			def _get_collection(self):
217			return self.collection
218
219			def _set_experiment_for_current_plugin(self, count):
220			datasets_list = self.meta_data.plugin_list._get_datasets_list()[count:]
221			exp_coll = self._get_collection()
222			self.index['out_data'] = exp_coll['datasets'][count]
223			if datasets_list:
224			self._get_current_and_next_patterns(datasets_list)
225			self.meta_data.set('nPlugin', count)
226
227			def _get_current_and_next_patterns(self, datasets_lists):
228			""" Get the current and next patterns associated with a dataset
229			throughout the processing chain.
230			"""
231			current_datasets = datasets_lists[0]
232			patterns_list = {}
233			for current_data in current_datasets['out_datasets']:
234			current_name = current_data['name']
235			current_pattern = current_data['pattern']
236			next_pattern = self.__find_next_pattern(datasets_lists[1:],
237			current_name)
238			patterns_list[current_name] = \
239			{'current': current_pattern, 'next': next_pattern}
240			self.meta_data.set('current_and_next', patterns_list)
241
242			def __find_next_pattern(self, datasets_lists, current_name):
243			next_pattern = []
244			for next_data_list in datasets_lists:
245			for next_data in next_data_list['in_datasets']:
246			if next_data['name'] == current_name:
247			next_pattern = next_data['pattern']
248			return next_pattern
249			return next_pattern
250
251			def _set_nxs_file(self):
252			folder = self.meta_data.get('out_path')
253			fname = self.meta_data.get('datafile_name') + '_processed.nxs'
254			filename = os.path.join(folder, fname)
255			self.meta_data.set('nxs_filename', filename)
256
257			if self.meta_data.get('process') == 1:
258			if self.meta_data.get('bllog'):
259			log_folder_name = self.meta_data.get('bllog')
260			with open(log_folder_name, 'a') as log_folder:
261			log_folder.write(os.path.abspath(filename) + '\n')
262
263			self._create_nxs_entry()
264
265			def _create_nxs_entry(self): # what if the file already exists?!
266			logging.debug("Testing nexus file")
267			if self.meta_data.get('process') == len(
268			self.meta_data.get('processes')) - 1 and not self.checkpoint:
269			with h5py.File(self.meta_data.get('nxs_filename'), 'w') as nxs_file:
270			entry_group = nxs_file.create_group('entry')
271			entry_group.attrs['NX_class'] = 'NXentry'
272
273			def _clear_data_objects(self):
274			self.index["out_data"] = {}
275			self.index["in_data"] = {}
276
277			def _merge_out_data_to_in(self, plugin_dict):
278			out_data = self.index['out_data'].copy()
279			for key, data in out_data.items():
280			if data.remove is False:
281			self.index['in_data'][key] = data
282			self.collection['datasets'].append(out_data)
283			self.collection['plugin_dict'].append(plugin_dict)
284			self.index["out_data"] = {}
285
286			def _finalise_experiment_for_current_plugin(self):
287			finalise = {'remove': [], 'keep': []}
288			# populate nexus file with out_dataset information and determine which
289			# datasets to remove from the framework.
290
291			for key, data in self.index['out_data'].items():
292			if data.remove is True:
293			finalise['remove'].append(data)
294			else:
295			finalise['keep'].append(data)
296
297			# find in datasets to replace
298			finalise['replace'] = []
299			for out_name in list(self.index['out_data'].keys()):
300			if out_name in list(self.index['in_data'].keys()):
301			finalise['replace'].append(self.index['in_data'][out_name])
302
303
304			return finalise
305
306			def _reorganise_datasets(self, finalise):
307			# unreplicate replicated in_datasets
308			self.__unreplicate_data()
309
310			# delete all datasets for removal
311			for data in finalise['remove']:
312			del self.index["out_data"][data.data_info.get('name')]
313
314			# Add remaining output datasets to input datasets
315			for name, data in self.index['out_data'].items():
316			data.get_preview().set_preview([])
317			self.index["in_data"][name] = copy.deepcopy(data)
318			self.index['out_data'] = {}
319
320			def __unreplicate_data(self):
321			in_data_list = self.index['in_data']
322			from savu.data.data_structures.data_types.replicate import Replicate
323			for in_data in list(in_data_list.values()):
324			if isinstance(in_data.data, Replicate):
325			in_data.data = in_data.data._reset()
326
327			def _set_all_datasets(self, name):
328			data_names = []
329			for key in list(self.index["in_data"].keys()):
330			if 'itr_clone' not in key:
331			data_names.append(key)
332			return data_names
333
334			def _barrier(self, communicator=MPI.COMM_WORLD, msg=''):
335			comm_dict = {'comm': communicator}
336			if self.meta_data.get('mpi') is True:
337			logging.debug("Barrier %d: %d processes expected: %s",
338			self._barrier_count, communicator.size, msg)
339			comm_dict['comm'].barrier()
340			self._barrier_count += 1
341
342			def log(self, log_tag, log_level=logging.DEBUG):
343			"""
344			Log the contents of the experiment at the specified level
345			"""
346			logging.log(log_level, "Experimental Parameters for %s", log_tag)
347			for key, value in self.index["in_data"].items():
348			logging.log(log_level, "in data (%s) shape = %s", key,
349			value.get_shape())
350			for key, value in self.index["in_data"].items():
351			logging.log(log_level, "out data (%s) shape = %s", key,
352			value.get_shape())
353

DiamondLightSource / Savu

Pull Request — master (#827)

Experiment._set_initial_datasets() A

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